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. 2022 Aug 25;23(17):9658.
doi: 10.3390/ijms23179658.

Gut Microbiome and Metabolome Modulation by Maternal High-Fat Diet and Thermogenic Challenge

Henry A Paz  1   2 , Anna-Claire Pilkington  1   2 , Ying Zhong  1   2 , Sree V Chintapalli  1   2 , James Sikes  1 , Renny S Lan  1   2 , Kartik Shankar  3 , Umesh D Wankhade  1   2
Affiliations

Gut Microbiome and Metabolome Modulation by Maternal High-Fat Diet and Thermogenic Challenge

Henry A Paz et al. Int J Mol Sci. .

Abstract

The gut microbiota plays a critical role in energy homeostasis and its dysbiosis is associated with obesity. Maternal high-fat diet (HFD) and β-adrenergic stimuli alter the gut microbiota independently; however, their collective regulation is not clear. To investigate the combined effect of these factors on offspring microbiota, 20-week-old offspring from control diet (17% fat)- or HFD (45% fat)-fed dams received an injection of either vehicle or β3-adrenergic agonist CL316,243 (CL) for 7 days and then cecal contents were collected for bacterial community profiling. In a follow-up study, a separate group of mice were exposed to either 8 °C or 30 °C temperature for 7 days and blood serum and cecal contents were used for metabolome profiling. Both maternal diet and CL modulated the gut bacterial community structure and predicted functional profiles. Particularly, maternal HFD and CL increased the Firmicutes/Bacteroidetes ratio. In mice exposed to different temperatures, the metabolome profiles clustered by treatment in both the cecum and serum. Identified metabolites were enriched in sphingolipid and amino acid metabolism in the cecum and in lipid and energy metabolism in the serum. In summary, maternal HFD altered offspring's response to CL and altered microbial composition and function. An independent experiment supported the effect of thermogenic challenge on the bacterial function through metabolome change.

Keywords: CL316,243; cold exposure; gut microbiota; maternal high fat diet; metabolome; thermogenesis.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Overview of the conducted studies. In Study 1, CCV = offspring from control diet-fed dams treated with vehicle (n = 5), CCCL = offspring from control diet-fed dams treated with CL316,243 (n = 5), HCV = offspring from high fat diet-fed dams treated with vehicle (n = 4), HCCL = offspring from high fat diet-fed dams treated with CL316,243 (n = 4). In Study 2, mice were exposed to either 8 °C (n = 5) or 30 °C (n = 5) for 7 days.
Figure 2
Figure 2
Murine gut bacterial community composition driven by maternal diet and CL316,243 challenge. (A) Genus-level principal coordinate analysis plot based on Bray–Curtis dissimilarities. Axes show the first two principal components and their corresponding percentage of variance explained. Ellipses define the 95% confidence level. Two-way PERMANOVA was used to determine the main effects of maternal diet, CL316,243 challenge and their interaction (statistically significant p-values are bolded). (B) Hierarchical dendrogram based on Ward’s method displaying clustering by treatment. CCV = offspring of control diet-fed dams treated with vehicle (n = 5); CCCL = offspring of control diet-fed dams treated with CL316,243 (n = 5); HCV = offspring of high-fat diet-fed dams treated with vehicle (n = 4); HCCL = offspring of high-fat diet-fed dams treated with CL316,243 (n = 4).
Figure 3
Figure 3
Phylum-level differences across treatments. CCV = offspring of control diet-fed dams treated with vehicle (n = 5); CCCL = offspring of control diet-fed dams treated with CL316,243 (n = 5); HCV = offspring of high-fat diet-fed dams treated with vehicle (n = 4); HCCL = offspring of high-fat diet-fed dams treated with CL316,243 (n = 4). Nonparametric multiple comparisons (* p < 0.05, *** p < 0.001).
Figure 4
Figure 4
Family- and genus-level differences across treatments. Relative abundance of specific (A) families and (B) genera. CCV = offspring of control diet-fed dams treated with vehicle (n = 5); CCCL = offspring of control diet-fed dams treated with CL316,243 (n = 5); HCV = offspring of high-fat diet-fed dams treated with vehicle (n = 4); HCCL = offspring of high-fat diet-fed dams treated with CL316,243 (n = 4). Nonparametric multiple comparisons (*** p < 0.001).
Figure 5
Figure 5
Distinct PICRUSt-predicted functional profiles in the mice gut driven by maternal diet and CL316,243 challenge. (A) Principal coordinate analysis plot based on Bray-Curtis dissimilarities generated from the pathways relative abundance. Axes show the first two principal components and their corresponding percentage of variance explained. Ellipses define the 95% confidence level. (B) Heatmap of pathways related to carbohydrate, energy, and lipid metabolism. (C) Significantly different metabolic-related pathways across treatments. Statistically significant p-values are bolded. CCV = offspring from control diet-fed dams treated with vehicle (n = 5); CCCL = offspring from control diet-fed dams treated with CL316,243 (n = 5); HCV = offspring from high fat diet-fed dams treated with vehicle (n = 4); HCCL = offspring from high fat diet-fed dams treated with CL316,243 (n = 4). Nonparametric multiple comparisons (*** p < 0.001).
Figure 6
Figure 6
Beta diversity and phylum-level differences from the cecal bacterial community of mice exposed to different temperatures. (A) Genus-level principal coordinate analysis plot based on Bray-Curtis dissimilarities. Axes show the first two principal components and their corresponding percentage of variance explained. Ellipses define the 95% confidence level. (B) Hierarchical dendrogram based on Ward’s method displaying clustering by temperature. (C) Phylum-level classification of the cecal bacterial community. Mice exposed to either 8 °C (n = 5) or 30 °C (n = 5) for 7 days. Wilcoxon rank sum test (* p < 0.05).
Figure 7
Figure 7
Metabolome profiles of the cecum and serum in mice exposed to different temperatures. Principal coordinate analysis plot based on Bray-Curtis dissimilarities including all metabolite features and showing clustering by temperature in the (A) cecum (n = 5) and (B) serum (n = 5). Ellipses define the 95% confidence level. Heatmaps from identified metabolite features in the (C) cecum and (D) serum.
Figure 8
Figure 8
Metabolic pathways (top 15) in the (A) cecum and (B) serum associated with enriched metabolites in mice exposed to cold (8 °C) or thermoneutral (30 °C) temperatures. Quantitative enrichment analysis was performed on identified metabolites in the cecum (n = 108) and serum (n = 75) using MetaboAnalyst v5.0.
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References

    1. Rowland I., Gibson G., Heinken A., Scott K., Swann J., Thiele I., Tuohy K. Gut microbiota functions: Metabolism of nutrients and other food components. Eur. J. Nutr. 2018;57:1–24. doi: 10.1007/s00394-017-1445-8. - DOI - PMC - PubMed
    1. Heiss C.N., Olofsson L.E. Gut Microbiota-Dependent Modulation of Energy Metabolism. J. Innate Immun. 2018;10:163–171. doi: 10.1159/000481519. - DOI - PMC - PubMed
    1. Aron-Wisnewsky J., Warmbrunn M.V., Nieuwdorp M., Clément K. Metabolism and Metabolic Disorders and the Microbiome: The Intestinal Microbiota Associated with Obesity, Lipid Metabolism, and Metabolic Health—Pathophysiology and Therapeutic Strategies. Gastroenterology. 2021;160:573–599. doi: 10.1053/j.gastro.2020.10.057. - DOI - PubMed
    1. David L.A., Maurice C.F., Carmody R.N., Gootenberg D.B., Button J.E., Wolfe B.E., Ling A.V., Devlin A.S., Varma Y., Fischbach M.A., et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–563. doi: 10.1038/nature12820. - DOI - PMC - PubMed
    1. Carmody R.N., Gerber G.K., Luevano J.M., Jr., Gatti D.M., Somes L., Svenson K.L., Turnbaugh P.J. Diet Dominates Host Genotype in Shaping the Murine Gut Microbiota. Cell Host Microbe. 2015;17:72–84. doi: 10.1016/j.chom.2014.11.010. - DOI - PMC - PubMed

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