Metagenomic next-generation sequencing (mNGS) for pan-pathogen detection has been successfully tested in proof-of-concept case studies in patients with acute illness of unknown etiology but to date has been largely confined to research settings. Here, we developed and validated a clinical mNGS assay for diagnosis of infectious causes of meningitis and encephalitis from cerebrospinal fluid (CSF) in a licensed microbiology laboratory. A customized bioinformatics pipeline, SURPI+, was developed to rapidly analyze mNGS data, generate an automated summary of detected pathogens, and provide a graphical user interface for evaluating and interpreting results. We established quality metrics, threshold values, and limits of detection of 0.2-313 genomic copies or colony forming units per milliliter for each representative organism type. Gross hemolysis and excess host nucleic acid reduced assay sensitivity; however, spiked phages used as internal controls were reliable indicators of sensitivity loss. Diagnostic test accuracy was evaluated by blinded mNGS testing of 95 patient samples, revealing 73% sensitivity and 99% specificity compared to original clinical test results, and 81% positive percent agreement and 99% negative percent agreement after discrepancy analysis. Subsequent mNGS challenge testing of 20 positive CSF samples prospectively collected from a cohort of pediatric patients hospitalized with meningitis, encephalitis, and/or myelitis showed 92% sensitivity and 96% specificity relative to conventional microbiological testing of CSF in identifying the causative pathogen. These results demonstrate the analytic performance of a laboratory-validated mNGS assay for panpathogen detection, to be used clinically for diagnosis of neurological infections from CSF.
6Metagenomic next-generation sequencing (mNGS) for pan-pathogen detection has been 3 7 successfully tested in proof-of-concept case studies in patients with acute illness of unknown 3 8 etiology, but to date has been largely confined to research settings. Here we developed and 3 9 validated an mNGS assay for diagnosis of infectious causes of meningitis and encephalitis from 4 0 cerebrospinal fluid (CSF) in a licensed clinical laboratory. A clinical bioinformatics pipeline, 4 1 SURPI+, was developed to rapidly analyze mNGS data, automatically report detected 4 2 pathogens, and provide a graphical user interface for evaluating and interpreting results. We 4 3 established quality metrics, threshold values, and limits of detection of between 0.16 -313 4 4 genomic copies or colony forming units per milliliter for each representative organism type. 4 5 Gross hemolysis and excess host nucleic acid reduced assay sensitivity; however, a spiked 4 6 phage used as an internal control was a reliable indicator of sensitivity loss. Diagnostic test 4 7 accuracy was evaluated by blinded mNGS testing of 95 patient samples, revealing 73% 4 8 sensitivity and 99% specificity compared to original clinical test results, with 81% positive 4 9
Centers for Disease Control and Prevention.
Background Current public health efforts often use molecular technologies to identify and contain communicable disease networks, but not for HIV. Here, we investigate how molecular epidemiology can be used to identify highly-related HIV networks within a population and how voluntary contact tracing of sexual partners can be used to selectively target these networks. Methods We evaluated the use of HIV-1 pol sequences obtained from participants of a community-recruited cohort (n=268) and a primary infection research cohort (n=369) to define highly related transmission clusters and the use of contact tracing to link other individuals (n=36) within these clusters. The presence of transmitted drug resistance was interpreted from the pol sequences (Calibrated Population Resistance v3.0). Results Phylogenetic clustering was conservatively defined when the genetic distance between any two pol sequences was <1%, which identified 34 distinct transmission clusters within the combined community-recruited and primary infection research cohorts containing 160 individuals. Although sequences from the epidemiologically-linked partners represented approximately 5% of the total sequences, they clustered with 60% of the sequences that clustered from the combined cohorts (O.R. 21.7; p=<0.01). Major resistance to at least one class of antiretroviral medication was found in 19% of clustering sequences. Conclusions Phylogenetic methods can be used to identify individuals who are within highly related transmission groups, and contact tracing of epidemiologically-linked partners of recently infected individuals can be used to link into previously-defined transmission groups. These methods could be used to implement selectively targeted prevention interventions.
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