Sukjoon Yoon

Tumor necrosis factor-α and interleukin-1β increases CTRP1 expression in adipose tissue

CTRP1, a member of the CTRP superfamily, consists of an N‐terminal signal peptide sequence followed by a variable region, a collagen repeat domain, and a C‐terminal globular domain. CTRP1 is expressed at high levels in adipose tissues of LPS‐stimulated Sprague‐Dawley rats. The LPS‐induced increase in CTRP1 gene expression was found to be mediated by TNF‐α and IL‐1β. Also, a high level of expression of CTRP1 mRNA was observed in adipose tissues of Zucker diabetic fatty (fa/fa) rats, compared to Sprague‐Dawley rats in the absence of LPS stimulation. These findings indicate that CTRP1 expression may be associated with a low‐grade chronic inflammation status in adipose tissues.

Somatic Mutaome Profile in Human Cancer Tissues

Somatic mutation is a major cause of cancer progression and varied responses of tumors against anticancer agents. Thus, we must obtain and characterize genome-wide mutational profiles in individual cancer subtypes. The Cancer Genome Atlas database includes large amounts of sequencing and omics data generated from diverse human cancer tissues. In the present study, we integrated and analyzed the exome sequencing data from ~3,000 tissue samples and summarized the major mutant genes in each of the diverse cancer subtypes and stages. Mutations were observed in most human genes (~23,000 genes) with low frequency from an analysis of 11 major cancer subtypes. The majority of tissue samples harbored 20-80 different mutant genes, on average. Lung cancer samples showed a greater number of mutations in diverse genes than other cancer subtypes. Only a few genes were mutated with over 5% frequency in tissue samples. Interestingly, mutation frequency was generally similar between non-metastatic and metastastic samples in most cancer subtypes. Among the 12 major mutations, the TP53, USH2A, TTN, and MUC16 genes were found to be frequent in most cancer types, while BRAF, FRG1B, PBRM1, and VHL showed lineage-specific mutation patterns. The present study provides a useful resource to understand the broad spectrum of mutation frequencies in various cancer types.

Systematic analysis of genotype-specific drug responses in cancer

A systematic understanding of genotype‐specific sensitivity or resistance to anticancer agents is required to provide improved patient therapy. The availability of an expansive panel of annotated cancer cell lines enables comparative surveys of associations between genotypes and compounds of various target classes. Thus, one can better predict the optimal treatment for a specific tumor. Here, we present a statistical framework, cell line enrichment analysis (CLEA), to associate the response of anticancer agents with major cancer genotypes. Multilevel omics data, including transcriptome, proteome and phosphatome data, were integrated with drug data based on the genotypic classification of cancer cell lines. The results reproduced known patterns of compound sensitivity associated with particular genotypes. In addition, this approach reveals multiple unexpected associations between compounds and mutational genotypes. The mutational genotypes led to unique protein activation and gene expression signatures, which provided a mechanistic understanding of their functional effects. Furthermore, CLEA maps revealed interconnections between TP53 mutations and other mutations in the context of drug responses. The TP53 mutational status appears to play a dominant role in determining clustering patterns of gene and protein expression profiles for major cancer genotypes. This study provides a framework for the integrative analysis of mutations, drug responses and omics data in cancers.

Sanguinarine is an allosteric activator of AMP-activated protein kinase.

We found that a natural product, Sanguinarine, directly interacts with AMPK and enhances its enzymatic activity. Cell-based assays confirmed that cellular AMPK and the downstream acetyl-CoA carboxylase (ACC) were phosphorylated after Sanguinarine treatment. Sanguinarine was shown to exclusively activate AMPK holoenzymes containing α1γ1 complexes, and it activated both β1- and β2-containing isotypes of AMPK. Furthermore, a docking study suggested that Sanguinarine binds AMPK at the cleft between the β and γ domains whereas the physiological activator, AMP, binds at the well-characterized γ domain pocket. In summary, we report that Sanguinarine is a novel, direct activator of AMPK that binds by a unique allosteric mechanism different from that of the natural AMPK ligand, AMP, and other known AMPK activators. These studies have direct applications to the pharmacological study of AMPK activation and the potential development of new therapeutics.

Large scale data mining approach for gene-specific standardization of microarray gene expression data

MOTIVATION The identification of the change of gene expression in multifactorial diseases, such as breast cancer is a major goal of DNA microarray experiments. Here we present a new data mining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of genes behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes between samples. RESULTS The availability of a large collection of array samples sharing the same platform in public databases, such as NCBI GEO, enabled us to re-standardize the expression intensity of a gene using its mean and variation in the wide variety of experimental conditions. This approach was evaluated via the re-identification of breast cancer-specific gene expression. It successfully prioritized several genes associated with breast tumor, for which the expression difference between normal and breast cancer cells was marginal and thus would have been difficult to recognize using conventional analysis methods. Maximizing the utility of microarray data in the public database, it provides a valuable tool particularly for the identification of previously unrecognized disease-related genes. AVAILABILITY A user friendly web-interface (http://compbio.sookmyung.ac.kr/~lage/) was constructed to provide the present large-scale approach for the analysis of GEO microarray data (GS-LAGE server).

QCanvas: An Advanced Tool for Data Clustering and Visualization of Genomics Data

We developed a user-friendly, interactive program to simultaneously cluster and visualize omics data, such as DNA and protein array profiles. This program provides diverse algorithms for the hierarchical clustering of two-dimensional data. The clustering results can be interactively visualized and optimized on a heatmap. The present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization. Furthermore, the heatmaps allow the selective display of data points satisfying user-defined criteria. For example, a clustered heatmap of experimental values can be differentially visualized based on statistical values, such as p-values. Including diverse menu-based display options, QCanvas provides a convenient graphical user interface for pattern analysis and visualization with high-quality graphics.

Identification of (β-carboxyethyl)-rhodanine derivatives exhibiting peroxisome proliferator-activated receptor γ activity

We applied an improved virtual screening scheme combining ligand-centric and receptor-centric methods for the identification of a new series of PPARgamma agonists known as (beta-carboxyethyl)-rhodanine derivatives which include a thiazolidin-based core structure, 2-thioxo-thiazolidine-4-one. An in vitro assay confirmed the nanomolar binding affinity in one of the (beta-carboxyethyl)-rhodanine derivatives, SP1818. It showed a PPARgamma agonistic activity similar to that of a known PPARgamma drug, pioglitazone, in a cell-based transactivation assay. Furthermore, the structure-activity relationships of the rhodanine derivatives were investigated through comparative molecular field analysis. We also characterized the inconsistency between the in vitro binding affinity and cell-based transactivation ability by using a set of property-based molecular descriptors. The binding mode analysis provided new insight concerning their agonistic effect on PPARgamma.

1,3-Diphenyl-1H-pyrazole derivatives as a new series of potent PPARγ partial agonists

A new series of PPARγ partial agonists, 1,3-diphenyl-1H-pyrazole derivatives, were identified using an improved virtual screening scheme combining ligand-centric and receptor-centric methods. An in vitro assay confirmed the nanomolar binding affinity of 1,3-diphenyl-1H-pyrazole derivatives such as SP3415. We also characterized the competitive antagonism of SP3415 against rosiglitazone at micromolar concentrations. They showed a PPARγ partial agonistic activity similar to that of a known PPARγ drug, pioglitazone, in a cell-based transactivation assay. Furthermore, the structure-activity relationships of the pyrazole derivatives were investigated through comparative molecular field analysis and binding mode analysis, which provided new insight concerning their partial agonistic effect on PPARγ.

Surrogate docking: structure-based virtual screening at high throughput speed

SummaryStructure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size – not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of 13 and 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself.

Cardiac glycosides display selective efficacy for STK11 mutant lung cancer.

Although STK11 (LKB1) mutation is a major mediator of lung cancer progression, targeted therapy has not been implemented due to STK11 mutations being loss-of-function. Here, we report that targeting the Na+/K+-ATPase (ATP1A1) is synthetic lethal with STK11 mutations in lung cancer. The cardiac glycosides (CGs) digoxin, digitoxin and ouabain, which directly inhibit ATP1A1 function, exhibited selective anticancer effects on STK11 mutant lung cancer cell lines. Restoring STK11 function reduced the efficacy of CGs. Clinically relevant doses of digoxin decreased the growth of STK11 mutant xenografts compared to wild type STK11 xenografts. Increased cellular stress was associated with the STK11-specific efficacy of CGs. Inhibiting ROS production attenuated the efficacy of CGs, and STK11-AMPK signaling was important in overcoming the stress induced by CGs. Taken together, these results show that STK11 mutation is a novel biomarker for responsiveness to CGs. Inhibition of ATP1A1 using CGs warrants exploration as a targeted therapy for STK11 mutant lung cancer.