IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-26798-4.html
   My bibliography  Save this article

Introns control stochastic allele expression bias

Author

Listed:
  • Bryan Sands

    (University of Washington)

  • Soo Yun

    (University of Washington)

  • Alexander R. Mendenhall

    (University of Washington)

Abstract

Monoallelic expression (MAE) or extreme allele bias can account for incomplete penetrance, missing heritability and non-Mendelian diseases. In cancer, MAE is associated with shorter patient survival times and higher tumor grade. Prior studies showed that stochastic MAE is caused by stochastic epigenetic silencing, in a gene and tissue-specific manner. Here, we used C. elegans to study stochastic MAE in vivo. We found allele bias/MAE to be widespread within C. elegans tissues, presenting as a continuum from fully biallelic to MAE. We discovered that the presence of introns within alleles robustly decreases MAE. We determined that introns control MAE at distinct loci, in distinct cell types, with distinct promoters, and within distinct coding sequences, using a 5’-intron position-dependent mechanism. Bioinformatic analysis showed human intronless genes are significantly enriched for MAE. Our experimental evidence demonstrates a role for introns in regulating MAE, possibly explaining why some mutations within introns result in disease.

Suggested Citation

  • Bryan Sands & Soo Yun & Alexander R. Mendenhall, 2021. "Introns control stochastic allele expression bias," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26798-4
    DOI: 10.1038/s41467-021-26798-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-26798-4
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-26798-4?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. Nikolay Burnaevskiy & Bryan Sands & Soo Yun & Patricia M. Tedesco & Thomas E. Johnson & Matt Kaeberlein & Roger Brent & Alexander Mendenhall, 2019. "Chaperone biomarkers of lifespan and penetrance track the dosages of many other proteins," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    2. Suzanne L. Rutherford & Susan Lindquist, 1998. "Hsp90 as a capacitor for morphological evolution," Nature, Nature, vol. 396(6709), pages 336-342, November.
    3. Alejandro Burga & M. Olivia Casanueva & Ben Lehner, 2011. "Predicting mutation outcome from early stochastic variation in genetic interaction partners," Nature, Nature, vol. 480(7376), pages 250-253, December.
    4. Christine Queitsch & Todd A. Sangster & Susan Lindquist, 2002. "Hsp90 as a capacitor of phenotypic variation," Nature, Nature, vol. 417(6889), pages 618-624, June.
    5. Alejandro Colman-Lerner & Andrew Gordon & Eduard Serra & Tina Chin & Orna Resnekov & Drew Endy & C. Gustavo Pesce & Roger Brent, 2005. "Regulated cell-to-cell variation in a cell-fate decision system," Nature, Nature, vol. 437(7059), pages 699-706, September.
    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. Kaushik Bhattacharya & Samarpan Maiti & Szabolcs Zahoran & Lorenz Weidenauer & Dina Hany & Diana Wider & Lilia Bernasconi & Manfredo Quadroni & Martine Collart & Didier Picard, 2022. "Translational reprogramming in response to accumulating stressors ensures critical threshold levels of Hsp90 for mammalian life," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Tracy Chih-Ting Koubkova-Yu & Jung-Chi Chao & Jun-Yi Leu, 2018. "Heterologous Hsp90 promotes phenotypic diversity through network evolution," PLOS Biology, Public Library of Science, vol. 16(11), pages 1-29, November.
    3. D. Blanco-Obregon & K. El Marzkioui & F. Brutscher & V. Kapoor & L. Valzania & D. S. Andersen & J. Colombani & S. Narasimha & D. McCusker & P. Léopold & L. Boulan, 2022. "A Dilp8-dependent time window ensures tissue size adjustment in Drosophila," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Casper J Breuker & James S Patterson & Christian Peter Klingenberg, 2006. "A Single Basis for Developmental Buffering of Drosophila Wing Shape," PLOS ONE, Public Library of Science, vol. 1(1), pages 1-7, December.
    5. Michael Chevalier & Ophelia Venturelli & Hana El-Samad, 2015. "The Impact of Different Sources of Fluctuations on Mutual Information in Biochemical Networks," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-21, October.
    6. Kemal Keseroglu & Oriana Q. H. Zinani & Sevdenur Keskin & Hannah Seawall & Eslim E. Alpay & Ertuğrul M. Özbudak, 2023. "Stochastic gene expression and environmental stressors trigger variable somite segmentation phenotypes," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    7. Kazunari Iwamoto & Yuki Shindo & Koichi Takahashi, 2016. "Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-18, November.
    8. Artémis Llamosi & Andres M Gonzalez-Vargas & Cristian Versari & Eugenio Cinquemani & Giancarlo Ferrari-Trecate & Pascal Hersen & Gregory Batt, 2016. "What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.
    9. Burton W Andrews & Pablo A Iglesias, 2007. "An Information-Theoretic Characterization of the Optimal Gradient Sensing Response of Cells," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-9, August.
    10. Bar Haim Y. & Booth James G. & Wells Martin T., 2012. "A Mixture-Model Approach for Parallel Testing for Unequal Variances," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-21, January.
    11. Zeina Shreif & Vipul Periwal, 2014. "A Network Characteristic That Correlates Environmental and Genetic Robustness," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-23, February.
    12. Masel, Joanna & Lyttle, David N., 2011. "The consequences of rare sexual reproduction by means of selfing in an otherwise clonally reproducing species," Theoretical Population Biology, Elsevier, vol. 80(4), pages 317-322.
    13. Steven S Andrews & Nathan J Addy & Roger Brent & Adam P Arkin, 2010. "Detailed Simulations of Cell Biology with Smoldyn 2.1," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-10, March.
    14. Klement Stojanovski & Helge Großhans & Benjamin D. Towbin, 2022. "Coupling of growth rate and developmental tempo reduces body size heterogeneity in C. elegans," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    15. Sergey Vakulenko & Dmitry Grigoriev, 2021. "Deep Gene Networks and Response to Stress," Mathematics, MDPI, vol. 9(23), pages 1-19, November.
    16. Zijuan Li & Yuyun Zhang & Ci-Hang Ding & Yan Chen & Haoyu Wang & Jinyu Zhang & Songbei Ying & Meiyue Wang & Rongzhi Zhang & Jinyi Liu & Yilin Xie & Tengfei Tang & Huishan Diao & Luhuan Ye & Yili Zhuan, 2023. "LHP1-mediated epigenetic buffering of subgenome diversity and defense responses confers genome plasticity and adaptability in allopolyploid wheat," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    17. Christoph Zechner & Heinz Koeppl, 2014. "Uncoupled Analysis of Stochastic Reaction Networks in Fluctuating Environments," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-9, 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:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26798-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.