Plasma cell- free RNA signatures of inflammatory syndromes in children

• 成果类型: Article
• 署名作者: Loy, Conor J.; Servellita, Venice; Sotomayor-Gonzalez, Alicia; Bliss, Andrew; Lenz, Joan S.; Belcher, Emma; Suslovic, Will; Nguyen, Jenny; Williams, Meagan E.; Oseguera, Miriam; Gardiner, Michael A.; Choi, Jong-Ha; Hsiao, Hui-Mien; Wang, Hao; Kim, Jihoon; Shimizu, Chisato; Tremoulet, Adriana H.; Delaney, Meghan; DeBiasi, Roberta L.; Rostad, Christina A.; Burns, Jane C.; Chiu, Charles Y.; De Vlaminck, Iwijn
• 署名单位: Cornell University; University of California System; University of California San Francisco; Children's National Health System; Rady Childrens Hospital San Diego; University of California System; University of California San Diego; Emory University; Children's Healthcare of Atlanta (CHOA); Yale University; George Washington University; University of California System; University of California San Francisco; Chan Zuckerberg Initiative (CZI)
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9742
• DOI: 10.1073/pnas.2403897121
• 发表日期: 2024-09-10
• 关键词: diagnosis; blood

摘要

Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circulating cell- free RNA (cfRNA) in plasma as an analyte for the differential diagnosis and characterization of pediatric inflammatory syndromes. We profiled cfRNA in 370 plasma samples from pediatric patients with a range of inflammatory conditions, including Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS- C), viral infections, and bacterial infections. We developed machine learning models based on these cfRNA profiles, which effectively differentiated KD from MIS-C-two conditions presenting with overlapping symptoms-with high performance [test area under the curve = 0.98]. We further extended this methodology into a multiclass machine learning framework that achieved 80% accuracy in distinguishing among KD, MIS- C, viral, and bacterial infections. We further demonstrated that cfRNA profiles can be used to quantify injury to specific tissues and organs, including the liver, heart, endothelium, nervous system, and the upper respiratory tract. Overall, this study identified cfRNA as a versatile analyte for the differential diagnosis and characterization of a wide range of pediatric inflammatory syndromes.