Biology subjects

Boulay, A.

Publications and source records attributed to Boulay, A..

5 recordsLinked to original sources

bioRxiv2025

PhaLP 2.0: extending the community-oriented phage lysin database with a SUBLYME pipeline for metagenomic discovery

As biology becomes increasingly data-driven, so too does the field of phage lysins, enzymes that degrade bacterial cell walls and hold promise as alternatives to traditional antibiotics. Five years ago, we introduced PhaLP, a centralized resource for Phage Lytic Protein sequences and associated metadata to support global research efforts. Here, we present PhaLP 2.0, a significantly enhanced database designed to overcome key challenges in the computational study of lysins by integrating newly identified lysins obtained from thousands of metagenomes. To expand the known diversity of lysins beyond those from cultured phages, we developed SUBLYME, a protein embedding-based machine learning Software designed to Uncover and classify Bacteriophage Lysins in Metagenomic datasets. Using embeddings derived from the prior well-curated protein sequences of the original PhaLP database, we trained support vector machines to distinguish lysins from non-lysins in viromes and classify them as either endolysins or virion-associated lysins. The models achieved an average F1-score of 98% on held-out lysin clusters. SUBLYME enabled the discovery of 743,000 new lysin sequences from EnVhogDB, a virome-derived protein database, increasing the number of known lysin clusters by a factor of 40, from 1,000 to 40,000. PhaLP 2.0 entries were annotated by integrating Pfam functional predictions to the refined delineations obtained with SPAED, an algorithm that leverages the predicted aligned error matrix from AlphaFold predictions to identify domain boundaries. Both SUBLYME and the PhaLP 2.0 database are accessible online at https://github.com/Rousseau-Team/sublyme and http://phalp.ugent.be, respectively. Together, these advances establish PhaLP 2.0 as a comprehensive and scalable portal for the discovery, classification, and sequence analysis of phage lysins, paving the way for future antibacterial applications and evolutionary insights. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=59 SRC="FIGDIR/small/692814v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@181e26borg.highwire.dtl.DTLVardef@37e099org.highwire.dtl.DTLVardef@7a7fccorg.highwire.dtl.DTLVardef@5c42f1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

A cold-water coral garden with co-occurring Antipathella subpinnata and Dendrophyllia cornigera in the mesophotic zone of the northern Bay of Biscay

Large aggregations of cold-water corals (CWC), often termed CWC gardens, are considered as biodiversity hotspots and recognized as vulnerable marine ecosystems. Their three-dimensional structure creates important habitat complexity, providing refuge, nursery and feeding areas for many deep-water species. Their vulnerability and limited current knowledge highlight the importance of describing the distribution and habitats of these gardens. Using remotely operated vehicles (ROV), we describe an unprecedented large aggregation of the co-occurring black (Antipathella subpinnata) and yellow (Dendrophyllia cornigera) coral gardens in the mesophotic in the northern Bay of Biscay. These distributions acknowledged their latitudinal and bathymetric distributions, in particular for A. subpinnata. Black and yellow corals presented mean densities of 2.0 {+/-} 1.8 colonies.10 m-2 and 1.5 {+/-} 1.3 colonies.10 m-2, respectively. Following the complex topography of this area, they were mostly fixed on hard substrates, particularly in narrow crests close to or surrounded by sand or mud planes. These findings are essential to develop appropriate local spatial management plans, ultimately contributing to the conservation of these vulnerable ecosystems.

ecology↗

bioRxiv2025

SPAED: Harnessing AlphaFold Output for Accurate Segmentation of Phage Endolysin Domains

SummarySPAED is an accessible tool for the accurate segmentation of protein domains that applies hierarchical clustering to the predicted aligned error (PAE) matrix obtained from AlphaFold predictions. It leverages information contained in the PAE matrix to better identify domain-linker boundaries and detect disordered regions. On a dataset of 376 bacteriophage endolysins (proteins that degrade the bacterial cell wall), SPAED achieves a mean intersect-over-union score of 96% and a domain-boundary-distance score of 89% compared to 94% and 70%, respectively, for the state-of-the-art tool Chainsaw. Availability and ImplementationSPAED is available on the web at http://spaed.ca and available for download at https://github.com/Rousseau-Team/spaed. ContactElsa Rousseau - elsa.rousseau@ift.ulaval.ca, Roberto Vazquez - rvazqf@gmail.com

bioinformatics↗

bioRxiv2025

Empathi: Embedding-based Phage Protein Annotation Tool by Hierarchical Assignment

Bacteriophages, viruses infecting bacteria, are estimated to outnumber their cellular hosts by 10-fold, acting as key players in all microbial ecosystems. Under evolutionary pressure by their host, they evolve rapidly and encode a large diversity of protein sequences. Consequently, the majority of functions carried by phage proteins remain elusive. Current tools to comprehensively identify phage protein functions from their sequence either lack sensitivity (those relying on homology for instance) or specificity (assigning a single coarse grain function to a protein). Here, we introduce Empathi, a protein-embedding-based classifier that assigns functions in a hierarchical manner - from general functional categories such as "structural" and "DNA-associated" proteins to more specific ones including "nucleases", "tail appendages" and "endolysins" to name only a few. These categories were specifically tailored for phage protein functions and organized such that molecular-level functions are respected in each category, making it well suited for training machine learning classifiers based on protein embeddings. We show on a dataset of cultured phage genomes that Empathi significantly outperforms homology-based methods, tripling the number of annotated homologous groups. On the EnVhog database, the most recent and extensive database of metagenomically-sourced phage proteins, Empathi doubled the annotated fraction of protein families from 16% to 33%. On complete genomes taken from new viromes, almost twice as many proteins are annotated using our method, predictions are consistent when compared to existing tools and Empathi predictions are highly colocalized. In addition, by leveraging Empathis ability to assign multiple labels to the same protein, it is possible to identify multifunctional proteins such as virion-associated lysins. Having a more global view of the repertoire of functions a phage possesses will assuredly help to understand them and their interactions with bacteria better.

bioinformatics↗

bioRxiv2024

Human plasma metabolic environment favours HIV replication in primary CD4 T lymphocytes

Cellular metabolism supports all viral replication steps and the metabolic state of infected cells is therefore a key factor influencing viral infections. Human Immunodeficiency virus (HIV) remains latent in resting CD4 T lymphocytes but actively replicates in activated CD4 T cells due to enhanced energy metabolism. Here, using the recently developed Human Plasma-Like Medium (HPLM) that mimics physiological plasma concentration of metabolites, we investigated how this near-physiologic environment modulates HIV-1 infection in primary CD4 T cells. Compared to the conventional culture medium (RPMI), HPLM enhanced HIV-1 infection in CD4 T cells despite similar levels of cell activation, proliferation and expression of viral receptor. In contrast with previous studies in RPMI, HPLM increased infection while decreasing energy metabolism and affecting other non-energetic metabolic pathways. Adjusting levels of several metabolites in RPMI and HPLM, we uncovered that the amino acids balance rather than the energy metabolism favoured HIV-1 replication in this system. Overall, our study used near-physiological conditions to better define metabolic dependencies of viral infections and highlights previously overlooked non-energetic metabolism pathways important for HIV-1 infection.

microbiology↗