Comprehensive Network Analysis of Anther-Expressed Genes in Rice by the Combination of 33 Laser Microdissection and 143 Spatiotemporal Microarrays

Co-expression networks systematically constructed from large-scale transcriptome data reflect the interactions and functions of genes with similar expression patterns and are a powerful tool for the comprehensive understanding of biological events and mining of novel genes. In Arabidopsis (a model dicot plant), high-resolution co-expression networks have been constructed from very large microarray datasets and these are publicly available as online information resources. However, the available transcriptome data of rice (a model monocot plant) have been limited so far, making it difficult for rice researchers to achieve reliable co-expression analysis. In this study, we performed co-expression network analysis by using combined 44 K agilent microarray datasets of rice, which consisted of 33 laser microdissection (LM)-microarray datasets of anthers, and 143 spatiotemporal transcriptome datasets deposited in RicexPro. The entire data of the rice co-expression network, which was generated from the 176 microarray datasets by the Pearson correlation coefficient (PCC) method with the mutual rank (MR)-based cut-off, contained 24,258 genes and 60,441 genes pairs. Using these datasets, we constructed high-resolution co-expression subnetworks of two specific biological events in the anther, “meiosis” and “pollen wall synthesis”. The meiosis network contained many known or putative meiotic genes, including genes related to meiosis initiation and recombination. In the pollen wall synthesis network, several candidate genes involved in the sporopollenin biosynthesis pathway were efficiently identified. Hence, these two subnetworks are important demonstrations of the efficiency of co-expression network analysis in rice. Our co-expression analysis included the separated transcriptomes of pollen and tapetum cells in the anther, which are able to provide precise information on transcriptional regulation during male gametophyte development in rice. The co-expression network data presented here is a useful resource for rice researchers to elucidate important and complex biological events

Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases.

Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10(-12)), while Dnmt3a KO signature does not (P = 0.017)

EgoNet: Identification of human disease ego-network modules

Background: Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks.Results: We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes.Conclusions: Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases

Network estimation in State Space Model with L1-regularization constraint

Biological networks have arisen as an attractive paradigm of genomic science ever since the introduction of large scale genomic technologies which carried the promise of elucidating the relationship in functional genomics. Microarray technologies coupled with appropriate mathematical or statistical models have made it possible to identify dynamic regulatory networks or to measure time course of the expression level of many genes simultaneously. However one of the few limitations fall on the high-dimensional nature of such data coupled with the fact that these gene expression data are known to include some hidden process. In that regards, we are concerned with deriving a method for inferring a sparse dynamic network in a high dimensional data setting. We assume that the observations are noisy measurements of gene expression in the form of mRNAs, whose dynamics can be described by some unknown or hidden process. We build an input-dependent linear state space model from these hidden states and demonstrate how an incorporated $L_{1}$ regularization constraint in an Expectation-Maximization (EM) algorithm can be used to reverse engineer transcriptional networks from gene expression profiling data. This corresponds to estimating the model interaction parameters. The proposed method is illustrated on time-course microarray data obtained from a well established T-cell data. At the optimum tuning parameters we found genes TRAF5, JUND, CDK4, CASP4, CD69, and C3X1 to have higher number of inwards directed connections and FYB, CCNA2, AKT1 and CASP8 to be genes with higher number of outwards directed connections. We recommend these genes to be object for further investigation. Caspase 4 is also found to activate the expression of JunD which in turn represses the cell cycle regulator CDC2.Comment: arXiv admin note: substantial text overlap with arXiv:1308.359

Mapping the genetic architecture of gene expression in human liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. © 2008 Schadt et al

A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules. © 2012 Rodrigo et al.This work was supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) grants BFU2009-06993 (S. F. E.) and BIO2006-13107 (C. L.) and by Generalitat Valenciana grant PROMETEO2010/016 (S. F. E.). G. R. is supported by a graduate fellowship from the Generalitat Valenciana (BFPI2007-160) and J.C. by a contract from MICINN grant TIN2006-12860. 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