iTRAQ-Based Quantitative Proteomic Analysis Reveals Cold Responsive Proteins Involved in Leaf Senescence in Upland Cotton (Gossypium hirsutum L.)

Abstract

Premature leaf senescence occurs in the ultimate phase of the plant, and it occurs through a complex series of actions regulated by stress, hormones and genes. In this study, a proteomic analysis was performed to analyze the factors that could induce premature leaf senescence in two cotton cultivars. We successfully identified 443 differential abundant proteins (DAPs) from 7388 high-confidence proteins at four stages between non-premature senescence (NS) and premature senescence (PS), among which 158 proteins were over-accumulated, 238 proteins were down-accumulated at four stages, and 47 proteins displayed overlapped accumulation. All the DAPs were mapped onto 21 different categories on the basis of a Clusters of Orthologous Groups (COG) analysis, and 9 clusters were based on accumulation. Gene Ontology (GO) enrichment results show that processes related to stress responses, including responses to cold temperatures and responses to hormones, are significantly differentially accumulated. More importantly, the enriched proteins were mapped in The Arabidopsis Information Resource (TAIR), showing that 58 proteins play an active role in abiotic stress, hormone signaling and leaf senescence. Among these proteins, 26 cold-responsive proteins (CRPs) are significantly differentially accumulated. The meteorological data showed that the median temperatures declined at approximately 15 days before the onset of aging, suggesting that a decrease in temperature is tightly linked to an onset of cotton leaf senescence. Because accumulations of H2O2 and increased jasmonic acid (JA) were detected during PS, we speculate that two pathways associated with JA and H2O2 are closely related to premature leaf senescence in cotton.

Genome-Wide Identification and Expression Analysis of the Dof Transcription Factor Gene Family in Gossypium hirsutum L.

“…Furthermore, in cotton production, abiotic stresses, such as alkalinity (pH = 11.0), cold, drought, and high salinity are environmental limiting factors for crop growth and yield [35]; therefore, we checked GhDof gene expression patterns under ABA, cold, pH, and salinity stresses. GhDofA5.7, GhDofA7.4, GhDofA8.2, GhDof11.1, GhDofD7.2, and GhDofD11.3 may play important functions in responding to cold, and Zheng et al (2017) reported that cold responsive proteins were involved in leaf senescence in cotton [22]. According to tissue-specific and leaf development analysis, GhDofD9.6 not only was expressed with differences between CCRI10 and Liao4086 but also responded to salinity stress which indicates that GhDofD9.6 may participate in the crosstalk of leaf senescence and salinity stress.…”

Identification and Comparative Analysis of Premature Senescence Leaf Mutants in Rice (Oryza sativa L.)

“…Chlorophyll plays a central role in photosynthesis by forming complexes with thylakoid-membrane proteins such as Photosystem I (PSI), Photosystem II (PSII), and the cytochrome b6f complex [ 3 ]. The most striking feature of premature senescence leaf mutants is the leaf yellowish phenotype due to the breakdown of chlorophyll during chloroplast degeneration and hydrolysis of macromolecules such as proteins and nucleic acids, which finally results in mitochondria and nuclei dissociation and cell death [ 4 , 5 ]. For example, rice pse(t) mutant displays brown spots and yellowish color on the upper leaves which ultimately wilt [ 6 ].…”

“…Natural variants or mutants that exhibit delayed senescence are generally called “stay-green” [ 21 ], such as nyc1 [ 10 ], nyc3 [ 3 ] sgr [ 22 ] and nol [ 23 ] mutants of rice. There are much more premature senescence mutants reported so far compared with the delayed senescence mutants in rice, for instance, the ospse1 [ 20 ], psd128 [ 7 ], es1-1 [ 24 ], lts [ 25 ], rls1 [ 9 ], ps1-D [ 5 ] and noe1 [ 26 ] mutants, which are involved in different complex regulatory networks of senescence.…”

Quantitative proteomic, physiological and biochemical analysis of cotyledon, embryo, leaf and pod reveals the effects of high temperature and humidity stress on seed vigor formation in soybean

“…The wiff files generated by the TripleTOF 5600 instrument were searched directly using a 20 ppm precursor mass tolerance and a 50 mmu fragment mass tolerance. All the peptides FDR were dynamically set as 1%, which were calculated by a decoy database search (using a reverse sequence version of the reference database), and each confident protein included at least one unique peptide [53]. Redundancy between the annotated proteins and proteins identified from translation of the soybean genome was identified using BLAST [54], and all the identified proteins was list in Additional file 12: S1-S9.…”