IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0085136.html
   My bibliography  Save this article

Global State Measures of the Dentate Gyrus Gene Expression System Predict Antidepressant-Sensitive Behaviors

Author

Listed:
  • Benjamin A Samuels
  • E David Leonardo
  • Alex Dranovsky
  • Amanda Williams
  • Erik Wong
  • Addie May I Nesbitt
  • Richard D McCurdy
  • Rene Hen
  • Mark Alter

Abstract

Background: Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common form of medication treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance. Methods: We take a novel approach to this problem by demonstrating that the gene expression system of the dentate gyrus responds to fluoxetine (FLX), a commonly used antidepressant medication, in a stereotyped-manner involving changes in the expression levels of thousands of genes. The aggregate behavior of this large-scale systemic response was quantified with principal components analysis (PCA) yielding a single quantitative measure of the global gene expression system state. Results: Quantitative measures of system state were highly correlated with variability in levels of antidepressant-sensitive behaviors in a mouse model of depression treated with fluoxetine. Analysis of dorsal and ventral dentate samples in the same mice indicated that system state co-varied across these regions despite their reported functional differences. Aggregate measures of gene expression system state were very robust and remained unchanged when different microarray data processing algorithms were used and even when completely different sets of gene expression levels were used for their calculation. Conclusions: System state measures provide a robust method to quantify and relate global gene expression system state variability to behavior and treatment. State variability also suggests that the diversity of reported changes in gene expression levels in response to treatments such as fluoxetine may represent different perspectives on unified but noisy global gene expression system state level responses. Studying regulation of gene expression systems at the state level may be useful in guiding new approaches to augmentation of traditional antidepressant treatments.

Suggested Citation

  • Benjamin A Samuels & E David Leonardo & Alex Dranovsky & Amanda Williams & Erik Wong & Addie May I Nesbitt & Richard D McCurdy & Rene Hen & Mark Alter, 2014. "Global State Measures of the Dentate Gyrus Gene Expression System Predict Antidepressant-Sensitive Behaviors," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
  • Handle: RePEc:plo:pone00:0085136
    DOI: 10.1371/journal.pone.0085136
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0085136
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0085136&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0085136?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. Zhang Bin & Horvath Steve, 2005. "A General Framework for Weighted Gene Co-Expression Network Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-45, August.
    2. Mark D Alter, 2013. "Studying Gene Expression System Regulation at the Program Level," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-8, April.
    3. Irina Voineagu & Xinchen Wang & Patrick Johnston & Jennifer K. Lowe & Yuan Tian & Steve Horvath & Jonathan Mill & Rita M. Cantor & Benjamin J. Blencowe & Daniel H. Geschwind, 2011. "Transcriptomic analysis of autistic brain reveals convergent molecular pathology," Nature, Nature, vol. 474(7351), pages 380-384, June.
    4. Michael J Gandal & Addie May Nesbitt & Richard M McCurdy & Mark D Alter, 2012. "Measuring the Maturity of the Fast-Spiking Interneuron Transcriptional Program in Autism, Schizophrenia, and Bipolar Disorder," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.
    5. Matthias Böck & Soichi Ogishima & Hiroshi Tanaka & Stefan Kramer & Lars Kaderali, 2012. "Hub-Centered Gene Network Reconstruction Using Automatic Relevance Determination," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-17, May.
    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. Yixuan Qiu & Jing Lei & Kathryn Roeder, 2023. "Gradient-based sparse principal component analysis with extensions to online learning," Biometrika, Biometrika Trust, vol. 110(2), pages 339-360.
    2. Ruiz Vargas, E. & Mitchell, D.G.V. & Greening, S.G. & Wahl, L.M., 2014. "Topology of whole-brain functional MRI networks: Improving the truncated scale-free model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 151-158.
    3. Yan Guo & Hui Yu & Haocan Song & Jiapeng He & Olufunmilola Oyebamiji & Huining Kang & Jie Ping & Scott Ness & Yu Shyr & Fei Ye, 2021. "MetaGSCA: A tool for meta-analysis of gene set differential coexpression," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-15, May.
    4. Yudong Gao & Daichi Shonai & Matthew Trn & Jieqing Zhao & Erik J. Soderblom & S. Alexandra Garcia-Moreno & Charles A. Gersbach & William C. Wetsel & Geraldine Dawson & Dmitry Velmeshev & Yong-hui Jian, 2024. "Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. Xue Jiang & Han Zhang & Xiongwen Quan & Zhandong Liu & Yanbin Yin, 2017. "Disease-related gene module detection based on a multi-label propagation clustering algorithm," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
    6. Mandel, Antoine & Landini, Simone & Gallegati, Mauro & Gintis, Herbert, 2015. "Price dynamics, financial fragility and aggregate volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 257-277.
    7. Peter Langfelder & Rui Luo & Michael C Oldham & Steve Horvath, 2011. "Is My Network Module Preserved and Reproducible?," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-29, January.
    8. Glenn N Saxe & Alexander Statnikov & David Fenyo & Jiwen Ren & Zhiguo Li & Meera Prasad & Dennis Wall & Nora Bergman & Ernestine C Briggs & Constantin Aliferis, 2016. "A Complex Systems Approach to Causal Discovery in Psychiatry," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-20, March.
    9. Elva María Novoa-del-Toro & Efrén Mezura-Montes & Matthieu Vignes & Morgane Térézol & Frédérique Magdinier & Laurent Tichit & Anaïs Baudot, 2021. "A multi-objective genetic algorithm to find active modules in multiplex biological networks," PLOS Computational Biology, Public Library of Science, vol. 17(8), pages 1-24, August.
    10. Matias Nehuen Iglesias, 2021. "The Overlooked Insights from Correlation Structures in Economic Geography," Papers in Evolutionary Economic Geography (PEEG) 2105, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jan 2021.
    11. Lingxue Zhang & Seyoung Kim, 2014. "Learning Gene Networks under SNP Perturbations Using eQTL Datasets," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-20, February.
    12. Tingting Bo & Jie Li & Ganlu Hu & Ge Zhang & Wei Wang & Qian Lv & Shaoling Zhao & Junjie Ma & Meng Qin & Xiaohui Yao & Meiyun Wang & Guang-Zhong Wang & Zheng Wang, 2023. "Brain-wide and cell-specific transcriptomic insights into MRI-derived cortical morphology in macaque monkeys," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    13. Chang Su & Zichun Xu & Xinning Shan & Biao Cai & Hongyu Zhao & Jingfei Zhang, 2023. "Cell-type-specific co-expression inference from single cell RNA-sequencing data," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    14. Sahra Uygun & Cheng Peng & Melissa D Lehti-Shiu & Robert L Last & Shin-Han Shiu, 2016. "Utility and Limitations of Using Gene Expression Data to Identify Functional Associations," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-27, December.
    15. Li, Jie & Wang, Lidan & Zhou, Zhong-Qiang & Zhang, Yongjie, 2021. "Monitoring or tunneling? Information interaction among large shareholders and the crash risk of the stock price," Pacific-Basin Finance Journal, Elsevier, vol. 65(C).
    16. Khang Tsung Fei & Yap Von Bing, 2010. "The Apportionment of Total Genetic Variation by Categorical Analysis of Variance," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-34, January.
    17. Shaoshuo Li & Baixing Chen & Hao Chen & Zhen Hua & Yang Shao & Heng Yin & Jianwei Wang, 2021. "Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-18, September.
    18. Peter Langfelder & Fuying Gao & Nan Wang & David Howland & Seung Kwak & Thomas F Vogt & Jeffrey S Aaronson & Jim Rosinski & Giovanni Coppola & Steve Horvath & X William Yang, 2018. "MicroRNA signatures of endogenous Huntingtin CAG repeat expansion in mice," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-20, January.
    19. Renaud Tissier & Jeanine Houwing-Duistermaat & Mar Rodríguez-Girondo, 2018. "Improving stability of prediction models based on correlated omics data by using network approaches," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-23, February.
    20. Shujuan Zhao & Kedous Y. Mekbib & Martijn A. Ent & Garrett Allington & Andrew Prendergast & Jocelyn E. Chau & Hannah Smith & John Shohfi & Jack Ocken & Daniel Duran & Charuta G. Furey & Le Thi Hao & P, 2023. "Mutation of key signaling regulators of cerebrovascular development in vein of Galen malformations," Nature Communications, Nature, vol. 14(1), pages 1-23, December.

    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:pone00:0085136. 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.