The gut microbiome is dynamic, with cyclical changes in composition and diversity

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.

Since the relationship between the gut microbiome and metabolism is unclear, and dietary changes lead to rapid shifts in its composition, we sought to determine whether the gut microbiome is affected by cyclical fluctuations in feeding. Natural feeding and fasting cycles result in a fluid gut milieu in terms of nutrients, pH, and secondary metabolites, but it is unclear whether the gut microbiome is similarly dynamic, and if so whether this dynamism plays a role in host metabolism.

Previous studies in murine models have shown that age (), host genetics (), and diet () can affect the composition of the gut microbiome. Long-term ecological studies of the gut microbiome have revealed longitudinal stability (). This has led to the hypothesis that early gut colonizers, likely acquired from parents, play a vital role in determining the composition of the host microbiota and the physiological and metabolic fate of the host (). However, longitudinal consistencies in gut-microbiome composition are strongly associated with long-term dietary patterns (). A change in diet can shift the composition of the gut microbiome rapidly, often within 24 hr, in both humans and mice (). Finally, there is an intimate relationship between the gut microbiome and host IEC circadian regulators. This is exemplified by microbiome perturbations in phase-shifted mice () and IEC circadian gene dysregulation with antibiotic-induced microbiome depletion (). This suggests a far more dynamic environment than previously thought.

Homeostasis in intestinal epithelium is orchestrated by the circadian clock and microbiota cues transduced by TLRs.

Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models.

Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models.

Obese humans and mice have gut microbiomes that are different from their lean controls (). In particular, obesity is associated with a reduction in bacteria from the Bacteroidetes phylum and an increase in bacteria from the Firmicutes phylum. Metagenomic analysis of the obese microflora shows that it is enriched for genes associated with lipid and carbohydrate metabolism (). Although it initially appeared that obesogenic microbiota (i.e., Firmicutes) contribute to obesity by harvesting more energy from the diet, more recent studies have challenged this notion. A high-fat diet (HFD) can increase Firmicutes in the gut microbiome without altering host metabolism, suggesting that many observed shifts in the microflora result from dietary changes and may not have metabolic consequences (). Human studies investigating the ratio of Firmicutes and Bacteroidetes have yielded inconsistent results (). More comprehensive analyses of the gut microbiome suggest that changes in the gut microbiome at the subphylum level (and involving a limited number of species) could account for metabolic changes observed between different cohorts ().

Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models.

The gut microbiome plays an important role in host metabolic homeostasis (). However, the mechanistic basis for this metabolic effect is not well understood. Transcriptional activity in intestinal epithelial cells (IECs) is highly dynamic, characterized by cyclical gene expression that is responsive to feeding and to the host’s central circadian clock (). Whether the gut microbiome exhibits similar fluctuations has not yet been investigated. Characterizing this feature of the gut microbiome is necessary to better understand the relationship it may have with other drivers of host metabolism.

Another way in which the gut microbiome may affect host metabolism is through altering luminal bile acid signaling (). Fecal/stool bile acids are structurally complex, and this complexity is driven by microbial species within the intestinal tract (). We therefore assessed stool bile acids from mice in the three conditions. More primary bile acids were found in FT mice compared to NA and FA mice ( Figures 6 C and S6 B). Furthermore, there tended to be more secondary and tauro-conjugated bile acids in FT mice compared to NA and FA mice, although these trends were not significant ( Figures 6 C and S6 B). Hence, alterations in the gut microbiome of FT mice led to increased excretion of bile acids, and likely increased concentrations of bile acids within the gut lumen. High levels of bile acids in FT mice could affect metabolism through luminal bile acid signaling.

We first investigated stool metabolites that would suggest that FA and FT mice extract different amounts of energy from chow. Hemicellulose, which is a component of the plant cell wall, is a nonabsorbable complex sugar present in both normal and HFD murine chow. It is normally broken down into more absorbable sugars, xylose and galactose, with the aid of cellulase enzymes from gut bacteria. FT mice excreted significantly more xylose, but there was no difference in the stool collected in the light and dark periods ( Figure 6 A). FT also excreted more galactose, with a significantly higher amount excreted in the stool collected during the light period ( Figure 6 B). Thus, FT mice excreted rather than absorbed these calories. NA mice excreted much higher amounts of xylose and galactose than FA and FT mice ( Figure S6 A). However, it is difficult to perform direct comparisons between mice fed normal or HFD chow, since the diets contain different amounts of hemicellulose.

(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.

Average relative quantification of (A) xylose and (B) galactose (±SEM) in the feces of mice fed a HFD (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 S6 A for NA results.

Metabolites Processed by Gut Microbes Are Differentially Excreted in the Feces of Mice in Different Conditions

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

It is unclear how cyclical (or TRF-induced) variation in the gut microbiome affects host metabolism. To better understand the specific effects of the gut microbiome, we analyzed stool metabolites that are altered by gut microbes (not by host enzymes) from dark/feeding and light/fasting cages. Pooled stool was collected from fresh nighttime and daytime cages from each condition. Samples were analyzed for specific metabolites.

Changes in the Gut Microbiome Are Accompanied by Changes in Stool Metabolites

Differences in β-diversity (i.e., the measure of dissimilarity between two samples; intersample diversity) were also measured. We first analyzed the difference between the gut microbiome of mice subjected to the same condition, but at different time points (i.e., within-condition; Figure 5 F). The within-condition β-diversity was significantly different between all three conditions (NAvNA, 0.845 ± 0.010; FAvFA, 0.581 ± 0.008; FTvFT, 0.625 ± 0.009; one-way ANOVA, p < 0.0001). This indicates that the NA gut microbiome undergoes dramatic temporal fluctuations compared to the HFD conditions ( Figures 5 F and S5 B). Furthermore, the gut microbiome of FT mice also experience significant fluctuations when compared to those of the FA mice. Dissimilarity between NA gut microbiomes and those of the FA and FT condition (i.e., outside condition) was nearly as high as that measured for the NA within-condition dissimilarity (NAvFA, 0.946 ± 0.002; NAvFT, 0.949 ± 0.002; FAvFT, 0.632 ± 0.006; one-way ANOVA, p < 0.0001; Figures 5 G and S5 B).

α-diversity can be affected by species richness (i.e., the number of unique species/OTUs) and the abundance of each species. Across all time points, NA mice had significantly higher species richness compared to the two HFD conditions (NA, 165.2 ± 11.6; FA, 122.7 ± 2.7; FT, 126.2 ± 4.5; p = 0.009; Figure 5 C). Levels of richness for FT and FA mice were indistinguishable ( Figure 5 C). These results were confirmed using a rarefaction curve analysis ( Figure 5 D). However, although the rarefaction curves of the averaged FA and FT samples appeared similar, there was much more variability in species obtained from different time points. For example rarefaction plots of FT samples from ZT21 and ZT9 were remarkably different from each other. These plots were also different from plots obtained from FA mice at the same time points ( Figure S5 A). Rank-abundance analysis of samples obtained from FA, FT, and NA suggests that the main cause of the rise in the variability of FT α-diversity was an increase in the relative abundance of species, rather than an increase in the number of species ( Figure 5 E).

We observed that the α-diversity of the gut microbiome of NA mice varied widely. There were usually higher levels of diversity during nighttime feeding and lower levels during daytime fasting ( Figure 5 A). However, gut microbiome α-diversity for FA and FT mice remained constant, without much temporal fluctuation ( Figure 5 A). Although NA and FT mice had significantly higher variance in their α-diversity compared to FA mice (Bartlett’s test for equal variance, p = 6.0 × 10), when all time points were averaged together there were no significant differences between the three conditions (NA, 21.70 ± 5.08; FA, 15.73 ± 0.81; FT, 18.75 ± 1.40; one-way ANOVA, p = 0.41; Figures 5 A and 5B).

(D–G) (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 25th–75th percentile, and the line is the median. ∗ p < 0.05.

(B and C) (B) Box-and-whisker plot of Shannon effective species (α-diversity) and (C) richness averaged across all time points (n = 18).

Dysbiosis, as a result of HFD, leads to alterations in microbiome ecology, particularly reductions in diversity (). Reduced diversity, particularly α-diversity (i.e., the types and relative amounts of species within a sample; intrasample diversity), is thought to play a significant role in host metabolism and global increase in obesity (), but it is unclear whether changes in microbial diversity result from changes in the nutritional composition of the diet or from cyclical changes in luminal content.

Gut Microbial Ecology Is Dynamic in the Gut Microbiome of Mice in NA and FT Condition

Principle component analysis of NA mice revealed that the ZT9 gut microbiome was quite distinct from gut microbiomes measured at any other time point ( Figure 4 E). Time within a 24 hr light:dark cycle is reported as Zeitgeber time (ZT), or “time since lights on,” where ZT0 is when the light turns on/dawn and ZT12 is when the light turns off. By definition, ZT9 is 9 hr after lights have been on (and 3 hr before the nocturnal feeding bout begins). After NA mice had commenced nighttime feeding, the composition of their microbiomes at ZT13 shifted toward the FA microbiome. In NA mice, feeding changed the gut microbiome so dramatically that some time point clusters were more different from each other than they were from the FA gut microbiome (e.g., compare ZT9 and ZT17; Figure 4 E). Principle component analysis of FA and FT gut microbiomes showed clusters that were not as dramatically different. Since Lachnospiraceae species accounted for >50% of the reads in both FA and FT mice ( Figure S3 ), these species accounted for much of the observed variance in these cohorts ( Figure S4 B). However, concerning second and third principle components, the FA and FT conditions exhibited some degree of separation ( Figure 4 F). FT gut microbiome clusters were similar to FA gut microbiome clusters during fasting. During feeding, however, the FT clusters deviated from the “baseline” FA clusters. This suggests that if the gut microbiome directly affects metabolism in FT mice, this impact likely occurs during a narrow window of time.

High relative levels of Oscillibacter and other Ruminococcaceae species protect against obesity and nonalcoholic fatty liver disease (). Here we found a significantly higher percentage of Oscillibacter species in FT gut microflora than we did in FA mice (0.40% ± 0.08% versus 0.13% ± 0.04%, respectively; p < 0.05, Figure 4 C). The relative reads of Oscillibacter species were cyclical in NA, but not in FT or FA mice (ADJ.Ps are 2.1 × 10for NA, 1 for FA, 0.09 for FT; Figures 4 C and S4 A). Furthermore, Ruminococcaceae species comprised a higher percentage of reads in FT mice compared to FA mice (6.69% ± 1.03% versus 3.96% ± 0.42%, respectively; p < 0.05; Figure 4 D). Ruminococcaceae were highly cyclical in NA mice, but not in FT and FA mice (ADJ.Ps are 3.3 × 10for NA, 1 for FA, 0.09 for FT; Figure 4 D). TRF increased the relative amounts of Ruminococcaceae species in the dark/feeding time of FT mice, which was significantly higher than the relative amounts in the FA mice during the same time points (8.79% ± 1.64% versus 4.15% ± 0.71%, respectively; p < 0.05; Figure 4 D). Relative reads for some of the other bacterial genera in NA, FA, and FT mice are shown in Figure S3

Previous studies have shown that the amount of Lactococcus species directly correlates with body fat percentage in mice consuming a high-fat/high-sugar diet (). We have shown that FA mice have a much higher body fat percentage than NA mice, but body fat percentages are similar between FT and NA mice (). Consistent with previous results, the current study shows that Lactococcus species were barely detectable in the gut microbiome of NA mice at any time points and were much higher in the FA mice (NA, 0.00% ± 0.00%; FA, 1.81% ± 0.69%; FT, 0.43% ± 0.15%; one-way ANOVA, p < 0.0001; Figure 4 B). FT mice had a significantly lower percentage of Lactococcus species in their gut microbiome than FA mice did ( Figure 4 B). Lactococcus was only cyclical in the FA mice (ADJ.p = 4.9 × 10). In particular the main difference in Lactococcus species between FA and FT is the amount found during the light/inactive phase. The percent reads of Lactococcus species were significantly higher in the FA mice than in the FT ones (2.66% ± 0.84% versus 0.45% ± 0.16%, respectively; p < 0.05), but not during the dark/active phase (0.96% ± 0.34% versus 0.41% ± 0.15%, respectively; p = 0.16; Figure 4 B).

The relationship between several Lactobacillus species and obesity, as well as associated metabolic disorders, has been studied extensively (). In particular, a decrease in Lactobacillus species protects against metabolic disorders associated with obesity, perhaps by altering bile acids in the lumen (). In both NA and FT mice, the Lactobacillus genera were cyclical, whereas in FA mice they were not (ADJ.Ps are 4.2 × 10for NA, 1 for FA, 1.2 × 10for FT; Figure 4 A). Furthermore, Lactobacillus species comprised a lower percentage of reads in FT mice compared to the combined ad libitum cohorts (0.97% ± 0.49% versus 3.70% ± 1.01%, respectively, p < 0.05; Figure 4 A). However, since the percentage reads were similar between FA and NA mice, it is likely that the temporal profile of these species, rather than the relative abundance, exerts greater influence on host metabolism. In particular, FA mice had a higher amount of Lactobacillus species during the dark/active phase compared to FT mice (3.62% ± 1.49% versus 0.06% ± 0.04%, respectively, p < 0.05; Figure 4 A).

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).

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 S4 A. 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 (ZT17, ZT21, and ZT1) and during relative fasting (ZT5, ZT9, and ZT13; see Figure 2 and).

In contrast to the analysis at the phylum level, subphylum analysis revealed that the microbiome of FT mice was distinct from that of FA mice. Although species in the Firmicutes phyla (and the Bacilli and Clostridia classes) dominated the gut microbiomes of both HFD cohorts, the families and genera were quite dissimilar, with differences in percent reads and cyclical activity ( Figures 3 D, S3 , and 4 A–4D). Many microfloral differences between FA and FT mice involved families and genera previously hypothesized to play roles in metabolism. Four of these will be discussed in greater detail.

Firmicutes species within the gut microbiome of NA mice belonged predominantly to the class Erysipelotrichia, which was far less common in the gut microbiome of mice fed the HFD, especially the FT condition (NA, 33.4% ± 10.4%; FA 7.6% ± 2.3%; FT, 1.1% ± 0.5%; one-way ANOVA, p < 0.0003; Figure 3 C). This phylogenetic class was only cyclical in the FT condition (ADJ.p = 2.0 × 10), where it was 12 times more prevalent in the light/inactive phase than in the dark/active phase (1.95% ± 0.63% versus 0.17% ± 0.05%, p < 0.05; Figure 3 C).

In addition, NA mice had a lower percentage of Clostridia species compared to the FA and FT conditions (35.2% ± 10.0%, 83.4% ± 4.0%, 90.9% ± 3.0%, respectively; one-way ANOVA, p < 0.0001; Figure 3 B). Clostridia as a phylogenetic class was cyclical in the NA condition, with a peak-to-trough of 9:1, but not in either of the HFD conditions (ADJ.P is 3.3 × 10for NA, 0.45 for FA, 0.03—but BH.Q was 0.08—for FT). In both the NA condition and the FT condition, Clostridia were more prevalent in the dark/active phase than in the light/inactive phase (for NA, 49.1% ± 9.6% versus 21.3% ± 8.3%, p < 0.05; for FT, 95.4% ± 1.0% versus 86.4% ± 3.6%, p < 0.05; Figure 3 B).

To further characterize differences between mice in three conditions, we performed subphylum analyses of the gut microbiome, focusing on classes, families, and genera. These analyses revealed further differences between NA mice and those in the HFD conditions. In NA mice Bacilli species were cyclical (ADJ.p = 5.0 × 10) with a peak that occurred after feeding (peak-to-trough 7:1; Figure 3 A). On average across all time points, these species were significantly more prevalent in the gut microbiome of mice in the HFD conditions than in the NA mice (6.4% ± 1.4% versus 2.9% ± 1.4%, respectively; p < 0.05). Unlike the NA condition, Bacilli were not cyclical in the two HFD conditions (ADJ.p = 1 for both FA and FT).

(D) Stacked bar graphs showing average percent reads of each family that comprised >5% of total reads for each condition.

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) shows percent reads (±SEM, n = 9) that are expressed when light off/light on. Histogram (bottom right of each panel) shows percent reads (±SEM, n = 9) that are expressed when food from nighttime feeding has reached the cecum (ZT17, ZT21, and ZT1) and during relative fasting (ZT5, ZT9, and ZT13; see Figure 2 and).p < 0.05.

Species from the Firmicutes, Bacteroidetes, and Verrucomicrobia comprised ≥98% of the reads in all three conditions. Analyses of other phyla revealed cyclical activity for Actinobacteria species only in the FA condition (ADJ.Ps are 0.09 for NA, 6.5 × 10for FA, 0.60 for FT; Figure S2 H). In contrast, Proteobacteria species did not cycle, comprising less than 0.1% of reads in most mice (ADJ.Ps are 1 for NA, FA, and FT; Figure S2 I).

FT mice had fewer OTUs compared to NA mice ( Figure S2 D; see also Figure 5 C). Like FA mice, FT mice had fewer cycling OTUs than NA mice ( Figures 2 G, S2 E, and S2G), and phylum-level cyclical changes were diminished (ADJ.Ps are 1 for Firmicutes, 0.06 for Bacteroidetes, and 1 for Verrucomicrobia; Figure 2 H). Unlike NA and FA mice, peaks in cycling OTUs in FT mice were related to the feeding schedule, occurring several hours after food was given or at the end of the fasting period ( Figure S2 G). Unlike NA mice and similar to FA mice, the maximum percentage of reads that belonged to cycling OTUs at any time point was 27%, and each time point was dominated by Firmicutes species ( Figure 2 I).

In a previous study () we showed that using TRF to impose a natural feeding rhythm on mice fed a HFD (FT mice; Figure 1 A) protects against diet-induced obesity and other metabolic disorders associated with FA mice. We replicated this finding in the current study ( Figures 1 B–1E). TRF provides an ideal backdrop for studying intestinal microflora, because FT mice consume (1) the same nutritional quality as FA mice, and (2) the same caloric quantity as NA mice ( Figures S1 A and S1B) (). In FT mice, therefore, microbiome differences that have metabolic consequences (e.g., obesity protective or obesogenic) are not obscured by alterations in nutritional/dietary intake. To better understand the relationship between cyclical fluctuations in the gut microbiome and diet, feeding phase, and metabolism, we expanded the study to include the FT condition.

Mice that have ad libitum access to HFD (FA mice; Figure 1 A) spread their caloric intake, feeding during the dark/active phase and the light/inactive phase (). They were obese ( Figure 1 B) and had dysfunctional glucose homeostasis ( Figure 1 C), gross steatosis ( Figure 1 D), and hypercholesterolemia ( Figure 1 E). FA mice had fewer OTUs on 16S rRNA sequencing compared to NA mice ( Figure S2 D; see also Figure 5 C), and fewer of these OTUs were cyclical ( Figures 2 D, S2 E, and S2F). Interestingly, phylum-level cyclical changes observed in NA mice were dampened and did not approach significance in FA mice (ADJ.Ps are 0.03—but BH.Q was 0.08—for Firmicutes, 0.24 for Bacteroidetes, 1 for Verrucomicrobia; Figure 2 E). The peak-to-trough ratio of Firmicutes species in FA mice was 1:1. Unlike NA mice, the maximum percentage of reads that belonged to cycling OTUs at any time point was 30%, and Firmicutes species were dominant at every time point ( Figure 2 F).

Mice with ad libitum access to normal chow (NA mice; Figure 1 A) have a cyclical feeding pattern, eating most of their diet during their nighttime active phase and less during their daytime inactive phase (). In order to determine whether a particular phylogenetic group was cyclical, we used JTK analysis, a nonparametric test that detects cycling elements (). In order for an OTU to be considered cyclical, its adjusted, permutation-based p value (ADJ.P) and Benjamini-Hochberg q values (BH.Q) both had to be less than 0.05. In NA mice, 17% of detected OTUs were cyclical ( Figure 2 A). Cyclical changes in the gut microbiome were apparent for all major phyla (ADJ.Ps are 2.0 × 10for Firmicutes, 0.019 for Bacteroidetes, and 2.9 × 10for Verrucomicrobia; Figures 2 B and 2C). The proportion of Firmicutes species peaked during nocturnal feeding and bottomed out during daytime fasting, with a peak-to-trough ratio of 3:1 in NA mice. Bacteroidetes and Verrucomicrobia species peaked during daytime fasting and bottomed out during nocturnal feeding ( Figures 2 B and 2C). At any point in time, 20%–83% of the reads belonged to OTUs that cycled. For NA animals, OTU peaks were not restricted to a particular time of day. They were distributed across all the time points, but with a gradual rise after feeding ( Figure S2 A). A majority of OTUs that cycled in NA mice (80%) were underrepresented in HFD microbiomes (p < 2.0 × 10 Figures S2 B and S2C).

(C) The top ten cyclical 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.

(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 hr for a food bolus to reach the cecum (), lower bar graphs show the average percent reads (±SEM, n = 9) for the dark/active feeding phase (ZT17, ZT21, and ZT1), and the light/inactive fasting phase (ZT5, ZT9, and ZT13).p < 0.05.

(A) Pie chart showing the percentage of cycling and noncycling OTUs (across all conditions) in NA mice (n = 18).

We fed 12-week-old male wild-type C57BL/6 mice a normal chow or a HFD for 8 weeks. These mice were allowed ad libitum access to food or were subjected to time-restricted feeding (TRF; Figure 1 and see Figure S1 available online). To assess the stability of the gut microbiome within a 24 hr period, mice were sacrificed every 4 hr and metagenomic DNA was extracted from the cecal contents. We then sequenced 16S rRNA and created microbial community profiles by clustering 16S rRNA sequences into operational taxonomic units (OTU; ≥97% sequence match) and used the Ribosomal Database Project classifier (with a threshold ≥80% bootstrap value) to assign sequences to taxonomic groups. Across mice in all conditions, we identified 298 OTUs ( Table S1 Table S2 , and Table S3 ).

(E) Serum quantification of cholesterol (n = 6 per group). Measurements (mean ± SEM) were performed twice. ∗ p < 0.05 (compared to NA and FT).

(C) Intraperitoneal glucose tolerance tests (mean ± SEM) show that TRF was protective against diabetes (n = 6 per condition). ∗ p < 0.05 (compared to NA).

(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).

(A) Study design. NA mice had ad libitum access to normal chow. FA mice had ad libitum access to HFD. FT mice had 8 hr access (ZT13–ZT21) to HFD. Results from our previous study were replicated ().

Discussion

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et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Previous studies have shown long-term stability of the gut microbiome, although variation driven by age, diet, and the environment has also been reported (). This long-term stability has been tempered with recent evidence that the gut microbiome rapidly changes when the diet is altered (). Here we have shown that the gut microbiome exhibited daily cyclical variation in a variety of dietary and feeding pattern conditions, with the assumption that the changes in the gut microbiome are similar to those in host gene expression to make 24 hr a sufficient amount of time to assess circadian changes. Of note, during the review of this manuscript, another group also reported diurnal changes in the mammalian gut microbiome ().

NA mice showed cyclical fluctuation in the composition of the gut microbiome. Firmicutes species were most abundant with feeding during the dark/active phase and reached their low point with fasting during the light/inactive phase. Conversely, Bacteroidetes and Verrucomicrobia species rose during fasting and fell during feeding. These cyclical fluctuations in specific members of the gut microbiome were accompanied by changes in the diversity of the microflora environment. The α-diversity of the gut microbiome fluctuated with the time of day and was highly variable, rising with feeding and falling with fasting. The wide fluctuations between time points were confirmed with significantly higher β-diversity between NA samples taken at different time points.

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Bass J. High-fat diet disrupts behavioral and molecular circadian rhythms in mice. Mice given ad libitum access to a HFD (a model of diet-induced obesity) lose diurnal feeding (). As a consequence, their gut microbiome contained half as many cycling OTUs, and cycling OTUs comprised less of the overall gut microbiome. Firmicutes species, particularly of the Clostridia order, dominated the gut microbiome of FA mice, and overall species numbers were reduced. In these mice α-diversity did not fluctuate; rather it stayed constantly low. Likewise, within-condition β-diversity of the gut microbiome remained low.

We originally hypothesized that TRF in FT mice would make the gut microbiome highly dynamic, similar to the gut microbiome of NA mice. However, this was not the case. Superficially, FT and FA gut microbiomes were very similar, highlighting the important role of diet in forming the gut microbial environment. At the subphylum level, however, there were key differences between FT and FA gut microbiomes. In addition, FT mice had a higher β-diversity, but not α-diversity, compared to FA mice. These differences in microbial ecology reinforce the fact that the gut microbiome of FA and FT mice are quite different in subtle ways.

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Lozupone C.A.

Knight R.D.

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et al. A core gut microbiome in obese and lean twins. Lozupone et al., 2012 Lozupone C.A.

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Knight R. Diversity, stability and resilience of the human gut microbiota. Temporal changes in the diversity are particularly important in understanding the contribution of the gut microbiome to obesity and metabolic disease (). Many have observed a decrease in α-diversity in diet-induced obesity/FA mice compared to control/NA mice and hence have hypothesized that increasing it can be protective against obesity (). However, our data show that α-diversity can vary widely throughout the day in NA mice and that, when all time points are taken into account, there are no significant differences between any conditions that we have tested. Changes in β-diversity, however, are significant, further showing that fluctuations in the gut microbiome are important for host metabolism, not necessarily richness of species which is affected by diet.

Joyce et al., 2014 Joyce S.A.

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Gahan C.G. Regulation of host weight gain and lipid metabolism by bacterial bile acid modification in the gut. Li et al., 2013 Li F.

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Gonzalez F.J. Microbiome remodelling leads to inhibition of intestinal farnesoid X receptor signalling and decreased obesity. Million et al., 2012 Million M.

Maraninchi M.

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Valero R.

Raccah D.

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Raoult D. Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and depleted in Bifidobacterium animalis and Methanobrevibacter smithii. Million et al., 2013 Million M.

Angelakis E.

Maraninchi M.

Henry M.

Giorgi R.

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Vialettes B.

Raoult D. Correlation between body mass index and gut concentrations of Lactobacillus reuteri, Bifidobacterium animalis, Methanobrevibacter smithii and Escherichia coli. Parks et al., 2013 Parks B.W.

Nam E.

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Pan C.

Civelek M.

Rau C.D.

Bennett B.J.

et al. Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Raman et al., 2013 Raman M.

Ahmed I.

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et al. Fecal microbiome and volatile organic compound metabolome in obese humans with nonalcoholic fatty liver disease. Million et al., 2012 Million M.

Maraninchi M.

Henry M.

Armougom F.

Richet H.

Carrieri P.

Valero R.

Raccah D.

Vialettes B.

Raoult D. Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and depleted in Bifidobacterium animalis and Methanobrevibacter smithii. Raman et al., 2013 Raman M.

Ahmed I.

Gillevet P.M.

Probert C.S.

Ratcliffe N.M.

Smith S.

Greenwood R.

Sikaroodi M.

Lam V.

Crotty P.

et al. Fecal microbiome and volatile organic compound metabolome in obese humans with nonalcoholic fatty liver disease. A key finding of the paper is that the feeding pattern affected the composition of the luminal microflora, even when animals were fed the same diet. Analysis of the gut microbiome of FA and FT mice revealed differences when analyzed at the subphylum level. Many of the OTUs that regained cycling or were significantly different in the FT gut microbiome (compared to the FA one) belong to genera that have been hypothesized to play a role in host metabolism. Lactobacillus species, which are thought to be obesogenic (), were cyclical in the gut microbiome of NA and FT mice, but not in FA mice. Compared to FA mice, FT mice had lower levels of these species. Lactococcus are also thought to be obesogenic (). FT mice had significant reduction in Lactococcus species compared to FA mice, especially during the light phase, suggesting that fasting may play a crucial role in keeping these species at check. In addition, protective species, such as those belonging to the Ruminococcacea family, including the genus Ocillibacter (), were elevated in the gut microbiome of NA and FT mice, but not in FA mice. These results suggest that the benefits of TRF could be due at least in part to an alteration in the gut microbiome.

Million et al., 2012 Million M.

Maraninchi M.

Henry M.

Armougom F.

Richet H.

Carrieri P.

Valero R.

Raccah D.

Vialettes B.

Raoult D. Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and depleted in Bifidobacterium animalis and Methanobrevibacter smithii. Everard et al., 2013 Everard A.

Belzer C.

Geurts L.

Ouwerkerk J.P.

Druart C.

Bindels L.B.

Guiot Y.

Derrien M.

Muccioli G.G.

Delzenne N.M.

et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Other species of gut bacteria have been hypothesized to play a role in host metabolism. These include Bifidobacterium () and Akkermansia (), both of which are highly cyclical species. Bifidobacterium species did not seem to be affected by the different dietary conditions (once data were averaged over all time points). Akkermansia seemed to thrive most in the fasting lumen of NA mice. Our findings suggest that changes in these genera may not be sufficient to improve metabolism, since they were not different between FA and FT conditions.

The mechanism by which the gut microbiome affects host metabolism is not well understood. Modification and fluctuation of the gut microbiome do not indicate that the host metabolism has changed, since this association may be correlative. However, our analysis of stool metabolites revealed how differences in the gut microbiome between FA and FT mice could explain their distinct phenotypes. For example, analysis of sugars revealed high levels of xylose and galactose in the stool of FT mice. This suggests that these sugars were more readily absorbed by the gut of FA mice than that of FT mice.