Biology subjects

Gibson, T. E.

Publications and source records attributed to Gibson, T. E..

5 recordsLinked to original sources

bioRxiv2026

Attomolar fecal cytokine profiling reveals gut immune dynamics and disease states

The gut modulates systemic health, influencing immune, neurological, and cardiovascular processes. While fecal sequencing of microbial nucleic acids provides a non-invasive view of microbial composition, sensitive measurement of host-derived signals in stool remains limited. Here we introduce DIGEST (Digital Immunoassay for Gut-Environment Single-molecule Targets), an ultrasensitive digital immunoassay that quantifies proteins in fecal extracts to attomolar levels. In mice, longitudinal profiling during a high-fat diet perturbation revealed coordinated host cytokine responses that occurred within 24 hours, with sustained elevation after diet withdrawal, enabling non-invasive tracking of within-subject immune dynamics. Application of DIGEST to quantify a panel of host inflammatory cytokines in patients with inflammatory bowel disease distinguished active ulcerative colitis from quiescent disease and non-IBD controls (AUC=0.98). In advanced melanoma patients receiving PD-1 blockade, pretreatment fecal IL-23 concentrations discriminated responders from non-responders with an AUC of 0.87. Together, these results establish DIGEST as a generalizable platform for sensitive, non-invasive quantification of host protein activity at the gut interface, with broad applications in basic science discovery, disease surveillance, and therapy response prediction.

systems biology↗

bioRxiv2023

Strain Tracking with Uncertainty Quantification

The ability to detect and quantify microbiota over time has a plethora of clinical, basic science, and public health applications. One of the primary means of tracking microbiota is through sequencing technologies. When the microorganism of interest is well characterized or known a priori, targeted sequencing is often used. In many applications, however, untargeted bulk (shotgun) sequencing is more appropriate; for instance, the tracking of infection transmission events and nucleotide variants across multiple genomic loci, or studying the role of multiple genes in a particular phenotype. Given these applications, and the observation that pathogens (e.g. Clostridioides difficile, Escherichia coli, Salmonella enterica) and other taxa of interest can reside at low relative abundance in the gastrointestinal tract, there is a critical need for algorithms that accurately track low-abundance taxa with strain level resolution. Here we present a sequence quality- and time-aware model, ChronoStrain, that introduces uncertainty quantification to gauge low-abundance species and significantly outperforms the current state-of-the-art on both real and synthetic data. ChronoStrain leverages sequences quality scores and the samples temporal information to produce a probability distribution over abundance trajectories for each strain tracked in the model. We demonstrate Chronostrains improved performance in capturing post-antibiotic Escherichia coli strain blooms among women with recurrent urinary tract infections (UTIs) from the UTI Microbiome (UMB) Project. Other strain tracking models on the same data either show inconsistent temporal colonization or can only track consistently using very coarse groupings. In contrast, our probabilistic outputs can reveal the relationship between low-confidence strains present in the sample that cannot be reliably assigned a single reference label (either due to poor coverage or novelty) while simultaneously calling high-confidence strains that can be unambiguously assigned a label. We also analyze samples from the Early Life Microbiota Colonisation (ELMC) Study demonstrating the algorithms ability to correctly identify Enterococcus faecalis strains using paired sample isolates as validation.

bioinformatics↗

bioRxiv2021

Intrinsic instability of the dysbiotic microbiome revealed through dynamical systems inference at scale

Dynamical systems models are a powerful tool for analyzing interactions, stability, resilience, and other key properties in biomedically important microbial ecosystems, such as the gut microbiome. Challenges to modeling and inference in this setting include the large number of species present, and data sparsity/noise characteristics. Here, we introduce a Bayesian statistical method, the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), which infers compact and interpretable ecosystems-scale dynamical systems models from microbiome time-series data. We model microbial dynamics as stochastic processes driven by inferred interaction modules, or groups of microbes with similar interaction structure and responses to perturbations. Additionally, we model the noise characteristics of sequencing and qPCR measurements to provide uncertainty quantification for all outputs. To evaluate MDSINE2, and provide a benchmarking resource for the community, we generated the most densely sampled microbiome time-series to date, which consists of a cohort of mice that received fecal transplants from a human donor and were then subjected to dietary and antibiotic perturbations. Benchmarking on simulated and real data demonstrate that MDSINE2 significantly outperforms state-of-the-art methods, and moreover identifies interaction modules that shed new light on ecosystems-scale interactions in the gut microbiome. We provide MDSINE2 as an open-source Python package at: https://github.com/gerberlab/MDSINE2.

systems biology↗

bioRxiv2018

Bacteriophages dynamically modulate the gut microbiota and metabolome

The human gut microbiome is comprised of densely colonizing micro-organisms in dynamic interaction with each other and the host. While the bacterial component of the microbiome is under intense investigation, far less is known about how bacteriophages impact bacterial communities in the gut. We investigated the dynamic effects of phages on a model microbiome using gnotobiotic mice colonized by commensal bacteria that colonize the human infant gut, and found that phage predation not only directly impacts susceptible bacteria but also leads to cascading effects on other bacterial species via inter-bacterial interactions. Using metabolomic profiling, we also found that the shifts in the microbiome caused by phage predation have a direct consequence on the gut metabolome. Our work provides insight into the ecological importance of phages as modulators of bacterial colonization, and additionally suggests the potential impact of gut phages on the host with implications for the use of phages as therapeutic tools to rationally and precisely modulate the microbiome.

microbiology↗

bioRxiv2017

On the Stability Landscape of the Human Gut Microbiome: Implications for Microbiome-based Therapies

Understanding how gut microbial species determine their abundances is crucial in developing any microbiome-based therapy. Towards that end, we show that the compositions of our gut microbiota have characteristic and attractive steady states, and hence respond to perturbations in predictable ways. This is achieved by developing a new method to analyze the stability landscape of the human gut microbiome. In order to illustrate the efficacy of our method and its ecological interpretation in terms of asymptotic stability, this novel method is applied to various human cohorts, including large cross-sectional studies, long longitudinal studies with frequent sampling, and perturbation studies via fecal microbiota transplantation, antibiotic and probiotic treatments. These findings will facilitate future ecological modeling efforts in human microbiome research. Moreover, the method allows for the prediction of the compositional shift of the gut microbiome during the fecal microbiota transplantation process. This result holds promise for translational applications, such as, personalized donor selection when performing fecal microbiota transplantations.\n\nOne Sentence SummaryA new method for analyzing the stability landscape of the human gut microbiome and predicting its steady-state composition is developed.

ecology↗