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Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms

Nature Methods volume 6, pages 767–772 (2009)Cite this article

Abstract

Biological pathways are structured in complex networks of interacting genes. Solving the architecture of such networks may provide valuable information, such as how microorganisms cause disease. Here we present a method (Tn-seq) for accurately determining quantitative genetic interactions on a genome-wide scale in microorganisms. Tn-seq is based on the assembly of a saturated Mariner transposon insertion library. After library selection, changes in frequency of each insertion mutant are determined by sequencing the flanking regions en masse. These changes are used to calculate each mutant's fitness. Using this approach, we determined fitness for each gene of Streptococcus pneumoniae, a causative agent of pneumonia and meningitis. A genome-wide screen for genetic interactions of five query genes identified both alleviating and aggravating interactions that could be divided into seven distinct categories. Owing to the wide activity of the Mariner transposon, Tn-seq has the potential to contribute to the exploration of complex pathways across many different species.

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Figure 1: Schematic depiction of Tn-seq.
Figure 2: Fitness determination and classification.
Figure 3: Validation of Tn-seq fitness measurements.
Figure 4: Genetic interaction network of query genes ccpA (SP_1999), malR (SP_2112), malX (SP_2108), regR (SP_0330) and hypothetical ABC transporter gene SP_1683.
Figure 5: Validation of genetic interactions.

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Acknowledgements

We thank L. Sonenshein, R. Isberg, R. Iyer and N. Vastenhouw for both discussions and comments on the manuscript, members of the Tufts University core facility for Illumina sequencing and members of the Camilli lab, particularly D. Lazinski, and A. Boorsma for helpful discussions. This work was supported by a grant from the Netherlands Organization for Scientific Research (NWO-Rubicon) to T.v.O. and by the Howard Hughes Medical Institute.

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  1. and Department of Molecular Biology and Microbiology, Howard Hughes Medical Institute, Tufts University School of Medicine, Boston, Massachusetts, USA

    Tim van Opijnen, Kip L Bodi & Andrew Camilli

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  1. Tim van Opijnen
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  2. Kip L Bodi
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  3. Andrew Camilli
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Contributions

T.v.O. and A.C. designed research, analyzed the data and wrote the paper. T.v.O. performed research and K.L.B. contributed to data analysis.

Corresponding author

Correspondence to Andrew Camilli.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1,5–7 (PDF 193 kb)

Supplementary Table 2

Wild-type library fitness values (XLS 426 kb)

Supplementary Table 3

GSEA enrichment analysis (XLS 26 kb)

Supplementary Table 4

Genetic interactions and fitness values for five query genes (XLS 47 kb)

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van Opijnen, T., Bodi, K. & Camilli, A. Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nat Methods 6, 767–772 (2009). https://doi.org/10.1038/nmeth.1377

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