Biology subjects

Kolling, G. L.

Publications and source records attributed to Kolling, G. L..

11 recordsLinked to original sources

bioRxiv2026

Environment-mediated metabolic constraints shape antimicrobial resistance evolution in Pseudomonas aeruginosa

Pseudomonas aeruginosa rapidly evolves resistance to multiple antibiotics, complicating treatment of chronic infections. While resistance mechanisms are well described, the influence of host-relevant environmental conditions on resistance evolution remains unclear. Here, we investigated how nutrient composition, pH, and oxygen availability influence resistance evolution to piperacillin, tobramycin, and ciprofloxacin. We evolved P. aeruginosa in different synthetic media mimicking different infection niches as well as variations in pH and oxygen availability. We also performed whole-genome sequencing to identify mutational adaptive strategies. Our findings reveal that environmental context profoundly affects both the rate and mechanisms of resistance evolution. Nutrient-rich conditions and aerobic metabolism favored canonical resistance mutations, while nutrient-limited or anaerobic environments promoted adaptations involving efflux regulation, metabolic remodeling, and biofilm-associated traits. pH and oxygen availability acted as selective pressures, modulating which pathways were accessible as well as the stability of resistance phenotypes. Additionally, the mutations that arose in the lab-evolved isolates were also actively maintained across a large population of clinical P. aeruginosa genomes, confirming that spatial oxygen gradients in host tissues drive parallel evolutionary pathways in vivo. These results underscore that resistance evolution is not uniform but highly contingent on the metabolic and redox environment. Understanding how infection-specific conditions shape evolutionary trajectories provides insight into the emergence of multidrug resistance and highlights potential avenues for therapeutic intervention.

microbiology↗

bioRxiv2026

Host-like Temperature Unlocks Glucose Uptake and Metabolism in Pathogenic Leptospira

Pathogenic Leptospira transitions between environmental reservoirs and mammalian hosts, expose the bacterium to distinct temperature conditions. Although Leptospira is generally considered to rely its growth primarily on long-chain fatty acids and to have limited capacity to use glucose, whether host-like temperature alters glucose-associated metabolic capacity remains unclear. Here, we used transcriptome-integrated genome-scale metabolic modeling to examine temperature-dependent metabolic states in pathogenic Leptospira. Models contextualized with transcriptomic data from cultures at 37{degrees}C predicted increased flux through glucose transport, glucose phosphorylation, and downstream glucose-associated reactions than it did at 29-30{degrees}C. Similar temperature-dependent predictions were obtained for another pathogenic Leptospira species under in vitro conditions. Consistent with these findings, analysis of leptospires cultivated intraperitoneally in dialysis membrane chambers also predicted enhanced glucose uptake and metabolism under host-temperature conditions. In vitro validation experiments showed increased bacterial uptake or accumulation of a fluorescent glucose analogue, elevated expression of candidate glucose transporter genes, and glucose-dependent enhancement of bacterial growth at 37{degrees}C. Treatment with a glucose degradation pathway inhibitor, 2-deoxy-D-glucose further supported a contribution of glucose-associated processes to proliferation at 37{degrees}C. Together, these findings indicate that pathogenic Leptospira displays condition-dependent glucose uptake and glucose-associated metabolic activity that become apparent at host-like temperature, opposed to glucose-incompetent in an environmental-like temperature. This prediction-driven framework refines the conventional view of Leptospira carbon metabolism and provides a basis for future studies of glucose-associated metabolic capacity during mammalian infection. ImportanceEnvironmental reservoirs play a central role in the transmission of many bacterial pathogens, yet the metabolic mechanisms that enable adaptation to both environmental and host-associated niches remain largely unknown. Using a prediction-driven framework that combines contextualized genome-scale metabolic modeling with multi-omics analyses and experimental validation, we discovered that pathogenic Leptospira activates glucose uptake and metabolism only under host-like conditions. This finding resolves a long-standing misconception arising from studies performed under conventional laboratory conditions and fundamentally revises our understanding of Leptospira physiology. More importantly, our study establishes an integrative systems biology strategy for uncovering condition-dependent metabolic traits that would be difficult to identify experimentally alone, with broad applicability to diverse environmentally transmitted pathogens.

microbiology↗

bioRxiv2026

Soil bacterium Massilia secretes metabolites that promote Leptospira growth

Understanding pathogen metabolism is critical for identifying key functions for drug targeting, establishing effective in vitro experimental systems, etc., particularly for metabolically unique organisms such as Leptospira. Pathogenic Leptospira are thought to infect humans from environmental sources; however, direct isolation from environmental samples remains technically challenging and is not yet well established. Here, we report that a ubiquitous environmental bacterium, Massilia sp., produces metabolites that promote the growth of Leptospira interrogans, encountered through an incidental contamination event, and identified in this study. Gas chromatography-tandem mass spectrometry (GC-MS/MS) analysis showed demonstrated that cultivating of Massilia sp. in R2A medium resulted in the accumulation of metabolites, including branched-chain amino acid (BCAA) intermediates, compared to fresh medium. By combining genome-scale metabolic modeling with experimental validation using cell-free culture supernatant supplementation assays, we demonstrate that BCAA intermediates, particularly 2-ketoisocaproic acid (4-methyl-2-oxopentanoate; 4MOP), a leucine biosynthetic intermediate produced by Massilia sp., enhance Leptospira growth. To investigate the metabolic role of 4MOP, we incorporated transcriptomic data into a genome-scale metabolic network model to generate condition-specific models. Resulted flux distributions indicated that Leptospira catabolized imported 4MOP to produce acetyl-CoA. Our results reveal a previously unrecognized metabolic interaction where metabolites produced by environmental bacteria support the growth of pathogenic Leptospira, offering mechanistic insight into its metabolic requirement. These findings have implications to understand the environmental persistence of Leptospira through its metabolic dependencies on coexisting microbes, and they also help develop better strategies for this pathogen. ImportancePathogenic Leptospira persist in environmental reservoirs, yet the mechanisms supporting their growth remain poorly defined. Here, we find that metabolites produced by common environmental bacteria, Massilia sp., can promote Leptospira growth, suggesting a previously unrecognized metabolic dependency on coexisting microbes. Importantly, this study indicates that combining genome-scale metabolic modeling with experimental validation provides a useful framework for identifying metabolic interactions that are otherwise difficult to resolve using conventional culture-based approaches. Current strategy may facilitate the systematic identification of growth-supporting metabolites and provide a basis for improving selective cultivation for uncultured or difficult to culture organisms. Determination of growth promoting metabolites advances our understanding of pathogen persistence in natural environments and offers a generalized framework to study metabolically dependent microorganisms.

microbiology↗

bioRxiv2025

A systems biology approach to evaluate potential probioticcandidates for womens vaginal health

Probiotic supplements are marketed for diverse health benefits, yet species inclusion often lacks functional rationale. We surveyed 352 U.S. probiotic products and found 36 unique microbial species, with most supplements containing only one species and no clear link between species and intended health benefit. To evaluate probiotic function, we developed CoPaPro, a collection of 1,012 genome-scale metabolic models spanning commensal, pathogenic, and probiotic bacteria. Flux balance analysis revealed that current probiotic species fail to capture the metabolic diversity of native commensals. Focusing on vaginal health, we identified commensals with metabolic profiles overlapping Gardnerella vaginalis, a key pathobiont. In vitro spent media assays using 11 vaginal isolates showed variable inhibition of G. vaginalis, primarily driven by D-lactic acid production rather than metabolic similarity. Several non-Lactobacillus species produced inhibitory levels of D-lactate. These findings highlight the need for function-based probiotic design and demonstrate a scalable framework integrating metabolic modeling with experimental validation.

systems biology↗

bioRxiv2023

Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multi-locus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection. Impact statementPseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. The treatment of these infections is complicated by the presence of a variety of virulence mechanisms and metabolic uniqueness among clinically relevant strains. This study is an attempt to understand the relationships between isolate phenotypic diversity and the functional metabolic landscape within a representative group of P. aeruginosa clinical isolates. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences in infection.

systems biology↗

bioRxiv2023

Systems-ecology designed bacterial consortium protects from severe Clostridioides difficile infection

Fecal Microbiota Transplant (FMT) is an emerging therapy that has had remarkable success in treatment and prevention of recurrent Clostridioides difficile infection (rCDI). FMT has recently been associated with adverse outcomes such as inadvertent transfer of antimicrobial resistance, necessitating development of more targeted bacteriotherapies. To address this challenge, we developed a novel systems biology pipeline to identify candidate probiotic strains that would be predicted to interrupt C. difficile pathogenesis. Utilizing metagenomic characterization of human FMT donor samples, we identified those metabolic pathways most associated with successful FMTs and reconstructed the metabolism of encoding species to simulate interactions with C. difficile. This analysis resulted in predictions of high levels of cross-feeding for amino acids in species most associated with FMT success. Guided by these in silico models, we assembled consortia of bacteria with increased amino acid cross-feeding which were then validated in vitro. We subsequently tested the consortia in a murine model of CDI, demonstrating total protection from severe CDI through decreased toxin levels, recovered gut microbiota, and increased intestinal eosinophils. These results support the novel framework that amino acid cross-feeding is likely a critical mechanism in the initial resolution of CDI by FMT. Importantly, we conclude that our predictive platform based on predicted and testable metabolic interactions between the microbiota and C. difficile led to a rationally designed biotherapeutic framework that may be extended to other enteric infections.

systems biology↗

bioRxiv2021

Identifying metabolic adaptations during cardiotoxicity using paired transcriptomics andmetabolomics data integrated with a model of heart metabolism

Improvements in the diagnosis and treatment of cancer has revealed the long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we present paired transcriptomics and metabolomics data characterizing in vitro cardiotoxicity to three compounds: 5-fluorouracil, acetaminophen, and doxorubicin. Standard gene enrichment and metabolomics approaches identify some commonly affected pathways and metabolites but are not able to readily identify metabolic adaptations in response to cardiotoxicity. The paired data was integrated with a genome-scale metabolic network reconstruction (GENRE) of the heart to identify shifted metabolic functions, unique metabolic reactions, and changes in flux in metabolic reactions in response to these compounds. Using this approach, we confirm known mechanisms of doxorubicin-induced cardiotoxicity and provide hypotheses for metabolic adaptations in cardiotoxicity for 5-fluorouracil, doxorubicin, and acetaminophen.

bioengineering↗

bioRxiv2020

Identifying functional metabolic shifts in heart failure with the integration of omics data and a cardiomyocyte-specific, genome-scale model

The heart is a metabolic omnivore, known to consume many different carbon substrates in order to maintain function. In diseased states, the hearts metabolism can shift between different carbon substrates; however, there is some disagreement in the field as to the metabolic shifts seen in end-stage heart failure and whether all heart failure converges to a common metabolic phenotype. Here, we present a new, validated cardiomyocyte-specific GEnome-scale metabolic Network REconstruction (GENRE), iCardio, and use the model to identify common shifts in metabolic functions across heart failure omics datasets. We demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying Tasks Inferred from Differential Expression (TIDEs) which represent metabolic functions associated with changes in gene expression. We identify decreased NO and Neu5Ac synthesis as common metabolic markers of heart failure across datasets. Further, we highlight the differences in metabolic functions seen across studies, further highlighting the complexity of heart failure. The methods presented for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissue and diseases of interest.

bioengineering↗

bioRxiv2019

Functional Anabolic Network Analysis of Human-associated Lactobacillus Strains

Members of the Lactobacillus genus are frequently utilized in the probiotic industry with many species conferring demonstrated health benefits; however, these effects are largely strain-dependent. We designed a method called PROTEAN (Probabilistic Reconstruction Of constituent Anabolic Networks) to computationally analyze the genomic annotations and predicted metabolic production capabilities of 144 strains across 16 species of Lactobacillus isolated from human intestinal, oral, and vaginal body sites. Using PROTEAN we conducted a genome-scale metabolic network comparison between strains, revealing that metabolic capabilities differ by isolation site. Notably, PROTEAN does not require a well-curated genome-scale metabolic network reconstruction to provide biological insights. We found that predicted metabolic capabilities of lactobacilli isolated from the vaginal microbiota cluster separately from intestinal and oral isolates, and we also uncovered an overlap in the predicted metabolic production capabilities of intestinal and oral isolates. Using machine learning, we determined the most informative metabolic products driving the difference between predicted metabolic capabilities of intestinal, oral, and vaginal isolates. Notably, intestinal and oral isolates were predicted to have a higher likelihood of producing D-alanine, D/L-serine, and L-proline, while the vaginal isolates were distinguished by a higher predicted likelihood of producing L-arginine, citrulline, and D/L-lactate. We found the distinguishing products to be consistent with published experimental literature. This study showcases a systematic technique, PROTEAN, for comparing the predicted functional metabolic output of microbes using genome-scale metabolic network analysis and computational modeling and provides unique insight into human-associated Lactobacillus biology.\n\nImportanceThe Lactobacillus genus has been shown to be important for human health. Lactobacilli have been isolated from human intestinal, oral, and vaginal sites. Members of the genus contribute significantly to the maintenance of vaginal health by providing colonization resistance to invading pathogens. A wide variety of clinical studies have indicated that Lactobacillus-based probiotics confer health benefits for several gut- and immune-associated diseases. Microbes interact with the human body in several ways, including the production of metabolites that influence physiology or other surrounding microbes. We have conducted a strain-level genome-scale metabolic network reconstruction analysis of human-associated Lactobacillus strains, revealing that predicted metabolic capabilities differ when comparing intestinal/oral isolate to vaginal isolates. The technique we present here allows for direct interpretation of discriminating features between the experimental groups.

systems biology↗

bioRxiv2018

A Novel Mouse Model of Campylobacter jejuni Enteropathy and Diarrhea

Campylobacter infections are among the leading bacterial causes of diarrhea and of environmental enteropathy (EE) and growth failure worldwide. However, the lack of an inexpensive small animal model of enteric disease with Campylobacter has been a major limitation for understanding its pathogenesis, interventions or vaccine development. We describe a robust standard mouse model that can exhibit reproducible bloody diarrhea or growth failure, depending on the zinc or protein deficient diet and on antibiotic alteration of normal microbiota prior to infection. Zinc deficiency and the use of antibiotics create a niche for Campylobacter infection to establish by narrowing the metabolic flexibility of these mice for pathogen clearance and by promoting intestinal and systemic inflammation. Several biomarkers and intestinal pathology in this model also mimic those seen in human disease. This model provides a novel tool to testing specific hypotheses regarding disease pathogenesis as well as vaccine development that is currently in progress.\n\nAuthor SummaryCampylobacter jejuni has been identified as one of the leading causes of enteropathy and diarrhea. In developing countries, these repeated enteric infections often result in growth deficits and cognitive impairment. There is a lack of small animal models of Campylobacter infection. This is a major hurdle in understanding the pathogenesis of Campylobacter infection in order to lead to therapeutic treatments and vaccines. We have developed a highly reproducible mouse model of Campylobacter infection that has clinical outcomes that match those of malnourished children. We hope that these insights into Campylobacter susceptibility will lead to the development of treatments against this major cause of diarrheal illness.

microbiology↗

bioRxiv2017

Novel Co-Culture Plate Enables Growth Dynamic-Based Assessment Of Contact-Independent Microbial Interactions

Interactions between microbes are central to the dynamics of microbial communities. Understanding these interactions is essential for the characterization of communities, yet challenging to accomplish in practice. There are limited available tools for characterizing diffusion-mediated, contact-independent microbial interactions. A practical and widely implemented technique in such characterization involves the simultaneous co-culture of distinct bacterial species and subsequent analysis of relative abundance in the total population. However, distinguishing between species can be logistically challenging. In this paper, we present a low-cost, vertical membrane, co-culture plate to quantify contact-independent interactions between distinct bacterial populations in co-culture via real-time optical density measurements. These measurements can be used to facilitate the analysis of the interaction between microbes that are physically separated by a semipermeable membrane yet able to exchange diffusible molecules. We show that diffusion across the membrane occurs at a sufficient rate to enable effective interaction between physically separate cultures. Two bacterial species commonly found in the cystic fibrotic lung, Pseudomonas aeruginosa and Burkholderia cenocepacia, were co-cultured to demonstrate how this plate may be implemented to study microbial interactions. We have demonstrated that this novel co-culture device is able to reliably generate real-time measurements of optical density data that can be used to characterize interactions between microbial species.

microbiology↗