Biology subjects

Graham, N. A.

Publications and source records attributed to Graham, N. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Nutrient content estimation of the world's fishes

Seafood nutrients from global fisheries are of increasing importance for research and policy in food security and nutrition. As the chemical composition of fish is determined by what they eat, their energetic demands, and the environment in which they live, nutrient content reflects aspects of physiology and life history, ecological and environmental traits, as well as evolutionary history. Here we present data from Bayesian model estimates of 12 key nutrients (calcium, iron, phosphorus, magnesium, selenium, zinc, vitamin A, vitamin B9, vitamin B12, vitamin D, omega-3 fatty acids, and protein) in wild fish, using a database of reported nutrient content for freshwater and marine species. We then predict the nutrient content of 5588 fish species with traits available from FishBase. We compare our previous model using traits alone with a new model of both traits and phylogeny, and present the data, code, and predictions for models coded in PyMC. These models and predictions, made freely available through FishBase, can be used to explore the historical, current, and future nutrient content of fisheries catch.

ecology↗

bioRxiv2026

Coral reef ecosystem functions in a human-dominated world

The metabolic processes sustaining coral reefs, from carbonate and primary production to secondary production, remain poorly integrated and rarely quantified simultaneously at global scales. This hampers our ability to predict global responses to accelerating human pressures and manage coral reef functioning. Using metabolic scaling and bioenergetic models applied to surveys from 1,100 reefs worldwide, we provide a global, standardized quantification of 14 ecosystem functions spanning benthic (corals and algae) and fish communities. Our analysis reveals a continuous functional spectrum of global coral reefs organized along four dominant axes: 1) primary production, 2) calcification and habitat structure, 3) secondary biomass production and consumption, and 4) biomass turnover. Functions mediated by fish and benthic communities show weak associations at the global scale rather than tight coupling. Climate stressors reduced calcification and local human impacts lowered secondary production. Yet these directional effects unfolded against a backdrop of substantial natural variability in reef functional configurations, such that heavily and minimally impacted reefs overlap substantially in the global functional space. Temporal analyses across three representative reef systems further revealed that functional trajectories following disturbance are context-dependent, with no universal pattern of recovery across locations. This continuous and context-dependent functional spectrum challenges the notion of universal functional benchmarks and supports locally tailored conservation strategies.

ecology↗

bioRxiv2026

Disruption of the SYNGAP1 PDZ ligand motif accelerates differentiation of human iPSC-derived GABAergic neurons

SYNGAP1 haploinsufficiency is a leading genetic cause of neurodevelopmental disorders (NDD), including intellectual disability and epileptic encephalopathy. While most studies on SYNGAP1 function have focused on glutamatergic neurons, its role in GABAergic neurons and during early neuronal development is unclear. Using human iPSC-derived GABAergic neurons, we demonstrate that SYNGAP1 haploinsufficiency accelerates neuronal maturation, characterized by increased dendritic length, synaptic density, and maturation of synaptic structures. Disruption of the isoform-specific SYNGAP1 PDZ binding motif reproduces these phenotypes, highlighting the critical role of PDZ-mediated interactions in regulating GABAergic neuronal differentiation. Proteomic and phosphoproteomic analyses reveal significant dysregulation of synaptic proteins, RNA processing, and transcriptional control, with a significant increase in postsynaptic density proteins content. RNA-seq analysis suggest that the acceleration in neuronal differentiation starts few hours after neuronal induction setting a path to a faster neuronal and synapse maturation. These findings establish that SYNGAP1 acts as a key regulator of neuronal differentiation across both excitatory and inhibitory neurons. Our work underscores the importance of the SYNGAP1 PDZ ligand motif for normal neuronal development and suggests translational strategies targeting SYNGAP1 alpha1 isoform levels to mitigate SYNGAP1-related NDD.

neuroscience↗

bioRxiv2026

LC-MS profiling of prmt-1 and prmt-5 knockout C. elegans reveals PRMT-1 substrates and global proteome remodeling

Although protein arginine methylation regulates diverse biological processes, it remains understudied relative to other post-translational modifications. Here, we analyzed C. elegans prmt-1 and prmt-5 null mutants using LC-MS proteomics to map PRMT methylation substrates and to quantify the effects of PRMT knockout on global protein abundance. High-pH strong cation exchange fractionation was used to enrich methylated peptides, and parallel analysis of whole cell lysates was used to measure global protein abundance. Quantitative methyl-proteomics identified 31 PRMT-1-dependent methyl-arginine peptides from 15 proteins with several arginine residues demonstrating dramatic decrease in both monomethyl- and asymmetric dimethyl-arginine abundance. Whole-proteome profiling revealed that prmt-1 knockout caused broad remodeling of the worm proteome with changes linked to DNA replication/cell-cycle programs, protein folding, and amino acid metabolism. Although prmt-5 knockout affected similar biological pathways to prmt-1 knockout, the effects on the C. elegans proteome were more modest. Together, these data connect PRMT-dependent methylation changes to proteome remodeling in a whole-animal model, support previous work suggesting that PRMT-1 is the dominant Type I PRMT in C. elegans, and provide a resource for studying how PRMT-1 and PRMT-5 shape protein regulation in vivo. All raw data have been deposited in the PRIDE database with accession number PXD074042.

systems biology↗

bioRxiv2025

Potential yield and food provisioning gains from rebuilding the worlds coral reef fish stocks

Many coral reefs have fish stocks that are depleted below the level at which sustainable production is maximized. Lower production means that millions of people are losing out on potential food, income and livelihoods. Rebuilding these stocks to maximize sustainable production can contribute towards ending hunger and malnutrition but requires active and effective fisheries management. Yet, for fish stock recovery plans to be implemented, recovery benefits, targets and timeframes need to be quantified. Here, using 1211 individual reef sites and 23 jurisdictions identified globally as being below maximum sustainable production levels, we show that reefs have the potential to increase sustainable yields by nearly 50% if allowed to recover towards their maximum production levels. For individual jurisdictions this recovery represents from 20,000 up to 162 million additional sustainable servings of reef fish per year in comparison to current sustainable production, meeting recommended seafood intake for up to 1. 4 million additional people a year. However, such growth and food provisioning will require fish stocks to double their standing biomass (increase by a median of 32 t/km2). Recovery timeframes range from 6.4 years under the most stringent scenario (a seascape moratorium) to 49.7 years under the maximum harvest scenario that results in recovery. We find that locations with the greatest potential for sustainable gains in yield are among those with the greatest food and micronutrient deficiencies, underscoring both the challenges and opportunities in recovering fish assemblages to achieve their maximum sustainable potential. Significance StatementCoral reef fisheries are a critical food source for people throughout the tropics. However, most reefs around the globe have fish biomass values below those enabling maximal sustainable production, risking food availability, income, and livelihoods. Rebuilding fish assemblages can increase sustainable food supplies, and, if these are well distributed, directly contribute towards enhanced food security. We show that recovering fish stocks on coral reefs can significantly increase the number of sustainable fish servings produced per year and the number of people meeting fish intake recommendations, particularly for countries with high malnutrition. Our study highlights the sustainable food provisioning potential of recovering reef fisheries and quantifies how much recovery would be needed and the time such recovery would take.

ecology↗

bioRxiv2024

Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as a crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.

systems biology↗

bioRxiv2023

Seabird traits and seasonality modulate nutrient dynamics of terrestrial and marine habitats on atolls

Marine nutrients underpin productivity and functioning of oceanic island ecosystems. Seabirds represent a primary source of marine nutrients. In tropical regions, some of the largest seabird populations nest on atolls, yet there is limited information available on seabird contributions to atoll ecosystem nutrient dynamics. To investigate the spatial and seasonal dynamics of seabird contributions, we assessed seabird colonies of different taxa, including red-footed boobies and terns, nesting on separate islands of Farquhar Atoll, Seychelles. We assessed nutrient concentrations of guano, soil, coastal plants and nearby seagrass in seabird colonies and at a control island with no seabirds, during the wet and dry seasons. Sooty terns contributed the highest quantities of nutrients, estimated at 71.2 N tonne.yr-1 and 52.2 P tonne.yr-1. Seabird-derived nutrient transfer occurred year-round from seabird colonies to soil, coastal plants and seagrass. Soil macro- and inorganic nutrients were higher in the high-density, ground-nesting tern colony and during the dry season, coinciding with the breeding period of sooty terns. Both red-footed booby and tern colonies maintained high nitrogen levels (%N) in coastal plants year-round, while phosphorus levels (%P) did not differ between islands or seasons. Seabird-derived nitrogen reversed nitrogen limitation (log C:N) of seagrass during the dry season. We provide the first insights into seabird nutrient contributions to atoll ecosystems in Seychelles, with recommendations for seabird conservation to boost and support atoll and island ecosystem resilience. Our results from a relatively undisturbed atoll serve as a baseline with which more impacted atolls and future changes can be assessed.

ecology↗

bioRxiv2022

Sustainable reference points for multispecies coral reef fisheries

Sustainably managing fisheries requires regular and reliable evaluation of stock status. However, most multispecies reef fisheries around the globe tend to be data-poor and lack research and monitoring capacity (e.g., long-term fishery data), preventing the estimation of sustainable reference points against which stocks can be assessed. Here, combining fish biomass data for more than 2000 coral reefs with catch estimates from 99 jurisdictions, we estimate site-specific sustainable reference points for coral reef fisheries and use these to assess the status of coral reef fish stocks. We reveal that more than half of jurisdictions with available information have stocks of conservation concern, having failed at least one fisheries sustainability benchmark. We quantify the trade-offs between biodiversity, mean fish length, and ecosystem functions relative to key benchmarks and highlight the ecological benefits of increasing sustainability. Our approach yields multispecies sustainable reference points for coral reef fisheries using environmental conditions, a promising means for enhancing the sustainability of the worlds coral reef fisheries. TeaserA global assessment of the sustainability of multispecies reef fisheries indicates that more than half of jurisdictions have failed at least one of two key sustainability benchmarks.

ecology↗

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↗

bioRxiv2022

Phospho-proteomic analysis of CAR-T cell signaling following activation by antigen-presenting cancer cells

Chimeric antigen receptors (CARs) are synthetic biomolecules comprised of an extracellular antigen recognition domain and intracellular signaling domains. When expressed in immune cells, CARs direct their host cells to kill diseased cells expressing the antigen recognized by the CAR. Although signaling pathways downstream of CAR activation control the cytotoxic function of CAR-expressing cells, phospho-proteomic studies of CAR signaling have been limited. Most approaches have used antibodies or soluble ligands, rather than cell-displayed antigens, to activate CAR signaling. Here, we demonstrate an efficient and cost-effective label-free phospho-proteomic approach to analyze CAR signaling in immune cells stimulated with antigen-presenting cancer cells. Following co-culture of CAR-T cells with cancer cells, we first preserve phospho-signaling by cross-linking proteins with formalin. Then, we use magnet-activated cell sorting (MACS) to isolate CAR-T cells from the co-culture. Validation experiments demonstrated that formalin fixation did not alter the phospho-proteome and that MACS achieved >90% CAR-T cell purity. Next, we compared the phospho-proteome in CAR-T cells stimulated with either CD19-expressing or non-CD19-expressing SKOV3 ovarian cancer cells. This analysis revealed that CAR signaling activated known pathways including the mitogen- activated protein kinases (MAPKs) ERK1/2. Bioinformatic approaches further showed that CAR activation induced other signaling pathways including the MAPK p38, protein kinase A, and checkpoint kinase 1 (CHK1). Taken together, this work presents an easy and inexpensive method to better understand CAR immunotherapy by label-free phospho-proteomic analysis of CAR signaling in immune cells stimulated by antigen- presenting cancer cells.

immunology↗

bioRxiv2021

Phospho-proteomics reveals that RSK signaling is required for proliferation of natural killer cells stimulated with IL-2 or IL-15

Natural killer (NK) cells are cytotoxic lymphocytes that play a critical role in the innate immune system. Although cytokine signaling is crucial for the development, expansion, and cytotoxicity of NK cells, the signaling pathways stimulated by cytokines are not well understood. Here, we sought to compare the early signaling dynamics induced by the cytokines interleukin (IL)-2 and IL-15 using liquid chromatography-mass spectrometry (LC-MS)-based phospho-proteomics. Following stimulation of the immortalized NK cell line NK-92 with IL-2 or IL-15 for 5, 10, 15, or 30 minutes, we identified 8,692 phospho-peptides from 3,023 proteins. Comparing the kinetic profiles of 3,619 fully quantified phospho-peptides, we found that IL-2 and IL-15 induced highly similar signaling in NK-92 cells. Among the IL-2/IL-15-regulated phospho-sites were both well-known signaling events like the JAK/STAT pathway and novel signaling events with potential functional significance including LCP1 Ser5, PAK2 Ser141, and STK17B Ser12. Using bioinformatic approaches, we sought to identify kinases regulated by IL-2/IL-15 stimulation and found that the p90 ribosomal S6 kinase (p90RSK) family was activated by both cytokines. Using pharmacological inhibitors, we then discovered that RSK signaling is required for IL-2 and IL-15-induced proliferation in NK-92 cells. Taken together, our analysis represents the first phospho-proteomic characterization of cytokine signaling in NK cells and increases our understanding of how cytokine signaling regulates NK cell function.

immunology↗

bioRxiv2021

Kinetic and data-driven modeling of pancreatic β-cell central carbon metabolism and insulin secretion

Pancreatic {beta}-cells respond to increased extracellular glucose levels by initiating a metabolic shift. That change in metabolism is part of the process of glucose-stimulated insulin secretion and is of particular interest in the context of diabetes. However, we do not fully understand how the coordinated changes in metabolic pathways and metabolite products influence insulin secretion. In this work, we apply systems biology approaches to develop a detailed kinetic model of the intracellular central carbon metabolic pathways in pancreatic {beta}-cells upon stimulation with high levels of glucose. The model is calibrated to published metabolomics datasets for the INS1 823/13 cell line, accurately capturing the measured metabolite fold-changes. We first employed the calibrated mechanistic model to estimate the stimulated cells fluxome. We then used the predicted network fluxes in a data-driven approach to build a partial least squares regression model. By developing the combined kinetic and data-driven modeling framework, we gain insights into the link between {beta}-cell metabolism and glucose-stimulated insulin secretion. The combined modeling framework was used to predict the effects of common anti-diabetic pharmacological interventions on metabolite levels, flux through the metabolic network, and insulin secretion. Our simulations reveal targets that can be modulated to enhance insulin secretion. The model is a promising tool to contextualize and extend the usefulness of metabolomics data and to predict dynamics and metabolite levels that are difficult to measure in vitro. In addition, the modeling framework can be applied to identify, explain, and assess novel and clinically-relevant interventions that may be particularly valuable in diabetes treatment. Author SummaryDiabetes is among the most common chronic illnesses, occurring when the {beta}-cells in the pancreas are unable to produce enough insulin to properly manage the bodys blood sugar levels. {beta}-cells metabolize nutrients to produce energy needed for insulin secretion in response to high glucose, and there is a potential to harness {beta}-cell metabolism for treating diabetes. However, {beta}-cell metabolism is not fully characterized. We have developed a computational modeling framework to better understand the relationship between cellular metabolism and insulin production in the pancreatic {beta}-cell. With this modeling framework, we are able to simulate metabolic perturbations, such as the knockdown of the activity of a metabolic enzyme, and predict the effect on the metabolic network and on insulin production. This work can therefore be applied to investigate, in a time- and cost-efficient manner, {beta}-cell metabolism and predict effective therapies that target the cells metabolic network.

systems biology↗

bioRxiv2021

Defining dysfunction due to loss of MECP2 in Rett Patient Brain

Rett Syndrome is characterized by a postnatal loss of neurophysiological function and regression of childhood development. Because the syndrome is X-linked and males with MECP2 mutations generally do not survive birth, the study of this syndrome has been complicated by the fact that in female brain, a portion of neurons express wild type MECP2, and another portion express a non-functional allele of MECP2. Therefore, bulk-RNA-sequencing of Rett brain is confounded by the presence of chimerism of neurons for functional MECP2 in neurons. We developed an approach that allows for single-nuclei transcriptional profiling of individual neurons and a direct comparison between neurons that express functional MECP2 with those that express the disease-causing allele. We found that mutant neurons from Rett brain show patterns of aberrant expression of synaptic and metabolic genes, both of which can be detected in in vitro models of Rett Syndrome. We used these resources to identify a role for POU2F1/OCT1 transcription factor in mediating the response to stress due to loss of MECP2, highlighting a potential key molecular regulator of stress in Rett neurons. Together, our new sorting approach enables us to highlight defective molecular and metabolic pathways in Rett brain neurons and suggests that in vitro models could serve as valuable tools to further study this syndrome and potentially for development of novel therapeutics.

molecular biology↗

bioRxiv2021

Elucidating Tumor-stromal Metabolic Crosstalk in Colorectal Cancer through Integration of Constraint-Based Models and LC-MS Metabolomics

Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling. Using metabolomics data, we perform unsteady-state parsimonious flux balance analysis to infer flux distributions for central carbon metabolism in CRC cells treated with or without CAF-conditioned media. We find that CAFs reprogram CRC metabolism through stimulation of glycolysis, the oxidative arm of the pentose phosphate pathway (PPP), and glutaminolysis as well as inhibition of the tricarboxylic acid cycle. To identify potential therapeutic targets, we simulate enzyme knockouts and find that inhibiting the hexokinase and glucose-6-phosphate dehydrogenase reactions exploits the CAF-induced dependence of CRC cells on glycolysis and oxidative PPP. Our work gives mechanistic insights into the metabolic interactions between CRC cells and CAFs and provides a framework for testing hypotheses towards CRC-targeted therapies.

systems biology↗

bioRxiv2021

Bayesian metamodeling of complex biological systems across varying representations

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide-and-conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are (i) converted to a standardized statistical representation relying on Probabilistic Graphical Models, (ii) coupled by modeling their mutual relations with the physical world, and (iii) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic {beta}-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic {beta}-Cell Consortium. Significance StatementCells are the basic units of life, yet their architecture and function remain to be fully characterized. This work describes Bayesian metamodeling, a modeling approach that divides-and-conquers a large problem of modeling numerous aspects of the cell into computing a number of smaller models of different types, followed by assembling these models into a complete map of the cell. Metamodeling enables a facile collaboration of multiple research groups and communities, thus maximizing the sharing of expertise, resources, data, and models. A proof-of-principle is provided by a model of glucose-stimulated insulin secretion produced by the Pancreatic {beta}-Cell Consortium.

cell biology↗

bioRxiv2021

Targeting CTP Synthetase 1 to Restore Interferon Induction and Impede Nucleotide Synthesis in SARS-CoV-2 Infection

The newly emerged SARS-CoV-2 caused a global pandemic with astonishing mortality and morbidity. The mechanisms underpinning its highly infectious nature remain poorly understood. We report here that SARS-CoV-2 exploits cellular CTP synthetase 1 (CTPS1) to promote CTP synthesis and suppress interferon (IFN) induction. Screening a SARS-CoV-2 expression library identified ORF7b and ORF8 that suppressed IFN induction via inducing the deamidation of interferon regulatory factor 3 (IRF3). Deamidated IRF3 fails to bind the promoters of classic IRF3-responsible genes, thus muting IFN induction. Conversely, a shRNA-mediated screen focused on cellular glutamine amidotransferases corroborated that CTPS1 deamidates IRF3 to inhibit IFN induction. Functionally, ORF7b and ORF8 activate CTPS1 to promote de novo CTP synthesis while shutting down IFN induction. De novo synthesis of small-molecule inhibitors of CTPS1 enabled CTP depletion and IFN induction in SARS-CoV-2 infection, thus impeding SARS-CoV-2 replication. Our work uncovers a strategy that a viral pathogen couples immune evasion to metabolic activation to fuel viral replication. Inhibition of the cellular CTPS1 offers an attractive means for developing antiviral therapy that would be resistant to SARS-CoV-2 mutation.

microbiology↗

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↗