Temporal Gut Microbial Changes Predict Recurrent<i>Clostridium difficile</i>in Patients with and without Ulcerative Colitis

Lee, Allen; K, Rao; Limsrivilai, Julajak; Gillilland, Merritt; Malamet, Benjamin; Briggs, Emily; Vb, Young; Pd, Higgins

doi:10.1101/632778

Cited by 2 publications

(1 citation statement)

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“…For example, the Lachnospiraceae family, predominantly found in the gut microbiota of mammals and humans, is associated with IBD disease. [15,34]. At the species level, while significant groups appear more sparsely, they align well with significant family-level groups.…”

Section: Miostone Identifies Microbiome-disease Associations With Hig...mentioning

confidence: 54%

MIOSTONE: Modeling microbiome-trait associations with taxonomy-adaptive neural networks

Jiang,

Atton,

Zhu

et al. 2023

Preprint

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The human microbiome, a complex ecosystem of microorganisms inhabiting the body, plays a critical role in human health. Investigating its association with host traits is essential for understanding its impact on various diseases. Although shotgun metagenomic sequencing technologies have produced vast amounts of microbiome data, analyzing such data is highly challenging due to its sparsity, noisiness, and high dimensionality. In this paper, we address these challenges by introducing MIOSTONE, a novel machine learning method that leverages the intercorrelation of microbiome features due to their taxonomic relationships. MIOSTONE integrates prior knowledge from the “Web of Life” (WoL), a comprehensive reference phylogeny with taxonomic annotations, into a deep neural network (DNN) framework. It processes microbiome data by feeding it into a neural network organized in layers to emulate the hierarchy of the WoL taxonomy, reducing overparameterization, and offering natural explanations for microbiometrait associations. We empirically assessed MIOSTONE’s accuracy and interpretability on real datasets, demonstrating its superior performance compared to other methods. MIOSTONE offers a powerful tool for robust analysis of microbiome data, shedding light on the microbiome’s impact on human health and disease, and facilitating disease diagnosis and treatment.2

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Section: Miostone Identifies Microbiome-disease Associations With Hig...mentioning

confidence: 54%

MIOSTONE: Modeling microbiome-trait associations with taxonomy-adaptive neural networks

Jiang,

Atton,

Zhu

et al. 2023

Preprint

View full text Add to dashboard Cite

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The initial gut microbiota and response to antibiotic perturbation influenceClostridioides difficilecolonization in mice

Tomkovich

Stough

Bishop

et al. 2020

Preprint

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The gut microbiota has a key role in determining susceptibility to Clostridioides difficile infections (CDIs). However, much of the mechanistic work examining CDIs in mouse models use animals obtained from a single source. We treated mice from 6 sources (2 University of Michigan colonies and 4 commercial vendors) with clindamycin, followed by a C. difficile challenge and then measured C. difficile colonization levels throughout the infection. The microbiota were profiled via 16S rRNA gene sequencing to examine the variation across sources and alterations due to clindamycin treatment and C. difficile challenge. While all mice were colonized 1-day post-infection, variation emerged from days 3-7 post-infection with animals from some sources colonized with C. difficile for longer and at higher levels. We identified bacteria that varied in relative abundance across sources and throughout the experiment. Some bacteria were consistently impacted by clindamycin treatment in all sources of mice including Lachnospiraceae, Ruminococcaceae, and Enterobacteriaceae. To identify bacteria that were most important to colonization regardless of the source, we created logistic regression models that successfully classified mice based on whether they cleared C. difficile by 7 days post-infection using community composition data at baseline, post-clindamycin, and 1-day post-infection. With these models, we identified 4 bacteria that were predictive of whether C. difficile cleared. They varied across sources (Bacteroides), were altered by clindamycin (Porphyromonadaceae), or both (Enterobacteriaceae and Enterococcus). Allowing for microbiota variation across sources better emulates human inter-individual variation and can help identify bacterial drivers of phenotypic variation in the context of CDIs.ImportanceClostridioides difficile is a leading nosocomial infection. Although perturbation to the gut microbiota is an established risk, there is variation in who becomes asymptomatically colonized, develops an infection, or has adverse infection outcomes. Mouse models of C. difficile infection (CDI) are widely used to answer a variety of C. difficile pathogenesis questions. However, the inter-individual variation between mice from the same breeding facility is less than what is observed in humans. Therefore, we challenged mice from 6 different breeding colonies with C. difficile. We found that the starting microbial community structures and C. difficile persistence varied by the source of mice. Interestingly, a subset of the bacteria that varied across sources were associated with how long C. difficile was able to colonize. By increasing the inter-individual diversity of the starting communities, we were able to better model human diversity. This provided a more nuanced perspective of C. difficile pathogenesis.

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Temporal Gut Microbial Changes Predict RecurrentClostridium difficilein Patients with and without Ulcerative Colitis

Cited by 2 publications

References 50 publications

MIOSTONE: Modeling microbiome-trait associations with taxonomy-adaptive neural networks

MIOSTONE: Modeling microbiome-trait associations with taxonomy-adaptive neural networks

The initial gut microbiota and response to antibiotic perturbation influenceClostridioides difficilecolonization in mice

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