Gene set predictor for post-treatment Lyme disease 2022, Clarke et al.

https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(22)00375-5


Highlights

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  RNA-seq of PBMCs from 152 individuals with post-treatment Lyme disease
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  Differential expression between acute, post-treatment, and uninfected
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  Machine learning to identify most relevant genes
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  35 genes identified as potentially useful biomarker set

Summary
Lyme disease (LD) is tick-borne disease whose post-treatment sequelae are not well understood. For this study, we enrolled 152 individuals with symptoms of post-treatment LD (PTLD) to profile their peripheral blood mononuclear cells (PBMCs) with RNA sequencing (RNA-seq). Combined with RNA-seq data from 72 individuals with acute LD and 44 uninfected controls, we investigated differences in differential gene expression. We observe that most individuals with PTLD have an inflammatory signature that is distinguished from the acute LD group. By distilling gene sets from this study with gene sets from other sources, we identify a subset of genes that are highly expressed in the cohorts but are not already established as biomarkers for inflammatory response or other viral or bacterial infections. We further reduce this gene set by feature importance to establish an mRNA biomarker set capable of distinguishing healthy individuals from those with acute LD or PTLD as a candidate for translation into an LD diagnostic.

 

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