Methylome and transcriptome data integration reveals potential roles of DNA methylation and candidate biomarkers of cow Streptococcus uberis subclinical mastitis

Background:Mastitis caused by different pathogens including Streptococcus uberis(S.uberis)is responsible for huge economic losses to the dairy industry.In order to investigate the potential genetic and epigenetic regulatory mecha‑nisms of subclinical mastitis due to S.uberis,the DNA methylome(whole genome DNA methylation sequencing)and transcriptome(RNA sequencing)of milk somatic cells from cows with naturally occurring S.uberis subclinical mastitis and healthy control cows(n=3/group)were studied.Results:Globally,the DNA methylation levels of CpG sites were low in the promoters and first exons but high in inner exons and introns.The DNA methylation levels at the promoter,first exon and first intron regions were nega‑tively correlated with the expression level of genes at a whole‑genome‑wide scale.In general,DNA methylation level was lower in S.uberis‑positive group(SUG)than in the control group(CTG).A total of 174,342 differentially methylated cytosines(DMCs)(FDR<0.05)were identified between SUG and CTG,including 132,237,7412 and 34,693 DMCs in the context of CpG,CHG and CHH(H=A or T or C),respectively.Besides,101,612 methylation haplotype blocks(MHBs)were identified,including 451 MHBs that were significantly different(dMHB)between the two groups.A total of 2130 differentially expressed(DE)genes(1378 with up‑regulated and 752 with down‑regulated expression)were found in SUG.Integration of methylome and transcriptome data with MethGET program revealed 1623 genes with signifi‑cant changes in their methylation levels and/or gene expression changes(MetGDE genes,MethGET P‑value<0.001).Functional enrichment of genes harboring≥15 DMCs,DE genes and MetGDE genes suggest significant involvement of DNA methylation changes in the regulation of the host immune response to S.uberis infection,especially cytokine activities.Furthermore,discriminant correlation analysis with DIABLO method identified 26 candidate biomarkers,including 6 DE genes,15 CpG‑DMCs and 5 dMHBs that discriminated between SUG and CTG.Conclusion:The integration of methylome and transcriptome of milk somatic cells suggests the possible involve‑ment of DNA methylation changes in the regulation of the host immune response to subclinical mastitis due to S.uberis.The presented genetic and epigenetic biomarkers could contribute to the design of management strategies of subclinical mastitis and breeding for mastitis resistance.

The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments

The Genome Analysis Toolkit(GATK) is a popular set of programs for discovering and genotyping variants from next-generation sequencing data.The current GATK recommendation for RNA sequencing(RNA-seq) is to perform variant calling from individual samples,with the drawback that only variable positions are reported.Versions 3.0 and above of GATK offer the possibility of calling DNA variants on cohorts of samples using the HaplotypeCaller algorithm in Genomic Variant Call Format(GVCF) mode.Using this approach,variants are called individually on each sample,generating one GVCF file per sample that lists genotype likelihoods and their genome annotations.In a second step,variants are called from the GVCF files through a joint genotyping analysis.This strategy is more flexible and reduces computational challenges in comparison to the traditional joint discovery workflow.Using a GVCF workflow for mining SNP in RNA-seq data provides substantial advantages,including reporting homozygous genotypes for the reference allele as well as missing data.Taking advantage of RNA-seq data derived from primary macrophages isolated from 50 cows,the GATK joint genotyping method for calling variants on RNA-seq data was validated by comparing this approach to a so-called "per-sample" method.In addition,pair-wise comparisons of the two methods were performed to evaluate their respective sensitivity,precision and accuracy using DNA genotypes from a companion study including the same 50 cows genotyped using either genotyping-by-sequencing or with the Bovine SNP50 Beadchip(imputed to the Bovine high density).Results indicate that both approaches are very close in their capacity of detecting reference variants and that the joint genotyping method is more sensitive than the per-sample method.Given that the joint genotyping method is more flexible and technically easier,we recommend this approach for variant calling in RNA-seq experiments.