Pub Date : 2024-09-06Epub Date: 2024-08-22DOI: 10.1021/acs.jproteome.4c00319
Qiong Li, Fanrong Liu, Xiaoyu Ma, Feifei Chen, Ziying Yi, Yangyang Du, Anxin Huang, Chenyang Zhao, Da Wang, Yanran Chen, Xiongwen Cao
Ribosome profiling and mass spectrometry have revealed thousands of previously unannotated small and alternative open reading frames (sm/alt-ORFs) that are translated into micro/alt-proteins in mammalian cells. However, their prevalence across human tissues and biological roles remains largely undefined. The placenta is an ideal model for identifying unannotated microproteins and alt-proteins due to its considerable protein diversity that is required to sustain fetal development during pregnancy. Here, we profiled unannotated microproteins and alt-proteins in human placental tissues from preeclampsia patients or healthy individuals by proteomics, identified 52 unannotated microproteins or alt-proteins, and demonstrated that five microproteins can be translated from overexpression constructs in a heterologous cell line, although several are unstable. We further demonstrated that one microprotein, XRCC6P1, associates with translation initiation factor eIF3 and negatively regulates translation when exogenously overexpressed. Thus, we revealed a hidden sm/alt-ORF-encoded proteome in the human placenta, which may advance the mechanism studies for placenta development as well as placental disorders such as preeclampsia.
{"title":"Proteomic Profiling of Unannotated Microproteins in Human Placenta Reveals XRCC6P1 as a Potential Negative Regulator of Translation.","authors":"Qiong Li, Fanrong Liu, Xiaoyu Ma, Feifei Chen, Ziying Yi, Yangyang Du, Anxin Huang, Chenyang Zhao, Da Wang, Yanran Chen, Xiongwen Cao","doi":"10.1021/acs.jproteome.4c00319","DOIUrl":"10.1021/acs.jproteome.4c00319","url":null,"abstract":"<p><p>Ribosome profiling and mass spectrometry have revealed thousands of previously unannotated small and alternative open reading frames (sm/alt-ORFs) that are translated into micro/alt-proteins in mammalian cells. However, their prevalence across human tissues and biological roles remains largely undefined. The placenta is an ideal model for identifying unannotated microproteins and alt-proteins due to its considerable protein diversity that is required to sustain fetal development during pregnancy. Here, we profiled unannotated microproteins and alt-proteins in human placental tissues from preeclampsia patients or healthy individuals by proteomics, identified 52 unannotated microproteins or alt-proteins, and demonstrated that five microproteins can be translated from overexpression constructs in a heterologous cell line, although several are unstable. We further demonstrated that one microprotein, XRCC6P1, associates with translation initiation factor eIF3 and negatively regulates translation when exogenously overexpressed. Thus, we revealed a hidden sm/alt-ORF-encoded proteome in the human placenta, which may advance the mechanism studies for placenta development as well as placental disorders such as preeclampsia.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142015515","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}
Leptospirosis, a notifiable endemic disease in Malaysia, has higher mortality rates than regional dengue fever. Diverse clinical symptoms and limited diagnostic methods complicate leptospirosis diagnosis. The demand for accurate biomarker-based diagnostics is increasing. This study investigated the plasma proteome of leptospirosis patients with leptospiraemia and seroconversion compared with dengue patients and healthy subjects using isobaric tags for relative and absolute quantitation (iTRAQ)-mass spectrometry (MS). The iTRAQ analysis identified a total of 450 proteins, which were refined to a list of 290 proteins through a series of exclusion criteria. Differential expression in the plasma proteome of leptospirosis patients compared to the control groups identified 11 proteins, which are apolipoprotein A-II (APOA2), C-reactive protein (CRP), fermitin family homolog 3 (FERMT3), leucine-rich alpha-2-glycoprotein 1 (LRG1), lipopolysaccharide-binding protein (LBP), myosin-9 (MYH9), platelet basic protein (PPBP), platelet factor 4 (PF4), profilin-1 (PFN1), serum amyloid A-1 protein (SAA1), and thrombospondin-1 (THBS1). Following a study on a verification cohort, a panel of eight plasma protein biomarkers was identified for potential leptospirosis diagnosis: CRP, LRG1, LBP, MYH9, PPBP, PF4, SAA1, and THBS1. In conclusion, a panel of eight protein biomarkers offers a promising approach for leptospirosis diagnosis, addressing the limitations of the "one disease, one biomarker" concept.
{"title":"The Potential of Eight Plasma Proteins as Biomarkers in Redefining Leptospirosis Diagnosis.","authors":"Cheng-Yee Fish-Low, Leslie Thian Lung Than, King-Hwa Ling, Zamberi Sekawi","doi":"10.1021/acs.jproteome.4c00376","DOIUrl":"10.1021/acs.jproteome.4c00376","url":null,"abstract":"<p><p>Leptospirosis, a notifiable endemic disease in Malaysia, has higher mortality rates than regional dengue fever. Diverse clinical symptoms and limited diagnostic methods complicate leptospirosis diagnosis. The demand for accurate biomarker-based diagnostics is increasing. This study investigated the plasma proteome of leptospirosis patients with leptospiraemia and seroconversion compared with dengue patients and healthy subjects using isobaric tags for relative and absolute quantitation (iTRAQ)-mass spectrometry (MS). The iTRAQ analysis identified a total of 450 proteins, which were refined to a list of 290 proteins through a series of exclusion criteria. Differential expression in the plasma proteome of leptospirosis patients compared to the control groups identified 11 proteins, which are apolipoprotein A-II (APOA2), C-reactive protein (CRP), fermitin family homolog 3 (FERMT3), leucine-rich alpha-2-glycoprotein 1 (LRG1), lipopolysaccharide-binding protein (LBP), myosin-9 (MYH9), platelet basic protein (PPBP), platelet factor 4 (PF4), profilin-1 (PFN1), serum amyloid A-1 protein (SAA1), and thrombospondin-1 (THBS1). Following a study on a verification cohort, a panel of eight plasma protein biomarkers was identified for potential leptospirosis diagnosis: CRP, LRG1, LBP, MYH9, PPBP, PF4, SAA1, and THBS1. In conclusion, a panel of eight protein biomarkers offers a promising approach for leptospirosis diagnosis, addressing the limitations of the \"one disease, one biomarker\" concept.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986813","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}
Pub Date : 2024-09-06Epub Date: 2024-08-16DOI: 10.1021/acs.jproteome.4c00389
Simeng Ma, Ruiling Li, Qian Gong, Honggang Lv, Zipeng Deng, Beibei Wang, Lihua Yao, Lijun Kang, Dan Xiang, Jun Yang, Zhongchun Liu
Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.
{"title":"Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.","authors":"Simeng Ma, Ruiling Li, Qian Gong, Honggang Lv, Zipeng Deng, Beibei Wang, Lihua Yao, Lijun Kang, Dan Xiang, Jun Yang, Zhongchun Liu","doi":"10.1021/acs.jproteome.4c00389","DOIUrl":"10.1021/acs.jproteome.4c00389","url":null,"abstract":"<p><p>Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression <i>n</i> = 4,479, healthy control <i>n</i> = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986815","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}
Pub Date : 2024-09-06Epub Date: 2024-08-23DOI: 10.1021/acs.jproteome.4c00177
Cassandra N Fuller, Lilian Valadares Tose, Francisca N L Vitorino, Natarajan V Bhanu, Erin M Panczyk, Melvin A Park, Benjamin A Garcia, Francisco Fernandez-Lima
The amino acid position within a histone sequence and the chemical nature of post-translational modifications (PTMs) are essential for elucidating the "Histone Code". Previous work has shown that PTMs induce specific biological responses and are good candidates as biomarkers for diagnostics. Here, we evaluate the analytical advantages of trapped ion mobility (TIMS) with parallel accumulation-serial fragmentation (PASEF) and tandem mass spectrometry (MS/MS) for bottom-up proteomics of model cancer cells. The study also considered the use of nanoliquid chromatography (LC) and traditional methods: LC-TIMS-PASEF-ToF MS/MS vs nLC-TIMS-PASEF-ToF MS/MS vs nLC-MS/MS. The addition of TIMS and PASEF-MS/MS increased the number of detected peptides due to the added separation dimension. All three methods showed high reproducibility and low RSD in the MS domain (<5 ppm). While the LC, nLC and TIMS separations showed small RSD across samples, the accurate mobility (1/K0) measurements (<0.6% RSD) increased the confidence of peptide assignments. Trends were observed in the retention time and mobility concerning the number and type of PTMs (e.g., ac, me1-3) and their corresponding unmodified, propionylated peptide that aided in peptide assignment. Mobility separation permitted the annotation of coeluting structural and positional isomers and compared with nLC-MS/MS showed several advantages due to reduced chemical noise.
{"title":"Bottom-up Histone Post-translational Modification Analysis using Liquid Chromatography, Trapped Ion Mobility Spectrometry, and Tandem Mass Spectrometry.","authors":"Cassandra N Fuller, Lilian Valadares Tose, Francisca N L Vitorino, Natarajan V Bhanu, Erin M Panczyk, Melvin A Park, Benjamin A Garcia, Francisco Fernandez-Lima","doi":"10.1021/acs.jproteome.4c00177","DOIUrl":"10.1021/acs.jproteome.4c00177","url":null,"abstract":"<p><p>The amino acid position within a histone sequence and the chemical nature of post-translational modifications (PTMs) are essential for elucidating the \"Histone Code\". Previous work has shown that PTMs induce specific biological responses and are good candidates as biomarkers for diagnostics. Here, we evaluate the analytical advantages of trapped ion mobility (TIMS) with parallel accumulation-serial fragmentation (PASEF) and tandem mass spectrometry (MS/MS) for bottom-up proteomics of model cancer cells. The study also considered the use of nanoliquid chromatography (LC) and traditional methods: LC-TIMS-PASEF-ToF MS/MS vs nLC-TIMS-PASEF-ToF MS/MS vs nLC-MS/MS. The addition of TIMS and PASEF-MS/MS increased the number of detected peptides due to the added separation dimension. All three methods showed high reproducibility and low RSD in the MS domain (<5 ppm). While the LC, nLC and TIMS separations showed small RSD across samples, the accurate mobility (1/K<sub>0</sub>) measurements (<0.6% RSD) increased the confidence of peptide assignments. Trends were observed in the retention time and mobility concerning the number and type of PTMs (e.g., ac, me<sub>1-3</sub>) and their corresponding unmodified, propionylated peptide that aided in peptide assignment. Mobility separation permitted the annotation of coeluting structural and positional isomers and compared with nLC-MS/MS showed several advantages due to reduced chemical noise.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142034500","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}
Pub Date : 2024-09-06Epub Date: 2024-08-12DOI: 10.1021/acs.jproteome.3c00751
Kamalika Roy Choudhury, Priya Verma, Aleepta Guha Ray, Sandip Samanta, Asish Manna, Arun Bandyopadhyay, Shanta Dutta, Provash C Sadhukhan
Dengue fever is a rapidly emerging tropical disease and an important cause of morbidity in its severe form worldwide. A wide spectrum of the pathophysiology is associated with the transition of dengue fever to severe dengue, which is driven by the host immune response and might reflect in patients' proteome profile. This study aims to analyze the plasma from different phases of dengue-infected patients at two time points. A mass-spectrometry-based proteomic approach was utilized to understand the involvement of probable candidate proteins toward developing a more severe, hemorrhagic form of dengue fever. Dengue-infected hospital-admitted patients with <5 days of fever were included in this study. Patient samples from the acute phase were screened for the presence of NS1 antigen using ELISA and subjected to molecular serotyping. Dengue molecular serotype-confirmed patient samples, pairwise from acute and critical phases with healthy control were subjected to qualitative and quantitative proteomic analysis, and then pathway analysis was performed. The protein-protein interaction network between the dengue virus and host proteins was depicted in the search for proteins associated with severe dengue pathophysiology. An array of apolipoprotein, cytokines, and endothelial proteins in association with virus replication and endothelial dysfunction were validated as biomolecules involved in severe dengue pathophysiology.
{"title":"Differential Proteomic Profiling at Different Phases of Dengue Infection: An Intricate Insight from Proteins to Pathogenesis.","authors":"Kamalika Roy Choudhury, Priya Verma, Aleepta Guha Ray, Sandip Samanta, Asish Manna, Arun Bandyopadhyay, Shanta Dutta, Provash C Sadhukhan","doi":"10.1021/acs.jproteome.3c00751","DOIUrl":"10.1021/acs.jproteome.3c00751","url":null,"abstract":"<p><p>Dengue fever is a rapidly emerging tropical disease and an important cause of morbidity in its severe form worldwide. A wide spectrum of the pathophysiology is associated with the transition of dengue fever to severe dengue, which is driven by the host immune response and might reflect in patients' proteome profile. This study aims to analyze the plasma from different phases of dengue-infected patients at two time points. A mass-spectrometry-based proteomic approach was utilized to understand the involvement of probable candidate proteins toward developing a more severe, hemorrhagic form of dengue fever. Dengue-infected hospital-admitted patients with <5 days of fever were included in this study. Patient samples from the acute phase were screened for the presence of NS1 antigen using ELISA and subjected to molecular serotyping. Dengue molecular serotype-confirmed patient samples, pairwise from acute and critical phases with healthy control were subjected to qualitative and quantitative proteomic analysis, and then pathway analysis was performed. The protein-protein interaction network between the dengue virus and host proteins was depicted in the search for proteins associated with severe dengue pathophysiology. An array of apolipoprotein, cytokines, and endothelial proteins in association with virus replication and endothelial dysfunction were validated as biomolecules involved in severe dengue pathophysiology.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915473","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}
Retinal artery occlusion (RAO), which is positively correlated with acute ischemic stroke (IS) and results in severe visual impairment, lacks effective intervention drugs. This study aims to perform integrated analysis using UK Biobank plasma proteome data of RAO and IS to identify potential targets and preventive drugs. A total of 7191 participants (22 RAO patients, 1457 IS patients, 8 individuals with both RAO and IS, and 5704 healthy age-gender-matched controls) were included in this study. Unique 1461 protein expression profiles of RAO, IS, and the combined data set, extracted from UK Biobank Plasma proteomics projects, were analyzed using both differential expression analysis and elastic network regression (Enet) methods to identify shared key proteins. Subsequent analyses, including single cell type expression assessment, pathway enrichment, and druggability analysis, were conducted for verifying shared key proteins and discovery of new drugs. Five proteins were found to be shared among the samples, with all of them showing upregulation. Notably, adhesion G-protein coupled receptor G1 (ADGRG1) exhibited high expression in glial cells of the brain and eye tissues. Gene set enrichment analysis revealed pathways associated with lipid metabolism and vascular regulation and inflammation. Druggability analysis unveiled 15 drug candidates targeting ADGRG1, which demonstrated protective effects against RAO, especially troglitazone (-8.5 kcal/mol). Our study identified novel risk proteins and therapeutic drugs associated with the rare disease RAO, providing valuable insights into potential intervention strategies.
视网膜动脉闭塞(RAO)与急性缺血性中风(IS)呈正相关,会导致严重的视力损伤,但缺乏有效的干预药物。本研究旨在利用英国生物库中的 RAO 和 IS 血浆蛋白质组数据进行综合分析,以确定潜在的靶点和预防药物。本研究共纳入7191名参与者(22名RAO患者、1457名IS患者、8名同时患有RAO和IS的患者以及5704名年龄性别匹配的健康对照者)。研究人员使用差异表达分析和弹性网络回归(Enet)方法分析了从英国生物库血浆蛋白质组学项目中提取的 RAO、IS 和合并数据集的 1461 个独特蛋白质表达谱,以确定共有的关键蛋白质。随后进行的分析包括单细胞类型表达评估、通路富集和可药性分析,以验证共有的关键蛋白并发现新药。结果发现,样本中有五种蛋白质是共有的,而且都出现了上调。值得注意的是,粘附 G 蛋白偶联受体 G1(ADGRG1)在脑和眼组织的胶质细胞中表现出高表达。基因组富集分析揭示了与脂质代谢、血管调节和炎症相关的通路。可药性分析揭示了15种以ADGRG1为靶点的候选药物,这些药物对RAO具有保护作用,尤其是曲格列酮(-8.5 kcal/mol)。我们的研究发现了与罕见病 RAO 相关的新型风险蛋白和治疗药物,为潜在的干预策略提供了宝贵的见解。
{"title":"Plasma Proteomics to Identify Drug Targets and Potential Drugs for Retinal Artery Occlusion: An Integrated Analysis in the UK Biobank.","authors":"Jiahui Cao, Minjing Zhuang, Huiqian Kong, Chunran Lai, Ting Su, Anyi Liang, Zicheng Wang, Qiaowei Wu, Ying Fang, Yijun Hu, Xiayin Zhang, Miao Lin, Honghua Yu","doi":"10.1021/acs.jproteome.4c00044","DOIUrl":"10.1021/acs.jproteome.4c00044","url":null,"abstract":"<p><p>Retinal artery occlusion (RAO), which is positively correlated with acute ischemic stroke (IS) and results in severe visual impairment, lacks effective intervention drugs. This study aims to perform integrated analysis using UK Biobank plasma proteome data of RAO and IS to identify potential targets and preventive drugs. A total of 7191 participants (22 RAO patients, 1457 IS patients, 8 individuals with both RAO and IS, and 5704 healthy age-gender-matched controls) were included in this study. Unique 1461 protein expression profiles of RAO, IS, and the combined data set, extracted from UK Biobank Plasma proteomics projects, were analyzed using both differential expression analysis and elastic network regression (Enet) methods to identify shared key proteins. Subsequent analyses, including single cell type expression assessment, pathway enrichment, and druggability analysis, were conducted for verifying shared key proteins and discovery of new drugs. Five proteins were found to be shared among the samples, with all of them showing upregulation. Notably, adhesion G-protein coupled receptor G1 (ADGRG1) exhibited high expression in glial cells of the brain and eye tissues. Gene set enrichment analysis revealed pathways associated with lipid metabolism and vascular regulation and inflammation. Druggability analysis unveiled 15 drug candidates targeting ADGRG1, which demonstrated protective effects against RAO, especially troglitazone (-8.5 kcal/mol). Our study identified novel risk proteins and therapeutic drugs associated with the rare disease RAO, providing valuable insights into potential intervention strategies.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141873497","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}
Pub Date : 2024-09-06Epub Date: 2024-07-27DOI: 10.1021/acs.jproteome.4c00060
Yu Zhang, Ning Ding, Jinkang Cao, Jing Zhang, Jianfeng Liu, Chun Zhang, Li Jiang
Although seminal plasma extracellular vesicles (SPEVs) play important roles in sperm function, little is known about their metabolite compositions and roles in sperm motility. Here, we performed metabolomics and proteomics analysis of boar SPEVs with high or low sperm motility to investigate specific biomarkers affecting sperm motility. In total, 140 proteins and 32 metabolites were obtained through differentially expressed analysis and weighted gene coexpression network analysis (WGCNA). Seven differentially expressed proteins (DEPs) (ADIRF, EPS8L1, PRCP, CD81, PTPRD, CSK, LOC100736569) and six differentially expressed metabolites (DEMs) (adenosine, beclomethasone, 1,2-benzenedicarboxylic acid, urea, 1-methyl-l-histidine, and palmitic acid) were also identified in WGCNA significant modules. Joint pathway analysis revealed that three DEPs (GART, ADCY7, and NTPCR) and two DEMs (urea and adenosine) were involved in purine metabolism. Our results suggested that there was significant correlation between proteins and metabolites, such as IL4I1 and urea (r = 0.86). Furthermore, we detected the expression level of GART, ADCY7, and CDC42 in sperm of two groups, which further verified the experimental results. This study revealed that several proteins and metabolites in SPEVs play important roles in sperm motility. Our results offered new insights into the complex mechanism of sperm motility and identified potential biomarkers for male reproductive diseases.
{"title":"Proteomics and Metabolic Characteristics of Boar Seminal Plasma Extracellular Vesicles Reveal Biomarker Candidates Related to Sperm Motility.","authors":"Yu Zhang, Ning Ding, Jinkang Cao, Jing Zhang, Jianfeng Liu, Chun Zhang, Li Jiang","doi":"10.1021/acs.jproteome.4c00060","DOIUrl":"10.1021/acs.jproteome.4c00060","url":null,"abstract":"<p><p>Although seminal plasma extracellular vesicles (SPEVs) play important roles in sperm function, little is known about their metabolite compositions and roles in sperm motility. Here, we performed metabolomics and proteomics analysis of boar SPEVs with high or low sperm motility to investigate specific biomarkers affecting sperm motility. In total, 140 proteins and 32 metabolites were obtained through differentially expressed analysis and weighted gene coexpression network analysis (WGCNA). Seven differentially expressed proteins (DEPs) (ADIRF, EPS8L1, PRCP, CD81, PTPRD, CSK, LOC100736569) and six differentially expressed metabolites (DEMs) (adenosine, beclomethasone, 1,2-benzenedicarboxylic acid, urea, 1-methyl-l-histidine, and palmitic acid) were also identified in WGCNA significant modules. Joint pathway analysis revealed that three DEPs (GART, ADCY7, and NTPCR) and two DEMs (urea and adenosine) were involved in purine metabolism. Our results suggested that there was significant correlation between proteins and metabolites, such as IL4I1 and urea (<i>r</i> = 0.86). Furthermore, we detected the expression level of GART, ADCY7, and CDC42 in sperm of two groups, which further verified the experimental results. This study revealed that several proteins and metabolites in SPEVs play important roles in sperm motility. Our results offered new insights into the complex mechanism of sperm motility and identified potential biomarkers for male reproductive diseases.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11385425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141786379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we utilized the Olink Cardiovascular III panel to compare the expression levels of 92 cardiovascular-related proteins between patients with dilated cardiomyopathy combined with heart failure (DCM-HF) (n = 20) and healthy normal people (Normal) (n = 18). The top five most significant proteins, including SPP1, IGFBP7, F11R, CHI3L1, and Plaur, were selected by Olink proteomics. These proteins were further validated using ELISA in plasma samples collected from an additional cohort. ELISA validation confirmed significant increases in SPP1, IGFBP7, F11R, CHI3L1, and Plaur in DCM-HF patients compared to healthy controls. GO and KEGG analysis indicated that NT-pro BNP, SPP1, IGFBP7, F11R, CHI3L1, Plaur, BLM hydrolase, CSTB, Gal-4, CCL15, CDH5, SR-PSOX, and CCL2 were associated with DCM-HF. Correlation analysis revealed that these 13 differentially expressed proteins have strong correlations with clinical indicators such as LVEF and NT-pro BNP, etc. Additionally, in the GEO-DCM data sets, the combined diagnostic value of these five core proteins AUC values of 0.959, 0.773, and 0.803, respectively indicating the predictive value of the five core proteins for DCM-HF. Our findings suggest that these proteins may be useful biomarkers for the diagnosis and prediction of DCM-HF, and further research is prompted to explore their potential as therapeutic targets.
{"title":"Plasma Olink Proteomics Reveals Novel Biomarkers for Prediction and Diagnosis in Dilated Cardiomyopathy with Heart Failure.","authors":"Shuai Xu, Ge Zhang, Xin Tan, Yiyao Zeng, Hezi Jiang, Yufeng Jiang, Xiangyu Wang, Yahui Song, Huimin Fan, Yafeng Zhou","doi":"10.1021/acs.jproteome.4c00522","DOIUrl":"10.1021/acs.jproteome.4c00522","url":null,"abstract":"<p><p>In this study, we utilized the Olink Cardiovascular III panel to compare the expression levels of 92 cardiovascular-related proteins between patients with dilated cardiomyopathy combined with heart failure (DCM-HF) (n = 20) and healthy normal people (Normal) (n = 18). The top five most significant proteins, including SPP1, IGFBP7, F11R, CHI3L1, and Plaur, were selected by Olink proteomics. These proteins were further validated using ELISA in plasma samples collected from an additional cohort. ELISA validation confirmed significant increases in SPP1, IGFBP7, F11R, CHI3L1, and Plaur in DCM-HF patients compared to healthy controls. GO and KEGG analysis indicated that NT-pro BNP, SPP1, IGFBP7, F11R, CHI3L1, Plaur, BLM hydrolase, CSTB, Gal-4, CCL15, CDH5, SR-PSOX, and CCL2 were associated with DCM-HF. Correlation analysis revealed that these 13 differentially expressed proteins have strong correlations with clinical indicators such as LVEF and NT-pro BNP, etc. Additionally, in the GEO-DCM data sets, the combined diagnostic value of these five core proteins AUC values of 0.959, 0.773, and 0.803, respectively indicating the predictive value of the five core proteins for DCM-HF. Our findings suggest that these proteins may be useful biomarkers for the diagnosis and prediction of DCM-HF, and further research is prompted to explore their potential as therapeutic targets.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11385702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06Epub Date: 2024-08-14DOI: 10.1021/acs.jproteome.4c00255
Brianna Ball, Arjun Sukumaran, Jonathan R Krieger, Jennifer Geddes-McAlister
Accurate and reliable detection of fungal pathogens presents an important hurdle to manage infections, especially considering that fungal pathogens, including the globally important human pathogen, Cryptococcus neoformans, have adapted diverse mechanisms to survive the hostile host environment and moderate virulence determinant production during coinfections. These pathogen adaptations present an opportunity for improvements (e.g., technological and computational) to better understand the interplay between a host and a pathogen during disease to uncover new strategies to overcome infection. In this study, we performed comparative proteomic profiling of an in vitro coinfection model across a range of fungal and bacterial burden loads in macrophages. Comparing data-dependent acquisition and data-independent acquisition enabled with parallel accumulation serial fragmentation technology, we quantified changes in dual-perspective proteome remodeling. We report enhanced and novel detection of pathogen proteins with data-independent acquisition-parallel accumulation serial fragmentation (DIA-PASEF), especially for fungal proteins during single and dual infection of macrophages. Further characterization of a fungal protein detected only with DIA-PASEF uncovered a novel determinant of fungal virulence, including altered capsule and melanin production, thermotolerance, and macrophage infectivity, supporting proteomics advances for the discovery of a novel putative druggable target to suppress C. neoformans pathogenicity.
对真菌病原体进行准确可靠的检测是控制感染的一个重要障碍,特别是考虑到真菌病原体,包括全球重要的人类病原体--新生隐球菌,已经适应了多种机制,以便在敌对的宿主环境中生存,并在合并感染期间适度产生毒力决定因子。这些病原体的适应性为改进(如技术和计算)提供了机会,以更好地了解宿主和病原体在疾病期间的相互作用,从而发现克服感染的新策略。在这项研究中,我们对体外共感染模型进行了比较蛋白质组图谱分析,研究了巨噬细胞中真菌和细菌负载的范围。通过比较依赖数据的采集和利用平行累积串行片段技术的独立数据采集,我们量化了双视角蛋白质组重塑的变化。我们报告了利用数据独立采集-平行累积序列片段技术(DIA-PASEF)对病原体蛋白的增强和新颖检测,尤其是在巨噬细胞单次感染和双重感染期间对真菌蛋白的检测。对仅用 DIA-PASEF 检测到的真菌蛋白的进一步鉴定发现了真菌毒力的一个新的决定因素,包括改变的胶囊和黑色素生成、耐热性和巨噬细胞感染性,这支持了蛋白质组学在发现抑制 C. neoformans 致病性的新的药物靶点方面取得的进展。
{"title":"Comparative Cross-Kingdom DDA- and DIA-PASEF Proteomic Profiling Reveals Novel Determinants of Fungal Virulence and a Putative Druggable Target.","authors":"Brianna Ball, Arjun Sukumaran, Jonathan R Krieger, Jennifer Geddes-McAlister","doi":"10.1021/acs.jproteome.4c00255","DOIUrl":"10.1021/acs.jproteome.4c00255","url":null,"abstract":"<p><p>Accurate and reliable detection of fungal pathogens presents an important hurdle to manage infections, especially considering that fungal pathogens, including the globally important human pathogen, <i>Cryptococcus neoformans</i>, have adapted diverse mechanisms to survive the hostile host environment and moderate virulence determinant production during coinfections. These pathogen adaptations present an opportunity for improvements (e.g., technological and computational) to better understand the interplay between a host and a pathogen during disease to uncover new strategies to overcome infection. In this study, we performed comparative proteomic profiling of an in vitro coinfection model across a range of fungal and bacterial burden loads in macrophages. Comparing data-dependent acquisition and data-independent acquisition enabled with parallel accumulation serial fragmentation technology, we quantified changes in dual-perspective proteome remodeling. We report enhanced and novel detection of pathogen proteins with data-independent acquisition-parallel accumulation serial fragmentation (DIA-PASEF), especially for fungal proteins during single and dual infection of macrophages. Further characterization of a fungal protein detected only with DIA-PASEF uncovered a novel determinant of fungal virulence, including altered capsule and melanin production, thermotolerance, and macrophage infectivity, supporting proteomics advances for the discovery of a novel putative druggable target to suppress <i>C. neoformans</i> pathogenicity.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11385706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141974456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06Epub Date: 2024-08-27DOI: 10.1021/acs.jproteome.4c00263
Yu Yan, Baradwaj Simha Sankar, Bilal Mirza, Dominic C M Ng, Alexander R Pelletier, Sarah D Huang, Wei Wang, Karol Watson, Ding Wang, Peipei Ping
Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.
{"title":"Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation.","authors":"Yu Yan, Baradwaj Simha Sankar, Bilal Mirza, Dominic C M Ng, Alexander R Pelletier, Sarah D Huang, Wei Wang, Karol Watson, Ding Wang, Peipei Ping","doi":"10.1021/acs.jproteome.4c00263","DOIUrl":"10.1021/acs.jproteome.4c00263","url":null,"abstract":"<p><p>Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11385379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142071404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}