Integrated host-microbe plasma metagenomics for sepsis diagnosis in a prospective cohort of critically ill adults

We carried out integrated host and pathogen metagenomic RNA and DNA next generation sequencing (mNGS) of whole blood (n = 221) and plasma (n = 138) from critically ill patients following hospital admission. We assigned patients into sepsis groups on the basis of clinical and microbiological criteria. From whole-blood gene expression data, we distinguished patients with sepsis from patients with non-infectious systemic inflammatory conditions using a trained bagged support vector machine (bSVM) classifier (area under the receiver operating characteristic curve (AUC) = 0.81 in the training set; AUC = 0.82 in a held-out validation set). Plasma RNA also yielded a transcriptional signature of sepsis with several genes previously reported as sepsis biomarkers, and a bSVM sepsis diagnostic classifier (AUC = 0.97 training set; AUC = 0.77 validation set). Pathogen detection performance of plasma mNGS varied on the basis of pathogen and site of infection. To improve detection of virus, we developed a secondary transcriptomic classifier (AUC = 0.94 training set; AUC = 0.96 validation set). We combined host and microbial features to develop an integrated sepsis diagnostic model that identified 99% of microbiologically confirmed sepsis cases, and predicted sepsis in 74% of suspected and 89% of indeterminate sepsis cases. In summary, we suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis

Metagenomic next-generation sequencing of samples from pediatric febrile illness in Tororo, Uganda.

Febrile illness is a major burden in African children, and non-malarial causes of fever are uncertain. In this retrospective exploratory study, we used metagenomic next-generation sequencing (mNGS) to evaluate serum, nasopharyngeal, and stool specimens from 94 children (aged 2-54 months) with febrile illness admitted to Tororo District Hospital, Uganda. The most common microbes identified were Plasmodium falciparum (51.1% of samples) and parvovirus B19 (4.4%) from serum; human rhinoviruses A and C (40%), respiratory syncytial virus (10%), and human herpesvirus 5 (10%) from nasopharyngeal swabs; and rotavirus A (50% of those with diarrhea) from stool. We also report the near complete genome of a highly divergent orthobunyavirus, tentatively named Nyangole virus, identified from the serum of a child diagnosed with malaria and pneumonia, a Bwamba orthobunyavirus in the nasopharynx of a child with rash and sepsis, and the genomes of two novel human rhinovirus C species. In this retrospective exploratory study, mNGS identified multiple potential pathogens, including 3 new viral species, associated with fever in Ugandan children

Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens.

The burden of meningitis in low-and-middle-income countries remains significant, but the infectious causes remain largely unknown, impeding institution of evidence-based treatment and prevention decisions. We conducted a validation and application study of unbiased metagenomic next-generation sequencing (mNGS) to elucidate etiologies of meningitis in Bangladesh. This RNA mNGS study was performed on cerebrospinal fluid (CSF) specimens from patients admitted in the largest pediatric hospital, a World Health Organization sentinel site, with known neurologic infections (n = 36), with idiopathic meningitis (n = 25), and with no infection (n = 30), and six environmental samples, collected between 2012 and 2018. We used the IDseq bioinformatics pipeline and machine learning to identify potentially pathogenic microbes, which we then confirmed orthogonally and followed up through phone/home visits. In samples with known etiology and without infections, there was 83% concordance between mNGS and conventional testing. In idiopathic cases, mNGS identified a potential bacterial or viral etiology in 40%. There were three instances of neuroinvasive Chikungunya virus (CHIKV), whose genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause febrile illness in 2017. CHIKV-specific qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 (n = 472) revealed 17 additional CHIKV meningitis cases, exposing an unrecognized meningitis outbreak. Orthogonal molecular confirmation, case-based clinical data, and patient follow-up substantiated the findings. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis, conduct surveillance, and predict outbreaks. The improved patient- and population-level data can inform evidence-based policy decisions.IMPORTANCE Globally, there are an estimated 10.6 million cases of meningitis and 288,000 deaths every year, with the vast majority occurring in low- and middle-income countries. In addition, many survivors suffer from long-term neurological sequelae. Most laboratories assay only for common bacterial etiologies using culture and directed PCR, and the majority of meningitis cases lack microbiological diagnoses, impeding institution of evidence-based treatment and prevention strategies. We report here the results of a validation and application study of using unbiased metagenomic sequencing to determine etiologies of idiopathic (of unknown cause) cases. This included CSF from patients with known neurologic infections, with idiopathic meningitis, and without infection admitted in the largest children's hospital of Bangladesh and environmental samples. Using mNGS and machine learning, we identified and confirmed an etiology (viral or bacterial) in 40% of idiopathic cases. We detected three instances of Chikungunya virus (CHIKV) that were >99% identical to each other and to a strain previously recognized to cause systemic illness only in 2017. CHIKV qPCR of all remaining stored 472 CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital uncovered an unrecognized CHIKV meningitis outbreak. CSF mNGS can complement conventional diagnostic methods to identify etiologies of meningitis, and the improved patient- and population-level data can inform better policy decisions

Pulmonary Metagenomic Sequencing Suggests Missed Infections in Immunocompromised Children

This article is made available for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing.BACKGROUND: Despite improved diagnostics, pulmonary pathogens in immunocompromised children frequently evade detection, leading to significant mortality. Therefore, we aimed to develop a highly sensitive metagenomic next-generation sequencing (mNGS) assay capable of evaluating the pulmonary microbiome and identifying diverse pathogens in the lungs of immunocompromised children. METHODS: We collected 41 lower respiratory specimens from 34 immunocompromised children undergoing evaluation for pulmonary disease at 3 children's hospitals from 2014-2016. Samples underwent mechanical homogenization, parallel RNA/DNA extraction, and metagenomic sequencing. Sequencing reads were aligned to the National Center for Biotechnology Information nucleotide reference database to determine taxonomic identities. Statistical outliers were determined based on abundance within each sample and relative to other samples in the cohort. RESULTS: We identified a rich cross-domain pulmonary microbiome that contained bacteria, fungi, RNA viruses, and DNA viruses in each patient. Potentially pathogenic bacteria were ubiquitous among samples but could be distinguished as possible causes of disease by parsing for outlier organisms. Samples with bacterial outliers had significantly depressed alpha-diversity (median, 0.61; interquartile range [IQR], 0.33-0.72 vs median, 0.96; IQR, 0.94-0.96; P < .001). Potential pathogens were detected in half of samples previously negative by clinical diagnostics, demonstrating increased sensitivity for missed pulmonary pathogens (P < .001). CONCLUSIONS: An optimized mNGS assay for pulmonary microbes demonstrates significant inoculation of the lower airways of immunocompromised children with diverse bacteria, fungi, and viruses. Potential pathogens can be identified based on absolute and relative abundance. Ongoing investigation is needed to determine the pathogenic significance of outlier microbes in the lungs of immunocompromised children with pulmonary disease

Combined Host and Microbial Metagenomic Next-Generation Sequencing: Applying Integrated Analysis Approaches for a Comprehensive Evaluation of Infectious Disease Response to Inform Diagnosis, Surveillance, and Treatment

Infectious diseases are a leading cause of morbidity and mortality worldwide. Despite significant advancement in our understanding of infectious disease biology, existing microbiologic diagnostic tests often fail to identify etiologic pathogens in cases of suspected infection. Metagenomic next-generation sequencing (mNGS) offers the potential for a universal pathogen detection method, but analysis and interpretation of findings are challenging. This is especially true for lower respiratory tract infections (LRTIs) where mNGS data interpretation is complicated by the existence of a respiratory microbiome composed of pathobionts present in both health and disease. To address the need for improved LRTI diagnostics, we first compared two fluid types commonly used for diagnosis of LRTI, showing that despite moderate microbiome differences, both mini-bronchioalveolar lavage (mBAL) and tracheal aspirate (TA) samples are suitable for identification of pathogens in the context of an infection. Then, we evaluated the utility of mNGS as a diagnostic for LRTI in a cohort of 92 TA samples from adults with acute respiratory failure. We developed methods for sifting putative pathogens from commensal microbiota as well as pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with noninfectious acute respiratory illnesses. We applied the models developed for evaluation of LRTI status to several other cohorts and disease contexts to show their broad applicability. The low sensitivity of existing clinical diagnostics results in an imperfect gold standard, complicating the development of mNGS-based biomarkers. We explored the impact of label noise on host gene expression classifiers and methods for circumventing the issue. First, we tested whether label-noise robust logistic regression approaches could improve classifier performance by enabling the use of a larger training set. Then, we tested whether variational autoencoders, an unsupervised dimensionality reduction approach, could generate novel insight from combined host and microbial mNGS data. Altogether, this work suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome, and host transcriptome may hold promise as a tool for diagnosis of infections and contextualization of patient response

Combined host and microbial metagenomic next-generation sequencing: Applying integrated analysis approaches for a comprehensive evaluation of infectious disease response to inform diagnosis, surveillance, and treatment

Infectious diseases are a leading cause of morbidity and mortality worldwide. Despite significant advancement in our understanding of infectious disease biology, existing microbiologic diagnostic tests often fail to identify etiologic pathogens in cases of suspected infection. Metagenomic next-generation sequencing (mNGS) offers the potential for a universal pathogen detection method, but analysis and interpretation of findings are challenging. This is especially true for lower respiratory tract infections (LRTIs) where mNGS data interpretation is complicated by the existence of a respiratory microbiome composed of pathobionts present in both health and disease.To address the need for improved LRTI diagnostics, we first compared two fluid types commonly used for diagnosis of LRTI, showing that despite moderate microbiome differences, both mini-bronchioalveolar lavage (mBAL) and tracheal aspirate (TA) samples are suitable for identification of pathogens in the context of an infection. Then, we evaluated the utility of mNGS as a diagnostic for LRTI in a cohort of 92 TA samples from adults with acute respiratory failure. We developed methods for sifting putative pathogens from commensal microbiota as well as pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with noninfectious acute respiratory illnesses. We applied the models developed for evaluation of LRTI status to several other cohorts and disease contexts to show their broad applicability. The low sensitivity of existing clinical diagnostics results in an imperfect gold standard, complicating the development of mNGS-based biomarkers. We explored the impact of label noise on host gene expression classifiers and methods for circumventing the issue. First, we tested whether label-noise robust logistic regression approaches could improve classifier performance by enabling the use of a larger training set. Then, we tested whether variational autoencoders, an unsupervised dimensionality reduction approach, could generate novel insight from combined host and microbial mNGS data. Altogether, this work suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome, and host transcriptome may hold promise as a tool for diagnosis of infections and contextualization of patient response

Host-Microbe Metagenomics: a Lens To Refocus Our Perspective on Infectious and Inflammatory Diseases.

A dynamic relationship involving pathogen, host immune response, and microbiome characterizes the biological framework of many infectious and inflammatory diseases. Combined host/microbe metagenomics (mNGS) enables simultaneous assessment of all three features, enabling the study and diagnosis of diverse infectious and inflammatory processes ranging from pneumonia to sepsis to inflammatory diseases such as rheumatoid arthritis. Host/microbe mNGS holds promise for new mechanistic insights, diagnostic approaches, and precision medicine interventions

NCBI-Hackathons/GeneExpressionAging: Initial test release

Testing Zenodo integratio