Proteomics最新文献

{"title":"Metaproteomic Profiling of the Secretome of a Granule-forming Ca. Accumulibacter Enrichment","authors":"Berdien van Olst,&nbsp;Simon A. Eerden,&nbsp;Nella A. Eštok,&nbsp;Samarpita Roy,&nbsp;Ben Abbas,&nbsp;Yuemei Lin,&nbsp;Mark C. M. van Loosdrecht,&nbsp;Martin Pabst","doi":"10.1002/pmic.70046","DOIUrl":"10.1002/pmic.70046","url":null,"abstract":"<div>\u0000 \u0000 <p><b>The following article for this Special Issue was published in an earlier Issue</b>.</p>\u0000 <p>B. van Olst, S. A. Eerden, N. A. Eštok et al.“ Metaproteomic Profiling of the Secretome of a Granule-forming Ca. Accumulibacter Enrichment”. <i>Proteomic</i>. 25 e202400189, https://doi.org/10.1002/pmic.202400189. https://onlinelibrary.wiley.com/doi/10.1002/pmic.202400189</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 17-18","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding Microbial Plastic Colonisation: Multi-Omic Insights Into the Fast-Evolving Dynamics of Early-Stage Biofilms","authors":"Charlotte E. Lee,&nbsp;Lauren F. Messer,&nbsp;Ruddy Wattiez,&nbsp;Sabine Matallana-Surget","doi":"10.1002/pmic.70048","DOIUrl":"10.1002/pmic.70048","url":null,"abstract":"<div>\u0000 \u0000 <p><b>The following article for this Special Issue was published in an earlier Issue</b>.</p>\u0000 <p>C. E. Lee, L. F. Messer, R. Wattiez, S. Matallana-Surget, (2025). Decoding Microbial Plastic Colonisation: Multi-Omic Insights Into the Fast-Evolving Dynamics of Early-Stage Biofilms. <i>Proteomics</i>. 25, e202400208, https://doi.org/10.1002/pmic.202400208. https://onlinelibrary.wiley.com/doi/10.1002/pmic.202400208</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 17-18","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome","authors":"","doi":"10.1002/pmic.70049","DOIUrl":"10.1002/pmic.70049","url":null,"abstract":"<div>\u0000 \u0000 <p><b>The following article for this Special Issue was published in an earlier Issue</b>.</p>\u0000 <p>A. T. Rajczewski, J. Alfredo Blakeley-Ruiz, A. Meyer et al. “Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome.” <i>Proteomics</i>, 25, e70049, https://doi.org/10.1002/pmic.202400187. https://onlinelibrary.wiley.com/doi/10.1002/pmic.202400187</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 17-18","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of Cytokine Treatment on Human Pancreatic Islets by Top-Down Proteomics.","authors":"Ashley N Ives, Tyler J Sagendorf, Lorenz Nierves, Tai-Tu Lin, Ercument Dirice, Rohit N Kulkarni, Ljiljana Paša-Tolić, Wei-Jun Qian, James M Fulcher","doi":"10.1002/pmic.70044","DOIUrl":"10.1002/pmic.70044","url":null,"abstract":"<p><p>Type 1 diabetes (T1D) results from autoimmune-mediated destruction of insulin-producing β cells in the pancreatic islet. This process is modulated by pro-inflammatory cytokine signaling, which has been previously shown to alter protein expression in ex vivo islets. Herein, we applied top-down proteomics to globally evaluate proteoforms from human islets treated with proinflammatory cytokines (interferon-γ and interleukin-1β). We measured 1636 unique proteoforms across six donors and two time points (control and 24 h post-treatment) and observed consistent changes in abundance across the glicentin-related pancreatic polypeptide (GRPP) and major proglucagon fragment regions of glucagon, as well as the LF-19/catestatin and vasostatin-1/2 region of chromogranin-A. We also observe several proteoforms that increase after cytokine-treatment or are exclusively observed after cytokine-treatment, including forms of beta-2 microglobulin (B2M), high-mobility group N2 protein (HMGN2), and chemokine (C-X-C motif) ligands (CXCL). Together, our quantitative results provide a baseline proteoform profile for human islets and identify several proteoforms that may serve as interesting candidate markers for T1D progression or therapeutic intervention. SUMMARY: This work applies a top-down proteomics workflow for the characterization and label-free quantification of proteoforms from human islets in the context of inflammation. The workflow is optimized for challenges unique to the islet proteome including high disulfide-linkage content and frequent truncation events, resulting in many proteoforms < 5kDa. There are limited examples of top-down proteomics characterization of human islets, thus this study provides a baseline characterization of the proteoforms of major hormones including chromogranin-A (CHGA), chromogranin-B/ secretogranin-1 (CHGB/SCG1), chromogranin-C/ secretogranin-2 (CHGC/SCG2), islet amyloid polypeptide (amylin/IAPP), insulin (INS), glucagon (GCG), pancreatic polypeptide prohormone (PPY), somatostatin (SST), and neurosecretory protein VGF (VGF). The quantitative results of proteoform abundances before and after cytokine treatment, which mimics the proinflammatory environment during T1D progression, provides interesting insights on how prohormone processing is altered under a proinflammatory environment.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e70044"},"PeriodicalIF":3.9,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FLASHApp: Interactive Data Analysis and Visualization for Top-Down Proteomics.","authors":"Tom David Müller, Jihyung Kim, Andrew Almaguer, Ayesha Feroz, Jaekwan Kim, Axel Walter, Wonhyeuk Jung, Oliver Kohlbacher, Kyowon Jeong","doi":"10.1002/pmic.70042","DOIUrl":"https://doi.org/10.1002/pmic.70042","url":null,"abstract":"<p><p>Top-down proteomics (TDP) is increasingly being applied in proteoform-resolved biomedical and clinical research. However, the complexity of TDP data demands flexible visualization tools integrated with analysis workflows to streamline interpretation and validation. Existing tools lack adaptability and interactivity, often requiring researchers to invest considerable resources on additional manual processing and analysis to generate publication-ready results and figures. This added layer of manual intervention impacts reproducibility, posing a significant challenge to consistent scientific outcomes. FLASHApp addresses these challenges by offering a web-based, platform-independent application for TDP data analysis and visualization. It integrates key tools like FLASHDeconv, featuring automated processing, interactive publication-ready visualizations, and direct team collaboration via shareable URLs. FLASHApp is open-source software as part of OpenMS and available at https://www.openms.org/FLASHApp/.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e70042"},"PeriodicalIF":3.9,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to Nutrient Stress in the Industrial Model Bacterium Cupriavidus necator: A Thermal Proteome Profiling (TPP) Investigation.","authors":"Kate McKeever, Jia-Lynn Tham, Manuel Bruch, Tania Narancic, Kevin O' Connor, Swathi Ramachandra Upadhya, Colm Ryan, Eugene T Dillon, Kieran Wynne, Gerard Cagney","doi":"10.1002/pmic.70045","DOIUrl":"https://doi.org/10.1002/pmic.70045","url":null,"abstract":"<p><p>The facultative chemolithoautotroph Cupriavidus necator is capable of heterotrophic growth on diverse carbon sources, or of autotrophic growth using CO₂ fixation with H₂ as an energy source. Under stress conditions, it produces biodegradable polyesters (polyhydroxyalkanoates, PHAs) as a storage material occupying a high proportion of the total biomass. This metabolic versatility means that C. necator is under intense study for sustainable biotechnology processes; however, a relative lack of understanding of the overall regulatory architecture has limited its application. The major mechanisms by which proteins can respond to shifting cellular demands are protein expression change and/or allosteric regulation. Here, we use two powerful proteomics methods to investigate these responses in C. necator cells grown on balanced or low nitrogen (PHA-inducing) media. Using quantitative proteomics and protein stability analysis (which can report on conformation change), we find that proteins across different pathways respond through one or both of these regulatory modes, including coordinated adaptation to nutrient stress by the PHA pathway, the Calvin cycle and ribosomal proteins. Overall, the study offers a valuable overview of global protein changes evoked by nutritional stress, and shows how the combined use of both proteomics approaches can identify key responsive proteins that would otherwise be undetected. SUMMARY: We report a comprehensive proteomics analysis of the important industrial bacterium Cupriavidus necator, using two state-of-the-art approaches: expression proteomics and thermal proteome profiling. With intense interest worldwide in finding substitutes for petrochemical based plastics, organisms such as C. necator are under active investigation, since they produce a storage bioplastic material (PHA) and have a versatile metabolism including growth on carbon dioxide. To our knowledge, this is the first thermal proteome analysis of a lithoautotrophic organism. We compared global protein expression the under conditions that induce PHA production, and we analysed the thermal proteome under the same conditions. Each experiment yielded novel, interesting results pertinent to individual proteins or pathways; moreover, by combining both approaches, proteins regulated by expression change and/or conformation change were highlighted.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e70045"},"PeriodicalIF":3.9,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and Opportunities in State-of-the-Art Proteomics Analysis for Biomarker Development From Plasma Extracellular Vesicles.","authors":"Panshak P Dakup, Ivo Diaz Ludovico, Youngki You, Chaitra Rao, Javier Flores, Lisa M Bramer, Marian Rewers, Bobbie-Jo M Webb-Robertson, Thomas O Metz, Raghavendra G Mirmira, Emily K Sims, Ernesto S Nakayasu","doi":"10.1002/pmic.70036","DOIUrl":"https://doi.org/10.1002/pmic.70036","url":null,"abstract":"<p><p>Extracellular vesicles (EVs) are membrane-bound particles secreted by cells, playing crucial roles in intercellular communication. The composition of EVs can undergo changes in response to stress and disease conditions, making them excellent biomarker candidates. However, extracting protein information from EVs can be challenging due to their low abundance in complex biofluids and copurification with contaminant proteins and particles. Techniques to enrich EVs have their strengths and limitations, without one being able to purify EVs to complete homogeneity. This can lead to compromised recovery rates and increased complexity, making data interpretation difficult. In this viewpoint article, we explore the concept that better characterization of EV composition, followed by quantification of EV proteins in complex samples, might be a more viable route for biomarker development. Mass spectrometers can provide reproducible deep coverage of the EV proteome, despite sample impurities. This paradigm shift presents opportunities to integrate advanced bioinformatics tools to refine the EV proteome landscape, identify novel biomarkers, and streamline validation processes in biomarker development. By focusing on leveraging technology rather than achieving absolute purity, this approach can transform current practices and open opportunities for robust biomarker discovery. Herein, we highlight not only such opportunities but also challenges to implement this concept. SUMMARY: Extracellular vesicles (EVs) have enormous potential as biomarkers of diseases, as they can carry signatures of the cells they are derived from and the pathogenesis process. Biofluids, such as blood plasma, are highly complex and contain many components with physicochemical properties similar to those of EVs, making it challenging to obtain pure EV fractions. Challenges in obtaining pure preparations represent a main hurdle for studying EVs, and their components are potential biomarkers. This article explores the concept of studying EV proteins within complex samples, discussing opportunities and needs to move this field forward.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e70036"},"PeriodicalIF":3.9,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deconvolution Methods to Link Multi-Omics Data to Cell Type-Specific Extracellular Vesicle Abundances.","authors":"Iben Skov Jensen, Jannik Hjortshøj Larsen, Per Svenningsen","doi":"10.1002/pmic.70043","DOIUrl":"https://doi.org/10.1002/pmic.70043","url":null,"abstract":"<p><p>Extracellular vesicles (EVs) provide non-invasive information on cellular health and disease. Yet, with the small size of EVs and more than 200 cell types contributing EVs to the extracellular fluids, it is challenging to determine whether changes in EV-associated lipids, RNAs, and proteins occur because of differences in expression or cell type-specific EV abundances. This limits our use of EV-based biomarkers and our understanding of how EVs contribute to health and diseases. In recent decades, next-generation RNA sequencing methods have fueled the development of transcriptome deconvolution methods to determine cell type proportions in tissue RNA samples. These methods can also estimate cell type-specific EV abundances using the EV's RNA \"fingerprint\"; however, differences between cell and EV RNA composition can significantly bias the estimates. Based on a recent benchmarking study of transcriptome deconvolution methods, we will review technical and biological factors that drive the most accurate deconvolution, focusing on mRNA sequencing data from EVs. Moreover, we will describe biological factors that can affect the interpretation of the deconvolution methods of cell type-specific EV abundance estimates in acute and chronic conditions and give a perspective on how deconvolution can be used to monitor physiological and disease processes in the human body.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e70043"},"PeriodicalIF":3.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145062912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupling CZE, Liquid-Phase Ion Mobility, to MS/MS for Quantitative Top-Down Proteomics: Revealing Significant Proteoform Differences Between Healthy and Alzheimer's Disease Brains.","authors":"Mehrdad Falamarzi Askarani, Fei Fang, Scott E Counts, Liangliang Sun","doi":"10.1002/pmic.70041","DOIUrl":"https://doi.org/10.1002/pmic.70041","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline and pathological protein aggregation. Comprehensive quantitative proteomics of brain tissues from AD patients is critical for pursuing a better understanding of the molecular mechanisms that drive AD progression. Here, we present one of the first quantitative top-down proteomics (TDP) studies of postmortem cortex samples from AD patients and healthy controls to profile their proteoform differences by coupling capillary zone electrophoresis (CZE, liquid-phase ion mobility) to tandem mass spectrometry (MS/MS). We identified 3191 unique proteoforms and uncovered a drastic transformation in the proteoform profile in AD compared to healthy controls. Over 2200 proteoforms were exclusively identified in either AD or healthy control samples, and 157 proteoforms identified in both AD and control samples showed statistically significant abundance differences between the two conditions. Gene Ontology and pathway analysis of the genes associated with those proteoforms revealed broad changes in biological processes in AD brains, for example, telomere organization, substantia nigra development, amyloid fibril formation, microtubule cytoskeleton organization, progressive neurological disorders, long-term synaptic potentiation, and axogenesis. These biological processes are highly associated with the development of AD. Our study revealed a pool of potential novel proteoform biomarkers of AD in human brain samples for early diagnosis and therapy development. SUMMARY: Alzheimer's disease (AD) is a chronic neurodegenerative disease, destroying brain cells and causing thinking ability and memory to decline over time. Proteins (e.g., amyloid and tau) play key roles in the development of AD. Global and accurate protein measurement of human brains of AD patients and healthy controls will shed new light on the molecular mechanisms driving AD progression and discover new biomarkers for AD diagnosis and therapeutic development. Here, we performed the first CZE-MS/MS-based quantitative top-down proteomics (TDP) of a small cohort of AD human brain samples and healthy controls (5 AD and 5 control) to determine the differentially quantified proteoforms between the two health conditions. Over 3000 proteoforms were identified, and only about 700 proteoforms were detected in both conditions, indicating drastically different proteoform profiles between the two conditions. The differentially quantified proteoforms (e.g., tau, neurogranin, and calmodulin-1 proteoforms) are associated with biological processes relevant to AD development, for example, amyloid fibril formation, microtubule disruption, synaptic transmission, and axogenesis. The results offer a deep view of the proteoform transformation in the AD human brain compared to the healthy control, providing potential proteoform biomarkers for AD diagnosis and proteoform targets for therapeutic development.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e70041"},"PeriodicalIF":3.9,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145062949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Omics Insights Into the Effects of Highbush Blueberry and Cranberry Crop Agroecosystems on Honey Bee Health and Physiology.","authors":"Huan Zhong, Yuming Shi, Aleksandra Kozlova, Renata Moravcova, Jason C Rogalski, Aidan Jamieson, Lance Lansing, Kyung-Mee Moon, Xiaojing Yuan, Amanda S Gregoris, Heather Higo, Julia Common, Ida M Conflitti, Mateus Pepinelli, Lan Tran, Morgan Cunningham, Hosna Jabbari, Syed Abbas Bukhari, Sarah K French, Rodrigo Ortega Polo, Shelley E Hoover, Stephen F Pernal, Pierre Giovenazzo, M Marta Guarna, Amro Zayed, Leonard J Foster","doi":"10.1002/pmic.70033","DOIUrl":"https://doi.org/10.1002/pmic.70033","url":null,"abstract":"<p><p>Honey bees (Apis mellifera) are vital pollinators in fruit-producing agroecosystems like highbush blueberry (HBB) and cranberry (CRA). However, their health is threatened by multiple interacting stressors, including pesticides, pathogens, and nutritional changes. We tested the hypothesis that distinct agricultural ecosystems-with different combinations of agrochemical exposure, pathogen loads, and floral resources-elicit ecosystem-specific, tissue-level molecular responses in honey bees. We conducted an integrated multi-omics analysis using RNA-sequencing (RNA-seq), proteomics, and gut microbiome profiling across three key tissue types (head, abdomen, and gut) of honey bees collected from two agroecosystems over two field seasons. Quantification was performed for pesticide residues, pathogen loads (Nosema spp., Varroa destructor, and multiple viruses), and gut microbiota. Weighted gene co-expression network analysis (WGCNA) revealed tissue-specific protein modules with ecosystem-associated patterns, which differed from RNA co-expression networks. Microbiome composition also varied, with key genera like Gilliamella, Snodgrassella, and Bartonella correlating with metabolic modules. These findings underscore the complex, environment-dependent impacts of agroecosystem conditions on bee health. Our study provides a system-level understanding of how combined pesticide, pathogen, and parasitic stressors, mediated by diet and microbiome, shape molecular phenotypes in honey bees-informing strategies for pollinator protection in managed landscapes. SUMMARY: This study provides a comprehensive multi-omics analysis of honey bees foraging in blueberry and cranberry agroecosystems, offering novel insights into the molecular mechanisms underlying pollinator health in managed crop environments. By integrating transcriptomic, proteomic, and microbiome profiling across key tissues-head, abdomen, and gut-we reveal how environmental stressors, including pesticide exposure, pathogen infections, and parasitic infestations (e.g., Varroa destructor), differentially impact bee physiology and microbiome composition. Our findings highlight tissue-specific responses to these stressors, with distinct metabolic pathway alterations observed in each tissue. Proteomic and transcriptomic analyses uncovered dysregulated pathways linked to oxidative phosphorylation and protein synthesis, while microbiome analysis revealed crop-dependent shifts in gut bacterial communities, suggesting potential roles in pesticide detoxification and immune modulation. Notably, we identified key molecular biomarkers associated with stress adaptation, which may serve as early indicators of colony health deterioration. This research underscores the need for a system-level approach to understanding pollinator stress in agricultural landscapes. By elucidating the interactions between diet, pesticide residues, pathogen loads, and molecular stress responses, our study provides a foundation for targete","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e70033"},"PeriodicalIF":3.9,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}