<i>Spermatogenesis Associated 4</i> Promotes Sertoli Cell Proliferation Modulated Negatively by Regulatory Factor X1

<div><p>Spermatogenesis associated 4 (<i>Spata4</i>), a testis-specific and CpG island associated gene, is involved in regulating cell proliferation, differentiation and apoptosis. To obtain insight into the role of <i>Spata4</i> in cell cycling control, we characterized the promoter region of <i>Spata4</i> and investigated its transcriptional regulation mechanism. The <i>Spata4</i> promoter is unidirectional transcribed and possesses multiple transcription start sites. Moreover, we present evidence that regulatory factor X1 (RFX1) could bind the typical 14-bp <i>cis</i>-elements of <i>Spata4</i> promoter, modulate transcriptional activity and endogenous expression of <i>Spata4</i>, and further regulate the proliferation of Sertoli cells. Overexpression of RFX1 was shown to down-regulate both the promoter activity and mRNA expression of Spata4, whereas knockdown of RFX1 demonstrated the opposite effects. Our studies provide insight into <i>Spata4</i> gene regulation and imply the potential role of RFX1 in growth of Sertoli cells. RFX1 may have negative effect on cell proliferation of Sertoli cells via modulating <i>Spata4</i> expression levels by binding the conserved 14-bp <i>cis</i>-elements of <i>Spata4</i> promoter.</p></div

RFX1 regulates <i>Spata4</i> mRNA expression in mouse Sertoli cells.

<p>After transient transfection of pcDNA3.1-RFX1 for 36 h, the overexpressed RFX1 protein was detected by immunocytochemistry staining and Western blot (<b>A</b>), overexpression of RFX1 was shown to down-regulate the promoter activity (<b>B</b>) and <i>Spata4</i> mRNA expression (<b>C</b>) and in a dose-dependent manner. The <i>Spata4</i> mRNA expression was analyzed by quantitative PCR and normalized to GAPDH. **<i>P</i><0.01. ***<i>P</i><0.001.</p

RFX1 binds to the <i>Spata4</i> promoter through its DNA binding domain.

<p><b>(A)</b> Alignment of the conserved 14-bp sequences of human, rat and mouse <i>Spata4</i> promoter with other RFX1 binding sequences shows high similarity to the RFX1 consensus binding sites. (<b>B</b>) Chromatin immunoprecipitation assay confirm that RFX1 could bind the conserved 14-bp sequence in mouse Sertoli cells. (<b>C</b>) DNA binding domain is essential for the binding of RFX1 to the conserved sequence of <i>Spata4</i> promoter. Vector, pcDNA3.1 plasmid; WT-RFX1, pcDNA3.1-RFX1 plasmid; ΔDBD-RFX1, pcDNA3.1-RFX1 plasmid with deletion of the sequences encoding the DNA binding domain. **<i>P</i><0.01. ***<i>P</i><0.001.</p

Knockdown of RFX1 could up-regulate endogenous <i>Spata4</i> expression.

<p>Treatment of mouse RFX1-RNAi-I, -II and -III for 48 h could efficiently knock down the endogenous RFX1 expression (<b>A, B</b>). (<b>C</b>) <i>Spata4</i> mRNA expression level was elevated upon RFX1 knockdown. Control, cells treated with nonspecific siRNA. (<b>D</b>) <i>RFX1</i> mRNA expression level was decreased by RFX1-RNAi-III treatment. (<b>E</b>) <i>Spata4</i> mRNA expression level was significantly elevated by RFX1-RNAi-III treatment in a time-dependent manner. *<i>P</i><0.05. ***<i>P</i><0.001.</p

Structure of the mouse <i>Spata4</i> gene and identification of the promoter region.

<p>(<b>A</b>) Exon-intron structure of the <i>Spata4</i> gene (1–9318) and mRNA (1–1057) as deduced by MGAlignIt using accession numbers NC_000074.5 and NM_133711.3, respectively. The colored boxes indicate the different exons. The top series of gray colored bars corresponds to individual exons drawn according to their size and position within the genomic sequence. Intron phases (0, 1 or 2) and exon borders are indicated on the bottom series of blue colored bars. (<b>B</b>) Mouse <i>Spata4</i> promoter contains a CpG island with a length of approximately 400 bp. (<b>C</b>) The transcription start sites of mouse <i>Spata4</i> gene. The frequency of transcription initiation from different sites is shown. The <i>Spata4</i> promoter contains dispersed transcription start sites located within a 90 bp region. The first transcription start site is underlined. (<b>D</b>) Reporter activity assay of serially deleted promoter constructs containing different lengths of the 5′-flanking sequence of mouse <i>Spata4</i> gene. The plasmids were transiently transfected into TM4 cells, and the reporter activity was analyzed 18 h after transfection. Gray bar, reverse promoter sequences; Black bar, forward promoter sequences. The values are shown as relative ratio to that of pGL4.17 control vector.</p

Pre-Exposure Prophylaxis for the Prevention of HIV Infection in High Risk Populations: A Meta-Analysis of Randomized Controlled Trials

<div><p>Background</p><p>Nearly ten randomized controlled trials (RCTs) of pre-exposure prophylaxis (PrEP) have been completed or are ongoing worldwide to evaluate the effectiveness of PrEP in HIV transmission among HIV-uninfected high risk populations. The purpose of this study was to evaluate the role of PrEP to prevent HIV transmission through a Mata-analysis.</p><p>Methods</p><p>A comprehensive computerized literature search was carried out in PubMed, EMbase, Ovid, Web of Science, Science Direct, Wan Fang, CNKI and related websites to collect relevant articles (from their establishment date to August 30, 2013). The search terms were “pre-exposure prophylaxis”, “high risk population”, “HIV infection”, “reduction”, “relative risk” and “efficacy”. We included any RCT assessing PrEP for the prevention of HIV infection in high risk populations. Interventions of the studies were continuously daily or intermittent doses of single or compound antiretrovirals (ARVs) before HIV exposure or during HIV exposure. A meta-analysis was conducted using Stata 10.0. A random-effects method was used to calculate the pooled relative risk (RR) and 95% confidence intervals (CI) for all studies included.</p><p>Results</p><p>Seven RCTs involving 14,804 individuals in high risk populations were eligible for this study. The number of subjects in the experimental groups was 8,195, with HIV infection rate of 2.03%. The number of subjects in the control groups was 6,609, with HIV infection rate of 4.07%. The pooled RR was 0.53 (95% CI = 0.40∼0.71, <i>P</i><0.001). The re-analyzed pooled RR were 0.61 (95% CI = 0.48∼0.77, <i>P</i><0.001), 0.49 (95% CI = 0.38∼0.63, <i>P</i><0.001), respectively, by excluding the largest study or two studies without statistical significance. Publication bias analysis revealed a symmetry funnel plot. The fail-safe number was 1,022.</p><p>Conclusion</p><p>These results show that PrEP is an effective strategy for reducing new HIV infections in high risk populations.</p></div

Funnel plot for the publication bias of all seven included trials.

<p>Funnel plot for the publication bias of all seven included trials.</p

Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China

<div><p>Background</p><p>Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease.</p><p>Methods</p><p>The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models.</p><p>Results</p><p>The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)₁₂ model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models.</p><p>Conclusions</p><p>The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County.</p></div

Quality assessment of the included trials.

<p>Note: Adequate if the allocation sequence was generated by a computer or random number table. Unclear if the trial was described as randomized, but the method used for the allocation sequence generation was not described; N/A, not available.</p

The funnel plots for publication bias.

<p>The red solid circle represents for PCR-based RDTMs, blue for ELISA-based RDTMs and green for other RDTMs. The areas of the circles stand for the number of cases. The Deek test was not significant (p = 0.69).</p