Medical Data Analysis

@article{Ribeiro2025,

title = {Identifying potential novel widespread determinants of bacterial pathogenicity using phylogenetic-based orthology analysis},

author = {Sara Ribeiro and Karine Alves and Julien Nourikyan and Jean-Pierre Lavergne and Simon de Bernard and Laurent Buffat},

doi = {10.3389/fmicb.2025.1494490},

issn = {1664-302X},

year  = {2025},

date = {2025-05-01},

urldate = {2025-05-01},

journal = {Front. Microbiol.},

volume = {16},

publisher = {Frontiers Media SA},

abstract = {<jats:sec><jats:title>Introduction</jats:title><jats:p>The global rise in antibiotic resistance and emergence of new bacterial pathogens pose a significant threat to public health. Novel approaches to uncover potential novel diagnostic and therapeutic targets for these pathogens are needed.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>In this study, we conducted a large-scale, phylogenetic-based orthology analysis (OA) to compare the proteomes of pathogenic to humans (HP) and non-pathogenic to humans (NHP) bacterial strains across 734 strains from 514 species and 91 families.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Using a dedicated workflow, we identified 4,383 hierarchical orthologous groups (HOGs) significantly associated with the HP label, many of which are linked to critical factors such as stress tolerance, metabolic versatility, and antibiotic resistance. Both known virulence factors (VFs) and potential novel widespread pathogenicity determinants were uncovered, supported by both statistical testing and complementary protein domain analysis.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>By integrating curated strain-level pathogenicity annotations from BacSPaD with phylogeny-based OA, we introduce a novel approach and provide a novel resource for bacterial pathogenicity research.</jats:p></jats:sec>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39204272,

title = {BacSPaD: A Robust Bacterial Strains' Pathogenicity Resource Based on Integrated and Curated Genomic Metadata},

author = {Sara Ribeiro and Guillaume Chaumet and Karine Alves and Julien Nourikyan and Lei Shi and Jean-Pierre Lavergne and Ivan Mijakovic and Simon de Bernard and Laurent Buffat},

doi = {10.3390/pathogens13080672},

issn = {2076-0817},

year  = {2024},

date = {2024-08-01},

urldate = {2024-08-01},

journal = {Pathogens},

volume = {13},

number = {8},

abstract = {The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains' Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {P-228 Les modèles mathématiques peuvent prédire l'activité des plaquettes humaines et les expressions protéiques en réponse à diverses stimulations},

author = {Fabrice Cognasse and Kim Anh Nguyen and Marco Heestermans and Charles-Antoine Arthaud and Marie-Ange Eyraud and Amélie Prier and Simon De Bernard and Julien Nourikyan and Anne-Claire Duchez and Stéphane Avril and Olivier Garraud and Hind Hamzeh-Cognasse},

doi = {10.1016/j.tracli.2023.09.272},

year  = {2023},

date = {2023-11-10},

urldate = {2023-11-10},

journal = {Transfusion Clinique et Biologique},

volume = {30},

issue = {S1},

pages = {S147},

keywords = {},

pubstate = {published},

tppubtype = {article}

}