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. 2023 Oct 5:10:1272940.
doi: 10.3389/fvets.2023.1272940. eCollection 2023.

Characterizing the influence of various antimicrobials used for metaphylaxis against bovine respiratory disease on host transcriptome responses

Rebecca A Bigelow  1 , John T Richeson  1 , Molly McClurg  2 , Robert Valeris-Chacin  2 , Paul S Morley  2 , Jenna L Funk  2 , Matthew A Scott  2
Affiliations

Characterizing the influence of various antimicrobials used for metaphylaxis against bovine respiratory disease on host transcriptome responses

Rebecca A Bigelow et al. Front Vet Sci. .

Abstract

Currently, control against bovine respiratory disease (BRD) primarily consists of mass administration of an antimicrobial upon arrival to facility, termed "metaphylaxis." The objective of this study was to determine the influence of six different antimicrobials used as metaphylaxis on the whole blood host transcriptome in healthy steers upon and following arrival to the feedlot. One hundred and five steers were stratified by arrival body weight (BW = 247 ± 28 kg) and randomly and equally allocated to one of seven treatments: negative control (NC), ceftiofur (CEFT), enrofloxacin (ENRO), florfenicol (FLOR), oxytetracycline (OXYT), tildipirosin (TILD), or tulathromycin (TULA). On day 0, whole blood samples and BW were collected prior to a one-time administration of the assigned antimicrobial. Blood samples were collected again on days 3, 7, 14, 21, and 56. A subset of cattle (n = 6) per treatment group were selected randomly for RNA sequencing across all time points. Isolated RNA was sequenced (NovaSeq 6,000; ~35 M paired-end reads/sample), where sequenced reads were processed with ARS-UCD1.3 reference-guided assembly (HISAT2/StringTie2). Differential expression analysis comparing treatment groups to NC was performed with glmmSeq (FDR ≤ 0.05) and edgeR (FDR ≤ 0.1). Functional enrichment was performed with KOBAS-i (FDR ≤ 0.05). When compared only to NC, unique differentially expressed genes (DEGs) found within both edgeR and glmmSeq were identified for CEFT (n = 526), ENRO (n = 340), FLOR (n = 56), OXYT (n = 111), TILD (n = 3,001), and TULA (n = 87). At day 3, CEFT, TILD, and OXYT shared multiple functional enrichment pathways related to T-cell receptor signaling and FcεRI-mediated NF-kappa beta (kB) activation. On day 7, Class I major histocompatibility complex (MHC)-mediated antigen presentation pathways were enriched in ENRO and CEFT groups, and CEFT and FLOR had DEGs that affected IL-17 signaling pathways. There were no shared pathways or Gene Ontology (GO) terms among treatments at day 14, but TULA had 19 pathways and eight GO terms enriched related to NF- κβ activation, and interleukin/interferon signaling. Pathways related to cytokine signaling were enriched by TILD on day 21. Our research demonstrates immunomodulation and potential secondary therapeutic mechanisms induced by antimicrobials commonly used for metaphylaxis, providing insight into the beneficial anti-inflammatory properties antimicrobials possess.

Keywords: RNA-Seq; T-cell; antimicrobial; bovine respiratory disease; cattle; immune; metaphylaxis; transcriptome.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Heatmap of nine clusters of differentially expressed genes identified at all timepoints across all 252 samples. Expression patterns are shown using hierarchical clustering of genes in the rows and samples in the columns. Timepoint and treatment group are shown above the heat map under the hierarchical tree to identify the sample in each column. Gene-wise variation was standardized using z-score statistics. Color scale (yellow-to-purple) represents gene expression levels per sample; yellow and purple colors indicate increased expression and decreased expression, respectively. Note that gene hierarchical clustering of gene expression profiles segregates timepoints and treatment groups.
Figure 2
Figure 2
Screeplot of principle components 1:12 explaining 47.5% of the variance within the dataset. The Elbow and Horn’s parallel analysis methods were used to determine the optimum number of components to retain.
Figure 3
Figure 3
Spearman’s correlation coefficients associated with metadata components for the first 12 PCs. Each animal’s average daily gain (ADG) over the course of the trial, time, treatment group (Group), and individual animal tag number (ID) were aspects that possessed significant association with one or more PCs.
Figure 4
Figure 4
Biplot of PC1:PC2 of time across all 252 samples. The ellipses are colored by timepoint as indicated by the legend. Clustering by each timepoint can be seen, indicating time played an important role in explaining the variance within this dataset.
Figure 5
Figure 5
Pairwise intersections between the number of DEGs found for every treatment group at each timepoint. Color scale (green-to-blue) shows the number of DEGs in common between treatment groups; green and blue represents decreased number of overlapping DEGs and increased number of overlapping DEGs, respectively.
Figure 6
Figure 6
FcεRI-mediated NF- κβ activation gene expression for CEFT, OXYT, and TILD. (A) Trended normalized averages calculated from log10 transformed gene expression over time of RSP27A for CEFT. (B) Trended normalized averages calculated from log10 transformed gene expression over time of RSP27A for OXYT. (C) Trended normalized averages calculated from log10 transformed gene expression over time of TAB2 for CEFT. (D) Trended normalized averages calculated from log10 transformed gene expression over time of TAB2 for TILD. The dots represent the average gene expression level at that timepoint, and the bars represent the standard error of gene expression found at each timepoint in each group. NC is represented by the black lines.
Figure 7
Figure 7
T-cell receptor signaling gene expression for CEFT, OXYT, and TILD. (A) Trended normalized averages calculated from log10 transformed gene expression over time of ITK for CEFT. (B) Trended normalized averages calculated from log10 transformed gene expression over time of ITK for OXYT. (C) Trended normalized averages calculated from log10 transformed gene expression over time of TAB2 for CEFT. (D) Trended normalized averages calculated from log10 transformed gene expression over time of TAB2 for TILD. The dots represent the average gene expression level at that timepoint, and the bars represent the standard error of gene expression found at each timepoint in each group. NC is represented by the black lines.
Figure 8
Figure 8
Th17 cell differentiation gene expression for OXYT and TILD. (A) Trended normalized averages calculated from log10 transformed gene expression over time of CD3G for OXYT. (B) Trended normalized averages calculated from log10 transformed gene expression over time of NFATC2 for OXYT. (C) Trended normalized averages calculated from log10 transformed gene expression over time of JAK1 for TILD. (D) Trended normalized averages calculated from log10 transformed gene expression over time of NFATC2 for TILD. The dots represent the average gene expression level at that timepoint, and the bars represent the standard error of gene expression found at each timepoint in each group. NC is represented by the black lines.
Figure 9
Figure 9
Class I MHC mediated antigen processing and presentation gene expression for CEFT and ENRO. (A) Trended normalized averages calculated from log10 transformed gene expression over time of SKP1 for CEFT. (B) Trended normalized averages calculated from log10 transformed gene expression over time of HERC3 for ENRO. (C) Trended normalized averages calculated from log10 transformed gene expression over time of KLHL42 for ENRO. (D) Trended normalized averages calculated from log10 transformed gene expression over time of RNF6 for ENRO. The dots represent the average gene expression level at that timepoint, and the bars represent the standard error of gene expression found at each timepoint in each group. NC is represented by the black lines.
Figure 10
Figure 10
IL-17 signaling gene expression for CEFT and FLOR. (A) Trended normalized averages calculated from log10 transformed gene expression over time of CXCL8 for CEFT. (B) Trended normalized averages calculated from log10 transformed gene expression over time of TNFAIP3 for CEFT. (C) Trended normalized averages calculated from log10 transformed gene expression over time of CEBPB for FLOR. The dots represent the average gene expression level at that timepoint, and the bars represent the standard error of gene expression found at each timepoint in each group. NC is represented by the black lines.
Figure 11
Figure 11
Gene expression downregulated by TULA at T4, day 14, related to NF – κβ activation, macrophage activation, and cytokine production. (A) Trended normalized averages calculated from log10 transformed gene expression over time of CD48 for TULA. (B) T Trended normalized averages calculated from log10 transformed gene expression over time of IFNG for TULA. (C) Trended normalized averages calculated from log10 transformed gene expression over time of RPS27A for TULA. The dots represent the average gene expression level at that timepoint, and the bars represent the standard error of gene expression found at each timepoint in each group. NC is represented by the black lines.
Figure 12
Figure 12
Gene expression downregulated by TILD at T5, day 21, related to cytokine signaling, Toll-like receptor/T-cell receptor cascades, and FcεRI-mediated NF- κβ activation. (A) Trended normalized averages calculated from log10 transformed gene expression over time of PIK3CA for TILD. (B) Trended normalized averages calculated from log10 transformed gene expression over time of RAP1B for TILD. (C) Trended normalized averages calculated from log10 transformed gene expression over time of SUMO1 for TILD. (D) Trended normalized averages calculated from log10 transformed gene expression over time of WWP1 for TILD. The dots represent the average gene expression level at that timepoint, and the bars represent the standard error of gene expression found at each timepoint in each group. NC is represented by the black lines.
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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was internally funded in part by Texas A&M University, School of Veterinary Medicine and Biomedical Sciences and West Texas A&M University, Department of Agricultural Sciences. Additionally, this study was externally funded in part by the Texas Cattle Feeders Association (TCFA).