Fast-forwarding evolution—Accelerated adaptation in a proofreading-deficient hypermutator herpesvirus

Evolution relies on the availability of genetic diversity for fitness-based selection. However, most deoxyribonucleic acid (DNA) viruses employ DNA polymerases (Pol) capable of exonucleolytic proofreading to limit mutation rates during DNA replication. The relative genetic stability produced by high-fidelity genome replication can make studying DNA virus adaptation and evolution an intensive endeavor, especially in slowly replicating viruses. Here, we present a proofreading-impaired Pol mutant (Y547S) of Marek’s disease virus that exhibits a hypermutator phenotype while maintaining unimpaired growth in vitro and wild-type (WT)-like pathogenicity in vivo. At the same time, mutation frequencies observed in Y547S virus populations are 2–5-fold higher compared to the parental WT virus. We find that Y547S adapts faster to growth in originally non-permissive cells, evades pressure conferred by antiviral inhibitors more efficiently, and is more easily attenuated by serial passage in cultured cells compared to WT. Our results suggest that hypermutator viruses can serve as a tool to accelerate evolutionary processes and help identify key genetic changes required for adaptation to novel host cells and resistance to antiviral therapy. Similarly, the rapid attenuation achieved through adaptation of hypermutators to growth in cell culture enables identification of genetic changes underlying attenuation and virulence, knowledge that could practically exploited, e.g. in the rational design of vaccines

Phenotypic Characterization of Recombinant Marek’s Disease Virus in Live Birds Validates Polymorphisms Associated with Virulence

Marek’s disease (MD) is a highly infectious lymphoproliferative disease in chickens with a significant economic impact. Mardivirus gallidalpha 2, also known as Marek’s disease virus (MDV), is the causative pathogen and has been categorized based on its virulence rank into four pathotypes: mild (m), virulent (v), very virulent (vv), and very virulent plus (vv+). A prior comparative genomics study suggested that several single-nucleotide polymorphisms (SNPs) and genes in the MDV genome are associated with virulence, including nonsynonymous (ns) SNPs in eight open reading frames (ORF): UL22, UL36, UL37, UL41, UL43, R-LORF8, R-LORF7, and ICP4. To validate the contribution of these nsSNPs to virulence, the vv+MDV strain 686 genome was modified by replacing nucleotides with those observed in the vMDV strains. Pathogenicity studies indicated that these substitutions reduced the MD incidence and increased the survival of challenged birds. Furthermore, using the best-fit pathotyping method to rank the virulence, the modified vv+MDV 686 viruses resulted in a pathotype similar to the vvMDV Md5 strain. Thus, these results support our hypothesis that SNPs in one or more of these ORFs are associated with virulence but, as a group, are not sufficient to result in a vMDV pathotype, suggesting that there are additional variants in the MDV genome associated with virulence, which is not surprising given this complex phenotype and our previous finding of additional variants and SNPs associated with virulence

The immune cell landscape and response of Marek’s disease resistant and susceptible chickens infected with Marek’s disease virus

Abstract Genetically resistant or susceptible chickens to Marek’s disease (MD) have been widely used models to identify the molecular determinants of these phenotypes. However, these prior studies lacked the basic identification and understanding of immune cell types that could be translated toward improved MD control. To gain insights into specific immune cell types and their responses to Marek’s disease virus (MDV) infection, we used single-cell RNA sequencing (scRNAseq) on splenic cells from MD resistant and susceptible birds. In total, 14,378 cells formed clusters that identified various immune cell types. Lymphocytes, specifically T cell subtypes, were the most abundant with significant proportional changes in some subtypes upon infection. The largest number of differentially expressed genes (DEG) response was seen in granulocytes, while macrophage DEGs differed in directionality by subtype and line. Among the most DEG in almost all immune cell types were granzyme and granulysin, both associated with cell-perforating processes. Protein interactive network analyses revealed multiple overlapping canonical pathways within both lymphoid and myeloid cell lineages. This initial estimation of the chicken immune cell type landscape and its accompanying response will greatly aid efforts in identifying specific cell types and improving our knowledge of host response to viral infection

The immune cell landscape and response of Marek’s disease resistant and susceptible chickens infected with Marek’s disease virus

Genetically resistant or susceptible chickens to Marek’s disease (MD) have been widely used models to identify the molecular determinants of these phenotypes. However, these prior studies lacked the basic identification and understanding of immune cell types that could be translated toward improved MD control. To gain insights into specific immune cell types and their responses to Marek’s disease virus (MDV) infection, we used single-cell RNA sequencing (scRNAseq) on splenic cells from MD resistant and susceptible birds. Totally, 14,378 cells formed clusters that identified various immune cell types. Lymphocytes, specifically T cell subtypes, were the most abundant with significant proportional changes in some subtypes upon infection. The largest number of differentially expressed genes (DEG) response was seen in granulocytes, while macrophage DEGs differed in directionality by subtype and line. Among the most DEG in almost all immune cell types were granzyme and granulysin, both associated with cell-perforating processes. Protein interactive network analyses revealed multiple overlapping canonical pathways within both lymphoid and myeloid cell lineages. This initial estimation of the chicken immune cell type landscape and its accompanying response will greatly aid efforts in identifying specific cell types and improving our knowledge of host response to viral infection.This preprint is made available through Research Square at doi:10.21203/rs.3.rs-1858513/v1.This work is licensed under a CC BY 4.0 License