A giant armoured skink from Australia expands lizard morphospace and the scope of the Pleistocene extinctions.

There are more species of lizards and snakes (squamates) alive today than any other order of land vertebrates, yet their fossil record has been poorly documented compared with other groups. Here, we describe a gigantic Pleistocene skink from Australia based on extensive material that includes much of the skull and postcranial skeleton, and spans ontogenetic stages from neonate to adult. Tiliqua frangens substantially expands the known ecomorphological diversity of squamates. At approximately 2.4 kg, it was more than double the mass of any living skink, with an exceptionally broad, deep skull, squat limbs and heavy, ornamented body armour. It probably filled the armoured herbivore niche that land tortoises (testudinids), absent from Australia, occupy on other continents. Tiliqua frangens and other giant Plio-Pleistocene skinks suggest that small-bodied groups that dominate vertebrate biodiversity might have lost their largest and often most morphologically extreme representatives in the Late Pleistocene, expanding the scope of these extinctions.

Impact of crowding on the diversity of expanding populations.

Crowding effects critically impact the self-organization of densely packed cellular assemblies, such as biofilms, solid tumors, and developing tissues. When cells grow and divide, they push each other apart, remodeling the structure and extent of the population's range. Recent work has shown that crowding has a strong impact on the strength of natural selection. However, the impact of crowding on neutral processes, which controls the fate of new variants as long as they are rare, remains unclear. Here, we quantify the genetic diversity of expanding microbial colonies and uncover signatures of crowding in the site frequency spectrum. By combining Luria-Delbrück fluctuation tests, lineage tracing in a novel microfluidic incubator, cell-based simulations, and theoretical modeling, we find that the majority of mutations arise behind the expanding frontier, giving rise to clones that are mechanically "pushed out" of the growing region by the proliferating cells in front. These excluded-volume interactions result in a clone-size distribution that solely depends on where the mutation first arose relative to the front and is characterized by a simple power law for low-frequency clones. Our model predicts that the distribution depends on a single parameter-the characteristic growth layer thickness-and hence allows estimation of the mutation rate in a variety of crowded cellular populations. Combined with previous studies on high-frequency mutations, our finding provides a unified picture of the genetic diversity in expanding populations over the whole frequency range and suggests a practical method to assess growth dynamics by sequencing populations across spatial scales.

Superinfection exclusion: A viral strategy with short-term benefits and long-term drawbacks.

Viral superinfection occurs when multiple viral particles subsequently infect the same host. In nature, several viral species are found to have evolved diverse mechanisms to prevent superinfection (superinfection exclusion) but how this strategic choice impacts the fate of mutations in the viral population remains unclear. Using stochastic simulations, we find that genetic drift is suppressed when superinfection occurs, thus facilitating the fixation of beneficial mutations and the removal of deleterious ones. Interestingly, we also find that the competitive (dis)advantage associated with variations in life history parameters is not necessarily captured by the viral growth rate for either infection strategy. Putting these together, we then show that a mutant with superinfection exclusion will easily overtake a superinfecting population even if the latter has a much higher growth rate. Our findings suggest that while superinfection exclusion can negatively impact the long-term adaptation of a viral population, in the short-term it is ultimately a winning strategy.

Branching structure of genealogies in spatially growing populations and its implications for population genetics inference.

Spatial models where growth is limited to the population edge have been instrumental to understanding the population dynamics and the clone size distribution in growing cellular populations, such as microbial colonies and avascular tumours. A complete characterization of the coalescence process generated by spatial growth is still lacking, limiting our ability to apply classic population genetics inference to spatially growing populations. Here, we start filling this gap by investigating the statistical properties of the cell lineages generated by the two dimensional Eden model, leveraging their physical analogy with directed polymers. Our analysis provides quantitative estimates for population measurements that can easily be assessed via sequencing, such as the average number of segregating sites and the clone size distribution of a subsample of the population. Our results not only reveal remarkable features of the genealogies generated during growth, but also highlight new properties that can be misinterpreted as signs of selection if non-spatial models are inappropriately applied.

Collective motion conceals fitness differences in crowded cellular populations.

Many cellular populations are tightly packed, such as microbial colonies and biofilms, or tissues and tumours in multicellular organisms. The movement of one cell in these 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. However, 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 neighbouring 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 neighbours through inevitable mechanical interactions. This effect has to be considered when predicting evolutionary outcomes, such as the emergence of drug resistance or cancer evolution.

Learning about Biomolecular Solvation from Water in Protein Crystals.

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.

Convection shapes the trade-off between antibiotic efficacy and the selection for resistance in spatial gradients.

Since penicillin was discovered about 90 years ago, we have become used to using drugs to eradicate unwanted pathogenic cells. However, using drugs to kill bacteria, viruses or cancer cells has the serious side effect of selecting for mutant types that survive the drug attack. A crucial question therefore is how one could eradicate as many cells as possible for a given acceptable risk of drug resistance evolution. We address this general question in a model of drug resistance evolution in spatial drug gradients, which recent experiments and theories have suggested as key drivers of drug resistance. Importantly, our model takes into account the influence of convection, resulting for instance from blood flow. Using stochastic simulations, we study the fates of individual resistance mutations and quantify the trade-off between the killing of wild-type cells and the rise of resistance mutations: shallow gradients and convection into the antibiotic region promote wild-type death, at the cost of increasing the establishment probability of resistance mutations. We can explain these observed trends by modeling the adaptation process as a branching random walk. Our analysis reveals that the trade-off between death and adaptation depends on the relative length scales of the spatial drug gradient and random dispersal, and the strength of convection. Our results show that convection can have a momentous effect on the rate of establishment of new mutations, and may heavily impact the efficiency of antibiotic treatment.

Excess of mutational jackpot events in expanding populations revealed by spatial Luria-Delbrück experiments.

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.

Soft matter perspective on protein crystal assembly.

Crystallography may be the gold standard of protein structure determination, but obtaining the necessary high-quality crystals is also in some ways akin to prospecting for the precious metal. The tools and models developed in soft matter physics to understand colloidal assembly offer some insights into the problem of crystallizing proteins. This topical review describes the various analogies that have been made between proteins and colloids in that context. We highlight the explanatory power of patchy particle models, but also the challenges of providing guidance for crystallizing specific proteins. We conclude with a presentation of possible future research directions. This review is intended for soft matter scientists interested in protein crystallization as a self-assembly problem, and as an introduction to the pertinent physics literature for protein scientists more generally.

Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms.

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.

Competition between monomeric and dimeric crystals in schematic models for globular proteins.

Advances in experimental techniques and in theoretical models have improved our understanding of protein crystallization. However, they have also left open questions regarding the protein phase behavior and self-assembly kinetics, such as why (nearly) identical crystallization conditions can sometimes result in the formation of different crystal forms. Here, we develop a patchy particle model with competing sets of patches that provides a microscopic explanation of this phenomenon. We identify different regimes in which one or two crystal forms can coexist with a low-density fluid. Using analytical approximations, we extend our findings to different crystal phases, providing a general framework for treating protein crystallization when multiple crystal forms compete. Our results also suggest different experimental routes for targeting a specific crystal form, and for reducing the dynamical competition between the two forms, thus facilitating protein crystal assembly.

Characterizing protein crystal contacts and their role in crystallization: rubredoxin as a case study.

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 the protein 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 protein­protein interactions that can be leveraged to crystallize proteins more generally.

Crystallization of asymmetric patchy models for globular proteins in solution.

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.

Effects of polymorphism for locally adapted genes on rates of neutral introgression in structured populations.

Adaptation to local conditions within demes balanced by migration can maintain polymorphisms for variants that reduce fitness in certain ecological contexts. Here, we address the effects of such polymorphisms on the rate of introgression of neutral marker genes, possibly genetically linked to targets of selection. Barriers to neutral gene flow are expected to increase with linkage to targets of local selection and with differences between demes in the frequencies of locally adapted alleles. This expectation is borne out under purifying and disruptive selection, regimes that promote monomorphism within demes. In contrast, overdominance within demes induces minimal barriers to neutral introgression even in the face of very large differences between demes in the frequencies of locally adapted alleles. Further, segregation distortion, a phenomenon observed in a number of interspecific hybrids, can in fact promote transmission by migrants to future generations at rates exceeding those of residents.

Sex-specific incompatibility generates locus-specific rates of introgression between species.

Disruption of interactions among ensembles of epistatic loci has been shown to contribute to reproductive isolation among various animal and plant species. Under the Bateson-Dobzhansky-Muller model, such interspecific incompatibility arises as a by-product of genetic divergence in each species, and the Orr-Turelli model indicates that the number of loci involved in incompatible interactions may "snowball" over time. We address the combined effect of multiple incompatibility loci on the rate of introgression at neutral marker loci across the genome. Our analysis extends previous work by accommodating sex specificity: differences between the sexes in the expression of incompatibility, in rates of crossing over between neutral markers and incompatibility loci, and in transmission of markers or incompatibility factors. We show that the evolutionary process at neutral markers in a genome subject to incompatibility selection is well approximated by a purely neutral process with migration rates appropriately scaled to reflect the influence of selection targeted to incompatibility factors. We confirm that in the absence of sex specificity and functional epistasis among incompatibility factors, the barrier to introgression induced by multiple incompatibility factors corresponds to the product of the barriers induced by the factors individually. A new finding is that barriers to introgression due to sex-specific incompatibility depart in general from multiplicativity. Our partitioning of variation in relative reproductive rate suggests that such departures derive from associations between sex and incompatibility and between sex and neutral markers. Concordant sex-specific incompatibility (for example, greater impairment of male hybrids or longer map lengths in females) induces lower barriers (higher rates of introgression) than expected under multiplicativity, and discordant sex-specific incompatibility induces higher barriers.

Identity and divergence of protein domain architectures after the yeast whole-genome duplication event.

Gene duplication is a key mechanism in evolution for generating new functionality, and it is known to have produced a large proportion of genes. Duplication mechanisms include small-scale, or "local", events such as unequal crossing over and retroposition, together with global events, such as chromosomal or whole genome duplication (WGD). In particular, different studies confirmed that the yeast S. cerevisiae arose from a 100-150 million-year old whole-genome duplication. Detection and study of duplications are usually based on sequence alignment, synteny and phylogenetic techniques, but protein domains are also useful in assessing protein homology. We develop a simple and computationally efficient protein domain architecture comparison method based on the domain assignments available from public databases. We test the accuracy and the reliability of this method in detecting instances of gene duplication in the yeast S. cerevisiae. In particular, we analyze the evolution of WGD and non-WGD paralogs from the domain viewpoint, in comparison with a more standard functional analysis of the genes. A large number of domains is shared by genes that underwent local and global duplications, indicating the existence of a common set of "duplicable" domains. On the other hand, WGD and non-WGD paralogs tend to have different functions. We find evidence that this comes from functional migration within similar domain superfamilies, but also from the existence of small sets of WGD and non-WGD specific domain superfamilies with largely different functions. This observation gives a novel perspective on the finding that WGD paralogs tend to be functionally different from small-scale paralogs. WGD and non-WGD superfamilies carry distinct functions. Finally, the Gene Ontology similarity of paralogs tends to decrease with duplication age, while this tendency is weaker or not observable by the comparison of the domain architectures of paralogs. This suggests that the set of domains composing a protein tends to be maintained, while its function, cellular process or localization diversifies. Overall, the gathered evidence gives a different viewpoint on the biological specificity of the WGD and at the same time points out the validity of domain architecture comparison as a tool for detecting homology.

Ordered structure of the transcription network inherited from the yeast whole-genome duplication.

BACKGROUND: Gene duplication, a major evolutionary path to genomic innovation, can occur at the scale of an entire genome. One such "whole-genome duplication" (WGD) event among the Ascomycota fungi gave rise to genes with distinct biological properties compared to small-scale duplications. RESULTS: We studied the evolution of transcriptional interactions of whole-genome duplicates, to understand how they are wired into the yeast regulatory system. Our work combines network analysis and modeling of the large-scale structure of the interactions stemming from the WGD. CONCLUSIONS: The results uncover the WGD as a major source for the evolution of a complex interconnected block of transcriptional pathways. The inheritance of interactions among WGD duplicates follows elementary "duplication subgraphs", relating ancestral interactions with newly formed ones. Duplication subgraphs are correlated with their neighbours and give rise to higher order circuits with two elementary properties: newly formed transcriptional pathways remain connected (paths are not broken), and are preferentially cross-connected with ancestral ones. The result is a coherent and connected "WGD-network", where duplication subgraphs are arranged in an astonishingly ordered configuration.