Dataset Information

Transcriptome- Assisted Label-Free Quantitative Proteomics Analysis Reveals Novel Insights into Piper nigrum-Phytophthora capsici Phytopathosystem.

ABSTRACT: Black pepper (Piper nigrum L.), a tropical spice crop of global acclaim, is susceptible to Phytophthora capsici, an oomycete pathogen which causes the highly destructive foot rot disease. A systematic understanding of this phytopathosystem has not been possible owing to lack of genome or proteome information. In this study, we explain an integrated transcriptome-assisted label-free quantitative proteomics pipeline to study the basal immune components of black pepper when challenged with P. capsici. We report a global identification of 532 novel leaf proteins from black pepper, of which 518 proteins were functionally annotated using BLAST2GO tool. A label-free quantitation of the protein datasets revealed 194 proteins common to diseased and control protein datasets of which 22 proteins showed significant up-regulation and 134 showed significant down-regulation. Ninety-three proteins were identified exclusively on P. capsici infected leaf tissues and 245 were expressed only in mock (control) infected samples. In-depth analysis of our data gives novel insights into the regulatory pathways of black pepper which are compromised during the infection. Differential down-regulation was observed in a number of critical pathways like carbon fixation in photosynthetic organism, cyano-amino acid metabolism, fructose, and mannose metabolism, glutathione metabolism, and phenylpropanoid biosynthesis. The proteomics results were validated with real-time qRT-PCR analysis. We were also able to identify the complete coding sequences for all the proteins of which few selected genes were cloned and sequence characterized for further confirmation. Our study is the first report of a quantitative proteomics dataset in black pepper which provides convincing evidence on the effectiveness of a transcriptome-based label-free proteomics approach for elucidating the host response to biotic stress in a non-model spice crop like P. nigrum, for which genome information is unavailable. Our dataset will serve as a useful resource for future studies in this plant. Data are available via ProteomeXchange with identifier PXD003887.

SUBMITTER: Mahadevan C

PROVIDER: S-EPMC4913111 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Transcriptome- Assisted Label-Free Quantitative Proteomics Analysis Reveals Novel Insights into Piper nigrum-Phytophthora capsici Phytopathosystem.

Mahadevan Chidambareswaren C Krishnan Anu A Saraswathy Gayathri G GG Surendran Arun A Jaleel Abdul A Sakuntala Manjula M

Frontiers in plant science 20160620

Black pepper (Piper nigrum L.), a tropical spice crop of global acclaim, is susceptible to Phytophthora capsici, an oomycete pathogen which causes the highly destructive foot rot disease. A systematic understanding of this phytopathosystem has not been possible owing to lack of genome or proteome information. In this study, we explain an integrated transcriptome-assisted label-free quantitative proteomics pipeline to study the basal immune components of black pepper when challenged with P. capsi ...[more]

PMID: 27379110

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Project description:Black pepper (Piper nigrum L.) is a prominent spice that is an indispensable ingredient in cuisine and traditional medicine. Phytophthora capsici, the causative agent of footrot disease, causes a drastic constraint in P. nigrum cultivation and productivity. To counterattack various biotic and abiotic stresses, plants employ a broad array of mechanisms that includes the accumulation of pathogenesis-related (PR) proteins. Through a genome-wide survey, eleven PR-1 genes that belong to a CAP superfamily protein with a caveolin-binding motif (CBM) and a CAP-derived peptide (CAPE) were identified from P. nigrum. Despite the critical functional domains, PnPR-1 homologs differ in their signal peptide motifs and core amino acid composition in the functional protein domains. The conserved motifs of PnPR-1 proteins were identified using MEME. Most of the PnPR-1 proteins were basic in nature. Secondary and 3D structure analyses of the PnPR-1 proteins were also predicted, which may be linked to a functional role in P. nigrum. The GO and KEGG functional annotations predicted their function in the defense responses of plant-pathogen interactions. Furthermore, a transcriptome-assisted FPKM analysis revealed PnPR-1 genes mapped to the P. nigrum-P. capsici interaction pathway. An altered expression pattern was detected for PnPR-1 transcripts among which a significant upregulation was noted for basic PnPR-1 genes such as CL10113.C1 and Unigene17664. The drastic variation in the transcript levels of CL10113.C1 was further validated through qRT-PCR and it showed a significant upregulation in infected leaf samples compared with the control. A subsequent analysis revealed the structural details, phylogenetic relationships, conserved sequence motifs and critical cis-regulatory elements of PnPR-1 genes. This is the first genome-wide study that identified the role of PR-1 genes during P. nigrum-P. capsici interactions. The detailed in silico experimental analysis revealed the vital role of PnPR-1 genes in regulating the first layer of defense towards a P. capsici infection in Panniyur-1 plants.

Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Dataset Information

Transcriptome- Assisted Label-Free Quantitative Proteomics Analysis Reveals Novel Insights into Piper nigrum-Phytophthora capsici Phytopathosystem.

Publications

Transcriptome- Assisted Label-Free Quantitative Proteomics Analysis Reveals Novel Insights into Piper nigrum-Phytophthora capsici Phytopathosystem.

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