Clustering algorithm based on DINNSM and its application in gene expression data analysis.

BACKGROUND: Selecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions. OBJECTIVE: This study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method. METHODS: A new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson's or Spearman's correlation. It was then used to construct a nearest-neighbor table based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes. RESULTS: Experiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques. CONCLUSIONS: DINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules.

The alleles of AGT and HIF1A gene affect the risk of hypertension in plateau residents.

Plateau essential hypertension is a common chronic harmful disease of permanent residents in plateau areas. Studies have shown some single nucleotide polymorphisms (SNPs) associations with hypertension, but few have been verified in plateau area-lived people. In this paper, we examined some hypertension-related gene loci to analyze the relationship between risk SNPs and plateau essential hypertension in residents in Qinghai-Tibet plateau area. We screened hypertension-related SNPs from the literature, Clinvar database, GHR database, GTR database, and GWAS database, and then selected 101 susceptible SNPs for detection. Illumina MiSeq NGS platform was used to perform DNA sequencing on the blood samples from 185 Tibetan dwellings of Qinghai, and bioinformatic tools were used to make genotyping. Genetic models adjusted by gender and age were used to calculate the risk effects of genotypes. Four known SNPs as well as a new locus were found associated with PHE, which were rs2493134 (AGT), rs9349379 (PHACTR1), rs1371182 (CYP2C56P-PRPS1P1), rs567481079 (CYP2C56P-PRPS1P1), and chr14:61734822 (HIF1A). Among them, genotypes of rs2493134, rs9349379, and rs567481079 were risk factors, genotypes of rs1371182 and chr14:61734822 were protective factors. The rs2493134 in AGT was found associated with an increased risk of the plateau essential hypertension by 3.24-, 3.24-, and 2.06-fold in co-dominant, dominant, and Log-additive models, respectively. The rs9349379 in PHACTR1 is associated with a 2.61-fold increased risk of plateau essential hypertension according to the dominant model. This study reveals that the alleles of AGT, HIF1A, and PHACTR1 are closely related to plateau essential hypertension risk in the plateau Tibetan population.

Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis.

BACKGROUND Hypertension is one of the most widespread health conditions in the world, and the molecular mechanism of it is still unclear. In this study, we identified the hub genes (hub miRNA genes) associated with hypertension and explored the relationship between hypertension miRNA-gene by constructing a mRNA co-expression network and a miRNA co-expression network, which can help to reveal the mechanism and predict the prognosis of hypertension progression. MATERIAL AND METHODS Based on gene expression profile data of hypertensive samples from the Gene Expression Omnibus database, WGCNA was used to detect hypertension-related biomarkers and key mRNA and miRNA modules. Then, DAVID was used to perform gene-annotation enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) and miRPath were used for pathway analysis of mRNA and miRNAs genes. RESULTS We identified 3 key modules relating to hypertension, 2 mRNA modules named Msaddlebrown and Mgreenyellow and 1 miRNA module named Msalmon. In addition, 12 hub genes (RPL21, RPS28, LOC442727/PTGAP10, LOC100129599/RPS29P14, TBXAS1, FCER1G, CFP, FURIN, PECAM1, IGSF6, NCF1C, and LOC285296/UNC93B3) and 7 hub miRNAs (hsa-miR-1268a/b, hsa-miR-513c-3p, hsa-miR-4799-5p, hsa-miR-296-3p, hsa-miR-5195-5p, hsa-miR-219-2-3p, and hsa-miR-548d-5p) relating to hypertension were identified. HIF-1 signaling pathway and insulin signaling pathway were closely related to the 3 key modules. We also discovered 4 miRNAs (hsa-miR-548am-3p, hsa-miR-513c-3p, hsa-miR-182-5p, and hsa-miR-548d-5p) and 6 genes (IGF1R, GSK3B, FOXO1, PRKAR2B, HIF1A, and PIK3R1) were the core nodes in the hypertension-related miRNA-gene network, and hsa-miR-548am-3p was at the center of the network. CONCLUSIONS These findings will help improve the understanding of the pathogenesis of hypertension, and the discovered genes can serve as signatures for early diagnosis of hypertension.

Origin and transmission of bovine viral diarrhea virus type 1 in China revealed by phylodynamic analysis.

Bovine viral diarrhea virus type 1 (BVDV-1) is prevalent worldwide and causes significant economic loss in animal husbandry. Since its first report in the 1980s in China, several genotypes of BVDV-1 had been reported, but an in-depth phylogenetic analysis on the BVDV isolates from China is lacking. To investigate the molecular evolution and phylodynamics of BVDV-1 genotypes circulating in China, comprehensive phylogenetic and phylodynamic analyses were performed to reconstruct the origin and spatial-temporal distribution, and to trace main viral flows among different areas. BVDV-1 5'-UTR sequences from China and Mongolia were collected from Genbank, and the phylogeny was built using the maximum likelihood method. The Bayesian Skygrid was used to estimate the evolution and population dynamics of BVDV-1. Eight BVDV-1 genotypes were identified, of which 1b and 1 m are the main genotypes. The results indicated that BVDV-1 might be introduced in China in the 1960s, and after a long period of population growth, it gradually leveled off after 2010. The phylodynamic inference clearly shows a more steady BVDV-1 population growth, and the transmission of BVDV-1 may be confined to specific regions. This study will help to understand the molecular epidemiology and long-term evolutionary dynamics of BVDV-1 in China, therefore providing a scientific basis for the prevention and controlof the virus.

Phylodynamic and transmission pattern of rabies virus in China and its neighboring countries.

Rabies is a fatal disease caused by infection with rabies virus (RABV), and human rabies is still a critical public-health concern in China. Although there have been some phylogenetic studies about RABV transmission patterns, with the accumulation of more rabies sequences in recent years, there is an urgent need to update and clarify the spatial and temporal patterns of RABV circulating in China on a national scale. In this study, we collected all available RABV nucleoprotein gene sequences from China and its neighboring countries and performed comparative analysis. We identified six significant subclades of RABV circulating in China and found that each of them has a specific geographical distribution, reflecting possible physical barriers to gene flow. The phylogeographic analysis revealed minimal viral movement among different geographical locations. An analysis using Bayesian coalescent methods indicated that the current RABV strains in China may come from a common ancestor about 400 years ago, and currently, China is amid the second event of increasing RABV population since the 1950s, but the population has decreased gradually. We did not detect any evidence of recombination in the sequence dataset, nor did we find any evidence for positive selection during the expansion of RABV. Overall, geographic location and neutral genetic drift may be the main factors in shaping the phylogeography of RABV transmission in China.

A meta-analysis on the EBV DNA and VCA-IgA in diagnosis of Nasopharyngeal Carcinoma.

OBJECTIVE: We conducted a meta-analysis to compare the EBV DNA and VCA-IgA in diagnosis of Nasopharyngeal Carcinoma, and provide important evidence for screening method of NPC. METHODOLOGY: Three databases, Medline (from Jan. 1966 to Jan. 2012), EMBASE (from January 1988 to Jan. 2012) and Chinese Biomedical Database (from January 1980 to Jan. 2012) were used to detect the role of EBV DNA and VCA-IgA in diagnosis of NPC. Meta-DiSc statistical software was used for analysis. RESULTS: Twenty seven case-control and cohort studies were included in final analysis. A total of 1554 cases and 2932 controls were included in our meta-analysis. The Sensitivity specificity, positive likelihood (+LR) and likelihood negative (-LR) of EBV-DNA in diagnosis of NPC were 0.75(0.72-0.76), 0.87(0.85-0.88), 6.98(4.50-10.83) and 0.18(0.11-0.29), respectively, and they were 0.83(0.81-0.85), 0.85(0.83-0.86), 10.89(5.41-21.93) and 0.20(0.14-0.29) for VCA-IgA. The SROC for EBV DNA detection was 0.939, while this was 0.936 for VCA-IgA detection. The subgroup analysis showed EBV-DNA had larger areas under the summary receiver operator curve when compared with VCA-IgA in high quality and low quality studies. CONCLUSION: Our meta-analysis indicated the EBV DNA had higher sensitivity and specificity in diagnosis of NPC.

[Hidden Markov model used in protein sequence analysis].

Hidden Markov model (HMM) used in the research of protein is a new field of bioinformatics. Nowadays large amount of data about protein sequences and structures have been obtained. Traditional methods of protein analysis are no longer used. Biologists have updated their research methods with computer technology and statistics, which can deal with large amount of data. HMM can be used to distinguish protein sequence with the same characteristics. A family of protein from SCOP database was selected, through which a HMM model representing the family was obtained, and then the model was utilized to analyze protein sequences. Results indicate that HMM can express particular family of protein, and recognize the given protein sequences of the family from many sequences.