Host-associated microbiomes play important roles in host health and pathogen defense. In amphibians, the skin-associated microbiota can contribute to innate immunity with potential implications for disease management. Few studies have examined season-long temporal variation in the amphibian skin-associated microbiome, and the interactions between bacteria and fungi on amphibian skin remain poorly understood. We characterize season-long temporal variation in the skin-associated microbiome of the western tiger salamander ( Ambystoma mavortium ) for both bacteria and fungi between sites and across salamander life stages. Two hundred seven skin-associated microbiome samples were collected from salamanders at two Rocky Mountain lakes throughout the summer and fall of 2018, and 127 additional microbiome samples were collected from lake water and lake substrate. We used 16S rRNA and ITS amplicon sequencing with Bayesian Dirichlet-multinomial regression to estimate the relative abundances of bacterial and fungal taxa, test for differential abundance, examine microbial selection, and derive alpha diversity. We predicted the ability of bacterial communities to inhibit the amphibian chytrid fungus Batrachochytrium dendrobatidis ( Bd ), a cutaneous fungal pathogen, using stochastic character mapping and a database of Bd -inhibitory bacterial isolates. For both bacteria and fungi, we observed variation in community composition through time, between sites, and with salamander age and life stage. We further found that temporal trends in community composition were specific to each combination of salamander age, life stage, and lake. We found salamander skin to be selective for microbes, with many taxa disproportionately represented relative to the environment. Salamander skin appeared to select for predicted Bd -inhibitory bacteria, and we found a negative relationship between the relative abundances of predicted Bd -inhibitory bacteria and Bd . We hope these findings will assist in the conservation of amphibian species threatened by chytridiomycosis and other emerging diseases.
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Multisample estimation of bacterial composition matrices in metagenomics data
Summary Metagenomics sequencing is routinely applied to quantify bacterial abundances in microbiome studies, where bacterial composition is estimated based on the sequencing read counts. Due to limited sequencing depth and DNA dropouts, many rare bacterial taxa might not be captured in the final sequencing reads, which results in many zero counts. Naive composition estimation using count normalization leads to many zero proportions, which tend to result in inaccurate estimates of bacterial abundance and diversity. This paper takes a multisample approach to estimation of bacterial abundances in order to borrow information across samples and across species. Empirical results from real datasets suggest that the composition matrix over multiple samples is approximately low rank, which motivates a regularized maximum likelihood estimation with a nuclear norm penalty. An efficient optimization algorithm using the generalized accelerated proximal gradient and Euclidean projection onto simplex space is developed. Theoretical upper bounds and the minimax lower bounds of the estimation errors, measured by the Kullback–Leibler divergence and the Frobenius norm, are established. Simulation studies demonstrate that the proposed estimator outperforms the naive estimators. The method is applied to an analysis of a human gut microbiome dataset.
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- Award ID(s):
- 1811868
- NSF-PAR ID:
- 10166376
- Date Published:
- Journal Name:
- Biometrika
- Volume:
- 107
- Issue:
- 1
- ISSN:
- 0006-3444
- Page Range / eLocation ID:
- 75 to 92
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Objectives Establishment and development of the infant gastrointestinal microbiome (GIM) varies cross‐culturally and is thought to be influenced by factors such as gestational age, birth mode, diet, and antibiotic exposure. However, there is little data as to how the composition of infants' households may play a role, particularly from a cross‐cultural perspective. Here, we examined relationships between infant fecal microbiome (IFM) diversity/composition and infants' household size, number of siblings, and number of other household members.
Materials and methods We analyzed 377 fecal samples from healthy, breastfeeding infants across 11 sites in eight different countries (Ethiopia, The Gambia, Ghana, Kenya, Peru, Spain, Sweden, and the United States). Fecal microbial community structure was determined by amplifying, sequencing, and classifying (to the genus level) the V1–V3 region of the bacterial 16S rRNA gene. Surveys administered to infants' mothers identified household members and composition.
Results Our results indicated that household composition (represented by the number of cohabitating siblings and other household members) did not have a measurable impact on the bacterial diversity, evenness, or richness of the IFM. However, we observed that variation in household composition categories did correspond to differential relative abundances of specific taxa, namely:
Lactobacillus ,Clostridium ,Enterobacter , andKlebsiella .Discussion This study, to our knowledge, is the largest cross‐cultural study to date examining the association between household composition and the IFM. Our results indicate that the social environment of infants (represented here by the proxy of household composition) may influence the bacterial composition of the infant GIM, although the mechanism is unknown. A higher number and diversity of cohabitants and potential caregivers may facilitate social transmission of beneficial bacteria to the infant gastrointestinal tract, by way of shared environment or through direct physical and social contact between the maternal–infant dyad and other household members. These findings contribute to the discussion concerning ways by which infants are influenced by their social environments and add further dimensionality to the ongoing exploration of social transmission of gut microbiota and the “old friends” hypothesis.
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Summary In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. We introduce a surprisingly simple, interpretable and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional log-error-in-variable regression model. The proposed method provides corrections on sequencing data with possible overdispersion and simultaneously avoids any subjective imputation of zero read counts. We provide theoretical justifications with matching upper and lower bounds for the estimation error. The merit of the procedure is illustrated through real data analysis and simulation studies.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/biomet/asz062
