In SilicoAnalysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites

Abstract

Color variation provides the opportunity to investigate the genetic basis of evolution and selection. Reptiles are less studied than mammals. Comparative genomics approaches allow for knowledge gained in one species to be leveraged for use in another species. We describe a comparative vertebrate analysis of conserved regulatory modules in pythons aimed at assessing bioinformatics evidence that transcription factors important in mammalian pigmentation phenotypes may also be important in python pigmentation phenotypes. We identified 23 python orthologs of mammalian genes associated with variation in coat color phenotypes for which we assessed the extent of pairwise protein sequence identity between pythons and mouse, dog, horse, cow, chicken, anole lizard, and garter snake. We next identified a set of melanocyte/pigment associated transcription factors (CREB, FOXD3, LEF-1, MITF, POU3F2, and USF-1) that exhibit relatively conserved sequence similarity within their DNA binding regions across species based on orthologous alignments across multiple species. Finally, we identified 27 evolutionarily conserved clusters of transcription factor binding sites within ~200-nucleotide intervals of the 1500-nucleotide upstream regions of AIM1, DCT, MC1R, MITF, MLANA, OA1, PMEL, RAB27A, and TYR from Python bivittatus. Our results provide insight into pigment phenotypes in pythons.

Identification and characterization of microRNAs in white and black coated yak

“…Furthermore, the results of KEGG analysis revealed many potential genes from the melanogenesis pathway. At present, many candidate genes in the melanogenesis pathway have already been shown to be related to coat color, including melanocortin 1 receptor (MC1R) (e.g., San-Jose et al, 2017), melanocyte-stimulating hormone (MSH) (e.g., Jo et al, 2017), and microphthalmia-associated transcription factor (MITF) (e.g., Irizarry and Bryden, 2016;Wang et al, 2017;Xia et al, 2017). Here, a series of DEMs whose targets are involved in the melanogenesis pathway were identified (Table 1), including Frizzled-7 (FZD7) and Frizzled-6 (FZD6), which are found in a variety of cancer cells and related to signal transduction (Cao et al, 2017;Liu et al, 2017;Yang et al, 2011;Corda et al, 2017).…”

A nonsense mutation in TFEC is the likely cause of the recessive piebald phenotype in ball pythons (Python regius)

“…Over the last thirty years, ball python breeders have discovered and successfully propagated in captivity more than three hundred Mendelian phenotypes. Yet, researchers working on the genetics of vertebrate colouration have been slow to appreciate the ball python as a powerful new model (but see Irizarry and Bryden 2016). We set out to bridge this gap.…”

“…We propose that the ball python ( Python regius ; Figure 1a) is an excellent model for studying the genetic basis of vertebrate colouration (Irizarry and Bryden 2016). Ball pythons are native to western Sub-Saharan Africa, with a range extending from Senegal to Uganda.…”

Blue, Black, and Stripes: Evolution and Development of Color Production and Pattern Formation in Lizards and Snakes

“…In this paper, we review recent advances in our understanding of the roles of pigment cells in the production of various skin colors and patterns in reptilian sauropsids (Kuriyama et al, 2006(Kuriyama et al, , 2013(Kuriyama et al, , 2016aMurakami et al, 2017;Alibardi, 2011Alibardi, , 2012Alibardi, , 2013Alibardi, , 2015Kikuchi and Pfennig, 2012;Saenko et al, 2013;Kikuchi et al, 2014;Murakami et al, 2016Murakami et al, , 2017Szydłowski et al, 2016;Jindřich et al, 2019). We approach this topic from the viewpoint of theoretical studies of pattern formation (Murray and Myerscough, 1991;Chang et al, 2009;Allen et al, 2013) and molecular studies of gene systems involved in generating color patterns (Rosenblum et al, 2004;Manceau et al, 2010;Saenko et al, 2015;Irizarry and Bryden, 2016;Iwanishi et al, 2018). Owing to the lack of extensive genomic resources, early studies relied on sequence analyses of a few candidate genes, such as MC1R (Rosenblum et al, 2004;Cox et al, 2013).…”