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Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

Understanding the demographic history of populations is a key goal in population genetics, and with improving methods and data, ever more complex models are being proposed and tested. Demographic models of current interest typically consist of a set of discrete populations, their sizes and growth rates, and continuous and pulse migrations between those populations over a number of epochs, which can require dozens of parameters to fully describe. There is currently no standard format to define such models, significantly hampering progress in the field. In particular, the important task of translating the model descriptions in published work into input suitable for population genetic simulators is labor intensive and error prone. We propose the Demes data model and file format, built on widely used technologies, to alleviate these issues. Demes provide a well-defined and unambiguous model of populations and their properties that is straightforward to implement in software, and a text file format that is designed for simplicity and clarity. We provide thoroughly tested implementations of Demes parsers in multiple languages including Python and C, and showcase initial support in several simulators and inference methods. An introduction to the file format and a detailed specification are available at https://popsim-consortium.github.io/demes-spec-docs/.

The pharmacological profile of cannabigerol (CBG), which acid form constitutes the main precursor of the most abundant cannabinoids, has been scarcely studied. It has been reported to target a2-adrenoceptor and 5-HT1A receptor. The locus coeruleus (LC) and the dorsal raphe nucleus (DRN) are the main serotonergic (5-HT) and noradrenergic (NA) areas in the rat brain, respectively. We aimed to study the effect of CBG on the firing rate of LC NA cells and DRN 5-HT cells and on a2-adrenergic and 5-HT1A autoreceptors by electrophysiological techniques in male Sprague-Dawley rat brain slices. The effect of CBG on the novelty-suppressed feeding test (NSFT) and the elevated plus maze test (EPMT) and the involvement of the 5-HT1A receptor was also studied. CBG (30 µM, 10 min) slightly changed the firing rate of NA cells but failed to alter the inhibitory effect of NA (1-100 µM). However, in the presence of CBG the inhibitory effect of the selective a2- adrenoceptor agonist UK14304 (10 nM) was decreased. Perfusion with CBG (30 µM, 10 min) did not change the firing rate of DRN 5-HT cells or the inhibitory effect of 5-HT (100 µM, 1 min) but it reduced the inhibitory effect of ipsapirone (100 nM). CBG failed to reverse ipsapirone-induced inhibition whereas perfusion with the 5-HT1A receptor antagonist WAY100635 (30 nM) completely restored the firing rate of DRN 5-HT cells. In the EPMT, CBG (10 mg/kg, i.p.) significantly increased the percentage of time the rats spent on the open arms and the number of head-dipping but it reduced the anxiety index. In the NSFT, CBG decreased the time latency to eat in the novel environment but it did not alter home-cage consumption. The effect of CBG on the reduction of latency to feed was prevented by pretreatment with WAY100635 (1 mg/kg, i.p.). In conclusion,CBG hinders the inhibitory effect produced by selective a2-adrenoceptor and 5-HT1A receptor agonists on the firing rate of NA-LC and 5-HT-DRN neurons by a yet unknown indirect mechanism in rat brain slices and produces anxiolytic-like effects through 5-HT1A receptor. [ABSTRACT FROM AUTHOR]

Background Inference of complex demographic histories is a source of information about events that happened in the past of studied populations. Existing methods for demographic inference typically require input from the researcher in the form of a parameterized model. With an increased variety of methods and tools, each with its own interface, the model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find model parameters sometimes turn out to be inefficient, for instance, by being not properly tuned or highly dependent on a user-provided initialization. The open-source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several likelihood engines and provides global parameters optimization based on a genetic algorithm. Results Here, we introduce the new GADMA2 software and provide a detailed description of the added and expanded features. It has a renovated core code base, new likelihood engines, an updated optimization algorithm, and a flexible setup for automatic model construction. We provide a full overview of GADMA2 enhancements, compare the performance of supported likelihood engines on simulated data, and demonstrate an example of GADMA2 usage on 2 empirical datasets. Conclusions We demonstrate the better performance of a genetic algorithm in GADMA2 by comparing it to the initial version and other existing optimization approaches. Our experiments on simulated data indicate that GADMA2's likelihood engines are able to provide accurate estimations of demographic parameters even for misspecified models. We improve model parameters for 2 empirical datasets of inbred species. [ABSTRACT FROM AUTHOR]

Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model's parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware resources, critically more so for larger population counts. Although genetic algorithm-based solution has proven efficient for demographic inference in the past, it struggles to deal with log-likelihoods in the setting of more than three populations. Different tools are therefore needed to handle such scenarios. We introduce a new optimization pipeline for demographic inference with time consuming log-likelihood evaluations. It is based on Bayesian optimization, a prominent technique for optimizing expensive black box functions. Comparing to the existing widely used genetic algorithm solution, we demonstrate new pipeline's superiority in the limited time budget setting with four and five populations, when using the log-likelihoods provided by the moments tool., Competing Interests: Conflicts of interest The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

The pharmacological profile of cannabigerol (CBG), which acid form constitutes the main precursor of the most abundant cannabinoids, has been scarcely studied. It has been reported to target α 2- adrenoceptor and 5-HT 1A receptor. The locus coeruleus (LC) and the dorsal raphe nucleus (DRN) are the main serotonergic (5-HT) and noradrenergic (NA) areas in the rat brain, respectively. We aimed to study the effect of CBG on the firing rate of LC NA cells and DRN 5-HT cells and on α 2 -adrenergic and 5-HT 1A autoreceptors by electrophysiological techniques in male Sprague-Dawley rat brain slices. The effect of CBG on the novelty-suppressed feeding test (NSFT) and the elevated plus maze test (EPMT) and the involvement of the 5-HT 1A receptor was also studied. CBG (30 μM, 10 min) slightly changed the firing rate of NA cells but failed to alter the inhibitory effect of NA (1-100 µM). However, in the presence of CBG the inhibitory effect of the selective α 2 -adrenoceptor agonist UK14304 (10 nM) was decreased. Perfusion with CBG (30 μM, 10 min) did not change the firing rate of DRN 5-HT cells or the inhibitory effect of 5-HT (100 μM, 1 min) but it reduced the inhibitory effect of ipsapirone (100 nM). CBG failed to reverse ipsapirone-induced inhibition whereas perfusion with the 5-HT 1A receptor antagonist WAY100635 (30 nM) completely restored the firing rate of DRN 5-HT cells. In the EPMT, CBG (10 mg/kg, i.p.) significantly increased the percentage of time the rats spent on the open arms and the number of head-dipping but it reduced the anxiety index. In the NSFT, CBG decreased the time latency to eat in the novel environment but it did not alter home-cage consumption. The effect of CBG on the reduction of latency to feed was prevented by pretreatment with WAY100635 (1 mg/kg, i.p.). In conclusion, CBG hinders the inhibitory effect produced by selective α 2 -adrenoceptor and 5-HT 1A receptor agonists on the firing rate of NA-LC and 5-HT-DRN neurons by a yet unknown indirect mechanism in rat brain slices and produces anxiolytic-like effects through 5-HT 1A receptor., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mendiguren, Aostri, Rodilla, Pujana, Noskova and Pineda.)

The blue shark Prionace glauca is a top predator with one of the widest geographical distributions of any shark species. It is classified as Critically Endangered in the Mediterranean Sea, and Near Threatened globally. Previous genetic studies did not reject the null hypothesis of a single global population. The blue shark was proposed as a possible archetype of the "grey zone of population differentiation," coined to designate cases where population structure may be too recent or too faint to be detected using a limited set of markers. Here, blue shark samples collected throughout its global range were sequenced using a specific RAD method (DArTseq), which recovered 37,655 genome-wide single nucleotide polymorphisms (SNPs). Two main groups emerged, with Mediterranean Sea and northern Atlantic samples (Northern population) differentiated significantly from the Indo-west Pacific samples (Southern population). Significant pairwise F ST values indicated further genetic differentiation within the Atlantic Ocean, and between the Atlantic Ocean and the Mediterranean Sea. Reconstruction of recent demographic history suggested divergence between Northern and Southern populations occurred about 500 generations ago and revealed a drastic reduction in effective population size from a large ancestral population. Our results illustrate the power of genome scans to detect population structure and reconstruct demographic history in highly migratory marine species. Given that the management plans of the blue shark (targeted or bycatch) fisheries currently assume panmictic regional stocks, we strongly recommend that the results presented here be considered in future stock assessments and conservation strategies., (© 2022 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.)