Jeewon Kim

Bio: Jeewon Kim is an academic researcher from Stanford University. The author has contributed to research in topics: Metastasis & Cancer. The author has an hindex of 14, co-authored 21 publications receiving 5970 citations. Previous affiliations of Jeewon Kim include University of California, Berkeley & Seoul National University.

Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis

Abstract: Large intervening non-coding RNAs (lincRNAs) are pervasively transcribed in the genome yet their potential involvement in human disease is not well understood. Recent studies of dosage compensation, imprinting, and homeotic gene expression suggest that individual lincRNAs can function as the interface between DNA and specific chromatin remodelling activities. Here we show that lincRNAs in the HOX loci become systematically dysregulated during breast cancer progression. The lincRNA termed HOTAIR is increased in expression in primary breast tumours and metastases, and HOTAIR expression level in primary tumours is a powerful predictor of eventual metastasis and death. Enforced expression of HOTAIR in epithelial cancer cells induced genome-wide re-targeting of Polycomb repressive complex 2 (PRC2) to an occupancy pattern more resembling embryonic fibroblasts, leading to altered histone H3 lysine 27 methylation, gene expression, and increased cancer invasiveness and metastasis in a manner dependent on PRC2. Conversely, loss of HOTAIR can inhibit cancer invasiveness, particularly in cells that possess excessive PRC2 activity. These findings indicate that lincRNAs have active roles in modulating the cancer epigenome and may be important targets for cancer diagnosis and therapy.

Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data

Abstract: The application of established drug compounds to novel therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning employ high-throughput experimental approaches to assess a compound’s potential therapeutic qualities. Here we present a systematic computational approach to predict novel therapeutic indications based on comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds yielding predicted therapeutic potentials for these drugs. We demonstrate the ability to recover many known drug and disease relationships using computationally derived therapeutic potentials, and also predict many new indications for these drugs. We experimentally validated a prediction for the anti-ulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate both in vitro and in vivo using mouse xenograft models. This novel computational method provides a novel and systematic approach to reposition established drugs to treat a wide range of human diseases.

Measurement in vivo of proliferation rates of slow turnover cells by 2H2O labeling of the deoxyribose moiety of DNA.

Abstract: We describe here a method for measuring DNA replication and, thus, cell proliferation in slow turnover cells that is suitable for use in humans. The technique is based on the incorporation of 2 H 2 O into the deoxyribose (dR) moiety of purine deoxyribonucleotides in dividing cells. For initial validation, rodents were administered 4% 2 H 2 O in drinking water. The proliferation rate of mammary epithelial cells in mice was 2.9% per day and increased 5-fold during pregnancy. Administration of estradiol pellets (0–200 μg) to ovariectomized rats increased mammary epithelial cell proliferation, according to a dose–response relationship up to the 100 μg dose. Similarly, proliferation of colon epithelial cells was stimulated in a dose–response manner by dietary cholic acid in rats. Bromodeoxyuridine labeling correlated with the 2 H 2 O results. Proliferation of slow turnover cells was then measured. Vascular smooth muscle cells isolated from mouse aorta divided with a half-life in the range of 270–400 days and die-away values after 2 H 2 O wash-out confirmed these slow turnover rates. The proliferation rate of an adipocyte-enriched fraction from mouse adipose tissue depots was 1–1.5% new cells per day, whereas obese ad libitum-fed ob/ob mice exhibited markedly higher fractional and absolute proliferation rates. In humans, stable long-term 2 H 2 O enrichments in body water were achieved by daily 2 H 2 O intake, without toxicities. Labeled dR from fully turned-over blood cells (monocytes or granulocytes) exhibited a consistent amplification factor relative to body 2 H 2 O enrichment (≈3.5-fold). The fraction of newly divided naive-phenotype T cells after 9 weeks of labeling with 2 H 2 O was 0.056 (CD4 + ) and 0.043 (CD8 + ) (replacement rate 2 H 2 O labeling of dR in DNA allows safe, convenient, reproducible, and inexpensive measurement of cell proliferation in humans and experimental animals and is well suited for slow turnover cells.

Non-coding RNAs in human disease

Abstract: The role of non-coding RNAs (ncRNAs) in disease is best understood for microRNAs in cancer. However, there is increasing interest in the disease-related roles of other ncRNAs — including piRNAs, snoRNAs, T-UCRs and lncRNAs — and in using this knowledge for therapy.

starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

Abstract: Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA-RNA and protein-RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285,000 protein-RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10,000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

Genome Regulation by Long Noncoding RNAs

Abstract: The central dogma of gene expression is that DNA is transcribed into messenger RNAs, which in turn serve as the template for protein synthesis. The discovery of extensive transcription of large RNA transcripts that do not code for proteins, termed long noncoding RNAs (lncRNAs), provides an important new perspective on the centrality of RNA in gene regulation. Here, we discuss genome-scale strategies to discover and characterize lncRNAs. An emerging theme from multiple model systems is that lncRNAs form extensive networks of ribonucleoprotein (RNP) complexes with numerous chromatin regulators and then target these enzymatic activities to appropriate locations in the genome. Consistent with this notion, lncRNAs can function as modular scaffolds to specify higher-order organization in RNP complexes and in chromatin states. The importance of these modes of regulation is underscored by the newly recognized roles of long RNAs for proper gene control across all kingdoms of life.