Biology subjects

Olayo-Alarcon, R.

Publications and source records attributed to Olayo-Alarcon, R..

9 recordsLinked to original sources

bioRxiv2026

Human-targeted drugs impact antibiotic sensitivity and growth of Campylobacter jejuni

Antibiotic resistance is increasingly recognized as a major threat to global health, yet the contribution of non-antibiotics to this problem remains poorly defined. Here, we established a transcriptional chemical genomics approach to map how 2,415 host-relevant small molecules influence stress-responsive promoters and fitness of the foodborne pathogen Campylobacter jejuni. This screen uncovered widespread activation of the CmeABC efflux pump by diverse compounds, including several human-targeted drugs. Notably, ibuprofen, one of the most widely used non-steroidal anti-inflammatory drugs worldwide, induced efflux pump expression and thereby enhanced bacterial tolerance to ciprofloxacin. We also identified multiple commensal-sparing human-targeted drugs with growth-inhibitory activity against C. jejuni, highlighting opportunities for pathogen-targeted drug repurposing. Together, our findings reveal that non-antibiotic drugs can both promote resistance-associated phenotypes and selectively suppress pathogen growth. This underscores the need to consider collateral drug effects on bacterial physiology when designing strategies to curb the burden of antimicrobial resistance.

microbiology↗

bioRxiv2026

Human-targeted drugs extensively reshape regulatory networks controlling virulence and antibiotic resistance in Salmonella Typhimurium

Human-targeted drugs are increasingly recognized to affect bacterial physiology, yet their impact beyond growth inhibition, and the underlying molecular mechanisms, remain poorly understood. Here, we systematically mapped how chemically diverse host-associated compounds influence virulence and intrinsic antibiotic resistance in Salmonella enterica serovar Typhimurium. Using a high-throughput reporter approach, we quantified transcriptional response of 26 key stress promoters in Salmonella against 2,415 compounds, including FDA-approved drugs, food-derived molecules, and human metabolites. We found that non-antibiotic compounds induce transcriptional responses distinct from those elicited by antibiotics, uncovering a regulatory shift from general stress responses to compound-specific regulation driven by ligand-responsive MarR- and TetR-family regulators. In particular, more than 200 non-antibiotic drugs modulate virulence or intrinsic resistance pathways through the central regulators SlyA or RamR, respectively. By integrating our large-scale dataset with machine learning, we further defined chemical features associated with ramA transcriptional activation in Salmonella, and predicted ~200 additional candidate inducers of intrinsic antibiotic resistance in this pathogen. Together, our results uncovered a large and previously unrecognized chemical landscape that reshapes bacterial stress responses through ligand-responsive regulatory mechanisms, with implications for how host-associated compounds influence pathogen virulence and antibiotic resistance.

microbiology↗

bioRxiv2026

BATTLE-AMP: Benchmarking Antimicrobial Peptide Predictors

As antimicrobial resistance outpaces antibiotic development, antimicrobial peptides (AMPs) have emerged as a promising class of alternative antibacterials, and computational predictors are increasingly used to prioritize AMP candidates. Such predictors are typically evaluated on binary AMP/non-AMP classification, which does not test whether they can identify peptides with clinically relevant potency against specific pathogens. We present BATTLE-AMP, a benchmarking framework that evaluates AMP predictors against experimentally measured minimum inhibitory concentrations (MICs) across clinically relevant bacterial species and strains. We surveyed 48 published methods, finding fewer than 25% reproducible, and benchmarked 10 model families (21 variants) using experimental MIC data, synthetic sequence perturbations, activity cliff analyses, and all-atom molecular dynamics (MD) simulations. Four findings emerge: (i) models trained on MIC data outperform binary classifiers regardless of architecture; (ii) the best model depends on the target pathogen, so model selection must be guided by the biological question; (iii) most models cannot distinguish active peptides from inactive sequences with identical amino acid composition; and (iv) activity cliffs remain unresolved by both machine learning and MD, marking a limit of current computational methods. BATTLE-AMP is released as an open Snakemake framework at https://github.com/szczurek-lab/battleamp-snakemake for benchmarking new models and scoring novel candidate libraries.

bioinformatics↗

bioRxiv2025

Statistical end-to-end analysis of large-scale microbial growth data with DGrowthR

Quantitative analysis of microbial growth curves is essential for understanding how bacterial populations respond to environmental cues. Traditional analysis approaches make parametric assumptions about the functional form of these curves, limiting their usefulness for studying conditions that distort standard growth curves. In addition, modern robotics platforms enable the high-throughput collection of large volumes of growth data, thus requiring strategies that can analyze large-scale growth data in a flexible and efficient manner. Here, we introduce DGrowthR, a statistical R framework and standalone app with a no-code interface for the integrative analysis of large growth experiments. DGrowthR comprises methods for data pre-processing and standardization, exploratory functional data analysis, and non-parametric modeling of growth curves using Gaussian Process regression. Importantly, DGrowthR includes a rigorous statistical testing framework for differential growth (DG) analysis. To illustrate the range of application scenarios of DGrowthR, we analyzed three large-scale bacterial growth datasets targeting distinct scientific inquiries. On an in-house dataset comprising more than 20, 000 growth curves of two pathogens that were subjected to chemical perturbations, DGrowthR enabled the discovery of compounds with significant growth inhibitory effects as well as compounds that induce non-canonical growth dynamics. On two publicly available perturbation datasets (> 100, 000 growth curves), DG analysis recovered reported adjuvants and antagonists of antibiotic activity, as well as bacterial genetic factors that determine susceptibility to specific antibiotic treatments. We anticipate DGrowthR to streamline the analysis of high-volume growth experiments, enabling researchers to make biological discoveries in a standardized and reproducible manner.

microbiology↗

bioRxiv2024

Systematic Characterization of Transport Regulation in Escherichia coli across defined environmental cues

Transport of small molecules across the bacterial cell envelope is essential to ensure nutrient uptake and protect bacteria from toxic compounds. Due to its decisive role, transport is controlled by complex regulatory networks, knowledge of which - in particular across external cues - remains poorly understood. Here we investigate transcriptional regulation of seven prominent transport genes in Escherichia coli across 94 defined chemical cues, and simultaneously map the contributions of the key regulators MarA, SoxS and Rob to promoter activity. One third of all tested compounds triggered transcriptional changes, the vast majority of which previously unknown. Importantly, we exposed main drivers of transport control in E. coli, e.g. bacteriostatic, but not bactericidal, antibiotics trigger expression of efflux pumps, and that Rob contributes to [~]1/3 of all measured transcriptional changes, thereby emerging as a more prominent transport regulator than previously thought. We showcase the potential of our resource by elucidating the molecular mechanism of antibiotic antagonisms with widely consumed caffeine in E. coli. Altogether, our resource capitalizes on providing a quantitative overview of transport determinants across environments, and brings perspective to long-term prevailing concepts in the field.

microbiology↗

bioRxiv2024

BacSC: A general workflow for bacterial single-cell RNA sequencing data analysis

Bacterial single-cell RNA sequencing has the potential to elucidate within-population heterogeneity of prokaryotes, as well as their interaction with host systems. Despite conceptual similarities, the statistical properties of bacterial single-cell datasets are highly dependent on the protocol, making proper processing essential to tap their full potential. We present BacSC, a fully data-driven computational pipeline that processes bacterial single-cell data without requiring manual intervention. BacSC performs data-adaptive quality control and variance stabilization, selects suitable parameters for dimension reduction, neighborhood embedding, and clustering, and provides false discovery rate control in differential gene expression testing. We validated BacSC on a broad selection of bacterial single-cell datasets spanning multiple protocols and species. Here, BacSC detected subpopulations in Klebsiella pneumoniae, found matching structures of Pseudomonas aeruginosa under regular and low-iron conditions, and better represented subpopulation dynamics of Bacillus subtilis. BacSC thus simplifies statistical processing of bacterial single-cell data and reduces the danger of incorrect processing.

bioinformatics↗

bioRxiv2024

Pre-trained molecular representations enable antimicrobial discovery

The rise in antimicrobial resistance poses a worldwide threat, reducing the efficacy of common antibiotics. Determining the antimicrobial activity of new chemical compounds through experimental methods is still a time-consuming and costly endeavor. Compound-centric deep learning models hold the promise to speed up this search and prioritization process. Here, we introduce a lightweight computational strategy for antimicrobial discovery that builds on MolE(Molecular representation through redundancy reduced Embedding), a deep learning framework that leverages unlabeled chemical structures to learn task-independent molecular representations. By combining MolE representation learning with experimentally validated compound-bacteria activity data, we design a general predictive model that enables assessing compounds with respect to their antimicrobial potential. The model correctly identified recent growth-inhibitory compounds that are structurally distinct from current antibiotics and discovered de novo three human-targeted drugs as Staphylococcus aureus growth inhibitors which we experimentally confirmed. Our framework offers a viable cost-effective strategy to accelerate antibiotics discovery.

microbiology↗

bioRxiv2024

RNF43 is a gatekeeper for colitis-associated cancer

Somatic mutations in the tumor suppressor Ring finger protein 43 (RNF43) were frequently found in colitis-associated cancer (CAC) and related to the duration of chronic inflammation, but their significance in inflammation and inflammation-associated carcinogenesis remained elusive. We assessed the onset of RNF43 mutations at different stages of human CAC development by exome sequencing, and comprehensively characterized RNF43 loss-of-function-driven malignant transformation in mice by RNA sequencing, flow cytometry, immunohistochemistry, computational transcriptome-microbiome associations, and determined the underlying mechanisms by performing functional stem-cell derived organoid studies and fecal microbiota transfers. Mutations in RNF43 were frequent (12.9 %) in precancerous lesions of ulcerative colitis (UC) patients and eventually detectable in 24.4 % of CAC patients. In a bacterial-induced colitis mouse model, Rnf43 mutations caused invasive colorectal carcinomas by aggravating and perpetuating inflammation due to impaired epithelial barrier integrity and pathogen control. We could demonstrate that Rnf43 loss-of-function-mutations were even sufficient to cause spontaneous intestinal inflammation, resulting in UC-typical pathological features and subsequent invasive carcinoma development. In detail, mutant Rnf43 impaired intestinal epithelial and particularly goblet cell homeostasis in a cell-intrinsic manner, and caused dysbiosis. The altered microbiota composition induced epithelial DNA damage and spontaneous mucosal inflammation characterized by TGF-{beta}-activating dendritic cells and pro-inflammatory (IL-17+, IL-22+, TNF+) T cells. Over time, the continuous epithelial and goblet cell dysfunction, combined with pro-tumorigenic and pro-inflammatory microbiota, resulted in accumulated epithelial damage with transformation into inflammation-associated cancer in the presence of constitutive WNT signaling activation. We identified mutant RNF43 as susceptibility gene for UC and bona fide driver of CAC.

cancer biology↗

bioRxiv2019

GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learning

MotivationThe genetic mechanisms involved in human diseases are fundamental in biomedical research. Several databases with curated associations between genes and diseases have emerged in the last decades. Although, due to the demanding and time consuming nature of manual curation of literature, they still lack large amounts of information. Current automatic approaches extract associations by considering each abstract or sentence independently. This approach could potentially lead to contradictions between individual cases. Therefore, there is a current need for automatic strategies that can provide a literature consensus of gene-disease associations, and are not prone to making contradictory predictions. ResultsHere, we present GeDex, an effective and freely available automatic approach to extract consensus gene-disease associations from biomedical literature based on a predictive model trained with four simple features. As far as we know, it is the only system that reports a single consensus prediction from multiple sentences supporting the same association. We tested our approach on the curated fraction of DisGeNet (f-score 0.77) and validated it on a manually curated dataset, obtaining a competitive performance when compared to pre-existing methods (f-score 0.74). In addition, we effectively recovered associations from an article collection of chronic pulmonary diseases, and discovered that a large proportion is not reported in current databases. Our results demonstrate that GeDex, despite its simplicity, is a competitive tool that can successfully assist the curation of existing databases. AvailabilityGeDex is available at https://bitbucket.org/laigen/gedex/src/master/ and can be used as a docker image https://hub.docker.com/r/laigen/gedex Contactcmendezc@ccg.unam.mx Supplementary informationSupplementary material are available at bioRxiv online.

bioinformatics↗