The Multivariate Nonparametric Methods for Identifying Gene Sets with Differential Expression

Background

Gene Set Analysis (GSA) identifies differential expression gene sets amid the different phenotypes. The results of published papers in this filed are inconsistent and there is no consensus on the best method. In this paper two new methods, in comparison to the previous ones, are introduced for GSA.

Methods

The MMGSA and MRGSA methods based on multivariate nonparametric techniques were presented. The implementation of five GSA methods (Hotelling's T², Globaltest, Abs_Cat, Med_Cat and Rs_Cat) and the novel methods to detect differential gene expression between phenotypes were compared using simulated and real microarray data sets.

Results

In a real dataset, the results showed that the powers of MMGSA and MRGSA were as well as Globaltest and Tsai. The MRGSA method has not a good performance in the simulation dataset.

Conclusions

The Globaltest method is the best method in the real or simulation datasets. The performance of MMGSA in simulation dataset is good in small-size gene sets. The GLS methods are not good in the simulated data, except the Med_Cat method in large-size gene sets.     

Random forests-based differential analysis of gene sets for gene expression data

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients.     

Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication)

The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.     

Investigating the effect of paralogs on microarray gene-set analysis

Background  

In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge to group genes into sets and then aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. We suspect that the presence of paralogs affects the ability of GSA methods to accurately identify the most important sets of genes for subsequent research.     

META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies

Introduction

Gene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a predefined set of genes are either contrasted with those of all remaining genes or with a null non-associated background. To pool the p-values from several GSAs, it is important to take into account the concordance of the observed patterns resulting from single marker association point estimates across any given gene set. Here we propose an enhanced version of Fisher’s inverse χ²-method META-GSA, however weighting each study to account for imperfect correlation between association patterns.

Simulation and Power

We investigated the performance of META-GSA by simulating GWASs with 500 cases and 500 controls at 100 diallelic markers in 20 different scenarios, simulating different relative risks between 1 and 1.5 in gene sets of 10 genes. Wilcoxon’s rank sum test was applied as GSA for each study. We found that META-GSA has greater power to discover truly associated gene sets than simple pooling of the p-values, by e.g. 59% versus 37%, when the true relative risk for 5 of 10 genes was assume to be 1.5. Under the null hypothesis of no difference in the true association pattern between the gene set of interest and the set of remaining genes, the results of both approaches are almost uncorrelated. We recommend not relying on p-values alone when combining the results of independent GSAs.

Application

We applied META-GSA to pool the results of four case-control GWASs of lung cancer risk (Central European Study and Toronto/Lunenfeld-Tanenbaum Research Institute Study; German Lung Cancer Study and MD Anderson Cancer Center Study), which had already been analyzed separately with four different GSA methods (EASE; SLAT, mSUMSTAT and GenGen). This application revealed the pathway GO0015291 “transmembrane transporter activity” as significantly enriched with associated genes (GSA-method: EASE, p = 0.0315 corrected for multiple testing). Similar results were found for GO0015464 “acetylcholine receptor activity” but only when not corrected for multiple testing (all GSA-methods applied; p≈0.02).     

Normalization and quantification of differential expression in gene expression microarrays

Array-based gene expression studies frequently serve to identify genes that are expressed differently under two or more conditions. The actual analysis of the data, however, may be hampered by a number of technical and statistical problems. Possible remedies on the level of computational analysis lie in appropriate preprocessing steps, proper normalization of the data and application of statistical testing procedures in the derivation of differentially expressed genes. This review summarizes methods that are available for these purposes and provides a brief overview of the available software tools.     

Rotation gene set testing for longitudinal expression data

Gene set analysis methods are popular tools for identifying differentially expressed gene sets in microarray data. Most existing methods use a permutation test to assess significance for each gene set. The permutation test's assumption of exchangeable samples is often not satisfied for time‐series data and complex experimental designs, and in addition it requires a certain number of samples to compute p‐values accurately. The method presented here uses a rotation test rather than a permutation test to assess significance. The rotation test can compute accurate p‐values also for very small sample sizes. The method can handle complex designs and is particularly suited for longitudinal microarray data where the samples may have complex correlation structures. Dependencies between genes, modeled with the use of gene networks, are incorporated in the estimation of correlations between samples. In addition, the method can test for both gene sets that are differentially expressed and gene sets that show strong time trends. We show on simulated longitudinal data that the ability to identify important gene sets may be improved by taking the correlation structure between samples into account. Applied to real data, the method identifies both gene sets with constant expression and gene sets with strong time trends.     

Comparative Sensitivity of PCR Primer Sets for Detection of Cryptosporidium parvum

Improved methods for detection of Cryptosporidium oocysts in environmental and clinical samples are urgently needed to improve detection of cryptosporidiosis. We compared the sensitivity of 7 PCR primer sets for detection of Cryptosporidium parvum. Each target gene was amplified by PCR or nested PCR with serially diluted DNA extracted from purified C. parvum oocysts. The target genes included Cryptosporidium oocyst wall protein (COWP), small subunit ribosomal RNA (SSU rRNA), and random amplified polymorphic DNA. The detection limit of the PCR method ranged from 10³ to 10⁴ oocysts, and the nested PCR method was able to detect 10⁰ to 10² oocysts. A second-round amplification of target genes showed that the nested primer set specific for the COWP gene proved to be the most sensitive one compared to the other primer sets tested in this study and would therefore be useful for the detection of C. parvum.     

Detection of Ammonia-Oxidizing Archaea in Fish Processing Effluent Treatment Plants

Ammonia oxidation is the rate limiting step in nitrification and thus have an important role in removal of ammonia in natural and engineered systems with participation of both ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB). However, their relative distribution and activity in fish processing effluent treatment plants (FPETPs) though significant, is hitherto unreported. Presence of AOA in sludge samples obtained from FPETPs was studied by amplification and sequencing of thaumarchaeal ammonia monooxygenase subunit A (AOA-amoA) gene. Different primer sets targeting 16S rRNA and AOA-amoA gene were used for the detection of AOA in FPETPs. Phylogenetic analysis of the gene revealed that the AOA was affiliated with thaumarchaeal group 1.1a lineage (marine cluster). Quantitative real time PCR of amoA gene was used to study the copy number of AOA and AOB in FPETPs. The AOA-amoA and AOB-amoA gene copy numbers of sludge samples ranged from 2.2 × 10⁶ to 4.2 × 10⁸ and 1.1 × 10⁷ to 8.5 × 10⁸ mg⁻¹ sludge respectively. Primer sets Arch-amoAF/Arch-amoAR and 340F/1000R were found to be useful for the sensitive detection of AOA-amoA and Archaeal 16S rRNA genes respectively in FPETPs. Their presence suggests the widespread occurrence and possible usefulness in removing ammonia from FPETPs which is in line with reports from other waste water treatment plants.

Electronic supplementary material

The online version of this article (doi:10.1007/s12088-014-0484-6) contains supplementary material, which is available to authorized users.     

Molecular evolution of bacterial indoleamine 2,3-dioxygenase

Indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO) are tryptophan-degrading enzymes that catalyze the first step in L-Trp catabolism via the kynurenine pathway. In mammals, TDO is mainly expressed in the liver and primarily supplies nicotinamide adenine dinucleotide (NAD⁺). TDO is widely distributed from mammals to bacteria. Active IDO enzymes have been reported only in vertebrates and fungi. In mammals, IDO activity plays a significant role in the immune system while in fungal species, IDO is constitutively expressed and supplies NAD⁺, like mammalian TDO. A search of genomic databases reveals that some bacterial species also have a putative IDO gene. A phylogenetic analysis clustered bacterial IDOs into two groups, group I or group II bacterial IDOs. The catalytic efficiencies of group I bacterial IDOs were very low and they are suspected not to contribute significantly to L-Trp metabolism. The bacterial species bearing the group I bacterial IDO are scattered across a few phyla and no phylogenetically close relationship is observed between them. This suggests that the group I bacterial IDOs might be acquired by horizontal gene transmission that occurred in each lineage independently. In contrast, group II bacterial IDOs showed rather high catalytic efficiency. Particularly, the enzymatic characteristics (K_m, V_max and inhibitor selectivity) of the Gemmatimonas aurantiaca IDO are comparable to those of mammalian IDO1, although comparison of the IDO sequences does not suggest a close evolutionary relationship. In several bacteria, TDO and the kynureninase gene (kynU) are clustered on their chromosome suggesting that these genes could be transcribed in an operon. Interestingly, G. aurantiaca has no TDO, and the IDO is clustered with kynU on its chromosome. Although the G. aurantiaca also has NadA and NadB to synthesize a quinolinic acid (a precursor of NAD⁺) via the aspartate pathway, the high activity of the G. aurantiaca IDO flanking the kynU gene suggests its IDO has a function similar to eukaryotic enzymes.     

Identification of a panel of mitotic spindle-related genes as a signature predicting survival in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most malignant tumor types worldwide. Our objective was to identify a genetic signature that could predict the prognosis of patients with LUAD. We extracted gene data sets from The Cancer Genome Atlas and obtained differentially expressed genes that were highly expressed at every stage. These genes were analyzed using gene set enrichment analysis to obtain four biological processes associated with LUAD. Subsequently, Cox univariate and multivariate analyses were performed to generate four optimized models (G2M checkpoint, E2F targets, mitotic spindle, and glycolysis). We identified a mitotic spindle-related signature (KIF15, BUB1, CCNB2, CDK1, KIF4A, DLGAP5, ECT2, and ANLN), which could be an independent prognostic indicator, to predict the prognosis of patients with LUAD. This new discovery should offer opportunities to explore the pathogenesis of LUAD and prove clinically useful in predicting LUAD patient prognosis.     

Genetic Manipulation in Δku80 Strains for Functional Genomic Analysis of Toxoplasma gondii

Targeted genetic manipulation using homologous recombination is the method of choice for functional genomic analysis to obtain a detailed view of gene function and phenotype(s). The development of mutant strains with targeted gene deletions, targeted mutations, complemented gene function, and/or tagged genes provides powerful strategies to address gene function, particularly if these genetic manipulations can be efficiently targeted to the gene locus of interest using integration mediated by double cross over homologous recombination.Due to very high rates of nonhomologous recombination, functional genomic analysis of Toxoplasma gondii has been previously limited by the absence of efficient methods for targeting gene deletions and gene replacements to specific genetic loci. Recently, we abolished the major pathway of nonhomologous recombination in type I and type II strains of T. gondii by deleting the gene encoding the KU80 protein^1,2. The Δku80 strains behave normally during tachyzoite (acute) and bradyzoite (chronic) stages in vitro and in vivo and exhibit essentially a 100% frequency of homologous recombination. The Δku80 strains make functional genomic studies feasible on the single gene as well as on the genome scale^1-4.Here, we report methods for using type I and type II Δku80Δhxgprt strains to advance gene targeting approaches in T. gondii. We outline efficient methods for generating gene deletions, gene replacements, and tagged genes by targeted insertion or deletion of the hypoxanthine-xanthine-guanine phosphoribosyltransferase (HXGPRT) selectable marker. The described gene targeting protocol can be used in a variety of ways in Δku80 strains to advance functional analysis of the parasite genome and to develop single strains that carry multiple targeted genetic manipulations. The application of this genetic method and subsequent phenotypic assays will reveal fundamental and unique aspects of the biology of T. gondii and related significant human pathogens that cause malaria (Plasmodium sp.) and cryptosporidiosis (Cryptosporidium).     

新基因水稻 OsLSD1 的克隆及拟南芥和水稻类 LSD1 基因家族的生物信息学分析

类 LSD1 (LSD1-like) 基因家族是一类特殊的 C2C2 型锌指蛋白基因,编码植物特有的转录因子 . 目前已经研究的 2 个成员拟南芥 LSD1 (lesions stimulating disease resistance 1) 和 LOL1 (LSD-One-Like 1) 基因均参与植物细胞程序化死亡 (programmed cell death, PCD) 的调控 . 从水稻 cDNA 文库中克隆到 1 个类 LSD1 基因,命名为 OsLSD1. 该基因长 988 bp ,包含一个 432 bp 的开放阅读框,推导的氨基酸序列 (143 个氨基酸 ) 含有 3 个内部保守的锌指结构域 . DNA 印迹结果表明 OsLSD1 基因在水稻基因组中为单拷贝,且在根、茎和叶中表达 . 借助于生物信息学分析技术,从拟南芥和水稻数据库中各识别出 5 个和 7 个 ( 包括 OsLSD1) 类 LSD1 基因 . 分析了这些类 LSD1 基因的结构,蛋白质结构域组成 . 系统进化分析表明,无论基于编码区的核苷酸或氨基酸序列都可以将这些类 LSD1 基因分为 2 类 . 虽然不存在拟南芥或水稻特有的类 LSD1 蛋白,但有些结构域是水稻所特有的,也有些基因是来源于复制事件 .