Gut response induced by weaning in piglet features marked changes in immune and inflammatory response

At weaning, piglets are exposed to many stressors, such as separation from the sow, mixing with other litters, end of lactational immunity, and a change in their environment and gut microbiota. The sudden change of feeding regime after weaning causes morphological and histological changes in the small intestine which are critical for the immature digestive system. Sixteen female piglets were studied to assess the effect of sorbic acid supplementation on the small intestine tissue transcriptome. At weaning day (T0, piglet age 28 days), four piglets were sacrificed and ileal tissue samples collected. The remaining 12 piglets were weighed and randomly assigned to different postweaning (T5, piglet age 33 days) diets. Diet A (n = 6) contained 5 g/kg of sorbic acid. In diet B (n = 6), the organic acids were replaced by barley flour. Total RNA was isolated and then hybridized to CombiMatrix CustomArray™ 90-K platform microarrays, screening about 30 K genes. Even though diet had no detectable effect on the transcriptome during the first 5 days after weaning, results highlighted some of the response mechanisms to the stress of weaning occurring in the piglet gut. A total of 205 differentially expressed genes were used for functional analysis using the bioinformatics tools BLAST2GO, Ingenuity Pathway Analysis 8.0, and Dynamic Impact Approach (DIA). Bioinformatic analysis revealed that apoptosis, RIG-I-like, and NOD-like receptor signaling were altered as a result of weaning. Interferons and caspases gene families were the most activated after weaning in response to piglets to multiple stressors. Results suggest that immune and inflammatory responses were activated and likely are a cause of small intestine atrophy as revealed by a decrease in villus height and villus/crypt ratio

Genomic Variants Revealed By Invariably Missing Genotypes In Nelore Cattle.

High density genotyping panels have been used in a wide range of applications. From population genetics to genome-wide association studies, this technology still offers the lowest cost and the most consistent solution for generating SNP data. However, in spite of the application, part of the generated data is always discarded from final datasets based on quality control criteria used to remove unreliable markers. Some discarded data consists of markers that failed to generate genotypes, labeled as missing genotypes. A subset of missing genotypes that occur in the whole population under study may be caused by technical issues but can also be explained by the presence of genomic variations that are in the vicinity of the assayed SNP and that prevent genotyping probes from annealing. The latter case may contain relevant information because these missing genotypes might be used to identify population-specific genomic variants. In order to assess which case is more prevalent, we used Illumina HD Bovine chip genotypes from 1,709 Nelore (Bos indicus) samples. We found 3,200 missing genotypes among the whole population. NGS re-sequencing data from 8 sires were used to verify the presence of genomic variations within their flanking regions in 81.56% of these missing genotypes. Furthermore, we discovered 3,300 novel SNPs/Indels, 31% of which are located in genes that may affect traits of importance for the genetic improvement of cattle production