Exome sequencing and metabolomic analysis of a chronic kidney disease and hearing loss patient family revealed RMND1 mutation induced sphingolipid metabolism defects.

Mitochondrial disorders (MIDs) shows overlapping clinical presentations owing to the genetic and metabolic defects of mitochondria. However, specific relationship between inherited mutations in nuclear encoded mitochondrial proteins and their functional impacts in terms of metabolic defects in patients is not yet well explored. Therefore, using high throughput whole exome sequencing (WES), we screened a chronic kidney disease (CKD) and sensorineural hearing loss (SNHL) patient, and her family members to ascertain the mode of inheritance of the mutation, and healthy population controls to establish its rare frequency. The impact of mutation on biophysical characteristics of the protein was further studied by mapping it in 3D structure. Furthermore, LC-MS tandem mass spectrophotometry based untargeted metabolomic profiling was done to study the fluctuations in plasma metabolites relevant to disease causative mutations and kidney damage. We identified a very rare homozygous c.631G > A (p.Val211Met) pathogenic mutation in RMND1 gene in the proband, which is inherited in an autosomal recessive fashion. This gene is involved in the mitochondrial translational pathways and contribute in mitochondrial energy metabolism. The p.Val211Met mutation is found to disturb the structural orientation (RMSD is -2.95 Å) and stability (ΔΔG is -0.552 Kcal/mol) of the RMND1 protein. Plasma metabolomics analysis revealed the aberrant accumulation of metabolites connected to lipid and amino acid metabolism pathways. Of these metabolites, pathway networking has discovered ceramide, a metabolite of sphingolipids, which plays a role in different signaling cascades including mitochondrial membrane biosynthesis, is highly elevated in this patient. This study suggests that genetic defects in RMND1 gene alters the mitochondrial energy metabolism leading to the accumulation of ceramide, and subsequently promote dysregulated apoptosis and tissue necrosis in kidneys.

LC-MS/MS-based metabolic profiling of Escherichia coli under heterologous gene expression stress.

Escherichia coli is frequently exploited for genetic manipulations and heterologous gene expression studies. We have evaluated the metabolic profile of E. coli strain BL21 (DE3) RIL CodonPlus after genetic modifications and subjecting to the production of recombinant protein. Three genetically variable E. coli cell types were studied, normal cells (susceptible to antibiotics) cultured in simple LB medium, cells harboring ampicillin-resistant plasmid pET21a (+), grown under antibiotic stress, and cells having recombinant plasmid pET21a (+) ligated with bacterial lactate dehydrogenase gene grown under ampicillin and standard isopropyl thiogalactoside (IPTG)-induced gene expression conditions. A total of 592 metabolites were identified through liquid chromatography-mass spectrometry/mass spectrometry analysis, feature and peak detection using XCMS and CAMERA followed by precursor identification by METLIN-based procedures. Overall, 107 metabolites were found differentially regulated among genetically modified cells. Quantitative analysis has shown a significant modulation in DHNA-CoA, p-aminobenzoic acid, and citrulline levels, indicating an alteration in vitamin K, folic acid biosynthesis, and urea cycle of E. coli cells during heterologous gene expression. Modulations in energy metabolites including NADH, AMP, ADP, ATP, carbohydrate, terpenoids, fatty acid metabolites, diadenosine tetraphosphate (Ap4A), and l-carnitine advocate major metabolic rearrangements. Our study provides a broader insight into the metabolic adaptations of bacterial cells during gene manipulation experiments that can be prolonged to improve the yield of heterologous gene products and concomitant production of valuable biomolecules.

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.