Research highlight by Pedro Beltrao, University of California, San Francisco
In a recent opinion piece, Andreas Wagner tries to reconcile the tension between proponents of neutral evolution and selectionism (Wagner 2008). He argues that “neutral mutations prepare the ground for later evolutionary innovation”. Wagner illustrates this point using a network model of genotype-phenotype relationships (Wagner 2005). In a so-called ‘neutral network’, nodes correspond to distinct genotypes associated with the same phenotype and are connected by an edge if the respective genotypes differ only by a single mutation event (eg point mutation). Examples of neutral networks include different genotypes coding for RNA or protein structures. In this representation, highly connected networks correspond to robust phenotypes that are not very sensitive to changes in genotype. Wagner notes the zinc finger fold as an impressive example of a highly connected neutral network as its structure remains essentially the same even after mutating all but seven of its 26 residues to alanine.
Using this model, Wagner describes how highly robust phenotypes can lead to faster exploration of the genotype space. He further proposes that evolution of innovation occurs via cycles of exploration of nearly neutral spaces (dubbed neutralist regime) followed by a reduction in diversity once a new phenotype of higher fitness is discovered (selectionist regime).
Although these models and ideas were mostly developed using models of sequence to structure relationships, Wagner cites several examples suggesting that these concepts are equally valid for cellular phenotypes that depend on molecular interactions (ex. gene expression patterns).
As Wagner points out, in order to understand the evolution of innovation we must fully understand the mapping between genotypes to phenotypes. This is why it is important to continue to develop richer evolutionary models to link changes at the DNA level with changes in molecular structures, interactions and ultimately phenotypes with a quantifiable impact on fitness. This is an area where systems biology should play an important role.
Models of RNA and protein structure stability upon mutation have existed now for some time (Hofacker et al. 1994, Guerois et al. 2002). More recently the study of large amounts of genomic information and/or systematic interactions studies are providing us with accurate models for different types of molecular interactions (Berger et al. 2008, Burger & van Nimwegen 2008, Chen et al. 2008). In parallel to these, theoretical analysis has been use to aid in the understanding of cellular phenotypes (i.e. cell-cycle, signaling pathways etc) (Tyson et al. 2003). Connecting these different layers of abstraction is an important challenge that will allow us to better understand the origins of biological innovation.
Berger MF et al. (2008). Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences. Cell 133:1266-76
Burger L & van Nimwegen E (2008). Accurate prediction of protein-protein interactions from sequence alignments using a Bayesian method. Mol Syst Biol 4:165
Chen JR et al. (2008). Predicting PDZ domain-peptide interactions from primary sequences. Nat Biotechnol 26:1041-5
Guerois R, Nielsen JE & Serrano L (2002). Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 320:369-87
Hofacker IL et al. (1994). Fast folding and comparison of RNA secondary structures. Monatshefte für Chemie / Chemical Monthly 125:167-188
Tyson JJ, Chen KC & Novak B (2003). Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 15:221-31
Wagner A (2005). Robustness and Evolvability in Living Systems. Princeton University Press
Wagner A (2008). Neutralism and selectionism: a network-based reconciliation. Nat Rev Genet 9:965-974