Characterization of GM events by insert knowledge adapted re-sequencing approaches

Abstract

Detection methods and data from molecular characterization of genetically modified (GM) events are needed by stakeholders of public risk assessors and regulators. Generally, the molecular characteristics of GM events are incomprehensively revealed by current approaches and biased towards detecting transformation vector derived sequences. GM events are classified based on available knowledge of the sequences of vectors and inserts (insert knowledge). Herein we present three insert knowledge-adapted approaches for characterization GM events (TT51-1 and T1c-19 rice as examples) based on paired-end re-sequencing with the advantages of comprehensiveness, accuracy, and automation. The comprehensive molecular characteristics of two rice events were revealed with additional unintended insertions comparing with the results from PCR and Southern blotting. Comprehensive transgene characterization of TT51-1 and T1c-19 is shown to be independent of a priori knowledge of the insert and vector sequences employing the developed approaches. This provides an opportunity to identify and characterize also unknown GM events.

Figure 1. The three modules proposed for molecular characterization of transgenic lines using paired-end whole genome re-sequencing and data analysis.

Module 1 (Fig. 1a–1b): the complete DNA sequence of the transformation vector is available (corresponding to insert sequence knowledge [ISK] class 2 scenarios). The paired-end reads are characterized into five types (A to E). Type A: both paired-ends perfectly map back to the host genome. Type B: one end matches to the host genome, the other to the transgene. Type C: both paired-ends match to transgene. Types D and E: one end matches to host genome or transgene, and the other spans the junction region between host genome and transgene. Module 2 (Fig. 1c): the DNA sequence of the transformation vector is not available but the transgene insert is expected to contain at least one genetic element that is included in a transgene element sequence library (database; corresponding to ISK-class 3 scenarios). Successful detection and characterization of the transgene depends on matches between the transgene and the sequence library. Module 3 (Fig. 1d): no DNA sequence information on the transgene insert is available and a transgene element sequence library is expected to be of limited use (cf. ISK-class 4). Successful detection and characterization depends on efficient de novo assembly and contig analyses.

Figure 2. Deduced transgene loci and PCR confirmation of the insertions in GM rice T1c-19.

(a) transgene locus on Chr 04; (b) transgene locus on Chr 11.

Figure 3. Comparison of inferred transgene maps for the rice events T1c-19 (Figs. 3a–3c and TT51-1 (Figs. 3d–3f), and for the mimic insert spiked in silico into the T1c-19 rice event raw sequence data reads (Figs. 3g–3h).

Top: the correct maps, obtained with module 1 for the two rice events (Figs. 3a and 3d), and spiked in silico (Fig. 3g). Middle: the results inferred with module 2 for the two rice events (Figs. 3b and 3e). Bottom: the results inferred with module 3 for the two rice events (Figs. 3c and 3f) and the spike (Fig. 3h).

Figure 4. Deduced transgene loci and PCR confirmation of the insertions in GM rice TT51-1.

(a) transgene locus on Chr 04; (b) transgene locus on Chr 10.