Plasma cell-free RNA signatures of inflammatory syndromes in children

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 121; no. 37; p. e2403897121
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 10.09.2024
• Subjects: Adolescent; Bacterial diseases; Bacterial infections; Bacterial Infections - blood; Bacterial Infections - diagnosis; Biological Sciences; Biomarkers - blood; Cell-Free Nucleic Acids - blood; Cell-Free Nucleic Acids - genetics; Child; Child, Preschool; Children; COVID-19 - complications; Diagnosis; Diagnosis, Differential; Differential diagnosis; Disorders; Endothelium; Female; Humans; Infant; Infections; Inflammation - blood; Injury analysis; Learning algorithms; Machine Learning; Male; Mucocutaneous lymph node syndrome; Mucocutaneous Lymph Node Syndrome - blood; Mucocutaneous Lymph Node Syndrome - diagnosis; Mucocutaneous Lymph Node Syndrome - genetics; Multisystem inflammatory syndrome in children; Nervous system; Pediatrics; Respiratory tract; Systemic Inflammatory Response Syndrome - blood; Systemic Inflammatory Response Syndrome - diagnosis; Viral infections; Virus Diseases - blood; Virus Diseases - diagnosis; Virus Diseases - genetics; diagnostics; cell-free RNA; machine learning; inflammation; pediatrics
• DOI: 10.1073/pnas.2403897121
• ISSN: 0027-8424; 1091-6490

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.