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F U L L T E X T S O U R C E : Aging Cell
Abstract
Composition of the gut microbiota changes during ageing, but questions remain about whether age is also associated with deficits in microbiome function and whether these changes occur sharply or progressively. The ability to define these deficits in populations of different ages may help determine a chronological age threshold at which deficits occur and subsequently identify innovative dietary strategies for active and healthy ageing. Here, active gut microbiota and associated metabolic functions were evaluated using shotgun proteomics in three well‐defined age groups consisting of 30 healthy volunteers, namely, ten infants, ten adults and ten elderly individuals. Samples from each volunteer at intervals of up to 6 months (n = 83 samples) were used for validation. Ageing gradually increases the diversity of gut bacteria that actively synthesize proteins, that is by 1.4‐fold from infants to elderly individuals. An analysis of functional deficits consistently identifies a relationship between tryptophan and indole metabolism and ageing (p < 2.8e−8). Indeed, the synthesis of proteins involved in tryptophan and indole production and the faecal concentrations of these metabolites are directly correlated (r2 > .987) and progressively decrease with age (r2 > .948). An age threshold for a 50% decrease is observed ca. 11–31 years old, and a greater than 90% reduction is observed from the ages of 34–54 years. Based on recent investigations linking tryptophan with abundance of indole and other “healthy” longevity molecules and on the results from this small cohort study, dietary interventions aimed at manipulating tryptophan deficits since a relatively “young” age of 34 and, particularly, in the elderly are recommended.
1 INTRODUCTION
The microbiota is now considered an additional organ in our body (Moya & Ferrer, 2016). Therefore, it undergoes changes throughout development, similar to other organs. Moreover, its physiological status, whether healthy or dysbiotic, influences the general health of individuals, although the directionality of this effect is not completely clear and is sometimes confusing (Kundu, Blacher, Elinav, & Pettersson, 2017). The numerous factors to which our body is exposed are also reflected by changes in the microbiota (Rojo et al., 2017). Various natural physiological changes are among the agents involved in modifying the microbial structure, both temporary (pregnancy or lactation) and permanent (the ageing process) (O'Toole & Jeffery, 2015). One of these changes is caused by chronological age. Humans are not free of microbes at birth, and the microbial community is continuously enriched and diversified with ageing (Odamaki et al., 2016). Indeed, it is generally accepted that a healthy and stable microbiota is established at the age of 3 years that remains similar until the adult stage, despite periodic fluctuations of various types (Odamaki et al., 2016). However, this conclusion should be re‐examined because the microbiota can be healthy throughout development but changes dynamically with age (Martí et al., 2017).
Based on 16S rDNA sequencing of the total gut microbiota, a relationship has been observed between clinical phenotypes in the elderly and an “aged microbiota” (Fransen et al., 2017). This microbiota is particularly enriched in pathobionts and displays a decreased abundance of bacteria with anti‐inflammatory and immunomodulatory properties (Fransen et al., 2017; García‐Peña, Álvarez‐Cisneros, Quiroz‐Baez, & Friedland, 2017; Kim & Jazwinski, 2018; Komanduri, Gondalia, Scholey, & Stough, 2019; Lu & Wang, 2018; Mangiola, Nicoletti, Gasbarrini, & Ponziani, 2018; Nagpal et al., 2018; Pasolli et al., 2019; Ramos‐Molina, Queipo‐Ortuño, Lambertos, Tinahones, & Peñafiel, 2019; Reveles, Patel, Forney, & Ross, 2019; Riaz Rajoka et al., 2018; Vaiserman, Koliada, & Marotta, 2017). These bacteria include the genera Bacteroides, Alistipes, Parabacteroides, Faecalibacterium, Ruminococcus, Clostridium clusters IV and XIVa, Coprococcus, Roseburia, Coprobacillus, Anaerotruncus, Escherichia, Lactonifactor, Eubacterium, Lactobacillus, Bifidobacterium, and Akkermansia, and families such as Enterobacteriaceae, Eubacteriaceae, Porphyromonadaceae and Christensenellaceae. However, most of these bacteria also represent microbial signatures of a number of diseases and disorders (Rojo et al., 2017). Therefore, researchers have yet to disentangle the dysbiotic alterations in those bacteria and their involvement in the ageing process.
We would such as focus on the existing difference between the total and active microbiota (Moya & Ferrer, 2016). The total amount of bacteria at a given moment is different from the active working fraction, as this fraction has a functional role and is more relevant to the human health (Mills et al., 2019). In other words, although an examination of the temporal changes in the total (active and inactive) microbiota is interesting at any time scale, an evaluation of changes in the active members is more important in a broad sense (Moya & Ferrer, 2016) or in relation to ageing, life expectancy and age‐related diseases (Zierer, Menni, Kastenmüller, & Spector, 2015). As example, in their study of ageing and the microbiota, Sonowal et al. (2017) found that indoles produced from commensal active microbiota do not affect the fitness of young individuals but extend the healthspan of older individuals in diverse organisms such as Caenorhabditis elegans, Drosophila melanogaster and mice. During ageing, indoles induce the expression of host genes that promote healthy ageing. Thus, an assessment of microbial functions associated with active components of the human microbiota in well‐defined age groups is necessary. However, these investigations are still rare.
Our goal in the present manuscript was to identify the association between the functional gut microbiome and ageing and to identify potential functional deficits associated with ageing. Studies using proteomics and metabolomics provide direct valuable insights into these deficits compared to other “omics” techniques, but due to technicalities, studies in ageing research are limited to a few examples and small (n = 12) sample sizes (Gelfi et al., 2006; Zierer et al., 2015). In the present study, total proteins from bacterial cells isolated from the faecal material of three well‐defined age groups (n = 30) were subjected to shotgun proteomics; this approach allowed us to define the active fraction of the microbiota that synthesizes proteins. Subsequently, a functional analysis of the identified proteins was performed to assess presumptive age‐dependent functional deficits. Finally, using liquid chromatography coupled with mass spectrometry, functional deficits were experimentally validated in an extended set of replicate samples collected over time (n = 83). The combined analysis identified a reproducible microbiome biomarker associated with ageing, namely, a link between an elderly age and tryptophan and indole deficits. The relevance of tryptophan and indole to healthy ageing (Sonowal et al., 2017) and the results reported in this study will provide opportunities for the development of putative and innovative dietary strategies for healthy ageing.
2 RESULTS
2.1 General characteristics of the study population and study design
Faeces from ten infants (I), ten adults (A) and ten elderly individuals (E) were collected and analysed with a proteomic approach using a pooling strategy. Generally, sample pooling results in some unforeseen methodological and statistical bias. A recent proteomic study using individual and pooled serum samples from controls and patients with Creutzfeldt‐Jakob disease (CJD) revealed that compared to the analysis of individual samples, sample pooling affected the coefficients of variation of the minimum, maximum and mean values in both the control and CJD groups. However, the authors were able to identify biomarkers that significantly differed among groups (Molinari et al., 2018), which were then subjected to an in‐depth analysis using independent samples. This strategy was used in the present study because our main objective was to identify biomarkers of protein and functional deficits that substantially differed among the three well‐defined age groups (see Table 1). Briefly, pooling reduced the number of samples to analyse from 10 individual samples to two pools of five individuals for each of the three well‐defined age groups. Figure S1 summarizes the experimental groups analysed in this study. The idea was to establish a proteomic analysis with low resource and time requirements that would allow us to detect substantial differences among the three groups, which would be further validated using individual samples. (NOTE: For the initial analysis, only time zero samples from each of the volunteers were measured, whereas for the validation analysis, samples collected at 0, 3 and 6 months were measured; see the Section 4 for a description of the sampling procedure).
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