E. coli and S. aureus are the most predominant Gramnegative and Gram-positive bacterial pathogens that infect the mammary cells in dairy cattle respectively (Jensen et al., 2013; Pal et al., 2019).

E. coli, the cause of acute clinical mastitis, is amongst the most common environmental pathogens (Ju et al., 2020). The E. coli-infected mastitic cows undergo serious systemic clinical symptoms; in the more severe cases, the disease can cause many deaths each year (Buitenhuis et al., 2011; Lippolis et al., 2018). This gram-negative bacterium constitutes the normal bovine intestinal flora and contaminates the environment through faeces, mostly bedding material. The cow is particularly vulnerable to coliform infections during the puerperal phase because the immune defence reaction is lower than normal at this time. Gram-negative bacteria produce endotoxin and the intramammary infection (IMI) normally results in extreme inflammatory reactions. Sometimes E. coli also can possibly cause subclinical mastitis, but these strains differ from those which cause clinical mastitis (Pal et al., 2019). E. coli produces lipopolysaccharide (LPS) endotoxin, the most potent immunostimulant (Arenas, 2012; Xu et al., 2019), and the key virulence factor eliciting clinical symptoms which is responsible for many of the inflammatory changes observed during coliform mastitis. The detection of LPS by Toll-like receptor 4 (TLR4) (Panigrahi et al., 2014), mediated by a cluster of differentiation 14 (CD14), LPS Binding protein, and myeloid differentiation protein, is interconnected with the development of acute immune-response, enhanced SCC, activation of different leukocytes and immune related genes, release of cytokines (IL-6 and TNF-α), chemokines (CXCL5, CXCL8, and RANTES) and leukocytic chemoattractant (IL-8) (Alnakip et al., 2014; ThompsonCrispi et al., 2014).

S. aureus, a gram-positive bacterium, goes on being the major cause of subclinical mainly but as well as, clinical mastitis worldwide (Murphy et al., 2019; Pal et al., 2019). The bacterium can quickly invade all types of cells in the MG and due to its intracellular localization in MG epithelial cells, it has a poor response to traditional antibiotic therapy. The intramammary infection induced with S. aureus progresses to chronic lifelong infection (Gilbert et al., 2013). The infected cows act as a reservoir from which transmission to humans can occur both from physical contact such as while milking by hand method and consumption of unpasteurized milk. IMIs by S. aureus are therefore not only potentially harmful to the animal but also to humans. S. aureus isolated from intramammary infections produces many potential virulence factors including enterotoxins, hemolysin, hyaluronidase, and leukocidins (Pal et al., 2019). Among the main immunostimulatory molecules, peptidoglycan (PGN) and lipoteichoic acid (LTA) have been proved to accelerate the development of inflammatory cytokines and chemokines by immune cells (Xu et al., 2019), including monocytes and macrophages. In vitro trials have also shown that LTA itself can trigger the expression in mammary epithelial cells (MECs) of many cytokines, few of which are IL1β, IL-8, IL-6 and TNF-α (Bougarn et al., 2011). LTA has also been shown to greatly induce the release of CXCL1, CXCL2, CXCL3 and CXCL8 chemokines, which specifically target neutrophils (Kiku et al., 2016) and it increases the expression of IL-17 driven immune response genes in MECs in vitro (Bougarn et al., 2011). Toll-like receptor 2 (TLR2) plays a major part in the identification of LTA (Zhang et al., 2016).

Adequate immune functions are important for the protection of the host against IMIs. MG immunity is the consequence of the integration between non-specific and specific protective factors, along with the anatomical characteristics of its gland and the defence factors of both cellular as well as humoral aspects (Schukken et al., 2011). However, MG immune response differs through different lactation stages that occurs in dairy animals and is usually impaired via stress exposure and at the period of parturition and drying-off, thereby intensifying susceptibility to mastitis (Moosavi et al., 2014; Wu et al., 2020).

Innate and adaptive immunity are two essential components of MG host defence to defend against infections. The adaptive immune system (AIS) responds more competently to previously exposed threats yet responding to new threats is sluggish. However, the innate immune response is the very first line of defence towards the bacteria after violating the physical boundary of the teat canal and prior to when the AIS comes into action (Sordillo, 2018). This mechanism is implicated in a variety cascades of intracellular signal transduction that induce acute upregulation of many other innate immune elements, namely different leukocytes, molecules of adhesion and cytokinesis (Prince et al., 2011; Wellnitz et al., 2011).

The activation of an efficient and timely innate immune response (IIR) largely relies on recognising the infectious agent. MG IIR is prompted as pattern recognition receptors (PRRs) bind to pathogen-associated molecular patterns (PAMPs) considered bacterial motifs (Wellnitz and Bruckmaier, 2012). These motifs may be revealed during a microorganism’s multiplication or degradation. A signalling transduction cascade is triggered by activation of these PRRs which contributes a vital part in controlling multiple signals and coordinating the expression of effector response genes, like cytokines, thus further stimulating local and systemic immune responses (Schukken et al., 2011; Stevens et al., 2011). Increases in PRR expression in the epithelia and tissues of affected bovine MGs and TLRs are the well-versed studied bovine PRRs and identify a large spectrum of PAMPs (Alnakip et al., 2014).

As pathogens enter the udder lumen via the teat channel, both immune and non-immune cells sense them, and the production of chemoattractant follows. Consequently, neutrophils migrate from the bloodstream to the area of infection. These cells possibly can generate phagocytes and exercise bactericidal activity by production of potent oxidative products (Prince et al., 2011). This huge recruitment of udder neutrophils brings about a drastic rise in the somatic cells count in the milk (SCC). Consequently, SCC were been employed widely as simple to use method for recognising mastitis and distinguishing among chronically infected and healthy or non-infected animals (Rainard et al., 2018).

There is ample evidence that the reaction of the host to IMI is subject to genetic control, but the underlying genetics for the resistance remains largely unclear. New progress in high throughput technology (RNA-Seq), which now facilitates the analysis of several thousands of infection-associated genes expression, has offered considerable insight into the host response to pathogens. Gene expression profiles for challenged mammary tissue (Kosciuczuk et al., 2017), milk cells (Wickramasinghe et al., 2012) and peripheral blood leukocytes (Wang et al., 2020) were studied using high throughput techniques such as RNA-Seq.

RNA molecules are highly vital components of all living cells. The primary aim of RNA investigation is to comprehend the identity and quantification of each RNA molecule in each cell under a particular condition. Most of what we know about RNA comes from biochemical examination of which a few numbers of specific molecules are studied. High-throughput approaches have also emerged which enable large-scale interrogation of RNA sequences (Hrdlickova et al., 2016).

Early gene expression analysis methods such as the expressed sequence tag (EST) technique described during 1990s analysis showed both the sequence and quantification of corresponding RNAs through partially sequencing complementary DNA (cDNA) clones. As in early 1990s, EST data played a crucial role. The high cost of the sequencing process, however, narrowed down the use of such expression processing. The Serial Analysis of Gene Expression (SAGE) technique was then introduced by significantly reducing the high cost of expression analysis per gene. However, with the advent of DNA microarray technology in the mid to later 1990s, replaced EST and SAGE techniques for gene expression research, primarily due to their much greater affordability for large-scale studies (Farkas et al., 2015; Hrdlickova et al., 2016). On the contrary, microarrays allow only the relative quantitation of transcripts and often depend on a properly annotated genome. It can only analyse transcripts of previously known, with no capability of recognizing alternative splice sites or new exons. Subsequently, the Next Generation Sequencing (NGS) or so-called deep sequencing i.e RNA-Seq, a paradigm shift in bio-medicine due to its ability to produce an incredible amount of data in a relatively short period has since then rapidly transformed RNA research. RNA-Seq provides low background signal, much more quantifiable and not limited to genomic sequences as contrast to DNA microarray-based methods (Nookaew et al., 2012; Adiconis et al., 2013; Van Dijk et al., 2014; Li et al., 2014; Han et al., 2015).

RNA-Seq has emerged as a widely used and important approach for characterizing transcripts, analysing gene expression and detecting RNA biogenesis and metabolism, offering powerful tools to uncover molecular pathways in development, differentiation and disease (Costa-Silva et al., 2017). RNA-Seq helps us to examine and discover transcriptome, the complete cellular content of RNAs, namely mRNA, rRNA and tRNA are few examples.

Understanding the transcriptome is fundamental if we are to link information about our genome to the functional protein expression. RNA-Seq can show us which genes are switched on in a cell, their level of expression and when they are activated or shut (Ozsolak and Milos, 2011). Initially, the technique converts the RNA population to cDNA fragments (a cDNA library). This enables the RNA to be integrated into the NGS procedure. Then NGS analyses the library, generating short sequences that match to either one or both ends of the fragment. Subsequently, reads can be matched with a reference genome and assembled to generate an RNA sequence map that covers the transcriptome (Zhao et al., 2015), and the levels of expression for each gene are calculated.

Many tools and pipelines for the research of differential gene expression were been built from these sequenced data with the popularisation of RNA-Seq technology. Numerous studies have also been conducted lately on the transcriptome analysis of RNA-Seq-based mastitis in cattle (Chen et al., 2016; Fang et al., 2017; Lai et al., 2020; Wang et al., 2020). To state the obvious, RNA-Seq is excellently known as the superior microarray alternative and is possibly to persist as the favoured available option setting the foundations for modern genetic investigations.

Some studies related to mammary transcriptome profiling performed through RNA-Seq are explained briefly in table 1.

Mastitis is a result of the consequence of several cellular and soluble factors that operate together to remove invading microorganisms. Therefore, it may be necessary to integrate more biological information related to the studied complex traits to correlate the flow of biological information underlying this trait, which will supplement us find the appropriate genomic characteristics that are highly enriched for causal variants (Fang et al., 2017). High throughput RNA-Seq has become one such important instrument for resolving complex biological issues by simultaneously evaluating thousands of genes expression in the tissue. Attribution of gene expression patterns would provide insight into their encoded proteins’ biological roles and interacting proteins networks that influence cellular responses to extracellular stimuli.

The assessment of these studies would certainly require a lot of effort to make highly concrete conclusions about the functional utility of this knowledge to develop the standard approach of treatment or control of the animal disease. Moreover, cattle diseases like mastitis have a large effect on the immune reaction expression and other genes.

The diversity in the prevalence and abundance of mastitiscausing organisms and the variation in host responses make mastitis a complex disease that continues to be a burden on the dairy sector. Generally, a disease management can be approached either of the two ways, by therapeutic approach that incorporates the high cost of veterinary diagnosis and services, antibiotic treatments, use of vaccine, etc., and the other way is via genetic approach of breeding for mastitis resistance. The earlier approach has instead contributed to the proliferation of numerous drug resistant strains of causative species (Chandrasekaran et al., 2014; Algammal et al., 2020) and failure of the vaccine to mount immunity against enormous mastitis-causing etiological agents (Mata, 2013), whereas the later approach was viewed to be an economic and prophylactic solution comparatively (Karthikeyan et al., 2016).

The initial step for such a strategy would be to correctly classify DEGs in recognising phenotypic variation under complex mastitis-related conditions. The discovery of the DEGs among the susceptible and resistant individuals offers key information for identifying candidates or marker for the underlying genetic basis of mastitis susceptibility or resistance, this lays the foundation for further modern genetic and mechanistic studies or approaches including genomic selection, marker-assisted selection or genomewide association studies (Bonnefont et al., 2011; Thompson-Crispi et al., 2014; Fang et al., 2017; Yan et al., 2020). To cite an example, Usman et al. (2017) also conducted a study suggesting that cytokines: IL-17F and IL-17A could be effective mastitis resistance candidate genes, and in both milk and dual-purpose animals, large SNPs may deemed as useful genetic markers to manage mastitis. Likewise, the peripheral blood leukocyte (PBL) obtained from S. aureus positive mother daughter pairs and S. aureus control mother daughter pairs were subjected to transcriptomic evaluation and characterized using RNASeq by Wang et al. (2020). As follows, differential gene expression was assessed in a consecutive generation of dairy cattle: revealing contrasting DEGs. The study suggested that the genes outlined in the analysis may qualify as biomarkers of mastitis expression and may also include variations in the sequence which could be used for genetic enhancement of dairy cattle in mastitis resistance.

MG immune response is complicated as mastitis is subjected to a wide variety of infectious agents with various pathogenic mechanisms requiring complex pathogen-linked immune response. And the prevalence in dairy cattle is lesser with improved and balanced immune response and can be identified using high immune response (HIR) technology. Increased immune response is often characterised by significantly higher immunisation response, higher production of milk and colostrum, strengthened animal welfare, and higher quality of food to sustain the increasing population. As immunity is a major fitness trait, beneficial correlations with survival and reproduction are also widely noted (ThompsonCrispi et al., 2014). Strengthened immune response can be achieved by selecting disease resistance in livestock, which is taken into consideration to be the most relevant prophylactic strategy for improving animal welfare.

Therefore, in the near term, the task is to get a full discovery and detailed study of a significant systemic response in the mammary gland that affect the interaction among the host and various etiological agents. As it is expected that the understanding of RNA sequencing data and the position of functional SNPs or biomarkers in the host and the pathogen would connect the dots among the immune function and the relevant genes with potential to enhance resistance to pathogens specifically. The expertise of host-pathogen relationship in mastitis will probably be essential for the formation of new therapeutics and for performing genetic selection approach for mastitis resistance. The inclusion of mastitis resistance during the selection programs in Scandinavian countries has resulted in a greater economic response to selection (Sulabh, 2016). Such strategy can result in animal herd developing with increased resistance or tolerance to mastitis.