Background Unlike mammals, zebrafish have a remarkable capacity to regenerate a variety of tissues, including central nervous system tissue. The function of macrophages in tissue regeneration is of great interest, as macrophages respond and participate in the landscape of events that occur following tissue injury in all vertebrate species examined. Understanding macrophage populations in regenerating tissue (such as in zebrafish) may inform strategies that aim to regenerate tissue in humans. We recently published an RNA-seq experiment that identified genes enriched in microglia/macrophages in regenerating zebrafish retinas. Interestingly, a small number of transcripts differentially expressed by retinal microglia/macrophages during retinal regeneration did not have predicted orthologs in human or mouse. We reasoned that at least some of these genes could be functionally important for tissue regeneration, but most of these genes have not been studied experimentally and their functions are largely unknown. To reveal their possible functions, we performed a variety of bioinformatic analyses aimed at identifying the presence of functional protein domains as well as orthologous relationships to other species. Results Our analyses identified putative functional domains in predicted proteins for a number of selected genes. For example, we confidently predict kinase function for one gene, cytokine/chemokine function for another, and carbohydrate enzymatic function for a third. Predicted orthologs were identified for some, but not all, genes in species with described regenerative capacity, and functional domains were consistent with identified orthologs. Comparison to other published gene expression datasets suggest that at least some of these genes could be important in regenerative responses in zebrafish and not necessarily in response to microbial infection. Conclusions This work reveals previously undescribed putative function of several genes implicated in regulating tissue regeneration. This will inform future work to experimentally determine the function of these genes in vivo, and how these genes may be involved in microglia/macrophage roles in tissue regeneration.

Summary The mechanisms underlying trait conservation over long evolutionary time scales are poorly known. These mechanisms fall into the two broad and nonmutually exclusive categories of constraint and selection. A variety of factors have been hypothesized to constrain trait evolution. Alternatively, selection can maintain similar trait values across many species if the causes of selection are also relatively conserved, while many sources of constraint may be overcome over longer periods of evolutionary divergence. An example of deep trait conservation is tetradynamy in the large family Brassicaceae, where the four medial stamens are longer than the two lateral stamens. Previous work has found selection to maintain this difference in lengths, which we call anther separation, in wild radish, Raphanus raphanistrum. Here, we test the constraint hypothesis using five generations of artificial selection to reduce anther separation in wild radish. We found a rapid linear response to this selection, with no evidence for depletion of genetic variation and correlated responses to this selection in only four of 15 other traits, suggesting a lack of strong constraint. Taken together, available evidence suggests that tetradynamy is likely to be conserved due to selection, but the function of this trait remains unclear.

Statistical estimation of parameters in large models of evolutionary processes using SNP data is often too computationally inefficient to pursue using exact model likelihoods. Approximate Bayesian Computation (ABC) to perform statistical inference about parameters of large models takes the advantage of simulations to bypass direct evaluation of model likelihoods. We use forward-in-time simulations of a mechanistic model of divergent selection with variable migration rates, modes of reproduction (sexual, asexual), length and number of migration-selection cycles, and investigate the computational feasibility of ABC to perform statistical inference and study the quality of estimates on the position of loci under selection and the strength of selection. We evaluate usefulness of summary statistics well-known to capture the strength of selection, and assess their informativeness under divergent selection. We also evaluate the effect of genetic drift with respect to an idealized deterministic model with single-locus selection. We discuss the role of the recombination rate as a confounding factor in estimating the strength of divergent selection, and we answer the question for which part of the parameter space of the model we recover strong signal for estimating the selection and make recommendations which summary statistics perform well in estimating selection.