Evolutionary dynamics of the most populated genotype on rugged fitness landscapes

We consider an asexual population evolving on rugged fitness landscapes which are defined on the multi-dimensional genotypic space and have many local optima. We track the most populated genotype as it changes when the population jumps from a fitness peak to a better one during the process of adaptation. This is done using the dynamics of the shell model which is a simplified version of the quasispecies model for infinite populations and standard Wright-Fisher dynamics for large finite populations. We show that the population fraction of a genotype obtained within the quasispecies model and the shell model match for fit genotypes and at short times, but the dynamics of the two models are identical for questions related to the most populated genotype. We calculate exactly several properties of the jumps in infinite populations some of which were obtained numerically in previous works. We also present our preliminary simulation results for finite populations. In particular, we measure the jump distribution in time and find that it decays as $t^{-2}$ as in the quasispecies problem.Comment: Minor changes. To appear in Phys Rev

Influenza vaccine effectiveness in adults 65 years and older, Denmark, 2015/16:a rapid epidemiological and virological assessment

In Denmark, both influenza A(H1N1)pdm09 and influenza B co-circulated in the 2015/16 season. We estimated the vaccine effectiveness (VE) of the trivalent influenza vaccine in patients 65 years and older using the test-negative case–control design. The adjusted VE against influenza A(H1N1)pdm09 was 35.0% (95% confidence interval (CI): 11.1–52.4) and against influenza B 4.1% (95% CI: −22.0 to 24.7). The majority of influenza A(H1N1)pdm09 circulating in 2015/16 belonged to the new genetic subgroup subclade 6B.1.</jats:p

Nonlinear acousto-electric transport in a two-dimensional electron system

We study both theoretically and experimentally the nonlinear interaction between an intense surface acoustic wave and a two-dimensional electron plasma in semiconductor-piezocrystal hybrid structures. The experiments on hybrid systems exhibit strongly nonlinear acousto-electric effects. The plasma turns into moving electron stripes, the acousto-electric current reaches its maximum, and the sound absorption strongly decreases. To describe the nonlinear phenomena, we develop a coupled-amplitude method for a two-dimensional system in the strongly nonlinear regime of interaction. At low electron densities the absorption coefficient decreases with increasing sound intensity, whereas at high electron density the absorption coefficient is not a monotonous function of the sound intensity. High-harmonic generation coefficients as a function of the sound intensity have a nontrivial behavior. Theory and experiment are found to be in a good agreement.Comment: 27 pages, 6 figure

Altered thymic differentiation and modulation of arthritis by invariant NKT cells expressing mutant ZAP70

Various subsets of invariant natural killer T (iNKT) cells with different cytokine productions develop in the mouse thymus, but the factors driving their differentiation remain unclear. Here we show that hypomorphic alleles of Zap70 or chemical inhibition of Zap70 catalysis leads to an increase of IFN-gamma-producing iNKT cells (NKT1 cells), suggesting that NKT1 cells may require a lower TCR signal threshold. Zap70 mutant mice develop IL-17-dependent arthritis. In a mouse experimental arthritis model, NKT17 cells are increased as the disease progresses, while NKT1 numbers negatively correlates with disease severity, with this protective effect of NKT1 linked to their IFN-gamma expression. NKT1 cells are also present in the synovial fluid of arthritis patients. Our data therefore suggest that TCR signal strength during thymic differentiation may influence not only IFN-gamma production, but also the protective function of iNKT cells in arthritis

Molecular mechanisms of drug resistance in natural Leishmania populations vary with genetic background

The evolution of drug-resistance in pathogens is a major global health threat. Elucidating the molecular basis of pathogen drug-resistance has been the focus of many studies but rarely is it known whether a drug-resistance mechanism identified is universal for the studied pathogen; it has seldom been clarified whether drug-resistance mechanisms vary with the pathogen's genotype. Nevertheless this is of critical importance in gaining an understanding of the complexity of this global threat and in underpinning epidemiological surveillance of pathogen drug resistance in the field. This study aimed to assess the molecular and phenotypic heterogeneity that emerges in natural parasite populations under drug treatment pressure. We studied lines of the protozoan parasite Leishmania (L.) donovani with differential susceptibility to antimonial drugs; the lines being derived from clinical isolates belonging to two distinct genetic populations that circulate in the leishmaniasis endemic region of Nepal. Parasite pathways known to be affected by antimonial drugs were characterised on five experimental levels in the lines of the two populations. Characterisation of DNA sequence, gene expression, protein expression and thiol levels revealed a number of molecular features that mark antimonial-resistant parasites in only one of the two populations studied. A final series of in vitro stress phenotyping experiments confirmed this heterogeneity amongst drug-resistant parasites from the two populations. These data provide evidence that the molecular changes associated with antimonial-resistance in natural Leishmania populations depend on the genetic background of the Leishmania population, which has resulted in a divergent set of resistance markers in the Leishmania populations. This heterogeneity of parasite adaptations provides severe challenges for the control of drug resistance in the field and the design of molecular surveillance tools for widespread applicability

Prioritising cat-owner behaviours for a campaign to reduce wildlife depredation

Behavior prioritisation is underutilised but critical to the success of conservation campaigns. It provides an understanding of the target audience’s values to transcend conflict and informs the design of achievable and effective advocacy campaigns. Depredation by domestic cats may depress wildlife populations, leading to conflict between cat owners and conservationists. We surveyed veterinarians and cat owners at veterinary clinics to prioritise a list of nine cat-management behaviours. Cat-owner behaviours were ranked by their (i) likelihood of implementation and (ii) current adoption rate by cat owners, (iii) perceived effectiveness at reducing predation on wildlife, and (iv) veterinarians’ opinions about their impact on cat welfare. Bringing cats in at night, from before dusk until after dawn, was revealed to be the behaviour most suited to a campaign to reduce cats’ hunting. Behaviours ranked as more effective for conservation (e.g., 24-hour cat confinement) were unlikely to be adopted by cat owners or not supported by veterinarians, whose expert and normative support may be critical to a campaign. Although more conservation-effective behaviours received a lower priority, we discuss the repeated use of behaviour prioritisation to achieve incremental reductions in cat depredation by engaging with cat owners

Deep diversification of an AAV capsid protein by machine learning.

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12-29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved viral vectors and protein therapeutics