THE EFFECT OF COLLECTIVE DISPERSAL ON THE GENETIC STRUCTURE OF A SUBDIVIDED POPULATION

Correlated dispersal paths between two or more individuals are widespread across many taxa. The population genetic implications of this collective dispersal have received relatively little attention. Here we develop two‐sample coalescent theory that incorporates collective dispersal in a finite island model to predict expected coalescence times, genetic diversities, and F‐statistics. We show that collective dispersal reduces mixing in the system, which decreases expected coalescence times and increases F_ST. The effects are strongest in systems with high migration rates. Collective dispersal breaks the invariance of within‐deme coalescence times to migration rate, whatever the deme size. It can also cause F_ST to increase with migration rate because the ratio of within‐ to between‐deme coalescence times can decrease as migration rate approaches unity. This effect is most biologically relevant when deme size is small. We find qualitatively similar results for diploid and gametic dispersal. We also demonstrate with simulations and analytical theory the strong similarity between the effects of collective dispersal and anisotropic dispersal. These findings have implications for our understanding of the balance between drift–migration–mutation in models of neutral evolution. This has applied consequences for the interpretation of genetic structure (e.g., chaotic genetic patchiness) and estimation of migration rates from genetic data.     

Detecting Signatures of Selection Through Haplotype Differentiation Among Hierarchically Structured Populations

The detection of molecular signatures of selection is one of the major concerns of modern population genetics. A widely used strategy in this context is to compare samples from several populations and to look for genomic regions with outstanding genetic differentiation between these populations. Genetic differentiation is generally based on allele frequency differences between populations, which are measured by F_ST or related statistics. Here we introduce a new statistic, denoted hapFLK, which focuses instead on the differences of haplotype frequencies between populations. In contrast to most existing statistics, hapFLK accounts for the hierarchical structure of the sampled populations. Using computer simulations, we show that each of these two features—the use of haplotype information and of the hierarchical structure of populations—significantly improves the detection power of selected loci and that combining them in the hapFLK statistic provides even greater power. We also show that hapFLK is robust with respect to bottlenecks and migration and improves over existing approaches in many situations. Finally, we apply hapFLK to a set of six sheep breeds from Northern Europe and identify seven regions under selection, which include already reported regions but also several new ones. We propose a method to help identifying the population(s) under selection in a detected region, which reveals that in many of these regions selection most likely occurred in more than one population. Furthermore, several of the detected regions correspond to incomplete sweeps, where the favorable haplotype is only at intermediate frequency in the population(s) under selection.     

Coexistence of competing juvenile–adult structured populations

The Leslie-Gower model is a discrete time analog of the competition Lotka–Volterra model and is known to possess the same dynamic scenarios of that famous model. The Leslie–Gower model played a historically significant role in the history of competition theory in its application to classic laboratory experiments of two competing species of flour beetles (carried out by Park in the 1940s–1960s). While these experiments generally supported what became the Competitive Exclusion Principle, Park observed an anomalous coexistence case. Recent literature has discussed Park’s ‘coexistence case’ by means of non-Lotka–Volterra, non-equilibrium dynamics that occur in a high dimensional model with life cycle stages. We study this dynamic possibility in the lowest possible dimension, that is to say, by means of a model involving only two species each with two life cycle stages. We do this by extending the Leslie–Gower model so as to describe the competitive interaction of two species with juvenile and adult classes. We give a complete account of the global dynamics of the resulting model and show that it allows for non-equilibrium competitive coexistence as competition coefficients are increased. We also show that this phenomenon occurs in a general class of models for competing populations structured by juvenile and adult life cycle stages.     

Variable prey development time suppresses predator–prey cycles and enhances stability

Although theoretical models have demonstrated that predator–prey population dynamics can depend critically on age (stage) structure and the duration and variability in development times of different life stages, experimental support for this theory is non‐existent. We conducted an experiment with a host–parasitoid system to test the prediction that increased variability in the development time of the vulnerable host stage can promote interaction stability. Host–parasitoid microcosms were subjected to two treatments: Normal and High variance in the duration of the vulnerable host stage. In control and Normal‐variance microcosms, hosts and parasitoids exhibited distinct population cycles. In contrast, insect abundances were 18–24% less variable in High‐ than Normal‐variance microcosms. More significantly, periodicity in host–parasitoid population dynamics disappeared in the High‐variance microcosms. Simulation models confirmed that stability in High‐variance microcosms was sufficient to prevent extinction. We conclude that developmental variability is critical to predator–prey population dynamics and could be exploited in pest‐management programs.     

Nanoscale Subsynaptic Domains Underlie the Organization of the Inhibitory Synapse

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Testing and interpreting the shared space‐environment fraction in variation partitioning analyses of ecological data

Variation partitioning analyses combined with spatial predictors (Moran's eigenvector maps, MEM) are commonly used in ecology to test the fractions of species abundance variation purely explained by environment and space. However, while these pure fractions can be tested using a classical residuals permutation procedure, no specific method has been developed to test the shared space‐environment fraction (SSEF). Yet, the SSEF is expected to encompass a major driver of community assembly, that is, an induced spatial dependence effect (ISD; i.e. the reflection of a spatially structured habitat filter on a species distribution). A reliable test of this fraction is therefore crucial to properly test the presence of an ISD on ecological data. To bridge the gap, we propose to test the SSEF through spatially‐constrained null models: torus‐translations, and Moran spectral randomisations. We investigated the type I error rate and statistical power of our method based on two real environmental datasets and simulations of tree distributions. Ten types of tree distribution displaying contrasted aggregation properties were simulated, and their abundances were sampled in 153 regularly‐distributed 20 × 20 m quadrats. The SSEF was tested for 1000 simulated tree distributions either unrelated to the environment, or filtered by environmental variables displaying contrasting spatial structures. The method proposed provided a correct type I error rate (< 0.05). The statistical power was high (> 0.9) when abundances were filtered by an environmental variable structured at broad scale. However, the spatial resolution allowed by the sampling design limited the power of the method when using a fine‐scale filtering variable. This highlighted that an ISD can be properly detected providing that the spatial pattern of the filtering process is correctly captured by the sampling design of the study. An R function to apply the SSEF testing method is provided and detailed in a tutorial.     

Molecular interaction of manganese based carbon monoxide releasing molecule (MnCORM) with human serum albumin (HSA)

In the present study, the interaction between the HSA and MnCORM in vitro under physiological conditions, was investigated through ultraviolet-visible (UV-vis) absorption, fluorescence, time-resolved fluorescence, circular dichroism (CD), Fourier transform infrared (FT-IR) spectroscopic techniques and in silico molecular docking methods. Binding parameters such as the binding constant, number of binding sites and binding force were obtained from the fluorescence data. Thermodynamic interaction revealed that the reaction was spontaneous (ΔG < 0) and hydrogen bond and van der Waals interaction were primarily involved in the binding. The changes induced in the secondary structure conformation due to the MnCORM interaction were monitored using CD and FT-IR spectroscopic techniques. The results showed reduction in α-helix conformation and corresponding increase in β-sheet and unordered structures due to slight unfolding. The time-resolved fluorescence decay confirmed the static quenching mechanism of the MnCORM. The molecular docking studies revealed that the MnCORM interacted at Sudlow’s site II of domain IIIA through hydrogen bond and van der Waals interactions. In order to understand the drug distribution and elimination, studies on the drug molecule interaction with HSA are vital. Therefore, it is evident that MnCORM interacts with HSA through ground state complex formation and thus suitable for in vivo delivery.     

基于PDB数据库的三个RNA二级结构预测软件评估

随着21世纪分子生物学研究的蓬勃发展,RNA二级结构预测成为其中一项重要内容。由于RNA二级结构预测的准确性最为关键,因此寻找高精度且易操作的二级结构预测工具显得非常重要。本文选取三种简单且易操作的二级结构预测软件,先基于PDB数据库收录的318个RNA发夹序列进行二级结构预测,进而通过比较预测结果与实验测定结果进行软件预测性能评估。比较结果显示,RNAstructure为三个软件中性能最优的RNA二级结构预测软件。     

An experimental and theoretical study of the inhibition of Escherichia coli lac operon gene expression by antigene oligonucleotides

Previously, we have developed a genetically structured mathematical model to describe the inhibition of Escherichia coli lac operon gene expression by antigene oligos. Our model predicted that antigene oligos targeted to the operator region of the lac operon would have a significant inhibitory effect on beta-galactosidase production. In this investigation, the E. coli lac operon gene expression in the presence of antigene oligos was studied experimentally. A 21-mer oligo, which was designed to form a triplex with the operator, was found to be able to specifically inhibit beta-galactosidase production in a dose-dependent manner. In contrast to the 21-mer triplex-forming oligonucleotide (TFO), several control oligos showed no inhibitory effect. The ineffectiveness of the various control oligos, along with the fact that the 21-mer oligo has no homology sequence with lacZYA, and no mRNA is transcribed from the operator, suggests that the 21-mer oligo inhibits target gene expression by an antigene mechanism. To simulate the kinetics of lac operon gene expression in the presence of antigene oligos, a genetically structured kinetic model, which includes transport of oligo into the cell, growth of bacteria cells, and lac operon gene expression, was developed. Predictions of the kinetic model fit the experimental data quite well after adjustment of the value of the oligonucleotide transport rate constant (9.0 x 10(-)(3) min(-)(1)) and oligo binding affinity constant (1.05 x 10(6) M(-)(1)). Our values for these two adjusted parameters are in the range of reported literature values.     

Completeness of NOEs in protein structures: A statistical analysis of NMR data

The completeness of experimentally observed NOE restraints of a set of 97 NMR protein structures deposited in the PDB has been assessed. Completeness is defined as the ratio of the number of experimentally observed NOEs and the number of 'expected NOEs'. A practical definition of 'expected NOEs' based on inter-proton distances in the structures up to a given cut-off distance is proposed. The average completeness for the set of 97 structures is 68, 48, and 26% up to 3, 4, and 5 Å cut-off distances, respectively. For recent state-of-the-art structures these numbers are approximately 90, 75, and 45%. Almost 20% of the observed NOEs are between atoms that are further than 5 Å apart in the final structures. The completeness is independent of the relative surface accessibility and does not depend strongly on residue type, secondary structure or local precision, although the number of observed NOEs in these classes varies considerably. The completeness of NOE restraints is a useful quality criterion in the course of structure refinement. The completeness per residue is more informative than the number of NOEs per residue, which makes it a useful tool to assess the quality of the NMR data set in relation to the resulting structures.