Search bioRxiv⌕ Search

Biology subjects

Rehas, R.

Publications and source records attributed to Rehas, R..

2 recordsLinked to original sources

Threonine Supplementation Reduces Methylglyoxal Overflow by Increasing Glycolysis Flux at the Payoff Phase: A Metabolic Modeling Analysis

Methylglyoxal (MGO) is a metabolic byproduct of sugar metabolism involved in the formation of advanced glycation end products (AGEs) and its accumulation disrupts protein function, redox balance and cellular viability. Yet the metabolic rewiring required to divert MGO overflow and prevent these cytotoxic effects remains largely unknown. To address these gaps, we used flux sampling and flux balance analysis in genome scale metabolic model iJO1366 of E. coli. High glucose increased MGO flux from 0.16 mmol/gDCW/h to 1.87 mmol/gDCW/h in simulation. Large scale in silico screening of metabolic reactions identified that increasing threonine uptake, thereby increasing ethanol flux, reduced glucose induced increase in MGO flux from 1.87 mmol/gDCW/h to 0.405 mmol/gDCW/h. In silico inhibition of ethanol production (NAD+ regeneration) inhibited the MGO lowering potential of threonine. Threonine supplementation actively drives the acetaldehyde to ethanol flux to provide localized NAD+ relief, which subsequently enhances the flux of payoff phase in glycolysis (glyceraldehyde 3-phosphate), thereby efficiently draining the stagnated DHAP pool and shutting down the overflow towards MGO production. Unexpectedly, under reduced oxygen conditions, high glucose caused only a minimal increase in MGO flux, which was not reduced by threonine supplementation. Our study also decoded the mechanistic details behind this paradox. Though this model driven hypothesis requires further validation in vivo, these stoichiometric predictions have applications in metabolic engineering, for commercial MGO production. Furthermore, in vivo studies focused on bacterial stress, gut microbiome imbalances and AGE related diseases might benefit from these simulations

systems biology↗

BC-Predict Database: A Curated Resource of Experimentally Validated Markers in Multidrug Resistance in Breast Cancer

BackgroundIn this study, we aim to develop a yearly updatable database that could predict chemotherapeutic drug resistance and overall survival probability in breast cancer patients. Existing drug sensitivity databases depend on correlation-based predictions. In our study, candidates involved in drug resistance are chosen based on cell line validation (overexpression or downregulation or inhibition of candidates) studies, curated manually. Method28,773 mRNA expression signatures from 914 breast cancer patients were extracted from cProsite. 106 of these patients had clinical information and log2 fold change information required for this study. We categorized these patients into deceased and surviving groups from TCGA. To prepare a database that can predict drug resistance and overall survival, we included mRNAs that were over-expressed in at least 80% of the breast cancer patients and mRNAs over-expressed in deceased and surviving groups. In addition, we also reported breast cancer-associated drug resistance candidates which have been reported in cell-line based studies. The database matrix preparation involved an approximate of 15000 manual searches of cell validated studies. (750 candidates x 20 drugs). The database was validated using a publicly available breast cancer patient proteomics data. ResultsOur analysis identified a list of top priority candidates associated with multidrug resistance, categorized based on their resistance to >15 drugs, 5-15 drugs, and 2-4 drugs. Analysis of patient profiles in the database revealed that the number of proteins contributing to drug resistance was high in the poor prognosis category compared to the good prognosis category. ConclusionsOur study highlights the probable gaps in breast cancer drug resistance research, as only a small subset of overexpressed mRNA candidates found in patients are studied in vitro or in vivo experiments focusing on drug resistance. We also identified candidates involved in multidrug resistance, whose role in drug resistance has not been studied in more than 15 drugs. After further validations, this will benefit the clinicians and upcoming CRISPR gene therapeutics.

cancer biology↗