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.This work was supported by the Ministerio de Sanidad, Consumo y Bienestar Social. Delegación del Gobierno para el Plan Nacional Sobre Drogas, PND 2018I025, (PND18/04) and University of the Basque Country (UPV/EHU) (Grant GIU19/076). EA and IR were supported by a predoctoral fellowship from the Basque and Spanish Government, respectively

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.This work is partially supported by the Basque Government, Bikaintek #010-B2/2021, and the Spanish Innovation Agency, AIE, Doctorados Industriales, #DIN2020-011349

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.This work has been partially supported by a grant from the Ministry of Economy and Competitiveness (DIN2019-010902 and DIN2020-011349) and the Basque Government Department of Economic Development, Sustainability and Environment (Bikaintek program: 005-B2/2021)

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

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.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

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.ISSN:2160-183

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., ai) 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