A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort

A two sample mendelian randomization analysis investigates causal effects between gut microbiome and immune related Vasculitis

Observational data suggest a link between gut microbiota and immune-related vasculitis, but causality remains unclear. A bidirectional mendelian randomization study was conducted using public genome-wide data. The inverse-variance-weighted (IVW) method identified associations and addressed heterogeneity.Families Clostridiaceae 1 and Actinomycetaceae correlated positively with granulomatosis with polyangiitis risk, while classes Lentisphaeria and Melainabacteria, and families Lachnospiraceae and Streptococcaceae showed negative associations. Behçet's disease was positively associated with the risk of family Streptococcaceae abundance. And other several gut microbiota constituents were identified as potential risk factors for immune-related vasculitis. Furthermore, combining positive association results from the IVW analysis revealed numerous shared gut microbiota constituents associated with immune-related vasculitis. MR analysis demonstrated a causal association between the gut microbiota and immune-related vasculitis, offering valuable insights for subsequent mechanistic and clinical investigations into microbiota-mediated immune-related vasculitis.

Causal effects of gut microbiota on the prognosis of ischemic stroke: evidence from a bidirectional two-sample Mendelian randomization study

Background

Increasing research has implicated the possible effect of gut microbiota (GM) on the prognosis of ischemic stroke (IS). However, the precise causal relationship between GM and functional outcomes after IS remains unestablished.

Methods

Data on 211 GM taxa from the MiBioGen consortium and data on prognosis of IS from the Genetics of Ischemic Stroke Functional Outcome (GISCOME) network were utilized as summary-level data of exposure and outcome. Four kinds of Mendelian randomization (MR) methods were carried out to ascertain the causal effect of GM on functional outcomes following IS. A reverse MR analysis was performed on the positive taxa identified in the forward MR analysis to determine the direction of causation. In addition, we conducted a comparative MR analysis without adjusting the baseline National Institute of Health Stroke Scale (NIHSS) of post-stroke functional outcomes to enhance confidence of the results obtained in the main analysis.

Results

Four taxa were identified to be related to stroke prognosis in both main and comparative analyses. Specifically, genus Ruminococcaceae UCG005 and the Eubacterium oxidoreducens group showed significantly negative effects on stroke prognosis, while the genus Lachnospiraceae NK4A136 group and Lachnospiraceae UCG004 showed protective effects against stroke prognosis. The reverse MR analysis did not support a causal role of stroke prognosis in GM. No evidence of heterogeneity, horizontal pleiotropy, and outliers was found.

Conclusion

This MR study provided evidence that genetically predicted GM had a causal link with post-stroke outcomes. Specific gut microbiota taxa associated with IS prognosis were identified, which may be helpful to clarify the pathogenesis of ischemic stroke and making treatment strategies.

The gut-joint axis: Genetic evidence for a causal association between gut microbiota and seropositive rheumatoid arthritis and seronegative rheumatoid arthritis

This study aimed to assess the causal relationship between GM and RA (seropositive RA and seronegative RA). A two-sample Mendelian randomization (MR) analysis was performed to assess the causality of GM on seropositive RA and seronegative RA. GM's genome-wide association study (GWAS) was used as the exposure, whereas the GWAS datasets of seropositive RA and seronegative RA were the outcomes. The primary analysis approach was used as inverse-variance weighted (IVW), followed by 3 additional MR methods (MR-Egger, weighted median, and weighted mode). Cochran's Q test was used to identify heterogeneity. The MR-Egger intercept test and leave-one-out analyses were used to assess horizontal pleiotropy. All statistical analyses were performed in R software. We discovered that Alloprevotella (IVW OR 0.84, 95% CI 0.71-0.99, P = .04) and Christensenellaceae R 7 group (IVW OR 0.71, 95% CI 0.52-0.99, P = .04) were negatively correlated with seropositive RA, Ruminococcaceae UCG002 (IVW OR 1.30, 95% CI 1.10-1.54, P = .002) was positively associated with seropositive RA. Actinomyces (IVW OR 0.73, 95% CI 0.54-0.99, P = .04), Christensenellaceae R 7 group (IVW OR 0.62, 95% CI 0.39-0.97, P = .04), Terrisporobacter (IVW OR 0.64, 95% CI 0.44-0.93, P = .02), Lactobacillales (IVW OR 0.65, 95% CI 0.47-0.90, P = .01) were negatively correlated with seronegative RA. The present MR analysis showed a protective effect of Alloprevotella and Christensenellaceae R 7 group and a potentially anti-protective effect of Ruminococcaceae UCG002 on seropositive RA; and a protective effect of Actinomyces, Christensenellaceae R 7 group, Terrisporobacter, and Lactobacillales on seronegative RA. Further experimental studies and randomized controlled trials are needed to validate these findings.

Associations between gut microbiota and three prostate diseases: a bidirectional two-sample Mendelian randomization study

According to previous observational researches and clinical trials, the gut microbiota is related to prostate diseases. However, the potential association between gut microbiota and prostate disorders is still uncertain. We first identified groups of gut microbiota based on the phylum, class, order, family, and genus levels from consortium MiBioGen. And we acquired prostate diseases statistics from the FINNGEN study and PRACTICAL consortium. Next, two-sample Mendelian randomization was used to investigate the potential associations between three prevalent prostate disease and gut microbiota. In addition, we performed a reverse MR analysis and Benjamini-Hochberg (BH) test for further research. We investigated the connection between 196 gut microbiota and three prevalent prostate diseases. We identified 42 nominally significant associations and 2 robust causative links. Upon correction for multiple comparisons using the Benjamini-Hochberg procedure, our analysis revealed a positive correlation between the risk of prostatitis and the presence of the taxonomic order Gastranaerophilales. Conversely, the risk of prostate cancer exhibited an inverse correlation with the presence of the taxonomic class Alphaproteobacteria. Our study revealed the potential association between gut microbiota and prostate diseases. The results may be useful in providing new insights for further mechanistic and clinical studies of prostate diseases.

mb-PHENIX: diffusion and supervised uniform manifold approximation for denoizing microbiota data

MOTIVATION

Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure.

RESULTS

We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information of count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion with supervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails.

AVAILABILITY AND IMPLEMENTATION

The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub).

The Causal Impact of the Gut Microbiota on Respiratory Tuberculosis Susceptibility

INTRODUCTION

Recent cross-sectional research has demonstrated a substantial link between tuberculosis (TB) and gut microbiota. Nevertheless, the causal impact of the gut microbiota on TB susceptibility in humans remains unknown.

METHODS

The Mendelian randomization (MR) method was utilized for investigating the causality between them. The main method used for MR analysis was the inverse variance weighted (IVW) test, with the MR-Egger, weighted median, weighted mode, and simple median methods serving as supplements. And several sensitivity tests were carried out to validate the MR findings.

RESULTS

The IVW outcomes suggested that three bacterial traits exhibited associations with susceptibility to respiratory TB after Bonferroni correction, namely Lachnospiraceae UCG010 (odds ratio [OR] 1.73, 95% confidence interval [CI] 1.17-2.55, P = 0.005), Eubacterium (brachy group) (OR 1.33, 95% CI 1.07-1.65, P = 0.009), and Ruminococcaceae UCG005 (OR 0.71, 95% CI 0.52-0.98, P = 0.034). Sensitivity analyses demonstrated that horizontal pleiotropy and heterogeneity were absent, thereby guaranteeing the reliability of the results.

CONCLUSION

This research sheds light on the causal impact of gut microbiota on respiratory tuberculosis susceptibility, improving our knowledge of therapeutic strategies for managing TB.

Type-2 Diabetes Mellitus and the Gut Microbiota: Systematic Review

The gut microbiota is a community situated in the gastrointestinal tract that consists of bacteria thriving and contributing to the functions of our body. It is heavily influenced by what individuals eat, as the quality, amount, and frequency of food consumed can favor and inhibit specific bacteria. Type-2 diabetes mellitus (T2DM) is a common but detrimental condition that arises from excessive hyperglycemia, leading to either insulin resistance or damage to the B-cells that produce insulin in the pancreas. A poor diet high in sugar and fats leads to hyperglycemia, and as this persists, it can lead to the development of T2DM. Both insulin resistance and damage to B-cells are greatly affected by the diet an individual consumes, but is there a more involved relationship between the gut microbiota and T2DM? This paper aimed to evaluate the changes in the gut microbiota in patients with T2DM and the impacts of the changes in gut microbiota. Bacteroides, Proteobacteria, Firmicutes, and Actinobacteria prevailed in patients with T2DM and healthy control, but their abundance varied greatly. There was also a significant decrease in bacteria like Lactobacilli spp.and F. prausnitizii associated with insulin resistance. High levels of BMI in patients with T2DM have also been associated with increased levels of A. muciniphilia, which has been associated with decreased fat metabolism and increased BMI. Metabolites such as butyrates and melatonin have also been identified as influencing the development and progression of T2DM. Testosterone levels have also been greatly influenced by the gut microbiota changes in T2DM, such that males with lower testosterone have a greater abundance of bacteria like Gemella, Lachnospiraceae, and Massiia. Identifying these changes and how they impact the body may lead to a treatment addressing insulin dysfunction and the changes that the altered gut microbiota leads to. Future research should address how treatment methods such as healthy diets, exercise, and anti-diabetics affect the gut microbiota and see if they influence sustained changes and reduced hyperglycemia.

Causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomization study

Background

Studies showed that development of gut microbial dysbiosis has a close association with type 2 diabetes (T2D). It is not yet clear if there is a causal relationship between gut microbiota and T2D.

Methods

The data collected from the published genome-wide association studies (GWASs) on gut microbiota and T2D were analyzed. Two-sample Mendelian randomization (MR) analyses were performed to identify causal relationship between bacterial taxa and T2D. Significant bacterial taxa were further analyzed. To confirm the findings' robustness, we performed sensitivity, heterogeneity, and pleiotropy analyses. A reverse MR analysis was also performed to check for potential reverse causation.

Results

By combining the findings of all the MR steps, we identified six causal bacterial taxa, namely, Lachnoclostridium, Oscillospira, Roseburia, Ruminococcaceae UCG003, Ruminococcaceae UCG010 and Streptococcus. The risk of T2D might be positively associated with a high relative abundance of Lachnoclostridium, Roseburia and Streptococcus but negatively associated with Oscillospira, Ruminococcaceae UCG003 and Ruminococcaceae UCG010. The results of MR analyses revealed that there were causal relationships between the six different genera and T2D. And the reverse MR analysis did not reveal any evidence of a reverse causality.

Conclusion

This study implied that Lachnoclostridium, Roseburia and Streptococcus might have anti-protective effect on T2D, whereas Oscillospira, Ruminococcaceae UCG003 and Ruminococcaceae UCG010 genera might have protective effect on T2D. Our study revealed that there was a causal relationship between specific gut microbiota genera and T2D.

Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort

Introduction

The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual's gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D.

Methods

To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls.

Results

We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Allisonella, Slackia, Ruminococus_2, Megaspgaera, Escherichia/Shigella, and Prevotella, among them. Besides, we concluded that Anaerostipes, Intestinibacter, Prevotella_9, Blautia, Granulicatella, and Veillonella were the relevant genus in patients with prediabetes compared to normoglycemic subjects.

Discussion

These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease.