Search for: All records

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. 

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.