Developmentally programmed genome rearrangements are rare in vertebrates, but have been reported in scattered lineages including the bandicoot, hagfish, lamprey, and zebra finch (Taeniopygia guttata) [1]. In the finch, a well-studied animal model for neuroendocrinology and vocal learning [2], one such programmed genome rearrangement involves a germline-restricted chromosome, or GRC, which is found in germlines of both sexes but eliminated from mature sperm [3, 4]. Transmitted only through the oocyte, it displays uniparental female-driven inheritance, and early in embryonic development is apparently eliminated from all somatic tissue in both sexes [3, 4]. The GRC comprises the longest finch chromosome at over 120 million base pairs [3], and previously the only known GRC-derived sequence was repetitive and non-coding [5]. Because the zebra finch genome project was sourced from male muscle (somatic) tissue [6], the remaining genomic sequence and protein-coding content of the GRC remain unknown. Here we report the first protein-coding gene from the GRC: a member of the α-soluble N-ethylmaleimide sensitive fusion protein (NSF) attachment protein (α-SNAP) family hitherto missing from zebra finch gene annotations. In addition to the GRC-encoded α-SNAP, we find an additional paralogous α-SNAP residing in the somatic genome (a somatolog)-making the zebra finch the first example in which α-SNAP is not a single-copy gene. We show divergent, sex-biased expression for the paralogs and also that positive selection is detectable across the bird α-SNAP lineage, including the GRC-encoded α-SNAP. This study presents the identification and evolutionary characterization of the first protein-coding GRC gene in any organism.

With the present work we quantitatively studied the modellability of the inactive state of Class A G protein-coupled receptors (GPCRs). Specifically, we constructed models of one of the Class A GPCRs for which structures solved in the inactive state are available, namely the β2 AR, using as templates each of the other class members for which structures solved in the inactive state are also available. Our results showed a detectable linear correlation between model accuracy and model/template sequence identity. This suggests that the likely accuracy of the homology models that can be built for a given receptor can be generally forecasted on the basis of the available templates. We also probed whether sequence alignments that allow for the presence of gaps within the transmembrane domains to account for structural irregularities afford better models than the classical alignment procedures that do not allow for the presence of gaps within such domains. As our results indicated, although the overall differences are very subtle, the inclusion of internal gaps within the transmembrane domains has a noticeable a beneficial effect on the local structural accuracy of the domain in question.