Biology subjects

Ilia, G.

Publications and source records attributed to Ilia, G..

5 recordsLinked to original sources

bioRxiv2024

POLYGENIC DETERMINANTS OF H5N1 ADAPTATION TO BOVINE CELLS

Avian influenza H5N1 clade 2.3.4.4b viruses caused a global panzootic and, unexpectedly, widespread outbreaks in dairy cattle, therefore representing a pandemic threat. To inform effective control strategies, it is critical to determine whether the potential to adapt to bovine cells is a generalised feature of H5N1 viruses, or is specific to clade 2.3.4.4b, or even more restricted to specific genotypes within this clade (e.g., B3.13 and D1.1). Using a large panel of H5N1 viruses representing >60 years of their natural history and other IAV for comparative purposes, we demonstrate that virus adaptation to bovine cells is: (i) highly variable across 2.3.4.4b genotypes, (ii) limited in viruses predating the global expansion of this clade, (iii) determined by the viral internal gene cassette, and (iv) not restricted to udder epithelial cells. Mutations in the PB2 polymerase subunit, particularly M631L, emerge as key determinants of adaptation, although their phenotypic effects are context dependent and have limited enhanced viral polymerase activity in human cells. Bovine B3.13 and some avian genotypes also exhibit enhanced modulation of bovine interferon-induced antiviral responses, determined by at least the viral PB2, nucleoprotein, and the non-structural protein NS1. Our results highlight the polygenic nature of IAV host range and reveal that the potential to cross the species barrier varies during the evolutionary trajectory of H5N1, with some avian viruses more predisposed to spillover than others.

microbiology↗

bioRxiv2024

Lung structural cell dynamics are altered by influenza virus infection experience leading to rapid immune protection following viral re-challenge

Lung structural cells, including epithelial cells and fibroblasts, form barriers against pathogens and trigger immune responses following infections such as influenza A virus. This response leads to the recruitment of innate and adaptive immune cells required for viral clearance. Some of these recruited cells remain within the lung following infection and contribute to enhanced viral control following subsequent infections. There is growing evidence that structural cells can also display long-term changes following infection or insults. Here we investigate long-term changes to mouse lung epithelial cells, fibroblasts, and endothelial cells following influenza virus infection and find that all three cell types maintain an imprint of the infection, particularly in genes associated with communication with T cells. Lung epithelial cells from IAV-infected mice display functional changes by more rapidly controlling influenza virus than cells from naive animals. This rapid anti-viral response and increased expression of molecules required to communicate with T cells demonstrates sustained and enhanced functions following infection. These data suggest lung structural cells could be effective targets for vaccines to boost durable protective immunity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/604410v5_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@2774e0org.highwire.dtl.DTLVardef@6a39e6org.highwire.dtl.DTLVardef@1ff5863org.highwire.dtl.DTLVardef@103df12_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LILung epithelial cells, fibroblasts, and blood endothelial cells maintain an inflammatory imprint of influenza A virus (IAV) infection for at least 40 days post-infection. C_LIO_LIIn vivo re-infection leads to a more spatially restricted anti-viral response compared to primary IAV-infected animals. C_LIO_LIT cells are not required for enhanced viral control early after re-infection in vivo C_LIO_LIEx vivo lung epithelial cells from IAV-infected mice more rapidly control IAV than cells from naive animals in the absence of immune cells. C_LI

immunology↗

bioRxiv2024

A Machine Learning Framework to Identify the Correlates of Disease Severity in 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 identify the key viral and host processes associated with disease pathogenesis. We identified 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, our study using an agnostic machine learning approach, can be used to prioritise the different pathogenetic mechanisms affecting the disease outcome of an arbovirus infection.

microbiology↗

bioRxiv2023

The SARS-CoV-2 Omicron sub-variant BA.2.86 is attenuated in hamsters

SARS-CoV-2 variants have emerged throughout the COVID-19 pandemic. There is a need to risk-assess newly emerged variants in near "real-time" to estimate their potential threat to public health. The recently emerged Omicron sub-variant BA.2.86 raised concerns as it carries a high number of mutations compared to its predecessors. Here, we assessed the virulence of BA.2.86 in hamsters. We compared the pathogenesis of BA.2.86 and BA.2.75, as the latter is one of the most virulent Omicron sub-variants in this animal model. Using digital pathology pipelines, we quantified the extent of pulmonary lesions measuring T cell and macrophage infiltrates, in addition to alveolar epithelial hyperplasia. We also assessed body weight loss, clinical symptoms, virus load in oropharyngeal swabs, and virus replication in the respiratory tract. Our data show that BA.2.86 displays an attenuated phenotype in hamsters, suggesting that it poses no greater risk to public health than its parental Omicron sub-variants. Article summary lineThe newly emerged Omicron sub-variant BA.2.86 is attenuated in hamsters.

microbiology↗

bioRxiv2023

Phenotyping the virulence of SARS-CoV-2 variants in hamsters by digital pathology and machine learning

SARS-CoV-2 has continued to evolve throughout the COVID-19 pandemic, giving rise to multiple variants of concern (VOCs) with different biological properties. As the pandemic progresses, it will be essential to test in near real time the potential of any new emerging variant to cause severe disease. BA.1 (Omicron) was shown to be attenuated compared to the previous VOCs like Delta, but it is possible that newly emerging variants may regain a virulent phenotype. Hamsters have been proven to be an exceedingly good model for SARS-CoV-2 pathogenesis. Here, we aimed to develop robust quantitative pipelines to assess the virulence of SARS-CoV-2 variants in hamsters. We used various approaches including RNAseq, RNA in situ hybridization, immunohistochemistry, and digital pathology, including software assisted whole section imaging and downstream automatic analyses enhanced by machine learning, to develop methods to assess and quantify virus-induced pulmonary lesions in an unbiased manner. Initially, we used Delta and Omicron to develop our experimental pipelines. We then assessed the virulence of recent Omicron sub-lineages including BA.5, XBB, BQ.1.18, BA.2 and BA.2.75. We show that in experimentally infected hamsters, accurate quantification of alveolar epithelial hyperplasia and macrophage infiltrates represent robust markers for assessing the extent of virus-induced pulmonary pathology, and hence virus virulence. In addition, using these pipelines, we could reveal how some Omicron sub-lineages (e.g., BA.2.75) have regained virulence compared to the original BA.1. Finally, to maximise the utility of the digital pathology pipelines reported in our study, we developed an online repository containing representative whole organ histopathology sections that can be visualised at variable magnifications (https://covid-atlas.cvr.gla.ac.uk). Overall, this pipeline can provide unbiased and invaluable data for rapidly assessing newly emerging variants and their potential to cause severe disease.

microbiology↗