Research Article

T cell–mediated regulation of the microbiota protects against obesity

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Science  26 Jul 2019:
Vol. 365, Issue 6451, eaat9351
DOI: 10.1126/science.aat9351

T cells help keep you lean

The gut microbiota is a critical factor regulating mammalian metabolism. The host immune system, in turn, can shape the microbiome, in part via immunoglobulin A (IgA) antibodies. Petersen et al. report that mice defective in T follicular helper cell development and gut IgA production show hallmarks of metabolic syndrome with age (see the Perspective by Wang and Hooper). These mice gain more weight, accumulate more fat, and show greater insulin resistance compared with controls. IgA in these mice inappropriately targets Clostridia species and allows for the outgrowth of Desulfovibrio. Clostridia suppress and Desulfovibrio enhance host lipid absorption by modulating CD36 expression. A better understanding of the microbial products that modulate lipid absorption may open the door to future therapies for obesity and metabolic disease.

Science, this issue p. eaat9351; see also p. 316

Structured Abstract


The gut microbial metagenome encodes a diverse array of functions that complement host genes involved in metabolism. In support of this idea, the gut microbiomes of obese individuals are characterized by reduced species richness and metabolic capacity. Thus, maintenance of the diversity and collective functional capacity of the microbiota is likely vital to the promotion of optimal metabolic health throughout life. One mechanism to maintain a diverse microbiota is through T cell–dependent immunoglobulin A (IgA) production. However, the relationship between IgA and the development of obesity remains unknown. Although studies of obesity and metabolic syndrome have highlighted a direct role for inflammation, overweight individuals also evince some diminished immune responses, such as reduced levels of mucosal IgA. Thus, microbiota maintenance through IgA may affect functions within both the microbiome and, subsequently, the host metabolism.


Young mice whose T cells have disabled Myd88 signaling (T-Myd88–/– mice) exhibit reduced follicular T cell responses and defective IgA targeting of their gut bacteria that altered the composition of the microbiota. As these knockout mice aged, they developed obesity and insulin resistance despite eating a normal mouse diet. We identified that the development of age-dependent obesity was reliant on the microbiota. On the basis of the similarities of the metabolic phenotype with human disease, we reasoned that this model would provide a platform to understand how the interaction between the host immune system and the microbiota influences metabolic disease.


T-Myd88–/– mice experienced noticeable weight gain between 5 and 6 months of age. Like in humans, weight gain was accompanied by fatty liver disease, inflammatory adipose tissue, and insulin resistance that could be accelerated by placing mice on a high-fat diet. The depletion of the microbiota through antibiotic treatment rescued this weight gain. The cohousing of T-Myd88–/– mice transferred the weight gain to wild-type mice, suggesting that the composition of the microbiota was driving the obesogenic phenotype. The major feature of the microbiota formed within T-Myd88–/– mice was a reduction in Clostridia colonization. Metatranscriptomic analysis further revealed the reduced functional capacity of several Clostridia species. Cohousing experiments demonstrated that outgrowth of the bacterial genus Desulfovibrio could lead to decreased colonization of Clostridia. The replacement of Clostridia into knockout animals rescued weight gain. The composition of obesogenic microbiota was driven by defective T follicular helper cell (TFH cell) responses and inappropriate IgA targeting of Clostridia. The colonization of germ-free mice with the protective Clostridia resulted in leanness, which was opposed by the addition of Desulfovibrio. T-Myd88–/– mice absorbed more long-chain fatty acids (LCFAs), suggesting that the microbiota regulates lipid absorption. Clostridia-colonized germ-free animals down-regulated CD36, a receptor that mediates the binding to and uptake of LCFAs, whereas the addition of Desulfovibrio up-regulated the expression of this receptor. Last, incubation of cell-free supernatant isolated from Clostridia with intestinal epithelial cells was able to down-regulate the expression of CD36 in culture, suggesting that a secreted bacterial molecule regulates host gene expression.


Reduced T cell responses within the guts of T-Myd88–/– mice results in loss of Clostridia colonization and function, as well as the outgrowth of Desulfovibrio, which collectively leads to metabolic disease. We observed similar changes to the microbiota in individuals with metabolic syndrome and obesity. Our findings reveal opposing microbial regulators of CD36 expression and fat accumulation within the gut. The mechanism by which Clostridia and Desulfovibrio alter CD36 remains unknown, and future studies will be needed to elucidate this interaction. Much work in the area of immunity in metabolic disease has been focused on the role of chronic inflammation. However, these studies highlight the importance of a robust immune response within the gastrointestinal tract to prevent metabolic disease, which remains unexplored in humans. Future investigations should be focused on studying the interaction between gut immunity and the microbiota in individuals with metabolic disease as these studies could uncover additional biomarkers and therapeutic interventions.

Innate immune signaling within T cells regulates the microbiota to protect from obesity.

TFH cells regulate IgA production, which appropriately sculpts the microbiota. Loss of this instruction leads to expansion of Desulfovibrio that negatively affects the colonization of beneficial Clostridia. Clostridia function to temper expression of CD36 and lipid absorption. Thus, a reduction in beneficial Clostridia can lead to obesity.


The microbiota influences obesity, yet organisms that protect from disease remain unknown. During studies interrogating host-microbiota interactions, we observed the development of age-associated metabolic syndrome (MetS). Expansion of Desulfovibrio and loss of Clostridia were key features associated with obesity in this model and are present in humans with MetS. T cell–dependent events were required to prevent disease, and replacement of Clostridia rescued obesity. Inappropriate immunoglobulin A targeting of Clostridia and increased Desulfovibrio antagonized the colonization of beneficial Clostridia. Transcriptional and metabolic analysis revealed enhanced lipid absorption in the obese host. Colonization of germ-free mice with Clostridia, but not Desulfovibrio, down-regulated genes that control lipid absorption and reduced adiposity. Thus, immune control of the microbiota maintains beneficial microbial populations that constrain lipid metabolism to prevent MetS.

Over the past century, obesity and metabolic syndrome (MetS) have developed into a global epidemic. Currently, more than 1.9 billion people are obese and at risk of developing metabolic dysfunctions such as type 2 diabetes and cardiovascular and liver disease (1). Multiple studies have highlighted a role for immune-system regulation of metabolic disease. These reports have largely focused on the role of inflammatory responses during obesity. Increased macrophage infiltration and a reduction in regulatory T cells have been reported within the adipose tissue during weight gain (2, 3). However, a number of human studies suggest that suboptimal immune responses are also associated with MetS and obesity. Obese adults show deficient immune responses to immunizations, increased incidence of infection, and reduced mucosal immunoglobulin A (IgA) levels, suggesting that effective immunity cannot be mounted within these individuals (49). The mechanisms by which defective immune reactions influence metabolic disease remain unclear.

The microbiota has emerged as a key regulator of metabolism within the mammalian host, and the composition of the microbiota in obese individuals is sufficient to confer metabolic defects when transferred into mice (10). In particular, reductions in the gene richness of the microbiota have been reported during metabolic disease, including decreased butyrate and methane production. Conversely, some microbiota functions, such as hydrogen sulfide and mucus degradation, are enhanced in individuals with metabolic disease (11). We and others have recently shown that gut immune responses are critical in regulating the composition of the microbiota (12, 13). IgA in particular functions to constrain the outgrowth of certain microbes and diversify the microbiota; changes in IgA binding of microbes or even slight reductions in gut IgA can negatively affect diversity (1214). Thus, defective immune control of the microbiota may contribute to metabolic disease.

Age-associated metabolic syndrome develops in mice with defective T cell signaling

We recently identified a molecular pathway that instructs the appropriate development of T cell–dependent IgA targeting of the microbiota. Mice that possess a T cell–specific ablation of the innate adaptor molecule Myd88 (T-Myd88–/– mice) have defective T follicular helper (TFH) cell development and IgA production within the gut. This was associated with dysregulated IgA targeting of gut microbes and compositional differences within the microbiota between genotypes (12, 14). During these studies, we observed that older T-Myd88–/– mice were consistently obese compared with their wild-type (WT) controls (Fig. 1A). Despite being fed a standard chow diet, T-Myd88–/– mice exhibited significantly increased weight gain and fat accumulation as they aged (Fig. 1, B and C, and fig. S1, A and B). By 1 year of age, male T-Myd88–/– mice weighed up to 60 g and exhibited a 50% body fat composition according to nuclear magnetic resonance (NMR) analysis (Fig. 1, D and E).

Fig. 1 Defective T cell signaling in the gut leads to age-associated obesity.

(A) Representative image of 6-month-old WT and T-Myd88–/– mice. (B) Percentage of weight gained as mice age, starting at 2 months of age (WT, n = 8; T-Myd88–/–, n = 7 plotted). Representative of three independent experiments. (C) Fat accumulation as mice age, starting at 2 months of age (WT, n = 8; T-Myd88–/–, n = 7 plotted.) Representative of three independent experiments. (D) Total weight of 1-year-old WT and T-Myd88–/– mice (n = 6). Representative of three independent experiments. (E) Total fat percentage as measured by NMR of 1-year-old WT and T-Myd88–/– mice (n = 6). Representative of three independent experiments. (F) Fasting serum insulin concentrations from 1-year-old WT and T-Myd88–/– mice (WT, n = 9; T-Myd88–/–, n = 10). Data pooled from three independent experiments. (G) HOMA-IR of 1-year-old WT and T-Myd88–/– mice. (WT, n = 9; T-Myd88–/–, n = 10). Data pooled from three independent experiments. (H) Blood glucose levels measured over time after intraperitoneal insulin (0.75 U/kg) injection during insulin-resistance test (WT, n = 9; T-Myd88–/–, n = 10). Data pooled from three independent experiments. (I) Representative hematoxylin and eosin staining of liver and VAT tissue from WT and T-Myd88–/– mice, taken with 20× magnification. Scale bars, 100 μm. n = 12 mice for each genotype. (J) Percentage of weight gained of WT and T-Myd88–/– mice fed a control or HFD (WT CTRL, n = 8; WT HFD, n = 15; T-Myd88–/– CTRL, n = 9; T-Myd88–/– HFD, n = 13). P < 0.05 (*); P < 0.01 (**); P <0.001 (***); P <0.0001 (****) using a two-tailed, unpaired Student’s t-test [(D) to (G)] and a repeated-measures analysis of variance (ANOVA) with Sidak’s correction for multiple comparisons [(B), (C), (H), and (J)]. Error bars indicate SD.

T-Myd88–/– mice developed many of the metabolic disease comorbidities found in humans (15). Although 1-year-old T-Myd88–/– mice raised on a standard diet cleared glucose to similar levels as those of their WT counterparts (fig. S1C), they had higher levels of circulating insulin, resulting in a higher homeostasis model assessment of insulin resistance (HOMA-IR) index (Fig. 1, F and G). Moreover, when challenged with additional insulin, T-Myd88–/– mice failed to clear glucose with similar kinetics as those of WT mice, indicating the development of insulin resistance (Fig. 1H). Food intake was decreased in T-Myd88–/– mice at 2 months of age compared with WT controls but was equivalent in 1-year-old mice (fig. S1, D and E). Additionally, although energy expenditure was decreased in young mice, these changes did not persist over time (fig. S1D). Movement was also similar between WT and T-Myd88–/– mice at both ages, and only a modest increase in heat production was measured in older T-Myd88–/– mice compared with WT controls, suggesting that these are not the primary cause of increased weight gain as seen in other models (fig. S1, F and G) (16). T-Myd88–/– mice also developed fatty liver disease and displayed inflammatory phenotypes within the adipose tissue that were marked by crown-like structures and dysregulated adipocyte size (Fig. 1I). Obesity on a standard mouse chow diet requires months to develop. By contrast, when mice were placed on a high-fat diet (HFD) (45% fat), T-Myd88–/– mice accumulated more weight and visceral adipose tissue (VAT) mass than did WT mice as early as 8 weeks after initiation of the diet (Fig. 1J and fig. S2, A and B). Thus, T-Myd88–/– mice are prone to developing MetS and obesity, which can be accelerated by the increased intake of dietary fat.

Development of obesity is dependent on the microbiota

We previously reported that the composition of the T-Myd88–/– microbiota is distinct from that of wild type in young mice (12). The microbiota is a known contributor to metabolic function and has been linked with the development of human obesity (17, 18). To initially determine whether the microbiota was involved in the MetS seen in T-Myd88–/– mice, we placed WT and T-Myd88–/– mice on broad-spectrum antibiotics while feeding them a HFD. WT mice exhibited no difference in weight gain on antibiotics. By contrast, weight gain was completely rescued by antibiotic treatment in T-Myd88–/– mice (Fig. 2, A and B). This was accompanied by a reduction in their body fat percentage and VAT mass to levels similar to the fat accumulation observed in lean mice (Fig. 2, C and D).

Fig. 2 The microbiota is required for weight gain associated with T-Myd88–/– mice.

(A) Grams of weight gained measured over time (mean ± SD), (B) total weight gained [area under the curve (AUC)], (C) grams of VAT, and (D) final body fat percentage when WT and T-Myd88–/– mice were fed HFD with or without antibiotics (WT CTRL, n = 5; TMYD CTRL, n = 4; WT ABX, n = 5, TMYD ABX, n = 5). Representative of two independent experiments. P < 0.05 (*); P < 0.01 (**); P < 0.001 (***); P < 0.0001 (****) by using a repeated-measures (A) ANOVA and a two-tailed, unpaired [(B) to (D)] Student’s t test.

Reductions in diversity and function within the Clostridia are associated with obesity

In order to determine the features of the microbiota that affect MetS in T-Myd88–/– mice, we performed 16S ribosomal RNA (rRNA) gene sequencing on aged mice fed normal chow to assess the taxonomic composition and diversity of the microbiota in obese T-Myd88–/– mice. There were significantly different communities in the ileum and fecal contents of aged WT and T-Myd88–/– mice (Fig. 3A and fig. S3A). Additionally, there was a slightly reduced species richness in the feces of aged mice (Fig. 3B). In order to identify organisms that could explain the major differences between WT and T-Myd88–/– microbiota communities, we performed a random forest analysis on the 16S rRNA data. The fecal microbiota was able to accurately classify genotype with 86% accuracy, whereas the ileal microbiota predicted genotype with 100% accuracy. Members of the microbiota that had the strongest influence on accuracy mostly belonged to the broad taxonomic class Clostridia and were enriched in WT mice compared with T-Myd88–/– mice (Fig. 3C and fig. S3B). An additional random forest approach indicated that fecal and ileal microbiota could predict total weight with coefficient of determination (R2) = 0.5 and R2 = 0.76, respectively, with many members of Clostridia strongly influencing this prediction (Fig. 3D and fig. S3B). Compared with WT mice, T-Myd88–/– mice showed broad reductions in diversity and overall abundance of multiple Clostridia taxa, including Dorea, SMB53, unclassified Peptostreptococcaceae, and Clostridium (fig. S3C).

Fig. 3 Loss of diversity and Clostridia abundance are associated with weight gain in T-Myd88–/– mice.

(A) PCoA plot (based on weighted UniFrac distances) and (B) number of observed OTUs from the ileal microbiota of indicated mice (WT, n = 8; T-Myd88–/–, n = 7). (C) Top 10 bacterial genera influencing mean accuracy of random forest classification between WT and T-Myd88–/– ileal microbiota. Genera with enriched relative abundance in WT mice are shaded blue, genera with enriched relative abundance in T-Myd88–/– mice are shaded red (WT, n = 8; T-Myd88–/–, n = 7). (D) Top 10 bacterial genera influencing standard error in random forest linearization of weight gain and ileal microbiota. Genera with enriched relative abundance in WT mice are shaded blue, genera with enriched relative abundance in T-Myd88–/– mice are shaded red (WT, n = 8; T-Myd88–/–, n = 7) (E) Volcano plot of ratio of bacterial UniRef90 gene family transcript abundances in ileal samples (n = 6 per cohort). (F) Mapped reads per million of significantly different species from WT and T-Myd88–/– ileal microbiota transcripts (n = 6 per genotype). Error bars indicate SD. Data in (A), (B), (C), and (D) are from one experiment, and data from (E) and (F) are from one experiment. P < 0.05 (*); P < 0.01 (**); P < 0.001 (***); P < 0.0001 (****) by using (A) PERMANOVA and [(B) and (F)] a two-tailed unpaired Student’s t test.

Compositional shifts in the microbiota, including reduced microbial diversity, can have negative effects on the functionality of the microbiota and have been correlated with a number of Western lifestyle–associated diseases, including MetS (16, 17). Additionally, individuals who harbor a microbiota with lower gene richness are more likely to be obese (18). In fecal and ileal microbial transcriptomes, the representation of transcripts from a number of gene families within T-Myd88–/– mice was generally reduced. Because there were the same number of organisms detected within the ileum by means of 16S rRNA gene sequencing, this supports the hypothesis that the microbiota at these sites has reduced metabolic functionality (Fig. 3E and fig. S4A). A comparable proportion of total reads mapped specifically to reference genomes between the two genotypes, suggesting the same coverage of transcriptomes in all mice. However, the proportion of reads mapped to the Clostridiaceae reference genomes in ileal and fecal transcriptomes of T-Myd88–/– mice was strikingly reduced in particular (Fig. 3F and fig. S4, B and C). Thus, Clostridia present in the obese mice have a reduced functional contribution to the microbiome. Furthermore, obesity is associated with a loss of microbial functional diversity within the Clostridia, as has similarly been reported in humans with metabolic disease (11).

Replacement of Clostridia reduces adiposity

Because loss of critical Clostridia organisms may play a role during disease, we performed a cohousing experiment to determine whether microbial transfer could rescue obesity (fig. S5A). Because mice are coprophagic, cohousing allows for the efficient and frequent transfer of microbes between genotypes and has known homogenizing effects on the microbiota. WT or T-Myd88–/– mice were either housed together with mice of the same genotype or cohoused with mice of the opposite genotype upon weaning. Before co-housing, T-Myd88–/– mice had a distinct microbiota composition, and 1 week of cohousing caused mixing of the two communities (fig. S6A). After 1 week, mice were placed on a HFD and monitored for signs of fat accumulation. Compared with separated WT mice, T-Myd88–/– mice and any animal cohoused with them gained significantly more weight, developed insulin resistance, and had increased VAT and total body fat (Fig. 4A and fig. S5, B to E). Furthermore, after 3 months, the microbiota from cohoused WT mice became significantly distinct from separately housed WT mice and showed greater similarity to the microbiota of separately housed T-Myd88–/– mice (fig. S6B). Thus, a transferable component of the microbiota formed in a T-Myd88–/– animal that could cause metabolic syndrome in an otherwise healthy WT animal.

Fig. 4 Manipulation of gut microbiota influences T-Myd88–/––associated weight gain.

(A) AUC of weight gained and (B) relative abundance of Desulfovibrio in WT and T-Myd88–/– mice maintained in separate cages or cohoused and fed a HFD (n = 4 mice per genotype). Representative of two independent experiments. (C) Relative abundance of indicated bacteria within fecal samples from SPF mice colonized with or without D. desulfuricans (n = 5 mice per genotype). Representative of three independent experiments. (D) Relative abundance of indicated bacteria from 16S sequencing in germ-free mice colonized with the Clostridia consortium alone or together with D. desulfuricans (n = 5 mice per cohort). Error bars indicate SD. Representative of two independent experiments. (E to G), T-Myd88–/– mice were gavaged with vehicle control or spore-forming Clostridia consortium [Vehicle (CTRL), n = 4; Clostridia consortium, n = 5]. Representative of two independent experiments. (E) AUC of weight gained. (F) Total fat percentage as measured with NMR. (G) Grams of VAT. *P < 0.05; **P <0.01; ***P <0.001; ****P < 0.0001 by using [(A) and (E) to (G)] a two-tailed, unpaired Student’s t test and [(B) to (D)] a Mann–Whitney U test.

Because differences in weight gain of cohoused mice were detected within the first 3 weeks, we focused on differences in microbial composition that were detectable at both the early and final time points. After 3 months of cohousing, Desulfovibrio, Lactobacillales, and Bifidobacterium pseudolongum were all present at greater abundances within cohoused WT mice (Fig. 4B and fig. S6, C and D). However, only the Desulfovibrio genus showed significantly greater abundance in separately housed T-Myd88–/– mice and cohoused mice after just 1 week of cohousing (fig. S6E). Desulfovibrio are mucolytic δ-proteobacteria, which produce hydrogen sulfide as a by-product of disulfide-bond degradation within mucin (1922). In addition to its association with inflammatory bowel disease (IBD), increased colonization of Desulfovibrio and genes associated with hydrogen sulfide production is detected in patients with type 2 diabetes and obesity (11). Thus, the community changes in obese mice mimic much of what is seen in humans and suggest that loss of Clostridia and increases in Desulfovibrio are highly relevant to metabolic disease (11, 23, 24).

Cohousing of WT mice with T-Myd88–/– mice that leads to obesity is also associated with the reduced colonization of members of Clostridia in WT mice (fig. S6F). Therefore, we hypothesized that Desulfovibrio colonization may reduce the abundance of these organisms. Specific-pathogen-free (SPF) mice were colonized for 1 week with Desulfovibrio desulfuricans subsp. desulfuricans, a strain that has a 16S rRNA gene sequence similarity of greater than 97% to the Desulfovibrio identified in our mice. Consistent with our hypothesis, WT SPF mice had significant reductions in the Clostridiales family Lachnospiraceae and genus Dorea (Fig. 4C and fig. S7A). Colonization with Desulfovibrio did not result in an overall reduction to all organisms; there was a significant increase in Bifidobacterium (Fig. 4C). Because these changes to the community could be an indirect effect of Desulfovibrio colonization, we tested whether Desulfovibrio could influence the colonization of Clostridia members in a germ-free system. Germ-free mice colonized with chloroform-treated fecal slurries were enriched for Clostridiaceae and Lachnospiraceae (fig. S8). We then analyzed this community in the presence or absence of D. desulfuricans. Desulfovibrio colonization led to a significant reduction in Clostridium, a genus that strongly influenced the predictive accuracy of both genotype and weight (Fig. 4D). Thus, an expansion of Desulfovibrio species, as seen in T-Myd88–/– mice and humans with type 2 diabetes, can antagonize the colonization of microbes associated with leanness.

We sought to identify whether reintroducing these lean-associated microbes could protect against obesity within T-Myd88–/– mice. Treatment of obesity-prone T-Myd88–/– mice every other day with a cocktail of spore-forming bacteria significantly reduced weight gain and fat accumulation (Fig. 4, E and F). At the end of 3 months, T-Myd88–/– mice treated with spore-forming microbes had a lower body fat percentage and a reduced VAT mass when compared with untreated T-Myd88–/– mice (Fig. 4, F and G). Thus, the loss of Clostridia is causally associated with obesity and MetS in T-Myd88–/– mice.

T cell–dependent immunity protects from obesity

Microbiota formed during defective gut immunity appears to result in MetS. Although cohousing of mice for 12 weeks led to the transmission of obesity into WT hosts, fecal transplants from T-Myd88–/– into WT germ-free recipients was insufficient to transfer obesity (fig. S9A). Additionally, when either SPF WT or T-Myd88–/– pregnant dams were cohoused with germ-free WT pregnant dams, the resulting colonized pups separated at weaning did not transmit the obese phenotype (fig. S9A). We hypothesized that immune defects in T-Myd88–/– mice were necessary to allow the persistence of the obesogenic microbiota. By contrast, the microbiota was appropriately controlled in the presence of a fully intact immune system. Tcrb–/– mice, which lack endogenous T cells, were depleted of endogenous microbiota with broad-spectrum antibiotic treatment and then colonized with a 1:1 mixture of WT and T-Myd88–/– microbiota before adoptive transfer of either WT or T-Myd88–/– CD4+ T cells (fig. S9B). Mice were separated into individually housed cages so that microbiota formation would not be influenced by the presence of other mice within the cage and each microbial community would be shaped independently. Even though these mice were initially colonized with the same microbiota, Tcrb–/– mice given T-Myd88–/– CD4+ T cells gained significantly more weight when compared with Tcrb–/– mice administered WT CD4+ T cells (Fig. 5A). Thus, defects in Myd88 signaling within T cells drives the metabolic defects in mice. Only 10% of bacteria were coated by IgA within Tcrb–/– mice, demonstrating the importance of T cells for IgA targeting of the microbiota (Fig. 3B). However, 1 week after T cell transfer, mice administered WT T cells showed a threefold increase in IgA-bound microbes (fig. S9C). IgG1 or IgG3 responses against the microbiota took longer to develop but were detectable 8 weeks after T cell transfer (fig. S9, D to F). Although total IgA levels were similar in all mice within this experimental setting, IgA and IgG1 binding to the microbiota was defective in mice receiving T cells in which Myd88 was deleted (Fig. 5B and fig. S9, E and G). Targeting of the microbiota by IgG3, which is believed to be governed by T cell–independent mechanisms, was not defective in Tcrb–/– mice receiving T-Myd88–/– T cells (fig. S9H) (25). The microbiota composition increasingly differed between genotypes over time (Fig. 5C). Moreover, community changes in mice receiving T cells from obesogenic mice were similar to those observed in T-Myd88–/– mice. Indeed, there was a significant negative correlation between the abundance of Desulfovibrionaceae and Clostridiaceae in both genotypes. Mice receiving T-Myd88–/– T cells were ultimately colonized with significantly fewer Clostridiaceae despite starting with the same microbiota admixture (Fig. 5, D and E). Three taxa at the genus level were differentially targeted by IgA, including the Oscillospira genus of Clostridia, whereas most Clostridia genera were highly variable at this level of taxonomic resolution (fig. S10A). We assessed the IgA-binding index at the finer operational taxonomic unit (OTU) level (97% similarity) and found an enrichment of Clostridia-classified OTUs that were differentially targeted by IgA in mice receiving T-Myd88–/– T cells (Fig. 5F). We observed only trending increases in IgA targeting of Desulfovibrio (fig. S10, A and B). Thus, reductions in Clostridia and their functional contributions may arise from a combination of inappropriate targeting by IgA and the expansion of Desulfovibrio.

Fig. 5 TFH cell regulation of the microbiota prevents obesity.

(A to H) Tcrb–/– mice were administered a mixture of WT and T-Myd88–/– microbiota 1 week before being given either WT or T-Myd88–/– T cells. Mice were then individually housed for 8 weeks and measured for weight gain and microbiota composition while being fed a normal chow (n = 6 per cohort). (A) Area under the curve (AUC) analysis of weight gained. (B) Representative flow cytometry plot was previously gated on SYBR Green+ cells in order to quantify the percentage of antibody-bound bacteria at 8 weeks. Rag1–/– feces control (gray shaded area); Tcrb–/– feces (gray line); WT (blue line); T-Myd88–/– (red line). Quantitation of multiple mice to the right. (C) Violin plot of Bray–Curtis distances between microbiota of Tcrb−/− mice administered WT or T-Myd88–/– T cells at days 0, 7, and 28. (D) Correlation between Desulfovibrionaceae abundance and Clostridiaceae abundance in Tcrb–/– mice given WT or T-Myd88–/– CD4+ T cells (n = 12). (E) Relative abundance of Clostridiaceae (4 weeks). Error bars indicate SD. (F) IgA-bound and -unbound bacteria were analyzed from cecal contents of Tcrb–/– mice given WT or T-Myd88–/– CD4+ T cells. An IgA index was calculated for each OTU to show differences in binding. Positive values indicate enrichment in the bound fraction and negative values enrichment in the unbound fraction. All OTUs with statistically significant differences are shown (P < 0.05, Wilcoxon signed-rank test). Each panel groups OTUs with the same taxonomic call according to their finest classification level [genus (g), family (f), or order (o)]. Each dot represents an individual animal, while different colors within a panel distinguish OTUs within a taxa, and each line connects the means from each OTU. Data from one experiment. (G) Percent weight gained in Rag1–/– mice colonized with WT or T-Myd88–/– fecal microbiota (n = 7 per cohort). Representative of two independent experiments. (H) AUC of weight gained in T-Myd88–/– mice receiving donor WT or Bcl6–/– T cells and fed a normal chow (WT donor, n = 5; T-Myd88–/– donor, n = 6). Representative of two independent experiments. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 by using [(A), (B), and (H)] a two-tailed unpaired Student’s t test, (C) repeated measures ANOVA with Tukey’s multiple comparison, (D) Spearman’s rank-order correlation, (G) a repeated measures ANOVA Sidak’s correction for multiple comparisons, and (E) a Mann–Whitney U test to calculate statistical significance, respectively. In (D) and (G), error bars indicate SD.

To support the hypothesis that antibody responses influence metabolic defects, we transferred the obesogenic microbiota into either antibiotic-treated WT or Rag1–/– mice. The transfer of the obesogenic microbiota to WT mice did not confer the phenotype, whereas transfer into Rag1–/– mice, which lack antibodies, resulted in significantly greater weight gain as compared with that of mice receiving WT microbiota (Fig. 5G and fig. S9I). TFH are T cells that function to instruct antibody class switching and affinity maturation in germinal center B cells. We previously established that the only T cell developmental defect in T-Myd88–/– mice was within TFH cells. T-Myd88–/– mice receiving Bcl6–/– T cells, which cannot differentiate into TFH cells, weighed significantly more as compared with mice receiving WT T cells (Fig. 5H) (26). Thus, T cells that do not have the capacity to develop into TFH cells fail to rescue the obese phenotype. Appropriate TFH cell function is therefore required to regulate the microbiota to prevent obesity.

Clostridia prevent lipid absorption

Short-chain fatty acids (SCFAs) are a well-studied microbiota-dependent mechanism that influences host metabolism. However, SCFA production did not differ between WT and T-Myd88–/– mice (fig. S11A). Increased intestinal permeability and leakage of bacterial products that induce low-grade inflammation within adipose tissue has also been proposed (21, 27). However, we failed to detect differences in bacterial ligands within the serum of T-Myd88–/– mice. Furthermore, when T-Myd88–/– were placed on a diet infused with an anti-inflammatory, 5-ASA, (28) they failed to rescue weight gain (fig. S11B). Liver RNA-sequencing (RNA-seq) and gene set enrichment analysis (GSEA) revealed that despite mice being fed a standard mouse chow, pathways involved in lipid metabolism, including glycerolphospholipid and glycerolipid metabolism, were the most significantly enriched pathways within T-Myd88–/– mice compared with WT controls (Fig. 6A). Particularly, the expression of genes required for the synthesis of lipids—including Fasn, Dgat2, and Srebpf1 and genes involved in lipid absorption including Slc27a4 and Cd36—were all highly up-regulated within the liver of T-Myd88–/– mice (Fig. 6B). Although Cd36 was up-regulated in T-Myd88–/– mice, antibiotic treatment significantly down-regulated Cd36 expression (Fig. 6C). Moreover, Clostridia treatment of obese T-Myd88–/– mice produced a significant down-regulation of Cd36, suggesting that Clostridia function to reduce lipid uptake (Fig. 6D). Gnotobiotic mice colonized with the Clostridia consortia alone had significant reductions in hepatic Cd36 expression when compared with germ-free mice (Fig. 6E). Thus, lipid uptake in T-Myd88–/– appears to occur in a microbiota-dependent manner.

Fig. 6 Clostridia inhibit lipid absorption within the intestine.

(A) GSEA analysis from RNA expression in livers from 1-year-old WT and T-Myd88–/– mice. Pathways that had a significant FDR of 0.25 or smaller were included. (B) Volcano plot of ratio of liver transcripts. Highlighted genes are involved in lipid metabolism. (C) Cd36 RNA expression within livers of WT and T-Myd88–/– mice fed HFD with or without antibiotics (ABX) (WT, n = 5; T-Myd88–/–, n = 4; WT ABX, n = 5, T-Myd88–/– ABX, n = 5). Representative of two independent experiments. (D) Cd36 RNA expression in livers of T-Myd88–/– mice gavaged with vehicle control or spore-forming Clostridia consortium (control n = 4; Clostridia consortium, n = 5). Representative of two independent experiments. (E to G) Germ-free mice with or without colonization of a Clostridia consortium (GF, n = 8; Clostridia, n = 10). (E) Cd36 RNA expression in the liver. (F) Cd36 RNA expression in the small intestines (SI). (G) Fasn RNA expression in the SI. (H) Cd36 RNA expression in MODE-K cells incubated for 4 hours with media or bacterial cell-free supernatant (CFS). Representative of three independent experiments. (I) Germ-free mice were associated with the Clostridia consortia or two Desulfovibrio species (D. piger and D. desulfuricans). Body fat percentage was measured by NMR analysis. (Germ-free mice n = 12; Clostridia, n = 16, Desulfovibrio, n = 14). (J and K) Germ-free mice were associated with the Clostridia consortia with or without D. piger and D. desulfuricans (DSV). Body fat percentage was measured by NMR (Clostridia alone n = 16; Clostridia+DSV n = 21) (J) and Cd36 within the small intestine by qPCR (K). (L and M) GC–MS-detected metabolites within serum and cecum contents of WT and T-Myd88–/– mice fed HFD (n = 6 per cohort). ϕP of <0.06, *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; [(C) to (G) and (I) to (M)] a two-tailed, unpaired Student’s t test, and (H) one-way ANOVA Sidak’s correction for multiple comparisons were used to calculate statistical significance. Data are presented as mean ± SD.

The colonization of germ-free mice with Clostridia significantly down-regulates both Cd36 and Fasn within the small intestine (Fig. 6, F and G), suggesting that Clostridia influence lipid absorption and metabolism within the gut. Moreover, cell-free supernatants (CFS) collected from the cultured Clostridia consortia significantly down-regulated Cd36 in cultured intestinal epithelial cells (IECs) (Fig. 6H). By contrast, CFS collected from cultured Desulfovibrio species directly elevated the expression of Cd36 on IECs (Fig. 6H). Furthermore, germ-free mice colonized with the Clostridia consortia showed a significant decrease in body fat percentage compared with that of mice mono-associated with Desulfovibrio or germ-free mice (Fig. 6I). The addition of Desulfovibrio to germ-free mice colonized with the Clostridia consortia alone led to an increase in body fat percentage and Cd36 expression in the small intestine (Fig. 6, J and K). Thus, the microbiota can directly regulate lipid metabolism within gut epithelia.

Supporting increased lipid absorption, HFD-fed T-Myd88–/– had significant decreases in several long-chain fatty acids (LCFAs) within the cecum and concomitant increases in the serum (Fig. 6, L and M). Comparison of lumenal lipid profiles and 16S sequencing revealed opposing correlations between Desulfovibrio and members of Clostridia and the abundance of LCFAs and other lipids. The depletion of LCFAs within the cecal content was significantly correlated with the presence of Desulfovibrio. By contrast, multiple members of Clostridia, including SMB53 and Dorea, were associated with LCFA accumulation (fig. S11C), further supporting the hypothesis that microbial composition can regulate lipid absorption. Thus, the loss of particular Clostridia species seen in individuals with obesity and T2D may lead to increased intestinal absorption and metabolism of fats, highlighting the importance of an appropriate microbiota composition to health.


The microbiota has been implicated in a wide variety of autoimmune and metabolic conditions. However, these diseases are not always associated with the acquisition of a pathogenic organism, and instead, the loss of beneficial species has been proposed to be a causative factor (29). Mechanisms leading to the loss of beneficial bacteria can include antibiotic use, increased sanitation, and a low-fiber diet (30). Our studies indicate that another mechanism to maintain healthy microbial communities is through appropriate immune control of these populations within the intestine. The microbiota formed within T-Myd88–/– mice mirrors the dysbiosis seen in individuals with type 2 diabetes and obesity, including an expansion of Desulfovibrio and a loss of Clostridia (11). Although comprehensive human studies are lacking, individuals with obesity and type 2 diabetes have also been reported to have lower mucosal IgA and decreased responses to immunizations. This suggests that these individuals have a suboptimal, but not completely deficient, immune response to their gut microbiota that, coupled with dietary deficiencies, leads to metabolic disease. Our data suggest that T cell–dependent targeting of the microbiota is important for the maintenance of a healthy community. Although IgA binding of bacteria is typically thought to lead to its eradication, IgA can regulate the functional gene expression of certain bacteria and even aid in mucosal association of certain commensals (3133). We found that despite lower levels of IgA in T-Myd88–/– mice, Desulfovibrio and several Clostridia species display increased IgA coating. Thus, the inappropriate targeting of Clostridia by IgA may either reduce their colonization or change their metabolic functions to influence development of obesity. Additionally, several Clostridia are targeted less by IgA. A recent evaluation of the microbiota within individuals with IgA deficiency showed a significant reduction in colonization by several Clostridia (34). Therefore, IgA may also function to enhance colonization of some Clostridia species, as has been shown for Bacteroides fragilis (31). The mechanism by which Desulfovibrio expands in this model and in individuals with MetS is still unclear. Our studies, however, indicate that this expansion can directly influence the colonization of specific Clostridia members, although how this occurs remains enigmatic. Understanding how IgA targeting of gut microbes influences their colonization and function in a germ-free setting may provide insight into how the immune system influences this microbial relationship. Because members of Clostridia are increasingly recognized in several settings (24), it will be important to determine how colonization by other microorganisms and the immune system together influence the function of Clostridia.

CD36 is a critical regulator of lipid absorption within the intestine, and its deficiency results in resistance to the development of obesity and MetS upon HFD feeding (35, 36). Increased expression of CD36 within the human liver is associated with fatty liver disease. Furthermore, individuals with polymorphisms in CD36 are resistant to metabolic disease (37). Thus, relative expression levels of CD36 are important for lipid absorption and homeostasis within mammals. Recent studies have demonstrated that the microbiota can up-regulate host absorption of lipids within the intestine through enhanced CD36 expression (38). However, we found that bacteria may also be able to restrain host lipid absorption.

Thus, gut bacteria can differentially regulate lipid metabolism. Products secreted by Desulfovibrio up-regulate CD36 expression, whereas products produced by Clostridia can down-regulate CD36 expression. Therefore, the loss of organisms that function to temper CD36 expression may lead to the inappropriate absorption of lipids, which can accumulate over time, leading to obesity and MetS. Further characterization of the interaction of organisms such as Desulfovibrio and Clostridia, as well as the identification of secreted molecules that influence CD36 expression, may inform future targeted therapies.

Materials and methods


C57Bl/6 Myd88LoxP/LoxP mice (Jackson Laboratories) were crossed to C57Bl/6 CD4-Cre mice (Taconic) to produce Myd88+/+ ; CD4-Cre+ mice (WT) and Myd88LoxP/LoxP; CD4-Cre+ (T-Myd88–/–) mice. Age-matched male mice were used to compare the spontaneous weight phenotype, including immune and microbiota responses, on a standard diet. Age-matched male and female mice were used to compare the weight phenotype, including immune and microbiota responses, on a high-fat diet (HFD). To measure T cell-dependent shaping of the microbiota, 4-week-old Tcrb–/– mice (Jackson Laboratories) were used. To investigate Desulfovibrio desulfuricans-dependent shaping of the microbiota, 6-week-old WT C57Bl/6 mice (Jackson Laboratories) or age-matched CD4-Cre+ (WT) mice from our facility were used. To measure microbiota effects on weight gain in immunodeficient mice, 4-week-old Rag1–/– mice (Jackson Laboratories) were used. GF mice were maintained in sterile isolators and verified monthly for GF status by plating and PCR of feces. GF C57Bl/6 mice were used in this study. The use of mice in all experiments was in strict adherence to federal regulations as well as the guidelines for animal use set forth by the University of Utah Institutional Animal Care and Use Committee.

Colonization of mice with spore-forming microbes

Fecal pellets were taken from WT mice and incubated in reduced PBS containing 3% chloroform (v/v) for 1 hour at 37°C in an anaerobic chamber. A control tube containing only reduced PBS and 3% chloroform was also incubated for 1 hour at 37°C in an anaerobic chamber. After incubation, tubes were gently mixed and fecal material was allowed to settle for 10 s. Supernatant was transferred to a fresh tube and chloroform was removed by forcing CO2 into the tube. For spore-forming (SF) experiments in conventional conditions, mice within the SF cohort were orally gavaged with 100 μL of spore forming fecal fraction, and mice within the CTRL cohort were orally gavaged with 100 μL of PBS that also had chloroform removed every third day. For spore-forming associations with germ-free mice, tubes containing gavage material were sterilized in the port of a germfree isolator for 1 hour before pulling them into the isolator for gavage. Breeder pairs were then orally gavaged with 100 μL of the spore-forming cocktail. Their offspring were sacrificed at 8 weeks of age for analysis of the small intestine and liver.

T cell transfer into T-Myd88–/– mice

T-Myd88–/– mice were sublethally irradiated with 500 rads the day before T cell transfer. Spleens from WT(CD4-cre+) and BCL6KO (Bcl6LoxP/LoxP CD4-cre+) mice were provided by S. Crotty from La Jolla Institute, and T cells were isolated using the CD4+ T Cell Isolation Kit (Miltenyi). T-Myd88–/– were retro-orbitally injected with 5×106 of either the WT or Bcl6–/– MACS-enriched T cells and weighed weekly for 5 weeks.

Diet treatment

Mice housed within the SPF facility were fed a standard chow of irradiated 2920x (Envigo). Mice were fed a high fat diet of 45 kcal% fat DIO mouse feed (Research Diets) or a diet of 10 kcal% fat DIO mouse feed (Research Diets) as a control during HFD experiments. Mice were also fed a custom diet containing irradiated standard 2020 chow containing 1% 5-ASA (Envigo) or a control diet lacking the 5-ASA (Envigo) during 5-ASA inflammation experiments.

Antibiotics treatment

WT and T-Myd88–/– mice were maintained on drinking water containing 0.5 mg/mL of ampicillin (Fisher Scientific), neomycin (Fisher Scientific), erythromycin (Fisher Scientific), and gentamicin (GoldBio) for 14 weeks while being fed a HFD in order to determine the relative contribution of the microbiota to the weight gain phenotype. Tcrb–/– and Rag1–/– mice were maintained on drinking water containing 0.5 mg/mL of ampicillin (Fisher Scientific), neomycin (Fisher Scientific), erythromycin (Fisher Scientific), and gentamicin (GoldBio) for 1 week to reduce the endogenous microbiota before being recolonized by fecal transfers.

T cell shaping of the microbiota within Tcrb–/– mice

Three separate cages of four Tcrb–/– mice were placed on drinking water containing the antibiotic cocktail described above for 1 week. Antibiotics was removed for 24 hours before any further treatment. One fecal pellet from a WT donor and one fecal pellet from a T-Myd88–/– donor were reconstituted in oxygen-free PBS containing 0.1% cysteine, which was immediately gavaged into Tcrb–/– mice orally. This oral gavage was repeated every other day for 1 week. Forty-eight hours following the final gavage, mice were placed into individually housed cages and retro-orbitally injected with 5 × 106 CD4+ MACS-enriched WT or T-Myd88–/– cells. This was labeled D0.

Glucose tolerance test

Mice were fasted for 6 hours prior to being challenged with glucose. Fasting levels of glucose were detected using a Contour Glucose Meter (Bayer) and Contour Glucose Strips (Bayer). One milligram of glucose per gram of body weight was injected intraperitoneally into mice at timepoint zero. Blood levels of glucose were measured at 5-, 15-, 30-, 60-, and 120-min time points using the glucose meter and strips.

Insulin ELISA

Serum was collected from 6-hour-fasted mice, and insulin was measured using a mouse insulin enzyme-linked immunosorbent assay (ELISA) kit (Crystal Chem). Serum samples were run in duplicate according to the manufacturer’s instructions.

Insulin resistance test

Mice were fasted for 6 hours prior to being challenged with glucose. Fasting levels of glucose were detected using a Contour Glucose Meter (Bayer) and Contour Glucose Strips (Bayer). Insulin (0.75 U/kg of body weight) was injected intraperitoneally into mice at timepoint zero. Blood levels of glucose were measured at 5-, 10-, 15-, 20-, 25-, 30-, 40-, and 60-min time points using the glucose meter and strips. Mice were removed from the experiment following a 150-μL intraperitoneal injection of 25% glucose if blood glucose levels dropped below 30 mg/dL.

In vitro experiments using mouse intestinal epithelial cells (MODE-K cells)

Mouse intestinal epithelial cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM), with 2 mM L-glutamine and 1 mM sodium pyruvate. DMEM was supplemented with 10% FBS, 1% (v/v) glutamine, penicillin–streptomycin, and 1% HEPES. To determine if bacteria regulated gene expression, a confluent monolayer of cells was incubated with a 1:1 mixture of penicillin–streptomycin-free DMEM and CFS collected from either cultured Clostridia consortia or Desulfovibrio species for 4 hours. The media was then aspirated and cells were placed in 600 μL of RiboZol (VWR) for later analysis.

RNA isolation from small intestine, cell culture, and liver tissue for qPCR and RNA-seq

Tissue sections 0.5 cm in length or 1 × 105 cells were stored at –70°C in 700 μL of RiboZol (VWR). RNA was isolated using the Direct-zol RNA MiniPrep Kit (Zymoresearch). cDNA was synthesized using qScript cDNA synthesis kit (Quanta Biosciences). qPCR was conducted using LightCycler 480 SYBR Green I Master (Roche). qPCR experiments were conducted on a Lightcycler LC480 instrument (Roche). For liver RNA sequencing, RNA was prepped following QC via an Illumina TruSeq Stranded RNA Sample Prep with RiboZero treatment (human, mouse, rat, etc.) and analyzed using Illumina HiSeq Sequencing.

Quantification of fecal immunoglobulins

To quantify luminal IgA, fecal pellets were collected in 1.5-mL microcentrifuge tubes and weighed. Luminal contents were resuspended in 10 μL of sterile 1X HBSS per mg of fecal weight and spun at 100×g for 5 min to remove course material. Supernatants were then placed in new 1.5-mL microcentrifuge tubes and centrifuged at 8,000×g for 5 min to pellet bacteria.

Supernatants (containing IgA) were then placed in new-1.5 mL microcentrifuge tubes and used as samples (1/10 and 1/100 (v/v) dilutions) for an IgA-specific ELISA kit (eBioscience; performed according to the manufacturer’s instructions). Absorbance was read at 450 nm and IgA concentrations were calculated using a standard curve. Concentrations were normalized to fecal weight.

Bacterial pellets were resuspended in 500 μL of sterile PBS and washed twice by spinning at 8,000×g for 5 min. The washed bacterial pellet was then resuspended in 10 μL of sterile PBS per mg of feces. Five microliters of each sample was plated onto a 96-well round-bottom plate. Bacteria were blocked for 15 min at room temperature with 100 μL of sterile HBSS containing 10% (v/v) FBS. Without washing cells, 4 ng/mL of rat anti-mouse IgA (eBioscience clone mA-6E1, PE), rat anti-mouse IgG1(Santa Cruz, CruzFluor 555), or rat anti- mouse IgG3 (Santa Cruz, CruzFluor 555) diluted at 1:500 in sterile HBSS containing 10% (v/v) FBS was added to the wells. Wells were incubated at 4°C for 30 min. The plate was washed twice by spinning at 2,500×g for 5 min before removing the supernatant and resuspending cells in sterile HBSS. After final wash, bacterial wells were resuspended in 250 μL of HBSS containing 5 μL of 1X SYBR green stain (Invitrogen cat #S7563). Wells were incubated for 20 min at 4°C before immediate enumeration on a BD LSR Fortessa. Flow data were analyzed using FlowJo software (Tree Star). Rag1–/– fecal pellets were included in all experiments as negative controls.

Growth of Desulfovibrio desulfuricans ATCC 27774 and Desulfovibrio piger ATCC 29098

The bacterial species Desulfovibrio desulfuricans was purchased from ATCC (#27774). The bacterial species Desulfovibrio piger was purchased from ATCC (#29098). The vial was handled and opened per ATCC instructions for anaerobic bacteria and cells were grown in Desulfovibrio media described previously (21). Media was composed of NH4Cl (1 g/L) (Fisher Chemical), Na2SO4 (2 g/L) (Fisher Chemical), Na2S2O3•5H2O (1 g/L) (Sigma), MgSO4•7H2O (1 g/L) (Fisher Chemical), CaCl2•2H2O (0.1 g/L) (Fisher Chemical), KH2PO4 (0.5 g/L) (Fisher Bioreagents), Yeast Extract (1 g/L) (Amresco), Resazurin (0.5 mL/L) (Sigma), cysteine (0.6 g/L) (Sigma), DTT (0.6 g/L) (Sigma), NaHCO3 (1 g/L) (Fisher Chemical), pyruvic acid (3 g/L) (Acros Organics), malic acid (3 g/L) (Acros Organics), ATCC Trace Mineral Mix (10 mL/L), ATCC Vitamin Mix (10 mL/L) and adjusted to pH of 7.2. Bacteria were grown for 48 hours in an anaerobic chamber (Coy Labs) and stored in growth media containing 25% glycerol at 70°C. 2.5×108 bacterial CFUs were added to 250 μL of mouse drinking water for 1 week.

Isolation and 16S sequencing of fecal, ileal, and IgA-bound microbial DNA

Mice were sacrificed and their entire lower digestive tract (from duodenum to rectum) was removed and longitudinally sectioned. One fecal pellet and luminal content from lower 10 cm of small intestine were collected from each animal to characterize the fecal and ileal microbiota communities, respectively. Fecal and ileal samples were immediately frozen at –70°C in 2-mL screw cap tubes containing ~250 mg of 0.15 mm garnet beads (MoBio, cat# 13122-500). DNA was extracted using the Power Fecal DNA Isolation Kit (MoBio), according to manufacturer instructions. IgA-bound and -unbound bacteria from T cell transfer experiments were isolated from cecal contents and frozen at –70 C before processing. IgA-bound bacteria separation, 16S rDNA amplification, sequencing, and sequence processing was performed as previously described (12), using paired-end 300 cycle MiSeq reads. The IgA index was calculated as previously described (39).


Fecal pellets or lumenal ileal contents were placed directly into Trizol and stored at –20°C until RNA extraction. Total RNA was extracted from samples using Direct-zol (Zymo Research, #R2052), then prepared for Illumina sequencing by the University of Utah high-throughput genomics core facility using the Ribo-Zero Gold rRNA (epidemiology) removal kit (Illumina, #MRZE724). Illumina libraries were multiplexed and sequenced on a HiSeq 2500 with single-end 50 cycle sequencing. The humann2(v 0.9.9) analysis framework was used for all subsequent sequencing processing and data analysis (40). First, using the knead data script implemented in Humann2, raw sequences were quality trimmed and filtered using Trimmomatic (41), then filtered to remove host reads against the Mus musculus genome build GRCm38 using bowtie2 (42). No significant difference in quality-filtered reads was observed among genotypes, although across all samples many more reads from ileal samples mapped to the mouse genome, providing less bacterial transcript coverage. Then, to improve mapping of these short reads, we restricted mapping of the quality-filtered reads to a custom database of mouse isolated bacterial reference genomes with UniRef90 gene annotations. This custom database consisted of 53 organisms isolated and sequenced recently as part of the mouse intestinal bacterial collection (miBC) (43), as well as nine reference genomes included in humman2’s chocophlan database, representing species we detected in 16S sequencing but that were not included in the miBC collection already. These nine genomes were: Bifidobacterium pseudolongum, Bifidobacterium animalis, Bifidobacterium longum, Bacteroides fragilis, Mucispirillum schaedleri, Lactobacillus reuteri, Clostridium perfringens, Desulfovibrio_desulfuricans, and Candidatus Arthromitus. To create the custom database with Uniref90 annotations, we aligned the amino acid sequences from the miBC genomes to the Uniref90 database using the Diamond aligner (44) and requiring 50% query coverage and 90% identity. Then, these uniref90-annotated miBC amino acid sequences were used to annotate each corresponding gene’s nucleotide sequences and combined with the nine genomes already annotated to create our custom nucleotide mapping reference containing mouse-specific bacterial genomes. For mapping filtered sequence reads to the custom reference using Humann2, we used only the nucleotide alignments (no translated alignments) due to the short read length. The counts of aligned reads per kilobase for uniref90 gene families output from humann2 were then normalized to counts per million (within a sample), or regrouped to Gene Ontology (GO) terms then normalized, for all subsequent analyses.

Metabolic phenotyping

Total body fat composition was measured on an NMR Bruker Minispec. CLAMS Metabolic Cages were used to measure indirect calorimetry. Both services were provided by the Metabolic Phenotyping Core, a part of the Health Sciences Cores at the University of Utah. Energy expenditure (EE) was calculated using the following formulas. Calorific Value (CV) = 3.815 + (1.232 × RER). EE = CV × VO2

Liver and adipose tissue microscopy

Liver and adipose tissue were fixed in formalin, embedded in wax. Seven-to-ten micron sections were then stained with hematoxylin and eosin. Microscopy images were collected using an EVOS core XL imaging system from Thermofisher.

Serum and cecal content metabolomics (excluding SCFA measurements)

Sample extraction and preparation

Cecal contents were stored at –70°C prior to analysis. Five mililiters of 75% ethanol solution containing internal standards (1 μg of d4-succinic acid and 5 μg of labeled amino acids (13C, 15N-labeled) mixture per sample) was added to each sample. Samples were vigorously vortexed and then incubated in boiling water for 10 min. Cooled samples were spun down at 5,000×g for 5 min. Supernatants were transferred to fresh tubes and then speed-vacuumed overnight to dry.

GC–MS analysis

All GC–MS analysis was performed with a Waters GCT Premier mass spectrometer fitted with an Agilent 6890 gas chromatograph and a Gerstel MPS2 autosampler. Dried samples were suspended in 40 μL of a 40 mg/mL O-methoxylamine hydrochloride (MOX) in pyridine and incubated for 1 hour at 30°C. To autosampler vials was added 25 μL of this solution. Forty microliters of N-methyl-N-trimethylsilyltrifluoracetamide (MSTFA) was added automatically via the autosampler and incubated for 60 min at 37°C with shaking. After incubation, 3 μL of a fatty acid methyl ester standard (FAMES) solution was added via the autosampler then 1 μL of the prepared sample was injected to the gas chromatograph inlet in the split mode with the inlet temperature held at 250°C. A 10:1 split ratio was used for analysis. The gas chromatograph had an initial temperature of 95°C for 1 min followed by a 40°C/min ramp to 110°C and a hold time of 2 min. This was followed by a second 5°C/min ramp to 250°C, a third ramp to 350°C, then a final hold time of 3 min. A 30-m Phenomex ZB5-5 MSi column with a 5-m long guard column was employed for chromatographic separation. Helium was used as the carrier gas at 1 mL/min. Due to the high amounts of several metabolites the samples were analyzed once more at a tenfold dilution.

Analysis of GC–MS data

Data were collected using MassLynx 4.1 software (Waters). Metabolites were identified and their peak area was recorded using QuanLynx. This data was transferred to an Excel spread sheet (Microsoft, Redmond WA). Metabolite identity was established using a combination of an in-house metabolite library developed using pure purchased standards and the commercially available NIST library. Not all metabolites are observed using GC–MS. This was due to several reasons. For example, some metabolites were present at very low concentrations. Second, metabolites may not be amenable to GC-MS due to either being too large to volatilize, are a quaternary amine such as carnitine, or just do not ionize well. Metabolites that do not ionize well include oxaloacetate, histidine, and arginine. Cysteine is observed depending upon cellular conditions, often forms disulfide bonds with proteins, and is found at a low intracellular concentration.

Short-chain fatty acid detection of cecal contents

Sample extraction and preparation

Samples were removed from the freezer and allowed to thaw at RT for 5 min. To these samples were added 400 μL of dd-H2O, 10 μL of 5-sulfosalicylic acid (1 mg/μL), and 2 μL of internal standard (1 M pivalic acid). Samples were vortexed for 30 s and rested on ice for 30 min. Samples were then centrifuged at 2,000×g for 10 min at 4°C. The supernatants were then transferred to glass vials with PTFE lined caps containing 10 μL of concentrated HCl. Next, 3 mL of ether was added and the samples were vortexed for 30 s, then centrifuged at 1200×g for 10 min at 4°C. The supernatants were then transferred to new glass vials with PTFE-lined caps and derivatized with 50 μL of N-Methyl-N-(tert-butyldimethylsilyl) trifluoroacetamide, tertbutyldimetheylchlorosilane (MTBSTFA; Thermo Scientific). Samples were vortexed and placed in a 60°C sand bath for 30 min. Samples were allowed to cool to RT and partially evaporated under a gentle stream of nitrogen to a volume of approximately 250 μL and transferred to glass GC–MS vials.

GC–MS analyses

GC–MS analyses were conducted on an Agilent 6890 gas chromatograph coupled to an Agilent 5793 mass spectrometer and an Agilent 7683 (Santa Clara, CA, USA) auto-injector equipped with a DB-1 column (15-m×0.25-mm internal diameter × 0.25-μm film thickness; J&W Scientific, Folsom, CA, USA). Helium carrier gas was used with a flow rate of 1.0 mL/min. Split ratio 10:1 with injections of 1-μL samples were made into an inlet held at 250°C. The GC oven ramp used was 40°C (hold 1 min); ramp at 5°C/min to 70°C (hold 3 min); ramp at 20°C/min to 160°C (hold 0 min); ramp at 40°C/min to 330°C (hold 6 min). Data were acquired in scan mode with a mass range of 44–200 m/z, targets were quantitated using m/z 117.0 for acetic acid, m/z 131.0 for butanoic acid, m/z 145.0 for propanoic acid and m/z 159.0 pivalic acid.

Supplementary Materials

References and Notes

Acknowledgments: We thank members of the Round and O’Connell laboratories for their critical reviews of the manuscript. We thank S. Crotty for the CD4-cre BCL6fl/fl mice, and B. Dalley and T. Mosbruger in the High-Throughput Genomics and Bioinformatics Analysis core facility performed the liver RNA-seq and analysis, respectively. Funding: Some of the germ-free mice used in this publication were provided from the University of North Carolina Gnotobiotic Facility, which is supported by grants 5-P39-DK034987 and 5-P40-OD010995. R.M.O. is supported by the NIH New Innovator Award DP2GM111099-01, the NHLBI R00HL102228-05, an American Cancer Society Research Grant, a Kimmel Scholar Award, and R01AG047956. S.R.C. is supported by NIGMS RO1 grant GM114817. Support for this project came from the Pews Scholar Program and NIH R56AI107090. Other support for the laboratory came from the Edward Mallinckrodt Jr. Foundation, NSF CAREER award (IOS-1253278), Packard Fellowship in Science and Engineering, NIAID K22 (AI95375), Burroughs Wellcome Investigator in Pathogenesis Award, the American Asthma Foundation, Margolis Foundation, the MS Society Center grant, and the Crohn’s and Colitis Foundation Senior Research Award to J.L.R. Author contributions: C.P. conceived of the study, performed most of the experiments, analyzed the data, and helped write the manuscript. R.B. performed all germ-free experiments and analysis on CD36. K.A.K. helped with the germ-free experiments. S.-H.L. performed glucose tolerance test and metabolic chambers experiments. R.S., A.G., and K.B. helped with mouse harvests with C.P.; H.A.E. helped with bioinformatic analysis. K.S.O. performed some of the IgA assays. S.B. helped with interpretation of metabolic chamber experiments and consulted on insulin resistant test and edited the manuscript. R.M.O. consulted on the immunological analysis and edited the manuscript. J.E.C. performed and analyzed the mass spectrometry analysis. C.J.V. consulted on all metabolic aspects of the manuscript and edited the manuscript. W.Z.S. performed all 16S and metatranscriptomic analyses, IgA-binding assays, and analyses; directed experiments; and helped write the manuscript. J.L.R. conceived of the study, directed experiments, analyzed the data, and helped write the manuscript. Competing interests: None of the authors of this manuscript declare a conflict. Data and materials availability: All sequence data are accessible under NCBI archives under the BioProject accession no. PRJNA542126. All other data needed to evaluate the conclusions in the paper are available within the main text or supplementary materials.

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