An untargeted LC-MS investigation of South African children with respiratory chain deficiencies

Abstract

Mitochondria are the main site of cellular adenosine triphosphate (ATP) generation which is achieved by a series of multi-subunit complexes and electron carriers which together create the oxidative hosphorylation system (OXPHOS). Whenever a defect in any of the numerous mitochondrial pathways occurs it is commonly referred to as a mitochondrial disorder. Mitochondrial disorders are a heterogeneous group of disorders characterised by impaired energy production and include a wide range of defects of either mitochondrial DNA (mtDNA) or nuclear DNA (nDNA) encoded proteins. In cases of dysfunction in the respiratory chain (complex I to IV) it is known to be a respiratory chain deficiency (RCD) which presents a huge challenge for routine diagnosis largely due to the lack of a specific and sensitive biomarker(s). One sure way of confirming the suspicion of a RCD is by performing enzyme analysis on a muscle sample obtained through a biopsy. However, due to the lack of theatre time available to clinicians and the relative large number of false positive patients that are being selected for biopsies, it was decided to develop a biosignature to limit the number of false positive patients from the diagnostic workflow. An untargeted liquid chromatography mass spectrometry (LC-MS) metabolomics approach was used to investigate RCDs in children from South Africa. Sample preparation, a liquid chromatography time-of-flight mass spectrometry method and data processing methods were standardised. Furthermore the developed methodology made use of reverse phase chromatography in conjunction with positive electrospray ionisation (ESI) and a hydrophilic interaction chromatography (HILIC) in negative electrospray ionisation. Urine samples of 61 patients representing three different experimental groups were analysed. The three experimental groups comprised of patients with respiratory chain deficiencies, clinical referred controls (CRC) and patients suffering from various neuromuscular disorders (NMD). After a variety of data mining steps and statistical analysis a list of 12 features were compiled with the ability to distinguish between patients with RCDs and CRCs. The proposed signature was also tested on the neuromuscular disorder group, but this result indicated that the biosignature performed better when used to differentiate between patients with RCDs and CRCs, since the model was designed with this purpose. An alternative validation study is required to identify the features found with this proposed biosignature, to ensure that this biosignature can be practically implemented as a non-invasive screening method.