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. Author manuscript; available in PMC: 2023 Nov 30.
Published in final edited form as: Science. 2023 Aug 24;381(6660):851–857. doi: 10.1126/science.ade0522

The gut microbiota reprograms intestinal lipid metabolism through long non-coding RNA Snhg9

Yuhao Wang 1,2,3,4,*, Meng Wang 1, Jiaxin Chen 5, Yun Li 4, Zheng Kuang 4,†, Chaitanya Dende 4, Prithvi Raj 4, Gabriella Quinn 4, Zehan Hu 4,‡, Tarun Srinivasan 4, Brian Hassell 4, Kelly Ruhn 4, Cassie L Behrendt 4, Tingbo Liang 1,3, Xiaobing Dou 2, Zhangfa Song 6, Lora V Hooper 4,7,*
PMCID: PMC10688608  NIHMSID: NIHMS1941736  PMID: 37616368

One sentence summary:

The intestinal microbiota promotes lipid absorption and storage by repressing the expression of long non-coding RNA Snhg9.

The intestinal microbiota regulates mammalian lipid absorption, metabolism, and storage. Here we report that the microbiota reprograms intestinal lipid metabolism in mice by repressing the expression of long non-coding RNA (lncRNA) Snhg9 in small intestinal epithelial cells. Snhg9 suppressed the activity of peroxisome proliferator–activated receptor γ (PPARγ) – a central regulator of lipid metabolism – by dissociating the PPARγ inhibitor Sirtuin 1 from cell cycle and apoptosis protein 2 (CCAR2). Forced expression of Snhg9 in the intestinal epithelium of conventional mice impaired lipid absorption, reduced body fat, and protected against diet-induced obesity. The microbiota repressed Snhg9 expression through an immune relay encompassing myeloid cells and group 3 innate lymphoid cells. Our findings thus identify an unanticipated role for a lncRNA in microbial control of host metabolism.

The intestinal microbiota has a significant impact on mammalian metabolism. In mice, resident intestinal bacteria enhance digestion of dietary polysaccharides (1), promote dietary lipid absorption by intestinal epithelial cells (2–5), and alter the function of adipose tissue (6–9). Consistent with the findings in mice, alterations in the composition of the human intestinal microbiota are associated with metabolic disorders including obesity, type 2 diabetes and cardiovascular disease (10–12). Given the rapidly increasing worldwide prevalence of metabolic disease (13), there is a pressing need to understand the mechanisms by which the intestinal microbiota affects host metabolism.

Long non-coding RNAs (lncRNAs) are RNA transcripts that are not translated but are nevertheless biologically functional. LncRNAs regulate biological processes such as cell proliferation, cell death, tumorigenesis, and immunity (14–17), but little is known about their involvement in the regulation of host metabolism by the gut microbiota. We therefore investigated whether the presence of the gut microbiota impacts the transcription of lncRNAs in small intestinal epithelial cells, which are central to microbial modulation of lipid absorption and metabolism (2–5).

Results

Expression of lncRNA Snhg9 is repressed by the microbiota

Whole transcriptome sequencing (RNA-seq) of small intestinal epithelial cells from conventionally raised (conventional) and germ-free mice identified 60 differentially expressed non-protein coding genes, including 42 genes that encode lncRNAs (Fig. 1A, fig. S1, A and B). In particular, the lncRNA encoded by Snhg9 (small nucleolar RNA host gene 9) showed reduced abundance in epithelial cells from conventional as compared to germ-free mice (Fig. 1B). Although lncRNAs are generally defined as >200 nucleotides (18) and mouse Snhg9 RNA is only 183 nucleotides long, human SNHG9 RNA is 233 nucleotides long and thus both the mouse and human RNAs are designated as lncRNAs (19).

Figure 1: Expression of lncRNA Snhg9 is repressed by the microbiota.

Figure 1:

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(A) Whole transcriptome sequencing of small intestinal epithelial cells recovered by laser capture microdissection from conventional and germ-free mice. Genes differentially expressed between conventional and germ-free mice are summarized and grouped based on transcript type. (B) Volcano plot visualizing the changes in lncRNA gene expression between conventional and germ-free mice. Snhg9 is highlighted in red. (C) qPCR analysis of Snhg9 expression in small intestinal epithelial cells recovered by laser capture microdissection from conventional (CV), germ-free (GF) and antibiotic treated (Abx) mice. Results are representative of at least two independent experiments. Means ± SEM are plotted; each data point represents one mouse. *p<0.05; **p<0.01; two-tailed Student’s t test.

Snhg9 transcripts were detected in small intestinal epithelial cells of antibiotic-treated mice by in situ hybridization (fig. S2A). Analysis of published single cell RNA sequencing data from mouse intestinal epithelial cells indicated that Snhg9 transcripts were mostly from stem cells, enterocytes and enteroendocrine cells (a specialized epithelial cell lineage) (20) (fig. S2B). Using quantitative PCR (qPCR), we confirmed that epithelial cells from conventional mice express less Snhg9 RNA when compared with epithelial cells from both germ-free and antibiotic treated mice (Fig. 1C). Thus, Snhg9 expression in small intestinal epithelial cells is reduced in the presence of the microbiota.

LncRNA Snhg9 binds to CCAR2

We next sought to illuminate the biological function(s) of Snhg9. Since other lncRNAs bind to and regulate the activity of proteins (21–23), we screened for Snhg9-protein interactions in small intestinal epithelial cells. We performed an RNA-protein pull-down assay in epithelial cell lysates using in vitro transcribed Snhg9 or antisense Snhg9. Mass spectrometry identified the most abundant interacting protein as cell cycle and apoptosis protein 2 (CCAR2; also known as deleted in breast cancer 1, or DBC1) (24) (Fig. 2, A and B). This finding was supported by immunoblot detection of CCAR2 among the proteins precipitated by Snhg9 (Fig. 2C).

Figure 2: LncRNA Snhg9 binds to CCAR2.

Figure 2:

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(A) Schematic of RNA-protein pull-downs in small intestinal epithelial cell (IEC) lysates in combination with mass spectrometry analysis. Created at BioRender.com. (B) Ten most abundant Snhg9 binding proteins identified by mass spectrometry. CCAR2 is highlighted in red. (C) Upper panel: representative immunoblot of CCAR2 in proteins pulled down from small intestinal IEC lysates by polyA RNA (negative control), Snhg9 or antisense Snhg9. Lower panel: Band intensities were quantified by densitometry and normalized to input. N=3 experimental replicates per group. (D) Upper panel: representative immunoblot of recombinant CCAR2 pulled down by polyA RNA, Snhg9 or antisense Snhg9. Lower panel: intensities were quantified by densitometry and normalized to input. N=3 experimental replicates per group. (E) Upper panel: representative immunoblot of CCAR2 in proteins pulled down from small intestinal IEC lysates by polyA RNA, antisense Snhg9, Snhg9, Snhg9 with 28 nucleotides deleted from the middle of the sequence (Snhg9-Δmid), Snhg9 with 3’-deletion of 24 nucleotides (Snhg9-Δ3’), Snhg9 with 5’-deletion of 24 nucleotides (Snhg9-Δ5’) or Snhg9 with both 3’- and 5’-deletion of 24 nucleotides (Snhg9-Δ3’Δ5’). Lower panel: band intensities were quantified by densitometry and normalized to input. N=3 experimental replicates per group. All experiments are representative of at least two independent experiments. Means ± SEM are plotted. **p<0.01; ***p<0.001; ns, not significant; two-tailed Student’s t test.

To test whether Snhg9 binds directly to CCAR2, we performed pull-down assays with in vitro transcribed Snhg9 and recombinant CCAR2. Consistent with our findings in epithelial cell lysates, recombinant CCAR2 was precipitated by Snhg9 RNA but not by antisense Snhg9 RNA or polyA RNA (Fig. 2D), indicating that CCAR2 binds directly to Snhg9 RNA. In addition, mutations in Snhg9 impacted binding to CCAR2. Deletion of 28 nucleotides in the middle of the Snhg9 sequence did not affect Snhg9 RNA binding to CCAR2. However, binding was reduced by 24-nucleotide deletions in the 3’ and 5’ regions, which target a predicted loop in the Snhg9 secondary structure (Fig. 2E; fig. S3, A and B). These data support a direct binding interaction and indicate that Snhg9 is a protein-binding lncRNA that binds directly to CCAR2.

LncRNA Snhg9 dissociates CCAR2 from the PPARγ inhibitor SIRT1, repressing PPARγ activity

We next investigated the cellular and physiological consequences of lncRNA Snhg9 binding to CCAR2. CCAR2 is an endogenous inhibitor of the deacetylase Sirtuin 1 (SIRT1) (25, 26). Among its several functions, SIRT1 regulates lipid metabolism by interacting with the transcription factor peroxisome proliferator-activated receptor gamma (PPARγ), which directs transcription of lipid metabolic genes (27–29). SIRT1 represses PPARγ activity through two mechanisms: by deacetylating PPARγ, or by docking with nuclear receptor corepressor 1 (NcoR1), a PPARγ cofactor. Both mechanisms decrease Pparg expression and reduce lipid metabolism (30, 31). We reasoned that by binding to CCAR2, Snhg9 might inhibit its interaction with SIRT1, thereby rescuing SIRT1 deacetylase and NcoR1 binding activities from CCAR2 inhibition. Indeed, overexpression of Snhg9 in HEK-293T cells largely abolished CCAR2 binding to SIRT1 (Fig. 3, A and B), and increased both SIRT1 deacetylase activity (Fig. 3C) and binding to NcoR1 (Fig. 3, D and E). These data indicate that Snhg9 promotes SIRT1 activity by sequestering the inhibitory protein CCAR2.

Figure 3: LncRNA Snhg9 dissociates CCAR2 from the PPARγ inhibitor SIRT1, repressing PPARγ activity.

Figure 3:

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(A) Co-immunoprecipitation (co-IP) of CCAR2 and SIRT1 with anti-SIRT1 antibody or IgG isotype control. HEK-293T cells were transfected with empty vector or Snhg9-encoding vector. Proteins were detected by immunoblot. (B) Band intensities in (A) were quantified by densitometry and normalized to input. N=3 experimental replicates per group. (C) Relative SIRT1 deacetylase activity in HEK-293T cells transfected with empty vector or Snhg9-encoding vector. N=5 experimental replicates per group. (D) Co-immunoprecipitation of NcoR1 and SIRT1 with anti-SIRT1 antibody or IgG isotype control. HEK-293T cells were transfected with empty vector or Snhg9-encoding vector. Proteins were detected by immunoblot. (E) Band intensities in (D) were quantified by densitometry and normalized to input. N=3 experimental replicates per group. (F) qPCR analysis of Pparg expression in 3T3-L1 cells with stable expression of Snhg9 or co-expression of Snhg9 and Ccar2. Cells were transduced with empty vector as a control. N=4 experimental replicates per group. (G) Immunoblot detection of PPARγ and β-actin (control) in 3T3-L1 cells from (F). (H) qPCR analysis of Pparg expression in Snhg9−/− 3T3-L1 cells that were untreated or rescued by Snhg9 expression, and in cells edited with non-targeting sgRNA. N=4 experimental replicates per group. (I) Immunoblot detection of PPARγ and β-actin (control) in 3T3-L1 cells from (H). (J) Snhg9 was stably expressed in 3T3-L1 cells and their differentiation to adipocytes was assessed by measuring glycerol as a readout of triglyceride accumulation. Cells were transduced with empty vector as a control. N=5 experimental replicates per group. (K) Lipids were detected by Oil Red O staining of differentiated cells from (J). Scale bar=30μm. (L) Snhg9−/− 3T3-L1 cells and cells edited with non-targeting sgRNA were assessed for differentiation to adipocytes as in (J). N=5 experimental replicates per group. Note that the multiple cell passages required by the CRISPR mutant selection process results in suppression of Pparg expression in the cells edited with non-targeting sgRNA (32). (M) Lipids were detected by Oil Red O staining of differentiated cells from (L). Scale bar=30 μm. All experiments are representative of at least two independent experiments. Means ± SEM are plotted. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001; two-tailed Student’s t test.

Since Snhg9 rescued SIRT1 activity from CCAR2 inhibition, we predicted that Snhg9 would repress PPARγ expression and activity. We therefore evaluated the impact of Snhg9 on Pparg expression and lipid metabolism in cells. PPARγ controls differentiation of the mouse fibroblast cell line 3T3-L1 into adipocytes following chemical induction (30). We generated 3T3-L1 cells that stably express Snhg9 (fig. S4A) and confirmed binding of Snhg9 to CCAR2 by RNA immunoprecipitation (RIP) assay (fig. S4B). Stable expression of Snhg9 inhibited expression of Pparg and its protein product PPARγ, whereas co-expression of Ccar2 rescued expression of both Pparg and PPARγ (Fig. 3, F and G). Accordingly, there was reduced transcription of PPARγ-controlled genes, including Cd36 (encoding a fatty acid transporter), Fabp4 (encoding a fatty acid binding protein), and Lpl (encoding lipoprotein lipase) (fig. S4, C to E). Similarly, stable expression of Snhg9 in mouse small intestinal organoids reduced expression of Pparg and PPARγ-controlled genes (fig. S4, F to J). These effects were not due to expression of the putative Snhg9 open-reading frame (ORF) (fig. S5A), since expression of the ORF alone failed to reduce expression of Pparg and its target genes, and Snhg9 with mutated start and stop codons retained the ability to reduce Pparg expression (fig. S5B). Together, these results indicate that Snhg9 represses PPARγ expression by binding to CCAR2.

Further supporting this idea, inactivation of Snhg9 in 3T3-L1 cells (Snhg9−/− cells) by CRISPR-Cas9 genome editing (fig. S6, A to D) increased expression of Pparg, PPARγ, and downstream target genes (Fig. 3, H and I, fig. S6E). This increase occurred despite the high frequency of cell passage during the selection process, which tends to suppress Pparg expression. In comparison, cells edited with a non-targeting single guide RNA (sgRNA) showed minimal Pparg expression as a result of a high number of cell passages (32) (Fig. 3, H and I), and re-expression of Snhg9 in Snhg9−/− 3T3-L1 cells largely reversed Pparg expression (Fig. 3, H and I). Of note, overexpression of Snora78, which is flanked by the Snhg9 exons, did not alter Pparg expression (fig. S6F), suggesting that Snora78 deletion did not cause the increased expression of Pparg in Snhg9−/− cells, and deleting the Snhg9 locus did not interfere with the expression of a nearby gene, Rps2 (fig. S6G). These data support the idea that Snhg9 released SIRT1 from CCAR2 inhibition.

Consistent with the reduced Pparg expression, Snhg9 overexpression restrained the differentiation of 3T3-L1 cells to adipocytes (Fig. 3, J and K). Conversely, Snhg9−/− 3T3-L1 cells maintained the ability to differentiate and form lipid droplets (Fig. 3, L and M). However, cells edited with non-targeting sgRNA lost the ability to differentiate as a result of insufficient expression of Pparg arising from multiple cell passages (32) (Fig. 3, L and M). Taken together, our results reveal that lncRNA Snhg9 inhibits Pparg expression and lipid metabolism by dissociating the CCAR2-SIRT1 complex.

Villin-Snhg9 transgenic mice have reduced lipid absorption

The presence of the intestinal microbiota enhances dietary lipid absorption and promotes obesity in mice fed a high fat diet. Germ-free mice, which are microbiologically sterile and thus lack an intestinal microbiota, tend to absorb less lipid than conventional mice and thus are largely protected from high fat diet-induced obesity (2, 3, 7, 8). Since Snhg9 suppresses lipid metabolism in cells (Fig. 3) and is upregulated in the intestines of germ-free mice (Fig. 1C), we hypothesized that Snhg9 limits body fat accumulation in mice.

To test this hypothesis, we used the Villin promoter to force expression of Snhg9 in intestinal epithelial cells of conventional mice (Villin-Snhg9 transgenic mice; fig. S7, A to D). Consistent with our findings in cultured cells, SIRT1 deacetylase activity was higher in intestinal epithelial cells from conventional Villin-Snhg9 transgenic mice as compared to wild-type littermates, with activity levels comparable to those of wild-type germ-free mice (Fig. 4A).

Figure 4: Villin-Snhg9 transgenic mice have reduced lipid absorption and are protected from high fat diet-induced metabolic disorders.

Figure 4:

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(A) Relative SIRT1 deacetylation activity in IECs from conventional wild-type, germ-free wild-type and conventional Villin-Snhg9 transgenic (Tg) mice. (B) RNA-seq of intestines of wild-type and Villin-Snhg9 Tg littermates. KEGG pathway analysis identifies pathways affected by Snhg9 overexpression. (C) Heatmap visualizing expression levels of selected lipid metabolic genes with altered expression in the small intestines of wild-type (WT) and Villin-Snhg9 Tg littermates. (D) LipidTox detection of fatty acids in the small intestines of wild-type and Villin-Snhg9 Tg littermates fed a high fat diet. Scale bar=100 μm. (E) Relative total lipid concentrations in isolated IECs from wild-type and Villin-Snhg9 Tg littermates fed a high fat diet. (F) Relative total neutral lipid concentrations in the feces of wild-type and Villin-Snhg9 Tg littermates fed a high fat diet. (G to I) Wild-type and Villin-Snhg9 Tg littermates were fed a high fat diet for 10 weeks and were assessed for body fat percentage (G), epididymal fat pad weight (H) and liver fat accumulation (examples are indicated with arrowheads) as indicated by hematoxylin and eosin staining (scale bar=100 μm) (I). (J and K) Wild-type and Villin-Snhg9 Tg littermates fed a high fat diet were assessed for glucose tolerance (J) and insulin tolerance (K). N=5 mice per group. (L) Body fat percentages of wild-type and Villin-Snhg9 Tg littermates that were treated with antibiotics after switching to a high fat diet for 10 weeks. (M) Body fat percentages of wild-type and Snhg9−/− littermates that were treated with antibiotics after switching to a high fat diet for 10 weeks. All experiments are representative of at least two independent experiments. Means ± SEM are plotted; each data point represents one mouse. *p<0.05; **p<0.01; ns, not significant; two-tailed Student’s t test.

To investigate whether Snhg9 overexpression impacts lipid metabolism in vivo, we compared the small intestinal transcriptomes of Villin-Snhg9 transgenic mice and their wild-type littermates by RNA-seq. KEGG pathway analysis confirmed that Snhg9 overexpression suppressed expression of genes involved in metabolic pathways including the PPAR signaling pathway (Fig. 4B). Villin-Snhg9 transgenic mice showed reduced expression not only of Pparg but also genes involved in fatty acid absorption (such as Cd36), transport (such as Fabp4), and synthesis (such as Scd1) (Fig. 4C). Expression of these genes and their protein products was also reduced in germ-free mice in comparison to conventional wild-type mice (fig. S7, E and F). Consequently, Villin-Snhg9 transgenic mice were similar to germ-free mice in that they had less lipid in their intestinal epithelial cells and more in their feces when compared to wild-type littermates (Fig. 4, D to F).

Villin-Snhg9 transgenic mice are protected from high fat diet-induced metabolic disorders

When fed a normal chow diet, Villin-Snhg9 transgenic mice had body weights similar to their wild-type littermates (fig. S7G). However, their body fat percentages and epididymal fat pad weights were reduced (fig. S7, H and I) and they were more glucose tolerant (fig. S7J). When switched to a high fat diet for 10 weeks, Villin-Snhg9 transgenic mice exhibited decreased body weights (fig. S7K), lower overall body fat percentages (Fig. 4G), smaller epididymal fat pads (Fig. 4H, fig. S7L) and milder liver steatosis (Fig. 4I) than their wild-type littermates. They also had lower serum triglycerides and free fatty acids (fig. S7, M and N), increased glucose tolerance, and decreased insulin resistance (Fig. 4, J and K). These phenotypes did not result from altered food intake, physical activity, respiratory exchange ratio or microbiota composition (fig. S8, A to C).

Because expression of intestinal Snhg9 is suppressed by the microbiota (Fig. 1C), we further assessed the requirement for the microbiota in Snhg9-regulated lipid metabolism. When we depleted the microbiota by antibiotic treatment, wild-type mice fed a high fat diet had lowered body fat percentages similar to those of Villin-Snhg9 transgenic littermates (Fig. 4L). This is consistent with the increased Snhg9 expression in antibiotic treated mice (Fig. 1C). For comparison, we generated Snhg9−/− mice by CRISPR-Cas9-mediated gene targeting (fig. S9, A and B). Although the Snhg9−/− mice had body weights and body fat percentages similar to wild-type littermates when fed a normal chow diet (fig. S9, C and D), they had increased body fat percentages and weight gain when fed a high fat diet even when their microbiota were depleted with antibiotics (Fig. 4M, fig. S9E). These data support the conclusion that the microbiota promotes body fat accumulation in part by repressing intestinal Snhg9 expression.

The microbiota suppresses Snhg9 expression through a myeloid cell-ILC3 relay

Bacteria activate intestinal epithelial cell gene expression through Toll-like receptors (TLRs) and their common signaling adaptor MyD88 (33). Expression of intestinal Snhg9 was increased in Myd88−/− mice as compared to wild-type controls (Fig. 5A), suggesting that MyD88 is required for microbial repression of intestinal Snhg9 expression. Although epithelial cell Myd88 was dispensable for repression of Snhg9 expression (Fig. 5B), mice with Myd88 selectively deleted in CD11c+ cells showed elevated Snhg9 expression (Fig. 5C), suggesting a role for CD11c+ cells in repressing Snhg9 expression. This idea was supported by studies of a mouse model of CD11c+ cell depletion in which Diphtheria toxin receptor (DTR) is expressed from the Cd11c promoter (34). Selective depletion of CD11c+ cells by Diphtheria toxin administration increased Snhg9 expression relative to controls (Fig. 5D). Since CD11c marks myeloid cells, including dendritic cells and macrophages, these results indicate that myeloid cells are required for microbial repression of intestinal Snhg9 expression.

Figure 5: The microbiota suppresses Snhg9 expression through a myeloid cell-ILC3 relay.

Figure 5:

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Snhg9 expression was measured by qPCR analysis of the small intestines of (A) conventional wild-type, germ-free wild-type and conventional Myd88−/− mice; (B) Myd88fl/fl and Myd88ΔIEC (epithelial cell-specific knockout) mice; (C) Myd88fl/fl and Myd88ΔCd11c (Cd11c+ cell-specific knockout) mice; (D) Cd11c-DTR mice untreated or treated with Diphtheria toxin (DT); (E) wild-type (WT) and Rag1−/− mice; (F) Rag1−/− mice injected via the intraperitoneal route with anti-CD90.2 antibody or IgG isotype control; (G) Rag1−/− and Rag2−/−;Il2rg−/− mice; (H) Rorc+/+ and Rorcgfp/gfp mice that were untreated (CV) or treated with antibiotics (Abx); (I) Myd88−/− mice treated with recombinant IL-22, IL-23 or vehicle. All experiments are representative of at least two independent experiments. Means ± SEM are plotted; each data point represents one mouse. *p<0.05; **p<0.01; ns, not significant; two-tailed Student’s t test.

Bacteria regulate the expression of several key intestinal epithelial cell genes through an immune cell signaling relay involving primarily myeloid cells and group 3 innate lymphoid cells (ILC3) (2, 35–37). In this relay, bacteria activate myeloid cells via TLRs and MyD88, which then signal to ILC3 through the cytokine interleukin 23 (IL-23). Activated ILC3 then signal to intestinal epithelial cells through IL-22 (2, 35–37). Having established a role for myeloid cells in microbial repression of Snhg9 expression, we next tested for the involvement of ILCs. Rag1−/− mice, which lack T and B cells, had decreased expression of intestinal Snhg9 as compared to wild-type mice (Fig. 5E). This indicated that T and B cells are dispensable for the microbial repression of Snhg9, while decreased expression of Snhg9 may be a result of the increased bacterial loads in the intestines of Rag1−/− mice (36). In contrast, depleting ILCs in Rag1−/− mice with the CD90.2 antibody (38) elevated Snhg9 expression (Fig. 5F), indicating a requirement for ILCs in microbial repression of Snhg9 expression. Similarly, Rag2−/−;Il2rg−/− mice, which lack immune cells (including ILCs) that dependent on the IL-2 receptor γ chain, showed increased expression of intestinal Snhg9 as compared to Rag1−/− mice (Fig. 5G).

To further assess the requirement for ILC3, we analyzed Snhg9 expression in Rorcgfp/gfp mice, which lack IL-22 producing cells including ILC3 (38). Rorcgfp/gfp mice showed increased Snhg9 expression as compared to wild-type littermates, and antibiotic depletion of gut microbiota abolished this difference (Fig. 5H), supporting the idea that ILC3s relay microbial signals that repress Snhg9 expression. In addition, supplementing Myd88−/− mice with either IL-22 or IL-23 repressed Snhg9 expression to the levels observed in conventional wild-type mice (Fig. 5I), consistent with the known involvement of these cytokines in the myeloid cell-ILC3 signaling circuit (2, 35–37). Further, Snhg9 expression was repressed by monocolonization of germ-free mice with bacterial species known to activate myeloid cell-ILC3 signaling (2, 35, 37), including Salmonella enterica Serovar Typhimurium (S. Typhimurium), a Gram-negative intestinal pathogen, and segmented filamentous bacteria (SFB), Gram-positive members of the intestinal microbiota (fig. S10, A and B). Thus, the microbiota suppresses Snhg9 expression through a myeloid cell-ILC3 signaling relay.

Discussion and limitations

This study shows that the gut microbiota promotes lipid absorption and metabolism by repressing expression of lncRNA Snhg9. This finding raises several additional questions for future study. First, are there specific components of the gut microbiota that promote lipid absorption through Snhg9 repression? We found that Snhg9 expression was selectively repressed by bacterial species that activate intestinal myeloid cell-ILC3 signaling, providing a potential clue. However, more studies are needed to determine what bacterial components or characteristics enable activation of myeloid cell-ILC3 signaling, and how microbial community composition impacts Snhg9 expression and lipid metabolism. Second, what is the evolutionary rationale for microbial regulation of lipid absorption? It is possible that intestinal lipid metabolism is linked to innate immune sensing of microbes in order to cope with an increased energy demand during colonization or infection, to provide lipid substrates or mediators that regulate intestinal immune cell development, or to enhance epithelial barrier function through reactive oxygen species production via lipid oxidation. Third, do these findings provide insight into the regulation of human lipid metabolism? SNHG9 is conserved and expressed in humans (19), raising the possibility that human SNHG9 functions in a similar manner.

Conclusion

In this study, we show that Snhg9 RNA regulates PPARγ activity by dissociating SIRT1 from CCAR2, providing insight into how a lncRNA regulates intestinal lipid metabolism. These findings advance our understanding of the complex epithelial cell networks that regulate lipid metabolism in response to microbial signals (2, 3)(fig. S11). Ultimately, these results could suggest strategies for treating metabolic disease by targeting Snhg9 and the microbiota.

Supplementary Material

Supplementary material

Acknowledgements

We thank N. Salinas for assistance with mouse experiments, A. Lemoff (UT Southwestern Proteomics Core) for assistance with mass spectrometry, C. Liang (UT Southwestern Microarray Core) for assistance with RNA sequencing, and A. Gewirtz (Georgia State University) for providing SFB-monocolonized mice.

Funding:

This work was supported by grant 2021YFA0805600 from the Ministry of Science and Technology of China (Y.W.); grants 32271213 (Y.W.) and 82188102 (T.L.) from the National Natural Science Foundation of China; grant 2020R01006 from the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (Y.W.); grant 226-2023-00134 from the Fundamental Research Funds for the Central Universities (Y.W.); NIH grants R01 DK070855 (L.V.H.) and R00 DK120897 (Z.K.); Welch Foundation Grant I-1874 (L.V.H.); the Walter M. and Helen D. Bader Center for Research on Arthritis and Autoimmune Diseases (L.V.H.); and the Howard Hughes Medical Institute (L.V.H.).

Footnotes

Competing interests: The authors declare no competing interests.

Data and materials availability:

RNA-seq data and 16S rRNA gene sequencing data are available from the Gene Expression Omnibus (GEO) repository under accession number GSE208020. All other data are available in the main text or the supplementary materials.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material
NIHMS1941736-supplement-Supplementary_material.pdf (3.3MB, pdf)

Data Availability Statement

RNA-seq data and 16S rRNA gene sequencing data are available from the Gene Expression Omnibus (GEO) repository under accession number GSE208020. All other data are available in the main text or the supplementary materials.

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