Genome Duplication Increases Meiotic Recombination Frequency: A Saccharomyces cerevisiae Model

Genetic recombination characterized by reciprocal exchange of genes on paired homologous chromosomes is the most prominent event in meiosis of almost all sexually reproductive organisms. It contributes to genome stability by ensuring the balanced segregation of paired homologs in meiosis, and it is also the major driving factor in generating genetic variation for natural and artificial selection. Meiotic recombination is subjected to the control of a highly stringent and complex regulating process and meiotic recombination frequency (MRF) may be affected by biological and abiotic factors such as sex, gene density, nucleotide content, and chemical/temperature treatments, having motivated tremendous researches for artificially manipulating MRF. Whether genome polyploidization would lead to a significant change in MRF has attracted both historical and recent research interests; however, tackling this fundamental question is methodologically challenging due to the lack of appropriate methods for tetrasomic genetic analysis, thus has led to controversial conclusions in the literature. This article presents a comprehensive and rigorous survey of genome duplication-mediated change in MRF using Saccharomyces cerevisiae as a eukaryotic model. It demonstrates that genome duplication can lead to consistently significant increase in MRF and rate of crossovers across all 16 chromosomes of S. cerevisiae, including both cold and hot spots of MRF. This ploidy-driven change in MRF is associated with weakened recombination interference, enhanced double-strand break density, and loosened chromatin histone occupation. The study illuminates a significant evolutionary feature of genome duplication and opens an opportunity to accelerate response to artificial and natural selection through polyploidization.     

The Impact of Recombination Hotspots on Genome Evolution of a Fungal Plant Pathogen

Recombination has an impact on genome evolution by maintaining chromosomal integrity, affecting the efficacy of selection, and increasing genetic variability in populations. Recombination rates are a key determinant of the coevolutionary dynamics between hosts and their pathogens. Historic recombination events created devastating new pathogens, but the impact of ongoing recombination in sexual pathogens is poorly understood. Many fungal pathogens of plants undergo regular sexual cycles, and sex is considered to be a major factor contributing to virulence. We generated a recombination map at kilobase-scale resolution for the haploid plant pathogenic fungus Zymoseptoria tritici. To account for intraspecific variation in recombination rates, we constructed genetic maps from two independent crosses. We localized a total of 10,287 crossover events in 441 progeny and found that recombination rates were highly heterogeneous within and among chromosomes. Recombination rates on large chromosomes were inversely correlated with chromosome length. Short accessory chromosomes often lacked evidence for crossovers between parental chromosomes. Recombination was concentrated in narrow hotspots that were preferentially located close to telomeres. Hotspots were only partially conserved between the two crosses, suggesting that hotspots are short-lived and may vary according to genomic background. Genes located in hotspot regions were enriched in genes encoding secreted proteins. Population resequencing showed that chromosomal regions with high recombination rates were strongly correlated with regions of low linkage disequilibrium. Hence, genes in pathogen recombination hotspots are likely to evolve faster in natural populations and may represent a greater threat to the host.     

Effect of the metal ion and the medium on the electronic structure of anthranilic acid: a modelling study on the Li and the BeH derivatives

This computational organic chemistry study illustrates that the size of the metal ion is a critical point in determining the bonding mode of the anthranilate (2-aminobenzoate). The beryllium model structure is the first example in the chemical literature on the ability of this amino acid to bond as N–O chelate. The medium has variable effect on the energies and the dipole moments of the studied models, which was found originating essentially from the differences in the atomic charges of the metals. Analysis of the molecular charge distribution allowed stating a new theory on the effect of the medium on the two pairs of isomers.     

The use of biomarkers in Daphnia magna toxicity testing. IV. Cellular Energy Allocation: a new methodology to assess the energy budget of toxicant-stressed Daphnia populations

The Cellular Energy Allocation (CEA) methodology wasdeveloped as biomarker technique to assess the effectof toxic stress on the energy budget of testorganisms. This short-term assay is based on thebiochemical assessment of changes in the energyreserves (total carbohydrate, protein and lipidcontent) and the energy consumption (electrontransport activity). The CEA methodology was evaluatedusing Daphnia magna juveniles exposed for 96hto sublethal lindane and mercury chlorideconcentrations. The ecological relevance of the CEAassay was assessed by comparing the sub-organismalresponse with population level parameters (obtainedfrom 21 day life table experiments) such as theintrinsic rate of natural increase (r_m) and themean total offspring per female. Two differentmethodologies were used to assess the effect levels:the no (lowest) observed effect level (NOAECs-LOAECs)approach and the regression-based approach. Bothtoxicants caused a significant decrease in the netenergy budget of D. magna, with a LowestObserved (Adverse) Effect Concentration (LOAEC) of0.18 mg/l and 5.6 µg/l for lindane andHgCl₂,respectively. Changes in the lipid content of theorganisms were detected at toxicant concentrationslower than those affecting the total carbohydrate andprotein content. Toxicant specific effects wereobserved on the electron transport activity.Comparison of the CEA results with those of thepopulation level tests revealed that for mercury theCEA based LOAEC was a three times lower than thatbased on r_m and the total brood size(18 µg/l). For lindane the CEA based LOAEC was twotimes lower than the LOAEC based on r_m(0.32 mg/l) but was higher than that based on thetotal number of offspring produced (0.1 mg/l).Using the regression-based approach, EC₁₀ valueswere calculated using three parameter sigmoid orlogistic models. Comparison between the CEA andr_m based EC₁₀ values demonstrates that forboth chemicals similar effect concentrations areobtained: the CEA-based EC₁₀ (0.20 mg/l) forlindane is 1.5 times higher than the r_m-basedEC₁₀ threshold (0.13 mg/l), while for mercury thebiomarker-based EC₁₀ value (9 µg/l) was 1.4times lower than the population-based EC₁₀ value(12.5 µg/l).From these results, we suggest that the short-term CEAassay may be useful for predicting long-term effectsat the population level. The consequences of theobserved effects on the energy budget of the testorganism are discussed in the context of the effectsemerging at the population and community level.     

Selection for territory acquisition is modulated by social network structure in a wild songbird

The social environment may be a key mediator of selection that operates on animals. In many cases, individuals may experience selection not only as a function of their phenotype, but also as a function of the interaction between their phenotype and the phenotypes of the conspecifics they associate with. For example, when animals settle after dispersal, individuals may benefit from arriving early, but, in many cases, these benefits will be affected by the arrival times of other individuals in their local environment. We integrated a recently described method for calculating assortativity on weighted networks, which is the correlation between an individual's phenotype and that of its associates, into an existing framework for measuring the magnitude of social selection operating on phenotypes. We applied this approach to large‐scale data on social network structure and the timing of arrival into the breeding area over three years. We found that late‐arriving individuals had a reduced probability of breeding. However, the probability of breeding was also influenced by individuals’ social networks. Associating with late‐arriving conspecifics increased the probability of successfully acquiring a breeding territory. Hence, social selection could offset the effects of nonsocial selection. Given parallel theoretical developments of the importance of local network structure on population processes, and increasing data being collected on social networks in free‐living populations, the integration of these concepts could yield significant insights into social evolution.