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  1. Abstract Background

    Despite the long-established importance of zebrafish (Danio rerio) as a model organism and their increasing use in microbiome-targeted studies, relatively little is known about how husbandry practices involving diet impact the zebrafish gut microbiome. Given the microbiome’s important role in mediating host physiology and the potential for diet to drive variation in microbiome composition, we sought to clarify how three different dietary formulations that are commonly used in zebrafish facilities impact the gut microbiome. We compared the composition of gut microbiomes in approximately 60 AB line adult (129- and 214-day-old) zebrafish fed each diet throughout their lifespan.

    Results

    Our analysis finds that diet has a substantial impact on the composition of the gut microbiome in adult fish, and that diet also impacts the developmental variation in the gut microbiome. We further evaluated how 214-day-old fish microbiome compositions respond to exposure of a common laboratory pathogen,Mycobacterium chelonae, and whether these responses differ as a function of diet. Our analysis finds that diet determines the manner in which the zebrafish gut microbiome responds toM. chelonaeexposure, especially for moderate and low abundance taxa. Moreover, histopathological analysis finds that male fish fed different diets are differentially infected byM. chelonae.

    Conclusions

    Overall, our results indicate that diet drives the successional development of the gut microbiome as well as its sensitivity to exogenous exposure. Consequently, investigators should carefully consider the role of diet in their microbiome zebrafish investigations, especially when integrating results across studies that vary by diet.

     
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  2. Drinking water is one of numerous sources of human exposure to microscale and nanoscale plastic particles. Here, using a mouse model, we tested enteric and hepatic cellular responses to nanoplastic ingestion. At 1.5 or 25.5 h after an oral dose of 70 mg polystyrene nanospheres (PSNS)/kg (nominal diameters of 20 and 200 nm) in aqueous suspension female mice exhibit no overt signs of toxicity. Routine histopathology on small intestine and liver reveals no acute toxicity. Immunohistochemistry detects an increase in the number of enterocytes with cleaved caspase-3 (active form) after PSNS exposure ( p ≤ 0.05) indicating progression toward lytic cell death via a proinflammatory pathway. This is not evident in liver after PSNS exposure. Transmission electron microscopy detects lytic cell death in enterocytes at 25.5 h after 200 nm PSNS exposure. Putative endosomes in liver appear to sequester 20 and 200 nm particles 25.5 h after exposure. Both 20 and 200 nm PSNS appear in putative perinuclear autolysosomes 25.5 h after treatment. No significant changes in gene expression in the small intestine or liver 25.5 h were observed after dosing, but there was a trend toward altered expression of cyp1b1 in the liver. Analysis of the fecal microbiome shows loss of diversity after exposure to both 20 and 200 nm particles after 25.5 h. Taken together, these results suggest risk from ingestion of nanoscale plastic particles from drinking water, which deserves systematic investigation. 
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  3. Kinkel, Linda (Ed.)
    ABSTRACT A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts. 
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