Effects of Different Preparation Methods on Microbiota Composition of Fecal Suspension.

Fecal microbiota transplantation is an emerging disease-modifying therapy. The viability of the microbiome in feces and its successful transfer depends on the preparation of fecal microbiota suspension. However, currently, no standard operation procedure is proposed for fecal suspension preparation. This study aims to compare the effect of different preparation methods on the composition of fecal microbiota composition in the rat. Four methods were used to collect the fecal suspension from fresh rat fecal (Group A), including stirring with normal saline (Group B), stirring with normal saline and then standing (Group C), stirring with normal saline and filtered with gauze (Group D), and stirring with normal saline and centrifuged (Group E). 16S ribosomal RNA gene (16S rDNA) sequencing technology was used to analyze the microbiota diversity and composition of each group of samples. Compared with fresh feces, the bacterial richness of the fecal suspension obtained by the four methods was significantly decreased (P < 0.05). The structural similarity with fresh fecal microbiota from high to low is groups B, D, C, and E. All four methods changed the microbiota structure to varying degrees, thus may affect the effect of FMT. In conclusion, choosing different methods to prepare fecal suspensions may help to better optimize the application of FMT.

Gene co-expression analysis identifies modules related to insufficient sleep in humans.

BACKGROUND: Insufficient sleep and circadian rhythm disruption may cause cancer, obesity, cardiovascular disease, and cognitive impairment. The underlying mechanisms need to be elucidated. METHOD: Weighted gene co-expression network analysis (WGCNA) was used to identify co-expressed modules. Connectivity Map tool was used to identify candidate drugs based on top connected genes. R ptestg package was utilized to detected module rhythmicity alteration. A hypergeometric test was used to test the enrichment of insomnia SNP signals in modules. Google Scholar was used to validate the modules and hub genes by literature. RESULTS: We identified a total of 45 co-expressed modules. These modules were stable and preserved. Eight modules were correlated with sleep restriction duration. Module rhythmicity was disrupted in sleep restriction subjects. Hub genes that involve in insufficient sleep also play important roles in sleep disorders. Insomnia GWAS signals were enriched in six modules. Finally, eight drugs associated with sleep disorders were identified. CONCLUSION: Systems biology method was used to identify sleep-related modules, hub genes, and candidate drugs. Module rhythmicity was altered in sleep insufficient subjects. Thiamphenicol, lisuride, timolol, and piretanide are novel candidates for sleep disorders.