Biology subjects

Weimann, A.

Publications and source records attributed to Weimann, A..

10 recordsLinked to original sources

bioRxiv2026

Structural basis of biofilm formation mediated by the Pseudomonas aeruginosa fibrillar adhesin CdrA

Many bacteria, including the important human pathogen Pseudomonas aeruginosa, are naturally found in antibiotic-tolerant, multicellular biofilms. Cell-cell interactions within P. aeruginosa biofilms are mediated by a large fibrillar adhesin called CdrA in an extracellular polysaccharide-dependent manner. Here, we report an electron cryomicroscopy structure of the 60 kDa CdrA adhesive N-terminus, which combined with electron cryotomography of focused-ion beam milled specimens, allows us to derive a complete in situ model of the native adhesin. Our structure reveals a small adhesive domain (called ADEPT) at the distal tip of CdrA that is nearly perfectly conserved across the P. aeruginosa pangenome, with structural similarity to previously reported sugar-binding domains in multiple bacterial species. Inhibitory nanobodies targeting CdrA that reduce biofilm formation bind to epitopes in, or close to, the ADEPT on bacterial cells. Furthermore, structure-guided mutagenesis of residues within the ADEPT abolishes bacterial aggregation, and genomic deletion of the whole ADEPT leads to strong attenuation of biofilm formation. Our data forms a rational basis for future targeted inhibition of pathogenic P. aeruginosa biofilms and elucidates the mechanism of biofilm formation mediated by fibrillar adhesins that are widespread in bacteria.

microbiology↗

bioRxiv2026

Pseudomonas aeruginosa balances cytotoxicity and motility to counter phagocytosis by macrophages

During chronic lung infections, Pseudomonas aeruginosa diversifies under selection from antibiotics, metabolic constraints, and host defenses. Macrophages are key sentinels of the innate immune system and play a central role in clearing airway pathogens. Yet, how they process heterogeneous bacterial populations remains poorly understood. Here, we investigate how P. aeruginosa evades phagocytosis under conditions that mimic chronic infection. We use an attenuated mutant lacking a functional type III secretion system (T3SS), which reduces macrophage killing, allowing us to isolate determinants of bacterial susceptibility to phagocytosis. Using transposon insertion sequencing (Tn-seq), we identify bacterial fitness factors under phagocytic selection. Our screen reveals that disruption of genes involved in swimming and twitching motility reduces uptake by macrophages. We find that motility defects interfere with the physical interactions between bacteria and macrophages. Live-cell imaging shows that motility-deficient bacteria exhibit reduced surface exploration and unstable attachment to macrophages, limiting their internalization. Clinical isolates with reduced swimming or twitching motility display similarly impaired uptake. Restoring T3SS activity in these motility mutants rescues cytotoxicity toward macrophages, with one notable exception: flagellum-less, hyper-piliated P. aeruginosa remains avirulent and resistant to phagocytosis due to their lack of engagement with macrophages. Together, these results support two distinct immune evasion strategies: during chronic infection, reduced motility promotes a "freeze"-like state that limits detection and engulfment, whereas during acute infection, P. aeruginosa adopts a "fight"-like strategy by activating its T3SS to eliminate macrophages. SummaryHow immune cells recognize and eliminate bacteria is typically explained by molecular signaling, yet the role of physical interactions remains unclear. We show that bacterial motility is a key determinant of phagocytosis by macrophages. Using functional genomics and live imaging, we find that swimming and twitching motility promote bacterial uptake by enabling effective surface exploration and stable physical engagement with macrophages. Loss of motility commonly observed in chronic P. aeruginosa infections reduces these interactions and allows bacteria to evade engulfment. In contrast, during acute infection, bacteria rely on T3SS-mediated killing of macrophages, independently of motility. These findings reveal that phagocytosis is governed not only by immune recognition but also by bacterial mechanical behavior, and identify a shift in host-pathogen interactions associated with chronic versus acute infection. More broadly, this work establishes mechanics as an important dimension of immune evasion.

microbiology↗

bioRxiv2025

A contextualised protein language model reveals the functional syntax of bacterial evolution

Bacteria have evolved a vast diversity of functions and behaviours that are currently incompletely understood and poorly predicted from DNA sequence alone. To understand the syntax of bacterial evolution and discover genome-to-phenotype relationships, we curated over 1.3 million genomes spanning bacterial phylogenetic space, represented each as an ordered sequence of proteins, and used these sequences to train a transformer-based, contextualised protein language model, Bacformer. By pretraining on genome-wide evolutionary patterns, Bacformer captures the compositional and positional relationships of proteins and thereby provides a whole-genome framework for linking genomic organisation and content to measurable bacterial traits. We demonstrate the ability of Bacformer to accurately predict protein-protein interactions; uncover operon structure, which we validated experimentally; infer important phenotypic traits, including antimicrobial resistance, while revealing likely causal genes; and design template synthetic proteomes with desirable properties. Thus, Bacformer establishes a genomic foundation model that reveals the evolutionary rules governing bacterial gene organisation, function, and phenotype, opening a route to systematic whole-genome engineering.

microbiology↗

bioRxiv2025

Defence systems drive accessory genome interactions in Pseudomonas aeruginosa.

Bacterial genomes represent dynamic ecological systems in which mutational selection and dynamic accessory genome element complements drive evolution. Emerging evidence suggests that bacterial defence systems, which protect against phages and other mobile genetic elements, interact through cooperative, competitive, and antagonistic relationships. These interactions can influence horizontal gene transfer, shape phage susceptibility, and the diversification of genome composition across environments. Recent ecological studies describe non-random co-occurrence and avoidance of defence systems, suggesting that they may emerge from ecological and evolutionary interactions, rather than by chance. This necessitates exploring whether these patterns reflect functional compatibility, shared selection pressures imposed by ecological niche, or if they merely arise from co-localisations of convenience. Understanding these patterns is key to elucidating how the accessory genome evolves and how defence systems constrain or facilitate genome plasticity. To investigate these patterns, in their genomic context, and provide a resource for future investigations, we analysed the distributions of defence systems and other accessory genome elements in a recently curated global dataset of 2,940 Pseudomonas aeruginosa genomes. Genomic defence system content varied by ecological niche, with higher numbers per genome in non-cystic fibrosis derived isolates (average n=7.9) compared to cystic fibrosis-derived isolates (average n=6.5). We identified multiple associations (n=426) and dissociations (n=50) among defence systems, and among other accessory genome elements, many of which had a plausible biological explanation. We also quantitated the relative interactions among accessory genome elements which revealed that defence systems and anti-defence systems engage in the most accessory genome interactions, compared with e.g. antimicrobial resistance genes, plasmids, phages, suggesting that systems are a major driving force in the ecological dynamics of the bacterial accessory genome. Together, these patterns provide new insights into the evolutionary forces shaping bacterial genomes, provide a valuable resource of quantitated interactions in a highly curated Pseudomonas aeruginosa dataset, and establish a framework for future mechanistic and ecological investigations of defence system interactions.

microbiology↗

bioRxiv2024

Sequence-based modelling of bacterial genomes enables accurate antibiotic resistance prediction

AbstractRapid detection of antibiotic-resistant bacteria and understanding the mecha- nisms underlying antimicrobial resistance (AMR) are major unsolved problems that pose significant threats to global public health. However, existing methods for predicting antibiotic resistance from genomic sequence data have had lim- ited success due to their inability to model epistatic effects and generalize to novel variants. Here, we present GeneBac, a deep learning method for predicting antibiotic resistance from DNA sequence through the integration of interactions between genes. We apply GeneBac to two distinct bacterial species and show that it can successfully predict the minimum inhibitory concentration (MIC) of multiple antibiotics. We use the WHO Mycobacterium tuberculosis mutation cat- alogue to demonstrate that GeneBac accurately predicts the effects of different variants, including novel variants that have not been observed during training. GeneBac is a modular framework which can be applied to a number of tasks including gene expression prediction, resistant gene identification and strain clus- tering. We leverage this modularity to transfer learn from the transcriptomic data to improve performance on the MIC prediction task.

microbiology↗

bioRxiv2022

Mutational spectra analysis reveals bacterial niche and transmission routes

As observed in cancers, individual mutagens and defects in DNA repair create distinctive mutational signatures that combine to form context-specific spectra within cells. We reasoned that similar processes must occur in bacterial lineages, potentially allowing decomposition analysis to identify disrupted DNA repair processes and niche-specific mutagen exposure. Here we reconstructed mutational spectra for 84 clades from 31 diverse bacterial species, assigned signatures to specific DNA repair pathways using hypermutator lineages, and, by comparing mutational spectra of clades from different environmental and biological locations, extracted reproducible niche-associated mutational signatures. We show that mutational spectra can predict general and specific bacterial niches and therefore reveal the site of infection and types of transmission routes for established and emergent human bacterial pathogens. One sentence summaryVariable mutagen exposure and DNA repair drive differential mutational spectra between bacteria and enable niche inference

microbiology↗

bioRxiv2022

Phagosomal RNA sensing through TLR8 controls susceptibility to tuberculosis

Genetic determinants of susceptibility to Mycobacterium tuberculosis (Mtb) are poorly understood but could provide insights into critical pathways involved in infection, informing host-directed therapies and enabling risk stratification at individual and population levels. Through a genome-wide forward genetic screen, we identify the Toll-like Receptor 8 (TLR8), as a key regulator of intracellular killing of Mtb. Pharmacological TLR8 activation enhances killing of phylogenetically diverse clinical isolates of drug-susceptible and multidrug-resistant Mtb by macrophages and during in vivo infection in mice. TLR8 is activated by phagosomal mycobacterial RNA released by extracellular membrane vesicles, and enhances xenophagy-dependent Mtb killing. We find that the TLR8 variant, M1V, common in far eastern populations, enhances intracellular killing of Mtb through preferential signal-dependent trafficking to phagosomes. TLR8 signalling may therefore both regulate susceptibility to tuberculosis and provide novel drug targets. Single sentence summaryRNA released from Mycobacterium tuberculosis in the macrophage phagosome is sensed by the pattern recognition receptor TLR8 controlling host susceptibility to tuberculosis and revealing a druggable pathway for host-directed therapy.

immunology↗

bioRxiv2021

The pathobiology of Mycobacterium abscessus revealed through phenogenomic analysis

The medical and scientific response to emerging pathogens is often severely hampered by ignorance of the genetic determinants of virulence, drug resistance, and clinical outcomes that could be used to identify therapeutic drug targets and forecast patient trajectories 1-5. Taking the newly emergent multidrug-resistant bacteria Mycobacterium abscessus as an example 6, we show that combining high dimensional phenotyping with whole genome sequencing in a phenogenomic analysis can rapidly reveal actionable systems-level insights into bacterial pathobiology. Using in vitro and in vivo phenotyping, we discovered three distinct clusters of isolates, each associated with a different clinical outcome. We combined genome-wide association studies (GWAS) with proteome-wide computational structural modelling 7 to define likely causal variants, and employed direct coupling analysis (DCA) 8 to identify co-evolving, and therefore potentially epistatic, gene networks. We then used in vivo CRISPR-based silencing to validate our findings, defining a novel secretion system controlling virulence in M. abscessus, and illustrating how phenogenomics can reveal critical pathways within emerging pathogenic bacteria.

microbiology↗

bioRxiv2020

Producing Polished Prokaryotic Pangenomes with the Panaroo Pipeline

Population-level comparisons of prokaryotic genomes must take into account the substantial differences in gene content, resulting from frequent horizontal gene transfer, gene duplication and gene loss. However, the automated annotation of prokaryotic genomes is imperfect, and errors due to fragmented assemblies, contamination, diverse gene families and mis-assemblies accumulate over the population, leading to profound consequences when analysing the set of all genes found in a species. Here we introduce Panaroo, a graph based pangenome clustering tool that is able to account for many of the sources of error introduced during the annotation of prokaryotic genome assemblies. We verified our approach through extensive simulations of de novo assemblies using the infinitely many genes model and by analysing a number of publicly available large bacterial genome datasets. Using a highly clonal Mycobacterium tuberculosis dataset as a negative control case, we show that failing to account for annotation errors can lead to pangenome estimates that are dominated by error. We additionally demonstrate the utility of the improved graphical output provided by Panaroo by performing a pan-genome wide association study in Neisseria gonorrhoeae and by analysing gene gain and loss rates across 51 of the major global pneumococcal sequence clusters. Panaroo is freely available under an open source MIT licence at https://github.com/gtonkinhill/panaroo.

genomics↗

bioRxiv2019

Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

The growing importance of antibiotic resistance on clinical outcomes and cost of care underscores the need for optimization of current diagnostics. For a number of bacterial species antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and gene expression profiles, we generated predictive models and identified biomarkers of susceptibility or resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (>0.9) sensitivity and predictive values, where the relative contribution of the different categories of biomarkers strongly depended on the antibiotic. For all drugs except for ciprofloxacin, gene expression information substantially improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine clinical microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

microbiology↗