Biology subjects

Gibbons, S.

Publications and source records attributed to Gibbons, S..

9 recordsLinked to original sources

bioRxiv2026

The microbial soy metabolite S-equol mitigates cadmium neurotoxicity in mice

Environmental exposures are major but understudied determinants of chronic disease. Cadmium is a pervasive toxic metal encountered through diet and tobacco smoke that has been associated with cognitive impairment and Alzheimer's disease mortality, yet strategies to mitigate its neurotoxicity remain limited. S-equol is a gut microbial metabolite of soy daidzein that crosses the blood brain barrier and preferentially activates estrogen receptor beta. Here, we show that cadmium disrupts an axis linking the equol producing gut bacterium Adlercreutzia equolifaciens to circulating S-equol and hippocampal estrogen receptor expression in mice, that S-equol attenuates cadmium-induced cytotoxicity and restores neuronal, glutamatergic and synaptic transcriptional programs in mouse neural progenitor cells, and that circulating S-equol in older humans is lower in apolipoprotein E4 carriers and covaries with body mass index, identifying S-equol as a microbiome-derived, host-modulated candidate countermeasure against cadmium neurotoxicity.

neuroscience↗

bioRxiv2025

Industrialization drives convergent microbial and physiological shifts in the human metaorganism

Understanding how host lifestyle and industrialization shape the human gut microbiome and intestinal physiology requires multimodal analyses across diverse global host contexts. Here, we generate multivariate data from the Global Microbiome Conservancy cohort, including gut microbiome, IgA-sequencing, host genotyping, diet, lifestyle and fecal biomarker profiles, to investigate host-microbiome interactions across gradients of industrialization and geography. We show that industrialization is associated with homogenized microbial compositions, reduced microbial diversity, and lower community stability, independent of host confounders. We further show that industrialization is linked to elevated markers of gut stress, increased IgA secretion, and altered patterns of IgA-bacteria interactions. Finally, we show that microbiome-based disease predictors trained on industrialized populations lose accuracy in less industrialized cohorts, highlighting limited cross-population transferability. Together, our results suggest profound restructuring of host-microbiome interactions due to industrialized lifestyles, and emphasize the need for inclusive, globally representative data to improve translational microbiome applications across diverse human populations.

microbiology↗

bioRxiv2025

MICOMWeb: a website for microbial community metabolic modeling of the human gut

SummaryMICOMWeb is a user-friendly website for easy microbial community metabolic modeling of the human gut. This website tackles three constraints when generating in silico metagenome-scale metabolic models: (i) the prior Python user knowledge for metabolic modeling using flux balance analysis with MICOM Python package, (ii) predefined and user-defined diets to generate ad hoc metabolic models, and (iii) the high- throughput computational infrastructure required to obtain the simulated growth and metabolic exchange fluxes, using real abundance from metagenomic shotgun or 16S amplicon sequencing; we present MICOMWebs features to easily run in silico experiments as a functional hypothesis generator for experimental validation on previously published data. Availability and ImplementationMICOMWeb is freely accessible for academic purposes at https://micomweb.c3.unam.mx.

systems biology↗

bioRxiv2024

Natural products from food sources can alter the spread of AMR plasmids in Enterobacterales

Antimicrobial resistance (AMR) poses a significant threat to global public health. Notably, resistance to carbapenem and extended-spectrum {beta}-lactam antibiotics in Gram-negative bacteria is a major impediment for the treatment of infections. Genes responsible for resistance to these antibiotics are frequently carried on plasmids, which can transfer between bacteria. Therefore, exploring strategies to prevent this transfer and/or the prevalence of AMR plasmids is timely and pertinent. Here, we show that certain natural product extracts and associated pure compounds can reduce the transmission of AMR plasmids into new bacterial hosts. Using our established high-throughput fluorescence-based screen we found that the natural products were more active in reducing transmission of the IncK plasmid pCT in Escherichia coli ST131, compared to Klebsiella pneumoniae Ecl8 carrying the IncFII plasmid pKpQIL. Furthermore, we found that the natural product rottlerin was more active in K. pneumoniae than in E. coli. Importantly, rottlerin was also associated with a reduced number of transconjugant bacteria in a clinical K. pneumoniae isolate harbouring a blaNDM-1 plasmid. Together, these results demonstrate the potential of natural products as promising anti-plasmid agents.

microbiology↗

bioRxiv2023

Metal complexes and conjugation: Harnessing the power of cobalt complexes to curtail plasmid transfer

BackgroundAntimicrobial resistance genes (ARG), such as extended spectrum {beta}-lactamase (ESBL) and carbapenemase genes, are commonly carried on plasmids. Plasmids can transmit between bacteria, disseminate globally, and cause clinically important resistance. Therefore, targeting plasmids could reduce ARG prevalence, and restore the efficacy of existing antibiotics. Here, we assessed the effect of four previously characterised bis(N-picolinamido)cobalt(II) complexes on the conjugative transfer of plasmids in Escherichia coli and Klebsiella pneumoniae. MethodsLiquid broth and solid agar conjugation assays were used to measure complex activity on four plasmids in E. coli. Additionally, the effect of cobalt complexes was tested on the transmission of the fluorescently tagged extended spectrum {beta}-lactamase encoding pCTgfp plasmid in E. coli and carbapenemase encoding pKpQILgfp plasmid in K. pneumoniae, using flow cytometry. ResultsAntimicrobial susceptibility testing of cobalt complexes revealed no antibacterial activity. The cobalt complexes significantly reduced conjugative transfer of RP4, R6K, and R388 plasmids on solid agar in E. coli and pKpQILgfp transmission in K. pneumoniae. None affected conjugative transfer of pKM101 or transmission of fluorescently tagged pCT in E. coli. The cobalt complexes had no effect on plasmid persistence, suggesting that they target conjugation rather than plasmid prevalence. ConclusionsTo the best of our knowledge, this is the first study to report reduced transmission of clinically relevant plasmids with cobalt complexes. These cobalt complexes are not cytotoxic towards mammalian cells and are not antibacterial, therefore they could be optimised and employed as conjugation inhibitors to reduce prevalence of AMR and/or virulence genes in animals and humans. SignificanceAntimicrobial resistance is a growing problem that poses a significant threat to modern medicine. Some of the most problematic resistance genes are carried on genetic elements, called plasmids, that can spread between bacteria. While our understanding of the mechanisms and drivers of gene transfer amongst bacteria is increasing, we lack effective tools to slow down/control these processes. Here we demonstrate for the first time that novel cobalt-based compounds have anti-plasmid activity on a subset of E. coli plasmids, and are extremely potent in K. pneumoniae carrying a clinical carbapenem-resistance plasmid, without impacting plasmid maintenance. This finding forms the foundations of a potential strategy to control the transfer of genes within Gram-negative bacteria, which has implications for AMR and virulence.

microbiology↗

bioRxiv2019

Blood metabolome signature predicts gut microbiome α-diversity in health and disease

Defining a healthy gut microbiome has been a challenge in the absence of detailed information on both host health and microbiome composition. Here, we analyzed a multi-omics dataset from hundreds of individuals (discovery n=399, validation n=540) enrolled in a consumer scientific wellness program to identify robust associations between host physiology and gut microbiome structure. We attempted to predict gut microbiome -diversity from nearly 1000 analytes measured from blood, including clinical laboratory tests, proteomics and metabolomics. While a broad panel of 77 standard clinical laboratory tests and a set of 263 proteins from blood could not accurately predict gut microbial -diversity, we found that 45% of the variance in microbial community diversity was explained by a subset of 40 blood metabolites, many of microbial origin. This relationship between the host metabolome and gut microbiome -diversity was very robust, persisting across disease conditions and antibiotics use. Several of these novel metabolic biomarkers of gut microbial diversity were previously associated with host health (e.g. cardiovascular disease risk, diabetes, and kidney function). A subset of 11 metabolites classified participants with potentially problematic low -diversity (ROC AUC=0.88, Precision-Recall AUC=0.76). Relationships between host metabolites and -diversity remained consistent across most of the Body Mass Index (BMI) spectrum, but were modified in extreme obesity (class II/III, but not class I), suggesting a significant metabolic shift. Out-of-sample prediction accuracy of -diversity from the 40 identified blood metabolites in a validation cohort, whose microbiome samples were analyzed by a different vendor, confirmed the robust correspondence between gut microbiome structure and host physiology. Collectively, our results reveal a strong coupling between the human blood metabolome and gut microbial diversity, with implications for human health.

bioinformatics↗

bioRxiv2018

Orthogonal dietary niche enables reversible engraftment of a gut bacterial commensal

Interest in manipulating the gut microbiota to treat disease has led to a need for understanding how organisms can establish themselves when introduced into a host with an intact microbial community. While probiotic or prebiotic approaches typically lead to a transient pulse in an organisms abundance, persistent establishment of an introduced species may require alternative strategies. Here, we introduce the concept of orthogonal niche engineering in the gut, where we include a resource typically absent from the diet, seaweed, to establish a customized niche for an introduced organism. We show that in the short term, co-introduction of this resource at 1% in the diet along with an organism with exclusive access to this resource, B. plebeius DSM 17135, enables it to colonize at a median abundance of 1%, frequently increasing in abundance to 10 or more percent. We construct a mathematical model of the system to infer that B. plebeius competitively acquires endogenous resources. We provide evidence that it competes with native commensals to achieve its observed abundance. We observe a diet-dependent loss in seaweed responsiveness of B. plebeius in the long term and show the potential for IgA-mediated control of putative invaders by the immune system. These results point to the potential for diet-based intervention as a means to introduce target organisms, but also indicate potential modes for failure of this strategy in the long term.

microbiology↗

bioRxiv2017

Predictability and persistence of prebiotic dietary supplementation in a healthy human cohort

Dietary interventions to manipulate the human gut microbiome for improved health have received increasing attention. However, their design has been limited by a lack of understanding of the quantitative impact of diet on a hosts microbiota. We present a highly controlled diet perturbation experiment in a healthy, human cohort in which individual micronutrients are spiked in against a standardized background. We identify strong and predictable responses of specific microbes across participants consuming prebiotic spike-ins, at the level of both strains and functional genes, suggesting fine-scale resource partitioning in the human gut. No predictable responses to non-prebiotic micronutrients were found. Surprisingly, we did not observe decreases in day-to-day variability of the microbiota compared to a complex, varying diet, and instead found evidence of diet-induced stress and an associated loss of biodiversity. Our data offer insights into the effect of a low complexity diet on the gut microbiome, and suggest that effective personalized dietary interventions will rely on functional, strain-level characterization of a patients microbiota.

microbiology↗

bioRxiv2017

Correcting for batch effects in case-control microbiome studies

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.\n\nAuthor SummaryBatch effects are obstacles to comparing results across studies. Traditional meta-analysis techniques for combining p-values from independent studies, like Fishers method, are effective but statistically conservative. If batch-effects can be corrected, then statistical tests can be performed on data pooled across studies, increasing sensitivity to detect differences between treatment groups. Here, we show how a simple, model-free approach corrects for batch effects in case-control microbiome datasets.

bioinformatics↗