Biology subjects

Acencio, M. L.

Publications and source records attributed to Acencio, M. L..

5 recordsLinked to original sources

bioRxiv2025

Association of ulcerative colitis with atopic dermatitis: identification of shared and unique mechanisms by construction and computational analysis of disease maps

Background and AimsUlcerative colitis (UC) and atopic dermatitis (AD) are immune-mediated inflammatory diseases with limited treatment options. They are known to be related which may explain higher risk of development of UC in patients with AD. The goal of this work is to review and analyse molecular mechanisms of UC in comparison to AD towards insights into UC complexity, potential comorbidities and novel therapies. MethodsWe developed graphical computational models of UC and AD molecular mechanisms (disease maps) by integrating information from over 800 manually curated articles. The maps are available online at https://imi-immuniverse.elixir-luxembourg.org. Disease-specific risk variants and gene expression profiles are visualised to identify signatures specific to UC, and shared with AD. Computational analysis shows key proteins, their interactions and pathways shared between UC and AD. ResultsUC and AD maps include more than 2000 molecular interactions. Systematic computational comparison shows that both disorders exhibit epithelial barrier dysfunction, immune dysregulation involving abnormal Th2, Th1 and ILC response, common inflammatory pathways and biomarkers such as IL-13, IL-4R, IFNG, and IL-18. Visualisation and analysis of omics data demonstrates UC map usability. ConclusionsWe developed the first computational graphic model of UC molecular mechanisms. Its content focuses on mechanisms of epithelial barrier disruption and downstream immune dysregulation through cytokines and other mediators. The comparison of UC to AD mechanisms demonstrates common signalling pathways and biomarkers, and supports potential drug repurposing and treatment options. The workflow can be reused and new findings can be dynamically integrated into the maps. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/650149v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@1c4c80eorg.highwire.dtl.DTLVardef@2a1e66org.highwire.dtl.DTLVardef@131ece3org.highwire.dtl.DTLVardef@b74971_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

bioinformatics↗

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

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

systems biology↗

bioRxiv2020

A GO catalogue of human DNA-binding transcription factors

DNA-binding transcription factors recognise genomic addresses, specific sequence motifs in gene regulatory regions, to control gene transcription. A complete and reliable catalogue of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such a catalogue of proteins is demonstrated by the many lists of DNA-binding transcription factors that have been produced over the past decade. The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF. In addition, this curation effort has led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science.

bioinformatics↗

bioRxiv2018

CausalTab: PSI-MITAB 2.8 updated format for signaling data representation and dissemination

Combining multiple layers of information underlying biological complexity into a structured framework, and in particular deciphering the molecular mechanisms behind cellular phenotypes, represent two challenges in systems biology. A key task is the formalisation of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signaling information, such as SIGNOR, SignaLink and IntAct, focus on capturing, organising and displaying signaling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources, through the development of a standardized exchange format, ontologies and controlled vocabularies supporting the domain of causal interactions. Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signaling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated MITAB2.8. We define the new term \"causal interaction\", and related child terms, which are children of the PSI-MI \"molecular interaction\" term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signaling networks more precisely and with higher coverage than before.

systems biology↗