There is a growing body of evidence that microbes residing in the human intestine represent a critical environmental factor that influences virtually all aspects of human health and disease (Brown and Hazen, 2015; Sonnenburg and Bäckhed, 2016). Our gut microbiome plays a central role in vital processes such as energy harvest from our diet, entraining our immune system during early life, xenobiotic metabolism, and the production of a diverse array of small molecule metabolites that are essential to human life (Brown and Hazen, 2015; Sonnenburg and Bäckhed, 2016). Although the microbiome field has uncovered many correlative relationships with human health and disease, there are few examples whereby alterations in the gut microbiome have been causally linked. One of the earliest relationships established between gut microbes and human disease was the link between alterations in gut microbial phyla (Bacteroidetes and Firmicutes) and obesity susceptibility, and the microbial transplantation studies revealing obesity susceptibility as a transmissible trait (Bäckhed et al., 2004; Ley et al., 2005; Turnbaugh et al., 2006; Turnbaugh et al., 2009). Specifically, obese mice harbor gut microbial communities with enhanced capacity to harvest energy from indigestible carbohydrates (Turnbaugh et al., 2006), and transplantation of either cecal contents or feces from either obese mice or humans is sufficient to promote obesity and related insulin resistance in germ-free mouse recipients (Bäckhed et al., 2004; Ley et al., 2005; Turnbaugh et al., 2006; Turnbaugh et al., 2009). There is also accumulating evidence that antibiotic exposure in early life can predispose children to become overweight or obese later in life (Ajslev et al., 2011), and antibiotic treatment in mice prior to weaning increases obesity and related insulin resistance in adulthood (Cho et al., 2012). It is interesting to note that nearly all diseases where obesity is a predisposing comorbidity (diabetes, liver disease, cardiovascular disease, hypertension, chronic kidney disease, and diverse cancers) have been shown to have a clear gut microbiome link (Canfora et al., 2019; Aron-Wisnewsky et al., 2020; Brown and Hazen, 2018; Knauf et al., 2019; Marques et al., 2018; McQuade et al., 2019).

Although there is now ample evidence that gut microbes play a contributory role in the development of obesity and related metabolic disorders, obesity-targeted drug discovery to this point has focused solely on targets encoded by the human genome. Our knowledge is rapidly expanding regarding what types of microbes are associated with obesity and related disorders, including the repertoire of microbe-associated molecule patterns they harbor and the vast array of metabolites that they produce. However, there are very few examples where this information has been leveraged into therapeutic strategies. The microbiome-targeted therapeutic field has primarily focused on either prebiotic or probiotic approaches, yet thus far these approaches have resulted in very modest or non-significant effects in obesity-related disorders in human studies (Aron-Wisnewsky et al., 2019; Asgharian et al., 2020; Reijnders et al., 2016; Madjd et al., 2016; Koutnikova et al., 2019). As an alternative microbiome-targeted approach, we and others have begun developing non-lethal selective small molecule inhibitors of bacterial enzymes with the hopes of reducing levels of disease-associated microbial metabolites (Roberts et al., 2018; Wang et al., 2015; Gupta et al., 2020; Organ et al., 2020; Orman et al., 2019). We have recently shown that small molecule inhibition of the gut microbial transformation of choline into trimethylamine (TMA), the initial and rate-limiting step in trimethylamine N-oxide (TMAO) generation (Wang et al., 2011), can significantly reduce atherosclerosis, thrombosis, and adverse ventricular and kidney remodeling in mice (Roberts et al., 2018; Wang et al., 2015; Gupta et al., 2020; Organ et al., 2020). Since 2011 the gut microbe-associated TMAO pathway has been associated with many human diseases associated with obesity including atherosclerosis (Wang et al., 2011; Koeth et al., 2013), thrombosis (Zhu et al., 2016; Zhu et al., 2017), chronic kidney disease (Bell et al., 1991; Tang et al., 2015), heart failure (Tang et al., 2014; Trøseid et al., 2015), cancer (Bae et al., 2014; Xu et al., 2015), and diabetes (Shan et al., 2017; Miao et al., 2015). Given the fact that obesity is a comorbidity in all of these human diseases, here we set out to determine whether selective small molecule inhibition of the gut microbial choline transformation into TMA, a metabolic activity catalyzed by the microbial choline TMA lyase CutC (Craciun and Balskus, 2012), can protect against metabolic disturbance in preclinical mouse models of obesity. Although there is clear evidence that TMAO can directly impact cell signaling in host platelets and immune cells (Zhu et al., 2016; Seldin et al., 2016), it is still incompletely understood how the TMAO pathway is mechanistically linked to obesity-related cardiometabolic diseases. It is well appreciated that manipulation of the gut microbiome can impact obesity in part via rewiring of the host circadian clock, and reciprocally it has been shown that an intact circadian clock in the host can impact oscillatory behavior of gut microbiota (Org et al., 2015; Seldin et al., 2016; Thaiss et al., 2014; Liang et al., 2015). Here, we hypothesized that gut microbe-driven TMA production may serve as a high fat diet (HFD)-induced signal that integrates diet-microbe-host interactions to shape obesity-related circadian dysfunction and metabolic disturbance.

Although obesity drug discovery has historically targeted pathways in the human host, a fertile period in biomedical research lies ahead where we instead target the microorganisms that live within us to improve obesity and related disorders. This paradigm shift in drug discovery is needed in light of the clear and reproducible associations between the gut microbiome and almost every human disease. Now we are faced with both the challenge and opportunity to test whether microbe-targeted therapeutic strategies can improve health in the human metaorganism without negatively impacting the symbiotic relationships that have co-evolved. Although traditional microbiome manipulating approaches such as antibiotics, prebiotics, probiotics, and fecal microbial transplantation have shown their own unique strengths and weaknesses, each of these presents unique challenges particularly for use in chronic diseases such as obesity. As we move toward selective non-lethal small molecule therapeutics, the hope is to have exquisite target selectivity and limited systemic drug exposure given the targets are microbial in nature. This natural progression parallels the paradigm shifts in oncology which have transitioned from broadly cytotoxic chemotherapies to target-selective small molecule and biologics-based therapeutics. Here, we provide the first evidence that a selective non-lethal inhibitor to the microbial choline TMA lyase CutC can have advantageous effects on host energy metabolism, adiposity, and insulin sensitivity. The current study demonstrates that gut microbe-targeted suppression of choline to TMA generation: (1) protects the host from DIO and glucose intolerance, (2) protects Lep^ob/ob mice from obesity, (3) increases energy expenditure and alters the expression of lipid metabolic genes in WAT, and (4) rapidly reorganizes gut microbial communities to reduce the Firmicutes to Bacteroidetes ratio and increase abundance of A. muciniphila. This study also reveals that nodes within the gut microbial TMAO pathway exhibit circadian oscillatory behavior, and that inhibition of microbial choline to TMA transformation is associated with alterations in: (1) the expression of core clock machinery in metabolic tissues; (2) the diurnal rhythmic behavior of gut microbial communities; and (3) circadian oscillation in choline-containing phospholipids; (4) and that these observations are at least in part transferred in a gnotobiotic context. Collectively, our findings reinforce the notion that the gut microbial TMAO pathway is a strong candidate for therapeutic intervention across a spectrum of obesity-related diseases and have uncovered metaorganismal crosstalk between gut microbe-derived TMA and circadian metabolic oscillations in mice. Interestingly, recent meta-analysis of clinical studies demonstrated that circulating TMAO levels are dose-dependently associated with obesity in humans (Moreira Júnior et al., 2019), highlighting the potential translation of this work.

As drug discovery advances the area of small molecule non-lethal bacterial enzyme inhibitors it is key to understand how these drugs impact microbial ecology in the gut and other microenvironments. As we have previously reported (Roberts et al., 2018; Gupta et al., 2020; Organ et al., 2020), IMC treatment does promote a significant remodeling of the cecal microbiome in mice. These IMC-induced alterations occur as early as 1 week (Figure 2—figure supplement 1b, c, e) and persist even after 20 weeks (Figure 2). It is important to note that the observed IMC-induced alterations in the gut microbiome are generally expected to be favorable for the host. For instance, IMC lowers the ratio of Firmicutes to Bacteroidetes (Figures 2c, 5b and c, Figure 2—figure supplement 1e), which is known to be elevated in obese humans and rodents (Bäckhed et al., 2004; Ley et al., 2005; Turnbaugh et al., 2006; Turnbaugh et al., 2009). Of the taxa that were significantly correlated with circulating TMA levels (Akkermansia, Bilophila, Clostridiales, and Allobaculum), only Akkermansia was likewise correlated with body weight in Lep^ob/ob mice (Figure 2i). It is important to note that previous studies have shown favorable effects of A. muciniphila in obesity and diabetes, which has prompted its development as a probiotic (Miao et al., 2015; Craciun and Balskus, 2012; Everard et al., 2013). Supplementation with A. muciniphila can improve glucose tolerance in obese mice (Everard et al., 2013; Shin et al., 2014; Org et al., 2015). Furthermore, increases in A. muciniphila are consistently seen in mice treated with TMA lyase inhibitor (Roberts et al., 2018; Gupta et al., 2020; Organ et al., 2020), suggesting that small molecule inhibition of gut microbial choline TMA lyase may be an alternative means to enrich gut microbiomes with A. muciniphila. As small molecule bacterial enzymes inhibitors are developed, it will be extremely important to understand their effects on microbial ecology, and it is expected that some of the favorable effects of these drugs will indeed originate from the restructuring of gut microbiome communities. This is not an uncommon mechanism by which host-targeted drugs impact human health. A recent study showed that nearly a quarter of commonly used host-targeted drugs have microbiome-altering properties (Maier et al., 2018), and in the context of diabetes therapeutics, it is important to note that metformin’s anti-diabetic effects are partially mediated by the drug’s microbiome-altering properties (Dehghan et al., 2020). Given the strong association between gut microbiome and obesity and diabetes (Bäckhed et al., 2004; Ley et al., 2005; Turnbaugh et al., 2006; Turnbaugh et al., 2009), it will likely be advantageous to find therapeutics that beneficially remodel the gut microbiome as well as engage either their microbe or host target of interest.

One burgeoning area of microbe-host crosstalk relevant to human disease is the intersection between gut microbes and host circadian rhythms (Org et al., 2015; Seldin et al., 2016; Thaiss et al., 2014; Liang et al., 2015). It is well appreciated that a transcriptional-translational feedback loop (TTFL) exists in mammalian cells to orchestrate an approximately 24 hr oscillatory rhythm in the expression of thousands of genes (Org et al., 2015; Seldin et al., 2016; Thaiss et al., 2014; Liang et al., 2015). The mammalian clock is coordinated by core transcription factors CLOCK and BMAL1, which peak during light phases, and crytochromes (CRYs) and period genes (PERs), which are most active during dark phases. Under normal conditions, the clock regulated TTFL maintains cell autonomous homeostatic responses to environmental ‘zeitgebers’ including light, food, xenobiotics, and exercise (Org et al., 2015; Seldin et al., 2016; Thaiss et al., 2014; Liang et al., 2015). However, disruption of normal circadian rhythms induced by abnormal light exposure, sleep-activity, or eating patterns has been associated with the development of many human diseases including obesity, diabetes, CVD, kidney disease, cancer, and neurological disease (Org et al., 2015; Seldin et al., 2016; Thaiss et al., 2014; Liang et al., 2015). Therefore, ‘chronotherapies’ or therapies that prevent circadian disruption hold promise across a wide spectrum of diseases. Interestingly, it has been discovered that circadian disruption is associated with a marked reorganization of gut microbial communities, and microbial abundance in the gut exhibits circadian rhythmicity (Org et al., 2015; Seldin et al., 2016; Thaiss et al., 2014; Liang et al., 2015). However, whether gut microbial metabolites contribute to circadian disruption is not well understood. Here, we show that multiple nodes with the metaorganismal TMAO pathway (choline, TMA, Fmo3, and Taar5) oscillate in a circadian manner, and that inhibition of the choline TMA lyase CutC rewires the host circadian clock itself, circadian rhythms in the cecal microbiome, and clock-driven reorganization of host lipid metabolic processes. The impact of gut microbial choline TMA lyase inhibition on the expression of core clock genes is modest in metabolic tissues such as the liver or WAT (Figure 3b; Figure 3—figure supplement 1b, d). However, in skeletal muscle where the host TMA receptor Taar5 exhibits oscillatory behavior (Figure 3a), IMC treatment elicits profound alterations in the expression of Nr1d1 (RevErbα) and cryptochrome 1 (Cry1) (Figure 3b). Furthermore, gnotobiotic mice receiving a cecal content transplant from IMC-treated mice had a marked suppression of Cry1 and Per2 in the skeletal muscle after 8 weeks (Figure 5j). The oscillatory behavior of the TMA receptor Taar5 is specific to both skeletal and cardiac muscle, and is not seen in the olfactory bulb where Taar5 is essential to sense the fish-like odor of TMA (Thaiss et al., 2016; Wang et al., 2017). A recent report demonstrated that TMAO, but not TMA, can bind to and activate the endoplasmic reticulum stress (ER stress)-related kinase PERK (Eif2ak3), and that TMAO binding to PERK regulates the expression of the forkhead transcription factor Foxo1 (Chen et al., 2019a). It is interesting to note that PERK was recently shown to regulate circadian rhythms that support sleep/wake patterns and cancer growth (Wu et al., 2017; Chen et al., 2019b). We also observed tissue-specific alterations in the circadian expression of Foxo1 with IMC treatment (Figure 3—figure supplement 1b,d; Figure 4—figure supplement 1d, Figure 5k). However, additional studies are needed to understand whether the TMAO-PERK-FOXO1 signaling axis is mechanistically linked to oscillatory behavior in metabolism or associated metabolic disease. Additionally, several independent metabolomics studies have found that circulating levels of TMAO exhibit circadian oscillatory behavior (Ly et al., 2020; Bu et al., 2018; Beli et al., 2019). In a similar manner, the expression of flavin-containing monooxygenase enzymes which convert TMA to TMAO are also under direct circadian clock transcriptional regulation (Giskeødegård et al., 2015; Bollard et al., 2001; Dixit and Roche, 1984). Collectively, these emerging data suggest that gut microbe-derived TMA and potentially its metabolic co-metabolite TMAO may be underappreciated microbe-derived signals that impact host circadian rhythms in metabolism. Further studies are now warranted to test whether TMA lyase inhibitors of the host liver TMAO-producing enzyme FMO3 can act as chronotherapies to improve circadian misalignment-associated diseases.

The metaorganismal TMAO pathway represents only one of many microbial metabolic circuits that have been associated with human disease. Many microbe-associated metabolites such as short chain fatty acids, secondary bile acids, phenolic acids, and polyamines have been associated with many human diseases (Brown and Hazen, 2015; Brown and Hazen, 2018). In a circadian and/or meal-related manner, gut microbes produce a diverse array of metabolites that reach micromolar to millimolar concentrations in the blood, making the collective gut microbiome an active endocrine organ (Brown and Hazen, 2015). Small molecule metabolites are well known to be mediators of signaling interactions in the host, and this work provides evidence that diet-microbe-host metabolic interplay can shape normal diurnal rhythms in metabolism and obesity susceptibility in mice. Our work, along with that of many others, demonstrates clear evidence of bi-directional crosstalk between the gut microbial endocrine organ and host metabolism. As drug discovery advances it will be important to move beyond targets based solely in the human host. This work highlights that non-lethal gut microbe-targeted enzyme inhibitors can serve as effective anti-obesity therapeutics in preclinical animal models and provides proof of concept of a generalizable approach to target metaorganismal crosstalk in other disease contexts. Selective mechanism-based inhibition of bacterial enzymes has the advantage over host targeting given that small molecules can be designed to avoid systemic absorption and exposure, thereby minimizing potential host off-target effects. As shown here with the gut microbial TMAO pathway, it is easy to envision that other microbe-host interactions are mechanistically linked to host disease pathogenesis, serving as the basis for the rational design of microbe-targeted therapeutics that improve human health.

RNA sequencing data has been deposited in GEO under accession code GSE157925. Microbiome data were submitted to the European Nucleotide Archive under accession code PRJEB48232.

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Decision letter after peer review:

Thank you for submitting your article "Gut Microbe-Targeted Choline Trimethylamine Lyase Inhibition Improves Obesity Via Rewiring of Host Circadian Rhythms" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Wendy Garrett as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

Schugar et al. present data on the impact of a small molecule inhibitor (iodomethylcholine, IMC) of bacterial choline metabolism. This extends prior work from this team of scientists to focus on obesity-related phenotypes. They report a decrease in body weight in a diet-induced obesity model accompanied by lower insulin and improved glucose control. Remarkably, they also observed phenotypes in the ob/ob (leptin-deficient) model, which has severe obesity. They go on to describe additional phenotypes in IMC treated and control mice: gut microbiota, gene expression, and metabolomics, with a focus on circadian rhythm. Taken together, these data support the potential therapeutic value of IMC for treating obesity and associated metabolic diseases.

Essential revisions:

While each of these datasets is intriguing, they haven't been integrated in such a way to explain the underlying mechanisms responsive for the initial phenotypes. As such, the extent to which these data change our understanding of obesity and associated metabolic diseases remains unclear.

Given the diverse datasets and concepts in the current manuscript, it would not be feasible to reduce each component to its mechanistic basis in a single paper. Instead, we propose that the authors choose one of the following options:

Option 1. Causal data that IMC effects on obesity are gut microbiome dependent.

It remains unclear if the phenotypes are related at all to CutC activity or a downstream consequence of the observed differences in the gut microbiota. There is also no explanation provided as to why IMC would impact the gut microbiota so much. Is choline metabolism really that important or is this potentially indicative of off-target effects? These questions could be addressed using gnotobiotic mice +/- CutC encoding strains in combination with pharmacological manipulation of choline or its downstream metabolites.

There is also no causal data presented to suggest that the IMC-associated gut microbiota impacts obesity. This could be readily done using microbiota transplantations to germ-free mice and/or through re-colonizing IMC treated mice with bacteria that are at decreased abundance following treatment. IMC treatment of germ-free or antibiotic-depleted mice would also help to test if there are direct effects of IMC on the host that impact body weight and adiposity; for example, decreasing food intake due to altered taste.

Option 2. Causal data that IMC effects are dependent on circadian networks.

The authors observe that a number of different genes are altered in a tissue-dependent manner upon IMC exposure, including shifts in Bmal1 and Rev-erba. Tissue-specific Bmal1 and global Rev-erba mice are available and could be used to determine the mechanistic basis of the interaction between the core clock machinery, TMA oscillations from gut microbes, as well as resulting downstream phospholipid species. Do tissue specific Bmal1 (Arntl) or Reverba knock-outs exhibit unique responsiveness to microbial CutC activity and TMA to TMAO levels/conversions at baseline or following HF-diet feeding?

Review Gréchez-Cassiau A, Feillet C, Guérin S, Delaunay F. The hepatic circadian clock regulates the choline kinase α gene through the BMAL1-REV-ERBα axis. (Chronobiol Int. 2015;32(6):774-84. doi: 10.3109/07420528.2015.1046601. Epub 2015 Jun 30. PMID: 26125130).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your article "Gut Microbe-Targeted Choline Trimethylamine Lyase Inhibition Improves Obesity Via Rewiring of Host Circadian Rhythms" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Sudha Biddinger (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

See the itemized comments from reviewer #3 for additional points to clarify in your resubmitted paper. Please note that we do not require any additional data or experiments at this time.

Reviewer #3:

Schugar et al., have presented studies examining how a small molecule (iodomethylcholine, IMC) prevents microbially-mediated TMA production via CutC inhibition to protect against diet-induced obesity. The authors use a variety of tools to interrogate the mechanistic underpinnings of IMC's impact on host metabolism, and suggest this is in part mediated by IMC-induced shifts in gut microbes that impact circadian outputs. The authors have generated additional data to address the initial set of reviewers' comments. In many cases, the manuscript has been strengthened. However, several points still remain regarding the connection between circadian aspects of the studies surrounding the capacity of IMC to shift circadian rhythms and subsequent metabolic outputs. For instance, many of the studies examining IMC's ability to shift circadian rhythms were performed in chow-fed animals. The meaning of shifts in circadian rhythms in regular chow-fed animals as they pertain to metabolic outputs in lean animals remains puzzling. Identical studies performed in HF-fed mice would clarify several key conclusions the authors are attempting to draw from these data.

1) Can the authors discuss what the implications are of IMC-induced shifts in circadian gene expression in regular chow-fed mice relative to regular chow untreated counterparts? Given that chow-fed mice aren't obese, the interpretation is a bit unclear regarding the protective metabolic impacts. This is also the case regarding phospholipid profiles presented in Figure 4 – what is the meaning of a 12-hour phase-shift in choline containing phospholipids in already lean animals?

2) Related to the comment #1, Figure 1—figure supplement 4 contains data where animals were fed HF diet followed by treatment w/ or w/o IMC to test therapeutic capacity – can the authors discuss the circadian aspects of gene expression? Are the phase and amplitude shifts induced by IMC also evident in HF-fed animals?

3) Despite the response to Reviewers' comments regarding the focus on adipose tissue vs. liver, the focus and findings in muscle are intriguing. Could the authors discuss and quantify the 12-hour phase shift in muscle Taar5 expression? Again, given that chow-fed animals are not obese, it is unclear what the gene expression changes indicate in already lean animals and it would be ideal to show under HF feeding conditions that this change in rhythmicity persists.

4) Line 279 – 282: The authors make a claim about microbial oscillations, however, the resolution of sample collections every 48 hours is not refined enough to make statements regarding microbial rhythms. It is possible the authors misinterpreted my initial comment regarding the need for 48-hour fecal collections (48-hour fecal collections refer to fecal collections from the same animal every 4 to 6 hours over 2 consecutive LD cycles, rather than fecal samples collected every 48 hours). Further, simply examining ZT2 vs. ZT14 does not provide enough sampling time points to identify rhythms. To truly examine microbial oscillations, resolution needs to be refined to every 4 hours over a 24 hour period at a minimum for cecal contents and every 4 to 6 hours over a 48 hour period for stool. These more resolved data are shown in a high-resolution figure at the end of the manuscript proof on page 81 and is presumably meant to be Figure 2—figure supplement 2 (although the high-resolution images are not labeled with figure #). This high-resolution figure seems to be missing from the figures shown beginning on page 55 of the proof. Can the authors resolve this discrepancy regarding this supplemental figure?

5) Similar to comment 4, it would be ideal to show that the microbial rhythmicity changes induced by IMC treatment also persist in HF-fed animals and are not just observed under chow-fed conditions.

6) In reading through the methods, germ-free mice used for conventionalization were maintained under a different light schedule (14:10LD) relative to the conventionally-raised mice (12:12LD). Yet, all animals were harvested at similar timepoints. Can the authors at a minimum, mention this in the discussion as a potential caveat in the interpretation of the data? Further, the authors are drawing broad claims regarding circadian rhythmicity of microbiota as well as host parameters based on two timepoints in the conventionalized study, which is not acceptable. A minimum of 3 timepoints (at least to make the initial claim) are needed to draw conclusions regarding rhythms in gut microbes and host parameters. Suggest softening claims regarding these data as they relate to circadian dynamics.

7) Regarding the conventionalization study – can the authors provide statistics for the engraftment efficiency relative to the donors? The stacked bars from the donors are shown in panel f in Figure 2—figure supplement 1, but it does not seem like all animals colonized to an equal extent (i.e., conventionalized mice at ZT2 from the HFD CCTx group shown in Figure 5, panel b appear to be missing Blautia). For the mice that were collected at ZT2 vs. ZT14, were all the animals harvested from a single cage? Or were they spread out across cages? This is important for conventionalization, since colonization efficiency can be unique from cage to cage, creating artifacts within a timepoint if not accounted for.

Essential revisions:

While each of these datasets is intriguing, they haven't been integrated in such a way to explain the underlying mechanisms responsive for the initial phenotypes. As such, the extent to which these data change our understanding of obesity and associated metabolic diseases remains unclear.

Given the diverse datasets and concepts in the current manuscript, it would not be feasible to reduce each component to its mechanistic basis in a single paper. Instead, we propose that the authors choose one of the following options:

We sincerely appreciate the reasonable nature of this request. We agree both options below are critically important questions. To make this revision feasible with one year, we chose option 1 below, and feel the results have strengthened the manuscripts conclusions.

Option 1. Causal data that IMC effects on obesity are gut microbiome dependent.

It remains unclear if the phenotypes are related at all to CutC activity or a downstream consequence of the observed differences in the gut microbiota. There is also no explanation provided as to why IMC would impact the gut microbiota so much. Is choline metabolism really that important or is this potentially indicative of off-target effects? These questions could be addressed using gnotobiotic mice +/- CutC encoding strains in combination with pharmacological manipulation of choline or its downstream metabolites.

There is also no causal data presented to suggest that the IMC-associated gut microbiota impacts obesity. This could be readily done using microbiota transplantations to germ-free mice and/or through re-colonizing IMC treated mice with bacteria that are at decreased abundance following treatment. IMC treatment of germ-free or antibiotic-depleted mice would also help to test if there are direct effects of IMC on the host that impact body weight and adiposity; for example, decreasing food intake due to altered taste.

We thank the reviewer for bringing up this point. We agree. To address the concerns raised in Option 1 above, we performed microbiota transplantation studies where we transferred control or IMC-treated mouse cecal contents into naïve germ-free mice and studied the downstream consequences on high fat diet-induced obesity and circadian disruption in re-colonized mice.

The results of this new study are included in main Figure 5 in the revised manuscript. Transplantion of cecal contents from mice previously treated with the TMA lyase inhibitor IMC was able to transmit reduced adiposity in recipient mice. Furthermore, cecal transplant from IMC-treated mice was also able to reorganize core circadian gene expression in the skeletal muscle of recipient mice. Collectively, our studies demonstrate for the first time that gut microbial TMA(O) production impacts core circadian metabolic pathways and obesity susceptibility in the host. Moreover, these new studies reveal the beneficial effects observed on adiposity and circadian rhythm alterations in the host following inhibition of gut microbial TMA production (i.e. with IMC) can be attributed, in part, to the microbiome altering properties of the drug.

Option 2. Causal data that IMC effects are dependent on circadian networks.

The authors observe that a number of different genes are altered in a tissue-dependent manner upon IMC exposure, including shifts in Bmal1 and Rev-erba. Tissue-specific Bmal1 and global Rev-erba mice are available and could be used to determine the mechanistic basis of the interaction between the core clock machinery, TMA oscillations from gut microbes, as well as resulting downstream phospholipid species. Do tissue specific Bmal1 (Arntl) or Reverba knock-outs exhibit unique responsiveness to microbial CutC activity and TMA to TMAO levels/conversions at baseline or following HF-diet feeding?

Review Gréchez-Cassiau A, Feillet C, Guérin S, Delaunay F. The hepatic circadian clock regulates the choline kinase α gene through the BMAL1-REV-ERBα axis. (Chronobiol Int. 2015;32(6):774-84. doi: 10.3109/07420528.2015.1046601. Epub 2015 Jun 30. PMID: 26125130).

We agree these are obvious next steps with this work. However, given the constraints of the COVID-19 pandemic, including restrictions on mouse housing, we have elected “option 1”. These follow up studies are planned in 2022 and beyond, and will allow us to further delineate mechanisms by which gut microbial TMA production may influence the host circadian clock and circadian-dependent regulation of lipid metabolism.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Reviewer #3:

Schugar et al., have presented studies examining how a small molecule (iodomethylcholine, IMC) prevents microbially-mediated TMA production via CutC inhibition to protect against diet-induced obesity. The authors use a variety of tools to interrogate the mechanistic underpinnings of IMC's impact on host metabolism, and suggest this is in part mediated by IMC-induced shifts in gut microbes that impact circadian outputs. The authors have generated additional data to address the initial set of reviewers' comments. In many cases, the manuscript has been strengthened. However, several points still remain regarding the connection between circadian aspects of the studies surrounding the capacity of IMC to shift circadian rhythms and subsequent metabolic outputs. For instance, many of the studies examining IMC's ability to shift circadian rhythms were performed in chow-fed animals. The meaning of shifts in circadian rhythms in regular chow-fed animals as they pertain to metabolic outputs in lean animals remains puzzling. Identical studies performed in HF-fed mice would clarify several key conclusions the authors are attempting to draw from these data.

We completely agree with this reviewer concern, and in fact have performed studies with IMC both in chow-fed mice as well as in mice fed matched low-fat and high-fat diets. Although the majority of circadian data presented are in chow-fed mice, we include new data in this revision in the high fat-fed cohorts. These data confirm that IMC alters circadian rhythms in metabolism in both chow and high fat-fed mice, and we importantly now show that the ability of IMC to alter host circadian rhythms can be transferred to germ-free mice implicating the gut microbiome in the drug’s anti-obesity effects.

1) Can the authors discuss what the implications are of IMC-induced shifts in circadian gene expression in regular chow-fed mice relative to regular chow untreated counterparts? Given that chow-fed mice aren't obese, the interpretation is a bit unclear regarding the protective metabolic impacts.

We thank the reviewer for bringing up this important point. We performed extensive circadian analysis in a chow-fed cohort simply due to the fact that >99% of all published studies in the literature use rodent chow as a base diet. We feel it is important to include these data in this initial description of IMC circadian links, but we also include new data in this revision showing the IMC can alter circadian oscillations in metabolism in high fat-fed mice (Figure 3—figure supplement 2).

This is also the case regarding phospholipid profiles presented in Figure 4 – what is the meaning of a 12-hour phase-shift in choline containing phospholipids in already lean animals?

Due to the complexity of the interorgan crosstalk (gut microbiome, liver, adipose, etc.) in IMC-treated mice, we hesitate to overinterpret the metabolic impact of altered oscillation in phosphatidylcholine (PC) metabolism in this manuscript. In fact, many follow up studies will be required to understand how TMA lyase inhibitors impact PC metabolism throughout the body. Although beyond the scope of this manuscript, we are actively investigating how the gut microbial TMAO pathway can alter hepatic phosphatidylcholine (PC) homeostasis. PC plays many key roles in the liver and systemic distribution of lipids, including its key role in hepatic biliary lipid secretion, very low density lipoprotein (VLDL) secretion, and PC is the major structural lipid in all mammalian cell membranes so can have key roles in lipid signaling and cell function. Therefore in follow up studies, we plan to examine the effect of exogenous TMA and/or TMA lyase drug treatment of biliary lipid section, VLDL production, and cell signaling. However, these are well beyond the scope of this current manuscript. The key new finding from this current work is that TMA lyase inhibition alters the normal circadian oscillations in circulating lysophosphatidylcholine (LPC) and PC, as well as circadian oscillation in the hepatic gene expression of key PC biosynthetic enzymes (choline kinase a, CKa and phosphatidylethanolamine methyltransferase, PEMT). It is important to note that we arose at this conclusion using unbiased LC-MS/MS-based metabolomic approaches (i.e. followed unbiased data to our conclusions), and we feel strongly that the PC altering aspect of TMA lyase inhibitors likely play an important role in shaping systemic lipid metabolism. Although we did not do an extensive dive in PC metabolism in high fat diet-fed mice here, we did find that the circadian expression of another PC metabolizing enzyme betaine homocysteine methyltransferase (BHMT) was altered in IMC-treated mice. These new data are included in the revised manuscript in Figure 3—figure supplement 2. We have now embarked on follow up studies examining the potential for bacterially-derived TMA to alter circadian rhythms in hepatic PC metabolism via the host GPCR trace amine-associated 5 (TAAR5).

2) Related to the comment #1, Figure 1—figure supplement 4 contains data where animals were fed HF diet followed by treatment w/ or w/o IMC to test therapeutic capacity – can the authors discuss the circadian aspects of gene expression? Are the phase and amplitude shifts induced by IMC also evident in HF-fed animals?

We completely agree with the reviewer’s interest about results from HFD cohorts. We also understand it is relevant for the reviewer to mention HFD studies in several contexts pertaining to this manuscript. Unfortunately, the samples collected for Figure 1 —figure supplement 4 were performed at a single timepoint and therefore cannot be used to address the question above. However, we have provided new data in the revised manuscript showing that IMC can alter circadian fluctuations in circulating metabolic hormones, as well as key metabolic genes in the liver and skeletal muscle (Figure 3—figure supplement 2).

3) Despite the response to Reviewers' comments regarding the focus on adipose tissue vs. liver, the focus and findings in muscle are intriguing. Could the authors discuss and quantify the 12-hour phase shift in muscle Taar5 expression? Again, given that chow-fed animals are not obese, it is unclear what the gene expression changes indicate in already lean animals and it would be ideal to show under HF feeding conditions that this change in rhythmicity persists.

We agree that circadian expression of Taar5 in muscle was an interesting finding, and worth further exploration. We performed cosinor analysis for skeletal muscle Taar5 gene expression (Figure 3—figure supplement 1 – Panel D). Acrophase shifted from about 20.4 hours in control mice to 0 in mice fed IMC. In the discussion we describe that it is tempting to speculate that the oscillation of muscle Taar5 may be sensitive to the oscillation of serum TMA levels, given that IMC inhibits the levels and oscillation of serum TMA. Also, as described above we have included new data in high fat fed mice, showing that IMC treatment can reorganize plasma hormone levels and circadian gene expression in skeletal muscle (Figure 3—figure supplement 2).

4) Line 279 – 282: The authors make a claim about microbial oscillations, however, the resolution of sample collections every 48 hours is not refined enough to make statements regarding microbial rhythms. It is possible the authors misinterpreted my initial comment regarding the need for 48-hour fecal collections (48-hour fecal collections refer to fecal collections from the same animal every 4 to 6 hours over 2 consecutive LD cycles, rather than fecal samples collected every 48 hours).

Indeed we did misinterpret the original reviewer concern to mean that we should collect feces every 48 hours and perform 16S microbiome analysis. The original comment was “Could the authors perform 48-hour fecal collections – (1) before, (2) early following IMC administration onset, and (3) late following IMC administration onset to show if gut microbiota oscillations observed at baseline pre-administration the same or unique post-administration and before overt metabolic and physiological changes are occurring?” We interpreted to mean “perform 48-hour fecal collections” immediately after the drug was given. Therefore, as requested we performed fecal collections after drug exposure for 4 48-hour time periods. This was not a trivial experiment and quite costly to perform longitudinal 16S sequencing. The resulting data clearly show that drug effect is seen quite rapidly and maintain throughout the time period assayed, which we feel help strengthen some of the microbiome-related conclusions of this work. On top of this experiment performed to be responsive to reviewer concerns, we performed another experiment where cecal microbiome analysis was performed every 4 hours over a 24 hour time course to examine more kinetic changes in the gut microbiome in the same mice (Figure 2—figure supplement 2). Although the mice have been exposed to drug for a week prior to cecum collection, these data clearly show oscillations in gut microbial populations that agrees with other published results, and show that IMC treatment significantly alters the normal circadian oscillations in gut microbial communities. To avoid confusion, we have removed any unintentional claims we may have made concerning microbial oscillation, unless we collected data every 4 hours over a 24-hour period, taking great care not to claim oscillation changes occurred for data measured only at one zeitgeber (ZT) time points.

Further, simply examining ZT2 vs. ZT14 does not provide enough sampling time points to identify rhythms. To truly examine microbial oscillations, resolution needs to be refined to every 4 hours over a 24 hour period at a minimum for cecal contents and every 4 to 6 hours over a 48 hour period for stool. These more resolved data are shown in a high-resolution figure at the end of the manuscript proof on page 81 and is presumably meant to be Figure 2—figure supplement 2 (although the high-resolution images are not labeled with figure #). This high-resolution figure seems to be missing from the figures shown beginning on page 55 of the proof. Can the authors resolve this discrepancy regarding this supplemental figure?

We apologize for confusion, but the experiment we did is exactly what the reviewer requests. The Reviewer suggests “resolution needs to be refined to every 4 hours over a 24 hour period at a minimum for cecal contents.…” In the experiment shown in Figure 2—figure supplement 2 we did exactly this, collecting cecal contents from experimental mice every 4 hours over a 24 hour period. This allowed us to utilize JTK_cycle to analyze the rhythmic oscillation within the gut microbiome.

5) Similar to comment 4, it would be ideal to show that the microbial rhythmicity changes induced by IMC treatment also persist in HF-fed animals and are not just observed under chow-fed conditions.

We unfortunately did not perform 16S analyses in the high fat diet-fed cohorts treated with IMC, and respectfully suggest that this is beyond the scope of this current manuscript. After last revision the editorial summary stated “Please note that we do not require any additional data or experiments at this time.” We feel strongly that even if these costly data were generated and included they will not dramatically alter any of the interpretations made. It is important to note that any additional 16S or metagenomic data generated would only allow for association – not causation.

6) In reading through the methods, germ-free mice used for conventionalization were maintained under a different light schedule (14:10LD) relative to the conventionally-raised mice (12:12LD). Yet, all animals were harvested at similar timepoints. Can the authors at a minimum, mention this in the discussion as a potential caveat in the interpretation of the data?

We agree that the light cycle is extremely important to consider designing any rodent experiment. The reason for this light cycle difference was due to institutional standard operating procedure. At the Cleveland Clinic, our mouse gnotobiotic facility is maintained on a 14:10 light cycle to maintain a diverse array of germ-free mouse lines. Therefore, we were not afforded the ability to switch the light:dark cycle in an area that serves many different laboratories. Given previous reviewer concerns to establish a gut microbial link to the observed IMC-induced metabolic reorganization, we felt it was imperative to perform a microbiome transfer experiment in germ-free mice under the most rigorous conditions allowed at our institution. The 2-hour light cycle difference is not a particular concern in our case, as we do not compare phase shifts between the germ-free and conventional mice. For us the consistency of the cycle was the critical variable, and in fact find similar alterations in circadian oscillation in host metabolism. We continued to compare ZT2 versus ZT14 in the germ-free mice, because although there were clear IMC-induced metabolomic alterations at every time point, diurnal patterns were the most significant alterations seen, and coincided with the transitions from light to dark phases. Comparing ZT2 versus ZT14 remained relevant and provided data that was sufficient to claim altered gene expression (not oscillations) in a tissue specific manner in alignment with our other studies. Importantly, we have fully disclosed the different light dark cycles in our experiments in our methods section, and have added the following statement in the discussion to address this comment:

“It is important to note that the cecal microbial transfer experiments into germ-free mice (Figure 5) were on a slightly different light cycle (14:10) compared to all other studies (12:12), which was necessitated by our gnotobiotic facility standard operating procedures. It remains possible that this lighting change may alter some of the circadian and metabolic phenotypes under study.”

Further, the authors are drawing broad claims regarding circadian rhythmicity of microbiota as well as host parameters based on two timepoints in the conventionalized study, which is not acceptable. A minimum of 3 timepoints (at least to make the initial claim) are needed to draw conclusions regarding rhythms in gut microbes and host parameters. Suggest softening claims regarding these data as they relate to circadian dynamics.

We understand the reviewer’s concern and agree the term oscillation should be used correctly. In the revised version here, we only discuss circadian rhythmicity in datasets where at least 6 time points were collected (i.e. samples collected every 4 hours over a consecutive 24-hour period). In the introduction we mention oscillation when describing previous literature. In the results, serum parameters and gene expression data were analyzed every 4-hours for 24-hours and analyzed for oscillations by the cosinor method. Therefore, oscillations in the data could be interpreted. Initial cecal bacterial communities and serum metabolite analyses were also conducted every 4-hours for 24-hours. If experiments were performed at ZT2 and ZT14 we changed any reference oscillation regarding the data. It is important to note that anywhere we make a circadian claim in the paper, at least 6 times points have been collected in the same experiment.

7) Regarding the conventionalization study – can the authors provide statistics for the engraftment efficiency relative to the donors? The stacked bars from the donors are shown in panel f in Figure 2—figure supplement 1, but it does not seem like all animals colonized to an equal extent (i.e., conventionalized mice at ZT2 from the HFD CCTx group shown in Figure 5, panel b appear to be missing Blautia). For the mice that were collected at ZT2 vs. ZT14, were all the animals harvested from a single cage? Or were they spread out across cages? This is important for conventionalization, since colonization efficiency can be unique from cage to cage, creating artifacts within a timepoint if not accounted for.

We appreciate the reviewer’s concern regarding the engraftment efficiency of donor cecal microbial pools. Due to the oscillatory nature of the gut microbial community, the donor cecal pools used for engraftment only represent a snapshot in time of the microbial community. For this reason, it is not feasible to calculate an exact engraftment efficiency. However, we can determine the engraftment efficiency summed over two timepoints (ZT2 and ZT14) as % donor pool OTUs present in the recipient:

Additionally, the data represented in Figure 5, panel b are relative (not absolute) abundances and consequently, underrepresented OTUs may be excluded from visual representation at any one timepoint. Regarding the possibility of cage effect, all samples sequenced from ZT2 vs. ZT14 gnotobiotic mice were from animals housed in different cages, so can not be attributed to cage effects. We would like to thank the reviewer for bringing this critical point to our attention. We have updated the Methods to reflect this distinction.

Author details

1. Rebecca C Schugar

   1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
   2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review and editing

   Contributed equally with

   Christy M Gliniak and Lucas J Osborn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5908-2213

2. Christy M Gliniak

   1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
   2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing – review and editing

   Contributed equally with

   Rebecca C Schugar and Lucas J Osborn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3806-6112

3. Lucas J Osborn

   1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
   2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Contributed equally with

   Rebecca C Schugar and Christy M Gliniak

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0077-9192

4. William Massey

   1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
   2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Data curation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2087-6048

5. Naseer Sangwan

   1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
   2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Anthony Horak

   1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
   2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Rakhee Banerjee

   1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
   2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. Danny Orabi

   1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
   2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Robert N Helsley

   1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
   2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Data curation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5000-3187

10. Amanda L Brown

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

11. Amy Burrows

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Chelsea Finney

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

13. Kevin K Fung

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. Frederick M Allen

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

15. Daniel Ferguson

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

16. Anthony D Gromovsky

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

17. Chase Neumann

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

18. Kendall Cook

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

19. Amy McMillan

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

20. Jennifer A Buffa

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    reports being eligible to receive royalty payments for inventions or discoveries related to cardiovascular therapeutics from the Proctor & Gamble Co

21. James T Anderson

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

22. Margarete Mehrabian

    Departments of Medicine, Microbiology, and Human Genetics, University of California, Los Angeles, Los Angeles, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

23. Maryam Goudarzi

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Proteomics and Metabolomics Core, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

24. Belinda Willard

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Proteomics and Metabolomics Core, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

25. Tytus D Mak

    Mass Spectrometry Data Center, National Institute of Standards and Technology (NIST), Gaithersburg, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

26. Andrew R Armstrong

    Department of Internal Medicine, Division of Gastroenterology, Rush University Medical Center, Chicago, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

27. Garth Swanson

    Departments of Medicine, Microbiology, and Human Genetics, University of California, Los Angeles, Los Angeles, United States

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

28. Ali Keshavarzian

    Department of Internal Medicine, Division of Gastroenterology, Rush University Medical Center, Chicago, United States

    Contribution

    Conceptualization, Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7969-3369

29. Jose Carlos Garcia-Garcia

    Life Sciences Transformative Platform Technologies, Procter & Gamble, Cincinnati, United States

    Contribution

    Conceptualization, Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    Employee of Procter & Gamble Company

30. Zeneng Wang

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Conceptualization, Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    reports being named as co-inventor on pending and issued patents 20200121615 held by the Cleveland Clinic relating to cardiovascular diagnostics and therapeutics. Reports being a paid consultant for Procter & Gamble, having received research funds from Procter & Gamble, Roche Diagnostics, and being eligible to receive royalty payments for inventions or discoveries related to cardiovascular diagnostics or therapeutics from Cleveland Heart Lab and Procter & Gamble

31. Aldons J Lusis

    Departments of Medicine, Microbiology, and Human Genetics, University of California, Los Angeles, Los Angeles, United States

    Contribution

    Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

32. Stanley L Hazen

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
    3. Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Resources, Supervision, Writing – review and editing

    Competing interests

    reports being named as co-inventor on pending and issued patents 20200121615 held by the Cleveland Clinic relating to cardiovascular diagnostics and therapeutics. Reports being a paid consultant for Procter & Gamble, having received research funds from Procter & Gamble, Roche Diagnostics, and being eligible to receive royalty payments for inventions or discoveries related to cardiovascular diagnostics or therapeutics from Cleveland Heart Lab and Procter & Gamble

33. Jonathan Mark Brown

    1. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, United States
    2. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing – review and editing

    For correspondence

    brownm5@ccf.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2708-7487

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