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Abstract: Understanding the processes of adaptive divergence, which may ultimately lead to speciation, is a major question in evolutionary biology. Allochronic differentiation refers to a particular situation where gene flow is primarily impeded by temporal isolation between early and late reproducers. This process has been suggested to occur in a large array of organisms, even though it is still overlooked in the literature. We here focused on a well‐documented case of incipient allochronic speciation in the winter pine processionary moth Thaumetopoea pityocampa. This species typically reproduces in summer and larval development occurs throughout autumn and winter. A unique, phenologically shifted population (SP) was discovered in 1997 in Portugal. It was proved to be strongly differentiated from the sympatric “winter population” (WP), but its evolutionary history could only now be explored. We took advantage of the recent assembly of a draft genome and of the development of pan‐genomic RAD‐seq markers to decipher the demographic history of the differentiating populations and develop genome scans of adaptive differentiation. We showed that the SP diverged relatively recently, that is, few hundred years ago, and went through two successive bottlenecks followed by population size expansions, while the sympatric WP is currently experiencing a population decline. We identified outlier SNPs that were mapped onto the genome, but none were associated with the phenological shift or with subsequent adaptations. The strong genetic drift that occurred along the SP lineage certainly challenged our capacity to reveal functionally important loci. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the susceptibility pattern of Staphylococcus aureus isolated from human and environmental surfaces in a research laboratory. A total of 320 samples from nostril (n=80), hand (n=80), door knob (n=80) and table surface (n=80) were collected for 16 weeks, before and after work. A total number of 256 samples were found positive for Staphylococcus aureus. Out of 80 randomly selected isolates, 50 (62.5%) isolates were resistant to methicillin (MRSA). Hence, the precautionary measures should be taken on self and environmental hygiene as MRSA may be transferred from humans and environmental surfaces. [ABSTRACT FROM AUTHOR]

Nowadays, the incidence rate of foodborne disease has increased and become one of the global burdens affecting all individual ages in South East Asia region. Foodborne disease is responsible for mortality and morbidity worldwide thus affecting socio-economic and quality of life. Major causes of foodborne hazards diseases include diarrheal and invasive infectious disease agent, helminthes and chemicals. However, in developing countries, data and record is insufficient with poor surveillance systems leading to incomplete information on the real burden of foodborne disease. The introduction of Actor Network Theory (ANT) as tools for assessing and analyzing the food safety issues has drawn attention from various researcher as it is proven to be able to point out and identify the human and non human actors which is directly and contingently involved. The interaction between the actors such as a worker in an organization, student in school, and peoples in public provide information that can be used to minimize the risk of foodborne disease. The ultimate use of ANT is it helps the researcher to draw a framework of the source of contamination, agent responsible, factors involved, and idea to control the spread. [ABSTRACT FROM AUTHOR]

Focusses on the isolation and characterization of microsatellite markers for Centaurea corymbosa, a narrow endemic plant species growing in the limestone Massif de la Clape, in southern France. Characterization of seven C. corymbosa microsatellite loci; Extreme rarity of the species; Highly restricted and fragmented distribution; Number of alleles per microsatellite locus.

Multipartite viruses have a segmented genome, with each segment encapsidated separately. In all multipartite virus species for which the question has been addressed, the distinct segments reproducibly accumulate at a specific and host-dependent relative frequency, defined as the 'genome formula'. Here, we test the hypothesis that the multipartite genome organization facilitates the regulation of gene expression via changes of the genome formula and thus via gene copy number variations. In a first experiment, the faba bean necrotic stunt virus (FBNSV), whose genome is composed of eight DNA segments each encoding a single gene, was inoculated into faba bean or alfalfa host plants, and the relative concentrations of the DNA segments and their corresponding messenger RNAs (mRNAs) were monitored. In each of the two host species, our analysis consistently showed that the genome formula variations modulate gene expression, the concentration of each genome segment linearly and positively correlating to that of its cognate mRNA but not of the others. In a second experiment, twenty parallel FBNSV lines were transferred from faba bean to alfalfa plants. Upon host switching, the transcription rate of some genome segments changes, but the genome formula is modified in a way that compensates for these changes and maintains a similar ratio between the various viral mRNAs. Interestingly, a deep-sequencing analysis of these twenty FBNSV lineages demonstrated that the host-related genome formula shift operates independently of DNA-segment sequence mutation. Together, our results indicate that nanoviruses are plastic genetic systems, able to transiently adjust gene expression at the population level in changing environments, by modulating the copy number but not the sequence of each of their genes., (© The Author(s) 2022. Published by Oxford University Press.)

By capturing various patterns of the structuring of genetic variation across populations, f -statistics have proved highly effective for the inference of demographic history. Such statistics are defined as covariances of SNP allele frequency differences among sets of populations without requiring haplotype information and are hence particularly relevant for the analysis of pooled sequencing (Pool-Seq) data. We here propose a reinterpretation of the F (and D ) parameters in terms of probability of gene identity and derive from this unified definition unbiased estimators for both Pool-Seq data and standard allele count data obtained from individual genotypes. We implemented these estimators in a new version of the R package poolfstat, which now includes a wide range of inference methods: (i) three-population test of admixture; (ii) four-population test of treeness; (iii) F 4 -ratio estimation of admixture rates; and (iv) fitting, visualization and (semi-automatic) construction of admixture graphs. A comprehensive evaluation of the methods implemented in poolfstat on both simulated Pool-Seq (with various sequencing coverages and error rates) and allele count data confirmed the accuracy of these approaches, even for the most cost-effective Pool-Seq design involving relatively low sequencing coverages. We further analysed a real Pool-Seq data made of 14 populations of the invasive species Drosophila suzukii, which allowed refining both the demographic history of native populations and the invasion routes followed by this emblematic pest. Our new package poolfstat provides the community with a user-friendly and efficient all-in-one tool to unravel complex population genetic histories from large-size Pool-Seq or allele count SNP data., (© 2021 John Wiley & Sons Ltd.)

Resurrection studies are a useful tool to measure how phenotypic traits have changed in populations through time. If these trait modifications correlate with the environmental changes that occurred during the time period, it suggests that the phenotypic changes could be a response to selection. Selfing, through its reduction of effective size, could challenge the ability of a population to adapt to environmental changes. Here, we used a resurrection study to test for adaptation in a selfing population of Medicago truncatula , by comparing the genetic composition and flowering times across 22 generations. We found evidence for evolution toward earlier flowering times by about two days and a peculiar genetic structure, typical of highly selfing populations, where some multilocus genotypes (MLGs) are persistent through time. We used the change in frequency of the MLGs through time as a multilocus fitness measure and built a selection gradient that suggests evolution toward earlier flowering times. Yet, a simulation model revealed that the observed change in flowering time could be explained by drift alone, provided the effective size of the population is small enough (<150). These analyses suffer from the difficulty to estimate the effective size in a highly selfing population, where effective recombination is severely reduced., Competing Interests: The authors of this article declare that they have no financial conflict of interest with the content of this article., (© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Simulation-based methods such as approximate Bayesian computation (ABC) are well-adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. Random Forest allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user-friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of pseudo-observed and real data sets corresponding to pool-sequencing and individual-sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories., (© The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.)

Natural reservoirs of zoonotic pathogens generally seem to be capable of tolerating infections. Tolerance and its underlying mechanisms remain difficult to assess using experiments or wildlife surveys. High-throughput sequencing technologies give the opportunity to investigate the genetic bases of tolerance, and the variability of its mechanisms in natural populations. In particular, population genomics may provide preliminary insights into the genes shaping tolerance and potentially influencing epidemiological dynamics. Here, we addressed these questions in the bank vole Myodes glareolus , the specific asymptomatic reservoir host of Puumala hantavirus (PUUV), which causes nephropathia epidemica (NE) in humans. Despite the continuous spatial distribution of M. glareolus in Sweden, NE is endemic to the northern part of the country. Northern bank vole populations in Sweden might exhibit tolerance strategies as a result of coadaptation with PUUV. This may favor the circulation and maintenance of PUUV and lead to high spatial risk of NE in northern Sweden. We performed a genome-scan study to detect signatures of selection potentially correlated with spatial variations in tolerance to PUUV. We analyzed six bank vole populations from Sweden, sampled from northern NE-endemic to southern NE-free areas. We combined candidate gene analyses ( Tlr4 , Tlr7 , and Mx2 genes) and high-throughput sequencing of restriction site-associated DNA (RAD) markers. Outlier loci showed high levels of genetic differentiation and significant associations with environmental data including variations in the regional number of NE human cases. Among the 108 outliers that matched to mouse protein-coding genes, 14 corresponded to immune-related genes. The main biological pathways found to be significantly enriched corresponded to immune processes and responses to hantavirus, including the regulation of cytokine productions, TLR cascades, and IL-7, VEGF, and JAK-STAT signaling. In the future, genome-scan replicates and functional experimentations should enable to assess the role of these biological pathways in M. glareolus tolerance to PUUV.

The advent of high throughput sequencing and genotyping technologies enables the comparison of patterns of polymorphisms at a very large number of markers. While the characterization of genetic structure from individual sequencing data remains expensive for many nonmodel species, it has been shown that sequencing pools of individual DNAs (Pool-seq) represents an attractive and cost-effective alternative. However, analyzing sequence read counts from a DNA pool instead of individual genotypes raises statistical challenges in deriving correct estimates of genetic differentiation. In this article, we provide a method-of-moments estimator of [Formula: see text] for Pool-seq data, based on an analysis-of-variance framework. We show, by means of simulations, that this new estimator is unbiased and outperforms previously proposed estimators. We evaluate the robustness of our estimator to model misspecification, such as sequencing errors and uneven contributions of individual DNAs to the pools. Finally, by reanalyzing published Pool-seq data of different ecotypes of the prickly sculpin Cottus asper , we show how the use of an unbiased [Formula: see text] estimator may question the interpretation of population structure inferred from previous analyses., (Copyright © 2018 by the Genetics Society of America.)

Identifying the genomic bases of adaptation to novel environments is a long-term objective in evolutionary biology. Because genetic differentiation is expected to increase between locally adapted populations at the genes targeted by selection, scanning the genome for elevated levels of differentiation is a first step towards deciphering the genomic architecture underlying adaptive divergence. The pea aphid Acyrthosiphon pisum is a model of choice to address this question, as it forms a large complex of plant-specialized races and cryptic species, resulting from recent adaptive radiation. Here, we characterized genomewide polymorphisms in three pea aphid races specialized on alfalfa, clover and pea crops, respectively, which we sequenced in pools (poolseq). Using a model-based approach that explicitly accounts for selection, we identified 392 genomic hotspots of differentiation spanning 47.3 Mb and 2,484 genes (respectively, 9.12% of the genome size and 8.10% of its genes). Most of these highly differentiated regions were located on the autosomes, and overall differentiation was weaker on the X chromosome. Within these hotspots, high levels of absolute divergence between races suggest that these regions experienced less gene flow than the rest of the genome, most likely by contributing to reproductive isolation. Moreover, population-specific analyses showed evidence of selection in every host race, depending on the hotspot considered. These hotspots were significantly enriched for candidate gene categories that control host-plant selection and use. These genes encode 48 salivary proteins, 14 gustatory receptors, 10 odorant receptors, five P450 cytochromes and one chemosensory protein, which represent promising candidates for the genetic basis of host-plant specialization and ecological isolation in the pea aphid complex. Altogether, our findings open new research directions towards functional studies, for validating the role of these genes on adaptive phenotypes., (© 2018 John Wiley & Sons Ltd.)

The relative female and male contributions to demography are of great importance to better understand the history and dynamics of populations. While earlier studies relied on uniparental markers to investigate sex-specific questions, the increasing amount of sequence data now enables us to take advantage of tens to hundreds of thousands of independent loci from autosomes and the X chromosome. Here, we develop a novel method to estimate effective sex ratios or ESR (defined as the female proportion of the effective population) from allele count data for each branch of a rooted tree topology that summarizes the history of the populations of interest. Our method relies on Kimura's time-dependent diffusion approximation for genetic drift, and is based on a hierarchical Bayesian model to integrate over the allele frequencies along the branches. We show via simulations that parameters are inferred robustly, even under scenarios that violate some of the model assumptions. Analyzing bovine SNP data, we infer a strongly female-biased ESR in both dairy and beef cattle, as expected from the underlying breeding scheme. Conversely, we observe a strongly male-biased ESR in early domestication times, consistent with an easier taming and management of cows, and/or introgression from wild auroch males, that would both cause a relative increase in male effective population size. In humans, analyzing a subsample of non-African populations, we find a male-biased ESR in Oceanians that may reflect complex marriage patterns in Aboriginal Australians. Because our approach relies on allele count data, it may be applied on a wide range of species.

Understanding the processes of adaptive divergence, which may ultimately lead to speciation, is a major question in evolutionary biology. Allochronic differentiation refers to a particular situation where gene flow is primarily impeded by temporal isolation between early and late reproducers. This process has been suggested to occur in a large array of organisms, even though it is still overlooked in the literature. We here focused on a well-documented case of incipient allochronic speciation in the winter pine processionary moth Thaumetopoea pityocampa. This species typically reproduces in summer and larval development occurs throughout autumn and winter. A unique, phenologically shifted population (SP) was discovered in 1997 in Portugal. It was proved to be strongly differentiated from the sympatric "winter population" (WP), but its evolutionary history could only now be explored. We took advantage of the recent assembly of a draft genome and of the development of pan-genomic RAD-seq markers to decipher the demographic history of the differentiating populations and develop genome scans of adaptive differentiation. We showed that the SP diverged relatively recently, that is, few hundred years ago, and went through two successive bottlenecks followed by population size expansions, while the sympatric WP is currently experiencing a population decline. We identified outlier SNPs that were mapped onto the genome, but none were associated with the phenological shift or with subsequent adaptations. The strong genetic drift that occurred along the SP lineage certainly challenged our capacity to reveal functionally important loci., (© 2017 John Wiley & Sons Ltd.)

In the version of this Article previously published, there was a typographical error ('4' instead of '2') in the equations relating F ST and effective population size (N e ) in the Methods section 'Genome-wide scan for selection based on temporal differentiation'. The correct equations are given below.[Formula: see text] [Formula: see text].

Rapid phenotypic evolution of quantitative traits can occur within years, but its underlying genetic architecture remains uncharacterized. Here we test the theoretical prediction that genes with intermediate pleiotropy drive adaptive evolution in nature. Through a resurrection experiment, we grew Arabidopsis thaliana accessions collected across an 8-year period in six micro-habitats representative of that local population. We then used genome-wide association mapping to identify the single-nucleotide polymorphisms (SNPs) associated with evolved and unevolved traits in each micro-habitat. Finally, we performed a selection scan by testing for temporal differentiation in these SNPs. Phenotypic evolution was consistent across micro-habitats, but its associated genetic bases were largely distinct. Adaptive evolutionary change was most strongly driven by a small number of quantitative trait loci (QTLs) with intermediate degrees of pleiotropy; this pleiotropy was synergistic with the per-trait effect size of the SNPs, increasing with the degree of pleiotropy. In addition, weak selection was detected for frequent micro-habitat-specific QTLs that shape single traits. In this population, A. thaliana probably responded to local warming and increased competition, in part mediated by central regulators of flowering time. This genetic architecture, which includes both synergistic pleiotropic QTLs and distinct QTLs within particular micro-habitats, enables rapid phenotypic evolution while still maintaining genetic variation in wild populations.