Characterization of Shallow Whole-Metagenome Shotgun Sequencing as a High-Accuracy and Low-Cost Method by Complicated Mock Microbiomes

Associations between gut microbiota and incident fractures in the FINRISK cohort

The gut microbiota (GM) can regulate bone mass, but its association with incident fractures is unknown. We used Cox regression models to determine whether the GM composition is associated with incident fractures in the large FINRISK 2002 cohort (n = 7043, 1092 incident fracture cases, median follow-up time 18 years) with information on GM composition and functionality from shotgun metagenome sequencing. Higher alpha diversity was associated with decreased fracture risk (hazard ratio [HR] 0.92 per standard deviation increase in Shannon index, 95% confidence interval 0.87-0.96). For beta diversity, the first principal component was associated with fracture risk (Aitchison distance, HR 0.90, 0.85-0.96). In predefined phyla analyses, we observed that the relative abundance of Proteobacteria was associated with increased fracture risk (HR 1.14, 1.07-1.20), while the relative abundance of Tenericutes was associated with decreased fracture risk (HR 0.90, 0.85-0.96). Explorative sub-analyses within the Proteobacteria phylum showed that higher relative abundance of Gammaproteobacteria was associated with increased fracture risk. Functionality analyses showed that pathways related to amino acid metabolism and lipopolysaccharide biosynthesis associated with fracture risk. The relative abundance of Proteobacteria correlated with pathways for amino acid metabolism, while the relative abundance of Tenericutes correlated with pathways for butyrate synthesis. In conclusion, the overall GM composition was associated with incident fractures. The relative abundance of Proteobacteria, especially Gammaproteobacteria, was associated with increased fracture risk, while the relative abundance of Tenericutes was associated with decreased fracture risk. Functionality analyses demonstrated that pathways known to regulate bone health may underlie these associations.

The Characteristics of Gut Microbiota and Its Relation with Diet in Postmenopausal Osteoporosis

The gut microbiome is linked to osteoporosis. Previous clinical studies showed inconsistent results. This study aimed to characterize the gut microbiota feature and reveal its relation with diet in postmenopausal osteoporosis. Fifty-five postmenopausal women with osteoporosis (Op group) and forty-four age-matched postmenopausal women (normal bone mineral density, Con group) were included in this study. Fecal microbiota was collected and analyzed by shallow shotgun sequencing. Food frequency questionnaires were collected from both groups, and Spearman analysis was used to clarify its correlation with gut microbiota. A total of 2671 species from 29 phyla, 292 families, 152 orders, 80 classes were detected in the study. The two groups had no significant difference in the α and β diversity (p > 0.05). At the genus level, Anaerostipes was enriched in Op group (p < 0.05). At species level, Methanobrevibacter smithii, Bifidobacterium animalis, Rhodococcus defluvii, Lactobacillus plantarum, and Carnobacterium mobile were enriched in the Op group, while Bacillus luciferensis, Acetivibrio cellulolyticus, Citrobacter amalonaticus, and Bifidobacterium breve were differentially enriched in the Con group. Food frequency questionnaire showed that postmenopausal women with osteoporosis intaked more red meat, beer, white and red wine (p < 0.05), and the Con group had more yogurt, fruit, and tea consumption. Red meat consumption had a significant negative correlation with Streptosporangiales (p < 0.01) and Actinomadura (p < 0.05). Fruits intake negatively correlated with Nocardiaceae, Rhodococcus, and Rhodococcus defluvii (p < 0.05). More yogurt intake was consistently correlated with a greater abundance of Streptosporangiales. This study suggests that gut microbiota is significantly altered in the postmenopausal osteoporosis population at genus and species levels, and specific dietary intake might relate to these changes.

Phage-inclusive profiling of human gut microbiomes with Phanta

Due to technical limitations, most gut microbiome studies have focused on prokaryotes, overlooking viruses. Phanta, a virome-inclusive gut microbiome profiling tool, overcomes the limitations of assembly-based viral profiling methods by using customized k-mer-based classification tools and incorporating recently published catalogs of gut viral genomes. Phanta's optimizations consider the small genome size of viruses, sequence homology with prokaryotes and interactions with other gut microbes. Extensive testing of Phanta on simulated data demonstrates that it quickly and accurately quantifies prokaryotes and viruses. When applied to 245 fecal metagenomes from healthy adults, Phanta identifies ~200 viral species per sample, ~5× more than standard assembly-based methods. We observe a ~2:1 ratio between DNA viruses and bacteria, with higher interindividual variability of the gut virome compared to the gut bacteriome. In another cohort, we observe that Phanta performs equally well on bulk versus virus-enriched metagenomes, making it possible to study prokaryotes and viruses in a single experiment, with a single analysis.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

Gut microbiota changes associated with low-carbohydrate diet intervention for obesity

Low-carbohydrate diets (LCDs) are frequently recommended for alleviating obesity, and the gut microbiota plays key roles in energy metabolism and weight loss. However, there is limited in-human research on how LCD changes gut microbiota. In this before-after study, 43 participants were assigned to the LCD intervention for 4 weeks. The main objective was to investigate the specific changes that occur in the participants' microbiome in response to the LCD. Changes in gut microbiota were analyzed using 16s rRNA sequencing. Body composition was measured using InBody 770. Remarkably, 35 participants (79.07%) lost more than 5% of their body weight; levels of BMI, body fat, and total cholesterol were significantly decreased, indicating the effectiveness of the LCD intervention. The richness of microbiota significantly increased after the intervention. By taking the intersection of ANOVA and linear discriminant analysis effect size (LEfSe) analysis results, we identified three phyla, three classes, four orders, five families, and six genera that were differentially enriched between baseline and week-4 time points. Among the three phyla, relative abundances of Firmicutes and Actinobacteriota decreased significantly, while Bacteroidetes increased significantly. At the genus level, Ruminococcus, Agathobacter, Streptococcus, and Bifidobacterium showed a significant reduction in relative abundances, whereas Parabacteroides and Bacteroides increased steadily. Our results demonstrate that LCD can effectively alleviate obesity and modify certain taxa of gut microbiota, providing potential insights for personalized dietary interventions against obesity.

Lactobacillus oris improves non-alcoholic fatty liver in mice and inhibits endogenous cholesterol biosynthesis

We previously confirmed that a strain of Lactobacillus oris isolated from the fecal samples of healthy Hainan centenarian having potent lipid-lowering ability in HepG2 cells; and this study was to investigate the effect of the stain on non-alcoholic fatty liver in mice in vivio. The Lactobacillus oris strain isolated from Hainan centenarian fecal samples were frozen stored in our laboratory. Thirty ob/ob mice (10 in each group) were orally gavaged with Lactobacillus oris (Lactobacillus, 5 × 109 cfu), mixed probiotics (Mixed, 5 × 109 cfu, a mixture with known lipid-lowering ability), or culture medium (Control) respectively. Lactobacillus oris isolated from fecal samples of Hainan centenarians showed significantly in vivo lipid lowering ability compared with the controls, and the ability was comparable with mixed probiotics strains in mice The possible mechanisms of lipid-lowering of probiotics and Lactobacillus oris may be associated with HMGR inhibition to suppress the synthesis of endogenous cholesterol; bile acids reabsorption, and intestinal FXR-FGF15 signaling pathways promoting the cholesterol conversion into bile acids secretion.

Identification of colorectal cancer progression-associated intestinal microbiome and predictive signature construction

OBJECTIVE

The relationship between intestinal microbiome and colorectal cancer (CRC) progression is unclear. This study aims to identify the intestinal microbiome associated with CRC progression and construct predictive labels to support the accurate assessment and treatment of CRC.

METHOD

The 192 patients included in the study were divided into stage I-II and stage III-IV CRC patients according to the pathological stages, and preoperative stools were collected from both groups for 16S rDNA sequencing of the intestinal microbiota. Pearson correlation and Spearman correlation coefficient analysis were used to analyze the differential intestinal microbiome and the correlation with tumor microenvironment and to predict the functional pathway. XGBoost model (XGB) and Random Forest model (RF) were used to construct the microbiome-based signature. The total RNA extraction from 17 CRC tumor simples was used for transcriptome sequencing.

RESULT

The Simpson index of intestinal microbiome in stage III-IV CRC were significantly lower than those in stage I-II CRC. Proteus, Parabacteroides, Alistipes and Ruminococcus etc. are significantly enriched genus in feces of CRC patients with stage III-IV. ko00514: Other types of O - glycan biosynthesis pathway is relevant with CRC progression. Alistipes indistinctus was positively correlated with mast cells, immune activators IL-6 and IL6R, and GOBP_PROTEIN_FOLDING_IN_ENDOPLASMIC_RETICULUM dominantly. The Random Forest (RF) model and eXtreme Gradient Boosting (XGBoost) model constructed with 42 CRC progression-associated differential bacteria were effective in distinguishing CRC patients between stage I-II and stage III-IV.

CONCLUSIONS

The abundance and diversity of intestinal microbiome may increase gradually with the occurrence and progression of CRC. Elevated fetal abundance of Proteus, Parabacteroides, Alistipes and Ruminococcus may contribute to CRC progression. Enhanced synthesis of O - glycans may result in CRC progression. Alistipes indistinctus may play a facilitated role in mast cell maturation by boosting IL-6 production. Alistipes indistinctus may work in the correct folding of endoplasmic reticulum proteins in CRC, reducing ER stress and prompting the survival and deterioration of CRC, which may owe to the enhanced PERK expression and activation of downstream UPR by Alistipes indistinctus. The CRC progression-associated differential intestinal microbiome identified in our study can be served as potential microbial markers for CRC staging prediction.

Comparison of commercial DNA extraction kits for whole metagenome sequencing of human oral, vaginal, and rectal microbiome samples

Introduction

Advancements in DNA extraction and sequencing technologies have been fundamental in deciphering the significance of the microbiome related to human health and pathology. Whole metagenome shotgun sequencing (WMS) is gaining popularity in use compared to its predecessor (i.e., amplicon-based approaches). However, like amplicon-based approaches, WMS is subject to bias from DNA extraction methods that can compromise the integrity of sequencing and subsequent findings. The purpose of this study was to evaluate systematic differences among four commercially available DNA extraction kits frequently used for WMS analysis of the microbiome.

Methods

Oral, vaginal, and rectal swabs were collected in replicates of four by a healthcare provider from five participants and randomized to one of four DNA extraction kits. Two extraction blanks and three replicate mock community samples were also extracted using each extraction kit. WMS was completed with NovaSeq 6000 for all samples. Sequencing and microbial communities were analyzed using nonmetric multidimensional scaling and compositional bias analysis.

Results

Extraction kits differentially biased the percentage of reads attributed to microbial taxa across samples and body sites. The PowerSoil Pro kit performed best in approximating expected proportions of mock communities. While HostZERO was biased against gram-negative bacteria, the kit outperformed other kits in extracting fungal DNA. In clinical samples, HostZERO yielded a smaller fraction of reads assigned to Homo sapiens across sites and had a higher fraction of reads assigned to bacterial taxa compared to other kits. However, HostZERO appears to bias representation of microbial communities and demonstrated the most dispersion by site, particularly for vaginal and rectal samples.

Conclusions

Systematic differences exist among four frequently referenced DNA extraction kits when used for WMS analysis of the human microbiome. Consideration of such differences in study design and data interpretation is imperative to safeguard the integrity of microbiome research and reproducibility of results.

The coral microbiome: towards an understanding of the molecular mechanisms of coral-microbiota interactions

Corals live in a complex, multipartite symbiosis with diverse microbes across kingdoms, some of which are implicated in vital functions, such as those related to resilience against climate change. However, knowledge gaps and technical challenges limit our understanding of the nature and functional significance of complex symbiotic relationships within corals. Here, we provide an overview of the complexity of the coral microbiome focusing on taxonomic diversity and functions of well-studied and cryptic microbes. Mining the coral literature indicate that while corals collectively harbour a third of all marine bacterial phyla, known bacterial symbionts and antagonists of corals represent a minute fraction of this diversity and that these taxa cluster into select genera, suggesting selective evolutionary mechanisms enabled these bacteria to gain a niche within the holobiont. Recent advances in coral microbiome research aimed at leveraging microbiome manipulation to increase coral's fitness to help mitigate heat stress-related mortality are discussed. Then, insights into the potential mechanisms through which microbiota can communicate with and modify host responses are examined by describing known recognition patterns, potential microbially derived coral epigenome effector proteins and coral gene regulation. Finally, the power of omics tools used to study corals are highlighted with emphasis on an integrated host-microbiota multiomics framework to understand the underlying mechanisms during symbiosis and climate change-driven dysbiosis.

Effect of Husbandry Practices on the Fecal Microbiota of C57BL/6J Breeding Colonies Housed in 2 Different Barrier Facilities in the Same Institution

Evidence showing a relationship between the mouse gut microbiome and properties such as phenotype and reaction to therapeutic agents and other treatments has increased significantly over the past 20 to 30 y. Recent concerns regarding the reproducibility of animal experiments have underscored the importance of understanding this relationship and how differences in husbandry practices can affect the gut microbiome. The current study focuses on effects of different barrier practices in 2 barrier facilities at the same institution on the fecal microbiome of breeding C57Bl/6J mice. Ten female and 10 male C57Bl/6J mice were obtained in one shipment from Jackson Laboratories and were housed under different barrier conditions upon arrival. Fecal samples were collected on arrival and periodically thereafter and were sent to TransnetYX for microbiome analysis. Mice used for collection of feces were housed as breeding pairs, with a total of 5 breeding pairs per barrier. An additional fecal sample was collected from these mice at 8 wk after arrival. One F1 female and one F1 male from each breeding cage were housed as brother-sister breeding pairs and a fecal sample was collected from them at 8 wk of age. Brother-sister breeding colonies were continued through F3, with fecal samples for microbiome analysis were collected from each generation at 8 wk of age. Breeding colonies in the 2 barriers showed differences in relative abundance, α -diversity, and β -diversity. Our data indicate that differences in barrier husbandry practices, including the use of autoclaved cages, the degree of restricted access, feed treatment practices, and water provision practices, can affect fecal microbiome divergence in both the parental and filial generations of different breeding colonies. To our knowledge, this is the first study to examine the effect of barrier husbandry practices on the microbiome of breeding colonies through the F3 generation.

Oral Microbiome in Nonsmoker Patients with Oral Cavity Squamous Cell Carcinoma, Defined by Metagenomic Shotgun Sequencing

Objectives: Smoking is the commonest cause of oral cavity squamous cell carcinoma (OC-SCC), but the etiology of OC-SCC in nonsmokers is unknown. Our primary goal was to use metagenomic shotgun sequencing (MSS) to define the taxonomic composition and functional potential of oral metagenome in nonsmokers with OC-SCC. Methods: We conducted a case-control study with 42 OC-SCC case and 45 control nonsmokers. MSS was performed on DNA extracted from mouthwash samples. Taxonomic analysis and pathway analysis were done using MetaPhlAn2 and HUMAnN2, respectively. Statistical difference was determined using the Mann-Whitney test controlling false discovery rate. Results: There was no significant difference in age, sex, race, or alcohol consumption between OC-SCC and control patients. There was a significant difference in beta diversity between OC-SCC and controls. At the phylum level, Bacteroidetes and Synergistetes were overly represented in OC-SCC while Actinobacteria and Firmicutes were overly represented in controls. At the genus level, Fusobacterium was overly represented in OC-SCC compared with controls, while Corynebacterium, Streptococcus, Actinomyces, Cryptobacterium, and Selenomonas were overly represented in controls. Bacterial pathway analysis identified overrepresentation in OC-SCC of pathways related to metabolism of flavin, biotin, thiamin, heme, sugars, fatty acids, peptidoglycans, and tRNA and overrepresentation of nucleotides and essential amino acids in controls. Conclusions: The oral microbiome in nonsmoker patients with OC-SCC is significantly different from that of nonsmoker control patients in taxonomic compositions and functional potentials. Our study's MSS findings matched with previous 16S-based methods in taxonomic differentiation but varied greatly in functional differentiation of microbiomes in OC-SCC and controls.

Caecal microbial communities, functional diversity, and metabolic pathways in Ross 308 broiler chickens fed with diets containing different levels of Marama (Tylosema esculentum) bean meal

The caecum of a chicken harbors complex microbial communities that play vital roles in feed digestion, nutrient absorption, and bird health. Understanding the caecal microbial communities could help improve feed utilization efficiency and chicken product quality and, ultimately, deliver sustainable poultry production systems. Thus, this study assessed the caecal microbial communities and their functional diversity and metabolic pathways in broilers reared on diets containing different levels of marama (Tylosema esculentum) bean meal (MBM). A total of 350, day-old male Ross 308 broiler chicks were randomly allocated to five dietary treatments formulated as follows: a soybean-based standard broiler diet (Con_BC); Con_BC in which soybean products were substituted with 7 (M7_BC), 14 (M14_BC), 21 (M21_BC), and 28% (M28_BC) MBM. The dietary treatments were distributed to 35 replicate pens (10 birds each). After 42 days of feeding, the birds were slaughtered and thereafter caecal samples were collected from each replicate pen. Subsequently, the samples were pooled per treatment group for metagenomics sequence analysis. The results revealed that the bacteria domain (99.11%), with Bacteroides, Firmicutes and Proteobacteria being the most prominent phyla (48.28, 47.52, and 4.86%, respectively). Out of 846 genera obtained, the most abundant genera were Bacteroides, Clostridium, Alistipes, Faecalibacterium, Ruminococcus, Eubacterium, and Parabacterioides. At the genus level, the alpha-diversity showed significant (p < 0.05) difference across all treatment groups. Based on the SEED subsystem, 28 functional categories that include carbohydrates (14.65%), clustering-based subsystems (13.01%), protein metabolism (10.12%) were obtained. The KO analysis revealed 183 endogenous pathways, with 100 functional pathways associated with the metabolism category. Moreover, 15 pathways associated with carbohydrates were observed. The glycolysis/gluconeogenesis, galactose metabolism, pyruvate metabolism (15.32, 12.63, and 11.93%) were the most abundant pathways. Moreover, glycoside hydrolases (GH1, GH5, and GH13) were the most prominent carbohydrates-active enzymes. Therefore, results presented in this study suggest that dietary MB meal can improve microbial communities and their functional and metabolic pathways, which may help increase poultry production.

Taxonomic and functional alterations in the salivary microbiota of children with and without severe early childhood caries (S-ECC) at the age of 3

Background

Primary dental caries is the most prevalent oral disease among preschool children, which can cause severe damage to teeth and even affect the mental well-being of children. Various studies have demonstrated that the oral microbiome plays a pivotal role in the onset and development of dental caries. However, it remains uncertain about the key microbial markers associated with caries, owing to the limited evidence.

Methods

Fifteen S-ECC children and fifteen healthy controls were selected from three-year-old children in this study. Their clinical data and oral saliva samples were collected. Shotgun sequencing was conducted to investigate the microbial differences and the relevant functions between the two groups.

Results

We observed no apparent difference in oral microbial community diversity between the two groups. Still, at the genus/species levels, several characteristic genera/species such as Propionibacterium, Propionibacterium acidifaciens, Prevotella denticola, Streptococcus mutans and Actinomyces sp. oral taxon 448/414 increased significantly in S-ECC children, compared with the oral health group. Furthermore, we found that functional pathways involving glycolysis and acid production, such as starch and sucrose metabolism, fructose and mannose metabolism, glycolysis/gluconeogenesis, were prominently up-regulated in the high-caries group.

Conclusions

Our study showed that dental caries in children were associated with the alterations in the oral microbiota at the composition and functional levels, which may potentially inspire the exploration of microbial diagnosis or therapeutic treatments.

Gut Microbiota Feature of Senile Osteoporosis by Shallow Shotgun Sequencing Using Aged Rats Model

Senile osteoporosis is defined as an age-related bone metabolic disorder, which is characterized by bone loss and decreased bone fragility. Gut microbiota (GM) could regulate the bone metabolic process and be closely related to senile osteoporosis. Several genus-level GM were found to increase in osteoporotic animals and patients. However, to reveal the pathogenic bacteria in senile osteoporosis, further studies are still needed to investigate the complete characteristics of bacteria species. In the present study, the rats were equally divided into two groups: the control group (Con, 6-month-old) and the osteoporosis group (OP, 22-month-old). Fecal samples were freshly collected to conduct the shallow shotgun sequencing. Then, we compared the species numbers, microbial diversity, GM composition at genus and species-level, and functional metabolic pathways in the two groups. The results showed that the species number was lower in the OP group (1272) than in the control group (1413), and 1002 GM species were shared between the two groups. The OP group had the decreased α diversity compared with the control group. As for β diversity, The PCA revealed that samples in the two groups had distinguishable ecological distance in each coordinate. At the species level, Bacteroide coprocola (B. coprocola), Acinetobacter baumannii (A. baumannii), Parabacteroides distasonis (P. distasonis), and Prevotella copri (P. copri) were higher in the OP group, while Corynebacterium stationis (C. stationis), Akkermansia muciniphila (A. muciniphila), and Alistipes indistinctus (A. indistinctus) were decreased. Moreover, functional metabolic analysis revealed that metabolic pathways of fatty acid biosynthesis, valine/isoleucine biosynthesis, GABA biosynthesis, and ubiquinone biosynthesis were enriched in the senile osteoporotic rats. In conclusion, GM at the species level in senile osteoporotic rats was significantly altered in structure, composition, and function. The altered GM structure, increased GM species such as P. copri, and decreased GM species such as A. muciniphila might be linked with the development of senile osteoporosis.

AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite

We introduce AGAMEMNON ( https://github.com/ivlachos/agamemnon ) for the acquisition of microbial abundances from shotgun metagenomics and metatranscriptomic samples, single-microbe sequencing experiments, or sequenced host samples. AGAMEMNON delivers accurate abundances at genus, species, and strain resolution. It incorporates a time and space-efficient indexing scheme for fast pattern matching, enabling indexing and analysis of vast datasets with widely available computational resources. Host-specific modules provide exceptional accuracy for microbial abundance quantification from tissue RNA/DNA sequencing, enabling the expansion of experiments lacking metagenomic/metatranscriptomic analyses. AGAMEMNON provides an R-Shiny application, permitting performance of investigations and visualizations from a graphics interface.

The Interaction between the Gut Microbiome and Bile Acids in Cardiometabolic Diseases

Cardio-metabolic diseases (CMD) are a spectrum of diseases (e.g., type 2 diabetes, atherosclerosis, non-alcohol fatty liver disease (NAFLD), and metabolic syndrome) that are among the leading causes of morbidity and mortality worldwide. It has long been known that bile acids (BA), which are endogenously produced signalling molecules from cholesterol, can affect CMD risk and progression and directly affect the gut microbiome (GM). Moreover, studies focusing on the GM and CMD risk have dramatically increased in the past decade. It has also become clear that the GM can function as a "new" endocrine organ. BA and GM have a complex and interdependent relationship with several CMD pathways. This review aims to provide a comprehensive overview of the interplay between BA metabolism, the GM, and CMD risk and progression.