Integrating Gene Expression Profiles Research Articles

Abstract 810: Integrative genomic and transcriptomic analysis in idenfitication of biomarkers of chemoresistance in serous epithelial ovarian cancer.

Abstract Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression and copy number profiling to delineate major deregulated pathways and biomarker networks associated with the development of intrinsic chemotherapy resistance with exposure to standard first-line therapy for ovarian cancer. The study cohort comprised 28 high grade serous ovarian cancer patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. Twelve patient tumors demonstrating relative resistance to platinum based chemotherapy corresponding to shorter PFS (less than 6 months) were compared to 16 tumors from platinum-sensitive patients (PFS more than 18months). Molecular profiling was performed using Affymetrix high-resolution microarray platforms to permit global comparisons of gene expression levels and copy number profiles between tumors from the resistant group with the sensitive group. Microarray data analysis revealed a set of 227 discriminating genes of which expression levels may be influencing differential chemotherapy response between the two groups. Pathway analysis of these genes showed the,PI3K,NFkB and IGF1 networks as some of the significant networks distinguishing the chemotherapy resistant with the sensitive group. Copy number analysis performed using Nexus copy number version 6.1 revealed differences in the chromosomal regions, 4q31.22, 5q13.2, 9p24.3, 2p23.2, 16q21, 6q14.1, 7p22.3, 12p13 and Xq. Integrative copy number and gene expression profiling will delineate the drivers of chemotherapy resistance in patients undergoing standard platinum-based treatment of ovarian cancer. Future studies to validate these markers are necessary to apply this knowledge to biomarker-based clinical trials. Citation Format: Madhuri Koti, Robert J. Gooding, Paulo Nuin, Alexandria Haslehurst, Colleen Crane, Johanne Weberpals, Timothy Childs, Peter Bryson, Moyez Dharsee, Kenneth Evans, Harriet E. Feilotter, Paul C. Park, Jeremy A. Squire. Integrative genomic and transcriptomic analysis in idenfitication of biomarkers of chemoresistance in serous epithelial ovarian cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 810. doi:10.1158/1538-7445.AM2013-810

Molecular interplay between Δ5/Δ6 desaturases and long-chain fatty acids in the pathogenesis of non-alcoholic steatohepatitis

ObjectiveThe mechanisms underlying non-alcoholic steatohepatitis (NASH) are not completely elucidated. In the current study we integrated gene expression profiling of liver biopsies from NASH patients with translational studies in mouse...

Integrative Genome-Wide Gene Expression Profiling of Clear Cell Renal Cell Carcinoma in Czech Republic and in the United States

Gene expression microarray and next generation sequencing efforts on conventional, clear cell renal cell carcinoma (ccRCC) have been mostly performed in North American and Western European populations, while the highest incidence rates are found in Central/Eastern Europe. We conducted whole-genome expression profiling on 101 pairs of ccRCC tumours and adjacent non-tumour renal tissue from Czech patients recruited within the “K2 Study”, using the Illumina HumanHT-12 v4 Expression BeadChips to explore the molecular variations underlying the biological and clinical heterogeneity of this cancer. Differential expression analysis identified 1650 significant probes (fold change ≥2 and false discovery rate <0.05) mapping to 630 up- and 720 down-regulated unique genes. We performed similar statistical analysis on the RNA sequencing data of 65 ccRCC cases from the Cancer Genome Atlas (TCGA) project and identified 60% (402) of the downregulated and 74% (469) of the upregulated genes found in the K2 series. The biological characterization of the significantly deregulated genes demonstrated involvement of downregulated genes in metabolic and catabolic processes, excretion, oxidation reduction, ion transport and response to chemical stimulus, while simultaneously upregulated genes were associated with immune and inflammatory responses, response to hypoxia, stress, wounding, vasculature development and cell activation. Furthermore, genome-wide DNA methylation analysis of 317 TCGA ccRCC/adjacent non-tumour renal tissue pairs indicated that deregulation of approximately 7% of genes could be explained by epigenetic changes. Finally, survival analysis conducted on 89 K2 and 464 TCGA cases identified 8 genes associated with differential prognostic outcomes. In conclusion, a large proportion of ccRCC molecular characteristics were common to the two populations and several may have clinical implications when validated further through large clinical cohorts.

Biomarker Discovery in Non–Small Cell Lung Cancer: Integrating Gene Expression Profiling, Meta-analysis, and Tissue Microarray Validation

Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multigene signatures in clinical practice is unclear, and the biologic importance of individual genes is difficult to assess, as the published signatures virtually do not overlap. Here, we describe a novel single institute cohort, including 196 non-small lung cancers (NSCLC) with clinical information and long-term follow-up. Gene expression array data were used as a training set to screen for single genes with prognostic impact. The top 450 probe sets identified using a univariate Cox regression model (significance level P < 0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n = 860). The meta-analysis revealed 14 genes that were significantly associated with survival (P < 0.001) with a false discovery rate <1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on tissue microarrays from 2 independent NSCLC cohorts, altogether including 617 NSCLC samples. Low CADM1 protein expression was significantly associated with shorter survival, with particular influence in the adenocarcinoma patient subgroup. Using a novel NSCLC cohort together with a meta-analysis validation approach, we have identified a set of single genes with independent prognostic impact. One of these genes, CADM1, was further established as an immunohistochemical marker with a potential application in clinical diagnostics.

A novel strategy for integrating gene expression profiles derived from multiple publicly available datasets highlights novel hub genes and pathways in mouse models of allergic asthma

Event Abstract Back to Event A novel strategy for integrating gene expression profiles derived from multiple publicly available datasets highlights novel hub genes and pathways in mouse models of allergic asthma Michela Riba1*, Jose M. Garcia Manteiga1, Berislav Bosniak2, Michelle Epstein2 and Elia Stupka1 1 San Raffaele Scientific Institute, Center for Translational Genomics and Bioinformatics, Italy 2 University of Vienna, Department of Dermatology, Austria There are 30 studies of gene expression profiling of lungs from mice with allergic asthma, which shed new light on the molecular mechanisms of disease, but the experimental designs between them differ. Nevertheless, relevant genes and pathways underlying disease pathogenesis may emerge from the integration of these datasets. From 12 publically available datasets, we selected 6 for meta-­analysis on the basis of the microarray platform and in vivo experimental protocol. Our strategy was to combine a top down pathway­centered and a bottom up gene­centered analyses. The first approach consisted of selecting asthma­specific pathways from a combination of ‘enriched biological terms’ in each individual study. Whereas, secondly, we combined differentially expressed genes from individual datasets to create a gene list. From 22,690 genes and 131 samples, we obtained a core-­network of more than 300 interconnected genes unraveling known (STAT1, JAK2) and unknown hubs (PU.1, CDK1). Additionally, we successfully selected known and unknown disease-­related pathways, such as T­cell activation during an immune response, B­cell mediated immunity, phagocytosis and complement activation. The power of this strategy is the possibility of uncovering key molecules underlying disease pathogenesis utilizing datasets derived from disparate experiments Keywords: Asthma, Gene Expression, Network analysis, Meta-analysis, pathway analysis Conference: 15th International Congress of Immunology (ICI), Milan, Italy, 22 Aug - 27 Aug, 2013. Presentation Type: Abstract Topic: Immune-mediated disease pathogenesis Citation: Riba M, Garcia Manteiga JM, Bosniak B, Epstein M and Stupka E (2013). A novel strategy for integrating gene expression profiles derived from multiple publicly available datasets highlights novel hub genes and pathways in mouse models of allergic asthma. Front. Immunol. Conference Abstract: 15th International Congress of Immunology (ICI). doi: 10.3389/conf.fimmu.2013.02.00881 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 20 Jun 2013; Published Online: 22 Aug 2013. * Correspondence: Dr. Michela Riba, San Raffaele Scientific Institute, Center for Translational Genomics and Bioinformatics, Milano, Italy, riba.michela@hsr.it Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Michela Riba Jose M Garcia Manteiga Berislav Bosniak Michelle Epstein Elia Stupka Google Michela Riba Jose M Garcia Manteiga Berislav Bosniak Michelle Epstein Elia Stupka Google Scholar Michela Riba Jose M Garcia Manteiga Berislav Bosniak Michelle Epstein Elia Stupka PubMed Michela Riba Jose M Garcia Manteiga Berislav Bosniak Michelle Epstein Elia Stupka Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Comparative motif discovery combined with comparative transcriptomics yields accurate targetome and enhancer predictions

The identification of transcription factor binding sites, enhancers, and transcriptional target genes often relies on the integration of gene expression profiling and computational cis-regulatory sequence analysis. Methods for the prediction of cis-regulatory elements can take advantage of comparative genomics to increase signal-to-noise levels. However, gene expression data are usually derived from only one species. Here we investigate tissue-specific cross-species gene expression profiling by high-throughput sequencing, combined with cross-species motif discovery. First, we compared different methods for expression level quantification and cross-species integration using Tag-seq data. Using the optimal pipeline, we derived a set of genes with conserved expression during retinal determination across Drosophila melanogaster, Drosophila yakuba, and Drosophila virilis. These genes are enriched for binding sites of eye-related transcription factors including the zinc-finger Glass, a master regulator of photoreceptor differentiation. Validation of predicted Glass targets using RNA-seq in homozygous glass mutants confirms that the majority of our predictions are expressed downstream from Glass. Finally, we tested nine candidate enhancers by in vivo reporter assays and found eight of them to drive GFP in the eye disc, of which seven colocalize with the Glass protein, namely, scrt, chp, dpr10, CG6329, retn, Lim3, and dmrt99B. In conclusion, we show for the first time the combined use of cross-species expression profiling with cross-species motif discovery as a method to define a core developmental program, and we augment the candidate Glass targetome from a single known target gene, lozenge, to at least 62 conserved transcriptional targets.

Abstract 88: Systems Biology Approaches to Exploring Molecular Mechanisms Underlying Blood Pressure Regulation

Blood pressure (BP) is a complex trait that is influenced by both genetic and environment factors. Recent genome-wide association studies (GWAS) have identified about 30 genetic loci that are associated with systolic and diastolic blood pressure (SBP, DBP) and hypertension. However, the molecular mechanisms underlying the GWAS associations remain unclear. We hypothesize that BP-associated genetic variants with both strong and subtle effects drive shifts in gene subnetworks that in turn affect BP. We surveyed BP-associated molecular interactions in Framingham offspring cohort participants (n=2461; 55% women; age range 40-92 yrs.) by integrating gene expression profiles, expression-associated SNPs (eSNPs), and BP GWAS with network approaches. Peripheral whole blood samples were collected and large-scale transcriptomic microarray analysis was performed on all available participants who attended a clinic visit in 2005-2008. Based on pedigrees the samples were split into a discovery set (n=1421) and a replication set (n=820). The expression levels of 661 genes (FDR &lt; 20%) and the eigengenes of 3 coexpression modules (CoEMs), which were found to be significantly correlated with SBP or DBP in the discovery set, were tested in the replication set. Seventy-two genes were replicated at FDR &lt; 20%. The 72-gene BP-correlated signature and 2 of the three CoEMs were found to be significantly enriched for eSNPs with low p value associations with BP in the International Consortium of Blood Pressure (ICBP) GWAS at p&lt;0.001, suggesting that these gene sets play causal roles in BP regulation. The putative causal BP gene sets were in turn integrated with tissue-specific Bayesian networks and a protein-protein interaction network to identify central network nodes (key drivers or KDs) that drive these BP-related gene sets. One hundred and ten KDs including TSPAN2, ECT2 and KCNK3 were identified. The BP causal gene sets and their KDs are enriched for BP-related biological processes including ion transport, nervous system development, and blood coagulation. Our systems biology analysis not only predicts novel BP risk genes, but also uncovers a network structure that entails the molecular interactions among BP and cardiovascular risk genes in a tissue-specific fashion.

Integrative Gene Expression Profiling Reveals G6PD-Mediated Resistance to RNA-Directed Nucleoside Analogues in B-Cell Neoplasms

The nucleoside analogues 8-amino-adenosine and 8-chloro-adenosine have been investigated in the context of B-lineage lymphoid malignancies by our laboratories due to the selective cytotoxicity they exhibit toward multiple myeloma (MM), chronic lymphocytic leukemia (CLL), and mantle cell lymphoma (MCL) cell lines and primary cells. Encouraging pharmacokinetic and pharmacodynamic properties of 8-chloro-adenosine being documented in an ongoing Phase I trial in CLL provide additional impetus for the study of these promising drugs. In order to foster a deeper understanding of the commonalities between their mechanisms of action and gain insight into specific patient cohorts positioned to achieve maximal benefit from treatment, we devised a novel two-tiered chemoinformatic screen to identify molecular determinants of responsiveness to these compounds. This screen entailed: 1) the elucidation of gene expression patterns highly associated with the anti-tumor activity of 8-chloro-adenosine in the NCI-60 cell line panel, 2) characterization of altered transcript abundances between paired MM and MCL cell lines exhibiting differential susceptibility to 8-amino-adenosine, and 3) integration of the resulting datasets. This approach generated a signature of seven unique genes including G6PD which encodes the rate-determining enzyme of the pentose phosphate pathway (PPP), glucose-6-phosphate dehydrogenase. Bioinformatic analysis of primary cell gene expression data demonstrated that G6PD is frequently overexpressed in MM and CLL, highlighting the potential clinical implications of this finding. Utilizing the paired sensitive and resistant MM and MCL cell lines as a model system, we go on to demonstrate through loss-of-function and gain-of-function studies that elevated G6PD expression is necessary to maintain resistance to 8-amino- and 8-chloro-adenosine but insufficient to induce de novo resistance in sensitive cells. Taken together, these results indicate that G6PD activity antagonizes the cytotoxicity of 8-substituted adenosine analogues and suggests that administration of these agents to patients with B-cell malignancies exhibiting normal levels of G6PD expression may be particularly efficacious.

Integrating Genomic Analysis with the Genetic Basis of Gene Expression: Preliminary Evidence of the Identification of Causal Genes for Cardiovascular and Metabolic Traits Related to Nutrition in Mexicans

AbstractWhole-transcriptome expression profiling provides novel phenotypes for analysis of complex traits. Gene expression measurements reflect quantitative variation in transcript-specific messenger RNA levels and represent phenotypes lying close to the action of genes. Understanding the genetic basis of gene expression will provide insight into the processes that connect genotype to clinically significant traits representing a central tenet of system biology. Synchronous in vivo expression profiles of lymphocytes, muscle, and subcutaneous fat were obtained from healthy Mexican men. Most genes were expressed at detectable levels in multiple tissues, and RNA levels were correlated between tissue types. A subset of transcripts with high reliability of expression across tissues (estimated by intraclass correlation coefficients) was enriched for cis-regulated genes, suggesting that proximal sequence variants may influence expression similarly in different cellular environments. This integrative global gene expression profiling approach is proving extremely useful for identifying genes and pathways that contribute to complex clinical traits. Clearly, the coincidence of clinical trait quantitative trait loci and expression quantitative trait loci can help in the prioritization of positional candidate genes. Such data will be crucial for the formal integration of positional and transcriptomic information characterized as genetical genomics.

Identification of retinoblastoma related genes with shortest path in a protein–protein interaction network

This paper presents a new method for identifying retinoblastoma related genes by integrating gene expression profile and shortest path in a functional linkage graph. With the existing protein-protein interaction data from STRING, a weighted functional linkage graph is constructed. 119 consistently differentially expressed genes between retinoblastoma and normal retina were obtained from the overlap of two gene expression studies of retinoblastoma. Then the shortest paths between each pair of these 119 genes were determined with Dijkstra's algorithm. Finally, all the genes present on the shortest paths were extracted and ranked according to their betweenness and the 119 shortest genes with a betweenness greater than 100 and with a p-value less than 0.05 were selected for further analysis. We also identified 53 retinoblastoma related miRNAs from published miRNA array data and most of the 238 (119 consistently differentially expressed genes and 119 shortest path genes) retinoblastoma genes were shown to be target genes of these 53 miRNAs. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network included more cancer genes than did the genes identified from the gene expression profiles alone. In addition, these genes also had greater functional similarity to the reported cancer genes than did the genes identified from the gene expression profiles alone. This study shows promising results and proves the efficiency of the proposed methods.

A network perspective on metabolic inconsistency

BackgroundIntegrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation.ResultsHere, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns.ConclusionsThe methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical function. Finally, we demonstrate how physiologically relevant insights into the structure and dynamics of metabolic networks can be obtained using this novel approach.

Overexpression of the oncostatin M receptor in cervical squamous cell carcinoma cells is associated with a pro‐angiogenic phenotype and increased cell motility and invasiveness

Oncostatin M receptor (OSMR) shows frequent copy number gain and overexpression in advanced cervical squamous cell carcinoma (SCC). We used cell-based in vitro assays, RNA interference, and integrative gene expression profiling to investigate the functional significance of this observation. CaSki and SW756 were selected as representative cervical SCC cells that overexpressed OSMR, and ME180 and MS751 as cells that did not. The STAT-dependent pro-angiogenic factors VEGF-A and ID1 were rapidly induced by OSM in CaSki/SW756 but not in ME180/MS751. However, rapid induction did occur in MS751 following forced OSMR overexpression, while depleting OSMR in CaSki abrogated VEGF-A expression. Conditioned medium from both CaSki and SW756 stimulated endothelial tube formation in vitro, effects that were inhibited by depleting OSMR in the SCC cells. For both CaSki and SW756, migration in a wound healing assay and invasion through Matrigel were stimulated by OSM and consistently inhibited by OSMR depletion. The phenotype was rescued by transfection with OSMR containing a silent mutation that provided specific siRNA resistance. Overall, there was a positive correlation between OSMR levels and invasiveness. We used gene expression profiling to identify genes induced by OSM in CaSki/SW756 but not in ME180/MS751. The most prominent gene ontology category groups for the differentially expressed genes were cell motility/invasion, angiogenesis, signal transduction, and apoptosis. We also profiled 23 cervical SCC samples, identifying genes that were differentially expressed in cases with OSMR overexpression versus those without. Integration of the datasets identified 15 genes that showed consistent differential expression in association with OSMR levels in vitro and in vivo. We conclude that OSMR overexpression in cervical SCC cells provides increased sensitivity to OSM, which induces pro-malignant changes. OSMR is a potential prognostic and therapeutic target in cervical SCC. The genes that mediate OSM:OSMR effects will be valuable indicators of the effectiveness of antibody blockade in pre-clinical systems.

Integrated analyses of copy number variations and gene expression in lung adenocarcinoma.

Numerous efforts have been made to elucidate the etiology and improve the treatment of lung cancer, but the overall five-year survival rate is still only 15%. Identification of prognostic biomarkers for lung cancer using gene expression microarrays poses a major challenge in that very few overlapping genes have been reported among different studies. To address this issue, we have performed concurrent genome-wide analyses of copy number variation and gene expression to identify genes reproducibly associated with tumorigenesis and survival in non-smoking female lung adenocarcinoma. The genomic landscape of frequent copy number variable regions (CNVRs) in at least 30% of samples was revealed, and their aberration patterns were highly similar to several studies reported previously. Further statistical analysis for genes located in the CNVRs identified 475 genes differentially expressed between tumor and normal tissues (p<10−5). We demonstrated the reproducibility of these genes in another lung cancer study (p = 0.0034, Fisher's exact test), and showed the concordance between copy number variations and gene expression changes by elevated Pearson correlation coefficients. Pathway analysis revealed two major dysregulated functions in lung tumorigenesis: survival regulation via AKT signaling and cytoskeleton reorganization. Further validation of these enriched pathways using three independent cohorts demonstrated effective prediction of survival. In conclusion, by integrating gene expression profiles and copy number variations, we identified genes/pathways that may serve as prognostic biomarkers for lung tumorigenesis.

Integrative network analysis identifies key genes and pathways in the progression of hepatitis C virus induced hepatocellular carcinoma

BackgroundIncidence of hepatitis C virus (HCV) induced hepatocellular carcinoma (HCC) has been increasing in the United States and Europe during recent years. Although HCV-associated HCC shares many pathological characteristics with other types of HCC, its molecular mechanisms of progression remain elusive.MethodsTo investigate the underlying pathology, we developed a systematic approach to identify deregulated biological networks in HCC by integrating gene expression profiles with high-throughput protein-protein interaction data. We examined five stages including normal (control) liver, cirrhotic liver, dysplasia, early HCC and advanced HCC.ResultsAmong the five consecutive pathological stages, we identified four networks including precancerous networks (Normal-Cirrhosis and Cirrhosis-Dysplasia) and cancerous networks (Dysplasia-Early HCC, Early-Advanced HCC). We found little overlap between precancerous and cancerous networks, opposite to a substantial overlap within precancerous or cancerous networks. We further found that the hub proteins interacted with HCV proteins, suggesting direct interventions of these networks by the virus. The functional annotation of each network demonstrates a high degree of consistency with current knowledge in HCC. By assembling these functions into a module map, we could depict the stepwise biological functions that are deregulated in HCV-induced hepatocarcinogenesis. Additionally, these networks enable us to identify important genes and pathways by developmental stage, such as LCK signalling pathways in cirrhosis, MMP genes and TIMP genes in dysplastic liver, and CDC2-mediated cell cycle signalling in early and advanced HCC. CDC2 (alternative symbol CDK1), a cell cycle regulatory gene, is particularly interesting due to its topological position in temporally deregulated networks.ConclusionsOur study uncovers a temporal spectrum of functional deregulation and prioritizes key genes and pathways in the progression of HCV induced HCC. These findings present a wealth of information for further investigation.

Identifying the key genes and pathways in the progression of hepatitis C virus induced hepatocellular carcinoma using a systems biology approach

Materials and methods We developed a systematic approach to identify deregulated biological networks in HCC by integrating gene expression profiles [1] with high-throughput proteinprotein interaction data [2]. Samples were grouped into five disease stages including normal, cirrhotic, dysplastic, early and advanced HCC. For each pair of consecutive stages, we compared gene expressions and then mapped these measures to the protein interaction network. Responsive subnetworks were then identified from these node weighted networks. The searching algorithm is adapted from a previous study [3], which expands the seed graphs under constrains of several parameters.

Use of bioinformatic SNP predictions in differentially expressed genes to find SNPs associated with Salmonella colonization in swine

Asymptomatic Salmonella-carrier pigs present a major problem in preharvest food safety, with a recent survey indicating >50% of swine herds in the United States have Salmonella-positive animals. Salmonella-carrier pigs serve as a reservoir for contamination of neighbouring pigs, abattoir pens and pork products. In addition, fresh produce as well as water can be contaminated with Salmonella from manure used as fertilizer. Control of Salmonella at the farm level could be through genetic improvement of porcine disease resistance, a potentially powerful method of addressing preharvest pork safety. In this research, we integrate gene expression profiling data and sequence alignment-based prediction of single nucleotide polymorphisms (SNPs) to successfully identify SNPs in functional candidate genes to test for the associations with swine response to Salmonella. A list of 2527 genes that were differentially regulated in porcine whole blood in response to infection with Salmonella enterica serovar Typhimurium were selected. In those genes, SNPs were predicted using ANEXdb alignments based on stringent clustering of all publically available porcine cDNA and expressed sequence tag (EST) sequences. A set of 30 mostly non-synonymous SNPs were selected for genotype analysis of four independent populations (n = 750) with Salmonella faecal shedding or tissue colonization phenotypes. Nine SNPs segregated with minor allele frequency ≥15% in at least two populations. Statistical analysis revealed SNPs associated with Salmonella shedding, such as haptoglobin (HP, p = 0.001, q = 0.01), neutrophil cytosolic factor 2 (NCF2 #2, p = 0.04, q = 0.21) and phosphogluconate dehydrogenase (p = 0.066, q = 0.21). These associations may be useful in identifying and selecting pigs with improved resistance to this bacterium.

Abstract 4914: A novel integrative network study identifies key genes and pathways in the progression of Hepatitis C virus induced hepatocellular carcinoma

Abstract Hepatocellular carcinoma (HCC) is the third most common cause of cancer mortality in the world. Incidence of hepatitis C virus (HCV) induced hepatocellular carcinoma (HCC) has been increasing in the United States and Europe during recent years. Although HCV-associated HCC shares many pathological characteristics with other types of HCC, the molecular mechanisms underlying its progression remain elusive. To investigate the underlying pathology, we developed a systematic approach to identify deregulated biological networks in HCC by integrating gene expression profiles with high-throughput protein-protein interaction data. This integrative approach to searching for deregulated networks during each pathological stage is different from typical search of common pathway resources like Gene Ontology or KEGG biochemical pathways. It has at least two advantages: 1) a high throughput protein interaction network covers more genes; and 2) the resulting network gives gene interaction information, which provides a simpler framework to explore the functional associations between genes and generate new biological hypotheses. By comparing sets of consecutive pathological stages, we identified four networks in five groups of samples corresponding to normal, cirrhosis, dysplasia, early HCC and advanced HCC. The functional annotation of each network demonstrates a high degree of consistency with current knowledge of HCC. By assembling these functions into a modular map, we were able to depict the stepwise pathways that are dysregulated in HCV-induced hepatocarcinogenesis. Additionally, these networks enable us to identify important genes and pathways by developmental stage, such as LCK signaling pathways in cirrhosis, MMP genes and TIMP genes in dysplastic liver, and CDC2-mediated cell cycle signaling in early and advanced HCC. CDC2 (alternative symbol CDK1), a regulatory gene in cell cycle control, is particularly interesting due to its topological position in temporally deregulated networks. Our study uncovers a temporal spectrum of functional deregulation, and prioritizes key genes and pathways in the progression of HCV induced HCC. These findings present a wealth of information for further investigation. 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 4914. doi:10.1158/1538-7445.AM2011-4914

Statistical Test of Expression Pattern (STEPath): a new strategy to integrate gene expression data with genomic information in individual and meta-analysis studies

BackgroundIn the last decades, microarray technology has spread, leading to a dramatic increase of publicly available datasets. The first statistical tools developed were focused on the identification of significant differentially expressed genes. Later, researchers moved toward the systematic integration of gene expression profiles with additional biological information, such as chromosomal location, ontological annotations or sequence features. The analysis of gene expression linked to physical location of genes on chromosomes allows the identification of transcriptionally imbalanced regions, while, Gene Set Analysis focuses on the detection of coordinated changes in transcriptional levels among sets of biologically related genes.In this field, meta-analysis offers the possibility to compare different studies, addressing the same biological question to fully exploit public gene expression datasets.ResultsWe describe STEPath, a method that starts from gene expression profiles and integrates the analysis of imbalanced region as an a priori step before performing gene set analysis. The application of STEPath in individual studies produced gene set scores weighted by chromosomal activation. As a final step, we propose a way to compare these scores across different studies (meta-analysis) on related biological issues. One complication with meta-analysis is batch effects, which occur because molecular measurements are affected by laboratory conditions, reagent lots and personnel differences. Major problems occur when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. We evaluated the power of combining chromosome mapping and gene set enrichment analysis, performing the analysis on a dataset of leukaemia (example of individual study) and on a dataset of skeletal muscle diseases (meta-analysis approach).In leukaemia, we identified the Hox gene set, a gene set closely related to the pathology that other algorithms of gene set analysis do not identify, while the meta-analysis approach on muscular disease discriminates between related pathologies and correlates similar ones from different studies.ConclusionsSTEPath is a new method that integrates gene expression profiles, genomic co-expressed regions and the information about the biological function of genes. The usage of the STEPath-computed gene set scores overcomes batch effects in the meta-analysis approaches allowing the direct comparison of different pathologies and different studies on a gene set activation level.

Sequencing the fungal tree of life

Sequencing the Fungal Tree of Life F. Martin 1 , D. Cullen 2 , D. Hibbett 3 , A. Pisabarro 4 , J. W. Spatafora 5 , S. E. Baker 6,7, I. V. Grigoriev 7 UMR INRA/UHP 1136, Interactions Arbres/Micro-Organismes, INRA-Nancy, 54280 Champenoux, France University of Wisconsin-Madison, Madison, WI, USA Clark University, Worcester, MA, USA Department of Agrarian Production, Public University of Navarre, 31006 Pamplona, Spain Dept. Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331 Chemical and Biological Process Development Group, Pacific Northwest National Laboratory, Richland, Washington, USA, 99352 US DOE Joint Genome Institute, Walnut Creek, California, USA March 2011 The work conducted by the U.S. Department of Energy Joint Genome Institute is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02- 05CH11231 DISCLAIMER This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or The Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or The Regents of the University of California.

Network Perspectives on HDM2 Inhibitor Chemotherapy Combinations

The discovery of small molecule inhibitors of HDM2-p53 interaction is considered one of the most significant therapeutic developments in the area p53 research. Intensive work on different classes of HDM2 inhibitors has proven their therapeutic utility as activators of p53 in multiple tumor models. Many laboratories have shown that HDM2 inhibitors can synergize with chemotherapeutic agents resulting in enhanced efficacy through both p53-dependent and independent mechanisms. In our hands HDM2 inhibitor and platinum drug combination showed remarkable antitumor activity that led tumor free survival in one of the most resistant and complex pancreatic xenograft models. Although antitumor efficacy of such combinations has been studied in detail, not much is known on the molecular mechanisms governing this synergy. This is partly due to complexity of multiple pathways modulated by p53 and HDM2. We are of the view that in order to decode this complexity, an integrated approach is needed that considers both HDM2 and p53 as components of a network and not in isolation. This review highlights recent advancements in our understanding of HDM2 inhibitor combination therapy based on network modeling and systems biology driven science. Our recent findings support such a network view as integrated gene expression profiling and pathway network modeling on MI-219-oxaliplatin treated cells revealed activation of multiple and closely knit biological networks. We anticipate that in the near future such network-centric approaches will benefit clinical development of HDM2 inhibitors for genetically predefined subsets of cancer patients and this will be a step towards personalized medicine.