A diagnostic classifier for gene expression-based identification of early Lyme disease.
Bacterial host response
Bacterial infection
Diagnostic markers
RNA sequencing
Targeted resequencing
Journal
Communications medicine
ISSN: 2730-664X
Titre abrégé: Commun Med (Lond)
Pays: England
ID NLM: 9918250414506676
Informations de publication
Date de publication:
2022
2022
Historique:
received:
09
11
2021
accepted:
17
05
2022
entrez:
26
7
2022
pubmed:
27
7
2022
medline:
27
7
2022
Statut:
epublish
Résumé
Lyme disease is a tick-borne illness that causes an estimated 476,000 infections annually in the United States. New diagnostic tests are urgently needed, as existing antibody-based assays lack sufficient sensitivity and specificity. Here we perform transcriptome profiling by RNA sequencing (RNA-Seq), targeted RNA-Seq, and/or machine learning-based classification of 263 peripheral blood mononuclear cell samples from 218 subjects, including 94 early Lyme disease patients, 48 uninfected control subjects, and 57 patients with other infections (influenza, bacteremia, or tuberculosis). Differentially expressed genes among the 25,278 in the reference database are selected based on ≥1.5-fold change, ≤0.05 We identify a 31-gene Lyme disease classifier (LDC) panel that can discriminate between early Lyme patients and controls, with 23 genes (74.2%) that have previously been described in association with clinical investigations of Lyme disease patients or in vitro cell culture and rodent studies of These results highlight the potential clinical utility of a gene expression classifier for diagnosis of early Lyme disease, including in patients negative by conventional serologic testing.
Sections du résumé
Background
UNASSIGNED
Lyme disease is a tick-borne illness that causes an estimated 476,000 infections annually in the United States. New diagnostic tests are urgently needed, as existing antibody-based assays lack sufficient sensitivity and specificity.
Methods
UNASSIGNED
Here we perform transcriptome profiling by RNA sequencing (RNA-Seq), targeted RNA-Seq, and/or machine learning-based classification of 263 peripheral blood mononuclear cell samples from 218 subjects, including 94 early Lyme disease patients, 48 uninfected control subjects, and 57 patients with other infections (influenza, bacteremia, or tuberculosis). Differentially expressed genes among the 25,278 in the reference database are selected based on ≥1.5-fold change, ≤0.05
Results
UNASSIGNED
We identify a 31-gene Lyme disease classifier (LDC) panel that can discriminate between early Lyme patients and controls, with 23 genes (74.2%) that have previously been described in association with clinical investigations of Lyme disease patients or in vitro cell culture and rodent studies of
Conclusions
UNASSIGNED
These results highlight the potential clinical utility of a gene expression classifier for diagnosis of early Lyme disease, including in patients negative by conventional serologic testing.
Identifiants
pubmed: 35879995
doi: 10.1038/s43856-022-00127-2
pii: 127
pmc: PMC9306241
doi:
Types de publication
Journal Article
Langues
eng
Pagination
92Informations de copyright
© The Author(s) 2022.
Déclaration de conflit d'intérêts
Competing interestsC.Y.C. and J.A. are on the scientific advisory board for the Bay Area Lyme Foundation. The other authors declare no competing interests.
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