Project description:Ducks and wild aquatic birds are the natural reservoirs of avian influenza viruses. However, the host proteome response that causes disease in vivo during infection by the highly pathogenic avian influenza (HPAI) H5N1 virus is still not well understood. In the present study, we compared the proteome response in Muscovy duck lung tissue during 3 day of infection with either a highly virulent or an avirulent H5N1 virus. During infection, proteins involved in immune response of neutrophils and size of cells were increased markedly in the lung by the virulent strain, while the avirulent strain evoked a distinct response, characterized by an increase in proteins involved in cell movement, maturation of dendritic cells, adhesion of phagocytes, and immune response of macrophages.
Project description:Transcriptional profiling was carried out on lung and ileum samples at 1dpi and 3dpi from chickens infected with either low pathogenic (H5N2) or highly pathogenic (H5N1) avian influenza. Infected birds were compared to control birds at each time point.
Project description:Transcriptional profiling was carried out on lung and ileum samples at 1dpi and 3dpi from ducks infected with either low pathogenic (H5N2) or highly pathogenic (H5N1) avian influenza. Infected birds were compared to control birds at each time point.
Project description:Transcriptional profiling was carried out on lung and ileum samples at 1dpi and 3dpi from quail infected with either low pathogenic (H5N2) or highly pathogenic (H5N1) avian influenza. Infected birds were compared to control birds at each time point.
Project description:Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We utilized deep learning to characterize these enhancer codes and devised three novel metrics to compare cell types in the telencephalon between mammals and birds. To this end, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous non-neuronal and GABAergic cell types show a high degree of similarity across vertebrates, while excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types solely based on genomic sequences.
Project description:Comparative genomic hybridisation of genomic DNA from avian species to the Roche NimbleGen chicken whole genome oligonucleotide array
Project description:Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We utilized deep learning to characterize these enhancer codes and devised three novel metrics to compare cell types in the telencephalon between mammals and birds. To this end, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous non-neuronal and GABAergic cell types show a high degree of similarity across vertebrates, while excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types solely based on genomic sequences.
