Proteomic and metabolomic analysis of Nicotiana benthamiana under dark stress.

Exposure to extended periods of darkness is a common source of abiotic stress that significantly affects plant growth and development. To understand how Nicotiana benthamiana responds to dark stress, the proteomes and metabolomes of leaves treated with darkness were studied. In total, 5763 proteins and 165 primary metabolites were identified following dark treatment. Additionally, the expression of autophagy-related gene (ATG) proteins was transiently upregulated. Weighted gene coexpression network analysis (WGCNA) was utilized to find the protein modules associated with the response to dark stress. A total of four coexpression modules were obtained. The results indicated that heat-shock protein (HSP70), SnRK1-interacting protein 1, 2A phosphatase-associated protein of 46 kDa (Tap46), and glutamate dehydrogenase (GDH) might play crucial roles in N. benthamiana's response to dark stress. Furthermore, a protein-protein interaction (PPI) network was constructed and top-degreed proteins were predicted to identify potential key factors in the response to dark stress. These proteins include isopropylmalate isomerase (IPMI), eukaryotic elongation factor 5A (ELF5A), and ribosomal protein 5A (RPS5A). Finally, metabolic analysis suggested that some amino acids and sugars were involved in the dark-responsive pathways. Thus, these results provide a new avenue for understanding the defensive mechanism against dark stress at the protein and metabolic levels in N. benthamiana.

Proteomic and Phosphoproteomic Analysis in Tobacco Mosaic Virus-Infected Tobacco (Nicotiana tabacum).

Tobacco mosaic virus (TMV) is a common source of biological stress that significantly affects plant growth and development. It is also useful as a model in studies designed to clarify the mechanisms involved in plant viral disease. Plant responses to abiotic stress were recently reported to be regulated by complex mechanisms at the post-translational modification (PTM) level. Protein phosphorylation is one of the most widespread and major PTMs in organisms. Using immobilized metal ion affinity chromatography (IMAC) enrichment, high-pH C18 chromatography fraction, and high-accuracy mass spectrometry (MS), a set of proteins and phosphopeptides in both TMV-infected tobacco and control tobacco were identified. A total of 4905 proteins and 3998 phosphopeptides with 3063 phosphorylation sites were identified. These 3998 phosphopeptides were assigned to 1311 phosphoproteins, as some proteins carried multiple phosphorylation sites. Among them, 530 proteins and 337 phosphopeptides corresponding to 277 phosphoproteins differed between the two groups. There were 43 upregulated phosphoproteins, including phosphoglycerate kinase, pyruvate phosphate dikinase, protein phosphatase 2C, and serine/threonine protein kinase. To the best of our knowledge, this is the first phosphoproteomic analysis of leaves from a tobacco cultivar, K326. The results of this study advance our understanding of tobacco development and TMV action at the protein phosphorylation level.

Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics.

Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2.

Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability.

Untargeted metabolic profiling analysis is employed to screen metabolites for specific purposes, such as geographical origin discrimination. However, the data analysis remains a challenging task. In this work, a new automatic untargeted metabolic profiling analysis coupled with a chemometric strategy was developed to improve the metabolite identification results and to enhance the geographical origin discrimination capability. Automatic untargeted metabolic profiling analysis with chemometrics (AuMPAC) was used to screen the total ion chromatographic (TIC) peaks that showed significant differences among the various geographical regions. Then, a chemometric peak resolution strategy is employed for the screened TIC peaks. The retrieved components were further analyzed using ANOVA, and those that showed significant differences were used to build a geographical origin discrimination model by using two-way encoding partial least squares. To demonstrate its performance, a geographical origin discrimination of flaxseed samples from six geographical regions in China was conducted, and 18 TIC peaks were screened. A total of 19 significant different metabolites were obtained after the peak resolution. The accuracy of the geographical origin discrimination was up to 98%. A comparison of the AuMPAC, AMDIS, and XCMS indicated that AuMPACobtained the best geographical origin discrimination results. In conclusion, AuMPAC provided another method for data analysis.

AntDAS: Automatic Data Analysis Strategy for UPLC-QTOF-Based Nontargeted Metabolic Profiling Analysis.

High-quality data analysis methodology remains a bottleneck for metabolic profiling analysis based on ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. The present work aims to address this problem by proposing a novel data analysis strategy wherein (1) chromatographic peaks in the UPLC-QTOF data set are automatically extracted by using an advanced multiscale Gaussian smoothing-based peak extraction strategy; (2) a peak annotation stage is used to cluster fragment ions that belong to the same compound. With the aid of high-resolution mass spectrometer, (3) a time-shift correction across the samples is efficiently performed by a new peak alignment method; (4) components are registered by using a newly developed adaptive network searching algorithm; (5) statistical methods, such as analysis of variance and hierarchical cluster analysis, are then used to identify the underlying marker compounds; finally, (6) compound identification is performed by matching the extracted peak information, involving high-precision m/z and retention time, against our compound library containing more than 500 plant metabolites. A manually designed mixture of 18 compounds is used to evaluate the performance of the method, and all compounds are detected under various concentration levels. The developed method is comprehensively evaluated by an extremely complex plant data set containing more than 2000 components. Results indicate that the performance of the developed method is comparable with the XCMS. The MATLAB GUI code is available from http://software.tobaccodb.org/software/antdas .

Mass-spectra-based peak alignment for automatic nontargeted metabolic profiling analysis for biomarker screening in plant samples.

Nontargeted metabolic profiling analysis is a difficult task in a routine investigation because hundreds of chromatographic peaks are eluted within a short time, and the time shift problem is severe across samples. To address these problems, the present work developed an automatic nontargeted metabolic profiling analysis (anTMPA) method. First, peaks from the total ion chromatogram were extracted using modified multiscale Gaussian smoothing method. Then, a novel peak alignment strategy was employed based on the mass spectra and retention times of the peaks in which the maximum mass spectral correlation coefficient path was extracted using a modified dynamic programming method. Moreover, an automatic landmark peak-searching strategy was employed for self-adapting time shift modification. Missing peaks across samples were grouped and registered into the aligned peak list table for final refinement. Finally, the aligned peaks across samples were analyzed using statistical methods to identify potential biomarkers. Mass spectral information on the screened biomarkers could be directly imported into the National Institute of Standards and Technology library to select the candidate compounds. The performance of the anTMPA method was evaluated using a complicated plant gas chromatography-mass spectrometry dataset with the aim of identifying biomarkers between the growth and maturation stages of the tested plant.