Gene expression pattern and molecular mechanisms involved in Shal and Sangsari sheep fertility using RNA-Seq.

Ovulation rate and litter size are the main reproductive traits with high economic value in the sheep breeding industry. In this study, three Shal ewes (multiparous) and three Sangsari ewes (uniparous) at the age of 5 were used. The live weight was between 45 and 50 kg at an extremely body condition score of 3. These breeds are marked seasonal reproduction activity and are often bred in semi-closed breeding systems. Total RNAs were extracted from the ovarian tissues, and RNA sequencing was carried out. The DAVID (Database for Annotation, Visualization and Integrated Discovery) database was then used to annotate genes, and the string database and the Cytoscape software were used to investigate their interactions. Then path-act network analysis and gene-act network analysis were investigated. The results indicated that 19,932 genes were differentially expressed. The 5968 differentially expressed genes were identified in Shal ewe's ovarian tissue compared to Sangsari ewes (FDR < 0.05), of which 2921 genes were up-regulated and 3047 genes were down-regulated. Bioinformatics analysis exhibited that most of the biological processes and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways associated with significant DEGs (Differentially Expressed Genes) in the two studied breeds are associated with oocyte maturation and metabolism. MAPK signalling pathways and Ubiquitin-mediated proteolysis are the most important biological pathways associated with reproductive and fertility traits in the Shal breed. AKT3, MAPK8, MAPK9 and RELA genes are also important genes related to the fertility of multiparous sheep. Analysis of ovarian RNA-seq data identified that most of the differentially expressed genes were involved in various reproductive processes including folliculogenesis, ovulation, ovarian and embryonic development. The MAPK signalling pathway had the most interaction with other pathways, and the AKT3 gene could be a powerful candidate gene in the reproduction and fertility of Shal sheep. These results could pave the way for future efforts to address sheep prolificacy barriers.

An association of CEP78, MEF2C, VPS13A and ARRDC3 genes with survivability to heat stress in an F2 chicken population.

Heat stress is a serious problem in the poultry industry. An effective tool for improving heat tolerance can be genomic selection based on single nucleotide polymorphisms. This study was performed to identify genomic regions controlling survivability to heat stress in a population of F2 chickens that accidentally experienced acute heat stress, using Illumina 60K Chicken SNP Bead Chip. After quality control in markers, 47,730 SNPs remained for genome-wide association study (GWAS). The GWAS results indicated that markers Gga_rs16111480 (p = 8.503e-08), GGaluGA354375 (p = 5.99e-07) and Gga_rs14748694 (p = 7.085e-07) located on Z chromosome showed significant association with heat stress tolerance trait. The Gga_rs16111480 marker was located inside the CEP78 gene. The marker GGaluGA354375 was located inside the LOC101752071 gene and next to the MEF2C gene. The Gga_rs14748694 marker was adjacent to LOC101752071 and MEF2C genes. Moreover, the SNP maker of Gga_rs16111480 was located on 243 kb downstream of the VPS13A gene, and the GGaluGA354375 and Gga_rs14748694 SNPs were located on 947 kb and 888 kb downstream of the ARRDC3 gene, respectively. The results of this study suggest that apart from the gene LOC101752071, which its function was unknown, each of the two MEF2C and CEP78 genes were found to be closely related to heat stress resistance in bird.

Deciphering Metabolic Pathways in High-Seeding-Density Fed-Batch Processes for Monoclonal Antibody Production: A Computational Modeling Perspective.

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high-seeding-density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells, focused on identifying significant metabolites and their interactions. We observed high glycolytic fluxes, the depletion of asparagine, and a shift from lactate production to consumption. Using a metabolic network and flux analysis, we compared the standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched with amino acids. We reconstructed a metabolic network and kinetic models based on the observations and explored the effects of different feeding strategies on CHO cell metabolism. Our findings revealed that the addition of a bolus medium (BM) containing asparagine improved final titers. However, increasing the asparagine concentration in the feed further prevented the lactate shift, indicating a need to find a balance between increased asparagine to counteract limitations and lower asparagine to preserve the shift in lactate metabolism.

Copy number variations affecting growth curve parameters in a crossbred chicken population.

Copy number variations (CNVs) are key structural variations in the genome and may contribute to phenotypic differences. In this study, we used a F2 chicken population created from reciprocal crossing between fast-growing Arian broiler line and Urmia native chickens. The chickens were genotyped by 60 K SNP BeadChip, and PennCNV algorithm was used to detect genome-wide CNVs. The growth curve parameters of W0, k, L, Wf, Wi, ti and average GR were used as phenotypic data. The association between CNV and growth curve parameters was carried out using the CNVRanger R/Bioconductor package. Five CNV regions (CNVRs) were chosen for the validation experiment using qPCR. Gene enrichment analysis was done using WebGestalt. The STRING database was used to search for significant pathways. The results identified 966 CNVs and 600 CNVRs including 468 gains, 67 losses, and 65 both events on autosomal chromosomes. Validation of the CNVRs obtained from the qPCR assay were 79 % consistent with the prediction by PennCNV. A total of 43 significant CNVs were obtained for the seven growth curve parameters. The 416 genes annotated for significant CNVs. Six genes out of 416 genes were most related to growth curve parameters. These genes were LCP2, Dock2, CD80, CYFIP1, NIPA1 and NIPA2. Some of these genes in their biological process were associated with the growth, reproduction and development of cells or organs that ultimately lead to the growth of the body. The results of the study could pave the way for better understanding the molecular process of CNVs and growth curve parameters in birds.

Trade-off between feed efficiency and immunity in specialized high-performing chickens.

Based on resource allocation theory, ignoring importance of immunity, and focus on growth and feed efficiency (FE) traits in breeding plans may lead to serious weakness in immune system performance. However, in poultry the adverse effects of selection for FE on the immune system are unclear. Therefore, an experiment was conducted to study the trade-off between FE and immunity using a total of 180 high-performing specialized male chickens from a commercial broiler line which were selected over 30 generations for growth (body weight gain, BWG) and FE (residual feed intake, RFI). Birds were reared for 42 d and 5 FE-related traits of the birds in the last week were considered including daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual BW gain (RG), and residual intake and gain (RIG). For all 180 chickens, immune system performance including humoral immune response, cell-mediated immunity (CMI), and the activity of lysozyme enzyme (L. activity) as innate immunity was measured. After ascending sort of each FE records, 10% of higher records (H-FE: N = 18) and 10% of lower records (L-FE: N = 18) were determined, and immunity between L-FE and H-FE groups were compared. Moreover, L-BWG and H-BWG were analyzed because BWG is one of components in the FE formula. Performance of the immune system was not statistically different for CMI in none of the studied FE groups. Moreover, high and low groups for DFI and BWG were not different regarding the immunity of the birds. Antibody titers against Newcastle disease virus (NDV) were different between low and high groups of FCR, RG, and RIG. Likewise, SRBC-derived antibodies were significantly different between RFI groups. Rather than humoral immunity, RIG had adversely effect on the innate immunity. Results of the present study showed that although RIG is a more appropriate indicator for FE, choosing for high RIG can weaken the performance of the both humoral and innate immune systems, while RFI had fewer adverse effects.

Inferring genetic architecture of complex traits using Bayesian integrative analysis of genome and transcriptome data.

BACKGROUND: To understand the genetic architecture of complex traits and bridge the genotype-phenotype gap, it is useful to study intermediate -omics data, e.g. the transcriptome. The present study introduces a method for simultaneous quantification of the contributions from single nucleotide polymorphisms (SNPs) and transcript abundances in explaining phenotypic variance, using Bayesian whole-omics models. Bayesian mixed models and variable selection models were used and, based on parameter samples from the model posterior distributions, explained variances were further partitioned at the level of chromosomes and genome segments. RESULTS: We analyzed three growth-related traits: Body Weight (BW), Feed Intake (FI), and Feed Efficiency (FE), in an F2 population of 440 mice. The genomic variation was covered by 1806 tag SNPs, and transcript abundances were available from 23,698 probes measured in the liver. Explained variances were computed for models using pedigree, SNPs, transcripts, and combinations of these. Comparison of these models showed that for BW, a large part of the variation explained by SNPs could be covered by the liver transcript abundances; this was less true for FI and FE. For BW, the main quantitative trait loci (QTLs) are found on chromosomes 1, 2, 9, 10, and 11, and the QTLs on 1, 9, and 10 appear to be expression Quantitative Trait Locus (eQTLs) affecting gene expression in the liver. Chromosome 9 is the case of an apparent eQTL, showing that genomic variance disappears, and that a tri-modal distribution of genomic values collapses, when gene expressions are added to the model. CONCLUSIONS: With increased availability of various -omics data, integrative approaches are promising tools for understanding the genetic architecture of complex traits. Partitioning of explained variances at the chromosome and genome-segment level clearly separated regulatory and structural genomic variation as the areas where SNP effects disappeared/remained after adding transcripts to the model. The models that include transcripts explained more phenotypic variance and were better at predicting phenotypes than a model using SNPs alone. The predictions from these Bayesian models are generally unbiased, validating the estimates of explained variances.

Identification of differentially expressed long noncoding RNAs in the ovarian tissue of Shal and Sangsari ewes using RNA-seq.

BACKGROUND: The ovary has an important role in reproductive function. Animal reproduction is dominated by numerous coding genes and noncoding elements. Although long noncoding RNAs (LncRNAs) are important in biological activity, little is known about their role in the ovary and fertility. METHODS: Three adult Shal ewes and three adult Sangsari ewes were used in this investigation. LncRNAs in ovarian tissue from two breeds were identified using bioinformatics analyses, and then target genes of LncRNAs were discovered. Target genes were annotated using the DAVID database, and their interactions were examined using the STRING database and Cytoscape software. The expression levels of seven LncRNAs with their target genes were assessed by real-time PCR to confirm the RNA-seq. RESULTS: Among all the identified LncRNAs, 124 LncRNAs were detected with different expression levels between the two breeds (FDR < 0.05). According to the DAVID database, target genes were discovered to be engaged in one biological process, one cellular component, and 21 KEGG pathways (FDR < 0.05). The PES1, RPS9, EF-1, Plectin, SURF6, CYC1, PRKACA MAPK1, ITGB2 and BRD2 genes were some of the most crucial target genes (hub genes) in the ovary. CONCLUSION: These results could pave the way for future efforts to address sheep prolificacy barriers.

Genetic variance of metabolomic features and their relationship with body weight and body weight gain in Holstein cattle1.

In recent years, metabolomics has been used to clarify the biology underlying biological samples. In the field of animal breeding, investigating the magnitude of genetic control on the metabolomic profiles of animals and their relationships with quantitative traits adds valuable information to animal improvement schemes. In this study, we analyzed metabolomic features (MFs) extracted from the metabolomic profiles of 843 male Holstein calves. The metabolomic profiles were obtained using nuclear magnetic resonance (NMR) spectroscopy. We investigated 2 alternative methods to control for peak shifts in the NMR spectra, binning and aligning, to determine which approach was the most efficient for assessing genetic variance. Series of univariate analyses were implemented to elucidate the heritability of each MF. Furthermore, records on BW and ADG from 154 to 294 d of age (ADG154-294), 294 to 336 d of age (ADG294-336), and 154 to 336 d of age (ADG154-336) were used in a series of bivariate analyses to establish the genetic and phenotypic correlations with MFs. Bivariate analyses were only performed for MFs that had a heritability significantly different from zero. The heritabilities obtained in the univariate analyses for the MFs in the binned data set were low (<0.2). In contrast, in the aligned data set, we obtained moderate heritability (0.2 to 0.5) for 3.5% of MFs and high heritability (more than 0.5) for 1% of MFs. The bivariate analyses showed that ~12%, ~3%, ~9%, and ~9% of MFs had significant additive genetic correlations with BW, ADG154-294, ADG294-336, and ADG154-336, respectively. In all of the bivariate analyses, the percentage of significant additive genetic correlations was higher than the percentage of significant phenotypic correlations of the corresponding trait. Our results provided insights into the influence of the underlying genetic mechanisms on MFs. Further investigations in this field are needed for better understanding of the genetic relationship among the MFs and quantitative traits.