Hai Wu

Association Study of the β2-Adrenergic Receptor Gene Polymorphisms and Hypertension in the Northern Han Chinese

Background The β2-adrenergic receptor (ADRB2) gene has been widely researched as a candidate gene for essential hypertension (EH), but no consensus has been reached in different ethnicities. The aim of the present study was to evaluate the possible association between the ADRB2 gene polymorphisms and the EH risk in the Northern Han Chinese population. Methodology/Principal Findings This study included 747 hypertensive subjects and 390 healthy volunteers as control subjects in the Northern Han Chinese. Genotyping was performed to identify the C-47T, A46G and C79G polymorphisms of the ADRB2 gene. G allelic frequency of A46G polymorphism was significantly higher in hypertensive subjects (Pu200a=u200a0.011, ORu200a=u200a1.287, 95%CI [1.059–1.565]) than that in controls. Significant association could also be found in dominant genetic model (GG+AG vs. AA, Pu200a=u200a0.006, ORu200a=u200a1.497, 95%CI [1.121–1.998]), in homozygote comparison (GG vs. AA, Pu200a=u200a0.025, ORu200a=u200a1.568, 95%CI [1.059–2.322]), and in additive genetic model (GG vs. AG vs. AA, Pu200a=u200a0.012, ORu200a=u200a1.282, 95%CI [1.056–1.555]). Subgroup analyses performed by gender suggested that this association could be found in male, but not in female. Stratification analyses by obesity showed that A46G polymorphism was related to the prevalence of hypertension in the obese population (GG vs. AG vs. AA, P<0.001, ORu200a=u200a1.645, 95%CI [1.258–2.151]). Significant interaction was found between A46G genotypes and body mass index on EH risk. No significant association could be found between C-47T or C79G polymorphism and EH risk. Linkage disequilibrium was detected between the C-47T, A46G and C79G polymorphisms. Haplotype analyses observed that the T-47-A46-C79 haplotype was a protective haplotype for EH, while the T-47-G46-C79 haplotype increased the risk. Conclusions/Significances We revealed that the ADRB2 A46G polymorphism might increase the risk for EH in the Northern Han Chinese population.

Construction and application of a co-expression network in Mycobacterium tuberculosis

Because of its high pathogenicity and infectivity, tuberculosis is a serious threat to human health. Some information about the functions of the genes in Mycobacterium tuberculosis genome was currently available, but it was not enough to explore transcriptional regulatory mechanisms. Here, we applied the WGCNA (Weighted Gene Correlation Network Analysis) algorithm to mine pooled microarray datasets for the M. tuberculosis H37Rv strain. We constructed a co-expression network that was subdivided into 78 co-expression gene modules. The different response to two kinds of vitro models (a constant 0.2% oxygen hypoxia model and a Wayne model) were explained based on these modules. We identified potential transcription factors based on high Pearson’s correlation coefficients between the modules and genes. Three modules that may be associated with hypoxic stimulation were identified, and their potential transcription factors were predicted. In the validation experiment, we determined the expression levels of genes in the modules under hypoxic condition and under overexpression of potential transcription factors (Rv0081, furA (Rv1909c), Rv0324, Rv3334, and Rv3833). The experimental results showed that the three identified modules related to hypoxia and that the overexpression of transcription factors could significantly change the expression levels of genes in the corresponding modules.

Genome-Wide De Novo Prediction of Cis-Regulatory Binding Sites in Mycobacterium tuberculosis H37Rv.

The transcription regulatory system of Mycobacterium tuberculosis (M. tb) remains incompletely understood. In this study, we have applied the eGLECLUBS algorithm to a group of related prokaryotic genomes for de novo genome-wide prediction of cis-regulatory binding sites (CRBSs) in M. tb H37Rv. The top 250 clusters from our prediction recovered 83.3% (50/60) of all known CRBSs in extracted inter-operonic sequences of this strain. We further demonstrated that the integration of our prediction results with the ChIP-Seq datasets is very effective in identifying true binding sites of TFs. Using electrophoretic mobility shift assays and real-time RT-PCR, we experimentally verified our prediction of CRBSs for Rv0081, an important transcription factor thought to be involved in regulation of M. tb under hypoxia.