Proteomics最新文献_第5页

Benefits and limits of decellularization on mass-spectrometry-based extracellular matrix proteome analysis of mouse kidney 脱细胞对基于质谱的小鼠肾脏细胞外基质蛋白质组分析的益处和局限。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-19 DOI: 10.1002/pmic.202400052

Teresa Frattini, Hanne Devos, Manousos Makridakis, Maria G. Roubelakis, Agnieszka Latosinska, Harald Mischak, Joost P. Schanstra, Antonia Vlahou, Jean-Sébastien Saulnier-Blache

{"title":"Benefits and limits of decellularization on mass-spectrometry-based extracellular matrix proteome analysis of mouse kidney","authors":"Teresa Frattini, Hanne Devos, Manousos Makridakis, Maria G. Roubelakis, Agnieszka Latosinska, Harald Mischak, Joost P. Schanstra, Antonia Vlahou, Jean-Sébastien Saulnier-Blache","doi":"10.1002/pmic.202400052","DOIUrl":"10.1002/pmic.202400052","url":null,"abstract":"The extracellular matrix (ECM) is composed of collagens, ECM glycoproteins, and proteoglycans (also named core matrisome proteins) that are critical for tissue structure and function, and matrisome-associated proteins that balance the production and degradation of the ECM proteins. The identification and quantification of core matrisome proteins using mass spectrometry is often hindered by their low abundance and their propensity to form macromolecular insoluble structures. In this study, we aimed to investigate the added value of decellularization in identifying and quantifying core matrisome proteins in mouse kidney. The decellularization strategy combined freeze-thaw cycles and sodium dodecyl sulphate treatment. We found that decellularization preserved 95% of the core matrisome proteins detected in non-decellularized kidney and revealed few additional ones. Decellularization also led to an average of 59 times enrichment of 96% of the core matrisome proteins as the result of the successful removal of cellular and matrisome-associated proteins. However, the enrichment varied greatly among core matrisome proteins, resulting in a misrepresentation of the native ECM composition in decellularized kidney. This should be brought to the attention of the matrisome research community, as it highlights the need for caution when interpreting proteomic data obtained from a decellularized organ.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202400052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial Board: Proteomics 12–13'24 编辑委员会：蛋白质组学 12-13'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-14 DOI: 10.1002/pmic.202470092

引用次数: 0

Contents: Proteomics 12–13'24 内容：蛋白质组学 12-13'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-14 DOI: 10.1002/pmic.202470093

引用次数: 0

Improved breast milk proteome coverage by DIA based LC-MS/MS method 基于 DIA 的 LC-MS/MS 方法提高了母乳蛋白质组的覆盖率。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-14 DOI: 10.1002/pmic.202300340

Jenni Viitaharju, Lauri Polari, Otto Kauko, Johannes Merilahti, Anne Rokka, Diana M. Toivola, Kirsi Laitinen

{"title":"Improved breast milk proteome coverage by DIA based LC-MS/MS method","authors":"Jenni Viitaharju, Lauri Polari, Otto Kauko, Johannes Merilahti, Anne Rokka, Diana M. Toivola, Kirsi Laitinen","doi":"10.1002/pmic.202300340","DOIUrl":"10.1002/pmic.202300340","url":null,"abstract":"The breast milk composition includes a multitude of bioactive factors such as viable cells, lipids and proteins. Measuring the levels of specific proteins in breast milk plasma can be challenging because of the large dynamic range of protein concentrations and the presence of interfering substances. Therefore, most proteomic studies of breast milk have been able to identify under 1000 proteins. Optimised procedures and the latest separation technologies used in milk proteome research could lead to more precise knowledge of breast milk proteome. This study (n = 53) utilizes three different protein quantification methods, including direct DIA, library-based DIA method and a hybrid method combining direct DIA and library-based DIA. On average we identified 2400 proteins by hybrid method. By applying these methods, we quantified body mass index (BMI) associated variation in breast milk proteomes. There were 210 significantly different proteins when comparing the breast milk proteome of obese and overweight mothers. In addition, we analysed a small cohort (n = 5, randomly selected from 53 samples) by high field asymmetric waveform ion mobility spectrometry (FAIMS). FAIMS coupled with the Orbitrap Fusion Lumos mass spectrometer, which led to 41.7% higher number of protein identifications compared to Q Exactive HF mass spectrometer.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202300340","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141316295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Standard abbreviations 标准缩写。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-14 DOI: 10.1002/pmic.202470094

引用次数: 0

Leishmaniinae: Evolutionary inferences based on protein expression profiles (PhyloQuant) congruent with phylogenetic relationships among Leishmania, Endotrypanum, Porcisia, Zelonia, Crithidia, and Leptomonas 利什曼病科基于蛋白质表达谱（PhyloQuant）的进化推论与利什曼原虫、Endotrypanum、Porcisia、Zelonia、Crithidia 和 Leptomonas 之间的系统发育关系一致。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-08 DOI: 10.1002/pmic.202100313

Simon Ngao Mule, Evaristo Villalba Alemán, Livia Rosa-Fernandes, Joyce S. Saad, Gilberto Santos de Oliveira, Deivid Martins, Claudia Blanes Angeli, Deborah Brandt-Almeida, Mauro Cortez, Martin Røssel Larsen, Jeffrey J. Shaw, Marta M. G. Teixeira, Giuseppe Palmisano

{"title":"Leishmaniinae: Evolutionary inferences based on protein expression profiles (PhyloQuant) congruent with phylogenetic relationships among Leishmania, Endotrypanum, Porcisia, Zelonia, Crithidia, and Leptomonas","authors":"Simon Ngao Mule, Evaristo Villalba Alemán, Livia Rosa-Fernandes, Joyce S. Saad, Gilberto Santos de Oliveira, Deivid Martins, Claudia Blanes Angeli, Deborah Brandt-Almeida, Mauro Cortez, Martin Røssel Larsen, Jeffrey J. Shaw, Marta M. G. Teixeira, Giuseppe Palmisano","doi":"10.1002/pmic.202100313","DOIUrl":"10.1002/pmic.202100313","url":null,"abstract":"Evolutionary relationships among parasites of the subfamily Leishmaniinae, which comprises pathogen agents of leishmaniasis, were inferred based on differential protein expression profiles from mass spectrometry-based quantitative data using the PhyloQuant method. Evolutionary distances following identification and quantification of protein and peptide abundances using Proteome Discoverer and MaxQuant software were estimated for 11 species from six Leishmaniinae genera. Results clustered all dixenous species of the genus Leishmania, subgenera L. (Leishmania), L. (Viannia), and L. (Mundinia), sister to the dixenous species of genera Endotrypanum and Porcisia. Placed basal to the assemblage formed by all these parasites were the species of genera Zelonia, Crithidia, and Leptomonas, so far described as monoxenous of insects although eventually reported from humans. Inferences based on protein expression profiles were congruent with currently established phylogeny using DNA sequences. Our results reinforce PhyloQuant as a valuable approach to infer evolutionary relationships within Leishmaniinae, which is comprised of very tightly related trypanosomatids that are just beginning to be phylogenetically unraveled. In addition to evolutionary history, mapping of species-specific protein expression is paramount to understand differences in infection processes, tissue tropisms, potential to jump from insects to vertebrates including humans, and targets for species-specific diagnostic and drug development.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292869","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 efficient hybrid deep learning architecture for predicting short antimicrobial peptides 预测短抗菌肽的高效混合深度学习架构。

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-04 DOI: 10.1002/pmic.202300382

Quang H. Nguyen, Thanh-Hoang Nguyen-Vo, Trang T. T. Do, Binh P. Nguyen

{"title":"An efficient hybrid deep learning architecture for predicting short antimicrobial peptides","authors":"Quang H. Nguyen, Thanh-Hoang Nguyen-Vo, Trang T. T. Do, Binh P. Nguyen","doi":"10.1002/pmic.202300382","DOIUrl":"10.1002/pmic.202300382","url":null,"abstract":"Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various types of antimicrobial drugs or treatments. In addition to experimental approaches, computational methods have been developed to improve screening efficiency. Although existing computational methods have achieved satisfactory performance, there is still much room for model improvement. In this study, we proposed iAMP-DL, an efficient hybrid deep learning architecture, for predicting short AMPs. The model was constructed using two well-known deep learning architectures: the long short-term memory architecture and convolutional neural networks. To fairly assess the performance of the model, we compared our model with existing state-of-the-art methods using the same independent test set. Our comparative analysis shows that iAMP-DL outperformed other methods. Furthermore, to assess the robustness and stability of our model, the experiments were repeated 10 times to observe the variation in prediction efficiency. The results demonstrate that iAMP-DL is an effective, robust, and stable framework for detecting promising short AMPs. Another comparative study of different negative data sampling methods also confirms the effectiveness of our method and demonstrates that it can also be used to develop a robust model for predicting AMPs in general. The proposed framework was also deployed as an online web server with a user-friendly interface to support the research community in identifying short AMPs.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202300382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141260038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extracellular vesicle proteins as breast cancer biomarkers: Mass spectrometry-based analysis 作为乳腺癌生物标记物的细胞外囊泡蛋白：基于质谱的分析

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-03 DOI: 10.1002/pmic.202300062

Raju Bandu, Jae Won Oh, Kwang Pyo Kim

{"title":"Extracellular vesicle proteins as breast cancer biomarkers: Mass spectrometry-based analysis","authors":"Raju Bandu, Jae Won Oh, Kwang Pyo Kim","doi":"10.1002/pmic.202300062","DOIUrl":"10.1002/pmic.202300062","url":null,"abstract":"Extracellular vesicles (EVs) are membrane-surrounded vesicles released by various cell types into the extracellular microenvironment. Although EVs vary in size, biological function, and components, their importance in cancer progression and the potential use of EV molecular species to serve as novel cancer biomarkers have become increasingly evident. Cancer cells actively release EVs into surrounding tissues, which play vital roles in cancer progression and metastasis, including invasion and immune modulation. EVs released by cancer cells are usually chosen as a gateway in the search for biomarkers for cancer. In this review, we mainly focused on molecular profiling of EV protein constituents from breast cancer, emphasizing mass spectrometry (MS)-based proteomic approaches. To further investigate the potential use of EVs as a source of breast cancer biomarkers, we have discussed the use of these proteins as predictive marker candidates. Besides, we have also summarized the key characteristics of EVs as potential therapeutic targets in breast cancer and provided significant information on their implications in breast cancer development and progression. Information provided in this review may help understand the recent progress in understanding EV biology and their potential role as new noninvasive biomarkers as well as emerging therapeutic opportunities and associated challenges.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202300062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141198570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial Board: Proteomics 11'24 编辑委员会：蛋白质组学 11'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-03 DOI: 10.1002/pmic.202470082

引用次数: 0

Contents: Proteomics 11'24 内容蛋白质组学 11'24

IF 3.4 4区生物学

Proteomics Pub Date : 2024-06-03 DOI: 10.1002/pmic.202470083

引用次数: 0