An Integrative Analysis to Identify Driver Genes in Esophageal Squamous Cell Carcinoma

BackgroundFew driver genes have been well established in esophageal squamous cell carcinoma (ESCC). Identification of the genomic aberrations that contribute to changes in gene expression profiles can be used to predict driver genes.MethodsWe searched for driver genes in ESCC by integrative analysis of gene expression microarray profiles and copy number data. To narrow down candidate genes, we performed survival analysis on expression data and tested the genetic vulnerability of each genes using public RNAi screening data. We confirmed the results by performing RNAi experiments and evaluating the clinical relevance of candidate genes in an independent ESCC cohort.ResultsWe found 10 significantly recurrent copy number alterations accompanying gene expression changes, including loci 11q13.2, 7p11.2, 3q26.33, and 17q12, which harbored CCND1, EGFR, SOX2, and ERBB2, respectively. Analysis of survival data and RNAi screening data suggested that GRB7, located on 17q12, was a driver gene in ESCC. In ESCC cell lines harboring 17q12 amplification, knockdown of GRB7 reduced the proliferation, migration, and invasion capacities of cells. Moreover, siRNA targeting GRB7 had a synergistic inhibitory effect when combined with trastuzumab, an anti-ERBB2 antibody. Survival analysis of the independent cohort also showed that high GRB7 expression was associated with poor prognosis in ESCC.ConclusionOur integrative analysis provided important insights into ESCC pathogenesis. We identified GRB7 as a novel ESCC driver gene and potential new therapeutic target.     

Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes

De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF) mutations falling in the same gene, and found that de novo LoF mutations occurred at a twofold higher rate than expected by chance. However successful these studies were, they used only a small fraction of the data, excluding other types of de novo mutations and inherited rare variants. Moreover, such analyses cannot readily incorporate data from case-control studies. An important research challenge in gene discovery, therefore, is to develop statistical methods that accommodate a broader class of rare variation. We develop methods that can incorporate WES data regarding de novo mutations, inherited variants present, and variants identified within cases and controls. TADA, for Transmission And De novo Association, integrates these data by a gene-based likelihood model involving parameters for allele frequencies and gene-specific penetrances. Inference is based on a Hierarchical Bayes strategy that borrows information across all genes to infer parameters that would be difficult to estimate for individual genes. In addition to theoretical development we validated TADA using realistic simulations mimicking rare, large-effect mutations affecting risk for ASD and show it has dramatically better power than other common methods of analysis. Thus TADA''s integration of various kinds of WES data can be a highly effective means of identifying novel risk genes. Indeed, application of TADA to WES data from subjects with ASD and their families, as well as from a study of ASD subjects and controls, revealed several novel and promising ASD candidate genes with strong statistical support.     

An Efficient Weighted Graph Strategy to Identify Differentiation Associated Genes in Embryonic Stem Cells

In the past few decades, embryonic stem cells (ESCs) were of great interest as a model system for studying early developmental processes and because of their potential therapeutic applications in regenerative medicine. However, the underlying mechanisms of ESC differentiation remain unclear, which limits our exploration of the therapeutic potential of stem cells. Fortunately, the increasing quantity and diversity of biological datasets can provide us with opportunities to explore the biological secrets. However, taking advantage of diverse biological information to facilitate the advancement of ESC research still remains a challenge. Here, we propose a scalable, efficient and flexible function prediction framework that integrates diverse biological information using a simple weighted strategy, for uncovering the genetic determinants of mouse ESC differentiation. The advantage of this approach is that it can make predictions based on dynamic information fusion, owing to the simple weighted strategy. With this approach, we identified 30 genes that had been reported to be associated with differentiation of stem cells, which we regard to be associated with differentiation or pluripotency in embryonic stem cells. We also predicted 70 genes as candidates for contributing to differentiation, which requires further confirmation. As a whole, our results showed that this strategy could be applied as a useful tool for ESC research.     

pDAWG: An Integrated Database for Plant Cell Wall Genes

We have recently developed a database, pDAWG, focused on information related to plant cell walls. Currently, pDAWG contains seven complete plant genomes, 12 complete algal genomes, along with computed information for individual proteins encoded in these genomes of the following types: (a) carbohydrate active enzyme (CAZy) family information when applicable; (b) phylogenetic trees of cell wall-related CAZy family proteins; (c) protein structure models if available; (d) physical and predicted interactions among proteins; (e) subcellular localization; (f) Pfam domain information; and (g) homology-based functional prediction. A querying system with a graphical interface allows a user to quickly compose information of different sorts about individual genes/proteins and to display the composite information in an intuitive manner, facilitating comparative analyses and knowledge discovery about cell wall genes. pDAWG can be accessed at http://csbl1.bmb.uga.edu/pDAWG/.     

Localization of Human Cadherin Genes to Chromosome Regions Exhibiting Cancer-Related Loss of Heterozygosity

This report presents the chromosomal localization of cadherin genes. Cadherins are cellular adhesion molecules. Since disturbance of intracellular adhesion is important for invasion and metastasis of tumor cells, cadherins are considered prime candidates for tumor suppressor genes. A variety of solid tumors show loss of heterozygosity of the long arm of chromosome 16, which is indicative of the potential location of tumor suppressor genes. Refined and new localizations of six cadherin genes (CDH3, 5, 8, 11, 13, and 15) to the long arm of chromosome 16 are shown. CDH15 was localized to 16q24.3, in a region that exhibits loss of heterozygosity in a number of sporadic breast cancer tumors. Previous localization of CDH13 (H-cadherin) to 16q24 suggested this gene as a tumor suppressor candidate in the 16q24.3 loss of heterozygosity region; however, refined mapping presented in this report localizes CDH13 proximal to this region. A human EST homologous to the chicken cadherin-7 was partially sequenced and found to represent a new human cadherin. This cadherin mapped to chromosome 18q22–q23, a region that exhibits loss of heterozygosity in head and neck squamous cell carcinomas. CDH16 was localized to 8q22.1, a region exhibiting loss of heterozygosity in adult acute myeloid leukemia.     

cDNA-AFLP技术在植物耐盐基因鉴定上的应用

cDNA-AFLP技术是近年来广泛应用于植物基因分离与表达研究的mRNA指纹图谱技术,在分离植物特异性基因等生物技术研究中发挥了巨大作用.在植物耐盐基因鉴定方面,该技术的应用处于新兴初始阶段,但已取得了诸多成效.本文在概述植物耐盐响应机制的基础上,着重对利用cDNA-AFLP技术鉴定的植物耐盐相关基因及其相应作用机制进行了阐述,并对其在植物耐盐分子生物学研究上的应用前景进行了展望.     

Suppressive Subtraction Libraries to Identify Interferon-Inducible Genes in Fish

Previous attempts to identify genes in fish that respond to virus infection or interferon induction have not been particularly productive. Since these genes are very important in developing strategies to control disease outbreaks in aquaculture, we began a study of interferon-inducible genes in fish using suppressive subtraction hybridization to construct cDNA libraries enriched for interferon-inducible genes. Subtraction hybridization libraries were constructed with cDNA obtained from the kidney, spleen, and liver of Chinook salmon (Oncorhynchus tshawytscha) and staghorn sculpin (Hemilepidotus spinosus) before and after injection with poly IC, a potent interferon inducer. The ``identified' genes in both cDNA libraries corresponded to previously identified genes of the fish complement system, the interferon-inducible proteins observed in mammalian cells, and the Vig-1 gene, identified in fish cells after infection with fish rhabdoviruses. Received June 19, 2001; accepted July 13, 2001     

An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes

Background

Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”.

Results

Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer.

Conclusion

The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.     

Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy,Identify Key Nodes and Predict Network Dynamics

Background

Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult.

Methodology/Principal Findings

Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics.

Conclusions/Significance

The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.     

An RNA Interference Lethality Screen of the Human Druggable Genome to Identify Molecular Vulnerabilities in Epithelial Ovarian Cancer

Targeted therapies have been used to combat many tumor types; however, few have effectively improved the overall survival in women with epithelial ovarian cancer, begging for a better understanding of this deadly disease and identification of essential drivers of tumorigenesis that can be targeted effectively. Therefore, we used a loss-of-function screening approach to help identify molecular vulnerabilities that may represent key points of therapeutic intervention. We employed an unbiased high-throughput lethality screen using a 24,088 siRNA library targeting over 6,000 druggable genes and studied their effects on growth and/or survival of epithelial ovarian cancer (EOC) cell lines. The top 300 “hits” affecting the viability of A1847 cells were rescreened across additional EOC cell lines and non-tumorigenic, human immortalized ovarian epithelial cell lines. Fifty-three gene candidates were found to exhibit effects in all tumorigenic cell lines tested. Extensive validation of these hits refined the list to four high quality candidates (HSPA5, NDC80, NUF2, and PTN). Mechanistic studies show that silencing of three genes leads to increased apoptosis, while HSPA5 silencing appears to alter cell growth through G1 cell cycle arrest. Furthermore, two independent gene expression studies show that NDC80, NUF2 and PTN were significantly aberrantly overexpressed in serous adenocarcinomas. Overall, our functional genomics results integrated with the genomics data provide an important unbiased avenue towards the identification of prospective therapeutic targets for drug discovery, which is an urgent and unmet clinical need for ovarian cancer.     

Use of Two-Dimensional Electrophoresis to Identify and Map New Mouse Genes

Cytosol polypeptides from mouse liver have been examined using two-dimensional electrophoresis. About 250 spots were readily discernible. When cytosols from strains BALB/cBy and C57BL/6By were compared eight genetically determined differences were observed. Other strain pairs show comparable numbers of differences. These eight phenotypes were scored in seven recombinant inbred lines derived from the two parental strains, and their strain distribution patterns were compared with previously determined patterns for other genetic markers that differ between the two progenitor strains. Using this information, tentative chromosomes assignments for the genes controlling five of the variant phenotypes have been made, and two of the assignments have been confirmed using congenic resistant strains. These eight genes will be useful reference markers in future crosses designed to map new genes.     

Experimental Strategy to Identify Genes Susceptible to Oxidative Stress in Nigral Dopaminergic Neurons

Neuropathological evidence from both human and experimental models of Parkinson's disease (PD) firmly supports a significant role for oxidative stress (OS) in the death of dopaminergic (DA) neurons in substantia nigra. Largely unknown are the genes underlying selective susceptibility of nigral DA neuron to OS and how they effect nigral DA cell death. The major barriers to high-throughput identification of candidate genes are the paucity of nigral DA neurons as well as the dilution effect of non-DA cells both in primary cultures and brain tissues. To overcome these barriers, we have developed a DA cell line model, SN4741, appropriate for cDNA microarray analysis. Candidate genes were selected from both the microarray analysis and the molecular implication of their pathological mechanisms (i.e., decreased mitochondrial complex I activity and proteasomal dysfunction) of PD. Subsequent secondary validation tests were devised to characterize genes including clone #45 that may underlie selective vulnerability of nigral DA neuron to OS.     

Genes Responsive to Elevated CO2 Concentrations in Triploid White Poplar and Integrated Gene Network Analysis

Background

The atmospheric CO₂ concentration increases every year. While the effects of elevated CO₂ on plant growth, physiology and metabolism have been studied, there is now a pressing need to understand the molecular mechanisms of how plants will respond to future increases in CO₂ concentration using genomic techniques.

Principal Findings

Gene expression in triploid white poplar ((Populus tomentosa ×P. bolleana) ×P. tomentosa) leaves was investigated using the Affymetrix poplar genome gene chip, after three months of growth in controlled environment chambers under three CO₂ concentrations. Our physiological findings showed the growth, assessed as stem diameter, was significantly increased, and the net photosynthetic rate was decreased in elevated CO₂ concentrations. The concentrations of four major endogenous hormones appeared to actively promote plant development. Leaf tissues under elevated CO₂ concentrations had 5,127 genes with different expression patterns in comparison to leaves under the ambient CO₂ concentration. Among these, 8 genes were finally selected for further investigation by using randomized variance model corrective ANOVA analysis, dynamic gene expression profiling, gene network construction, and quantitative real-time PCR validation. Among the 8 genes in the network, aldehyde dehydrogenase and pyruvate kinase were situated in the core and had interconnections with other genes.

Conclusions

Under elevated CO₂ concentrations, 8 significantly changed key genes involved in metabolism and responding to stimulus of external environment were identified. These genes play crucial roles in the signal transduction network and show strong correlations with elevated CO₂ exposure. This study provides several target genes, further investigation of which could provide an initial step for better understanding the molecular mechanisms of plant acclimation and evolution in future rising CO₂ concentrations.