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.

Citation: Voigt RM, Forsyth CB, Green SJ, Mutlu E, Engen P, Vitaterna MH, et al. (2014) Circadian Disorganization Alters Intestinal Microbiota. PLoS ONE 9(5): e97500. https://doi.org/10.1371/journal.pone.0097500 Editor: Nicolas Cermakian, McGill University, Canada Received: December 17, 2013; Accepted: April 16, 2014; Published: May 21, 2014 Copyright: © 2014 Voigt et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Work was supported by the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under award number R01AA020216 to A.K. and F.W.T. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction High throughput DNA/RNA-based technology has led to a revolution in our understanding of the complexity and diversity of the microbiota communities that inhabit many parts of the human body, including those in the intestine. It is now recognized that the number of microbial cells in the gastrointestinal tract outnumber the total human cells in the body, with an estimated 1014 cells, most of them being bacteria [1], [2]. Microbiota community composition and diversity are highly malleable and sensitive to changes in the host including environmental factors such as diet, and disruption of normal bacterial communities in the intestine (i.e., dysbiosis) has been linked to several chronic inflammatory diseases and pathological conditions [3], [4]. It has been known for over 40 years that the suprachiasmatic nucleus (SCN) in the mammalian hypothalamus contains the central circadian clock that regulates a myriad of cellular, physiological, and behavioral 24-hr rhythms. The discovery of circadian clock genes in mammals has led to a revolution in our understanding of how most, if not all, cells in the body contain the molecular machinery necessary to generate circadian rhythms. In fact, the genes and proteins comprising the core molecular clock regulate the timing of the expression of about 10% of all transcripts produced in any given tissue or organ [5]. Recently, a variety of human diseases, typically those associated with chronic inflammation (e.g., obesity, inflammatory bowel disease (IBD), cancer, and neurological disorders), have been found to occur in conjunction with either disruption of normal microbiota communities or circadian organization. It is not known if a common mechanism mediates the effects of disrupted microbiota or circadian rhythms, nor how disrupting intestinal microbiota or the circadian rhythms of the host might impact the other. The current study stemmed from our previous publication demonstrating that 12 weeks of circadian rhythm disruption promoted intestinal hyperpermeability, an effect that was exacerbated when mice were fed a high-fat, high-sugar diet for 10 weeks. This effect seems to be, at least in part, due to effects on the tight junction protein occludin [6]. In an effort to better understand the mechanism by which circadian rhythm disruption may influence intestinal permeability, stool collected from mice in the previous study was analyzed. As a first step to elucidate how the intestinal microbiota and circadian systems might interact, we examined the impact of chronic circadian rhythm disruption on the intestinal microbiota composition of mice fed a standard laboratory chow diet or a high-fat, high-sugar diet. Since we have previously demonstrated that repeated phase shifts in the light:dark cycle can disrupt intestinal barrier function [6] and render the colon more susceptible to injury in an experimentally induced model of ulcerative colitis [7], and because changes in microbiota are known to occur during both pathological conditions [3], [8], [9], we used this phase-shift model to determine if circadian rhythm disruption impacted intestinal microbiota composition.

Methods Ethics Statement All mice were housed and handled in accordance with federal animal welfare guidelines and in compliance with the Public Health Service Policy on Humane Care and Use of Laboratory Animals (2002) and the Guide for the Use and Care of Laboratory Animals (8th Edition). All experiments were reviewed and approved prior to being conducted by the Institutional Animal Care and Use Committee at Rush University Medical Center (Animal Study Protocol #10-083). Mice and Housing Studies were conducted at Rush University Medical Center in accordance with Federal animal welfare guidelines and in compliance with the Public Health Service Policy on Humane Care and Use of Laboratory Animals and the Guide for the Use and Care of Laboratory Animals. All experimental protocols were reviewed and approved by the Institutional Animal Care and Use Committee of Rush University Medical Center. Thirty-three young adult (6–8 week), male wild-type C57BL/6J mice obtained from Jackson Laboratory (Bar Harbor, ME) were housed individually in cages stored within ventilated, light-tight cabinets. Mice were acclimated to the facility for one week prior to initiating the experiment. Locomotor activity, food intake, and body weight were measured throughout the experiment. Circadian Manipulation The circadian manipulation protocol was conducted as described in our previous publication [6]. The phase shift in the light:dark cycle every week (Figure 1A) is intended to disrupt the normal synchrony of the circadian clock system to the entraining light:dark cycle in the same way that flying rapidly across time zones (for mice in this study, 12 time zones) leads to jet lag in humans. In brief, following the acclimation period, the mice were randomized into one of two groups: (1) the non-shifted group, maintained on a constant light:dark cycle or (2) the shifted group, underwent weekly reversals of the light:dark cycle. These circadian protocols were maintained for the duration of the experiment (i.e., 22 weeks). Twelve weeks of circadian rhythm disruption is sufficient to promote intestinal hyperpermeability [6] and exacerbate dextran sodium sulfate (DSS)-induced effects in a mouse model of ulcerative colits [10] and 22 weeks of circadian rhythm disruption is sufficient to markedly increase alcohol-induced intestinal hyperpermeability [6]; thus, the 22 week study period parallels studies we have already established negatively impact health. Locomotor activity records of a representative non-shifted (Figure 1B) and shifted (Figure 1C) mouse indicate the stable entrainment (non-shifted) or the repeated re-entrainment/disturbance (shifted) of the circadian locomotor behavioral rhythm. In this example (Figure 1C), it took an average of six days for the mouse to re-entrain to the weekly 12-hour phase shift, that is, it was phase delaying about two hours each day. This phenomenon can give the appearance that the mouse was free running with a period of approximately 26 hours. The free running period of C57BL/6J mice is approximately 23.7 hours; we have never observed a period greater than 24.3 hours in C57BL/6J mice unless the mouse is carrying a genetic mutation in a core clock gene. Figure 1D depicts altered RNA expression of the circadian gene Per2 in the proximal colon following 22 weeks of 12 hour light:dark inversions supporting that our chronic shifting paradigm disrupted circadian rhythmicity not only behaviorally but also circadian gene expression in the intestine. PPT PowerPoint slide

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larger image TIFF original image Download: 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). https://doi.org/10.1371/journal.pone.0097500.g001 In our study, individual mice took from four to seven days to re-entrain to the light:dark cycle. At the time when tissue samples were collected (seven days after the last phase shift) the phase angles between activity onset and lights off were significantly clustered (p<0.0001, Rayleigh test) with a mean of 1.31±0.53 h, indicative of their re-entrainment (Figure 1E). While the distributions of phases were different between non-shifted and shifted mice at the times of stool collection (Figure 1E), these distributions were attributable to differences in variance (or concentration parameter) rather than mean phase [11]. Although no significant differences in mean phase angle were detected, we still examined the potential impact of entrainment on microbiota composition (described in Results). Feeding Protocol The feeding protocol was conducted as described in our previous publication [6]. Briefly, all mice were maintained on standard rodent chow (Harlan Teklad Global 18% Protein Rodent Diet; 18.6% protein, 44.2% carbohydrate, 6.2% fat, 3.5% crude fiber) for the first 12 weeks of the study. After this time, mice were switched to a high-fat, high-sugar diet (36% protein, 29% carbohydrate (dextrose), and 35% fat) that contained: mineral mix, vitamin mix, choline bitartrate, DL-methionine, lactalalbumin, xanthan gum, dextrose (Dyets, Bethlehem, PA), fish oil from menhaden (Sigma, St. Louis, MO), and Hersey's chocolate syrup. Fish oil was selected as the source of fat based on data demonstrating that unsaturated fat (i.e., fish oil) promotes the development of endotoxemia and alcoholic steatohepatitis by increasing cytochrome P450 2E1 induction and lipid peroxidation [12], [13]. The diet was prepared fresh daily and supplied to mice in specialized graduated sipper tubes (Bio-Serv, Frenchtown, NJ), for daily monitoring of food intake. Tissue Collection & RNA analysis The tissue collection protocol was conducted as described in our previous publication [6]. Briefly, at the end of the experiment, mice were euthanized by conscious decapitation. Proximal colon was harvested and placed into RNALater (Qiagen, Valencia, CA) and frozen. Tissues were stored at −80°C until use. Tissues were collected every four hours across the diurnal cycle, starting at ZT0 (i.e., ZT0, ZT4, ZT8, ZT12, ZT16, and ZT20). Clock mRNA was measured in proximal colon samples prepared as tissue homogenates using Affymetrix lysis buffer and processed according to manufacturer's instructions. mRNA levels were determined using a Luminex platform-based custom multiplex bead array (Affymetrix, Inc., Santa Clara, CA). Expression was normalized to the housekeeping gene RPLPO. Data are expressed as mean fluorescent intensity (MFI). Stool Collection. Spontaneously voided stool pellets were collected at two times: (1) after 12 weeks on the standard chow diet (Week 12) and (2) after 10 weeks on the high-fat, high-sugar diet (Figure 1A). These time points were selected as they corresponded to times when intestinal permeability was measured, a phenomenon that may be directly or indirectly influenced by intestinal microbiota composition [6]. Mice were placed into a metabolic cage for five hours beginning at lights on (ZT0-ZT5, with ZT0 = lights on) and stool produced during this five hour period was collected and stored at −80°C. Microbial Community Analysis Genomic DNA was obtained using an extraction kit according to manufacturer's instructions (MP Biomedicals FastDNA SPIN Kit for Soil, #116560200). Genomic DNA samples were sent to Research and Testing Laboratory (Lubbock, TX) for amplification and sequencing of fragments of bacterial small subunit (SSU or 16S) ribosomal RNA (rRNA) genes. Briefly, PCR amplification was performed using the primers Gray28F and Gray519r [14] and sequencing reactions were performed on a Roche 454 FLX instrument (Roche, Indianapolis, IN) with Titanium reagents, titanium procedures, a one-step PCR, and a mixture of Hot Start and Hot Star high-fidelity Taq polymerases. After sequencing, all failed sequence reads and low-quality sequences were removed, and sequences were depleted of any nonbacterial ribosome sequences and chimeras using custom software [15], as described previously [16]. Clustering sequences into taxonomic groups from species to phylum was performed with the software package USEARCH [17], and taxonomic affiliation of sequences was performed by query of representative sequences against a curated 16S rRNA gene database, as described previously (e.g. Andreotti et al. 2011). For determination of diversity, raw sequences were quality filtered at a Q20 level within the software package CLC genomics workbench (CLC bio, Denmark), and sequences shorter than 275 bases were removed. Subsequently, each sample set was randomly sub-sampled to the same number of sequences (800 sequences/sample, ∼75% of the smallest library) to allow for direct comparison of calculated diversity indices. Sequences from each sample were pooled and processed through the Ribosomal Database Project's (RDP) Pyrosequencing pipeline [18] (http://pyro.cme.msu.edu/). Briefly, all sequences were aligned, and clustered using a 0.03 similarity threshold for complete linkage clustering. The number of sequences from each sample cluster was identified and a biological observation matrix (BIOM) was generated [19]. The BIOM was analyzed using the software package Primer6 (v6.1.15; PrimerE). Univariate diversity analysis (i.e., Shannon index) was implemented using the DIVERSE function in Primer6. Species-Taxonomic affiliation of clustered sequence data were determined as described above, and these data were used to generate multiple BIOMs at multiple taxonomic levels, including species, genus, and family. These BIOMs were subsequently used for analysis of microbial community structure and statistical analyses. For hypothesis testing, species-level data organized in a BIOM were pre-treated to standardize taxon abundance within each sample, and the data were subsequently square-root transformed to down-weight the impact of high abundance species. The transformed data were then used to generate a pair-wise resemblance matrix based on Bray-Curtis similarity (Primer6) that was analyzed using hierarchical clustering (group average) and non-metric multi-dimensional scaling (NMDS). Analysis of similarities (ANOSIM) was used to determine statistically significant changes in microbial community structure in a priori defined sample grouping. To do so, a test statistic “R” was calculated from the average distance of all members of a group and contrasted with average distances between replicates of different groups. The R statistic is calculated so that R = 1 if all replicates within groups are more similar than any replicates from different samples and R values approaching zero indicate dissimilarity. To determine significance, the R statistic was calculated under permutations of the sample labels, and the measured R value was compared to the permutation distribution [20]. Similarity percentage for each group (5-10/group) was independently analyzed. Community dissimilarity between treatment groups was calculated using the ‘similarity percentages' (SIMPER) function within Primer6, employing resemblance based on Bray Curtis similarity. Treatment effects were also analyzed at higher taxonomic level, employing a BIOM generated from family-level clustered sequence data. Principal coordinates analysis (PCA) was performed within the software package Canoco (v5) [21]. Data were standardized by sample, and log-transformed prior to PCA. Data Access The 16S rRNA gene amplicon sequence data from this study have been submitted to the NCBI Sequence ReadArchive (http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi) under accession number SRP029435.

Discussion It is well-established that diet impacts intestinal microbiota composition and diversity. Diets high in fat tend to decrease Bacteroides and increase Firmicutes with increases in Proteobacteria also reported [22], [23]. Similar effects are observed when a high-fat diet is combined with a high-sugar diet to comprise a “Western” diet [24], [25]. The “Western” diet-induced microbiome is distinct from that occurring as a consequence of diets high in fat or sugar [25]. The “Western” diet microbiome is dominated by Firmicutes of the Mollicute class whereas diets high in fat or sugar are comprised of Firmicutes of other classes [25]. In the current study, stool collected from mice fed a high-fat, high-sugar diet demonstrated a significant relative decrease in the abundance of Bacteroidetes and an increase in Firmicutes (i.e., non-Mollicute). Ruminococcus (a Firmicute of Clostridiales lineage) was particularly enriched in the stool of high-fat, high-sugar diet-fed mice. Ruminococcus spp. increase in response to diets high in resistant starch [26], [27] and certain species are increased in inflammatory bowel disease (i.e., ulcerative colitis, Crohn's disease) and colon cancer [28]–[30] suggesting that the increase may reflect mucolytic activity which may disrupt barrier function and cause inflammation. Likewise, Akkermansia, a mucolytic bacteria, increased in response to the high-fat, high-sugar diet [31]. Therefore, the diet-induced changes may have negative pro-inflammatory consequences in the intestine. It has been suggested that Akkermansia mucinphila has anti-inflammatory effects and improves intestinal barrier function [32]; however, in the context of our recently published paper [6] demonstrating that circadian rhythm disruption promotes intestinal hyperpermeability the likelihood of Akkermansia playing a beneficial role in the current study is unlikely. Disturbing the circadian clock system negatively impacts health and it is noteworthy that repeated phase shifts only altered the intestinal microbiota composition in circadian disrupted mice fed a high-fat, high-sugar diet but not in mice that were fed the standard chow diet. Indeed, our group as well as others have demonstrated that repeated phase shifts in the light:dark cycle have minimal health effects in an "unchallenging" environment under which laboratory animals are usually maintained; however, negative consequences of chronic circadian disruption are revealed when animals are put into a “challenging” environment. For example: repeated phase shifts in the entraining light:dark cycle results in increased gut leakiness when mice are challenged with an alcohol-containing diet [6] and chronic phase shifts increase mortality and cause severe colonic inflammation only when mice are challenged with dextran sodium sulfate (DSS) [7]. Our current findings and these examples support that a second environmental insult is often necessary to reveal the deleterious effects of circadian disruption. It will be of interest to determine if other conditions that impact the microbiota community such as obesity, metabolic syndrome, inflammatory bowel disease, and stress [3], [4] have a greater effect on the microbiota environment under conditions where the circadian organization is simultaneously disrupted. Inflammation appears to be the central mechanism of the deleterious health consequences of circadian disorganization, as exemplified by increased risk and severity of obesity, metabolic syndrome, cardiovascular disease, and cancer [33]–[35]. Since the intestinal microbiota is a trigger for systemic inflammation, we hypothesize that circadian-induced changes in microbiota composition play a role in mediating the deleterious health effects of circadian disorganization. There is significant interplay between the intestinal microbiota and intestinal epithelial cells [36] and this may be driving the intestinal hyperpermeability that we have observed [6] to promote intestinal and systemic inflammation. Although several circadian-induced effects on the microbiota were noted, the most dramatic effect was to augment the relative average abundance of the pro-inflammatory bacteria Ruminococcus [28]–[30], suggesting that circadian-induced changes may augment mucolytic activity to disrupt intestinal barrier function and promote inflammation. In addition, it is now established that the microbiota impact intestinal epithelial barrier integrity via a downstream mechanism involving NF-ΚB [37] with members of the genus Lactobacillus inhibiting NF-KB [38], [39]. In the present study, relative abundance of Lactobacillus was decreased in the stool of high-fat, high-sugar diet-fed, circadian disrupted mice. This finding aligns with our previous studies demonstrating that circadian rhythm disruption promotes DSS-induced colitis and intestinal hyperpermeability [6], [7], both pathological states where barrier integrity is disrupted. Thus, there are multiple mechanisms by which microbiota can disrupt intestinal barrier integrity, trigger systemic inflammation, and promote pathologies (e.g., metabolic syndrome, cancer, cardiovascular disease) [40], [41]. Stress (i.e., physical, psychological) affects the intestinal microbiome in laboratory animals [42]–[45] and humans [46], [47]. For example, social disruption increases both Bacteroidetes and Clostridia (i.e., Firmicutes) whereas grid-floor-induced stress increases Bacteroidetes and Actinobacteria and physical restraint stress has been reported to reduce overall bacterial community diversity and richness [48]. Our current study distinguishes itself from these previous studies in that significant circadian-induced effects were limited to bacteria from the phylum Firmicutes. One interpretation of these data is that chronic circadian disruption is a form of long-term biological stress capable of inducing changes in microbiota that differentiates itself from other types of physical or psychological stress. It is noteworthy that in humans, chronic circadian disruption, which occurs in shift workers, is associated with reduced sleep time and disturbance in the timing of sleep relative to other 24-hour biological rhythms [49]. Sleep loss is associated with disturbances of hormone secretion and metabolism, and voluntary partial sleep curtailment is a widespread practice in modern societies. Indeed chronic short sleep has been associated with obesity, diabetes, and cardiometabolic disorders, which themselves have been linked with the composition of the microbiota and intestinal hyperpermeability [50], [51]. For example, we have reported that disruption of sleep in mice increases the severity of symptoms in a DSS model of colitis [10]. Furthermore, sleep disruption is a feature commonly reported in patients with inflammatory bowel disease [52], [53] and a recent study found that patients with disrupted sleep have a higher risk of disease flare-up during the following six months indicating the deleterious impact of poor sleep [54]. The role of microbiota and dysbiosis in pathogenesis of colitis is now well-established [3]; thus, the negative impact of disruption of sleep and circadian rhythms on IBD course could be due to dysbiosis induced by circadian misalignment. The changes in the microbiota that we observed in the present study could be due, at least in part, to chronic sleep loss. Further studies are needed to differentiate the effects of chronic circadian disruption and chronic sleep loss as possible contributing factors to microbiota dysbiosis. The finding that the intestinal microbiome can be altered following chronic disruption of normal circadian function raises the possibility that the composition of the microbiome at other anatomical sites (e.g., skin, vagina) might also be impacted by disturbances in the local or organism-wide circadian organization. Given the rapidly growing evidence that different microbiome structures may underlie a variety of pathological disorders, it is of great importance to determine how disruption of normal circadian time structure, such as that which occurs in the elderly, in shift workers, during jet lag, and in many individuals living on different sleep-wake schedules during the week compared to weekends and holidays (i.e., social jet lag) impacts the microbiota at various anatomical sites. Indeed, it may well be that many of the adverse effects that have been observed in circadian rhythm disrupted humans may be due in part to dysbiosis. There are some limitations associated with or study. (1) Our study did not include age matched chow-fed mice therefore it is difficult to assess what changes may have occurred as a consequence of age. To the best of our knowledge, there are no studies demonstrating age-related effects on the microbiota but establishing this in our model will be useful to best interpret our results. (2) Likewise, the lack of an age-matched chow fed control group means it is difficult to assess the potential impact of the liquid diet itself or the content (e.g., availability of fermentable fiber/carbohydrates) on our outcomes. Future studies evaluating the effect of a high-fat diet in solid form will certainly be informative on this matter. (3) Finally, future studies evaluating microbial community function will be informative in determining the effects circadian rhythm-induced changes in microbiome function including short chain fatty acid production, metabolites linked to stress, obesity, insulin resistance, inflammation, and the gut-brain axis. Despite these limitations we have, for the first time, determined that circadian disorganization impacts the intestinal microbiome in high-fat, high-sugar fed mice. The present results add a new environmental factor that can alter the intestinal microbiota; however, it is not clear how disruption of circadian rhythms induces changes in the intestinal microbiota community. Our hypothesis is that disrupted host circadian organization alters the circadian clock of the microbiota leading to a change in the intestinal microbiota community structure. However, at the present time, there is no evidence that the intestinal microbiota possess intrinsic circadian organization that is independent from the host circadian structure although it has been established that cyanobacteria harbors a circadian timing mechanism. It has been conservatively estimated that animals have had a symbiotic relationship with microbes for 500 million years, and given the recent evidence that circadian clocks may have evolved in prokaryotes 2.5 billion years ago [55], [56], the host and microbiota circadian clock systems may have co-evolved with nutrient availability and/or the type of nutrient composition in the diet acting to entrain circadian rhythms in the intestinal microbiota. Our findings provide a rationale to study intrinsic circadian clock in intestinal microbiota and the potential impact of host circadian organization on the bacterial circadian clock system.

Author Contributions Conceived and designed the experiments: AK RMV CBF FWT. Performed the experiments: RMV CBF PE. Analyzed the data: RMV SJG EM MHV. Contributed reagents/materials/analysis tools: SJG AK. Wrote the paper: RMV CBF SJG EM PE MHV FWT AK.