The possibility of epileptiform activity generation by the thalamocortical neuronal network after focal brain injuries, including traumatic brain injury (TBI), is actively debated. Presumably, posttraumatic spike-wave discharges (SWDs) involve a cortico-thalamocortical neuronal network. Differentiation of posttraumatic and idiopathic (i.e., spontaneously generated) SWDs is imperative for understanding posttraumatic epileptogenic mechanisms. Experiments were performed on male Sprague-Dawley rats with electrodes implanted into the somatosensory cortex and the thalamic ventral posterolateral nucleus. Local field potentials were recorded for 7 days before and 7 days after TBI (lateral fluid percussion injury, 2.5 atm). The morphology of 365 SWDs (89 idiopathic before craniotomy, and 262 posttraumatic that appeared only after TBI) and their appearance in the thalamus were analyzed. The occurrence of SWDs in the thalamus determined their spike-wave form and bilateral lateralization in the neocortex. Posttraumatic discharges were characterized by more "mature" characteristics as compared to spontaneously generated discharges: higher proportions of bilateral spreading, well-defined spike-wave form, and thalamus involvement. Based on SWD parameters, the etiology could be established with an accuracy of 75% (AUC 0.79). Our results support the hypothesis that the formation of posttraumatic SWDs involves a cortico-thalamocortical neuronal network. The results form a basis for further research of mechanisms associated with posttraumatic epileptiform activity and epileptogenesis.
Acute and chronic corticosterone (CS) elevations after traumatic brain injury (TBI) may be involved in distant hippocampal damage and the development of late posttraumatic behavioral pathology. CS-dependent behavioral and morphological changes were studied 3 months after TBI induced by lateral fluid percussion in 51 male Sprague-Dawley rats. CS was measured in the background 3 and 7 days and 1, 2 and 3 months after TBI. Tests including open field, elevated plus maze, object location, new object recognition tests (NORT) and Barnes maze with reversal learning were used to assess behavioral changes in acute and late TBI periods. The elevation of CS on day 3 after TBI was accompanied by early CS-dependent objective memory impairments detected in NORT. Blood CS levels > 860 nmol/L predicted delayed mortality with an accuracy of 0.947. Ipsilateral neuronal loss in the hippocampal dentate gyrus, microgliosis in the contralateral dentate gyrus and bilateral thinning of hippocampal cell layers as well as delayed spatial memory deficits in the Barnes maze were revealed 3 months after TBI. Because only animals with moderate but not severe posttraumatic CS elevation survived, we suggest that moderate late posttraumatic morphological and behavioral deficits may be at least partially masked by CS-dependent survivorship bias.
Brain Injuries, Traumatic , Corticosterone , Rats , Male , Animals , Rats, Sprague-Dawley , Survivorship , Brain Injuries, Traumatic/pathology , Hippocampus/pathology , Memory Disorders/pathology , Maze Learning/physiology
Changes in the accuracy of the genomic estimates obtained by the ssGBLUP and wssGBLUP methods were evaluated using different reference groups. The weighting procedure's reasonableness of application Pwas considered to improve the accuracy of genomic predictions for meat, fattening and reproduction traits in pigs. Six reference groups were formed to assess the genomic data quantity impact on the accuracy of predicted values (groups of genotyped animals). The datasets included 62,927 records of meat and fattening productivity (fat thickness over 6-7 ribs (BF1, mm)), muscle depth (MD, mm) and precocity up to 100 kg (age, days) and 16,070 observations of reproductive qualities (the number of all born piglets (TNB) and the number of live-born piglets (NBA), according to the results of the first farrowing). The wssGBLUP method has an advantage over ssGBLUP in terms of estimation reliability. When using a small reference group, the difference in the accuracy of ssGBLUP over BLUP AM is from -1.9 to +7.3 percent points, while for wssGBLUP, the change in accuracy varies from +18.2 to +87.3 percent points. Furthermore, the superiority of the wssGBLUP is also maintained for the largest group of genotyped animals: from +4.7 to +15.9 percent points for ssGBLUP and from +21.1 to +90.5 percent points for wssGBLUP. However, for all analyzed traits, the number of markers explaining 5% of genetic variability varied from 71 to 108, and the number of such SNPs varied depending on the size of the reference group (79-88 for BF1, 72-81 for MD, 71-108 for age). The results of the genetic variation distribution have the greatest similarity between groups of about 1000 and about 1500 individuals. Thus, the size of the reference group of more than 1000 individuals gives more stable results for the estimation based on the wssGBLUP method, while using the reference group of 500 individuals can lead to distorted results of GEBV.
The wild boar is the wild ancestor of the domestic pig and one of the most common species of ungulates. At the beginning of the 20th century, the wild boar was practically exterminated in the European part of Russia. In the period 1935-1988, 7705 boars were caught in various regions of the European part of Russia, the Far East, Ukraine, Belarus, Kyrgyzstan, Kazakhstan, Latvia, Lithuania, Estonia, Tajikistan and resettled in the territory of Russia. Asian and European wild boars dwell the territory of Russia. The aim of our research was to study the genetic diversity and structure of wild boar populations in different regions of Russia using genome-wide genotyping. We have determined the genetic distances, population structure, parameters of genetic diversity and significantly expanded our understanding of the genetic state of the Russian wild boar. For the first time, we calculated autozygosity of the wild boar of the European and Asian subspecies using Homozygous-by-Descent (HBD) Segments analysis, which is important in terms of population recovery. We also found evidence of hybridization between Russian wild boar and domestic pigs. A group of European wild boars showed introgression of the Asian boar into population. The mean level of the inbreeding coefficient in European wild boar was higher than in Asian wild boar, and combined groups of the European boar had higher inbreeding coefficient than Russian wild boars. These results obtained can be used in population management.
Pigs are strategically important animals for the agricultural industry. An assessment of genetic differentiation between pigs, undergone and not undergone to selection intensification, is of particular interest. Our research was conducted on two groups of Large White pigs grown on the same farm but in different years. A total of 165 samples were selected with 78 LW_Ð (n = 78, the Russian selection) and LW_B (n = 87, a commercial livestock). For genotyping, we used GeneSeek® GGP Porcine HD Genomic Profiler v1 (Illumina Inc, San Diego, CA, USA). To define breeding characteristics of selection, we used smoothing FST and segment identification of HBD (Homozygous-by-Descent). The results of smoothing FST showed 20 areas of a genome with strong ejection regions of the genome located on all chromosomes except SSC2, SSC3, and SSC8. The average realized autozygosity in Large White pigs of native selection was in (LW_A)-0.21, in LW_Ð-0.29. LW_Ð showed 13,338 HBD segments, 171 per one animal, and LW_B-15,747 HBD segments, 181 per one animal. The ejections found by the smoothing FST method were partially localized in the HBD regions. In these areas, the genes ((NCBP1, PLPPR1, GRIN3A, NBEA, TRPC4, HS6ST3, NALCN, SMG6, TTC3, KCNJ6, IKZF2, OBSL1, CARD10, ETV6, VWF, CCND2, TSPAN9, CDH13, CEP128, SERPINA11, PIK3CG, COG5, BCAP29, SLC26A4) were defined. The revealed genes can be of special interest for further studying their influence on an organism of an animal since they can act as candidate genes for selection-significant traits.
Genome , Genomics , Animals , Genomics/methods , Homozygote , Phenotype , Russia , Swine/genetics
Intensive selection raises the efficiency of pig farming considerably, but it also promotes the accumulation of homozygosity, which can lead to an increase in inbreeding and the accumulation of deleterious variation. The analysis of segments homozygous-by-descent (HBD) and non-HBD segments in purebred and crossbred pigs is of great interest. Research was carried out on 657 pigs, of which there were Large White (LW, n = 280), Landrace (LR, n = 218) and F1 female (âLR × âLW) (F1, n = 159). Genotyping was performed using the GeneSeek® GGP Porcine HD Genomic Profiler v1 (Illumina Inc., USA). To identify HBD segments and estimate autozygosity (inbreeding coefficient), we used the multiple HBD classes model. LW pigs exhibited 50,420 HBD segments, an average of 180 per animal; LR pigs exhibited 33,586 HBD segments, an average of 154 per animal; F1 pigs exhibited 21,068 HBD segments, an average of 132 per animal. The longest HBD segments in LW were presented in SSC1, SSC13 and SSC15; in LR, in SSC1; and in F1, in SSC15. In these segments, 3898 SNPs localized in 1252 genes were identified. These areas overlap with 441 QTLs (SSC1-238 QTLs; SSC13-101 QTLs; and SSC15-102 QTLs), including 174 QTLs for meat and carcass traits (84 QTLs-fatness), 127 QTLs for reproduction traits (100 QTLs-litter traits), 101 for production traits (69 QTLs-growth and 30 QTLs-feed intake), 21 QTLs for exterior traits (9 QTLs-conformation) and 18 QTLs for health traits (13 QTLs-blood parameters). Thirty SNPs were missense variants. Whilst estimating the potential for deleterious variation, six SNPs localized in the NEDD4, SEC11C, DCP1A, CCT8, PKP4 and TENM3 genes were identified, which may show deleterious variation. A high frequency of potential deleterious variation was noted for LR in DCP1A, and for LW in TENM3 and PKP4. In all cases, the genotype frequencies in F1 were intermediate between LR and LW. The findings presented in our work show the promise of genome scanning for HBD as a strategy for studying population history, identifying genomic regions and genes associated with important economic traits, as well as deleterious variation.
Breeding practices adopted at different farms are aimed at maximizing the profitability of pig farming. In this work, we have analyzed the genetic diversity of Large White pigs in Russia. We compared genomes of historic and modern Large White Russian breeds using 271 pig samples. We have identified 120 candidate regions associated with the differentiation of modern and historic pigs and analyzed genomic differences between the modern farms. The identified genes were associated with height, fitness, conformation, reproductive performance, and meat quality.
BACKGROUND: A significant proportion of perinatal losses in pigs occurs due to congenital malformations. The purpose of this study is the identification of genomic loci associated with fetal malformations in piglets. METHODS: The malformations were divided into two groups: associated with limb defects (piglet splay leg) and associated with other congenital anomalies found in newborn piglets. 148 Landrace and 170 Large White piglets were selected for the study. A genome-wide association study based on the gradient boosting machine algorithm was performed to identify markers associated with congenital anomalies and piglet splay leg. RESULTS: Forty-nine SNPs (23 SNPs in Landrace pigs and 26 SNPs in Large White) were associated with congenital anomalies, 22 of which were localized in genes. A total of 156 SNPs (28 SNPs in Landrace; 128 in Large White) were identified for piglet splay leg, of which 79 SNPs were localized in genes. We have demonstrated that the gradient boosting machine algorithm can identify SNPs and their combinations associated with significant selection indicators of studied malformations and productive characteristics. DATA AVAILABILITY: Genotyping and phenotyping data are available at http://www.compubioverne.group/data-and-software/.
Replacement pigs' genomic prediction for reproduction (total number and born alive piglets in the first parity), meat, fatness and growth traits (muscle depth, days to 100 kg and backfat thickness over 6-7 rib) was tested using single-step genomic best linear unbiased prediction ssGBLUP methodology. These traits were selected as the most economically significant and different in terms of heritability. The heritability for meat, fatness and growth traits varied from 0.17 to 0.39 and for reproduction traits from 0.12 to 0.14. We confirm from our data that ssGBLUP is the most appropriate method of genomic evaluation. The validation of genomic predictions was performed by calculating the correlation between preliminary GEBV (based on pedigree and genomic data only) with high reliable conventional estimates (EBV) (based on pedigree, own phenotype and offspring records) of validating animals. Validation datasets include 151 and 110 individuals for reproduction, meat and fattening traits, respectively. The level of correlation (r) between EBV and GEBV scores varied from +0.44 to +0.55 for meat and fatness traits, and from +0.75 to +0.77 for reproduction traits. Average breeding value (EBV) of group selected on genomic evaluation basis exceeded the group selected on parental average estimates by 22, 24 and 66% for muscle depth, days to 100 kg and backfat thickness over 6-7 rib, respectively. Prediction based on SNP markers data and parental estimates showed a significant increase in the reliability of low heritable reproduction traits (about 40%), which is equivalent to including information about 10 additional descendants for sows and 20 additional descendants for boars in the evaluation dataset.
The article highlighted the problem of meat cattle genetic defects. The aim was the development of DNA tests for some genetic defects diagnostics, the determination of the animal carriers and their frequencies tracking in time. The 1490 DNA samples from the Aberdeen Angus (n = 701), Hereford (n = 385), Simmental (n = 286) and Belgian Blue (n = 118) cattle have been genotyped on the genetic defects by newly created and earlier developed DNA tests based on AS-PCR and PCR-RFLP methods. The Aberdeen Angus cattle genotyping has revealed 2.38 ± 0.31% AMC-cows and 1.67 ± 0.19 % AMC-bulls, 0.65 ± 0.07% DDC-cows and 0.90 ± 0.10% DDC-bulls. The single animals among the Hereford cattle were carriers of MSUD and CWH (on 0.27 ± 0.05%), ICM and HY (on 0.16 ± 0.03%). The Simmental cattle were free from OS. All Belgian Blue livestock were M1- and 0.84%-CMD1-carriers. The different ages Aberdeen Angus cattle genotyping has shown the tendency of the AMC- and DDC frequencies to increase in the later generations. The statistically significant increase of DDC of 1.17% in the cows' population born in 2019 compared to those born in 2015 allows concluding the further development of the DNA analysis-based measures preventing the manifestation of the genetic anomalies in meat cattle herds is necessary.
Cattle Diseases/genetics , Cattle/genetics , Genetic Carrier Screening/veterinary , Animals , Cattle Diseases/diagnosis , Genetic Carrier Screening/methods , Genetic Carrier Screening/standards , Genotyping Techniques/methods , Genotyping Techniques/standards , Genotyping Techniques/veterinary , Polymerase Chain Reaction/methods , Polymerase Chain Reaction/standards , Sensitivity and Specificity
The Large White pig is the most commonly raised commercial pig breed in the world. The aim of this work was to investigate D-loop mtDNA in Large White pigs (n = 402) of various selections bred in the Russian Federation from 2000 to 2019. The general sample consisted of three groups: Old (n = 78) (Russian selection, 2000-2010); Imp (n = 123) (imported to Russia in 2008-2014); New (n = 201) (2015-2019). The synthesized score (Fz) was calculated by analyzing the main PCA (principal component analysis components). An affiliation to Asian or European haplogroups was determined according to the NCBI (National Center for Biotechnology Information). In the study, we defined 46 polymorphic sites and 42 haplotypes. Significant distinctions between groups Old, Imp and New in frequencies of haplotypes and haplogroups were established. The distribution of Asian and European haplotypes in the groups was Old: 50%/50%, Imp: 43%/57%, New: 75%/25%, respectively. The variety of haplotypes and haplogroups in the pigs of the group New is related to the farms in which they breed. Haplotype frequencies significantly differ between the clusters Old_Center, Old_Siberia and Old_South. This study will provide information on the genetic diversity of Large White breed pigs. The results will be useful for the conservation and sustainable use of these resources.
Genomic selection is routinely used worldwide in agricultural breeding. However, in Russia, it is still not used to its full potential partially due to high genotyping costs. The use of genotypes imputed from the low-density chips (LD-chip) provides a valuable opportunity for reducing the genotyping costs. Pork production in Russia is based on the conventional 3-tier pyramid involving 3 breeds; therefore, the best option would be the development of a single LD-chip that could be used for all of them. Here, we for the first time have analyzed genomic variability in 3 breeds of Russian pigs, namely, Landrace, Duroc, and Large White and generated the LD-chip that can be used in pig breeding with the negligible loss in genotyping quality. We have demonstrated that out of the 3 methods commonly used for LD-chip construction, the block method shows the best results. The imputation quality depends strongly on the presence of close ancestors in the reference population. We have demonstrated that for the animals with both parents genotyped using high-density panels high-quality genotypes (allelic discordance rate < 0.05) could be obtained using a 300 single nucleotide polymorphism (SNP) chip, while in the absence of genotyped ancestors at least 2,000 SNP markers are required. We have shown that imputation quality varies between chromosomes, and it is lower near the chromosome ends and drops with the increase in minor allele frequency. Imputation quality of the individual SNPs correlated well across breeds. Using the same LD-chip, we were able to obtain comparable imputation quality in all 3 breeds, so it may be suggested that a single chip could be used for all of them. Our findings also suggest that the presence of markers with extremely low imputation quality is likely to be explained by wrong mapping of the markers to the chromosomal positions.
Reproductive productivity depend on a complex set of characteristics. The number of piglets at birth (Total number born, Litter size, TNB) and the number of alive piglets at birth (Total number born alive, NBA) are the main indicators of the reproductive productivity of sows in pig breeding. Great hopes are pinned on GWAS (Genome-Wide Association Studies) to solve the problems associated with studying the genetic architecture of reproductive traits of pigs. This paper provides an overview of international studies on SNP (Single nucleotide polymorphism) associated with TNB and NBA in pigs presented in PigQTLdb as "Genome map association". Currently on the base of Genome map association results 306 SNPs associated with TNB (218 SNPs) and NBA (88 SNPs) have been identified and presented in the Pig QTLdb database. The results are based on research of pigs such as Large White, Yorkshire, Landrace, Berkshire, Duroc and Erhualian. The presented review shows that most SNPs found in chromosome areas where candidate genes or QTLs (Quantitative trait locus) have been identified. Further research in the given direction will allow to obtain new data that will become an impulse for creating breakthrough breeding technologies and increase the production efficiency in pig farming.
Litter Size/genetics , Quantitative Trait Loci/genetics , Reproduction/genetics , Swine/genetics , Animals , Breeding , Chromosomes/genetics , Female , Genome-Wide Association Study , Genotype , Phenotype , Polymorphism, Single Nucleotide/genetics , Pregnancy , Sus scrofa/genetics
Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.
The use of specialized meat breeds in cattle breeding programs is considered very promising for improving herds' productivity. However, in animal genotype, along with genes that positively affect the productivity signs, there are genes whose mutations, known as genetic defects, negatively affect the health of animals. The aim of the study was the screening of the Russian populations of Aberdeen Angus, Hereford and Belgian Blue cattle on gene mutations associated with the genetic defects of arthrogryposis multiplex (AM), osteopetrosis (OS), developmental duplication (DD), double muscling (M1), hypotrichosis (HY) and maple syrup urine disease (MSUD) as well as the F94L polymorphism of myostatin gene (MSTN) linked with the gene responsible for less fat content in the carcass by means of DNA analysis. In the article, test systems based on the polymerase chain reaction method are presented. The analysis of the Aberdeen Angus ( n = 4480 ) population has revealed 0.19⯠± â¯0.09â¯% animal M1 carriers, 0.53⯠± â¯0.03â¯% OS carriers, 1.92⯠± â¯0.09â¯% AM carriers and 9.00⯠± â¯0.20â¯% DD carriers. The genotyping of Hereford cattle of Russian populations ( n = 525 ) has not revealed any individual carriers of MSUD or HY genetic defects. All of the Belgian Blue population ( n = 92 ) animals were heterozygous M1 carriers. The study of the F94L MSTN polymorphism has demonstrated extremely high frequencies of the desirable A allele (0.93 and 0.90) in two Aberdeen Angus populations with an average mean of 0.63⯠± â¯0.08, which was 32â¯% higher compared to the Belgian Blue population. The results suggest the high genetic potential of the Aberdeen Angus and Belgian Blue cattle, but the existence in the genotypes of the mutant alleles associated with hereditary diseases indicates the risk of uncontrolled use of these breeds.
BACKGROUND: The origin of native and locally developed Russian cattle breeds is linked to the historical, social, cultural, and climatic features of the diverse geographical regions of Russia. In the present study, we investigated the population structure of nine Russian cattle breeds and their relations to the cattle breeds from around the world to elucidate their origin. Genotyping of single nucleotide polymorphisms (SNPs) in Bestuzhev (n = 26), Russian Black-and-White (n = 21), Kalmyk (n = 14), Kholmogor (n = 25), Kostromsky (n = 20), Red Gorbatov (n = 23), Suksun (n = 20), Yakut (n = 25), and Yaroslavl cattle breeds (n = 21) was done using the Bovine SNP50 BeadChip. SNP profiles from an additional 70 breeds were included in the analysis as references. RESULTS: The observed heterozygosity levels were quite similar in eight of the nine studied breeds (HO = 0.337-0.363) except for Yakut (Ho = 0.279). The inbreeding coefficients FIS ranged from -0.028 for Kalmyk to 0.036 for Russian Black-and-White and were comparable to those of the European breeds. The nine studied Russian breeds exhibited taurine ancestry along the C1 axis of the multidimensional scaling (MDS)-plot, but Yakut was clearly separated from the European taurine breeds on the C2 axis. Neighbor-Net and admixture analyses, discriminated three groups among the studied Russian breeds. Yakut and Kalmyk were assigned to a separate group because of their Turano-Mongolian origin. Russian Black-and-White, Kostromsky and Suksun showed transboundary European ancestry, which originated from the Holstein, Brown Swiss, and Danish Red breeds, respectively. The lowest level of introgression of transboundary breeds was recorded for the Kholmogor, Yaroslavl, Red Gorbatov and Bestuzhev breeds, which can be considered as an authentic genetic resource. CONCLUSIONS: Whole-genome SNP analysis revealed that Russian native and locally developed breeds have conserved authentic genetic patterns in spite of the considerable influence of Eurasian taurine cattle. In this paper, we provide fundamental genomic information that will contribute to the development of more accurate breed conservation programs and genetic improvement strategies.
Cattle/classification , Genotyping Techniques/veterinary , Polymorphism, Single Nucleotide , Whole Genome Sequencing/veterinary , Animals , Cattle/genetics , Genetics, Population , Heterozygote , Inbreeding , Russia
BACKGROUND: It is generally accepted that domestication of pigs took place in multiple locations across Eurasia; the breeds that originated in Europe and Asia have been well studied. However, the genetic structure of pig breeds from Russia, Belorussia, Kazakhstan and Ukraine, which represent large geographical areas and diverse climatic zones in Eurasia, remains largely unknown. RESULTS: This study provides the first genomic survey of 170 pigs representing 13 breeds from Russia, Belorussia, Kazakhstan and Ukraine; 288 pigs from six Chinese and seven European breeds were also included for comparison. Our findings show that the 13 novel breeds tested derived mainly from European pigs through the complex admixture of Large White, Landrace, Duroc, Hampshire and other breeds, and that they display no geographic structure based on genetic distance. We also found a considerable Asian contribution to the miniature Siberian pigs (Minisib breed) from Russia. Apart from the Minisib, Urzhum, Ukrainian Spotted Steppe and Ukrainian White Steppe breeds, which may have undergone intensive inbreeding, the breeds included in this study showed relatively high genetic diversity and low levels of homozygosity compared to the Chinese indigenous pig breeds. CONCLUSIONS: This study provides the first genomic overview of the population structure and genetic diversity of 13 representative pig breeds from Russia, Belorussia, Kazakhstan and Ukraine; this information will be useful for the preservation and management of these breeds.
Genetic Variation , Metagenomics , Sus scrofa/genetics , Alleles , Animals , Europe, Eastern , Genome-Wide Association Study/methods , Genotype , Inbreeding , Phylogeny , Sus scrofa/classification
