Chao Ning

Identification of reference microRNAs for quantitative expression analysis in porcine peripheral blood mononuclear cells treated with polyinosinic-polycytidylic acid.

Peripheral blood mononuclear cells (PBMCs) are clinically important cells. Detection of microRNAs (miRNAs) expression in PBMCs can be useful for miRNA biomarker discovery for various diseases. Quantitative real‐time PCR (qRT‐PCR) has become an important method used for measuring miRNAs expression. However, the reliability of qRT‐PCR data critically depends on proper selection of reference genes. Here, we performed qRT‐PCR to quantify the expression levels of nine miRNAs (Ssc‐miR‐16, Hsa‐miR‐25, Ssc‐miR‐34a, Hsa‐miR‐93, Bta‐miR‐92b, Ssc‐miR‐103, Ssc‐miR‐106a, Ssc‐miR‐128 and Ssc‐miR‐107) and one small nuclear RNA (U6) in PBMCs treated with polyinosinic–polycytidylic acid [poly (I:C)] that widely used for simulating viral infection. We used the four statistical algorithms (GeNorm 3.5, NormFinder, BestKeeper and comparative ∆ Ct method) to evaluate gene expression stability and observed that Ssc‐miR‐34a was the best single reference gene and the pair of Ssc‐miR‐107 and Ssc‐miR‐103 was the best combination of reference miRNAs for porcine PBMCs treated with poly (I:C). Our study shows the first evidence of careful selection of reference miRNAs in porcine PBMCs and maybe helpful for discovering miRNA biomarkers for double‐stranded RNA‐induced disease.

Profiling long noncoding RNA of multi-tissue transcriptome enhances porcine noncoding genome annotation

AIMnTo construct a comprehensive pig noncoding transcriptome and further enhance porcine noncoding genome annotation.nnnMATERIALS & METHODSnWe performed a tissue-based long noncoding RNA (lncRNA) profiling via exploiting 32,212 nonredundant lncRNA isoforms corresponding to 18,676 lncRNA loci across 34 normal pig tissues using high-throughput sequencing. Furthermore, the potential relationship between our identified lncRNAs and known protein-coding genes were globally assessed via a comprehensive computation-based strategy, developing a genome-wide lncRNA-targeted genome draft for further functional studies on noncoding genes.nnnRESULTS & CONCLUSIONnAmong these lncRNAs, ubiquitously expressed lncRNA appeared at a higher level compared with tissue-specific one. Findings herein provide insight into comprehensive knowledge of porcine noncoding RNAs and further enhance pig noncoding annotation. For ease of accessing the information of the identified lncRNAs, we deposited those with high confidence in the publicly available NONCODE database, providing a valuable resource for facilitating pig noncoding genomic studies.

Functional validation of GPIHBP1 and identification of a functional mutation in GPIHBP1 for milk fat traits in dairy cattle

In a previous genome-wide association study (GWAS) on milk production traits in a Chinese Holstein population, we revealed that GPIHBP1 is a novel promising candidate gene for milk fat content traits. In this study, we performed over-expression and RNAi experiments on GPIHBP1 in bovine primary mammary epithelial cells. The results showed that the expression of several important milk fat-related genes (LPL, CD36, VLDLR, ACACA and FASN) increased or decreased when the expression of GPIHBP1 was up- or down-regulated. To identify the potential functional SNP involved, we explored the genetic variants of GPIHBP1 and found that a G/A mutation (chr14:2553998) in the promoter region of GPIHBP1 significantly reduced promoter activity and had an effect on transcription factor binding sites. This finding was consistent with the lower expression of GPIHBP1 observed in the mammary gland tissue of cows harboring the homozygous AA mutation compared with wild-type homozygous GG or heterozygous AG. Furthermore, association analysis showed that cows with the AA genotype outperformed those with the GG and AG genotypes in terms of the milk fat percentage. Our study demonstrates that GPIHBP1 could be a strong candidate gene for milk fat content traits and, in particular, the G to A mutation at chr14:2553998 within GPIHBP1 could be a functional mutation related to its effects.

Genome-wide DNA methylation and transcriptome analyses reveal genes involved in immune responses of pig peripheral blood mononuclear cells to poly I:C

DNA methylation changes play essential roles in regulating the activities of genes involved in immune responses. Understanding of variable DNA methylation linked to immune responses may contribute to identifying biologically promising epigenetic markers for pathogenesis of diseases. Here, we generated genome-wide DNA methylation and transcriptomic profiles of six pairs of polyinosinic-polycytidylic acid-treated pig peripheral blood mononuclear cell (PBMC) samples and corresponding controls using methylated DNA immunoprecipitation sequencing and RNA sequencing. Comparative methylome analyses identified 5,827 differentially methylated regions and 615 genes showing differential expression between the two groups. Integrative analyses revealed inverse associations between DNA methylation around transcriptional start site and gene expression levels. Furthermore, 70 differentially methylated and expressed genes were identified such as TNFRSF9, IDO1 and EBI3. Functional annotation revealed the enriched categories including positive regulation of immune system process and regulation of leukocyte activation. These findings demonstrated DNA methylation changes occurring in immune responses of PBMCs to poly I:C stimulation and a subset of genes potentially regulated by DNA methylation in the immune responses. The PBMC DNA methylome provides an epigenetic overview of this physiological system in response to viral infection, and we expect it to constitute a valuable resource for future epigenetic epidemiology studies in pigs.

Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects

Complex traits with multiple phenotypic values changing over time are called longitudinal traits. In traditional genome-wide association studies (GWAS) for longitudinal traits, a combined/averaged estimated breeding value (EBV) or deregressed proof (DRP) instead of multiple phenotypic measurements per se for each individual was frequently treated as response variable in statistical model. This can result in power losses or even inflate false positive rates (FPRs) in the detection due to failure of exploring time-dependent relationship among measurements. Aiming at overcoming such limitation, we developed two random regression-based models for functional GWAS on longitudinal traits, which could directly use original time-dependent records as response variable and fit the time-varied Quantitative Trait Nucleotide (QTN) effect. Simulation studies showed that our methods could control the FPRs and increase statistical powers in detecting QTN in comparison with traditional methods where EBVs, DRPs or estimated residuals were considered as response variables. Besides, our proposed models also achieved reliable powers in gene detection when implementing into two real datasets, a Chinese Holstein Cattle data and the Genetic Analysis Workshop 18 data. Our study herein offers an optimal way to enhance the power of gene detection and further understand genetic control of developmental processes for complex longitudinal traits.

Quantitative proteome analysis of bovine mammary gland reveals protein dynamic changes involved in peak and late lactation stages

Mammary gland is an important organ for milk synthesis and secretion. It undergoes dramatic physiological changes to adapt the shift from peak to late lactation stage. Protein plays a final very vital role in many life functions, and the protein changes during different lactation stages potentially reflect the biology of lactation and the functions of mammary gland in cows. In current study, we adopted tandem mass tags label-based quantitative analysis technique and to investigate proteome changes occurring in bovine mammary gland from peak to late lactation stages. A total of 3753 proteins from mammary tissues taken at two lactation points from four individual cows by biopsy were quantified, out of which 179 proteins were expressed differentially between two stages. We observed five new DEPs (AACS, DHCR7, GSTM3, SFRP1 and SFRP4) and nine functional well-studies known proteins (PLIN2, LPIN1, PLIN3, GSN, CD74, MMP2, SOD1, SOD3 and GPX3) related to milk performance and mammary morphology. Bioinformatics analyses of the DEPs showed a majority of the up-regulated proteins during late lactation stage were related to apoptosis and immune process, while the downregulated proteins were mainly involved in localization, lipid metabolic and transport process. This suggests that the mammary gland can adapt to different molecular functions according to the biological need of the animal. From the integrated analysis of the differentially expressed proteins with known quantitative trait loci and genome-wide association study data, we identified 95 proteins may potentially affect milking performance. We expect findings in this study could be a valuable resource for future studies investigating the bovine proteome and functional studies.

Characterization of genome-wide segmental duplications reveals a common genomic feature of association with immunity among domestic animals

BackgroundSegmental duplications (SDs) commonly exist in plant and animal genomes, playing crucial roles in genomic rearrangement, gene innovation and the formation of copy number variants. However, they have received little attention in most livestock species.ResultsAiming at characterizing SDs across the genomes of diverse livestock species, we mapped genome-wide SDs of horse, rabbit, goat, sheep and chicken, and also enhanced the existing SD maps of cattle and pig genomes based on the most updated genome assemblies. We adopted two different detection strategies, whole genome analysis comparison and whole genome shotgun sequence detection, to pursue more convincing findings. Accordingly we identified SDs for each species with the length of from 21.7 Mb to 164.1 Mb, and 807 to 4,560 genes were harboured within the SD regions across different species. More interestingly, many of these SD-related genes were involved in the process of immunity and response to external stimuli. We also found the existence of 59 common genes within SD regions in all studied species except goat. These common genes mainly consisted of both UDP glucuronosyltransferase and Interferon alpha families, implying the connection between SDs and the evolution of these gene families.ConclusionsOur findings provide insights into livestock genome evolution and offer rich genomic sources for livestock genomic research.

Efficient Multivariate Analysis Algorithms for Longitudinal Genome-wide Association Studies

Motivation Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. Results We herein developed efficient genome-wide multivariate association algorithms (GMA) for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed new method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. Availability and Implementation We wrote a software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA), which is available freely for interested users in relevant fields.

Mining unknown porcine protein isoforms by tissue-based map of proteome enhances the pig genome annotation

A lack of the complete pig proteome has left a gap in our knowledge of the pig genome and has restricted the feasibility of using pigs as a biomedical model. We developed the tissue-based proteome maps using 34 major normal pig tissues. A total of 7,319 unknown protein isoforms were identified and systematically characterized, including 3,703 novel protein isoforms, 669 protein isoforms from 460 genes symbolized beginning with LOC, and 2,947 protein isoforms without clear NCBI annotation in current pig reference genome. These newly identified protein isoforms were functionally annotated through profiling the pig transcriptome with high-throughput RNA sequencing (RNA-seq) of the same pig tissues, further improving the genome annotation of corresponding protein coding genes. Combining the well-annotated genes that having parallel expression pattern and subcellular witness, we predicted the tissue related subcellular components and potential function for these unknown proteins. Finally, we mined 3,656 orthologous genes for 49.95% of unknown protein isoforms across multiple species, referring to 65 KEGG pathways and 25 disease signaling pathways. These findings provided valuable insights and a rich resource for enhancing studies of pig genomics and biology as well as biomedical model application to human medicine.

Transcriptome of Porcine PBMCs over Two Generations Reveals Key Genes and Pathways Associated with Variable Antibody Responses post PRRSV Vaccination

Porcine reproductive and respiratory syndrome virus (PRRSV) is a virus susceptible to antibody dependent enhancement, causing reproductive failures in sows and preweaning mortality of piglets. Modified-live virus (MLV) vaccines are used to control PRRS in swine herds. However, immunized sows and piglets often generate variable antibody levels. This study aimed to detect significant genes and pathways involved in antibody responsiveness of pregnant sows and their offspring post-PRRSV vaccination. RNA sequencing was conducted on peripheral blood-mononuclear cells (PBMCs), which were isolated from pregnant sows and their piglets with high (HA), median (MA), and low (LA) PRRS antibody levels following vaccination. 401 differentially expressed genes (DEGs) were identified in three comparisons (HA versus MA, HA versus LA, and MA versus LA) of sow PBMCs. Two novel pathways (complement and coagulation cascade pathway; and epithelial cell signaling in H. pylori infection pathway) revealed by DEGs in HA versus LA and MA versus LA were involved in chemotactic and proinflammatory responses. TNF-α, CCL4, and NFKBIA genes displayed the same expression trends in subsequent generation post-PRRS-MLV vaccination. Findings of the study suggest that two pathways and TNF-α, CCL4, and NFKBIA could be considered as key pathways and potential candidate genes for PRRSV vaccine responsiveness, respectively.