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. 2016 Feb 16:6:20127.
doi: 10.1038/srep20127.

Hepatic circadian clock oscillators and nuclear receptors integrate microbiome-derived signals

Alexandra Montagner  1 , Agata Korecka  2 , Arnaud Polizzi  1 , Yannick Lippi  1 , Yuna Blum  3 , Cécile Canlet  1 , Marie Tremblay-Franco  1 , Amandine Gautier-Stein  4 , Rémy Burcelin  5 , Yi-Chun Yen  6   7 , Hyunsoo Shawn Je  6   7 , Maha Al-Asmakh, Gilles Mithieux  4 , Velmurugesan Arulampalam  2 , Sandrine Lagarrigue  8   9   10 , Hervé Guillou  1 , Sven Pettersson  2   11   12 , Walter Wahli  1   11   13
Affiliations

Hepatic circadian clock oscillators and nuclear receptors integrate microbiome-derived signals

Alexandra Montagner et al. Sci Rep. .

Erratum in

Abstract

The liver is a key organ of metabolic homeostasis with functions that oscillate in response to food intake. Although liver and gut microbiome crosstalk has been reported, microbiome-mediated effects on peripheral circadian clocks and their output genes are less well known. Here, we report that germ-free (GF) mice display altered daily oscillation of clock gene expression with a concomitant change in the expression of clock output regulators. Mice exposed to microbes typically exhibit characterized activities of nuclear receptors, some of which (PPARα, LXRβ) regulate specific liver gene expression networks, but these activities are profoundly changed in GF mice. These alterations in microbiome-sensitive gene expression patterns are associated with daily alterations in lipid, glucose, and xenobiotic metabolism, protein turnover, and redox balance, as revealed by hepatic metabolome analyses. Moreover, at the systemic level, daily changes in the abundance of biomarkers such as HDL cholesterol, free fatty acids, FGF21, bilirubin, and lactate depend on the microbiome. Altogether, our results indicate that the microbiome is required for integration of liver clock oscillations that tune output activators and their effectors, thereby regulating metabolic gene expression for optimal liver function.

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Figures

Figure 1
Figure 1. Gut microbiome influences hepatic core clock and clock-controlled gene expression.
(a) Hepatic mRNA levels were measured at ZT0, ZT6, ZT12, and ZT18 by real-time quantitative PCR. Data presented are the mean ± SEM of relative expression values measured in germ-free (GF; black line) and specific pathogen–free (SPF; black dotted line) mice (n = 5 animals/group/ZT time point). Statistical analyses were performed with Student’s t-test at each time point between SPF and GF mice. In case of unequal variances between the two group samples, the Wehch’s two sample t-test was used. P values were corrected for multiple testing using BH procedure and FDR < 5% threshold is considered for significant difference. ***FDR < 0.005, **FDR < 0.01, *FDR < 0.05. (b) Graphical representation of acrophase (mode of expression) of hepatic core clock genes estimated by JTK_Cycle analysis for SPF (red cross) and GF (green cross) mice. The arrow indicates the shift for each gene expression tested between SPF and GF condition.
Figure 2
Figure 2. Gut microbiome affects the hepatic transcriptome and the daily hepatic gene expression.
(a) Hepatic transcriptome was analysed at ZT0, ZT6, ZT12, and ZT18 in GF and SPF mice using Affymetrix Mouse Gene 2.0 ST arrays. Each group comprised 5 mice (a total of 40 animals). A model was fitted using the limma lmFit function. Probes with FDR < 1% (BH procedure) were considered significantly regulated. A hierarchical clustering was obtained from individual’s expression values of 4429 significantly regulated ProbeSets overall comparisons using 1-Pearson correlation coefficient as distance and the Ward’s criterion for agglomeration. Red and green colors presented in the heatmap indicate values above and below the mean centred and scaled expression values, respectively. Black indicates values close to the mean. The ProbeSets clustering and individuals clustering are illustrated on left panel dendrogram and top panel dendrogram, respectively. (b) Gene expression profiles of the 9 ProbeSets clusters. Mean expression values centred and scaled (average Z-score) are plotted for each sanitary status along the Zeitgeber time (ZT) (GF, black line and SPF, black dotted line). Data are scaled so that values for SPF mice at ZT0 equal zero (reference group). Error bars represent the 95% confidence interval (n = 5 animals/group/ZT time point). (c) Rhythmicity analysis (JTK_Cycle) on ProbeSets. For each ProbeSets cluster, the rhythmicity classes distribution is illustrated as pie charts. ProbeSets are categorized into 6 classes according to rhythmicity significance and parameters (period and phase lag) results regarding SPF and GF mice. Class 1 (dark grey): significant rhythmic ProbeSets with identical rhythmicity parameters for both SPF and GF mice; Class 2 (dark blue): significant ProbeSets in both SPF and GF mice but with a phase lag for GF mice; Class 3 (blue): significant rhythmic ProbeSets in both SPF and GF mice but with a different period for GF mice; Class 4 (light blue): significant rhythmic ProbeSets in SPF but not in GF mice; Class 5 (beige): significant rhythmic ProbeSets in GF but not in SPF mice; Class 6 (light grey): no significant rhythmic ProbeSets neither in SPF nor in GF mice.
Figure 3
Figure 3. Daily expression of fatty acid, xenobiotic, sterol, and glucose sensors and their target genes under SPF and GF conditions.
Hepatic mRNA levels of transcription factors and their respective target genes: (a) Pxr and Cyp3a11, (b) Car and Cyp2b10, (c) Lxrα and Fasn, (d) Pparα, Fgf21, and Cyp4a14, and (e) Chrebp α and β and Lpk measured at ZT0, ZT6, ZT12, and ZT18 by real-time quantitative PCR. Data are the mean ± SEM of relative expression values measured in GF and SPF mice (n = 5 animals/group/ZT time point). Statistical analyses were performed with Student’s t-test at each time point between SPF and GF mice. In case of unequal variances between the two samples, the Wehch’s two sample t-test was used. P values were corrected for multiple testing using BH procedure and FDR < 5% threshold is considered for significant difference. ***FDR < 0.005, **FDR < 0.01, *FDR < 0.05.
Figure 4
Figure 4. Gut microbiome strongly impacts hepatic gene expression correlation networks of LXRβ and PPARα.
Networks of the 50 genes showing the highest absolute correlation with each gene of interest, Pxr, Car, Lxrβ and α, Chrebp, and Pparα, β, and γ (red node) under SPF condition (n = 20 mice) are presented as circle plots. The edges corresponding to significant correlations are represented (Bonferroni-adjusted P value < 5%). Another network circle plot based on these 51 genes is then presented in GF mice. Magenta nodes correspond to genes significantly correlated with the gene of interest. The thickness of the edges reflects the absolute correlation, and red/blue were used for positive/negative correlations, respectively. The size of each node indicates the connectivity in the circle plots.
Figure 5
Figure 5. Intestinal gluconeogenesis does not impact liver clock, PPARα, LXR, ChREBP, CAR, and PXR activities.
Hepatic mRNA levels of (a) Rev-erbα, Bmal1, Per2, (b) Cyp4a14 and Fgf21, (c) Fasn and Lpk, and (d) Cyp2b10 and Cyp3a11 were measured at ZT6 and ZT18 by real-time quantitative PCR. Data are the mean ± SEM of values measured in wild-type mice (WT) and in mice with intestine-specific deletion of G6Pase (I-G6PcKO) (n = 6 animals/genotype/ZT time point). Statistical analyses were performed with Student’s t-test. In case of unequal variances between the two samples, the Wehch’s two sample t-test was used. P values were corrected for multiple testing using BH procedure and FDR < 5% threshold is considered for significant difference. * represents difference between ZT time points within a genotype; ***FDR < 0.005, **FDR < 0.01, *FDR < 0.05. #represents difference between genotype for a ZT time point; #FDR < 0.05.
Figure 6
Figure 6. Gut microbiome influences hepatic metabolites and plasma biomarkers.
(a) Two-dimensional PLS-DA scores plot of liver extract integrated 1H-NMR spectra. Each dot represents an observation (animal), projected onto first (horizontal axis) and second (vertical axis) PLS-DA variables. Time points are shown in different colors: ZT0 in black, ZT6 in blue, ZT12 in red and ZT18 in green. The black ellipse determines the 95% confidence interval, which is drawn using Hotelling’s T2 statistic. Left: SPF mice: A = 5, R2 = 94.6%, Q2 = 0.654; Right GF mice: A = 4, R2 = 87.5%, Q2 = 0.616. (b) Periodic changes in metabolites detected by NMR profiling of liver metabolites from GF and SPF mice are presented as a heatmap. Red and green indicate values above and below the mean, respectively. Black indicates values close to the mean. Individual values for each group are represented in the heatmap, and the hierarchical clustering was obtained from individual values using 1-Pearson correlation coefficient as distance and the Ward’s criterion for agglomeration. (c) Plasma biochemistry in GF (black line) and SPF (black dotted line) mice. Data are the mean ± SEM (n = 5 animals/group/time point) Statistical analyses were performed with Student’s t-test at each time point between SPF and GF mice. In case of unequal variances between the two samples, the Wehch’s two sample t-test was used. P values were corrected for multiple testing using BH procedure and FDR < 5% threshold is considered for significant difference. ***FDR < 0.005, **FDR < 0.01, *FDR < 0.05.
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