Biology subjects

Gomez-Perez, D.

Publications and source records attributed to Gomez-Perez, D..

8 recordsLinked to original sources

bioRxiv2026

Widespread detection of Polyethylene glycol reveals chronic human exposure through pharmaceutical drugs

Polyethylene glycol (PEG) is a synthetic polymer ubiquitous in pharmaceuticals, personal care products, food additives, and industrial manufacturing. Despite its widespread use and potential importance as an exposure chemical, the prevalence of PEG exposure and its excretion in human populations remain largely uncharacterized. Moreover, PEG detected in human biofluids is frequently assumed to arise from analytical contamination during sample preparation, potentially obscuring its contribution to the human exposome and confounding metabolic phenotyping studies. Here, we show that PEG is as component of the human xenobiotic exposome and is further metabolized into PEG hydoxy acid and diacid metabolites in humans. We further find that PEG exposure is associated with alterations in microbiome composition and short-chain fatty acid metabolism, suggesting its biological impact of its exposure. We identify PEG exposure in approximately 2.3% of publicly available metabolomics data files and provide a reusable 85,484 candidate PEG and PEGylated MS/MS spectral library for future use for the metabolomic community. Together, these findings establish PEG signal in human biofluids can reflect genuine exposure, and that PEG exposure is neither metabolically inert nor biologically silent.

biochemistry↗

bioRxiv2026

Intrinsic disorder in elicitin-like effectors: Molecular shields in the arms race of biotrophic pathogens

Plant pathogens establish colonization through effector secretion to modulate host immune responses. Within bacterial effectors intrinsically disordered regions (IDRs) contribute to diverse functions and pathogenicity, but their role in effectors of phytopathogenic oomycetes remains poorly understood. Here, analysis of oomycete secretomes across lifestyles reveal widespread intrinsic disorder in secreted proteins, particularly in apoplastic elicitin and elicitin-like effectors (ELLs). Combining in vitro and in planta experiments, we show that IDRs in ELLs from the biotrophic pathogen Albugo candida reduce immune recognition, consistent with an IDR-mediated shielding mechanism. This function is transferable, as fusion of an ELL-derived IDR to the immunogenic elicitin INF1 from Phytophthora infestans abolishes its recognition. These findings identify intrinsic disorder as a functional feature of apoplastic effectors that modulates host detection and promotes pathogen fitness. Graphical summary O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/717489v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@1363e05org.highwire.dtl.DTLVardef@21f34dorg.highwire.dtl.DTLVardef@ec6e5borg.highwire.dtl.DTLVardef@41327b_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

Cooperative siderophore use stabilizes a protective leaf microbiome

Plant-associated microbial communities provide crucial protection against pathogens. Specialized metabolites play key roles in plant-microbe and microbe-microbe interactions and, ultimately, in plant health; however, the molecular mechanisms underlying their plant-protecting properties remain largely unknown. Nutrient deficiency (e.g., iron) on leaf surfaces creates intense competition among microbes, driving both antagonism and cooperation. Using a gnotobiotic Arabidopsis thaliana model and a synthetic leaf microbial community, we show that community stability and plant protection depend on cooperative siderophore exchange between the basidiomycete yeast Rhodotorula kratochvilovae and commensal Pseudomonas species. Removal of Pseudomonas caused a strong shift in the community metabolome and accumulation of the yeast siderophore rhodotorulic acid (RA). RA selectively promoted the growth of commensal Pseudomonas via TonB-dependent transporters, which are absent in pathogenic Pseudomonas strains. Inactivation of these transporter genes abolished RA uptake, destabilized the synthetic community, and eliminated protection against Pseudomonas syringae infection. RA and Rhodotorula also induced host iron-deficiency and jasmonate-related defense metabolites, linking microbial cooperation to plant stress responses. These findings reveal that microbial siderophore exchange acts as a key mechanism that maintains stability in the phyllosphere microbiome. Rather than solely promoting competition, iron-binding compounds can serve as cooperative currencies that align microbial fitness with host protection.

microbiology↗

bioRxiv2026

REMAG: recovery of eukaryotic genomes from metagenomic data using contrastive learning

Metagenome-assembled genomes (MAGs) are central to exploring microbial communities. Yet, despite the relevance of protists and fungi to diverse ecosystems, eukaryotic MAG recovery lags behind that of prokaryotes. A major bottleneck is that most state-of-the-art binning pipelines exclusively rely on prokaryotic single-copy core gene reference databases and are optimized for smaller genomes. To address this gap, we present REMAG (Recovery of Eukaryotic MAGs), a tool designed to recover high-quality eukaryotic genomes suited for longread metagenomic data. REMAG leverages finetuned HyenaDNA genomic foundation models to efficiently filter eukaryotic contigs. It then employs a dual-encoder Siamese network trained with Barlow Twins contrastive loss to learn a shared embedding space by integrating contig composition and differential coverage. Finally, high-quality bins are extracted using greedy iterative Leiden clustering optimized with eukaryotic single-copy core gene constraints. In benchmarks based on simulated mixed prokaryotic/eukaryotic communities and real datasets of varying sizes and origin, we demonstrate REMAGs ability to recover more near-complete eukaryotic genomes than existing state-of-the-art tools, which often produce highly fragmented eukaryotic bins. REMAG provides an automated eukaryotic binning method that scales effectively with the increasing size and sequencing depth of metagenomic datasets.

bioinformatics↗

bioRxiv2024

Integrating natural language processing and genome analysis enables accurate bacterial phenotype prediction

Understanding microbial phenotypes from genomic data is crucial in areas of research including co-evolution, ecology and pathology. This study proposes a new approach to integrate literature-derived information with genomic data to study microbial traits, combining natural language processing (NLP) with functional genome analysis. We applied this methodology to publicly available data to overcome current limitations and provide novel insights into microbial phenotype prediction. We fine-tuned specialized transformer-based large language models to analyze 3.3 million open-access scientific articles, extracting a network of phenotypic information linked to bacterial strains. The network maps relationships between bacterial strains and traits such as pathogenicity, metabolic capacity, and host and biome preference. By functionally annotating reference genome assemblies for strains in the phenotypic network, we were able to predict key genes influencing phenotypes. Our findings align with known phenotypes and reveal novel correlations, leading to the identification of microbial genes relevant in particular disease and host-association phenotypes. The interconnectivity of strains within the network provided further understanding of microbial community interactions, leading to the identification of hub species by inferring trophic connections--insights challenging to extract by means of experimental work. This study demonstrates the potential of machine learning methods to uncover cross-species patterns in microbial gene-phenotype correlations. As the number of sequenced strains and literature descriptions grows exponentially, such methods become crucial for extracting meaningful information and advancing microbiology research.

bioinformatics↗

bioRxiv2022

Cell specialization in cyanobacterial biofilm development revealed by expression of a cell-surface and extracellular matrix protein

Cyanobacterial biofilms are ubiquitous and play important roles in diverse environments, yet, understanding of the processes underlying development of these aggregates is just emerging. Here we report cell specialization in formation of Synechococcus elongatus PCC 7942 biofilms - a hitherto unknown characteristic of cyanobacterial multicellularity. We show that only a quarter of the cell population expresses at high levels the four-gene ebfG-operon that is required for biofilm formation. Almost all cells, however, are assembled in the biofilm. Detailed characterization of EbfG4 encoded by this operon revealed cell-surface localization as well as its presence in the biofilm matrix. Moreover, EbfG1-3 were shown to form amyloid structures such as fibrils and are thus likely to contribute to the matrix structure. These data suggest a beneficial division of labour during biofilm formation where only some of the cells allocate resources to produce matrix proteins - public goods that support robust biofilm development by the majority of the cells. Additionally, previous studies revealed the operation of a self-suppression mechanism that depends on an extracellular inhibitor, which supresses transcription of the ebfG-operon. Here we revealed inhibitor activity at an early growth stage and its gradual accumulation along the exponential growth phase in correlation with cell density. Data, however, do not support a threshold-like phenomenon known for quorum-sensing in heterotrophs. Together, data presented here demonstrate cell specialization and imply density-dependent regulation thereby providing novel insights into cyanobacterial communal behaviour. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/498973v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@7758f8org.highwire.dtl.DTLVardef@172396aorg.highwire.dtl.DTLVardef@176612org.highwire.dtl.DTLVardef@2e320c_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2022

Proteins released into the plant apoplast by the obligate parasitic protist Albugo selectively repress phyllosphere-associated bacteria

Biotic and abiotic interactions shape natural microbial communities. The mechanisms behind microbe-microbe interactions, particularly those protein-based, are not well understood. We hypothesize that secreted proteins are a powerful and highly specific toolset to shape and defend plant niches. We have studied Albugo candida, an obligate plant parasite from the protist Oomycota phylum, for its potential to modulate the growth of bacteria through secretion of proteins into the apoplast. Amplicon sequencing and network analysis of Albugo-infected and uninfected samples revealed an abundance of negative correlations between Albugo and other phyllosphere microbes. Analysis of the apoplastic proteome of Albugo colonized leaves combined with machine-learning predictors enabled the selection of candidates for heterologous expression and study of their inhibitory activity. We found that three of the candidate proteins show selective antimicrobial activity on gram-positive bacteria isolated from Arabidopsis thaliana and that these inhibited bacteria are important for the stability of the community structure. We could ascribe the antibacterial activity of the candidates to intrinsically disordered regions and positively correlate it with net charge. This is the first report of protist proteins with antimicrobial activity under apoplastic conditions that therefore are potential biocontrol tools for targeted manipulations of the microbiome.

microbiology↗

bioRxiv2021

Predicting lifestyle and host from positive selection data and genome properties in oomycetes

BackgroundHost and niche shifts are a source of genomic and phenotypic diversification as evidenced in parasitism. Exemplary is core metabolism reduction as parasites adapt to a particular host, while the accessory genome often maintains a high degree of diversification. However, selective pressures acting on the genome of organisms that have undergone lifestyle or host change have not been fully investigated. ResultsHere, we developed a comparative genomics approach to study underlying adaptive trends in oomycetes, a eukaryotic phylum with a broad range of economically important plant and animal parasitic lifestyles. Our analysis reveals converging evolution on biological processes for oomycetes that have similar lifestyle. Besides, we find that certain functions, in particular carbohydrate metabolism, transport, and signaling, are important for host and environmental adaption in oomycetes. DiscussionGiven the high correlation between lifestyle and genome properties in our oomycete dataset and the convergent evolution of fungal and oomycete genomes, we have developed a model that predicts plant pathogen lifestyles with high accuracy based on functional annotations. Understanding how genomes and selective pressures correlate with lifestyle may be crucial to identify new emerging diseases and pandemic threats.

evolutionary biology↗