Correlates of disease severity in bluetongue as a model of acute arbovirus infection

Most viral diseases display a variable clinical outcome due to differences in virus strain virulence and/or individual host susceptibility to infection. Understanding the biological mechanisms differentiating a viral infection displaying severe clinical manifestations from its milder forms can provide the intellectual framework toward therapies and early prognostic markers. This is especially true in arbovirus infections, where most clinical cases are present as mild febrile illness. Here, we used a naturally occurring vector-borne viral disease of ruminants, bluetongue, as an experimental system to uncover the fundamental mechanisms of virus-host interactions resulting in distinct clinical outcomes. As with most viral diseases, clinical symptoms in bluetongue can vary dramatically. We reproduced experimentally distinct clinical forms of bluetongue infection in sheep using three bluetongue virus (BTV) strains (BTV-1IT2006, BTV-1IT2013 and BTV-8FRA2017). Infected animals displayed clinical signs varying from clinically unapparent, to mild and severe disease. We collected and integrated clinical, haematological, virological, and histopathological data resulting in the analyses of 332 individual parameters from each infected and uninfected control animal. We subsequently used machine learning to select the key viral and host processes associated with disease pathogenesis. We identified and experimentally validated five different fundamental processes affecting the severity of bluetongue: (i) virus load and replication in target organs, (ii) modulation of the host type-I IFN response, (iii) pro-inflammatory responses, (iv) vascular damage, and (v) immunosuppression. Overall, we showed that an agnostic machine learning approach can be used to prioritise the different pathogenetic mechanisms affecting the disease outcome of an arbovirus infection.

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Publication Details

Journal
PLoS Pathogens
Published
2024-08-16
DOI
https://doi.org/10.1371/journal.ppat.1012466
Citations
4
Primary Topic
Vector-Borne Animal Diseases
Type
article
Field-Weighted Citation Impact
4.12

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article

Correlates of disease severity in bluetongue as a model of acute arbovirus infection

Noemı́ Sevilla, Meredith Stewart, Oscar A. MacLean, Mauro Di Ventura et al.
4 citations
PLoS Pathogens
Vector-Borne Animal Diseases
4.12
article

Correlates of disease severity in bluetongue as a model of acute arbovirus infection

Noemı́ Sevilla, Meredith Stewart, Oscar A. MacLean, Mauro Di Ventura, Chris Boutell, Giantonella Puggioni, Sara Capista, Maria Teresa Mercante, Kyriaki Nomikou, Wilhelm Furnon, Daniela Antonucci, Gaetano Federico Ronchi, Quan Gu, Davide Pintus, Natasha Palmalux, Massimo Palmarini, Ciriaco Ligios, Jenna Nichols, Daniele Giansante, Marco Caporale, Ana da Silva Filipe, Vanessa Herder, Stéphan Zientara, Emmanuel Bréard, Aislynn Taggart, Rosario Scivoli, Antonio Cocco, Jay Allan, Georgios Ilia, Xinyi Huang, Haris Malik
article en
4 citations

Abstract

Most viral diseases display a variable clinical outcome due to differences in virus strain virulence and/or individual host susceptibility to infection. Understanding the biological mechanisms differentiating a viral infection displaying severe clinical manifestations from its milder forms can provide the intellectual framework toward therapies and early prognostic markers. This is especially true in arbovirus infections, where most clinical cases are present as mild febrile illness. Here, we used a naturally occurring vector-borne viral disease of ruminants, bluetongue, as an experimental system to uncover the fundamental mechanisms of virus-host interactions resulting in distinct clinical outcomes. As with most viral diseases, clinical symptoms in bluetongue can vary dramatically. We reproduced experimentally distinct clinical forms of bluetongue infection in sheep using three bluetongue virus (BTV) strains (BTV-1IT2006, BTV-1IT2013 and BTV-8FRA2017). Infected animals displayed clinical signs varying from clinically unapparent, to mild and severe disease. We collected and integrated clinical, haematological, virological, and histopathological data resulting in the analyses of 332 individual parameters from each infected and uninfected control animal. We subsequently used machine learning to select the key viral and host processes associated with disease pathogenesis. We identified and experimentally validated five different fundamental processes affecting the severity of bluetongue: (i) virus load and replication in target organs, (ii) modulation of the host type-I IFN response, (iii) pro-inflammatory responses, (iv) vascular damage, and (v) immunosuppression. Overall, we showed that an agnostic machine learning approach can be used to prioritise the different pathogenetic mechanisms affecting the disease outcome of an arbovirus infection.

PLoS PathogensVol. 20(8)
Consejo Superior de Investigaciones Científicas (ES), École Nationale Vétérinaire d'Alfort (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (ES), Département Santé Animale (FR), Istituto Zooprofilattico Sperimentale della Sardegna (IT), Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail (FR), Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise G. Caporale (IT), MRC University of Glasgow Centre for Virus Research (GB)
Wellcome, Wellcome Trust, University of Glasgow, Deutsche Forschungsgemeinschaft, Ministero della Salute, Horizon 2020 Framework Programme, Medical Research Council
Good health and well-being
Openalex Percentile: Top 7%
Vector-Borne Animal Diseases
4.12
DOIOpen Access PDFOpenAlex
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