Abstract
Transcriptome profiles established using high-throughput sequencing can be effectively used for screening genome-wide differentially expressed genes (DEGs). RNA sequences (from RNA-seq) and microRNA sequences (from miRNA-seq) from the tissues of longissimus dorsi muscle of two indigenous Chinese pig breeds (Diannan Small-ear pig [DSP] and Tibetan pig [TP]) and two introduced pig breeds (Landrace [LL] and Yorkshire [YY]) were examined using HiSeq 2000 to identify and compare the differential expression of functional genes related to muscle growth and lipid deposition. We obtained 27.18 G clean data through the RNA-seq and detected that 18,208 genes were positively expressed and 14,633 of them were co-expressed in the muscle tissues of the four samples. In all, 315 DEGs were found between the Chinese pig group and the introduced pig group, 240 of which were enriched with functional annotations from the David database and significantly enriched in 27 Gene Ontology (GO) terms that were mainly associated with muscle fiber contraction, cadmium ion binding, response to organic substance and contractile fiber part. Based on functional annotation, we identified 85 DEGs related to growth traits that were mainly involved in muscle tissue development, muscle system process, regulation of cell development, and growth factor binding, and 27 DEGs related to lipid deposition that were mainly involved in lipid metabolic process and fatty acid biosynthetic process. With miRNA-seq, we obtained 23.78 M reads and 320 positively expressed miRNAs from muscle tissues, including 271 known pig miRNAs and 49 novel miRNAs. In those 271 known miRNAs, 20 were higher and 10 lower expressed in DSP-TP than in LL-YY. The target genes of the 30 miRNAs were mainly participated in MAPK, GnRH, insulin and Calcium signaling pathway and others involved cell development, growth and proliferation, etc. Combining the DEGs and the differentially expressed (DE) miRNAs, we drafted a network of 46 genes and 18 miRNAs for regulating muscle growth and a network of 15 genes and 16 miRNAs for regulating lipid deposition. We identified that CAV2, MYOZ2, FRZB, miR-29b, miR-122, miR-145-5p and miR-let-7c, etc, were key genes or miRNAs regulating muscle growth, and FASN, SCD, ADORA1, miR-4332, miR-182, miR-92b-3p, miR-let-7a and miR-let-7e, etc, were key genes or miRNAs regulating lipid deposition. The quantitative expressions of eight DEGs and seven DE miRNAs measured with real-time PCR certified that the results of differential expression genes or miRNAs were reliable. Thus, 18,208 genes and 320 miRNAs were positively expressed in porcine longissimus dorsi muscle. We obtained 85 genes and 18 miRNAs related to muscle growth and 27 genes and 16 miRNAs related to lipid deposition, which provided new insights into molecular mechanism of the economical traits in pig.
Highlights
Growth rate, meat quality, and meat flavor are the main economic traits in pig production that can influence human consumption of meat products
The results indicated that the muscle tissues were right for searching genes related to muscle growth and lipid deposition
62.83 M to 77.17 M raw reads were generated for each sample, 79.31% to 82.21% of the clean reads were aligned with the pig reference genome (Sus scrofa 10.2) (Table 4), and 56.73% to 62.76% of the clean reads were distributed in coding regions (S1 Fig)
Summary
Meat quality, and meat flavor are the main economic traits in pig production that can influence human consumption of meat products. Fatness traits such as back fat thickness and intramuscular fat content (IMF), which have positive correlations with meat tenderness, juiciness, and taste[1], are economically important in pig breeding because these can influence meat quality and carcass composition. Different patterns of muscle growth among these breeds make them a good model for identifying the functional genes responsible for the molecular mechanisms that control the aforementioned economical traits. Novel and low-abundance transcripts can be efficiently identified via transcriptome profiling.
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