Production Technologies for Recombinant Antibodies: Insights into Eukaryotic, Prokaryotic, and Transgenic Expression Systems.

Recombinant antibodies, a prominent class of recombinant proteins, are witnessing substantial growth in research and diagnostics. Recombinant antibodies are being produced employing diverse hosts ranging from highly complex eukaryotes, for instance, mammalian cell lines (and insects, fungi, yeast, etc.) to unicellular prokaryotic models like gram-positive and gram-negative bacteria. This review delves into these production methods, highlighting approaches like antibody phage display that employs bacteriophages for gene library creation. Recent studies emphasize monoclonal antibody generation through hybridoma technology, utilizing hybridoma cells from myeloma and B-lymphocytes. Transgenic plants and animals have emerged as sources for polyclonal and monoclonal antibodies, with transgenic animals preferred due to their human-like post-translational modifications and reduced immunogenicity risk. Chloroplast expression offers environmental safety by preventing transgene contamination in pollen. Diverse production technologies, such as stable cell pools and clonal cell lines, are available, followed by purification via techniques like affinity chromatography. The burgeoning applications of recombinant antibodies in medicine have led to their large-scale industrial production.

Multivariate genome-wide associations for immune traits in two maternal pig lines.

BACKGROUND: Immune traits are considered to serve as potential biomarkers for pig's health. Medium to high heritabilities have been observed for some of the immune traits suggesting genetic variability of these phenotypes. Consideration of previously established genetic correlations between immune traits can be used to identify pleiotropic genetic markers. Therefore, genome-wide association study (GWAS) approaches are required to explore the joint genetic foundation for health biomarkers. Usually, GWAS explores phenotypes in a univariate (uv), trait-by-trait manner. Besides two uv GWAS methods, four multivariate (mv) GWAS approaches were applied on combinations out of 22 immune traits for Landrace (LR) and Large White (LW) pig lines. RESULTS: In total 433 (LR: 351, LW: 82) associations were identified with the uv approach implemented in PLINK and a Bayesian linear regression uv approach (BIMBAM) software. Single Nucleotide Polymorphisms (SNPs) that were identified with both uv approaches (n = 32) were mostly associated with immune traits such as haptoglobin, red blood cell characteristics and cytokines, and were located in protein-coding genes. Mv GWAS approaches detected 647 associations for different mv immune trait combinations which were summarized to 133 Quantitative Trait Loci (QTL). SNPs for different trait combinations (n = 66) were detected with more than one mv method. Most of these SNPs are associated with red blood cell related immune trait combinations. Functional annotation of these QTL revealed 453 immune-relevant protein-coding genes. With uv methods shared markers were not observed between the breeds, whereas mv approaches were able to detect two conjoint SNPs for LR and LW. Due to unmapped positions for these markers, their functional annotation was not clarified. CONCLUSIONS: This study evaluated the joint genetic background of immune traits in LR and LW piglets through the application of various uv and mv GWAS approaches. In comparison to uv methods, mv methodologies identified more significant associations, which might reflect the pleiotropic background of the immune system more accurately. In genetic research of complex traits, the SNP effects are generally small. Furthermore, one genetic variant can affect several correlated immune traits at the same time, termed pleiotropy. As mv GWAS methods consider strong dependencies among traits, the power to detect SNPs can be boosted. Both methods revealed immune-relevant potential candidate genes. Our results indicate that one single test is not able to detect all the different types of genetic effects in the most powerful manner and therefore, the methods should be applied complementary.

Genome-wide associations for immune traits in two maternal pig lines.

BACKGROUND: In recent years, animal welfare and health has become more and more important in pig breeding. So far, numerous parameters have been considered as important biomarkers, especially in the immune reaction and inflammation. Previous studies have shown moderate to high heritabilities in most of these traits. However, the genetic background of health and robustness of pigs needs to be extensively clarified. The objective of this study was to identify genomic regions with a biological relevance for the immunocompetence of piglets. Genome-wide Association Studies (GWAS) in 535 Landrace (LR) and 461 Large White (LW) piglets were performed, investigating 20 immune relevant traits. Besides the health indicators of the complete and differential blood count, eight different cytokines and haptoglobin were recorded in all piglets and their biological dams to capture mediating processes and acute phase reactions. Additionally, all animals were genotyped using the Illumina PorcineSNP60v2 BeadChip. RESULTS: In summary, GWAS detected 25 genome-wide and 452 chromosome-wide significant SNPs associated with 17 immune relevant traits in the two maternal pig lines LR and LW. Only small differences were observed considering the maternal immune records as covariate within the statistical model. Furthermore, the study identified across- and within-breed differences as well as relevant candidate genes. In LR more significant associations and related candidate genes were detected, compared with LW. The results detected in LR and LW are partly in accordance with previously identified quantitative trait loci (QTL) regions. In addition, promising novel genomic regions were identified which might be of interest for further detailed analysis. Especially putative pleiotropic regions on SSC5, SSC12, SSC15, SSC16 and SSC17 are of major interest with regard to the interacting structure of the immune system. The comparison with already identified QTL gives indications on interactions with traits affecting piglet survival and also production traits. CONCLUSION: In conclusion, results suggest a polygenic and breed-specific background of immune relevant traits. The current study provides knowledge about regions with biological relevance for health and immune traits. Identified markers and putative pleiotropic regions provide first indications in the context of balancing a breeding-based modification of the porcine immune system.

Transformation of animal genomics by next-generation sequencing technologies: a decade of challenges and their impact on genetic architecture.

For more than a quarter of a century, sequencing technologies from Sanger's method to next-generation high-throughput techniques have provided fascinating opportunities in the life sciences. The continuing upward trajectory of sequencing technologies will improve livestock research and expedite the development of various new genomic and technological studies with farm animals. The use of high-throughput technologies in livestock research has increased interest in metagenomics, epigenetics, genome-wide association studies, and identification of single nucleotide polymorphisms and copy number variations. Such studies are beginning to provide revolutionary insights into biological and evolutionary processes. Farm animals, such as cattle, swine, and horses, have played a dual role as economically and agriculturally important animals as well as biomedical research models. The first part of this study explores the current state of sequencing methods, many of which are already used in animal genomic studies, and the second part summarizes the state of cattle, swine, horse, and chicken genome sequencing and illustrates its achievements during the last few years. Finally, we describe several high-throughput sequencing approaches for the improved detection of known, unknown, and emerging infectious agents, leading to better diagnosis of infectious diseases. The insights from viral metagenomics and the advancement of next-generation sequencing will strongly support specific and efficient vaccine development and provide strategies for controlling infectious disease transmission among animal populations and/or between animals and humans. However, prospective sequencing technologies will require further research and in-field testing before reaching the marketplace.

Lessons learnt on the analysis of large sequence data in animal genomics.

The 'omics revolution has made a large amount of sequence data available to researchers and the industry. This has had a profound impact in the field of bioinformatics, stimulating unprecedented advancements in this discipline. Mostly, this is usually looked at from the perspective of human 'omics, in particular human genomics. Plant and animal genomics, however, have also been deeply influenced by next-generation sequencing technologies, with several genomics applications now popular among researchers and the breeding industry. Genomics tends to generate huge amounts of data, and genomic sequence data account for an increasing proportion of big data in biological sciences, due largely to decreasing sequencing and genotyping costs and to large-scale sequencing and resequencing projects. The analysis of big data poses a challenge to scientists, as data gathering currently takes place at a faster pace than does data processing and analysis, and the associated computational burden is increasingly taxing, making even simple manipulation, visualization and transferring of data a cumbersome operation. The time consumed by the processing and analysing of huge data sets may be at the expense of data quality assessment and critical interpretation. Additionally, when analysing lots of data, something is likely to go awry-the software may crash or stop-and it can be very frustrating to track the error. We herein review the most relevant issues related to tackling these challenges and problems, from the perspective of animal genomics, and provide researchers that lack extensive computing experience with guidelines that will help when processing large genomic data sets.

James Cossar Ewart and the Origins of the Animal Breeding Research Department in Edinburgh, 1895-1920.

In 1919 the Animal Breeding Research Department was established in Edinburgh. This Department, later renamed the Institute of Animal Genetics, forged an international reputation, eventually becoming the centrepiece of a cluster of new genetics research units and institutions in Edinburgh after the Second World War. Yet despite its significance for institutionalising animal genetics research in the UK, the origins and development of the Department have not received as much scholarly attention as its importance warrants. This paper sheds new light on Edinburgh's place in early British genetics by drawing upon recently catalogued archival sources including the papers of James Cossar Ewart, Regius Professor of Natural History at the University of Edinburgh between 1882 and 1927. Although presently a marginal figure in genetics historiography, Ewart established two sites for experimental animal breeding work between 1895 and 1911 and played a central role in the founding of Britain's first genetics lectureship, also in 1911. These early efforts helped to secure government funding in 1913. However, a combination of the First World War, bureaucratic problems and Ewart's personal ambitions delayed the creation of the Department and the appointment of its director by another six years. This paper charts the institutionalisation of animal breeding and genetics research in Edinburgh within the wider contexts of British genetics and agriculture in the early twentieth century.

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics.

Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the Tharparkar cattle breed using genomics tools combined with machine learning (ML) techniques. By leveraging data from the Bovine SNP 50K chip, we developed a breed-specific panel of single nucleotide polymorphisms (SNPs) for Tharparkar cattle and integrated data from seven other Indian cattle populations to enhance panel robustness. Genome-wide association studies (GWAS) and principal component analysis were employed to identify 500 SNPs, which were then refined using ML models-AdaBoost, bagging tree, gradient boosting machines, and random forest-to determine the minimal number of SNPs needed for accurate breed identification. Panels of 23 and 48 SNPs achieved accuracy rates of 95.2-98.4%. Importantly, the identified SNPs were associated with key productive and adaptive traits, thus attesting to the value and potentials of digital transformation in livestock genomics. The ML-aided ultra-low-density SNP panel approach reported here not only facilitates breed identification but also contributes to preserving genetic diversity and guiding future breeding programs.

The take-off of Drosophila research in 1930-1950s Edinburgh.

The fruit fly, Drosophila melanogaster, is a simple and powerful model organism. It has played a critical role over more than a century, for example in establishing the field of genetics, and in foundational insights into the molecular basis of development. From the 1930s until today, researchers at the University of Edinburgh have used Drosophila to tackle questions in basic and biomedical science. Here the history of the initial decades of this research is explored, beginning with the introduction of Drosophila research to Edinburgh by Francis Albert Eley Crew, in the newly established Institute of Animal Genetics. This period of research includes the discovery that chemicals can cause genetic mutation. This was demonstrated by research of the effects of mustard gas on flies by Charlotte Auerbach and colleagues, guided by the future Nobel laureate Hermann Muller. Drosophila research was also formative in Conrad Hal Waddington's conceptual thinking about developmental biology, including in his vision of the epigenetic landscape. This holistic, systems-level view of the control of development was far before its time and has continued to be influential to this day in our conceptualisation of developmental biology and in the increasingly important field of systems biology. Waddington's experiments with Drosophila in Edinburgh also gave rise to the evolutionary concept of genetic assimilation, in which an environmentally induced phenotype subsequently becomes genetically encoded.

The Innovative Informatics Approaches of High-Throughput Technologies in Livestock: Spearheading the Sustainability and Resiliency of Agrigenomics Research.

For more than a decade, next-generation sequencing (NGS) has been emerging as the mainstay of agrigenomics research. High-throughput technologies have made it feasible to facilitate research at the scale and cost required for using this data in livestock research. Scale frameworks of sequencing for agricultural and livestock improvement, management, and conservation are partly attributable to innovative informatics methodologies and advancements in sequencing practices. Genome-wide sequence-based investigations are often conducted worldwide, and several databases have been created to discover the connections between worldwide scientific accomplishments. Such studies are beginning to provide revolutionary insights into a new era of genomic prediction and selection capabilities of various domesticated livestock species. In this concise review, we provide selected examples of the current state of sequencing methods, many of which are already being used in animal genomic studies, and summarize the state of the positive attributes of genome-based research for cattle (Bos taurus), sheep (Ovis aries), pigs (Sus scrofa domesticus), horses (Equus caballus), chickens (Gallus gallus domesticus), and ducks (Anas platyrhyncos). This review also emphasizes the advantageous features of sequencing technologies in monitoring and detecting infectious zoonotic diseases. In the coming years, the continued advancement of sequencing technologies in livestock agrigenomics will significantly influence the sustained momentum toward regulatory approaches that encourage innovation to ensure continued access to a safe, abundant, and affordable food supplies for future generations.

Analysis of genetic diversity and structure of Mongolian horse using microsatellite markers.

Mongolian horses are one of the oldest horse breeds, and are very important livestock in Mongolia as they are used in various fields such as transportation, food (milk, meat), and horse racing. In addition, research and preservation on pure Mongolian breeds are being promoted under the implementation of the new Genetics of Livestock Resources' act in Mongolia. However, despite the implementation of this act, genetic research on Mongolian horses using microsatellites (MS) has not progressed enough. Therefore, this study was conducted to analyze the genetic polymorphism of five breeds (Gobi shankh, Tes, Gal shar, Darkhad, and Undurshil) using 14 MS markers recommended by International Society for Animal Genetics (ISAG). The mean number of alleles (MNA) was 8.29, expected heterozygosity frequency (HExp) was 0.767, observed heterozygosity frequency (HObs) was 0.752, and polymorphism information content (PIC) was 0.729. The Nei's genetic distance analysis showed that the genetic distance between Gobi shankh and Darkhad horses was the farthest, and the other three breeds, Tes, Gal shar, and Undurshil were found to be close to each other. Similarly, the principal coordinate analysis (PCoA) and factorial correspondence analysis (FCA) showed that the Gobi shankh and Darkhad horses were genetically distinct from other breeds. On the other hand, it appears that Tes, Gal shar, and Undurshil horses, which are genetically similar, most likely interbred with each other. Therefore, it is expected that these results will help the conservation of genetic resources in Mongolia and the establishment of policies related to Mongolian horses.

Heat stress on cattle embryo: gene regulation and adaptation.

Global warming has been affecting animal husbandry and farming production worldwide via changes in organisms and their habitats. In the tropics, these conditions are adverse for agriculture and animal production in some areas, due to high temperatures and relative humidity, affecting competitiveness related to economic activities. These environments have deteriorated livestock production, due to periods of drought, reduction in forage quality and heat stress, eliciting negative effects on reproduction, weight gain, and reduced meat and milk production. However, the use of animals adapted to tropics such as breeds derived from subspecies Bos primigenius indicus and native breeds from tropical countries or their crossings, is an alternative to improve production under high-temperature conditions. Therefore, physiological adaptation including gene expression induced by heat stress have been studied to understand the response of animals and to improve cross-breeding between cattle breeds to maintain high productivity in adverse weather conditions. Heat stress has been associated with lower reproductive performance in cows, due to the impact on blastocyst production, decreased implantation and increased embryonic death. Thus, for decades, in vitro fertilization and embryo transfer techniques have focused on studying the optimal conditions for production of high-quality embryos to transfer. The aim of this review is to discuss the effects of heat stress in bovine embryos, and their physiological and genetic modulation, focusing on the genes that are related with major adaptability to heat stress conditions and their relationship with different embryonic stages.

Embryonic lethality and defective mammary gland development of activator-function impaired conditional knock-in Erbb3 V943R mice.

ERBB3 is a pseudokinase domain-containing member of the ERBB family of receptor tyrosine kinases (RTKs). Following ligand binding, ERBB receptors homo- or hetero-dimerize, leading to a head-to-tail arrangement of the intracellular kinase domains, where the "receiver" kinase domain of one ERBB is activated by the "activator" domain of the other ERBB in the dimer. In ERBB3, a conserved valine at codon 943 (V943) in the kinase C-terminal domain has been shown to be important for its function as an "activator" kinase in vitro. Here we report a knock-in mouse model where we have modified the endogenous Erbb3 allele to allow for tissue-specific conditional expression of Erbb3 V943R (Erbb3 CKI-V943R ). Additionally, we generated an Erbb3 D850N (Erbb3 CKI-D850N ) conditional knock-in mouse model where the conserved aspartate in the DFG motif of the pseudokinase domain was mutated to abolish any potential residual kinase activity. While Erbb3 D850N/D850N animals developed normally, homozygous Erbb3 V943R/V943R expression during development resulted in embryonic lethality. Further, tissue specific expression of Erbb3 V943R/V943R in the mammary gland epithelium following its activation using MMTV-Cre resulted in delayed elongation of the ductal network during puberty. Single-cell RNA-seq analysis of Erbb3 V943R/V943R mammary glands showed a reduction in a specific subset of fibrinogen-producing luminal epithelial cells.

Association of Common Genetic Variants in the CPSF7 and SDHAF2 Genes with Canine Idiopathic Pulmonary Fibrosis in the West Highland White Terrier.

Canine idiopathic pulmonary fibrosis (CIPF) is a chronic fibrotic lung disease that is observed at a higher frequency in the West Highland White Terrier dog breed (WHWT) and may have molecular pathological overlap with human lung fibrotic disease. We conducted a genome-wide association study (GWAS) in the WHWT using whole genome sequencing (WGS) to discover genetic variants associated with CIPF. Saliva-derived DNA samples were sequenced using the Riptide DNA library prep kit. After quality controls, 28 affected, 44 unaffected, and 1,843,695 informative single nucleotide polymorphisms (SNPs) were included in the GWAS. Data were analyzed both at the single SNP and gene levels using the GEMMA and GATES methods, respectively. We detected significant signals at the gene level in both the cleavage and polyadenylation specific factor 7 (CPSF7) and succinate dehydrogenase complex assembly factor 2 (SDHAF2) genes (adjusted p = 0.016 and 0.024, respectively), two overlapping genes located on chromosome 18. The top SNP for both genes was rs22669389; however, it did not reach genome-wide significance in the GWAS (adjusted p = 0.078). Our studies provide, for the first time, candidate loci for CIPF in the WHWT. CPSF7 was recently associated with lung adenocarcinoma, further highlighting the potential relevance of our results because IPF and lung cancer share several pathological mechanisms.

Using quantile regression methodology to evaluate changes in the shape of growth curves in pigs selected for increased feed efficiency based on residual feed intake.

Growth rate is a major component of feed efficiency when estimating residual feed intake (RFI). Quantile regression (QR) methodology can be used to identify animals with different growth trajectories. The objective of this study was to evaluate the use of QR to identify phenotypic and genetic differences in pigs selected for low RFI. Using performance data on 750 Yorkshire pigs selected for low RFI, individual average daily gain (ADG), average daily feed intake (ADFI), RFI and Gompertz growth curve parameters (asymptotic weight (a), inflection point (b) and decay parameter (c)) were estimated for each pig. Using QR methodology, three Gompertz growth curves were estimated for the whole population for three quantiles (0.1, 0.5 and 0.9) of the BW data. Each animal was classified into one of the quantile regression groups (QRG) based on their overall Euclidian distance between each observed and estimated BW from the quantile growth curves. These three curves were also estimated using only part of the data (generations -1 to 3, and -1 to 4) in order to evaluate the agreement classification rate of animals from later generations into QRGs. We evaluated the effect of QRG on growth parameters and performance traits. Genetic parameters were estimated for these traits, as well as for QRG. In addition, genetic trends for each QRG were estimated. Three distinct growth curves were observed for animals classified into either quantiles 0.1 (QRG0.1), 0.5 (QRG0.5) or 0.9 (QRG0.9). When only part of the data was used to estimate quantile growth curves, all animals from QRG0.1 were correctly classified in their group. Animals in QRG0.1 had significantly lower ADFI, ADG and RFI, and greater a, b and c than animals in the other groups. Quantile regression groups analysed as a trait was highly heritable (0.41) and had high (0.8) and moderate (0.46) genetic correlations with ADG and RFI, respectively. Selection for reduced RFI increased the number of animals classified as QRG0.1 in the population. Overall, downward genetic trends were observed for all traits as a function of selection for reduced RFI. However, QRG0.1 was the only group that had a positive genetic trend for ADG. Altogether, these results indicate that selection for reduced RFI changes the shape of growth curves in Yorkshire in pigs, and that QR methodology was able to identify animals having different genetic potential for feed efficiency, bringing a new opportunity to improve selection for reduced RFI.

The Evolution of Science in a Latin-American Country: Genetics and Genomics in Brazil.

This article begins with a brief overview of the history of Brazil and that of Brazilian science, from the European discovery of the country in 1500 up to the early 21st century. The history of the fields of genetics and genomics, from the 1930s, is then first examined from the focal point of the lives and publications of the three persons who are generally considered to be the founders of genetics in Brazil (C. A. Krug, F. G. Brieger, and A. Dreyfus), and then by 12 other researchers up to 1999. The area of molecular genetics and genomics from 2000 to present is then described. Despite the problems of underdevelopment and the periodical political and economic crises that have affected life in Brazil, the fields of genetics and genomics in Brazil can be regarded as having developed at an appropriate pace, and have contributed in several major ways to world science.

Comparison of random regression models, traditional animal model and with the inclusion of molecular markers in the estimation of genetic parameters in Colombian Holstein cattle / Comparação entre modelos de regressão aleatória, modelo animal tradicional e com a inclusão de marcadores moleculares na estimativa de parâmetros genéticos em bovinos da raça Holandesa Colombiana

The use of molecular markers to identify desirable genes in animal production is known as marker-assisted selection. The traditional genetic evaluation model uses the BLUP methodology; when genetic markers are included in the evaluation model, the methodology is known as M-BLUP. In contrast, random regression models (RRM), unlike the models based on production at 305 days, consider factors that change for each animal from one test to another. The objective of this study was to compare variance components, genetic parameters and breeding values for milk production, protein percentage and somatic cell score in Colombian Holstein cattle using BLUP, M-BLUP and RRM. For the estimation of genetic parameters and values, 2003 lactations corresponding to 1417 cows in 55 herds were used, and effects of the order of delivery, herd, and contemporary group were included. The three traits presented greater heritability under the MBLUP model: 0.44 for protein percentage, 0.27 for milk production and 0.28 for somatic cell score. This was because the genetic variance was greater when M-BLUP was used, which allowed a greater accuracy of the breeding value estimation in the three traits. Therefore, the model that includes information on molecular markers is more suitable for genetic evaluation in Colombian Holstein cattle.(AU)

O uso de marcadores moleculares para identificar genes desejáveis na produção animal é conhecido como seleção assistida por marcadores. O modelo tradicional de avaliação genética utiliza a metodologia BLUP, e quando os marcadores genéticos são incluídos no modelo de avaliação, a metodologia é conhecida como M-BLUP. Por outro lado, os modelos de regressão aleatória (RRM), ao contrário dos modelos baseados em produções a 305 dias, consideram fatores que mudam para cada indivíduo de um controle para outro. O objetivo deste estudo foi comparar componentes de variância, parâmetros e valores genéticos para produção de leite, percentagem de proteína e escore de células somáticas em gado holandês de Colômbia utilizando BLUP, M-BLUP e RRM. Para a estimativa de parâmetros e valores genéticos, foram utilizadas 2.003 lactações correspondentes a 1.417 vacas de 55 rebanhos e efeitos da ordem de parto, rebanho e grupo contemporâneo. Os três traços apresentaram maior hereditariedade no modelo MBLUP, 0,44 para percentagem de proteína, 0,27 para produção de leite e 0,28 para escore de células somáticas. Isso por causa da variância genética foi maior quando o M-BLUP foi utilizado, o que permitiu estimar maior precisão do valor genético nos três traços, portanto, o modelo que inclui informações sobre marcadores moleculares é mais adequado para avaliação genética em gado holandês colombiano.(AU)

A consideration of resistance and tolerance for ruminant nematode infections.

Debates on the relative merits of resistance (the ability of the host to control the parasite lifecycle) and tolerance (the net impact of infection on host performance) are often lively and unhindered by data or evidence. Resistance generally shows continuous, heritable variation but data are sparser for tolerance, the utility of which will depend upon the disease prevalence. Prevalence is a function of group mean resistance and infection pressure, which itself is influenced by mean resistance. Tolerance will have most value for endemic diseases with a high prevalence but will be of little value for low prevalence diseases. The conditionality of tolerance on infection status, and hence resistance, makes it difficult to estimate independently of resistance. Tolerance is potentially tractable for nematode infections, as the prevalence of infection is ca. 100% in animals grazing infected pasture, and infection level can be quantified by faecal egg count (FEC). Whilst individual animal phenotypes for tolerance are difficult to estimate, breeding values are estimable if related animals graze pastures of different contamination levels. Selection for resistance, i.e., FEC, provides both direct and indirect benefits from ever decreased pasture contamination and hence decreased infectious challenge. Modeling and experimental studies have shown that such reductions in pasture contamination may lead to substantially increased performance. It is proposed that selection goals addressing nematode infections should include both resistance and performance under challenging conditions. However, there may be benefits from exploiting large datasets in which sires are used across cohorts differing in infection level, to further explore tolerance. This may help to customise breeding objectives, with tolerance given greater weight in heavily parasitized environments.

IDENTIFICAÇÃO DE VARIAÇÕES ESTRUTURAIS DO GENOMA DE BOVINOS GIR LEITEIRO

O Gir Leiteiro resulta da seleção fenotípica de animais da raça zebuína Gir com maior aptidão para produção de leite. Esses animais possuem tolerância ao calor, às doenças e aos parasitas tropicais. Essas características adaptativas tornam o Gir Leiteiro um recurso genético importante para a produção de leite nos trópicos. O estudo e caracterização de variantes estruturais (SV) do DNA desses indivíduos relacionadas a características adaptativas, de sanidade e produtivas são de interesse para o melhoramento genético da raça. Um exemplo de variações estruturais estudadas é a variação no número de cópias (CNV), que compreende deleção ou duplicação de um segmento de DNA em relação ao genoma referência. Esse tipo de SV envolve muitos pares de bases, sendo capaz de alterar a expressão gênica e de ocasionar alterações fenotípicas. Os objetivos deste trabalho foram: (1) detectar CNV no genoma de bovinos Gir Leiteiro; (2) identificar regiões de CNV (CNVR) de alta confiabilidade; (3) determinar as regiões genômicas em que ocorrem as CNV que coincidem com regiões de interesse, tais como genes e Quantitative trait loci (QTL) previamente relacionados às características de sanidade, reprodução e produção de leite; (4) categorizar funcionalmente os genes sobrepostos às CNV e identificar as vias biológicas enriquecidas. Após o controle de qualidade, dados de intensidade de sinal de genotipagem de SNP (polimorfismo de nucleotídeo único) provenientes do ensaio Bovine HD SNPChip de 545 indivíduos e dados oriundos de sequenciamento de genoma completo de 38 touros foram utilizados para a detecção de CNV. Ao total, 547 animais foram utilizados, dos quais 36 possuíam informação de painel de SNP e sequenciamento. Para a identificação das CNV nos dados de genotipagem foi utilizado o modelo oculto de Markov por meio do programa PennCNV e, nos dados de sequenciamento, foram utilizadas as metodologias de Read Depth, por meio do programa CNVnator e, Split-Read e Read Pair, pelo programa DELLY. O genoma referência ARS_UCD1.2 foi utilizado para o alinhamento das amostras de sequenciamento e para o mapa de SNP. Dois conjuntos de regiões de CNV (CNVR) de alta confiança foram definidos, contendo variantes encontradas nos dados de painéis de SNP e de sequenciamento. Esses conjuntos foram relativos às CNVR presentes em, no mínimo, 5% da população estudada (CNVR_POP) e aos indivíduos representativos da população Gir Leiteiro (CNVR_ANI). No conjunto CNVR_POP, foram encontradas dez CNVR, que representam 1,04 Mb do genoma bovino. No conjunto CNVR_ANI, foram encontradas 45 CNVR, que somadas representam mais de 4,4 Mb do genoma bovino. Os dois conjuntos de CNVR de alta confiança foram unidos para análise funcional resultando em 48 CNVR únicas e de alta confiança. Essas se sobrepuseram a 69 genes, incluindo FILIP1, SENP6, CA5A, BANP, HERC2, RHOU, GBP2, GBP4, GBP6, BLA-DQB, ENSBTAG00000037605 e genes de receptores olfativos. Na análise de enriquecimento, dois termos de Gene Ontology (GO) (FDR<0,05) e 17 termos da plataforma Medical Subject Headings (MeSH) (pajustado< 0,05) foram enriquecidos significativamente. Nas CNVR únicas e de alta confiança foram encontrados 44 Quantitative trait loci (QTL) significativamente associados (p<0,05) a produção (29,54%), reprodução (22,73%), conformação (18,18%), sanidade (13,64%), leite (13,63%), e carne e carcaça (2,27%). As CNVR iv presentes no conjunto CNVR_POP podem ser consideradas como regiões de polimorfismos no número de cópia, pois estão presentes em mais de 1% da população estudada. Em adição, esse conjunto pode ser utilizado como critério de escolha de CNV a serem validadas, como por exemplo, por PCR. Este estudo poderá auxiliar na escolha de SNP que estejam sobrepostos às CNV para o desenvolvimento de painéis de genotipagem para o Gir leiteiro. Ainda, a inclusão de CNVR na avaliação genômica poderá trazer benefícios para o processo de seleção e deve ser verificada. Nossos resultados abrangem a identificação e caracterização de 48 CNVR de alta confiança no genoma de bovinos Gir Leiteiro, o que contribui para a elaboração de um mapa de SV na raça Gir e para o melhor entendimento do genoma dos zebuínos. As CNVR identificadas neste estudo podem afetar potencialmente genes que estão envolvidos no processo evolutivo e no controle fenotípico de característica de interesse para a cadeia produtiva leiteira, como imunidade, lactação, reprodução, reconhecimento de estímulos e sanidade.

The Dairy Gir cattle results from the selection of animals from the Zebu Gir breed with greater aptitude for milk production. These animals are tolerant of heat, disease, and tropical parasites. These adaptive traits make the Dairy Gir an important genetic resource for milk production in the tropics. The study and characterization of structural variants (SV) of the DNA of these animals related to adaptive, healthy, and productive traits are interesting to the animal breeding of the breed. An example of structural variants is copy number variation (CNV), which comprises deletion or duplication of a DNA segment compared to the reference genome. The CNV can alter the gene expression and consequently change phenotypes because it encompasses many base pairs. The objectives of the work were: (1) detect CNV in the genome of Dairy Gir cattle; (2) identify high-confidence CNVR; (3) determine the genomic regions overlapping CNVR that coincide with the interest regions, such as genes and Quantitative Trait Loci (QTL) previously related to health, reproduction, and milk production traits; (4) functionally categorize genes overlapped with CNVR and identify the enriched biological pathways. After the quality control, luminosity data from 545 individuals genotyped with Illumina BovineHD BeadChip and whole-genome sequencing data from 38 bulls were used to detect CNV. In total, 547 Dairy Gir animals were used, and 36 animals had both types of information. To identify the CNV in the SNP (single nucleotide polymorphism) array data, the hidden Markov model methodology was used by the PennCNV software and, in the sequencing data, the Read Depth methodology was used by the CNVnator software, and Split-Read and Read Pair methodologies by the DELLY software. The reference genome ARS_UCD1.2 was used for the alignment and the SNP map. Two sets of high confidence CNV regions (CNVR) were defined, containing variants found in the SNP array and sequencing data. These sets are related to the CNVR present in at least 5% of the studied population (CNVR_POP) and the representative animals of the Dairy Gir population (CNVR_ANI). In the CNVR_POP set, ten CNVR were found, covering 1.04 Mb of the bovine genome. In the CNVR_ANI set, 45 CNVR were found, which together cover more than 4.4 Mb of the bovine genome. The two high confidence CNVR sets were merged for functional analysis, resulting in 48 unique and high-confidence CNVR. These overlapped 69 genes, including FILIP1, SENP6, CA5A, BANP, HERC2, RHOU, GBP2, GBP4, GBP6, BLA-DQB, ENSBTAG00000037605, and odorant receptors genes. In the enrichment analysis, two Gene Ontology (GO) terms (FDR<0.05), and 17 terms (padjusted< 0.05) from the Medical Subject Headings (MeSH) platform were significant. In the unique and high confidence CNVR, 44 Quantitative trait loci (QTL) were significantly associated (p<0,05) with production (29.54%), reproduction (22.73%), conformation (18.18%), health (13.64%), milk (13, 63%), and meat and carcass (2.27%). The CNVR in the CNVR_POP set may be considered regions of copy number polymorphism since they are present in more than 1% of the studied population. In addition, this set can be used to choose the CNV to be validated, such as by PCR. This study may support the selection of markers that flanks CNV to the development of SNP arrays for the Dairy Gir. Also, the inclusion of CNVR in the genomic evaluation could benefit the selection process and must be verified. Our results include the identification and characterization of 48 high-confidence CNVR in the genome of Dairy Gir cattle. These data contribute to elaborate an SV map in the vi Gir breed and a better understanding of the genome of the zebu cattle. The CNVR identified in this study may potentially affect genes involved in the evolutionary process and the phenotypic control of traits of interest to the dairy products market, such as immunity, lactation, reproduction, stimulus recognition, and health.

Características Físico Químicas y Composición de la Leche de Cabras Mestizas Canarias en Condiciones Tropicales / Physics and Chemistry Characteristics, and Composition of Goat Milk of Canarian Goats on Tropical Condition

Los factores que afectan las características y composición de la leche de cabra han sido bien estudiados en países de clima templado, pero no en países tropicales como Venezuela. El presente trabajo evaluó el efecto de grupo racial y de algunos factores no genéticos sobre las características fisicoquímicas y composición de la leche de cabras mestizas canarias, para lo cual, se analizaron 758 muestras de leche. Los datos se recolectaron en un periodo de nueve años. Las muestras fueron tomadas de un rebaño caprino multirracial confinado, ubicado en Maracay, en el estado Aragua, Venezuela (10º 16’ 25,30’’ N y 67º 36’ 35’’ O). Los resultados se analizaron mediante un modelo estadístico lineal aditivo, con el padre como efecto aleatorio y como efectos fijos, la edad al parto (EP), año (AP) y mes (MP) de parto, tipo de parto (TP), grupo racial (GR); y las covariables días en producción (efectos lineal y cuadrático). Las medias de las características fueron: crioscopía (ºC): -0,56; acidez (mLNaOH 0,1N/100 mL leche): 19,12; cloruros: (%): 0,22; WMT (mm): 10,75; pH: 6,55; composición de la leche (%): grasa: 4,32; proteína: 4,00; caseína: 2,63; humedad: 86,69; sólidos totales: 13,30; sólidos no grasos: 9,04; cenizas: 0,77 y lactosa: 4,27. Los resultados del estudio, muestran que todos los efectos fijos estudiados, afectaron en forma variable las características y la composición de la leche. El año de parto de la cabra, fue el factor que más influyó sobre la variación de las características de la leche; mientras que el GR ½ Canarias ½ Alpino Francés, estuvo por encima de los demás genotipos en cuanto al contenido porcentual de los diferentes sólidos de la leche.

Factors affecting the characteristics and composition of goat milk have been well studied in template countries, but not in tropical countries, such as Venezuela. The purpose of the present investigation was to evaluate the effect of breed group and some non-genetic factors on both characteristics and composition of milk from Canary crossbred goats in tropical conditions. To carry out the study, a total of 758 milk samples were analyzed. Data were collected in a period of nine years and milk samples were taken from a multiracial confined goat herd, located in Maracay, the State of Aragua, Venezuela (10° 16’ 25.30’’ N and 67º 36’ 35’’ W). Data were analyzed by a linear additive statistical model, with sire as random effect; age at calving (EC), year of calving (YC), month of calving (MC), prolificity (P), and breed group (BG), as fixed effects, and the covariates production days, as linear and quadratic effects. The means of the characteristics and composition (%) of goat milk were: cryoscopy (°C): -0.56; acidity (0.1 mL NaOH N/100 mL of milk): 19.12; chlorides (%): 0.22; WMT (mm): 10.75; pH: 6.55. Milk composition (%): fat: 4.32; protein: 4.00; casein: 2.63; humidity: 86.69; total solids: 13.30; non-fat solids: 9.04; ash: 0.77; and lactose: 4.27. The results of this study showed that all the fixed effects studied affected in a variable form, the characteristics and components of milk, being YC the most influential factor on the variation of the characteristic of milk. The BG “½ Canarian ½ Alpine” highlighted above all other genotypes, regarding the percentage content of the different solids of milk.