Motivation: Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities. Results: Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions. Availability and implementation: The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.

Gut microbiota plays a crucial role in inflammatory bowel diseases (IBD) and can potentially prevent IBD through microbial-derived metabolites, making it a promising therapeutic avenue. Recent evidence suggests that despite an unclear underlying mechanism, red cabbage juice (RCJ) alleviates Dextran Sodium Sulfate (DSS)-induced colitis in mice. Thus, the study aims to unravel the molecular mechanism by which RCJ modulates the gut microbiota to alleviate DSS-induced colitis in mice. Using C57BL/6J mice, we evaluated RCJ’s protective role in DSS-induced colitis through two cycles of 3% DSS. Mice were daily gavaged with PBS or RCJ until the endpoint, and gut microbiota composition was analyzed via shotgun metagenomics. RCJ treatment significantly improved body weight (p ≤ 0.001), survival in mice (p < 0.001) and reduced disease activity index (DAI) scores. Further, RCJ improved colonic barrier integrity by enhancing the expression of protective colonic mucins (p < 0.001) and tight junction proteins (p ≤ 0.01) in RCJ + DSS-treated mice compared to the DSS group. Shotgun metagenomic analysis revealed an enrichment of short-chain fatty acids (SCFAs)-producing bacteria (p < 0.05), leading to increased Peroxisome Proliferator-Activated Receptor Gamma (PPAR-γ) activation (p ≤ 0.001). This, in turn, resulted in repression of the nuclear factor κB (NFκB) signaling pathway, causing decreased production of inflammatory cytokines and chemokines. Our study demonstrates colitis remission in a DSS-induced mouse model, showcasing RCJ as a potential modulator for gut microbiota and metabolites, with promising implications for IBD prevention and treatment.

Strain-resolved metagenomics characterizes microbial communities at nucleotide-level resolution, enabling researchers to differentiate identical from closely related organisms and characterize population structure and gene content variation. Here we introduce ZipStrain, a program that performs highly accurate strain-resolved metagenomics over 500× faster than available methods while offering superior RAM management. Applied to a dataset of 2,754 samples spanning human populations, we identify a strain-sharing gradient across social relationships, reveal striking variation in clonal structure across bacteria and bacteriophage, and pinpoint genes whose nucleotide identity deviates from genome-wide expectations. ZipStrain is distributed as an open-source Python package and accompanying Nextflow pipeline at https://github.com/OlmLab/ZipStrain .

Handling the global food waste requires sustainable solutions. Anaerobic digestion (AD) is a common approach for waste remediation and bioenergy production which has been traditionally used mainly for methane production. Shifting the focus from methane to volatile fatty acids (VFAs), such as acetic, propionic, and butyric acids, offers promising alternatives due to their diverse applications and higher added value. Achieving desired AD product distributions requires a comprehensive understanding of factors like temperature, pH, feedstock composition, and the complex microbial dynamics inherent in AD. Various AD modeling approaches exist, from simple equations to complex flux balance analysis (FBA) and machine learning (ML) model. The Anaerobic Digestion Model No. 1 (ADM 1) is a commonly used kinetic model, striking a reasonable balance between parameter requirements and biochemical details involved in the model. Yet, it falls short in capturing specific VFAs like caproic acid and integrating microbial information directly. We present ADToolbox, a Python package for modeling AD metabolism. ADToolbox incorporates metagenomic information into an enhanced ADM model. The model accommodates a more detailed feedstock degradation model and VFA and methanogenesis metabolism. ADToolbox provides a variety of interfaces such as command line interface and an interactive web interface line interface, and a Python API, facilitating large-scale metagenomic analyses and modeling simulations. In this article we indicate that prioritizing the microbial aspect of AD enhances flexibility and predictive power in terms of VFA production accuracy, contributing to sustainable waste management strategies. Explore ADToolbox at https://chan-csu.github.io/ADToolbox for detailed documentation.

Background: IgA is the dominant antibody in the human gut and a key regulator of host-microbe interactions. Infants begin to produce IgA at around 6 months old and receive large quantities of IgA via human milk, but technical limitations have prevented species-level characterization of IgA binding in early life. This has left basic knowledge gaps about which species are targeted by IgA in infancy, and how modifiable lifestyle factors like breastfeeding and complementary feeding impact IgA targeting. Results: Here we adapt Metagenomic Immunoglobulin Sequencing (MIg-Seq) for low-biomass infant fecal samples and apply this optimized protocol to 32 longitudinal samples from 16 infants enrolled in the MINT trial, a four-arm randomized controlled trial comparing meat-based, dairy-based, plant-based, and reference complementary feeding patterns, with fecal sampling at 6 and 12 months (pre and post intervention). Infant IgA targeting mirrors adults at the phylum level, with both age groups showing significantly higher IgA targeting of Pseudomonadota and lower targeting of Bacteroidota relative to other phyla. During the substantial microbiome compositional shifts noted between 6 and 12 months, IgA targeting is significantly more stable than the microbiome itself. Among persistent colonizers, IgA targeting strengthens significantly from 6 to 12 months, with the most pronounced effect observed for Bifidobacterium , a finding robust across all dietary arms and feeding modes. The feeding arm to which infants were enrolled was not significantly associated with IgA binding, but several nutrient-specific associations were discovered. Animal-derived nutrients, particularly cholesterol, are strongly positively correlated with IgA targeting of Bifidobacterium longum , while plant-derived carotenoids are positively associated with IgA targeting of Flavonifractor plautii and Ruminococcus gnavus . Conclusions: This study introduces an experimental and computational framework for species-level IgA profiling in the infant gut. The progressive strengthening of IgA targeting of Bifidobacterium and other beneficial persistent colonizers suggests a role for IgA in reinforcing beneficial microbes during infancy. The nutrient-specific dietary effects on IgA targeting reveal the immunological consequences of the complementary feeding period, and highlight a contrast between animal-versus plant-based diets. Together, these findings point to early nutritional interventions and IgA-based therapeutics as promising tools for promoting healthy immune-microbiome development.

Arrested anaerobic digestion (AAD) offers a promising route for producing fatty acids (FAs) from organic residues, yet optimal conditions for selectively generating medium-chain fatty acids (MCFAs) remain poorly defined. Here, we systematically evaluated the main and interaction effects of pH (5, 7, 9), feedstock (food waste, manure), temperature (35 and 45 °C), and inoculum source on microbiome composition and FA production. Anaerobic digester sludge and a novel bison rumen inoculum were compared. Significant higher-order interactions among operating parameters governed FA profiles and microbiome structure. Butyric acid production was driven by a three-way interaction among pH, feedstock, and temperature (p < 0.001), with maximum concentrations achieved in food waste reactors at pH 5.0 and 35 °C (1.2 ± 0.1 g L^-1 with sludge and 1.1 ± 0.3 g L^-1 with rumen). MCFA production exhibited significant four-way interactions (p < 0.1 to p < 0.001). At 45 °C and pH 5.0, inoculum source tuned MCFA selectivity: sludge favored pentanoic acid (0.4 ± 0.1 g L^-1), whereas rumen favored hexanoic and heptanoic acids (up to 0.4 ± 0.2 g L^-1). Manure reactors produced < 0.2 g L^-1 MCFAs under all conditions. Genera, including Megasphaera, Prevotella, and Lactobacillus, were associated with production of specific MCFAs. PICRUSt2-based pathway predictions were consistent with MCFA production patterns and suggested a potential role for lactic acid-driven chain elongation pathways. This study provides insights into how interacting operating conditions shape AAD microbiomes, their FA profiles, and advances the trajectory of research aimed at engineering robust and controllable microbiomes for waste valorization.