Microbial risk score for capturing microbial characteristics, integrating multi-omics data, and predicting disease risk

α-Methyl-Tryptophan Inhibits SLC6A14 Expression and Exhibits Immunomodulatory Effects in Crohn's Disease

Introduction

Crohn's disease (CD) is a chronic inflammatory condition of the intestines with a rising global incidence. Traditional diagnostic and therapeutic methods have limitations, necessitating the exploration of more effective strategies.

Methods

In this study, we employed the Gene Expression Omnibus database to identify genes that are differentially expressed in CD. RT-PCR and immunohistochemical analysis were used to SLC6A14 RNA and protein expression in the colons of CD mice and CD tissues from patients. The mouse model of CD was induced by dextran sodium sulfate (DSS). Infiltrating immune cells in mouse model were screened by flow cytometry.

Results

We discovered that SLC6A14 is significantly overexpressed in CD samples, and its expression is positively correlated with the degree of infiltration by CD4+ and CD8+ T cells. The elevated levels of SLC6A14 RNA and protein were confirmed in clinical CD tissues. The SLC6A14 inhibitor α-methyl-tryptophan (α-MT) significantly decreased the expression of SLC6A14 RNA and protein in the colons of CD mice. The α-MT treatment group also exhibited reduced levels of cytokines involved in T cell differentiation (IFN-γ and TNF-α) and the expression of immune cell surface markers CXCR-3 and LAG-3. Flow cytometry analysis revealed a significant increase in the infiltration of CD4+ and CD8+ T cells in the DSS-treated group compared to the control group. Conversely, the α-MT treatment group showed a significant reduction in CD4+ and CD8+ T cell infiltration and the restoration of intestinal parameters in CD mice. These findings underscore the role of SLC6A14 in regulating intestinal immune cell infiltration during CD progression.

Discussion

Our findings suggest that SLC6A14 could serve as a potential diagnostic biomarker and therapeutic target for CD. Furthermore, α-MT offers a novel approach for the clinical diagnosis and treatment of CD by targeting SLC6A14 for therapeutic intervention.

Cervicovaginal microbiome and natural history of Chlamydia trachomatis in adolescents and young women

This study investigated the cervicovaginal microbiome's (CVM's) impact on Chlamydia trachomatis (CT) infection among Black and Hispanic adolescent and young adult women. A total of 187 women with incident CT were matched to 373 controls, and the CVM was characterized before, during, and after CT infection. The findings highlight that a specific subtype of bacterial vaginosis (BV), identified from 16S rRNA gene reads using the molBV algorithm and community state type (CST) clustering, is a significant risk factor for CT acquisition. A microbial risk score (MRS) further identified a network of bacterial genera associated with increased CT risk. Post treatment, the CVM associated with CT acquisition re-emerged in a different subset of cases leading to reinfection. Additionally, the analysis showed a connection between post-treatment CVM and the development of pelvic inflammatory disease (PID) and miscarriage, further underscoring the CVM's contributing role to incident CT natural history and highlighting its consideration as a therapeutic target.

Predicting host health status through an integrated machine learning framework: insights from healthy gut microbiome aging trajectory

The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach for predicting host health status. Previous research has underscored the potential of microbiome-based predictions, and the rapid advancements of machine learning techniques have introduced new opportunities for exploiting microbiome data. To predict various host nonhealthy conditions, this study proposed an integrated machine learning-based estimation pipeline of Gut Age Index (GAI) by establishing a health aging baseline with the gut microbiome data from healthy individuals. We assessed the performance of GAI pipeline on two extensive cohorts - the Guangdong Gut Microbiome Project (GGMP) and the American Gut Project (AGP). In the GGMP cohort, for 20 common chronic diseases such as metabolic syndrome, obesity, and cardiovascular diseases, the proposed GAI achieved a balanced accuracy, ranging from 66 to 75%, with the prediction performance for atherosclerosis being the highest. In the AGP cohort, the balanced accuracy of GAI ranged from 58 to 72% for 10 diseases. Based on the results from these two datasets, we conclude that our proposed approach in this study can be used to predict individual health status, which offers the potential for scalable, cost-effective, and personalized health insights.

Oral Microbiome and Subsequent Risk of Head and Neck Squamous Cell Cancer

Importance

The oral microbiota may be involved in development of head and neck squamous cell cancer (HNSCC), yet current evidence is largely limited to bacterial 16S amplicon sequencing or small retrospective case-control studies.

Objective

To test whether oral bacterial and fungal microbiomes are associated with subsequent risk of HNSCC development.

Design, Setting, and Participants

Prospective nested case-control study among participants providing oral samples in 3 epidemiological cohorts, the American Cancer Society Cancer Prevention Study II Nutrition Cohort, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, and the Southern Community Cohort Study. Two hundred thirty-six patients who prospectively developed HNSCC were identified during a mean (SD) of 5.1 (3.6) years of follow-up. Control participants who remained HNSCC free were selected by 2:1 frequency matching on cohort, age, sex, race and ethnicity, and time since oral sample collection. Data analysis was conducted in 2023.

Exposures

Characterization of the oral bacterial microbiome using whole-genome shotgun sequencing and the oral fungal microbiome using internal transcribed spacer sequencing. Association of bacterial and fungal taxa with HNSCC was assessed by analysis of compositions of microbiomes with bias correction. Association with red and orange oral pathogen complexes was tested by logistic regression. A microbial risk score for HNSCC risk was calculated from risk-associated microbiota.

Main Outcomes and Measures

The primary outcome was HNSCC incidence.

Results

The study included 236 HNSCC case participants with a mean (SD) age of 60.9 (9.5) years and 24.6% women during a mean of 5.1 (3.6) years of follow-up, and 485 matched control participants. Overall microbiome diversity at baseline was not related to subsequent HNSCC risk; however 13 oral bacterial species were found to be differentially associated with development of HNSCC. The species included the newly identified Prevotella salivae, Streptococcus sanguinis, and Leptotrichia species, as well as several species belonging to beta and gamma Proteobacteria. The red/orange periodontal pathogen complex was moderately associated with HNSCC risk (odds ratio, 1.06 per 1 SD; 95% CI, 1.00-1.12). A 1-SD increase in microbial risk score (created based on 22 bacteria) was associated with a 50% increase in HNSCC risk (multivariate odds ratio, 1.50; 95% CI, 1.21-1.85). No fungal taxa associated with HNSCC risk were identified.

Conclusions and Relevance

This case-control study yielded compelling evidence that oral bacteria are a risk factor for HNSCC development. The identified bacteria and bacterial complexes hold promise, along with other risk factors, to identify high-risk individuals for personalized prevention of HNSCC.

Multiomics Assessment of the Gut Microbiome in Rare Hyperoxaluric Conditions

Introduction

Hyperoxaluria is a risk factor for kidney stone formation and chronic kidney disease progression. The microbiome is an important protective factor against oxalate accumulation through the activity of its oxalate-degrading enzymes (ODEs). In this cross-sectional study, we leverage multiomics to characterize the microbial community of participants with primary and enteric hyperoxaluria, as well as idiopathic calcium oxalate kidney stone (CKS) formers, focusing on the relationship between oxalate degrading functions of the microbiome.

Methods

Patients diagnosed with type 1 primary hyperoxaluria (PH), enteric hyperoxaluria (EH), and CKS were screened for inclusion in the study. Participants completed a food frequency questionnaire recording their dietary oxalate content while fecal oxalate levels were ascertained. DNA and RNA were extracted from stool samples and sequenced. Metagenomic (MTG) and metatranscriptomic (MTT) data were processed through our bioinformatics pipelines, and microbiome diversity, differential abundance, and networks were subject to statistical analysis in relationship with oxalate levels.

Results

A total of 38 subjects were recruited, including 13 healthy participants, 12 patients with recurrent CKS, 8 with PH, and 5 with EH. Urinary and fecal oxalate were significantly higher in the PH and the EH population compared to healthy controls. At the community level, alpha-diversity and beta-diversity indices were similar across all populations. The respective contributions of single bacterial species to the total oxalate degradative potential were similar in healthy and PH subjects. MTT-based network analysis identified the most interactive bacterial network in patients with PH. Patients with EH had a decreased abundance of multiple major oxalate degraders.

Conclusion

The composition and inferred activity of oxalate-degrading microbiota were differentially associated with host clinical conditions. Identifying these changes improves our understanding of the relationships between dietary constituents, microbiota, and oxalate homeostasis, and suggests new therapeutic approaches protecting against hyperoxaluria.

Identification of commensal gut bacterial strains with lipogenic effects contributing to NAFLD in children

Gut microbiota is known to have a significant impact on nonalcoholic fatty liver disease (NAFLD), particularly in children with obesity. However, the specific functions of microbiota at the strain level in this population have not been fully elucidated. In this study, we successfully isolated and identified several commensal gut bacterial strains that were dominant in children with obesity and NAFLD. Among these, four novel isolates were found to have significant lipogenic effects in vitro. These strains exhibited a potential link to hepatocyte steatosis by regulating the expression of genes involved in lipid metabolism and inflammation. Moreover, a larger cohort analysis confirmed that these identified bacterial strains were enriched in the NAFLD group. The integrated analysis of these strains effectively distinguished NASH from NAFL. These four strains might serve as potential biomarkers in children with NAFLD. These findings provided new insights into the exploration of therapeutic targets for NAFLD.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Statistical modeling of gut microbiota for personalized health status monitoring

BACKGROUND

The gut microbiome is closely associated with health status, and any microbiota dysbiosis could considerably impact the host's health. In addition, many active consortium projects have generated many reference datasets available for large-scale retrospective research. However, a comprehensive monitoring framework that analyzes health status and quantitatively present bacteria-to-health contribution has not been thoroughly investigated.

METHODS

We systematically developed a statistical monitoring diagram for personalized health status prediction and analysis. Our framework comprises three elements: (1) a statistical monitoring model was established, the health index was constructed, and the health boundary was defined; (2) healthy patterns were identified among healthy people and analyzed using contrast learning; (3) the contribution of each bacterium to the health index of the diseased population was analyzed. Furthermore, we investigated disease proximity using the contribution spectrum and discovered multiple multi-disease-related targets.

RESULTS

We demonstrated and evaluated the effectiveness of the proposed monitoring framework for tracking personalized health status through comprehensive real-data analysis using the multi-study cohort and another validation cohort. A statistical monitoring model was developed based on 92 microbial taxa. In both the discovery and validation sets, our approach achieved balanced accuracies of 0.7132 and 0.7026, and AUC of 0.80 and 0.76, respectively. Four health patterns were identified in healthy populations, highlighting variations in species composition and metabolic function across these patterns. Furthermore, a reasonable correlation was found between the proposed health index and host physiological indicators, diversity, and functional redundancy. The health index significantly correlated with Shannon diversity ([Formula: see text]) and species richness ([Formula: see text]) in the healthy samples. However, in samples from individuals with diseases, the health index significantly correlated with age ([Formula: see text]), species richness ([Formula: see text]), and functional redundancy ([Formula: see text]). Personalized diagnosis is achieved by analyzing the contribution of each bacterium to the health index. We identified high-contribution species shared across multiple diseases by analyzing the contribution spectrum of these diseases.

CONCLUSIONS

Our research revealed that the proposed monitoring framework could promote a deep understanding of healthy microbiomes and unhealthy variations and served as a bridge toward individualized therapy target discovery and precise modulation. Video Abstract.

The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans

Exposure to environmental pollution influences respiratory health. The role of the airway microbial ecosystem underlying the interaction of exposure and respiratory health remains unclear. Here, through a province-wide chronic obstructive pulmonary disease surveillance program, we conducted a population-based survey of bacterial (n = 1,651) and fungal (n = 719) taxa and metagenomes (n = 1,128) from induced sputum of 1,651 household members in Guangdong, China. We found that cigarette smoking and higher PM_2.5 concentration were associated with lung function impairment through the mediation of bacterial and fungal communities, respectively, and that exposure was associated with an enhanced inter-kingdom microbial interaction resembling the pattern seen in chronic obstructive pulmonary disease. Enrichment of Neisseria was associated with a 2.25-fold increased risk of high respiratory symptom burden, coupled with an elevation in Aspergillus, in association with occupational pollution. We developed an individualized microbiome-based health index, which covaried with exposure, respiratory symptoms and diseases, with potential generalizability to global datasets. Our results may inform environmental risk prevention and guide interventions that harness airway microbiome.

Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis

Objective

To conduct the first thorough bibliometric analysis to evaluate and quantify global research regarding to the gut microbiota and type 1 diabetes (T1D).

Methods

A literature search for research studies on gut microbiota and T1D was conducted using the Web of Science Core Collection (WoSCC) database on 24 September 2022. VOSviewer software and the packages Bibliometrix R and ggplot used in RStudio were applied to perform the bibliometric and visualization analysis.

Results

A total of 639 publications was extracted using the terms "gut microbiota" and "type 1 diabetes" (and their synonyms in MeSH). Ultimately, 324 articles were included in the bibliometric analysis. The United States and European countries are the main contributors to this field, and the top 10 most influential institutions are all based in the United States, Finland and Denmark. The three most influential researchers in this field are Li Wen, Jorma Ilonen and Mikael Knip. Historical direct citation analysis showed the evolution of the most cited papers in the field of T1D and gut microbiota. Clustering analysis defined seven clusters, covering the current main topics in both basic and clinical research on T1D and gut microbiota. The most commonly found high-frequency keywords in the period from 2018 to 2021 were "metagenomics," "neutrophils" and "machine learning."

Conclusion

The application of multi-omics and machine learning approaches will be a necessary future step for better understanding gut microbiota in T1D. Finally, the future outlook for customized therapy toward reshaping gut microbiota of T1D patients remains promising.

Relationship between gut microbiota and rheumatoid arthritis: A bibliometric analysis

Introduction

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease. Recently, growing evidence demonstrates that gut microbiota (GM) plays an important role in RA. But so far, no bibliometric studies pertaining to GM in RA have ever been published. This study attempts to depict the knowledge framework in this field from a holistic and systematic perspective based on the bibliometric analysis.

Methods

Literature related to the involvement of GM in RA was searched and picked from the Web of Science Core Collection (WOSCC) database. The annual output, cooperation, hotspots, research status and development trend of this field were analyzed by bibliometric software (VOSviewer and Bibliometricx).

Results

255 original research articles and 204 reviews were included in the analysis. The articles in this field that can be retrieved in WOSCC were first published in 2004 and increased year by year since then. 2013 is a growth explosion point. China and the United States are the countries with the most contributions, and Harvard University is the affiliation with the most output. Frontiers in Immunology (total citations = 603) is the journal with the most publications and the fastest growth rate. eLife is the journal with the most citations (total citations = 1248). Scher, Jose U. and Taneja, Veena are the most productive and cited authors. The research in this field is mainly distributed in the evidence, mechanism and practical application of GM participating in RA through the analysis of keywords and documents. There is sufficient evidence to prove the close relationship between GM and RA, which lays the foundation for this field. This extended two colorful and tender branches of mechanism research and application exploration, which have made some achievements but still have broad exploration space. Recently, the keywords "metabolites", "metabolomics", "acid", "b cells", "balance", "treg cells", "probiotic supplementation" appeared most frequently, which tells us that research on the mechanism of GM participating in RA and exploration of its application are the hotspots in recent years.

Discussion

Taken together, these results provide a data-based and objective introduction to the GM participating in RA, giving readers a valuable reference to help guide future research.