Cattle possess three IgG subclasses. However, the key immune functions, including complement and NK cell activation, and enhancement of phagocytosis, are not fully described for bovine IgG1, 2 and 3. We produced chimeric monoclonal antibodies (mAbs) consisting of a defined variable region linked to the constant regions of bovine IgG1, 2 and 3, and expressed His-tagged soluble recombinant bovine Fc gamma receptors (FcγRs) IA (CD64), IIA (CD32A), III (CD16) and Fcγ2R. Functional assays using bovinized mAbs were developed. IgG1 and IgG3, but not IgG2, activated complement-dependent cytotoxicity. Only IgG1 could activate cattle NK cells to mobilize CD107a after antigen crosslinking, a surrogate assay for antibody-dependent cell cytotoxicity. Both IgG1 and IgG2 could trigger monocyte-derived macrophages to phagocytose fluorescently labelled antigen-expressing target cells. IgG3 induced only weak antibody-dependent cellular phagocytosis (ADCP). By contrast, monocytes only exhibited strong ADCP when triggered by IgG2. IgG1 bound most strongly to recombinant FcγRs IA, IIA and III, with weaker binding by IgG3 and none by IgG2, which bound exclusively to Fcγ2R. Immune complexes containing IgG1, 2 and 3 bound differentially to leukocyte subsets, with IgG2 binding strongly to neutrophils and monocytes and all subclasses binding platelets. Differential expression of the FcγRs on leukocyte subsets was demonstrated by surface staining and/or RT-qPCR of sorted cells, e.g., Fcγ2R mRNA was expressed in monocytes/macrophages, neutrophils, and platelets, potentially explaining their strong interactions with IgG2, and FcγRIII was expressed on NK cells, presumably mediating IgG1-dependent NK cell activation. These data reveal differences in bovine IgG subclass functionality, which do not correspond to those described in humans, mice or pigs, which is relevant to the study of these IgG subclasses in vaccine and therapeutic antibody development.

Chickens genetically resistant to avian influenza could prevent future outbreaks. In chickens, influenza A virus (IAV) relies on host protein ANP32A. Here we use CRISPR/Cas9 to generate homozygous gene edited (GE) chickens containing two ANP32A amino acid substitutions that prevent viral polymerase interaction. After IAV challenge, 9/10 edited chickens remain uninfected. Challenge with a higher dose, however, led to breakthrough infections. Breakthrough IAV virus contained IAV polymerase gene mutations that conferred adaptation to the edited chicken ANP32A. Unexpectedly, this virus also replicated in chicken embryos edited to remove the entire ANP32A gene and instead co-opted alternative ANP32 protein family members, chicken ANP32B and ANP32E. Additional genome editing for removal of ANP32B and ANP32E eliminated all viral growth in chicken cells. Our data illustrate a first proof of concept step to generate IAV-resistant chickens and show that multiple genetic modifications will be required to curtail viral escape.

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

Type I interferons (IFN) are the first line of immune response against infection. In this study, we explore the interaction between Type I IFN and foot-and-mouth disease virus (FMDV), focusing on the effect of this interaction on epithelial cell death. While several mathematical models have explored the interaction between interferon and viruses at a systemic level, with most of the work undertaken on influenza and hepatitis C, these cannot investigate why a virus such as FMDV causes extensive cell death in some epithelial tissues leading to the development of lesions, while other infected epithelial tissues exhibit negligible cell death. Our study shows how a model that includes epithelial tissue structure can explain the development of lesions in some tissues and their absence in others. Furthermore, we show how the site of viral entry in an epithelial tissue, the viral replication rate, IFN production, suppression of viral replication by IFN and IFN release by live cells, all have a major impact on results.