Cross-platform expression microarray performance in a mouse model of mitochondrial disease therapy.
Cross-platform expression microarray performance in a mouse model of mitochondrial disease therapy.
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DOI：
10.1016/j.ymgme.2009.10.179
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发表时间：
2010-03
影响因子：
3.8
通讯作者：
Falk, Marni J.
中科院分区：
文献类型：
--
作者：
Zhang, Zhe;Gasser, David L.;Rappaport, Eric F.;Falk, Marni J.
微阵列表达谱已成为一个有价值的工具，在评估代谢性疾病的遗传后果。虽然3′-偏倚基因表达微阵列平台是第一代广泛可用的平台，但具有更先进内容和/或更高成本效率的新平台正在逐渐出现。解读这些用于代谢途径水平分析的各种平台的相对优势和劣势可能是令人生畏的。我们试图确定四个领先的商用表达阵列平台相对于生物学研究的实际优势和劣势，以及评估跨平台数据集成用于生化途径分析的可行性。在基线或用抗氧化剂/抗高血压药普罗布考治疗后，提取原发性辅酶Q缺乏的B6.Alb/cre，Pdss 2loxP/loxP小鼠的肝脏RNA。制备靶RNA样品，并与Affyphase 430 2.0、Affyphase Gene 1.0 ST、Affyphase Exon 1.0 ST和Illumina小鼠WG-6表达阵列杂交。将所有平台上的探针重新映射到当前版本的小鼠基因组中的编码序列。数据处理和统计分析由R/Bioconductor函数进行，通路分析由KEGG Atlas和GSEA进行。表达测量在不同平台之间通常是一致的。然而，密集的探针水平比较表明，探针位置的差异是平台间方差的主要来源。此外，在低或中等水平表达的基因比高表达的基因具有更低的平台间重现性。所有平台在样品组之间显示出相似的差异表达模式，其中类固醇生物合成一致地被鉴定为普罗布考处理下调最多的代谢途径。这项工作为代谢疾病研究人员提供了及时的指导，使最终用户能够做出明智的决定，选择最适合特定研究项目目标的表达微阵列平台。还证明了生物化学途径表达数据的成功跨平台整合，特别是对于注释良好和高表达的基因。然而，基因水平表达数据的整合受到个体平台探针设计和靶基因表达水平的限制。生化途径数据的跨平台分析将需要额外的数据处理和新的计算生物信息学工具，以解决独特的统计挑战。
Microarray expression profiling has become a valuable tool in the evaluation of the genetic consequences of metabolic disease. Although 3′-biased gene expression microarray platforms were the first generation to have widespread availability, newer platforms are gradually emerging that have more up-to-date content and/or higher cost efficiency. Deciphering the relative strengths and weaknesses of these various platforms for metabolic pathway level analyses can be daunting. We sought to determine the practical strengths and weaknesses of four leading commercially-available expression array platforms relative to biologic investigations, as well as assess the feasibility of cross-platform data integration for purposes of biochemical pathway analyses. Liver RNA from B6.Alb/cre,Pdss2loxP/loxP mice having primary Coenzyme Q deficiency was extracted either at baseline or following treatment with an antioxidant/antihyperlipidemic agent, probucol. Target RNA samples were prepared and hybridized to Affymetrix 430 2.0, Affymetrix Gene 1.0 ST, Affymetrix Exon 1.0 ST, and Illumina Mouse WG-6 expression arrays. Probes on all platforms were re-mapped to coding sequences in the current version of the mouse genome. Data processing and statistical analysis were performed by R/Bioconductor functions, and pathway analyses were carried out by KEGG Atlas and GSEA. Expression measurements were generally consistent across platforms. However, intensive probe-level comparison suggested that differences in probe locations were a major source of inter-platform variance. In addition, genes expressed at low or intermediate levels had lower inter-platform reproducibility than highly expressed genes. All platforms showed similar patterns of differential expression between sample groups, with steroid biosynthesis consistently identified as the most down-regulated metabolic pathway by probucol treatment. This work offers a timely guide for metabolic disease investigators to enable informed end-user decisions regarding choice of expression microarray platform best-suited to specific research project goals. Successful cross-platform integration of biochemical pathway expression data is also demonstrated, especially for well-annotated and highly-expressed genes. However, integration of gene-level expression data is limited by individual platform probe design and the expression level of target genes. Cross-platform analyses of biochemical pathway data will require additional data processing and novel computational bioinformatics tools to address unique statistical challenges.
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PDSS2突变小鼠的原发性辅酶缺乏会引起孤立的肾脏疾病。
影响因子：
4.5
作者：
Peng, Min;Falk, Marni J.;Haase, Volker H.;King, Rhonda;Polyak, Erzsebet;Selak, Mary;Yudkoff, Marc;Hancock, Wayne W.;Meade, Ray;Saiki, Ryoichi;Lunceford, Adam L.;Clarke, Catherine F.;Gasser, David L.
通讯作者：
Gasser, David L.
影响因子：
3.7
作者：
Xing, Yi;Kapur, Karen;Wong, Wing Hung
通讯作者：
Wong, Wing Hung
影响因子：
14.9
作者：
Barnes M;Freudenberg J;Thompson S;Aronow B;Pavlidis P
通讯作者：
Pavlidis P
影响因子：
4.4
作者：
McHale, Cliona M.;Zhang, Luoping;Lan, Qing;Li, Guilan;Hubbard, Alan E.;Forrest, Matthew S.;Vermeulen, Roel;Chen, Jinsong;Shen, Min;Rappaport, Stephen M.;Yin, Songnian;Smith, Martyn T.;Rothman, Nathaniel
通讯作者：
Rothman, Nathaniel
DOI：
10.1073/pnas.0401994101
发表时间：
2004-06-22
影响因子：
11.1
作者：
Rhodes, DR;Yu, JJ;Chinnaiyan, AM
通讯作者：
Chinnaiyan, AM