Biology subjects

Menden, M. P.

Publications and source records attributed to Menden, M. P..

16 recordsLinked to original sources

bioRxiv2026

miR-940 suppresses ferroptosis by controlling expression of key regulatory genes

Ferroptosis is a form of regulated cell death that is characterized by iron-dependent lipid peroxidation. This process is regulated by specific metabolites, the lipid composition of the cells, redox-active iron, and antioxidant mechanisms. Although numerous regulators have been identified over the past decade, exploring other mechanisms, particularly from non-coding genomic regions, can build a thorough understanding of the multifaceted regulatory processes underlying ferroptosis. MicroRNAs (miRNAs) play a crucial role in gene regulation and cellular functions. Through a CRISPR KO screen, we identified miR-940 as a negative regulator of ferroptosis. Overexpression of miR-940 in several cell lines consistently suppressed ferroptosis induced by system xc- inhibition. Notably, multiple cancer patient cohorts with elevated miR-940 levels exhibit reduced survival. Integrated bioinformatic, transcriptomic, and proteomic analyses revealed that miR-940 decreases the expression of ACSL4, LPCAT3, DMT1, and NCOA4, and simultaneously increases levels of GPX4. Pharmacological inhibition of GPX4 attenuated the protective effect of miR-940, indicating that its primary anti-ferroptotic activity is mediated through GPX4. Overall, this gene rewiring is associated with reduced levels of redox-active iron and diminished lipid peroxidation, consistent with ferroptosis suppression. These findings suggest that miR-940 coordinates ferroptosis inhibition, which presents a novel regulatory layer for therapeutic exploration in susceptible cancers.

cell biology↗

bioRxiv2025

Decoding Helicobacter pylori Resistance: Machine Learning Enhanced Prediction of Antibiotic Susceptibility using Whole-Genome Sequencing

BackgroundHelicobacter pylori is a significant risk factor for gastric cancer, peptic ulcers, and MALT lymphoma. Rising antibiotic resistance rates complicate treatment strategies. While nucleotide sequence based assays are reliable in predicting clarithromycin and levofloxacin resistance, predicting metronidazole resistance is more challenging due to diverse metabolic pathways contributing to resistance, and high genomic variability. MethodsWe assembled a cohort of 483 H. pylori clinical isolates, combining whole-genome sequencing with phenotypic susceptibility testing. Machine learning models (SVM, XGBoost, FNN) were trained on genomic variants to predict resistance phenotypes. A sliding-window approach and SHAP-based importance scoring were used for feature selection to identify biologically relevant mutations, improving prediction accuracy, particularly for metronidazole resistance. ResultsThe best-performing FNN model improved metronidazole resistance prediction by 16% compared to conventional (non-ML, single polymorphisms) sequence-based detection methods applied to the same strain collection. Feature selection identified 32 feature sets, with 11 sets significantly improving F1-scores over the baseline. Combining 2-4 feature sets revealed 53 synergistic combinations across all models. Validation showed that 87% of these combinations significantly outperformed non-ML molecular testing, with 16 combinations achieving F1-scores above 0.65. ConclusionMachine-learning can significantly improve the performance of sequence-based susceptibility testing for metronidazole in H. pylori. Novel candidate predictive markers identified from whole-genome data offer testable hypotheses about yet unexplored mechanisms of metronidazole resistance. These findings support the potential for ML-based approaches to enable more accurate susceptibility-guided therapies.

microbiology↗

bioRxiv2025

Systematic identification of pan-cancer single-gene expression biomarkers in drug high-throughput screens

Precision oncology relies on molecular biomarkers to stratify patients into responders and non-responders to a given treatment. Although gene expression profiles have historically been explored for biomarker discovery, fewer studies investigated single-gene expression biomarkers. Additionally, many approaches are limited to cancer type-specific associations, which constrain statistical power. To address these limitations, we developed a regression-based framework that corrects for tissue-specific biases and enhances detection of pan-cancer single-gene expression biomarkers of drug sensitivity in cancer cell line high-throughput drug screens. Our method maintains predictive performance post-correction, and successfully recovers established biomarkers, such as SLFN11 expression for DNA damaging agents. Notably, we identified SPRY4 and NES expression as biomarkers of sensitivity for compounds targeting ERK/MAPK signaling (adjusted p-value=4.016x10- and 7.221x10-, respectively). This approach offers a scalable strategy for biomarker discovery and holds potential for translation to more complex biological models and patient-derived datasets. Ultimately, pan-cancer single-gene expression biomarkers may improve patient stratification and clinical outcomes in precision oncology.

cancer biology↗

bioRxiv2025

Single cell spatial transcriptomics integration deciphers the morphological heterogeneity of atherosclerotic carotid arteries

The process of arterial atherosclerosis is characterised by accumulation of lipids and fibrous material with accompanying inflammation. As plaques progress, they restrict blood flow and cause rupture, which results in life threatening organ ischemia and dysfunction. Although extensively studied, a clear understanding of plaque heterogeneity and mechanisms that trigger their destabilization remains elusive. Our study reveals the molecular microarchitecture of human carotid artery plaques, using bulk and single-cell RNA sequencing combined with single cell spatial transcriptomics, for which we present optimized cell segmentation algorithms. We identified distinct plaque morphologies linked to different cell type compositions, impacting early and advanced lesion formation, as well as destabilization. Spatial transcriptomics enabled us further to determine an inflammatory smooth muscle cell subtype, localize regions of neovascularization, and assign hotspots for macrophage activity within distinct cellular neighbourhoods across lesions. For different macrophage substates, we propose gradual and locally contained transdifferentiation of subluminal inflammatory HMOX1+ macrophages into a lipid-handling TREM2+ phenotype within border zones of the fibrous cap and necrotic core. Our findings provide insight into the complex heterogeneity of human atherosclerosis by unravelling location and proximity of different mural and immune cell substates involved in plaque progression and vulnerability.

cell biology↗

bioRxiv2024

Enhancing Gene Expression Representation and Drug Response Prediction with Data Augmentation and Gene Emphasis

Representation learning for tumor gene expression (GEx) data with deep neural networks is limited by the large gene feature space and the scarcity of available clinical and preclinical data. The translation of the learned representation between these data sources is further hindered by inherent molecular differences. To address these challenges, we propose GExMix (Gene Expression Mixup), a data augmentation method, which extends the Mixup concept to generate training samples accounting for the imbalance in both data classes and data sources. We leverage the GExMix-augmented training set in encoder-decoder models to learn a GEx latent representation. Subsequently, we combine the learned representation with drug chemical features in a dual-objective enhanced gene-centric drug response prediction, i.e., reconstruction of GEx latent embeddings and drug response classification. This dual-objective design strategically prioritizes gene-centric information to enhance the final drug response prediction. We demonstrate that augmenting training samples improves the GEx representation, benefiting the gene-centric drug response prediction model. Our findings underscore the effectiveness of our proposed GExMix in enriching GEx data for deep neural networks. Moreover, our proposed gene-centricity further improves drug response prediction when translating preclinical to clinical datasets. This highlights the untapped potential of the proposed framework for GEx data analysis, paving the way toward precision medicine.

bioinformatics↗

bioRxiv2024

MetaIBS: large-scale amplicon-based meta analysis of irritable bowel syndrome

BackgroundIrritable Bowel Syndrome (IBS) is a chronic functional bowel disorder causing abdominal discomfort, as well as transit deregulation with constipation and/or diarrhea. The pathophysiology of IBS is poorly understood and believed to be multifactorial. The role of gut microbiota in IBS has been investigated in several case-control studies, in particular via 16S rRNA amplicon sequencing surveys. These studies, however, have not yet led to a consistent picture of significant changes in gut microbial compositions across health and disease. One key bottleneck is the modest cohort sizes of most individual studies and a high diversity of experimental, bioinformatics, and statistical analysis approaches across studies. ResultsWe address these shortcomings by presenting MetaIBS, an open-access data repository and associated meta-analysis workflow of thirteen 16S rRNA amplicon datasets comprising both fecal matter and sigmoid biopsy samples spanning {bsim}2,500 IBS and healthy individuals. MetaIBS includes a tailored computational framework that (i) enables coherent de novo processing and taxonomic assignments of the raw 16S rRNA amplicon reads across experimental protocols and sequencing technologies, and (ii) statistical workflows for visualization and analysis at different taxonomic ranks and data granularity. Our statistical meta-analysis shows that popular high-level microbiome summary statistics, including Firmicutes/Bacteroidota ratios or diversity indices, are insufficient for reliable discrimination between IBS patients and healthy controls. Fine-grained multi-method differential abundance and classification analysis, however, can identify sets of differentially abundant taxa that replicate across multiple datasets, including Coprococcus eutactus and Alistipes finegoldii. ConclusionsMetaIBS provides a curated and reproducible data and (meta-)analysis resource for amplicon-based IBS research at unprecedented scale. MetaIBS allows assessing the heterogeneity of IBS cohorts across multiple experimental protocols, sample types, and IBS phenotypes. Our framework will likely contribute to more coherent insights into the role of the microbiome in IBS and the discovery of reliable microbial IBS biomarkers for follow-up functional and translational studies.

microbiology↗

bioRxiv2024

The pharmacogenomic assessment of molecular epithelial-mesenchymal transition signatures reveals drug susceptibilities in cancer cell lines

The epithelial-mesenchymal transition (EMT) is characterised by the loss of cell-cell adhesion and cell polarity, which is often exploited by cancer cells to adopt a motile, invasive and metastatic phenotype. Whilst EMT is often linked with cancer progression and therapy resistance, strategies for its selective targeting remain limited. In order to address this, we infer EMT states of cancer cell lines from their molecular signatures and use predictive and causal modelling to estimate the effect of EMT on drug susceptibility in high-throughput drug screens. For example, we show that EMT signatures in melanoma cells can predict favourable responses to the HSP90 inhibitor luminespib and demonstrate that epithelial-like melanoma cells can be sensitised to luminespib upon stimulation of EMT by TGF-{beta}. Thus, we provide an analysis that systematically yields a set of potent drugs by exploiting vulnerabilities of cancer cells undergoing EMT, which may pave the way for therapies to target these cells.

cancer biology↗

bioRxiv2023

The pharmacoepigenomic landscape of cancer cell lines reveals the epigenetic component of drug sensitivity

Aberrant DNA methylation accompanies genetic alterations during oncogenesis and tumour homeostasis and contributes to the transcriptional deregulation of key signalling pathways in cancer. Despite increasing efforts in DNA methylation profiling of cancer patients, there is still a lack of epigenetic biomarkers to predict treatment efficacy. To address this, we analysed 721 cancer cell lines across 22 cancer types treated with 453 anti-cancer compounds. We systematically detected the predictive component of DNA methylation in the context of transcriptional and mutational patterns, i.e., in total 19 DNA methylation biomarkers across 17 drugs and five cancer types. DNA methylation constituted drug sensitivity biomarkers by mediating the expression of proximal genes, thereby enhancing biological signals across multi-omics data modalities. Our method reproduced anticipated associations, and in addition, we found that the NEK9 promoter hypermethylation may confer sensitivity to the NEDD8-activating enzyme (NAE) inhibitor pevonedistat in melanoma through downregulation of NEK9. In summary, we envision that epigenomics will refine existing patient stratification, thus empowering the next generation of precision oncology.

cancer biology↗

bioRxiv2023

Nfe2l1-mediated proteasome function controls muscle energy metabolism in obesity

Muscle function is an important denominator of energy metabolism and metabolic health. Adapting the myocyte proteome to energetic challenges, in response to diet or fasting, is facilitated by programs of proteostasis, but the adaptive role of the ubiquitin-proteasome system (UPS) in muscle remains unclear. Here, we show that myocyte Nuclear factor erythroid derived 2,-like 1 (Nfe2l1, also known as Nrf1) is a key regulator of skeletal muscle proteostasis and function. In mice and humans, Nfe2l1 is highly expressed in skeletal myocytes, and its loss diminishes proteasomal activity and leads to hyperubiquitylation. Mice lacking myocyte Nfe2l1 display muscle fiber type switching and insulin resistance when fed a high-fed diet. Nfe2l1 protects myocytes from ferroptosis, which is enhanced in the presence of excess lipids. In conclusion, we define a new adaptive role for the Nfe2l1-ubiquitin proteasome system in the control of skeletal muscle function and energy metabolism.

physiology↗

bioRxiv2023

Suppression of ferroptosis by vitamin A or antioxidants is essential for neuronal development

Development of functional neurons is a complex orchestration of several signaling pathways controlling cell proliferation, differentiation, and homeostasis1. However, details about the involved factors are not fully understood. The balance of antioxidants and vitamins is important for neuronal survival, synaptic plasticity, and early neuronal development; thus, we hypothesized that ferroptosis--a lipid peroxidation dependent cell death modality that is inhibited by antioxidanats2,3--needs to be suppressed to gain neurons. Our study shows that removal of antioxidants diminishes neuronal development and laminar organization of cortical organoids. Intriguingly, impaired neuronal development in conditions lacking antioxidants can be fully restored when ferroptosis is specifically inhibited by ferrostatin-1, or neuronal differentiation occurs in the presence of sufficient amounts of vitamin A. Mechanistically, vitamin A activates the heterodimeric nuclear receptor complex Retinoic Acid Receptor (RAR)/Retinoid X Receptor (RXR)4, which upregulates expression of the ferroptosis regulators GPX4, FSP1, GCH1, and ACSL3, amongst others. Therefore, our study reveals that above a certain threshold, vitamin A increases expression of essential cellular gatekeepers of lipid peroxidation. This study uncovers a critical process during early neuronal development, where suppression of ferroptosis by radical-trapping antioxidants or vitamin A is required to obtain maturing neurons and proper laminar organization in cortical organoids.

cell biology↗

bioRxiv2021

Low numbers of cytokine transcripts drive inflammatory skin diseases by initiating amplification cascades in localized epidermal clusters

Abundant polyclonal T cells infiltrate chronic inflammatory diseases and characterization of these cells is needed to distinguish disease-driving from bystander immune cells. Here, we investigated 52,000 human cutaneous transcriptomes of non-communicable inflammatory skin diseases (ncISD) using spatial transcriptomics. Despite the expected T cell infiltration, we observed only 1-10 pathogenic T cell cytokine per skin section. Cytokine expression was limited to lesional skin and presented in a disease-specific pattern. In fact, we identified responder signatures in direct proximity of cytokines, and showed that single cytokine transcripts initiate amplification cascades of thousands of specific responder transcripts forming localized epidermal clusters. Thus, within the abundant and polyclonal T cell infiltrates of ncISD, only a few T cells drive disease by initiating an inflammatory amplification cascade in their local microenvironment.

immunology↗

bioRxiv2020

A statistical framework for assessing pharmacological response and biomarkers with confidence

Drug high-throughput screenings across large molecular-characterised cancer cell line panels enable the discovery of biomarkers, and thereby, cancer precision medicine. The ability to experimentally generate drug response data has accelerated. However, this data is typically quantified by a summary statistic from a best-fit dose response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage this uncertainty for identifying associated biomarkers with a new statistical framework based on Bayesian testing. Applied to the Genomics of Drug Sensitivity in Cancer, in vitro screening data on 265 compounds across 1,074 cell lines, our uncertainty models identified 24 clinically established drug response biomarkers, and in addition provided evidence for 6 novel biomarkers. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to drug high-throughput screens without replicates, and enables robust biomarker discovery for new cancer therapies.

cancer biology↗

bioRxiv2019

Stratification and prediction of drug synergy based on target functional similarity

Targeted mono-therapies in cancer are hampered by the ability of tumor cells to escape inhibition through rewiring or alternative pathways. Drug combination approaches can provide a means to overcome these resistance mechanisms. Effective use of combinations requires strategies to select combinations from the enormous space of combinations, and to stratify patients according to their likelihood to respond. We here introduce two complementary workflows: One prioritising experiments in high-throughput screens for drug synergy enrichment, and a consecutive workflow to predict hypothesis-driven synergy stratification. Both approaches only need data of efficacy of single drugs. They rely on the notion of target functional similarity between two target proteins. This notion reflects how similarly effective drugs are on different cancer cells as a function of cancer signaling pathways activities on those cells. Our synergy prediction workflow revealed that two drugs targeting either the same or functionally opposite pathways are more likely to be synergistic. This enables experimental prioritisation in high-throughput screens and supports the notion that synergy can be achieved by either redundant pathway inhibition or targeting independent compensatory mechanisms. We tested the synergy stratification workflow on seven target protein pairs (AKT/EGFR, AKT/MTOR, BCL2/MTOR, EGFR/MTOR, AKT/BCL2, AKT/ALK and AKT/PARP1, representing 29 combinations and predicted their synergies in 33 breast cancer cell lines (Pearsons correlation r=0.27). Additionally, we experimentally validated predicted synergy of the BRAF/Insulin Receptor combination (Dabrafenib/BMS-754807) in 48 colorectal cancer cell lines (r=0.5). In conclusion, our synergy prediction workflow can support compound prioritization in large scale drug screenings, and our synergy stratification workflow can select where the efficacy of drugs already known for inducing synergy is higher.

bioinformatics↗

bioRxiv2018

Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

We describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consistently quantified 3,171 proteotypic proteins annotated in the SwissProt database across all cell lines, generating a data matrix with 0.1% missing values, allowing analyses of protein complexes and pathway activities across all the cancer cells. Systematic and integrative analysis of the genetic variation, mRNA expression and proteomic data of the NCI-60 cancer cell lines uncovered complementarity between different types of molecular data in the prediction of the response to 240 drugs. We additionally identified novel proteomic drug response determinants for clinically relevant chemotherapeutic and targeted therapies. We anticipate that this study represents a significant advance toward the translational application of proteotypes, which reveal biological insights that are easily missed in the absence of proteomic data.

systems biology↗

bioRxiv2017

Community assessment of cancer drug combination screens identifies strategies for synergy prediction

The effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patients tumor. AstraZeneca carried out 11,576 experiments on 910 drug combinations across 85 cancer cell lines, recapitulating in vivo response profiles. These data, the largest openly available screen, were hosted by DREAM alongside deep molecular characterization from the Sanger Institute for a Challenge to computationally predict synergistic drug pairs and associated biomarkers. 160 teams participated to provide the most comprehensive methodological development and subsequent benchmarking to date. Winning methods incorporated prior knowledge of putative drug target interactions. For >60% of drug combinations synergy was reproducibly predicted with an accuracy matching biological replicate experiments, however 20% of drug combinations were poorly predicted by all methods. Genomic rationale for synergy predictions were identified, including antagonism unique to combined PIK3CB/D inhibition with the ADAM17 inhibitor where synergy is seen with other PI3K pathway inhibitors. All data, methods and code are freely available as a resource to the community.

bioinformatics↗

bioRxiv2017

GDSCTools for Mining Pharmacogenomic Interactions in Cancer

MotivationLarge pharmacogenomic screenings integrate heterogeneous cancer genomic data sets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces.\n\nResultsWe have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic data sets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (ANOVA) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC, to analyse their own drug responses or genomic datasets, and to implement new analytical methods.\n\nContactthomas.cokelaer@pasteur.fr

cancer biology↗