Previously, we investigated the susceptibility of muscles in rats to age-related decline [ 20 ]. In the current study, we characterized the same rats to determine a metabolic fingerprint of the aging phenotype and investigated the associated alterations of the microbiome as a contributor to age-related physiology and sarcopenia. We identified age-specific features in the gut microbial communities for 8, 18 and 24-month rats and age-related alterations of the microbial metabolic potential. At the physiological level, we observed decreased gastrocnemius muscle mass and sciatic response amplitude that correlated with vitamin B12 levels and lipid metabolism. We identified several known and novel associations between gut microbiota and physiological parameters in aging. In summary, the data pointed towards changes in nutrient processing, musculoskeletal status and inflammatory/immune status with aging. This enabled us to define a consensus phenotype of age-related alterations in gut microbiome, muscle physiology, and biochemical protein and lipid markers of aging.

The microbiome has been implicated in several aspects associated with aging including rate of aging [ 15 ], inflammation [ 16 ], immunity [ 17 ], and muscle status [ 18 ]. Previously, it was shown that there is a significant relationship between age and the taxonomic and altered metabolic potential of the microbiome in mice [ 8 ]. Associations between microbiome, age and pro-inflammatory status (serum MCP-1 status) mice have also been identified [ 19 ]. However, an integrated view of age-related alterations in gut microbiome, muscle physiology, and biochemical protein and lipid markers would help to define areas of further investigation and potential intervention to support ‘healthy aging’.

The concept of 'healthy aging' addresses the need to combat the economic burden of an aging population, its physiological and social consequences, and subsequent reduced quality of life. Microbes and multicellular organisms have co-evolved over millennia. The microbial counterpart has been shaped in terms of their composition and metabolic potential by taking cues from the state of their hosts’ physiology and immediate external environments [ 1 ]. Aging is characterized by alterations in distinct sets of host functions including cellular function (leading to oxidative stress and senescence) and a pathophysiological decline of most organs and metabolic homeostasis [ 2 - 4 ]. In particular, there is decline of the musculoskeletal system [ 5 , 6 ], which contributes to alterations in the quality of life. The gut microbiota may directly or indirectly impact several age-related aspects [ 7 - 12 ] and is an under-explored area of investigation [ 13 , 14 ].

Results

In this study we sought to investigate the association of age and sarcopenia (AAS)-related gut microbial changes with host physiology and identify the potential molecular mechanisms underlying these associations to evaluate the potential of targeting the microbiome in AAS-related health. Using aged Wistar rats of ages 8, 18 and 24 months (subsequently referred to as 8M for adult, 18M for adult-pre-sarcopenic and 24M for adult-sarcopenic respectively (similar framework as our previous work [20]), we determined the ecological states of the microbiome at the various ages, identified potential metabolic functions of these states and integrated this analysis with the biochemical and physiological phenotypes (Figure 1A).

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.



Gut microbial diversity in aged rats The gut microbiome is a complex ecosystem that reflects the contribution of multiple environmental (such as diet, drugs and pathogens) and host-related (immunity) factors. We first sought to understand the composition of the microbiota across the ages to determine key bacteria associated with the aging phenotype. We began by conducting a 16S rRNA-based OTU survey of the ecological state of the fecal microbiomes (S1.xlsx, ). Our observations of the microbial ecology (using NMDS plots of the community structures Beta diversity) suggest that the microbiome is affected by aging. The 18M communities appeared unique to the 8M and 24M communities that could not be distinguished from one another (Figure 1B). The composition (alpha diversity) of the microbiome at the three ages demonstrated unique OTUs at each age, but generally did not distinguish one age-related community from another (Figure 1C). The OTUs identified in the 16S survey were organised into categories based on the observed abundance across the ages (Figure 1D) (further described in Supplementary Materials). We observed 7 of the possible 18 categories (theoretically possible) that would demonstrate a significant change. In particular, categories 9 and 10 demonstrated the same pattern as seen in the NMDS plots identifying potential OTUs that drive the distinction of the 18M samples. We also used indicator analysis [21] to investigate the contributions of different OTUs to the differences between the age groups. Thirty-nine unique indicator OTUs with P values < 0.001 and statistically different levels upon comparison of two age groups were identified for ages 8M, 18M and 24M (Supplementary Materials, S1.xlsx–IndicatorAnalysis Sheet). Selected indicator OTUs (indicator values > 65), their average relative abundance, their categorization and the age group they indicate are depicted in Figure 2A. OTUs indicating 24M were the most abundant. The only indicator OTU for 18M, with an indicator value (69.32), was OTU0560 (Thermotalea) which was also significantly upregulated at 18M vs 8M. Focussing on the category 9 and 10 OTUs, OTU 0499 (Lachnospiraceae insertae sedis) and OTU 0809 (Lachnobacterium) were both identified as indicators of the 24M group, consistent with the observed decrease at 18M (Figure 1D). Based on Spearman correlations, these two OTUs were also some of the most connected OTUs based on their positive correlation with other OTUs across the ages (Figure 2B, Supplementary Figure 1). OTU 0914 (Lactonifactor), the only OTU found in category 9, was also well connected but negatively correlated with a number of OTUs including OTUs 0499 and 0809. Based on the composition of the microbes across the ages, these three OTUs appear to be markers and keystones of the 18M microbial ecology. 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 correlation values > 0.6 and < -0.6 are shown. The OTUs are sorted and plotted anticlockwise starting at 0 degrees, based on the number of correlations (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.



Association of bacteria genera and rat sarcopenia We next examined the association between the microbial ecology and the observed physiological changes in the rats. Aged rats gradually lose muscle mass and function (i.e. sarcopenia) through multi-factorial mechanisms involving mitochondrial and neuromuscular dysfunction [20,22]. Having characterized the genetic composition of the microbiome, we investigated whether the age-related changes observed in the ecology of the gut microbiota could account for some of the physiological parameters associated with aging and sarcopenia. We previously demonstrated in the same rats that they demonstrated selective age-related sarcopenia (Table 1, complete data provided in Supplementary Materials S3.xlsx). Using age window comparisons of the physiological measurements we observed that body weight and fat mass (%) increased with age (statistically different in 8M-24M and 18M-24M comparisons) while gastrocnemius muscle mass (statistically different in 8M-18M, 8M-24M, 18M-24M comparisons), lean mass (%) and sciatic response amplitude (statistically different in 8M-24M and 18M-24M comparisons) decreased with age (Figure 3A). To investigate the potential associations between the microbiome and the host, we analysed the correlations (details in materials and methods) between the physiological measurements and the OTUs significantly correlated with age (Figure 3B). We focussed on the indicator OTUs for the 24M samples consistent with the development of the sarcopenia phenotype. In general, these OTUs (24M) correlated with increased fat mass, decreased lean and gastrocnemius muscle mass. Several of these OTUs also correlated with vitamin B12 and folate levels. The group 10 OTU (OTU0499) positively correlated with both folate levels in the plasma and heart mass. Although folate levels did not significantly change across the ages, its role in the context of cardiovascular disease has been studied extensively. Vitamin B12, as well as folate, have been similarly studied in the context of lipid physiology. Considering the association of these bacteria with these vitamins as well as the physiological impact of aging, we next sought to determine the underlying mechanistic links between the microbiota and host physiological responses to aging. Table 1. Physiological parameters measured in Aged Rats. Std Dev – refers to standard deviation. Physiological Parameters Average 8M Std Dev 8M Average 18M Std Dev

18M Average 24M Std Dev 24M Body weight (g) 518.72 52.82 555.50 36.20 606.92 37.69 Lean mass (%) 72.75 2.12 71.21 2.34 66.88 1.44 Fat mass (%) 12.54 2.43 13.32 2.42 18.58 1.76 Gastrocnemius muscle mass (mg/g) 4.84 0.76 3.92 0.55 2.32 0.39 Sciatic response amplitude (mV) 65.86 14.88 60.26 15.18 26.66 11.63 Triceps muscle mass (mg/g) 3.88 0.67 3.92 0.23 3.81 0.35 Radial response amplitude (mV) 69.32 14.79 70.96 11.61 63.51 17.03 Heart muscle mass (mg/g) 2.71 0.46 2.74 0.30 3.07 0.45 B12 total (pmol/L) 1074.00 183.97 965.00 167.10 912.50 258.94 Folate level (nmol/L) 169.05 27.83 170.75 24.18 205.50 47.08 Figure 3. Correlations between microbiome and host physiology. (A) Age group comparisons of statistical differences for 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.



Metagenomic functional content analysis We next tried to identify potential molecular mechanisms implicated in the aging phenotype by applying PICRUSt [23] to identify the Metagenomic Functional Content (MFC) for different groups using the 16S rRNA data (Figure 4A). The tabulated entries for the entire list of statistically different MFC’s are available in the Supplementary Materials (S2.xlsx). 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.

To help focus the analysis, the MFCs were put into the same 18 categories previously used for the OTUs in Figure 1 (Figure 4B, 4C and Figure 1D). All the statistically different MFC’s could be classified into 9 categories, with the majority (82% or 307 KO IDs, categorised into categories 1, 2, 4 and 7). Most of the physiological responses (Figure 3A) could be categorised into Category 7 or 8 (significantly higher/lower at the 24M vs 8/18M). Since there were no MFCs observed in category 8, we focussed our attention on the Category 7 observations. These can be summarised in three main categories: secretion systems (often associated with pathogenic mechanisms), ABC transporters (often associated with uptake of essential nutrients) and dietary metabolism (protein, carbohydrate and lipid metabolism) suggesting that the host/microbiome interactions contributing to the aging phenotype involves immune and dietary components. Microbial NMDS analysis and the community analysis summarised in Figures 1 and 2 indicated that the 18M microbiota was distinct from the 8M and 24M communities. While the composition of the 8M and 24M communities could not be distinguished based on 16S rRNA survey, their functional capacity was distinct as demonstrated in Figure 4. One interpretation of this is that there was a transition in the community membership that led to a functional alteration of the microbiota. The 18M community MFC therefore may give an indication of the molecular mechanisms that underlie this transition. Figure 1 identified Category 9 and 10 OTUs as associated with the 18M samples. While no category 10 MFCs were observed, there were an abundance of category 9 MFCs. We further summarized the MFC’s into their corresponding KEGG pathways to hypothesize the molecular mechanisms that may underlie the transition to the aged phenotype (Figure 4D). Category 9 pathways were primarily related to diet including metabolism of carbohydrate, protein, lipids and vitamin biosynthesis (Supplementary Materials, S2.xlsx) suggesting that the microbiome contributes in part to aging through their established role in digestion. We observed a general increase in MFC’s assigned to Amino Acid (25/29) and Carbohydrate (12/15) metabolism with a concurrent general decrease (6/9) in those assigned to Lipid Metabolism suggesting a shift in the potential to digest, process, and synthesize these dietary components. One of the increased MFCs was cholesterol oxidase (steroid biosynthesis) indicating a predicted change towards cholesterol versus fatty acid synthesis. Furthermore, consistent with the analysis of MFCs related to Metabolism of Cofactors and Vitamins (Figure 4D), statistically reduced levels of serum Vitamin B12 were observed. We also explored the correlations between specific MFC’s and the physiological measurements (Table 1, Supplemental Figure 2). None of the Category 9 MFCs associated with physiological parameters. However, there were several associations of other diet-related MFCs on many physiological factors including gastrocnemius muscle mass and sciatic response amplitude (Supplementary Figure 2, S2.xlsx; MFC_Metadata_Correlation Sheet). Fat mass was positively correlated to Cobalamin biosynthetic protein CobC (K02225), carnitinyl-CoA dehydratase (K08299) and allantoin permease (K10975). Furthermore, there was a strong negative correlation between lean mass and allantoin permease (K10975). Allantoin has previously been demonstrated to increase lifespan when administered to the nematode worm Caenorhabditis elegans [24,25]. The negative association could relate to microbial scavenging of this purine metabolism biomarker of oxidative stress [26]. We considered the following key observations: 1) analysis of the aging microbiome identified a diet-related ecology specifically associated with the transition at 18M to the sarcopenic phenotype, 2) the OTUs characterized at the 18M correlated with observed Vitamin B12 and folate levels, and 3) the MFC analysis identified candidate molecular mechanisms including pathogenesis and dietary metabolic function associated with the sarcopenic phenotype. Taking these observations together, they indicate an important role for microbial-derived dietary metabolic pathways in aging and sarcopenia including carbohydrate, lipid and vitamin metabolism that could alter the metabolic status of the host and contribute to the physiological state in aging.

Serum proteomics of aged rats As shown by the analysis above, microbial ecology is a complex process that involves the integration of many factors leading to an outcome in the host. We performed proteomic analysis of the serum to identify the potential host response to the changes in the microbiota and their association with the physiological state. Serum proteins for the 3 groups of rats (n=9 at 8M, n=10 at 18M and n=8 at 24M) were analysed using aptamer-based detection [27]. Similar to the OTUs and MFC analysis, we performed pairwise comparisons to identify proteins that were significantly altered between two age groups (8M-18M, 8M-24M and 18M-24M) and put them into the categories previously described. (Figure 5A, Supplementary Materials S4.xlsx). 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.

Since we were looking specifically at the host response, we focussed on categories that demonstrated a significant association with the host physiological measurements shown in Figure 3. In general, fat mass, lean mass and sciatic response amplitude demonstrated a 'Category 7 or 8' pattern. There were only two serum proteins detected demonstrating this pattern; Amnionless (AMN) and Insulin-like growth factor I receptor (IGF1R). IGF1R did not show any significant correlation with the physiological measurements. However, AMN correlated with most of the physiological measurements with the exception of folate (Figure 5B). AMN plays a key role in Cobalamin (Cbl, vitamin B12) transport as well as for lipid metabolism, HDL-mediated lipid transport, lipoprotein metabolism and lipid digestion. We also detected an association of AMN with cholesterol oxidase and cobalamin biosynthetic protein CobC in the MFCs (Supplementary Figure 4). Subsequently, we used Reactome [28,29] (www.Reactome.org) to identify participation of the differentially observed proteins in different pathways in rats (S4.xlsx). Overall, protein levels which consistently increased with age (Cat 1), i.e. Neutral ceramidase (ASAH2), Nuclear receptor subfamily 1 group D member 1 (NR1D1), Parathyroid hormone (PTH) and X-linked ectodysplasin-A2 receptor (EDA2R) were implicated in glycosphingolipid metabolism, sphingolipid metabolism, GPCR signalling, TNFR2 non-canonical NF-kB signalling and the immune system. Summarizing the genetic analysis of the microbiome and the serum protein response of the host there was a common theme associated with the microbiome and aging. Alterations in dietary metabolism, possibly via their influence on Vitamin B12 and folate, seemed to be involved in the aging phenotype.