ArticleNature communications2022
Integrating central nervous system metagenomics and host response for diagnosis of tuberculosis meningitis and its mimics.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed, 79 citations in OpenAlex.
- Safety and Tolerability of a Short Course of Linezolid for the Treatment of Predominantly Moderate to Severe Tuberculous Meningitis in Adults With Human Immunodeficiency Virus.The Journal of infectious diseases · 2025Trial
- Cerebrospinal Fluid Transcriptional Immune Pathways Linked to Survival in HIV-Associated Tuberculous Meningitis.The Journal of infectious diseases · 2026Article
- Etiologic diagnosis of suspected tuberculous meningitis by multiplex PCR of cerebrospinal fluid.Journal of clinical microbiology · 2026Article
- Review
- Clinical metagenomics for diagnosis and surveillance of viral pathogens.Nature reviews. Microbiology · 2026Review
- Central Nervous System Toxoplasmosis is an Under-Recognized Opportunistic infection in Uganda.Journal of tropical medicine · 2026Article
- Metagenomic next-generation sequencing: new horizons in microbiology.Frontiers in cellular and infection microbiology · 2026Review
- Transforming tuberculosis diagnosis with clinical metagenomics: progress and roadblocks.Journal of clinical microbiology · 2025Review
- Bronchoalveolar lavage fluid metagenomic datasets: a multidimensional clinical biomolecular resource.Scientific data · 2025Article
- A case of enterovirus D68-associated acute flaccid myelitis in an immunocompromised adult, Australia.ASM case reports · 2025Article
- Metagenomic fingerprints in bronchoalveolar lavage differentiate pulmonary diseases.NPJ digital medicine · 2025Article
- Bridging the Gap: Opsoclonus-Myoclonus Syndrome: Human Pegivirus Encephalomyelitis Diagnosed Through Metagenomic Next-Generation Sequencing.Neurology · 2025Article
- Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics.Scientific reports · 2025Article
- Novel techniques for the diagnosis of neurological infections.Current opinion in neurology · 2025Review
- Improving meningitis surveillance and diagnosis with machine learning: Insights from São Paulo.PLOS digital health · 2025Article
- Etiology and Outcomes of Meningitis among Adults in Three Ugandan Referral Hospitals, 2018-2023: A Prospective Cohort Study in a High-HIV Endemic Setting.The American journal of tropical medicine and hygiene · 2025Observational
- Metagenomic Next-generation Sequencing in Patients With Infectious Meningoencephalitis: A Comprehensive Systematic Literature Review and Meta-analysis.Open forum infectious diseases · 2025Article
- Clinical implement of Probe-Capture Metagenomics in sepsis patients: A multicentre and prospective study.Clinical and translational medicine · 2025Article
- Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.BMC medical imaging · 2025Article
- Tuberculosis: An Update for the Clinician.Respirology (Carlton, Vic.) · 2025Review
Corrections and comments
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Authors and funding
31 authors at 7 institutions in 4 countries.
Funding
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.
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