https://orcid.org/0000-0001-7653-4621
Figures
Abstract
Bovine alphaherpesvirus one (BoHV-1) is a primary cause of bovine respiratory disease (BRD), and a leading cause of morbidity and mortality in cattle. The transcriptomic responses of key respiratory and immune associated tissues of dairy calves following experimental challenge with BoHV-1 are unknown. Thus, the study objective was to examine the gene expression profiles of multiple tissue types from dairy calves following an infectious challenge with BoHV-1. Holstein-Friesian bull calves (mean age ± SD 149.2 days ± 23.8; mean weight ± SD 174.6 kg ± 21.3 kg were challenged with either BoHV-1 inoculate (6.3 × 107/mL × 1.35mL) (n = 12) or sterile phosphate buffered saline (n = 6). Animals were euthanised on day 6 post-challenge and tissue samples collected, including bronchial (BLN) and mediastinal lymph nodes (MLN), pharyngeal tonsil (PGT) and healthy (HL) and lesioned right cranial lung (LL). Total RNA was extracted and libraries sequenced on an Illumina NovaSeq 6000. Differential expression analysis was conducted using edgeR and pathways analysed using DAVID. A weighted gene co-expression network analysis (WGCNA) was conducted separately for each tissue type to identify networks significantly associated with BoHV-1 infection. Differentially expressed genes (DEGs) were identified in all tissues (P < 0.05, FDR < 0.1, FC > 2). Thirty-three DEGs were common to all tissues and enriched pathways included Influenza A and Herpes simplex 1 infection (P < 0.05, FDR < 0.05). Modules enriched for antiviral and innate immune processes were identified for each tissue type. Of the 33 DEGs common to all tissues, 26 were also identified as hub genes in the blood (blue) module. Our use of a controlled experimental challenge allowed for improved understanding of the immune response of dairy calves to a BoHV-1 infection. Furthermore, discovering DEGs that are common to all tissues, including whole blood, indicates future focus areas in research surrounding BRD diagnostic biomarkers.
Citation: O’Donoghue S, Earley B, Johnston D, Finnie MS, Cosby SL, Lemon K, et al. (2025) Examination of the lung and lymphoid tissue mRNA transcriptome response in dairy calves following experimental challenge with bovine alphaherpesvirus one (BoHV-1). PLoS One 20(5): e0319575. https://doi.org/10.1371/journal.pone.0319575
Editor: Gianmarco Ferrara, University of Messina: Universita degli Studi di Messina, ITALY
Received: September 9, 2024; Accepted: February 4, 2025; Published: May 2, 2025
Copyright: © 2025 O’Donoghue et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All sequence data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) and are publicly available under the following accession numbers: GSE153242 (BLN data), GSE153429 (cranial lung lobe data), GSE227344 (PGT data), and GSE229115 (MLN data).
Funding: This project was funded by the Irish Department of Agriculture, Food and the Marine (DAFM) and the Department of Agriculture, Environment and Rural Affairs (DAERA), Northern Ireland, as part of the US-Ireland R&D partnership call (RMIS_0033 Project 16/RD/US-ROI/11). JT and JK were supported by Grant No. 2017-67015-26760 from the United States Department for Agriculture’s National Institute for Food and Agriculture. There was no additional external funding received for this study.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Bovine respiratory disease (BRD) is a multifactorial syndrome affecting cattle of all ages in beef and dairy industries not only in Ireland [1,2] but internationally [3], often responsible for reduced animal performance and economic losses [4]. Stress resulting from management and environmental factors as well as infections caused by bacterial and viral agents influence the onset of BRD. A key BRD-associated virus, BoHV-1 is an alphaherpesvirus which causes the clinical disease infectious bovine rhinotracheitis (IBR) in cattle [5]. BoHV-1 has been shown to remain latent within the sensory neurons within trigeminal ganglia [6] and also in the pharyngeal tonsil of infected cattle [7]. As a result, despite recovering from acute infection, the dormant virus can reactivate causing the emergence of symptoms and viral shedding [8,9]. Although vaccines may reduce clinical symptoms of IBR, they have not been fully successful at preventing infection with BoHV-1 or the establishment of BoHV-1 latency [5].
Next-generation sequencing methodologies have been utilised in efforts to gain an improved understanding of the response of animals to infection by BRD pathogens at the molecular level. RNA sequencing (RNA-Seq) has been used to analyse the gene expression profiles of lung, pharyngeal tonsil, retropharyngeal lymph node, nasopharyngeal lymph node [10] and bronchial lymph node [11] in beef steers following experimental challenge with BoHV-1, resulting in the identification of key genes influencing the activation of key immune networks across lymphoid tissues. We have previously identified key biological pathways and processes such as inflammatory response, and viral pathways such as Influenza A and defence response to virus in the whole blood transcriptomes of calves experimentally challenged with BoHV-1 [12]. Furthermore, other studies have utilised a network-based approach to analyse the blood transcriptome profiles of cattle with BRD and have found networks and hub genes involved in the immune response to infection [13–15]. However, in these studies the BRD causal pathogen was unknown. One possible application for the discovery of important genes related to the immune response in whole blood is the creation of a BRD diagnostic that can be used “ante-mortem”.
The mechanisms by which BoHV-1 establishes infection and the host response to disease at the molecular level requires further study in weaned dairy calves. In particular, gene and network regulation in respiratory and immune tissues should be studied to complement findings in whole blood [12]. Studies focusing on pan-tissue transcriptomics in response to key pathogens allow for a better understanding of the immune system functionality at the molecular level. Furthermore, the use of network based approaches allows for the examination of interactions between genes governing a particular phenotypic outcome [16], and the identification of hub genes involved in the regulation of other network genes, may enable the development of biomarkers for traits such as BRD [17]. Hub genes are highly connected genes within a co-expression module that help to explain the module functioning [14,18].
The objectives of this study were threefold; first, to characterise the gene expression profiles of key immune and respiratory associated tissues in dairy calves experimentally infected with BoHV-1 and identify the biological processes that are enriched. Second, to cross-analyse the DEGs identified with those previously identified in whole blood from the same animals in response to a BoHV-1 challenge [12]. Finally, to perform WGCNA on the analysed tissues augmented by the whole blood data from O’Donoghue et al. (2023) and to determine if any modules and associated pathways identified in the key respiratory and immune tissues are also detected in the blood.
Materials and methods
Animal model
All animal studies were carried out in accordance with the UK Animals (Scientific Procedures) Act 1986 and with the approval of the Agri-Food and Biosciences Institute Northern Ireland Ethical Review Committee. The details of the study have been reported in accordance with the ARRIVE guidelines (https://arriveguidelines.org/).
The model and sampling protocol used in this study have previously been described (O’Donoghue et al., 2023). In brief, Holstein-Friesian bull calves (mean ± SD age 149.2 ± 23.82 days) were selected based on low BoHV-1 specific maternally derived antibody levels and negative BoHV-1 PCR status. Calves were either challenged with BoHV-1 inoculum (strain 2011-426) (dose = 6.3 × 107/mL in a volume of 1.35mL per animal) (n = 12) or mock-challenged with sterile phosphate buffered saline (PBS) (n = 6). Clinical signs such as nasal discharge, ocular discharge, general demeanour, size of mandibular lymph nodes, presence of a cough, respiratory rate, respiratory character, mouth breathing, dyspnoea, presence of an expiratory grunt and rectal temperature were recorded daily from the day of challenge until euthanasia, and scores were calculated by a trained veterinarian blinded to the calves treatment status, using the scoring system described by Johnston et al. (2019). On day 6 post-challenge, calves were euthanised by captive bolt. At post-mortem, a trained veterinary pathologist examined and scored the lungs using an AFBI standardised scoring system, which assigns the percentages of lesions present on the total lung area and on component parts of the lungs. The workspace and implements were thoroughly cleaned and disinfected with bleach and 75% ethanol and sprayed with RNaseZap for RNase inhibition, before tissue collection and between the processing of each animal. Tissue samples BLN, MLN, PGT, HL and LL were harvested, immediately flash frozen in liquid nitrogen, and placed on dry ice before storage at − 80°C. BoHV-1 viral load was assessed using qPCR and was performed on BLN, MLN, PGT and lung tissue samples (data not shown). BoHV1 PCR was performed using the exopol EXOone oneMIX BoHV-1 kit (exopol, Spain), according to the manufacturer’s instructions.
RNA extraction
Total RNA was extracted using the Qiagen RNeasy Plus Universal Mini Kit (Qiagen LTD, Manchester, UK), according to the manufacturer’s instructions. The quantity of the extracted RNA was determined by measuring the absorbance at 260 nm with a Nanodrop spectrophotometer (Nano Drop technologies; Wilmington, DE, USA). The quality of the extracted RNA was examined with the Agilent 2100 Bioanalyzer (Agilent Technologies Ireland Ltd; Dublin, Ireland) using the RNA 6000 Nano LabChip kit (Agilent Technologies Ireland Ltd; Dublin, Ireland). The mean ± SD RNA Integrity Number for BLN, MLN, PGT, HL and LL samples were 9.07 ± 0.11, 9.32 ± 0.21, 9.4 ± 0.17, 8.83 ± 0.16 and 9.14 ± 0.30 respectively.
RNA-Seq library preparation and sequencing
RNA samples were shipped frozen at − 80°C on dry ice to the University of Missouri’s Genomics Technology Core (Columbia Missouri, USA). Library preparation was performed using the TruSeq stranded mRNA kit (Illumina, San Diego, California, USA) and libraries sequenced (150 bp (BLN, lung) or 100 bp (MLN, PGT) paired-end) on an Illumina NovaSeq 6000. All sequence data produced in this study have been deposited to the NCBI GEO repository and are available under the following accession codes: GSE153242 for BLN, GSE153429 for cranial lung lobe, GSE227344 for PGT and GSE229115 for MLN. Whole blood RNA-Seq data from O’Donoghue et al. (2023) are available at GEO with accession number GSE199108.
Read processing
Sequence reads were received in FASTQ format and were assessed for quality using FastQC (version 0.11.8). Reads were trimmed at the 3’ end using cutadapt (version 1.18) [19] for the removal of short reads ( < 10 bases), ambiguous nucleotides and poly-G-artefacts. Trimmed reads were then re-assessed using FastQC to ensure that all reads passed the basic quality statistics. Sequence reads were aligned to the ARS-UCD1.2 bovine reference genome [20] and read counts generated using the STAR (Spliced Transcripts Alignment to a Reference) alignment tool (version 2.6.1b).
Differential expression analysis
Raw read counts were input into R (version 4.3.2 (2023-10-31)) and differential expression analysis performed using the edgeR package (version 4.0.5) which uses an over-dispersed model to account for arising biological and technical variation [21]. Genes with less than one read count per million in the smallest number of samples per treatment were removed from the analysis. The trimmed-mean of M values method was used to normalise the data and the negative binomial dispersion parameters were estimated using the quantile-adjusted conditional maximum likelihood estimator. Differentially expressed (DE) genes between the treatment (control and challenged) groups were detected using exact tests, with genes considered DE if they had a Benjamini- Hochberg false discovery rate (FDR) of ≤ 0.1 and a fold change of ≥ 2. To analyse the DEGs common across tissues, DEGs from each tissue type were input into the Venn diagram tool on BioTools.fr https://www.biotools.fr/misc/venny. Genes common to all tissue types, which had the same direction of regulation (upregulated) between treatments were ranked using a methodology similar to that described by Laighneach et al. (2021) [22]. For each tissue, genes were ordered by statistical significance for DE and assigned a rank (with the most significant DEG ranked first). The rank for each DEG within each tissue type were then summed across tissues to produce a sum rank. A spearman correlation was performed using the cor() function in R (version 4.3.2 (2023-10-31)) to assess the relationship between viral load (qPCR data) and the common DEGs in each tissue type. Correlation (effect size and the strength of the correlation) was described using the following: 0.00–0.19 “very weak”; 0.20–0.39 “weak”; 0.40–0.59 “moderate”; 0.60–0.79 “strong”; 0.80–1.0 “very strong” [23,24].
Pathway and gene ontology analysis
For the detection of perturbed biological pathways and gene ontologies, DEGs were input into the Database for Annotation, Visualization and Integrated Discovery (DAVID) [25,26]. The lists of DEGs identified within each tissue type were separately uploaded to DAVID and Kyoto Encyclopaedia of Genes and Genomes (KEGG) and gene ontology (GO) terms for biological processes (BP), cellular components (CC) and molecular functions (MF) were interrogated. Pathways and ontologies with a P ≤ 0.05 and FDR ≤ 0.05 were considered enriched for DEGs. This analysis was also performed for the DEGs found to be common to all tissue types.
Gene co-expression analysis
For each tissue dataset, raw RNA-seq read counts were filtered for lowly expressed genes, by retaining only genes that had greater than one count per million in at least 12 samples (BoHV-1 challenged) or 6 samples (control). The filtered reads were normalised in edgeR using the trimmed mean of M – values normalisation method. Normalised counts were then Log2(x + 1) transformed in R. Filtered reads were input into R and weighted gene co-expression network analysis (WGCNA) was performed on the data for each tissue type individually using the WGCNA R package (version 1.72-5) which identifies modules (or networks) of co-expressed genes [27]. First, a similarity matrix was constructed to estimate the similarity between genes with similar expression profiles. The similarity matrix was next transformed into an adjacency matrix using a soft thresholding power corresponding to R2 ≥ 0.8 [14]. The adjacency matrix, which encodes the strength of the connection between pairs of genes, was finally transformed into a topological overlap matrix (TOM) and the corresponding dissimilarity (distance) of genes was calculated to minimize the effects of noise and false associations [28]. Average linkage hierarchical clustering was applied to the TOM for each tissue dataset using the hclust function in R to produce a hierarchically clustered tree of genes for each tissue type. The dynamicTreeCut package (version 1.63-1) 10.32614/CRAN.package.dynamicTreeCut was then used to identify modules (groups of co-expressed genes). Module eigenegenes were calculated using the moduleEigengenes function in the WGCNA package, to quantify the co-expression similarity of the entire module. Modules with genes possessing highly similar expression profiles were merged based on their correlation and assigned a colour identifier by the software [27].
Pearson’s correlation was used to analyse the relationship between identified modules of co-expressed genes and selected clinical traits (treatment (BoHV-1-challenged), clinical score and rectal temperature). A positive Pearson correlation indicates that a module is associated with BoHV-1 infection and a negative Pearson correlation indicates that a module is not associated with BoHV-1 infection. Modules with statistically significant (P ≤ 0.05) correlations were selected for further analysis as biologically interesting modules associated with BoHV-1 infection.
Identification of hub genes within modules of interest
The module membership (MM) can take a positive or negative value, indicating that genes are positively or negatively related to the module eigengene, respectively. Genes which have a MM value closer to an absolute value of 1 are highly connected to the other genes within a module (Langfelder and Horvath, 2008). We considered genes with MM > 0.9 to be hub genes [29]. Hub genes identified in the significant modules for each tissue data set were then cross-analysed against the identified DEGs to reveal the genes identified in both analyses.
Pathway analysis and functional enrichment of modules of interest
Pathway analysis was conducted on genes present in modules significantly associated with the examined traits (BoHV-1-challenged, clinical score and rectal temperature). To perform these analyses the list of genes from each significant module were input into DAVID. The databases queried through DAVID were the KEGG, GO_BP, GO_CC and GO_MF. DAVID employs Fisher’s exact test to determine the enrichment of pathways and ontology terms. Significant pathways and terms were considered those with a P-value < 0.05 and FDR < 0.05.
Protein-protein interaction (PPI) analysis
Genes that exist in close proximity to each other in a genome can have functional associations [30]. DEGs common to all tissue types were input into the search tool for recurring instances of neighbouring genes (STRING) (version 12.0) [30,31], for the examination of functional associations between their encoded proteins.
Results
Clinical scores, haematology variables and lung pathology
Clinical scores and lung pathology measures have been described previously [12]. Briefly, there was a significant treatment × day (d) interaction for both clinical scores (P < 0.05) and rectal temperature (P < 0.01) between control and challenged calves, whereby clinical scores and rectal temperatures were greater for BoHV-1 challenged compared to control calves on d 4, 5, and 6 post-challenge. BoHV-1 challenged calves had higher clinical scores (P < 0.001) and rectal temperatures (P < 0.0001) on d 3, 4, 5, and 6 relative to d -1. There were no differences in lung scores in the overall lung or the right cranial lung lobe lung between BoHV-1 challenged and control calves (P > 0.05).
Differential gene expression analysis
The mean sequence reads ± SD obtained for the tissues were; 40,815,975 ± 4,282,489 (BLN), 44,308,241 ± 4,896,483 (MLN), 81,200,000 ± 8,920,000 (PGT), 41,181,735 ± 4, 308, 861 (HL) and 40,502,064 ± 5,048,646 (LL) of which 94%, 86%, 86%, 94% and 94% of reads were uniquely aligned to the ARD UCD 1.2 bovine reference genome respectively.
There were 337, 81, 1833, 334, and 67 DEGs identified (P < 0.05, FDR < 0.1, FC > 2) between control and challenged calves in the BLN, MLN, PGT, HL and LL samples respectively (S1 Table). Multi-dimensional scaling based on global gene expression, revealed a clear separation between the control and challenged groups in the BLN, PGT and HL (Supplementary Figures 1–5 S2 File). Multi-dimensional scaling of all samples based on global gene expression revealed four distinct clusters (Fig 1). The gene expression profiles of the whole blood and PGT separated into two distinct clusters. The MLN and BLN samples formed a cluster, as did samples from the LH and LL.
Samples separated into four clusters; whole blood (pink), PGT (green), MLN and BLN (red and blue, respectively) and LH and LL (orange and purple, respectively).
A Venn diagram was used to display the DEGs in common across tissue types (Fig 2). There were 35 DEGs common to all tissue types, and all were upregulated in response to BoHV-1 (S2 Table). Strikingly, thirty-three of these genes were also DE and upregulated in the whole blood data from O’Donoghue et al. (2023). Table 1 shows the sum ranks of p-values for these DEGs across all tissues with the lowest sum rank representing the most significant gene.
The corresponding colour and label for each node are; green, bronchial lymph node (BLN), blue, mediastinal lymph node (MLN), pink, pharyngeal tonsil (PGT), yellow, healthy lung (LH) and orange, lesioned lung (LL).
Significant relationships were found between the expression of these genes and the BoHV-1 viral load in the PGT and MLN, with 19 and 1 gene(s) moderately significantly correlated respectively (S1 File).
PPI analysis
An examination of the functional connections among the proteins encoded by the 33 genes common to all tissue types in STRING (version 12) revealed that this gene group had significantly more interactions than expected if the genes had been sampled at random (P < 1.0e-16). This suggests that these proteins are biologically connected as a group. A PPI network for these genes is presented in Fig 3 (S3 Table). Table 2 presents the STRING functional analysis of the PPI network.
Nodes represent each protein where coloured nodes represent the first shell of interactors and white nodes represent second shell of interactors. Within some nodes the known or predicted 3D structure is given and in blank nodes the 3D structure of the protein is unknown. Edges represent the protein-protein associations and are colour coded; light blue = known interactions from curated databases, pink = known interactions determined experimentally, green = predicted interactions gene neighbourhood, red = predicted interaction gene fusion, dark blue = predicted interaction gene co-occurrence. The light green, black and lilac edges represent other interactions such as text mining, co-expression and protein homology respectively.
Pathway and gene ontology analysis
Analysis of the DEGs from each tissue type identified pathways and ontology terms associated with the innate and antiviral immune responses across tissue types (S4–S8 Tables). Analysis of the 33 DEGs common to all tissue types revealed enriched pathways related viral diseases such as Influenza A, Hepatitis C and Herpes simplex virus 1 infection. Other identified pathways were related to pathogen recognition receptor signalling such as NOD-like receptor signalling (S9 Table). Fig 4 displays the KEGG pathways enriched for the 33 DEGs common to all tissue types.
The significance (P-value) for each term is given on the X-axis and the term name is given on Y-axis.
Tissue specific weighted gene co-expression analysis
After filtering, there were a total of 14341, 14205, 14760, 14908, 14690 and 14743 genes remaining in the BLN, MLN, PGT, LL, LH and blood data sets respectively, for downstream analysis. A soft-thresholding power as determined by the scale-free topology of each network (R2 > 0.8) was used to construct the adjacency matrix. The blood, LL and PGT datasets failed to reach a scale-free topology fit index above 0.8 and so a power of 9 was used for each of these analyses, as advised by the package information https://github.com/edo98811/WGCNA_official_documentation/blob/main/faq.html. A soft-thresholding power of 20 (BLN), 10 (MLN) and 9 (PGT, LH, LL and blood) were used for each analysis. Modules with a correlation > 0.5 and P ≤ 0.05 were considered significantly associated with each analysed trait, in this case, treatment (BoHV-1 challenge), clinical score and rectal temperature.
WGCNA identified modules significantly associated with BoHV-1 infection in each tissue dataset, and in some cases modules significantly associated with clinical score and rectal temperature were also identified. A heatmap detailing the modules significant for each tissue type is provided in S6 Fig. A complete list of the genes within each significant module for BLN, MLN, PGT, LH, LL and whole blood are provided in S10–S15 Tables, respectively.
Pathway analysis of genes within modules
Analysis of the genes from each significant module for each tissue type through DAVID revealed enriched pathways and gene ontology terms (P < 0.05, FDR < 0.05). There were no significant KEGG pathways identified for the genes in significant modules in the BLN or MLN data. A list of the KEGG pathways enriched for significant modules in the PGT, LH, LL and whole blood data are provided in S16–S19 Tables, respectively.
The blue module identified for the blood, was significantly and positively associated with BoHV-1 infection. Many of the KEGG pathways enriched for the genes in this module were also enriched for genes in significant modules across the other tissue types. Fig 5 shows the KEGG pathways from this module that are also enriched in other tissue modules.
Each KEGG term is listed on the left with the tissue codes given across the top of each column. The green squares represent the terms from the blue module that overlap in that tissue type. The module within each tissue is given in each green square as well as their respective P-value (in brackets).
Hub gene identification and overlap with DEGs
Across all the tissue types, 3472 hub genes (MM > 0.9) were identified. A list of the hub genes identified for each tissue type is provided in S20 Table. The blue module identified in the blood analysis had many overlaps in enriched pathways with significant modules across other tissue types. Five hundred and seventeen hub genes (MM > 0.9) were identified in this module, with 125 also identified as DEGs by O’Donoghue et al. (2023). Of these, 26 were amongst the 33 genes common to all tissues (Fig 6), demonstrating that these genes are centrally important to the processes enriched in this module. A list of the hubs genes also found to be DE in each tissue type is provided in S21 Table.
Discussion
In the present study, DE and WGCNA approaches were used to analyse the transcriptome profiles of bronchial, and mediastinal lymph node, pharyngeal tonsil, healthy and lesioned lung and whole blood samples collected from dairy calves experimentally infected with BoHV-1. Previous studies by our groups have examined the transcriptome response of various tissue types from animals following a single pathogen challenge with BRD causative agents (Tizioto et al., 2015; Behura et al., 2017; Johnston et al; 2019; Johnston et al., 2021; O’Donoghue et al; 2023). These studies used a functional genomics approach, RNA-Seq, to identify DEGs between different phenotypes (e.g., control versus infected animals). This methodology has been successful in uncovering genes and biological pathways involved in the host immune response to BRD, with our group identifying differentially regulated genes in response to experimental challenges with BRSV (Johnston et al., 2019; Johnston et al., 2021) and BoHV-1 (O’Donoghue et al, 2023). The use of co-expression networks for the examination of correlation patterns across tissues to find clusters of highly correlated genes and regulatory genes within these networks is crucial [18,27], given that genes and gene products operate in association with many other genes, connected in larger, more complex networks. For example, Behura et al. (2017) utilised a network approach to identify key genes and networks enriched in various lung and lymphoid tissue in beef calves following experimental challenge with a range of viral and bacterial BRD causative agents. Other studies have utilised network-based analyses to investigate the regulatory networks in whole blood that may be involved in naturally contracted BRD infection [13–15]. In studies of naturally infected animals, clinical signs are often used as a means of BRD diagnosis, making it difficult to identify the causative agent and thus, the initial cause of infection.
We found 33 DEGs in common among all of the analysed tissues in response to BoHV-1 infection. The upregulation of all these genes in all tissues in response to infection suggests that they play a key role in mounting the across-tissue immune response to BoHV-1. Furthermore, the fact that these genes are also upregulated in circulating whole blood suggests that they may have potential as ‘ante-mortem’ biomarkers of BRD, allowing earlier diagnosis of BoHV-1 infection. Many of these genes (DHX58, EIF2AK2, GZMB, IFI44L, IFI6, IFITM3, ISG15, MX1, MX2, OASIY, OAS2 and RSAD2) were also identified as hub-hub genes in the whole blood of cattle with BRD and were suggested to be critical in the immune response to BRD infections [14]. Further, Johnston et al. (2019) found several of these genes (EIF2AK2, IFIT1, IFIT5, ISG15, MX1, OAS2 and RSAD2) to be DE in the bronchial lymph node of dairy calves following an experimental challenge with BRSV [64]. A study analysing the bronchial lymph node transcriptome profiles of beef cattle following single pathogen challenges with a range of BRD associated bacterial and viral pathogens also found some of these genes (HERC6, ZBP1, IFIT1, ISG15 and RSAD2) to be DE in response to all challenge pathogens [11]. These findings also support the utility of these genes as potential biomarkers for the presence of BRD infection. Scott et al. (2022a) discussed the potential of rapid diagnostics for testing cattle for BRD upon arrival to the feedlot. Whole blood, as a tissue source, would be ideal for this application as collection is relatively non-invasive, economical and practical [65]. Moreover, the usage of antibiotics in herds might potentially be decreased with testing, which has applications in both the dairy and beef industries.
The blue module identified in the whole blood data was enriched for several KEGG pathways that were also activated in some of the other tissue types. The pathways for N-glycan biosynthesis and protein processing in the endoplasmic reticulum were enriched in this module and were also enriched for module genes identified for the PGT. Glycans are an essential part of cell-cell interactions and N-glycans have been described as fine-tuners of immunological responses and potential molecular targets for the manipulation of immune tolerance and activation in a wide range of diseases and pathological conditions [66]. N- glycans, although involved in the immune response, can also be involved in viral attachment and entry to cells [67].
Analysis of the clinical scores found differences between the BoHV-1 challenged and control calves, as previously described [12]. Disease severity was not included as a variable in the DE analysis, representing a possible study limitation. However, clinical score was included in the WGCNA with the antiquewhite2 and sienna3 module significantly correlated with this trait in the BLN and MLN tissues respectively. Despite being significantly correlated, no KEGG pathways or ontology terms were enriched for the genes or hub genes in either module.
The pathways for ‘Cytokine-cytokine receptor interaction’ and ‘Viral protein interaction with cytokine and cytokine receptor’ were the most significant for DEGs in the PGT and were also enriched for genes in modules identified in the blood, LH and PGT. Cytokines are non-structural proteins, which play a regulatory role in processes such as inflammation, immunity and haematopoiesis [68], and often act as protectors against dangerous stimuli such as invading pathogens [69]. As a strategy to evade the host immune response, large DNA viruses, such as herpesviruses, encode homologues of cytokines, chemokines and their receptors [70], targeting chemokine-signalling networks to disrupt immune surveillance and defence of vertebrates [71]. The molecular mimicry of cytokines and cytokine receptors is known to be an immune modulation strategy adopted by herpesviruses [71]. Glycoprotein G (gG) is encoded in alphaherpesviruses and homologues of gG were found in BoHV-1 [72]. Recombinant gG from BoHV-1 was found to bind to a broad range of chemokines with high affinity and inhibit their biological activity in vitro [73]. These findings illuminate the nature of host-virus interactions occurring during BoHV-1 infection.
The most highly upregulated gene across all tissues analysed in the current study was PRSS2 or serine protease 2. This gene was identified as the second most upregulated gene in whole blood in response to BoHV-1 [12]. Serine proteases are involved in a diverse range of biological processes including cellular and humoral immunity [74]. Other serine protease genes have been found to be differentially regulated in BRD cases with PRSS45 downregulated in feedlot cattle at arrival that went on to develop BRD [75] while PRSS50 was upregulated in feedlot cattle with BRD [76]. In humans, PRSS2 is involved in inflammatory diseases [77,78] as well as tumour growth [79]. A recent study found that PRSS2 is a key gene involved the regulation of lipid metabolism in dairy cows [80]. We found PRSS2 to be amongst the DEGs involved in the protein digestion and absorption pathway in addition to several collagen genes that were downregulated in the bronchia lymph node, which may suggest it plays a role in collagen degradation. Additionally, we found this gene was also involved in the Influenza A pathway in other tissues. The significant upregulation of this gene across all our datasets in response to BoHV-1 infection suggests that it plays a key role in response BoHV-1 infection in dairy calves, and further investigation of its mechanism of action is warranted. Validation of key DEGs through qRT-PCR would strengthen the claim that these genes are central to the immune response to BoHV-1 infection, and we acknowledge that the absence of this validation could be a study limitation. Several pathways related to various viral infections such as Influenza A and Herpes Simplex infection were also identified. The Herpes Simplex infection pathway was enriched for the DEGs common to all tissue types as well as for genes in several significant modules. BoHV-1 and HSV1 share several biological properties, with both initiating infection in mucosal surfaces [81] and both are able to establish latency in the host sensory neurons [82]. DEGs and the gene modules involved in this pathway provide insight into the genes associated with the response to BoHV-1 infection.
Upon infection, pathogens express molecules known as pathogen-associated molecular patterns, which are recognised by host sensors called pathogen recognition receptors [83]. Nucleotide-binding oligomerization domain-like receptors (NLRs) are one such family of these receptors involved in the host response to BRD [84] and play a critical role in pathogen recognition and immune signalling [85]. Retinoic acid-inducible gene I (RIGI-I) like receptors are RNA sensors crucial in innate antiviral immunity [86] and the detection of viral replication in the cytoplasm during early infection [87]. The NLR and RIG-I-like receptor signalling pathways were enriched for the DEGs common to all tissue types, suggesting that they play a role in the cross-tissue host response to infection, a finding similar to that of Behura et al. (2017). With their role in innate and adaptive immunity, and their influence on downstream signalling pathways, NLRs have been suggested as targets for therapeutic strategies for auto-inflammatory disorders [88]. In humans certain micro-RNAs have been shown to promote RIG-I-like signalling and enhance the antiviral immune response to certain viruses [89]. Genes involved in these pathways may serve as potential targets for BRD therapies.
Respiratory disease causing viruses often spread to the mediastinal lymph nodes from the lungs [90,91]. Of the identified DEGs, 11 were unique to this tissue type. Of these, the antimicrobial peptides CATHL2 and PGLYRP1 were also found to be upregulated in Holstein-Friesian cows with clinical mastitis [92]. The AGRN gene was also found to be uniquely upregulated in the MLN and a hypothesis surrounding its role in immune system regulation depicts that agrin expression in T cells may be upregulated during pathogenic infection and plays a role in cell-cell adhesion that is required for successful T cell activation [93]. The serine protease PRSS45 was uniquely DE in the MLN and was found to be downregulated in the current study. Interestingly, this gene was found to be upregulated in the whole blood of cattle that had resisted BRD infection [75]. To our knowledge, this is the first study to examine the transcriptomic changes in the mediastinal lymph node in response to a BoHV-1 challenge in dairy calves.
We found the pharyngeal tonsil to possess the greatest number of DEGs in response to BoHV-1 infection, with 1833 genes DE. Further examination of these DEGs, revealed that 1573 ( ~ 86%) were unique to the PGT. The IL-17 signalling pathway was one of the most significant for the unique DEGs. Although we did not find an altered expression of 1L-17 in this tissue, we did detect the downregulation of both IL17RE and IL17A. In early stages of the host response to a virus, the host immune system induces the production of interferons and pro-inflammatory cytokines [94]. IL-17, which is primarily produced by γδ T cells and CD4 + or Th17 cells [95], is a ‘master regulator’ of downstream cytokine and chemokine activities [96] and plays a key role in the maintenance of tissue integrity and the induction of protective immune responses to pathogens, particularly at epithelial barrier sites [97]. An increase in the expression of IL-17 was observed in the lungs of calves infected with the BRD–associated virus, BRSV [96]. Although a critical component of the host defence to viral infections, it has also been thought to play a role in the promotion of viral infection and related illness [98]. The ontology term for interleukin-27-mediated signalling pathway was also found to be enriched in the STRING analysis of the 33 DEGs common to all tissues. Interestingly, IL-17 has become a major drug target in a range of autoimmune and inflammatory diseases [99], and so could have potential as a target for BRD therapies.
The pathway for protein digestion and absorption was enriched in the BLN and many of the DEGs involved in this pathway (ATP1A2, COL5A3, ELN, COL21A1, COL4A1, COL4A2, COL3A1, COL5A1 and COL8A1) were unique to this tissue type. All of these genes were found to be downregulated in response to BoHV-1 infection. A link exists between the extracellular matrix (ECM) and innate and adaptive immunity and the degradation and deposition of collagen is related to immune cell activity [100]. Collagen degradation in the extracellular matrix is known to result from tissue damage during infection and can enhance inflammation [100]. Interestingly, degraded collagen has also shown to be a chemoattractant for immune cells [101]. The unique differential expression of these genes in the BLN suggest this tissue to be involved in a specific aspect of the overall immune response to BoHV-1.
The right cranial lung lobes are a primary site for lesion development during BoHV-1infection. Despite appearing healthy, we were able to identify over 300 DEGs in the LH, of which 130 were unique to this tissue. Genes in the phospholipase A2 group (PLAG27 and PLA2G2D5) were found to be unique to this tissue and were upregulated in response to BoHV-1. Phospholipases are enzymes responsible for the cleavage of ester bonds within phospholipids [102] and have been shown to play a role in host defence against bacteria, parasites and viruses [103]. IRF9 was also uniquely upregulated in the healthy lung and is involved in virus-mediated activation of interferon [104]. In humans, it was found that mutations in IRF9 resulted in increased susceptibility to viral infection and that IRF9 deficient cells were unable to induce multiple interferon-stimulated genes (ISGs) [105]. This could suggest that IRF9 plays a role in the regulation of ISGs in the lung during BoHV-1 infection.
Conclusion
Our use of a controlled experimental challenge allowed for an improved understanding of the specific immune responses to BoHV-1. Building on the work of others in this field, this study has successfully identified DEGs and important gene networks associated with BoHV-1 infection. PRSS2 was found to be highly upregulated in all analysed tissues and therefore warrants further investigation into its role in BoHV-1 infection. A better understanding of the biological mechanisms underlying the host response to BoHV-1 was made possible by the application of a systems biology approach as opposed to a DE analysis alone. The discovery of DEGs that are present in all tissue types, including whole blood, suggests prospective future targets for BRD therapies and diagnostics.
Supporting information
S1 Table. A list of the DEGs identified for each tissue type.
https://doi.org/10.1371/journal.pone.0319575.s001
(XLSX)
S2 Table. A list of the DEGS in each section of the VENN diagram.
https://doi.org/10.1371/journal.pone.0319575.s002
(XLSX)
S3 Table. A PPI network of the 33 DEGS common to all tissue types.
https://doi.org/10.1371/journal.pone.0319575.s003
(XLSX)
S4 Table. The DAVID results for the BLN tissue.
https://doi.org/10.1371/journal.pone.0319575.s004
(XLSX)
S5 Table. The DAVID results for the MLN tissue.
https://doi.org/10.1371/journal.pone.0319575.s005
(XLSX)
S6 Table. The DAVID results for the PGT tissue.
https://doi.org/10.1371/journal.pone.0319575.s006
(XLSX)
S7 Table. The DAVID results for the HL tissue.
https://doi.org/10.1371/journal.pone.0319575.s007
(XLSX)
S8 Table. The DAVID results for the LL tissue.
https://doi.org/10.1371/journal.pone.0319575.s008
(XLSX)
S9 Table. The DAVID results for the 33 DEGs common to all tissues.
https://doi.org/10.1371/journal.pone.0319575.s009
(XLSX)
S10 Table. A list of the genes in the significant modules in the BLN.
https://doi.org/10.1371/journal.pone.0319575.s010
(XLSX)
S11 Table. A list of the genes in the significant modules in the MLN.
https://doi.org/10.1371/journal.pone.0319575.s011
(XLSX)
S12 Table. A list of the genes in the significant modules in the PGT.
https://doi.org/10.1371/journal.pone.0319575.s012
(XLSX)
S13 Table. A list of the genes in the significant modules in the LH.
https://doi.org/10.1371/journal.pone.0319575.s013
(XLSX)
S14 Table. A list of the genes in the significant modules in the LL.
https://doi.org/10.1371/journal.pone.0319575.s014
(XLSX)
S15 Table. A list of the genes in the significant modules in the blood.
https://doi.org/10.1371/journal.pone.0319575.s015
(XLSX)
S16 Table. Enriched KEGG pathways for genes in the significant modules in the PGT.
https://doi.org/10.1371/journal.pone.0319575.s016
(XLSX)
S17 Table. Enriched KEGG pathways for genes in the significant modules in the LH.
https://doi.org/10.1371/journal.pone.0319575.s017
(XLSX)
S18 Table. Enriched KEGG pathways for genes in the significant modules in the LL.
https://doi.org/10.1371/journal.pone.0319575.s018
(XLSX)
S19 Table. The DAVID results for genes in the significant modules in the blood.
https://doi.org/10.1371/journal.pone.0319575.s019
(XLSX)
S20 Table. The hub genes identified for each tissue type.
https://doi.org/10.1371/journal.pone.0319575.s020
(XLSX)
S21 Table. A list of the hubs genes also found to be DE in each tissue type.
https://doi.org/10.1371/journal.pone.0319575.s021
(XLSX)
S2 File. Figures 1-5. MDS plots from the DE analysis performed for each tissue type.
https://doi.org/10.1371/journal.pone.0319575.s023
(DOCX)
S6 Figure. Module trait association heatmaps for the modules detected in each tissue type (A = BLN, B = MLN, C = PGT, D = HL, E = LL and F = Blood). The module-trait relationships were colour coded based on correlation between the module and trait with red = strong positive correlation and green = strong negative correlation. The correlation coefficient and p- value (in parentheses) are given for each association. The traits are listed along the X-axis (treatment, clinical score and rectal temperature) and each module colour is given on the Y-axis.
https://doi.org/10.1371/journal.pone.0319575.s024
(TIF)
Acknowledgments
Thanks is given to Dr Catherine Duffy in AFBI for assisting with the animal experiments.
References
- 1. Murray GM, More SJ, Sammin D, Casey MJ, McElroy MC, O’Neill RG, et al. Pathogens, patterns of pneumonia, and epidemiologic risk factors associated with respiratory disease in recently weaned cattle in Ireland. J Vet Diagn Invest. 2017;29(1):20–34. pmid:28074713
- 2. Cuevas-Gómez I, McGee M, Sánchez JM, O’Riordan E, Byrne N, McDaneld T, et al. Association between clinical respiratory signs, lung lesions detected by thoracic ultrasonography and growth performance in pre-weaned dairy calves. Ir Vet J. 2021;74(1):7. pmid:33766106
- 3. Dubrovsky SA, Van Eenennaam AL, Karle BM, Rossitto PV, Lehenbauer TW, Aly SS. Epidemiology of bovine respiratory disease (BRD) in preweaned calves on California dairies: The BRD 10K study. J Dairy Sci. 2019;102(8):7306–19. pmid:31202655
- 4. Peel DS. The effect of market forces on bovine respiratory disease. Vet Clin North Am Food Anim Pract. 2020;36(2):497–508. pmid:32451038
- 5. Muylkens B, Thiry J, Kirten P, Schynts F, Thiry E. Bovine herpesvirus 1 infection and infectious bovine rhinotracheitis. Vet Res. 2007;38(2):181–209. pmid:17257569
- 6. Jones C. Bovine herpesvirus 1 counteracts immune responses and immune-surveillance to enhance pathogenesis and virus transmission. Frontiers in Immunology. 2019;10.
- 7. Toomer G, Workman A, Harrison K, Stayton E, Hoyt P, Jones C. Stress triggers expression of bovine herpesvirus 1 infected cell protein 4 (bICP4) RNA during early stages of reactivation from latency in pharyngeal tonsil. J Virol. 2022;96(23):e01010-22.
- 8. Castrucci G, Cilli V, Frigeri F, Ferrari M, Ranucci S, Rampichini L. Reactivation of Bovid herpesvirus 1 and 2 and parainfluenza-3 virus in calves latently infected. Comp Immunol Microbiol Infect Dis. 1983;6(3):193–9. pmid:6313287
- 9. Winkler M, Doster A, Jones C. Persistence and reactivation of bovine herpesvirus 1 in the tonsils of latently infected calves. J Virol. 2000;74(11):5337–46.
- 10. Behura SK, Tizioto PC, Kim J, Grupioni NV, Seabury CM, Schnabel RD, et al. Tissue tropism in host transcriptional response to members of the bovine respiratory disease complex. Sci Rep. 2017;7(1):17938. pmid:29263411
- 11. Tizioto PC, Kim J, Seabury CM, Schnabel RD, Gershwin LJ, Van Eenennaam AL, et al. Immunological response to single pathogen challenge with agents of the bovine respiratory disease complex: an rna-sequence analysis of the bronchial lymph node transcriptome. PLoS One. 2015;10(6):e0131459. pmid:26121276
- 12. O’Donoghue S, Earley B, Johnston D, McCabe M, Kim J, Taylor J, et al. Whole blood transcriptome analysis in dairy calves experimentally challenged with bovine herpesvirus 1 (BoHV-1) and comparison to a bovine respiratory syncytial virus (BRSV) challenge. Frontiers in Genetics. 2023;14.
- 13. Sun H-Z, Srithayakumar V, Jiminez J, Jin W, Hosseini A, Raszek M, et al. Longitudinal blood transcriptomic analysis to identify molecular regulatory patterns of bovine respiratory disease in beef cattle. Genomics. 2020;112(6):3968–77. pmid:32650099
- 14. Hasankhani A, Bahrami A, Sheybani N, Fatehi F, Abadeh R, Ghaem Maghami Farahani H. Integrated network analysis to identify key modules and potential hub genes involved in bovine respiratory disease: A systems biology approach. Frontiers in Genetics. 2021;12.
- 15. Scott MA, Woolums AR, Swiderski CE, Finley A, Perkins AD, Nanduri B, et al. Hematological and gene co-expression network analyses of high-risk beef cattle defines immunological mechanisms and biological complexes involved in bovine respiratory disease and weight gain. PLoS One. 2022;17(11):e0277033. pmid:36327246
- 16. Kogelman LJ, Cirera S, Zhernakova DV, Fredholm M, Franke L, Kadarmideen HN. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model. BMC medical genomics. 2014;7:1–16.
- 17. Keogh K, Kenny DA. Gene co-expression networks contributing to reproductive development in holstein-friesian bull calves. Animal. 2022;16(5):100527. pmid:35500509
- 18. van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform. 2018;19(4):575–92. pmid:28077403
- 19. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. Bioinformatics. 2011;17(1):3.
- 20. Rosen BD, Bickhart DM, Schnabel RD, Koren S, Elsik CG, Tseng E, et al. De novo assembly of the cattle reference genome with single-molecule sequencing. Gigascience. 2020;9(3):giaa021. pmid:32191811
- 21. Robinson MD, McCarthy DJ, Smyth GK. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40. pmid:19910308
- 22. Laighneach A, Desbonnet L, Kelly JP, Donohoe G, Morris DW. Meta-analysis of brain gene expression data from mouse model studies of maternal immune activation using poly(I:C). Genes (Basel). 2021;12(9):1363. pmid:34573345
- 23.
Evans J. Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co; 1996.
- 24. Papageorgiou SN. On correlation coefficients and their interpretation. J Orthod. 2022;49(3):359–61. pmid:36017900
- 25. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1–13. pmid:19033363
- 26. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. pmid:19131956
- 27. Langfelder P, Horvath S. WGCNA: An r package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. pmid:19114008
- 28. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Statist Appl Genetics Mole Biol. 2005;4(1):Article17.
- 29. Horvath S, Dong J. Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol. 2008;4(8):e1000117. pmid:18704157
- 30. Snel B, Lehmann G, Bork P, Huynen MA. STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res. 2000;28(18):3442–4. pmid:10982861
- 31. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638–46. pmid:36370105
- 32. Upton JW, Kaiser WJ, Mocarski ES. DAI/ZBP1/DLM-1 complexes with RIP3 to mediate virus-induced programmed necrosis that is targeted by murine cytomegalovirus vIRA. Cell Host Microbe. 2012;11(3):290–7. pmid:22423968
- 33. Hao Y, Yang B, Yang J, Shi X, Yang X, Zhang D, et al. ZBP1: a powerful innate immune sensor and double-edged sword in host immunity. Int J Mol Sci. 2022;23(18):10224. pmid:36142136
- 34. Smith S, Weston S, Kellam P, Marsh M. IFITM proteins-cellular inhibitors of viral entry. Curr Opin Virol. 2014;4:71–7. pmid:24480526
- 35. Wang G, Zheng C. Zinc finger proteins in the host-virus interplay: multifaceted functions based on their nucleic acid-binding property. FEMS Microbiol Rev. 2021;45(3):fuaa059. pmid:33175962
- 36. Chan YK, Gack MU. RIG-I-like receptor regulation in virus infection and immunity. Curr Opin Virol. 2015;12:7–14. pmid:25644461
- 37. O’Callaghan E, Sánchez JM, Rabaglino MB, McDonald M, Liu H, Spencer TE, et al. Influence of sire fertility status on conceptus-induced transcriptomic response of the bovine endometrium. Front Cell Dev Biol. 2022;10:950443. pmid:36072344
- 38. Zhang B, Liu X, Chen W, Chen L. IFIT5 potentiates anti-viral response through enhancing innate immune signaling pathways. Acta Biochim Biophys Sin (Shanghai). 2013;45(10):867–74. pmid:23942572
- 39. Liao X, Xie H, Li S, Ye H, Li S, Ren K, et al. 2’, 5’-Oligoadenylate synthetase 2 (OAS2) inhibits zika virus replication through activation of type ι ifn signaling pathway. Viruses. 2020;12(4):418. pmid:32276512
- 40. DeDiego ML, Martinez-Sobrido L, Topham DJ. Novel functions of ifi44l as a feedback regulator of host antiviral responses. J Virol. 2019;93(21):e01159-19. pmid:31434731
- 41. Vitour D, Meurs EF. Regulation of interferon production by RIG-I and LGP2: a lesson in self-control. Sci STKE. 2007;2007(384):pe20. pmid:17473309
- 42. Scott MA, Woolums AR, Swiderski CE, Thompson AC, Perkins AD, Nanduri B, et al. Use of nCounter mRNA profiling to identify at-arrival gene expression patterns for predicting bovine respiratory disease in beef cattle. BMC Vet Res. 2022;18(1):77. pmid:35197051
- 43. Betancor G. You shall not pass: MX2 proteins are versatile viral inhibitors. Vaccines (Basel). 2023;11(5):930. pmid:37243034
- 44. de Jong LC, Crnko S, Ten Broeke T, Bovenschen N. Noncytotoxic functions of killer cell granzymes in viral infections. PLoS Pathog. 2021;17(9):e1009818. pmid:34529743
- 45.
Cesar A. Identification of genes associated with intramuscular fat deposition and composition in Nellore breed. Universidade de São Paulo; 2014.
- 46. Liu B-Q, Liu R-B, Li W-P, Mao X-T, Li Y-N, Huang T, et al. XAF1 prevents hyperproduction of type I interferon upon viral infection by targeting IRF7. EMBO Rep. 2023;24(1):e55387. pmid:36394357
- 47.
Sweett H. Genetic mechanisms of male and female cattle fertility. University of Guelph; 2022.
- 48. Villamayor L, Rivero V, López-García D, Topham DJ, Martínez-Sobrido L, Nogales A, et al. Interferon alpha inducible protein 6 is a negative regulator of innate immune responses by modulating RIG-I activation. Frontiers in Immunology. 2023;14.
- 49. Sánchez JM, Mathew DJ, Behura SK, Passaro C, Charpigny G, Butler ST, et al. Bovine endometrium responds differentially to age-matched short and long conceptuses†. Biol Reprod. 2019;101(1):26–39. pmid:30977805
- 50. Villamayor L, López-García D, Rivero V, Martínez-Sobrido L, Nogales A, DeDiego ML. The IFN-stimulated gene IFI27 counteracts innate immune responses after viral infections by interfering with RIG-I signaling. Front Microbiol. 2023;14:1176177. pmid:37187533
- 51. Larcombe SD, Capewell P, Jensen K, Weir W, Kinnaird J, Glass EJ, et al. Susceptibility to disease (tropical theileriosis) is associated with differential expression of host genes that possess motifs recognised by a pathogen DNA binding protein. PLoS One. 2022;17(1):e0262051. pmid:35061738
- 52. Verhelst J, Hulpiau P, Saelens X. Mx proteins: antiviral gatekeepers that restrain the uninvited. Microbiol Mol Biol Rev. 2013;77(4):551–66. pmid:24296571
- 53. Garcia-Ortega MB, Lopez GJ, Jimenez G, Garcia-Garcia JA, Conde V, Boulaiz H, et al. Clinical and therapeutic potential of protein kinase PKR in cancer and metabolism. Expert Rev Mol Med. 2017;19:e9. pmid:28724458
- 54. Ramana CV. Regulation of a metabolic gene signature in response to respiratory viruses and type i interferon signaling. JMP. 2024;5(1):133–52.
- 55. Morales DJ, Lenschow DJ. The antiviral activities of ISG15. J Mol Biol. 2013;425(24):4995–5008. pmid:24095857
- 56. Fensterl V, Sen GC. Interferon-induced Ifit proteins: their role in viral pathogenesis. J Virol. 2015;89(5):2462–8. pmid:25428874
- 57. Jang J-S, Lee J-H, Jung N-C, Choi S-Y, Park S-Y, Yoo J-Y, et al. Rsad2 is necessary for mouse dendritic cell maturation via the IRF7-mediated signaling pathway. Cell Death Dis. 2018;9(8):823. pmid:30068989
- 58. Tang Z, Mao Y, Ruan P, Li J, Qiu X, Meng Y, et al. Drugs targeting CMPK2 inhibit pyroptosis to alleviate severe pneumonia caused by multiple respiratory viruses. J Med Virol. 2024;96(5):e29643. pmid:38695269
- 59. Feng C, Tang Y, Liu X, Zhou Z. CMPK2 of triploid crucian carp is involved in immune defense against bacterial infection. Dev Comp Immunol. 2021;116:103924. pmid:33186560
- 60. Talker SC, Barut GT, Lischer HEL, Rufener R, von Münchow L, Bruggmann R, et al. Monocyte biology conserved across species: Functional insights from cattle. Front Immunol. 2022;13:889175. pmid:35967310
- 61. Hu Y, Wang J, Yang B, Zheng N, Qin M, Ji Y, et al. Guanylate binding protein 4 negatively regulates virus-induced type I IFN and antiviral response by targeting IFN regulatory factor 7. J Immunol. 2011;187(12):6456–62. pmid:22095711
- 62. Zhao P, Jiang T, Zhong Z, Zhao L, Yang S, Xia X. Inhibition of rabies virus replication by interferon-stimulated gene 15 and its activating enzyme UBA7. Infection, Genetics and Evolution. 2017;56:44–53.
- 63. Boys IN, Xu E, Mar KB, De La Cruz-Rivera PC, Eitson JL, Moon B, et al. RTP4 is a potent ifn-inducible anti-flavivirus effector engaged in a host-virus arms race in bats and other mammals. Cell Host Microbe. 2020;28(5):712-723.e9. pmid:33113352
- 64. Johnston D, Earley B, McCabe MS, Lemon K, Duffy C, McMenamy M, et al. Experimental challenge with bovine respiratory syncytial virus in dairy calves: bronchial lymph node transcriptome response. Sci Rep. 2019;9(1):14736. pmid:31611566
- 65. Liew C-C, Ma J, Tang H-C, Zheng R, Dempsey AA. The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. J Lab Clin Med. 2006;147(3):126–32. pmid:16503242
- 66. Pinho SS, Alves I, Gaifem J, Rabinovich GA. Immune regulatory networks coordinated by glycans and glycan-binding proteins in autoimmunity and infection. Cell Mol Immunol. 2023;20(10):1101–13. pmid:37582971
- 67. Pandey VK, Sharma R, Prajapati GK, Mohanta TK, Mishra AK. N-glycosylation, a leading role in viral infection and immunity development. Mol Biol Rep. 2022;49(8):8109–20. pmid:35364718
- 68. Satyesh CR. Cytokine, cytokine storm and their role in fatalities of COVID-19. J Infect Dis Immun. 2021;13(2):20–9.
- 69. Righi C, Franzoni G, Feliziani F, Jones C, Petrini S. The Cell-Mediated Immune Response against Bovine alphaherpesvirus 1 (BoHV-1) Infection and Vaccination. Vaccines (Basel). 2023;11(4):785. pmid:37112697
- 70. Alcami A. Viral mimicry of cytokines, chemokines and their receptors. Nat Rev Immunol. 2003;3(1):36–50. pmid:12511874
- 71. Benko M, Lenhartová S, Kempová V, Betáková T, Kúdelová M. Chemokine-binding proteins encoded by herpesviruses. Acta Virol. 2020;64(2):233–44. pmid:32551791
- 72. Keil GM, Engelhardt T, Karger A, Enz M. Bovine herpesvirus 1 U(s) open reading frame 4 encodes a glycoproteoglycan. J Virol. 1996;70(5):3032–8. pmid:8627780
- 73. Bryant NA, Davis‐Poynter N, Vanderplasschen A, Alcami A. Glycoprotein G isoforms from some alphaherpesviruses function as broad‐spectrum chemokine binding proteins. The EMBO Journal. 2003;22(4):833–46.
- 74. Krem MM, Di Cera E. Molecular markers of serine protease evolution. EMBO J. 2001;20(12):3036–45. pmid:11406580
- 75. Scott MA, Woolums AR, Swiderski CE, Perkins AD, Nanduri B, Smith DR, et al. Whole blood transcriptomic analysis of beef cattle at arrival identifies potential predictive molecules and mechanisms that indicate animals that naturally resist bovine respiratory disease. PLoS One. 2020;15(1):e0227507. pmid:31929561
- 76. Jiminez J, Timsit E, Orsel K, van der Meer F, Guan L, Plastow G. Whole-blood transcriptome analysis of feedlot cattle with and without bovine respiratory disease. Frontiers in Genetics. 2021;12(257):257.
- 77. Witt H, Sahin-Tóth M, Landt O, Chen J-M, Kähne T, Drenth JP, et al. A degradation-sensitive anionic trypsinogen (PRSS2) variant protects against chronic pancreatitis. Nat Genet. 2006;38(6):668–73. pmid:16699518
- 78. Wang W, Wu L, Wu X, Li K, Li T, Xu B, et al. Combined analysis of serum SAP and PRSS2 for the differential diagnosis of CD and UC. Clin Chim Acta. 2021;514:8–14. pmid:33333044
- 79. Sui L, Wang S, Ganguly D, El Rayes TP, Askeland C, Børretzen A, et al. PRSS2 remodels the tumor microenvironment via repression of Tsp1 to stimulate tumor growth and progression. Nat Commun. 2022;13(1):7959. pmid:36575174
- 80. Lu H, Zhao Z, Yu H, Iqbal A, Jiang P. The serine protease 2 gene regulates lipid metabolism through the LEP/ampkα1/SREBP1 pathway in bovine mammary epithelial cells. Biochem Biophys Res Commun. 2024;698:149558. pmid:38271832
- 81. Jones C. Bovine herpes virus 1 (BHV-1) and herpes simplex virus type 1 (HSV-1) promote survival of latently infected sensory neurons, in part by inhibiting apoptosis. J Cell Death. 2013;6:1–16. pmid:25278776
- 82. Jones C. Herpes simplex virus type 1 and bovine herpesvirus 1 latency. Clin Microbiol Rev. 2003;16(1):79–95. pmid:12525426
- 83. Kawai T, Akira S. Innate immune recognition of viral infection. Nat Immunol. 2006;7(2):131–7. pmid:16424890
- 84. Ackermann MR, Derscheid R, Roth JA. Innate immunology of bovine respiratory disease. Vet Clin North Am Food Anim Pract. 2010;26(2):215–28. pmid:20619180
- 85. Kim Y, Shin J-S, Nahm M. NOD-like receptors in infection, immunity, and diseases. Yonsei Med J. 2016;57(1):5–14.
- 86. Onomoto K, Onoguchi K, Yoneyama M. Regulation of RIG-I-like receptor-mediated signaling: interaction between host and viral factors. Cell Mol Immunol. 2021;18(3):539–55. pmid:33462384
- 87. Song J, Li M, Li C, Liu K, Zhu Y, Zhang H. Friend or foe: RIG- I like receptors and diseases. Autoimmun Rev. 2022;21(10):103161. pmid:35926770
- 88. Geddes K, Magalhães JG, Girardin SE. Unleashing the therapeutic potential of NOD-like receptors. Nat Rev Drug Discov. 2009;8(6):465–79. pmid:19483708
- 89. Yeh Y-J, Tseng C-P, Hsu S-D, Huang H-Y, Lai MMC, Huang H-D, et al. Dual effects of let-7b in the early stage of hepatitis c virus infection. J Virol. 2021;95(4):e01800-20. pmid:33208444
- 90. Rosendahl A, Bergmann S, Hammerschmidt S, Goldmann O, Medina E. Lung dendritic cells facilitate extrapulmonary bacterial dissemination during pneumococcal pneumonia. Front Cell Infect Microbiol. 2013;3:21. pmid:23802100
- 91. Johnston D, Earley B, Cormican P, Murray G, Kenny DA, Waters SM, et al. Illumina MiSeq 16S amplicon sequence analysis of bovine respiratory disease associated bacteria in lung and mediastinal lymph node tissue. BMC Vet Res. 2017;13(1):118. pmid:28464950
- 92. Cheng Z, Buggiotti L, Salavati M, Marchitelli C, Palma-Vera S, Wylie A, et al. Global transcriptomic profiles of circulating leucocytes in early lactation cows with clinical or subclinical mastitis. Mol Biol Rep. 2021;48(5):4611–23. pmid:34146201
- 93. Jury EC, Kabouridis PS. New role for Agrin in T cells and its potential importance in immune system regulation. Arthritis Res Ther. 2010;12(2):205. pmid:20398335
- 94. Iwasaki A, Medzhitov R. Toll-like receptor control of the adaptive immune responses. Nat Immunol. 2004;5(10):987–95. pmid:15454922
- 95. Bystrom J, Al-Adhoubi N, Al-Bogami M, Jawad AS, Mageed RA. Th17 lymphocytes in respiratory syncytial virus infection. Viruses. 2013;5(3):777–91.
- 96. McGill JL, Rusk RA, Guerra-Maupome M, Briggs RE, Sacco RE. Bovine gamma delta t cells contribute to exacerbated il-17 production in response to co-infection with bovine rsv and mannheimia haemolytica. PLoS One. 2016;11(3):e0151083. pmid:26942409
- 97. Veldhoen M. Interleukin 17 is a chief orchestrator of immunity. Nat Immunol. 2017;18(6):612–21. pmid:28518156
- 98. Ma W-T, Yao X-T, Peng Q, Chen D-K. The protective and pathogenic roles of IL-17 in viral infections: friend or foe?. Open Biol. 2019;9(7):190109.
- 99. Mills KHG. IL-17 and IL-17-producing cells in protection versus pathology. Nat Rev Immunol. 2023;23(1):38–54. pmid:35790881
- 100. Schwarz D, Lipoldová M, Reinecke H, Sohrabi Y. Targeting inflammation with collagen. Clin Transl Med. 2022;12(5):e831. pmid:35604877
- 101. Tomlin H, Piccinini AM. A complex interplay between the extracellular matrix and the innate immune response to microbial pathogens. Immunology. 2018;155(2):186–201. pmid:29908065
- 102. Balboa MA, Balsinde J. Phospholipases: from structure to biological function. Biomolecules. 2021;11(3).
- 103. Pungerčar J, Bihl F, Lambeau G, Križaj I. What do secreted phospholipases A2 have to offer in combat against different viruses up to SARS-CoV-2?. Biochimie. 2021;189:40–50. pmid:34097986
- 104. Paul A, Tang TH, Ng SK. Interferon Regulatory factor 9 structure and regulation. Front Immunol. 2018;9.
- 105. Bravo García-Morato M, Calvo Apalategi A, Bravo-Gallego LY, Blázquez Moreno A, Simón-Fuentes M, Garmendia JV, et al. Impaired control of multiple viral infections in a family with complete IRF9 deficiency. J Allergy Clin Immunol. 2019;144(1):309-312.e10. pmid:30826365