Characterization of Single Nucleotide Polymorphisms (SNPs) associated with intestinal microbiota in pigs fed diets rich in oleic acid

Abstract

Gut microbiota modulation through the dietary fatty acid profile may play a crucial role in the prevention and treatment of obesity and comorbidities. However, it remains unclear whether the cecal or fecal microbiome is more suitable to understanding its link with obesity onset. Therefore, this study will be conducted to evaluate the bacterial community composition, as well as identify Single Nucleotide Polymorphisms (SNPs) associated with bacterial abundance in both cecal content and feces of pig fed diets enriched with oleic acid in different ages. Cecal and fecal samples from 48 immunologically castrated barrows with 28.5 ± 2.7 kg initial body weight (71 days of age) and 133.9 ± 9.4 kg final body weight (169 days of age) will be used in this study. Pigs were arranged in a randomized complete block design, with six replicates per treatment and four pigs per pen. Treatments consisted of corn-soybean meal-based diets with 3% of degummed soybean oil (SOJ) or 3% of canola oil (CAN), being the last one rich in oleic acid. During the 98-day experimental period, all pigs had ad libitum access to feed and water. Fecal samples were collected from all pigs through rectal palpation at 71, 134 e 166 days of age. Cecal content and muscle tissue samples were taken from each pig after exsanguination. Bacterial genomic DNA will be extracted from cecal and fecal samples and V3-V4 region of bacterial 16S ribosomal RNA (rRNA) genes will be amplified. The muscle tissue will be used for the extraction of genomic DNA for the study of SNPs. The 16S rRNA libraries will be sequenced by the Illumina MiSeq platform. The relative abundance of each taxonomic group will be generated in QIIME2 platform and PERMANOVA tests will be performed to compare beta diversity between each group. Genomic DNA from muscle samples will be isolated, genotyped using the Illumina PorcineSNP60 BeadChip, and then association analyses will be performed by Bayesian method using GenSel software. (AU)