Mutation of SOCS2 induces structural and functional changes in mammary development.

Lactation is an essential process for mammals. In sheep, the R96C mutation in suppressor of cytokine signaling 2 (SOCS2) protein is associated with greater milk production and increased mastitis sensitivity. To shed light on the involvement of R96C mutation in mammary gland development and lactation, we developed a mouse model carrying this mutation (SOCS2KI/KI). Mammary glands from virgin adult SOCS2KI/KI mice presented a branching defect and less epithelial tissue, which were not compensated for in later stages of mammary development. Mammary epithelial cell (MEC) subpopulations were modified, with mutated mice having three times as many basal cells, accompanied by a decrease in luminal cells. The SOCS2KI/KI mammary gland remained functional; however, MECs contained more lipid droplets versus fat globules, and milk lipid composition was modified. Moreover, the gene expression dynamic from virgin to pregnancy state resulted in the identification of about 3000 differentially expressed genes specific to SOCS2KI/KI or control mice. Our results show that SOCS2 is important for mammary gland development and milk production. In the long term, this finding raises the possibility of ensuring adequate milk production without compromising animal health and welfare.

Assessment of the zoonotic potential of atypical scrapie prions in humanized mice reveals rare phenotypic convergence but not identity with sporadic CJD prions.

BACKGROUND: Atypical/Nor98 scrapie (AS) is an idiopathic infectious prion disease affecting sheep and goats. Recent findings suggest that zoonotic prions from bovine spongiform encephalopathy (C-BSE) may co-propagate with atypical/Nor98 prions in AS sheep brains. Investigating the risk AS poses to humans is crucial. METHODS: To assess the risk of sheep/goat-to-human transmission of AS, we serially inoculated brain tissue from field and laboratory isolates into transgenic mice overexpressing human prion protein (Met129 allele). We studied clinical outcomes as well as presence of prions in brains and spleens. RESULTS: No transmission occurred on the primary passage, with no clinical disease or pathological prion protein in brains and spleens. On subsequent passages, one isolate gradually adapted, manifesting as prions with a phenotype resembling those causing MM1-type sporadic Creutzfeldt-Jakob disease in humans. However, further characterization using in vivo and in vitro techniques confirmed both prion agents as different strains, revealing a case of phenotypic convergence. Importantly, no C-BSE prions emerged in these mice, especially in the spleen, which is more permissive than the brain for C-BSE cross-species transmission. CONCLUSIONS: The results obtained suggest a low the zoonotic for AS. Rare adaptation may allow the emergence of prions phenotypically resembling those spontaneously forming in humans.

Targeted mutation and inactivation of the kinesin light chain 3 protein-encoding gene have no impact on mouse fertility†.

The kinesin light chain 3 protein (KLC3) is the only member of the kinesin light chain protein family that was identified in post-meiotic mouse male germ cells. It plays a role in the formation of the sperm midpiece through its association with both spermatid mitochondria and outer dense fibers (ODF). Previous studies showed a significant correlation between its expression level and sperm motility and quantitative semen parameters in humans, while the overexpression of a KLC3-mutant protein unable to bind ODF also affected the same traits in mice. To further assess the role of KLC3 in fertility, we used CRISPR/Cas9 genome editing in mice and investigated the phenotypes induced by the invalidation of the gene or of a functional domain of the protein. Both approaches gave similar results, i.e. no detectable change in male or female fertility. Testis histology, litter size and sperm count were not altered. Apart from the line-dependent alterations of Klc3 mRNA levels, testicular transcriptome analysis did not reveal any other changes in the genes tested. Western analysis supported the absence of KLC3 in the gonads of males homozygous for the inactivating mutation and a strong decrease in expression in males homozygous for the allele lacking one out of the five tetratricopeptide repeats. Overall, these observations raise questions about the supposedly critical role of this kinesin in reproduction, at least in mice where its gene mutation or inactivation did not translate into fertility impairment.

Individualized multi-omic pathway deviation scores using multiple factor analysis.

Malignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.

Specific Milk Composition of miR-30b Transgenic Mice Associated with Early Duodenum Maturation in Offspring with Lasting Consequences for Growth.

BACKGROUND: Milk composition is complex and includes numerous components essential for offspring growth and development. In addition to the high abundance of miR-30b microRNA, milk produced by the transgenic mouse model of miR-30b-mammary deregulation displays a significantly altered fatty acid profile. Moreover, wild-type adopted pups fed miR-30b milk present an early growth defect. OBJECTIVE: This study aimed to investigate the consequences of miR-30b milk feeding on the duodenal development of wild-type neonates, a prime target of suckled milk, along with comprehensive milk phenotyping. METHODS: The duodenums of wild-type pups fed miR-30b milk were extensively characterized at postnatal day (PND)-5, PND-6, and PND-15 using histological, transcriptomic, proteomic, and duodenal permeability analyses and compared with those of pups fed wild-type milk. Milk of miR-30b foster dams collected at mid-lactation was extensively analyzed using proteomic, metabolomic, and lipidomic approaches and hormonal immunoassays. RESULTS: At PND-5, wild-type pups fed miR-30b milk showed maturation of their duodenum with 1.5-fold (P < 0.05) and 1.3-fold (P < 0.10) increased expression of Claudin-3 and Claudin-4, respectively, and changes in 8 duodenal proteins (P < 0.10), with an earlier reduction in paracellular and transcellular permeability (183 ng/mL fluorescein sulfonic acid [FSA] and 12 ng/mL horseradish peroxidase [HRP], respectively, compared with 5700 ng/mL FSA and 90 ng/mL HRP in wild-type; P < 0.001). Compared with wild-type milk, miR-30b milk displayed an increase in total lipid (219 g/L compared with 151 g/L; P < 0.05), ceramide (17.6 µM compared with 6.9 µM; P < 0.05), and sphingomyelin concentrations (163.7 µM compared with 76.3 µM; P < 0.05); overexpression of 9 proteins involved in the gut barrier (P < 0.1); and higher insulin and leptin concentrations (1.88 ng/mL and 2.04 ng/mL, respectively, compared with 0.79 ng/mL and 1.06 ng/mL; P < 0.01). CONCLUSIONS: miR-30b milk displays significant changes in bioactive components associated with neonatal duodenal integrity and maturation, which could be involved in the earlier intestinal closure phenotype of the wild-type pups associated with a lower growth rate.

Fine-tuned adaptation of embryo-endometrium pairs at implantation revealed by transcriptome analyses in Bos taurus.

Interactions between embryo and endometrium at implantation are critical for the progression of pregnancy. These reciprocal actions involve exchange of paracrine signals that govern implantation and placentation. However, it remains unknown how these interactions between the conceptus and the endometrium are coordinated at the level of an individual pregnancy. Under the hypothesis that gene expression in endometrium is dependent on gene expression of extraembryonic tissues and genes expressed in extraembryonic tissues are dependent of genes expressed in the endometrium, we performed an integrative analysis of transcriptome profiles of paired extraembryonic tissue and endometria obtained from cattle (Bos taurus) pregnancies initiated by artificial insemination. We quantified strong dependence (|r| > 0.95, empirical false discovery rate [eFDR] < 0.01) in transcript abundance of genes expressed in the extraembryonic tissues and genes expressed in the endometrium. The profiles of connectivity revealed distinct coexpression patterns of extraembryonic tissues with caruncular and intercaruncular areas of the endometrium. Notably, a subset of highly coexpressed genes between extraembryonic tissue (n = 229) and caruncular areas of the endometrium (n = 218, r > 0.9999, eFDR < 0.001) revealed a blueprint of gene expression specific to each pregnancy. Gene ontology analyses of genes coexpressed between extraembryonic tissue and endometrium revealed significantly enriched modules with critical contribution for implantation and placentation, including "in utero embryonic development," "placenta development," and "regulation of transcription." Coexpressing modules were remarkably specific to caruncular or intercaruncular areas of the endometrium. The quantitative association between genes expressed in extraembryonic tissue and endometrium emphasize a coordinated communication between these two entities in mammals. We provide evidence that implantation in mammalian pregnancy relies on the ability of the extraembryonic tissue and the endometrium to develop a fine-tuned adaptive response characteristic of each pregnancy.

Potential genetic robustness of Prnp and Sprn double knockout mouse embryos towards ShRNA-lentiviral inoculation.

The Shadoo and PrP prion protein family members are thought to be functionally related, but previous knockdown/knockout experiments in early mouse embryogenesis have provided seemingly contradictory results. In particular, Shadoo was found to be indispensable in the absence of PrP in knockdown analyses, but a double-knockout of the two had little phenotypic impact. We investigated this apparent discrepancy by comparing transcriptomes of WT, Prnp0/0 and Prnp0/0Sprn0/0 E6.5 mouse embryos following inoculation by Sprn- or Prnp-ShRNA lentiviral vectors. Our results suggest the possibility of genetic adaptation in Prnp0/0Sprn0/0 mice, thus providing a potential explanation for their previously observed resilience.

A unified framework for the integration of multiple hierarchical clusterings or networks from multi-source data.

BACKGROUND: Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are generally heterogeneous: it is a common place to gather data in the form of trees, networks or factorial maps, as these representations all have an appealing visual interpretation that helps to study grouping patterns and interactions between entities. The question we aim to answer in this paper is that of the integration of such representations. RESULTS: To this end, we provide a simple procedure to compare data with various types, in particular trees or networks, that relies essentially on two steps: the first step projects the representations into a common coordinate system; the second step then uses a multi-table integration approach to compare the projected data. We rely on efficient and well-known methodologies for each step: the projection step is achieved by retrieving a distance matrix for each representation form and then applying multidimensional scaling to provide a new set of coordinates from all the pairwise distances. The integration step is then achieved by applying a multiple factor analysis to the multiple tables of the new coordinates. This procedure provides tools to integrate and compare data available, for instance, as tree or network structures. Our approach is complementary to kernel methods, traditionally used to answer the same question. CONCLUSION: Our approach is evaluated on simulation and used to analyze two real-world data sets: first, we compare several clusterings for different cell-types obtained from a transcriptomics single-cell data set in mouse embryos; second, we use our procedure to aggregate a multi-table data set from the TCGA breast cancer database, in order to compare several protein networks inferred for different breast cancer subtypes.

Fast tree aggregation for consensus hierarchical clustering.

BACKGROUND: In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues. RESULTS: We propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same cluster for all the trees at height h. The method is exact and proven to be [Formula: see text], n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real -omics data sets, introducing a spectral variant as an efficient and robust by-product. CONCLUSIONS: Our tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas.

Molecular signatures of muscle growth and composition deciphered by the meta-analysis of age-related public transcriptomics data.

The lean-to-fat ratio is a major issue in the beef meat industry from both carcass and meat production perspectives. This industrial perspective has motivated meat physiologists to use transcriptomics technologies to decipher mechanisms behind fat deposition within muscle during the time course of muscle growth. However, synthetic biological information from this volume of data remains to be produced to identify mechanisms found in various breeds and rearing practices. We conducted a meta-analysis on 10 transcriptomic data sets stored in public databases, from the longissimus thoracis of five different bovine breeds divergent by age. We updated gene identifiers on the last version of the bovine genome (UCD1.2), and the 715 genes common to the 10 studies were subjected to the meta-analysis. Of the 238 genes differentially expressed (DEG), we identified a transcriptional signature of the dynamic regulation of glycolytic and oxidative metabolisms that agrees with a known shift between those two pathways from the animal puberty. We proposed some master genes of the myogenesis, namely MYOG and MAPK14, as probable regulators of the glycolytic and oxidative metabolisms. We also identified overexpressed genes related to lipid metabolism (APOE, LDLR, MXRA8, and HSP90AA1) that may contribute to the expected enhanced marbling as age increases. Lastly, we proposed a transcriptional signature related to the induction (YBX1) or repression (MAPK14, YWAH, ERBB2) of the commitment of myogenic progenitors into the adipogenic lineage. The relationships between the abundance of the identified mRNA and marbling values remain to be analyzed in a marbling biomarkers discovery perspectives.

Bovine and murine models highlight novel roles for SLC25A46 in mitochondrial dynamics and metabolism, with implications for human and animal health.

Neuropathies are neurodegenerative diseases affecting humans and other mammals. Many genetic causes have been identified so far, including mutations of genes encoding proteins involved in mitochondrial dynamics. Recently, the "Turning calves syndrome", a novel sensorimotor polyneuropathy was described in the French Rouge-des-Prés cattle breed. In the present study, we determined that this hereditary disease resulted from a single nucleotide substitution in SLC25A46, a gene encoding a protein of the mitochondrial carrier family. This mutation caused an apparent damaging amino-acid substitution. To better understand the function of this protein, we knocked out the Slc25a46 gene in a mouse model. This alteration affected not only the nervous system but also altered general metabolism, resulting in premature mortality. Based on optic microscopy examination, electron microscopy and on biochemical, metabolic and proteomic analyses, we showed that the Slc25a46 disruption caused a fusion/fission imbalance and an abnormal mitochondrial architecture that disturbed mitochondrial metabolism. These data extended the range of phenotypes associated with Slc25a46 dysfunction. Moreover, this Slc25a46 knock-out mouse model should be useful to further elucidate the role of SLC25A46 in mitochondrial dynamics.

Puberty is a critical window for the impact of diet on mammary gland development in the rabbit.

BACKGROUND: Nutritional changes can affect future lactation efficiency. In a rabbit model, an obesogenic diet initiated before puberty and pursued throughout pregnancy enhances mammary differentiation, but when started during the neonatal period can cause abnormal mammary development in early pregnancy. The aim of this study was to investigate the impact of an unbalanced diet administered during the pubertal period only. RESULTS: Consuming an obesogenic diet at puberty did not affect either metabolic parameters or certain maternal reproductive parameters at the onset of adulthood. In contrast, at Day 8 of pregnancy, epithelial tissue showed a lower proliferation rate in obesogenic-diet fed rabbits than in control-diet fed rabbits. Wap and Cx26 genes, mammary epithelial cell differentiation markers, were upregulated although Wap protein level remained unchanged. However, the expression of genes involved in lipid metabolism and in alveolar formation was not modified. CONCLUSION: Taken together, our results demonstrate that the consumption for 5 weeks of an obesogenic diet during the pubertal period initiates mammary structure modifications and affects mammary epithelial cell proliferation and differentiation. Our findings highlight the potentially important role played by unbalanced nutrition during critical early-life windows in terms of regulating mammary epithelial cell differentiation and subsequent function in adulthood.

DNAJC2 is required for mouse early embryonic development.

DNAJC2 protein, also known as ZRF1 or MPP11, acts both as chaperone and as chromatin regulator. It is involved in stem cell differentiation and its expression is associated with various cancer malignancies. However, the role of Dnajc2 gene during mouse embryogenesis has not been assessed so far. To this aim, we invalidated Dnajc2 gene in FVB/Nj mice using the CrispR/Cas9 approach. We showed that this invalidation leads to the early post-implantation lethality of the nullizygous embryos. Furthermore, using siRNAs against Dnajc2 in mouse 1-cell embryos, we showed that maternal Dnajc2 mRNAs may allow for the early preimplantation development of these embryos. Altogether, these data demonstrate for the first time the requirement of DNAJC2 for early mouse embryogenesis.

Genetic and transcriptomic analyses provide new insights on the early antiviral response to VHSV in resistant and susceptible rainbow trout.

BACKGROUND: The viral hemorrhagic septicemia virus (VHSV) is a major threat for salmonid farming and for wild fish populations worldwide. Previous studies have highlighted the importance of innate factors regulated by a major quantitative trait locus (QTL) for the natural resistance to waterborne VHSV infection in rainbow trout. The aim of this study was to analyze the early transcriptomic response to VHSV inoculation in cell lines derived from previously described resistant and susceptible homozygous isogenic lines of rainbow trout to obtain insights into the molecular mechanisms responsible for the resistance to the viral infection. RESULTS: We first confirmed the presence of the major QTL in a backcross involving a highly resistant fish isogenic line (B57) and a highly susceptible one (A22), and were able to define the confidence interval of the QTL and to identify its precise position. We extended the definition of the QTL since it controls not only resistance to waterborne infection but also the kinetics of mortality after intra-peritoneal injection. Deep sequencing of the transcriptome of B57 and A22 derived cell lines exposed to inactivated VHSV showed a stronger response to virus inoculation in the resistant background. In line with our previous observations, an early and strong induction of interferon and interferon-stimulated genes was correlated with the resistance to VHSV, highlighting the major role of innate immune factors in natural trout resistance to the virus. Interestingly, major factors of the antiviral innate immunity were much more expressed in naive B57 cells compared to naive A22 cells, which likely contributes to the ability of B57 to mount a fast antiviral response after viral infection. These observations were further extended by the identification of several innate immune-related genes localized close to the QTL area on the rainbow trout genome. CONCLUSIONS: Taken together, our results improve our knowledge in virus-host interactions in vertebrates and provide novel insights in the molecular mechanisms explaining the resistance to VHSV in rainbow trout. Our data also provide a collection of potential markers for resistance and susceptibility of rainbow trout to VHSV infection.

Epithelial response to a high-protein diet in rat colon.

BACKGROUND: High-protein diets (HPD) alter the large intestine microbiota composition in association with a metabolic shift towards protein degradation. Some amino acid-derived metabolites produced by the colon bacteria are beneficial for the mucosa while others are deleterious at high concentrations. The aim of the present work was to define the colonic epithelial response to an HPD. Transcriptome profiling was performed on colonocytes of rats fed an HPD or an isocaloric normal-protein diet (NPD) for 2 weeks. RESULTS: The HPD downregulated the expression of genes notably implicated in pathways related to cellular metabolism, NF-κB signaling, DNA repair, glutathione metabolism and cellular adhesion in colonocytes. In contrast, the HPD upregulated the expression of genes related to cell proliferation and chemical barrier function. These changes at the mRNA level in colonocytes were not associated with detrimental effects of the HPD on DNA integrity (comet assay), epithelium renewal (quantification of proliferation and apoptosis markers by immunohistochemistry and western blot) and colonic barrier integrity (Ussing chamber experiments). CONCLUSION: The modifications of the luminal environment after an HPD were associated with maintenance of the colonic homeostasis that might be the result of adaptive processes in the epithelium related to the observed transcriptional regulations.

A model selection criterion for model-based clustering of annotated gene expression data.

In co-expression analyses of gene expression data, it is often of interest to interpret clusters of co-expressed genes with respect to a set of external information, such as a potentially incomplete list of functional properties for which a subset of genes may be annotated. Based on the framework of finite mixture models, we propose a model selection criterion that takes into account such external gene annotations, providing an efficient tool for selecting a relevant number of clusters and clustering model. This criterion, called the integrated completed annotated likelihood (ICAL), is defined by adding an entropy term to a penalized likelihood to measure the concordance between a clustering partition and the external annotation information. The ICAL leads to the choice of a model that is more easily interpretable with respect to the known functional gene annotations. We illustrate the interest of this model selection criterion in conjunction with Gaussian mixture models on simulated gene expression data and on real RNA-seq data.

A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.

During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.

Differential meta-analysis of RNA-seq data from multiple studies.

BACKGROUND: High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question. RESULTS: We demonstrate how p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies. CONCLUSIONS: The p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the CRAN (http://cran.r-project.org/web/packages/metaRNASeq).

Data-based filtering for replicated high-throughput transcriptome sequencing experiments.

MOTIVATION: RNA sequencing is now widely performed to study differential expression among experimental conditions. As tests are performed on a large number of genes, stringent false-discovery rate control is required at the expense of detection power. Ad hoc filtering techniques are regularly used to moderate this correction by removing genes with low signal, with little attention paid to their impact on downstream analyses. RESULTS: We propose a data-driven method based on the Jaccard similarity index to calculate a filtering threshold for replicated RNA sequencing data. In comparisons with alternative data filters regularly used in practice, we demonstrate the effectiveness of our proposed method to correctly filter lowly expressed genes, leading to increased detection power for moderately to highly expressed genes. Interestingly, this data-driven threshold varies among experiments, highlighting the interest of the method proposed here. AVAILABILITY: The proposed filtering method is implemented in the R package HTSFilter available on Bioconductor.

Multi-omics data integration for the identification of biomarkers for bull fertility.

Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify infertile bulls, no tools are currently available to enable precise predictions and prevent the commercialization of subfertile semen. Because male fertility is a multifactorial phenotype that is dependent on genetic, epigenetic, physiological and environmental factors, we hypothesized that an integrative analysis might help to refine our knowledge and understanding of bull fertility. We combined -omics data (genotypes, sperm DNA methylation at CpGs and sperm small non-coding RNAs) and semen parameters measured on a large cohort of 98 Montbéliarde bulls with contrasting fertility levels. Multiple Factor Analysis was conducted to study the links between the datasets and fertility. Four methodologies were then considered to identify the features linked to bull fertility variation: Logistic Lasso, Random Forest, Gradient Boosting and Neural Networks. Finally, the features selected by these methods were annotated in terms of genes, to conduct functional enrichment analyses. The less relevant features in -omics data were filtered out, and MFA was run on the remaining 12,006 features, including the 11 semen parameters and a balanced proportion of each type of-omics data. The results showed that unlike the semen parameters studied the-omics datasets were related to fertility. Biomarkers related to bull fertility were selected using the four methodologies mentioned above. The most contributory CpGs, SNPs and miRNAs targeted genes were all found to be involved in development. Interestingly, fragments derived from ribosomal RNAs were overrepresented among the selected features, suggesting roles in male fertility. These markers could be used in the future to identify subfertile bulls in order to increase the global efficiency of the breeding sector.