GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph.

BACKGROUND: Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery. RESULTS: We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs. CONCLUSIONS: GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo's dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

Associations of muscle mass and strength with depression among US adults: A cross-sectional NHANES study.

OBJECTIVES: The aim of our study was to assess the association between muscle mass and strength and depression through a cross-sectional study of the National Health and Nutrition Examination Survey from 2011 to 2014. METHODS: Muscle mass was calculated by summing the lean body mass of the limbs and muscle strength was assessed by grip strength. Depression was determined by The 9-item Patient Health Questionnaire. We used weighted multivariate logistic regression models to explore the relationship between muscle mass and strength and depression. Generalized additive models were used to test for the presence of nonlinear associations. We then constructed a two-piece-wise linear regression model and performed a recursive algorithm to calculate inflection points. In addition, subgroup analyses and interaction tests were performed. RESULTS: The study recruited 4871 adults from the United States. In regression models adjusted for all confounding variables, the OR (95 % CI) for the association between grip strength and appendicular lean mass (ALM) and depression were 0.943 (0.903, 0.985), 0.945 (0.908, 0.983), respectively. There was a non-linear association between grip strength and depression with a turning point of 46.3. The OR (95 % CI) before the turning point was 0.920 (0.872, 0.972). The interaction was statistically significant only in the age analysis. There was also a nonlinear association between ALM and depression, but no significant turning point was found. The interaction was statistically significant in the gender and BMI analyses. CONCLUSION: Grip strength and ALM are negatively associated with an increased likelihood of depression in US adults. Exercises for muscle mass and strength may help prevent depression.

Association of systemic immune inflammatory index with obesity and abdominal obesity: A cross-sectional study from NHANES.

BACKGROUND AND AIM: Our aim was to explore the potential relationship between SII and obesity, as well as abdominal obesity. METHODS AND RESULTS: We utilized a weighted multivariable logistic regression model to investigate the relationship between SII and obesity, as well as abdominal obesity. Generalized additive models were employed to test for non-linear associations. Subsequently, we constructed a two-piecewise linear regression model and conducted a recursive algorithm to calculate inflection points. Additionally, subgroup analyses and interaction tests were performed. A total of 7,880 U.S. adult participants from NHANES 2011-2018 were recruited for this study. In the regression model adjusted for all confounding variables, the odds ratios (95% confidence intervals) for the association between SII/100 and obesity, as well as abdominal obesity, were 1.03 (1.01, 1.06) and 1.04 (1.01, 1.08) respectively. There was a non-linear and reverse U-shaped association between SII/100 and obesity, as well as abdominal obesity, with inflection points at 7.32 and 9.98 respectively. Significant positive correlations were observed before the inflection points, while significant negative correlations were found after the inflection points. There was a statistically significant interaction in the analysis of age, hypertension, and diabetes. Moreover, a notable interaction is observed between SII/100 and abdominal obesity within non-Hispanic Asian populations. CONCLUSIONS: In adults from the United States, there is a positive correlation between SII and the high risk of obesity, as well as abdominal obesity. Further large-scale prospective studies are needed to analyze the role of SII in obesity and abdominal obesity.

Nonlinear association of triglyceride-glucose index with hyperuricemia in US adults: a cross-sectional study.

BACKGROUND: Despite abundant evidence on the epidemiological risk factors of metabolic diseases related to hyperuricemia, there is still insufficient evidence regarding the nonlinear relationship between triglyceride-glucose (TyG) index and hyperuricemia. Thus, the purpose of this research is to clarify the nonlinear connection between TyG and hyperuricemia. METHODS: From 2011 to 2018, a cross-sectional study was carried out using data from the National Health and Nutrition Examination Survey (NHANES). This study had 8572 participants in all. TyG was computed as Ln [triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The outcome variable was hyperuricemia. The association between TyG and hyperuricemia was examined using weighted multiple logistic regression, subgroup analysis, generalized additive models, smooth fitting curves, and two-piecewise linear regression models. RESULTS: In the regression model adjusting for all confounding variables, the OR (95% CI) for the association between TyG and hyperuricemia was 2.34 (1.70, 3.21). There is a nonlinear and reverse U-shaped association between TyG and hyperuricemia, with a inflection point of 9.69. The OR (95% CI) before the inflection point was 2.64 (2.12, 3.28), and after the inflection point was 0.32 (0.11, 0.98). The interaction in gender, BMI, hypertension, and diabetes analysis was statistically significant. CONCLUSION: Additional prospective studies are required to corroborate the current findings, which indicate a strong positive connection between TyG and hyperuricemia among adults in the United States.

Role of Traditional Chinese Medicine Syndrome Type, Gut Microbiome, and Host Immunity in Predicting Early and Advanced Stage Colorectal Cancer.

OBJECTIVE: To investigate the role of Traditional Chinese Medicine (TCM) syndrome type, gut microbiome distribution, and host immunity function in predicting the early and advanced clinical stages of colorectal cancer (CRC). METHODS: A cross-sectional case-control study was performed which included 48 early stage and 48 advanced patients with CRC enrolled from March 2018 to December 2020. 16S rRNA gene sequencing was performed to analyze the gut microbiomes of the patients, while T and B lymphocyte subsets in peripheral blood were assessed using flow cytometry. TCM syndrome type was measured using the spleen deficiency syndrome (SDS) scale. RESULTS: The abundance levels of Prevotella, Escherichia-Shigella, and Faecalibacterium in the gut microbiota were significantly increased in the advanced group, while Bacteroides was significantly decreased. Phascolarctobacterium was detectable only in the early metaphase group, whereas Alistipes was detectable only in the advanced group. The lymphocyte (P = .006), T helper cell (TH) (P = .002), cytotoxic T cell (TC) (P = .003), double positive T cell (DPT) (P = .02), and total T counts (P = .001) were significantly higher in the early metaphase group than in the advanced metaphase group. Compared with patients with early stage CRC, the advanced group had a higher SDS score. After adjusting for clinical stage, Spearman's correlation analysis showed interactions among gut microbiome abundance, T cell level, and SDS score. Multivariate logistic analysis showed that after controlling for the SDS score, abundance of Alistipes and Faecalibacterium, and double negative T cell (DNT) level, DPT was significantly associated with a lower risk of advanced-stage disease (hazard ratio, 0.918; P = .022). CONCLUSION: Our study suggested associations between clinical stage, SDS, gut microbiota, and T lymphocytes, which provided insights for a potential prediction model for the disease progression of CRC.

Comprehensive and functional analyses reveal the genomic diversity and potential toxicity of Microcystis.

Microcystis is a cyanobacteria that is widely distributed across the world. It has attracted great attention because it produces the hepatotoxin microcystin (MC) that can inhibit eukaryotic protein phosphatases and pose a great risk to animal and human health. Due to the high diversity of morphospecies and genomes, it is still difficult to classify Microcystis species. In this study, we investigated the pangenome of 23 Microcystis strains to detect the genetic diversity and evolutionary dynamics. Microcystis revealed an open pangenome containing 22,009 gene families and exhibited different functional constraints. The core-genome phylogenetic analysis accurately differentiated the toxic and nontoxic strains and could be used as a taxonomic standard at the genetic level. We also investigated the functions of HGT events, of which were mostly conferred from cyanobacteria and closely related species. In order to detect the potential toxicity of Microcystis, we searched and characterized MC biosynthetic gene clusters and other secondary metabolite gene clusters. Our work provides insights into the genetic diversity, evolutionary dynamics, and potential toxicity of Microcystis, which could benefit the species classification and development of new methods for drinking water quality control and management of bloom formation in the future.

Multi-Omics Approach Reveals the Potential Core Vaccine Targets for the Emerging Foodborne Pathogen Campylobacter jejuni.

Campylobacter jejuni is a leading cause of bacterial gastroenteritis in humans around the world. The emergence of bacterial resistance is becoming more serious; therefore, development of new vaccines is considered to be an alternative strategy against drug-resistant pathogen. In this study, we investigated the pangenome of 173 C. jejuni strains and analyzed the phylogenesis and the virulence factor genes. In order to acquire a high-quality pangenome, genomic relatedness was firstly performed with average nucleotide identity (ANI) analyses, and an open pangenome of 8,041 gene families was obtained with the correct taxonomy genomes. Subsequently, the virulence property of the core genome was analyzed and 145 core virulence factor (VF) genes were obtained. Upon functional genomics and immunological analyses, five core VF proteins with high antigenicity were selected as potential core vaccine targets for humans. Furthermore, functional annotations indicated that these proteins are involved in important molecular functions and biological processes, such as adhesion, regulation, and secretion. In addition, transcriptome analysis in human cells and pig intestinal loop proved that these vaccine target genes are important in the virulence of C. jejuni in different hosts. Comprehensive pangenome and relevant animal experiments will facilitate discovering the potential core vaccine targets with improved efficiency in reverse vaccinology. Likewise, this study provided some insights into the genetic polymorphism and phylogeny of C. jejuni and discovered potential vaccine candidates for humans. Prospective development of new vaccines using the targets will be an alternative to the use of antibiotics and prevent the development of multidrug-resistant C. jejuni in humans and even other animals.