Biology subjects

von Mering, C.

Publications and source records attributed to von Mering, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Autoinducer-2 functions as both a quorum sensing and metabolic signal in Escherichia coli

Bacteria integrate diverse environmental signals to coordinate behavior, yet the relationship between nutrient sensing and quorum sensing (QS) remains incompletely understood. Autoinducer-2 (AI-2) is unique among QS signals in that its production is tightly linked to central metabolism, raising fundamental questions about the boundary between metabolic and signaling functions. In Escherichia coli, AI-2 coordinates collective behaviors through the lsr operon, whose expression is controlled not only by the AI-2-responsive repressor LsrR but also by the cAMP receptor protein (CRP), placing it at the intersection of carbon sensing and population-level signaling. While inhibition of lsr operon expression by PTS sugars such as glucose was previously established, we demonstrate that non-PTS sugars similarly suppress lsr expression through CRP, further decoupling QS activation from cell density and coupling it to carbon source availability. Systematic analysis of Enterobacteriaceae genomes reveals that CRP binding sites in the lsr promoter region are broadly conserved, indicating that metabolic modulation of AI-2 signaling is an ancestral regulatory feature. Importantly, using a FRET-based biosensor, we show that AI-2 uptake modulates intracellular cAMP levels in a manner resembling non-PTS carbon source transport, suggesting that AI-2 may have originally functioned as a nutrient substrate, with its signaling role emerging subsequently or co-evolving alongside. In support of this hypothesis, we isolated soil- and phyllosphere-associated bacteria capable of utilizing AI-2 as a sole carbon source. Our findings reveal an underappreciated metabolic dimension of AI-2 QS and suggest an evolutionary trajectory in which AI-2 signaling emerged from ancestral carbon utilization pathways. ImportanceQS allows bacteria to coordinate collective behaviors by detecting secreted signaling molecules, yet the evolutionary origins of these systems remain poorly understood. AI-2, one of the most broadly conserved bacterial signals, is derived from central metabolism and processed by machinery in E. coli that strikingly resembles a sugar utilization system. Here, we show that nutrient availability overrides cell density as the primary determinant of AI-2 responsiveness, that this regulatory logic is conserved among Enterobacteriaceae genomes, and that environmental bacteria can grow on AI-2 as a sole carbon source. These findings reframe AI-2 as a signal embedded within, and potentially evolved from, nutrient sensing pathways, with direct implications for understanding how byproducts of cellular metabolism can acquire signaling functions.

microbiology↗

bioRxiv2026

Susagi: A Microbiome World Model

MotivationAccurately modelling how microbial communities assemble and change across hosts and environments is essential for analysis and intervention. Typical pipelines capture limited generalisable structure and often depend on fixed ecological unit definitions. ResultsWe present Susagi (Set Unsupervised Assessment of Genetic Imposters), a permutation-invariant denoising transformer that operates directly on sets of bacterial SSU rRNA gene embeddings to learn a member-level stability function. The model was trained on [~] 2 x 106 bacterial community samples. We show that it reliably predicts community composition dynamics in a zero-shot (no training) setting, demonstrated here across three challenging microbiomes for which traditional ML methods do not exceed random expectation. The models stability scores capture biological structure: across datasets, higher scores are enriched for agricultural, cropland, and soil-associated habitats, consistent with microbial communities in these environments supporting positive diversity-stability relationships. The highest stability scores are only attained by communities with high Pielou evenness and large size, despite the fact that the model has never seen abundances suggesting it can recognise community dysbiosis from presence absence alone. Furthermore, they also track biological gradients such as subject age. Susagi is competitive with another Large Microbiome Model (Microbial General Model, MGM) on diverse classification tasks, without task-specific fine-tuning and with an increased parameter efficiency. Ultimately, our model will facilitate hypothesis generation for complex microbial processes, including deterministic assembly and microbial interactions, crucial for instance in the design of communities in silico. Availability and implementationEvaluation code and model weights can be found from https://github.com/the-puzzler/Microbiome-Modelling. Model weights can also be downloaded directly from https://huggingface.co/basilboy/microbiome-model. Interactive demo can be found here: https://huggingface.co/spaces/basilboy/microbiome-space.

systems biology↗

bioRxiv2026

WISH-barcoding of Salmonella Typhimurium ATCC14028s strains for population dynamics studies in vivo

BackgroundBarcoding of isogenic strains is a powerful approach to assess pathogen population dynamics during infection. Here, we adapted WISH-barcoding to Salmonella Typhimurium ATCC14028s to evaluate its suitability for pooled infection experiments in streptomycin-pretreated mouse models. ResultsWISH-barcoded wild-type pools showed pronounced population instability, characterized by stochastic strain loss and segregation into high- and low-fitness subpopulations. Whole-genome sequencing identified recurrent mutations in the methyltransferase rsmG and loss of the P3 plasmid carrying streptomycin resistance in low-fitness strains; neither was observed in {Delta}invG or {Delta}ssaV pools. We propose that rsmG mutations were enriched during strain construction carried out under streptomycin selection, following loss of the P3 plasmid. Control experiments demonstrated that rsmG mutations and P3 loss are counter-selected in vivo and attenuate gut-luminal colonization in streptomycin-pretreated mice. ConclusionWhile population dynamics experiments with ATCC14028s are feasible in principle, wild-type strains are prone to acquiring fitness-altering mutations during in vitro construction when using the P3 plasmid and streptomycin, highlighting the need for careful pool validation prior to use.

microbiology↗

bioRxiv2026

D-gluconate drives Salmonella growth during acute and chronic infection

Monosaccharides support Salmonella enterica serovar Typhimurium colonization of the gut, yet the role of their oxidized derivatives remains understudied. Sugar acids are largely diet-independent carbon sources generated by host-driven oxidative processes, but their contribution during infection - particularly that of less oxidized aldonic and uronic acids - has not been defined. Here, we systematically assess the role of sugar acids derived from D-glucose and D-galactose in S. Typhimurium SL1344 colonization. Among D-glucose-derived acids, D-gluconate accumulated to the highest levels and was the dominant substrate supporting luminal expansion in streptomycin-pretreated mice, exceeding the more oxidized acids D-glucuronate and D-glucarate. During chronic infection, D-glucose-derived sugar acids became increasingly important for pathogen persistence. Ecological niche invasion assays identified these compounds as a principal metabolic niche, whereas D-galactose-derived acids contributed minimally. Consistent with a transient, inflammation-linked nutrient niche, sugar acid utilization pathways were similarly prevalent in Escherichia coli from individuals with and without inflammatory bowel disease. Together, these findings identify D-gluconate as a key inflammation-dependent nutrient source that fuels Enterobacteriaceae expansion in the inflamed gut.

microbiology↗

bioRxiv2025

Uncovering Cas9 PAM diversity through metagenomic mining and machine learning

Recognition of protospacer adjacent motifs (PAMs) is crucial for target site recognition by CRISPR-Cas systems. In genome editing applications, the requirement for specific PAM sequences at the target locus imposes substantial constraints, driving efforts to search for novel Cas9 orthologs with extended or alternative PAM compatibilities. Here, we present CRISPR-PAMdb, a comprehensive and publicly accessible database compiling Cas9 protein sequences from 3.8 million bacterial and archaeal genomes and PAM profiles from 7.4 million phage and plasmid sequences. Through spacer-protospacer alignment, we inferred consensus PAM preferences for 8,003 unique Cas9 clusters. To extend PAM discovery beyond traditional alignment-based approaches, we developed CICERO, a machine learning model predicting PAM preferences directly from Cas9 protein sequences. Built on the ESM2 protein language model and trained on the CRISPR-PAMdb database, CICERO achieved an average accuracy of 0.68 on test data and 0.75 on experimentally validated Cas9 orthologs. For Cas9 clusters where alignment-based predictions were infeasible, CICERO generated PAM profiles for an additional 50,308 Cas9 proteins, including 17,453 high-confidence predictions with accuracies above 0.86. CRISPR-PAMdb, alongside CICERO models, enables large-scale exploration of PAM diversity across Cas9 proteins, accelerating design of next-generation CRISPR-Cas9 tools for precise genome engineering.

synthetic biology↗

bioRxiv2025

The MicrobeAtlas database: Global trends and insights into Earth's microbial ecosystems

Environmental DNA sequencing has revolutionized our understanding of microbial diversity and ecology. Microbiomes have now been sequenced across the entire planet--from the deep subsurface to the mountain tops--covering a myriad of hosts, biomes, and conditions. Yet, the diversity of sequencing and processing strategies hampers universal insights. MicrobeAtlas unifies more than two million microbiome samples in a single resource, harmonized to facilitate discoveries across technologies. Communities are hierarchically quantified at adjustable SSU rRNA marker gene resolution and feature detailed metadata, including rich geographic information. Connections to genome, phenotype, and ecological resources enable multimodal insights. Microbial lineages can be reliably tracked across environments, including a long tail of rare, uncharacterized species. Recurring community structures and geographic preferences become apparent, and global, taxonomy-specific generalism trends emerge. With MicrobeAtlas (www.microbeatlas.org), both known and newly described species and communities can readily be placed into ecological context, taking full advantage of earlier work.

bioinformatics↗

bioRxiv2025

Microbiome variations in osteoarthritis reflect aging and metabolic factors, not the disease

The gut microbiome is crucial for human health. Its disruption has been linked to several chronic diseases, including joint disorders. The gut-joint axis has been implicated in the pathogenesis of osteoarthritis (OA), but conflicting findings and study limitations have led to uncertainty regarding the role of microbiota. We conducted a multi-cohort gut microbiota analysis in 1,395 OA patients from four European cohorts (Lifelines, EstMB, FINRISK 2002, TwinsUK), using stringent exclusion criteria and matched controls. When assessing microbial diversity, taxa, functional gene profiles, and gut permeability biomarkers, no significant differences were found between OA and controls. Although this does not exclude a causal contribution of the microbiota in the gut-joint-axis, its dysbiosis does not seem to be linked with OA disease progression. Instead, age and BMI appeared as the main contributing factors to microbiome changes. Microbiome studies in complex diseases often face challenges such as small sample sizes, batch effects, and limited ability to match appropriate controls, particularly in single-cohort designs. By combining data from multiple large cohorts, we were able to mitigate these limitations and provide a more robust assessment of microbiome association with OA. Our findings emphasize the need for rigorous study design in microbiome research and challenge the OA-gut dysbiosis hypothesis.

microbiology↗

bioRxiv2025

Enhanced semantic classification of microbiome sample origins using Large Language Models (LLMs)

Over the past decade, central sequence repositories have expanded significantly in size. This vast accumulation of data holds value and enables further studies, provided that the data entries are well annotated. However, the submitter-provided metadata of sequencing records can be of heterogeneous quality, presenting significant challenges for re-use. Here, we test to what extent large language models (LLMs) can be used to cost-effectively automate the re-annotation of sequencing records against a simplified classification scheme of broad ecological environments with relevance to microbiome studies, without retraining. We focused on sequencing samples taken from the environment, for which metadata is important. We employed OpenAI Generative Pretrained Transformer (GPT) models, and assessed scalability, time and cost-effectiveness, as well as performance against a diverse, hand-curated ground-truth benchmark with 1000 examples, that span a wide range of complexity in metadata interpretation. We observed that annotation performance markedly outperforms that of a baseline, manually curated, non-machine-learning keyword-based approach. Changing models (or model parameters) has only minor effects on performance, but prompts need to be carefully designed to match the task. We applied the optimized pipeline to more than 3.8 million sequencing records from the environment, providing coarse-grained yet standardized sampling site annotations covering the globe. Our work demonstrates the effective use of LLMs to simplify and standardize annotation from complex biological metadata.

bioinformatics↗

bioRxiv2025

Unravelling the Core and Accessory Genome Diversity of Enterobacteriaceae in Carbon Metabolism

Enterobacteriaceae are commonly colonizing animal guts and impact health. While strain-specific metabolic features can promote gut colonization, we lack systematic knowledge regarding metabolic diversity and the core metabolism shared among Enterobacteriaceae. To address this gap, we have analyzed the pan-genome of nearly 20,000 genomes. We found that genes necessary for monosaccharide-fuelled mixed acid fermentation are part of the Enterobacteriaceae core genome, while most genes for anaerobic respiration and most carbohydrate utilization genes belong to the accessory genome. Understanding Enterobacteriaceaes metabolic capacity helps clarify the distinction of nutrients consumed by all Enterobacteriaceae, and niche-defining nutrient sources, which are genus-, species- or strain-specific. This knowledge sheds light on bacterial nutrient exploitation during gut colonization in health and disease, aiding in the development of targeted interventions for microbiome research and infectious disease control. The theoretical framework described here can also be adapted to analyze core physiological characteristics of other microbiota taxa.

microbiology↗

bioRxiv2024

Habitat filtering but not microbiota origin controls microbiome transplant outcomes in soil

Human activities cause global losses of soil microbiome diversity and functionality. Microbiota transplants offer a potential solution, but the factors influencing transplant success remain unclear. We investigated how microbiota origin affects microbiome mergers, hypothesizing that native strains through niche preference are better adapted to their habitat and will outcompete non-native ones. To test this, we contrasted transplants between soil microcosm-cultured topsoil or lakewater communities with a community of 21 soil bacteria (SynCom). In both cases, SynCom transplant increased resident productivity but permanently shifted compositions, although its abundance dropped from an initial 50-80% to <1% within two months. Both merged and non-merged communities resembled natural soil microbiota in comparisons with over 81,000 soil, sediment and lake compositional data. Our results show that habitat filtering and niche competition, more than microbiota origin, determine transplant outcomes. Despite the limited proliferation of SynCom transplants, their capacity to instill lasting community trajectory changes opens new paths for microbiome engineering. TEASEREven transiently present microbiota transplants can alter resident microbiome composition through processes governed by habitat filtering.

microbiology↗

bioRxiv2024

Community conservatism is widespread throughout microbial phyla and environments

Evolution gives rise to various long-term phenomena, including Phylogenetic signal, which describes the tendency of related biological taxa to resemble each other in morphology and function. Related taxa tend to also live in similar ecological niches - this trend is termed Niche conservatism. Both concepts are widely used to understand crucial aspects of evolu1on and specia1on and are well-established in animals and plants. The extension of these concepts to microorganisms is however challenging and thus far only assumed. Here, we hypothesize that two closely related microbial species, if indeed similar in both their morphology and their ecological niche, should be found in samples with similar community compositions. We propose "community conservatism" to refer to this, and leverage a database with millions of samples and hundreds of thousands of pairs of microbial taxa to assess their relatedness and the similarity of the communities they occupy. Our findings reveal that community conservatism can be observed globally in all environments and phyla tested, to varying extents. Ecologically specialized taxa show a stronger community conservatism signal than generalists, and the signal can be discerned over nearly all taxonomic ranks. Analyzing community conservatism shows promise to advance our understanding of evolution, speciation, and the mechanisms governing community assembly in microorganisms. Furthermore, we propose it can be used to reintegrate ecological parameters into Operational Taxonomic Unit delimitation.

ecology↗

bioRxiv2024

BGC Atlas: A Web Resource for Exploring the Global Chemical Diversity Encoded in Bacterial Genomes

Secondary metabolites are compounds not essential for an organisms development, but provide significant ecological and physiological benefits. These compounds have applications in medicine, biotechnology, and agriculture. Their production is encoded in biosynthetic gene clusters (BGCs), groups of genes collectively directing their biosynthesis. The advent of metagenomics has allowed researchers to study BGCs directly from environmental samples, identifying numerous previously unknown BGCs encoding unprecedented chemistry. Here, we present the BGC Atlas (https://bgc-atlas.cs.uni-tuebingen.de), a web resource that facilitates the exploration and analysis of BGC diversity in metagenomes. The BGC Atlas identifies and clusters BGCs from publicly available datasets, offering a centralized database and a web interface for metadata-aware exploration of BGCs and gene cluster families (GCFs). We analyzed over 35,000 datasets from MGnify, identifying nearly 1.8 million BGCs, which were clustered into GCFs. The analysis showed that ribosomally synthesized and post-translationally modified peptides (RiPPs) are the most abundant compound class, with most GCFs exhibiting high environmental specificity. We believe that our tool will enable researchers to easily explore and analyze the BGC diversity in environmental samples, significantly enhancing our understanding of bacterial secondary metabolites, and promote the identification of ecological and evolutionary factors shaping the biosynthetic potential of microbial communities.

bioinformatics↗

bioRxiv2024

Monosaccharides Drive Salmonella Gut Colonization in a Context-Dependent Manner

The carbohydrates that fuel gut colonization by S. Typhimurium are not fully known. To investigate this, we designed a quality-controlled mutant pool to probe the metabolic capabilities of this enteric pathogen. Using WISH-barcoding, we tested 35 metabolic mutants across five different mouse models, allowing us to differentiate between context-dependent and context-independent nutrient sources. Results showed that S. Typhimurium uses D-glucose, D-mannose, D-fructose, and D-galactose as context-independent carbohydrates across all models. The utilization of N-acetylglucosamine and hexuronates, on the other hand, was context-dependent. Furthermore, we showed that D-fructose is important in strain-to-strain competition between Salmonella serovars. Complementary experiments confirmed that D-glucose, D-fructose, and D-galactose are excellent niches for S. Typhimurium to exploit during colonization. Quantitative measurements revealed sufficient amounts of D-glucose and D-galactose in the murine cecum to drive S. Typhimurium colonization. Understanding these key substrates and their context-dependent use by enteric pathogens will inform the future design of probiotics and therapeutics to prevent diarrheal infections such as non-typhoidal salmonellosis.

microbiology↗

bioRxiv2024

Heterogeneous and Novel Transcript Expression in Single Cells of Patient-Derived ccRCC Organoids

Splicing is often dysregulated in cancer, leading to alterations in the expression of canonical and alternative splice isoforms. This complex phenomenon can be revealed by an in-depth understanding of cellular heterogeneity at the single-cell level. Recent advances in single-cell long- read sequencing technologies enable comprehensive transcriptome sequencing at the single-cell level. In this study, we have generated single-cell long-read sequencing of Patient-Derived Organoid (PDO) cells of clear-cell Renal Cell Carcinoma (ccRCC), an aggressive and lethal form of cancer that arises in kidney tubules. We have used the Multiplexed Arrays Sequencing (MAS-ISO-Seq) protocol of PacBio to sequence full-length transcripts exceptionally deep across 2,599 single cells to obtain the most comprehensive view of the alternative landscape of ccRCC to date. On average, we uncovered 86,182 transcripts across PDOs, of which 31,531 (36.6%) were previously uncharacterized. In contrast to known transcripts, many of these novel isoforms appear to exhibit cell-specific expression. Nonetheless, >50% of these novel transcripts were predicted to possess a complete protein-coding open reading frame. This finding suggests a biological role for these transcripts within kidney cells. Moreover, an analysis of the most dominant transcript switching events between ccRCC and non-ccRCC cells revealed that many switching events were cell and sample-specific, underscoring the heterogeneity of alternative splicing events in ccRCC. Overall, our research elucidates the intricate transcriptomic architecture of ccRCC, potentially exposing the mechanisms underlying its aggressive phenotype and resistance to conventional cancer therapies.

cancer biology↗

bioRxiv2023

Clade-wise alignment integration improves co-evolutionary signals for protein-protein interaction prediction

BackgroundProtein-protein interactions play essential roles in almost all biological processes. The binding interfaces between interacting proteins impose evolutionary constraints, leading to co-evolutionary signals that have successfully been employed to predict protein interactions from multiple sequence alignments (MSAs). During the construction of MSAs for this purpose, critical choices have to be made: how to ensure the reliable identification of orthologs, how to deal with paralogs, and how to optimally balance the need for large alignments versus sufficient alignment quality. ResultsHere, we propose a divide-and-conquer strategy for MSA generation: instead of building a single, large alignment for each protein, multiple distinct alignments are constructed, each covering only a single clade in the tree of life. Co-evolutionary signals are searched separately within these clades, and are only subsequently integrated into a final interaction prediction using machine learning. We find that this strategy markedly improves overall prediction performance, concomitant with better alignment quality. Using the popular DCA algorithm to systematically search pairs of such alignments, a genome-wide all-against-all interaction scan in a bacterial genome is demonstrated. ConclusionsGiven the recent successes of AlphaFold in predicting protein-protein interactions at atomic detail, a discover-and-refine approach is proposed: our method could provide a fast and accurate strategy for pre-screening the entire genome, submitting to AlphaFold only promising interaction candidates - thus reducing false positives as well as computation time.

bioinformatics↗

bioRxiv2023

Discovery of Theta Ribozymes in Gut Phages-Implications for tRNA and Alternative Genetic Coding

Ribozymes, relics of the "RNA world", are essential across all domains of life. Nonetheless, the functions and genomic contexts of recently discovered small ribozymes, such as minimal hepatitis delta virus (HDV)-like ribozymes, remain elusive. Using bioinformatic analyses, we identified a novel subfamily of minimal HDV-like ribozymes, coined theta ribozymes. Hundreds of unique examples were found adjacent to viral tRNAs within Caudoviricetes bacteriophages of the mammalian gut virome. In vitro experiments confirm site-specific self-scission activity, suggesting their involvement in processing tRNA 3-trailers. Intriguingly, a significant fraction of theta ribozymes is associated with viral suppressor tRNAs, potentially regulating the late-stage assembly of recoded bacteriophages. These findings advance the understanding of RNA-based mechanisms underlying the intricate interplay between the bacterial and viral parts of the mammalian gut microbiome. One-Sentence SummaryNewly unveiled theta ribozymes associate with suppressor tRNAs of alternatively coded gut phages: a potential lytic switch.

bioinformatics↗

bioRxiv2023

PaxDB 5.0: curated protein quantification data suggests adaptive proteome changes

The "Protein Abundances Across Organisms" database (PaxDB) is an integrative meta-resource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDB focuses on computing best-estimate abundances for proteins in normal/healthy contexts, and expresses abundance values for each protein in "parts per million" (ppm) in relation to all other protein molecules in the cell. The uniform data re-processing, quality scoring, and integrated orthology relations have made PaxDB one of the preferred tools for comparisons between individual datasets, tissues or organisms. In describing the latest version 5.0 of PaxDB, we particularly emphasise the data integration from various types of raw data, and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDB includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria and 67 Eukaryota. Apart from detailing the data update, we also show a comparative analysis of the human proteome subset of PaxDB against the two most widely-used human proteome data resources: Human Protein Atlas (HPA) and Genotype-Tissue Expression (GTEx). Lastly, we present a use case of PaxDB, showing how protein abundance data can be used to study the evolution of relative amino acid usage in Fungi.

bioinformatics↗

bioRxiv2021

Reproducible Propagation of Species-Rich Soil Microbiomes Suggests Robust Underlying Deterministic Principles of Community Formation

Microbiomes are typically characterised by high species diversity but it is poorly understood how such system-level complexity can be generated and propagated. Here, we used soils as a relevant model to study microbiome development. Despite inherent stochastic variation in manipulating species-rich communities, both laboratory-mixed medium complexity (21 soil bacterial isolates in equal proportions) and high-diversity natural top-soil communities followed highly reproducible succession paths, maintaining distinct soil microbiome signatures. Development trajectories and compositional states were different for communities propagated in soils than in liquid suspension. Microbiome states were maintained over multiple renewed growth cycles but could be diverged by short-term pollutant exposure. The different but robust trajectories demonstrated that deterministic taxa-inherent characteristics underlie reproducible development and self-organized complexity of soil microbiomes within their environmental boundary conditions. Our findings also have direct implications for potential strategies to achieve controlled restoration of desertified land. TEASERSpecies-rich soil microbiomes grow and propagate reproducibly despite inherent stochastic complexity, paving the way for soil restoration.

microbiology↗