Methods for Optimising Metabolite Production in Genetically Modified Plants and for Processing These Plants

Abstract

The present invention relates to the field of producing particular metabolites of interest by engineered crop plants such as transgenic crop plants. Provided are methods that are easily applicable by farmers to determine when the metabolites of interest have reached an optimal content in the plant. These methods also help to facilitate decisions about the timeframe for preparing harvest or harvesting the engineered crop plant.

Claims

1. A method for determining a plant property at which an optimal content of a metabolite of interest is reached for harvesting an engineered plant or a harvestable part thereof, wherein the synthesis of said metabolite of interest is modulated through a genetic modification, and which method comprises: (i) cultivating the engineered plant and making a correlation between an accumulation pattern of the metabolite of interest and the plant growth stage or other plant property; (ii) determining a time at which the optimal content for harvest of said metabolite of interest in the harvestable part of the engineered plant is reached; and (iii) identifying a corresponding plant property as an indicator of the optimal metabolite of interest content for harvest in the engineered plant.

2. The method of claim 1, wherein the engineered plant is a transgenic oil crop plant.

3. The method of claim 2, wherein the transgenic oil crop plant is a transgenic Brassica sp.

4. The method of claim 2, wherein the harvestable part is a seed.

5. The method according to claim 1 te-4, wherein the metabolite of interest is a Very Long Chain Polyunsaturated Fatty Acid (VLCPUFA).

6. The method of claim 5, wherein the VLCPUFA comprises eicosapentaenoic acid (EPA) and/or docosahexaenoic acid (DHA).

7. The method according to claim 1, wherein the optimal content of the metabolite of interest is increased content as compared to control plants.

8. The method according to claim 1, wherein said plant property is a growth stage of the engineered plant.

9. A method for determining a growth stage at which an optimal content of a metabolite of interest is reached in harvestable parts of an engineered plant and expressing it as a Growing Degree Day (GDD) value, wherein the synthesis of said metabolite of interest is modulated through a genetic modification, and which method comprises: (i) choosing a suitable planting day; (ii) recording the daily temperatures during growth of the engineered plant; (iii) determining a time during plant growth at which the optimal content of the metabolite of interest in the harvestable parts of the engineered plant is reached; (iv) determining a suitable starting point during plant growth for calculating the GDD to the time when the optimal content of the metabolite of interest in the harvestable parts of the engineered plant is reached; and (v) determining the accumulated GDD from the start of step (iii) to the time when the optimal content of the metabolite of interest in the harvestable parts is reached.

10. The method of claim 9, wherein the engineered plant is a transgenic oil crop, preferably a transgenic Brassica sp.

11. The method according to claim 9, wherein the harvestable parts are seeds.

12. The method according to claim 9, wherein the metabolite of interest comprises a VLCPUFA, preferably EPA and/or DHA.

13. The method of claim 9, wherein the GDD is used as a predictor for a suitable swathing date.

14. A method for determining the optimal time for swathing or harvesting of genetically engineered Brassica species that synthesize VLCPUFAs, and in particular EPA and/or DHA in seeds, which method comprises (i) choosing a suitable planting day to allow the seeds to reach maturity, (ii) recording the daily temperatures during growth of the transformed Brassica sp. for calculating the GDD; (iii) sampling the transformed Brassica sp. plants, determining the accumulation of EPA and/or DHA in the developing and/or mature seeds, and defining the point in time at which the maximal level of EPA and/or DHA in the seeds is reached; (iv) monitoring the GDD from the start of flowering to the point in time where the maximal content of EPA and/or DHA in the seeds is reached; and (v) calculating the accumulated GDD from the start of flowering to the point in time where the maximal content of EPA and/or DHA in the seeds is reached.

15. The method of claim 14 wherein the GDD value corresponding to the point in time where the maximal content of EPA and/or DHA in the seeds is reached is the bottom limit of a range of GDD values representing the optimal time window for swathing or harvesting.

16. (canceled)

17. (canceled)

18. A method for the commercial production of oil enriched with a VLCPUFA from seeds of a transgenic Brassica napus variety capable of producing said VLCPUFA, which method comprises: (i) calculating the Growing Degree Days (GDD) in °F, starting from the appearance of the first open flower, (ii) swathing the plants when the GDD reaches a value of at least 1600, (iii) harvesting the seeds at a suitable maturation stage and processing the seeds to produce oil enriched in said VLCPUFA.

19. The method of claim 18, wherein the VLCPUFA comprises EPA and/or DHA.

20. The method of claim 18,wherein the plants are swathed in a range of growing GDD between, with increasing order of preference, 1600 and 2200 GDD, between 1600 and 2100 GDD, between 1600 and 2000 GDD, between 1600 and 1900 GDD, between 1600 and 1800 GDD.

Description

DESCRIPTION OF FIGURES

[0159] The present invention is described with reference to the following figures in which:

[0160] FIG. 1: Change in the concentration of 18:1 n-9 and EPA + DHA over GDD accumulation.

[0161] FIG. 2: Plots of EPA + DHA content vs accumulated GDD41 for various developmental periods. Data from event LBFLFK from 2014 and 2015 are included. Each data point represents average data for a single location in a single year. GDD41 refers to accumulated GDD calculated using T-base value of 41° F. The content of fatty acids is expressed as percentage (weight of a particular fatty acid) of the (total weight of all fatty acids).

[0162] FIG. 3: Plots of EPA + DHA content vs accumulated GDD41 for various developmental periods. Data from event LBFDAU from 2014 and 2015 are included. Each data point represents average data for a single location in a single year. GDD41 refers to accumulated GDD calculated using T-base value of 41° F. The content of fatty acids is expressed as percentage (weight of a particular fatty acid) of the (total weight of all fatty acids).

[0163] FIG. 4: Plots of Oil content vs accumulated GDD41 for various developmental periods. Data from event LBFLFK from 2014 and 2015 are included. Each data point represents average data for a single location in a single year. GDD41 refers to accumulated GDD calculated using T-base value of 41° F. The content of oil is expressed as percentage (weight of oil) of the total seed weight.

[0164] FIG. 5: Plots of Oil content vs accumulated GDD41 for various developmental periods. Data from event LBFDAU from 2014 and 2015 are included. Each data point represents average data for a single location in a single year. GDD41 refers to accumulated GDD calculated using T-base value of 41° F. The content of oil is expressed as percentage (weight of oil) of the total seed weight.

[0165] FIG. 6: EPA+DHA accumulation during the course of seed development. EPA+DHA data shown is the mean of all technical replicates. GDD41 is the accumulated growing degree days calculated based on a T-base of 41° F. 25 DAF and 35 DAF refers to the immature samples. BBCH63 refers to samples taken from the lower portion of the main raceme and BBCH67 refers to samples taken from the upper portion of the main raceme.

[0166] FIG. 7: EPA+DHA and Oil content in mature and late mature seed samples.

EXAMPLES

Example 1. PUFA Accumulation During Canola Seed Development

Plant Growth and Sampling

[0167] All plant vectors and events are described in PCT/EP2015/076631. Homozygous T3 or T4 plants of event LBFLFK, LBFGKN, LANPMZ and LAODDN were sown in the field in Hawaii in January. In the week following the date of first flower, individual racemes were visibly marked on the stem just above the most recently opened flower. For every raceme, the three pods immediately below the mark were considered to be the same age (i.e. flowered or were pollinated on the same day). Starting at 14 days after marking and until 46 days after marking, the three pods below the mark on each raceme were collected at various time points. At each time point, approximately 150 pods from 50 individual plants were sampled. Each individual plant was sampled only once in its lifespan. Immature seeds were dissected from the pods immediately after removal from the raceme and were promptly frozen on dry ice. The age of the seeds was determined by the age of the mark on the raceme, meaning that the three pods (and the seeds inside) taken from immediately below a 15 day-old mark were assumed to be 15 days after flowering. For each event, at each time point, seeds from about 150 pods were pooled into a single sample. For analysis, each seed sample was pulverized to powder while still frozen, and the powder was dispensed into aliquot amounts to be used as technical replicates for lipid analysis.

Lipid Extraction and Lipid Analysis of Plant Oils

[0168] Extraction of oil from canola seed samples was carried out by adding 800 .Math.L of methyl tert-butyl ether (MTBE) to the samples followed by extraction in a swing mill for 2 × 30 sec at 30 Hz. After centrifugation at 4000 rpm for 10 min, 40 .Math.L of the clear supernatant was transferred into a 96 well micro rack and diluted using 260 .Math.L MTBE. Lipids were derivatized into fatty acid methyl esters (FAMEs) by adding 20 .Math.L trimethylsulfonium hydroxide solution (TMSH, 0.2 M in methanol) into each sample. The rack was closed using silicone/PTFE cap mats and incubated for 20 min at room temperature.

[0169] An Agilent 7890A gas chromatograph coupled to Agilent flame ionization detector was used for FAME analysis. Separation of FAMEs was carried out on a DB-225 capillary column (20 m × 180 .Math.m × 0.2 .Math.m, Agilent) using H2 as carrier gas with a flow rate of 0.8 mL/min. The GC was operated in split mode using a split ratio of 1:50 at an injector temperature of 250° C., injection volume was 1 .Math.L. Oven temperature was held at 190° C. for 3 min and increased to 220° C. with 15° C. min-1. Temperature was held at 220° C. for another 6 min. Peak detection and integration was carried out using Agilent GC ChemStation software (Rev. B.04.02 SP1).

[0170] The content (levels) of fatty acids is expressed throughout the present invention as percentage (weight of a particular fatty acid) of the (total weight of all fatty acids).

Calculation of GDD

[0171] Growing degree day (GDD) accumulation was calculated using atmospheric data from the nearest weather station to each experimental plot. The GDD daily value = [(Tmax+Tmin)/2] - Tbase, where Tmax is the maximum daily temperature in degrees F. This value can be constrained to minimize the impact of high temperatures that can minimize growth. For calculating canola GDD, there is typically no constraint placed on Tmax. Tmin is the minimum daily temperature in degrees F. Tbase is related to the minimum temperature at which a particular plant grows and is calculated by region. A typically accepted value of Tbase for canola is 41° F. The accumulated GDD value is then the sum of all GDD daily values from a defined time to another defined time. GDD values may also be calculated using degrees C. A typical Tbase for canola is 5° C. One can convert GDD from F to C by using the following conversion rate 9 GDD F = 5 GDD C.

PUFA Production in Developing Canola Seeds

[0172] The fatty acid profiles of developing canola seeds is shown in Table 1. The age of each seed sample is indicated with days after flowering and with accumulated GDD from flowering to sampling calculated both with Tbase of 50 degrees (GDD50) F and with Tbase of 41° F. (GDD41). Individual fatty acids have different accumulation patterns. For example, the precursor fatty acid for the transgenic biosynthetic pathway, 18:1n-9, declines rapidly and appears to reach a steady state at around 1000 GDD41 (FIG. 1). Between zero and 1005 GDD41 18:1 n-9 decreased from 32% to 23.8%, which is a relative decrease of 26%. From 1005 to 1604 GDD41 18:1n-9 decreased from 23.8% to 22.6%, which is a relative decrease of just 5%. However, EPA + DHA accumulates throughout developmental time, peaking at around 1600 GDD41 (FIG. 1). While 18:1n-9 changed by just 5% relative between 1005 and 1604 GDD41, EPA + DHA increased from 7 to 10%, which is a relative increase of 43%. This trend was observed for all four events examined, regardless of which construct(s) were used for transformation. The timing of EPA and DHA accumulation is not consistent with the accumulation of naturally occurring PUFAs in canola, namely linoleic (18:2n-6) and linolenic (18:3n-3) acids. In general, the amount of 18:2n-6 and 18:3n-3 decreases as canola seeds age and reaches a minimum in fully mature seeds (Baux et al. 2008 Europ. J. Agronomy 29:102-107, Deng and Scarth 1998 JAOCS 75:759-766, and Fowler and Downey, 1970 Can. J. Plant. Sci. 50:233-247).

TABLE-US-00001 Accumulated GDD from first flower to sampling and fatty acid profile from developing seeds of four canola events. The content fatty acids is expressed as percentage (weight of a particular fatty acid) of the (total weight of all fatty acids) Accumulated GDD first flower to sampling Fatty Acid Composition (% total FA) Event Days After Tbase=50 Tbase=41 16:0 16:1n-7 16:3n-3 18:0 18:1n-7 18:1n-9 18:2n-6 (LA) 18:2n-9 18:3n-3 (ALA) 18:3n-6 (GLA) 18:4n-3 (SDA) 20:0 20:1n-9 20:2n-6 LANPMZ 14 394 520 7.4 1.2 0.3 4.3 13.1 33.7 29.6 0.0 7.6 0.0 0.0 1.0 0.5 0.1 LANPMZ 18 497 659 6.3 0.6 0.2 3.5 7.6 29.9 38.0 0.1 7.7 0.2 0.1 0.9 0.7 0.2 LANPMZ 21 575 764 5.7 0.5 0.1 3.2 6.0 26.7 40.6 0.2 6.8 0.5 0.1 0.8 0.7 0.3 LANPMZ 25 673 898 5.1 0.3 0.1 2.9 5.0 26.1 39.7 0.3 5.9 0.7 0.1 0.7 0.7 0.4 LANPMZ 28 751 1003 5.2 0.3 0.1 2.8 4.8 26.2 37.9 0.3 5.6 0.8 0.1 0.7 0.7 0.4 LANPMZ 32 832 1120 5.0 0.3 0.1 2.9 4.6 26.0 37.0 0.3 5.2 0.8 0.1 0.7 0.7 0.5 LANPMZ 35 907 1222 5.0 0.3 0.1 2.8 4.4 25.5 36.1 0.3 5.1 0.8 0.1 0.7 0.7 0.5 LANPMZ 39 1005 1356 5.0 0.3 0.1 2.7 4.4 24.7 36.2 0.3 5.1 0.9 0.2 0.8 0.7 0.5 LANPMZ 42 1080 1458 4.9 0.3 0.1 2.8 4.2 24.7 36.2 0.3 5.2 0.9 0.2 0.7 0.7 0.5 LANPMZ 46 1176 1590 5.1 0.4 0.1 2.8 4.3 23.8 36.4 0.3 5.0 0.9 0.2 0.7 0.7 0.5 LAODDN 14 399 525 7.5 1.5 0.3 4.4 15.3 31.2 29.7 0.0 7.2 0.0 0.0 1.0 0.4 0.1 LAODDN 17 472 625 7.2 0.8 0.2 4.1 9.2 32.2 34.8 0.1 7.6 0.1 0.0 1.0 0.5 0.1 LAODDN 21 575 764 5.8 0.5 0.1 3.1 5.9 27.4 42.0 0.2 7.3 0.4 0.1 0.8 0.7 0.1 LAODDN 24 653 869 5.1 0.4 0.1 2.8 4.8 27.5 42.1 0.2 6.9 0.6 0.1 0.7 0.7 0.1 LAODDN 28 777 1029 4.9 0.3 0.1 2.6 4.4 28.0 40.5 0.3 6.8 0.8 0.2 0.6 0.7 0.1 LAODDN 31 880 1159 4.8 0.3 0.1 2.6 4.2 28.7 38.1 0.3 6.8 1.1 0.3 0.6 0.7 0.1 LAODDN 35 988 1303 4.9 0.3 0.1 2.6 4.3 26.9 38.8 0.3 6.3 1.3 0.3 0.6 0.7 0.1 LAODDN 38 1085 1427 4.9 0.3 0.1 2.6 4.3 26.7 38.2 0.3 6.5 1.4 0.4 0.7 0.7 0.1 LAODDN 42 1150 1528 4.8 0.3 0.1 2.6 4.0 27.0 38.2 0.3 6.4 1.5 0.4 0.6 0.7 0.1 LAODDN 45 1225 1630 4.8 0.3 0.1 2.6 3.8 28.0 38.0 0.3 6.6 1.4 0.4 0.6 0.7 0.1 LBFGKN 14 391 517 7.5 1.5 0.4 3.9 16.3 32.1 27.7 0.0 7.8 0.0 0.0 0.9 0.4 0.1 LBFGKN 17 474 627 7.2 0.9 0.3 3.6 10.5 32.9 33.2 0.1 7.5 0.1 0.0 1.0 0.6 0.1 LBFGKN 21 574 763 6.1 0.6 0.2 3.1 7.1 27.6 39.7 0.2 6.7 0.6 0.1 0.8 0.6 0.1 LBFGKN 24 647 863 5.3 0.4 0.1 3.1 5.6 26.3 40.5 0.3 6.3 0.9 0.2 0.8 0.7 0.2 LBFGKN 28 752 1004 4.9 0.4 0.1 2.8 4.8 26.9 37.9 0.4 5.7 1.1 0.2 0.7 0.7 0.2 LBFGKN 31 828 1107 5.0 0.3 0.1 2.8 4.7 25.2 36.8 0.5 5.2 1.4 0.2 0.7 0.7 0.2 LBFGKN 35 927 1242 4.9 0.3 0.1 2.9 4.7 24.7 36.4 0.5 5.1 1.4 0.2 0.7 0.7 0.2 LBFGKN 38 1005 1347 4.9 0.3 0.1 2.9 4.5 24.8 36.4 0.5 5.1 1.4 0.2 0.7 0.7 0.2 LBFGKN 42 1105 1483 4.7 0.3 0.1 2.9 4.3 24.6 35.8 0.5 5.1 1.4 0.2 0.8 0.7 0.2 LBFGKN 45 1182 1587 4.8 0.3 0.1 2.8 4.3 24.1 35.6 0.5 5.2 1.5 0.2 0.8 0.7 0.2 LBFLFK 14 399 525 7.6 1.4 0.4 4.2 15.0 32.0 29.4 0.0 7.4 0.0 0.0 1.0 0.4 0.1 LBFLFK 17 475 628 7.1 0.8 0.2 3.7 8.9 30.2 36.2 0.2 6.8 0.4 0.1 1.0 0.5 0.1 LBFLFK 21 572 761 5.9 0.4 0.2 3.3 5.9 24.6 39.2 0.5 6.0 1.2 0.2 0.8 0.6 0.1 LBFLFK 24 651 867 5.5 0.3 0.1 2.9 4.9 23.9 38.1 0.6 5.4 1.5 0.2 0.7 0.6 0.1 LBFLFK 28 753 1005 5.1 0.3 0.1 2.9 4.5 23.8 34.4 0.8 4.6 2.0 0.2 0.7 0.6 0.1 LBFLFK 31 827 1106 5.1 0.3 0.1 2.8 4.5 23.4 33.3 0.7 4.3 2.1 0.3 0.7 0.7 0.1 LBFLFK 35 935 1250 5.1 0.3 0.1 2.9 4.4 22.4 33.1 0.8 4.0 2.2 0.3 0.7 0.6 0.1 LBFLFK 38 1015 1357 4.9 0.3 0.1 2.9 4.2 22.3 32.2 0.8 4.0 2.4 0.3 0.7 0.7 0.1 LBFLFK 45 1160 1565 5.0 0.3 0.1 2.9 4.2 22.5 32.1 0.8 4.1 2.2 0.3 0.8 0.6 0.1 LBFLFK 46 1190 1604 4.9 0.3 0.1 2.8 4.1 22.6 31.8 0.8 4.1 2.2 0.3 0.7 0.7 0.1

TABLE-US-00002 Accumulated GDD from first flower to sampling and fatty acid profile from developing seeds of four canola events. The content fatty acids is expressed as percentage (weight of a particular fatty acid) of the (total weight of all fatty acids) Accumulated GDD first flower to sampling Fatty Acid Composition (% total FA) Event Days After Tbase=50 Tbase=41 20:2n-9 20:3n-3 20:3n-6 (DGLA) 20:3n-9 20:4n-3 (ETA) 20:4n-6 (ARA) 20:5n-3 (EPA) 22:0 22:4n-3 22:4n-6 22:5n-3(DPA) 22:5n-6 22:6n-3 (DHA) LANPMZ 14 394 520 0.0 0.0 0.1 0.0 0.0 0.1 0.1 0.4 0.0 0.0 0.1 0.0 0.0 LANPMZ 18 497 659 0.0 0.1 0.3 0.0 0.1 0.9 0.7 0.4 0.1 0.4 0.7 0.0 0.1 LANPMZ 21 575 764 0.1 0.1 0.7 0.0 0.3 2.0 1.7 0.3 0.2 0.6 1.3 0.0 0.3 LANPMZ 25 673 898 0.2 0.1 1.0 0.1 0.5 2.8 3.2 0.3 0.2 1.0 2.0 0.0 0.5 LANPMZ 28 751 1003 0.2 0.1 1.2 0.1 0.6 3.1 3.9 0.3 0.2 1.1 2.3 0.0 0.7 LANPMZ 32 832 1120 0.2 0.1 1.4 0.1 0.7 3.3 4.7 0.3 0.2 1.2 2.5 0.0 0.9 LANPMZ 35 907 1222 0.2 0.2 1.6 0.1 0.8 3.6 5.2 0.3 0.3 1.2 2.8 0.0 1.1 LANPMZ 39 1005 1356 0.2 0.1 1.6 0.1 0.8 3.6 5.3 0.3 0.3 1.4 2.9 0.0 1.2 LANPMZ 42 1080 1458 0.2 0.2 1.7 0.1 0.9 3.6 5.5 0.3 0.3 1.3 3.0 0.0 1.3 LANPMZ 46 1176 1590 0.2 0.2 1.7 0.1 0.9 3.6 5.6 0.3 0.3 1.4 3.0 0.0 1.3 LAODDN 14 399 525 0.0 0.0 0.1 0.0 0.0 0.1 0.2 0.4 0.0 0.0 0.1 0.0 0.0 LAODDN 17 472 625 0.0 0.0 0.1 0.0 0.1 0.3 0.4 0.4 0.0 0.2 0.3 0.0 0.0 LAODDN 21 575 764 0.0 0.0 0.4 0.0 0.2 0.9 1.6 0.3 0.1 0.4 1.0 0.0 0.2 LAODDN 24 653 869 0.1 0.1 0.6 0.0 0.3 1.2 2.4 0.3 0.2 0.6 1.4 0.0 0.3 LAODDN 28 777 1029 0.1 0.1 0.6 0.0 0.4 1.5 3.2 0.3 0.2 0.8 1.9 0.0 0.5 LAODDN 31 880 1159 0.1 0.1 0.7 0.0 0.5 1.5 4.0 0.3 0.3 0.8 2.3 0.0 0.7 LAODDN 35 988 1303 0.1 0.1 0.8 0.0 0.5 1.5 4.5 0.3 0.3 0.9 2.4 0.0 0.9 LAODDN 38 1085 1427 0.1 0.1 0.8 0.0 0.6 1.4 4.6 0.3 0.3 1.0 2.6 0.0 1.0 LAODDN 42 1150 1528 0.1 0.1 0.8 0.0 0.6 1.4 4.9 0.3 0.3 0.9 2.6 0.0 1.0 LAODDN 45 1225 1630 0.1 0.1 0.7 0.0 0.6 1.3 4.5 0.3 0.3 0.8 2.5 0.0 0.9 LBFGKN 14 391 517 0.0 0.0 0.0 0.0 0.0 0.2 0.1 0.4 0.0 0.0 0.0 0.0 0.0 LBFGKN 17 474 627 0.0 0.0 0.1 0.0 0.1 0.3 0.4 0.4 0.0 0.2 0.2 0.0 0.1 LBFGKN 21 574 763 0.0 0.1 0.7 0.0 0.3 1.2 2.0 0.4 0.1 0.3 0.8 0.0 0.4 LBFGKN 24 647 863 0.1 0.1 1.0 0.0 0.4 1.8 3.2 0.3 0.1 0.4 1.2 0.0 0.5 LBFGKN 28 752 1004 0.1 0.1 1.4 0.0 0.6 2.6 4.7 0.3 0.1 0.5 1.7 0.0 0.8 LBFGKN 31 828 1107 0.1 0.1 1.7 0.1 0.7 3.0 6.1 0.3 0.2 0.6 2.0 0.1 1.1 LBFGKN 35 927 1242 0.1 0.1 1.8 0.1 0.9 2.8 6.7 0.3 0.2 0.6 2.1 0.1 1.4 LBFGKN 38 1005 1347 0.1 0.1 1.9 0.1 0.9 2.6 6.8 0.3 0.2 0.6 2.1 0.1 1.4 LBFGKN 42 1105 1483 0.1 0.1 1.9 0.1 1.0 2.6 7.3 0.3 0.2 0.6 2.3 0.1 1.6 LBFGKN 45 1182 1587 0.1 0.1 2.0 0.1 1.1 2.5 7.6 0.3 0.2 0.6 2.4 0.1 1.7 LBFLFK 14 399 525 0.0 0.0 0.1 0.0 0.0 0.2 0.0 0.4 0.0 0.0 0.0 0.0 0.0 LBFLFK 17 475 628 0.0 0.0 0.4 0.0 0.2 0.6 1.0 0.4 0.1 0.3 0.4 0.0 0.2 LBFLFK 21 572 761 0.1 0.1 1.9 0.0 0.7 1.9 3.3 0.3 0.3 0.5 1.5 0.0 0.4 LBFLFK 24 651 867 0.1 0.1 2.4 0.0 0.9 2.5 4.8 0.3 0.4 0.6 2.1 0.1 0.6 LBFLFK 28 753 1005 0.2 0.1 3.6 0.1 1.3 3.0 6.2 0.3 0.6 0.8 2.7 0.2 0.8 LBFLFK 31 827 1106 0.2 0.1 3.2 0.1 1.3 3.3 7.5 0.3 0.5 0.9 3.1 0.2 1.0 LBFLFK 35 935 1250 0.2 0.1 3.6 0.1 1.5 3.1 7.8 0.3 0.6 1.0 3.3 0.2 1.1 LBFLFK 38 1015 1357 0.2 0.1 4.2 0.1 1.8 2.8 8.1 0.3 0.7 0.9 3.5 0.2 1.2 LBFLFK 45 1160 1565 0.2 0.1 4.0 0.1 1.9 2.6 8.3 0.3 0.7 0.9 3.5 0.2 1.3 LBFLFK 46 1190 1604 0.2 0.1 3.9 0.1 1.9 2.5 8.6 0.3 0.7 0.9 3.6 0.2 1.4

Example 2. Correlation of GDD With Fatty Acid Profile

[0173] Example 1 demonstrates that EPA and DHA accumulation in transgenic canola does not follow the known pattern of PUFA accumulation in non-transgenic canola. Therefore, experimental field trials were conducted to further examine the accumulation pattern of EPA and DHA with respect to swathing time, with the goal of discovering means to optimize the production of EPA and DHA in seed oil. Experimental field trials were conducted in 2014 at six different sites, spanning four different states in USDA growth zones 4 and 6. Homozygous T3 plants of independent transgenic events LBFLFK, LBFDAU, LBFDGG, LBFGKN, LBFIHE, and LBFPRA (described in PCT/EP2015/076631) were grown in each location in replicated plots. Plants were managed according to standard agricultural practices for canola. All plants at a given location were swathed on the same date. Seeds were harvested and subjected to fatty acid profiling as described in Example 1. Accumulated GDD for each location was calculated as described in Example 1. Location level accumulated GDD and fatty acid profile data for each transgenic event is shown in Table 2. Accumulated GDD values were calculated for various developmental intervals including the time from planting to flowering, from flowering to swathing, and from planting to swathing.

TABLE-US-00003 Accumulated GDD and fatty acid profile data for each transgenic event grown at six different field sites in the continental US in 2014. GDD41 refers to accumulated GDD calculated using T-base value of 41° F. The content fatty acids is expressed as percentage (weight of a particular fatty acid) of the (total weight of all fatty acids) Event Location GDD41 Planting to Flowering GDD41 First Flower to Swathing GDD41 Planting to Swathing EPA+DHA EPA (20:5n3) ARA (20:4n-6) DHA (22:6n-3) DPA (22:5n-3) 18:1n-9 LBFLFK 1 1112 1344 2430 7.7 6.7 1.6 1.0 2.6 32.4 LBFLFK 2 1398 1694 3064 9.6 8.4 1.8 1.3 3.3 30.8 LBFLFK 3 1097 1791 2864 9.2 7.9 1.9 1.3 3.1 29.4 LBFLFK 4 979 1528 2486 9.4 8.2 2.2 1.2 3.0 31.8 LBFLFK 5 1037 1778 2799 10.2 8.7 2.0 1.5 3.6 28.0 LBFLFK 6 1009 1704 2692 10.9 9.3 2.2 1.6 3.5 29.4 LBFDAU 1 1112 1344 2430 10.9 9.4 1.7 1.5 2.5 29.4 LBFDAU 2 1398 1694 3064 11.9 10.5 1.7 1.4 2.7 30.1 LBFDAU 3 1097 1791 2864 12.5 10.8 2.2 1.7 3.0 27.9 LBFDAU 4 979 1528 2486 12.8 11.2 2.3 1.6 2.6 31.3 LBFDAU 5 1037 1778 2799 13.8 11.8 2.1 2.0 3.2 26.2 LBFDAU 6 1009 1704 2692 11.8 10.2 2.2 1.5 2.6 29.5 LBFDGG 1 1112 1344 2430 6.0 5.1 1.6 0.9 1.8 36.0 LBFDGG 2 1398 1694 3064 7.0 6.1 1.7 0.9 2.2 35.2 LBFDGG 3 1097 1791 2864 7.1 5.9 1.9 1.2 2.2 32.8 LBFDGG 4 979 1528 2486 7.2 6.2 2.1 1.0 2.0 35.7 LBFDGG 5 1037 1778 2799 7.2 6.2 1.8 1.1 2.2 33.0 LBFDGG 6 1009 1704 2692 7.1 6.0 2.0 1.1 2.0 35.2 LBFGKN 1 1112 1344 2430 6.0 5.0 1.6 0.9 1.7 35.2 LBFGKN 2 1398 1694 3064 7.6 6.6 1.9 1.0 2.1 33.4 LBFGKN 3 1097 1791 2864 6.8 5.7 1.9 1.1 2.0 33.4 LBFGKN 4 979 1528 2486 6.8 5.8 2.0 0.9 1.9 35.7 LBFGKN 5 1037 1778 2799 7.8 6.5 1.8 1.2 2.2 31.7 LBFGKN 6 1009 1704 2692 7.1 6.0 2.0 1.1 2.0 34.1 LBFIHE 1 1112 1344 2430 7.3 6.2 2.2 1.1 1.8 32.2 LBFIHE 2 1398 1694 3064 7.4 6.3 1.8 1.1 2.0 32.4 LBFIHE 3 1097 1791 2864 8.1 6.8 2.6 1.3 2.0 30.7 LBFIHE 4 979 1528 2486 7.9 6.8 2.5 1.1 1.9 32.3 LBFIHE 5 1037 1778 2799 7.1 6.0 2.1 1.0 1.8 32.0 LBFIHE 6 1009 1704 2692 8.0 6.8 2.6 1.2 1.9 31.1 LBFPRA 1 1112 1344 2430 9.2 8.3 3.2 0.9 2.0 30.3 LBFPRA 2 1398 1694 3064 10.1 9.1 3.2 1.0 2.4 30.9 LBFPRA 3 1097 1791 2864 10.4 9.3 3.7 1.1 2.3 28.0 LBFPRA 4 979 1528 2486 11.0 10.0 4.3 1.0 2.2 28.9 LBFPRA 5 1037 1778 2799 11.4 10.1 4.0 1.2 2.7 25.9 LBFPRA 6 1009 1704 2692 11.5 10.3 4.1 1.2 2.5 27.5

[0174] The accumulation of GDD at each location was distinct and allowed for a correlation analysis to be performed between the fatty acid profile and the accumulated GDD. Pearson correlation coefficients of various parameters are presented in Table 3. For each event, the highest correlation coefficient value for EPA+DHA is with GDD41 First Flower to Swathing (italicized cells in Table 3). This observation is also true for EPA and DHA individually, as well as for DPA. Therefore, in 2014 the accumulated GDD from flowering to swathing is the best indicator of EPA, DHA, and DPA accumulation in seed oil.

TABLE-US-00004 Pearson correlation coefficients (R value) between accumulated GDD41 and fatty acid content in mature canola seeds from six different transgenic events grown at various locations in 2014 GDD Event EPA+DHA EPA (20:5n-3) ARA (20:4n-6) DHA (22:6n-3) DPA (22:5n-3) 18:1n-9 GDD41 Planting to First Flower LBFDAU -0.32 -0.26 -0.76 -0.45 -0.11 0.15 LBFDGG -0.12 -0.04 -0.64 -0.39 0.27 0.11 LBFGKN 0.29 0.36 -0.25 -0.15 0.14 -0.20 LBFIHE -0.32 -0.31 -0.78 -0.32 0.32 0.33 LBFLFK -0.16 -0.14 -0.52 -0.26 -0.04 0.16 LBFPRA -0.51 -0.51 -0.80 -0.49 -0.06 0.67 GDD41 First Flower to Swathing LBFDAU 0.62 0.62 0.41 0.49 0.79 -0.55 LBFDGG 0.81 0.74 0.32 0.80 0.94 -0.79 LBFGKN 0.80 0.74 0.52 0.81 0.93 -0.79 LBFIHE 0.20 0.17 0.01 0.30 0.54 -0.53 LBFLFK 0.76 0.72 0.39 0.82 0.87 -0.89 LBFPRA 0.63 0.60 0.27 0.71 0.82 -0.60 GDD41 Planting to Swathing LBFDAU 0.26 0.29 -0.17 0.09 0.51 -0.31 LBFDGG 0.52 0.51 -0.16 0.34 0.85 -0.51 LBFGKN 0.77 0.77 0.22 0.50 0.77 -0.70 LBFIHE -0.06 -0.08 -0.48 0.01 0.59 -0.18 LBFLFK 0.45 0.45 -0.04 0.43 0.62 -0.55 LBFPRA 0.14 0.12 -0.30 0.22 0.57 -0.02

[0175] Experimental field trials were conducted in 2015 at six different sites, spanning four different states in USDA growth zone 4. Homozygous T4 plants of events LBFDAU and LBFLFK were grown in each location in replicated plots. Plants were managed according to standard agricultural practices for canola. All plants at a given location were swathed on the same date. Seeds were harvested and subjected to fatty acid profiling as described in Example 1. Accumulated GDD for each location was calculated as described in Example 1. Location level accumulated GDD and fatty acid profile data for each transgenic event grown in 2015 is shown in Table 4. Accumulated GDD values were calculated for the same developmental intervals as in 2014.

TABLE-US-00005 Accumulated GDD and fatty acid profile data for each transgenic event grown at six different field sites in the continental US in 2015. GDD41 refers to accumulated GDD calculated using T-base value of 41° F. The content of fatty acids is expressed as percentage (weight of a particular fatty acid) of the (total weight of all fatty acids) Event Location GDD41 Platning to Flowering GDD41 First Flower to GDD41Planting to Swathing EPA + DHA EPA (20:5n-3) ARA (20:4n-6) DHA (22:6n-3) DPA (22:5n-3) 18:1n-9 LBFDAU 1 800 1539 2325 10.38 8.89 2.12 1.48 2.80 27.33 LBFDAU 2 1157 1246 2380 11.30 9.96 2.81 1.34 2.68 28.74 LBFDAU 3 1096 1387 2473 10.02 8.84 2.04 1.17 2.79 30.67 LBFDAU 4 1173 1171 2319 8.73 7.59 2.25 1.14 2.15 30.17 LBFDAU 5 1181 1200 2361 10.83 9.40 2.84 1.43 2.84 27.29 LBFDAU 6 962 1423 2361 10.64 9.24 3.07 1.40 2.42 30.20 LBFLFK 1 800 1539 2325 6.76 6.09 1.86 0.68 3.12 28.62 LBFLFK 2 1157 1246 2380 7.19 6.54 2.59 0.66 3.01 31.29 LBFLFK 3 1096 1387 2473 6.64 6.07 1.75 0.57 3.15 32.23 LBFLFK 4 1173 1171 2319 6.80 6.15 2.36 0.65 2.97 32.08 LBFLFK 5 1181 1200 2361 7.00 6.33 2.42 0.67 3.09 29.63 LBFLFK 6 962 1423 2361 8.44 7.51 3.32 0.93 3.16 30.89 indicates text missing or illegible when filed

[0176] The accumulation of GDD at each location was distinct and allowed for a correlation analysis to be performed between the fatty acid profile and the accumulated GDD. Pearson correlation coefficients of various parameters are presented in Table 5. Again, the highest positive correlation values for EPA and DHA were with GDD41 from flowering to swathing. The strength of the correlation in 2015 was not as high as in 2014, but the trend was similar. Environmental factors that are not part of the GDD calculation, such as rainfall, humidity, and field location, may be the reason why the correlation is not as strong in 2015 compared to 2014.

TABLE-US-00006 Pearson correlation coefficients (R value) between accumulated GDD41 and fatty acid content in mature canola seeds from six different transgenic events grown at various locations in 2015 GDD Event EPA+DHA EPA(20:5n-3) ARA (20:4n-6) DHA (22:6n-3) DPA (22:5n-3) 18:1n-9 GDD41 Planting to First Flower LBFDAU -0.12 -0.03 0.21 -0.56 -0.20 0.24 LBFLFK -0.19 -0.15 0.10 -0.37 -0.59 0.61 GDD41 First Flower to Swathing LBFDAU 0.20 0.14 -0.26 0.44 0.35 -0.08 LBFLFK 0.17 0.14 -0.19 0.28 0.76 -0.45 GDD41 Planting to Swathing LBFDAU 0.20 0.29 -0.19 -0.38 0.45 0.45 LBFLFK -0.15 -0.10 -0.30 -0.36 0.44 0.49

[0177] An across-year analysis was performed combining the data from 2014 and 2015 for events LBFLFK and LBFDAU. FIG. 2 shows plots of EPA+DHA content in seed oil vs accumulated GDD values for various developmental intervals for event LBFLFK and FIG. 3 shows the corresponding plots for event LBFDAU. For each event and for each developmental interval a correlation was calculated between EPA + DHA and GDD, and the resulting R2 values are shown on the plots. For both LBFLFK and LBFDAU the highest R2 value is for EPA +DHA vs GDD41 from flowering to swathing. This means that the accumulated GDD from flowering to swathing is the best indicator of EPA + DHA content in seeds and may be useful as a predictor. FIGS. 4 and 5 show plots of oil content vs accumulated GDD for various developmental intervals for events LBFLFK and LBFDAU, respectively. For both events oil content is most highly correlated with the accumulated GDD from flowering to swathing. The correlation is negative in both cases, meaning that higher accumulated GDD correlates with lower oil content. The influence of temperature during seed development has been studied in crop plants (e.g. Deng and Scarth 1998 JAOCS 75:759-766, Schulte et al. 2013 Industrial Crops and Products 51:212- 219, Vera et al. 2007 Canadian J. Plant Sci 87:13-26). For canola, 18:1n-9 content increases during seed development reaching a maximum in mature seed, and reaching a higher maximum when temperatures are relatively high during seed development. Similar observations were made for camelina and soybean seeds. Together, these results suggest that in oil seed crops a higher GDD would result in higher 18:1n-9 content. 18:1n-9 is the precursor for the synthesis of EPA and DHA, and based on the literature one may predict that EPA and DHA content as a proportion of total fatty acids would decline with higher GDD accumulation. On the contrary, we discovered that EPA and DHA content tends to reach a higher concentration in seeds that have accumulated the most GDD from flowering to swathing, as shown in FIGS. 2 and 3. A grower may use this information to maximize EPA + DHA content in field grown plants, for example, by swathing plants only after the accumulated GDD41 from flowering has reached at least 1600 units. The impact of such a cutoff can be evaluated by applying it to the 2014 and 2015 field data presented in this example. The average EPA + DHA content of seed oil from all 2014 and 2015 field sites for event LBFLFK is 8.29%. Selecting sites where GDD41 from flowering to swathing was greater than 1600 units gives an EPA + DHA concentration of 9.99%, which is a relative increase of 20.5%. Applying the same criteria to event LBFDAU would result in an EPA + DHA content of 12.49%, compared to 11.3% from all field sites. This is a relative increase in EPA + DHA content of 10.6%.

[0178] In practice, a grower may achieve a desired number of GDD units in several ways. A planting date may be chosen to increase the likelihood of achieving a desired GDD from flowering to swathing. Once the first flowers are present in the field, the GDD can be actively monitored and swathing can be done only once the GDD has reached the desired value. The transgenic event may also be bred into germplasm with differing flowering dates or maturity times such that at least 1600 accumulated GDD41 from flowering to swathing can be achieved in any given location. Most likely, a combination of approaches would be taken to achieve the desired GDD from flowering to swathing in order to maximize EPA + DHA content.

Example 3. Analysis of Correlation of GDD With EPA and DHA Content Over Multiple Generations

[0179] Five seed lots of EPA+DHA canola event LBFLFK (event described in PCT/EP2015/076631) representing three different generations (T3-T5) and two different production environments (greenhouse vs field) were grown in a single field in Hawaii. Seeds were sown in late December 2015. A weather station was deployed at the edge of the field to record atmospheric data. Flowering racemes were marked and immature seed samples were harvested at 25 days after flowering (DAF) and 35 DAF as described in Example 1. All immature seed samples were collected and pooled from 12-14 different plants per seed lot. Seed samples were also collected at maturity and at late maturity. Maturity was defined as the BBCH 86 stage, when 60% of pods are ripe with dark, hard seeds. Late maturity is defined as 2 weeks after maturity. At maturity, four different types of samples were harvested from plants of each seed lot: [0180] 1) Seeds the lower portion of the main raceme [0181] 2) Seeds from the upper portion of the main raceme [0182] 3) All pods from the main raceme [0183] 4) All pods from the branches

[0184] Each sampling consisted of pooling pods from 12-14 individual plants per entry. Sample types 1 and 2 were collected from the same plants for each seed lot. Sample types 3 and 4 were collected from the same plants for each seed lot. All immature, mature, and late mature seed samples were subject to GC-FID for determination of fatty acid composition, as described in Example 1. The mature and late mature samples were subject to Near Infrared Spectroscopy (NIRS) to determine the approximate oil content of the seeds.

[0185] The temperature data was used to calculate growing degree days (GDDs) as described in Example 1, using T-base of 41° F. FIG. 6 is a plot of EPA+DHA content in all samples as a function of GDD accumulation from the onset of flowering to the time of sample collection. The figure shows that EPA+DHA content increases as seeds mature. EPA+DHA accumulates at a high rate during the middle of seed development, doubling from 25 DAF to 35 DAF. From 35 DAF to maturity, EPA+DHA increases slightly and then does not change between maturity and late-maturity. This data shows that EPA+DHA content can be maximized by insuring that the seed have reached full maturity prior to harvest. It is therefore recommended to use GDD, particularly GDD accumulation from the onset of flowering to swathing/harvest, to estimate seed maturity. In this experiment, we confirmed the findings in Example 2 that a GDD of at least 1600 units correlated with seed maturity and maximum EPA+DHA content. The percentage of EPA+DHA in seed oil does not decrease from maturity to late maturity (FIG. 6). There were no patterns in EPA+DHA content that correlated with source seed production environment. There was also not a consistent increase or decrease in EPA+DHA content when progressing from one generation to the next, even when looking at three generations where the source seeds were produced in the same greenhouse environment. Therefore, the EPA+DHA production trait appears to be stable over at least three generations of EPA+DHA canola event LBFLFK. The degree of seed maturity, defined by days after flowering or by GDD accumulation, does appear to correlate strongly with EPA+DHA content. However, total oil content does decrease from maturity to late-maturity (FIG. 7), which is known phenomenon for most oil seeds, including canola. Therefore, to maximize EPA+DHA yield per acre, it is critical to harvest seeds not until EPA+DHA reaches a maximum, but before total oil content begins to decline.