Table of Contents Author Guidelines Submit a Manuscript
Mediators of Inflammation
Volume 2018, Article ID 7026198, 15 pages
https://doi.org/10.1155/2018/7026198
Review Article

Gut Dysbiosis and Muscle Aging: Searching for Novel Targets against Sarcopenia

1Department of Geriatrics, Neurosciences and Orthopedics, Catholic University of the Sacred Heart, Rome, Italy
2Institute of Sciences of Food Production, National Research Council, Bari, Italy
3Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari, Bari, Italy

Correspondence should be addressed to Riccardo Calvani; moc.liamg@inavlac.odraccir

Received 3 August 2017; Revised 28 November 2017; Accepted 5 December 2017; Published 30 January 2018

Academic Editor: Jorg Fritz

Copyright © 2018 Anna Picca et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Advanced age is characterized by several changes, one of which is the impairment of the homeostasis of intestinal microbiota. These alterations critically influence host health and have been associated with morbidity and mortality in older adults. “Inflammaging,” an age-related chronic inflammatory process, is a common trait of several conditions, including sarcopenia. Interestingly, imbalanced intestinal microbial community has been suggested to contribute to inflammaging. Changes in gut microbiota accompanying sarcopenia may be attenuated by supplementation with pre- and probiotics. Although muscle aging has been increasingly recognized as a biomarker of aging, the pathophysiology of sarcopenia is to date only partially appreciated. Due to its development in the context of the age-related inflammatory milieu, several studies favor the hypothesis of a tight connection between sarcopenia and inflammaging. However, conclusive evidence describing the signaling pathways involved has not yet been produced. Here, we review the current knowledge of the changes in intestinal microbiota that occur in advanced age with a special emphasis on findings supporting the idea of a modulation of muscle physiology through alterations in gut microbial composition and activity.

1. Introduction

Advances in medicine have led to worldwide population aging with an ever-growing proportion of elderly individuals. In such a scenario, strategies able to extend healthy lifespan and to foster active aging are a top public health priority. Indeed, advanced age is associated with an extraordinarily high prevalence of chronic disease conditions (e.g., cardiovascular disease, diabetes, cancer, and neurodegeneration), which in turn contribute to a number of negative health-related events (e.g., poor quality of life, morbidity, loss of independence, institutionalization, and mortality) [1].

The progressive loss of skeletal muscle mass and strength/function, referred to as sarcopenia, is increasingly recognized as a relevant determinant of negative health outcomes in late life [2]. As such, sarcopenia is endorsed as a meaningful biomarker allowing for the discrimination, at a clinical level, of biological from chronological age [3]. Despite growing interest surrounding the sarcopenia phenomenon, several limitations exist that impede its full appreciation in the clinical arena. Indeed, the lack of a univocal operational definition of sarcopenia and unbiased methods for assessing muscle mass and function represent major limitations in the field [3]. In addition to this, the incomplete knowledge of the pathophysiology of sarcopenia hampers the identification of targets that could be exploited for drug development [4].

A growing body of evidence suggests that the innumerable microorganisms that populate the mammalian gastrointestinal tract (gut microbiota) are tightly linked to the aging process of their host [5, 6]. Indeed, this microbial community, mostly composed of bacteria, participates in crucial activities of the gut barrier including the generation of metabolites essential for several host functions [7] and the mediation of exogenous chemical effects on their host [8].

Age-related changes in the bacterial composition of the microbiota are well known, and alterations of gut microbiota driven by the diet may affect the health of elderly people [9, 10]. However, the complexity of mammalian gut microbiota and the technical challenges in isolating specific “prolongevity” microbial variants limit the knowledge of the microbiota to taxonomic and metagenomic profiling. The functions of individual microbial genes and the molecular mechanisms through which they intervene in host aging are yet to be elucidated. Even less is known about the implications of microbiota-immune system crosstalk on muscle aging.

Here, we overview the current evidence supporting the involvement of gut microbiota in muscle aging. Special focus is placed on the analytical tools that may help capture the complexity of human microbiota and its crosstalk with several body systems in advanced age.

2. Microbiota in Health and Aging

The human gut microbiota is a complex ecosystem existing in a symbiotic and commensal relationship with 10–100 trillion microbial cells, mostly bacteria but also yeast, virus/phages, fungi, archaea, microeukaryotes, protozoa, helminths, and parasites [11].

Being frequently confused with the microbiota, the term microbiome indicates the gene catalogues these microbes harbor [12], sometimes referred to as our second genome [13].

The gut represents the largest contributor to the human microbiota. Although rich in variety throughout its segments, the human gastrointestinal tract harbors about 1014 bacterial cells, which is ten times the number of human cells in the body [14]. Such a bulk of biomass, with 3.3 million nonredundant genes, is almost 150 times the 23 thousand genes present in the cells of the human body and plays a central role in health [15, 16]. Despite a high degree of interindividual variability in gut microbiota composition [17, 18], there is a remarkable similarity in the basal gene metabolic activities across individuals [17].

The human gut controls luminal gastrointestinal content at the interface with the external environment and is involved in several host functions. The microbial ecosystem can impact nutrient absorption through bioconversion of food compounds and is also responsible for the nutritional status of the organism [19]. Indeed, among its activities, gut microbiota is involved in the production of micronutrients, such as essential vitamins and cofactors; regulation of the immune system; transformation of xenobiotics; breakdown of complex lipids, proteins, and polysaccharides into metabolite intermediates [e.g., short-chain fatty acids (SCFA)]; and waste product detoxification and finally represents a barrier against the spread of pathogens [20, 21].

In addition, the gut microbiota participates in host metabolism by contributing to bile acid metabolism and recirculation; absorption of calcium, magnesium, and iron; regulation of fat storage; and activation of bioactive compounds [22, 23].

The gut also serves as an endocrine, immune, and neuronal organ. As the largest endocrine organ, it releases hormones by means of enteroendocrine cells [24], but its role goes well beyond. Besides its barrier-like role that protects the host from pathogen colonization [25], the intestinal microbiota also participates in the development and homeostasis of the host immune system [26, 27]. Indeed, 70% of the body immune cells reside in the gut-associated lymphoid tissue. Immune cells can sense changes in the microbiota through specific gastrointestinal cells and receptors and, in turn, trigger lymphocyte accumulation and differentiation in the gastrointestinal tract [28]. The interaction between gastrointestinal cells and commensal bacteria fosters immunological tolerance or inflammatory responses to pathogens by regulating immune homeostasis in the gut [29]. This crosstalk between microbiota and gut mucosal cells (enterocytes, dendritic cells, lymphocytes, macrophages, and M cells) modulates the production of various cytokines and chemokines. These can be proinflammatory, such as interleukin (IL) 1 and 8, or anti-inflammatory, such as IL10 and transforming growth factor [30].

A bidirectional gut-brain communication involving the microbiota has also been recognized and comprises neural [e.g., enteric nervous system (ENS), vagus, and sympathetic and spinal nerves] and humoral pathways (e.g., cytokines, hormones, and neuropeptides as signaling molecules) [31]. Such a communication network is referred to as the “microbiome-gut-brain axis” [32] and signals gastrointestinal perception to the brain which in turn elaborates a gastrointestinal response.

Through this gut-brain homeostasis axis, the microbiota is able to influence numerous aspects of host health, including organ morphogenesis, immune system and gastrointestinal tract development and maturation, intestinal vascularization, tissue regeneration, carcinogenesis, metabolism [33], bone homeostasis [34], memory formation, emotional arousal, affective behavior [35], intuitive decision-making, and a range of neurological disease [32, 36].

The composition of the gut microbiota drastically changes during the first 2-3 years of life [37]. Primarily dominated by Bifidobacteria [38, 39], its development and intraindividual variation in healthy individuals is highly influenced by several factors including mode of delivery (vaginal or cesarean), diet, use of antibiotics, geography, and environmental exposure [4042]. In adults (<65 years), the interindividual microbial diversity of the gut microbiota reaches its maximum, but a plateau effect is observed afterwards as a consequence of the aging process [37, 43, 44]. An adult-associated core microbiome comprising 66 dominant operational taxonomic units (OTUs) [45] that differs from the core and diversity levels of younger counterparts has been identified [30].

Among the age-associated changes in the microbial population, a reduced abundance of several butyrate producers (Clostridium clusters XIVa and IV) has been reported by both 16S targeted Sanger sequencing and next-generation pyrosequencing [46, 47].

In addition, analysis of the microbial composition of 161 Irish people aged 65 years and older compared to nine younger controls showed that, even if possessing a unique individual microbiota profile, microbiota of older people was represented predominantly by Bacteroidetes population [48], as inferred by pyrosequencing of 16S rRNA. Using the same sequencing approach, a reduction of Ruminococcus and Blautia species and an increase in the abundance of Escherichia were also found [9]. However, the more evident age-associated trait within the microbial population was the lower Firmicutes/Bacteroides ratio (F/B ratio) reported by Mariat et al. [49] via qPCR analysis and confirmed by Claesson et al. [9] by pyrosequencing of 16S rRNA. A schematic representation of the microbial changes associated with unhealthy microbiota occurring during aging and leading to host inflammation is depicted in Figure 1.

Figure 1: Healthy microbiota is a balanced community of symbiont, commensal, and pathobiont microorganisms. Each microbial class confers distinct characteristics to the host. Either imbalance in alpha-diversity or variations of relative abundance of single microbial taxa results in microbiota imbalance. As such, a sterile inflammation occurs and may predispose the host to opportunistic infections, ultimately leading to acute inflammation.

Most gut microbial changes observed during aging are attributable to diet composition. Both environmental and behavioral factors, including loss of sensation, tooth loss, chewing difficulties, changes in lifestyle, increased consumption of high sugar-fat foods and reduction in plant-based foods, and location of residence (community, long-term care, etc.), have been suggested to influence age-associated diet variations. Furthermore, reduced intestinal motility has been indicated to unfavorably affect gut fermentative processes in advanced age. Results from the ELDERMET project, aimed at investigating the association between diet, gut bacteria, and health status in a large cohort of elderly by pyrosequencing of 16S rRNA, showed that the setting of long-term care living represents a major factor affecting diet composition [9]. The authors identified a relationship between diet, microbiota, and health status. In particular, microbial population composition was mainly affected by the consumption of vegetables, fruits, and meat. Furthermore, in elderly people living in long-term care facilities, a higher proportion of Bacteroidetes was found compared with a higher Firmicutes population in community-dwelling persons within the same ethnogeographic region [9].

Taken as a whole, these results support a new hypothetical link between aging and microbiota alterations relying on a proinflammatory loop. In this context, the age-related decline in masticatory function together with a reduction of appetite and gastrointestinal motility induces dietary changes (reduction in fruits and vegetables) that is reflected in microbiota rearrangement (dysbiosis). This alteration, in turn, can activate a proinflammatory loop fueled by the immunosenescence of gut-associated lymphoid tissue releasing proinflammatory mediators which further favors microbiota rearrangements [50].

Regardless of diet, microbiota may also vary in older age in relation to several physiological and immunological statuses, such as antibiotic exposure [51, 52], decreased responsiveness of the immune system, and the existence of a chronic low-grade inflammatory status [53], as well as lifestyle and geographical location [54]. Indeed, bacterial 16S ribosomal RNA genes analyzed by next-generation sequencing in stool samples of Korean women aged 65+ with similar genetic background showed different gut microbial composition according to the location they are living, in island or inland areas [54]. Interestingly, the same approach on fecal samples of Italian elderly inpatients revealed an association between changes in microbial composition and polypharmacy, but not multimorbidity and frailty [55]. Notably, these changes were reported to impact mortality, rehospitalizations, and incident sepsis [55].

Besides the association with aging, dysbiosis has also been related to several undesirable conditions including obesity [56], inflammatory bowel disease [57], type 1 [58] and type 2 diabetes [59, 60], and nonalcoholic steatohepatitis [61] but has also recently been proposed to be involved in nonmetabolic syndromes such as age-related frailty [9], autism [62, 63], Alzheimer’s disease [64], and depression [65].

3. The Importance of Dietary Supplementation on Microbiota

The analysis of microbial community composing human fecal samples of healthy individuals indicated that it is possible to distinguish the human gut microbiota into three main enterotypes based on the abundance of specific bacterial genera (i.e., Bacteroides, Prevotella, or members of the order Clostridiales) [66]. However, recent studies revisited this categorization and proposed the concept of bacterial communities being distributed as a continuum of abundance gradients between microbial genera [67].

Regardless of the exact microbial distribution, distortion of normal microbial balance has been implicated in several chronic conditions, including obesity and metabolic syndrome. Interestingly, antiaging strategies involving dietary manipulations addressing either variation in calorie intake or diet composition have been reported to affect the composition of gut microbiota. Changes in intestinal microbiota composition have been observed after weight loss following calorie restriction (CR), the only life-extending strategy available to date. In particular, an increase in the F/B ratio in obesity and a reduction of the same index with weight loss-producing CR-based interventions were found [68]. Obese people undergoing surgical (laparoscopic sleeve gastrectomy) or diet-based weight loss were also analyzed for changes in gut microbiota composition associated with weight loss interventions. Interestingly, in this case, differences in energy-reabsorbing potential were found to be associated with variation in F/B ratio [69]. A profile of weight gain-associated bacteria has been identified as related to the promotion of the expression of genes linked to carbohydrate and lipid metabolism thereby influencing dietary energy harvest [70]. Structural variations of gut microbiota have also been reported in animal models undergoing CR. For instance, a life-long low-fat diet significantly reshaped the overall structure of the intestinal microbiota in C57BL/6J mice. In particular, enrichment in phylotypes (genus Lactobacillus) positively correlated with longevity and a reduction in phylotypes negatively associated with lifespan was found in CR-treated mice [71].

Apart from strategies acting on calorie intake, diet composition (protein-rich versus fiber-rich dietary supplementation) represents a significant modulator of the microbial population of the gut [9, 72]. As such, diet is indicated as the main culprit responsible for metabolic diseases linked to gut dysbiosis. Even for short-term changes in consumption (4 days), animal-based and plant-based diets alter microbial community structure in a specific manner [72]. This change in food intake reflects the exchange between carbohydrate and protein fermentation existing between carnivore and herbivore mammals [72, 73].

Interestingly, Wu et al. [74], although reporting changes in microbiome composition within 24 h of high-fat/low-fiber or high-fiber/low-fat diet, showed that enterotype identity remained stable over 10 days of nutritional intervention. This suggests that food ingredients (e.g., dietary fibers) that are not digested by host enzymes but fermented by gut bacteria could modulate the gut microbiome composition in a relatively short period of time, independent of the effect of changes in transit time [74, 75]. This ability to resist disturbances and restore changes occurring in its composition (e.g., after short-term variations in dietary habits) is referred to as resilience [74, 75].

Beyond their primary role as dietary supplements ensuring the minimum nutritional requirements for maintenance and growth, some food components exert several beneficial effects on the host. This is achieved through the interaction with and modification of the gut microbiota. Among these, nutraceutical polyphenols, pre- and probiotics, vitamins, and polyunsaturated fatty acid (PUFA) supplementation have been recently investigated.

The administration of pre- and probiotics has been recommended as a dietary supplement to mitigate some of the age-related alterations in the intestinal microbiota associated with several gastrointestinal and respiratory diseases [76].

Probiotics defined as “live microorganisms which, when administered in adequate amounts, confer a health benefit on the host” [77] exert their beneficial effects on the host by improving gut barrier function, immunomodulation, and production of neurotransmitters as well as by modulating cellular components of the gut-brain axis [78]. On the other hand, prebiotics are “selectively fermented ingredient that allows specific changes, both in the composition and/or activity in the gastrointestinal microflora that confers benefits upon host well-being and health” [79].

The impact of probiotics, prebiotics, PUFAs, and phytochemicals, including flavonoids and phenolic compounds, on the gut microbiota is well characterized [78, 8082].

Probiotics, particularly those containing Bifidobacterium and Lactobacillus, are among the most actively investigated microbiota-targeted interventions aimed at improving health status in advanced age [83, 84]. Results from a clinical trial showed that administration of Lactobacillus rhamnosus GG ATCC 53103 in healthy individuals, aged 65 to 80 years, was able to modulate the transcriptional response of the microbiota [85]. Oral supplementation of probiotics containing Bifidobacterium brevis B-3 and Lactobacillus plantarum HY7714, instead, has been shown to prevent skin photoaging induced by chronic ultraviolet irradiation in both mice and humans [8688]. Similarly, oral administration of Lactobacillus brevis OW38 to aged mice ameliorated both age-associated colitis and memory impairments through the inhibition of lipopolysaccharide (LPS) production by the gut microbiota, p16 expression, and NF-κB activation [89].

It is worth nothing that, when analyzing probiotic-mediated effects, host benefits are mediated through the promotion of microbiota homeostasis, rather than through changes in its composition [90].

Targeting gut microbiota has been indicated as a tool to modulate lean tissue mass. Bindels et al. showed that leukemic mouse model were cachectic mice with gut dysbiosis characterized by selective modulation of Lactobacillus spp. [91]. Following the administration of oral probiotic containing Lactobacillus reuteri and L. gasseri, an inverse association among serum levels of inflammatory cytokines [IL6 and monocyte chemoattractant protein-1 (MCP-1); the expression of protein associated with muscle atrophy, muscle RING-finger protein-1 (MuRF1); and atrogin-1] and muscle mass was found in these animals. Increased muscle mass and function (grip strength and swim time) have also been found in healthy young mice supplemented with L. plantarum [92]. The existence of a relationship between Lactobacillus species and skeletal muscle size found in this preclinical model needs to be confirmed in human studies.

The downside of probiotic usage including the potential risk of inducing gastrointestinal side effects, an unfavorable metabolic profile, excessive immune stimulation, and systemic infections in susceptible individuals, as well as horizontal gene transfer, needs also to be considered [93]. Therefore, a more comprehensive evaluation of the incidence and severity of adverse outcomes linked to probiotic consumption needs to be assessed.

Fermented nondigestible compounds, referred to as prebiotics, favor the proliferation of health-promoting bacteria [94] that may positively affect muscle health. Cani et al. [95] reported decreased levels of circulating LPS and inflammation and increased muscle mass in obese mice supplemented with prebiotics (i.e., fiber oligofructose) [95]. As a confirmation of the beneficial effect of prebiotic administration on gut microbiota, a shift in B/F ratio in addition to increased levels of Lactobacillus and Bifidobacterium spp. were found in follow-up analysis [96]. Further evidence supporting a link between prebiotic administration and effects on muscle mass is that association of proliferation of Lactobacillus and Bifidobacterium in leukemic mice with restoration of intestinal homeostasis (e.g., increase tight junction proteins) and reduced muscle wasting following administration of symbiotic inulin-type fructans and Lactobacillus reuteri [97]. These findings suggest that Lactobacillus and Bifidobacterium may influence gut-muscle communication and regulate muscle size. Interestingly, Bifidobacterium decrease with age [98] and are associated with lower circulating LPS levels [99]. Thus, an age-related decrease in gut Bifidobacterium content may underlie increases in circulating endotoxin that are shown to induce muscle atrophy [100]. While no conclusive data show increased muscle mass as an effect of Bifidobacterium supplementation especially in humans, there is evidence linking butyrate (associated with Bifidobacterium [101]) treatment as a protective strategy to counteract age-related muscle atrophy [102].

Indeed, 50+-year-old persons supplemented with galactooligosaccharides (GOS, 2 × 4 g/d for 3 weeks) in a randomized, double-blinded, placebo-controlled trial showed attenuation in age-associated Bifidobacteria reduction. In particular, an increase in the number of Bifidobacteria, together with higher Lactobacilli and butyrate levels, was obtained following GOS treatment. Moreover, SCFA concentration was increased whereas branched chain fatty acid concentrations were decreased by the same treatment. Thus, a more saccharolytic environment was achieved [103]. This and other studies based on GOS supplementation lead one to hypothesize that the administration of the GOS mixture in advanced age might positively affect the microbiota and age-associated markers of immune function [104].

The administration of symbiotic, comprising the probiotic Bifidobacterium longum and an inulin-based prebiotic component, has also been demonstrated to have an effect on the age-related changes in the intestinal microbiota. Indeed, an elevation in the number of Bifidobacteria as well as increasing members of the phyla Actinobacteria and Firmicutes together with a reduction of Proteobacteria was observed. Furthermore, treatment with this symbiotic caused an enhancement in butyrate production and a reduction in proinflammatory responses [105]. These findings might explain, at least in part, why probiotics have been successfully implemented as strategies to treat respiratory and gastrointestinal infections and enhance responses to vaccinations in older people [106].

Taken as a whole, these findings support the idea that pre- and/or probiotic supplementation may prevent age-related muscle loss by increasing the abundance of Bifidobacterium and butyrate producers in old individuals [85, 107].

Although the supply and conversion of nutrients are highly dependent on the composition of gut microbiota, bidirectional interactions between the microbiome, nutrient availability, and gastrointestinal function have also been proposed [108]. The metabolic activity of gut microbiota can modify the exposure, absorption, and potential health-promoting effects exerted by bioactive compounds, functional foods, or nutraceuticals.

Nutraceuticals are recognized among nutrients to beneficially modulate the growth, composition, and functions of the microbial host community in several animal models and recently also in humans [81, 109112]. However, causality between bioactive compound assumptions and their benefits on host gut microbiota [113] is yet to be established and is challenging especially due to the complexity of endogenous and environmental factors affecting its equilibrium. Nevertheless, functional diets are proposed to prevent or attenuate metabolic diseases in view of their ability to elicit anti-inflammatory responses [114, 115].

As a whole, these findings support the hypothesis of a link between diet, microbiota, metabolism, and inflammation in several conditions and especially in advanced age [116]. Changes in microbiota have recently become the subject of intensive research because of their possible involvement in several conditions associated with inflammation, such as aging. However, a gap exists in the knowledge of how this could influence the variation of muscle mass and strength that accompanies aging. Here, we track some of the molecular pathways shared by age-related microbial alterations, metabolic changes, and sarcopenia in order to identify possible candidates and provide arguments in support of their exploitation in the management of muscle wasting.

4. Sarcopenia: A “Bacterial” Perspective

Muscle wasting is a key feature of several age-related conditions (e.g., sarcopenia, cachexia, and diabetes) leading to functional impairment and disability. Animal studies suggest a relationship between muscle wasting and alterations in the gut microbiome. Interestingly, muscle wasting induced in a mice model of acute leukemia was attenuated by oral supplementation with specific Lactobacillus species [91]. The authors suggested an influence of gut microbiota on muscle physiology through the regulation of amino acid availability.

Muscle protein metabolism is a multifactorial process resulting from the dynamic balance of protein synthesis and breakdown. Muscle protein synthesis is regulated by several anabolic stimuli (e.g., physical activity, food ingestion). Both amino acid (AA) composition of dietary proteins (e.g., prevalence of essential amino acids such as leucine) and their absorption kinetics (i.e., protein digestion speed and AA absorption) influence muscle protein synthesis [117]. Changes in the gut microbiota induced by clinically relevant interventions impact the bioavailability of dietary AAs [118]. Along the gastrointestinal tract, dietary and endogenous proteins are hydrolyzed into peptides and AAs by host- and bacteria-derived proteases and peptidases [119, 120]. The resulting peptides are subsequently released and support the growth and survival of bacteria in the gastrointestinal tract [121], but also regulate energy and protein homeostasis of the whole organism [122, 123]. AAs can also serve as precursors for the synthesis of SCFA by bacteria, thus suggesting an interplay between microbial activity and host AA and SCFA homeostasis [124]. The most abundant SCFA are propionate, butyrate, and acetate. The latter is mainly utilized by muscle cells to generate energy [125]. In addition to this, modulation of proinflammatory responses associated with microbial changes can be triggered by pathogens, various diseases, and malnutrition [126].

The presence of chronic, low-grade systemic inflammation, called “inflammaging,” also represents the substrate of aging and a highly significant risk factor for both morbidity and mortality in elderly people [127]. The inflammaging process is characterized by the persistent activation of innate immunity mediated by the NF-κB transcription factor [128] and loss of CD4+ T cells.

Gut microbiota plays a crucial role in maintaining the balance of pro- and anti-inflammatory responses [129]. Aged gut microbiota may elicit an inflammatory response and display lower capability of counteracting adverse microbes or removing their metabolites [30]. The entrance of pathogens into the intestinal mucosa is also facilitated by the secretion of mucins by intestinal epithelial cells [130], which is triggered by a reduction in SCFA levels (especially acetate, n-butyrate, and n-propionate) in the intestines [30]. SCFA serves within the gut not only as an energy source for colonic epithelial cells but also as strong anti-inflammatory molecules regulating host metabolism and immunity [131]. In particular, butyrate modulates intestinal homeostasis through several actions, including the differentiation of CD4+ T cells into regulatory T cells, the induction of tumor growth factor- (TGF-) β secretion by epithelial cells, and the triggering of IL10 and retinoic acid production by dendritic cells and macrophages [131]. These actions allow for resolving local intestinal inflammation and avoiding its dissemination through leakage of bacteria and bacteria-derived inflammatory compounds into the blood [131].

Increased intestinal permeability to LPS is another element in support of a mechanistic link between microbial dysbiosis and systemic inflammation. Indeed, in young mice, high-fat feeding, which is known to compromise epithelial tight junctions and increase intestinal permeability [132], has been associated with decreased glucose tolerance and increased inflammation markers through LPS leakage from the intestine into the circulation [133].

In such a context, chronic inflammation may represent the trait d’union of microbial alterations and the development of muscle-wasting conditions in advanced age through a gut microbiota-muscle crosstalk. The molecular players involved in this process are not yet fully understood. Bäckhed et al. [134] showed that germ-free mice are protected from diet-induced obesity through increased fatty acid metabolism. This pathway involves AMP-activated protein kinase (AMPK), which monitors cellular energy status; increased muscular activity of carnitine:palmitoyltransferase-1 (CPT-1), which promotes the entry of long-chain fatty acylCoA into the mitochondria; and higher levels of the fasting-induced adipocyte factors linked to the peroxisome proliferator-activated receptor, gamma coactivator 1-alpha (PGC-1α), the regulator of mitochondrial content and oxidative metabolism. These increased activities counteract the impact of denervation and fasting on muscle atrophy.

The possible involvement of mitochondria in this crosstalk is not surprising if one considers that the maintenance of mitochondrial function is crucial to myocyte viability. Mitochondrial impairment and systemic inflammation play a central role in both cachexia and sarcopenia. Indeed, a role for proinflammatory cytokines [e.g., TNF-α, IL1β, IL6, TNF-like weak inducer of apoptosis (TWEAK)] in the induction of muscle catabolism has been previously reported [135].

Only one study has focused on the interface between chronic inflammation and mitochondrial clearance in skeletal muscle in the context of aging and physical frailty [136]. This investigation made use of IL10-null mice (IL-10tm/tm), a rodent model of chronic inflammation and frailty, and reported severe mitochondrial damage with disrupted organelle ultrastructure and abnormal autophagosomes in skeletal muscle [136]. Although these findings support the existence of a connection among mitochondrial dysfunction, cellular quality control failure, and inflammation, the signaling pathways responsible for such a link have yet to be fully elucidated. Circulating mtDNA is a prominent candidate for such a role, being an important damage-associated molecular pattern (DAMP) associated with inflammation and arising directly from mitochondrial damage [137].

Recent findings by our group also support the idea that mitochondrial impairments in muscle occur in both sarcopenia and cachexia [138, 139]. Trigger candidates of inflammation in sarcopenia and cachexia could be represented by oxidized cell free-mtDNA or nucleoids extruded from damaged mitochondria (Figure 2). These DAMPs would activate the innate immune system and induce the subsequent production of inflammatory mediators. The release of the latter could sustain a vicious circle in myocytes through impaired quality control signaling, resulting in further mitochondrial impairment, increased reactive oxygen species generation, and the release of mitochondrial vesicles enriched with DAMPs. This series of events would fuel sterile inflammation, ultimately contributing to muscle wasting [140].

Figure 2: Proposed crosstalk between mitochondrial dysfunction and inflammation in muscle wasting. Imbalanced gut microbiota contributes to host inflammation and fuels the age-associated impairment of mitochondrial quality control in myocytes. This may lead to the release of mitochondrial damage-associated molecular patterns (DAMPs), such as mtDNA and ATP. The subsequent recruitment of local macrophages may maintain a persistent inflammatory milieu by alerting circulating immune cell and mounting a systemic response through the activation of mtDNA-induced inflammatory pathways. Cytokines, chemokines, nitric oxide (NO), and reactive oxygen species (ROS), released in the circulation by inflammatory cells, can induce further mitochondrial damage, thereby establishing a vicious circle and eventually contributing to muscle wasting. ETC: electron transport chain; mtDNA: mitochondrial DNA; TFAM: mitochondrial transcription factor A; PAMPs: pathogen-associated molecular patterns.

Due to its crucial role in host physiology and health status, age-related differences in the gut microbiota composition have been suggested to relate to the progression of diseases and frailty in old age. The first study correlating gut microbiota composition with frailty severity was conducted by van Tongeren et al. [141]. The authors demonstrated a significant reduction in the proportion of Lactobacilli, Bacteroides/Prevotella, and Faecalibacterium prausnitzii and an increase in the proportion of Ruminococcus, Atopobium, and Enterobacteriacae in older persons with high frailty scores [141].

The finding of dysbiotic shifts of gut microbiota towards a greater abundance of butyrate-producing bacteria such as Faecalibacterium prausnitzii in higher functioning persons suggests a positive role for these microbes in muscle function. Indeed, butyrate, by enhancing intestinal barrier function through the reinforcement of tight junction assembly [142], should prevent endotoxin translocation and reduce circulating inflammation [143].

Evidence from metagenomic analysis in a large sample of older adults (), the ELDERMET study, clearly linked butyrate-generating bacteria with functional capacity by showing that community-dwelling elderly have more butyrate-producing microbes than those in long-stay residence [9]. This finding, together with a greater abundance of Enterobacteriaceae and Escherichia/Shigella and reduced gut microbial diversity among institutionalized elderly, highlights the need of nutritional strategies aimed at preventing the loss of “healthy” microbes (e.g., butyrate-producing bacteria) for those individuals entering long-term care facilities. Notably, prebiotic supplementation (inulin plus fructooligosaccharides) has been shown to increase muscle strength and endurance in frail older adults [144], thereby highlighting the potential of prebiotic supplementation as a treatment for age-associated deficits in muscle function.

Such findings, although indicating gut microbial changes among the factors affecting muscle mass and quality during aging, are not yet conclusive. Further research aimed at deciphering the pathways involved in microbiota-immune system crosstalk and its implication in muscle aging is warranted.

5. Catching the Microbiota Complexity: Opportunities from Next-Generation Sequencing Approaches

The advent of sequencing technologies has revolutionized the analysis of complex microbiomes and their functions and has allowed for upgrading fundamental theories of evolution [145].

The next-generation sequencing (NGS) revolution has enabled the genomic and functional characterization of novel microbial species, especially pathogens, revealing the diverse composition of microbial communities in several environments and the association of microbial groups with specific activities [146]. Technological advances in sequencing platforms have ensured increasingly long-read lengths that have helped cut down the cost of sequencing, one of the major limitations of the technology [147]. This has led to a dramatic increase in the amount of sequencing data generated. Such a burst in big data production and the parallel exponential increase in computational power have introduced new challenges and bottlenecks related to handling the complexity of the information generated and storing it [148, 149], especially in medicine [150].

Metagenomic studies, among other methods, have taken advantage of increasing computational power to address more complex questions compared with traditional genomic approaches.

Since its inception in 1998 [151], metagenomics has allowed for culture-independent analysis of several complex microbial populations, thus capturing the variability of microbial ecosystems that could not be identified under standard laboratory conditions [152]. This approach has revealed structural diversity, functionality, microbial interactions with the environment, other microbes and the host, and evolutionary processes [13, 153155].

Targeted metagenomics, known as metagenetic [156], is based on marker gene amplification and the sequencing of 16S ribosomal RNA gene (16S libraries), the domain of which is restricted to Bacteria and Archaea [157]. Whole-metagenome shotgun analyses, instead, are accomplished by unrestricted sequencing of the collective microbial genomes present in the sample (shotgun libraries). While 16S sequencing approach aims at reconstructing the taxonomic content of the microbial population, the shotgun approach can address the question of how the collective microbial genomes interact in the sample. This allows for a functional microbial characterization by retrieving the complete sequences of protein-coding genes in the sequenced genomes [158]. The choice between these two methods depends on the nature of the study. The 16S approach is generally used with large sample sizes and in longitudinal studies, while the shotgun approach is preferred when a functional characterization within the samples is required [159].

Although being more expensive, the shotgun approach generates more informative libraries when performed with appropriate sequencing depth [154, 160]. Li et al. [161] published a nonredundant reference catalogue of 9,879,896 genes by combining 249 newly sequenced samples in the Metagenomics of the Human Intestinal Tract (MetaHit) project with 1018 previously sequenced samples (data available at http://meta.genomics.cn). Likewise, a shotgun approach was recently used by Xie et al. [162] to construct a comprehensive gut microbial reference gene catalogue from a metagenomic analysis of fecal samples of 250 adult twins from the UK. In this study, the authors demonstrated the heritability of many microbial taxa and functional modules in the gut microbiome, including disease-associated ones. However, the application of shotgun metagenomics to overcome the limited taxonomic resolution and functional inference of metagenetic approaches and to reveal the functional association of gut microbiota in disease conditions is still limited [163].

Especially in relation to human health, the study of 16S is of critical importance, since several disease conditions have been associated with decreased microbiome diversity or with the abundance of specific microbial species. The binning process, which is defined as the assignment of sequences to the corresponding taxonomic group, referred to as operational taxonomic unit (OTU), is pivotal in defining the diversity of the sample and its taxonomic composition. In addition, it facilitates genome assembly and the evaluation of gene association with different taxonomic groups and derived metatranscriptomic or metabolomic analyses [164, 165]. The binning process, the accuracy of which depends mainly on the clustering algorithm and on the preprocessing of the reads [166], is usually carried out with taxonomy-dependent and independent methods: the first performs a standard homology inference against a reference database to classify DNA fragments [167171], while the second is a reference-free method which applies clustering techniques on features extracted from the sequences [172176].

Though, when assessing gut microbiota composition by 16S analysis, many sources of bias have been recognized. These include adequacy of the experimental design and data analysis. In particular, the choice of the extraction kit [177], primer selection and hence the regions to be amplified [178], library preparation methodology [179], sequencing errors [180], and sequencing throughput as well as the choice of pipelines and reference databases for data analysis [181] strongly impact the results.

For all these reasons, the need for a standardized method is required in order to compare datasets generated by different platforms, especially for clinical and diagnostic purposes.

Despite these criticalities, large-scale projects [The NIH Human Microbiome Project, the Metagenomes of the Human Intestinal Tract (MetaHIT), and the ELDERMET project] succeeded in paving the way to a comprehensive determination of the microbial composition of the gut microbiota and its relationship with health and diseases [9, 16, 161, 182184]. These findings are further supported by advanced computational tools and dedicated pipelines for the analysis of microbial community data [181, 185, 186] including mothur [187], w.A.T.E.R.S [188], the RDP classifier [189], mOTU [190], and QIIME [191], defined as the “gold standard” for 16S metagenomic datasets [192].

Several software programs have been developed to infer metabolic capacity and functionality from 16S libraries. PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) is the first and most used software that associates representative sequences from OTUs to nodes of a reference phylogenetic tree [193]. In addition, it can predict gene content even in sequenced genomes not available by using ancestral state reconstruction algorithms. Other examples are Tax4Fun [194], which relies on the KEGG database, the SILVA SSU Ref NR database, and Piphillin [195], which has implemented an inference tool that works with any current genome database and has improved correlation and accuracy for clinical samples compared with PICRUSt and Tax4Fun [166].

Metagenomics is one of the most powerful tools available to unravel the complexity of gut microbiota. The integration of metagenomic data and other “omic” techniques (e.g., proteomics, metabolomics), within a multidimensional approach, will be crucial to define the determinants of several clinical conditions and thus identify complementary biomarkers [196, 197] and new therapeutic targets [198] based on nutritional and transplantation interventions.

6. Conclusion and Future Perspectives

The identification of specific biomarkers that may aid in the development of noninvasive tools for the assessment and monitoring of the relationship between inflammation and muscle wasting conditions has been sought for a long time. Current research efforts on specific “danger molecules” that stimulate sterile inflammation and link this process with muscular mitochondrial dysfunction could enhance our understanding of muscle wasting pathophysiology. Results from several studies indicate the relevant contribution of microbial changes and activity in the gut to the repertoire of inflammatory molecules involved in the milieu characterizing muscle aging. This represents an important matter to be addressed by future investigations to unravel the signaling pathways that may serve as targets for interventions.

Conflicts of Interest

Emanuele Marzetti, Francesco Landi, and Riccardo Calvani are partners of the SPRINTT consortium, which is partly funded by the European Federation of Pharmaceutical Industries and Associations (EFPIA). Emanuele Marzetti served as a consultant for Huron Consulting Group, Genactis, Novartis, and Nutricia. Riccardo Calvani served as a consultant from Novartis and Nutricia. All other authors have no competing financial interests to declare.

Acknowledgments

This work was supported by the Fondazione Roma (NCDs Call for Proposals 2013), the Innovative Medicine Initiative-Joint Undertaking (IMI-JU no. 115621), intramural research grant from the Catholic University of the Sacred Heart (D3.2 2013 and D3.2 2015), and the nonprofit research foundation “Centro Studi Achille e Linda Lorenzon.”

References

  1. M. Cesari, E. Marzetti, U. Thiem et al., “The geriatric management of frailty as paradigm of “the end of the disease era”,” European Journal of Internal Medicine, vol. 31, pp. 11–14, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. E. Marzetti, R. Calvani, M. Tosato et al., “Sarcopenia: an overview,” Aging Clinical and Experimental Research, vol. 29, no. 1, pp. 11–17, 2017. View at Publisher · View at Google Scholar
  3. F. Landi, R. Calvani, M. Cesari et al., “Sarcopenia: an overview on current definitions, diagnosis and treatment,” Current Protein & Peptide Science, vol. 18, 2017. View at Publisher · View at Google Scholar
  4. M. Altun, M. Grönholdt-Klein, L. Wang, and B. Ulfhake, “Cellular degradation machineries in age-related loss of muscle mass (sarcopenia),” in Senescence, T. Nagata, Ed., InTech, 2012. View at Publisher · View at Google Scholar
  5. I. Cho and M. J. Blaser, “The human microbiome: at the interface of health and disease,” Nature Reviews Genetics, vol. 13, no. 4, pp. 260–270, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Heintz and W. Mair, “You are what you host: microbiome modulation of the aging process,” Cell, vol. 156, no. 3, pp. 408–411, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. W. J. Lee and K. Hase, “Gut microbiota-generated metabolites in animal health and disease,” Nature Chemical Biology, vol. 10, no. 6, pp. 416–424, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. F. Cabreiro, C. Au, K. Y. Leung et al., “Metformin retards aging in C. elegans by altering microbial folate and methionine metabolism,” Cell, vol. 153, no. 1, pp. 228–239, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. M. J. Claesson, I. B. Jeffery, S. Conde et al., “Gut microbiota composition correlates with diet and health in the elderly,” Nature, vol. 488, no. 7410, pp. 178–184, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. T. Yatsunenko, F. E. Rey, M. J. Manary et al., “Human gut microbiome viewed across age and geography,” Nature, vol. 486, no. 7402, pp. 222–227, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. S. M. Bakhtiar, J. G. LeBlanc, E. Salvucci et al., “Implications of the human microbiome in inflammatory bowel diseases,” FEMS Microbiology Letters, vol. 342, no. 1, pp. 10–17, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Lederberg and A. T. McCray, “Ome sweet omics: a genealogical treasury of words,” The Scientist, vol. 15, p. 8, 2001. View at Google Scholar
  13. L. K. Ursell, J. L. Metcalf, L. Wegener Parfrey, and R. Knight, “Defining the human microbiome,” Nutrition Reviews, vol. 70, Supplement 1, pp. S38–S44, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Xu and J. I. Gordon, “Honor thy symbionts,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 18, pp. 10452–10459, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. S. R. Gill, M. Pop, R. T. DeBoy et al., “Metagenomic analysis of the human distal gut microbiome,” Science, vol. 312, no. 5778, pp. 1355–1359, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Qin, R. Li, J. Raes et al., “A human gut microbial gene catalogue established by metagenomic sequencing,” Nature, vol. 464, no. 7285, pp. 59–65, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Huttenhower, D. Gevers, R. Knight et al., “Structure, function and diversity of the healthy human microbiome,” Nature, vol. 486, no. 7402, pp. 207–214, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. S. M. Huse, Y. Ye, Y. Zhou, and A. A. Fodor, “A core human microbiome as viewed through 16S rRNA sequence clusters,” PLoS One, vol. 7, no. 6, article e34242, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. E. M. O’Connor, “The role of gut microbiota in nutritional status,” Current Opinion in Clinical Nutrition and Metabolic Care, vol. 16, no. 5, pp. 509–516, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. J. G. LeBlanc, C. Milani, G. S. de Giori, F. Sesma, D. van Sinderen, and M. Ventura, “Bacteria as vitamin suppliers to their host: a gut microbiota perspective,” Current Opinion in Biotechnology, vol. 24, no. 2, pp. 160–168, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. L. V. Hooper, D. R. Littman, and A. J. Macpherson, “Interactions between the microbiota and the immune system,” Science, vol. 336, no. 6086, pp. 1268–1273, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. T. D. Leser and L. Molbak, “Better living through microbial action: the benefits of the mammalian gastrointestinal microbiota on the host,” Environmental Microbiology, vol. 11, no. 9, pp. 2194–2206, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. V. Tremaroli and F. Bäckhed, “Functional interactions between the gut microbiota and host metabolism,” Nature, vol. 489, no. 7415, pp. 242–249, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. J. F. Rehfeld, “A centenary of gastrointestinal endocrinology,” Hormone and Metabolic Research, vol. 36, no. 11/12, pp. 735–741, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Stecher and W. D. Hardt, “The role of microbiota in infectious disease,” Trends in Microbiology, vol. 16, no. 3, pp. 107–114, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. A. J. Macpherson, M. B. Geuking, and K. D. McCoy, “Immune responses that adapt the intestinal mucosa to commensal intestinal bacteria,” Immunology, vol. 115, no. 2, pp. 153–162, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. J. L. Round and S. K. Mazmanian, “The gut microbiota shapes intestinal immune responses during health and disease,” Nature Reviews Immunology, vol. 9, no. 5, pp. 313–323, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Yamanaka, L. Helgeland, I. N. Farstad, H. Fukushima, T. Midtvedt, and P. Brandtzaeg, “Microbial colonization drives lymphocyte accumulation and differentiation in the follicle-associated epithelium of Peyer’s patches,” The Journal of Immunology, vol. 170, no. 2, pp. 816–822, 2003. View at Publisher · View at Google Scholar
  29. D. Artis, “Epithelial-cell recognition of commensal bacteria and maintenance of immune homeostasis in the gut,” Nature Reviews Immunology, vol. 8, no. 6, pp. 411–420, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. E. Biagi, L. Nylund, M. Candela et al., “Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians,” PLoS One, vol. 5, no. 5, article e10667, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. J. F. Cryan and S. M. O’Mahony, “The microbiome-gut-brain axis: from bowel to behavior,” Neurogastroenterology & Motility, vol. 23, no. 3, pp. 187–192, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. E. A. Mayer, “Gut feelings: the emerging biology of gut-brain communication,” Nature Reviews Neuroscience, vol. 12, no. 8, pp. 453–466, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. F. Sommer and F. Bäckhed, “The gut microbiota - masters of host development and physiology,” Nature Reviews Microbiology, vol. 11, no. 4, pp. 227–238, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. K. Sjögren, C. Engdahl, P. Henning et al., “The gut microbiota regulates bone mass in mice,” Journal of Bone and Mineral Research, vol. 27, no. 6, pp. 1357–1367, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. G. G. Berntson, M. Sarter, and J. T. Cacioppo, “Ascending visceral regulation of cortical affective information processing,” European Journal of Neuroscience, vol. 18, no. 8, pp. 2103–2109, 2003. View at Publisher · View at Google Scholar · View at Scopus
  36. S. M. Collins, M. Surette, and P. Bercik, “The interplay between the intestinal microbiota and the brain,” Nature Reviews Microbiology, vol. 10, no. 11, pp. 735–742, 2012. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Kumar, P. Babaei, B. Ji, and J. Nielsen, “Human gut microbiota and healthy aging: recent developments and future prospective,” Nutrition and Healthy Aging, vol. 4, no. 1, pp. 3–16, 2016. View at Publisher · View at Google Scholar
  38. C. F. Favier, E. E. Vaughan, W. M. De Vos, and A. D. Akkermans, “Molecular monitoring of succession of bacterial communities in human neonates,” Applied and Environmental Microbiology, vol. 68, no. 1, pp. 219–226, 2002. View at Publisher · View at Google Scholar · View at Scopus
  39. C. Palmer, E. M. Bik, D. B. DiGiulio, D. A. Relman, and P. O. Brown, “Development of the human infant intestinal microbiota,” PLoS Biology, vol. 5, no. 7, article e177, 2007. View at Publisher · View at Google Scholar · View at Scopus
  40. S. Mueller, K. Saunier, C. Hanisch et al., “Differences in fecal microbiota in different European study populations in relation to age, gender, and country: a cross-sectional study,” Applied and Environmental Microbiology, vol. 72, no. 2, pp. 1027–1033, 2006. View at Publisher · View at Google Scholar · View at Scopus
  41. A. Huurre, M. Kalliomäki, S. Rautava, M. Rinne, S. Salminen, and E. Isolauri, “Mode of delivery - effects on gut microbiota and humoral immunity,” Neonatology, vol. 93, no. 4, pp. 236–240, 2008. View at Publisher · View at Google Scholar · View at Scopus
  42. T. A. Ajslev, C. S. Andersen, M. Gamborg, T. I. A. Sørensen, and T. Jess, “Childhood overweight after establishment of the gut microbiota: the role of delivery mode, pre-pregnancy weight and early administration of antibiotics,” International Journal of Obesity, vol. 35, no. 4, pp. 522–529, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. M. J. Hopkins, R. Sharp, and G. T. Macfarlane, “Variation in human intestinal microbiota with age,” Digestive and Liver Disease, vol. 34, Supplement 2, pp. S12–S18, 2002. View at Publisher · View at Google Scholar · View at Scopus
  44. S. Shoaie, P. Ghaffari, P. Kovatcheva-Datchary et al., “Quantifying diet-induced metabolic changes of the human gut microbiome,” Cell Metabolism, vol. 22, no. 2, pp. 320–331, 2015. View at Publisher · View at Google Scholar · View at Scopus
  45. J. Tap, S. Mondot, F. Levenez et al., “Towards the human intestinal microbiota phylogenetic core,” Environmental Microbiology, vol. 11, no. 10, pp. 2574–2584, 2009. View at Publisher · View at Google Scholar · View at Scopus
  46. H. Mäkivuokko, K. Tiihonen, S. Tynkkynen, L. Paulin, and N. Rautonen, “The effect of age and non-steroidal anti-inflammatory drugs on human intestinal microbiota composition,” British Journal of Nutrition, vol. 103, no. 2, pp. 227–234, 2010. View at Publisher · View at Google Scholar · View at Scopus
  47. M. Centanni, S. Turroni, C. Consolandi et al., “The enterocyte-associated intestinal microbiota of breast-fed infants and adults responds differently to a TNF-α-mediated pro-inflammatory stimulus,” PLoS One, vol. 8, no. 11, article e81762, 2013. View at Publisher · View at Google Scholar · View at Scopus
  48. M. J. Claesson, S. Cusack, O. O'Sullivan et al., “Composition, variability, and temporal stability of the intestinal microbiota of the elderly,” Proceedings of the National Academy of Sciences of the United States of America, vol. 108, Supplement 1, pp. 4586–4591, 2011. View at Publisher · View at Google Scholar · View at Scopus
  49. D. Mariat, O. Firmesse, and F. Levenez, “The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age,” BMC Microbiology, vol. 9, no. 1, p. 123, 2009. View at Publisher · View at Google Scholar · View at Scopus
  50. M. Candela, E. Biagi, P. Brigidi, P. W. O’Toole, and W. M. De Vos, “Maintenance of a healthy trajectory of the intestinal microbiome during aging: a dietary approach,” Mechanisms of Ageing and Development, vol. 136-137, pp. 70–75, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. A. E. Pérez-Cobas, M. J. Gosalbes, A. Friedrichs et al., “Gut microbiota disturbance during antibiotic therapy: a multi-omic approach,” Gut, vol. 62, no. 11, pp. 1591–1601, 2013. View at Publisher · View at Google Scholar · View at Scopus
  52. E. J. Woodmansey, M. E. T. McMurdo, G. T. Macfarlane, and S. Macfarlane, “Comparison of compositions and metabolic activities of fecal microbiotas in young adults and in antibiotic-treated and non-antibiotic-treated elderly subjects,” Applied and Environmental Microbiology, vol. 70, no. 10, pp. 6113–6122, 2004. View at Publisher · View at Google Scholar · View at Scopus
  53. Y. Guigoz, J. Doré, and E. J. Schiffrin, “The inflammatory status of old age can be nurtured from the intestinal environment,” Current Opinion in Clinical Nutrition and Metabolic Care, vol. 11, no. 1, pp. 13–20, 2008. View at Publisher · View at Google Scholar · View at Scopus
  54. J. H. Shin, M. Sim, J. Y. Lee, and D. M. Shin, “Lifestyle and geographic insights into the distinct gut microbiota in elderly women from two different geographic locations,” Journal of Physiological Anthropology, vol. 35, no. 1, p. 31, 2016. View at Publisher · View at Google Scholar
  55. A. Ticinesi, C. Milani, F. Lauretani et al., “Gut microbiota composition is associated with polypharmacy in elderly hospitalized patients,” Scientific Reports, vol. 7, no. 1, article 11102, 2017. View at Publisher · View at Google Scholar
  56. P. J. Turnbaugh, M. Hamady, T. Yatsunenko et al., “A core gut microbiome in obese and lean twins,” Nature, vol. 457, no. 7228, pp. 480–484, 2009. View at Publisher · View at Google Scholar · View at Scopus
  57. G. Holleran, L. R. Lopetuso, G. Ianiro et al., “Gut microbiota and inflammatory bowel disease: an update,” Minerva Gastroenterologica e Dietologica, vol. 63, no. 4, pp. 373–384, 2017. View at Publisher · View at Google Scholar
  58. L. Wen, R. E. Ley, P. Y. Volchkov et al., “Innate immunity and intestinal microbiota in the development of type 1 diabetes,” Nature, vol. 455, no. 7216, pp. 1109–1113, 2008. View at Publisher · View at Google Scholar · View at Scopus
  59. N. Larsen, F. K. Vogensen, F. W. J. van den Berg et al., “Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults,” PLoS One, vol. 5, no. 2, article e9085, 2010. View at Publisher · View at Google Scholar · View at Scopus
  60. J. Qin, Y. Li, Z. Cai et al., “A metagenome-wide association study of gut microbiota in type 2 diabetes,” Nature, vol. 490, no. 7418, pp. 55–60, 2012. View at Publisher · View at Google Scholar · View at Scopus
  61. J. Henao-Mejia, E. Elinav, C. Jin et al., “Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity,” Nature, vol. 482, no. 7384, pp. 179–185, 2012. View at Publisher · View at Google Scholar · View at Scopus
  62. J. F. Cryan and T. G. Dinan, “Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour,” Nature Reviews Neuroscience, vol. 13, no. 10, pp. 701–712, 2012. View at Publisher · View at Google Scholar · View at Scopus
  63. Q. Li, Y. Han, A. B. C. Dy, and R. J. Hagerman, “The gut microbiota and autism spectrum disorders,” Frontiers in Cellular Neuroscience, vol. 11, article 120, 2017. View at Publisher · View at Google Scholar
  64. C. Jiang, G. Li, P. Huang, Z. Liu, and B. Zhao, “The gut microbiota and Alzheimer’s disease,” Journal of Alzheimer's Disease, vol. 58, no. 1, pp. 1–15, 2017. View at Publisher · View at Google Scholar
  65. G. Clarke, S. Grenham, P. Scully et al., “The microbiome-gut-brain axis during early life regulates the hippocampal serotonergic system in a sex-dependent manner,” Molecular Psychiatry, vol. 18, no. 6, pp. 666–673, 2013. View at Publisher · View at Google Scholar · View at Scopus
  66. M. Arumugam, J. Raes, E. Pelletier et al., “Enterotypes of the human gut microbiome,” Nature, vol. 473, no. 7346, pp. 174–180, 2011. View at Publisher · View at Google Scholar · View at Scopus
  67. I. B. Jeffery, M. J. Claesson, P. W. O'Toole, and F. Shanahan, “Categorization of the gut microbiota: enterotypes or gradients?” Nature Reviews Microbiology, vol. 10, no. 9, pp. 591-592, 2012. View at Publisher · View at Google Scholar
  68. R. Mathur and G. M. Barlow, “Obesity and the microbiome,” Expert Review of Gastroenterology & Hepatology, vol. 9, no. 8, pp. 1087–1099, 2015. View at Publisher · View at Google Scholar · View at Scopus
  69. A. Damms-Machado, S. Mitra, A. E. Schollenberger et al., “Effects of surgical and dietary weight loss therapy for obesity on gut microbiota composition and nutrient absorption,” BioMed Research International, vol. 2015, Article ID 806248, 12 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  70. G. K. John and G. E. Mullin, “The gut microbiome and obesity,” Current Oncology Reports, vol. 18, no. 7, p. 45, 2016. View at Publisher · View at Google Scholar · View at Scopus
  71. C. Zhang, S. Li, L. Yang et al., “Structural modulation of gut microbiota in life-long calorie-restricted mice,” Nature Communications, vol. 4, article 2163, 2013. View at Publisher · View at Google Scholar · View at Scopus
  72. L. A. David, A. C. Materna, J. Friedman et al., “Host lifestyle affects human microbiota on daily timescales,” Genome Biology, vol. 15, no. 7, article R89, 2014. View at Publisher · View at Google Scholar · View at Scopus
  73. B. D. Muegge, J. Kuczynski, D. Knights et al., “Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans,” Science, vol. 332, no. 6032, pp. 970–974, 2011. View at Publisher · View at Google Scholar · View at Scopus
  74. G. D. Wu, J. Chen, C. Hoffmann et al., “Linking long-term dietary patterns with gut microbial enterotypes,” Science, vol. 334, no. 6052, pp. 105–108, 2011. View at Publisher · View at Google Scholar · View at Scopus
  75. A. Zhernakova, A. Kurilshikov, M. J. Bonder et al., “Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity,” Science, vol. 352, no. 6285, pp. 565–569, 2016. View at Publisher · View at Google Scholar · View at Scopus
  76. S. C. Bischoff, “Microbiota and aging,” Current Opinion in Clinical Nutrition and Metabolic Care, vol. 19, no. 1, pp. 26–30, 2016. View at Publisher · View at Google Scholar · View at Scopus
  77. Food and Agricultural Organization of the United Nations and World Health Organization and World Health Organization, Health and Nutritional Properties of Probiotics in Food Including Powder Milk with Live Lactic Acid Bacteria, 2001, ftp://ftp.fao.org/docrep/fao/009/a0512e/a0512e00.pdf.
  78. B. Sánchez, S. Delgado, A. Blanco-Míguez, A. Lourenço, M. Gueimonde, and A. Margolles, “Probiotics, gut microbiota, and their influence on host health and disease,” Molecular Nutrition & Food Research, vol. 61, no. 1, 2017. View at Publisher · View at Google Scholar
  79. G. R. Gibson and M. B. Roberfroid, “Dietary modulation of the human colonic microbiota: introducing the concept of prebiotics,” The Journal of Nutrition, vol. 125, no. 6, pp. 1401–1412, 1995. View at Google Scholar
  80. H. C. Lee, A. M. Jenner, C. S. Low, and Y. K. Lee, “Effect of tea phenolics and their aromatic fecal bacterial metabolites on intestinal microbiota,” Research in Microbiology, vol. 157, no. 9, pp. 876–884, 2006. View at Publisher · View at Google Scholar · View at Scopus
  81. F. F. Anhê, D. Roy, G. Pilon et al., “A polyphenol-rich cranberry extract protects from diet-induced obesity, insulin resistance and intestinal inflammation in association with increased Akkermansia spp. population in the gut microbiota of mice,” Gut, vol. 64, no. 6, pp. 872–883, 2015. View at Publisher · View at Google Scholar · View at Scopus
  82. D. K. Dahiya, Renuka, M. Puniya et al., “Gut microbiota modulation and its relationship with obesity using prebiotic fibers and probiotics: a review,” Frontiers in Microbiology, vol. 8, article 563, 2017. View at Publisher · View at Google Scholar
  83. M. Rondanelli, A. Giacosa, M. A. Faliva, S. Perna, F. Allieri, and A. M. Castellazzi, “Review on microbiota and effectiveness of probiotics use in older,” World Journal of Clinical Cases, vol. 3, no. 2, pp. 156–162, 2015. View at Publisher · View at Google Scholar
  84. S. Saraswati and R. Sitaraman, “Aging and the human gut microbiota—from correlation to causality,” Frontiers in Microbiology, vol. 5, article 764, 2015. View at Publisher · View at Google Scholar · View at Scopus
  85. E. A. Eloe-Fadrosh, A. Brady, J. Crabtree et al., “Functional dynamics of the gut microbiome in elderly people during probiotic consumption,” MBio, vol. 6, no. 2, article e00231-15, 2015. View at Publisher · View at Google Scholar · View at Scopus
  86. H. M. Kim, D. E. Lee, S. D. Park et al., “Oral administration of Lactobacillus plantarum HY7714 protects hairless mouse against ultraviolet B-induced photoaging,” Journal of Microbiology and Biotechnology, vol. 24, no. 11, pp. 1583–1591, 2014. View at Publisher · View at Google Scholar · View at Scopus
  87. J. Ra, D. E. Lee, S. H. Kim et al., “Effect of oral administration of Lactobacillus plantarum HY7714 on epidermal hydration in ultraviolet B-irradiated hairless mice,” Journal of Microbiology and Biotechnology, vol. 24, no. 12, pp. 1736–1743, 2014. View at Publisher · View at Google Scholar · View at Scopus
  88. D. E. Lee, C. S. Huh, J. Ra et al., “Clinical evidence of effects of Lactobacillus plantarum HY7714 on skin aging: a randomized, double blind, placebo-controlled study,” Journal of Microbiology and Biotechnology, vol. 25, no. 12, pp. 2160–2168, 2015. View at Publisher · View at Google Scholar · View at Scopus
  89. J. J. Jeong, K. A. Kim, Y. J. Hwang, M. J. Han, and D. H. Kim, “Anti-inflammaging effects of Lactobacillus brevis OW38 in aged mice,” Beneficial Microbes, vol. 7, no. 5, pp. 707–718, 2016. View at Publisher · View at Google Scholar · View at Scopus
  90. M. E. Sanders, “Probiotics and microbiota composition,” BMC Medicine, vol. 14, no. 1, p. 82, 2016. View at Publisher · View at Google Scholar · View at Scopus
  91. L. B. Bindels, R. Beck, O. Schakman et al., “Restoring specific lactobacilli levels decreases inflammation and muscle atrophy markers in an acute leukemia mouse model,” PLoS One, vol. 7, no. 6, article e37971, 2012. View at Publisher · View at Google Scholar · View at Scopus
  92. Y. M. Chen, L. Wei, Y. S. Chiu et al., “Lactobacillus plantarum TWK10 supplementation improves exercise performance and increases muscle mass in mice,” Nutrients, vol. 8, no. 4, p. 205, 2016. View at Publisher · View at Google Scholar · View at Scopus
  93. S. Doron and D. R. Snydman, “Risk and safety of probiotics,” Clinical Infectious Diseases, vol. 60, Supplement 2, pp. S129–S134, 2015. View at Publisher · View at Google Scholar · View at Scopus
  94. R. W. Hutkins, J. A. Krumbeck, L. B. Bindels et al., “Prebiotics: why definitions matter,” Current Opinion in Biotechnology, vol. 37, pp. 1–7, 2016. View at Publisher · View at Google Scholar · View at Scopus
  95. P. D. Cani, S. Possemiers, T. Van de Wiele et al., “Changes in gut microbiota control inflammation in obese mice through a mechanism involving GLP-2-driven improvement of gut permeability,” Gut, vol. 58, no. 8, pp. 1091–1103, 2009. View at Publisher · View at Google Scholar · View at Scopus
  96. A. Everard, V. Lazarevic, M. Derrien et al., “Responses of gut microbiota and glucose and lipid metabolism to prebiotics in genetic obese and diet-induced leptin-resistant mice,” Diabetes, vol. 60, no. 11, pp. 2775–2786, 2011. View at Publisher · View at Google Scholar · View at Scopus
  97. L. B. Bindels, A. M. Neyrinck, S. P. Claus et al., “Synbiotic approach restores intestinal homeostasis and prolongs survival in leukaemic mice with cachexia,” The ISME Journal, vol. 10, no. 6, pp. 1456–1470, 2016. View at Publisher · View at Google Scholar · View at Scopus
  98. P. Enck, K. Zimmermann, K. Rusch, A. Schwiertz, S. Klosterhalfen, and J. S. Frick, “The effects of maturation on the colonic microflora in infancy and childhood,” Gastroenterology Research and Practice, vol. 2009, Article ID 752401, 7 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  99. P. D. Cani, A. M. Neyrinck, F. Fava et al., “Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia,” Diabetologia, vol. 50, no. 11, pp. 2374–2383, 2007. View at Publisher · View at Google Scholar · View at Scopus
  100. M. G. Morales, H. Olguín, G. Di Capua, E. Brandan, F. Simon, and C. Cabello-Verrugio, “Endotoxin-induced skeletal muscle wasting is prevented by angiotensin-(1–7) through a p38 MAPK-dependent mechanism,” Clinical Science, vol. 129, no. 6, pp. 461–476, 2015. View at Publisher · View at Google Scholar · View at Scopus
  101. A. Belenguer, S. H. Duncan, A. G. Calder et al., “Two routes of metabolic cross-feeding between Bifidobacterium adolescentis and butyrate-producing anaerobes from the human gut,” Applied and Environmental Microbiology, vol. 72, no. 5, pp. 3593–3599, 2006. View at Publisher · View at Google Scholar · View at Scopus
  102. M. E. Walsh, A. Bhattacharya, K. Sataranatarajan et al., “The histone deacetylase inhibitor butyrate improves metabolism and reduces muscle atrophy during aging,” Aging Cell, vol. 14, no. 6, pp. 957–970, 2015. View at Publisher · View at Google Scholar · View at Scopus
  103. G. E. Walton, E. G. van den Heuvel, M. H. Kosters, R. A. Rastall, K. M. Tuohy, and G. R. Gibson, “A randomised crossover study investigating the effects of galacto-oligosaccharides on the faecal microbiota in men and women over 50 years of age,” British Journal of Nutrition, vol. 107, no. 10, pp. 1466–1475, 2012. View at Publisher · View at Google Scholar · View at Scopus
  104. J. Vulevic, A. Juric, G. E. Walton et al., “Influence of galacto-oligosaccharide mixture (B-GOS) on gut microbiota, immune parameters and metabonomics in elderly persons,” British Journal of Nutrition, vol. 114, no. 4, pp. 586–595, 2015. View at Publisher · View at Google Scholar · View at Scopus
  105. S. Macfarlane, S. Cleary, B. Bahrami, N. Reynolds, and G. T. Macfarlane, “Synbiotic consumption changes the metabolism and composition of the gut microbiota in older people and modifies inflammatory processes: a randomised, double-blind, placebo-controlled crossover study,” Alimentary Pharmacology and Therapeutics, vol. 38, no. 7, pp. 804–816, 2013. View at Publisher · View at Google Scholar · View at Scopus
  106. E. Biagi, M. Candela, S. Turroni, P. Garagnani, C. Franceschi, and P. Brigidi, “Ageing and gut microbes: perspectives for health maintenance and longevity,” Pharmacological Research, vol. 69, no. 1, pp. 11–20, 2013. View at Publisher · View at Google Scholar · View at Scopus
  107. J. Vulevic, A. Drakoularakou, P. Yaqoob, G. Tzortzis, and G. R. Gibson, “Modulation of the fecal microflora profile and immune function by a novel trans-galactooligosaccharide mixture (B-GOS) in healthy elderly volunteers,” The American Journal of Clinical Nutrition, vol. 88, no. 5, pp. 1438–1446, 2008. View at Google Scholar
  108. D. M. Jonkers, “Microbial perturbations and modulation in conditions associated with malnutrition and malabsorption,” Best Practice & Research Clinical Gastroenterology, vol. 30, no. 2, pp. 161–172, 2016. View at Publisher · View at Google Scholar · View at Scopus
  109. A. Fiesel, D. K. Gessner, E. Most, and K. Eder, “Effects of dietary polyphenol-rich plant products from grape or hop on pro-inflammatory gene expression in the intestine, nutrient digestibility and faecal microbiota of weaned pigs,” BMC Veterinary Research, vol. 10, p. 196, 2014. View at Publisher · View at Google Scholar · View at Scopus
  110. O. Rom, H. Korach-Rechtman, T. Hayek et al., “Acrolein increases macrophage atherogenicity in association with gut microbiota remodeling in atherosclerotic mice: protective role for the polyphenol-rich pomegranate juice,” Archives of Toxicology, vol. 91, no. 4, pp. 1709–1725, 2017. View at Publisher · View at Google Scholar · View at Scopus
  111. S. Jafari, G. Y. Meng, M. A. Rajion, M. F. Jahromi, and M. Ebrahimi, “Manipulation of rumen microbial fermentation by polyphenol rich solvent fractions from papaya leaf to reduce green-house gas methane and biohydrogenation of C18 PUFA,” Journal of Agricultural and Food Chemistry, vol. 64, no. 22, pp. 4522–4530, 2016. View at Publisher · View at Google Scholar · View at Scopus
  112. J. Most, J. Penders, M. Lucchesi, G. H. Goossens, and E. E. Blaak, “Gut microbiota composition in relation to the metabolic response to 12-week combined polyphenol supplementation in overweight men and women,” European Journal of Clinical Nutrition, vol. 71, no. 9, pp. 1040–1045, 2017. View at Publisher · View at Google Scholar
  113. J. M. Laparra and Y. Sanz, “Interactions of gut microbiota with functional food components and nutraceuticals,” Pharmacological Research, vol. 61, no. 3, pp. 219–225, 2010. View at Publisher · View at Google Scholar · View at Scopus
  114. T. Magrone, F. Perez de Heredia, E. Jirillo, G. Morabito, A. Marcos, and M. Serafini, “Functional foods and nutraceuticals as therapeutic tools for the treatment of diet-related diseases,” Canadian Journal of Physiology and Pharmacology, vol. 91, no. 6, pp. 387–396, 2013. View at Publisher · View at Google Scholar · View at Scopus
  115. L. S. Zhang and S. S. Davies, “Microbial metabolism of dietary components to bioactive metabolites: opportunities for new therapeutic interventions,” Genome Medicine, vol. 8, no. 1, p. 46, 2016. View at Publisher · View at Google Scholar · View at Scopus
  116. E. M. M. Quigley, “Commentary: synbiotics and gut microbiota in older people – a microbial guide to healthy ageing,” Alimentary Pharmacology and Therapeutics, vol. 38, no. 9, pp. 1141-1142, 2013. View at Publisher · View at Google Scholar · View at Scopus
  117. A. M. Martone, E. Marzetti, R. Calvani et al., “Exercise and protein intake: a synergistic approach against sarcopenia,” BioMed Research International, vol. 2017, Article ID 2672435, 7 pages, 2017. View at Publisher · View at Google Scholar
  118. P. Puiman, B. Stoll, L. Mølbak et al., “Modulation of the gut microbiota with antibiotic treatment suppresses whole body urea production in neonatal pigs,” American Journal of Physiology-Gastrointestinal and Liver Physiology, vol. 304, no. 3, pp. G300–G310, 2013. View at Publisher · View at Google Scholar · View at Scopus
  119. G. T. Macfarlane, C. Allison, S. A. W. Gibson, and J. H. Cummings, “Contribution of the microflora to proteolysis in the human large intestine,” Journal of Applied Bacteriology, vol. 64, no. 1, pp. 37–46, 1988. View at Publisher · View at Google Scholar · View at Scopus
  120. G. T. Macfarlane, J. H. Cummings, S. Macfarlane, and G. R. Gibson, “Influence of retention time on degradation of pancreatic enzymes by human colonic bacteria grown in a 3-stage continuous culture system,” Journal of Applied Bacteriology, vol. 67, no. 5, pp. 521–527, 1989. View at Publisher · View at Google Scholar · View at Scopus
  121. M. J. Morowitz, E. M. Carlisle, and J. C. Alverdy, “Contributions of intestinal bacteria to nutrition and metabolism in the critically ill,” Surgical Clinics of North America, vol. 91, no. 4, pp. 771–785, 2011. View at Publisher · View at Google Scholar · View at Scopus
  122. C. C. Metges, “Contribution of microbial amino acids to amino acid homeostasis of the host,” The Journal of Nutrition, vol. 130, no. 7, pp. 1857S–1864S, 2000. View at Google Scholar
  123. W. G. Bergen and G. Wu, “Intestinal nitrogen recycling and utilization in health and disease,” The Journal of Nutrition, vol. 139, no. 5, pp. 821–825, 2009. View at Publisher · View at Google Scholar · View at Scopus
  124. A. M. Davila, F. Blachier, M. Gotteland et al., “Re-print of “intestinal luminal nitrogen metabolism: role of the gut microbiota and consequences for the host”,” Pharmacological Research, vol. 69, no. 1, pp. 114–126, 2013. View at Publisher · View at Google Scholar · View at Scopus
  125. J. M. W. Wong, R. de Souza, C. W. C. Kendall, A. Emam, and D. J. A. Jenkins, “Colonic health: fermentation and short chain fatty acids,” Journal of Clinical Gastroenterology, vol. 40, no. 3, pp. 235–243, 2006. View at Publisher · View at Google Scholar · View at Scopus
  126. T. Hashimoto, T. Perlot, A. Rehman et al., “ACE2 links amino acid malnutrition to microbial ecology and intestinal inflammation,” Nature, vol. 487, no. 7408, pp. 477–481, 2012. View at Publisher · View at Google Scholar · View at Scopus
  127. C. Franceschi, M. Bonafè, S. Valensin et al., “Inflamm-aging. An evolutionary perspective on immunosenescence,” Annals of the New York Academy of Sciences, vol. 908, no. 1, pp. 244–254, 2000. View at Publisher · View at Google Scholar
  128. A. Salminen, J. Huuskonen, J. Ojala, A. Kauppinen, K. Kaarniranta, and T. Suuronen, “Activation of innate immunity system during aging: NF-kB signaling is the molecular culprit of inflamm-aging,” Ageing Research Reviews, vol. 7, no. 2, pp. 83–105, 2008. View at Publisher · View at Google Scholar · View at Scopus
  129. J. D. Forbes, G. Van Domselaar, and C. N. Bernstein, “The gut microbiota in immune-mediated inflammatory diseases,” Frontiers in Microbiology, vol. 7, article 1081, 2016. View at Publisher · View at Google Scholar · View at Scopus
  130. E. J. Schiffrin, J. E. Morley, A. Donnet-Hughes, and Y. Guigoz, “The inflammatory status of the elderly: the intestinal contribution,” Mutation Research, vol. 690, no. 1-2, pp. 50–56, 2010. View at Publisher · View at Google Scholar · View at Scopus
  131. H. Shapiro, C. A. Thaiss, M. Levy, and E. Elinav, “The cross talk between microbiota and the immune system: metabolites take center stage,” Current Opinion in Immunology, vol. 30, pp. 54–62, 2014. View at Publisher · View at Google Scholar · View at Scopus
  132. S. Guo, R. Al-Sadi, H. M. Said, and T. Y. Ma, “Lipopolysaccharide causes an increase in intestinal tight junction permeability in vitro and in vivo by inducing enterocyte membrane expression and localization of TLR-4 and CD14,” The American Journal of Pathology, vol. 182, no. 2, pp. 375–387, 2013. View at Publisher · View at Google Scholar · View at Scopus
  133. J. J. Senn, P. J. Klover, I. A. Nowak, and R. A. Mooney, “Interleukin-6 induces cellular insulin resistance in hepatocytes,” Diabetes, vol. 51, no. 12, pp. 3391–3399, 2002. View at Publisher · View at Google Scholar
  134. F. Bäckhed, J. K. Manchester, C. F. Semenkovich, and J. I. Gordon, “Mechanisms underlying the resistance to diet-induced obesity in germ-free mice,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 3, pp. 979–984, 2007. View at Publisher · View at Google Scholar · View at Scopus
  135. J. Zhou, B. Liu, C. Liang, Y. Li, and Y. H. Song, “Cytokine signaling in skeletal muscle wasting,” Trends in Endocrinology and Metabolism, vol. 27, no. 5, pp. 335–347, 2016. View at Publisher · View at Google Scholar · View at Scopus
  136. F. Ko, P. Abadir, R. Marx et al., “Impaired mitochondrial degradation by autophagy in the skeletal muscle of the aged female interleukin 10 null mouse,” Experimental Gerontology, vol. 73, pp. 23–27, 2016. View at Publisher · View at Google Scholar · View at Scopus
  137. A. Picca, A. Lezza, C. Leeuwenburgh et al., “Fueling inflamm-aging through mitochondrial dysfunction: mechanisms and molecular targets,” International Journal of Molecular Sciences, vol. 18, no. 5, article E933, 2017. View at Publisher · View at Google Scholar
  138. E. Marzetti, R. Calvani, M. Lorenzi et al., “Association between myocyte quality control signaling and sarcopenia in old hip-fractured patients: results from the sarcopenia in HIp FracTure (SHIFT) exploratory study,” Experimental Gerontology, vol. 80, pp. 1–5, 2016. View at Publisher · View at Google Scholar · View at Scopus
  139. E. Marzetti, M. Lorenzi, F. Landi et al., “Altered mitochondrial quality control signaling in muscle of old gastric cancer patients with cachexia,” Experimental Gerontology, vol. 87, Part A, pp. 92–99, 2016. View at Publisher · View at Google Scholar
  140. A. Picca, A. M. S. Lezza, C. Leeuwenburgh et al., “Circulating mitochondrial DNA at the crossroads of mitochondrial dysfunction and inflammation during aging and muscle wasting disorders,” Rejuvenation Research, 2017. View at Publisher · View at Google Scholar
  141. S. P. van Tongeren, J. P. J. Slaets, H. J. M. Harmsen, and G. W. Welling, “Fecal microbiota composition and frailty,” Applied and Environmental Microbiology, vol. 71, no. 10, pp. 6438–6442, 2005. View at Publisher · View at Google Scholar · View at Scopus
  142. L. Peng, Z. R. Li, R. S. Green, I. R. Holzman, and J. Lin, “Butyrate enhances the intestinal barrier by facilitating tight junction assembly via activation of AMP-activated protein kinase in Caco-2 cell monolayers,” Journal of Nutrition, vol. 139, no. 9, pp. 1619–1625, 2009. View at Publisher · View at Google Scholar · View at Scopus
  143. M. A. Cox, J. Jackson, M. Stanton et al., “Short-chain fatty acids act as antiinflammatory mediators by regulating prostaglandin E2 and cytokines,” World Journal of Gastroenterology, vol. 15, no. 44, pp. 5549–5557, 2009. View at Publisher · View at Google Scholar · View at Scopus
  144. C. Buigues, J. Fernández-Garrido, L. Pruimboom et al., “Effect of a prebiotic formulation on frailty syndrome: a randomized, double-blind clinical trial,” International Journal of Molecular Sciences, vol. 17, no. 6, article E932, 2016. View at Publisher · View at Google Scholar · View at Scopus
  145. I. Zilber-Rosenberg and E. Rosenberg, “Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution,” FEMS Microbiology Reviews, vol. 32, no. 5, pp. 723–735, 2008. View at Publisher · View at Google Scholar · View at Scopus
  146. R. Tripathi, P. Sharma, P. Chakraborty, and P. K. Varadwaj, “Next-generation sequencing revolution through big data analytics,” Frontiers in Life Science, vol. 9, no. 2, pp. 119–149, 2016. View at Publisher · View at Google Scholar · View at Scopus
  147. P. Muir, S. Li, S. Lou et al., “The real cost of sequencing: scaling computation to keep pace with data generation,” Genome Biology, vol. 17, no. 1, article 53, 2016. View at Publisher · View at Google Scholar · View at Scopus
  148. V. Marx, “Biology: the big challenges of big data,” Nature, vol. 498, no. 7453, pp. 255–260, 2013. View at Publisher · View at Google Scholar · View at Scopus
  149. C. A. Mattmann, “Computing: a vision for data science,” Nature, vol. 493, no. 7433, pp. 473–475, 2013. View at Publisher · View at Google Scholar · View at Scopus
  150. C. Auffray, R. Balling, I. Barroso et al., “Making sense of big data in health research: towards an EU action plan,” Genome Medicine, vol. 8, no. 1, p. 71, 2016. View at Publisher · View at Google Scholar · View at Scopus
  151. J. Handelsman, M. R. Rondon, S. F. Brady, J. Clardy, and R. M. Goodman, “Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products,” Chemistry & Biology, vol. 5, no. 10, pp. R245–R249, 1998. View at Publisher · View at Google Scholar
  152. J. Handelsman, J. M. Tiedje, L. Alvarez-Cohen et al., The New Science of Metagenomics: Revealing the Secrets of Our Microbial Planet, The National Academies Press, Washington, DC, 2007. View at Publisher · View at Google Scholar · View at Scopus
  153. L. Jostins, S. Ripke, R. K. Weersma et al., “Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease,” Nature, vol. 491, no. 7422, pp. 119–124, 2012. View at Publisher · View at Google Scholar · View at Scopus
  154. J. K. Nicholson, E. Holmes, J. Kinross et al., “Host-gut microbiota metabolic interactions,” Science, vol. 336, no. 6086, pp. 1262–1267, 2012. View at Publisher · View at Google Scholar · View at Scopus
  155. S. D. Hooper, J. Raes, K. U. Foerstner, E. D. Harrington, D. Dalevi, and P. Bork, “A molecular study of microbe transfer between distant environments,” PLoS One, vol. 3, no. 7, article e2607, 2008. View at Publisher · View at Google Scholar · View at Scopus
  156. J. Handelsman, “Metagenetics: spending our inheritance on the future,” Microbial Biotechnology, vol. 2, no. 2, pp. 138-139, 2009. View at Publisher · View at Google Scholar · View at Scopus
  157. J. M. Janda and S. L. Abbott, “16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls,” Journal of Clinical Microbiology, vol. 45, no. 9, pp. 2761–2764, 2007. View at Publisher · View at Google Scholar · View at Scopus
  158. D. D. Roumpeka, R. J. Wallace, F. Escalettes, I. Fotheringham, and M. Watson, “A review of bioinformatics tools for bio-prospecting from metagenomic sequence data,” Frontiers in Genetics, vol. 8, article 23, 2017. View at Publisher · View at Google Scholar
  159. J. Jovel, J. Patterson, W. Wang et al., “Characterization of the gut microbiome using 16S or shotgun metagenomics,” Frontiers in Microbiology, vol. 7, article 459, 2016. View at Publisher · View at Google Scholar · View at Scopus
  160. J. Ni, Q. Yan, and Y. Yu, “How much metagenomic sequencing is enough to achieve a given goal?” Scientific Reports, vol. 3, no. 1, article 1968, 2013. View at Publisher · View at Google Scholar · View at Scopus
  161. J. Li, H. Jia, X. Cai et al., “An integrated catalog of reference genes in the human gut microbiome,” Nature Biotechnology, vol. 32, no. 8, pp. 834–841, 2014. View at Publisher · View at Google Scholar · View at Scopus
  162. H. Xie, R. Guo, H. Zhong et al., “Shotgun metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome,” Cell Systems, vol. 3, no. 6, pp. 572–584.e3, 2016. View at Publisher · View at Google Scholar · View at Scopus
  163. Q. He, Y. Gao, Z. Jie et al., “Two distinct metacommunities characterize the gut microbiota in Crohn’s disease patients,” Gigascience, vol. 6, no. 7, pp. 1–11, 2017. View at Publisher · View at Google Scholar
  164. V. Aguiar-Pulido, W. Huang, V. Suarez-Ulloa, T. Cickovski, K. Mathee, and G. Narasimhan, “Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis,” Evolutionary Bioinformatics Online, vol. 12, Supplement 1, pp. 5–16, 2016. View at Publisher · View at Google Scholar · View at Scopus
  165. S. Bikel, A. Valdez-Lara, F. Cornejo-Granados et al., “Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome,” Computational and Structural Biotechnology Journal, vol. 13, pp. 390–401, 2015. View at Publisher · View at Google Scholar · View at Scopus
  166. H. H. Lin and Y. C. Liao, “Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes,” Scientific Reports, vol. 6, no. 1, article 24175, 2016. View at Publisher · View at Google Scholar · View at Scopus
  167. D. H. Huson, A. F. Auch, J. Qi, and S. C. Schuster, “MEGAN analysis of metagenomic data,” Genome Research, vol. 17, no. 3, pp. 377–386, 2007. View at Publisher · View at Google Scholar · View at Scopus
  168. D. H. Huson, S. Mitra, H.-J. Ruscheweyh, N. Weber, and S. C. Schuster, “Integrative analysis of environmental sequences using MEGAN4,” Genome Research, vol. 21, no. 9, pp. 1552–1560, 2011. View at Publisher · View at Google Scholar · View at Scopus
  169. D. E. Wood and S. L. Salzberg, “Kraken: ultrafast metagenomic sequence classification using exact alignments,” Genome Biology, vol. 15, no. 3, article R46, 2014. View at Publisher · View at Google Scholar · View at Scopus
  170. R. Ounit, S. Wanamaker, T. J. Close, and S. Lonardi, “CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers,” BMC Genomics, vol. 16, no. 1, article 236, 2015. View at Publisher · View at Google Scholar · View at Scopus
  171. N. Segata, L. Waldron, A. Ballarini, V. Narasimhan, O. Jousson, and C. Huttenhower, “Metagenomic microbial community profiling using unique clade-specific marker genes,” Nature Methods, vol. 9, no. 8, pp. 811–814, 2012. View at Publisher · View at Google Scholar · View at Scopus
  172. V. Vinh le, T. V. Lang, T. Binh le, and T. V. Hoai, “A two-phase binning algorithm using l-mer frequency on groups of non-overlapping reads,” Algorithms for Molecular Biology, vol. 10, no. 1, p. 2, 2015. View at Publisher · View at Google Scholar · View at Scopus
  173. J. Wang, J. Li, J. Doré, S. D. Ehrlich, A. Stamatakis, and P. Bork, “Metagenomic species profiling using universal phylogenetic marker genes,” Nature Methods, vol. 10, no. 12, pp. 1196–1199, 2013. View at Publisher · View at Google Scholar · View at Scopus
  174. B. Yang, Y. Peng, H. C. M. Leung et al., “MetaCluster: unsupervised binning of environmental genomic fragments and taxonomic annotation,” in BCB '10 Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, pp. 170–179, Niagara Falls, New York, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  175. Y. W. Wu and Y. Ye, “A novel abundance-based algorithm for binning metagenomic sequences using l-tuples,” Journal of Computational Biology, vol. 18, no. 3, pp. 523–534, 2011. View at Publisher · View at Google Scholar · View at Scopus
  176. S. Chatterji, I. Yamazaki, Z. Bai, and J. A. Eisne, “CompostBin: a DNA composition-based algorithm for binning environmental shotgun reads,” Research in Computational Molecular Biology, vol. 4955, pp. 17–28, 2008. View at Publisher · View at Google Scholar · View at Scopus
  177. N. A. Kennedy, A. W. Walker, S. H. Berry et al., “The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing,” PLoS One, vol. 9, no. 2, article e88982, 2014. View at Publisher · View at Google Scholar · View at Scopus
  178. D.-P. Mao, Q. Zhou, C.-Y. Chen, and Z.-X. Quan, “Coverage evaluation of universal bacterial primers using the metagenomic datasets,” BMC Microbiology, vol. 12, no. 1, p. 66, 2012. View at Publisher · View at Google Scholar · View at Scopus
  179. M. B. Jones, S. K. Highlander, E. L. Anderson et al., “Library preparation methodology can influence genomic and functional predictions in human microbiome research,” Proceedings of the National Academy of Sciences of the United States of America, vol. 112, no. 45, pp. 14024–14029, 2015. View at Publisher · View at Google Scholar
  180. J. C. Dohm, C. Lottaz, T. Borodina, and H. Himmelbauer, “Substantial biases in ultra-short read data sets from high-throughput DNA sequencing,” Nucleic Acids Research, vol. 36, no. 16, article e105, 2008. View at Publisher · View at Google Scholar · View at Scopus
  181. L. Siegwald, H. Touzet, Y. Lemoine, D. Hot, C. Audebert, and S. Caboche, “Assessment of common and emerging bioinformatics pipelines for targeted metagenomics,” PLoS One, vol. 12, no. 1, article e0169563, 2017. View at Publisher · View at Google Scholar
  182. The Human Microbiome Project Consortium, “A framework for human microbiome research,” Nature, vol. 486, no. 7402, pp. 215–221, 2012. View at Publisher · View at Google Scholar · View at Scopus
  183. A. B. Shreiner, J. Y. Kao, and V. B. Young, “The gut microbiome in health and in disease,” Current Opinion in Gastroenterology, vol. 31, no. 1, pp. 69–75, 2015. View at Publisher · View at Google Scholar · View at Scopus
  184. P. W. O’Toole and M. J. Claesson, “Gut microbiota: changes throughout the lifespan from infancy to elderly,” International Dairy Journal, vol. 20, no. 4, pp. 281–291, 2010. View at Publisher · View at Google Scholar · View at Scopus
  185. N. Segata, D. Boernigen, T. L. Tickle, X. C. Morgan, W. S. Garrett, and C. Huttenhower, “Computational meta’omics for microbial community studies,” Molecular Systems Biology, vol. 9, no. 1, p. 666, 2013. View at Publisher · View at Google Scholar · View at Scopus
  186. A. Oulas, C. Pavloudi, P. Polymenakou et al., “Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies,” Bioinformatics and Biology Insights, vol. 9, pp. 75–88, 2015. View at Publisher · View at Google Scholar · View at Scopus
  187. P. D. Schloss, S. L. Westcott, T. Ryabin, J. R. Hall, M. Hartmann, and E. B. Hollister, “Introducing mothur: open-source platform-independent community-supported software for describing and comparing microbial communities,” Applied and Environmental Microbiology, vol. 75, no. 23, pp. 7537–7541, 2009. View at Publisher · View at Google Scholar · View at Scopus
  188. A. L. Hartman, S. Riddle, T. McPhillips, B. Ludäscher, and J. A. Eisen, “Introducing W.A.T.E.R.S.: a workflow for the alignment., taxonomy., and ecology of ribosomal sequences,” BMC Bioinformatics, vol. 11, no. 1, p. 317, 2010. View at Publisher · View at Google Scholar · View at Scopus
  189. Q. Wang, G. M. Garrity, J. M. Tiedje, and J. R. Cole, “Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy,” Applied and Environmental Microbiology, vol. 73, no. 16, pp. 5261–5267, 2007. View at Publisher · View at Google Scholar · View at Scopus
  190. S. Sunagawa, D. R. Mende, G. Zeller et al., “Metagenomic species profiling using universal phylogenetic marker genes,” Nature Methods, vol. 10, no. 12, pp. 1196–1199, 2013. View at Publisher · View at Google Scholar · View at Scopus
  191. J. G. Caporaso, J. Kuczynski, J. Stombaugh et al., “QIIME allows analysis of high-throughput community sequencing data,” Nature Methods, vol. 7, no. 5, pp. 335-336, 2010. View at Publisher · View at Google Scholar · View at Scopus
  192. H. Nilakanta, K. L. Drews, S. Firrell, M. A. Foulkes, and K. A. Jablonski, “A review of software for analyzing molecular sequences,” BMC Research Notes, vol. 7, no. 1, p. 830, 2014. View at Publisher · View at Google Scholar · View at Scopus
  193. M. G. I. Langille, J. Zaneveld, J. G. Caporaso et al., “Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences,” Nature Biotechnology, vol. 31, no. 9, pp. 814–821, 2013. View at Publisher · View at Google Scholar · View at Scopus
  194. K. P. Aßhauer, B. Wemheuer, R. Daniel, and P. Meinicke, “Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data,” Bioinformatics, vol. 31, no. 17, pp. 2882–2884, 2015. View at Publisher · View at Google Scholar · View at Scopus
  195. S. Iwai, T. Weinmaier, B. L. Schmidt et al., “Piphillin: improved prediction of metagenomic content by direct inference from human microbiomes,” PLoS One, vol. 11, no. 11, article e0166104, 2016. View at Publisher · View at Google Scholar · View at Scopus
  196. R. Calvani, F. Marini, M. Cesari et al., “Biomarkers for physical frailty and sarcopenia: state of the science and future developments,” Journal of Cachexia, Sarcopenia and Muscle, vol. 6, no. 4, pp. 278–286, 2015. View at Publisher · View at Google Scholar · View at Scopus
  197. R. Calvani, A. Miccheli, F. Landi et al., “Current nutritional recommendations and novel dietary strategies to manage sarcopenia,” The Journal of Frailty & Aging, vol. 2, no. 1, pp. 38–53, 2013. View at Google Scholar
  198. L. B. Bindels and N. M. Delzenne, “Muscle wasting: the gut microbiota as a new therapeutic target?” The International Journal of Biochemistry & Cell Biology, vol. 45, no. 10, pp. 2186–2190, 2013. View at Publisher · View at Google Scholar · View at Scopus