Hui-Rong Qian

Transcriptome signature of resistance exercise adaptations: mixed muscle and fiber type specific profiles in young and old adults

This investigation examined the effects of acute resistance exercise (RE), progressive resistance training (PRT), and age on the human skeletal muscle Transcriptome. Two cohorts of young and old adults [study A: 24 yr, 84 yr (n = 28); study B: 25 yr, 78 yr (n = 36)] were studied. Vastus lateralis biopsies were obtained pre- and 4 h post-RE in conjunction with the 1st and 36th (last) training session as part of a 12-wk PRT program in study A, whereas biopsies were obtained in the basal untrained state in study B. Additionally, the muscle fiber type specific (MHC I and MHC IIa) Transcriptome response to RE was examined in a subset of young and old women from study A. Transcriptome profiling was performed using HG U133 Plus 2.0 Arrays. The main findings were 1) there were 661 genes affected by RE during the 1st and 36th training bout that correlated with gains in muscle size and strength with PRT (termed the Transcriptome signature of resistance exercise adaptations); 2) the RE gene response was most pronounced in fast-twitch (MHC IIa) muscle fibers and provided additional insight into the skeletal muscle biology affected by RE; 3) skeletal muscle of young adults is more responsive to RE at the gene level compared with old adults and age also affected basal level skeletal muscle gene expression. These skeletal muscle Transcriptome findings provide further insight into the molecular basis of sarcopenia and the impact of resistance exercise at the mixed muscle and fiber type specific level.

Identification of blood biomarkers of rheumatoid arthritis by transcript profiling of peripheral blood mononuclear cells from the rat collagen-induced arthritis model

Rheumatoid arthritis (RA) is a chronic debilitating autoimmune disease that results in joint destruction and subsequent loss of function. To better understand its pathogenesis and to facilitate the search for novel RA therapeutics, we profiled the rat model of collagen-induced arthritis (CIA) to discover and characterize blood biomarkers for RA. Peripheral blood mononuclear cells (PBMCs) were purified using a Ficoll gradient at various time points after type II collagen immunization for RNA preparation. Total RNA was processed for a microarray analysis using Affymetrix GeneChip technology. Statistical comparison analyses identified differentially expressed genes that distinguished CIA from control rats. Clustering analyses indicated that gene expression patterns correlated with laboratory indices of disease progression. A set of 28 probe sets showed significant differences in expression between blood from arthritic rats and that from controls at the earliest time after induction, and the difference persisted for the entire time course. Gene Ontology comparison of the present study with previous published murine microarray studies showed conserved Biological Processes during disease induction between the local joint and PBMC responses. Genes known to be involved in autoimmune response and arthritis, such as those encoding Galectin-3, Versican, and Socs3, were identified and validated by quantitative TaqMan RT-PCR analysis using independent blood samples. Finally, immunoblot analysis confirmed that Galectin-3 was secreted over time in plasma as well as in supernatant of cultured tissue synoviocytes of the arthritic rats, which is consistent with disease progression. Our data indicate that gene expression in PBMCs from the CIA model can be utilized to identify candidate blood biomarkers for RA.

DNA microarray data integration by ortholog gene analysis reveals potential molecular mechanisms of estrogen-dependent growth of human uterine fibroids

BackgroundUterine fibroids or leiomyoma are a common benign smooth muscle tumor. The tumor growth is well known to be estrogen-dependent. However, the molecular mechanisms of its estrogen-dependency is not well understood.MethodsDifferentially expressed genes in human uterine fibroids were either retrieved from published papers or from our own statistical analysis of downloaded array data. Probes for the same genes on different Affymetrix chips were mapped based on probe comparison information provided by Affymetrix. Genes identified by two or three array studies were submitted for ortholog analysis. Human and rat ortholog genes were identified by using ortholog gene databases, HomoloGene and TOGA and were confirmed by synteny analysis with MultiContigView tool in the Ensembl genome browser.ResultsBy integrated analysis of three recently published DNA microarray studies with human tissue, thirty-eight genes were found to be differentially expressed in the same direction in fibroid compared to adjacent uterine myometrium by at least two research groups. Among these genes, twelve with rat orthologs were identified as estrogen-regulated from our array study investigating uterine expression in ovariectomized rats treated with estrogen. Functional and pathway analyses of the twelve genes suggested multiple molecular mechanisms for estrogen-dependent cell survival and tumor growth. Firstly, estrogen increased expression of the anti-apoptotic PCP4 gene and suppressed the expression of growth inhibitory receptors PTGER3 and TGFBR2. Secondly, estrogen may antagonize PPARγ signaling, thought to inhibit fibroid growth and survival, at two points in the PPAR pathway: 1) through increased ANXA1 gene expression which can inhibit phospholipase A2 activity and in turn decrease arachidonic acid synthesis, and 2) by decreasing L-PGDS expression which would reduce synthesis of PGJ2, an endogenous ligand for PPARγ. Lastly, estrogen affects retinoic acid (RA) synthesis and mobilization by regulating expression of CRABP2 and ALDH1A1. RA has been shown to play a significant role in the development of uterine fibroids in an animal model.ConclusionIntegrated analysis of multiple array datasets revealed twelve human and rat ortholog genes that were differentially expressed in human uterine fibroids and transcriptionally responsive to estrogen in the rat uterus. Functional and pathway analysis of these genes suggest multiple potential molecular mechanisms for the poorly understood estrogen-dependent growth of uterine fibroids. Fully understanding the exact molecular interactions among these gene products requires further study to validate their roles in uterine fibroids. This work provides new avenues of study which could influence the future direction of therapeutic intervention for the disease.

Developing and applying a gene functional association network for anti-angiogenic kinase inhibitor activity assessment in an angiogenesis co-culture model

BackgroundTumor angiogenesis is a highly regulated process involving intercellular communication as well as the interactions of multiple downstream signal transduction pathways. Disrupting one or even a few angiogenesis pathways is often insufficient to achieve sustained therapeutic benefits due to the complexity of angiogenesis. Targeting multiple angiogenic pathways has been increasingly recognized as a viable strategy. However, translation of the polypharmacology of a given compound to its antiangiogenic efficacy remains a major technical challenge. Developing a global functional association network among angiogenesis-related genes is much needed to facilitate holistic understanding of angiogenesis and to aid the development of more effective anti-angiogenesis therapeutics.ResultsWe constructed a comprehensive gene functional association network or interactome by transcript profiling an in vitro angiogenesis model, in which human umbilical vein endothelial cells (HUVECs) formed capillary structures when co-cultured with normal human dermal fibroblasts (NHDFs). HUVEC competence and NHDF supportiveness of cord formation were found to be highly cell-passage dependent. An enrichment test of Biological Processes (BP) of differentially expressed genes (DEG) revealed that angiogenesis related BP categories significantly changed with cell passages. Built upon 2012 DEGs identified from two microarray studies, the resulting interactome captured 17226 functional gene associations and displayed characteristics of a scale-free network. The interactome includes the involvement of oncogenes and tumor suppressor genes in angiogenesis. We developed a network walking algorithm to extract connectivity information from the interactome and applied it to simulate the level of network perturbation by three multi-targeted anti-angiogenic kinase inhibitors. Simulated network perturbation correlated with observed anti-angiogenesis activity in a cord formation bioassay.ConclusionWe established a comprehensive gene functional association network to model in vitro angiogenesis regulation. The present study provided a proof-of-concept pilot of applying network perturbation analysis to drug phenotypic activity assessment.

Assessing the Variability in GeneChip® Data

AbstractIntroduction: Oligonucleotide and cDNA microarray experiments are now common practice in biological science research. The goal of these experiments is generally to gain clues about the functions of genes by measuring how their expression levels rise and fall in response to changing experimental conditions. Measures of gene expression are affected, however, by a variety of factors. This paper introduces statistical methods to assess the variability of Affymetrix GeneChip® data due to randomness. Methods: The variation of Affymetrix’s GeneChip® signal data are quantified at both chip level and individual gene level, respectively, by the agreement study method and variance components method. Three agreement measurement methods are introduced to assess the variability among chips. Variation sources for gene expression data are decomposed into four categories: systematic experiment variation, treatment effect, biological variation, and chip variation. The focus of this paper is on evaluating and comparing the last two kinds of variations. Results: Measurement of agreement and variance components methods were applied to an experimental data, and the calculation and interpretation were exemplified. The variability between biological samples were shown to exist and were assessed at both the chip level and individual gene level. Using the variance components method, it was found that the biological and chip variation are roughly comparable. The Statistical Analysis System (SAS) program for doing the agreement studies can be obtained from the correspondence author.