Finding disease candidate genes by liquid association

A novel approach to finding candidate genes by using gene expression data through liquid association is developed and used to identify multiple sclerosis susceptibility candidate genes.     

Iroquois complex genes induce co-expression of rhodopsins in Drosophila

The Drosophila eye is a mosaic that results from the stochastic distribution of two ommatidial subtypes. Pale and yellow ommatidia can be distinguished by the expression of distinct rhodopsins and other pigments in their inner photoreceptors (R7 and R8), which are implicated in color vision. The pale subtype contains ultraviolet (UV)-absorbing Rh3 in R7 and blue-absorbing Rh5 in R8. The yellow subtype contains UV-absorbing Rh4 in R7 and green-absorbing Rh6 in R8. The exclusive expression of one rhodopsin per photoreceptor is a widespread phenomenon, although exceptions exist. The mechanisms leading to the exclusive expression or to co-expression of sensory receptors are currently not known. We describe a new class of ommatidia that co-express rh3 and rh4 in R7, but maintain normal exclusion between rh5 and rh6 in R8. These ommatidia, which are localized in the dorsal eye, result from the expansion of rh3 into the yellow-R7 subtype. Genes from the Iroquois Complex (Iro-C) are necessary and sufficient to induce co-expression in yR7. Iro-C genes allow photoreceptors to break the "one receptor-one neuron" rule, leading to a novel subtype of broad-spectrum UV- and green-sensitive ommatidia.     

Finding genes underlying risk of complex disease by linkage disequilibrium mapping

Identification of genes that harbor variation associated with inter-individual differences in risk of complex diseases remains one of the most challenging and important problems in human genetics. For genetic variants that are sufficiently common and have sufficiently large effects, direct tests of association through linkage disequilibrium with anonymous SNPs may prove effective. But the two critical parameters - the frequency of risk-inflating alleles and the magnitudes of their effect on risk - remain largely unknown. In this review we consider the latest information regarding the likely efficacy of the linkage disequilibrium mapping approach.     

Alzheimer disease periventricular white matter lesions exhibit specific proteomic profile alterations

The white matter (WM) represents approximately half the cerebrum volume and is profoundly affected in Alzheimer’s disease (AD). However, both the WM responses to AD as well as potential influences of this compartment to dementia pathogenesis remain comparatively neglected. Neuroimaging studies have revealed WM alterations are commonly associated with AD and renewed interest in examining the pathologic basis and importance of these changes.     

基于已知疾病基因构建共表达网络识别胃癌进展及预后相关非编码基因

本研究旨在总结整理已有胃癌研究的基础上,进一步挖掘出非编码基因在胃癌的进展及预后中起的关键作用。通过胃癌患者编码及非编码基因的表达数据,结合已知胃癌致病基因,进行编码基因与非编码基因的共表达计算,识别出由miRNA介导的并且与已知胃癌基因互作的lncRNA,挖掘出三者(mRNA-miRNA-lncRNA)相互作用的模块,进而对模块进行筛选,并对疾病相关的显著模块的基因进行生存分析。除已知的胃癌相关基因外,研究也使用了差异表达的胃癌基因,这些基因显著的富集在细胞增殖、细胞粘附、肌肉收缩、血管重塑、细胞分裂、染色体分离等生物过程,这些生物过程都与胃的基础功能及胃癌发生发展密切相关。分值最高的三元组模块内核心基因BGN在胃癌患者中显著高表达,而且和胃癌患者的预后显著相关;同时也发现该模块内的miRNA has-miRNA-153-5p和has-miRNA-5001-5p均为已证实的胃癌相关基因;模块内的mRNA和miRNA的表达异常可能是由于与他们显著相关的lncRNA的表达异常导致的。本研究为胃癌已知致病基因的表达异常研究找到了新突破口,潜在的胃癌相关的非编码基因的发现为胃癌预防与治疗提供了新的靶点,为未来的临床应用提供了依据。     

Intronic positioning maximizes co-expression and co-amplification of nonselectable heterologous genes

The overproduction of heterologous gene products in mammalian cells is often a prerequisite for studies of protein structure, function, and therapeutic efficacy. We report that recombinant fusion of a nonselectable reporter template into the intronic sequences of an amplifiable minigene gives dramatically enhanced co-expression efficiency in stable, primary transformants. Further incremental selective pressure for marker gene amplification results in the rapid acquisition of very high expression levels from the intronically positioned reporter. Nested within a constitutively expressing gene, these templates can be independently regulated at moderate gene dosage levels. Intronic positioning may therefore be of general utility for a variety of recombinant studies.     

Conserved co-expression for candidate disease gene prioritization

Background  

Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone.     

Finding edging genes from microarray data

MOTIVATION: A set of genes and their gene expression levels are used to classify disease and normal tissues. Due to the massive number of genes in microarray, there are a large number of edges to divide different classes of genes in microarray space. The edging genes (EGs) can be co-regulated genes, they can also be on the same pathway or deregulated by the same non-coding genes, such as siRNA or miRNA. Every gene in EGs is vital for identifying a tissue's class. The changing in one EG's gene expression may cause a tissue alteration from normal to disease and vice versa. Finding EGs is of biological importance. In this work, we propose an algorithm to effectively find these EGs. RESULT: We tested our algorithm with five microarray datasets. The results are compared with the border-based algorithm which was used to find gene groups and subsequently divide different classes of tissues. Our algorithm finds a significantly larger amount of EGs than does the border-based algorithm. As our algorithm prunes irrelevant patterns at earlier stages, time and space complexities are much less prevalent than in the border-based algorithm. AVAILABILITY: The algorithm proposed is implemented in C++ on Linux platform. The EGs in five microarray datasets are calculated. The preprocessed datasets and the discovered EGs are available at http://www3.it.deakin.edu.au/~phoebe/microarray.html.     

Finding novel genes in bacterial communities isolated from the environment

MOTIVATION: Novel sequencing techniques can give access to organisms that are difficult to cultivate using conventional methods. When applied to environmental samples, the data generated has some drawbacks, e.g. short length of assembled contigs, in-frame stop codons and frame shifts. Unfortunately, current gene finders cannot circumvent these difficulties. At the same time, the automated prediction of genes is a prerequisite for the increasing amount of genomic sequences to ensure progress in metagenomics. RESULTS: We introduce a novel gene finding algorithm that incorporates features overcoming the short length of the assembled contigs from environmental data, in-frame stop codons as well as frame shifts contained in bacterial sequences. The results show that by searching for sequence similarities in an environmental sample our algorithm is capable of detecting a high fraction of its gene content, depending on the species composition and the overall size of the sample. The method is valuable for hunting novel unknown genes that may be specific for the habitat where the sample is taken. Finally, we show that our algorithm can even exploit the limited information contained in the short reads generated by 454 technology for the prediction of protein coding genes. AVAILABILITY: The program is freely available upon request.     

Etiology of specific molecular alterations in human malignancies

Cancer results from multiple genomic changes that affect DNA and its gene expression. The DNA sequences may be gained, lost or amplified, or translocated into different parts of the genome to form a fusion gene with oncogenic properties. The occurrence of specific chromosomal aberrations may be restricted to only one cancer type and it may be considered a primary carcinogenic event. Furthermore, the aberration profiles may be used to cluster tumors with similar origins. A variety of techniques exist for the detection of specific chromosomal and gene expression changes. However, the etiology of these molecular alterations remains unclear. Here we discuss the roles of Helicobacter pylori and asbestos burden as carcinogens that cause gastric cancer, mesothelioma and lung cancer.     

Mining the key genes for ventilator-induced lung injury using co-expression network analysis

Mechanical ventilation is extensively adopted in general anesthesia and respiratory failure management, but it can also induce ventilator-induced lung injury (VILI). Therefore, it is of great urgency to explore the mechanisms involved in the VILI pathogenesis, which might contribute to its future prevention and treatment. Four microarray datasets from the GEO database were selected in our investigation, and were subjected to the Weighted Gene Co-Expression Network Analysis (WGCNA) to identify the VILI-correlated gene modules. The limma package in R software was used to identify the differentially expressed genes (DEGs) between the VILI and control groups. WGCNA was constructed by merging the {"type":"entrez-geo","attrs":{"text":"GSE9314","term_id":"9314"}}GSE9314, {"type":"entrez-geo","attrs":{"text":"GSE9368","term_id":"9368"}}GSE9368, {"type":"entrez-geo","attrs":{"text":"GSE11434","term_id":"11434"}}GSE11434 and {"type":"entrez-geo","attrs":{"text":"GSE11662","term_id":"11662"}}GSE11662 datasets. A total of 49 co-expression network modules were determined as associated with VILI. The intersected genes between hub genes screened from DEGs for VILI and those identified using WGCNA were as follows: Tlr2, Hmox1, Serpine1, Mmp9, Il6, Il1b, Ptgs2, Fos and Atf3, which were determined to be key genes for VILI. Those key genes were validated by {"type":"entrez-geo","attrs":{"text":"GSE86229","term_id":"86229"}}GSE86229 and quantitative PCR (qPCR) experiment to have significantly statistical difference in their expression between the VILI and control groups. In a nutshell, nine key genes with expression differences in VILI were screened by WGCNA by integrating multiple datasets.