Biomedical subjects

Eric W Su

Publications and source records attributed to Eric W Su.

7 recordsLinked to original sources

PubMed2006

Comparative analysis and integrative classification of NCI60 cell lines and primary tumors using gene expression profiling data.

BACKGROUND: NCI60 cell lines are derived from cancers of 9 tissue origins and have been invaluable in vitro models for cancer research and anti-cancer drug screen. Although extensive studies have been carried out to assess the molecular features of NCI60 cell lines related to cancer and their sensitivities to more than 100,000 chemical compounds, it remains unclear if and how well these cell lines represent or model their tumor tissues of origin. Identification and confirmation of correct origins of NCI60 cell lines are critical to their usage as model systems and to translate in vitro studies into clinical potentials. Here we report a direct comparison between NCI60 cell lines and primary tumors by analyzing global gene expression profiles. RESULTS: Comparative analysis suggested that 51 of 59 cell lines we analyzed represent their presumed tumors of origin. Taking advantage of available clinical information of primary tumor samples used to generate gene expression profiling data, we further classified those cell lines with the correct origins into different subtypes of cancer or different stages in cancer development. For example, 6 of 7 non-small cell lung cancer cell lines were classified as lung adenocarcinomas and all of them were classified into late stages in tumor progression. CONCLUSION: Taken together, we developed and applied a novel approach for systematic comparative analysis and integrative classification of NCI60 cell lines and primary tumors. Our results could provide guidance to the selection of appropriate cell lines for cancer research and pharmaceutical compound screenings. Moreover, this gene expression profile based approach can be generally applied to evaluate experimental model systems such as cell lines and animal models for human diseases.

Carcinoma, Non-Small-Cell Lung↗

PubMed2006

Intrinsic disorder in transcription factors.

Intrinsic disorder (ID) is highly abundant in eukaryotes, which reflect the greater need for disorder-associated signaling and transcriptional regulation in nucleated cells. Although several well-characterized examples of intrinsically disordered proteins in transcriptional regulation have been reported, no systematic analysis has been reported so far. To test for the general prevalence of intrinsic disorder in transcriptional regulation, we used the predictor of natural disorder regions (PONDR) to analyze the abundance of intrinsic disorder in three transcription factor datasets and two control sets. This analysis revealed that from 94.13 to 82.63% of transcription factors possess extended regions of intrinsic disorder, relative to 54.51 and 18.64% of the proteins in two control datasets, which indicates the significant prevalence of intrinsic disorder in transcription factors. This propensity of transcription factors to intrinsic disorder was confirmed by cumulative distribution function analysis and charge-hydropathy plots. The amino acid composition analysis showed that all three transcription factor datasets were substantially depleted in order-promoting residues and significantly enriched in disorder-promoting residues. Our analysis of the distribution of disorder within the transcription factor datasets revealed that (a) the AT-hooks and basic regions of transcription factor DNA-binding domains are highly disordered; (b) the degree of disorder in transcription factor activation regions is much higher than that in DNA-binding domains; (c) the degree of disorder is significantly higher in eukaryotic transcription factors than in prokaryotic transcription factors; and (d) the level of alpha-MoRF (molecular recognition feature) prediction is much higher in transcription factors. Overall, our data reflected the fact that eukaryotes with well-developed gene transcription machinery require transcription factor flexibility to be more efficient.

Amino Acid Sequence↗

PubMed2006

A flexible integration and visualisation system for biomarker discovery.

Biological data have accumulated at an unprecedented pace as a result of improvements in molecular technologies. However, the translation of data into information, and subsequently into knowledge, requires the intricate interplay of data access, visualisation and interpretation. Biological data are complex and are organised either hierarchically or non-hierarchically. For non-hierarchically organised data, it is difficult to view relationships among biological facts. In addition, it is difficult to make changes in underlying data storage without affecting the visualisation interface. Here, we demonstrate a platform where non-hierarchically organised data can be visualised through the application of a customised hierarchy incorporating medical subject headings (MeSH) classifications. This platform gives users flexibility in updating and manipulation. It can also facilitate fresh scientific insight by highlighting biological impacts across different hierarchical branches. An example of the integration of biomarker information from the curated Proteome database using MeSH and the StarTree visualisation tool is presented.

Algorithms↗

PubMed2005

Niacin mediates lipolysis in adipose tissue through its G-protein coupled receptor HM74A.

A G-protein coupled receptor to niacin (nicotinic acid) was identified recently but the physiological/pharmacological role of the receptor remains poorly defined. We present our studies to demonstrate that HM74A, but not HM74, binds niacin at high affinities and effectively mediates Gi signaling events in human embryonic kidney HEK293 cells as well as in 3T3L1 adipocytes expressing HM74A. Furthermore, HM74A, but not HM74, expressed in differentiated 3T3L1 adipocytes effectively mediated inhibition of lipolysis by niacin. Our results provided direct evidence indicating that HM74A, but not HM74, was sufficient to mediate anti-lipolytic effect of niacin in adipose tissue.

3T3-L1 Cells↗

PubMed2005

Cloning and functional characterization of the rabbit C-C chemokine receptor 2.

BACKGROUND: CC-family chemokine receptor 2 (CCR2) is implicated in the trafficking of blood-borne monocytes to sites of inflammation and is implicated in the pathogenesis of several inflammatory diseases such as rheumatoid arthritis, multiple sclerosis and atherosclerosis. The major challenge in the development of small molecule chemokine receptor antagonists is the lack of cross-species activity to the receptor in the preclinical species. Rabbit models have been widely used to study the role of various inflammatory molecules in the development of inflammatory processes. Therefore, in this study, we report the cloning and characterization of rabbit CCR2. Data regarding the activity of the CCR2 antagonist will provide valuable tools to perform toxicology and efficacy studies in the rabbit model. RESULTS: Sequence alignment indicated that rabbit CCR2 shares 80 % identity to human CCR2b. Tissue distribution indicated that rabbit CCR2 is abundantly expressed in spleen and lung. Recombinant rabbit CCR2 expressed as stable transfectants in U-937 cells binds radiolabeled 125I-mouse JE (murine MCP-1) with a calculated Kd of 0.1 nM. In competition binding assays, binding of radiolabeled mouse JE to rabbit CCR2 is differentially competed by human MCP-1, -2, -3 and -4, but not by RANTES, MIP-1alpha or MIP-1beta. U-937/rabbit CCR2 stable transfectants undergo chemotaxis in response to both human MCP-1 and mouse JE with potencies comparable to those reported for human CCR2b. Finally, TAK-779, a dual CCR2/CCR5 antagonist effectively inhibits the binding of 125I-mouse JE (IC50 = 2.3 nM) to rabbit CCR2 and effectively blocks CCR2-mediated chemotaxis. CONCLUSION: In this study, we report the cloning of rabbit CCR2 and demonstrate that this receptor is a functional chemotactic receptor for MCP-1.

Amino Acid Sequence↗

PubMed2005

A statistical analysis of the TRANSFAC database.

Transcription factors are key regulatory elements that control gene expression. The TRANSFAC database represents the largest repository for experimentally derived transcription factor binding sites (TFBS). Understanding TFBS, which are typically conserved during evolution, helps us identify genomic regions related to human health and disease, and regions that might be predictive of patient outcomes. Here we present a statistical analysis of all TFBS in the TRANSFAC database. Our analysis suggests that current definition of TFBS core regions in TRANSFAC should be re-examined so as to capture a more precise notion of "cores." We offer insight into more appropriate definitions of TFBS consensus sequences and core regions. These revised definitions provide a better understanding of the nature of transcription factor-DNA binding and assist with developing algorithms for de novo TFBS discovery as well as finding novel variants of known TFBS.

Algorithms↗

PubMedDate not supplied

Alternative splicing within the ligand binding domain of the human constitutive androstane receptor.

The human constitutive androstane receptor (hCAR; NR1I3) is a member of the nuclear receptor superfamily. The activity of hCAR is regulated by a variety of xenobiotics including clotrimazole and acetaminophen metabolites. hCAR, in turn, regulates a number of genes responsible for xenobiotic metabolism and transport including several cytochrome P450s (CYP 2B5, 2C9, and 3A4) and the multidrug resistance-associated protein 2 (MRP2, ABCC2). Thus, hCAR is believed to be a mediator of drug-drug interactions. We identified two novel hCAR splice variants: hCAR2 encodes a receptor in which alternative splice acceptor sites are utilized resulting in a 4 amino acid insert between exons 6 and 7, and a 5 amino acid insert between 7 and 8, and hCAR3 encodes a receptor with exon 7 completely deleted resulting in a 39 amino acid deletion. Both hCAR2 and hCAR3 mRNAs are expressed in a pattern similar to the initially described MB67 (hCAR1) with some key distinctions. Although the levels of expression vary depending on the tissue examined, hCAR2 and hCAR3 contribute 6-8% of total hCAR mRNA in liver. Analysis of the activity of these variants indicates that both hCAR2 and hCAR3 lose the ability to heterodimerize with RXR and lack transactivation activity in cotransfection experiments where either full-length receptor or GAL4 DNA-binding domain/CAR ligand binding domain chimeras were utilized. Although the role of hCAR2 and hCAR3 is currently unclear, these additional splice variants may provide for increased diversity in terms of responsiveness to xenobiotics.

Alternative Splicing↗