Improved Quantitative Plant Proteomics via the Combination of Targeted and Untargeted Data Acquisition

Assessing the Impacts of Cu and Mo Engineered Nanomaterials on Crop Plant Growth Using a Targeted Proteomics Approach

In this study, we investigated the effects of molybdenum (Mo)-based nanofertilizer and copper (Cu)-based nanopesticide exposure on wheat through a multifaceted approach, including physiological measurements, metal uptake and translocation analysis, and targeted proteomics analysis. Wheat plants were grown under a 16 h photoperiod (light intensity 150 μmol·m-2·s-1) for 4 weeks at 22 °C and 60% humidity with 6 different treatments, including control, Mo, and Cu exposure through root and leaf. The exposure dose was 6.25 mg of element per plant through either root or leaf. An additional low-dose (0.6 mg Mo/plant) treatment of Mo through root was added after phytotoxicity was observed. Using targeted proteomics approach, 24 proteins involved in 12 metabolomic pathways were quantitated to understand the regulation at the protein level. Mo exposure, particularly through root uptake, induced significant upregulation of 16 proteins associated with 11 metabolic pathways, with the fold change (FC) ranging from 1.28 to 2.81. Notably, a dose-dependent response of Mo exposure through the roots highlighted the delicate balance between nutrient stimulation and toxicity as a high Mo dose led to robust protein upregulation but also resulted in depressed physiological measurements, while a low Mo dose resulted in no depression of physiological measurements but downregulations of proteins, especially in the first leaf (0.23 < FC < 0.68) and stem (0.13 < FC < 0.68) tissues. Conversely, Cu exposure exhibited tissue-specific effects, with pronounced downregulation (18 proteins involved in 11 metabolic pathways) particularly in the first leaf tissues (root exposure: 0.35 < FC < 0.74; leaf exposure: 0.49 < FC < 0.72), which indicated the quick response of plants to Cu-induced stress in the early stage of exposure. By revealing the complexities of plants' response to engineered nanomaterials at both physiological and molecular levels, this study provides insights for optimizing nutrient management practices in crop production and advancing toward sustainable agriculture.

Stressing the importance of plant specialized metabolites: omics-based approaches for discovering specialized metabolism in plant stress responses

Plants produce a diverse range of specialized metabolites that play pivotal roles in mediating environmental interactions and stress adaptation. These unique chemical compounds also hold significant agricultural, medicinal, and industrial values. Despite the expanding knowledge of their functions in plant stress interactions, understanding the intricate biosynthetic pathways of these natural products remains challenging due to gene and pathway redundancy, multifunctionality of proteins, and the activity of enzymes with broad substrate specificity. In the past decade, substantial progress in genomics, transcriptomics, metabolomics, and proteomics has made the exploration of plant specialized metabolism more feasible than ever before. Notably, recent advances in integrative multi-omics and computational approaches, along with other technologies, are accelerating the discovery of plant specialized metabolism. In this review, we present a summary of the recent progress in the discovery of plant stress-related specialized metabolites. Emphasis is placed on the application of advanced omics-based approaches and other techniques in studying plant stress-related specialized metabolism. Additionally, we discuss the high-throughput methods for gene functional characterization. These advances hold great promise for harnessing the potential of specialized metabolites to enhance plant stress resilience in the future.

Optimization of Targeted Plant Proteomics Using Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS)

This study was conducted to optimize a targeted plant proteomics approach from signature peptide selection and liquid chromatography with tandem mass spectrometry (LC-MS/MS) analytical method development and optimization to sample preparation method optimization. Three typical protein extraction and precipitation methods, including trichloroacetic acid (TCA)/acetone method, phenol method, and TCA/acetone/phenol method, and two digestion methods, including trypsin digestion and LysC/trypsin digestion, were evaluated for selected proteins related to the impact of engineered nanomaterials (ENMs) on wheat (Triticum aestivum) plant growth. In addition, we compared two plant tissue homogenization methods: grinding freeze-dried tissue and fresh tissue into a fine powder using a mortar and pestle aided with liquid nitrogen. Wheat plants were grown under a 16 h photoperiod (light intensity 150 μmol·m^-2·s^-1) for 4 weeks at 22 °C with a relative humidity of 60% and were watered daily to maintain a 70-90% water content in the soil. Processed samples were analyzed with an optimized LC-MS/MS method. The concentration of selected signature peptides for the wheat proteins of interest indicated that the phenol extraction method using fresh plant tissue, coupled with trypsin digestion, was the best sample preparation method for the targeted proteomics study. Overall, the optimized approach yielded the highest total peptide concentration (68,831 ng/g, 2.4 times the lowest concentration) as well as higher signature peptide concentrations for most peptides (19 out of 28). In addition, three of the signature peptides could only be detected using the optimized approach. This study provides a workflow for optimizing targeted proteomics studies.

Targeted and explorative profiling of kallikrein proteases and global proteome biology of pancreatic ductal adenocarcinoma, chronic pancreatitis, and normal pancreas highlights disease-specific proteome remodelling

Pancreatic ductal adenocarcinoma (PDAC) represents one of the most aggressive and lethal malignancies worldwide with an urgent need for new diagnostic and therapeutic strategies. One major risk factor for PDAC is the pre-indication of chronic pancreatitis (CP), which represents highly inflammatory pancreatic tissue. Kallikreins (KLKs) are secreted serine proteases that play an important role in various cancers as components of the tumor microenvironment. Previous studies of KLKs in solid tumors largely relied on either transcriptomics or immunodetection. We present one of the first targeted mass spectrometry profiling of kallikrein proteases in PDAC, CP, and normal pancreas. We show that KLK6 and KLK10 are significantly upregulated in PDAC (n=14) but not in CP (n=7) when compared to normal pancreas (n=16), highlighting their specific intertwining with malignancy. Additional explorative proteome profiling identified 5936 proteins in our pancreatic cohort and observed disease-specific proteome rearrangements in PDAC and CP. As such, PDAC features an enriched proteome motif for extracellular matrix (ECM) and cell adhesion while there is depletion of mitochondrial energy metabolism proteins, reminiscent of the Warburg effect. Although often regarded as a PDAC hallmark, the ECM fingerprint was also observed in CP, alongside with a prototypical inflammatory proteome motif as well as with an increased wound healing process and proteolytic activity, thereby possibly illustrating tissue autolysis. Proteogenomic analysis based on publicly accessible data sources identified 112 PDAC-specific and 32 CP-specific single amino acid variants, which among others affect KRAS and ANKHD1. Our study emphasizes the diagnostic potential of kallikreins and provides novel insights into proteomic characteristics of PDAC and CP.

Absolute and relative quantitation of amylase/trypsin-inhibitors by LC-MS/MS from wheat lines obtained by CRISPR-Cas9 and RNAi

Quantitation of wheat proteins is still a challenge, especially regarding amylase/trypsin-inhibitors (ATIs). A selection of ATIs was silenced in the common wheat cultivar Bobwhite and durum wheat cultivar Svevo by RNAi and gene editing, respectively, in order to reduce the amounts of ATIs. The controls and silenced lines were analyzed after digestion to peptides by LC-MS/MS with different approaches to evaluate changes in composition of ATIs. First, a targeted method with stable isotope dilution assay (SIDA) using labeled peptides as internal standards was applied. Additionally, four different approaches for relative quantitation were conducted, in detail, iTRAQ labeled and label free quantitation (LFQ) combined with data dependent acquisition (DDA) and data independent acquisition (DIA). Quantitation was performed manually (Skyline and MASCOT) and with different proteomics software tools (PLGS, MaxQuant, and PEAKS X Pro). To characterize the wheat proteins on protein level, complementary techniques as high-performance liquid chromatography (HPLC) and gel electrophoresis were performed. The targeted approach with SIDA was able to quantitate all ATIs, even at low levels, but an optimized extraction is necessary. The labeled iTRAQ approach revealed an indistinct performance. LFQ with low resolution equipment (IonTrap) showed similar results for major ATIs, but low abundance ATIs as CM1, were not detectable. DDA measurements with an Orbitrap system and evaluation using MaxQuant showed that the relative quantitation was dependent on the wheat species. The combination of manual curation of the MaxQuant search with Skyline revealed a very good performance. The DIA approach with analytical flow found similar results compared to absolute quantitation except for some minor ATIs, which were not detected. Comparison of applied methods revealed that peptide selection is a crucial step for protein quantitation. Wheat proteomics faces challenges due to the high genetic complexity, the close relationship to other cereals and the incomplete, redundant protein database requiring sensitive, precise and accurate LC-MS/MS methods.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Application of Parallel Reaction Monitoring in 15N Labeled Samples for Quantification

Accurate relative quantification is critical in proteomic studies. The incorporation of stable isotope ¹⁵N to plant-expressed proteins in vivo is a powerful tool for accurate quantification with a major advantage of reducing preparative and analytical variabilities. However, ¹⁵N labeling quantification has several challenges. Less identifications are often observed in the heavy-labeled samples because of incomplete labeling, resulting in missing values in reciprocal labeling experiments. Inaccurate quantification can happen when there is contamination from co-eluting peptides or chemical noise in the MS¹ survey scan. These drawbacks in quantification can be more pronounced in less abundant but biologically interesting proteins, which often have very few identified peptides. Here, we demonstrate the application of parallel reaction monitoring (PRM) to ¹⁵N labeled samples on a high resolution, high mass accuracy Orbitrap mass spectrometer to achieve reliable quantification even of low abundance proteins in samples.

Metaproteomics insights into fermented fish and vegetable products and associated microbes

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Increasing global population means higher demand for healthy food.

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Fish and vegetables are healthy foods, but overproduction leads to spoilage.

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Fermentation of fish/vegetables elongate their shelf lives, improved flavour and functions.

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Microbes associated with Fish/vegetable fermentation produce health conferring peptides.

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There is little review on peptides elicited during fish/vegetable fermentations.

The interest in proteomic studies of fermented food is increasing; the role of proteins derived from fermentation extends beyond preservation, they also improve the organoleptic, anti-pathogenic, anti-cancer, anti-obesogenic properties, and other health conferring properties of fermented food. Traditional fermentation processes are still in use in certain cultures, but recently, the controlled process is gaining wider acceptance due to consistency and predictability. Scientists use modern biotechnological approaches to evaluate reactions and component yields from fermentation processes. Pieces of literature on fermented fish and vegetable end-products are scanty (compared to milk and meat), even though fish and vegetables are considered health conferring diets with high nutritional contents. Evaluations of peptides from fermented fish and vegetables show they have anti-obesity, anti-oxidative, anti-inflammatory, anti-pathogenic, anti-anti-nutrient, improves digestibility, taste, nutrient content, texture, aroma properties, etc. Despite challenges impeding the wider applications of the metaproteomic analysis of fermented fish and vegetables, their potential benefits cannot be underestimated.

Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of analytical, biochemical, and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes accompanying plant development, senescence, and response to biotic or abiotic stress. Among individual post-genomic methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. During the recent decades, multiple methodological advances were introduced in different branches of life science, although only some of them were established in seed proteomics so far. Therefore, here we discuss main methodological approaches already employed in seed proteomics, as well as those still waiting for implementation in this field of plant research, with a special emphasis on sample preparation, data acquisition, processing, and post-processing. Thereby, the overall goal of this review is to bring new methodologies emerging in different areas of proteomics research (clinical, food, ecological, microbial, and plant proteomics) to the broad society of seed biologists.

Combining Targeted and Untargeted Data Acquisition to Enhance Quantitative Plant Proteomics Experiments

Most quantitative proteomics experiments either target a limited number of selected proteins for quantification or quantify proteins on a broad scale in an untargeted manner. However, we recently demonstrated that experiments that have both targeted and untargeted components can be particularly advantageous. Using a combined targeted and untargeted liquid chromatography-tandem mass spectrometry data acquisition strategy termed TDA/DDA (shorthand for targeted data acquisition/data-dependent acquisition), which we applied to a model quantitative plant proteomics experiment performed on Arabidopsis, we demonstrated improved quantification of both targeted and untargeted proteins relative to purely untargeted experiments performed using conventional data-dependent acquisition (Hart-Smith et al. Front Plant Sci 8:1669, 2017). This suggests that many quantitative proteomics datasets earmarked for collection using data-dependent acquisition are likely to benefit from the use of TDA/DDA instead.This chapter describes how TDA/DDA liquid chromatography-tandem mass spectrometry methods can be created on commonly used mass spectrometric instrument platforms. It described how, using freely available software, tandem mass spectrometry inclusion lists designed to target proteins of hypothesized interest can be generated. Best practice implementation of these inclusion lists in TDA/DDA strategies is then described. Relative to conventional data-dependent acquisition, the liquid chromatography-tandem mass spectrometry methods created using these guidelines increase the chances of quantifying targeted proteins and can produce widespread improvements in the reproducibility of untargeted protein quantification, without compromising the total numbers of proteins quantified. They are compatible with different quantitative proteomics methodologies, including metabolic labeling, chemical labeling and label-free approaches, and can be used to create tailored assay libraries to aid the interpretation of quantitative proteomics data collected using data-independent acquisition.

Control of Cognate Sense mRNA Translation by cis-Natural Antisense RNAs

Cis-Natural Antisense Transcripts (cis-NATs), which overlap protein coding genes and are transcribed from the opposite DNA strand, constitute an important group of noncoding RNAs. Whereas several examples of cis-NATs regulating the expression of their cognate sense gene are known, most cis-NATs function by altering the steady-state level or structure of mRNA via changes in transcription, mRNA stability, or splicing, and very few cases involve the regulation of sense mRNA translation. This study was designed to systematically search for cis-NATs influencing cognate sense mRNA translation in Arabidopsis (Arabidopsis thaliana). Establishment of a pipeline relying on sequencing of total polyA⁺ and polysomal RNA from Arabidopsis grown under various conditions (i.e. nutrient deprivation and phytohormone treatments) allowed the identification of 14 cis-NATs whose expression correlated either positively or negatively with cognate sense mRNA translation. With use of a combination of cis-NAT stable over-expression in transgenic plants and transient expression in protoplasts, the impact of cis-NAT expression on mRNA translation was confirmed for 4 out of 5 tested cis-NAT:sense mRNA pairs. These results expand the number of cis-NATs known to regulate cognate sense mRNA translation and provide a foundation for future studies of their mode of action. Moreover, this study highlights the role of this class of noncoding RNAs in translation regulation.