Severity of bovine tuberculosis is associated with innate immune-biased transcriptional signatures of whole blood in early weeks after experimental Mycobacterium bovis infection.

Mycobacterium bovis, the causative agent of bovine tuberculosis, is a pathogen that impacts both animal and human health. Consequently, there is a need to improve understanding of disease dynamics, identification of infected animals, and characterization of the basis of immune protection. This study assessed the transcriptional changes occurring in cattle during the early weeks following a M. bovis infection. RNA-seq analysis of whole blood-cell transcriptomes revealed two distinct transcriptional clusters of infected cattle at both 4- and 10-weeks post-infection that correlated with disease severity. Cattle exhibiting more severe disease were transcriptionally divergent from uninfected animals. At 4-weeks post-infection, 25 genes had commonly increased expression in infected cattle compared to uninfected cattle regardless of disease severity. Ten weeks post-infection, differential gene expression was only observed when severely-affected cattle were compared to uninfected cattle. This indicates a transcriptional divergence based on clinical status following infection. In cattle with more severe disease, biological processes and cell type enrichment analyses revealed overrepresentation of innate immune-related processes and cell types in infected animals. Collectively, our findings demonstrate two distinct transcriptional profiles occur in cattle following M. bovis infection, which correlate to clinical status.

Introduction

Mycobacterium bovis is a member of the Mycobacterium tuberculosis complex that preferentially infects cattle (Bos taurus) and is the primary causative agent of bovine tuberculosis (bTB). Though most cases of human tuberculosis (hTB) are attributed to Mycobacterium tuberculosis infection, M. bovis can also cause infection in humans, known as zoonotic TB. Eradication programs in the United States, primarily based on test and slaughter, have generally had great success in controlling bTB, but total eradication has not been achieved and remains unlikely [1, 2]. A variety of factors including the movement of infected livestock [3], the presence of wildlife reservoirs [1, 3], the cost of managing infected cattle [1], and the lack of effective vaccines [4, 5] have contributed to the continued disease presence in both livestock and wildlife species. In 1995, it was estimated that worldwide, >50 million cattle were infected with M. bovis, resulting in a financial loss of $3 billion USD annually [6]. Despite efforts to reduce bTB incidence worldwide, disease caused by M. bovis remains a significant economic burden and has a major impact on animal and human health. Improving our understanding of disease dynamics, identifying infected animals, and characterizing the basis of immune protection are key to reducing the incidence and severity of disease caused by M. bovis.

Transcriptome analyses can provide insights that help us understand complex diseases including the underlying mechanisms of pathogenesis, and the genes responsible for protective immune responses. Genes associated with specific diseases, known as biomarkers, once identified, can be used in disease diagnosis, determination of clinical status, and assessment of disease progression. Previous bTB studies employing microarrays or RNA sequencing (RNA-seq) have identified over 3,000 differentially expressed genes in cattle naturally-infected with M. bovis compared to uninfected cattle, and pathway analyses revealed enrichment for genes involved with immune function [7-11].

However, analysis of transcriptional dynamics of bTB at early post-infection timepoints is lacking. In cattle, pulmonary lesions associated with experimental M. bovis infection via aerosol have been noted as early as 15 days after infection [12] and robust interferon-gamma (IFN-γ) responses to both complex and specific M. bovis antigens are seen 3 to 4 weeks after infection [13-15]. Studies using a non-human primate model of human M. tuberculosis infection have clearly demonstrated that early post-exposure interactions between pathogen and host are critical in determining the eventual outcome of infection [16-18].

To gain a deeper knowledge into the transcriptional dynamics occurring in the early weeks after M. bovis infection, we performed differential gene expression analysis of RNA-seq data from whole blood leukocyte populations collected with a cell filter-based system in cattle experimentally-infected with aerosolized M. bovis. In this manuscript, we present gene expression profiles, pathway and cell type enrichment analysis, and correlate our transcriptional findings to lesion severity and clinical disease presentation.

Materials and methods

Animals

Holstein steers (n = 13) of 2–3 months of age were obtained from Kline Dairy (State Center, Iowa), a source with no history of bTB. Cattle were housed outdoors for ~1.5 months, then transitioned to a biosafety level 3 (BSL-3) indoor facility where they were randomly assigned to 2 rooms corresponding to treatment: infected (n = 8) or uninfected (n = 5). After ~2 additional months of acclimation to BSL-3 housing, cattle in the infected group (~5–6 months of age) were experimentally infected with M. bovis. Cattle were monitored daily for development of clinical signs (coughing, labored breathing, fever).

Calves were humanely euthanized by intravenous administration of sodium pentobarbital at 6 (n = 1), 10 (n = 2), and 12 months (n = 3) after infection. Animals displaying all three clinical signs (coughing, labored breathing, and a body temperature >39.3°C) (n = 2) concurrently, were euthanized at 2.5–3 months post-infection, within 24 hours of meeting the criteria. No animals died before meeting the criteria for humane endpoint euthanasia.

All experimental animal procedures were conducted in accordance with recommendations in the Care and Use of Laboratory Cattle of the National Institutes of Health and the Guide for the Care and Use of Agricultural Cattle in Research and Teaching [19, 20]. All procedures performed were approved by the USDA-National Animal Disease Center Animal Care and Use Committee, under prococol number ARS-2015-451.

Inoculum preparation and aerosol challenge

Mycobacterium bovis strain 10–7428 was used in this experiment [21]. This field strain was of low passage (less than 3 passages) and has been previously shown to be virulent in the calf aerosol model [22]. Inoculum was prepared using standard techniques [23] in Middlebrook’s 7H9 liquid media (Becton Dickinson, Franklin Lakes, NJ, USA) supplemented with 10% oleic acid-albumin-dextrose complex (OADC; Difco, Detroit, MI, USA) plus 0.05% Tween 80 (Sigma Chemical Co., St. Louis, MO, USA). Mid log-phase growth bacilli were pelleted by centrifugation at 750 x g, washed twice with phosphate buffered saline (PBS) (0.01 M, pH 7.2) and stored at -80°C until used. Frozen stock was warmed to room temperature and diluted to the appropriate cell density in 2 ml of 7H9 liquid media. Bacilli were enumerated by serial dilution plate counting on Middlebrook’s 7H11 selective media (Becton Dickinson). A single dose was determined to be 1.77 X 106 CFU per calf.

Aerosol infection of calves with virulent M. bovis has been described in detail previously [22, 24, 25]. Briefly, 8 Holstein steer calves (5–6 months of age) were infected with a single dose of virulent M. bovis strain 10–7428 by nebulization of inoculum into a mask (Equine AeroMask®, Trudell Medical International, London, ON, Canada) covering the nostrils and mouth.

Post-mortem examination

Following euthanasia, tissues were examined for gross lesions and processed for microscopic analysis as described previously [22]. Tissues collected included: lung, liver, and, lymph nodes (medial retropharyngeal, mediastinal, tracheobronchial, hepatic, and mesenteric). Grossly, lymph nodes and lung lobes were examined on cut-surface in 0.5–1 cm sections. Lungs and lymph nodes were scored using a semiquantitative gross lesion scoring system [26]. Lung lobes (left cranial, left caudal, right cranial, right caudal, middle and accessory) were assessed individually based on the following scoring system: 0, no visible lesions; 1, no external gross lesions but lesions seen on slicing; 2, < 5 gross lesions of < 10 mm in diameter; 3, > 5 gross lesions of < 10 mm in diameter; 4, > 1 distinct gross lesion of > 10 mm in diameter; and 5, gross coalescing lesions. Cumulative mean scores were then calculated for each entire lung. Lung-associated lymph nodes (tracheobronchial and mediastinal) were weighed and scored. Scoring of lymph node pathology was based on the following system: 0, no necrosis or visible lesions; 1, small focus (1–2 mm diameter); 2, several small foci; 3, extensive necrosis.

Tissues collected for microscopic analysis were fixed in 10% neutral buffered formalin (3.7% formaldehyde) for ~24 hours, transferred to 70% ethanol and processed routinely. Formalin-fixed, paraffin embedded sections (4 μm) were stained with hematoxylin and eosin (H&E). Adjacent sections from samples containing lesions consistent with tuberculosis were stained by the Ziehl-Neelsen technique for identification of acid-fast bacteria (AFB).

The same tissues collected for microscopic analysis were collected for mycobacterial isolation as described previously [27] using Middlebrook 7H11 selective agar plates (Becton Dickinson) incubated for 8 weeks at 37°C. Colonies were confirmed as M. bovis using IS6110 real time PCR as described previously [28].

Leukocyte RNA sample collection

Total leukocyte populations were isolated from whole blood using the LeukoLOCKTM Fractionation & Stabilization Kit (Ambion, Life Technologies, Carlsbad, CA, USA) according to manufacturer’s instructions (Life Technologies). Briefly, 9 to 10 mL of whole blood were collected from each animal via jugular venipuncture into a K2 EDTA vacutainer tube (Becton, Dickinson and Company, Franklin Lakes, NJ, USA). Blood was immediately filtered through a LeukoLOCKTM Filter apparatus to retain total leukocyte populations while excluding red blood cells. Filters were then rinsed with 3 mL of PBS and flushed with 3 mL of RNAlaterTM (Ambion) to stabilize cells on the filter. Filters containing leukocytes were left saturated in RNAlaterTM and stored at -80°C up to 25 months before isolating RNA.

RNA isolation

Isolation of total RNA directly from stored leukocyte filters was performed according to manufacturer’s instructions for the alternative protocol for extraction of RNA from cells captured on LeukoLOCKTM Filters using TRI Reagent® (Life Technologies). Briefly, LeukoLOCKTM Filters were thawed to room temperature, flushed using an empty retracted syringe to expel RNAlaterTM, with 4 mL TRI Reagent® Solution (Ambion) to lyse cells, collecting the lysate. As a phase separation reagent, 800 μL bromo-3-chloro-propane (BCP) (Acros Organics, Thermo Fisher Scientific, Waltham, MA, USA) was added to the expelled lysate. The lysate/BCP mixture was shaken vigorously for 30 sec, incubated at room temperature for 5 min, and centrifuged at 2,000 x g for 10 min to allow separation of the aqueous and organic phases. The upper aqueous phase containing the RNA was collected, and 0.5 volumes of nuclease-free water and 1.25 volumes ACS reagent grade 100% ethanol (Sigma-Aldrich, St. Louis, MO, USA) were added to the extracted aqueous phase and mixed thoroughly to recover total RNA. The mixture was then filtered through a silica Filter Cartridge (Ambion) using brief centrifugations, and flow-through was discarded. Filters containing RNA were next washed once with 750 μL Wash 1 (a 3:7 ratio of Denaturing Lysis Solution (Ambion) and 100% ethanol, respectively) and twice with 750 μL Wash 2 (an 80:19:1 ratio of 100% ethanol, nuclease-free water, and 5 M NaCl (Ambion), respectively), discarding flow-through after each brief centrifugation. As a stabilization reagent, 250 μL 0.1 mM ethylenediaminetetraacetic acid (EDTA) (Ambion) warmed to 80°C was added to the center of the filter and incubated at room temperature for 1 min. The filter was centrifuged at max speed for 1 min, and the flow-through containing the eluted RNA was retained.

The RNA elution was treated with DNase from the DNA-freeTM Kit (Ambion) according to manufacturer’s instructions (Life Technologies). To deplete DNA, 0.1 volumes 10X DNase I Buffer and 2 μL rDNase were added to the RNA elution and incubated at 37°C for 30 min. Next, 0.1 volumes resuspended DNase Inactivation Reagent were added and incubated at room temperature for 2 min. Tubes were centrifuged at 10,000 x g for 1.5 min, and the supernatant containing the DNase-treated RNA was collected. Samples were stored at -80°C.

RNA quality assessment and quantification

RNA quality was assessed via the Agilent RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, CA, USA) run on an Agilent 2100 Bioanalyzer System (Agilent Technologies) according to manufacturer’s instructions (Agilent Technologies) and assessed for purity and quantity via NanoDropTM 2000 Spectrophotometer (Thermo Fisher Scientific).

RNA-seq library preparation, sequencing, and primary analysis

Isolated total RNA was submitted to the Iowa State University DNA Facility for library preparation, sequencing, and primary analysis. Sample libraries were prepared using the Universal Plus mRNA-Seq kit (NuGEN Technologies, San Carlos, CA, USA) according to the manufacturer’s instructions (NuGEN Technologies) and were sequenced as 2x100 paired end, stranded mRNA-seq libraries using an Illumina HiSeq 3000 (Illumina, San Diego, CA, USA). Sequencing lanes and multiplexing barcodes were randomly assigned to samples to avoid confounding nuisance factors (sequencing lane and barcode assignment) with treatment variables (individual animal, infection group, collection timepoint). Image analysis and sequence base calling were performed, and samples were demultiplexed to obtain processed raw data in fastq file format for both forward and reverse strands. Samples were sequenced to an average raw depth of 25,259,821± 6,231,306 SD, an adequate sequencing depth based on previous findings [8, 29–32].

RNA-seq pipeline

Read quality was assessed using FastQC (version 0.11.5) [33]. Following quality analysis, reads were trimmed to remove low quality and adapter sequences using Trimmomatic (version 0.36) [34]. FastQC analysis of trimmed-read quality analysis indicated that further read filtering and trimming was not required. After trimming, an average of 20.5 M ± 5.5 M SD paired reads remained in each sample. Paired-end reads were aligned to the Bos taurus reference genome (B. taurus UMD 3.1.1) [35] obtained from Ensembl [36] using the STAR RNA-seq aligner package (version 2.5.3a) [37]. On average, 92.47% of paired-end reads were uniquely aligned, and 3.96% of read pairs mapped to multiple loci. The number of aligned reads mapping to each gene annotated in the B. taurus genome was determined for each library using featureCounts from the Subread package (version 3.22.0) [38]. On average, 82.9% of aligned read pairs could be assigned to an annotation feature, resulting in an average of 18.0 M ± 4.6 M SD assigned read pairs per sample.

Differential gene expression analysis

Analysis of differential gene expression (DGE) was performed using the gene-wise count tables and the edgeR Bioconductor package (version 3.24.3) [39] based on a negative binomial model. The edgeR package was used to: 1) filter out genes with under 1 count per million (cpm) reads in a minimum threshold of samples; 2) obtain a normalization factor (the relative sequencing depth of each library using the trimmed mean of M-values (TMM) method of normalization) [40]; 3) create a design matrix using group-means parameterization to obtain a coefficient for expression to describe each treatment group; 4) estimate the dispersion parameter, ϕ, used to estimate variances by squeezing gene-specific tagwise dispersions towards trended dispersions using empirical Bayes estimations under the Cox-Reid adjusted profile likelihood method [41]; 5) fit a generalized linear model (GLM) [41] to the non-normally distributed data based on the design matrix and ϕ; 6) test for differential expression using the likelihood ratio test (LRT) [41], specifying contrasts for the comparisons of interest; and 7) control false discovery rate (FDR) using the Benjamini-Hochberg method [42] to account for multiple testing error.

Comparisons were made between all infected, clusters of infected, and uninfected cattle at single timepoints. Genes with an FDR value (adjusted p-value) of less than 0.05 were considered to be differentially expressed.

Biological process enrichment analysis

Lists of genes with increased (log2FC > 0 and FDR < 0.05) or decreased (log2FC < 0 and FDR < 0.05) expression obtained through differential gene expression analysis were individually analyzed using ClueGO (version 2.5.5) in Cytoscape (Cytoscape Consortium) [43, 44]. Enriched GO biological processes within the B. taurus gene database were detected with the following parameters: medium network specificity, GO term fusion enabled, GO tree interval between 5 and 20, a minimum of 2 genes and 3% of genes, a kappa score of 0.4, and right-sided hypergeometric testing for enrichment. The list of reference genes was further reduced by only considering genes analyzed during differential gene expression analysis after filtering out of low count genes. Significance values were calculated using Benjamini-Hochberg correction. Corrected p-values < 0.05 were considered significant. Enriched GO immune system processes were detected using previous parameters with the following modifications: GO tree interval between 2 and 20, a minimum of 1.5% of genes, and a p-value < 0.1.

Cell type enrichment analysis

Cell type enrichment analysis was completed using Cten [45] by inputting lists of genes with increased (log2FC > 0 and FDR < 0.05) and decreased (log2FC < 0 and FDR < 0.05) expression obtained through differential gene expression analysis. Genes were compared to mouse gene symbols in order to obtain lists of enriched cell types. Significance values were obtained from -log10 Benjamini-Hochberg adjusted p-values. Enrichment scores greater than 2 were considered significant. A heatmap of enrichment scores of cell types showing significant enrichment in at least one comparison was created using GraphPad Prism 8.3.0 for Mac OSX, (GraphPad Software, San Diego, CA, USA).

Data availability

Sequence fastq files used for pipeline analysis of RNA-seq data can be found at SRA database (accession number PRJNA600004). RNA-seq pipeline bash and R scripts as well as output and input data for DGE analyses can be obtained at https://github.com/jwiarda/EarlyTB_RNAseq.

Results

Study overview

The purpose of this study was to assess transcriptional changes during the early stages of M. bovis infection in cattle and how these corresponded to disease outcome, as indicated by clinical signs of disease and post-mortem pathology (Fig 1). RNA from whole blood of experimenally-infected cattle and age-matched uninfected cohorts was collected prior to infection (0 wpi), and at 4 and 10 weeks-post infection (wpi) using a filter-based system [46, 47]. Concurrently, clinical signs of disease were monitored, and cattle were euthanized as disease became evident. Upon euthanasia, post-mortem analysis was performed to assess severity of disease pathology.

Fig 1

Overview of experimental timeline and workflow.

Cattle were infected with M. bovis (n = 8) or left uninfected (n = 5). Following infection, whole blood was collected, clinical signs of disease were monitored, and pathology grading was assessed at necropsies. Whole blood was collected, and RNA was stored on filters from M. bovis-infected (n = 8) and uninfected (n = 5) cattle at 0 wpi, 4 wpi, and 10 wpi. RNA was later isolated and processed to create cDNA libraries that were sequenced, and RNA-seq data were analyzed for differential gene expression, biological process enrichment, and cell type enrichment. Clinical signs of disease were assessed throughout the study, and animals were euthanized as signs of disease became evident. Immediately following euthanasia, necropsies were performed to assess severity of post-mortem pathology.

RNA was later isolated from stored filters using methods shown to yield high quality RNA for high-throughput analyses [48, 49] and analyzed for integrity, purity, and quantity. All samples displayed RINs ≥ 7.9 (mean RIN score of 9.09 ± 0.49 SD) and 260/280 ratios ranging from 1.98 to 2.11 (mean absorbance ratio of 2.04 ± 0.03 SD), characteristic of high RNA integrity and purity. Total RNA concentrations ranged between 65.05 and 177.35 ng/μL (mean concentration of 108.41 ± 29.74 ng/μL SD), sufficient for downstream sequencing and analysis. High-quality RNA was used to create cDNA libraries, perform RNA-seq, and analyze data for differential gene expression, biological process enrichment, and cell type enrichment.

Transcriptionally-distinct clusters following infection

A multidimensional scaling (MDS) plot was used to visualize the transcriptional relationship between samples and observe differences between samples based on infection status and timepoint post-infection (Fig 2A). Gene counts from each timepoint were also analyzed to obtain lists of differentially expressed genes (FDR < 0.05) between infected and uninfected cattle. Of the 14,279 analyzed genes, a total of 1 (0.01%), 1060 (7.42%), and 162 (1.13%) genes were differentially expressed between infected and uninfected cattle at 0 wpi, 4 wpi, and 10 wpi, respectively (Fig 2B–2D and S1 File).

Fig 2

Differential gene expression observed between M. bovis-infected and uninfected cattle revealed two transcriptional clusters of infected animals.

Whole blood from M. bovis experimentally infected (n = 8) and uninfected (n = 5) cattle at 0 wpi, 4 wpi, and 10 wpi was collected and analyzed for differential gene expression. (A) MDS plot of infected and uninfected samples at 0 wpi, 4 wpi, and 10 wpi based on the top 500 genes with highest standard deviations between treatment groups. Each point represents an individual sample, and smaller distances between points represent greater similarities. (B-D) Gene expression in infected compared to uninfected cattle at 0 wpi (B), 4 wpi (C), and 10 wpi (D). Positive logFC indicates genes with increased expression in infected samples, while negative logFC indicates genes with decreased expression in infected samples. Each point represents an individual gene. Red points indicate differential gene expression reaching statistical significance (FDR < 0.05); orange points correspond to genes with zero counts in all samples of one treatment group. Black points indicate genes that were not differentially expressed and did not have all-zero counts in one treatment group. (E-F) Hierarchical clustering of samples based on 1,060 genes at 4 wpi (E) and 162 genes at 10 wpi (F) differentially expressed (FDR < 0.05) between infected and uninfected cattle. (G-I) MDS plots of infected animals only at 0 wpi (G), 4 wpi (H), and 10 wpi (I) based on the top 500 genes with highest standard deviations. Each point represents an individual sample, and smaller distances between points represent greater similarities. Samples from the 2 infected transcriptional clusters are denoted by green or blue text, while the sample clustering differently at each post-infection timepoint is denoted by orange text. n = 3 green (clustering more closely with uninfected samples), n = 4 blue (clustering more distantly from uninfected samples), and n = 1 sample that did not consistently cluster (Inf1).

Clustering of samples based on differentially expressed genes revealed the existence of two distinct groups of infected cattle, observed at both 4 wpi and 10 wpi (Fig 2E and 2F). This clustering was not present at 0 wpi, suggesting that these changes in gene expression were driven by infection (Fig 2G–2I). While one cluster shared more transcriptional similarities with uninfected samples, the other appeared more distantly related to the uninfected samples. Interestingly, samples from one animal, Inf1, clustered to the more distantly related cluster at 4 wpi but with the more closely related cluster at 10 wpi. All other samples corresponding to one animal were found within the same cluster at both timepoints.

Correlation between disease severity and transcriptional clustering

We evaluated whether the divergence in gene expression profiles of infected animals correlated to clinical presentation and/or disease-associated pathology by assessing disease severity via clinical and post-mortem parameters. Of the 8 infected cattle, 4 developed clinical signs characterized by intermittent coughing (Table 1). Additionally, 2 of these calves became febrile (> 39.3°C) and clinical signs progressed to labored breathing. These animals were then humanely euthanized at approximately 2.5–3 months post-infection. The 6 remaining calves were euthanized and examined at approximately 6 months (n = 1), 10 months (n = 2) and 12 months (n = 3) post-infection. Interestingly, the 4 animals exhibiting clinical signs were the same animals clustering more distantly from uninfected cohorts (Fig 2E and 2F).

Table 1

Clinical signs of cattle inoculated via aerosol with 1.77 X 106 CFU of M. bovis strain 10–7428.

	Animal Number
Clinical Signs	Inf9	Inf8	Inf6	Inf5	Inf4	Inf3	Inf2	Inf1
Coughing	Yes	Yes	No	Yes	Yes	No	No	No
Labored breathing	Yes	Yes	No	No	No	No	No	No
Febrile	Yes	Yes	No	No	No	No	No	No
Time of Necropsy (PID)	77	78	165	294	301	355	361	370

PID: post infection day.

Post-mortem, gross and microscopic lesions (caseonecrotic granulomas with AFB) of variable severity were present in pulmonary lymph nodes (mediastinal and tracheobronchial) in all lung lobes from all experimentally-infected calves (Fig 3A and 3B and Table 2). Extrapulmonary lesions were also observed in the liver and/or kidneys in 3 of 8 calves (Fig 3C and Table 2). In all infected animals, viable M. bovis was recovered from all pulmonary lymph nodes, all lung lobes, and extrathoracic lesions. Although all calves had tuberculous lesions, based on lesion scores and pulmonary lymph node weights, cattle could be grouped into 2 categories: severely affected and moderately affected. Severely affected cattle had higher lymph node weights and lesion scores than did those classified as moderately affected (Fig 3D–3G and S1 Table). As with clinical presentation, differences in lesion severity correlated to the distinct transcriptional clustering observed. The severely affected group contained all 4 cattle that developed clinical signs and extrathoracic lesions. Collectively, these observations show a correlation between a transcriptional divergence from uninfected animals and disease severity.

Fig 3

Cattle experimentally infected with M. bovis develop moderate or severe disease.

(A-C) Gross pathology of M. bovis-infected cattle. Lung from moderately-affected (A) or severely-affected (B) calf following experimental infection. Kidney with tuberculous lesions from extrapulmonary dissemination in a severely-affected calf (C). (D-E) Weight of tracheobronchial (D) and mediastinal (E) lymph nodes from moderately-affected (n = 3) vs. severely-affected (n = 4) cattle. Black bars represent standard error of the mean. (F-G) Disease pathology scores of respiratory-associated lymph nodes (F) and lung lobes (G) in moderately- (n = 3) vs. severely-affected (n = 4) cattle. Black bars represent standard error of the mean. LN = lymph node; TBLN = tracheobronchial lymph node; MSLN = mediastinal lymph node.

Table 2

Lesion scores and lymph node weights from cattle inoculated via aerosol with 1.77 X 106 CFU M. bovis strain 10–7428.

Group Assignment	Severe	Severe	Severe	Severe	Moderate	Moderate	Moderate	Undetermined
Tissue	Inf9	Inf8	Inf5	Inf4	Inf6	Inf3	Inf2	Inf1
Tracheobronchial LN	3	3	3	3	1	3	3	3
Mediastinal LN	3	3	3	3	1	3	3	3
Right Cranial Lobe	5	5	5	5	3	4	3	4
Right Caudal Lobe	5	5	5	5	3	4	4	4
Left Cranial Lobe	5	5	5	5	3	4	4	4
Left Caudal Lobe	5	5	5	5	3	4	4	4
Accessory Lobe	5	5	5	5	2	4	3	4
Total LN Score	6	6	6	6	2	6	6	6
Total Lung Score	25	25	25	25	14	20	18	20
Total Lesion Score	31	31	31	31	16	26	24	26
Mediastinal LN wt (g)	360.5	170.8	266.1	140.4	18.2	157.2	43.5	123.0
Tracheobronchial LN wt (g)	90.65	55.0	57.2	32.4	11.4	48.4	20.2	58.1
Extrapulmonary lesions	Yes	Yes	No	Yes	No	No	No	No

LN = lymph node.

Transcriptional divergence following infection

Additional differential gene expression analysis was conducted at each timepoint to further assess differences between the two distinct clusters of infected cattle. Samples from animal Inf1 were excluded from all further cluster analysis because of the unmatched clustering observed for this animal at 4 wpi compared to 10 wpi. Both moderately- and severely-affected cattle were compared to uninfected cohorts to assess differential gene expression from a total of 14,525 genes (S2 File). When compared to uninfected controls, moderately-affected cattle showed only 35 (0.24%) differentially-expressed genes at 4 wpi, while no differential gene expression was observed at 0 wpi or 10 wpi (Fig 4A–4C and S2 File). Conversely, severely-affected cattle showed much greater differential gene expression, with a total of 8 (0.06%), 2,488 (17.13%) and 2,015 (13.87%) genes being differentially expressed at 0 wpi, 4 wpi, and 10 wpi, respectively, when compared to uninfected controls (Fig 4D–4F and S2 File). When compared to each other, moderately- and severely-affected cattle demonstrated 3 (0.02%), 604 (4.16%), and 916 (6.31%) genes differentially expressed genes at 0 wpi, 4 wpi, and 10 wpi, respectively (Fig 4G–4I and S2 File). Altogether, the data suggests that of the three groups (uninfected, moderately-, and severely-affected cattle), severely-affected cattle showed the most divergent transcriptional profile at 4 and 10 wpi. Additionally, direct comparison of moderately- and severely-affected cattle showed that a greater number of differentially-expressed genes was observed at 10 wpi compared to 4 wpi, suggesting that the 2 groups became increasingly divergent later in infection.

Fig 4

Clusters of infected cattle showed differential gene expression compared to uninfected cohorts and diverge from each other over time.

Whole blood from severely-affected (n = 4), moderately-affected (n = 3), and uninfected (n = 5) cattle at 0 wpi, 4 wpi, and 10 wpi was collected and analyzed for differential gene expression. Gene expression in moderately-affected compared to uninfected cattle (A-C) at 0 wpi (A), 4 wpi (B), and 10 wpi (C); in severely-affected compared to uninfected cattle (D-F) at 0 wpi (D), 4 wpi (E), and 10 wpi (F); and in moderately-affected compared to severely-affected cattle (G-I) at 0 wpi (G), 4 wpi (H), and 10 wpi (I). Positive logFC indicates greater expression in infected samples (A-F) or moderately-affected samples (G-I), while negative logFC indicates greater expression in infected samples (A-F) or severely-affected samples (G-I). Each point represents an individual gene. Red points indicate significant differential gene expression (FDR < 0.05); orange points correspond to genes with zero counts in all samples of one treatment group. Black points indicate genes that were not differentially expressed and did not have all-zero counts in one treatment group.

Lists of differentially expressed genes between infected and uninfected cattle at 4 wpi and 10 wpi were compared to further characterize the transcriptional differences between moderately- and severely-affected cattle. At 4 wpi, 25 genes had commonly increased expression in both moderately- and severely-affected cattle when compared to uninfected cattle (Fig 5A and S3 File). When comparing severely-affected to uninfected cattle at both 4 and 10 wpi, we also observed common increased gene expression of 791 genes (Fig 5A and S3 File) and common decreased gene expression of 408 genes (Fig 5B and S4 File).

Fig 5

Common differential gene expression in infected clusters at 4 wpi.

Venn diagram of differentially expressed genes with increased (A) and decreased (B) expression between moderately or severely affected cattle compared to uninfected cattle at 4 wpi and 10 wpi. Uninfected cattle n = 5, moderately affected cattle n = 3, severely affected cattle n = 4. wpi = weeks post-infection.

These comparisons indicate that while some genes are differentially expressed following infection with M. bovis, a more distinct transcriptional profile between uninfected and infected animals is observed in animals with clinical disease.

Innate immune bias in severely-affected cattle

Enrichment analyses were performed using lists of genes with increased or decreased expression. This allowed us to summarize the biological implications of the transcriptional differences observed between uninfected, moderately- and severely-affected groups at 4 and 10 wpi. We were unable to analyze genes with increased expression at 10 wpi or decreased expression at 4 wpi or 10 wpi in moderately-affected compared to uninfected cattle due to low numbers or the absence of differentially expressed genes. Top biological processes enriched in severely-affected cattle compared to uninfected (S1 Fig and S5 File) and to moderately-affected (S2 Fig and S5 File) were processes related to host defense and immune response that were highly overlapping at 4 wpi and 10 wpi. These same processes were absent in moderately-affected cattle compared to uninfected (S1 Fig and S5 File) cattle at 4 wpi and compared to severely-affected cattle (S2 Fig and S5 File) at both 4 wpi and 10 wpi, as well as in uninfected compared to severely-affected cattle at both post-infection timepoints (S3 Fig and S5 File).

Since many of the biological processes enriched in severely-affected cattle were associated with host defense and the immune response, gene sets were reanalyzed for enrichment of only immune-related processes to further examine immune associations. Immune processes enriched in moderately-affected compared to uninfected animals at 4 wpi involved regulation of T cell proliferation. Immune processes enriched in severely- affected compared to uninfected animals involved innate immune pathways and responses to interferon-gamma (Fig 6A and S6 File). Only one immune process was enriched in uninfected compared to severely-affected cattle and involved type IV hypersensitivity at 10 wpi (S6 File). By comparing moderately- to severely-affected cattle at post-infection timepoints, enrichment of immune processes relating to T cells, neutrophils, complement, and cytokine production was observed in moderately-affected cattle, while responses to interferon-gamma were enriched in severely-affected cattle (Fig 6B and S6 File). Cell type enrichment further elucidated the immune shifts observed in moderately- and severely-affected cattle at post-infection timepoints. Severely-affected cattle showed enrichment of innate immune cells compared to either moderately-affected or uninfected cohorts, while T cells were enriched in uninfected compared to severely-affected cattle (Fig 6C and S2 Table). These results indicate severely-affected cattle have a transcriptional bias towards innate immune processes and innate cell types compared to moderately-affected or uninfected cohorts.

Fig 6

Severely-affected cattle show biases towards innate immune biological processes and cell types.

(A-B) Significantly enriched immune biological processes in infected compared to uninfected cattle (A) and one cluster of infected cattle compared to the other infected cluster (B). Processes with corrected p-values < 0.1 were included. P-values are indicated for processes with a corrected p-value < 0.05. (C) Heat map of cell type enrichment scores from comparisons of uninfected, moderately-affected (moderate), and severely-affected (severe) cattle at 4 or 10 weeks post-infection. Cell types that showed significant enrichment in at least one of the comparisons are displayed on the y axis. Comparisons are denoted on the x axis. Heatmap color indicates cell enrichment scores, with larger values indicating higher enrichment scores. Cell enrichment scores greater than 2 were considered significant. Black boxes indicate cell enrichment scores less than 2. Uninfected cattle (n = 5), moderately-affected cattle (n = 3), severely-affected cattle (n = 4).

Discussion

This study characterizes the transcriptional changes that occur in peripheral blood leukocytes in early weeks following M. bovis infection in cattle. We found three distinct transcriptional profiles: one in uninfected animals, and two within infected animals, which corresponded to clinical presentation of disease (i.e. moderately-affected and severely-affected animals).

Moderately-affected animals did not show any clinical signs of disease, such as coughing, labored breathing, or pyrexia. On post-mortem evaluation, we observed lesions consistent with M. bovis infection, which were found primarily in the lungs and in lung-associated lymph nodes. The transcriptional profile of these animals closely resembled that of uninfected cohorts, especially at the later 10 wpi timepoint. This would suggest that for this experimental model, early timepoints after infection may be a critical window for detecting the transcriptional biomarkers of infection. Information from very early timepoints following M. bovis infection could provide insight into transcriptional dynamics involved in the early interactions between mycobacteria and the host, and how this is associated with disease control versus progression.

In comparison, severely-affected animals displayed coughing, labored breathing and a febrile response as early as 35 days post-infection. Post-mortem examination revealed that these animals had increased lesion severity in the lungs and pulmonary lymph nodes, as well as extra-thoracic lesions in the liver and/or kidneys. The early onset of clinical signs observed in this study correlates with peripheral dissemination of M. bovis. Therefore, it is likely that the transcriptional profile observed in these animals, distinct from that of uninfected and moderately-affected animals, is related to extra-thoracic dissemination.

In this study, we investigated the ex vivo gene expression profile of leukocytes from whole blood samples from M. bovis-infected cattle using RNA-seq. Similar to previous findings from transcriptomic analysis of PBMC or PBL from infected and control cattle [10-12], we observed a clear transcriptional difference between uninfected and infected animals (regardless of clinical status) at 4 wpi, despite the lack of in vitro antigen stimulation. Enrichment analyses indicated that the top biological processes enriched in infected cattle compared to uninfected cattle involved processes related to host defense and immune responses. This is consistent with previous work [11, 12]. However, in our data set, these differences diminished with time, and were minimal by 10 wpi. Furthermore, these transcriptional differences become less obvious if animals were segregated by clinical status, with moderately-affected animals becoming indiscriminate from controls by 10 wpi.

Severely-affected animals demonstrated the most transcriptionally distinct profile, as expected. In-depth analysis of pathways within host defense and immune response categories revealed an enrichment for upregulated genes associated with innate immune responses. This is in contrast to work that reported transcriptional suppression of genes involved in innate function following M. bovis infection in cattle [9, 10, 50]. Work in human and mouse models show that M. bovis down-modulates specific aspects of innate immunity including toll-like receptor (TLR)-mediated signaling [51-53], dendritic cell function [54] and antigen presentation by macrophages [55, 56]. However, despite this suppression in genes associated with innate immunity, infection with M. bovis also triggers a shift in the ratio of circulating monocytes to lymphocytes. This increase in lymphocyte numbers is usually correlated with protection [11]. Consistent with these findings, we observed that moderately-affected animals showed an enrichment of immune processes related to T cells, which was not seen in severely-affected animals; however, whether the enrichment can be attributed to increased proportions of circulating T cells, transcriptional alteration to existing cells, or a combination of both is unknown. It should be noted that, to our knowledge, the relationship between M. bovis infection and innate immune function has not been analyzed in the context of animals exhibiting clinical signs, as shown in this work. Our findings could suggest that, not unexpectedly, enhanced and sustained innate immune activation are detrimental to the host, and likely contribute to disease progression and bacterial dissemination. However, whether innate immune biases noted in our data are due to shifts in proportions of circulating cells, transcriptional alterations within the existing cells of circulation, or a combination of both is again unknown.

Further evidence of innate immune bias in severely-affected animals was observed in the cell type enrichment analysis. Severely-affected animals showed an enrichment in innate immune cells (primarily dendritic cells and macrophages), as compared to moderately-affected and uninfected cohorts. Interestingly, a study of transcriptional profiling of human patients with active tuberculosis showed there is a decrease in the abundance of B and T cell transcripts and an increase in myeloid-related transcripts, as compared to latently-infected patients [57]. Based on this report, there appears to be a correlation between clinical disease and innate immune bias. Lacking hematological information pertaining to cell subpopulations from our samples, we cannot determine if this enrichment in innate cells is due to transcriptional changes within cells or due to a change in cell numbers. Nevertheless, the observed transcriptional profile of clinical animals showing innate immune bias, and its similarities to human patients with active TB, provide insights into the immune responses that fail to control mycobacterial infection.

The infectious dose and aerosol route used in this study delivered a high dose of bacteria into the respiratory tract. Natural infection with M. bovis results in a slow-progressing disease, with animals showing limited clinical signs. In contrast, while all animals were inoculated at the same time and with the same inoculum preparation, clinical signs occurred only in some animals. At this time, we cannot explain why some animals developed more severe clinical disease as compared to others. While we cannot rule out the possibility of variability during the experimental infection process, this raises some important questions regarding individual susceptibility to infection. Furthermore, this variation allowed for an unique assessment of gene expression profiles in animals with distinct clinical presentations.

Experimental, high-dose infection may not fully recapitulate the transcriptional changes that occur following natural infection where the dose is likely lower and may involve multiple exposure events. However, the data obtained from this study indicates: 1) the quiescent nature of M. bovis infection, as moderately-affected animals appear transcriptionally similar to uninfected controls, 2) identifying biomarkers of infection presents a challenge, especially in subclinical animals, and 3) clinical severity has a unique transcriptional gene pathway profile, characterized by enhanced innate responses. To our knowledge, this is the first report of transcriptomic analysis of clinical cattle and the first to examine such early time-points.

(XLSX)

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Mean lesion scores and lymph node weights (g) with 95% confidence intervals.

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Cell type enrichment scores from comparisons of uninfected, moderately-affected, and severely-affected cattle.

Cell type enrichment scores were obtained from lists of differentially expressed genes. Scored > 2 were considered significant. AvB10down = enriched in severe compared to moderate at 10 wpi; AvB10up = enriched in moderate compared to severe at 10 wpi; AvB4down = enriched in severe compared to moderate at 4 wpi; AvB4up = enriched in moderate compared to severe at 4 wpi; BvC10down = enriched in control compared to severe at 10 wpi; BvC10up = enriched in severe compared to control at 10 wpi; BvC4down = enriched in control compared to severe at 4 wpi; BvC4up = enriched in severe compared to control at 4 wpi.

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Enriched biological processes in M. bovis infected compared to uninfected cattle.

Top 5 enriched biological processes from each group based on lowest p values. P-values all < 0.05. Uninfected cattle n = 5, moderately affected cattle n = 3, severely affected cattle n = 4.

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Enriched biological processes in one cluster of M. bovis infected cattle compared to the other cluster of infected cattle.

Top 5 enriched biological processes from each group based on lowest p values. P-values all < 0.05. Uninfected cattle n = 5, moderately affected cattle n = 3, severely affected cattle n = 4.

(TIF)

Click here for additional data file.

Enriched biological processes in uninfected compared to M. bovis infected cattle.

Top 5 enriched biological processes from each group based on lowest p values. P-values all < 0.05. Uninfected cattle n = 5, moderately affected cattle n = 3, severely affected cattle n = 4.

(TIF)

Click here for additional data file.

19 May 2020

PONE-D-20-09070

Severity of bovine tuberculosis is associated with innate immune-biased transcriptional signatures of whole blood in early weeks after experimental Mycobacterium bovis infection

PLOS ONE

Dear Dr. Boggiatto,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

This is a well-designed, well-implemented and interesting study with the potential to provide new insights on early events following M.bovis infection of cattle. However, Reviewer 2 has highlighted several aspects which need addressing. In particular the FASTQ files generated for the RNA-seq analysis and Supplementary Files S1-S6 need to be supplied and it needs to be clarified which background gene set was used for GO term enrichment analyses. Other comments regarding interpretation of the data should also be corrected such as use of the terms “upregulated”, “downregulated” or “activated” when there is no direct evidence for these processes other than changes in gene expression. Please carefully respond to the other specific points made by reviewer 2.

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Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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5. Review Comments to the Author

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Reviewer #1: The study is a well thought out thorough investigation into the transcriptional response of cattle experimentally infected with M. bovis. It builds on other previous studies in this area but brings new insights regarding early time points and the correlation between transcriptional changes and the severity of infection.

Reviewer #2: Severity of bovine tuberculosis is associated with innate immune-biased transcriptional signatures of whole blood in early weeks after experimental Mycobacterium bovis infection

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

This is a well-designed, well-implemented and scientifically interesting study of transcriptional responses in Holstein cattle experimentally infected with Mycobacterium bovis.

I have several general points and then some specific points about the study and the manuscript describing the work.

General Points

+++++++++

The manuscript is well-written, easy to understand and scientifically authoritative. There are a small number of typographical errors that I detail in the Specific Points section.

This is a suggestion, but I think the overall experimental design, purpose and outputs from the study could be usefully represented in a summary overview diagram (new Figure 1) that shows the experimental workflow from start to finish. This could include the infection time course, the tissue sampling for post-mortem pathology, the RNA-seq analysis of peripheral blood leukocytes and the basic analyses of DE genes plus the downstream data mining using GO term categories and cell type enrichment etc. This would make it easier for the reader to understand exactly what was done and appreciate the importance of the work.

The FASTQ files generated for the RNA-seq analysis are not currently available from the NCBI SRA repository. There is a BioProject accession entry (PRJNA600004) but not data is available. This must be made available when the paper is published. It should really be made available to the manuscript Reviewers and this is possible through the NCBI BioProject/SRA system - see the following link:

https://www.ncbi.nlm.nih.gov/sra/docs/submitquestions/#question3gen

Supplementary Files S1-S6 are not available to Reviewers with the submitted manuscript files on the PLOS ONE Editorial Manager website. Most of these files presumably contain the "meat" of the RNA-seq results (e.g. gene ID, log2FC, P value, FDR-adjusted P value etc.), which need to be made available to Reviewers to properly assess the results obtained and the biological relevance of the differentially expressed genes and gene set enrichment analyses etc. Can the authors ensure that these files are available with revised version of the manuscript?

t is not really appropriate to use the terms “upregulated” or “downregulated” in the context of the results reported here. The peripheral blood leukocytes examined are a heterogeneous cell mixture and the authors do not have clear evidence that genes increased or decreased in expression are actively upregulated or downregulated (e.g. through chromatin state changes, transcription factors, microRNAs etc.). The changes could be due to changes in cell composition; therefore, it is better to use the terms “increased in expression” or “decreased in expression”. As it happens, the CTen paper (Shoemaker et al. 2012) has a good overview of why this is important.

Shoemaker J.E., et al. (2012) CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data. BMC Genomics 13, 460.

Specific Points

+++++++++

Line 54: The $3 billion dollar figure used for the global financial loss associated with bovine tuberculosis is 25 years old now (quarter of a century!). We’re all guilty of casually citing this reference, but at this stage it should really be qualified as a “conservative” or “long-standing” estimate.

Lines 102-103: Is there a specific Animal Ethics Committee approval code or number for this project?

Lines 223-224: I am surprised that the authors did not use the new bovine genome assembly for their reference genome (ARS-UCD1.2 - www.ncbi.nlm.nih.gov/assembly/GCF_002263795.1). This resource has been available for more than two years (since April 2018) and is now formally published in Gigascience. In our experience, it provides a much better reference genome than UMD3.1 for RNA-seq studies in cattle.

A more pedantic or bolshy Reviewer might insist that the analyses be re-done using the newer assembly. However, I would be interested to know the authors’ reasons/justification for not using ARS-UCD1.2.

Rosen B.D., et al. (2020) De novo assembly of the cattle reference genome with single-molecule sequencing. Gigascience 9.

Lines 253-266: The authors do not make it clear in this Methods section, but what background gene set did they used for these gene set GO term enrichment analyses? It is important that the appropriate background gene set is used, which should be the detectable expressed gene set, not the complete bovine transcriptome. Based on the results obtained in this study, the background set should be the 14,279 genes reported on line 304. Can the authors explicitly state what background gene set was used? If it is not the detectable gene set, they should consider re-doing the analyses because the GO enrichment results obtained using the complete bovine gene set as the background will be biased towards processes in peripheral blood leukocytes that may not be a consequence of M. bovis infection. See Timmons et al. 2015 for a more detailed explanation of why this is important.

Timmons J.A., et al. (2015) Multiple sources of bias confound functional enrichment analysis of global -omics data. Genome Biol. 16, 186.

Lines 263-266: Could the authors explain why the parameters for detection of enriched GO immune system processes were relaxed (e.g. P < 0.1 – is this an FDR-adjusted P value? – it is not clear). Also, there is a typo on line 265: “1” should be “a”.

Lines 308-331: Figure 1 and legend to Figure 1. The axes labels and text for Fig1B, Fig1C and Fig1D are far too small – they need to be increased in size to make them legible. The term “LogFC” is not specific enough and needs to be replaced with Log2FC (with appropriate subscripting) of “2” (in the legend and on the axes labels).

Lines 333-341 and elsewhere in the manuscript. It’s probably a long shot, but could a genetic explanation at least partially account for the two clusters of animals that exhibit distinct clinical and transcriptional profiles? For example, do the calves coded inf4, inf5, inf8 and inf9 share the same sire, with a genetic background that might account for low resilience to bovine TB and a concomitant severe disease clinical phenotype and expression profile? Although the MDS plot in Figure 1G does not indicate sharing of basal transcriptional profiles among inf4, inf5, inf8 and inf9 that could be due to close genetic relationship.

In this regard, it would be useful if the authors provided some more information in the Materials and Methods concerning the genetic relationships among the calves used for the study.

Lines 414-428: Figure 3 and legend to Figure 3. The axes labels and text for Fig3A to Fig3I are far too small – they need to be increased in size to make them legible. The term “LogFC” is not specific enough and needs to be replaced with Log2FC (with appropriate subscripting) of “2” (in the legend and on the axes labels).

Line 436: Typo: “File 3S” should be “File S3”.

Line 525: Reference 1 seems to be out of place here?

Line 535-538 and earlier lines: The following statement may not be correct.

“In-depth analysis of pathways within host defense and immune response categories revealed an enrichment for genes associated with innate immune responses. This is in contrast to work that reported transcriptional suppression of genes involved in innate function following M. bovis infection in cattle (9, 10, 51).”

Statistically significant enrichment of genes corresponding to particular biological processes does not necessarily correspond to activation (opposite of suppression) of innate immune responses. This may be because the enrichment is due to overrepresentation of genes that are decreased in expression in infected animals. Also, all of these analyses are suspect if the incorrect background gene set was used (see my previous point relating to the Timmons et al. 2015 Genome Biology paper).

The enrichment of genes associated with innate immune responses would indicate the opposite pattern to what was observed previously if the input data sets corresponded to genes exhibiting increased expression (note: not upregulation). The authors do not make it clear which input gene sets gave these results; was it just the set of genes exhibiting increased expression or was it the combined sets of genes exhibiting both increased and decreased expression? The authors need to be clearer on how they describe these different input data sets. It’s not even clear whether they segregated genes into two lists (increased in expression and decreased in expression), or whether it was a single list containing genes showing both increased and decreased expression in infected animals versus control non-infected animals (lines 450-451). From lines 254-256, it seems that it was the just the combined list of DE genes (FDR-adjusted P value <0.05).

Line 545: Again, enrichment of immune processes related to T cells does not necessarily mean that these processes are “activated”. For example, the enrichment could be due to genes that are both increased and decreased in expression in infected animals compared to control non-infected animals.

Line 550: “innate immune activation” – again, unless the input gene sets were only those exhibiting increased expression, then this statement cannot be supported by the data.

Overall, the point I am trying to make here is that “Enrichment” of DE genes in particular GO term categories or biological pathways does not automatically mean “Activation”.

It would be much easier for the Reviewer to evaluate these results and look at specific sets of genes if the Supplementary Files S1-S6 were actually available on the PLOS ONE Editorial Manager website. This absence of supporting files (and the possibility that the gene set GO term enrichment analyses were not performed with the appropriate background gene set) is the reason I selected "Major Revision" as my recommendation.

I have indicated that the statistical analyses have been conducted appropriately in the single pull-down menu because the statistical analyses of DE genes using RNA-seq data and the post-mortem pathology data have been performed correctly. It's only the gene set GO term enrichment that might be suspect.

**********

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Reviewer #1: Yes: Sharon Louise Kendall

Reviewer #2: Yes: David E. MacHugh

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25 Jun 2020

Dear PLOS ONE Editor,

We thank you for your review of the manuscript entitled “Severity of bovine tuberculosis is associated with innate immune-biased transcriptional signatures of whole blood in early weeks after experimental Mycobacterium bovis infection.” We have addressed all changes requested either in the manuscript and/or through further clarification in this letter.

Journal requirements

1. Please ensure that your manuscript meets PLOS ONE’s style requirements, including those for file naming.

We have edited the manuscript to make sure that all requirements were met.

2. In your methods section, please provide additional details regarding the animals used in your study and ensure you have described the source.

We have added further information on the source of the animals used in the study.

3. We note that you are reporting an analysis of a microarray, next-generation sequencing, or deep sequencing data sets. PLOS requires that authors comply with field-specific standards for preparation, recording and deposition of data in repositories appropriate to their field. Please upload these data to a stable, public repository…please provide the relevant accession numbers that may be used to access these data.

The data sets have been uploaded to a repository and the accession number is provided. Unfortunately, a delay had been placed on the data availability, but that issues has now been resolved. This information included in the original manuscript, under Materials and Methods, Data Availability is the correct information and the data should now be accessible.

Reviewer Comments

"This is a suggestion, but I think the overall experimental design, purpose and outputs from the study could be usefully represented in a summary overview diagram (new Figure 1) that shows the experimental workflow from start to finish. This could include the infection time course, the tissue sampling for post-mortem pathology, the RNA-seq analysis of peripheral blood leukocytes and the basic analyses of DE genes plus the downstream data mining using GO term categories and cell type enrichment etc. This would make it easier for the reader to understand exactly what was done and appreciate the importance of the work."

Thank you for this suggestion. We have created a new Figure 1 depicting our experimental outline. All other figure numbers have also been changed throughout the text, accordingly.

"The FASTQ files generated for the RNA-seq analysis are not currently available from the NCBI SRA repository. There is a BioProject accession entry (PRJNA600004) but not data is available. This must be made available when the paper is published. It should really be made available to the manuscript Reviewers and this is possible through the NCBI BioProject/SRA system - see the following link:https://www.ncbi.nlm.nih.gov/sra/docs/submitquestions/#question3gen."

We thank the reviewer for their comment. The data files were uploaded and the accession number is correct. Unfortunately, we had a hold on the file accessibility, and therefore, we apologize for not having the data available at the time of the review.

"Supplementary Files S1-S6 are not available to Reviewers with the submitted manuscript files on the PLOS ONE Editorial Manager website. Most of these files presumably contain the "meat" of the RNA-seq results (e.g. gene ID, log2FC, P value, FDR-adjusted P value etc.), which need to be made available to Reviewers to properly assess the results obtained and the biological relevance of the differentially expressed genes and gene set enrichment analyses etc. Can the authors ensure that these files are available with revised version of the manuscript?"

We apologize these files not being available during the initial submission of this manuscript. This was an unfortunate mistake, and we apologize for that. The figures are now available.

"It is not really appropriate to use the terms “upregulated” or “downregulated” in the context of the results reported here. The peripheral blood leukocytes examined are a heterogeneous cell mixture and the authors do not have clear evidence that genes increased or decreased in expression are actively upregulated or downregulated (e.g. through chromatin state changes, transcription factors, microRNAs etc.). The changes could be due to changes in cell composition; therefore, it is better to use the terms “increased in expression” or “decreased in expression”. As it happens, the CTen paper (Shoemaker et al. 2012) has a good overview of why this is important."

We thank the reviewer for their comment, as it is a critical distinction to make. We have taken the advice and made the necessary adjustments throughout the text and we hope these changes are suitable.

"Line 54: The $3 billion dollar figure used for the global financial loss associated with bovine tuberculosis is 25 years old now (quarter of a century!). We’re all guilty of casually citing this reference, but at this stage it should really be qualified as a “conservative” or “long-standing” estimate."

We thank the reviewer for this comment, however, we have not been able to find a better estimate of the global economic burden for bovine tuberculosis. We agree that this is a rather old estimate now, so we have made a modification in the text to reflect this in the manuscript.

"Lines 102-103: Is there a specific Animal Ethics Committee approval code or number for this project?"

We have now provided the protocol number associated with this research study.

"Lines 223-224: I am surprised that the authors did not use the new bovine genome assembly for their reference genome (ARS-UCD1.2 - www.ncbi.nlm.nih.gov/assembly/GCF_002263795.1). This resource has been available for more than two years (since April 2018) and is now formally published in Gigascience. In our experience, it provides a much better reference genome than UMD3.1 for RNA-seq studies in cattle. A more pedantic or bolshy Reviewer might insist that the analyses be re-done using the newer assembly. However, I would be interested to know the authors’ reasons/justification for not using ARS-UCD1.2.Rosen B.D., et al. (2020) De novo assembly of the cattle reference genome with single-molecule sequencing. Gigascience 9."

We appreciate the reviewer’s recognition of this fact. The reason for not having used the new bovine genome assembly is that the initial processing of RNA-seq reads to obtain gene counts and do general infected versus uninfected DGE analysis was performed in early 2018, before the new genome was made available. It was not only until later that we received all pathology and culture results to continue analysis and writing.

"Lines 253-266: The authors do not make it clear in this Methods section, but what background gene set did they used for these gene set GO term enrichment analyses? It is important that the appropriate background gene set is used, which should be the detectable expressed gene set, not the complete bovine transcriptome. Based on the results obtained in this study, the background set should be the 14,279 genes reported on line 304. Can the authors explicitly state what background gene set was used? If it is not the detectable gene set, they should consider re-doing the analyses because the GO enrichment results obtained using the complete bovine gene set as the background will be biased towards processes in peripheral blood leukocytes that may not be a consequence of M. bovis infection. See Timmons et al. 2015 for a more detailed explanation of why this is important."

Thank you for your insight. We do state in lines 265-266 of the methods that the reference list of genes used was only genes that survived filtering during DGE analysis. This then would be our list of 14,525 genes found in File S2 that were used for DGE between severely affected, moderately affected, and uninfected animals. This list of genes is mentioned in line 458 as the filtered genes used for this DGE analysis. Please let us know if we can make this any clearer to the reader.

"Timmons J.A., et al. (2015) Multiple sources of bias confound functional enrichment analysis of global -omics data. Genome Biol. 16, 186."

The gene set used for the GO term enrichment analyses was indeed the filtered genes, not the complete bovine transcriptome. This has been revised in the Materials and Methods for clarification.

"Lines 263-266: Could the authors explain why the parameters for detection of enriched GO immune system processes were relaxed (e.g. P < 0.1 – is this an FDR-adjusted P value? – it is not clear). Also, there is a typo on line 265: “1” should be “a”."

We thank the reviewer for their comment. Yes, it is an FDR corrected P value. We relaxed the parameters for detection since we did not have a lot of significant FDRs. However, we felt these findings could still be biologically meaningful, even if not statistically significant. It should be noted that we do denote these with exact p-values as a cautionary measure for the reader.

"Lines 308-331: Figure 1 and legend to Figure 1. The axes labels and text for Fig1B, Fig1C and Fig1D are far too small – they need to be increased in size to make them legible. The term “LogFC” is not specific enough and needs to be replaced with Log2FC (with appropriate subscripting) of “2” (in the legend and on the axes labels)."

We thank the reviewer for this feedback. The figure has been adjusted in order to make the legible.

"Lines 333-341 and elsewhere in the manuscript. It’s probably a long shot, but could a genetic explanation at least partially account for the two clusters of animals that exhibit distinct clinical and transcriptional profiles? For example, do the calves coded inf4, inf5, inf8 and inf9 share the same sire, with a genetic background that might account for low resilience to bovine TB and a concomitant severe disease clinical phenotype and expression profile? Although the MDS plot in Figure 1G does not indicate sharing of basal transcriptional profiles among inf4, inf5, inf8 and inf9 that could be due to close genetic relationship. In this regard, it would be useful if the authors provided some more information in the Materials and Methods concerning the genetic relationships among the calves used for the study."

We thank the reviewer for this observation, as we too had some thoughts about the individual genetic susceptibility to infection. We make a brief mention of this in the discussion, lines 667-668. Unfortunately, we do not have individual calf information in terms of genetic relationships, as we do not breed animals on site. Since this study was performed several years ago, we are unsure whether or not we could obtain this information. We agree with the reviewer that genetic relationships may provide an interesting perspective on the data. However, even if we had this information, we feel we do not have a large enough number of calves in the analysis in order to make such conclusions.

"Lines 414-428: Figure 3 and legend to Figure 3. The axes labels and text for Fig3A to Fig3I are far too small – they need to be increased in size to make them legible. The term “LogFC” is not specific enough and needs to be replaced with Log2FC (with appropriate subscripting) of “2” (in the legend and on the axes labels)."

As above, thank you for the comment and we have adjusted the labels accordingly.

"Line 436: Typo: “File 3S” should be “File S3”."

Typo has been changed in the text.

"Line 525: Reference 1 seems to be out of place here?"

We apologize for the oversight. This has been fixed.

"Line 535-538 and earlier lines: The following statement may not be correct. “In-depth analysis of pathways within host defense and immune response categories revealed an enrichment for genes associated with innate immune responses. This is in contrast to work that reported transcriptional suppression of genes involved in innate function following M. bovis infection in cattle (9, 10, 51).”

Statistically significant enrichment of genes corresponding to particular biological processes does not necessarily correspond to activation (opposite of suppression) of innate immune responses. This may be because the enrichment is due to overrepresentation of genes that are decreased in expression in infected animals. Also, all of these analyses are suspect if the incorrect background gene set was used (see my previous point relating to the Timmons et al. 2015 Genome Biology paper). The enrichment of genes associated with innate immune responses would indicate the opposite pattern to what was observed previously if the input data sets corresponded to genes exhibiting increased expression (note: not upregulation). The authors do not make it clear which input gene sets gave these results; was it just the set of genes exhibiting increased expression or was it the combined sets of genes exhibiting both increased and decreased expression? The authors need to be clearer on how they describe these different input data sets. It’s not even clear whether they segregated genes into two lists (increased in expression and decreased in expression), or whether it was a single list containing genes showing both increased and decreased expression in infected animals versus control non-infected animals (lines 450-451). From lines 254-256, it seems that it was the just the combined list of DE genes (FDR-adjusted P value <0.05)."

We thank the reviewer for their comment. We have tried to clarify that we used either lists of genes with increased expression or lists of genes with decreased expression for inputs of GO analysis. We have added text to clarify this in lines 258-259 of the Methods. Additionally, we made this clear in the discussion, line 626, and 637-639, to better reflect our findings. These individual gene lists are also available in File S5 and specify increased or decreased expression between the various pairwise comparisons.

"Line 545: Again, enrichment of immune processes related to T cells does not necessarily mean that these processes are “activated”. For example, the enrichment could be due to genes that are both increased and decreased in expression in infected animals compared to control non-infected animals."

We have considered this comment and carefully reworded our conclusions to avoid making assumptions from our data. Please refer to lines 632-636 of the revised manuscript.

"Line 550: “innate immune activation” – again, unless the input gene sets were only those exhibiting increased expression, then this statement cannot be supported by the data."

In line with the previous comment, we have adjusted our wording to avoid making assumptions. Please see lines 639-644.

"Overall, the point I am trying to make here is that “Enrichment” of DE genes in particular GO term categories or biological pathways does not automatically mean “Activation”."

Thank you for this observation. We have tried to adjust our wording and conclusions to be aware of this in our revised manuscript.

"It would be much easier for the Reviewer to evaluate these results and look at specific sets of genes if the Supplementary Files S1-S6 were actually available on the PLOS ONE Editorial Manager website. This absence of supporting files (and the possibility that the gene set GO term enrichment analyses were not performed with the appropriate background gene set) is the reason I selected "Major Revision" as my recommendation."

We apologize for the lack of availability of these files. All supplementary material has been made available. We are unsure why all supplementary information was not available, but these files will be submitted with the resubmission.

"I have indicated that the statistical analyses have been conducted appropriately in the single pull-down menu because the statistical analyses of DE genes using RNA-seq data and the post-mortem pathology data have been performed correctly. It's only the gene set GO term enrichment that might be suspect."

Thank you for this. We have addressed the reviewer’s concerns about our GO term enrichment in previous comments.

Again, we thank the editor and reviewers for their time and effort in reviewing our manuscript. We appreciate the comments and suggestions, which can only strengthen and make the message we are trying to convey clearer. We hope that we have addressed all concerns either in the text and/or through our explanations in this letter and hope that you will find this manuscript suitable for publication.

Thank you,

Paola M. Boggiatto

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

30 Jul 2020

PONE-D-20-09070R1

Severity of bovine tuberculosis is associated with innate immune-biased transcriptional signatures of whole blood in early weeks after experimental Mycobacterium bovis infection

PLOS ONE

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Please address the minor comments raised by Reviewer 2

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: I had no major comments on the manuscript but reviewer 2 highlighted deficiencies that needed to be addressed. These have been addressed.

Reviewer #2: The authors have improved the manuscript and associated files significantly in Revision 1.

The new Figure 1 is an excellent graphical representation of the work and make it much easier for readers to understand how the study was conducted.

There are just several small issues the authors should correct in a minor revision.

Line 54-55: This statement doesn't currently make any sense. The citation is from 1995 and the year being referred to 2004. Is there a citation for the $50 million cattle statistic? If so, the text could be re-worded as follows - note insertion of new citation and "at least" before "$3 billion"

"In 2004, it was estimated that worldwide, an estimated >50 million cattle are infected with M. bovis (CITATION), resulting in a financial loss of at least $3 billion USD annually (6)."

Lines 258 and 259: Please make it clear you are using Log2FC values (with subscript "2"). "LogFC" is not sufficient - a non-expert reader will assume this is Log10.

Lines 275 and 276: Same as previous comment.

Supplementary Files 3 and 4: The terms "upregulated" and "downregulated" are still being used inappropriately in these files. Please change to "increased expression" or "decreased expression" etc. as detailed in the review of the first version of the manuscript.

PLEASE NOTE: I do not need to check that these minor edits are completed. The PLOS ONE Editorial staff should be able to do this on my behalf. They are very minor edits and can be completed in 15-20 minutes by the authors.

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Reviewer #1: Yes: Sharon L Kendall

Reviewer #2: Yes: David E MacHugh

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8 Sep 2020

Dear PLOS ONE Editor,

We thank you for your review of the manuscript entitled “Severity of bovine tuberculosis is associated with innate immune-biased transcriptional signatures of whole blood in early weeks after experimental Mycobacterium bovis infection.” We have addressed the second round of comments suggested by the reviewer and hope that we have taken care of all concerns.

Reviewer comments

The new Figure 1 is an excellent graphical representation of the work and make it much easier for readers to understand how the study was conducted.

We hoped this figure would improve the manuscript and thank the reviewer for their comment.

Line 54-55: This statement doesn't currently make any sense. The citation is from 1995 and the year being referred to 2004. Is there a citation for the $50 million cattle statistic? If so, the text could be re-worded as follows - note insertion of new citation and "at least" before "$3 billion"

"In 2004, it was estimated that worldwide, an estimated >50 million cattle are infected with M. bovis (CITATION), resulting in a financial loss of at least $3 billion USD annually (6)."

We apologize for the confusion with this statement. The “2004” date is a mistake, so that has been corrected. We have not been able to find a more recent citation for the number of cattle and cost associated with bovine tuberculosis worldwide. While old, this is the citation that is still currently used by ourselves and others.

Lines 258 and 259: Please make it clear you are using Log2FC values (with subscript "2"). "LogFC" is not sufficient - a non-expert reader will assume this is Log10.

Lines 275 and 276: Same as previous comment.

We apologize for the oversight, this has been addressed.

Supplementary Files 3 and 4: The terms "upregulated" and "downregulated" are still being used inappropriately in these files. Please change to "increased expression" or "decreased expression" etc. as detailed in the review of the first version of the manuscript.

We apologize for the oversight, we have made these changes accordingly.

Again, we thank the editor and reviewers for their time and effort in reviewing our manuscript. We hope that we have addressed all concerns and hope that you will find this manuscript suitable for publication.

Thank you,

Paola M. Boggiatto

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

16 Sep 2020

Severity of bovine tuberculosis is associated with innate immune-biased transcriptional signatures of whole blood in early weeks after experimental Mycobacterium bovis infection

PONE-D-20-09070R2

Dear Dr. Boggiatto,

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Ann Rawkins, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

22 Sep 2020

PONE-D-20-09070R2

Severity of bovine tuberculosis is associated with innate immune-biased transcriptional signatures of whole blood in early weeks after experimental Mycobacterium bovis infection

Dear Dr. Boggiatto:

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.

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on behalf of

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Academic Editor

PLOS ONE