The Human Microbiome and Obesity: Moving beyond Associations

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Perspective The Human Microbiome and Obesity: Moving beyond Associations Padma Maruvada,1 Vanessa Leone,2 Lee M. Kaplan,3 and Eugene B. Chang2,* 1NIH,

National Institute of Diabetes and Digestive and Kidney Diseases, Division of Digestive Diseases and Nutrition, Bethesda, MD, USA of Medicine, Knapp Center for Biomedical Discovery, University of Chicago, Chicago, IL, USA 3Obesity, Metabolism, and Nutrition Institute, Massachusetts General Hospital, Boston, MA, USA *Correspondence: [email protected] https://doi.org/10.1016/j.chom.2017.10.005 2Department

Mounting evidence indicates that the gut microbiome responds to diet, antibiotics, and other external stimuli with speed and high precision and in ways that impact a variety of metabolic conditions including obesity and non-alcoholic fatty liver disease. Despite a decade of research establishing a strong association between the gut microbiota and obesity in humans, a causal relationship and the underlying mechanism remain outstanding. Several technological and methodological limitations in obesity and microbiome research have made it difficult to establish causality in this complex relationship. Additionally, limited collaborative interaction between microbiome and obesity researchers has delayed progress. Here, we discuss the current status of microbiome research as it relates to understanding obesity from the perspective of both communities, outline the underlying research challenges, and suggest directions to advance the obesity-microbiome field as a whole, with particular emphasis on the development of microbiome-targeted therapies for obesity prevention and treatment. Introduction The gut microbiome can be viewed as a ‘‘microbial organ’’—one that is sensitive to environmental, dietary, and host factors—with its functions intricately intertwined with host physiology and pathophysiology. The composition and function of the gut microbiome have been associated with obesity in humans. However, establishing the causal and consequential actions of the gut microbiome in driving obesity and metabolic diseases in humans has been challenging. Significant limitations in the existing models, technologies, bioinformatics, and experimental platforms employed in microbiome research have only added to the challenge. These limitations, coupled with the many inconsistent and inadequate vetting of methods and approaches associated with clinical study design, and large variations in sample acquisition, have contributed to the lack of clarity. Consequently, our understanding of the interrelationships between the gut microbiome and the development of obesity remains descriptive and large gaps between clinical and experimental knowledge persist. The following perspective highlights the current state of knowledge and provides specific recommendations and actionable solutions to advance microbiome research in the areas of obesity and metabolic diseases. What Is the Current State of Knowledge of Host Physiological Roles in Obesity Development? Fat serves as the body’s primary energy depot and is highly defended through physiological regulation of food intake (by appetitive and hedonic drives) and energy expenditure (EE) (primarily by thermogenesis). Obesity has been construed as an abnormal deviation from the normal energy steady state that could potentially be treated by simply reducing energy intake (EI) and/or increasing EE. However, increasing evidence indicates that EI and EE are highly interconnected and regulated by complex and coordinated mechanisms that ultimately

influence hypothalamic, limbic, brain stem, and other CNS centers to regulate food intake and EE. Metabolic and physiological energy demands and maintenance of ‘‘adequate’’ energy stores serve as positive signals that determine food intake and non-activity-associated EE. Multiple hormones and neuronal circuits appear to control these regulatory processes, including leptin providing feedback from fat itself; ghrelin secreted by the gastric mucosa; GLP-1, PYY, and other intestinal peptides; and several appetite-regulating neuropeptides. Perturbations that reduce fat mass, such as acute illness, starvation, or calorie-restricting diets, cause a decline in circulating leptin levels, resulting in the reduction of inhibitory signals on appetite by several mechanisms including attenuation of the sympathetic nervous response, blunting of thyroid activity, and direct effects on hypothalamic and hindbrain appetite regulatory centers (Rosenbaum et al., 2005). With obesity, these inhibitory signals are blunted by leptin resistance, resulting in hyperphagia despite elevated circulating leptin levels (Rosenbaum and Leibel, 2014). Research examining the effects of bariatric surgical procedures, including vertical sleeve gastrectomy (VSG) and Roux-en-Y gastric bypass (RYGB), demonstrates that these operations do not change the physiological response to calorie restriction. Rather, they change the relationship between fat mass and these physiological responses, reducing the fat mass at which these counter-regulatory phenomena are activated (Stefater et al., 2010; Hao et al., 2016). Current understanding of the mechanisms underlying the defense of fat mass and the alteration in the ‘‘set point’’ for this defense in response to bariatric surgery is limited. Multiple alterations of neuroendocrine and immune signaling pathways are suspected to play a role in the metabolic effects of surgery. Effective metabolic surgical procedures vary substantially in their specific anatomic alterations, suggesting that multiple GI segments contribute. These diverse procedures share the characteristic of changing luminal contents throughout Cell Host & Microbe 22, November 8, 2017 ª 2017 Elsevier Inc. 589

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Figure 1. Environmental Factors and the Bidirectional Interaction with Host Organ Systems Shape the Intestinal Microbiome Studies over the past decade have revealed that many environmental factors, including diet, antibiotic exposure, energy intake (EI), and exercise, can dramatically influence the intestinal microbiome (both membership and functional capacity). In addition to environment, further research has revealed a bidirectional interaction between host organ systems and the intestinal microbiome in shaping host metabolic outcomes.

the gut, and these altered contents could be the proximal source of the post-operative changes in metabolic signals emanating from the GI tract after bariatric surgery. Luminal changes after bariatric surgery include the profound and specific changes to microbiota composition. These changes are specific to the surgical procedures (i.e., not from the associated weight loss), and transfer experiments into germ-free (GF) mice demonstrate that they can transmit many of the phenotypes induced by surgery, suggesting their functional contribution to the regulation of energy balance and body fat mass (Liou et al., 2013). The gut plays a major role in providing episodic stimuli in response to food/nutrient availability in the lumen by secreting a number of gastric peptide hormones (CCK, GLP-1, PYY, oxyntomodulin, and amylin as satiation- or satiety-promoting hormones, and ghrelin as an appetite-stimulating signal) and stimulating parasympathetic neuronal activity to reduce appetitive drives in the post-prandial period (Blundell et al., 2012). These neuroendocrine signals from the gut also influence the brain centers responsible for ingestive behavior, thermogenesis, and energy balance through multiple mechanisms including reward-learned aspects of food eating behavior, food-seeking behaviors, and altered sensitivity to and preference for specific nutrients and flavors. In addition, bile acids secreted into the GI tract influence energy balance by regulating hepatic lipid metabolism via FXR signaling, and appetitive and energy expenditure. G protein-coupled TGR5 receptors mediate EI by stimulating L cells to secrete GLP-1 and PYY, while EE is stimulated by muscle and brown (thermogenic) fat (Penney et al., 2015). Total daily EE (TEE) includes three primary components: resting EE (REE), mostly associated with metabolic activity of the vital organs and resting thermogenesis; diet-induced 590 Cell Host & Microbe 22, November 8, 2017

thermogenesis (DIT), EE associated with digestion of food; and activity-associated EE (AE). Of these components, REE is the largest determinant of TEE and is largely determined by fatfree mass (primarily muscle), with fat playing a lesser role. When challenged with caloric restriction (CR), such as starvation or restrictive dieting, the body responds with metabolic adaptation- decreasing EE by reducing TEE and switching fuel oxidation from glycogen to fat, thereby mobilizing fat mass. Metabolic adaptation, including changes in adaptive thermogenesis (AT), continues after the CR has ceased, until restoration of baseline fat mass and body weight, suggesting that the adaptive process serves as a protective mechanism to preserve body weight and fat mass. Specific regulatory signals and drivers of €ller et al., metabolic adaptation are not well understood (Mu 2015). In addition to AT, decreased nutrient intake alters taste, perception, food-seeking behaviors, hunger, and satiety cues, presumably by changing GI peptide levels and the activity of CNS circuits that regulate hunger, satiety, reward value of food, and, ultimately, food intake. These mechanisms remain active even in the setting of obesity, where they are activated at a total body fat mass that is far greater than normal (Carneiro et al., 2016). Consequently, dietary restriction strategies for treating obesity are generally met with long-term failure as the body adjusts to restore the pre-diet (abnormally elevated) fat mass (Blundell et al., 2012). Bariatric surgical procedures that result in dramatic loss of fat mass, such as RYGB and VSG, provide a good framework for studying altered energy states. Studies using animal models of these operations have demonstrated that after post-operative weight stabilization, if the animals are calorie-restricted to lose additional weight and then allowed to eat ad libitum, they behave like unoperated animals, overeating and conserving TEE to regain the acutely lost weight (Stefater et al., 2010; Hao et al., 2016). These operations thus appear to establish a new defended fat mass that is protected from both weight regain and further weight loss. Dietary intervention studies have been difficult to conduct and challenging to interpret (Alhassan et al., 2008). Many long-term studies in free-living populations have failed—failures that historically have been ascribed to inadequate subject adherence (Alhassan et al., 2008). However, large inter-individual biological variation in their response to diets and ability to lose weight is observed that cannot easily be explained by mere lack of compliance. While heterogeneity in the regulation and activity of host-derived mechanisms is a likely contribution, the plasticity of the gut microbiota and its high response sensitivity to changes in diet and other environmental influences suggest that it is a major source of both host metabolic regulation and the observed inter-individual variability of metabolic phenotype. As described below, the gut microbiota appears to influence multiple aspects of host energy balance and adiposity (fat mass). What Is the Evidence that the Gut Microbiome, Host Metabolism, and Obesity Are Linked? A plethora of environmental factors and both uni- and bidirectional interactions with specific host tissues and responses that result in associated changes in the gut microbiota have been reported (highlighted in Figure 1). For instance, studies indicate that the interaction between bile acids and the microbiota may contribute to the bidirectional communication

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Perspective between the gut microbiota and the host (Joyce and Gahan, 2016). Indeed, selected gut bacteria are responsible for several phases of bile acid metabolism, and in turn, luminal bile acids influence gut microbial ecology. The role of other intra- and extra-luminal factors in this communication, including gut peptides, autonomic nerves, and immune cells, remains unclear €ckhed, 2016; Wichmann (Arora et al., 2016; Greiner and Ba et al., 2013). Even less well understood is how these mechanisms fit into the broader picture of host energy balance and metabolic function. Several studies in animal models have demonstrated an immediate and significant impact of diet on gut microbial membership and function that likely alters critical host-microbe interactions on a real-time basis (Carmody et al., 2015). Distinct microbial communities and metabolites associated with lean body composition have been identified, and deviations from such communities have been associated with weight gain and metabolic disease (Turnbaugh and Gordon, 2009; Turnbaugh et al., 2009). Animal studies have shown that gut microbes may influence adiposity and weight gain by affecting host gene expression and impacting metabolic and inflammatory pathways as well as the gut-brain axis (Bauer et al., 2016; Ussar et al., 2015). A variety of weight loss intervention studies in both animals and humans are associated with shifts in microbiota composition following treatment and have been implicated in the resulting reduction in weight and improvement in metabolic function (Palleja et al., 2016; Ryan et al., 2014). Moreover, fecal microbiota transfer (FMT) into recipient human and rodent hosts has been associated with the recapitulation of the functional metabolic phenotype observed in donor subjects (Liou et al., 2013; Vrieze et al., 2012). While these observations suggest a connection between the microbiota and obesity development, a definitive causal role and the underlying set of mechanisms and mediators have yet to be been clearly established. FMT into GF mice, using microbiota from obese donors, leads to increased fat mass, and the inverse is also true. The observation that transfer of fecal material from RYGB-operated mice to non-operated GF mice successfully transfers a lean, reduced fat mass phenotype provides further support for a causal relationship between gut microbes and specific metabolic outcomes (Liou et al., 2013). Microbial shifts induced by VSG appear to enhance circulating bile salts by targeting FXR signaling and improving glucose tolerance (Ryan et al., 2014). Secondary bile acid metabolism influenced by gut microbiota has also been implicated in regulating host inflammation and thermogenesis (Jones et al., 2014; Parse´us et al., 2017; Zie˛tak et al., 2016). While not fully elucidated, emerging evidence indicates that inflammation mediated by gut microbes may exacerbate adipose tissue inflammation via increased gut permeability and enhanced circulating lipopolysaccharide (LPS) levels (Chassaing et al., 2014). However, the exact significance of this low-grade chronic inflammation commonly observed in obesity is not clearly understood. Certain bacterial taxa have been shown to be anti-inflammatory and protective against host visceral fat, whereas absence of such bacterial taxa has been implicated and demonstrated in obesity (Dao et al., 2016; Goodrich et al., 2016). While some attempts have been made to study the role of the microbiome on energy balance in animals, human studies are

still lacking to demonstrate the impact of microbiota in this arena (Rosenbaum et al., 2015; Turnbaugh and Gordon, 2009). When considering environmental influences on the gut microbiome, diet is one of the most important factors that affect microbial community membership, diversity, patterns, and structure, which in turn may impact a variety of host metabolic responses (Caesar et al., 2015; Leone et al., 2015; Sonnenburg and €ckhed, 2016). Modulation of the gut microbiota by dietary Ba shifts is complex and further confounded by host metabolic responses to specific diets. High-fat diets have consistently been shown to alter composition and richness of the microbiota (Martinez et al., 2017). In addition, many studies indicate that microbial metabolism of undigested dietary components yields a variety of microbial metabolites with biological functions (Ben€ckhed, 2016; Wu et al., nett et al., 2013; Sonnenburg and Ba 2016). While only a few potential microbial metabolites such as short-chain fatty acids and secondary bile acids with bioactive properties, along with the associated bacterial strains, have been widely studied, many other microbial metabolites are currently being explored or remain unknown (Donia and Fischbach, 2015; Guo et al., 2017). Together, these observations reveal that diet-induced effects on gut microbes are profound, with a lasting impact on host phenotype in animal models. In humans, however, lasting effects are seen only in long-term intervention studies, and these types of studies have been limited to cross-sectional studies that have been unable to establish causality because of large variability in inter-individual metadata and absence of time-sequence sampling (Carmody et al., 2015; Rosenbaum et al., 2015). Finally, it has been demonstrated that gut microbiota alter nutrient metabolism and can respond to host circadian rhythms in a diet-dependent manner (Leone et al., 2015; Thaiss et al., 2014; Zarrinpar et al., 2014). Circadian rhythms have been shown to be an integral component to metabolic function in nearly all life forms. This system allows for tuning of biological clocks to a 24-hr cycle of burning versus storing energy based, in part, on environmental cues, including light:dark cycles, sleeping versus waking, and fasting versus refeeding, which in turn promotes metabolic homeostasis (Bass, 2012; Dibner et al., 2010). Shifts in behavioral rhythms, including changes in dietary intake, shift-work disorder, and jet lag, have been shown to contribute to development of obesity and other co-morbidities (Buxton et al., 2012; Depner et al., 2014). In mice, recent evidence suggests gut microbes play an integral role in maintaining circadian alignment. When this host-microbe interaction is disrupted by exposure to antibiotics, diet changes, genetic manipulation of the circadian gene network, or induction of chronic jet lag, metabolic homeostasis is disrupted (Leone et al., 2015; Liang et al., 2015; Mukherji et al., 2013; Thaiss et al., 2014, 2016; Zarrinpar et al., 2014). Precisely how this occurs is unclear, although shifts in diurnal patterns of the microbially derived metabolome have recently been implicated in host circadian dysfunction (Leone et al., 2015; Thaiss et al., 2016). Initial work using high-fat diet in conventionally raised animals showed disruption of the host molecular clock components, but did not consider the contribution of gut microbes to this observed phenomenon (Kohsaka et al., 2007). More recently, research shows that gut microbes play an integral role in driving both amplitude and rhythmicity of core circadian clock genes, in that lack of gut Cell Host & Microbe 22, November 8, 2017 591

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Perspective microbes (i.e., GF) or depletion via antibiotics shifts the expression, in both the intestinal epithelium and the liver, of a core set of genes that regulate the mammalian circadian clock (Leone et al., 2015; Mukherji et al., 2013; Wang et al., 2017). Antibiotic depletion revealed changes not only in core circadian clock genes in the intestinal epithelium, but also observed alterations in Rev-Erba and the retinoid-related orphan receptor RORa (Mukherji et al., 2013). Furthermore, Wang et al. (2017) showed that specific gut microbial components from Gram-negative microbes could interact with innate immune cells, kicking off cytokine release, which alters intestinal epithelial cell STAT activation. Circadian dynamics and interactions between REV-ERBa and NFIL3 are shifted, promoting lipid uptake in intestinal epithelial cells, and lead to energy balance disruption in the host. Ultimately, these genes participate in positive and negative control of the core circadian clock genes and downstream oscillatory networks important for metabolic regulation. Finally, work reveals that high-fat-diet-induced gut microbes may mediate reprogramming of the overall liver transcriptional oscillatory program through regulation of PPARg (Murakami et al., 2016). Not only does the gut microbiota influence the host circadian system, but gut microbes themselves undergo oscillations over a 24-hr period (Leone et al., 2015; Liang et al., 2015; Thaiss et al., 2014, 2016; Zarrinpar et al., 2014). Using 16S rRNA amplicon sequencing, 15% of the bacterial taxa oscillate under low-fat, high-fiber regular chow feeding in mice (Leone et al., 2015; Thaiss et al., 2014; Zarrinpar et al., 2014). These oscillations are diminished by high-fat, high-simple-carbohydrate diet, and can be partially restored by time-restricted feeding of high-fat diet only in the dark phase (Leone et al., 2015; Zarrinpar et al., 2014). However, while feeding behavior appears to be the predominant driver of microbial oscillations, mice administered constant infusion of parenteral nutrition still exhibited oscillations within the microbial community, albeit of very different members relative to enterally fed controls, suggesting that some host factors could drive oscillations in specific microbial community members (Leone et al., 2015). Despite these compelling findings, one limitation is the reliance on relative abundance to describe oscillatory behavior of specific taxa rather than absolute measurements. More refined methods are needed to determine absolute abundance of microbes in order to make more definitive conclusions in regard to the diurnal dynamics of the microbial community. To work around this complication, several groups have gone beyond examining community membership via 16S rRNA amplicon sequencing and have examined oscillations in microbial function either via shotgun metagenomics or using targeted metabolomics (Leone et al., 2015; Thaiss et al., 2014, 2016). Notably, on a low-fat regular chow diet, the shortchain fatty acid butyrate exhibits diurnal patterns, and timed exposure in vitro (hepanoids) or in vivo (GF mice) elicited a direct impact on host circadian clock gene expression, suggesting that gut microbial metabolites can indeed impact the host circadian system (Leone et al., 2015). The connection between hostmicrobe circadian dynamics has been demonstrated mostly in murine models, and data in humans and the translatability of the murine findings to humans remain limited. One study examined the gut microbes of two humans before, during, and after experiencing jet lag, which revealed alterations in microbial composition and loss of oscillatory gut microbiota (Thaiss 592 Cell Host & Microbe 22, November 8, 2017

et al., 2014). In the same study, FMT using stool from jet-lagged individuals into GF mice induced obesity and increased insulin resistance as compared to FMT with non-jet-lagged gut microbiota (Thaiss et al., 2014). This suggests a causal role for circadian-disrupted gut microbes in human obesity outcomes. While more work is needed to clearly define the translatability of these findings to humans and how they can be leveraged to improve disease outcomes, time of day when sampling both human stool or luminal contents, as well as blood collection for targeted metabolomics, should be carefully considered and controlled for to improve reproducibility. Several factors impact the microbiota at critical time points during an organism’s lifespan that may increase vulnerability for obesity risk. Animal studies indicate an association between prenatal and perinatal antibiotic use and increased risk for childhood obesity (Cox and Blaser, 2015). Early infant gut microbiota is affected by maternal diet during and after pregnancy, mode of delivery, breastfeeding practices, and early nutrition (Ma et al., 2014a). Recent studies indicate that the microbiota undergoes a distinct maturation process, which is highly influenced by early nutrition and host development, characterized by distinct age discriminatory microbial taxa. Interestingly, these microbial taxa signatures may predict normal growth and nourishment status of infants (Blanton et al., 2016). However, it remains unclear what other factors determine microbial colonization and maturation. Recent observations in microbiome-targeted therapies (MTTs) such as prebiotic-resistant starches (fiber), probiotics, and FMTs provide novel and highly tractable opportunities to prevent and treat diseases, complementing traditional pharmacological €ckhed, regimens rooted in host biology (Sonnenburg and Ba 2016). Dietary fiber has been shown to improve metabolic syndrome both in humans and animals and is considered a promising approach to manage disease, presumably acting by increasing short-chain fatty acids through microbial fermenta€ckhed, tion (Rosenbaum et al., 2015; Sonnenburg and Ba 2016). Studies further indicate that reduced fiber consumption by Western populations may be one of the possible mechanisms for the observed loss of microbial diversity, which in turn has been implicated in a variety of chronic clinical conditions including metabolic disease (Sonnenburg et al., 2016). Mice maintained on low microbiome-accessible carbohydrate (MAC) diets over multiple generations undergo long-term microbial shifts and permanently lose certain microbial taxa. These taxa are not regained, even after switching to high-MAC diets (Sonnenburg et al., 2016). Beneficial effects of probiotics have been demonstrated in animal studies and a limited number of human studies. However, some of the beneficial effects demonstrated in rodents are not necessarily recapitulated in humans (Khan et al., 2014), limiting their utility in the clinical realm. While FMT has been shown to be a highly effective treatment option for Clostridium difficile infection (Khoruts and Sadowsky, 2016), its efficacy in metabolic disease and obesity requires further exploration (Vrieze et al., 2012). It will be important to understand the nature of the clusters of key microbial community members and/or key representative disease-specific community structures that are altered by FMT to help with facilitating metabolic improvements. Furthermore, regardless of disease, little is known about the stability of the transplanted communities, the

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Perspective Figure 2. Top-Down versus Bottom-Up Approaches in Defining Causal Relationships for the Role of Intestinal Microbes in Human Disease Data from in vitro systems and in vivo animal models suggest that gut microbes triggered by environmental exposures such as diet and antibiotics play a mechanistic role in disrupting molecular metabolism and impact obesity outcomes. These findings cannot be readily translated to human obesity, in part due to limitations in the experimental systems, thereby preventing the establishment of clear causality. Much of the current findings in obesity-microbiome research remain at the association level in relation to human disease. Establishing causation, and realizing the promise of microbially derived therapies aimed at restoring metabolic health in humans, requires cross-cutting research between major stakeholders who can dissect the complex interaction of environmental influences on host-microbe interactions.

necessity for repeated FMT interventions, the duration of the FMT, the need for concomitant dietary intervention that facilitates maintenance of the explanted taxa, and finally, its relevance in obesity treatment. The exact mode of action and molecular targets for the majority of microbe-centric therapeutics in obesity remain to be determined. Challenges and Opportunities for Microbiome and Obesity Research In the last decade, the microbiome field has made tremendous strides in identifying a link between intestinal dysbiosis and obesity. However, as outlined in Figure 2, specific mechanisms through which gut microbes affect host metabolism in states of health and disease remain elusive. This has been due to inherent difficulties with human subject research, including lack of appropriate clinical design, sample acquisition practices, subject compliance, and an overwhelming variation in interpersonal response. Additionally, many technological limitations and challenges in meta-omic technologies and bioinformatics analyses, and lack of experimental models have created barriers for microbiome research. Further hindering progress in this area has been the lack of dialog and interaction between investigators pursuing microbiome and those immersed in obesity research. These gaps and limitations are further elaborated below. Clinical and Translational Studies Clinical studies are critical for establishing the physiological and pathophysiological relationships between microbiota and obesity, for validating findings from animal and experimental studies, and for the development of effective microbiome-based intervention strategies for metabolic disease and obesity. Well-designed mechanistic studies that systematically examine various components of metabolic players and pathways in the context of the microbiome can yield the knowledge necessary to establish a causal role. Yet most microbiome-obesity clinical

studies have been cross-sectional and performed with little or no attention to critical clinical metadata (host physiology, disease subtype stratification, age, race, demography, and other biological variables). Consequently, large amounts of observational data with limited mechanistic insights have been generated. Moreover, given the tremendous inter-individual variation in human gut microbial composition and function, it is unclear to what level these types of studies need to be powered. Finally, most microbiome studies have relied upon fecal samples for 16S rRNA and metagenomic analyses, which likely consist of an admixture of planktonic microbiota that does not speak to the mucosa-associated microbiota, which are perhaps more relevant and stable. In some cases, it is uncertain how relevant fecal collections are to the underlying research question, and perhaps regional sampling would be more prudent and informative to host obesity outcomes. Another limitation of fecal collections and, for that matter, nearly any microbiome sampling approach, including regional sampling in humans, is that an accurate time series that correlates with changes in host physiological functions is nearly impossible to achieve. This can be particularly challenging in complex disorders, including obesity, where a true baseline is rarely ever achieved, particularly in cross-section studies. To gain a better understanding of the role gut microbes play in obesity relative to individual host responses and clinical outcomes, longitudinal and prospective studies, in which subjects serve as their own controls, are superior. These types of studies differ significantly from cross-sectional population studies in which general trends are sought, often requiring large numbers of subjects to achieve an acceptable false discovery rate. Rather, the goal of longitudinal and prospective studies is to establish a baseline along with temporal relationships between events and measured outcomes, with the understanding that these associations can vary from one individual to another. Thus, the number of subjects required to achieve this goal is likely to be far smaller than that required for larger cross-sectional population studies, yielding data that include individualspecific trends and relationships. Counterintuitively, the one-off Cell Host & Microbe 22, November 8, 2017 593

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Perspective study involving single individuals may be highly informative if the metadata are well captured, curated, and analyzed over time. The success of this approach requires rigid subject compliance, careful attention to sample collections, and a study design in which the incidence of disease or the outcome of an intervention can be tracked for a feasible, yet meaningful period (David et al., 2014; Zeevi et al., 2015). Importantly, the role of the gut microbiota in modulating energy balance, metabolic function, and changes in body weight will need to be assessed in interventional studies employing deep metabolic phenotyping strategies and well-characterized study populations, ensuring highly curated clinical metadata and microbial/metabolic metrics. However, follow-ups using large cross-sectional studies aimed at assessing human variability could be helpful for validating controlled longitudinal studies, by serving as a reference to gauge the generalizability of their findings. For example, age-dependent drift in gut microbiome community membership and function could provide a reference base to assess deviations of an individual’s microbiota, and may serve as a predictor for the onset of disease or response to therapeutic interventions. Along these lines, studies that address early microbial colonization and transgenerational effects of the gut microbiome relative to obesity risk can strengthen our knowledge of microbial maturation and interventional windows of time, adding to the utility of using gut microbes as biomarkers for disease. Finally, advances in technologies that enable regional sampling of the GI tract in humans without requiring expensive and time-consuming endoscopic strategies to explore site-specific host microbial interactions would be most beneficial to avoid using fecal material as the primary endpoint. Experimental Models Animal models, especially those involving gnotobiotic approaches, have been used successfully to investigate the connections between microbiota and obesity. Experiments can be performed under conditions in which host genetics, environmental factors, diet, chronobiological measures, gut microbiota, and regional/mucosal sampling, etc. are carefully controlled. Gnotobiotic models currently provide the best available systems to characterize the functional profiles of complex microbial communities as well as specific microbial strains, enabling studies that would be difficult to perform in humans. However, the major limitation of animal models is that, in most cases, they may not completely recapitulate human study observations (Khan et al., 2014). Because of inherent differences in conditions for gut microbial assemblage and function, immune and metabolic homeostasis, and species-specific physiology and environmental factors, it may not be possible to faithfully replicate human conditions. This is particularly true for gnotobiotic mice, where physiological abnormalities, behavioral differences, and developmental immaturity are evident as reviewed in Al-Asmakh and Zadjali (2015). The inability to translate experimental findings to clinical observation and responses represents a major gap that remains a challenge in the field. Another concern is that gut microbiota in animal models can vary from one lab to another, possibly as a function of the husbandry, breeding, and maintenance conditions, as well as their diet compositions. These differences can significantly impact the reproducibility of studies, interpretation of experimental data, and generalizability 594 Cell Host & Microbe 22, November 8, 2017

of the experimental results. This is a particularly important challenge in regard to the scientific community’s call for and NIH’s efforts to enhance rigor and reproducibility in scientific research in general. To overcome some of these challenges, improved experimental models and systems are needed for studies of gut microbiota in the context of metabolic diseases and obesity. In this regard, several approaches are worth mentioning. Instead of performing studies on indigenous murine microbiota, conventionalization of GF animal models with human microbiota (through FMT) can provide useful information on the functional impact and potential mechanisms of action involving gut microbiota on host metabolism. However, a great deal of care will need to be taken to ensure the physiological/pathophysiological effects observed in humans are recapitulated. Animal studies that incorporate the conditions simulating the study population/subjects eliminate confounding factors, while allowing the exploration of mechanistic and functional aspects of host-microbiota interactions (Blanton et al., 2016). Several animal models provide additional and alternate means to GF mice that could fill the gaps related to discrepancies in human and animal studies. On one hand, smaller organisms such as Drosophila, zebrafish, and C. elegans, with simplified host physiology and defined microbiota, provide a powerful means to study hostmicrobe interactions (Melancon et al., 2017; Trinh and Boulianne, 2013; Zheng and Greenway, 2012). Here, experimental conditions can be easier to control than in GF mice. On the other hand, larger animal models such as piglets (conventional, GF, or gnotobiotic) and non-human primates are closer to humans in physiology and microbiota, and may provide more direct relevance for mechanistic insight into host-microbe relationships. Replication of studies in multiple gnotobiotic animal models is likely to strengthen the findings and may help with better prediction of clinical outcomes. Alternative experimental approaches can also be useful to address specific hypotheses and questions under controlled reductionist conditions. Emerging opportunities for microbiome research involve a variety of in vitro platforms that offer excellent model systems for studying the role of microbiota. Parallel minibioreactors that mimic the human GI tract enable in vitro culture of human microbiota, allowing for investigation under a variety of physiological conditions in a highly reproducible manner (Arnold et al., 2016; Ma et al., 2014b). Ex vivo organoid models can be derived from differentiated cells such as those obtained via biopsies (intestinal epithelial organoids) or from induced pluripotent stem cells (iPSCs)/embryonic stem cells (human intestinal organoids; HIOs) (Bartfeld and Clevers, 2017; Sachs et al., 2017; Yu et al., 2017). These organoids can either be treated with conditioned media or micro-injected with live microorganisms to examine how microbes or their products can impact epithelial-based inflammatory parameters or pathways involved in macro-nutrient uptake (Leslie et al., 2015). Interestingly, HIOs resemble fetal intestines and provide an excellent opportunity to study early microbial colonization and the impact of nutrition on the maturation of microbiota. Organoids can be made not only from the intestine, but also from hepatic stem cells derived from biliary ducts (hepanoids) (Huch et al., 2013). These cells can also be treated with known microbial metabolites that are transported to the liver via portal circulation to observe their

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Perspective impact on a variety of mechanisms known to be involved with obesity development (Leone et al., 2015). While these technologies are still emerging, organoids and bioreactors provide powerful tools for microbiome-obesity research, but will require a team-science approach to actualize their utility in the translational arena to find microbe-centric avenues to treat or prevent obesity. Technology and Informatics Continued development of affordable, high-throughput technologies is essential for building the toolbox to address fundamental questions in microbiome-obesity research. A great deal of variability exists across human studies. Interpersonal variability is often the largest confounder in obesity intervention studies. Tools that enable the identification of the sources of this variability and the development of improved models that integrate behavioral, psychosocial, environmental, and biological factors impacting treatment responses are necessary to integrate complex outcomes. Compounded with this variability, monitoring dietary intake and compliance in long-term clinical studies that involve subjects in free-living conditions remains a challenge. Improved biomarkers and crowd-sourcing apps to assess dietary intake and exposure are critically needed to better monitor dietary compliance, even outside of the clinic (Wu et al., 2016). Wearables and other new biosensors that are emerging will also provide detailed real-time information about personal and physiological metadata. While subject compliance is a major challenge to these types of studies, any human study that utilizes unique dietary formulations limits the comparisons between datasets in regard to gut microbiota outcomes. Moreover, diets are very complex. For instance, dietary fibers and fats, which can be derived from various sources, are not the same and can differ significantly in their composition and impact on both gut microbiota and the host. Here, the challenge is to better define dietary compositions of foods that are used in clinical studies to make meaningful comparisons across many different trials. A practical and affordable solution to this problem, however, is still wanting. To gain better insight into the critical phenomena of bodily metabolic adaptation following changes in energy balance, an expansion of the current knowledge of the factors that affect AT and the strategies to follow the dynamic changes in body €ller weight perturbations are needed (Geisler et al., 2016; Mu €ller et al., 2016; Schweitzer and Bosy-Westphal, 2013; Mu et al., 2016). This will involve designing integrative approaches with deep physical and metabolic phenotyping strategies to improve our understanding of the mechanisms in subtle dynamic changes in body weight and composition over time. It is evident that most obesity interventions can affect the host directly and indirectly via microbial metabolism. However, current strategies do not distinguish the subtle microbiome-mediated effects from robust host physiological effects in response to the interventions. Technologies that can parse direct versus indirect effects would be powerful in identifying causality in the role of the microbiome in obesity development. Since the advancement of microbiome research through nextgeneration sequencing, the field has steadily progressed; however, improved methods are needed to functionally define the impact of gut microbes in the context of obesity. Unfortunately, our ability to resolve the structure and function of complex microbial communities remains limited. Reliance on 16S

rRNA marker gene profiles is highly dependent on regions selected for amplicon sequencing and limited to a genus-level resolution at best. Moreover, 16S rRNA marker gene profiles provide limited, if any, information about functional properties of microbial communities. New tools such as oligotyping or minimal entropy decomposition (MED) show promise to increase utility and resolution of 16S rRNA gene amplicon sequencing (Eren et al., 2013, 2015a). Moving beyond amplicon sequencing, metagenomic and meta-transcriptomic analyses hold greater promise to examine functionality, but are hampered by an incomplete database for functional annotation, differences among investigators in the way data are curated, and inadequate attempts to validate inferential conclusions made through bioinformatics analysis. However, novel high-resolution bioinformatics are currently in development to incorporate such datasets. For instance, Anvi’o is an analysis and visualization platform for -omics data that brings together many aspects of today’s cutting-edge genomic, metagenomic, and meta-transcriptomic analysis practices to address a wide array of needs (Eren et al., 2015b). Using these sophisticated data visualization platforms, it is possible to integrate multiomic datasets from microbial functional and metagenomic assessments with extensive longitudinal host datasets from weight gain or loss studies to understand how the microbiome impacts the dynamic regulation of energy balance. Other -omic technologies, including proteomics, targeted and non-targeted metabolomics, and lipidomics, are still evolving; however, these approaches can potentially yield highly informative readouts in regard to microbial function. Bioinformatic approaches such as metabolic flux and metabolite flux that integrate large datasets from microbial community function and composition over time may enable a comprehensive perspective on systems-level temporal metabolic perturbations (Embree et al., 2015). However, while many labs can generate large datasets using these multiomic platforms, the bottleneck for these types of studies remains in data analysis and integration. Yet without functional measures of the gut microbiome, it remains a challenge to determine the meaning of gut microbial shifts on host energy balance, both in humans and animal models. Current culture-free sequencing approaches, while powerful and informative, often fail to support exploration of functional dynamics of microbial communities with the host, especially with low-abundance microbes. Beyond advancements of -omics technologies, new technologies are on the horizon, including novel culturing techniques and better simulation of the host environment. These tools may help to further understand microbial function and transform microbiome and obesity research (Arnold et al., 2016). To better define microbial strainlevel interactions with host machinery, new methodologies and platforms for cultivating uncultivable microbial strains are needed (Ma et al., 2014b). For instance, microbial community structure and assemblage remain difficult to assess within the host GI tract. Justin Sonnenburg’s team has demonstrated a novel immuno-staining combined with confocal microscopic approach to examine these interactions (Sonnenburg and €ckhed, 2016). However, further expansion on innovative Ba approaches that combine microscopy with sophisticated computational image processing would allow for visualization of spatiotemporal host-microbiota interactions, which are Cell Host & Microbe 22, November 8, 2017 595

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Perspective needed in the context of the microbiome and obesity (Earle et al., 2015). Furthermore, because fecal microbiome composition may not accurately reflect regional microbiota composition, development of technologies that allow bio-sampling of microbiota along the entire length of the gut would allow for characterization of region-specific microbial community structure/function-associated host interactions (Kotula et al., 2014; Ohigashi et al., 2013; Ohta et al., 2015). Finally, effective methods to tease out the host effects from microbial effects in response to weight loss interventions are also needed. To this end, application of stable isotopically labeled dietary components such as 13C-labeled fiber or vitamins may enable differentiation of some of the diet, microbe, and host interactions (Dotz et al., 2014; Lakoff et al., 2014; Lytle et al., 2001). Expansion and improved characterization of ex vivo organoid and in vitro models, such as bioreactors that simulate the GI tract and novel tools such as gut-on-a-chip, could increase capabilities to explore microbial-host metabolic interactions in an efficient and cost-effective system (Kim and Ingber, 2013; Ma et al., 2014b). Application of synthetic biology approaches exploiting novel synthetic bacteria may be able to enable visualization and identification of microbial communities and other critical colonization factors that are clinically relevant and lead to development of novel microbe-centric therapeutic strategies for obesity (Earle et al., 2015; Guo et al., 2017). Microbiome-Targeted Therapies for Obesity and Metabolic Disease: Potential and Pitfalls Microbial manipulation may offer a means to prevent/treat weight gain and associated co-morbidities. Managing microbes and microbial metabolism may be more tractable than modulating host physiology due, in part, to the apparent plasticity of microbial communities. Targeted approaches could range from simple dietary manipulation suited to an individual’s microbiota, to designer probiotics and FMT, which could be individualized with high precision. While we are a long way to fully understanding the mechanisms of action, a variety of empirical approaches are already underway for targeting microbiota in metabolic disease. Dietary approaches such as inclusion of fiber or foods rich in polyphenols, and abstinence from high-fat foods or specific food additives to preserve beneficial microbial functions, present potentially effective long-term preventive strategies that can be incorporated into one’s lifestyle (Anheˆ et al., 2015; Chassaing et al., 2015; Roopchand et al., 2015). However, to develop better therapeutic strategies, the impact of diet on gut microbes needs to be fully explored and understood. Successful microbiome-targeted drug development depends on numerous factors, such as the individualized nature of the resident microbial community composition and structure, detailed understanding of the dynamic alterations of the microbial communities, and the nature of their genetic composition over time. Exciting innovative approaches such as synthetic biology methods are emerging in which microbiota themselves may be used to treat several host conditions, although their potential, efficacy, and utility for obesity remain to be determined. There is limited to no information available on the safety and efficacy of probiotic formulations, and commonly their mode of action remains unknown. Therapeutics based on live bacteria and other 596 Cell Host & Microbe 22, November 8, 2017

designer probiotics (both live microbes and/or their small-molecule metabolites) need to be thoroughly tested for their safety in both animal and controlled human studies. Currently, very few human studies have shown efficacy of FMT for metabolic disorders and since all patients do not respond in a similar manner, the applicability of FMT in obesity is likely to remain limited (Hartstra et al., 2015; Vrieze et al., 2012). Many factors affect outcome of FMT, including donor selection and recipient conditioning, as well as the diet and other lifestyle choices of both donor and recipient. In this regard, it is necessary to identify keystone taxa (specific taxa that are required to support the microbial community) needed for the beneficial effects of FMT and their community dynamics with recipient gut microbes. Issues related to FMT need to be further addressed to maximize its therapeutic effects and minimize adverse outcomes. Future Directions Research Partnerships Microbiome research with interdisciplinary teams is often expensive and requires a variety of resources that are often difficult to assemble. Clinical studies addressing the effectiveness of MTTs for weight loss and metabolic disease are difficult to design as well as implement and can often be cost forbidding. It is important to engage public-private partnerships to facilitate and support the research projects when possible and appropriate. It is also important to inform and engage the community to increase voluntary participation and improve compliance, which can oftentimes be cumbersome in clinical studies. This field will immensely benefit from engaging all the stakeholders to join forces ranging from citizens to scientists, the food industry, pharma, non-profits, and academics, as well as private and public sectors, in order to fill various research gaps and encourage participation in microbiome research (Debelius et al., 2016). Engaging industrial partners early on will help with the development of effective and marketable therapeutic strategies. Involving regulatory agencies is necessary to educate them on the nature of MTTs and their mechanisms of action, which will aid in expedited approvals of Investigational New Drugs (INDs) for their testing in humans and subsequent speedy drug approval process for MTTs for treatment of health conditions. Research Directions While empirical strategies can provide immediate proof of principle for MTTs, a causal role for microbiota in obesity development and energy balance needs to be established beyond doubt with well-characterized host metabolic targets and pathways as well as detailed functional metagenomic characterization. Basic, translational, and human mechanistic studies that specifically explore and establish functional connections between the microbiome and obesity, which involve cross-cutting interdisciplinary teams, are needed to drive the field forward. Development of innovative technologies and sophisticated bioinformatics tools that integrate the data from both host and microbiome will facilitate a systems-level understanding. Finally, issues that relate to MTT strategies and their mode of action need to be addressed in a meaningful way to move from the bench to the clinic within a reasonable time frame. Taken together, these strategies will allow us to make meaningful progress in the quest to understand the role of gut microbes in the development of obesity.

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Perspective ACKNOWLEDGMENTS The concepts and ideas presented in this perspective were conceived by the organizers and participants in a 2016 NIH/NIDDK-sponsored workshop entitled ‘‘Gastrointestinal Microbiome and Energy Balance: Obesity Development and Treatment—Beyond Metagenomic Associations.’’ The authors are grateful for the commitment and dedication from the speakers and moderators for participating in pre-workshop conversations to develop the individual sessions and overall themes. They would also like to express their gratitude to the workshop attendees, speakers, and moderators for providing insightful and interactive discussions and recommendations regarding gut microbes and obesity outcomes throughout the workshop. We also acknowledge NIDDK DDRCC P30 42086. L.M.K. is a consultant to and member of scientific advisory boards of the following companies: Ethicon, Fractyl, Gelesis, GI Dynamics, Janssen, Novo Nordisk, and Rhythm. P.M. is an employee of the United States Government. REFERENCES Al-Asmakh, M., and Zadjali, F. (2015). Use of germ-free animal models in microbiota-related research. J. Microbiol. Biotechnol. 25, 1583–1588. Alhassan, S., Kim, S., Bersamin, A., King, A.C., and Gardner, C.D. (2008). Dietary adherence and weight loss success among overweight women: results from the A TO Z weight loss study. Int. J. Obes. 32, 985–991. Anheˆ, F.F., Roy, D., Pilon, G., Dudonne´, S., Matamoros, S., Varin, T.V., Garofalo, C., Moine, Q., Desjardins, Y., Levy, E., and Marette, A. (2015). A polyphenol-rich cranberry extract protects from diet-induced obesity, insulin resistance and intestinal inflammation in association with increased Akkermansia spp. population in the gut microbiota of mice. Gut 64, 872–883. Arnold, J.W., Roach, J., and Azcarate-Peril, M.A. (2016). Emerging technologies for gut microbiome research. Trends Microbiol. 24, 887–901.

Chassaing, B., Ley, R.E., and Gewirtz, A.T. (2014). Intestinal epithelial cell toll-like receptor 5 regulates the intestinal microbiota to prevent low-grade inflammation and metabolic syndrome in mice. Gastroenterology 147, 1363– 1377.e17. Chassaing, B., Koren, O., Goodrich, J.K., Poole, A.C., Srinivasan, S., Ley, R.E., and Gewirtz, A.T. (2015). Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome. Nature 519, 92–96. Cox, L.M., and Blaser, M.J. (2015). Antibiotics in early life and obesity. Nat. Rev. Endocrinol. 11, 182–190. Dao, M.C., Everard, A., Aron-Wisnewsky, J., Sokolovska, N., Prifti, E., Verger, E.O., Kayser, B.D., Levenez, F., Chilloux, J., Hoyles, L., et al.; MICRO-Obes Consortium (2016). Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut 65, 426–436. David, L.A., Materna, A.C., Friedman, J., Campos-Baptista, M.I., Blackburn, M.C., Perrotta, A., Erdman, S.E., and Alm, E.J. (2014). Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89. Debelius, J.W., Va´zquez-Baeza, Y., McDonald, D., Xu, Z., Wolfe, E., and Knight, R. (2016). Turning participatory microbiome research into usable data: lessons from the American Gut Project. J. Microbiol. Biol. Educ. 17, 46–50. Depner, C.M., Stothard, E.R., and Wright, K.P., Jr. (2014). Metabolic consequences of sleep and circadian disorders. Curr. Diab. Rep. 14, 507. Dibner, C., Schibler, U., and Albrecht, U. (2010). The mammalian circadian timing system: organization and coordination of central and peripheral clocks. Annu. Rev. Physiol. 72, 517–549. Donia, M.S., and Fischbach, M.A. (2015). HUMAN MICROBIOTA. Small molecules from the human microbiota. Science 349, 1254766.

Arora, T., Wegmann, U., Bobhate, A., Lee, Y.S., Greiner, T.U., Drucker, D.J., €ckhed, F. (2016). Microbially produced glucagon-like pepNarbad, A., and Ba tide 1 improves glucose tolerance in mice. Mol. Metab. 5, 725–730.

Dotz, V., Rudloff, S., Blank, D., Lochnit, G., Geyer, R., and Kunz, C. (2014). 13C-labeled oligosaccharides in breastfed infants’ urine: individual-, structure- and time-dependent differences in the excretion. Glycobiology 24, 185–194.

Bartfeld, S., and Clevers, H. (2017). Stem cell-derived organoids and their application for medical research and patient treatment. J. Mol. Med. (Berl.) 95, 729–738.

Earle, K.A., Billings, G., Sigal, M., Lichtman, J.S., Hansson, G.C., Elias, J.E., Amieva, M.R., Huang, K.C., and Sonnenburg, J.L. (2015). Quantitative imaging of gut microbiota spatial organization. Cell Host Microbe 18, 478–488.

Bass, J. (2012). Circadian topology of metabolism. Nature 491, 348–356.

Embree, M., Liu, J.K., Al-Bassam, M.M., and Zengler, K. (2015). Networks of energetic and metabolic interactions define dynamics in microbial communities. Proc. Natl. Acad. Sci. USA 112, 15450–15455.

Bauer, P.V., Hamr, S.C., and Duca, F.A. (2016). Regulation of energy balance by a gut-brain axis and involvement of the gut microbiota. Cell. Mol. Life Sci. 73, 737–755. Bennett, B.J., de Aguiar Vallim, T.Q., Wang, Z., Shih, D.M., Meng, Y., Gregory, J., Allayee, H., Lee, R., Graham, M., Crooke, R., et al. (2013). TrimethylamineN-oxide, a metabolite associated with atherosclerosis, exhibits complex genetic and dietary regulation. Cell Metab. 17, 49–60. Blanton, L.V., Charbonneau, M.R., Salih, T., Barratt, M.J., Venkatesh, S., Ilkaveya, O., Subramanian, S., Manary, M.J., Trehan, I., Jorgensen, J.M., et al. (2016). Gut bacteria that prevent growth impairments transmitted by microbiota from malnourished children. Science 351, 1–10. €slund, E., King, N.A., Blundell, J.E., Caudwell, P., Gibbons, C., Hopkins, M., Na and Finlayson, G. (2012). Body composition and appetite: fat-free mass (but not fat mass or BMI) is positively associated with self-determined meal size and daily energy intake in humans. Br. J. Nutr. 107, 445–449. Buxton, O.M., Cain, S.W., O’Connor, S.P., Porter, J.H., Duffy, J.F., Wang, W., Czeisler, C.A., and Shea, S.A. (2012). Adverse metabolic consequences in humans of prolonged sleep restriction combined with circadian disruption. Sci. Transl. Med. 4, 129ra43. €ckhed, Caesar, R., Tremaroli, V., Kovatcheva-Datchary, P., Cani, P.D., and Ba F. (2015). Crosstalk between gut microbiota and dietary lipids aggravates WAT inflammation through TLR signaling. Cell Metab. 22, 658–668. Carmody, R.N., Gerber, G.K., Luevano, J.M., Jr., Gatti, D.M., Somes, L., Svenson, K.L., and Turnbaugh, P.J. (2015). Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17, 72–84. Carneiro, I.P., Elliott, S.A., Siervo, M., Padwal, R., Bertoli, S., Battezzati, A., and Prado, C.M. (2016). Is obesity associated with altered energy expenditure? Adv. Nutr. 7, 476–487.

Eren, A.M., Maignien, L., Sul, W.J., Murphy, L.G., Grim, S.L., Morrison, H.G., and Sogin, M.L. (2013). Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119. Eren, A.M., Morrison, H.G., Lescault, P.J., Reveillaud, J., Vineis, J.H., and Sogin, M.L. (2015a). Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J. 9, 968–979. Eren, A.M., Esen, O¨.C., Quince, C., Vineis, J.H., Morrison, H.G., Sogin, M.L., and Delmont, T.O. (2015b). Anvi’o: an advanced analysis and visualization platform for ’omics data. PeerJ 3, e1319. €er, C.-C., BosyGeisler, C., Braun, W., Pourhassan, M., Schweitzer, L., Glu €ller, M.J. (2016). Age-dependent changes in resting Westphal, A., and Mu energy expenditure (REE): insights from detailed body composition analysis in normal and overweight healthy caucasians. Nutrients 8, E322. Goodrich, J.K., Davenport, E.R., Beaumont, M., Jackson, M.A., Knight, R., Ober, C., Spector, T.D., Bell, J.T., Clark, A.G., and Ley, R.E. (2016). Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 19, 731–743. €ckhed, F. (2016). Microbial regulation of GLP-1 and L-cell Greiner, T.U., and Ba biology. Mol. Metab. 5, 753–758. Guo, C.-J., Chang, F.-Y., Wyche, T.P., Backus, K.M., Acker, T.M., Funabashi, M., Taketani, M., Donia, M.S., Nayfach, S., Pollard, K.S., et al. (2017). Discovery of reactive microbiota-derived metabolites that inhibit host proteases. Cell 168, 517–526.e18. €nzberg, H., Ye, Hao, Z., Mumphrey, M.B., Townsend, R.L., Morrison, C.D., Mu J., and Berthoud, H.-R. (2016). Reprogramming of defended body weight after

Cell Host & Microbe 22, November 8, 2017 597

Cell Host & Microbe

Perspective Roux-En-Y gastric bypass surgery in diet-induced obese mice. Obesity (Silver Spring) 24, 654–660.

derivation and gnotobiotic zebrafish husbandry. Methods Cell Biol. 138, 61–100.

€ckhed, F., and Nieuwdorp, M. (2015). Insights Hartstra, A.V., Bouter, K.E.C., Ba into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care 38, 159–165.

Mukherji, A., Kobiita, A., Ye, T., and Chambon, P. (2013). Homeostasis in intestinal epithelium is orchestrated by the circadian clock and microbiota cues transduced by TLRs. Cell 153, 812–827.

Huch, M., Dorrell, C., Boj, S.F., van Es, J.H., Li, V.S.W., van de Wetering, M., Sato, T., Hamer, K., Sasaki, N., Finegold, M.J., et al. (2013). In vitro expansion of single Lgr5+ liver stem cells induced by Wnt-driven regeneration. Nature 494, 247–250.

€ller, M.J., and Bosy-Westphal, A. (2013). Adaptive thermogenesis with Mu weight loss in humans. Obesity (Silver Spring) 21, 218–228.

Jones, M.L., Martoni, C.J., Ganopolsky, J.G., Labbe´, A., and Prakash, S. (2014). The human microbiome and bile acid metabolism: dysbiosis, dysmetabolism, disease and intervention. Expert Opin. Biol. Ther. 14, 467–482. Joyce, S.A., and Gahan, C.G.M. (2016). Bile acid modifications at the microbehost interface: potential for nutraceutical and pharmaceutical interventions in host health. Annu. Rev. Food Sci. Technol. 7, 313–333.

€ller, M.J., Enderle, J., Pourhassan, M., Braun, W., Eggeling, B., Lagerpusch, Mu €er, C.-C., Kehayias, J.J., Kiosz, D., and Bosy-Westphal, A. (2015). M., Glu Metabolic adaptation to caloric restriction and subsequent refeeding: the Minnesota Starvation Experiment revisited. Am. J. Clin. Nutr. 102, 807–819. €ller, M.J., Enderle, J., and Bosy-Westphal, A. (2016). Changes in energy Mu expenditure with weight gain and weight loss in humans. Curr. Obes. Rep. 5, 413–423.

€ckhed, F. (2014). Microbial modulation of Khan, M.T., Nieuwdorp, M., and Ba insulin sensitivity. Cell Metab. 20, 753–760.

Murakami, M., Tognini, P., Liu, Y., Eckel-Mahan, K.L., Baldi, P., and SassoneCorsi, P. (2016). Gut microbiota directs PPARg-driven reprogramming of the liver circadian clock by nutritional challenge. EMBO Rep. 17, 1292–1303.

Khoruts, A., and Sadowsky, M.J. (2016). Understanding the mechanisms of faecal microbiota transplantation. Nat. Rev. Gastroenterol. Hepatol. 13, 508–516.

Ohigashi, S., Sudo, K., Kobayashi, D., Takahashi, T., Nomoto, K., and Onodera, H. (2013). Significant changes in the intestinal environment after surgery in patients with colorectal cancer. J. Gastrointest. Surg. 17, 1657–1664.

Kim, H.J., and Ingber, D.E. (2013). Gut-on-a-Chip microenvironment induces human intestinal cells to undergo villus differentiation. Integr. Biol. 5, 1130–1140.

Ohta, H., Izumi, S., and Yoshimoto, M. (2015). A more acceptable endoluminal implantation for remotely monitoring ingestible sensors anchored to the stomach wall. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2015, 4089–4092.

Kohsaka, A., Laposky, A.D., Ramsey, K.M., Estrada, C., Joshu, C., Kobayashi, Y., Turek, F.W., and Bass, J. (2007). High-fat diet disrupts behavioral and molecular circadian rhythms in mice. Cell Metab. 6, 414–421.

Palleja, A., Kashani, A., Allin, K.H., Nielsen, T., Zhang, C., Li, Y., Brach, T., Liang, S., Feng, Q., Jørgensen, N.B., et al. (2016). Roux-en-Y gastric bypass surgery of morbidly obese patients induces swift and persistent changes of the individual gut microbiota. Genome Med. 8, 67.

Kotula, J.W., Kerns, S.J., Shaket, L.A., Siraj, L., Collins, J.J., Way, J.C., and Silver, P.A. (2014). Programmable bacteria detect and record an environmental signal in the mammalian gut. Proc. Natl. Acad. Sci. USA 111, 4838–4843. Lakoff, A., Fazili, Z., Aufreiter, S., Pfeiffer, C.M., Connolly, B., Gregory, J.F., 3rd, Pencharz, P.B., and O’Connor, D.L. (2014). Folate is absorbed across the human colon: evidence by using enteric-coated caplets containing 13C-labeled [6S]-5-formyltetrahydrofolate. Am. J. Clin. Nutr. 100, 1278–1286. Leone, V., Gibbons, S.M., Martinez, K., Hutchison, A.L., Huang, E.Y., Cham, C.M., Pierre, J.F., Heneghan, A.F., Nadimpalli, A., Hubert, N., et al. (2015). Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism. Cell Host Microbe 17, 681–689. Leslie, J.L., Huang, S., Opp, J.S., Nagy, M.S., Kobayashi, M., Young, V.B., and Spence, J.R. (2015). Persistence and toxin production by Clostridium difficile within human intestinal organoids result in disruption of epithelial paracellular barrier function. Infect. Immun. 83, 138–145. Liang, X., Bushman, F.D., and FitzGerald, G.A. (2015). Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock. Proc. Natl. Acad. Sci. USA 112, 10479–10484. Liou, A.P., Paziuk, M., Luevano, J.-M., Jr., Machineni, S., Turnbaugh, P.J., and Kaplan, L.M. (2013). Conserved shifts in the gut microbiota due to gastric bypass reduce host weight and adiposity. Sci. Transl. Med. 5, 178ra41. Lytle, C.A., Fuller, M.E., Gan, Y.M., Peacock, A., DeFlaun, M.F., Onstott, T.C., and White, D.C. (2001). Utility of high performance liquid chromatography/ electrospray/mass spectrometry of polar lipids in specifically Per-13C labeled Gram-negative bacteria DA001 as a tracer for acceleration of bioremediation in the subsurface. J. Microbiol. Methods 44, 271–281. Ma, J., Prince, A.L., Bader, D., Hu, M., Ganu, R., Baquero, K., Blundell, P., Alan Harris, R., Frias, A.E., Grove, K.L., and Aagaard, K.M. (2014a). High-fat maternal diet during pregnancy persistently alters the offspring microbiome in a primate model. Nat. Commun. 5, 3889. Ma, L., Kim, J., Hatzenpichler, R., Karymov, M.A., Hubert, N., Hanan, I.M., Chang, E.B., and Ismagilov, R.F. (2014b). Gene-targeted microfluidic cultivation validated by isolation of a gut bacterium listed in Human Microbiome Project’s Most Wanted taxa. Proc. Natl. Acad. Sci. USA 111, 9768–9773. Martinez, K.B., Leone, V., and Chang, E.B. (2017). Western diets, gut dysbiosis, and metabolic diseases: are they linked? Gut Microbes 8, 130–142. Melancon, E., Gomez De La Torre Canny, S., Sichel, S., Kelly, M., Wiles, T.J., Rawls, J.F., Eisen, J.S., and Guillemin, K. (2017). Best practices for germ-free

598 Cell Host & Microbe 22, November 8, 2017

Parse´us, A., Sommer, N., Sommer, F., Caesar, R., Molinaro, A., Sta˚hlman, M., €ckhed, F. (2017). Microbiota-induced obesity Greiner, T.U., Perkins, R., and Ba requires farnesoid X receptor. Gut 66, 429–437. Penney, N.C., Kinross, J., Newton, R.C., and Purkayastha, S. (2015). The role of bile acids in reducing the metabolic complications of obesity after bariatric surgery: a systematic review. Int. J. Obes. 39, 1565–1574. Roopchand, D.E., Carmody, R.N., Kuhn, P., Moskal, K., Rojas-Silva, P., Turnbaugh, P.J., and Raskin, I. (2015). Dietary polyphenols promote growth of the gut bacterium Akkermansia muciniphila and attenuate high-fat diet-induced metabolic syndrome. Diabetes 64, 2847–2858. Rosenbaum, M., and Leibel, R.L. (2014). 20 years of leptin: role of leptin in energy homeostasis in humans. J. Endocrinol. 223, T83–T96. Rosenbaum, M., Goldsmith, R., Bloomfield, D., Magnano, A., Weimer, L., Heymsfield, S., Gallagher, D., Mayer, L., Murphy, E., and Leibel, R.L. (2005). Low-dose leptin reverses skeletal muscle, autonomic, and neuroendocrine adaptations to maintenance of reduced weight. J. Clin. Invest. 115, 3579–3586. Rosenbaum, M., Knight, R., and Leibel, R.L. (2015). The gut microbiota in human energy homeostasis and obesity. Trends Endocrinol. Metab. 26, 493–501. Ryan, K.K., Tremaroli, V., Clemmensen, C., Kovatcheva-Datchary, P., Myrono€ckhed, F., vych, A., Karns, R., Wilson-Pe´rez, H.E., Sandoval, D.A., Kohli, R., Ba and Seeley, R.J. (2014). FXR is a molecular target for the effects of vertical sleeve gastrectomy. Nature 509, 183–188. Sachs, N., Tsukamoto, Y., Kujala, P., Peters, P.J., and Clevers, H. (2017). Intestinal epithelial organoids fuse to form self-organizing tubes in floating collagen gels. Development 144, 1107–1112. €er, C.C., BosySchweitzer, L., Geisler, C., Pourhassan, M., Braun, W., Glu €ller, M.J. (2016). Estimation of skeletal muscle mass Westphal, A., and Mu and visceral adipose tissue volume by a single magnetic resonance imaging slice in healthy elderly adults. J. Nutr. 146, 2143–2148. €ckhed, F. (2016). Diet-microbiota interactions as Sonnenburg, J.L., and Ba moderators of human metabolism. Nature 535, 56–64. Sonnenburg, E.D., Smits, S.A., Tikhonov, M., Higginbottom, S.K., Wingreen, N.S., and Sonnenburg, J.L. (2016). Diet-induced extinctions in the gut microbiota compound over generations. Nature 529, 212–215.

Cell Host & Microbe

Perspective Stefater, M.A., Pe´rez-Tilve, D., Chambers, A.P., Wilson-Pe´rez, H.E., Sandoval, D.A., Berger, J., Toure, M., Tscho¨p, M., Woods, S.C., and Seeley, R.J. (2010). Sleeve gastrectomy induces loss of weight and fat mass in obese rats, but does not affect leptin sensitivity. Gastroenterology 138, 2426–2436, 2436.e1–2436.e3. Thaiss, C.A., Zeevi, D., Levy, M., Zilberman-Schapira, G., Suez, J., Tengeler, A.C., Abramson, L., Katz, M.N., Korem, T., Zmora, N., et al. (2014). Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell 159, 514–529. Thaiss, C.A., Levy, M., Korem, T., Dohnalova´, L., Shapiro, H., Jaitin, D.A., David, E., Winter, D.R., Gury-BenAri, M., Tatirovsky, E., et al. (2016). Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495–1510.e12. Trinh, I., and Boulianne, G.L. (2013). Modeling obesity and its associated disorders in Drosophila. Physiology (Bethesda) 28, 117–124. Turnbaugh, P.J., and Gordon, J.I. (2009). The core gut microbiome, energy balance and obesity. J. Physiol. 587, 4153–4158. Turnbaugh, P.J., Hamady, M., Yatsunenko, T., Cantarel, B.L., Duncan, A., Ley, R.E., Sogin, M.L., Jones, W.J., Roe, B.A., Affourtit, J.P., et al. (2009). A core gut microbiome in obese and lean twins. Nature 457, 480–484. Ussar, S., Griffin, N.W., Bezy, O., Fujisaka, S., Vienberg, S., Softic, S., Deng, L., Bry, L., Gordon, J.I., and Kahn, C.R. (2015). Interactions between gut microbiota, host genetics and diet modulate the predisposition to obesity and metabolic syndrome. Cell Metab. 22, 516–530. €rvi, J., Kootte, R.S., Bartelsman, Vrieze, A., Van Nood, E., Holleman, F., Saloja J.F.W.M., Dallinga-Thie, G.M., Ackermans, M.T., Serlie, M.J., Oozeer, R., et al. (2012). Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 143, 913–916.e7.

Wang, Y., Kuang, Z., Yu, X., Ruhn, K.A., Kubo, M., and Hooper, L.V. (2017). The intestinal microbiota regulates body composition through NFIL3 and the circadian clock. Science 357, 912–916. Wichmann, A., Allahyar, A., Greiner, T.U., Plovier, H., Lunde´n, G.O¨., Larsson, €ckhed, F. (2013). MicroT., Drucker, D.J., Delzenne, N.M., Cani, P.D., and Ba bial modulation of energy availability in the colon regulates intestinal transit. Cell Host Microbe 14, 582–590. Wu, G.D., Compher, C., Chen, E.Z., Smith, S.A., Shah, R.D., Bittinger, K., Chehoud, C., Albenberg, L.G., Nessel, L., Gilroy, E., et al. (2016). Comparative metabolomics in vegans and omnivores reveal constraints on diet-dependent gut microbiota metabolite production. Gut 65, 63–72. Yu, H., Hasan, N.M., In, J.G., Estes, M.K., Kovbasnjuk, O., Zachos, N.C., and Donowitz, M. (2017). The contributions of human mini-intestines to the study of intestinal physiology and pathophysiology. Annu. Rev. Physiol. 79, 291–312. Zarrinpar, A., Chaix, A., Yooseph, S., and Panda, S. (2014). Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017. Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., Ben-Yacov, O., Lador, D., Avnit-Sagi, T., Lotan-Pompan, M., et al. (2015). Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094. Zheng, J., and Greenway, F.L. (2012). Caenorhabditis elegans as a model for obesity research. Int. J. Obes. 36, 186–194. Zie˛tak, M., Kovatcheva-Datchary, P., Markiewicz, L.H., Sta˚hlman, M., Kozak, €ckhed, F. (2016). Altered microbiota contributes to reduced L.P., and Ba diet-induced obesity upon cold exposure. Cell Metab. 23, 1216–1223.

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