Characterization of the serum and liver proteomes in gut-microbiota-lacking mice

Abstract

Current nutrition research is focusing on health promotion, disease prevention, and performance improvement for individuals and communities around the world. The humans with required nutritional ingredients depend on both how well the individual is provided with balanced foods and what state of gut microbiota the host has. Studying the mutually beneficial relationships between gut microbiome and host is an increasing attention in biomedical science. The purpose of this study is to understand the role of gut microbiota and to study interactions between gut microbiota and host. In this study, we used a shotgun proteomic approach to reveal the serum and liver proteomes in gut-microbiota-lacking mice. For serum, 15 and 8 proteins were uniquely detected in specific-pathogen-free (SPF) and germ-free (GF) mice, respectively, as well as the 3 and 20 proteins were significantly increased and decreased, respectively, in GF mice compared to SPF mice. Among the proteins of the serum, major urinary protein 1 (MUP-1) of GF mice was significantly decreased compared to SPF mice. In addition, MUP-1 expression is primarily regulated by testosterone. Lacking in gut flora has been implicated in many adverse effects, and now we have found its pathogenic root maybe gut bacteria can regulate the sex-hormone testosterone levels. In the liver, 8 and 22 proteins were uniquely detected in GF mice and SPF mice, respectively, as well as the 14 and 30 proteins were significantly increased and decreased, respectively, in GF mice compared to SPF mice. Furthermore, ingenuity pathway analysis (IPA) indicated that gut microbiota influence the host in cancer, organismal injury and abnormalities, respiratory disease; cell cycle, cellular movement and tissue development; cardiovascular disease, reproductive system disease; and lipid metabolism, molecular transport and small molecule biochemistry. Our findings provide more detailed information of the role of gut microbiota and will be useful to help study gut bacteria and disease prevention.

Keywords: Biomarker; Endurance swimming; Exercise; Germ-free; Gut flora; Metabolism.

Figure 1

Effects of SPF and GF mice on the (A) exhaustive swimming time of exercise performance, and serum levels of the (B) glucose, (B) TG, (D) glycogen, (E) AST, (F) ALT, (G) CK, and (E) ALP after an acute exercise challenge. Data were mean ± SEM (n = 12). Different letters indicated significant difference at p < 0.05 by one-way ANOVA.

Figure 2

Venn diagrams comparing the common, only detected, or overlap in significant regulated proteins for serum samples from SPF and GF mice. (A) There was a 91% common and a 9% significant regulated proteins between two groups. When the 91% un-changed proteins were removed and the other significant regulated proteins were normalized to 100%, there was a 17.4% unique proteins in GF mice, a 32.6% unique proteins in SPF mice, and an 50% overlap in significant regulated proteins between SPF mice and GF mice. (B) The percent distributions for the 8 proteins were only detected in GF mice. (C) The percent distributions for the 15 proteins were only detected in SPF mice. (D) The percent distributions for the 3 proteins were significantly increased in GF mice compared to SPF mice. (E) The percent distributions for the 20 proteins were significantly decreased in GF mice compared to SPF mice.

Figure 3

Effects of SPF and GF mice on MUP-1 protein expression and testosterone level. (A) Protein expression of MUP-1 in the serum of SPF and GF mice as measured by western blot. (B) Testosterone expression in the serum of SPF and GF mice as measured by an automatic analyzer (Hitachi 7060, Hitachi, Tokyo, Japan).

Figure 4

Venn diagrams comparing the common, only detected, or overlap in significant regulated proteins for liver samples from SPF and GF mice. (A) There was a 95% common and a 5% significant regulated proteins between two groups. When the 95% un-changed proteins were removed and the other significant regulated proteins were normalized to 100%, there was a 10.8% unique proteins in GF mice, a 29.7% unique proteins in SPF mice, and an 59.9% overlap in significant regulated proteins between SPF mice and GF mice. (B) The percent distributions for the 8 proteins were only detected in GF mice. (C) The percent distributions for the 22 proteins were only detected in SPF mice. (D) The percent distributions for the 14 proteins were significantly increased in GF mice compared to SPF mice. (E) The percent distributions for the 30 proteins were significantly decreased in GF mice compared to SPF mice.

Figure 5

Classification of the differentially expressed proteins in liver tissues were identified from SPF and GF mice. (A) Pie charts representing the distribution of the differential proteins in SPF (left panel) and GF (right panel) mice according to their cellular component. (B) Pie charts representing the distribution of the differential proteins in SPF (left panel) and GF (right panel) mice according to their biological process. (C) Pie charts representing the distribution of the differential proteins in SPF (left panel) and GF (right panel) mice according to their molecular function.