Metabolites: a converging node of host and microbe to explain meta-organism

Abstract

Meta-organisms encompassing the host and resident microbiota play a significant role in combatting diseases and responding to stress. Hence, there is growing traction to build a knowledge base about this ecosystem, particularly to characterize the bidirectional relationship between the host and microbiota. In this context, metabolomics has emerged as the major converging node of this entire ecosystem. Systematic comprehension of this resourceful omics component can elucidate the organism-specific response trajectory and the communication grid across the ecosystem embodying meta-organisms. Translating this knowledge into designing nutraceuticals and next-generation therapy are ongoing. Its major hindrance is a significant knowledge gap about the underlying mechanisms maintaining a delicate balance within this ecosystem. To bridge this knowledge gap, a holistic picture of the available information has been presented with a primary focus on the microbiota-metabolite relationship dynamics. The central theme of this article is the gut-brain axis and the participating microbial metabolites that impact cerebral functions.

Introduction

The total number of resident microbiota, or the “collection of microorganisms living in or on the human body,” (Merriam-Webster, n.d.) marginally exceeds the number of human cells (Sender et al., 2016). A 70 kg adult male is estimated to have 39 trillion bacteria that live with 30 trillion human cells, which includes both nucleated and non-nucleated cells, e.g., red blood cells (Sender et al., 2016). The ratio between bacterial and host cells varies from 1.3 in adult males to 2.2 in adult females (Sender et al., 2016). The higher microbial cellular load in adult females is attributed to a unique and complex ecosystem of microbes colonized in the female genital tract (Punzon-Jimenez and Labarta, 2021) and vagina (Fu et al., 2020; Srinivasan et al., 2022), and this ecosystem alters with the menstruation cycle (Krog et al., 2022) and during pregnancy (Punzon-Jimenez and Labarta, 2021). Hence, it is essential to consider this diverse microbial ecosystem across the genders (Auriemma et al., 2021) to make any inference in this field of study.

The endogenous microbiota of the human body is mostly concentrated inside the intestine. The gut microbial community is predominantly enriched by bacteria (10⁹–10¹¹ cells/g) (Sender et al., 2016; Cani, 2018) and archaea (10⁸–10¹⁰ cells/g) (Kim et al., 2020). Another major microbe that colonizes the intestinal lumen is the virus, an intracellular parasite, and the bacteriophage is a major viral species that controls the bacterial diversity in the host (Scanlan, 2017). The bacteria outnumber viruses by approximately 10 to 1, as there are 10⁹–10¹⁰ virus-like particles (VLP) per gram of human feces (Shkoporov and Hill, 2019). While the human genome contains approximately 20,000 genes, the hologenome, a combination of the host and resident microbes, contains over 33 million genes (Lloyd-Price et al., 2016). Ninety-three percent of these genes belong to bacteria (Sender et al., 2016), while viruses claim the second largest share, e.g., 5.8% of total DNA (Arumugam et al., 2011). Together, this gut microbiota fosters a balanced ecosystem with its host, and this interactive milieu is the core feature of the meta-organism (Theis et al., 2016) or holobiont concept (Simon et al., 2019). In theory, the meta-organism or holobiont concept is about studying the holistic host-microbiota interactive sphere that includes biology, ecology, and evolution of both host and resident-microbiota. For the present purpose, we will focus on the biology of meta-organisms.

The functional microbiome (Lam et al., 2015; Berg et al., 2020) is constituted by different omics layers linked to the microbe, namely metagenomics, metatranscriptomics, metaproteomics, and meta-metabolomics, often called metabolomics (Zhang et al., 2019; Zou et al., 2019; Salvato et al., 2021). One of the significant operations of a functional microbiome is to maintain a robust crosstalk between the resident microbe and the host’s peripheral tissues, such as the heart, lungs, kidney, and brain. The gut-brain axis, possibly the most studied subject in this context, embodies the bidirectional communication between the host’s brain and gut commensals that control several brain functions, such as neuroinflammation, neurodegeneration, neurotoxicity, and behavioral, emotional, and memory constructs (Mayer et al., 2015; Cryan et al., 2020).

Eubiosis, or the balanced abundance profile of resident microbiota (Iebba et al., 2016; Al-Rashidi, 2022) fosters a symbiotic relationship with the host when the resources available to the host become systematically shared with its resident microbiota. Eubiotic microbial composition controls inflammation and maintains energy homeostasis and a robust gut-brain axis (Lloyd-Price et al., 2016). In contrast, stressful conditions, such as changes in lifestyle or challenges from foreign elements, elevate the host’s demand for the resources, eventually forcing the host and microbiota to compete for resources from a shared pool. Consequently, as the microbial diversity alters, the adaptive and facultative microbiota proliferate, and the overall ecosystem shifts into dysbiosis that could disrupt the host-microbial communication, including the gut-brain axis (Chen and Devaraj, 2018; Dabke et al., 2019; Louis-Jean and Martirosyan, 2019; Cryan et al., 2020; Xu et al., 2020).

Metabolites are the key information hub of meta-organisms since the host-microbiota communication grid, including the gut-brain axis, is built upon the exchange of metabolites (Ramautar et al., 2013; Wishart, 2019; Wachsmuth et al., 2022). Being the intermediate and derivatives of the biological networks in host cells and microorganisms alike, metabolites appear to be the converging node of the ecosystem (Krautkramer et al., 2021). Illustrating this concept, Figure 1A depicts metabolites as the major node of the crosstalk between the host and its resident microbiota. Figure 1B shows the sizes of various metabolite superfamilies; these metabolite superfamilies are linked to the host, environment, resident microbiome, and their interphases. Microbial metabolites are the smallest in number (Krautkramer et al., 2021).

Figure 1

Rather than curating citations via preset exclusion-inclusion criteria, the present review article selected citations driven by its hypothesis and crafted the review in the following order. The first chapter briefly describes metabolomics, focusing on the available detection tools and pertinent databases to illuminate the metabolites’ association with other omics components. The second chapter describes the resident microbiome, its diversity profile, and how this ecosystem controls the host’s health. The third chapter focuses on the microbial metabolites, e.g., those synthesized by commensals colonized in or on the host. The fourth chapter discusses the gut-brain axis; the potential roles of metabolites and microbiome in supporting this communication framework, and the pathogenesis of the diseases linked to the central nervous system (CNS) are discussed in adjunct. The concluding section highlights the knowledge gap in comprehending meta-organisms and suggests future applications in healthcare and beyond. Significant terms used in this review article are defined in Box 1.

Metabolites: current perspective

Metabolites are the substrate, intermediate, and end product of the biological activities at the cells, tissues, or organ levels (Ramautar et al., 2013; Wishart, 2019). Metabolites are the typical downstream products of the host’s genomic, epigenomic, and proteomic activities. Present capabilities can detect more than 200,000 endogenous metabolites linked to ~1,900 metabolic enzymes encoded in the human genome (Kanehisa and Goto, 2000). In comparison, microbes produce approximately 16,000 metabolites, of which nearly 1,800 are exclusively produced by the microbe community, and the rest are produced by both humans and microbes. In addition, many metabolites are linked to the environment. For instance, ~32,000, ~900, and ~160 metabolites are associated with diet, toxins, and drugs, respectively (Metabolite Statistics, n.d.). Approximately 1 to 3 million xenobiotic metabolites or synthetic products represent drugs, cosmetics, food supplements, pollutants, etc. (Idle and Gonzalez, 2007; Johnson et al., 2012), and many overlap with those linked to the environment. A subset of these metabolites is classified as toxic substances and foreign stimulants to meta-organisms (Gonzalez-Sanchez and DeNicola, 2021).

Emerging knowledge has associated the metabolites’ roles with many biological functions, such as disease pathogenesis (Mamas et al., 2011), immune modulations (Levy et al., 2016), and trans-organ communications (Levy et al., 2016; Frezza, 2017). Indeed, the alterations of biochemical activities due to physiological and psychological stress, exposure to external stimulants, or shifts in diet or lifestyle changes alter the metabolite expressions (Liu et al., 2021) and ultimately influence the overall health of the meta-organism (Milovic et al., 2000; Amaral et al., 2009). Therefore, metabolomics holds time-sensitive information on the molecular activities that co-occur across multiple organisms comprising the host, its resident microbiota, and the overarching environment.

The technical capability of detecting the metabolite has reached a high standard of resolution and robustness in recent years due to the development of cutting-edge tools and highly enriched databases. Table 1 lists the leading tools available to detect and characterize metabolites. Mass spectroscopy-based tools are most popular due to their high throughput and highly sensitive detection capabilities (Dührkop et al., 2021). Recent technological developments in the miniaturization of spectrophotometers have revolutionized their applicability since these tools have become increasingly portable, affordable, and easy to operate in austere conditions (Alseekh et al., 2021). There are supplementary tools available that can further enrich our knowledge of metabolites. For instance, the nuclear magnetic resonance (NMR) spectroscopy can divulge the metabolites’ structures (Wishart et al., 2022). The capability of Raman spectroscopy in detecting metabolites with spatial resolution has recently been adapted to study single-cell metabolomics (Berry and Loy, 2018). A low throughput option, such as histochemistry, presents a unique scope to detect the spatial enrichment of targeted metabolites. The spatial information could be vital in mapping metabolites to the central nervous system (CNS). It can illuminate the differential metabolomic expressions across the blood-brain barrier (BBB). Speculatively, this knowledge can help to understand the roles of metabolites in the gut-brain axis (Mayer et al., 2015).

Development of pertinent databases remains an ongoing effort since the technological capabilities in identifying the metabolites remain a limiting step for constructing the databases. Table 2 lists the leading databases that can help in characterizing the metabolites in three ways. For instance, these databases can (1) find the association of the metabolites to the host’s bio functions, (2) find the association of the metabolites to microbial functions, and (3) facilitate systems integration to link the host and microbiota via metabolite-enriched bio networks and pathways. Of these three types of databases, the host-specific database is possibly at its most mature phase. Part of the reason is that we have yet to fully comprehend the taxonomic determinants of the entire microbial community (Zhu et al., 2019).

There are mounting efforts to comprehend the biological functions of these metabolites and integrate them across various kingdoms (mammals, bacteria, etc.) and viruses based on their functional and biological relationships. These relationships are typically deduced either by statistical methods (e.g., correlative or enrichment analysis of the co-expressed metabolites) or by garnering the biochemical information (Amara et al., 2022) via curating the available literature that helps linking the metabolites to certain diseases (e.g., carcinoma) or biological networks (e.g., HPA axis abnormalities) based on a priori information. Subsequently, this information converges to build ab initio metabolite network topology (Naake and Fernie, 2019). For instance, the IPA and KEGG pipeline listed in Table 2 statistically integrate a priori knowledge to infer a multi-omics associative matrix (Subramanian et al., 2020) with metabolomics as one of the network layers. Next, this network topology could be mapped across the meta-organism to gain insight. Table 2 lists MIMOSA (Noecker et al., 2016), BioTransformer, and gutMGene (Cheng et al., 2022) databases that can inform about meta-organisms by integrating metabolomics across different kingdoms and organisms. This knowledge can lead us to design therapeutic strategies (Shi et al., 2021).

To summarize this chapter, metabolomics is a key sub-discipline of the pan-omics family (Brestoff and Artis, 2015; Bae et al., 2019; Krautkramer et al., 2021; Spivak et al., 2022), and microbial metabolomics is one of its impactful, though small in size components (Krautkramer et al., 2021). Ongoing efforts aim to link the metabolites to their upstream and downstream regulators that could be potential therapeutic targets (Olivotto et al., 1984; Lee and Finkel, 2013). This information-gathering process needs customization since the expression levels of the metabolites are susceptible to the host’s disease pathology, diet, geographical location, and circadian rhythm (Jones et al., 2021). To elaborate, the abundance of trimethylamine-oxide (TMAO), a liver-oxidized product of gut bacteria-derived TMA, is significantly over-expressed not only by certain disease types, such as type-2 diabetes and hepatic/renal diseases but also among the population who consumes fish-enriched diet (Subramaniam and Fletcher, 2018). Further, the sensitivity of metabolite profile to geographic location is underlined by a cross-continent diversification of breast milk-induced metabolite and microbiota (Gómez-Gallego et al., 2018). The compositions and characteristics of fecal microbiota and corresponding metabolites are highly susceptible to many indigenous and exogenous factors. Hence, the accuracy and reproducibility of detecting fecal metabolites critically depend on the study design and sampling protocol (Weinstock, 2012; Scherz et al., 2022). Potentially optimized protocols for fecal sample collection are discussed here (Mathay et al., 2015; Vogtmann et al., 2017; Jones et al., 2021). It is possibly essential to longitudinally collect fecal samples (Jones et al., 2021; Zheng et al., 2022) since it is unlikely that a single sampling of fecal materials can accurately represent the dynamic nature of microbiota.

All these factors should be considered to understand the true impacts of metabolites. In conclusion, systems knowledge integration could be the key to elucidating the relationship dynamics among different kingdoms, ecosystems, and the longitudinal profile of functional microbiota. The following chapters illuminate how metabolites play key roles in determining functional metagenomics—the bidirectional relationship between the microbiota and the host.

Microbial ecosystem: diversity profile and disease pathogenesis

The hologenome encompasses nearly 1,600 times more microbial genes than the host genome (Lloyd-Price et al., 2016). This estimation alone can underscore the inherent complexity of meta-organism, which thrives on a symbiotic bidirectional relationship between host and resident microbiota. This host-microbiota crosstalk is shaped by and contributed to the host and its resident microbe’s coevolution, synchronized interactions with foreign elements, commensalistic association, and ecological or mutualistic collaboration (Ogunrinola et al., 2020); and its cumulative impacts are manifested in the microbial diversity profile (Manor et al., 2020). Therefore, it is contemplated that the quantitation of microorganismal diversity could throw light on disease etiology.

The estimation of microbial diversity primarily depends on the following two factors: richness (measures the number of independent species) and evenness (quantifies the relative abundances of different species). Alpha diversity measures the evenness and richness of microbial profiles within a community (Baczkowski et al., 1998). The routines, namely the Shannon diversity, Simpson diversity, and Chao 1 quantify different features of alpha diversity (GitHub, n.d.). Shannon diversity estimates the effective number of species colonized in a particular community, hence quantifying both evenness and richness (with a weight to evenness) of the microbial profile. Chao 1 estimates the number of species or the total richness of a particular community. Simpson diversity is primarily a dominance index, as this estimation gives more weight to the common or dominant species. On the other hand, beta diversity is the characteristic of the trans-community microbial profile (Baczkowski et al., 1998). The unweighted Unifrac quantifies the presence or absence of different taxa across the communities, whereas the weighted Unifrac considers the abundance of different taxa. The Bray–Curtis index estimates the abundance-based dissimilarity across the communities, while the Jaccard index measures the occurrence (presence vs. absence)-based diversity across the communities (GitHub, n.d.).

A shift from a balanced ecosystem or the dysbiotic ecosystem could be attributed to diet and lifestyle alterations (Ghosh et al., 2013), age (Mariat et al., 2009), obesity (Magne et al., 2020), circadian rhythm (Thaiss et al., 2014, 2015), and disease pathologies, including cancer (Sheflin et al., 2014; Biragyn and Ferrucci, 2018), cardiovascular disorder (Lau et al., 2017), immune dysfunction (de Oliveira et al., 2021), and several psychological illnesses (Sarkar et al., 2018; Parker et al., 2020). For instance, a reduced alpha diversity of gut microbiota was found in young adults (mean age: ~13 years) with attention-deficit hyperactivity disorder (ADHD). The measurement was estimated by Shannon diversity and Chao 1 index, which likely indicated a diminished richness of gut microbe linked to ADHD (Prehn-Kristensen et al., 2018). A contrasting picture emerged from an independent younger (mean age ~8 years) cohort, where Shannon diversity and Chao 1 index of subjects with ADHD emerged higher than that of the healthy baseline (Wang L. J. et al., 2020). These studies highlighted how multiple factors concurrently influence the microbial diversity.

Often, a combinatory analysis of alpha and beta diversity metrics is used to characterize the holistic changes in the microbial ecosystem. For instance, the alpha diversity in the fecal microbiota of cervical cancer patients showed no differences. Still, the beta diversity measured by the weighted Unifrac and Bray–Curtis algorithm revealed a significant difference that underscored a shift in trans-community microbial abundance, but not within a community (Wang Z. et al., 2019). Similarly, physiological stress caused significant alpha and beta diversity in gut microbes among young men. The Shannon dissimilarity and Chao 1 index suggested a shift in microbial richness within a community, while the Bray–Curtis analysis suggested a trans-community shift (Karl et al., 2017).

With the advancement of high-resolution detection technologies, we can now probe individual community members of the gut commensals. The abundance profile of a single microbe or its associative abundance profile with neighboring commensal(s) can deliver highly precise information. For instance, the shifting ratio of Bacteroidetes and Firmicutes has been linked to age (Mariat et al., 2009), obesity (Magne et al., 2020), and so forth. Linking the microbial abundance profile with circadian rhythm, the relative abundance of Lactobacillus was reported to escalate during the resting phase than during the active phase (Thaiss et al., 2014).

There is a growing appreciation for using the microbial diversity profile to develop the next generation intervention strategy. The torchbearer of the success story is the fecal microbiota transplantation (FMT) method that stalled tumor growth (Riquelme et al., 2019), ameliorated cardiovascular illness (Hu et al., 2019), and eliminated pathogenic insults (Hui et al., 2019) by maneuvering the microbial diversity. However, the potential of FMT as a treatment option is possibly limited due to the concerns about this intervention method’s traceability, safety, and standardization process (Osman et al., 2022; Vaughn et al., 2023). Driven by the hypothesis that the hypoxic condition inside the tumor is favorable for anaerobic microorganisms, systematic colonization of anaerobic bacteria successfully arrested the growth and metastasis of tumor cells (Drozdz et al., 2020). Further, the knowledge of dysbiosis helped to customize the diet supplements for immunotherapy to treat carcinoma (Routy et al., 2018).

Systematic manipulation of microbial colonies has emerged as a potential therapeutic option to combat several ailments; nevertheless, a comprehensive understanding of its molecular underpinnings is warranted to make this intervention process robust and effective. In this context, the next chapter highlights the microbial metabolites, which could play a critical role in designing a therapeutic strategy based on microbes. Once integrated with host metabolites, this knowledge could illuminate the biological underpinnings of their symbiotic relationship and lead to novel therapeutic options.

Microbial metabolites and their spectrum of bio functions

In a homeostatic condition, the expression level of microbial metabolites is controlled by the host’s genetic predisposition, age (Connell et al., 2022), and other environmental factors, such as geographical location, food habits, and various lifestyle traits (Gupta et al., 2020; Krautkramer et al., 2021). Interestingly, there are less than two thousand microbial-specific metabolites in human compared to more than two hundred thousand host metabolites. However, the size of the microbial genome far outnumbers the human genome (Lloyd-Price et al., 2016; Metabolite Statistics, n.d.). A small number of microbial metabolites is potentially attributed to a rather streamlined metabolic function performed by the microorganisms. Ahmed et al. (2022) and Krautkramer et al. (2021) extensively reviewed these microbial-derived metabolites and reported how their biological functions reach various peripheral tissues to ensure the host’s health and fitness.

Shifting microbial diversity from its eubiotic state is effectively mirrored by the microbial metabolite profile. Therefore, multiple ongoing efforts aim to manipulate the microbial metabolites to improve host’s health; however, these undertakings meet a significant challenge due to the lack of pertinent knowledge (Entwistle et al., 2019; Brunet et al., 2020). After screening several available literature (Yano et al., 2015; Rath et al., 2017; Fung et al., 2019; Wang S. P. et al., 2020; Krautkramer et al., 2021; Mou et al., 2021; Otaru et al., 2021; Ahmed et al., 2022), a bacteria-metabolite relationship network (Figure 2) was developed. This network informs the biological sites (e.g., bacteria) where a particular family of metabolites is synthesized. It is important to note that these networks are primarily built upon a priori knowledge; hence, the validity of such networks depends on continuous cross-checking of the literature and experimental feedback (Amara et al., 2022). Secondly, similar associative networks linking metabolites with other organisms, such as viruses and archaea, are essential to fully characterize the microbial metabolites’ functional outreach, and this aspect is yet underdeveloped. Table 2 presents the databases that aim to bridge this knowledge gap, and Figure 2 maps these microbial metabolites to their bacterial source.

Figure 2

Secondary bile acid (SBA) and short-chain fatty acids (SCFAs) are two major microbial metabolites in human. High-fat diets trigger the biosynthesis of SBA in the gut lumen (Ridlon et al., 2016; Zeng et al., 2019). Deoxycholic acid and lithocholic acid are two important SBAs produced by the dehydroxylation of primary bile acids (Ridlon et al., 2016; Zeng et al., 2019). In addition, gut microbiota epimerizes and oxidizes the primary and secondary bile acids to produce SBAs of iso-, alo-, oxo- and keto- families, which are comparatively low expressed entities, and their functional attributes are yet to be fully comprehended (Wahlstrom et al., 2016).

SCFAs are primarily comprised of acetate, propionate, and butyrate in an approximate molar ratio of 60:20:20, respectively, in a healthy host. Additional low-abundant SCFAs include fumarate, succinate, lactate, and pyruvate, which are used for cross-feeding among the different microbes (Macfarlane and Macfarlane, 2003; Silva et al., 2020). Anaerobic fermentation of non-digestible carbohydrates is the primary course of SCFAs production, as the pentose phosphate pathway and the Embden–Meyerhof–Parnas Glycolytic pathway catabolize five-carbon (e.g., xylene, pectins) and six-carbon (e.g., fructose, sucrose, starch, cellulose, etc.) substrates, respectively, to produce SCFA (Krautkramer et al., 2021). Briefly, the common end-product of the pentose phosphate pathway and Glycolytic pathway is phosphoenolpyruvate, which converts to pyruvate by endothermic cyclic conversion between NADPH and NAD+. Pyruvate is essentially the upstream substrate of all major SCFAs. For instance, the acetate and butyrate are produced from pyruvate via acetyl Co-A intermediate, with carbon dioxide and ethanol being the major byproducts, and propionate is generated via the succinate pathway utilizing carbon dioxide as the major co-factor (Fernandez-Veledo and Vendrell, 2019). Lactate and formate are additional downstream products from pyruvate metabolism. The production of these metabolites in microbes is typically controlled by access to dietary resources and stress factors. For instance, acetate and lactate are typically produced when carbohydrates are in excess; on the other hand, limited energy to the microbial community generally escalates the synthesis of propionate (Macfarlane and Macfarlane, 2003).

The scarcity of carbohydrates and/or high colonic pH induces the generation of SCFAs via protein fermentation (Neis et al., 2015). In addition to SCFAs, the protein fermentation in microbes produces folates, phenols, and indoles. Catabolism of branched-chain amino acids, such as leucine, isoleucine, and valine, produce isobutyrate and isovalerate, which are usually accumulated in low concentrations (Macfarlane and Macfarlane, 2003; Silva et al., 2020). Kynurenines and serotonin are the major products of the catabolism of tryptophan, an essential amino acid (Ostapiuk and Urbanska, 2022). Nearly 90% of the serotonin in the human body is produced by the colon, particularly the enterochromaffin cells on colonic epithelia. Currently, there is no direct evidence that microbiota produces serotonin, although such a possibility cannot be overruled since the microbiome encodes some contigs that typically contribute to the serotonergic network (Yano et al., 2015; Fung et al., 2019). In addition, a reduced concentration of serotonin was reported in concurrence with an increased abundance of tryptophan in germ-free mice, which indirectly suggests a microbial influence on serotonin production (Strasser et al., 2016). Another essential amino acid, namely histidine, undergoes decarboxylation in various bacterial species to produce histamine, and this recent discovery could have great potential in the field of allergic and immune therapy (Mou et al., 2021). Non-essential amino acids, such as tyrosine and L-dopa, and SCFAs, particularly butyrate, are metabolized in various bacterial species to produce dopamine, a key modulator of the gut-brain axis (Villageliu and Lyte, 2018).

Microbial metabolites could have beneficial or toxic roles depending on the metabolites’ expression levels and the target organ. For instance, low expression of SBAs (5–50 μM) promotes proliferation and invasiveness of colon cancer cells. Still, at higher expression levels (>50 μM), SBAs inhibit the colonic cell cycle and activate cancer cell apoptosis (Milovic et al., 2000; Amaral et al., 2009). Kynurenic acid operates differentially in different organs; in the CNS, kynurenic acid acts as a neuroprotective agent but inhibits insulin synthesis in the liver and kidney (Ostapiuk and Urbanska, 2022). SCFAs have various functions in bioenergy production, maintaining gut integrity, and promoting anti-inflammation via reactive oxygen species production (Tan et al., 2014). On the other hand, TMAO, a phosphatidylcholine derivative, is linked to oxidative stress, hyperlipidemia, and pro-inflammation (Agus et al., 2021).

In this context, there is growing traction about how gut microbiota communicate with peripheral organs. For instance, the dysbiosis of gut commensal has been linked to pulmonary health and asthma (Hufnagl et al., 2020), liver immunology (Trebicka et al., 2021), kidney failure (Zaky et al., 2021), and multiple cancer pathogenesis (Alexander and Turnbaugh, 2020; Sánchez-Alcoholado et al., 2020; Zheng et al., 2020; Kandalai et al., 2023; Xia et al., 2023). Indeed, an in vivo model suggested how microbial metabolites are associated with circadian rhythm and its disruption (Tahara et al., 2018). The roles of microbial metabolites in bioenergy production, somatic inflammation (Alexander and Turnbaugh, 2020), and physiological performances (Borrego-Ruiz and Borrego, 2024) have been studied extensively. Because psychological issues have become a prevalent health concern in the modern world, the study on the gut-brain axis is at the epicenter of the field of host-microbiome study. Figure 3 illustrates the functional association of these microbial metabolites to various brain diseases and co-morbidities, and the next chapter is focused on the gut microbiome-metabolite-brain axis.

Figure 3

Gut microbiota-metabolite-brain axis

The gut microbiota-brain axis is a feedback-controlled circuit that recruits metabolites as the primary messenger between the gut and brain (Mayer et al., 2015; Ahmed et al., 2022). Due to the limited access to the human brain, the concept of the gut-brain axis has thus far been primarily built upon animal models. Mounting evidence shows that the gut microbiota-metabolite-brain axis controls the neurophysiology and signaling networks and regulates a spectrum of behavioral constructs. The association between the resident commensals and cerebral health was suggested by interrogating a colony of germ-free mice that demonstrated inhibited expression of tight junction proteins on BBB coupled with escalated BBB permeability (Braniste et al., 2014; Parker et al., 2020). Similarly, longitudinal antibiotic treatment reduced the abundance of gut commensals in mice, and this cohort demonstrated a cognitive deficiency and memory loss, as well as diminished neurogenesis in the hippocampus, the primary brain region to process memory (Fröhlich et al., 2016). Increased activity of a major component of the sympathetic nervous system in germ-free mice, namely the hypothalamic-pituitary-adrenal (HPA) axis, potentially orchestrated elevated Bdnf gene expressions (Manca et al., 2020) and accumulation of corticosterone in the bloodstream (Neufeld et al., 2011). Linking the gut commensal to social skill development, Desbonnet et al. (2014) reported that the germ-free mice showed increased social avoidance and self-grooming along with decreased social engagement. This negative outcome was reversed by administrating probiotic diets that helped growing the commensals in the host (Desbonnet et al., 2014; Fröhlich et al., 2016).

Differential microbial signatures, such as a shift in the diversity of the entire ecosystem (Uronis et al., 2011; Prehn-Kristensen et al., 2018; Tran et al., 2019; Wang L. J. et al., 2020) or a change in the comparative abundance of selected taxonomic groups (e.g., Firmicutes and Bacteroidetes ratio) (Mariat et al., 2009; Magne et al., 2020) are linked to various psychological deficiencies and co-morbidities. To elaborate, traumatic brain injury and post-traumatic stress disorder—the two most prevalent psychological disorders of modern warfare—are linked to fecal microbiota. For instance, a social stress mouse model stimulating PTSD-like traits caused a time-independent shift in the overall abundance profile of gut bacteria alongside a time-resolved alteration of the Firmicutes and Bacteroidetes ratio (Gautam et al., 2018; Hoke et al., 2022). Concurrently, these stressed mice displayed behavioral deficiencies, metabolic dysfunction, neurotoxicity (Muhie et al., 2017), and a shift in the neuronal morphology in the hippocampus, amygdala, and prefrontal cortex (Anilkumar et al., 2022). In fecal samples, the total abundance of bacterial phyla, namely Actinobacteria, Lentisphaerae, and Verrucomicrobia, was directly associated with the increased PTSD index (Hemmings et al., 2017). The proliferation of obligate anaerobic bacteria such as Deferribacteres in fecal samples of traumatic brain injury (TBI) patients potentially highlights that TBI triggers a hypoxic condition in the colonic lumen due to endogenous energy deprivation (Nicholson et al., 2019; Angoa-Pérez et al., 2020).

Gut-brain communication is facilitated by transferring neurotransmitters and neuromodulators via the bloodstream or lymphatic systems and synaptic transmission via the vagus nerve (VN). Most bloodborne microbial metabolites cross the BBB via carrier proteins and receptors expressed on BBB or by compromising BBB integrity (Ahmed et al., 2022). VN, a significant component of the parasympathetic nervous system, constitutes a parallel communication channel between the gut microbiota and the nervous system, encompassing the CNS and enteric nervous system (ENS) (Parker et al., 2020). In vivo studies observed that the microbial population was not directly connected to the VN because the nerve fibers do not penetrate the gut epithelia. Instead, the changes in the microbial ecosystem and corresponding shifts in metabolite levels perturb the neuroendocrine and endocrine signals of the host gastrointestinal tract; downstream signal travels to the brain via a series of receptors on the vagal afferents and neuropod cell-mediated synaptic transmission (Parker et al., 2020; Ahmed et al., 2022).

The intestinal epithelia play an integral role in the communication between brain and gut microbiota. Perturbed by the stress and environmental shifts, the neurons, immune cells, and mucosal cells of the intestinal epithelia release catecholamines, serotonin, dynorphin, and cytokines into the gut lumen. Playing a critical role in this process, the enterochromaffin cells in intestinal epithelia potentially act as the first line of the recipient of information about food intake, as it rapidly transmits this knowledge to the brain via activating VNs [sometimes via a single synapse within a millisecond timeframe (Kaelberer et al., 2018)] and/or sending bloodborne messengers to the hypothalamus; henceforth these cells regulate food intake and glycemia (Bohórquez et al., 2015; Gribble and Reimann, 2016). This enterochromaffin cell-mediated serotonin production is modulated by microorganisms such as Clostridium spp. and their metabolites, such as indole, norepinephrine, and butyric acid (Yano et al., 2015; Fung et al., 2019). This serotonin pool, often called the peripheral serotonin (El-Merahbi et al., 2015) functions independently of the serotonin pool generated inside the CNS since serotonin cannot cross BBB (Berger et al., 2009). The role of brain serotonin in controlling mood, sleep, and stress response is comparatively well studied (Berger et al., 2009; Strasser et al., 2016), while we have not fully comprehended the role of peripheral serotonin (El-Merahbi et al., 2015). The SBAs are another major microbial metabolite that cannot cross the BBB, although preliminary indications suggest certain roles for SBAs in modulating the brain functions (Monteiro-Cardoso et al., 2021).

The stress signals released into the intestinal lumen via the gut epithelial cells alter the pH and viscosity of gastrointestinal fluid along with the intestinal lumen’s temperature and pressure (Parker et al., 2020; Ahmed et al., 2022). As a result, the microbial compositions and their motility adapt to best utilize the available resources, such as excess lactate or reduced bioenergy, to survive in unfavorable environments. If the changes in the gut microbial ecosystem exceed normal homeostatic ranges, a therapeutic intervention becomes necessary; furthermore, if these processes are left unchecked, the intestinal epithelia become permeable, allowing the gut microbiome to enter the bloodstream- this condition is known as gut leakage (Chakaroun et al., 2020). Given changes in the functional metagenomics is mirrored by the differentially expressed microbial metabolites.

The altered ecology of commensals differentially secrete a wide variety of neurotransmitters derived from aromatic amino acids, which act on the brain functions as excitatory (e.g., glutamate, dopamine, and acetylcholine) or inhibitory neurotransmitters (e.g., GABA and glycine) (Spivak et al., 2022). It is also important to note that the synthesis and biofunctions of these neurotransmitters are controlled by various co-factors (e.g., age) and multiple peripheral tissues (e.g., kidney and liver). Still, the degree of influences of these co-factors on the gut commensals remains largely unexplained.

Glutamate, the most prominent neurotransmitter in the cortex region of the brain, is regulated by kynurenic acid, and colonic Lactobacillus and Pseudomonas spp. contribute a significant portion of whole-body kynurenic acid accumulation via tryptophan metabolism (Ostapiuk and Urbanska, 2022). The synthesis of dopamine, another excitatory neurotransmitter, is controlled by p-cresol and synthesized in several peripheral tissues and colonic Lactobacillus and Clostridium spp. (Pascucci et al., 2020). In addition to modulating the dopaminergic network, p-cresol regulates the oxytocinergic and opioidergic networks and guides behavioral plasticity (Putnam and Chang, 1858). On the other hand, Bacteroidetes, one of the most abundant fecal microorganisms, encodes glutamate decarboxylase-encoding gene, which is linked to the production of GABA, the major inhibitory neurotransmitter of brain (Otaru et al., 2021).

Clostridia, Fusobacterium, and Acidaminobacter are the primary producers of SCFAs, while Firmicutes, a highly abundant fecal microorganism, mainly secretes butyrate (Eeckhaut et al., 2011). SCFAs, primarily propionate and butyrate, participate in many health-beneficial function (Louis and Flint, 2017); for instance, they control the adaptive immune systems (Louis and Flint, 2017) and, in this process, mitigate neuroinflammation by inhibiting the production of histone deacetylase 1, a proinflammatory cytokine (Song et al., 2022).

In a healthy gut milieu, lactate is used to cross-feed the microbiota for unrelenting SCFA production. In homeostatic conditions, many gut microbes, including Firmicutes, convert lactate to propionate and butyrate (Abedi and Hashemi, 2020), which helps maintain a low accumulation of lactate in the intestinal lumen (Duncan et al., 2004). Increased lactate production is typically pH mediated and often identified as the marker of dysbiosis when Firmicutes and Bacteroidetes get replaced by lactate-producing Actinobacteria, Lactobacillus, and Proteobacteria. This event inhibits the synthesis of butyrate and propionate (Abedi and Hashemi, 2020; Wang S. P. et al., 2020). Interestingly, increased lactate production by gut anaerobes during exercise potentially supports the host’s mitochondrial respiration (Brooks, 2020) and indirectly facilitates cerebral BDNF production via upregulating the Sirtuin1 network. This is likely a dose-dependent impact of lactate on brain, as the mice under the regime of physical exercise showed increased learning and memory retention power (El Hayek et al., 2019).

In addition to lactate, several other microbial metabolites actively participate in neuronal bioenergetics and glucose homeostasis (Zhang et al., 2021). Potentially directed by enterochromaffin cells, microorganisms inside the intestinal lumen produce glucose via gluconeogenesis (Soty et al., 2017), which switches to lactate production during energy deficiency and hypoxia (Brooks, 2020). Although the microbe-induced glucose metabolism has been comparatively well studied (van Olden et al., 2015; Utzschneider et al., 2016), some pertinent aspects still need additional probing. For instance, the energy-modulating hormones, such as peptide YY and GLP-1, are found to be expressed on intestinal epithelia, and corresponding receptors are reportedly expressed in the hypothalamus (Steinert et al., 2011), although the mode of exchange of these hormones between the gut and brains is yet obscured.

Finally, recent studies have suggested a potential association between the host microbial community and the glymphatic system (Hablitz and Nedergaard, 2021), a novel concept that explains the waste clearance pathways exclusively from the mammalian CNS (Meyerhoff et al., 2022). Glymphatic systems’ close associations with BBB and sleep construction, and therefore with the tryptophan pathway and vagal retrograde signaling network, intrigue us to link the gut microbiome to glymphatic systems (Hablitz and Nedergaard, 2021); however, much work is needed to completely understand this mechanism.

Challenges and path forward

Systems interrogation and knowledge integration of functional metagenome, meta-organism, and host omics are gaining traction to uncover the holistic molecular mechanism that drives the host’s stress response. Most of the available data, particularly those focused on the gut-brain axis, are built upon animal models due to the limited availability of the human brain. However, the translational potential of rodent metagenomic data is often contested. The brain morphology of humans and rodents is fundamentally different since the human gyrecephalic brain has distinct convolutions and expansions in the cortex, which facilitate handling a much wider range of emotional structures than that by the smooth-surfaced cortex in the lissencephalic brain of rodent (Sun and Hevner, 2014). Furthermore, the ecosystem of rodent microorganisms is distant from that of human; conversely, domestic animals, such as dogs, have higher metagenomic homology with humans (Wardeh et al., 2015). On the positive side, the enterochromaffin cells of rodents were found genetically homologous to that of human, although their functional homology, particularly under stress, is yet to be fully characterized (Roberts et al., 2019). Some of the customized rodent models, such as the germ-free model and gnotobiotic humanized model, are promoted for greater translational potential (Uzbay, 2019). These models could reduce the high variability of metagenomic data among the research laboratories. Nevertheless, germ-free mice are predisposed to various unique characteristics due to their atypical habitat, limited maternal care, and distinct lifestyles and diets (Mayer et al., 2015). Altogether, these traits potentially limit the translational potential of the outcome derived from germ-free mouse colonies (Mayer et al., 2015).

The true characteristics of the host-gut microbe relationship are still obscure; to begin with, we are still unsure if this relationship is causative or correlative in nature. Comprehension of the host-microbe relationship possibly depends on the following two fundamental pieces of information: (i) how the gut microbe communicates with peripheral organs and (ii) how different microorganisms in the gut lumen, such as bacteria, viruses, etc. crosstalk among themselves. To shed light on the gut microbe-organ axis, this review article has discussed in detail the topic of the gut microbe-brain axis, while the other relationship matrix with gut microbes and other peripheral organs, such as lungs, liver, and kidneys, were cursorily discussed. The inter-organism relationship, particularly the bacteria-virus mutualistic association, has been considered to be the controlling factor of host defense. Enteric virus is mostly represented by bacteriophage and eukaryotic virus (Li et al., 2021), although the characterization of the virus composition has been challenged by limited capability in mapping the virus gene sequences (Minot et al., 2011; Manrique et al., 2016). Given that the bacteriophage can obliterate bacteria, their relationship and comparative abundance in the intestine remains an interesting subject to moot. A significantly high ratio of bacteriophage-to-bacteria at the enteric mucosal surface in comparison to the rest of gut lumen (Barr et al., 2013) is a potential frontline of defense against bacterial infection and thereby regulates many antagonists and beneficial actions including the host defense and immune response (Almeida et al., 2019; Kirsch et al., 2021). Our understanding of additional inter-kingdom relationships, such as bacteria-archaea (Hoegenauer et al., 2022) and bacteria-protozoa (Dubik et al., 2022), are still at their early stages, although emerging studies identified their concerted efforts in disease pathogenesis (Kodio et al., 2020; Mafra et al., 2022).

Needless to say, we have yet to fully characterize the associations between microbial metabolites and disease pathology. One of the possible modalities to meet the knowledge gap is to systematically dissociate a sick gut from its sick host. The null hypothesis could be that the fecal microbe of a sick host cannot adversely affect a healthy host. To support this hypothesis, fecal samples could be collected from the stressed or sick rodents and allowed to colonize them in healthy gnotobiotic rodents using FMT (Wang J. W. et al., 2019). Subsequent analysis can throw light on how a sick microbiota can control the host’s health in the absence of the adverse condition. Concurrent FMT of healthy commensals (i.e., the fecal samples collected from healthy cohort) into sick rodents will help in getting the full scenario about how microbiota communicate with the host to regulate its overall health.

Following the same concept, a surgical deletion or chemical manipulation of VNs can highlight the role of this nervous system in sensing the dietary intake (Brown et al., 2011; Yao et al., 2018) and consequent impacts on humans, such as the change in body weight (Burneo et al., 2002).

The outcome of the abovementioned modalities has been used to customize the traditional nutraceuticals, such as prebiotic, postbiotic, and symbiotic diets, and more novel supplements, namely parabiotic (made from non-viable microorganisms) and postbiotic biotherapies (made from microbial derivatives, such as metabolites) (Nataraj et al., 2020); although these nomenclatures are contested in past (Zolkiewicz et al., 2020; Salminen et al., 2021). Additional therapeutic approaches include genetic domestication of the microbe of interest using gene-editing technology (e.g., CRISPR) or by implanting synthetic promoters in the gut lumen (Mimee et al., 2016; Inda et al., 2019). In the recent past, Bacteroidetes, one of the most abundant gut commensals, was successfully systems-engineered (Mimee et al., 2016). It is a step toward designing a universally applicable tool that can systematically alter the microorganism along with its neighbors (e.g., Firmicutes that co-habitats Bacteroidetes) and/or co-factors (e.g., diet source) to reinforce the host’s response (Wexler, 2007; Inda et al., 2019).

Disease diagnostic and prognostic capabilities based on meta-organism data have shown significant progress. A sophisticated colonoscopy with a miniature camera can monitor a wide range of physiological attributes of the colon, such as pH, temperatures, etc., in real-time (Yung et al., 2016; Kalantar-Zadeh et al., 2017). Ingestible sensor prototypes, such as the “digital pill,” have emerged at the forefront to the disease diagnosis platforms (Kalantar-Zadeh et al., 2017; Beardslee et al., 2020). These advanced sensors can determine the colon oxidation potentials due to the shifts in microbial abundance (Baltsavias et al., 2020) and profile the gases, such as oxygen and hydrogen, emitted inside the colon lumen (Kalantar-Zadeh et al., 2018). Box 2 documents these novel diagnostic concepts along with the more conventional prototypes that monitor microbe and microbial metabolites.

Shifting the focus to its surrounding environment, metagenome and metabolites have shown great potential to enable 360° surveillance outreach. Wastewater surveillance of the microbiome gave a longitudinal profile of spreading COVID-19 infection across different communities (Brumfield et al., 2022). Metabolite monitoring in bryophytes and fish larvae (Sanches-Fernandes et al., 2022) can act as biosensors of toxins or radio-biological attacks on the communities.

Microbial metabolites are the primary intermediatory of the crosstalk between host and microbiota. This is an emerging concept essentially refining the traditional view of postprandial neuronal and hormonal exchange between the brain and gut (Wachsmuth et al., 2022). The microbiome is critically associated with the pathophysiology of several diseases and the host’s adaptive response to stress. Hence, one can anticipate that systematic modulation and monitoring of the microbiome could be the key enabler to combat diseases. Overall, meta-organisms and functional microbiota are the subject of very active research, and we hope to see significant progress in the coming years.

Box 2 An outlook of the conventional to futuristic tools/technologies that could readily benefit from the knowledge of meta-organisms and metabolites.

References

• 1

  AbediE.HashemiS. M. B. (2020). Lactic acid production-producing microorganisms and substrates sources-state of art. Heliyon6:e04974. doi: 10.1016/j.heliyon.2020.e04974

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2

  AgusA.ClementK.SokolH. (2021). Gut microbiota-derived metabolites as central regulators in metabolic disorders. Gut70, 1174–1182. doi: 10.1136/gutjnl-2020-323071

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3

  AhmedH.LeyrolleQ.KoistinenV.KärkkäinenO.LayéS.DelzenneN.et al. (2022). Microbiota-derived metabolites as drivers of gut-brain communication. Gut Microbes14:2102878. doi: 10.1080/19490976.2022.2102878

  • CrossRef
  • Google Scholar

• 4

  AlexanderM.TurnbaughP. J. (2020). Deconstructing mechanisms of diet-microbiome-immune interactions. Immunity53, 264–276. doi: 10.1016/j.immuni.2020.07.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5

  AlmeidaG. M. F.LaantoE.AshrafiR.SundbergL. R. (2019). Bacteriophage adherence to mucus mediates preventive protection against pathogenic bacteria. mBio10:e01984. doi: 10.1128/mBio.01984-19

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6

  Al-RashidiH. E. (2022). Gut microbiota and immunity relevance in eubiosis and dysbiosis. Saudi J. Biol. Sci.29, 1628–1643. doi: 10.1016/j.sjbs.2021.10.068

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  AlseekhS.AharoniA.BrotmanY.ContrepoisK.D’AuriaJ.EwaldJ.et al. (2021). Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat. Methods18, 747–756. doi: 10.1038/s41592-021-01197-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8

  AmaraA.FrainayC.JourdanF.NaakeT.NeumannS.Novoa-del-ToroE. M.et al. (2022). Networks and graphs discovery in metabolomics data analysis and interpretation. Front. Mol. Biosci.9:841373. doi: 10.3389/fmolb.2022.841373

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  AmaralJ. D.VianaR. J.RamalhoR. M.SteerC. J.RodriguesC. M. (2009). Bile acids: regulation of apoptosis by ursodeoxycholic acid. J. Lipid Res.50, 1721–1734. doi: 10.1194/jlr.R900011-JLR200

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10

  Angoa-PérezM.ZagoracB.AnnekenJ. H.BriggsD. I.WintersA. D.GreenbergJ. M.et al. (2020). Repetitive, mild traumatic brain injury results in a progressive white matter pathology, cognitive deterioration, and a transient gut microbiota dysbiosis. Sci. Rep.10:8949. doi: 10.1038/s41598-020-65972-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  AnilkumarS.ChakrabortyN.MeyerhoffJ.CampbellR.MuhieS.HammamiehR.et al. (2022). Longitudinally divergent dendritic spine densities of the hippocampus and amygdala: an impact of aggressor-exposed social stress. Horz. Neurosci. Res.46.

  • Google Scholar

• 12

  ArumugamM.RaesJ.PelletierE.Le PaslierD.YamadaT.MendeD. R.et al. (2011). Enterotypes of the human gut microbiome. Nature473, 174–180. doi: 10.1038/nature09944

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  AuriemmaR. S.ScairatiR.Del VecchioG.LiccardiA.VerdeN.PirchioR.et al. (2021). The vaginal microbiome: a long urogenital colonization throughout woman life. Front. Cell. Infect. Microbiol.11:686167. doi: 10.3389/fcimb.2021.686167

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14

  BaczkowskiA. J.JoanesD. N.ShamiaG. M. (1998). Range of validity of alpha and beta for a generalized diversity index H (alpha, beta) due to good. Math. Biosci.148, 115–128. doi: 10.1016/s0025-5564(97)10013-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  BaeS. A.FangM. Z.RustgiV.ZarblH.AndroulakisI. P. (2019). At the interface of lifestyle, behavior, and circadian rhythms: metabolic implications. Front. Nutr.6:132. doi: 10.3389/fnut.2019.00132

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16

  BaltsaviasS.van TreurenW.WeberM. J.CharthadJ.BakerS.SonnenburgJ. L.et al. (2020). In vivo wireless sensors for gut microbiome redox monitoring. IEEE Trans. Biomed. Eng.67, 1–1830. doi: 10.1109/TBME.2019.2948575

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17

  BarrJ. J.AuroR.FurlanM.WhitesonK. L.ErbM. L.PoglianoJ.et al. (2013). Bacteriophage adhering to mucus provide a non-host-derived immunity. Proc. Natl. Acad. Sci. U.S.A.110, 10771–10776. doi: 10.1073/pnas.1305923110

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 18

  BeardsleeL. A.BanisG. E.ChuS.LiuS.ChapinA. A.StineJ. M.et al. (2020). Ingestible sensors and sensing systems for minimally invasive diagnosis and monitoring: the next frontier in minimally invasive screening. ACS Sens.5, 891–910. doi: 10.1021/acssensors.9b02263

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19

  BergG.RybakovaD.FischerD.CernavaT.VergèsM. C. C.CharlesT.et al. (2020). Microbiome definition re-visited: old concepts and new challenges. Microbiome8:103. doi: 10.1186/s40168-020-00875-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20

  BergerM.GrayJ. A.RothB. L. (2009). The expanded biology of serotonin. Annu. Rev. Med.60, 355–366. doi: 10.1146/annurev.med.60.042307.110802

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  BerryD.LoyA. (2018). Stable-isotope probing of human and animal microbiome function. Trends Microbiol.26, 999–1007. doi: 10.1016/j.tim.2018.06.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22

  BiragynA.FerrucciL. (2018). Gut dysbiosis: a potential link between increased cancer risk in ageing and inflammaging. Lancet Oncol.19, e295–e304. doi: 10.1016/S1470-2045(18)30095-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23

  BohórquezD. V.ShahidR. A.ErdmannA.KregerA. M.WangY.CalakosN.et al. (2015). Neuroepithelial circuit formed by innervation of sensory enteroendocrine cells. J. Clin. Invest.125, 782–786. doi: 10.1172/JCI78361

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24

  Borrego-RuizA.BorregoJ. J. (2024). An updated overview on the relationship between human gut microbiome dysbiosis and psychiatric and psychological disorders. Prog. Neuro-Psychopharmacol. Biol. Psychiatry128:110861. doi: 10.1016/j.pnpbp.2023.110861

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25

  BranisteV.Al-AsmakhM.KowalC.AnuarF.AbbaspourA.TóthM.et al. (2014). The gut microbiota influences blood-brain barrier permeability in mice. Sci. Transl. Med.6:263ra158. doi: 10.1126/scitranslmed.3009759

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  BrestoffJ. R.ArtisD. (2015). Immune regulation of metabolic homeostasis in health and disease. Cell161, 146–160. doi: 10.1016/j.cell.2015.02.022

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27

  BrooksG. A. (2020). Lactate as a fulcrum of metabolism. Redox Biol.35:101454. doi: 10.1016/j.redox.2020.101454

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28

  BrownT. A.WashingtonM. C.MetcalfS. A.SayeghA. I. (2011). The feeding responses evoked by cholecystokinin are mediated by vagus and splanchnic nerves. Peptides32, 1581–1586. doi: 10.1016/j.peptides.2011.06.024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  BrumfieldK. D.LeddyM.UsmaniM.CotruvoJ. A.TienC. T.DorseyS.et al. (2022). Microbiome analysis for wastewater surveillance during COVID-19. mBio13:e0059122. doi: 10.1128/mbio.00591-22

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  BrunetM. A.LeblancS.RoucouX. (2020). Reconsidering proteomic diversity with functional investigation of small ORFs and alternative ORFs. Exp. Cell Res.393:112057. doi: 10.1016/j.yexcr.2020.112057

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  BurneoJ. G.FaughtE.KnowltonR.MorawetzR.KuznieckyR. (2002). Weight loss associated with vagus nerve stimulation. Neurology59, 463–464. doi: 10.1212/wnl.59.3.463

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  CaniP. D. (2018). Human gut microbiome: hopes, threats and promises. Gut67, 1716–1725. doi: 10.1136/gutjnl-2018-316723

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33

  CaspiR.BillingtonR.KeselerI. M.KothariA.KrummenackerM.MidfordP. E.et al. (2020). The MetaCyc database of metabolic pathways and enzymes—a 2019 update. Nucleic Acids Res.48, D445–D453. doi: 10.1093/nar/gkz862

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  ChakarounR. M.MassierL.KovacsP. (2020). Gut microbiome, intestinal permeability, and tissue bacteria in metabolic disease: perpetrators or bystanders?Nutrients12:1082. doi: 10.3390/nu12041082

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35

  ChenX.DevarajS. (2018). Gut microbiome in obesity, metabolic syndrome, and diabetes. Curr. Diab. Rep.18:129. doi: 10.1007/s11892-018-1104-3

  • CrossRef
  • Google Scholar

• 36

  ChengL.QiC.YangH.LuM.CaiY.FuT.et al. (2022). gutMGene: a comprehensive database for target genes of gut microbes and microbial metabolites. Nucleic Acids Res.50, D795–D800. doi: 10.1093/nar/gkab786

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  ConnellE.Le GallG.PontifexM. G.SamiS.CryanJ. F.ClarkeG.et al. (2022). Microbial-derived metabolites as a risk factor of age-related cognitive decline and dementia. Mol. Neurodegener.17:43. doi: 10.1186/s13024-022-00548-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38

  CottretL.FrainayC.ChazalvielM.CabanettesF.GloaguenY.CamenenE.et al. (2018). MetExplore: collaborative edition and exploration of metabolic networks. Nucleic Acids Res.46, W495–W502. doi: 10.1093/nar/gky301

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39

  CryanJ. F.O’RiordanK. J.SandhuK.PetersonV.DinanT. G. (2020). The gut microbiome in neurological disorders. Lancet Neurol.19, 179–194. doi: 10.1016/S1474-4422(19)30356-4

  • CrossRef
  • Google Scholar

• 40

  DabkeK.HendrickG.DevkotaS. (2019). The gut microbiome and metabolic syndrome. J. Clin. Invest.129, 4050–4057. doi: 10.1172/JCI129194

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41

  de OliveiraG. L. V.CardosoC. R. B.TanejaV.FasanoA. (2021). Editorial: intestinal dysbiosis in inflammatory diseases. Front. Immunol.12:727485. doi: 10.3389/fimmu.2021.727485

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  DesbonnetL.ClarkeG.ShanahanF.DinanT. G.CryanJ. F. (2014). Microbiota is essential for social development in the mouse. Mol. Psychiatry19, 146–148. doi: 10.1038/mp.2013.65

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43

  DrozdzM.MakuchS.CieniuchG.WozniakM.ZiolkowskiP. (2020). Obligate and facultative anaerobic bacteria in targeted cancer therapy: current strategies and clinical applications. Life Sci.261:118296. doi: 10.1016/j.lfs.2020.118296

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 44

  DubikM.PileckiB.MoellerJ. B. (2022). Commensal intestinal protozoa-underestimated members of the gut microbial community. Biology11:1742. doi: 10.3390/biology11121742

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  DührkopK.NothiasL. F.FleischauerM.ReherR.LudwigM.HoffmannM. A.et al. (2021). Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat. Biotechnol.39, 462–471. doi: 10.1038/s41587-020-0740-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46

  DuncanS. H.LouisP.FlintH. J. (2004). Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product. Appl. Environ. Microbiol.70, 5810–5817. doi: 10.1128/AEM.70.10.5810-5817.2004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 47

  EeckhautV.Van ImmerseelF.CroubelsS.De BaereS.HaesebrouckF.DucatelleR.et al. (2011). Butyrate production in phylogenetically diverse Firmicutes isolated from the chicken caecum. Microb. Biotechnol.4, 503–512. doi: 10.1111/j.1751-7915.2010.00244.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48

  El HayekL.KhalifehM.ZibaraV.Abi AssaadR.EmmanuelN.KarnibN.et al. (2019). Lactate mediates the effects of exercise on learning and memory through SIRT1-dependent activation of hippocampal brain-derived neurotrophic factor (BDNF). J. Neurosci.39, 2369–2382. doi: 10.1523/JNEUROSCI.1661-18.2019

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49

  El-MerahbiR.LofflerM.MayerA.SumaraG. (2015). The roles of peripheral serotonin in metabolic homeostasis. FEBS Lett.589, 1728–1734. doi: 10.1016/j.febslet.2015.05.054

  • CrossRef
  • Google Scholar

• 50

  EntwistleS.LiX.YinY. (2019). Orphan genes shared by pathogenic genomes are more associated with bacterial pathogenicity. mSystems4:e00290. doi: 10.1128/mSystems.00290-18

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51

  Fernandez-VeledoS.VendrellJ. (2019). Gut microbiota-derived succinate: friend or foe in human metabolic diseases?Rev. Endocr. Metab. Disord.20, 439–447. doi: 10.1007/s11154-019-09513-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52

  FrezzaC. (2017). Mitochondrial metabolites: undercover signalling molecules. Interface Focus7:20160100. doi: 10.1098/rsfs.2016.0100

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  FröhlichE. E.FarziA.MayerhoferR.ReichmannF.JačanA.WagnerB.et al. (2016). Cognitive impairment by antibiotic-induced gut dysbiosis: analysis of gut microbiota-brain communication. Brain Behav. Immun.56, 140–155. doi: 10.1016/j.bbi.2016.02.020

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 54

  FuM.ZhangX.LiangY.LinS.QianW.FanS. (2020). Alterations in vaginal microbiota and associated metabolome in women with recurrent implantation failure. mBio11:e03242. doi: 10.1128/mBio.03242-19

  • CrossRef
  • Google Scholar

• 55

  FungT. C.VuongH. E.LunaC. D. G.PronovostG. N.AleksandrovaA. A.RileyN. G.et al. (2019). Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat. Microbiol.4, 2064–2073. doi: 10.1038/s41564-019-0540-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 56

  GautamA.KumarR.ChakrabortyN.MuhieS.HokeA.HammamiehR.et al. (2018). Altered fecal microbiota composition in all male aggressor-exposed rodent model simulating features of post-traumatic stress disorder. J. Neurosci. Res.96, 1311–1323. doi: 10.1002/jnr.24229

  • CrossRef
  • Google Scholar

• 57

  GhoshS.MolcanE.DeCoffeD.DaiC.GibsonD. L. (2013). Diets rich in n−6 PUFA induce intestinal microbial dysbiosis in aged mice. Br. J. Nutr.110, 515–523. doi: 10.1017/S0007114512005326

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58

  GitHub. (n.d.). Chapter 6 microbiome diversity. Available at: https://microbiome.github.io/OMA/microbiome-diversity.html

  • Google Scholar

• 59

  Gómez-GallegoC.MoralesJ. M.MonleónD.Du ToitE.KumarH.LinderborgK.et al. (2018). Human breast milk NMR metabolomic profile across specific geographical locations and its association with the milk microbiota. Nutrients10:1355. doi: 10.3390/nu10101355

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 60

  Gonzalez-SanchezP.DeNicolaG. M. (2021). The microbiome(s) and cancer: know thy neighbor(s). J. Pathol.254, 332–343. doi: 10.1002/path.5661

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61

  GribbleF. M.ReimannF. (2016). Enteroendocrine cells: chemosensors in the intestinal epithelium. Annu. Rev. Physiol.78, 277–299. doi: 10.1146/annurev-physiol-021115-105439

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62

  GuirimandT.DelmotteS.NavratilV. (2015). VirHostNet 2.0: surfing on the web of virus/host molecular interactions data. Nucleic Acids Res.43, D583–D587. doi: 10.1093/nar/gku1121

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63

  GuptaA.OsadchiyV.MayerE. A. (2020). Brain-gut-microbiome interactions in obesity and food addiction. Nat. Rev. Gastroenterol. Hepatol.17, 655–672. doi: 10.1038/s41575-020-0341-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 64

  HablitzL. M.NedergaardM. (2021). The glymphatic system: a novel component of fundamental neurobiology. J. Neurosci.41, 7698–7711. doi: 10.1523/JNEUROSCI.0619-21.2021

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 65

  HemmingsS. M. J.Malan-MüllerS.van den HeuvelL. L.DemmittB. A.StanislawskiM. A.SmithD. G.et al. (2017). The microbiome in posttraumatic stress disorder and trauma-exposed controls: an exploratory study. Psychosom. Med.79, 936–946. doi: 10.1097/PSY.0000000000000512

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66

  HoegenauerC.HammerH. F.MahnertA.Moissl-EichingerC. (2022). Methanogenic archaea in the human gastrointestinal tract. Nat. Rev. Gastroenterol. Hepatol.19, 805–813. doi: 10.1038/s41575-022-00673-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67

  HokeA.ChakrabortyN.GautamA.HammamiehR.JettM. (2022). Acute and delayed effects of stress eliciting post-traumatic stress-like disorder differentially alters fecal microbiota composition in a male mouse model. Front. Cell. Infect. Microbiol.12:810815. doi: 10.3389/fcimb.2022.810815

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68

  HuX. F.ZhangW. Y.WenQ.ChenW. J.WangZ. M.ChenJ.et al. (2019). Fecal microbiota transplantation alleviates myocardial damage in myocarditis by restoring the microbiota composition. Pharmacol. Res.139, 412–421. doi: 10.1016/j.phrs.2018.11.042

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69

  HufnaglK.Pali-SchollI.Roth-WalterF.Jensen-JarolimE. (2020). Dysbiosis of the gut and lung microbiome has a role in asthma. Semin. Immunopathol.42, 75–93. doi: 10.1007/s00281-019-00775-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70

  HuiW.LiT.LiuW.ZhouC.GaoF. (2019). Fecal microbiota transplantation for treatment of recurrent C. difficile infection: an updated randomized controlled trial meta-analysis. PLoS One14:e0210016. doi: 10.1371/journal.pone.0210016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71

  IdleJ. R.GonzalezF. J. (2007). Metabolomics. Cell Metab.6, 348–351. doi: 10.1016/j.cmet.2007.10.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  IebbaV.TotinoV.GagliardiA.SantangeloF.CacciottiF.TrancassiniM.et al. (2016). Eubiosis and dysbiosis: the two sides of the microbiota. New Microbiol.39, 1–12. PMID:

  • Pubmed Abstract
  • Google Scholar

• 73

  IndaM. E.BrosetE.LuT. K.de la Fuente-NunezC. (2019). Emerging frontiers in microbiome engineering. Trends Immunol.40, 952–973. doi: 10.1016/j.it.2019.08.007

  • CrossRef
  • Google Scholar

• 74

  JohnsonC. H.PattersonA. D.IdleJ. R.GonzalezF. J. (2012). Xenobiotic metabolomics: major impact on the metabolome. Annu. Rev. Pharmacol. Toxicol.52, 37–56. doi: 10.1146/annurev-pharmtox-010611-134748

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75

  JonesJ.ReinkeS. N.AliA.PalmerD. J.ChristophersenC. T. (2021). Fecal sample collection methods and time of day impact microbiome composition and short chain fatty acid concentrations. Sci. Rep.11:13964. doi: 10.1038/s41598-021-93031-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76

  KaelbererM. M.BuchananK. L.KleinM. E.BarthB. B.MontoyaM. M.ShenX.et al. (2018). A gut-brain neural circuit for nutrient sensory transduction. Science361:eaat5236. doi: 10.1126/science.aat5236

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77

  Kalantar-ZadehK.BereanK. J.HaN.ChrimesA. F.XuK.GrandoD.et al. (2018). A human pilot trial of ingestible electronic capsules capable of sensing different gases in the gut. Nat. Electron.1, 79–87. doi: 10.1038/s41928-017-0004-x

  • CrossRef
  • Google Scholar

• 78

  Kalantar-ZadehK.HaN.OuJ. Z.BereanK. J. (2017). Ingestible sensors. ACS Sens.2, 468–483. doi: 10.1021/acssensors.7b00045

  • CrossRef
  • Google Scholar

• 79

  KandalaiS.LiH.ZhangN.PengH.ZhengQ. (2023). The human microbiome and cancer: a diagnostic and therapeutic perspective. Cancer Biol. Ther.24:2240084. doi: 10.1080/15384047.2023.2240084

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80

  KanehisaM.FurumichiM.TanabeM.SatoY.MorishimaK. (2017). KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res.45, D353–D361. doi: 10.1093/nar/gkw1092

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  KanehisaM.GotoS. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res.28, 27–30. doi: 10.1093/nar/28.1.27

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 82

  KanehisaM.SatoY.MorishimaK. (2016). BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol.428, 726–731. doi: 10.1016/j.jmb.2015.11.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83

  KarlJ. P.MargolisL. M.MadslienE. H.MurphyN. E.CastellaniJ. W.GundersenY.et al. (2017). Changes in intestinal microbiota composition and metabolism coincide with increased intestinal permeability in young adults under prolonged physiological stress. Am. J. Physiol. Gastrointest. Liver Physiol.312, G559–G571. doi: 10.1152/ajpgi.00066.2017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84

  KimJ. Y.WhonT. W.LimM. Y.KimY. B.KimN.KwonM. S.et al. (2020). The human gut archaeome: identification of diverse haloarchaea in Korean subjects. Microbiome8:114. doi: 10.1186/s40168-020-00894-x

  • CrossRef
  • Google Scholar

• 85

  KirschJ. M.BrzozowskiR. S.FaithD.RoundJ. L.SecorP. R.DuerkopB. A. (2021). Bacteriophage-bacteria interactions in the gut: from invertebrates to mammals. Annu. Rev. Virol.8, 95–113. doi: 10.1146/annurev-virology-091919-101238

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86

  KodioA.MenuE.RanqueS. (2020). Eukaryotic and prokaryotic microbiota interactions. Microorganisms8:12. doi: 10.3390/microorganisms8122018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87

  KrautkramerK. A.FanJ.BackhedF. (2021). Gut microbial metabolites as multi-kingdom intermediates. Nat. Rev. Microbiol.19, 77–94. doi: 10.1038/s41579-020-0438-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  KrogM. C.HugerthL. W.FranssonE.BashirZ.Nyboe AndersenA.EdfeldtG.et al. (2022). The healthy female microbiome across body sites: effect of hormonal contraceptives and the menstrual cycle. Hum. Reprod.37, 1525–1543. doi: 10.1093/humrep/deac094

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89

  LamK. N.ChengJ.EngelK.NeufeldJ. D.CharlesT. C. (2015). Current and future resources for functional metagenomics. Front. Microbiol.6:1196. doi: 10.3389/fmicb.2015.01196

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90

  LauK.SrivatsavV.RizwanA.NashedA.LiuR.ShenR.et al. (2017). Bridging the gap between gut microbial dysbiosis and cardiovascular diseases. Nutrients9:859. doi: 10.3390/nu9080859

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91

  LeeI. H.FinkelT. (2013). Metabolic regulation of the cell cycle. Curr. Opin. Cell Biol.25, 724–729. doi: 10.1016/j.ceb.2013.07.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92

  LevyM.ThaissC. A.ElinavE. (2016). Metabolites: messengers between the microbiota and the immune system. Genes Dev.30, 1589–1597. doi: 10.1101/gad.284091.116

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93

  LiY.HandleyS. A.BaldridgeM. T. (2021). The dark side of the gut: virome-host interactions in intestinal homeostasis and disease. J. Exp. Med.218:e20201044. doi: 10.1084/jem.20201044

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94

  LiuK. H.OwensJ. A.SaeediB.CohenC. E.BellissimoM. P.NaudinC.et al. (2021). Microbial metabolite delta-valerobetaine is a diet-dependent obesogen. Nat. Metab.3, 1694–1705. doi: 10.1038/s42255-021-00502-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95

  Lloyd-PriceJ.Abu-AliG.HuttenhowerC. (2016). The healthy human microbiome. Genome Med.8:51. doi: 10.1186/s13073-016-0307-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96

  LouisP.FlintH. J. (2017). Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol.19, 29–41. doi: 10.1111/1462-2920.13589

  • CrossRef
  • Google Scholar

• 97

  Louis-JeanS.MartirosyanD. (2019). Nutritionally attenuating the human gut microbiome to prevent and manage metabolic syndrome. J. Agric. Food Chem.67, 12675–12684. doi: 10.1021/acs.jafc.9b04879

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  MacfarlaneS.MacfarlaneG. T. (2003). Regulation of short-chain fatty acid production. Proc. Nutr. Soc.62, 67–72. doi: 10.1079/PNS2002207

  • CrossRef
  • Google Scholar

• 99

  MafraD.RibeiroM.FonsecaL.RegisB.CardozoL. F. M. F.Fragoso dos SantosH.et al. (2022). Archaea from the gut microbiota of humans: could be linked to chronic diseases?Anaerobe77:102629. doi: 10.1016/j.anaerobe.2022.102629

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100

  MagneF.GottelandM.GauthierL.ZazuetaA.PesoaS.NavarreteP.et al. (2020). The Firmicutes/Bacteroidetes ratio: a relevant marker of gut dysbiosis in obese patients?Nutrients12:1474. doi: 10.3390/nu12051474

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101

  MagnúsdóttirS.HeinkenA.KuttL.RavcheevD. A.BauerE.NoronhaA.et al. (2017). Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol.35, 81–89. doi: 10.1038/nbt.3703

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102

  MallickH.RahnavardA.McIverL. J.MaS.ZhangY.NguyenL. H.et al. (2021). Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol.17:e1009442. doi: 10.1371/journal.pcbi.1009442

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103

  MamasM.DunnW. B.NeysesL.GoodacreR. (2011). The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch. Toxicol.85, 5–17. doi: 10.1007/s00204-010-0609-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104

  MancaC.ShenM.BoubertakhB.MartinC.FlamandN.SilvestriC.et al. (2020). Alterations of brain endocannabinoidome signaling in germ-free mice. Biochim. Biophys. Acta Mol. Cell Biol. Lipids1865:158786. doi: 10.1016/j.bbalip.2020.158786

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 105

  ManorO.DaiC. L.KornilovS. A.SmithB.PriceN. D.LovejoyJ. C.et al. (2020). Health and disease markers correlate with gut microbiome composition across thousands of people. Nat. Commun.11:5206. doi: 10.1038/s41467-020-18871-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 106

  ManriqueP.BolducB.WalkS. T.van der OostJ.de VosW. M.YoungM. J. (2016). Healthy human gut phageome. Proc. Natl. Acad. Sci. U.S.A.113, 10400–10405. doi: 10.1073/pnas.1601060113

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 107

  MariatD.FirmesseO.LevenezF.GuimarăesV. D.SokolH.DoréJ.et al. (2009). The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol.9:123. doi: 10.1186/1471-2180-9-123

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 108

  MathayC.HamotG.HenryE.GeorgesL.BelloraC.LebrunL.et al. (2015). Method optimization for fecal sample collection and fecal DNA extraction. Biopreserv. Biobank.13, 79–93. doi: 10.1089/bio.2014.0031

  • CrossRef
  • Google Scholar

• 109

  MayerE. A.TillischK.GuptaA. (2015). Gut/brain axis and the microbiota. J. Clin. Invest.125, 926–938. doi: 10.1172/JCI76304

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 110

  Merriam-Webster. (n.d.). Merriam-Webster dictionary: definition of microbiome. Available at: https://www.merriam-webster.com/dictionary/microbiome

  • Google Scholar

• 111

  Metabolite Statistics. (n.d.). Available at: https://hmdb.ca/statistics

  • Google Scholar

• 112

  MeyerhoffJ.ChakrabortyN.HammamiehR. (2022). Glymphatics: a transformative development in medical neuroscience relevant to injuries in military central nervous system. Mil. Med.187, e1086–e1090. doi: 10.1093/milmed/usab344

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113

  MilovicV.SteinJ.OderaG.GilaniS.MurphyG. M. (2000). Low-dose deoxycholic acid stimulates putrescine uptake in colon cancer cells (Caco-2). Cancer Lett.154, 195–200. doi: 10.1016/s0304-3835(00)00400-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 114

  MimeeM.TuckerA. C.VoigtC. A.LuT. K. (2016). Programming a human commensal bacterium, Bacteroides thetaiotaomicron, to sense and respond to stimuli in the murine gut microbiota. Cell Syst.2:214. doi: 10.1016/j.cels.2016.03.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115

  MinotS.SinhaR.ChenJ.LiH.KeilbaughS. A.WuG. D.et al. (2011). The human gut virome: inter-individual variation and dynamic response to diet. Genome Res.21, 1616–1625. doi: 10.1101/gr.122705.111

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 116

  Monteiro-CardosoV. F.CorlianoM.SingarajaR. R. (2021). Bile acids: a communication channel in the gut-brain axis. Neuromolecular Med.23, 99–117. doi: 10.1007/s12017-020-08625-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117

  MouZ.YangY.HallA. B.JiangX. (2021). The taxonomic distribution of histamine-secreting bacteria in the human gut microbiome. BMC Genomics22:695. doi: 10.1186/s12864-021-08004-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 118

  MuhieS.GautamA.ChakrabortyN.HokeA.MeyerhoffJ.HammamiehR.et al. (2017). Molecular indicators of stress-induced neuroinflammation in a mouse model simulating features of post-traumatic stress disorder. Transl. Psychiatry7:e1135. doi: 10.1038/tp.2017.91

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 119

  NaakeT.FernieA. R. (2019). MetNet: metabolite network prediction from high-resolution mass spectrometry data in R aiding metabolite annotation. Anal. Chem.91, 1768–1772. doi: 10.1021/acs.analchem.8b04096

  • CrossRef
  • Google Scholar

• 120

  NatarajB. H.AliS. A.BehareP. V.YadavH. (2020). Postbiotics-parabiotics: the new horizons in microbial biotherapy and functional foods. Microb. Cell Factories19:168. doi: 10.1186/s12934-020-01426-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 121

  NeisE. P.DejongC. H.RensenS. S. (2015). The role of microbial amino acid metabolism in host metabolism. Nutrients7, 2930–2946. doi: 10.3390/nu7042930

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 122

  NeufeldK. M.KangN.BienenstockJ.FosterJ. A. (2011). Reduced anxiety-like behavior and central neurochemical change in germ-free mice. Neurogastroenterol. Motil.23, 255–e119. doi: 10.1111/j.1365-2982.2010.01620.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 123

  NicholsonS. E.WattsL. T.BurmeisterD. M.MerrillD.ScrogginsS.ZouY.et al. (2019). Moderate traumatic brain injury alters the gastrointestinal microbiome in a time-dependent manner. Shock52, 240–248. doi: 10.1097/SHK.0000000000001211

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124

  NoeckerC.EngA.SrinivasanS.TheriotC. M.YoungV. B.JanssonJ. K.et al. (2016). Metabolic model-based integration of microbiome taxonomic and metabolomic profiles elucidates mechanistic links between ecological and metabolic variation. mSystems1:e00013. doi: 10.1128/mSystems.00013-15

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 125

  OgunrinolaG. A.OyewaleJ. O.OshamikaO. O.OlasehindeG. I. (2020). The human microbiome and its impacts on health. Int. J. Microbiol.2020, 8045646–8045647. doi: 10.1155/2020/8045646

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 126

  OlivottoM.ArcangeliA.CaldiniR.ChevanneM.CipolleschiM. G.Dello SbarbaP. (1984). Metabolic aspects of cell cycle regulation in normal and cancer cells. Toxicol. Pathol.12, 369–373. doi: 10.1177/019262338401200411

  • CrossRef
  • Google Scholar

• 127

  OsmanM.BudreeS.KellyC. R.PanchalP.AllegrettiJ. R.KassamZ.et al. (2022). Effectiveness and safety of fecal microbiota transplantation for Clostridioides difficile infection: results from a 5344-patient cohort study. Gastroenterology163, 319–322. doi: 10.1053/j.gastro.2022.03.051

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 128

  OstapiukA.UrbanskaE. M. (2022). Kynurenic acid in neurodegenerative disorders-unique neuroprotection or double-edged sword?CNS Neurosci. Ther.28, 19–35. doi: 10.1111/cns.13768

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129

  OtaruN.YeK.MujezinovicD.BerchtoldL.ConstanciasF.CornejoF. A.et al. (2021). GABA production by human intestinal Bacteroides spp.: prevalence, regulation, and role in acid stress tolerance. Front. Microbiol.12:656895. doi: 10.3389/fmicb.2021.656895

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 130

  ParkerA.FonsecaS.CardingS. R. (2020). Gut microbes and metabolites as modulators of blood-brain barrier integrity and brain health. Gut Microbes11, 135–157. doi: 10.1080/19490976.2019.1638722

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 131

  PascucciT.ColamartinoM.FioriE.SaccoR.CovielloA.VenturaR.et al. (2020). P-cresol alters brain dopamine metabolism and exacerbates autism-like behaviors in the BTBR mouse. Brain Sci.10:233. doi: 10.3390/brainsci10040233

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 132

  PokusaevaK.JohnsonC.LukB.UribeG.FuY.OezguenN.et al. (2017). GABA-producing Bifidobacterium dentium modulates visceral sensitivity in the intestine. Neurogastroenterol. Motil.29:e12904. doi: 10.1111/nmo.12904

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133

  Prehn-KristensenA.ZimmermannA.TittmannL.LiebW.SchreiberS.BavingL.et al. (2018). Reduced microbiome alpha diversity in young patients with ADHD. PLoS One13:e0200728. doi: 10.1371/journal.pone.0200728

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 134

  Punzon-JimenezP.LabartaE. (2021). The impact of the female genital tract microbiome in women health and reproduction: a review. J. Assist. Reprod. Genet.38, 2519–2541. doi: 10.1007/s10815-021-02247-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 135

  PutnamP. T.ChangS. W. C. (1858). Interplay between the oxytocin and opioid systems in regulating social behaviour. Philos. Trans. R. Soc. B377:20210050. doi: 10.1098/rstb.2021.0050

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 136

  RamautarR.BergerR.van der GreefJ.HankemeierT. (2013). Human metabolomics: strategies to understand biology. Curr. Opin. Chem. Biol.17, 841–846. doi: 10.1016/j.cbpa.2013.06.015

  • CrossRef
  • Google Scholar

• 137

  RathS.HeidrichB.PieperD. H.VitalM. (2017). Uncovering the trimethylamine-producing bacteria of the human gut microbiota. Microbiome5:54. doi: 10.1186/s40168-017-0271-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 138

  RidlonJ. M.HarrisS. C.BhowmikS.KangD. J.HylemonP. B. (2016). Consequences of bile salt biotransformations by intestinal bacteria. Gut Microbes7, 22–39. doi: 10.1080/19490976.2015.1127483

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 139

  RiquelmeE.ZhangY.ZhangL.MontielM.ZoltanM.DongW.et al. (2019). Tumor microbiome diversity and composition influence pancreatic cancer outcomes. Cell178, 795–806.e12. doi: 10.1016/j.cell.2019.07.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 140

  RobertsG. P.LarraufieP.RichardsP.KayR. G.GalvinS. G.MiedzybrodzkaE. L.et al. (2019). Comparison of human and murine enteroendocrine cells by transcriptomic and peptidomic profiling. Diabetes68, 1062–1072. doi: 10.2337/db18-0883

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 141

  RoutyB.Le ChatelierE.DerosaL.DuongC. P. M.AlouM. T.DaillèreR.et al. (2018). Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science359, 91–97. doi: 10.1126/science.aan3706

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 142

  SalminenS.ColladoM. C.EndoA.HillC.LebeerS.QuigleyE. M. M.et al. (2021). The International Scientific Association of Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of postbiotics. Nat. Rev. Gastroenterol. Hepatol.18, 649–667. doi: 10.1038/s41575-021-00440-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 143

  SalvatoF.HettichR. L.KleinerM. (2021). Five key aspects of metaproteomics as a tool to understand functional interactions in host-associated microbiomes. PLoS Pathog.17:e1009245. doi: 10.1371/journal.ppat.1009245

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 144

  Sanches-FernandesG. M. M.CalifanoG.CastanhoS.SoaresF.RibeiroL.Pousão-FerreiraP.et al. (2022). Effects of live feed manipulation with algal-derived antimicrobial metabolites on fish larvae microbiome assembly: a molecular-based assessment. Aquac. Res.3, 1062–1083. doi: 10.1111/are.15648

  • CrossRef
  • Google Scholar

• 145

  Sánchez-AlcoholadoL.Ramos-MolinaB.OteroA.Laborda-IllanesA.OrdóñezR.MedinaJ. A.et al. (2020). The role of the gut microbiome in colorectal cancer development and therapy response. Cancers12:1406. doi: 10.3390/cancers12061406

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 146

  SarkarA.HartyS.LehtoS. M.MoellerA. H.DinanT. G.DunbarR. I. M.et al. (2018). The microbiome in psychology and cognitive neuroscience. Trends Cogn. Sci.22, 611–636. doi: 10.1016/j.tics.2018.04.006

  • CrossRef
  • Google Scholar

• 147

  ScanlanP. D. (2017). Bacteria-bacteriophage coevolution in the human gut: implications for microbial diversity and functionality. Trends Microbiol.25, 614–623. doi: 10.1016/j.tim.2017.02.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 148

  ScherzV.GreubG.BertelliC. (2022). Building up a clinical microbiota profiling: a quality framework proposal. Crit. Rev. Microbiol.48, 356–375. doi: 10.1080/1040841X.2021.1975642

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 149

  SenderR.FuchsS.MiloR. (2016). Revised estimates for the number of human and bacteria cells in the body. PLoS Biol.14:e1002533. doi: 10.1371/journal.pbio.1002533

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 150

  SheflinA. M.WhitneyA. K.WeirT. L. (2014). Cancer-promoting effects of microbial dysbiosis. Curr. Oncol. Rep.16:406. doi: 10.1007/s11912-014-0406-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 151

  ShiZ.LiX.WangX.ZhangL.LiL.FuX.et al. (2021). Characteristics and clinical implications of the nasal microbiota in extranodal NK/T-cell lymphoma nasal type. Front. Cell Infect. Microbiol.11:686595. doi: 10.3389/fcimb.2021.686595

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 152

  ShkoporovA. N.HillC. (2019). Bacteriophages of the human gut: the “known unknown” of the microbiome. Cell Host Microbe25, 25, 195–209. doi: 10.1016/j.chom.2019.01.017

  • CrossRef
  • Google Scholar

• 153

  SilvaY. P.BernardiA.FrozzaR. L. (2020). The role of short-chain fatty acids from gut microbiota in gut-brain communication. Front. Endocrinol.11:25. doi: 10.3389/fendo.2020.00025

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 154

  SimonJ. C.MarchesiJ. R.MougelC.SelosseM. A. (2019). Host-microbiota interactions: from holobiont theory to analysis. Microbiome7:5. doi: 10.1186/s40168-019-0619-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 155

  SongL.SunQ.ZhengH.ZhangY.WangY.LiuS.et al. (2022). Roseburia hominis alleviates neuroinflammation via short-chain fatty acids through histone deacetylase inhibition. Mol. Nutr. Food Res.66:e2200164. doi: 10.1002/mnfr.202200164

  • CrossRef
  • Google Scholar

• 156

  SotyM.Gautier-SteinA.RajasF.MithieuxG. (2017). Gut-brain glucose signaling in energy homeostasis. Cell Metab.25, 1231–1242. doi: 10.1016/j.cmet.2017.04.032

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 157

  SpivakI.FluhrL.ElinavE. (2022). Local and systemic effects of microbiome-derived metabolites. EMBO Rep.23:e55664. doi: 10.15252/embr.202255664

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 158

  SrinivasanS.HuaX.WuM. C.ProllS.ValintD. J.ReedS. D.et al. (2022). Impact of topical interventions on the vaginal microbiota and metabolome in postmenopausal women: a secondary analysis of a randomized clinical trial. JAMA Netw. Open5:e225032. doi: 10.1001/jamanetworkopen.2022.5032

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 159

  SteinertR. E.GerspachA. C.GutmannH.AsarianL.DreweJ.BeglingerC. (2011). The functional involvement of gut-expressed sweet taste receptors in glucose-stimulated secretion of glucagon-like peptide-1 (GLP-1) and peptide YY (PYY). Clin. Nutr.30, 524–532. doi: 10.1016/j.clnu.2011.01.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 160

  StrasserB.GostnerJ. M.FuchsD. (2016). Mood, food, and cognition: role of tryptophan and serotonin. Curr. Opin. Clin. Nutr. Metab. Care19, 55–61. doi: 10.1097/MCO.0000000000000237

  • CrossRef
  • Google Scholar

• 161

  SubramaniamS.FletcherC. (2018). Trimethylamine N-oxide: breathe new life. Br. J. Pharmacol.175, 1344–1353. doi: 10.1111/bph.13959

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 162

  SubramanianI.VermaS.KumarS.JereA.AnamikaK. (2020). Multi-omics data integration, interpretation, and its application. Bioinform. Biol. Insights14:1177932219899051. doi: 10.1177/1177932219899051

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 163

  SunT.HevnerR. F. (2014). Growth and folding of the mammalian cerebral cortex: from molecules to malformations. Nat. Rev. Neurosci.15, 217–232. doi: 10.1038/nrn3707

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 164

  TaharaY.YamazakiM.SukigaraH.MotohashiH.SasakiH.MiyakawaH.et al. (2018). Gut microbiota-derived short chain fatty acids induce circadian clock entrainment in mouse peripheral tissue. Sci. Rep.8:1395. doi: 10.1038/s41598-018-19836-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 165

  TanJ.McKenzieC.PotamitisM.ThorburnA. N.MackayC. R.MaciaL. (2014). The role of short-chain fatty acids in health and disease. Adv. Immunol.121, 91–119. doi: 10.1016/B978-0-12-800100-4.00003-9

  • CrossRef
  • Google Scholar

• 166

  ThaissC. A.ZeeviD.LevyM.SegalE.ElinavE. (2015). A day in the life of the meta-organism: diurnal rhythms of the intestinal microbiome and its host. Gut Microbes6, 137–142. doi: 10.1080/19490976.2015.1016690

  • CrossRef
  • Google Scholar

• 167

  ThaissC. A.ZeeviD.LevyM.Zilberman-SchapiraG.SuezJ.TengelerA. C.et al. (2014). Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell159, 514–529. doi: 10.1016/j.cell.2014.09.048

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 168

  TheisK. R.DheillyN. M.KlassenJ. L.BruckerR. M.BainesJ. F.BoschT. C. G.et al. (2016). Getting the hologenome concept right: an eco-evolutionary framework for hosts and their microbiomes. mSystems1. doi: 10.1128/mSystems.00028-16

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 169

  TranT. T. T.CousinF. J.LynchD. B.MenonR.BrulcJ.BrownJ. R. M.et al. (2019). Prebiotic supplementation in frail older people affects specific gut microbiota taxa but not global diversity. Microbiome7:39. doi: 10.1186/s40168-019-0654-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 170

  TrebickaJ.BorkP.KragA.ArumugamM. (2021). Utilizing the gut microbiome in decompensated cirrhosis and acute-on-chronic liver failure. Nat. Rev. Gastroenterol. Hepatol.18, 167–180. doi: 10.1038/s41575-020-00376-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 171

  UronisJ. M.ArthurJ. C.KekuT.FodorA.CarrollI. M.CruzM. L.et al. (2011). Gut microbial diversity is reduced by the probiotic VSL#3 and correlates with decreased TNBS-induced colitis. Inflamm. Bowel Dis.17, 289–297. doi: 10.1002/ibd.21366

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 172

  UtzschneiderK. M.KratzM.DammanC. J.HullarM. (2016). Mechanisms linking the gut microbiome and glucose metabolism. J. Clin. Endocrinol. Metab.101, 1445–1454. doi: 10.1210/jc.2015-4251

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 173

  UzbayT. (2019). Germ-free animal experiments in the gut microbiota studies. Curr. Opin. Pharmacol.49, 6–10. doi: 10.1016/j.coph.2019.03.016

  • CrossRef
  • Google Scholar

• 174

  van OldenC.GroenA. K.NieuwdorpM. (2015). Role of intestinal microbiome in lipid and glucose metabolism in diabetes mellitus. Clin. Ther.37, 1172–1177. doi: 10.1016/j.clinthera.2015.03.008

  • CrossRef
  • Google Scholar

• 175

  VaughnB. P.FischerM.KellyC. R.AllegrettiJ. R.GraizigerC.ThomasJ.et al. (2023). Effectiveness and safety of colonic and capsule fecal microbiota transplantation for recurrent Clostridioides difficile infection. Clin. Gastroenterol. Hepatol.21, 1330–1337.e2. doi: 10.1016/j.cgh.2022.09.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 176

  VillageliuD.LyteM. (2018). Dopamine production in Enterococcus faecium: a microbial endocrinology-based mechanism for the selection of probiotics based on neurochemical-producing potential. PLoS One13:e0207038. doi: 10.1371/journal.pone.0207038

  • CrossRef
  • Google Scholar

• 177

  VogtmannE.ChenJ.AmirA.ShiJ.AbnetC. C.NelsonH.et al. (2017). Comparison of collection methods for fecal samples in microbiome studies. Am. J. Epidemiol.185, 115–123. doi: 10.1093/aje/kww177

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 178

  WachsmuthH. R.WeningerS. N.DucaF. A. (2022). Role of the gut-brain axis in energy and glucose metabolism. Exp. Mol. Med.54, 377–392. doi: 10.1038/s12276-021-00677-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 179

  WahlstromA.SayinS. I.MarschallH. U.BackhedF. (2016). Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab.24, 41–50. doi: 10.1016/j.cmet.2016.05.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 180

  WangJ. W.KuoC. H.KuoF. C.WangY. K.HsuW. H.YuF. J.et al. (2019). Fecal microbiota transplantation: review and update. J. Formos. Med. Assoc.118, S23–S31. doi: 10.1016/j.jfma.2018.08.011

  • CrossRef
  • Google Scholar

• 181

  WangS. P.RubioL. A.DuncanS. H.DonachieG. E.HoltropG.LoG.et al. (2020). Pivotal roles for pH, lactate, and lactate-utilizing bacteria in the stability of a human colonic microbial ecosystem. mSystems5:e00645. doi: 10.1128/mSystems.00645-20

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 182

  WangZ.WangQ.ZhaoJ.GongL.ZhangY.WangX.et al. (2019). Altered diversity and composition of the gut microbiome in patients with cervical cancer. AMB Express9:40. doi: 10.1186/s13568-019-0763-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 183

  WangL. J.YangC. Y.ChouW. J.LeeM. J.ChouM. C.KuoH. C.et al. (2020). Gut microbiota and dietary patterns in children with attention-deficit/hyperactivity disorder. Eur. Child Adolesc. Psychiatry29, 287–297. doi: 10.1007/s00787-019-01352-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 184

  WardehM.RisleyC.McIntyreM. K.SetzkornC.BaylisM. (2015). Database of host-pathogen and related species interactions, and their global distribution. Sci. Data2:150049. doi: 10.1038/sdata.2015.49

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 185

  WeinstockG. M. (2012). Genomic approaches to studying the human microbiota. Nature489, 250–256. doi: 10.1038/nature11553

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 186

  WexlerH. M. (2007). Bacteroides: the good, the bad, and the nitty-gritty. Clin. Microbiol. Rev.20, 593–621. doi: 10.1128/CMR.00008-07

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 187

  WishartD. S. (2019). Metabolomics for investigating physiological and pathophysiological processes. Physiol. Rev.99, 1819–1875. doi: 10.1152/physrev.00035.2018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 188

  WishartD. S.ChengL. L.CopiéV.EdisonA. S.EghbalniaH. R.HochJ. C.et al. (2022). NMR and metabolomics-a roadmap for the future. Metabolites12:678. doi: 10.3390/metabo12080678

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 189

  XiaB.WangJ.ZhangD.HuX. (2023). The human microbiome links to prostate cancer risk and treatment (review). Oncol. Rep.49:123. doi: 10.3892/or.2023.8560

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 190

  XuC.LeeS. K.ZhangD.FrenetteP. S. (2020). The gut microbiome regulates psychological-stress-induced inflammation. Immunity53, 417–428.e4. doi: 10.1016/j.immuni.2020.06.025

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 191

  YanoJ. M.YuK.DonaldsonG. P.ShastriG. G.AnnP.MaL.et al. (2015). Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell161, 264–276. doi: 10.1016/j.cell.2015.02.047

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 192

  YaoG.KangL.LiJ.LongY.WeiH.FerreiraC. A.et al. (2018). Effective weight control via an implanted self-powered vagus nerve stimulation device. Nat. Commun.9:5349. doi: 10.1038/s41467-018-07764-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 193

  YungD. E.RondonottiE.KoulaouzidisA. (2016). Review: capsule colonoscopy-a concise clinical overview of current status. Ann. Transl. Med.4:398. doi: 10.21037/atm.2016.10.71

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 194

  ZakyA.GlastrasS. J.WongM. Y. W.PollockC. A.SaadS. (2021). The role of the gut microbiome in diabetes and obesity-related kidney disease. Int. J. Mol. Sci.22:9641. doi: 10.3390/ijms22179641

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 195

  ZengH.UmarS.RustB.LazarovaD.BordonaroM. (2019). Secondary bile acids and short chain fatty acids in the colon: a focus on colonic microbiome, cell proliferation, inflammation, and cancer. Int. J. Mol. Sci.20:1214. doi: 10.3390/ijms20051214

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 196

  ZhangX.LiL.ButcherJ.StintziA.FigeysD. (2019). Advancing functional and translational microbiome research using meta-omics approaches. Microbiome7:154. doi: 10.1186/s40168-019-0767-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 197

  ZhangL.LiuC.JiangQ.YinY. (2021). Butyrate in energy metabolism: there is still more to learn. Trends Endocrinol. Metab.32, 159–169. doi: 10.1016/j.tem.2020.12.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 198

  ZhengY.FangZ.XueY.ZhangJ.ZhuJ.GaoR.et al. (2020). Specific gut microbiome signature predicts the early-stage lung cancer. Gut Microbes11, 1030–1042. doi: 10.1080/19490976.2020.1737487

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 199

  ZhengW.ZhaoS.YinY.ZhangH.NeedhamD. M.EvansE. D.et al. (2022). High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome. Science376:eabm1483. doi: 10.1126/science.abm1483

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 200

  ZhuZ.RenJ.MichailS.SunF. (2019). MicroPro: using metagenomic unmapped reads to provide insights into human microbiota and disease associations. Genome Biol.20:154. doi: 10.1186/s13059-019-1773-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 201

  ZolkiewiczJ.MarzecA.RuszczynskiM.FeleszkoW. (2020). Postbiotics-a step beyond pre- and probiotics. Nutrients12:2189. doi: 10.3390/nu12082189

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 202

  ZouY.XueW.LuoG.DengZ.QinP.GuoR.et al. (2019). 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses. Nat. Biotechnol.37, 179–185. doi: 10.1038/s41587-018-0008-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar