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.

Integrity of RNA in long-term-stored cervical liquid-based cytology samples: implications for biomarker research.

Biobanks of cervical screening (LBC) samples annotated with disease status are an invaluable resource to support the development of tools for the risk stratification of disease. Although there is growing interest in the assessment of RNA-based biomarkers, little is known on the suitability and durability of stored clinical samples (commonly used in cervical screening) to support RNA-based research. RNA was extracted from 260 stored LBC samples. Storage at -80°C or -25°C allowed isolation of sufficient RNA for further analysis. RNA was found to be substantially degraded according to Agilent Bioanalyser data. Despite this, RT-qPCR was successful in 95% samples tested. These data suggest that biobanked LBC samples are suitable for RNA-based assessment even if stored for up to 14 years.

RNA was extracted from 260 cervical screening samples stored at either -80 or -25°C. An Agilent Bioanalyser was used to examine the level of degradation of a convenience sample of RNAs. Reverse transcriptase quantitative PCR (RT-qPCR) was used to quantify levels of two cellular mRNAs in all samples as a practical means of assessing suitability of the samples for mRNA biomarker analysis.