Dataset Information

A Diagnostic Classifier for Gene Expression-Based Identification of Early Lyme Disease

ABSTRACT: Lyme disease is a tickborne 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. Using transcriptome profiling by RNA-Seq, targeted RNA sequencing, and machine learning (ML)-based classification of 218 subjects, we identified a 31-gene Lyme disease classifier (LDC) to discriminate early Lyme patients from “non-Lyme” controls infected with influenza, bacteremia, or tuberculosis or uninfected asymptomatic controls. Among the 31 classifier genes, 23 (74.2%) had previously been described in association with clinical investigations of Lyme disease patients or in vitro cell culture and rodent studies of Borrelia burgdorferi infection. Evaluation of the LDC using an independent test set of samples from 63 subjects (16 Lyme seropositive patients, 14 Lyme seronegative patients, and 33 controls) yielded an overall sensitivity of 90.0%, specificity of 100%, and accuracy of 95.2%. The LDC was positive in 85.7% of seronegative patients and persisted for ≥3 weeks in available longitudinal samples from 9 of 12 (75%) patients. These results highlight the potential clinical utility of a gene expression classifier for diagnosis of early Lyme disease.

ORGANISM(S): Homo sapiens

PROVIDER: GSE196192 | GEO | 2022/05/01

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Chemokines and cytokines are key signaling molecules that orchestrate the trafficking of immune cells, direct them to sites of tissue injury and inflammation and modulate their states of activation and effector cell function. We have measured, using a multiplex-based approach, the levels of 58 immune mediators and 7 acute phase markers in sera derived from of a cohort of patients diagnosed with acute Lyme disease and matched controls. This analysis identified a cytokine signature associated with the early stages of infection and allowed us to identify two subsets (mediator-high and mediator-low) of acute Lyme patients with distinct cytokine signatures that also differed significantly (p<0.0005) in symptom presentation. In particular, the T cell chemokines CXCL9 (MIG), CXCL10 (IP-10) and CCL19 (MIP3B) were coordinately increased in the mediator-high group and levels of these chemokines could be associated with seroconversion status and elevated liver function tests (p=0.027 and p=0.021 respectively). There was also upregulation of acute phase proteins including CRP and serum amyloid A. Consistent with the role of CXCL9/CXCL10 in attracting immune cells to the site of infection, CXCR3+ CD4 T cells are reduced in the blood of early acute Lyme disease (p=0.01) and the decrease correlates with chemokine levels (p=0.0375). The levels of CXCL9/10 did not relate to the size or number of skin lesions but elevated levels of serum CXCL9/CXCL10 were associated with elevated liver enzymes levels. Collectively these results indicate that the levels of serum chemokines and the levels of expression of their respective chemokine receptors on T cell subsets may prove to be informative biomarkers for Lyme disease and related to specific disease manifestations. A total of 65 immune and inflammatory mediators were profiled in serum samples derived from early Lyme disease patients and age- and sex-matched controls. These samples have been generated as part of a prospective cohort study that includes a well-defined cohort of patients with acute Lyme disease enrolled from a Lyme endemic area of the mid-Atlantic United States. Only patients with untreated, confirmed early Lyme disease manifesting an active EM skin lesion at the time of enrollment, as defined by CDC case criteria are eligible. Patients with a history of prior Lyme disease or the presence of confounding preexisting medical conditions associated with prolonged fatigue, pain or neurocognitive symptoms are excluded. Controls are nonhospitalized age- and sex-matched and have no prior history of Lyme disease or any exclusionary medical conditions including lack of inflammatory disorders.

Dataset Information

A Diagnostic Classifier for Gene Expression-Based Identification of Early Lyme Disease

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets