Real-time metagenomics-based diagnosis of community-acquired meningitis: A prospective series, southern France

Morsli, Madjid; Boudet, Agathe; Kerharo, Quentin; Stéphan, Robin; Salipante, Florian; Dunyach-Rémy, Catherine; Houhamdi, Linda; Fournier, Pierre-Édouard; Lavigne, Jean Philippe; Drancourt, Michel

doi:10.1016/j.ebiom.2022.104247

Cited by 11 publications

(11 citation statements)

References 27 publications

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“…Therefore, the diagnostic efficacy of mNGS in patients with diabetes requires further verification in large-scale studies. For more rapid etiological diagnosis of pulmonary infection, real-time metagenomics sequencing can be used in further studies to detect pathogens within a few hours, as well as bacterial analysis and genotyping [24].…”

Section: Discussionmentioning

confidence: 99%

Metagenomic next-generation sequencing for pulmonary infections diagnosis in patients with diabetes

Zhang¹,

Ou²,

Tan³

et al. 2023

BMC Pulm Med

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Background Diabetes mellitus is a major cause of high mortality and poor prognosis in patients with pulmonary infections. However, limited data on the application of metagenomic next-generation sequencing (mNGS) are available for diabetic patients. This study aimed to evaluate the diagnostic performance of mNGS in diabetic patients with pulmonary infections. Methods We retrospectively reviewed 184 hospitalized patients with pulmonary infections at Guizhou Provincial People’s Hospital between January 2020 to October 2021. All patients were subjected to both mNGS analysis of bronchoalveolar lavage fluid (BALF) and conventional testing. Positive rate by mNGS and the consistency between mNGS and conventional testing results were evaluated for diabetic and non-diabetic patients. Results A total of 184 patients with pulmonary infections were enrolled, including 43 diabetic patients and 141 non-diabetic patients. For diabetic patients, the microbial positive rate by mNGS was significantly higher than that detected by conventional testing methods, primarily driven by bacterial detection (microbes: 95.3% vs. 67.4%, P = 0.001; bacteria: 72.1% vs. 37.2%, P = 0.001). mNGS and traditional tests had similar positive rates with regard to fungal and viral detection in diabetic patients. Klebsiella pneumoniae was the most common pathogen identified by mNGS in patients with diabetes. Moreover, mNGS identified pathogens in 92.9% (13/14) of diabetic patients who were reported negative by conventional testing. No significant difference was found in the consistency of the two tests between diabetic and non-diabetic groups. Conclusions mNGS is superior to conventional microbiological tests for bacterial detection in diabetic patients with pulmonary infections. mNGS is a valuable tool for etiological diagnosis of pulmonary infections in diabetic patients.

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Section: Discussionmentioning

confidence: 99%

Metagenomic next-generation sequencing for pulmonary infections diagnosis in patients with diabetes

Zhang¹,

Ou²,

Tan³

et al. 2023

BMC Pulm Med

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“…mNGS is an unbiased culture-independent and hypothesis-free sequencing technology that has shown tremendous clinical application particularly in the diagnosis of CNS infections, bloodstream infections, and respiratory tract infections ( Blauwkamp et al, 2019 ; Miller et al, 2019 ; Wilson et al, 2019 ; Chen H. et al, 2020 ; Hasan et al, 2020 ; Haston et al, 2020 ; Li et al, 2020 ; Chen et al, 2021b ; Hogan et al, 2021 ; Jing et al, 2021 ; Liu et al, 2021 ; Mu et al, 2021 ; Pollock et al, 2021 ; Zhou et al, 2021 ; Deng et al, 2022 ; Fu et al, 2022 ; Guo et al, 2022 ; Li et al, 2022 ; Wang et al, 2022 ; Zhang et al, 2022 ; Morsli et al, 2022b ). Examples of recent applications of mNGS in the diagnosis of these infections are provided in Table 2 .…”

Section: Detection Of Pathogens Via Clinical Metagenomicsmentioning

confidence: 99%

“…However, prior antibiotic therapy may reduce the sensitivity of CSF cultures, increasing the possibility of false-negatives ( Greenberg and Herrera, 2019 ). mNGS has potential to detect pathogens of unknown etiology as evidenced by clinical series demonstrating the success of mNGS in the detection of hard-to-diagnose CNS infection cases ( Miller et al, 2019 ; Wilson et al, 2019 ; Hasan et al, 2020 ; Haston et al, 2020 ; Chen et al, 2021b ; Morsli et al, 2022b ). Recently published studies have confirmed the diagnostic sensitivity of mNGS ranging 22% to 95% in patients with CNS infections ( Miller et al, 2019 ; Wilson et al, 2019 ; Hasan et al, 2020 ).…”

Section: Detection Of Pathogens Via Clinical Metagenomicsmentioning

confidence: 99%

“…mNGS can capture millions to billions of nucleic acids sequences at once and detect multiple organisms including novel pathogens that may be present in a clinical specimen ( John et al, 2021 ). The time required for sample preparation, sequencing, and preliminary bioinformatic analysis depends on the nature of sequencing platform being used ( Morsli et al, 2021a , b , 2022a , b ). For example, newly available long-read sequencing platforms, such as Oxford Nanopore sequencing, provide real time pathogen detection within minutes and additional information regarding genotyping and bacterial profiling in less than 6 h ( Morsli et al, 2021a , b , 2022a , b ).…”

Section: Introductionmentioning

confidence: 99%

“…The time required for sample preparation, sequencing, and preliminary bioinformatic analysis depends on the nature of sequencing platform being used ( Morsli et al, 2021a , b , 2022a , b ). For example, newly available long-read sequencing platforms, such as Oxford Nanopore sequencing, provide real time pathogen detection within minutes and additional information regarding genotyping and bacterial profiling in less than 6 h ( Morsli et al, 2021a , b , 2022a , b ). Also, Oxford Nanopore Technologies is currently the most prevalent and cost-effective mNGS platform in low- and middle-income countries ( Yek et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Clinical metagenomics—challenges and future prospects

Batool

Galloway-Peña

2023

Front. Microbiol.

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Infections lacking precise diagnosis are often caused by a rare or uncharacterized pathogen, a combination of pathogens, or a known pathogen carrying undocumented or newly acquired genes. Despite medical advances in infectious disease diagnostics, many patients still experience mortality or long-term consequences due to undiagnosed or misdiagnosed infections. Thus, there is a need for an exhaustive and universal diagnostic strategy to reduce the fraction of undocumented infections. Compared to conventional diagnostics, metagenomic next-generation sequencing (mNGS) is a promising, culture-independent sequencing technology that is sensitive to detecting rare, novel, and unexpected pathogens with no preconception. Despite the fact that several studies and case reports have identified the effectiveness of mNGS in improving clinical diagnosis, there are obvious shortcomings in terms of sensitivity, specificity, costs, standardization of bioinformatic pipelines, and interpretation of findings that limit the integration of mNGS into clinical practice. Therefore, physicians must understand the potential benefits and drawbacks of mNGS when applying it to clinical practice. In this review, we will examine the current accomplishments, efficacy, and restrictions of mNGS in relation to conventional diagnostic methods. Furthermore, we will suggest potential approaches to enhance mNGS to its maximum capacity as a clinical diagnostic tool for identifying severe infections.

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Metagenomic sequencing of cerebrospinal fluid from children with meningitis

Grose

2022

eBioMedicine

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Real-time metagenomics-based diagnosis of community-acquired meningitis: A prospective series, southern France

Cited by 11 publications

References 27 publications

Metagenomic next-generation sequencing for pulmonary infections diagnosis in patients with diabetes

Metagenomic next-generation sequencing for pulmonary infections diagnosis in patients with diabetes

Clinical metagenomics—challenges and future prospects

Metagenomic sequencing of cerebrospinal fluid from children with meningitis

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