Biology subjects

Martens, M.

Publications and source records attributed to Martens, M..

9 recordsLinked to original sources

bioRxiv2026

Ontology-Driven Annotation of Key Events Enables Transcriptomics-Based Assessment of Adverse Outcome Pathway Networks

Chemical safety assessment is moving towards new approach methodologies (NAMs) to improve human safety and reduce animal testing. The Adverse Outcome Pathway (AOP) is a NAM framework describing toxicity as a chain of Key Events (KEs). Many AOP-Wiki KEs describe molecular, cellular, or tissue-level processes and can therefore be assessed with gene expression data, making transcriptomics of human in vitro models a NAM data generator. In AOP-Wiki, KEs are described by Key Event Components (KECs). A gene set can be derived when the biological process (BP) component carries a Gene Ontology (GO) term. Most KEs carry no such term, so their perturbation cannot be assessed using omics data. We assigned GO BP terms to the unmapped BP component across the brain, lung, liver and kidney AOP networks. We also replaced obsolete and direction-carrying terms in the pre-existing mapping with current, direction-neutral ones. For liver, we reviewed all pre-existing terms and replaced overly broad terms with better-matched alternatives. We then applied this approach to pesticide-induced hepatotoxicity, using Open TG-GATEs transcriptomics data from primary human hepatocytes exposed to cycloheximide, 2,4-dinitrophenol, hexachlorobenzene, and rotenone. KE perturbation was scored using single-sample Gene Set Enrichment Analysis (ssGSEA). Annotation increased the proportion of transcriptomics-linkable KEs in the liver network from 53% to 77%, enhancing identification of perturbed KEs after exposure. The KEs perturbed by all four compounds fell into five functional categories associated with hepatotoxicity. Our approach resulted in a more relevant AOP network for transcriptomics-based assessment of KE perturbation. These findings show molecular perturbation rather than direct proof of KE activation or causal progression through an AOP. When validated on larger and independent datasets, such networks will support mechanistic interpretation of transcriptomics data in chemical safety assessment.

bioinformatics↗

bioRxiv2026

PlantMetWiki: a FAIR knowledge graph for plant metabolic pathway cross-species representation and integration

Plants produce a vast diversity of specialized metabolites with extensive potential ecological, agro-industrial, and pharmaceutical applications. Discovery of novel plant natural products relies on combining multi-omics evidence with biochemical transformations. However, pathway-level annotations are fragmented across individual species, databases, and publications, limiting comparative cross-species pathway analyses and systematic generation of hypotheses. To support integration and reuse of plant metabolic knowledge, we developed PlantMetWiki, a FAIR Linked Open Data semantically enriched knowledge graph built on infrastructure adapted from WikiPathways. Our approach extends on the highly curated pathway information from Plant Metabolic Network with crosslinks to biosynthetic gene clusters resources (MIBiG and plantiSMASH), metabolite annotations, and cross-species modelling. This way, our resource captures pathway genomic context, increases metabolomics data interoperability via federated queries, and supports cross-species analysis to identify annotation gaps. PlantMetWiki represents 1,162 plant metabolic pathways as Resource Description Framework (RDF) graphs, preserving pathway structure, literature provenance, annotations, and taxonomic information from PlantCyc 17.0. PlantMetWiki is distributed through a public SPARQL endpoint with open-source reproducible data transformation and validation workflows. By modelling pathways as a multispecies graph, PlantMetWiki enables comparative analyses across taxa, integration with external chemical knowledge resources through federated queries, and identification of metabolic, genomic, and chemical annotation gaps. As a result, PlantMetWiki provides a foundation for FAIR reuse and integration of plant pathway knowledge.

bioinformatics↗

bioRxiv2026

Ketamine attenuates habenula activity in response to aversive outcomes during Pavlovian learning

Ketamine is an NMDA receptor antagonist with rapid-antidepressant properties when administered at a sub-anesthetic dose. Preclinical models indicate that a direct injection of ketamine into lateral habenula (Hb), a small midbrain structure with an evolutionarily preserved role in aversive learning across mammals, can rapidly relieve depression-like behavior. However, there is limited evidence to explain how ketamine acts on the function of the human habenula. In a translational computational neuroscience study, 70 healthy adult volunteers were randomised in a 1:1 ratio to receive ketamine or placebo (NaCl 0.9%). We used an aversive Pavlovian conditioning paradigm combined with 7-Tesla functional neuroimaging to show that ketamine attenuates habenula response during aversive stimuli expectations and outcomes 24 hours post-infusion. We further present preliminary evidence suggesting that when aversive learning occurs after ketamine infusion, reduced habenula activity during the learning process may lead to downstream effects that diminish the aversive impact of negative affective memories. These findings provide translational support for preclinical models of ketamines mechanisms in humans.

neuroscience↗

bioRxiv2025

Synaptic histamine shapes the neurocomputational dynamics of human learning

Histamine was the first canonical monoamine identified in the mammalian brain 1-6, yet arguably remains the least understood in its mechanistic contributions to human behaviour. Using a first-in-class causal probe (H3R inverse agonist pitolisant), we show how increased synaptic histamine shapes offline and online temporal-hippocampal dynamics, sustaining learning-related activity and polarising retrieval computations. At a broader level, elevated histamine adaptively shifts neurocomputational strategy under high cognitive load, while stabilising value updates during aversive reinforcement learning. These findings uncover a mechanistically grounded influence of this underexplored system on human neurocomputation, supporting its therapeutic potential in psychiatry.

neuroscience↗

bioRxiv2025

Two Transport Routes for Fetuin-A-based Mineral Complexes in Osteogenic Matrix Mineralization

Living organisms must transport calcium and phosphate at high concentrations to enable bone formation without triggering uncontrolled mineral precipitation. Fetuin-A binds calcium phosphate to form soluble calciprotein complexes, but how these complexes contribute to physiological mineralization has remained unclear. Here we show that Fetuin-A-based mineral complexes exist in functionally distinct states that determine mineral bioavailability. Using cryogenic and liquid-phase electron microscopy, biochemical analysis, and osteoblast cell cultures, we demonstrate that small, chemically labile calciprotein monomers directly mineralize collagen fibrils, whereas larger, chemically matured primary calciprotein particles cannot. Instead, these particles require cellular uptake and lysosomal processing to release mineral for matrix deposition. This functional divergence arises from a irreversible chemical transformation of the mineral phase that drives the primary CPP assembly. Together, our findings establish nanoscale chemical maturation as a key control point that separates mineral transport from mineralization, reframing our understanding both physiological bone formation and pathological calcification.

biochemistry↗

bioRxiv2023

Unravelling the relationship between isolated bone matrix vesicles and forming mineral at the nanometer scale

Matrix vesicles (MVs) are involved in the initial deposition of hydroxyapatite (HAp) during bone mineralization, but their mechanism of action is not yet fully understood. In vitro studies propose two pathways by which MVs can trigger HAp precipitation: the first is mediated by their enhanced phosphatase activity, and the second suggested to depend on structural components present in MVs to mediate nucleation directly from soluble ions without the requirement of phosphatase activity. However, the relevance of these two pathways for bone mineralization and the relationship between MVs and forming mineral in such in vitro experiments remains unclear. Here, we used near-native cryoTEM nanoscale imaging in combination with bulk characterizations to disentangle the content and action of MVs during in vitro mineralization. We show that MVs isolation by conventional ultracentrifugation results in heterogeneous dispersions containing non-vesicular particles, including collagens and proteoglycans, in addition to bilayered vesicles. The separation of phosphatase-enriched MVs from non-vesicular particles and comparative mineralization experiments demonstrated that the ability of MVs to induce fast mineralization, independently of phosphatase activity, depends on the presence of non-vesicular particles. Therefore, we conclude that the primary pathway by which MVs trigger mineralization is through the action of their resident phosphatase enzymes, with the direct mineral nucleation to be a secondary event consequential of their membrane components. Lastly, we observed mineral formation restricted to the extravesicular space or in close proximity to the membrane interface, suggesting that the relationship between MVs and forming mineral is more intricate than previously understood.

biophysics↗

bioRxiv2023

Molecular Adverse Outcome Pathways: towards the implementation of transcriptomics data in risk assessments

Adverse Outcome Pathways (AOPs) are designed to provide mechanistic insights into toxicological processes after exposure to a stressor and facilitate the replacement of animal studies with in vitro testing systems. Starting with a Molecular Initiating Event (MIE) and ending with an Adverse Outcome, the sequence of Key Events (KEs) spans across biological levels, but the majority of KEs in AOP-Wiki describe molecular and cellular processes. That suggests that established transcriptome-wide studies can be used to validate or measure a multitude of KEs simultaneously and be a goldmine of useful information about cellular responses. Currently, in toxicology, omics technologies are not widely applied in risk assessments of chemicals because of their complexity and lack of consensus on aspects such as standardization, analysis and interpretation. Given their value in hypothesis generation and ability to provide a vast amount of information about a biological response, the challenge lies in the acceptance of using transcriptomics data in regulatory risk assessments. We here introduce molecular AOPs to define the connections between KEs of the AOP-Wiki and curated biological pathways in WikiPathways, thereby providing a new method for the analysis and interpretation of transcriptomics data to identify KE activation. To study this, we performed case studies on liver steatosis and mitochondrial complex I inhibition, for which molecular AOPs were developed and public transcriptomics datasets were selected. Upon extension of the molecular AOP networks in Cytoscape, we mapped and analysed transcriptomics data, and calculated an enrichment score for individual KEs. Further interpretation of the data was done through the visualisation of the data on the specific molecular pathways. Two molecular AOPs were developed and KEs were linked to the appropriate molecular pathways, allowing a detailed exploration of molecular processes with the selected transcrip-tomics datasets. This has shown us that we can verify the activation of specific MIEs and KEs, and assess progression across the AOP in the steatosis case study through variables in exposure time and dose. These case studies have shown that transcriptomics data can be used for identifying the potential activation of KEs. However, it is also clear that extensive datasets are required to fully test the capabilities of molecular AOPs, and the process of linking molecular pathways and KEs can be challenging, not always allowing one-to-one mapping. While proven valuable to analyse and understand transcriptomic data, pathways linked to KEs appear to show inconsistent levels of activation and should be looked into and refined. More case studies are required to optimize the approaches used for the development and use of molecular AOPs with transcriptomics datasets.

bioinformatics↗

bioRxiv2017

Genome-wide strategies identify molecular niches regulated by connective tissue-associated transcription factors

BackgroundConnective tissues support, connect and separate tissues and organs, playing crucial roles in development, homeostasis and fibrosis. Cell specification and differentiation is triggered by the activity of specific transcription factors. While key transcription factors have been identified for differentiation processes of most tissues, connective tissue differentiation remains largely unstudied.\n\nResultsTo gain insight into the regulatory cascades involved in connective tissue differentiation, we selected five zinc finger transcription factors - OSR1, OSR2, EGR1, KLF2 and KLF4 - based on their expression patterns and/or known involvement in the differentiation of mesenchymal cells into connective tissue subtypes. We combined RNA-seq with ChIP-seq profiling in chick limb cells following overexpression of individual transcription factors. We identified a set of common genes regulated by all five transcription factors, which constitutes a connective tissue core expression set. This common core was enriched in genes associated with axon guidance and myofibroblast signature. In addition, each of the transcription factors regulated a different set of extracellular matrix components and signalling molecules, which define local molecular niches important for connective tissue development and function.\n\nConclusionsThe established regulatory network identifies common and distinct molecular signatures downstream of five connective tissue-associated transcription factors and provides insight into the signalling pathways governing limb connective tissue differentiation. It also suggests a concept whereby local molecular niches can be created via the expression of specific transcription factors impinging on the specification of microenvironments.

developmental biology↗

bioRxiv2017

A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model

The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads and the gene annotation that defines gene features must also be taken into account. Partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.

bioinformatics↗