Molecular analysis of human tick-bitten skin yields signatures associated with distinct spatial and temporal trajectories
Tick-associated diseases pose significant challenges due to multifaceted interactions among various biological entities from host-specific responses to tick-introduced toxins, salivary proteins, and inoculated microbes. Clinically, host skin responses vary between tick-bitten patients, however the underlying cellular and molecular mechanisms dictating these clinical variations remain elusive. This hinders prognosis and impedes identification of potential therapeutic interventions post tick-bite. We hypothesized that analysis of the tick-bitten skin could yield molecular signatures to bridge this gap and uncover prognostic or therapeutic biomarkers. As a first step towards this overall goal, we conducted a technical pilot to assess if spatial transcriptomic analysis using the NanoString GeoMx Digital Spatial Profiler on skin tissues from tick-bitten samples (cases) vs contralateral samples (controls) collected simultaneously from the same participants can detect tick bite-specific signatures. Comparative analysis between cases and control samples revealed 274 upregulated and 840 downregulated genes. Key pathway perturbations associated with tick bites comprised keratinization and immune system regulation. This tissue-based approach was informative not only across space (i.e. location) but also across time (i.e. longitudinal). Samples of skin biopsies taken within 72 hours of a bite displayed differentially expressed genes (DEGs) focused on protein metabolism and viral infection pathways as compared to samples taken 3 months post tick-bite, which instead displayed significant perturbations in several epigenetic regulatory pathways, highlighting the temporal nature of the host response following tick bites. These skin biopsies allowed concurrent assessment of tick bite-associated bacteria, notably Rickettsia, which were detectable but without consistent associations between microbe host response. Importantly within-individual signatures can distinguish tick-bitten samples from controls and identify between-individual signatures. Such variation in signatures promise to support future identification of biomarkers guiding prognosis and therapeutic intervention.