Dataset Information

Proteome data associated with the leaf senescence in Glycine max.

ABSTRACT: The data presented in this article are associated with the article "Coupling of gel-based 2-DE and 1-DE shotgun proteomics approaches to dig deep into the leaf senescence proteome of Glycine max" (R. Gupta, S.J. Lee, C.W. Min, S.W. Kim, K.-H. Park, D.-W. Bae, et al., 2016) [1]. Leaf senescence is one of the important aspects of the life cycle of a plant that leads to the recycling of nutrients from source to sink cells. To understand the leaf senescence-associated proteins, we used a combination of gel-based 2-DE and 1-DE shotgun proteomic approaches. Here, we display the 2-DE, Mass spectrometry, and Gene ontology data related with the leaf senescence in soybean [1].

SUBMITTER: Gupta R

PROVIDER: S-EPMC5013252 | biostudies-other | 2016 Dec

REPOSITORIES: biostudies-other

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Proteome data associated with the leaf senescence in Glycine max.

Gupta Ravi R Lee Su Ji SJ Min Cheol Woo CW Kim So Wun SW Park Ki-Hun KH Bae Dong-Won DW Lee Byong Won BW Agrawal Ganesh Kumar GK Rakwal Randeep R Kim Sun Tae ST

Data in brief 20160828

The data presented in this article are associated with the article "Coupling of gel-based 2-DE and 1-DE shotgun proteomics approaches to dig deep into the leaf senescence proteome of Glycine max" (R. Gupta, S.J. Lee, C.W. Min, S.W. Kim, K.-H. Park, D.-W. Bae, et al., 2016) [1]. Leaf senescence is one of the important aspects of the life cycle of a plant that leads to the recycling of nutrients from source to sink cells. To understand the leaf senescence-associated proteins, we used a combination ...[more]

PMID: 27631020

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Project description:BACKGROUND:Intercropping and close planting are important cultivation methods that increase soybean yield in agricultural production. However, plant shading is a major abiotic stress factor that influences soybean growth and development. Although shade affects leaf morphological parameters and decreases leaf photosynthesis capacity, information on the responses of soybean leaf photosynthesis to shading at proteomic level is still lacking. RESULTS:Compared with leaves under normal light (CK) treatment, leaves under shading treatment exhibited decreased palisade and spongy tissue thicknesses but significantly increased cell gap. Although shade increased the number of the chloroplast, the thickness of the grana lamella and the photosynthetic pigments per unit mass, but the size of the chloroplast and starch grains and the rate of net photosynthesis decreased compared with those of under CK treatment. A total of 248 differentially expressed proteins, among which 138 were upregulated, and 110 were downregulated, in soybean leaves under shading and CK treatments were detected via isobaric tags for relative and absolute quantification labeling in the three biological repeats. Differentially expressed proteins were classified into 3 large and 20 small groups. Most proteins involved in porphyrin and chlorophyll metabolism, photosynthesis-antenna proteins and carbon fixation in photosynthetic organisms were upregulated. By contrast, proteins involved in photosynthesis were downregulated. The gene family members corresponding to differentially expressed proteins, including protochlorophyllide reductase (Glyma06g247100), geranylgeranyl hydrogenase (Ggh), LHCB1 (Lhcb1) and ferredoxin (N/A) involved in the porphyrin and chlorophyll metabolism, photosynthesis-antenna proteins and photosynthesis pathway were verified with real-time qPCR. The results showed that the expression patterns of the genes were consistent with the expression patterns of the corresponding proteins. CONCLUSIONS:This study combined the variation of the soybean leaf structure and differentially expressed proteins of soybean leaves under shading. These results demonstrated that shade condition increased the light capture efficiency of photosystem II (PSII) in soybean leaves but decreased the capacity from PSII transmitted to photosystem II (PSI). This maybe the major reason that the photosynthetic capacity was decreased in shading.

Project description:BackgroundSmall non-coding RNAs (21 to 24 nucleotides) regulate a number of developmental processes in plants and animals by silencing genes using multiple mechanisms. Among these, the most conserved classes are microRNAs (miRNAs) and small interfering RNAs (siRNAs), both of which are produced by RNase III-like enzymes called Dicers. Many plant miRNAs play critical roles in nutrient homeostasis, developmental processes, abiotic stress and pathogen responses. Currently, only 70 miRNA have been identified in soybean.MethodsWe utilized Illumina's SBS sequencing technology to generate high-quality small RNA (sRNA) data from four soybean (Glycine max) tissues, including root, seed, flower, and nodules, to expand the collection of currently known soybean miRNAs. We developed a bioinformatics pipeline using in-house scripts and publicly available structure prediction tools to differentiate the authentic mature miRNA sequences from other sRNAs and short RNA fragments represented in the public sequencing data.ResultsThe combined sequencing and bioinformatics analyses identified 129 miRNAs based on hairpin secondary structure features in the predicted precursors. Out of these, 42 miRNAs matched known miRNAs in soybean or other species, while 87 novel miRNAs were identified. We also predicted the putative target genes of all identified miRNAs with computational methods and verified the predicted cleavage sites in vivo for a subset of these targets using the 5' RACE method. Finally, we also studied the relationship between the abundance of miRNA and that of the respective target genes by comparison to Solexa cDNA sequencing data.ConclusionOur study significantly increased the number of miRNAs known to be expressed in soybean. The bioinformatics analysis provided insight on regulation patterns between the miRNAs and their predicted target genes expression. We also deposited the data in a soybean genome browser based on the UCSC Genome Browser architecture. Using the browser, we annotated the soybean data with miRNA sequences from four tissues and cDNA sequencing data. Overlaying these two datasets in the browser allows researchers to analyze the miRNA expression levels relative to that of the associated target genes. The browser can be accessed at http://digbio.missouri.edu/soybean_mirna/.

Dataset Information

Proteome data associated with the leaf senescence in Glycine max.

Publications

Proteome data associated with the leaf senescence in Glycine max.

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