Cannabigerol modulates α2-adrenoceptor and 5-HT1A receptor-mediated electrophysiological effects on dorsal raphe nucleus and locus coeruleus neurons and anxiety behavior in rat.

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 α2-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 α2-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 α2-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 α2-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.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations.

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.

Bayesian optimization for demographic inference.

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.

Acute Stress-Induced Changes in the Lipid Composition of Cow's Milk in Healthy and Pathological Animals.

Producers of milk and dairy products have been faced with the challenge of responding to European society's demand for guaranteed animal welfare production. In recent years, measures have been taken to improve animal welfare conditions on farms and evaluation systems have been developed to certify them, such as the Welfare Quality® protocol. Among the markers used for this purpose, acute phase proteins stand out, with haptoglobin being one of the most relevant. However, the diagnostic power of these tools is limited and more sensitive and specific technologies are required to monitor animal health status. Different factors such as diet, stress, and diseases modify the metabolism of the animals, altering the composition of the milk in terms of oligosaccharides, proteins, and lipids. Thus, in order to study oxidative-stress-associated lipids, a collection of well-characterized milk samples, both by veterinary diagnosis and by content of the acute stress biomarker haptoglobin, was analyzed by mass spectrometry and artificial intelligence. Two lipid species (sphingomyelin and phosphatidylcholine) were identified as potential biomarkers of health status in dairy cows. Both lipids allow for the discrimination of milk from sick animals and also milk from those with stress. Moreover, lipidomics revealed specific lipid profiles depending on the origin of the samples and the degree of freedom of the animals on the farm. These data provide evidence for specific lipid changes in stressed animals and open up the possibility that haptoglobin could also affect lipid metabolism in cow's milk.

Characterization of the Antitumor Potential of Extracts of Cannabis sativa Strains with High CBD Content in Human Neuroblastoma.

Cannabis has been used for decades as a palliative therapy in the treatment of cancer. This is because of its beneficial effects on the pain and nausea that patients can experience as a result of chemo/radiotherapy. Tetrahydrocannabinol and cannabidiol are the main compounds present in Cannabis sativa, and both exert their actions through a receptor-mediated mechanism and through a non-receptor-mediated mechanism, which modulates the formation of reactive oxygen species. These oxidative stress conditions might trigger lipidic changes, which would compromise cell membrane stability and viability. In this sense, numerous pieces of evidence describe a potential antitumor effect of cannabinoid compounds in different types of cancer, although controversial results limit their implementation. In order to further investigate the possible mechanism involved in the antitumoral effects of cannabinoids, three extracts isolated from Cannabis sativa strains with high cannabidiol content were analyzed. Cell mortality, cytochrome c oxidase activity and the lipid composition of SH-SY5Y cells were determined in the absence and presence of specific cannabinoid ligands, with and without antioxidant pre-treatment. The cell mortality induced by the extracts in this study appeared to be related to the inhibition of the cytochrome c oxidase activity and to the THC concentration. This effect on cell viability was similar to that observed with the cannabinoid agonist WIN55,212-2. The effect was partially blocked by the selective CB1 antagonist AM281, and the antioxidant α-tocopherol. Moreover, certain membrane lipids were affected by the extracts, which demonstrated the importance of oxidative stress in the potential antitumoral effects of cannabinoids.

Screening System of Cannabis sativa Extracts Based on Their Mitochondrial Safety Profile Using Cytochrome c Oxidase Activity as a Biomarker

The development of Cannabis sativa strains with high cannabidiol (CBD) and low tetrahydrocannabinol (THC) content is a growing field of research, both for medical and recreational use. However, the mechanisms behind clinical actions of cannabinoids are still under investigation, although there is growing evidence that mitochondria play an important role in many of them. Numerous studies have described that cannabinoids modulate mitochondrial activity both through activation of mitochondrial cannabinoid receptors and through direct action on other proteins such as mitochondrial complexes involved in cellular respiration. Thus, the aim of this study was to determine the actions of a panel of extracts, isolated from high-CBD varieties of Cannabis sativa, on the activity of the mitochondrial electron transport chain complex IV, cytochrome c oxidase (CCO), in order to select those with a safer profile. After demonstrating that Cannabis sativa strains could be identified by cannabinoids content, concentration-response curves were performed with a collection of extracts from strains with high-CBD and low-THC content using bovine CCO. The CCO rate was clearly modified by specific extracts of Cannabis sativa plants compared to others. Half maximal inhibitory concentrations (IC50) of extracts and the inhibitory effects evoked at 1 × 10-4 g/mL displayed a significant correlation with the THC. Therefore, the screening of extracts based on CCO activity provides a powerful and rapid methodology to identify those plants with higher mitochondrial toxicity or even mito-protective actions.

Stepping up to genome scan allows stock differentiation in the worldwide distributed blue shark Prionace glauca.

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 FST 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.

Demes: a standard format for demographic models.

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/.

GADMA2: more efficient and flexible demographic inference from genetic data.

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.

A community-maintained standard library of population genetic models.

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data.

BACKGROUND: The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum (AFS), the distribution of allele frequencies in populations. The joint AFS is commonly used to reconstruct the demographic history of multiple populations, and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. RESULTS: Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA). CONCLUSIONS: We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history.

Genome-wide sequence analyses of ethnic populations across Russia.

The Russian Federation is the largest and one of the most ethnically diverse countries in the world, however no centralized reference database of genetic variation exists to date. Such data are crucial for medical genetics and essential for studying population history. The Genome Russia Project aims at filling this gap by performing whole genome sequencing and analysis of peoples of the Russian Federation. Here we report the characterization of genome-wide variation of 264 healthy adults, including 60 newly sequenced samples. People of Russia carry known and novel genetic variants of adaptive, clinical and functional consequence that in many cases show allele frequency divergence from neighboring populations. Population genetics analyses revealed six phylogeographic partitions among indigenous ethnicities corresponding to their geographic locales. This study presents a characterization of population-specific genomic variation in Russia with results important for medical genetics and for understanding the dynamic population history of the world's largest country.