Circadian disorganization alters intestinal microbiota

Abstract

Intestinal dysbiosis and circadian rhythm disruption are associated with similar diseases including obesity, metabolic syndrome, and inflammatory bowel disease. Despite the overlap, the potential relationship between circadian disorganization and dysbiosis is unknown; thus, in the present study, a model of chronic circadian disruption was used to determine the impact on the intestinal microbiome. Male C57BL/6J mice underwent once weekly phase reversals of the light:dark cycle (i.e., circadian rhythm disrupted mice) to determine the impact of circadian rhythm disruption on the intestinal microbiome and were fed either standard chow or a high-fat, high-sugar diet to determine how diet influences circadian disruption-induced effects on the microbiome. Weekly phase reversals of the light:dark (LD) cycle did not alter the microbiome in mice fed standard chow; however, mice fed a high-fat, high-sugar diet in conjunction with phase shifts in the light:dark cycle had significantly altered microbiota. While it is yet to be established if some of the adverse effects associated with circadian disorganization in humans (e.g., shift workers, travelers moving across time zones, and in individuals with social jet lag) are mediated by dysbiosis, the current study demonstrates that circadian disorganization can impact the intestinal microbiota which may have implications for inflammatory diseases.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Protocol and timeline for circadian disruption, dietary changes, and stool collection.

A. Non-shifted mice were kept on a constant light:dark schedule for the entirety of the experiment whereas Shifted mice underwent once weekly light:dark inversion. Mice were maintained on a standard chow diet for the first 12 weeks of the study followed by a stool collection at the end of week 12. Mice were subsequently placed on a high-fat, high-sugar diet for 10 weeks and stool was collected at the end Week 22. Representative behavioral activity recording for a Non-shifted (B) and Shifted mouse (C). Activity is represented as dark areas in the actogram. These data demonstrate that non-shifted mice have stable behavioral patterns while the shifted mice have disrupted behavioral patterns. D. Expression of Per2 mRNA in the proximal colon one day after the week 22 stool collection demonstrates altered circadian expression in the intestine following the 22 weeks of phase shifting. Significant effect of light condition, (F(1,58) = 5.84, p = 0.02), Zeitgeber Time (ZT) (F(5,58) = 8.38, p<0.0001), and light condition x ZT interaction (F(5,58) = 9.67, p = <0.0001). E. Activity onset phase distribution on the day of stool collection for standard chow-fed and (left) and high-fat, high-sugar diet (right). Individual mouse onset times relative to the light:dark cycle are depicted by the circles, and vector means depicted by the lines. There were no significant differences between the mean phase angle of entrainment between the shifted and non-shifted mice at the time of stool sample collection, but shifted mice exhibited a greater dispersal of phases in both diets (p<0.05).

Figure 2. Effect of diet and circadian rhythm disruption on mouse gut microbial communities.

The non-metric multidimensional scaling (NMDS) plot demonstrates the effect of treatments on the overall mouse fecal microbial community structure, as assessed by bacterial small subunit ribosomal RNA gene amplicon sequencing . The NMDS plot is based on sample-standardized and square-root transformed abundance data. The NMDS plot and the hierarchical cluster overlay are based on a resemblance matrix calculated using S17 Bray-Curtis similarity. 2D stress values ranged from 0.04 to 0.17. ANOSIM: Analysis of similarities. SIMPER: Similarity percentages.

Figure 3. Mouse gut microbial community structure at the phylum level.

The bar graph graphically represents the average relative abundance of classified bacteria SSU rRNA gene amplicons belonging to the most abundant phyla.

Figure 4. Mouse gut microbial community structure at the class level.

The bar graph graphically represents the average relative abundance of classified bacteria SSU rRNA gene amplicons belonging to the most abundant taxon at the class level.

Figure 5. Mouse gut microbial community structure at the order level.

The bar graph graphically represents the average relative abundance of classified bacteria SSU rRNA gene amplicons belonging to the most abundant taxon at the order level.

Figure 6. Mouse gut microbial community structure at the family level.

The bar graph graphically represents the average relative abundance of classified bacteria SSU rRNA gene amplicons belonging to the most abundant taxon at the family level.

Figure 7. Family-level gut microbial community analysis.

The composition of bacterial communities from each sample, grouped at the family level, was analyzed using principal component analysis of log-transformed and standardized data, as described in the text. Vectors, or arrows, point in the direction of the steepest increase of values for the corresponding family. The angle between arrows indicates approximated correlation (>90° indicates negative correlation). The samples are indicated with individual symbols, according to treatment, and the distance between symbols approximates the dissimilarity of their microbial communities, as measured by Euclidean distance. PCA axes 1 and 2 explain 47.51% of the variation.

Figure 8. Genus-level gut microbial community analysis.

A dual hierarchical dendogram describes the 40 most abundant genera detected in the amplicon sequence study (y-axis) across the mouse fecal samples. The heat map indicates the relative abundance of sequences derived from bacteria belonging to each genus, scaled to each sample (red = most abundant; green = no sequences) (x-axis). The clustering of samples was performed on the full dataset of sequences, and sequence data abundance values were standardized by sample, square-root transformed, and a resemblance matrix was generated using Bray-Curtis similarity. Similarly, hierarchical clustering was performed on the 40 most abundant species, using standardized abundance data and Bray-Curtis similarity. Group average hierarchical clustering was performed on both matrices.

Figure 9. Circadian disruption has a significant effect on body weight.

Mice were weighed once weekly and the body weights of mice consuming either the Standard chow diet (A) or the high-fat, high-sugar diet (B) are depicted over the last six weeks of each diet calculated as a percent of either Week 7 or Week 17, respectively. (A) Both circadian rhythm disruption (F(1,90) = 4.93, p = 0.04) and time (F(5,90) = 20.24, p<0.0001) had a significant impact on body weight when mice were consuming the standard chow diet. (B) There was no effect of circadian disruption on body weight in the high-fat, high-sugar diet-fed mice (F(1,159) = 0.03, p = 0.85) while time did have a significant impact (F(9,159) = 43.14, p<0.0001).