ArticleFrontiers in immunology2021
Predicting Lyme Disease From Patients' Peripheral Blood Mononuclear Cells Profiled With RNA-Sequencing.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Monocyte-Amplified Transcriptional Signatures of Human Diseases.bioRxiv : the preprint server for biology · 2026Article
- Application of Mass Spectrometry-Based Metabolomics and Machine Learning in the Diagnostics of Lyme Neuroborreliosis.ACS omega · 2026Article
- Identification and Validation of Differentially Expressed mRNAs From Peripheral Leukocytes for Acute Stanford Type A Aortic Dissection.Journal of the American Heart Association · 2025Article
- Protocol for a longitudinal cohort study of Lyme disease with physical, mental and immunological assessment.BMJ open · 2023Article
- Gene set predictor for post-treatment Lyme disease.Cell reports. Medicine · 2022Article
- The Troublesome Ticks Research Protocol: Developing a Comprehensive, Multidiscipline Research Plan for Investigating Human Tick-Associated Disease in Australia.Pathogens (Basel, Switzerland) · 2022Article
- Persistent Symptoms After Treatment of Lyme Disease.Infectious disease clinics of North America · 2022Review
- Biomarker selection and a prospective metabolite-based machine learning diagnostic for lyme disease.Scientific reports · 2022Article
- A diagnostic classifier for gene expression-based identification of early Lyme disease.Communications medicine · 2022Article
- Recent Progress in Lyme Disease and Remaining Challenges.Frontiers in medicine · 2021Review
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 1 country.
Funding
Abstract
Although widely prevalent, Lyme disease is still under-diagnosed and misunderstood. Here we followed 73 acute Lyme disease patients and uninfected controls over a period of a year. At each visit, RNA-sequencing was applied to profile patients' peripheral blood mononuclear cells in addition to extensive clinical phenotyping. Based on the projection of the RNA-seq data into lower dimensions, we observe that the cases are separated from controls, and almost all cases never return to cluster with the controls over time. Enrichment analysis of the differentially expressed genes between clusters identifies up-regulation of immune response genes. This observation is also supported by deconvolution analysis to identify the changes in cell type composition due to Lyme disease infection. Importantly, we developed several machine learning classifiers that attempt to perform various Lyme disease classifications. We show that Lyme patients can be distinguished from the controls as well as from COVID-19 patients, but classification was not successful in distinguishing those patients with early Lyme disease cases that would advance to develop post-treatment persistent symptoms.
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Registered trials
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