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. 2017 Jul 17;9(7):1698-1720.
doi: 10.18632/aging.101262.

Aging and sarcopenia associate with specific interactions between gut microbes, serum biomarkers and host physiology in rats

Jay Siddharth #  1 , Anirikh Chakrabarti #  1 , Alice Pannérec #  1 , Sonia Karaz  1 , Delphine Morin-Rivron  1 , Mojgan Masoodi  1 , Jerome N Feige  1 , Scott James Parkinson  1
Affiliations

Aging and sarcopenia associate with specific interactions between gut microbes, serum biomarkers and host physiology in rats

Jay Siddharth et al. Aging (Albany NY). .

Abstract

The microbiome has been demonstrated to play an integral role in the maintenance of many aspects of health that are also associated with aging. In order to identify areas of potential exploration and intervention, we simultaneously characterized age-related alterations in gut microbiome, muscle physiology and serum proteomic and lipidomic profiles in aged rats to define an integrated signature of the aging phenotype. We demonstrate that aging skews the composition of the gut microbiome, in particular by altering the Sutterella to Barneseilla ratio, and alters the metabolic potential of intestinal bacteria. Age-related changes of the gut microbiome were associated with the physiological decline of musculoskeletal function, and with molecular markers of nutrient processing/availability, and inflammatory/immune status in aged versus adult rats. Altogether, our study highlights that aging leads to a complex interplay between the microbiome and host physiology, and provides candidate microbial species to target physical and metabolic decline during aging by modulating gut microbial ecology.

Keywords: aging; lipidomics; microbiome; muscle physiology; proteomics; sarcopenia.

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Conflict of interest statement

CONFLICTS OF INTEREST

All authors are employees of the Nestle Institute of Health Sciences SA.

Figures

Figure 1
Figure 1. Gut microbial diversity in aging rats
(A) Study design highlighting the experimental plan and the measured parameters. (B) NMDS plot of OTUs using Jclass calculator for the 16S data. The points show a distinct cloud for age group 18M (green circles), while ages 8M (orange circles) and 24M (blue circles) show more overlap. (C) Overlap of observed OTUs between the different age groups. (D) Comparison of statistically different OTUs across different age groups and classification into categories. Vignette: Categorization/feature based classification of members based on statistical increase/decrease across different age windows. Abbreviations: SU – Statistically Up, SD – Statistically Down, 8M – 8 months, 18M – 18 months and 24M – 24 months.
Figure 2
Figure 2. Inter-species correlations in the aging rat microbiome
(A) Indicator analysis for different age groups. Indicator OTUs with P values < 0.001 for different age groups – orange for 8M, green for 18M and blue for 24M are shown. Categorization of the indicator OTUs and their corresponding indicator values are also indicated. (B) Correlations between the statistically different OTUs. Only correlations values > 0.6 and < -0.6 are shown. The OTUs are sorted and plotted anticlockwise starting at 0 degrees, based on the number of correlation (statistically relevant and with R values > 0.6 or < -0.6) across the entire set. The OTU classified as Clostridium XIVa (highlighted in red text) at the genus level is the most correlated while the OTU classified as Acidaminobacter (highlighted in blue text) is the least correlated. Details of one to one OTU correlations are in Supplementary Figure 1 and Supplementary Materials. Abbreviations: SU – Statistically Up, SD – Statistically Down, Cat – Category, 8M – 8 months, 18M – 18 months and 24M – 24 months.
Figure 3
Figure 3. Correlations between microbiome and host physiology
(A) Age group comparisons for statistical differences of measured physiological parameters, body weight (g), lean mass (%), fat mass (%), gastrocnemius muscle mass (mg/g), sciatic response amplitude (mV), triceps muscle mass (mg/g), radial response amplitude (mV), heart muscle mass (mg/g), Vitamin B12 total (pmol/L) and folate levels (nmol/L). (B) Correlations between statistically different OTUs and physiological measurements. Correlations shown are after FDR correction with Q values < 0.05. Abbreviations: SU – Statistically Up, SD – Statistically Down, Cat – Category, 8M – 8 months, 18M – 18 months and 24M – 24 months.
Figure 4
Figure 4. Analysis of predicted Metagenomic Functional Content (MFC) obtained from PICRUSt
(A) Statistically different MFC's for each comparison (8M vs 18M, 8M vs 24M, 18M vs 24M) and their overlaps are depicted. (B) Comparison of the cumulatively unique statistically different MFC's identified in panel A is depicted across each of the studied comparisons. Red shaded boxes indicates increase in MFC levels, Grey boxes indicate no statistical difference and Blue boxes indicates decrease in MFC levels. The MFC's are sorted according to different categories as explained in panel C. (C) Explanation of the categories or feature based classes for the statistically different MFC's. (D) Membership of the statistically different MFC's in each category in different pathways. Correlations shown are after FDR correction with Q values < 0.05. Abbreviations: SU – Statistically Up, SD – Statistically Down, Cat – Category, 8M – 8 months, 18M – 18 months and 24M – 24 months.
Figure 5
Figure 5. Comparative analysis of proteomics data from the serum of aging rats (8M, 18M and 24M) obtained using aptamer-based detection method
(A) Based on the pattern of statistically significant increase (SU) and statistically significant decrease (SD) between the different ages, the proteins were classified into categories. Protein full names, Entrez Gene Names, UniProt IDs and corresponding categorical classifications of the statistically different proteins identified in the serum of the aging rats. Abbreviations: SU – Statistically Up, SD – Statistically Down, Cat – Category, 8M – 8 months, 18M – 18 months and 24M – 24 months. (B) Correlations between statistically different serum proteins and physiological measurements. Correlations shown are after FDR correction with Q values < 0.05.
Figure 6
Figure 6. Serum Lipidomic analysis of aged rats
(A) NMDS plot of the lipid species measured across all the samples. Overall, we see a separation between 18M and 24M but not between 8M-18M and 8M-24M. (B) Statistically different lipid species and demarcation of up/downregulation in different comparisons. (C) Correlations between lipid species and measured physiological parameters. (D) Correlations between lipid species and OTUs. (E) Correlations between lipid species and proteins. Only statistically significant correlations are shown. Correlations shown are after FDR correction with Q values < 0.05. Abbreviations: SU – Statistically Up, SD – Statistically Down, Cat – Category, 8M – 8 months, 18M – 18 months and 24M – 24 months.
Figure 7
Figure 7. Summary of host-microbial interactions in Aging and Sarcopenia
(A) Different aspects of host physiology impacted directly or indirectly by gut microbiome include nutrients, musculoskeletal and inflammation/immunity. (B) Illustration highlighting the shift within the microbial community in terms of members of Sutterella and Barnesiella with aging. Barnesiella is positively correlated to Clostridium XIVa and Papillibacter, which are all similarly correlated to aging phenotypes, specifically at the level of Lean Mass, Vitamin B12 levels, Lipid metabolism and Gastrocnemius muscle mass.
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