Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers

A universal oral microbiome-based signature for periodontitis

We analyzed eight oral microbiota shotgun metagenomic sequencing cohorts from five countries and three continents, identifying 54 species biomarkers and 26 metabolic biomarkers consistently altered in health and disease states across three or more cohorts. Additionally, machine learning models based on taxonomic profiles achieved high accuracy in distinguishing periodontitis patients from controls (internal and external areas under the receiver operating characteristic curves of 0.86 and 0.85, respectively). These results support metagenome-based diagnosis of periodontitis and provide a foundation for further research and effective treatment strategies.

Characteristics of gut microbiome in patients with pediatric solid tumor

Background

Pediatric solid tumors are a common malignant disease in children, and more and more studies have proved that there is an inseparable relationship between adult tumors and intestinal microbiome, but the changes in the intestinal microbiota of pediatric solid tumor (PST) patients have been scarcely examined. This study aims to examine the differences in the intestinal microbiota features between patients diagnosed with PST and healthy controls (HCs).

Methods

To elucidate the unique characteristics of the gut microbiota in pediatric patients with solid tumors, we recruited 23 PST patients and 20 HCs. A total of 43 stool samples were gathered, and then 16S rRNA sequencing was performed.

Results

We noticed a noticeable pattern of elevated diversity in the gut microbiota within the PST groups. The differences in microbial communities among two groups were remarkable, regarding the analysis at the class level, the abundance of Bacilli was markedly increased in PST patients compared to HCs (P < 0.05), regarding the analysis at the genus level, The presence of Enterococcus was significantly higher in PST cases compared to HCs (P < 0.01), while Lachnospiraceae unclassified, Lachnospira, Haemophilus and Colidextribacter in PST cases, the abundance was significantly reduced. (P < 0.05), 6 genera, including Bacilli, Lactobacillales, Enterococcaceae and Morganella, showed a significant enrichment compared to healthy controls, while 10 genera, including Bilophila, Colidextribacter, Pasteurellales, Haemophilus, Lachnospiraceae unclassified, Lachnospira and Fusobacteriales, were significant reduction in the PST groups.

Conclusion

Our research conducted the characterization analysis of the gut microbiota in PST patients for the first time. More importantly, there are some notable differences in the gut microbiota between PST patients and healthy controls, which we believe is an interesting finding.

Compared to histamine-2 receptor antagonist, proton pump inhibitor induces stronger oral-to-gut microbial transmission and gut microbiome alterations: a randomised controlled trial

OBJECTIVE

We aim to compare the effects of proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs) on the gut microbiota through longitudinal analysis.

DESIGN

Healthy volunteers were randomly assigned to receive either PPI (n=23) or H2RA (n=26) daily for seven consecutive days. We collected oral (saliva) and faecal samples before and after the intervention for metagenomic next-generation sequencing. We analysed intervention-induced alterations in the oral and gut microbiome including microbial abundance and growth rates, oral-to-gut transmissions, and compared differences between the PPI and H2RA groups.

RESULTS

Both interventions disrupted the gut microbiota, with PPIs demonstrating more pronounced effects. PPI usage led to a significantly higher extent of oral-to-gut transmission and promoted the growth of specific oral microbes in the gut. This led to a significant increase in both the number and total abundance of oral species present in the gut, including the identification of known disease-associated species like Fusobacterium nucleatum and Streptococcus anginosus. Overall, gut microbiome-based machine learning classifiers could accurately distinguish PPI from non-PPI users, achieving an area under the receiver operating characteristic curve (AUROC) of 0.924, in contrast to an AUROC of 0.509 for H2RA versus non-H2RA users.

CONCLUSION

Our study provides evidence that PPIs have a greater impact on the gut microbiome and oral-to-gut transmission than H2RAs, shedding light on the mechanism underlying the higher risk of certain diseases associated with prolonged PPI use.

TRIAL REGISTRATION NUMBER

ChiCTR2300072310.

Autism spectrum disorders and the gastrointestinal tract: insights into mechanisms and clinical relevance

Autism spectrum disorders (ASDs) are recognized as central neurodevelopmental disorders diagnosed by impairments in social interactions, communication and repetitive behaviours. The recognition of ASD as a central nervous system (CNS)-mediated neurobehavioural disorder has led most of the research in ASD to be focused on the CNS. However, gastrointestinal function is also likely to be affected owing to the neural mechanistic nature of ASD and the nervous system in the gastrointestinal tract (enteric nervous system). Thus, it is unsurprising that gastrointestinal disorders, particularly constipation, diarrhoea and abdominal pain, are highly comorbid in individuals with ASD. Gastrointestinal problems have also been repeatedly associated with increased severity of the core symptoms diagnostic of ASD and other centrally mediated comorbid conditions, including psychiatric issues, irritability, rigid-compulsive behaviours and aggression. Despite the high prevalence of gastrointestinal dysfunction in ASD and its associated behavioural comorbidities, the specific links between these two conditions have not been clearly delineated, and current data linking ASD to gastrointestinal dysfunction have not been extensively reviewed. This Review outlines the established and emerging clinical and preclinical evidence that emphasizes the gut as a novel mechanistic and potential therapeutic target for individuals with ASD.

Analyzing lung cancer risks in patients with impaired pulmonary function through characterization of gut microbiome and metabolites

BACKGROUND

Lung cancer (LC) is one of the most devastating diseases worldwide, there is growing studies confirm the role of impaired lung function in LC susceptibility. Moreover, gut microbiota dysbiosis is associated with LC severity. Whether alterations in gut microbiota and metabolites are associated with long-term lung dysfunction in LC patients remain unclear. Our study aimed to analyze the risk factors in LC patients with impaired pulmonary function based on the characteristics of the gut microbiome and metabolites.

METHODS

Fecal samples from 55 LC patients and 28 benign pulmonary nodules patients were collected. Pulmonary ventilation function was graded according to the American Thoracic Society/ European Respiratory Society (ATS/ERS) method. LC patients were divided into 3 groups, including 20 patients with normal lung ventilation, 23 patients with mild pulmonary ventilation dysfunction and 12 patients with moderate or above pulmonary ventilation dysfunction. The fecal samples were analyzed using 16 S rRNA gene amplicon sequencing and metabolomics.

RESULTS

The gut microbiome composition between LC patients and benign pulmonary nodules patients presented clearly differences based on Partial Least Squares Discriminant Analysis (PLS-DA). Pulmonary ventilation function was positively correlated with LC tumor stage, the richness and diversity of the gut microbiota in LC patients with moderate or above pulmonary ventilation dysfunction increased significantly, characterized by increased abundance of Subdoligranulum and Romboutsia. The metabolomics analysis revealed 69 differential metabolites, which were mainly enriched in beta-Alanine metabolism, styrene degradation and pyrimidine metabolism pathway. The area under the curve (AUC) combining the gut microbiome and metabolites was 90% (95% CI: 79-100%), indicating that the two species and four metabolites might regarded as biomarkers to assess the prediction of LC patients with impaired pulmonary function.

CONCLUSIONS

Our results showed that microbiome and metabolomics analyses provide important candidate to be used as clinically diagnostic biomarkers and therapeutic targets related to lung cancer with impaired pulmonary function.

Human Gut Microbiota in Heart Failure: Trying to Unmask an Emerging Organ

There is a bidirectional relationship between the heart and the gut. The gut microbiota, the community of gut micro-organisms themselves, is an excellent gut-homeostasis keeper since it controls the growth of potentially harmful bacteria and protects the microbiota environment. There is evidence suggesting that a diet rich in fatty acids can be metabolized and converted by gut microbiota and hepatic enzymes to trimethyl-amine N-oxide (TMAO), a product that is associated with atherogenesis, platelet dysfunction, thrombotic events, coronary artery disease, stroke, heart failure (HF), and, ultimately, death. HF, by inducing gut ischemia, congestion, and, consequently, gut barrier dysfunction, promotes the intestinal leaking of micro-organisms and their products, facilitating their entrance into circulation and thus stimulating a low-grade inflammation associated with an immune response. Drugs used for HF may alter the gut microbiota, and, conversely, gut microbiota may modify the pharmacokinetic properties of the drugs. The modification of lifestyle based mainly on exercise and a Mediterranean diet, along with the use of pre- or probiotics, may be beneficial for the gut microbiota environment. The potential role of gut microbiota in HF development and progression is the subject of this review.

Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis

BACKGROUND

The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large-scale meta-analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine-learning classifier.

METHODS

In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony-related functions.

RESULTS

The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top-ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota-associated functions between cancer-affected and healthy individuals that were primarily associated with metabolic disturbances.

CONCLUSIONS

GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC-specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.