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Altered expression of RNA splicing proteins in Alzheimer's disease patients: evidence from two microarray studies.

ABSTRACT: BACKGROUND/AIMS:Dysregulation of pre-mRNA splicing from an altered expression of RNA splice-regulatory proteins may act as the convergence point underlying aberrant gene expression changes in Alzheimer's disease (AD). METHODS:Two microarray datasets from a control/AD postmortem brain cohort of 31 subjects - 9 controls and 22 AD subjects (National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database) - were used. RESULTS:Between the two microarray studies, the expression of six splice-regulatory protein genes showed concordant changes in AD. These genes were then correlated with gene expression changes of transcripts reported to be altered in AD. Amyloid beta (A4) precursor protein and tropomyosin receptor kinase B transcripts were found to correlate significantly with the same splice-regulatory proteins in the two studies. CONCLUSION:This study highlights a susceptibility network that can potentially link a number of susceptibility genes.

SUBMITTER: Wong J

PROVIDER: S-EPMC3617979 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Altered expression of RNA splicing proteins in Alzheimer's disease patients: evidence from two microarray studies.

Wong Jenny J

Dementia and geriatric cognitive disorders extra 20130101 1

<h4>Background/aims</h4>Dysregulation of pre-mRNA splicing from an altered expression of RNA splice-regulatory proteins may act as the convergence point underlying aberrant gene expression changes in Alzheimer's disease (AD).<h4>Methods</h4>Two microarray datasets from a control/AD postmortem brain cohort of 31 subjects - 9 controls and 22 AD subjects (National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database) - were used.<h4>Results</h4>Between the two microarr ...[more]

PMID: 23637700

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Project description:BackgroundThe complicated cellular and biochemical changes that occur in brain during Alzheimer's disease are poorly understood. In a previous study we used an unbiased label-free quantitative mass spectrometry-based proteomic approach to analyze these changes at a systems level in post-mortem cortical tissue from patients with Alzheimer's disease (AD), asymptomatic Alzheimer's disease (AsymAD), and controls. We found modules of co-expressed proteins that correlated with AD phenotypes, some of which were enriched in proteins identified as risk factors for AD by genetic studies.MethodsThe amount of information that can be obtained from such systems-level proteomic analyses is critically dependent upon the number of proteins that can be quantified across a cohort. We report here a new proteomic systems-level analysis of AD brain based on 6,533 proteins measured across AD, AsymAD, and controls using an analysis pipeline consisting of isobaric tandem mass tag (TMT) mass spectrometry and offline prefractionation.ResultsOur new TMT pipeline allowed us to more than double the depth of brain proteome coverage. This increased depth of coverage greatly expanded the brain protein network to reveal new protein modules that correlated with disease and were unrelated to those identified in our previous network. Differential protein abundance analysis identified 350 proteins that had altered levels between AsymAD and AD not caused by changes in specific cell type abundance, potentially reflecting biochemical changes that are associated with cognitive decline in AD. RNA binding proteins emerged as a class of proteins altered between AsymAD and AD, and were enriched in network modules that correlated with AD pathology. We developed a proteogenomic approach to investigate RNA splicing events that may be altered by RNA binding protein changes in AD. The increased proteome depth afforded by our TMT pipeline allowed us to identify and quantify a large number of alternatively spliced protein isoforms in brain, including AD risk factors such as BIN1, PICALM, PTK2B, and FERMT2. Many of the new AD protein network modules were enriched in alternatively spliced proteins and correlated with molecular markers of AD pathology and cognition.ConclusionsFurther analysis of the AD brain proteome will continue to yield new insights into the biological basis of AD.

Project description:BackgroundThe increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.ResultsFor each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios.ConclusionsWe present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.

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Altered expression of RNA splicing proteins in Alzheimer's disease patients: evidence from two microarray studies.

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Altered expression of RNA splicing proteins in Alzheimer's disease patients: evidence from two microarray studies.

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