Biology subjects

Puigbo, P.

Publications and source records attributed to Puigbo, P..

7 recordsLinked to original sources

bioRxiv2026

Glyphosate as a driver of antimicrobial resistance evolution in bacteria

Glyphosate, the worlds most widely used herbicide, targets the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), which is conserved across plants and many bacteria. While its environmental effects are increasingly recognized, its role on antimicrobial resistance (AMR) remains incompletely understood. In particular, the link between intrinsic glyphosate sensitivity and AMR gene content or evolutionary dynamics has not been systematically explored. We examined the relationship between bacterial sensitivity to glyphosate, AMR profiles, and the evolution of AMR genes. We analyzed genome datasets from the human gut microbiota and the Alignable Tight Genomic Clusters (ATGC). EPSPS sequences were identified via BLAST and annotations and classified based on the intrinsic sensitivity to glyphosate using the EPSPSClass webserver. AMR genes, including associated drug classes and resistance mechanisms, were annotated using the Comprehensive Antibiotic Resistance Database (CARD). Across datasets, glyphosate-sensitive bacteria carried a greater diversity of AMR genes and mechanisms. In contrast, probabilistic modeling revealed that glyphosate-resistant bacteria accumulate AMR genes at significantly higher rates. Phylogenetic birth-and-death analyses and stochastic mapping further revealed elevated AMR gene gain, loss, expansion, and reduction in resistant strains. These results indicate a decoupling between AMR gene diversity and evolutionary dynamics: sensitive bacteria maintain more resistance genes, whereas resistant bacteria display accelerated AMR gene turnover. This suggests that glyphosate resistance is linked to increased genome dynamics, potentially enhancing bacterias adaptability under combined herbicide and antimicrobial pressures. Given glyphosates extensive agricultural use and potential human exposure, these findings highlight an underappreciated link between herbicide resistance and the evolution of AMR in bacterial populations.

microbiology↗

bioRxiv2025

Plastic-Degrading Clusters of Orthologous Groups in Prokaryotes

Micro- and nanoplastic pollution (MNPP) is an increasing environmental threat due to the persistence, dispersal, and potential toxicity of plastic particles. Although microbial biodegradation offers a sustainable mitigation strategy, a comprehensive understanding of plastic-degrading proteins across microbial taxa is lacking. Here, we present the Plastic-Degrading Clusters of Orthologous Groups (PDCOGs) database (https://phylobone.com/microworld/PDCOG), comprising 625,616 potential plastic-degrading proteins (PPDPs) from free-living prokaryotes organized into 51 orthologous groups. The database/PDCOGs enable systematic analysis of microbial plastic-degrading capacity across ecosystems and phylogenetic lineages. Notably, PPDPs constitute [~]3.5% of all prokaryotic proteins, with over 95% of the species having the potential to biodegrade at least one plastic polymer type. This resource provides a genomic tool/framework for exploring the ecological and evolutionary importance of plastic biodegradation and supports future efforts to mitigate the global MNPP crisis.

microbiology↗

bioRxiv2024

Bone mechano-response is driven by locomotion transitions during vertebrate evolution

The skeleton supports the muscles in keeping the body upright against gravity while enduring thousands of daily loads. In this study, we investigated non-collagenous bone matrix proteins using osteoblast cell cultures and phylogenetic analyses to identify the molecular mechanisms involved in mechanical loading. The results indicate that the bone mechano-response is an evolutionary-driven process and that several non-collagenous proteins may significantly regulate the bones response to mechanical stress. According to our results, two significant evolutionary transitions in vertebrate locomotion shaped the roles of non-collagenous proteins in humans: the water-to-land transition, which increased mechanical stress on the limbs, and the evolution to bipedalism in humans, which altered the distribution of stress on the lower and upper limbs. Fetuin A, positively selected in both evolutionary transitions, showed the most significant expression change during mechanical stimulation.

evolutionary biology↗

bioRxiv2023

Phylobone: a comprehensive database of bone extracellular matrix proteins in human and model organisms

The bone extracellular matrix (ECM) contains minerals deposited on highly crosslinked collagen fibrils and hundreds of non-collagenous proteins. Some of these proteins are key to the regulation of bone formation and regeneration via signaling pathways, and play important regulatory and structural roles. However, the complete list of bone extracellular matrix proteins, their roles, and the extent of individual and cross-species variations have not been fully captured in both humans and model organisms. Here, we introduce the most comprehensive resource of bone extracellular matrix (ECM) proteins that can be used in research fields such as bone regeneration, osteoporosis, and mechanobiology. The Phylobone database (available at https://phylobone.com) includes 255 proteins potentially expressed in the bone extracellular matrix (ECM) of humans and 30 species of vertebrates. A bioinformatics pipeline was used to identify the evolutionary relationships of bone ECM proteins. The analysis facilitated the identification of potential model organisms to study the molecular mechanisms of bone regeneration. A network analysis showed high connectivity of bone ECM proteins. A total of 214 functional protein domains were identified, including collagen and the domains involved in bone formation and resorption. Information from public drug repositories was used to identify potential repurposing of existing drugs. The Phylobone database provides a platform to study bone regeneration and osteoporosis in light of (biological) evolution, and will substantially contribute to the identification of molecular mechanisms and drug targets.

cell biology↗

bioRxiv2020

Adaptation of bacteria to glyphosate: a microevolutionary perspective of the enzyme 5-enolpyruvylshikimate 3-phosphate (EPSP) synthase

Glyphosate is the leading herbicide worldwide, but it also affects prokaryotes because it targets the central enzyme (EPSPS) of the shikimate pathway in the synthesis of the three essential aromatic amino acids in autotrophs. Our results reveal that bacteria easily become resistant to glyphosate through changes in the EPSPS active site. This indicates the importance of examining how glyphosate affects microbe-mediated ecosystem functions and human microbiomes.

microbiology↗

bioRxiv2020

Classification of the glyphosate target enzyme (5-enolpyruvylshikimate-3-phosphate synthase)

Glyphosate is the most common broad-spectrum herbicide. It targets the key enzyme of the shikimate pathway, 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), which synthesizes three essential aromatic amino acids (phenylalanine, tyrosine and tryptophan) in plants. Because the shikimate pathway is also found in many prokaryotes and fungi, the widespread use of glyphosate may have unsuspected impacts on the diversity and composition of microbial communities, including the human gut microbiome. Here, we introduce the first bioinformatics method to assess the potential sensitivity of organisms to glyphosate based on the type of EPSPS enzyme. We have precomputed a dataset of EPSPS sequences from thousands of species that will be an invaluable resource to advancing the research field. This novel methodology can classify sequences from >90% of eukaryotes and >80% of prokaryotes. A conservative estimate from our results shows that 54% of species in the core human gut microbiome are sensitive to glyphosate.

bioinformatics↗

bioRxiv2020

An unsupervised algorithm for host identification in flaviviruses

Early characterization is essential to control the spread of emerging viruses, such as the Zika Virus outbreak in 2014. A major challenge is the identification of potential hosts for novel viruses. We introduce an algorithm to identify the host range of a virus from its raw genome sequence that will be a useful tool to understand host-virus relationships.

bioinformatics↗