This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ http://www.biomedcentral.com/bmcmedgenomics/ The latest articles from BMC Medical Genomics (ISSN 1755-8794) published by BioMed Central Background: Genistein is an isoflavonoid present in soybeans that exhibits anti-carcinogenic properties. In the present study, we treated primary glioblastoma, rhabdomyosarcoma, hepatocellular carcinoma and human embryonic carcinoma cells (NCCIT) with u-molar concentrations of Genistein and assessed mitotic index, cell morphology, global gene expression, and specific cell-cycle regulating genes. We compared the expression profiles of NCCIT cells with that of the cancer cell lines in order to identify common Genistein-dependent transcriptional changes and accompanying signaling cascades. Results: We found that cancer cells treated with Genistein undergo cell-cycle arrest at different checkpoints. This arrest was associated with a decrease in the mRNA levels of core regulatory genes, PBK, BUB1, and CDC20 as determined by microarray-analysis and verified by Real-Time PCR. In contrast, human NCCIT cells showed over-expression of GADD45 A and G (growth arrest- and DNA-damage-inducible proteins 45A and G), as well as down-regulation of OCT4, and NANOG protein. Furthermore, Genistein induced the expression of apoptotic and anti-migratory proteins p53 and p38 in all cell lines. Genistein also up-regulated steady-state levels of both CYCLIN A and B. Conclusions: These results suggest that the major cell-cycle guarding checkpoints may be involved in Genistein-induced cell-cycle arrest in low passage, primary cancer cells. http://www.biomedcentral.com/1755-8794/1/49 Christian RA Regenbrecht, Marc Jung, Hans Lehrach and James Adjaye BMC Medical Genomics 2008, 1:49 2008-10-10 doi:10.1186/1755-8794-1-49 BMC Medical Genomics 1755-8794 1 49 2008-10-10 Background: The etiology of many chronic diseases involves interactions between environmental factors and genes that modulate physiological processes. Understanding interactions between environmental chemicals and genes/proteins may provide insights into the mechanisms of chemical actions, disease susceptibility, toxicity, and therapeutic drug interactions. The Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org) provides these insights by curating and integrating data describing relationships between chemicals, genes/proteins, and human diseases. To illustrate the scope and application of CTD, we present an analysis of curated data for the chemical arsenic. Arsenic represents a major global environmental health threat and is associated with many diseases. The mechanisms by which arsenic modulates these diseases are not well understood. Methods: Curated interactions between arsenic compounds and genes were downloaded using export and batch query tools at CTD. The list of genes was analyzed for molecular interactions, Gene Ontology (GO) terms, KEGG pathway annotations, and inferred disease relationships. Results: CTD contains curated data from the published literature describing 2,738 molecular interactions between 21 different arsenic compounds and 1,456 genes and proteins. Analysis of these genes and proteins provide insight into the biological functions and molecular networks that are affected by exposure to arsenic, including stress response, apoptosis, cell cycle, and specific protein signaling pathways. Integrating arsenic-gene data with gene-disease data yields a list of diseases that may be associated with arsenic exposure and genes that may explain this association. Conclusions: CTD data integration and curation strategies yield insight into the actions of environmental chemicals and provide a basis for developing hypotheses about the molecular mechanisms underlying the etiology of environmental diseases. While many reports describe the molecular response to arsenic, CTD integrates these data with additional curated data sets that facilitate construction of chemical-gene-disease networks and provide the groundwork for investigating the molecular basis of arsenic-associated diseases or toxicity. The analysis reported here is extensible to any environmental chemical or therapeutic drug. http://www.biomedcentral.com/1755-8794/1/48 Allan P Davis, Cynthia G Murphy, Michael C Rosenstein, Thomas C Wiegers and Carolyn J Mattingly BMC Medical Genomics 2008, 1:48 2008-10-09 doi:10.1186/1755-8794-1-48 BMC Medical Genomics 1755-8794 1 48 2008-10-09 Background: Hypermethylation of promoter CpG islands with associated loss of gene expression, and hypomethylation of CpG-rich repetitive elements that may destabilize the genome are common events in most, if not all, epithelial cancers. Methods: The methylation of 6,502 CpG-rich sequences spanning the genome was analyzed in 137 ovarian samples (ten normal, 23 low malignant potential, 18 stage I, 16 stage II, 54 stage III, and 16 stage IV) ranging from normal tissue through to stage IV cancer using a sequence-validated human CpG island microarray. The microarray contained 5' promoter-associated CpG islands as well as CpG-rich satellite and Alu repetitive elements. Results: Results showed a progressive de-evolution of normal CpG methylation patterns with disease progression; 659 CpG islands showed significant loss or gain of methylation. Satellite and Alu sequences were primarily associated with loss of methylation, while promoter CpG islands composed the majority of sequences with gains in methylation. Since the majority of ovarian tumors are late stage when diagnosed, we tested whether DNA methylation profiles could differentiate between normal and low malignant potential (LMP) compared to stage III ovarian samples. We developed a class predictor consisting of three CpG-rich sequences that was 100% sensitive and 89% specific when used to predict an independent set of normal and LMP samples versus stage III samples. Bisulfite sequencing confirmed the NKX-2-3 promoter CpG island was hypermethylated with disease progression. In addition, 5-aza-2'-deoxycytidine treatment of the ES2 and OVCAR ovarian cancer cell lines re-expressed NKX-2-3. Finally, we merged our CpG methylation results with previously published ovarian expression microarray data and identified correlated expression changes. Conclusion: Our results show that changes in CpG methylation are cumulative with ovarian cancer progression in a sequence-type dependent manner, and that CpG island microarrays can rapidly discover novel genes affected by CpG methylation in clinical samples of ovarian cancer. http://www.biomedcentral.com/1755-8794/1/47 George S Watts, Bernard W Futscher, Nicholas Holtan, Koen DeGeest, Frederick E Domann and Stephen L Rose BMC Medical Genomics 2008, 1:47 2008-09-30 doi:10.1186/1755-8794-1-47 BMC Medical Genomics 1755-8794 1 47 2008-09-30 Background: Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions. Results: In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC) on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs) such as NF-kB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin). Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection) of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network. Conclusion: In this study, Data mining and dynamic network analyses were integrated to examine the gene regulatory network in the inflammatory response system. Compared with previous methodologies reported in the literatures, the proposed gene network perturbation method has shown a great improvement in analyzing the systemic inflammation. http://www.biomedcentral.com/1755-8794/1/46 Bor-Sen Chen, Shih-Kuang Yang, Chung-Yu Lan and Yung-Jen Chuang BMC Medical Genomics 2008, 1:46 2008-09-30 doi:10.1186/1755-8794-1-46 BMC Medical Genomics 1755-8794 1 46 2008-09-30 Background: While BMPR2 mutation strongly predisposes to pulmonary arterial hypertension (PAH), only 20% of mutation carriers develop clinical disease. This finding suggests that modifier genes contribute to FPAH clinical expression. Since modifiers are likely to be common alleles, this problem is not tractable by traditional genetic approaches. Furthermore, examination of gene expression is complicated by confounding effects attributable to drugs and the disease process itself. Methods: To resolve these problems, B-cells were isolated, EBV-immortalized, and cultured from familial PAH patients with BMPR2 mutations, mutation positive but disease-free family members, and family members without mutation. This allows examination of differences in gene expression without drug or disease-related effects. These differences were assayed by Affymetrix array, with follow-up by quantitative RT-PCR and additional statistical analyses. Results: By gene array, we found consistent alterations in multiple pathways with known relationship to PAH, including actin organization, immune function, calcium balance, growth, and apoptosis. Selected genes were verified by quantitative RT-PCR using a larger sample set. One of these, CYP1B1, had tenfold lower expression than control groups in female but not male PAH patients. Analysis of overrepresented gene ontology groups suggests that risk of disease correlates with alterations in pathways more strongly than with any specific gene within those pathways. Conclusion: Disease status in BMPR2 mutation carriers was correlated with alterations in proliferation, GTP signaling, and stress response pathway expression. The estrogen metabolizing gene CYP1B1 is a strong candidate as a modifier gene in female PAH patients. http://www.biomedcentral.com/1755-8794/1/45 James West, Joy Cogan, Mark Geraci, Linda Robinson, John Newman, John A Phillips, Kirk Lane, Barbara Meyrick and Jim Loyd BMC Medical Genomics 2008, 1:45 2008-09-29 doi:10.1186/1755-8794-1-45 BMC Medical Genomics 1755-8794 1 45 2008-09-29 Background: Late-onset Alzheimer's disease (LOAD) is an age related neurodegenerative disease with a high prevalence that places major demands on healthcare resources in societies with increasingly aged populations. The only extensively replicable genetic risk factor for LOAD is the apolipoprotein E gene. In order to identify additional genetic risk loci we have conducted a genome-wide association (GWA) study in a large LOAD case - control sample, reducing costs through the use of DNA pooling. Methods: DNA samples were collected from 1,082 individuals with LOAD and 1,239 control subjects. Age at onset ranged from 60 to 95 and Controls were matched for age (mean = 76.53 years, SD = 6.33), gender and ethnicity. Equimolar amounts of each DNA sample were added to either a case or control pool. The pools were genotyped using Illumina HumanHap300 and Illumina Sentrix HumanHap240S arrays testing 561,494 SNPs. 114 of our best hit SNPs from the pooling data were identified and then individually genotyped in the case - control sample used to construct the pools. Results: Highly significant association with LOAD was observed at the APOE locus confirming the validity of the pooled genotyping approach. For 109 SNPs outside the APOE locus, we obtained uncorrected p-values [less than or equal to] 0.05 for 74 after individual genotyping. To further test these associations, we added control data from 1400 subjects from the 1958 Birth Cohort with the evidence for association increasing to 3.4x10-6 for our strongest finding, rs727153. rs727153 lies 13kb from the start of transcription of lecithin retinol acyltransferase (phosphatidylcholine--retinol O-acyltransferase, LRAT). Five of seven tag SNPs chosen to cover LRAT showed significant association with LOAD with a SNP in intron 2 of LRAT, showing greatest evidence of association (rs201825, p-value = 6.1 x 10-7 ). Conclusions: We have validated the pooling method for GWA studies by both identifying the APOE locus and by observing a strong enrichment for significantly associated SNPs. We provide evidence for LRAT as a novel candidate gene for LOAD. LRAT plays a prominent role in the Vitamin A cascade, a system that has been previously implicated in LOAD. http://www.biomedcentral.com/1755-8794/1/44 Richard Abraham, Valentina Moskvina, Rebecca Sims, Paul Hollingworth, Angharad Morgan, Lyudmila Georgieva, Kimberley Dowzell, Sven Cichon, Axel M Hillmer, Michael C O'Donovan, Julie Williams, Michael J Owen and George Kirov BMC Medical Genomics 2008, 1:44 2008-09-29 doi:10.1186/1755-8794-1-44 BMC Medical Genomics 1755-8794 1 44 2008-09-29 Background: The regulatory subunit of cAMP-dependent protein kinase (PKA) exists in two isoforms, RI and RII, which distinguish the PKA isozymes, type I (PKA-I) and type II (PKA-II). Evidence obtained from a variety of different experimental approaches has shown that the relative levels of type I and type II PKA in cells can play a major role in determining the balance between cell growth and differentiation. In order to characterize the effect of PKA type I and type II regulatory subunits on gene transcription at a global level, the PKA regulatory subunit genes for RIalpha and RIIbeta were stably transfected into cells of the ovarian cancer cell line (OVCAR8). Results: RIalpha transfected cells exhibit hyper-proliferative growth and RIIbeta transfected cells revert to a relatively quiescent state. Profiling by microarray revealed equally profound changes in gene expression between RIalpha, RIIbeta, and parental OVCAR cells. Genes specifically up-regulated in RIalpha cells were highly enriched for pathways involved in cell growth while genes up-regulated in RIIbeta cells were enriched for pathways involved in differentiation. A large group of genes (~3600) was regulated along an axis of proliferation/differentiation between RII+/-, parental, and RIIbeta cells. RIalpha/wt and RIIbeta/wt gene regulation was shown by two separate and distinct gene set analytical methods to be strongly cross-correlated with a generic model of cellular differentiation. Conclusion: Overexpression of PKA regulatory subunits in an ovarian cancer cell line dramatically influences the cell phenotype. The proliferation phenotype is strongly correlated with recently identified clinical biomarkers predictive of poor prognosis in ovarian cancer biomarkers suggesting a possible pivotal role for PKA regulation in disease progression. http://www.biomedcentral.com/1755-8794/1/43 Chris Cheadle, Maria Nesterova, Tonya Watkins, Kathleen C Barnes, John C Hall, Antony Rosen, Kevin G Becker and Yoon S Cho-Chung BMC Medical Genomics 2008, 1:43 2008-09-26 doi:10.1186/1755-8794-1-43 BMC Medical Genomics 1755-8794 1 43 2008-09-26 Background: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. Results: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. Conclusion: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power. http://www.biomedcentral.com/1755-8794/1/42 Andrew H Sims, Graeme J Smethurst, Yvonne Hey, Michal J Okoniewski, Stuart D Pepper, Anthony Howell, Crispin J Miller and Robert B Clarke BMC Medical Genomics 2008, 1:42 2008-09-21 doi:10.1186/1755-8794-1-42 BMC Medical Genomics 1755-8794 1 42 2008-09-21 Background: The immense diagnostic potential of human plasma has prompted great interest and effort in cataloging its contents, exemplified by the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) pilot project. Due to challenges in obtaining a reliable blood plasma protein list, HUPO later re-analysed their own original dataset with a more stringent statistical treatment that resulted in a much reduced list of high confidence (at least 95%) proteins compared with their original findings. In order to facilitate the discovery of novel biomarkers in the future and to realize the full diagnostic potential of blood plasma, we feel that there is still a need for an ultra-high confidence reference list (at least 99% confidence) of blood plasma proteins. Methods: To address the complexity and dynamic protein concentration range of the plasma proteome, we employed a linear ion-trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) for mass spectrometry (MS) analysis. Both instruments allow the measurement of peptide masses in the low ppm range. Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3). The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved. Results: Herein we established a high confidence set of 697 blood plasma proteins and achieved a high 'average sequence coverage' of more than 14 peptides per protein and a median of 6 peptides per protein. All proteins annotated as belonging to the immunoglobulin family as well as all hypothetical proteins whose peptides completely matched immunoglobulin sequences were excluded from this protein list. We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches. Furthermore, we characterized the plasma proteins using cellular localization information, as well as comparing our list of proteins to data from other sources, including the HUPO PPP dataset. Conclusion: Superior instrumentation combined with rigorous validation criteria gave rise to a set of 697 plasma proteins in which we have very high confidence, demonstrated by an exceptionally low false peptide identification rate of 0.29%. http://www.biomedcentral.com/1755-8794/1/41 Susann Schenk, Gary J Schoenhals, Gustavo de Souza and Matthias Mann BMC Medical Genomics 2008, 1:41 2008-09-15 doi:10.1186/1755-8794-1-41 BMC Medical Genomics 1755-8794 1 41 2008-09-15 Background: The rexinoid bexarotene (LGD1069, Targretin) is a highly selective retinoid × receptor (RXR) agonist that inhibits the growth of pre-malignant and malignant breast cells. Bexarotene was shown to suppress the development of breast cancer in transgenic mice models without side effects. The chemopreventive effects of bexarotene are due to transcriptional modulation of cell proliferation, differentiation and apoptosis. Our goal in the present study was to obtain a profile of the genes modulated by bexarotene on mammary gland from three transgenic mouse mammary cancer models in an effort to elucidate its molecular mechanism of action and for the identification of biomarkers of effectiveness. Methods: Serial analysis of gene expression (SAGE) was employed to profile the transcriptome of p53-null, MMTV-ErbB2, and C3(1)-SV40 mammary cells obtained from mice treated with bexarotene and their corresponding controls. Results: This resulted in a dataset of approximately 360,000 transcript tags representing over 20,000 mRNAs from a total of 6 different SAGE libraries. Analysis of gene expression changes induced by bexarotene in mammary gland revealed that 89 genes were dysregulated among the three transgenic mouse mammary models. From these, 9 genes were common to the three models studied. Conclusion: Analysis of the indicated core of transcripts and protein-protein interactions of this commonly modulated genes indicate two functional modules significantly affected by rexinoid bexarotene related to protein biosynthesis and bioenergetics signatures, in addition to the targeting of cancer-causing genes related with cell proliferation, differentiation and apoptosis. http://www.biomedcentral.com/1755-8794/1/40 Martin C Abba, Yuhui Hu, Carla C Levy, Sally Gaddis, Frances S Kittrell, Yun Zhang, Jamal Hill, Reid P Bissonnette, Daniel Medina, Powel H Brown and C Marcelo Aldaz BMC Medical Genomics 2008, 1:40 2008-09-11 doi:10.1186/1755-8794-1-40 BMC Medical Genomics 1755-8794 1 40 2008-09-11