The gut microbiota promotes hepatic fatty acid desaturation and elongation in mice - PubMed (original) (raw)

doi: 10.1038/s41467-018-05767-4.

Gerhard Liebisch 3, Thomas Clavel 4 5, Dirk Haller 5 6, Gabriele Hörmannsperger 5 6, Hongsup Yoon 5 6, Daniela Kolmeder 7, Alexander Sigruener 3, Sabrina Krautbauer 3, Claudine Seeliger 7, Alexandra Ganzha 7, Sabine Schweizer 7, Rosalie Morisset 7, Till Strowig 8, Hannelore Daniel 7, Dominic Helm 9, Bernhard Küster 9, Jan Krumsiek 10 11 12, Josef Ecker 13

Affiliations

The gut microbiota promotes hepatic fatty acid desaturation and elongation in mice

Alida Kindt et al. Nat Commun. 2018.

Abstract

Interactions between the gut microbial ecosystem and host lipid homeostasis are highly relevant to host physiology and metabolic diseases. We present a comprehensive multi-omics view of the effect of intestinal microbial colonization on hepatic lipid metabolism, integrating transcriptomic, proteomic, phosphoproteomic, and lipidomic analyses of liver and plasma samples from germfree and specific pathogen-free mice. Microbes induce monounsaturated fatty acid generation by stearoyl-CoA desaturase 1 and polyunsaturated fatty acid elongation by fatty acid elongase 5, leading to significant alterations in glycerophospholipid acyl-chain profiles. A composite classification score calculated from the observed alterations in fatty acid profiles in germfree mice clearly differentiates antibiotic-treated mice from untreated controls with high sensitivity. Mechanistic investigations reveal that acetate originating from gut microbial degradation of dietary fiber serves as precursor for hepatic synthesis of C16 and C18 fatty acids and their related glycerophospholipid species that are also released into the circulation.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1

Fig. 1

Transcriptomic, proteomic, and phosphoproteomic analyses of liver samples from SPF and GF mice. a Transcriptome data analyzed by _t_-tests, (n = 6/6). b KEGG pathway enrichment on transcriptome data. c Proteome data, analyzed by _t_-tests, (n = 5/5). d KEGG pathway enrichment in proteome data. e Transcriptome–proteome correlation. f GO enrichment analysis for biological processes of Q2 from e. g Phosphoproteome analyzed by _t_-tests, (n = 5/5). h Proteome–phosphoproteome comparison. i Venn diagram showing the overlap of detected genes, proteins, and phosphoproteins. e, h Q1, Q2, Q3, and Q4 mark the quadrants of the correlation plots; directed _p_-values are defined as–log10 (p) times the direction of the effect. b, d, f Red dots indicate lipid-related pathways. FDR: false discovery rate, GF: germfree, SPF: specific pathogen-free

Fig. 2

Fig. 2

Quantitative lipidome analyses of liver and plasma from GF and SPF mice. Data from experiment 1 are shown (n = 6/6). Candidates verified in experiment 2 (SPF: n = 14; GF: n = 12) are displayed in blue (high in SPF) or orange (high in GF). a Total fatty acids (FA) in liver. b PC species in liver. c Total FA in plasma. d PC species in plasma. a–d Volcano plots: significance and log2 fold change in individual lipid species; boxplots: concentrations of saturated (SA), monounsaturated (MU), and polyunsaturated (PU) lipid species in GF and SPF mice; barplots: molecular lipid species profile of GF animals. In boxplots the thick lines represent the medians, the upper and lower lines of the boxes show the 25 and 75% quartiles and the whiskers are 1.5 times the interquartile range of the data. Error bars in the bar plots show the standard deviation. e Lipid class composition of liver samples, experiment 1. f Sphingolipid composition of liver samples, experiment 1. g Lipid class composition of plasma, experiment 1. GF: germfree, PC: phosphatidylcholine (diacyl), PC: O phosphatidylcholine (alkyl-acyl), SPF: specific pathogen-free, for other abbreviations, see Table 1

Fig. 3

Fig. 3

Reconstruction of FA metabolic pathways from multi-omics data and FA-glycerophospholipid species correlations. a Cellular de novo synthesis of palmitate, elongation to saturated and desaturation to monounsaturated FA. b Metabolism of _n_-3 and _n_-6 PUFA. (I–III) indicate the key processes altered in GF compared to SPF mice. c Transcription factors and regulators controlling a and b. d Correlation of product to precursor ratios with the appropriate gene or protein expression for reactions (II) and (III). e Correlation of FA species ratios with corresponding PC or LPC ratios for reactions (II) and (III). f Legend for the figure highlighting the strength and direction of the association for all molecular entities shown. FA: fatty acid, LPC: Lyso-PC, MUFA: monounsaturated fatty acids, PC: phosphatidylcholine, PUFA: polyunsaturated fatty acids, SAFA: saturated fatty acids, for other abbreviations, see Table 1

Fig. 4

Fig. 4

Effect of antibiotics on hepatic FA metabolism and association of cecal microbial species with FA metabolic consequences. SPF mice were treated with ampicillin (A, n = 6), metronidazole (M, n = 5), vancomycin (V, n = 6) or a combination of V and M (VM, n = 6). a Classification sensitivity and specificity of the calculated score separating GF and antibiotic-treated from the SPF mice. b Score for GF and SPF mice (GF: n = 5, SPF: n = 5) from a third experiment to validate the classification score. c Score for antibiotic-treated and untreated mice. d Relative mRNA expression of Fasn, Scd1, and Elovl5, mean and standard deviation are shown. e Alpha-diversity analysis shown as richness counts, and f Shannon effective counts. Samples from A-treated mice reproducibly generated too few sequences and thus could not be included in 16 S rRNA gene amplicon analysis. g Multidimensional scaling showing differences in the phylogenetic makeup of microbiota between samples (β-diversity) based on general UniFrac distances. h Microbiota composition at the phylum level. i-k Most significantly different OTUs after a Kruskal–Wallis analysis in untreated compared to antibiotics treated mice. N.S.: not significant, OTU: operational taxonomic unit. In boxplots the thick lines represent the medians, the upper and lower lines of the boxes show the 25 and 75% quartiles and the whiskers are 1.5 times the interquartile range of the data. In barplots the error bars show the standard deviation

Fig. 5

Fig. 5

Fiber-derived FA 2:0 is precursor for hepatic synthesis of fatty acids and glycerophospholipids. SPF mice were supplemented with 13C-FA 2:0 via oral gavage with the indicated amount (n = 3/group), samples were taken 4 h after gavage. a Isotopologue distribution of FA 16:0 in liver and b plasma. c Fractional alterations (%) of M0, M1–M6 isotopologues relative to unlabeled control in liver and e plasma. d Fraction of de novo synthesized FA 16:0 in liver. SPF mice were treated for 2 days with VM (TP2), additional 2 (TP4) or 10 days (TP14) without antibiotics (n = 6/TP); control SPF mice did not receive antibiotics (n = 6/TP). f Alpha-diversity analysis shown as richness counts and g Shannon effective counts. h Multidimensional scaling showing differences in the phylogenetic makeup of microbiota between samples (β-diversity) based on general UniFrac distances. i Composition of major phylas determined in cecum content. j Portal vein SCFA levels. k Levels of selected MUFA, PUFA and MUPC in liver and l plasma. AB: antibiotics, Ac: acetate, VM: vancomycin in combination with metronidazole. Red dots and error bars in i–l indicate a significant difference (p < 0.05) between the control and VM group. m Experimental setup for the antibiotics experiment. In boxplots the thick lines represent the medians, the upper and lower lines of the boxes show the 25% and 75% quartiles and the whiskers are 1.5 times the interquartile range of the data. In barplots the error bars show the standard deviations. The dot plots in i–l show the mean and the standard deviation per group and condition

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