5.9
CiteScore
5.9
Impact Factor
Volume 46 Issue 8
Aug.  2019
Turn off MathJax
Article Contents

The expression tractability of biological traits shaped by natural selection

doi: 10.1016/j.jgg.2019.08.001
More Information
  • Corresponding author: E-mail address: hexiongl@mail.sysu.edu.cn (Xionglei He)
  • Received Date: 2019-05-29
  • Accepted Date: 2019-08-01
  • Rev Recd Date: 2019-07-31
  • Available Online: 2019-08-13
  • Publish Date: 2019-08-20
  • Understanding how gene expression is translated to phenotype is central to modern molecular biology, and the success is contingent on the intrinsic tractability of the specific traits under examination. However, an a priori estimate of trait tractability from the perspective of gene expression is unavailable. Motivated by the concept of entropy in a thermodynamic system, we here propose such an estimate (ST) by gauging the number (N) of expression states that underlie the same trait abnormality, with large ST corresponding to large N. By analyzing over 200 yeast morphological traits, we show that ST predicts the tractability of an expression-trait relationship. We further show thatST is ultimately determined by natural selection, which builds co-regulated gene modules to minimize possible expression states.
  • These authors contribute equally to this work.
  • loading
  • [1]
    Allu, T.K., Oprea, T.I., 2005. Rapid evaluation of synthetic and molecular complexity for in silico chemistry. Cheminform 45, 1237-1243.
    [2]
    Amin, S.B., Yip, W., Minvielle, S., Broyl, A., Li, Y., Hanlon, B.M., Swanson, D., Shah, P.K., Moreau, P., Der Holt, B.V., 2014. Gene expression profile alone is inadequate in predicting complete response in multiple myeloma. Leukemia 28, 2229-2234.
    [3]
    Ayoub, R.G., 1982. On the nonsolvability of the general polynomial. Am. Math. Mon. 89, 397.
    [4]
    Ayroles, J.F., Carbone, M.A., Stone, E.A., Jordan, K.W., Lyman, R.F., Magwire, M.M., Rollmann, S.M., Duncan, L., Lawrence, F., Anholt, R.R.H., 2009. Systems genetics of complex traits in Drosophila melanogaster. Nat. Genet. 41, 299-307.
    [5]
    Benschop, J.J., Brabers, N., Van Leenen, D., Bakker, L., Van Deutekom, H.W.M., Van Berkum, N.L., Apweiler, E., Lijnzaad, P., Holstege, F.C.P., Kemmeren, P., 2010. A Consensus of Core Protein Complex Compositions for Saccharomyces cerevisiae. Mol. Cell 38, 916-928.
    [6]
    Bodenhofer, U., Kothmeier, A., Hochreiter, S., 2011. APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463-2464.
    [7]
    Chen, B., Causton, H.C., Mancenido, D., Goddard, N.L., Perlstein, E.O., Peer, D., 2009. Harnessing gene expression to identify the genetic basis of drug resistance. Mol. Syst. Biol. 5, 310-310.
    [8]
    Chen, H., Wu, C.-I., He, X., 2016. The regulator-executor-phenotype architecture shaped by natural selection. BioRxiv. doi: https://doi.org/10.1101/026443.
    [9]
    Civelek, M., Lusis, A.J., 2014. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15, 34-48.
    [10]
    Feldman, D.P., Crutchfield, J.P., 1998. Measures of statistical complexity: why? Phys. Lett. A 238, 244-252.
    [11]
    Gamazon, E.R., Wheeler, H.E., Shah, K.P., Mozaffari, S.V., Aquinomichaels, K., Carroll, R.J., Eyler, A.E., Denny, J.C., Nicolae, D.L., Cox, N.J., 2015. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091-1098.
    [12]
    Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B.W.J.H., Jansen, R., De Geus, E.J.C., Boomsma, D.I., Wright, F.A., 2016. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245.
    [13]
    Ho, W.C., Zhang, J., 2014. The genotype-phenotype map of yeast complex traits: basic parameters and the role of natural selection. Mol. Biol. Evol. 31, 1568-1580.
    [14]
    Janes, K.A., Albeck, J.G., Gaudet, S., Sorger, P.K., Lauffenburger, D.A., Yaffe, M.B., 2005. A Systems Model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310, 1646-1653.
    [15]
    Kemmeren, P., Sameith, K., De Pasch, L.V., Benschop, J.J., Lenstra, T.L., Margaritis, T., Duibhir, E.O., Apweiler, E., Van Wageningen, S., Ko, C.W., 2014. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell 157, 740-752.
    [16]
    Komili, S., Silver, P.A., 2008. Coupling and coordination in gene expression processes: a systems biology view. Nat. Rev. Genet. 9, 38-48.
    [17]
    Ladyman, J., Lambert, J., Wiesner, K., 2013. What is a complex system? Eur. J. Philos. Sci. 3, 33-67.
    [18]
    Lee, I., Lehner, B., Crombie, C., Wong, W., Fraser, A.G., Marcotte, E.M., 2008. A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans. Nat. Genet. 40, 181-188.
    [19]
    Lloyd, K., Cree, I.A., Savage, R.S., 2015. Prediction of resistance to chemotherapy in ovarian cancer: a systematic review. BMC Cancer 15, 117-117.
    [20]
    Lopezruiz, R., Mancini, H.L., Calbet, X., 2010. A statistical measure of complexity. Phys. Lett. A 209, 321-326.
    [21]
    Ohnomachado, L., 2001. Modeling medical prognosis: survival analysis techniques. Comput.Biomed. Res. 34, 428-439.
    [22]
    Ohya, Y., Sese, J., Yukawa, M., Sano, F., Nakatani, Y., Saito, T., Saka, A., Fukuda, T., Ishihara, S., Oka, S., 2005. High-dimensional and large-scale phenotyping of yeast mutants. Proc. Natl. Acad. Sci. U. S. A. 102, 19015-19020.
    [23]
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., 2011. Scikit-learn: Machine Learning in Python. J. Mach. Learn Res. 12, 2825-2830.
    [24]
    Qian, W., Ma, D., Xiao, C., Wang, Z., Zhang, J., 2012. The Genomic Landscape and Evolutionary Resolution of Antagonistic Pleiotropy in Yeast. Cell Rep. 2, 1399-1410.
    [25]
    Ritchie, M.D., Holzinger, E.R., Li, R., Pendergrass, S.A., Kim, D., 2015. Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 16, 85-97.
    [26]
    Rosen, M., 2016. Niels Hendrik Abel and Equations of the Fifth Degree. Am. Math.Mon. 102, 495-505.
    [27]
    Segal, E., Friedman, N., Kaminski, N., Regev, A., Koller, D., 2005. From signatures to models: understanding cancer using microarrays. Nat. Genet. 37, S38-S45.
    [28]
    Teichmann, S.A., Babu, M.M., 2002. Conservation of gene co-regulation in prokaryotes and eukaryotes. Trends Biotechnol. 20, 407-410.
    [29]
    Veer, L.J.V.T., Bernards, R., 2008. Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature 452, 564-570.
    [30]
    Volm, M., Efferth, T., 2015. Prediction of cancer drug resistance and implications for personalized medicine. Front. Oncol. 5, 282.
    [31]
    Zhu, J., Zhang, B., Smith, E.N., Drees, B., Brem, R.B., Kruglyak, L., Bumgarner, R.E., Schadt, E.E., 2008. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat. Genet. 40, 854-861.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (86) PDF downloads (1) Cited by ()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return