Genotype alters the composition and functional repertoire of intestinal microbiota

We first confirmed that HFD affected the composition of the gut microbiota (Fig. S1A). Interestingly, when comparing the effect of genotype, we found that the effect was most profound under HFD feeding, based on 16 s rRNA sequences of unweighted UniFrac distance metric (Fig. 1A, S1B and S1C) (PERMANOVA, p = 0.04). Moreover, supervised learning using Random Forests, a machine learning method using OTUs as predictive features, accurately assigned samples to their source population based on taxonomic profiles at the OTU level (83.3% accuracy, 3 times better than the baseline error rate for random guessing).

Figure 1 Macrophage RIP140 level alters the composition and functional repertoire of intestinal microbiota. (A) Beta diversity comparisons of the gut microbiomes of the fecal samples collected from WT and RIP140mϕKD mice receiving High Fat Diet. Analyses were performed on 16 S rRNA V4 regions data with a rarefaction depth of 66677 reads per sample. Principal coordinates analysis of Unweighted UniFrac distances. Proportion of variance explained by each principal coordinate axis is denoted in the corresponding axis label. The plot shows a separation between samples from WT and RIP140mϕKD mice receiving High Fat Diet (PERMANOVA, p = 0.04). (B) Summary of the taxa that differentiate WT from RIP140mϕKD mice receiving HFD using Linear discriminant analysis Effect Size analysis (LEfSe). (C) Left: KD microbiome index corresponding to the sum of number of genera among the differentiating taxa. Data were presented with Mann–Whitney U test: p-value = 0.007. Right: KD microbiome index corresponding to the total relative abundance of the differentiating taxa. Data were presented with Mann–Whitney U test: p-value = 0.002. (D) ROC curve analysis for KD microbiome index. (E) Beta-diversity plots from Bray-Curtis distance matrices for genome analysis. (F) CAZY GH assignments for glycoside hydrolase families analysis. Full size image

Using LEfSe19, we found that 6 genera were significantly different between WT and RIP140mϕKD mice fed a HFD (LDA log10 score >2). Specifically, RIP140mϕKD profile was associated with a significant gain in Odoribacter, Coprococcus, Lautropia, Luteimonas, Candidatus Arthromitus and Pseudomonadaceae when compared to WT mice. (Fig. 1B, S1D). We then constructed a KD microbiome index from this panel of taxa that highly differentiated between WT and RIP140mϕKD mice under HFD. This KD index corresponded to the sum of relative abundances of the 6 differentiating taxa. We found that the median KD index was 0.30 (IQR = 0.05) in RIP140mϕKD mice and 0.17 (IQR = 0.01) in WT mice (Mann-Whitney U test, p = 0.002) (Fig. 1C, right panel). Moreover, ROC curve analysis showed that our KD index was a strong predictor of the genotype, with an area under the curve of 0.91 (Fig. 1D). In order to determine the KD index threshold that best predicts genotype, we performed leave-one-out cross-validation on our KD indices. Each held-out KD index was treated as a new sample, independently from the initial cohort, on whom we tested and subsequently refined the optimal index cutoff to separate WT and RIP140mϕKD. This Leave-one-out (LOO) cross-validation procedure demonstrated that the taxon panel was able to predict the genotype in a new sample, with an accuracy of 83% at a specificity of 80%. Thus, our LOO analysis predicted genotype with reasonable accuracy and identified taxa associated with resistance to diet induced metabolic diseases that can serve as future biomarkers. These data demonstrated that genotype, that is WT vs. RIP140mϕKD profile, is strongly associated with a specific taxonomic repertoire in mice fed a HFD and that only 6 taxa can be used to predict the genotype of a mouse

Using PICRUSt20, we found that the gut microbiota functional repertoire is significantly different between WT and RIP140mϕKD mice. This algorithm estimates the functional potential of microbial communities given the current 16 S rRNA gene survey and a set of currently sequenced reference genomes. PICRUSt predictions in human and mouse gut microbiomes are expected to have 80–90% accuracy. First, beta-diversity plots generated from Bray–Curtis distance matrices showed a separation between WT and RIP140mϕKD mice fed a HFD (PERMANOVA, p = 0.041) (Fig. 1E). Moreover, supervised learning using Random Forests, with the predicted metagenome table collapsed at level 3 KEGG Orthology groups as predictive features, accurately assigned samples to their source population based on predicted metagenomic profiles (75% accuracy, 2 times better than the baseline error rate for random guessing). We also identified significant differences in microbial functional pathways in the fecal samples of WT and RIP140mϕKD mice fed a HFD (level 3 KEGG Orthology groups, Mann-Whitney U test, False Discovery Risk corrected p-value <0.20). The fecal microbiome of RIP140mϕKD mice is enriched in functional categories associated with fatty acid metabolism, lysine, valine, leucine and isoleucine degradation, caprolactam degradation, styrene and atrazine degradation, and depleted in categories associated with galactose metabolism, purine metabolism, cysteine and methionine metabolism, D-Glutamine and D-glutamate metabolism, nicotinate and nicotinamide metabolism, amino sugar and nucleotide sugar, thiamine metabolism and primary bile acid biosynthesis (Table S1).

Using CAZY GH assignments, we found that 5 glycoside hydrolase families were increased in RIP140mϕKD mice as compared to WT mice, including GH17, PL5, GT4, GT9 and AA1, whereas 10 glycoside hydrolase families showed a loss of abundance in KD mice as compared to WT mice, including GT14, GH36, GH3, GH18, GH115, GH78, GH65, GH130, GT32 and GH127 (LDA score (log10) >2) (Fig. 1F)

Together, these findings demonstrate that, in addition to a change in taxonomy, the genotype is associated with marked change in functional profile and glycoside hydrolase repertoire in animals fed a HFD.

Fecal microbiota transplantation transfers gut microbiota from donor to recipient mice

Our previous studies showed that RIP140mϕKD mice are resistant to diet-induced metabolic diseases16,21. Since gut microbiota are strongly associated with hosts’ health, we proposed that gut microbiota in RIP140mϕKD mice could be associated with their metabolic protection features particularly under HFD. We thus performed reciprocal FMTs, from WT or RIP140mϕKD fed a HFD into WT or RIP140mϕKD mice in four groups: WT → WT (WT receiving WT), KD → WT (WT receiving KD), WT → KD (KD receiving WT) and KD → KD (KD receiving KD). The experimental design is depicted in Fig. 2A.

Figure 2 FMT transfers gut microbiota from donor to recipient. (A) A scheme showing FMT experiment. (B) Left: Unweighted UniFrac based PCoA from RIP140mϕKD mice receiving FMT from WT (WT → KD) mice. Right: Unweighted UniFrac based PCoA from WT mice receiving FMT from RIP140mϕKD (KD → WT) mice. (C) Left panel: KD microbiome index in RIP140mϕKD mice receiving FMT from WT (WT → KD) mice. Right panel: KD microbiome index in WT mice receiving FMT from RIP140mϕKD (KD → WT) mice. (D) Representative pie chart of Bayesian source-tracking analysis of taxonomy, predicted metagenome and predicted GH of WT → KD mice post FMT 4 weeks (left panel) and KD → WT mice post FMT 4 weeks (right panel). Source contributions were averaged across samples within the population. WT → WT: WT receiving WT, KD → WT: WT receiving KD, WT → KD: KD receiving WT and KD → KD: KD receiving KD. Full size image

To first validate FMT efficiency, we examined changes in the diversity of recipient KD mice receiving FMT from donor WT mice (WT → KD). Unweighted UniFrac based PCoA showed differences between fecal samples collected from recipient RIP140mϕKD mice before FMT (i.e. native RIP140mϕKD mice) and four weeks after FMT (WT → KD) (PERMANOVA, p = 0.04), but did not show significant differences between fecal samples of WT → KD mice four weeks after FMT and native WT mice (PERMANOVA, p = 0.19). Moreover, fecal samples of recipient RIP140mϕKD mice after FMT (WT → KD) differed in terms of overall diversity when comparing to fecal samples of native RIP140mϕKD mice (PERMANOVA, p = p 0.048), but did not differ when comparing to fecal samples of native WT mice (PERMANOVA, p = 0.192) (Fig. 2B, left panel). We also examined changes in diversity of recipient WT mice receiving FMT from donor RIP140mϕKD mice (KD → WT). Unweighted UniFrac PCoA showed differences between fecal samples of recipient WT mice before FMT (i.e. native WT mice) and four weeks after FMT (KD → WT) (PERMANOVA, p = 0.02), but did not show significant differences between fecal samples of KD → WT mice and native RIP140mϕKD mice (PERMANOVA, p = 0.06). Moreover, fecal samples of recipient WT mice after FMT (KD → WT) differed in terms of diversity when comparing to fecal samples of native WT mice (PERMANOVA, p = p 0.043), but did not differ when comparing to fecal samples of native RIP140mϕKD mice (PERMANOVA, p = 0.071) (Fig. 2B, right panel).

Using the KD microbiome index described above, we found that WT → KD mice acquired a WT fecal microbiota signature; KD → WT mice acquired a KD fecal microbiota signature. The KD index differed between native RIP140ϕKD mice (i.e. fecal sample collected before FMT) and WT → KD mice post FMT (MWU test, p = 0.012) but the KD index did not differ between native WT mice and WT → KD mice post FMT (MWU test, p = 0.28) (Fig. 2C, left panel). Following the same trend, the KD index differed between native WT mice (i.e. fecal sample collected before FMT) and KD → WT mice post FMT (MWU test, p = 0.0018) but the KD index did not differ between native KD mice and KD → WT mice post FMT (MWU test, p = 0.28) (Fig. 2C, right panel).

To further explore this trend, we performed Bayesian source tracking on each of the post FMT fecal samples. This allows us to estimate the contribution of bacteria from the native WT mice, native RIP140mϕKD mice or from ‘unknown’ sources (one or more sources absent from the training data) in the post FMT samples. We found that the fecal samples of WT → KD mice were dominated by bacteria, predicted genes or CAZY GH of native WT mice (Fig. 2D, left panel); on the other hand, fecal samples of KD → WT mice were dominated by bacteria, predicted genes or CAZY GH of native RIP140mϕKD mice (Fig. 2D, right panel).

Based on these findings, we conclude that WT → KD mice post FMT acquired the microbiota signature of the WT genotype and KD → WT mice acquired that of the KD genotype.

Healthy gut microbiota ameliorate diet-induced metabolic syndrome

To determine if gut microbiota from RIP140mϕKD mice could benefit recipient mice under HFD, we performed a series of metabolic tests on post-FMT mice. RIP140mϕKD typically have elevated anti-inflammatory activities in their adipose tissues17,22. We therefore first examined if adipose innate immunity was affected by FMT. It appeared that mice receiving FMT from RIP140mϕKD mice (KD → WT) indeed have reduced M1 inflammatory marker (Tnfα) and elevated level of M2 anti-inflammatory marker (Arg1) in white adipose tissue (Fig. 3A). Furthermore, these mice express stronger beige (Cd137) and brown (Ucp1) fat markers (Fig. 3B). Mice receiving FMT from KD mice (KD → WT or KD → KD) were resistant to HFD-induced weight gain and insulin resistance compared to mice receiving FMT from WT mice (WT → WT or WT → KD) (Fig. 3C, Fig. S2). KD → WT mice show a higher rate of energy expenditure (O 2 consumption) in both the light and dark phases (Fig. 3D). Taken together, our data show that FMT could transfer not only the “good” gut microbiota, but also the HFD-resistant, protective phenotype of the donor, RIP140mϕKD.

Figure 3 Receiving FMT from RIP140mϕKD mice ameliorates diet-induced diabetic traits. (A) qPCR of Tnfa (M1) and Arg1 (M2) in visceral white adipose tissues. Student test (n = 3) was used and presented as mean ± SD, *P < 0.05. (B) qPCR of Ucp1 (brown fat) and Cd137 (beige fat) in visceral white adipose tissues. Student test (n = 3) was used and presented as mean ± SD, *P < 0.05; **P < 0.01. (C) GTT (left) and ITT (right) determined after 14 weeks of HFD feeding Student test (n = 3) was used and presented as mean ± SD, *P < 0.05, **P < 0.01 (WT → WT vs. KD → WT); †P < 0.05 (WT → KD vs. KD → KD). (D) Energy expenditure of FMT mice. O 2 consumption was measured in the dark and light phases and presented as vO 2 (ml/kg/hr) (n = 3 in each group). Student test was used and presented as mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001 (WT → WT vs. KD → WT), †P < 0.05; ††P < 0.01; †††P < 0.001 (WT → KD vs. KD → KD). Full size image

To gain insights into the possible mechanism, we examined the innate immune potential and tissue integrity of the gastrointestinal (GI) tract, because gut microbiota make direct contact with GI tract, affecting the microenvironment. We first monitored intestinal permeability (Fig. 4A) and found that both KD → WT and KD → KD mice have decreased intestinal permeability, indicating that they are less susceptible to low-grade inflammation that could contribute to the development of metabolic syndrome23. These mice also exhibit fewer pathological features in the colon, with apparently decreased hyperplasia (Fig. 4B). In terms of local (intestinal) innate immunity as monitored by M1 vs. M2 ratio (Fig. 4C), these mice have elevated M2 and lowered M1 markers and a reduced M1/M2 ratio indicative of improved anti-inflammation in the GI tract.