Biology subjects

Bucci, V.

Publications and source records attributed to Bucci, V..

11 recordsLinked to original sources

bioRxiv2026

Analysis of gut bacterial communities leveraging AI methods unveils novel relations between species in Alzheimers disease

The gut microbiome has been increasingly implicated in Alzheimers disease (AD), with studies reporting numerous species- and genus-level differences. These findings established a growing catalog of AD-associated taxa, yet they typically evaluate taxa individually or in small sets rather than across microbial communities. Gut microorganisms act collectively through cross-feeding, competition, and metabolic exchange. Hence, jointly analyzing co-occurring species can reveal community structure and biological insights that a taxon-by-taxon analysis may miss. In 274 stool metagenomes from 119 older adults (18 with AD), we used our Alzheimers disease Analysis Model (ADAM) framework to run Latent Dirichlet Allocation (LDA) 1,000 times with other bioinformatics tools, decomposing species abundances into communities and aligning them into 22 reproducible ones. Of the 50 species that define these communities, 15 showed an AD-associated shift in relative abundance (10 depleted, 5 enriched; Cohens d from -0.91 to +0.67, each 95% CI excluding zero), spanning the depletion of Phocaeicola vulgatus (d - 0.91, 95% CI [-1.23, -0.59]) and the enrichment of Bacteroides fragilis (d +0.67, 95% CI [0.35, 0.99]), the two ends of an AD-associated balance. Separately, P. vulgatus competitively excludes its congener Phocaeicola dorei. In AlzBiom, an independent amyloid-defined cohort, the exclusion reproduced (within the Bacteroidaceae, r = -0.43 vs -0.57 in GAINS) and held in both control and AD participants, a conserved, disease-independent property. The P. vulgatus/B. fragilis balance also reproduced but more modestly (d = -0.24, permutation p = 0.038), whereas the substitution toward P. dorei did not. IMPORTANCEThe gut microbiome, the collection of bacteria living in the human gut, is organized into interacting communities whose members rise and fall together. Reading them jointly is more faithful but yields complex, high-dimensional patterns that standard analysis cannot resolve. Making sense of them means weighing how species move together, what they do, and what the literature reports, a task suited to artificial intelligence. We used our previously developed Alzheimers disease Analysis Model, an artificial intelligence framework customized here for species-community analysis, to integrate this evidence and turn patterns into biological findings. These findings are relationships, not single species, and they separate conserved ecology, shared with and without the disease, such as the competition between Phocaeicola vulgatus and its relatives, from shifts specific to Alzheimers disease. This keeps a general relationship from being mistaken for a disease marker, because the signal lies in the community, not in a single microbe.

microbiology↗

bioRxiv2026

Dynamical Systems-Constrained Metabolic Modeling Enables Forecasting of Host-Microbiome Dynamics

Forecasting how microbiome-host ecosystems evolve through time simultaneously at the compositional and functional level remains a central challenge in biology. While dynamical systems models (DSMs) can infer and predict community composition from longitudinal abundance data, and constraint-based metabolic models (CBMMs) can estimate metabolic fluxes from genome-scale reconstructions, no existing framework unifies these approaches to generate mechanistically grounded, time-resolved forecasts of both microbial abundances and metabolite dynamics from ecological data alone. Here, we introduce the Dynamical Systems Constrained Metabolic Modeling (DySCoMeMo) framework, a new hybrid computational pipeline that integrates ecological DSMs with CBMMs to predict temporal dynamics of biomass and metabolites across microbial communities and hosts. DySCoMeMo leverages parameters inferred from application of DSMs to microbiome time series data to constrain metabolic modeling over time, thereby bridging ecological interaction networks with genome-scale metabolic modeling. DySCoMeMo is able to predict future community and metabolite dynamics in vitro with accuracy that is superior or on-par compared to that achieved with established methods that require actual microbial abundances and/or metabolites data for metabolite network inference or for estimating the per-microbe contribution to the extracellular metabolic pool. DySCoMeMo also generalizes to in vivo data as it is capable of accurately forecasting microbial and metabolite dynamics in response to dietary perturbations even when host metabolism is included. Finally, DySCoMeMo uniquely enables the identification of keystone species by quantifying their contributions to sustaining metabolic environments. Together, our work establishes a generalizable, mechanistically grounded framework for time-resolved forecasting of microbiome-host microbial and metabolic dynamics, bridging ecological interaction inference with genome-scale metabolism of communities.

systems biology↗

bioRxiv2025

Microbiome functional gene pathways predict cognitive performance in older adults with Alzheimers disease

Disturbances in the gut microbiome is increasing correlated with neurodegenerative disorders, including Alzheimers Disease. The microbiome may in fact influence disease pathology in AD by triggering or potentiating systemic and neuroinflammation, thereby driving disease pathology along the "microbiota-gut-brain-axis". Currently, drivers of cognitive decline and symptomatic progression in AD remain unknown and understudied. Changes in gut microbiome composition may offer clues to potential systemic physiologic and neuropathologic changes that contribute to cognitive decline. Here, we recruited a cohort of 260 older adults (age 60+) living in the community and followed them over time, tracking objective measures of cognition, clinical information, and gut microbiomes. Subjects were classified as healthy controls or as having mild cognitive impairment based on cognitive performance. Those with a diagnosis of Alzheimers Diseases with confirmed using serum biomarkers. Using metagenomic sequencing, we found that relative species abundances correlated well with cognition status (MCI or AD). Furthermore, gene pathways analyses suggest certain microbial metabolic pathways to either be correlated with cognitive decline or maintaining cognitive function. Specifically, genes involved in the urea cycle or production of methionine and cysteine predicted worse cognitive performance. Our study suggests that gut microbiome composition may predict AD cognitive performance.

microbiology↗

bioRxiv2024

Microbiota encoded fatty-acid metabolism expands tuft cells to protect tissues homeostasis during Clostridioides difficile infection in the large intestine

Metabolic byproducts of the intestinal microbiota are crucial in maintaining host immune tone and shaping inter-species ecological dynamics. Among these metabolites, succinate is a driver of tuft cell (TC) differentiation and consequent type 2 immunity-dependent protection against invading parasites in the small intestine. Succinate is also a growth enhancer of the nosocomial pathogen Clostridioides difficile in the large intestine. To date, no research has shown the role of succinate in modulating TC dynamics in the large intestine, or the relevance of this immune pathway to C. difficile pathophysiology. Here we reveal the existence of a three-way circuit between commensal microbes, C. difficile and host epithelial cells which centers around succinate. Through selective microbiota depletion experiments we demonstrate higher levels of type 2 cytokines leading to expansion of TCs in the colon. We then demonstrate the causal role of the microbiome in modulating colonic TC abundance and subsequent type 2 cytokine induction using rational supplementation experiments with fecal transplants and microbial consortia of succinate-producing bacteria. We show that administration of a succinate-deficient Bacteroides thetaiotaomicron knockout ({Delta}frd) significantly reduces the enhanced type 2 immunity in mono-colonized mice. Finally, we demonstrate that mice prophylactically administered with the consortium of succinate-producing bacteria show reduced C. difficile-induced morbidity and mortality compared to mice administered with heat-killed bacteria or the vehicle. This effect is reduced in a partial tuft cell knockout mouse, Pou2f3+/-, and nullified in the tuft cell knockout mouse, Pou2f3-/-, confirming that the observed protection occurs via the TC pathway. Succinate is an intermediary metabolite of the production of short-chain fatty acids, and its concentration often increases during dysbiosis. The first barrier to enteric pathogens alike is the intestinal epithelial barrier, and host maintenance and strengthening of barrier integrity is vital to homeostasis. Considering our data, we propose that activation of TC by the microbiota-produced succinate in the colon is a mechanism evolved by the host to counterbalance microbiome-derived cues that facilitate invasion by intestinal pathogens.

immunology↗

bioRxiv2023

Expanding the toolbox: Novel class IIb microcins show activity against gram-negative ESKAPE and plant pathogens

Interspecies interactions involving direct competition via bacteriocin production play a vital role in shaping ecological dynamics within microbial ecosystems. For instance, the ribosomally-produced siderophore bacteriocins, known as class IIb microcins, affect the colonization of host-associated pathogenic Enterobacteriaceae species. Notably, to date, only five of these antimicrobials have been identified, all derived from specific Escherichia coli and Klebsiella pneumoniae strains. We hypothesized that class IIb microcin production extends beyond these specific compounds and organisms. With a customized informatics-driven approach, screening bacterial genomes in public databases with BLAST and manual curation, we have discovered twelve previously unknown class IIb microcins in seven additional Enterobacteriaceae species, encompassing phytopathogens and environmental isolates. We introduce three novel clades of microcins (MccW, MccX, and MccZ), while also identifying eight new variants of the five known class IIb microcins. To validate their antimicrobial potential, we heterologously expressed these microcins in E. coli and demonstrated efficacy against a variety of bacterial isolates, including plant pathogens from the genera Brenneria, Gibbsiella, and Rahnella. Two newly discovered microcins exhibit activity against Gram-negative ESKAPE pathogens, i.e. Acinetobacter baumannii or Pseudomonas aeruginosa, providing the first evidence that class IIb microcins can target bacteria outside of the Enterobacteriaceae family. This study underscores that class IIb microcin genes are more prevalent in the microbial world than previously recognized and that synthetic hybrid microcins can be a viable tool to target clinically relevant drug-resistant pathogens. Our findings hold significant promise for the development of innovative engineered live biotherapeutic products tailored to combat these resilient bacteria.

microbiology↗

bioRxiv2021

Microcin MccI47 selectively inhibits enteric bacteria and reduces carbapenem-resistant Klebsiella pneumoniae colonization in vivo when administered via an engineered live biotherapeutic

BackgroundThe gastrointestinal (GI) tract is the reservoir for multidrug-resistant (MDR) pathogens, specifically carbapenem-resistant (CR) Klebsiella pneumoniae and other Enterobacteriaceae, which often lead to the spread of antimicrobial resistance genes, severe extraintestinal infections, and lethal outcomes. Selective GI decolonization has been proposed as a new strategy for preventing transmission to other body sites and minimizing spreading to susceptible individuals. ResultsHere, we purify the to-date uncharacterized class IIb microcin I47 (MccI47) and demonstrate potent inhibition of numerous Enterobacteriaceae, including multidrug-resistant clinical isolates, in vitro at concentrations resembling those of commonly prescribed antibiotics. We then genetically modify the probiotic bacterium Escherichia coli Nissle 1917 (EcN) to produce MccI47 from a stable multicopy plasmid by using MccI47 toxin production in a counterselection mechanism to engineer one of the native EcN plasmids, which renders provisions for inducible expression and plasmid selection unnecessary. We then test the clinical relevance of the MccI47-producing engineered EcN in a murine CR K. pneumoniae colonization model and demonstrate significant MccI47-dependent reduction of CR K. pneumoniae abundance after seven days of daily oral live biotherapeutic administration without disruption of the resident microbiota. ConclusionsThis study provides the first demonstration of MccI47 as a potent antimicrobial against certain Enterobacteriaceae, and its ability to significantly reduce the abundance of CR K. pneumoniae in a preclinical animal model, when delivered from an engineered live biotherapeutic product. This study serves as the foundational step towards the use of engineered live biotherapeutic products aimed at the selective removal of MDR pathogens from the GI tract

bioengineering↗

bioRxiv2021

MDITRE: scalable and interpretable machine learning for predicting host status from temporal microbiome dynamics

Longitudinal microbiome datasets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. Yet, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software package, MDITRE, which implements a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing datasets, we demonstrate that in almost all cases, MDITRE performs on par or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through use cases can readily derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes.

bioinformatics↗

bioRxiv2021

Proanthocyanidin-enriched cranberry extract induces resilient bacterial community dynamics in a gnotobiotic mouse model

Cranberry consumption has numerous health benefits, with experimental reports showing its anti-inflammatory and anti-tumor properties. Importantly, microbiome research has demonstrated that the gastrointestinal bacterial community modulates host immunity, raising the question whether the cranberry-derived effect may be related to its ability to modulate the microbiome. Only a few studies have investigated the effect of cranberry products on the microbiome to date. Especially because cranberry is rich in dietary fibers, we do not know the extent of microbiome modulation that is caused solely by polyphenols, particularly proanthocyanidins (PACs). Since previous work has only focused on the long-term effects of cranberry extracts, in this study we investigated the effect of a water-soluble, polyphenol-rich cranberry juice extract (CJE) on the short-term dynamics of human-derived bacterial community in a gnotobiotic mouse model. CJE characterization revealed a high enrichment in PACs (57% PACs), the highest ever utilized in a microbiome study. In a 37-day experiment with a 10-day CJE intervention and 14-day recovery time, we profiled the microbiota via 16 rDNA sequencing and applied diverse time-series analytics methods to identify individual bacterial responses. We show that daily administration of CJE induces distinct dynamical patterns in bacterial abundances during and after treatment before recovering resiliently to pre-treatment levels. Specifically, we observed an increase of the immunomodulatory mucin degrading Akkermansia muciniphila after treatment, suggesting intestinal mucus accumulation due to CJE. Interestingly, this expansion coincided with an increase in the abundance of butyrate-producing Clostridia, a group of microbes known to promote numerous adaptive and innate anti-inflammatory phenotypes.

microbiology↗

bioRxiv2020

Scalable learning of interpretable rules for the dynamic microbiome domain

The microbiome, which is inherently dynamic, plays essential roles in human physiology and its disruption has been implicated in numerous human diseases. Linking dynamic changes in the microbiome to the status of the human host is an important problem, which is complicated by limitations and complexities of the data. Model interpretability is key in the microbiome field, as practitioners seek to derive testable biological hypotheses from data or develop diagnostic tests that can be understood by clinicians. Interpretable structure must take into account domainspecific information key to biologists and clinicians including evolutionary relationships (phylogeny) and dynamic behavior of the microbiome. A Bayesian model was previously developed in the field, which uses Markov Chain Monte Carlo inference to learn human interpretable rules for classifying the status of the human host based on microbiome time-series data, but that approach is not scalable to increasingly large microbiome datasets being produced. We present a new fully-differentiable model that also learns human-interpretable rules for the same classification task, but in an end-to-end gradient-descent based framework. We validate the performance of our model on human microbiome data sets and demonstrate our approach has similar predictive performance to the fully Bayesian method, while running orders-of-magnitude faster and moreover learning a larger set of rules, thus providing additional biological insight into the effects of diet and environment on the microbiome.

bioinformatics↗

bioRxiv2019

Evolutionary dynamics of phage resistance in bacterial biofilms

Interactions among bacteria and their viral predators, the bacteriophages, are likely among the most common ecological phenomena on Earth. The constant threat of phage infection to bacterial hosts, and the imperative of achieving infection on the part of phages, drives an evolutionary contest in which phage-resistant bacteria emerge, often followed by phages with new routes of infection. This process has received abundant theoretical and experimental attention for decades and forms an important basis for molecular genetics and theoretical ecology and evolution. However, at present, we know very little about the nature of phage-bacteria interaction - and the evolution of phage resistance - inside the surface-bound communities that microbes usually occupy in natural environments. These communities, termed biofilms, are encased in a matrix of secreted polymers produced by their microbial residents. Biofilms are spatially constrained such that interactions become limited to neighbors or near-neighbors; diffusion of solutes and particulates is reduced; and there is pronounced heterogeneity in nutrient access and therefore physiological state. These factors can dramatically impact the way phage infections proceed even in simple, single-strain biofilms, but we still know little of their effect on phage resistance evolutionary dynamics. Here we explore this problem using a computational simulation framework customized for implementing phage infection inside multi-strain biofilms. Our simulations predict that it is far easier for phage-susceptible and phage-resistant bacteria to coexist inside biofilms relative to planktonic culture, where phages and hosts are well-mixed. We characterize the negative frequency dependent selection that underlies this coexistence, and we then test and confirm this prediction using an experimental model of biofilm growth measured with confocal microscopy at single-cell and single-phage resolution.

microbiology↗

bioRxiv2016

Theory for the spatiotemporal interaction between lytic phages and biofilm-dwelling bacteria

Many bacteria are adapted for attaching to surfaces and for building complex communities, termed biofilms. The biofilm mode of life is predominant in bacterial ecology. So, too, is exposure of bacteria to ubiquitous viral pathogens, termed bacteriophages. Although biofilmphage encounters are likely to be very common in nature, little is known about how phages might interact with biofilm-dwelling bacteria. It is also unclear how the ecological dynamics of phages and their hosts depend on the biological and physical properties of the biofilm environment. To make headway in this area, here we develop the first biofilm simulation framework that captures key mechanistic features of biofilm growth and phage infection. Using these simulations, we find that the equilibrium state of interaction between biofilms and phages is governed largely by nutrient availability to biofilms, infection likelihood per host encounter, and the ability of phages to diffuse through biofilm populations. Interactions between the biofilm matrix and phage particles are thus likely to be of fundamental importance, controlling the extent to which bacteria and phages can coexist in natural contexts. Our results open avenues to new questions of host-parasite coevolution and horizontal gene transfer in spatially structured biofilm contexts.

microbiology↗