Pre-weaning heat stress alters liver transcriptome and DNA methylation in dairy calves | Laporta et al., JDS
Description
This study investigated the impact of pre-weaning heat stress on dairy calves’ hepatic gene expression and DNA methylation. Holstein dairy calves were exposed to summer heat stress (daily average THI > 78) for 42 d postnatally (i.e., birth to weaning) with access to active fan heat abatement (postnatal cooling, post-CL; calf-height airspeed 2.05 m/s, n = 12) or not (postnatal heat stress, post-HS; calf-height airspeed 0.15 m/s, n = 12). Liver tissue was harvested via biopsy (n = 6 per treatment) at 42 d of age for RNA sequencing and reduced representation bisulfite sequencing. The data sets in this repository contain the raw data for these analyses, including a list of differentially expressed genes and associated pathways and a list of differentially methylated genes and their related pathways and upstream regulators.
Files
Steps to reproduce
RNA-Sequencing and bioinformatics analysis Libraries were constructed using the NEBNext Ultra II RNA Library Prep Kit for Illumina (#E7775; New England BioLabs, Ipswich, MA). Libraries were sequenced using the Illumina NovaSeq 6000 platform (Illumina Inc., CA), generating 150 base-pair paired-end reads. The RNA-Seq data can be accessed by NCBI GEO with accession number GSE227323. Sequencing reads were tested for quality before and after trimming using the software FastQC (version 0.11.7, Babraham Bioinformatics, UK). Read trimming was performed using Trim Galore software (version 0.4.4, Babraham Bioinformatics, UK) with the options --paired, --length 50, --clip_R1 12, --clip_R2 12, --three_prime_clip_R1 15, and --three_prime_clip_R2 15. After edition, sequencing reads were mapped to the latest bovine reference genome (ARS-UCD1.2) using the software Hisat2 (v2.1.0) (Kim et al., 2015). The number of reads that mapped to each annotated gene in the ARS-UCD1.2 gtf file was obtained using the python script htseq-count (v0.6.1p1) using the option intersection-nonempty (Anders et al., 2015). Differentially expressed genes between treatments were detected using the R package edgeR (Robinson et al., 2010). This R package combines the use of the trimmed mean of M-values as a normalization method, an empirical Bayes approach for estimating tag wise negative binomial dispersion values, and finally, generalized linear models and likelihood ratio tests for detecting differentially expressed genes. Methylation bioinformatic analysis Genomic DNA was extracted from 10 mg of homogenized liver tissue (#OSR-M40, Tiangen Biotech, Beijing). DNA methylation was analyzed by double restriction enzyme Mspl digestion& NEBNext® Ultra™ II DNA Library Prep Kit for Illumina® (NEB #E7645) and the EZ DNA Methylation Gold Kit (Zymo Research #D5006) according to the manufacturer’s protocol. Purified DNA (50 nM) libraries were sequenced using the Illumina HiSeq. 4000 system. Quality control of the Illumina raw reads was performed using FastQC software (Babraham Bioinformatics, UK). Low-quality base removal and adapter trimming was done using Trim Galore (Babraham Bioinformatics, UK). Sequencing reads were mapped to the bovine reference genome (ARS-UCD1.2) using Bismark software (version 0.16.1, Babraham Bioinformatics, UK). Methylation information was extracted at the base resolution using the Bismark methylation extractor (Babraham Bioinformatics, UK). Differential methylation analysis was performed using the R package methylKit. Only cytosines in a CpG context with a coverage ≥ 10 reads were considered. Differentially methylated cytosines between treatments were identified via logistic regression (cut-off of at least 20% methylation difference and a q-value < 0.05). Differentially methylated cytosines were mapped to different genomic features, including coding and non-coding regions, using the R package rtracklayer. The RRBS data can be accessed by NCBI GEO: GSE227249.