Plant material and environments
One hundred recombinant inbred lines derived from a cross between S. lycopersicum cv. Moneymaker and S. pimpinellifolium GI.1554 were used for resistance screenings (Table 1). We screened in three different environments (Table 2).
Table 1
Genetic map based on a RIL population derived from a cross between S. lycopersicum cv. Moneymaker and S. pimpinellifolium G1.1554 .
Chromosome
|
Number of linkage groups
|
Average cM distance between markers
|
1
|
2
|
1.3
|
2
|
1
|
1.2
|
3
|
3
|
2.2
|
4
|
1
|
1.4
|
5
|
1
|
1.7
|
6
|
1
|
1.3
|
7
|
1
|
2.7
|
8
|
2
|
2.1
|
9
|
1
|
1.1
|
10
|
2
|
3.0
|
11
|
1
|
1.2
|
12
|
1
|
1.1
|
Table 2
Screening conditions and scored phenotypic traits of the recombinant inbred population.
Screening conditions
|
Measured traits
|
Environments
|
Population size
|
Growing conditions
|
Bacterial strain
|
Wilting
|
Stem dis- coloration
|
Bacterialtiter
|
Dutch winter
|
100
|
Soilless
|
Cmm 542
|
done
|
done
|
done
|
Dutch summer
|
80
|
Soilless
|
Cmm 542
|
done
|
done
|
done
|
Antalya
|
100
|
Soil
|
Mix of strains
|
done
|
not done
|
not done
|
The first environment was a greenhouse in winter in the Netherlands where outside conditions are cold (-10 to 10 °C), cloudy and shortdays. The second environment was a greenhouse in Dutch summer when it is relatively warmer (10 to 25 °C) and long days that might even be sunny. A third screening was done in a greenhouse in Antalya, in the south of Turkey where the growing season is from January to May with temperatures varying from 5 to 30°C and at least half of the season is sunny. In the first and second screening, conditions in the greenhouse were controlled (18 °C at night and 24 °C at day with 60% humidity) whereas in the third environment this was not the case. In the first and second environment, each line and parents were represented by 4 plants, in the third environment each line and parent was represented by 8 plants and 2 control plants.
Disease test
In the Netherlands we have used the aggressive bacterial strain Cmm542, in Turkey we have used a mix of 14 local aggressive strains of which specificity is not known. Inoculation was done at the sixth leaf stage by removing the second leaf with scissors and injecting 5μl of 106 cfu/ml bacterial suspension in the wound. In the first and second environment, after inoculation, plants were kept for one week under high relative humidity (100%), then conditions were changed to 60%, 12 hours daylight, 24°C day and 18°C night temperature. In Turkey (3rd environment) plants were kept under greenhouse conditions after inoculation. Wilting symptoms were recorded using the following scale: 0.5 stands for 12.5% wilting, 1 for 25% and scale continues until 100% wilting. No symptoms, score = 0 and when the whole plant is wilted and dead, score = 4. The final wilting symptoms were recorded at 65 days after inoculation. Stem discoloration was measured on the inoculation area of the stem after the experiment finished based on following scale: 0.5 scale was used for each 12.5% discoloration stem clean, score = 0; and stem is totally rotten, score = 4.
Quantification of bacteria by TaqMan PCR
To quantify bacteria, three plants from each line and 3 parts of each plant, hereafter refered to as lower, middle part and upper part, were used. The extraction of bacteria from this material was done using PBS buffer (3 times the weight of the stem part). DNA extraction was done using the Quick Pick SML Plant DNA purification kit provided by Bio-Nobile in combination with a Kingfisher processor and followed by a purification step on a PVPP column. RT-PCR amplification was done as follows; The 25 µl reaction includes 10 µl DNA template, 12,5 µl 10x Takara mix, 0,5 µl Rox and 2 µl mix of 4 µM Forward primer (GGG GCC GAA GGT GCT GGTG), 4 µM Reverse primer (CGT CGC CCG CCC GCTG) and 1 µM TaqMan probe (modified) (6-FAM/TGG TCG TCC /ZEN/TCG GCG CC/IABkFQ) (Berendsen et al. 2011). The real-time PCR temperature regime was as follows: 95 ºC for 30 seconds followed by 50 cycles of 95 ºC for 3 seconds and 60 ºC for 35 seconds using a Bio-Rad CFX thermocycler. To obtain a standard curve, 3 independent replications of ten-fold serial dilutions of bacteria wereused as a template and a water control was included as negative control.
Genetic map
Custom-made Infinium Bead arrays containing 5528 SNPs were used for genotyping the population (Viquez- Zamora et al. 2013). A genetic map was constructed using Joinmap 4.1 software (Stam, 1993) using regression algorithm with Kosambi mapping function.
QTL analysis
Data were analyzed by two approaches; single trait single environment and multitrait single environment. Single trait single environment analysis of data was done by MapQTL 6.0 software (Van Oojien et al. 2009) using interval mapping. In order to convert scale type data to continuous style data that allow interval mapping, data were transformed to log scale prior QTL analysis. The Q-Q plot test was used to inspect the distribution of residual data. For interval mapping, a permutation test (10000 times) was done to determine the genome wide threshold for QTL detection. The logarithm-of-odds (LOD) profiles from interval mapping were inspected and the marker closest to each LOD peak was selected as cofactor and the backward elimination procedure was used to select the significant cofactors. This backward elimination procedure was performed until stable cofactor subsets had been obtained. Remaining cofactors were used for further rMQM mapping analysis. For multi-trait single environment analysis, data were standardized according to formula: XA= (x-x̅)/SD.
Here each value is subtracted from the mean and divided by the standard deviation.We have used a multi-trait single environment model per environment using GenStatversion 14.0 (International 2011). A mixed model composite interval mapping algorithm was used to detect QTLs assuming QTLs as fixed effects in the model, and an unstructured variance covariance model for the residual multi-normal polygenic effect. Details about models and methods can be found in (International 2011).
Heritability Estimates
Total genotypic variance were obtained from a one-way random effects analysis of variance using GenStatversion 14.0 (International 2011). Total variances was partitioned in two components; variations between lines (Vg) and variation within lines, or error variance (Ve). Broad-sense heritability was (H2) estimated using both variances according to the formula;
H2 =Vg/(Vg +Ve/n ) n= number of replicates.
Retrieving candidate genes
We have used the Marker2 sequence program (Chibon et al. 2012) to retrieve candidate genes in the QTL hot spot region.