Sung‐Joon Park

Fertilization precisely choreographs parental genomes by using gamete-derived cellular factors and activating genome regulatory programs. However, the mechanism remains elusive owing to the technical difficulties of preparing large numbers of high-quality preimplantation cells. Here, we collected >14 × 10(4) high-quality mouse metaphase II oocytes and used these to establish detailed transcriptional profiles for four early embryo stages and parthenogenetic development. By combining these profiles with other public resources, we found evidence that gene silencing appeared to be mediated in part by noncoding RNAs and that this was a prerequisite for post-fertilization development. Notably, we identified 817 genes that were differentially expressed in embryos after fertilization compared with parthenotes. The regulation of these genes was distinctly different from those expressed in parthenotes, suggesting functional specialization of particular transcription factors prior to first cell cleavage. We identified five transcription factors that were potentially necessary for developmental progression: Foxd1, Nkx2-5, Sox18, Myod1, and Runx1. Our very large-scale whole-transcriptome profile of early mouse embryos yielded a novel and valuable resource for studies in developmental biology and stem cell research. The database is available at http://dbtmee.hgc.jp.

DBTMEE (http://dbtmee.hgc.jp/) is a searchable and browsable database designed to manipulate gene expression information from our ultralarge-scale whole-transcriptome analysis of mouse early embryos. Since integrative approaches with multiple public analytical data have become indispensable for studying embryogenesis due to technical challenges such as biological sample collection, we intend DBTMEE to be an integrated gateway for the research community. To do so, we combined the gene expression profile with various public resources. Thereby, users can extensively investigate molecular characteristics among totipotent, pluripotent and differentiated cells while taking genetic and epigenetic characteristics into consideration. We have also designed user friendly web interfaces that enable users to access the data quickly and easily. DBTMEE will help to promote our understanding of the enigmatic fertilization dynamics.

To examine transcriptional alterations associated with aging in skeletal muscle and the heart, we and others have used DNA microarrays to compare the gene expression profile of young and old animals. Aging results in a differential gene expression pattern specific to each tissue, and most alterations can be completely or partially prevented by caloric restriction (CR) in both heart and skeletal muscle. Transcriptional patterns of tissues from calorie-restricted animals suggests that CR retards the aging process by reducing endogenous damage and by inducing metabolic shifts associated with specific transcriptional profiles. These studies demonstrate that DNA microarrays can be used in cardiovascular aging research to generate panels of hundreds of transcriptional biomarkers, providing a new tool to measure biological age of cardiac and skeletal muscles and to test interventions designed to retard aging in these tissues.

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

BACKGROUND: Drug combination therapy, which is considered as an alternative to single drug therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug combination therapies are widely used in the clinic for hypertension, asthma, and AIDS, they have also been proposed for the treatment of cancer. However, it is difficult to select and experimentally evaluate effective combinations because not only is the number of cancer drug combinations extremely large but also the effectiveness of drug combinations varies depending on the genetic variation of cancer patients. A computational approach that prioritizes the best drug combinations considering the genetic information of a cancer patient is necessary to reduce the search space. RESULTS: We propose an in-silico method for personalized drug combination therapy discovery. We predict the synergy between two drugs and a cell line using genomic information, targets of drugs, and pharmacological information. We calculate and predict the synergy scores of 583 drug combinations for 31 cancer cell lines. For feature dimension reduction, we select the mutations or expression levels of the genes in cancer-related pathways. We also used various machine learning models. Extremely Randomized Trees (ERT), a tree-based ensemble model, achieved the best performance in the synergy score prediction regression task. The correlation coefficient between the synergy scores predicted by ERT and the actual observations is 0.738. To compare with an existing drug combination synergy classification model, we reformulate the problem as a binary classification problem by thresholding the synergy scores. ERT achieved an F1 score of 0.954 when synergy scores of 20 and -20 were used as the threshold, which is 8.7% higher than that obtained by the state-of-the-art baseline model. Moreover, the model correctly predicts the most synergistic combination, from approximately 100 candidate drug combinations, as the top choice for 15 out of the 31 cell lines. For 28 out of the 31 cell lines, the model predicts the most synergistic combination in the top 10 of approximately 100 candidate drug combinations. Finally, we analyze the results, generate synergistic rules using the features, and validate the rules through the literature survey. CONCLUSION: Using various types of genomic information of cancer cell lines, targets of drugs, and pharmacological information, a drug combination synergy prediction pipeline is proposed. The pipeline regresses the synergy level between two drugs and a cell line as well as classifies if there exists synergy or antagonism between them. Discovering new drug combinations by our pipeline may improve personalized cancer therapy.