Melting curve analysis reveals false-positive norovirus detection in a molecular syndromic panel.

BACKGROUND: Molecular syndromic panels can improve rapidity of results and ease clinical laboratory workflow, although caution has been raised for potential false-positive results. Upon implementation of a new panel for infectious diarrhea (BioFire® FilmArray® Gastrointestinal [GI] Panel, bioMérieux) in our clinical laboratory, a higher than expected number of stool samples with norovirus were detected. OBJECTIVES: The goal of this study was to investigate positive percent agreement and the false-positive rate of norovirus detected by the multiplex BioFire GI panel compared to a singleplex commercial assay. STUDY DESIGN: From October 2023 to January 2024, all prospective stool samples with a positive norovirus result by BioFire had melting curves reviewed manually using the BioFire FilmArray Torch System. Stool samples further underwent testing by a supplementary real-time RT-PCR assay (Xpert® Norovirus, Cepheid) for comparative analysis. RESULTS: Of the 50 stool samples with norovirus detected by BioFire, 18 (36 %) tested negative by Xpert (deemed "false-positives"). Furthermore, melting curve analysis revealed nearly all of these samples had atypical melting curve morphologies for the "Noro-1" target on BioFire (16/18, 89 %), which was statistically significant (Odds Ratio 173.2, 95 % CI [22.2, 5326.9], p < 0.0001). Stool samples with multiple pathogens detected by BioFire including norovirus were not more likely to produce false-positive norovirus results (Odds Ratio 1, 95 % CI [0.3, 3.3], p = 1). CONCLUSIONS: Although not described in the manufacturer's Instructions for Use, we propose routine manual review of melting curves for the BioFire GI panel prior to reporting, to mitigate potential false-positive norovirus results.

WGS of a cluster of MDR Shigella sonnei utilizing Oxford Nanopore R10.4.1 long-read sequencing.

OBJECTIVES: To utilize long-read nanopore sequencing (R10.4.1 flowcells) for WGS of a cluster of MDR Shigella sonnei, specifically characterizing genetic predictors of antimicrobial resistance (AMR). METHODS: WGS was performed on S. sonnei isolates identified from stool and blood between September 2021 and October 2022. Bacterial DNA from clinical isolates was extracted on the MagNA Pure 24 and sequenced on the GridION utilizing R10.4.1 flowcells. Phenotypic antimicrobial susceptibility testing was interpreted based on CLSI breakpoints. Sequencing data were processed with BugSeq, and AMR was assessed with BugSplit and ResFinder. RESULTS: Fifty-six isolates were sequenced, including 53 related to the cluster of cases. All cluster isolates were identified as S. sonnei by sequencing, with global genotype 3.6.1.1.2 (CipR.MSM5), MLST 152 and PopPUNK cluster 3. Core genome MLST (cgMLST, examining 2513 loci) and reference-based MLST (refMLST, examining 4091 loci) both confirmed the clonality of the isolates. Cluster isolates were resistant to ampicillin (blaTEM-1), trimethoprim/sulfamethoxazole (dfA1, dfrA17; sul1, sul2), azithromycin (ermB, mphA) and ciprofloxacin (gyrA S83L, gyrA D87G, parC S80I). No genomic predictors of resistance to carbapenems were identified. CONCLUSIONS: WGS with R10.4.1 enabled rapid sequencing and identification of an MDR S. sonnei community cluster. Genetic predictors of AMR were concordant with phenotypic antimicrobial susceptibility testing.