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Identification of Differentially Expressed Genes through Integrated Study of Alzheimer’s Disease Affected Brain Regions

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  • Nisha Puthiyedth
  • Carlos Riveros
  • Regina Berretta
  • Pablo Moscato

Abstract

Background: Alzheimer’s disease (AD) is the most common form of dementia in older adults that damages the brain and results in impaired memory, thinking and behaviour. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. In the past decade, several studies have reported many genes that are associated with AD. This wealth of information has become difficult to follow and interpret as most of the results are conflicting. In that case, it is worth doing an integrated study of multiple datasets that helps to increase the total number of samples and the statistical power in detecting biomarkers. In this study, we present an integrated analysis of five different brain region datasets and introduce new genes that warrant further investigation. Methods: The aim of our study is to apply a novel combinatorial optimisation based meta-analysis approach to identify differentially expressed genes that are associated to AD across brain regions. In this study, microarray gene expression data from 161 samples (74 non-demented controls, 87 AD) from the Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX) brain regions were integrated and analysed using our method. The results are then compared to two popular meta-analysis methods, RankProd and GeneMeta, and to what can be obtained by analysing the individual datasets. Results: We find genes related with AD that are consistent with existing studies, and new candidate genes not previously related with AD. Our study confirms the up-regualtion of INFAR2 and PTMA along with the down regulation of GPHN, RAB2A, PSMD14 and FGF. Novel genes PSMB2, WNK1, RPL15, SEMA4C, RWDD2A and LARGE are found to be differentially expressed across all brain regions. Further investigation on these genes may provide new insights into the development of AD. In addition, we identified the presence of 23 non-coding features, including four miRNA precursors (miR-7, miR570, miR-1229 and miR-6821), dysregulated across the brain regions. Furthermore, we compared our results with two popular meta-analysis methods RankProd and GeneMeta to validate our findings and performed a sensitivity analysis by removing one dataset at a time to assess the robustness of our results. These new findings may provide new insights into the disease mechanisms and thus make a significant contribution in the near future towards understanding, prevention and cure of AD.

Suggested Citation

  • Nisha Puthiyedth & Carlos Riveros & Regina Berretta & Pablo Moscato, 2016. "Identification of Differentially Expressed Genes through Integrated Study of Alzheimer’s Disease Affected Brain Regions," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-29, April.
    • Handle: RePEc:plo:pone00:0152342
    DOI: 10.1371/journal.pone.0152342
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    References listed on IDEAS

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    1. Martín Gómez Ravetti & Pablo Moscato, 2008. "Identification of a 5-Protein Biomarker Molecular Signature for Predicting Alzheimer's Disease," PLOS ONE, Public Library of Science, vol. 3(9), pages 1-12, September.
    2. Nisha Puthiyedth & Carlos Riveros & Regina Berretta & Pablo Moscato, 2015. "A New Combinatorial Optimization Approach for Integrated Feature Selection Using Different Datasets: A Prostate Cancer Transcriptomic Study," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-26, June.
    3. Anne Maass & Hartmut Schütze & Oliver Speck & Andrew Yonelinas & Claus Tempelmann & Hans-Jochen Heinze & David Berron & Arturo Cardenas-Blanco & Kay H. Brodersen & Klaas Enno Stephan & Emrah Düzel, 2014. "Laminar activity in the hippocampus and entorhinal cortex related to novelty and episodic encoding," Nature Communications, Nature, vol. 5(1), pages 1-12, December.
    4. Ahmed Shamsul Arefin & Luke Mathieson & Daniel Johnstone & Regina Berretta & Pablo Moscato, 2012. "Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-25, September.
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    1. Chitradevi Dhakhinamoorthy & Sathish Kumar Mani & Sandeep Kumar Mathivanan & Senthilkumar Mohan & Prabhu Jayagopal & Saurav Mallik & Hong Qin, 2023. "Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease," Mathematics, MDPI, vol. 11(5), pages 1-17, February.

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