Various tomato cultivars display contrasting morphological and molecular responses to a chronic heat stress.

Climate change is one of the biggest threats that human society currently needs to face. Heat waves associated with global warming negatively affect plant growth and development and will increase in intensity and frequency in the coming years. Tomato is one of the most produced and consumed fruit in the world but remarkable yield losses occur every year due to the sensitivity of many cultivars to heat stress (HS). New insights into how tomato plants are responding to HS will contribute to the development of cultivars with high yields under harsh temperature conditions. In this study, the analysis of microsporogenesis and pollen germination rate of eleven tomato cultivars after exposure to a chronic HS revealed differences between genotypes. Pollen development was either delayed and/or desynchronized by HS depending on the cultivar considered. In addition, except for two, pollen germination was abolished by HS in all cultivars. The transcriptome of floral buds at two developmental stages (tetrad and pollen floral buds) of five cultivars revealed common and specific molecular responses implemented by tomato cultivars to cope with chronic HS. These data provide valuable insights into the diversity of the genetic response of floral buds from different cultivars to HS and may contribute to the development of future climate resilient tomato varieties.

Comparison of whole-genome (13X) and capture (87X) resequencing methods for SNP and genotype callings.

The number of polymorphisms identified with next-generation sequencing approaches depends directly on the sequencing depth and therefore on the experimental cost. Although higher levels of depth ensure more sensitive and more specific SNP calls, economic constraints limit the increase of depth for whole-genome resequencing (WGS). For this reason, capture resequencing is used for studies focusing on only some specific regions of the genome. However, several biases in capture resequencing are known to have a negative impact on the sensitivity of SNP detection. Within this framework, the aim of this study was to compare the accuracy of WGS and capture resequencing on SNP detection and genotype calling, which differ in terms of both sequencing depth and biases. Indeed, we have evaluated the SNP calling and genotyping accuracy in a WGS dataset (13X) and in a capture resequencing dataset (87X) performed on 11 individuals. The percentage of SNPs not identified due to a sevenfold sequencing depth decrease was estimated at 7.8% using a down-sampling procedure on the capture sequencing dataset. A comparison of the 87X capture sequencing dataset with the WGS dataset revealed that capture-related biases were leading with the loss of 5.2% of SNPs detected with WGS. Nevertheless, when considering the SNPs detected by both approaches, capture sequencing appears to achieve far better SNP genotyping, with about 4.4% of the WGS genotypes that can be considered as erroneous and even 10% focusing on heterozygous genotypes. In conclusion, WGS and capture deep sequencing can be considered equivalent strategies for SNP detection, as the rate of SNPs not identified because of a low sequencing depth in the former is quite similar to SNPs missed because of method biases of the latter. On the other hand, capture deep sequencing clearly appears more adapted for studies requiring great accuracy in genotyping.