The genetic diversity of growing cellular populations, such as biofilms, solid tumours or developing embryos, is thought to be dominated by rare, exceptionally large mutant clones. Yet, the emergence of these mutational jackpot events is only understood in well-mixed populations, where they stem from mutations that arise during the first few cell divisions. To study jackpot events in spatially structured populations, we track mutant clones in microbial populations using fluorescence microscopy and population sequencing. High-frequency mutations are found to be massively enriched in microbial colonies compared with well-shaken liquid cultures, as a result of late-occurring mutations surfing at the edge of range expansions. Thus, jackpot events can be generated not only when mutations arise early but also when they occur at favourable locations, which exacerbates their role in adaptation and disease. In particular, because spatial competition with the wild type keeps most mutant clones in a quiescent state, strong selection pressures that kill the wild type promote drug resistance.

The fields of structural biology and soft matter have independently sought out fundamental principles to rationalize protein crystallization. Yet the conceptual differences and the limited overlap between the two disciplines have thus far prevented a comprehensive understanding of the phenomenon to emerge. We conduct a computational study of proteins from the rubredoxin family that bridges the two fields. Using atomistic simulations, we characterize their crystal contacts, and accordingly parameterize patchy particle models. Comparing the phase diagrams of these schematic models with experimental results enables us to critically examine the assumptions behind the two approaches. The study also reveals features of protein-protein interactions that can be leveraged to crystallize proteins more generally.

Asymmetric patchy particle models have recently been shown to describe the crystallization of small globular proteins with near-quantitative accuracy. Here, we investigate how asymmetry in patch geometry and bond energy generally impacts the phase diagram and nucleation dynamics of this family of soft matter models. We find the role of the geometry asymmetry to be weak, but the energy asymmetry to markedly interfere with the crystallization thermodynamics and kinetics. These results provide a rationale for the success and occasional failure of the proposal of George and Wilson for protein crystallization conditions as well as physical guidance for developing more effective protein crystallization strategies.

No abstract

Many cellular populations are tightly-packed, such as microbial colonies and biofilms, or tissues and tumors in multicellular organisms. Movement of one cell in those crowded assemblages requires motion of others, so that cell displacements are correlated over many cell diameters. Whenever movement is important for survival or growth, these correlated rearrangements could couple the evolutionary fate of different lineages. Yet, little is known about the interplay between mechanical forces and evolution in dense cellular populations. Here, by tracking slower-growing clones at the expanding edge of yeast colonies, we show that the collective motion of cells prevents costly mutations from being weeded out rapidly. Joint pushing by neighboring cells generates correlated movements that suppress the differential displacements required for selection to act. This mechanical screening of fitness differences allows slower-growing mutants to leave more descendants than expected under non-mechanical models, thereby increasing their chance for evolutionary rescue. Our work suggests that, in crowded populations, cells cooperate with surrounding neighbors through inevitable mechanical interactions. This effect has to be considered when predicting evolutionary outcomes, such as the emergence of drug resistance or cancer evolution.

Many cellular populations are tightlypacked, for example microbial colonies and biofilms [39,10,41], or tissues and tumors in multi-cellular organisms [11,29]. Movement of one cell inside such crowded assemblages requires movement of others, so that cell displacements are correlated over many cell diameters [28,6,31]. Whenever movement is important for survival or growth [15,34,38,9], such correlated rearrangements could couple the evolutionary fate of di↵erent lineages. Yet, little is known about the interplay between mechanical stresses and evolution in dense cellular populations. Here, by tracking deleterious mutations at the expanding edge of yeast colonies, we show that crowding-induced collective motion prevents costly mutations from being weeded out rapidly. Joint pushing by neighboring cells generates correlated movements that suppress the di↵erential displacements required for selection to act. Such mechanical screening of fitness di↵erences allows the mutants to leave more descendants than expected under non-mechanical models, thereby increasing their chance for evolutionary rescue [2,5]. Our work suggests that mechanical interactions generally influ-ence evolutionary outcomes in crowded cellular populations, which has to be considered when modeling drug resistance or cancer evolution [1,22,34,30,36,42]. As a model system for crowded cellular populations, we focused on colonies of the budding yeast Saccharomyces cerevisiae [33]. Since yeast cells lack motility, colony expansion is fueled purely by the pushing forces generated by cellular growth and division [14,7]. To explore how these pushing forces a↵ect the strength of natural selection, we competed "mutant" cells, carrying a growth-rate deficit, with faster-growing "wild-type" cells in expanding colonies (Fig. 1a-c). Mutant and wild-type cells remained in well-segregated sectors during the expansion [15], which allowed us to use time-resolved fluorescence microscopy to monitor the gradual demise of the mutant fraction. We then measured the rate at which mutant cells are out-competed by faster-growing wildtype cells at the expanding front of a linear colony (Fig. 1c).We found that mutants were weeded out from the expanding frontier in two main stages. In the first stage, the width of mutant sectors decreased at a constant rate. This is in line with a minimal model where local front expansion velocities depend only on the cell-type specific growth rates of the "pioneer" 1

Water occupies typically 50% of a protein crystal and thus significantly contributes to the diffraction signal in crystallography experiments. Separating its contribution from that of the protein is, however, challenging because most water molecules are not localized and are thus difficult to assign to specific density peaks. The intricateness of the protein-water interface compounds this difficulty. This information has, therefore, not often been used to study biomolecular solvation. Here, we develop a methodology to surmount in part this difficulty. More specifically, we compare the solvent structure obtained from diffraction data for which experimental phasing is available to that obtained from constrained molecular dynamics (MD) simulations. The resulting spatial density maps show that commonly used MD water models are only partially successful at reproducing the structural features of biomolecular solvation. The radial distribution of water is captured with only slightly higher accuracy than its angular distribution, and only a fraction of the water molecules assigned with high reliability to the crystal structure is recovered. These differences are likely due to shortcomings of both the water models and the protein force fields. Despite these limitations, we manage to infer protonation states of some of the side chains utilizing MD-derived densities.

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.