BackgroundAdvances in genetic tools applied to livestock breeding has prompted research into the previously neglected breeds adapted to harsh local environments. One such group is the Welsh mountain sheep breeds, which can be farmed at altitudes of 300 m above sea level but are considered to have a low productive value because of their poor wool quality and small carcass size. This is contrary to the lowland breeds which are more suited to wool and meat production qualities, but do not fare well on upland pasture. Herein, medium-density genotyping data from 317 individuals representing 15 Welsh sheep breeds were used alongside the whole-genome resequencing data of 14 breeds from the same set to scan for the signatures of selection and candidate genetic variants using haplotype- and SNP-based approaches.ResultsHaplotype-based selection scan performed on the genotyping data pointed to a strong selection in the regions of GBA3, PPARGC1A, APOB, and PPP1R16B genes in the upland breeds, and RNF24, PANK2, and MUC15 in the lowland breeds. SNP-based selection scan performed on the resequencing data pointed to the missense mutations under putative selection relating to a local adaptation in the upland breeds with functions such as angiogenesis (VASH1), anti-oxidation (RWDD1), cell stress (HSPA5), membrane transport (ABCA13 and SLC22A7), and insulin signaling (PTPN1 and GIGFY1). By contrast, genes containing candidate missense mutations in the lowland breeds are related to cell cycle (CDK5RAP2), cell adhesion (CDHR3), and coat color (MC1R).ConclusionWe found new variants in genes with potentially functional consequences to the adaptation of local sheep to their environments in Wales. Knowledge of these variations is important for improving the adaptative qualities of UK and world sheep breeds through a marker-assisted selection.
SummaryRussian sheep breeds represent an important economic asset by providing meat and wool, whilst being adapted to extreme climates. By resequencing two Russian breeds from Siberia: Tuva (n = 20) and Baikal (n = 20); and comparing them with a European (UK) sheep outgroup (n = 14), 41 million variants were called, and signatures of selection were identified. High‐frequency missense mutations on top of selection peaks were found in genes related to immunity (LOC101109746) in the Baikal breed and wool traits (IDUA), cell differentiation (GLIS1) and fat deposition (AADACL3) in the Tuva breed. In addition, genes found under selection owing to haplotype frequency changes were related to wool traits (DSC2), parasite resistance (CLCA1), insulin receptor pathway (SOCS6) and DNA repair (DDB2) in the Baikal breed, and vision (GPR179) in the Tuva breed. Our results present candidate genes and SNPs for future selection programmes, which are necessary to maintain and increase socioeconomic gain from Siberian breeds.
As interest in antibody-based drug development continues to increase, the biopharmaceutical industry has begun to focus on complex multi-specific antibodies (MsAbs) as an up-and-coming class of biologic that differ from natural monoclonal antibodies through their ability to bind to more than one type of antigen. As techniques to generate such molecules have diversified, so have their formats and the need for standard notation. Previous efforts to develop a notation language for macromolecule drugs have been insufficient, or too complex, for MsAbs. Here, we present Antibody Markup Language (AbML), a new notation language specifically for antibody formats that overcomes the limitations of existing languages and can annotate all current antibody formats, including fusions, fragments, standard antibodies and MsAbs, as well as all currently conceivable future formats. AbML V1.1 also provides explicit support for T-cell receptor domains. To assist users of this language we have also developed a tool, abYdraw, that can draw antibody schematics from AbML strings or generate an AbML string from a drawn antibody schematic. AbML has the potential to become a standardized notation for describing new MsAb formats entering clinical trials. Abbreviations: AbML: Antibody Markup Language; ADC: Antibody-drug conjugate; CAS: Chemical Abstracts Service; CH: Constant heavy; CL: Constant light; Fv: Variable fragment; HELM: Hierarchical Editing Language for Macromolecules; HSA: Human serum albumin; INN: International Nonproprietary Names; KIH: Knobs-into-holes; mAbs: Monoclonal antibodies; MsAb: Multi-specific antibody; WHO: World Health Organization; PEG: Poly-ethylene glycol; scFv: Single-chain variable fragment; SMILES: Simplified Molecular-Input Line-Entry System; VH: Variable heavy; VHH: Single-domain (Camelid) variable heavy; VL: Variable light
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