The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging. Here, we present a Ranking-system of Anti-Cancer Synergy (RACS) that combines features of targeting networks and transcriptomic profiles, and validate it on three types of cancer. Using data on human β-cell lymphoma from the Dialogue for Reverse Engineering Assessments and Methods consortium we show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm. We confirm 63.6% of our breast cancer predictions through experiment and literature, including four strong synergistic pairs. Further in vivo screening in a zebrafish MCF7 xenograft model confirms one prediction with strong synergy and low toxicity. Validation using A549 lung cancer cells shows similar results. Thus, RACS can significantly improve drug synergy prediction and markedly reduce the experimental prescreening of existing drugs for repurposing to cancer treatment, although the molecular mechanism underlying particular interactions remains unknown.

RNA interference (RNAi) is a powerful tool to silence gene expression posttranscriptionally. In this study, we evaluated the antiviral potential of small interfering RNA (siRNA) targeting VP1 of foot-and-mouth disease virus (FMDV), which is essential during the life cycle of the virus and plays a key role in virus attachment to susceptible cells. We investigated in vivo the inhibitory effect of VP1-specific siRNAs on FMDV replication in BHK-21 cells and suckling mice, a commonly used small animal model. The results showed that transfection of siRNA-expressing plasmids gave an 80 to 90% reduction in the expression of FMDV VP1 in BHK-21 cells. Moreover, BHK-21 cells transiently transfected with siRNA-expressing plasmids were specifically resistant to FMDV infection when exposed to 100 50% tissue culture infective doses of virus, and the antiviral effects extended to almost 48 h postinfection. Furthermore, subcutaneous injection of siRNA-expressing plasmids in the neck made suckling mice significantly less susceptible to FMDV. In conclusion, our data suggests that RNAi may provide a viable therapeutic approach to treat FMDV infection.

BackgroundPhysic nut (Jatropha curcas L.) is an oilseed plant species with high potential utility as a biofuel. Furthermore, following recent sequencing of its genome and the availability of expressed sequence tag (EST) libraries, it is a valuable model plant for studying carbon assimilation in endosperms of oilseed plants. There have been several transcriptomic analyses of developing physic nut seeds using ESTs, but they have provided limited information on the accumulation of stored resources in the seeds.Methodology/Principal FindingsWe applied next-generation Illumina sequencing technology to analyze global gene expression profiles of developing physic nut seeds 14, 19, 25, 29, 35, 41, and 45 days after pollination (DAP). The acquired profiles reveal the key genes, and their expression timeframes, involved in major metabolic processes including: carbon flow, starch metabolism, and synthesis of storage lipids and proteins in the developing seeds. The main period of storage reserves synthesis in the seeds appears to be 29–41 DAP, and the fatty acid composition of the developing seeds is consistent with relative expression levels of different isoforms of acyl-ACP thioesterase and fatty acid desaturase genes. Several transcription factor genes whose expression coincides with storage reserve deposition correspond to those known to regulate the process in Arabidopsis.Conclusions/SignificanceThe results will facilitate searches for genes that influence de novo lipid synthesis, accumulation and their regulatory networks in developing physic nut seeds, and other oil seeds. Thus, they will be helpful in attempts to modify these plants for efficient biofuel production.