Proteomic and Phytohormone Analysis of the Response of Maize (Zea mays L.) Seedlings to Sugarcane Mosaic Virus

<div><p>Background</p><p>Sugarcane mosaic virus (SCMV) is an important virus pathogen in crop production, causing serious losses in grain and forage yields in susceptible cultivars. Control strategies have been developed, but only marginal successes have been achieved. For the efficient control of this virus, a better understanding of its interactions and associated resistance mechanisms at the molecular level is required.</p><p>Methodology/Principal Findings</p><p>The responses of resistant and susceptible genotypes of maize to SCMV and the molecular basis of the resistance were studied using a proteomic approach based on two-dimensional polyacrylamide gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS/MS) analysis. Ninety-six protein spots showed statistically significant differences in intensity after SCMV inoculation. The classification of differentially expressed proteins showed that SCMV-responsive proteins were mainly involved in energy and metabolism, stress and defense responses, and photosynthesis. Most of the proteins identified were located in chloroplasts, chloroplast membranes, and the cytoplasm. Analysis of changes in phytohormone levels after virus inoculation suggested that salicylic acid, abscisic acid, jasmonic acid, and azelaic acid may played important roles in the maize response to SCMV infection.</p><p>Conclusions/Significance</p><p>Among these identified proteins, 19 have not been identified previously as virus-responsive proteins, and seven were new and did not have assigned functions. These proteins may be candidate proteins for future investigation, and they may present new biological functions and play important roles in plant-virus interactions. The behavioural patterns of the identified proteins suggest the existence of defense mechanisms operating during the early stages of infection that differed in two genotypes. In addition, there are overlapping and specific phytohormone responses to SCMV infection between resistant and susceptible maize genotypes. This study may provide important insights into the molecular events during plant responses to virus infection.</p></div

Phenotypic analysis of resistant Siyi and susceptible Mo17 plants to SCMV infection.

<p>The plant phenotypes were observed at six days post inoculation. (A) Siyi plants: left, mock-inoculated; right, SCMV-inoculated. (B) Mo17 plants: left, mock-inoculated; right, SCMV-inoculated. Typical mosaic symptoms are visible in Mo17 with SCMV infection, while Siyi is symptomless.</p

Differentially expressed maize leaf protein spots observed by 2-DE analysis.

<p>Arrows indicate spots showing significantly regulated proteins in the resistant genotype Siyi (A) and susceptible genotype Mo17 (B). The numbers correspond to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070295#pone-0070295-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070295#pone-0070295-t002" target="_blank">2</a>, respectively.</p

Western blot analysis of ascorbate peroxidase, peroxiredoxin and superoxide dismutase levels in SCMV-inoculated and mock-inoculated samples.

<p>Expression level of β-actin was used as loading control. The changes in these three proteins were in good agreement with the proteomic results. These experiments were repeated two times, with similar results.</p

Distribution of functional categories of differentially expressed proteins.

<p>(A) Functional categories of differentially expressed proteins in Siyi. (B) Functional categories of differentially expressed proteins in Mo17. (C) Contributions to molecular functions from up-(red) and down-(green) regulated proteins in Siyi. (D) Contributions to molecular functions from up-(red) and down-(green) regulated proteins in Mo17.</p

Phytohormone accumulation after inoculation of maize leaves with SCMV.

<p>Maize plants (Siyi or Mo17) were mock-inoculated or inoculated with SCMV and leaf samples were collected at 2, 4, 6, 8, 10 and 12 dpi. (A) SA accumulation after inoculation; (B) ABA accumulation after inoculation; (C) JA accumulation after inoculation; (D) AZA accumulation after inoculation. Data are representative of three independent biological experiments. Bars show SE (<i>n</i> = 3). FW, fresh weight; dpi, days post inoculation.</p

Genetic map positions for genes encoding differentially expressed proteins.

<p>Genetic map positions were determined <i>in silico</i> using the Maize GDB <a href="http://www.maizegdb.org/" target="_blank">http://www.maizegdb.org/</a>. Chr, Chromosome.</p

Protein–protein interaction network analyzed by String software.

<p>(A) Network analyzed from differentially expressed proteins in sample group Siyi_SCMV/Siyi_CK. (B) Network analyzed from differentially expressed proteins in sample group Mo17_SCMV/Mo17_CK. Different line colors represent types of evidence for association: green line, neighborhood evidence; red line, fusion evidence; purple line, experimental evidence; light blue line, database evidence; black line, coexpression evidence; blue line, co-occurrence evidence; and yellow line, text-mining evidence.</p

List of proteins identified by MALDI-TOF-MS/MS analysis in resistant maize genotype Siyi’s response to SCMV infection.

a<p>The spot ID was determined at the beginning of the analysis of the gel.</p>b<p>Accession number from NCBI database of the matched protein.</p>c<p>Theoretical MW/pI was calculated using DNAStar software.</p>d<p>NP: the number of matched peptides.</p>e<p>Fold change was calculated by imagemaster 2D software.</p>f<p>P value obtained from ANOVA test analysis.</p>g<p>Map positions were determined <i>in silico</i> using the Maize GDB.</p

DataSheet1_A novel necroptosis-related lncRNA based signature predicts prognosis and response to treatment in cervical cancer.PDF

Background: Necroptosis has been demonstrated to play a crucial role in the prognosis prediction and assessment of treatment outcome in cancers, including cervical cancer. The purpose of this study was to explore the potential prognostic value of necroptosis-related lncRNAs and their relationship with immune microenvironment and response to treatment in cervical cancer.Methods: Data from The Cancer Genome Atlas (TCGA) were collected to obtain synthetic data matrices. Necroptosis-related lncRNAs were identified by Pearson Correlation analysis. Univariate Cox and multivariate Cox regression analysis and Lasso regression were used to construct a necroptosis-related LncRNAs signature. Kaplan-Meier analysis, univariate and multivariate Cox regression analyses, receiver operating characteristic (ROC) curve, nomogram, and calibration curves analysis were performed to validate this signature. Gene set enrichment analyses (GSEA), immunoassays, and the half-maximal inhibitory concentration (IC50) were also analyzed.Results: Initially, 119 necroptosis-related lncRNAs were identified based on necroptosis-related genes and differentially expressed lncRNAs between normal and cervical cancer samples. Then, a prognostic risk signature consisting of five necroptosis-related lncRNAs (DDN-AS1, DLEU1, RGS5, RUSC1-AS1, TMPO-AS1) was established by Cox regression analysis, and LASSO regression techniques. Based on this signature, patients with cervical cancer were classified into a low- or high-risk group. Cox regression confirmed this signature as an independent prognostic predictor with an AUC value of 0.789 for predicting 1-year OS. A nomogram including signature, age, and TNM stage grade was then established, and showed an AUC of 0.82 for predicting 1-year OS. Moreover, GSEA analysis showed that immune-related pathways were enriched in the low-risk group; immunoassays showed that most immune cells, ESTIMAT scores and immune scores were negatively correlated with risk score and that the expression of immune checkpoint-proteins (CD27, CD48, CD200, and TNFRSF14) were higher in the low-risk group. In addition, patients in the low-risk group were more sensitive to Rucaparib, Navitoclax and Crizotinib than those in the high-risk group.Conclusion: We established a novel necroptosis-related lncRNA based signature to predict prognosis, tumor microenvironment and response to treatment in cervical cancer. Our study provides clues to tailor prognosis prediction and individualized immunization/targeted therapy strategies.</p