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. 

/pdf/integrating-central-nervous-system-metagenomics-and-host-sjo3wppt.pdf

Integrating central nervous system metagenomics and host response for diagnosis of tuberculosis meningitis and its mimics

A patient in California, USA, with rare and usually fatal Balamuthia mandrillaris granulomatous amebic encephalitis survived after receiving treatment with a regimen that included the repurposed drug nitroxoline. Nitroxoline, which is a quinolone typically used to treat urinary tract infections, was identified in a screen for drugs with amebicidal activity against Balamuthia.

/pdf/successful-treatment-of-balamuthia-mandrillaris-1drymc2t.pdf

Successful Treatment of Balamuthia mandrillaris Granulomatous Amebic Encephalitis with Nitroxoline

Metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) is an agnostic method for broad-based diagnosis of central nervous system (CNS) infections. Here we analyzed the 7-year performance of clinical CSF mNGS testing of 4,828 samples from June 2016 to April 2023 performed by the University of California, San Francisco (UCSF) clinical microbiology laboratory. Overall, mNGS testing detected 797 organisms from 697 (14.4%) of 4,828 samples, consisting of 363 (45.5%) DNA viruses, 211 (26.4%) RNA viruses, 132 (16.6%) bacteria, 68 (8.5%) fungi and 23 (2.9%) parasites. We also extracted clinical and laboratory metadata from a subset of the samples (n = 1,164) from 1,053 UCSF patients. Among the 220 infectious diagnoses in this subset, 48 (21.8%) were identified by mNGS alone. The sensitivity, specificity and accuracy of mNGS testing for CNS infections were 63.1%, 99.6% and 92.9%, respectively. mNGS testing exhibited higher sensitivity (63.1%) than indirect serologic testing (28.8%) and direct detection testing from both CSF (45.9%) and non-CSF (15.0%) samples (P < 0.001 for all three comparisons). When only considering diagnoses made by CSF direct detection testing, the sensitivity of mNGS testing increased to 86%. These results justify the routine use of diagnostic mNGS testing for hospitalized patients with suspected CNS infection. 

Seven-year performance of a clinical metagenomic next-generation sequencing test for diagnosis of central nervous system infections

Diagnosis of tuberculous meningitis (TBM) remains challenging due to a paucity of high-performance diagnostics. Even those that have reasonable sensitivity are not adequate to ‘rule out' TBM. Therefore, a combination of clinical factors alongside microbiological, molecular, and radiological investigations are utilized, depending on availability. A low threshold for starting empiric therapy in the appropriate clinical scenario remains crucial for good outcomes in many cases. Herein, we review the current TBM diagnostics landscape with a focus on limitations frequently encountered, such as diagnostic test performance, cost, laboratory infrastructure, and clinical expertise. Though molecular technologies, particularly GeneXpert MTB/Rif Ultra, have been a step forward, diagnosis of TBM remains difficult. We also provide an overview of promising technologies, such as cerebrospinal fluid (CSF) lactate, a new lipoarabinomannan test (FujiLAM), metagenomic next-generation sequencing, and transcriptomics that may further improve our TBM diagnostic capacity and lead to better outcomes.

/pdf/improving-technology-to-diagnose-tuberculous-meningitis-are-2ghblqbf.pdf

Improving Technology to Diagnose Tuberculous Meningitis: Are We There Yet?

Metagenomic next-generation sequencing (mNGS) is the process of sequencing all genetic material in a biological sample. The technique is growing in popularity with myriad applications including outbreak investigation, biosurveillance, and pathogen detection in clinical samples. However, mNGS programs are costly to build and maintain, and additional obstacles faced by low- and middle-income countries (LMICs) may further widen global inequities in mNGS capacity. Over the past two decades, several important infectious disease outbreaks have highlighted the importance of establishing widespread sequencing capacity to support rapid disease detection and containment at the source. Using lessons learned from the COVID-19 pandemic, LMICs can leverage current momentum to design and build sustainable mNGS programs, which would form part of a global surveillance network crucial to the elimination of infectious diseases.

Metagenomic pathogen sequencing in resource-scarce settings: Lessons learned and the road ahead

Minimalistic Transcriptomic Signatures Permit Accurate Early Prediction of COVID-19 Mortality

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Integrating central nervous system metagenomics and host response for diagnosis of tuberculosis meningitis and its mimics

Minimalistic Transcriptomic Signatures Permit Accurate Early Prediction of COVID-19 Mortality