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Ghadermazi, P.

Publications and source records attributed to Ghadermazi, P..

6 recordsLinked to original sources

ZipStrain Enables Rapid and Precise Strain-Resolved Metagenomics

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 500x 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.

bioinformatics↗

IgA Targeting in the Infant Gut Is Modulated by Diet and Increasingly Directed Towards Persistent Species

BackgroundIgA 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. ResultsHere 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. ConclusionsThis 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.

microbiology↗

ADToolbox: Incorporating Metagenomics Data for Improved Prediction of Anaerobic Digestion Dynamics

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.

bioengineering↗

Red cabbage juice-mediated gut microbiota modulation improves intestinal epithelial homeostasis and ameliorates colitis

Gut microbiota plays a crucial role in inflammatory bowel disease (IBD) and has therapeutic benefits. Thus, targeting the gut microbiota is a promising therapeutic approach for IBD treatment. We recently found that red cabbage juice (RCJ) ameliorates dextran sulfate sodium (DSS)-induced colitis in mice. However, the underlying mechanisms remain unknown. The current study investigated the modulation of gut microbiota in response to treatment with RCJ to ameliorate the DSS colitis. The initial results demonstrated that mice treated with DSS + RCJ showed increased body weight and decreased diarrhea and blood in feces compared to the DSS alone group. RCJ ameliorated colitis by regulating the intestinal barrier function by reducing the number of apoptotic cells, improving colonic protective mucin, and increasing tight junction protein in RCJ + DSS groups compared to the DSS group. Short-gun metagenomic analysis revealed significant enrichment of short-chain fatty acid (SCFAs)-producing bacteria (Butyrivibrio, Ruminococcaceae, Acetatifactor muris, Rosburia Sp. CAG:303, Dorea Sp. 5-2) increased PPAR-(C) activation, leading to repression of the nuclear factor {kappa}B (NF{kappa}B) signaling pathway, thus decreasing the production of crucial inflammatory cytokines and chemokines in the RCJ + DSS groups compared to the DSS group. Pathway abundance analysis showed an increased abundance of the SCFA pathway, reduced histidine degradation (Bacteroides sartorii, and Bacteroides caecimuris), and LCFA production in the RCJ+DSS treated group, suggesting the promotion of good colonic health. Furthermore, increased T-reg (FOXP3+) cells in the colon were due to SCFAs produced by the gut microbiota, which was corroborated by an increase in IL-10, a vital anti-inflammatory cytokine. Thus, our study provides the first evidence that RCJ ameliorates colonic inflammation by modulating the gut microbiota.

pathology↗

Microbial Interactions from a New Perspective: Reinforcement Learning Reveals New insights into microbiome evolution

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. Here we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, that 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 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.

systems biology↗

kb_DRAM: Annotating and functional profiling of genomes with DRAM in KBase

SummaryAnnotation is predicting the location of and assigning function to genes in a genome. DRAM is a tool developed to annotate bacterial, archaeal and viral genomes derived from pure cultures or metagenomes. DRAM distills multiple gene annotations to summaries of functional potential. Despite these benefits, a downside of DRAM is processing requires large computation resources, which limits its accessibility, and it did not integrate with downstream metabolic modelling tools. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated in the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point and click interface, as well as generate genome scale metabolic models from these DRAM annotations. Availability and ImplementationThe kb_DRAM software is available at https://github.com/shafferm/kb_DRAM. The kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. A narrative with examples of running all KBase apps is available at https://kbase.us/n/88325/84/. ContactMichael Shaffer, michael.t.shaffer@colostate.edu; Kelly Wrighton, kelly.wrighton@colostate.edu Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗