Predicting Lyme Disease From Patients' Peripheral Blood Mononuclear Cells Profiled With RNA-Sequencing

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 e...

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Bibliographic Details
Published in:Frontiers in immunology 2021-03, Vol.12, p.636289-636289
Main Authors: Clarke, Daniel J B, Rebman, Alison W, Bailey, Allison, Wojciechowicz, Megan L, Jenkins, Sherry L, Evangelista, John E, Danieletto, Matteo, Fan, Jinshui, Eshoo, Mark W, Mosel, Michael R, Robinson, William, Ramadoss, Nitya, Bobe, Jason, Soloski, Mark J, Aucott, John N, Ma'ayan, Avi
Format: Article
Language:English
Subjects:
Adult
COVID-19 - genetics
COVID-19 - immunology
Cytokines - genetics
Cytokines - immunology
data mining
Female
Follow-Up Studies
Humans
Immunology
Leukocytes, Mononuclear - chemistry
Leukocytes, Mononuclear - immunology
Lyme disease
Lyme Disease - blood
Lyme Disease - genetics
Lyme Disease - immunology
Machine Learning
Male
Middle Aged
PBMCs
Prospective Studies
PTLDS
RNA-Seq
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Summary: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.
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2021.636289