Biology subjects

Dugourd, A.

Publications and source records attributed to Dugourd, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Turning Domain Expertise into Multi-Dimensional Evaluation of Biomedical AI with Karenina

Language models and agents are increasingly used in biomedicine, but current benchmarks reward correct answers even when the underlying reasoning is flawed. Here we introduce Karenina, an open-source framework that turns expert knowledge into multi-dimensional evaluations of questions, conversations and autonomous agents. Illustrated in Question-Answer pairs, multi-turn conversations and autonomous data-analysis, these dimensions together moves evaluation beyond scoring, enabling trustworthy decision-making with AI in biomedicine.

bioinformatics↗

bioRxiv2026

Function-driven geometry directs human pilosebaceous unit development

Single-cell technologies have generated cell censuses of tissues, however, how tissue geometry reflects functional needs remains poorly characterized. The human pilosebaceous unit offers a tractable model, a prenatally-formed complex mini-organ combining hair and sebum production with a stem cell reservoir. Using histomorphology, spatial transcriptomics, and single-cell multiomics on the same human prenatal scalp skin samples (8-19 post-conception weeks), integrated and analyzed using machine learning approaches, we built a spatiotemporal map of pilosebaceous unit development. We demonstrate that epithelial-mesenchymal interactions coordinate cellular fate and organogenesis, using an in vitro hair-bearing skin organoid model to validate this tissue-patterning. In addition, we show sebaceous gland developmental programmes are overcome during tumor formation. Our large-scale multi-modal analysis provides a unique framework for understanding form and function of tissues with applications in tissue engineering and pathology.

developmental biology↗

bioRxiv2026

Combining prior knowledge and transcriptomics data for logic models of patient subgroups

Computational modeling provides a powerful framework for in silico exploration of anti-cancer therapeutic targets and tumor response mechanisms. Oncogenic signaling pathways play a central role in tumor behavior and represent promising targets for personalized combination therapies. However, these pathways are complex, and although logic-based models are well suited for representing signaling dynamics, they are often constrained by model-specific data requirements, limited scalability, and time-consuming manual curation. Here, we introduce Functional Integration of Contextualized Omics for Unraveling regulatory dynamicS (FICUS), a framework that integrates omics-driven network contextualization with dynamic Boolean and logic-ODE modeling. FICUS enables automated, data-driven protein network inference and patient stratification, allowing shared signaling mechanisms to be identified across patient subgroups while preserving patient-specific dynamic responses. We applied FICUS to the SU2C-MARK lung cancer cohort and the The Cancer Genome Atlas kidney cancer cohort, demonstrating its utility for post-hoc analyses and downstream interrogation of dynamic tumor models. Overall, our results highlight the flexibility of FICUS in capturing heterogeneous signaling mechanisms across patient subgroups, addressing a key challenge in precision oncology.

Systems Biology↗

bioRxiv2026

OmniPath Metabo: chemical structures, interactions and mechanisms to study the metabolome

Mechanistic and functional analysis of omics data largely relies on the incorporation of prior knowledge; however, connecting metabolomics data and knowledge is a major methodological challenge. This is largely driven by the diverse prior knowledge being fragmented across many databases requiring the merging of different database records across chemical structures, identifiers, and varying levels of structural specificity. Hence, this limits mechanistic interpretation and functional characterisation of the metabolome. Here, we present OmniPath Metabo, a comprehensive, harmonized, metabolome-centric database covering metabolites, lipids, food-derived compounds, and small molecule drugs, along with their associated receptors, transporters, enzymes, reactions, allosteric regulators, and disease associations. OmniPath Metabo harmonizes attributes using controlled vocabularies and ontologies, structures and built-in cheminformatics to map identifiers and track ambiguity. OmniPath Metabo is built directly from 40+ original resources and is freely accessible via an interactive web app and API at metabo.omnipathdb.org. OmniPath Metabo enables dynamic, context-specific construction of subnetworks to serve dedicated purposes, such as cell-cell communication or integrated multi-omics metabolite-driven regulation, connecting reactions, allosteric regulation, metabolite-receptor and metabolite-transporter interactions. Combining it with the over 170 other resources in OmniPath, it can be used for integrated networks of signaling, gene regulation, and metabolism. We showcase the application of OmniPath Metabo by analysing publicly available metabolomics data of lung cancer cell lines and metabolic footprints to mutational patterns. In summary, OmniPath Metabo transforms fragmented resources into a harmonised prior knowledge framework for a mechanistic and functional analysis of the metabolome.

bioinformatics↗

bioRxiv2025

Bioinformatic inference of the exercise-responsive control of p70 S6 kinase through RPS6KB1 expression

Previous research suggests that the absolute levels of p70 S6 kinase (p70S6K) are a key determinant of the rate of skeletal muscle protein synthesis (MPS). p70S6K levels are in part determined by the transcriptional control of the gene encoding p70S6K, RPS6KB1, but the molecular mechanisms governing its expression are poorly understood. The purpose of this study was to infer the molecular regulatory network governing RPS6KB1 expression. We applied a novel bioinformatic network inference algorithm called CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) to infer the signaling network downstream of canonical exercise sensors controlling RPS6KB1-specific transcription factors (TFs) after acute aerobic (AE) or resistance exercise (RE). CARNIVAL integrates a prior knowledge network, TF and signaling pathway activities inferred from transcriptomic data, and perturbation targets to predict the network that best explains the data. The networks revealed intracellular sensors and hormone receptors controlling RPS6KB1-specific TFs. Both exercise types resulted in AMPK-mediated SNAI1 regulation, but HIF1A was distinctly controlled (AE: PHD1-3, FIH; RE: AMPK). AE controlled FOXA1 via insulin, TGF-{beta}, and myostatin signalling, while RE controlled CEBPA via MAP3Ks. Our study is the first to apply a comprehensive bioinformatic network inference algorithm to infer causal exercise-responsive signaling networks. The results of our analysis motivate experimentally testable hypotheses pertaining to the molecular control of RPS6KB1 transcription in human skeletal muscle in response to aerobic and resistance exercise.

bioinformatics↗

bioRxiv2025

OmniPath: integrated knowledgebase for multi-omics analysis

Analysis and interpretation of omics data largely benefit from the use of prior knowledge. However, this knowledge is fragmented across resources and often is not directly accessible for analytical methods. We developed OmniPath (https://omnipathdb.org/), a database combining diverse molecular knowledge from 168 resources. It covers causal protein-protein, gene regulatory, miRNA, and enzyme-PTM (post-translational modification) interactions, cell-cell communication, protein complexes, and information about the function, localization, structure, and many other aspects of biomolecules. It prioritizes literature curated data, and complements it with predictions and large scale databases. To enable interactive browsing of this large corpus of knowledge, we developed OmniPath Explorer, which also includes a large language model (LLM) agent that has direct access to the database. Python and R/Bioconductor client packages and a Cytoscape plugin create easy access to customized prior knowledge for omics analysis environments, such as scverse. OmniPath can be broadly used for the analysis of bulk, single-cell and spatial multi-omics data, especially for mechanistic and causal modeling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/675512v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17c2b6borg.highwire.dtl.DTLVardef@1069835org.highwire.dtl.DTLVardef@1f2ce76org.highwire.dtl.DTLVardef@1d0b34f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

A knowledge graph and topological data analysis framework to disentangle the tomato-multi pathogens complex gene regulatory network

Global population is rapidly increasing, representing a major challenge for food supply, exacerbated by climate change and environmental degradation. Despite the pivotal role of agriculture, plant health and survival are threatened by various biotic stressors. Although how plants respond to each of these individual stresses is well studied, little is known about how they respond to a combination of many of these bio-aggressors occurring together. To tackle this question, first, we built TomTom, a knowledge graph gathering molecular interactions from nine publicly available databases, including transcription factors- or microRNAs-targets, protein-protein interactions, and functional terms. Then, we selected transcriptomics data of tomato subjected to six distinct pathogens and performed an integrative analysis. We found 5561 candidate genes involved in the multi-stress response of tomato. To study how the response is orchestrated, we mapped those genes in TomTom and extracted a comprehensive gene regulatory network (GRN) composed of 71 transcription factors (TF) and 1786 target genes. By estimating the TF activity, we identified 43 TFs responding either specifically to one or multiple bio-aggressors. GRN analyses with a topological data analysis approach allowed to identify 18 clusters of TFs with similar properties, yielding four main configurations localized in specific regions of the GRN. Finally, we found one NAC and four ERF hubs which cooperatively coordinate the tomato response to multiple pathogens. Our findings allowed to study the complex molecular reprogramming in tomato upon interaction with different biotic agents, providing tools scalable to other questions involving tomato molecular interactions and beyond.

systems biology↗

bioRxiv2024

Modeling causal signal propagation in multi-omic factor space with COSMOS

Understanding complex diseases requires approaches that jointly analyze omics data across multiple biological layers, including signaling, gene regulation, and metabolism. Existing data-driven multi-omics analysis methods, such as multi-omics factor analysis (MOFA), can identify associations between molecular features and phenotypes, but they are not designed to integrate existing mechanistic molecular knowledge, which can provide further actionable insights. We introduce an approach that connects data-driven analysis of multi-omics data with systematic integration of mechanistic prior knowledge using COSMOS+ (Causal Oriented Search of Multi-Omics Space). We show how factor analysis output can be used to estimate activities of transcription factors and kinases as well as ligand-receptor interactions, which in turn are integrated with network-level prior-knowledge to generate mechanistic hypotheses about paths connecting deregulated molecular features. We apply this approach on a novel multi-omics dataset of cell line models of breast cancer resistance to evaluate the ability of such mechanistic hypotheses to identify resistance drivers, as well as a breast cancer patient cohort. Our approach offers an interpretable framework to generate actionable insights from multi-omic data particularly suited for high dimensional datasets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/603538v3_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1de0eb1org.highwire.dtl.DTLVardef@19690a7org.highwire.dtl.DTLVardef@1f2f0ddorg.highwire.dtl.DTLVardef@a36d5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Kinases play a central role in regulating cellular processes, making their study essential for understanding cellular function and disease mechanisms. To investigate the regulatory state of a kinase, numerous methods have been, and continue to be, developed to infer kinase activities from phosphoproteomics data. These methods usually rely on a set of kinase targets collected from various kinase-substrate libraries. However, only a small percentage of measured phosphorylation sites can usually be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. In addition, the inferred activities from different methods can vary making it crucial to evaluate them for accurate interpretation. Here, we present a comprehensive evaluation of kinase activity inference methods using multiple kinase-substrate libraries combined with different inference algorithms. Additionally, we try to overcome the coverage limitations for measured targets in kinase substrate libraries by adding predicted kinase-substrate interactions for activity inference. For the evaluation, in addition to classical cell-based perturbation experiments, we introduce a tumor-based benchmarking approach that utilizes multi-omics data to identify highly active or inactive kinases per tumor type. We show that while most computational algorithms perform comparably regardless of their complexity, the choice of kinase-substrate library can highly impact the inferred kinase activities. Hereby, manually curated libraries, particularly PhosphoSitePlus, demonstrate superior performance in recapitulating kinase activities from phosphoproteomics data. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance, while normalizing sites to host protein levels reduces kinase activity inference performance. We then showcase how kinase activity inference can help in characterizing the response to kinase inhibitors in different cell lines. Overall, the selection of reliable kinase activity inference methods is important in identifying deregulated kinases and novel drug targets. Finally, to facilitate the evaluation of novel methods in the future, we provide both benchmarking approaches in the R package benchmarKIN. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/601117v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@44de1org.highwire.dtl.DTLVardef@9510a3org.highwire.dtl.DTLVardef@775225org.highwire.dtl.DTLVardef@1b2256f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions

Interactions between proteins and metabolites are key for cellular function, from the catalytic breakdown of nutrients to signaling. An important case is cell-cell communication, where cellular metabolites are secreted into the microenvironment and initiate a signaling cascade by binding to an intra- or extracellular receptor of another cell. While protein-protein mediated cell-cell communication is routinely inferred from transcriptomic data, for metabolite-protein interactions this is challenging due to the limitations of high-throughput single-cell and spatial metabolomics technologies, together with the absence of comprehensive prior knowledge resources that include metabolites. Here we report MetalinksDB, a comprehensive and flexible database of intercellular metabolite-protein interactions that is a magnitude larger than existing ones. MetalinksDB can be tailored to specific biological contexts such as diseases, pathways, or tissue/cellular locations by querying subsets of interactions using the web interface (https://metalinks.omnipathdb.org/) or the knowledge graph adapters. We showcase the use of MetalinksDB by identifying deregulated processes in renal cancer patients from multi-omics data as well as inferring metabolite-mediated cell-cell communication events driving acute kidney injury from spatial transcriptomic data. We anticipate that MetalinksDB will facilitate the study of metabolite-mediated communication processes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/573715v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@18c78aaorg.highwire.dtl.DTLVardef@145f21borg.highwire.dtl.DTLVardef@119d16borg.highwire.dtl.DTLVardef@3febb_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens

Understanding the interplay of the proteome and the metabolome aids in understanding cellular phenotypes. To enable more robust inferences from such multi-omics analyses, combining proteomic and metabolomic datasets from the same sample provides major benefits by reducing technical variation between extracts during the pre-analytical phase, decreasing sample variation due to varying cellular content between aliquots, and limiting the required sample amount. We evaluated the advantages, practicality and feasibility of a single-sample workflow for combined proteome and metabolome analysis. In the workflow, termed MTBE-SP3, we combined a fully automated protein lysis and extraction protocol (autoSP3) with a semi-automated biphasic 75% EtOH/MTBE extraction for quantification of polar/non-polar metabolites. Additionally, we compared the resulting proteome of various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) between autoSP3 and MTBE-SP3. Our analysis revealed that the single-sample workflow provided similar results to those obtained from autoSP3 alone, with an 85-98% overlap of proteins detected across the different biological matrices. Additionally, it provides distinct advantages by decreasing (tissue) heterogeneity by retrieving metabolomics and proteomic data from the identical biological material, and limiting the total amount of required material. Lastly, we applied MTBE-SP3 to a lung adenocarcinoma cohort of 10 patients. Integrating the metabolic and proteomic alterations between tumour and non-tumour adjacent tissue yielded consistent data independent of the method used. This revealed mitochondrial dysfunction in tumor tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and confident parallel measurement of proteins and metabolites obtained from the same sample. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

cancer biology↗

bioRxiv2023

LIANA+: an all-in-one cell-cell communication framework

The growing availability of single-cell and spatially-resolved transcriptomics has led to the rapidly growing popularity of methods to infer cell-cell communication. Many approaches have emerged, each capturing only a partial view of the complex landscape of cell-cell communication. Here, we present LIANA+, a scalable framework to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially-resolved data. Beyond integrating and extending established methodologies and a rich knowledge base, LIANA+ enables novel analyses using diverse molecular mediators, including those measured in multi-omics data. Accessible as an open-source Python package at https://github.com/saezlab/liana-py, LIANA+ provides a comprehensive set of synergistic components to study cell-cell communication. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/553863v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@16486ceorg.highwire.dtl.DTLVardef@ce6821org.highwire.dtl.DTLVardef@1165e54org.highwire.dtl.DTLVardef@18add5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

systems biology↗

bioRxiv2022

Multi-omic profiling of breast cancer cells uncovers stress MAPK-associated sensitivity to AKT degradation

Over 50% of human tumors display hyperactivation of the serine/threonine kinase AKT. Despite evidence of clinical efficacy, there remains scope to improve upon the therapeutic window of the current generation of AKT inhibitors. Here we report the development of a second-generation AKT degrader, INY-05-040, which outperformed catalytic AKT inhibition with respect to cellular suppression of AKT-driven phenotypes in breast cancer cell lines. A systematic growth inhibition screen across 288 cancer cell lines confirmed a substantially higher potency for INY-05-040 (median GI50adj = 1.1 {micro}M) compared to our first-generation AKT degrader (INY-03-041; median GI50adj = 3.1 {micro}M), with both compounds outperforming catalytic AKT inhibition with GDC-0068 (median GI50adj > 10 {micro}M). Using multi-omic profiling and causal network integration in breast cancer cells, we demonstrate that the enhanced efficacy of INY-05-040 is associated with sustained suppression of AKT signaling, followed by a potent induction of the stress mitogen activated protein kinase (MAPK) c-Jun N-terminal kinase (JNK). Further integration of growth inhibition assays with publicly available transcriptomic, proteomic, and reverse phase protein array (RPPA) measurements established low baseline JNK signaling as a biomarker for breast cancer sensitivity to AKT degradation. Collectively, our study presents a systematic framework for mapping the network-wide signaling effects of therapeutically relevant compounds, and identifies INY-05-040 as a potent pharmacological suppressor of AKT signaling.

cancer biology↗

bioRxiv2022

Deep Phosphoproteomic Elucidation of Metformin-Signaling in Heterogenous Colorectal Cancer Cells

The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials were set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). To interrogate cell signaling events and networks in CRC and explore the druggability of the metformin-rewired phosphorylation network, we performed a proteomic and phosphoproteomic analysis on a panel of 12 molecularly heterogeneous CRC cell lines. Using in-depth data-independent analysis mass spectrometry (DIA-MS), we profiled a total of 10,142 proteins and 56,080 phosphosites (P-sites) in CRC cells treated with metformin for 30 minutes and 24 hours. Our results indicate that metformin does not directly trigger or inhibit any immediate phosphorylation events. Instead, it primarily remodels cell signaling in the long-term. Strikingly, the phosphorylation response to metformin was highly heterogeneous in the CRC panel, uncovering four groups of metformin responsivity. We further performed a network analysis to systematically estimate kinase/phosphatase activities and reconstruct signaling cascades in each cell line. We created a "MetScore" which catalogs the most consistently perturbed P-sites among CRC cells for future studies. Finally, we leveraged the metformin P-site signature to identify pharmacodynamic interactions revealing a number of candidate metformin-interacting drugs. Together, we provide a data resource using state-of-the-art phosphoproteomics to understand the metformin-induced cell signaling for potential cancer therapeutics.

cancer biology↗

bioRxiv2022

Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis

Metabolomic and proteomic analyses of human plasma and serum samples harbour the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implement a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4{degrees}C) are imperative for metabolic profiling, the analysed proteome showed variability when exposed to temperatures of 4{degrees}C for more than 2 hours, highlighting the need for compromises in a combined analysis. We formalised a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonised standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies we suggest to keep blood samples up to 2 hours on ice (4{degrees}C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open source R package allowing the systematic scoring of proteomics and metabolomics datasets to assess the stability of plasma and serum samples.

biochemistry↗

bioRxiv2021

decoupleR: Ensemble of computational methods to infer biological activities from omics data

SummaryMany methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across methods perform better than other methods at predicting perturbed regulators. Availability and ImplementationdecoupleR is open source available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html). The code to reproduce the results is in Github (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208). ContactJulio Saez-Rodriguez at pub.saez@uni-heidelberg.de.

bioinformatics↗

bioRxiv2021

Nitrogen partitioning between branched-chain amino acids and urea cycle enzymes sustains renal cancer progression

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we combined multi-omics datasets of primary and metastatic clonally related clear cell renal cancer cells (ccRCC) and generated a computational tool to explore the metabolic landscape during cancer progression. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism is required to maintain the aspartate pool in cancer cells across all tumor stages. We also provide evidence that metastatic renal cancer cells reactivate argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, to enable invasion in vitro and metastasis in vivo. Overall, our study provides the first comprehensive elucidation of the molecular mechanisms responsible for metabolic flexibility in ccRCC, paving the way to the development of therapeutic strategies based on the specific metabolism that characterizes each tumor stage. HighlightsO_LIBranched-chain amino acids catabolism is reprogrammed in ccRCC tumors C_LIO_LIBCAT-dependent transamination supplies nitrogen for de novo biosynthesis of amino acids including aspartate and asparagine in ccRCC C_LIO_LIAspartate produced downstream of BCAT is used specifically by metastatic cells through argininosuccinate synthase (ASS1) and argininosuccinate lyase (ASL) to generate arginine, providing a survival advantage in the presence of microenvironments with rate limiting levels of arginine C_LIO_LIASS1 is re-expressed in metastatic 786-M1A through epigenetic remodeling and it is sensitive to arginine levels C_LIO_LISilencing of ASS1 impairs the metastatic potential in vitro and in vivo of ccRCC cells C_LI

cancer biology↗