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

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.

A diagnostic classifier for gene expression-based identification of early Lyme disease

“…However, the LDC would have been near impossible to construct a priori given that selection of an optimal set of genes would have been difficult and that 8 of the 31 (25.8%) genes had not been previously described in the literature. Notably, only 7 (22.5%) genes in the panel were associated with immune cell signaling, of which 3 (9.7%) were related to interferon signaling, in contrast with prior reports demonstrating strong immune and inflammatory responses in early Lyme disease 20 , 21 , 37 , 38 . Unlike these previous studies, here we incorporated controls from patients with acute febrile infections from viruses (influenza) or other bacteria, potentially explaining why only a minority of LDC genes were associated with immune cell signaling.…”

“…All these classifiers are limited by the absence of controls from other viral and bacterial infections to exclude overlapping immune and inflammatory response genes. In fact, only two genes in our LDC classifier, TYMS, a DNA replication and repair gene, and GRN, a cell proliferation gene, are shared with these prior classifiers 37 , 38 . Other “omics” technologies have been used to develop classifiers for Lyme disease.…”

“…Prior studies have used gene expression to profile Lyme disease patients from PBMCs 20 , 37 , 38 , although our study incorporates larger numbers of Lyme disease cases and controls. The three previously reported studies present similar findings showing an increase in immune and inflammatory response genes, particularly those interferon-regulated, in Lyme disease cases relative to uninfected controls.…”

“…The three previously reported studies present similar findings showing an increase in immune and inflammatory response genes, particularly those interferon-regulated, in Lyme disease cases relative to uninfected controls. The study by Clarke, et al 37 also reported the development of a diagnostic classifier of 20 genes for early Lyme disease, but the performance was not evaluated with an independent test set. The study by Petzke, et al 38 reported two kinds of classifiers for discriminating between Lyme disease cases and controls and between Lyme disease cases that resolve after treatment and those that progress to having persistent symptoms.…”

Cytokine Hub Classification of PASC, ME-CFS and other PASC-like Conditions

“…Both share features that include T cell receptor signaling and involvement of monocytes/CD4+ T cells. The rst cluster characterized by a type 1 in ammatory response associated with post-infectious Lyme arthritis and autoimmune joint disease that are associated with IL-8 10,11 . The second cluster includes activation of neutrophils and IL-4/IL-13 signaling 11 which aligns more with post-treatment neurological disease 10 .…”