Society: AGA

The molecular circadian clock helps optimize host physiology to the environment by influencing the timing and coordination of metabolic processes. Host-microbe interactions also play an important role in determining metabolic responses to environmental challenges. The gut microbiome helps break down diet-derived nutrients indigestible by the host, thereby generating metabolites that influence host metabolism and immune function. Host-microbe interactions can be influenced by dietary quality and timing, as well as daily light/dark cycles that entrain circadian rhythms in the host. Together, the gut microbiome and the circadian clock play a coordinated role in determining host metabolism and response to environmental stimuli. In this session, you will learn about the bidirectional interactions between the circadian clock, gut microbiota, and host metabolic systems and their effects on energy homeostasis, obesity and immune dysfunction.

BACKGROUND: Diurnal rhythmicity in the gut is important for the maintenance of gut integrity, peripheral circadian rhythms, and host metabolic homeostasis. However, studies exploring these rhymicities have focused on host transcription and 16S compositional data. Metatranscriptomics is a method by which we can determine the RNA transcriptional expression of microbial communities. In this study, we characterize the normal diurnal fluctuation of the mice cecal metatranscriptome and metagenome as well as the effects of diet induced obesity (DIO) and time restricted feeding (TRF) feeding. We hypothesize that the metatranscriptome will be more dynamic compared to metagenomic and 16S compositional data in addition to being more disrupted by DIO, but corrected with TRF.
METHODS: We collected cecum samples from mice in three diet and feeding regimens: normal chow diet ad libitum (control), high-fat diet (HFD) ad libitum (DIO), and HFD with TRF, every four hours for 24 hours. Metatranscriptome and metagenomic sequencing was performed on the cecal content and the transcripts and genes quantified using Woltka2. We analyzed these genes and transcripts for circadian rhythmicity and differential expressions.
RESULTS: HFD, regardless of feeding condition, completely altered the functional landscape of the metatranscriptome as well its dynamics, suggesting that diet is the main driver of microbial functional change. In the normal chow control mice, cycling functions included translation and regulation of transcription, which were lost in the HFD conditions. TRF featured cyclical fluctuation in carbohydrate metabolic processes and proteolysis that did not exist in the DIO mice. More transcripts are differentially expressed between DIO and control mice than they are between TRF and control mice, suggesting eating patterns can potentially serve a functional restorative effect. A function of particular interest that showed microbe dependent diurnal differences was bile salt hydrolase (BSH). BSH is a microbial enzyme that deconjugates bile acids, and can maintain insulin sensitivity. Our metabolomics results suggest that differential BSH activity could explain the protective effects of TRF against obesity, NAFLD, and diabetes. Though a number of microbes have diurnal changes in BSH in the normal chow control mice, Dubosiella newyorkensis exhibited change in the TRF group, suggesting that the metabolic benefits of TRF may be conveyed by the action of few bacterial species.
CONCLUSIONS: The cecum metatranscriptome pool exhibits similar HFD changes in cycling as observed in its compositional counterpart with DIO being destructive to cyclical functional rhythms and TRF partially restoring effects on luminal dynamics. Diet is the main driver of differential expression changes in the metatranscriptome.

BACKGROUND: Dysregulation of the gut microbiome is a major contributor to a variety of diseases. Yet, there is a lack of understanding of what constitutes a normal gut microbiota, in part due to inter-individual variability and a plethora of factors that impact microbiome signatures.Temporal oscillations in microbiome composition are underestimated factors that can dramatically impact microbiome study results. Importantly, microbiome rhythms are essential for entraining peripheral circadian rhythms that can ultimately impact host health. Most studies to date showing microbiome oscillations focus on model organisms, although there is some evidence that the human gut microbiome also exhibits these fluctuations.
METHODS: Here we investigate gut microbiome oscillations in the publicly available American Gut Project dataset (AGP). A total of 19335 adult human stool samples were selected for analysis upon filtering out samples with low counts and without an associated time of collection, and limiting 10 samples per participant. We employed cosinor-based rhythmometry and generalized additive mixed models to assess diurnal and seasonal oscillations, respectively.
RESULTS: Both diurnal and seasonal gut microbiome rhythms are present in the AGP dataset. We determined that diurnal signals are present at various taxonomic levels, with ~ 57% of phyla displaying 24h cyclicity. Analysis of small subsets of data impacts detection of these rhythms within the AGP. This suggests that human diurnal rhythms might not be apparent in studies with small sample sizes and less controlled confounding variables, though diurnal rhythms may still influence study results. Seasonal microbiome oscillations are widespread, with some taxa following one of two distinct patterns annually. These rhythms occur across multiple regions across the globe, suggesting that seasonality has a strong effect on stool microbiome across regions.
CONCLUSIONS: Diurnal and seasonal gut microbiome rhythms are observed at a population-level scale across the world. The levels of human gut microbes vary throughout the day and year, and can impact microbiome study interpretability. Furthermore, dataset size dramatically impacts diurnal rhythmicity assessment of sparse and unbalanced human microbiome data. Seasonal patterns are detected at a regional and global scale. Given the dynamic nature of the microbiome in relation to time, this may help explain the difficulty in defining what is a normal gut microbiome.

Shubham Singh

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