Bioremediation of heavy metals polluted 
soil of active gold mines using bacteria 
biopolymers 
 
AS AYANGBENRO 
orcid.org/ 0000-0002-3220-1873 
 
 
Thesis accepted in fulfilment of the requirements for the 
degree Doctor of Philosophy in Biology at the North-West 
University 
 
 
Promoter:  Prof OO Babalola 
 
 
Graduation: April 2019 
Student number: 28072693 
i 
 
DECLARATION 
I, the undersigned, declare that this thesis submitted to the North-West University for the degree 
of Doctor of Philosophy in Biology in the Faculty of Natural and Agricultural Sciences, School of 
Biological Sciences, and the work contained herein is my original work with exception of the 
citations and that this work has not been submitted at any other University in part or entirety for 
the award of any degree. 
 
STUDENT NAME 
Ayansina Segun AYANGBENRO 
SIGNATURE:  
 
DATE: 20th February, 2019. 
 
SUPERVISOR’S NAME 
Professor Olubukola Oluranti BABALOLA 
 
 
 
 
 
 
ii 
 
DEDICATION 
This work is dedicated to the giver of life, who has directed my path this far and to all who have 
contributed to the success of this work.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
iii 
 
ACKNOWLEDGMENTS 
I have learnt many things through this journey and every little achievement has been through the 
help of people surrounding me. I will not pass on this opportunity without expressing my gratitude 
to people and organizations who have helped me thus far.  
I would like to thank my supervisor, Prof. O.O. Babalola, for her support and constant 
encouragement during the course of this research work. I am indeed grateful. I equally want to 
thank all members and staff of the Department of Microbiology and staff of the Faculty of Natural 
and Agricultural Sciences for their support. 
I also want to acknowledge the financial assistance of the National Research Foundation (NRF) of 
South Africa and The World Academy of Science (TWAS) for NRF-TWAS African Renaissance 
Doctoral Scholarship (Grant UID 99779). The financial support of North-West University 
postgraduate bursary is also gratefully appreciated. 
I would also like to appreciate people who provided technical support: Mr. K. Mokalane, Mr. S. 
Loyilani and Dr. O.S. Aremu of the Department of Chemistry for the supply of chemicals and 
chemical analysis of my samples, Dr. B.R. Aremu for whole genome analysis and Mr. G. van 
Rensburg of the School of Chemical and Mineral Engineering, Potchefstroom Campus for ICP-
OES analysis.  
I would like to thank past and current members of Microbial Biotechnology Research Group and 
friends for all their support and for being who they are. Thank you for keeping me sane, especially 
during frustrating periods in the laboratory. Dr. M.F. Adegboye, Dr. C.F. Ajilogba, Dr. K.O. 
Afolabi, Dr. O.B. Ojuederie, Dr. M.O. Fashola, Dr. A.A. Adeniji, Dr. A.O. Yusuf, Dr. A.E. Amoo, 
Mrs. O.S. Abiodun-Salawu, Mrs. F.A. Chukwuneme, Mr. O.S. Olanrewaju, Mr. Simon Isaiah, 
Miss H.K. Mongadi and Miss. M. Khantsi. Thank you all for contributing to this success. 
iv 
 
My gratitude also goes to my pastors, Revd. (Dr.) and Mrs. E.S. Ojo, Revd. O.I. Adegboyega, 
Prof. Abiodun Salawu and Prof. and Prof. (Mrs). Akpovire Oduaran. Thank you for your prayers 
and words of encouragement. 
Lastly, to my parents, Mr. and Mrs. Theophilus A. Ayangbenro, thank you for your support and 
unconditional love. You have given me the best you could afford and I will always strive to make 
you proud. To my siblings, Ayanniyi, Ayanwale and Ayandele, thank you for giving me this 
opportunity to succeed. To my wife, Oluwadamilola, your unflinching support and love has been 
my motivation. You are a treasure indeed. 
Thank you all for helping me succeed. 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
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TABLE OF CONTENTS 
 
DECLARATION ........................................................................................................................... ii 
DEDICATION ..............................................................................................................................iii 
ACKNOWLEDGMENTS ............................................................................................................ iv 
TABLE OF CONTENTS ............................................................................................................. vi 
LIST OF TABLES ........................................................................................................................ xi 
LIST OF FIGURES ....................................................................................................................xiii 
GENERAL ABSTRACT .......................................................................................................... xvii 
LIST OF PUBLICATIONS ........................................................................................................ xx 
CHAPTER ONE ............................................................................................................................ 1 
GENERAL INTRODUCTION .................................................................................................... 1 
1.1 Introduction to this chapter ................................................................................................ 1 
1.2 Problem statement ................................................................................................................. 4 
1.3 Research aims and objectives ................................................................................................ 5 
CHAPTER TWO ........................................................................................................................... 6 
NEW STRATEGY FOR HEAVY METAL POLLUTED ENVIRONMENT: A REVIEW 
OF MICROBIAL BIOSORBENTS ............................................................................................. 6 
Abstract .......................................................................................................................................... 6 
2.1 Introduction ............................................................................................................................. 6 
2.2 Sources of heavy metal pollution in the environment .......................................................... 9 
2.3 Toxicity of heavy metals to life forms .................................................................................. 10 
2.4 Bioremediation of heavy metals by microorganisms ......................................................... 16 
vi 
 
2.5 Mechanisms of heavy metal uptake by microorganisms ................................................... 20 
2.6 Biosorption capacity of various microbial biosorbents...................................................... 23 
Conclusions .................................................................................................................................. 29 
CHAPTER THREE .................................................................................................................... 30 
METAL(LOID) BIOREMEDIATION: STRATEGIES EMPLOYED BY MICROBIAL 
POLYMERS ................................................................................................................................ 30 
Abstract ........................................................................................................................................ 30 
3.1 Introduction ........................................................................................................................... 30 
3.2 Biosurfactant as a metal(loid)-complexing biopolymer ..................................................... 35 
3.2.1 Mechanism of biosurfactant removal of metal(loid)s ....................................................... 37 
3.3 Metal(loid) removal by microbial flocculants ..................................................................... 41 
3.3.1 Mechanism of metal(loid) removal by flocculation ......................................................... 42 
3.4 Biofilms and metal(loid) removal ......................................................................................... 46 
Conclusions .................................................................................................................................. 52 
CHAPTER FOUR ....................................................................................................................... 53 
IDENTIFICATION AND CHARACTERIZATION OF HEAVY METAL RESISTANT 
BACTERIAL ISOLATES FROM GOLD MINING SOIL ..................................................... 53 
Abstract ........................................................................................................................................ 53 
4.1 Introduction ........................................................................................................................... 54 
4.2 Materials and methods .......................................................................................................... 55 
4.2.1 Soil sampling and analysis................................................................................................ 55 
4.2.2 Isolation and screening of heavy metal resistant bacteria isolates ................................... 58 
4.2.3 Biochemical characterization of bacteria isolates ............................................................. 58 
vii 
 
4.2.4 Genomic DNA extraction ................................................................................................. 59 
4.2.5 Amplification of 16S rDNA and detection of heavy metal resistance genes ................... 59 
4.2.6 Sequencing reaction .......................................................................................................... 60 
4.2.7 Screening of biosurfactant production by heavy metal resistant isolates ......................... 60 
4.2.8 Screening of metal resistant isolates for bioflocculating activity ..................................... 62 
4.2.9 Statistical analysis ........................................................................................................... 63 
4.3 Results ..................................................................................................................................... 63 
4.3.1 Physicochemical properties of the soil samples ............................................................... 63 
4.3.2. Diversity of bacterial isolates from the study sites .......................................................... 72 
4.3.3. Biochemical profile of heavy metal resistant isolates ..................................................... 74 
4.3.4. Molecular characterization of resistant isolates ............................................................... 74 
4.3.5. Disstribution of heavy metal resistance genes among the resistant isolates .................... 84 
4.3.6. The effect of heavy metals on bacteria growth ................................................................ 88 
4.3.7. Biosurfactant production by heavy metal resistant isolates ............................................. 91 
4.3.8. Flocculating activity of heavy metal resistant bacterial isolates ..................................... 93 
4.4. Discussion .............................................................................................................................. 94 
Conclusion .................................................................................................................................. 102 
CHAPTER FIVE ....................................................................................................................... 103 
HEAVY METAL REMOVAL FROM CONTAMINATED SOIL BY LIPOPEPTIDE 
BIOSURFACTANT PRODUCED BY BACILLUS CEREUS NWUAB01 .......................... 103 
Abstract ...................................................................................................................................... 103 
5.1 Introduction ......................................................................................................................... 103 
viii 
 
5.2 Materials and method ......................................................................................................... 105 
5.2.1 Isolation and screening of heavy metal resistant bacterial isolates ................................ 105 
5.2.2 Identification of strain NWUAB01 ................................................................................ 106 
5.2.3 Amplification of 16S rDNA and detection of heavy metal resistance genes ................. 106 
5.2.4 Screening and characterization of biosurfactant produced by strain NWUAB01 .......... 108 
5.2.5 Remediation of heavy metal contaminated soil with biosurfactant ................................ 110 
5.3 Results and Discussion ........................................................................................................ 110 
Conclusion .................................................................................................................................. 123 
CHAPTER SIX .......................................................................................................................... 124 
DRAFT GENOME SEQUENCE OF HEAVY METAL RESISTANT BACILLUS CEREUS 
STRAIN NWUAB01 .................................................................................................................. 124 
Abstract ...................................................................................................................................... 124 
6.1 Introduction ......................................................................................................................... 124 
6.2 Materials and Method ......................................................................................................... 124 
6.3 Results and Discussion ........................................................................................................ 125 
CHAPTER SEVEN ................................................................................................................... 127 
BIOFLOCCULANT PRODUCTION AND HEAVY METAL SORPTION BY METAL 
RESISTANT BACTERIAL ISOLATES FROM MINING SOIL ........................................ 127 
Abstract ...................................................................................................................................... 127 
7.1 Introduction ......................................................................................................................... 127 
7.2 Materials and methods ........................................................................................................ 129 
7.2.1 Soil sampling and isolation of heavy metal resistant bacteria isolates ........................... 129 
7.2.2 Biochemical characterization of bacteria isolates ........................................................... 130 
ix 
 
7.2.3 Genomic DNA extraction ............................................................................................... 130 
7.2.4 Molecular characterization and detection of heavy metal resistance genes ................... 130 
7.2.5 Sequencing reaction ........................................................................................................ 131 
7.2.6 Screening of metal resistant isolates for bioflocculant production ................................. 131 
7.2.7 Bioflocculant purification ............................................................................................... 132 
7.2.8 Characterization of the bioflocculant produced .............................................................. 132 
7.2.9 Effect of culture conditions on flocculation activity ...................................................... 133 
7.2.10 Heavy metal sorption by bioflocculant produced by bacterial isolates ........................ 133 
7.2.11 Statistical analysis ......................................................................................................... 133 
7.3 Results and Discussion ........................................................................................................ 133 
Conclusion .................................................................................................................................. 148 
CHAPTER EIGHT ................................................................................................................... 149 
8.0 SUMMARY, CONCLUSION AND RECOMMENDATIONS ....................................... 149 
REFERENCES .......................................................................................................................... 152 
APPENDICES ........................................................................................................................... 184 
 
 
 
 
 
 
 
 
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LIST OF TABLES 
Table 2.1: Toxicity of heavy metals to life forms………………………………….……….12 
Table 2.2: Metal biosorption by different microbial biosorbents…………………..…….26 
Table 3.1: Biosurfactant washing of some metal polluted environment………………...….43 
Table 3.2: Metal(loid) removal by bioflocculant produced by different bacterial species….48 
Table 4.1: Primer sets used in PCR amplification of heavy metal resistance genes………..61 
Table 4.2: Sampling location and particle size distribution of the soil samples…………….65 
Table 4.3: Physical and chemical properties of the soil samples……………………….…...68 
Table 4.4: Correlation matrix between the physicochemical properties of the soil samples..69 
Table 4.5: Heavy metal concentration (mg/kg) of the soil samples..…………………….…71 
Table 4.6: Correlation matrix between pH and heavy metals concentration of the soil 
samples………………………………………………………………………………………72 
Table 4.7: Enumeration of heavy metal resistant bacterial isolates from each sample….…76 
Table 4.8: Growth pattern of heavy metal resistant isolates on different concentrations 
of heavy metal..…………………………………………………………………..............…77 
Table 4.9: Biochemical profile of heavy metal resistant bacterial isolates………….……..79 
Table 4.10: 16S rRNA sequence identification of the isolates and their accession numbers.81 
Table 4.11: Screening of bacterial isolates for biosurfactant production………………..…92 
Table 4.12: Flocculating activity by resistant bacterial isolates……………………..……..93 
Table 5.1: Growth of B. cereus NWUAB01 on different concentrations of heavy metal…113 
Table 5.2: The biochemical properties of B. cereus NWUAB01………………………….113 
Table 5.3: Evaluation of B. cereus NWUAB01 for biosurfactant production…………......120 
xi 
 
Table 5.4: Heavy metal removal by strain NWUAB01 and its biosurfactant…………..122 
Table 7.1: Heavy metal tolerance of the two bioflocculant producing isolates………...137 
Table 7.2: Biochemical profile of heavy metal resistant bacterial isolates…………..…137 
Table 7.3: Flocculating activity using different carbon sources…………………….….139 
Table 7.4: Kinetics of bioflocculant producing isolates and their flocculating activity..141 
Table 7.5: Metal removal efficiency by bioflocculant produced by the two isolates…...147 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
xii 
 
LIST OF FIGURES 
Figure 2.1: Mechanisms of heavy metal uptake by microorganisms .....................…..…21 
Figure 3.1: Mechanism of metal(loid) sequestration ….……………………………………33 
Figure 4.1: Map showing the soil sampling location………………….....…………………64 
Figure 4.2: The pH of the soil samples from different sampling locations……….....……..66 
Figure 4.3: Cation exchange capacity of the soil samples…………………….....…………66 
Figure 4.4: Distribution of bacterial isolates from each soil sample…................………….72 
Figure 4.5: Plates showing bacterial counts and appearance of heavy metal resistant 
isolates………………….................................................................................................…..74 
Figure 4.6: Photograph of the agarose gel showing 16S rRNA gene amplicons of 1500  
bp of metal resistant bacteria.............................................................................................…80 
Figure 4.7: Evolutionary relationships of taxa tree based on partial 16S rDNA  
sequences using maximum likelihood method showing relationships between the  
resistant Bacillus species isolates and closely related strains from NCBI GenBank……....82 
Figure 4.8: Evolutionary relationships of taxa tree based on partial 16S rDNA  
sequences using neighbor-joining method showing relationships between  
the resistant Acinetobacter calcoaceticus and its closely related strains from  
NCBI GenBank………….....................................................................................................83 
Figure 4.9: Evolutionary relationships of taxa tree based on partial 16S rDNA  
sequences using maximum likelihood based on the Jukes-Cantor model showing  
relationships between the resistant Enterobacter asburiae and its closely related strains  
from NCBI GenBank…................................................................................................……83 
Figure 4.10: Evolutionary relationships of taxa tree based on partial 16S rDNA  
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sequences using maximum likelihood based on the Jukes-Cantor model showing  
relationships between the resistant Citrobacter freundii and its closely related strains  
from NCBI GenBank…..................................................................................................…83 
Figure 4.11: Evolutionary relationships of taxa tree based on partial 16S rDNA  
sequences using maximum likelihood based on the Jukes-Cantor model showing  
relationships between the resistant Pseudomonas species and its closely related strains  
from NCBI GenBank…..................................................................................................…84 
Figure 4.12: Evolutionary relationships of taxa tree based on partial 16S rDNA  
sequences using maximum parsimony method based on the Jukes-Cantor  
model showing relationships between the resistant Pantoea sp. and its  
closely related strains from NCBI GenBank………..........................................................84 
Figure 4.13: Photograph of the agarose gel showing cadA gene amplicons of 600 bp  
for cadmium resistance...........................................................................................………85 
Figure 4.14: Photograph of the agarose gel showing CzcD gene amplicons of 398  
bp for cadmium, cobalt and zinc resistance…...........................................................…….86 
Figure 4.15: Photograph of the agarose gel showing PbrA gene amplicons of 766 bp  
for lead resistance…….......................................................................................................86 
Figure 4.16: Photograph of the agarose gel showing PbrT gene amplicons of 740 bp  
for lead resistance…….............................................................................................……..87 
Figure 4.17: Photograph of the agarose gel showing chrA gene amplicons of 1292  
bp for chromium resistance……….....................................................................…………87 
Figure 4.18: The growth patterns of B. cereus (AB4), B. toyonensis (AB5),  
A. calcoaceticus (AB6), and E. asburiae (AB9) in the presence of heavy metals…….….88 
Figure 4.19: The growth pattern of B. wiedmannii (AB14), B. cereus (AB18),  
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B. cereus (AB19), and B. thuringiensis (AB21) in the presence of Cd, Cr and Pb….....…89 
Figure 4.20: The growth patterns of B. subtilis (AB22), A. tumefaciens (AB23),  
P. fluorescens (AB30), and P. korensis (AB36) in the presence of Cd, Cr and Pb…....….89 
Figure 4.21: The pattern of growth of C. freundii (AB58), B. cereus (AB61),  
B. wiedmannii (AB66), and Pantoea sp. (AB77) in the presence of heavy metals…....….90 
Figure 4.22: The growth patterns of B. megaterium (AB79), B. aryabhattai (AB82),  
B. megaterium (AB91), and Bacillus sp. (AB92) in the presence of Cd, Cr and Pb………90 
Figure 5.1: Time course growth of B. cereus NWUAB01 on different heavy metals…....113 
Figure 5.2: The 16S rRNA and heavy metal resistant genes amplification of DNA  
sequence of strain NWUAB01….........................................................................................115 
Figure 5.3: Phylogenetic tree using Maximum Likelihood method of strain NWUAB01  
based on 16S rRNA gene sequence…..................................................................................116 
Figure 5.4: The phylogenetic relationship of the pangenome sequence of strain  
NWUAB01 with other closely related organisms generated from KBase database…........116 
Figure 5.5: Emulsification of (a) kerosene (b) kerosene in the presence of heavy  
metals (c) vegetable oil (d) engine oil by strain NWUAB01…...........................................118 
Figure 5.6: Stability of the emulsion produced from kerosene at different pH and  
temperature….......................................................................................................................119 
Figure 5.7: The SEM image of the biosurfactant produced be strain NWUAB01….........120 
Figure 5.8: The FTIR spectra of biosurfactant produced by strain NWUAB01….............121 
Figure 5.9: The mass spectrum of the purified biosurfactant produced by strain NWUAB01..121 
xv 
 
Figure 6.1: The subsystem information of the genome of Bacillus cereus NWUAB01 as 
predicted by SEED Viewer v2.0. Genomic features are colored according to their  
functional classification types…..........................................................................................….126 
Figure 7.1: Maximum likelihood tree showing the phlogenetic relationship  
of the two bioflocculant producing isolates based on 16S rRNA gene sequence…..............…135 
Figure 7.2: The growth of P. korensis in the presence of heavy metals……......................….136 
Figure 7.3: The growth of Pantoea sp. in the presence of heavy metals…….....................….138 
Figure 7.4: The effect of pH on flocculating potential of Pantoea sp….............................….141 
Figure 7.5: The effect of pH on flocculating activity of Pseudomonas korensis….................142 
Figure 7.6: The effect of culture temperature on flocculating activity…............................…143 
Figure 7.7: Bioflocculant yield from each isolate at optimum condition (a) P. korensis  
and (b) Pantoea sp...........................................................................................................……144 
Figure 7.8: The scanning electron micrograph of the purified bioflocculant produced  
by (a) P. korensis and (b) Pantoea sp…..............................................................................…144 
Figure 7.9: The FTIR spectroscopy of the purified bioflocculant produced by P. korensis..146 
Figure 7.10: The FTIR spectroscopy of the purified bioflocculant from Pantoea sp…........147 
 
 
 
 
 
 
 
 
 
xvi 
 
GENERAL ABSTRACT 
Mining activities have increased environmental pollution which has consequently resulted in the 
release of large quantities of heavy metals into the environmental media. These metal species tend 
to accumulate in the environmental media because they are non-degradable causing several toxic 
effects on biological systems. Hence, there is a need for appropriate treatment techniques for 
effective removal of heavy metals from contaminated media. This study was designed to screen 
for prospective biopolymer producing heavy metal resistant bacterial isolates that can be used for 
metal removal from polluted media. Soil samples were collected from gold mining sites in 
Vryburg, South Africa. The soil physicochemical properties and metal concentrations were 
determined. Each soil sample was digested and the metal concentration determined through 
inducible conductivity plasma-optical emission spectroscopy. The pH values of the soil samples 
ranged from 5.9 to 7.5 which is slightly acidic to slightly alkaline. The properties of the soil 
revealed that the soil samples are sandy in nature and deficient in nutrients for microbial activities. 
Heavy metal concentrations for the soil samples ranged between 0.03-0.36 mg/kg for Cd, 0.22-
0.41 mg/kg for Cr, 0.12-0.64 mg/kg for Cu, 127.9-1213.2 mg/kg for Fe, 0.2-1.9 mg/kg for Pb and 
0.07-1.2 mg/kg for Zn. These values fall within the recommended limit for South African soil and 
sediments, but still have potential impacts on biological systems. Ninety-eight heavy metal (Cd, 
Cr and Pb) resistant bacterial isolates were isolated from the soil samples using Luria-Bertani agar 
supplemented with each of the heavy metals. These isolates were screened for biopolymer 
production and a total of 20 isolates (20.4%), that were positive for biopolymer production were 
selected for further studies. The metal tolerance of the biopolymer producing isolates were 
determined and 55% of the isolates grew on 1000 mg/L of Pb. Few isolates tolerated up to 300 
mg/L of cadmium and chromium. All isolates were characterized biochemically and molecularly 
and the results showed that the phylum Firmicutes were the dominant organisms. The isolates were 
further characterized by 16S rRNA gene sequence analysis and identified as belonging to the 
genera Acinetobacter, Agrobacterium, Bacillus, Citrobacter, Enterobacter, Pantoea and 
xvii 
 
Pseudomonas. The isolates were screened for heavy metal resistance genes encoding for Cd (cadA, 
CzcA, CzcB and CzcD), Cr (chrA and chrB) and Pb (PbrA and PbrT). Multi- metal resistance was 
found in most of the isolates, notably, Bacillus cereus MH399230, B. toyonensis MH399231, 
Pseudomonas korensis MH399240 and Pantoea sp. MH399244. B. cereus MH399230 was 
positive for all the screening test for biosurfactant production and was selected for biosurfactant 
production and metal removal. B. cereus MH399230 has emulsification of 22%, 24%, 37% and 
54% on hexadecane, vegetable oil, kerosene and engine oil respectively, and was able to reduce 
the surface tension of 39.5 mN/m. The biosurfactant produced by B. cereus MH399230 was 
identified as a lipopeptide with stability over a wide range of pH and temperature. The 
biosurfactant produced by B. cereus MH399230 was able to remove 78% of Pb, 56% of Cd and 
35% of Cr from polluted soil after single washing with biosurfactant. The genome of B. cereus 
MH399230 was mined and revealed the presence of 44 gene clusters involved in antibiotic and 
secondary metabolite biosynthesis. These included non-ribosomal peptide synthetase (NRPS) gene 
clusters, lipoprotein, lipopolysaccharides, binding proteins, proteins related to degradation of toxic 
compounds and biofilm secretion genes. Genes responsible for resistance to toxic heavy metals 
such as arsenic, cadmium, chromium, cobalt, copper, lead, mercury and zinc were also detected in 
the genome of B. cereus MH399230. P. korensis MH399240 and Pantoea sp. MH399244 were 
further screened for bioflocculant production based on the high flocculating activity produced with 
kaolin clay. Maximum flocculating activity of 71.3% and 51.7% with glucose and yield of 2.98 
g/L and 3.26 g/L was obatanied for Pantoea sp. and P. korensis respectively at optimum pH (7.5) 
and temperature (30˚C). Characterization of the partially purified bioflocculant using FTIR 
revealed the presence of carboxyl, hydroxyl and amino groups. These groups are responsible for 
metal binding. Metal sorption by the partially purified bioflocculants of Pantoea sp. removed 
51.2%, 52.5%and 80.5% of Cd, Cr and Pb respectively while that of P. korensis removed 48.5%, 
42.5% and 73.7% of Cd, Cr and Pb respectively from aqueous solution. This study shows the 
xviii 
 
potentials of these heavy metal resistant isolates for biopolymer production and removal of heavy 
metals from polluted media. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
xix 
 
LIST OF PUBLICATIONS 
Chapter Two: New strategy for heavy metal polluted environment: A review of microbial 
biosorbents. Published in International Journal of Environmental Research and Public Health. 
(2017) 14: 94 DOI: 10.3390/ijerph14010094 
Authors: Ayansina Segun Ayangbenro and Olubukola Oluranti Babalola  
Candidate‘s Contributions: managed the literature searches and wrote the first draft of the 
manuscript.  
Chapter Three: Metal(loid) bioremediation: strategies employed by microbial polymers. 
Published in Sustainability. (2018) 10: 3028 DOI: 10.3390/su10093028 
Authors: Ayansina Segun Ayangbenro and Olubukola Oluranti Babalola  
Candidate’s Contributions: managed the literature searches and wrote the first draft of the 
manuscript.  
Chapter Four: Identification and characterization of heavy metal resistant bacterial isolates from gold 
mining soil. This chapter has been submitted for publication in Applied Microbiology and 
Biotechnology.  
Authors: Ayansina Segun Ayangbenro and Olubukola Oluranti Babalola  
Candidate’s Contributions: designed the study, managed the literature searches, wrote the protocol, 
carry out the laboratory work, performed all the analyses, interpretation of results and wrote the 
first draft of the manuscript.  
Chapter Five: Heavy metal removal from contaminated soil by lipopeptide biosurfactant 
produced by Bacillus cereus NWUAB01. This chapter has been submitted in this format for 
publication in Environmental Science and Pollution Research.  
Authors: Ayansina Segun Ayangbenro and Olubukola Oluranti Babalola  
xx 
 
Candidate’s Contributions: designed the study, managed the literature searches, wrote the protocol, 
carry out the laboratory work, performed all the analyses, interpretation of results and wrote the 
first draft of the manuscript.  
Chapter Six: Draft genome sequence of heavy metal resistant Bacillus cereus strain NWUAB01. 
Published in Micriobiology Resource Announcement. (2019)8(7): e01706-18. DOI: 
10.1128/mra.01706-18. 
Authors: Olubukola Oluranti Babalola, Bukola Rhoda Aremu and Ayansina Segun Ayangbenro 
Candidate’s Contributions: managed the literature searches, wrote the protocol, performed part of 
the analyses and wrote the first draft of the manuscript.  
Chapter Seven: Bioflocculant production and heavy metal sorption by metal resistant bacterial 
isolates from mining soil. This chapter has been submitted in this format for publication in 
Chemosphere. 
Authors: Ayansina Segun Ayangbenro, Samuel Oluwole Aremu and Olubukola Oluranti Babalola  
Candidate’s Contributions: managed the literature searches, wrote the protocol, carry out the 
laboratory work, performed part of the analyses and wrote the first draft of the manuscript.
xxi 
 
CHAPTER ONE 
GENERAL INTRODUCTION 
1.1 Introduction to this chapter 
Industrial activities have increased environmental pollution challenges and degradation of  
ecosystems with the buildup of many pollutants, such as toxic metals. Pollution with heavy metals 
is a consequnece of anthropogenic activities and thus becomes an important environmental and 
health problem (Siddiquee et al., 2015). Heavy metals are not degradable, unlike organic 
contaminants, and able to build up in the food web via bioaccumulation (Siddiquee et al., 2015). 
The toxic properties of metals are attributed to their ability to interfere with critical biological 
processes.  
Heavy metals and metalloids are natural components of the earth’s crust and are referred to as 
metallic elements. They have relatively high density and are toxic even at low concentration  
(Elekofehinti et al., 2012). They have been defined with a number of criteria which include 
cationic-hydroxide formation, specific gravity greater than 5 g/ml, complex formation, hard-soft 
acids and bases, and environmental toxicity (Rajendran et al., 2003). The stable oxidation states 
of these metals are the most toxic forms which react with body bio-molecules to form extremely 
stable bio-toxic compounds which are difficult to dissociate (Duruibe et al., 2007).  
Natural occurence of heavy metals in the environment has been attributed to pedogenesis, a process 
of weathering of parent materials  (Dixit et al., 2015). Examples of natural sources of metals 
includes volcanic activity, erosion and weathering of minerals. The anthropogenic sources are as 
a result of human activities such as biosolids, mining, atmospheric disposition, electroplating, 
smelting, pesticides and phosphate fertilizers (Fulekar et al., 2009). 
Mining has been identified as one of the anthropogenic activities which have a destructive impact 
on environmental quality. The impacts range from physical destruction with accompanying loss 
1 
 
of biodiversity to accumulation of pollutants in different media of the environment (Getaneh and 
Alemayehu, 2006). Mining sites are usually contaminated with several kinds of heavy metals that 
come primarily from ore processing, disposal of tailings and wastewaters around the mines 
(Grimalt et al., 1999). These metals are then released into the environmental media, especially 
water, sediment and soil (Eisler, 2003; Getaneh and Alemayehu, 2006). Heavy metals in tailings 
can be transported to and accumulate in plants and animals, and can then be passed on to human 
beings in the food web, as a final consumer (Kim et al., 2001; Patel et al., 2005) and may also 
cause adverse effects on the ecosystem (Kim et al., 1998). Heavy metals associated with mining 
are of particular interest because they show a tendency to accumulate in sediments and soils and 
have a long persistence in the environment, as well as being ubiquitous in sediments and soils 
(Getaneh and Alemayehu, 2006).  
The threat heavy metals pose to the health of living organisms is aggravated by their long-term 
persistence in the environment. The toxicity becomes more severe in acidic medium and nutrient-
deficient ecosystems (Mukhopadhyay and Maiti, 2010). Most heavy metals are known to be 
carcinogenic, mutagenic, cause cardiovascular diseases, disrupt the endocrine system, and cause 
coordination problems (Wuana and Okieimen, 2011). They can accumulate in plant tissues to a 
level that affects growth and development, inhibits photosynthesis, interferes with metabolic 
functions and respiration, degenerates cell organelles and even leads to plant death (Liu et al., 
2008). 
Heavy metals cannot be degraded to harmless products and hence persist in the environment 
indefinitely (Rajkumar and Freitas, 2008). As a result, many different remediation methods have 
been employed to address the increase of heavy metal contaminated sites. Techniques for heavy 
metal removal from a polluted environment include chemical precipitation and sludge separation, 
chemical oxidation or reduction, and ion exchange and filtration. These conventional 
physicochemical methods are costly, non-specific, and become ineffective when metal 
2 
 
concentrations are less than 100 mg/L in such media (Ahluwalia and Goyal, 2007). The toxic 
sludge released from these methods of remediation can also pose adverse effects on biological 
activity; hence a biological approach for remediation of heavy metal contamination has been 
proposed (Congeevaram et al., 2007).  
The biological approach for heavy metal removal is an attractive alternative to conventional 
techiques. The use of microorganisms for remediation purposes is a solution for heavy metal 
pollution because it is a sustainable technique to rectify and re-establish the natural soil condition 
(Kapoor and Viraraghavan, 1995). Due to high concentration of heavy metals in mining 
environments, resident microorganisms have developed resistance mechanisms that falicitate 
effective detoxification of toxic metals. They have developed strategies for their continued 
existence in heavy metal-polluted habitats and are known to develop and adopt different 
detoxifying mechanisms such as biosorption, bioaccumulation, biotransformation and 
biomineralization, which can be exploited for bioremediation processes (Lin and Lin, 2005). They 
secrete various kinds of metal-binding metabolites, biopolymers, capsules, extracellular polymeric 
substances, slimes and sheaths (Fomina and Gadd, 2014).  
Microbial polymers are generating increasing attention in recent years due to their biodegradable 
nature, low toxicity, and diversity, which makes them superior to their chemical counterparts. 
These polymers provide defensive mechanisms and increase metal bioavailability in the soil (Valls 
and De Lorenzo, 2002; Vaishnav and Demain, 2011). One of the major impacts of microbial 
metabolic processes has been the exploitation of pathways for degradation and consequent 
remediation of pollutants in the environment. In contrast to organic pollutants, heavy metals 
presents a difficult challenge; metal ions can only be converted to base metal, precipitated, 
methlylated, volatilized or complexed with an organic ligand. One such approach to heavy metal 
removal involves the formation of stable complexes between metals and microbial biomass which 
3 
 
is a result of electrostatic interaction between metal ligands and negatively charged cellular 
biopolymers (Gutnick and Bach, 2000). 
1.2 Problem statement 
Mining activities generate different kinds of waste which are harmful to the environment. These 
wastes include mine waste, tailings, dump heap leach, and acid mine water (Yassir et al., 2015). A 
major source of the metal pollution is metal-rich acid mine drainage (AMD) from waste rock piles 
dumped in the surrounding soil. Heavy metals generated from mine wastes have been reported to 
travel several kilometers and impact downstream ecosystems. This is of great concern due to toxic 
effects on flora and fauna. The fate of metallic species after they enter the ecosystem becomes very 
difficult to track and they start to inflict damage as they move through from one ecological trophic 
level to another. Heavy metals such as chromium, cadmium and lead have always been present in 
the environment, but exploration and exploitation, and anthropogenic activities have increased 
their concentration in the biosphere (Prabhu et al., 2018). These metals have also been listed among 
toxic elements within the first twenty pollutants priority list that are of public health significance 
(Cáliz et al., 2013).  
Microbial polymers are the first line of defense against metal toxicity and protect the interior of 
the cell (Li and Yu, 2014). The use of isolated biopolymers from microorganisms is desired due 
to their easy availability in heavy metal removal and circumvention of pathogenicity concerns of 
some producing organisms. Therefore, this research work becomes imperative to ascertain the 
removal of these toxic metals by biopolymers produced by heavy metal resistant bacterial isolates 
from polluted mine soil. 
 
 
 
 
4 
 
1.3 Research aims and objectives 
The aims of this research are to isolate and characterize heavy metal resistant bacteria in soil 
samples from active mines,  to screen these bacteria for biopolymer production and to use these 
microbial polymers for heavy metal removal. 
The objectives of the study include:  
1. To determine the concentrations of heavy metals in active mine soil samples. 
2. To isolate and characterize heavy metal resistant bacteria isolates present in mine soil 
samples and to determine their phylogenetic relationship. 
3. To screen heavy metal resistant bacterial isolates for biopolymer (biosurfactant and 
bioflocculant) production. 
4. To remediate heavy metal (Cd, Cr, Pb) polluted soil with biopolymer produced.  
 
 
 
 
 
 
 
 
 
 
 
 
5 
 
CHAPTER TWO 
NEW STRATEGY FOR HEAVY METAL POLLUTED ENVIRONMENT: A REVIEW 
OF MICROBIAL BIOSORBENTS 
Abstract 
Persistent heavy metal pollution poses a major threat to all life forms in the environment due to 
its toxic effects. These metals are very reactive at low concentrations and can accumulate in the 
food web, causing severe public health concerns. Remediation using conventional physical and 
chemical methods is uneconomical and generates large volumes of chemical waste. 
Bioremediation of hazardous metals has received considerable and growing interest over the 
years. The use of microbial biosorbents is eco-friendly and cost effective; hence, it is an 
efficient alternative for the remediation of heavy metal contaminated environments. Microbes 
have various mechanisms of metal sequestration that hold greater metal biosorption capacities. 
The goal of microbial biosorption is to remove and/or recover metals and metalloids from 
solutions, using living or dead biomass and their components. This review discusses the sources 
of toxic heavy metals and describes the groups of microorganisms with biosorbent potential for 
heavy metal removal. 
Keywords: bioremediation; biosorbent; biosorption; heavy metals; microorganisms; 
remediation. 
2.1 Introduction 
Industrialization and technological advancement have put an increasing burden on the 
environment by releasing large quantities of hazardous waste, heavy metals (cadmium, 
chromium, and lead) and metalloids (elements with intermediate properties between those of 
typical metals and non-metals, such as arsenic and antimony), and organic contaminants that 
have inflicted serious damage on the ecosystem. The build-up of heavy metals and metalloids 
6 
 
in soils and waters continues to create serious global health concerns, as these metals and 
metalloids cannot be degraded into non-toxic forms, but persist in the ecosystem. 
Contamination of the environment with heavy metals has increased beyond the recommended 
limit and is detrimental to all life forms (Tak et al., 2013; Gaur et al., 2014; Dixit et al., 2015). 
The maximum permissible concentration of some heavy metals in water, as stated by the 
Comprehensive Environmental Response Compensation and Liability Act (CERCLA), USA, is 
0.01, 0.05, 0.01, 0.015, 0.002, and 0.05 mg/L for Ar, Cd, Cr, Pb, Hg, and Ag respectively 
(Chaturvedi et al., 2015). The standard for soil, as established by the Indian standards for heavy 
metals, is 3–6, 135–270, 75–150, 250–500, and 300–600 mg/kg for Cd, Cu, Ni, Pb, and Zn 
respectively (Nagajyoti et al., 2010). The standard for soil as stipulated by the National Norms 
and Standards for Remediation of Contaminated Land and Soil Quality in South Africa is 5.8, 
37, 80, 16, 1.0, 740, 91, 20, and 240 for As, Cd, Cr, Cu, Hg, Mn, Ni, Pb and Zn respectively 
(Olobatoke and Mathuthu, 2016). 
Heavy metal pollution is currently a major environmental problem because metal ions persist 
in the environment due to their non-degradable nature. The toxicity and bioaccumulation 
tendency of heavy metals in the environment is a serious threat to the health of living organisms. 
Unlike organic contaminants, heavy metals cannot be broken down by chemical or biological 
processes. Hence, they can only be transformed into less toxic species. 
The majority of heavy metals are toxic at low concentrations and are capable of entering the 
food chain, where they accumulate and inflict damage to living organisms. All metals have the 
potential to exhibit harmful effects at higher concentrations and the toxicity of each metal 
depends on the amount available to organisms, the absorbed dose, the route and the duration of 
exposure (Mani and Kumar, 2014). Due to the noxious effects of these metals, there are growing 
environmental and public health concerns, and a consequent need for increased awareness in 
order to remediate the heavy metal polluted environment. Thus, it is imperative to remove or 
7 
 
reduce heavy metal contamination in order to prevent or reduce contaminating the environment 
and the possibility of uptake in the food web. To achieve this, bioremediation is employed in 
order to increase metal stability (speciation), which in turn reduces the bioavailability of metal 
(Abbas et al., 2014; Akcil et al., 2015; Ndeddy Aka and Babalola, 2016). Speciation is defined 
as the identification and quantification of the different, defined species, forms, or phases, in 
which a metal occurs, while bioavailability is the portion of the total amount of a metal in an 
environment, within a time frame, that is available or made available for uptake by living 
organisms in their direct surroundings. Speciation of metal and its bioavailability determines 
the physiological and toxic effects of a metal on living organisms (Olaniran et al., 2013). 
Bioremediation is a state-of-the-art technique used for heavy metal removal and/or recovery 
from polluted environments. The technique utilizes inherent biological mechanisms to eradicate 
hazardous contaminants using microorganisms and plants, or their products, to restore polluted 
environments to their original condition (Mani and Kumar, 2014; Akcil et al., 2015; Dixit et 
al., 2015). It is an environmentally friendly and cost-effective technique for heavy metal 
removal/recovery, when compared to the conventional chemical and physical techniques, which 
are often more expensive and ineffective, especially for low metal concentrations. In addition, 
these conventional methods generate significant amounts of toxic sludge.  
Microbial remediation is described as the use of microorganisms to perform the absorption, 
precipitation, oxidation, and reduction of heavy metals in the soil (Su, 2014). Microorganisms 
possess astonishing metabolic pathways which utilize various toxic compounds as a source of 
energy for growth and development, through respiration, fermentation, and cometabolism. Due 
to their characteristic degradative enzymes for a particular contaminant, they have evolved 
diverse mechanisms for maintaining homeostasis and resistance to heavy metals, in order to 
adapt to toxic metals in the ecosystem (Brar et al., 2006; Wei et al., 2014). Strategies developed 
by microorganisms for continued existence in heavy metal polluted environments, include 
8 
 
mechanisms such as bioaccumulation, biomineralization, biosorption, and biotransformation. 
These mechanisms are exploited for in situ (treatment at the site of contamination), or ex situ 
(the contaminated site can be excavated or pumped and treated away from the point of 
contamination), remediation. Owing to these abilities, they have been effectively used as 
biosorbents for heavy metal removal and recovery. The majority of heavy metals disrupt 
microbial cell membranes, but microorganisms can develop defense mechanisms that assist 
them in overcoming the toxic effect. Thus, the response of microorganisms to heavy metal 
toxicity is of importance for re-establishing polluted sites.  
This article presents insights into the use of microbial biosorbents for removing heavy metals 
from industrial waste and contaminated environments, as well as the sources and toxicity of 
these metals in the food web. 
2.2 Sources of heavy metal pollution in the environment 
Naturally occurring heavy metals are present in forms that are not readily available for uptake 
by plants. They are typically present in insoluble forms, like in mineral structures, or in 
precipitated or complex forms that are not readily available for plant uptake. Naturally 
occurring heavy metals have a great adsorption capacity in soil and are thus not readily available 
for living organisms. The bonding energy between naturally occurring heavy metals and soil is 
very high compared to that with anthropogenic sources. Examples of natural processes that 
bring about the occurrence of heavy metals in the environment are comets, erosion, volcanic 
eruptions, and the weathering of minerals. Heavy metals from anthropogenic sources typically 
have a high bioavailability due to their soluble and mobile reactive forms. These anthropogenic 
sources include alloy production, atmospheric deposition, battery production, biosolids, 
coating, explosive manufacturing, improper stacking of industrial solid waste, leather tanning, 
mining, pesticides, phosphate fertilizer, photographic materials, printing pigments, sewage 
irrigation, smelting, steel and electroplating industries, textiles, and dyes and wood preservation 
9 
 
(Fulekar et al., 2009; Dixit et al., 2015) (Table 2.1). Sources of heavy metals, concentrations in 
soil, soil properties, the degree and extent of uptake by plants, and the extent of absorption by 
animals, are the factors that influence the accumulation of metal ions in the food web (Bolan et 
al., 2014). According to D'amore et al. (2005), the geochemical cycle of heavy metals results 
in the buildup of heavy metals in the environment, which could cause risk to all life forms when 
they are above permitted levels. The routes of entry into the environment usually include the 
weathering of parent materials, the alteration of the geochemical cycle by man, soil ingestion 
(which is the primary exposure route to humans of soil-borne metals), the transfer from mines 
to other locations, and the discharge of high concentrations of metal waste by industries.  
Mining has negatively impacted the environment, causing destruction and an alteration of the 
ecosystem, including a loss of biodiversity and an accumulation of pollutants in the 
environment. Mining and ore processing are major sources of heavy metal pollution in the soil, 
and the recovery of ecosystems from mining activities could take several decades. These 
activities produced large quantities of stockpiles and dumps, which are frequently abandoned 
without treatment. Abandoned mines contaminate water bodies through chemical run-off and 
particulates that accumulate in water sources (Adler et al., 2007), hence creating a need to treat 
wastewaters contaminated with heavy metals, before discharge into the environment occurs.  
2.3 Toxicity of heavy metals to life forms 
Although some heavy metals play important roles in the physiological, biochemical, and 
metabolic processes of living organisms, functioning as co-factors for some enzymes, 
micronutrients, regulators of osmotic pressure, and stabilization of molecules, the majority of 
them have no known biological function in living organisms and are toxic when generated in 
excess (Fashola et al., 2016). The toxicity of metals is the ability of a metal to cause undesirable 
effects on organisms. This depends on the heavy metal bioavailability and the absorbed dose 
(Rasmussen et al., 2000). The threat posed by heavy metals to the health of living organisms is 
10 
 
worsened by their continuously persistent nature in the environment. Toxicity increases when 
the medium becomes acidic and nutrient-deficient, and when the soil structure is poor, 
especially in mining environments (Mukhopadhyay and Maiti, 2010). 
At acidic pH levels, heavy metals tend to form free ionic species, with more protons available 
to saturate metal binding sites. This means that at higher hydrogen ion concentrations, the 
adsorbent surface is further positively charged, thus reducing the attraction between adsorbent 
and metal cations. Therefore, the heavy metal becomes more bioavailable, thereby increasing 
its toxicity to microorganisms and plants. At basic conditions, metal ions replace protons to 
form other species, such as hydroxo-metal complexes. These complexes are soluble in some 
cases (Cd, Ni, Zn), while those of Cr and Fe are insoluble. The solubility and bioavailability of 
heavy metals can be influenced by a small change in the pH level. Variations in soil 
composition, such as the organic matter content of a soil, also affect the toxicity of heavy 
metals. In soil with relatively low organic matter content, high contamination by heavy metals 
is usually observed. Organic matter content has a strong influence on the cation exchange 
capacity, buffer capacity, as well as on the retention of heavy metals. Thus, metals present in 
organic soils contaminated with a combination of heavy metals are less mobile and less 
bioavailable to microorganisms and plants, than metals present in mineral soils (Olaniran et al., 
2013). 
11 
 
Table 2.1: Toxicity of heavy metals to life forms 
Metal Source Effects on human Effects on plants Effects on Reference 
microorganisms 
Antimony Coal combustion, Cancer, cardiovascular Decreases synthesis of some Inhibit enzyme Blais et al. (2008); 
mining, smelting, soil diseases, conjunctivitis, metabolites, growth inhibition, activities, reduced An and Kim (2009) 
erosion, volcanic dermatitis, liver diseases, nasal inhibit chlorophyll synthesis growth rate 
eruption ulceration, respiratory diseases 
Arsenic Atmospheric Brain damage, cardiovascular Damage cell membrane, inhibition Deactivation of Bissen and Frimmel (2003);  
deposition, mining, and respiratory disorder, of growth, inhibits roots extension enzymes Abdul-Wahab and Marikar (2012); 
pesticides, rock conjunctivitis, dermatitis, skin and proliferation, interferes with Finnegan and Chen (2012) 
sedimentation, smelting cancer,  critical metabolic processes, loss 
 of fertility, yield and fruit 
production, oxidative stress, 
physiological disorders 
Beryllium Coal and oil Allergic reactions, berylliosis, Inhibits seed germination Chromosomal Gordon and Bowser (2003); Blais et 
combustion, volcanic cancer, heart diseases, lung aberration, al. (2008)  
dust diseases mutation 
Cadmium Fertilizer, mining, Bone disease, coughing, Chlorosis, decrease in plant Damage nucleic Nagajyoti et al. (2010); Sebogodi 
pesticide, plastic, emphysema, headache, nutrient content, growth acid, denature and Babalola (2011); Chibuike and 
refining, welding hypertension, itai-itai, kidney inhibition, reduced seed protein, inhibit Obiora (2014); (Sankarammal et 
 diseases, lung and prostate germination cell division and al., 2014); Fashola et al. (2016) 
cancer, lymphocytosis,  transcription, 
microcytic hypochromic inhibits carbon 
anemia, testicular atrophy, and nitrogen 
vomiting mineralization 
Chromium  Dyeing, electroplating, Bronchopneumonia, chronic Chlorosis, delayed, senescence, Elongation of lag Cervantes et al. (2001); Barakat 
paints production, steel bronchitis, diarrhea, wilting, biochemical lesions, phase, growth (2011); Mohanty et al. (2012) 
fabrication, tanning, emphysema, headache, reduced biosynthesis germination, inhibition, 
textile irritation of the skin, itching of stunted growth, oxidative stress inhibition of 
respiratory tract, liver diseases, oxygen uptake 
lung cancer, nausea, renal 
failure, reproductive toxicity, 
vomiting 
12 
 
Copper Copper polishing, Abdominal pain, anemia, Chlorosis, oxidative stress, retard Disrupt cellular Salem et al. (2000); Nagajyoti et 
mining, paint, plating, diarrhea, headache, liver and growth function, inhibit al. (2010); Dixit et al. (2015); 
printing operations kidney damage, metabolic enzyme activities Fashola et al. (2016) 
disorders, nausea, vomiting 
Mercury Batteries, coal Ataxia, attention deficit, Affects antioxidative system, Decrease Wang et al. (2012); Ali et al. 
combustion, blindness, deafness, decrease affects photosynthesis, enhance population size, (2013); Fashola et al. (2016) 
geothermal activities, rate of fertility, dementia, lipid peroxidation, induced denature protein, 
mining, paint dizziness, dysphasia, genotoxic effect, inhibit plant disrupt cell 
industries, paper gastrointestinal irritation, growth, yield, nutrient uptake and membrane, 
industry, volcanic gingivitis, kidney problem, loss homeostasis, oxidative stress inhibits enzyme 
eruption, weathering of of memory, pulmonary edema, function 
rocks reduced immunity, sclerosis  
Lead Coal combustion, Anorexia, chronic Affects photosynthesis and Denatures nucleic Nagajyoti et al. (2010); Wuana and 
electroplating, nephropathy, damage to growth, chlorosis, inhibit enzyme acid and protein, Okieimen (2011); Mupa (2013); 
manufacturing of neurons, high blood pressure, activities and seed germination, inhibits enzymes Fashola et al. (2016) 
batteries, mining, paint, hyperactivity, insomnia, oxidative stress activities and  
pigments  learning deficits, reduced  transcription  
 fertility, renal system damage,   
risk factor for Alzheimer’s 
disease, shortened attention 
span 
Nickel Electroplating, non- Cardiovascular diseases, chest Decrease chlorophyll content, Disrupt cell Malik (2004); Chibuike and 
ferrous metal, paints, pain, dermatitis, dizziness, dry inhibit enzyme activities and membrane, inhibit Obiora (2014); Fashola et al. 
porcelain enameling cough and shortness of breath, growth, reduced nutrient uptake enzyme activities, (2016) 
 headache, kidney diseases, lung oxidative stress 
and nasal cancer, nausea  
Selenium Coal combustion, Dysfunction of the endocrine Alteration of protein properties, Inhibits growth Germ et al. (2007); Dixit et al. 
mining system, gastrointestinal reduction of plant biomass rate (2015) 
disturbances, impairment of  
natural killer cells activity, liver  
damage 
Silver Battery Argyria and argyrosis, Affects homeostasis, decrease Cell lysis, inhibit Prabhu and Poulose (2012); Qian 
manufacture, mining, bronchitis, cytopathological chlorophyll content, inhibits cell transduction et al. (2013)  
photographic effects in fibroblast and growth and growth   
processing, smelting keratinocytes, emphysema, 
 knotting of cartilage, mental 
13 
 
fatigue, nose, throat and chest 
irritation, rheumatism 
Thallium Cement production, Alopecia, ataxia, burning feet Inhibits enzyme activities, Damages DNA, Babula et al. (2008); Blais et al. 
combustion of fossil syndrome, coma, convulsions, reduced growth inhibits enzyme (2008) 
fuels, metal smelting, delirium, fatigue, activities and  
oil refining  gastroenteritis, hair fall, growth  
 hallucinations, headache, 
hypotension, insomnia, nausea, 
tachycardia, vomiting 
Zinc Brass manufacturing, Ataxia, depression, Affects photosynthesis, inhibits Death, decrease Chibuike and Obiora (2014); 
mining, oil refinery, gastrointestinal irritation, growth rate, reduced chlorophyll in biomass, Gumpu et al. (2015) 
plumbing hematuria, icterus, impotence, content, germination rate and inhibits growth  
 kidney and liver failure, plant biomass  
lethargy, macular  
degeneration, metal fume 
fever, prostate cancer, seizures, 
vomiting  
14 
 
Temperature also plays an important role in the adsorption of heavy metals. It has two major 
effects on the adsorption process. Increasing the temperature will also increase the rate of 
adsorbate diffusion across the external boundary layer and in the internal pores of the adsorbate 
particles, because liquid viscosity decreases as temperature increases. It also affects the 
equilibrium capacity of the adsorbate, depending on whether the process is exothermic or 
endothermic. Temperature changes affect the stability of the metal ion species initially placed 
in solution; stability of the microorganism–metal complex depends on the biosorption sites, 
microbial cell wall configuration, and ionization of chemical moieties on the cell wall. An 
increase in the sorption capacity of lead, from 0.596 to 0.728 mg/g, was obtained when the 
temperature was raised from 25 to 40 °C by olive stone (Arjoon et al., 2013). 
Metal toxicity is also shown in their ability to disrupt enzyme structures and functions by 
binding with thiol and protein groups, or by replacing co-factors in prosthetic groups of 
enzymes. Exposure to lead and mercury can cause the development of autoimmunity, which 
can result in joint diseases, such as rheumatoid arthritis, kidney diseases, circulatory and 
nervous system disorders, and the damaging of the fetal brain in humans. Exposure to lead and 
mercury in children causes reduced intelligence, impaired development, and an increased risk 
of cardiovascular disease. Cadmium is known to be carcinogenic and mutagenic, and can 
disrupt the endocrine system, damage fragile bones and lungs, and affect the regulation of 
calcium in biological systems. Chromium causes hair loss, headaches, diarrhea, nausea, and 
vomiting in humans (Table 2.1). 
Heavy metal contaminated soils limit plant habitats due to toxicity, resulting in ecological, 
evolutionary, and nutritional problems, as well as severe selection pressures (Abdul-Wahab and 
Marikar, 2012; Mani and Kumar, 2014). The toxicity of heavy metals in plants varies, 
depending on the plant species, specific metal involved, concentration of metal, chemical form 
of the metal, and soil composition and pH (Nagajyoti et al., 2010). There can be a build-up of 
15 
 
heavy metals in plant tissues that affects or inhibits nutrient uptake, homoeostasis, growth, and 
development. They disrupt metabolic functions, such as physiological and biochemical 
processes, biochemical lesions, cell organelles destruction, chlorosis, delayed germination, 
induced genotoxicity, inhibition of photosynthesis and respiration, loss of enzyme activities, 
oxidative stress, premature leaf fall, reduced biomass, reduced crop yield, senescence, stunted 
growth, wilting and can even cause the death of plants (Table 2.1). 
Heavy metal toxicity affects microbial population size, diversity, and activity, as well as their 
genetic structure. It affects the morphology, metabolism, and growth of microorganisms by 
altering the nucleic acid structure, disrupting the cell membranes, causing functional 
disturbance, inhibiting enzyme activity and oxidative phosphorylation, and causing lipid 
peroxidation, osmotic balance alteration, and protein denaturation  (Fashola et al., 2016; Xie et 
al., 2016) (Table 2.1). 
2.4 Bioremediation of heavy metals by microorganisms 
Several techniques have been used for the removal and/or recovery of heavy metals from 
polluted environments. Some established conventional procedures for heavy metal removal 
and/or recovery from solution, include adsorption processes, chemical oxidation or reduction 
reactions, chemical precipitation, electrochemical techniques, evaporative recovery, ion 
exchange, reverse osmosis, and sludge filtration (Siddiquee et al., 2015). However, these 
techniques are expensive, sometimes impracticable, and are not specific for metal -binding 
properties. Furthermore, the generation of toxic waste, the high reagent requirement, and the 
unpredictable nature of metal ion removal, highlights some of the disadvantages of these 
methods. The majority of these methods are ineffective when metal concentrations in solution 
are less than 100 mg/L (Ahluwalia and Goyal, 2007). Separation by physical and chemical 
techniques is also challenging due to the high solubility of most heavy metal salts in solution. 
Thus, there is a need to evaluate alternative techniques for a given procedure and such an 
16 
 
approach should be suitable, appropriate, and applicable to the local conditions, and must be 
able to meet the established permissible limits. 
Bioremediation is an innovative technique for the removal and recovery of heavy metal ions 
from polluted areas, and involves using living organisms to reduce and/or recover heavy metal 
pollutants into less hazardous forms, using the activities of algae, bacteria, fungi, or plants. It 
has been employed for the removal of heavy metals from contaminated wastewaters and soils. 
This method is an appealing alternative to physical and chemical techniques, and the use of 
microorganisms plays a significant role in heavy metal remediation. Similarly, the use of 
microorganisms to remediate polluted environments is sustainable and helps to restore the 
natural state of the polluted environment with long term environmental benefits and cost 
effectiveness (Dixit et al., 2015). These organisms help to detoxify hazardous components in 
the environment. The process can function naturally or can be improved through the addition 
of electron acceptors, nutrients, or other factors. 
Detoxification can occur through the valence transformation mechanism. This is particularly 
applicable in the case of metals whose different valence states vary in toxicity. In mercury-
resistant bacteria, organomercurial lyase converts methyl mercury to Hg(II), which is one 
hundred-fold less toxic than methyl mercury (Wu et al., 2010a). The reduction of Cr(VI) to 
Cr(III) is widely studied, with Cr(III) having less mobility and toxicity. Other detoxification 
mechanisms of heavy metals are accomplished through metal binding, vacuole 
compartmentalization, and volatilization. Metal binding involves chelators, such as 
metallothein, glutathione-derived-peptides called phytochelatin, and metal binding peptides. 
These chelators bind to heavy metals and facilitate microbial absorption and the transportation 
of metal ions. Volatilization mechanisms involve turning metal ions into a volatile state. This 
is only possible with Se and Hg, which have volatile states. Mercury-resistant bacteria utilize 
the MerA enzyme to reduce Hg(II) to the volatile form Hg(0) (Wu et al., 2010a). The reduction 
17 
 
of Se(V) to elemental Se(0) has been employed to remediate contaminated waters and soils. 
The metabolic processes of these organisms help to transform pollutants in the environment 
(Siddiquee et al., 2015). 
Biosorption, bioaccumulation, biotransformation, and biomineralization are the techniques 
employed by microorganisms for their continued existence in metal polluted environment. 
These strategies have been exploited for remediation procedures (Gadd, 2000; Lin and Lin, 
2005). Heavy metal removal can be carried out by living organisms or dead biological materials. 
Large scale feasibility applications of biosorptive processes have shown that dead biomass is 
more applicable than the bioaccumulation approach, which involves the use of living organisms 
and thus requires nutrient supply and a complicated bioreactor system. Also, the toxicity of 
pollutants, as well as other unfavorable environmental conditions, can contribute to the inability 
to maintain a healthy microbial population. However, many characteristic attributes of living 
microorganisms have not been exploited in large scale applications (Park et al., 2010). The 
choice organism must develop resistance towards metal ions as it comes into contact with the 
heavy metal pollutant to achieve the goal of remediation. The organism of choice may be native 
to the polluted environment, or isolated from another environment and brought to the 
contaminated site (Sharma et al., 2000). 
Advances in the understanding of metabolic pathways of microorganisms are responsible for 
metal sequestration, improving microbial survival rates, and their stability. This has led to the 
manipulation of metal adsorption (Gavrilescu, 2004). Adsorption is the physical adherence of 
ions and molecules onto the surface of another molecule. The material accumulated at the 
interface is the adsorbate and the solid surface is the adsorbent. If adsorption occurs and results 
in the formation of a stable molecular phase at the interface, this can be described as a surface 
complex. Most solids, including microorganisms, possess functional groups like –SH, –OH, 
and –COOH on their surfaces, that help in the adsorption of metals (Gadd, 2009). It has been 
18 
 
reported that a microbial cell develops resistance to heavy metals through the excretion of metal 
chelating substances, or through a problem in a particular transport system, which results in a 
reduced cell accumulation of the metal ion. Another resistance mechanism includes the binding 
of a metal ion to intracellular molecules, such as metallothionein, vacuole, or mitochondria, 
which results in changes in the distribution of metal ion (Siddiquee et al., 2015). 
Microorganisms interact with metal ions through cell wall associated metals, intracellular 
accumulation, metalsiderophore, extracellular polymeric reactions with transformation, 
extracellular mobilization or immobilization of metal ions, and volatilization of metals 
(Siddiquee et al., 2015). 
Various factors influence the microbial remediation of metals. They include the bioavailability 
of the metal to the microbe, concentration of pollutants, electron acceptors, moisture content, 
nutrients, osmotic pressure, oxygen, pH, redox potential, soil structure, temperature, and water 
activity. The bioavailability of each metal in soil is influenced by factors such as the buffering 
capacity, cation exchange capacity, clay minerals content, metal oxide, and organic matter (Brar 
et al., 2006; Tak et al., 2013; Mani and Kumar, 2014). In general, remediation of heavy metal 
is achieved through the removal of the metal ion from substratum to reduce the risk posed by 
exposure to such heavy metals. 
The environmental conditions, prehistory, and pretreatment required for the removal of heavy 
metals need to be established in order to select the most appropriate biosorbent for a speci fic 
situation, from the extremely large pool of organisms that are readily available. Sometimes, the 
interest may be to recover a specific metal regardless of equilibrium concentration attained, or 
on the other hand, the interest may be to curtail levels of pollution in the effluent, so that they 
fall within the acceptable containment limit. Also, priority may be given to the recovery of a 
large quantity of metal, while also achieving low equilibrium concentrations. Whatever the 
case, the biosorbent used should have a high sorption capacity (Romera et al., 2007). 
19 
 
2.5 Mechanisms of heavy metal uptake by microorganisms 
The cellular structure of a microorganism can trap heavy metal ions and subsequently sorb them 
onto the binding sites of the cell wall  (Malik, 2004). This process is called biosorption or 
passive uptake, and is independent of the metabolic cycle. The amount of metal sorbed depends 
on the kinetic equilibrium and composition of the metal at the cellular surface. The mechanism 
involves several processes, including electrostatic interaction, ion exchange, precipitation, the 
redox process, and surface complexation (Yang et al., 2015) (Figure 1). The process is fast and 
can reach equilibrium within a few minutes. Biosorption can be carried out by fragments of 
cells and tissues, or by dead biomass or living cells as passive uptake via surface complexation 
onto the cell wall and other outer layers (Fomina and Gadd, 2014). The other method is a 
process in which the heavy metal ions pass across the cell membrane into the cytoplasm, 
through the cell metabolic cycle. This is referred to as bioaccumulation or active uptake. 
Bioaccumulation is a process of a living cell that is dependent on a variety of physical, chemical, 
and biological mechanisms (Figure 1). These factors include intracellular and extracellular 
processes, where biosorption plays a limited and ill-defined role (Fomina and Gadd, 2014). The 
organism that will accumulate heavy metals should have a tolerance to one or more metals at 
higher concentrations, and must exhibit enhanced transformational abilities, changing toxic 
chemicals to harmless forms that allows the organism to lessen the toxic effect of the metal, 
and at the same time, keep the metal contained (Mosa et al., 2016). 
20 
 
 
Figure 2.1: Mechanisms of heavy metal uptake by microorganisms 
Metal uptake mechanisms by various biosorbents depend on the cellular surface of the 
microbes, as well as the exchange of metal ions and complex formations with the metal ions on 
the reactive chemical sites of the cell surface. These have been extensively studied with respect 
to various biosorption isotherms, derived from sorption experiments and the effect of various 
factors, such as pH, biomass pretreatment, and the biomass of the organisms. Precipitation of 
the excess metal ions, through nucleation reactions, then occurs at the cell surface. All 
microorganisms have a negative charge on their cell surface due to the presence of anionic 
structures, which enable them to bind to metal cations. The negatively charged groups that are 
involved in metal adsorption are the alcohol, amine, carboxyl, ester, hydroxyl, sulfhydryl, 
phosphoryl, sulfonate, thioether, and thiol groups (Gavrilescu, 2004). 
An analysis of the cell wall components, which vary among the different microorganisms, helps 
in assessing metal uptake by different microorganisms. The peptidoglycan layer in Gram-
positive bacteria, which contains alanine, glutamic acid, meso-di-aminopimelic acid, polymer 
of glycerol and teichoic acid, and that of the Gram-negative bacteria, which contains enzymes, 
glycoproteins, lipopolysaccharides, lipoproteins, and phospholipids, are the active sites 
21 
 
involved in metal binding processes (Lesmana et al., 2009; Fomina and Gadd, 2014; Gupta et 
al., 2015). Metals and metalloids are attached to these ligands on cell surfaces, which displace 
essential metals from their normal binding sites. Once the metal and metalloid are bound, 
microbial cells can transform them from one oxidation state to another, thus reducing their 
toxicity (Chaturvedi et al., 2015). Gavrilescu (2004) reported that the cell walls of bacteria are 
polyelectrolyte, which interacts with metal ions to maintain electro-neutrality by mechanisms 
of covalent bonding, extracellular precipitations, redox interactions, and van der Waals forces. 
The rigid cell wall of fungi is made up of chitin, inorganic ions, lipids, nitrogen-containing 
polysaccharide, polyphosphates, and proteins. They can tolerate and detoxify metal ions by 
active uptake, extracellular and intracellular precipitation, and valence transformation, with 
many absorbing heavy metals into their mycelium and spores. The surface of their cell wall acts 
as a ligand for binding metal ions, resulting in the removal of metals (Gupta et al., 2015). The 
first barrier includes excreted substances like organic acids or/and proteins with an ability to 
immobilize heavy metals. The second barrier includes the (unspecific) binding of heavy metals 
by the cell wall and melanins located in the cell wall. Toxic heavy metals that could not be 
detained outside the cell must be detoxified inside the cell (Mishra and Malik, 2013). 
The cell wall of all classes of algae is composed of cellulose with sulfonated polysaccharides 
present in the cell wall of brown and red algae. Other binding sites in algae are polysaccharides 
such as alginic acid, glycan, mannan, proteins, and xylans. The cell wall of cyanobacteria is 
composed of peptidoglycan, and some species also produce sheaths and extracellular polymeric 
substances, which are used for sorption. Characteristics of the biomass, chemical and physical 
properties of the metal of interest, and pH of the solution, influence the sorption capacity of 
algae (Lesmana et al., 2009). 
Non-essential metal uptake usually consists of transporters which are committed to the 
acquisition of vital organic and inorganic ions. These transporters assist in either the co-
22 
 
transport of these metals in complexes with low-molecular-mass ligands, or in the direct uptake 
of non-essential metals (Lemire et al., 2013). Microorganisms can also secrete many kinds of 
metal-binding metabolites, produce extracellular polymeric substances, which are made up of 
polysaccharide, capsules, slimes and sheaths, and biofilms, depending on the make-up of the 
polysaccharide and associated components. Biofilms bind substantial quantities of heavy metals 
under pristine conditions and serve as a medium for the precipitation of insoluble mineral 
phases (Fomina and Gadd, 2014). 
2.6 Biosorption capacity of various microbial biosorbents 
Various microbial biomasses have different biosorptive abilities, which also vary considerably 
within each group. However, the biosorption capacity of each biosorbent depends on its 
prehistory and pretreatment, as well as the experimental conditions. The biosorbent should be 
cheap, effective, and easy to grow and harvest. The organism should also lend itself to alteration 
of the bioreactor configuration, as well as physical and chemical conditions to enhance 
biosorption (Fomina and Gadd, 2014). 
Bacteria have been used as biosorbents owing to their ubiquity, size, ability to grow under 
controlled conditions, and resilience to an extensive range of environmental conditions (Wang 
and Chen, 2009; Srivastava et al., 2015). Various heavy metals have been tested on bacteria 
species such as Pseudomonas, Enterobacter, Bacillus, and Micrococcus species (Table 2.2). 
Their excellent sorption capacity is due to their high surface-to-volume ratios and their 
numerous potential active chemosorption sites, such as the teichoic acid on the cell wall (Mosa 
et al., 2016).  
Sinha et al. (2012) designed a laboratory scale sequential bioreactor for the removal of mercury 
from synthetic effluent (10 mg/L of Hg). The efficiency of mercury removal by Bacillus 
cereus (immobilized on alginate) was 104.1 mg/g on the third day. Micrococcus luteus was 
used to remove a large amount of Pb from a synthetic medium. Under optimal conditions, the 
23 
 
removal capacity was 1965 mg/g (Puyen et al., 2012). Kim et al. (2015) also designed a batch 
system using zeolite-immobilized Desulfovibrio desulfuricans for Cu, Cr, and Ni removal from 
contaminated seawater (Table 2.2). The removal efficiency was 98.2, 99.8, and 90.1 mg/g, 
respectively, after about seven days. 
Yeasts and molds are easy to cultivate, can be genetically and morphologically manipulated, 
and can produce a high biomass yield. They are widely used in a variety of large-scale industrial 
fermentation processes, producing ferrichrome, gallic and kojic acid, and enzymes like lipases, 
glucose isomerase, pectinases, amylases, and glucanases  (Wang and Chen, 2009). They are 
extensively used as biosorbents for the removal of toxic metals from polluted wastewaters, with 
excellent abilities for metal uptake and recovery (Dursun et al., 2003; Akar et al., 2005; Fu et 
al., 2012). They have developed a complex defense system to neutralize heavy metal toxicity. 
Akar et al. (2005) evaluated the Pb removal potential of Botrytis cinerea in a batch reactor. 
Lead (II) ions were found to be extracellularly accumulated on the cell surface and the rate of 
accumulation was affected by the pH, contact time, and initial metal concentration. The sorption 
capacity of Pb by B. cinerea was found to be 107.1 mg/g at an initial Pb concentration of 350 
mg/L, after 180 min. Fu et al. (2012) recently reported the biosorption of Cu(II) ions by mycelial 
pellets of Rhizopus oryzae. The effects of pellet diameter, solution pH, contact time, initial 
metal concentration, and temperature were evaluated. Metal removal efficiency of Cu(II) ions 
using mycelial pellets was observed to be 34 mg/g after two hours (Table 2.2). Sharma and 
Adholeya (2011) reported that Paecilomyces lilacinus fungi accumulate only 24% of chromium 
from spent chrome effluent supplemented with cane sugar, while 100% removal was observed 
from a synthetic medium. Srivastava and Thakur (2006) also reported the efficiency of 
chromium removal by Aspergillus sp. from tannery effluent. Eighty-five percent of the 
chromium was removed at pH 6 in a bioreactor system from the synthetic medium, compared 
to a 65% removal from the tannery effluent. This is because of the presence of organic pollutants 
that inhibit the growth of the fungal species. 
24 
 
Algae have also been used as biosorbents for heavy metal removal. Brown algae have gained 
prominence as good biosorbents because of their high sorption capacity. Red, green, and brown 
algae have been used for adsorption studies and are all readily available in marine and fresh 
water environments (Srivastava et al., 2015). Algae are autotrophic, thus require a low number 
of nutrients and produce a large biomass compared to other microbial biosorbents. They have a 
high sorption capacity and are readily available in large quantities (Abbas et al., 2014). The 
sorption capacity of six different algae were evaluated for the recovery of Cd, Cu, Ni, Pb, and 
Zn from an aqueous solution by Romera et al. (2007). The maximum sorption of Cd (32.3 
mg/g), Pb (63.7 mg/g), and Zn (21.6 mg/g), were recorded for Asparagopsis armata, while the 
maximum Cd (21.8 mg/g), Pb (63.3 mg/g), and Zn (23.8 mg/g) uptake, occurred in Codium 
vermilara (Table 2.2). Algae are effective biosorbents for the removal of Sb(III) from aqueous 
solutions (Ungureanu et al., 2015). The maximum adsorption capacity of Sb(III) by the 
algae Sargassum muticum was 5.5 mg/g, at pH 5. A slight effect of pH was observed in the 
removal efficiency of Sb by S. muticum. 
25 
 
Table 2.2: Metal biosorption by different microbial biosorbents 
 Microbial biosorbent Metal pH Temperature Time (h) Initial metal ion Sorption capacity Reference 
(◦C) conc. (mg/L) (mg/g) 
Bacteria Bacillus cereus Hg 7 30 72 30 140.1 Sinha et al. (2012) 
(Immobilize on alginate) 
 B. laterosporus Cd 7 25 2 1000 159.5 Zouboulis et al. 
 Cr (VI) 2.5    72.6 (2004) 
 B. licheniformis Cd 7 25 2 1000 142.7 
 Cr (VI) 2.5    62 
 Desulfovibrio desulfuricans Cu 7.8 37 168 100 98.2 Kim et al. (2015) 
 (immobilize on zeolite) Ni    100 90.1 
 Cr(VI)    100 99.8 
 Enterobacter cloacae  Pb - 30 48 7.2 2.3 Kang et al. (2015) 
 Kocuria rhizophila Cd 8 35 1 150 9.07 Haq et al. (2015) 
 Cr 4   150 14.4  
 Micrococcus luteus Cu 7 27 12 80.24 408 Puyen et al. (2012) 
 Pb    272.39 1965 
 Pseudomonas aeruginosa Co 5.2 25 10 58.93 8.92 Kang et al. (2005) 
 Ni 5.5   58.69 8.26 
 Cr(III) 3.4   52 6.42 
26 
 
 P. jessenii  Ni - 25 6 275 1.36 Rajkumar and 
Cu    300 10.22 Freitas (2008) 
Zn    400 4.39 
 Pseudomonas sp. Ni  25 6 275 2.79 
Cu    300 5.52 
Zn    275 3.66 
 Sulphate-reducing bacteria As (III) 6.9 - 24 1 0.07 Teclu et al. (2008) 
As(V)     0.11  
Fungi Aspergillus niger Cu 5 30 1 100 15.6 Dursun et al. (2003) 
 Pb 4.5   100 34.4 
 Cr (VI) 3.5   50 6.6 
 Botrytis cinerea Pb 4 25 1.5 350 107.1 Akar et al. (2005) 
 Phanerochaete Pb 6 20 1 100 88.16 Iqbal and Edyvean 
 chrysosporium (immobilized Cu    100 68.73 (2004) 
 on loofa sponge) Zn     100 39.62 
 Pleurotus platypus Ag 6 20 2 200 46.7 Das et al. (2010) 
 Rhizopus oryzae Cu 4 35 2 100 34 Fu et al. (2012) 
Algae Asparagopsis armata Cd 6 - 2 135 32.3 Romera et al. (2007) 
 Ni 6   141 17.7 
 Zn 6   182 21.6 
27 
 
 Cu 5   134.4 21.3 
 Pb 4   124 63.7 
 Codium vermilara Cd 6 - 2 135 21.8 Romera et al. (2007) 
 Ni 6   147 13.2 
 Zn 6   182 23.8 
 Cu 5   140 16.9 
 Pb 5   83 63.3 
 Cystoseira barbata Cd 4 20 1 117.4 37.6 Yalçın et al. (2012) 
Ni    224.8 78.7 
Pb    414 196.7 
 Lessonia nigrescens Ar(V) 2.5 20 5 200 45.2 Hansen et al. (2006) 
 Sargassum muticum Sb 5 23 4 10 5.5 Ungureanu et al. 
(2015) 
 Spirogyra sp. Pb 5 25 1.6 200 140 Gupta and Rastogi 
(2008) 
28 
 
Conclusions 
This review revealed the contributions of the various biosorbents which are potentially effective 
and readily available for heavy metal removal. These biosorbents present attractive 
opportunities as low cost means of protecting the environment from pollution. Biosorbent 
selection and implementation for industrial wastewater management and soil remediation 
requires more effort, as most reported adsorption studies have been confined to laboratory 
investigations in a batch system. A sustainable approach needs to be developed in order to select 
the most appropriate biosorbent, operating conditions, and efficient mechanism of heavy metal 
removal in industrial effluent, to sufficiently address the major challenges involved. Also, in 
order to develop a reliable biosorption process, more research is needed in biosorbent 
characterization, in terms of surface morphology and area, zeta potential, functional groups, 
and particle size, as these are important in biosorption experiments, influenced by the 
pretreatment of the biosorbents. Equally, growing microbial biomass with the potential for 
metal uptake needs further investigation, with the aim of exploring the metabolic potential of 
these growing biomasses and their application in industrial wastewater management. 
 
 
 
 
 
 
 
 
29 
 
CHAPTER THREE 
METAL(LOID) BIOREMEDIATION: STRATEGIES EMPLOYED BY MICROBIAL 
POLYMERS 
Abstract 
Environmental pollution arising from metal(loid)s is a result of industrialization, and has led to 
serious health issues. Conventional methods of metal(loid) removal often results in generation of 
secondary waste which is toxic to the environment. Bioremediation in combination with 
physicochemical techniques offers an excellent and effective means of removal. The use of 
secondary metabolites and extracellular polymers produced by microorganisms is an effective 
procedure employed in metal(loid) sequestration and reduction in toxicity of contaminated 
environments. These biopolymers have different chemical structures and have shown varied 
selectivity to different metal(loid)s. This review discusses various microbial polymers, their 
mechanism of metal(loid) removal and their potential application in remediation of contaminated 
environment. 
Keywords: biodegradation; biofilms; bioflocculant; biopolymers; biosurfactant; secondary     
metabolites; pollution. 
3.1 Introduction 
Environmental pollution due to swift growth in chemical industries, battery production, 
electroplating and steel industries, fertilizer and pesticide production, and mining activities is one 
of the most important challenges that are of human health concern. Pollution caused by 
metal(loid)s poses a health risk to living organisms. The accumulation of metal(loid)s in the 
environment as a result of natural and anthropogenic activities has significantly altered the nature 
of the environment (Sarubbo et al., 2015; Luna et al., 2016). In the midst of diverse metal ions, 
cadmium, chromium, lead and mercury in wastewater and soil are hazardous to the environment 
30 
 
and have been linked to certain ailments such as birth defects, damage of internal organs such as 
kidney and liver, certain learning disabilities, and a host of other diseases. Due to their 
carcinogenic, genotoxic, mutagenic or toxic effects, their presence in the environment poses health 
risks and harmful effects on living organisms (Das et al., 2009; Sriram et al., 2011; Fashola et al., 
2016; Ayangbenro and Babalola, 2017). 
Metal(loid)s are natural components of the earth’s crust and are also produced from anthropogenic 
sources. Some metal(loid)s act as essential micro nutrients for living organisms, but at higher 
concentrations they can cause severe poisoning. Metal(loid) ions are non-degradable and thus 
persist in the ecosystem. They become bioavailable for removal as there is decrease in the surface 
tension of water at polluted sites (Hashim et al., 2011). 
Metal(loid) removal from contaminated environments usually involves three technologies namely: 
(1) Chemical, (2) physicochemical, and (3) biological techniques (Hashim et al., 2011; Sarubbo et 
al., 2015). The techniques can overlap in some cases as a result of multidisciplinary research and 
paradigms shift in technology. However, there can be limitations to a particular technique as the 
operation condition might not be effective in a particular environment (Sarubbo et al., 2015). 
Conventional techniques for metal(loid) removal involve treatment of polluted soil with 
surfactants, organic and inorganic acids, water, reverse osmosis, adsorption processes, and 
metal(loid) chelating agents. However, these physicochemical techniques are expensive and do 
not guarantee effective removal of metal(loid) ions from the soil especially when metal(loid) 
concentrations are less than 100 mg/L. Other disadvantages include non-specificity of the 
techniques for metal(loid) binding, high energy demand, land space requirements, and sometimes 
the methods are impracticable and often exchange one problem for  another (Das et al., 2009; 
Voica et al., 2016). Effective sequestration of metal(loid) from polluted environments demands a 
good metal(loid) complexing agent. Such a complexing agent must possess characteristics such as 
31 
 
environmental stability, high solubility and good metal(loid) complexation potentials (Das et al., 
2009). 
Bioremediation is a cost-effective alternative for metal(loid) decontamination of polluted 
environments. It offers a long term recovery and/or removal of metal(loid) ions from contaminated 
environments and can be used in combination with other techniques (Sarubbo et al., 2015). Various 
strategies have been developed by microbes for their continued existence in metal(loid) polluted 
environments. They adopt diverse detoxifying mechanisms that include bioaccumulation, 
biomineralization, biosorption and biotransformation for their survival (Lin and Lin, 2005) (Figure 
1). These mechanisms are often triggered and activated when the concentration of metal(loid)s in 
their environment increases. They secrete many kinds of metal(loid)-binding metabolites, ranging 
from simple organic acids, alcohols to extracellular polymeric substances (EPS), slimes and 
sheaths, humic and fulvic acids, capsules, and biofilms which help in metal(loid) uptake from 
solution (Wu et al., 2010a; Fomina and Gadd, 2014). Metal sulfides and oxides are also trapped 
and absorbed by these extracellular polymers and proteins produced by microorganisms (Wu et 
al., 2010a). 
Microbial products have generated increasing attention lately due to their biodegradable nature, 
diversity, and low toxicity. This makes them superior to their chemical counterparts. These 
metabolites provide defensive mechanisms, increase metal(loid) bioavailability and facilitate 
reproductive processes (Valls and De Lorenzo, 2002; Vaishnav and Demain, 2011). The metabolic 
pathways of these microorganisms have been exploited for degradation and removal of pollutants 
in the environment. In contrast to organic pollutants, metal(loid) contamination presents a difficult 
challenge; metal(loid) ions are not biodegradable but can be converted to base metal(loid), 
complexed with an organic ligand, methylated, precipitated or volatilized. An approach to 
metal(loid) removal involves stable complex formation between metal(loid) ion and microbial 
32 
 
polymers. This is as a consequence of the electrostatic interaction between metal(loid) ions and 
negatively charged microbial polymers (Gutnick and Bach, 2000). 
 
Figure 3.1: Mechanism of metal(loid) sequestration 
From the beginning of cellular life, microorganisms are periodically exposed to metal(loid)s. This 
is evident in many of their enzymes that are composed of different essential divalent and transition 
metals at their active sites. These essential metal(loid)s are involved in maintaining protein 
structure and catalyzing key enzymatic reactions. They are thus needed in small quantity for 
cellular metabolism. The uptake of these metal(loid)s is regulated via the homeostatic mechanisms 
that ensure sufficient, but not excessive intake (Valls and De Lorenzo, 2002). However, other 
metal(loid)s have no known biological function, but instead, they damage, inactivate or block 
enzyme functions because of their affinity for the sulfhydryl groups of proteins. Resistance to 
metal(loid)s most likely evolved shortly after life started, in an early metal(loid)-polluted world 
(Valls and De Lorenzo, 2002). The synthesis of microbial polymers is driven by the need to survive 
in hostile environments and to assist in neutralization of toxic elements by sequestration (Van 
Hamme et al., 2006). 
33 
 
Microbial processes that facilitate the detoxification and metal(loid) mobility using resistant 
microbial strains can be used for removal of metal(loid)s from soil, effluents and wastewaters. The 
use of microbial polymers is evolving as a promising technique to improve and enhance the 
effectiveness of metal(loid) removal. These compounds are often economic, versatile, 
environmentally friendly and can reduce the concentration and toxicity of pollutants.  
In addition, these biopolymers have the capability to survive a wide range of temperature and pH 
and can affect interfaces. pH affects the functional groups involved in metal(loid) binding; 
therefore providing varying binding sites on the surface of the organisms at different pH values. 
At high pH, there are less H+ to compete with metal(loid)s for binding sites on the biopolymers. 
At neutral pH, they have functional groups that are negatively charged and are capable of forming 
stable organo-metallic complexes with metal cations by means of diverse interactions (Li and Yu, 
2014). A high temperature favors biopolymer-metal adsorption (Kiran and Kaushik, 2008). As the 
temperature increases, the affinity for the binding sites increases which favors direct contact 
between the biopolymer functional groups and metal ions (Kiran and Kaushik, 2008). However a 
very high temperature can cause structural damage of the biopolymer and some functional groups, 
which can reduce the efficiency of the polymer (Li and Yu, 2014). The metal binding ability of 
these biopolymers depends on the nature of biomass, specificity, affinity, pH, temperature, 
hydrodynamics and functional groups present (Pal and Paul, 2008; Li and Yu, 2014). 
The nature of the biopolymer varies depending on the origin, extraction procedure and adsorption 
characteristics (Li and Yu, 2014). Guibaud et al. (2009) made a comparison of the origin of some 
biopolymers. Their results showed that biopolymers from activated sludge have higher affinity for 
Cd2+ and Pb2+ than polymers from pure strains of bacteria. Polymers from anaerobic granular 
sludge were found to have lower affinity and proton exchange capability (Guibaud et al., 2012). 
The functional groups present on these biopolymers also vary in their affinity for metal(loid) ions 
(Li and Yu, 2014). The carboxyl and the phosphate groups are the main functional groups that are 
34 
 
involved in binding of metal ions and are effective at neutral pH. Other functional groups include 
the amides, hydroxyl groups and nucleic acids (Zhang et al., 2010). These groups showed weaker 
binding ability compared to carboxyl and phosphate groups due to their high pKa values (Guibaud 
et al., 2003).  
Microbial polymers are usually the first line of defense against metal(loid)s, and are important in 
protecting the interior of the microbial cell (Li and Yu, 2014). The use of isolated biopolymers is 
desired for easy availability in metal(loid) removal and circumvention of pathogenicity concerns 
of some producing organisms. This review article discusses the activities of useful microbial 
polymers in metal(loid)-binding, their mechanism of action and applications in bioremediation 
processes. 
3.2 Biosurfactant as a metal(loid)-complexing biopolymer 
One of such microbial polymers is biosurfactant, which has been reported to be an effective 
metal(loid) complexing agent. They are surface-active metabolites with amphiphilic configuration. 
This amphiphilic metabolite has both hydrophilic and hydrophobic moieties, causing them to 
aggregate or form micelles at interfaces between fluids of different polarities, hence why it 
decreases interfacial surface tension between such fluids (Mazaheri and Tabatabaee, 2010). 
Typical hydrophilic groups of biosurfactants are amino acids, anions or cations, carbohydrates 
(mono-, di-, or polysaccharides) and peptides. The hydrophobic groups are the fatty acids which 
include branched, saturated, unsaturated, or hydroxylated fatty acids (Singh and Cameotra, 2004; 
Kitamoto et al., 2009).  
Surfactants reduce the interfacial and surface tension, and increase the bioavailability, mobility, 
solubility and subsequent degradation of pollutants. There has been growing interest in this 
biopolymer due to several advantages it has over chemical surfactants. These characteristics 
include: (a) One or more chiral centers and functional groups; (b) complex structural diversity; (c) 
enhanced activity at extreme conditions of pH, salinity and temperature; (d) excellent ability to 
35 
 
form molecular assembly and liquid crystals; (e) gradual adsorption and continuing activity; (f) 
advanced surface activity and lower critical micelle concentration; (g) superior biodegradability 
and lower toxicity; (h) versatile biological activity; and (i) production from renewable feedstocks 
(Kitamoto et al., 2009; Sriram et al., 2011; Dadrasnia and Ismail, 2015; Bezza and Chirwa, 2016; 
Henkel et al., 2017). 
Biosurfactants are extracted constituents of the bacterial or yeast cell membrane with structural 
variability. These secondary metabolites include high and low molecular-weight polymers that 
have the ability to effectively reduce interfacial and surface tension, and act as highly efficient 
emulsifiers. This stimulates the bioavailability of hydrophobic molecules by dissolving and 
emulsifying these hydrophobic compounds or by altering the cell’s surface properties. Their 
production is considered as an important strategy to overcome pollutant-related stress by 
microorganisms (Bezza and Chirwa, 2016). They speed up the rate of mass transfer of hydrophobic 
compounds by making them readily available to microbes and thus acting as mediators. 
Furthermore, they induce changes in cell membrane properties which increases microbial 
adherence. This process is important when two immiscible fluids are present and direct substrate 
uptake is plausible (Dadrasnia and Ismail, 2015). Consequently, they help to surmount the 
diffusion-related mass transfer limitations (Bezza and Chirwa, 2016). 
Biosurfactants are produced by different microorganisms and have unique metal(loid)binding 
capacities and selectivity (Arjoon et al., 2013). The selectivity in metal(loid)—binding is species 
specific (Miller, 1995) and it is important to remove the target metal(loid) as other metal(loid)s in 
solution compete with the target metal(loid). The selectivity depends on the ionic radius and the 
stability of the surfactant-metal(loid) complex (Yuan et al., 2008). It has been shown that cadmium 
and lead have higher affinities for rhamnolipids from P. aeruginosa than any other soil components 
to which they are bound. Rhamnolipids have special affinity for non-essential and essential 
36 
 
metallic cations, potentially leading to greater toxicity when in excess in exposure pathways, than 
with macronutrient cations (Torrens et al., 1998; Singh and Cameotra, 2004).  
Biosurfactants with anionic, cationic, or neutral charge are employed as washing agents. They can 
solubilize, disperse and desorb metal(loid)s from excavated sediments or soils in a washing unit 
(Dahrazma and Mulligan, 2007). Anionic biosurfactants have been proposed to bind metal(loid) 
ions by complexation or electrostatic techniques. These complexations result in an increase in the 
apparent metal(loid) solubility (Rufino et al., 2012). Therefore, the bioavailability of metal(loid)s 
is affected through their reduction by this biopolymer, which leads to the formation of less soluble 
metal salts including phosphate and sulfide precipitates (Mosa et al., 2016). By adsorbing onto the 
surface of sediments, biosurfactants form complexes with metal(loid) ions, and thus detaching 
metal(loid) ions from the sediment into the pore water. Therefore, they are able to remove 
metal(loid)s adsorbed to the sediment in association with surfactant micelles (Mazaheri and 
Tabatabaee, 2010; Franzetti et al., 2014).  
Biosurfactants are produced by a variety of actinobacteria, bacteria and fungi with different 
classes. The classes include glycolipids, fatty acids, lipopeptides, lipoproteins, phospholipids, 
particulate and polymeric surfactants (Singh and Cameotra, 2004; Sriram et al., 2011). They have 
achieved significant importance with their activities and structural characteristics. Several 
bacterial genera such as Arthrobacter, Bacillus and Pseudomonas have been reported to produce 
surfactants with metal(loid) binding characteristics (Table 3.1). 
3.2.1 Mechanism of biosurfactant removal of metal(loid)s 
Most microorganisms utilize the reductive pathway as an alternative to the oxidative pathway 
during transformation of metal(loid)s. This is due to the less toxic nature of the reduced form of 
metal(loid)s. The processes used include accumulation, complexation, efflux system and reduction 
mechanisms to tolerate metal(loid) exposure. Resistance to metal(loid) ions can also be conferred 
37 
 
by gene clustering on plasmid when exposed to stress conditions (Gnanamani et al., 2010). 
Biosurfactants act as a link between the fluid interfaces, due to their amphiphilic nature, and thus 
reduce the surface tension. Decrease in the surface tension of water increases mobilization of 
metal(loid) from unsaturated soils, consequently making removal of metal(loid)s possible. 
Biosurfactants have been proposed to utilize two fundamental principles for metal(loid) removal 
from solution. Metal sorption mediated by biosurfactant is promoted through complexation of the 
free metal ion. This process employs Le Chatelier’s principle and the solution phase activity of 
metal(loid) ion diminishes to promote desorption of metal(loid). Another mechanism is the direct 
contact with metal(loid) under reduced interfacial tension at solid-solution interface. This permits 
the biosurfactant to accumulate at solid-solution interface which aids metal(loid) binding (Singh 
and Cameotra, 2004; Chakraborty and Das, 2014).  
Soils contaminated with metal(loid)s are treated by two basic techniques. The first technique 
involves metal(loid) immobilization on a strongly bound solid matrix to reduce migration. 
Nevertheless, the process does not permanently solve the problem as soil reuse is limited and also 
requires long-term monitoring. As a result, solidification is often limited to highly toxic or 
radioactive wastes. The other technique promotes mobility and migration of metal(loid) into liquid 
state by desorption and solubilization. This procedure offers a permanent solution to the pollution 
challenge. In addition, the treated soil can be recycled, which also improves further land-use 
alternative (Hong et al., 2002).  
The mechanism of metal(loid) extraction by biosurfactant includes ion exchange, counter binding 
and precipitation. Ion exchange mechanism involves negatively charged anionic biosurfactants 
which bind with positively charged metal cations to form a bond that is stronger than the bond 
formed between metal ion and soil. The cell surface is conferred with a negative charge by 
biosurfactant. This process supports the electrostatic interaction with metal ion. The polar head 
moieties of surfactant micelles bind with the metal ions making them water soluble. The surfactant 
38 
 
micelles then move the metal ions into solution, thus making it easier for metal recovery by 
flushing, washing, or pumping (Mulligan, 2005; Aşçı et al., 2010). The metal(loid)-biosurfactant 
complex forms a strong bond and flushing with water removes the complex from soil matrix 
(Mulligan, 2005). Cationic biosurfactants replace the same charged ions by competing with 
negatively charged surfaces (Franzetti et al., 2014; Sarubbo et al., 2015). The metal(loid) ion can 
then be recovered from the complex and used for different applications. The carboxyl group of the 
biosurfactant mono-rhamnolipid produced by P. aeruginosa has preference for metals such as 
Cd2+, Pb2+ and Zn2+ (Juwarkar et al., 2007; Franzetti et al., 2014). The complex formed between 
metal and biosurfactant is non-toxic to bacteria. In the precipitation process, biosurfactants are 
added to soil during washing. As a result of the foaming characteristics of biosurfactants, the 
biosurfactant-metal complex can be removed from soil by the addition of air to cause foaming. 
The biosurfactant can then be recovered and recycled through precipitation by reducing the pH 
(Mulligan, 2005). 
Surfactant concentration at which aggregation of ions or molecules (micelles) first begins to form 
is called the critical micelle concentration (CMC) where the surfactants assemble into an organized 
molecular arrangement (Chakraborty and Das, 2014). Above the CMC, the surfactant molecules 
assemble together, forming aggregates and a range of micellar and vesicular configuration which 
depend on the pH of the solution (Aşçı et al., 2010). The surfactants exist as single molecules or 
monomers at low concentration in aqueous solutions. The CMC depends on the composition and 
structure of the surfactant, temperature, ionic strength, and the presence and nature of organic 
compounds in the solutions. At the CMC of surfactant solutions, there is a radical change that 
occurs in many physico-chemical properties of the surfactant (Bustamante et al., 2012). 
Biosurfactant is applied to contaminated soil for metal removal in a small quantity by using a 
cement mixer. The complex formed between the biopolymer and metal ion is then flushed out and 
the soil deposited back on site. Biosurfactant is then precipitated out of the complex formed leaving 
39 
 
behind the metal ion. The bond formed by biosurfactant-metal ion is so strong that flushing with 
water eliminates the complex from soil matrix. Likewise, the process can be repeated for deep 
subsurface (in situ) contamination, but with increased flushing activities. Biosurfactants in cell 
free broth can be used directly or in dilute concentrations on polluted sites. They are stable and 
effective in the production medium. Biosurfactants also enhance the degradation of organic 
contaminant in the presence of metal(loid)s by lowering the toxicity of metal(loid) ions and can 
be simultaneously used to eliminate metal(loid)s and organic pollutants (Pacwa-Płociniczak et al., 
2011).  
Metal(loid)s form a strong bond with soil, thus making its bioavailability difficult for microbes. 
The activities of biosurfactant-metal(loid) ion complexes in soil are influenced by the soil’s cation 
exchange capacity, soil composition and pH, particle size, type and time of contamination, and 
geological pattern (Singh and Cameotra, 2004; Marchant and Banat, 2012; Chakraborty and Das, 
2014). Long duration of metal(loid) contamination provides sufficient time for metal(loid) ion to 
stabilize and attach to soil, thus making removal difficult (Singh and Cameotra, 2004; Chakraborty 
and Das, 2014). 
Decontamination tests have been performed on metal contaminated soil and sludge with different 
biosurfactants (Table 3.1). Studies have revealed the potential of lipopeptides, rhamnolipids, 
sophorolipids and surfactin in metal removal (Table 3.1). Dahrazma and Mulligan (2007) 
examined the capability of rhamnolipids to remove metal ions from contaminated soil sediments. 
The rhamnolipid was able to remove up to 37% of Cu, 33% of Ni, and 7.5% of Zn when applied 
in a continuous flow configuration. Rhamnolipid from Pseudomonas was shown to remove 19% 
of Fe and 52% of Zn in a cyclic treatment (Table 3.1).  
Other biosurfactants that have been effective soil washing agents for metal(loid) removal include 
lipopeptide from Bacillus subtilis. This surfactant has been demonstrated to remove metals like 
Cd, Cu, Co, Pb, Ni and Zn from metal and hydrocarbon polluted soil (Singh and Cameotra, 2013). 
40 
 
The efficiency ranged from 26% for Cu to 44% for Cd (Table 3.1). Sophorolipids produced by 
Torulopsis bombicola have also been employed successfully in the removal of metal ions 
(Mulligan et al., 2001b). It has demonstrated the capacity to remove 60% of Zn and 25% of Cu in 
a soil sediment washing experiment (Table 3.1). 
Wang and Mulligan (2009) used a commercially available rhamnolipid biosurfactant JBR425 
produced by P. aeruginosa to remove arsenic and other metals (copper, lead and zinc) from mine 
tailings. They observed that 0.1% of the surfactant removed 148, 74, 2379 and 259 mg/kg of As, 
Cu, Pb and Zn respectively under alkaline condition. They concluded that the mobilization of 
arsenic by the rhamnolipid surfactant is enhanced by the presence of other metals under alkaline 
condition. Consequently, allowing metal bridging through the formation of the biosurfactant-metal 
complex. 
3.3 Metal(loid) removal by microbial flocculants 
Substances used in the separation of suspended solid-liquid are called flocculants (Okaiyeto et al., 
2016). Flocculation involves the floc formation through colloid aggregation. This is stimulated by 
a chemical substance called flocculant. There are three types of flocculating agents namely 
inorganic (alum, aluminum chloride and sulfate, ferrous sulfate, ferric chloride and polyaluminium 
chloride), natural (cellulose, chitosan, gum and mucilage, starch, tannin and microbial flocculant), 
and organic polymeric (polyacrylamide and polyethylene amine) flocculants (Lee et al., 2014; 
Salehizadeh and Yan, 2014; Sajayan et al., 2017). Chemically synthesized flocculants possess 
inherent drawbacks of being toxic, cause serious health issues, produce large quantities of sludge, 
and are not readily degradable (Zhai et al., 2012; Farag et al., 2014; Lee et al., 2014). Therefore, 
bioflocculants have gained increased attraction because they are environmentally friendly, non-
toxic, free of secondary pollution risks, and they provide effective elimination of metal(loid)s and 
suspended solids from wastewaters (Guo, 2015).  
41 
 
Bioflocculants are polymers produced by bacteria during growth and contain complex mixtures of 
macromolecular polyelectrolytes produced by different organisms. They have various organic 
groups, such as aspartic and glutamic acid in the protein component, galacturonic and glucuronic 
acid in the polysaccharide constituent and uronic acids (containing a carbonyl and carboxylic acid 
component). These groups are responsible for metal binding (Lin and Harichund, 2011). The 
carboxyl groups on the molecular chain of this biopolymer allow the chain to stretch out due to 
electrostatic repulsion, and the extended molecular chains offer more effectual sites for attachment 
of particles. Metals form complexes with the carboxyl and amino functional groups of the 
bioflocculant by neutralizing and stabilizing the residual charge as the distance of binding is 
shortened (Pathak et al., 2014).  
The flocculating efficiency depends on the contact area between the adsorbate and bioflocculant, 
suspended particle size, concentration and structure of adsorbate, contact time, ionic strength, pH, 
temperature and the type of producing organism (Okaiyeto et al., 2016). The flocculant choice has 
a significant effect on the flocculation performance, strength of particle aggregation, and the 
strength and number of bonds formed due to flocculation (Zhang et al., 2014).  
3.3.1 Mechanism of metal(loid) removal by flocculation 
The formation of interface with metal(loid) ions was attributed to be the mechanism of metal(loid) 
removal by bioflocculant. The interface formation depends on ionic groups such as amino, 
carboxyl and hydroxyl groups in the biopolymer which enable metal-floc interactions (Deng et al., 
2005; Sathiyanarayanan et al., 2016). It is a metabolic-independent process in which 
communication between metal ions and the flocculant functional groups can be established by 
complexation, immobilization, ion exchange, physical adsorption and precipitation processes 
(Sathiyanarayanan et al., 2016).  
 
42 
 
Table 3.1: Biosurfactant washing of some metal polluted environment 
Organism Biosurfactant Contaminated pH Temperature Metals Efficiency (%) Reference 
Type Environment (°C) 
Bacillus subtilis A21 Lipopeptide  Soil 9 25 Cd (989.8 mg/kg) 44.2 Singh and Cameotra (2013) 
(50 × CMC) 
Co (166.8 mg/kg) 35.4 
Cu (173.6 mg/kg) 26.2 
Ni (227.9 mg/kg) 32.2 
Pb (143.7 mg/kg) 40.3 
Zn (404.7 mg/kg) 32.07 
Bacillus subtilis Surfactin  Soil - - Cd (1 mM) 15 Mulligan et al. (1999) 
(0.1%) 
Cu (1 mM) 70 
Zn (1 mM) 25 
Pseudomonas aeruginosa Rhamnolipid  Soil 8 25 Fe 19 Diaz et al. (2015) 
CVCM 411 (1 mg/mL) 
Zn 52 
Commercial Rhamnolipid  Soil 6.5 25 Cu (140 mg/kg) 37 Dahrazma and Mulligan 
(5%) (2007) 
Ni (76 mg/kg) 33.2 
Zn (4854 mg/kg) 7.5 
Bacillus circulans Crude surfactant  Soil - - Cd (100 ppm) 97.66 Das et al. (2009) 
(5 × CMC) 
Pb (100 ppm) 100 
Commercial  Saponin  Sludge 3 25 Cr (1000 ppm) 56.1 Gao et al. (2012) 
(30 g/dm3) 
Ni (1000 ppm) 64.2 
Pb (1360 ppm) 73.2 
Candida sphaerica UCP  Soil  27 Fe (1877 mg/kg) 95 Luna et al. (2016) 
0995 
Pb (3038 mg/kg) 79 
43 
 
Zn (1470 mg/kg) 90 
Torulopsis bombicola Sophorolipid Soil 5.4 - Cu 25 Mulligan et al. (2001b) 
(4% and 0.7% 
Zn 60 
HCl) 
Candida lipolytica UCP Lipoprotein Soil   Cd (0.9 mg/kg) 50 Rufino et al. (2011) 
0988 
Cu (13.3 mg/kg) 96 
Fe (52.5 g/kg) 16.5 
Pb (14 mg/kg) 15.4 
Zn (19.1 mg/kg) 96 
44 
 
Similarly, charge neutralization, electrostatic patch and polymer bridging are other mechanisms of 
flocculation. Metal ions are positively charged and most bioflocculants are negatively charged 
which makes charge neutralization applicable. The cation exchange ability makes the electrostatic 
forces between metal ions and bioflocculant possible (Lee et al., 2014). During the process of 
charge neutralization, the surface charge density of the particle is reduced by bioflocculant 
adsorption and the particles sufficiently aggregate for the effectiveness of the attractive forces 
(Salehizadeh and Shojaosadati, 2001). 
In a bridging mechanism, the bioflocculant serves as a bridge in the complex formed by particle-
polymer-particle. Some functional groups of the biopolymer adsorb to the metal ions when the 
biopolymer comes in contact with the metal ion, thus leaving the unabsorbed metal ions to extend 
out into the solution. The second biopolymer with unattached adsorption sites then comes in 
contact with the extended segments for attachment to occur. A metal-biopolymer-metal complex 
is thus formed with the biopolymer serving as a bridge (Lee et al., 2014). Before flocculation can 
occur, the biopolymer must not only come in contact with the particles, it must also conquer the 
electrostatic repulsive forces between particles (Wang et al., 2011; Okaiyeto et al., 2016). Efficient 
bridging mechanisms are attributed to the following characteristics: The ionic strength of the 
suspension, the molecular weight of the biopolymer, the net charge of the biopolymer, the 
suspended particles, and the kind of mixing (Okaiyeto et al., 2016). 
The producing microorganisms determine the structural characteristics of the biopolymer 
produced. Metal(loid) ions demonstrate varying effects on different biopolymers. These effects 
depend on the concentration and valence and metal(loid) ions (Wu and Ye, 2007b; Okaiyeto et al., 
2016). The metal(loid)-binding interactions are influenced by various factors such as bioflocculant 
dosage, physico-chemical characteristics of metal(loid) ions, initial metal(loid) concentration, 
ionic strength, charge density, appropriate positioning with the binding site, conformation of 
45 
 
polymer with adsorbed ions and tertiary structure of floc (Deng et al., 2005; Lin and Harichund, 
2011; Gomaa, 2012; Salehizadeh and Yan, 2014). 
Several bioflocculants have been reported in literature with strong metal(loid) adsorption 
capability (Table 3.2). Many of them have been isolated from soil and wastewaters and have 
various metal(loid) biosorption capacity. 
3.4 Biofilms and metal(loid) removal 
Bacteria exist in colonies in nature, accumulating at interfaces to form cell mass as seen in biofilms, 
mats, flocs and sludge. Singh et al. (2006), described biofilms as clusters of microbial cells 
attached to a surface. This usually occurs in moist environments where there is sufficient flow of 
nutrients. They can be formed by single or several species of organisms consisting of algae, 
bacteria, fungi and protozoa. Biofilm is a stable, highly structured ecosystem usually enclosed in 
a matrix adhering to a dormant or living surface (Farasin et al., 2017). Biofilms develop on 
different surfaces such as living tissues, soil and aquatic environments, medical devices, industrial 
or potable water piping systems (Vu et al., 2009). Microbial biofilms develop an increased 
preformed resistance to hard-surface disinfectants, antibiotics, extreme pH and oxygen levels, 
desiccation and metal(loid) ions (Morel et al., 2009).  
Biofilm offers the advantage of availability of nutrient, persistence in different metabolic states, 
and communication and exchange of genetic materials. Biofilm-mediated removal of metal(loid)s 
is crucial as high concentration of metal(loid)s is detrimental to planktonic organisms. Planktonic 
communities usually have a problem when they come in contact with metal(loid)s because 
metal(loid) ions have unique chemistries, and function via varied biochemical routes of toxicity. 
Biofilm increases the tolerance of microorganisms to metal(loid)s and acidic environment, due to 
better adaptation and survival as they are protected within a matrix (Edwards and Kjellerup, 2013). 
The macromolecules within the biofilm matrix exhibit properties such as electrostatic interactions, 
hydrogen bonding and London dispersion forces. The extracellular polymeric substances (EPS) 
46 
 
plays an active role in metal(loid) sorption as many of them are anionic in nature (van Hullebusch 
et al., 2003). The close proximity within the biofilm and the physiological interactions among the 
organisms enhance metal(loid) tolerance and removal (Singh et al., 2006). 
Metal(loid) sorption sites on biofilm include cell membrane and cell wall, extracellular polymeric 
substances and cytoplasm. Extracellular polymers consist mainly of polysaccharides, proteins, 
uronic acids (D-galacturonic, D-glucuronic and mannuronic acids), humic acids, ketal-linked 
pyruvates, lipids, nucleic acids, ionizable functional groups, and cell fragments (Quintelas et al., 
2006; Pal and Paul, 2008; Vu et al., 2009). Removal of metal(loid) mediated by microbial biofilm 
offers an effective and innocuous alternative to sequestration by planktonic organisms. This is due 
to the better chance of adaptation that cells in biofilm offer and their subsequent survival in a 
protected matrix (Singh et al., 2006; Vu et al., 2009). The mineralization process is improved in 
biofilm mediated removal by maintaining optimum pH conditions, localized solute and redox 
potential (Vu et al., 2009). Acquired traits through horizontal gene transfer processes from the high 
cell density in the biofilm community also enhance their resistance abilities to metal(loid) ions 
(Farasin et al., 2017). 
The factors that influenced biofilm EPS metal(loid) binding include the surrounding pH, ionic 
strength, metal(loid) concentration, EPS composition, physical gel state, organic matter 
component, and biomass. Furthermore, the ratio of carbohydrate to protein in the EPS also plays 
a major role in metal(loid) removal from wastewaters and in monitoring the state of the biofilm. 
A higher pH results in metal(loid) chelation with maximum binding affinity between pH 8–8.2, 
while a lower pH favors the release of meta(loid) ions from a bound state (van Hullebusch et al., 
2003; Pal and Paul, 2008). At acidic pH, the binding anions such as COO- groups are not readily 
available, thus a few metallic ions are bounded (Dominguez-Benetton et al., 2018). The encasing 
EPS protects microbial cells in biofilm from metal(loid) stress through metal(loid) ion binding or 
by preventing metal(loid) diffusion within the biofilm (Pal and Paul, 2008).  
47 
 
Table 3.2: Metal(loid) removal by bioflocculant produced by different bacterial species 
Isolate  Source Flocculating Metals removed Metal removal pH Reference 
efficiency (%) (%) 
  
Pseudomonas 62.25 Cu (20 ppm) 87.39 7 Gomaa (2012) 
aeruginosa Hg (20 ppm) 89.09 
Pb (40 ppm) 79.7 
Cd (40 ppm) 79.73 
As (60 ppm) 72.96 
Zn (60 ppm) 80.59 
Rhodococcus  Swine wastewater 44.7 Cu (100 mg/L) 96.9 6 Guo (2015) 
erythropolis 
Bacillus  Activated sludge 90.69 Cd (60 ppm) 90.9 8 Kaewchai and Prasertsan (2002) 
subtilis WD 
Ni (10 ppm) 90.7 
90 
Enterobacter  Activated sludge 87.84 Cd (60 ppm) 84.2 7 
agglomerans 
Ni (10 ppm) 92.8 
48 
 
Paenibacillus  Soil 87 Al (2 mM) 72 7.2 Li et al. (2013) 
elgii Co (2 mM) 49 
Cu (2 mM) 53 
Pb (2 mM) 60 
Bacillus  Marine sponge 94 Cu (1-3 mM) Significant 7 Sajayan et al. (2017) 
cereus Hg (1-3 mM) reduction 
(Dendrilla nigra) 
Zn (1-3 mM) 
Bacillus  Soil  Cu (1000 mg/L) 74.9 4 Salehizadeh and Shojaosadati 
firmus Pb (1000 mg/L) 98.3 4.5 (2003) 
Zn (1000 mg/L) 61.8 6 
Rothia sp.  Ruditapes philippinarum 86.22 Cr (1 mg/L) 69.3 9 Gao et al. (2009)  
ZHT4-13 conglutination Ni (20 mg/L) 19.2 
mud 
49 
 
Colonization of microorganisms on metal(loid)s is mediated by biofilms which play a crucial role 
in their attachment. Metal sulfide dissolution is proposed to take place in the EPS layer of biofilm, 
and the method of occurrence is based on two postulates. It is first presumed that the complex 
formed by the metal ion and the EPS is decreased as a result of an electron tunneling effect. In 
addition, the glucuronic acid-metal ion complex is unstable, thus permitting metal ions to move 
inside the EPS space. If the metal ions migrate near the outer membrane, they will be re-oxidized 
by the cell’s enzymatic system and can then reenter the oxidation/reduction cycle (Vu et al., 2009). 
Pseudomonas aeruginosa biofilm was demonstrated to be more resistant to Cu, Pb and Zn ions 
than the free planktonic cells in a rotating-disk biofilm reactor (Teitzel and Parsek, 2003). The 
EPS encasing the biofilm was found to protect against metal(loid) by binding metal(loid) ions and 
preventing metal(loid) ion diffusion into the biofilm. In like manner, Au, Fe and La were 
demonstrated to bind to P. aeruginosa PAO1 biofilm in a more significant amount than the 
planktonic cells (Langley and Beveridge, 1999). The binding of metals by the biofilm of P. 
aeruginosa led to an increase in the electron density of the cells’ surfaces when observed with a 
transmission electron micrograph. The shift in the production of lipopolysaccharide in P. 
aeruginosa PAO1 biofilm could be responsible for metal(loid) resistance. This showed that the 
mode of growth (biofilm or planktonic) affects the extent to which cells bind and precipitate 
metal(loid) ions (Langley and Beveridge, 1999). 
The planktonic and biofilm cells of Candida tropicalis were exposed to a diverse range of 
metal(loid) ions (Ag, Al, Au, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Se, Te and Zn) in a time-dependent 
experiment (Harrison et al., 2006). The biofilm of C. tropicalis was found to be highly resistant to 
metal(loid) toxicity and was also found to be 65 times more tolerant than planktonic cells. The 
extracellular matrix of the biofilm was observed to be the contributing factor responsible for 
metal(loid) resistance. The EPS restricted the movement of metal(loid) ions into the biofilm of C. 
tropicalis. The densely packed cells formed by the biofilm, which later developed into dome-
50 
 
shaped microcolonies, could also be responsible for the metal(loid) resistance observed in the 
study (Harrison et al., 2006). 
The negatively charged carboxyl, phosphoryl and sulfhydryl groups existing in the EPS of biofilm 
produced Escherichia coli PHL628 was suggested to be responsible for sorption of copper (Hu et 
al., 2007). In addition, increased exopolysaccharide produced per cell within the biofilm and the 
tortuosity that possibly impede transport of Cu into biofilms were also reported to be responsible 
for Cu sorption. The study showed that Cu sorption by the biofilm is 6 times more than planktonic 
cells of E. coli PHL628. 
Quintelas et al. (2006) investigated the removal of chromium by biofilm of Arthrobacter viscosus 
supported on granular activated carbon from wastewater. They observed a simultaneous increase 
in chromium uptake by A. viscosus biofilm in the presence of organic compounds (o-cresol, phenol 
and chlorophenol). The biofilm can be applied in wastewater removal of metal(loid)s. 
Von Canstein et al. (2002) evaluated the diversity of species and the efficiency of mercury-
reducing biofilm in a packed-bed reactor. They found that biofilms composed of multi-species 
organisms retained large quantities of mercury compared to mono-species biofilms in a changing 
environment. The diversity of the microbial species inhabiting the biofilm resulted in superior 
bioreactor performance in a rapidly changing environment, hence leading to efficient metal(loid) 
removal (Von Canstein et al., 2002). Environmental and metal(loid) stress related issues that affect 
planktonic organisms and monoculture biofilms were reduced using multi-species biofilms. 
Biofilms with multi-species offer a superior survival niche, and increase the metabolic abilities in 
the presence of metal(loid)s (Singh and Cameotra, 2004). There has been growing interest in 
metal(loid) removal by biofilm, and with genetic engineering, it could be manipulated and 
improved upon for efficient metal(loid) removal. 
51 
 
Conclusions 
The exploration of the properties of microbial polymers for in situ bioremediation will enhance 
metal(loid) removal from contaminated environments as most experiments are still laboratory 
based. The metabolic properties of microorganisms can be exploited for this purpose since the 
polymers are specific, tolerant to extreme conditions and have low toxicity. Techniques to 
elucidate the mechanisms of action of these polymers are needed to further understand their role 
in metal(loid)-binding and metal(loid) decontamination. Molecular techniques that enable the 
construction and improvement of the production of these biopolymers with specific metal(loid)-
binding characteristics from robust environmental strains hold future promise. This can be 
accomplished through alteration of metabolic pathways, and insertion of new genes and plasmids 
into the genome of robust organisms. The description and characterization of enzymes, genes, and 
proteins responsible for the synthesis and metal(loid)-binding mechanism of these biopolymers 
will also provide insight on the level of alteration needed under different environmental conditions.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
52 
 
CHAPTER FOUR 
IDENTIFICATION AND CHARACTERIZATION OF HEAVY METAL RESISTANT 
BACTERIAL ISOLATES FROM GOLD MINING SOIL 
Abstract 
The selection of tolerant bacterial species for remediation of contaminated environments is based 
on their response and growth kinetics in polluted medium. Microbial population and diversity in 
heavy metal polluted environment are influenced by the bioavailable concentration of the heavy 
metals. This study investigated bacterial diversity in gold mining soil, their metal tolerance, growth 
kinetics and biopolymer production potential. Ninety-eight (98) heavy metal resistant bacteria 
were isolated and tested for their ability to tolerate cadmium, chromium and lead. Fifty-five 
percent of the isolates were resistant to Pb (1000 mg/L) and 10% and 20% of the isolates were 
able to resist Cd and Cr at level greater than 200 mg/L respectively. The tolerance pattern showed 
by the resistant isolates was the trend of Pb>Cr >Cd. Fifty percent of the isolates are multi-metal 
tolerant. Bacillus species were the dominant organisms with Acinetobacter, Citrobacter, 
Enterobacter, Pantoea and Pseudomonas species also isolated. Metal resistance genes: cadA and 
czcD (Cd), chrA (Cr), PbrA and PbrT (Pb) were shown to be present in some of the isolates using 
PCR. Fifty percent of the isolates showed α-hemolysis with emulsification of various 
hydrocarbons. The highest flocculating efficiency of 71.3% was shown by Pantoea sp. The multi-
metal tolerance and growth kinetics assays revealed the potential of these isolates in heavy metal 
removal from contaminated environment. 
Keywords: bacteria resistance, bioflocculant, bioremediation, biosurfactant, metal tolerance, 
pollution  
53 
 
4.1 Introduction 
Incautious use of natural resources coupled with industrialization, mining, and urbanization have 
resulted in the buildup of pollutants in the environment. While mining activities have advanced 
the socio-economic development of national economies around the world, they have impacted 
negatively on the environment resulting in buildup of pollutants (Ayangbenro et al., 2018). 
Amongst such pollutants are metal(loid)s which have generated wide interest because of their 
toxicity and the threat they pose to the health of biological systems  (Ayangbenro and Babalola, 
2018; Prabhu et al., 2018). Metal pollution arising from mining activities has negative impacts on 
the environment, and is of particular interest because of the persistent nature of these metals in the 
environment (Abdul-Wahab and Marikar, 2012), and are often irreversible resulting in difficulties 
in remediation (Deng et al., 2018). These metals are toxic at low concentration and could 
accumulate in the food web, causing damage to biological systems (Ayangbenro and Babalola, 
2017). 
The mobility, bioavailability, leaching and toxicity of metals and metalloids in soil is dependent 
on the soil properties. The mobility and toxicity of metals in soil is greatly influenced by pH, 
presence and amount of organic and inorganic components, redox reactions, sorbent nature, and 
nutrients (Violante et al., 2010). High concentration of metals in the soil are highly toxic to 
microorganisms and impact on the ecosystem functioning (Singh et al., 2014).  
Metal toxicity affects microbial diversity and population size, morphology, activities, metabolism 
and growth, enzyme activities, and genetic and nucleic acid structure (Fashola et al., 2016; Xie et 
al., 2016; Ayangbenro and Babalola, 2017). This suppresses bacterial population and diversity in 
soil that plays a key role in cycling of nutrients in the environment, consequently, impacting 
negatively on the health and productivity of such environment (Zampieri et al., 2016). 
54 
 
Microorganisms have developed defense strategies for their continued existence in metal polluted 
environments and stress combating mechanisms against heavy metals. The coping mechanisms 
include complex formation, efflux of metals out of the cell, reduction of metals to less toxic forms, 
precipitation, and transformation  (Bolan et al., 2014; Zampieri et al., 2016; Ayangbenro and 
Babalola, 2017). Due to the transformation of metal species to less toxic species by bacteria, they 
have potential application in removal of metals and metalloids from polluted environments. 
Studies have shown the potential application of metal resistant bacterial isolates from 
contaminated soil in the removal of metals (Shin et al., 2012; Chihomvu et al., 2014; Oladipo et 
al., 2018).  
With increasing mining activities in the North West province of South Africa, there is the need to 
study indigenous resistant bacterial isolates associated with mining activities and their diversity. 
Thus, this study aimed to isolate and characterize heavy metal resistant bacteria from gold mining 
sites in Vryburg, South Africa and to screen the isolates for biopolymer production. 
4.2 Materials and methods 
4.2.1 Soil sampling and analysis 
Soil samples were collected from active gold mines in Vryburg, North West Province, South 
Africa. Control soil samples were collected around the mines. Soil samples were collected in 
triplicate at depths of 10-30 cm with a soil auger in June 2016 and transported to the laboratory 
for analysis in sterile plastic bags. 
Some of the samples were dried at 80˚C and sieved through a 2 mm sieve and used to determine 
the heavy metal composition (Freitas et al., 2004). The pH of the soil samples was determined in 
soil/water suspension (1:2.5) with a pH meter (Jenway, Bibby Scientific Ltd, Stone, Staffs, UK).  
55 
 
Soil samples were analyzed for heavy metals by digesting with acids (HCl-HNO3) using a 
microwave reaction system (Multiwave 3000, Anton Paar GmbH, Graz, Austria). The digested 
samples were analyzed for total concentration of metals using Inducible Conductivity Plasma-
Optical Emission Spectroscopy (725 ICP-OES, Agilent Technologies, Palo Alto, CA, USA) (Wei 
and Yang, 2010; Ali and Shakrani, 2014). 
The ammonium acetate method was used in determining the Cation Exchange Capacity (CEC) and 
extractable ions of the soil samples. Soil samples (25 g each) were weighed into Erlenmeyer flasks 
(500 ml) and 125 ml 1 M ammonium acetate was added. The mixture was shaken and allowed to 
stand for 5 h. The solution was allowed to leach through a leaching tube and the filtrate was used 
for determination of Ca, K, Mg and Na in the samples.  The ammonium saturated sample in the 
leaching tube was washed with 50% ethyl alcohol (150 ml) and then by 90% ethyl alcohol (30 ml) 
to remove the excess ammonium. The leachate was then transferred into a 250 ml volumetric flask 
and brought to volume with 1 M potassium chloride. The adsorbed ammonium was leached with 
1 M potassium chloride (25 ml). The CEC was calculated as: 
CEC (cmol (+)/Kg) = (NH +4  in extract - NH
+
4  in blank)/18. 
The procedure of Filgueira et al. (2006) was used for the determination of the particle size. Forty 
gram of air dried and sieved soil sample was weighed into 500 ml beaker. One hundred millilitre 
(100 ml) of 50 g/L sodium-hexametaphosphate solution and 250 ml distilled water were added. 
The mixture was stirred with a glass rod, left overnight and transferred to a dispersing cup, and 
homogenized for 5 min with an electric mixer. The mixture was then transferred to a sedimentation 
cylinder and distilled water added to make up one litre. A plunger was used to mix the suspension 
thoroughly before inserting a hydrometer into the suspension. The reading was taken after 40 s. 
The suspension was homogenized again and the reading was taken after 4 min. The hydrometer 
was then removed and cleaned. The hydrometer reading was taken again after 6 h to determine the 
56 
 
clay particle density. The following equation was used to calculate the percentage of clay, sand 
and silt in each of the samples: 
𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑 ℎ𝑦𝑑𝑟𝑜𝑚𝑒𝑡𝑒𝑟 𝑟𝑒𝑎𝑑𝑖𝑛𝑔 𝑎𝑓𝑡𝑒𝑟 6 ℎ
% 𝑐𝑙𝑎𝑦 =  × 100 
𝑚𝑎𝑠𝑠 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒
𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑 ℎ𝑦𝑑𝑟𝑜𝑚𝑒𝑡𝑒𝑟 𝑟𝑒𝑎𝑑𝑖𝑛𝑔 𝑎𝑓𝑡𝑒𝑟 40 𝑠
% 𝑠𝑖𝑙𝑡 =   × 100 − % 𝑐𝑙𝑎𝑦  
𝑚𝑎𝑠𝑠 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒
% 𝑆𝑎𝑛𝑑 = 100 − (% 𝑠𝑖𝑙𝑡 − % 𝑐𝑙𝑎𝑦). 
Organic carbon of the soil samples was determined using the Walkley-Black method (Abollino et 
al., 2002). The organic carbon was oxidized to CO2 in the presence of K2Cr2O7 in the presence of 
H2SO4. The unreacted K2Cr2O7 was titrated with FeSO4.7H2O. The average organic carbon in soil 
organic matter is equal to 58%, then the conversion factor of 1.724 was used to calculate the 
organic carbon content. 
The soil moisture content was determined by oven-drying the soil samples at 105 °C for 24 h while 
the soil nitrate was extracted using potassium chloride solution (Colombo et al., 2016). Five gram 
of the fresh soil sample and 50 ml of 2 M KCl solution were mixed and shaken for 60 min at 150 
rpm and the mixture was filtered. The filtrate was analyzed for nitrate. The extractable phosphorus 
was determined using Bray-1 reagent. The phosphorus was extracted with 0.025 N HCl and 0.03 
N NH4F. One gram of the air-dried soil sample was extracted with 10 ml of Bray-1 reagent and 
shaken for 5 min. The phosphorus extracted was determined by measuring the intensity of blue 
color developed by the filtrate when treated with molybadate- ascorbic acid reagent using a 
Brinkman PC 900 probe colorimeter at 880 nm. 
Sulfate was measured by weighing 10 g of each of the soil sample separately into a 100 ml 
Erlenmeyer flask and extracting with 25 ml of the extracting solution (39 g NH4OAC and 1 litre 
of 0.25 M acetic acid). The mixture was mixed and shaken at 200 rpm for 30 min and 0.25 g 
activated charcoal was added. The resultant mixture was then shaken for 3 min and filtered using 
sulfate free filter paper and washed with the extracting solution. The filtrate (10 ml) was pipetted 
57 
 
into an Erlenmeyer flask (50 ml) and 1 ml of acid seed (6 M HCl + K2SO4 (20 mg) and 50 ml of 
40 mg standard solution plus 50 ml of concentrated HCl) was added. The mixture was shaken and 
0.5 g of BaCl2. H20 crystals was added and allowed to stand for 1 min and shaken regularly to 
dissolve the crystals. The sulfate content of the mixture was determined by measuring the optical 
density using a UV spectrophotometer at 420 nm.  
4.2.2 Isolation and screening of heavy metal resistant bacteria isolates 
Heavy metal (Cd, Cr, Pb) resistant bacteria were isolated as described by Rajkumar and Freitas 
(2008). One gram of each of the collected soil samples were serially diluted and 10-4 plated out on 
Luria-Bertani (LB) agar that had been supplemented with 50 mg/L of each metal solution and a 
mixture of all the metals. The metal solutions (CdSO4, K2CrO4 and Pb(NO3)2) were filter sterilized 
through 0.22 µm membrane filter (Millipore Corporation, Bedford, MA, USA). The plates were 
incubated at 30˚C for 48 h. Resistant isolates were then screened for ability to grow in different 
metal concentrations (100 to 1000 mg/L) on LB agar plates (Srinath et al., 2002). Growth of 
resistant isolates was monitored in the presence of heavy metals (100 mg/L of each metal) in LB broth 
and the optical density was measured at 24 h interval for 6 days. 
4.2.3 Biochemical characterization of bacteria isolates 
The following biochemical tests were carried out on the heavy metal resistant isolates: Gram 
reaction, catalase test, citrate utilization, gelatin liquefaction, hydrogen sulfide production, 
oxidase, nitrate reduction, starch hydrolysis, sugar fermentation test (glucose, arabinose, fructose, 
maltose, sucrose, lactose, mannose, melibiose, sorbitol, maltose), urease, and Voges-Proskauer 
test. Pure cultures of the isolates were identified according to the Bergey’s manual of 
Determinative Bacteriology (Garrity et al., 2004). 
 
58 
 
4.2.4 Genomic DNA extraction  
The DNA of the selected heavy metal resistant bacterial isolates were extracted from pure cultures 
grown on LB agar using a ZR soil microbe DNA mini prep TM DNA extraction kit (Zymo Research, 
CA, USA) according to the kit’s protocol. The DNA quality and quantity was determined using a 
NanoDrop Lite spectrophotometer (Thermo Fischer Scientific, CA, USA). 
4.2.5 Amplification of 16S rDNA and detection of heavy metal resistance genes 
The 16S rRNA gene was amplified for each isolate using universal primers F1 (forward, 5′-
AGAGTTTGATCCTGGCTCAG-3′) and R2 (reverse, 5′-ACGGCTACCTTGTTACGACTT-3′) with 
approximately 1500 base pair amplicon (Marin et al., 2011). The PCR was performed with a total 
volume of 25 μl of a reaction mixture consisting of 12.5 μl 2X master mix (One Taq® Hot Start 
Quick Load, Biolabs, England), 1 μl of the DNA template, 0.5 μl (10 μM working solution) of 
each of the forward and reverse primer, and 11 μl nuclease free water. The PCR was performed in 
a C1000 thermal cycler (Bio-Rad, CA, USA) with preincubation step of 95˚C for 5 min and 35 
cycles of denaturation at 94˚C for 1 min, annealing at 50˚C for 30 s, and extension at 72˚C for 1 
min, followed by a final extension step of 72˚C for 10 min. The PCR amplicons were analyzed by 
electrophoresis on agarose gel (1% w/v) containing ethidium bromide (1 μl) and the size of the 
band was determined using 1 kb molecular marker. The gel images were captured with gel 
documentation system (Gel Doc 2000, Bio-Rad, USA).  
Analysis of heavy metal encoding genes were done with primers encoding for cadmium (cadA, 
CzcA, CzcB and CzcD), chromium (chrA and chrB) and lead (PbrA and PbrT) for each isolate. 
The sequences of each primer set are presented in Table 4.1 with their expected band size. All the 
primer sets used for PCR amplifications were synthesized by Whitehead Scientific, Integrated 
DNA Technologies, South Africa.  
 
59 
 
4.2.6 Sequencing reaction 
Polymerase chain reaction amplicons were sequenced with PRISM TM Ready Reaction Dye 
Terminator Cycle Sequencing Kit using dideoxy chain termination method and electrophoresed 
with a model ABI PRISM® 3500XL DNA Sequencer (Applied Bio systems, USA) at Inqaba 
Biotechnical Industrial (Pty) Ltd, Pretoria, South Africa. The forward and reverse primers were 
used in sequencing of the purified PCR products. Purified sequencing products (Zymo Research, 
ZR-96 DNA Sequencing Clean-up Kit™) were analyzed using CLC Main Workbench 7. 
The 16S rRNA gene sequences were aligned and compared to other 16S rRNA genes in the NCBI 
GenBank database. The most similar sequences were aligned, analyzed and the phlogenetic 
analysis was done using MEGA 7.0 software  (Kumar et al., 2016b). 
4.2.7 Screening of biosurfactant production by heavy metal resistant isolates  
Biosurfactant production was quantified in pure cultures of the isolates by different methods which 
include hemolytic activity, drop collapse test, and emulsification activity. All tests were carried 
out in duplicate. The hemolytic test on blood agar was carried out as described by Bicca et al. 
(1999). 
The cultivation medium for biosurfactant production contains (g/L): yeast extract, 0.5; sucrose, 
5.0; Na2HPO4·12H2O, 1.4; MgSO4·7H2O, 0.02; KH2PO4, 0.4 and peptone, 20.0 (Sriram et al., 
2011). The medium was seeded with 3% inoculum prepared from LB broth into 50 ml cultivation 
medium in a 250 ml Erlenmeyer flask and incubated at 37˚C for 7 days at 160 rpm. 
Hemolysis, drop collapse and emulsification tests were taken with supernatant obtained by 
centrifuging the cultures at 10,000 rpm for 20 min. 
The drop collapse test was determined as described by Sriram et al. (2011). Mineral oil (2 µl) was 
added to each well of a 96-well microtiter plate and allowed to equilibrate for one hour at 30˚C. 
Thereafter, 5 µl of the culture supernatant was added to the center of each well over the oil layer. 
60 
 
After one minute, the shape of the oil drop was examined. A flattened drop was recorded as 
positive for biosurfactant production. Water was used as negative control.  
Table 4.1: Primer sets used in PCR amplification of heavy metal resistance genes 
Gene Primer sequence (5ʹ →3ʹ)  PCR conditions PCR Reference 
 product 
size (bp) 
CzcA F- GTTTGAACGTATCATTAGTTTC Initial denaturation at 95 C for 5 1885 Nies et al. (1989) 
R - GTAGCCATCCGAAATATTCG min, 34 cycles at 94 C for 90 s, 
57 C for 90 s, 72 C for 2 min 
and a final extension at 72 C for 
7 min. 
CzcD  F- CAGGTCACTGACACGACCAT Initial denaturation at 95 C for 5 398 Nies et al. (1989) 
R- CATGCTGATGAGATTGATGATC min, 34 cycles at 94 C for 90 s, 
52 C for 90 s, 72 C for 2 min 
and a final extension at 72 C for 
7 min. 
CzcB F- CTATTTCGAACAAACAAAAGG Initial denaturation at 95 C for 5 1520 Abou-Shanab et al. 
R- CTTCAGAACAAAACTGTTGG (2007) 
min, 35 cycles at 94 C for 90 s, 
57 C for 1 min, 72 C for 90 s 
and a final extension at 72 C for 
3 min. 
PbrT F- ATGGTGATTGCTTTAGTT Initial denaturation at 95 C for 4  Shin et al. (2012) 
R- TTAGGCTTGCTTCTTTTT 
min, 35 cycles at 95 C for 30 s, 
50 C for 90 s, 72 C for 2 min 
and a final extension at 72 C for 
7 min. 
PbrA F- ATGAGCGAATGTGGCTCGAAG  Initial denaturation at 95 C for 766 Shin et al. (2012) 
R- TCATCGACGCAACAGCCTCAA 4 min, 35 cycles at 95 C for 30 
s, 60 C for 90 s, 72 C for 2 
min and a final extension at of 
72 C for 7 min 
chrA F- CTTATACGCTACGCCAACTG Initial denaturation at 95 C for 1292 Nies et al. (1990) 
R- GTAATGGCATTCAGTCGCTTG 5 min, 35 cycles at 94 C for 30 
s, 58 C for 90 s, 72 C for 2 
min and a final extension step 
of 72 C for 5 min 
chrB   F- GTCGTTAGCTTGCCAACATC  Initial denaturation at 95 C for 450 Nies et al. (1990) 
R- CGAAAGCAAGATGTCGATCG 5 min, 30 cycles at 94 C for 30 
s, 57 C for 60 s, 72 C for 2 
min and a final extension at 72
C for 3 min 
61 
 
cadA F- GACAAGACYGGMACYMTCAC Initial denaturation at 95 C for 600 Dell’Amico et al. 
(2008) 
R- GCRTGGTTRATSCGTC 5 min, 34 cycles at 94 C for 90 
s, 57 C for 60 s, 72 C for 2 
min and a final extension at 72
C for 7 min 
 
The emulsification activity of resistant isolates was determined by measuring the emulsification 
index (E24) after 24 h. Two millitre (2 ml) of the culture supernatant was mixed with kerosene (2 
ml) in a test tube. The mixture was vortexed at high speed for 2 minutes. The E24 was calculated 
as the percentage of height of emulsified layer divided by total height of the liquid column (Rizzo 
et al., 2015). The test was also performed using engine oil and vegetable oil in place of kerosene.  
4.2.8 Screening of metal resistant isolates for bioflocculating activity  
Heavy metal resistant isolates were pre-cultured in 50 ml LB broth on a rotary shaker (200 rpm) 
at 30˚C for 24 hours. One mililitre (0.5 McFarland) of the culture broth was used to inoculate 100 
ml fermentation medium after washing with sterile water. The fermentation medium contains 
(g/L): K2HPO4, 2; NH4Cl, 1; MgSO4, 0.5; CaCO3 0.2; KH2PO4, 5; glucose, 2; peptone, 5 and yeast 
extract, 5 (Subudhi et al., 2014). The medium was maintained at pH of 7.2 and incubated for 72 h 
at 30˚C on a rotary shaker (200 rpm). After incubation, the culture broth was centrifuged at 6000 
xg for 30 min and the supernatant was evaluated for flocculating activity. The flocculating activity 
was measured using kaolin clay suspension (0.5 g of kaolin clay in 100 ml distilled water). Forty-
five millilitre (45 ml) of the kaolin clay suspension was mixed with 0.5 ml of the supernatant and 
4.5 ml of 1% CaCl2 was added to the mixture. The mixture was vortexed for 2 minutes and left 
standing for 5 min at room temperature. The optical density of the supernatant and the blank 
(distilled water) was measured at 550 nm (Gomaa, 2012). The flocculating activity was defined as 
𝐴−𝐵
follows: flocculating activity =  × 100 
𝐴
Where A and B are the optical densities of the control and sample respectively at 550 nm.  
 
62 
 
4.2.9 Statistical analysis 
The principal component analysis was used to determine the relationship between 
physicochemical parameters using SPSS statistical package (v 25.0). Analysis of variance 
(ANOVA) with Least significant difference test (LSD) and Duncan multiple test (p < 0.05) were 
used to compare treatment means using SPSS.  
4.3 Results 
4.3.1 Physicochemical properties of the soil samples 
The sampling location is shown in Table 4.2 and the geographical map of the sampling sites is 
presented in Figure 4.1. A total of seven gold mining soil samples (sampling sites 1-7) and a control 
soil sample (sampling site 8) were collected from Vryburg, South Africa in June 2016. The particle 
size distribution of the soil samples is presented in Table 4.2 The soil samples were generally 
sandy in nature which accounts for over 60% in each sample. The range of the particle size is 
between 12-26%, 62-86% and 0-26% for clay, sand and silt respectively for the soil samples. The 
soil sample from site 3 has the highest sand distribution (86%) while the sample from site 4 has 
the lowest (62%). The silt composition is very low in all samples. Site 5 (26%) has the highest 
clay composition while sites 3 and 4 have the lowest (12%). The mean moisture content of the soil 
samples is also shown in Table 4.2. The moisture content ranged between 0.153-3.89%. Sampling 
site 4 had the highest moisture content of 3.89% and also had the lowest sand composition (62%). 
The particle size of the samples were statistically significant (P <0.05).  
The average pH of the samples ranged between 5.9-7.5 (Figure 4.2). This indicates that the soil 
samples are slightly acidic to neutral. The soil sample from sampling site 3 has the highest pH of 
7.5 which falls within the neutral pH range. Soils from sampling sites 1, 2, 5, 6 and 7 are slightly 
acidic in nature (Figure 4.2). Samples 3, 4 and 8 fall within the neutral pH range.  
63 
 
 
Figure 4.1: Map showing the soil sampling location 
The cation exchange capacity (CEC) of the soil samples varied from each sampling location. It 
ranged between 2.71 cmol(+)/kg from sampling site 8 to 12.98 cmol(+)/kg from sampling site 2 
(Figure 4.3). The CEC values were statistically significant (P <0.05) with each samples. 
The other chemical and physical properties of the soil samples are presented in Table 4.3. The 
organic carbon ranged between 0.09 to 1.07% (Table 4.3). This showed that the soil samples have 
low organic content as a result of mining activities in that environment which had impacted on the 
activities of plants and microorganisms in the environment. The control soil sample also had a 
very low amount of organic carbon (0.3%) which can be attributed to the parent material of the 
soil sample. 
The soil samples are rich in sulfur (Table 4.3). The range of the sulfur value is between 1.973 
mg/kg in the control (sample 8) to 30.78 mg/kg in sample 4. High sulfate content in the samples 
can be attributed to sulfur minerals in gold mining which has been exposed to atmosphere and 
water after excavation. Sulfate values ranged between 2.583 mg/kg (in sample 1) to 2312.858 
kg/mg (in sample 4) (Table 4.3). In all samples, the clay content and organic carbon are low which 
makes the soil to be permeable. 
 
64 
 
Table 4.2: Sampling location and particle size distribution of the soil samples 
Sampling site Location Sand (2.0-0.05 mm) (%) Silt (0.05-0.002 mm) (%) Clay (<0.002 mm) (%) Moisture content (%) 
1 S 26.1681354 E 25.246582 84.00 (0.57) ab 2.00 (0.58) de 14.00 (0.00) cd 0.153 (0.00) h 
2 S 26.1706352 E 25.237386 84.00 (1.16) ab 0.00 (0.00) e 16.00 (1.16) c 0.325 (0.00) e 
3 S 26.1675545 E 25.255411 86.00 (0.58) a 2.00 (0.00) de 12.00 (0.58) d 0.234 (0.00) f 
4 S 26.1672729 E 25.255739 62.00 (0.58) e 26.00 (0.58) a 12.00 (1.56) d 3.890 (0.03) a 
5 S 26.1863994 E 25.261444 68.00 (2.31) de 6.00 (1.73) bc 26.00 (0.58) a 2.317 (0.11) b 
6 S 26.1863937 E 25.261355 78.00 (2.89) bc 8.00 (1.16) b 14.00 (0.57) cd 0.188 (0.00) g 
7 S 26.1611553 E 25.248723 74.00 (3.46) cd 4.00 (1.16) cd 22.00 (1.73) b 1.469 (0.00) d 
8 S 26.1118325 E 25.156812 84.00 (2.31) ab 2.00 (0.57) de 14.00 (0.57) cd 2.043 (0.01) c 
Values are means of triplicate readings and values in parenthesis are ± standard error 
Values with the same letter within a column are not significantly different (P < 0.05) 
 
65 
 
 
 
 
8
7
6
5
4
3
2
1
0
1 2 3 4 5 6 7 8
Sample
 
Figure 4.2: The pH of the soil samples from different sampling locations 
 
 
 
12.975
12.364
9.865
7.313
4.892
3.72
3.074
2.713
1 2 3 4 5 6 7 8
Sample
 
 
Figure 4.3: Cation exchange capacity of the soil samples 
 
66 
 
Cation exchange capacity (cmol (+)/kg) pH
 
Low nitrogen contents were observed in all samples which is between 0.027 mg/kg of soil in 
sample 4 to 0.101 mg/kg of soil in sample 7 (Table 4.3). Nitrate concentration in the samples is 
within the range of 0.63 mg/kg of soil in sample 2 to 11.28 mg/kg of soil in sample 4. 
Low concentrations of sodium, potassium and magnesium were observed in the collected soil 
samples (Table 4.3). The concentration of sodium ranged between 0.073 mg/kg in the control soil 
sample to 1.173 mg/kg of soil in sample 4. The values recorded for potassium is between 0.173 
mg/kg in sample 2 to 0.73 mg/kg in sample 7. Magnesium is between 0.51 mg/kg in sample 8 to 
8 mg/kg in sample 4. The calcium content is relatively high compared to sodium, potassium and 
magnesium. The concentration is between 0.983 mg/kg in sample 2 to 21.4 mg/kg in sample 4. 
These values are significantly different (P <0.05) with each sampling location. Sample 4 generally 
had the highest concentration of SO4, S, Mg, Ca, Na and NO3, while sample 2 had the lowest 
concentration of organic carbon, NO3, K and Ca (Table 4.3). 
The correlation between all the physicochemical characteristics of the soil samples is presented in 
Table 4.4. The correlation matrix showed that there were positive correlations between organic 
carbon and P, N, NO3, CEC, K, Ca, Mg, and S. Negative correlations were observed between 
organic carbon and pH, Na and SO4, phosphorus and pH, Na, Ca, Mg, S and SO4, and nitrogen and 
pH, Na, Na, Ca, Mg, S and SO4. Strong correlations were observed between sodium and sulfate (r 
= 0.999), calcium and sulfur (r = 0.999), calcium and magnesium (r = 0.989) and, magnesium and 
sulfur (r = 0.994) (Table 4.4).  
67 
 
Table 4.3: Physical and chemical properties of the soil samples 
Sample Organic Phosphorus Total Nitrogen Nitrate Sodium Potassium Calcium Magnesium Sulfur (mg/kg) Sulfate (mg/kg) 
carbon (%) (mg/kg) (%) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) 
1 0.240 (0.12) d 1.020 (0.01) a 0.045 (0.00) d 2.800 (0.04) d 0.084 (0.00) de 0.345 (0.00) c 1.491 (0.00) e 0.774 (0.00) f 2.693 (0.00) f 2.583 (0.06) f 
2 0.090 (0.06) e 0.300 (0.00) a 0.032 (0.00) f 0.630 (0.01) h 0.079 (0.00) ef 0.173 (0.03) e 0.983 (0.00) g  0.769 (0.01) f 2.021 (0.01) g 5.150 (0.03) e 
3 0.230 (0.06) d 0.663 (0.02) a 0.040 (0.02) e 2.260 (0.06) g 0.107 (0.00) c 0.198 (0.00) e 3.169 (0.00) d 1.249 (0.00) e 4.724 (0.01) e 5.820 (0.04) e 
4 0.350 (0.23) c 0.450 (0.03) a 0.027 (0.00) g 11.283 (0.01) a 1.173 (0.00) a 0.258 (0.00) d 21.436 (0.03) a 7.957 (0.01) a 30.784 (0.00) a 2312.857 (1.19) a 
5 0.750 (0.11) b 2.333 (0.02) a 0.061 (0.00) b 6.640 (0.23) b 0.089 (0.00) d 0.540 (0.00) b 8.132 (0.00) b 2.322 (0.00) d 11.085 (0.00) d 11.080 (0.02) c 
6 0.340 (0.01) c 17.340 (16.83) a 0.054 (0.00) c 4.747 (0.01) c 0.312 (0.00) b 0.361 (0.00) c 8.035 (0.00) c 2.954 (0.00) b 11.677 (0.01) b 537.793 (0.50) b 
7 1.070 (0.04) a 9.323 (0.01) a 0.101 (0.00) a 2.440 (0.04) f 0.078 (0.00) fg 0.732 (0.00) a 8.034 (0.00) c 2.387 (0.00) c 11.255 (0.03) c 8.403 (0.30) d 
8 0.300 (0.01) c 7.707 (0.01) a 0.046 (0.00) d 2.570 (0.04) e 0.073 (0.00) g 0.363 (0.00) c 1.029 (0.00) f 0.506 (0.00) g 1.973 (0.00) h 3.097 (0.00) f 
Values are means of triplicate readings and values in parenthesis are ± standard error 
Values with the same letter within a column are not significantly different (P < 0.05) 
68 
 
Table 4.4: Correlation matrix between the physicochemical properties of the soil samples 
 
 Organic Phosphorus Total Nitrate Cation pH Sodium Potassium Calcium Magnesium  Sulfur Sulfate 
carbon nitrogen exchange 
capacity 
Organic 1.000 0.656 0.888 0.144 0.133 -0.044 -0.115 0.949 0.281 0.146 0.252 -0.110 
carbon 
Phosphorus  0.656 1.000 0.728 -0.267 0.104 0.211 -0.331 0.727 -0.152 -0.231 -0.165 -0.322 
Total 0.888 0.728 1.000 -0.228 0.323 -0.094 -0.409 0.934 -0.055 -0.177 -0.081 -0.400 
nitrogen 
Nitrate 0.144 -0.267 -0.228 1.000 -0.635 0.182 0.870 0.015 0.924 0.916 0.923 0.869 
Cation 0.133 0.104 0.323 -0.635 1.000 -0.502 -0.465 0.141 -0.419 -0.424 -0.420 -0.469 
exchange 
capacity 
pH -0.044 0.211 -0.094 0.182 -0.502 1.000 0.293 -0.163 0.241 0.253 0.244 0.283 
Sodium -0.115 -0.331 -0.409 0.870 -0.465 0.293 1.000 -0.260 0.908 0.958 0.922 0.999 
Potassium 0.949 0.727 0.934 0.015 0.141 -0.163 -0.260 1.000 0.109 -0.022 0.082 -0.249 
Calcium 0.281 -0.152 -0.055 0.924 -0.419 0.241 0.908 0.109 1.000 0.989 0.999 0.908 
Magnesium 0.146 -0.231 -0.177 0.916 -0.424 0.253 0.958 -0.022 0.989 1.000 0.994 0.959 
Sulfur 0.252 -0.165 -0.081 0.923 -0.420 0.244 0.922 0.082 0.999 0.994 1.000 0.923 
Sulfate -0.110 -0.322 -0.400 0.869 -0.469 0.283 0.999 -0.249 0.908 0.959 0.923 1.000 
 Positive correlation means that as one data set increases, the other data set increases as well. Negative correlation means that as one data set increases, the other 
decreases. 
  
69 
 
Table 4.5 showed the heavy metal concentration in each sampling site by total acid digestion. The 
concentration of cadmium in the samples ranged between 0.024 mg/kg to 0.36 mg/kg, chromium is 
between 0.215 mg/kg to 0.413 mg/kg in the digested samples. Lead concentration is between 0.2 
mg/kg in sample 6 to 1.9 mg/kg in sample 1, copper is between 0.12 mg/kg in sample 5 to 0.64 mg/kg 
in sample 5. Iron had the highest concentration among the samples collected with values between 
127.9 mg/kg in sample 5 to 1213.2 mg/kg in sample 4. Sample 4 generally has the highest 
concentration of heavy metals (Table 4.5). 
The correlation between heavy metal concentration soil samples and pH of the soil samples is 
presented in Table 4.6. There is a positive correlation between all metals and pH. This indicates that 
the bioavailability of these metals is pH dependent and these metals are associated with one another. 
4.3.2. Diversity of bacterial isolates from the study sites 
A total of ninety-eight (98) heavy metal resistant bacteria were isolated from all the soil samples. 
Their distribution is shown in Figure 4.4. Twenty-six percent of the bacterial isolates were isolated 
from sample 3 while sample 1 accounted for 24% of the isolates. The lowest number of isolates were 
from sample 4 with 5% (Figure 4.4), which also has the highest concentration of the metals 
determined in the digested samples (Table 4.5).  Figure 4.5 showed the pictures of some of the plates 
used for isolation and the appearance of some of the heavy metal resistant isolates on nutrient agar. 
 
 
70 
 
 
Table 4.5: Heavy metal concentration (mg/kg) of the digested soil samples 
Sample Cadmium Chromium Copper Lead Nickel Iron  Zinc 
1 0.2616 (0.001) b 0.3474 (0.003) c 0.5360 (0.001) b 1.8576 (0.003) b 0.6993 (0.003) b 980.0427 (1.592) b 0.9603 (0.001) b 
2 0.0213 (0.003) d 0.2671 (0.013) f 0.2638 (0.025) c 0.2323 (0.014) de 0.1666 (0.013) c 96.6412 (3.814) f 0.3012 (0.021) c 
3 0.0362 (0.000) c 0.3708 (0.001) b 0.2204 (0.001) d 0.2979 (0.010) c 0.1154 (0.002) d 178.5447 (0.813) c 0.1631 (0.002) d 
4 0.3579 (0.002) a 0.4126 (0.004) a 0.6360 (0.001) a 2.4015 (0.040) a 0.7454 (0.002) a 1213.2267 (1.080) a 1.1965 (0.001) a 
5 0.0245 (0.001) d 0.2872 (0.002) e 0.1209 (0.000) f 0.2093 (0.005) de 0.1247 (0.003) d 127.8600 (0.0352) e 0.0698 (0.001) g 
6 0.0235 (0.001) d 0.2154 (0.001) g 0.1366 (0.000) f 0.1972 (0.002) e 0.0845 (0.001) e 130.5643 (0.360) e 0.0701 (0.001) g 
7 0.0241 (0.001) d 0.2668 (0.001) f 0.1926 (0.000) e 0.2518 (0.006) cd 0.0937 (0.003) e 128.7373 (0.204) e 0.1356 (0.000) e 
8 0.0343 (0.000) c 0.3262 (0.002) d 0.1459 (0.001) f 0.2842 (0.008) c 0.1281 (0.005) d 138.3280 (0.452) d 0.1086 (0.002) f 
*Ref. 7.50 Cr (IV) 6.50 16.00 20.00 91.00 N/A 240.00 
value 
Values are means of triplicate readings and values in parenthesis are ± standard error 
Values with the same letter within a column are not significantly different (P < 0.05) 
*National Norms and Standards for Remediation of Contaminated Land and Soil Quality in South Africa. N/A- Not available
71 
 
Table 4.6: Correlation matrix between soil pH and heavy metals concentration of the acid 
digested soil samples 
 Cd Cr Pb Ni Zn Cu Fe  pH 
Cd 1.000 0.714 0.999 0.984 0.983 0.967 0.998 0.520 
Cr 0.714 1.000 0.707 0.677 0.687 0.703 0.704 0.554 
Pb 0.999 0.707 1.000 0.988 0.986 0.971 0.999 0.518 
Ni 0.984 0.677 0.988 1.000 0.988 0.973 0.988 0.467 
Zn 0.983 0.687 0.986 0.988 1.000 0.994 0.982 0.486 
Cu 0.967 0.703 0.971 0.973 0.994 1.000 0.966 0.533 
Fe 0.998 0.704 0.999 0.988 0.982 0.966 1.000 0.541 
pH 0.520 0.554 0.518 0.467 0.486 0.533 0.541 1.000 
Positive correlation means that as one data set increases, the other data set increases as well. Negative 
correlation means that as one data set increases, the other decreases. 
 
 
10%
24%
12%
7% 6%
10%
5% 26%
1 2 3 4 5 6 7 8
 
Figure 4.4:  Distribution of bacterial isolates from each soil sample 
72 
 
The bacterial counts from each soil sample are presented in Table 4.7. The total bacterial counts 
for the samples varied between 1.25-6.3 × 106 CFU/g. The population of the heterotrophic 
bacteria from the soil samples were sensitive to heavy metals as the number decreases when 
heavy metals were added to the medium for isolation. The counts (in CFU/g) obtained for heavy 
metal resistant isolates are between 1.6-7.6 × 103, 0.15-2.37 × 103, and 1.28-6.95 × 103 for 
cadmium, chromium, and lead respectively. The total bacterial counts obtained in the presence 
of the combination of all metals (Cd, Cr and Pb) is between 0.13-2.08 × 102. The presence of 
heavy metals in the growth  medium had a negative effect on the bacterial counts. The 
combination of all metals also had a negative effect on the bacterial counts (Table 4.7). The 
resistant isolates were screened for their ability to grow on increasing concentration of heavy 
metals. Twenty isolates that showed appreciable growth were selected for further studies. Table 
4.8 showed metal resistance pattern of the twenty resistant isolates on different concentrations of 
heavy metals. Most of the bacterial isolates were able to grow on 400 mg/L of Pb and about 55% 
of the isolates were able to grow in the presence of 1 g/L of Pb. Forty percent of the isolates were 
able to grow on 200 mg/L of Cd with 10% grew on 300 mg/L of Cd. Limited growth was observed 
on medium containing chromium. Fifty percent of the isolates were able to grow on 100 mg/L of 
Cr and 20% on 300 mg/L of Cr. As the metal concentration increases, there was a decrease in the 
number of organisms that were able to grow on them (Table 4.8). 
 
 
 
 
 
 
 
 
 
 
 
73 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4.5: Plates showing bacterial counts and appearance of heavy metal resistant isolates 
 
4.3.3. Biochemical profile of heavy metal resistant isolates 
Most of the isolates are rod-like in shape with 70% Gram positive organisms (Table 4.9). All 
isolates were catalase positive, oxidase negative, and did not produce hydrogen sulfide and indole. 
Sixty-four percent of the isolates utilize citrate as a carbon source while 5% produce urease. 
Eighty-five percent of the isolates liquefy gelatin, 80% reduce nitrate to nitrite while 65% of the 
isolates hydrolyze starch. Five percent of the isolates ferment lactose, 10% ferment maltose, 65% 
ferment sucrose while 80% of the isolates ferment glucose (Table 4.9).  
4.3.4. Molecular characterization of resistant isolates 
The 16S rRNA gene amplification of the genomic DNA of the resistant isolates yielded the 
expected 1500 bp fragments using universal primers (F1, R2) as shown in Figure 4.6. The 
organisms were identified based on the 16S rRNA gene sequence using the BLAST search of the 
74 
 
NCBI database (Table 4.10). The organisms were grouped into Acinetobacter (1 isolate), 
Agrobacterium (1 isolate), Bacillus (13 isolates), Citrobacter (1 isolate), Enterobacter (1 isolate), 
Pantoea (1 isolate) and Pseudomonas (2 isolates) (Table 4.10). Bacillus species accounts for 65% 
of the metal resistant isolates while Pseudomonas is 10% of the isolated metal resistant organisms. 
The phylogenetic relationship of the 16S rRNA gene of the identified heavy metal resistant 
bacteria with their aligned reference sequences of related taxa are presented in Figures 4.7- 4.12. 
The relationship between the Bacillus species, Enterobacter asburiae, Citrobacter freundii, 
Pseudomonas species and their related taxa in the NCBI GenBank database using maximum 
likelihood method based on Jukes-Cantor model (Jukes and Cantor, 1969) is presented in Figures 
4.7, 4.9, 4.10, and 4.11 respectively. The bootstrap consensus tree inferred from 1000 replicates is 
taken to represent the evolutionary history of the taxa analyzed. The percentage of replicate trees 
in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown 
next to the branches (Felsenstein, 1985). All positions containing gaps and missing data were 
eliminated. 
75 
 
Table 4.7: Enumeration of heavy metal resistant bacterial isolates from each sample 
Sample Total bacterial Cd (103 CFU/g) Cr (103 CFU/g) Pb (104 CFU/g) MM (102 CFU/g) 
counts (106 CFU/g) 
1 6.30 (0.20) a 2.50 (0.20) bc 0.26 (0.06) d 2.53 (0.13) c 0.13 (0.03) f 
2 6.20 (0.30) a 6.75 (0.75) a 0.78 (0.13) c 1.28 (0.75) d 0.38 (0.02) e 
3 1.35 (0.25) c 1.65 (0.25) c 1.53 (0.13) b 1.75 (0.15) cd 0.23 (0.03) f 
4 1.25 (0.05) c 7.60 (0.30) a 0.15 (0.05) d 6.95 (0.56) a 1.23 (0.07) b 
5 2.15 (0.15) b 3.30 (0.10) b 2.37 (0.75) a 2.17 (0.17) cd 0.86 (0.04) d 
6 1.40 (0.10) c 2.60 (0.20) bc 1.23 (0.23) b 5.25 (0.45) b 2.08 (0.02) a 
7 1.80 (0.20) bc 1.60 (0.30) c 0.84 (0.04) c 6.51 (0.41) a 1.06 (0.05) c 
8 1.65 (0.25) bc 1.95 (0.15) c 0.38 (0.02) d 1.28 (0.02) d 0.86 (0.15) d 
MM is multi-metal (Cd, Cr, Pb) of mixture of 50 mg/kg of each metal  
Values are means of duplicate readings and values in parenthesis are ± standard error 
Values with the same letter within a column are not significantly different (P < 0.05) 
 
 
 
 
 
76 
 
Table 4.8: Growth pattern of heavy metal resistant isolates on different concentrations of heavy metals 
 
Isolate Cd (mg/L) Cr (mg/L) Pb (mg/L) 
code 
 100 200 300 100 200 300 100 200 300 400 500 1000 
AB4 + + - + + - + + + + + + 
AB5 + + - - + - + + + + + + 
AB6 + + - + + + + + + + + + 
AB9 + + - + - - + + + + + + 
AB14 - - - - - - + + + + + + 
AB18 + - - + + - + + + + + - 
AB19 - - - - - - + + + + + - 
AB21 - - - - - - + + + + + - 
AB22 - - - - - - + + + + + + 
AB23 + + + - - - + + + + + + 
AB30 + - - - - - + + + + + + 
AB36 + + - + - - + + + + + + 
AB58 - - - - - - + + + + + + 
AB61 + - - - - - + + + + + + 
AB66 - - - - - - + + + - - - 
AB77 + - - + + + + + + + - - 
AB79 + + - + + + + + + + - - 
AB82 + - - + + - + + + + - - 
AB91 - - - + + + + + + + - - 
AB92 + + + + + - + + + + - - 
(+) = Growth in the presence of metals; (-) =  No  growth in the presence of metal
77 
 
The relationship between Acinetobacter calcoaceticus and its related taxa in the NCBI GenBank 
database using neighbor-joining method (Saitou and Nei, 1987) is presented in Figure 4.8. The 
percentage of replicate trees in which the associated taxa clustered together in the bootstrap test 
(1000 replicates) are shown next to the branches (Felsenstein, 1985). The tree is drawn to scale, 
with branch lengths in the same units as those of the evolutionary distances used to infer the 
phylogenetic tree. The evolutionary distances were computed using the Jukes-Cantor method 
(Jukes and Cantor, 1969) and are in the units of the number of base substitutions per site.  
The relationship between Pantoea sp. and its closely related taxa in the NCBI GenBank database 
was determined using the maximum parsimony method. The bootstrap consensus tree inferred 
from 1000 replicates is taken to represent the evolutionary history of the taxa analyzed 
(Felsenstein, 1985). Branches corresponding to partitions reproduced in less than 50% bootstrap 
replicates are collapsed. The maximum parsimony tree was obtained using the subtree-pruning-
regrafting algorithm (Nei and Kumar, 2000) with search level 2 in which the initial trees were 
obtained by the random addition of sequences.  
78 
 
Table 4.9: Biochemical profile of heavy metal resistant bacterial isolates 
TESTS 
ISOLATE 
CODE 
AB4 + Rod + - - - + - + - + - + + + + - - - - - 
AB5 + Rod + - - + + - + - + - + + + + - - - - - 
AB6 + Coccobacilli + - - + - - - - + - + + + - - - + - - 
AB9 - Rod + - - + - - + - + +G + +G + +G + +G + +G - 
AB14 + Rod + - - - + - + - + - + +G - + - - - - - 
AB18 + Rod + - - - + - + - + - + + - + - - - - - 
AB19 + Rod + - - - + - + - + - + + - + - - - - - 
AB21 + Rod + - - - + - + - + - + +G - + - - - - - 
AB22 + Rod + - - - + - + - + - + + - + - - - - - 
AB23 - Rod + - - + + - - - + - + - - - - - - - - 
AB30 - Rod + - - + + - - - + - - - + - - - - - - 
AB36 - Rod + - - + + - - - + - + + - + - - - - - 
AB58 - Rod + - - - - - - - - +G + +G + +G + +G + +G +G 
AB61 + Rod + - - - + - + - + - - - - + - - - - - 
AB66 + Rod + - - - + - + - + - + + - + - - - - - 
AB77 - Rod + - - + + - - - - + + + + + + - - - - 
AB79 + Rod + - - - - - + - + - - - + - - - - - - 
AB82 + Rod + - - + - - + - + - - + - - - - - - - 
AB91 + Rod + - - - - - + - + - + + - + - - - - - 
AB92 + Rod + - - + - + - - - - + + + - - - - - - 
Key:  + (Positive), - (Negative), G (Gas production), CAT (Catalase), OXI (Oxidase), IN (Indole), VP (Voges Proskaeur), CIT (Citrate), URE (Urease), SH (Starch hydrolysis), GL (Gelatin 
liquefaction), MAN (Mannose), NIT(Nitrate), GLU (Glucose), ARA (Arabinose), SUC (Sucrose), MAN (Mannose), SOR (Sorbitol), MEL (Melibiose), MAL (Maltose), LAC (Lactose)
79 
 
 
GRAM 
RXT 
SHAPE  
CAT  
OXI  
IN  
VP  
CIT  
URE  
SH  
H2S   
GL  
MAN  
NIT  
GLU  
ARA  
SUC  
MAN  
SOR  
MEL  
MAL  
LAC  
 
Figure 4.6: Photograph of the agarose gel showing 16S rRNA gene amplicons of 1500 bp of metal 
resistant bacteria. M= 1Kb molecular weight marker. Lane 1: AB04, Lane 2: AB05, Lane 3: AB06, 
Lane 4: AB09, Lane 5: AB14, Lane 6: AB18, Lane 7: AB19, Lane 8: AB21, Lane 9: AB22, Lane 
10: AB23, Lane 11: AB30, Lane 12: AB36, Lane 13: AB58, Lane 14: AB61, Lane 15: AB66, Lane 
16: AB77, Lane 17: AB79, Lane 18: AB82, Lane 19: AB91, Lane 20: AB92, Lane 21: Control 
 
 
 
 
 
 
 
80 
 
Table 4.10: 16S rRNA  gene sequence-based identification of the isolates and their accession numbers 
Isolate Strain code Isolate name Accession BLAST ID (closest cultured representative) Percentage E value 
code number similarity (%) 
AB4 NWUAB01 Bacillus cereus MH399230 Bacillus cereus (MH021873 100 0.0 
AB5 NWUAB02 Bacillus toyonensis MH399231 Bacillus sp. (KY064069) 99 0.0 
AB6 NWUAB03 Acinetobacter calcoaceticus MH399232 Acinetobacter calcoaceticus (KM114923) 100 0.0 
AB9 NWUAB04 Enterobacter asburiae MH399233 Enterobacter sp.  (MG754444) 95 0.0 
AB14 NWUAB05 Bacillus wiedmannii MH399234 Bacillus wiedmannii (MF681995) 100 0.0 
AB18 NWUAB06 Bacillus cereus MH399235 Bacillus cereus (KJ612536) 99 0.0 
AB19 NWUAB07 Bacillus cereus MH399236 Bacillus sp. (KP992119) 99 0.0 
AB21 NWUAB08 Bacillus thuringiensis MH399237 Bacillus thuringiensis (MH260380) 100 0.0 
AB22 NWUAB09 Bacillus subtilis MH399238 Bacillus subtilis (KY495216) 100 0.0 
AB23 NWUAB10 Agrobacterium tumefaciens  Agrobacterium tumefaciens (KP050793) 99 0.0 
AB30 NWUAB11 Pseudomonas fluorescens MH399239 Pseudomonas fluorescens (KU977136) 99 0.0 
AB36 NWUAB12 Pseudomonas korensis MH399240 Pseudomonas korensis (MH211310) 99 0.0 
AB58 NWUAB13 Citrobacter freundii MH399241 Citrobacter freundii (KM406465) 99 0.0 
AB61 NWUAB14 Bacillus cereus MH399242 Bacillus cereus (MH356588) 99 0.0 
AB66 NWUAB15 Bacillus wiedmannii MH399243 Bacillus wiedmannii (MF681995) 99 0.0 
AB77 NWUAB16 Pantoea sp. MH399244 Pantoea sp. (JN082730) 91 0.0 
AB79 NWUAB17 Bacillus megaterium MH399245 Bacillus megaterium (MH368091) 100 0.0 
AB82 NWUAB18 Bacillus aryabhattai MH399246 Bacillus aryabhattai (MF 681982) 99 0.0 
AB91 NWUAB19 Bacillus megaterium  Bacillus megaterium (MF681883) 99 0.0 
AB92 NWUAB20 Bacillus sp. MH399247 Bacillus sp. (MH191109) 99 0.0 
 
81 
 
 
Figure 4.7: Evolutionary relationships of taxa tree based on partial 16S rDNA sequences using 
maximum likelihood method showing relationships between the resistant Bacillus species isolates 
and closely related strains from NCBI GenBank
82 
 
 
 
 
Figure 4.8: Evolutionary relationships of taxa tree based on partial 16S rDNA sequences using 
neighbor-joining method showing relationships between the resistant Acinetobacter calcoaceticus 
and its closely related strains from NCBI GenBank 
 
 
Figure 4.9: Evolutionary relationships of taxa tree based on partial 16S rDNA sequences using 
maximum likelihood based on the Jukes-Cantor model showing relationships between the resistant 
Enterobacter asburiae and its closely related strains from NCBI GenBank 
 
 
 
Figure 4.10: Evolutionary relationships of taxa tree based on partial 16S rDNA sequences using 
maximum likelihood based on the Jukes-Cantor model showing relationships between the resistant 
Citrobacter freundii and its closely related strains from NCBI GenBank 
 
83 
 
 
Figure 4.11: Evolutionary relationships of taxa tree based on partial 16S rDNA sequences using 
maximum likelihood based on the Jukes-Cantor model showing relationships between the resistant 
Pseudomonas species and its closely related strains from NCBI GenBank 
 
 
 
Figure 4.12: Evolutionary relationships of taxa tree based on partial 16S rDNA sequences using 
maximum parsimony method based on the Jukes-Cantor model showing relationships between the 
resistant Pantoea sp. and its closely related strains from NCBI GenBank 
4.3.5. Disstribution of heavy metal resistance genes among the resistant isolates 
The amplification of metal resistance genes responsible for Cd, Cr, and Pb were performed with 
specific primers for PCR amplification of the genomic DNA. No amplification products were 
obtained for primers representing CzcA, CzcB and chrB. The amplification of cadmium resistance 
genes (cadA and CzcD) with the expected band size are shown in Figures 4.13 and 4.14. Of the 20 
isolates, 9 (45%) were positive for cadA and one unexpected band size was observed at Lanes 4, 
5, and 12 (Figure 4.13). cadA primers produce fragments of 600 base pairs in Bacillus cereus (Lane 
84 
 
1, 6 and 14), B. toyonensis (Lane 2), Citrobacter freundii (Lane 13), Pantoea sp. (Lane 16), 
Bacillus aryabhattai (Lane 18), B. megaterium (Lane 17 and 19). Ninety percent of the isolates 
had the CzcD gene which is responsible for cadmium, cobalt and zinc resistance (Figure 4.14). 
There was no band in Lane 9 and an unexpected band size was observed at Lane 15. All the isolates 
produce CzcD fragments of 398 bp except B. subtilis (Lane 9) and B. wiedmannii (Lane 15) (Figure 
4.14).  
 
Figure 4.13: Photograph of the agarose gel showing cadA gene amplicons of 600 bp for cadmium 
resistance. M= 1Kb molecular weight marker. Lane 1: AB04, Lane 2: AB05, Lane 3: AB06, Lane 
4: AB09, Lane 5: AB14, Lane 6: AB18, Lane 7: AB19, Lane 8: AB21, Lane 9: AB22, Lane 10: 
AB23, Lane 11: AB30, Lane 12: AB36, Lane 13: AB58, Lane 14: AB61, Lane 15: AB66, Lane 
16: AB77, Lane 17: AB79, Lane 18: AB82, Lane 19: AB91, Lane 20: AB92 
The amplification of lead resistance genes (PbrA and PbrT) are presented in Figures 4.15 and 4.16. 
Five of the isolates produce PbrA primer fragments at 766 bp in B. cereus (Lane 1), B. toyonensis 
(Lane 2), A. calcoaceticus (Lane 3), Pantoea sp. (Lane 16) and B. megaterium (Lane 17). There 
was unexpected band size in Lanes 9,10, 14,15, 19 and 20 (Figure 4.15). PbrT primers produce 
fragments of 740 bp in two isolates (Figure 4.16): Bacillus cereus (Lane 7) and Bacillus 
aryabhattai (Lane 18). 
85 
 
 
Figure 4.14: Photograph of the agarose gel showing CzcD gene amplicons of 398 bp for cadmium, 
cobalt and zinc resistance. M= 1Kb molecular weight marker. Lane 1: AB04, Lane 2: AB05, Lane 
3: AB06, Lane 4: AB09, Lane 5: AB14, Lane 6: AB18, Lane 7: AB19, Lane 8: AB21, Lane 9: 
AB22, Lane 10: AB23, Lane 11: AB30, Lane 12: AB36, Lane 13: AB58, Lane 14: AB61, Lane 
15: AB66, Lane 16: AB77, Lane 17: AB79, Lane 18: AB82, Lane 19: AB91, Lane 20: AB92 
 
Figure 4.15: Photograph of the agarose gel showing PbrA gene amplicons of 766 bp for lead 
resistance. M= 1Kb molecular weight marker. Lane 1: AB04, Lane 2: AB05, Lane 3: AB06, Lane 
4: AB09, Lane 5: AB14, Lane 6: AB18, Lane 7: AB19, Lane 8: AB21, Lane 9: AB22, Lane 10: 
AB23, Lane 11: AB30, Lane 12: AB36, Lane 13: AB58, Lane 14: AB61, Lane 15: AB66, Lane 
16: AB77, Lane 17: AB79, Lane 18: AB82, Lane 19: AB91, Lane 20: AB92 
86 
 
 
Figure 4.16: Photograph of the agarose gel showing PbrT gene amplicons of 740 bp for lead 
resistance. M= 1Kb molecular weight marker. Lane 1: AB04, Lane 2: AB05, Lane 3: AB06, Lane 
4: AB09, Lane 5: AB14, Lane 6: AB18, Lane 7: AB19, Lane 8: AB21, Lane 9: AB22, Lane 10: 
AB23, Lane 11: AB30, Lane 12: AB36, Lane 13: AB58, Lane 14: AB61, Lane 15: AB66, Lane 
16: AB77, Lane 17: AB79, Lane 18: AB82, Lane 19: AB91, Lane 20: AB92 
Chromium resistance gene (chrA) produce a fragment of 1292 bp in B. cereus (Lane 6), P. 
fluorescens (Lane 11), P. korensis (Lane 12) and Bacillus sp. (Lane 20) (Figure 4.17). There were 
multiple fragments at Lanes 2 and 4, and unexpected band size at Lane 15 (Figure 4.17). 
 
Figure 4.17: Photograph of the agarose gel showing chrA gene amplicons of 1292 bp for 
chromium resistance. M= 1Kb molecular weight marker. Lane 1: AB04, Lane 2: AB05, Lane 3: 
AB06, Lane 4: AB09, Lane 5: AB14, Lane 6: AB18, Lane 7: AB19, Lane 8: AB21, Lane 9: AB22, 
Lane 10: AB23, Lane 11: AB30, Lane 12: AB36, Lane 13: AB58, Lane 14: AB61, Lane 15: AB66, 
Lane 16: AB77, Lane 17: AB79, Lane 18: AB82, Lane 19: AB91, Lane 20: AB92 
 
87 
 
4.3.6. The effect of heavy metals on bacteria growth 
The growth of metal resistant isolates was monitored in LB broth for six days in the presence of the metal 
tested (Cd, Cr, Pb; 100 mg/L of each metal) and the results are presented in Figures 4.18 to 4.22. In all, 
a decrease in optical density value was observed in the presence of heavy metals compared with 
the metal-free medium for each isolate (Figure 4.18-4.22). Also, most of the isolates were able to 
utilize Pb better than Cd and Cr. Generally, the growth of the bacterial isolates in the presence of 
the metals tested follows this pattern, Pb > Cr > Cd.  
 
Figure 4.18: The growth patterns of B. Cereus (AB4), B. Toyonensis (AB5), A. Calcoaceticus 
(AB6), and E. Asburiae (AB9) in the presence of heavy metals 
88 
 
 
Figure 4.19: The growth patterns of B. wiedmannii (AB14), B. cereus (AB18), B. cereus 
(AB19), and B. thuringiensis (AB21) in the presence of Cd, Cr and Pb 
 
Figure 4.20: The growth patterns of B. subtilis (AB22), A. tumefaciens (AB23), P. fluorescens 
(AB30), and P. korensis (AB36) in the presence of Cd, Cr and Pb 
89 
 
 
 
Figure 4.21: The pattern of growth of C. freundii (AB58), B. cereus (AB61), B. wiedmannii 
(AB66), and Pantoea sp. (AB77) in the presence of heavy metals 
 
Figure 4.22: The growth patterns of B. megaterium (AB79), B. aryabhattai (AB82), B. 
megaterium (AB91), and Bacillus sp. (AB92) in the presence of Cd, Cr and Pb 
90 
 
4.3.7. Biosurfactant production by heavy metal resistant isolates 
Biosurfactant production profiles by the heavy metal resistant isolates are presented in Table 4.11. 
Of the twenty isolates, 50% showed complete hemolysis of red blood cells on blood agar (Figure 
4.23 and Table 4.11). Bacillus sp., B. cereus (2), B. toyonensis, B. wiedimannii, B. thuringiensis, 
B. substilis, P. fluorescens A. calcoaceticus and Pantoea sp. showed complete hemolysis of red 
blood cells. Forty-five percent of the isolates were positive for drop collapse with most hemolytic 
organisms also showing positive results for drop collapse. 
The emulsification of different hydrocarbons and vegetable oil by different strains showed 
contrasting results as shown in Table 4.11. Most of the organisms were able to produce emulsion 
with engine oil at high percentage after 24 hours. The range of the emulsification of engine oil is 
between 0 – 93%. The emulsification of vegetable oil by different isolates is between 0 – 46% and 
that of kerosene is between 0 – 50.6%. Most of the isolates are able to form a stable emulsion with 
at least two of the oils used as the substrate. 
Organisms such as B. cereus (AB4), A. calcoaceticus (AB6), B. wiedmannii (AB14), B. cereus 
(AB19), B. thuringiensis (AB21), Pantoea sp. (AB77), and Bacillus sp. (AB92) were positive for 
all the biosurfactant production tests. They are also able to completely hemolyse red blood cells, 
are positive for drop collapse and are able to emulsify engine oil, vegetable oil, and kerosene. 
91 
 
Table 4.11: Biosurfactant production profiles of the bacterial isolates 
Isolate Organism Hemolysis Emulsification index (%) Drop collapse 
code 
   Engine oil Vegetable oil Kerosene  
AB4 Bacillus cereus + 53.40 (0.50) 23.50 (0.50) 37.25 (0.25) + 
AB5 Bacillus toyonensis + 49.00 (1.00) 20.50 (0.50) 36.90 (0.90) - 
AB6 Acinetobacter calcoaceticus + 44.00 (2.00) 14.00 (2.00) 40.85 (0.85) + 
AB9 Enterobacter asburiae - 13.25 (0.75) 22.50 (1.50) 48.5 (1.50) - 
AB14 Bacillus wiedmannii + 81.50 (1.50) 17.50 (1.50) 4.50 (0.50) + 
AB18 Bacillus cereus - 87.25 (0.25) 25.00 (1.00) 14.00 (2.00) - 
AB19 Bacillus cereus + 14.75 (0.75) 25.00 (1.50)  5.50 (1.50) + 
AB21 Bacillus thuringiensis + 4.50 (0.50) 42.00 (2.00) 0.00 (0.00) + 
AB22 Bacillus subtilis + 28.70 (0.70) 40.50 (0.50) 5.15 (1.15) - 
AB23 Agrobacterium tumefaciens - 0.00 (0.00) 26.50 (0.50) 36.75 (0.75) - 
AB30 Pseudomonas fluorescens + 76.75 (0.25) 46.00 (2.00) 45.50 (2.50) - 
AB36 Pseudomonas korensis - 80.50 (0.50) 28.65 (0.65) 13.25 (0.75)  + 
AB58 Citrobacter freundii - 74.80 (0.20) 0.00 (0.0) 37.60 (1.60) + 
AB61 Bacillus cereus - 87.25 (0.25) 28.50 (0.50) 0.00 (0.00) - 
AB66 Bacillus wiedmannii - 93.00 (4.24) 14.00 (2.00) 0.00 (0.00) - 
AB77 Pantoea sp. + 10.50 (0.50) 29.80 (0.60) 29.60 (0.40) + 
AB79 Bacillus megaterium - 41.30 (0.60) 30.30 (0.70) 37.75 (1.35) - 
AB82 Bacillus aryabhattai - 83.80 (0.50) 24.05 (0.95) 39.30 (4.20) - 
AB91 Bacillus megaterium - 82.05 (0.95) 23.55 (0.45) 5.15 (0.95) - 
AB92 Bacillus sp. + 23.75 (0.25) 39.7 (0.60) 50.60 (1.70) + 
  Values are means of duplicate readings at p < 0.05  
  Values in parenthesis are ± standard error 
92 
 
   
4.3.8. Flocculating activity of heavy metal resistant bacterial isolates 
The flocculating activities of heavy metal resistant isolates are presented in Table 4.12. The 
flocculating activity of the different isolates ranged between 0 – 71.3%. Pantoea sp. had the 
highest flocculating activity of 71.3% while some of the Bacillus species, Acinetobacter 
calcoaceticus and  Enterobacter asburiae did not show any activity. 
Table 4.12: Flocculating activity of the heavy metal resistant bacterial isolates 
Isolate code Organism Flocculating activity (%) 
AB4 Bacillus cereus 0.00 (0.00) 
AB5 Bacillus toyonensis 0.00 (0.00) 
AB6 Acinetobacter calcoaceticus 0.00 (0.00) 
AB9 Enterobacter asburiae 0.00 (0.00) 
AB14 Bacillus wiedmannii 17.5 (0.25) 
AB18 Bacillus cereus 21.35 (0.65) 
AB19 Bacillus cereus 32.70 (0.30) 
AB21 Bacillus thuringiensis 23.10 (0.90) 
AB22 Bacillus subtilis 25.75 (0.45) 
AB23 Agrobacterium tumefaciens 21.90 (0.60) 
AB30 Pseudomonas fluorescens 22.90 (0.60) 
AB36 Pseudomonas korensis 51.70 (1.70) 
AB58 Citrobacter freundii 27.85 (0.75) 
AB61 Bacillus cereus 13.00 (0.50) 
AB66 Bacillus wiedmannii 20.00 (1.00) 
AB77 Pantoea sp. 71.25 (0.65) 
AB79 Bacillus megaterium 3.80 (1.30) 
AB82 Bacillus aryabhattai 0.00 (0.00) 
AB91 Bacillus megaterium 42.05 (0.95) 
AB92 Bacillus sp. 16.60 (1.40) 
  Values are means of duplicate readings (P < 0.05) 
  Values in parenthesis are ± standard error 
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4.4. Discussion 
The bioavailability and mobility of metals in soil is controlled by the physical and chemical 
properties of soil that determine their biding ability to soil (Dube et al., 2001). Metal availability 
and behavior in mining environment is influenced by pH, cation exchange capacity (CEC), organic 
matter, particle size, moisture content, and sulfate composition of such soil (Dube et al., 2001; 
Finzgar et al., 2007). Soil type and pH, particle size, CEC and permeability of the soil affect metal 
removal efficiency from soil with high organic matter and clay content having a detrimental effect 
on metal removal (Mulligan et al., 2001a). The soil samples used in this study have a low organic 
matter and clay content that will make metal removal from the soil samples effective for 
remediation purposes.  
The soil samples are sandy in nature which increases their permeability. These soil samples have 
numerous pore volumes that makes removal of metals easier and effective. Soil with high clay 
content and layered systems are difficult to remediate because they are less permeable (Mulligan 
et al., 2001a), but they accommodate more diverse and large populations of microorganisms than 
sandy soils (Deng et al., 2018). Organic carbon is often correlated with microbial biomass, with 
clay soils having higher number of biomass than sandy soils  (Reeve et al., 2010). The low organic 
carbon causes disruption in the function of the ecosystem. Ashraf et al. (2012) reported that mining 
soils are low in organic carbon and organic matter which also corroborate the results of this study. 
The sandy nature of the soil samples indicates that these soils have poor nutrient retention capacity 
and do not support good microbial habitats. This impacts negatively on the diversity and function 
of microbial populations in the soil environment (Reeve et al., 2010). The sandy nature of the soils 
also makes metals readily bioavailable in the environment as a result of low binding capacity of 
sandy soils, consequently resulting in high metal mobility in such environments. 
Low soil pH tends to increase metal solubility and toxicity and thus has a strong effect on the 
microbial population (Deng et al., 2018). The soil samples in this study are slightly acidic to neutral 
94 
 
in nature which accounts for the low diversity of bacteria obtained in this study. The dominant 
microbial population isolated in this study (Bacillus and Pseudomonas species) have been known 
to grow and survive at the pH range obtained for soil samples in this study (Das and Mukherjee, 
2007). The results showed that slightly acidic to slightly alkaline environment is suitable for 
isolation of these dominant organisms. This correlates with similar findings reported by Ndeddy 
Aka and Babalola (2017) who reported slightly alkaline soil from mining soil with Bacillus and 
Pseudomonas species as the dominant organisms, and Wahl (2014), who reported slightly acidic 
to slightly neutral pH for mining soils from North West province of South Africa in their study. 
Maiti (2006) reported that moisture content of mining soil is a fluctuating parameter that depends 
on sampling period, organic carbon content, stone content, dump height, texture, and thickness of 
litter layers. It is an important parameter for biogeochemical, biological and hydrological processes 
(Sadhu et al., 2012). Low moisture content from this study can be attributed to low organic carbon 
content and the sandy nature of the soil samples. Low moisture content of between 2-3% during 
summer and 5% during winter was reported by Maiti et al. (2002). The moisture content of soil 
samples in this study are lower than those reported by Sadhu et al. (2012) except for sampling site 
4 with moisture content of 3.890%. 
The cation exchange capacity of a soil is the capacity of the soil to absorb or release cations (Ashraf 
et al., 2012), and a measure of soil fertility and nutrient retention (Mukhopadhyay et al., 2014). 
Clay content and organic matter of the soil are responsible for the CEC. The CEC of soil decreases 
with increasing soil size distribution (Dube et al., 2001) with clay soils having high CEC values. 
This suggest that negatively charged humic acids serve as binding sites for organic matter that 
helps in the retention of cations (Abollino et al., 2002). The CEC of metal species depends on the 
ionic strength of the soil colloid surfaces and relative charges of metal ions in solution (Finzgar et 
al., 2007). Sandy soils typically have low CEC as reflected in this study and indicates the poor 
ability of the soil to retain cations and hold water, consequently increasing metal availability 
95 
 
potential in the soil. The sandy nature of the soil samples is due to high leaching caused by porous 
soil samples. Similar results were reported by Sadhu et al. (2012) for sandy soils with low CEC 
values. 
Organic carbon, phosphorus and nitrogen have been reported as growth limiting factors in mining 
soils (Mukhopadhyay et al., 2014). The total nitrogen and phosphorus in the soil samples are very 
low except for sample 6 which has high phosphorus content. The low nitrogen content of mining 
soil is as a result of inadequate organic input, nitrogen fixation and lack of mineralizable organic 
nitrogen (Saviour and Stalin, 2012; Mukhopadhyay et al., 2014). The low nitrogen content of the 
soil can be attributed to lack of microbial activity and removal of natural vegetation in mining soil. 
Phosphorus content of soil is influenced by pH, organic matter and weathering processes 
(Mukhopadhyay et al., 2014) and has an important effect on metal bioavailability as a result of 
sparingly soluble salts (Finzgar et al., 2007).  
The microbial activities are reduced in soil affected by high sulfur content which ultimately affects 
biogeochemical cycles and mineral nutrition (Menz and Seip, 2004).  
The iron content of the soil samples showed that they are adequate for mine soil samples as they 
contain concentrations greater than 4.5 mg/kg of soil samples (Maiti, 2006). The copper content 
of the soil samples is also low and within the toxic limits of the National Norms and Standards for 
Remediation of Contaminated Land and Soil Quality in South Africa of 16 mg/kg of soil for all 
land uses. The low copper content can be attributed to the low organic matter content of the soils 
that possibly immobilize metals (Maiti, 2006). The copper content of the soil samples were lower 
than those reported by Wahl (2014) from gold mining soils in North West province of South 
Africa.  
Mining activities have resulted in destruction and alteration of the ecosystem through metal 
pollution of the surrounding soil (Ayangbenro and Babalola, 2017). Although the concentration of 
96 
 
heavy metals in the soil samples falls within the permissible limits of National Norms and 
Standards for Remediation of Contaminated Land and Soil Quality in South Africa, these metals 
still have potential significant impacts on biological systems (Guo et al., 2009). This was observed 
in the lower microbial diversity that were found in the polluted sites compared with the control 
soil. The findings of this study are similar to the concentrations of heavy metals reported by Wahl 
(2014) from gold mining soil from North West province of South Africa which also falls below 
the permissible limits. The heavy metal concentration recorded in this study is lower than those in 
the study conducted by Olobatoke and Mathuthu (2016) from gold mining sites in Johannesburg, 
South Africa. Metal concentrations were found to exceed the recommended limits set by the South 
African regulatory body. Soil particle size influences the heavy metal content of such soil (Dube 
et al., 2001).  Hence, metal content is higher in clay soil than in sandy soil because clay minerals 
have large surface area and weak pH dependence of CEC (Dube et al., 2001). The low metal 
contents in the soil samples in this study can be attributed its sandy nature. This implies that high 
amounts of metals will be in soil solution, thereby increasing metal bioavailability in the 
environment. The binding forces between soil particles and metal ions are dependent on the pH 
and the ionic properties of the metal ions (Dube et al., 2001). Increase in pH of the environment 
decreases the binding forces between soil particles and metal ions. The slightly acidic pH to 
slightly alkaline pH of the soil samples can also be attributed to the low heavy metal concentrations 
obtained from the soil samples. Likewise, the affinity of each metal varies between different soil 
mineral components and organic matter. 
Bacteria in polluted environments have been exposed to high concentrations of heavy metals from 
nearby environments which have occurred steadily over a long period (Oliveira et al., 2010; 
Ayangbenro and Babalola, 2018). Mining soils contaminated with metals and metalloids are 
sources of metal resistant bacteria isolates belonging to Acinetobacter, Bacillus, Pseudomonas, 
and Enterobacter species (Fan et al., 2011; Ndeddy Aka and Babalola, 2017), with Bacillus species 
reported to be the most abundant in metal contaminated soil (Ellis et al., 2003). These soils are 
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considered an interesting model for the development of metal resistant bacteria isolates (Rajkumar 
and Freitas, 2008). The isolated organisms have been previously isolated from mining and metal 
contaminated environments (Sharma and Fulekar, 2009; Zhang et al., 2011; Mohite et al., 2018; 
Oladipo et al., 2018). The ability of these isolates to survive in metal polluted environment can be 
attributed to their cell wall composition that binds and interacts with metal ions and their genetic 
makeup (Abou-Shanab et al., 2007; Shin et al., 2012). 
The total bacterial counts from each sample were higher than the counts obtained in the presence 
of heavy metals from each sample. This can be attributed to growth inhibition in the presence of 
heavy metals which suppresses the diversity and population of bacteria in the growth medium 
(Chihomvu et al., 2014; Zampieri et al., 2016). Screening of metal resistant bacterial isolates is a 
continuous process in the search for isolates that have the potential to remediate polluted 
environments. Hence, there is a need to screen for resistant isolates from mining soils to harness 
the potential benefits of these organisms. The metal tolerances reported in this study are higher 
than those reported by Rajesh and Rajesh (2016), Mohite et al. (2018), Oladipo et al. (2018) and 
Marzan et al. (2017). However, they are lower than those reported by Zhang et al. (2011). The 
different responses of each isolate to different concentrations of heavy metals suggests different 
action mechanisms, unique chemistry and toxicity of each metal ion  (Edwards and Kjellerup, 
2013; Oladipo et al., 2018).  
Multi-metal resistance by microbial strains gives mutual benefits to the single components and are 
suitable for metal removal (Alisi et al., 2009). Multi-metal resistance showed the presence of 
various genetic determinants for metal resistance, which could have evolved in the natural 
environment of the organism. The genetic determinants encode for specific metal transport 
proteins involved in sequestration of metal ions and regulating active efflux (Pal et al., 2005). 
Multi-metal resistance could also be attributed to existence of concomitant regulating factors for 
co-selection of metals that is widespread in the environment (Rahman and Singh, 2016). 
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Bacteria have adapted to environmental changes by physiological and genetic mechanisms that 
confer new traits, such as metal resistance and xenobiotic degradation, to survive and colonize 
their environment (Anjum et al., 2011). Metal resistance is known to be achieved through efflux 
systems as found in Gram-negative organisms (Blair et al., 2014) that pump metals out of the 
cytoplasm into the periplasmic space, and other mechanisms such as complexation, accumulation, 
transformation, sorption, oxido-reduction reactions and mineralization (Govarthanan et al., 2013; 
Ayangbenro and Babalola, 2017). Bacterial isolates were screened for metal resistance genes by 
PCR amplification of genomic DNA for genes responsible for Cd, Cr, and Pb resistance. These 
genes have the potential to reduce or eliminate metal toxicity (Wei et al., 2009).  
Most of the isolates harbor the CzcD operon that is responsible for multi-metal resistance (Cd, Co 
and Zn), which is widespread in most isolates. It is necessary for the activation of Czc determinant 
known to encode for Cd, Co and Zn resistance together (Nies et al., 1989). It is a protein involved 
in detoxification of these metals, by exporting these metal ions  from the periplasm and/or from 
the cytoplasm directly into the extracellular medium and regulation of CzcCBA pump (Legatzki et 
al., 2003). cadA is a cadmium efflux and that is involved in soft-metal-transporting ATPases. This 
protein binds the inducing cations with high affinity to cysteine residues and interacts directly with 
the promoter or operator region of the ATPase-encoding gene (Legatzki et al., 2003). The 
expression of cadA by bacteria isolates in this study is controlled by the cytoplasmic concentration 
of zinc and cadmium in their cells. The cadA and Czc are energy dependent efflux systems, cadA 
is constitutively expressed while Czc is inducible (Nies et al., 1989). cadA appears to be the main 
detoxification mechanism of Cd in this study as most of the isolates that have CzcD operon do not 
possess the cadA gene. 
The PbrA is an efflux transporter (P-type ATPases) protein and PbrT is an uptake protein that is 
involved in lead resistance (Shin et al., 2012).The organisms were screened for efflux transporter 
PbrA and lead uptake protein PbrT and some of the isolates harbor these genes. ChrA confers 
99 
 
chromate resistance on the organisms which results from the accumulation of CrO 2-4  (Nies et al., 
1990). It is an efflux pump responsible for chromate extrusion (Pimentel et al., 2002). Therefore, 
organisms possessing metal resistance genes are ideal isolates for metal removal in contaminated 
environments and potential sources of novel metal resistant genes.  The organisms that did not 
harbor any of the metal resistance genes could be explained by the lack of genetic systems 
responsible for metal resistance or the presence of other efflux mechanisms for metal resistance 
(Shin et al., 2012). Furthermore, the primers used for resistant gene amplification may be 
inappropriate.  
The low tolerance for Cd and Cr which also follows the decrease in growth rate of the bacterial 
isolates in the presence of these metals can be explained by the slow utilization efficiency as a 
result of high energy cost of the organisms in the presence of metal stress (Giller et al., 2009). This 
is also a common phenomenon as isolates from metal polluted environment only proliferate in the 
presence of small amounts or the absence of metal in the growth medium under laboratory 
conditions (Gadd, 1990). Other factors that could be responsible for this are media composition, 
dosage and incubation conditions. Whatever the case, heavy metals usually suppress microbial 
population and species diversity in a particular environment, thus selecting for resistant isolates  
(Zampieri et al., 2016). The growth rate of all the isolates in the presence of heavy metals was 
generally lower than that of the control. This is similar to the pattern observed by Raja et al. (2006) 
and Pal et al. (2004). 
Microbial metabolites are generating increasing attention and are usually the first barrier against 
metal toxicity that is essential in protecting the internal cell (Li and Yu, 2014; Ayangbenro and 
Babalola, 2018). These biopolymers are desirable to circumvent the pathogenicity concerns of 
some of the producing isolates. Metal resistant isolates were screened for biopolymer production 
that are useful in metal(loid) removal from contaminated environment. The presence of 
100 
 
extracellular substances which serve as barrier in Gram-positive bacteria enhances resistance to 
metal compared to Gram-negative organisms (Ianeva, 2009). 
The screening for biosurfactant production was based on the concept that contaminated 
environment serves as a source of potential biosurfactant-producing isolates and the isolates were 
evaluated under laboratory conditions (Eddouaouda et al., 2012). Hemolysis of red blood cells on 
nutrient agar has been used to screen for biosurfactant production by bacteria (Bicca et al., 1999; 
Kumar et al., 2016a). Carrillo et al. (1996) and Kumar et al. (2016a) found a correlation between 
hemolysis of red blood cell and surfactant production, and suggested its use as a screening 
procedure for biosurfactant production. However, not all biosurfactant producing isolates have the 
ability to lyse red blood cells. It could exclude some potential isolates, but has helped in the initial 
screening of surfactant producing strains (Youssef et al., 2004). 
Drop collapse method was suggested  as an easy and sensitive method for biosurfactant production 
by Jain et al. (1991). The organisms were therefore screened for drop collapse and it was observed 
that some isolates that were able to hemolyze red blood cells were negative for drop collapse. This 
can be attributed to the fact that some isolates act as biosurfactants themselves and do not produce 
extracellular polymer (Thavasi et al., 2011). The drop collapse is a criterion that excludes 
hemolytic organisms that do not produce biosurfactant.  
Emulsification index is another criterion used in the selection of surface active producing bacterial 
isolates. Satpute et al. (2008) suggested that more than one screening method should be used in 
the primary screening of potential surface-active agents. Emulsification enhances the 
bioavailability of metals and thus increases the removal rate. Emulsification test is a reliable test 
for surfactant production which makes an organism a suitable surface-active agent. It was however 
observed that some bacterial isolates were able to emulsify some of the oils tested but did not 
produce hemolysis or are positive for drop collapse. 
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Biosurfactant production by bacteria isolated from mining environment was reported by Toribio-
Jim et al. (2014) and Wang and Mulligan (2009) and were found to be useful in metal removal 
from contaminated environment. Production of biosurfactant could be a mechanism of metal 
uptake and metal resistance by the isolated organisms. 
Bioflocculant is another biopolymer produced by bacterial isolates during growth. The flocculating 
properties of the metal resistant isolates were determined  to access the aggregation of particles 
and cells (Yim et al., 2007). Bacterial isolates that were able to flocculate suspended kaolin clay 
can adsorb to other particles to form flocs (Tang et al., 2014). The flocs are capable of effective 
metal removal from solution. 
Conclusion 
This study has identified many heavy metal resistant bacterial isolates with high tolerance and 
capable of producing biopolymers which are metal complexing agents for future biotechnological 
use. The study also showed that mining soils are deficient in nutrients which affects the 
distribution, population and diversity of microorganisms in the environment.  Moreover, Bacillus 
species were the dominant organisms in the areas and they showed high metal resistance. 
 
 
 
 
 
 
 
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CHAPTER FIVE 
HEAVY METAL REMOVAL FROM CONTAMINATED SOIL BY LIPOPEPTIDE 
BIOSURFACTANT PRODUCED BY BACILLUS CEREUS NWUAB01 
Abstract 
Microbial polymers, such as biosurfactants, are good metal(loid) complexing agents and have been 
used for the removal of metal(loids) from polluted environmental media. Biosurfactant from 
Bacillus cereus NWUAB01 isolated from a gold mining soil was evaluated for heavy metal (Cd, 
Cr and Pb) removal from contaminated soil. Heavy metal resistance of the isolate was determined 
on Luria Bertani agar. Biosurfactant production was determined by conventional screening 
methods that include hemolysis, drop collapse, emulsification and surface tension measurement. 
Strain NWUAB01 was able to grow on 200 mg/L of Cd and Cr, and was resistant to 1 g/L of Pb. 
The surfactant produced is a lipopeptide with a metal complexing property. It has emulsification 
of 22%, 24%, 37% and 54% on hexadecane, vegetable oil, kerosene and engine oil respectively. 
The surfactant had a surface tension of 39.5 mN/m. The crude biosurfactant was able to remove 
35%, 56% and 78% of Cr, Cd and Pb respectively from contaminated soil. The biosurfactant 
produced by strain NWUAB01 is a promising metabolite for remediation of heavy metal 
contaminated soil. 
Keywords: bioremediation, biosurfactant, metal(loid)s, mining, pollution, secondary metabolite 
5.1 Introduction 
Industrialization and mining activities have continued to put an increasing burden on the 
environment as a result of metal(loid) pollution (Das et al., 2009; Ayangbenro et al., 2018). The 
unrestrained release of metal(loid)s into the environment from these activities poses a serious 
threat to the ecosystem and the health of living organisms. Heavy metals have been known to cause 
various diseases and ailments in humans, such as allergic reactions, birth defects, damage to 
103 
 
internal organs, cardiovascular and respiratory disorder, learning disabilities and cancer 
(Ayangbenro and Babalola, 2017). 
Conventional methods of heavy metal removal involve treatment with chelating agents, organic 
and inorganic acids, reverse osmosis, surfactants and water. However, these techniques are often 
expensive and ineffective for low heavy metal concentration removal. Other challenges often 
encountered with the use of these conventional techniques include non-specificity of these 
methods, high energy demand, space requirements and the impractical nature of some of the 
techniques  (Das et al., 2009; Voica et al., 2016).  
Bioremediation of heavy metals offers an alternative and effective means of decontaminating 
metal polluted environments. Heavy metal remediation of contaminated environment mediated by 
microorganisms is efficient and cost effective (Voica et al., 2016). Microorganisms have 
developed various mechanisms for detoxifying heavy metals. These mechanisms include 
bioaccumulation, biomineralization, biosorption and biotransformation (Lin and Lin, 2005). These 
organisms also secrete a range of metal sequestering biopolymers that are employed in metal 
uptake (Wu et al., 2010a; Fomina and Gadd, 2014). These biopolymers also trap and absorb metal 
sulfides and oxides (Wu et al., 2010a). 
Biopolymers of microbial origin are emerging as a promising technique to enhance the 
effectiveness of metal(loid) removal. Similarly, these polymers can survive in a wide range of pH 
and temperature. Their metal binding ability depends on the producing organism, metal affinity 
and specificity, functional groups on the biopolymer, temperature and pH  (Pal and Paul, 2008; Li 
et al., 2014; Ayangbenro and Babalola, 2018). They are environmentally friendly, versatile and 
economic compared to chemical polymers. 
One of the numerous polymers of microbial origin is biosurfactant, with numerous applications in 
detergents, cosmetics, medicine, food industries, petroleum and bioremediation (Valls and De 
104 
 
Lorenzo, 2002). Biosurfactants have been reported as a metal complexing agent for removal of 
metal(loid)s from polluted soil and wastewaters (Singh and Cameotra, 2004; Mulligan, 2005; 
Dahrazma and Mulligan, 2007; Ayangbenro and Babalola, 2018). They solubilize metal ions 
through surface tension reduction and increase in wettability, thereby bringing metal ions out of 
the soil matrix. Biosurfactants of microbial origin are good metal complexing agents due to their 
stability, degradability, low toxicity and environmental compatibility (Rizzo et al., 2015). They 
form stable complexes with metal ions as a result of electrostatic interaction between charged 
polymers.  
Cadmium, chromium and lead have been listed among toxic elements within the first twenty 
pollutants priority list that are of significance to public health (Cáliz et al., 2013). The use of 
isolated biopolymers from microorganisms is desired to circumvent the pathogenicity concerns of 
some producing organisms. Therefore, this research work investigate the removal of these toxic 
metals (Cd, Cr, and Pb) by biosurfactants produced by heavy metal resistant B. cereus NWUAB01 
isolated from gold mining polluted soil in Vryburg, South Africa. 
5.2 Materials and method 
5.2.1 Isolation and screening of heavy metal resistant bacterial isolates 
Soil samples were collected from active gold mine in Vryburg (26˚10’5.28” S, 25˚14’47.69” E), 
North West Province, South Africa. Control soil samples were also collected around the mines. 
Soil samples were collected in triplicates at depth of 10-30 cm with a soil auger in June 2016 and 
transported to the laboratory for analysis in sterile plastic bags. Isolation of heavy metal (Cd, Cr, 
Pb) resistant isolates was done as described by Rajkumar and Freitas (2008). One gram of each of 
the collected soil samples was serially diluted and plated out on Luria-Bertani agar that has been 
supplemented with 50 mg/L of each metal solution and a mixture of metals. The metal solutions: 
CdSO  4 (Sigma-Aldrich, India), K2CrO4 and Pb(NO3)2) (Sigma-Aldrich, USA) were filter-
105 
 
sterilized through 0.22 µm membrane filter (Millipore Corporation, Bedford, MA, USA). The 
plates were incubated at 37˚C for 48 h. Resistant isolates were then screened for ability to grow 
on different concentrations (100 to 1000 mg/L) of each metal salt on LB agar plates (Srinath et al., 
2002). The plates were incubated at 37˚C for 48 h and observed for growth. The lowest 
concentration of metal that completely inhibits the growth of bacterial isolates was considered the 
minimum inhibitory concentration (MIC). Metal sorption by strain NWUAB01 was performed with 
100 mg/L of each metal and optical density measured at 24 h interval.  
5.2.2 Identification of strain NWUAB01 
The following biochemical tests were carried out on strain NWUAB01: Gram reaction, catalase 
test, citrate utilization, indole production, hydrogen sulfide production, oxidase, nitrate reduction, 
methyl red, sugar fermentation test (fructose, glucose, galactose, lactose, starch, sorbitol, sucrose, 
maltose, mannitol), and Voges-Proskauer test. The organism was identified according to the 
Bergey’s manual of determinative bacteriology (Garrity et al., 2004). 
The DNA of the strain NWUAB01 was extracted from pure culture grown on LB agar using a ZR 
soil microbe DNA mini prep TM DNA extraction kit (Zymo Research, CA, USA) according to the 
kit’s protocol. The quality and quantity of the DNA was determined using a NanoDrop Lite 
spectrophotometer (Thermo Fischer Scientific, CA, USA). 
5.2.3 Amplification of 16S rDNA and detection of heavy metal resistance genes 
Universal primers F1 (forward, 5′- AGAGTTTGATCCTGGCTCAG -3′) and R2 (reverse, 5′- 
ACGGCTACCTTGTTACGACTT -3′), with approximately 1500 base pair amplicon, were used 
to amplify the 16S rRNA gene of strain NWUAB01 (Marin et al., 2011). The PCR was performed 
with a total volume of 25 μl of a reaction mixture consisting of 12.5 μl 2X master mix (One Taq® 
Hot Start Quick Load, Biolabs, England), 0.5 μl (10 μM working solution) of each of the forward 
and reverse primer, 1 μl of the DNA template, and 11 μl nuclease free water. The PCR was 
106 
 
performed with a thermal cycler (Bio-Rad, CA, USA) with preincubation step of 95˚C for 5 min 
and 35 cycles of denaturation at 94˚C for 1 min, annealing at 50˚C for 30 s, and extension at 72˚C 
for 1 min, followed by a final extension step of 72˚C for 10 min. Polymerase chain reaction 
amplicons were analyzed by electrophoresis on agarose gel (1% w/v) containing ethidium bromide 
(1%) and the band size was determined using 1 kb molecular marker.  
The sequencing method was as described in section 4.2.6. The nucleotide BLAST of the 16S rRNA 
gene sequence homology was searched on the NCBI GenBank database. The sequence was aligned 
and analyzed by Molecular Evolutionary Genetics Analysis (MEGA 7.0) software (Kumar et al., 
2016b). (Kumar et al., 2016b)(Kumar et al., 2016b)(Kumar et al., 2016b)(Kumar et al., 
2016b)(Kumar et al., 2016b)(Kumar et al., 2016b)The phylogenetic tree was constructed based on 
the 16S rDNA using the maximum parsimony method. The sequence was deposited in the NCBI 
GenBank database. 
Analysis of heavy metals encoding genes was done with primers encoding for cadmium (cadA, 
CzcA, CzcB and CzcD), chromium (chrA and chrB) and lead (PbrA and PbrT) for the isolate. The 
sequences of each primer set and their PCR conditions are presented in Table 4.1. All the primer 
set used for PCR amplifications were synthesized by Whitehead Scientific, Integrated DNA 
Technologies, South Africa.  
The isolate was sequenced on the Illumina Miseq platform. Sample DNA was fragmented by 
ultrasonication procedure (Covaris), and the fragments selected by size with AMPure XP beads 
and the end was repaired. Illumina specific adapter sequences were ligated to each fragment. The 
fluorometric method was used for quantification of the fragment and then diluted to a standard 
concentration (4 nM). A MiSeq v3 kit was used for sequencing of the fragment on the Illumina 
Miseq platform. The obtained sequences were processed and the quality of the reads were checked 
using FastQC v.1.0.1 of the Kbase platform (Arkin et al., 2016). The reads were trimmed to remove 
low quality sequences and adapter sequences using Trimmomatic v0.36. The CLC de novo 
107 
 
assembly was used to assemble the genome using the CLC platform. The Rapid Annotations using 
Subsystems Technology (RAST v2.0) server (Aziz et al., 2008) and the NCBI Prokaryotic Genome 
Annotation Pipeline (PGAP v4.5) (Haft et al., 2017) were used for genome annotation. 
5.2.4 Screening and characterization of biosurfactant produced by strain NWUAB01 
Pure strain of NWUAB01 was used to quantify biosurfactant production by different methods 
which include hemolytic activity, drop collapse test, oil displacement test, and emulsification 
activity. All tests were carried out in triplicates. The hemolytic test on blood agar was carried out 
as described by Bicca et al. (1999). 
The cultivation medium for biosurfactant production contains (g/l): yeast extract, 0.5; sucrose, 5.0; 
Na2HPO4·12H2O, 1.4; MgSO4·7H2O, 0.02; KH2PO4, 0.4 and peptone, 20.0 (Sriram et al., 2011). 
The medium was seeded with 3% inoculum prepared from LB broth into 50 ml cultivation medium 
in a 250 ml Erlenmeyer flask and incubated at 37˚C for 7 days at 160 rpm. 
All readings were taken with supernatant obtained by centrifuging the cultures at 10,000 rpm for 
20 min. 
The drop collapse test was determined as described by Sriram et al. (2011). Mineral oil (2 µl) was 
added to each well of a 96-well microtiter plate and allowed to equilibrate for one hr at 37˚C. 
Thereafter, 5 µl of the culture supernatant was added to the center of each well over the oil layer. 
After one min, the shape of the oil drop was examined. A flattened drop was recorded as positive 
for biosurfactant production. Water was used as negative control. 
The oil displacement test was carried out as described by Sriram et al. (2011). A petri dish (150 
mm diameter) was filled with 40 ml sterile distilled water and engine oil (15 µl) was added. 
Thereafter, 10 µl of the supernatant was added to the center of the oil film and the halo zone was 
measured after 30 s of incubation. 
108 
 
The emulsification activity of strain NWUAB01 was determined by measuring the emulsification 
index (E24) after 24 h. A 2 ml volume of the culture supernatant was added to 2 ml of kerosene in 
a test tube and the mixture was vortexed at high speed for 2 min. The E24 was calculated as the 
percentage of height of emulsified layer divided by total height of the liquid column (Rizzo et al., 
2015). The test was also performed using engine oil, hexadecane and vegetable oil in place of 
kerosene. The stability of the emulsion produced was determined at different temperature and pH. 
Surface tension of cell free supernatant was determined in a force tensiometer (Sigma 702, Biolin 
Scientific, Sweden) using the du Nouy ring method at room temperature.  
The biosurfactant produced was extracted and purified as described by Gond et al. (2015). Strain 
NWUAB01 was grown in 1 L cultivation medium for 7 days at 37˚C at 200 rpm. The cell free 
supernatant obtained by centrifuging at 5000 rpm for 15 min at 4˚C was precipitated by adding 
conc. HCl to reduce the pH to 2 and incubated overnight at 4˚C. The precipitate was collected by 
centrifugation at 10000 rpm for 15 min at 4˚C and dissolved in methanol and then filtered using a 
membrane filter (0.22 µm PTFE) to remove cell components and larger particles. The resulting 
mixture was then concentrated using a vacuum evaporator at 30˚C and lyophilized. 
The lyophilized biosurfactant was characterized by Fourier transform infrared spectroscopy 
(FTIR). The FTIR spectra, with a resolution of 4 cm-1, were collected from 400 to 4000 
wavenumbers (cm_1) with an average of 32 scans using an Alpha II Platinum-ATR IR 
spectrophotometer (Brucker, USA). 
The molecular mass of the surfactant was determined using Micromass ToFSpec matrix-assisted 
laser desorption ionization time-of-flight mass spectrometry (MICRO-TOF-MS). Applied 
Biosystems 4800 Plus MICRO-TOF/TOF analyzer (AB Sciex, USA) was used to obtain a purified 
sample. The analyzer was operated in the positive ion mode with 337 nm nitrogen laser for 
ionization, accelerating voltage of 20 kV and a-cyano-4-hydroxycinnamic acid for matrix. The 
109 
 
molecular weight was determined by mass spectrum smart formula tools and the mass 
spectrometry was determined using the Bruker compass data analysis. 
5.2.5 Remediation of heavy metal contaminated soil with biosurfactant 
Biosurfactant washing of heavy metal polluted soil was performed in a batch experiment as 
described by Singh and Cameotra (2013). A gram of heavy metal (Cd, Cr, Pb: 100 mg/L of each 
metal salt) spiked soil sample was placed in a series of 50 ml centrifuge tubes with 25 ml of the 
biosurfactant solution. The suspensions were centrifuged at 5000 rpm for 12 min and the 
supernatant was filtered. The metal composition of the filtered supernatant was analyzed by 
Inducible Conductivity Plasma-Optical Emission Spectroscopy (725 ICP-OES, Agilent 
Technologies, Palo Alto, CA, USA). The positive control used is strain NWUAB01 while sterile 
distilled water served as the negative control. The metal removal efficiency was calculated using 
the formula: 
𝐶𝑖 −𝐶𝑒  
𝑅𝑒𝑚𝑜𝑣𝑎𝑙 𝑟𝑎𝑡𝑒 (%) =  𝑋 100  
𝐶𝑖
Where Ci and Ce are the initial and final concentrations of metal respectively. 
5.3 Results and Discussion 
Soils contaminated with heavy metals are usually sources of metal resistant bacteria isolates (Xie 
et al., 2016; Ndeddy Aka and Babalola, 2017). Mining soils are rich sources of potential bacterial 
populations that are resistant to heavy metals but with reduced bacteria diversity, population size 
and metabolic activities (Choudhary and Sar, 2009; Zampieri et al., 2016). Metal resistance might 
have developed as a result of the presence of metals in their growth medium (Çolak et al., 2011). 
In this study, soil samples from a gold mining environment, with natural occurrence of heavy 
metals, were used for isolation of metal resistant isolates. Thus, the metal-containing environment 
might have led to the evolvement of mechanisms of resistance to heavy metals in the isolated 
organisms. Ninety-eight heavy metal resistant bacteria isolates were isolated from the collected 
110 
 
soil samples. Elevated levels of tolerance to metals is an important criterion for metal removal by 
bacterial strains (Ndeddy Aka and Babalola, 2017). Strain NWUAB01 showed multiple resistance 
to the metals tested and good preliminary metal removal properties, with the ability to grow on all 
the concentrations of Pb tested while it showed growth on 100 and 200 mg/L of Cd and Cr (Table 
5.1). The organisms showed the ability to withstand varying metal concentrations as reported in 
different studies (Gnanamani et al., 2010; Shim et al., 2014) from different polluted sites and 
higher tolerance compared to those observed by Oladipo et al. (2018). Varied responses of strain 
NWUAB01 to different metal ions observed in this study could be attributed to different mode of 
action, unique chemistry and level of toxicity of each metal (Edwards and Kjellerup, 2013; Oladipo 
et al., 2018).  
Multi-metal resistance by microbial strains gives mutual benefits to the single components and are 
suitable for metal removal (Alisi et al., 2009). Multi-metal resistance showed the presence of 
various combinations of genetic determinants for metal resistance. This could probably have 
evolved in the natural environment of the organism. The genetic determinants encode for specific 
metal transport proteins involved in sequestration of metal ions and regulating active efflux (Pal 
et al., 2005). The resistance pattern to the tested heavy metals follows the order Pb > Cd = Cr. Our 
results showed that Cd and Cr are more toxic to strain NWUAB01 than Pb due to the toxicity of 
the metals on the organism. Many reports had also reported many bacteria with multi-metal 
resistance abilities (Abou-Shanab et al., 2007; Dell’Amico et al., 2008; Ndeddy Aka and Babalola, 
2017). Multi-metal tolerance in Bacillus species has been well documented.  
The presence of extracellular substances which serve as a barrier in Gram-positive bacteria 
enhances metal resistance compared to Gram-negative organisms (Ianeva, 2009). A direct 
comparison of metal resistance by strain NWUAB01 with other studies is difficult due to the  
composition and strength of medium, nature of the medium that influences metal bioavailability, 
111 
 
complexation, organic constituents, diffusion rate and incubation period which causes variations 
in inhibitory concentrations (Govarthanan et al., 2013; Shim et al., 2014). 
Various mechanisms are employed by microbial cells for metal removal (Lin and Lin, 2005). 
These include bioaccumulation, biomineralization, biosorption and biotransformation. The growth 
inhibition curve of strain NWUAB01 on 100 mg/L of each metal at pH 7, agitation of 150 rpm 
and 25 ˚C is presented in Figure 5.1. The highest optical density was obtained on the sixth day of 
growth for each of the metals tested and the control. The optical density generally increases with 
time for all the metals and control (Figure 5.1). In summary, a decrease in the optical density of 
strain NWUAB01 was observed in the presence of heavy metals compared with the metal-free 
medium. This is similar to the pattern observed by Shim et al. (2014) and Raja et al. (2006). The 
presence of heavy metal in the growth medium is consistently less than that observed in the control. 
The decrease in growth of B. cereus in the presence of heavy metals might be due to metal ion 
interaction with cell membrane and thus increases metal binding sites which makes it less effective 
for transport of materials essential for growth (Achal et al., 2012). To understand the mechanism 
of resistance to metals, the growth kinetics is used as an index of adaptation (Oladipo et al., 2018). 
The inverse growth rate relationship observed between metal concentrations and growth rate in 
tolerant bacteria are characteristic of bacteria growth in response to external stress (Bloem and 
Breure, 2003). The high inhibitory values obtained for Cd and Cr along with decrease in growth 
rate in the presence of these metals could be attributed to decline in efficiency of substrate 
utilization as a result of high energy cost of the organism subjected to metal stress (Giller et al., 
2009). 
The biochemical profile of strain NWUAB01 is presented in Table 5.2. The organism was able to 
ferment glucose, fructose, sucrose, and starch; utilize citrate and is catalase positive (Table 5.2). 
 
112 
 
Table 5.1: Growth of B. cereus NWUAB01 on different concentrations of heavy metals 
Metal 100 200 300 400 500 600 700 800 900 1000 
concentration 
(mg/L) 
Cd + + - - - - - - - - 
Cr + + - - - - - - - - 
Pb + + + + + + + + + + 
(+) Positive reactions; (-) negative reactions 
  
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0 24 48 72 96 120 144
Time (hours)
Cd Cr Pb Control
Figure 5.1: Time course growth of B. cereus NWUAB01 on different heavy metals 
Table 5.2: The biochemical properties of B. cereus NWUAB01 
Test Result Test Result 
Gram reaction + Sugar fermentation  
Shape Rod Glucose + 
Catalase + Galactose - 
Citrate utilization + Lactose - 
H2S production - Starch + 
Indole - Sucrose + 
Methyl red - Sorbitol - 
Nitrate reduction + Maltose - 
Oxidase - Mannitol - 
(+) Positive reactions; (-) negative reactions 
113 
 
Optical density at 600 nm
The amplification of the 16S rRNA gene of strain NWUAB01 yielded the expected 1500 bp 
amplicon (Figure 5.2). The amplicon sequence was compared with the 16S rRNA gene sequences 
in NCBI database and it showed that strain NWUAB01 had 100% similarity with Bacillus cereus 
strain BS16 (MH021873), B. wiedmanni strain F23 (MF681995), B. thuringiensis strain FDB-6 
(MH260380), Bacillus sp strain SP9 (MH191109) and 99% similarity with B. proteolticus strain 
SPB3 (MG280785) with E-value of 0.0. The organism has a 94.6% relationship with B. cereus 
172560W and B. cereus BAG30-2 genomes (Figure 5.4). B. cereus NWUAB01 has been deposited 
at the NCBI database under the 16S rRNA gene accession number MH399230 and whole genome 
accession number QNGD00000000. Bacillus cereus has been reported to have a strong 
relationship with gold mines (Reith et al., 2005) which is evident in this study. 
Genes encoding metal resistance can eliminate or reduce metal toxicity (Shin et al., 2012). Hence, 
strain NWUAB01 was screened for metal resistance genes. The amplification of primer-specific 
heavy metal resistance genes of chromosomal DNA of strain NWUAB01 yielded amplicons of the 
expected band size of 600 bp for cadA, 398 bp for CzcD, and 766 bp for PbrA (Figure 5.2). PbrA  
is the gene responsible for lead uptake and down-regulation of the metal concentration which occur 
in response to high levels of lead (Wu et al., 2016). It thus revealed that isolate NWUAB01 has a 
functional gene that is key in lead resistance. PbrA is an active efflux pump gene that transports 
Pb ions against concentration gradient using energy provided by ATP hydrolysis (Shin et al., 
2012). cadA which is a P-type ATPase, was also found to be present on the organism. cadA is 
cadmium specific ATPase used for Cd efflux and confers metal resistance to strain NWUAB01. 
CzcD is responsible for efflux of cobalt, zinc and cadmium. Both CzcD and cadA operons are 
energy dependent efflux and confer cadmium resistance (Nies et al., 1989). The efflux systems are 
actively involved in the pumping out of toxic metal ions that enter the cell through ATPase 
diffusion. Metal transport proteins are involved in transporting metal ions outside the cell 
membrane (Naik and Dubey, 2013). These metal transporting proteins are a group of PIB-type 
ATPases which governs metal resistance. cadA, CzcD and PbrA belong to these groups of proteins 
114 
 
present in strain NWUAB01 which are involved in metal resistance. These proteins prevent metal 
accumulation of highly reactive and toxic metals within the cell membrane and also play a key 
role in metal resistance by strain NWUAB01 (Naik and Dubey, 2013). 
No amplification was observed for CzcA, CzcB, PbrT, chrA, and chrB. This might be as a result 
of the lack of mechanisms responsible for metal resistance in the genetic system of the organism. 
The organism may also use other mechanisms different from the efflux system for metal tolerance. 
It is likely that amplification of the genes was done with inappropriate primers or the genes were 
not present in the organism. 
 M M M M 
 
 
    1500 
bp 
     600 
bp 
 398 bp 
766 bp 
 
 
 16S cadA CzcD PbrA 
rRNA 
                Lane M-1kb molecular marker 
Figure 5.2: The 16S rRNA and heavy metal resistance genes amplification of DNA sequence of 
strain NWUAB01 
The evolutionary relationship of strain NWUAB01 was deduced by the Maximum Likelihood 
method based on the Jukes-Cantor model (Jukes and Cantor, 1969). The phylogenetic relationship 
of strain NWUAB01 is presented in Figure 5.3, which shows the relationship of the organisms 
with closely related strains from the GenBank. The percentage of trees in which the associated 
taxa clustered together is shown next to the branches. Initial tree for the heuristic search was 
115 
 
obtained automatically by applying the maximum parsimony method. The tree is drawn to scale, 
with branch lengths measured in the number of substitutions per site. The analysis involved 6 
nucleotide sequences. Evolutionary analyses were conducted in MEGA7 (Kumar et al., 2016b). 
 
Figure 5.3: Phylogenetic tree using maximum likelihood method of strain NWUAB01 based on 
16S rRNA gene sequence 
 
Figure 5.4: The phylogenetic relationship of the pangenome sequence of strain NWUAB01 with 
other closely related organisms generated from KBase database 
The emulsification of different hydrocarbons and vegetable oil by strain NWUAB01 is presented 
in Figure 5.5 and the biosurfactant properties of the organism are presented in Table 5.3. The 
organism was able to completely hemolyze red blood cells (α-hemolysis), was positive for drop 
collapse test and reduced the surface tension of the medium to 39.48 mN/m (Table 5.3). Blood 
agar has been used to quantify and screen for biosurfactant production by bacteria (Bicca et al., 
116 
 
1999; Kumar et al., 2016a). Carrillo et al. (1996) and Kumar et al. (2016a) found an association 
between surfactant production and hemolytic activity, and recommended the use of lysis of blood 
agar as screening method for biosurfactant production. Although lysis of red blood cells could 
exclude some biosurfactant producing organisms, it has helped in initial screening of biosurfactant 
producing organisms. Strain NWUAB01 showed complete hemolysis on red blood cells and was 
used as the initial screening test for its selection. The reduction in surface tension of water has 
been reported by several studies (Tuleva et al., 2005; Sriram et al., 2011; Kumar et al., 2016a) for 
various biosurfactant producing Bacillus species. The reduction in surface tension confirmed the 
production of biosurfactant by strain NWUAB01. The ability to reduce the surface tension of water 
from 72 to 35 mN/m has been considered as a characteristic of a good surfactant (Mulligan, 2005). 
Strain NWUAB01 has a surface tension that is similar to that produced by B. cereus NK1 which 
has a value of 38 mN/m (Sriram et al., 2011). It has a better surface tension compared to B. cereus, 
B. sphaericus and B. fusiformis, which has surface tension of 50, 55.2 and 56.4 mN/m respectively 
(Bento et al., 2005), and B. amyloliquefaciens and B. thuringiensis with surface tension values of 
57.7 m/Nm each (Barakat et al., 2017). Strain NWUAB01 has lower surface tension potential 
compared to Bacillus sp reported by Heryani and Putra (2017), that had a value of 27.1 mN/m. 
The differences in the surface tension values can be attributed to different production medium, 
condition of growth and uniqueness of individual organisms. 
Emulsification index is another criterion used in the selection of surface active producing bacterial 
isolates. Satpute et al. (2008) suggested that more than one screening method should be used in 
the primary screening of potential surface-active agents. Strain NWUAB01 produced stable 
emulsions with various hydrocarbons and vegetable oil with the highest E24 of 54% obtained with 
engine oil and lowest E24 of 22% with hexadecane (Table 5.3). This appreciable emulsifying 
property makes the organism a suitable surface-active agent. The ability of biosurfactant to 
emulsify different hydrocarbons and vegetable oil had been reported for Bacillus species with 
different results. Sriram et al. (2011), reported B. cereus NK1 to emulsify motor oil, diesel oil, 
117 
 
crude oil, petrol and vegetable oil with E24 of 80.36, 55.5, 70, 44 and 50.47% respectively. Strain 
NWUAB01 had lower emulsification index compared to what was reported for B. cereus NK1. 
Likewise, Barakat et al. (2017) reported emulsification index of 60 and 69% with paraffin oil for 
B. amyloliquefaciens and B. thuringiensis respectively. This might be as a result of the different 
components of the production medium and different carbon sources used for producing 
biosurfactant (Pereira et al., 2013). 
The production of bioactive metabolites that confer resistance to microorganisms growing in 
polluted environments is an important defense mechanism against environmental stress and for 
survival (Rizzo et al., 2015). Biosurfactants are applied in several fields and the applicability 
depends on their stability at different temperatures and pH values (Khopade et al., 2012). The 
stability of the emulsion produced by strain NWUAB01 was tested at different temperatures and 
pH (Figure 5.6). Gradual increase in emulsion stability was observed and optimum pH for 
emulsion stability of 37.5% was obtained at pH 7 at 30˚C. At 40˚C, the organism gave a stable 
emulsion with E24 of 30% (Figure 5.6). Reduction in the stability of the emulsion was observed at 
extreme temperature and pH. As the pH increases, there was an increase in the stability of the 
emulsion until pH 7, after which, the stability begins to reduce. The result indicated that increase 
in pH had a positive effect on stability of emulsion. This could be as a result of the precipitation 
of biosurfactant at high pH values (Khopade et al., 2012). Lower stability at reduced pH (<4) can 
be attributed to distortion of the biosurfactant structure and precipitation (Zou et al., 2014). 
 
Figure 5.5: Emulsification of (a) kerosene (b) kerosene in the presence of heavy metals (c) 
vegetable oil (d) engine oil by strain NWUAB01 
118 
 
 
Figure 5.6: Stability of the emulsion produced from kerosene at different pH and temperature 
The scanning electron micrograph (SEM) of the surfactant produced by strain NWUAB01 is 
presented in Figure 5.7. This shows the morphology of the lipopeptide surfactant. The FTIR 
characterization of biosurfactant produced by strain NWUAB01 is presented in Figure 5.8. The 
spectra had characteristic absorbance band of peptides at 3273 cm-1 (stretching mode N-H), 1624 
cm-1 (stretching mode CO-N) and 1526 cm-1 (N-H deformation and C-N stretching mode). The 
bands obtained at 2961-2878 cm-1 and 1624-1447 cm-1 represent the presence of aliphatic chains. 
This suggest that the surfactant produced by strain NWUAB01 contained peptide-like moieties 
which is typical of lipopeptides surfactants produced by Bacillus species described in literature 
(Rivardo et al., 2009; Sriram et al., 2011; Pereira et al., 2013). 
The MALDI-TOF spectra of the surfactant produced by strain NWUAB01 is shown in Figure 5.9. 
There are only well resolved groups of peaks at m/z values between 182.76 and 696.37. The 
detected groups could be attributed to the iturin variants as described by Jasim et al. (2016) and 
Cho et al. (2003). The lack of some specific iturin homologs can be attributed to loss of some of 
the amino acids such as asparagine, glycine and tyrosine in the structure of iturin, which makes 
many homologs of the lipopeptide difficult to identify (Chen et al., 2014). The composition of the 
medium of production of lipopeptides can be attributed to some of the variations in the structure 
of the lipopeptide produced (Akpa et al., 2001). This showed that different compounds could be 
expressed by Bacillus species during changes in growth condition (Chen et al., 2014). 
119 
 
Table 5.3: Evaluation of B. cereus NWUAB01 for biosurfactant production 
Test Result 
Hemolysis test Positive 
Oil displacement Negative 
Drop collapse Positive 
Surface tension 39.48 ± 0.25 mN/m 
Emulsification index Engine oil 54.0 ± 0.58 
(E24) (%) Hexadecane 22.4 ± 0.6 
Kerosene 37.5 ± 0.29 
Vegetable oil 24.0 ± 0.58 
Biosurfactant yield  0.38 g/L 
Values are means of triplicate readings ± standard error  
 
 
 
Figure 5.7: The SEM image of the biosurfactant produced be strain NWUAB01 
120 
 
 
 
Figure 5.8: The FTIR spectra of biosurfactant produced by strain NWUAB01 
 
 
Figure 5.9: The mass spectrum of the purified biosurfactant produced by strain NWUAB01 
 
 
121 
 
Table 5.4: Heavy metal removal by strain NWUAB01 and its biosurfactant 
Biosurfactant Strain NWUAB01 
Metal Percentage removal (%) Percentage removal (%) 
Cd 56.06 60.90 
Cr 35.00 30.85 
Pb 78.73 83.83 
Multi-metal 
Cd 35.34 45.80 
Cr 32.23 33.72 
Pb 73.84 72.39 
Biosurfactant soil washing has been used for the removal of metals from contaminated soils and 
sediments  due to their biodegradability environmental compatibility and low toxicity (Mulligan, 
2005; Singh and Cameotra, 2013). In this study, we evaluated the metal removal capability of 
biosurfactant produced by strain NWUAB01. This study showed that the crude biosurfactant was 
able to remove 78% of Pb, 56% of Cd and 35% of Cr from the batch experiment (Table 5.4). We 
found that biosurfactant produced by strain NWUAB01 is efficient in removing metal from 
contaminated soil. In a multi-metal system, the percentage removal of each metal decreases 
compared to a single metal system. The ability of the biosurfactant to remove metals from 
contaminated soil was also examined in comparison with strain NWUAB01. It was observed that 
the percentage metal removal was higher for the organisms than the surfactant with significant 
removal of cadmium. The results of this study showed highest removal of Pb overall followed by 
Cd and Cr. This could be attributed to the affinity of the biosurfactant to different metals (Ochoa-
Loza et al., 2007; da Rocha Junior et al., 2018). The efficiency of metal removal of biosurfactants 
also depends on the type of biosurfactant and its concentration, soil characteristics and other 
additives such as acids and bases that may be added (da Rocha Junior et al., 2018). Metal removal 
efficiency of strain NWUAB01 biosurfactant is higher than those reported by Singh and Cameotra 
(2013). However, the metal removal  efficiency of lipopeptides of marine origin reported by Das 
122 
 
et al. (2009) was higher than those of strain NWUAB01. The metal removal potential of strain 
NWUAB01 corroborated the work of Mulligan et al. (2001b), who reported the use of lipopeptide 
from B. subtilis for the removal of Cd, Cu and Zn. Lipopeptides which are anionic in nature have 
better metal sequestration properties and are more effective in metal removal (Mulligan, 2005). 
Removal of metals by biosurfactant has been proposed to occur by surfactant sorption to soil 
surface which is followed by complexation with metals, thus leading to metal detachment from 
soil surface by the reduction in interfacial tension (Mulligan, 2005).  
Conclusion  
Biosurfactant is a promising technique for enhanced remediation of polluted environments. The 
present study shows the feasibility of using biosurfactant produced by strain NWUAB01 in 
remediation of heavy metal contaminated soil. Findings of this study demonstrate the potential of 
the biosurfactant produced for effective removal and recovery of heavy metals for environmental 
applications. 
 
 
 
 
 
 
 
 
 
 
 
 
 
123 
 
CHAPTER SIX 
DRAFT GENOME SEQUENCE OF HEAVY METAL RESISTANT BACILLUS CEREUS 
STRAIN NWUAB01 
Abstract  
Bacillus cereus NWUAB01 was isolated from a gold mining site from Vryburg, South Africa for 
its multiple heavy metal resistance properties. Here we report the draft genome sequence of B. 
cereus NWUAB01, obtained by Illumina sequencing. 
6.1 Introduction 
Bacteria from polluted environments have evolved resistance and adaptive genes required for their 
survival in hostile environments. The search for novel molecular mechanisms from polluted 
environments provides strong informational resources that lead to new technologies (Astolfi et al., 
2018). As a result, the draft genome of B. cereus NWUAB01 isolated from gold mining soil in 
Vryburg, South Africa is hereby reported.  
Bacillus cereus is a spore forming Gram positive bacterium that is ubiquitous in nature and always 
detected in hostile environments (Rasko et al., 2005). The spore forming ability helps them to 
survive in different habitats (Singh et al., 2010). B. cereus NWUAB01 is a metal resistant spore 
forming isolate. Microbial degradation is a major route of recovery of pollutants from polluted 
environments by means of transformation and detoxification mechanisms (Lin and Lin, 2005).  
6.2 Materials and Method 
B. cereus NWUAB01 was identified from 98 bacterial isolates from soil samples obtained from a 
gold mining site (Section 4.3.2). The isolate was screened for heavy metal (Cd, Cr, Pb) resistance, 
and antibiotic and secondary metabolites production. 
124 
 
The draft genome of B. cereus NWUAB01 was obtained by whole genome shotgun sequencing 
using one Illumina paired-end library with an average insert size of ~300 bp. The sample was 
fragmented using an ultrasonication approach (Covaris). The resulting DNA fragments were 
size selected (300 – 800bp), using AMPure XP beads. The fragments were end repaired and 
Illumina specific adapter sequences were ligated to each fragment. The sample was indexed, and 
a second size selection step was performed. The sample was then quantified, using a fluorometric 
method, diluted to a standard concentration (4 nM) and then sequenced on Illumina’s MiSeq 
platform, using a MiSeq v3 (600 cycle) kit as described in the kit protocol.   
6.3 Results and Discussion 
The raw sequence was processed to obtain high-quality reads using the Kbase (Arkin et al., 2016) 
platform to check quality of reads (FastQC v.1.0.1), trim reads and to remove adapter and low-
quality sequences, and ambiguous reads (Trimmomatic v0.36). The CLC de novo assembly was 
used to assemble the genome using the CLC platform. The Rapid Annotations using Subsystems 
Technology (RAST v 2.0) server (Aziz et al., 2008) and the NCBI Prokaryotic Genome Annotation 
Pipeline (PGAP v4.5) (Haft et al., 2017) was used for genome annotation. 
 The genome of B. cereus NWUAB01 consists of a linear chromosome (5,859,863 bp) with GC 
content of 35.7%, 849 contigs, 6094 protein coding genes, 2 rRNA operons, 12 tRNA genes and 
286 pseudogenes. The subsystem information of B. cereus NWUAB01 was predicted with SEED 
viewer (Overbeek et al., 2014) (Figure 6.1).  
Analysis of the secondary metabolite biosynthesis gene clusters with antiSMASH v3.0 (Weber et 
al., 2015) revealed that B. cereus NWUAB01 had 44 gene clusters involved in antibiotic and 
secondary metabolite biosynthesis. These include non-ribosomal peptide synthetase (NRPS) gene 
clusters, lipoprotein, lipopolysaccharides, petrobactin, siderophore, binding proteins, bacillibactin, 
proteins related to degradation of toxic compounds and biofilm secretion genes. Genes responsible 
125 
 
for resistance to toxic heavy metals such as arsenic, cadmium, chromium, cobalt, copper, lead, 
mercury and zinc were also detected. Five questionable clustered regularly interspaced short 
palindromic repeats (CRISPR) sequences were identified by CRISPRFinder (Grissa et al., 2007). 
Further investigation will provide additional information into the mechanism involved in multi-
metal resistance role of B. cereus NWUAB01 that facilitates environmental applications. 
 
Figure 6.1: The subsystem information of the genome of Bacillus cereus NWUAB01 as predicted 
by SEED Viewer v2.0. Genomic features are colored according to their functional classification 
types 
Accession number(s). This Whole Genome Shotgun project has been deposited at 
DDBJ/ENA/GenBank under the accession QNGD00000000 and BioProject number 
PRJNA476495. The version described in this paper is version QNGD01000000. 
 
 
126 
 
CHAPTER SEVEN 
BIOFLOCCULANT PRODUCTION AND HEAVY METAL SORPTION BY METAL 
RESISTANT BACTERIAL ISOLATES FROM MINING SOIL 
Abstract 
Two bioflocculant producing bacterial isolates from mining soil samples were investigated for 
their application in heavy metal removal. The bacterial isolates were identified as Pseudomonas 
korensis and Pantoea sp. Cadmium resistance genes cadA and CzcD were detected in Pantoea sp. 
while P. korensis harbor CzcD and chrA responsible for Cd and Cr resistance respectively. The 
isolates showed maximum flocculating activity of 71.3% and 51.7% with glucose and yield of 2.98 
g/L and 3.26 g/L for Pantoea sp. and P. korensis respectively. The optimum flocculating activity 
was achieved at pH 7.5 and temperature of 30˚C. Fourier transform infrared analysis of the 
bioflocculants produced by the two isolates showed the presence of carboxyl, hydroxyl and amino 
groups characteristic of polysaccharide and protein. Heavy metal sorption by bioflocculant of 
Pantoea sp. removed 51.2%, 52.5% and 80.5% of Cd, Cr and Pb respectively while that of P. 
korensis removed 48.5%, 42.5% and 73.7% of Cd, Cr and Pb respectively from aqueous solution. 
The bioflocculants produced have potential application in metal removal from industrial wastes. 
Keyword: Biopolymer, biosorption, flocculation, pollution, remediation  
7.1 Introduction 
Heavy metal pollution as a result of industrialization and urbanization has put an increasing burden 
on the ecosystem due to the deleterious effect on living organisms (Sajayan et al., 2017). Industries 
such as mining and metallurgy release large tons of heavy metal containing waste into the 
surrounding environment. These metals are of serious global health concern because of their non-
degradable and persistent nature (Subudhi et al., 2016; Ayangbenro and Babalola, 2017). These 
127 
 
metals exert toxic effects on living organisms even at low concentrations, thus there is a need for 
removal of these metals from the environment (Subudhi et al., 2016; Sajayan et al., 2017).  
Flocculation involves floc formation through aggregation of colloids and suspended solids with 
the help of a stimulant (Sajayan et al., 2017; Ayangbenro and Babalola, 2018). Flocculants are 
materials used in the separation of suspended substances and have several industrial applications. 
Flocculants are of two types: chemically synthesized organic and inorganic flocculants, and natural 
flocculants (Lee et al., 2014; Subudhi et al., 2014; Okaiyeto et al., 2016). Chemically synthesized 
organic and inorganic flocculants are toxic, cause health complications, produce a large quantity 
of waste and are difficult to degrade (Zhai et al., 2012; Farag et al., 2014). Thus, bioflocculants 
are receiving increasing attention due to their biodegradable nature, and because they are 
environmentally benign, non-toxic and lead to efficient removal of suspended particles, dye and 
heavy metals from wastewaters (Sajayan et al., 2017).  
In the last few decades, much attention has been given to environmental pollution management of 
hazardous pollutants (Umrania, 2006). Heavy metal removal from water and soil has been a 
challenge in/and around mining environments. Current chemical and physical approaches of metal 
removal from polluted environments are not environmentally friendly, they are expensive, some 
of the methods are non-specific, have high energy demands, and impracticable nature (Das et al., 
2009; Voica et al., 2016). Biosorption is one of the promising techniques for heavy metal removal 
from wastewater. The technique is simple and economically feasible (Subudhi et al., 2016). 
Secondary metabolites of microbial origin are generating increasing attention lately as a result of 
their diversity, low toxicity and biodegradable nature, which makes them superior to chemical 
products. Microbial products provide defensive mechanisms and increase bioavailability of metals 
(Vaishnav and Demain, 2011; Ayangbenro and Babalola, 2018). One of these bioproducts is 
bioflocculant, an extracellular polymer that is made up of exopolysaccharides, proteins, 
glycoproteins, lipids and glycolipids (More et al., 2014; Subudhi et al., 2016). Bioflocculant is a 
128 
 
promising alternative to synthetic flocculant and is generally recognized as being a non-toxic, 
environmentally-friendly and highly biodegradable substitute with no secondary pollution 
(Bukhari et al., 2018). Different studies have reported on the various applications of bioflocculants 
in remediation processes to treat wastewater, removal of heavy metals and toxic materials, 
potential emulsifier, synthesis of nanoparticles and biomass recovery (Buthelezi et al., 2009; 
Gomaa, 2012; Sathiyanarayanan et al., 2013; Drakou et al., 2017; Bukhari et al., 2018). 
Mining industries discharge waste that contains heavy metals into the surrounding environment. 
High concentrations of these metals are hazardous and tend to form toxic complex compounds 
which affects biological functions (Subudhi et al., 2014). Thus, it becomes imperative to explore 
effective processes of metal removal from contaminated soil and wastewater. This study 
investigates the isolation and bioflocculant production of heavy metal resistant bacterial isolates 
from mining soil and their potential in heavy metal (Cd, Cr and Pb) biosorption. 
7.2 Materials and methods 
7.2.1 Soil sampling and isolation of heavy metal resistant bacteria isolates 
Soil samples were collected from active gold mines in Vryburg, North West Province, South 
Africa. Control soil samples were also collected around the mines. Soil samples were collected in 
triplicate at depths of 10-30 cm with a soil auger in sterile plastic bags. 
Heavy metal (Cd, Cr, Pb) resistant bacterial isolates were isolated as described by Rajkumar and 
Freitas (2008). One gram of each of the collected soil samples were serially diluted and plated out 
on Luria-Bertani (LB) agar that has been supplemented with 50 mg/L of each metal solution and 
a mixture of all the metals. The metal solutions (CdSO4, K2CrO4 and Pb(NO3)2) were filter 
sterilized through 0.22 µm membrane filters (Millipore Corporation, Bedford, MA, USA). The 
plates were incubated at 30˚C for 48 h. Resistant isolates were then screened for ability to grow 
on different metal concentrations (100 to 1000 mg/L) on LB agar plates (Srinath et al., 2002). 
129 
 
Growth of resistant isolates was monitored in the presence of heavy metals (100 mg/L of each metal) in 
LB broth and the optical density measured at 24 h intervals for 6 days. 
7.2.2 Biochemical characterization of bacteria isolates 
The following biochemical tests were carried out on heavy metal resistant isolates: Gram reaction, 
catalase test, citrate utilization, gelatin liquefaction, hydrogen sulfide production, oxidase, nitrate 
reduction, starch hydrolysis, sugar fermentation test (glucose, arabinose, fructose, maltose, 
sucrose, lactose, mannose, melibiose, sorbitol, maltose), urease, and Voges-Proskauer test. Pure 
cultures of the isolates were identified according to the Bergey’s manual of Determinative 
Bacteriology (Garrity et al., 2004). 
7.2.3 Genomic DNA extraction  
The DNA of the selected heavy metal resistant bacterial isolates were extracted from pure cultures 
grown on LB agar using a ZR soil microbe DNA mini prep TM DNA extraction kit (Zymo Research, 
CA, USA) according to the kit’s protocol. The DNA quality and quantity of the bacterial isolates 
wee determined using NanoDrop Lite spectrophotometer (Thermo Fischer Scientific, CA, USA). 
7.2.4 Molecular characterization and detection of heavy metal resistance genes 
The 16S rRNA gene was amplified for each isolate using universal primers F1 (forward, 5′-
AGAGTTTGATCCTGGCTCAG-3′) and R2 (reverse, 5′-ACGGCTACCTTGTTACGACTT-3′) 
with approximately 1500 base pair amplicon (Marin et al., 2011). The PCR was performed with a 
total volume of 25 μl of a reaction mixture consisting of 12.5 μl 2X master mix (One Taq® Hot 
Start Quick Load, Biolabs, England), 1 μl of the DNA template, 0.5 μl (10 μM working solution) 
of each of the forward and reverse primer, and 11 μl nuclease free water. The PCR was performed 
in a C1000 thermal cycler (Bio-Rad, CA, USA) with a preincubation step of 95˚C for 5 min and 
35 cycles of denaturation at 94˚C for 1 min, annealing at 50˚C for 30 s, and extension at 72˚C for 
130 
 
1 min, followed by a final extension step of 72˚C for 10 min. Polymerase chain reaction amplicons 
were analyzed by electrophoresis on agarose gel (1% w/v) containing ethidium bromide and the 
band size was determined using 1 kb molecular marker. Gel Doc 2000 (Bio-Rad, USA) was used 
for gel visualization and photograph.  
Analysis of heavy metal encoding genes were done with primers encoding for cadmium (cadA, 
CzcA, CzcB and CzcD), chromium (chrA and chrB) and lead (PbrA and PbrT) for each isolate. 
The sequences of each primer set are presented in Table 4.1 with their expected band size. All the 
primer sets used for PCR amplifications were synthesized by Whitehead Scientific, Integrated 
DNA Technologies, South Africa.  
7.2.5 Sequencing reaction 
Sequencing of the PCR amplicons was done at Inqaba Biotechnical industrial (Pty) Ltd, Pretoria, 
South Africa with PRISM TM Ready Reaction Dye Terminator Cycle Sequencing Kit using 
dideoxy chain termination method and electrophoresed with a model ABI PRISM® 3500XL DNA 
Sequencer (Applied Bio systems, USA) by following the manufacturer’s instructions. The forward 
and reverse primers were used in sequencing of the purified PCR products. Purified sequencing 
products (Zymo Research, ZR-96 DNA Sequencing Clean-up Kit™) were analyzed using CLC 
Main Workbench 7. 
The 16S rRNA gene sequences were blast searched on the NCBI GenBank database. Aligned 
sequences were analyzed using MEGA 7.0 software (Kumar et al., 2016b). 
7.2.6 Screening of metal resistant isolates for bioflocculant production 
Heavy metal resistant isolates were pre-cultured in 50 ml LB broth on a rotary shaker (200 rpm) 
at 30˚C for 24 hours. The culture medium was used to inoculate 100 ml fermentation medium. The 
fermentation medium contains (g/L):  K2HPO4, 2; NH4Cl, 1; MgSO4, 0.5; CaCO3 0.2; KH2PO4, 5; 
glucose, 2; peptone, 5 and yeast extract, 5 at pH 7.2 (Subudhi et al., 2014). The medium was 
131 
 
incubated for 72 hours at 30˚C on a rotary shaker at 200 rpm. After incubation, the medium was 
centrifuged at 6000 g for 30 minutes and the supernatant was evaluated for flocculating activity.  
The flocculating activity was measured using kaolin clay suspension (0.5 g of kaolin clay in 100 
ml distilled water). Kaolin suspension (45 ml) was mixed with 0.5 ml of the supernatant and 4.5 
ml of CaCl2 (1%) was added to the mixture. The mixture was vortexed for 2 minutes and left 
standing for 5 minutes at room temperature. The optical density of the supernatant and the blank 
(distilled water) was measured at 550 nm (Gomaa, 2012). The flocculating activity was defined as 
𝐴−𝐵
follows: flocculating activity =  × 100 
𝐴
Where A and B are the optical densities of the control and sample respectively at 550 nm. 
7.2.7 Bioflocculant purification 
The purification of the crude bioflocculant was done as described by Nwodo et al. (2012). The 
fermentation broth was centrifuged at 3000 rpm for 30 minutes at 15˚C. The supernatant was 
mixed with cold ethanol at a ratio of 1:4 (v/v) respectively in order to precipitate the bioflocculant. 
The mixture was kept for 16 hours overnight at 4˚C. The mixture was then centrifuged at 1000 
rpm for 30 minutes at 15˚C to collect the resulting precipitate. The precipitate was dissolved in 
distilled water at a ratio of 1:4 (v/v) and the bioflocculant produced was lyophilized. 
7.2.8 Characterization of the bioflocculant produced 
The lyophilized bioflocculant was characterized by Fourier transform infrared spectroscopy 
(FTIR). The FTIR spectra, with a resolution of 4 cm-1, were collected from 400 to 4000 
wavenumbers (cm_1) with an average of 32 scans under ambient conditions using Alpha II 
Platinum-ATR IR spectrophotometer (Brucker, USA). 
The lyophilized bioflocculant was subjected to scanning electron microscope (JSM-6390LV, 
JEOL, Tokyo, Japan). 
132 
 
7.2.9 Effect of culture conditions on flocculation activity 
The flocculating activities of the isolates were determined as described by Subudhi et al. (2014) at 
different pH (5-9), temperature (25, 30, 37 and 50˚C) and carbon source (glucose, fructose, lactose 
and sucrose).  
7.2.10 Heavy metal sorption by bioflocculant produced by bacterial isolates 
The purified bioflocculant produced by each isolate was used for heavy metal (Cd, Cr and Pb) 
removal as described by Subudhi et al. (2016) with little modifications. A 0.1 g of the lyophilized 
partially purified bioflocculant was added to 100 ml of solution of 100 mg/L of each of the metal 
salt (CdSO4, K2CrO4 and Pb(NO3)2). The mixture was incubated at 30˚C for 24 hours at 150 rpm. 
Afterwards, the mixture was centrifuged at 10,000 rpm for 30 mins to remove insoluble 
bioflocculants. The metal concentrations in the supernatant were measured using Inducible 
Conductivity Plasma-Optical Emission Spectroscopy (725 ICP-OES, Agilent Technologies, Palo 
Alto, CA, USA). The metal removal efficiency was calculated using the formula: 
𝐶𝑖 −𝐶𝑒  
𝑅𝑒𝑚𝑜𝑣𝑎𝑙 𝑟𝑎𝑡𝑒 (%) =  𝑋 100  
𝐶𝑖
Where Ci and Ce are the initial and final concentrations of metal respectively. 
7.2.11 Statistical analysis 
Data were means values of replicate readings and were subjected to analysis of variance (ANOVA) 
at significant level of p < 0.05 using SPSS statistical package (v 25.0).  
7.3 Results and Discussion 
The heavy metal tolerance of the two bioflocculant producing strains is presented in Table 7.1. 
The two isolates showed appreciable growth on Pb, with Pseudomonas korensis growing on 1000 
mg/L of Pb and Pantoea sp. was able to tolerate 400 mg/L of Pb. However, the two organisms 
showed limited growth on Cd and Cr. P. korensis was able to tolerate 200 mg/L of Cd and 100 
133 
 
mg/L of Cr while Pantoea sp. was able to tolerate 100 mg/L of Cd and 200 mg/L of Cr (Table 
7.1).  
The biochemical characteristics of the two isolates are presented in Table 7.2. Both isolates are 
Gram negative, rod-like in shape, catalase positive and have the ability to utilize citrate, glucose 
and sucrose. Pantoea sp. was able to produce nitrate, ferment arabinose and mannose while P. 
korensis has the ability to liquefy gelatin (Table 7.2).  
Bacterial isolates from heavy metal contaminated environments are usually more resistant to high 
concentrations of heavy metals than those isolated from pristine environments (Umrania, 2006). 
Soils from mining environments are potential sources of heavy metal resistant isolates (Choudhary 
and Sar, 2009; Zampieri et al., 2016) and these organisms may have developed resistance as a 
result of the presence of metals in their medium of growth (Çolak et al., 2011). 
The 16S rRNA gene amplification of the genomic DNA of the resistant isolates yielded 1500 bp 
fragments using universal primers (F1, R2) as shown in Figure 4.6. (Lane 12: Pseudomonas 
korensis and Lane 16: Pantoea sp.). The organisms were identified based on the 16S rRNA gene 
sequence using the BLAST search of the NCBI database that identified the organisms as 
Pseudomonas korensis and Pantoea sp. The results showed that Pantoea sp. had maximum 
homology of 91% with Pantoea sp. (JN082730) and Pseudomonas korensis showed maximum 
homology of 99% with Pseudomonas korensis (MH211310). 
Bacteria have adapted to environmental changes by physiological and genetic mechanisms that 
confer new traits, such as metal resistance and xenobiotic degradation, to survive and colonize 
their environment (Anjum et al., 2011). Metal resistance is known to be achieved through an efflux 
system as found in Gram-negative organisms (Blair et al., 2014) that pumps metals out of the 
cytoplasm into the periplasmic space and other mechanisms such as complexation, accumulation, 
transformation, sorption, oxido-reduction reactions and mineralization (Govarthanan et al., 2013; 
134 
 
Ayangbenro and Babalola, 2017). The isolates were screened for metal resistance genes by PCR 
amplification of genomic DNA for genes responsible Cd, Cr, and Pb. These genes have the 
potential to reduce or eliminate metal toxicity (Wei et al., 2009). The amplification of genes 
responsible for Cd, Cr, and Pb resistance were performed with specific primers. No amplification 
products were obtained for primers representing CzcA, CzcB and chrB, PbrA and PbrT for each 
isolate. Pseudomonas korensis possess resistance genes for cadmium (CzcD) (Figure 4.14; Lane 
12) and chromium (chrA) (Figure 4.17; Lane 12) while Pantoea sp. possess genes responsible for 
cadmium resistance (cadA) (Figure 4.13; Lane 16) and (CzcD) (Figure 4.14; Lane 16). 
The two isolates possess the CzcD operon that is responsible for Cd, Co and Zn resistance. The 
CzcD operon is used in the detoxification of Cd, Co and Zn by exporting them from the cytoplasm 
and/or periplasm into the extracellular medium (Legatzki et al., 2003). The cadA found in Pantoea 
sp. is responsible for Cd efflux which is energy dependent (Nies et al., 1989). 
ChrA confers chromate resistance on P. korensis which results from the accumulation of Cr (Nies 
et al., 1990). It is an efflux pump responsible for chromate extrusion (Pimentel et al., 2002). 
Therefore, these bacterial isolates are ideal for heavy metal removal from polluted environments. 
 
 
Figure 7.1: Maximum likelihood tree showing the phylogenetic relationship of the two 
bioflocculant producing isolates based on 16S rRNA gene sequence 
135 
 
The evolutionary history was inferred by using the maximum likelihood method based on the 
Hasegawa-Kishino-Yano model (Hasegawa et al., 1985). The bootstrap consensus tree inferred 
from 1000 replicates is taken to represent the evolutionary history of the taxa analyzed 
(Felsenstein, 1985).  
The growth pattern of the isolates in the presence of heavy metals is presented in Figures 7.2 and 
7.3. Both isolates showed a decrease in optical density while growing in heavy metal containing 
medium compared to a metal-free medium (Figure 7.2 and 7.3). The two isolates have similar 
growth patterns in the presence of heavy metals and follow this order Cr > Pb > Cd. The highest 
optical density of 0.283, 0.947 and 1.087 were observed on the sixth day of growth for P. korensis 
for Cd, Pb and Cr respectively (Figure 7.2). Similar results were also observed for Pantoea sp. on 
the sixth day with highest optical density of 1.089, 1.364 and 1.366 for Cd, Pb and Cr respectively 
(Figure 7.3). The optical densities observed for Pantoea sp. in the presence of Cr and Pb on the 
sixth day were higher than that of the control. 
 
1.4
1.2
1
0.8
0.6
0.4
0.2
0
24 48 72 96 120 144
Time (Hours)
Cd Cr Pb Control
 
Figure 7.2: The growth pattern of P. korensis in the presence of heavy metals 
136 
 
Optical density at 600 nm
 
 
Table 7.1: Heavy metal tolerance of the two bioflocculant producing isolates  
Isolate  Cd (mg/L) Cr (mg/L) Pb (mg/L) 
 100 200 300 100 200 300 100 200 300 400 500 1000 
Pseudomonas + + - + - - + + + + + + 
korensis 
Pantoea sp. + - - + + + + + + + - - 
(+) Positive reactions; (-) negative reactions 
 
 
Table 7.2: Biochemical profile of heavy metal resistant bacterial isolates 
TESTS 
ISOLATE 
Pseudomonas - Rod + - - + + - - - + - + + - + - - - - - 
korensis 
Pantoea sp. - Rod + - - + + - - - - + + + + + + - - - - 
Key:  + (Positive), - (Negative), G (Gas production), CAT (Catalase), OXI (Oxidase), IN (Indole), VP (Voges Proskaeur), CIT (Citrate), URE (Urease), SH (Starch hydrolysis), GL (Gelatin 
liquefaction), MAN (Mannose), NIT(Nitrate), GLU (Glucose), ARA (Arabinose), SUC (Sucrose), MAN (Mannose), SOR (Sorbitol), MEL (Melibiose), MAL (Maltose), LAC (Lactose)
137 
 
 
GRAM 
RXT 
SHAPE  
CAT  
OXI  
IN  
VP  
CIT  
URE  
SH  
H2S   
GL  
MAN  
NIT  
GLU  
ARA  
SUC  
MAN  
SOR  
MEL  
MAL  
LAC  
 
1.4
1.2
1
0.8
0.6
0.4
0.2
0
24 48 72 96 120 144
Time (Hours)
Cd Cr Pb Control
 
Figure 7.3: The growth pattern of Pantoea sp. in the presence of heavy metals 
Bioflocculant is another biopolymer produced by bacterial isolates during growth. The flocculating 
properties of the metal resistant isolates were determined  to access the aggregation of particles 
and cells (Yim et al., 2007). Bacterial isolates that were able to flocculate suspended kaolin clay 
can adsorb to other particles to form flocs (Tang et al., 2014). The flocs are capable of effective 
metal removal from solution. 
Flocculation is an effective technique used in the treatment of wastewater for removal of 
suspended particles and heavy metals (Aljuboori et al., 2013). The metal resistant isolates were 
screened for bioflocculant production. Synthesis of bioflocculant is influenced by the 
composition of culture medium and other parameters. The carbon source for synthesis of 
bioflocculant was investigated. Glucose gave the highest flocculating activity of 71.3% for 
Pantoea sp. while sucrose served as the best carbon source for P. korensis resulting in 
flocculating activity of 56% (Table 7.3). Conversely, lactose and maltose showed the lowest 
flocculating properties. The least flocculating activity was observed when lactose was used as a 
carbon source for each isolate. The flocculating activity were 30.5% and 32% for P. korensis and 
Pantoea sp. respectively (Table 7.3).  
138 
 
Optical density at 600 nm
Gomaa (2012), reported that changing carbon and nitrogen sources of the production medium 
influences flocculating activities of bacteria. Sugars, such as glucose, fructose and sucrose have 
been reported to be favorable for bioflocculant production by bacterial isolates (Nwodo et al., 
2012; Nwodo and Okoh, 2013). The two organisms in this study preferred sucrose, glucose and 
fructose and the flocculating activity was higher compared to what was observed for lactose and 
maltose. Glucose was used as carbon source for further experiments. Sucrose was reported to be 
the most favorable carbon source for bioflocculant production by Achromobacter xylosoxidans 
(Subudhi et al., 2016) which is similar to what was observed for Pseudomonas korensis in this 
study. Nwodo and Okoh (2013), and Xia et al. (2008) reported glucose to be the best carbon 
source for bioflocculant production and yield by Cellulomonas sp. (Okoh) and Proteus mirabilis 
TJ-1 respectively. There is variation in carbon requirement by different organisms. Ethanol was 
reported to be preferred by Pseudomonas aeruginosa (Gomaa, 2012) while lactose was preferred 
by Serratia ficaria (Gong et al., 2008). 
Table 7.3: Flocculating activity of the bacterial isolates using different carbon sources 
Organism Flocculating activity (%) 
 Glucose Fructose Sucrose Maltose Lactose 
Pseudomonas 51.70 (1.70) 49.38 (1.39) 56.02 (0.22) 40.15 (1.15) 30.51 (0.84) 
korensis 
Pantoea sp. 71.25 (0.65) 68.99 (0.02) 67.19 (0.82) 41.12 (0.88) 32.00 (1.00) 
  Values are means of duplicate readings (P < 0.05) 
  Values in parenthesis are ± standard error 
The kinetics of flocculating activity was monitored for 5 days and presented in Figure 7.4. A 
continuous increase in flocculating activity was observed from 39.9% for Pantoea sp. and 22.5% 
for P. korensis after 24 hours of fermentation to a peak of 71.3% and 51.7% for Pantoea sp. and 
P. korensis respectively after 120 hours of fermentation. The bioflocculant synthesis increases 
during of growth. This can be attributed to synthesis during growth and not cell lysis (Gao et al., 
2006). This is consistent with the report of Gong et al. (2008), Wu et al. (2010b) and Okaiyeto et 
al. (2013) that shows a link between bioflocculant synthesis and bacterial growth. There was a 
139 
 
decrease in pH with increase in flocculating activity which can be attributed to the production of 
organic acids from glucose during the growth phase (Gong et al., 2008; Xiong et al., 2010). The 
maximum flocculating activity was obtained at pH 6.9 and 7.1 for P. korensis and Pantoea sp. 
respectively (Table 7.4). In general, the flocculating activity increases with growth and peaked at 
the stationary phase.  
The effect of initial pH on flocculating activities of the two isolates are presented in Figures 7.4 
and 7.5. Increase in flocculating activity was observed as pH increases for each isolate with the 
optimum around neutral pH and then decreases with further increase in pH. This implied that the 
bioflocculant production occurred in the range between neutral to slightly alkaline pH. The 
optimum flocculating activity of 74.4% and 60.3% was observed at pH 7.5 for Pantoea sp. and P. 
korensis respectively and then decreases with increase in initial pH (Figures 7.4 and 7.5). It has 
been reported that the initial pH of the fermentation medium determines bioflocculant production, 
electric charge on the cell surface and the redox potential that consequently affects nutrient 
absorption, enzymatic reactions, growth and flocculating activity (Zhang et al., 2007; Xia et al., 
2008). Similar results were observed for Bacillus licheniformis and A. xylosoxidans that has 
optimum flocculating activity at pH 7.5 (Xiong et al., 2010; Subudhi et al., 2016), while P. 
aeruginosa, Cellulomonas sp. and S. ficaria were reported to have optimum flocculating activity 
at pH 7 (Gong et al., 2008; Gomaa, 2012; Nwodo and Okoh, 2013). On the contrary, optimum 
flocculating pH of 4 was reported for Gyrodinium impudicum (Yim et al., 2007). Thus, different 
strains have different optimum pH for flocculating activity.  
 
 
 
 
140 
 
Table 7.4: Kinetics of bioflocculant producing isolates and their flocculating activity 
Time (Hours) 24 48 72 96 120 
 pH 
Pseudomonas 
6.2 6.2 6.5 6.7 6.9 
korensis 
Pantoea sp. 6.4 6.3 6.5 6.8 7.1 
 Optical density 
Pseudomonas 
1.01 (0.75) 1.45 (1.17) 2.12 (1.56) 1.85 (0.59) 2.05 (1.78) 
korensis 
Pantoea sp. 1.73 (0.89) 1.90 (1.63) 2.04 (1.50) 1.99 (2.01) 2.36 (0.51) 
 Flocculating activity 
Pseudomonas 
22.54 (2.35) 32.33 (2.01) 50.5 (1.50) 50.81 (1.49) 51.70 (1.70) 
korensis 
Pantoea sp. 39.89 (0.57) 47.90 (1.11) 68.40 (1.61) 69.15 (1.15) 71.25 (0.65) 
  Values are means of duplicate readings (P < 0.05) 
  Values in parenthesis are ± standard error 
 
80
70
60
50
40
30
20
10
0
5 5.5 6 6.5 7.2 7.5 8 8.5 9
pH
 
Figure 7.4: The effect of pH on flocculating potential of Pantoea sp. 
 The exponential condition is between pH 5.5-7.5 
141 
 
Flocculating activity (%)
70
60
50
40
30
20
10
0
5 5.5 6 6.5 7.2 7.5 8 8.5 9
pH
 
Figure 7.5: The effect of pH on flocculating activity of Pseudomonas korensis 
Temperature affects enzymatic reactions and bacteria growth, and consequently the flocculating 
activity (Subudhi et al., 2016). The effect of culture temperature on flocculating activities of the 
two bacterial strains is presented in Figure 7.6. The maximum flocculating activity of 71.3% and 
51.7% for Pantoea sp. and P. korensis respectively was observed at 30˚C and slightly above what 
was observed at 37˚C. Increase in initial culture temperature increases flocculating activity until 
the optimum was reached at 30˚C. Afterwards, flocculating activities were observed to steadily 
decrease as temperature increases due to decrease in enzymatic activities. This suggests that the 
synthesis of bioflocculant is strongly influenced by cultivation temperature and optimum 
enzymatic activation can only be obtained at optimum temperature (Zhang et al., 2007). This result 
corroborates the work of Nwodo and Okoh (2013) that reported optimum flocculating activity at 
30˚C for Cellulomonas sp. However, Wu and Ye (2007a) and Gomaa (2012) reported optimum 
flocculating activity at 35˚C for Bacillus subtilis DYU1 and P. aeruginosa, while optimum 
flocculating activity at 37˚C was reported for A. xylosoxidans by Subudhi et al. (2016). 
. 
142 
 
Flocculating activity (%)
90
80
70
60
50
40
30
20
10
0
25 30 37 50
Temperature (˚C)
Pantoea sp. Pseudomonas korensis
 
Figure 7.6: The effect of culture temperature on flocculating activity 
The bioflocculant yield for each isolate is presented in Figure 7.7. The optimum yield of 3.26 g/L 
for P. korensis and 2.98 g/L for Pantoea sp. was obtained. The yield was lower than 5 g/L reported 
for  A. xylosoxidans by Subudhi et al. (2016) using the same medium and culture conditions. This 
could be attributed to the different producing strains. The bioflocculant yield was also lower than 
those reported for Paenibacillus elgii B69 (25.63 g/L) (Sun et al., 2012), Cellulomonas sp. (4.04 
g/L) (Nwodo and Okoh, 2013) and a consortium of Streptomyces and Cellulomonas species with 
a yield of 4.45 g/L (Nwodo et al., 2014). However, the yield produced by P. korensis and Pantoea 
sp. in this study was higher than those reported for Halomonas sp. (1.213 g/L) and Micrococcus 
sp. (0.738 g/L) (Okaiyeto et al., 2013). Pseudomonas korensis from this study also has a higher 
yield compared to Bacillus megaterium which was reported to have a yield of 3.11 g/L (Guo and 
Chen, 2017). 
 
143 
 
Flocculatingactivity (%)
 
Figure 7.7: Bioflocculant yield from each isolate at optimum condition (a) P. korensis and (b) 
Pantoea sp. 
 
Figure 7.8: The scanning electron micrograph of the purified bioflocculant produced by (a) P. 
korensis and (b) Pantoea sp. 
The scanning electron micrograph of the purified bioflocculant produced by the two isolates is 
shown in Figure 7.8. The micrograph of P. korensis revealed the irregular shaped amorphous 
144 
 
biopolymer (Figure 7.8a) while the Pantoea sp. showed particles with random size and shape with 
a large degree of aggregation. 
The functional groups of bioflocculant produced differs for each organism. The flocculating 
activity depends on the functional groups and chemical structure of the bioflocculant (Okaiyeto et 
al., 2013). The partially purified bioflocculant produced by the two bacterial isolates were 
characterized by FTIR and are presented in Figures 7.9 and 7.10. The FTIR spectrum showed the 
presence of various functional groups. The spectrum in Figure 7.9 showed a weak absorption peak 
at 3471 cm-1 characteristic of hydroxyl group that could be as a result of vibration of -OH or -NH 
in the sugar ring of polysaccharide (He et al., 2010; Kavita et al., 2013). The broad weak peak at 
2879 cm-1 is characteristic of C-H stretching vibration. In addition, the stretching peak at 1640 cm-
1 showed the presence of carbonyl group vibration in the peptide (Okaiyeto et al., 2013; Okaiyeto 
et al., 2015). A strong carboxylic group vibration band was observed at 1432 cm-1 (Kavita et al., 
2013).  
The FTIR spectrum of the purified bioflocculant produced by Pantoea sp. (Figure 7.10) showed a 
broad, intense medium peak at 3231 cm-1 that is typical of either -OH or -NH stretching vibration. 
This indicates the presence of hydroxyl group (Subudhi et al., 2014). The band observed at 2335 
cm-1 is attributed to C-H stretching. The asymmetric stretching band at 1637 cm-1 and peak at 1431 
cm-1 revealed the presence of carboxyl groups (Yin et al., 2014).   
Furthermore, the peaks between 1200 and 800 cm-1 for the two isolates were attributed to different 
polysaccharide derivatives (Pathak et al., 2017). In addition, the presence of strong absorption 
bands obtained at 983 cm-1 (Figure 7.8) and 979 cm-1 (Figure 7.9) showed stretching of =C-H 
bends of alkanes. The peaks between 690–515 cm-1 correspond to N-H vibrations of primary amine 
and C-X stretch of alkyl halides (Kavita et al., 2013). The FTIR spectrum of the bioflocculant 
produced by P. korensis and Pantoea sp. revealed the presence of carboxyl, hydroxyl groups (the 
main groups used for flocculation), amino acid and sugar derivative groups. The carboxyl 
145 
 
functional groups in the bioflocculant structure provides adequate binding sites for attachment of 
particles which makes bridging mechanisms between particles and flocculants effective (Pathak et 
al., 2017). Thus, it can be inferred that the two bioflocculants are polysaccharides containing 
proteins.  
 
Figure 7.9: The FTIR spectroscopy of the purified bioflocculant produced by P. korensis 
Microorganism develop resistance mechanisms, such as producing secondary metabolites, to 
thrive and survive in harsh environments. These metabolites have important ecological function 
and application in industrial waste treatment (Okaiyeto et al., 2016). Thus, the heavy metal 
removal potential of the bioflocculants produced by the two isolates were investigated and results 
is presented in Table 7.5. Lead had the highest percentage removal by the partially purified 
bioflocculants (Table 7.5). The bioflocculant from Pantoea sp. was able to remove 80.5% of Pb, 
52.5% of Cr and 51.2% of Cd from aqueous solution of each metal. Pseudomonas korensis had 
removal efficiency of 42.5%, 48.5% and 73.7% for Cr, Cd and Pb respectively.  
 
 
146 
 
 
Figure 7.10: The FTIR spectroscopy of the purified bioflocculant from Pantoea sp. 
Table 7.5: Metal removal efficiency by bioflocculant produced by the two isolates 
Heavy metal removal (%) 
 Cd Cr Pb 
Pseudomonas korensis 48.50 (0.50) 42.50 (2.50) 73.70 (0.50) 
Pantoea sp. 51.20 (1.20) 52.50 (1.50) 80.50 (1.50) 
  Values are means of duplicate readings (P < 0.05) 
  Values in parenthesis are ± standard error 
 
The metal removal efficiencies observed in this study were lower than those reported for 
Achromobacter xylosoxidans for the removal of Pb from aqueous solution (Subudhi et al., 2016) 
and Cd removal by Bacillus subtilis WD 90 and B. subtilis SM 29 (Kaewchai and Prasertsan, 
2002). Achromobacter xylosoxidans achieved 95% removal efficiency from 1500 mg/L of Pb 
while B. subtilis SM 29 and B. subtilis WD 90 were reported to remove 91.4% and 90.9% of Cd 
147 
 
(60 mg/L) respectively. However, the removal efficiency of bioflocculant produced by the two 
isolates were higher that those reported for Bacillus sp., Halomonas sp., and Herbaspirillium sp. 
for removal of Cd, Cr and Pb from different dyes and chemical industry effluents (Lin and 
Harichund, 2011). Pantoea sp. was observed to remove a slightly higher percentage of Pb 
compared to that reported for Pseudomonas aeruginosa that had 79.7% Pb removal efficiency 
(Gomaa, 2012).  
The differences in metal removal efficiency by each bioflocculant can be attributed to their binding 
specificity, charge density, attractive interaction, types of conformation of the bioflocculant with 
the adsorbed metal ion (Gomaa, 2012) and bioflocculant concentration (Lin and Harichund, 2011).  
Conclusion 
Bioflocculants are produced by different bacterial species and have received increasing attention 
lately as a result of their applications in metal removal. The bioflocculants produced by the two 
isolates demostrate high metal removal potential from aqueous solution. The structure of the 
bioflocculants revealed the presence of polysaccharides and proteins which are responsible for 
metal binding. This study demonstrates possible evironmental and biotechnological applications 
of this biopolymer in heavy metal removal.  
 
 
 
 
 
148 
 
CHAPTER EIGHT 
8.0 SUMMARY, CONCLUSION AND RECOMMENDATIONS 
Mining activities have led to the release of large quantities of heavy metals into the environmental 
media, which pose significant health hazards to the ecosystem. These metals exert their toxic effect 
on microorganisms by disrupting cell membranes, causing functional disturbance, inhibiting 
enzyme activity, causing protein denaturation, loss of diversity and blocking essential functional 
groups and essential ions. However, polluted media harbor microorganisms that have the potential 
to remove these heavy metals from those environments. The surviving organisms have developed 
intrinsic or induced resistance mechanisms that counter the effect of high concentrations of heavy 
metals in their environment. These organisms secrete various kinds of metal-binding polymers that 
are important in metal removal. 
This study showed that mining activities constitute a threat to the ecosystem. The soil samples 
collected had low moisture content, organic matter and nutrients needed for survival of microbial 
communities. Consequently, this affects microbial diversity, nutrient cycling and soil fertility. The 
soil samples are generally sandy in nature which also has effects on metal mobility and availability, 
thus leading to lower microbial population and diversity. Furthermore, the soil samples have low 
cation exchange capacity (CEC) and have high sulfur and sulfate content. The low CEC values 
also contribute to low water holding capacity of the soil samples and its attendant leaching of 
cations and nutrients in the soil samples. High sulfur content affects calcium and magnesium ions 
from sorption complexes, thereby increasing acidification of the soil. This impacts negatively on 
the microbial population in the soil samples. 
Despite the low heavy metal concentrations in the digested mine soil samples, the microbial 
diversity was low as Bacillus species were the dominant organisms. These metals still have a 
potential significant impact on biological systems. A total of 98 heavy metal resistant bacterial 
isolates were recovered from the sampling sites and were clustered within a few genera which 
149 
 
corroborates the concept that bacterial diversity is affected by pollutants in contaminated 
environment when compared to pristine environment. The resistant organisms showed active 
growth, suggesting that they have been able to adapt to the metal polluted environment. A 
significant number of the isolates were able to grow at high concentration of heavy metals, but a 
few were susceptible to high metal concentration, perhaps owing to changes in the laboratory 
conditions they were subjected to. However, a few of the resistant organisms were able to secrete 
secondary metabolites that are involved in metal binding. The metabolites are usually the first line 
of defense against toxic metals.  
As a result of the secretion of metal binding polymers and the multi-metal resistance of some of 
the isolates, a few of them were selected for further studies. Bacillus cereus was investigated for 
biosurfactant production and its metal removal potential. The organism was able to produce 
biosurfactant using different hydrocarbons as carbon source and was able to emulsify different 
hydrocarbons. The biosurfactant produced by the isolate had a good stability over a wide range of 
temperature and pH and was able to reduce the surface tension of water from 72 to 39.5 mN/m. 
Spectrometric analysis of the biosurfactant revealed that it was a lipopeptide with the potential to 
remove 35%, 56% and 78% of Cr, Cd and Pb respectively from contaminated soil. The genomic 
richness of the organism can be tapped into for biotechnological applications. 
Similarly, two strains that showed potential flocculating activity were investigated for their 
bioflocculant production. The spectrometric analysis of the bioflocculant produced by the Pantoea 
sp. and P. korensis revealed the presence of carboxyl, hydroxyl and amino groups typical of 
polysaccharides and proteins involved in metal binding. The bioflocculant of Pantoea sp. was able 
to remove 51.2%, 52.5% and 80.5% of Cd, Cr and Pb respectively while that of P. korensis 
removed 48.5%, 42.5% and 73.7% of Cd, Cr and Pb respectively from aqueous solution. 
The use of these biopolymers has provided valuable insight to their potential metal removal 
abilities. These polymers produced by frequently designated opportunistic pathogens bear a great 
150 
 
role for removal of heavy metals from polluted media. These polymers have also helped in 
circumventing the pathogenicity concerns that may arise from the use of these organisms as 
microbial inoculants for metal removal and have immense possibilities in treatment of soil and 
wastewaters.  
Further investigation needs to be carried out in detail on the promising industrial application, large 
scale production through optimization and their ability for reuse. The economics of their 
production should also be evaluated. The exploitation of the metabolic pathways of the producing 
organisms and the roles of the biosynthetic genes would also give insight about other obscure 
benefits of these polymers.  
 
 
 
 
 
 
 
 
 
 
 
151 
 
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APPENDICES 
Appendix 1: Excerpt of research work presented at the ICIEM conference 2018 
 
 
 
 
 
 
184 
 
 
 
Appendix 2: Hemolysis of red blood cells by resistant bacteria isolates  
 
 
 
 
 
 
 
 
 
185 
 
Appendix 3: Description and location of major BGC in B. cereus NWUAB01 identified in silico  
Cluster ID Type Position/Region Most Similar BGC Percentage 
predicted similarity (%) 
Cluster 1  Saccharide 162620-187499 Undefined - 
Cluster 5  Putative  145-7560 Citrulline 18 
Cluster 7  Putative 91503-105724 Burkholderic acid 6 
Cluster 11  Saccharide 221494-253173 S-layer glycan 9 
Cluster 15  Siderophore 120072-133779 Petrobactin 100 
Cluster 18  Lassopeptide 35492-57743 Undefined - 
Cluster 19 Fatty acid 175528-197730 Undefined - 
Cluster 20 Saccharide 208427-233412 S-layer glycan  19 
Cluster 21 Bacteriocin 253699-267564 Undefined - 
Cluster 22  Putative 310537-317336 Polyhydroxyalkaoates 100 
Cluster 25 Nrps 101717-148733 Undefined  
Cluster 26 Bacteriocin 166579-176845 Undefined - 
Cluster 28 Nrps 72712-122437 Bacillibactin 46 
Cluster 36 Other  76321-119902 Undefined - 
Cluster 40  Terpene 139251-161104 Molybdenum cofactor 17 
Cluster 41 Lantipeptide 1-20956 Undefined  - 
Cluster 43 Saccharide-Nrps 3687-62177 Polyoxypeptin 5 
 
186 
 
Appendix 4: Molecular annotation of the predicted clusters identified in the Bacillus cereus 
NWUAB01 genome 
 
Cluster 1: saccharide annotation 
 
 
Cluster 2: putative annotation for predicted citrulline synthesis 
187 
 
Cluster 15: siderophore annotation for petrobactin synthesis 
 
 
Cluster 21: bacteriocin annotation 
 
188 
 
 
 
Cluster 22: putative annotation for polyhdroxyalkanoates synthesis 
 
Cluster 28: Nrps annotation for bacillibactin synthesis 
189 
 
 
Cluster 40: terpene annotation for the synthesis of molybdenum cofactor 
 
Cluster 41: lantipeptide annotation 
190 
 
 
Cluster 43: saccharide-Nrps annotation for the synthesis of polyoxpeptin 
 
 
 
 
 
 
 
191 
 
Appendix 5: In silico annotation of heavy metals present in the genome of B. cereus NWUAB01 
with their protein translations  
 
1:  MSIEIWITIIVFFLTMIVIFWRPRGLNEAWPAAIGAGIILITGLVSKPDV 
51:  IDIVSKIGGASITILATIVMAVILESFGFFHWSAAKLANLAKGSGRRLYW 
101:  YIQLLCFLMTLLFNNDGSILITTPILILLLKNLQLKPHQQIPYLLSGALI 
151:  ATASSAPIGVSNIVNLIALNIVNMTLYMHTAMMFVPATLGLLFMSWLMYT 
201:  VLKKKLPKKLPVSSYDIEEIFFTKNFHPLKGNNSVDTKQKRTRFMLKVLG 
251:  FVFLMRCLLFVASFLSIPIEIVAVLGSLVLLIWRWYYLRTNPVDILKKTP 
301:  WHILIFAFSMYVIIYGLHNAGLTAVLVQWLEPIVNQHLLYASFAMGGLVS 
351:  ILSNVFNNHPALMIGTITLTEMGLDPVTLKTIYLANIIGSDIGSLLLPIG 
401:  TLASLIWMYILKQNKIKVKWKDYLSVSLIVIPLTTVVTLFLLFYWVHLFF 
451:  AL 
Arsenic efflux pump protein 
 
 
1:  MAGNKVETPQETCSQTIIHEEVVEQVKQTIPTDESLSKVAELFKVLGDRT 
51:  RTRILHALFEAEMCVCDLAYLLGMTQSSISHQLRVLKQAKLVKNRKEGKV 
101:  VYYSLADQHVIHIFEQAFEHVNEEE 
Cadmium efflux system accessory protein 
 
 
 
 
 
 
192 
 
 
1:  MNEKKEANLQISGMTCAACANRIEKGLKKVEGVHEANVNFALEKTKIMYD 
51:  PTKTNPQQFKEKVESLGYGIVSDKAEFTVSGMTCAACANRVEKRLNKLDG 
101:  VNKATVNFALESATVDFNPNEVNVNEMKSAITKLGYKLEVKPDDQDASTD 
151:  HRLQEIERQKKKFIISFILSFPLLWAMVSHFSFTSFIYLPDMLMNPWVQL 
201:  ALATPVQFIIGGQFYVGAYKALRNKSANMDVLVALGTSAAYFYSVYLSIQ 
251:  SIGSSKHMTDLYFETSAVLITLIILGKLFEAKAKGRSSEAIKKLMGLQAK 
301:  TATVVRDGTEIKILIEEVVAGDIVYVKPGEKIPVDGEIVEGKSAIDESML 
351:  TGESIPVDKSIGDVVIGSTINKNGFLKVKATKVGRDTALAQIIKVVEEAQ 
401:  GSKAPIQRVADQISGIFVPVVVVIAIITFAVWMIFVTPGDFGGALEKMIA 
451:  VLVIACPCALGLATPTSIMAGSGRSAEYGILFKGGEHLEATHRLDTVILD 
501:  KTGTVTNGKPVLTDVIVADRFNENELLRLVGAAERNSEHPLAEAIVEGIK 
551:  EKKIDIPSSETFEAIPGFGIESVVEGKHLLIGTRRLMKKFNIDIEEVSKS 
601:  MEALEREGKTAMLIAIDKEYAGIVAVADTVKDTSKAAIARLKKMGLDVVM 
651:  ITGDNTQTAQAIAKQVGIDHVIAEVLPEGKAEEVKKLQANGKKVAMVGDG 
701:  INDAPALATANIGMAIGTGTDVAMEAADITLIRGDLNSIADAIFMSKMTI 
751:  RNIKQNLFWALAYNALGIPIAALGFLAPWVAGAAMAFSSVSVVLNALRLQ 
801:  RVKLKP 
Lead, cadmium, zinc and mercury transporting ATPase 
 
 
1:  MKKNKHIFHTLLEIFLISFKLGLTSFGGPVAHLGYFHHEYVQKRKWMDER 
51:  SYGDLVALCQFLPGPASSQVGMGVGLLRGGLLGAIISWIGFTLPSVLVLV 
101:  FFASFLNQFDLGSAGWIHGLKLVAVAIVAHAIWGMARKLTPDRNRATIAI 
151:  VTAAIALLWPSSWTQVILIILCGFIGWFLYRNQPISQSQNIKVPISKKIA 
201:  VSCLVLFFGLLLLLPILRPFSYYIALFDSFYRSGALVFGGGHVVLPLLEG 
251:  EFVQNGMMTKEQFLAGYGLTQAVPGPLFTFASYIGAVLNGTLGATLATIA 
301:  IFLPAFLLVIGVLPFWNSVRKISFIQGALLGVNAAVVGILIAAFYDPIWT 
351:  STIINASDFVFASLLFCLLAFWKTPPWVIVILGAFGGYILSIL 
Chromate transport protein 
 
193 
 
 
1:  MGHSHDHGHSKNKKALLIAFLLTTSFMIAEVVGGFVTNSLALLSDAGHML 
51:  SDAVSLALSLLAFKLGEKTATTAKTYGYKRVEMLAALCNGVVLIVISVYI 
101:  FIEAIRRFKEPVEIASNGMLIIAVLGLLINILSAWILMRGGDVKGNLNLR 
151:  SAFLHVLGDLLGSVGAIIAALLIKFFGWTAADAIASILVSILVIISGWRV 
201:  TRDTVHILMEGAPQHINVEEVKSTLLNIPIVKEVHDLHIWSVTSDFQVLT 
251:  CHLIIKGNETQSVLKEATYVLKEKFHVEHVTIQVEIDGEFHHDETTCKV 
Cobalt-zinc-cadmium resistance protein CzcD 
 
 
 
1:  MITTIISSVVAFATTNIDDIFILLVLFSQVRTEGRAVREKAMRKKVYIVI 
51:  GQYVGFSMIIFLSIVGSLSSFFIPVSWIGLLGFVPIYMGIKGLFSIRSNK 
101:  SNEVIDNTSESLFKVAAITLANGADNISIYIPMFASQSLEANIVTLMIFF 
151:  P 
Cadmium resistance transporter 
 
 
 
 
 
 
194 
 
 
1:  MAEALVKKKLMLEGLDCANCAMKIEKGVGNIEGVNSCSVNFATKTMILET 
51:  AQNKENEVVTEAKQLVTKLEPHIKVQEEQKNKIAKEVFILEGLDCANCAM 
101:  KIENKVKEMPAVSEATVDFVSKKLRVEVANKRELEATVANITNVVQKLEP 
151:  DVKVVREEKNGHDHGHSHDHGEANVKKMVGRLVVGGILTAIAALAGLPQM 
201:  VTIPLFVLAYLLIGGDIVWRAVRNITRGQVFDENFLMAIATVGAFAIQQY 
251:  SEAVAVMLFYQVGELFQSIAVNRSRKSITSLMDIRPDYANVKVGNETKQV 
301:  SPEDVQIGDYIIVKPGEKVPLDGKVIEGTSMVDTSALTGESVPREVEVGN 
351:  DVLSGFVNQNGVLTIEVTKEFGESTVSKILDLVQNASSKKAPTENFITKF 
401:  ARYYTPVVVITAAIMAFIPPLILEGATFSEWIYRALVFLVISCPCALVVS   
451:  IPLGFFGGIGGASKSGVLVKGSNYLEALNDVKYIVFDKTGTLTKGVFKVT 
501:  KMEPSEGTTSEELLEYAAFAEVYSNHPIAQSIRKAYGKSIDEKIIDDYNE 
551:  ISGHGTVVKVQGKEIFAGNAKLMRKENIEFKQPETVGTLVHVAVDGRYAG 
601:  YIVISDEVKEDSKQAIQKLKELGIKKTVMLTGDAKPVGEAVGKELGLDEV 
651:  HAELLPQQKVEEIEKIDAAKHGKEKIAFVGDGINDTPVLARADVGIAMGG 
701:  LGSDAAIEAADIVIMTDEPSKIATAVKIAKRTRSIVWQNIIFALGVKGIV 
751:  LLLGAFGIATMWEAVFSDVGVTLLAVLNAMRVLRVKDL 
Cadmium-transporting ATPase 
 
 
1:  MRRLGTWLLLACVLIILIPKSASAHAYVVKSNPMENETLKRAPSVVKIEF 
51:  DEDIQVSRFNTLYVRDTSGKRVDLKDAHIDKKNKKLLEAGLKENLKNGLY 
101:  SIQWKAISADGHPIQGVIPFRIGLAEAGTDDVQVEEMGYVPQIDMIMERG 
151:  ILYASFSLFLGVLFFNLIMYKGNATSVRSRSKNIIWISLFGIFNSLLFNL 
201:  PLQAKINADVSWLEAFNPLLLKETLQLSVFGYVWITQMVLISTLIIVTYF 
251:  AVKHEKFSSFKVWSIPILLFIGLLVMKAFNSHAYGLKFKDIAVVMDFLHL 
301:  FAASLWVGGLSSIILLLRKEDNKWHMYWDMIKRFSPWATGAVIVILITGL 
351:  FNSTFFIPTIHSLFDTKYGLALLTKILLFIFMGILGIIHYMKGRMRAKQG 
401:  LGATVKVEFIIGIIIFVIVAFMTNVQTPPMPPTGPFTESKQLDNGYEMTL 
451:  NVSPNRVGQNIFHITLKDENGQPVTDMEQIILTTQSLDMNMGKGLFKVSA 
501:  VSPGEYEAEGMYINMTGNWNIQVHGLTKSLDSFDTDYKFIVGGR 
Copper resistance protein CopC / Copper resistance protein CopD 
 
 
 
 
 
 
 
 
 
195 
 
 
Appendix 6: Published review article from this study 
 
196