NinimHMDA: neural integration of neighborhood information on a multiplex heterogeneous network for multiple types of human Microbe-Disease association.

MOTIVATION: Many computational methods have been recently proposed to identify differentially abundant microbes related to a single disease; however, few studies have focused on large-scale microbe-disease association prediction using existing experimentally verified associations. This area has critical meanings. For example, it can help to rank and select potential candidate microbes for different diseases at-scale for downstream lab validation experiments and it utilizes existing evidence instead of the microbiome abundance data which usually costs money and time to generate. RESULTS: We construct a multiplex heterogeneous network (MHEN) using human microbe-disease association database, Disbiome and other prior biological databases, and define the large-scale human microbe-disease association prediction as link prediction problems on MHEN. We develop an end-to-end graph convolutional neural network-based mining model NinimHMDA which can not only integrate different prior biological knowledge but also predict different types of microbe-disease associations (e.g. a microbe may be reduced or elevated under the impact of a disease) using one-time model training. To the best of our knowledge, this is the first method that targets on predicting different association types between microbes and diseases. Results from large-scale cross validation and case studies show that our model is highly competitive compared to other commonly used approaches. AVAILABILITYAND IMPLEMENTATION: The codes are available at Github https://github.com/yuanjing-ma/NinimHMDA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel normalization and differential abundance test framework for microbiome data.

MOTIVATION: Microbial communities have been proved to have close relationship with many diseases. The identification of differentially abundant microbial species is clinically meaningful for finding disease-related pathogenic or probiotic bacteria. However, certain characteristics of microbiome data have hurdled the accuracy and effectiveness of differential abundance analysis. The abundances or counts of microbiome species are usually on different scales and exhibit zero-inflation and over-dispersion. Normalization is a crucial step before the differential abundance test. However, existing normalization methods typically try to adjust counts on different scales to a common scale by constructing size factors with the assumption that count distributions across samples are equivalent up to a certain percentile. These methods often yield undesirable results when differentially abundant species are of low to medium abundance level. For differential abundance analysis, existing methods often use a single distribution to model the dispersion of species which lacks flexibility to catch a single species' distinctiveness. These methods tend to detect a lot of false positives and often lack of power when the effect size is small. RESULTS: We develop a novel framework for differential abundance analysis on sparse high-dimensional marker gene microbiome data. Our methodology relies on a novel network-based normalization technique and a two-stage zero-inflated mixture count regression model (RioNorm2). Our normalization method aims to find a group of relatively invariant microbiome species across samples and conditions in order to construct the size factor. Another contribution of the paper is that our testing approach can take under-sampling and over-dispersion into consideration by separating microbiome species into two groups and model them separately. Through comprehensive simulation studies, the performance of our method is consistently powerful and robust across different settings with different sample size, library size and effect size. We also demonstrate the effectiveness of our novel framework using a published dataset of metastatic melanoma and find biological insights from the results. AVAILABILITY AND IMPLEMENTATION: The R package 'RioNorm2' can be installed from Github athttps://github.com/yuanjing-ma/RioNorm2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic-Based Hypertension Subtype Identification Using Informative SNPs.

In this work, we proposed a process to select informative genetic variants for identifying clinically meaningful subtypes of hypertensive patients. We studied 575 African American (AA) and 612 Caucasian hypertensive participants enrolled in the Hypertension Genetic Epidemiology Network (HyperGEN) study and analyzed each race-based group separately. All study participants underwent GWAS (Genome-Wide Association Studies) and echocardiography. We applied a variety of statistical methods and filtering criteria, including generalized linear models, F statistics, burden tests, deleterious variant filtering, and others to select the most informative hypertension-related genetic variants. We performed an unsupervised learning algorithm non-negative matrix factorization (NMF) to identify hypertension subtypes with similar genetic characteristics. Kruskal-Wallis tests were used to demonstrate the clinical meaningfulness of genetic-based hypertension subtypes. Two subgroups were identified for both African American and Caucasian HyperGEN participants. In both AAs and Caucasians, indices of cardiac mechanics differed significantly by hypertension subtypes. African Americans tend to have more genetic variants compared to Caucasians; therefore, using genetic information to distinguish the disease subtypes for this group of people is relatively challenging, but we were able to identify two subtypes whose cardiac mechanics have statistically different distributions using the proposed process. The research gives a promising direction in using statistical methods to select genetic information and identify subgroups of diseases, which may inform the development and trial of novel targeted therapies.

Regulatory T Cell Plasticity and Stability and Autoimmune Diseases.

CD4+CD25+ regulatory T cells (Tregs) are a class of CD4+ T cells with immunosuppressive functions that play a critical role in maintaining immune homeostasis. However, in certain disease settings, Tregs demonstrate plastic differentiation, and the stability of these Tregs, which is characterized by the stable expression or protective epigenetic modifications of the transcription factor Foxp3, becomes abnormal. Plastic Tregs have some features of helper T (Th) cells, such as the secretion of Th-related cytokines and the expression of specific transcription factors in Th cells, but also still retain the expression of Foxp3, a feature of Tregs. Although such Th-like Tregs can secrete pro-inflammatory cytokines, they still possess a strong ability to inhibit specific Th cell responses. Therefore, the plastic differentiation of Tregs not only increases the complexity of the immune circumstances under pathological conditions, especially autoimmune diseases, but also shows an association with changes in the stability of Tregs. The plastic differentiation and stability change of Tregs play vital roles in the progression of diseases. This review focuses on the phenotypic characteristics, functions, and formation conditions of several plastic Tregs and also summarizes the changes of Treg stability and their effects on inhibitory function. Additionally, the effects of Treg plasticity and stability on disease prognosis for several autoimmune diseases were also investigated in order to better understand the relationship between Tregs and autoimmune diseases.

Yangyin Qingre Huoxue Method in Traditional Chinese Medicine Ameliorates Atherosclerosis in ApoE-/- Mice Suffering from High-Fat Diet and HSP65 Aggression.

Atherosclerosis (AS) is a complicated arterial disease resulting from abnormal lipid deposition and inflammatory injury, which is attributed to Yin deficiency, accumulation of heat materials, and stasis of blood flow in Traditional Chinese Medicine (TCM) theory. Thus, according to TCM theory, the method of nourishing Yin (Yangyin), clearing away heat (Qingre), and promoting blood circulation (Huoxue) is a reasonable strategy, which has achieved remarkable clinical efficacy in the treatment of AS, but the mechanisms remain to be known. In this study, we evaluated the effects of Yangyin Qingre Huoxue Prescription (YQHP) on AS in ApoE-/- mice suffering from a high-fat diet and heat shock protein (HSP65) attack. YQHP regulated levels of blood lipids and inflammation-linked cytokines as well as Th17/Treg ratio in peripheral blood. Suppressed IL-6-p-STAT3 signaling and restored IL-2-p-STAT5 signaling in the presence of YQHP may partake in the regulation of Th17 and Treg differentiation. Moreover, YQHP modulated transcriptional levels of costimulator CD80 in aortas as well corresponding to the downregulation of GM-CSF in serum and CD3 expression in CD4+ T cells, which might indicate the potential of YQHP to regulate antigen presenting cells. All these effects eventually promoted the improvement of atherosclerotic lesions. In addition, YQHP promoted less monocyte infiltration in the liver and lower levels of AST, ALT, and AKP production than simvastatin. Conclusively, lipid-regulating and anti-inflammatory functions mediated by YQHP with lower hepatotoxicity than simvastatin hindered the progression of HSP65 aggravated AS in ApoE-/- mice, indicating the effectiveness of Yangyin Qingre Huoxue Method in the treatment of AS.

Draft Genome Sequence of Pseudarthrobacter sp. Strain AG30, Isolated from a Gold and Copper Mine in China.

Here, we report the features and draft genome sequence of Pseudarthrobacter sp. strain AG30, isolated from the Zijin gold and copper mine in China. The genome size of Pseudarthrobacter sp. AG30 was 4,618,494 bp, with a G+C content of 66.2%. Interesting genes and operons putatively conferring resistance to copper and arsenic were identified.