Transcriptomic profiling of cotton Gossypium hirsutum challenged with low-temperature gradients stress.

Gossypium hirsutum, a cotton species widely cultivated around the world, is a typical cold-sensitive crop. Low-temperature (LT) stress is one of the main environmental stressors that can affect growth and the quality of cotton fibers. LT is also a major challenge for cotton survival, growth maturity and geographical distribution. However, few genome-wide transcriptional response and profiling datasets are available to explore the LT-tolerant mechanism of cotton. This study treated G. hirsutum with four LT gradients (control at 25 °C and cold temperatures at 4 °C, 10 °C and 15 °C) for 24 hour to generate 12 RNA-Seq datasets (three biological replicates per treatment) with approximately 280 million clean reads per dataset. The quality of the datasets obtained in the current study was validated through a series of quality checks including verification of RNA sample quality and RNA-Seq read quality. Data analyses included novel gene discovery, global gene expression profiling and quantitative real-time PCR. This is the first study to report genome-wide transcriptomic datasets for cotton in response to LT exposure.

[Dynamic QTL and correlated characters of tomato soluble solid content.].

Two hundred and six F2:3 families from the cross between TD22 and HT-1-1-1-1 were used for dynamic QTL research of tomato soluble solid content and correlative traits, and correlation analysis of soluble solid content (SSC) with fruit weight (FW), fruit shape index (FSI), soluble sugar, vitamin C (VC), and organic acid at three different development stages. The results showed that there were differences in QTL loci for soluble solid content during the three stages of tomato fruit development. Four and eight QTLs were detected in green ripe stage and red ripe stage, respectively. These QTLs showed dynamic changes, and two markers LEaat006 and Tomato|TC162363 were detected in two stages, which might be useful in molecular-marker assisted selection (MAS). The result also showed that there was extremely significant difference in SSC at the three different stages, and its main correlative traits were different at different stages. Soluble solid content was positively correlated with soluble sugar, but negatively correlated with FW at green ripe stage; SSC was positively correlated with soluble sugar and organic acid at yellow ripe stage; SSC was positively correlated with soluble sugar and organic acid, but negatively correlated with fruit weight at red ripe stage. Based on correlation analysis of these traits, linear regression model was constructed. Non-tested varieties were used to test the fitness, and the result showed that it is well fitted, and the fitness is above 95%.