DEVELOPMENT OF INTEGRATED CATTLE GENOMICS KNOWLEDGE BASE

Systems biology approaches being applied to animal breeding represent an opportunity to derive greater benefits from animal production systems. The increasingly detailed investigations in systems biology have led to a large amount of data dispersed over various sources; therefore, a centralized knowledge base is in demand. In this study, we have integrated cattle genomics data of heterogeneous sources and types and developed a bioinformatics tool to study genotype-phenotype associations in cattle: http://integromics-time.com/integromics-database/. The tool enables revealing genomic overlaps within trait-associated loci and identification of potential functional candidates. It might be also used as a tool for planning genotype– phenotype research in cattle

Mining for Structural Variations in Next-Generation Sequencing Data

Genomic structural variations (SVs) are genetic alterations that result in duplications, insertions, deletions, inversions, and translocations of segments of DNA covering 50 or more base pairs. By changing the organization of DNA, SVs can contribute to phenotypic variation or cause pathological consequences as neurobehavioral disorders, autoimmune diseases, obesity, and cancers. SVs were first examined using classic cytogenetic methods, revealing changes down to 3 Mb. Later techniques for SV detection were based on array comparative genome hybridization (aCGH) and single-nucleotide polymorphism (SNP) arrays. Next-generation sequencing (NGS) approaches enabled precise characterization of breakpoints of SVs of various types and sizes at a genome-wide scale. Dissecting SVs from NGS presents substantial challenge due to the relatively short sequence reads and the large volume of the data. Benign variants and reference errors in the genome present another dimension of problem complexity. Even though a wide range of tools is available, the usage of SV callers in routine molecular diagnostic is still limited. SV detection algorithms relay on different properties of the underlying data and vary in accuracy and sensitivity; therefore, SV detection process usually utilizes multiple variant callers. This chapter summarizes strengths and limitations of different tools in effective NGS SV calling

Development of an In Vitro Goat Mammary Gland Model: Establishment, Characterization, and Applications of Primary Goat Mammary Cell Cultures

Alternatives to animal experiments, based on in vitro methodologies, have been suggested and adopted in the last decades in order to completely substitute or to reduce animal numbers in in vivo assays. In this chapter we describe methods for establishment, maintenance, and characterization of primary goat mammary epithelial cell cultures (pgMECs) and possible applications for which the derived primary cell model can be used instead of in vivo experiments. The established cell lines were grown in vitro for several passages and remained hormone and immune responsive and capable of milk protein synthesis. Knowledge on goat mammary cells and their manipulation is applicable to different fields of research; for example, it could be used in basic research to study mammary development and lactation biology, in agriculture to enhance lactation yield and persistency or to produce milk with special characteristics, in biopharma to express recombinant proteins in goat milk, or in biomedicine to study lactation, mammary development, and pathology, including neoplasia. The established cells represent an adequate surrogate for mammary gland; were successfully used to study mammary gland immunity, lactation, and mammary stem/progenitor cells; and have a potential to be used for other purposes

Genome sequence variation in two subspecies of western honeybee, A.m.carnica and A.m.ligustica

Submitted 2020-08-09 | Accepted 2020-09-21 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.331-337Populations of western honeybee (Apis mellifera) show differences in morphology, physiology and behaviour as a result of adaptation to various habitats. A. m. carnica, which inhabits the South-East and Central Europe, and A. m. ligustica, which is endemic on Apennine peninsula, represent 2 closely related honeybee subspecies living in the neighbouring climatic regions. In the current study, 3,655,618 polymorphisms were identified from the whole genome sequences of 37 individual drone genomes, from A. m. carnica (n=27) and A. m. ligustica (n=10). The analysis revealed variation in genes involved in biological pathways associated with energy production and conversion, cell cycle and cytokinesis.Keywords: A. m. carnica, A. m. ligustica, genomics, honeybee, whole genome sequencingReferencesBoch, R. (1957). Rassenmässige Unterschiede bei den Tänzen der Honigbiene (Apis mellifica L.). Zeitschrift für vergleichende Physiologie, 40(3), 289-320. https://doi.org/10.1007/BF00340572Bozic, J. et al. (2016). Novel aspects in characterisation of Carniolan honey bee (Apis mellifera carnica, Pollmann 1879). Acta Agriculturae Slovenica, Supplement 5, 18-27.Caron, D. M. and Connor, L. J. (2013). Honey Bee Biology and Beekeeping: Wicas Press.Chan, Q. W. T. and Foster, L. J. (2008). Changes in protein expression during honey bee larval development. Genome Biology, 9(10), R156. https://doi.org/10.1186/gb-2008-9-10-r156Cridland, J. M., Tsutsui, N. D. and Ramírez, S. R. (2017). The complex demographic history and evolutionary origin of the Western honey bee, Apis Mellifera. Genome Biology and Evolution, 9(2), 457-472. https://doi.org/10.1093/gbe/evx009Dall'Olio, R. et al. (2007). Genetic characterization of Italian honeybees, Apis mellifera ligustica, based on microsatellite DNA polymorphisms. Apidologie, 38(2), 207-217. https://doi.org/10.1051/apido:2006073De la Rúa, P. et al. (2009). Biodiversity, conservation and current threats to European honeybees. Apidologie, 40(3), 263-284. https://doi.org/10.1051/apido/2009027Engel, M. S. (1999). The taxonomy of recent and fossil honey bees (Hymenoptera: Apidae; Apis). Journal of Hymenoptera Research, 8, 165-196.Gissi, C., Iannelli, F. and Pesole, G. (2008). Evolution of the mitochondrial genome of Metazoa as exemplified by comparison of congeneric species. Heredity, 101(4), 301-320. https://doi.org/10.1038/hdy.2008.62Gonzalez, A. N., Ing, N. and Rangel, J. (2018). Upregulation of antioxidant genes in the spermathecae of honey bee (Apis mellifera) queens after mating. Apidologie, 49(2), 224-234. https://doi.org/10.1007/s13592-017-0546-yHoefer, I. and Lindauer, M. (1975). Das Lernverhalten zweier Bienenrassen unter veränderten Orientierungsbedingungen. Journal of Comparative Physiology, 99(2), 119-138. https://doi.org/10.1007/BF00618179Holzmann, J. et al. (2008). RNase P without RNA: identification and functional reconstitution of the human mitochondrial tRNA processing enzyme. Cell, 135(3), 462-474. https://doi.org/10.1016/j.cell.2008.09.013Iqbal, J. et al. (2019). Olfactory associative behavioral differences in three honey bee Apis mellifera L. races under the arid zone ecosystem of central Saudi Arabia. Saudi Journal of Biological Sciences, 26(3), 563-568. https://doi.org/10.1016/j.sjbs.2018.08.002Isserlin, R. et al. (2014). Enrichment Map - a Cytoscape app to visualize and explore OMICs pathway enrichment results. F1000Research, 3, 141. https://doi.org/10.12688/f1000research.4536.1Jackman, J. E. and Alfonzo, J. D. (2013). Transfer RNA modifications: nature's combinatorial chemistry playground. Wiley Interdisciplinary Reviews RNA, 4(1), 35-48. https://doi.org/10.1002/wrna.1144Kovac, H. et al. (2014). Metabolism and upper thermal limits of Apis mellifera carnica and A. m. ligustica. Apidologie, 45(6), 664-677. https://doi.org/10.1007/s13592-014-0284-3Kriventseva, E. V. et al. (2019). OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucleic Acids Research, 47(D1), D807-D811. https://doi.org/10.1093/nar/gky1053Kunieda, T. et al. (2006). Carbohydrate metabolism genes and pathways in insects: insights from the honey bee genome. Insect Molecular Biology, 15(5), 563-576. https://doi.org/10.1111/j.1365-2583.2006.00677.xLe Conte, Y. and Navajas, M. (2008). Climate change: impact on honey bee populations and diseases. Revue Sentifique et technique, 27(2), 485-497, 499-510.Li, H. and Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754-1760. https://doi.org/10.1093/bioinformatics/btp324Li, J. et al. (2007). Proteomic analysis of royal jelly from three strains of Western Honeybees (Apis mellifera). Journal of Agricultural and Food Chemistry, 55(21), 8411-8422. https://doi.org/10.1021/jf0717440Okonechnikov, K., Conesa, A. and Garcia-Alcalde, F. (2016). Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics, 32(2), 292-294. https://doi.org/10.1093/bioinformatics/btv566Patel, M. S. et al. (2014). The pyruvate dehydrogenase complexes: structure-based function and regulation. The Journal of Biological Chemistry, 289(24), 16615-16623. https://doi.org/10.1074/jbc.R114.563148Perham, R. N. (1991). Domains, motifs, and linkers in 2-oxo acid dehydrogenase multienzyme complexes: a paradigm in the design of a multifunctional protein. Biochemistry, 30(35), 8501-8512. https://doi.org/10.1021/bi00099a001Personnic, N. et al. (2014). A role for Ral GTPase-activating protein subunit β in mitotic regulation. The FEBS Journal, 281(13), 2977-2989. https://doi.org/10.1111/febs.12836Raudvere, U. et al. (2019). g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Research, 47(W1), W191-W198. https://doi.org/10.1093/nar/gkz369Rinkevich, F. D. et al. (2015). Genetics, synergists, and age affect insecticide sensitivity of the honey bee, Apis mellifera. PloS One, 10(10), e0139841. https://doi.org/10.1371/journal.pone.0139841Ruttner, F. (1988). Biogeography and taxonomy of honeybees. Springer-Verlag, Berlin.Sen, A. et al. (2016). Loss of the mitochondrial protein-only ribonuclease P complex causes aberrant tRNA processing and lethality in Drosophila. Nucleic Acids Research, 44(13), 6409-6422. https://doi.org/10.1093/nar/gkw338Shannon, P. et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498-2504. https://doi.org/10.1101/gr.1239303Shirakawa, R. et al. (2009). Tuberous sclerosis tumor suppressor complex-like complexes act as GTPase-activating proteins for Ral GTPases. Journal of Biological Chemistry, 284(32), 21580-21588. https://doi.org/10.1074/jbc.M109.012112Singhi, P. et al. (2013). Pyruvate dehydrogenase-e1α deficiency presenting as recurrent demyelination: an unusual presentation and a novel mutation. JIMD Reports, 10, 107-111. https://doi.org/10.1007/8904_2012_211Todeschini, V. et al. (2017). In vitro study on the inhibition of enzymatic systems in Italian and Carniolan honey bees by piperonyl butoxide new derivatives. Bulletin of Insectology, 70, 237-244.Vilardo, E. et al. (2012). A subcomplex of human mitochondrial RNase P is a bifunctional methyltransferase--extensive moonlighting in mitochondrial tRNA biogenesis. Nucleic Acids Research, 40(22), 11583-11593. https://doi.org/10.1093/nar/gks910Wallberg, A. et al (2014). A worldwide survey of genome sequence variation provides insight into the evolutionary history of the honeybee Apis mellifera. Nature Genetics, 46(10), 1081-1088. https://doi.org/10.1038/ng.3077Webster, T. C. (2008). Honey Bee, Apis mellifera (Hymenoptera: Apidae). In J. L. Capinera (Ed.), Encyclopedia of Entomology (pp. 1835-1840). Dordrecht: Springer Netherlands.Wragg, D. et al. (2016). Whole-genome resequencing of honeybee drones to detect genomic selection in a population managed for royal jelly. Scientific Reports, 6, 27168. https://doi.org/10.1038/srep2716

The new bovine reference genome assembly provides new insight into genomic organization of the bovine major histocompatibility complex

The reference genome sequence represents a key resource for genetic studies of the target species. In 2009, two reference assemblies of the cattle (Bos taurus) genome, were published (Btau 4.0 and UMD 2.0). Both assemblies were upgraded several times since then. Highly polymorphic major histocompatibility complex (MHC) encodes proteins crucial for immune recognition and regulation of immune response in vertebrates. It is characterised by extensive nucleotide diversity, copy number variation of paralogous genes, and long repetitive sequences. In cattle, MHC is designated as BoLA (bovine leucocyte antigen), located on the chromosome 23. Its organisation differs from typical mammalian MHCs. The structural complexity makes it difficult to assemble a reliable reference sequence of this genomic region. Therefore, this region represents a good genomic model region to compare the accuracy of different assembly strategies. Recent advances in long-read sequence technology, combined with new scaffolding technologies, enabled issuing of the new bovine reference genome assembly build ARS-UCD 1.2, which is significantly improved over previous bovine genome assembly releases. In the current study the software tool Mauve for multiple alignment of conserved genomic sequences with rearrangements was used to identify the differences of genomic organization in the BoLA region assembled in three bovine reference genomes, Btau 5.0.1, UMD 3.1.1, and ARS-UCD 1.2. Multiple alignment of the bovine chromosome 23 sequences extracted from three genome assemblies revealed differences in the structure of the BoLA region. Segments encoding genes BOLA-DMA and BOLA-DQB are rearranged and inverted in the new assembly relative to the previous builds

Genomic characterization of the three Balkan Livestock Guardian Dogs

Balkan Livestock Guardian Dogs (LGD) were bred to help protect sheep flocks in sparsely populated, remote mountainous areas in the Balkans. The aim of this study was genomic characterization (107,403 autosomal SNPs) of the three LGD breeds from the Balkans (Karst Shepherd, Sharplanina Dog, and Tornjak). Our analyses were performed on 44 dogs representing three Balkan LGD breeds, as well as on 79 publicly available genotypes representing eight other LGD breeds, 70 individuals representing seven popular breeds, and 18 gray wolves. The results of multivariate, phylogenetic, clustering (STRUCTURE), and FST differentiation analyses showed that the three Balkan LGD breeds are genetically distinct populations. While the Sharplanina Dog and Tornjak are closely related to other LGD breeds, the Karst Shepherd is a slightly genetically distinct population with estimated influence from German Shepard (Treemix analysis). Estimated genomic diversity was high with low inbreeding in Sharplanina Dog (Ho = 0.315, He = 0.315, and FROH>2Mb = 0.020) and Tornjak (Ho = 0.301, He = 0.301, and FROH>2Mb = 0.033) breeds. Low diversity and high inbreeding were estimated in Karst Shepherds (Ho = 0.241, He = 0.222, and FROH>2Mb = 0.087), indicating the need for proper diversity management. The obtained results will help in the conservation management of Balkan LGD dogs as an essential part of the specific grazing biocultural system and its sustainable maintenance

Pivotal role of the muscle-contraction pathway in cryptorchidism and evidence for genomic connections with cardiomyopathy pathways in RASopathies

BACKGROUND: Cryptorchidism is the most frequent congenital disorder in male children; however the genetic causes of cryptorchidism remain poorly investigated. Comparative integratomics combined with systems biology approach was employed to elucidate genetic factors and molecular pathways underlying testis descent. METHODS: Literature mining was performed to collect genomic loci associated with cryptorchidism in seven mammalian species. Information regarding the collected candidate genes was stored in MySQL relational database. Genomic view of the loci was presented using Flash GViewer web tool (http://gmod.org/wiki/Flashgviewer/). DAVID Bioinformatics Resources 6.7 was used for pathway enrichment analysis. Cytoscape plug-in PiNGO 1.11 was employed for protein-network-based prediction of novel candidate genes. Relevant protein-protein interactions were confirmed and visualized using the STRING database (version 9.0). RESULTS: The developed cryptorchidism gene atlas includes 217 candidate loci (genes, regions involved in chromosomal mutations, and copy number variations) identified at the genomic, transcriptomic, and proteomic level. Human orthologs of the collected candidate loci were presented using a genomic map viewer. The cryptorchidism gene atlas is freely available online: http://www.integratomics-time.com/cryptorchidism/. Pathway analysis suggested the presence of twelve enriched pathways associated with the list of 179 literature-derived candidate genes. Additionally, a list of 43 network-predicted novel candidate genes was significantly associated with four enriched pathways. Joint pathway analysis of the collected and predicted candidate genes revealed the pivotal importance of the muscle-contraction pathway in cryptorchidism and evidence for genomic associations with cardiomyopathy pathways in RASopathies. CONCLUSIONS: The developed gene atlas represents an important resource for the scientific community researching genetics of cryptorchidism. The collected data will further facilitate development of novel genetic markers and could be of interest for functional studies in animals and human. The proposed network-based systems biology approach elucidates molecular mechanisms underlying co-presence of cryptorchidism and cardiomyopathy in RASopathies. Such approach could also aid in molecular explanation of co-presence of diverse and apparently unrelated clinical manifestations in other syndromes

Mitochondrial variability of Small Međimurje dog

There are six native dog breeds in Croatia recognized by the Federation Cynologique International (FCI) and one national dog breed Small Međimurje dog (MEDI) still unrecognized by the FCI. To promote breed we have analysed mitochondrial DNA control region (CR-mtDNA) sequence (551-bp) in 35 Small Međimurje dogs sampled in Međimurje County. After comparison with 33 worldwide distributed dog breeds (N=115 samples), three main canine CR-mtDNA haplogroups (A, B and C) were observed in Small Međimurje dogs. Median-joining tree showed that MEDI forms six haplotypes presented in haplogroup C (H3 is the most frequent in MEDI population), haplogroup A (haplotypes H2, H5 and H7) and in haplogroup B (haplotypes H4 and H6). The results presented in this study correspond to other mtDNA studies of native dog breeds. For the better genetic description of MEDI and for the optimal future breeding management, further analyses of nuclear genome are recommended