Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data. The ability of cells to survive and grow depends on their ability to metabolize nutrients and create products vital for cell function. This is done through a complex network of reactions controlled by many genes. Changes in cellular metabolism play a role in a wide variety of diseases. However, despite the availability of genome sequences and of genome-scale expression data, which give information about which genes are present and how active they are, our ability to use these data to understand changes in cellular metabolism has been limited. We present a new approach to this problem, linking gene expression data with models of cellular metabolism. We apply the method to predict the effects of drugs and agents on Mycobacterium tuberculosis (M. tb). Virulence, growth in human hosts, and drug resistance are all related to changes in M. tb's metabolism. We predict the effects of a variety of conditions on the production of mycolic acids, essential cell wall components. Our method successfully identifies seven of the eight known mycolic acid inhibitors in a compendium of 235 conditions, and identifies the top anti-TB drugs in this dataset. We anticipate that the method will have a range of applications in metabolic engineering, the characterization of disease states, and drug discovery.

Maha Farhat, Megan Murray and colleagues report whole-genome sequencing of 116 Mycobacterium tuberculosis strains selected to be representative of both global diversity and drug resistance. The authors develop a new method to search for resistance markers in microbial genomes based on reconstructing a genome-wide phylogeny and identifying regions showing convergent evolution, and they use this method to identify 39 new candidate drug resistance regions in the M. tuberculosis genome. M. tuberculosis is evolving antibiotic resistance, threatening attempts at tuberculosis epidemic control. Mechanisms of resistance, including genetic changes favored by selection in resistant isolates, are incompletely understood. Using 116 newly sequenced and 7 previously sequenced M. tuberculosis whole genomes, we identified genome-wide signatures of positive selection specific to the 47 drug-resistant strains. By searching for convergent evolution—the independent fixation of mutations in the same nucleotide position or gene—we recovered 100% of a set of known resistance markers. We also found evidence of positive selection in an additional 39 genomic regions in resistant isolates. These regions encode components in cell wall biosynthesis, transcriptional regulation and DNA repair pathways. Mutations in these regions could directly confer resistance or compensate for fitness costs associated with resistance. Functional genetic analysis of mutations in one gene, ponA1, demonstrated an in vitro growth advantage in the presence of the drug rifampicin.

Introduction: Our aim was to investigate the impact of early versus late initiation of renal replacement therapy (RRT) on clinical outcomes in critically ill patients with acute kidney injury (AKI). Methods: Systematic review and meta-analysis were used in this study. PUBMED, EMBASE, SCOPUS, Web of Science and Cochrane Central Registry of Controlled Clinical Trials, and other sources were searched in July 2010. Eligible studies selected were cohort and randomised trials that assessed timing of initiation of RRT in critically ill adults with AKI. Results: We identified 15 unique studies (2 randomised, 4 prospective cohort, 9 retrospective cohort) out of 1,494 citations. The overall methodological quality was low. Early, compared with late therapy, was associated with a significant improvement in 28-day mortality (odds ratio (OR) 0.45; 95% confidence interval (CI), 0.28 to 0.72). There was significant heterogeneity among the 15 pooled studies (I² = 78%). In subgroup analyses, stratifying by patient population (surgical, n = 8 vs. mixed, n = 7) or study design (prospective, n = 10 vs. retrospective, n = 5), there was no impact on the overall summary estimate for mortality. Meta-regression controlling for illness severity (Acute Physiology And Chronic Health Evaluation II (APACHE II)), baseline creatinine and urea did not impact the overall summary estimate for mortality. Of studies reporting secondary outcomes, five studies (out of seven) reported greater renal recovery, seven (out of eight) studies showed decreased duration of RRT and five (out of six) studies showed decreased ICU length of stay in the early, compared with late, RRT group. Early RRT did not; however, significantly affect the odds of dialysis dependence beyond hospitalization (OR 0.62 0.34 to 1.13, I² = 69.6%). Conclusions: Earlier institution of RRT in critically ill patients with AKI may have a beneficial impact on survival. However, this conclusion is based on heterogeneous studies of variable quality and only two randomised trials. In the absence of new evidence from suitably-designed randomised trials, a definitive treatment recommendation cannot be made.

Phylogenetics of superspreading: One important characteristic of coronavirus epidemiology is the occurrence of superspreading events. These are marked by a disproportionate number of cases originating from often-times asymptomatic individuals. Using a rich sequence dataset from the early stages of the Boston outbreak, Lemieuxet al.identified superspreading events in specific settings and analyzed them phylogenetically (see the Perspective by Alizon). Using ancestral trait inference, the authors identified several importation events, further investigated the context and contribution of particular superspreading events to the establishment of local and wider SARS-CoV-2 transmission, and used viral phylogenies to describe sustained transmission.Science, this issue p.eabe3261; see also p.574