IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0069768.html
   My bibliography  Save this article

Hippocampal Gene Expression Meta-Analysis Identifies Aging and Age-Associated Spatial Learning Impairment (ASLI) Genes and Pathways

Author

Listed:
  • Raihan K Uddin
  • Shiva M Singh

Abstract

A number of gene expression microarray studies have been carried out in the past, which studied aging and age-associated spatial learning impairment (ASLI) in the hippocampus in animal models, with varying results. Data from such studies were never integrated to identify the most significant ASLI genes and to understand their effect. In this study we integrated these data involving rats using meta-analysis. Our results show that proper removal of batch effects from microarray data generated from different laboratories is necessary before integrating them for meta-analysis. Our meta-analysis has identified a number of significant differentially expressed genes across age or across ASLI. These genes affect many key functions in the aged compared to the young rats, which include viability of neurons, cell-to-cell signalling and interaction, migration of cells, neuronal growth, and synaptic plasticity. These functional changes due to the altered gene expression may manifest into various neurodegenerative diseases and disorders, some of which leading into syndromic memory impairments. While other aging related molecular changes can result into altered synaptic plasticity simply causing normal aging related non-syndromic learning or spatial learning impairments such as ASLI.

Suggested Citation

  • Raihan K Uddin & Shiva M Singh, 2013. "Hippocampal Gene Expression Meta-Analysis Identifies Aging and Age-Associated Spatial Learning Impairment (ASLI) Genes and Pathways," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-16, July.
    • Handle: RePEc:plo:pone00:0069768
    DOI: 10.1371/journal.pone.0069768
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0069768
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0069768&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0069768?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Tao Lu & Ying Pan & Shyan-Yuan Kao & Cheng Li & Isaac Kohane & Jennifer Chan & Bruce A. Yankner, 2004. "Gene regulation and DNA damage in the ageing human brain," Nature, Nature, vol. 429(6994), pages 883-891, June.
    2. Alina Sîrbu & Heather J Ruskin & Martin Crane, 2010. "Cross-Platform Microarray Data Normalisation for Regulatory Network Inference," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    3. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    4. Chao Chen & Kay Grennan & Judith Badner & Dandan Zhang & Elliot Gershon & Li Jin & Chunyu Liu, 2011. "Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-10, February.
    5. Adaikalavan Ramasamy & Adrian Mondry & Chris C Holmes & Douglas G Altman, 2008. "Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets," PLOS Medicine, Public Library of Science, vol. 5(9), pages 1-13, September.
    6. Dillon Y. Chen & Sarah A. Stern & Ana Garcia-Osta & Bernadette Saunier-Rebori & Gabriella Pollonini & Dhananjay Bambah-Mukku & Robert D. Blitzer & Cristina M. Alberini, 2011. "A critical role for IGF-II in memory consolidation and enhancement," Nature, Nature, vol. 469(7331), pages 491-497, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cherif Ben Hamda & Raphael Sangeda & Liberata Mwita & Ayton Meintjes & Siana Nkya & Sumir Panji & Nicola Mulder & Lamia Guizani-Tabbane & Alia Benkahla & Julie Makani & Kais Ghedira & H3ABioNet Consor, 2018. "A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-21, July.
    2. Atle van Beelen Granlund & Arnar Flatberg & Ann E Østvik & Ignat Drozdov & Bjørn I Gustafsson & Mark Kidd & Vidar Beisvag & Sverre H Torp & Helge L Waldum & Tom Christian Martinsen & Jan Kristian Damå, 2013. "Whole Genome Gene Expression Meta-Analysis of Inflammatory Bowel Disease Colon Mucosa Demonstrates Lack of Major Differences between Crohn's Disease and Ulcerative Colitis," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-14, February.
    3. Aaron C Ericsson & J Wade Davis & William Spollen & Nathan Bivens & Scott Givan & Catherine E Hagan & Mark McIntosh & Craig L Franklin, 2015. "Effects of Vendor and Genetic Background on the Composition of the Fecal Microbiota of Inbred Mice," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    4. Melissa G Naylor & Xihong Lin & Scott T Weiss & Benjamin A Raby & Christoph Lange, 2010. "Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants," PLOS ONE, Public Library of Science, vol. 5(5), pages 1-6, May.
    5. Gustavo Glusman & Juan Caballero & Max Robinson & Burak Kutlu & Leroy Hood, 2013. "Optimal Scaling of Digital Transcriptomes," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-12, November.
    6. Hossain, Ahmed & Beyene, Joseph & Willan, Andrew R. & Hu, Pingzhao, 2009. "A flexible approximate likelihood ratio test for detecting differential expression in microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3685-3695, August.
    7. Xiaohong Li & Guy N Brock & Eric C Rouchka & Nigel G F Cooper & Dongfeng Wu & Timothy E O’Toole & Ryan S Gill & Abdallah M Eteleeb & Liz O’Brien & Shesh N Rai, 2017. "A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-22, May.
    8. Mike Males, 2015. "Age, Poverty, Homicide, and Gun Homicide," SAGE Open, , vol. 5(1), pages 21582440155, March.
    9. Kerr Kathleen F., 2012. "Optimality Criteria for the Design of 2-Color Microarray Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-9, January.
    10. Ambroise Jérôme & Bearzatto Bertrand & Robert Annie & Macq Benoit & Gala Jean-Luc, 2012. "Combining Multiple Laser Scans of Spotted Microarrays by Means of a Two-Way ANOVA Model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-20, February.
    11. J. McClatchy & R. Strogantsev & E. Wolfe & H. Y. Lin & M. Mohammadhosseini & B. A. Davis & C. Eden & D. Goldman & W. H. Fleming & P. Conley & G. Wu & L. Cimmino & H. Mohammed & A. Agarwal, 2023. "Clonal hematopoiesis related TET2 loss-of-function impedes IL1β-mediated epigenetic reprogramming in hematopoietic stem and progenitor cells," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    12. Xia Qing & Thompson Jeffrey A. & Koestler Devin C., 2021. "Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 20(4-6), pages 101-119, December.
    13. Alexandra Gyurdieva & Stefan Zajic & Ya-Fang Chang & E. Andres Houseman & Shan Zhong & Jaegil Kim & Michael Nathenson & Thomas Faitg & Mary Woessner & David C. Turner & Aisha N. Hasan & John Glod & Ro, 2022. "Biomarker correlates with response to NY-ESO-1 TCR T cells in patients with synovial sarcoma," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    14. Sora Yoon & Seon-Young Kim & Dougu Nam, 2016. "Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-16, November.
    15. Yu Lianbo & Gulati Parul & Fernandez Soledad & Pennell Michael & Kirschner Lawrence & Jarjoura David, 2011. "Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, September.
    16. Miranda van Uitert & Perry D Moerland & Daniel A Enquobahrie & Hannele Laivuori & Joris A M van der Post & Carrie Ris-Stalpers & Gijs B Afink, 2015. "Meta-Analysis of Placental Transcriptome Data Identifies a Novel Molecular Pathway Related to Preeclampsia," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-15, July.
    17. Sinan Xiong & Jianbiao Zhou & Tze King Tan & Tae-Hoon Chung & Tuan Zea Tan & Sabrina Hui-Min Toh & Nicole Xin Ning Tang & Yunlu Jia & Yi Xiang See & Melissa Jane Fullwood & Takaomi Sanda & Wee-Joo Chn, 2024. "Super enhancer acquisition drives expression of oncogenic PPP1R15B that regulates protein homeostasis in multiple myeloma," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    18. Chaofeng Yuan & Wensheng Zhu & Xuming He & Jianhua Guo, 2019. "A mixture factor model with applications to microarray data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 60-76, March.
    19. Nan Li & Matthew N. McCall & Zhijin Wu, 2017. "Establishing Informative Prior for Gene Expression Variance from Public Databases," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 160-177, June.
    20. Brian Caffo & Liu Dongmei & Giovanni Parmigiani, 2004. "Power Conjugate Multilevel Models with Applications to Genomics," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1062, Berkeley Electronic Press.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0069768. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.