Using transcriptomics to predict and visualize disease status in bighorn sheep (Ovis canadensis). Issue 1 (3rd July 2022)

• Record Type: Journal Article
• Authors: Bowen, Lizabeth; Manlove, Kezia; Roug, Annette; Waters, Shannon; LaHue, Nate; Wolff, Peregrine
• Editors: Cooke, Steven
• Is Part Of: Conservation physiology. Volume 10:Issue 1(2022)
• Publication Date: 2022-07-03
• Subjects: Nature -- Effect of human beings on -- Periodicals; Conservation biology -- Periodicals; 577.05
• DOI: 10.1093/conphys/coac046
• Languages: English
• ISSNs: 2051-1434

Abstract

Abstract : Wildlife disease is a major conservation issue worldwide. New diagnostic approaches and strategies offering insights into mechanisms shaping disease dynamics are required. We show how a relatively novel combination of molecular and analytical approaches could advance understanding of disease processes in one wildlife disease system—pneumonia in bighorn sheep. Abstract: Increasing risk of pathogen spillover coupled with overall declines in wildlife population abundance in the Anthropocene make infectious disease a relevant concern for species conservation worldwide. While emerging molecular tools could improve our diagnostic capabilities and give insight into mechanisms underlying wildlife disease risk, they have rarely been applied in practice. Here, employing a previously reported gene transcription panel of common immune markers to track physiological changes, we present a detailed analysis over the course of both acute and chronic infection in one wildlife species where disease plays a critical role in conservation, bighorn sheep ( Ovis canadensis ). Differential gene transcription patterns distinguished between infection statuses over the course of acute infection and differential correlation (DC) analyses identified clear changes in gene co-transcription patterns over the early stages of infection, with transcription of four genes—TGFb, AHR, IL1b and MX1—continuing to increase even as transcription of other immune-associated genes waned. In a separate analysis, we considered the capacity of the same gene transcription panel to aid in differentiating between chronically infected animals and animals in other disease states outside of acute disease events (an immediate priority for wildlife management in this system). We found that this transcription panel was capable of accurately identifying chronically infected animals in the test dataset, though additional data will be required to determine how far this ability extends. Taken together, our results showcase the successful proof of concept and breadth of potential utilities that gene transcription might provide to wildlife disease management, from direct insight into mechanisms associated with differential disease response to improved diagnostic capacity in the field.