Conserved syntenic clusters of protein coding genes are missing in birds

BACKGROUND: Birds are one of the most highly successful and diverse groups of vertebrates, having evolved a number of distinct characteristics, including feathers and wings, a sturdy lightweight skeleton and unique respiratory and urinary/excretion systems. However, the genetic basis of these traits is poorly understood. RESULTS: Using comparative genomics based on extensive searches of 60 avian genomes, we have found that birds lack approximately 274 protein coding genes that are present in the genomes of most vertebrate lineages and are for the most part organized in conserved syntenic clusters in non-avian sauropsids and in humans. These genes are located in regions associated with chromosomal rearrangements, and are largely present in crocodiles, suggesting that their loss occurred subsequent to the split of dinosaurs/birds from crocodilians. Many of these genes are associated with lethality in rodents, human genetic disorders, or biological functions targeting various tissues. Functional enrichment analysis combined with orthogroup analysis and paralog searches revealed enrichments that were shared by non-avian species, present only in birds, or shared between all species. CONCLUSIONS: Together these results provide a clearer definition of the genetic background of extant birds, extend the findings of previous studies on missing avian genes, and provide clues about molecular events that shaped avian evolution. They also have implications for fields that largely benefit from avian studies, including development, immune system, oncogenesis, and brain function and cognition. With regards to the missing genes, birds can be considered ‘natural knockouts’ that may become invaluable model organisms for several human diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-014-0565-1) contains supplementary material, which is available to authorized users

A New Chicken Genome Assembly Provides Insight into Avian Genome Structure

The importance of the Gallus gallus (chicken) as a model organism and agricultural animal merits a continuation of sequence assembly improvement efforts. We present a new version of the chicken genome assembly (Gallus_gallus-5.0; GCA_000002315.3), built from combined long single molecule sequencing technology, finished BACs, and improved physical maps. In overall assembled bases, we see a gain of 183 Mb, including 16.4 Mb in placed chromosomes with a corresponding gain in the percentage of intact repeat elements characterized. Of the 1.21 Gb genome, we include three previously missing autosomes, GGA30, 31, and 33, and improve sequence contig length 10-fold over the previous Gallus_gallus-4.0. Despite the significant base representation improvements made, 138 Mb of sequence is not yet located to chromosomes. When annotated for gene content, Gallus_gallus-5.0 shows an increase of 4679 annotated genes (2768 noncoding and 1911 protein-coding) over those in Gallus_gallus-4.0. We also revisited the question of what genes are missing in the avian lineage, as assessed by the highest quality avian genome assembly to date, and found that a large fraction of the original set of missing genes are still absent in sequenced bird species. Finally, our new data support a detailed map of MHC-B, encompassing two segments: one with a highly stable gene copy number and another in which the gene copy number is highly variable. The chicken model has been a critical resource for many other fields of study, and this new reference assembly will substantially further these efforts

Cell Culture on MEMS Platforms: A Review

Microfabricated systems provide an excellent platform for the culture of cells, and are an extremely useful tool for the investigation of cellular responses to various stimuli. Advantages offered over traditional methods include cost-effectiveness, controllability, low volume, high resolution, and sensitivity. Both biocompatible and bioincompatible materials have been developed for use in these applications. Biocompatible materials such as PMMA or PLGA can be used directly for cell culture. However, for bioincompatible materials such as silicon or PDMS, additional steps need to be taken to render these materials more suitable for cell adhesion and maintenance. This review describes multiple surface modification strategies to improve the biocompatibility of MEMS materials. Basic concepts of cell-biomaterial interactions, such as protein adsorption and cell adhesion are covered. Finally, the applications of these MEMS materials in Tissue Engineering are presented.Institute of Bioengineering and Nanotechnology (Singapore)Singapore. Biomedical Research CouncilSingapore. Agency for Science, Technology and ResearchSingapore. Agency for Science, Technology and Research (R-185-001-045-305)Singapore. Ministry of EducationSingapore. Ministry of Education (Grant R-185- 000-135-112)Singapore. National Medical Research CouncilSingapore. National Medical Research Council (Grant R-185-000-099-213)Jassen Cilag (Firm)Singapore-MIT Alliance (Computational and Systems Biology Flagship Project)Global Enterprise for Micro-Mechanics and Molecular Medicin

The constitutive differential transcriptome of a brain circuit for vocal learning

Abstract Background The ability to imitate the vocalizations of other organisms, a trait known as vocal learning, is shared by only a few organisms, including humans, where it subserves the acquisition of speech and language, and 3 groups of birds. In songbirds, vocal learning requires the coordinated activity of a set of specialized brain nuclei referred to as the song control system. Recent efforts have revealed some of the genes that are expressed in these vocal nuclei, however a thorough characterization of the transcriptional specializations of this system is still missing. We conducted a rigorous and comprehensive analysis of microarrays, and conducted a separate analysis of 380 genes by in situ hybridizations in order to identify molecular specializations of the major nuclei of the song system of zebra finches (Taeniopygia guttata), a songbird species. Results Our efforts identified more than 3300 genes that are differentially regulated in one or more vocal nuclei of adult male birds compared to the adjacent brain regions. Bioinformatics analyses provided insights into the possible involvement of these genes in molecular pathways such as cellular morphogenesis, intrinsic cellular excitability, neurotransmission and neuromodulation, axonal guidance and cela-to-cell interactions, and cell survival, which are known to strongly influence the functional properties of the song system. Moreover, an in-depth analysis of specific gene families with known involvement in regulating the development and physiological properties of neuronal circuits provides further insights into possible modulators of the song system. Conclusion Our study represents one of the most comprehensive molecular characterizations of a brain circuit that evolved to facilitate a learned behavior in a vertebrate. The data provide novel insights into possible molecular determinants of the functional properties of the song control circuitry. It also provides lists of compelling targets for pharmacological and genetic manipulations to elucidate the molecular regulation of song behavior and vocal learning

Additional file 1: of The constitutive differential transcriptome of a brain circuit for vocal learning

Table S1. Summary of the numbers of oligos and cDNAs analyzed for each nucleus. Table S2. Summary of the subsets of differential vs. non-differential genes analyzed for each nucleus to establish the p-value cutoffs in Fig. 2. Table S3. List of genes that are differentially regulated in HVC vs. Shelf. Table S4. List of genes that are differentially regulated in RA vs. VLA. Table S5. List of genes that are differentially regulated in nXIIts vs. SSP. Table S6. List of genes that are differentially regulated in Area X vs. VSP. Table S7. Pathways significantly over-represented in HVC. Table S8. Pathways significantly over-represented in RA. Table S9. Pathways significantly over-represented in nXIIts. Table S10. Pathways significantly over-represented in Area X. Table S11. Level 5 Gene Ontology (GO) terms significantly over-represented in HVC. Table S12. Level 5 Gene Ontology (GO) terms significantly over-represented in RA. Table S13. Level 5 Gene Ontology (GO) terms significantly over-represented in nXIIts. Table S14. Level 5 Gene Ontology (GO) terms significantly over-represented in Area X. Table S15. Unique and shared markers of song system nuclei. Table S16. Summary of in situ and array data analyzed to assess gene regulation in Figs. 4-9. (XLSX 244 kb