Pub Date : 2024-07-03DOI: 10.1021/acs.jproteome.4c00111
Leila Pirayeshfard, Shu Luo, John Maringa Githaka, Arashdeep Saini, Nicolas Touret, Ing Swie Goping, Olivier Julien
Protein-protein interaction studies using proximity labeling techniques, such as biotin ligase-based BioID, have become integral in understanding cellular processes. Most studies utilize conventional 2D cell culture systems, potentially missing important differences in protein behavior found in 3D tissues. In this study, we investigated the protein-protein interactions of a protein, Bcl-2 Agonist of cell death (BAD), and compared conventional 2D culture conditions to a 3D system, wherein cells were embedded within a 3D extracellular matrix (ECM) mimic. Using BAD fused to the engineered biotin ligase miniTurbo (BirA*), we identified both overlapping and distinct BAD interactomes under 2D and 3D conditions. The known BAD binding proteins 14-3-3 isoforms and Bcl-XL interacted with BAD in both 2D and 3D. Of the 131 BAD-interactors identified, 56% were specific to 2D, 14% were specific to 3D, and 30% were common to both conditions. Interaction network analysis demonstrated differential associations between 2D and 3D interactomes, emphasizing the impact of the culture conditions on protein interactions. The 2D-3D overlap interactome encapsulated the apoptotic program, which is a well-known role of BAD. The 3D unique pathways were enriched in ECM signaling, suggestive of hitherto unknown functions for BAD. Thus, exploring protein-protein interactions in 3D provides novel clues into cell behavior. This exciting approach has the potential to bridge the knowledge gap between tractable 2D cell culture and organoid-like 3D systems.
利用近距离标记技术(如基于生物素连接酶的 BioID)进行蛋白质-蛋白质相互作用研究,已成为了解细胞过程不可或缺的一部分。大多数研究利用传统的二维细胞培养系统,可能会忽略三维组织中蛋白质行为的重要差异。在这项研究中,我们研究了一种蛋白质--Bcl-2 细胞死亡激动剂(BAD)的蛋白质-蛋白质相互作用,并将传统的二维培养条件与三维系统进行了比较,后者将细胞嵌入三维细胞外基质(ECM)模拟体中。利用与工程生物素连接酶 miniTurbo(BirA*)融合的 BAD,我们确定了二维和三维条件下重叠和不同的 BAD 相互作用组。已知的 BAD 结合蛋白 14-3-3 异构体和 Bcl-XL 在二维和三维条件下都与 BAD 相互作用。在鉴定出的131个BAD相互作用因子中,56%对二维具有特异性,14%对三维具有特异性,30%对两种条件具有共通性。相互作用网络分析显示了二维和三维相互作用组之间不同的关联,强调了培养条件对蛋白质相互作用的影响。2D-3D重叠相互作用组包含了凋亡程序,这是众所周知的BAD作用。三维独特途径富含 ECM 信号转导,这表明 BAD 具有迄今未知的功能。因此,在三维环境中探索蛋白质之间的相互作用为了解细胞行为提供了新的线索。这种令人兴奋的方法有望弥合可操作的二维细胞培养与类器官三维系统之间的知识鸿沟。
{"title":"Comparing the BAD Protein Interactomes in 2D and 3D Cell Culture Using Proximity Labeling.","authors":"Leila Pirayeshfard, Shu Luo, John Maringa Githaka, Arashdeep Saini, Nicolas Touret, Ing Swie Goping, Olivier Julien","doi":"10.1021/acs.jproteome.4c00111","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00111","url":null,"abstract":"<p><p>Protein-protein interaction studies using proximity labeling techniques, such as biotin ligase-based BioID, have become integral in understanding cellular processes. Most studies utilize conventional 2D cell culture systems, potentially missing important differences in protein behavior found in 3D tissues. In this study, we investigated the protein-protein interactions of a protein, Bcl-2 Agonist of cell death (BAD), and compared conventional 2D culture conditions to a 3D system, wherein cells were embedded within a 3D extracellular matrix (ECM) mimic. Using BAD fused to the engineered biotin ligase miniTurbo (BirA*), we identified both overlapping and distinct BAD interactomes under 2D and 3D conditions. The known BAD binding proteins 14-3-3 isoforms and Bcl-XL interacted with BAD in both 2D and 3D. Of the 131 BAD-interactors identified, 56% were specific to 2D, 14% were specific to 3D, and 30% were common to both conditions. Interaction network analysis demonstrated differential associations between 2D and 3D interactomes, emphasizing the impact of the culture conditions on protein interactions. The 2D-3D overlap interactome encapsulated the apoptotic program, which is a well-known role of BAD. The 3D unique pathways were enriched in ECM signaling, suggestive of hitherto unknown functions for BAD. Thus, exploring protein-protein interactions in 3D provides novel clues into cell behavior. This exciting approach has the potential to bridge the knowledge gap between tractable 2D cell culture and organoid-like 3D systems.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141496266","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 : 2024-07-01DOI: 10.1021/acs.jproteome.4c00233
Weifen Sun, Ao Huang, Shubo Wen, Ruicong Yang, Xiling Liu
The use of protein biomarkers in blood for clinical settings is limited by the cost and accessibility of traditional venipuncture sampling. The dried blood spot (DBS) technique offers a less invasive and more accessible alternative. However, protein stability in DBS has not been well evaluated. Herein, we deployed a quantitative LC-MS/MS system to construct proteomic atlases of whole blood, DBSs, plasma, and blood cells. Approximately 4% of detected proteins' abundance was significantly altered during blood drying into blood spots, with overwhelming disturbances in cytoplasmic fraction. We also reported a novel finding suggesting a decrease in the level of membrane/cytoskeletal proteins (SLC4A1, RHAG, DSC1, DSP, and JUP) and an increase in the level of proteins (ATG3, SEC14L4, and NRBP1) related to intracellular trafficking. Furthermore, we identified 19 temporally dynamic proteins in DBS samples stored at room temperature for up to 6 months. There were three declined cytoskeleton-related proteins (RDX, SH3BGRL3, and MYH9) and four elevated proteins (XPO7, RAN, SLC2A1, and SLC29A1) involved in cytoplasmic transport as representatives. The instability was governed predominantly by hydrophilic proteins and enhanced significantly with an increasing storage time. Our analyses provide comprehensive knowledge of both short- and long-term storage stability of DBS proteins, forming the foundation for the widespread use of DBS in clinical proteomics and other analytical applications.
{"title":"Temporal Assessment of Protein Stability in Dried Blood Spots.","authors":"Weifen Sun, Ao Huang, Shubo Wen, Ruicong Yang, Xiling Liu","doi":"10.1021/acs.jproteome.4c00233","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00233","url":null,"abstract":"<p><p>The use of protein biomarkers in blood for clinical settings is limited by the cost and accessibility of traditional venipuncture sampling. The dried blood spot (DBS) technique offers a less invasive and more accessible alternative. However, protein stability in DBS has not been well evaluated. Herein, we deployed a quantitative LC-MS/MS system to construct proteomic atlases of whole blood, DBSs, plasma, and blood cells. Approximately 4% of detected proteins' abundance was significantly altered during blood drying into blood spots, with overwhelming disturbances in cytoplasmic fraction. We also reported a novel finding suggesting a decrease in the level of membrane/cytoskeletal proteins (SLC4A1, RHAG, DSC1, DSP, and JUP) and an increase in the level of proteins (ATG3, SEC14L4, and NRBP1) related to intracellular trafficking. Furthermore, we identified 19 temporally dynamic proteins in DBS samples stored at room temperature for up to 6 months. There were three declined cytoskeleton-related proteins (RDX, SH3BGRL3, and MYH9) and four elevated proteins (XPO7, RAN, SLC2A1, and SLC29A1) involved in cytoplasmic transport as representatives. The instability was governed predominantly by hydrophilic proteins and enhanced significantly with an increasing storage time. Our analyses provide comprehensive knowledge of both short- and long-term storage stability of DBS proteins, forming the foundation for the widespread use of DBS in clinical proteomics and other analytical applications.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141475418","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}
Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing significant health problems. Early diagnosis of the disease is quite inadequate. To screen urine biomarkers of DN and explore its potential mechanism, this study collected urine from 87 patients with type 2 diabetes mellitus (which will be classified into normal albuminuria, microalbuminuria, and macroalbuminuria groups) and 38 healthy subjects. Twelve individuals from each group were then randomly selected as the screening cohort for proteomics analysis and the rest as the validation cohort. The results showed that humoral immune response, complement activation, complement and coagulation cascades, renin-angiotensin system, and cell adhesion molecules were closely related to the progression of DN. Five overlapping proteins (KLK1, CSPG4, PLAU, SERPINA3, and ALB) were identified as potential biomarkers by machine learning methods. Among them, KLK1 and CSPG4 were positively correlated with the urinary albumin to creatinine ratio (UACR), and SERPINA3 was negatively correlated with the UACR, which were validated by enzyme-linked immunosorbent assay (ELISA). This study provides new insights into disease mechanisms and biomarkers for early diagnosis of DN.
{"title":"Application of Proteomics and Machine Learning Methods to Study the Pathogenesis of Diabetic Nephropathy and Screen Urinary Biomarkers.","authors":"Xi Yan, Xinglai Zhang, Haiying Li, Yongdong Zou, Wei Lu, Man Zhan, Zhiyuan Liang, Hongbin Zhuang, Xiaoqian Ran, Guanwei Ma, Xixiao Lin, Hongbo Yang, Yuhan Huang, Hanghang Wang, Liming Shen","doi":"10.1021/acs.jproteome.4c00267","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00267","url":null,"abstract":"<p><p>Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing significant health problems. Early diagnosis of the disease is quite inadequate. To screen urine biomarkers of DN and explore its potential mechanism, this study collected urine from 87 patients with type 2 diabetes mellitus (which will be classified into normal albuminuria, microalbuminuria, and macroalbuminuria groups) and 38 healthy subjects. Twelve individuals from each group were then randomly selected as the screening cohort for proteomics analysis and the rest as the validation cohort. The results showed that humoral immune response, complement activation, complement and coagulation cascades, renin-angiotensin system, and cell adhesion molecules were closely related to the progression of DN. Five overlapping proteins (KLK1, CSPG4, PLAU, SERPINA3, and ALB) were identified as potential biomarkers by machine learning methods. Among them, KLK1 and CSPG4 were positively correlated with the urinary albumin to creatinine ratio (UACR), and SERPINA3 was negatively correlated with the UACR, which were validated by enzyme-linked immunosorbent assay (ELISA). This study provides new insights into disease mechanisms and biomarkers for early diagnosis of DN.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141464323","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 : 2024-06-29DOI: 10.1021/acs.jproteome.4c00035
Daniel Radford-Smith, Tang T Ng, Abi G Yates, Isobel Dunstan, Timothy D W Claridge, Daniel C Anthony, Fay Probert
Tumor necrosis factor (TNF) has well-established roles in neuroinflammatory disorders, but the effect of TNF on the biochemistry of brain cells remains poorly understood. Here, we microinjected TNF into the brain to study its impact on glial and neuronal metabolism (glycolysis, pentose phosphate pathway, citric acid cycle, pyruvate dehydrogenase, and pyruvate carboxylase pathways) using 13C NMR spectroscopy on brain extracts following intravenous [1,2-13C]-glucose (to probe glia and neuron metabolism), [2-13C]-acetate (probing astrocyte-specific metabolites), or [3-13C]-lactate. An increase in [4,5-13C]-glutamine and [2,3-13C]-lactate coupled with a decrease in [4,5-13C]-glutamate was observed in the [1,2-13C]-glucose-infused animals treated with TNF. As glutamine is produced from glutamate by astrocyte-specific glutamine synthetase the increase in [4,5-13C]-glutamine reflects increased production of glutamine by astrocytes. This was confirmed by infusion with astrocyte substrate [2-13C]-acetate. As lactate is metabolized in the brain to produce glutamate, the simultaneous increase in [2,3-13C]-lactate and decrease in [4,5-13C]-glutamate suggests decreased lactate utilization, which was confirmed using [3-13C]-lactate as a metabolic precursor. These results suggest that TNF rearranges the metabolic network, disrupting the energy supply chain perturbing the glutamine-glutamate shuttle between astrocytes and the neurons. These insights pave the way for developing astrocyte-targeted therapeutic strategies aimed at modulating effects of TNF to restore metabolic homeostasis in neuroinflammatory disorders.
{"title":"Ex-Vivo <sup>13</sup>C NMR Spectroscopy of Rodent Brain: TNF Restricts Neuronal Utilization of Astrocyte-Derived Metabolites.","authors":"Daniel Radford-Smith, Tang T Ng, Abi G Yates, Isobel Dunstan, Timothy D W Claridge, Daniel C Anthony, Fay Probert","doi":"10.1021/acs.jproteome.4c00035","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00035","url":null,"abstract":"<p><p>Tumor necrosis factor (TNF) has well-established roles in neuroinflammatory disorders, but the effect of TNF on the biochemistry of brain cells remains poorly understood. Here, we microinjected TNF into the brain to study its impact on glial and neuronal metabolism (glycolysis, pentose phosphate pathway, citric acid cycle, pyruvate dehydrogenase, and pyruvate carboxylase pathways) using <sup>13</sup>C NMR spectroscopy on brain extracts following intravenous [1,2-<sup>13</sup>C]-glucose (to probe glia and neuron metabolism), [2-<sup>13</sup>C]-acetate (probing astrocyte-specific metabolites), or [3-<sup>13</sup>C]-lactate. An increase in [4,5-<sup>13</sup>C]-glutamine and [2,3-<sup>13</sup>C]-lactate coupled with a decrease in [4,5-<sup>13</sup>C]-glutamate was observed in the [1,2-<sup>13</sup>C]-glucose-infused animals treated with TNF. As glutamine is produced from glutamate by astrocyte-specific glutamine synthetase the increase in [4,5-<sup>13</sup>C]-glutamine reflects increased production of glutamine by astrocytes. This was confirmed by infusion with astrocyte substrate [2-<sup>13</sup>C]-acetate. As lactate is metabolized in the brain to produce glutamate, the simultaneous increase in [2,3-<sup>13</sup>C]-lactate and decrease in [4,5-<sup>13</sup>C]-glutamate suggests decreased lactate utilization, which was confirmed using [3-<sup>13</sup>C]-lactate as a metabolic precursor. These results suggest that TNF rearranges the metabolic network, disrupting the energy supply chain perturbing the glutamine-glutamate shuttle between astrocytes and the neurons. These insights pave the way for developing astrocyte-targeted therapeutic strategies aimed at modulating effects of TNF to restore metabolic homeostasis in neuroinflammatory disorders.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141464324","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 : 2024-06-29DOI: 10.1021/acs.jproteome.4c00014
Ryan M Riley, Gian Luca Negri, S-W Grace Cheng, Sandra E Spencer Miko, Ryan D Morin, Gregg B Morin
Proteome coverage and accurate protein quantification are both important for evaluating biological systems; however, compromises between quantification, coverage, and mass spectrometry (MS) resources are often necessary. Consequently, experimental parameters that impact coverage and quantification must be adjusted, depending on experimental goals. Among these parameters is offline prefractionation, which is utilized in MS-based proteomics to decrease sample complexity resulting in higher overall proteome coverage upon MS analysis. Prefractionation leads to increases in required MS analysis time, although this is often mitigated by isobaric labeling using tandem-mass tags (TMT), which allow samples to be multiplexed. Here we evaluate common prefractionation schemes, TMT variants, and MS acquisition methods and their impact on protein quantification and coverage. Furthermore, we provide recommendations for experimental design depending on the experimental goals.
蛋白质组覆盖率和准确的蛋白质定量对于评估生物系统都很重要;然而,定量、覆盖率和质谱(MS)资源之间的折衷往往是必要的。因此,必须根据实验目标调整影响覆盖率和定量的实验参数。在这些参数中,离线预分馏是一种基于质谱的蛋白质组学方法,它可以降低样品的复杂性,从而在质谱分析时提高蛋白质组的整体覆盖率。预分馏会导致所需的质谱分析时间增加,不过使用串联质量标签(TMT)进行等位标记通常可以缓解这一问题,因为这样可以对样品进行多重分析。在此,我们对常见的预分馏方案、TMT 变体和 MS 采集方法及其对蛋白质定量和覆盖率的影响进行了评估。此外,我们还根据实验目标提供了实验设计建议。
{"title":"Mass Spectrometry Acquisition and Fractionation Recommendations for TMT11 and TMT16 Labeled Samples.","authors":"Ryan M Riley, Gian Luca Negri, S-W Grace Cheng, Sandra E Spencer Miko, Ryan D Morin, Gregg B Morin","doi":"10.1021/acs.jproteome.4c00014","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00014","url":null,"abstract":"<p><p>Proteome coverage and accurate protein quantification are both important for evaluating biological systems; however, compromises between quantification, coverage, and mass spectrometry (MS) resources are often necessary. Consequently, experimental parameters that impact coverage and quantification must be adjusted, depending on experimental goals. Among these parameters is offline prefractionation, which is utilized in MS-based proteomics to decrease sample complexity resulting in higher overall proteome coverage upon MS analysis. Prefractionation leads to increases in required MS analysis time, although this is often mitigated by isobaric labeling using tandem-mass tags (TMT), which allow samples to be multiplexed. Here we evaluate common prefractionation schemes, TMT variants, and MS acquisition methods and their impact on protein quantification and coverage. Furthermore, we provide recommendations for experimental design depending on the experimental goals.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141464325","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 : 2024-06-27DOI: 10.1021/acs.jproteome.4c00289
Shenyu Zhang, Zhengyu Ma
Plasma membrane proteins (PMPs) play critical roles in a myriad of physiological and disease conditions. A unique subset of PMPs functions through interacting with each other in trans at the interface between two contacting cells. These trans-interacting PMPs (tiPMPs) include adhesion molecules and ligands/receptors that facilitate cell-cell contact and direct communication between cells. Among the tiPMPs, a significant number have apparent extracellular binding domains but remain orphans with no known binding partners. Identification of their potential binding partners is therefore important for the understanding of processes such as organismal development and immune cell activation. While a number of methods have been developed for the identification of protein binding partners in general, very few are applicable to tiPMPs, which interact in a two-dimensional fashion with low intrinsic binding affinities. In this review, we present the significance of tiPMP interactions, the challenges of identifying binding partners for tiPMPs, and the landscape of method development. We describe current avidity-based screening approaches for identifying novel tiPMP binding partners and discuss their advantages and limitations. We conclude by highlighting the importance of developing novel methods of identifying new tiPMP interactions for deciphering the complex protein interactome and developing targeted therapeutics for diseases.
{"title":"<i>trans</i>-Interacting Plasma Membrane Proteins and Binding Partner Identification.","authors":"Shenyu Zhang, Zhengyu Ma","doi":"10.1021/acs.jproteome.4c00289","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00289","url":null,"abstract":"<p><p>Plasma membrane proteins (PMPs) play critical roles in a myriad of physiological and disease conditions. A unique subset of PMPs functions through interacting with each other <i>in trans</i> at the interface between two contacting cells. These <i>trans</i>-interacting PMPs (tiPMPs) include adhesion molecules and ligands/receptors that facilitate cell-cell contact and direct communication between cells. Among the tiPMPs, a significant number have apparent extracellular binding domains but remain orphans with no known binding partners. Identification of their potential binding partners is therefore important for the understanding of processes such as organismal development and immune cell activation. While a number of methods have been developed for the identification of protein binding partners in general, very few are applicable to tiPMPs, which interact in a two-dimensional fashion with low intrinsic binding affinities. In this review, we present the significance of tiPMP interactions, the challenges of identifying binding partners for tiPMPs, and the landscape of method development. We describe current avidity-based screening approaches for identifying novel tiPMP binding partners and discuss their advantages and limitations. We conclude by highlighting the importance of developing novel methods of identifying new tiPMP interactions for deciphering the complex protein interactome and developing targeted therapeutics for diseases.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141464322","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 : 2024-06-27DOI: 10.1021/acs.jproteome.4c00188
Douwe Schulte, Joost Snijder
Antibody sequences can be determined at 99% accuracy directly from the polypeptide product by using bottom-up proteomics techniques. Sequencing accuracy at the peptide level is limited by the isobaric residues leucine and isoleucine, incomplete fragmentation spectra in which the order of two or more residues remains ambiguous due to lacking fragment ions for the intermediate positions, and isobaric combinations of amino acids, of potentially different lengths, for example, GG = N and GA = Q. Here, we present several updates to Stitch (v1.5), which performs template-based assembly of de novo peptides to reconstruct antibody sequences. This version introduces a mass-based alignment algorithm that explicitly accounts for mass coincidence errors. In addition, it incorporates a postprocessing procedure to assign I/L residues based on secondary fragments (satellite ions, i.e., w-ions). Moreover, evidence for sequence assignments can now be directly evaluated with the addition of an integrated spectrum viewer. Lastly, input data from a wider selection of de novo peptide sequencing algorithms are allowed, now including Casanovo, PEAKS, Novor.Cloud, pNovo, and MaxNovo, in addition to flat text and FASTA. Combined, these changes make Stitch compatible with a larger range of data processing pipelines and improve its tolerance to peptide-level sequencing errors.
利用自下而上的蛋白质组学技术,可直接从多肽产物中确定抗体序列,准确率高达 99%。肽水平的测序准确性受到以下因素的限制:等位残基亮氨酸和异亮氨酸;不完整的片段谱,其中两个或更多残基的顺序因中间位置缺乏片段离子而模糊不清;氨基酸的等位组合,其长度可能不同,例如 GG = N 和 GA = Q。该版本引入了基于质量的比对算法,明确考虑了质量重合误差。此外,它还加入了一个后处理程序,根据二级碎片(卫星离子,即 w 离子)分配 I/L 残基。此外,现在还增加了综合光谱查看器,可以直接评估序列分配的证据。最后,除了平面文本和 FASTA 之外,现在还允许从更广泛的肽测序算法中输入数据,包括 Casanovo、PEAKS、Novor.Cloud、pNovo 和 MaxNovo。这些变化使 Stitch 能够兼容更多的数据处理管道,并提高了对肽段测序错误的容错能力。
{"title":"A Handle on Mass Coincidence Errors in <i>De Novo</i> Sequencing of Antibodies by Bottom-up Proteomics.","authors":"Douwe Schulte, Joost Snijder","doi":"10.1021/acs.jproteome.4c00188","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00188","url":null,"abstract":"<p><p>Antibody sequences can be determined at 99% accuracy directly from the polypeptide product by using bottom-up proteomics techniques. Sequencing accuracy at the peptide level is limited by the isobaric residues leucine and isoleucine, incomplete fragmentation spectra in which the order of two or more residues remains ambiguous due to lacking fragment ions for the intermediate positions, and isobaric combinations of amino acids, of potentially different lengths, for example, GG = N and GA = Q. Here, we present several updates to Stitch (v1.5), which performs template-based assembly of <i>de novo</i> peptides to reconstruct antibody sequences. This version introduces a mass-based alignment algorithm that explicitly accounts for mass coincidence errors. In addition, it incorporates a postprocessing procedure to assign I/L residues based on secondary fragments (satellite ions, <i>i.e.</i>, <i>w-</i>ions). Moreover, evidence for sequence assignments can now be directly evaluated with the addition of an integrated spectrum viewer. Lastly, input data from a wider selection of <i>de novo</i> peptide sequencing algorithms are allowed, now including Casanovo, PEAKS, Novor.Cloud, pNovo, and MaxNovo, in addition to flat text and FASTA. Combined, these changes make Stitch compatible with a larger range of data processing pipelines and improve its tolerance to peptide-level sequencing errors.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141453711","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}
Cancer cachexia is an involuntary loss of body weight, mostly of skeletal muscle. Previous research favors the existence of a microbiota-muscle crosstalk, so the aim of the study was to evaluate the impact of microbiota alterations induced by antibiotics on skeletal muscle proteins expression. Skeletal muscle proteome changes were investigated in control (CT) or C26 cachectic mice (C26) with or without antibiotic treatment (CT-ATB or C26-ATB, n = 8 per group). Muscle protein extracts were divided into a sarcoplasmic and myofibrillar fraction and then underwent label-free liquid chromatography separation, mass spectrometry analysis, Mascot protein identification, and METASCAPE platform data analysis. In C26 mice, the atrogen mafbx expression was 353% higher than CT mice and 42.3% higher than C26-ATB mice. No effect on the muscle protein synthesis was observed. Proteomic analyses revealed a strong effect of antibiotics on skeletal muscle proteome outside of cachexia, with adaptative processes involved in protein folding, growth, energy metabolism, and muscle contraction. In C26-ATB mice, proteome adaptations observed in CT-ATB mice were blunted. Differentially expressed proteins were involved in other processes like glucose metabolism, oxidative stress response, and proteolysis. This study confirms the existence of a microbiota-muscle axis, with a muscle response after antibiotics that varies depending on whether cachexia is present.
{"title":"Skeletal Muscle Proteome Modifications following Antibiotic-Induced Microbial Disturbances in Cancer Cachexia","authors":"Mathilde Simonson, Gwendal Cueff, Morgane M. Thibaut, Christophe Giraudet, Jérôme Salles, Christophe Chambon, Yves Boirie, Laure B. Bindels, Marine Gueugneau, Christelle Guillet","doi":"10.1021/acs.jproteome.4c00143","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00143","url":null,"abstract":"Cancer cachexia is an involuntary loss of body weight, mostly of skeletal muscle. Previous research favors the existence of a microbiota-muscle crosstalk, so the aim of the study was to evaluate the impact of microbiota alterations induced by antibiotics on skeletal muscle proteins expression. Skeletal muscle proteome changes were investigated in control (CT) or C26 cachectic mice (C26) with or without antibiotic treatment (CT-ATB or C26-ATB, <i>n</i> = 8 per group). Muscle protein extracts were divided into a sarcoplasmic and myofibrillar fraction and then underwent label-free liquid chromatography separation, mass spectrometry analysis, Mascot protein identification, and METASCAPE platform data analysis. In C26 mice, the atrogen <i>mafbx</i> expression was 353% higher than CT mice and 42.3% higher than C26-ATB mice. No effect on the muscle protein synthesis was observed. Proteomic analyses revealed a strong effect of antibiotics on skeletal muscle proteome outside of cachexia, with adaptative processes involved in protein folding, growth, energy metabolism, and muscle contraction. In C26-ATB mice, proteome adaptations observed in CT-ATB mice were blunted. Differentially expressed proteins were involved in other processes like glucose metabolism, oxidative stress response, and proteolysis. This study confirms the existence of a microbiota-muscle axis, with a muscle response after antibiotics that varies depending on whether cachexia is present.","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516641","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 : 2024-06-19DOI: 10.1021/acs.jproteome.4c00107
Guusje van Schaick, Manfred Wuhrer, Constantin Blöchl, Radboud J. E. M. Dolhain, Elena Domínguez-Vega
Alpha-1-acid glycoprotein (AGP) is a heterogeneous glycoprotein fulfilling key roles in many biological processes, including transport of drugs and hormones and modulation of inflammatory and immune responses. The glycoform profile of AGP is known to change depending on (patho)physiological states such as inflammatory diseases or pregnancy. Besides complexity originating from five N-glycosylation sites, the heterogeneity of the AGP further expands to genetic variants. To allow in-depth characterization of this intriguing protein, we developed a method using anion exchange chromatography (AEX) coupled to mass spectrometry (MS) revealing the presence of over 400 proteoforms differing in their glycosylation or genetic variants. More precisely, we could determine that AGP mainly consists of highly sialylated higher antennary structures with on average 16 sialic acids and 0 or 1 fucose per protein. Interestingly, a slightly higher level of fucosylation was observed for AGP1 variants compared to that of AGP2. Proteoform assignment was supported by integrating data from complementary MS-based approaches, including AEX–MS of an exoglycosidase-treated sample and glycopeptide analysis after tryptic digestion. The developed analytical method was applied to characterize AGP from plasma of women during and after pregnancy, revealing differences in glycosylation profiles, specifically in the number of antennae, HexHexNAc units, and sialic acids.
{"title":"Anion Exchange Chromatography–Mass Spectrometry to Characterize Proteoforms of Alpha-1-Acid Glycoprotein during and after Pregnancy","authors":"Guusje van Schaick, Manfred Wuhrer, Constantin Blöchl, Radboud J. E. M. Dolhain, Elena Domínguez-Vega","doi":"10.1021/acs.jproteome.4c00107","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00107","url":null,"abstract":"Alpha-1-acid glycoprotein (AGP) is a heterogeneous glycoprotein fulfilling key roles in many biological processes, including transport of drugs and hormones and modulation of inflammatory and immune responses. The glycoform profile of AGP is known to change depending on (patho)physiological states such as inflammatory diseases or pregnancy. Besides complexity originating from five N-glycosylation sites, the heterogeneity of the AGP further expands to genetic variants. To allow in-depth characterization of this intriguing protein, we developed a method using anion exchange chromatography (AEX) coupled to mass spectrometry (MS) revealing the presence of over 400 proteoforms differing in their glycosylation or genetic variants. More precisely, we could determine that AGP mainly consists of highly sialylated higher antennary structures with on average 16 sialic acids and 0 or 1 fucose per protein. Interestingly, a slightly higher level of fucosylation was observed for AGP1 variants compared to that of AGP2. Proteoform assignment was supported by integrating data from complementary MS-based approaches, including AEX–MS of an exoglycosidase-treated sample and glycopeptide analysis after tryptic digestion. The developed analytical method was applied to characterize AGP from plasma of women during and after pregnancy, revealing differences in glycosylation profiles, specifically in the number of antennae, HexHexNAc units, and sialic acids.","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141530875","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 : 2024-06-18DOI: 10.1021/acs.jproteome.4c00322
Lauren N. Lopez, Blythe Durbin-Johnson, Chenoa R. Vargas, John Ruzinski, Anne Goodling, Rajnish Mehrotra, Tomas Vaisar, David M. Rocke, Maryam Afkarian
To our knowledge, calibration curves or other validations for thousands of SomaScan aptamers are not publicly available. Moreover, the abundance of urine proteins obtained from these assays is not routinely validated with orthogonal methods (OMs). We report an in-depth comparison of SomaScan readout for 23 proteins in urine samples from patients with diabetic kidney disease (n = 118) vs OMs, including liquid chromatography-targeted mass spectrometry (LC-MS), ELISA, and nephelometry. Pearson correlation between urine abundance of the 23 proteins from SomaScan 3.2 vs OMs ranged from −0.58 to 0.86, with a median (interquartile ratio, [IQR]) of 0.49 (0.18, 0.53). In multivariable linear regression, the SomaScan readout for 6 of the 23 examined proteins (26%) was most strongly associated with the OM-derived abundance of the same (target) protein. For 3 of 23 (13%), the SomaScan and OM-derived abundance of each protein were significantly associated, but the SomaScan readout was more strongly associated with OM-derived abundance of one or more “off-target” proteins. For the remaining 14 proteins (61%), the SomaScan readouts were not significantly associated with the OM-derived abundance of the targeted proteins. In 6 of the latest group, the SomaScan readout was not associated with urine abundance of any of the 23 quantified proteins. To sum, over half of the SomaScan results could not be confirmed by independent orthogonal methods.
{"title":"Comparative Analysis of Protein Quantification by the SomaScan Assay versus Orthogonal Methods in Urine from People with Diabetic Kidney Disease","authors":"Lauren N. Lopez, Blythe Durbin-Johnson, Chenoa R. Vargas, John Ruzinski, Anne Goodling, Rajnish Mehrotra, Tomas Vaisar, David M. Rocke, Maryam Afkarian","doi":"10.1021/acs.jproteome.4c00322","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00322","url":null,"abstract":"To our knowledge, calibration curves or other validations for thousands of SomaScan aptamers are not publicly available. Moreover, the abundance of urine proteins obtained from these assays is not routinely validated with orthogonal methods (OMs). We report an in-depth comparison of SomaScan readout for 23 proteins in urine samples from patients with diabetic kidney disease (<i>n</i> = 118) vs OMs, including liquid chromatography-targeted mass spectrometry (LC-MS), ELISA, and nephelometry. Pearson correlation between urine abundance of the 23 proteins from SomaScan 3.2 vs OMs ranged from −0.58 to 0.86, with a median (interquartile ratio, [IQR]) of 0.49 (0.18, 0.53). In multivariable linear regression, the SomaScan readout for 6 of the 23 examined proteins (26%) was most strongly associated with the OM-derived abundance of the same (target) protein. For 3 of 23 (13%), the SomaScan and OM-derived abundance of each protein were significantly associated, but the SomaScan readout was more strongly associated with OM-derived abundance of one or more “off-target” proteins. For the remaining 14 proteins (61%), the SomaScan readouts were not significantly associated with the OM-derived abundance of the targeted proteins. In 6 of the latest group, the SomaScan readout was not associated with urine abundance of any of the 23 quantified proteins. To sum, over half of the SomaScan results could not be confirmed by independent orthogonal methods.","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516642","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}