脱发相关差异表达基因的生物信息学分析

利用生物信息学方法分析脱发相关差异表达基因,有望帮助了解脱发发生发展的分子机制。本研究从NCBI的子数据库GEO中选择基因表达谱GSE45512和GSE45513数据集,利用R语言limma工具包,筛选出两个物种斑秃样本与正常样本的共同显著差异表达基因。对这部分基因进行功能注释和蛋白互作网络分析,同时对全部差异表达基因进行基因集富集分析。结果发现,人头皮斑秃样本共筛选出225个差异表达基因;C3H/HeJ小鼠自发斑秃皮肤样本共筛选出337个差异表达基因;两个物种的共同显著差异表达基因有23个。GO功能富集分析和蛋白互作网络分析显示,这部分差异基因显著富集于免疫相关功能,并且彼此间存在蛋白互作关系。基因集富集分析显示两个物种的差异基因都能显著富集到趋化因子信号通路、细胞因子受体相互作用、金葡菌感染及抗原加工与呈递通路;而且人的下调差异基因不仅映射到了人类表型数据库的脱发表型,也映射到皮肤附属物病理相关表型。综上所述,本研究通过生物信息方法分析脱发皮肤组织与正常皮肤组织的差异表达基因,最终筛选出23个在人和小鼠中共同存在的显著差异表达基因;此外,分析发现脱发与免疫过程及皮肤附属物病变密切相关,这些结果为脱发的诊断和治疗提供了新思路。     

Similarities of ordered gene lists

MOTIVATION: Many applications of microarray technology in clinical cancer studies aim at detecting molecular features for refined diagnosis. In this paper, we follow an opposite rationale: we try to identify common molecular features shared by phenotypically distinct types of cancer using a meta-analysis of several microarray studies. We present a novel algorithm to uncover that two lists of differentially expressed genes are similar, even if these similarities are not apparent to the eye. The method is based on the ordering in the lists. RESULTS: In a meta-analysis of five clinical microarray studies we were able to detect significant similarities in five of the ten possible comparisons of ordered gene lists. We included studies, where not a single gene can be significantly associated to outcome. The detection of significant similarities of gene lists from different microarray studies is a novel and promising approach. It has the potential to improve upon specialized cancer studies by exploring the power of several studies in one single analysis. Our method is complementary to previous methods in that it does not rely on strong effects of differential gene expression in a single study but on consistent ones across multiple studies.     

桑树桑黄菌丝体和子实体基因差异表达分析

通过对桑树桑黄Sanghuangporus sanghuang菌丝体和子实体2个不同生长阶段的转录组进行分析,为研究桑黄子实体生长发育相关机制奠定基础。采用Illumina测序技术,对桑树桑黄菌株S23菌丝体和子实体2个不同生长发育阶段进行了全转录组测序。将转录组测序reads比对到参考序列上,菌丝体测序样本的reads比对率为82.89%;子实体测序样本的reads比对率为83%。基因差异表达分析显示,与菌丝体相比,子实体中显著上调表达基因为2 898个,显著下调表达基因为1 965个。经过Blast nr比对发现,桑黄菌在子实体阶段表达量上升的基因主要与各种氧化酶活性、疏水蛋白等相关;表达量下降的基因主要与糖类、氨基酸结合、运输等相关。基因本体（gene ontology,GO）富集分析表明,菌丝体及子实体两个阶段与跨膜转运相关的差异表达基因富集明显。代谢通路（pathway）富集分析表明,类固醇生物合成、精氨酸生物合成、丝裂原活化蛋白激酶（mitogen-activated protein kinase,MAPK）信号通路等差异基因富集明显。     

Identifying Gene Interaction Enrichment for Gene Expression Data

Gene set analysis allows the inclusion of knowledge from established gene sets, such as gene pathways, and potentially improves the power of detecting differentially expressed genes. However, conventional methods of gene set analysis focus on gene marginal effects in a gene set, and ignore gene interactions which may contribute to complex human diseases. In this study, we propose a method of gene interaction enrichment analysis, which incorporates knowledge of predefined gene sets (e.g. gene pathways) to identify enriched gene interaction effects on a phenotype of interest. In our proposed method, we also discuss the reduction of irrelevant genes and the extraction of a core set of gene interactions for an identified gene set, which contribute to the statistical variation of a phenotype of interest. The utility of our method is demonstrated through analyses on two publicly available microarray datasets. The results show that our method can identify gene sets that show strong gene interaction enrichments. The enriched gene interactions identified by our method may provide clues to new gene regulation mechanisms related to the studied phenotypes. In summary, our method offers a powerful tool for researchers to exhaustively examine the large numbers of gene interactions associated with complex human diseases, and can be a useful complement to classical gene set analyses which only considers single genes in a gene set.     

Chronic Ethanol Exposure Produces Time- and Brain Region-Dependent Changes in Gene Coexpression Networks

Repeated ethanol exposure and withdrawal in mice increases voluntary drinking and represents an animal model of physical dependence. We examined time- and brain region-dependent changes in gene coexpression networks in amygdala (AMY), nucleus accumbens (NAC), prefrontal cortex (PFC), and liver after four weekly cycles of chronic intermittent ethanol (CIE) vapor exposure in C57BL/6J mice. Microarrays were used to compare gene expression profiles at 0-, 8-, and 120-hours following the last ethanol exposure. Each brain region exhibited a large number of differentially expressed genes (2,000-3,000) at the 0- and 8-hour time points, but fewer changes were detected at the 120-hour time point (400-600). Within each region, there was little gene overlap across time (~20%). All brain regions were significantly enriched with differentially expressed immune-related genes at the 8-hour time point. Weighted gene correlation network analysis identified modules that were highly enriched with differentially expressed genes at the 0- and 8-hour time points with virtually no enrichment at 120 hours. Modules enriched for both ethanol-responsive and cell-specific genes were identified in each brain region. These results indicate that chronic alcohol exposure causes global ‘rewiring‘ of coexpression systems involving glial and immune signaling as well as neuronal genes.     

Novel transcriptome data analysis implicates circulating microRNAs in epigenetic inheritance in mammals

Experimental evidence supports a role of mobile small non-coding RNAs in mediating soma to germline hereditary information transfer in epigenetic inheritance in plants and worms. Similar evidence in mammals has not been reported so far. In this bioinformatic analysis, differentially expressed microRNAs (miRNAs) or mRNAs reported previously in genome level expression profiling studies related to or relevant in epigenetic inheritance in mammals were examined for circulating miRNA association. The reported sets of differentially expressed miRNAs or miRNAs that are known to target the reported sets of differentially expressed genes, in that order, showed enrichment of circulating miRNAs across environmental factors, tissues, life cycle stages, generations, genders and species. Circulating miRNAs commonly representing the expression profiles enriched various epigenetic processes. These results provide bioinformatic evidence for a role of circulating miRNAs in epigenetic inheritance in mammals.     

A global view of the oncogenic landscape in nasopharyngeal carcinoma: an integrated analysis at the genetic and expression levels

Previous studies have reported that the tumour cells of nasopharyngeal carcinoma (NPC) exhibit recurrent chromosome abnormalities. These genetic changes are broadly assumed to lead to changes in gene expression which are important for the pathogenesis of this tumour. However, this assumption has yet to be formally tested at a global level. Therefore a genome wide analysis of chromosome copy number and gene expression was performed in tumour cells micro-dissected from the same NPC biopsies. Cellular tumour suppressor and tumour-promoting genes (TSG, TPG) and Epstein-Barr Virus (EBV)-encoded oncogenes were examined. The EBV-encoded genome maintenance protein EBNA1, along with the putative oncogenes LMP1, LMP2 and BARF1 were expressed in the majority of NPCs that were analysed. Significant downregulation of expression in an average of 76 cellular TSGs per tumour was found, whilst a per-tumour average of 88 significantly upregulated, TPGs occurred. The expression of around 60% of putative TPGs and TSGs was both up-and down-regulated in different types of cancer, suggesting that the simplistic classification of genes as TSGs or TPGs may not be entirely appropriate and that the concept of context-dependent onco-suppressors may be more extensive than previously recognised. No significant enrichment of TPGs within regions of frequent genomic gain was seen but TSGs were significantly enriched within regions of frequent genomic loss. It is suggested that loss of the FHIT gene may be a driver of NPC tumourigenesis. Notwithstanding the association of TSGs with regions of genomic loss, on a gene by gene basis and excepting homozygous deletions and high-level amplification, there is very little correlation between chromosomal copy number aberrations and expression levels of TSGs and TPGs in NPC.     

Impact of DNA microarray data transformation on gene expression analysis - comparison of two normalization methods

Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory results. In general, our results provide an additional piece of evidence that the normalization method can profoundly influence final results of DNA microarray-based analysis. The impact of the normalization method depends greatly on the algorithm employed. Consequently, the normalization procedure must be carefully considered and optimized for each individual data set.     

A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors

MOTIVATION: The analysis of gene expression data in its chromosomal context has been a recent development in cancer research. However, currently available methods fail to account for variation in the distance between genes, gene density and genomic features (e.g. GC content) in identifying increased or decreased chromosomal regions of gene expression. RESULTS: We have developed a model-based scan statistic that accounts for these aspects of the complex landscape of the human genome in the identification of extreme chromosomal regions of gene expression. This method may be applied to gene expression data regardless of the microarray platform used to generate it. To demonstrate the accuracy and utility of this method, we applied it to a breast cancer gene expression dataset and tested its ability to predict regions containing medium-to-high level DNA amplification (DNA ratio values >2). A classifier was developed from the scan statistic results that had a 10-fold cross-validated classification rate of 93% and a positive predictive value of 88%. This result strongly suggests that the model-based scan statistic and the expression characteristics of an increased chromosomal region of gene expression can be used to accurately predict chromosomal regions containing amplified genes. AVAILABILITY: Functions in the R-language are available from the author upon request. CONTACT: fcouples@umich.edu.     

Prevalent function of genome loci associated with development of multiple sclerosis as revealed by GWAS and eQTL analysis

We studied genome distribution of single-nucleotide polymorphisms (SNP) associated with development of multiple sclerosis and identified genome segments enriched in such polymorphisms. Some SNPs observed in identified segments are also local or distal eQTLs (expression quantitative trait loci) for a number of genes expressed in the blood or the nervous system. We analyzed lists of genes expression of which depends on these eQTLs, separately for the blood and the nervous system, and identified GO functions overrepresented in such gene lists. An antigen processing and presentation via MHC class II appeared to be the main gene functions either in the blood or in the nervous system. We identified a set of SNPs genetically linked with at least three SNPs associated with multiple sclerosis in GWAS, which includes eQTLs for all overrepresented functions. These SNPs and genes are located in a rather short locus on chromosome 14 presumably containing IGHG genes. SNPs from this genome segment affect expression of the HLA-DOB, HLA-DQA1, HLA-DQA2, and HLA-DQB1 genes both in the blood and in the nervous system. The results we obtained made it possible to suggest the mechanisms of multiple sclerosis development.     

A brain region-specific predictive gene map for autism derived by profiling a reference gene set

Molecular underpinnings of complex psychiatric disorders such as autism spectrum disorders (ASD) remain largely unresolved. Increasingly, structural variations in discrete chromosomal loci are implicated in ASD, expanding the search space for its disease etiology. We exploited the high genetic heterogeneity of ASD to derive a predictive map of candidate genes by an integrated bioinformatics approach. Using a reference set of 84 Rare and Syndromic candidate ASD genes (AutRef84), we built a composite reference profile based on both functional and expression analyses. First, we created a functional profile of AutRef84 by performing Gene Ontology (GO) enrichment analysis which encompassed three main areas: 1) neurogenesis/projection, 2) cell adhesion, and 3) ion channel activity. Second, we constructed an expression profile of AutRef84 by conducting DAVID analysis which found enrichment in brain regions critical for sensory information processing (olfactory bulb, occipital lobe), executive function (prefrontal cortex), and hormone secretion (pituitary). Disease specificity of this dual AutRef84 profile was demonstrated by comparative analysis with control, diabetes, and non-specific gene sets. We then screened the human genome with the dual AutRef84 profile to derive a set of 460 potential ASD candidate genes. Importantly, the power of our predictive gene map was demonstrated by capturing 18 existing ASD-associated genes which were not part of the AutRef84 input dataset. The remaining 442 genes are entirely novel putative ASD risk genes. Together, we used a composite ASD reference profile to generate a predictive map of novel ASD candidate genes which should be prioritized for future research.