Identification of the Tyrosine-Protein Phosphatase Non-Receptor Type 2 as a Rheumatoid Arthritis Susceptibility Locus in Europeans

Objectives

Genome-wide association studies have facilitated the identification of over 30 susceptibility loci for rheumatoid arthritis (RA). However, evidence for a number of potential susceptibility genes have not so far reached genome-wide significance in studies of Caucasian RA.

Methods

A cohort of 4286 RA patients from across Europe and 5642 population matched controls were genotyped for 25 SNPs, then combined in a meta-analysis with previously published data.

Results

Significant evidence of association was detected for nine SNPs within the European samples. When meta-analysed with previously published data, 21 SNPs were associated with RA susceptibility. Although SNPs in the PTPN2 gene were previously reported to be associated with RA in both Japanese and European populations, we show genome-wide evidence for a different SNP within this gene associated with RA susceptibility in an independent European population (rs7234029, P = 4.4×10⁻⁹).

Conclusions

This study provides further genome-wide evidence for the association of the PTPN2 locus (encoding the T cell protein tyrosine phosphastase) with Caucasian RA susceptibility. This finding adds to the growing evidence for PTPN2 being a pan-autoimmune susceptibility gene.     

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.     

Genome-wide eQTLs and heritability for gene expression traits in unrelated individuals

Background

While the possible sources underlying the so-called ‘missing heritability’ evident in current genome-wide association studies (GWAS) of complex traits have been actively pursued in recent years, resolving this mystery remains a challenging task. Studying heritability of genome-wide gene expression traits can shed light on the goal of understanding the relationship between phenotype and genotype. Here we used microarray gene expression measurements of lymphoblastoid cell lines and genome-wide SNP genotype data from 210 HapMap individuals to examine the heritability of gene expression traits.

Results

Heritability levels for expression of 10,720 genes were estimated by applying variance component model analyses and 1,043 expression quantitative loci (eQTLs) were detected. Our results indicate that gene expression traits display a bimodal distribution of heritability, one peak close to 0% and the other summit approaching 100%. Such a pattern of the within-population variability of gene expression heritability is common among different HapMap populations of unrelated individuals but different from that obtained in the CEU and YRI trio samples. Higher heritability levels are shown by housekeeping genes and genes associated with cis eQTLs. Both cis and trans eQTLs make comparable cumulative contributions to the heritability. Finally, we modelled gene-gene interactions (epistasis) for genes with multiple eQTLs and revealed that epistasis was not prevailing in all genes but made a substantial contribution in explaining total heritability for some genes analysed.

Conclusions

We utilised a mixed effect model analysis for estimating genetic components from population based samples. On basis of analyses of genome-wide gene expression from four HapMap populations, we demonstrated detailed exploitation of the distribution of genetic heritabilities for expression traits from different populations, and highlighted the importance of studying interaction at the gene expression level as an important source of variation underlying missing heritability.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-13) contains supplementary material, which is available to authorized users.     

Clustered gene expression changes flank targeted gene loci in knockout mice

Background

Gene expression profiling using microarrays is a powerful technology widely used to study regulatory networks. Profiling of mRNA levels in mutant organisms has the potential to identify genes regulated by the mutated protein.

Methodology/Principle Findings

Using tissues from multiple lines of knockout mice we have examined genome-wide changes in gene expression. We report that a significant proportion of changed genes were found near the targeted gene.

Conclusions/Significance

The apparent clustering of these genes was explained by the presence of flanking DNA from the parental ES cell. We provide recommendations for the analysis and reporting of microarray data from knockout mice     

Gene network landscape of the ciliate Tetrahymena thermophila

Background

Genome-wide expression data of gene microarrays can be used to infer gene networks. At a cellular level, a gene network provides a picture of the modules in which genes are densely connected, and of the hub genes, which are highly connected with other genes. A gene network is useful to identify the genes involved in the same pathway, in a protein complex or that are co-regulated. In this study, we used different methods to find gene networks in the ciliate Tetrahymena thermophila, and describe some important properties of this network, such as modules and hubs.

Methodology/Principal Findings

Using 67 single channel microarrays, we constructed the Tetrahymena gene network (TGN) using three methods: the Pearson correlation coefficient (PCC), the Spearman correlation coefficient (SCC) and the context likelihood of relatedness (CLR) algorithm. The accuracy and coverage of the three networks were evaluated using four conserved protein complexes in yeast. The CLR network with a Z-score threshold 3.49 was determined to be the most robust. The TGN was partitioned, and 55 modules were found. In addition, analysis of the arbitrarily determined 1200 hubs showed that these hubs could be sorted into six groups according to their expression profiles. We also investigated human disease orthologs in Tetrahymena that are missing in yeast and provide evidence indicating that some of these are involved in the same process in Tetrahymena as in human.

Conclusions/Significance

This study constructed a Tetrahymena gene network, provided new insights to the properties of this biological network, and presents an important resource to study Tetrahymena genes at the pathway level.     

A new platform for ultra-high density Staphylococcus aureus transposon libraries

Background

Staphylococcus aureus readily develops resistance to antibiotics and achieving effective therapies to overcome resistance requires in-depth understanding of S. aureus biology. High throughput, parallel-sequencing methods for analyzing transposon mutant libraries have the potential to revolutionize studies of S. aureus, but the genetic tools to take advantage of the power of next generation sequencing have not been fully developed.

Results

Here we report a phage-based transposition system to make ultra-high density transposon libraries for genome-wide analysis of mutant fitness in any Φ11-transducible S. aureus strain. The high efficiency of the delivery system has made it possible to multiplex transposon cassettes containing different regulatory elements in order to make libraries in which genes are over- or under-expressed as well as deleted. By incorporating transposon-specific barcodes into the cassettes, we can evaluate how null mutations and changes in gene expression levels affect fitness in a single sequencing data set. Demonstrating the power of the system, we have prepared a library containing more than 690,000 unique insertions. Because one unique feature of the phage-based approach is that temperature-sensitive mutants are retained, we have carried out a genome-wide study of S. aureus genes involved in withstanding temperature stress. We find that many genes previously identified as essential are temperature sensitive and also identify a number of genes that, when disrupted, confer a growth advantage at elevated temperatures.

Conclusions

The platform described here reliably provides mutant collections of unparalleled genotypic diversity and will enable a wide range of functional genomic studies in S. aureus.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1361-3) contains supplementary material, which is available to authorized users.     

The phytocyanin gene family in rice (Oryza sativa L.): genome-wide identification, classification and transcriptional analysis

Background

Phytocyanins (PCs) are plant-specific blue copper proteins involved in electron transport, and a large number of known PCs are considered to be chimeric arabinogalactan proteins (AGPs). To date there has not been a genome-wide overview of the OsPC gene family. Therefore, as the first step and a useful strategy to elucidate the functions of OsPCs, there is an urgent need for a thorough genome-wide analysis of this gene family.

Methodology/Principal Findings

In this study, a total of 62 OsPC genes were identified through a comprehensive bioinformatics analysis of the rice (Oryza sativa L.) genome. Based on phylogeny and motif constitution, the family of OsPCs was classified into three subclasses: uclacyanin-like proteins (OsUCLs), stellacyanin-like proteins (OsSCLs) and early nodulin-like proteins (OsENODLs). Structure and glycosylation prediction indicated that 46 OsPCs were glycosylphosphatigylinositol-anchored proteins and 38 OsPCs were chimeric AGPs. Gene duplication analysis revealed that chromosomal segment and tandem duplications contributed almost equally to the expansion of this gene family, and duplication events were mostly happened in the OsUCL subfamily. The expression profiles of OsPC genes were analyzed at different stages of vegetative and reproductive development and under abiotic stresses. It revealed that a large number of OsPC genes were abundantly expressed in the various stages of development. Moreover, 17 genes were regulated under the treatments of abiotic stresses.

Conclusions/Significance

The genome-wide identification and expression analysis of OsPC genes should facilitate research in this gene family and give new insights toward elucidating their functions in higher plants.     

Characterization of the MLO gene family in Rosaceae and gene expression analysis in Malus domestica

Background

Powdery mildew (PM) is a major fungal disease of thousands of plant species, including many cultivated Rosaceae. PM pathogenesis is associated with up-regulation of MLO genes during early stages of infection, causing down-regulation of plant defense pathways. Specific members of the MLO gene family act as PM-susceptibility genes, as their loss-of-function mutations grant durable and broad-spectrum resistance.

Results

We carried out a genome-wide characterization of the MLO gene family in apple, peach and strawberry, and we isolated apricot MLO homologs through a PCR-approach. Evolutionary relationships between MLO homologs were studied and syntenic blocks constructed. Homologs that are candidates for being PM susceptibility genes were inferred by phylogenetic relationships with functionally characterized MLO genes and, in apple, by monitoring their expression following inoculation with the PM causal pathogen Podosphaera leucotricha.

Conclusions

Genomic tools available for Rosaceae were exploited in order to characterize the MLO gene family. Candidate MLO susceptibility genes were identified. In follow-up studies it can be investigated whether silencing or a loss-of-function mutations in one or more of these candidate genes leads to PM resistance.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-618) contains supplementary material, which is available to authorized users.     

Sortin2 enhances endocytic trafficking towards the vacuole in Saccharomyces cerevisiae

Background

A highly regulated trafficking of cargo vesicles in eukaryotes performs protein delivery to a variety of cellular compartments of endomembrane system. The two main routes, the secretory and the endocytic pathways have pivotal functions in uni- and multi-cellular organisms. Protein delivery and targeting includes cargo recognition, vesicle formation and fusion. Developing new tools to modulate protein trafficking allows better understanding the endomembrane system mechanisms and their regulation. The compound Sortin2 has been described as a protein trafficking modulator affecting targeting of the vacuolar protein carboxypeptidase Y (CPY), triggering its secretion in Saccharomyces cerevisiae.

Results

A reverse chemical-genetics approach was used to identify key proteins for Sortin2 bioactivity. A genome-wide Sortin2 resistance screen revealed six yeast deletion mutants that do not secrete CPY when grown at Sortin2 condition where the parental strain does: met18, sla1, clc1, dfg10, dpl1 and yjl175w. Integrating mutant phenotype and gene ontology annotation of the corresponding genes and their interactome pointed towards a high representation of genes involved in the endocytic process. In wild type yeast endocytosis towards the vacuole was faster in presence of Sortin2, which further validates the data of the genome-wide screen. This effect of Sortin2 depends on structural features of the molecule, suggesting compound specificity. Sortin2 did not affect endocytic trafficking in Sortin2-resistant mutants, strongly suggesting that the Sortin2 effects on the secretory and endocytic pathways are linked.

Conclusions

Overall, the results reveal that Sortin2 enhances the endocytic transport pathway in Saccharomyces cerevisiae. This cellular effect is most likely at the level where secretory and endocytic pathways are merged. Them Sortin2 specificity over the endomembrane system places it as a powerful biological modulator for cell biology.

Electronic supplementary material

The online version of this article (doi:10.1186/s40659-015-0032-9) contains supplementary material, which is available to authorized users.     

Prioritization and evaluation of depression candidate genes by combining multidimensional data resources

Background

Large scale and individual genetic studies have suggested numerous susceptible genes for depression in the past decade without conclusive results. There is a strong need to review and integrate multi-dimensional data for follow up validation. The present study aimed to apply prioritization procedures to build-up an evidence-based candidate genes dataset for depression.

Methods

Depression candidate genes were collected in human and animal studies across various data resources. Each gene was scored according to its magnitude of evidence related to depression and was multiplied by a source-specific weight to form a combined score measure. All genes were evaluated through a prioritization system to obtain an optimal weight matrix to rank their relative importance with depression using the combined scores. The resulting candidate gene list for depression (DEPgenes) was further evaluated by a genome-wide association (GWA) dataset and microarray gene expression in human tissues.

Results

A total of 5,055 candidate genes (4,850 genes from human and 387 genes from animal studies with 182 being overlapped) were included from seven data sources. Through the prioritization procedures, we identified 169 DEPgenes, which exhibited high chance to be associated with depression in GWA dataset (Wilcoxon rank-sum test, p = 0.00005). Additionally, the DEPgenes had a higher percentage to express in human brain or nerve related tissues than non-DEPgenes, supporting the neurotransmitter and neuroplasticity theories in depression.

Conclusions

With comprehensive data collection and curation and an application of integrative approach, we successfully generated DEPgenes through an effective gene prioritization system. The prioritized DEPgenes are promising for future biological experiments or replication efforts to discoverthe underlying molecular mechanisms for depression.     

Performance of Polygenic Scores for Predicting Phobic Anxiety

Context

Anxiety disorders are common, with a lifetime prevalence of 20% in the U.S., and are responsible for substantial burdens of disability, missed work days and health care utilization. To date, no causal genetic variants have been identified for anxiety, anxiety disorders, or related traits.

Objective

To investigate whether a phobic anxiety symptom score was associated with 3 alternative polygenic risk scores, derived from external genome-wide association studies of anxiety, an internally estimated agnostic polygenic score, or previously identified candidate genes.

Design

Longitudinal follow-up study. Using linear and logistic regression we investigated whether phobic anxiety was associated with polygenic risk scores derived from internal, leave-one out genome-wide association studies, from 31 candidate genes, and from out-of-sample genome-wide association weights previously shown to predict depression and anxiety in another cohort.

Setting and Participants

Study participants (n = 11,127) were individuals from the Nurses'' Health Study and Health Professionals Follow-up Study.

Main Outcome Measure

Anxiety symptoms were assessed via the 8-item phobic anxiety scale of the Crown Crisp Index at two time points, from which a continuous phenotype score was derived.

Results

We found no genome-wide significant associations with phobic anxiety. Phobic anxiety was also not associated with a polygenic risk score derived from the genome-wide association study beta weights using liberal p-value thresholds; with a previously published genome-wide polygenic score; or with a candidate gene risk score based on 31 genes previously hypothesized to predict anxiety.

Conclusion

There is a substantial gap between twin-study heritability estimates of anxiety disorders ranging between 20–40% and heritability explained by genome-wide association results. New approaches such as improved genome imputations, application of gene expression and biological pathways information, and incorporating social or environmental modifiers of genetic risks may be necessary to identify significant genetic predictors of anxiety.     

DISC1 conditioned GWAS for psychosis proneness in a large Finnish birth cohort

Background

Genetic evidence implicates the DISC1 gene in the etiology of a number of mental illnesses. Previously, we have reported association between DISC1 and measures of psychosis proneness, the Revised Social Anhedonia Scale (RSAS) and Revised Physical Anhedonia Scale (RPAS), in the Northern Finland Birth Cohort 1966 (NFBC66). As part of the studies of this Finnish birth cohort genome-wide association analysis has recently been performed.

Methodology

In the present study, we re-analyzed the genome-wide association data with regard to these two measures of psychosis proneness, conditioning on our previous DISC1 observation. From the original NFBC66 sample (N = 12 058), 4 561 individuals provided phenotype and genotype data. No markers were significant at the genome-wide level. However, several genes with biological relevance to mental illnesses were highlighted through loci displaying suggestive evidence for association (≥3 SNP with P<10E-4). These included the protein coding genes, CXCL3, KIAA1128, LCT, MED13L, TMCO7, TTN, and the micro RNA MIR620.

Conclusions

By conditioning a previous genome-wide association study on DISC1, we have been able to identify eight genes as associating to psychosis proneness. Further, these molecules predominantly link to the DISC1 pathway, strengthening the evidence for the role of this gene network in the etiology of mental illness. The use of quantitative measures of psychosis proneness in a large population cohort will make these findings, once verified; more generalized to a broad selection of disorders related to psychoses and psychosis proneness.     

Optimal In Silico Target Gene Deletion through Nonlinear Programming for Genetic Engineering

Background

Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized.

Methodology/Principal Findings

Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy.

Significance

Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial-and-error procedure.     

tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities

Background

To determine which changes in the host cell genome are crucial for cervical carcinogenesis, a longitudinal in vitro model system of HPV-transformed keratinocytes was profiled in a genome-wide manner. Four cell lines affected with either HPV16 or HPV18 were assayed at 8 sequential time points for gene expression (mRNA) and gene copy number (DNA) using high-resolution microarrays. Available methods for temporal differential expression analysis are not designed for integrative genomic studies.

Results

Here, we present a method that allows for the identification of differential gene expression associated with DNA copy number changes over time. The temporal variation in gene expression is described by a generalized linear mixed model employing low-rank thin-plate splines. Model parameters are estimated with an empirical Bayes procedure, which exploits integrated nested Laplace approximation for fast computation. Iteratively, posteriors of hyperparameters and model parameters are estimated. The empirical Bayes procedure shrinks multiple dispersion-related parameters. Shrinkage leads to more stable estimates of the model parameters, better control of false positives and improvement of reproducibility. In addition, to make estimates of the DNA copy number more stable, model parameters are also estimated in a multivariate way using triplets of features, imposing a spatial prior for the copy number effect.

Conclusion

With the proposed method for analysis of time-course multilevel molecular data, more profound insight may be gained through the identification of temporal differential expression induced by DNA copy number abnormalities. In particular, in the analysis of an integrative oncogenomics study with a time-course set-up our method finds genes previously reported to be involved in cervical carcinogenesis. Furthermore, the proposed method yields improvements in sensitivity, specificity and reproducibility compared to existing methods. Finally, the proposed method is able to handle count (RNAseq) data from time course experiments as is shown on a real data set.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-327) contains supplementary material, which is available to authorized users.