Biology subjects

Meunier, L.

Publications and source records attributed to Meunier, L..

12 recordsLinked to original sources

bioRxiv2026

Tumour architecture shapes polarized epithelial states that predict survival in high-grade serous ovarian cancer

Epithelial heterogeneity defines high-grade serous ovarian carcinoma (HGSC), yet principles that generate this diversity within and across tumours remain unclear. Integrating single-cell RNA sequencing (scRNA-seq) data from 13 studies (1,980,703 cells, 371 samples), we resolve a dominant axis of secretory cell polarization spanning proliferative, progenitor-like SecA cells and quiescent SecB cells expressing a mucosal injury response program. Targeted spatial transcriptomics across 8 whole HGSC tissues and a 97-patient tissue microarray shows this axis is spatially deterministic: tumour architecture shapes a hypoxic gradient along which SecB cells localize to avascular, luminal regions, where HIF/NF-{kappa}B-driven survival and glycolysis displace the mitogenic signalling of SecA. Within this niche, SecB cells rewire adhesion, ECM-remodelling, and immune-regulatory programs. Transcriptionally reprogrammed macrophages are enriched in this niche, while lymphocytes are excluded or dysfunctional. These cells assemble a coordinated multicellular niche poised for dissemination. SecB cells are enriched both in ascites and after chemotherapy, and are progressively lost in patient-derived organoids, only partially restored by IFN{gamma}, suggesting SecB is environmentally programmed rather than clonally fixed. SecB proportion independently predicts worse overall (HR = 1.31, p = 0.023) and progression-free survival (HR = 1.28, p = 0.011). Tumour architecture is thus a primary axis of malignant identity in HGSC, coupling microenvironment, cell state, and immune niche to clinical outcomes.

cancer biology↗

bioRxiv2026

CMS4-focused multi-omic integration enhances antigen target identification in colorectal cancer

Colorectal cancer (CRC) remains a major cause of cancer mortality, with limited options for poor-prognosis subtypes such as CMS4. Antigen-targeted therapies show promise but tend to fail due to inadequate target selection and insufficient patient stratification. Effective prioritization requires large harmonized data capturing CRC heterogeneity - a resource that is currently lacking. To address this need, we built a harmonized multi-omic CRC knowledge base and applied a scalable discovery pipeline to identify antigen targets specifically associated with CMS4 biology and with strong translational potential. We constructed a harmonized CRC atlas by integrating 79 transcriptomics datasets (5,033 tumors, 161 normal samples) using proprietary AI-powered data scouting, integration, and curation technologies. Consensus Molecular Subtypes (CMS) were inferred to capture CMS4-specific expression patterns and this atlas was then combined with 3 bulk RNA-seq reference datasets, 2 single-cell atlases, and 8 protein annotation databases to form a unified multi-omic CRC knowledge base of unmatched scale. From this integrated system, we identified genes differentially expressed in CMS4 patients encoding druggable cell-surface proteins, which we then prioritized using a weighted efficacy- and safety-based scoring model. We identified 236 CMS4-enriched candidates, including 124 not detectable at the CRC-wide level, demonstrating the added resolution gained through subtype stratification. Recovery of known investigational CRC (LGR5, MET, TACSTD2) and CMS4-associated targets of clinical emerging interest (PDGFRB, ALK5/TGFBR1, FAP) support the biological and methodological validity of our approach. Benchmarking against thresholds from FDA-approved pan-cancer targets and terminated trials identified 32 candidates with comparable or superior therapeutic profiles. Among these, 11 were enriched for CMS4-defining pathways, including epithelial-mesenchymal transition, angiogenesis, and stromal invasion, and 5 showed strong profile similarity to established CRC and CMS4 benchmarks. After extensive data exploration, particularly promising candidates were shortlisted for further validation. This work shows that CMS4-focused molecular stratification, when combined with an unprecedentedly large harmonized multi-omic knowledge base, yields a refined set of antigen candidates with enhanced specificity, safety, and biological relevance. The prioritized targets illustrate the power of subtype-resolved discovery to uncover clinically actionable insights. Our pipelines modular design can extend to other tumor contexts, offering a robust foundation for accelerating targeted therapy development.

cancer biology↗

bioRxiv2025

Cognitive Load Impairs Professional Scepticism in Decision-Making: The Mitigating Role of Default Nudges

Cognitive overload can impair professional scepticism in high-stakes contexts such as auditing. In these settings, sustaining professional scepticism is essential. Default nudges, or pre-selected options, may offset these effects by reducing cognitive demands. We conducted two online experiments to examine how cognitive load and default nudges influence professional scepticism in auditing decisions. Experiment 1 validated a dot memory task manipulation of cognitive load and identified low and high load conditions for subsequent testing. Experiment 2 embedded this manipulation in Phillips audit task, used for measuring professional scepticism in audit. Results showed that cognitive load slowed responses and reduced accuracy. Default nudges accelerated responding and improved accuracy under load, but only when aligned with the most probable response; misaligned nudges reduced accuracy. These findings suggest that defaults act as conditional scaffolds under cognitive strain, supporting judgment and decision-making in some contexts but introducing risks in others. Misaligned defaults reduced accuracy, indicating that they can exploit intuitive responding rather than enhance deliberation.

neuroscience↗

bioRxiv2024

Bulk Transcriptomic Analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python

We introduce InMoose, an open-source Python environment aimed at omic data analysis. We illustrate its capabilities for bulk transcriptomic data analysis. Due to its wide adoption, Python has grown as a de facto standard in fields increasingly important for bioinformatic pipelines, such as data science, machine learning, or artificial intelligence (AI). As a general-purpose language, Python is also recognized for its versatility and scalability. InMoose aims at bringing state-of-the-art tools, historically written in R, to the Python ecosystem. Our intent is to provide a drop-in replacement for R tools, so our approach focuses on the faithfulness to the original tools outcomes. The first development phase has focused on bulk transcriptomic data, with current capabilities encompassing data simulation, batch effect correction, and differential analysis and meta-analysis.

bioinformatics↗

bioRxiv2024

Differential Expression Analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python

We present the differential expression features of InMoose, a Python implementation of R tools limma, edgeR, and DESeq2. We experimentally show that InMoose stands as a drop-in replacement for those tools, with nearly identical results. This ensures reproducibility when interfacing both languages in bioinformatic pipelines. InMoose is an open source software released under the GPL3 license, available at www.github.com/epigenelabs/inmoose and https://inmoose.readthedocs.io.

bioinformatics↗

bioRxiv2023

A BAC-guided haplotype assembly pipeline increases the resolution of the virus resistance locus CMD2 in cassava

Cassava is an important crop for food security in the tropics where its production is jeopardized by several viral diseases, including the cassava mosaic disease (CMD) which is endemic in Sub-Saharan Africa and the Indian subcontinent. Resistance to CMD is linked to a single dominant locus, namely CMD2. The cassava genome contains highly repetitive regions making the accurate assembly of a reference genome challenging. In the present study, we generated BAC libraries of the CMD- susceptible cassava cultivar (cv.) 60444 and the CMD-resistant landrace TME3. We subsequently identified and sequenced BACs belonging to the CMD2 region in both cultivars using high-accuracy long-read PacBio circular consensus sequencing (ccs) reads. We then sequenced and assembled the complete genomes of cv. 60444 and TME3 using a combination of ONT ultra-long reads and optical mapping. Anchoring the assemblies on cassava genetic maps revealed discrepancies in our, as well as in previously released, CMD2 regions of the cv. 60444 and TME3 genomes. A BAC guided approach to assess cassava genome assemblies significantly improved the synteny between the assembled CMD2 regions of cv. 60444 and TME3 and the CMD2 genetic maps. We then performed repeat-unmasked gene annotation on CMD2 assemblies and identified 81 stress resistance proteins present in the CMD2 region, amongst which 31 were previously not reported in publicly available CMD2 sequences.

genomics↗

bioRxiv2022

The GEN-ERA toolbox: unified and reproducible workflows for research in microbial genomics .

BackgroundMicrobial culture collections play a key role in taxonomy by studying the diversity of their accessions and providing well characterized strains to the scientific community for fundamental and applied research. These microbial resource centers thus need to implement new standards in species delineation, including whole-genome sequencing and phylogenomics. In this context, the genomic needs of the Belgian Coordinated Collections of Microorganisms (BCCM) were studied, resulting in the GEN-ERA toolbox. The latter is a unified cluster of bioinformatic workflows dedicated to both bacteria and small eukaryotes (i.e. yeasts). FindingsThis public toolbox allows researchers without a specific training in bioinformatics to perform robust phylogenetic analyses. Hence, it facilitates all steps from genome downloading and quality assessment, including genomic contamination estimation, to tree reconstruction. It also offers workflows for average nucleotide identity comparisons and metabolic modeling. Technical detailsNextflow workflows are launched by a single command and are available on the GEN-ERA GitHub repository (https://github.com/Lcornet/GENERA). All the workflows are based on Singularity containers to increase reproducibility. TestingThe toolbox was developed for a diversity of microorganisms, including bacteria and fungi. It was further tested on an empirical dataset of 18 (meta)genomes of early-branching Cyanobacteria, providing the most up-to-date phylogenomic analysis of the Gloeobacterales order, the first group to diverge in the evolutionary tree of Cyanobacteria. ConclusionThe GEN-ERA toolbox can be used to infer completely reproducible comparative genomic and metabolic analyses on prokaryotes and small eukaryotes. Although designed for routine bioinformatics of culture collections, it can also be useful for other applications, as shown by our case study on Gloeobacterales.

bioinformatics↗

bioRxiv2021

AMAW: automated gene annotation for non-model eukaryotic genomes

SummaryTo support small and large-scale genome annotation projects, we present AMAW (Automated MAKER2 Annotation Wrapper), a program devised to annotate non-model unicellular eukaryotic genomes by automating the acquisition of evidence data (transcripts and proteins) and facilitating the use of MAKER2, a widely adopted software suite for the annotation of eukaryotic genomes. Moreover, AMAW exists as a Singularity container recipe easy to deploy on a grid computer, thereby overcoming the tricky installation of MAKER2. AvailabilityAMAW is released both as a Singularity container recipe and a standalone Perl script (https://bitbucket.org/phylogeno/amaw/). Contactlmeunier@uliege.be or luc.cornet@sciensano.be Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

The hormone Fibroblast Growth Factor 19 stimulates water intake

Fibroblast growth factor 19 (FGF19) is a hormone with pleiotropic metabolic functions, leading to ongoing development of analogues for the treatment of metabolic disorders. On the other hand, FGF19 is overexpressed in a sub-group of hepatocellular carcinoma (HCC) patients and has oncogenic properties. It is therefore crucial to precisely define FGF19 effects, notably chronic exposure to elevated concentrations of the hormone. We used hydrodynamic gene transfer approach to generate a transgenic mouse model with long-term FGF19 hepatic overexpression. Here we describe a novel effect of FGF19, namely stimulation of water intake. This phenotype, lasting at least over a 6-month period, depends on signaling in the central nervous system and is independent of FGF21, although it mimics some of its features. We further show that HCC patients with high levels of circulating FGF19 have a reduced natremia, indicating dispogenic features. The present study provides evidence of a new activity for FGF19, which could be clinically relevant in the context of FGF19 overexpressing cancers and treatment of metabolic disorders by FGF19 analogues.

physiology↗

bioRxiv2021

A synthetic receptor platform enables rapid and portable monitoring of liver dysfunction via engineered bacteria.

Bacterial biosensors, or bactosensors, are promising field-deployable agents for medical and environmental diagnostics. However, the lack of scalable frameworks to systematically program ligand detection limits their applications. Here we present a synthetic receptor platform, termed EMeRALD (Engineered Modularized Receptors Activated via Ligand-induced Dimerization) which supports the modular assembly of sensing modules onto a high-performance, generic signaling scaffold controlling gene expression in E. coli. We applied EMeRALD to detect bile salts, a biomarker of liver dysfunction, by repurposing sensing modules from enteropathogenic Vibrio species. We improved the sensitivity and lowered the limit-of-detection of the sensing module by directed evolution. We then engineered a colorimetric bactosensor detecting pathological bile salt levels in serum from patients having undergone liver transplant, providing an output detectable by the naked-eye. The EMeRALD technology enables functional exploration of natural sensing modules and rapid engineering of synthetic receptors for diagnostics, environmental monitoring, and control of therapeutic microbes.

synthetic biology↗

bioRxiv2019

MS.liverK: an R package for transcriptome-based computation of molecular subtypes and functional signatures in liver cancer

SummaryLiver cancer is a highly heterogeneous disease in terms of etiology, tissue and cellular morphology, tumor molecular characteristics, microenvironment composition and prognosis. Several studies, based on tumor gene-expression profiling (GEP) data, have dissected the molecular heterogeneity of liver cancer. They resulted in various tools, either delineating homogeneous tumor subtypes or calculating molecular scores of prognostic or biological functions. Here, we present MS.liverK, an easy-to-use R package providing a comprehensive implementation of these tools, for research use. Availability and implementationThe MS.liverK R package is available from GitHub (https://github.com/cit-bioinfo/MS.liverK).

bioinformatics↗

bioRxiv2018

Consensus assessment of the contamination level of publicly available cyanobacterial genomes

BACKGROUNDPublicly available genomes are crucial for phylogenetic and metagenomic studies, in which contaminating sequences can be the cause of major problems. This issue is expected to be especially important for Cyanobacteria because axenic strains are notoriously difficult to obtain and keep in culture. Yet, despite their great scientific interest, no data are currently available concerning the quality of publicly available cyanobacterial genomes.\n\nRESULTSAs reliably detecting contaminants is a complex task, we designed a pipeline combining six methods in a consensus strategy to assess the contamination level of 440 genome assemblies of Cyanobacteria. Two methods are based on published reference databases of ribosomal genes (SSU rRNA 16S and ribosomal proteins), one is indirectly based on a reference database of marker genes (CheckM), and three are based on complete genome analysis. Among those genome-wide methods, Kraken and DIAMOND blastx share the same reference database that we derived from Ensembl Bacteria, whereas CONCOCT does not require any reference database, instead relying on differences in DNA tetramer frequencies. Given that all the six methods appear to have their own strengths and limitations, we used the consensus of their rankings to infer that >5% of cyanobacterial genome assemblies are highly contaminated by foreign DNA (i.e., contaminants were detected by 5 or 6 methods).\n\nCONCLUSIONSOur results will help researchers to check the quality of publicly available genomic data before use in their own analyses. Moreover, we argue that journals should make mandatory the submission of raw read data along with genome assemblies in order to facilitate the detection of contaminants in sequence databases.

genomics↗