The ISME JournalMicrobiologyPeer-reviewed journal article
Interplay between particle size and microbial ecology in the gut microbiome
Jeffrey LetourneauiD, Verónica M Carrion, Jun Zeng, Sharon Jiang, Olivia OsborneiD, Zachary C. HolmesiD, Aiden Fox, Piper Epstein, Chin Yee TaniD, Michelle C. KirtleyiD, Neeraj K. SuranaiD, Lawrence A. DavidiD
Abstract
Physical particles can serve as critical abiotic factors that structure the ecology of microbial communities. For non-human vertebrate gut microbiomes, fecal particle size (FPS) has been known to be shaped by chewing efficiency and diet. However, little is known about what drives FPS in the human gut. Here, we analyzed FPS by laser diffraction across a total of 76 individuals and found FPS to be strongly individualized. Surprisingly, a behavioral intervention with 41 volunteers designed to increase chewing efficiency did not impact FPS. Dietary patterns could also not be associated with FPS. Instead, we found evidence that mammalian and human gut microbiomes shaped FPS. Fecal samples from germ-free and antibiotic-treated mice exhibited increased FPS relative to colonized mice. In humans, markers of longer transit time were correlated with smaller FPS. Gut microbiota diversity and composition were also associated with FPS. Finally, ex vivo culture experiments using human fecal microbiota from distinct donors showed that differences in microbiota community composition can drive variation in particle size. Together, our results support an ecological model in which the human gut microbiome plays a key role in reducing the size of food particles during digestion, and that the microbiomes of individuals vary in this capacity. These new insights also suggest FPS in humans to be governed by processes beyond those found in other mammals and emphasize the importance of gut microbiota in shaping their own abiotic environment.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: experimental assay
- Citations
- 11
- FWCI
- 1.27
- Field percentile
- top 21%
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