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With the molecular revolution in Biology, a mechanistic understanding of the genotype–phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype–fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype–phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Molecular examples of evolutionary innovation are scarce and generally involve point mutations. Innovation can occur through larger rearrangements, but here experimental data is extremely limited. Integron integrases innovated from double-strand- toward single-strand-DNA recombination through the acquisition of the I2 α-helix. To investigate how this transition was possible, we have evolved integrase IntI1 to what should correspond to an early innovation state by selecting for its ancestral activity. Using synonymous alleles to enlarge sequence space exploration, we have retrieved 13 mutations affecting both I2 and the multimerization domains of IntI1. We circumvented epistasis constraints among them using a combinatorial library that revealed their individual and collective fitness effects. We obtained up to 10 4 -fold increases in ancestral activity with various asymmetrical trade-offs in single-strand-DNA recombination. We show that high levels of primary and promiscuous functions could have initially coexisted following I2 acquisition, paving the way for a gradual evolution toward innovation.

The effect of mutations in a protein may depend on the presence of others—a phenomenon known as epistasis. Epistasis plays a key role in evolution and complicates predictions of mutational effects, as effects can be context-dependent. Yet, despite its importance, the mechanistic basis of epistasis remains poorly understood. To better characterize epistasis, we focused on an 11-residue α-helix in TEM-1 β-lactamase and constructed a comprehensive library of over 14,000 double mutants. Fitness and minimum inhibitory concentration, two contrasted measure of protein efficiency, reveal consistent widespread epistasis. A non-linear two-state protein stability model in which destabilizing, neutral, or stabilizing mutations contribute additively to the stability phenotype, largely explain the data. Most epistatic effects are consequently predictable from single-mutation effects. However, systematic deviations from the model occur when both mutated residues directly interact in the 3D structure—a fold conserved across distant TEM-1 homologs. We therefore investigated the predictive power of statistical models trained on distant homologous sequences and found that they could partially recover the observed epistatic interactions. Our results, built on a short structural element of a protein, shed light on multiple determinants of the epistatic landscape that have shaped the evolutionary trajectory of β-lactamase proteins over long timescales. Analyzing fitness and function of 14,000 double mutants in an α-helix of TEM-1 β-lactamase shows that widespread epistasis is largely explained by a two-state stability model, consistent with constraints inferred from distant homologous proteins.

Our ability to predict the impact of mutations on traits relevant for disease and evolution remains severely limited by the dependence of their effects on the genetic background and environment. Even when molecular interactions between genes are known, it is unclear how these translate to organism-level interactions between alleles. We therefore characterized the interplay of genetic and environmental dependencies in determining fitness by quantifying ~4,000 fitness interactions between expression variants of two metabolic genes, in different environments. We detect a remarkable variety of environment-dependent interactions, and demonstrate they can be quantitatively explained by a mechanistic model accounting for catabolic flux, metabolite toxicity and expression costs. Complex fitness interactions between mutations can therefore be predicted simply from their simultaneous impact on a few connected molecular phenotypes.

Epistasis affects genome evolution together with our ability to predict individual mutation effects. The mechanistic basis of epistasis remains, however, largely unknown. To quantify and better understand interactions between fitness-affecting mutations, we focus on a 11 amino-acid α-helix of the protein β-lactamase TEM-1, and build a comprehensive library of more than 15,000 double mutants. Analysis of the growth rates of these mutants shows pervasive epistasis, which can be largely explained by a non-linear two-state model, where inactivating, destabilizing, neutral, or stabilizing mutations additively contribute to the phenotype. Hence, most epistatic interactions can be predicted by a non-linear model informed by single-point mutational measurements only. Deviations from the two-state model are consistently found for few pairs of residues, in particular when they are in contact. This result, as well as single-point mutation parameters, can be quantitatively found back through direct-coupling-analysis-based statistical models inferred from homologous sequence data. Our results thus shed light on the existence and the origins of the multiple determinants of the epistatic landscape, even at the level of small structural components of a protein, and suggest that the corresponding constraints shape the entire β-lactamase family.