In-depth characterization of the tomato fruit pericarp proteome

Multi-omics provide insights into the regulation of DNA methylation in pear fruit metabolism

BACKGROUND

Extensive research has been conducted on fruit development in crops, but the metabolic regulatory networks underlying perennial fruit trees remain poorly understood. To address this knowledge gap, we conduct a comprehensive analysis of the metabolome, proteome, transcriptome, DNA methylome, and small RNAome profiles of pear fruit flesh at 11 developing stages, spanning from fruitlet to ripening. Here, we systematically investigate the metabolic landscape and regulatory network involved.

RESULTS

We generate an association database consisting of 439 metabolites and 14,399 genes to elucidate the gene regulatory network of pear flesh metabolism. Interestingly, we detect increased DNA methylation in the promoters of most genes within the database during pear flesh development. Application of a DNA methylation inhibitor to the developing fruit represses chlorophyll degradation in the pericarp and promotes xanthophyll, β-carotene, and abscisic acid (ABA) accumulation in the flesh. We find the gradual increase in ABA production during pear flesh development is correlated with the expression of several carotenoid pathway genes and multiple transcription factors. Of these transcription factors, the zinc finger protein PbZFP1 is identified as a positive mediator of ABA biosynthesis in pear flesh. Most ABA pathway genes and transcription factors are modified by DNA methylation in the promoters, although some are induced by the DNA methylation inhibitor. These results suggest that DNA methylation inhibits ABA accumulation, which may delay fruit ripening.

CONCLUSION

Our findings provide insights into epigenetic regulation of metabolic regulatory networks during pear flesh development, particularly with regard to DNA methylation.

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.

Proteomics of Reproductive Development, Fruit Ripening, and Stress Responses in Tomato

The fruits of the tomato crop (Solanum lycopersicum L.) are increasingly consumed by humans worldwide. Due to their rich nutritional quality, pharmaceutical properties, and flavor, tomato crops have gained a salient role as standout crops among other plants. Traditional breeding and applied functional research have made progress in varying tomato germplasms to subdue biotic and abiotic stresses. Proteomic investigations within a span of few decades have assisted in consolidating the functional genomics and transcriptomic research. However, due to the volatility and dynamicity of proteins in the regulation of various biosynthetic pathways, there is a need for continuing research in the field of proteomics to establish a network that could enable a more comprehensive understanding of tomato growth and development. With this view, we provide a comprehensive review of proteomic studies conducted on the tomato plant in past years, which will be useful for future breeders and researchers working to improve the tomato crop.

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.

Translating the Arabidopsis thaliana Peroxisome Proteome Insights to Solanum lycopersicum: Consensus Versus Diversity

Peroxisomes are small, single-membrane specialized organelles present in all eukaryotic organisms. The peroxisome is one of the nodal centers of reactive oxygen species homeostasis in plants, which are generated in a high amount due to various stress conditions. Over the past decade, there has been extensive study on peroxisomal proteins and their signaling pathways in the model plant Arabidopsis thaliana, and a lot has been deciphered. However, not much impetus has been given to studying the peroxisome proteome of economically important crops. Owing to the significance of peroxisomes in the physiology of plants during normal and stress conditions, understating its proteome is of much importance. Hence, in this paper, we have made a snapshot of putative peroxisomal matrix proteins in the economically important vegetable crop tomato (Solanum lycopersicum, (L.) family Solanaceae). First, a reference peroxisomal matrix proteome map was generated for Arabidopsis thaliana using the available proteomic and localization studies, and proteins were categorized into various groups as per their annotations. This was used to create the putative peroxisomal matrix proteome map for S. lycopersicum. The putative peroxisome proteome in S. lycopersicum retains the basic framework: the bulk of proteins had peroxisomal targeting signal (PTS) type 1, a minor group had PTS2, and the catalase family retained its characteristic internal PTS. Apart from these, a considerable number of S. lycopersicum orthologs did not contain any “obvious” PTS. The number of PTS2 isoforms was found to be reduced in S. lycopersicum. We further investigated the PTS1s in the case of both the plant species and generated a pattern for canonical and non-canonical PTS1s. The number of canonical PTS1 proteins was comparatively lesser in S. lycopersicum. The non-canonical PTS1s were found to be comparable in both the plant species; however, S. lycopersicum showed greater diversity in the composition of the signal tripeptide. Finally, we have tried to address the lacunas and probable strategies to fill those gaps.

In Depth Exploration of the Alternative Proteome of Drosophila melanogaster

Recent studies have shown that hundreds of small proteins were occulted when protein-coding genes were annotated. These proteins, called alternative proteins, have failed to be annotated notably due to the short length of their open reading frame (less than 100 codons) or the enforced rule establishing that messenger RNAs (mRNAs) are monocistronic. Several alternative proteins were shown to be biologically active molecules and seem to be involved in a wide range of biological functions. However, genome-wide exploration of the alternative proteome is still limited to a few species. In the present article, we describe a deep peptidomics workflow which enabled the identification of 401 alternative proteins in Drosophila melanogaster. Subcellular localization, protein domains, and short linear motifs were predicted for 235 of the alternative proteins identified and point toward specific functions of these small proteins. Several alternative proteins had approximated abundances higher than their canonical counterparts, suggesting that these alternative proteins are actually the main products of their corresponding genes. Finally, we observed 14 alternative proteins with developmentally regulated expression patterns and 10 induced upon the heat-shock treatment of embryos, demonstrating stage or stress-specific production of alternative proteins.

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.

Solanum lycopersicum (tomato) possesses mitochondrial and plastidial lipoyl synthases capable of increasing lipoylation levels when expressed in bacteria

Lipoic acid (LA) and its reduced form (dihydrolipoic acid, DHLA) have unique antioxidant properties among such molecules. Moreover, after a process termed lipoylation, LA is an essential prosthetic group covalently-attached to several key multi-subunit enzymatic complexes involved in primary metabolism, including E2 subunits of pyruvate dehydrogenase (PDH). The metabolic pathway of lipoylation has been extensively studied in Escherichia coli and Arabidopsis thaliana in which protein modification occurs via two routes: de novo synthesis and salvage. Common to both pathways, lipoyl synthase (LIP1 in plants, LipA in bacteria, EC 2.8.1.8) inserts sulphur atoms into the molecule in a final, activating step. However, despite the detection of LA and DHLA in other plant species, including tomato (Solanum lycopersicum), no plant LIP1s have been characterised to date from species other than Arabidopsis. In this work, we present the identification and characterisation of two LIPs from tomato, SlLIP1 and SlLIP1p. Consistent with in silico data, both are widely-expressed, particularly in reproductive organs. In line with bioinformatic predictions, we determine that yellow fluorescent protein tagged versions of SlLIP1 and SlLIP1p are mitochondrially- and plastidially-localised, respectively. Both possess the molecular hallmarks and domains of well-characterised bacterial LipAs. When heterologously-expressed in an E. coli lipA mutant, both are capable of complementing specific growth phenotypes and increasing lipoylation levels of E2 subunits of PDH in vivo, demonstrating that they do indeed function as lipoyl synthases.

Dissection of complex traits of tomato in the post-genome era

We present the main advances of dissection of complex traits in tomato by omics, the genes identified to control complex traits and the application of CRISPR/Cas9 in tomato breeding. Complex traits are believed to be under the control of multiple genes, each with different effects and interaction with environmental factors. Advance development of sequencing and molecular technologies has enabled the recognition of the genomic structure of most organisms and the identification of a nearly limitless number of markers that have made it to accelerate the speed of QTL identification and gene cloning. Meanwhile, multiomics have been used to identify the genetic variations among different tomato species, determine the expression profiles of genes in different tissues and at distinct developmental stages, and detect metabolites in different pathways and processes. The combination of these data facilitates to reveal mechanism underlying complex traits. Moreover, mutants generated by mutagens and genome editing provide relatively rich genetic variation for deciphering the complex traits and exploiting them in tomato breeding. In this article, we present the main advances of complex trait dissection in tomato by omics since the release of the tomato genome sequence in 2012. We provide further insight into some tomato complex traits because of the causal genetic variations discovered so far and explore the utilization of CRISPR/Cas9 for the modification of tomato complex traits.

Changes in the microsomal proteome of tomato fruit during ripening

The variations in the membrane proteome of tomato fruit pericarp during ripening have been investigated by mass spectrometry-based label-free proteomics. Mature green (MG30) and red ripe (R45) stages were chosen because they are pivotal in the ripening process: MG30 corresponds to the end of cellular expansion, when fruit growth has stopped and fruit starts ripening, whereas R45 corresponds to the mature fruit. Protein patterns were markedly different: among the 1315 proteins identified with at least two unique peptides, 145 significantly varied in abundance in the process of fruit ripening. The subcellular and biochemical fractionation resulted in GO term enrichment for organelle proteins in our dataset, and allowed the detection of low-abundance proteins that were not detected in previous proteomic studies on tomato fruits. Functional annotation showed that the largest proportion of identified proteins were involved in cell wall metabolism, vesicle-mediated transport, hormone biosynthesis, secondary metabolism, lipid metabolism, protein synthesis and degradation, carbohydrate metabolic processes, signalling and response to stress.

Quantitative assay of targeted proteome in tomato trichome glandular cells using a large-scale selected reaction monitoring strategy

BACKGROUND

Glandular trichomes found in vascular plants are called natural cell factories because they synthesize and store secondary metabolites in glandular cells. To systematically understand the metabolic processes in glandular cells, it is indispensable to analyze cellular proteome dynamics. The conventional proteomics methods based on mass spectrometry have enabled large-scale protein analysis, but require a large number of trichome samples for in-depth analysis and are not suitable for rapid and sensitive quantification of targeted proteins.

RESULTS

Here, we present a high-throughput strategy for quantifying targeted proteins in specific trichome glandular cells, using selected reaction monitoring (SRM) assays. The SRM assay platform, targeting proteins in type VI trichome gland cells of tomato as a model system, demonstrated its effectiveness in quantifying multiple proteins from a limited amount of sample. The large-scale SRM assay uses a triple quadrupole mass spectrometer connected online to a nanoflow liquid chromatograph, which accurately measured the expression levels of 221 targeted proteins contained in the glandular cell sample recovered from 100 glandular trichomes within 120 min. Comparative quantitative proteomics using SRM assays of type VI trichome gland cells between different organs (leaves, green fruits, and calyx) revealed specific organ-enriched proteins.

CONCLUSIONS

We present a targeted proteomics approach using the established SRM assays which enables quantification of proteins of interest with minimum sampling effort. The remarkable success of the SRM assay and its simple experimental workflow will increase proteomics research in glandular trichomes.

Transcriptional regulation of the genes involved in protein metabolism and processing in Saccharomyces cerevisiae

Topological analysis of large networks, which focus on a specific biological process or on related biological processes, where functional coherence exists among the interacting members, may provide a wealth of insight into cellular functionality. This work presents an unbiased systems approach to analyze genetic, transcriptional regulatory and physical interaction networks of yeast genes possessing such functional coherence to gain novel biological insight. The present analysis identified only a few transcriptional regulators amongst a large gene cohort associated with the protein metabolism and processing in yeast. These transcription factors are not functionally required for the maintenance of these tasks in growing cells. Rather, they are involved in rewiring gene transcription in response to such major challenges as starvation, hypoxia, DNA damage, heat shock or the accumulation of unfolded proteins. Indeed, only a subset of these proteins were captured empirically in the nuclear-enriched fraction of non-stressed yeast cells, suggesting that the transcriptional regulation of protein metabolism and processing in yeast is primarily concerned with maintaining cellular robustness in the face of threat by either internal or external stressors.

Targeted Proteomics Allows Quantification of Ethylene Receptors and Reveals SlETR3 Accumulation in Never-Ripe Tomatoes

Ethylene regulates fruit ripening and several plant functions (germination, plant growth, plant-microbe interactions). Protein quantification of ethylene receptors (ETRs) is essential to study their functions, but is impaired by low resolution tools such as antibodies that are mostly nonspecific, or the lack of sensitivity of shotgun proteomic approaches. We developed a targeted proteomic method, to quantify low-abundance proteins such as ETRs, and coupled this to mRNAs analyses, in two tomato lines: Wild Type (WT) and Never-Ripe (NR) which is insensitive to ethylene because of a gain-of-function mutation in ETR3. We obtained mRNA and protein abundance profiles for each ETR over the fruit development period. Despite a limiting number of replicates, we propose Pearson correlations between mRNA and protein profiles as interesting indicators to discriminate the two genotypes: such correlations are mostly positive in the WT and are affected by the NR mutation. The influence of putative post-transcriptional and post-translational changes are discussed. In NR fruits, the observed accumulation of the mutated ETR3 protein between ripening stages (Mature Green and Breaker + 8 days) may be a cause of NR tomatoes to stay orange. The label-free quantitative proteomics analysis of membrane proteins, concomitant to Parallel Reaction Monitoring analysis, may be a resource to study changes over tomato fruit development. These results could lead to studies about ETR subfunctions and interconnections over fruit development. Variations of RNA-protein correlations may open new fields of research in ETR regulation. Finally, similar approaches may be developed to study ETRs in whole plant development and plant-microorganism interactions.

Application of a Sensitive and Reproducible Label-Free Proteomic Approach to Explore the Proteome of Individual Meiotic-Phase Barley Anthers

Meiosis is a highly dynamic and precisely regulated process of cell division, leading to the production of haploid gametes from one diploid parental cell. In the crop plant barley (Hordeum vulgare), male meiosis occurs in anthers, in specialized cells called meiocytes. Barley meiotic tissue is scarce and not easily accessible, making meiosis study a challenging task. We describe here a new micro-proteomics workflow that allows sensitive and reproducible genome-wide label-free proteomic analysis of individual staged barley anthers. This micro-proteomic approach detects more than 4,000 proteins from such small amounts of material as two individual anthers, covering a dynamic range of protein relative abundance levels across five orders of magnitude. We applied our micro-proteomics workflow to investigate the proteome of the developing barley anther containing pollen mother cells in the early stages of meiosis and we successfully identified 57 known and putative meiosis-related proteins. Meiotic proteins identified in our study were found to be key players of many steps and processes in early prophase such as: chromosome condensation, synapsis, DNA double-strand breaks or crossover formation. Considering the small amount of starting material, this work demonstrates an important technological advance in plant proteomics and can be applied for proteomic examination of many size-limited plant specimens. Moreover, it is the first insight into the proteome of individual barley anther at early meiosis. The proteomic data have been deposited to the ProteomeXchange with the accession number PXD010887.

Ethylene Receptors, CTRs and EIN2 Target Protein Identification and Quantification Through Parallel Reaction Monitoring During Tomato Fruit Ripening

Ethylene, the plant ripening hormone of climacteric fruit, is perceived by ethylene receptors which is the first step in the complex ethylene signal transduction pathway. Much progress has been made in elucidating the mechanism of this pathway, but there is still a lot to be done in the proteomic quantification of the main proteins involved, particularly during fruit ripening. This work focuses on the mass spectrometry based identification and quantification of the ethylene receptors (ETRs) and the downstream components of the pathway, CTR-like proteins (CTRs) and ETHYLENE INSENSITIVE 2 (EIN2). We used tomato as a model fruit to study changes in protein abundance involved in the ethylene signal transduction during fruit ripening. In order to detect and quantify these low abundant proteins located in the membrane of the endoplasmic reticulum, we developed a workflow comprising sample fractionation and MS analysis using parallel reaction monitoring. This work shows the feasibility of the identification and absolute quantification of all seven ethylene receptors, three out of four CTRs and EIN2 in four ripening stages of tomato. In parallel, gene expression was analyzed through real-time qPCR. Correlation between transcriptomic and proteomic profiles during ripening was only observed for three of the studied proteins, suggesting that the other signaling proteins are likely post-transcriptionally regulated. Based on our quantification results we were able to show that the protein levels of SlETR3 and SlETR4 increased during ripening, probably to control ethylene sensitivity. The other receptors and CTRs showed either stable levels that could sustain, or decreasing levels that could promote fruit ripening.

Postnovo: Postprocessing Enables Accurate and FDR-Controlled de Novo Peptide Sequencing

De novo sequencing offers an alternative to database search methods for peptide identification from mass spectra. Since it does not rely on a predetermined database of expected or potential sequences in the sample, de novo sequencing is particularly appropriate for samples lacking a well-defined or comprehensive reference database. However, the low accuracy of many de novo sequence predictions has prevented the widespread use of the variety of sequencing tools currently available. Here, we present a new open-source tool, Postnovo, that postprocesses de novo sequence predictions to find high-accuracy results. Postnovo uses a predictive model to rescore and rerank candidate sequences in a manner akin to database search postprocessing tools such as Percolator. Postnovo leverages the output from multiple de novo sequencing tools in its own analyses, producing many times the length of amino acid sequence information (including both full- and partial-length peptide sequences) at an equivalent false discovery rate (FDR) compared to any individual tool. We present a methodology to reliably screen the sequence predictions to a desired FDR given the Postnovo sequence score. We validate Postnovo with multiple data sets and demonstrate its ability to identify proteins that are missed by database search even in samples with paired reference databases.

Spectral Libraries for SWATH-MS Assays for Drosophila melanogaster and Solanum lycopersicum

Quantitative proteomics methods have emerged as powerful tools for measuring protein expression changes at the proteome level. Using MS-based approaches, it is now possible to routinely quantify thousands of proteins. However, prefractionation of the samples at the protein or peptide level is usually necessary to go deep into the proteome, increasing both MS analysis time and technical variability. Recently, a new MS acquisition method named SWATH is introduced with the potential to provide good coverage of the proteome as well as a good measurement precision without prior sample fractionation. In contrast to shotgun-based MS however, a library containing experimental acquired spectra is necessary for the bioinformatics analysis of SWATH data. In this study, spectral libraries for two widely used models are built to study crop ripening or animal embryogenesis, Solanum lycopersicum (tomato) and Drosophila melanogaster, respectively. The spectral libraries comprise fragments for 5197 and 6040 proteins for S. lycopersicum and D. melanogaster, respectively, and allow reproducible quantification for thousands of peptides per MS analysis. The spectral libraries and all MS data are available in the MassIVE repository with the dataset identifiers MSV000081074 and MSV000081075 and the PRIDE repository with the dataset identifiers PXD006493 and PXD006495.

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.