Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli

Abstract

Individual genetic variation affects gene responsiveness to stimuli, often by influencing complex molecular circuits. Here we combine genomic and intermediate-scale transcriptional profiling with computational methods to identify variants that affect the responsiveness of genes to stimuli (responsiveness quantitative trait loci or reQTLs) and to position these variants in molecular circuit diagrams. We apply this approach to study variation in transcriptional responsiveness to pathogen components in dendritic cells from recombinant inbred mouse strains. We identify reQTLs that correlate with particular stimuli and position them in known pathways. For example, in response to a virus-like stimulus, a trans-acting variant responds as an activator of the antiviral response; using RNA interference, we identify Rgs16 as the likely causal gene. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in circuits that control responses to stimuli.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Multistimulus reQTL analysis.
Figure 2: Experimental design of the study in dendritic cells.
Figure 3: cis-reQTLs in the response of dendritic cells to three pathogenic components.
Figure 4: Stimulus-specific, pleiotropic, trans-acting reQTLs.
Figure 5: Positioning reQTLs in regulatory circuits.
Figure 6: Rgs16 may be the causal variant in the reQTL of module no.2.

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. Smith, E.N. & Kruglyak, L. Gene-environment interaction in yeast gene expression. PLoS Biol. 6, e83 (2008).

    PubMed  PubMed Central  Google Scholar 

  2. Heinig, M. et al. A trans-acting locus regulates an antiviral expression network and type 1 diabetes risk. Nature 467, 460–464 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Zhong, H. et al. Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes. PLoS Genet. 6, e1000932 (2010).

    PubMed  PubMed Central  Google Scholar 

  4. Barreiro, L.B. et al. Deciphering the genetic architecture of variation in the immune response to Mycobacterium tuberculosis infection. Proc. Natl. Acad. Sci. USA 109, 1204–1209 (2012).

    CAS  PubMed  Google Scholar 

  5. Gargalovic, P.S. et al. Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids. Proc. Natl. Acad. Sci. USA 103, 12741–12746 (2006).

    CAS  PubMed  Google Scholar 

  6. Smirnov, D.A., Morley, M., Shin, E., Spielman, R.S. & Cheung, V.G. Genetic analysis of radiation-induced changes in human gene expression. Nature 459, 587–591 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Yang, I.V. et al. Identification of novel genes that mediate innate immunity using inbred mice. Genetics 183, 1535–1544 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Dombroski, B.A. et al. Gene expression and genetic variation in response to endoplasmic reticulum stress in human cells. Am. J. Hum. Genet. 86, 719–729 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Romanoski, C.E. et al. Systems genetics analysis of gene-by-environment interactions in human cells. Am. J. Hum. Genet. 86, 399–410 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Maranville, J.C. et al. Interactions between glucocorticoid treatment and cis-regulatory polymorphisms contribute to cellular response phenotypes. PLoS Genet. 7, e1002162 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Peirce, J.L., Lu, L., Gu, J., Silver, L.M. & Williams, R.W. A new set of BXD recombinant inbred lines from advanced intercross populations in mice. BMC Genet. 5, 7 (2004).

    PubMed  PubMed Central  Google Scholar 

  12. Takeda, K. & Akira, S. Toll-like receptors in innate immunity. Int. Immunol. 17, 1–14 (2005).

    CAS  PubMed  Google Scholar 

  13. Kawai, T. & Akira, S. The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat. Immunol. 11, 373–384 (2010).

    CAS  PubMed  Google Scholar 

  14. Wang, J., Williams, R.W. & Manly, K.F. WebQTL: web-based complex trait analysis. Neuroinformatics 1, 299–308 (2003).

    PubMed  Google Scholar 

  15. Chesler, E.J. et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat. Genet. 37, 233–242 (2005).

    CAS  PubMed  Google Scholar 

  16. Bystrykh, L. et al. Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'. Nat. Genet. 37, 225–232 (2005).

    CAS  PubMed  Google Scholar 

  17. Fairfax, B.P. et al. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Gerrits, A. et al. Expression quantitative trait loci are highly sensitive to cellular differentiation state. PLoS Genet. 5, e1000692 (2009).

    PubMed  PubMed Central  Google Scholar 

  19. Dimas, A.S. et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325, 1246–1250 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Mackay, T.F., Stone, E.A. & Ayroles, J.F. The genetics of quantitative traits: challenges and prospects. Nat. Rev. Genet. 10, 565–577 (2009).

    CAS  PubMed  Google Scholar 

  21. Hubner, N. et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat. Genet. 37, 243–253 (2005).

    CAS  PubMed  Google Scholar 

  22. Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257–263 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Garber, M. et al. A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals. Mol. Cell 47, 810–822 (2012).

    CAS  PubMed  Google Scholar 

  24. O'Neill, L.A. When signaling pathways collide: positive and negative regulation of toll-like receptor signal transduction. Immunity 29, 12–20 (2008).

    CAS  PubMed  Google Scholar 

  25. Barbalat, R., Lau, L., Locksley, R.M. & Barton, G.M. Toll-like receptor 2 on inflammatory monocytes induces type I interferon in response to viral but not bacterial ligands. Nat. Immunol. 10, 1200–1207 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Ulitsky, I. et al. Expander: from expression microarrays to networks and functions. Nat. Protoc. 5, 303–322 (2010).

    CAS  PubMed  Google Scholar 

  27. Chevrier, N. et al. Systematic discovery of TLR signaling components delineates viral-sensing circuits. Cell 147, 853–867 (2011).

    CAS  PubMed  Google Scholar 

  28. Baum, A. & Garcia-Sastre, A. Differential recognition of viral RNA by RIG-I. Virulence 2, 166–169 (2011).

    PubMed  PubMed Central  Google Scholar 

  29. Diebold, S.S., Kaisho, T., Hemmi, H., Akira, S. & Reis e Sousa, C. Innate antiviral responses by means of TLR7-mediated recognition of single-stranded RNA. Science 303, 1529–1531 (2004).

    CAS  PubMed  Google Scholar 

  30. Kato, H. et al. Differential roles of MDA5 and RIG-I helicases in the recognition of RNA viruses. Nature 441, 101–105 (2006).

    CAS  PubMed  Google Scholar 

  31. Frazer, K.A. et al. Segmental phylogenetic relationships of inbred mouse strains revealed by fine-scale analysis of sequence variation across 4.6 mb of mouse genome. Genome Res. 14, 1493–1500 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Chen, C., Wang, H., Fong, C.W. & Lin, S.C. Multiple phosphorylation sites in RGS16 differentially modulate its GAP activity. FEBS Lett. 504, 16–22 (2001).

    CAS  PubMed  Google Scholar 

  33. Hackett, C.A., Meyer, R.C. & Thomas, W.T. Multi-trait QTL mapping in barley using multivariate regression. Genet. Res. 77, 95–106 (2001).

    CAS  PubMed  Google Scholar 

  34. Xu, C., Li, Z. & Xu, S. Joint mapping of quantitative trait Loci for multiple binary characters. Genetics 169, 1045–1059 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Banerjee, S., Yandell, B.S. & Yi, N. Bayesian quantitative trait loci mapping for multiple traits. Genetics 179, 2275–2289 (2008).

    PubMed  PubMed Central  Google Scholar 

  36. Gilbert, H. & Le Roy, P. Methods for the detection of multiple linked QTL applied to a mixture of full and half sib families. Genet. Sel. Evol. 39, 139–158 (2007).

    PubMed  PubMed Central  Google Scholar 

  37. Lee, S.I., Pe'er, D., Dudley, A.M., Church, G.M. & Koller, D. Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. Proc. Natl. Acad. Sci. USA 103, 14062–14067 (2006).

    CAS  PubMed  Google Scholar 

  38. Xie, S. et al. IL-17 activates the canonical NF-kappaB signaling pathway in autoimmune B cells of BXD2 mice to upregulate the expression of regulators of G-protein signaling 16. J. Immunol. 184, 2289–2296 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Shankar, S.P. et al. RGS16 Attenuates Pulmonary Th2/Th17 Inflammatory Responses. J. Immunol. 188, 6347–6356 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Shi, G.X., Harrison, K., Han, S.B., Moratz, C. & Kehrl, J.H. Toll-like receptor signaling alters the expression of regulator of G protein signaling proteins in dendritic cells: implications for G protein-coupled receptor signaling. J. Immunol. 172, 5175–5184 (2004).

    CAS  PubMed  Google Scholar 

  41. Park, H. et al. Discovery of common Asian copy number variants using integrated high-resolution array CGH and massively parallel DNA sequencing. Nat. Genet. 42, 400–405 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Addona, T.A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27, 633–641 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Bendall, S.C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    PubMed  Google Scholar 

  45. Wilks, S.S. The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat. 9, 60–62 (1938).

    Google Scholar 

  46. Geiss, G.K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol. 26, 317–325 (2008).

    CAS  PubMed  Google Scholar 

  47. Gaglani, S.M., Lu, L., Williams, R.W. & Rosen, G.D. The genetic control of neocortex volume and covariation with neocortical gene expression in mice. BMC Neurosci. 10, 44 (2009).

    PubMed  PubMed Central  Google Scholar 

  48. Scheffe, H. The Analysis of Variance (John Wiley and Sons, Inc., 1959).

  49. Falconer, D. & Mackay, T.F.C. Introduction to Quantitative Genetics (Pearson, 1996).

  50. Root, D.E., Hacohen, N., Hahn, W.C., Lander, E.S. & Sabatini, D.M. Genome-scale loss-of-function screening with a lentiviral RNAi library. Nat. Methods 3, 715–719 (2006).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank C. (Jimmie) Ye, J. Pickerel, M. Daly and E. Lander for comments and discussions. I.G.-V. and I.A. were supported by Human Frontiers Science Program postdoctoral fellowships. Work was supported by Howard Hughes Medical Institute, a US National Institutes of Health PIONEER award, a Burroughs-Wellcome Fund Career Award at the Scientific Interface (A.R.), a Center for Excellence in Genome Science grant 5P50HG006193-02 from the National Human Genome Research Institute (N.H. and A.R.), the Klarman Cell Observatory at the Broad Institute (A.R.), the New England Regional Center for Excellence/Biodefense and Emerging Infectious Disease U54 AI057159 (N.H.), the Israeli Centers of Research Excellence (I-CORE) Gene Regulation in Complex Human Disease, Center No. 41/11 (I.G.-V., R.W. and Y.S.), the Human Frontiers Science Program Career Development Award and an Israeli Science Foundation Bikura Institutional Research Grant Program (I.A.) and the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University (R.W. and Y.S.). A.R. is a fellow of the Merkin Foundation for Stem Cell Research at the Broad Institute. I.G.-V. is a Faculty Fellow of the Edmond J. Safra Center for Bioinformatics at Tel Aviv University and an Alon Fellow.

Author information

Authors and Affiliations

Authors

Contributions

I.G.-V., I.A. and A.R. conceived and designed the study. N.C., T.E., R.R., A.S. and I.A. conducted the experiments. I.G.-V. and A.R. conceived computational methods. I.G.-V., R.W. and Y.S. conceived, developed and implemented the computational methods. N.H. participated in study design and interpretation. I.G.-V., I.A. and A.R. wrote the manuscript with input from all the authors.

Corresponding authors

Correspondence to Irit Gat-Viks, Ido Amit or Aviv Regev.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 1–6 and Supplementary Notes 1–3 (PDF 8508 kb)

Supplementary Table 7

Dataset 1 (XLS 761 kb)

Supplementary Table 8

Dataset 2 (XLS 739 kb)

Supplementary Table 9

Dataset 3 (XLS 746 kb)

Supplementary Table 10

Dataset 4 (XLS 761 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Gat-Viks, I., Chevrier, N., Wilentzik, R. et al. Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli. Nat Biotechnol 31, 342–349 (2013). https://doi.org/10.1038/nbt.2519

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.2519

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing