Comparative study of gene expression and allele-specific expression in three tissues of the heart, muscle, and spleen of roosters adapted to low and high altitude regions based on open access RNA-Seq databases

Introduction: Gene regulation can be assessed by allele-specific expression (ASE), which has the potential to cause phenotypic variation. The variants associated with gene regulation can be found using ASE. Transcriptomic variation in gene expression is known to play an important role in the formation of the phenotype. Single base variations resulting from transitions (C/T or G/A) or transversions (C/G, C/A, T/A, T/G) of nucleotides at the same position between individual nucleotides in genomic DNA sequences are known as single nucleotide polymorphisms (SNPs). SNPs are important molecular markers used in breeding and genetic research. Next-generation sequencing is advancing rapidly and provides a high-throughput approach for SNP discovery in the transcriptome or genome. Transcriptome studies can bridge the gaps between genotypes and phenotypes and provide insights into the mechanisms linking sequence and function. An effective transcriptome mapping and quantification technique for studying global gene expression is RNA sequencing or RNA-Seq. For this reason, RNA-Seq is a next-generation sequencing technology that can analyze gene expression profiles and the entire transcriptome. This study aimed to identify differential gene expression and SNPs in three tissues of the heart, muscle, and spleen of roosters adapted to low and high altitude regions based on open access RNA-Seq databases. Materials and methods: RNA-Seq data from 54 samples were collected from the SRA database in NCBI for chickens (NCBI GEO accession: GSE119387). The samples included 33.4, 30.3, and 35.3 million paired final reads of heart, muscle, and spleen tissues, each 100 bp in length. Illumina Hiseq 2000 was used to perform mRNA sequencing. The fastq-dump command in Sratoolkit 2.11 was used to convert data from SRA to FASTQ format  FastQC (version 0.11) was used to assess data quality and Trimmomatic (version 0.33) was used to trim the reads to eliminate adapters and low-quality sequences. Trimmed reads were aligned to the reference genome of the chicken species (Gallus gallus domesticus) and the gene annotation data (GRC6a) from Tophat2, whose core host is Bowtie2. By independently aligning and mapping the RNA-Seq reads of each sample to the chicken reference genome, the transcriptome was assembled. Then, the differential gene expression analysis was performed with cufflinks. The Samtools program performed SNP detection. Finally, using the R software (version 4.2.2), the chi-square test was used to compare the amount of expressed reference and alternative alleles in the polymorphic regions of heterozygous individuals to find the notable variations. Results and discussion: As a result of gene expression analysis, 2260 genes were significantly differential expression (P<0.0002). Gene ontology analysis showed that these genes are in pathways related to heat stress and immune responses to the cause of trying to maintain body temperature involved and this cause was the activation of immune pathways. Identification of 1,473,176, 388,224, and 1,169,394 SNPs in the heart, muscle, and spleen tissues was enabled by SNP calling and discovery in the assembled transcriptome. The chi-square test revealed that the ASE-SNPs in the heart, muscle, and spleen tissues were 48,906 (10.3%), 28,529 (7.3%), and 76,251 (6.3%) SNPs (P<0.05). These SNPs were associated with 7,919, 6,182, and 10,590 genes in the heart, muscle, and spleen tissues. In three tissues, the number of reference and alternative alleles was shifted by 4.5% in favor of the reference allele. This suggests that the superiority of the reference allele over the reference genome during mapping may be related to mapping bias. Among the twelve potential SNPs and ASE-SNP types found, four were transition types (Ts) and eight were transformation types (Tv). The transition type accounted for 74% of the most common polymorphisms in the heart and 77% in the muscle and spleen. For ASE-SNPs, transition mutations accounted for 70% of all mutations in the heart tissue and 75% in the muscle and spleen tissue. These mutations were also the most common. The Ts/Tv ratios for the heart, muscle, and spleen tissues were 2.3, 3, and 3 in the ASE-SNP and 2.9, 3.3, and 2.2 in all SNPs, respectively. This indicated a decrease in Ts/Tv for the heart and muscle tissues in the ASE-SNP compared to all SNPs. Conclusions: The results of the current study support the validity of identifying SNPs in transcriptionally active regions of the genome using RNA-Seq data. Further research is needed to determine whether the expression differences between reference and alternative alleles found in heterozygous roosters raised in different environments are related to tolerance to environmental stressors such as low oxygen levels.