The Troublesome Ticks Research Protocol: Developing a Comprehensive, Multidiscipline Research Plan for Investigating Human Tick-Associated Disease in Australia.

In Australia, there is a paucity of data about the extent and impact of zoonotic tick-related illnesses. Even less is understood about a multifaceted illness referred to as Debilitating Symptom Complexes Attributed to Ticks (DSCATT). Here, we describe a research plan for investigating the aetiology, pathophysiology, and clinical outcomes of human tick-associated disease in Australia. Our approach focuses on the transmission of potential pathogens and the immunological responses of the patient after a tick bite. The protocol is strengthened by prospective data collection, the recruitment of two external matched control groups, and sophisticated integrative data analysis which, collectively, will allow the robust demonstration of associations between a tick bite and the development of clinical and pathological abnormalities. Various laboratory analyses are performed including metagenomics to investigate the potential transmission of bacteria, protozoa and/or viruses during tick bite. In addition, multi-omics technology is applied to investigate links between host immune responses and potential infectious and non-infectious disease causations. Psychometric profiling is also used to investigate whether psychological attributes influence symptom development. This research will fill important knowledge gaps about tick-borne diseases. Ultimately, we hope the results will promote improved diagnostic outcomes, and inform the safe management and treatment of patients bitten by ticks in Australia.

Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract.

Host biomarkers are increasingly being considered as tools for improved COVID-19 detection and prognosis. We recently profiled circulating host-encoded microRNA (miRNAs) during SARS-CoV-2 infection, revealing a signature that classified COVID-19 cases with 99.9% accuracy. Here we sought to develop a signature suited for clinical application by analyzing specimens collected using minimally invasive procedures. Eight miRNAs displayed altered expression in anterior nasal tissues from COVID-19 patients, with miR-142-3p, a negative regulator of interleukin-6 (IL-6) production, the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-30c-2-3p, miR-628-3p and miR-93-5p) independently classifies COVID-19 cases with 100% accuracy. This study further defines the host miRNA response to SARS-CoV-2 infection and identifies candidate biomarkers for improved COVID-19 detection.