Analysis Of Large-scale Expression Data Research Articles

Gene Expression Biomarkers in the Brain of a Mouse Model for Alzheimer's Disease: Mining of Microarray Data by Logic Classification and Feature Selection

The identification of early and stage-specific biomarkers for Alzheimer's disease (AD) is critical, as the development of disease-modification therapies may depend on the discovery and validation of such markers. The identification of early reliable biomarkers depends on the development of new diagnostic algorithms to computationally exploit the information in large biological datasets. To identify potential biomarkers from mRNA expression profile data, we used the Logic Mining method for the unbiased analysis of a large microarray expression dataset from the anti-NGF AD11 transgenic mouse model. The gene expression profile of AD11 brain regions was investigated at different neurodegeneration stages by whole genome microarrays. A new implementation of the Logic Mining method was applied both to early (1-3 months) and late stage (6-15 months) expression data, coupled to standard statistical methods. A small number of "fingerprinting" formulas was isolated, encompassing mRNAs whose expression levels were able to discriminate between diseased and control mice. We selected three differential "signature" genes specific for the early stage (Nudt19, Arl16, Aph1b), five common to both groups (Slc15a2, Agpat5, Sox2ot, 2210015, D19Rik, Wdfy1), and seven specific for late stage (D14Ertd449, Tia1, Txnl4, 1810014B01Rik, Snhg3, Actl6a, Rnf25). We suggest these genes as potential biomarkers for the early and late stage of AD-like neurodegeneration in this model and conclude that Logic Mining is a powerful and reliable approach for large scale expression data analysis. Its application to large expression datasets from brain or peripheral human samples may facilitate the discovery of early and stage-specific AD biomarkers.

Identifying subtle interrelated changes in functional gene categories using continuous measures of gene expression

Analysis of large-scale expression data is greatly facilitated by the availability of gene ontologies (GOs). Many current methods test whether sets of transcripts annotated with specific ontology terms contain an excess of 'changed' transcripts. This approach suffers from two main limitations. First, since gene expression is continuous rather than discrete, designating a gene as changed or unchanged is arbitrary and oblivious to the actual magnitude of the change. Second, by considering only the number of changed genes, finer changes in expression patterns associated with the category may be ignored. Since genes generally participate in multiple networks, widespread and subtle modifications in expression patterns are at least as important as extreme increases/decreases of a few genes. Numerical simulations confirm that incorporating continuous measures of gene expression for all measured transcripts yields detection of considerably more subtle changes. Applying continuous measures to microarray data from brains of mice injected with the Parkinsonian neurotoxin, MPTP, enables detection of changes in various biologically relevant GO terms, many of which are overlooked by discrete approaches.

Revealing modular organization in the yeast transcriptional network.

Standard clustering methods can classify genes successfully when applied to relatively small data sets, but have limited use in the analysis of large-scale expression data, mainly owing to their assignment of a gene to a single cluster. Here we propose an alternative method for the global analysis of genome-wide expression data. Our approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods. We use our method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements. By applying our algorithm systematically to all of the available expression data on Saccharomyces cerevisiae, we identify a comprehensive set of overlapping transcriptional modules. Our results provide functional predictions for numerous genes, identify relations between modules and present a global view on the transcriptional network.

Promoter analysis of co-regulated genes in the yeast genome

The use of high density DNA arrays to monitor gene expression at a genome-wide scale constitutes a fundamental advance in biology. In particular, the expression pattern of all genes in Saccharomyces cerevisiae can be interrogated using microarray analysis where cDNAs are hybridized to an array of more than 6000 genes in the yeast genome. In an effort to build a comprehensive Yeast Promoter Database and to develop new computational methods for mapping upstream regulatory elements, we started recently in an on going collaboration with experimental biologists on analysis of large-scale expression data. It is well known that complex gene expression patterns result from dynamic interacting networks of genes in the genetic regulatory circuitry. Hierarchical and modular organization of regulatory DNA sequence elements are important information for our understanding of combinatorial control of gene expression. As a bioinformatics attempt in this new direction, we have done some computational exploration of various initial experimental data. We will use cell-cycle regulated gene expression as a specific example to demonstrate how one may extract promoter information computationally from such genome-wide screening. Full report of the experiments and of the complete analysis will be published elsewhere when all the experiments are to be finished later in this year (Spellman, P.T., et al. 1998. Mol. Biol. Cell 9, 3273–3297).