Fresh fecal pellets were collected and analyzed every 8 days to ensure colonization, no contamination of unwanted bacterial groups, and examine the influence of TCDD on bacterial abundance. Fecal pellets were placed in RNA/DNA stabilizer (Zymo Research Corp, Irvine, CA, United States) and stored in a −20◦C freezer. Whole blood, spleen, mesenteric lymph nodes, and intestinal tissues were collected at the time of sacrifice. Mice were weighed prior to sacrifice. Two intestinal sections were removed and used for subsequent analysis, including the cecum and the ileum. For the ileum, a 0.25 cm segment proximal to the cecum was removed and stored for all animals. For the cecum, sections of content and tissue were both removed and placed in the same vial. Intestinal tissue samples were immediately placed on RNA stabilizer and stored at −80◦C.

### Transcriptomic Response Analysis with qPCR and nCounter R

RNA was extracted from ileum and cecum of all mice to evaluate the transcriptome response of SFB and B. fragilis in response to TCDD and the response of host immune cells. In detail, the PureLink RNA Mini Kit with Trizol (12183018A, Ambion/Thermo Fisher Scientific, Waltham, MA, United States) was used to extract RNA from mouse tissue and content. Two additional steps were used to digest DNA including the DNase 1 (Invitrogen/Thermo Fisher Scientific, Waltham, MA, United States) and Turbo DNase I Kit (Life Technologies/Thermo Fisher Scientific, Waltham, MA, United States). After each step, isolated RNA was quantified using a Qubit (Life Technologies), and assessed for purity using the Nanodrop ND-1000 UV– Vis spectrophotometer (Nanodrop Products, Wilmington, DE, United States).

2,3,7,8-Tetrachlorodibenzo-p-dioxin-induced expression of both bacterial members was monitored in the ileum and cecum using qPCR. For mRNA analysis of bacteria, qPCR primers were designed from sequences available for the colonized organisms. Genes targeting SFB functions were selected based on potential interaction with host immunity (Pamp et al., 2012) including the putative hemolysin A producing gene (BAK56433.1), and the putative rubrerythrin producing gene (BAK56170.1). Primers targeting B. fragilis gene clusters wcfQ (CAH07088.1) responsible for biosynthesis of PSA (Troutman et al., 2014) were selected due to known influence in signaling naïve T-cells differentiation into Treg cells (Troy and Kasper, 2010). Primers were designed as previously described (Stedtfeld et al., 2008). Briefly, qPCR primers were designed from sequences downloaded from NCBI using Primer Express (Applied Biosystems) default settings; with a maximum length of 150 bases, and a theoretical melting temperature of 59◦C. NCBI BLAST analysis (Altschul et al., 1997) was used to check theoretical specificity of designed primers against the GenBank database (Supplementary Table S1). cDNA was synthesized via random primers as instructed in the High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA, United States). qPCR was performed using a custom SmartChipTM (Wafergen Biosystems, Fremont, CA, United States) with species-specific 16S rRNA gene and functional gene primers (Supplementary Table S1). Briefly, Wafergen's Custom SmartChipTM array allows for 5,184 qPCR reactions with 100 nl volumes to be run in parallel. Sample/primers were dispensed into the SmartChipTM using a Multi-sample Nano-dispenser (Wafergen Biosystems, Fremont, CA, United States). PCR cycling conditions and initial data processing were performed as previously described (Wang et al., 2014; Stedtfeld et al., 2016). Amplification reactions on the SmartChipTM consisted of 1× LightCycler 480 SYBR R Green I Master Mix (Roche Inc., United States), nuclease-free PCR-grade water, 50 ng/µl cDNA template per sample, and 0.5 µM of each forward and reverse primer. Thermal cycling included an initial denaturation at 95◦C for 3 min, followed by 40 cycles of denaturation at 95◦C for 30 s, and annealing at 60◦C for 1 min. Each primer/sample combination was tested in triplicate. Genetic copies were estimated as previously described (Looft et al., 2012). Negative controls with no templates were also included on the array.

2,3,7,8-Tetrachlorodibenzo-p-dioxin-induced ileal expression of host maker genes related to immune function was quantified using the nCounter R mouse immunology panel. In detail, 200 ng of digested RNA was submitted per sample using the nCounter R (NanoString Technologies, Seattle, WA, United States). NanoString's nCounter R mouse immunology panel contains probes targeting 547 immunology-related mouse genes (Reis et al., 2011).

## qPCR of gDNA from Fecal Pellets

Genomic DNA was also extracted from fecal pellets 18–20 days after colonization and 21 days after initial treatment (following the sixth dose of TCDD) using the PowerSoil Extraction Kit (MoBio). qPCR was performed using species-specific 16S rRNA gene primers (Supplementary Table S1). Assays were performed in parallel using the Wafergen SmartChipTM with the same reaction conditions described above for cDNA; however, only 0.1 ng/µl of gDNA was used per sample. Results showed

colonization of SFB and B. fragilis in the groups expected to contain these populations.

## Flow Cytometry of Whole Blood, Splenocytes, and Mesenteric Lymph Nodes

Regulatory T-cell responses were measured in mesenteric lymph nodes, blood, and spleen. Trunk blood was collected into heparin-treated tubes followed by red blood cell lysis using a standard ACK lysing protocol (BD Biosciences, San Jose, CA, United States). Whole blood leukocytes were washed and counted, 1–2 × 106 leukocytes were stained with antibodies for analysis by flow cytometry. Spleens and mesenteric lymph nodes were isolated and made into single-cell suspensions by removing the capsule and connective tissue via mechanical disruption. Cells were then washed and 1–2 × 106 lymphocytes stained with antibodies for analysis by flow cytometry. Subsequently, Fc receptors on peripheral blood, lymph node, and spleen-derived leukocytes were blocked with an anti-mouse CD16/CD32 (BD Biosciences, San Jose, CA, United States), and the cells were stained for extracellular proteins using the following antibodies (Biolegend, San Diego, CA, United States) CD3, CD4, CD25, and NK1.1. Samples were incubated for 20 min at 4◦C, and washed twice with FACS buffer (1× HBSS containing 1% BSA and 0.1% sodium azide). Next, cells were fixed with Cytofix (BD Biosciences, San Jose, CA, United States) and resuspended in FACS buffer. For intercellular protein staining, cells that were previously fixed after surface staining were permeabilized with FoxP3 Perm Buffer (Biolegend, San Diego, CA, United States) by incubating the cells in the perm buffer for 10 min, followed by the addition of the antibodies (FoxP3 and IL-17F (Biolegend, San Diego, CA, United States) directly to the cells and incubated for an additional 30 min at room temperature. Cells were washed four times in the Perm Buffer and after the last wash cells were re-suspended in FACs buffer. Cells were analyzed using a FACSCanto II Flow Cytometer (BD Biosciences, San Jose, CA, United States) and the data analyzed with Flowjo software (ver. 8.8.7 Treestar Software, Ashland, OR, United States), with a gating strategy as shown in Supplementary Figure S2. The percentage of Th17 cells was near the detection limit in measured matrices, as the background ranged from 0.001 to 0.08% in whole blood samples for IL17 staining.

#### Statistical Analysis and Host/Bacterial Functional Gene Annotation

qPCR was analyzed for absolute abundance based on mass of tissue used to extract DNA and extraction yield, or normalized for relative expression of 16S rRNA gene specific to SFB or B. fragilis.

For data obtained using the nCounter R , expression counts were normalized to the geometric mean of multiple housekeeping genes including Rpl19, Ppia, G6pdx, Tubb5, Alas1, Tbp Gusb, Hprt, Gapdh (all had %CV <50) with an algorithm developed by NanoString Technologies. Genes with a maximum normalized expression count of 10 or less among all colonized groups were removed. The Shapiro–Wilk test was used to evaluate normality of data. If necessary, the variance between compared groups was corrected with the Geisser–Greenhouse method. For comparison of more than two groups, one-way ANOVA followed by a multiple comparison Sidak test was performed. When normal distribution was satisfied, a Student's t-test was used for comparing differences between two groups. Otherwise, the nonparametric Mann–Whitney test was used. Statistical analysis and some plots were generated using Prism (version 7 for Windows; GraphPad Software, San Diego, CA, United States), additional plots were rendered using Excel or Cytoscape v. 3.3.0 (Institute for Systems Biology, Seattle, WA, United States). Genes were deemed significantly different if p < 0.05.

Genes that responded to TCDD and colonization were analyzed for enriched functions using the database for annotation, visualization, and integrated discovery (DAVID) v6.8 (Huang et al., 2009a,b). Gene ontology-biological processes (GO-BPs) were used for clustering the enrichment analysis. Functional groups with an enrichment score (ES)≥1.3 were considered significantly enriched, representing the −log scale geometric mean p < 0.05.

## RESULTS

### Transcriptomic Response of Ileal Immune Cells

The influence of SFB on TCDD-induced transcriptomic response was measured using the nCounter R mouse immunology panel (**Figure 1** and Supplementary Table S2). Comparisons were made between groups of mice (Supplementary Figure S1) that remained uncolonized (UC), mice mono-colonized with B. fragilis (B), mice mono-colonized with SFB (SFB), and mice co-colonized with both bacteria (SFB+B) to examine: (i) influence of TCDD in presence of SFB (SFB+B, **Figure 1A**) and (ii) influence of TCDD in absence of SFB (UC and B. fragilis groups, **Figure 1B**). The influence of SFB colonization was determined by comparing vehicle dosed SFB+B vs B groups; and the influence of B. fragilis was determined by comparing vehicle dosed UC vs B groups. Results showed that 11 genes were significantly influenced by TCDD in the presence of SFB (**Figure 1A**). Nine out of 11 of these genes had an opposing response between TCDD and SFB colonization, in terms of transcript regulation. The two genes with similar responses between SFB colonization and TCDD (cfd, Il2rb) had a conflicting response between TCDD and colonization of B. fragilis. The influence of SFB on TCDDinduced host response was further verified in comparing UC with SFB mono-colonized groups (Supplementary Figure S3).

Nine genes were influenced by TCDD in the absence of SFB (**Figure 1B**), all of which were no longer significantly up/down regulated in the presence of SFB. All nine of the genes influenced by TCDD in the absence of SFB were up/down regulated in the same direction as SFB colonization. Influence of SFB colonization in **Figure 1B** was measured by comparing SFB vs UC groups. TCDD-induced responses in groups with and without SFB exhibited opposing modulation of genes associated with immune cell function.

A majority of genes that responded to TCDD in presence/absence of SFB are related to T-cell differentiation

including: Il1β, Ciita, H2-Eb1, and H2-Aa which were significantly upregulated in response to SFB, and were downregulated in response to TCDD. These genes encode the class II, major histocompatibility complex (MHC II), transactivator, which is required for SFB induction of Th17 cells (Lecuyer et al., 2012; Goto et al., 2014). The Il1β gene, which has also been shown to substitute TGFβ in differentiation of T-cells (Ghoreschi et al., 2010), was also upregulated with SFB and downregulated with TCDD (Supplementary Figure S3). In the group without SFB, TCDD induced downregulation of genes related to T-cells (Traf1) and activation of NF-κB (Tlr3) and genes related to regulation of IgA and IgM (Fcamr, Pdcd1) homeostasis (Kawamoto et al., 2012; Ouchida et al., 2012).

Colonization with SFB had a greater overall influence on immune gene expression compared to TCDD (**Figures 1A,B**, **2A**). In total, 93 genes were influenced by SFB colonization; and 83 of these were up-regulated. To identify the function of genes that responded to colonization and TCDD, the DAVID v6.8 (Huang et al., 2009a,b) was used. Seven and zero significant clusters were observed in response to SFB and B. fragilis colonization, respectively (**Figure 2B**). Three enriched clusters functionally associated with T-cell differentiation and inflammatory response (**Figure 2C**) were identified from the genes that responded to TCDD in one or more groups.

## Response of Regulatory T Cells

2,3,7,8-Tetrachlorodibenzo-p-dioxin-induced responses to Treg cells in the mesenteric lymph node, blood, and spleen were less impacted in the presence of SFB, which is known to promote differentiation of cytokines toward Th17 cells (**Figure 3**). TCDD-induced differentiation of Treg was greater in the group mono-colonized with PSA producing B. fragilis. The percent of CD3+ and CD4+ in the spleen and mesenteric lymph nodes of groups with SFB was also less influenced by TCDD (**Table 1**). Colonization and TCDD also tended to influence Th17 cells in an expected manner; however, Th17 measurements were near the limit of detection (Supplementary Figure S4).

## Response of Colonized Bacteria

qPCR analysis showed that TCDD significantly influenced the abundance of SFB and B. fragilis in the absence of other gut commensals (**Figure 4**). In detail, the absolute abundance of SFB was significantly higher in response to TCDD (**Figure 4B**). Increased SFB abundance was also verified with gDNA extracted from fecal pellets, and using assays targeting the SFB fliC functional gene (Supplementary Table S3). Overall, a 2.8-fold higher level of SFB (p = 0.038) was observed in response to TCDD. In contrast, the absolute abundance of B. fragilis, measured using species-specific rplB gene primers, significantly (p = 0.029) decreased 2.1-fold in mice dosed with TCDD (**Figure 4A**). The cecal and ileal abundance of B. fragilis and SFB did not differ among co-colonized or monocolonization groups (Supplementary Table S3). Thus, bacterial abundances in both groups of mice were plotted and analyzed concurrently.

FIGURE 2 | nCounter R mouse immunology panel analysis and functional response. (A) Genes in ileal tissue that responded to SFB colonization. Line color and direction indicate upregulated (red) and downregulated (blue) genes. Abbreviations include mice that were mono-colonized with SFB (SFB), mono-colonized with B. fragilis (B) and co-colonized (SFB+B). (B,C) Functional clusters identified using the DAVID using nCounter R mouse immunology panel analysis with gene expression of ileal tissue showing (B) clusters of genes that responded to colonization with SFB, and (C) clusters of genes that responded to TCDD in presence and absence of SFB. Functional clusters with scores =1.3 were included as significantly enriched.

percent and error bars represent standard error in presence/absence of SFB. P-values are shown between vehicle- (Veh) and TCDD-treated groups.

## Response of Select Functional Genes from Colonized Bacteria

The expression of select functional SFB and B. fragilis genes was also quantified via qPCR to examine responses putative to host interaction. In the ileum of the SFB colonized group, expression of the hemolysin A producing genes was downregulated in response to TCDD (**Figure 4D** and Supplementary Table S3). Hemolysin A has been shown to decrease the ability of host cells to phagocytize bacteria and undergo chemotaxis in vitro (Cavalieri and Snyder, 1982). Host macrophage recruiting genes were downregulated in the jejunum of mice dosed with TCDD in previously described studies (Fader et al., 2015). Down-regulation of this gene was only observed in the SFB mono-colonized group, as the expression was below the limit of detection in the group that also had B. fragilis.

Expression of the wcf gene involved in PSA production by B. fragilis was also measured. In detail, the wcfQ gene was significantly upregulated in the cecum of the B. fragilis colonized mice (**Figure 4C**) in response to TCDD. B. fragilis produce PSA as a control mechanism to induce regulatory T-cells and create a more self-tolerable growth environment (Round et al., 2011).

## DISCUSSION

Comparative analysis between gnotobiotic mice colonized with and without SFB was performed to examine modulatory responses to TCDD. Noting the reductionist approach of the mouse model colonized with individual populations, the presence of SFB had an opposing response to TCDD in terms of host ileal immune gene expression, and helped modulate levels of TCDDinduced regulatory T-cells in blood, spleen, and mesenteric lymph nodes.

#### Opposing Influence of SFB Colonization and TCDD on Host

Transcriptomic response fell into two categories including: (i) only responding to TCDD in opposition to SFB colonization and (ii) only responding to TCDD in the group without SFB. In the first category, genes influenced by TCDD would not have differed if had they not been expressed via colonization of SFB. This is evident in that none of the MHC II genes were differentially expressed in response to TCDD in groups without SFB. Within the second category, SFB reduced impact of TCDD on host immune gene expression. Verification of SFB influence in groups without B. fragilis further verifies opposing responses with TCDD (Supplementary Figure S3).

A majority of ileal immune genes were down-regulated with TCDD, with gene expression corresponding with differentiation of regulatory T-cells. In the presence of SFB, only two genes were significantly upregulated in response to TCDD (Pparg and cfd) in the group with background B. fragilis. The peroxisome proliferator-activated receptor-γ (Pparg) is a nuclear acceptor that inhibits NFκB activity. The downregulation of this gene has been associated with ulcerative colitis (Dubuquoy et al., 2003), and its increased expression correlates with anti-inflammatory responses, which is akin to what has been observed with TCDD (Benson and Shepherd, 2011). The genes that responded to TCDD in the absence of SFB (e.g., Il12rb2, Cd36) also regulate the number of regulatory T-cells and suppression of an inflammatory response (Zhao et al., 2008; Cecil et al., 2009).

#### Influence of T-Cell Response in Health

2,3,7,8-Tetrachlorodibenzo-p-dioxin-induced response of regulatory T-cells in the mesenteric lymph nodes was less impacted in groups colonized with SFB, extending to blood and spleen matrices beyond the gut (**Figure 3**). Differences in regulatory T-cell responses among the colonized groups further indicate the influence of gut commensals on host disposition to TCDD. The impact of TCDD on regulatory T-cells has been shown to influence susceptibility to infection in the gut (Thigpen et al., 1975). However, the modulatory influence of SFB or Th17 inducers isolated from human stool (Tan et al., 2016) may help modulate susceptibility to bacterial infection and abrogate TCDD-induced response.

### TCDD-Induced Host Response Favors SFB

2,3,7,8-Tetrachlorodibenzo-p-dioxin-induced down-regulation of host immune genes that were activated with SFB colonization may provide a more favorable environment for SFB proliferation. This is evident in opposing functional inflammatory responses. Previous studies have found that mice deficient in mucosal

TABLE 1 | Body weight and T-cell number (CD3+ and CD4+) in spleen, blood, and mesenteric lymph nodes (lymph) after TCDD (30 µg/kg) or vehicle (sesame oil) treatment of mice by oral gavage once every 4 days for 28 days.

Numbers indicate mean and standard error. Abbreviations identify mice that remained uncolonized (UC) or were mono-colonized with B. fragilis (B) and co-colonized (SFB+B). Vehicle dosed mice (Veh) and TCDD dosed mice (TCDD). ∗Significant difference between TCDD and vehicle-dosed groups (p < 0.05).

immunity maintainers displayed a 10-fold increase in SFB abundance in fecal pellets (Upadhyay et al., 2012). Studies have also shown that mice lacking an enzyme critical to the production of IgA had an overgrowth of SFB despite the presence of other commensal organisms (Suzuki et al., 2004). TCDD also tended to reduce expression of the flagella receptor Toll-like receptor 5 gene TLR5 gene 2.0-fold (p = 0.06) in the ileum of UC mice. Increased levels of flagella producing bacterial members have previously been observed in mice lacking the TLR5 gene (Cullender et al., 2013). As such, TCDD appears to repress host immune cell gene expression in the gut, permitting proliferation of SFB. Downregulation of mRNA from measured SFB functional genes putative to evasion from host defense has similar implications.

A similar shift in SFB abundance was also observed in mice with a traditional gut microbiome; however, it was unknown if this was due to the expansion of other bacterial populations or changes in nutrients caused by commensal fluctuations. In detail, previous studies described an increased ratio of Firmicutes to Bacteroidetes in response to TCDD (Lefever et al., 2016; Stedtfeld et al., 2017a), and higher levels of Proteobacteria (Stedtfeld et al., 2017b). The expansion in these predominantly flagella producing groups (Lozupone et al., 2012) may in part influence the relative levels of Bacteroidetes and SFB. However, similar shifts in both bacteria in the gnotobiotic mice indicate this response can occur solely via TCDD-induced modulation of the host.

### TCDD-Induced Host Response Influences B. fragilis

The abundance of B. fragilis decreased in response to TCDD in the gnotobiotic mice. While the reduced abundance of Bacteroidetes was previously observed in TCDD-dosed mice with a traditional gut microbiome (Bhaduri, 2015; Lefever et al., 2016), the causation for this dysregulation is unclear. Previous studies performed by Round et al. (2011) suggest that B. fragilis favors an immunosuppressed environment, and that expression of B. fragilis PSA is a defense or colonization mechanism to increase differentiations of Treg cells. In accordance, it would seem that PSA-producing genes should be downregulated to balance the levels of Treg cells induced by TCDD. However, the upregulation of mRNA genes clusters related to the production of PSA indicates that B. fragilis are in a state of distress in response to TCDD. In addition, TCDD did not significantly influence the abundance of Bacteroidetes or other key commensals when tested in vitro in this (Supplementary Figure S5) or previously

FIGURE 4 | Bacteroides fragilis and SFB abundance and selected functional gene expression based on isolated RNA. (A) Absolute abundance of B. fragilis expression measured with specific rplB gene primers in cecum content from respective groups. (B) Absolute abundance of SFB expression using species-specific 16S rRNA gene primers from ileum of respective groups. (C) PSA biosynthesizing wcfQ gene normalized expression, measured with cecum content from respective groups. (D) SFB functional genes putative to interaction with host immunity (hemolysin A producing gene) normalized expression, measured with ileal RNA from SFB mono-colonized mice. SFB functional genes in co-colonized group are not included as the genes were expressed below detection limit in two or more biological replicates. Bars represent mean normalized abundance. Open dots indicate mono-colonized groups, closed dots indicate co-colonized groups. Abbreviations include vehicle dosed (Veh) and TCDD treated (TCDD).

described studies (Lefever et al., 2016). Collectively, shifts in B. fragilis function and abundance also appear to be the result of TCDD-elicited changes in host.

## Relevance of TCDD Dose

fmicb-08-01708 September 5, 2017 Time: 16:57 # 9

Considering a half-life of 7–11 days for TCDD (Gasiewicz et al., 1983; Birnbaum, 1986), mice in this study had an estimated concentration of 60.5–90.7 µg/kg TCDD at sacrifice. One previously measured human exposure event resulted in comparable levels; with lipid-adjusted blood levels of 56 µg/kg TCDD in children near a 1976 plant accident in Seveso, Italy (Bertazzi et al., 1993). In addition, the lowest observable effect level of TCDD on inhibiting an immune response (IgM secretion) was lower in human (0.3 nM) than mice (3 nM) in lymphocytes tissue tested in vitro (Wood et al., 1993). We have also observed significantly higher levels of SFB at doses ≥1.0 µg/kg TCDD in other studies (Bhaduri, 2015). Thus, similar responses to those described here may occur with 10- to 100-fold less TCDD, warranting additional experiments. While environmental levels of TCDD are typically lower than that used in this study and are thought to be decreasing in the environment, cumulative exposure to other AhR ligands such as polyaromatic hydrocarbons and flame retardants is increasing (Lim et al., 2008; Wiseman et al., 2011).

## TCDD versus Other AhR Ligands

The AhR ligand response demonstrated in this study differs from those published by Zhang et al. (2015). In detail, their study with wild-type mice orally dosed with 2,3,7,8 tetrachlorodibenzofuran (TCDF), a less potent AhR activator, heightened host inflammation, reduced levels of SFB, and increased abundance of Bacteroidetes over Firmicutes. However, our results showed reduced levels of host immune gene expression, which has also been previously described in the jejunum in response to TCDD (Fader et al., 2015) and AhR activation (Monteleone et al., 2013). Dissimilar responses may be due to differences in AhR ligand potency, which has been reported in a study comparing TCDD and 6-formylindolo[3,2 b]carbazole (Farmahin et al., 2016). Future studies addressing AhR activity and influence to host and gut microbes in response to different ligands throughout the gut are necessary (Zhang et al., 2017).

#### Differences in Murine and Human Models

Before inferring if TCDD and presence/absence of SFB would have a similar influence on humans compared to the murine model, both the host and microbial phenotype must be considered. Differences in host phenotype have been described previously (Kreisman and Cobb, 2011), with varied gene expression in response to TCDD depending on the measured tissue and host species (Kovalova et al., 2017). In regards to microbiota, recent meta-analysis has shown that 90 and 89% of bacterial phyla and genera, respectively, are shared between mice and the human gut microbiota (Krych et al., 2013), with the most abundant bacterial species being common in both. Studies examining the presence of SFB in human microbiome have conflicting results (Klaasen et al., 1993; Qin et al., 2010; Caselli et al., 2013; Yin et al., 2013; Shukla et al., 2015), which may be due to variability in the methods or matrices used for measuring SFB. However, immune-activating bacterial groups isolated from human gut microbiome appear to have a similar response to SFB in the capacity to differentiate T-cells in mice (Tan et al., 2016). Commercially available probiotic consortia have also been shown to contain Th17 cell inducers in mice (Tan et al., 2016), highlighting the therapeutic potential of immuneactivating bacteria to modulate host response to TCDD.

Collectively, the presence of SFB significantly influenced host response to TCDD in the matrices measured. TCDD exposure also appeared to provide a more favorable environment for SFB. B. fragilis also responded to TCDD-induced shifts in the host, expressing higher levels of the wcfQ gene, a PSA-producing gene cluster. TCDD did not appear to influence abundance of these bacterial groups in vitro in TCDD-dosed batch reactors (Supplementary Figure S5), further suggesting that the bacterial response of SFB and B. fragilis is in part due to TCDD induced influences on the host.

Follow-up studies are underway to further elucidate causation of functional dysbiosis with B. fragilis. This will include the analysis of additional genes related to host interaction and immune tolerance. Further identification of key bacterial species and functional genes may facilitate attempts to manipulate the intestinal microbiome toward a protective and healthy state. Future studies will also examine the ability of other human bacterial commensals to influence TCDD-induced response in the absence of SFB. These studies may lead to new therapeutic approaches to treat intestinal pathogens and resulting autoimmune diseases.

## AUTHOR CONTRIBUTIONS

RS, TS, and SH wrote the manuscript; RS, SH, JT, NK, RC, JC, and TK contributed to experimental design; and SH, BC, RC, TS, RS, SX, and MW aided in collection of tissue, measurements, and data analysis.

## FUNDING

This work was supported by the National Institute of Environmental Health Sciences Superfund Basic Research Program (NIEHS SBRP P42ES04911) with contributions from Project 1, 4, 5, and Core-B.

## ACKNOWLEDGMENT

We would like to thank Kathryn Eaton and her laboratory team at the University of Michigan Germ Free Facility.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2017. 01708/full#supplementary-material

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Stedtfeld, Chai, Crawford, Stedtfeld, Williams, Xiangwen, Kuwahara, Cole, Kaminski, Tiedje and Hashsham. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Human Gut Symbiont *Roseburia hominis* Promotes and Regulates Innate Immunity

*Angela M. Patterson1†‡, Imke E. Mulder1‡, Anthony J. Travis1 , Annaig Lan1 , Nadine Cerf-Bensussan2,3, Valerie Gaboriau-Routhiau2,3,4, Karen Garden1 , Elizabeth Logan1 , Margaret I. Delday1 , Alistair G. P. Coutts1 , Edouard Monnais1 , Vanessa C. Ferraria1 , Ryo Inoue5 , George Grant1,6 and Rustam I. Aminov1,7\**

*1Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, United Kingdom, 2INSERM, UMR1163, Lab Intestinal Immunity, Paris, France, 3Université Paris Descartes-Sorbonne Paris Cité and Institut Imagine, Paris, France, 4Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France, 5Kyoto Prefectural University, Kyoto, Japan, 6School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom, 7 Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia*

#### *Edited by:*

*Laurel L. Lenz, University of Colorado Denver School of Medicine, United States*

#### *Reviewed by:*

*Erguang Li, Nanjing University, China Ricardo Silvestre, Instituto de Pesquisa em Ciências da Vida e da Saúde (ICVS), Portugal*

#### *\*Correspondence:*

*Rustam I. Aminov<EMAIL_ADDRESS>
#### *†Present address:*

*Angela M. Patterson, Quadram Institute Biosciences, Norwich Research Park, Norwich, United Kingdom and Norwich Medical School, University of East Anglia, Norwich, United Kingdom*

*‡ These authors have contributed equally to this work.*

#### *Specialty section:*

*This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology*

*Received: 17 May 2017 Accepted: 04 September 2017 Published: 26 September 2017*

#### *Citation:*

*Patterson AM, Mulder IE, Travis AJ, Lan A, Cerf-Bensussan N, Gaboriau-Routhiau V, Garden K, Logan E, Delday MI, Coutts AGP, Monnais E, Ferraria VC, Inoue R, Grant G and Aminov RI (2017) Human Gut Symbiont Roseburia hominis Promotes and Regulates Innate Immunity. Front. Immunol. 8:1166. doi: 10.3389/fimmu.2017.01166*

Objective: *Roseburia hominis* is a flagellated gut anaerobic bacterium belonging to the *Lachnospiraceae* family within the Firmicutes phylum. A significant decrease of *R. hominis* colonization in the gut of ulcerative colitis patients has recently been demonstrated. In this work, we have investigated the mechanisms of *R. hominis*–host cross talk using both murine and *in vitro* models.

Design: The complete genome sequence of *R. hominis* A2-183 was determined. C3H/ HeN germ-free mice were mono-colonized with *R. hominis*, and the host–microbe interaction was studied using histology, transcriptome analyses and FACS. Further investigations were performed *in vitro* and using the TLR5KO and DSS-colitis murine models.

Results: In the bacterium, *R. hominis*, host gut colonization upregulated genes involved in conjugation/mobilization, metabolism, motility, and chemotaxis. In the host cells, bacterial colonization upregulated genes related to antimicrobial peptides, gut barrier function, toll-like receptors (TLR) signaling, and T cell biology. CD4+CD25+FoxP3+ T cell numbers increased in the *lamina propria* of both mono-associated and conventional mice treated with *R. hominis.* Treatment with the *R. hominis* bacterium provided protection against DSSinduced colitis. The role of flagellin in host–bacterium interaction was also investigated.

Conclusion: Mono-association of mice with *R. hominis* bacteria results in specific bidirectional gene expression patterns. A set of genes thought to be important for host colonization are induced in *R. hominis*, while the host cells respond by strengthening gut barrier function and enhancing Treg population expansion, possibly *via* TLR5-flagellin signaling. Our data reveal the immunomodulatory properties of *R. hominis* that could be useful for the control and treatment of gut inflammation.

Keywords: *Roseburia*, T lymphocytes, immune tolerance, inflammatory bowel disease, flagellin, TLR5

#### INTRODUCTION

The human gut microbiota consists of more than 500–1,000 different phylotypes the majority of which belong to the Bacteroidetes and Firmicutes bacterial phyla (1). Successful symbiotic relationships arising from bacterial colonization of the human gut yield a wide variety of metabolic, structural, protective, and other beneficial functions. The immunological importance of the gut

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microbiota is also well recognized; it is particularly apparent in germ-free (GF) animals that have an impaired immune system, which, however, can be functionally reconstituted by the introduction of gut commensal bacteria (2–4).

In sharp contrast to the production of secretory intestinal IgA, which is mainly driven by microbial colonization *per se* (5, 6), the development and differentiation of T cells require colonization by specific commensal bacteria. Some species of clostridia, such as segmented filamentous bacteria (SFB), appear to be potent inducers of differentiation and maturation of intestinal Th1, Th17, and Treg cell lineages (7, 8). Recent studies have demonstrated that the clostridia clusters IV and XIVa and the Altered Schaedler Flora can induce *de novo* generation of Treg cells, while mono-colonization with *Bacteroides fragilis* can correct the Th1/Th2 imbalance in germ-free mice by promoting the expansion of Treg cells (4, 9, 10). The effects of commensal bacteria on T cell differentiation pathways are variable and may be influenced by a specific array of toll-like receptors (TLR) ligands associated with particular bacteria (11). For instance, the Treg-enhancing effects of *B. fragilis* are implemented *via* TLR2 signaling by polysaccharide A (12).

Dramatic changes in microbiota composition affecting the balance of symbionts and pathobionts have been documented in gastrointestinal disorders such as inflammatory bowel disease. Crohn's disease (CD), for example, is characterized by a greater relative abundance of the Proteobacteria and a reduction of other bacteria such as the Bacteroidales and Clostridiales (13–17). A specific decrease of *Roseburia* spp*.* in patients with CD has also been noted (16, 18). Interestingly, this microbial dysbiosis is also associated with imbalances in T effector cell populations. The gut microbiota alterations in ulcerative colitis (UC) have not been characterized to the same extent. Only recently, a study of UC patients has demonstrated that *Roseburia hominis*, together with *Faecalibacterium prausnitzii*, is significantly decreased in this disease (16, 19). Both species display an inverse correlation with disease activity. Previous studies have shown anti-inflammatory effects of *F. prausnitzii* colonization on the host (20). Much less is known about *R. hominis*, which is a member of the clostridia cluster XIVa.

The complete DNA sequence and annotation of the *R. hominis* genome has been previously described (21). In this paper, bacterial and host transcriptome responses to *R. hominis* colonization were investigated*.* Bacterial responses included expression of genes involved in colonization of and adaptation to the environment of the murine gut, while host responses to colonization included genes of immunity and gut function.

## MATERIALS AND METHODS

#### Bacterial Cultures

*Roseburia hominis* A2-183T (=DSM 16839T = NCIMB 14029T) and other *Roseburia* species were maintained and grown on synthetic YCFA media as described before (22). *Escherichia coli* and *Salmonella enterica* strains were cultivated in LB medium. All *Roseburia* culture manipulations were performed in a MACS-MG-1000 anaerobic workstation (Don Whitley Scientific) under an atmosphere of 80% N2, 10% CO2 and 10% H2 at 37°C.

## Animals, Experimental Design, and Sampling

Germ-free animal experiments were performed in the gnotobiotic rodent breeding facility of INRA (ANAXEM platform, Institut Micalis, INRA, Jouy-en-Josas, France). The GF C3H/ HeN male mice were allocated into the control (*N* = 8) and treatment (*N* = 10) groups and caged individually. *R. hominis* A2-183T (=DSM 16839T = NCIMB 14029T) was grown anaerobically at 37°C in YCFA media. At days 0, 1, and 2, animals in the treatment group were given 109 colony-forming units (CFU) of *R*. *hominis* culture by gavage, while control animals were given 100 µL of YCFA medium. The ileal, ascending colonic, and cecal samples were collected at days 14 and 28. GF TLR5KO (C57BL/6 genetic background, *N* = 3) and C57BL/6 (*N* = 3) animals (Jouy-en-Josas), and conventional TLR5KO (C57BL/6 genetic background, *N* = 6) and Boy/J (C57BL/6 congenic, *N* = 6) animals (Medical Research Facility, University of Aberdeen) were gavaged with the live cultures of *R. hominis* to evaluate its functional importance. Boy/J (C57BL/6 B6 Cd45.1) mice were the background controls for conventional TLR5K0. They carry a CD45.1 pan leukocyte marker but are otherwise equivalent to C57BL/6 wild-type mice (23, 24). Twelve female C57BL/6 (6 weeks old) mice were used to evaluate the effect of *R. hominis* during DSS-induced mild colitis. Three mice were dosed daily by direct administration of a culture of *R. hominis* for a total of 8 days. Six mice were dosed with culture medium. Exposure to DSS was for 4 days (from day 3 to day 6). Three *R. hominis*-dosed and three control mice were offered sterile water containing DSS (20 g/L) over this period. On day 7, these mice were switched back to sterile water with no DSS. The mice were euthanased and dissected aseptically on day 9. Twenty-two female C57BL/6 mice (6 weeks old) were used to evaluate the effect of *R. hominis* during DSSinduced pathological colitis. After the acclimatization period of 7–10 days, the mice were dosed daily with 50 µL (109 CFU) of *R. hominis* in growth culture medium for 14 days. Control animals were given growth culture medium. From day 9, mice were given DSS (MW 50 kDa, 30 g/L) in their drinking water for 6 days. The animals were euthanized on day 15 and tissue sampling was performed as described earlier. The management and experimental procedures with animals were approved by the respective Local Ethical Review Committees.

#### Transcriptome Analyses

Bacterial RNA from the mouse cecum contents was isolated and labeled with dCTP-Cy3 or dCTP-Cy5 during cDNA synthesis. PCR products amplified from an *R. hominis* small fragment library (6,000 clones) were used to create duplicate microarrays on glass slides using a MicroGrid II TAS array-spotting robot (BioRobotics). Microarray hybridization was performed in a GeneTAC hybridization station (Genomic Solutions). Dye-swap and separate RNA purification protocols were used to avoid potential biases.

Murine RNA was extracted from the ileum and ascending colon tissues and hybridized to the GeneChip "NuGO Mouse Array" and GeneChip "Mouse Genome Array" (Affymetrix). Data analysis was performed using R1 and Bioconductor.2 The microarray data were submitted to NCBI GEO (Gene Expression Omnibus) with accession number GSE25544.

The *R. hominis*-specific primers 5′-CCCACTGACAGAGTA TGTAATGTAC-3′ and 5′-GCACCACCTGTCACCAC-3′ were used for qPCR analyses of fecal samples to validate the efficiency of colonization. The analyses were performed using a 7500 Fast Real-Time PCR System (Applied Biosystems) with a Power SYBR Green PCR Master Mix (Applied Biosystems). RT-qPCR analyses were performed on the ileal and colonic RNA samples for host gene expression studies using a QuantiFast SYBR Green PCR Kit (Qiagen) and QuantiTect Primer Assays (Qiagen).

#### Immunofluorescence and Histology

FISH analyses were performed with neutral buffered, formalinfixed gut tissue sections (2 µm thick) with 16S rRNA probes, specific for the domain of Bacteria (Cy3-labeled Eub338) and specific for the *R. hominis* strain A2-183 (FITC-labeled GTACATTACATACTCTGTCAGTG). Bacteria were visualized at x630 magnification. Immunolocalization of *R. hominis* flagellin was examined in methanol-fixed colon content smears using rabbit antisera anti-FlaA1 or anti-FlaA2 (Covalab) and Alexa donkey anti-rabbit 488 (Molecular Probes). T cell markers were examined on sequential intestinal tissue sections (8 µm) using Ly6G-FITC, CD3-FITC, isospecific IgG (BD Biosciences), and double-labeled FoxP3 Alexa Fluor 594 (Abcam) and CD3-FITC. Intestinal tissue sections (4 µm) were also stained with hematoxylin/eosin. A complete transverse cross-section of the colon from each animal was visualized at ×200 magnification. Each field of view was scored from 0 to 4 according to the method based on Ref. (25).

#### Cell Experiments

Caco-2 and HT29 cells were grown at 37°C in a 75% humidified atmosphere with 5% CO2 in 24-well plates. Before any treatment, cells were washed twice with Hanks' Balanced Salt Solution and kept in DMEM supplemented with l-glutamine, selenium, and transferrin for 24 h. Epithelial cells were harvested after incubation for 2 or 4 h and then stored in RNAlater for further analyses.

Bone marrow cells were harvested from the femur and tibia of C3H/HeN and C57BL/6 mice. Bone marrow-derived dendritic cells (BMDCs) were generated by Flt3L or GM-CSF culture, stimulated with 100 ng/mL flagellin, and analyzed by flow cytometry. Supernatants were collected and tested for IL-10 and IL-12 by cytometry bead arrays (BD Bioscience).

Cells from the intestine and mesenteric lymph nodes (MLNs) were isolated as described previously (26), with minor modifications. In brief, cell suspensions were incubated with 100 U/mL of collagenase VIII (Sigma-Aldrich) in RPMI supplemented with 20% of FBS at 37o C for 20 min (MLN) or 60 min (intestine). Single cell suspensions were analyzed by flow cytometry. For mixed leukocyte reactions, naïve CD4+ T cells were purified from the major lymph nodes and spleens of OTII transgenic mice using MACs Miltenyi CD4+ T cell Isolation Kit as per manufactures instruction. Purified CD4+ T cells were co-incubated with flagellin activated Flt3L-derived BMDCs in the absence or presence of OVA323–339 peptide (1 µg/ml). After 5 days, T cell differentiation was determined by flow cytometry.

#### Flow Cytometry

Intestinal lamina propria cells and MLN cells were labeled with CD4-FITC, CD25-APC (eBioscience), CD8-APC-Cy7, CD3- PerCP (Biolegend), and B220-BV570 (BD Biosciences). Labeling of intracellular FoxP3 was performed after extracellular staining and fixation/permeabilization of cells. GM-CSF-derived dendritic cells were labeled with CD11b-PerCP Cy5.5 (BD Biosciences), CD11c-PE-Cy7, I-A/I-E-APC-Cy7, CD80-PE, CD86-APC, CD8- FITC, and B220-BV570 (Biolegend). Flt3L-derived dendritic cells were labeled with CD11c-PE-Cy7, CD11b- or Siglec-H-PerCP Cy5.5 (Biolegend), I-A/I-E-APC-Cy7, CD317-PE, CD40-Alexa Fluor 647, CD103-FITC, and B220-BV570. Coculture T cells were labeled with CD4-PerCP Cy5.5 (BD Biosciences), CD127- PE-Cy7, CD73-APC, CD195 (CCR5)-FITC, CD62L-BV570, and CD25-APC-Cy7 (Biolegend).

#### Statistical Analyses

Spot intensities on the microarrays were log-transformed and Loess normalization was applied to the microarray results. RT-qPCR data were transformed on a base-2 logarithmic scale and analyzed by one-way analysis of variance with a significance threshold of *P* < 0.05. Statistical significance between the treatment groups was evaluated using Student's *t*-test.

More detailed protocols are available in SI Materials and Methods in Supplementary Material.

#### RESULTS

#### *R. hominis* Responds to the Gut Environment by Upregulating Chemotaxis, Motility, and Mobilization Genes

Genome analysis (**Figure 1A**) identified about 5% of all genes were related to chemotaxis and motility function including four different flagellin genes of which one located within the flagellar operon is associated with flagellar motility.

A biomass of *R. hominis* was given by gavage to GF C3H/HeN mice on three consecutive days. *R. hominis*-colonized both the ileum and colon but was found in much higher numbers in the colon, up to 1 × 1010 bacteria/g feces (Figure S1 in Supplementary Material) and was found to be closely associated with the colonic mucosa.

Differential gene expression in the bacterium in response to association with the host was investigated using the *R. hominis* microarray. Bacterial RNA was isolated from three different experimental conditions to distinguish between the effects of the gut environment and animal dietary components, (i) *in vivo,* from the cecum of mono-associated mice; (ii) *in vitro*, from bacteria grown in the presence of dietary components; and (iii) *in vitro*, from bacteria grown in culture.

1http://www.r-project.org.

2http://www.bioconductor.org.

(*N* = 4) identified with the Affymetrix microarray. Bar graphs represent the number of genes up- or downregulated in mice after 14 and 28 days of colonization. (C) Heatmap generated from differentially expressed genes with functional significance between GF and *R. hominis*-colonized mice at 14 and 28 days.

Colonization of GF mice with *R. hominis* correlated with the increased host gene expression, which was highest in the colon, particularly at day 28 post-colonization (**Figure 1B**). The number of differentially expressed genes in the ileum was very low at day 28, consistent with the reduced bacterial numbers. Differential transcriptome responses in the two tissue sites and time points were illustrated by a clear separation of significant transcripts in the heatmap analysis (**Figure 1C**).

Differentially expressed genes were identified *(in vivo* vs*. in vitro*) with RT-qPCR validation performed on 42 differentially expressed genes (Tables S1 and S2 in Supplementary Material). The *mobA*- and *mobL*-like genes that are involved in conjugation/ mobilization transfer showed strong upregulation *in vivo* (**Figure 2A**). Other subsystems induced by the gut environment included membrane transport, in particular magnesium transport, and motility and chemotaxis including multiple methyl-accepting chemotaxis proteins and genes of the flagellar operon (**Figure 2B**). *R. hominis* possesses multiple flagellin genes: *flaA1*, *flaA2*, *flaA3*, and *flaB.* The expression of *flaA1*and *flaA2* in the murine gut environment was verified by Western–blot and immunocytochemistry (**Figure 2C**).

## *R. hominis* Affects Host Innate Signaling Pathways

Genes involved in innate immunity and gut barrier function of the host were significantly induced by *R. hominis* colonization [**Figure 1C**; NCBI GEO (Gene Expression Omnibus), accession number GSE25544]. The GO process "innate immune response" (GO:0045087) was upregulated and included the TLR-related genes *Tlr5*, *Tlr1*, and *Vnn1*. The upregulation of *Tlr5* was of particular interest, given the presence of flagellar genes in the genome of *R. hominis* and the expression of the corresponding proteins in the gut of mono-colonized mice. The flagellins may have a role in this innate signaling pathway to mediate innate and adaptive immune responses. Other innate immunity genes affected by *R. hominis* colonization included the antimicrobial peptides *Defb37*, *Pla2g3*, *Muc16*, and *Itln* and the gut barrier function genes *Sprr1a*, *Cldn4*, *Pmp22*, *Crb3*, and *Magi3.* Innate immunity genes that were upregulated in the ileum in response to *R. hominis* included *Defcr20*, *Pcgf2*, *Ltbp4, Igsf8*, and *Tcfe2a*. We also found a negative regulation of the NF-κB pathway (GO:0043124) by *R. hominis*, which may contribute to the immune homeostasis by downregulating this inflammatory cascade.

Figure 2 | Responses of *Roseburia hominis* to the gut environment. (A) RT-qPCR quantification of *R. hominis* transcripts involved in conjugation/mobilization transfer. *R. hominis* RNA samples: *in vivo*—isolated from mono-associated animals; *in vitro*—isolated from YCFA medium-grown cultures; *in vitro* + diet—isolated from cultures grown on YCFA medium with addition of murine chow. (B) RT-qPCR quantification of *R. hominis* transcripts involved in motility and chemotaxis. *R. hominis* RNA samples: *in vivo*—isolated from mono-associated animals; *in vitro*—isolated from YCFA medium-grown cultures; *in vitro* + diet—isolated from cultures grown on YCFA medium with addition of murine chow. (C) Western blot of *R. hominis* proteins grown *in vitro* in the presence of UV-irradiated standard murine chow. The membrane was immunostained with affinity-purified 1 antibody, Fla2 specific antiserum, and anti-DNA gyrase A antibody (lane 1: no diet, lane 2: 0.01 g diet/10 mL of *R. hominis* culture, lane 3: 0.02 g diet/10 mL, lane 4: 0.05 g diet/10 mL, lane 5: 0.1 g diet/10 mL, lane 6: 0.2 g diet/10 mL, lane 7: 0.5 g diet/10 mL, and lane 8: 1 g diet/10 mL). Electron microscopy of *R. hominis* showing flagella (black arrows). (D) Immunocytochemistry with FlaA1 and FlaA2 specific antisera performed on *R. hominis* from the luminal contents of mono-colonized mice and on *R. hominis* grown *in vitro*. Original magnification is ×1,000. RT-qPCR results are presented as fold change. Statistical significance: \**P* < 0.05, \*\**P* < 0.01, and \*\*\**P* < 0.001.

## *R. hominis* Directs T Cell Pathways

Immune response was a major pathway induced by *R. hominis* at day 28 in the ascending colon of mono-associated mice. The pathways significantly affected in this category were mostly involved in T cell function, including IL-10 signaling and regulation of T cell function by CTLA-4 (Table S3 in Supplementary Material). The Ly6 gene family was also induced in the ascending colon. In particular, the GPI-anchored gene product of *Ly6g6c* was upregulated 25-fold, and the related gene *Ly6g6e* was upregulated twofold at day 28. The majority of hematopoietic cells, including neutrophils and plasmacytoid dendritic cells, express one or more members of the Ly6 family. The increased presence of Ly6G+ cells in *R. hominis*-colonized mice was confirmed by immunocytochemistry (**Figure 3A**).

While the pathways of T cell regulation were significantly influenced by association with *R. hominis*, the corresponding effects on T cell differentiation required further investigation. We therefore assessed the number of double-positive CD3+FoxP3+ cells in the colonic lamina propria of monoassociated C3H/HeN mice. A significant increase in the number of regulatory T cells (CD3+FoxP3+ cells) was detected in the germfree C3H/HeN and C56BL/6 mice colonized with *R. hominis* (**Figures 3A,B**).

Conventional C3H/HeN and C57BL/6 mice were gavaged with *R. hominis* for 14 days to determine the impact of the bacterium on Treg cells in the *lamina propria* of animals with a normal microbiota. This treatment resulted in a small but significant increase in the population of CD3+CD4+CD25+FoxP3+ T cells in the C3H/HeN (*P* = 0.04) and a smaller increase in the C57BL/6 mice (*P* = 0.05) when compared to non-colonized mice (**Figure 3C**). While the population of Tregs was induced in conventional mice after colonization with *R. hominis* under homeostatic conditions, it was unknown if this induction could be maintained under conditions of mild colitis. We therefore investigated the impact of the bacterium on Treg cells in the lamina propria of animals that were given low dose DSS. FoxP3-tagged mice of C57BL/6 background were colonized with *R. hominis* for 2 days before treatment with low dose of DSS for 4 days followed by 2 days of pure drinking water. DSS treatment alone significantly increased the population of CD3+CD4+CD25+FoxP3+ T cells (*P* = 0.02) (**Figure 3D**). When the animals were additionally treated with *R. hominis*, there was a further significant increase in the population of CD3+CD4+CD25+FoxP3+ T cells (*P* = 0.02) versus DSS (**Figures 3D,E**).

## The Role of *R. hominis* Flagellins in Modulation of T Cell Differentiation

The influence of bacteria on the differentiation of T cells may reflect a specific array of TLR ligands displayed by a particular bacterium. TLR5KO (GF and conventional) and WT murine strains were gavaged with the bacterium to evaluate the functional importance of *R. hominis* and its flagellins. Analysis of the differentially expressed genes in TLR5KO and WT Boy/J mice colonized with *R. hominis* revealed that although T cell pathways were still influenced by the colonization event in TLR5KO mice, responses were more related to IL-4, IL-5, IL-6, IL-9 pathways and not to IL-10 and CTLA-4 (Table S4 in Supplementary Material). Furthermore, in contrast to mono-associated C3H/HeN and C57BL/6 animals (**Figure 3B**) and conventional Boy/J animals (**Figures 4B,C**), the numbers of double-positive CD3+FoxP3+ cells in the *lamina propria* of TLR5KO mice were not increased due to *R. hominis* treatment (**Figure 4A**).

A set of flagellins from pathogenic and commensal bacteria, including recombinant *R. hominis* flagellins, was used to compare their effect on activation of signaling responses in IECs and BMDCs. Caco-2 cells were treated with the identical concentrations of different bacterial flagellins (**Figure 5A**). The flagellin of pathogenic *S. enterica* serotype Enteritidis (SE) induced a larger gene panel than the flagellins of commensal *E. coli* K12 or *R. hominis* FlaA1. *E. coli* and *R. hominis* induced a strong response and, with a similar gene complement between the two, formed a separate clade distinct from SE. In contrast, FlaA2 was generally neither pro-inflammatory nor did it activate the conserved gene signature (*IL8*, *CXCL1*, *CXCL2*, and *CXCL10*) that was induced by the other recombinant bacterial flagellins. In fact, the set of genes affected was closer to that of the control cells and formed a cluster separated from the other flagellins. FlaA1 from *R. hominis* induced different responses in Flt3L- and GM-CSFderived BMDCs compared to SE and K12 (**Figures 5B,C**). In particular, FlaA1 was uniquely able to activate Flt3L-expanded DCs, with the upregulation of I-A/I-E and CD40 and production of IL-10 by BMDCs from both C3H/HeN and C57BL/6 mice. The IL-10/IL-12 ratio was particularly elevated in C57BL/6 DCs (**Figure 5D**), which were found to be CD103+Siglec-H+. To test whether flagellin activated BMDCs could differentially modulate naïve T cell differentiation, mixed leukocyte cultures of CD4+ OTII cells with flagellin stimulated Flt3-derived BMDC were performed. The data show that the production of IL-10 and IL-12 by the flagellin stimulated Flt3-derived BMDC ratio triggered naïve T cell differentiation toward a Tregs profile (**Figure 5E**). In contrast to FlaA2, FlaA1 significantly increased the Treg population in the T cell cocultures.

## *R. hominis* Attenuates Colitis in DSS-Treated Mice

The effects of *R. hominis* on innate and adaptive immunity prompted us to test its therapeutic efficacy using the murine DSS model (Figure S2 in Supplementary Material; **Figure 6**). The treatment group was dosed daily with *R. hominis* (~50 μL, ca. 109 CFU) *via* gavage for a period of 14 days while the untreated group received the same amount of bacterial growth medium. The two groups were then given DSS in drinking water (MW 50 kDa, 30 g/L) beginning from day 8 onward. On day 15, DSS-treated mice without *R. hominis* addition had a strong elevation of a panel of pro-inflammatory biomarkers compared to control mice, with gene induction levels ranging from 4- to 49-fold (**Figure 6A**). Induction of pro-inflammatory genes was significantly lower in the *R. hominis*-treated mice compared to the control group, indicating a strong therapeutic benefit of oral administration of *R. hominis*. On day 15, severe inflammation was detected in the ascending colon of DSS-treated mice without an *R. hominis* supplement, while the colonic mucosa in the *R. hominis*-supplemented group displayed only low-level inflammation, consistent with the reduced expression of pro-inflammatory genes (**Figures 6B,C**). Treatment with *R. hominis* also alleviated the weight loss in DSS-treated mice (Figure S3 and Table S5 in Supplementary Material).

## *R. hominis* Colonization Influences Body Composition and Expression of Satiety Genes

In the next set of experiments, we investigated metabolic changes in the host and bacterium due to host–microbe association. Mice mono-associated with *R. hominis* displayed significant

Figure 3 | Induction of FoxP3+ Treg cells by *Roseburia hominis.* (A) Immunofluorescence analysis of lamina propria cells labeled with (i) anti-Ly6G, (ii) anti-CD3, and (iii) anti-CD3/anti-FoxP3 in the ascending colon of germ-free (GF) and *R. hominis*-treated C3H/HeN mice. Data are shown as the number of positive cells per field of view in GF (*N* = 7–8) and *R. hominis*-treated mice (*N* = 8–10). Original magnification ×630. Statistical significance: \**P* < 0.05. (B) Immunofluorescence analysis of lamina propria cells in the ascending colon labeled with anti-CD3 and anti-FoxP3 in germ-free and *R. hominis* mono-colonized C3H/HeN (*N* = 8) and C57BL/6 (*N* = 3) mice. (C) Flow cytometry analysis of FoxP3+ Treg cells in the lamina propria of conventional mice treated for 14 days with *R. hominis*: C3H/HeN mice (\**P* = 0.04 between control and *R. hominis* treatment) and C57Bl/6 mice (\*\**P* = 0.05 between control and *R. hominis* treatment)*.* (D) Flow cytometry analysis of FoxP3+ Tregs in lamina propria of FoxP3-tagged conventional mice treated for 8 days with *R. hominis*, with 4 days of low dose DSS treatment or untreated. Statistical significance: \**P* < 0.005 control versus DSS treatment and \*\**P* = 0.02 DSS treatment versus DSS+ *R. hominis* treatment. (E) Flow cytometry plots of FoxP3+ Treg cells in the colonic lamina propria of DSS-treated mice and DSS+ *R. hominis*-treated mice. Plots are representative of three experiments. The gating strategy for the FACS plots was as follows: FSC/SSC (removal of cell debris), live/dead-SSC, singlets (FSC-A/FSC-H), CD3+CD8−, and CD25+FoxP3+. The percentages were calculated as CD25+FoxP3+ cells of total CD4+ T cells in each preparation. CD4+ cells were derived from the gating strategy of CD3+CD8−.

metabolic changes compared to control GF mice. In particular, the GO processes "negative regulation of response to food" (GO:0032096), "negative regulation of appetite" (GO:0032099), and "regulation of catecholamine secretion" (GO:0050433) were all downregulated in the ascending colon after colonization by *R. hominis* [NCBI GEO (Gene Expression Omnibus), accession

number GSE25544]. The genes involved in these processes were *Agt*, *Cartpt*, *Cck*, and *Cxcl12*, with the corresponding mRNA changes induced ranging from 2- to 12-fold. *Cck*, in particular, plays a major role in digestion and satiety as a hunger suppressant. *Gcg* expression was also downregulated at this gut site.

To establish whether the transcriptional changes of these genes have any physiological relevance in terms of body composition, analyses of dry carcass weight and lipid composition were performed. Dry carcass weights of *R. hominis*-associated mice were significantly heavier than those of GF animals (**Figure 7A**). Carcass lipid analysis showed that the total adiposity was also significantly higher in *R. hominis*-colonized animals at day 14 (**Figure 7B**).

Significant metabolic changes were also detected on the other side of the host–microbe interaction spectrum. In particular, expression of *R. hominis* genes involved in butyrate production was upregulated by the gut environment (**Figure 7C**). These were the genes encoding acetyl-CoA acetyltransferase, 3-hydroxyacyl-CoA dehydrogenase, butyryl-CoA dehydrogenase, and phosphoenolpyruvate carboxykinase. The stimulatory effect of host epithelial cells on production of acetate and butyrate by *R. hominis* was further confirmed in separate *in vitro* experiments (Figure S4 in Supplementary Material). Bacterial cells incubated with Caco-2 and HT29 cells produced significantly higher amounts of these metabolites compared to *R. hominis* control cells incubated under the same conditions but without the epithelial cells.

#### DISCUSSION

A long-term co-evolution of host–microbe interaction has likely driven the selection of functionally important bacterial species in the gut, the majority of which are not encountered in other ecosystems. There is only limited information available at present about the contribution of individual members of the microbial community to intestinal functions, in particular to the development and regulation of mucosal immunity.

The specific functions of certain intestinal bacteria such as *B. fragilis* and SFB have been investigated in the mouse gut to define their individual contributions to T cell biology, and these bacteria have been shown to be potent inducers of Tregs and Th17 cells (4, 7, 8). Members of the *Clostridium* clusters IV and XIVa have recently received more attention. For instance, their presence in the altered Schaedler flora and the contribution of a

Figure 5 | Effects of *Roseburia hominis* flagellin FlaA1 on intestinal epithelial cells and murine bone marrow-derived dendritic cells. (A) Heatmap of differentially expressed genes in Caco-2 cells treated with different bacterial flagellins: from *Salmonella enterica* serotype Enteritidis (SE), *Escherichia coli* K12 (K12), and *R. hominis* (FlaA1 and FlaA2). (B) Expression of CD40; I-A/I-E and CD103 by CD11c+B220+CD317+ Flt3L-derived dendritic cells from conventional C3H/HeN, control (blue) and after 24 h incubation with recombinant flagellins (SE, K12, FlaA1, FlaA2; all in green) determined by flow cytometry. Histogram represents data from three experiments. (C) Frequencies of Flt3L- and GM-CSF-derived dendritic cells from conventional C3H/HeN gated on CD11c+B220+CD317+ cells and CD11c+CD11b+B220− cells treated with different recombinant flagellins (SE, K12, FlaA1, and FlaA2). Results are shown as the percentage of the total, live and singlet cells, mean ± SD from three experiments. (D) The level of IL-10 and IL-12 cytokines was measured by cytometry bead arrays in supernatants from control (unstimulated DCs; *N* = 3) and FlaA1-treated DCs (*N* = 3) derived from C3H/HeN and C57BL/6. Data are presented as mean ± SD. Statistical significance: \*\*\**P* < 0.001. (E) Flagellin stimulated Flt3-derived dendritic cells were cocultured with CD4+ T cells for 5 days, and T cell were stained for the expression of T cell differentiation markers. Graph indicates the percentage of activated Treg cells. Statistical significance: \*\**P* < 0.01.

mixed culture of *Clostridium* spp. to T cell differentiation has also been noted (9, 10).

We report here the successful and stable mono-association of *R. hominis*, a strictly anaerobic flagellated member of the Firmicutes phylum, in the gut of GF mice. The transcriptional responses of *R. hominis* following mono-colonization could be attributed to both the host gut environment and diet. These included chemotaxis and motility subsystems. The role of motility and the flagellar apparatus in host colonization is well understood for pathogenic bacteria but less is known

presented as a fold change compared to control. Statistical significance: \*\*\**P* < 0.001. (B) Histopathology tissue scoring (25) presented as mean percentage of fields of view at a given grade. DSS treatment significantly altered all fields of view at grades 0, 2, 3, and 4. *R. hominis* significantly reduced the % fields of view for grade 4 pathology (*P* = 0.02) and increased the % fields of view for grade 0. Data obtained on day 15 are presented as mean ± SD. (C) Ascending colon (hematoxylin/eosin stained) of control, DSS+ *R. hominis*-treated and DSS-treated animals. Images shown are the representative fields of view for each treatment group with the allocated score in parenthesis.  L, lumen. Original magnification ×200. Scale bar represents 50 µm.

about the role of flagellins in commensal bacteria. Genes encoding cell motility functions, and flagellins in particular, are generally expressed at variable and low abundance rates in the gut microbiome (27). In addition, only a subset of human gut microbiomes contains detectable flagellin genes (28). We showed that *R. hominis* flagellins are expressed in the mouse intestinal environment. Flagellin gene expression appears to be partly dependent on the presence of certain dietary constituents. Previous work has shown that flagellin gene expression of the related species *Roseburia inulinivorans* is indeed substrate dependent; its expression is higher in the presence of starch compared to glucose, inulin, or fructan (29).

The presence of *R. hominis* in the gut induces genes involved in promoting gut barrier function and innate immunity. Tight junctions, gap junctions, and adherens junctions operate to limit bacterial translocation to the subepithelial layer of the gut, and these functions may be promoted by intestinal bacteria (30). Both CD and UC are characterized by the loss of barrier function and the integrity of tight junctions. Interestingly, dysbiosis of the gut microbiota in UC and CD is associated with a significant reduction of *R. hominis* and *F. prausnitzii* (16, 19). Our results concerning the active contribution of *R. hominis* to gut barrier function support the view that its loss in patients with CD or UC may have significant consequences. Tight junction complexes can be activated by a number of other commensal and probiotic bacteria (31), potentially contributing to the amelioration of the "leaky gut" condition in these diseases.

*Roseburia hominis* induced the expression of genes such as *Ly6g6c*, as well as pathways involved in the regulation of T cells. The most affected T cell pathways included those related to IL-10, ICOS, and CTLA-4, which are all involved in the differentiation

(*N* = 8) and treatment (*N* = 10) groups. At days 0, 1, and 2, animals in the treatment group were dosed by ~109 gavage, while control animals were given 100 µL of YCFA medium alone. Dry body weight and lipid content of carcasses were analyzed. (A) Weight of dry carcasses of *R. hominis*-associated mice was significantly heavier compared to GF animals. (B) Carcass lipid analyses showed that the total adiposity was also significantly higher in *R. hominis*-associated animals at day 14. Data are presented as mean ± SD. (C) RT-qPCR analysis of genes involved in butyrate metabolism.

of Treg cells. Significant increases in colonic CD3+CD4+CD2 5+FoxP3+ cells were observed in mice either mono-colonized with *R. hominis* or in conventional mice fed a supplement of live *R. hominis* culture. Our findings complement recent reports on Treg differentiation effects by other *Clostridium* species (10, 32). We have shown here that the single bacterial strain *R. hominis* A2-183 is able to promote mucosal T cell expansion and impact T cell differentiation in mono-associated, conventional and DSS-treated mice.

Members of the *Roseburia* genus are some of the most prevalent motile bacterial species in the normal healthy human intestinal microbiota (33). Flagellins are potent immunomodulatory proteins: flagellated bacteria, such as *R. hominis*, could interact actively with the host immune system. Another important consequence of motility among the *Roseburia* species is their ability to penetrate the mucus layer and attach to the host gut epithelial cell surfaces (34). This property is considered to be a very desirable characteristic enhancing the probiotic potential of bacteria (35). Close proximity to the host cells allows commensal gut bacteria to exert potent physiological effects, potentially reversing metabolic disorders and controlling inflammation, gut barrier function, and gut peptide secretion.

Flagellin signaling by many pathogenic bacteria through the host's TLR5 receptors induces a strong pro-inflammatory response, mainly driven by the activation of the NF-κB signaling pathway (36). However, the numerical prevalence of flagellated commensal bacteria makes a strong pro-inflammatory scenario seem unlikely. These bacteria are capable of flagellin/ TLR5 signaling, which is important for host defense and disease protection, because deletion of TLR5 results in colitis (37). The presence of flagellin genes in the SFB genomes (38, 39) may explain the potent induction of Th17 cells by these bacteria (7, 8).

Differential expression of *R. hominis* flagellins *in vitro* has effects on IECs and BMDCs. The panel of flagellins tested affected IECs and BMDCs differently, although all the flagellin structures included the conserved Arg90 domain associated with flagellins that bind and activate TLR5 (40), which suggests that other sequence/structural properties might account for the signaling responses mediated by FlaA1 and FlaA2. Certain commensal flagellin structures may help to direct immune tolerance responses through TLR5 expressed on either CD103+DC or Treg subsets (23, 41, 42). The significance of flagellin-TLR5 signaling in Treg responses induced by *R. hominis* was further investigated using TLR5KO mice. We showed that *R. hominis*-induced CD3+CD4+CD25+FoxP3+ cells were significantly lower in TLR5KO mice, indicating that TLR5/flagellin signaling is an important mediator for the expansion of this T cell subset. The immunomodulatory effects of *R. hominis* were shown in DSS-treated mice. Although the DSS model of colitis is generally a T cell-independent model, we showed that *R. hominis* induced regulatory T cell populations in these animals. Other anti-inflammatory molecules such as butyrate may also contribute to immune tolerance and limit tissue damage (43, 44). Another effect of short-chain fatty acids produced by the bacterium is the improved food conversion efficiency. The dietary constituents such as polysaccharides that are poorly utilized by the host are degraded by the bacterium and used by the host resulting in a better energy recovery from the diet. Differential expression of satiety genes may be another factor contributing to the heavier weight and higher adiposity in *R. hominis*-colonized animals.

In summary, gut colonization with *R. hominis* caused the induction of specific subsets of genes on both the bacterial and host sides of the interaction. In this study, we have focused on signaling amongst other molecular mechanisms to explain the nature of this cross talk. TLR5/flagellin signaling could, potentially, drive the expansion of Treg cells *via* a TLR5-dependent mechanism, and our results suggest a potential therapeutic benefit of *R. hominis* in the treatment of UC, which is characterized by the loss of this bacterium from the gut (16).

#### ETHICS STATEMENT

The management and experimental procedures with animals were approved by the respective Local Ethical Review Committees at Institut Micalis, INRA, Jouy-en-Josas, France and Medical Research Facility, University of Aberdeen, United Kingdom.

## AUTHOR CONTRIBUTIONS

AP, IM, and RA designed research; AP, IM, AT, AL, NC-B, VG-R, KG, EL, MD, AC, EM, VF, RI, GG, and RA performed research; AP, IM, AL, GG, and RA analyzed data; AP, IM, GG, and RA wrote the manuscript.

## ACKNOWLEDGMENTS

The authors would like to thank Andrew Gewirtz (Georgia State University, Atlanta, GA, USA) and Adam Cunningham (University of Birmingham, Edgbaston, Birmingham, UK) for generously providing the germ-free and conventional TLR5KO mice, respectively. The authors also wish to acknowledge a significant contribution to the inception of the work, experimental work, interpretation of results, and writing of the manuscript by Denise Kelly, as well as the support from RESAS (Rural and Environmental Science and Analytical Services) of the Scottish Government. EM and VF were supported by the Marie Curie Initial training network Fellowships funded by the EU (grant #215532). The authors acknowledge a generous support of Prof. Harry Flint and Dr. Sylvia Duncan in the microbiological part of this work. The skilled technical support of Gillian Campbell and Pauline Young at RINH Genomics is also gratefully acknowledged.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at http://journal.frontiersin.org/article/10.3389/fimmu.2017.01166/ full#supplementary-material.

## REFERENCES

extensive auxotrophy and adaptations to the intestinal environment. *Cell Host Microbe* (2011) 10(3):260–72. doi:10.1016/j.chom.2011.08.005

44. Furusawa Y, Obata Y, Fukuda S, Endo TA, Nakato G, Takahashi D, et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. *Nature* (2013) 504(7480):446–50. doi:10.1038/ nature12721

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Patterson, Mulder, Travis, Lan, Cerf-Bensussan, Gaboriau-Routhiau, Garden, Logan, Delday, Coutts, Monnais, Ferraria, Inoue, Grant and Aminov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Free Fatty acids Profiles are related to gut Microbiota signatures and short-chain Fatty acids

*Javier Rodríguez-Carrio1 , Nuria Salazar1 , Abelardo Margolles1 , Sonia González2 , Miguel Gueimonde1 , Clara G. de los Reyes-Gavilán1 \* and Ana Suárez3*

*1Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias (IPLA-CSIC), Villaviciosa, Asturias, Spain, 2Area of Physiology, Department of Functional Biology, University of Oviedo, Oviedo, Asturias, Spain, 3Area of Immunology, Department of Functional Biology, University of Oviedo, Oviedo, Asturias, Spain*

#### *Edited by:*

*Marina I. Arleevskaya, Kazan State Medical Academy, Russia*

#### *Reviewed by:*

*Mario M. D'Elios, University of Florence, Italy Francois-Pierre Martin, Nestle Institute of Health Sciences, Switzerland Arne Yndestad, Oslo University Hospital, Norway*

*\*Correspondence:*

*Clara G. de los Reyes-Gavilán<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology*

*Received: 07 April 2017 Accepted: 29 June 2017 Published: 24 July 2017*

#### *Citation:*

*Rodríguez-Carrio J, Salazar N, Margolles A, González S, Gueimonde M, de los Reyes-Gavilán CG and Suárez A (2017) Free Fatty Acids Profiles Are Related to Gut Microbiota Signatures and Short-Chain Fatty Acids. Front. Immunol. 8:823. doi: 10.3389/fimmu.2017.00823*

A growing body of evidence highlights the relevance of free fatty acids (FFA) for human health, and their role in the cross talk between the metabolic status and immune system. Altered serum FFA profiles are related to several metabolic conditions, although the underlying mechanisms remain unclear. Recent studies have highlighted the link between gut microbiota and host metabolism. However, although most of the studies have focused on different clinical conditions, evidence on the role of these mediators in healthy populations is lacking. Therefore, we have addressed the analysis of the relationship among gut microbial populations, short-chain fatty acid (SCFA) production, FFA levels, and immune mediators (IFNγ, IL-6, and MCP-1) in 101 human adults from the general Spanish population. Levels of selected microbial groups, representing the major phylogenetic types present in the human intestinal microbiota, were determined by quantitative PCR. Our results showed that the intestinal abundance of *Akkermansia* was the main predictor of total FFA serum levels, displaying a negative association with total FFA and the pro-inflammatory cytokine IL-6. Similarly, an altered FFA profile, identified by cluster analysis, was related to imbalanced levels of *Akkermansia* and *Lactobacillus* as well as increased fecal SCFA, enhanced IL-6 serum levels, and higher prevalence of subclinical metabolic alterations. Although no differences in nutritional intakes were observed, divergent patterns in the associations between nutrient intakes with intestinal microbial populations and SCFA were denoted. Overall, these findings provide new insights on the gut microbiota–host lipid metabolism axis and its potential relevance for human health, where FFA and SCFA seem to play an important role.

Keywords: free fatty acids, microbiota, *Akkermansia*, short-chain fatty acids, serum lipids, subclinical metabolic alterations

#### INTRODUCTION

Free fatty acids (FFA) are lipid species released from the adipose tissue and several cell types upon lipolysis. Apart from their classical roles in energy supply or as structural components, FFA are emerging as active players of a number of biological processes. FFA can affect gene expression of macrophages (1), adipocytes (2), or endothelial cells (3). In addition, FFA can modulate the production of chemokines and cytokines (3–5), the expression of genes coding for adhesion molecules (6, 7) and they give rise to pro-inflammatory and inflammation pro-resolving lipid-derived species (8).

Therefore, a growing body of evidence emphasizes a role for FFA as common mediators between metabolic conditions and the immune system. FFA have been proposed as a mechanistic explanation for the relationship among obesity, inflammation, altered glucose homeostasis, and cardiovascular disease (9). Similarly, some FFA have been reported as regulators of systemic metabolism homeostasis in mice (10). Importantly, quantitative and qualitative differences among the effects of individual FFA have been revealed (11). Thus, not only increased levels but also an altered FFA pool composition may be associated with the risk of developing a range of disorders in which the immune system plays a role (12–17). However, the underlying causes of the altered FFA pool composition remain unknown.

Compelling evidence from recent years has shed some light on the effects that gut microbiota can exert on the host health. Alterations in the composition of the intestinal microbiota have been related with the development of metabolic disorders both in mice and in human studies (18), whereas some dietary factors are also known to modulate these microbial communities (19). Recent studies indicate that the gut microbiota is also involved in the host energy metabolism by regulating the absorption of nutrients, local production of hormones and immune mediators, fat storage, and gut permeability (20–22). However, to what extent host lipid metabolism is associated with the intestinal microbial populations, and whether the gut microbiota could be related to FFA levels, remains largely unknown. Short-chain fatty acids (SCFA) are produced in the gut by the metabolic activity of the intestinal microbiota as catabolic end-products from the fermentation of undigested dietary components, mainly complex carbohydrates. These compounds are pivotal in the interactions between the host and intestinal microbial populations (23), but their actual links with the metabolism of fatty acids in humans are controversial. In addition, since most studies have been performed with relatively low sample sizes and mainly with *a priori* established conditions, evidence for their potential relevance in healthy adults is lacking in the literature. However, due to their pivotal role in shaping the host metabolism, it is tempting to speculate that the intestinal microbiota could be related to FFA levels in healthy populations.

Based on these lines of evidence, we hypothesize that alterations in the composition and metabolic activity of the gut microbiota may be associated with altered levels of FFA, which can be in turn be related with some mediators of inflammation. With the aim to gain insight into the joint impact of these players in human health, we have studied the relationships among selected microbial populations, fecal SCFA, serum FFA levels, and composition, as well as inflammatory mediators. In this way, the main objective of the present study was to evaluate whether specific gut microbial signatures can be related to altered levels of FFA species in adults from the general population.

#### MATERIALS AND METHODS

#### Ethical Approval

Ethical approval for this study was obtained from the Institutional Review Board (Comité de Ética de Investigación Clínica del Principado de Asturias) in compliance with the Declaration of Helsinki. All participants were informed and gave a signed informed consent prior their inclusion in the study.

#### Subjects

A group of 101 individuals was recruited from the general population in the Asturias region, northern Spain. Exclusion criteria were the diagnosis of chronic or immune-mediated diseases, recent infections, cancer, or altered metabolic conditions, as well as the current usage (within the previous 6 months) of immunomodulatory drugs, metabolic agents, probiotics, or antibiotics. Demographical parameters of the analyzed population are summarized in **Table 1**.

Table 1 | Description of the study population.

*Variables are summarized as median (interquartile range), mean* ± *SD, or n (%), as appropriated, unless otherwise stated.*

Subjects were asked to participate in this study and, upon acceptance, an overnight fast blood sample was drawn by venipuncture in tubes without anticoagulant. After blood clotting, serum was collected and stored at −80°C until further analyses. In addition, basic serum biochemical analyses were performed by standardized procedures. Subjects were considered to exhibit a subclinical metabolic alteration if they meet any of the following criteria: fasting glucose levels higher than 100 mg/dl or that of triglycerides over 150 mg/dl. These objectives cut offs were obtained from clinical national guidelines.

#### Total FFA Assessment

Total FFA serum levels were quantified by means of a colorimetric assay using a commercial kit (NEFA kit half-microtest, Roche Life Sciences, Penzberg, Germany) following the protocol from the manufacturer. Final absorbance was measured at 546 nm, and detection limit was 0.02 mM.

#### Individual FFA Quantification

Free fatty acids were analyzed after a methyl-*tert*-butylether (MTBE)-based extraction protocol as previously described (24) with slight modifications. Briefly, 100 µl serum samples were spiked with 5 µl of internal standard (600 ppm heptadecanoic acid). Then, protein precipitation was performed by adding 200 µl methanol chromasolv grade (Sigma Aldrich, MO, USA), and tubes were vortexed for 30 s. Then, 1,200 µl MTBE chromasolv grade (Sigma) was added, vortexed, and an incubation in an ultrasound water bath at 15°C was performed for 30 min. Finally, organic phase was separated after the addition of 200 µl milliQ water and a centrifugation step for 7 min at 5,000 rpm (15°C). After collecting the upper layer, the extraction protocol was repeated once with 100 µl MetOH, 500 µl MTBE, and 100 µl milliQ H2O.

Lipid extracts were dried in a miVac centrifugal evaporator (Genevac Ltd., UK) and redissolved in 100 µl of water:acetonitrile 38:62.

For the determination of the fatty acids, a Dionex Ultimate 3000 HPLC system (Thermo Scientific, Bremen, Germany), consisting of a high pressure binary pump, an autosampler and a column oven, was used. The column was a Zorbax Eclipse Plus C18, 50 mm × 2.1 mm, 1.8 μm from Agilent. Mobile phases A and B were water and acetonitrile, respectively, both containing 0.1% of formic acid. Fatty acids separation was carried out by the following gradient program: 62% B (held for 4.5 min) followed by a linear increase up to 100% B in 10 min (held for 1 min). The column temperature was set at 45°C and the injection volume was 2 µl.

Mass detection was performed using a Bruker Impact II q-ToF mass spectrometer with electrospray ionization, operated in the negative mode. The settings of the mass spectrometer were as follows: spray voltage, 4.5 kV; drying gas flow 12 l/min; drying gas temperature 250°C; nebulizer pressure 44°psi.

For quantitation, calibration curves for each compound were prepared by proper dissolution of the pure standards in methanol to encompass the expected concentration of the analytes in the sample. The calibration ranges were as follows: 0.4–12.5 µg/ml for eicosapentaenoic (EPA) and γ-linolenic; 1.2–37.5 µg/ml for docosahexaenoic acid (DHA) and linolenic; 2.3–75 µg/ml for arachidonic (AA) and palmitoleic; 3.9–125 µg/ml for linoleic; and 7.8–250 µg/ml for oleic, palmitic, and stearic. A good linearity was observed in all cases (*r*2 > 0.994). Heptadecanoic acid, added at a level of 30 µg/ml, was used as internal standard to compensate for possible biases during the sample preparation step.

#### Quantification of Cytokines

Serum levels of IFNγ, MCP-1, and IL-6 were determined by immunoassays using commercial kits (IFNγ OptEIA kit) from BD Biosciences (NJ, USA) and MCP-1 and IL-6 mini-EDK kits from Peprotech (NJ, USA), following manufacturer's instructions. Detection limits were 0.58, 8, and 5.8 pg/ml for IFNγ, MCP-1, and IL-6, respectively.

#### Nutritional Assessments

Dietary intake was assessed by means of an annual semi quantitative validated food frequency questionnaire including 160 items (25). Trained dieticians asked about cooking practices, number and amount of ingredients used in each recipe, as well as enquiring about menu preparation (e.g., type of oil used and type of milk) and other relevant information to the study. During an interview, subjects were asked item by item whether they usually ate each food and, if so, how much they usually ate. For this purpose, three different serving sizes of each cooked food were presented in pictures to the participants so that they could choose from up to seven serving sizes (from "less than the small one" to "more than the large one"). For some of the foods consumed, amounts were recorded in household units, by volume, or by measuring with a ruler. Methodological issues concerning dietary assessment have been detailed elsewhere (25). Food intake was analyzed for energy, macronutrients, and total fiber content by using the nutrient Food Composition Tables developed by the Centro de Enseñanza Superior de Nutrición Humana y Dietética (26).

#### Anthropometric Measures

Height was measured using a stadiometer with an accuracy of ±1 mm (Año-Sayol, Barcelona, Spain). The subjects stood barefoot, in an upright position and with the head positioned in the Frankfort horizontal plane. Weight was measured on a scale with an accuracy of ±100 g (Seca, Hamburg, Germany). Quetelet index was calculated using the formula: weight (kg)/height (m2 ).

#### Analysis of Fecal Microbiota

Fecal samples from individuals participating in the study were collected at home in sterile containers, kept at 4°C in the domestic refrigerator (maximum 2–3 h from deposition) and frozen at −80°C until analyses just right on arrival to the laboratory, as previously reported (27, 28). One gram of fecal sample was employed for DNA extraction by using the QIAamp DNA stool mini kit (Qiagen, Hilden, Germany) as previously described (28). Quantification of bacterial groups (*Akkermansia, Bacteroides* group, *Bifidobacterium*, *Clostridium* cluster XIVa, *Lactobacillus* group, and *Faecalibacterium*) in feces was performed with a 7500 Fast Real Time PCR System (Applied Biosystems, Foster City, CA, USA) using SYBR Green PCR Master Mix (Applied Biosystems) as previously described (29–31). These microbial groups include the main representatives of the human intestinal microbiota, which all together represent over 95% of the total bacteria in the human intestine (32, 33). For qPCR analysis, 1 µl of template fecal DNA (~5 ng) and 0.2 µM of each primer were added to the reaction mixture (25 µl). PCR cycling was as follows: an initial cycle of 95°C 10 min, 40 cycles of 95°C 15 s, and 1 min at the appropriate primer temperature. We compared the *C*t values obtained from a standard curve constructed as previously indicated (30) to estimate the number of cells. Standard cultures, primers, and annealing temperatures used for qPCR were the same as those recently reported (31).

## Analysis of Short-Chain Fatty Acids in Fecal Samples

Analysis of short chain fatty acids (SCFA) was performed by gas chromatography to determine the concentrations of acetate, propionate, and butyrate. One gram of fecal samples was weighed, diluted 1:10 in sterile PBS, and homogenized in a LabBlender 400 stomacher (Seward Medical, London, UK) at full speed for 4 min. Supernatants were then obtained by centrifugation (10,000 *g*, 30 min, 4°C), filtered through 0.2-µm filters, mixed with 1/10 of ethyl butyric (2 mg/ml) as an internal standard, and stored at −80°C until analysis. A gas chromatograph 6890N (Agilent Technologies Inc., Palo Alto, CA, USA) connected to a mass spectrometry (MS) 5973N detector (Agilent Technologies) and to a flame ionization detector was used for identification and quantification of SCFA.

Data were collected using the Enhanced ChemStation G1701DA software (Agilent). Samples (1 µl) were directly injected into the gas chromatograph equipped with an HP-Innowax capillary column (60-m length by 0.25-mm internal diameter, with a 0.25 µm film thickness; Agilent) using He as gas carrier and a constant flow rate of 1.5 ml/min. The temperature of the injector was kept at 220°C, and the split ratio was 50:1. Chromatographic conditions were as follows: initial oven temperature of 120°C, 5°C/min up to 180°C, 1 min at 180°C, and a ramp of 20°C/min up to 220°C to clean the column. In the MS detector, the electron impact energy was set at 70 eV. The data collected were in the range of 25–250 atomic mass units (at 3.25 scans/s).

SCFA were identified by comparison of their mass spectra with those held in the HP-Wiley 138 library (Agilent) and by comparison of their retention times with those of the corresponding standards (Sigma Aldrich, St. Louis, MO, USA). The peaks were quantified as relative abundances with respect to the internal standard. The concentration (in mM) of each SCFA was calculated using the linear regression equations (*r*2 ≥ 0.99) from the corresponding standard curves obtained with six different concentrations.

#### Statistical Analysis

The study design was a cross-sectional, one-group, observational analysis. Continuous variables were summarized as median (interquartile range) or mean ± SD, whereas *n* (%) was used for the categorical ones. Mann–Whitney *U* or *t*-tests were used to assess statistical differences, when appropriate. Correlations were analyzed by Spearman's rank test. After univariate correlation analyses, multiple regression analyses were conducted to assess the strength of the association between these variables including other (continuous) variables as potential confounders. Multiple regression analyses were also performed to identify the main predictors of a candidate independent variable. When coefficients of determination correspond to multiple regression analyses they are indicated as R2 . For each analysis, dependent and independent variables were indicated and the β coefficient, *B* coefficient with 95% confidence intervals (CIs), and *p*-values were computed. The association between two categorical variables was first studied by χ2 tests in univariate models and then was analyzed by multiple logistic regression models to include potential confounders as independent variables. For this analysis, odds ratio (OR) and 95% CI were computed. Principal component analysis (PCA) with Varimax rotation was performed to reduce sample dimensionality and potential collinearity effects. The number of components retained was based on eigenvalues (>1), and loadings greater than 0.5 were used to identify the variables comprising a single component. For cluster analysis, squared Euclidean distances were calculated, and Ward's Minimum Variance Method was used to identify clusters minimizing the loss of information. R package *heatmap.2* was used to generate heatmaps for visualization purposes. Some statistical analyses were performed independently in each cluster to evaluate whether differences among the studied variables independently arise on each cluster. The statistical approach is summarized in **Figure 1**. SPSS 19.0, R 3.0.3, and GraphPad Prism 5.0 for Windows were used.

## RESULTS

#### FFA and Microbial Populations

Total FFA serum levels and fecal microbial groups were analyzed in 101 human adults from the general population (**Table 1**). High total FFA serum levels were considered as a surrogate marker of impaired lipid metabolism. Then, the associations between FFA levels and intestinal microbial populations were determined by univariate and multivariate analyses adjusted for socio-demographical [age, gender, and body mass index (BMI)] and nutritional parameters (total energy and intakes of carbohydrates, lipids, proteins, and fiber). Interestingly, only *Akkermansia* abundance was correlated with FFA serum levels (*r* = -0.383, *p* < 0.001) (**Figure 2**). Moreover, this association remained statistically significant in a multiple regression analysis adjusted for age, gender, BMI, microbial groups analyzed, total energy as well as carbohydrates, lipids, and proteins intake as potential confounders (**Table 2**). Although a slight correlation between total FFA and the serum levels of the pro-inflammatory cytokine IL-6 was observed (*r* = 0.240, *p* = 0.020), no effect was evidenced when IL-6 was introduced in the previous multiple regression analysis.

Next, we aimed to evaluate whether this finding was due to a general effect on total FFA levels or it could be also associated with individual FFA species. To this end, associations between *Akkermansia* and the levels of individual FFA (γ-linolenic, palmitic, oleic, stearic, linoleic, palmitoleic, linoleic, AA, EPA, and

(left) indicates groups analyzed and sample sizes, whereas statistical methods for analysis and stratification of the study population are indicated at the left side.

DHA) were assessed. *Akkermansia* abundance was negatively associated with some FFA [stearic (*r* = −0.218, *p* = 0.039), palmitic (*r* = −0.321, *p* = 0.002), oleic (*r* = −0.261, *p* = 0.013), palmitoleic (*r* = −0.297, *p* = 0.004), linoleic (*r* = −0.272, *p* = 0.010), and γ-linolenic (*r* = −0.232, *p* = 0.028)], but no association was observed with EPA, DHA, AA, or linoleic levels. As important differences among FFA exist, individual FFA were grouped according to their chemical structure: saturated (SFA), monounsaturated (MUFA), w3-poly-unsaturated (w3-PUFA), and w6-PUFA. Interestingly, *Akkermansia* exhibited a strong negative association with SFA (*r* = −0.314, *p* = 0.003) as well as with

Table 2 | *Akkermansia* as predictor of free fatty acids (FFA) serum levels.

*The associations between FFA serum levels (as dependent variable) and microbial populations were studied by multiple lineal regression analysis including sociodemographical parameters and nutritional intakes as potential confounders (all included as independent variables in the model).* β *and B coefficients [with 95% confidence intervals (CI)] and p-values are calculated for each parameter within the same model. R2 (model)* = *0.303. The p-value highlighted in bold represent statistically significant differentes.*

MUFA and w6-PUFA to a lower degree (*r* = −0.266, *p* = 0.011 and *r* = −0.244, *p* = 0.020, respectively), whereas no association was observed for w3-PUFA (*r* = −0.104, *p* = 0.330). Thus, our findings point to a negative association between *Akkermansia* abundance and levels of SFA, MUFA, and w6-PUFA, all of them pro-inflammatory. Since some collinearity was noted among individual FFA levels, a PCA was carried out to avoid potential biases. PCA was then conducted with the serum levels of specific FFA, given that all of them exhibited communalities higher than 0.5, thus supporting the appropriateness of this analysis. The Kaiser–Meyer–Olkin test provided a good adequacy of the data (0.858) as the Bartlett test of sphericity (*p* = 10−144) did. PCA identified two main components (explaining 72.4% of the total variance): C1 (including loadings from γ-linolenic, palmitic, oleic, stearic, linoleic, palmitoleic, linoleic, AA; explaining 59.81% of the total variance) and C2 (including EPA and DHA; explaining 13.09% of the total variance). Notably, *Akkermansia* was negatively associated with C1 (*r* = −0.308, *p* = 0.003), thus supporting an opposite relationship between the levels of this microorganism and those of the saturated, monounsaturated, and w6-PUFA retained in this component, whereas no association was found with anti-inflammatory w3-PUFA (C2). These results from the PCA strengthen our previous findings on the separated analysis of FFA according to their chemical structure. Moreover, since PUFA are present in lower levels, and taking into account that nine FFA loaded on C1 whereas only two did in C2, this association was studied by regression analyses including total FFA serum levels as confounder to rule out the possibility that decreased FFA abundance may bias the analysis. Interestingly, *Akkermansia* levels remained associated with C1 after performing this adjustment (β [95% CI], *p*: −0.286 [−1.999, −0.315], *p* = 0.008), whereas no association was observed with C2 (−0.052 [−0.152, −0.092], *p* = 0.625), thereby confirming our previous findings. In addition, the analysis of inflammatory mediators showed that *Akkermansia* was negatively associated with IL-6 serum levels (*r* = −0.233, *p* = 0.032), whereas IL-6 levels and C1 were in turn associated (*r* = 0.220, *p* = 0.032). No associations were found for *Akkermansia* with IFNγ (*r* = 0.056, *p* = 0.604) or MCP-1 (*r* = −0.028, *p* = 0.802) serum levels.

Therefore, all these results suggest a relationship between intestinal *Akkermansia* and serum FFA levels in humans. Moreover, *Akkermansia* was negatively associated with specific FFA species, mainly those saturated and/or with an attributed pro-inflammatory role, such as MUFA and w6-PUFA. This finding is in line with the negative association observed between *Akkermansia* and IL-6 serum levels.

#### Associations between FFA Profiles, Microbial Populations, and SCFA

Our results from the PCA analysis and the differences found in the associations with *Akkermansia* and IL-6 levels point to the existence of different FFA profiles among individuals. To address this hypothesis, an unsupervised cluster analysis was performed for the individual FFA levels. Interestingly, this approach confirmed the existence of two independent groups of individuals based on FFA levels, thereafter referred to as cluster I (*n* = 62) and cluster II (*n* = 39) (**Figure 3A**).

Although all FFA species were higher in cluster II, mainly saturated and/or pro-inflammatory fatty acids were associated with this group, whereas a strikingly different pattern was observed for EPA and DHA, which seem not to play a role in clusters definition on our analysis (**Figure 3B**). No significant differences in age, gender or BMI were observed between individuals from clusters I and II (**Table 3**). Nutrient intakes were then compared between clusters in order to evaluate whether distinct daily intakes could account for the differences observed in FFA clusters. Interestingly, no differences in daily intakes were found (**Table 4**), hence supporting that additional factors other than demographic and nutritional parameters may underlie the differences found between FFA clusters.

Notably, important differences in intestinal microbial populations and fecal SCFA levels were observed between clusters (**Table 5**). Cluster II was characterized by decreased levels of *Akkermansia* and increased *Lactobacillus* group counts as well as higher acetate, propionate and total fecal SCFA concentrations than cluster I. Moreover, increased total FFA [0.48 (0.24) vs 0.26 (0.13) mM] and IL-6 [752.40 (1,155.22) vs 411.77 (636.85) pg/ml], but not IFNγ (*p* = 0.252) or MCP-1 (*p* = 0.541) serum levels were also registered in cluster II (**Figure 3C**).

Finally, some statistical analyses performed independently within each cluster revealed particular interesting features for each group, thereby stressing the existence of important differences between groups. On the one hand, IL-6 serum levels were negatively correlated to *Akkermansia* abundance (*r* = −0.312, *p* = 0.034) and acetate (*r* = −0.344, *p* = 0.014) in subjects included in cluster I, whereas no associations were observed in cluster II, characterized by higher IL-6 and acetate levels. Conversely, acetate and total fecal SCFA were strongly associated with higher total FFA serum levels in subjects included in cluster II (*r* = 0.459, *p* = 0.006; and *r* = 0.410, *p* = 0.016, respectively). Similarly, total energy intake was positively associated in this group with total SCFA production (*r* = 0.438, *p* = 0.008), but not in their cluster I counterparts (*r* = −0.044, *p* = 0.748), hence supporting a cross talk between host and microbial metabolisms, in which SCFA are involved.

Overall, our findings support the existence of homeostatic relationship between microbial populations, host lipid metabolism and inflammatory mediators. Thus, imbalanced microbial populations and/or increased fecal SCFA production could be related to an altered lipid metabolism as well as a serum IL-6 shift.

#### Analysis of the Associations among Impaired FFA Profile, Subclinical Metabolic Alterations, and Microbial Populations

Then, we addressed in our group of adult subjects, whether an altered gut microbiota composition and/or serum inflammatory mediators may have a negative impact on the host metabolism and if a clinical relevance of these players could be expected.

Although no significant differences in serum levels of glucose, cholesterol or triglycerides were found between groups, individuals within cluster II were more likely to exhibit hyperglycemic (>100 mg/dl) or hypertriglycemic (>150 mg/dl) states than those within cluster I (22/39 vs 22/62, *p* = 0.039 and 7/39 vs 4/62, *p* = 0.070). Consequently, metabolic alterations (either hyperglycemia or hypertriglycemia, as stated in the Methods section) were more prevalent in cluster II (23/39 vs 24/62, *p* = 0.047).

The strength of these associations was further analyzed by multiple logistic regression. By selecting subclinical metabolic

specific FFA serum levels (columns). Colors in the vertical bar at the right of the heatmap identify clusters I (black) and II (gray). Tiles are colored based on serum FFA concentrations, red and blue indicating low or high levels, respectively. (B) Biplot obtained in the cluster analysis of specific FFA serum levels. Red arrows represent the vectors showing the associations among the original variables entered and the outcome of the cluster analysis. Numbers represent the final cluster assigned to each subject (1: cluster I, 2: cluster II). Whereas saturated and mainly pro-inflammatory FA were closely related, EPA and DHA were not involved in clusters definition. (C) Comparison of serum levels of FFA, IL-6, IFNγ, and MCP-1 between subjects of both clusters. Boxes represent median and interquartile range, whereas whiskers represent minimum and maximum values. Differences were assessed by Mann–Whitney *U* tests.

*Demographical parameters from the whole population (n* = *101) and from the study participants classified according to clusters computed from individual FFA levels are shown. Differences between clusters were analyzed by Mann–Whitney U or* χ*2 tests. Variables are expressed as mean* ± *SD, median (interquartile range) or n, as appropriate.*

alterations as the dependent variable and adjusting the model for confounders (age, gender, BMI, total energy, as well as lipids, carbohydrates, protein, and fiber intakes, all introduced as independent variables), we found that individuals within cluster II were more likely to exhibit metabolic alterations (OR [95% CI], *p*: 3.064 [1.127, 8.330], 0.028). Interestingly, this association remained significant when the microbial groups were entered in the model, appearing *Akkermansia* abundance also associated with metabolic alterations (*p* = 0.029). This highlights the interplay between altered FFA profile, microbial populations and impaired host metabolism.

Moreover, additional observations reinforced the finding commented just above. Since our initial results point to a cross

#### Table 4 | Nutritional parameters according to free fatty acids (FFA) clusters.

*Dietary intakes from the whole population (n* = *101) and from the study participants classified according to clusters computed from individual FFA levels are shown. Differences in daily intakes between clusters were analyzed by Mann–Whitney U tests (unadjusted) or linear regression models to adjust for confounders (adjusted). Energy was adjusted by gender and age, whereas the rest of the nutrients were adjusted by gender, age, and energy. Variables are expressed as mean* ± *SD.*

Table 5 | Intestinal microbial populations, fecal short-chain fatty acid (SCFA), and serum cytokine levels according to free fatty acids (FFA) clusters.

*Microbial populations and SCFA fecal levels and serum cytokines from the whole population (n* = *101) and from the study participants classified according to clusters computed from individual FFA levels are shown. Differences in microbial populations, fecal SCFA, and serum cytokine levels between clusters were analyzed by t or Mann– Whitney U tests. The p-values highlighted in bold represent statistically significant differences. Variables are expressed as mean* ± *SD or median (interquartile range).*

talk between host and microbial metabolism, we further deepen into this idea focusing on possible differential associations between microbial groups or SCFA with nutrient intakes. In subjects with subclinical metabolic alterations, *Lactobacillus* abundance was correlated with carbohydrates intake in both cluster I (*r* = 0.603, *p* = 0.004) and II (*r* = 0.765, *p* < 0.001), but with fiber intakes only in cluster I (*r* = 0.609, *p* = 0.003). No associations were observed in those subjects free of metabolic alterations. On the other hand, the association between total SCFA and total FFA levels mirrored this situation, a positive correlation being found in individuals with metabolic alterations in both cluster I (*r* = 0.524, *p* = 0.018) and II (*r* = 0.432, *p* = 0.045), but not in those individuals without subclinical metabolic alterations.

In sum, these results support an association between microbial populations, nutritional factors, and impaired host metabolism. Subjects exhibiting an altered FFA profile, which also display imbalanced *Akkermansia* and *Lactobacillus* populations, were more frequently associated with subclinical pathogenic metabolic states. Divergent patterns in the associations between dietary intakes with microbial populations and SCFA were also observed.

#### Microbial Imbalanced Populations As Predictors of Impaired FFA Levels

Finally, although *Akkermansia* abundance was identified as the only microbial predictor of serum FFA levels in the whole population analyzed (*n* = 101), striking differences in *Akkermansia* levels were evidenced between clusters I and II. Since the results commented just above suggest that altered microbial populations and their interactions with nutrient intakes may underlie impairment in the host metabolism and that SCFA can have relevance in such interactions, we included all these parameters (microbial populations, SCFA, dietary intakes, age, gender, and BMI) as independent variables in a multivariate regression analysis, FFA levels being selected as the dependent variable, after stratifying our populations by the clusters obtained in the previous analysis.

Interestingly, whereas only age (*p* = 0.038) and BMI (*p* = 0.050) were identified as predictors of FFA levels in individuals within cluster I (*R*2 (model) = 0.407), *Lactobacillus* abundance (*p* = 0.032), fiber intake (*p* = 0.021), SCFA (acetate *p* = 0.008, propionate *p* = 0.018, and butyrate *p* = 0.003), and gender (*p* = 0.008) were associated with FFA levels in subjects within cluster II (*R*2 (model) = 0.646).

In summary, different factors were found to be associated with total FFA levels depending on the levels of *Akkermansia*. Above a threshold of *Akkermansia* levels, anthropometric factors such as BMI and age were the main predictors of serum FFA. However, when *Akkermansia* abundance is low, factors other than the anthropometric characteristics (as imbalanced microbial populations or SCFA production) seem to impact the FFA pool.

## DISCUSSION

Despite the important research advances in recent years, the links between human metabolism and gut microbiota are far from being completely understood, especially in the field concerning the lipid metabolism. The present study addresses a multilevel analysis of this scenario, by assessing different surrogate biomarkers of the lipid metabolism, in addition to some relevant intestinal microbial groups and SCFA production as well as inflammatory mediators; the study was performed in a sample of adult subjects from the general population in order to gain some insight into the relationship among these parameters and their potential impact on the human health. Our results revealed an association between the abundance of the intestinal microorganism *Akkermansia* and circulating FFA. This association was restricted to a specific group of FFA species, *Akkermansia* and serum IL-6. In addition, cluster analyses revealed that imbalanced intestinal microbial groups and levels of SCFA production may be related to an impaired FFA profile, pro-inflammatory and saturated fatty acids mainly hallmarking this group. Interestingly, individuals who exhibit these features were more likely to show metabolic alterations. Therefore, the results herein presented provide valuable information on the gut microbiota-host metabolism axis and its involvement in human health.

A major finding of our study was the association between *Akkermansia* and serum FFA levels. *Akkermansia* has been reported to participate in the maintenance of gut integrity and energy harvest by the host (34). It has been confirmed that *Akkermansia* has a causative role in lowering body fat mass and in glucose homeostasis in mice models (35, 36), although evidence in humans is limited. Our results demonstrate for the first time an association between *Akkermansia* and serum FFA in a cohort of human adults from the general population. Interestingly, we were able to cluster these healthy adults into two independent groups on the basis of serum individual FFA species, one of these groups showing an increased prevalence of subclinical metabolic alterations. In our study *Akkermansia* was negatively associated mainly with saturated FFA, in line with the differences that have been previously reported by other authors between lard-like and fish oil-enriched diets in the gut microbiota of mice (36, 37). Moreover, differences in *Akkermansia* abundance found in our study between FFA clusters were associated in turn with striking differences in total FFA levels. This observation supports a gradual relationship between *Akkermansia* abundance and FFA serum levels and therefore aligns with the concept proposed by other authors (36, 38) that a "threshold" for *Akkermansia* levels may exist. In line with this hypothesis, below certain levels of *Akkermansia* abundance, gut barrier, and other functions developed by this microbe may become insufficient, thus promoting a shift from a healthy toward a pathologic-prone status. As previously commented, different parameters were found to be predictors of total FFA serum levels depending on the *Akkermansia* levels. Therefore, low *Akkermansia* levels may render the host metabolism more sensitive to a number of factors which can lead to an imbalanced FFA profile and, potentially, altered metabolism, hence supporting this notion.

*Akkermansia* is known to reside within the mucus layer of the intestine, thus contributing to strengthen the intestinal wall (34). Then, it is feasible that decreased *Akkermansia* levels may lead to a compromised barrier function and increased gut permeability, hence promoting metabolic endotoxemia, which has been related to the development of obesity and associated disorders (35, 39). Interestingly, we have found that decreased *Akkermansia* levels were associated with elevated IL-6 serum concentrations and impaired FFA profile, and the subpopulation with such profile exhibited a bias toward the enrichment in pro-inflammatory and saturated fatty acids, as well as an increased prevalence of metabolic disturbances, thus supporting those previous findings. Therefore, these results suggest that *Akkermansia* may be linked to the inflammatory milieu by modulating the FFA profile in the host.

Previous studies have revealed reduced levels of *Akkermansia* in patients suffering from inflammatory bowel disease or other metabolic impaired conditions (40, 41), although some controversy exist on the role of *Akkermansia* in these disorders. Interestingly, a recent study reported decreased counts of *Akkermansia* in pre-diabetic individuals (42), suggesting an early involvement of this microorganism in metabolic disorders. Thus, we decided to focus on subclinical metabolic features (suggestive of an impaired metabolism), which can be detected in healthy subjects, in order to improve our knowledge on the potential role of *Akkermansia* as an early marker of impaired metabolic conditions in the general population. These metabolic features were related to an increased risk of metabolic complications in the long-term (43–48). Thus, these subjects showing such subclinical alterations could be classified as "at risk" population according to the scientific literature. Based on this approach, it may be hypothesized that imbalanced microbial populations can underlie the subclinical stage of some metabolic conditions. Actually, the lack of association in our study between *Akkermansia* and other clinical hard end-points, such as obesity or related immune mediators, such as MCP-1 or IFNγ, may support a very early role of these features. However, long-term studies are warranted.

Importantly, we found an opposite behavioral pattern between *Akkermansia* and *Lactobacillus* groups, the latter being overrepresented in subjects with impaired FFA profile and increased prevalence of metabolic traits. Although classically regarded as "beneficial" microbes, some recent evidence highlights a positive association between *Lactobacillus* and BMI (49, 50) as well as with some pathological outcomes (51–53). In fact, a slight increase in the relative abundance of *Lactobacillales* has been reported in the context of the inflammatory condition of Behçet syndrome (54), related to an altered SCFA production and aberrant immune responses. Thus, these pieces of evidence point to a *Lactobacillus* within-group heterogeneity with relevance for the human health, as suggested by other authors (40, 55). In agreement with this hypothesis our findings on the interactions between *Lactobacillus* abundance and nutrient parameters, evidenced different association patterns as depending on the metabolic "health" status of the subject. Controversy observed in mice studies and clinical interventions in humans is also consistent with this idea. A recent work shows opposite patterns upon mucin usage as substrate by *Lactobacillus* and *Akkermansia*

(56), thereby suggesting that trophic interactions may underlie, at least in part, these opposite trends of both microorganisms. Actually, a negative effect on *Akkermansia* levels in the gut upon administration of a probiotic mixture was observed when *Lactobacillus*, but not other probiotic bacteria, were added to the mixture (57). Conversely, *Akkermansia* has been shown to increase the production of the antimicrobial peptide RegIIIγ by colonic epithelial cells (35). This peptide specifically targets Gram-positive bacteria, thus potentially accounting for the opposite trends between *Akkermansia* and *Lactobacillus* groups. In sum, it is feasible that *Akkermansia* may modulate the gut environment and some intestinal microbial populations through several mechanisms (58).

human health may be suggested, SCFA production playing an important role.

It has been reported that *Akkermansia* and *Lactobacillus* exhibited diverging trends in twins discordant for metabolic syndrome (40). Our results herein presented are in line with these findings and suggest that the altered composition of the intestinal microbiota may be found in subclinical stages in healthy subjects. A recent study in mice revealed that whereas the colonization by *Akkermansia muciniphila* shifted the intestinal mucosa gene expression profile toward gene pathways involved in immune tolerance and metabolic homeostasis, colonization by *Lactobacillus plantarum* resulted in an overexpression of genes involved in the metabolism of fatty acids, lipoprotein lipase being one of the most up-regulated genes (59). An enhanced enzymatic activity of this enzyme may contribute to explain the striking increase in serum FFA found in association with increased *Lactobacillus* abundance in our study. Actually, overexpression of lipoprotein lipase gene has been related to fatty acid accumulation and insulin resistance (60, 61). On the other hand, the balanced immune responses promoted upon *Akkermansia* colonization may also account for the increased IL-6 levels found in our study in subjects with diminished abundance of *Akkermansia*. This is also in accordance with the positive effect on the induction of regulatory T cells reported in mice administered *Akkermansia* (62). It must be noted that IL-6 can promote a number of pleiotropic functions other than triggering inflammation. However, since we have focused our analysis in subjects with no previous diagnosis of chronic or immune-mediated conditions, our approach allowed us to gain insight into the relationships between *Akkermansia*, IL-6 and low-grade inflammation in healthy states. Nevertheless, these associations cannot be directly translated into pathological frameworks. Therefore, a study of the gut microbiota composition— IL-6 axis in disease stages remains to be elucidated.

Modulation by *Akkermansia* of genes involved in lipid metabolism seems to be mediated, at least in part, by the production of SCFA (63). Imbalanced microbial populations can have an impact on SCFA production, which can elicit different responses in the host metabolism and immune system (64). Interestingly, several authors have found increased SCFA in obesity and metabolic syndrome [reviewed in Ref. (65)], acetate and propionate being especially relevant in such alterations (66, 67). The associations found in our study between acetate and IL-6 and that of the total SCFA with FFA serum levels strongly support the cross talk between gut microbiota, host metabolism and immune networks, highlighting a role for SCFA as important elements of this interplay.

The present study has a number of limitations that can be remarked. On the one hand, we have performed a targeted analysis of the gut microbial composition instead of a global profiling by 16S rRNA gene sequences analysis. As a consequence, whether a reduced diversity underlies the present findings cannot be concluded. Similarly, a number of selected FFA and the major SCFA species were chosen for the analysis, based on their outmost relevance in several biological processes. In addition, the same concerns apply to the analysis of inflammatory mediators. Finally, a more precise characterization of dietary intakes may be needed to precisely account for the exact contribution of short-term nutrition to the FFA levels.

In conclusion, we reported an association between some intestinal microbial populations and the characteristics of the FFA profile in healthy middle-aged subject. *Akkermansia* and *Lactobacillus* groups seem to be connected to the health metabolic status of the host, the interplay with nutritional parameters playing a potential role (**Figure 4**). Finally, our findings provide some evidence on the role of SCFA as mediators of the cross talk in the gut microbiota-host lipid metabolism axis. Although our results point to a very early role of an altered microbial composition in the further development of metabolic disorders, prospective and long-term studies are needed to accurately address this possibility.

#### AUTHOR CONTRIBUTIONS

All the authors listed made substantial contributions to the design of the work, analysis, or interpretation of the results obtained;

#### REFERENCES

participated in the study design and data interpretation, reviewed the manuscript, and approved the final version; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. JR-C and NS performed most of the experimental procedures. SG was involved in the nutritional assessments, anthropometrical measurements, and collection of samples. AM, MG, CR-G, and AS provided biological samples and financial support. JR-C and AS drafted the manuscript.

#### ACKNOWLEDGMENTS

The authors acknowledge the excellent technical assistance of Ana M. Hernández-Barranco (IPLA) with the analysis of SCFA in fecal samples as well as of the staff of the scientific core facilities from the University of Oviedo (Unidad de Espectrometría y Espectrofotometría, Servicios Científico-Técnicos, Universidad de Oviedo). The authors also show their deepest gratitude to all the study volunteers. The authors acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).

#### FUNDING

This work was funded through the Grant GRUPIN14-043 "Microbiota Humana, Alimentación y Salud" funded by "Plan Regional de Investigación del Principado de Asturias," Asturias, Spain and by the grants AGL2010-14952 from Spanish "Plan Nacional I+D+I" and by Biopolis SL within the framework of the e-CENIT Project SENIFOOD from the Spanish Ministry of Science and Innovation. JR-C and NS benefit from postdoctoral contracts supported by the Grant GRUPIN14-043 and by a Clarín regional contract cofinanced by the Marie Curie CoFund European Program, respectively. Regional grants received cofounding from European Union FEDER funds.

lupus erythematosus: a pilot study. *JPEN J Parenter Enteral Nutr* (2011) 35:198–208. doi:10.1177/0148607110386378

**Conflict of Interest Statement:** The authors declared no potential competing financial interests concerning this study. Funders had no role in study conception, design, analysis of the results, or decision to publish.

*Copyright © 2017 Rodríguez-Carrio, Salazar, Margolles, González, Gueimonde, de los Reyes-Gavilán and Suárez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Dietary and Microbial Metabolites in the Regulation of Host Immunity

Naoko Shibata1,2,3, Jun Kunisawa1,3,4,5 \* and Hiroshi Kiyono1,6