Lin Xi, Xuna Wu, Jiahui Wang, Zhaoxia Zhang, Mingjie He, Zeeshan Zeeshan, Thorsten Stefan, Waltraud X Schulze
{"title":"AE-MS方法中的结构域交换嵌合体揭示了BRI1和SIRK1的受体激酶信号通过它们的相互作用组紧密平衡。","authors":"Lin Xi, Xuna Wu, Jiahui Wang, Zhaoxia Zhang, Mingjie He, Zeeshan Zeeshan, Thorsten Stefan, Waltraud X Schulze","doi":"10.1016/j.mcpro.2024.100857","DOIUrl":null,"url":null,"abstract":"<p><p>At the plasma membrane, in response to biotic and abiotic cues, specific ligands initiate the formation of receptor kinase heterodimers, which regulate the activities of plasma membrane proteins and initiate signaling cascades to the nucleus. In this study, we utilized affinity enrichment mass spectrometry to investigate the stimulus-dependent interactomes of LRR receptor kinases in response to their respective ligands, with an emphasis on exploring structural influences and potential cross-talk events at the plasma membrane. BRI1 and SIRK1 were chosen as receptor kinases with distinct coreceptor preference. By using interactome characteristic of domain-swap chimera following a gradient boosting learning algorithm trained on SIRK1 and BRI1 interactomes, we attribute contributions of extracellular domain, transmembrane domain, juxtamembrane domain, and kinase domain of respective ligand-binding receptors to their interaction with their coreceptors and substrates. Our results revealed juxtamembrane domain as major structural element defining the specific substrate recruitment for BRI1 and extracellular domain for SIRK1. Furthermore, the learning algorithm enabled us to predict the phenotypic outcomes of chimeric receptors based on different domain combinations, which was verified by dedicated experiments. As a result, our work reveals a tightly controlled balance of signaling cascade activation dependent on ligand-binding receptors domains and the internal ligand status of the plant. Moreover, our study shows the robust utility of machine learning classification as a quantitative metric for studying dynamic interactomes, dissecting the contribution of specific domains and predicting their phenotypic outcome.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Receptor Kinase Signaling of BRI1 and SIRK1 Is Tightly Balanced by Their Interactomes as Revealed From Domain-Swap Chimaera in AE-MS Approaches.\",\"authors\":\"Lin Xi, Xuna Wu, Jiahui Wang, Zhaoxia Zhang, Mingjie He, Zeeshan Zeeshan, Thorsten Stefan, Waltraud X Schulze\",\"doi\":\"10.1016/j.mcpro.2024.100857\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>At the plasma membrane, in response to biotic and abiotic cues, specific ligands initiate the formation of receptor kinase heterodimers, which regulate the activities of plasma membrane proteins and initiate signaling cascades to the nucleus. In this study, we utilized affinity enrichment mass spectrometry to investigate the stimulus-dependent interactomes of LRR receptor kinases in response to their respective ligands, with an emphasis on exploring structural influences and potential cross-talk events at the plasma membrane. BRI1 and SIRK1 were chosen as receptor kinases with distinct coreceptor preference. By using interactome characteristic of domain-swap chimera following a gradient boosting learning algorithm trained on SIRK1 and BRI1 interactomes, we attribute contributions of extracellular domain, transmembrane domain, juxtamembrane domain, and kinase domain of respective ligand-binding receptors to their interaction with their coreceptors and substrates. Our results revealed juxtamembrane domain as major structural element defining the specific substrate recruitment for BRI1 and extracellular domain for SIRK1. Furthermore, the learning algorithm enabled us to predict the phenotypic outcomes of chimeric receptors based on different domain combinations, which was verified by dedicated experiments. As a result, our work reveals a tightly controlled balance of signaling cascade activation dependent on ligand-binding receptors domains and the internal ligand status of the plant. Moreover, our study shows the robust utility of machine learning classification as a quantitative metric for studying dynamic interactomes, dissecting the contribution of specific domains and predicting their phenotypic outcome.</p>\",\"PeriodicalId\":18712,\"journal\":{\"name\":\"Molecular & Cellular Proteomics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular & Cellular Proteomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.mcpro.2024.100857\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular & Cellular Proteomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.mcpro.2024.100857","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Receptor Kinase Signaling of BRI1 and SIRK1 Is Tightly Balanced by Their Interactomes as Revealed From Domain-Swap Chimaera in AE-MS Approaches.
At the plasma membrane, in response to biotic and abiotic cues, specific ligands initiate the formation of receptor kinase heterodimers, which regulate the activities of plasma membrane proteins and initiate signaling cascades to the nucleus. In this study, we utilized affinity enrichment mass spectrometry to investigate the stimulus-dependent interactomes of LRR receptor kinases in response to their respective ligands, with an emphasis on exploring structural influences and potential cross-talk events at the plasma membrane. BRI1 and SIRK1 were chosen as receptor kinases with distinct coreceptor preference. By using interactome characteristic of domain-swap chimera following a gradient boosting learning algorithm trained on SIRK1 and BRI1 interactomes, we attribute contributions of extracellular domain, transmembrane domain, juxtamembrane domain, and kinase domain of respective ligand-binding receptors to their interaction with their coreceptors and substrates. Our results revealed juxtamembrane domain as major structural element defining the specific substrate recruitment for BRI1 and extracellular domain for SIRK1. Furthermore, the learning algorithm enabled us to predict the phenotypic outcomes of chimeric receptors based on different domain combinations, which was verified by dedicated experiments. As a result, our work reveals a tightly controlled balance of signaling cascade activation dependent on ligand-binding receptors domains and the internal ligand status of the plant. Moreover, our study shows the robust utility of machine learning classification as a quantitative metric for studying dynamic interactomes, dissecting the contribution of specific domains and predicting their phenotypic outcome.
期刊介绍:
The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action.
The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data.
Scope:
-Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights
-Novel experimental and computational technologies
-Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes
-Pathway and network analyses of signaling that focus on the roles of post-translational modifications
-Studies of proteome dynamics and quality controls, and their roles in disease
-Studies of evolutionary processes effecting proteome dynamics, quality and regulation
-Chemical proteomics, including mechanisms of drug action
-Proteomics of the immune system and antigen presentation/recognition
-Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease
-Clinical and translational studies of human diseases
-Metabolomics to understand functional connections between genes, proteins and phenotypes