Biology subjects

Poulet, C.

Publications and source records attributed to Poulet, C..

3 recordsLinked to original sources

bioRxiv2026

Conserved metabolic vulnerabilities across pathogenic coronaviruses nominate host-directed therapeutic targets

Pathogenic coronaviruses profoundly rewire host cell metabolism to support viral replication, yet whether these metabolic alterations expose shared and actionable vulnerabilities remains unclear. By integrating transcriptomic profiles from cells infected with SARS-CoV, SARS-CoV-2, and MERS-CoV with genome-scale metabolic models, we identify conserved and virus-specific metabolic perturbations affecting mitochondrial transport, nucleotide biosynthesis, fatty acid metabolism, and redox balance. Despite distinct transcriptional responses, all three viruses converge on a limited set of metabolic reactions whose flux ranges deviate strongly from healthy states. Using a network-based predictive framework, we systematically identify gene-pair perturbations that restore perturbed reaction fluxes toward non-infected metabolic states. Predicted rescue mechanisms reveal shared metabolic dependencies across coronaviruses, as well as time-dependent virus-specific vulnerabilities, and nominate druggable host targets. Notably, several top predictions align with independent experimental and clinical evidence, including metabolic interventions shown to reduce viral replication or disease severity in COVID-19 patients. Together, our results define conserved metabolic rescue pathways in coronavirus infection and provide a general strategy for identifying host-directed therapeutic opportunities from transcriptomic data. HighlightsO_LICoronaviruses converge on shared metabolic vulnerabilities in host cells C_LIO_LINiTRO predicts gene pairs that rescue viral-induced metabolic states C_LIO_LIMitochondrial transport emerges as a key pan-coronavirus target C_LIO_LITop predictions validated by clinical trials and in vitro evidence C_LI eTOC BlurbDohai et al. develop NiTRO, a network-based algorithm that integrates coronavirus-induced transcriptomic changes with genome-scale metabolic models to identify gene-pair perturbations capable of rescuing infected metabolic states. The approach reveals shared and virus-specific druggable metabolic vulnerabilities, with top predictions corroborated by clinical evidence.

systems biology↗

bioRxiv2023

Assessing Random Forest self-reproducibility for optimal short biomarker signature discovery

Biomarker signature discovery remains the main path to develop clinical diagnostic tools when the biological knowledge on a pathology is weak. Shortest signatures are often preferred to reduce the cost of the diagnostic. The ability to find the best and shortest signature relies on the robustness of the models that can be built on such set of molecules. The classification algorithm that will be used is selected based on the average performance of its models, often expressed via the average AUC. However, it is not garanteed that an algorithm with a large AUC distribution will keep a stable performance when facing data. Here, we propose two AUC-derived hyper-stability scores, the HRS and the HSS, as complementary metrics to the average AUC, that should bring confidence in the choice for the best classification algorithm. To emphasize the importance of these scores, we compared 15 different Random Forests implementation. Additionally, the modelization time of each implementation was computed to further help deciding the best strategy. Our findings show that the Random Forest implementation should be chosen according to the data at hand and the classification question being evaluated. No Random Forest implementation can be used universally for any classification and on any dataset. Each of them should be tested for both their average AUC performance and AUC-derived stability, prior to analysis. Author summaryTo better measure the performance of a Machine Learning (ML) implementation, we introduce a new metric, the AUC hyper-stability, to be used in parallel with the average AUC. This AUC hyper-stability is able to discriminate ML implementations that show the same AUC performance. This metric can therefore help researchers in choosing the best ML method to get stable short predictive biomarker signatures. More specifically, we advocate a tradeoff between the average AUC performance, the hyper-stability scores, and the modeling time.

bioinformatics↗

bioRxiv2020

Deeper insights into long-term survival heterogeneity of Pancreatic Ductal Adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

BackgroundPancreatic ductal adenocarcinoma (PDAC) is categorized as the seventh leading cause of cancer mortality worldwide. Its predictive markers for long-term survival are not well known. Therefore, it is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors, and to exploit the integrative individual- and group-based transcriptome profiling. MethodUsing a discovery cohort of 19 PDAC patients from CHU-Liege (Belgium), we first performed differential gene expression (DGE) analysis comparing LT to ST survivor. Second, we adopted unsupervised systems biology approaches to obtain gene modules linked to clinical features. Third, we created individual-specific perturbation profiles and identified key regulators across the LT patients. Furthermore, we applied two gene prioritization approaches: random walk-based Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. FindingsWe identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits such as tumor size and chemotherapy. DGE analysis identified differences in genes involved in metabolic and cell cycle activity. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Individual-specific omics changes across LT survivors revealed biological signatures such as focal adhesion and extracellular matrix receptors, implying a potential role in molecular-level heterogeneity of LT PDAC survivors. Via NetICS and DADA we not only identified various known oncogenes such as CUL1, SCF62, EGF, FOSL1, MMP9, and TGFB1, but also highlighted novel genes (TAC1, KCNH7, IRS4, DKK4). InterpretationOur proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling. It suggested novel potential transcriptome marks of LT survival heterogeneity in PDAC. FundingTelevie-FRS-FNRS

genomics↗