Differential gene expression in peripheral leukocytes of pre-weaned Holstein heifer calves with respiratory disease

Lily A. Elder; Holly R. Hinnant; Chris M. Mandella; Rachel A. Claus-Walker; Lindsay M. Parrish; Giovana S. Slanzon; Craig S. McConnel

doi:10.1371/journal.pone.0285876

Abstract

Bovine respiratory disease (BRD) is a leading cause of calf morbidity and mortality, and prevalence remains high despite current management practices. Differential gene expression (DGE) provides detailed insight into individual immune responses and can illuminate enriched pathways and biomarkers that contribute to disease susceptibility and outcomes. The aims of this study were to investigate differences in peripheral leukocyte gene expression in Holstein preweaned heifer calves 1) with and without BRD, and 2) across weeks of age. Calves were enrolled for this short-term longitudinal study on two commercial dairies in Washington State. Calves were assessed every two weeks throughout the pre-weaning period using clinical respiratory scoring (CRS) and thoracic ultrasonography (TUS), and blood samples were collected. Calves were selected that were either healthy (n = 10) or had BRD diagnosed by CRS (n = 7), TUS (n = 6), or both (n = 6) in weeks 5 or 7 of life). Three consecutive time point samples were analyzed for each BRD calf consisting of PRE, ONSET, and POST samples. Nineteen genes of interest were selected based on previous gene expression studies in cattle: ALOX15, BPI, CATHL6, CXCL8, DHX58, GZMB, HPGD, IFNG, IL17D, IL1R2, ISG15, LCN2, LIF, MX1, OAS2, PGLYRP1, S100A8, SELP, and TNF. Comparisons were made between age and disease time point matched BRD and healthy calves as well as between calf weeks of age. No DGE was observed between diseased and healthy calves; however, DGE was observed between calf weeks of age regardless of disease state. Developmental differences in leukocyte gene expression, phenotype, and functionality make pre-weaned calves immunologically distinct from mature cattle, and early life shifts in calf leukocyte populations likely contribute to the age-related gene expression differences we observed. Age overshadows disease impacts to influence gene expression in young calves, and immune development progresses upon a common trajectory regardless of disease during the preweaning period.

Citation: Elder LA, Hinnant HR, Mandella CM, Claus-Walker RA, Parrish LM, Slanzon GS, et al. (2023) Differential gene expression in peripheral leukocytes of pre-weaned Holstein heifer calves with respiratory disease. PLoS ONE 18(5): e0285876. https://doi.org/10.1371/journal.pone.0285876

Editor: Angel Abuelo, Michigan State University, UNITED STATES

Received: March 21, 2023; Accepted: May 3, 2023; Published: May 16, 2023

Copyright: © 2023 Elder 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 relevant data are within the paper and its Supporting information files.

Funding: This project was supported by Agriculture and Food Research Initiative Competitive Grant no. 2021-68014-34144 from the USDA National Institute of Food and Agriculture, and the USDA National Institute of Food and Agriculture, Animal Health & Disease Research Capacity Grant project 1014680. This work was also supported by funds from the USDA National Institute of Food and Agriculture, Animal Health and Disease Research Program project NI21AHDRXXXXG028. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Bovine respiratory disease (BRD) is a leading cause of calf morbidity and mortality in both beef and dairy sectors of the United States cattle industry resulting in financial losses and compromised welfare [1–3]. BRD is a multifactorial disease complex with varied etiology including viral and bacterial pathogens as well as environmental and husbandry factors, and it ultimately manifests as bronchopneumonia [4, 5]. BRD is currently managed with prophylactic vaccination and antimicrobial treatment of clinically affected animals; however, morbidity and mortality attributed to this disease complex have increased over the past decades [6–10].

The immune response of individual animals plays a significant role in determining susceptibility to and severity of BRD as the host inflammatory process is largely responsible for the clinical signs and tissue damage that leads to negative sequelae [11–13]. Differential gene expression (DGE) provides detailed insight into individual immune responses and can illuminate enriched pathways and biomarkers that contribute to disease susceptibility and outcomes [10, 14, 15]. Pathways associated with innate immunity were found to be consistently enriched in bronchial lymph node tissue from single BRD pathogen challenged cattle suggesting the potential for a diagnostic gene expression signature of BRD [16, 17]. Differential expression of genes related to innate immune function were also demonstrated in peripheral leukocytes from both beef and dairy breeds with experimentally induced or naturally occurring BRD [18–20]. Additionally, differential expression of genes involved in inflammatory regulation has been found prior to clinical disease in cattle that were subsequently diagnosed with BRD, and expression differences were associated with the eventual severity of disease [14, 15, 21]. These findings further indicate the possibility of identifying biomarkers for use in early disease diagnosis, susceptibility screening tools, or novel targeted therapeutic modalities.

Prior gene expression studies related to naturally occurring BRD have been conducted solely in post-weaned beef cattle. Baseline transcriptomic differences have been demonstrated between cattle breeds highlighting the necessity of testing these findings in dairy cattle breeds [22, 23]. Additionally, considerable differences in gene expression based on age have been demonstrated in human studies [24, 25]. This indicates the need to test the findings from feedlot and adult cattle studies in pre-weaned Holstein calves.

In this study we analyzed the expression of 19 genes in peripheral leukocytes of preweaned Holstein calves with and without BRD. Selected genes were previously shown to be differentially expressed in cattle with BRD and other common diseases of dairy cattle [14, 16–19, 21, 26–30]. Genes were chosen based on the antibacterial or antiviral action of their protein products or their role in the inflammatory process with the goal of identifying indicators of early or subclinical disease, markers of innate disease resistance and tolerance, and common expression patterns across disease states.

The aims of this study were to investigate differences in peripheral leukocyte gene expression in Holstein preweaned calves 1) with and without BRD, and 2) across weeks of age. We hypothesized that both disease status and temporal changes would lead to differential expression of the selected genes.

Materials and methods

Ethics statement

The research protocol was reviewed and approved by the Institutional Animal Care and Use Committee of Washington State University (IACUC protocol #6859).

Cohort enrollment and management

For this short-term longitudinal study, a cohort of 60 Holstein heifer calves was enrolled on two commercial dairies (Farms A and B) in Washington State with a convenience sample of 30 individuals per farm selected based on age and adjacent housing. Calves were enrolled at one week (±4 days) of age and sampled every other week through 11 weeks of age spanning May-August 2021.

Both dairies housed calves in individual pens separated by removable, solid panels within an open-sided, covered housing unit containing a total of 60 calves (30 per side). Calves were grouped in pairs at nine weeks of age on Farm A by removing solid panels between each pair of calves, whereas calves remained individually housed throughout the study on Farm B. On both farms calves received 1 gallon of colostrum (Brix refractometer ≥ 22%) via oral intubation within 30 minutes of birth and a second gallon (Brix refractometer ≤ 22%) 8–12 hours later. All the calves were bucket fed and weaned at eight weeks of age. On Farm A all calves received a single, intranasal dose of Inforce 3^® (Zoetis Animal Health, Parsippany, NJ) at four weeks of age. On Farm B all calves received a single, intranasal dose of Nasalgen^® 3 (Merck Animal Health, Kenilworth, NJ) at two weeks of age.

Assessments and sampling

Clinical assessments (CRS) and thoracic ultrasonography (TUS) for BRD diagnosis were performed at each sampling with TUS assessment starting at the second sampling in week three of life. Calves were scored for clinical signs of respiratory disease using the Wisconsin Calf Health Scoring Chart [31]. The respiratory parameters in this system consist of nose, eyes, cough, and temperature, and they are scored using a 0–3 scale with 0 indicating normal and 1–3 indicating mild, moderate, or severe clinical signs. The navel, joint, ears and fecal parameters included in the scoring system also were assessed.

TUS was performed and lung lesions scored using the technique previously described by Ollivett and Buczinski [32]. Briefly, the lung fields were imaged by scanning the tenth through first intercostal spaces from dorsal to ventral on both sides of the calf. The seven peripheral lung lobes were scored using a 0–5 scale with 0 indicating healthy lungs, 1 indicating diffuse comet tailing, 2 indicating lobular consolidation of ≥1 cm but not full thickness, 3 indicating full thickness lobar consolidation of one lung lobe, 4 indicating full thickness lobar consolidation of two lobes, and 5 indicating full thickness lobar consolidation of three or more lobes. All clinical assessments and TUS examinations were performed under the supervision of the PI, McConnel, including secondary reviews of recorded video for all identified lung lesions. TUS was performed using IBEX^® EVO^® II and IBEX^® PRO/r ultrasounds both with a L7HD linear probe for large animal exams (E.I. Medical Imaging, Loveland, CO).

Blood samples were collected at each sampling. Blood was collected from the jugular vein into Tempus Blood RNA Tubes (Thermo Fisher, Waltham, MA) and shaken vigorously before being placed on ice. Samples were refrigerated for up to 48 hours, frozen at -20° C for up to four weeks, and stored at -80° C.

Sample selection for differential gene expression (DGE)

Calves were retrospectively selected for DGE analysis based on CRS and TUS scoring and farm treatment records. Individuals were categorized at each sampling as CRS- or CRS+ and TUS- or TUS+. CRS- was defined as a score of ≤ 1 in all the respiratory parameters (nasal, eyes, and cough). CRS+ was defined as a score of ≥ 2 in one or more of the respiratory parameters. Rectal temperature was not considered in the CRS definition due to its lack of specificity regarding respiratory disease, and because a period of abnormally hot weather occurred that affected both farms equally during the sampling period (late June to mid-July) resulting in hyperthermia in otherwise healthy calves. Calves with a navel score of ≥ 2 were not considered for DGE.

Considerable variation of lobular lung lesion sizes, number, and distribution was encompassed by a TUS score of 2; therefore, TUS- was defined as scores of ≤ 1and TUS+ was defined as lobar scores of ≥ 3 (i.e., full thickness lobar consolidation of ≥ 1 lung lobe) to eliminate the ambiguity of lesion severity associated with a lobular score of 2. Calves were then further categorized into one of the following four categories: CRS-/TUS- (healthy), CRS+/TUS-, CRS-/TUS+, and CRS+/TUS+. Calves were selected for DGE analysis that were CRS-/TUS- at one sampling and then progressed to one of the other categories at the next sampling indicating the onset of respiratory disease. A healthy comparison group was selected that remained CRS-/TUS- throughout the sampling period. Calves that were treated with antimicrobial and/or anti-inflammatory drugs by the farm personnel prior to the ONSET sample were not considered for DGE analysis.

Three consecutive samples were analyzed for each calf to assess the full progression of disease. These samples spanned either weeks 3–7 or 5–9 of life depending on the timing of the onset of respiratory disease. The three time points were defined as follows. At the first sample time point (PRE) the calf was CRS- and TUS-. At the second time point (ONSET) the calf fit one of the three BRD categories (CRS+/TUS-, CRS-/TUS+, or CRS+/TUS+). The third time point (POST) was not defined by diagnostic parameters but was intended to capture the residual effects of the disease event and the continuation or resolution phase of the inflammatory cascade. PRE was either week 3 or 5 of life, ONSET was either week 5 or 7, and POST was either week 7 or 9. This timeframe was chosen in order to span the pre-weaning period. Four consecutive samples spanning weeks 3–9 of life were analyzed for most of the healthy calves to maximize the numbers of healthy comparisons at each age time point. The distribution of individuals in each disease category by week of age at ONSET are shown in Fig 1. The individual CRS and TUS scores for each calf by sampling week can be found in the S1 Table.

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Fig 1. Age and number of calves at the onset of disease for each of the BRD categories and for age-matched healthy calves.

https://doi.org/10.1371/journal.pone.0285876.g001

The distribution of individuals in each BRD category by week of age at ONSET is shown. Calves were selected that had the onset of BRD in either week 5 or 7 of life. PRE and POST samples were also analyzed for each individual with PRE occurring two weeks before ONSET (week 3 or 5) and POST occurring two weeks after ONSET (week 7 or 9).

Sample size

Previous DGE studies have shown that a minimum of eight animals should be targeted per group (diseased, healthy), with the sample size based on expectations for a multivariate analysis estimating the log fold-change in a target gene’s DGE following a negative binomial distribution [33]. Historical precedent on the participating dairies suggested that on average 15% of calves would be diagnosed and treated for respiratory disease through 12 weeks of age. Therefore, expectations were for quality RNA samples from a minimum of 9 calves with evidence of respiratory disease (CRS+ and/or TUS+), and at least equal numbers of consistently healthy calves.

Gene selection

Nineteen genes of interest were selected based on previous studies of gene expression in cattle: ALOX15, BPI, CATHL6, CXCL8, DHX58, GZMB, HPGD, IFNG, IL17D, IL1R2, ISG15, LCN2, LIF, MX1, OAS2, PGLYRP1, S100A8, SELP, and TNF [14, 16–19, 21] (Table 1). All the genes were found to be differentially expressed in two or more studies focused on BRD, and at least one study each that used RNA extracted from whole blood samples. Ten of the genes (CATHL6, CXCL8, DHX58, IFNG, IL17D, LCN2, LIF, PGLYRP1, SELP, and TNF) also were differentially expressed in dairy cattle diagnosed with metritis, mastitis, or Johne’s Disease [26–30], and these genes have the potential to provide insight into conserved elements bridging disease states and target tissues in diverse dairy populations. Genes were prioritized that have strong evidence for their role in amplification or attenuation of inflammatory processes, the antibacterial or antiviral actions of their protein products, or their involvement in disease resistance and tolerance mechanisms. Five internal control genes (ACTB, GOLGA5, OSBPL2, SMUG1, and YWHAZ) were also selected based on their previously demonstrated suitability as controls in bovine peripheral leukocytes [29].

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Table 1. Genes of interest examined for differential expression in Holstein heifer calf peripheral leukocytes.

https://doi.org/10.1371/journal.pone.0285876.t001

Leukocyte RNA extraction

Extraction of RNA from peripheral blood leukocytes was accomplished using the Tempus Spin RNA isolation kit (Thermo Fisher, Waltham, MA). RNA was quantified using a NanoDrop One Spectrophotometer (Thermo Fisher Scientific, Waltham, MA USA). Samples were selected at random to test RNA integrity using a Qubit 4 Fluorometer (Invitrogen, Waltham, MA, USA). 100 μl of eluted RNA was stored at -80°C until submission for DGE analysis. Prior to submission, RNA was diluted to a working concentration of 20 ng/μl (15.0–35.3 ng/μl) measured using a NanoDrop Spectrophotometer (Thermo Fisher Scientific, Waltham, MA). RNA samples were processed by the NanoString Technologies Proof of Principle Services in Seattle, WA. Gene expression analysis of the 19 target genes and five housekeeping genes was performed using the Nanostring System (Nanostring Technologies, Seattle, Washington, USA), as previously described [29]. Gene expression was measured using a custom CodeSet for the selected genes in the Nanostring nCounter Analysis System 2.0 (NanoString Technologies, Seattle, WA). The NanoString technology uses a digital color-coded barcode tag with single-molecule imaging that can detect and count hundreds of unique transcripts per reaction. The nCounter run was performed at NanoString using a MAX analyzer, with 555 maximum possible fields of view. There were two consecutive normalizations: 1) A Positive Control Normalization took into account the linearity of the positive controls, using the geometric mean of the positive controls to compute the normalization factor. 2) CodeSet Content (HouseKeeping Normalization) used the geometric mean of the designated housekeeper genes to compute a normalization factor.

Gene expression analysis

The data analysis was performed using nCounter Analysis Software 2.0, and nSolver Advanced Analysis 4.0 (NanoString Technologies, Seattle, WA). The nCounter software detects probes based on a doubling of the counts relative to the median count value of the negative control. Expression data for the 19 genes of interest was normalized using the expression values for the five internal control genes. Fold changes and p-values were calculated using a Benjamini-Yekutieli (B-Y) correction for multiple comparisons. The B-Y correction assumes that there may be a biological connection between genes and returns moderately conservative estimates of false discovery rate (FDR). The FDR is the proportion of genes with equal or greater evidence for differential expression (i.e., equal or lower raw p-value) than are expected to be “false discoveries” due to chance.

Comparisons were made based on farm, disease, and age. First, age matched healthy calves (CRS-/TUS-) were compared between farms A and B. Second, comparisons were made between age matched BRD and healthy calves over the course of disease progression with each BRD group compared against the healthy baseline. The CRS-/TUS+ and CRS+/TUS+ groups were then combined to create a larger TUS+ group and compared to the healthy baseline. Lastly, calves were compared solely by week of age (3, 5, 7, or 9) in groupings of healthy, TUS+, and all calves with the younger age serving as the baseline in each comparison.

Results

Calves and clinical outcomes

The calves selected for DGE analysis (n = 29) were either diseased (n = 19) or healthy (n = 10). The diseased calves fit one of three case definitions for BRD at the onset of disease: CRS+/TUS- (n = 7); CRS-/TUS+ (n = 6); or CRS+/TUS+ (n = 6). Three consecutive samples were analyzed for each diseased calf: PRE, ONSET, and POST. The PRE sample was from two weeks prior to the onset of disease when the calf was healthy. The ONSET sample was from when the calf was identified as diseased by one of the three BRD case definitions above. The POST sample was from two weeks after ONSET and was not defined by diagnostic parameters. The numbers of calves with onset of disease in each week were as follows and are shown in Fig 1. Week 5 (n = 10): CRS+/TUS- (n = 4); CRS-/TUS+ (n = 2); and CRS+/TUS+ (n = 4). Week 7 (n = 9): CRS+/TUS- (n = 3); CRS-/TUS+ (n = 4); and CRS+/TUS+ (n = 2). Three or four consecutive samples were also analyzed for each of the healthy calves with the total number of healthy samples in each week as follows: week 3 (n = 9), week 5 (n = 10), week 7 (n = 10), and week 9 (n = 10).

The POST sample time point was not defined by diagnostic parameters, and a variety of disease progression or resolution was observed at this sampling. Additionally, five calves received treatment by farm personnel using the antibiotic florfenicol in combination with the nonsteroidal anti-inflammatory flunixin meglumine (Resflor, Merck Animal Health, Kenilworth, NJ) according to farm protocols between the ONSET and POST sample time points. Of the calves that were CRS+/TUS- at ONSET (n = 7), four resolved to CRS-/TUS-, two remained CRS+/TUS- (one of which was treated with Resflor) but had a score of ≥ 2 in a different one of the three respiratory parameters (nasal, eyes, and cough), and one progressed to CRS- with a TUS score of 2. Of the calves that were CRS-/TUS+ at ONSET (n = 6), four remained CRS-/TUS+, one was treated with Resflor and resolved to CRS-/TUS-, and one progressed to CRS+/TUS-. Of the calves that were CRS+/TUS+ at ONSET (n = 6), two progressed to CRS- with a TUS score of 2 (one of which was treated with Resflor), two progressed to CRS-/TUS+ (one of which was treated with Resflor), and two remained CRS+/TUS+ with one of those having CRS+ defined by a different respiratory parameter than at ONSET and the other one receiving treatment with Resflor (S1 Table). Of all the calves that were TUS+ at ONSET (n = 12), eight calves remained TUS+ at POST with at least one lung lobe fully consolidated (two of these having received treatment with Resflor), two calves partially resolved to have lobular consolidation (TUS score of 2; one of these having received treatment with Resflor), and two calves fully resolved their visible lung lesions (TUS-; one of these having received treatment with Resflor).

Differential gene expression.

RNA integrity number values ranged from 9.1–10.0 (mean = 9.7; StDev = 0.4) for the randomly selected samples, indicating intact RNA and sufficient storage and extraction methods [34]. RNA quality control metrics were assessed for all the samples (n = 96) using nSolver 4.0 (NanoString Technologies, Seattle, WA). None were flagged for imaging, binding density, positive control, or limit of detection; therefore, expression data from all the samples were included in the analyses. The expression of the internal control genes was likewise found to be within the expected range for consistent background expression, and expression data for the 19 genes of interest was normalized using the values for the five internal control genes. Differential expression was analyzed with nSolver’s advanced analysis feature using normalized data in a mixture negative binomial model. Low count data was omitted by removing probes that fell below a standard threshold count value of 20, and three genes (LIF, IFNG, and TNF) were omitted from the analysis for this reason.

Age-matched healthy calves on farm A (n = 6) and farm B (n = 4) were compared and showed no DGE between the populations, including in week 5 of life following intranasal vaccination on farm A (B-Y p > 0.3). Calves were not differentiated by farm in further analyses. Healthy and BRD calves were matched by age (week 3, 5, 7, or 9) and disease progression (PRE, ONSET, or POST) with healthy calves used as the baseline for DGE analyses. The CRS+/TUS- group compared with the healthy group showed differences in two genes, ISG15 and MX1, in week 7 POST (B-Y p = 0.04), with no differential expression observed in the rest of the analysis (B-Y p > 0.05). The CRS-/TUS+ and CRS+/TUS+ groups were each likewise compared to the healthy group, and no differential expression was observed in either analysis (B-Y p > 0.05). We considered the comparisons involving calves with ultrasonographic evidence of lobar consolidation (TUS+) to be the most meaningful from a clinical perspective since TUS has greater sensitivity and specificity as a diagnostic modality than does clinical scoring for BRD diagnosis [35]. Therefore, CRS-/TUS+ and CRS+/TUS+ classifications were combined to create a larger, more statistically powerful TUS+ group (n = 12) and compared to the healthy baseline. No differential expression was observed in this analysis either (B-Y p > 0.05; Table 2).

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Table 2. Log₂ fold changes in peripheral leukocyte gene expression in Holstein calves with lobar lung consolidation (TUS+; n = 12) as compared to healthy calves (CRS-/TUS-; n = 10).

https://doi.org/10.1371/journal.pone.0285876.t002

Calves were compared by week of age as follows: 3 vs. 5, 3 vs. 7, 3 vs. 9, 5 vs. 7, 5 vs. 9, and 7 vs. 9. Age comparisons were made using groupings of healthy (CRS-/TUS-) (Table 3), TUS+ (Table 4), and all calves (Table 5). Gene expression differences were observed for one or more of the genes of interest in all the age comparisons (B-Y p < 0.05; Tables 3–5). Consistent trends were observed across age comparisons regarding which genes were up-regulated (ALOX15, HPGD, GZMB, IL17D, LCN2), down-regulated (BPI, OAS2, SELP, S100A8, ISG15, IL1R2, DHX58, MX1), or not differentially expressed (CATHL6, PGLYRP1, CXCL8) in the older age samples as compared to younger ages. Relative magnitudes of log fold changes differed for each gene across comparisons; however, there were no instances of a gene being differentially expressed with a positive log fold change in one comparison and a negative log fold change in another. The greatest number of differentially expressed genes were found with the grouping of all calves which also had the most robust sample size. The direction and magnitude of the expression differences across age comparisons for this grouping are presented in Fig 2.

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Fig 2. Differential gene expression magnitude and direction for calves with and without clinical (CRS) or ultrasonographic (TUS) evidence of respiratory disease (n = 29) compared by week of age.

Volcano plots showing differential gene expression between calf ages. (A) week 5 vs. baseline week 3. (B) week 7 vs. baseline week 3. (C) week 9 vs. baseline week 3. (D) week 7 vs. baseline week 5. (E) week 9 vs. baseline week 5. (F) week 9 vs. baseline week 7. In each plot the x-axis is the log₂ fold change of the comparison (older) group relative to the baseline (younger) group. Zero indicates no difference between the groups with the positive or negative values indicating increased or decreased expression of the gene in the comparison group relative to the baseline group. The y-axis represents statistical significance with the horizontal lines representing various B-Y p-value cut-offs. Adj. p-value: Benjamini-Yekutieli (B-Y) method for p-value adjustment.

https://doi.org/10.1371/journal.pone.0285876.g002

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Table 3. Log₂ fold changes in peripheral leukocyte gene expression in healthy Holstein calves (CRS-/TUS-; n = 10) compared by week of age.

https://doi.org/10.1371/journal.pone.0285876.t003

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Table 4. Log₂ fold changes in peripheral leukocyte gene expression in Holstein calves with lobar lung consolidation (TUS+; n = 12) compared by week of age.

https://doi.org/10.1371/journal.pone.0285876.t004

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Table 5. Log₂ fold changes in peripheral leukocyte gene expression in Holstein calves with and without clinical (CRS) or ultrasonographic (TUS) evidence of respiratory disease (n = 29) compared by week of age.

https://doi.org/10.1371/journal.pone.0285876.t005

Discussion

To the authors’ knowledge, this is the first study to investigate DGE relative to BRD in preweaned Holstein calves. We did not identify differential expression of the genes of interest in age-matched calves relative to lobar lung consolidation as determined via TUS. However, we did observe consistent patterns of differential expression relative to calf age throughout the preweaning period. This suggests that age-related factors and immune system development may be more influential to gene expression in peripheral leukocytes in young calves than inflammatory disease processes.

We considered the TUS+ vs healthy analysis to be the most clinically meaningful of the disease-based analyses. TUS has greater sensitivity and specificity for diagnosis of BRD than does clinical scoring [35]. Although differential expression of ISG15 and MX1 was observed in the CRS+/TUS- vs healthy analysis in the week 7 POST comparison, we did not consider this finding to be clinically significant. The POST time point was expected to demonstrate residual effects of the inflammatory event initiated at ONSET, and the interpretation of differential expression at POST in the absence of differential expression at ONSET in terms of inflammatory gene expression differences was unclear. Additionally, we did not observe a consistent progression of clinical respiratory signs from ONSET to POST. Calves that had a nasal, eye, or cough score of 2 at ONSET either had improvement of their clinical signs or had an elevated score in a different one of the respiratory parameters two weeks later at POST. There were no instances of the same respiratory parameter being scored ≥ 2 at both ONSET and POST in the absence of TUS lesions. These findings caused us to suspect that at least some of the clinical respiratory signs we observed were in response to transient environmental conditions or upper respiratory irritation and not indicative of a lower respiratory disease process. Conversely, there was evident continuity between the TUS lesions at ONSET and POST. Although two of the TUS+ calves were resolved at POST, one of these having been treated by farm personnel, the remaining ten remained diseased at the same level, increased in lesion severity, or marginally improved while retaining at least a lobular lesion, and three of these had received treatment (S1 Table).

The genes of interest in this study were based on previous research in postweaned or adult cattle. Multiple studies in feedlot cattle with BRD have identified common differentially expressed genes associated with disease presence, severity, and outcomes [14, 15, 19, 21]. Likewise, studies aimed at identifying biomarkers of diseases affecting adult dairy cows such as mastitis, metritis, and Johne’s disease have identified consistent gene expression changes associated with subclinical or clinical disease that may bridge disease states [28–30]. The differential expression patterns observed in these studies involving older animals were not observed in our cohort of pre-weaned calves, suggesting that there are additional factors affecting leukocyte behavior in young calves.

Early life shifts in calf leukocyte populations are a likely contributing factor to the age-related gene expression changes we observed. In a study of Norwegian Red calves, substantial changes in leukocyte cell type absolute numbers, lymphocyte subpopulation proportions, and neutrophil functions occurred in the first 5–8 weeks of life with ongoing changes up to 6 months of age [36]. More recently, 30-day old Holstein calves were shown to have distinctly different lymphocyte subpopulation proportions as compared to adults including fewer B cells and comparatively more γδ T cells [37]. Furthermore, monocyte subpopulations have been found to be phenotypically and functionally distinct between 10-day old and 18–24 month old Holstein-Friesian cattle both at baseline and following an in vitro Neospora caninum challenge [38]. Given these early life changes, it is unsurprising that leukocyte gene expression profiles of young calves would differ from those expected in more mature animals in response to an inflammatory disease process such as BRD.

These differences in leukocyte profiles between calves and mature cattle are also the likely reason that three of our genes of interest (LIF, IFNG, and TNF) had consistently low copy numbers in our samples and were unable to be reliably included in DGE analysis. Although these genes are well documented inflammatory mediators in older cattle [14, 17, 29, 30], they are evidently not prolifically expressed by peripheral leukocytes of young calves.

Gene expression differences between neonates and immunologically mature animals are supported by studies in human medical fields. Olin et al. [25] analyzed serial samples of peripheral blood leukocytes from babies throughout the first 3 months of life and found substantial age-related phenotypic and transcriptomic changes whereas adult parameters remained relatively stable over a similar period. Additionally, pediatric immune system development occurred on a shared trajectory despite considerable differences in pathology, maturity at birth, environment, and other variables. Similarly, Wynn et al. [24] demonstrated age-related transcriptomic changes in pediatric patients with the degree of difference increasing in proportion to age difference and with the neonatal group being the most distinctive.

Developmental differences in leukocyte gene expression, surface protein profiles, and functionality make pre-weaned calves immunologically distinct from adult cattle. Indeed, the rate and magnitude of change in the early weeks of life arguably prevent pre-weaned calves from being considered comparable as a group with respect to these parameters. This developmental homeorhesis is the likely reason for our failure to demonstrate differential expression of genes of interest relative to the presence of BRD as was expected based on prior findings in older cattle.

This developmental progression may also account for the expression trend we observed with the number of differentially expressed genes and the magnitude of the difference being greatest with younger samples and with increasing difference in age (Fig 2). This expression trend was clearly observed with respect to ALOX15 which was strongly differentially expressed in comparisons of week 3 to older weeks in our three age-related analyses, and to a lesser extent HPGD in the comparison of all calves by week of age (Tables 3–5, Fig 2). Additionally, a marked increase in log fold-change was observed from the 3 vs. 5 to the 3 vs. 7 comparisons across the analyses (Tables 3–5). This trend continued from the 3 vs. 7 to the 3 vs. 9 comparisons in the analysis of TUS+ calves (Table 4).

ALOX15 encodes an enzyme involved in production of specialized pro-resolving mediators (SPMs). This class of bioactive lipids is instrumental to the resolution phase of the inflammatory cascade and return of the affected tissue to homeostasis through the activity of M2 macrophages [39–41]. In beef calves SPM expression, including ALOX15 and HPGD, has been shown to increase consistently throughout the pre-weaning period in calves undergoing a variety of management and vaccination programs [42]. This aligns with our findings suggesting that changes in SPM expression are an integral part of calf immune development during early life. Consequently, as biomarkers of inflammatory disease these genes are unlikely to be as reliable in pre-weaned calves as in immunologically mature cattle.

Overall, this study of pre-weaned Holstein heifer calves with BRD did not demonstrate notable differential expression of 19 genes that are candidate biomarkers of inflammatory disease in postweaned and adult cattle, and that in some cases are indicative of resistance to or tolerance of disease. Transcriptomic profiling provides a plausible alternative for identifying innate immune system genes and pathways that are differentially expressed with respect to young calf BRD. Given that biomarkers may not be stable throughout the pre-weaning period, narrow age ranges may be necessary to observe consistent results. Furthermore, knowledge of gene expression profiles in healthy pre-weaned calves is lacking and will be necessary to evaluate disease-related changes. Preweaned calves are immunologically distinct from older cattle, and baseline values established in adults must be applied with caution to younger age groups.

Conclusions

Nineteen genes that are candidate biomarkers of inflammatory disease in postweaned and adult cattle were not differentially expressed in pre-weaned Holstein heifer calves with BRD. However, differential expression was observed relative to calf age between three and nine weeks of life. Factors related to age and immune system development overshadow disease impacts to influence gene expression patterns in young calves, and immune development progresses upon a common trajectory during the preweaning period regardless of respiratory disease.

Supporting information

S1 Table. Individual CRS and TUS scores for each calf by sampling week.

https://doi.org/10.1371/journal.pone.0285876.s001

(XLSX)

S2 Table. Raw counts for endogenous, housekeeping, negative, and positive probes.

https://doi.org/10.1371/journal.pone.0285876.s002

(XLSX)

S3 Table. Probe sequences.

https://doi.org/10.1371/journal.pone.0285876.s003

(XLSX)

Acknowledgments

The authors thank the participating dairies and associated personnel for their invaluable assistance with this project.

References

1. USDA. Feedlot 2011 Part IV: Health and Health Management on U.S. Feedlots with a Capacity of 1,000 or More Head. Fort Collins, CO, USA; 2013 Sep pp. 18–27. Report No.: 638.0913.
2. USDA. Dairy 2014: Health and Management Practices on U.S. Dairy Operations, 2014. Fort Collins, CO, USA; 2018 Feb pp. 35–36. Report No.: 696.0218.
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6. Apley M. Bovine respiratory disease: pathogenesis, clinical signs, and treatment in lightweight calves. Vet Clin North Am Food Anim Pract. 2006;22: 399–411. pmid:16814024
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7. Chamorro MF, Palomares RA. Bovine respiratory disease vaccination against viral pathogens: modified-live versus inactivated antigen vaccines, intranasal versus parenteral, what is the evidence? Vet Clin North Am Food Anim Pract. 2020;36: 461–472. pmid:32451035
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10. Smith RA, Step DL, Woolums AR. Bovine respiratory disease: looking back and looking forward, what do we see? Vet Clin North Am Food Anim Pract. 2020;36: 239–251. pmid:32451026
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- PubMed/NCBI
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12. Malazdrewich C, Thumbikat P, Maheswaran SK. Protective effect of dexamethasone in experimental bovine pneumonic mannheimiosis. Microb Pathog. 2004;36: 227–236. pmid:15001229
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- PubMed/NCBI
- Google Scholar
13. Rhodes V, Ryan EG, Hayes CJ, McAloon C, O’Grady L, Hoey S, et al. Diagnosis of respiratory disease in preweaned dairy calves using sequential thoracic ultrasonography and clinical respiratory scoring: temporal transitions and association with growth rates. J Dairy Sci. 2021;104: 11165–11175. pmid:34275625
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- Google Scholar
15. Scott MA, Woolums AR, Swiderski CE, Perkins AD, Nanduri B, Smith DR, et al. Comprehensive at-arrival transcriptomic analysis of post-weaned beef cattle uncovers type I interferon and antiviral mechanisms associated with bovine respiratory disease mortality. PloS One. 2021;16: e0250758. pmid:33901263
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- PubMed/NCBI
- Google Scholar
18. Johnston D, Earley B, McCabe MS, Kim J, Taylor JF, Lemon K, et al. Messenger RNA biomarkers of bovine respiratory syncytial virus infection in the whole blood of dairy calves. Sci Rep. 2021;11: 9392. pmid:33931718
- View Article
- PubMed/NCBI
- Google Scholar
19. Jiminez J, Timsit E, Orsel K, van der Meer F, Guan LL, Plastow G. Whole-blood transcriptome analysis of feedlot cattle with and without bovine respiratory disease. Front Genet. 2021;12: 627623. pmid:33763112
- View Article
- PubMed/NCBI
- Google Scholar
20. Lindholm-Perry AK, Kuehn LA, McDaneld TG, Miles JR, Workman AM, Chitko-McKown CG, et al. Complete blood count data and leukocyte expression of cytokine genes and cytokine receptor genes associated with bovine respiratory disease in calves. BMC Res Notes. 2018;11: 786. pmid:30390697
- View Article
- PubMed/NCBI
- Google Scholar
21. 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: 3968–3977. pmid:32650099
- View Article
- PubMed/NCBI
- Google Scholar
22. Huang W, Nadeem A, Zhang B, Babar M, Soller M, Khatib H. Characterization and comparison of the leukocyte transcriptomes of three cattle breeds. PloS One. 2012;7: e30244. pmid:22291923
- View Article
- PubMed/NCBI
- Google Scholar
23. Johnston D, Earley B, Cormican P, Kenny DA, McCabe MS, Kelly AK, et al. Characterisation of the whole blood mRNA transcriptome in Holstein-Friesian and Jersey calves in response to gradual weaning. PloS One. 2016;11: e0159707. pmid:27479136
- View Article
- PubMed/NCBI
- Google Scholar
24. Wynn JL, Cvijanovich NZ, Allen GL, Thomas NJ, Freishtat RJ, Anas N, et al. The influence of developmental age on the early transcriptomic response of children with septic shock. Mol Med. 2011;17: 1146–1156. pmid:21738952
- View Article
- PubMed/NCBI
- Google Scholar
25. Olin A, Henckel E, Chen Y, Lakshmikanth T, Pou C, Mikes J, et al. Stereotypic immune system development in newborn children. Cell. 2018;174: 1277–1292. pmid:30142345
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- PubMed/NCBI
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26. Cai Z, Guldbrandtsen B, Lund MS, Sahana G. Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle. BMC Genomics. 2018;19: 656. pmid:30189836
- View Article
- PubMed/NCBI
- Google Scholar
27. Chen X, Cheng Z, Zhang S, Werling D, Wathes DC. Combining genome wide association studies and differential gene expression data analyses identifies candidate genes affecting mastitis caused by two different pathogens in the dairy cow. Open J Anim Sci. 2015;5: 358–393.
- View Article
- Google Scholar
28. McConnel C, Crisp S, Biggs T, Parrish L, Sischo W, Adams-Progar A, et al. Transcriptomic analysis of peripheral leukocytes in dairy cows with and without evidence of metritis and associated early postpartum disease. Appl Anim Sci. 2020;36: 784–798.
- View Article
- Google Scholar
29. McConnel CS, Crisp SA, Biggs TD, Ficklin SP, Parrish LM, Trombetta SC, et al. A Fixed Cohort Field Study of Gene Expression in Circulating Leukocytes From Dairy Cows With and Without Mastitis. Front Vet Sci. 2020;7: 559279. pmid:33195534
- View Article
- PubMed/NCBI
- Google Scholar
30. Skovgaard K, Grell SN, Heegaard PMH, Jungersen G, Pudrith CB, Coussens PM. Differential expression of genes encoding CD30L and P-selectin in cattle with Johne’s disease: progress toward a diagnostic gene expression signature. Vet Immunol Immunopathol. 2006;112: 210–224. pmid:16621022
- View Article
- PubMed/NCBI
- Google Scholar
31. McGuirk SM. Disease Management of Dairy Calves and Heifers. Vet Clin North Am Food Anim Pract. 2008;24: 139–153. pmid:18299036
- View Article
- PubMed/NCBI
- Google Scholar
32. Ollivett TL, Buczinski S. On-farm use of ultrasonography for bovine respiratory disease. Vet Clin North Am Food Anim Pract. 2016;32: 19–35. pmid:26922110
- View Article
- PubMed/NCBI
- Google Scholar
33. Wang H, Horbinski C, Wu H, Liu Y, Sheng S, Liu J, et al. NanoStringDiff: a novel statistical method for differential expression analysis based on NanoString nCounter data. Nucleic Acids Res. 2016;44: e151. pmid:27471031
- View Article
- PubMed/NCBI
- Google Scholar
34. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006;7: 3. pmid:16448564
- View Article
- PubMed/NCBI
- Google Scholar
35. Buczinski S, Ollivett TL, Dendukuri N. Bayesian estimation of the accuracy of the calf respiratory scoring chart and ultrasonography for the diagnosis of bovine respiratory disease in pre-weaned dairy calves. Prev Vet Med. 2015;119: 227–231. pmid:25794838
- View Article
- PubMed/NCBI
- Google Scholar
36. Kampen AH, Olsen I, Tollersrud T, Storset AK, Lund A. Lymphocyte subpopulations and neutrophil function in calves during the first 6 months of life. Vet Immunol Immunopathol. 2006;113: 53–63. pmid:16772096
- View Article
- PubMed/NCBI
- Google Scholar
37. Casaro S, Marrero MG, Madrid DMC, Prim JG, Nelson CD, Galvão KN, et al. Flow cytometry panels for immunophenotyping dairy cattle peripheral blood leukocytes. Vet Immunol Immunopathol. 2022;248: 110417. pmid:35413495
- View Article
- PubMed/NCBI
- Google Scholar
38. Sharma P, Hartley CS, Haque M, Coffey TJ, Egan SA, Flynn RJ. Bovine neonatal monocytes display phenotypic differences compared with adults after challenge with the infectious abortifacient agent Neospora caninum. Front Immunol. 2018;9: 3011. pmid:30619358
- View Article
- PubMed/NCBI
- Google Scholar
39. Basil MC, Levy BD. Specialized pro-resolving mediators: endogenous regulators of infection and inflammation. Nat Rev Immunol. 2016;16: 51–67. pmid:26688348
- View Article
- PubMed/NCBI
- Google Scholar
40. Bannenberg G, Serhan CN. Specialized pro-resolving lipid mediators in the inflammatory response: An update. Biochim Biophys Acta. 2010;1801: 1260–1273. pmid:20708099
- View Article
- PubMed/NCBI
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- View Article
- PubMed/NCBI
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42. Scott MA, Woolums AR, Karisch BB, Harvey KM, Capik SF. Impact of preweaning vaccination on host gene expression and antibody titers in healthy beef calves. Front Vet Sci. 2022;9: 1010039. pmid:36225796
- View Article
- PubMed/NCBI
- Google Scholar

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[ref2] 2. USDA. Dairy 2014: Health and Management Practices on U.S. Dairy Operations, 2014. Fort Collins, CO, USA; 2018 Feb pp. 35–36. Report No.: 696.0218.

[ref3] 3. Mellor DJ, Stafford KJ. Animal welfare implications of neonatal mortality and morbidity in farm animals. Vet J. 2004;168: 118–133. pmid:15301760
View Article
PubMed/NCBI
Google Scholar

[4] View Article

[5] PubMed/NCBI

[6] Google Scholar

[ref4] 4. Taylor JD, Fulton RW, Lehenbauer TW, Step DL, Confer AW. The epidemiology of bovine respiratory disease: what is the evidence for predisposing factors? Can Vet J. 2010;51: 1095–1102. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2942046/ pmid:21197200
View Article
PubMed/NCBI
Google Scholar

[8] View Article

[9] PubMed/NCBI

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View Article
Google Scholar

[12] View Article

[13] Google Scholar

[ref6] 6. Apley M. Bovine respiratory disease: pathogenesis, clinical signs, and treatment in lightweight calves. Vet Clin North Am Food Anim Pract. 2006;22: 399–411. pmid:16814024
View Article
PubMed/NCBI
Google Scholar

[15] View Article

[16] PubMed/NCBI

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View Article
PubMed/NCBI
Google Scholar

[19] View Article

[20] PubMed/NCBI

[21] Google Scholar

[ref8] 8. Binversie ES, Ruegg PL, Combs DK, Ollivett TL. Randomized clinical trial to assess the effect of antibiotic therapy on health and growth of preweaned dairy calves diagnosed with respiratory disease using respiratory scoring and lung ultrasound. J Dairy Sci. 2020;103: 11723–11735. pmid:33222860
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref9] 9. Gorden PJ, Plummer P. Control, management, and prevention of bovine respiratory disease in dairy calves and cows. Vet Clin Food Anim Pract. 2010;26: 243–259. pmid:20619182
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref10] 10. Smith RA, Step DL, Woolums AR. Bovine respiratory disease: looking back and looking forward, what do we see? Vet Clin North Am Food Anim Pract. 2020;36: 239–251. pmid:32451026
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref11] 11. Bassel LL, Tabatabaei S, Caswell JL. Host tolerance to infection with the bacteria that cause bovine respiratory disease. Vet Clin North Am Food Anim Pract. 2020;36: 349–359. pmid:32451029
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref12] 12. Malazdrewich C, Thumbikat P, Maheswaran SK. Protective effect of dexamethasone in experimental bovine pneumonic mannheimiosis. Microb Pathog. 2004;36: 227–236. pmid:15001229
View Article
PubMed/NCBI
Google Scholar

[39] View Article

[40] PubMed/NCBI

[41] Google Scholar

[ref13] 13. Rhodes V, Ryan EG, Hayes CJ, McAloon C, O’Grady L, Hoey S, et al. Diagnosis of respiratory disease in preweaned dairy calves using sequential thoracic ultrasonography and clinical respiratory scoring: temporal transitions and association with growth rates. J Dairy Sci. 2021;104: 11165–11175. pmid:34275625
View Article
PubMed/NCBI
Google Scholar

[43] View Article

[44] PubMed/NCBI

[45] Google Scholar

[ref14] 14. 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: e0227507. pmid:31929561
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref15] 15. Scott MA, Woolums AR, Swiderski CE, Perkins AD, Nanduri B, Smith DR, et al. Comprehensive at-arrival transcriptomic analysis of post-weaned beef cattle uncovers type I interferon and antiviral mechanisms associated with bovine respiratory disease mortality. PloS One. 2021;16: e0250758. pmid:33901263
View Article
PubMed/NCBI
Google Scholar

[51] View Article

[52] PubMed/NCBI

[53] Google Scholar

[ref16] 16. Tizioto PC, Kim J, Seabury CM, Schnabel RD, Gershwin LJ, Eenennaam ALV, 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: e0131459. pmid:26121276
View Article
PubMed/NCBI
Google Scholar

[55] View Article

[56] PubMed/NCBI

[57] Google Scholar

[ref17] 17. 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: 14736. pmid:31611566
View Article
PubMed/NCBI
Google Scholar

[59] View Article

[60] PubMed/NCBI

[61] Google Scholar

[ref18] 18. Johnston D, Earley B, McCabe MS, Kim J, Taylor JF, Lemon K, et al. Messenger RNA biomarkers of bovine respiratory syncytial virus infection in the whole blood of dairy calves. Sci Rep. 2021;11: 9392. pmid:33931718
View Article
PubMed/NCBI
Google Scholar

[63] View Article

[64] PubMed/NCBI

[65] Google Scholar

[ref19] 19. Jiminez J, Timsit E, Orsel K, van der Meer F, Guan LL, Plastow G. Whole-blood transcriptome analysis of feedlot cattle with and without bovine respiratory disease. Front Genet. 2021;12: 627623. pmid:33763112
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref20] 20. Lindholm-Perry AK, Kuehn LA, McDaneld TG, Miles JR, Workman AM, Chitko-McKown CG, et al. Complete blood count data and leukocyte expression of cytokine genes and cytokine receptor genes associated with bovine respiratory disease in calves. BMC Res Notes. 2018;11: 786. pmid:30390697
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref21] 21. 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: 3968–3977. pmid:32650099
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref22] 22. Huang W, Nadeem A, Zhang B, Babar M, Soller M, Khatib H. Characterization and comparison of the leukocyte transcriptomes of three cattle breeds. PloS One. 2012;7: e30244. pmid:22291923
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref23] 23. Johnston D, Earley B, Cormican P, Kenny DA, McCabe MS, Kelly AK, et al. Characterisation of the whole blood mRNA transcriptome in Holstein-Friesian and Jersey calves in response to gradual weaning. PloS One. 2016;11: e0159707. pmid:27479136
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref24] 24. Wynn JL, Cvijanovich NZ, Allen GL, Thomas NJ, Freishtat RJ, Anas N, et al. The influence of developmental age on the early transcriptomic response of children with septic shock. Mol Med. 2011;17: 1146–1156. pmid:21738952
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref25] 25. Olin A, Henckel E, Chen Y, Lakshmikanth T, Pou C, Mikes J, et al. Stereotypic immune system development in newborn children. Cell. 2018;174: 1277–1292. pmid:30142345
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref26] 26. Cai Z, Guldbrandtsen B, Lund MS, Sahana G. Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle. BMC Genomics. 2018;19: 656. pmid:30189836
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref27] 27. Chen X, Cheng Z, Zhang S, Werling D, Wathes DC. Combining genome wide association studies and differential gene expression data analyses identifies candidate genes affecting mastitis caused by two different pathogens in the dairy cow. Open J Anim Sci. 2015;5: 358–393.
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref28] 28. McConnel C, Crisp S, Biggs T, Parrish L, Sischo W, Adams-Progar A, et al. Transcriptomic analysis of peripheral leukocytes in dairy cows with and without evidence of metritis and associated early postpartum disease. Appl Anim Sci. 2020;36: 784–798.
View Article
Google Scholar

[102] View Article

[103] Google Scholar

[ref29] 29. McConnel CS, Crisp SA, Biggs TD, Ficklin SP, Parrish LM, Trombetta SC, et al. A Fixed Cohort Field Study of Gene Expression in Circulating Leukocytes From Dairy Cows With and Without Mastitis. Front Vet Sci. 2020;7: 559279. pmid:33195534
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref30] 30. Skovgaard K, Grell SN, Heegaard PMH, Jungersen G, Pudrith CB, Coussens PM. Differential expression of genes encoding CD30L and P-selectin in cattle with Johne’s disease: progress toward a diagnostic gene expression signature. Vet Immunol Immunopathol. 2006;112: 210–224. pmid:16621022
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref31] 31. McGuirk SM. Disease Management of Dairy Calves and Heifers. Vet Clin North Am Food Anim Pract. 2008;24: 139–153. pmid:18299036
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref32] 32. Ollivett TL, Buczinski S. On-farm use of ultrasonography for bovine respiratory disease. Vet Clin North Am Food Anim Pract. 2016;32: 19–35. pmid:26922110
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref33] 33. Wang H, Horbinski C, Wu H, Liu Y, Sheng S, Liu J, et al. NanoStringDiff: a novel statistical method for differential expression analysis based on NanoString nCounter data. Nucleic Acids Res. 2016;44: e151. pmid:27471031
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref34] 34. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006;7: 3. pmid:16448564
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

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View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref36] 36. Kampen AH, Olsen I, Tollersrud T, Storset AK, Lund A. Lymphocyte subpopulations and neutrophil function in calves during the first 6 months of life. Vet Immunol Immunopathol. 2006;113: 53–63. pmid:16772096
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref37] 37. Casaro S, Marrero MG, Madrid DMC, Prim JG, Nelson CD, Galvão KN, et al. Flow cytometry panels for immunophenotyping dairy cattle peripheral blood leukocytes. Vet Immunol Immunopathol. 2022;248: 110417. pmid:35413495
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref38] 38. Sharma P, Hartley CS, Haque M, Coffey TJ, Egan SA, Flynn RJ. Bovine neonatal monocytes display phenotypic differences compared with adults after challenge with the infectious abortifacient agent Neospora caninum. Front Immunol. 2018;9: 3011. pmid:30619358
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref39] 39. Basil MC, Levy BD. Specialized pro-resolving mediators: endogenous regulators of infection and inflammation. Nat Rev Immunol. 2016;16: 51–67. pmid:26688348
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref40] 40. Bannenberg G, Serhan CN. Specialized pro-resolving lipid mediators in the inflammatory response: An update. Biochim Biophys Acta. 2010;1801: 1260–1273. pmid:20708099
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref41] 41. Snodgrass RG, Benatzy Y, Schmid T, Namgaladze D, Mainka M, Schebb NH, et al. Efferocytosis potentiates the expression of arachidonate 15-lipoxygenase (ALOX15) in alternatively activated human macrophages through LXR activation. Cell Death Differ. 2021;28: 1301–1316. pmid:33177619
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref42] 42. Scott MA, Woolums AR, Karisch BB, Harvey KM, Capik SF. Impact of preweaning vaccination on host gene expression and antibody titers in healthy beef calves. Front Vet Sci. 2022;9: 1010039. pmid:36225796
View Article
PubMed/NCBI
Google Scholar

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Figures

Abstract

Introduction

Materials and methods

Ethics statement

Cohort enrollment and management

Assessments and sampling

Sample selection for differential gene expression (DGE)

Sample size

Gene selection

Leukocyte RNA extraction

Gene expression analysis

Results

Calves and clinical outcomes

Differential gene expression.

Discussion

Conclusions

Supporting information

S1 Table. Individual CRS and TUS scores for each calf by sampling week.

S2 Table. Raw counts for endogenous, housekeeping, negative, and positive probes.

S3 Table. Probe sequences.

Acknowledgments

References