Article | Published:

Massively parallel high-order combinatorial genetics in human cells

Nature Biotechnology volume 33, pages 952961 (2015) | Download Citation

Abstract

The systematic functional analysis of combinatorial genetics has been limited by the throughput that can be achieved and the order of complexity that can be studied. To enable massively parallel characterization of genetic combinations in human cells, we developed a technology for rapid, scalable assembly of high-order barcoded combinatorial genetic libraries that can be quantified with high-throughput sequencing. We applied this technology, combinatorial genetics en masse (CombiGEM), to create high-coverage libraries of 1,521 two-wise and 51,770 three-wise barcoded combinations of 39 human microRNA (miRNA) precursors. We identified miRNA combinations that synergistically sensitize drug-resistant cancer cells to chemotherapy and/or inhibit cancer cell proliferation, providing insights into complex miRNA networks. More broadly, our method will enable high-throughput profiling of multifactorial genetic combinations that regulate phenotypes of relevance to biomedicine, biotechnology and basic science.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    , , , & Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).

  2. 2.

    & Molecular roadblocks for cellular reprogramming. Mol. Cell 47, 827–838 (2012).

  3. 3.

    , & Combinatorial drug therapy for cancer in the post-genomic era. Nat. Biotechnol. 30, 679–692 (2012).

  4. 4.

    , , & The mystery of missing heritability: Genetic interactions create phantom heritability. Proc. Natl. Acad. Sci. USA 109, 1193–1198 (2012).

  5. 5.

    et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010).

  6. 6.

    et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

  7. 7.

    et al. A melanocyte lineage program confers resistance to MAP kinase pathway inhibition. Nature 504, 138–142 (2013).

  8. 8.

    et al. A genetic screen implicates miRNA-372 and miRNA-373 as oncogenes in testicular germ cell tumors. Cell 124, 1169–1181 (2006).

  9. 9.

    & Building mammalian signaling pathways with RNAi screens. Nat. Rev. Mol. Cell Biol. 7, 177–187 (2006).

  10. 10.

    et al. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells. Nature 509, 487–491 (2014).

  11. 11.

    , , , & Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).

  12. 12.

    et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

  13. 13.

    , , & Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).

  14. 14.

    Sequencing technologies - the next generation. Nat. Rev. Genet. 11, 31–46 (2010).

  15. 15.

    et al. Next-generation sequencing to generate interactome datasets. Nat. Methods 8, 478–480 (2011).

  16. 16.

    et al. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell 152, 909–922 (2013).

  17. 17.

    , & A one pot, one step, precision cloning method with high throughput capability. PLoS ONE 3, e3647 (2008).

  18. 18.

    et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

  19. 19.

    et al. Spanning high-dimensional expression space using ribosome-binding site combinatorics. Nucleic Acids Res. 41, e98 (2013).

  20. 20.

    et al. MicroRNA-mediated conversion of human fibroblasts to neurons. Nature 476, 228–231 (2011).

  21. 21.

    & Exploiting and antagonizing microRNA regulation for therapeutic and experimental applications. Nat. Rev. Genet. 10, 578–585 (2009).

  22. 22.

    et al. RPN2 gene confers docetaxel resistance in breast cancer. Nat. Med. 14, 939–948 (2008).

  23. 23.

    et al. Proteomic and transcriptomic profiling reveals a link between the PI3K pathway and lower estrogen-receptor (ER) levels and activity in ER+ breast cancer. Breast Cancer Res. 12, R40 (2010).

  24. 24.

    et al. MicroRNA expression profiles for the NCI-60 cancer cell panel. Mol. Cancer Ther. 6, 1483–1491 (2007).

  25. 25.

    et al. Expression of microRNAs in the NCI-60 cancer cell-lines. PLoS ONE 7, e49918 (2012).

  26. 26.

    et al. Global proteome analysis of the NCI-60 cell line panel. Cell Reports 4, 609–620 (2013).

  27. 27.

    et al. MiRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 42, D78–D85 (2014).

  28. 28.

    et al. Optimized PCR conditions and increased shRNA fold representation improve reproducibility of pooled shRNA screens. PLoS ONE 7, e42341 (2012).

  29. 29.

    et al. MiR-15a and MiR-16 control Bmi-1 expression in ovarian cancer. Cancer Res. 69, 9090–9095 (2009).

  30. 30.

    , & Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics. Proc. Natl. Acad. Sci. USA 111, 12462–12467 (2014).

  31. 31.

    , & Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells. Proc. Natl. Acad. Sci. USA 110, E2317–E2326 (2013).

  32. 32.

    , , & Genome-wide analysis of barcoded Saccharomyces cerevisiae gene-deletion mutants in pooled cultures. Nat. Protoc. 2, 2958–2974 (2007).

  33. 33.

    & Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

  34. 34.

    et al. miR-15b and miR-16 modulate multidrug resistance by targeting BCL2 in human gastric cancer cells. Int. J. Cancer 123, 372–379 (2008).

  35. 35.

    , & MiRNA-34a is associated with docetaxel resistance in human breast cancer cells. Breast Cancer Res. Treat. 131, 445–454 (2012).

  36. 36.

    et al. Combinatorial microRNA target predictions. Nat. Genet. 37, 495–500 (2005).

  37. 37.

    et al. Analysis of microRNA-target interactions across diverse cancer types. Nat. Struct. Mol. Biol. 20, 1325–1332 (2013).

  38. 38.

    , & Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005).

  39. 39.

    et al. Cdc14b regulates mammalian RNA polymerase II and represses cell cycle transcription. Sci. Rep. 1, 189 (2011).

  40. 40.

    & Gene silencing by microRNAs: contributions of translational repression and mRNA decay. Nat. Rev. Genet. 12, 99–110 (2011).

  41. 41.

    , & Applying synthetic lethality for the selective targeting of cancer. N. Engl. J. Med. 371, 1725–1735 (2014).

  42. 42.

    The Cancer Genome Atlas Research Network. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

  43. 43.

    et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

  44. 44.

    FANTOM Consortium and the RIKEN PMI and CLST (DGT). et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

  45. 45.

    & Genome-wide association studies: the key to unlocking neurodegeneration? Nat. Neurosci. 13, 789–794 (2010).

  46. 46.

    & Thirty-five common variants for coronary artery disease: the fruits of much collaborative labour. Hum. Mol. Genet. 20, R198–R205 (2011).

  47. 47.

    , , & A genome-wide shRNA screen identifies GAS1 as a novel melanoma metastasis suppressor gene. Genes Dev. 22, 2932–2940 (2008).

  48. 48.

    et al. RAS–MAPK–MSK1 pathway modulates ataxin 1 protein levels and toxicity in SCA1. Nature 498, 325–331 (2013).

  49. 49.

    et al. A genome-wide RNAi screen reveals determinants of human embryonic stem cell identity. Nature 468, 316–320 (2010).

  50. 50.

    , & MicroRNA sponges: competitive inhibitors of small RNAs in mammalian cells. Nat. Methods 4, 721–726 (2007).

  51. 51.

    et al. lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature 477, 295–300 (2011).

  52. 52.

    et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

  53. 53.

    , & Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 1262–1278 (2014).

  54. 54.

    , & ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 31, 397–405 (2013).

  55. 55.

    et al. DICE, an efficient system for iterative genomic editing in human pluripotent stem cells. Nucleic Acids Res. 42, e34 (2014).

  56. 56.

    et al. Identification of a microRNA that activates gene expression by repressing nonsense-mediated RNA decay. Mol. Cell 42, 500–510 (2011).

  57. 57.

    et al. Homeostatic regulation of MeCP2 expression by a CREB-induced microRNA. Nat. Neurosci. 10, 1513–1514 (2007).

  58. 58.

    et al. Cortical representations of olfactory input by trans-synaptic tracing. Nature 472, 191–196 (2011).

  59. 59.

    et al. Targeted tumor-penetrating siRNA nanocomplexes for credentialing the ovarian cancer oncogene ID4. Sci. Transl. Med. 4, 147ra112 (2012).

  60. 60.

    et al. Derivation of genetic interaction networks from quantitative phenotype data. Genome Biol. 6, R38 (2005).

  61. 61.

    et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004).

Download references

Acknowledgements

We thank members of the Lu laboratory and H. Ding for helpful discussions. We thank S. Patnaik of the Roswell Park Cancer Institute for processing miRNA expression data from the ArrayExpress database of European Bioinformatics Institute, the Massachusetts Institute of Technology BioMicroCenter for technical support on Illumina HiSeq, J. Weis for assisting computational analysis of next-generation sequencing data and C. Cui for technical assistance on cell viability assays. This work was supported by the US National Institutes of Health (DP2 OD008435 and P50 GM098792), the Office of Naval Research (N00014-13-1-0424), the Ellison Foundation New Scholar in Aging Award, the Defense Advanced Research Projects Agency and the Defense Threat Reduction Agency (HDTRA1-15-1-0050). A.S.L.W. was supported by the Croucher Foundation. The pAWp6 vector backbone (pFUGW-UBCp-GFP) was a gift from L. Nissim of the T.K. Lu laboratory, MIT; miR-128 was a gift from M.F. Wilkinson, University of California San Diego, USA and miR-132 was a gift from R.H. Goodman, Oregon Health and Science University, USA; HOSE 11-12 and HOSE 17-1 cells were gifts from G.S.W. Tsao, University of Hong Kong, Hong Kong; OVCAR8 and OVCAR8-ADR cells were gifts from S.N. Bhatia, MIT, and T. Ochiya, Japanese National Cancer Center Research Institute, Japan, respectively.

Author information

Affiliations

  1. Synthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Alan S L Wong
    • , Gigi C G Choi
    • , Allen A Cheng
    • , Oliver Purcell
    •  & Timothy K Lu
  2. Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Alan S L Wong
    • , Gigi C G Choi
    • , Allen A Cheng
    • , Oliver Purcell
    •  & Timothy K Lu
  3. Department of Biological Engineering and Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Alan S L Wong
    • , Gigi C G Choi
    • , Allen A Cheng
    • , Oliver Purcell
    •  & Timothy K Lu

Authors

  1. Search for Alan S L Wong in:

  2. Search for Gigi C G Choi in:

  3. Search for Allen A Cheng in:

  4. Search for Oliver Purcell in:

  5. Search for Timothy K Lu in:

Contributions

A.S.L.W., G.C.G.C., A.A.C. and T.K.L. conceived the work. A.S.L.W. and G.C.G.C. performed experiments. A.S.L.W., G.C.G.C., A.A.C. and O.P. performed computational analyses on next-generation sequencing data. A.S.L.W., G.C.G.C. and T.K.L. designed the experiments, interpreted and analyzed the data. A.S.L.W. and T.K.L. wrote the paper.

Competing interests

T.K.L., A.W. and G.C. have filed a provisional patent application (U.S. Provisional Application No.: 62/102,255) on this work.

Corresponding author

Correspondence to Timothy K Lu.

Integrated supplementary information

Supplementary figures

  1. 1.

    Lentiviral Delivery of Combinatorial miRNA Expression Constructs Provides Efficient Target Gene Repression.

  2. 2.

    Efficient Lentiviral Delivery of a Dual-Fluorescent Protein Reporter Construct in Human Cells.

  3. 3.

    Identification of the Exponential Phase During PCR for CombiGEM Barcode Amplification.

  4. 4.

    High Reproducibility of Barcode Quantitation in Biological Replicates for Combinatorial miRNA Screens.

  5. 5.

    Consistent Fold Changes of Barcodes among Same miRNA Combinations Arranged in Different Orders in the Expression Constructs.

  6. 6.

    Docetaxel Dose-Response Curves for the OVCAR8 Cell Line and the Docetaxel-Resistant OVCAR8-ADR Cell Line.

  7. 7.

    Log2 Fold-Changes in Barcode Representation Between Biological Replicates for All Individual Combinations in the Pooled Screens.

  8. 8.

    Three-dimensional Plots Depicting the Docetaxel-Sensitizing (a) and Proliferation-Modulating (b) Effects of Three-Wise miRNA Combinations.

  9. 9.

    Definitions of Genetic Interactions (GIs) in This Study.

  10. 10.

    GI Scores Between Biological Replicates for All Individual Combinations in the Pooled Screens.

  11. 11.

    Synergistic Interactions between the miR-16-1/15a Cluster, miR-128b, and the let-7e/miR-99b Cluster Modulate Cell Proliferation Phenotypes.

  12. 12.

    Three-Wise miRNA Combinations Display Distinct Docetaxel Sensitivity and Anti-Proliferation Phenotypes.

  13. 13.

    Combinatorial Expression of the miR-16-1/15a Cluster, miR-128b, and the let-7e/miR-99b Cluster Inhibits Colony Formation by Viable OVCAR8-ADR Cells.

  14. 14.

    High Consistency between Pooled Screens and Validation Data for Individual Hits.

  15. 15.

    Combinatorial Expression of the miR-16/15a Cluster, miR-128b, and/or the let-7e/miR-99b Cluster Reduce mRNA Levels of Targeted Genes in OVCAR8-ADR Cells.

Supplementary information

PDF files

  1. 1.

    Supplementary Figures

    Supplementary Figures 1-15

  2. 2.

    Supplementary Text and Figures

    Supplementary Figures 16, 17 and legends; Supplementary Tables 1, 2, 4–14

Excel files

  1. 1.

    Supplementary Table 3

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nbt.3326

Further reading