Improving a biosignature for respiratory chain deficiencies in a South African cohort

Abstract

Mitochondria are the cell’s main energy producing site, found in the cytoplasm of nearly all eukaryotic cells. These organelles generate cellular energy in the form of adenosine triphosphate (ATP), mostly by means of the oxidative phosphorylation (OXPHOS) system, consisting of the respiratory chain (RC) and ATP synthase complex. When one of the four complexes (which form the RC) becomes impaired, it is called a respiratory chain deficiency (RCD). Diagnosing RCDs is a major challenge and requires a multi-disciplinary approach, which includes clinical, histochemical, molecular and biochemical assessment. The golden standard for diagnosing a RCD is enzyme analyses on a muscle sample obtained from a muscle biopsy, which is an invasive procedure. A urinary biosignature was proposed, that has the potential to be used as a screening tool for selecting patients that have a potential RCD. The proposed biosignature however consisted of 12 features that still required verification. The aim of this study was to improve this proposed biosignature, by verifying the 12 features and expanding the biosignature by analysing the identical sample cohort with alternative analytical platforms in order to discover additional features. This study was conducted in two separate phases, a verification phase and an expanding phase. Two levels of verification were performed for the LC-MS features of the proposed biosignature. Five of the 12 features could be verified and only one could be identified to a certain extent. For the expansion part of this study, two additional platforms were used, a gas-chromatography mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) analysis. Following evaluation of the methods used and sample analysis, a number of data mining steps and statistical analyses were performed to compile a list of top ranked features for each platform. The LC-MS, GC-MS and NMR features were considered for the improved biosignature, by using a variety of statistical analyses the number and best combination of features were selected. A list of five features was compiled and the discriminative power was evaluated. Results indicated that the improved biosignature was unable to classify samples 100% accurately with some of the clinical control samples classified as RCDs, however the biosignature could still be helpful in limiting the inclusion of CRC patients in the biopsy process which gives it the potential to be used in the diagnostic workflow.