Verticillium Wilt Resistance In Upland Cotton: Biochemical Responses And Disease Resistance QTLs Mapping

Cotton production is affected by abiotic and biotic stresses among which Verticillium wilt(VW) caused by the soil-borne fungus Verticillium dahlia Kleb is one of the most destructive diseases, occurring in all the cotton-growing areas in the world. Wilt disease in plants is generally the result of the restriction of water movement added to complex interactions between enzymes, hormones and some others biochemical compounds. Disease resistance in plants is associated with the activation of a wide array of defense responses that slow down or halt the infection at certain stages of the host pathogen-pathogen interaction. Comprehensive studies and understanding the different biochemical mechanisms underlying the survival of cotton plants to the VW disease could be very useful for breeding programs. Development and planting of Verticillium wilt resistant cultivars is the most practical, cost-effective economical way to control the disease. Numerous efforts have been made to improve the resistance of upland cotton against VW, however little achievement has been made due to the paucity of upland cotton breeding lines and cultivars with high level of resistance to VW. Among the four cultivated cottons, G. barbadense has the highest level of resistance to VW, however the transfer of resistance from G. barbadense to G. hirsutum have been unsuccessful mainly because of the hybrid breakdown in the F2 and successive generations of interspecific populations.In the first part of this study, a recombinant inbred lines(RILs) population derived from an intraspecific cross between two upland cotton cultivars, sGK9708 and 0-153 was used to detect QTLs related to VW resistance. A high density genetic map was constructed through recently developed Illumina Infinium array(cottonSNP70K) and was used to identify QTLs. This map covered all 26 chromosomes and spanned a total distance of 2865.73 cM almost 64% of the upland cotton genome. In total 2393 loci were mapped, among which 2316 were SNP while 77 were SSRs. The VW phenotypic data was collected for two disease parameters(disease index and disease incidence) at three stages of growth: seedling stage(in the greenhouse), mature stage(in 4-year replicated test) and flowering stage(one year 2015). In total, 144 QTLs were identified for the 2 parameters which were mapped on all 26 chromosomes, among which 71 were mapped on the At-subgenome and 73 in the Dt-subgenome. Sixty five QTLs explaining 2.5-12.2% of the phenotypic variation were detected on 24 chromosomes(except on chromosome 11 and chromosome 13) for disease index while a total of 79 QTLs explaining 1.5-21.30% of the observed phenotypic variation were identified on 24 chromosomes(C1-C26 except C5 and C19) for disease incidence parameter. Eight stables QTLs were detected for VW disease index(DI), among which six have sGK9708 allele. Forty two stable QTLs were identified for disease incidence(DInc). Twenty three QTL clusters containing 58 QTLs were identified on 18 chromsomes(C1-C10, C14-C15, C17, C20-C22, C24-C25). Most of the stable QTLs aggregated into these cluster regions. Twelve clusters in the At subgenome comprised 31 QTLs while 11 clusters in the Dt genome comprised 27 QTLs. C1 harbored the highest number of clusters(3 clusters). QTLs of the two different parameters sharing the same genomic region were identified on C3, C15; C17, C21, C22 and C24. These QTLs are potentially tagging causative disease resistance genes. In summary, the high-density genetic map used in the first part of our study provides a higher coverage of the upland cotton genome than all of the previous studies allowing a high QTL mapping resolution in our RILs population and provides useful information for the development of VW resistant upland cotton cultivars. It is also strengthen the basis of VW polygenic inheritance.In an attempt to understand the molecular basis of the antioxidant enzymes and biochemical compounds acting against the oxidative stress caused by the VW disease, the activities of some defense related enzymes and the content of some biochemical substances of the leaves of the RILs population were investigated at 30 days after inoculation in the greenhouse and after the disease scoring in the field(2 years experiments). The greenhouse results showed that the resistance was significantly positively correlated with the activity of superoxide dismutase(SOD) and peroxidase dismutase(POD) but negatively correlated with the catalase activity, the total soluble sugar content(TSS) and the protein content(PrC). The correlation between resistance and the phenylalanine ammonia lyase(PAL) activity and the proline content were not detected. Low to moderate significant correlations were found between most of the measured parameters. No consistent results were found for the correlation between the disease and the SOD activity, TSS and Pro contents in the field, however moderate to strong correlation were found between some biochemical parameters across the two years. Seventy nine QTLs were detected for all the traits measured in three experiments. For the parameters measured only in the greenhouse, eleven QTLs explaining 4-10.3% of the PV were found for the PAL activity, eight QTLs were detected for Protein content, explained 4.2-8.8% of the observed PV, and seven QTLs for CAT activity, explained 4-9.3% of the observed PV. Only one QTL explaining 4.2% of the observed PV was found for the POD activity. For the parameters measured in three experiments(2 years field experiments and one greenhouse experiment), 14 QTLs were identified for the SOD activity with 3.3-11.4% of the observed PV, 25 QTLs were detected for the proline content explaining 3.6-9.5% of the observed PV and 14 QTLs were identified for the TSS activity explaining 4.1-8% of the observed PV. Furthermore, ten clusters(each harboring 2 QTLs of different parameters) were identified on C1, C2, C4, C7, C9, C15, C19 and C24.Most of the biochemical parameters QTLs were gathered with the disease parameters stable QTLs. Moreover, some of the disease QTLs share the same genomic location with some biochemical parameters QTLs suggesting a common regulation at these loci. The information provided by the correlation analysis and the quantitative trait loci identification might be useful for breeding programs aiming to develop cotton cultivar resistant to the Verticillium wilt disease.