RFtest: A Robust and Flexible Community-Level Test for Microbiome Data Powerfully Detects Phylogenetically Clustered Signals

New bitongling regulates gut microbiota to predict angiogenesis in rheumatoid arthritis via the gut-joint axis: a deep neural network approach

Background

Rheumatoid arthritis (RA) is a persistent autoimmune disorder marked by inflammation and joint damage. Although current treatments, such as disease-modifying antirheumatic drugs (DMARDs), help control symptoms, they frequently cause substantial side effects, highlighting the urgent need for safer and more effective alternatives. Recent research indicates that gut microbiota might be pivotal in RA development through the "gut-joint axis," presenting novel therapeutic possibilities.

Purpose

This study seeks to explore the therapeutic potential of the traditional Chinese medicine (TCM) compound new bitongling (NBTL) for RA, with an emphasis on its capacity to regulate gut microbiota and suppress angiogenesis via the vascular endothelial growth factor (VEGF) signaling pathway.

Methods

We utilized a collagen-induced arthritis (CIA) rat model to assess the impact of NBTL. The study employed 16S ribosomal DNA (16S rDNA) sequencing to analyze gut microbiota composition, machine learning techniques to identify characteristic microbial taxa, and transcriptomic analysis (GSVA) to assess the impact on the VEGF signaling pathway. The findings were further validated through analysis with deep neural network models and in vivo/in vitro experiments, including western blot, immunofluorescence, and miRNA analysis.

Results

NBTL treatment markedly diminished inflammation in RA rats, evidenced by the reduced expression of TNF-α, IL-17, IL-6, and ASC in synovial tissues. Histopathological analysis confirmed alleviation of joint damage. Five characteristic microbial taxa, including f_Mycoplasmataceae, s_Metamycoplasma_sualvi, and g_Prevotellaceae_Ga6A1_group, were identified and associated with NBTL's modulation of the VEGF pathway. Gene set variation analysis (GSVA) revealed significant downregulation of the VEGF signaling pathway following NBTL treatment. Subsequent experiments confirmed that NBTL inhibited VEGF and its receptors, VEGFR1 and VEGFR2, along with HIF-1α (hypoxia-inducible factor 1-alpha), thereby reducing angiogenesis. Additionally, NBTL upregulated miR-20a-5p and miR-223-3p, contributing to its anti-angiogenic effects.

Conclusion

NBTL exhibits significant therapeutic potential in RA by modulating gut microbiota and inhibiting the VEGF signaling pathway. These findings support NBTL's use as a promising candidate for RA treatment, emphasizing the need for further research on its mechanisms and clinical application.

Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks

Background

To construct a diagnostic model for Bipolar Disorder (BD) depressive phase using peripheral tissue RNA data from patients and combining Random Forest with Feedforward Neural Network methods.

Methods

Datasets GSE23848, GSE39653, and GSE69486 were selected, and differential gene expression analysis was conducted using the limma package in R. Key genes from the differentially expressed genes were identified using the Random Forest method. These key genes' expression levels in each sample were used to train a Feedforward Neural Network model. Techniques like L1 regularization, early stopping, and dropout layers were employed to prevent model overfitting. Model performance was then validated, followed by GO, KEGG, and protein-protein interaction network analyses.

Results

The final model was a Feedforward Neural Network with two hidden layers and two dropout layers, comprising 2345 trainable parameters. Model performance on the validation set, assessed through 1000 bootstrap resampling iterations, demonstrated a specificity of 0.769 (95 % CI 0.571-1.000), sensitivity of 0.818 (95 % CI 0.533-1.000), AUC value of 0.832 (95 % CI 0.642-0.979), and accuracy of 0.792 (95 % CI 0.625-0.958). Enrichment analysis of key genes indicated no significant enrichment in any known pathways.

Conclusion

Key genes with biological significance were identified based on the decrease in Gini coefficient within the Random Forest model. The combined use of Random Forest and Feedforward Neural Network to establish a diagnostic model showed good classification performance in Bipolar Disorder.

Human Gut Microbiota for Diagnosis and Treatment of Depression

Nowadays, depressive disorder is spreading rapidly all over the world. Therefore, attention to the studies of the pathogenesis of the disease in order to find novel ways of early diagnosis and treatment is increasing among the scientific and medical communities. Special attention is drawn to a biomarker and therapeutic strategy through the microbiota-gut-brain axis. It is known that the symbiotic interactions between the gut microbes and the host can affect mental health. The review analyzes the mechanisms and ways of action of the gut microbiota on the pathophysiology of depression. The possibility of using knowledge about the taxonomic composition and metabolic profile of the microbiota of patients with depression to select gene compositions (metagenomic signature) as biomarkers of the disease is evaluated. The use of in silico technologies (machine learning) for the diagnosis of depression based on the biomarkers of the gut microbiota is given. Alternative approaches to the treatment of depression are being considered by balancing the microbial composition through dietary modifications and the use of additives, namely probiotics, postbiotics (including vesicles) and prebiotics as psychobiotics, and fecal transplantation. The bacterium Faecalibacterium prausnitzii is under consideration as a promising new-generation probiotic and auxiliary diagnostic biomarker of depression. The analysis conducted in this review may be useful for clinical practice and pharmacology.

Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance

Random forests have emerged as a promising tool in comparative metagenomics because they can predict environmental characteristics based on microbial composition in datasets where β-diversity metrics fall short of revealing meaningful relationships between samples. Nevertheless, despite this efficacy, they lack biological insight in tandem with their predictions, potentially hindering scientific advancement. To overcome this limitation, we leverage a geometric characterization of random forests to introduce a data-driven phylogenetic β-diversity metric, the adaptive Haar-like distance. This new metric assigns a weight to each internal node (i.e., split or bifurcation) of a reference phylogeny, indicating the relative importance of that node in discerning environmental samples based on their microbial composition. Alongside this, a weighted nearest-neighbors classifier, constructed using the adaptive metric, can be used as a proxy for the random forest while maintaining accuracy on par with that of the original forest and another state-of-the-art classifier, CoDaCoRe. As shown in datasets from diverse microbial environments, however, the new metric and classifier significantly enhance the biological interpretability and visualization of high-dimensional metagenomic samples.

Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning

In the last few years, investigation of the gut-brain axis and the connection between the gut microbiota and the human nervous system and mental health has become one of the most popular topics. Correlations between the taxonomic and functional changes in gut microbiota and major depressive disorder have been shown in several studies. Machine learning provides a promising approach to analyze large-scale metagenomic data and identify biomarkers associated with depression. In this work, machine learning algorithms, such as random forest, elastic net, and You Only Look Once (YOLO), were utilized to detect significant features in microbiome samples and classify individuals based on their disorder status. The analysis was conducted on metagenomic data obtained during the study of gut microbiota of healthy people and patients with major depressive disorder. The YOLO method showed the greatest effectiveness in the analysis of the metagenomic samples and confirmed the experimental results on the critical importance of a reduction in the amount of Faecalibacterium prausnitzii for the manifestation of depression. These findings could contribute to a better understanding of the role of the gut microbiota in major depressive disorder and potentially lead the way for novel diagnostic and therapeutic strategies.

multiMiAT: an optimal microbiome-based association test for multicategory phenotypes

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.