Functional Gene Sets Research Articles

Human HLA-G+ extravillous trophoblasts: immune activating cells that interact with decidual leucocytes (MUC2P.921)

Abstract Invading HLA-G+ extravillous trophoblasts (EVT) are believed to play a key role in the prevention of a maternal immune attack on foreign fetal tissues. EVT are difficult to study due to their low frequency and lack of proliferative capacity. Here highly purified HLA-G+ EVT and HLA-G- villous trophoblast (VT) were isolated. Culture on fibronectin increased HLA-G expression on EVT but differentiation from VT into EVT was not observed. Moreover microarray analysis demonstrated that VT and EVT have more than 4200 differentially expressed genes. Functional gene set enrichment analysis (GSEA) revealed a striking immune activating potential for EVT that is absent in VT. Co-culture of HLA-G+ EVT with sample matched decidual NK, macrophages, CD4+ and CD8+ T cells were established and demonstrated interactions of all leukocyte types with EVT. Interaction of EVT with CD4+ T cells resulted in increased proportion of CD4+CD25hiFOXP3+ Tregs and increased FOXP3 protein level in these cells. However, EVT did not enhance cytokine secretion in dNK whereas stimulation of dNK using mitogens or classical NK targets confirmed the distinct cytokine profiles of dNK and pNK. Thus EVT are specialized cells with an immune activating profile whose properties are not imitated by HLA-G expressing cell lines. Careful validation of EVT-leukocyte interactions using primary HLA-G+ EVT needs to be carried out to understand the unique contribution of EVT to the decidual immune response in human pregnancy.

Identifications of genetic differences between metastatic and non-metastatic osteosarcoma samples based on bioinformatics analysis.

To investigate the differences in gene expression level between metastatic and non-metastatic osteosarcoma (OS) samples and the potential mechanism. Gene expression profile data GSE9508 were downloaded from Gene Expression Omnibus database to identify the differentially expressed genes (DEGs) between metastatic, non-metastatic OS samples, and normal control samples via SAM method. Function expression matrix of the DEGs was constructed by calculating the functional node scores based on the genes sets collected from the pathways recorded in MsigDB database. Next, t test was applied to screen the differentially expressed functional nodes between each two kinds of samples. Finally, we compared the significant genes between selected DEGs and genes in differentially expressed functional nodes. There were 79 up-regulated DEGs between non-metastatic OS and normal samples, 380 up-regulated and 134 down-regulated DEGs between the metastatic OS and normal samples, and 761 up-regulated plus 186 down-regulated DEGs between metastatic and non-metastatic OS samples. A total of 3846 functional gene sets were collected to form the function expression profile matrix. The numbers of differentially expressed functional nodes between non-metastatic OS and normal samples, metastatic OS and normal samples, and metastatic and non-metastatic OS samples were 8, 39, and 5, respectively. The gene level difference between metastatic and non-metastatic OS samples can be distinguished using bioinformatics analysis. TGFB1, LFT3, KDM1A, and KRAS genes have the potential to be used as biomarkers for OS; however, further analysis is needed to verify the current results.

Systematic computation with functional gene-sets among leukemic and hematopoietic stem cells reveals a favorable prognostic signature for acute myeloid leukemia

BackgroundGenes that regulate stem cell function are suspected to exert adverse effects on prognosis in malignancy. However, diverse cancer stem cell signatures are difficult for physicians to interpret and apply clinically. To connect the transcriptome and stem cell biology, with potential clinical applications, we propose a novel computational “gene-to-function, snapshot-to-dynamics, and biology-to-clinic” framework to uncover core functional gene-sets signatures. This framework incorporates three function-centric gene-set analysis strategies: a meta-analysis of both microarray and RNA-seq data, novel dynamic network mechanism (DNM) identification, and a personalized prognostic indicator analysis. This work uses complex disease acute myeloid leukemia (AML) as a research platform.ResultsWe introduced an adjustable “soft threshold” to a functional gene-set algorithm and found that two different analysis methods identified distinct gene-set signatures from the same samples. We identified a 30-gene cluster that characterizes leukemic stem cell (LSC)-depleted cells and a 25-gene cluster that characterizes LSC-enriched cells in parallel; both mark favorable-prognosis in AML. Genes within each signature significantly share common biological processes and/or molecular functions (empirical p = 6e-5 and 0.03 respectively). The 25-gene signature reflects the abnormal development of stem cells in AML, such as AURKA over-expression. We subsequently determined that the clinical relevance of both signatures is independent of known clinical risk classifications in 214 patients with cytogenetically normal AML. We successfully validated the prognosis of both signatures in two independent cohorts of 91 and 242 patients respectively (log-rank p < 0.0015 and 0.05; empirical p < 0.015 and 0.08).ConclusionThe proposed algorithms and computational framework will harness systems biology research because they efficiently translate gene-sets (rather than single genes) into biological discoveries about AML and other complex diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0510-7) contains supplementary material, which is available to authorized users.

A Genome-wide Screen Identifies PAPP-AA-Mediated IGFR Signaling as a Novel Regulator of Habituation Learning

Habituation represents a fundamental form of learning, yet the underlying molecular genetic mechanisms are not well defined. Here we report on a genome-wide genetic screen, coupled with whole-genome sequencing, that identified 14 zebrafish startle habituation mutants including mutants of the vertebrate-specific gene pregnancy-associated plasma protein-aa (pappaa). PAPP-AA encodes an extracellular metalloprotease known to increase IGF bioavailability, thereby enhancing IGF receptor signaling. We find that pappaa is expressed by startle circuit neurons, and expression of wild-type but not a metalloprotease-inactive version of pappaa restores habituation in pappaa mutants. Furthermore, acutely inhibiting IGF1R function in wild-type reduces habituation, while activation of IGF1R downstream effectors in pappaa mutants restores habituation, demonstrating that pappaa promotes learning by acutely and locally increasing IGF bioavailability. In sum, our results define the first functional gene set for habituation learning in a vertebrate and identify PAPPAA-regulated IGF signaling as a novel mechanism regulating habituation learning.

Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome.

BackgroundTranscriptogram profiling is a method to present and analyze transcription data in a genome-wide scale that reduces noise and facilitates biological interpretation. An ordered gene list is produced, such that the probability that the genes are functionally associated exponentially decays with their distance on the list. This list presents a biological logic, evinced by the selective enrichment of successive intervals with Gene Ontology terms or KEGG pathways. Transcriptograms are expression profiles obtained by taking the average of gene expression over neighboring genes on this list. Transcriptograms enhance reproducibility and precision for expression measurements of functionally correlated gene sets.ResultsHere we present an ordering list for Homo sapiens and apply the transcriptogram profiling method to different datasets. We show that this method enhances experiment reproducibility and enhances signal. We applied the method to a diabetes study by Hwang and collaborators, which focused on expression differences between cybrids produced by the hybridization of mitochondria of diabetes mellitus donors with osteosarcoma cell lines, depleted of mitochondria. We found that the transcriptogram method revealed significant differential expression in gene sets linked to blood coagulation and wound healing pathways, and also to gene sets that do not represent any metabolic pathway or Gene Ontology term. These gene sets are connected to ECM-receptor interaction and secreted proteins.ConclusionThe transcriptogram profiling method provided an automatic way to define sets of genes with correlated expression, reduce noise in genome-wide transcription profiles, and enhance measure reproducibility and sensitivity. These advantages enabled biologic interpretation and pointed to differentially expressed gene sets in diabetes mellitus which were not previously defined.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1181) contains supplementary material, which is available to authorized users.

Transcriptional changes in African clawed frogs (Xenopus laevis) exposed to 17α-ethynylestradiol during early development.

Although the past two decades have witnessed a significant increase in the number of studies investigating effects of estrogenic chemicals on amphibians, to date little is known about specific molecular interactions of estrogens with the hypothalamus-pituitary-gonadal-hepatic axis in developing amphibians. Here, tissue-specific functional sets of genes, derived previously from studies of fishes exposed to endocrine active chemicals, were evaluated in Xenopus laevis exposed to 17α-ethynylestradiol (EE2) throughout their early development. Specifically, transcriptional responses of X. laevis exposed to 0.09, 0.84, or 8.81µg EE2/L were characterized during sexual differentiation [31day post hatch (dph)] and after completion of metamorphosis during the juvenile stage (89dph). While at 31dph there were no consistent effects of EE2 on abundances of transcripts,at 89dph X. laevis exhibited significant alterations in expression of genes involved in steroid signaling and metabolism, synthesis of cholesterol, and vitellogenesis. Specifically, expression of androgen receptor, farnesyl diphosphate synthase, estrogen receptor α, and vitellogenin A2 was significantly greater (>2-fold) than in controls while expression of farnesoid x-activated receptors α and β was significantly less (>2-fold reduction) than in controls. These results support the hypothesis that sets of genes derived from studies in teleost fish can be extrapolated for use in amphibians during the juvenile stage but not in sexually undifferentiated individuals. Furthermore, changes in abundances of transcripts of the here utilized sets of genes in animals sampled post sexual differentiation were in accordance with developmental effects and alterations of gonadal histology reported in a parallel study. This set of genes might be useful for predicting potential adverse outcomes at later life-stages.

Key regulators in prostate cancer identified by co-expression module analysis.

BackgroundProstate cancer (PrCa) is the most commonly diagnosed cancer in men in the world. Despite the fact that a large number of its genes have been investigated, its etiology remains poorly understood. Furthermore, most PrCa candidate genes have not been rigorously replicated, and the methods by which they biologically function in PrCa remain largely unknown.ResultsAiming to identify key players in the complex prostate cancer system, we reconstructed PrCa co-expressed modules within functional gene sets defined by the Gene Ontology (GO) annotation (biological process, GO_BP). We primarily identified 118 GO_BP terms that were well-preserved between two independent gene expression datasets and a consequent 55 conserved co-expression modules within them. Five modules were then found to be significantly enriched with PrCa candidate genes collected from expression Quantitative Trait Loci (eQTL), somatic copy number alteration (SCNA), somatic mutation data, or prognostic analyses. Specifically, two transcription factors (TFs) (NFAT and SP1) and three microRNAs (hsa-miR-19a, hsa-miR-15a, and hsa-miR-200b) regulating these five candidate modules were found to be critical to the development of PrCa.ConclusionsCollectively, our results indicated that genes with similar functions may play important roles in disease through co-expression, and modules with different functions could be regulated by similar genetic components, such as TFs and microRNAs, in a synergistic manner.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1015) contains supplementary material, which is available to authorized users.

An integrated cell purification and genomics strategy reveals multiple regulators of pancreas development.

The regulatory logic underlying global transcriptional programs controlling development of visceral organs like the pancreas remains undiscovered. Here, we profiled gene expression in 12 purified populations of fetal and adult pancreatic epithelial cells representing crucial progenitor cell subsets, and their endocrine or exocrine progeny. Using probabilistic models to decode the general programs organizing gene expression, we identified co-expressed gene sets in cell subsets that revealed patterns and processes governing progenitor cell development, lineage specification, and endocrine cell maturation. Purification of Neurog3 mutant cells and module network analysis linked established regulators such as Neurog3 to unrecognized gene targets and roles in pancreas development. Iterative module network analysis nominated and prioritized transcriptional regulators, including diabetes risk genes. Functional validation of a subset of candidate regulators with corresponding mutant mice revealed that the transcription factors Etv1, Prdm16, Runx1t1 and Bcl11a are essential for pancreas development. Our integrated approach provides a unique framework for identifying regulatory genes and functional gene sets underlying pancreas development and associated diseases such as diabetes mellitus.

Niche and host-associated functional signatures of the root surface microbiome.

Plant microbiomes are critical to host adaptation and impact plant productivity and health. Root-associated microbiomes vary by soil and host genotype, but the contribution of these factors to community structure and metabolic potential has not been fully addressed. Here we characterize root microbial communities of two disparate agricultural crops grown in the same natural soil in a controlled and replicated experimental system. Metagenomic (genetic potential) analysis identifies a core set of functional genes associated with root colonization in both plant hosts, and metatranscriptomic (functional expression) analysis revealed that most genes enriched in the root zones are expressed. Root colonization requires multiple functional capabilities, and these capabilities are enriched at the community level. Differences between the root-associated microbial communities from different plants are observed at the genus or species level, and are related to root-zone environmental factors.

Gene Set- and Pathway- Centered Knowledge Discovery Assigns Transcriptional Activation Patterns in Brain, Blood, and Colon Cancer

Genome-wide ‘omics'-assays provide a comprehensive view on the molecular landscapes of healthy and diseased cells. Bioinformatics traditionally pursues a ‘gene-centered' view by extracting lists of genes differentially expressed or methylated between healthy and diseased states. Biological knowledge mining is then performed by applying gene set techniques using libraries of functional gene sets obtained from independent studies. This analysis strategy neglects two facts: (i) that different disease states can be characterized by a series of functional modules of co-regulated genes and (ii) that the topology of the underlying regulatory networks can induce complex expression patterns that require analysis methods beyond traditional genes set techniques. The authors here provide a knowledge discovery method that overcomes these shortcomings. It combines machine learning using self-organizing maps with pathway flow analysis. It extracts and visualizes regulatory modes from molecular omics data, maps them onto selected pathways and estimates the impact of pathway-activity changes. The authors illustrate the performance of the gene set and pathway signal flow methods using expression data of oncogenic pathway activation experiments and of patient data on glioma, B-cell lymphoma and colorectal cancer.

First Gene Expression Profiling of the Chronic Transplant Arteriopathy On FFPE Renal Biopsies.

Background: Chronic rejection shows varying combination of lesions such as transplant glomerulopathy, interstitial fibrosis/tubular atrophy and chronic transplant arteriopathy (CTA) which is characterized by larger arteries with intimal fibrosis, thickening of intima, invasion of proliferated smooth muscle cells and infiltrates of mononuclear cells. Several independent coexisting factors contribute to the characteristic morphological features of CTA which are the final event of different non-immunological and immunological pathways. Since smooth muscle cells respond to growth factors and vasoactive peptides we applied transcriptomics on archival formalin-fixed, paraffin-embedded (FFPE) renal tissue of patient with CTA for searching genes responsible for the production of humoral factors. Materials and Methods: Total RNA from 21 CTA biopsies was processed using cDNA-mediated Annealing, Selection, extension, and Ligation (DASL) Assay (Illumina). Fifty-two renal tissue samples obtained from cadaveric donors were used as controls. Results: Using fold change ≥2 and a false discovery rate<0.05, we identify 191 differentially modulated genes in CTA respect to cadaveric donors. Principal Component Analysis 3-D analysis showed that the two groups had significant differences in gene expression distribution indicating two transcriptionally distant populations. A Gene Set Enrichment Analysis approach showed functional gene set differentially expressed such as allograft rejection, and graft versus host disease. In particular, we focus on 9 modulated genes belonging to the leukocyte endothelial transmigration pathway, 16 up-regulated genes belonging to cell adhesion molecules pathway and 16 modulated genes belonging to natural killer mediated cytotoxicity, that were modulated more than 1.5 fold. Discussion and Conclusions: This study shows, for the first time, that a characteristic gene expression profile occurs in CTA. The in vivo and in vitro validation studies of some identified genes may help in evolving strategies for diagnosis and treatment targeted to specific mechanisms.

Integrated pathway-based approach identifies association between genomic regions at CTCF and CACNB2 and schizophrenia.

In the present study, an integrated hierarchical approach was applied to: (1) identify pathways associated with susceptibility to schizophrenia; (2) detect genes that may be potentially affected in these pathways since they contain an associated polymorphism; and (3) annotate the functional consequences of such single-nucleotide polymorphisms (SNPs) in the affected genes or their regulatory regions. The Global Test was applied to detect schizophrenia-associated pathways using discovery and replication datasets comprising 5,040 and 5,082 individuals of European ancestry, respectively. Information concerning functional gene-sets was retrieved from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and the Molecular Signatures Database. Fourteen of the gene-sets or pathways identified in the discovery dataset were confirmed in the replication dataset. These include functional processes involved in transcriptional regulation and gene expression, synapse organization, cell adhesion, and apoptosis. For two genes, i.e. CTCF and CACNB2, evidence for association with schizophrenia was available (at the gene-level) in both the discovery study and published data from the Psychiatric Genomics Consortium schizophrenia study. Furthermore, these genes mapped to four of the 14 presently identified pathways. Several of the SNPs assigned to CTCF and CACNB2 have potential functional consequences, and a gene in close proximity to CACNB2, i.e. ARL5B, was identified as a potential gene of interest. Application of the present hierarchical approach thus allowed: (1) identification of novel biological gene-sets or pathways with potential involvement in the etiology of schizophrenia, as well as replication of these findings in an independent cohort; (2) detection of genes of interest for future follow-up studies; and (3) the highlighting of novel genes in previously reported candidate regions for schizophrenia.

Complete Genome Sequence of Paenibacillus polymyxa SQR-21, a Plant Growth-Promoting Rhizobacterium with Antifungal Activity and Rhizosphere Colonization Ability.

Here we report the complete genome sequence of a plant growth-promoting rhizobacterium (PGPR), Paenibacillus polymyxa SQR-21, which consists of one circular chromosome of 5,828,438 bp with 5,024 coding sequences (CDS). The data presented highlight multiple sets of functional genes associated with its plant-beneficial characteristics.

DFLAT: functional annotation for human development

BackgroundRecent increases in genomic studies of the developing human fetus and neonate have led to a need for widespread characterization of the functional roles of genes at different developmental stages. The Gene Ontology (GO), a valuable and widely-used resource for characterizing gene function, offers perhaps the most suitable functional annotation system for this purpose. However, due in part to the difficulty of studying molecular genetic effects in humans, even the current collection of comprehensive GO annotations for human genes and gene products often lacks adequate developmental context for scientists wishing to study gene function in the human fetus.DescriptionThe Developmental FunctionaL Annotation at Tufts (DFLAT) project aims to improve the quality of analyses of fetal gene expression and regulation by curating human fetal gene functions using both manual and semi-automated GO procedures. Eligible annotations are then contributed to the GO database and included in GO releases of human data. DFLAT has produced a considerable body of functional annotation that we demonstrate provides valuable information about developmental genomics. A collection of gene sets (genes implicated in the same function or biological process), made by combining existing GO annotations with the 13,344 new DFLAT annotations, is available for use in novel analyses. Gene set analyses of expression in several data sets, including amniotic fluid RNA from fetuses with trisomies 21 and 18, umbilical cord blood, and blood from newborns with bronchopulmonary dysplasia, were conducted both with and without the DFLAT annotation.ConclusionsFunctional analysis of expression data using the DFLAT annotation increases the number of implicated gene sets, reflecting the DFLAT’s improved representation of current knowledge. Blinded literature review supports the validity of newly significant findings obtained with the DFLAT annotations. Newly implicated significant gene sets also suggest specific hypotheses for future research. Overall, the DFLAT project contributes new functional annotation and gene sets likely to enhance our ability to interpret genomic studies of human fetal and neonatal development.

Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

Liver transcriptome analysis of Atlantic cod (Gadus morhua) exposed to PCB 153 indicates effects on cell cycle regulation and lipid metabolism

BackgroundPolychlorinated biphenyls (PCBs) are persistent organic pollutants (POPs) with harmful effects in animals and humans. Although PCB 153 is one of the most abundant among PCBs detected in animal tissues, its mechanism of toxicity is not well understood. Only few studies have been conducted to explore genes and pathways affected by PCB 153 by using high throughput transcriptomics approaches. To obtain better insights into toxicity mechanisms, we treated juvenile Atlantic cod (Gadus morhua) with PCB 153 (0.5, 2 and 8 mg/kg body weight) for 2 weeks and performed gene expression analysis in the liver using oligonucleotide arrays.ResultsWhole-genome gene expression analysis detected about 160 differentially regulated genes. Functional enrichment, interactome, network and gene set enrichment analysis of the differentially regulated genes suggested that pathways associated with cell cycle, lipid metabolism, immune response, apoptosis and stress response were among the top significantly enriched. Particularly, genes coding for proteins in DNA replication/cell cycle pathways and enzymes of lipid biosynthesis were up-regulated suggesting increased cell proliferation and lipogenesis, respectively.ConclusionsPCB 153 appears to activate cell proliferation and lipogenic genes in cod liver. Transcriptional up-regulation of marker genes for lipid biosynthesis resembles lipogenic effects previously reported for persistent organic pollutants (POPs) and other environmental chemicals. Our results provide new insights into mechanisms of PCB 153 induced toxicity.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-481) contains supplementary material, which is available to authorized users.

A Comparison of Gene Set Analysis Methods in Terms of Sensitivity, Prioritization and Specificity

Identification of functional sets of genes associated with conditions of interest from omics data was first reported in 1999, and since, a plethora of enrichment methods were published for systematic analysis of gene sets collections including Gene Ontology and biological pathways. Despite their widespread usage in reducing the complexity of omics experiment results, their performance is poorly understood. Leveraging the existence of disease specific gene sets in KEGG and Metacore® databases, we compared the performance of sixteen methods under relaxed assumptions while using 42 real datasets (over 1,400 samples). Most of the methods ranked high the gene sets designed for specific diseases whenever samples from affected individuals were compared against controls via microarrays. The top methods for gene set prioritization were different from the top ones in terms of sensitivity, and four of the sixteen methods had large false positives rates assessed by permuting the phenotype of the samples. The best overall methods among those that generated reasonably low false positive rates, when permuting phenotypes, were PLAGE, GLOBALTEST, and PADOG. The best method in the category that generated higher than expected false positives was MRGSE.

Specific glial functions contribute to schizophrenia susceptibility.

Schizophrenia is a highly polygenic brain disorder. The main hypothesis for disease etiology in schizophrenia primarily focuses on the role of dysfunctional synaptic transmission. Previous studies have therefore directed their investigations toward the role of neuronal dysfunction. However, recent studies have shown that apart from neurons, glial cells also play a major role in synaptic transmission. Therefore, we investigated the potential causal involvement of the 3 principle glial cell lineages in risk to schizophrenia. We performed a functional gene set analysis to test for the combined effects of genetic variants in glial type-specific genes for association with schizophrenia. We used genome-wide association data from the largest schizophrenia sample to date, including 13 689 cases and 18 226 healthy controls. Our results show that astrocyte and oligodendrocyte gene sets, but not microglia gene sets, are associated with an increased risk for schizophrenia. The astrocyte and oligodendrocyte findings are related to astrocyte signaling at the synapse, myelin membrane integrity, glial development, and epigenetic control. Together, these results show that genetic alterations underlying specific glial cell type functions increase susceptibility to schizophrenia and provide evidence that the neuronal hypothesis of schizophrenia should be extended to include the role of glia.

Voting-Based Cancer Module Identification by Combining Topological and Data-Driven Properties

Recently, computational approaches integrating copy number aberrations (CNAs) and gene expression (GE) have been extensively studied to identify cancer-related genes and pathways. In this work, we integrate these two data sets with protein-protein interaction (PPI) information to find cancer-related functional modules. To integrate CNA and GE data, we first built a gene-gene relationship network from a set of seed genes by enumerating all types of pairwise correlations, e.g. GE-GE, CNA-GE, and CNA-CNA, over multiple patients. Next, we propose a voting-based cancer module identification algorithm by combining topological and data-driven properties (VToD algorithm) by using the gene-gene relationship network as a source of data-driven information, and the PPI data as topological information. We applied the VToD algorithm to 266 glioblastoma multiforme (GBM) and 96 ovarian carcinoma (OVC) samples that have both expression and copy number measurements, and identified 22 GBM modules and 23 OVC modules. Among 22 GBM modules, 15, 12, and 20 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Among 23 OVC modules, 19, 18, and 23 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Similarly, we also observed that 9 and 2 GBM modules and 15 and 18 OVC modules were enriched with cancer gene census (CGC) and specific cancer driver genes, respectively. Our proposed module-detection algorithm significantly outperformed other existing methods in terms of both functional and cancer gene set enrichments. Most of the cancer-related pathways from both cancer data sets found in our algorithm contained more than two types of gene-gene relationships, showing strong positive correlations between the number of different types of relationship and CGC enrichment -values (0.64 for GBM and 0.49 for OVC). This study suggests that identified modules containing both expression changes and CNAs can explain cancer-related activities with greater insights.

Molecular analysis of human HLA-G+ trophoblasts reveals their strong immune regulatory potential (P2172)

Abstract INTRODUCTION: During pregnancy HLA-G+ extravillous trophoblasts (EVTs) play a key role in the prevention of a maternal immune attack to allogeneic fetal tissue. Human EVTs are extremely difficult to study due to their low frequency and lack of proliferative capacity. In this study we obtained robust villous trophoblast (VT) and EVT preparations and performed microarray analysis on RNA isolates. RESULTS: Comparison of the expression profiles revealed &amp;gt;4000 differentially expressed genes based on a 2-fold difference. To identify functional differences a Gene Set Enrichment Analysis (GSEA) was performed. In EVTs, 14 functional gene sets out of the 20 most significantly enriched gene sets were associated with direct immune activation. The core genes of these immune pathways include cytokines (e.g. EBi3, TGFβ and IL8) and cell surface molecules (e.g. B7-H3 and CRTAM) which can all directly influence lymphocyte binding and activation. To evaluate the implications of this immune activating profile, co-cultures of EVT with decidual NK (dNK) cells and decidual T (dT) cells were established. Low percentages of dNK and dT cells form immune synapses with HLA-G+ EVTs. Preliminary analysis of dNK-EVT and dT-EVT co-cultures demonstrates that EVTs influence the expression of activation markers on decidual lymphocytes. CONCLUSION: Our data supports the hypothesis that proper immune activation, rather than general immune suppression, facilitates the establishment of fetal-maternal tolerance.