Upon accounting for the impact of isoenzyme loss, gene deletion costs anticorrelate with their evolutionary rates

System-level metabolic network models enable the computation of growth and metabolic phenotypes from an organism’s genome. In particular, flux balance approaches have been used to estimate the contribution of individual metabolic genes to organismal fitness, offering the opportunity to test whether such contributions carry information about the evolutionary pressure on the corresponding genes. Previous failure to identify the expected negative correlation between such computed gene-loss cost and sequence-derived evolutionary rates in Saccharomyces cerevisiae has been ascribed to a real biological gap between a gene’s fitness contribution to an organism “here and now” and the same gene’s historical importance as evidenced by its accumulated mutations over millions of years of evolution. Here we show that this negative correlation does exist, and can be exposed by revisiting a broadly employed assumption of flux balance models. In particular, we introduce a new metric that we call “function-loss cost”, which estimates the cost of a gene loss event as the total potential functional impairment caused by that loss. This new metric displays significant negative correlation with evolutionary rate, across several thousand minimal environments. We demonstrate that the improvement gained using function-loss cost over gene-loss cost is explained by replacing the base assumption that isoenzymes provide unlimited capacity for backup with the assumption that isoenzymes are completely non-redundant. We further show that this change of the assumption regarding isoenzymes increases the recall of epistatic interactions predicted by the flux balance model at the cost of a reduction in the precision of the predictions. In addition to suggesting that the gene-to-reaction mapping in genome-scale flux balance models should be used with caution, our analysis provides new evidence that evolutionary gene importance captures much more than strict essentiality.This work was supported by the National Science Foundation, grant CCF-1219007 to YX; the Natural Sciences and Engineering Research Council of Canada, grant RGPIN-2014-03892 to YX; the National Institute of Health, grants 5R01GM089978 and 5R01GM103502 to DS; the Army Research Office - Multidisciplinary University Research Initiative, grant W911NF-12-1-0390 to DS; the US Department of Energy, grant DE-SC0012627 to DS; and by the Canada Research Chairs Program (YX). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (CCF-1219007 - National Science Foundation; RGPIN-2014-03892 - Natural Sciences and Engineering Research Council of Canada; 5R01GM089978 - National Institute of Health; 5R01GM103502 - National Institute of Health; W911NF-12-1-0390 - Army Research Office - Multidisciplinary University Research Initiative; DE-SC0012627 - US Department of Energy; Canada Research Chairs Program)Published versio

Metabolic gatekeeper function of B-lymphoid transcription factors.

B-lymphoid transcription factors, such as PAX5 and IKZF1, are critical for early B-cell development, yet lesions of the genes encoding these transcription factors occur in over 80% of cases of pre-B-cell acute lymphoblastic leukaemia (ALL). The importance of these lesions in ALL has, until now, remained unclear. Here, by combining studies using chromatin immunoprecipitation with sequencing and RNA sequencing, we identify a novel B-lymphoid program for transcriptional repression of glucose and energy supply. Our metabolic analyses revealed that PAX5 and IKZF1 enforce a state of chronic energy deprivation, resulting in constitutive activation of the energy-stress sensor AMPK. Dominant-negative mutants of PAX5 and IKZF1, however, relieved this glucose and energy restriction. In a transgenic pre-B ALL mouse model, the heterozygous deletion of Pax5 increased glucose uptake and ATP levels by more than 25-fold. Reconstitution of PAX5 and IKZF1 in samples from patients with pre-B ALL restored a non-permissive state and induced energy crisis and cell death. A CRISPR/Cas9-based screen of PAX5 and IKZF1 transcriptional targets identified the products of NR3C1 (encoding the glucocorticoid receptor), TXNIP (encoding a glucose-feedback sensor) and CNR2 (encoding a cannabinoid receptor) as central effectors of B-lymphoid restriction of glucose and energy supply. Notably, transport-independent lipophilic methyl-conjugates of pyruvate and tricarboxylic acid cycle metabolites bypassed the gatekeeper function of PAX5 and IKZF1 and readily enabled leukaemic transformation. Conversely, pharmacological TXNIP and CNR2 agonists and a small-molecule AMPK inhibitor strongly synergized with glucocorticoids, identifying TXNIP, CNR2 and AMPK as potential therapeutic targets. Furthermore, our results provide a mechanistic explanation for the empirical finding that glucocorticoids are effective in the treatment of B-lymphoid but not myeloid malignancies. Thus, B-lymphoid transcription factors function as metabolic gatekeepers by limiting the amount of cellular ATP to levels that are insufficient for malignant transformation

Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network

Understanding how evolution of antimicrobial resistance increases resistance to other drugs is a challenge of profound importance. By combining experimental evolution and genome sequencing of 63 laboratory-evolved lines, we charted a map of cross-resistance interactions between antibiotics in Escherichia coli, and explored the driving evolutionary principles. Here, we show that (1) convergent molecular evolution is prevalent across antibiotic treatments, (2) resistance conferring mutations simultaneously enhance sensitivity to many other drugs and (3) 27% of the accumulated mutations generate proteins with compromised activities, suggesting that antibiotic adaptation can partly be achieved without gain of novel function. By using knowledge on antibiotic properties, we examined the determinants of cross-resistance and identified chemogenomic profile similarity between antibiotics as the strongest predictor. In contrast, cross-resistance between two antibiotics is independent of whether they show synergistic effects in combination. These results have important implications on the development of novel antimicrobial strategies

Metabolomics : a tool for studying plant biology

In recent years new technologies have allowed gene expression, protein and metabolite profiles in different tissues and developmental stages to be monitored. This is an emerging field in plant science and is applied to diverse plant systems in order to elucidate the regulation of growth and development. The goal in plant metabolomics is to analyze, identify and quantify all low molecular weight molecules of plant organisms. The plant metabolites are extracted and analyzed using various sensitive analytical techniques, usually mass spectrometry (MS) in combination with chromatography. In order to compare the metabolome of different plants in a high through-put manner, a number of biological, analytical and data processing steps have to be performed. In the work underlying this thesis we developed a fast and robust method for routine analysis of plant metabolite patterns using Gas Chromatography-Mass Spectrometry (GC/MS). The method was performed according to Design of Experiment (DOE) to investigate factors affecting the extraction and derivatization of the metabolites from leaves of the plant Arabidopsis thaliana. The outcome of metabolic analysis by GC/MS is a complex mixture of approximately 400 overlapping peaks. Resolving (deconvoluting) overlapping peaks is time-consuming, difficult to automate and additional processing is needed in order to compare samples. To avoid deconvolution being a major bottleneck in high through-put analyses we developed a new semi-automated strategy using hierarchical methods for processing GC/MS data that can be applied to all samples simultaneously. The two methods include base-line correction of the non-processed MS-data files, alignment, time-window determinations, Alternating Regression and multivariate analysis in order to detect metabolites that differ in relative concentrations between samples. The developed methodology was applied to study the effects of the plant hormone GA on the metabolome, with specific emphasis on auxin levels in Arabidopsis thaliana mutants defective in GA biosynthesis and signalling. A large series of plant samples was analysed and the resulting data were processed in less than one week with minimal labour; similar to the time required for the GC/MS analyses of the samples

A network-based target overlap score for characterizing drug combinations: High correlation with cancer clinical trial results

Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as cancer, diabetes, arthritis and hypertension. Most currently used combinations were found in empirical ways, which limits the speed of discovery for new and more effective combinations. Therefore, there is a substantial need for efficient and fast computational methods. Here, we present a principle that is based on the assumption that perturbations generated by multiple pharmaceutical agents propagate through an interaction network and can cause unexpected amplification at targets not immediately affected by the original drugs. In order to capture this phenomenon, we introduce a novel Target Overlap Score (TOS) that is defined for two pharmaceutical agents as the number of jointly perturbed targets divided by the number of all targets potentially affected by the two agents. We show that this measure is correlated with the known effects of beneficial and deleterious drug combinations taken from the DCDB, TTD and Drugs.com databases. We demonstrate the utility of TOS by correlating the score to the outcome of recent clinical trials evaluating trastuzumab, an effective anticancer agent utilized in combination with anthracycline- and taxane-based systemic chemotherapy in HER2-receptor (erb-b2 receptor tyrosine kinase 2) positive breast cancer. © 2015 Ligeti et al