The feather pattern autosomal barring in chicken is strongly associated with segregation at the MC1R locus.

Color patterns within individual feathers are common in birds but little is known about the genetic mechanisms causing such patterns. Here, we investigate the genetic basis for autosomal barring in chicken, a horizontal striping pattern on individual feathers. Using an informative backcross, we demonstrate that the MC1R locus is strongly associated with this phenotype. A deletion at SOX10, underlying the dark brown phenotype on its own, affects the manifestation of the barring pattern. The coding variant L133Q in MC1R is the most likely causal mutation for autosomal barring in this pedigree. Furthermore, a genetic screen across six different breeds showing different patterning phenotypes revealed that the most striking shared characteristics among these breeds were that they all carried the MC1R alleles Birchen or brown. Our data suggest that the presence of activating MC1R mutations enhancing pigment synthesis is an important mechanism underlying pigmentation patterns on individual feathers in chicken. We propose that MC1R and its antagonist ASIP play a critical role for determining within-feather pigmentation patterns in birds by acting as activator and inhibitor possibly in a Turing reaction-diffusion model.

Self-assembly of biological networks via adaptive patterning revealed by avian intradermal muscle network formation.

Networked structures integrate numerous elements into one functional unit, while providing a balance between efficiency, robustness, and flexibility. Understanding how biological networks self-assemble will provide insights into how these features arise. Here, we demonstrate how nature forms exquisite muscle networks that can repair, regenerate, and adapt to external perturbations using the feather muscle network in chicken embryos as a paradigm. The self-assembled muscle networks arise through the implementation of a few simple rules. Muscle fibers extend outward from feather buds in every direction, but only those muscle fibers able to connect to neighboring buds are eventually stabilized. After forming such a nearest-neighbor configuration, the network can be reconfigured, adapting to perturbed bud arrangement or mechanical cues. Our computational model provides a bioinspired algorithm for network self-assembly, with intrinsic or extrinsic cues necessary and sufficient to guide the formation of these regenerative networks. These robust principles may serve as a useful guide for assembling adaptive networks in other contexts.