Label-free Proteomic Reveals that Cowpea Severe Mosaic Virus Transiently Suppresses the Host Leaf Protein Accumulation During the Compatible Interaction with Cowpea (Vigna unguiculata [L.] Walp.)

Bidirectional de novo peptide sequencing using a transformer model

In proteomics, a crucial aspect is to identify peptide sequences. De novo sequencing methods have been widely employed to identify peptide sequences, and numerous tools have been proposed over the past two decades. Recently, deep learning approaches have been introduced for de novo sequencing. Previous methods focused on encoding tandem mass spectra and predicting peptide sequences from the first amino acid onwards. However, when predicting peptides using tandem mass spectra, the peptide sequence can be predicted not only from the first amino acid but also from the last amino acid due to the coexistence of b-ion (or a- or c-ion) and y-ion (or x- or z-ion) fragments in the tandem mass spectra. Therefore, it is essential to predict peptide sequences bidirectionally. Our approach, called NovoB, utilizes a Transformer model to predict peptide sequences bidirectionally, starting with both the first and last amino acids. In comparison to Casanovo, our method achieved an improvement of the average peptide-level accuracy rate of approximately 9.8% across all species.

Plasmodesmal endoplasmic reticulum proteins regulate intercellular trafficking of cucumber mosaic virus in Arabidopsis

Plasmodesmata (PD) are plasma membrane-lined cytoplasmic nanochannels that mediate cell-to-cell communication across the cell wall. A range of proteins are embedded in the PD plasma membrane and endoplasmic reticulum (ER), and function in regulating PD-mediated symplasmic trafficking. However, knowledge of the nature and function of the ER-embedded proteins in the intercellular movement of non-cell-autonomous proteins is limited. Here, we report the functional characterization of two ER luminal proteins, AtBiP1/2, and two ER integral membrane proteins, AtERdj2A/B, which are located within the PD. These PD proteins were identified as interacting proteins with cucumber mosaic virus (CMV) movement protein (MP) in co-immunoprecipitation studies using an Arabidopsis-derived plasmodesmal-enriched cell wall protein preparation (PECP). The AtBiP1/2 PD location was confirmed by TEM-based immunolocalization, and their AtBiP1/2 signal peptides (SPs) function in PD targeting. In vitro/in vivo pull-down assays revealed the association between AtBiP1/2 and CMV MP, mediated by AtERdj2A, through the formation of an AtBiP1/2-AtERdj2-CMV MP complex within PD. The role of this complex in CMV infection was established, as systemic infection was retarded in bip1/bip2w and erdj2b mutants. Our findings provide a model for a mechanism by which the CMV MP mediates cell-to-cell trafficking of its viral ribonucleoprotein complex.

Critical evaluation of the use of artificial data for machine learning based de novo peptide identification

Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art de novo peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of de novo peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of de novo algorithms as well as alleviating the reliance on database searches for training data.

Application of a Novel Hybrid CNN-GNN for Peptide Ion Encoding

Almost all state-of-the-art de novo peptide sequencing algorithms now use machine learning models to encode fragment peaks and hence identify amino acids in mass spectrometry (MS) spectra. Previous work has highlighted how the inherent MS challenges of noise and missing peptide peaks detrimentally affect the performance of these models. In the present research we extracted and evaluated the encoding modules from 3 state-of-the-art de novo peptide sequencing algorithms. We also propose a convolutional neural network-graph neural network machine learning model for encoding peptide ions in tandem MS spectra. We compared the proposed encoding module to those used in the state-of-the-art de novo peptide sequencing algorithms by assessing their ability to identify b-ions and y-ions in MS spectra. This included a comprehensive evaluation in both real and artificial data across various levels of noise and missing peptide peaks. The proposed model performed best across all data sets using two different metrics (area under the receiver operating characteristic curve (AUC) and average precision). The work also highlighted the effect of including additional features such as intensity rank in these encoding modules as well as issues with using the AUC as a metric. This work is of significance to those designing future de novo peptide identification algorithms as it is the first step toward a new approach.

Regulation of heat shock proteins 70 and their role in plant immunity

Heat shock proteins 70 (HSP70s) are steadily gaining more attention in the field of plant biotic interactions. Though their regulation and activity in plants are much less well characterized than are those of their counterparts in mammals, accumulating evidence indicates that the role of HSP70-mediated defense mechanisms in plant cells is indispensable. In this review, we summarize current knowledge of HSP70 post-translational control in plants. We comment on the phytohormonal regulation of HSP70 expression and protein abundance, and identify a prominent role for cytokinin in HSP70 control. We outline HSP70s' subcellular localizations, chaperone activity, and chaperone-mediated protein degradation. We focus on the role of HSP70s in plant pathogen-associated molecular pattern-triggered immunity and effector-triggered immunity, and discuss the contribution of different HSP70 subfamilies to plant defense against pathogens.

Identification, characterization, and expression analysis of cowpea (Vigna unguiculata [L.] Walp.) miRNAs in response to cowpea severe mosaic virus (CPSMV) challenge

Cowpea miRNAs and Argonaute genes showed differential expression patterns in response to CPSMV challenge Several biotic stresses affect cowpea production and yield. CPSMV stands out for causing severe negative impacts on cowpea. Plants have two main induced immune systems. In the basal system (PTI, PAMP-triggered immunity), plants recognize and respond to conserved molecular patterns associated with pathogens (PAMPs). The second type (ETI, Effector-triggered immunity) is induced after plant recognition of specific factors from pathogens. RNA silencing is another important defense mechanism in plants. Our research group has been using biochemical and proteomic approaches to learn which proteins and pathways are involved and could explain why some cowpea genotypes are resistant whereas others are susceptible to CPSMV. This current study was conducted to determine the role of cowpea miRNA in the interaction between a resistant cowpea genotype (BRS-Marataoã) and CPSMV. Previously identified and deposited plant microRNA sequences were used to find out all possible microRNAs in the cowpea genome. This search detected 617 mature microRNAs, which were distributed in 89 microRNA families. Next, 4 out of these 617 miRNAs and their possible target genes that encode the proteins Kat-p80, DEAD-Box, GST, and SPB9, all involved in the defense response of cowpea to CPSMV, had their expression compared between cowpea leaves uninoculated and inoculated with CPSMV. Additionally, the differential expression of genes that encode the Argonaute (AGO) proteins 1, 2, 4, 6, and 10 is reported. In summary, the studied miRNAs and AGO 2 and AGO4 associated genes showed differential expression patterns in response to CPSMV challenge, which indicate their role in cowpea defense.

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework

MOTIVATION

De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.

RESULTS

In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29-102% more correct spectra, and the precision was 11-89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21-50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences.

AVAILABILITY AND IMPLEMENTATION

pNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

H2O2Accumulation, Host Cell Death and Differential Levels of Proteins Related to Photosynthesis, Redox Homeostasis, and Required for Viral Replication Explain the Resistance of EMS-mutagenized Cowpea to Cowpea Severe Mosaic Virus

Infection with Cowpea severe mosaic virus (CPSMV) represents one of the main limitations for cowpea (Vigna unguiculata L. Walp.) productivity due to the severity of the disease symptoms, frequency of incidence, and difficulties in dissemination control. This study aimed to identify the proteins and metabolic pathways associated with the susceptibility and resistance of cowpea plants to CPSMV. Therefore, we treated the seeds of a naturally susceptible cowpea genotype (CE-31) with the mutagenic agent ethyl methane sulfonate (EMS) and compared the secondary leaf proteomic profile of the mutagenized resistant plants inoculated with CPSMV (MCPI plant group) to those of the naturally susceptible cowpea genotype CE-31 inoculated (CPI) and noninoculated (CPU) with CPSMV. MCPI responded to CPSMV by accumulating proteins involved in the oxidative burst, increasing H₂O₂ generation, promoting leaf cell death (LCD), increasing the synthesis of defense proteins, and decreasing host factors important for the establishment of CPSMV infection. In contrast, CPI accumulated several host factors that favor CPSMV infection and did not accumulate H₂O₂ or present LCD, which allowed CPSMV replication and systemic dissemination. Based on these results, we propose that the differential abundance of defense proteins and proteins involved in the oxidative burst, LCD, and the decrease in cowpea protein factors required for CPSMV replication are associated with the resistance trait acquired by the MCPI plant group.

Cucumber mosaic virus resistance: Comparative proteomics of contrasting Cucumis sativus cultivars after long-term infection

Plant viruses are a significant threat to a wide range of host species, causing substantial losses in agriculture. Particularly, Cucumber mosaic virus (CMV) evokes severe symptoms, thus dramatically limiting yield. Activation of plant defense reactions is associated with changes in the cellular proteome to ensure virus resistance. Herein, we studied two cultivars of cucumber (Cucumis sativus) resistant host Heliana and susceptible host Vanda. Plant cotyledons were mechanically inoculated with CMV isolate PK1, and systemic leaves were harvested at 33 days post-inoculation. Proteome was profiled by ultrahigh-performance liquid chromatography and comprehensively quantified by ion mobility enhanced mass spectrometry. From 1516 reproducibly quantified proteins using a label-free approach, 133 were differentially abundant among cultivars or treatments by strict statistic and effect size criteria. Pigments and hydrogen peroxide measurements corroborated proteomic findings. Comparison of both cultivars in the uninfected state highlighted more abundant photosynthetic and development-related proteins in resistant cucumber cultivar. Long-term CMV infection caused worse preservation of energy processes and less robust translation in the susceptible cultivar. Contrary, compatible plants had numerous more abundant stress and defense-related proteins. We proposed promising targets for functional validation in transgenic lines: A step toward durable virus resistance in cucurbits and other crops. SIGNIFICANCE: Sustainable production of crops requires an understanding of natural mechanisms of resistance/susceptibility to ubiquitous viral infections. We report original findings of comparative analysis of plant genotypes exposed to CMV. Deep discovery proteomics of resistant and susceptible cucumber cultivars, inoculated with widespread phytovirus, allowed to suggest several novel molecular targets for functional testing in plant protection strategies.

What proteomics can reveal about plant-virus interactions? Photosynthesis-related proteins on the spotlight

Plant viruses are responsible for losses in worldwide production of numerous economically important food and fuel crops. As obligate cellular parasites with very small genomes, viruses rely on their hosts for replication, assembly, intra- and intercellular movement, and attraction of vectors for dispersal. Chloroplasts are photosynthesis and are the site of replication for several viruses. When viruses replicate in chloroplasts, photosynthesis, an essential process in plant physiology, is inhibited. The mechanisms underlying molecular and biochemical changes during compatible and incompatible plants-virus interactions, are only beginning to be elucidated, including changes in proteomic profiles induced by virus infections. In this review, we highlight the importance of proteomic studies to understand plant-virus interactions, especially emphasizing the changes in photosynthesis-related protein accumulation. We focus on: (a) chloroplast proteins that differentially accumulate during viral infection; (b) the significance with respect to chloroplast-virus interaction; and (c) alterations in plant's energetic metabolism and the subsequently the plant defense mechanisms to overcome viral infection.

A resistant cowpea (Vigna unguiculata [L.] Walp.) genotype became susceptible to cowpea severe mosaic virus (CPSMV) after exposure to salt stress

In nature, plants are simultaneously challenged by biotic and abiotic stresses. However, little is known about the effects of these combined stresses for most crops. This work aimed to evaluate the responsed of the virus-resistant cowpea genotype BRS-Marataoã to the exposure of salt stress combined with CPSMV infection. Cowpea plants were exposed to 200 mM NaCl either simultaneously (SV plant group) or 24 h prior to the CPSMV infection [S(24 h)V plant group]. Physiological, biochemical, and proteomic analyses at 2 and 6 days post salt stress (DPS) revealed that cowpea significantly reprogrammed its cellular metabolism. Indeed, plant size, photosynthetic parameters (net photosynthesis, transpiration rate, stomatal conductance, and internal CO₂ partial pressure) and chlorophyll and carotenoid contents were reduced in S(24 h)V compared to SV. Moreover, accumulation of viral particles at 6 DPS in S(24 h)V was observed indicating that the salt stress imposed prior to virus infection favors viral particle proliferation. Proteomic analysis showed differential contents of 403 and 330 proteins at 2 DPS and 6 DPS, respectively, out of 733 differentially abundant proteins between the two plant groups. The altered leaf proteins are involved in energy and metabolism, photosynthesis, stress response, and oxidative burst. BIOLOGICAL SIGNIFICANCE: This is an original study in which a virus-resistant cowpea genotype (BRS-Marataoã) was (i) exposed simultaneously to 200 mM NaCl and inoculation with CPSMV (SV plant group) or (ii) exposed to 200 mM NaCl stress 24 h prior to inoculation with CPSMV [S(24 h)V plant group]. The purpose was to shed light on how this CPSMV resistant cowpea responded to the combined stresses. Numerous key proteins and associated pathways were altered in the cowpea plants challenged with both stresses, but unexpectedly, the salt stress imposed 24 h prior to CPSMV inoculation allowed viral proliferation, turning the cowpea genotype from resistant to susceptible.

Evaluation of sample preparation methods for mass spectrometry-based proteomic analysis of barley leaves

BACKGROUND

Sample preparation is a critical process for proteomic studies. Many efficient and reproducible sample preparation methods have been developed for mass spectrometry-based proteomic analysis of human and animal tissues or cells, but no attempt has been made to evaluate these protocols for plants. We here present an LC-MS/MS-based proteomics study of barley leaf aimed at optimization of methods to achieve efficient and unbiased trypsin digestion of proteins prior to LC-MS/MS based sequencing and quantification of peptides. We evaluated two spin filter-aided sample preparation protocols using either sodium dodecyl-sulphate or sodium deoxycholate (SDC), and three in-solution digestion (ISD) protocols using SDC or trichloroacetic acid/acetone precipitation.

RESULTS

The proteomics workflow identified and quantified up to 1800 barley proteins based on sequencing of up to 6900 peptides per sample. The two spin filter-based protocols provided a 12-38% higher efficiency than the ISD protocols, including more proteins of low abundance. Among the ISD protocols, a simple one-step reduction and S-alkylation method (OP-ISD) was the most efficient for barley leaf sample preparation; it identified and quantified 1500 proteins and displayed higher peptide-to-protein inference ratio and higher average amino acid sequence coverage of proteins. The two spin filter-aided sample preparation protocols are compatible with TMT labelling for quantitative proteomics studies. They exhibited complementary performance as about 30% of the proteins were identified by either one or the other protocol, but also demonstrated a positive bias for membrane proteins when using SDC as detergent.

CONCLUSIONS

We provide detailed protocols for efficient plant protein sample preparation for LC-MS/MS-based proteomics studies. Spin filter-based protocols are the most efficient for the preparation of leaf samples for MS-based proteomics. However, a simple protocol provides comparable results although with different peptide digestion profile.

Silicon and Nitrate Differentially Modulate the Symbiotic Performances of Healthy and Virus-Infected Bradyrhizobium-nodulated Cowpea (Vigna unguiculata), Yardlong Bean (V. unguiculata subsp. sesquipedalis) and Mung Bean (V. radiata)

The effects of 2 mM silicon (Si) and 10 mM KNO₃ (N)-prime signals for plant resistance to pathogens-were analyzed in healthy and Cowpea chlorotic mottle virus (CCMV) or Cowpea mild mottle virus (CMMV)-infected Bradyrhizobium-nodulated cowpea, yardlong bean and mung bean plants. In healthy plants of the three Vigna taxa, nodulation and growth were promoted in the order of Si + N > N > Si > controls. In the case of healthy cowpea and yardlong bean, the addition of Si and N decreased ureide and α-amino acids (AA) contents in the nodules and leaves in the order of Si + N> N > Si > controls. On the other hand, the addition of N arrested the deleterious effects of CCMV or CMMV infections on growth and nodulation in the three Vigna taxa. However, the addition of Si or Si + N hindered growth and nodulation in the CCMV- or CMMV-infected cowpea and yardlong bean, causing a massive accumulation of ureides in the leaves and nodules. Nevertheless, the AA content in leaves and nodules of CCMV- or CMMV-infected cowpea and yardlong bean was promoted by Si but reduced to minimum by Si + N. These results contrasted to the counteracting effects of Si or Si + N in the CCMV- and CMMV-infected mung bean via enhanced growth, nodulation and levels of ureide and AA in the leaves and nodules. Together, these observations suggest the fertilization with Si + N exclusively in virus-free cowpea and yardlong bean crops. However, Si + N fertilization must be encouraged in virus-endangered mung bean crops to enhance growth, nodulation and N-metabolism. It is noteworthy to see the enhanced nodulation of the three Vigna taxa in the presence of 10 mM KNO₃.

De novo peptide sequencing by deep learning

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.

Proteomics in commercial crops: An overview

Proteomics is a rapidly growing area of biological research that is positively affecting plant science. Recent advances in proteomic technology, such as mass spectrometry, can now identify a broad range of proteins and monitor their modulation during plant growth and development, as well as during responses to abiotic and biotic stresses. In this review, we highlight recent proteomic studies of commercial crops and discuss the advances in understanding of the proteomes of these crops. We anticipate that proteomic-based research will continue to expand and contribute to crop improvement.

SIGNIFICANCE

Plant proteomics study is a rapidly growing area of biological research that is positively impacting plant science. With the recent advances in new technologies, proteomics not only allows us to comprehensively analyses crop proteins, but also help us to understand the functions of the genes. In this review, we highlighted recent proteomic studies in commercial crops and updated the advances in our understanding of the proteomes of these crops. We believe that proteomic-based research will continue to grow and contribute to the improvement of crops.