Picasso-Risso, C. | bioRxiv Science

bioRxiv2022

Standardization of Data Analysis for RT-QuIC-based Detection of Chronic Wasting Disease

Chronic wasting disease (CWD) is a disease affecting cervids and is caused by prions accumulating as pathogenic fibrils in lymphoid tissue and the central nervous system. Approaches for detecting CWD prions historically relied on antibody-based assays. However, recent advancements in protein amplification technology provided the foundation for a new class of CWD diagnostic tools. In particular, real-time quaking-induced conversion (RT-QuIC) has rapidly become a feasible option for CWD diagnosis. Despite its increased usage for CWD-focused research, there lacks consensus regarding the interpretation of RT-QuIC data for diagnostic purposes. It is imperative then to identify a standardized and replicable method for determining CWD status from RT-QuIC data. Here, we assessed variables that could impact RT-QuIC results and explored the use of maxpoint ratios (maximumRFU/backgroundRFU) to improve the consistency of RT-QuIC analysis. We examined a variety of statistical analyses to retrospectively analyze CWD status based on RT-QuIC and ELISA results from 668 white-tailed deer lymph nodes. Our results revealed an MPR threshold of 2.0 for determining the rate of amyloid formation, and MPR analysis showed excellent agreement with independent ELISA results. These findings suggest that the use of MPR is a statistically viable option for normalizing between RT-QuIC experiments and defining CWD status.

molecular biology↗

bioRxiv2019

Mapping changes in the spatiotemporal distribution of lumpy skin disease virus

Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vectors which results in substantial economic losses due to impact on production efficiency and profitability, and represents an emerging threat to international trade of livestock products and live animals. Since 2015, the disease has spread across many Eastern European countries as well as Russia and Kazakhstan. This rapid expansion highlights the emergent nature of the virus in more temperate regions than those in which LSDV traditionally occurred. The goal of this study was to assess the risk for further LSDV spread in Eurasia through a) analysis of environmental factors conducive for LSDV and b) estimate of the underlying LSDV risk using a fine spatiotemporally explicit Bayesian hierarchical model on LSDV outbreak occurrence information. We used ecological niche modeling to estimate the potential distribution of LSDV outbreaks for 2014-2016. This analysis resulted in a spatial representation of environmental limits where, if introduced, LSDV is expected to efficiently spread. The Bayesian space-time model incorporated both environmental factors and the changing spatiotemporal distribution of the disease to capture the dynamics of disease spread and predict areas in which there is an increased risk of LSDV occurrence. Variables related to the average temperature, precipitation, wind speed, as well as land cover and host densities were found to be important drivers explaining the observed distribution of LSDV in both modeling approaches. Areas of elevated LSDV risks were identified mainly in Russia, Turkey, Serbia, and Bulgaria. Results suggest that prevailing ecological conditions may be compatible with further spread of LSDV in Eurasia, though models should be continually updated to reflect the current epidemiologic conditions. The results presented here advance our understanding of the ecological requirements of LSDV in temperate regions and may help in the design and implementation of prevention and surveillance strategies in the region.

epidemiology↗