Regulatory Network of MiRNAs, IncRNAs, Transcription Factors and Target Genes Associated With Immune Response in Bovine Mastitis

Background: Bovine mastitis is a mammary gland infectious disease caused by a variety of pathogens with a devasting economic impact worldwide. Pre- and post-transcriptional modications, including transcription factors (TFs), altering gene expression are emerging foci of disease studies, with minimal studies revealing the importance of non-coding transcripts, like long non-coding RNAs (lncRNAs) and microRNAs (miRNAs). We hypothesize TFs, lncRNAs and miRNAs can modulate the immune response in bovine mastitis and can potentially serve as disease biomarkers and/or drug targets. Methods: With computational analyses, we aimed to identify candidate bovine mastitis genes and construct the networks of miRNA, lncRNA and TFs regulating the gene’s mRNA, affecting disease pathogenesis. Experimentally validated genes associated with bovine mastitis were obtained through an extensive search for signicantly mentioned genes, utilizing several databases. Prediction of miRNA and lncRNA binding bovine mastitis candidate genes were performed through several algorithms and software that relied on base pair complementation, evolutionary conservation, and thermodynamic stability of binding regions. Combined interactome network of lncRNAs, miRNAs, TFs and immune gene targets were constructed. Results: Sixteen of 923 genes were found to be highly signicant in bovine mastitis disease pathway including, CD4, IL-10, IFNγ, IL-4, TLR4, TNFα, and CD14. Remarkably, we found six miRNAs, two being bta-miR-223 and bta-miR-24-3p, to bind to several targets. Eight out of 22 lncRNA, such as NONBTAT027932.1 and XR_003029725.1, were identied as regulatory elements that target the genes based on the normalized binding free energies ranging from -0.1774 to -2.875. Similarly identied were 17 TFs, including JUN and CREB. Our functional and pathway analyses revealed several pathways like lipopolysaccharide-mediated signaling pathway, regulation of chemokine (C-X-C motif) ligand 2 production and regulation of IL-23 production among others.


Results
Dataset of candidate genes associated with immune response to bovine mastitis Our initial search on PubMed using the keywords "bovine," "mastitis," "immunity," and "genes" generated 276 articles. Following ltering, a total of 109 articles were selected and added to the pipeline for candidate genes identi cation. These articles, including experimental, meta-analysis, and reviews mentioned 919 individual genes associated with host immune response to bovine mastitis. Of importance to our analysis are genes with multiple mentions, including CXCL8 and IL-6 referenced in 41 and 29 articles, respectively (Fig 1b). Comparing the 919 genes found during our search with the 20 genes generated from Genomatix, yielded 16 genes that were common to both search methods ( Supplementary Fig 1). These genes are MYD88, CD4, IL-10, IFNγ, IL-4, ICAM1, CXCL8, TLR4, TNFα, IL-18, TLR2, CD86, CCL2, IL-6, CSF2, and CD14. The interactions between these candidate genes reveal TLR4 and TLR2 are considerably more linked to other genes (Fig 2), while CD4 on the other hand had the least interaction.

miRNAs bind to the candidate genes in bovine mastitis
Our analyses explored the complementarily binding of miRNAs to the 3'UTR, CDS, and 5'UTR regions of the candidate genes. We report a total of 768 miRNAs binding either the 3' UTR, CDS, or 5' UTR of the 16 target genes (Supplementary Table 2). With BEG, ve miRNAs were uncovered to bind all three regions within the same gene: bta-miR-2338 and bta-miR-2433 to CD14, bta-miR-2373-3p to ICAM1, bta-miR-2328-3p and bta-miR-2356 to TLR4 (Fig. 3). In addition, a substantial number of miRNAs bound to two or more regions within the same gene (Fig. 3), Displaying all the miRNA predicted to bind to three or more target genes is Fig. 4, which also depicts whether the miRNA binds one (green), two (blue), or three (red) of the possible regions (3'UTR, 5'UTR, and CDS). The complete list of miRNAs binding three or more target genes is depicted in Supplementary Table 3. Likewise, we found six miRNAs; bta-miR-24-3p, bta-miR-149-5p, bta-miR-223, bta-miR-185, bta-miR-874, and bta-miR-328, commonly predicted by the three software as shown ( Supplementary Fig 2; Supplementary Table 2). lncRNAs interact with mRNA target inferring their functionalities NONCODE database search revealed 22 bovine lncRNAs that matched our prediction criteria. Genomic location and strand sense for each lncRNAs are shown in Supplementary Table 4. From the analysis, we identi ed 20 lncRNAs that bind to our target mRNAs with a ndG < -0.05 (Fig 5 and Supplementary Table  4 Table 4). Eight lncRNAs that bind 13 or more candidate genes with lower ndGs were selected for network and conservational analysis ( Table 3). Most of the identi ed lncRNAs are not yet annotated, therefore, we inferred their gene ontology and pathways based on their potential mRNA targets/genes. Identi ed ontologies and associated pathways are shown in Table 4 and Figure 6a and 6b.
Biological, molecular and cellular function of target genes and pathway analysis The pathway analysis of 16 target genes identi ed 25 biological processes (Fig 6a). Myeloid leukocyte activation with a p-value of 4.92E-13 vascular endothelial growth factor (GF) production, regulation of chemokine (C-X-C motif) ligand 2 production and lipopolysaccharide (LPS)-mediated signaling pathway (p-value; 2.58E-13) ( Table 4). Of importance are the three signi cant biological processes concomitantly identi ed from the three databases including LPS-mediated signaling pathway, regulation of IL-23 production, and positive regulation of chemokine production. DAVID analysis identi ed eight biological processes, three molecular functions and one cellular component, while 11 disease pathways were identi ed including malaria and rheumatoid arthritis that featured most candidate genes during bovine mastitis with p-values of 9.20E-19 and 4.60E-16, respectively (Fig 6b; Supplementary Table 5).

Catalog of transcription factors, biological processes and molecular function
Seventeen TFs were identi ed as signi cant by at least two software (Table 5; Supplementary Table 6).
All 17 TFs appear to have the potential to regulate the transcription of the 16 genes including CD4, MYD88, ICAM1, TLR4, TLR2, IL-18, and CD86.The genomic location and other details of the 17 TFs are presented in Table 5. In addition, functional analysis identi ed 3 biological processes, 2 molecular functions, and 1 cellular component involving two to three TFs, with a p-value less than 0.0005 (Table 6).

Sequence conservation of bovine miRNA and lncRNAs in other species
Sequence analysis was performed to assess the evolutionary conservation of bovine lncRNAs and miRNAs across various species (Supplementary Table 1). We examined the signi cant miRNA (6) and lncRNA (8) identi ed previously with their homologous sequence from 14 other species. Our analysis of the six miRNAs reveal bta-miR-223 to be highly conserved in all 15 species, without a single deviant polymorphism (Supplementary Fig 3). The evolutionary closeness of the cow and wild yak is also seen in bta-miR-149-5p, bta-miR-328, and bta-miR-185, but are distantly related in bta-miR-24-3p and bta-miR-874 ( Supplementary Fig. 4a-c). The phylogenetic analysis of bta-miR-223 de nes cow having the closest identity with wild yak and the remaining ruminants (goat and sheep) as orthologs ( Supplementary Fig   4b). The alignment of bta-miR-874 showed high conservation between cow, mouse, and megabat, with phylogenetic analysis having these three sharing a common node, while no other tree had these three species this close in proximity.
The length of lncRNA allow for greater variation within the gene throughout evolution. The evolutionary alignment analysis revealed that each lncRNA was not completely conserved in the 15 species; however, the majority have similar base pair sequences in conserved regions. Out of the 8 lncRNA, 7 have bovine lncRNA to be most closely related to wild yak, followed by other ruminants (Supplementary Fig. 5a-d). The tree of NONBTAT027932.1 show wild yak to be relatively distant from cow, sheep, and goat compared to the species relation in the other lncRNA analysis. Five out of eight lncRNA have human, gorilla, and chimpanzee clustered with the same origin.

Dysregulation of lncRNA-miRNA interaction on immune response in bovine mastitis
Our analysis showed several miRNA potential binding sites on lncRNA, based on the complementary base pairing and normalized binding free energies (ndG) values. Of interest, we found 8 out of 22 lncRNAs possessing binding sites for the entire length of these miRNAs, with the exception of 4 out of the 48 pairings (Fig 7). The ndG values range from -0.1774 to -2.875. lncRNA XR_003033296.1 and bta-miR-223 had the greatest ndG while lncRNA XR_234647.4 and bta-miR-328 had the lowest ndG. The lower the ndG, the greater the possibility that the two RNA will bind. The vast majority of miRNA bind their entire length to the lncRNA except four, the shortest binding length is 10 (out of 22 base pairs) between bta-miR-223 and XR_234647.4. Additional results on lncRNA-miRNA binding are presented in Supplementary Table 7.
Combined network and molecular interactome of lncRNAs, miRNAs, TFs and immune genes Figures 8 through 11 show molecular interactions between miRNAs, lncRNAs, TFs, target genes and pathways. Figure 8 in particular show the connections between the bovine mastitis target genes, miRNA, and the pathways/biological processes of the genes. CD4 (36) and TLR4 (36) have the most miRNA targeting them, as opposed to IL-4, CSF2, and IL-18 (one, two and two miRNAs respectively). Some miRNAs are seen targeting more than one gene such as bta-miR-2294 (targeting CD4 and CD86) and bta-miR-2374 (targeting TLR4 and CD4). Interestingly, we found some miRNAs that bind to multiple genes involved in the same pathway, such as bta-miR-671 targeting TLR4 and IFNγ, both of which are involved in macrophage activation; bta-miR-2413 targeting IFNγ, IL-10, and TLR4, all of which are involved in the regulation of nitric oxide biosynthetic process.
The functional annotation of each lncRNA was inferred from the target genes using GO Enrichment Analysis and the DAVID KEGG algorithm program. Several of these lncRNAs were involved in multiple ontologies ( Figure 9). Speci cally, NONBTAT001181.2 could bind to IL-6 to regulate its expression, thereby perturbing functional pathways like vascular endothelial growth factor, regulation of cytokine production, which are involved in in ammatory response, and regulation of tyrosine phosphorylation of STAT protein pathways. LncRNA XR_003030515.1 targets TLR4 and is connected to cellular response to lipoteichoic acid, macrophage activation, NAD+ nucleosidase activity, and regulation of interleukin-23 production. NONBTAT027932.1 lncRNA targets CD14 and is involved in positive regulation of NIK/NF-κB signaling, regulation of interleukin-8 production, regulation of cytokine secretion, LPS-mediated signaling pathway, lipopeptide binding, and LPS receptor complex ( Figure 9). Figure 10 shows the interconnections between the 17 TFs, 16 target genes, and the pathways they are involved in. Both CREB1 and JUN have numerous connections directed toward the target genes and pathways.
The nal immunoregulatory integrated network in our analysis contains miRNAs, lncRNAs, TFs, target genes, and biological pathways and their extensive interconnections (Fig 11). We showed an important connectivity between lncRNA, miRNA, bovine mastitis immune genes and that their biological functions have all been mapped on the network. The outer circle contains mostly miRNAs, with the exception of 3 TFs, that bind only to a single target gene. The inner circle consists of miRNAs binding to more than one target gene, lncRNAs, TFs, target genes, and biological pathways. LncRNA are found densely in the top inner circle and can be sporadically located in the outer circle. Our results indicate that the majority of lncRNAs, miRNAs, and TFs interactions overlapped and were involved in several pathways including biological processes, cellular components, and molecular functions.

Discussion
Bovine mastitis is detrimental to the dairy industry with signi cant impacts on farms in developing countries [26,27]. Mastitis can become chronic and highly contagious, and the effective treatment options available are suboptimal, one reason being the cost accompanied with antibiotic residue in the milk [28]. Regulatory factors have been associated with other diseases, including atherosclerosis, identifying a lncRNA that may be an intuitive preventive and therapeutic treatment option [29] but not in bovine mastitis. Novel key lncRNA and TFs were identi ed in lung adenocarcinoma regulatory networks, offering new drug targets for therapeutic treatments [30]. In another study, miR-99b was found to repress the expression of IGF1R and mTOR, ultimately acting as an onco-suppressor in clear cell renal cell carcinoma [31]. To our knowledge, this is the rst report examining all three regulatory elements (miRNA, lncRNA and TFs) in the context of bovine mastitis, with the aim to identify elements in uencing expression of candidate genes regulating immune response.
Through our meta-analysis and computational mining, we generated a list of genes playing critical roles in immune response during bovine mastitis. TLR2 and TLR4 were substantially connected to other candidate genes through our network, which is expected given their involvement in pro-in ammatory and innate immune response [32]. Speci cally, TLR4 recognizes lipopolysaccharides found in the outer membrane of gram-negative bacteria and TLR2 recognizes lipoteichoic acid found in the cell wall of gram-positive bacteria [33]. Pathogen recognition is imperative to the initiation of the immune response, and without TLR4 and TLR2, identi cation of bacteria in bovine mammary epithelial cells would be impaired. IL-8, IL-6 and TNFα, the three most common genes in our meta-analysis, are all in ammatory cytokines or chemokines, and have been shown to be upregulated in bovine mammary epithelial cells stimulated with LPS [34].
Of importance, our results identi ed six miRNAs binding to several target genes with the potential to regulate their expression during bovine mastitis. Interestingly, previous studies have shown bta-miR-24-3p, bta-miR-328, bta-miR-223, and bta-miR-185to be associated with bovine immune response. Bta-miR-24-3p has been associated with serum antibody response to Mycoplasma bovis in beef cattle [35]. Bta-miR-185 had increased expression in milk from a S. aureus infected cow compared to a healthy cow. Ma et al.
[36] identi ed bta-miR-185 targeting ve genes including MLLT3, DYRK1B and NPR2. A few other studies have demonstrated the involvement of bta-miR-223 in bovine mastitis host immune response [37,32], but also in other immune responses to lung infection, bacterial peritonitis, and cutaneous leishmaniasis [38][39][40][41]. The other two miRNA identi ed in our study, bta-mir-149-5p and bta-miR-874, have been associated with fat levels in beef cattle [42,43]. Our ndings along with previous publications collectively demonstrate the involvement of these miRNAs in immune response and should be strongly considered for further laboratory studies. In addition, our study demonstrated that miRNAs that could bind to all three regions on a single gene had a greater probability of binding the target gene, and ultimately perturb the disease pathways the target genes are involved in.
Furthermore, we identi ed novel interaction between lncRNA and mRNAs that could mediate host immune response at the post-transcriptional level. lncRNAs are becoming known to be involved in several biogenetic processes that may regulate disease pathogenesis [44,14]. We hypothesize that lncRNAs that bind to multiple mRNA target sequences could serve as signi cant drug targets and have the potential to modify or regulate immune signaling pathways, ameliorating the over-or under-production of cytokines or chemokine during infection. Importantly, lncRNAs could directly bind to the mRNA thereby getting it degraded and prevent translation to the expected protein. Of interest, we also found several lncRNAs with binding sites for miRNAs, for example XR_003033296.1-bta-miR-223 and lncRNA XR_234647.4-bta-miR-328 interactions thereby providing a way to prevent the negative regulatory effect of miRNA on the mRNA targets during disease perturbation. Gene ontology analysis reveal several of these miRNAs and lncRNAs to be associated with myeloid leukocyte activation, LPS-mediated signaling pathway, and positive regulation of IL-8 production. These miRNAs and lncRNAs interactions may be an avenue to manipulate of the regulatory elements involved in immune response to bovine mastitis. The knowledge is potentially useful in disease detection/diagnosis, especially in subclinical cases and may be an ideal target for treatment [15,45].
Additionally, our pathway analysis revealed seven genes, IL-4, IFNγ, TLR4, CXCL8, IL-18, CSF2 and TNFα, are involved in the morphological and behavioral changes of macrophages, monocytes, granulocytes, and dendritic cells (DCs) and their recruitment into infection sites. Therefore, the miRNA binding to these seven genes transcripts and degrade their mRNA would have a deleterious effect on the immune response needed to eliminate the pathogen. Our study indicated bta-miR-328 and bta-miR-223 bind to three out of the seven genes involved in this pathway. A recent study found the overexpression of bta-miR-223 in Mac T cells attenuating the in ammatory response induced by lipoteichoic acid derived from S. aureus, by targeting the Cbl proto-oncogeneB (CBLB), positively correlated with the expression of P13K, AKT, and NF-κB p65 [37]. Fang and others concluded that bta-miR-223 is a central post-transcriptional regulator in mammary tissue when infected with S. aureus by altering the expression of key innate immune response genes, one being CXCL14 [32]. Other studies have also identi ed bta-miR-223 as a key miRNA expressed in mammary tissue during bovine mastitis [46]. Given our ndings and previous reports, we surmise that these six miRNAs, especially bta-miR-223 would play a signi cant dysregulatory role on immune response during bovine mastitis and could serve as biomarker or drug target, deserving further in vivo and in vitro investigation to clarify.
Our study showed seven signi cant transcription factors including SP1, SF1 and JUN targeting several genes within the biological pathways. The SP1, JUN and CREB are commonly found in almost all the signi cant biological pathways (molecular function, biological and cellular processes), resulting from their function as activators and repressors. SP1 has been identi ed as one of the rst eukaryotic trans activators to regulate numerous mammalian genes, often in association with other transcription factors, playing major roles, including cell cycle regulation, hormonal activation and apoptosis [47,48]. Jun directly transcribe the expression of epidermal growth factor receptor thereby positively regulating the proliferation and differentiation of keratinocyte [49].
Nuclear transcription factor-κ (NF-κB) signaling is a major player in the regulation of innate and adaptive immune responses and is widely studied for the role it plays in mastitis progression and pathogenesis. Many in ammatory genes contain binding sites for NF-κB in their promoter regions, thus active NF-κB regulates expression by directly binding to target genes in the nucleus [4]. Cytokines (IL-6, IL-1β, TNFα) and chemokine (CXCL8) are associated with increased expression in LPS-stimulated mammary glands [8, 50,51]. These pro-in ammatory cells disrupt the blood-milk barrier causing slowed milk production and painful clinical manifestations in cattle with mastitis. Given the immunological and in ammatory importance, lncRNAs targeting mediators (TLR2, TLR4, MYD88, CD14, ICAM-1, IL-6, IL-1β, TNFα, and CXCL8) of the NF-κB signaling pathway may act as biomarkers for disease diagnosis and drug therapeutics by blocking lncRNAs from interacting with the target genes.
A variety of growth factors and in ammatory signals induces CREB and subsequently mediate the transcription of genes that contain a cAMP-responsive element. Interestingly, our study identi ed that miRNAs could bind and regulate IL-6, IL-10, and TNFα, which possess the cAMP-responsive element [52]. IRF1, through the regulation of autoimmunity, in ammation, viral infections, and innate and adaptive immune responses, is also responsible for the protection of host cells [53].
Conservation analysis revealed all six miRNAs to be almost entirely conserved between the 15 species. The bta-miR-223 was highly conserved in all 15 species, without a single base pair difference. The conservation of these miRNAs throughout evolution indicates their importance and bene ts to the host, further validating our results. These ndings also suggest a drug targeting one of these miRNAs can be used across multiple species. This also suggests the possibility of drug experimental trials on smaller animals that have a higher rate of reproduction, such as a mouse or rat, following in vivo/in vitro elucidation.
Network interactome of miRNA, target genes, pathways, and cellular functions give a visualization of the connections between them, allowing for comprehension of the broad picture-regulatory element to target gene to biological pathway. For example, bta-miR-671 is predicted to bind to IFNγ and TLR4 in at least two of the three mRNA regions, and both IFNγ and TLR4 are involved in the positive regulation of tumor necrosis factor superfamily cytokine production. Therefore, bta-miR-671 may be a strong target gene as it affects a biological pathway through two different genes. One of our identi ed miRNAs, bta-miR-24-3p binds to CD14 in at least two regions. CD14 is involved in cellular response to lipoteichoic acid, Toll-like receptor signaling pathway, positive regulation of tumor necrosis factor superfamily cytokine production, positive regulation of cytokine secretion, positive regulation of NIK/NF-κB signaling, regulation of interleukin-8 production, and LPS-mediated signaling pathway, all of which play major roles in immune response. Bta-miR-149-3p targets both CXCL8 and TLR4, both of which are involved in myeloid leukocyte activity. This interactome ties our ndings together and allows for miRNA to be seen as possible therapeutic targets.

Conclusions
The present study provides computational evidence that miRNA, lncRNA, and TFs could regulate the host immune response to bovine mastitis. These regulatory element interactomes provide an insight into immune gene dysregulation during bovine mastitis. The knowledge, which is useful in disease detection/diagnosis, especially in subclinical cases, may be ideal targets as an antibiotic for treatment. Meta-analysis indicated 16 immune-related genes as key components involved in host immune response to bovine mastitis. We identi ed 6 miRNA, 8 lncRNA, and 17 TFs that could potentially regulate expression of our candidate genes and were deemed signi cant to our study. Of importance, we reported miRNA bta-miR-223 to be highly conserved across species and targeting multiple genes. Likewise, 6 lncRNAs including NONBTAT027932.1 and XR_003029725.1 bind to all candidate genes while Sp1, JUN, and CREB transcription factors were involved in a majority ontology processes. The crosstalk between these regulatory elements was particularly important to our study, providing a new direction for disease studies in farm animals. Functional network analyses revealed complex interactions, suggesting that our identi ed signi cant elements would be effective drug targets for treatment and prevention through vaccination. To validate these predictions, further work is needed with in vitro and in vivo analyses that examine the expression or regulatory role of miRNA, lncRNA and TFs on host immune response in bovine mastitis.

Extraction and veri cation of signi cantly regulated genes in bovine mastitis
The analytical pipeline employed for this study, starting from literature search to interaction networks (Fig. 1a), including some amendments has been described previously [7,54]. Brie y, to obtain experimentally validated genes associated with bovine mastitis, we carried out an extensive search for signi cantly mentioned genes, utilizing several databases. First, LitInspector (Genomatix version 3.10, Munich, Germany) was used to scan all literature-curated datasets derived from high and low throughput experiments with a Medical Subject Headings (MeSH) on bovine mastitis, as described [7]. We obtained a MeSH-Term ID of C22.196.5819 and a p-value of 48E-31. Identi ed gene sets were ltered based on the number of reports from PubMed articles, pathway enrichment and gene ontology (GO) [55]. Second, we carried out a meta-analysis on PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) with combination of keywords including bovine mastitis + gene expression, to catalog genes from published literature in the database. We matched the identi ed genes from both search methods with Bioinformatics and Evolutionary Genomics (BEG) Venn diagram generator (http://bioinformatics.psb.ugent.be/webtools/Venn/) to identify common genes. The overlapped genes between the two lists were selected as our candidate genes (total of 16 genes) and were included for further analysis (Table 1). We then constructed a protein-protein interaction network of the candidate genes with Search Tool for the Retrieval of Interacting Genes database (STRING-DB; string-db.org) to determine molecular interactions and possible mechanisms for co-expression and interconnectivity, from both text mining and experimentally determined datasets [56].

Prediction of miRNA for bovine mastitis candidate genes
The locations of the 16 identi ed genes from our analysis were determined within the Bos taurus reference genome (ARS-UCD1.2) and complete transcript sequences were downloaded from GenBank. The transcript information and accession numbers are listed in Table 1. Furthermore, using Bos taurus as the reference organism, we searched for miRNAs that could bind complementarily against the entire region of each of our candidate genes, including the 5'-UTR, CDS, and 3'-UTR, using the prediction software miRWalk [20]. MiRNA binding to any of the three regions with a maximum p-value of 1 was used for further selection. To evade false positive in our analysis, the generated list of miRNA were cross referenced with two other prediction software, miRNet [21] and TargetScan [22] as described [7]. Using Venn generator, the overlap of predicted miRNA from the three software were selected as signi cant for further analysis (Table 2).

Identi cation of lncRNAs associated with immune response during bovine mastitis
In order to identify putative lncRNAs within cattle genome that may be binding to the transcript sequences thereby regulating expression, we performed a genome-wide search based on the publicly available curated data from four databases: NONCODE [24] a comprehensive knowledge based ncRNA database that speci cally focuses on lncRNA; LncTar [19] a lncRNA-RNA prediction software using the minimum binding free energy between RNA sequences; Ensembl (http://useast.ensembl.org/index.html), a vertebrate genome browser supporting analysis software, and LncRNA2Target v2.0 [25], a comprehensive database of lncRNA-target relationships, manually curated from published lncRNAfocused articles. Brie y, we searched each database for lncRNAs using cow as reference. Due to the high number of lncRNAs within the genome, we made some assumptions and applied the most stringent lter criteria using: 1. The number of exons, believing more exons imply high quality transcripts; we selected only lncRNAs with exon number greater than two. 2. Longer transcript length gives better quality transcripts; transcript length >200 bp were selected. 3. The prediction score; a lower score means a higher quality transcript; prediction score less than -0.2 was used. 4. Finally, we selected only lncRNAs that meet criteria 1-3 and also found in minimum of three out of the four databases. Brie y, complete sequences of each lncRNA were retrieved from NONCODE and each sequence searched on the bovine genome ARS-UCD1.2. using the Ensembl BLAT/BLAST genomic sequencing tools. NCBI BLAST was used to identify available accession numbers for each lncRNA (Table 3). Using LncTar, each mRNA gene was used as target to determine if bovine lncRNAs binds to the mRNA target. Based on the normalized free binding energy (ndG) value from the nucleotide base pairing, dataset with less than -0.05 were considered signi cant and used as candidates for further analysis [19].To avoid false positive lncRNAs, we used LncRNA2Target v2.0 as a second lncRNA-mRNA prediction software with human homology for the 16 target genes to determine bovine lncRNA binding sites. We located all identi ed human lncRNAs counterparts on the bovine genome ARS-UCD1.2. The lncRNAs found in bovine genome ARS-UCD1.2 and the human genome GRCh38 e were signi cant to our study.

Prediction of signi cant transcription factors
Transcription factors and their corresponding binding sites were predicted for the genes using different prediction software; AnimalTFDB [57] and GeneXplain [58], so as to increase result accuracy. AnimalTFDB was employed using the default settings with the p-value cut off at the minimum of e-06.
For GeneXplain, both Match and P-Match algorithms were adjusted to use the cut off selection "1.0 and 1.0" as matrix similarity cut-off and core similarity cut-off. We included only seventeen TFs that appeared in the results of at least two software for further analysis.
Functional and pathway analysis of candidate genes associated with immune response to bovine mastitis Functional enrichment analysis, using geneontology.org, was performed on the 16 candidate genes to determine the molecular functions, biological processes and cellular components the genes are involved in as described [14,59], with slight changes. Using the fold enrichment of greater than 100, the false discovery rate (FDR) of <10 -5 , and the p-value of <10 -7 , we catalogue the biological processes and cellular components. The molecular functions were selected based on the following criteria; fold enrichment >100, FDR <0.001, and the p-value was < 0.01. To validate these ndings, we carried out functional analysis with Database for Annotation, Visualization and Integrated Discovery (DAVID) [60], using the same parameters as stated previously. Results common in both programs were used for further analysis.
Signi cant pathways were generated using KEGG program algorithms with fold enrichment of >40, while the p-value and FDR were <10 -4 .

Sequence conservation analysis of signi cant miRNA and lncRNA
Using miRBase [61], candidate miRNAs sequences were retrieved and ascertained using the BLAST against cow, dog, cat, pig, human, gorilla, chicken, goat, rat, megabat, horse, mouse, wild yak, sheep, and chimpanzee for conservation analysis (Supplementary Table 1). MEGAX [62] and MultiAlin [63] were used to align miRNA sequences from each species to determine the regions of conservation and construct a phylogenetic tree. The tree was exported as a Newick text-le and uploaded to iTOL [64] for further editing. Furthermore, lncRNA sequences extracted from NONCODE were compared using multiple sequence alignment and phylogenetic analyses to detect evolutionary pro le across 15 species using MEGA X. The phylogenetic trees were also imported to iTOL for further editing and visualization.
Prediction of lncRNA-miRNA interactions associated with bovine mastitis pathogenesis We hypothesized that a complementary base pairing or binding of miRNA and lncRNA would be an important mechanism to manipulate the regulatory elements involved in immune response to bovine mastitis. To do this, we predicted the possible miRNA binding sites on the lncRNA sequences and determine their normalized binding free energy (ndG) based on the complementary base pairing, using LncTar [19]. Furthermore, miRNA, lncRNA, their target genes, and the biological processes were connected on a critical network using Cytoscape version 3.7.2, as described previously [54,65].
Combined interactome network of lncRNAs, miRNAs, TFs and immune gene targets To provide information on the connectivity as well as to identify the molecular mechanisms for gene regulation between the candidate genes, miRNAs, lncRNAs, and TFs, we created an interactome network with Cytoscape software, version 3.

Figure 9
A network incorporating lncRNA, their target genes and corresponding gene ontologies. The green triangles are the lncRNA, red circles represent the target bovine mastitis genes, and the pink rectangles are the biological processes, molecular functions, cellular components, and pathways.

Figure 10
Network interactome of TFs, bovine mastitis target genes, and gene ontologies. The red triangles are TFs while the green circles are the target bovine mastitis genes. The while ovals represent the biological processes, molecular functions, cellular components, and pathways.