Rhythmic Bacteria as Biomarkers for Circadian-Related Diseases

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Marquet This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5723754/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Recent studies suggest that the human circadian clock influences periodic changes in the composition of the gut microbiota, which is essential for maintaining host health. This connection has led researchers to hypothesize that the disruption of the circadian clock may impact human health via the gut microbiota. Here, we hypothesize that rhythmic bacteria—those whose abundance fluctuates in a circadian pattern—are key drivers of the differences in gut microbiota composition between healthy individuals and those with circadian-related diseases. Even in the absence of a causal relation, identifying rhythmic bacteria associated with circadian-related diseases can reveal disease biomarkers as well as intervention strategies. To test this, we first conducted a systematic review to identify rhythmic bacteria reported in the literature. Then, we mapped these bacteria onto a reference gut microbiota dataset of nearly 4,800 healthy individuals from a previously curated metagenomic database. We use this data to assess the prevalence and abundance of bacteria. To examine significant bacteria in samples from individuals with circadian-related diseases, including type 2 diabetes, hypertension, atherosclerotic cardiovascular disease, colorectal cancer, metabolic syndrome, and inflammatory bowel disease, we compared disease datasets from several previous studies with their respective healthy controls. Of the eight rhythmic bacteria identified in previous studies, seven were among the top 100 most prevalent and abundant species in the gut. We found the rhythmic bacterium Roseburia faecis to be strongly and exclusively associated with circadian-related diseases, suggesting its use as a biomarker and possibly coadjuvant in the treatment of these diseases. Clinical trial number: not applicable. Human gut microbiota Dysbiosis Circadian rhythms Rhythmic bacteria Circadian disruption Roseburia faecis Rhythmic bacteria in circadian-related diseases Figures Figure 1 Figure 2 1 Introduction The circadian clock drives periodic physiological changes and metabolic fluctuations critical for maintaining health [ 1 , 2 ]. This clock operates through two components: a central clock located in the suprachiasmatic nucleus (SCN) in the anterior hypothalamus, and peripheral clocks found in all body cells [ 3 ]. The central clock is regulated by light and coordinates signals to the peripheral clocks, which are also regulated by behavioral cues, such as feeding times [ 3 ]. This dual input allows for the decoupling of central and peripheral clocks, often observed in shift workers or individuals with sleep-wake disorders, such as jet lag [ 1 ]. Given that many metabolic pathways, such as glucose, glycogen and lipid metabolism, as well as blood pressure, heart rate, and body temperature, are regulated by the circadian clock [ 2 , 4 ], any decoupling or desynchronization, referred to as circadian disruption, has been linked to the onset and worsening of various diseases, including type 2 diabetes, obesity, cardiovascular diseases, hypertension, inflammatory bowel disease, and some types of cancer [ 2 , 4 ]. Since these diseases have also been associated with the gut microbiota [ 5 – 8 ], researchers have proposed that the gut microbial community may serve as a key, if not a causal factor linking circadian disruption to the metabolic imbalance that leads to these diseases [ 9 – 13 ]. Dysbiosis is commonly used to describe an imbalance in the gut microbiota associated with disease [ 14 ]. Differences in the gut microbiota compositions of healthy versus diseased individuals have been extensively reviewed and analyzed across several dimensions of biodiversity, including community richness, composition, and the abundance or proportion of specific taxonomic groups [ 5 , 15 – 18 ]. Although establishing a causal relationship between changes in gut microbiota and disease is difficult to prove [ 5 , 14 , 16 ], several studies have made an effort to explore bacterial production of key metabolites related to disease or conducted fecal transplant of gut microbiota from diseased individuals into animal models (primarily mice) to test for changes in disease biomarkers [ 5 , 16 , 17 ]. However, fecal transplantation experiments present methodological challenges and difficulties in standardization, raising questions about the generalizability of these studies’ conclusions (e.g., Gheorghe, 2021 [ 19 ]) and highlighting the need for further research on gut microbiota. Certain gut bacteria exhibit rhythmic fluctuations associated with the circadian clock, which may be altered under conditions of circadian disruption. In humans, it has been observed that rhythmic dynamics in the gut microbial community involve fluctuations of 10 to 15% of the detected bacteria [ 9 , 20 ], henceforth referred to as rhythmic bacteria. These bacteria demonstrate statistically detectable periodic changes in abundance over a 24-hour cycle, which significantly differ in mice under conditions of circadian disruption [ 9 ]. However, the underlying causes of these fluctuations in abundance remain unclear. Most evidence from mouse studies suggests that feeding time is the primary driver [ 9 , 10 , 21 ]. Nevertheless, one study that used parenteral nutrition found fluctuations in bacterial abundance even without the direct input of nutrients into the gut [ 22 ]. The effects of circadian disruption on gut microbiota have been primarily studied in mice [ 9 , 10 , 21 , 23 ], using various perturbations, including changes in the light-dark cycle [ 9 , 10 , 23 ], variations in feeding schedules [ 9 , 21 ], and gene knockouts in components of the molecular clock [ 9 , 10 ]. In humans, studies have examined the effects on the general composition of the gut microbiota after jet lag [ 9 ], sleep deprivation [ 24 – 26 ], and shifts in sleep patterns [ 27 ]. Although some studies found no significant differences in the overall richness of gut microbiota species or in the composition of genera [ 25 , 28 ], other studies reported significant changes in the abundance of certain taxonomic groups within the gut microbiota [ 9 , 26 , 27 ]. The importance of these changes was assessed by Thaiss et al., who compared disease biomarkers after fecal transfer experiments of germ-free mice colonized with human microbiota, before and after jetlag. Mice colonized with jetlag microbiota showed weight gain, higher glucose levels after an oral glucose challenge, and greater accumulation of body fat [ 9 ]. Given that the studies in mice have shown that circadian clock disruption leads to changes in the abundance of certain genera within the gut microbiota [ 21 ], we focus here on the relationship between circadian disruption and gut microbiota composition. We hypothesize that rhythmic bacteria are key drivers of the differences in gut microbiota composition between healthy individuals and those with circadian-related diseases. Specifically, we propose that these bacteria are critical due to fluctuations in their abundance throughout the day, reflecting their sensitivity to environmental changes driven by the components of the circadian clock. Consequently, they are likely to be more affected by circadian disruption and could play a key role in mediating its effects on disease development. If this hypothesis is correct, we expect rhythmic bacteria to show significant differences in abundance between healthy individuals and those with circadian-related diseases. While we anticipate rhythmic bacteria to be affected, broader changes in the gut microbiota may also occur as the microbial community structure becomes destabilized during disease onset. This study aims to contribute to the understanding of the interplay between circadian rhythms in the gut microbiota in the context of health and disease. To assess this hypothesis, we will test whether the abundance of rhythmic bacteria in the gut microbiota differs between healthy individuals and those suffering from circadian-related diseases. We first identify rhythmic bacteria previously reported in studies of the human gut microbiota. Second, to assess the overall importance of the gut microbiota composition, we study the abundance and prevalence of rhythmic bacteria using a reference human gut microbiota constructed from the taxonomic groups identified in nearly 4,800 samples compiled previously in a curated metagenomic database [ 29 ]. Additionally, we will examine gut microbiota composition from data in the same database for individuals with six circadian-related diseases (type 2 diabetes, hypertension, atherosclerotic cardiovascular disease, colorectal cancer, inflammatory bowel disease, metabolic syndrome, and one non-circadian disease). For each disease dataset, we compare the microbial composition and abundance with that of healthy controls from the same study, focusing specifically on identifying rhythmic bacteria that show significant differences between the two groups. 2 Methods 2.1 Published rhythmic bacteria We conducted a comprehensive search of published articles on PubMed and Web of Science (WOS) updated last August 2024, using the following search terms: (”Gastrointestinal Microbiome,” OR gut microbiome OR gut microbiota OR gut bacteria OR dysbiosis) AND (circadian clocks OR circadian rhythm) AND (Human) NOT (Review). based on the following inclusion criteria: i) human fecal samples taken at different times of the day, ii) analyses of bacterial abundances, and iii) identified taxa with circadian changes in abundance. Our search yielded two articles that met all criteria [ 9 , 20 ]. Taxonomic groups were assigned in both studies using 16S rRNA sequences, with rhythmicity determined using either the JTK-cycle algorithm [ 9 ] or cosine-wave fitting analysis [ 20 ]. In the first study conducted by Thaiss’ team, samples came from two volunteers. The overall health, feeding time, and use of antibiotics were not documented in this study [ 9 ]. In the second study by Reitmeier’s group, reported stool samples came from a German cohort of 1,943 volunteers with recorded times of defecation. This study tested gut microbiota from subjects with T2D and healthy individuals [ 20 ]. We considered only those bacterial species (87) that were rhythmic in healthy individuals (subjects with nonT2D, Prediabetes, or a BMI > 30) [ 20 ]. Considering the low number of studies that we were able to identify, we tagged bacteria as “rhythmic” based on two criteria: 1) if they were identified at the species level in both studies, or 2) the bacteria were identified as rhythmic at the species level in one study, and as rhythmic at the genus level in the second study. Although the limited number of studies means that we cannot conclusively state that these are the only rhythmic bacteria, our conservative approach provides a minimal set of candidates found in two distinct datasets, thereby accounting for variability in factors such as geography, age, and methodology [ 30 ]. 2.2 Human gut microbiota database filtering To construct a reference human gut microbiota, we obtained pre-processed metagenomic data from CuratedMetagenomicData (v.1.20.0) database [ 29 ], last accessed in January 2022. We used taxonomic composition and relative abundances of bacterial species identified and curated by the authors based on their metagenomic analyses [ 29 ]. We accessed 79 curated metagenomic datasets derived from different studies, each encompassing multiple stool samples from individuals across 38 different countries. For our reference human gut microbiota, we included only data from healthy adults aged 19 to 70 years who had not recently used antibiotics. In the same CuratedMetagenomicData database, we searched for studies that presented stool samples from patients with diseases previously associated and not associated with circadian disruption [ 2 , 4 ]. We utilized the taxonomic classification provided by the database and applied selection criteria to ensure that only samples from subjects who had not taken antibiotics, patients diagnosed with a single disease, and studies with a corresponding set of control subjects were included (Table 1 ). The circadian-related diseases analyzed in this study are atherosclerotic cardiovascular disease (ACVD), colorectal cancer (CRC), inflammatory bowel disease (IBD), hypertension (Ht), type 2 diabetes (T2D), and metabolic syndrome (MS). These disease samples come from nine studies. Here each study’s disease group was compared to its respective healthy controls in separate analyses to avoid confounding factors such as batch effects (Table 1 ). Cirrhosis, whose onset has not been previously linked to circadian disruption in humans, was used as a control disease (Table 1 ). Data compilation and analyses were conducted in R Studio v.4.2.1[ 31 ]. Table 1 Human gut microbiota samples of circadian-related and non-circadian-related diseases used in this revision obtained from CuratedMetagenomicBase [ 29 ]. The abbreviations correspond to atherosclerotic cardiovascular disease (ACVD), colorectal cancer (CRC), inflammatory bowel disease (IBD), hypertension (Ht), type 2 diabetes (T2D), and metabolic syndrome (MS). Disease n° Control individuals n° Disease individuals Reference ACVD 156 179 [ 32 ] CRC1 29 26 [ 33 ] CRC2 28 26 [ 34 ] CRC3 10 6 [ 35 ] Cohort A CRC4 29 32 [ 35 ] Cohort B IBD 14 46 [ 36 ] Hypertension 41 99 [ 37 ] T2D1 33 36 [ 38 ] T2D2 9 15 [ 39 ] MS 5 10 [ 40 ] Cirrhosis 114 8 [ 41 ] 2.3 Data analysis 2.3.1 Alpha diversity analysis The alpha diversity metrics provided insights into the overall diversity of gut microbiota across different health statuses. To validate differences in the disease datasets (Table 1 ) between healthy and diseased gut microbiota, we measured the richness and evenness of microbial species within individual samples. We assessed alpha diversity using the Phyloseq package v.1.40.0 in R v.4.2.1 [ 31 , 43 ]. 2.3.2 Beta diversity analysis We conducted a second analysis to validate differences between healthy and diseased individuals from the disease dataset comparing community structure. Beta diversity analysis was performed using the Bray-Curtis dissimilarity method using the Vegan R package v.2.6-2 dissimilarity test [ 44 ]. The significance of observed dissimilarities was statistically assessed using permutational multivariate analysis of variance (PERMANOVA) with adonis2 function from the Vegan R package [ 44 ]. 2.3.3 Species contribution analysis The contribution of individual microbial species to the differences in community composition was performed on datasets showing significant differences in alpha or beta diversities. We used a Similarity Percentage Analysis (SIMPER) using the simper function from Vegan R package v.2.6-2 [ 44 ]. This method identified the specific species driving the observed differences between healthy controls and patients with circadian-related diseases, providing a deeper understanding of the underlying shifts in the gut microbiota. 2.3.4 Significant differences in species abundance Differences in the abundance of microbial species between healthy and diseased groups were evaluated using the Wilcoxon rank-sum test (Mann-Whitney) implemented in R Stats Package v.4.2.1 [ 31 ]. To control for type I errors due to multiple comparisons, the false discovery rate (FDR) method was applied, ensuring the robustness of the statistical findings. 2.3.5 Data visualization Visual representations of alpha and beta diversity were created to illustrate the differences in microbial community structures using ggplot2 R package v.3.5.1 [ 45 ]. 3 Results 3.1 Identifying and characterizing rhythmic bacteria Our systematic review of articles focused on identifying rhythmic bacteria in the human gut microbiota retrieved 124 studies. Among these, two studies met our search criteria, namely: i) they used fecal human samples from different times of the day, ii) they calculated bacterial abundances, and iii) they searched for circadian patterns in variation of abundances of bacterial taxa. The limited number of studies underscores the need for a conservative approach to identify consistently rhythmic bacteria in the human gut (see Methods). A comparison of the bacteria reported as rhythmic in the studies by Thaiss’s [ 9 ] and Reitmeier’s[ 20 ] groups revealed eight rhythmic bacterial species (Table 2 ). Notably, only Bacteroides dorei was identified as rhythmic at the species level in both studies, while the other seven species were identified as rhythmic at the genus level by Thaiss’s group [ 9 ] and at the species level by Reitmeier’s group[ 20 ]. These eight rhythmic bacteria belong to the two most abundant phyla of the gut microbiota: Firmicutes (four species) and Bacteroidetes (four species). Table 2 Species identified as rhythmic by Reitmeier et al. [ 20 ] with genera identified as rhythmic by Thaiss et al. [ 9 ]. The columns report mean abundance (over the nearly 4800 samples) and prevalence in the reference human gut microbiota compiled from curatedMetagenomicBase [ 29 ]. Species Abundance (%) Prevalence % Abundance Rank Prevalence Rank Bacteroides vulgatus 4.758 87.74 3 18 Bacteroides uniformis 4.409 93.237 4 9 Roseburia faecis 2.454 85.717 11 21 Bacteroides dorei 2.157 69.534 12 47 Ruminococcus torques 1.317 88.353 19 16 Dorea formicigenerans 0.539 93.216 43 10 Ruminococcus gnavus 0.237 36.596 70 91 Bacteroides koreensis - - - - To explore the position (abundance and prevalence) of these bacteria in the human gut microbiota, we constructed a reference human gut microbiota database using data from the CuratedMetagenomicBase database [ 29 ]. After filtering the data to include only healthy adults with no history of antibiotic use, the final reference gut microbiota comprised stool samples from 4,783 unique healthy adults across 17 different countries. Mapping the rhythmic bacteria in this reference database revealed that they are among the hundred most prevalent and abundant species in the human gut microbiota (Table 2 and Fig. 1 ), with the exception of Bacteroides koreensis , which is not present in the database. B. koreensis was first isolated from the feces of a healthy Korean adult [ 46 ], and the name ”koreensis” denotes its association with Korea. We did not find samples from Korea in our database; however, since B. koreensis was detected in the German cohort reported by Reitmeier et al. [ 20 ], its absence from our reference database is likely due to its recent description (2017) relative to the data in the CuratedMetagenomicBase, rather than indicating an exclusive presence in Korean populations. 3.2 Microbiota-level differences between disease and healthy humans We hypothesized that rhythmic bacteria would have different abundances in individuals suffering from circadian-related diseases than in healthy individuals. To test this, our first step was to identify which disease datasets exhibited significant differences from their healthy control samples, as this would guide our search for rhythmic bacteria with differential abundances. It is important to note that all comparisons between healthy and diseased gut microbiota were restricted to samples from the same study. We began by evaluating beta-diversity using Bray-Curtis dissimilarity analysis. The results showed that dissimilarity values were comparable within, and between samples for each dataset (Supp. Table 1 (Additional file 3) and Supp. Figure 1 (Additional file 1)). Although the proportion of variance explained by disease status (R² in Supp. Table 2 (Additional file 3)) was generally low across all datasets, pairwise multivariate analysis revealed significant differences between healthy individuals and those with ACVD, CRC, metabolic syndrome, and T2D (Supp. Table 2 (Additional file 3)). In contrast, no significant differences were found for hypertension or IBD. Next, we looked for species diversity (alpha diversity) differences between diseased and healthy individuals (see Methods). Our analysis revealed that almost all circadian-related diseases differed significantly in Observed alpha diversity except for hypertension and metabolic syndrome (Supp. Figure 2 (Additional file 2) and Supp. Table 3 (Additional file 4)). Concerning the general values of the Observed alpha diversity, there is no trend of higher or lower diversity in the gut of diseased patients. It is important to emphasize that dysbiosis, by definition, does not necessarily imply a reduction or increase in species richness but is best described as an imbalance [ 14 ]. In summary, our findings reveal that four out of six sets of samples from circadian-related diseases exhibit a dysbiotic component, therefore, those are useful for identifying specific differences involving rhythmic species. The non-circadian disease, cirrhosis, was incorporated as a negative control to search for changes in bacteria belonging to the rhythmic group. Here, the cirrhosis patients also have a gut microbiota with a composition significantly different from the control subjects (Supp. Table 2 (Additional file 3)). These patients’ gut microbiota also have significantly different observed alpha diversity (Supp. Table 3 (Additional file 4)). Thus, this disease serves as a useful comparison with circadian-related diseases. 3.3 Differential abundance of rhythmic bacteria in health and disease Finally, to determine if rhythmic bacteria are involved in the differences observed in the previous section, we first identified bacteria that contribute to Bray-Curtis dissimilarity between samples by performing a SIMPER analysis (Supp. Table 4 (Additional file 5)). This analysis shows that among the species contributing to 70% of the dissimilarities, there is only one rhythmic bacterium, R. gnavus. We found this bacterium contributing to the dissimilarity of all circadian-related diseases, explaining between 2.9% and 5.9%. In the non-circadian-related disease, R. gnavus was also the only rhythmic bacteria contributing to differences with 3.9%, which suggests that R. gnavus is not exclusively associated with diseases due to circadian disruption. Next, we looked for differentially abundant species between groups using a Mann-Whitney test FDR corrected (Supp. Table 5A-E (Additional file 6)). Among the circadian-related diseases, only datasets from ACVD, T2D, and CRC showed significantly different abundances of bacteria between patients and controls. In ACVD, B. uniformis , R. gnavus , and R. faecis exhibited significant differences (Figs. 2 A and B). R. gnavus and R. faecis , along with B. vulgatus , are significantly different for CRC (Figs. 2 A and C). However, in T2D, no rhythmic bacteria showed significant differences. In the non-circadian related disease, we identified four significantly different rhythmic bacteria. Among them, we find three of the bacteria detected in ACVD and CRC (Figs. 2 A and D). Interestingly, R. gnavus , as in the SIMPER analysis, is significantly different in these three diseases. The mean abundance of this bacteria is always higher in the samples from volunteers with some of the three diseases. Still, as previously concluded, the association of R. gnavus is not exclusively related to circadian patterns, as it also differs in non-circadian-related diseases. The only bacterium significantly different exclusively in the circadian-related diseases assessed here is R. faecis , which shows lower abundances in both contexts. 4 Discussion In this study, we conducted a two-stage analysis: first, a systematic review to identify rhythmic bacteria in the human gut microbiota; second, an analysis aimed to test if these bacteria are significantly different in the gut microbiota from healthy individuals and those with circadian-related diseases. Our findings suggest weak support for a general link between rhythmic bacteria and circadian-related diseases. Only two diseases showed significant differences in rhythmic species compared to healthy controls, with two of these species also differing in cirrhosis, a non-circadian-related disease. This suggests that rhythmic bacteria, as a group, are not uniquely influenced by circadian disruptions that lead to disease. However, we did identify Roseburia faecis , which was uniquely altered in circadian-related diseases, hinting at a potential specific association with these conditions. Further research involving additional diseases would clarify these potential connections. Despite the weak overall association, our study provides valuable insight into rhythmic bacteria, a relatively unexplored group. The eight rhythmic species shared between the studies by Thaiss et al. and Reitmeier et al. belong to the Firmicutes and Bacteroidetes phyla [ 9 , 20 ]. These bacteria, with the exception of Bacteroides koreensis , which was not found in the reference database, rank among the top hundred most abundant and prevalent species in this reference gut microbiota dataset (Fig. 1 and Table 2 ). These seven mapped bacteria, identified at the species level in Reitmeier et al., were not in the top hundred abundant groups in that research, with the exception of B. dorei [ 20 ]. Moreover, only a few of the most abundant bacteria detected in that study showed a rhythmic pattern. This supports the idea that rhythmic patterns are not merely detectable in highly abundant bacteria, suggesting there is no detection bias influencing their detection. We tested gut microbiota differences by comparing diseased individuals to their respective healthy controls within each study before identifying bacteria that were differentially abundant. Our results largely aligned with previous studies reporting differences, with a few exceptions. Inconsistencies in findings regarding gut microbiota differences between healthy individuals and diseased patients are quite common [ 47 , 48 ]. These discrepancies can be attributed to various confounding factors such as diet, age, gender, and methodological differences between research groups [48– 50 ]. Nonetheless, by focusing on diseases where the gut microbiota shows consistent and significant differences, we can still leverage these findings to identify significantly different bacteria, including rhythmic bacteria. We focused on diseases that showed significant differences in alpha or beta diversity and analyzed the differential abundance of rhythmic bacteria. Of the eight rhythmic bacteria identified, four showed significant differences in abundance in two of these diseases (Fig. 2 A and B). Notably, Ruminococcus gnavus and Roseburia faecis differed significantly in both ACVD and CRC. Additionally, two species from the Bacteroides genus were found to differ significantly: B. uniformis in ACVD and B. vulgatus in CRC. Interestingly, these two species were also significantly altered in cirrhosis, a non-circadian-related disease included in our analysis. This suggests that these bacteria may respond to multiple physiological stressors beyond circadian disruption, further highlighting their potential relevance in disease contexts beyond the circadian framework. Among the rhythmic bacteria, we identified species known for producing short chain fatty acids (SCFAs), particularly those belonging to the Bacteroides and Roseburia genera [ 51 – 53 ]. SCFAs are related to functions in the host that involve the cellular barrier function, modulating immune responses through T regulatory and effector cells, and contributing to glucose metabolism and insulin sensitivity [ 8 ]. These metabolites exert positive effects on the host through anti-inflammatory activities, appetite regulation, and participation in synthesizing cholesterol [ 54 ]. Consistent with these beneficial effects, the three rhythmic species belonging to these SCFA-producing genera ( R. faecis, B. uniformis , and B. vulgatus ) are increased in the healthy gut microbiota compared to their diseased counterparts (Fig. 2 B-D). Notably, R. gnavus was the most outstanding bacterium that differentiated between healthy and diseased gut microbiota. It consistently contributed to Bray-Curtis dissimilarities (Sup. Table 4A and B) and was significantly more abundant in ACVD, CRC, and cirrhosis (Fig. 2 B-D). This bacterium has been linked to mucin degradation and inflammation in conditions such as Crohn’s disease and metabolic syndrome [ 55 , 56 ]. Its inflammatory role is further supported by its production of an inflammatory polysaccharide [ 57 ]. In a recent study, Grahnemo et al. also associated R. gnavus with multiple metabolic syndrome factors, including elevated C-reactive protein, a marker of systemic inflammation [ 58 ]. Importantly, the Grahnemo et al. study suggested that R. gnavus abundance increases prior to the onset of metabolic markers, implying a potential causal role. The reason for this elevated abundance remains unclear, but R. gnavus ’s ability to utilize mucin as a carbon source may provide an advantage during fasting periods, which are common in circadian disruptions. Its increase in non-circadian-related conditions like cirrhosis, however, suggests that multiple mechanisms likely drive its growth. Roseburia faecis , a SCFAs producer, was the only bacterium significantly different exclusively in circadian-related diseases, with reduced abundance in both ACVD and CRC (Fig. 2 A-C). R. faecis main SCFA product is butyrate [ 59 ], which is known to be involved in the modulation of blood pressure and in maintaining the intestinal barrier [ 8 ]. Furthermore, as mentioned earlier, SCFAs are anti-inflammatory metabolites [ 54 ] and have been shown to be protective in circadian-related diseases like CRC and IBD [ 60 ]. Thus, the reduction of R. faecis and its butyrate production in disease states aligns with the pathophysiology of these conditions. Although the precise causal direction between the decrease in R. faecis abundance and disease onset remains unclear, the protective function of butyrate suggests a scenario where reduced R. faecis levels may precede disease development, contributing to the disruption of gut homeostasis. The reasons behind this decrease are still uncertain. Nutritionally, R. faecis can utilize a wide range of substrates, from simple sugars like glucose to more complex polysaccharides [ 59 ], so it is unclear whether environmental nutrient shifts (derived from circadian variations) could significantly affect its growth rate. Ecologically, competitive interactions in the gut may influence its abundance. There is evidence of one gut strain of R. faecis producing a bacteriocin in the presence of Bacillus subtilis [ 61 ], that suggest it has competitive pressures in the gut. While advantageous, bacteriocin production is metabolically costly [ 62 ], and the energy expenditure required for it could negatively impact R. faecis growth in highly competitive environments. For instance, prolonged fasting due to circadian disruption could reduce nutrient availability, creating a highly competitive situation among gut microbes. Further exploration of these interactions could shed light on the dynamics of R. faecis in a disease context. We find particularly interesting to follow changes on R. faecis in the context of circadian-disruption to test its potential as a biomarker of changes in the gut microbiota generated by this disruption. The association of R. faecis with circadian-related diseases suggest that this bacteria could be progressively changing, for example, in individuals starting a shift work regime. In this context, specifically monitoring this bacteria as marker of the state of the gut microbiota, would allow early actions preventing major changes in the general composition of the gut microbiota. A major limitation of our study is the restricted availability of data for identifying rhythmic bacteria. We based our findings on only two studies that analyzed fecal samples collected at different times of day [ 9 , 20 ]. To mitigate the risk of overestimation and ensure scientific rigor, we adopted a conservative approach by cross-referencing the two datasets, prioritizing the avoidance of false positives (i.e., false rhythmic bacteria). The methodological differences (sample sizes, schedule strategies, circadian detection algorithms, and the nationality of the subjects), contributed to the robustness of our findings. These variations provided a unique opportunity to identify a consistent group of rhythmic bacteria while simultaneously reducing the risk of overestimating the number of rhythmic species. Nonetheless, two datasets are insufficient to definitively conclude that the eight bacteria identified here are the complete set of rhythmic bacteria in the human gut microbiota. Another significant limitation is the absence of detailed information regarding the disease etiology and microbiota progression in the subjects studied. We assumed that healthy controls represented the initial state of the gut microbiota and that disease subjects reflected the final state caused by circadian disruption. This assumption limits our understanding of the intermediate stages of disease progression and how these may influence changes in rhythmic bacteria. This consideration is particularly relevant when analyzing cirrhosis as a control disease. Although we found no evidence linking circadian disruption to the onset of cirrhosis, it is important to note that once cirrhosis develops, patients may experience circadian disruption [ 63 ], likely due to the liver’s robust circadian regulation [ 4 ]. As Bruyneel and Sersté point out, circadian disruption in cirrhosis typically occurs after the disease has developed rather than being a causative factor [ 63 ]. Overall, longitudinal studies tracking gut microbiota changes in individuals transitioning into circadian-disrupted conditions, such as shift work, would provide more definitive insights into these dynamics. Our study adds to the growing body of knowledge on the interaction between gut microbiota and circadian rhythms. While the overall evidence for a strong relationship between rhythmic bacteria and circadian-related diseases is limited, certain species, such as R. gnavus and R. faecis , may hold promise for further investigation into their roles in health and disease. In particular, R. faecis stands out as a promising marker of circadian disruption in the gut microbiota. Declarations This work was supported by National Agency of Research and Development of Chile ANID-Chile, grant PFCHA/DocNac/21180581. Competing interests The authors declare that they have no competing interests. Availability of data and materials Data and code used here are available at: https://github.com/pameubillag/Chap1_Rhythmic_bact_and_disease. Clinical trial number: not applicable. Ethics Declaration: not applicable. Author contribution Conceptualization: P.K.U., P.A.M., and E.F. Investigation: P.K.U. Methodology: P.K.U., P.A.M., and E.F. Formal analyses: P.K.U., P.A.M., and E.F. Visualization: P.K.U. Writing original draft: P.K.U. Writing, review, and editing: P.K.U., P.A.M., and E.F. Acknowledgments We would like to acknowledge the National Agency of Research and Development of Chile ANID-Chile for financing this research. P.A.M acknowledges support from Centro de Modelamiento Matemático (CMM), Grant FB210005, BASAL funds for centers of excellence from ANID-Chile, and Proyecto Exploración 13220168. References Potter, G.D., Skene, D.J., Arendt, J., Cade, J.E., Grant, P.J., Hardie, L.J.: Circadian rhythm and sleep disruption: causes, metabolic consequences, and countermeasures. Endocrine reviews 37 (6), 584–608 (2016) Fishbein, A.B., Knutson, K.L., Zee, P.C., et al.: Circadian disruption and human health. The Journal of clinical investigation 131 (19) (2021) Mohawk, J.A., Green, C.B., Takahashi, J.S.: Central and peripheral circadian clocks in mammals. 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Supplementary Files Additionalfile1.pdf Additionalfile2.pdf Additionalfile3.pdf Additionalfile4.pdf Additionalfile5.csv Additionalfile6.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5723754","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396212561,"identity":"2bee2c6a-e574-4d45-97ed-c7ca7359d4f5","order_by":0,"name":"Pamela Karina Ubilla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYDACHgY2hoQCCwZ+MK+AaC0GEgySDSCeAbFaGIBaDA4Qq8Wc54zZgwcGEvLGN5IfMPwgRotlb4+5AdBhhttupBkw9hCjxeA8j5kEUAvjths5DMxEOQymxX7zDKK1nO0Ba0ncIEGsFsueY+UgvyTPOPPM4CBRfjHnSd728EeFjW1/e/LDBz8qiHEYMucAERqIjO5RMApGwSgY4QAActYusMM0QDoAAAAASUVORK5CYII=","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":true,"prefix":"","firstName":"Pamela","middleName":"Karina","lastName":"Ubilla","suffix":""},{"id":396212562,"identity":"d60769bd-582c-48d3-98fb-6b6e30915c40","order_by":1,"name":"Evandro Ferrada","email":"","orcid":"","institution":"Universidad de Valparaíso","correspondingAuthor":false,"prefix":"","firstName":"Evandro","middleName":"","lastName":"Ferrada","suffix":""},{"id":396212563,"identity":"bee13efd-9dea-4c87-86e1-733cb0eca7ba","order_by":2,"name":"Pablo A. Marquet","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"A.","lastName":"Marquet","suffix":""}],"badges":[],"createdAt":"2024-12-27 23:08:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5723754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5723754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72809132,"identity":"a2d661a7-6a1a-4375-af30-9698fd799164","added_by":"auto","created_at":"2025-01-02 10:54:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155391,"visible":true,"origin":"","legend":"\u003cp\u003eMean relative abundance versus percentage of prevalence. Each dot represents bacterial species identified in the healthy adult subjects samples from the reference database (CuratedMetagenomicBase [29]). Rhythmic species are labeled and represented as red dots.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-5723754/v1/434dc225e847eb13445964fb.png"},{"id":72809133,"identity":"e6e1fcfb-0bc4-470d-9fbd-985af3d6a554","added_by":"auto","created_at":"2025-01-02 10:54:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":569214,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant bacterial species between healthy individuals and diseased patients. A) Venn diagram of diseases with rhythmic bacteria significantly different. Only rhythmic bacteria are individualized; numbers refer to the total significantly different bacteria observed in each disease. B) Relative abundance of bacteria significantly different in ACVD, C) CRC, and D) cirrhosis. 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This clock operates through two components: a central clock located in the suprachiasmatic nucleus (SCN) in the anterior hypothalamus, and peripheral clocks found in all body cells [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The central clock is regulated by light and coordinates signals to the peripheral clocks, which are also regulated by behavioral cues, such as feeding times [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This dual input allows for the decoupling of central and peripheral clocks, often observed in shift workers or individuals with sleep-wake disorders, such as jet lag [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Given that many metabolic pathways, such as glucose, glycogen and lipid metabolism, as well as blood pressure, heart rate, and body temperature, are regulated by the circadian clock [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], any decoupling or desynchronization, referred to as circadian disruption, has been linked to the onset and worsening of various diseases, including type 2 diabetes, obesity, cardiovascular diseases, hypertension, inflammatory bowel disease, and some types of cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since these diseases have also been associated with the gut microbiota [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], researchers have proposed that the gut microbial community may serve as a key, if not a causal factor linking circadian disruption to the metabolic imbalance that leads to these diseases [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDysbiosis is commonly used to describe an imbalance in the gut microbiota associated with disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Differences in the gut microbiota compositions of healthy versus diseased individuals have been extensively reviewed and analyzed across several dimensions of biodiversity, including community richness, composition, and the abundance or proportion of specific taxonomic groups [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although establishing a causal relationship between changes in gut microbiota and disease is difficult to prove [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], several studies have made an effort to explore bacterial production of key metabolites related to disease or conducted fecal transplant of gut microbiota from diseased individuals into animal models (primarily mice) to test for changes in disease biomarkers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, fecal transplantation experiments present methodological challenges and difficulties in standardization, raising questions about the generalizability of these studies\u0026rsquo; conclusions (e.g., Gheorghe, 2021 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]) and highlighting the need for further research on gut microbiota.\u003c/p\u003e \u003cp\u003eCertain gut bacteria exhibit rhythmic fluctuations associated with the circadian clock, which may be altered under conditions of circadian disruption. In humans, it has been observed that rhythmic dynamics in the gut microbial community involve fluctuations of 10 to 15% of the detected bacteria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], henceforth referred to as rhythmic bacteria. These bacteria demonstrate statistically detectable periodic changes in abundance over a 24-hour cycle, which significantly differ in mice under conditions of circadian disruption [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the underlying causes of these fluctuations in abundance remain unclear. Most evidence from mouse studies suggests that feeding time is the primary driver [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nevertheless, one study that used parenteral nutrition found fluctuations in bacterial abundance even without the direct input of nutrients into the gut [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe effects of circadian disruption on gut microbiota have been primarily studied in mice [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], using various perturbations, including changes in the light-dark cycle [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], variations in feeding schedules [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and gene knockouts in components of the molecular clock [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In humans, studies have examined the effects on the general composition of the gut microbiota after jet lag [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], sleep deprivation [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and shifts in sleep patterns [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although some studies found no significant differences in the overall richness of gut microbiota species or in the composition of genera [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], other studies reported significant changes in the abundance of certain taxonomic groups within the gut microbiota [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The importance of these changes was assessed by Thaiss et al., who compared disease biomarkers after fecal transfer experiments of germ-free mice colonized with human microbiota, before and after jetlag. Mice colonized with jetlag microbiota showed weight gain, higher glucose levels after an oral glucose challenge, and greater accumulation of body fat [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven that the studies in mice have shown that circadian clock disruption leads to changes in the abundance of certain genera within the gut microbiota [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], we focus here on the relationship between circadian disruption and gut microbiota composition. We hypothesize that rhythmic bacteria are key drivers of the differences in gut microbiota composition between healthy individuals and those with circadian-related diseases. Specifically, we propose that these bacteria are critical due to fluctuations in their abundance throughout the day, reflecting their sensitivity to environmental changes driven by the components of the circadian clock. Consequently, they are likely to be more affected by circadian disruption and could play a key role in mediating its effects on disease development. If this hypothesis is correct, we expect rhythmic bacteria to show significant differences in abundance between healthy individuals and those with circadian-related diseases. While we anticipate rhythmic bacteria to be affected, broader changes in the gut microbiota may also occur as the microbial community structure becomes destabilized during disease onset. This study aims to contribute to the understanding of the interplay between circadian rhythms in the gut microbiota in the context of health and disease.\u003c/p\u003e \u003cp\u003eTo assess this hypothesis, we will test whether the abundance of rhythmic bacteria in the gut microbiota differs between healthy individuals and those suffering from circadian-related diseases. We first identify rhythmic bacteria previously reported in studies of the human gut microbiota. Second, to assess the overall importance of the gut microbiota composition, we study the abundance and prevalence of rhythmic bacteria using a reference human gut microbiota constructed from the taxonomic groups identified in nearly 4,800 samples compiled previously in a curated metagenomic database [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, we will examine gut microbiota composition from data in the same database for individuals with six circadian-related diseases (type 2 diabetes, hypertension, atherosclerotic cardiovascular disease, colorectal cancer, inflammatory bowel disease, metabolic syndrome, and one non-circadian disease). For each disease dataset, we compare the microbial composition and abundance with that of healthy controls from the same study, focusing specifically on identifying rhythmic bacteria that show significant differences between the two groups.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Published rhythmic bacteria\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive search of published articles on PubMed and Web of Science (WOS) updated last August 2024, using the following search terms: (\u0026rdquo;Gastrointestinal Microbiome,\u0026rdquo; OR gut microbiome OR gut microbiota OR gut bacteria OR dysbiosis) AND (circadian clocks OR circadian rhythm) AND (Human) NOT (Review). based on the following inclusion criteria: i) human fecal samples taken at different times of the day, ii) analyses of bacterial abundances, and iii) identified taxa with circadian changes in abundance. Our search yielded two articles that met all criteria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaxonomic groups were assigned in both studies using 16S rRNA sequences, with rhythmicity determined using either the JTK-cycle algorithm [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] or cosine-wave fitting analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the first study conducted by Thaiss\u0026rsquo; team, samples came from two volunteers. The overall health, feeding time, and use of antibiotics were not documented in this study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the second study by Reitmeier\u0026rsquo;s group, reported stool samples came from a German cohort of 1,943 volunteers with recorded times of defecation. This study tested gut microbiota from subjects with T2D and healthy individuals [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We considered only those bacterial species (87) that were rhythmic in healthy individuals (subjects with nonT2D, Prediabetes, or a BMI\u0026thinsp;\u0026gt;\u0026thinsp;30) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the low number of studies that we were able to identify, we tagged bacteria as \u0026ldquo;rhythmic\u0026rdquo; based on two criteria: 1) if they were identified at the species level in both studies, or 2) the bacteria were identified as rhythmic at the species level in one study, and as rhythmic at the genus level in the second study. Although the limited number of studies means that we cannot conclusively state that these are the only rhythmic bacteria, our conservative approach provides a minimal set of candidates found in two distinct datasets, thereby accounting for variability in factors such as geography, age, and methodology [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Human gut microbiota database filtering\u003c/h2\u003e \u003cp\u003eTo construct a reference human gut microbiota, we obtained pre-processed metagenomic data from CuratedMetagenomicData (v.1.20.0) database [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], last accessed in January 2022. We used taxonomic composition and relative abundances of bacterial species identified and curated by the authors based on their metagenomic analyses [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We accessed 79 curated metagenomic datasets derived from different studies, each encompassing multiple stool samples from individuals across 38 different countries. For our reference human gut microbiota, we included only data from healthy adults aged 19 to 70 years who had not recently used antibiotics.\u003c/p\u003e \u003cp\u003eIn the same CuratedMetagenomicData database, we searched for studies that presented stool samples from patients with diseases previously associated and not associated with circadian disruption [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. We utilized the taxonomic classification provided by the database and applied selection criteria to ensure that only samples from subjects who had not taken antibiotics, patients diagnosed with a single disease, and studies with a corresponding set of control subjects were included (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The circadian-related diseases analyzed in this study are atherosclerotic cardiovascular disease (ACVD), colorectal cancer (CRC), inflammatory bowel disease (IBD), hypertension (Ht), type 2 diabetes (T2D), and metabolic syndrome (MS). These disease samples come from nine studies. Here each study\u0026rsquo;s disease group was compared to its respective healthy controls in separate analyses to avoid confounding factors such as batch effects (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cirrhosis, whose onset has not been previously linked to circadian disruption in humans, was used as a control disease (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Data compilation and analyses were conducted in R Studio v.4.2.1[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHuman gut microbiota samples of circadian-related and non-circadian-related diseases used in this revision obtained from CuratedMetagenomicBase\u003c/span\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe abbreviations correspond to atherosclerotic cardiovascular disease (ACVD), colorectal cancer (CRC), inflammatory bowel disease (IBD), hypertension (Ht), type 2 diabetes (T2D), and metabolic syndrome (MS).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eDisease\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003en\u0026deg; Control individuals\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003en\u0026deg; Disease individuals\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eReference\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eACVD\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e156\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e179\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCRC1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e29\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e26\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCRC2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e28\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e26\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCRC3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e6\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCohort A\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCRC4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e29\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e32\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCohort B\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eIBD\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e14\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e46\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHypertension\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e41\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e99\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eT2D1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e33\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e36\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eT2D2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e9\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e15\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMS\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCirrhosis\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e114\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e8\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Alpha diversity analysis\u003c/h2\u003e \u003cp\u003eThe alpha diversity metrics provided insights into the overall diversity of gut microbiota across different health statuses. To validate differences in the disease datasets (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) between healthy and diseased gut microbiota, we measured the richness and evenness of microbial species within individual samples. We assessed alpha diversity using the Phyloseq package v.1.40.0 in R v.4.2.1 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Beta diversity analysis\u003c/h2\u003e \u003cp\u003eWe conducted a second analysis to validate differences between healthy and diseased individuals from the disease dataset comparing community structure. Beta diversity analysis was performed using the Bray-Curtis dissimilarity method using the Vegan R package v.2.6-2 dissimilarity test [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The significance of observed dissimilarities was statistically assessed using permutational multivariate analysis of variance (PERMANOVA) with adonis2 function from the Vegan R package [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Species contribution analysis\u003c/h2\u003e \u003cp\u003eThe contribution of individual microbial species to the differences in community composition was performed on datasets showing significant differences in alpha or beta diversities. We used a Similarity Percentage Analysis (SIMPER) using the simper function from Vegan R package v.2.6-2 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This method identified the specific species driving the observed differences between healthy controls and patients with circadian-related diseases, providing a deeper understanding of the underlying shifts in the gut microbiota.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Significant differences in species abundance\u003c/h2\u003e \u003cp\u003eDifferences in the abundance of microbial species between healthy and diseased groups were evaluated using the Wilcoxon rank-sum test (Mann-Whitney) implemented in R Stats Package v.4.2.1 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To control for type I errors due to multiple comparisons, the false discovery rate (FDR) method was applied, ensuring the robustness of the statistical findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Data visualization\u003c/h2\u003e \u003cp\u003eVisual representations of alpha and beta diversity were created to illustrate the differences in microbial community structures using ggplot2 R package v.3.5.1 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identifying and characterizing rhythmic bacteria\u003c/h2\u003e \u003cp\u003eOur systematic review of articles focused on identifying rhythmic bacteria in the human gut microbiota retrieved 124 studies. Among these, two studies met our search criteria, namely: i) they used fecal human samples from different times of the day, ii) they calculated bacterial abundances, and iii) they searched for circadian patterns in variation of abundances of bacterial taxa. The limited number of studies underscores the need for a conservative approach to identify consistently rhythmic bacteria in the human gut (see Methods).\u003c/p\u003e \u003cp\u003eA comparison of the bacteria reported as rhythmic in the studies by Thaiss\u0026rsquo;s [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Reitmeier\u0026rsquo;s[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] groups revealed eight rhythmic bacterial species (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, only \u003cem\u003eBacteroides dorei\u003c/em\u003e was identified as rhythmic at the species level in both studies, while the other seven species were identified as rhythmic at the genus level by Thaiss\u0026rsquo;s group [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and at the species level by Reitmeier\u0026rsquo;s group[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These eight rhythmic bacteria belong to the two most abundant phyla of the gut microbiota: Firmicutes (four species) and Bacteroidetes (four species).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSpecies identified as rhythmic by Reitmeier et al.\u003c/span\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ewith genera identified as rhythmic by Thaiss et al.\u003c/span\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe columns report mean abundance (over the nearly 4800 samples) and prevalence in the reference human gut microbiota compiled from curatedMetagenomicBase\u003c/span\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSpecies\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAbundance (%)\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePrevalence %\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAbundance Rank\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePrevalence Rank\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eBacteroides vulgatus\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e4.758\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e87.74\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e18\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eBacteroides uniformis\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e4.409\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e93.237\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e9\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eRoseburia faecis\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e2.454\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e85.717\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e11\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e21\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eBacteroides dorei\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e2.157\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e69.534\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e12\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e47\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eRuminococcus torques\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e1.317\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e88.353\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e19\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e16\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eDorea formicigenerans\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e0.539\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e93.216\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e43\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eRuminococcus gnavus\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e0.237\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e36.596\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e70\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e91\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eBacteroides koreensis\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e-\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e-\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e-\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e-\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo explore the position (abundance and prevalence) of these bacteria in the human gut microbiota, we constructed a reference human gut microbiota database using data from the CuratedMetagenomicBase database [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. After filtering the data to include only healthy adults with no history of antibiotic use, the final reference gut microbiota comprised stool samples from 4,783 unique healthy adults across 17 different countries. Mapping the rhythmic bacteria in this reference database revealed that they are among the hundred most prevalent and abundant species in the human gut microbiota (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with the exception of \u003cem\u003eBacteroides koreensis\u003c/em\u003e, which is not present in the database. \u003cem\u003eB. koreensis\u003c/em\u003e was first isolated from the feces of a healthy Korean adult [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and the name \u0026rdquo;koreensis\u0026rdquo; denotes its association with Korea. We did not find samples from Korea in our database; however, since \u003cem\u003eB. koreensis\u003c/em\u003e was detected in the German cohort reported by Reitmeier et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], its absence from our reference database is likely due to its recent description (2017) relative to the data in the CuratedMetagenomicBase, rather than indicating an exclusive presence in Korean populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Microbiota-level differences between disease and healthy humans\u003c/h2\u003e \u003cp\u003eWe hypothesized that rhythmic bacteria would have different abundances in individuals suffering from circadian-related diseases than in healthy individuals. To test this, our first step was to identify which disease datasets exhibited significant differences from their healthy control samples, as this would guide our search for rhythmic bacteria with differential abundances. It is important to note that all comparisons between healthy and diseased gut microbiota were restricted to samples from the same study.\u003c/p\u003e \u003cp\u003eWe began by evaluating beta-diversity using Bray-Curtis dissimilarity analysis. The results showed that dissimilarity values were comparable within, and between samples for each dataset (Supp. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Additional file 3) and Supp. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Additional file 1)). Although the proportion of variance explained by disease status (R\u0026sup2; in Supp. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Additional file 3)) was generally low across all datasets, pairwise multivariate analysis revealed significant differences between healthy individuals and those with ACVD, CRC, metabolic syndrome, and T2D (Supp. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Additional file 3)). In contrast, no significant differences were found for hypertension or IBD.\u003c/p\u003e \u003cp\u003eNext, we looked for species diversity (alpha diversity) differences between diseased and healthy individuals (see Methods). Our analysis revealed that almost all circadian-related diseases differed significantly in Observed alpha diversity except for hypertension and metabolic syndrome (Supp. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Additional file 2) and Supp. Table\u0026nbsp;3 (Additional file 4)). Concerning the general values of the Observed alpha diversity, there is no trend of higher or lower diversity in the gut of diseased patients. It is important to emphasize that dysbiosis, by definition, does not necessarily imply a reduction or increase in species richness but is best described as an imbalance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In summary, our findings reveal that four out of six sets of samples from circadian-related diseases exhibit a dysbiotic component, therefore, those are useful for identifying specific differences involving rhythmic species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe non-circadian disease, cirrhosis, was incorporated as a negative control to search for changes in bacteria belonging to the rhythmic group. Here, the cirrhosis patients also have a gut microbiota with a composition significantly different from the control subjects (Supp. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Additional file 3)). These patients\u0026rsquo; gut microbiota also have significantly different observed alpha diversity (Supp. Table\u0026nbsp;3 (Additional file 4)). Thus, this disease serves as a useful comparison with circadian-related diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Differential abundance of rhythmic bacteria in health and disease\u003c/h2\u003e \u003cp\u003eFinally, to determine if rhythmic bacteria are involved in the differences observed in the previous section, we first identified bacteria that contribute to Bray-Curtis dissimilarity between samples by performing a SIMPER analysis (Supp. Table\u0026nbsp;4 (Additional file 5)). This analysis shows that among the species contributing to 70% of the dissimilarities, there is only one rhythmic bacterium, \u003cem\u003eR. gnavus.\u003c/em\u003e We found this bacterium contributing to the dissimilarity of all circadian-related diseases, explaining between 2.9% and 5.9%. In the non-circadian-related disease, \u003cem\u003eR. gnavus\u003c/em\u003e was also the only rhythmic bacteria contributing to differences with 3.9%, which suggests that \u003cem\u003eR. gnavus\u003c/em\u003e is not exclusively associated with diseases due to circadian disruption.\u003c/p\u003e \u003cp\u003eNext, we looked for differentially abundant species between groups using a Mann-Whitney test FDR corrected (Supp. Table\u0026nbsp;5A-E (Additional file 6)). Among the circadian-related diseases, only datasets from ACVD, T2D, and CRC showed significantly different abundances of bacteria between patients and controls. In ACVD, \u003cem\u003eB. uniformis\u003c/em\u003e, \u003cem\u003eR. gnavus\u003c/em\u003e, and \u003cem\u003eR. faecis\u003c/em\u003e exhibited significant differences (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). \u003cem\u003eR. gnavus\u003c/em\u003e and \u003cem\u003eR. faecis\u003c/em\u003e, along with \u003cem\u003eB. vulgatus\u003c/em\u003e, are significantly different for CRC (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and C). However, in T2D, no rhythmic bacteria showed significant differences. In the non-circadian related disease, we identified four significantly different rhythmic bacteria. Among them, we find three of the bacteria detected in ACVD and CRC (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and D). Interestingly, \u003cem\u003eR. gnavus\u003c/em\u003e, as in the SIMPER analysis, is significantly different in these three diseases. The mean abundance of this bacteria is always higher in the samples from volunteers with some of the three diseases. Still, as previously concluded, the association of \u003cem\u003eR. gnavus\u003c/em\u003e is not exclusively related to circadian patterns, as it also differs in non-circadian-related diseases. The only bacterium significantly different exclusively in the circadian-related diseases assessed here is \u003cem\u003eR. faecis\u003c/em\u003e, which shows lower abundances in both contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, we conducted a two-stage analysis: first, a systematic review to identify rhythmic bacteria in the human gut microbiota; second, an analysis aimed to test if these bacteria are significantly different in the gut microbiota from healthy individuals and those with circadian-related diseases. Our findings suggest weak support for a general link between rhythmic bacteria and circadian-related diseases. Only two diseases showed significant differences in rhythmic species compared to healthy controls, with two of these species also differing in cirrhosis, a non-circadian-related disease. This suggests that rhythmic bacteria, as a group, are not uniquely influenced by circadian disruptions that lead to disease. However, we did identify \u003cem\u003eRoseburia faecis\u003c/em\u003e, which was uniquely altered in circadian-related diseases, hinting at a potential specific association with these conditions. Further research involving additional diseases would clarify these potential connections.\u003c/p\u003e \u003cp\u003eDespite the weak overall association, our study provides valuable insight into rhythmic bacteria, a relatively unexplored group. The eight rhythmic species shared between the studies by Thaiss et al. and Reitmeier et al. belong to the Firmicutes and Bacteroidetes phyla [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These bacteria, with the exception of \u003cem\u003eBacteroides koreensis\u003c/em\u003e, which was not found in the reference database, rank among the top hundred most abundant and prevalent species in this reference gut microbiota dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These seven mapped bacteria, identified at the species level in Reitmeier et al., were not in the top hundred abundant groups in that research, with the exception of \u003cem\u003eB. dorei\u003c/em\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, only a few of the most abundant bacteria detected in that study showed a rhythmic pattern. This supports the idea that rhythmic patterns are not merely detectable in highly abundant bacteria, suggesting there is no detection bias influencing their detection.\u003c/p\u003e \u003cp\u003eWe tested gut microbiota differences by comparing diseased individuals to their respective healthy controls within each study before identifying bacteria that were differentially abundant. Our results largely aligned with previous studies reporting differences, with a few exceptions. Inconsistencies in findings regarding gut microbiota differences between healthy individuals and diseased patients are quite common [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These discrepancies can be attributed to various confounding factors such as diet, age, gender, and methodological differences between research groups [48\u0026ndash; \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Nonetheless, by focusing on diseases where the gut microbiota shows consistent and significant differences, we can still leverage these findings to identify significantly different bacteria, including rhythmic bacteria.\u003c/p\u003e \u003cp\u003eWe focused on diseases that showed significant differences in alpha or beta diversity and analyzed the differential abundance of rhythmic bacteria. Of the eight rhythmic bacteria identified, four showed significant differences in abundance in two of these diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). Notably, \u003cem\u003eRuminococcus gnavus\u003c/em\u003e and \u003cem\u003eRoseburia faecis\u003c/em\u003e differed significantly in both ACVD and CRC. Additionally, two species from the Bacteroides genus were found to differ significantly: \u003cem\u003eB. uniformis\u003c/em\u003e in ACVD and \u003cem\u003eB. vulgatus\u003c/em\u003e in CRC. Interestingly, these two species were also significantly altered in cirrhosis, a non-circadian-related disease included in our analysis. This suggests that these bacteria may respond to multiple physiological stressors beyond circadian disruption, further highlighting their potential relevance in disease contexts beyond the circadian framework.\u003c/p\u003e \u003cp\u003eAmong the rhythmic bacteria, we identified species known for producing short chain fatty acids (SCFAs), particularly those belonging to the \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eRoseburia\u003c/em\u003e genera [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. SCFAs are related to functions in the host that involve the cellular barrier function, modulating immune responses through T regulatory and effector cells, and contributing to glucose metabolism and insulin sensitivity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These metabolites exert positive effects on the host through anti-inflammatory activities, appetite regulation, and participation in synthesizing cholesterol [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Consistent with these beneficial effects, the three rhythmic species belonging to these SCFA-producing genera (\u003cem\u003eR. faecis, B. uniformis\u003c/em\u003e, and \u003cem\u003eB. vulgatus\u003c/em\u003e) are increased in the healthy gut microbiota compared to their diseased counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003eNotably, \u003cem\u003eR. gnavus\u003c/em\u003e was the most outstanding bacterium that differentiated between healthy and diseased gut microbiota. It consistently contributed to Bray-Curtis dissimilarities (Sup. Table\u0026nbsp;4A and B) and was significantly more abundant in ACVD, CRC, and cirrhosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D). This bacterium has been linked to mucin degradation and inflammation in conditions such as Crohn\u0026rsquo;s disease and metabolic syndrome [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Its inflammatory role is further supported by its production of an inflammatory polysaccharide [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In a recent study, Grahnemo et al. also associated \u003cem\u003eR. gnavus\u003c/em\u003e with multiple metabolic syndrome factors, including elevated C-reactive protein, a marker of systemic inflammation [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Importantly, the Grahnemo et al. study suggested that \u003cem\u003eR. gnavus\u003c/em\u003e abundance increases prior to the onset of metabolic markers, implying a potential causal role. The reason for this elevated abundance remains unclear, but \u003cem\u003eR. gnavus\u003c/em\u003e\u0026rsquo;s ability to utilize mucin as a carbon source may provide an advantage during fasting periods, which are common in circadian disruptions. Its increase in non-circadian-related conditions like cirrhosis, however, suggests that multiple mechanisms likely drive its growth.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRoseburia faecis\u003c/em\u003e, a SCFAs producer, was the only bacterium significantly different exclusively in circadian-related diseases, with reduced abundance in both ACVD and CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). \u003cem\u003eR. faecis\u003c/em\u003e main SCFA product is butyrate [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], which is known to be involved in the modulation of blood pressure and in maintaining the intestinal barrier [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, as mentioned earlier, SCFAs are anti-inflammatory metabolites [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and have been shown to be protective in circadian-related diseases like CRC and IBD [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Thus, the reduction of \u003cem\u003eR. faecis\u003c/em\u003e and its butyrate production in disease states aligns with the pathophysiology of these conditions.\u003c/p\u003e \u003cp\u003eAlthough the precise causal direction between the decrease in \u003cem\u003eR. faecis\u003c/em\u003e abundance and disease onset remains unclear, the protective function of butyrate suggests a scenario where reduced \u003cem\u003eR. faecis\u003c/em\u003e levels may precede disease development, contributing to the disruption of gut homeostasis. The reasons behind this decrease are still uncertain. Nutritionally, \u003cem\u003eR. faecis\u003c/em\u003e can utilize a wide range of substrates, from simple sugars like glucose to more complex polysaccharides [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], so it is unclear whether environmental nutrient shifts (derived from circadian variations) could significantly affect its growth rate. Ecologically, competitive interactions in the gut may influence its abundance. There is evidence of one gut strain of \u003cem\u003eR. faecis\u003c/em\u003e producing a bacteriocin in the presence of \u003cem\u003eBacillus subtilis\u003c/em\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], that suggest it has competitive pressures in the gut. While advantageous, bacteriocin production is metabolically costly [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], and the energy expenditure required for it could negatively impact \u003cem\u003eR. faecis\u003c/em\u003e growth in highly competitive environments. For instance, prolonged fasting due to circadian disruption could reduce nutrient availability, creating a highly competitive situation among gut microbes. Further exploration of these interactions could shed light on the dynamics of \u003cem\u003eR. faecis\u003c/em\u003e in a disease context.\u003c/p\u003e \u003cp\u003eWe find particularly interesting to follow changes on \u003cem\u003eR. faecis\u003c/em\u003e in the context of circadian-disruption to test its potential as a biomarker of changes in the gut microbiota generated by this disruption. The association of \u003cem\u003eR. faecis\u003c/em\u003e with circadian-related diseases suggest that this bacteria could be progressively changing, for example, in individuals starting a shift work regime. In this context, specifically monitoring this bacteria as marker of the state of the gut microbiota, would allow early actions preventing major changes in the general composition of the gut microbiota.\u003c/p\u003e \u003cp\u003eA major limitation of our study is the restricted availability of data for identifying rhythmic bacteria. We based our findings on only two studies that analyzed fecal samples collected at different times of day [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To mitigate the risk of overestimation and ensure scientific rigor, we adopted a conservative approach by cross-referencing the two datasets, prioritizing the avoidance of false positives (i.e., false rhythmic bacteria). The methodological differences (sample sizes, schedule strategies, circadian detection algorithms, and the nationality of the subjects), contributed to the robustness of our findings. These variations provided a unique opportunity to identify a consistent group of rhythmic bacteria while simultaneously reducing the risk of overestimating the number of rhythmic species. Nonetheless, two datasets are insufficient to definitively conclude that the eight bacteria identified here are the complete set of rhythmic bacteria in the human gut microbiota.\u003c/p\u003e \u003cp\u003eAnother significant limitation is the absence of detailed information regarding the disease etiology and microbiota progression in the subjects studied. We assumed that healthy controls represented the initial state of the gut microbiota and that disease subjects reflected the final state caused by circadian disruption. This assumption limits our understanding of the intermediate stages of disease progression and how these may influence changes in rhythmic bacteria. This consideration is particularly relevant when analyzing cirrhosis as a control disease. Although we found no evidence linking circadian disruption to the onset of cirrhosis, it is important to note that once cirrhosis develops, patients may experience circadian disruption [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], likely due to the liver\u0026rsquo;s robust circadian regulation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As Bruyneel and Serst\u0026eacute; point out, circadian disruption in cirrhosis typically occurs after the disease has developed rather than being a causative factor [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Overall, longitudinal studies tracking gut microbiota changes in individuals transitioning into circadian-disrupted conditions, such as shift work, would provide more definitive insights into these dynamics.\u003c/p\u003e \u003cp\u003eOur study adds to the growing body of knowledge on the interaction between gut microbiota and circadian rhythms. While the overall evidence for a strong relationship between rhythmic bacteria and circadian-related diseases is limited, certain species, such as \u003cem\u003eR. gnavus\u003c/em\u003e and \u003cem\u003eR. faecis\u003c/em\u003e, may hold promise for further investigation into their roles in health and disease. In particular, \u003cem\u003eR. faecis\u003c/em\u003e stands out as a promising marker of circadian disruption in the gut microbiota.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis work was supported by National Agency of Research and Development of Chile ANID-Chile, grant PFCHA/DocNac/21180581.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003e Availability of data and materials\u003c/h3\u003e\n\u003cp\u003eData and code used here are available at: https://github.com/pameubillag/Chap1_Rhythmic_bact_and_disease.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003ch3\u003eEthics Declaration: not applicable.\u003c/h3\u003e\n\u003ch3\u003e Author contribution\u003c/h3\u003e\n\u003cp\u003eConceptualization: P.K.U., P.A.M., and E.F. Investigation: P.K.U. Methodology: P.K.U., P.A.M., and E.F. Formal analyses: P.K.U., P.A.M., and E.F. Visualization: P.K.U. Writing original draft: P.K.U. Writing, review, and editing: P.K.U., P.A.M., and E.F.\u003c/p\u003e\n\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eWe would like to acknowledge the National Agency of Research and Development of Chile ANID-Chile for financing this research.\u003c/p\u003e\n\u003cp\u003eP.A.M acknowledges support from Centro de Modelamiento Matem\u0026aacute;tico (CMM), Grant FB210005, BASAL funds for centers of excellence from ANID-Chile, and Proyecto Exploraci\u0026oacute;n 13220168.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePotter, G.D., Skene, D.J., Arendt, J., Cade, J.E., Grant, P.J., Hardie, L.J.: Circadian rhythm and sleep disruption: causes, metabolic consequences, and countermeasures. 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Nature and science of sleep, 369\u0026ndash;375 (2018)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human gut microbiota, Dysbiosis, Circadian rhythms, Rhythmic bacteria, Circadian disruption, Roseburia faecis, Rhythmic bacteria in circadian-related diseases","lastPublishedDoi":"10.21203/rs.3.rs-5723754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5723754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent studies suggest that the human circadian clock influences periodic changes in the composition of the gut microbiota, which is essential for maintaining host health. This connection has led researchers to hypothesize that the disruption of the circadian clock may impact human health via the gut microbiota. Here, we hypothesize that rhythmic bacteria\u0026mdash;those whose abundance fluctuates in a circadian pattern\u0026mdash;are key drivers of the differences in gut microbiota composition between healthy individuals and those with circadian-related diseases. Even in the absence of a causal relation, identifying rhythmic bacteria associated with circadian-related diseases can reveal disease biomarkers as well as intervention strategies. To test this, we first conducted a systematic review to identify rhythmic bacteria reported in the literature. Then, we mapped these bacteria onto a reference gut microbiota dataset of nearly 4,800 healthy individuals from a previously curated metagenomic database. We use this data to assess the prevalence and abundance of bacteria. To examine significant bacteria in samples from individuals with circadian-related diseases, including type 2 diabetes, hypertension, atherosclerotic cardiovascular disease, colorectal cancer, metabolic syndrome, and inflammatory bowel disease, we compared disease datasets from several previous studies with their respective healthy controls. Of the eight rhythmic bacteria identified in previous studies, seven were among the top 100 most prevalent and abundant species in the gut. We found the rhythmic bacterium \u003cem\u003eRoseburia faecis\u003c/em\u003e to be strongly and exclusively associated with circadian-related diseases, suggesting its use as a biomarker and possibly coadjuvant in the treatment of these diseases.\u003c/p\u003e \u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e","manuscriptTitle":"Rhythmic Bacteria as Biomarkers for Circadian-Related Diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-02 10:54:09","doi":"10.21203/rs.3.rs-5723754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f5cee85-d77d-469b-aac0-809e15aeb3d6","owner":[],"postedDate":"January 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[{"value":"featured","date":"2025-01-02 18:25:43"}],"updatedAt":"2025-06-05T20:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-02 10:54:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5723754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5723754","identity":"rs-5723754","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}