Biology subjects

Kutmon, M.

Publications and source records attributed to Kutmon, M..

11 recordsLinked to original sources

bioRxiv2026

User-driven development and evaluation of an agentic framework for analysis of large pathway diagrams

As biomedical knowledge keeps growing, resources storing available information multiply and grow in size and complexity. Such resources can be in the format of molecular interaction maps, which represent cellular and molecular processes under normal or pathological conditions. However, these maps can be complex and hard to navigate, especially to novice users. Large Language Models (LLMs), particularly in the form of agentic frameworks, have emerged as a promising technology to support this exploration. In this article, we describe a user-driven process of prototyping, development, and user testing of Llemy, an LLM-based system for exploring these molecular interaction maps. By involving domain experts from the very first prototyping in the form of a hackathon and collecting both fine-grained and general feedback on more refined versions, we were able to evaluate the perceived utility and quality of the developed system, in particular for summarising maps and pathways, as well as prioritise the development of future features. We recommend continued user-driven development and benchmarking to keep the community engaged. This will also facilitate the transition towards open-weight LLMs to support the needs of the open research environment in an ever-changing technology landscape.

bioinformatics↗

bioRxiv2025

Vascular contribution to cognitive impairment in heart failure with preserved ejection fraction: TRPV4 and KLF2 as key mediators of neurovascular dysfunction in the ZSF1 model

Vascular cognitive impairment (VCI) shares major risk factors with heart failure with preserved ejection fraction (HFpEF), including obesity, diabetes and hypertension. Yet VCI research often relies on single-stimulus models, whereas patients experience combined risk factors. We therefore assessed cerebrovascular and cognitive phenotypes in an HFpEF model and investigated underlying mechanisms. Male Lean and Obese ZSF1 rats underwent longitudinal assessments of blood pressure, glucose, cardiac function, and behavioural performance. Cerebral blood flow and neurovascular coupling were assessed by laser speckle contrast imaging. White matter integrity, blood-brain barrier (BBB) permeability, and vascular density were analysed by (immuno)histochemistry. Cortical microvessels were isolated for transcriptomic profiling, and selected targets were validated using multiplex in-situ hybridization. Obese rats exhibited neurovascular uncoupling and impaired short- and long-term memory and spatial learning, accompanied by brain atrophy and reduced myelin. BBB permeability increased at 22-23 weeks and vascular density at 34-35 weeks in Obese vs Lean rats. Transcriptomic analysis of brain microvessels revealed altered processes related to angiogenesis, vasoreactivity, immune mechanisms and vascular remodelling, with consistent downregulation of Trpv4 and Klf2. Obese ZSF1 rats develop progressive neurovascular dysfunction associated with HFpEF onset and reduced Trpv4 and Klf2 expression in cerebral microvessels, two key vasoprotective genes.

neuroscience↗

bioRxiv2024

The guardians of mitochondrial dynamics: a novel role for intermediate filament proteins

Mitochondria are dynamic organelles and the main source of cellular energy. Their dynamic nature is crucial to meet cellular requirements. However, the processes and proteins involved in mitochondrial dynamics are not fully understood. Using a computational protein-protein interaction approach, we identified ITPRIPL2, which caused mitochondrial elongation upon knockdown. ITPRIPL2 co-localizes with the intermediate filament protein vimentin and interacts with vimentin according to protein simulations. ITPRIPL2 knockdown alters vimentin processing, disrupts intermediate filaments and transcriptomics analysis revealed changes in vimentin-related pathways. Our data illustrates that ITPRIPL2 is essential for vimentin related intermediate filament structure. Interestingly, like ITPRIPL2 knockdown, vimentin knockdown results in mitochondrial elongation. Our data highlights ITPRIPL2 as a vimentin-associated protein and reveals a role for intermediate filaments in mitochondrial dynamics, improving our understanding of mitochondrial dynamics regulators. Moreover, our study demonstrates that protein- protein interaction analysis is a powerful approach for identifying novel mitochondrial dynamics proteins.

systems biology↗

bioRxiv2024

GSEA and co-expression network approach to identify molecular processes affected in Porto-sinusoidal Vascular Disease

11.1 BackgroundPorto-sinusoidal vascular disease (PSVD) is a complex, rare liver disease characterized by the absence of cirrhosis, with or without the presence of portal hypertension or histological lesions. Given the knowledge gaps in the mechanisms involved in this disease with unknown etiology, we used omics-based approaches to further elucidate the pathways affected by PSVD, facilitating improvements in the prognosis, diagnosis, and treatment options for these patients. 1.2 MethodsWe applied gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) to identify pathways dysregulated in PSVD. Network construction and visualization were performed in Cytoscape to explore interconnectivity among enriched processes. Within key modules, candidate genes were prioritized by ranking approaches and cross-referenced with findings from previous studies. 1.3 Results and ConclusionThis study revealed that PSVD is characterized by coordinated dysregulation, with immune and signaling pathways activated alongside the suppression of metabolic, ribosomal, and mitochondrial programs. Alterations in ribosomal proteins, ATP synthase subunits, and serpin family members highlight translational, bioenergetic, and anticoagulant dysfunction as core mechanisms. Together, these findings define PSVD as a disorder of integrated immune, vascular, and metabolic imbalance.

bioinformatics↗

bioRxiv2022

Different B cell activation patterns in asymptomatic and symptomatic COVID-19 patients

Early and persistent defects in B cell subsets such as memory B cells were shown to be correlated with poor outcomes in COVID-19 patients. This research aimed to develop a molecular pathway model to understand the B cell development in COVID-19. A B cell transcriptomics dataset, obtained from COVID-19 patients, was analyzed on the resulting pathway model to study B cell activation. The pathway showed two distinct gene expression profiles between asymptomatic and symptomatic patients. In asymptomatic patients, there is an increase in transcript levels of antiviral interferon-stimulated genes such as ISG15, IFITM1, and NEAT1 and a driving gene for the extrafollicular pathway CXCR4 indicating a formation of plasmablast. In symptomatic patients, the results suggest an inhibition occurring at the germinal center hinting at a reduction in memory B cell production. Transcripts of driver gene CXCR5 involved in germinal center development is one of the most downregulated genes. This could contribute to the shortage in the formation of memory B cells in COVID-19. Concluding, in SARS-CoV-2 infection, B cells follow different activation routes in asymptomatic and symptomatic patients. In this study, we constructed a pathway that allowed us to analyze and interpret activation patterns of B cells in COVID-19 patients and their link to disease severity. Importantly, the pathway and approach can be reused for further research in COVID-19 or other diseases.

immunology↗

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↗

bioRxiv2020

ComMet: A method for comparing metabolic states in genome-scale metabolic models

Being comprehensive knowledge bases of cellular metabolism, Genome-scale metabolic models (GEMs) serve as mathematical tools for studying cellular flux states in various organisms. However, analysis of large-scale (human) GEMs, still presents considerable challenges with respect to objective selection and reaction flux constraints. In this study, we introduce a model-based method, ComMet (Comparison of Metabolic states), for comprehensive analysis of large metabolic flux spaces and comparison of various metabolic states. ComMet allows (a) an in-depth characterisation of achievable flux states, (b) comparison of flux spaces from several conditions of interest and (c) identification and visualization of metabolically distinct network modules. As a proof-of-principle, we employed ComMet to extract the biochemical differences in the human adipocyte network (iAdipocytes1809) arising due to unlimited/blocked uptake of branched-chain amino acids. Our study opens avenues for exploring several metabolic conditions of interest in both microbe and human models. ComMet is open-source and is available at https://github.com/macsbio/commet.

systems biology↗

bioRxiv2020

Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions

BackgroundTo grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of biological pathways, such as signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format that is easier for humans to interpret. It has already been established that these diagrams can be represented in machine readable formats, as done in KEGG, Reactome, and WikiPathways. However, while humans are good at interpreting the message of the creator of such a diagram, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles, and therefore needs to harmonize this to offer semantically enriched access via the Resource Description Framework format. Particularly challenging in the normalization of diagrams are the interactions between the biological entities, so that we can glean information about the connectivity of the entities represented. These interactions include information about the type of interaction (metabolic conversion, inhibition, etc.), the direction, and the participants. Availability of the interactions in a semantic and harmonized format enables searching the full network of biological interactions and integration with the linked data cloud. ResultsWe here study how the graphically modelled biological knowledge in diagrams can be semantified and harmonized efficiently, and exemplify how the resulting data can be used to programmatically answer biological questions. We find that we can translate graphically modelled biological knowledge to a sufficient degree into a semantic model of biological knowledge and discuss some of the current limitations. Furthermore, we show how this interaction knowledge base can be used to answer specific biological questions. ConclusionThis paper demonstrates that most of the graphical biological knowledge from WikiPathways is modelled in the semantic layer of WikiPathways with the semantic information intact and connectivity information preserved. The usability of the WikiPathways pathway and connectivity information has shown to be useful and has been integrated into other platforms. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified.

bioinformatics↗

bioRxiv2020

25 Years of Pathway Figures

BackgroundPathway diagrams are fundamental tools for describing biological processes in all aspects of science, including training, generating hypotheses, describing new knowledge and ultimately as communication tools in published work. Thousands of pathway diagrams are published each year as figures in papers. But as static images the pathway knowledge represented in figures is not accessible to researchers for computational queries and analyses. In this study, we aimed to identify pathway figures published in the past 25 years, to characterize the human gene content in figures by optical character recognition, and to describe their utility as a resource for pathway knowledge. ApproachTo identify pathway figures representing 25 years of published research, we trained a ma-chine learning service on manually-classified figures and applied it to 235,081 image query results from PubMed Central. Our previously described pipeline 1 was utilized to extract hu-man genes from the pathway figure images. These figures were characterized in terms of their parent papers, human gene content and enriched disease terms. Diverse use cases were explored for this newly accessible pathway resource. ResultsWe identified 64,643 pathway figures published between 1995 and 2019, depicting 1,112,551 instances of human genes (13,464 unique NCBI Genes) in various interactions and contexts. This represents more genes than found in the text of the same papers, as well as genes not found in any pathway database. We developed an interactive web tool to explore the results from the 65k set of figures, and used this tool to explore the history of scientific discovery of the Hippo Signaling pathway. We also defined a filtered set of 32k pathway figures useful for enrichment analysis.

bioinformatics↗

bioRxiv2020

A protocol for adding knowledge to Wikidata, a case report

Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a "commons". Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modelled with entity schemas represented by Shape Expressions. As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable, is demonstrated by integrating data from NCBI Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, Human Coronavirus NL63, Human coronavirus 229E, Human coronavirus HKU1, Human coronavirus OC4).

bioinformatics↗

bioRxiv2019

History of rare diseases and their genetic causes - a data driven approach

This dataset provides information about monogenic, rare diseases with a known genetic cause supplemented with manually extracted provenance of both the disease and the discovery of the underlying genetic cause of the disease.\n\nWe collected 4166 rare monogenic diseases according to their OMIM identifier, linked them to 3163 causative genes which are annotated with Ensembl identifiers and HGNC symbols. The PubMed identifier of the scientific publication, which for the first time describes the rare disease, and the publication which found the gene causing this disease were added using information from OMIM, Wikipedia, Google Scholar, Whonamedit, and PubMed. The data is available as a spreadsheet and as RDF in a semantic model modified from DisGeNET.\n\nThis dataset relies on publicly available data and publications with a PubMed IDs but this is to our knowledge the first time this data has been linked and made available for further study under a liberal license. Analysis of this data reveals the timeline of rare disease and causative genes discovery and links them to developments in methods and databases.

systems biology↗