Direct Diagnosis of Echovirus 12 Meningitis Using Metagenomic Next Generation Sequencing.
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ABSTRACT:
SUBMITTER: Morsli M
PROVIDER: S-EPMC8156364 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Current routine real-time PCR methods used for the point-of-care diagnosis of infectious meningitis do not allow for one-shot genotyping of the pathogen, as in the case of deadly Haemophilus influenzae meningitis. Real-time PCR diagnosed H. influenzae meningitis in a 22-year-old male patient, during his hospitalisation following a more than six-metre fall. Using an Oxford Nanopore Technologies real-time sequencing run in parallel to real-time PCR, we detected the H. influenzae genome directly from the cerebrospinal fluid sample in six hours. Furthermore, BLAST analysis of the sequence encoding for a partial DUF417 domain-containing protein diagnosed a non-b serotype, non-typeable H.influenzae belonging to lineage H. influenzae 22.1-21. The Oxford Nanopore metagenomic next-generation sequencing approach could be considered for the point-of-care diagnosis of infectious meningitis, by direct identification of pathogenic genomes and their genotypes/serotypes.
| S-EPMC8069228 | biostudies-literature
Project description:Abstract Background Metagenomic next-generation sequencing (mNGS) of CSF can identify nearly all pathogens in a single test. We previously validated a CSF mNGS assay in a licensed clinical laboratory. To date, the utility of mNGS for infectious disease diagnosis has been described in case reports and small case series, but not in a large-scale clinical trial. Methods The PDAID (“Precision Diagnosis of Acute Infectious Diseases”) study was a 1-year nationwide prospective study across 8 tertiary care hospitals to evaluate the performance and utility of a clinical metagenomic sequencing assay for diagnosis of meningitis, encephalitis, or myelitis from cerebrospinal fluid (CSF) (ClinicalTrials.gov number NCT02910037). We recruited acutely ill hospitalized inpatients lacking a diagnosis at the time of enrollment. CSF samples were processed and analyzed by mNGS testing within 1 week of receipt in the clinical microbiology laboratory, with sequencing results reported in the patient medical record and used to make contemporaneous treatment decisions. Weekly clinical microbial sequencing boards were convened to discuss mNGS results with treating physicians, and clinical impact evaluated by surveys, chart review, and direct clinician feedback. Results A total of 204 patients were enrolled. Patients were severely ill (ICU 48%, average length of stay 26 days, overall 30-day mortality 7.4%). Fifty-nine neurologic infections were diagnosed in 57 patients (27.9%). mNGS identified 15 (25.4%) infections that were missed by all conventional microbiological tests, including emerging and/or uncommon pathogens such as St. Louis encephalitis virus, hepatitis E virus acquired by lung transplant, and Nocardia farcinica. Twelve of the 15 mNGS-only diagnoses (80%) had clinical impact, with 9 of 15 (60%) guiding appropriate treatment. For diagnosis of infections by direct detection CSF testing, mNGS had 79.1% sensitivity and 98.8% specificity, versus 65.1% sensitivity and 99.4% specificity by conventional testing. Conclusion A significant proportion of neurologic infections are missed despite extensive diagnostic testing performed in tertiary care hospitals. Clinical metagenomic CSF testing was found to be useful in increasing the number of diagnosed neurologic infections and providing actionable information for physicians. Disclosures All authors: No reported disclosures.
| S-EPMC6252609 | biostudies-literature
Project description:Importance:Identifying infectious causes of subacute or chronic meningitis can be challenging. Enhanced, unbiased diagnostic approaches are needed. Objective:To present a case series of patients with diagnostically challenging subacute or chronic meningitis using metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) supported by a statistical framework generated from mNGS of control samples from the environment and from patients who were noninfectious. Design, Setting, and Participants:In this case series, mNGS data obtained from the CSF of 94 patients with noninfectious neuroinflammatory disorders and from 24 water and reagent control samples were used to develop and implement a weighted scoring metric based on z scores at the species and genus levels for both nucleotide and protein alignments to prioritize and rank the mNGS results. Total RNA was extracted for mNGS from the CSF of 7 participants with subacute or chronic meningitis who were recruited between September 2013 and March 2017 as part of a multicenter study of mNGS pathogen discovery among patients with suspected neuroinflammatory conditions. The neurologic infections identified by mNGS in these 7 participants represented a diverse array of pathogens. The patients were referred from the University of California, San Francisco Medical Center (n = 2), Zuckerberg San Francisco General Hospital and Trauma Center (n = 2), Cleveland Clinic (n = 1), University of Washington (n = 1), and Kaiser Permanente (n = 1). A weighted z score was used to filter out environmental contaminants and facilitate efficient data triage and analysis. Main Outcomes and Measures:Pathogens identified by mNGS and the ability of a statistical model to prioritize, rank, and simplify mNGS results. Results:The 7 participants ranged in age from 10 to 55 years, and 3 (43%) were female. A parasitic worm (Taenia solium, in 2 participants), a virus (HIV-1), and 4 fungi (Cryptococcus neoformans, Aspergillus oryzae, Histoplasma capsulatum, and Candida dubliniensis) were identified among the 7 participants by using mNGS. Evaluating mNGS data with a weighted z score-based scoring algorithm reduced the reported microbial taxa by a mean of 87% (range, 41%-99%) when taxa with a combined score of 0 or less were removed, effectively separating bona fide pathogen sequences from spurious environmental sequences so that, in each case, the causative pathogen was found within the top 2 scoring microbes identified using the algorithm. Conclusions and Relevance:Diverse microbial pathogens were identified by mNGS in the CSF of patients with diagnostically challenging subacute or chronic meningitis, including a case of subarachnoid neurocysticercosis that defied diagnosis for 1 year, the first reported case of CNS vasculitis caused by Aspergillus oryzae, and the fourth reported case of C dubliniensis meningitis. Prioritizing metagenomic data with a scoring algorithm greatly clarified data interpretation and highlighted the problem of attributing biological significance to organisms present in control samples used for metagenomic sequencing studies.
| S-EPMC5933460 | biostudies-literature
Project description:BackgroundMycoplasma hominis meningitis is a rare postoperative complication of neurosurgery. Accurate and early diagnosis of M. hominis remains challenging because of the limitations of traditional detection methods. Metagenomic next-generation sequencing (mNGS) is an advanced technique with high sensitivity and specificity for identifying infectious pathogens; however, its application in diagnosing M. hominis meningitis has not been widely studied.Case presentationWe report the case of a 61-year-old man who presented with fever and headache after neurosurgical treatment for a cerebral hemorrhage. Empiric antibiotic therapy was ineffective. Traditional culture of pathogens and serological testing yielded negative results, but M. hominis was detected in the cerebrospinal fluid by mNGS. After further verification by polymerase chain reaction (PCR), the patient's clinical treatment was adjusted accordingly. With targeted antibiotic intervention, the patient's symptoms were effectively alleviated, and clinical indicators returned to normal levels. Furthermore, the abundance of M. hominis decreased significantly compared to the initial mNGS reading after targeted treatment, indicating that the infection caused by M. hominis was effectively controlled.ConclusionUsing mNGS, we found that M. hominis may be a candidate causative agent of meningitis. The technique also has the advantage of timeliness and accuracy that traditional cultures cannot achieve. A combination of mNGS with PCR is recommended to identify pathogens in the early stages of infectious diseases to administer targeted clinical medication.
| S-EPMC9380824 | biostudies-literature
Project description:Leishmaniasis is a vector-borne disease caused by Leishmania. Although the incidence of leishmaniasis in China is currently low, it has not been completely eradicated. In 2019, visceral leishmaniasis was diagnosed in three patients using bone marrow microscopic examination and metagenomic next-generation sequencing (mNGS). The bone marrow mNGS results from the three patients indicated that 99.9, 99.6, and 30.3% of non-human reads matched the Leishmania genome, and plasma mNGS results from one of the patients revealed that 46.2% of non-human reads matched the Leishmania genome. In the second patient's plasma, no Leishmania sequences were detected by plasma mNGS, and the third patient's plasma was unavailable. The pathogen in all three patients was identified as Leishmania infantum. Leishmania amastigotes were observed by microscopic examination of bone marrow smears in all three patients, but were not found in peripheral blood smears. This indicates that the sensitivity of mNGS is higher than that of smear microscopy and that mNGS can be used to identify Leishmania at the species level. All three patients were elderly male farmers, two from Shanxi and one from Beijing. All three patients had splenomegaly and pancytopenia. Originally, these patients were misdiagnosed and treated for extended periods in other hospitals. Diagnoses of visceral leishmaniasis took place 6, 2, and 2 months after the onset of symptoms in the three patients. In conclusion, this study confirms that bone marrow mNGS can be used to quickly and accurately confirm a diagnosis in patients with suspected leishmaniasis.
| S-EPMC7538539 | biostudies-literature
Project description:BackgroundTuberculous meningitis (TBM) is a severe form of extrapulmonary tuberculosis and its early diagnosis is very difficult leading to present with severe disability or die. The current study aimed to assess the accuracy of metagenomic next generation sequencing (mNGS) for TBM, and to identify a new test for the early diagnosis of TBM.MethodsWe searched for articles published in Embase, PubMed, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data up to June 30, 2020 for studies that assessed the efficacy of mNGS for the diagnosis of TBM. Then, the accuracy between mNGS and a composite reference standard (CRS) in these articles was compared using the meta-analysis approach.ResultsFour independent studies with 342 samples comparing mNGS and a CRS were included in this study. The sensitivity of mNGS for TBM diagnosis ranged from 27% to 84%. The combined sensitivity of mNGS was 61%, and the I2 value was 92%. Moreover, the specificity of mNGS for TBM diagnosis ranged from 96% to 100%. The combined specificity of mNGS was 98%, and the I2 value was 74%. The heterogeneity between studies in terms of sensitivity and specificity was significant. The area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) of mNGS for TBM was 0.98.ConclusionsThe sensitivity of mNGS for TBM diagnosis was moderate. Furthermore, the specificity was extremely high, and the AUC of the SROC indicated a very good diagnostic efficacy. mNGS could be used as an early diagnostic method for TBM, however, the results should be treated with caution for the heterogeneity between studies was extremely significant.Systematic review registrationINPLASY202070100.
| S-EPMC7707562 | biostudies-literature
Project description:ObjectivesMetagenomic next-generation sequencing (mNGS) has been applied more and more widely for the diagnosis of infectious diseases, but its performance in the diagnosis of cryptococcal meningitis (CM) remains unclear.MethodsCerebrospinal fluid (CSF) samples from 197 HIV-negative patients with suspected central nervous system infections were tested simultaneously by mNGS and routine methods [India ink staining, fungal culture, or cryptococcal antigen (CrAg) tests]. The performance of mNGS was evaluated.ResultsOf the 197 enrolled cases, 46 (23.4%) cases were finally diagnosed with CM, including 43 (93.5%) Cryptococcus neoformans infections and 3 (6.5%) Cryptococcus gattii infections. The sensitivity, specificity, positive predictive value, negative predictive value, and concordance rate of mNGS were 93.5% [95% confidence interval (CI) at 86.4%~100.0%], 96.0% (95% CI at 92.9%~99.1%), 87.8%, 98.0%, and 95.4%, respectively. Comparing to the conventional diagnostic methods, the sensitivity and concordance rate of mNGS were slightly lower than those of CrAg tests (97.4%) but higher than those of India ink (63.0%) and culture (76.7%). Besides, mNGS had a sensitivity of 100.0% against culture. It should be noted that mNGS could identify Cryptococcus at species level; C. gattii of the 3 cases was only distinguished by mNGS.ConclusionsCSF mNGS can be considered as a supplementary test to diagnose CM and directly distinguish C. gattii from C. neoformans in clinical specimens.
| S-EPMC9069553 | biostudies-literature
Project description:BackgroundMetagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce.MethodsFrom July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared.ResultsThe sensitivity of mNGS in mixed pulmonary infection diagnosis was much higher than that of conventional test (97.2% vs 13.9%; P < 0.01), but the specificity was the opposite (63.2% vs 94.7%; P = 0.07). The positive predictive value of mNGS was 83.3% (95% CI, 68.0-92.5%), and the negative predictive value was 92.3% (95% CI, 62.1-99.6%). A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1 (1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1 (1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected by mNGS was Human cytomegalovirus in our study, which was identified in 19 (34.5%) cases of mixed pulmonary infection. Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7 (12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection.ConclusionsmNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity.Trial registration(retrospectively registered): ChiCTR1900023727. Registrated 9 JUNE 2019.
| S-EPMC6921575 | biostudies-literature
Project description:IntroductionEarly and accurate identification of pathogens is essential for improved outcomes in patients with viral encephalitis (VE) and/or viral meningitis (VM).MethodsIn our research, Metagenomic next-generation sequencing (mNGS) which can identify viral pathogens unbiasedly was performed on RNA and DNA to identify potential pathogens in cerebrospinal fluid (CSF) samples from 50 pediatric patients with suspected VEs and/or VMs. Then we performed proteomics analysis on the 14 HEV-positive CSF samples and another 12 CSF samples from health controls (HCs). A supervised partial least squaresdiscriminant analysis (PLS-DA) and orthogonal PLS-DA (O-PLS-DA) model was performed using proteomics data.ResultsTen viruses in 48% patients were identified and the most common pathogen was human enterovirus (HEV) Echo18. 11 proteins overlapping between the top 20 DEPs in terms of P value and FC and the top 20 proteins in PLS-DA VIP lists were acquired.DiscussionOur result showed mNGS has certain advantages on pathogens identification in VE and VM and our research established a foundation to identify diagnosis biomarker candidates of HEV-positive meningitis based on MS-based proteomics analysis, which could also contribute toward investigating the HEV-specific host response patterns.
| S-EPMC10161730 | biostudies-literature
Project description:Purpose: We assessed the performance of metagenomic next-generation sequencing (mNGS) in the diagnosis of infectious encephalitis and meningitis. Methods: This was a prospective multicenter study. Cerebrospinal fluid samples from patients with viral encephalitis and/or meningitis, tuberculous meningitis, bacterial meningitis, fungal meningitis, and non-central nervous system (CNS) infections were subjected to mNGS. Results: In total, 213 patients with infectious and non-infectious CNS diseases were finally enrolled from November 2016 to May 2019; the mNGS-positive detection rate of definite CNS infections was 57.0%. At a species-specific read number (SSRN) ≥2, mNGS performance in the diagnosis of definite viral encephalitis and/or meningitis was optimal (area under the curve [AUC] = 0.659, 95% confidence interval [CI] = 0.566-0.751); the positivity rate was 42.6%. At a genus-specific read number ≥1, mNGS performance in the diagnosis of tuberculous meningitis (definite or probable) was optimal (AUC=0.619, 95% CI=0.516-0.721); the positivity rate was 27.3%. At SSRNs ≥5 or 10, the diagnostic performance was optimal for definite bacterial meningitis (AUC=0.846, 95% CI = 0.711-0.981); the sensitivity was 73.3%. The sensitivities of mNGS (at SSRN ≥2) in the diagnosis of cryptococcal meningitis and cerebral aspergillosis were 76.92 and 80%, respectively. Conclusion: mNGS of cerebrospinal fluid effectively identifies pathogens causing infectious CNS diseases. mNGS should be used in conjunction with conventional microbiological testing. Trial Registration: Chinese Clinical Trial Registry, ChiCTR1800020442.
| S-EPMC7066979 | biostudies-literature