Microarray Expression Profiling Data Research Articles

Bioinformatics Study on Mechanism of Postnatal Development of Craniofacial Bone.

The postnatal development of craniofacial bone plays a crucial role in shaping the overall structure and functionality of the skull and face. Understanding the underlying mechanisms of this intricate process is essential for both clinical and research purposes. In this study, the authors conducted a bioinformatics analysis using the Gene Expression Omnibus database to investigate the molecular pathways and regulatory networks involved in the postnatal development of craniofacial bone. In this study, the online Gene Expression Omnibus microarray expression profiling data set GSE27976 was used to identify differentially expressed genes (DEGs) in different age groups. Protein-Protein Interaction network analyses, functional enrichment, and hub genes analysis were performed. The differences in immune infiltration and microenvironment among different types of cells were also analyzed. In total, 523 DEGs, including 287 upregulated and 236 downregulated genes, were identified. GO and KEGG analysis showed that the DEGs were significantly enriched in multiple signaling pathways, such as skeletal system morphogenesis, osteoblast differentiation, and stem cell differentiation. Immune infiltration and microenvironment characteristics analysis showed that there were significant differences in fibroblasts, mesenchymal stem cell, osteoblast, stroma score, and microenvironment score between the two groups. Five hub genes, including IGF1, IL1B, ICAM1, MMP2 , and brain-derived neurotrophic factor, were filled out. The findings of this study showed a significant shift in gene expression towards osteogenesis during the first 12 months after birth. These findings emphasize the critical role of the postnatal period in craniofacial bone development and provide valuable insights into the molecular mechanisms underlying this process.

A multitasking multi-objective differential evolution gene selection algorithm enhanced with new elite and guidance strategies for tumor identification

A key preprocessing step in tumor recognition based on microarray expression profile data and machine learning is to identify tumor marker genes. Gene selection aims to select the most relevant gene subset from the original ultra-high dimensional microarray expression profile data to improve tumor identification performance. Inspired by evolutionary multitasking (EMT) and multi-objective optimization, this paper puts forward a novel multitasking multi-objective differential evolution gene selection algorithm (MMODE) which uses new elite and guidance strategies to select the best gene subsets. MMODE initializes two different populations according to different filtering criteria to increase the diversity of the search. These two populations guide their respective populations to search in the optimal direction through knowledge transfer in the evolutionary process. In addition, MMODE employs new elite and guidance strategies that enables individuals to narrow the search range and jump out of local optima. The proposed algorithm is validated on 13 publicly available microarray expression datasets in comparison with state-of-the-art gene selection algorithms. The experimental results show that MMODE can find smaller gene subsets and achieve higher classification accuracy compared with other algorithms.

Identification of tumor-associated antigens and immune subtypes of lower-grade glioma and glioblastoma for mRNA vaccine development

BackgroundmRNA became a promising therapeutic approach in many diseases. This study aimed to identify the tumor antigens specifically expressed in tumor cells for lower-grade glioma (LGG) and glioblastoma (GBM) patients.MethodsIn this work, the mRNA microarray expression profile and clinical data were obtained from 301 samples in the Chinese Glioma Genome Atlas (CGGA) database, the mRNA sequencing data and clinical data of 701 samples were downloaded from The Cancer Genome Atlas (TCGA) database. Genetic alterations profiles were extracted from CGGA and cBioPortal datasets. R language and GraphPad Prism software were applied for the statistical analysis and graph work.ResultsPTBP1 and SLC39A1, which were overexpressed and indicated poor prognosis in LGG patients, were selected as tumor-specific antigens for LGG patients. Meanwhile, MMP9 and SLC16A3, the negative prognostic factors overexpressed in GBM, were identified as tumor-specific antigens for GBM patients. Besides, three immune subtypes (LGG1-LGG3) and eight WGCNA modules were identified in LGG patients. Meanwhile, two immune subtypes (GBM1–GBM2) and 10 WGCNA modules were selected in GBM. The immune characteristics and potential functions between different subtypes were diversity. LGG2 and GBM1 immune subtype were associated with longer overall survival than other subtypes.ConclusionIn this study, PTBP1 and SLC39A1 are promising antigens for mRNA vaccines development in LGG, and MMP9 and SLC16A3 were potential antigens in GBM. Our analyses indicated that mRNA vaccine immunotherapy was more suitable for LGG2 and GBM1 subtypes. This study was helpful for the development of glioma immunotherapies.

Integrated analysis revealing genome-wide chromosomal copy number variation in supraglottic laryngeal squamous cell carcinoma.

Laryngeal squamous cell carcinoma (LSCC) is a genetically complex tumor type and one of the leading causes of cancer-associated disability and mortality. Genetic instability, such as chromosomal instability, is associated with the tumorigenesis of LSCC. Copy number variations (CNVs) have been demonstrated to contribute to the genetic diversity of tumor pathogenesis. Comparative genomic hybridization (CGH) has emerged as a high-throughput genomic technology that facilitates the aggregation of high-resolution data of cancer-associated genomic imbalances. In the present study, a total of 38 primary supraglottic LSCC cases were analyzed by high-resolution array-based CGH (aCGH) to improve the understanding of the genetic alterations in LSCC. Additionally, integration with bioinformatic analysis of microarray expression profiling data from the Gene Expression Omnibus (GEO) database provided a fundamental method for the identification of putative target genes. Genomic CNVs were detected in all cases. The size of net genomic imbalances per case ranged between a loss of 682.3 Mb (~24% of the genome) and a gain of 1,958.6 Mb (~69% of the genome). Recurrent gains included 2pter-q22.1, 3q26.1-qter, 5pter-p12, 7p22.3p14.1, 8p12p11.22, 8q24.13q24.3, 11q13.2q13.4, 12pter-p12.2, 18pter-p11.31 and 20p13p12.1, whereas recurrent losses included 3pter-p21.32, 4q28.1-q35.2, 5q13.2-qter, 9pter-p21.3 and monosomy 13. Gains of 3q26.1-qter were associated with tumor stage, poor differentiation and smoking history. Additionally, through integration with bioinformatic analysis of data from the GEO database, putative target oncogenes, including sex-determining region Y-box 2, eukaryotic translation initiation factor 4 gamma 1, fragile X-related gene 1, disheveled segment polarity protein 3, defective n cullin neddylation 1 domain containing 1, insulin like growth factor 2 mRNA binding protein 2 and CCDC26 long non-coding RNA, and tumor suppressor genes, such as CUB and sushi multiple domains 1, cyclin dependent kinase inhibitor 2A, protocadherin 20, serine peptidase inhibitor Kazal type 5 and Nei like DNA glycosylase 3, were identified in supraglottic LSCC. Supraglottic LSCC is a genetically complex tumor type and aCGH was demonstrated to be effective in the determination of molecular profiles with higher resolution. The present results enable the identification of putative target oncogenes and tumor suppressor gene mapping in supraglottic LSCC.

Analysis of RNA Expression Profiles Identifies Dysregulated Vesicle Trafficking Pathways in Creutzfeldt-Jakob Disease.

Functional genomics applied to the study of RNA expression profiles identified several abnormal molecular processes in experimental prion disease. However, only a few similar studies have been carried out to date in a naturally occurring human prion disease. To better characterize the transcriptional cascades associated with sporadic Creutzfeldt-Jakob disease (sCJD), the most common human prion disease, we investigated the global gene expression profile in samples from the frontal cortex of 10 patients with sCJD and 10 non-neurological controls by microarray analysis. The comparison identified 333 highly differentially expressed genes (hDEGs) in sCJD. Functional enrichment Gene Ontology analysis revealed that hDEGs were mainly associated with synaptic transmission, including GABA (q value = 0.049) and glutamate (q value = 0.005) signaling, and the immune/inflammatory response. Furthermore, the analysis of cellular components performed on hDEGs showed a compromised regulation of vesicle-mediated transport with mainly up-regulated genes related to the endosome (q value = 0.01), lysosome (q value = 0.04), and extracellular exosome (q value < 0.01). A targeted analysis of the retromer core component VPS35 (vacuolar protein sorting-associated protein 35) showed a down-regulation of gene expression (p value= 0.006) and reduced brain protein levels (p value= 0.002). Taken together, these results confirm and expand previous microarray expression profile data in sCJD. Most significantly, they also demonstrate the involvement of the endosomal-lysosomal system. Since the latter is a common pathogenic pathway linking together diseases, such as Alzheimer's and Parkinson's, it might be the focus of future studies aimed to identify new therapeutic targets in neurodegenerative diseases.

Identification of key tumorigenesis‑related genes and their microRNAs in colon cancer.

Colorectal cancer(CRC) is one of the most common malignant tumors and its development involves multi‑gene driven processes that affect the digestive system. The objective of this study was to identify tumorigenesis‑associated gene signatures using microarray expression profiling data. The gene expression profiling of GSE39582, a dataset containing 566colon cancer samples and 19non‑tumoral colorectal mucosae was downloaded from Gene Expression Omnibus (GEO) database. A total of 439differentially expressed genes (DEGs) were extracted by GEO2R. Many of these DEGs were cancer‑related, involved in the regulation of cell proliferation, extracellular matrix (ECM)‑receptor interaction and phosphatidylinositol3‑kinase(PI3K)‑Akt signaling pathway according to the results of pathway enrichment analysis in Database for Annotation, Visualization and Integrated Discovery(DAVID). Then, 10genes were predicted to play an important role in the development of CRC. Transient receptor potential cation channel, subfamilyM, and member6 (TRPM6), a member of 10hub genes, was confirmed to be downregulated in 16(80%) of 20colon cancer tissues using quantitative polymerase chain reaction(qPCR) technology. Furthermore, high expression of TRPM6 was indicative of a prolonged overall survival(OS) in CRC patients through the analysis of GSE39582. Hsa‑let‑7g and hsa‑let‑7f‑1 were believed to be the regulatory miRNAs of TRPM6 by TCGA and miRanda database. In conclusion, this study may play a critical role in promoting the discovery of potential targets for diagnosis, treatment and prognosis of CRC.

Bioinformatic Analysis of Potential microRNAs in Ischemic Stroke

MicroRNAs (miRNAs) are part of the brain's response to ischemia. This study aimed to screen potential miRNAs for the prediction and novel treatments of ischemic stroke. Two mRNA and 1 miRNA microarray expression profile data were downloaded from the Gene Expression Omnibus database. Then, differentially expressed mRNAs and miRNAs were identified. Based on the miRNA-target pairs predicted, an miRNA-target interaction network was established. Bioinformatics analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment) was applied to interpret the function of the miRNA targets. In total, 16 differentially expressed miRNAs and 1345 differentially expressed genes were identified in ischemic stroke, respectively. Importantly, 445 miRNA-target pairs with an inverse correlation of expression were obtained, and the miRNA functional synergistic network was generated. Two miRNAs (miR-145 and miR-122) may represent potential biomarkers in ischemic stroke by being involved in the process of postischemic neuronal damage and thrombosis, respectively. Three novel miRNAs (miR-99b, miR-542-3p, and miR-455-5p) were deregulated, suggesting their roles in the pathological processes of ischemic stroke. Functional annotation indicated that apoptotic signaling cascades play an important role in patients with ischemic stroke. miR-145 and miR-122 represent new biomarkers and underlying targets for the prevention and treatment of ischemic stroke.

MiR-22 and miR-29a Are Members of the Androgen Receptor Cistrome Modulating LAMC1 and Mcl-1 in Prostate Cancer.

The normal prostate as well as early stages and advanced prostate cancer (PCa) require a functional androgen receptor (AR) for growth and survival. The recent discovery of microRNAs (miRNAs) as novel effector molecules of AR disclosed the existence of an intricate network between AR, miRNAs and downstream target genes. In this study DUCaP cells, characterized by high content of wild-type AR and robust AR transcriptional activity, were chosen as the main experimental model. By integrative analysis of chromatin immunoprecipitation-sequencing (ChIP-seq) and microarray expression profiling data, miRNAs putatively bound and significantly regulated by AR were identified. A direct AR regulation of miR-22, miR-29a, and miR-17-92 cluster along with their host genes was confirmed. Interestingly, endogenous levels of miR-22 and miR-29a were found to be reduced in PCa cells expressing AR. In primary tumor samples, miR-22 and miR-29a were less abundant in the cancerous tissue compared with the benign counterpart. This specific expression pattern was associated with a differential DNA methylation of the genomic AR binding sites. The identification of laminin gamma 1 (LAMC1) and myeloid cell leukemia 1 (MCL1) as direct targets of miR-22 and miR-29a, respectively, suggested a tumor-suppressive role of these miRNAs. Indeed, transfection of miRNA mimics in PCa cells induced apoptosis and diminished cell migration and viability. Collectively, these data provide additional information regarding the complex regulatory machinery that guides miRNAs activity in PCa, highlighting an important contribution of miRNAs in the AR signaling.

An algebra-based method for inferring gene regulatory networks

BackgroundThe inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.ResultsThis paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network.ConclusionsBoolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.

Abstract PR11: Dissecting signaling transduction network to infer master regulators of non small cell lung cancer

Abstract The dissection of signal transduction networks is lagging significantly with respect to their transcriptional and protein-complex counterparts. Innovative use of multiplex and microarray-based approaches, where multiple antibodies can be used to probe an ensemble of phosphoproteins, is starting to become sufficiently mature to allow characterization of small pathways. Yet, this is still far from providing an unbiased, genome-wide view of signal-transduction processes. Using data from the first genome-wide profiling of phosphopeptide abundance in normal and tumor-related lung tissue, we have designed a novel algorithm, pARACNe, to generate an unbiased, genome-wide network representing tyrosine kinase signal transduction pathways. Specifically, pARACNe was used to infer tyrosine kinase substrates in non-small cell lung cancer (NSCLC) from tandem mass-spec abundance profiling of phosphotyrosine-enriched peptides from ˜250 samples derived from primary tumors, cell lines, and normal lung tissue. SILAC validation of EGFR, MET and ALK substrates, confirmed &amp;gt;80% of the pARACNe-inferred interactions. Further analysis of large-scale NSCLC microarray expression profile data, using the MINDy algorithm, showed significant convergence between the signal transduction pathways inferred by proteomics and gene expression-based methods. The availability of an accurate NSCLC Tyrosine Kinase Interaction Network (TKIN) has significant implications for our ability to elucidate master regulators of oncogenesis and tumor progression and for the identification and prioritization of druggable targets. We used the MARINa algorithm and the TKIN to infer a complete repertoire of master regulator (MRs) kinases, both individual as well as synergistic, in 46 cell lines. Initial validation shows that the method could recapitulate both cancer-specific MRs, such as EGFR, as well as cell line-specific ones, such as ALK and PTK2 in H2228 cells. Follow-up experimental assays have shown that the PTK2, which is active in tumor-related but not in normal cells, is required for H2228 cell viability and may constitute a novel oncogene addiction in NSCLC and a candidate therapeutic target. We also validated synergistic addiction of EGFR and MET in several cell lines by showing no effect on colony formation after treating cell lines with inhibitors of individual genes but synergistic decrease in colony formation with dual treatment. The TKI suggests that current pathway models are incomplete and sets the stage for quantitative, network-based methods to identify key regulators of physiologic and pathologic cell phenotypes. This proffered talk is also presented as Poster A14. Citation Format: Mukesh Bansal, Michael Peyton, Manjunath Kustagi, Luc Girard, Klarisa Rikova, Michael Comb, Michael White, John Minna, Andrea Califano. Dissecting signaling transduction network to infer master regulators of non small cell lung cancer [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR11.

ChIP-Array: combinatory analysis of ChIP-seq/chip and microarray gene expression data to discover direct/indirect targets of a transcription factor

Chromatin immunoprecipitation (ChIP) coupled with high-throughput techniques (ChIP-X), such as next generation sequencing (ChIP-Seq) and microarray (ChIP–chip), has been successfully used to map active transcription factor binding sites (TFBS) of a transcription factor (TF). The targeted genes can be activated or suppressed by the TF, or are unresponsive to the TF. Microarray technology has been used to measure the actual expression changes of thousands of genes under the perturbation of a TF, but is unable to determine if the affected genes are direct or indirect targets of the TF. Furthermore, both ChIP-X and microarray methods produce a large number of false positives. Combining microarray expression profiling and ChIP-X data allows more effective TFBS analysis for studying the function of a TF. However, current web servers only provide tools to analyze either ChIP-X or expression data, but not both. Here, we present ChIP-Array, a web server that integrates ChIP-X and expression data from human, mouse, yeast, fruit fly and Arabidopsis. This server will assist biologists to detect direct and indirect target genes regulated by a TF of interest and to aid in the functional characterization of the TF. ChIP-Array is available at http://jjwanglab.hku.hk/ChIP-Array, with free access to academic users.

Abstract 4822: Nonsense mediated decay defines deleterious TP53 mutations and tumor aggressiveness in ovarian cancer

Abstract Genetic mutations leading to premature termination of protein translation, nonsense mutations, result in aberrant precursor mRNA processing and subsequent UPF1-dependent exosomal degradation of mRNA. The significance of this surveillance mechanism in controlling the expression of key mutated tumor suppressor genes in cancer in vivo has remained unclear. We have recently shown that TP53 mutations are almost invariably present in high-grade serous ovarian cancers (Ahmed at al., Journal of Pathology, 2010;221:49) and that about one third of such mutations are nonsense. We have now tested the hypothesis that nonsense mutations induce nonsense mediated decay resulting in TP53 mRNA depletion. We analyzed microarray expression profiling data from 136 epithelial ovarian cancers with known TP53 mutation status and found a strikingly statistically significant underexpression of TP53 mRNA transcript in cancers with truncating mutations (p&amp;lt;0.0001) compared to other types of mutations. This effect allowed the identification of a class of TP53-non-expressing serous epithelial cancers that comprised all TP53 truncating mutations. We next conducted a systematic analysis of 18 publically available expression profiling ovarian cancer studies published at the Gene Expression Omnibus database. We validated the results by showing that cancers that had nonsense mutations had a significantly lower TP53 expression irrespective of stage. Importantly, this systematic analysis of multiple independent data set established that lack of TP53 expression was, almost exclusively, a feature of high grade serous cancers and invasion as opposed to low grade disease or tumors of low malignant potential (p&amp;lt;0.0001 and p=0.002, respectively). Expression profiling analysis revealed that cancers with truncating mutations had a distinct gene expression signature compared to those with missense mutations. This signature was enriched for genes that regulated angiogenesis such as CSPG4 and metabolism. Surprisingly, however, the magnitude of expression differences was not high. Based on these results, we propose a model for the biogenesis of a subset of high-grade serous ovarian cancers that is defined by nonsense mediated decay of nonsense TP53 mutations. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4822. doi:10.1158/1538-7445.AM2011-4822

Abstract LB-27: Dissecting signaling transduction network to infer master regulators of non small cell lung cancer

Abstract Abstract The dissection of signal transduction networks is lagging significantly with respect to their transcriptional and protein-complex counterparts. Innovative use of multiplex and microarray-based approaches, where multiple antibodies can be used to probe an ensemble of phosphoproteins, is starting to become sufficiently mature to allow characterization of small pathways. Yet, this is still far from providing an unbiased, genome-wide view of signal-transduction processes. Using data from the first genome-wide profiling of phosphopeptide abundance in normal and tumor related lung tissue1, we have designed a novel algorithm, pARACNe, to generate an unbiased, genome-wide network representing tyrosine kinase signal transduction pathways. Specifically, pARACNe was used to infer tyrosine kinase substrates in non-small cell lung cancer (NSCLC) from tandem mass-spec abundance profiling of phosphotyrosine enriched peptides from ∼250 samples derived from primary tumors, cell lines, and normal lung tissue. SILAC validation of EGFR, MET and ALK substrates, confirmed &amp;gt;80% of the pARACNe inferred interactions. Further analysis of large-scale NSCLC microarray expression profile data, using the MINDy algorithm2, showed significant convergence between the signal transduction pathways inferred by proteomics and gene expression based methods. The availability of an accurate NSCLC Tyrosine Kinase Interaction Network (TKIN) has significant implications for our ability to elucidate master regulators of oncogenesis and tumor progression and for the identification and prioritization of druggable targets. We used the MARINa algorithm and the TKIN to infer a complete repertoire of master regulator (MRs) kinases of 46 cell lines. Initial validation shows that the method could recapitulate both cancer specific MRs, such as EGFR, as well as cell line specific ones, such as ALK and PTK2 in H2228 cells. Follow up experimental assays have shown that the PTK2, which is active in tumor-related but not in normal cells, is required for H2228 cell viability and may constitute a novel oncogene addiction in NSCLC and a candidate therapeutic target. The TKI suggests that current pathway models are incomplete and sets the stage for quantitative, network-based methods to identify key regulators of physiologic and pathologic cell phenotypes.

Emodin inhibits the growth of hepatoma cells: Finding the common anti-cancer pathway using Huh7, Hep3B, and HepG2 cells

Emodin—a major component of Rheum palmatum L.—exerts antiproliferative effects in cancer cells that are regulated by different signaling pathways. Hepatocellular carcinoma has high-incidence rates and is associated with poor prognosis and high mortality rates. This study was designed to evaluate the effects of emodin on human hepatocarcinoma cell viability and investigate its mechanisms of action in Huh7, Hep3B, and HepG2 cells. To define the molecular changes associated with this process, expression profiles were compared in emodin-treated hepatoma cells by cDNA microarray hybridization, quantitative RT-PCRs, and Western blot analysis. G2/M phase arrest was observed in all 3 cell lines. Cell cycle regulatory gene analysis showed increased protein levels of cyclin A, cyclin B, Chk2, Cdk2, and P27 in hepatoma cells after time courses of emodin treatment, and Western blot analysis showed decreased protein levels of Cdc25c and P21. Microarray expression profile data and quantitative PCR revealed that 15 representative genes were associated with emodin treatment response in hepatoma cell lines. The RNA expression levels of CYP1A1, CYP1B1, GDF15, SERPINE1, SOS1, RASD1, and MRAS were upregulated and those of NR1H4, PALMD, and TXNIP were downregulated in all three hepatoma cells. Moreover, at 6 h after emodin treatment, the levels of GDF15, CYP1A1, CYP1B1, and CYR61 were upregulated. Here, we show that emodin treatment caused G2/M arrest in liver cancer cells and increased the expression levels of various genes both in mRNA and protein level. It is likely that these genes act as biomarkers for hepatocellular carcinoma therapy.

IRF5 Genetic Risk Haplotype Influences Host B Cell Gene Responses to Epstein-Barr Virus (EBV) (49.14)

Abstract Both gene and environment interactions play keyroles in the development of systemic lupus erythematosus (SLE). We examined effects of the IRF5 lupus risk haplotype upon the host's B cell response to the binding or infection with EBV, a suspected environmental trigger for SLE. Whole genome microarray expression profile data was collected from cells exposed to EBV for 16 hours. Analysis of gene expression by gene set enrichment analysis revealed that key differences in expression between SLE patients and controls (or individuals carrying the IRF5 risk and non-risk haplotype) were in a subset of interferon response genes. Patients with the IRF5 risk haplotype have a heightened interferon signature under all experimental conditions; whereas, the patients with the IRF5 protective haplotype have a B cell interferon signature similar to that of unrelated, matched controls. Overexpression of interferon pathway genes in B cells following viral exposure in control individuals carrying the IRF5 risk haplotype suggests that the IRF5 risk alleles alone can modulate ones biological response to the environmental insult. Patients carrying either the IRF5 risk or non-risk alleles appear to already be predisposed to having a higher interferon signature even without exposure to virus, suggesting that other genetic factors are also influencing the interferon response, independent of virus. Support by NIH (AI007633 and AI31584).

S2050 Prostanglandin E2 Receptors Are Involved in Nicotine-Induced Gastric Tumor Growth

We have previously determined that the small GTPase Rab25 regulates membrane recycling in epithelial cells. However, little is known whether membrane recycling and vesicle trafficking pathways influence epithelial tissue transformation and neoplasia. Based on the hypothesis that membrane recycling is necessary to maintain epithelial cell polarity, we sought to determine the effects of Rab25 loss on the development of colonic neoplasia. We therefore bred Rab25 knockout mice in the C57BL/6 background onto the ApcMin/+ mouse. To understand the function of Rab25, we have developed a mouse model for deletion of the Rab25 gene product. To assess the effects of Rab25 loss on the development of colonic neoplasia, we have bred Rab25 knockout mice on the C57BL/6 background onto the ApcMin/ +mouse. At 4months of age, Rab25-/-ApcMin/+mice showed 175 intestinal polyps compared with 45.4 in Rab25+/+ApcMin/+ mice and 98.5 in Rab25+/-ApcMin/+ mice (p<0.01). In the colon, Rab25-/-ApcMin/+ 4.7 colon tumors compared with only 1.9 in Rab25+/+ApcMin/ + mice (p<0.01) and 3.5 in Rab25+/-ApcMin/+ mice. Histopathological analysis of the polyps in Rab25-/-ApcMin/+ mice showed diffuse and fused microadenomas with high mitotic index compared to control. We also sought to examine Rab25 gene expression in a large panel of colon cancer tumor isolates by mining a previously characterized series of mouse and human tumor gene microarray expression profile data. Expression of Rab25 was significantly decreased in colon adenocarcinomas (p<0.03) independent of Duke's stage. In addition, Kaplan-Meier analysis demonstrated that lower expression of Rab25 correlated with significantly lower survival (p<0.035). These results show that the loss of Rab25 promotes the initiation of intestinal polyposis inmice and reductions in Rab25 correlate with poor prognosis in human colorectal cancer. These findings indicate that aberrations in vesicle trafficking may promote intestinal neoplasia.

Analysis of the temporal expression of Trichoplusia ni single nucleopolyhedrovirus genes following transfection of BT1-Tn-5B1-4 cells

Trichoplusia ni single nucleopolyhedrovirus (TnSNPV), a 134,394-bp double-stranded DNA group II Nucleopolyhedrovirus, is pathogenic to the lepidopteran T. ni. TnSNPV transcription is temporally regulated and divided into three promoter sequence-dependent classes (early, late and very late genes). A viral oligonucleotide DNA microarray containing all potential (144) viral genes of TnSNPV was designed to investigate global viral gene expression during cell infection. Total BT1-Tn-5B1-4 cellular mRNAs extracted between 0 and 72 h posttransfection with TnSNPV genomic DNA were hybridized to the microarray. Initial average expression of early genes was detected between 12 and 24 h posttransfection while late genes were mainly detected between 24 and 72 h posttransfection. The microarray expression profiling data verified many computer predicted promoter assignments. K-means clustering was used to sort the 144 genes based on their temporal expression pattern similarities. This clustering resulted in the confirmation and temporal class assignment of previously unidentified genes and promoters.

Reverse engineering cellular networks

We describe a computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data. Similar to other algorithms, ARACNE predicts potential functional associations among genes, or novel functions for uncharacterized genes, by identifying statistical dependencies between gene products. However, based on biochemical validation, literature searches and DNA binding site enrichment analysis, ARACNE has also proven effective in identifying bona fide transcriptional targets, even in complex mammalian networks. Thus we envision that predictions made by ARACNE, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks. While the examples in this protocol use only gene expression profile data, the algorithm's theoretical basis readily extends to a variety of other high-throughput measurements, such as pathway-specific or genome-wide proteomics, microRNA and metabolomics data. As these data become readily available, we expect that ARACNE might prove increasingly useful in elucidating the underlying interaction models. For a microarray data set containing approximately 10,000 probes, reconstructing the network around a single probe completes in several minutes using a desktop computer with a Pentium 4 processor. Reconstructing a genome-wide network generally requires a computational cluster, especially if the recommended bootstrapping procedure is used.

The PEPR GeneChip data warehouse, and implementation of a dynamic time series query tool (SGQT) with graphical interface.

Publicly accessible DNA databases (genome browsers) are rapidly accelerating post-genomic research (see http://www.genome.ucsc.edu/), with integrated genomic DNA, gene structure, EST/ splicing and cross-species ortholog data. DNA databases have relatively low dimensionality; the genome is a linear code that anchors all associated data. In contrast, RNA expression and protein databases need to be able to handle very high dimensional data, with time, tissue, cell type and genes, as interrelated variables. The high dimensionality of microarray expression profile data, and the lack of a standard experimental platform have complicated the development of web-accessible databases and analytical tools. We have designed and implemented a public resource of expression profile data containing 1024 human, mouse and rat Affymetrix GeneChip expression profiles, generated in the same laboratory, and subject to the same quality and procedural controls (Public Expression Profiling Resource; PEPR). Our Oracle-based PEPR data warehouse includes a novel time series query analysis tool (SGQT), enabling dynamic generation of graphs and spreadsheets showing the action of any transcript of interest over time. In this report, we demonstrate the utility of this tool using a 27 time point, in vivo muscle regeneration series. This data warehouse and associated analysis tools provides access to multidimensional microarray data through web-based interfaces, both for download of all types of raw data for independent analysis, and also for straightforward gene-based queries. Planned implementations of PEPR will include web-based remote entry of projects adhering to quality control and standard operating procedure (QC/SOP) criteria, and automated output of alternative probe set algorithms for each project (see http://microarray.cnmcresearch.org/pgadatatable.asp).

Artefacts and Reliability of DNA Microarray Expression Profiling Data

Microarray expression profiling is a complex experimental methodology comprising sequential steps: fluorescent or radioactive labelling of extracted RNA, hybridisation to the array, washing, scanning, and image processing of the array and data analysis. Each of these steps is a possible source of artefacts that potentially can influence the final result, and hence the evaluation of differential gene expression. In addition, the quality of expression profiling experimental data also depends upon the design and quality of the arrays themselves. Here we review specific problems and pitfalls that may occur during expression profiling experiments and suggest possible remedies. Keywords: expression profiling, microarray artefacts, dna-chip, hybridisation, fluorescent labelling, image processing, cross-hybridisation, hybridisation specificity