A Diagnostic Classifier for Gene Expression-Based Identification of Early Lyme Disease
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA803869 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description: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.
2022-05-01 | GSE196192 | GEO
Project description:BackgroundLyme 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.MethodsHere 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 p value, and ≤0.001 false-discovery rate cutoffs. After gene selection using a k-nearest neighbor algorithm, the comparative performance of ten different classifier models is evaluated using machine learning.ResultsWe 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 Borrelia burgdorferi infection. Evaluation of the LDC using an independent test set of samples from 63 subjects yields an overall sensitivity of 90.0%, specificity of 100%, and accuracy of 95.2%. The LDC test is positive in 85.7% of seronegative patients and found to persist for ≥3 weeks in 9 of 12 (75%) patients.ConclusionsThese 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.
| S-EPMC9306241 | biostudies-literature
Project description:Lyme disease is challenging to diagnose, as clinical manifestations are variable and current tools to detect nucleic acid or antibody responses from Borrelia burgdorferi infection have low sensitivity. Here we conducted the first study of the global transcriptome of patients with Lyme disease to identify potential diagnostic biomarkers. Twenty-nine patients were enrolled and compared to 13 healthy controls at three time points after infection. Fifteen publicly available transcriptome datasets from patients in vivo or infection models in vitro were used to assess specificity of differentially expressed genes (DEGs). We found that Lyme disease results in profound and sustained changes in the patient transcriptomes, with a specific signature that shares 44% DEGs with other infections. Gene expression profile from peripheral mononuclear blood cells (PBMC) of Lyme disease patients against healthy controls was undertaken. A total of 29 Lyme disease patients were sampled at 3 time points: acute Lyme pre-treatment (V1), 3 weeks later, immediately following completion of a standard course of antibiotics (V2), and 6 months following treatment completion (V5). 13 healthy controls were also sampled at one time point. Total RNA was extracted from 10e7 PBMC, followed by mRNA purification, paired-end barcode library preparation and sequencing on an Illumina Hiseq 2000.
2016-02-15 | E-GEOD-63085 | biostudies-arrayexpress
Project description:Lyme disease is challenging to diagnose, as clinical manifestations are variable and current tools to detect nucleic acid or antibody responses from Borrelia burgdorferi infection have low sensitivity. Here we conducted the first study of the global transcriptome of patients with Lyme disease to identify potential diagnostic biomarkers. Twenty-nine patients were enrolled and compared to 13 healthy controls at three time points after infection. Fifteen publicly available transcriptome datasets from patients in vivo or infection models in vitro were used to assess specificity of differentially expressed genes (DEGs). We found that Lyme disease results in profound and sustained changes in the patient transcriptomes, with a specific signature that shares ≤44% DEGs with other infections.
2016-02-15 | GSE63085 | GEO
Project description:Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (Acute LD) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels six months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly acurate in distinvuishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infecitons. This computational approach offers the potential for more accurate diagnosis of early dissminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.
2020-04-03 | GSE145974 | GEO
Project description:BACKGROUND: In patients with suspicious pulmonary lesions, bronchoscopy is frequently non-diagnostic. This often results in additional invasive testing, including surgical biopsy, although many patients have benign disease. We sought to validate an airway gene-expression classifier for lung cancer in patients undergoing diagnostic bronchoscopy. METHODS: Two multicenter prospective studies (AEGIS 1 and 2) enrolled 1357 current or former smokers undergoing bronchoscopy for suspected lung cancer. Bronchial epithelial cells were collected from normal appearing mucosa in the mainstem bronchus during bronchoscopy. Patients without a definitive diagnosis from bronchoscopy were followed for 12 months. A gene-expression classifier was used to assess the risk of lung cancer, and its performance was evaluated. RESULTS: A total of 298 patients from AEGIS 1 and 341 from AEGIS 2 met criteria for analysis. Bronchoscopy was non-diagnostic for cancer in 272 of 639 patients (43%; 95%CI, 39-46%). The gene expression classifier correctly identified 431 of 487 patients with cancer (89% sensitivity; 95%CI, 85-91%), and 72 of 152 patients without cancer (47% specificity; 95%CI, 40-55%). The combination of the classifier and bronchoscopy had a sensitivity of 97% (95%CI, 95-98%), which was independent of size, location, stage, and histological subtype of lung cancer. In patients with an intermediate pre-test risk (10-60%) of lung cancer, the NPV of the classifier was 91% (95%CI 75-98%). CONCLUSIONS: In patients with an intermediate risk of lung cancer and a non-diagnostic bronchoscopy, a gene-expression classification of “low-risk” warrants consideration of a more conservative diagnostic approach that could reduce unnecessary invasive testing in patients with benign disease. 680 CEL files from 639 BEC specimens, 152 benign and 487 malignant samples
2015-05-17 | E-GEOD-66499 | biostudies-arrayexpress
Project description:BACKGROUND: In patients with suspicious pulmonary lesions, bronchoscopy is frequently non-diagnostic. This often results in additional invasive testing, including surgical biopsy, although many patients have benign disease. We sought to validate an airway gene-expression classifier for lung cancer in patients undergoing diagnostic bronchoscopy. METHODS: Two multicenter prospective studies (AEGIS 1 and 2) enrolled 1357 current or former smokers undergoing bronchoscopy for suspected lung cancer. Bronchial epithelial cells were collected from normal appearing mucosa in the mainstem bronchus during bronchoscopy. Patients without a definitive diagnosis from bronchoscopy were followed for 12 months. A gene-expression classifier was used to assess the risk of lung cancer, and its performance was evaluated. RESULTS: A total of 298 patients from AEGIS 1 and 341 from AEGIS 2 met criteria for analysis. Bronchoscopy was non-diagnostic for cancer in 272 of 639 patients (43%; 95%CI, 39-46%). The gene expression classifier correctly identified 431 of 487 patients with cancer (89% sensitivity; 95%CI, 85-91%), and 72 of 152 patients without cancer (47% specificity; 95%CI, 40-55%). The combination of the classifier and bronchoscopy had a sensitivity of 97% (95%CI, 95-98%), which was independent of size, location, stage, and histological subtype of lung cancer. In patients with an intermediate pre-test risk (10-60%) of lung cancer, the NPV of the classifier was 91% (95%CI 75-98%). CONCLUSIONS: In patients with an intermediate risk of lung cancer and a non-diagnostic bronchoscopy, a gene-expression classification of “low-risk” warrants consideration of a more conservative diagnostic approach that could reduce unnecessary invasive testing in patients with benign disease.
2015-05-17 | GSE66499 | GEO
Project description:Lyme disease is a tick-borne bacterial illness that occurs in areas of North America, Europe, and Asia. Early infection typically presents as generalized symptoms with an erythema migrans (EM) skin lesion. Bacterial dissemination can result in multiple EM skin lesions or in extracutaneous manifestations such as Lyme neuroborreliosis. Metabolic biosignatures of patients with early Lyme disease can potentially provide diagnostic targets, as well as highlight metabolic pathways that contribute to pathogenesis. Sera from well-characterized patients diagnosed with either early localized Lyme disease (ELL) or early disseminated Lyme disease (EDL), plus healthy individuals (HC), from the United States were analyzed by liquid chromatography-mass spectrometry (LC-MS). Comparative analyses were performed between ELL, or EDL, or ELL combined with EDL, and the HC to develop biosignatures present in early Lyme disease. A direct comparison between ELL and EDL was also performed to develop a biosignature for stages of early Lyme disease. Metabolic pathway analysis and chemical identification of metabolites with LC-tandem mass spectrometry (LC-MS/MS) demonstrated alterations of eicosanoid, bile acid, sphingolipid, glycerophospholipid, and acylcarnitine metabolic pathways during early Lyme disease . These metabolic alterations were confirmed using a separate set of serum samples for validation. The findings demonstrated the metabolic pathways altered in the host during early Lyme disease and provide evidence that the diversity in the type of early Lyme disease manifestations may be associated with particular metabolic alterations.
2019-07-02 | ST001223 | MetabolomicsWorkbench
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.
2014-04-25 | E-GEOD-55815 | biostudies-arrayexpress