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PLOS One logoLink to PLOS One
. 2023 May 16;18(5):e0285876. doi: 10.1371/journal.pone.0285876

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

Lily A Elder 1,#, Holly R Hinnant 1,#, Chris M Mandella 1,, Rachel A Claus-Walker 1,, Lindsay M Parrish 1,, Giovana S Slanzon 1,¤,, Craig S McConnel 1,*,#
Editor: Angel Abuelo2
PMCID: PMC10187941  PMID: 37192182

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.

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 [13]. 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 [610].

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 [1113]. 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 [1820]. 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, 1619, 21, 2630]. 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.

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

Fig 1

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, 1619, 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 [2630], 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].

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

Gene symbol Protein name Biological function Biological function and process
ALOX15 Polyunsaturated fatty acid lipoxygenase Lipid metabolism Production of specialized pro-resolving mediators for inflammatory resolution
BPI Bactericidal permeability increasing protein Antibacterial Antimicrobial against Gram negative organisms
CATHL6 Cathelicidin 6 Antibacterial Antimicrobial against Gram positive, Gram negative, and fungal organisms
CXCL8 Interleukin-8 Inflammatory response Chemotactic factor for neutrophil attraction and activation
DHX58 ATP-dependent RNA helicase Antiviral Regulation of antiviral signaling
GZMB Granzyme B Apoptosis Protease present in the granules of cytotoxic T-cells and NK-cells involved in induction of cytolysis and apoptosis
HPGD 15-hydroxyprostaglandin dehydrogenase Lipid metabolism Breakdown of pro-inflammatory prostaglandins and cytokines
IFNG Interferon gamma Inflammatory response Antiviral activity and immunoregulatory functions
IL17D Interleukin 17D Inflammatory response Pro-inflammatory cytokine
IL1R2 Interleukin 1 receptor type 2 Inflammatory response Binds and inactivates the pro-inflammatory cytokine IL1B
ISG15 Ubiquitin like protein Antiviral Binds intracellular viral proteins inducing recognition and destruction by the innate immune system
LCN2 Neutrophil gelatinase-associated lipocalin Apoptosis Binds and sequesters iron thereby inducing apoptosis and inhibiting bacterial proliferation
LIF Leukemia inhibitory factor Inflammatory response Cytokine that influences leukocyte differentiation and acute phase protein synthesis
MX1 Interferon-induced GTP-binding protein Antiviral Impedes transcription of the viral genome by inhibiting viral polymerase
OAS2 2’-5’-oligoadenylate synthase 2 Antiviral Detects double-stranded RNA and induces RNA degredation thereby inhibiting viral genome replication
PGLYRP1 Peptidoglycan recognition protein 1 Antibacterial Antimicrobial against Gram positive organisms
S100A8 Protein S100-A8 Inflammatory response A central regulator of the inflammatory response with activity including leukocyte recruitment and activation, zinc sequestration, and apoptosis
SELP P-selectin Cell adhesion Mediates the interraction of circulating leukocytes with endothelial cells allowing leukocyte tethering and rolling
TNF Tumor necrosis factor Inflammatory response Cytokine with a central role in the inflammatory response and apoptosis

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).

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).
Gene Name Week Log2 fold change Std error (log2) Lower CL (log2) Upper CL (log2) P-value B-Y p-valuea
ALOX15 3 Pre -1.03 0.65 -2.29 0.24 0.1150 1.0000
5 Pre -0.11 0.62 -1.33 1.11 0.8600 1.0000
5 Onset -0.84 0.62 -2.07 0.38 0.1800 1.0000
7 Onset -0.27 0.62 -1.49 0.95 0.6650 1.0000
7 Post -0.38 0.62 -1.60 0.84 0.5410 1.0000
9 Post 0.74 0.62 -0.48 1.96 0.2380 1.0000
BPI 3 Pre -0.57 0.47 -1.49 0.34 0.2240 1.0000
5 Pre 0.08 0.46 -0.82 0.98 0.8640 1.0000
5 Onset -0.89 0.46 -1.78 0.01 0.0566 0.5690
7 Onset -0.72 0.46 -1.62 0.18 0.1200 0.9280
7 Post -0.48 0.46 -1.38 0.42 0.3000 1.0000
9 Post -0.02 0.46 -0.91 0.88 0.9730 1.0000
CATHL6 3 Pre 0.36 0.22 -0.07 0.78 0.1040 1.0000
5 Pre 0.16 0.22 -0.26 0.59 0.4560 1.0000
5 Onset -0.08 0.22 -0.51 0.36 0.7290 1.0000
7 Onset 0.43 0.21 0.02 0.85 0.0435 0.6080
7 Post 0.43 0.21 0.02 0.85 0.0435 0.7850
9 Post 0.15 0.22 -0.28 0.57 0.4970 1.0000
CXCL8 3 Pre -0.17 0.51 -1.18 0.84 0.7380 1.0000
5 Pre -0.82 0.50 -1.81 0.17 0.1070 1.0000
5 Onset -0.13 0.50 -1.12 0.86 0.7950 1.0000
7 Onset -0.36 0.50 -1.35 0.63 0.4740 1.0000
7 Post -0.07 0.50 -1.05 0.92 0.8940 1.0000
9 Post -0.55 0.50 -1.54 0.44 0.2780 1.0000
DHX58 3 Pre -0.50 0.35 -1.19 0.19 0.1590 1.0000
5 Pre 0.50 0.34 -0.18 1.17 0.1540 1.0000
5 Onset -0.44 0.34 -1.11 0.24 0.2090 1.0000
7 Onset -0.06 0.34 -0.73 0.62 0.8670 1.0000
7 Post -0.14 0.34 -0.82 0.53 0.6840 1.0000
9 Post 0.13 0.34 -0.55 0.80 0.7150 1.0000
GZMB 3 Pre -0.91 0.37 -1.63 -0.18 0.0161 0.7870
5 Pre -0.34 0.36 -1.05 0.37 0.3470 1.0000
5 Onset -0.75 0.36 -1.46 -0.05 0.0394 0.5690
7 Onset -0.74 0.36 -1.45 -0.04 0.0427 0.6080
7 Post -0.86 0.36 -1.56 -0.15 0.0194 0.5250
9 Post -0.64 0.36 -1.34 0.07 0.0793 1.0000
HPGD 3 Pre -0.52 0.46 -1.42 0.38 0.2570 1.0000
5 Pre -0.37 0.44 -1.23 0.50 0.4090 1.0000
5 Onset -0.92 0.45 -1.79 -0.05 0.0412 0.5690
7 Onset -0.80 0.44 -1.66 0.07 0.0737 0.6650
7 Post -0.65 0.44 -1.51 0.22 0.1470 1.0000
9 Post 0.56 0.44 -0.31 1.42 0.2080 1.0000
IL17D 3 Pre 0.12 0.29 -0.45 0.68 0.6890 1.0000
5 Pre 0.41 0.26 -0.10 0.93 0.1200 1.0000
5 Onset 0.18 0.27 -0.35 0.70 0.5070 1.0000
7 Onset -0.03 0.26 -0.55 0.48 0.8980 1.0000
7 Post 0.00 0.26 -0.52 0.51 0.9950 1.0000
9 Post 0.44 0.26 -0.07 0.94 0.0938 1.0000
IL1R2 3 Pre -0.38 0.30 -0.97 0.22 0.2220 1.0000
5 Pre -0.56 0.30 -1.15 0.03 0.0665 0.8990
5 Onset -0.53 0.30 -1.12 0.07 0.0848 0.6550
7 Onset -0.02 0.30 -0.61 0.57 0.9500 1.0000
7 Post -0.98 0.30 -1.57 -0.38 0.0019 0.1000
9 Post 0.02 0.30 -0.57 0.61 0.9510 1.0000
ISG15 3 Pre -0.65 0.57 -1.76 0.46 0.2540 1.0000
5 Pre 0.76 0.55 -0.32 1.85 0.1710 1.0000
5 Onset -0.57 0.55 -1.65 0.52 0.3100 1.0000
7 Onset -0.06 0.55 -1.15 1.02 0.9090 1.0000
7 Post -0.53 0.55 -1.61 0.56 0.3420 1.0000
9 Post -0.05 0.55 -1.13 1.04 0.9320 1.0000
LCN2 3 Pre -0.02 0.27 -0.54 0.51 0.9490 1.0000
5 Pre 0.50 0.26 -0.01 1.01 0.0588 0.8990
5 Onset 0.49 0.26 -0.02 1.00 0.0631 0.5690
7 Onset 0.59 0.26 0.08 1.10 0.0270 0.6080
7 Post 0.03 0.26 -0.49 0.54 0.9210 1.0000
9 Post 0.13 0.26 -0.38 0.64 0.6170 1.0000
MX1 3 Pre -0.88 0.40 -1.66 -0.10 0.0291 0.7870
5 Pre 0.51 0.39 -0.25 1.27 0.1950 1.0000
5 Onset -0.63 0.39 -1.39 0.13 0.1080 0.7310
7 Onset 0.15 0.39 -0.61 0.91 0.7000 1.0000
7 Post -0.47 0.39 -1.23 0.30 0.2330 1.0000
9 Post 0.20 0.39 -0.56 0.96 0.6110 1.0000
OAS2 3 Pre -1.00 0.52 -2.03 0.02 0.0577 1.0000
5 Pre 0.05 0.51 -0.96 1.05 0.9300 1.0000
5 Onset -1.00 0.51 -2.00 0.00 0.0542 0.5690
7 Onset -0.13 0.51 -1.13 0.87 0.7960 1.0000
7 Post -0.74 0.51 -1.75 0.26 0.1510 1.0000
9 Post 0.22 0.51 -0.79 1.22 0.6740 1.0000
PGLYRP1 3 Pre 0.04 0.25 -0.45 0.52 0.8840 1.0000
5 Pre 0.06 0.24 -0.42 0.54 0.8070 1.0000
5 Onset 0.01 0.24 -0.47 0.49 0.9570 1.0000
7 Onset 0.26 0.24 -0.21 0.73 0.2790 1.0000
7 Post 0.21 0.24 -0.26 0.68 0.3890 1.0000
9 Post -0.26 0.25 -0.74 0.22 0.2850 1.0000
S100A8 3 Pre -0.01 0.31 -0.61 0.60 0.9860 1.0000
5 Pre 0.58 0.30 -0.01 1.16 0.0585 0.8990
5 Onset 0.68 0.30 0.09 1.27 0.0257 0.5690
7 Onset 0.61 0.30 0.02 1.20 0.0450 0.6080
7 Post -0.29 0.30 -0.88 0.30 0.3440 1.0000
9 Post -0.63 0.30 -1.21 -0.04 0.0401 1.0000
SELP 3 Pre -0.37 0.27 -0.89 0.16 0.1730 1.0000
5 Pre -0.76 0.26 -1.27 -0.25 0.0047 0.2520
5 Onset -0.02 0.26 -0.54 0.49 0.9280 1.0000
7 Onset -0.47 0.26 -0.99 0.04 0.0734 0.6650
7 Post -0.18 0.26 -0.69 0.34 0.5050 1.0000
9 Post -0.13 0.26 -0.64 0.39 0.6310 1.0000

Comparisons are made between TUS+ calves (any CRS score with a TUS score of ≥ 3 at ONSET) and healthy calves (CRS ≤ 1 in all the respiratory parameters, nasal, eyes, and cough, and TUS ≤ 1). Samples are matched by week of age (3–9) and disease progression time point (PRE, ONSET, or POST). There are twelve TUS+ calves with six having onset of disease in week 5 and six having onset of disease in week 7. The healthy group is comprised of nine individuals in week 3 and those same individuals plus one more for a total of ten in weeks 5, 7, and 9. The healthy calf samples are used as the baseline with the BRD calf samples serving as the comparison. The magnitude and direction of the log fold change can be interpreted as referring to the change in the BRD calves relative to the healthy. Genes IFNG, LIF, and TNF are not assessed due to consistently falling below the recommended expression minimum of 20 copies per sample.

aB-Y p-value: Benjamini-Yekutieli method for p-value adjustment.

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 35). 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.

Table 3. Log2 fold changes in peripheral leukocyte gene expression in healthy Holstein calves (CRS-/TUS-; n = 10) compared by week of age.
Gene Name Week Log2 fold change Std error (log2) Lower CL (log2) Upper CL (log2) P-value B-Y p-valuea
ALOX15 3 vs. 5 2.25 0.50 1.27 3.24 <0.0001 0.0012
3 vs. 7 2.78 0.50 1.80 3.77 <0.0001 <0.0001
3 vs. 9 2.02 0.50 1.03 3.00 0.0001 0.0044
5 vs. 7 0.53 0.49 -0.42 1.49 0.2760 1.0000
5 vs. 9 -0.24 0.49 -1.19 0.72 0.6300 1.0000
7 vs. 9 -0.77 0.49 -1.72 0.19 0.1180 1.0000
BPI 3 vs. 5 -0.81 0.41 -1.60 -0.02 0.0483 0.8210
3 vs. 7 -1.22 0.41 -2.01 -0.42 0.0035 0.0267
3 vs. 9 -1.47 0.41 -2.26 -0.67 0.0005 0.0054
5 vs. 7 -0.41 0.39 -1.18 0.37 0.3060 1.0000
5 vs. 9 -0.66 0.39 -1.43 0.12 0.1000 0.7750
7 vs. 9 -0.25 0.40 -1.02 0.53 0.5300 1.0000
CATHL6 3 vs. 5 -0.03 0.20 -0.42 0.36 0.8800 1.0000
3 vs. 7 0.03 0.20 -0.36 0.41 0.8970 1.0000
3 vs. 9 0.02 0.20 -0.37 0.40 0.9370 1.0000
5 vs. 7 0.06 0.19 -0.32 0.43 0.7740 1.0000
5 vs. 9 0.05 0.19 -0.33 0.42 0.8140 1.0000
7 vs. 9 -0.01 0.19 -0.38 0.36 0.9590 1.0000
CXCL8 3 vs. 5 -0.03 0.46 -0.93 0.87 0.9450 1.0000
3 vs. 7 -0.20 0.46 -1.09 0.70 0.6720 1.0000
3 vs. 9 -0.37 0.46 -1.27 0.53 0.4210 1.0000
5 vs. 7 -0.16 0.45 -1.04 0.71 0.7150 1.0000
5 vs. 9 -0.34 0.45 -1.21 0.53 0.4490 1.0000
7 vs. 9 -0.18 0.45 -1.05 0.70 0.6940 1.0000
DHX58 3 vs. 5 -0.17 0.33 -0.81 0.48 0.6130 1.0000
3 vs. 7 -1.00 0.33 -1.64 -0.35 0.0032 0.0267
3 vs. 9 -1.02 0.33 -1.67 -0.37 0.0027 0.0206
5 vs. 7 -0.83 0.32 -1.46 -0.20 0.0113 0.2040
5 vs. 9 -0.85 0.32 -1.48 -0.22 0.0094 0.1610
7 vs. 9 -0.02 0.32 -0.65 0.61 0.9470 1.0000
GZMB 3 vs. 5 0.37 0.33 -0.28 1.02 0.2630 1.0000
3 vs. 7 0.60 0.33 -0.05 1.24 0.0743 0.3350
3 vs. 9 0.79 0.33 0.14 1.44 0.0190 0.0935
5 vs. 7 0.22 0.32 -0.41 0.85 0.4870 1.0000
5 vs. 9 0.42 0.32 -0.21 1.05 0.1980 1.0000
7 vs. 9 0.19 0.32 -0.44 0.82 0.5500 1.0000
HPGD 3 vs. 5 0.74 0.39 -0.02 1.50 0.0607 0.8210
3 vs. 7 1.19 0.39 0.44 1.95 0.0027 0.0267
3 vs. 9 0.32 0.39 -0.45 1.08 0.4190 1.0000
5 vs. 7 0.46 0.37 -0.28 1.19 0.2250 1.0000
5 vs. 9 -0.42 0.38 -1.16 0.32 0.2660 1.0000
7 vs. 9 -0.88 0.38 -1.61 -0.14 0.0216 0.4370
IL17D 3 vs. 5 0.41 0.25 -0.08 0.90 0.1070 1.0000
3 vs. 7 0.69 0.25 0.20 1.17 0.0068 0.0456
3 vs. 9 0.62 0.25 0.13 1.10 0.0146 0.0791
5 vs. 7 0.28 0.23 -0.18 0.74 0.2380 1.0000
5 vs. 9 0.21 0.24 -0.25 0.67 0.3740 1.0000
7 vs. 9 -0.07 0.23 -0.52 0.39 0.7680 1.0000
IL1R2 3 vs. 5 -0.55 0.29 -1.12 0.01 0.0586 0.8210
3 vs. 7 -0.64 0.29 -1.20 -0.07 0.0302 0.1820
3 vs. 9 -0.78 0.29 -1.34 -0.21 0.0083 0.0499
5 vs. 7 -0.08 0.28 -0.63 0.47 0.7690 1.0000
5 vs. 9 -0.23 0.28 -0.78 0.33 0.4220 1.0000
7 vs. 9 -0.14 0.28 -0.70 0.41 0.6100 1.0000
ISG15 3 vs. 5 -0.46 0.55 -1.53 0.61 0.4000 1.0000
3 vs. 7 -1.98 0.55 -3.05 -0.91 0.0005 0.0126
3 vs. 9 -1.82 0.55 -2.89 -0.75 0.0013 0.0114
5 vs. 7 -1.52 0.53 -2.56 -0.48 0.0054 0.1450
5 vs. 9 -1.36 0.53 -2.40 -0.32 0.0124 0.1610
7 vs. 9 0.16 0.53 -0.88 1.20 0.7630 1.0000
LCN2 3 vs. 5 0.34 0.26 -0.17 0.85 0.1980 1.0000
3 vs. 7 0.56 0.26 0.05 1.08 0.0337 0.1820
3 vs. 9 0.97 0.26 0.46 1.48 0.0004 0.0048
5 vs. 7 0.23 0.25 -0.27 0.72 0.3790 1.0000
5 vs. 9 0.63 0.25 0.13 1.13 0.0149 0.1610
7 vs. 9 0.41 0.25 -0.09 0.90 0.1130 1.0000
MX1 3 vs. 5 -0.32 0.38 -1.08 0.43 0.4000 1.0000
3 vs. 7 -1.16 0.38 -1.91 -0.41 0.0033 0.0267
3 vs. 9 -1.08 0.38 -1.83 -0.33 0.0062 0.0416
5 vs. 7 -0.84 0.37 -1.57 -0.10 0.0279 0.3780
5 vs. 9 -0.75 0.37 -1.49 -0.02 0.0469 0.4220
7 vs. 9 0.08 0.37 -0.65 0.81 0.8270 1.0000
OAS2 3 vs. 5 -0.16 0.51 -1.15 0.83 0.7550 1.0000
3 vs. 7 -1.77 0.51 -2.77 -0.79 0.0007 0.0126
3 vs. 9 -1.93 0.51 -2.92 -0.94 0.0002 0.0044
5 vs. 7 -1.62 0.49 -2.58 -0.65 0.0015 0.0786
5 vs. 9 -1.78 0.49 -2.74 -0.81 0.0005 0.0184
7 vs. 9 -0.16 0.49 -1.12 0.81 0.7480 1.0000
PGLYRP1 3 vs. 5 -0.12 0.22 -0.55 0.31 0.5740 1.0000
3 vs. 7 -0.05 0.22 -0.48 0.38 0.8200 1.0000
3 vs. 9 0.05 0.22 -0.38 0.47 0.8220 1.0000
5 vs. 7 0.07 0.21 -0.34 0.49 0.7320 1.0000
5 vs. 9 0.17 0.21 -0.24 0.59 0.4200 1.0000
7 vs. 9 0.10 0.21 -0.32 0.51 0.6420 1.0000
S100A8 3 vs. 5 -0.32 0.27 -0.85 0.20 0.2320 1.0000
3 vs. 7 -0.54 0.27 -1.06 -0.01 0.0478 0.2350
3 vs. 9 0.06 0.27 -0.47 0.59 0.8230 1.0000
5 vs. 7 -0.22 0.26 -0.73 0.30 0.4110 1.0000
5 vs. 9 0.38 0.26 -0.13 0.90 0.1460 0.9860
7 vs. 9 0.60 0.26 0.09 1.11 0.0242 0.4370
SELP 3 vs. 5 -0.11 0.25 -0.60 0.37 0.6480 1.0000
3 vs. 7 -0.17 0.25 -0.65 0.32 0.5020 1.0000
3 vs. 9 -0.97 0.25 -1.45 -0.48 0.0002 0.0044
5 vs. 7 -0.05 0.24 -0.53 0.42 0.8250 1.0000
5 vs. 9 -0.85 0.24 -1.33 -0.38 0.0007 0.0184
7 vs. 9 -0.80 0.24 -1.27 -0.32 0.0014 0.0754

Comparisons are made between ages of healthy calves (CRS ≤ 1 in all the respiratory parameters, nasal, eyes, and cough, and TUS ≤ 1) using serial samples. There are nine total individuals in week 3 and those same individuals plus one more for a total of ten in weeks 5, 7, and 9. The younger age samples are used as the baseline with the older age samples serving as the comparison. The magnitude and direction of the log fold change can be interpreted as referring to the change in the older sample relative to the younger. Genes IFNG, LIF, and TNF are not assessed due to consistently falling below the recommended expression minimum of 20 copies per sample.

aB-Y p-value: Benjamini-Yekutieli method for p-value adjustment.

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.
Gene Name Week Log2 fold change Std error (log2) Lower CL (log2) Upper CL (log2) P-value B-Y p-valuea
ALOX15 3 vs. 5 2.85 0.61 1.64 4.05 <0.0001 0.0007
3 vs. 7 3.49 0.61 2.28 4.69 <0.0001 <0.0001
3 vs. 9 3.78 0.71 2.40 5.17 <0.0001 <0.0001
5 vs. 7 0.64 0.50 -0.33 1.61 0.2010 1.0000
5 vs. 9 0.94 0.61 -0.25 2.12 0.1270 0.8630
7 vs. 9 0.30 0.61 -0.89 1.49 0.6240 1.0000
BPI 3 vs. 5 -0.56 0.45 -1.45 0.32 0.2140 1.0000
3 vs. 7 -1.24 0.45 -2.12 -0.36 0.0073 0.0980
3 vs. 9 -0.91 0.52 -1.93 0.11 0.0839 0.5680
5 vs. 7 -0.68 0.37 -1.40 0.05 0.0699 0.5400
5 vs. 9 -0.35 0.45 -1.23 0.54 0.4450 1.0000
7 vs. 9 0.33 0.45 -0.56 1.21 0.4670 1.0000
CATHL6 3 vs. 5 -0.34 0.21 -0.74 0.07 0.1040 0.9390
3 vs. 7 0.10 0.20 -0.30 0.50 0.6220 1.0000
3 vs. 9 -0.20 0.24 -0.66 0.27 0.4160 1.0000
5 vs. 7 0.44 0.17 0.11 0.77 0.0109 0.1180
5 vs. 9 0.15 0.21 -0.27 0.56 0.4920 1.0000
7 vs. 9 -0.30 0.21 -0.70 0.11 0.1560 1.0000
CXCL8 3 vs. 5 -0.30 0.49 -1.26 0.67 0.5510 1.0000
3 vs. 7 -0.23 0.49 -1.19 0.73 0.6420 1.0000
3 vs. 9 -0.75 0.57 -1.86 0.37 0.1910 1.0000
5 vs. 7 0.07 0.40 -0.72 0.85 0.8720 1.0000
5 vs. 9 -0.45 0.49 -1.42 0.51 0.3590 1.0000
7 vs. 9 -0.52 0.49 -1.48 0.45 0.2950 1.0000
DHX58 3 vs. 5 0.44 0.34 -0.24 1.11 0.2070 1.0000
3 vs. 7 -0.60 0.34 -1.27 0.07 0.0848 0.4590
3 vs. 9 -0.39 0.40 -1.17 0.38 0.3220 1.0000
5 vs. 7 -1.03 0.28 -1.58 -0.49 0.0004 0.0070
5 vs. 9 -0.83 0.34 -1.50 -0.16 0.0175 0.2400
7 vs. 9 0.20 0.34 -0.47 0.88 0.5540 1.0000
GZMB 3 vs. 5 0.75 0.35 0.06 1.44 0.0369 0.3990
3 vs. 7 0.71 0.35 0.01 1.39 0.0485 0.2920
3 vs. 9 1.06 0.41 0.26 1.85 0.0109 0.1170
5 vs. 7 -0.04 0.29 -0.60 0.52 0.8840 1.0000
5 vs. 9 0.31 0.35 -0.38 1.00 0.3790 1.0000
7 vs. 9 0.35 0.35 -0.34 1.04 0.3180 1.0000
HPGD 3 vs. 5 0.64 0.44 -0.22 1.50 0.1460 0.9890
3 vs. 7 1.00 0.44 0.14 1.85 0.0249 0.2070
3 vs. 9 1.40 0.50 0.42 2.38 0.0065 0.0878
5 vs. 7 0.36 0.35 -0.34 1.05 0.3170 1.0000
5 vs. 9 0.76 0.43 -0.09 1.60 0.0821 0.7400
7 vs. 9 0.40 0.43 -0.44 1.24 0.3520 1.0000
IL17D 3 vs. 5 0.59 0.27 0.07 1.11 0.0282 0.3810
3 vs. 7 0.55 0.27 0.03 1.07 0.0405 0.2740
3 vs. 9 0.94 0.30 0.35 1.53 0.0023 0.0420
5 vs. 7 -0.04 0.21 -0.45 0.37 0.8490 1.0000
5 vs. 9 0.35 0.25 -0.14 0.84 0.1690 1.0000
7 vs. 9 0.39 0.25 -0.10 0.88 0.1260 1.0000
IL1R2 3 vs. 5 -0.72 0.30 -1.31 -0.13 0.0192 0.3460
3 vs. 7 -0.68 0.30 -1.27 -0.09 0.0267 0.2070
3 vs. 9 -0.39 0.35 -1.07 0.30 0.2710 1.0000
5 vs. 7 0.04 0.25 -0.45 0.53 0.8720 1.0000
5 vs. 9 0.33 0.30 -0.26 0.93 0.2720 1.0000
7 vs. 9 0.29 0.30 -0.30 0.89 0.3340 1.0000
ISG15 3 vs. 5 0.43 0.55 -0.64 1.51 0.4310 1.0000
3 vs. 7 -1.61 0.55 -2.68 -0.54 0.0042 0.0764
3 vs. 9 -1.22 0.63 -2.46 0.02 0.0573 0.4420
5 vs. 7 -2.04 0.45 -2.92 -1.17 <0.0001 0.0009
5 vs. 9 -1.65 0.55 -2.73 -0.58 0.0034 0.1270
7 vs. 9 0.39 0.55 -0.68 1.46 0.4780 1.0000
LCN2 3 vs. 5 0.85 0.26 0.35 1.36 0.0014 0.0372
3 vs. 7 0.91 0.26 0.41 1.42 0.0006 0.0169
3 vs. 9 1.12 0.30 0.54 1.70 0.0003 0.0082
5 vs. 7 0.06 0.21 -0.35 0.47 0.7660 1.0000
5 vs. 9 0.27 0.26 -0.24 0.77 0.3010 1.0000
7 vs. 9 0.20 0.26 -0.30 0.71 0.4280 1.0000
MX1 3 vs. 5 0.61 0.39 -0.16 1.37 0.1260 0.9760
3 vs. 7 -0.40 0.39 -1.17 0.36 0.3060 1.0000
3 vs. 9 0.00 0.45 -0.89 0.89 0.9960 1.0000
5 vs. 7 -1.01 0.32 -1.64 -0.38 0.0022 0.0300
5 vs. 9 -0.60 0.39 -1.37 0.17 0.1280 0.8630
7 vs. 9 0.41 0.39 -0.36 1.17 0.3030 1.0000
OAS2 3 vs. 5 0.46 0.51 -0.53 1.45 0.3630 1.0000
3 vs. 7 -1.18 0.51 -2.17 -0.19 0.0220 0.2070
3 vs. 9 -0.71 0.58 -1.86 0.43 0.2240 1.0000
5 vs. 7 -1.64 0.41 -2.45 -0.83 0.0001 0.0039
5 vs. 9 -1.18 0.51 -2.16 -0.19 0.0222 0.2400
7 vs. 9 0.46 0.51 -0.53 1.45 0.3620 1.0000
PGLYRP 3 vs. 5 -0.12 0.24 -0.58 0.34 0.6030 1.0000
3 vs. 7 0.15 0.23 -0.31 0.61 0.5230 1.0000
3 vs. 9 -0.25 0.27 -0.79 0.29 0.3620 1.0000
5 vs. 7 0.27 0.19 -0.10 0.65 0.1580 1.0000
5 vs. 9 -0.13 0.24 -0.59 0.34 0.5930 1.0000
7 vs. 9 -0.40 0.24 -0.86 0.06 0.0942 1.0000
S100A8 3 vs. 5 0.31 0.30 -0.28 0.90 0.3020 1.0000
3 vs. 7 -0.30 0.30 -0.89 0.29 0.3190 1.0000
3 vs. 9 -0.56 0.35 -1.24 0.12 0.1100 0.6640
5 vs. 7 -0.61 0.25 -1.10 -0.13 0.0143 0.1290
5 vs. 9 -0.87 0.30 -1.46 -0.28 0.0047 0.1270
7 vs. 9 -0.26 0.30 -0.85 0.33 0.3920 1.0000
SELP 3 vs. 5 -0.09 0.26 -0.61 0.42 0.7230 1.0000
3 vs. 7 -0.12 0.26 -0.63 0.40 0.6530 1.0000
3 vs. 9 -0.73 0.30 -1.32 -0.13 0.0187 0.1680
5 vs. 7 -0.03 0.21 -0.44 0.39 0.9070 1.0000
5 vs. 9 -0.63 0.26 -1.15 -0.12 0.0180 0.2400
7 vs. 9 -0.61 0.26 -1.12 -0.09 0.0229 1.0000

Comparisons are made between ages of TUS+ calves (any CRS score with a TUS score of ≥ 3 at ONSET; n = 12) using serial samples. The younger age samples are used as the baseline with the older age samples serving as the comparison. The magnitude and direction of the log fold change can be interpreted as referring to the change in the older sample relative to the younger. Genes IFNG, LIF, and TNF are not assessed due to consistently falling below the recommended expression minimum of 20 copies per sample.

aB-Y p-value: Benjamini-Yekutieli method for p-value adjustment.

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.
Gene Name Week Log2 fold change Std error (log2) Lower CL (log2) Upper CL (log2) P-value B-Y p-valuea
ALOX15 3 vs. 5 2.37 0.33 1.72 3.02 <0.0001 <0.0001
3 vs. 7 3.43 0.33 2.78 4.08 <0.0001 <0.0001
3 vs. 9 2.69 0.37 1.97 3.41 <0.0001 <0.0001
5 vs. 7 1.06 0.29 0.49 1.64 0.0005 0.0071
5 vs. 9 0.32 0.33 -0.32 0.97 0.3300 1.0000
7 vs. 9 -0.74 0.33 -1.39 -0.10 0.0269 0.4850
BPI 3 vs. 5 -0.83 0.26 -1.34 -0.31 0.0021 0.0383
3 vs. 7 -1.42 0.26 -1.93 -0.90 <0.0001 <0.0001
3 vs. 9 -1.41 0.29 -1.97 -0.85 <0.0001 0.0001
5 vs. 7 -0.59 0.23 -1.05 -0.13 0.0130 0.0880
5 vs. 9 -0.58 0.26 -1.10 -0.07 0.0280 0.1690
7 vs. 9 0.01 0.26 -0.51 0.52 0.9830 1.0000
CATHL6 3 vs. 5 -0.19 0.13 -0.45 0.06 0.1440 0.9750
3 vs. 7 0.02 0.13 -0.24 0.27 0.8900 1.0000
3 vs. 9 -0.17 0.14 -0.45 0.11 0.2400 0.9290
5 vs. 7 0.21 0.12 -0.02 0.44 0.0739 0.4440
5 vs. 9 0.02 0.13 -0.24 0.28 0.8650 1.0000
7 vs. 9 -0.19 0.13 -0.44 0.07 0.1550 1.0000
CXCL8 3 vs. 5 -0.13 0.30 -0.71 0.46 0.6760 1.0000
3 vs. 7 -0.16 0.30 -0.74 0.43 0.6060 1.0000
3 vs. 9 -0.29 0.33 -0.94 0.35 0.3760 1.0000
5 vs. 7 -0.03 0.27 -0.55 0.49 0.9120 1.0000
5 vs. 9 -0.17 0.30 -0.76 0.42 0.5790 1.0000
7 vs. 9 -0.14 0.30 -0.73 0.45 0.6470 1.0000
DHX58 3 vs. 5 0.17 0.22 -0.25 0.60 0.4300 1.0000
3 vs. 7 -0.54 0.22 -0.96 -0.11 0.0152 0.0750
3 vs. 9 -0.58 0.24 -1.05 -0.12 0.0163 0.0802
5 vs. 7 -0.71 0.19 -1.09 -0.33 0.0004 0.0071
5 vs. 9 -0.76 0.22 -1.18 -0.33 0.0008 0.0103
7 vs. 9 -0.05 0.22 -0.47 0.38 0.8290 1.0000
GZMB 3 vs. 5 0.61 0.22 0.18 1.04 0.0071 0.0742
3 vs. 7 0.90 0.22 0.47 1.33 0.0001 0.0014
3 vs. 9 1.12 0.24 0.64 1.59 <0.0001 0.0002
5 vs. 7 0.29 0.20 -0.10 0.67 0.1440 0.7100
5 vs. 9 0.51 0.22 0.08 0.94 0.0236 0.1590
7 vs. 9 0.22 0.22 -0.21 0.65 0.3240 1.0000
HPGD 3 vs. 5 0.83 0.26 0.33 1.33 0.0017 0.0383
3 vs. 7 1.55 0.26 1.06 2.05 <0.0001 <0.0001
3 vs. 9 0.83 0.28 0.28 1.38 0.0040 0.0243
5 vs. 7 0.73 0.23 0.29 1.17 0.0016 0.0176
5 vs. 9 0.00 0.25 -0.49 0.50 0.9930 1.0000
7 vs. 9 -0.73 0.25 -1.22 -0.23 0.0050 0.1350
IL17D 3 vs. 5 0.35 0.16 0.03 0.67 0.0330 0.2550
3 vs. 7 0.58 0.16 0.26 0.89 0.0005 0.0048
3 vs. 9 0.71 0.18 0.37 1.06 <0.0001 0.0011
5 vs. 7 0.23 0.14 -0.05 0.50 0.1100 0.5950
5 vs. 9 0.36 0.16 0.06 0.67 0.0221 0.1590
7 vs. 9 0.14 0.15 -0.17 0.44 0.3770 1.0000
IL1R2 3 vs. 5 -0.52 0.19 -0.88 -0.15 0.0067 0.0742
3 vs. 7 -0.68 0.19 -1.05 -0.32 0.0004 0.0046
3 vs. 9 -0.72 0.21 -1.12 -0.32 0.0007 0.0052
5 vs. 7 -0.17 0.17 -0.49 0.16 0.3220 1.0000
5 vs. 9 -0.21 0.19 -0.57 0.16 0.2750 1.0000
7 vs. 9 -0.04 0.19 -0.41 0.33 0.8320 1.0000
ISG1 3 vs. 5 0.17 0.36 -0.54 0.88 0.6360 1.0000
3 vs. 7 -0.99 0.36 -1.70 -0.28 0.0077 0.0417
3 vs. 9 -1.14 0.40 -1.92 -0.36 0.0053 0.0284
5 vs. 7 -1.16 0.32 -1.79 -0.53 0.0005 0.0071
5 vs. 9 -1.31 0.36 -2.02 -0.60 0.0005 0.0100
7 vs. 9 -0.15 0.36 -0.86 0.56 0.6760 1.0000
LCN2 3 vs. 5 0.46 0.17 0.13 0.79 0.0082 0.0742
3 vs. 7 0.60 0.17 0.27 0.93 0.0006 0.0048
3 vs. 9 0.88 0.19 0.51 1.24 <0.0001 0.0002
5 vs. 7 0.14 0.15 -0.15 0.44 0.3450 1.0000
5 vs. 9 0.42 0.17 0.09 0.75 0.0147 0.1320
7 vs. 9 0.28 0.17 -0.05 0.61 0.1040 1.0000
MX1 3 vs. 5 0.14 0.26 -0.36 0.64 0.5970 1.0000
3 vs. 7 -0.46 0.26 -0.96 0.04 0.0765 0.3450
3 vs. 9 -0.56 0.28 -1.11 -0.01 0.0494 0.2230
5 vs. 7 -0.59 0.23 -1.04 -0.15 0.0105 0.0815
5 vs. 9 -0.69 0.26 -1.19 -0.19 0.0078 0.0843
7 vs. 9 -0.10 0.26 -0.60 0.40 0.6920 1.0000
OAS2 3 vs. 5 0.11 0.33 -0.54 0.77 0.7360 1.0000
3 vs. 7 -1.11 0.33 -1.77 -0.46 0.0012 0.0074
3 vs. 9 -1.08 0.37 -1.80 -0.36 0.0041 0.0243
5 vs. 7 -1.23 0.30 -1.81 -0.64 <0.0001 0.0043
5 vs. 9 -1.20 0.33 -1.85 -0.54 0.0006 0.0100
7 vs. 9 0.03 0.33 -0.62 0.69 0.9250 1.0000
PGLYRP1 3 vs. 5 -0.18 0.14 -0.46 0.10 0.2030 1.0000
3 vs. 7 -0.08 0.14 -0.36 0.20 0.5680 1.0000
3 vs. 9 -0.14 0.16 -0.45 0.16 0.3560 1.0000
5 vs. 7 0.10 0.13 -0.15 0.35 0.4270 1.0000
5 vs. 9 0.04 0.14 -0.24 0.32 0.7930 1.0000
7 vs. 9 -0.06 0.14 -0.34 0.21 0.6560 1.0000
S100A8 3 vs. 5 -0.11 0.18 -0.46 0.25 0.5560 1.0000
3 vs. 7 -0.61 0.18 -0.96 -0.25 0.0012 0.0074
3 vs. 9 -0.32 0.20 -0.71 0.07 0.1140 0.4740
5 vs. 7 -0.50 0.16 -0.82 -0.18 0.0026 0.0232
5 vs. 9 -0.21 0.18 -0.57 0.14 0.2470 1.0000
7 vs. 9 0.29 0.18 -0.07 0.64 0.1140 1.0000
SELP 3 vs. 5 -0.10 0.16 -0.42 0.22 0.5410 1.0000
3 vs. 7 -0.10 0.16 -0.42 0.22 0.5320 1.0000
3 vs. 9 -0.70 0.18 -1.05 -0.34 0.0002 0.0019
5 vs. 7 0.00 0.15 -0.29 0.28 0.9870 1.0000
5 vs. 9 -0.60 0.16 -0.92 -0.27 0.0005 0.0100
7 vs. 9 -0.59 0.16 -0.91 -0.27 0.0005 0.0264

Comparisons are made between ages of all calves irrespective of disease state using serial samples. The number of individuals comprising each week are as follows: 19 in week 3, 29 in weeks 5 and 7, and 19 in week 9. The younger age samples are used as the baseline with the older age samples serving as the comparison. The magnitude and direction of the log fold change can be interpreted as referring to the change in the older sample relative to the younger. Genes IFNG, LIF, and TNF are not assessed due to consistently falling below the recommended expression minimum of 20 copies per sample.

aB-Y p-value: Benjamini-Yekutieli method for p-value adjustment.

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.

Fig 2

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.

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 [2830]. 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 35, 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 35). 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 [3941]. 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.

(XLSX)

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

(XLSX)

S3 Table. Probe sequences.

(XLSX)

Acknowledgments

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

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

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.

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PONE-D-23-08494Differential gene expression in peripheral leukocytes of pre-weaned Holstein heifer calves with respiratory diseasePLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This manuscript analyzed a panel of 19 genes as potential biomarkers for prediction of BRD. Authors compared pre, onset and post gene expression from whole blood samples in preweaned heifer calves across time. While they did not identify any differences between BRD cases (regardless of diagnoses), they noted changes in gene expression over time.

The paper is clearly written and the study is easy to follow. Although it is largely negative data, the results are relevant for future studies aimed at identifying biomarkers of disease in very young calves. A few comments:

1) Calves in the study received an intranasal vaccine. The vaccine was different and received at different time points. However, there is no discussion or consideration of the effects of the vaccine on gene expression. Because the authors determined early on that GE did not differ, this possibility was disregarded. I think some comparisons should be made to determine if there are effects of the vaccine and/or the different vaccines given at different ages.

2) The authors indicate a major heat event during the course of the study, but do not comment on the timing of this event. A clearer indication of when the heat event (and thus possible heat stress) occurred is important. Were both farms equally effected? Approximately how old were the animals and were major changes observed? this could be buried in the 'age' comparisons but likely has a major impact on interpretation.

3) Figure 1 is not mentioned in the text at all. Figure 2 is not discussed at all in the results section (only discussion). The quality of Figure 2 is very poor and there is no way to evaluate the usefulness of this figure or what it's telling us.

4) Authors mention treatment events in some BRD calves but not others. Were any effects of treatment considered (grouping as +/- treatment, rather than BRD as defined by study personnel)?

Reviewer #2: Thank you for the opportunity to review the manuscript titled “Differential gene expression in peripheral leukocytes of pre-weaned Holstein heifer calves with respiratory disease”. This manuscript performed a controlled time-course analysis of healthy and diseased heifer Holstein calves with NanoString nCounter methodology, further categorizing concurrent respiratory disease via semi-objective scoring assessment and transthoracic ultrasonography. I commend the authors for their novel approach and thoroughness regarding sampling, clinical assessment, and workflow. In its current state, the presented manuscript is well written and of high quality, and I recommend it for publication pending minor revisions and clarification. Below are comments which may provide further insights and improvements to the manuscript.

• Line 219-220: while the Benjamini-Yekutieli correction method for controlling type I error is appropriate, it may be argued that family-wise error correction methodology and false-discovery rates are too conservative due to the limited “independent” hypotheses being tested in nCounter datasets when compared to microarray or next-generation sequencing (DOI: 10.4137/CIN.S16343). This may be an analytical component as to why so few differences were found with these comparisons; however, this approach also provides a strength in demonstrating the differential expression of ALOX15.

• Lines 217-219 & 270-277: the authors should expand on normalization and analysis techniques. What type of nCounter analyzer was used (SPRINT, Pro?)? What was the maximum FOV of detection? How was codeset normalization performed (geometric mean of positive controls? Mean/median/max of negative control counts?)? How was differential gene expression evaluated (Fisher’s exact, Wilcoxon-Rank, etc.)? The aforementioned citation [29] does not provide detail regarding these elements of the project.

• Lines 228-231 & 278-280: I commend the authors for exploring possible effects between farms and rationalizing the lack of need for its use as a random effect in their testing parameters.

• Lines 382-387: I commend the authors on their discussion regarding the potential transient disease that may have occurred within this study; this speaks to the multifaceted nature, and frustration regarding research, of bovine respiratory disease. However, as stated by the authors, these cattle are of different age, breed, and systems, and it may be that the genes selected better represent a more infectious course of respiratory disease, especially of cattle placed into riskier environments (e.g., salebarns, feedlots, etc.). Could it be hypothesized that these cattle, even across two separate farms, were experiencing non-viral (ISG15, MX1, OAS2) or non-infectious (CATHL6, S100A8) course of disease, and that young Holsteins require their own, separate candidate biomarker identification research? Possibly, these distinct gene expression patterns (as detailed by the provided citations) and their associated mechanisms in association with BRD are specific to post-weaned beef systems.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2023 May 16;18(5):e0285876. doi: 10.1371/journal.pone.0285876.r002

Author response to Decision Letter 0


28 Apr 2023

Response to reviewers

Thank you for your insightful questions and comments. Your commentary has led to changes that we believe make the manuscript a stronger addition to the literature. Please find responses to specific questions below.

Reviewer #1: This manuscript analyzed a panel of 19 genes as potential biomarkers for prediction of BRD. Authors compared pre, onset and post gene expression from whole blood samples in preweaned heifer calves across time. While they did not identify any differences between BRD cases (regardless of diagnoses), they noted changes in gene expression over time.

The paper is clearly written and the study is easy to follow. Although it is largely negative data, the results are relevant for future studies aimed at identifying biomarkers of disease in very young calves.

A few comments:

1) Calves in the study received an intranasal vaccine. The vaccine was different and received at different time points. However, there is no discussion or consideration of the effects of the vaccine on gene expression. Because the authors determined early on that GE did not differ, this possibility was disregarded. I think some comparisons should be made to determine if there are effects of the vaccine and/or the different vaccines given at different ages.

AU: Thank you for bringing up the potential for the difference in vaccination protocols between farms impacting calf gene expression. Further analysis aimed at determining if there are gene expression effects of intranasal vaccination, differing by type of vaccine and timing of administration, was not performed for the following reasons.

Age matched healthy calves were compared between the farms, and no differential gene expression (DGE) was observed (lines 284-286). These comparisons consisted of samples from weeks 3, 5, 7, and 9 of calf life. Intranasal vaccination was done at four weeks of age on farm A, and at two weeks of age on farm B. Although our sampling did not span the timing around vaccination on farm B, no effects were observed in this analysis following vaccination on farm A. Additional text highlighting lack of effect of vaccination has been added on lines 285-286.

Unfortunately, due to the difference in timing of vaccine administration between farms, a comparison of calves at the post-vaccination time point, week 3 on farm B and week 5 on farm A, would require comparison of calves of different ages. We have found age, irrespective of farm, to be a major source of differential gene expression (lines 313-370), and, therefore, this analysis would be unrewarding for the purpose of identifying effects of vaccination. Additionally, all calves on both farms were vaccinated per farm protocols leaving no unvaccinated individuals to serve as a control group for an analysis of gene expression in age matched vaccinated vs. unvaccinated calves over time. Furthermore, both of the vaccine brands used by the farms are intranasal, modified live virus vaccines. Intranasal vaccination is known to produce a primarily local mucosal response. We are not aware of studies assessing systemic gene expression relative to mucosal vaccination. We recognize that there may have been a transient systemic inflammatory response to intranasal vaccination in the study calves, and this response may have been identifiable in a study utilizing transcriptomic techniques, but given our targeted gene set and conservative statistical approach effects of vaccination were not observed.

2) The authors indicate a major heat event during the course of the study, but do not comment on the timing of this event. A clearer indication of when the heat event (and thus possible heat stress) occurred is important. Were both farms equally effected? Approximately how old were the animals and were major changes observed? this could be buried in the 'age' comparisons but likely has a major impact on interpretation.

AU: We appreciate the request for clarification regarding the heat event that occurred during our sampling period resulting in hyperthermic but otherwise healthy calves. Farms A and B are located approximately 3 miles from each other and so were equally affected by local weather. There was pervasive heat in the region, outside of the thermoneutral zone, from late June through mid-July primarily corresponding with weeks 6-9 of life of the study calves. Major changes in the calves other than rectal temperature were not observed based on the parameters that we assessed. Additional text has been added to clarify the heat exposure across farms in lines 139-140.

3) Figure 1 is not mentioned in the text at all. Figure 2 is not discussed at all in the results section (only discussion). The quality of Figure 2 is very poor and there is no way to evaluate the usefulness of this figure or what it's telling us.

AU: Please see line 166 for the reference to Figure 1 and thank you for bringing your concerns to our attention regarding the image quality of Figure 2. There was debate among the authors about whether or not to include this figure in the manuscript. The data it represents is presented on lines 313-325, shown in Table 5, and discussed on lines 439-447. The decision was ultimately made to include the figure in order to provide a visual demonstration of differential gene expression relative to calf age over time and highlight the notable changes in expression magnitude of several of our genes of interest during the pre-weaning period. We also considered the possibility of only including a single volcano plot for the sake of increasing the size of the figure details; however, we concluded that a lone plot would not be informative and the series of six volcano plots is necessary to tell the whole story.

We are aware that the PDF version of Figure 2 is of poor quality. However, if it is downloaded as a TIF file it has much better resolution, and zooming in on the individual volcano plots allows thorough evaluation of the figure. We would prefer to leave Figure 2 in the paper given that the downloaded version is appropriately clear, digital publication will allow readers to zoom in on the plots, and there is utility in a visual demonstration when discussing the magnitude of DGE relative to calf age changing over time.

4) Authors mention treatment events in some BRD calves but not others. Were any effects of treatment considered (grouping as +/- treatment, rather than BRD as defined by study personnel)?

AU: Your point about consideration of treatment in conjunction with BRD in the DGE analysis is well taken. This study was designed with the intention to eliminate treatment as a variable prior to the onset of BRD in order to assess DGE in the absence of antimicrobial or anti-inflammatory drug effects. Although some of the study calves were treated between the ONSET and POST sampling time points, our methods allowed us to identify and sample calves at ONSET prior to any treatment.

An extensive explanation of the variability of clinical signs and ultrasonographic lesions as well as treatments occurring at the POST time point can be found on lines 252-270. Of the 19 BRD calves, the five that received treatment by farm personnel between ONSET and POST were assigned case definitions across all three BRD categories (CRS+/TUS-; CRS-/TUS+; CRS+/TUS+), and each individual had a different combination of CRS and TUS scores at POST. Consequently, an analysis of the POST time point comparing treated vs. untreated calves while taking into account the effects of disease would be unrewarding due to the exceedingly low sample size of treated calves that would be included in each comparison.

We agree that systemic treatment with a combination of antimicrobial and anti-inflammatory drugs may have an effect at some level on gene expression, and this was a point of discussion among the authors. As with intranasal vaccination, this response may have been identifiable in a study utilizing transcriptomic techniques. However, our finding of no DGE relative to disease state minimizes our concern for this effect substantially impacting the results of this study. Additionally, the PRE and ONSET sampling time points, which we consider to be the most meaningful aspects of the BRD related analysis, were not influenced by any treatment effects.

Reviewer #2: Thank you for the opportunity to review the manuscript titled “Differential gene expression in peripheral leukocytes of pre-weaned Holstein heifer calves with respiratory disease”. This manuscript performed a controlled time-course analysis of healthy and diseased heifer Holstein calves with NanoString nCounter methodology, further categorizing concurrent respiratory disease via semi-objective scoring assessment and transthoracic ultrasonography. I commend the authors for their novel approach and thoroughness regarding sampling, clinical assessment, and workflow. In its current state, the presented manuscript is well written and of high quality, and I recommend it for publication pending minor revisions and clarification. Below are comments which may provide further insights and improvements to the manuscript.

• Line 219-220: while the Benjamini-Yekutieli correction method for controlling type I error is appropriate, it may be argued that family-wise error correction methodology and false-discovery rates are too conservative due to the limited “independent” hypotheses being tested in nCounter datasets when compared to microarray or next-generation sequencing (DOI:10.4137/CIN.S16343). This may be an analytical component as to why so few differences were found with these comparisons; however, this approach also provides a strength in demonstrating the differential expression of ALOX15.

AU: We appreciate your thoughtful assessment of our statistical methods. We agree that although a less conservative approach may have allowed for additional differences in gene expression to be identified, the conservative approach afforded by the use of the B-Y p-value adjustment likely led to more trustworthy, defensible results such as those found with ALOX15 and reduced the potential noise produced by field conditions.

• Lines 217-219 & 270-277: the authors should expand on normalization and analysis techniques. What type of nCounter analyzer was used (SPRINT, Pro?)? What was the maximum FOV of detection? How was codeset normalization performed (geometric mean of positive controls? Mean/median/max of negative control counts?)? How was differential gene expression evaluated (Fisher’s exact, Wilcoxon-Rank, etc.)? The aforementioned citation [29] does not provide detail regarding these elements of the project.

AU: Thank you for your comments regarding the need for additional details about the gene expression normalization and analysis techniques. Test has been included within lines 213-218 and 280-281 to explain that 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. The geometric mean of the positive controls was used to compute the normalization factor. 2) CodeSet Content (HouseKeeping Normalization) used the geometric mean of our designated housekeepers to compute a normalization factor. Differential gene expression was evaluated within nSolver advanced analysis using a mixture negative binomial model and a Benjamini-Yekutieli p-value adjustment.

• Lines 228-231 & 278-280: I commend the authors for exploring possible effects between farms and rationalizing the lack of need for its use as a random effect in their testing parameters.

AU: Thank you. We appreciate the commendation.

• Lines 382-387: I commend the authors on their discussion regarding the potential transient disease that may have occurred within this study; this speaks to the multifaceted nature, and frustration regarding research, of bovine respiratory disease. However, as stated by the authors, these cattle are of different age, breed, and systems, and it may be that the genes selected better represent a more infectious course of respiratory disease, especially of cattle placed into riskier environments (e.g., salebarns, feedlots, etc.). Could it be hypothesized that these cattle, even across two separate farms, were experiencing non-viral (ISG15, MX1, OAS2) or non-infectious (CATHL6, S100A8) course of disease, and that young Holsteins require their own, separate candidate biomarker identification research? Possibly, these distinct gene expression patterns (as detailed by the provided citations) and their associated mechanisms in association with BRD are specific to post-weaned beef systems.

AU: Thank you for these comments. It seems that we are in agreement about the substantial differences between pre-weaned dairy calves and post-weaned beef cattle regarding candidate BRD biomarkers and the necessity for targeted research in young dairy calves. A combination of age, breed, and rearing system appears to have an impact on peripheral leukocyte gene expression in cattle, and caution is necessary in inferring findings from one population to another in the face of differences in these areas. As you speculated, the disease etiology, pathogens, and pathophysiology experienced by the animals may also impact gene expression. We look forward to continuing this area of study through a follow up transcriptomic study as well as specific BRD pathogen identification on these farms to provide additional detail about the disease processes calves are experiencing in these systems.

Attachment

Submitted filename: Response to Reviewers.docx

Decision Letter 1

Angel Abuelo

4 May 2023

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

PONE-D-23-08494R1

Dear Dr. McConnel,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Angel Abuelo, DVM, MRes, MSc, PhD, DABVP (Dairy), DECBHM

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: The authors have addressed all relevant points in their letter to the reviewers and additions within the revised manuscript. At this time, I recommend the revised manuscript be published in its current state.

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

Acceptance letter

Angel Abuelo

8 May 2023

PONE-D-23-08494R1

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

Dear Dr. McConnel:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Angel Abuelo

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

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

    (XLSX)

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

    (XLSX)

    S3 Table. Probe sequences.

    (XLSX)

    Attachment

    Submitted filename: Response to Reviewers.docx

    Data Availability Statement

    All relevant data are within the paper and its Supporting information files.


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