Biology subjects

Zengler, K.

Publications and source records attributed to Zengler, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Targeting catecholate siderophore systems enables precision depletion of gut enterobacteria during colitis

Adherent-invasive Escherichia coli (AIEC) is frequently isolated from the dysbiotic gut microbiome in inflammatory bowel disease and is implicated in disease progression, highlighting its potential as a therapeutic target. Here we show that iron acquisition is essential for AIEC colonization of the inflamed gut. Mechanistically, four catecholate siderophore receptors act redundantly to support AIEC growth under iron-limited conditions and during colitis. Immunization of colitis-prone Il10-/- mice against catecholate siderophores reduced intestinal AIEC colonization, particularly its association with the mucosa, while largely preserving overall gut microbiome composition. Longitudinal metagenomic profiling identified immunization-associated shifts in Akkermansiaceae and Lactobacillaceae, and experiments in gnotobiotic Il10-/- mice demonstrated that Limosilactobacillus reuteri, a member of the Lactobacillaceae, limited AIEC colonization, whereas Akkermansiaceae exacerbated disease. Our findings establish catecholate siderophore-mediated iron acquisition as a therapeutic target in AIEC and provide proof-of-principle that virulence-targeted immunization can selectively antagonize a disease-associated bacterium while preserving the gut microbiota

microbiology↗

bioRxiv2026

Rational engineering of facultative anaerobiosis enables commensal survival in the oxygenated gut

Life originated in the absence of oxygen. Despite its substantial energetic advantages, many modern microbes remain obligate anaerobes, confined to anoxic niches such as the mammalian gut. Why these organisms cannot tolerate oxygen has remained unresolved for more than two centuries. Here, using integrated multi-omics analyses, we identify a network of interlocking vulnerabilities in central metabolism, biosynthetic pathways, and redox homeostasis that together impose an aerobic growth barrier in the obligate anaerobic commensal Bacteroides thetaiotaomicron. Rational repair of these vulnerabilities restores metabolic integrity and progressively enhances oxygen tolerance, yielding engineered strains capable of robust growth at 10% O2 and markedly improved resilience in the oxygenated, inflamed gut. These findings define a molecular basis for obligate anaerobiosis and establish a framework for engineering commensal bacteria to function in oxidative environments, expanding their ecological range and therapeutic potential.

microbiology↗

bioRxiv2026

A Multi-Omics Processing Pipeline (MOPP) for Extracting Taxonomic and Functional Insights from Metaribosome Profiling (metaRibo-Seq) data

Metaribosome profiling (metaRibo-Seq) enables genome-wide measurement of translation across complex microbial communities by sequencing ribosome-protected mRNA fragments, but the short length of these footprints creates substantial nonspecific mapping against large reference genome collections, leading to spurious taxonomic and functional assignments. Here we present MOPP (Multi-Omics Processing Pipeline), a modular reference-based workflow that denoises metaRibo-Seq data by leveraging matched metagenomic coverage breadth to identify genomes likely to be truly present in a sample before aligning metatranslatomic and optional metatranscriptomic reads. MOPP generates taxon-by-gene count tables across genomic, transcriptional and translational layers, enabling integrated downstream analyses of microbial function. We evaluated MOPP using a defined 79-member synthetic human gut community profiled by metagenomics and metaRibo-Seq. Coverage breadth filtering markedly improved detection accuracy relative to a standard baseline workflow, with performance remaining robust across a broad intermediate threshold range and peaking at 92-95% coverage breadth. At a 92% threshold, MOPP reduced the number of distinct detected operational genomic units by 99.4% while retaining 87.8% of aligned metaRibo-Seq reads on average, and increased the F1 score from 0.02 to 0.61. Residual false positives were predominantly attributable to genomes with extremely high nucleotide similarity to true community members, whereas false negatives were enriched among low-abundance taxa, indicating that remaining errors are driven primarily by biological similarity and detection limits rather than widespread nonspecific mapping. Together, these results establish MOPP as a high-throughput workflow for robust processing of metaRibo-Seq in the context of matched metagenomics and position it as a scalable framework for integrated taxonomic and functional analysis of microbial communities across genomic, transcriptional and translational layers.

bioinformatics↗

bioRxiv2026

Multi-omics Analyses of Facial Skin in Acne Identify Distinct Microbial and Metabolic Features at Lesional and Non-lesional Sites

The microbial and biochemical landscape of clinically normal-appearing skin in individuals with acne remains uncharacterized. Here, we performed longitudinal multi-omics profiling of facial skin from 10 individuals with moderate acne and 10 healthy controls, integrating 16S rRNA gene sequencing, shotgun metagenomics, and untargeted metabolomics across lesional and non-lesional sites. Compositional tensor factorization revealed that non-lesional acne skin occupies a distinct intermediate state between healthy and lesional skin in both the microbiome and the metabolome. Machine learning models distinguished healthy from non-lesional acne skin with 70% accuracy, demonstrating that molecular dysbiosis occurs in areas of the skin without visible lesions. Non-lesional sites exhibited reduced microbial diversity, strain-level shifts in Corynebacterium and Lawsonella correlating with disease severity, and metabolic alterations, including elevated lipids and perturbed amino acid and dipeptide profiles. Microbe-metabolite co-occurrence network analyses revealed that healthy skin is enriched for protective metabolites such as urocanic acid, while acne-associated skin shows distinct co-occurrence patterns. These findings establish that acne represents a field effect disorder, with molecular alterations extending beyond visible lesions to encompass the entire facial skin ecosystem. This molecular signature of pre-lesional skin provides potential biomarkers for early intervention and suggests that effective acne treatment may require holistic approaches targeting the broader skin environment rather than individual lesions alone.

microbiology↗

bioRxiv2026

Synthesis-driven reverse metabolomics reveals 3-hydroxy N-acyl amides as gut microbial molecules

3-hydroxy N-acyl amides are bioactive lipids with reported anti-obesity and glucose-regulating effects, yet they are rarely detected in untargeted metabolomics studies because they are largely absent from existing spectral reference libraries. To address this gap, we synthesized an MS2 spectral resource comprising 436 structurally diverse 3-hydroxy N-acyl amides, spanning 3- to 18-carbon chains with a wide range of amine headgroups such as ornithine, valine, and dopamine. Using a synthesis-driven reverse metabolomics approach, we found 161,626 spectral matches across 54,744 publicly available files in untargeted metabolomics datasets revealing widespread occurrences in biological samples, including human-derived specimens. Of these molecules detected through MS2 spectral matching, 334 represent newly reported biological entities. We further confirmed their presence in human saliva, stool, and skin using retention time and ion mobility measurements. Frequent detection in microbial datasets and validation in communities of human-derived gut bacteria support microbial production. Several metabolites also showed altered abundance in individuals with diabetes mellitus, showing that this lipid class is modulated in human metabolic disease. Together, these findings establish 3-hydroxy N-acyl amides as a distinct and biologically relevant lipid class, and the accompanying MS2 spectral resource will enable their broader recognition and study in untargeted metabolomics data.

microbiology↗

bioRxiv2025

An Anaerobic Pathogen Rewires Host Metabolism to Fuel Oxidative Growth in the Inflamed Gut

To colonize their host and cause disease, enteric pathogens must deploy their virulence factors to establish distinct nutrient niches. How obligate anaerobic pathogens construct nutrient niches in the densely populated large intestine remains poorly understood. Enterotoxigenic Bacteroides fragilis (ETBF) is considered an obligate anaerobic bacterium and has been implicated in inflammation-associated diseases, including colitis and colorectal cancer. Here we show that ETBF uses its virulence factor, Bacteroides fragilis toxin, to reprogram colonic epithelial cell metabolism to colonize the inflamed gut. Bacteroides fragilis toxin activates colonic epithelial signaling and hijacks the host bile acid recycling pathway, inducing a metabolic shift in the epithelium from oxidative phosphorylation to glycolysis. This shift increases local concentrations of lactate and oxygen, nutrients that support an oxidative metabolism in ETBF. These findings reveal an unexpected strategy by which a pathogenic organism, previously considered to be an obligate anaerobic bacterium, generates and exploits an oxidative niche in the inflamed gut.

microbiology↗

bioRxiv2025

Evolved microbial diversity enables combinatoric biosensing in complex environments

Whole-cell biosensors (WCBs) offer rapid, cost-effective monitoring of environmental contamination and human disease. Current WCB efforts to optimize detection of single target analytes under laboratory conditions have achieved vastly improved performance, setting the stage for WCB deployment in complex environments. We propose a framework that leverages the cross-specificity of single-target WCBs to quantify multiple targets using supervised machine learning. Specifically, we engineer six sensors for heavy metal contaminants in laboratory E. coli. We then evolve the strain to generate five chassis with improved growth in seawater conditions. We transform the chassis with the sensors, creating a set of 30 variants. The variant dynamic responses are characterized with microfluidics, revealing significant diversity. Leveraging this diversity, we construct a consortium to combinatorically quantify multiple analytes with machine learning, outperforming single-target biosensors in over 90% of our test samples. These results form a generalizable framework that facilitates WCB translation toward settings beyond the laboratory.

bioengineering↗

bioRxiv2024

Proteome allocation of the microbiome reveals how diet and metabolic dysbiosis impact disease

The gut microbiome plays a critical role in human health, spurring extensive research into host-microbe interactions using multi-omic technologies. Although these tools offer valuable insights, they often fall short in capturing the complexity of microbial interactions and assigning causality to disease onset, progression, and treatment. Proteome allocation is intertwined with microbial interactions, yet there is little mechanistic knowledge. Here, we deploy models of metabolism and gene expression (ME-models) that predict proteome allocation. Despite their potential, their deployment has been limited by the laborious reconstruction process. To address this, we reconstructed 495 ME-models for human gut microbes using an automated pipeline. By integrating ME-models with multi-omics data from patients with inflammatory bowel disease (IBD), we identified taxa responsible for variations in amino acids, short-chain fatty acids, and pH in the gut of IBD patients. Thus, this approach provides a mechanistic understanding and paves the way for new intervention strategies.

systems biology↗

bioRxiv2024

The microbiome diversifies N-acyl lipid pools - including short-chain fatty acid-derived compounds

N-acyl lipids are important mediators of several biological processes including immune function and stress response. To enhance the detection of N-acyl lipids with untargeted mass spectrometry-based metabolomics, we created a reference spectral library retrieving N-acyl lipid patterns from 2,700 public datasets, identifying 851 N-acyl lipids that were detected 356,542 times. 777 are not documented in lipid structural databases, with 18% of these derived from short-chain fatty acids and found in the digestive tract and other organs. Their levels varied with diet, microbial colonization, and in people living with diabetes. We used the library to link microbial N-acyl lipids, including histamine and polyamine conjugates, to HIV status and cognitive impairment. This resource will enhance the annotation of these compounds in future studies to further the understanding of their roles in health and disease and highlight the value of large-scale untargeted metabolomics data for metabolite discovery.

bioinformatics↗

bioRxiv2024

Multi-laboratory Study Establishes Reproducible Methods for Plant-Microbiome Research in Fabricated Ecosystems

Inter-laboratory replicability is crucial yet challenging in microbiome research. Leveraging microbiomes to promote soil health and plant growth requires understanding underlying molecular mechanisms using reproducible experimental systems. In a global collaborative effort involving five laboratories, we aimed to help advance reproducibility in microbiome studies by testing our ability to replicate synthetic community assembly experiments. Our study compared fabricated ecosystems constructed using two different synthetic bacterial communities, the model grass Brachypodium distachyon, and sterile EcoFAB 2.0 devices. All participating laboratories observed consistent inoculum-dependent changes in plant phenotype, root exudate composition, and final bacterial community structure where Paraburkholderia sp. OAS925 could dramatically shift microbiome composition. Comparative genomics and exudate utilization linked the pH-dependent colonization ability of Paraburkholderia, which was further confirmed with motility assays. The study provides detailed protocols, benchmarking datasets, and best practices to help advance replicable science and inform future multi-laboratory reproducibility studies.

microbiology↗

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2024

Insights into the role of root exudates in bacteriophage infection dynamics

Bacteriophages impact soil bacteria through lysis, altering the availability of organic carbon and plant nutrients. However, the magnitude of nutrient uptake by plants from lysed bacteria remains unknown, partly because this process is challenging to investigate in the field. In this study, we extend ecosystem fabrication (EcoFAB 2.0) approaches to study plant-bacteria-phage interactions by comparing the impact of phage-lysed and uninfected 15N-labeled bacterial necromass on plant nitrogen acquisition and rhizosphere exometabolites composition. We show that grass Brachypodium distachyon derives some nitrogen from amino acids in uninfected Pseudomonas putida necromass but not from virocell necromass. Additionally, the bacterial necromass elicits the formation of rhizosphere exometabolites, some of which (guanosine), alongside tested aromatic acids (p-coumaric and benzoic acid), show distinct effects on bacteriophage-induced lysis when tested in vitro. The study highlights the dynamic feedback between bacterial necromass and plants and suggests that root exudate metabolites can impact bacteriophage infection dynamics.

plant biology↗

bioRxiv2024

Pathogenic Bacteroides fragilis strains can emerge from gut-resident commensals

Bacteroides fragilis is a prominent member of the human gut microbiota, playing crucial roles in maintaining gut homeostasis and host health. Although it primarily functions as a beneficial commensal, B. fragilis can become pathogenic. To determine the genetic basis of its duality, we conducted a comparative genomic analysis of 813 B. fragilis strains, representing both commensal and pathogenic origins. Our findings reveal that pathogenic strains emerge across diverse phylogenetic lineages, due in part to rapid gene exchange and the adaptability of the accessory genome. We identified 16 phylogenetic groups, differentiated by genes associated with capsule composition, interspecies competition, and host interactions. A microbial genome-wide association study identified 44 genes linked to extra-intestinal survival and pathogenicity. These findings reveal how genomic diversity within commensal species can lead to the emergence of pathogenic traits, broadening our understanding of microbial evolution in the gut.

microbiology↗

bioRxiv2024

A synthetic skin microbial community (SkinCom) enables reproducible investigations of the human skin microbiome

Existing models of the human skin have aided our understanding of skin health and disease. However, they currently lack a microbial component, despite microbes demonstrated connections to various skin diseases. Here we present a robust, standardized model of the skin bacterial community (SkinCom) to support complex in vitro and in vivo investigations. Our methods lead to an accurate, reproducible, and diverse community formation of aerobic and anaerobic bacteria. Subsequent testing of SkinCom on the dorsal skin of mice allowed for DNA and RNA recovery from both the applied SkinCom and from dorsal skin, highlighting its practicality for in vivo studies and -omics analyses. Furthermore, our results indicate that 65.6% of the responses to common cosmetic chemicals in vitro were recapitulated in a human trial. Therefore, SkinCom represents a valuable, standardized tool for investigating microbe-metabolite interactions and facilitates the experimental design of in vivo studies targeting host-microbe relationships.

microbiology↗

bioRxiv2024

Spatial chemistry of citrus reveals molecules bactericidal to Candidatus Liberibacter asiaticus

Huanglongbing (HLB), associated with the psyllid-vectored phloem-limited bacterium, Candidatus Liberibacter asiaticus (CLas), is a disease threat to all citrus production worldwide. Currently, there are no sustainable curative or prophylactic treatments available. In this study, we utilized mass spectrometry (MS)-based metabolomics in combination with 3D molecular mapping to visualize complex chemistries within plant tissues to explore how these chemistries change in vivo in HLB-impacted trees. We demonstrate how spatial information from molecular maps of branches and single leaves yields insight into the biology not accessible otherwise. In particular, we found evidence that flavonoid biosynthesis is disrupted in HLB-impacted trees, and an increase in the polyamine, feruloylputrescine, is highly correlated with an increase in disease severity. Based on mechanistic details revealed by these molecular maps, followed by metabolic modeling, we formulated and tested the hypothesis that CLas infection either directly or indirectly converts the precursor compound, ferulic acid, to feruloylputrescine to suppress the antimicrobial effects of ferulic acid and biosynthetically downstream flavonoids. Using in vitro bioassays, we demonstrated that ferulic acid and bioflavonoids are indeed highly bactericidal to CLas, with the activity on par with a reference antibiotic, oxytetracycline, recently approved for HLB management. We propose these compounds should be evaluated as therapeutics alternatives to the antibiotics for HLB treatment. Overall, the utilized 3D metabolic mapping approach provides a promising methodological framework to identify pathogen-specific inhibitory compounds in planta for potential prophylactic or therapeutic applications.

plant biology↗

bioRxiv2023

Guild and Niche Determination Enable Targeted Alteration of the Microbiome

Microbiome science has greatly contributed to our understanding of microbial life and its essential roles for the environment and human health1-5. However, the nature of microbial interactions and how microbial communities respond to perturbations remains poorly understood, resulting in an often descriptive and correlation-based approach to microbiome research6-8. Achieving causal and predictive microbiome science would require direct functional measurements in complex communities to better understand the metabolic role of each member and its interactions with others. In this study we present a new approach that integrates transcription and translation measurements to predict competition and substrate preferences within microbial communities, consequently enabling the selective manipulation of the microbiome. By performing metatranscriptomic (metaRNA-Seq) and metatranslatomic (metaRibo-Seq) analysis in complex samples, we classified microbes into functional groups (i.e. guilds) and demonstrated that members of the same guild are competitors. Furthermore, we predicted preferred substrates based on importer proteins, which specifically benefited selected microbes in the community (i.e. their niche) and simultaneously impaired their competitors. We demonstrated the scalability of microbial guild and niche determination to natural samples and its ability to successfully manipulate microorganisms in complex microbiomes. Thus, the approach enhances the design of pre- and probiotic interventions to selectively alter members within microbial communities, advances our understanding of microbial interactions, and paves the way for establishing causality in microbiome science.

microbiology↗

bioRxiv2022

Extensive plant use of exometabolites

Root exudation has been extensively studied due to its importance in soil carbon cycling and in supporting growth of soil microbes. However, the extent and dynamics of plant uptake of exogenous metabolites is poorly understood. To gain new insights into these processes we used 13C-tracing to characterize plant uptake of exometabolites across a panel of diverse plant species (Arabidopsis thaliana, Brachypodium distachyon, Lotus japonicus, Panicum virgatum, and Kalanchoe fedtschenkoi) grown in sterile hydroponic cultures. The uptake of exometabolites accounted for 23% of the overall B. distachyon carbon budget, and we identified 33 metabolites that were taken up by plants. Counterintuitively, many metabolites had higher uptake rates during the day vs. night. Thirteen of the metabolites from root exudates were found to promote root growth in A. thaliana, including hydroxybenzoate, threonate, N-acetyl-glucosamine, and uracil. Together these results indicate that the root uptake of organics can account for a significant portion of the plant carbon budget and that exogenous small molecules used by plants alter root growth with implications for plant nutrition, organic farming, soil nutrient cycling, and rhizosphere community dynamics.

plant biology↗

bioRxiv2022

A reproducible and tunable synthetic soil microbial community provides new insights into microbial ecology

Microbial soil communities form commensal relationships with plants to promote the growth of both parties. Optimization of plant-microbe interactions to advance sustainable agriculture is an important field in agricultural research. However, investigation in this field is hindered by a lack of model microbial community systems and efficient approaches for building these communities. Two key challenges in developing standardized model communities are maintaining community diversity over time and storing/resuscitating these communities after cryopreservation, especially considering the different growth rates of organisms. Here, a model community of 17 soil microorganisms commonly found in the rhizosphere of diverse plant species, isolated from soil surrounding a single switchgrass plant, has been developed and optimized for use with fabricated ecosystem devices (EcoFABs). EcoFABs allow reproducible research in model plant systems, with precise control of environmental conditions and easy measurement of plant-microbe metrics. The model soil community grows reproducibly in vitro between replicates and experiments, with high community -diversity achieved through growth in low-nutrient media and adjustment of starting composition ratios for the growth of individual organisms. The community additionally grows in EcoFAB devices and regrows with a similar composition to unfrozen communities following cryopreservation with glycerol, allowing for dissemination of the model community. Our results demonstrate the generation of a stable microbial community that can be used with EcoFAB devices and shared between research groups for maximum reproducibility. ImportanceMicrobes associate with plants in distinct soil communities, to the benefit of both the soil microbes and the plant. Interactions between plants and these microbes can improve plant growth and health and are therefore a field of study in sustainable agricultural research. In this study, a model community of 17 soil bacteria has been developed to further reproducible study of plant-soil microbe interactions. Preservation of the microbial community has been optimized for dissemination to other research settings. Overall, this work will advance soil microbe research through optimization of a robust, reproducible model community.

microbiology↗