Elevated ATG5 expression in autoimmune demyelination and multiple sclerosis

Multiple sclerosis (MS) is an inflammatory central nervous system (CNS) disorder characterized by T cell mediated demyelination. In MS, prolonged T cell survival and increased T cell proliferation have been linked to disease relapse and progression. Recently, the autophagy related gene 5 (Atg5) has been shown to modulate T cell survival. In this study, we examined the expression of Atg5 using both a mouse model of autoimmune demyelination as well as blood and brain tissues from MS cases. Quantitative real-time PCR analysis of RNA isolated from blood samples of experimental autoimmune encephalomyelitis (EAE) mice revealed a strong correlation between Atg5 expression and clinical disability. Analysis of protein extracted from these cells confirmed both upregulation and post-translational modification of Atg5 the latter of which was positively correlated with EAE severity. Analysis of RNA extracted from T cells isolated by negative selection, indicated that Atg5 expression was significantly elevated in individuals with active relapsing-remitting MS compared to non-diseased controls. Brain tissue sections from relapsing-remitting MS cases examined by immunofluorescent histochemistry suggested that encephalitogenic T cells are a source of Atg5 expression in MS brain samples. Together these data suggest that increased T cell expression of Atg5 may contribute to inflammatory demyelination in MS.     

Validation of rat reference genes for improved quantitative gene expression analysis using low density arrays

Real-time PCR has become increasingly important in gene expression profiling research, and it is widely agreed that normalized data are required for accurate estimates of messenger RNA (mRNA) expression. With increased gene expression profiling in preclinical research and toxicogenomics, a need for reference genes in the rat has emerged, and the studies in this area have not yet been thoroughly evaluated. The purpose of our study was to evaluate a panel of rat reference genes for variation of gene expression in different tissue types. We selected 48 known target genes based on their putative invariability. The gene expression of all targets was examined in 11 types of rat tissues using TaqMan low density array (LDA) technology. The variability of each gene was assessed using a two-step statistical model. The analysis of mean expression using multiple reference genes was shown to provide accurate and reliable normalized expression data. The least five variable genes from each specific tissue were recommended for future tissue-specific studies. Finally, a subset of investigated rat reference genes showing the least variation is recommended for further evaluation using the LDA platform. Our work should considerably enhance a researcher's ability to simply and efficiently identify appropriate reference genes for given experiments.     

Gene microarray analysis of multiple sclerosis lesions

Multiple sclerosis (MS) is an autoimmune disease affecting central nervous system white matter. The cause is unknown. It is thought that environmental factors trigger an immune response against myelin antigens in a genetically susceptible individual. The characteristic lesion of MS seen in the brain is a plaque, an area of inflammation, demyelination and glial reaction or ‘sclerosis’. Several recent studies have examined gene expression in MS plaques on a large scale using microarray technology. The involvement of immune-related genes has been confirmed, and many new genes not previously associated with MS lesions have been identified. Microarray studies are significant in identifying potential new targets for therapy.     

Predicting the Phenotypic Values of Physiological Traits Using SNP Genotype and Gene Expression Data in Mice

Predicting phenotypes using genome-wide genetic variation and gene expression data is useful in several fields, such as human biology and medicine, as well as in crop and livestock breeding. However, for phenotype prediction using gene expression data for mammals, studies remain scarce, as the available data on gene expression profiling are currently limited. By integrating a few sources of relevant data that are available in mice, this study investigated the accuracy of phenotype prediction for several physiological traits. Gene expression data from two tissues as well as single nucleotide polymorphisms (SNPs) were used. For the studied traits, the variance of the effects of the expression levels was more likely to differ among the genes than were the effects of SNPs. For the glucose concentration, the total cholesterol amount, and the total tidal volume, the accuracy by cross validation tended to be higher when the gene expression data rather than the SNP genotype data were used, and a statistically significant increase in the accuracy was obtained when the gene expression data from the liver were used alone or jointly with the SNP genotype data. For these traits, there were no additional gains in accuracy from using the gene expression data of both the liver and lung compared to that of individual use. The accuracy of prediction using genes that were selected differently was examined; the use of genes with a higher tissue specificity tended to result in an accuracy that was similar to or greater than that associated with the use of all of the available genes for traits such as the glucose concentration and total cholesterol amount. Although relatively few animals were evaluated, the current results suggest that gene expression levels could be used as explanatory variables. However, further studies are essential to confirm our findings using additional animal samples.     

Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo

MOTIVATION: Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods. RESULTS: We present a Bayesian semi-supervised learning method, called BGEN, that improves upon supervised and unsupervised methods by both capturing relevant expression profiles and using prior biological knowledge from literature and experimental validation. Unlike currently available semi-supervised learning methods, this new method trains a kernel classifier based on labeled and unlabeled gene expression examples. The semi-supervised trained classifier can then be used to efficiently classify the remaining genes in the dataset. Moreover, we model the confidence of microarray probes and probabilistically combine multiple probe predictions into gene predictions. We apply BGEN to identify genes involved in the development of a specific cell lineage in the C. elegans embryo, and to further identify the tissues in which these genes are enriched. Compared to K-means clustering and SVM classification, BGEN achieves higher sensitivity and specificity. We confirm certain predictions by biological experiments. AVAILABILITY: The results are available at http://www.csail.mit.edu/~alanqi/projects/BGEN.html.     

Gene-Expression Profiling of Experimental Autoimmune Encephalomyelitis

Experimental autoimmune encephalomyelitis (EAE) is a mouse model that serves as an experimental tool for studying the etiology, pathogenesis, as well as new therapeutic approaches of multiple sclerosis (MS). EAE is a polygenic chronic inflammatory demyelinating disease of the nervous system that involves the interaction between genetic and environmental factors. Previous studies have identified multiple quantitative trait loci (QTL) controlling different aspects of disease pathogenesis. However, progress in identifying new susceptibility genes outside the MHC locus has been slow. With the advent of new global methods for genetic analysis such as large-scale sequencing, gene expression profiling combined with classic linkage analysis and congenic and physical mapping progress is considerably accelerating. Here we review our preliminary work on the use of gene expression mapping to identify new putative genetic pathways contributing to the pathogenesis of EAE.     

Individual-specific variation of gene expression in peripheral blood leukocytes

DNA microarray technology is used to determine gene expression profiles of various cell types, especially abnormal cells, such as cancer. By contrast, relatively little attention has been given to expression profiling of normal tissues. Here we describe studies of gene expression in peripheral blood leukocytes (PBL) from normal individuals sampled multiple times over periods ranging from several weeks up to 6 months. We demonstrate stable patterns of gene expression that differ between individuals. Among the genes whose expression varies by individual is a group of genes responsive to interferon stimulation. Certain individuals ( approximately 10-20% of those tested) showed higher baseline levels and lower inducibility of these genes in response to in vitro interferon stimulation. These studies demonstrate the feasibility of using DNA microarrays to measure the variations in gene expression of PBL from different individuals in response to environmental and genetic factors.     

Multiplexed microsphere diagnostic tools in gene expression applications: factors and futures

Microarrays have received significant attention in recent years as scientists have firstly identified factors that can produce reduced confidence in gene expression data obtained on these platforms, and secondly sought to establish laboratory practices and a set of standards by which data are reported with integrity. Microsphere-based assays represent a new generation of diagnostics in this field capable of providing substantial quantitative and qualitative information from gene expression profiling. However, for gene expression profiling, this type of platform is still in the demonstration phase, with issues arising from comparative studies in the literature not yet identified. It is desirable to identify potential parameters that are established as important in controlling the information derived from microsphere-based hybridizations to quantify gene expression. As these evolve, a standard set of parameters will be established that are required to be provided when data are submitted for publication. Here we initiate this process by identifying a number of parameters we have found to be important in microsphere-based assays designed for the quantification of low abundant genes which are variable between studies.     

Redox regulation of cytokine-mediated inhibition of myelin gene expression in human primary oligodendrocytes

Multiple sclerosis (MS) is a chronic autoimmune demyelinating disorder of the central nervous system (CNS) of unknown etiology. Several studies have shown that demyelination in MS is caused by proinflammatory mediators which are released by perivascular infiltrates and/or activated glial cells. To understand if proinflammatory mediators such as IL (interleukin)-1beta and TNF (tumor necrosis factor)-alpha are capable of modulating the expression of myelin-specific genes, we investigated the effect of these cytokines on the expression of myelin basic protein (MBP), 2',3'-cyclic nucleotide 3'-phosphodiesterase (CNPase), myelin oligodendrocyte glycoprotein (MOG), and proteolipid protein (PLP) in human primary oligodendrocytes. Interestingly, both IL-1beta and TNF-alpha markedly inhibited the expression of MOG, CNPase, and PLP but not MBP, the effect that was blocked by antioxidants such as N-acetylcysteine (NAC) and pyrrolidine dithiocarbamate (PDTC). Consistently, oxidants and prooxidants like H(2)O(2) and diamide also markedly inhibited the expression of MOG, CNPase, and PLP. Furthermore, both IL-1beta and TNF-alpha induced the production of H(2)O(2). Taken together, these studies suggest that proinflammatory cytokines inhibit the expression of myelin genes in human primary oligodendrocytes through the alteration of cellular redox.     

Genomic Portraits of the Nervous System in Health and Disease

As the human genome project moves toward its goal of sequencing the entire human genome, gene expression profiling by DNA microarray technology is being employed to rapidly screen genes for biological information. In this review, we will introduce DNA microarray technology, outline the basic experimental paradigms and data analysis methods, and then show with some examples how gene expression profiling can be applied to the study of the central nervous system in health and disease.     

Use of SMART-generated cDNA for gene expression studies in multiple human tumors

We demonstrate here that SMART PCR-amplified cDNAs arrayed on a nylon membrane are suitable for high-throughput tissue expression profiling when starting biological materials are limited. We show that SMART cDNA accurately reflects gene expression patterns found in total RNA by comparing the expression level of several target genes in SMART PCR-amplified cDNAs and their corresponding total RNAs. We also arrayed cDNAs from 68 matched tumor and normal samples on a nylon membrane to determine whether SMART PCR-amplified cDNA could be used for detecting differentially expressed genes in these tissues. These arrays containing normalized tumor and normal cDNAs were hybridized with probes for glutathione peroxidase and gelsolin. The hybridization results revealed cancer-related and patient-specific gene expression differences between tumor and normal tissues for these genes. These studies show that SMART PCR-amplified cDNAs maintain the complexity of the original mRNA population and are thus suitable for high-throughput studies to compare the relative abundance of target genes and to detect differentially expressed genes in a wide variety of tissues simultaneously.     

基于单细胞转录组的多级别胶质瘤异质性及免疫微环境分析揭示了潜在的预后生物标志物

脑胶质瘤(glioma)是中枢神经系统最常见的内在肿瘤,具有发病率高、预后较差等特点。本研究旨在鉴定多形性胶质母细胞瘤(glioblastoma multiforme,GBM)和低级别胶质瘤(lower-grade gliomas, LGG)之间的差异表达基因(differentially expressed genes, DEGs),以探讨不同级别胶质瘤的预后影响因素。从NCBI基因表达综合数据库中收集了胶质瘤的单细胞转录组测序数据,其中包括来自3个数据集的共29 097个细胞样本。对于不同分级的人脑胶质瘤进行分析,经过滤得到21 071个细胞,通过基因本体分析、京都基因与基因组百科全书途径分析,从差异表达基因中筛选出70个基因,我们通过查阅文献,聚焦到delta样典型Notch配体3 (delta like canonical Notch ligand 3,DLL3)这个基因。基于TCGA的基因表达谱交互分析(gene expression profiling interactive analysis, GEPIA)数据库用于探索LGG和GBM中DLL3基因的表达差异,采用基因表达...