Longitudinal analyses reveal that aging-related alterations in the intestinal environment promote HFD-induced obesity

doi:10.21203/rs.3.rs-3464296/v1

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Longitudinal analyses reveal that aging-related alterations in the intestinal environment promote HFD-induced obesity

https://doi.org/10.21203/rs.3.rs-3464296/v1

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Background: Aging is a progressive decline of cellular functions that ultimately affects whole-body homeostasis. Alterations in the gut microbiota associated with aging have been reported, however, these studies were mainly based on the simple comparison of young and old subjects. In this study, we longitudinally analyzed fecal microbial community composition, together with fecal and plasma metabolomes, in C57BL/6J mice over a period of 72 weeks.

Result: By using longitudinal microbiome and metabolome analyses, we found aging-related alterations in fecal microbiota and metabolome profiles. There was a significant reduction in gut microbial diversity with ageand, in addition, the fecal metabolome showed a significant decrease in monosaccharides and an increase in the metabolites of energy metabolism. In the age-related change of plasma metabolites, amino acids were significantly increased. The features of age-related changes in the fecal bacteria and metabolites were consistent with those observed in obesity and diabetes studies. Consistently, fecal microbiota transplantation from agedspecific pathogen-free (SPF) mice into young germ-free (GF) mice resulted in increased weight gain and impaired glucose tolerance.

Conclusion: Our findings provide new insights into the relationships between host aging and gut environmental changes and may contribute to the development of a solution to aging-related diseases such as obesity.

Aging

Gut microbiota

Metabolites

Longitudinal analysis

Dysbiosis

Aging is characterized by the progressive functional decline of cellular functions in multiple organs and tissues, which results in reduced resistance to stress, increased vulnerability to diseases and eventually culminates in death[1, 2]. The aging process also seriously affects the human gut microbiota. It is accompanied by a reduction in gastrointestinal functions including intestinal motility, digestive and absorptive functions, as well as immune responses[3–9], which are further influenced by changes in diet and lifestyle in the elderly[10, 11]. Moreover, many recent studies have shown that compositional and functional abnormalities of the human gut microbiota (dysbiosis) are correlated with inflammatory disorders and metabolic syndrome, including inflammatory bowel disease[6], irritable bowel syndrome[12], type 2 diabetes (T2D)[13, 14], and obesity[15–17]. Notably, human studies have revealed that T2D and obesity increase in the elderly, suggesting a possible contribution of the alterations in gut microbiota to the onset and progression of aging-associated diseases.

The composition of the gut microbiota is subject to rapid change during early childhood, from breastfeeding to the weaning period. The composition becomes relatively stable at the species level during adult life, although this relative stability tends to be lost with aging[3, 11, 18–20]. Virtually all human studies include probable confounding factors, i.e. different genetic backgrounds in combination with changes in diet, medications and lifestyle. By contrast, animal studies, especially of mice, allow more controlled experiments and analyses with pure genetic backgrounds and uniform diet and breeding environments[21–23]. The average lifespan of mice is 24–30 months and their physiological state is defined by a frailty index[24]. Previous reports have defined age groups as ‘young’ (less than 6 months), ‘middle’ (around 20 months) and ‘old’ (around 28 months), which correlate with the frailty index and body weight changes[21, 24]. Body weight and fat mass increase until middle-age, despite a slight reduction of food intake, which is mostly due to the decrease in physical activity and muscle mass[21, 25, 26]. At older ages, body weight gradually decreases, probably because of appetite loss and reduced food digestion and absorption, resulting in diminished energy intake with eventual weight loss[27].

Murine gut microbial composition is correlated with aging[20, 23, 24]; recent reports show that older mice exhibit microbial dysbiosis, concomitantly with increased intestinal permeability (leaky gut), systemic inflammation, glucose intolerance, and insulin resistance compared to younger mice [7, 28–30]. Fecal microbiota transplantation from old donor mice to young germ-free mice promotes leaky gut-associated low-grade systemic inflammation[7], indicating that aging-associated microbiota can directly influence host physiology. Other reports showed that probiotic treatment could prevent aging-related leaky gut and inflammation in old mice [30] and that fecal microbiota transplantation from wild-type mice improved the health and lifespan in progeroid murine models[31] and from young mouse improved the aged phenotype[32, 33], suggesting that appropriate gut microbiota intervention in old mice may improve host health and lifespan.

However, these previous studies were mainly based on the simple comparison of young and old subjects, instead of sampling the same individuals repeatedly over time. In this study, we longitudinally analyzed fecal microbial community composition, together with fecal and plasma metabolomes, in C57BL/6J mice over a period of 72 weeks to explore the consequences of gut microbiota changes associated with aging and their influences on the host. To eliminate possible perturbation of microbial composition by external bacterial contamination, we took advantage of a radiation-sterilized diet and vinyl isolators to maintain GF and SPF mice. We found aging-associated correlations among microbial composition and the fecal and plasma metabolomes in aged mice. Fecal microbiota from the aged mice conferred an obese phenotype upon association with “young” GF mice, suggesting that the aging-associated gut microbial changes are not merely the consequence of host aging but rather could dictate aging-related phenotypes. These approaches thus allow us to achieve a comprehensive understanding of the relationships between host aging and the gut microbiota and may provide insights into the development of a solution to aging-related diseases such as obesity through manipulation of the intestinal microbiota.

Aging in SPF mice is associated with a longitudinal alterations in the structure of the gut microbiota

We monitored six SPF mice and six GF mice from three weeks to until 64 weeks (GF) and 72 weeks (SPF) in separate vinyl isolators. Six mice were divided into two group and housed in a cage by group. To characterize aging-associated gut microbiota and metabolic phenotype changes, fecal and plasma samples are repetitively collected from GF and SPF mice at 3, 4, 6, 8, and 12 weeks after birth, and every 4 weeks thereafter until 64 weeks (GF) and 72 weeks (SPF) (Fig. 1a). The increase of body weight in SPF mice during aging (Supplementary Fig. 1) is mostly due to an increase in fat mass, even though food intake is slightly decreased[21, 25, 26]. Weight gain in aged GF mice compared to aged SPF mice is probably due to the gradual cecum enlargement characteristic of GF mice because of the accumulation of hydrated dietary fiber components[34].

To assess the overall structure of the gut microbiota, we amplicon-sequenced the V1-V2 region of the 16S rRNA gene. Alpha diversities were negatively correlated with age in weeks (Fig. 1b), indicating that alpha diversities were decreased with age. Mean relative abundance values of multiple bacterial OTUs exhibited shifts with aging in SPF mice (Fig. 1c and Supplementary Fig. 2). Bray-Curtis distance to the data at 3 weeks of age is significantly changed according to age (Fig. 1d). Another longitudinal analysis, liner mixed effects modeling by the q2-longutidutinal package [35] showed that taxa abundance is significantly correlated with age in weeks, as evaluated by the analysis of the predictive accuracy of the regression model (R² = 0.64, P = 2.5E^− 06) (Fig. 1e). To identify taxa that are differentially abundant during aging, we applied the data of taxa to ANCOM[36] and found that the microbial genera Allobaculum, Turicibacter, Bifidobacterium, and an unclassified genus of the Coriobaceriaceae family were significantly increased, whereas the genus of Candidatus Arthromitus and Ruminococcus were significantly decreased with aging (Fig. 1f and 1g). Notably, the gut microbiota drastically changed between 3 and 6 weeks (wean-young periods); after that, it was stable from 8 to 72 weeks. Additionally, mice were transported to a new facility at 3 weeks of age, which might have had an influence on the gut microbiome. Since we expected the statistical tests and liner mixed effects modeling were probably affected by the data of wean-young periods, we reanalyzed using the 8 to 72 weeks data, excluding 3–6 weeks, and found that alpha and beta diversities were significantly changed with age (Supplementary Fig. 3a-c). We concluded from this result that fecal microbiota changed with age even after the weaning period. The data analysis from 8 to 72 weeks also showed that the ANCOM score was high only in the genera of Turicibacter, indicating that Turicibacter might be important bacteria in the young to aged phase (Supplementary Fig. 3e and f). In addition, considering the influence of experimental conditions on aging data, we evaluated the cage effect of SPF mice. We compared the Bray-Curtis distance between cages and found that there were no significant differences between cage 1 and cage 2 (Supplementary Fig. 4).

Aging-associated alterations in fecal metabolome profiles

We next performed metabolome analysis by capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify age-associated fecal metabolite changes in GF and SPF mice. This analysis identified a total of 204 metabolites in all murine feces, of which 183 and 195 metabolites were identified in GF and SPF mice, respectively. Principal component analysis (PCA) of the fecal metabolome data showed that the profiles were significantly separated in GF and SPF mice (Adonis, R² = 0.6, P < 0.001, Fig. 2a). PCA of GF and SPF mice revealed age-related significant changes in fecal metabolites in GF mice (Adonis, R² = 0.54, P < 0.001, Fig. 2b) and SPF mice (Adonis, R² = 0.55, P < 0.001, Fig. 2c).

To focus on metabolites correlated with age, we calculated Spearman’s rank correlations between fecal metabolites and age. In SPF mice, 58 metabolites (29.7% in total metabolites in feces) were positively correlated with age, whereas 65 metabolites (33.3% in total metabolites in feces) were negatively correlated with age. In GF mice, 46 metabolites (26.7% in total metabolites in feces) were positively correlated with age, whereas 62 metabolites (36.0% in total metabolites in feces) were negatively correlated with age. We showed metabolites significantly correlated with age on a heatmap with category of KEGG in SPF mice (Fig. 2d) and GF mice (Supplemental Fig. 5). Comparing the age-related change of fecal metabolites between GF and SPF mice (Fig. 2d, the right yellow- and green- colored heatmap), the correlation coefficients between GF and SPF mice were low and there were few metabolites with significant correlation, which corresponded to the PCA results. These results suggested that the fecal metabolites changed with age in both GF mice and SP mice, however the fluctuating metabolites differed between them.

To investigate which pathway associated with fecal metabolites correlated with age in SPF mice, we performed pathway enrichment analysis. Although there was no significant pathway for fecal metabolites that had a significant positive correlation with age, metabolites associated with degradation of amino acids, such as ammonia recycling and the urea cycle, were decreased with age (Fig. 2e). Indeed, except for aspartate, arginine and histidine, fecal amino acids were negatively correlated or showed no change with age (Supplemental Fig. 6).

Another feature of the fecal metabolites correlated with age in SPF mice is that monosaccharides, except for galactose, were decreased with age (Fig. 2f). In contrast, di- and tri- saccharides, except for raffinose, were increased with age (Fig. 2g). In relation to sugar and energy metabolism, monosaccharides and glucose were decreased with age, and citrate, fumarate, and malate in the downstream TCA cycle were increased with age (Supplemental Fig. 7), suggesting that energy metabolism was promoted with age. Focusing on the fermentative pathway, short-chain fatty acids were unchanged, however lactate was significantly increased with age. These results suggested that monosaccharides might be metabolized to the TCA cycle and lactate. Notably, maltose and maltotriose were detected mainly in SPF, but not in GF mice, suggesting that these sugars might be derived from microbial digestion of dietary starch. However, the concentration of soluble starch in feces was comparable across 8 to 72 weeks in SPF mice (Supplemental Fig. 7). It is possible that the intestinal bacteria involved in degradation of polysaccharides into monosaccharides were reduced with aging.

Aging-associated alterations of plasma metabolome profiles

We further analyzed the plasma metabolome of these mice to capture age-associated plasma metabolic dynamics. This analysis identified 157 metabolites in total across all the murine plasma samples, out of which 148 and 151 metabolites were identified in GF and SPF mice, respectively. PCA showed that the plasma metabolome profiles were significantly clustered into 2 groups of GF and SPF (Adonis, R² = 0.42, P < 0.001) and also, aging-associated changes of GF and SPF plasma metabolome profiles were observed (Adonis, R² = 0.44, P < 0.001) (Fig. 3a). Furthermore, PCA calculated for each type of mouse showed aging-associated changes in plasma metabolome profiles in both GF mice (Adonis, R² = 0.66, P < 0.001, Fig. 3b) and SPF mice (Adonis, R² = 0.73, P < 0.001, Fig. 3c). SPF mouse number 4 was an outlier in the PCA score at 44 weeks so it was excluded from the plasma analysis (Supplemental Fig. 8).

To focus on metabolites correlated with age, 69 metabolites (46.0% in total metabolites in plasma) had a significant positive correlation with age and 33 metabolites (22.0% in total metabolites in plasma) had a significant negative correlation with age. In GF mice, 35 metabolites (23.6% in total metabolites in plasma) were positively correlated with age, whereas 47 metabolites (31.8% in total metabolites in plasma) were negatively correlated with age. We showed metabolites significantly correlated with age on a heatmap with category of KEGG in SPF mice (Fig. 3d) and GF mice (Supplemental Fig. 9). In the comparison between GF and SPF mice, overall metabolites showed a positive correlation between SPF and GF (Fig. 3d, the right yellow- and green- colored heatmap), suggesting that relatively the same metabolites were changed with age in both GF and SPF mice.

A heatmap of plasma metabolites in SPF mice showed that metabolites associated with amino acid metabolism were significantly accumulated with aging (Fig. 3d). Indeed, total amino acids in plasma were gradually increased with age (Fig. 3e) and 15 amino acids were significantly correlated with age (Supplemental Fig. 10). The pathway enrichment analysis of the plasma metabolites showed that metabolites associated with amino acid metabolism, degradation of amino acids, were increased with age, in contrast, the citric acid cycle was decreased with age (Fig. 3e). These changes in plasma metabolites were opposite to that of fecal metabolites. The pathway analysis suggests that the urea cycle, which decomposes ammonia generated by amino acid catabolism, might be activated to metabolize the increase of amino acids in plasma.

Correlation among metabolites and microbes and transition time point analyses of SPF mice with aging

To examine aging-associated correlations among the fecal microbes, fecal metabolites, and plasma metabolites in SPF mice, we calculated Spearman’s rank correlation coefficients among fecal microbe-fecal metabolite and fecal microbe-plasma metabolite pairs in a total of 119 samples. Turicibacter, Allobaculum, Bifidobacterium, and an unclassified genus of the Muribaculaceae family were positively correlated with age and disaccharides (sucrose and trehalose), TCA cycle metabolites (citrate, fumarate, and malate) in feces (Fig. 4a). In contrast, an unclassified genus of the Ruminococcaceae family, Ruminococcus, an unclassified genus of the Lachnospiraceae family, Oscillospira, Desulfovibrio, and an unclassified genus of the Clostrididales family were negatively correlated with age. These bacteria also were positively and highly correlated with butyrate and negatively correlated with disaccharides and TCA cycle metabolites. In plasma-microbe correlation, few correlation pairs were detected (Fig. 4b). Turicibacter and Bifidobacterium showed a positive correlation and Ruminococcus showed a negative correlation with age.

Moreover, we also performed Procrustes analysis between Bray-Curtis dissimilarity and fecal metabolome, Bray-Curtis dissimilarity and plasma metabolome, and fecal metabolome and plasma metabolome, respectively (Supplementary Fig. 11). All comparisons had a significant correlation (P < 0.001, 999 permutations). The sum of square distances between paired points in the ordination space were 0.3891 (feces-microbiome), 0.8815 (plasma-microbiome), and 0.8754 (feces-plasma), respectively. This result indicated that the pair between feces-microbiome was highly related compared to other pairs. Therefore, the aging-related phenotype in the fecal metabolome might be related to taxonomic and structural changes in the gut microbiota.

We further conducted an estimation of the cluster of fecal profiles matching with age using change point analysis (CPA) based on a entropy based optimization[37]. We analyzed CPA for the fecal microbiome using scores of principal coordinate (PCo) 1 to 3 of Bray Curtis distance and fecal and plasma metabolome using scores of principal component (PC) 1 to 3. Sum of squared error (SSE) estimated the optimal number of clusters (Supplementary Fig. 12a-c). Interestingly, the clusters of CPA of the fecal microbiome coincided with the fecal metabolome at the three breaking points including 3 to 4 weeks, 6 to 36 weeks and 40 to 72 weeks (Supplementary Fig. 12d and e). The plasma metabolome profiles were categorized into 3 clusters, including 3 to 12 weeks, 16 to 56 weeks and 60 to 72 weeks (Supplementary Fig. 12f), which were little different from fecal microbiome and metabolome, implying that the transition toward host aging might be different from the intestinal environment. Next, we analyzed random forest to identify which metabolites and bacteria contribute to this grouping (Supplementary Fig. 13). Bacteria that have a high contribution to random forest were close to the result of ANCOM (Fig. 1g). Fecal metabolites with a large contribution in the random forest were also highly correlated to fecal microbiome (Fig. 4a), which might construct the common gaps of CPA. Looking at the estimation error of the random forest, the microbiome is 25.2% and the fecal metabolome is 3.4%, suggesting that the fecal metabolome could be used better for estimating the breaking points. Plasma metabolites with a large contribution in the random forest were also highly correlated with fecal bacteria (Fig. 4b). Although the breaking points are slightly different from the fecal microbiota, Allobaculum, Turicibacter, and Ruminococcus have a high contribution of random forest and highly correlated with plasma metabolites, which might influence host aging.

Aged gut microbiota appeared less resistant to HFD-induces obesity.

Our analysis highlighted the features of an aged murine intestinal environment, such as a decrease in monosaccharides and an increase in the metabolites of energy metabolism. In addition, plasma amino acids were increased in aged mice. These features are somewhat similar to the intestinal environment of mice with an obese phenotype[19, 38, 39]. To investigate whether the aged gut microbiota is related to the obese phenotype, we orally transferred fecal microbiota from young or aged SPF mice into young 8-week-old GF mice and maintained them on a high-fat diet (HFD) to develop HFD-induced obesity (Fig. 5a). The GF mice gavaged with aged SPF fecal microbiota displayed enhanced weight gain and higher blood glucose levels after oral glucose challenge compared to those colonized with a young SPF fecal microbiota (Fig. 5b-d), although these parameters were comparable on a normal diet (Supplementary Fig. 14). Blood insulin levels and the weight of epididymal white adipose tissue (WAT) were relatively higher but not significant, in recipients of aged compared to young fecal microbiota (Supplementary Fig. 15). We also assessed the inflammatory state of the liver, WAT, and small intestine, because it was recently reported that aged gut microbiota increases intestinal permeability and promotes age-associated inflammation[7]. The expression of tumor necrosis factor-α (TNF-α) mRNA in the small intestine was relatively higher but did not reach significance (P = 0.0611) in recipients of aged compared to young fecal microbiota, however there was no significant difference in the expression of cytokines in the liver and WAT (Supplementary Fig. 16). We additionally examined the expression of inflammatory genes in the small and large intestines, however there were no significant differences between recipients of aged and young fecal microbiota (data not shown). Next, we compared the composition of the fecal microbiota (Fig. 5e). Alpha diversity and beta diversity were comparable in recipients of aged and young fecal microbiota after 4 weeks on a HFD diet (Fig. 5f and 5g), however, the comparison of pairwise distance at 0 and 4 weeks after HFD feeding showed that recipients of aged fecal microbiota were altered compared to those of young fecal microbiota (Fig. 5h), suggesting that young fecal microbiota were more stable than aged fecal microbiota during HFD feeding. In addition, Parabacteroides, an unclassified genus of the Erysipelotrichaceae family, Sutterella, an unclassified genus of the Muribaculaceae family, Allobaculum, Candidatus Arthromitus, and an unclassified genus of the Peptostreptococcaceae family were significantly different at 4 weeks after HFD feeding (Fig. 5i). The increase of Allobaculum and the decrease of Candidatus Arthromitus were also observed in aging of SPF mice (Fig. 1g). From these results, some of the age-related changes in fecal bacteria were detected as features even in recipients of aged fecal microbiota under the HFD. Taken together, these data indicate that the aged SPF gut microbiota has the effect of promoting HFD-induced obesity.

In this study, we repetitively interrogated fecal microbiome and fecal and plasma metabolome profiles in the same mice over 72 weeks to understand the aging-associated alterations in the gut environment. The fecal microbiome changed dynamically from weaning to young periods and then changed gradually in later periods, consistent with previous reports [20, 23]. Although we analyzed the fecal microbiome of 8 to 72 weeks, excluding weaning to young periods, it was significantly changed with age (Supplementary Fig. 3). Additionally, in the CPA results, the fecal microbiome in the Bray-Curtis distance and the fecal metabolome have common gaps separated by 3 to 4 weeks, 6 to 36 weeks and 40 to 72 weeks. These data indicate that the transition time point of the intestinal environment in older age is around 36 to 40 weeks. On the other hand, the CPA gaps of the plasma metabolome were different from the fecal microbiome and metabolome, suggesting that age-related changes in the intestinal environment are not correlated with those in plasma. Previous studies have reported that inflammatory cytokines in the blood are correlated with aging in mice and humans[7, 10]. Plasma metabolome profiles are also thought to reflect the host physiological changes with aging. Thus, our correlation analysis between fecal microbes and plasma metabolites shows that Turicibacter, Bifidobacterium, and Ruminococcus have a significant correlation with plasma metabolites, implying that these bacteria may have an impact on host physiological aging.

We found that aging decreased the diversity and altered the composition of the gut microbiota. The microbial genera, Allobaculum, Turicibacter, Bifidobacterium, and an unclassified genus of the Coriobaceriaceae family were significantly increased with aging. The increase of Turicibacter associated with aging was in agreement with previous mouse studies[29, 40]. Other reports also showed that Turicibacter is related to inflammation and diseases [41–43]. The increase of Allobaculum was observed in HFD-treated mice[44] and obese model mice [45]. Conversely, Candidatus Arthromitus and Ruminococcus were significantly decreased in aging. Especially, Candidatus Arthromitus was high in the wean-young period. Candidatus Arthromitus is known as segmented filamentous bacteria and specifically modulates host immune responses, including T helper 17 cell differentiation and the induction of gut IgA plasma cells [46, 47]. Although no evidence existed regarding the role of Candidatus Arthromitus in aging, it was speculated that colonization by Candidatus Arthromitus in the wean-young period is important to develop innate and adaptive immune systems. Ruminococcus was highly and positively correlated with fecal butyrate. In addition, correlation analysis showed that an unclassified genus of the Ruminococcaceae family, an unclassified genus of the Lachnospiraceae family, and Ocillospira were also negatively corrected with age and positively correlated with butyrate in feces. These bacteria are members of the Clostridium cluster IV and XIVa and are known as butyrate-producing bacteria [2]. Although there were no significant changes in fecal butyrate in aging, fecal lactate increased with age. Bifidobacterium and Turicibacter were positively correlated with age and positively correlated with lactate. Bifidobacterium species can produce lactate [48] and Turicibacter sanguinis, which is found as a species of Turicibacter, can produce lactate [49]. Additionally, since butyrate is also produced from lactate [50], so the decrease in butyrate-producing bacteria may also be a factor in the increase of fecal lactate.

We found age-related changes of fecal metabolites that have been predicted to promote energy metabolism. Previous metagenomic studies reported that up regulated energy metabolism by intestinal bacteria was observed in obesity and prediabetes [14, 51]. Furthermore, a decrease in glutamate and glutamine was observed in the feces with age, which has been reported in previous metagenome studies in mammals including mice and humans[52, 53]. Metagenome analysis of cecal contents from mice fed a Western-type diet has shown that genes assigned to glutamate metabolic pathways were enriched[52]. In microbiome studies across mammals, glutamate biosynthesis pathways are significantly increased in herbivore microbiomes, whereas the catabolic reactions to break down glutamate and glutamine are increased in carnivores[53].

Plasma metabolome analysis revealed that amino acids and their derivatives were increased with age in SPF mice. Increased amino acids in human blood have been reported as a signature biomarker for the development of diabetes[39]. Also, branched-chain amino acids (BCAA) derived from food proteins contribute to the development of obesity-associated insulin resistance, as reported in animal experiments where rats were fed a HFD supplemented with BCAA[54]. A high flux rate through BCAA catabolic pathways and accumulation of glutamate may increase the transamination of pyruvate to alanine, and these highly gluconeogenic amino acids may contribute to the development of glucose intolerance in obesity[54]. The pathway analysis of plasma metabolites suggests that the urea cycle, which decomposes ammonia generated by amino acid catabolism, might be activated in order to metabolize the overflow of amino acids in plasma. Moreover, the pathway analysis of fecal metabolites showed a decrease of ammonia recycling and the urea cycle, suggesting that intestinal ammonia accumulated in aging. Therefore, we considered that the increase in ammonia absorption from the intestine might activate the host's urea cycle. Comparing GF and SPF mice, plasma glutamate and glutamine were higher in SPF mice, probably because ammonia produced by intestinal bacteria could be absorbed and converted to glutamate and glutamine by the host [55]. Taken together, activation of the urea cycle, including the increase of plasma glutamate and glutamine with age, might be caused by changes of intestinal bacteria involved in the metabolism of ammonia.

Some of the features of age-related changes in the gut microbial environment were consistent with those observed in obesity and diabetes. In our experiments, fecal microbiota transplantation from aged SPF mice into young GF mice resulted in enhanced weight gain and impaired glucose tolerance compared to mice colonized with microbiota from young SPF mice. These differences were not observed in the transplantation experiments under a normal diet, suggesting that the aged gut microbiota have an effect of promoting HFD-induced obesity. Recent reports have shown that age-associated microbial dysbiosis in mice promotes intestinal barrier dysfunction and enhances leakage of inflammatory bacterial components into the circulation, which may promote increased systemic inflammation [7, 56]. We examined expression of inflammatory genes by qPCR in liver, WAT, and small intestine and found that only TNF-α in the small intestine tended to be higher in recipients of aged compared to young fecal microbiota. A recent study showed that TNF-α was highly expressed in the intestinal ileum in mice transplanted with feces from old mice compared with young mice [56]. TNF-α is well known for its role in the pro-inflammatory response [57] and was also shown to increase intestinal epithelial permeability [58]. Therefore, TNF-α is predicted to be a key factor of age-associated inflammation involved in the changing microbiota. Comparing the changes in beta-diversity during HFD feeding, the aged microbiota was significantly altered compared to the young microbiota, suggesting that the aged microbiota is more susceptible to disturbance than the young microbiota. Allobaculum, which was increased with age, also increased under HFD feeding, implying that Allobaculum might be involved in age-related obesity. Turicibacter is one of the key bacteria in aging of our study, however it was not changed by HFD feeding. Turicibacter has been reported to be related to diseases and inflammation, suggesting that it might be involved in diseases related to aging.

Although many studies of aging have been reported, this study have the advantage of clarifying changes in fecal microbiota and metabolome profiles by analyzing the same mouse individuals over time. Some age-related changing bacteria were consistent with previous studies and some of the features of age-related changes in the fecal microbes and metabolites were consistent with those observed in obesity and diabetes. In addition, FMT experiments revealed that the aged gut microbiota itself has an effect of promoting HFD-induced obesity, suggesting that the aged gut microbiota may be a risk associated with obesity. In this study, we transplanted the aged fecal microbiota into young GF mice. However, there are other experiments on transplanting the aged fecal microbiota into aged GF mice and also of transplanting the young fecal microbiota into aged GF mice that will be performed in the future. This will allow us to demonstrate more clearly the functional differences between young and aged gut microbiota.

In this study, we have revealed how the murine gut microbiota and their metabolites are altered with aging. Our findings contribute to a mechanistic understanding of the changes associated with aging in the intestinal microbiome and metabolome profiles, an understanding that is crucial for the development of therapeutic strategies to prevent or treat aging-associated pathologies such as obesity.

Animal experiments with aging mice

Six SPF male C57BL/6J mice (3 weeks old) purchased from CLEA Japan, Inc. and six GF male C57BL/6J mice (3 weeks old) obtained from Central Institute for Experimental Animals (CIEA, Kanagawa, Japan) were separately maintained in sterilized vinyl isolators and fed sterilized normal diet (CA-1, CLEA Japan) until 64 (GF) or 72 weeks of age (SPF). Three of 6 GF mice were excluded at 24 weeks due to bacterial contamination of a vinyl isolator. One GF mouse at 52 weeks was dead due to the accumulation of hydrated dietary fiber components [34] and one SPF mouse at 72 weeks was dead for unknown reasons. (Supplementary Fig. 1). Fecal and plasma samples were collected from each mouse at the time points indicated in Fig. 1a. Samples were snap-frozen with liquid nitrogen upon collection and stored at − 80°C until further analysis.

Fecal transplantation experiments

GF C57BL/6N male mice (3 weeks old) obtained from the RIKEN Center for Integrative Medical Sciences (Yokohama, Japan) were maintained in a vinyl isolator and fed a sterilized normal diet. Twenty milligrams of stool samples from wean-young SPF mice (3 weeks old to 12 weeks old) or aged SPF mice (52 weeks-old to 72 weeks-old) were resuspended in 1 ml of phosphate-buffered saline. Recipient mice at 8 weeks-old were gavaged with 0.2 ml of the suspension and then separately maintained in vinyl isolators and fed with a normal diet for a week. After that, recipient mice were fed a high-fat (D12492, Research Diets) or a normal diet ad libitum for 4 weeks. We monitored body weight twice a week and collected feces every week during high-fat feeding. After high fat feeding for 4 weeks, we performed oral glucose tolerance tests (OGTT) and plasma insulin measurements.

Microbiome analysis

Fecal microbial genomic DNA was extracted from about 10 mg of fresh feces as described previously [59]. PCR amplification of the 16S rRNA gene V1 to V2 region and analysis pipeline for 16S data were performed using the protocol previously reported [60].

DNA sequence analyses and statistics

Sequence reads were demultiplexed using QIIME2 software (version QIIME 2 Core − 2022.2) [61], and errors were corrected by DADA2 [62].The analysis removed low-quality sequences that did not perfectly match the PCR primer, were shorter than 200 bp and longer than 400 bp, contained undetermined nucleotides (N), or did not match a barcode. A total of 3,185,009 reads (average of 26,764 per sample) remained after quality filtering. Sequences were binned by barcode and clustered into de novo operational taxonomic units (OTUs) using a 97% similarity. Taxonomic categories were classified using the Greengenes reference database [63] within QIIME2. The operational taxonomic units (OTUs) table was rarefied to the sample with 10,000 reads per sample. The alpha-diversity and the beta-diversity were analyzed using the QIIME2 software package. ANCOM was used to identify differentially abundant taxa [36]. Differences between the distances of Bray-Curtis dissimilarity were analyzed using a pairwise Permanova test. To evaluate whether the taxa abundance was significantly correlated to time points, we examined longitudinal analysis with liner mixed effects modeling by q2-longutidutinal package [35].

Metabolome analysis

Extraction for plasma metabolites was as described previously with slight modifications[64]. A volume of 20 µl of plasma was added in 230 µl of methanol containing internal standards (20 µmol/l each of methionine sulfone, camphor 10-sulfonic acid and ¹³C₆-d-glucose) and mixed well. Then 100 µl of Milli-Q water and 250 µl of chloroform were added, followed by centrifugation at 4,600 × g for 5 min at 4°C. The aqueous layer was transferred to a 5-kDa cutoff centrifugal filter tube (Millipore) to remove large molecules. The filtrate was centrifugally concentrated at 40°C and reconstituted with 100 µl of Milli-Q water. Fecal metabolites were extracted from fresh fecal samples (about 10 mg) with 400 µl 50% MeOH containing internal standards and about 100 mg of 0.1 mm and two 3 mm zirconia/silica beads (BioSpec Products) with vigorous shaking for 3 min on a Micro Smash (TOMY). After centrifugation at 4,600 × g for 15 min at room temperature, the supernatant was transferred to a 5-kDa cutoff centrifugal filter tube. The filtrate was centrifugally concentrated at 40°C and reconstituted with 40 µl of Milli-Q water. The extracted plasma and fecal samples were split into equal parts for analysis on the CE-TOFMS and LC-MS/MS platforms. Ionic metabolites were analyzed using CE-TOFMS in both positive and negative mode[64]. All CE-TOFMS experiments were performed using an Agilent CE capillary electrophoresis system (Agilent Technologies, Inc.). To identify peak annotation and quantification, the obtained data were processed using in-house software (MasterHands)[65]. Concentrations of sugars in murine plasma and fecal samples were determined by LC-MS/MS in negative multiple reaction monitoring (MRM) mode on an API3000 triple quadrupole mass spectrometer (AB Sciex) as described previously[66]. All LC-MS/MS data were acquired using Analyst Software (AB Sciex).

Starch assay

The concentrations of soluble starch in feces were measured by using an EnzyChrom™ Starch Assay Kit (Funakoshi) on an Infinite M200 microplate reader (Tecan). The assays were performed following the manufacturer's instructions.

Glucose tolerance test and plasma insulin measurement

After 6 hr of fasting, mice were orally administrated with glucose (1.5 g/kg b.w.). Blood glucose was monitored for 120 min using a glucometer (GLUCOCARD G Black, Arkray). Plasma insulin concentrations were determined using an ELISA kit (Morinaga) according to the manufacturer’s instructions.

RNA extraction and quantitative PCR analysis

The liver tissues, epididymal white adipose tissue, and small intestinal tissues were preserved in RNAlater (Thermo Fisher Scientific) at − 80℃. Total RNA was extracted with Trizol and purified with an RNeasy Mini Kit (Qiagen). DNA was synthesized through reverse-transcription PCR with ReverTra Ace (TOYOBO) and random primers (TOYOBO). Quantitative PCR was performed with SYBR Premix Ex Taq (Takara) on a LightCycler 480 (Roche). The primers used in this study were as follows. Tnfa forward: 5’-AGTTCTATGGCCCAGACCCT − 3’, Tnfa reverse: 5’-CTCCTCCACTTGGTGGTTTG-3’, Il-1b forward: 5’-CGACAAAATACCTGTGGCCT-3’, Il-1b reverse: 5’-TTCTTTGGGTATTGCTTGGG − 3’, Mcp-1 forward: 5’-GCTCAGCCAGATGCAGTTAA − 3’, Mcp-1 reverse: 5’-TCTTGAGCTTGGTGACAAAAACT − 3’, Lbp forward: 5’-GTCCTGGGAATCTGTCCTTG-3’, Lbp reverse: 5’-CCGGTAACCTTGCTGTTGTT-3’, Actb forward: 5'-GATCTGGCACCACACCTTCT-3', Actb reverse: 5'- GATCTGGCACCACACCTTCT − 3'.

Bioinformatics and statistics

Statistical and graphical analysis were performed by R version 4.2.1 using R studio version 2022.07.1 and GraphPad Prism version 8.0 (GraphPad Software). PCA on fecal and plasma metabolome datasets was conducted by prcomp function from R. PERMANOVA tests were applied to analyze group separation using adonis from R package “vegan”. Spearman's rank correlation coefficients (r) were calculated by R package “microbiome”. Heatmaps with Euclidean clustering were performed and visualized by R package “ComplexHeatmap”. The pathway enrichment analysis was performed by MetabAnalyst [67]. Procrustes analysis was conducted by procrustes from R package “vegan”. All metabolites were categorized based on the metabolic pathways of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Supplementary Data 1). Two-group comparison was analyzed by Wilcoxon rank-sum test, and multiple sample comparison was analyzed by two-way repeated ANOVA followed by the Benjamini and Hochberg method. P-values < 0.05 were considered statistically significant. We applied a repeated measures mixed-effects model for longitudinal data due to missing values in the aging mice experiments. CPA was performed by implementing a dynamic programming algorithm to identify optimal partitions with varying numbers of segments [37] on Mathematica 10.2. We used PCo1 to 3 and PC1 to 3 to divide the samples into multiple clusters. We used the k-means method for clustering and determined the number of clusters from the mean and standard deviation of the SSE normalized by the mean when the number of clusters was 1. The optimal number of clusters was defined as the smallest number of clusters that satisfies both the mean SSE less than 0.5 and the standard deviation less than 0.01. We then chose a partition common to all individuals that minimized the sum of the entropy of the cluster indices in each segment. The number of clusters does not necessarily correspond to the number of segments, as multiple states may represent a particular life stage. We then performed a random forest analysis using segment order as a label to identify factors that characterize the life stage shifts in the microbiome, fecal metabolome and plasma metabolome. We used the R package randomForest[68] and ranked the importance of components by mean decrease Gini.

SPF

specific pathogen-free

germ-free

T2D

type 2 diabetes

OTUs

operational taxonomic units

PCoA

principal coordinate analysis

CE-TOFMS

capillary electrophoresis-time-of-flight mass spectrometry

LC-MS/MS

liquid chromatography-tandem mass spectrometry

PCA

principal component analysis

SCFAs

short-chain fatty acids

CPA

change point analysis

BIC

Bayesian Information Criterion

HFD

high-fat diet

OGTT

oral glucose tolerance tests

AUC

area under the curve

WAT

white adipose tissue

BCAA

branched-chain amino acids

Availability of data and materials

Sequence data are available at the DDBJ database (http://getentry.ddbj.nig.ac.jp/) under accession number DRA010080 and DRA010096.

Acknowledgments

We would like to thank Drs. Peter D. Burrows and Wanping Aw for their critical reading and English editing of the manuscript. We also would like to thank Hirokuni Miyamoto, Hiroaki Kodama, Akira Watanabe, Yasunori Muraosa and Katsuhiko Kamei for their technical support with the animal experiments.

Authors’ contributions

Y.N., M.I., M.T. and S.F. designed the project. Y.N. performed a large part of the experiments, analyzed the data and wrote the paper. S.F. and H.O. supervised the project and wrote the paper. R.N., M.U., K.H., Y.K. T.O, A.I. and N.T. performed animal experiments. C.I., S.M. T.K. and K.S. analyzed the microbiome and metabolome data. A.H. M.S. and T.S. contributed to experimental design and data analysis. All members reviewed and edited the manuscript.

Funding

This work was supported in part by JSPS KAKENHI (17H05654, 18H04805, and 22H03541 to S.F., 20K06820 and 20H03010 to K.S. and 19K05923 to Y.N.), JST PRESTO (JPMJPR1537 to S.F.), AMED-CREST (JP23gm1010009 to S.F.), JST ERATO (JPMJER1902 to S.F.), the Food Science Institute Foundation (to S.F.), the Program for the Advancement of Research in Core Projects under Keio University’s Longevity Initiative (to S.F.) and the Yamagata Prefectural Government and the City of Tsuruoka (to M.T.).

Availability of data and materials

16S rRNA gene sequences of gut microbiota have been deposited in the DDBJ database (http://getentry.ddbj.nig.ac.jp/) under accession numbers DRA010080 and DRA010096.

Ethics approval and consent to participate

These long-term animal monitoring experiments were performed at CIEA and were carried out in accordance with Institutional Guidelines for Experimental Animal Welfare (09043). Fecal transplantation experiments were performed at Yokohama City University (Kanagawa, Japan) and were carried out in accordance with their Institutional Guidelines for Experimental Animal Welfare (T-A-17-001).

Consent for publication

Not applicable.

Competing interests

S.F. serves as CEO and holds equity in Metagen, Inc., a company involved in microbiome-based healthcare.

Author details

¹RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 2300045, Japan.²Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 9970052, Japan. ³Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, 3-25-13 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 2100821, Japan. ⁴Department of Medical Life Science, Graduate School of Medical Life Science, Yokohama City University, Kanagawa 2300045, Japan. ⁵Central Institute for Experimental Animals, 3-25-12 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 2100821, Japan. ⁶Integrated Bioresource Information Division, BioResource Research Center, RIKEN, 3-1-1 Koyadai, Tsukuba, Ibaraki 3050074 Japan. ⁷Transborder Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan. ⁸Laboratory for Regenerative Microbiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan. ⁹Metagen, Inc. 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, Japan.

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Competing interest reported. S.F. serves as CEO and holds equity in Metagen, Inc., a company involved in microbiome-based healthcare.

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Longitudinal analyses reveal that aging-related alterations in the intestinal environment promote HFD-induced obesity

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Abstract

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Introduction

Results

Aging-associated alterations in fecal metabolome profiles

Aging-associated alterations of plasma metabolome profiles

Correlation among metabolites and microbes and transition time point analyses of SPF mice with aging

Discussion

Conclusion

Materials & Methods

Animal experiments with aging mice

Fecal transplantation experiments

Microbiome analysis

DNA sequence analyses and statistics

Metabolome analysis

Starch assay

Glucose tolerance test and plasma insulin measurement

RNA extraction and quantitative PCR analysis

Bioinformatics and statistics

Abbreviations

Declarations

References

Additional Declarations

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