Biology subjects

Mueller, C. L.

Publications and source records attributed to Mueller, C. L..

8 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

The role of IgE patterns and their link to the gut microbiome in allergic sensitization

Allergic diseases are heterogeneous conditions shaped by immune pathways and environmental influences, including the gut microbiome. Using cross-sectional data from 508 adults in the KORA FF4 cohort (275 with IgE sensitization, 233 without), we provide a multimodal statistical analysis of deep IgE profiles and concomitant gut microbial amplicon sequencing variant (ASV) data. We identified three latent allergy components in the cohorts IgE profiles that reflects food, pollen, and house dust mite markers and enables interpretable stratification of the cohort. Contrary to prior studies, microbial diversity did not differ between sensitized and non-sensitized individuals across all cohort strata. Differential abundance analysis identified 61 ASVs, with enrichment in Bacteroidaceae, Oscillospiraceae, and Veillonellaceae families, and depletion in the Lachnospiraceae family. Microbial network analysis further identified altered family-level associations in pollen- and food-sensitized individuals. Taxon set enrichment highlighted folic acid- and vitamin A-producing taxa, with consistent signals from Prevotella copri (depleted) and Bacteroides massiliensis (enriched). Together, our analysis points toward specific microbial families and metabolic groups as correlates of IgE sensitization in an adult population. HIGHLIGHTSO_LIIgE sensitization clusters reflect allergen sources and cross-reactive proteins C_LIO_LIThree latent allergy components capture the IgE profile structure of the entire cohort C_LIO_LIDistinct IgE patterns are linked to changes in taxa compositions and associations C_LIO_LIFolic acid- and vitamin A-producing taxa are enriched in IgE-sensitized individuals C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/714318v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1d1c8fborg.highwire.dtl.DTLVardef@1e1fea7org.highwire.dtl.DTLVardef@1589fcorg.highwire.dtl.DTLVardef@160282_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

Target-driven optimization of feature representation and model selection for microbiome sequencing data with ritme

Microbiome sequencing datasets are sparse, high-dimensional, compositional, and hierarchically structured, and predictive modeling from them typically relies on ad hoc feature representation choices that obscure their impact on performance and interpretation. We present ritme, an open-source Python package that jointly optimizes microbiome-specific feature representation and model selection - combined algorithm selection and hyperparameter optimization - tailored to these data. ritme systematically searches taxonomic aggregation, sparsity-aware selection, compositional transforms, and metadata enrichment together with model class and hyperparameters, using state-of-the-art optimizers that scale from a laptop to a compute cluster. Across three real-world use cases, ritme outperformed the original study pipelines by 7-29% on the primary task metric and surpassed three AutoML baselines in six of seven comparisons, while selecting substantially fewer features and exposing how feature and model choices drive performance. Open-source and modular, ritme supports reproducible, parsimonious predictive modeling, downstream biological investigation, and extension to other multi-omics modalities.

bioinformatics↗

bioRxiv2025

biscot: an Optimal Transport framework for multimodal bacterial single-cell data analysis

Computational optimal transport-based approaches have emerged as promising tools for the integration and interpretation of complex single-cell data. In this study, we introduce an integrative Optimal Transport (OT) framework for spatiotemporal and multi-omics bacterial single-cell analysis using Gaussian Mixture Model (GMM) OT, termed biscot (bacterial integrative single-cell optimal transport). We show that biscot, equipped with a novel global-to-local GMM initialization, outperforms classical OT and entropically-regularized OT methods both in terms of speed and accuracy for disentangling complex bacterial communities mixtures from single-cell flow cytometry data. When applied to time-series flow cytometry data from Bacillus subtilis, our framework delivers robust and biologically meaningful results, effectively capturing subtle phenotypic shifts in spore populations transitioning from inactive to active growth states. biscot also allows multi-omics integration of flow cytometry and unpaired bacterial single-cell RNA sequencing (scRNA-seq) data, enabling the alignment of individual gene expression profiles to the cytometric data. For an unpaired flow cytometry/scRNA-seq dataset of Bacillus subtilis cells, we validate the biological plausibility of inferred gene expression patterns with relevant marker genes, including spoVID and nin, closely aligning with observed cellular states. Overall, our framework thus provides not only dynamic tracking of phenotypic cell states but aligns cell states with detailed transcriptomic information from scRNA-seq, demonstrating its potential to advance microbial single-cell research. biscot will be made publicly available on GitHub.

bioinformatics↗

bioRxiv2024

asteRIa enables robust interaction modeling between chromatin modifications and epigenetic readers

Chromatin, the nucleoprotein complex consisting of DNA and histone proteins, plays a crucial role in regulating gene expression by controlling access to DNA. Chromatin modifications are key players in this regulation, as they help to orchestrate DNA transcription, replication, and repair. These modifications recruit epigenetic "reader" proteins, which mediate downstream events. Most modifications occur in distinctive combinations within a nucleosome, suggesting that epigenetic information can be encoded in combinatorial chromatin modifications. A detailed understanding of how multiple modifications cooperate in recruiting such proteins has, however, remained largely elusive. Here, we integrate nucleosome affinity purification data with high-throughput quantitative proteomics and hierarchical interaction modeling to estimate combinatorial effects of chromatin modifications on protein recruitment. This is facilitated by the computational workflow asteRIa which combines hierarchical interaction modeling, stability-based model selection, and replicate-consistency checks for a stable estimation of Robust Interactions among chromatin modifications. asteRIa identifies several epigenetic reader candidates responding to specific interactions between chromatin modifications. For the polycomb protein CBX8, we independently validate our results using genome-wide ChIP-Seq and bisulphite datasets. We provide the first quantitative framework for identifying cooperative effects of chromatin modifications on protein binding.

molecular biology↗

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↗

bioRxiv2020

scCODA: A Bayesian model for compositional single-cell data analysis

Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA (https://github.com/theislab/scCODA), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance and identified experimentally verified cell type changes that were missed in original analyses.

bioinformatics↗