Proteomics最新文献

Prediction of Lung Adenocarcinoma Driver Genes Through Protein-Protein Interaction Networks Utilizing GenePlexus.

IF 3.4 4区生物学

Proteomics Pub Date : 2024-12-18 DOI: 10.1002/pmic.202400296

Fei Yuan, Yu-Hang Zhang, FeiMing Huang, Xiaoyu Cao, Lei Chen, JiaBo Li, WenFeng Shen, KaiYan Feng, YuSheng Bao, Tao Huang, Yu-Dong Cai

{"title":"Prediction of Lung Adenocarcinoma Driver Genes Through Protein-Protein Interaction Networks Utilizing GenePlexus.","authors":"Fei Yuan, Yu-Hang Zhang, FeiMing Huang, Xiaoyu Cao, Lei Chen, JiaBo Li, WenFeng Shen, KaiYan Feng, YuSheng Bao, Tao Huang, Yu-Dong Cai","doi":"10.1002/pmic.202400296","DOIUrl":"https://doi.org/10.1002/pmic.202400296","url":null,"abstract":"Lung adenocarcinoma, a subtype of lung cancer, is produced by uncontrolled proliferation of somatic cells affected by some tumorigenic factors. The origin of this disease can be attributed to the concept of \"cancer driver,\" which links the occurrence of tumor with specific changes in some key genes. These key genes can be identified at various molecular levels. Our innovative method uses a groundbreaking computing technology called GenePlexus to mine new genes related to lung adenocarcinoma. Initially, a vast network was synthesized from protein-protein interactions. Utilizing GenePlexus, we traversed paths interlinking aberrant genes across different layers and pinpointed emerging candidate genes situated on these trajectories. Finally, the candidate genes that were obtained underwent a series of filtering processes, including a permutation test, interaction test, and enrichment test. Compared with the shortest path method, GenePlexus has identified previously neglected genes involved in lung adenocarcinoma. For example, genes such as EGR2, EPHA3, FGFR4, HOXB1, and HEY1 play key roles at multiple molecular levels, including methylation, microRNA, mRNA and mutation, which affect tumorigenesis and lung cancer progression. These genes regulate various processes, from gene expression and cell proliferation to drug resistance to therapeutic drugs and the progress of lung adenocarcinoma.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400296"},"PeriodicalIF":3.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851688","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}

引用次数: 0

Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification.

IF 3.4 4区生物学

Proteomics Pub Date : 2024-12-18 DOI: 10.1002/pmic.202400087

Gul Muneer, Ciao-Syuan Chen, Yu-Ju Chen

{"title":"Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification.","authors":"Gul Muneer, Ciao-Syuan Chen, Yu-Ju Chen","doi":"10.1002/pmic.202400087","DOIUrl":"https://doi.org/10.1002/pmic.202400087","url":null,"abstract":"Protein phosphorylation introduces post-genomic diversity to proteins, which plays a crucial role in various cellular activities. Elucidation of system-wide signaling cascades requires high-performance tools for precise identification and quantification of dynamics of site-specific phosphorylation events. Recent advances in phosphoproteomic technologies have enabled the comprehensive mapping of the dynamic phosphoproteomic landscape, which has opened new avenues for exploring cell type-specific functional networks underlying cellular functions and clinical phenotypes. Here, we provide an overview of the basics and challenges of phosphoproteomics, as well as the technological evolution and current state-of-the-art global and quantitative phosphoproteomics methodologies. With a specific focus on highly sensitive platforms, we summarize recent trends and innovations in miniaturized sample preparation strategies for micro-to-nanoscale and single-cell profiling, data-independent acquisition mass spectrometry (DIA-MS) for enhanced coverage, and quantitative phosphoproteomic pipelines for deep mapping of cell and disease biology. Each aspect of phosphoproteomic analysis presents unique challenges and opportunities for improvement and innovation. We specifically highlight evolving phosphoproteomic technologies that enable deep profiling from low-input samples. Finally, we discuss the persistent challenges in phosphoproteomic technologies, including the feasibility of nanoscale and single-cell phosphoproteomics, as well as future outlooks for biomedical applications.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400087"},"PeriodicalIF":3.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851687","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}

引用次数: 0

Key Regulators of Parasite Biology Viewed Through a Post-Translational Modification Repertoire.

IF 3.4 4区生物学

Proteomics Pub Date : 2024-12-17 DOI: 10.1002/pmic.202400120

Naiwen Zhang, Ning Jiang, Qijun Chen

引用次数: 0

Standard abbreviations

IF 3.4 4区生物学

Proteomics Pub Date : 2024-12-16 DOI: 10.1002/pmic.202470184

引用次数: 0

Editorial Board: Proteomics 23–24'24 编委会：蛋白质组学 23-24'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-12-16 DOI: 10.1002/pmic.202470182

引用次数: 0

Contents: Proteomics 23–24'24 内容：蛋白质组学 23-24'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-12-16 DOI: 10.1002/pmic.202470183

引用次数: 0

Addressing Sample Mix-Ups: Tools and Approaches for Large-Scale Multi-Omics Studies.

IF 3.4 4区生物学

Proteomics Pub Date : 2024-12-10 DOI: 10.1002/pmic.202400271

Yingxue Fu, Zuo-Fei Yuan, Long Wu, Junmin Peng, Xusheng Wang, Anthony A High

{"title":"Addressing Sample Mix-Ups: Tools and Approaches for Large-Scale Multi-Omics Studies.","authors":"Yingxue Fu, Zuo-Fei Yuan, Long Wu, Junmin Peng, Xusheng Wang, Anthony A High","doi":"10.1002/pmic.202400271","DOIUrl":"https://doi.org/10.1002/pmic.202400271","url":null,"abstract":"Advances in high-throughput omics technologies have enabled system-wide characterization of biological samples across multiple molecular levels, such as the genome, transcriptome, and proteome. However, as sample sizes rapidly increase in large-scale multi-omics studies, sample mix-ups have become a prevalent issue, compromising data integrity and leading to erroneous conclusions. The interconnected nature of multi-omics data presents an opportunity to identify and correct these errors. This review examines the potential sources of sample mix-ups and evaluates the methodologies and tools developed for detecting and correcting these errors, with an emphasis on approaches applicable to proteomics data. We categorize existing tools into three main groups: expression/protein quantitative trait loci-based, genotype concordance-based, and gene/protein expression correlation-based approaches. Notably, only a handful of tools currently utilize the proteogenomics approach for correcting sample mix-ups at the proteomics level. Integrating the strengths of current tools across diverse data types could enable the development of more versatile and comprehensive solutions. In conclusion, verifying sample identity is a critical first step to reduce bias and increase precision in subsequent analyses for large-scale multi-omics studies. By leveraging these tools for identifying and correcting sample mix-ups, researchers can significantly improve the reliability and reproducibility of biomedical research.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400271"},"PeriodicalIF":3.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142805723","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}

引用次数: 0

An In-Depth Coho Salmon (Oncorhynchus kisutch) Ovarian Follicle Proteome Reveals Coordinated Changes Across Diverse Cellular Processes during the Transition From Primary to Secondary Growth.

IF 3.4 4区生物学

Proteomics Pub Date : 2024-12-08 DOI: 10.1002/pmic.202400311

Emma Timmins-Schiffman, Jennifer Telish, Chelsea Field, Chris Monson, José M Guzmán, Brook L Nunn, Graham Young, Kristy Forsgren

{"title":"An In-Depth Coho Salmon (Oncorhynchus kisutch) Ovarian Follicle Proteome Reveals Coordinated Changes Across Diverse Cellular Processes during the Transition From Primary to Secondary Growth.","authors":"Emma Timmins-Schiffman, Jennifer Telish, Chelsea Field, Chris Monson, José M Guzmán, Brook L Nunn, Graham Young, Kristy Forsgren","doi":"10.1002/pmic.202400311","DOIUrl":"https://doi.org/10.1002/pmic.202400311","url":null,"abstract":"Teleost fishes are a highly diverse, ecologically essential group of aquatic vertebrates that include coho salmon (Oncorhynchus kisutch). Coho are semelparous and all ovarian follicles develop synchronously. Owing to their ubiquitous distribution, teleosts provide critical sources of food worldwide through subsistence, commercial fisheries, and aquaculture. Enhancement of hatchery practices requires detailed knowledge of teleost reproductive physiology. Despite decades of research on teleost reproductive processes, an in-depth proteome of teleost ovarian development has yet to be generated. We have described a coho salmon ovarian proteome of over 5700 proteins, generated with data independent acquisition, revealing the proteins that change through the transition from primary to secondary ovarian follicle development. This transition is critical during the onset of puberty and for determining egg quality and embryonic development. Primary follicle development was marked by differential abundances of proteins in carbohydrate metabolism, protein turnover, and the complement pathway, suggesting elevated metabolism as the follicles develop through stages of oogenesis. The greatest proteomic shift occurred during the transition from primary to secondary follicle growth, with increased abundance of proteins underlying cortical alveoli formation, extracellular matrix reorganization, iron binding, and cell-cell signaling. This work provides a foundation for identifying biomarkers of salmon oocyte stage and quality.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400311"},"PeriodicalIF":3.4,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794071","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}

引用次数: 0

Reduction of Chemokine CXCL9 Expression by Omega-3 Fatty Acids via ADP-Ribosylhydrolase ARH3 in MIN6 Insulin-Producing Cells.

IF 3.4 4区生物学

Proteomics Pub Date : 2024-12-08 DOI: 10.1002/pmic.202400053

Youngki You, Soumyadeep Sarkar, Cailin Deiter, Emily C Elliott, Carrie D Nicora, Raghavendra G Mirmira, Lori Sussel, Ernesto S Nakayasu

{"title":"Reduction of Chemokine CXCL9 Expression by Omega-3 Fatty Acids via ADP-Ribosylhydrolase ARH3 in MIN6 Insulin-Producing Cells.","authors":"Youngki You, Soumyadeep Sarkar, Cailin Deiter, Emily C Elliott, Carrie D Nicora, Raghavendra G Mirmira, Lori Sussel, Ernesto S Nakayasu","doi":"10.1002/pmic.202400053","DOIUrl":"10.1002/pmic.202400053","url":null,"abstract":"Type 1 diabetes (T1D) results from the autoimmune destruction of the insulin-producing β cells of the pancreas. Omega-3 fatty acids protect β cells and reduce the incidence of T1D, but the mechanism is poorly understood. We have shown that omega-3 fatty acids reduce pro-inflammatory cytokine-mediated β-cell apoptosis by upregulating the expression of the ADP-ribosylhydrolase ARH3. Here, we further investigate the β-cell protection mechanism of ARH3 by performing siRNA analysis of its gene Adprhl2 in MIN6 insulin-producing cells, subsequent treatment with a cocktail of the pro-inflammatory cytokines IL-1β + IFN-γ + TNF-α, followed by proteomics analysis. ARH3 regulated proteins from several pathways related to the nucleus (splicing, RNA surveillance, and nucleocytoplasmic transport), mitochondria (metabolic pathways), and endoplasmic reticulum (protein folding). ARH3 also regulated the levels of proteins related to antigen processing and presentation, and the chemokine-signaling pathway. We further studied the role of ARH3 in regulating the chemokine CXCL9. We found that ARH3 reduces the cytokine-induced expression of CXCL9, which is dependent on omega-3 fatty acids. In conclusion, we demonstrate that omega-3 fatty acids regulate CXCL9 expression via ARH3, which may have a role in protecting β cells from immune attack thereby preventing T1D development. Significance of the Study: Omega-3 fatty acids have a variety of health benefits. In type 1 diabetes, omega-3 fatty acids reduce the islet autoimmune response and the disease development. Here, we studied the pathways regulated by the adenosine diphosphate (ADP)-ribosylhydrolase ARH3, a protein whose expression is regulated by omega-3 fatty acids. We showed that ARH3 reduces the expression of chemokines in response to omega-3 fatty acids. This represents an anti-inflammatory mechanism of omega-3 fatty acids that might be involved with protection against type 1 diabetes development.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400053"},"PeriodicalIF":3.4,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794073","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}

引用次数: 0

Prediction of Plant Resistance Proteins Using Alignment-Based and Alignment-Free Approaches. 利用基于配位和无配位方法预测植物抗性蛋白

IF 3.4 4区生物学

Proteomics Pub Date : 2024-11-24 DOI: 10.1002/pmic.202400261

Pushpendra Singh Gahlot, Shubham Choudhury, Nisha Bajiya, Nishant Kumar, Gajendra P S Raghava

{"title":"Prediction of Plant Resistance Proteins Using Alignment-Based and Alignment-Free Approaches.","authors":"Pushpendra Singh Gahlot, Shubham Choudhury, Nisha Bajiya, Nishant Kumar, Gajendra P S Raghava","doi":"10.1002/pmic.202400261","DOIUrl":"https://doi.org/10.1002/pmic.202400261","url":null,"abstract":"Plant disease resistance (PDR) proteins are critical in identifying plant pathogens. Predicting PDR protein is essential for understanding plant-pathogen interactions and developing strategies for crop protection. This study proposes a hybrid model for predicting and designing PDR proteins against plant-invading pathogens. Initially, we tried alignment-based approaches, such as Basic Local Alignment Search Tool (BLAST) for similarity search and MERCI for motif search. These alignment-based approaches exhibit very poor coverage or sensitivity. To overcome these limitations, we developed alignment-free or machine learning (ML)-based methods using compositional features of proteins. Our ML-based model, developed using compositional features of proteins, achieved a maximum performance area under the receiver operating characteristic curve (AUROC) of 0.91. The performance of our model improved significantly from AUROC of 0.91-0.95 when we used evolutionary information instead of protein sequence. Finally, we developed a hybrid or ensemble model that combined our best ML model with BLAST and obtained the highest AUROC of 0.98 on the validation dataset. We trained and tested our models on a training dataset and evaluated them on a validation dataset. None of the proteins in our validation dataset are more than 40% similar to proteins in the training dataset. One of the objectives of this study is to facilitate the scientific community working in plant biology. Thus, we developed an online platform for predicting and designing plant resistance proteins, \"PlantDRPpred\" (https://webs.iiitd.edu.in/raghava/plantdrppred).","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400261"},"PeriodicalIF":3.4,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708782","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}

引用次数: 0