作者
Prashanth Ramachandran,Akshaya Ramesh,Fiona Cresswell,Anne E. Wapniarski,R. Narendra,Carson M Quinn,Edwina B. Tran,Morris K Rutakingirwa,Ananta S Bangdiwala,Enock Kagimu,K. T. Kandole,Kelsey Zorn,Lillian Tugume,John Kasibante,Kenneth Ssebambulidde,Michael Okirwoth,Nathan C Bahr,Abdu K Musubire,Caleb P Skipper,Camille Fouassier,Amy Lyden,Paula Hayakawa Serpa,Gloria Castañeda,Saharai Caldera,Vida Ahyong,Joseph L. DeRisi,Charles Langelier,Emily Crawford,David R. Boulware,David B. Meya,Michael R. Wilson
摘要
Abstract The epidemiology of infectious causes of meningitis in sub-Saharan Africa is not well understood, and a common cause of meningitis in this region, Mycobacterium tuberculosis (TB), is notoriously hard to diagnose. Here we show that integrating cerebrospinal fluid (CSF) metagenomic next-generation sequencing (mNGS) with a host gene expression-based machine learning classifier (MLC) enhances diagnostic accuracy for TB meningitis (TBM) and its mimics. 368 HIV-infected Ugandan adults with subacute meningitis were prospectively enrolled. Total RNA and DNA CSF mNGS libraries were sequenced to identify meningitis pathogens. In parallel, a CSF host transcriptomic MLC to distinguish between TBM and other infections was trained and then evaluated in a blinded fashion on an independent dataset. mNGS identifies an array of infectious TBM mimics (and co-infections), including emerging, treatable, and vaccine-preventable pathogens including Wesselsbron virus, Toxoplasma gondii, Streptococcus pneumoniae , Nocardia brasiliensis , measles virus and cytomegalovirus. By leveraging the specificity of mNGS and the sensitivity of an MLC created from CSF host transcriptomes, the combined assay has high sensitivity (88.9%) and specificity (86.7%) for the detection of TBM and its many mimics. Furthermore, we achieve comparable combined assay performance at sequencing depths more amenable to performing diagnostic mNGS in low resource settings.