Gut-Microbiome Interactions: Characterization,Therapeutic Implications and Machine Learning

Introduction

The gastrointestinal tract is home to a dynamic and complex community of microorganisms known as the gut microbiome, which is crucial to managing illness and preserving human health. It consists of bacteria, viruses, fungi, and archaea that collectively affect homeostasis, immunological system modulation, and metabolic processes. ¹ We now better understand the gut microbiome’s microbial composition, which has revealed its vast biodiversity and functional potential (Figure 1).

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Figure 1.

Diversity of the human microbiota, depicting the main microbial kingdoms—bacteria, viruses, archaea, fungi, and protozoa/parasites—alongside their colonization of distinct anatomical sites, including the oral cavity, respiratory tract, skin, breast, gastrointestinal (gut), and urogenital tract. Each of these body sites harbors unique microbial communities, collectively known as the human microbiome, which play critical roles in maintaining health and influencing disease processes, including cancer development and progression. ²

Factors like diet, genetics, age, and environmental influences such as antibiotic use influence all these diversities in the microbial makeup. Firmicutes and Bacteroidetes are 2 of the most dominant phyla, while Actinobacteria and Proteobacteria comprise the least proportion of the human gut. ³ This microbial diversity depends on the breakdown of complex dietary fibers and the production of vital metabolites, such as short-chain fatty acids (SCFAs), which promote intestinal health.

Interactions within the gut microbiome and between the microbiome and host are mediated at the level of complex biochemical pathways. Such interactions determine nutrient uptake, resistance to infection, and immune function. The cross-feeding dependency among microbial species ensures metabolic efficiency, while host-microbe interaction regulates the integrity of the epithelial barrier and inflammatory responses.^4,5 With such methodologies, gut microbes’ important roles in metabolizing xenobiotics, producing neurotransmitters, and regulating epigenetic modifications have been revealed.

The gut microbiome function extends to therapeutic application, with microbial manipulation emerging as a therapeutic intervention for treating various diseases. Microbial restoration via probiotics, prebiotics, and fecal microbiota transplantation is explored for balancing microbes in disease conditions, including irritable bowel syndrome and inflammatory bowel disease.^6,7 Furthermore, microbiome diagnostics for disease susceptibility and therapeutic prediction are in development.

Medicinal plants have promising avenues for modulating gut microbiota due to the presence of bioactive compounds, capable of inducing beneficial microbial growth. ⁸ Flavonoids, alkaloids, and polyphenols in plants have a prebiotic function, enhancing microbial diversity and SCFA production. ⁹ The modulation of gut dysbiosis and its related diseases through medicinal plants is a promising avenue for future treatments. ¹⁰

Recent studies have implicated the gut microbiota in developing cancer through immune modularity, genotoxicity, and metabolite production. ¹¹ Colorectal cancer has been linked with dysbiosis, and specific microbial groups have been characterized as having an oncogenic driving role or an inhibitory role.^12,13 The gut microbiota also modulates the efficacy of cancer therapy, including immunotherapy, through immune checkpoint modularity. ¹¹

Machine learning (ML) transforms our understanding of gut-microbiota relations through its capacity to interpret large and complex datasets. ML algorithms detect patterns and can predict relationships between microbial communities and host phenotypes, enabling personalized interventions.^14,15 These approaches are critical for driving the development of precision medicine and discovering new therapeutic targets.

The gut microbiome remains a focal point of biomedical research due to its extensive impact on health and disease. Thus, a comprehensive understanding of the dynamic nature of the microbiome and its long-term implications is critical and integration of multiomics data and ML algorithms can potentially enhance microbiome-based therapies and diagnostics, paving the way for new healthcare solutions. ¹⁶ This review aims to explore the microbial diversity of the gut microbiome and its intricate interactions with the host. It examines the main mechanisms of microbiome interactions, molecular and biochemical characterizations, and medicinal and therapeutic applications. Furthermore, the review determines the role of medicinal plants on gut microbiota, disease progression and the understanding of the application of ML in gut-microbiome interaction. By integrating these aspects, this study offers insights into the modulation of health and disease caused by the microbiome.

Microbial Composition of the Gut Microbiome

The gut microbiome is a vast and intricate ecosystem that houses trillions of complex and diverse microorganisms, including bacteria, archaea, viruses, and fungi (Table 1). ¹⁷ These microorganisms play a fundamental role in maintaining human health by regulating metabolism, supporting the immune system, and even influencing neurological functions. ¹⁸ The composition of the gut microbiome is highly individualized, shaped by various factors such as genetics, diet, lifestyle, and geographic location. ¹⁹

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Table 1.

Biological Features and Functions of Some Gut Microbes.

Microbe	Type	Features	Biological role	Reference
Bacteroides spp.	Bacteria (Gram-negative)	Anaerobic, saccharolytic, dominant in the gut microbiota	Ferment polysaccharides, produce SCFAs, immune modulation	Odamaki et al 20
Firmicutes	Bacteria (Gram-positive)	Includes genera like Lactobacillus, Clostridium, Bacillus	Metabolism of dietary fiber, energy harvest, gut homeostasis	Turnbaugh et al 21
Lactobacillus spp.	Bacteria (Gram-positive)	Probiotic, acid-tolerant, produces lactic acid	Gut barrier integrity, immune regulation, antimicrobial production	Walter 22
Bifidobacterium spp.	Bacteria (Gram-positive)	Probiotic, high GC content, ferments oligosaccharides	SCFA production, protection against pathogens, enhances gut health	Arboleya et al 23
Escherichia coli	Bacteria (Gram-negative)	Facultative anaerobe, includes commensals and pathogens	Vitamin K production, gut colonization resistance, potential pathogenicity	Tenaillon et al 24
Akkermansia muciniphila	Bacteria (Gram-negative)	Mucin-degrading, anaerobic	Maintains mucus layer, regulates metabolism, anti-inflammatory	Everard et al 25
Faecalibacterium prausnitzii	Bacteria (Gram-positive)	Butyrate producer, anaerobic	Anti-inflammatory, gut homeostasis, SCFA production	Miquel et al 26
Methanobrevibacter smithii	Archaea	Methanogen, hydrogen consumer	Regulates fermentation, affects energy harvest and metabolism	Samuel et al 27
Saccharomyces boulardii	Fungi	Probiotic yeast, acid-resistant	Prevents pathogen colonization, immune modulation, enhances gut function	McFarland 28
Candida albicans	Fungi	Opportunistic pathogen, dimorphic	Gut homeostasis, potential pathogen in dysbiosis	Richardson and Moyes 29
Norovirus	Virus	Non-enveloped RNA virus	Influences immune response, gut inflammation	Karst 30
Bacteriophages	Virus	Infect bacteria, influence microbial balance	Regulate bacterial populations, horizontal gene transfer	Reyes et al 31
Entameba histolytica	Parasite	Protozoan pathogen	Causes dysentery, alters gut microbiota composition	Marie and Petri 32
Giardia lamblia	Parasite	Flagellated protozoan	Disrupts gut absorption, alters microbiome composition	Cotton et al 33

Mechanisms of Microbiome Interactions in the Gut

Due to its vast taxonomic, genetic, and metabolic variety, dissecting the mechanisms behind the microbiome’s impacts on the host is difficult.^71,72 These effects may be indirect, such as when interactions between microbes alter the composition and function of the microbiome, or direct, where microbiome composition and function impact the host. ⁷³ We offer 3 conceptual frameworks that are not mutually exclusive to aid in considering these kinds of interactions. ⁷⁴

The first frame examines the molecular makeup of the entity mediating the host impact, including anything from tiny metabolites to complex glycolipids. Within these are the processes that each item mediates, which can include genotoxic effects, signaling on host pathways, or energy substrates. ⁷⁵

The second framing, which is largely spatial, includes substances that move far through the blood or lymphatic system and those that operate locally at the lumenal junction with intestinal epithelium. For instance, the local action of a microbial product at the intestinal epithelium might have distant consequences through the production of endocrine factors like hormones or cytokines. These afferent neurons are related to the central nervous system or immune cell migration. ⁷⁶

Finally, the third frame is centered on function. When considering function, it’s critical to consider 2 facets of these relationships that are sometimes overlooked in this field. ⁷⁷ First, even though a microbial factor x may mediate host phenotypic y, this does not always mean that the bacterium has “intent”; when the microbial factor is only a waste product, for instance, the microbe may not benefit from the host phenotype in any way. ⁷⁸ The function’s impact on the microbe’s, host’s, and maybe both parties’ fitness comes in second. Giving a function a fit across life cycles and conditions enables us to identify the evolutionary mechanism. ⁷⁹ Particularly, the potential for co-evolution. Related to function are specificity and redundancy. Microbe-derived entities may be specific (or non-specific) to the individual microbe or community depending on the mechanism of each entity. On the microbial side, various microbes might make the same factor. ⁸⁰ On the host side, a common host signaling cascade might be activated by 2 different microbial entities imparting similar effects and, therefore, functional redundancy. With this framework, we now discuss 3 major categories of microbial entities mediating direct gut microbiome–host interactions: metabolites, ligands of pattern recognition, and molecules that act via effects on adaptive immunity. ⁸¹

Biochemical Features and Detection

Gut microorganisms enzymatically transform a wide range of carbohydrates for both catabolic and anabolic purposes. Their contribution to host metabolism is significantly tied to the fermentation of indigestible dietary and endogenous carbohydrates. These nutrients, primarily reaching the colon, are substrates for microbial fermentation. Such metabolic activity underpins the survival of microbiota and yields metabolites with profound impacts on host immunity, energy homeostasis, and gut integrity.

Carbohydrates escaping digestion in the upper gastrointestinal tract, such as resistant starches, non-starch polysaccharides (eg, cellulose, hemicellulose, and pectins), and oligosaccharides like inulin and fructo-oligosaccharides (FOS), serve as fermentation substrates. These collectively termed microbiota-accessible carbohydrates influence microbial community structure and function. ¹⁰⁸ Microbial carbohydrate-active enzymes (CAZymes) catalyze the degradation of these complex substrates into fermentable sugars, subsequently producing SCFAs including acetate, propionate, and butyrate. ¹⁰⁹ SCFAs serve as fuel for colonocytes and modulate host metabolic and immune functions. ¹¹⁰

Apart from exogenous sources, gut microbes also metabolize host-derived carbohydrates, notably mucin glycans present in the intestinal mucus layer. These glycans are composed of O-linked oligosaccharides rich in galactose, N-acetylglucosamine, and sialic acid. ¹¹¹ Specialized microbial taxa such as Akkermansia muciniphila and Bacteroides spp. possess mucin-degrading enzymatic machinery, enabling their proliferation in fiber-depleted environments.^112,113 While mucin catabolism supports microbial sustenance and contributes to immune modulation and barrier maintenance, excessive degradation in low-fiber diets may erode the mucus layer, increasing intestinal permeability and inflammation. ¹¹⁴ A subset of gut microbes produce exopolysaccharides (EPS) that enhance biofilm formation, microbial adherence, and resistance to stressors. ¹¹⁵ EPS also interacts with host immune receptors, modulating inflammation. Bifidobacterium and Lactobacillus spp. are notable producers, whose EPS support microbial cross-feeding and symbiosis. ¹¹⁶ Variations in EPS structure among strains influence their functional attributes.

Structural polysaccharides such as peptidoglycan and lipopolysaccharide are indispensable components of bacterial cell walls. Peptidoglycan, a polymer of N-acetylglucosamine and N-acetylmuramic acid, confers cell wall rigidity. Its fragments released during microbial turnover activate host NOD-like receptors, triggering immune signaling. ¹¹⁷ Lipopolysaccharides, specific to Gram-negative bacteria, harbors a variable O-antigen and lipid A domain, which can potentiate inflammatory responses via Toll-like receptor 4 (TLR4). ¹¹⁸

The integrity of the gastric and intestinal epithelium is a critical component of gut health, reliant on efficient repair mechanisms. Experimental acute gastric ulcerations (EAGU) are healed very rapidly. This healing process has 2 steps; mucosal restitution and delayed repair. Adenosine 5′-triphosphate (ATP)-dependent potassium channels (KATP) have a regulatory role in this process .KATP channel modulators like diazoxide (channel opener) and glibenclamide (channel antagonist) can influence the healing of EAGU, potentially interacting with microbiome-derived signals like SCFAs that are known to promote epithelial health. Furthermore, the effect of polyamine biosynthesis by difluoromethylornithine (DFMO) on diazoxide-induced alterations highlights a complex interplay between cellular energy channels and mediators responsible for restitution.^119,120

Microbial proteins are pivotal to the gut microbiome’s capacity to influence host physiology. These include enzymatic proteins responsible for macronutrient breakdown, structural and adhesion molecules, immunomodulatory proteins, and signaling peptides. They orchestrate nutrient processing, microbial homeostasis, immune response, and systemic metabolic regulation.

Carbohydrate-active enzymes (CAZymes), primarily from Bacteroidetes and Firmicutes, catalyze the degradation of plant polysaccharides into fermentable sugars. ¹²¹ The SCFAs generated through this process modulate epithelial health and energy homeostasis. ¹¹⁰ Other critical enzymes include proteases (for protein degradation), lipases (for lipid hydrolysis), and bile salt hydrolases (modulating host bile acids). ¹²² Additionally, biosynthetic enzymes underpin microbial cell wall assembly and structural integrity.

Bacteriocins—ribosomally synthesized antimicrobial peptides—are secreted by certain bacteria (eg, Lactobacillus, Enterococcus) to suppress competitor or pathogenic strains. ¹²³ These molecules sustain ecological balance and protect against gut infections. Mucin-degrading enzymes, such as glycosidases and sulfatases, allow bacteria like A. muciniphila to utilize host-derived glycans. ¹¹² Although generally beneficial, unregulated mucin degradation may compromise gut barriers. Adhesion proteins also mediate microbial attachment and biofilm formation. ¹²⁴ Microbial proteins with immunomodulatory roles include those from Bacteroides fragilis, which produces PSA-associated proteins that promote regulatory T cell (Treg) responses, curbing inflammation. Conversely, pro-inflammatory proteins like flagellin activate host immunity via TLR5. ¹²⁵ Proteomic studies have linked altered microbial protein expression to diseases such as inflammatory bowel disease, obesity, and diabetes and thus provides a global view of active microbial proteins and functions. ¹²⁶

Nucleic acids of the gut microbiome extend beyond genetic repositories to serve regulatory, communicative, and immunomodulatory functions. High-throughput sequencing has unveiled their immense diversity and functional significance. The microbial metagenome, vastly exceeding the human genome in gene content, encodes pathways critical for nutrient processing, immune modulation, and xenobiotic metabolism. ¹²⁷ CAZyme genes, for instance, equip microbes to metabolize complex carbohydrates. ¹²⁰ Horizontal gene transfer (HGT), mediated by plasmids, transposons, and phages, fosters microbial adaptation and disseminates antibiotic resistance. ¹²⁸ Extracellular DNA (eDNA), derived from lysis or secretion, promotes biofilm formation, interspecies communication, and immune activation via TLR9 recognition. ¹²⁹ Microbial RNA types, including mRNA, rRNA, and regulatory sRNAs, regulate microbial gene expression and interkingdom signaling. Metatranscriptomics captures dynamic microbial activity in situ. ¹³⁰ sRNAs govern stress and quorum responses ¹³¹ and may modulate host gene expression. ¹³² Microbial EVs can encapsulate nucleic acids, facilitating protected delivery to host targets. ¹³³

Microbial lipids in the gut include diverse molecular species such as SCFAs, sphingolipids, bile acid derivatives, lipopolysaccharides, and branched-chain fatty acids. Among these, SCFAs, notably acetate, propionate, and butyrate, are key metabolites derived from the microbial fermentation of dietary fibers. These metabolites not only serve as substrates for colonocytes but also modulate host gene expression, inflammation, and barrier integrity. ¹¹⁰ Butyrate, in particular, plays a central role in maintaining intestinal epithelial function and anti-inflammatory signaling. ¹³⁴ Sphingolipids, such as ceramides and sphingomyelins, synthesized by both host and specific bacterial taxa like Bacteroides spp., participate in cellular signaling pathways governing apoptosis, differentiation, and immune regulation. ¹³⁵ Microbial sphingolipids can mimic those of the host, thereby influencing host lipid metabolism and potentially contributing to pathologies such as inflammatory bowel disease and metabolic disorders. ¹³⁶

Secondary bile acids, produced through microbial transformation of hepatic bile acids, represent another critical lipid group. Gut bacteria including Clostridium and Bacteroides spp. deconjugate and modify bile acids into derivatives like deoxycholic acid and lithocholic acid. These molecules interact with host nuclear receptors such as FXR and TGR5, modulating lipid absorption, glucose metabolism, and gut motility. ¹³⁷ Lipopolysaccharides, integral to the outer membrane of Gram-negative bacteria like Escherichia coli, consist of lipid A moieties and carbohydrate-rich domains. Increased systemic levels of lipopolysaccharides due to compromised gut integrity or dysbiosis have been associated with metabolic endotoxemia, obesity, insulin resistance, and chronic inflammation. ¹¹⁸ Additionally, BCFAs, such as iso-butyrate and iso-valerate, are fermentation by-products of branched-chain amino acids. Though less explored, BCFAs are recognized for their roles in maintaining colonic pH and modulating microbial ecology. ¹³⁸

The field of lipidomics is uncovering novel microbial lipids with immunoregulatory and metabolic potential. For example, fatty acid esters of hydroxy fatty acids (FAHFAs), produced by gut microbes, have demonstrated anti-inflammatory and anti-diabetic properties in preclinical models. ¹³⁹ Such discoveries open promising avenues for lipid-based interventions and diagnostics in microbiome-centered health strategies.

The gut microbiome contributes significantly to human nutrition and metabolic homeostasis by facilitating not only the digestion of complex carbohydrates and immune regulation but also the de novo synthesis and biotransformation of essential micronutrients such as vitamins and cofactors. Various gut microbial taxa possess the biosynthetic capacity to produce B-complex vitamins, including biotin (B7), folate (B9), riboflavin (B2), cobalamin (B12), thiamine (B1), pantothenic acid (B5), and niacin (B3). ¹⁴⁰ These vitamins function as enzymatic cofactors vital to cellular processes such as energy metabolism, DNA synthesis, and signal transduction. For example, folate synthesized by Lactobacillus and Bifidobacterium species plays a pivotal role in nucleotide biosynthesis and methylation reactions, particularly in proliferative tissues such as the intestinal epithelium. ¹⁴¹

Among the B vitamins, vitamin B12 (cobalamin) is distinctive in being produced solely by microbial biosynthesis. While humans depend primarily on dietary intake for B12, specific gut microbes such as Propionibacterium and Lactobacillus reuteri can generate bioavailable forms. However, due to microbial competition for B12 and its analogs, not all synthesized forms are accessible to the host. ¹⁴² Despite this, microbial B12 production significantly influences microbial community dynamics and metabolic interactions. The gut microbiota also enhances the endogenous pool of vitamin K, primarily in the form of menaquinones (vitamin K2), which contribute to coagulation and skeletal health. Species such as Escherichia coli and Bacteroides fragilis are known producers of menaquinones, which may be absorbed in the colon, although the extent of host assimilation remains under investigation. ¹⁴³

In addition to vitamins, the gut microbiome supports the production and interconversion of critical cofactors such as coenzyme A, NAD, and FAD. These molecules are essential for oxidative-reductive reactions, fatty acid β-oxidation, and the citric acid cycle. Microbial synthesis of such cofactors not only sustains bacterial metabolism but may also influence host bioenergetics and mitochondrial activity. ¹⁴⁴ Microbial vitamin production is modulated by factors such as host diet, antibiotic exposure, and the overall composition of the gut microbiota. For instance, fiber-enriched diets that promote saccharolytic fermentation are often correlated with elevated synthesis of specific B vitamins. Conversely, antibiotic treatment may transiently deplete vitamin-producing species, potentially resulting in micronutrient deficiencies. ¹⁴⁵

The gut microbiota synthesizes a vast repertoire of metabolites and small signaling molecules that orchestrate intricate communication networks between microbes and the host. These molecular signals are integral to regulating immune activity, metabolic function, and the maintenance of gastrointestinal homeostasis. Their systemic influence extends to the neurological, cardiovascular, and metabolic systems, reflecting their broad physiological relevance. Among the most thoroughly characterized microbial metabolites are SCFAs—notably acetate, propionate, and butyrate—generated via fermentation of dietary fibers by anaerobic microbes such as Faecalibacterium prausnitzii and Roseburia spp. These SCFAs function as energy substrates for colonocytes, reinforce epithelial barrier integrity, and regulate inflammatory responses. ¹¹⁰ Butyrate, in particular, acts as a potent anti-inflammatory agent by inhibiting histone deacetylases and promoting the differentiation of regulatory T cells (Tregs). ¹¹⁷

Indole derivatives, arising from microbial metabolism of tryptophan, represent another critical class of signaling compounds. Molecules such as indole-3-propionic acid and indole-3-acetic acid modulate gut permeability, immune signaling, and host energy homeostasis through activation of host receptors like the aryl hydrocarbon receptor (AhR) and pregnane X receptor (PXR). ¹⁴⁶ These pathways are crucial for preserving mucosal integrity and mitigating inflammation. Gut bacteria also participate in the conversion of bile acids into secondary forms—such as deoxycholic acid and lithocholic acid—which serve not only in digestion but also as signaling molecules. These derivatives engage host nuclear receptors such as FXR and TGR5, thereby influencing lipid absorption, glucose metabolism, and energy expenditure. ¹³⁷ Other microbial communication tools include quorum sensing molecules like N-acyl homoserine lactones and autoinducing peptides. These molecules enable intra- and inter-species signaling that regulates behaviors such as biofilm formation, virulence, and competitive exclusion within the gut environment. ¹⁴⁷

The gut microbiome generates a diverse array of redox-active molecules and microbial antioxidants that play pivotal roles in maintaining oxidative balance within the gut and throughout the body. These compounds modulate cellular redox states, counteract reactive oxygen species (ROS), and influence host defense mechanisms and mitochondrial function. One of the most extensively studied microbial redox metabolites is hydrogen sulfide (H2S), produced by sulfate-reducing bacteria such as Desulfovibrio spp. and by bacteria metabolizing sulfur-containing amino acids. At physiological concentrations, H2S serves as a signaling molecule, promoting cytoprotection, modulating mitochondrial respiration, and regulating antioxidant pathways. ¹⁴⁸ However, excessive H2S production can be cytotoxic, damaging the gut epithelium and promoting inflammation. ¹⁴⁹ The microbiome also contributes to the gut’s antioxidant capacity through the production and recycling of glutathione, a tripeptide composed of glutamate, cysteine, and glycine. Although the host synthesizes glutathione, certain microbes, including Lactobacillus and Bifidobacterium can either produce or regenerate it, thereby enhancing the antioxidant defense system of the host. ¹⁵⁰ Additionally, microbial metabolites such as butyrate have been shown to stimulate the activation of the nuclear factor erythroid 2-related factor 2 (Nrf2), a transcription factor that upregulates the expression of key antioxidant enzymes, including glutathione peroxidase and superoxide dismutase. This Nrf2-mediated response is crucial for cellular resilience against oxidative damage. ¹⁵¹ Another significant group of microbial redox-active compounds includes phenolic metabolites, derived from the bacterial metabolism of dietary polyphenols. These microbial phenolics exhibit strong antioxidant properties and modulate redox-sensitive signaling cascades, thereby contributing to anti-inflammatory and cytoprotective effects. ¹⁵²

The gut microbiome synthesizes various structural biomolecules and secretes extracellular vesicles that are essential for microbial survival, interspecies communication, and host interaction. These components contribute to microbial architecture, immune modulation, and dynamic crosstalk between microbial communities and host tissues. Key structural biomolecules include integral elements of the bacterial cell envelope such as lipopolysaccharides, peptidoglycans, and capsular polysaccharides. Lipopolysaccharide, present in the outer membrane of Gram-negative bacteria like Escherichia coli, is a potent immunomodulator that engages Toll-like receptor 4 (TLR4) on host cells, activating pro-inflammatory signaling pathways. ¹⁵³ Similarly, peptidoglycans, found in both Gram-positive and Gram-negative organisms, interact with host pattern recognition receptors, shaping immune responses and supporting mucosal homeostasis. ¹⁵⁴ Notably, Bacteroides fragilis synthesizes a capsular polysaccharide A (PSA) that promotes the expansion of regulatory T cells (Tregs), thereby dampening intestinal inflammation. ¹⁵⁵

In addition to structural macromolecules, gut microbes release extracellular vesicles—including outer membrane vesicles from Gram-negative species and analogous vesicles from Gram-positive bacteria. These nano-sized particles encapsulate a variety of biomolecules such as proteins, lipids, nucleic acids, and metabolites. Extracellular vesicles serve as delivery systems, enabling microbial components to modulate host immune function, reinforce epithelial barrier integrity, and even exert effects on distal organs through systemic circulation. ¹³³ Furthermore, extracellular vesicles facilitate microbial communication and horizontal gene exchange, enhancing microbial adaptability and resilience under environmental stress. Their ability to traverse host barriers and deliver functional biomolecules positions them as promising tools for novel therapeutic delivery systems and diagnostic biomarkers.^156,157

The biochemical and molecular characterization of the gut microbiome have played a significant role in their detection. While molecular methods reveal microbial identity and genetic potential, biochemical analyses detect microbial activity and metabolite production. Molecular tools, particularly nucleic acid-based methods, are the basis of gut microbiome analysis. 16S rRNA gene sequencing is the most widely used technique for bacterial identification and phylogenetic profiling. It targets conserved regions of the 16S ribosomal RNA gene, enabling classification of bacteria to the genus or species level.^158-160 This method, while cost-effective, lacks functional resolution. To overcome this, shotgun metagenomic sequencing is employed to sequence all DNA in a sample, offering insight into microbial diversity, gene content, and functional potential. ¹²⁷ Metatranscriptomics takes this further by analyzing total RNA, providing information about genes actively expressed under different physiological conditions. ¹³⁰ Emerging molecular methods also include quantitative PCR (qPCR) and digital PCR (dPCR) for targeted detection of specific taxa or genes, and fluorescence in situ hybridization (FISH) for spatial visualization of microbes in the gut environment.^161,162 Additionally, metaproteomics and metabolomics allow for the detection of microbial proteins and metabolites, bridging the gap between gene content and functional expression. ¹⁶³

Biochemical detection focuses on the metabolic outputs of the gut microbiome. One major area of interest is the quantification of SCFAs—namely acetate, propionate, and butyrate—produced through fermentation of dietary fibers by gut bacteria. These can be measured using GC-MS or LC-MS and are associated with host energy metabolism, inflammation, and gut integrity. ¹⁶⁴ Other important biochemical markers include bile acid derivatives, phenolic compounds, and amino acid metabolites, which are influenced by microbial enzymatic activity and impact host immunity and neurological function. ¹⁶⁵ ELISAs are commonly used to detect microbial components such as lipopolysaccharides and flagellin, which interact with host immune receptors and serve as indicators of microbial load or inflammation. ¹⁶⁶ NMR and FTIR spectroscopy also allow for non-targeted, label-free detection of microbial metabolites, making them valuable tools for rapid gut microbiota screening in clinical and research settings. ¹⁶⁷

Medicinal and Therapeutic Implications

The gut microbiome has emerged as a critical factor in health and disease. Recent advances in microbiome research have unveiled its profound medicinal and therapeutic implications, offering novel approaches to treating various diseases, including metabolic disorders, autoimmune conditions, mental health disorders, and cancer. Understanding the mechanisms by which the gut microbiome influences human health opens new avenues for precision medicine, probiotics, prebiotics, fecal microbiota transplantation, and microbiome-targeted drug development.

One of the most well-documented roles of the gut microbiome is its influence on metabolic health, particularly in obesity and type 2 diabetes. Studies have shown that individuals with obesity often exhibit reduced microbial diversity and an altered ratio of Firmicutes to Bacteroidetes, 2 dominant bacterial phyla in the gut. ²¹ These microbial shifts enhance energy harvest from indigestible polysaccharides, contributing to weight gain. Additionally, gut bacteria produce SCFAs such as acetate, propionate, and butyrate through fermentation of dietary fiber. SCFAs serve as energy substrates for colonocytes, regulate appetite via gut-brain signaling, and improve insulin sensitivity. ¹⁶⁸

Therapeutic interventions targeting the gut microbiome for metabolic disorders include probiotics (eg, Lactobacillus and Bifidobacterium strains), prebiotics (eg, inulin and resistant starch), and synbiotics (combinations of probiotics and prebiotics). Fecal microbiota transplantation, which involves transferring stool from a healthy donor to a recipient, has shown promise in improving insulin sensitivity in patients with metabolic syndrome. ¹⁶⁹ Furthermore, next-generation microbiome-based therapies, such as engineered bacteria producing beneficial metabolites, are under investigation for their potential to treat obesity and diabetes.

The gut microbiome plays a crucial role in immune system development and regulation. Dysbiosis—an imbalance in microbial composition—has been linked to autoimmune diseases such as inflammatory bowel disease, rheumatoid arthritis, and multiple sclerosis. In inflammatory bowel disease, including Crohn’s disease and ulcerative colitis, alterations in beneficial bacteria (eg, Faecalibacterium prausnitzii) and pathogenic species (eg, Escherichia coli) may contribute to chronic inflammation. ¹⁷⁰

Microbiome-based therapies for autoimmune conditions include fecal microbiota transplantation, which has shown efficacy in inducing remission in ulcerative colitis.^171,172 Additionally, several probiotic strains have demonstrated anti-inflammatory effects by enhancing regulatory T-cell (Treg) activity and reducing pro-inflammatory cytokines. ¹⁷³ Beyond probiotics, postbiotics—bioactive compounds derived from microbial metabolism—are being explored for their immunomodulatory properties. For example, butyrate may enhance the intestinal mucosal barrier by directly inducing tight junctional proteins in the epithelium. ¹⁷⁴ Through interaction with GPCR 43/41, it may also inhibit proinflammatory cytokine secretion from neutrophils. ¹⁷⁴ It may also exhibit direct effects on macrophages and dendritic cells via GPCR and modulate T cell function by increasing Foxp3 T cells while inhibiting IFN-ɣ producing T cells. ¹⁷⁴ Moreover, it can increase serotonin production and is also an inhibitor of HDAC. Thus, by increasing IgA and IgG antibody response from B cells, butyrate could augment specific immunity and inhibit autoimmunity. ¹⁷⁴

The bidirectional communication between the gut microbiome and the central nervous system, known as the gut-brain axis, has significant implications for mental health and neurological disorders (Figure 2). Gut microbes produce neurotransmitters such as serotonin, dopamine, and gamma-aminobutyric acid (GABA), influencing mood and cognition. ¹⁷⁵ In depression, altered levels of gut microbiome may correlate with increased inflammation and altered serotonin production. Indeed, probiotic supplementation has shown antidepressant effects in clinical trials. ¹⁷⁶ In Parkinson’s disease, gut dysbiosis may contribute to alpha-synuclein aggregation, suggesting that microbiome modulation could slow disease progression. ¹⁷⁷

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Figure 2.

Schematic depiction of the gut-brain axis. It highlights how gut microbial metabolites, such as acetate and butyrate from bacteria, affect brain health. These metabolites may cross the blood-brain barrier, especially when it is disrupted by inflammation, influencing immune responses. Factors such as diet, antibiotics, and inflammation shape the gut microbiome, impacting metabolite production. Some bacterial metabolites (eg, colibactin) are linked to tumor promotion, while others, such as butyrate, have anti-inflammatory and tumor-suppressing properties.

The gut microbiome also plays a significant role in cancer progression, in terms of tumor behavior and immune response (Figure 3). Recent studies indicate microbial dysbiosis can contribute to tumorigenesis and affect treatment outcomes. Dysbiosis has been known to lead to chronic inflammation which is a known precursor for cancer development. ¹⁷⁸ Certain specific bacterial taxa have been identified to promote this inflammatory environment especially in colorectal cancer. ¹⁷⁹ The pro-inflammatory environment created promotes tumor growth and metastasis. Gut microbes also induce chronic inflammation by stimulating proinflammatory pathways such as NF-κB through Toll-like receptor (TLR) activation. For example, F. nucleatum releases outer membrane vesicles that activate TLR4 on colonic epithelial cells, triggering inflammation linked to tumorigenesis.^180,181 Chronic inflammation disrupts the mucosal barrier and creates a tumor-promoting microenvironment. The presence of virulence factors from certain bacteria in the tumor microenvironment further supports this notion.

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Figure 3.

Microbiota-derived signals modulate antitumor immunity by shaping both innate and adaptive immune responses. These signals influence the differentiation and function of regulatory T cells (Tregs) while enhancing the activation and cytotoxic capacity of CD8+ T cells. Concurrently, microbiome-induced production of interferon-γ (IFN-γ) promotes natural killer (NK) cell activation, collectively driving tumor cell elimination through perforin- and granzyme-mediated cytotoxicity. In parallel, tumor-associated macrophages respond to inflammatory cues such as tumor necrosis factor (TNF), adopting functional states that regulate immune signaling within the tumor microenvironment. Together, these interactions form a feedback loop linking the microbiome to immune modulation and tumor control. Schematic representation of microbiome-mediated modulation of anti-tumor immunity.

Bacteria such as Salmonella typhi can activate β-catenin and MAPK pathways, enhancing cell proliferation and survival.^182,183 Another metabolite known as fragilysin secreted by Bacteroides fragilis also stimulates the expression of inflammatory factors, the growth-related oncogene-α, and the oncogene c-Myc, thereby promoting the progression cancer in the intestines under chronic inflammatory stimulation. ¹⁸⁴ In addition, metabolites such as N-oxide and trimethylamine have been associated with promoting metastasis in some cancer types such as hepatocellular carcinoma. Diet-induced changes in the microbiome can thus modulate cancer risk by altering microbial metabolite profiles. ¹⁸⁵

Another way the gut microbiome can cause cancer initiation and progression is through promoting epithelial-to-mesenchymal transition (EMT) and angiogenesis. ¹⁸⁶ Certain microbial imbalances and changes in bacterial metabolites and toxins have been shown to enhance cancer progression by facilitating EMT, which increases cancer cell invasiveness, motility, and metastatic potential. This process supports the generation and migration of circulating tumor cells, contributing to metastasis capacity. ¹⁸⁷ Fusobacterium nucleatum have been shown to induce an EMT phenotype in cancer cells by not only impairing tight junctions but increasing permeability, thereby increasing their invasive potential and metastatic capacity. ¹⁸⁷ Also, the gut microbiome can promote angiogenesis, the formation of new blood vessels, which supplies tumors with nutrients and oxygen necessary for their growth and dissemination. ¹⁸⁸ This is done by the secretion of angiogenic factors such as VEGF. ¹⁸⁶ These mechanisms involve complex crosstalk between the gut microbiome, host immune system, and tumor microenvironment, and have been documented in preclinical and clinical studies.^1,189,190

Furthermore, the gut microbiome can modulate the immune response by suppressing anti-tumor immunity. For example, Fusobacterium nucleatum binds to the inhibitory receptor TIGIT on natural killer (NK) cells and T cells, inhibiting their cytotoxic activity and thus weakening the body’s anti-tumor immune function. ¹⁹¹ This immune evasion facilitates tumor occurrence and progression, particularly in colorectal cancer. The microbiome can also promote infiltration of immunosuppressive cells and downregulate effector immune cells, creating a tumor-favorable immune microenvironment that supports cancer growth. ¹⁹²

The gut microbiome also affects the efficacy and toxicity of various cancer treatment such as chemotherapy and immunotherapy. Several studies indicate that the gut microbiome modulates the metabolism and bioavailability of several chemotherapeutic agents hence affecting their efficacy and side effects.^{180,181,193,194} For instance, Fusobacterium nucleatum has been implicated in chemoresistance in colorectal cancer by activating autophagy pathways in cancer cells, reducing chemotherapy-induced apoptosis. ¹⁹⁵ Also certain gut bacteria, Bacteroides fragilis, Escherichia coli and Clostridium species, can degrade chemotherapy drugs, such as gemcitabine and irinotecan. This degradation can lead to reduced effectiveness of these treatments. ¹⁹⁶

Effects of Medicinal Plants on the Gut Microbiome

Medicinal plants contain bioactive substances like flavonoids, polyphenols, alkaloids, saponins, and essential oils, which have significant impact on the gut microbiome (Table 2). ¹⁹⁷ These substances have the ability to affect the gut microbiota’s diversity, composition, and function and can have an impact on inflammation, metabolism, and immune system performance, among other aspects of health. ¹⁹⁸ A number of therapeutic plants encourage the growth of beneficial bacterial species that are important for immunological and digestive processes. ¹⁹⁹ They also enhance microbial diversity which is linked to a healthier gut ecosystem and increased resistance to infections like colorectal cancer. ²⁰⁰ Some medicinal plants have fibers and polysaccharides that feed good gut bacteria by acting as prebiotics. ²⁰¹ They also have antimicrobial qualities that allow beneficial species to flourish while selectively inhibiting pathogenic bacteria and resisting microbes (such as Staphylococcus aureus and Escherichia coli). ²⁰²

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Table 2.

Medicinal Effects of Some Plant Species on the Gut Microbiome.

Family	Species	Medicinal effects of plant species	Reference
Zingiberaceae	Curcuma Longa L.	Contains curcumin, which has anti-inflammatory effects and support beneficial bacteria like Bifidobacterium and Lactobacillus and promotes immune function and decreases pathogenic bacteria like Prevotellaceae, and Rikenellaceae etc. in the gut.	Scazzocchio et al 203
Amaryllidaceae	Allium Sativum L.	Allicin has antimicrobial properties, selectively reducing harmful bacteria and promoting beneficial ones. It supports digestion, reduces infection risk, and maintains gut balance. It has antimicrobial effects on yogurt and probiotic bacteria.	Altuntas and Korukluoglu, 204 Bryan-Thomas et al, 205 Magryś et al 206
Theaceae	Camellia Sinensis (L.) Kuntze	Green tea contains catechins enhance microbial diversity, support beneficial bacteria like Bifidobacterium and Akkermansia, improve gut health, reduce inflammation, and support the immune system. Also increase the production of beneficial metabolites like short chain fatty acids.	Pérez-Burillo et al, 207 Huang et al 61
Berberidaceae	Berberis Vulgaris L.	Berberine has potent antimicrobial and anti-inflammatory properties, reducing pathogens like H. pylori and promoting beneficial bacteria. Supports digestive health, reduces gastrointestinal infections, influences the intestinal immune system, lowering inflammation by blocking certain factors like TNF-α and ILs	Gong et al, 208 Wang et al 209
Zingiberaceae	Zingiber officinale Roscoe	Gingerol and shogaol have anti-inflammatory and antioxidant properties, reducing the abundance of certain bacteria and restoring gut microbiota from high fat diets. Increase the prevalence of Bacteroidetes, reducing the Firmicutes/Bacteroidetes ratio, a factor linked to obesity and maintaining intestinal homeostasis.	Alhamoud et al, 210 Crichton et al, 211 Wang et al 39
Lamiaceae	Mentha × piperita L.	Menthol and rosmarinic acid have antimicrobial properties, reducing pathogens like S. aureus and maintaining microbial balance, while supporting digestion, reducing discomfort, and promoting gut health.	Noor 212
Apiaceae	Petroselinum crispum (Mill.) Fuss	Consuming parsley flavonoids boosts gut microbiota diversity, regulates fatigue-induced imbalance, and increases probiotics and SCFA-producing flora. Acetic acid reduces obesity-induced inflammation, increases glucose uptake, and provides energy for colonic epithelial cells.	Wang et al 213
Apiaceae	Pimpinella anisum L.	Eugenol and anethole stimulate the digestive tract, impacting feed conversion and live weight gain. Anise essential oil enhances digestion of lipids, proteins, and cellulose, improving nutrient absorption and intensifying amylase and pancreatic lipase effects.	Sultan et al 214
Asteraceae	Silybum marianum (L.) Gaertn.	Silymarin improves bile acid metabolism and support beneficial bacteria. It reduces short-chain fatty acids production, increases alpha-diversity, and decreases glucose utilization. Also shows increased catabolite levels of Oscillibacter sp.	Tomisova et al 215
Asteraceae	Inula helenium L.	Inulin and sesquiterpene lactones serve as prebiotics, promoting Bifidobacterium and Faecalibacterium growth, supporting SCFA production, reducing inflammation, and enhancing gut health.	Meral et al 216
Lamiaceae	Salvia rosmarinus Spenn.	Rosemary polyphenols cause improved mucus secretion, increased antioxidant enzymes, inhibition of inflammatory pathways, modulation of gut microbiota, and alterations in metabolism, all linked to improved gut barrier and gut microbiota.	Zhang and Lu 217
Asteraceae	Echinacea purpurea (L.) Moench	E. purpurea, a food with cichoric acid and polysaccharides, enhances immune function and gut microbial composition by supporting Lactobacillus and immune cells. Exposure increases total aerobic bacteria, Bacteroides group, and Bacteroides fragilis, and increases beneficial bacterial genera.	Hill et al, 218 Lü et al 93
Asphodelaceae	Aloe vera (L.) Burm.f.	Polysaccharides and aloin serve as prebiotics, promoting the growth of beneficial bacteria such as Bifidobacterium and Lactobacillus. Supports digestion, reduces constipation, and improves gut health especially in multiple sclerosis patients.	Miauolo et al
Asteraceae	Artemisia annua L.	Artemisinin, with its antimicrobial and anti-inflammatory properties, enhances the diversity of cecum microbiota, reduces inflammation, improves gut health, and lowers infection risks.	Cui et al 219
Hypericaceae	Hypericum perforatum L.	Hypericum perforatum, containing hypericin and hyperforin, affects gut-brain axis by modulating neurotransmitter levels and microbial composition, notably increasing beneficial bacterium like Akkermansia muciniphila and reducing gut inflammation.	Jiang et al 220
Lamiaceae	Ocimum caryophyllinum F.Muell.	Eugenol and ursolic acid in this product reduce oxidative stress, inflammation, and promotes higher prebiotic activity, supporting gut health, immune function, and inflammation reduction.	Narendra Babu et al 221
Solanaceae	Withania somnifera (L.) Dunal	Withanolides reduce stress, support beneficial bacteria, and maintain microbiome balance by enhancing hematological and biochemical profiles, raising L-citrulline, propionic acid, and SCFA levels, and reducing age-related alterations.	Bharani et al 222
Lamiaceae	Lavandula angustifolia Mill.	Contains linalool and linalyl acetate that promotes calmness, reduces stress, and supports gut-brain axis health by promoting overall microbial balance.	Amer et al 223
Asteraceae	Taraxacum fontanum Hand.-Mazz.	Inulin, a prebiotic, promotes beneficial bacteria like Bifidobacterium and Lactobacillus, enhances SCFA production, supports digestion, reduces inflammation, and restores gut balance, reversing bacterial imbalances.	Ma et al 224
Lamiaceae	Thymus vulgaris L.	Thymol and carvacrol have antimicrobial properties that reduce harmful bacteria like C. difficile, promoting balanced gut flora. Supports digestion, reduces gastrointestinal infections, and enhances gut health.	Lee et al 225
Araliaceae	Panax ginseng C.A.Mey.	Ginsenosides and saponins modulate the gut microbiota by promoting beneficial bacteria such as Bacteroides and regulates immune responses, supporting gut barrier integrity and immunity.	Zhao et al180,181

Berberine taken at 0.5 to 1 g daily, Bifidobacterium, and their combination were compared to a placebo over a 16-week period in a multi-center double-blind Randomized Control Trial (RCT) in adults with hyperglycemia. Significant improvements in fasting glucose, lipid profiles, and gut microbiota restructuring—including a rise in Blautia spp. and a decrease in Roseburia—as well as changes in bile acid metabolic pathways were observed in the berberine and combination groups. ²²⁶ Crucially, gut microbiota study showed that berberine changed the makeup of microbial communities and the gene richness of microorganisms, particularly when paired with probiotics. ²²⁶ This suggests that the metabolic effects of berberine were influenced by changes in the microbiome. By inhibiting pathogenic bacteria (Proteobacteria) and boosting beneficial SCFA-producing taxa (Lactobacillaceae, Akkermansia), berberine restored equilibrium in diabetic rat/mouse models, which is correlated with better glycemic control. Berberine rebalanced gut taxa (increased SCFA-producers, decreased pathobionts), calmed inflammation, and decreased tumor growth in colorectal cancer mice models.^182,183

During an 8-week treatment period, a double-blind RCT pilot trial evaluated daily turmeric root (1 g + piperine), the compound curcumin (1 g + piperine), and a placebo in healthy volunteers, the gut bacterial profiles of the turmeric and curcumin groups differed significantly from the placebo group. ²²⁷ In adults with digestive issues, a different randomized controlled pilot research showed that curcumin-enriched extract improved microbiota modulation and decreased gastrointestinal discomfort. ²²⁸ According to in vivo animal research, curcumin lowers intestinal inflammation, increases bile acid metabolism, and stimulates the growth of bacteria that produce SCFAs—all of which have positive impacts on overall health. ²²⁹ In a study, curcuma extracts were found to lessen gut bacterial microbiota dysbiosis and inhibit strains. The efficacy of the extracts was associated with their antioxidant potential and modulation of fingerprint microbiota. High concentrations of curcumin reduced adverse strains. The antioxidant capacity of curcuma extracts at the colon level determines their capacity to lessen oxidative stress. A dose-dependent reduction in the inflammatory process results from valuing beneficial strains. ²³⁰

Panax ginseng supplementation on a daily basis improved metabolic markers and significantly altered the composition of the gut microbiota in adults with metabolic syndrome, according to a randomized, double-blind, placebo-controlled clinical trial. ²³¹ In particular, it reduced inflammatory taxa and increased the abundance of advantageous genera like Bifidobacterium and Lactobacillus, promoting improved glucose and lipid metabolism, helped the mucosa heal, balanced metabolic processes, and restored the gut microbiota. ²³² Gut bacteria convert the ginsenosides and polysaccharides found in ginseng into bioactive compounds that aid in reestablishing metabolic equilibrium. Changes in the gut microbiome were directly associated with clinical improvements in metabolic syndrome, indicating that ginseng functions through a microbiome-mediated mechanism.^233,234 This is because ginseng polysaccharides improved intestinal metabolism and absorption of ginsenosides while also re-establishing a balanced gut microbiota. ²³⁵

Based on in vitro fermentation models using human microbiota, a brief (10-day) controlled human trial demonstrated that supplementing with cranberry extract significantly increased butyrate production and decreased levels of potentially harmful gut bacteria. ²³⁶ It was also seen to produce a potent bifidogenic effect and increased the number of bacteria that produce butyrate, including Clostridium and Anaerobutyricum. ²³⁷ While overall microbial diversity remained stable, drinking cranberry drinks was linked to decreases in urinary symptoms and trends toward increases in Bifidobacterium longum and Akkermansia muciniphila in a 24-week placebo-controlled study with women who were at risk for UTIs. ²³⁸

Proanthocyanidins (PACs), in particular, are cranberry polyphenols that function as prebiotic-like substances by encouraging the growth of good bacteria and raising the synthesis of SCFAs, such as butyrate. ²³⁶ These alterations suggest wider systemic advantages by supporting anti-inflammatory effects and possibly preserving gut integrity and showed intriguing prebiotic potential in vitro because host microorganisms selectively use it. ²³⁹

Applications of Machine Learning for Characterization of Gut Microbiome

Microbiome signatures, encompassing taxonomic, functional, metabolic, and disease-associated profiles, are increasingly being leveraged for ML-based predictions.^15,240,241 ML models trained on amplicon sequencing data at various taxonomic levels can identify microbial patterns correlated with specific conditions, aiding in biomarker discovery. ²⁴² This approach has gained prominence in gut microbiome interaction studies, as traditional statistical methods often struggle to handle the complexity and high-dimensionality of microbial data. ²⁴³ These conventional methods frequently fall short in feature selection, biomarker identification, distinguishing disease-related signatures from noise, and making treatment recommendations.^35,244 In contrast, ML offers a robust alternative by leveraging pattern recognition and predictive modeling to enhance disease detection and therapeutic strategies.^245,246

ML classifiers such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) have been widely applied in gut microbiome research for disease detection and prediction, particularly in metabolic disorders like diabetes and cancers. These models utilize data from 16S rRNA gene sequencing, shotgun metagenomics, or PCR-amplified microbiome genes.^242,247 For instance, Novielli et al ²⁴⁷ trained RF, XGBoost, and SVM using 16S rRNA sequencing data from 164 microbial taxa alongside host metadata (age, gender, BMI, and country) to classify colorectal cancer (CRC) cases and control subjects. Upon evaluation across multiple performance metrics, with AUPRC as the primary selection criterion, RF emerged as the best-performing classifier, achieving the highest precision (0.729 ± 0.038) and AUPRC (0.668 ± 0.016), outperforming SVM, which had the lowest performance across all metrics. While RF and XGBoost exhibited comparable classification accuracy, RF demonstrated superior precision. However, despite their efficiency in handling large datasets and performing complex decision-making, advanced ML models are often criticized for their opacity, which can lead to bias and challenges in translating outcomes into healthcare decision-making making. ²⁴⁸ To address this, SHapley Additive exPlanations (SHAP), a widely used explainable AI (XAI) technique, was applied to quantify the contribution of individual features to model predictions. This approach enhances transparency by attributing predictive importance to specific variables, ensuring that decisions are both interpretable and justifiable. SHAP analysis in the study by Novielli et al ²⁴⁷ identified Fusobacterium and Peptostreptococcus as key microbial biomarkers associated with CRC, as confirmed in other studies, ²⁴⁷ along with metadata features such as age, gender, country, and BMI. Similarly, Neri-Rosario et al ²⁴⁹ explored the role of gut microbiota in type 2 diabetes (T2D) by comparing six ML models—Binary Logistic Regression, Naïve Bayes, Decision Tree, RF, XGBoost, and Multilayer Perceptron (MLP)—for classifying individuals with normal glucose tolerance (NGT), prediabetes, and T2D based on blood glucose levels and 16S rRNA sequencing data. While both RF and XGBoost outperformed the other models, XGBoost demonstrated superior performance in multiclass classification, achieving an accuracy of 0.96 and a Cohen’s Kappa score of 0.93, compared to RF’s 0.95 accuracy and 0.90 Kappa. SHAP analysis further highlighted Escherichia, Shigella, Anaerostipes, and Collinsella as key microbial taxa linked to disease progression, reinforcing their potential as microbiome-based biomarkers for metabolic disorders.

Unlike traditional ML approaches like RF, which prioritize individual feature importance, Iterative Random Forest (iRF) identifies stable, high-order feature interactions by iteratively adjusting feature weights and using Random Intersection Trees (RIT) to extract key decision-path interactions. ²⁵⁰ This approach enhances microbiome-based disease prediction by detecting feature interactions. For example, Villani et al ²⁵¹ applied iRF to predict prostate cancer metastasis using gut microbial abundances, identifying Gram-negative bacteria, including butyrate producers (Anaerostipes hadrus, Roseburia inulinivorans, and Roseburia intestinalis), linked to angiogenesis via the LPS-TLR4-VEGF signaling pathway.

Beyond classification, ML-driven microbiome-based therapeutics have also gained significant traction.^15,252 Neri-Rosario et al ²⁴⁹ developed Enbiosis AI, an ML-driven system that integrates XGBoost to classify gut microbiome profiles and generate personalized dietary recommendations for gastrointestinal disorders, including irritable bowel syndrome and functional constipation. ²⁵³ This system analyzes alpha and beta diversity metrics and key microbial taxa such as Bacteroidaceae, Ruminococcaceae, and Clostridiaceae to provide personalized microbiome modulation recommendations.^249,253 Using Monte Carlo simulations, Enbiosis predicts microbiome shifts needed to improve irritable bowel syndrome, demonstrating the potential of ML in guiding precision medicine and gut microbiome interventions. While RF excels in binary classification, distinguishing healthy versus diseased microbiome signatures, XGBoost proves superior in multiclass classification, making it particularly useful for diseases that progress in stages, such as T2D and prediabetes, obesity, ulcerative colitis, and autoimmune diseases.^249,254 The ability to classify patients into distinct metabolic states allows for better risk assessment and targeted intervention strategies.

Leave-One-Dataset-Out Cross-Validation (LODOCV) improves model generalization by training on multiple datasets and testing on a separate one. Unlike K-fold cross-validation, which overestimates performance on new data, LODOCV prevents data leakage, reducing bias while providing realistic performance estimates. ²⁵⁵ Though it increases variability, it enhances predictive accuracy in microbiome studies, where dataset differences significantly impact results. Lee et al ³⁵ applied LODOCV to assess ML models on whole metagenome sequencing data, where XGBoost outperformed RF. Aiming to enhance cross-cohort generalizability in microbiome biomarker discovery for Crohn’s disease and CRC, they evaluated 5184 method combinations across 1.65 million training iterations, optimizing diagnostic accuracy and identifying microbial species with differential abundance that traditional statistical methods failed to detect.

While XGBoost excels in large datasets, it struggles with small, high-dimensional microbiome data, showing inconsistent superiority, higher computational cost, and longer training times compared to RF, Elastic Net (ENET), and SVM.^95,96 SVM improves classification by maximizing margins between class boundaries and nearest data points, using kernel functions to handle nonlinearity and epsilon-insensitive loss for robustness. ²⁴⁴ In predicting Type 2 Diabetes Mellitus (T2DM) using gut microbiome data, SVM outperformed XGBoost, demonstrating better generalization and higher predictive accuracy, particularly in distinguishing early T2DM stages. ²⁵⁶ A study by Ge et al ²⁵⁷ analyzed 207 fecal samples (118 T2DM patients, 89 controls), incorporating 6 clinical features and 10 bacterial species, including those in the genus Bifidobacterium, Roseburia, and Enterococcus. Models were trained on 80% of the data and tested on 20%, where SVM achieved an AUC of 0.72 compared to XGBoost’s 0.70. In the test set, only SVM improved to 0.77, suggesting better generalization.

Due to the limitations of classical ML in handling large volumes of high-dimensional and sparse data, as well as its challenges in capturing intricate patterns and relationships, Deep Learning (DL) has emerged as a better option for analyzing the human microbiome. ²⁴⁴ Unlike traditional ML, which often requires extensive feature engineering, DL can learn directly from raw data through multiple hidden layers, enabling more accurate and automated pattern recognition. ²⁵⁸ This capability is driven by advanced architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs), each playing a crucial role in microbiome research by enhancing feature extraction, predictive modeling, and data-driven discovery.^244,259 Image Microbiome (iMic) and Graph Microbiome (gMic) handle small datasets effectively and offer interpretable results compared to RF and XGBoost in microbiome research. ²⁴² These models use DL techniques, including CNNs and Graph Convolutional Networks (GCNs), to represent microbiome data as images and graphs, preserving phylogenetic relationships. This improves feature representation, aiding biomarker discovery and enhancing disease classification, particularly for CRC and T2D. ²⁴²

Beyond 16S rRNA sequencing, metagenomics, and PCR-amplified genes, metaproteomics data is increasingly integrated into DL models to enhance microbiome analysis. DeepFilter, a CNN-based DL model, improves peptide-spectrum matching (PSM) identification in metaproteomics, analyzing protein compositions in microbial communities such as environmental and human gut microbiomes. By identifying peptides, which reflect expressed proteins and microbial functionality, DeepFilter provides critical biological insights, excelling in PSM, peptide, and protein identification across diverse metaproteomic datasets. ²⁶⁰ When applied to human gut microbiome data, DeepFilter identified 6.2% more PSMs, 6.7% more peptides, and 4% more proteins than the best baseline method. It also excelled in cross-dataset generalization, outperforming baselines in marine and soil microbiome datasets. ²⁶⁰ However, unlike SHAP-based models, DeepFilter lacks feature attribution, limiting its interpretability despite its superior performance.

Conclusion and Future Directions

The microbiome is a complex and dynamic ecosystem that plays a central role in health and disease. Advances in sequencing technologies and computational biology have greatly expanded our understanding of microbial communities, particularly in the gut. However, translating this growing body of knowledge into mechanistic insight and clinical application requires more comprehensive, integrative, and multidisciplinary approaches.

Despite intensified research over the past two decades, a full characterization of the microbiome as an ecosystem remains incomplete. Addressing this gap requires the integration of theoretical frameworks and experimental methods from multiple disciplines. Evolutionary biology, for example, is essential for understanding how heredity and selective pressures shape host–microbe co-evolution. To establish robust cause–effect relationships, comprehensive profiling of both biotic components (microbes and host factors) and abiotic variables (diet, environment, and lifestyle) is necessary. Future studies should therefore focus on disentangling the relative contributions of environmental influences, host immune responses and tolerance mechanisms, and microbial community structure and function.

Identifying the functional links between diet, gut microbiota, and host health is particularly important for therapeutic development. Genome-scale metabolic models of human-associated microbes provide a powerful framework for integrating multi-omics datasets. Metabolomics, for instance, can detect system-wide biochemical changes that reveal molecular interactions between the microbiome, host metabolic homeostasis, and disease processes, thereby enabling the discovery of novel therapeutic targets. Fluxomics further complements this approach by quantifying metabolic fluxes, including the rates of metabolite production, consumption, and transformation within microbial communities. Together, these approaches position as systems biology platforms for studying diet–microbiome interactions, microbe–microbe dynamics, and host–microbiome crosstalk under physiological conditions, as well as for constructing personalized, condition-specific gut microbiome models to assess disease-associated microbial shifts.

Despite these advances, significant challenges hinder the clinical translation of microbiome research. High inter-individual variability, limited standardization of microbiota-based interventions such as fecal microbiota transplantation, and potential safety concerns underscore the need for well-designed and rigorously controlled clinical trials. Looking ahead, personalized microbiome-based therapies informed by metagenomic sequencing and AI-driven data analysis are likely to define the next phase of precision medicine. Given its broad influence across metabolic, autoimmune, neurological, and oncological diseases, the gut microbiome represents a major therapeutic frontier. Continued methodological refinement and interdisciplinary innovation will be essential for developing safe, effective, and clinically actionable microbiome-targeted treatments.