| Literature DB >> 31467865 |
Yong Zhang1, Yunting Zhang2, Yuanxiu Lin2, Ya Luo1, Xiaorong Wang2, Qing Chen1, Bo Sun1, Yan Wang2, Mengyao Li1, Haoru Tang1,2.
Abstract
Strawberry is often subjected to cold stress in temperate regions when insulation measures are not strictly applied in protected cultivation. Cold stress adversely influences plant growth and development by triggering a massive change to the transcriptome. To provide the potential strategies in improving strawberry cold tolerance and give a glimpse into the understanding of the complex cold signaling pathways in plants, this study identified attractive candidate genes and revealed diverse regulatory networks that responded to cold stress in strawberry (Fragaria×ananassa) by a transcriptomic analysis. Totally, there were 2397 differentially expressed genes (DEGs) under cold stress treatment (T1) vs. normal treatment (CK). Of these, 1180 DEGs were upregulated, while 1217 DEGs were downregulated. Functional enrichment analysis showed that DEGs were significantly (adjusted P value < 0.05) overrepresented in six pathways including plant hormone signal transduction, flavonoid biosynthesis, mitogen-activated protein kinase (MAPK) signaling, starch and sucrose metabolism, circadian rhythm, and alpha-linolenic acid metabolism. The cold signaling initiated expression of downstream cold-responsive (COR) genes with cis-acting element ABRE or CRT/DRE in the ABA-independent or ABA-dependent pathway to impel plant defense against the stress. Strikingly, GIGANTEA (gene id 101308922), two-component response regulator-like PRR95 (gene id 101295449), and ethylene-responsive transcription factor ERF105-like (gene id 101295082) were dramatically induced under low-temperature treatment, indicating that they played an important role in response to cold stress in strawberry.Entities:
Year: 2019 PMID: 31467865 PMCID: PMC6701341 DOI: 10.1155/2019/7106092
Source DB: PubMed Journal: Int J Genomics ISSN: 2314-436X Impact factor: 2.326
Figure 1Volcano plot of genome-wide differentially expressed genes of T1 vs. CK. Genes with P value < 10−5 and GFOLD > 1 were indicated with colored dots and defined as robust differentially expressed ones (see Materials and Methods). DEG: differentially expressed gene; FALSE: filtered by the criteria above.
Figure 2Functional enrichment analysis of differentially expressed genes. Pathways with adjusted P value < 0.05 were shown (see Materials and Methods).
Figure 3Hormone signaling pathways mapped with relative expression levels (T1 vs. CK). Gene ID of F. vesca is indicated at the corresponding gene node if there is.
Figure 4Starch and saccharide metabolism mapped with relative expression levels (T1 vs. CK). Gene ID of F. vesca is indicated at the corresponding gene node if there is.
Figure 5Circadian clock pathway mapped with relative expression levels (T1 vs. CK). Gene ID of F. vesca is indicated at the corresponding gene node if there is.
Figure 6Flavonoid biosynthesis pathways mapped with relative expression levels (T1 vs. CK). Gene ID of F. vesca is indicated at the corresponding gene node if there is.
Figure 7α-Linolenic acid metabolism pathways mapped with relative expression levels (T1 vs. CK). Gene ID of F. vesca is indicated at the corresponding gene node if there is.
Figure 8Transcriptional response of cold stress-involved components. Gene RPKM values of CK and T1 were plotted within the x-axis and y-axis, respectively. CBF-like genes and potential regulators in F. vesca were indicated by arrows.
Figure 9Verification of RNA-Seq results by qRT-PCR. (a) Comparison of the expression level of unique transcripts between RNA-Seq and qRT-PCR. Primers for qRT-PCR are listed in Supplementary Table 4. (b) Scatter diagram of log2 ratios from qRT-PCR and RNA-Seq results indicates the correlation between them. Gene ID of F. vesca is indicated at the corresponding gene node if there is.