[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-427710-105":59,"doc-detail-427710-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","systems-level-plasma-biomarkers-for-tissue-health-integrating-protein-networks-with-rag-biological-insights","SYSTEMS-LEVEL PLASMA BIOMARKERS FOR TISSUE HEALTH - Integrating Protein Networks with RAG Biological Insights","","Plasma proteomics enables inference of tissue-specific biological processes, yet the regulatory links between circulating plasma proteins and tissues are not well defined. This study analyzes paired plasma–tissue proteomics from 172 samples across muscle, ovary, and breast, identifying plasma protein signatures predictive of tissue protein levels. Correlation and Lasso modeling are complemented by a retrieval-augmented generation (RAG) framework to contextualize systemic interactions. Cross-tissue regulatory modules, sex- and age-dependent modules, and longevity-associated plasma–tissue interactions are characterized. Results provide a noninvasive systems view of tissue biology for aging research.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/systems-level-plasma-biomarkers-for-tissue-health-integrating-protein-networks-with-rag-biological-insights/427710/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/systems-level-plasma-biomarkers-for-tissue-health-integrating-protein-networks-with-rag-biological-insights/427710.png","ImageObject",300,407,{"name":92,"@type":93},"WPS_1786070896","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How did the study relate plasma proteins to tissue protein levels?","Question",{"text":112,"@type":113},"Using paired plasma–tissue proteomic data from 172 samples, the authors applied correlation analyses and Lasso regression to identify plasma protein signatures that predict tissue protein levels.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What role does the RAG model play in the research?",{"text":117,"@type":113},"A retrieval-augmented generation (RAG) model powered by large language models (LLMs) was used to enrich understanding of systemic protein interactions and contextualize the findings.",{"name":119,"@type":110,"acceptedAnswer":120},"What did the authors find about regulatory modules across tissues?",{"text":121,"@type":113},"They identified robust cross-tissue regulatory protein modules largely independent of age and sex, with primary mediation of immune system regulation and steroid hormone.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},427710,1791125305,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":81},549768072016,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Innovation in Aging, 2025, Vol. 9, No. S2 749  \nAbstract citation ID: igaf122.2547  \nSYSTEMS-LEVEL PLASMA BIOMARKERS FOR TISSUE HEALTH: INTEGRATING PROTEIN NETWORKS WITH RAG BIOLOGICAL INSIGHTS  \nQiongrong Huang1, John Earls1, Noa Rappaport2, Lance Pflieger3, Kengo Watanabe4, and Nathan Price1, 1. The Buck Institute, Navato, California, United States, 2. Institute for Systems Biology, Buck Institute, Phenome Health, Seattle, Washington, United States, 3. Phenome Health, Seattle,  \nWashington, United States, 4. Tokushima University Graduate School of Biomedical Sciences, Tokushima, Tokushima, Japan  \nPlasma proteomics offers a promising avenue to infer tissue-specific biological processes; however, the regulatory relationships between plasma and tissues remain unclear. Using paired plasma-tissue proteomic data from 172 samples spanning muscle, ovary, and breast tissues, we we analyzed 5,415 proteins and employed correlation analyses and Lasso regression to identify plasma protein signatures predictive of tissue protein levels. To validate and contextualize these findings, we integrated a retrieval-augmented generation (RAG) model powered by large language models (LLMs) to enrich our understanding of systemic protein interactions. We uncovered robust cross-tissue regulatory protein modules independent of age and sex, primarily mediating immune system regulation and steroid hormone. Sex- and age-dependent regulatory modules were identified using weighted gene co-expression network analysis (WGCNA), revealing distinct aging effects: in males, aging impacted modules related to muscular and reproductive system development, angiogenesis regulation, and non-canonical Wnt signaling (Spearman’s ρ = 0.66, p \u003C 2.2 × 10 16, whereas in females, aging primarily affected cell-cell interactions, angiogenesis, extracellular matrix organization, and nervous system development (Spearman’s ρ = 0.34, p \u003C 2.2 × 10 16) . Plasma-tissue modules exhibited functional parallels with minimal protein overlap, suggesting regulatory mediation via distinct proteins. Using Differential Rank Conservation (DIRAC), we further identified key plasma-tissue interactions associated with longevity. These findings provide a systems-level perspective on the plasma proteome as a noninvasive window into tissue biology, with implications for aging research.","cbCaiiybgwyGophE","https://ap.wps.com/l/cbCaiiybgwyGophE","pdf",685081,"English","# Abstract\n## Study design and data integration\n## Modeling plasma–tissue relationships\n## RAG-based contextualization and regulatory modules\n## Sex- and age-dependent effects and longevity associations","[{\"question\":\"How did the study relate plasma proteins to tissue protein levels?\",\"answer\":\"Using paired plasma–tissue proteomic data from 172 samples, the authors applied correlation analyses and Lasso regression to identify plasma protein signatures that predict tissue protein levels.\"},{\"question\":\"What role does the RAG model play in the research?\",\"answer\":\"A retrieval-augmented generation (RAG) model powered by large language models (LLMs) was used to enrich understanding of systemic protein interactions and contextualize the findings.\"},{\"question\":\"What did the authors find about regulatory modules across tissues?\",\"answer\":\"They identified robust cross-tissue regulatory protein modules largely independent of age and sex, with primary mediation of immune system regulation and steroid hormone.\"}]","SYSTEMS-LEVEL PLASMA BIOMARKERS FOR TISSUE HEALTH - Integrating Protein Networks with RAG Biological Insights | PDF",1790644643]