A semi-automated workflow for DIA-based global discovery to pathway-driven PRM analysis.

Targeted proteomics, which includes parallel reaction monitoring (PRM), is typically utilized for more precise detection and quantitation of key proteins and/or pathways derived from complex discovery proteomics datasets. Initial discovery-based analysis using data independent acquisition (DIA) can obtain deep proteome coverage with low data missingness while targeted PRM assays can provide additional benefits in further eliminating missing data and optimizing measurement precision. However, PRM method development from bioinformatic predictions can be tedious and time-consuming because of the DIA output complexity. We address this limitation with a Python script that rapidly generates a PRM method for the TIMS-TOF platform using DIA data and a user-defined target list. To evaluate the script, DIA data obtained from HeLa cell lysate (200 ng, 45-min gradient method) as well as canonical pathway information from Ingenuity Pathway Analysis was utilized to generate a pathway-driven PRM method. Subsequent PRM analysis of targets within the example pathway, regulation of apoptosis, resulted in improved chromatographic data and enhanced quantitation precision (100% peptides below 10% CV with a median CV of 2.9%, n = 3 technical replicates). The script is freely available at https://github.com/StevensOmicsLab/PRM-script and provides a framework that can be adapted to multiple DDA/DIA data outputs and instrument-specific PRM method types.

A Framework for Quality Control in Quantitative Proteomics.

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow, from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at the protein and peptide levels allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and ProteomeXchange under the identifier PXD051318.

Affinity purification mass spectrometry characterisation of the interactome of receptor tyrosine kinase proline-rich motifs in cancer.

Receptor tyrosine kinase (RTK) overexpression is linked to the development and progression of multiple cancers. RTKs are classically considered to initiate cytoplasmic signalling pathways via ligand-induced tyrosine phosphorylation, however recent evidence points to a second tier of signalling contingent on interactions mediated by the proline-rich motif (PRM) regions of non-activated RTKs. The presence of PRMs on the C-termini of >40 % of all RTKs and the abundance of PRM-binding proteins encoded by the human genome suggests that there is likely to be a large number of previously unexplored interactions which add to the RTK intracellular interactome. Here, we explore the RTK PRM interactome and its potential significance using affinity purification mass spectrometry and in silico enrichment analyses. Peptides comprising PRM-containing C-terminal tail regions of EGFR, FGFR2 and HER2 were used as bait to affinity purify bound proteins from different cancer cell line lysates. 490 unique interactors were identified, amongst which proteins with metabolic, homeostatic and migratory functions were overrepresented. This suggests that PRMs from RTKs may sustain a diverse interactome in cancer cells. Since RTK overexpression is common in cancer, RTK PRM-derived signalling may be an important, but as yet underexplored, contributor to negative cancer outcomes including resistance to kinase inhibitors.

optiPRM: A Targeted Immunopeptidomics LC-MS Workflow With Ultra-High Sensitivity for the Detection of Mutation-Derived Tumor Neoepitopes From Limited Input Material.

Personalized cancer immunotherapies such as therapeutic vaccines and adoptive transfer of T cell receptor-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen class I molecules to cytotoxic T cells. Such neoepitopes can for example arise from somatic mutations and their identification is crucial for the rational design of new therapeutic interventions. Liquid chromatography mass spectrometry (LC-MS)-based immunopeptidomics is the only method to directly prove actual peptide presentation and we have developed a parameter optimization workflow to tune targeted assays for maximum detection sensitivity on a per peptide basis, termed optiPRM. Optimization of collision energy using optiPRM allows for the improved detection of low abundant peptides that are very hard to detect using standard parameters. Applying this to immunopeptidomics, we detected a neoepitope in a patient-derived xenograft from as little as 2.5 × 106 cells input. Application of the workflow on small patient tumor samples allowed for the detection of five mutation-derived neoepitopes in three patients. One neoepitope was confirmed to be recognized by patient T cells. In conclusion, optiPRM, a targeted MS workflow reaching ultra-high sensitivity by per peptide parameter optimization, makes the identification of actionable neoepitopes possible from sample sizes usually available in the clinic.

Multiplexed quantitative proteomics in prostate cancer biomarker development.

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.

Koina: Democratizing machine learning for proteomics research.

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

The key role of Spain in the traffic of West Nile virus lineage 1 strains between Europe and Africa.

BACKGROUND: West Nile Virus (WNV) is a zoonotic arbovirus worldwide spread. Seasonal WNV outbreaks occur in the Mediterranean basin since the late 1990's with ever-increasing incidence. In Southern Spain WNV is endemic, as disease foci - caused by WNV lineage 1 (WNV-L1) strains - occur every year. On the contrary, WNV-L2 is the dominant lineage in Europe, so most European WNV sequences available belong to this lineage, WNV-L1 sequences being still scarce. METHODS: To fill this gap, this study reports the genetic characterisation of 27 newly described WNV-L1 strains, involved in outbreaks affecting wild birds and horses during the last decade in South-Western Spain. RESULTS: All strains except one belong to the Western Mediterranean-1 sub-cluster (WMed-1), related phylogenetically to Italian, French, Portuguese, Moroccan and, remarkably, Senegalese strains. This sub-cluster persisted, spread and evolved into three distinguishable WMed-1 phylogenetic groups that co-circulated, notably, in the same province (Cádiz). They displayed different behaviours: from long-term persistence and rapid spread to neighbouring regions within Spain, to long-distance spread to different countries, including transcontinental spread to Africa. Among the different introductions of WNV in Spain revealed in this study, some of them succeeded to get established, some extinguished from the territory shortly afterwards. Furthermore, Spain's southernmost province, Cádiz, constitutes a hotspot for virus incursion. CONCLUSION: Southern Spain seems a likely scenario for emergence of exotic pathogens of African origin. Therefore, circulation of diverse WNV-L1 variants in Spain prompts for an extensive surveillance under a One Health approach.

Differential Analysis of Cereblon Neosubstrates in Rabbit Embryos Using Targeted Proteomics.

Targeted protein degradation is the selective removal of a protein of interest through hijacking intracellular protein cleanup machinery. This rapidly growing field currently relies heavily on the use of the E3 ligase cereblon (CRBN) to target proteins for degradation, including the immunomodulatory drugs (IMiDs) thalidomide, lenalidomide, and pomalidomide which work through a molecular glue mechanism of action with CRBN. While CRBN recruitment can result in degradation of a specific protein of interest (e.g., efficacy), degradation of other proteins (called CRBN neosubstrates) also occurs. Degradation of one or more of these CRBN neosubstrates is believed to play an important role in thalidomide-related developmental toxicity observed in rabbits and primates. We identified a set of 25 proteins of interest associated with CRBN-related protein homeostasis and/or embryo/fetal development. We developed a targeted assay for these proteins combining peptide immunoaffinity enrichment and high-resolution mass spectrometry and successfully applied this assay to rabbit embryo samples from pregnant rabbits dosed with three IMiDs. We confirmed previously reported in vivo decreases in neosubstrates like SALL4, as well as provided evidence of neosubstrate changes for proteins only examined in vitro previously. While there were many proteins that were similarly decreased by all three IMiDs, no compound had the exact same neosubstrate degradation profile as another. We compared our data to previous literature reports of IMiD-induced degradation and known developmental biology associations. Based on our observations, we recommend monitoring at least a major subset of these neosubstrates in a developmental test system to improve CRBN-binding compound-specific risk assessment. A strength of our assay is that it is configurable, and the target list can be readily adapted to focus on only a subset of proteins of interest or expanded to incorporate new findings as additional information about CRBN biology is discovered.

Sodium arsenite impairs sperm quality via downregulating the ZMYND15 and ZMYND10.

Zinc finger MYND-type containing 15 (ZMYND15) has been documented to play important roles in spermatogenesis, and mutants contribute to recessive azoospermia, severe oligozoospermia, non-obstructive azoospermia, teratozoospermia, even male infertility. ZMYND10 is involved in sperm motility. Whether environmental pollutants impair male fertility via regulating the expression of ZMYND15 and ZMYND10 has not been studied. Arsenic exposure results in poor sperm quality and male infertility. In order to investigate whether arsenic-induced male reproductive toxicity is related to the expression of ZMYND15, ZMYND10 and their target genes, we established a male rat model of sodium arsenite exposure-induced reproductive injury, measured sperm quality, serum hormone levels, mRNA and protein expressions of intratesticular ZMYND15 and ZMYND10 as well as their target genes. The results showed that, in addition to the increased mRNA expression of Tnp1, sodium arsenite exposure reduced sperm quality, serum hormone levels, and mRNA and protein expression of intratesticular ZMYND15 and ZMYND10 and their target genes in male rats compared with the control group (p < .05). Therefore, our study first showed that the environmental pollutant arsenic impairs sperm quality in male rats by reducing the expression of ZMYND10 and ZMYND15 and their regulatory genes, which provides a possible diagnostic marker for environmental pollutants-induced male infertility.

Quantitative accuracy of lung function measurement using parametric response mapping: A virtual imaging study.

Parametric response mapping (PRM) is a voxel-based quantitative CT imaging biomarker that measures the severity of chronic obstructive pulmonary disease (COPD) by analyzing both inspiratory and expiratory CT scans. Although PRM-derived measurements have been shown to predict disease severity and phenotyping, their quantitative accuracy is impacted by the variability of scanner settings and patient conditions. The aim of this study was to evaluate the variability of PRM-based measurements due to the changes in the scanner types and configurations. We developed 10 human chest models with emphysema and air-trapping at end-inspiration and end-expiration states. These models were virtually imaged using a scanner-specific CT simulator (DukeSim) to create CT images at different acquisition settings for energy-integrating and photon-counting CT systems. The CT images were used to estimate PRM maps. The quantified measurements were compared with ground truth values to evaluate the deviations in the measurements. Results showed that PRM measurements varied with scanner type and configurations. The emphysema volume was overestimated by 3 ± 9.5 % (mean ± standard deviation) of the lung volume, and the functional small airway disease (fSAD) volume was underestimated by 7.5±19 % of the lung volume. PRM measurements were more accurate and precise when the acquired settings were photon-counting CT, higher dose, smoother kernel, and larger pixel size. This study demonstrates the development and utility of virtual imaging tools for systematic assessment of a quantitative biomarker accuracy.

Proteomics Defines Plasma Biomarkers for the Early Diagnosis of Biliary Atresia.

Early diagnosis of biliary atresia (BA) is crucial for improving the chances of survival and preserving the liver function of pediatric patients with BA. Herein, we performed proteomics analysis using data-independent acquisition (DIA) and parallel reaction monitoring (PRM) to explore potential biomarkers for the early diagnosis of BA compared to other non-BA jaundice cases. Consequently, we detected and validated differential protein expression in the plasma of patients with BA compared to the plasma of patients with intrahepatic cholestasis. Bioinformatics analysis revealed the enriched biological processes characteristic of BA by identifying the differential expression of specific proteins. Signaling pathway analysis revealed changes in the expression levels of proteins associated with an alteration in immunoglobulin levels, which is indicative of immune dysfunction in BA. The combination of polymeric immunoglobulin receptor expression and immunoglobulin lambda variable chain (IGL c2225_light_IGLV1-47_IGLJ2), as revealed via machine learning, provided a useful early diagnostic model for BA, with a sensitivity of 0.8, specificity of 1, accuracy of 0.89, and area under the curve value of 0.944. Thus, our study identified a possible effective plasma biomarker for the early diagnosis of BA and could help elucidate the underlying mechanisms of BA.

Plasma proteomic characterization of colorectal cancer patients with FOLFOX chemotherapy by integrated proteomics technology.

BACKGROUND: Colorectal Cancer (CRC) is a prevalent form of cancer, and the effectiveness of the main postoperative chemotherapy treatment, FOLFOX, varies among patients. In this study, we aimed to identify potential biomarkers for predicting the prognosis of CRC patients treated with FOLFOX through plasma proteomic characterization. METHODS: Using a fully integrated sample preparation technology SISPROT-based proteomics workflow, we achieved deep proteome coverage and trained a machine learning model from a discovery cohort of 90 CRC patients to differentiate FOLFOX-sensitive and FOLFOX-resistant patients. The model was then validated by targeted proteomics on an independent test cohort of 26 patients. RESULTS: We achieved deep proteome coverage of 831 protein groups in total and 536 protein groups in average for non-depleted plasma from CRC patients by using a Orbitrap Exploris 240 with moderate sensitivity. Our results revealed distinct molecular changes in FOLFOX-sensitive and FOLFOX-resistant patients. We confidently identified known prognostic biomarkers for colorectal cancer, such as S100A4, LGALS1, and FABP5. The classifier based on the biomarker panel demonstrated a promised AUC value of 0.908 with 93% accuracy. Additionally, we established a protein panel to predict FOLFOX effectiveness, and several proteins within the panel were validated using targeted proteomic methods. CONCLUSIONS: Our study sheds light on the pathways affected in CRC patients treated with FOLFOX chemotherapy and identifies potential biomarkers that could be valuable for prognosis prediction. Our findings showed the potential of mass spectrometry-based proteomics and machine learning as an unbiased and systematic approach for discovering biomarkers in CRC.

Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA.

BACKGROUND: Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. METHODS: Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. RESULTS: We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. CONCLUSIONS: Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

CircANXA4 (hsa_circ_0055087) regulates the miR-1256/PRM1 axis to promote tumor progression in colorectal cancer.

Colorectal cancer (CRC) incidence ranks third among malignant cancers with a high propensity for distant metastasis. Despite continuous efforts to improve treatment, the prognosis especially in patients with advanced distant metastasis is low. The mechanism of development and progression of CRC is not fully understood. Non-coding RNAs (ncRNAs) have emerged as essential regulators in cancer progression. Here, we aim to dissect the role of one critical ncRNA, circANXA4, in CRC progression. CircANXA4 expression was analyzed by the GEO database. Differentially expressed circRNAs were identified by the Limma package R software. Expression of circANXA4 and miR-1256 was detected by qRT-PCR. The regulation of circANXA4 on cell proliferation and progression was confirmed with the cell viability assay using cell counting kit-8 (CCK-8) and transwell migration assay. RNA pull-down assay, RNA immunoprecipitation (RIP), and western blot were used to determine the interaction between circANXA4, miR-1256, and protamine1 (PRM1). CircANXA4 was upregulated in both CRC tissues and cell lines. Knockdown of circANXA4 effectively reduced cell proliferation, progression, and migration. Additionally, silencing circANXA4 remarkably increased miR-1256 expression, while reducing PRM1 expression, thereby demonstrating that circANXA4 downregulates miR-1256 expression through a complementary binding site. Rescue experiments revealed the interactions between circANXA4, miR-1256, and PRM1. Pearson correlation analysis revealed that circANXA4 expression positively correlated with PRM1 expression and miR-1256 expression inversely correlated with PRM1 expression. In sum, we demonstrated that circANXA4 promotes cancer cell proliferation and progression by sponging miR-1256 and upregulating PRM1 in CRC.

A framework for quality control in quantitative proteomics.

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.

Hydrogen Sulfide Exerted a Pro-Angiogenic Role by Promoting the Phosphorylation of VEGFR2 at Tyr797 and Ser799 Sites in Hypoxia-Reoxygenation Injury.

The protective effects of hydrogen sulfide (H2S) against ischemic brain injury and its role in promoting angiogenesis have been established. However, the specific mechanism underlying these effects remains unclear. This study is designed to investigate the regulatory impact and mechanism of H2S on VEGFR2 phosphorylation. Following expression and purification, the recombinant His-VEGFR2 protein was subjected to LC-PRM/MS analysis to identify the phosphorylation sites of VEGFR2 upon NaHS treatment. Adenovirus infection was used to transfect primary rat brain artery endothelial cells (BAECs) with the Ad-VEGFR2WT, Ad-VEGFR2Y797F, and Ad-VEGFR2S799A plasmids. The expression of VEGFR2 and recombinant Flag-VEGFR2, along with Akt phosphorylation, cell proliferation, and LDH levels, was assessed. The migratory capacity and tube-forming potential of BAECs were assessed using wound healing, transwell, and tube formation assays. NaHS notably enhanced the phosphorylation of VEGFR2 at Tyr797 and Ser799 sites. These phosphorylation sites were identified as crucial for mediating the protective effects of NaHS against hypoxia-reoxygenation (H/R) injury. NaHS significantly enhanced the Akt phosphorylation, migratory capacity, and tube formation of BAECs and upregulated the expression of VEGFR2 and recombinant proteins. These findings suggest that Tyr797 and Ser799 sites of VEGFR2 serve as crucial mediators of H2S-induced pro-angiogenic effects and protection against H/R injury.

Single Isotopologue for In-Sample Calibration and Absolute Quantitation by LC-MS/MS.

Targeted mass spectrometry (MS)-based absolute quantitative analysis has been increasingly used in biomarker discovery. The ability to accurately measure the masses by MS enabled the use of isotope-incorporated surrogates having virtually identical physiochemical properties with the target analytes as calibrators. Such a unique capacity allowed for accurate in-sample calibration. Current in-sample calibration uses multiple isotopologues or structural analogues for both the surrogate and the internal standard. Here, we simplified this common practice by using endogenous light peptides as the internal standards and used a mathematical deduction of "heavy matching light, HML" to directly quantify an endogenous analyte. This method provides all necessary assay performance parameters in the authentic matrix, including the lower limit of quantitation (LLOQ) and intercept of the calibration curve, by using only a single isotopologue of the analyte. This method can be applied to the quantitation of proteins, peptides, and small molecules. Using this method, we quantified the efficiency of heart tissue digestion and recovery using sodium deoxycholate as a detergent and two spiked exogenous proteins as mimics of heart proteins. The results demonstrated the robustness of the assay.

A peptide dataset for target analysis of human complement system proteins.

The targeted LC-MS/MS method has been widely applied for peptide quantification, offering sensibility, specificity, and reproducibility to the analysis. However, it requires the prior selection of targets, including the construction of a spectral library. Here, we present a dataset comprising peptide mass spectra for targeted LC-MS/MS method setup, applied to a set of human complement system proteins. Additionally, we selected a group of peptides and demonstrated their stability and reproducibility in quantification. This dataset is invaluable for studies aiming at the quantification of the complement system proteins by targeted LC-MS/MS, as it provides data for spectral library construction and a list of selected peptides.

The proteome of Penicillium expansum during infection of postharvest apple is revealed using Label-Free and Parallel Reaction Monitoring(PRM)Techniques.

Penicillium expansum is the main pathogen in the postharvest storage of apples. Penicilliosis caused by P. expansum infection not only seriously affects the appearance and quality of fruits, but also the secondary metabolite Patulin (PAT) can cause harm to human health. Until now, little attention has been paid to the molecular mechanism of P. expansum infecting apples. Studying its molecular mechanism can help us better prevent and control apple postharvest blue mold. In this present investigation, we will use Label-Free technology to perform proteomic sequencing on apple samples at key time points of P. expansum infection, explore and screen key proteins and metabolic pathways during infection, and use Parallel Reaction Monitoring (PRM) technology to thoroughly validate proteomic data. The infection of P. expansum activates the MAPK signaling pathway, plant-pathogen interaction metabolic pathway and phenylpropanoid biosynthesis pathway of apple, participates in the regulation of ROS generation and oxidative stress process, promotes the synthesis of lignin and flavonoids, and the synthesis of Pathogenesis-Related Protein helps apple directly defend against P. expansum infection. This study provides the foundation for relevant postharvest control strategies, paving the way for further exploration of the proteome of pathogens infecting fruit and vegetables. SIGNIFICANCE: Proteins are macromolecules essential to the life of organisms, as they participate in the function and structure of cells. Proteomics technology is currently one of the important means to study the the response mechanism of pathogenic bacteria to plant infection, which can reveal the essence of physiological and pathological processes and help to clarify the possible relationship between protein abundance and plant stress. The present study essentially uses recent proteome analysis technology, namely label-free and PRM techniques, and lays the foundations for studying the of the infection response between P. expansum and apples. In particular, it provides a broad perspective on the molecular mechanism of P. expansum in the early stage of apple infection through detailed functional exploration and verification of associated proteins. Thus, it provides a theoretical basis for preventing and treating apple postharvest blue mold.

SECRET AGENT O-GlcNAcylates Hundreds of Proteins Involved in Diverse Cellular Processes in Arabidopsis.

O-GlcNAcylation is a critical post-translational modification of proteins observed in both plants and animals and plays a key role in growth and development. While considerable knowledge exists about over 3000 substrates in animals, our understanding of this modification in plants remains limited. Unlike animals, plants possess two putative homologs: SECRET AGENT (SEC) and SPINDLY, with SPINDLY also exhibiting O-fucosylation activity. To investigate the role of SEC as a major O-GlcNAc transferase in plants, we utilized lectin-weak affinity chromatography enrichment and stable isotope labeling in Arabidopsis labeling, quantifying at both MS1 and MS2 levels. Our findings reveal a significant reduction in O-GlcNAc levels in the sec mutant, indicating the critical role of SEC in mediating O-GlcNAcylation. Through a comprehensive approach, combining higher-energy collision dissociation and electron-transfer high-energy collision dissociation fragmentation with substantial fractionations, we expanded our GlcNAc profiling, identifying 436 O-GlcNAc targets, including 227 new targets. The targets span diverse cellular processes, suggesting broad regulatory functions of O-GlcNAcylation. The expanded targets also enabled exploration of crosstalk between O-GlcNAcylation and O-fucosylation. We also examined electron-transfer high-energy collision dissociation fragmentation for site assignment. This report advances our understanding of O-GlcNAcylation in plants, facilitating further research in this field.