Diet and feeding pattern affect the diurnal dynamics of the gut microbiome

Abstract

The gut microbiome and daily feeding/fasting cycle influence host metabolism and contribute to obesity and metabolic diseases. However, fundamental characteristics of this relationship between the feeding/fasting cycle and the gut microbiome are unknown. Our studies show that the gut microbiome is highly dynamic, exhibiting daily cyclical fluctuations in composition. Diet-induced obesity dampens the daily feeding/fasting rhythm and diminishes many of these cyclical fluctuations. Time-restricted feeding (TRF), in which feeding is consolidated to the nocturnal phase, partially restores these cyclical fluctuations. Furthermore, TRF, which protects against obesity and metabolic diseases, affects bacteria shown to influence host metabolism. Cyclical changes in the gut microbiome from feeding/fasting rhythms contribute to the diversity of gut microflora and likely represent a mechanism by which the gut microbiome affects host metabolism. Thus, feeding pattern and time of harvest, in addition to diet, are important parameters when assessing the microbiome's contribution to host metabolism.

Figure 1. Study Design and Metabolic Studies of Mice in Each Condition

(A) Study design. NA mice had ad libitum access to normal chow. FA mice had ad libitum access to HFD. FT mice had 8-hour access (ZT 13–21) to HFD. Results from our previous study were replicated (Hatori et al., 2012). (B) Line plot showing the average weekly weight (g ± SEM) of mice in different conditions (n = 24 per condition). FA mice gained weight, whereas FT mice, despite being on a HFD were indistinguishable from NA controls. *p < 0.05 (compared to NA). (C) Intraperitoneal glucose tolerance tests (mean ± SEM) show that TRF was protective against diabetes (n = 6 per condition). *p < 0.05 (compared to NA). (D) On gross inspection, FA livers had steatosis, whereas FT livers did not. (E) Serum quantification of cholesterol (n = 6 per group). Measurements (mean ± SEM) were performed twice. *p < 0.05 (compared to NA and FT).

Figure 2. Diurnal Rhythms of Gut Microbiome Phyla in Mice from Different Feeding Conditions

(A) Pie chart showing the percentage of cycling and non-cycling OTUs (across all conditions) in NA mice (n = 18). (B) Upper double-plot line graph – where the second cycle is a duplicate of the first cycle following the dashed line – shows the average percent read (± SEM) of the three most predominant phyla at each time point (n = 3 per time point). Black and white boxes indicate light off and light on, respectively. The yellow box shows when mice had access to food. Colored asterisks at the end of lines in line graph show which phyla were cycling based on JTK analysis (that is ADJ.P < 0.05 and BH.Q. < 0.05). Since it takes > 1 hour for a food bolus to reach the cecum (Padmanabhan et al., 2013), lower bar graphs show the average percent reads (± SEM, n = 9) for the dark/active feeding phase (ZT 17, 21, and 1), and the light/inactive fasting phase (ZT 5, 9, and 13). *p < 0.05. (C) The top ten OTUs (based on percent reads) are depicted in a polar plot. The radian indicates the phase of the OTU’s peak, the distance from center is the average percent read across all time points, and the radius of each point indicates the amplitude of cycling. The colors of the circles indicate the phylum of the OTU: Firmicutes (pink), Bacteroidetes (blue), and Verrucomicrobia (green). The black arc on the left side of the plot indicates the light/dark cycle. The yellow arc depicts access to food. The bottom polar plot shows a magnified view of the inner ring (10%) of the top polar plot. These descriptions also apply to panels for FA mice (D, E, F) and FT mice (G, H, I).

Figure 3. Subphylum Analysis of the Gut Microbiome in the Three Conditions

Diurnal activity of several Firmicutes classes including (A) Bacilli, (B) Clostridia, and (C) Erysipelotrichia. Line graphs (left) show a double-plot of percent reads (± SEM, n = 3 per time point) for a particular class from all three conditions. Conditions are color-coded (see legend). Colored asterisks at the end of lines in line graph show which conditions were cycling based on JTK analysis. Bar graphs (top right in each panel) show percent total reads (± SEM, n = 18) for a particular class in each condition averaged over all time points. Histogram (bottom left of each panel) show percent reads (± SEM, n = 9) that are expressed when light off/light on. Histogram (bottom right of each panel) show percent reads (± SEM, n = 9) that are expressed when food from nighttime feeding has reached the cecum (ZT 17, 21, 1) and during relative fasting (ZT 5, 9, 13; see Fig 2 and Padmanabhan et al., 2013). * p < 0.05. (D) Stacked bar graphs showing average percent reads of each family that comprised > 5% of total reads for each condition.

Figure 4. TRF Restores Cyclical Fluctuation in Genera Thought to be Involved in Metabolism

Diurnal activity of (A) genus Lactobacillus, (B) genus Lactococcus, (C) genus Oscillibacter, and (D) other genera in the Ruminococcaceae family. For each, there is a double-plot of percent reads (± SEM, n = 3 per time point) for the three conditions. Colored asterisks at the end of lines in line graph show which conditions were cycling based on JTK analysis. For (C) the NA condition is excluded from the line plot but can be seen in Figure S4C. This is followed by a histogram of percent total reads (± SEM, n = 18) that this genus comprises in each condition, a histogram of percent reads (± SEM, n = 9) that are expressed when light off/light on and a histogram of percent reads (± SEM, n = 9) that are expressed when food from nighttime feeding has reached the cecum (ZT 17, 21, 1) and during relative fasting (ZT 5, 9, 13; see Fig 2 and Padmanabhan et al., 2013). Genera-based principle component analysis (PCA) of NA and FA mice (E) and of FA and FT mice (F). Green vectors show the axis where that particular genus accounted for most of the variability. In (F), dotted lines show trend line of FA and FT mice in the PCA, which are significantly different (p < 0.05 by ANOVA of two populations).

Figure 5. Diversity Analysis of Gut Microbiomes at Different Time Points

(A) Shannon effective species (α-diversity or intra-sample diversity; ± SEM, n = 3 per time point) for each condition and time point. (B) Box and whisker plot of Shannon effective species (α-diversity) and (C) richness averaged across all time points (n = 18). (D) Rarefaction and (E) rank-abundance curves of average reads (n= 18 per condition). Dissimilary (β-diversity) of (F) samples within the same condition (except those from the same time point), and (G) samples from different conditions. In all box plots, whiskers show minimum and maximum, the box is the 25–75th percentile, and the line is the median. *p < 0.05.

Figure 6. Metabolites Processed by Gut Microbes are Differentially Excreted in the Feces of Mice in Different Conditions

Average relative quantification of (A) xylose and (B) galactose (± SEM) in the feces of mice fed a HFD (n = 4 per condition, from separate cages). Histogram on left shows average across all samples collected (n=8). Histogram on right shows differences between samples collected from dark and light (n = 4). See Figure S6A for NA results. (C) Average absolute quantification (± SEM) of primary, secondary, and tauro-conjugated bile acids within feces. Dashed line connects similar bile acids to allow easy comparison across conditions. CA: cholate, CDCA: chenodeoxycholate, MCA: Muricholate (a: alpha-, b: beta-, g: gamma-, w: omega-), DCA: deoxycholate, UDCA: ursodeoxycholate, LCA: lithocholate, T-: Tauro-. *p < 0.05, **p < 0.01.