Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing.

BACKGROUND: There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates. RESULTS: First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors. CONCLUSIONS: DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .

Glutamine-dependent signaling controls pluripotent stem cell fate.

Human pluripotent stem cells (hPSCs) can self-renew indefinitely or can be induced to differentiate. We previously showed that exogenous glutamine (Gln) withdrawal biased hPSC differentiation toward ectoderm and away from mesoderm. We revealed that, although all three germ lineages are capable of de novo Gln synthesis, only ectoderm generates sufficient Gln to sustain cell viability and differentiation, and this finding clarifies lineage fate restrictions under Gln withdrawal. Furthermore, we found that Gln acts as a signaling molecule for ectoderm that supersedes lineage-specifying cytokine induction. In contrast, Gln in mesoderm and endoderm is the preferred precursor of α-ketoglutarate without a direct signaling role. Our work raises a question about whether the nutrient environment functions directly in cell differentiation during development. Interestingly, transcriptome analysis of a gastrulation-stage human embryo shows that unique Gln enzyme-encoding gene expression patterns may also distinguish germ lineages in vivo. Together, our study suggests that intracellular Gln may help coordinate differentiation of the three germ layers.

The landscape of metabolic pathway dependencies in cancer cell lines.

The metabolic reprogramming of cancer cells creates metabolic vulnerabilities that can be therapeutically targeted. However, our understanding of metabolic dependencies and the pathway crosstalk that creates these vulnerabilities in cancer cells remains incomplete. Here, by integrating gene expression data with genetic loss-of-function and pharmacological screening data from hundreds of cancer cell lines, we identified metabolic vulnerabilities at the level of pathways rather than individual genes. This approach revealed that metabolic pathway dependencies are highly context-specific such that cancer cells are vulnerable to inhibition of one metabolic pathway only when activity of another metabolic pathway is altered. Notably, we also found that the no single metabolic pathway was universally essential, suggesting that cancer cells are not invariably dependent on any metabolic pathway. In addition, we confirmed that cell culture medium is a major confounding factor for the analysis of metabolic pathway vulnerabilities. Nevertheless, we found robust associations between metabolic pathway activity and sensitivity to clinically approved drugs that were independent of cell culture medium. Lastly, we used parallel integration of pharmacological and genetic dependency data to confidently identify metabolic pathway vulnerabilities. Taken together, this study serves as a comprehensive characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines.

MOTS-c is an exercise-induced mitochondrial-encoded regulator of age-dependent physical decline and muscle homeostasis.

Healthy aging can be promoted by enhanced metabolic fitness and physical capacity. Mitochondria are chief metabolic organelles with strong implications in aging that also coordinate broad physiological functions, in part, using peptides that are encoded within their independent genome. However, mitochondrial-encoded factors that actively regulate aging are unknown. Here, we report that mitochondrial-encoded MOTS-c can significantly enhance physical performance in young (2 mo.), middle-age (12 mo.), and old (22 mo.) mice. MOTS-c can regulate (i) nuclear genes, including those related to metabolism and proteostasis, (ii) skeletal muscle metabolism, and (iii) myoblast adaptation to metabolic stress. We provide evidence that late-life (23.5 mo.) initiated intermittent MOTS-c treatment (3x/week) can increase physical capacity and healthspan in mice. In humans, exercise induces endogenous MOTS-c expression in skeletal muscle and in circulation. Our data indicate that aging is regulated by genes encoded in both of our co-evolved mitochondrial and nuclear genomes.

Differential Gene Set Enrichment Analysis: a statistical approach to quantify the relative enrichment of two gene sets.

MOTIVATION: Gene Set Enrichment Analysis (GSEA) is an algorithm widely used to identify statistically enriched gene sets in transcriptomic data. However, GSEA cannot examine the enrichment of two gene sets or pathways relative to one another. Here we present Differential Gene Set Enrichment Analysis (DGSEA), an adaptation of GSEA that quantifies the relative enrichment of two gene sets. RESULTS: After validating the method using synthetic data, we demonstrate that DGSEA accurately captures the hypoxia-induced coordinated upregulation of glycolysis and downregulation of oxidative phosphorylation. We also show that DGSEA is more predictive than GSEA of the metabolic state of cancer cell lines, including lactate secretion and intracellular concentrations of lactate and AMP. Finally, we demonstrate the application of DGSEA to generate hypotheses about differential metabolic pathway activity in cellular senescence. Together, these data demonstrate that DGSEA is a novel tool to examine the relative enrichment of gene sets in transcriptomic data. AVAILABILITY AND IMPLEMENTATION: DGSEA software and tutorials are available at https://jamesjoly.github.io/DGSEA/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A synthetic lethal drug combination mimics glucose deprivation-induced cancer cell death in the presence of glucose.

Metabolic reprogramming in cancer cells can increase their dependence on metabolic substrates such as glucose. As such, the vulnerability of cancer cells to glucose deprivation creates an attractive opportunity for therapeutic intervention. Because it is not possible to starve tumors of glucose in vivo, here we sought to identify the mechanisms in glucose deprivation-induced cancer cell death and then designed inhibitor combinations to mimic glucose deprivation-induced cell death. Using metabolomic profiling, we found that cells undergoing glucose deprivation-induced cell death exhibited dramatic accumulation of intracellular l-cysteine and its oxidized dimer, l-cystine, and depletion of the antioxidant GSH. Building on this observation, we show that glucose deprivation-induced cell death is driven not by the lack of glucose, but rather by l-cystine import. Following glucose deprivation, the import of l-cystine and its subsequent reduction to l-cysteine depleted both NADPH and GSH pools, thereby allowing toxic accumulation of reactive oxygen species. Consistent with this model, we found that the glutamate/cystine antiporter (xCT) is required for increased sensitivity to glucose deprivation. We searched for glycolytic enzymes whose expression is essential for the survival of cancer cells with high xCT expression and identified glucose transporter type 1 (GLUT1). Testing a drug combination that co-targeted GLUT1 and GSH synthesis, we found that this combination induces synthetic lethal cell death in high xCT-expressing cell lines susceptible to glucose deprivation. These results indicate that co-targeting GLUT1 and GSH synthesis may offer a potential therapeutic approach for targeting tumors dependent on glucose for survival.