Biology subjects

Amstalden, M. K.

Publications and source records attributed to Amstalden, M. K..

5 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↗

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↗

bioRxiv2021

Drug repurposing to face Covid-19: Celastrol, a potential leading drug capable of inhibiting SARS-CoV-2 replication and induced inflammation

The global emergence of Covid-19 has caused huge human casualties. Clinical manifestations of the disease vary from asymptomatic to lethal, and the symptomatic form can be associated with cytokine storm and non-homeostatic inflammatory response. In face of the urgent demand for effective drugs to treat Covid-19, we have searched for candidate compounds using a drug repurposing approach based on in silico analysis followed by biological validation. Here we identified celastrol, a pentacyclic triterpene isolated from Tripterygium wilfordii Hook F - a plant used in traditional Chinese medicine - as one of the best compounds out of 39 repurposed drug candidates. Celastrol reverted gene expression signature from SARS-CoV-2-infected cells; bound with high-affinity energy to viral molecular targets such as main protease (Mpro) and receptor-biding domain (RBD); inhibited SARS-CoV-2 replication in monkey (Vero and Vero-ACE2) and human (Caco-2 and Calu-3) cell lines; and decreased interleukin-6 (IL-6) secretion in SARS-CoV-2-infected human cell lines. Interestingly, celastrol acted in a concentration-dependent manner, with undetectable signs of cytotoxicity. Therefore, celastrol is a promising lead drug candidate to treat Covid-19 due to its ability to suppress SARS-CoV-2 replication and IL-6 production in infected cells, two critical events in the pathophysiology of this disease.

immunology↗