Xiao YH, Luo ZX, Wu HW, Xu DR, Zhao R. Metagenomic next-generation sequencing for the identification of infections caused by Gram-negative pathogens and the prediction of antimicrobial resistance.
Lab Med 2024;
55:71-79. [PMID:
37253164 DOI:
10.1093/labmed/lmad039]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023] Open
Abstract
OBJECTIVE
The aim of this study was to evaluate the efficacy of metagenomic next-generation sequencing (mNGS) for the identification of Gram-negative bacteria (GNB) infections and the prediction of antimicrobial resistance.
METHODS
A retrospective analysis was conducted on 182 patients with diagnosis of GNB infections who underwent mNGS and conventional microbiological tests (CMTs).
RESULTS
The detection rate of mNGS was 96.15%, higher than CMTs (45.05%) with a significant difference (χ 2 = 114.46, P < .01). The pathogen spectrum identified by mNGS was significantly wider than CMTs. Interestingly, the detection rate of mNGS was substantially higher than that of CMTs (70.33% vs 23.08%, P < .01) in patients with but not without antibiotic exposure. There was a significant positive correlation between mapped reads and pro-inflammatory cytokines (interleukin-6 and interleukin-8). However, mNGS failed to predict antimicrobial resistance in 5 of 12 patients compared to phenotype antimicrobial susceptibility testing results.
CONCLUSIONS
Metagenomic next-generation sequencing has a higher detection rate, a wider pathogen spectrum, and is less affected by prior antibiotic exposure than CMTs in identifying Gram-negative pathogens. The mapped reads may reflect a pro-inflammatory state in GNB-infected patients. Inferring actual resistance phenotypes from metagenomic data remains a great challenge.
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