Functional roles of Aves class specific cis-regulatory elements on macroevolution of bird-specific features
Ontology highlight
ABSTRACT: We carried out a comparative genomic analysis of 48 avian species to identify avian-specific highly conserved elements (ASHCEs). We performed genome-wide chromatin immunoprecipitation sequencing (ChIP-seq) for three enhancer-associated histone modifications (H3K4me1, H3K27ac, H3K27me3), to investigate dynamic regulatory roles of ASHCEs in chicken development. We found that all three enhancer-associated histone marks are enriched in ASHCEs compared to the whole genome background.
Project description:Unlike microevolutionary processes, little is known about the genetic basis of macroevolutionary processes. One of these magnificent examples is the transition from non-avian dinosaurs to birds that has created numerous evolutionary innovations such as self-powered flight and its associated wings with flight feathers. By analysing 48 bird genomes, we identified millions of avian-specific highly conserved elements (ASHCEs) that predominantly (>99%) reside in non-coding regions. Many ASHCEs show differential histone modifications that may participate in regulation of limb development. Comparative embryonic gene expression analyses across tetrapod species suggest ASHCE-associated genes have unique roles in developing avian limbs. In particular, we demonstrate how the ASHCE driven avian-specific expression of gene Sim1 driven by ASHCE may be associated with the evolution and development of flight feathers. Together, these findings demonstrate regulatory roles of ASHCEs in the creation of avian-specific traits, and further highlight the importance of cis-regulatory rewiring during macroevolutionary changes.
Project description:The purpose of this microarray experiment was to validate the Del-Mar 14K Chicken Integrated Systems Microarray for different chicken tissues and to determine the utility of this chicken cDNA microarray for other closely related and more distant avian species. The Del-Mar 14 K array was constructed from cDNAs amplified from EST clones sequenced from five normalized chicken cDNA libraries derived from neuroendocrine (5,929), abdominal fat (4,800), liver (2,635), skeletal muscle (2,398), reproductive tract (2,008), 387 long (70mer) oligonucleotides and 72 quality control spots. The array contains 17,770 cDNA clones, where protein matches were found by BlastX analysis for 68% of chicken contigs and 46% of singleton sequences represented on the array. A reference RNA design was used for the hybridization of total RNA from four chicken tissues (liver, abdominal fat, breast muscle and hypothalamus) and the cross-species hybridization (CSH) of total RNA from tissue from birds representing four orders of the Class Aves [Galliformes (chicken, Coturnix quail and domestic turkey), Anseriformes (Peking duck), Falconiformes (American kestrel) and Passeriformes (American tree sparrow)]. A reference RNA pool was made from an equal amount of high-quality total RNA extracted from chicken liver, abdominal fat, breast muscle and hypothalamus. Each of the 43 microarrays was co-hybridized with Cy3-labeled cDNA targets from one of the avian tissue samples and Cy5-labled cDNA targets from the reference chicken RNA pool. Loess-normalized data were used to determine the number of cDNAs expressed in chicken tissues and the number of genes (cDNAs) detectable by cross-hybridization with various avian tissue samples. The Cy5-labeled reference samples were used to determine the coefficient of variation across the 43 microarrays. This study shows a remarkably high level of cross hybridization of Cy3-labeled cDNA targets from a wide range of avian species to the Del-Mar 14K microarray, where 38 to 62% of the cDNA probes on the chicken array (genes) were detectable. Keywords: Transcriptional profiling, Del-Mar 14K Chicken Integrated Systems Microarray validation, multi-tissues, cross-species hybridization, class Aves
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: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: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: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:Toxoplasma gondii is a ubiquitous protozoan pathogen able to infect both mammalian and avian hosts. Surprisingly, just three strains appear to account for the majority of isolates from Europe and N. America. To test the hypothesis that strain divergence might be driven by differences between mammalian and avian response to infection, we examine in vitro strain-dependent host responses in a representative avian host, the chicken.
Project description:Toxoplasma gondii is a ubiquitous protozoan pathogen able to infect both mammalian and avian hosts. Surprisingly, just three strains appear to account for the majority of isolates from Europe and N. America. To test the hypothesis that strain divergence might be driven by differences between mammalian and avian response to infection, we examine in vitro strain-dependent host responses in a representative avian host, the chicken.
Project description:Toxoplasma gondii is a ubiquitous protozoan pathogen able to infect both mammalian and avian hosts. Surprisingly, just three strains appear to account for the majority of isolates from Europe and N. America. To test the hypothesis that strain divergence might be driven by differences between mammalian and avian response to infection, we examine in vitro strain-dependent host responses in a representative avian host, the chicken. Chicken embryonic fibroblasts were cultivated in vitro and infected with different strains of Toxoplasma gondii; host transcriptional responses were then analyzed at 24 hours post-infection.
Project description:Toxoplasma gondii is a ubiquitous protozoan pathogen able to infect both mammalian and avian hosts. Surprisingly, just three strains appear to account for the majority of isolates from Europe and N. America. To test the hypothesis that strain divergence might be driven by differences between mammalian and avian response to infection, we examine in vitro strain-dependent host responses in a representative avian host, the chicken. To identify parasite drivers of strain-dependent host response, QTL mapping was used; analysis revealed a locus on Toxoplasma chromosome VIIb. To determine whether this was the parasite gene ROP16, array analysis was performed on chicken embryonic fibroblasts infected with Type I parasites and ROP16-KO parasites (of a Type I background).