Biology subjects

Zannoni, A.

Publications and source records attributed to Zannoni, A..

6 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

Genome-wide definition of the CosR regulon and DNA-binding properties in Campylobacter jejuni

CosR is an essential OmpR-family transcriptional regulator of Campylobacter jejuni, but its direct regulon and DNA-binding properties in vivo remain poorly defined. Here, we used ChIP-seq with a functional chromosomal CosR::3xFLAG allele to define the genome-wide CosR-binding landscape. CosR binding was strongly enriched at promoters and accumulated around transcription start sites, consistent with a primary role in transcriptional control. Functional analysis of promoter-bound targets revealed significant enrichment for translation- and transcription-related functions, identifying CosR as a regulator of core cellular processes. Motif analysis of summit-centered ChIP-seq regions identified a CosR-associated bipartite sequence signature characterized by TTAA-like elements separated by an A/T-rich spacer. DNase I footprinting confirmed direct promoter binding at nucleotide resolution and revealed heterogeneous architectures, including single, multipartite, and bidirectional binding arrangements. A footprint-derived motif was significantly similar to the ChIP-derived motif, supporting a shared recognition signature across in vivo-enriched regions and in vitro-protected segments. Footprinting also validated binding at non-coding RNA promoters and at the cosR promoter, indicating autoregulation. Hydrogen peroxide treatment differentially remodeled CosR promoter occupancy in vivo, reducing binding at translation-associated promoters while increasing enrichment at other targets. Redox-dependent footprinting showed that oxidative conditions directly impaired CosR binding at selected promoters, consistent with previously reported C218-dependent redox modulation. Despite these opposite occupancy patterns, most tested transcripts decreased after oxidative stress, indicating that CosR redox responsiveness is integrated with broader stress-dependent regulatory inputs. Together, these data define CosR as a condition-responsive regulator linking promoter recognition, core physiology and oxidative-stress-associated transcriptional remodeling. IMPORTANCECampylobacter jejuni is a major foodborne pathogen that must adapt to changing oxygen levels and stress conditions during transmission and infection. CosR is essential for this bacterium, but previous studies could not clearly distinguish direct regulation from secondary effects caused by perturbing an essential protein. By mapping CosR binding across the genome and validating binding at nucleotide resolution, this study shows that CosR directly targets promoters associated with fundamental cellular functions, particularly translation and RNA metabolism, showing that the role of CosR extends well beyond oxidative-stress regulation. We also show that a brief oxidative challenge redistributes CosR occupancy among promoters in a target-dependent manner. These findings identify CosR as a condition-responsive regulator linking the gene-expression machinery and other core physiological functions to stress adaptation, and provide a framework for understanding how C. jejuni adjusts its physiology in changing environments.

molecular biology↗

bioRxiv2026

Microencapsulated Vaccinium floribundum Kunth extract promotes angiogenesis and attenuates inflammation in in vitro and in vivo models.

Natural products, especially polyphenol-rich medicinal plants, are increasingly investigated as multitarget therapeutics in both human and veterinary medicine for angiogenic regenerative properties and for inflammation based-diseases. Recent developments in natural product formulation, notably microencapsulation, have been shown to improve the stability, bioavailability, and controlled release of bioactive compounds. The integration of complementary in vitro and in vivo models is critical for evaluating both efficacy and translational potential. In this context, the present study assessed the phytochemical composition and biological activity of a microencapsulated Ecuadorian Vaccinium floribundum extract (VFM), using a combination of in vitro and in vivo approaches. VFM biochemical characterization identified 15 compounds, including flavonoids, procyanidins, dihydrochalcones, and phenolic acids, with chlorogenic acid and quercetin as the most abundant metabolites. Anthocyanins ideain and petunidin were also detected, confirming a rich bioactive profile. Primary porcine thoracic aortic endothelial cells (pAECs) were treated with VFM to assess cell viability and angiogenic potential and challenged with bacterial lipopolysaccharide (LPS) in the presence or absence of the extract. Anti-inflammatory effects were further evaluated in vivo using a carrageenan-induced mouse paw edema model. VFM enhanced endothelial cell viability, promoted capillary-like network and modulated early angiogenic signaling pathways. It mitigated LPS-induced endothelial dysfunction by reducing pro-inflammatory cytokines and oxidative stress markers. In vivo, paw edema assays confirmed its anti-inflammatory efficacy, with microencapsulation likely sustaining bioactive release. These findings support the traditional use of Vaccinium floribundum and highlight its potential for developing nutraceutical formulations targeting vascular and inflammatory disorders.

pharmacology and toxicology↗

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

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↗