The latent reservoirs of HIV represent a major impediment to eradication of HIV/AIDS. To overcome this problem, agents that can activate latent HIV proviruses have been actively sought after, as they can potentially be used in combination with the highly active antiretroviral therapy (HAART) to eliminate the latent reservoirs. Although several chemical compounds have been shown to activate latency, they are of limited use due to high toxicity and poor clinical outcomes. In an attempt to identify natural products as effective latency activators from traditional Chinese medicinal herbs that have long been widely used in human population, we have isolated procyanidin C-13,3',3"-tri-O-gallate (named as REJ-C1G3) from Polygonum cuspidatum Sieb. et Zucc., that can activate HIV in latently infected Jurkat T cells. REJ-C1G3 preferentially stimulates HIV transcription in a process that depends on the viral encoded Tat protein and acts synergistically with prostratin (an activator of the NF-κB pathway) or JQ1 (an inhibitor of Brd4) to activate HIV latency. Our mechanistic analyses further show that REJ-C1G3 accomplishes these tasks by inducing the release of P-TEFb, a host cofactor essential for Tat-activation of HIV transcription, from the cellular P-TEFb reservoir 7SK snRNP.

Objective. Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods. Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results. Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa ( p < 0.05 ). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions. The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.