Assessment of DNA methylation in porcine immune cells reveals novel regulatory elements associated with cell-specific gene expression and immune capacity traits.

BACKGROUND: Genetics studies in the porcine immune system have enhanced selection practices for disease resistance phenotypes and increased the efficacy of porcine models in biomedical research; however limited functional annotation of the porcine immunome has hindered progress on both fronts. Among epigenetic mechanisms that regulate gene expression, DNA methylation is the most ubiquitous modification made to the DNA molecule and influences transcription factor binding as well as gene and phenotype expression. Human and mouse DNA methylation studies have improved mapping of regulatory elements in these species, but comparable studies in the pig have been limited in scope. RESULTS: We performed whole-genome bisulfite sequencing to assess DNA methylation patterns in nine pig immune cell populations: CD21+ and CD21- B cells, four T cell fractions (CD4+, CD8+, CD8+CD4+, and SWC6γδ+), natural killer and myeloid cells, and neutrophils. We identified 54,391 cell differentially methylated regions (cDMRs), and clustering by cDMR methylation rate grouped samples by cell lineage. 32,737 cDMRs were classified as cell lowly methylated regions (cLMRs) in at least one cell type, and cLMRs were broadly enriched in genes and regions of intermediate CpG density. We observed strong correlations between differential methylation and expression across immune cell populations, with cell-specific low methylation disproportionately impacting genes exhibiting enriched gene expression in the same cell type. Motif analysis of cLMRs revealed cell type-specific enrichment of transcription factor binding motifs, indicating that cell-specific methylation patterns may influence accessibility by trans-acting factors. Lastly, cDMRs were enriched for immune capacity GWAS SNPs, and many such overlaps occurred within genes known to influence immune cell development and function (CD8B, NDRG1). CONCLUSION: Our DNA methylation data improve functional annotation of the porcine genome through characterization of epigenomic regulatory patterns that contribute to immune cell identity and function, and increase the potential for identifying mechanistic links between genotype and phenotype.

Integrated Genome-Wide Analysis of MicroRNA Expression Quantitative Trait Loci in Pig Longissimus Dorsi Muscle.

Determining mechanisms regulating complex traits in pigs is essential to improve the production efficiency of this globally important protein source. MicroRNAs (miRNAs) are a class of non-coding RNAs known to post-transcriptionally regulate gene expression affecting numerous phenotypes, including those important to the pig industry. To facilitate a more comprehensive understanding of the regulatory mechanisms controlling growth, carcass composition, and meat quality phenotypes in pigs, we integrated miRNA and gene expression data from longissimus dorsi muscle samples with genotypic and phenotypic data from the same animals. We identified 23 miRNA expression Quantitative Trait Loci (miR-eQTL) at the genome-wide level and examined their potential effects on these important production phenotypes through miRNA target prediction, correlation, and colocalization analyses. One miR-eQTL miRNA, miR-874, has target genes that colocalize with phenotypic QTL for 12 production traits across the genome including backfat thickness, dressing percentage, muscle pH at 24 h post-mortem, and cook yield. The results of our study reveal genomic regions underlying variation in miRNA expression and identify miRNAs and genes for future validation of their regulatory effects on traits of economic importance to the global pig industry.

Weaning Induces Stress-Dependent DNA Methylation and Transcriptional Changes in Piglet PBMCs.

Changes to the epigenome, including those to DNA methylation, have been proposed as mechanisms by which stress can induce long-term physiological changes in livestock species. Pig weaning is associated with dietary and social stress, both of which elicit an immune response and changes to the hypothalamic-pituitary-adrenal (HPA) axis. While differential methylation following stress has been assessed in model organisms, it remains poorly understood how the pig methylome is altered by stressors in production settings. We quantified changes in CpG methylation and transcript abundance in piglet peripheral blood mononuclear cells (PBMCs) following weaning and also assessed differential patterns in pigs exhibiting high and low stress response as measured by cortisol concentration and lesion scores. Blood was collected from nine gilt piglets 24 h before and after weaning, and whole-genome bisulfite sequencing (WGBS) and RNA-sequencing were performed on six and nine animals, respectively, at both time points. We identified 2,674 differentially methylated regions (DMRs) that were enriched within promoters of genes associated with lymphocyte stimulation and transcriptional regulation. Stress groups displayed unique differential methylation and expression patterns associated with activation and suppression of T cell immunity in low and high stress animals, respectively. Differential methylation was strongly associated with differential expression; specifically, upregulated genes were enriched among hypomethylated genes. We observed post-weaning hypermethylation of the glucocorticoid receptor (NR3C1) promoter and a significant decrease in NR3C1 expression (n = 9, p = 6.1 × 10-3). Our results indicate that weaning-associated stress elicits genome-wide methylation changes associated with differential gene expression, reduced T cell activation, and an altered HPA axis response.

Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs.

BACKGROUND: Economically important growth and meat quality traits in pigs are controlled by cascading molecular events occurring during development and continuing throughout the conversion of muscle to meat. However, little is known about the genes and molecular mechanisms involved in this process. Evaluating transcriptomic profiles of skeletal muscle during the initial steps leading to the conversion of muscle to meat can identify key regulators of polygenic phenotypes. In addition, mapping transcript abundance through genome-wide association analysis using high-density marker genotypes allows identification of genomic regions that control gene expression, referred to as expression quantitative trait loci (eQTL). In this study, we perform eQTL analyses to identify potential candidate genes and molecular markers regulating growth and meat quality traits in pigs. RESULTS: Messenger RNA transcripts obtained with RNA-seq of longissimus dorsi muscle from 168 F2 animals from a Duroc x Pietrain pig resource population were used to estimate gene expression variation subject to genetic control by mapping eQTL. A total of 339 eQTL were mapped (FDR ≤ 0.01) with 191 exhibiting local-acting regulation. Joint analysis of eQTL with phenotypic QTL (pQTL) segregating in our population revealed 16 genes significantly associated with 21 pQTL for meat quality, carcass composition and growth traits. Ten of these pQTL were for meat quality phenotypes that co-localized with one eQTL on SSC2 (8.8-Mb region) and 11 eQTL on SSC15 (121-Mb region). Biological processes identified for co-localized eQTL genes include calcium signaling (FERM, MRLN, PKP2 and CHRNA9), energy metabolism (SUCLG2 and PFKFB3) and redox hemostasis (NQO1 and CEP128), and results support an important role for activation of the PI3K-Akt-mTOR signaling pathway during the initial conversion of muscle to meat. CONCLUSION: Co-localization of eQTL with pQTL identified molecular markers significantly associated with both economically important phenotypes and gene transcript abundance. This study reveals candidate genes contributing to variation in pig production traits, and provides new knowledge regarding the genetic architecture of meat quality phenotypes.

Profiling and characterization of a longissimus dorsi muscle microRNA dataset from an F2 Duroc × Pietrain pig resource population.

To elucidate the effects of microRNA (miRNA) regulation in skeletal muscle of adult pigs, miRNA expression profiling was performed with RNA extracted from longissimus dorsi (LD) muscle samples from 174 F2 pigs (~ 5.5 months of age) from a Duroc × Pietrain resource population. Total RNA was extracted from LD samples, and libraries were sequenced on an Illumina HiSeq 2500 platform in 1 × 50 bp format. After processing, 232,826,977 total reads were aligned to the Sus scrofa reference genome (v10.2.79), with 74.8% of total reads mapping successfully. The miRDeep2 software package was utilized to quantify annotated Sus scrofa mature miRNAs from miRBase (Release 21) and to predict candidate novel miRNA precursors. Among the retained 295 normalized mature miRNA expression profiles ssc-miR-1, ssc-miR-133a-3p, ssc-miR-378, ssc-miR-206, and ssc-miR-10b were the most abundant, all of which have previously been shown to be expressed in pig skeletal muscle. Additionally, 27 unique candidate novel miRNA precursors were identified exhibiting homologous sequence to annotated human miRNAs. The composition of classes of small RNA present in this dataset was also characterized; while the majority of unique expressed sequence tags were not annotated in any of the queried databases, the most abundantly expressed class of small RNA in this dataset was miRNAs. This data provides a resource to evaluate miRNA regulation of gene expression and effects on complex trait phenotypes in adult pig skeletal muscle. The raw sequencing data were deposited in the Sequence Read Archive, BioProject PRJNA363073.

Evidence for transcriptome-wide RNA editing among Sus scrofa PRE-1 SINE elements.

BACKGROUND: RNA editing by ADAR (adenosine deaminase acting on RNA) proteins is a form of transcriptional regulation that is widespread among humans and other primates. Based on high-throughput scans used to identify putative RNA editing sites, ADAR appears to catalyze a substantial number of adenosine to inosine transitions within repetitive regions of the primate transcriptome, thereby dramatically enhancing genetic variation beyond what is encoded in the genome. RESULTS: Here, we demonstrate the editing potential of the pig transcriptome by utilizing DNA and RNA sequence data from the same pig. We identified a total of 8550 mismatches between DNA and RNA sequences across three tissues, with 75% of these exhibiting an A-to-G (DNA to RNA) discrepancy, indicative of a canonical ADAR-catalyzed RNA editing event. When we consider only mismatches within repetitive regions of the genome, the A-to-G percentage increases to 94%, with the majority of these located within the swine specific SINE retrotransposon PRE-1. We also observe evidence of A-to-G editing within coding regions that were previously verified in primates. CONCLUSIONS: Thus, our high-throughput evidence suggests that pervasive RNA editing by ADAR can exist outside of the primate lineage to dramatically enhance genetic variation in pigs.

Association of Insulin-like growth factor binding protein 2 genotypes with growth, carcass and meat quality traits in pigs.

BACKGROUND: This study was conducted to investigate the potential association of variation in the insulin-like growth factor binding protein 2 (IGFBP2) gene with growth, carcass and meat quality traits in pigs. IGFBP2 is a member of the insulin-like growth factor binding protein family that is involved in regulating growth, and it maps to a region of pig chromosome 15 containing significant quantitative trait loci that affect economically important trait phenotypes. RESULTS: An IGFBP2 polymorphism was identified in the Michigan State University (MSU) Duroc × Pietrain F2 resource population (n = 408), and pigs were genotyped by MspI PCR-RFLP. Subsequently, a Duroc pig population from the National Swine Registry, USA, (n = 326) was genotyped using an Illumina Golden Gate assay. The IGFBP2 genotypic frequencies among the MSU resource population pigs were 3.43, 47.06 and 49.51 % for the AA, AB and BB genotypes, respectively. The genotypic frequencies for the Duroc pigs were 9.82, 47.85, and 42.33 % for the AA, AB and BB genotypes, respectively. Genotype effects (P < 0.05) were found in the MSU resource population for backfat thickness at 10(th) rib and last rib as determined by ultrasound at 10, 13, 16 and 19 weeks of age, ADG from 10 to 22 weeks of age, and age to reach 105 kg. A genotype effect (P < 0.05) was also found for off test Longissimus muscle area in the Duroc population. Significant effects of IGFBP2 genotype (P < 0.05) were found for drip loss, 24 h postmortem pH, pH decline from 45 min to 24 h postmortem, subjective color score, CIE L* and b*, Warner-Bratzler shear force, and sensory panel scores for juiciness, tenderness, connective tissue and overall tenderness in MSU resource population pigs. Genotype effects (P < 0.05) were found for 45-min pH, CIE L* and color score in the Duroc population. CONCLUSIONS: Results of this study revealed associations of the IGFBP2 genotypes with growth, carcass and meat quality traits in pigs. The results indicate IGFBP2 as a potential candidate gene for growth rate, backfat thickness, loin muscle area and some pork quality traits.

Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection.

BACKGROUND: The presence of variability in the response of pigs to Porcine Reproductive and Respiratory Syndrome virus (PRRSv) infection, and recent demonstration of significant genetic control of such responses, leads us to believe that selection towards more disease resistant pigs could be a valid strategy to reduce its economic impact on the swine industry. To find underlying molecular differences in PRRS susceptible versus more resistant pigs, 100 animals with extremely different growth rates and viremia levels after PRRSv infection were selected from a total of 600 infected pigs. A microarray experiment was conducted on whole blood RNA samples taken at 0, 4 and 7 days post infection (dpi) from these pigs. From these data, we examined associations of gene expression with weight gain and viral load phenotypes. The single nucleotide polymorphism (SNP) marker WUR10000125 (WUR) on the porcine 60 K SNP chip was shown to be associated with viral load and weight gain after PRRSv infection, and so the effect of the WUR10000125 (WUR) genotype on expression in whole blood was also examined. RESULTS: Limited information was obtained through linear modeling of blood gene differential expression (DE) that contrasted pigs with extreme phenotypes, for growth or viral load or between animals with different WUR genotype. However, using network-based approaches, molecular pathway differences between extreme phenotypic classes could be identified. Several gene clusters of interest were found when Weighted Gene Co-expression Network Analysis (WGCNA) was applied to 4 dpi contrasted with 0 dpi data. The expression pattern of one such cluster of genes correlated with weight gain and WUR genotype, contained numerous immune response genes such as cytokines, chemokines, interferon type I stimulated genes, apoptotic genes and genes regulating complement activation. In addition, Partial Correlation and Information Theory (PCIT) identified differentially hubbed (DH) genes between the phenotypically divergent groups. GO enrichment revealed that the target genes of these DH genes are enriched in adaptive immune pathways. CONCLUSION: There are molecular differences in blood RNA patterns between pigs with extreme phenotypes or with a different WUR genotype in early responses to PRRSv infection, though they can be quite subtle and more difficult to discover with conventional DE expression analyses. Co-expression analyses such as WGCNA and PCIT can be used to reveal network differences between such extreme response groups.

Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations.

BACKGROUND: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. RESULTS: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements.The following steps are used to locate genome segments displaying strong association. The marker with the highest - log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. CONCLUSIONS: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels.

BACKGROUND: F(2) resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F(2) populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F(2) individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F(2) cross to estimate imputation accuracy under several genotyping scenarios. RESULTS: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F(2), IA reaches 0.99. In order to attain such high imputation accuracy the F(0) and F(1) generations should be genotyped at high density. Alternatively, when only the F(0) is genotyped at HD, while F(1) and F(2) are genotyped with a 9K panel, IA drops to 0.90. CONCLUSIONS: Combining 60K and 9K panels with imputation in F(2) populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.

Evaluation of QTL for carcass merit and meat quality traits in a US commercial Duroc population.

Putative quantitative trait loci (QTL) regions on 5 chromosomes (SSC3, 6, 12, 15, and 18) were selected from our previous genome scans of a Duroc×Pietrain F(2) resource population for further evaluation in a US commercial Duroc population (n=331). A total of 81 gene-specific single nucleotide polymorphism (SNP) markers were genotyped and 33 markers were segregating. The MDH1 SNP on SSC3 was associated with 45-min and ultimate pH (pHu), and pH decline. PRKAG3 on SSC15 was associated with pHu. The HSPG2 SNP on SSC6 was associated with marbling score and days to 113kg. Markers for NUP88 and FKBP10 on SSC12 were associated with 45-min pH and L*, respectively. The SSC15 marker SF3B1 was associated with L* and LMA, and the SSC18 marker ARF5 was associated with pHu and color score. These results in a commercial Duroc population showed a general consistency with our previous genome scan.

Characterizing differential individual response to porcine reproductive and respiratory syndrome virus infection through statistical and functional analysis of gene expression.

We evaluated differences in gene expression in pigs from the Porcine Reproductive and Respiratory Syndrome (PRRS) Host Genetics Consortium initiative showing a range of responses to PRRS virus infection. Pigs were allocated into four phenotypic groups according to their serum viral level and weight gain. RNA obtained from blood at 0, 4, 7, 11, 14, 28, and 42 days post-infection (DPI) was hybridized to the 70-mer 20K Pigoligoarray. We used a blocked reference design for the microarray experiment. This allowed us to account for individual biological variation in gene expression, and to assess baseline effects before infection (0 DPI). Additionally, this design has the flexibility of incorporating future data for differential expression analysis. We focused on evaluating transcripts showing significant interaction of weight gain and serum viral level. We identified 491 significant comparisons [false discovery rate (FDR) = 10%] across all DPI and phenotypic groups. We corroborated the overall trend in direction and level of expression (measured as fold change) at 4 DPI using qPCR (r = 0.91, p ≤ 0.0007). At 4 and 7 DPI, network and functional analyses were performed to assess if immune related gene sets were enriched for genes differentially expressed (DE) across four phenotypic groups. We identified cell death function as being significantly associated (FDR ≤ 5%) with several networks enriched for DE transcripts. We found the genes interferon-alpha 1(IFNA1), major histocompatibility complex, class II, DQ alpha 1 (SLA-DQA1), and major histocompatibility complex, class II, DR alpha (SLA-DRA) to be DE (p ≤ 0.05) between phenotypic groups. Finally, we performed a power analysis to estimate sample size and sampling time-points for future experiments. We concluded the best scenario for investigation of early response to PRRSV infection consists of sampling at 0, 4, and 7 DPI using about 30 pigs per phenotypic group.

Genome-wide linkage analysis of global gene expression in loin muscle tissue identifies candidate genes in pigs.

BACKGROUND: Nearly 6,000 QTL have been reported for 588 different traits in pigs, more than in any other livestock species. However, this effort has translated into only a few confirmed causative variants. A powerful strategy for revealing candidate genes involves expression QTL (eQTL) mapping, where the mRNA abundance of a set of transcripts is used as the response variable for a QTL scan. METHODOLOGY/PRINCIPAL FINDINGS: We utilized a whole genome expression microarray and an F(2) pig resource population to conduct a global eQTL analysis in loin muscle tissue, and compared results to previously inferred phenotypic QTL (pQTL) from the same experimental cross. We found 62 unique eQTL (FDR <10%) and identified 3 gene networks enriched with genes subject to genetic control involved in lipid metabolism, DNA replication, and cell cycle regulation. We observed strong evidence of local regulation (40 out of 59 eQTL with known genomic position) and compared these eQTL to pQTL to help identify potential candidate genes. Among the interesting associations, we found aldo-keto reductase 7A2 (AKR7A2) and thioredoxin domain containing 12 (TXNDC12) eQTL that are part of a network associated with lipid metabolism and in turn overlap with pQTL regions for marbling, % intramuscular fat (% fat) and loin muscle area on Sus scrofa (SSC) chromosome 6. Additionally, we report 13 genomic regions with overlapping eQTL and pQTL involving 14 local eQTL. CONCLUSIONS/SIGNIFICANCE: Results of this analysis provide novel candidate genes for important complex pig phenotypes.

Identification of Carcass and Meat Quality QTL in an F(2) Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models.

A three-generation resource population was constructed by crossing pigs from the Duroc and Pietrain breeds. In this study, 954 F(2) animals were used to identify quantitative trait loci (QTL) affecting carcass and meat quality traits. Based on results of the first scan analyzed with a line-cross (LC) model using 124 microsatellite markers and 510 F(2) animals, 9 chromosomes were selected for genotyping of additional markers. Twenty additional markers were genotyped for 954 F(2) animals and 20 markers used in the first scan were genotyped for 444 additional F(2) animals. Three different Mendelian models using least-squares for QTL analysis were applied for the second scan: a LC model, a half-sib (HS) model, and a combined LC and HS model. Significance thresholds were determined by false discovery rate (FDR). In total, 50 QTL using the LC model, 38 QTL using the HS model, and 3 additional QTL using the combined LC and HS model were identified (q < 0.05). The LC and HS models revealed strong evidence for QTL regions on SSC6 for carcass traits (e.g., 10th-rib backfat; q < 0.0001) and on SSC15 for meat quality traits (e.g., tenderness, color, pH; q < 0.01), respectively. QTL for pH (SSC3), dressing percent (SSC7), marbling score and moisture percent (SSC12), CIE a* (SSC16), and carcass length and spareribs weight (SSC18) were also significant (q < 0.01). Additional marker and animal genotypes increased the statistical power for QTL detection, and applying different analysis models allowed confirmation of QTL and detection of new QTL.

Application of alternative models to identify QTL for growth traits in an F2 Duroc x Pietrain pig resource population.

BACKGROUND: A variety of analysis approaches have been applied to detect quantitative trait loci (QTL) in experimental populations. The initial genome scan of our Duroc x Pietrain F2 resource population included 510 F2 animals genotyped with 124 microsatellite markers and analyzed using a line-cross model. For the second scan, 20 additional markers on 9 chromosomes were genotyped for 954 F2 animals and 20 markers used in the first scan were genotyped for 444 additional F2 animals. Three least-squares Mendelian models for QTL analysis were applied for the second scan: a line-cross model, a half-sib model, and a combined line-cross and half-sib model. RESULTS: In total, 26 QTL using the line-cross model, 12 QTL using the half-sib model and 3 additional QTL using the combined line-cross and half-sib model were detected for growth traits with a 5% false discovery rate (FDR) significance level. In the line-cross analysis, highly significant QTL for fat deposition at 10-, 13-, 16-, 19-, and 22-wk of age were detected on SSC6. In the half-sib analysis, a QTL for loin muscle area at 19-wk of age was detected on SSC7 and QTL for 10th-rib backfat at 19- and 22-wk of age were detected on SSC15. CONCLUSIONS: Additional markers and animals contributed to reduce the confidence intervals and increase the test statistics for QTL detection. Different models allowed detection of new QTL which indicated differing frequencies for alternative alleles in parental breeds.

Gene expression profiling in hepatic tissue of newly weaned pigs fed pharmacological zinc and phytase supplemented diets.

BACKGROUND: Zinc (Zn) is an essential trace element. However, Zn bioavailability from commonly consumed plants may be reduced due to phytic acid. Zn supplementation has been used to treat diarrheal disease in children, and in the U.S. swine industry at pharmacological levels to promote growth and fecal consistency, but underlying mechanisms explaining these beneficial effects remain unknown. Moreover, adding supplemental phytase improves Zn bioavailability. Thus, we hypothesized that benefits of pharmacological Zn supplementation result from changes in gene expression that could be further affected by supplemental phytase. The goal of this study was to investigate the effects of feeding newly weaned pigs dietary Zn (150, 1,000, or 2,000 mg Zn/kg) as Zn oxide with or without phytase [500 phytase units (FTU)/kg] for 14 d on hepatic gene expression. Liver RNA from pigs fed 150, 1,000, or 2,000 mg Zn/kg, or 1,000 mg Zn/kg with phytase (n = 4 per treatment) was reverse transcribed and examined using the differential display reverse transcription polymerase chain reaction technique. Liver RNA from pigs fed 150 or 2,000 mg Zn/kg (n = 4 per treatment) was also evaluated using a 70-mer oligonucleotide microarray. RESULTS: Expressed sequence tags for 61 putatively differentially expressed transcripts were cloned and sequenced. In addition, interrogation of a 13,297 element oligonucleotide microarray revealed 650 annotated transcripts (FDR

Pharmacological zinc and phytase supplementation enhance metallothionein mRNA abundance and protein concentration in newly weaned pigs.

The swine industry feeds pharmacological zinc (Zn) to newly weaned pigs to improve health. Because most swine diets are plant-based with a high phytic acid content, we hypothesized that adding phytase to diets could reduce the amount of Zn required to obtain beneficial responses. The role of metallothionein (MT) in Zn homeostasis could be important in this positive response. Thus, the goal of this study was to investigate the effect of dietary Zn and phytase on relative MT mRNA abundance and protein concentration in newly weaned pigs. Diets containing adequate (150 mg Zn/kg) or pharmacological concentrations of Zn (1000 or 2000 mg Zn/kg), as zinc oxide, with or without phytase [0, 500 phytase units (FTU)/kg, Natuphos, BASF] were fed in a 3 x 2 factorial design. Plasma and tissue minerals were measured in pigs killed after 14 d of dietary intervention. Hepatic and renal relative MT mRNA abundance and protein were greater (P < 0.05) in pigs fed 1000 mg Zn/kg with phytase, or 2000 mg Zn/kg with or without phytase vs. the remaining treatments. Intestinal mucosa MT mRNA abundance and protein were greater (P < 0.05) in pigs fed 2000 mg Zn/kg with phytase than in pigs fed 2000 mg Zn/kg alone or 1000 mg Zn/kg with phytase. Pigs fed 1000 mg Zn/kg plus phytase or 2000 mg Zn/kg with or without phytase had higher plasma, hepatic, and renal Zn than those fed the adequate Zn diets or 1000 mg Zn/kg. We conclude that feeding 1000 mg Zn/kg with phytase enhances MT mRNA abundance and protein and Zn absorption to the same degree as 2000 mg Zn/kg with and without phytase.

Developmental changes in the fetal pig transcriptome.

Growth and development of pig fetuses is dependent on the coordinated expression of multiple genes. Between 21 and 45 days of gestation, fetuses experience increasing growth rates that can result in uterine crowding and increased mortality. We used differential display reverse transcription-PCR (DDRT-PCR) to identify differentially expressed genes in pig fetuses at 21, 35, and 45 days of gestation. Pig cDNAs were identified with homologies to CD3 gamma-subunit, collagen type XIV alpha1, complement component C6, craniofacial developmental protein 1, crystallin-gammaE, DNA binding protein B, epsilon-globin, formin binding protein 2, ribosomal protein L23, small acidic protein, secreted frizzled related protein 2, titin, vitamin D binding protein, and two hypothetical protein products. Two novel expressed sequence tags (ESTs) were also identified. Expression patterns were confirmed for eight genes, and spatiotemporal expression of three genes was evaluated. We identified novel transcriptome changes in fetal pigs during a period of rapid growth. These changes involved genes with a spectrum of proposed functions, including musculoskeletal growth, immune system function, and cellular regulation. This information can ultimately be used to enhance production efficiency through improved pig growth and survival.