Biology subjects

Joly, J. H.

Publications and source records attributed to Joly, J. H..

6 recordsLinked to original sources

bioRxiv2022

Drug Mechanism Enrichment Analysis: A tool to link molecular signatures with sensitivity to drug mechanisms of action

BACKGROUNDThere is a pressing need for improved methods to identify effective therapeutics for disease. 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. RESULTSFirst, 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. CONCLUSIONSDMEA is a fast and 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.

bioinformatics↗

bioRxiv2020

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. Author SummaryCancer cells rewire their metabolism, which creates targetable metabolic vulnerabilities. Previous analyses of metabolic vulnerabilities in cancer cells have been limited to the analysis of individual genes or metabolites. However, metabolic pathways exhibit significant cross talk and compensation for one another. We developed a computational method to answer the question: when a metabolic pathways activity is high, which other metabolic pathways become more essential or less essential? By integrating genetic screen data with drug response data from FDA approved drugs, we identified cancer cell line dependence on metabolic pathways as opposed to individual genes. For example, we found that identifying key regulators of metabolic pathways, such as the Pentose Phosphate Pathway, may serve as a biomarker to identify which patients may benefit from antifolate chemotherapies (e.g. methotrexate, 5-fluorouracil). The efforts outlined here serve as the first characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines. Our results demonstrate the benefit of analyzing dependencies on metabolic pathways as opposed to metabolic genes.

cancer biology↗

bioRxiv2020

Proteomic profiling enables prediction of novel senolytics from large-scale drug screens

Senescence is a permanent cell cycle arrest that occurs in response to cellular stress. Because senescent cells promote age-related disease, there has been considerable interest in defining the proteomic alterations in senescent cells. Because senescence differs greatly depending on cell type and senescence inducer, continued progress in the characterization of senescent cells is needed. Here, we analyzed primary human mammary epithelial cells (HMECs), a model system for aging, using mass spectrometry-based proteomics. By integrating data from replicative senescence, immortalization by telomerase reactivation, and drug-induced senescence, we identified a robust proteomic signature of HMEC senescence consisting of 77 upregulated and 36 downregulated proteins. This approach identified known biomarkers, such as downregulation of the nuclear lamina protein lamin-B1 (LMNB1), and novel upregulated proteins including the {beta}-galactoside-binding protein galectin-7 (LGALS7). Gene ontology enrichment analysis demonstrated that senescent HMECs upregulated lysosomal proteins and downregulated RNA metabolic processes. We additionally integrated our proteomic signature of senescence with transcriptomic data from senescent HMECs to demonstrate that our proteomic signature can discriminate proliferating and senescent HMECs even at the transcriptional level. Taken together, our results demonstrate the power of proteomics to identify cell type-specific signatures of senescence and advance the understanding of senescence in primary HMECs.

systems biology↗

bioRxiv2019

MOTS-c is an Exercise-Induced Mitochondrial-Encoded Regulator of Age-Dependent Physical Decline and Muscle Homeostasis

Healthy aging can be promoted by enhancing metabolic fitness and physical capacity (1, 2). Mitochondria are chief metabolic organelles with strong implications in aging (3-8). In addition to their prominent role in bioenergetics, mitochondria also coordinate broad physiological functions by communicating to other cellular compartments or distal cells using multiple factors (9, 10), including peptides that are encoded within their own independent genome (11, 12). However, it is unknown if aging is actively regulated by factors encoded in the mitochondrial genome. MOTS-c is a mitochondrial-encoded peptide that regulates metabolic homeostasis (13, 14), in part, by translocating to the nucleus to regulate adaptive nuclear gene expression in response to cellular stress (15-17). Here, we report that MOTS-c is an exercise-induced mitochondrial-encoded peptide that significantly enhanced physical performance when administered to young (2 mo.), middle-aged (12 mo.), and old (22 mo.) mice. In humans, we found that endogenous MOTS-c levels significantly increased in response to exercise in skeletal muscle (11.9-fold) and in circulation (1.5-fold). Systemic MOTS-c treatment in mice significantly enhanced the performance on a treadmill of all age groups (~2-fold). MOTS-c regulated (i) nuclear genes, including those related to metabolism and protein homeostasis, (ii) glucose and amino acid metabolism in skeletal muscle, and (iii) myoblast adaptation to metabolic stress. Late-life (23.5 mo.) initiated intermittent MOTS-c treatment (3x/week) improved physical capacity and trended towards increasing lifespan. Our data indicate that aging is regulated by genes that are encoded not only in the nuclear genome (18, 19), but also in the mitochondrial genome. Considering that aging is the major risk factor for multiple chronic diseases (20, 21), our study provides new grounds for further investigation into mitochondrial-encoded regulators of healthy lifespan.

cell biology↗

bioRxiv2019

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

Gene Set Enrichment Analysis (GSEA) is an algorithm widely used to identify statistically enriched gene sets in transcriptomic data. However, to our knowledge, there exists no method for examining the enrichment of two gene sets relative to one another. Here, we present Differential Gene Set Enrichment Analysis (DGSEA), an adaptation of GSEA that assesses the relative enrichment of two gene sets. Using the metabolic pathways glycolysis and oxidative phosphorylation as an example, we demonstrate that DGSEA accurately captures the hypoxia-induced shift towards glycolysis. 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. Furthermore, we demonstrate that DGSEA identifies novel metabolic dependencies not found by GSEA in cancer cell lines. Together, these data demonstrate that DGSEA is a novel tool to examine the relative enrichment of two gene sets.

bioinformatics↗

bioRxiv2019

NADPH consumption by L-cystine reduction creates a metabolic vulnerability upon glucose deprivation

The consequences of metabolic reprogramming in cancer can include an increased dependence on metabolic substrates such as glucose for survival. 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, we sought to identify the mechanisms regulating cancer cell death upon glucose deprivation and then design combinations of inhibitors 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 glutathione. Building on this observation, we show that glucose deprivation-induced cell death is driven not by lack of glucose but rather by L-cystine import. Following glucose deprivation, the import of L-cystine and subsequent reduction to L-cysteine depleted both NADPH and glutathione, thereby allowing toxic accumulation of reactive oxygen species. Consistent with this model, we found that the glutamate/cystine antiporter, xCT, was required for sensitivity to glucose deprivation. We searched for glycolytic enzymes whose expression is essential for survival of cancer cells with high xCT expression and identified the glucose transporter GLUT1. We therefore tested a drug combination co-targeting GLUT1 and glutathione synthesis and found that these drugs induced synthetic lethal cell death in high xCT-expressing cell lines susceptible to glucose deprivation. These results indicate that co-targeting GLUT1 and glutathione synthesis is a potential therapeutic approach in tumors dependent on glucose for survival.

systems biology↗