Identification of candidate genes affecting chronic subclinical mastitis in Norwegian Red cattle: combining genome-wide association study, topologically associated domains and pathway enrichment analysis

Multi-omics integration identifies regulatory factors underlying bovine subclinical mastitis

BACKGROUND

Mastitis caused by multiple factors remains one of the most common and costly disease of the dairy industry. Multi-omics approaches enable the comprehensive investigation of the complex interactions between multiple layers of information to provide a more holistic view of disease pathogenesis. Therefore, this study investigated the genomic and epigenomic signatures and the possible regulatory mechanisms underlying subclinical mastitis by integrating RNA sequencing data (mRNA and lncRNA), small RNA sequencing data (miRNA) and DNA methylation sequencing data of milk somatic cells from 10 healthy cows and 20 cows with naturally occurring subclinical mastitis caused by Staphylococcus aureus or Staphylococcus chromogenes.

RESULTS

Functional investigation of the data sets through gene set analysis uncovered 3458 biological process GO terms and 170 KEGG pathways with altered activities during subclinical mastitis, provided further insights into subclinical mastitis and revealed the involvement of multi-omics signatures in the altered immune responses and impaired mammary gland productivity during subclinical mastitis. The abundant genomic and epigenomic signatures with significant alterations related to subclinical mastitis were observed, including 30,846, 2552, 1276 and 57 differential methylation haplotype blocks (dMHBs), differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs), respectively. Next, 5 factors presenting the principal variation of differential multi-omics signatures were identified. The important roles of Factor 1 (DEG, DEM and DEL) and Factor 2 (dMHB and DEM), in the regulation of immune defense and impaired mammary gland functions during subclinical mastitis were revealed. Each of the omics within Factors 1 and 2 explained about 20% of the source of variation in subclinical mastitis. Also, networks of important functional gene sets with the involvement of multi-omics signatures were demonstrated, which contributed to a comprehensive view of the possible regulatory mechanisms underlying subclinical mastitis. Furthermore, multi-omics integration enabled the association of the epigenomic regulatory factors (dMHBs, DELs and DEMs) of altered genes in important pathways, such as 'Staphylococcus aureus infection pathway' and 'natural killer cell mediated cytotoxicity pathway', etc., which provides further insights into mastitis regulatory mechanisms. Moreover, few multi-omics signatures (14 dMHBs, 25 DEGs, 18 DELs and 5 DEMs) were identified as candidate discriminant signatures with capacity of distinguishing subclinical mastitis cows from healthy cows.

CONCLUSION

The integration of genomic and epigenomic data by multi-omics approaches in this study provided a better understanding of the molecular mechanisms underlying subclinical mastitis and identified multi-omics candidate discriminant signatures for subclinical mastitis, which may ultimately lead to the development of more effective mastitis control and management strategies.

Genomic clues of association between clinical mastitis and SNPs identified by ddRAD sequencing in Murrah buffaloes

The total milk production of India is 209.96 MT out of which 45% is contributed by the indigenous buffalo and due to their high producing virtue, the prevalence of mastitis is 5-20%. Despite the increasing level of technological advancement, mastitis is still an issue of concern for dairy industry in India as well as across the world. Therefore, the present study aimed to identify the SNPs and associate them with the incidence of clinical mastitis in Murrah buffalo using the ddRAD sequencing approach taking mastitis incidence data of 96 Murrah buffaloes. A total of 246 million quality controlled reads were obtained with an average alignment rate of 99.01% and at a read depth of 10, quality controlled SNPs obtained were 18,056. The logistic regression model was used and a total of seven SNPs were found significantly associated (p < 0.001) with mastitis incidence and seven genes were identified viz., NCBP1, FOXN3, TPK1, XYLT2, CPXM2, HERC1, and OPCML. The majority of them were having tumor suppressing action, related to immunogenetics or glycolytic and energy production. Conclusively, the SNPs identified in this study may be useful for future studies on mastitis incidence in Murrah buffalo and the SNP associations can be further validated.

Candidate genes for mastitis resistance in dairy cattle: a data integration approach

BACKGROUND

Inflammation of the mammary tissue (mastitis) is one of the most detrimental health conditions in dairy ruminants and is considered the most economically important infectious disease of the dairy sector. Improving mastitis resistance is becoming an important goal in dairy ruminant breeding programmes. However, mastitis resistance is a complex trait and identification of mastitis-associated alleles in livestock is difficult. Currently, the only applicable approach to identify candidate loci for complex traits in large farm animals is to combine different information that supports the functionality of the identified genomic regions with respect to a complex trait.

METHODS

To identify the most promising candidate loci for mastitis resistance we integrated heterogeneous data from multiple sources and compiled the information into a comprehensive database of mastitis-associated candidate loci. Mastitis-associated candidate genes reported in association, expression, and mouse model studies were collected by searching the relevant literature and databases. The collected data were integrated into a single database, screened for overlaps, and used for gene set enrichment analysis.

RESULTS

The database contains candidate genes from association and expression studies and relevant transgenic mouse models. The 2448 collected candidate loci are evenly distributed across bovine chromosomes. Data integration and analysis revealed overlaps between different studies and/or with mastitis-associated QTL, revealing promising candidate genes for mastitis resistance.

CONCLUSION

Mastitis resistance is a complex trait influenced by numerous alleles. Based on the number of independent studies, we were able to prioritise candidate genes and propose a list of the 22 most promising. To our knowledge this is the most comprehensive database of mastitis associated candidate genes and could be helpful in selecting genes for functional validation studies.

New Insights on Nucleotide Sequence Variants and mRNA Levels of Candidate Genes Assessing Resistance/Susceptibility to Mastitis in Holstein and Montbéliarde Dairy Cows

A major factor in the propagation of an infectious disease is host genetics. In this study, 180 dairy cows (90 of each breed: Holstein and Montbéliarde) were used. Each breed's tested dairy cows were divided into two groups of comparable size (45 cows each), mastitis-free and mastitis-affected groups. Each cow's jugular vein was punctured to obtain blood samples for DNA and RNA extraction. In the examined Holstein and Montbéliarde dairy cows, single nucleotide polymorphisms (SNPs) related with mastitis resistance/susceptibility were found in the RASGRP1, NFkB, CHL1, MARCH3, PDGFD, MAST3, EPS15L1, C1QTNF3, CD46, COX18, NEURL1, PPIE, and PTX3 genes. Chi-square analysis of identified SNPs revealed a significant difference in gene frequency between mastitic and healthy cows. Except for CHL1, mastitic dairy cows of two breeds had considerably higher mRNA levels of the examined genes than did healthy ones. Marker-assisted selection and monitoring of dairy cows' susceptibility to mastitis may be accomplished through the use of discovered SNPs and changes in the gene expression profile of the studied genes. These findings also point to a possible method for reducing mastitis in dairy cows through selective breeding of animals using genetic markers linked to an animal's ability to resist infection.

Understanding the genomic architecture of clinical mastitis in Bos indicus

This study elucidated potential genetic variants and QTLs associated with clinical mastitis incidence traits in Bos indicus breed, Sahiwal. Estimated breeding values for the traits (calculated using Bayesian inference) were used as pseudo-phenotypes for association with genome-wide SNPs and further QTL regions underlying the traits were identified. In all, 25 SNPs were found to be associated with the traits at the genome-wide suggestive threshold (p ≤ 5 × 10^-4) and these SNPs were used to define QTL boundaries based on the linkage disequilibrium structure. A total of 16 QTLs were associated with the trait EBVs including seven each for clinical mastitis incidence (CMI) in first and second lactations and two for CMI in third lactation. Nine out of sixteen QTLs overlapped with the already reported QTLs for mastitis traits, whereas seven were adjudged as novel ones. Important candidates for clinical mastitis in the identified QTL regions included DNAJB9, ELMO1, ARHGAP26, NR3C1, CACNB2, RAB4A, GRB2, NUP85, SUMO2, RBPJ, and RAB33B genes. These findings shed light on the genetic architecture of the disease in Bos indicus, and present potential regions for fine mapping and downstream analysis in future.

Genome-wide selection signatures detection in Shanghai Holstein cattle population identified genes related to adaption, health and reproduction traits

Background

Over several decades, a wide range of natural and artificial selection events in response to subtropical environments, intensive pasture and intensive feedlot systems have greatly changed the customary behaviour, appearance, and important economic traits of Shanghai Holstein cattle. In particular, the longevity of the Shanghai Holstein cattle population is generally short, approximately the 2nd to 3rd lactation. In this study, two complementary approaches, integrated haplotype score (iHS) and runs of homozygosity (ROH), were applied for the detection of selection signatures within the genome using genotyping by genome-reduced sequence data from 1092 cows.

Results

In total, 101 significant iHS genomic regions containing selection signatures encompassing a total of 256 candidate genes were detected. There were 27 significant |iHS| genomic regions with a mean |iHS| score > 2. The average number of ROH per individual was 42.15 ± 25.47, with an average size of 2.95 Mb. The length of 78 % of the detected ROH was within the range of 1–2 MB and 2–4 MB, and 99 % were shorter than 8 Mb. A total of 168 genes were detected in 18 ROH islands (top 1 %) across 16 autosomes, in which each SNP showed a percentage of occurrence > 30 %. There were 160 and 167 genes associated with the 52 candidate regions within health-related QTL intervals and 59 candidate regions within reproduction-related QTL intervals, respectively. Annotation of the regions harbouring clustered |iHS| signals and candidate regions for ROH revealed a panel of interesting candidate genes associated with adaptation and economic traits, such as IL22RA1, CALHM3, ITGA9, NDUFB3, RGS3, SOD2, SNRPA1, ST3GAL4, ALAD, EXOSC10, and MASP2. In a further step, a total of 1472 SNPs in 256 genes were matched with 352 cis-eQTLs in 21 tissues and 27 trans-eQTLs in 6 tissues. For SNPs located in candidate regions for ROH, a total of 108 cis-eQTLs in 13 tissues and 4 trans-eQTLs were found for 1092 SNPs. Eighty-one eGenes were significantly expressed in at least one tissue relevant to a trait (P value < 0.05) and matched the 256 genes detected by iHS. For the 168 significant genes detected by ROH, 47 gene-tissue pairs were significantly associated with at least one of the 37 traits.

Conclusions

We provide a comprehensive overview of selection signatures in Shanghai Holstein cattle genomes by combining iHS and ROH. Our study provides a list of genes associated with immunity, reproduction and adaptation. For functional annotation, the cGTEx resource was used to interpret SNP-trait associations. The results may facilitate the identification of genes relevant to important economic traits and can help us better understand the biological processes and mechanisms affected by strong ongoing natural or artificial selection in livestock populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08042-x.

MiR-125b regulates inflammation in bovine mammary epithelial cells by targeting the NKIRAS2 gene

Mastitis is a complex inflammatory disease caused by pathogenic infection of mammary tissue in dairy cows. The molecular mechanism behind its occurrence, development, and regulation consists of a multi-gene network including microRNA (miRNA). Until now, there is no report on the role of miR-125b in regulating mastitis in dairy cows. This study found that miR-125b expression is significantly decreased in lipopolysaccharide (LPS)-induced MAC-T bovine mammary epithelial cells. Also, its expression is negatively correlated with the expression of NF-κB inhibitor interacting Ras-like 2 (NKIRAS2) gene. MiR-125b target genes were identified using a double luciferase reporter gene assay, which showed that miR-125b can bind to the 3′ untranslated region (3′ UTR) of the NKIRAS2, but not the 3′UTR of the TNF-α induced protein 3 (TNFAIP3). In addition, miR-125b overexpression and silencing were used to investigate the role of miR-125b on inflammation in LPS-induced MAC-T. The results demonstrate that a reduction in miR-125b expression in LPS-induced MAC-T cells increases NKIRAS2 expression, which then reduces NF-κB activity, leading to low expression of the inflammatory factors IL-6 and TNF-α. Ultimately, this reduces the inflammatory response in MAC-T cells. These results indicate that miR-125b is a pro-inflammatory regulator and that its silencing can alleviate bovine mastitis. These findings lay a foundation for elucidating the molecular regulation mechanism of cow mastitis.

Opportunity to improve livestock traits using 3D genomics

The advent of high-throughput chromosome conformation capture and sequencing (Hi-C) has enabled researchers to probe the 3D architecture of the mammalian genome in a genome-wide manner. Simultaneously, advances in epigenomic assays, such as chromatin immunoprecipitation and sequencing (ChIP-seq) and DNase-seq, have enabled researchers to study cis-regulatory interactions and chromatin accessibility across the same genome-wide scale. The use of these data has revealed many unique insights into gene regulation and disease pathomechanisms in several model organisms. With the advent of these high-throughput sequencing technologies, there has been an ever-increasing number of datasets available for study; however, this is often limited to model organisms. Livestock species play critical roles in the economies of developing and developed nations alike. Despite this, they are greatly underrepresented in the 3D genomics space; Hi-C and related technologies have the potential to revolutionise livestock breeding by enabling a more comprehensive understanding of how production traits are controlled. The growth in human and model organism Hi-C data has seen a surge in the availability of computational tools for use in 3D genomics, with some tools using machine learning techniques to predict features and improve dataset quality. In this review, we provide an overview of the 3D genome and discuss the status of 3D genomics in livestock before delving into advancing the field by drawing inspiration from research in human and mouse. We end by offering future directions for livestock research in the field of 3D genomics.

Somatic cell score: gene polymorphisms and other effects in Holstein and Simmental cows

Objective

The aim of the study was to evaluate the influence of gene polymorphisms and nongenetic factors on the somatic cell score (SCS) in the milk of Holstein (n = 148) and Simmental (n = 73) cows and their crosses (n = 6).

Methods

The SCS was calculated by the formula SCS = log₂(SCC/100,000)+3, where SCC is the somatic cell count. Polymorphisms in the casein alpha S1 (CSN1S1), beta-casein (CSN2), kappa-casein (CSN3), beta-lactoglobulin (LGB), acyl-CoA diacylglycerol transferase 1 (DGAT1), leptin (LEP), fatty acid synthase (FASN), stearoyl CoA desaturase 1 (SCD1), and 1-acylglycerol-3-phosphate O-acyltransferase 6 (AGPAT6) genes were genotyped, and association analysis to the SCS in the cow’s milk was performed. Further, the impact of breed, farm, year, month of the year, lactation stage and parity on the SCS were analysed. Phenotype correlations among SCS and milk constituents were computed by Pearson correlation coefficients.

Results

Only CSN2 genotypes A¹/A² were found to have significant association with the SCS (p<0.05), and alleles of CSN1S1 and DGAT1 genes (p<0.05). Other polymorphisms were not found to be significant. SCS had significant association with the combined effect of farm and year, lactation stage and month of the year. Lactation parity and breed had not significant association with SCS. The phenotypic correlation of SCS to lactose content was negative and significant, while the correlation to protein content was positive and significant. The correlations of SCS to fat, casein, nonfat solids, urea, citric acid, acetone and ketones contents were very low and not significant.

Conclusion

Only CSN2 genotypes, CSN1S1 and DGAT1 alleles did show an obvious association to the SCS. The results confirmed the importance of general quality management of farms on the microbial milk quality, and effects of lactation stage and month of the year. The lactose content in milk reflects the health status of the udder.

Proofs for genotype by environment interactions considering pedigree and genomic data from organic and conventional cow reference populations

The aim of the present study was to prove genotype by environment interactions (G × E) for production, longevity, and health traits considering conventional and organic German Holstein dairy cattle subpopulations. The full data set included 141,778 Holstein cows from 57 conventional herds and 7,915 cows from 9 organic herds. The analyzed traits were first-lactation milk yield and fat percentage (FP), the length of productive life (LPL) and the health traits mastitis, ovarian cycle disorders, and digital dermatitis in first lactation. A subset of phenotyped cows was genotyped and used for the implementation of separate cow reference populations. After SNP quality controls, the cow reference sets considered 40,830 SNP from 19,700 conventional cows and the same 40,830 SNP from 1,282 organic cows. The proof of possible G × E was made via multiple-trait model applications, considering same traits from the conventional and organic population as different traits. In this regard, pedigree (A), genomic (G) and combined relationship (H) matrices were constructed. For the production traits, heritabilities were very similar in both organic and conventional populations (i.e., close to 0.70 for FP and close to 0.40 for milk yield). For low heritability health traits and LPL, stronger heritability fluctuations were observed, especially for digital dermatitis with 0.05 ± 0.01 (organic, A matrix) to 0.33 ± 0.04 (conventional, G matrix). Quite large genetic correlations between same traits from the 2 environments were estimated for production traits, especially for high heritability FP. For LPL, the genetic correlation was 0.67 (A matrix) and 0.66 (H matrix). The genetic correlation between LPL organic with LPL conventional was 0.94 when considering the G matrix, but only 213 genotyped cows were included. For health traits, genetic correlations were throughout lower than 0.80, indicating possible G × E. Genetic correlations from the different matrices A, G, and H for health and production traits followed the same pattern, but the estimates from G for health traits were associated with quite large standard errors. In genome-wide association studies, significantly associated SNP for production traits overlapped in the conventional and organic population. In contrast, for low heritability LPL and health traits, significantly associated SNP and annotated potential candidate genes differed in both populations. In this regard, significantly associated SNP for mastitis from conventional cows were located on Bos taurus autosomes 6 and 19, but on Bos taurus autosomes 1, 10, and 22 in the organic population. For the remaining health traits and LPL, different potential candidate genes were annotated, but the different genes reflect similar physiological pathways. We found evidence of G × E for low heritability functional traits, suggesting different breeding approaches in organic and conventional populations. Nevertheless, for a verification of results and implementation of alternative breeding strategies, it is imperative to increase the organic cow reference population.

Expression analysis of candidate genes for chronic subclinical mastitis in Norwegian Red cattle

Chronic subclinical mastitis (SCM) is characterized by a long-term inflammation in the udder with high somatic cell count (SCC) in milk. Previously, several novel alternative SCM traits for Norwegian Red (NR) cattle have been defined to improve breeding strategies against chronic SCM. Quantitative trait loci and candidate genes affecting chronic SCM in NR have been identified. The aim of this study was to analyze the expression profiles of 14 selected candidate genes (RAD17, ACOT2, ACOT4, FOS, CXCL1, CXCL8, CCNB1, CDK7, TGFB3, SEL1L, STAT4, C6, GLI2, and SLC18A2). Twenty healthy NR cows with official genomic estimated breeding values (GEBV) for lactation average somatic cell scores (LSCS) were selected. Ten cows had high GEBV for LSCS (cows with low probability to have high SCC in milk during lactation) and 10 cows had low GEBV for LSCS (cows with high probability of having high SCC in milk). We isolated RNA from unstimulated peripheral blood mononuclear cells from these. Two out of the 14 analyzed genes showed significantly different results between groups. The group with high GEBV for LSCS displayed significantly higher expression of the CXCL1 gene than the low GEBV group. Grouping by lactation stage revealed significant differential expression of the FOS gene, with higher expression in early lactation (2-3 mo after calving) compared with late lactation (7-8 mo after calving). In addition, flow cytometry was performed on the peripheral blood mononuclear cells samples to analyze if number and type of isolated cells influenced the gene expression in the groups. The results in the current study provide identified genes that can be considered as possible candidate genes for chronic SCM in NR cows.