Journal of proteomics最新文献

{"title":"Comparative secretome analysis of Oudemansiella raphanipes grown on different agricultural residues","authors":"Liping Zhu ,&nbsp;Shunan Ma ,&nbsp;Xia Gao ,&nbsp;Jiandong Han ,&nbsp;Weidong Lu ,&nbsp;Hao Yu ,&nbsp;Song Yang","doi":"10.1016/j.jprot.2025.105445","DOIUrl":"10.1016/j.jprot.2025.105445","url":null,"abstract":"<div><div><em>Oudemansiella raphanipes</em> can degrade lignocellulose-rich biomass, especially agricultural residues. However, its substrate utilization and degradation mechanisms remain poorly understood. To explore this, we cultured <em>O. raphanipes</em> mycelium in Kirk's liquid medium supplemented with eight distinct substrates and conducted studies on extracellular enzyme activities and secretome analysis. A total of 905 secreted proteins were identified, with the cornstalk group having the highest counts. Carbohydrate-active enzymes (CAZymes) were the predominant type (32.8–48.9 %), followed by oxidoreductases (2.8 %–13.3 %), while lipase and phosphatase were minor categories. Functional annotation of the secreted proteins comprehensively revealed their diversity in various biological processes. Among the 340 secreted proteins with Enzyme Commission codes, (Methyl)glyoxal oxidase, chitinase, and β-glucosidase were most prominent. Bran, cottonseed hulls, corncobs, and the mixture promoted mycelium growth and conserved CAZymes expression patterns. In contrast, sawdust, corn steep liquor, and cornstalk induced divergent secretome profiles. Sawdust led to a higher proportion of hemicellulose- and lignin-degrading enzymes. Corn steep liquor induced relatively high activities and abundances of laccase and MnP, while cornstalk induced a broad spectrum of oxidoreductases, lipases, and protease &amp; peptidases. In addition, redundancy analysis further indicated that the extracellular enzyme activities (notably laccase, MnP, and xylanase) induced by different substrates significantly impacted the secretome.</div></div><div><h3>Significance</h3><div><em>O. raphanipes</em> can efficiently utilize a variety of lignocellulosic materials, and genomic sequencing has confirmed the presence of abundant CAZymes in its genome. This study employed various agricultural residues as substrate inducers to elucidate the extracellular enzyme profiles of <em>O. raphanipes</em> involved in lignocellulose degradation, which indicated its metabolic plasticity in response to varying substrate composition. These findings facilitate further exploration of the biomass bioconversion mechanism of <em>O. raphanipes</em> and provide novel perspectives for the induction of combined agro-residues in its industrial cultivation.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"317 ","pages":"Article 105445"},"PeriodicalIF":2.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata","authors":"Ping Zheng ,&nbsp;Enrique Audain ,&nbsp;Henry Webel ,&nbsp;Chengxin Dai ,&nbsp;Joshua Klein ,&nbsp;Marc-Phillip Hitz ,&nbsp;Timo Sachsenberg ,&nbsp;Mingze Bai ,&nbsp;Yasset Perez-Riverol","doi":"10.1016/j.jprot.2025.105440","DOIUrl":"10.1016/j.jprot.2025.105440","url":null,"abstract":"<div><div>Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a protein's absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (<span><span>https://github.com/bigbio/ibaqpy</span><svg><path></path></svg></span>), a Python package designed to compute iBAQ values efficiently for experiments of any scale. Ibaqpy leverages the Sample and Data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpy's capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4921 samples and 5766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse.</div></div><div><h3>Significance</h3><div>Proteomics studies often rely on intensity-based absolute quantification (iBAQ) to assess protein abundance across various biological conditions. Despite its widespread use, computing iBAQ values at scale remains challenging due to the increasing complexity and volume of proteomics experiments. Existing tools frequently lack metadata integration, limiting their ability to handle experimental design intricacies such as replicates, fractions, and batch effects. Our work introduces ibaqpy, a scalable Python package that leverages the Sample and Data Relationship Format (SDRF) to compute iBAQ values efficiently while incorporating critical experimental metadata. By enabling automated normalization and batch correction, ibaqpy ensures reproducible and comparable quantification across large-scale datasets.</div><div>We validated the utility of ibaqpy through the reanalysis of 17 public HeLa datasets, comprising over 200 million peptide features and quantifying 11,000 proteins across thousands of samples. This comprehensive reanalysis highlights the robustness and scalability of ibaqpy, making it an essential tool for researchers conducting large-scale proteomics experiments. Moreover, by promoting FAIR principles for data reuse and interoperability, ibaqpy offers a transformative approach to baseline protein quantification, supporting reproducible research and data integration within the proteomics community.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"317 ","pages":"Article 105440"},"PeriodicalIF":2.8,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center","authors":"Henriett Oskolas ,&nbsp;Fábio C.N. Nogueira ,&nbsp;Gilberto B. Domont ,&nbsp;Kun-Hsing Yu ,&nbsp;Yevgeniy R. Semenov ,&nbsp;Peter Sorger ,&nbsp;Erik Steinfelder ,&nbsp;Les Corps ,&nbsp;Lesley Schulz ,&nbsp;Elisabet Wieslander ,&nbsp;David Fenyö ,&nbsp;Sarolta Kárpáti ,&nbsp;Péter Holló ,&nbsp;Lajos V. Kemény ,&nbsp;Balazs Döme ,&nbsp;Zsolt Megyesfalvi ,&nbsp;Krzysztof Pawłowski ,&nbsp;Toshihide Nishimura ,&nbsp;HoJeong Kwon ,&nbsp;Sergio Encarnación-Guevara ,&nbsp;Jeovanis Gil","doi":"10.1016/j.jprot.2025.105442","DOIUrl":"10.1016/j.jprot.2025.105442","url":null,"abstract":"<div><div>This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology.</div><div>Tumor and blood-based workflows, supported by automated fractionation systems and standardized protocols, ensure the collection of high-quality biospecimens suitable for proteomic, genomic, and metabolic studies. A robust informatics infrastructure, integrating LIMS, barcoding, and REDCap, supports end-to-end traceability and realtime data synchronization, thereby enriching each sample with critical clinical metadata. Proteogenomic integration lies at the core of this initiative, uncovering tumor- and blood-based molecular profiles that inform cancer heterogeneity, metastasis, and therapeutic resistance. Machine learning and AI-driven models further enhance these datasets by stratifying patient populations, predicting therapeutic responses, and expediting the discovery of actionable targets and companion biomarkers. This synergy between technology, automation, and high-dimensional data analytics enables individualized treatment strategies in melanoma, lung, and other cancer types. Aligned with international programs such as the Cancer Moonshot and the ICPC, the Lund Center's approach fosters open collaboration and data sharing on a global scale. This scalable, patient-centric biobanking paradigm provides an adaptable model for institutions aiming to unify clinical, molecular, and computational resources for transformative cancer research.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"316 ","pages":"Article 105442"},"PeriodicalIF":2.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomics analysis of the mechanism of the treatment of corneal injury in dry-eye mice","authors":"Zhirui Zhang ,&nbsp;Changxing Liu ,&nbsp;Jiadi Wang ,&nbsp;Yue Liu ,&nbsp;Yuhang Li ,&nbsp;Jing Yao","doi":"10.1016/j.jprot.2025.105443","DOIUrl":"10.1016/j.jprot.2025.105443","url":null,"abstract":"<div><div>Dry eye disease (DED) is a common ocular surface disorder affecting millions globally. Clinical and experimental studies have shown that the traditional Chinese medicine formula Qingxuan Runmu Yin decoction (QXRMY) is effective in treating DED. This study aimed to explore the molecular mechanisms of corneal damage in DED and evaluate QXRMY's therapeutic effects. A total of 120 C57BL/6 mice were divided into control, DED model, and QXRMY treatment groups. DIA sequencing of corneal tissue identified 2411 differentially expressed proteins. Enrichment analysis revealed these proteins were involved in RNA polymerase II regulation, apoptosis, and protein phosphorylation. KEGG pathway analysis highlighted key roles of the PI3K/AKT, HIF-1 signaling pathways, and cytoskeleton regulation in QXRMY's effects. FL, BUT, Schirmer I tests, HE, and PAS staining confirmed corneal damage in DED and the repair effects of QXRMY. ELISA showed QXRMY significantly reduced IL-1β, IL-6, and TNF-α levels, suggesting anti-inflammatory properties. PCR and Western blot further confirmed QXRMY repairs corneal damage via the PI3K/AKT/HIF1α pathway. This study provides new insights into the pathogenesis of DED and supports QXRMY's therapeutic potential in treating DED by alleviating inflammation and promoting corneal repair.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"316 ","pages":"Article 105443"},"PeriodicalIF":2.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative approaches for predicting protein network perturbations through machine learning and structural characterization","authors":"Bethany D. Bengs ,&nbsp;Jules Nde ,&nbsp;Sreejata Dutta ,&nbsp;Yanming Li ,&nbsp;Mihaela E. Sardiu","doi":"10.1016/j.jprot.2025.105439","DOIUrl":"10.1016/j.jprot.2025.105439","url":null,"abstract":"<div><div>Chromatin remodeling complexes, such as the <em>Saccharomyces cerevisiae</em> INO80 complex, exemplify how dynamic protein interaction networks govern cellular function through a balance of conserved structural modules and context-dependent functional partnerships, as revealed by integrative machine learning and structural mapping approaches. In this study, we explored the INO80 complex using machine learning to predict network changes caused by genetic deletions. Tree-based models outperformed linear approaches, highlighting non-linear relationships within the interaction network. Feature selection identified key INO80 components (e.g., Arp5, Arp8) and cross-compartment features from other remodeling complexes like SWR1 and NuA4, emphasizing shared functional pathways. Perturbation patterns aligned with biological modules, particularly those linked to telomere maintenance and aging, underscoring the functional coherence of these networks. Structural mapping revealed that not all interactions are predictable through proximity alone, particularly with Arp5 and Yta7. By combining structural insights with machine learning, we enhanced predictions of genetic perturbation effects, providing a template for analyzing cross-species homologs (e.g., human INO80) and their disease-associated variants. This integrative approach bridges the gap between static structural data and dynamic functional networks, offering a pathway to disentangle conserved mechanisms from context-dependent adaptations in chromatin biology.</div></div><div><h3>Significance</h3><div>By leveraging an innovative, integrative machine learning approach, we have successfully predicted and analyzed perturbations in the INO80 network with good accuracy and depth. Our novel combination of machine learning, perturbation analysis, and structural investigation approach has provided crucial insights into the complex's structure-function relationships, shedding new light on its pivotal roles in affected pathways such as telomere maintenance. Our findings not only enhance our understanding of the INO80 complex but also establish a powerful framework for future studies in chromatin biology and beyond. This work represents a step forward in our understanding of chromatin remodeling complexes and their diverse cellular functions, laying the groundwork for future studies that can further refine our computational approaches and experimental techniques in this field.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"316 ","pages":"Article 105439"},"PeriodicalIF":2.8,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel model organisms and proteomics for a better biological understanding","authors":"Jean Armengaud ,&nbsp;Tristan Cardon ,&nbsp;Susana Cristobal ,&nbsp;Sabine Matallana-Surget ,&nbsp;Fabrice Bertile","doi":"10.1016/j.jprot.2025.105441","DOIUrl":"10.1016/j.jprot.2025.105441","url":null,"abstract":"<div><div>The concept of « model organisms » is being revisited in the light of the latest advances in multi-omics technologies that can now capture the full range of molecular events that occur over time, regardless of the organism studied. Classic, well-studied models, such as <em>Escherichia coli, Saccharomyces cerevisiae</em>, to name a few, have long been valuable for hypothesis testing, reproducibility, and sharing common platforms among researchers. However, they are not suitable for all types of research. The complexity of unanswered questions in biology demands more elaborated systems, particularly to study plant and animal biodiversity, microbial ecosystems and their interactions with their hosts if any. More integrated systems, known as « holobionts », are emerging to describe and unify host organisms and associated microorganisms, providing an overview of all their possible interactions and trajectories. Comparative evolutionary proteomics offers interesting prospects for extrapolating knowledge from a few selected model organisms to others. This approach enables a deeper characterization of the diversity of proteins and proteoforms across the three branches of the tree of life, i.e. Bacteria, Archaea, and Eukarya. It also provides a powerful means to address remaining biological questions, such as identifying the key molecular players in organisms when they are confronted to environmental challenges, like anthropogenic toxicants, pathogens, dietary shifts or climate stressors, and proposing long-term sustainable solutions.</div></div><div><h3>Significance</h3><div>In this commentary, we reevaluated the concept of “model organisms” in light of advancements in multi-omics technologies. Traditional models have proven invaluable for hypothesis testing, reproducibility, and fostering shared research frameworks. However, we discussed that they are not universally applicable. To address complexities such as biodiversity and understand microbial ecosystems and their host interactions, integrated systems like “holobionts,” which encompass host organisms and their associated microbes, are gaining prominence. Comparative evolutionary proteomics further enhances our understanding by enabling detailed exploration of protein diversity across organisms. This approach also facilitates the identification of critical molecular players in organisms facing environmental challenges, such as pollutants, pathogens, dietary changes, or climate stress, and contributes to developing sustainable long-term solutions.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"316 ","pages":"Article 105441"},"PeriodicalIF":2.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial and development responses in the wheat leaf highlight the loss of chloroplast protein homeostasis during salt stress","authors":"Samalka Wijeweera,&nbsp;Owen Duncan,&nbsp;A. Harvey Millar","doi":"10.1016/j.jprot.2025.105438","DOIUrl":"10.1016/j.jprot.2025.105438","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Salinity stress in wheat affects physiological and biochemical parameters in tissues that alter plant development and ultimately lower crop yield. Shoot tissues can accumulate high concentrations of sodium over time through the transpiration stream coming from the roots. This imposes physiological responses that align salt effects with the basipetal developmental gradient of the monocot leaf. The role of metabolic processes in generating and responding to these increases in sodium concentration over time was explored by linking changes in ion distributions to those of enzyme abundance from the base to the tip of leaves under salt stress. We found that enzymes for methionine synthesis and lipid degradation pathways increase, concomitantly with proteins in jasmonate synthesis, which are key players in plant stress-induced responses. Combining the use of Differential Abundance of Protein analysis and Weighted Correlation Network Analysis we have focused on identifying key protein hubs associated with responses to salt stress or salt susceptibility, shedding light on potential sites of salt sensitivity as targets for enhancing salt tolerance in wheat. We found chloroplast protein synthesis machinery, including the 30S and 50S ribosomal proteins, and plastid localised protein synthesis elongation factors, were significantly reduced in abundance and correlated with the altered K&lt;sup&gt;+&lt;/sup&gt;/Na&lt;sup&gt;+&lt;/sup&gt; ratio along salt-stressed wheat leaves. Additionally, the plastid protease system including ATP-dependent caseinolytic protease and filamentous temperature-sensitive H proteases involved in chloroplast protein homeostasis, show decreased abundance with salt. The complex interplay of these processes in and across the leaf affects overall plant viability under salt stress mainly affecting the energy homeostasis in wheat shoot.&lt;/div&gt;&lt;div&gt;Data are available via ProteomeXchange with identifier &lt;span&gt;&lt;span&gt;PXD059765&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Significance&lt;/h3&gt;&lt;div&gt;Soil salinity is a major agricultural challenge that cause significant reduction in wheat yields, a staple crop vital for global food security. Despite extensive breeding efforts, developing salt-tolerant wheat remains challenging due to the complex, multi-genic nature of salinity tolerance. While numerous studies have explored molecular responses to salt stress making salt to control comparisons, there is little consensus on the primary points of metabolic disruptions that would determine the salt response in wheat. Our study addresses this gap by integrating proteomics with Weighted Correlation Network Analysis to examine metabolic responses along the developmental gradient of wheat leaves. By exploiting the natural base-to-tip progression of leaf maturation under salt stress, we identify key protein groups linked to salt response. These findings provide new insights into potential metabolic targets for enhancing wheat's resilience to salinity stress.&lt;/d","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"316 ","pages":"Article 105438"},"PeriodicalIF":2.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143795220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iTRAQ-based quantitative proteomics reveals dysregulation of fibronectin 1 contributes to impaired endometrial decidualization in recurrent implantation failure","authors":"Jingying Wang ,&nbsp;Xuehan Zhao ,&nbsp;Jiaqi Wu ,&nbsp;Cong Wang ,&nbsp;Qin Wang ,&nbsp;Ying Fang ,&nbsp;Xiaokui Yang","doi":"10.1016/j.jprot.2025.105437","DOIUrl":"10.1016/j.jprot.2025.105437","url":null,"abstract":"<div><div>Recurrent implantation failure (RIF) poses challenges to successful embryo implantation. In this study, we utilized isobaric tags for relative and absolute quantification (iTRAQ) to profile endometrial protein abundance in RIF patients. Through functional and pathway analyses, ECM-related proteins including fibronectin 1 (FN1), collagen type I alpha 2 chain (COL1A2), and integrin beta-1 (ITGB1) were revealed to be associated with RIF. Correlation analysis identified TGF-β1 as an upstream regulator of FN1. Knockdown experiments showed TGF-β1 downregulation could inhibit FN1 expression to inhibit decidualization markers. Our findings suggest a mechanistic link between TGF-β1/FN1 axis dysregulation and impaired decidualization observed in RIF.</div></div><div><h3>Significance</h3><div>Our study addresses the pressing issue of RIF, a significant obstacle in assisted reproductive technology. By employing isobaric tags for relative and absolute quantification (iTRAQ), we comprehensively analyzed endometrial protein abundance in RIF patients. Through functional and pathway enrichment analyses, we identified dysregulation in extracellular matrix (ECM)-related proteins, including FN1, COL1A2, and ITGB1, shedding light on their potential roles in implantation failure. Additionally, our correlation analysis revealed TGF-β1 as an upstream regulator of FN1, suggesting a novel regulatory axis involved in decidualization. Knockdown experiments further demonstrated the impact of TGF-β1 and FN1 on decidualization markers. This study contributes to a better understanding of the molecular mechanisms underlying RIF.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"316 ","pages":"Article 105437"},"PeriodicalIF":2.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing tissue analysis: Integrating mass tags with mass spectrometry imaging and immunohistochemistry","authors":"Dandan Zhang ,&nbsp;Hairong Zhang ,&nbsp;Yuexin Yang ,&nbsp;Ying Jin ,&nbsp;Yingjie Chen ,&nbsp;Caisheng Wu","doi":"10.1016/j.jprot.2025.105436","DOIUrl":"10.1016/j.jprot.2025.105436","url":null,"abstract":"<div><div>In biological and biomedical research, it's a crucial task to detect or quantify proteins or proteomes accurately across multiple samples. Immunohistochemistry (IHC) and spatial proteomics based on mass spectrometry imaging (MSI) are used to detect proteins in tissue samples. IHC can detect precisely but has a limited throughput, whereas MSI can simultaneously visualize thousands of specific chemical components but hindered by detailed protein annotation. Thereby, the introduction of mass tags may be adopted to expand the potential for integrating MSI and IHC. By enriching optical information for IHC and enhancing MS signals, mass tags can boost the accuracy of qualitative, localization, and quantitative detection of specific proteins in tissue sections, thereby widening the scope of protein detection and annotation results. Consequently, more comprehensive information regarding biological processes and disease states can be obtained, which aids in understanding complex biological processes and disease mechanisms and provides additional perspectives for clinical diagnosis and treatment. In the current review, we aim to discuss the role of different mass tags (e.g., mass tags based on inorganic molecules and organic molecules) in the combined application of MSI and IHC.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"316 ","pages":"Article 105436"},"PeriodicalIF":2.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143780320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomic insights into cell signaling and stress response mechanisms in Chaetoceros muelleri under nitrogen limitation","authors":"Damaristelma de Jesús-Campos ,&nbsp;Esaú Bojórquez-Velázquez ,&nbsp;Eliel Ruiz-May ,&nbsp;Diana Fimbres-Olivarría ,&nbsp;Corina Hayano-Kanashiro ,&nbsp;José Ángel Huerta-Ocampo","doi":"10.1016/j.jprot.2025.105435","DOIUrl":"10.1016/j.jprot.2025.105435","url":null,"abstract":"<div><div>Microalgae are often called “green factories” because they can perform photosynthesis, converting sunlight into biomass and high-value metabolites. Nitrogen concentration is a critical factor influencing protein accumulation. Unfortunately, nitrogen deprivation often negatively impacts biomass production. Understanding the relationship between nitrogen concentration and protein accumulation is crucial for harnessing the potential of microalgae in various industries and addressing environmental challenges. Here, we quantitatively compared the proteomic profiles of <em>Chaetoceros muelleri</em> diatom, grown in two Nitrogen-deficient conditions and control treatment by employing a Tandem Mass Tag-based quantitative proteomic approach. Proteins involved in photosynthesis were differentially accumulated under moderately nitrogen-deficient conditions. In contrast, proteins involved in cell signaling and protection mechanisms were differentially accumulated under severely nitrogen-limited conditions. Proteins associated with nitrogen metabolism, carbohydrate metabolism, and protein biosynthesis were differentially decreased in severely nitrogen-limited conditions, indicating differential response mechanisms of <em>C. muelleri</em> to varying nitrogen conditions. Our results show that <em>C. muelleri</em> employs distinct strategies in response to nitrogen limitation. These results provide valuable insights into the adaptive strategies of <em>C. muelleri</em> under nitrogen limitation, offering potential applications in optimizing microalgal cultures for the enhanced production of target metabolites in industrial bioreactors.</div></div><div><h3>Biological significance</h3><div>The marine diatom <em>Chaetoceros muelleri</em> accumulates lipids and carbohydrates under low nitrogen conditions without affecting its biomass. Response to nitrogen limitation in <em>C. muelleri</em> was examined by isobaric labelling-based proteomics. We identified changes mainly focused on photosynthesis pathways, cell signaling and protection mechanisms, nitrogen and carbohydrate metabolism, as well as protein biosynthesis. Our results indicate that <em>C. muelleri</em> activate unique strategies in response to different nitrogen concentrations, and this differential response represents a key factor for inducing metabolite accumulation without affecting biomass production.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"316 ","pages":"Article 105435"},"PeriodicalIF":2.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}