Dataset Information

Identification of Differentially Expressed Genes Associated with Litter Size in Berkshire Pig Placenta.

ABSTRACT: Improvement in litter size has become of great interest in the pig industry because fecundity is directly related to sow reproductive life. Improved reproduction has thus been achieved by elucidating the molecular functions of genes associated with fecundity. In the present study, we identified differentially expressed genes (DEGs) via transcriptomic analysis using RNA-sequencing (RNA-Seq) in Berkshire pig placentas from larger (LLG, mean litter size >12) and smaller (SLG, mean litter size < 6.5) litter size groups. In total 588 DEGs were identified (p < 0.05, > 1.5-fold change), of which 98 were upregulated, while 490 were downregulated in the LLG compared with the SLG. Gene Ontology (GO) enrichment was also performed. We concluded that 129 of the 588 DEGs were closely related to litter size according to reproduction related genes selected based on previous reports, as 110 genes were downregulated and 19 upregulated in the LLG compared with the SLG. RT-qPCR utilizing specific primers targeting the early growth response 2 (EGR2), pheromaxein c subunit (PHEROC) and endothelial lipase (LIPG) genes showed high accordance with RNA-Seq results. Furthermore, we investigated the upstream regulators of these three genes in the placenta. We found that WNT9B, a Wnt signaling pathway molecule, and IL-6, known inducers of EGR2 and LIPG, respectively, were significantly increased in LLG compared with SLG. We believe that the induction of IL-6 and LIPG may play an important role in increasing nutrition supply through the placenta from the sow to the piglet during gestation. These results provide novel molecular insights into pig reproduction.

SUBMITTER: Kwon SG

PROVIDER: S-EPMC4831801 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Identification of Differentially Expressed Genes Associated with Litter Size in Berkshire Pig Placenta.

Kwon Seul Gi SG Hwang Jung Hye JH Park Da Hye DH Kim Tae Wan TW Kang Deok Gyeong DG Kang Kyung Hee KH Kim Il-Suk IS Park Hwa Chun HC Na Chong-Sam CS Ha Jeongim J Kim Chul Wook CW

PloS one 20160414 4

Improvement in litter size has become of great interest in the pig industry because fecundity is directly related to sow reproductive life. Improved reproduction has thus been achieved by elucidating the molecular functions of genes associated with fecundity. In the present study, we identified differentially expressed genes (DEGs) via transcriptomic analysis using RNA-sequencing (RNA-Seq) in Berkshire pig placentas from larger (LLG, mean litter size >12) and smaller (SLG, mean litter size < 6.5 ...[more]

PMID: 27078025

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Project description:Seasonal reproductive inefficiency is still observed in modern swine facilities. We previously reported global placental methylation activity was reduced from summer breedings and tended to be less from semen collected during cooler periods. The objective of the current study was to evaluate chromatin modification marks within swine placenta in relationship to breeding season, semen collection season, and semen storage. White composite gilts were artificially inseminated in August or January using single-sire semen that was collected during warm or cool periods and stored as either cryopreserved or cooled-extended. Gilts were harvested 45 days post-breeding, and placental samples from the smallest, average, and largest fetus in each litter were collected and stored at -80°C until RNA extraction. An RT2 Profiler assay featuring 84 known chromatin modification enzyme targets was performed using placental RNA pooled by litter. Real-time quantitative polymerase chain reaction results were analyzed using the MIXED procedure, and P-values were Hochberg corrected using the MULTTEST procedure in SAS. The complete model included the fixed effects of breeding season (winter or summer), semen collection season (cool or warm), semen storage (cooled-extended or cryopreserved), interactions; boar as repeated effect; and plate as random effect. If interactions were not significant, only the main effects were tested. The genes, ATF2, AURKA, and KDM5B, were different (P < 0.05) by interaction of breeding season, semen collection season, and semen storage. In general, the greatest (P < 0.05) expression was in placentas derived from summer breedings. Expression of AURKA was also influenced by semen collection and storage. Expression of placental KDM5B from winter breedings was also greater (P < 0.05) from semen collected during cool periods. Placental expressions of ASH2L, DNMT3B, ESCO1, HDAC2, ING3, KDM6B, MYSM1, and SMYD3 were greater (P < 0.05) from summer breedings. Increased expressions of known chromatin modification genes, from placentas derived from summer breedings, are likely responsible for differences in gene transcription between summer- or winter-derived placentas.

Project description:Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.

Project description:BackgroundMicroarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).ResultsWe hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile.ConclusionsFactors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

Project description:BackgroundPostnatal muscle growth is largely depending on the number and size of muscle fibers. The number of myofibers and to a large extent their metabolic and contractile properties, which also influence their size, are determined prenatally during the process of myogenesis. Hence identification of genes and their networks governing prenatal development of skeletal muscles will provide insight into the control of muscle growth and facilitate finding the source of its variation. So far most of the genes involved in myogenesis were identified by in vitro studies using gene targeting and transgenesis. Profiling of transcriptome changes during the myogenesis in vivo promises to obtain a more complete picture. In order to address this, we performed transcriptome profiling of prenatal skeletal muscle using differential display RT-PCR as on open system with the potential to detect novel transcripts. Seven key stages of myogenesis (days 14, 21, 35, 49, 63, 77 and 91 post conception) were studied in two breeds, Pietrain and Duroc, differing markedly in muscularity and muscle structure.ResultsEighty prominent cDNA fragments were sequenced, 43 showing stage-associated and 37 showing breed-associated differences in the expression, respectively. Out of the resulting 85 unique expressed sequence tags, EST, 52 could be assigned to known genes. The most frequent functional categories represented genes encoding myofibrillar proteins (8), genes involved in cell adhesion, cell-cell signaling and extracellular matrix synthesis/remodeling (8), genes regulating gene expression (8), and metabolism genes (8). Some of the EST that showed no identity to any known transcripts in the databases are located in introns of known genes and most likely represent novel exons (e.g. HMGA2). Expression of thirteen transcripts along with five reference genes was further analyzed by means of real-time quantitative PCR. Nine of the target transcripts showed higher than twofold differences in the expression between the two breeds (GATA3, HMGA2, NRAP, SMC6L1, SPP1, RAB6IP2, TJP1 and two EST).ConclusionThe present study revealed several genes and novel transcripts not previously associated with myogenesis and expands our knowledge of genetic factors operating during myogenesis. Genes that exhibited differences between the divergent breeds represent candidate genes for muscle growth and structure.

Dataset Information

Identification of Differentially Expressed Genes Associated with Litter Size in Berkshire Pig Placenta.

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Identification of Differentially Expressed Genes Associated with Litter Size in Berkshire Pig Placenta.

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