Developmental processes regulate craniofacial variation in disease and evolution

Abstract

Variation in development mediates phenotypic differences observed in evolution and disease. Although the mechanisms underlying phenotypic variation are still largely unknown, recent research suggests that variation in developmental processes may play a key role. Developmental processes mediate genotype-phenotype relationships and consequently play an important role regulating phenotypes. In this review, we provide an example of how shared and interacting developmental processes may explain convergence of phenotypes in spliceosomopathies and ribosomopathies. These data also suggest a shared pathway to disease treatment. We then discuss three major mechanisms that contribute to variation in developmental processes: genetic background (gene-gene interactions), gene-environment interactions, and developmental stochasticity. Finally, we comment on evolutionary alterations to developmental processes, and the evolution of disease buffering mechanisms.

Keywords: craniofacial anomalies; evolution of development; genotype-phenotype relationships; morphological variation; ribosomopathies; spliceosomopathies.

Figure 1:. Developmental processes integrating ribosomopathies and spliceosomopathies

Relationships between genes and traits, shown in red boxes, are modeled to illustrate complexity and show processes integrating ribosomopathies and spliceosomopathies. Alternative splicing (left) and ribosome biogenesis (right) are two connected molecular processes upstream of protein synthesis. The spliceosomal small nuclear ribonucleoproteins (snRNPs), shown in purple, catalyze the splicing of exons, shown in yellow, in nascent mRNA molecules. Splicing factors associated with developmental defects are depicted as orange ovals inside their corresponding snRNPs. Other factors that link transcription and splicing are shown interacting with the spliceosome and RNA polymerase II. In the left panel, RNA polymerase I is shown synthesizing a strand of rRNA while interacting with TCOF1 and other co-factors. Both these molecular processes lead to protein synthesis, which is crucial for neural crest cell survival.

Figure 2:. Mechanisms regulating variation in developmental processes

Factors contributing to variation in developmental processes include A) gene-gene interactions, B) gene-environment interactions, and C) developmental stochasticity. A) Gene-gene interactions are modeled by BMP signaling in osteogenesis. Proteins in red are negative regulators of the pathway; proteins in green are positive regulators. Heterozygous mutations in SMAD6, a negative regulator of the BMP pathway, may be buffered if they occur in isolation. However, if they occur in a “risk allele” background in which BMP levels are increased or a second negative regulator (e.g., TCF12) is decreased, disease phenotypes are observed. Image modeled after Timberlake et al. 2018. B) Gene-environment interactions are modeled by ethanol (EtOH) influences on SHH signaling. EtOH may exacerbate mutations in CDO, a SHH co-receptor, by negatively interacting with SHH binding to its receptors. Image modeled after Kahn et al. 2016. C) Developmental stochasticity is modeled by Satb2-mediated variation in jaw size. Satb2 protein levels have a non-linear relationship with jaw size, where wild-type and homozygous mutant individuals exhibit little population variance in size (grey shaded rectangles). However, heterozygous mutants are highly variable in size, encompassing the range of variation between wild-type and mutant (purple shaded rectangles). This continuous morphological variation (upper panel) can be explained by discrete cellular variation (lower panel). Satb2^+/− cells are predicted to generate Satb2 protein levels that are at or near the threshold for Satb2 activation. Those cells that meet or surpass the threshold will proliferate and differentiate into osteoblasts; those cells that fall below the threshold will undergo apoptosis. Thus, random variation in the degree of heterogeneity in cell fate between individuals can explain variation in jaw size. Dotted lines indicate threshold for protein activity.

Figure 3:. Mechanisms contributing to the evolution of splicing patterns

Both A) cis-mediated and B) trans-mediated alterations contribute to the evolution of splicing patterns. A) Mutations affecting splice recognition sites contribute to increased exon skipping in mammals. Such mutations are enriched around exons containing intrinsic disordered regions (IDR) and under-represented around nuclei acid binding domains (NAB). In this example, mammals are able to produce two protein isoforms from the same mRNA, one containing and IDR and one lacking the IDR. B) IDRs contribute to protein-protein interactions. A protein complex assembled among IDR-containing RNPs may promote exon inclusion (left). In the absence of IDRs, such complexes are not formed and exon skipping occurs (right). Image modeled after Gueroussov et al. 2017.

Figure 4:. Evolution of buffering mechanisms

A) Non-linear model of genotype-phenotype relationships, where genotype is represented as functional protein produced by a gene (x-axis). Protein level variance is represented by the vertical bars; horizontal bars represent variation in phenotype. Note that, based on the threshold model, the same variance in protein levels has a significantly different effects on phenotype depending on protein levels relative to the threshold, where dark grey is wild-type (WT) and light gret is mutant (Het^+/−). Either heterometry or increases in GRN complexity may shift the position of a threshold value in a non-linear genotype-phenotype curve (black to green). B) Alterations to inputs regulating a gene of interest (GOI) may affect its levels (heterometry). C) Alterations to the number of co-regulators of protein complexes regulating a developmental process can increase gene regulatory network (GRN) complexity.