Cang, S. | bioRxiv Science

bioRxiv2024

Loss of Diurnal Oscillatory Rhythms in Gut Microbiota Correlates with Progression of Atherosclerosis

Circadian rhythms in gut microbiota composition are crucial for metabolic function and disease progression, yet the diurnal oscillation patterns of gut microbiota in atherosclerotic cardiovascular disease (ASCVD) and their role in disease progression remain unknown. Here, we investigate gut bacterial dynamics in ApoE-/- mice within a day, and elucidated the dynamic changes in fecal microbiota composition and function differences among C57BL/6 and ApoE-/- mice with standard chow diet or high-fat, high-cholesterol diet under ad libitum conditions. Compared with C57BL/6 mice, ApoE-/- mice exhibit significant differences in fecal microbial composition. Rhythmic analysis showed that the dynamic changes in the composition and function of fecal microbiota in ApoE-/- mice were significantly different from those in C57BL/6 mice. We further found that the rhythmic strains (Blautia Coccoides) inhibit the progression of ASCVD by improving the intestinal and endothelial barrier function. Our findings demonstrate that diurnal oscillations in gut microbiota are closely related to the progression of ASCVD, and provide a new insight for microbial-targeted therapies for ASCVD.

microbiology↗

bioRxiv2019

Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data

Clear cell renal cell carcinoma represents the most common type of kidney cancer. Precision medicine approach to ccRCC requires an accurate stratification of patients that can predict prognosis and guide therapeutic decision. Transcription factors are implicated in the initiation and progression of human carcinogenesis. However, no comprehensive analysis of transcription factor activity has been proposed so far to realize patient stratification. Here we propose a novel approach to determine the subtypes of ccRCC patients based on global transcription factor activity landscape. Using the TCGA cohort dataset, we identified different subtypes that have distinct upregulated biomarkers and altered biological pathways. More important, this subtype information can be used to predict the overall survival of ccRCC patients. Our results suggest that transcription factor activity can be harnessed to perform patient stratification.

systems biology↗