Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

Eukaryotic circadian oscillators consist of negative feedback loops that generate endogenous rhythmicities1. Natural antisense RNAs are found in a wide range of eukaryotic organisms2-5. Nevertheless, the physiological importance and mode of action of most antisense RNAs is not clear6-9. frequency (frq) encodes a component of the Neurospora core circadian negative feedback loop which was thought to generate sustained rhythmicity10. Transcription of qrf, the long non-coding frq antisense RNA, is light induced, and its level oscillates in antiphase to frq sense RNA3. Here we show that qrf transcription is regulated by both light-dependent and -independent mechanisms. Light-dependent qrf transcription represses frq expression and regulates clock resetting. qrf expression in the dark, on the other hand, is required for circadian rhythmicity. frq transcription also inhibits qrf expression and surprisingly, drives the antiphasic rhythm of qrf transcripts. The mutual inhibition of frq and qrf transcription thus forms a double negative feedback loop that is interlocked with the core feedback loop. Genetic and mathematical modeling analyses indicate that such an arrangement is required for robust and sustained circadian rhythmicity. Moreover, our results suggest that antisense transcription inhibits sense expression by mediating chromatin modifications and premature transcription termination. Together, our results established antisense transcription as an essential feature in a circadian system and shed light on the importance and mechanism of antisense action.

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Multidrug regimens are a promising strategy for improving therapeutic efficacy and reducing side effects, especially for complex disorders such as cancer. However, the use of multidrug therapies is very challenging, due to a lack of understanding of the mechanisms of drug interactions. We herein present a novel computational approach—Drug-Induced Genomic Residual Effect (DIGRE) Computational Model—to predict drug combination effects by explicitly modeling drug response curves and gene expression changes after drug treatments. The prediction performance of DIGRE was evaluated using two datasets: (i) OCI-LY3 B-lymphoma cells treated with 14 different drugs and (ii) MCF breast cancer cells treated with combinations of gefitinib and docetaxel at different doses. In both datasets, the predicted drug combination effects significantly correlated with the experimental results. The results indicated the model was useful in predicting drug combination effects, which may greatly facilitate the discovery of new, effective multidrug therapies.

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