Skip to main content

Genome-wide association analysis identified both RNA-seq and DNA variants associated to paratuberculosis in Canadian Holstein cattle ‘in vitro’ experimentally infected macrophages

Abstract

Background

Mycobacterium avium ssp. paratuberculosis (MAP) is the causative agent of paratuberculosis, or Johne’s disease (JD), an incurable bovine disease. The evidence for susceptibility to MAP disease points to multiple interacting factors, including the genetic predisposition to a dysregulation of the immune system. The endemic situation in cattle populations can be in part explained by a genetic susceptibility to MAP infection. In order to identify the best genetic improvement strategy that will lead to a significant reduction of JD in the population, we need to understand the link between genetic variability and the biological systems that MAP targets in its assault to dominate macrophages. MAP survives in macrophages where it disseminates. We used next-generation RNA (RNA-Seq) sequencing to study of the transcriptome in response to MAP infection of the macrophages from cows that have been naturally infected and identified as positive for JD (JD (+); n = 22) or negative for JD (healthy/resistant, JD (−); n = 28). In addition to identifying genetic variants from RNA-seq data, SNP variants were also identified using the Bovine SNP50 DNA chip.

Results

The complementary strategy allowed the identification of 1,356,248 genetic variants, including 814,168 RNA-seq and 591,220 DNA chip variants. Annotation using SnpEff predicted that the 2435 RNA-seq genetic variants would produce high functional effect on known genes in comparison to the 33 DNA chip variants. Significant variants from JD(+/−) macrophages were identified by genome-wide association study and revealed two quantitative traits loci: BTA4 and 11 at (P < 5 × 10− 7). Using BovineMine, gene expression levels together with significant genomic variants revealed pathways that potentially influence JD susceptibility, notably the energy-dependent regulation of mTOR by LKB1-AMPK and the metabolism of lipids.

Conclusion

In the present study, we succeeded in identifying genetic variants in regulatory pathways of the macrophages that may affect the susceptibility of cows that are healthy/resistant to MAP infection. RNA-seq provides an unprecedented opportunity to investigate gene expression and to link the genetic variations to biological pathways that MAP normally manipulate during the process of killing macrophages. A strategy incorporating functional markers into genetic selection may have a considerable impact in improving resistance to an incurable disease. Integrating the findings of this research into the conventional genetic selection program may allow faster and more lasting improvement in resistance to bovine paratuberculosis in dairy cattle.

Background

Mycobacterium avium subspecies paratuberculosis (MAP) is a causative agent for paratuberculosis or Johne’s disease (JD). Bovine paratuberculosis has been a global concern for many years. It inflicts a substantial economic burden on dairy and beef industries worldwide [1,2,3]. The prevalence of infected farms has been increasing worldwide, reaching 66% for farms in Western Canada [4], 68% in USA [5, 6] and 68% in Great Britain [7]. Paratuberculosis induces substantial economic losses ranging from early culling of JD cows to decreased milk production and lowered reproductive and feed efficiency [8].

Paratuberculosis is a slow and progressive chronic inflammatory bowel disease, leading to malfunction of the intestinal tract and persistent diarrhea [1, 3, 9]. These symptoms, coupled with serological tests (e.g., ELISA) and MAP detecting assays (e.g., fecal PCR), allow detecting clinical JD [3, 10]. With cows confirmed JD (+) by bacterial culture, the ability to detect actual MAP infections using ELISA is 96–99% [11]. However, for cows excreting a low amount of MAP in feces, ELISA assay’s sensitivity can be low as 4.8% [12] indicating a lack sensitivity for detecting subclinical JD probably because the subclinical cows shed MAP intermittently in their feces [13,14,15]. Besides, the culture of MAP is labor-intensive and often compromised by bacterial contamination [16]. In contrast, PCR’s fecal analysis is cost-effective, rapid and compensates for the weak sensitivity of culture for diagnosis [17,18,19,20]. Though direct fecal PCR outperformed ELISA in detecting cows excreting MAP in feces [12, 17], a better MAP testing strategy would be concurrent serological and fecal testing with the repeated diagnosis over time. Such a two-fold strategy would improve sensitivity because animals with subclinical diseases may shed MAP intermittently in their feces [17, 21].

Though routine farm management practices such as test and cull have shown limited performance, several modeling studies have demonstrated that vaccination is an economically attractive option for dairy producers [22, 23], resulting in benefits such as delay in the onset of clinical disease, reduced clinical cases, and reduced level of MAP shedding [24,25,26]. However, vaccination does not prevent new infection and must be administered to each new individual in each generation. The results of genetic improvement of disease resistance are permanent; genetic gains made in one generation remain in future generations, and under a program of continuous improvement, advances in genetic resistance accumulate generation upon generation [27]. In an ideal situation, vaccination should be combined with genetic improvement as both will contribute to eradicating the incurable disease. Furthermore, host genetic improvement of JD resistance is a good strategy for reducing new infections, but improvement is a slow, long-term process [28]. For bovine tuberculosis, the model predicted that the risk would be reduced by half after 4, 6, 9, and 15 generations for selection intensities corresponding to genetic selection of the 10, 25, 50, and 70% most resistant sires, respectively [29]. To develop a genetic improvement strategy that will lead to a significant reduction of JD in the population, a better understanding of the genetic components influencing the biological systems that MAP targets during its assault is required. Early events of MAP infection occur in two functional stages: (1) Invasion through the intestinal barrier via MAP discharge from epithelial M cells (2) phagocytosis and survival in macrophages [30,31,32]. It is known that MAP uses tissue resident macrophages as its primary reservoir for survival and for multiplication [33,34,35]. On the one hand, studies of the effects of age and dose on MAP infection susceptibility in experimental infection models and naturally infected calves reported significant individual variation, which could be explained by the host’s genetic difference in susceptibility/resistance to MAP disease [36, 37]. On the other hand, several genetic variations were associated with the susceptibility to develop clinical JD [38,39,40,41,42,43,44], notably in the macrophage BOLA-DRB2 gene [44]. Interestingly, genetic variations in numerous candidate genes expressed in macrophages are associated with resistance/susceptibility to MAP infection, notably the NOD2 [45, 46], IL10 [47,48,49,50], SLC11A1, and Toll-like receptor genes [51, 52]. Genetic variations in NOD2 associated with Crohn’s disease can predict impaired innate immunity [53, 54]. In JD ruminants this MAP-induced granulomatous infection shares many features with Crohn’s disease [55, 56] and has similarities with ileocecal tuberculosis [57].

Nowadays, sequencing technology is becoming more and more affordable and the accuracy of the SNPs called from the RNA-seq data, compared to whole-genome sequencing, is > 98% [58]. Augmenting association studies with RNA sequencing (RNA-seq) can detect subtle pathways that might be affected by genetic variations since the design of RNA-seq allows one to read the genome activity of a cell or a system in a defined environment and at given time points. In a previous study, we have defined the transcriptomic profiles of bovine macrophages from naturally MAP infected cows, i.e., JD(+) cows, and otherwise healthy cows, i.e., JD(−) [59]. We analyzed the phenotypic response to MAP infection and identified differentially expressed genes associated with inflammatory processes, the resolution of inflammation, and cellular metabolism, among others. Interestingly, the transcriptomic profiles of JD(+) macrophages differed distinctly from JD(−) macrophages. In the current study, we investigate the potential of an in vitro model of macrophages to identify individual genetic variations from the transcriptome associated with bovine paratuberculosis. We hypothesize that the genetic variations identified in the transcriptome associated with disease susceptibility could provide information on (1) the biological pathways leading to susceptibility to MAP infection’s susceptibility and (2) the genetic markers providing weakness to the host at the early stage of MAP infection.

This research’s originality comes from using two datasets: (1) SNP variants mined from differentially expressed in vitro infected macrophages from both healthy and JD cows, and (2) DNA chip data to provide high-resolution genomic analysis of macrophages. Taken together, the result of this functional genomics study provides useful information for predicting the genotype-to-phenotype relationship in this context of host-pathogen interaction where macrophages are targeted by MAP to survive and to escape the normal mycobacterial killing process.

Results

RNA-Seq variants and DNA-derived genotypes

The objective of our work was to use the genetic information from the transcripts and regulatory sequences of the macrophages from JD(−) and 22 JD(+) cows in the genetic association study. Genetic variants from the resting macrophages (unchallenged controls) and those called to respond to MAP infection were combined with the imputed SNPs to obtain a dense portray of the genetics variations of these innate immune sentinel cells. For each cow, RNA-seq samples from all infection time points were merged with the RNA-seq sample of the unchallenged controls. For each cow, the pool is a representation of all the genes expressed in the macrophages at one point or another during infection, including genes expressed in the resting unchallenged macrophages. The in vitro infection model makes it possible to include genes expressed in response to a MAP infection for the search for genetic variations. Overall, the transcriptome of the macrophages from 28 JD(−) and 22 JD(+) cows was sequenced, resulting in 9820 billion paired-end reads. To avoid false calls, only unique mapping reads were used and these included 473.3 ± 33.0 million mapped unique reads per cow for the first 12 cows (previous study [59]) and 84.26 ± 34.02 per cow for the 38 additional cows. After filtering to generate a non-redundant dataset (Supplementary Figure 1), we identified 1,356,248 variants, including 814,168 RNA-seq, and 591,220 DNA chip variants (Table 1). Of these 1,356,248 variants, 104,065 were insertions or deletions (indels). Comparing matched RNA and DNA sequences enables an assessment of the accuracy of SNP RNA calls. Correlation of genotype called between the common SNPs of RNA-seq and SNP50 was of 98.8 ± 0.7%. The RNA-seq data alone enabled discovery of 765,028 (56.4%) of all the genetic variants identified, while the imputed DNA chips represent 40% (542,060) of the identified SNPs (Fig. 1a). A total of 88% of the variants (Fig. 1b) were found annotated in dbSNP (version 150).

Table 1 Summary statistics of the identified variants using the respective methods, from the bovine genome using DNA chip and from the transcriptome of the macrophages using RNA-seq
Fig. 1
figure1

Functional characterization of all variants identified using DNA chips and RNA-seq. SNPs were identified from the bovine genome while RNA-seq variants were identified from the transcriptome of bovine macrophages. The distribution of variants by the different methods used to detect them is represented in (a) and the proportion of novel and annotated variants is represented in (b). The genomic distributions of variants from each methods is represented in (c)

Enrichment of genetic variants in functional categories

We annotated and predicted the variants’ effects using SnpEff. Table 1 summarizes the regulatory functions of the effect (high, low, or moderate). As expected, due to the nature of the sequencing method, RNA-seq dataset had the most SNP having a high functional impact. The SNPs identified from RNA-seq data are enriched in the expressed gene regions. This enrichment is advantageous compared to DNA genotyping methods because it increases the power to detect the SNPs responsible for regulating gene expression. A higher proportion of intergenic SNPs was found with the DNA chips, while intronic, exonic, and UTRs variants were primarily identified in the RNA-seq dataset (Fig. 1c).

Genome wide association study (GWAS) and pathway analysis of the significant variants

The main single cluster in the PCA plot (Supplementary Figure 2) and the deviation from the diagonal at the upper-right end of the Q-Q plot (Supplementary Figure 3) indicate absence of population stratification or other problems with the data, such as cryptic relatedness. Even though the pedigree information is known, inferring relationships through genomic marker data validates the absence of closely-related animals. The 1,356,248 SNPs called from the 50 cows, which includes the imputed RNA-sequencing and DNA chip datasets from the 28 JD(−) and 22 JD(+) cows, were used for performing the genome-wide association analysis (Fig. 2). A total of 787 variants (P ≤ 5 × 10− 4) were identified as significant (Table 2). Nine SNPs are within a distance of 1 Kb of the transcription start site (data not shown). Numerous eQTL were identified, mainly on BTA 4, 6, 7, 8, 10, 11, 12, 15, 27, and BTA28 (Fig. 2, blue line). The genomic distribution of these 787 variants (P ≤ 5 × 10− 4) suggests a higher enrichment in intronic compared to exonic sequences (Fig. 3a).

Fig. 2
figure2

Manhattan plot of SNPs associated with Johne’s disease infection in dairy bovine. The –log10 of the P-value of variants are plotted. The red line represents a p-value of 5 × 10−7 while the blue line represent a p-value of 5 × 10−4

Table 2 Number of variants identified using the respective genotyping methods, from the bovine genome using DNA chip and from the transcriptome of the macrophages using RNA-seq
Fig. 3
figure3

Functional characterization of variants significantly associated with JD in dairy bovine in the GWAS analysis at a p-value≤0.005. The distribution of the significant variants on the genome (a) and by the different method of sequencing used to detect them (b). The novel and annotated variants are represented in (c)

The 20 most significant variants are shown in Table 3. Only three variants passed the threshold of strong association (P < 1 × 10− 7). The two most significant SNPs were identified on BTA4 position 50 Mb (P = 3.26 × 10− 8). With an additional variant located on BTA11 (21.4 Mb; P = 9.25 × 10− 8) and two also on BTA11 (21.5 Mb, P = 1.26 × 10− 8; 12.4 Mb, P = 9.20 × 10− 7), genetic variants on BTA4 and 11 show a high association with JD-positive status. Considering the 143 SNP below the threshold of P < 5 × 10− 5, 15 and 82 additional SNP strengthened the association with BTA4 and BTA11, respectively. At this level of significance, the number of variants identified using the DNA chip (390 variants) and RNA-seq are similar. Only 28 variants were identified by both methods (Fig. 3b). In the 787 significant variants at P ≤ 5 × 10− 4, a total of 60 were not identified in the NCBI database, while the others (727) were already known (Fig. 3c).

Table 3 Top 20 most significant variants in the GWAS analysis

GWAS, however, empowered significant molecular biomarker identification, enabling the association of pathways in the context of JD resistance/susceptibility. Using BovineMine, analysis revealed interesting pathways that deserved investigation (Table 4). Among the top 15 pathways being significantly enriched by variants with a p-value ≤0.005, were identified the metabolism of vitamins, lipids, and endogenous sterols. Many pathway associated with different aspects of cell metabolism are significantly enriched. In addition, four are not related to amino acid/lipids metabolism or cellular energy production. These pathways are implicated in DNA repairs and cell signaling.

Table 4 Top 15 pathways significantly enriched by genes associated with SNPs significant (−log[p-value] ≥3) in GWAS a performed using the merged dataset of genotypes (DNA and RNA-seq)

Discussion

Currently, little is known about MAP’s mechanisms to restrain macrophages from becoming the main reservoir that ensures its multiplication and survival. Macrophages play a central role in mycobacterial pathogenesis. Therefore, macrophages are used as a model for M. tuberculosis and M. bovis [60,61,62,63], and to study MAP [59, 64, 65]. Because some cows are more resistant to paratuberculosis, we hypothesized that macrophages could be used as a sub-system to identified genetic variations associated to JD. Two-pronged issues arise, on one hand, MAP is able to subvert the functions of infected macrophages to establish its protective environment, and on the other hand, the host genetics influences MAP infection success. In a previous preliminary study, we depicted a significant difference in the transcriptome of primary bovine macrophages from JD (+) and (−) cows [57] where macrophages from JD(+) cows can reproduce complex phenotypes observed in tissue, like giant epithelioid cells, foam cells, and granulomatous appearance with high endogenous lipid droplets. In the current study, we used RNA-seq to provide a high-resolution extent of the genetic variations between the two groups, JD (+) and (−) cows. The result allowed the identification of novel genes and pathways involved in MAP infection. This study presents an opportunity to uncover the genetic aspect of both JD (+) and (−) cows using DNA and RNA-seq genotyping methods. In contrast to previous studies, UTR and intronic variants dominated most RNA-seq variants [58, 66]. One could attribute this skewed distribution to the limited knowledge about alternative splicing in bovine species or the absence of methylated cytosine in CpG dinucleotides in exonic regions, reported to bias SNP calling in DNA-based sequencing methods [67]. Moreover, numerically more variant exists in introns than in coding regions since selective pressure plays a role in variant distribution on the genome [68]. Indeed, alternative splicing contributes to producing numerous mature transcripts from the same primary RNA sequence. In other words, the pattern of whole-exome sequencing may exclude some intronic sequences with an essential functional role. Alternative splicing occurs in over 90% of genes [69], limiting the scope of whole-exome sequencing, whereas the RNA-seq method can still uncover the genetic variations from all alternative transcripts. Given the very high number of spliced variants and non-coding RNA in humans [58] and the lack of equivalent knowledge for the bovine species, RNA-seq remains an exciting approach to study the entire transcriptome and to uncover genetic variations from the non-coding and all alternative transcripts.

To our knowledge, this is the first GWAS for susceptibility to paratuberculosis using a dense RNA-seq dataset. The approach combines RNA-seq with a GWAS as it is an effective method for identifying expression quantitative trait loci (eQTL). We compared two cohorts of 22 JD (+) and 28 JD(−) cows. We identified a total of 1,356,248 markers, of which 52,360 variants had a high, low, or moderate impact on the transcriptomic machinery or the coding sequence. Among these, 4810 SNPs located in the vicinity of the alternative splicing sites are believed to exert control on the type of mRNA isoform. While most of these SNPs are more than 1 kb of a transcription start site, other effects such as epistasis or linkage equilibrium make it challenging to identify the SNPs with the main effect. Nonetheless, in this preliminary study, we demonstrate that this approach is a robust way to identify eQTL functional markers associated with paratuberculosis using macrophages as a subsystem.

Though most significant genetic variants were mapped to many genes, some SNPs were localized to specific regulatory regions, whereas others may influence gene expression and related pathways. Interestingly, many enriched pathways were associated with amino acid metabolism roles, energy production, and lipids metabolism. Only four of the top 15 pathways were not directly associated with those categories indicating that the variants linked to the JD susceptibility might impact genes with the host’s metabolism roles. Moreover, two of the genes mapped by highly-significant peak of variants on BTA4 (PNPLA8 gene) and BTA11 (CYP26B1 gene) are implicated in metabolism pathways. PNPLA8 gene, which encodes for the Phospholipase A2 Gamma, is located mainly in the peroxisome and mitochondria. These phospholipases help catalyze the cleavage of fatty acids from the membrane’s phospholipids. It also has a prominent effect on the modulation of energy storage and lipid utilization [70]. CYP26B1 gene encodes for the cytochrome P450 Family 26 Subfamily B Member 1 protein. This enzyme catalyzes reactions implicated in the synthesis of cholesterol and other lipids while also acting as a regulator of the retinoic acid level. However, GWAS significant SNPs had a putative impact on the NRCAM gene (Table 3), a β-catenin target gene. The NRCAM gene is a member of the immunoglobulin superfamily that encodes a cell adhesion molecule with multiple immunoglobulin-like C2-type domains and fibronectin type-III domains. Aberrant β-catenin signaling activation is associated with chronic inflammation and organ fibrosis [71, 72]. The failure to resolve acute inflammation leads to chronic inflammation, tissue remodeling, and fibrosis [73]. Defective β-catenin-NRCAM signaling may have a subsequent role in granulomatous formation. This adhesion molecule is downregulated in dendritic cells during inflammation [74]. In our previous study, NRCAM was found downregulated in macrophages in response to MAP infection but was not differentially expressed in JD(+) vs. JD(−) macrophages (data not shown). In other studies, the NRCAM gene overlapped with QTL found in association with milk composition [75], protein percentage (rs41650658) [76], and milking temperament (rs41587635) [77]. The impact of this gene on production and functional traits for dairy cattle is not known. Besides, the role of NRCAM in foam cell and fibrosis phenotypes observed in JD(+) macrophages requires further investigation. The change in fatty acid metabolism and resolution of inflammation through the foam cell and fibrosis phenotypes could be a way by which MAP survive and evade the immune system to establish a persistent infection, leading to JD.

Difficulties in studying JD include the identification of infected animals and the ability to match a non-infected animal in commercial herds. It is accepted that MAP infection’s susceptibility occurs early in life and that disease manifests in four stages: silent, subclinical, clinical, cachexia [1, 10]. Identifying an infected animal depends on the testing frequency and the ease with which the disease can be detected using current laboratory methods. Quantitative PCR was used as is more sensitive than MAP culture [17, 78]. The serological test we used (ELISA IDEXX) is highly specific (96–99%) and its sensitivity is 75% for MAP shedding cows [13] and 99.2% for combined tests [79]. Cows were tested every 6 months during the longitudinal study (3–5 yrs. period) using blood ELISA and fecal PCR methods to define JD status. ELISA and fecal PCR are well recognized phenotypes for the identification of MAP infected cows or cows diagnosed with paratuberculosis disease [44]. The concurrent positive detections (blood and feces) indicate that the 22 cows were JD positive, and no misleading false-positive diagnosis was attributed to JD(+) cows. The JD(−) cows were non-infected herd mates with no trace of MAP in feces and were concomitantly testing ELISA negative. Some non-infected cows might have been exposed but were free of MAP excretion throughout the study. Clinical signs appear 2–7 years after the infection. Because they tested negative every 6 months until they were culled at later age > 7 yrs. old, and since there were no intermittent shedding and absence of MAP-specific antibodies in blood during the longitudinal study (3–5 yrs. period), cows were not JD positive.

The population used in our study is small, owing to the challenge of collecting a substantial amount of blood on cows on commercial herds having received the diagnosis of JD and ready to be culled. Diagnosis and culling of JD animals are ineffective preventive measures. Because of slow progression, the disease is systematically diagnosed too late, while subclinical cows would have excreted MAP sporadically in the environment. As the infection and disease progress, the fecal shedding of MAP increases and contributes to its horizontal transmission. In combination with genetic improvement (innate protection), vaccination (acquired protection) will support eradicating this incurable disease. The Canadian dairy industry is aware of the importance of considering genetic resistance to MAP infection. To this end, molecular markers relevant to JD are needed to improve Holstein breeding programs, the dairy industry’s predominant breed. The importance of defining JD-associated markers having a significant variance effect on the trait is a challenge because of the limited study sample size, high linkage disequilibrium in dairy cattle, and the disease’s low heritability. For instance, some SNPs are linked with paratuberculosis disease and described as risk factors but could not be associated with the disease for two reasons: one reason concerns the relevance of the population tested (different breeds) [49], and the second concerns the functionalities of the SNPs, which have rarely been investigated. Our results suggest that the discovery of genetic variants using the transcriptome is an attractive complementary approach to the traditional DNA chip genotyping method. However, this strategy requires intensive laboratory manipulations (e.g., isolation of blood immune cells and macrophage culture). Alternatively, genotyping by sequencing could also be an exciting approach to discover variants in non-repetitive and single-copy gene-rich regions of the genome [80] that can complement DNA chip genotypes [81]. The macrophage subsystem used in this study allows studying early infection events in a controlled environment. Previous results indicate macrophages have been used as a model to understand the biological and molecular pathways affected during MAP infection [59, 82, 83]. It is also considered a new dimension in the infection model to study host genetics [84]. A strategy to discover functional markers associated with a phenotype is essential for the dairy industry. With the availability of the full bovine genomic sequence, numerous SNPs are available. However, we still have the daunting tasks of predicting the ‘genotype-to-phenotype’ effect, mainly because MAP resistance/susceptibility is lowly heritable and is a complex trait affected by many genes that have not yet been defined.

Conclusion

In this research, we have identified functional markers associated with JD. The SNP identified using macrophages as the subsystem can potentially explain the establishment of the foam cell and fibrosis phenotypes associated with the granulomatous appearance observed in JD(+) macrophages. The beneficial or unfavorable impact of these eQTLs remains to be investigated before been considered in genetic selection. If confirmed, the dissemination of beneficial allele(s) in the progeny should support the host immune system’s permanent effect. This research’s originality lies in the use of a simplified system that uses macrophages in vitro to control environmental parameters. RNA-seq provided data which, coupled with microarray genotypes, enabled high-resolution genomic analysis to identify new mutations, transcripts, and novel pathways associated with bovine paratuberculosis.

Methods

Animal selection and JD diagnosis

Fifteen dairy herds in Canada, located in the province of Quebec, were included in the study. Cows were sampled on commercial farms for the duration of the study. They were owned by dairy producers and were kept on farms beyond the end of sampling. Animals were treated according to local management practices and as recommended by the attending veterinarian. They were not euthanized as part of this study. A confidentiality agreement has been signed with all dairy producers. Cows had to be at least 30 months old at first sampling and paired blood and fecal samples (collected at the same time) were analyzed twice per year (every 5 to 7 months) as described [17]. They were sampled during a three-to-five-year period to exclude animals still in the silent period. JD(+) cows or JD(−) were selected and some of them were also part of a companion project [17, 59]. Cows that presented concordant serological and fecal culture or qPCR statuses, either positive or negative, were retained. As previously described [17], cows were considered to be shedders if the qPCR threshold cycle (Ct) was 35 or less, and non-shedders if Ct was 45. Regarding the ELISA testing kit (IDEXX Laboratories, Markham, ON, Canada), the threshold of 45 was used (S/P higher than 45% according to manufacturer). All samples with intermediate qPCR results (> 35) or serological results (> 5 and < 45) were considered to be of uncertain status and were excluded from the study. ELISA and PCR analyses were performed in a single laboratory with the same test kits throughout the experiment as described [17]. The culture of MAP was performed by the Quebec Animal Epidemiological Surveillance Laboratory (Saint-Hyacinthe, Québec, Canada), as described in [85]. Selected animals were confirmed JD(+) or JD(−) following concordant results for the two consecutive sampling periods. Fifty cows were selected including 6 JD(+) and 6 JD(−) cows analyzed previously [59], and totalized 28 JD(−) cows and 22 JD(+) confirmed using both tests. Negative cows for JD were 6.4 ± 1.5 years, and JD(+) cows were 5.1 ± 1.8 years.

Differentiation of monocyte-derived macrophages and in vitro MAP infection

For monocyte isolation, blood (700–750 mL) was collected in citrate–phosphate–dextrose–adenine anticoagulant transfusion bags (Animal Blood Resources International, Dixon, CA, USA). Monocytes were isolated peripheral blood mononuclear cells by adherence [86] with minor modifications as described [59].

To differentiate the monocytes into macrophages, cultures were maintained in a humid atmosphere at 39 °C with 5% CO2 for a period varying from a week to 10 days. Medium was refreshed every 3 days. The purity of the culture was analyzed by flow cytometry on the 3-laser FACSCanto II flow cytometer (BD Biosciences) using a maker of the macrophage population (anti-CD68 antibody). The anti-CD68 antibody is specific to a protein present on the lysosomal membrane of the macrophages [87]. The procedure includes a fixation and permeabilisation treatments performed using Cytofix/cytoperm kit (BD biosciences, Mississauga, Ontario, Canada). Immuno-labelling included the mouse primary anti-CD68 antibody (M071801–5; Agilent) following by the second anti-Mouse PE labelled IgG1 (P-21129; ThermoFisher, Waltham, MA, USA). The cells displayed macrophage morphology after 7–10 days. One day prior the MAP infection experiments, the culture was rinsed with RMPI 1640 medium (Wisent, Saint-Bruno, Québec, Canada) and incubated without the antibiotics/antimycotics with complete media including 10% bovine heterologous serum (heat-inactivated) collected from tested JD- cows of the local herd (Research and Development Center of Sherbrooke). A mix of GlutaMax (Life Technologies, Burlington, Ontario, Canada) and L-glutamine (Wisent) was added to the media. The culture of MAP was performed in Middlebrook 7H9 liquid media supplemented with mycobactin J as described previously [59]. The in vitro experiments were performed at 39 °C at a multiplicity of MAP infection of 10 bacteria per macrophage. Briefly, the fresh culture of the clinical MAP strain was vortexed, washed, and resuspended in PBS (pH 7.4). The suspension is then passed through a 26G needle to disperse clumps as described [59]. After 15 min of decantation, the concentration of the upper 5 mL layer was measured by absorbance at 600 nm as described elsewhere [88,89,90,91,92]. Macrophage infections were performed, as described elsewhere [59, 93, 94]. The MAP infection flasks were collected at 1 h, 4 h, 8 h, and 24 h. The experiment also included two uninfected control flasks incubated for 4 h and 24 h. Prior to harvest using the RLT buffer (Qiagen, Toronto, Ontario, Canada), the macrophages were washed twice with PBS. Cells were harvested by adding 1.8 mL of lysis buffer to the culture flask. Harvested cells were stored at − 80 °C until RNA extractions were performed.

RNA extraction, libraries and sequencing

Twelve cows were from a companion project [59]. The RNeasy kit (Qiagen) was used to extract RNA from those MDM. The DNase treatment was performed on-column according to the manufacturer’s recommendations. MDM was prepared from additional 38 animals. A different protocol was used to extract the RNA because the classic RNeasy protocol does not extract miRNA. To retain miRNA along with total RNA, the lysed MDM samples in the buffer RLT were treated using a phenol–chloroform extraction method. Both miRNA and total RNA from the lysed MDM samples were treated using TRIzol LS (Thermo Fisher) and chloroform separation. Both miRNA and total RNA part of the aqueous phase were then extracted using the miRNeasy kit according to the manufacturer’s recommendations. Quantification of the RNA concentration from the six RNA samples per animal (two controls and four infection time points) was performed using a NanoDrop (Thermo Fisher). Quality of the RNA extracts was evaluated using the Bioanalyzer RNA 6000 kit (Agilent Technologies, Santa Clara, CA, USA).

The ribosomal RNA was removed using the Ribo-Zero Gold kit (Illumina, Victoria, British Columbia, Canada). To construct each cDNA library, 250 ng of total RNA was processed using the Illumina TruSeq Stranded Total mRNA Sample Preparation kit (Illumina). In the previous study, one library was generated for each infection time points for the 12 cows [59]. For each additional 38 cows, equal quantity of total RNA from each time points were pooled. For each cow, all genes expressed in the unchallenged macrophages (CTL) were pooled to the transcripts harvested at each infection time points (1 h, 4 h, 8 h, and 24 h). Therefore, all genes that are expressed in the resting macrophages and the genes potentially upregulated during the infection are represented in each library, i.e. for each cow. RNA libraries were constructed as described above and submitted for sequencing to The Centre for Applied Genomics, The Hospital for Sick Children (Toronto, Canada) for quality control prior sequencing. Pool of RNA libraries was quantified by qPCR using the Kapa Library Quantification Illumina/ABI Prism Kit protocol (KAPA Biosystems), cluster on a cBot instrument and paired-end sequenced on 2 lanes of a High Throughput Run Mode flowcell with the V4 sequencing chemistry on an Illumina HiSeq 2500 platform running HCS software v2.2.68 following Illumina’s recommended protocol to generate paired-end reads of 126-bases in length.

RNA-seq-derived SNP calling and DNA genotyping using bovine SNP50 BeadChip

For each 12 cows previously analyzed [59], RNA-seq reads of the 6 distinct libraries (4 infection time points and 2 controls) were pooled to produce a FASTQ file containing roughly 360 million paired-end (PE) reads per cow. The FASTQ sequence files from the 38 additional cows contained a minimum of 60 million PE reads each. RNA-seq samples were processed in accordance with the Best Practices workflow for variant calling on RNA-seq data up to the variant calling step, after which we switched to the Joint Variant workflow (GVCF mode), a strategy that we validated previously [95]. Briefly, reads were firstly mapped against the UMD 3.1.1/bosTau8 assembly using the STAR aligner (version 2.4.0j, [96]). Next, duplicated reads were marked with Picard tools. The Split’N’Trim and indel realigments steps were performed (Genome Analysis Toolkit, GATK, version 3.3–0-g37228af [97];). The HaplotypeCaller algorithm was used to call variants (Supplementary file 1). With all the gVCFs files derived from the above mentioned step, we performed a joint genotyping analysis for all samples and produced a final VCF file (Supplementary file 2).

All animals were genotyped with the commercial DNA chip. Blood samples (EDTA vials) were collected at the first sampling with the registered animal ID. Extraction of the genomic DNA was performed using the Wizard Genomic DNA Purification Kit (Promega, Madison, WI) following the manufacturer’s instructions. Concentration was evaluated using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific, Wilmington, DE). Genotyping was performed using the Illumina BovineSNP50 BeadChip (Zoetis, Kalamazoo, MI, USA) by Zoetis (Sainte-Anne-de-Bellevue, Qc, Canada). Using a custom Perl script [81], a VCF file was generated from the Illumina ‘A/B’ allele genotypes, which contains 67,740 SNPs. This script removed SNPs not consistent with the nomenclature of ‘Allele A’ and ‘Allele B’ according to the Illumina genotyping system. The script remove also duplicate SNPs and SNPs that do not perfectly aligned (BLASTN) to the reference sequences of the mitochondrial genome, the bovine autosomes, or the X chromosome (UMD 3.1.1/bosTau8). Dataset of 50,539 was obtained SNPs from the BovineSNP50 BeadChip. Imputation to the BovineHD density was performed with FImpute v2.2 (Sargolzaei et al., 2014) using a large reference panel which we had access through the Center of Genetic Improvement of Livestock (CGIL) at Guelph University, Ontario, Canada.

Variant filtering, and annotation

RNA-seq-derived SNP and BovineSNP data were merged and imputed using Minimac3 (version 2.0.1) [98] software tools. SVS v8.7.1 (Golden Helix) and the BCFtools (Li et al. 2009) were used to obtain high quality variants. RNA-Seq genotypes supported by read depth < 4 reads or genotype quality score < 20 were filtered out. Additional variants were eliminated from downstream analysis: those with Minor allele frequency (MAF) < 0.05, those with a Call rate < 0.2, those harboring two or more alternative alleles, and those that were not in Hardy-Weinberg equilibrium (P < 0.001). Known variants were annotated with a bovine reference VCF file (SNPdb-version 150) downloaded from the NCBI ftp site [99]. The final constructed dataset used for GWAS analysis is summarized in Supplementary Figure 1.The GWAS statistical analysis was performed using the SVS software by doing a mixed linear model analysis (MLM) with identity by descent estimation and the first 20 principal components accounting for heterogeneous variances and cryptic relatedness. All parameter of the analysis used in the SVS software are consigned in the Supplementary file 3.

Functional analysis (pathways and networks)

The software SnpEff v.4.3 t [100] was used to predict the functional or biologically interpretable pathways and networks associated with significant variants. The final aim of this study is to characterize the variants that may be of special interest for the dairy industry. Considering that, we used SnpEff to detect variants associated with candidate genes related to JD. Using BovineMINE v1.4, functional analysis of the pathways and networks was performed [101].

Availability of data and materials

All sequencing data are available in the NCBI Gene Expression Omnibus (GEO) database under the accession number GSE98363 (https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/geo/query/acc.cgi?acc=GSE98363). The reference sequences of the mitochondrial genome, the bovine autosomes and the X chromosome are from the UMD 3.1.1/bosTau8 assembly and are publically available at NCBI under the accession number GCF_000003055.6 (https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/assembly/GCF_000003055.6) or alternatively at the Bovine Genome Database (http://128.206.12.216/drupal/sites/bovinegenome.org/files/data/umd3.1/UMD3.1_chromosomes.fa.gz).

Abbreviations

eQTL:

Expression quantitative trait loci

GWAS:

Genome-wide association study

HWE:

Hardy-Weinberg equilibrium

JD:

Johne’s disease

MAF:

Minor allele frequency

MAP:

Mycobacterium avium subsp. paratuberculosis

PCR:

Polymerase chain reaction

RNA-Seq:

RNA sequencing

SNP:

Single-nucleotide polymorphism

References

  1. 1.

    Tiwari A, VanLeeuwen JA, McKenna SLB, Keefe GP, Barkema HW. Johne’s disease in Canada: part I: clinical symptoms, pathophysiology, diagnosis, and prevalence in dairy herds. Can Vet J. 2006;47(9):874–82.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Whittington R, Donat K, Weber MF, Kelton D, Nielsen SS, Eisenberg S, Arrigoni N, Juste R, Saez JL, Dhand N, et al. Control of paratuberculosis: who, why and how. A review of 48 countries. BMC Vet Res. 2019;15(1):198.

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Stabel JR. Johne's disease: a hidden threat. J Dairy Sci. 1998;81(1):283–8.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Corbett CS, Naqvi SA, Bauman CA, De Buck J, Orsel K, Uehlinger F, Kelton DF, Barkema HW. Prevalence of Mycobacterium avium ssp. paratuberculosis infections in Canadian dairy herds. J Dairy Sci. 2018;101(12):11218–28.

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Lombard JE, Gardner IA, Jafarzadeh SR, Fossler CP, Harris B, Capsel RT, Wagner BA, Johnson WO. Herd-level prevalence of Mycobacterium avium subsp. paratuberculosis infection in United States dairy herds in 2007. Prev Vet Med. 2013;108(2–3):234–8.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Centers for E, Animal H. Johne’s disease on US dairies, 1991–2007. Fort Collins: CO: U.S. Dept. of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Centers for Epidemiology and Animal Health; 2008.

    Google Scholar 

  7. 7.

    Velasova M, Damaso A, Prakashbabu BC, Gibbons J, Wheelhouse N, Longbottom D, Van Winden S, Green M, Guitian J. Herd-level prevalence of selected endemic infectious diseases of dairy cows in Great Britain. J Dairy Sci. 2017;100(11):9215–33.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Garcia AB, Shalloo L. Invited review: the economic impact and control of paratuberculosis in cattle. J Dairy Sci. 2015;98(8):5019–39.

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Clarke CJ. The pathology and pathogenesis of paratuberculosis in ruminants and other species. J Comp Pathol. 1997;116(3):217–61.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  10. 10.

    Whitlock RH, Buergelt C. Preclinical and clinical manifestations of paratuberculosis (including pathology). Vet Clin North Am Food Anim Pract. 1996;12(2):345–56.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

    Nielsen SS, Toft N. Ante mortem diagnosis of paratuberculosis: a review of accuracies of ELISA, interferon-gamma assay and faecal culture techniques. Vet Microbiol. 2008;129(3–4):217–35.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12.

    Clark DL Jr, Koziczkowski JJ, Radcliff RP, Carlson RA, Ellingson JL. Detection of Mycobacterium avium subspecies paratuberculosis: comparing fecal culture versus serum enzyme-linked immunosorbent assay and direct fecal polymerase chain reaction. J Dairy Sci. 2008;91(7):2620–7.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  13. 13.

    Whitlock RH, Wells SJ, Sweeney RW, Van Tiem J. ELISA and fecal culture for paratuberculosis (Johne's disease): sensitivity and specificity of each method. Vet Microbiol. 2000;77(3–4):387–98.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Dargatz DA, Byrum BA, Barber LK, Sweeney RW, Whitlock RH, Shulaw WP, Jacobson RH, Stabel JR. Evaluation of a commercial ELISA for diagnosis of paratuberculosis in cattle. J Am Vet Med Assoc. 2001;218(7):1163–6.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15.

    Salgado M, Kruze J, Collins MT. Diagnosis of paratuberculosis by fecal culture and ELISA on milk and serum samples in two types of Chilean dairy goat herds. J Vet Diagn Investig. 2007;19(1):99–102.

    Article  Google Scholar 

  16. 16.

    Whittington RJ. Cultivation of Mycobacterium avium subsp. paratuberculosis, in: Paratuberculosis: organism, disease, control, Behr, M. A. Wallingford: CABI; 2010.

    Google Scholar 

  17. 17.

    Fock-Chow-Tho D, Topp E, Ibeagha-Awemu EA, Bissonnette N. Comparison of commercial DNA extraction kits and quantitative PCR systems for better sensitivity in detecting the causative agent of paratuberculosis in dairy cow fecal samples. J Dairy Sci. 2017;100(1):572–81.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  18. 18.

    Taddei S, Robbi C, Cesena C, Rossi I, Schiano E, Arrigoni N, Vicenzoni G, Cavirani S. Detection of Mycobacterium avium subsp. paratuberculosis in bovine fecal samples: comparison of three polymerase chain reaction-based diagnostic tests with a conventional culture method. J Vet Diagn Investig. 2004;16(6):503–8.

    Article  Google Scholar 

  19. 19.

    Bogli-Stuber K, Kohler C, Seitert G, Glanemann B, Antognoli MC, Salman MD, Wittenbrink MM, Wittwer M, Wassenaar T, Jemmi T, et al. Detection of Mycobacterium avium subspecies paratuberculosis in Swiss dairy cattle by real-time PCR and culture: a comparison of the two assays. J Appl Microbiol. 2005;99(3):587–97.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  20. 20.

    Herthnek D, Bolske G. New PCR systems to confirm real-time PCR detection of Mycobacterium avium subsp. paratuberculosis. BMC Microbiol. 2006;6:87.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  21. 21.

    Laurin EL, Chaffer M, McClure JT, SL MK, Keefe GP. The association of detection method, season, and lactation stage on identification of fecal shedding in Mycobacterium avium ssp. paratuberculosis infectious dairy cows. J Dairy Sci. 2015;98(1):211–20.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  22. 22.

    Cho J, Tauer LW, Schukken YH, Gómez MI, Smith RL, Lu Z, Grohn YT. Economic analysis of Mycobacterium avium subspecies paratuberculosis vaccines in dairy herds. J Dairy Sci. 2012;95(4):1855–72.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. 23.

    Lu Z, Schukken YH, Smith RL, Grohn YT. Using vaccination to prevent the invasion of Mycobacterium avium subsp. paratuberculosis in dairy herds: a stochastic simulation study. Prev Vet Med. 2013;110(3–4):335–45.

    PubMed  Article  PubMed Central  Google Scholar 

  24. 24.

    Bastida F, Juste RA. Paratuberculosis control: a review with a focus on vaccination. J Immune Based Ther Vaccines. 2011;9:8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Alonso-Hearn M, Molina E, Geijo M, Vazquez P, Sevilla IA, Garrido JM, Juste RA. Immunization of adult dairy cattle with a new heat-killed vaccine is associated with longer productive life prior to cows being sent to slaughter with suspected paratuberculosis. J Dairy Sci. 2012;95(2):618–29.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Tewari D, Hovingh E, Linscott R, Martel E, Lawrence J, Wolfgang D, Griswold D. Mycobacterium avium subsp. paratuberculosis antibody response, fecal shedding, and antibody cross-reactivity to Mycobacterium bovis in M. avium subsp. paratuberculosis-infected cattle herds vaccinated against Johne's disease. Clin Vaccine Immunol. 2014;21(5):698–703.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Kirkpatrick BW, Shi X, Shook GE, Collins MT. Whole-genome association analysis of susceptibility to paratuberculosis in Holstein cattle. Anim Genet. 2011;42(2):149–60.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  28. 28.

    Kirkpatrick BW, Shook GE. Genetic susceptibility to paratuberculosis. Vet Clin North Am Food Anim Pract. 2011;27(3):559–71 vi.

    PubMed  Article  PubMed Central  Google Scholar 

  29. 29.

    Raphaka K, Sanchez-Molano E, Tsairidou S, Anacleto O, Glass EJ, Woolliams JA, Doeschl-Wilson A, Banos G. Impact of genetic selection for increased cattle resistance to bovine tuberculosis on disease transmission dynamics. Front Vet Sci. 2018;5:237.

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Arsenault RJ, Maattanen P, Daigle J, Potter A, Griebel P, Napper S. From mouth to macrophage: mechanisms of innate immune subversion by Mycobacterium avium subsp. paratuberculosis. Vet Res. 2014;45:54.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. 31.

    Koets AP, Eda S, Sreevatsan S. The within host dynamics of Mycobacterium avium ssp. paratuberculosis infection in cattle: where time and place matter. Vet Res. 2015;46:61.

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Momotani E, Whipple DL, Thiermann AB, Cheville NF. Role of M cells and macrophages in the entrance of Mycobacterium paratuberculosis into domes of Ileal Peyer's patches in calves. Vet Pathol. 1988;25(2):131–7.

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Bannantine JP, Stabel JR. Killing of Mycobacterium avium subspecies paratuberculosis within macrophages. BMC Microbiol. 2002;2:2.

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Weiss DJ, Souza CD, Evanson OA, Sanders M, Rutherford M. Bovine monocyte TLR2 receptors differentially regulate the intracellular fate of Mycobacterium avium subsp. paratuberculosis and Mycobacterium avium subsp. avium. J Leukoc Biol. 2008;83(1):48–55.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Hussain T, Shah SZ, Zhao D, Sreevatsan S, Zhou X. The role of IL-10 in Mycobacterium avium subsp. paratuberculosis infection. Cell Commun Signal. 2016;14(1):29.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    Mortier RA, Barkema HW, De Buck J. Susceptibility to and diagnosis of Mycobacterium avium subspecies paratuberculosis infection in dairy calves: a review. Prev Vet Med. 2015;121(3–4):189–98.

    PubMed  Article  Google Scholar 

  37. 37.

    Mitchell RM, Medley GF, Collins MT, Schukken YH. A meta-analysis of the effect of dose and age at exposure on shedding of Mycobacterium avium subspecies paratuberculosis (MAP) in experimentally infected calves and cows. Epidemiol Infect. 2012;140(2):231–46.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Alpay F, Zare Y, Kamalludin MH, Huang X, Shi X, Shook GE, Collins MT, Kirkpatrick BW. Genome-wide association study of susceptibility to infection by Mycobacterium avium subspecies paratuberculosis in Holstein cattle. PLoS One. 2014;9(12):e111704.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39.

    Kupper J, Brandt H, Donat K, Erhardt G. Phenotype definition is a main point in genome-wide association studies for bovine Mycobacterium avium ssp. paratuberculosis infection status. Animal. 2014;8(10):1586–93.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    van Hulzen KJE, Schopen GCB, van Arendonk JAM, Nielen M, Koets AP, Schrooten C, Heuven HCM. Genome-wide association study to identify chromosomal regions associated with antibody response to Mycobacterium avium subspecies paratuberculosis in milk of Dutch Holstein-Friesians. J Dairy Sci. 2012;95(5):2740–8.

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  41. 41.

    Sallam AM, Zare Y, Alpay F, Shook GE, Collins MT, Alsheikh S, Sharaby M, Kirkpatrick BW. An across-breed genome wide association analysis of susceptibility to paratuberculosis in dairy cattle. J Dairy Res. 2017;84(1):61–7.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. 42.

    Kiser JN, White SN, Johnson KA, Hoff JL, Taylor JF, Neibergs HL. Identification of loci associated with susceptibility to Mycobacterium avium subspecies paratuberculosis (map) tissue infection in cattle. J Anim Sci. 2017;95(3):1080–91.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Zare Y, Shook GE, Collins MT, Kirkpatrick BW. Genome-wide association analysis and genomic prediction of Mycobacterium avium subspecies paratuberculosis infection in US Jersey cattle. PLoS One. 2014;9(2):e88380.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    Sanchez MP, Guatteo R, Davergne A, Saout J, Grohs C, Deloche MC, Taussat S, Fritz S, Boussaha M, Blanquefort P, et al. Identification of the ABCC4, IER3, and CBFA2T2 candidate genes for resistance to paratuberculosis from sequence-based GWAS in Holstein and Normande dairy cattle. Genet Sel Evol. 2020;52(1):14.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Pinedo PJ, Buergelt CD, Donovan GA, Melendez P, Morel L, Wu R, Langaee TY, Rae DO. Association between CARD15/NOD2 gene polymorphisms and paratuberculosis infection in cattle. Vet Microbiol. 2009;134(3–4):346–52.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46.

    Ruiz-Larranaga O, Garrido JM, Iriondo M, Manzano C, Molina E, Koets AP, Rutten VP, Juste RA, Estonba A. Genetic association between bovine NOD2 polymorphisms and infection by Mycobacterium avium subsp. paratuberculosis in Holstein-Friesian cattle. Anim Genet. 2010;41(6):652-5.

  47. 47.

    Verschoor CP, Pant SD, You Q, Schenkel FS, Kelton DF, Karrow NA. Polymorphisms in the gene encoding bovine interleukin-10 receptor alpha are associated with Mycobacterium avium ssp. paratuberculosis infection status. BMC Genet. 2010;11:23.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Korou LM, Liandris E, Gazouli M, Ikonomopoulos J. Investigation of the association of the SLC11A1 gene with resistance/sensitivity of goats (Capra hircus) to paratuberculosis. Vet Microbiol. 2010;144(3–4):353–8.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. 49.

    Pinedo PJ, Buergelt CD, Donovan GA, Melendez P, Morel L, Wu R, Langaee TY, Rae DO. Candidate gene polymorphisms (BoIFNG, TLR4, SLC11A1) as risk factors for paratuberculosis infection in cattle. Prev Vet Med. 2009;91(2–4):189–96.

    PubMed  Article  PubMed Central  Google Scholar 

  50. 50.

    Ruiz-Larranaga O, Garrido JM, Manzano C, Iriondo M, Molina E, Gil A, Koets AP, Rutten VP, Juste RA, Estonba A. Identification of single nucleotide polymorphisms in the bovine solute carrier family 11 member 1 (SLC11A1) gene and their association with infection by Mycobacterium avium subspecies paratuberculosis. J Dairy Sci. 2010;93(4):1713–21.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Cinar MU, Hizlisoy H, Akyüz B, Arslan K, Aksel EG, Gümüşsoy KS. Polymorphisms in toll-like receptor (TLR) 1, 4, 9 and SLC11A1 genes and their association with paratuberculosis susceptibility in Holstein and indigenous crossbred cattle in Turkey. J Genet. 2018;97(5):1147–54.

    CAS  Article  Google Scholar 

  52. 52.

    Juste RA, Vazquez P, Ruiz-Larranaga O, Iriondo M, Manzano C, Agirre M, Estonba A, Geijo MV, Molina E, Sevilla IA, et al. Association between combinations of genetic polymorphisms and epidemiopathogenic forms of bovine paratuberculosis. Heliyon. 2018;4(2):e00535.

    PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Verway M, Behr MA, White JH. Vitamin D, NOD2, autophagy and Crohn's disease. Expert Rev Clin Immunol. 2010;6(4):505–8.

    PubMed  Article  PubMed Central  Google Scholar 

  54. 54.

    Wang TT, Dabbas B, Laperriere D, Bitton AJ, Soualhine H, Tavera-Mendoza LE, Dionne S, Servant MJ, Bitton A, Seidman EG, et al. Direct and indirect induction by 1,25-dihydroxyvitamin D3 of the NOD2/CARD15-defensin beta2 innate immune pathway defective in Crohn disease. J Biol Chem. 2010;285(4):2227–31.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. 55.

    Sechi LA, Dow CT. Mycobacterium avium ss. paratuberculosis Zoonosis - The Hundred Year War - Beyond Crohn's Disease. Front Immunol. 2015;6:96.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56.

    Mucha R, Bhide MR, Chakurkar EB, Novak M, Mikula I Sr. Toll-like receptors TLR1, TLR2 and TLR4 gene mutations and natural resistance to Mycobacterium avium subsp. paratuberculosis infection in cattle. Vet Immunol Immunopathol. 2009;128(4):381–8.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  57. 57.

    Greenstein RJ. Is Crohn's disease caused by a mycobacterium? Comparisons with leprosy, tuberculosis, and Johne's disease. Lancet Infect Dis. 2003;3(8):507–14.

    PubMed  Article  PubMed Central  Google Scholar 

  58. 58.

    Piskol R, Ramaswami G, Li JB. Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet. 2013;93(4):641–51.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Ariel O, Gendron D, Dudemaine PL, Gevry N, Ibeagha-Awemu EM, Bissonnette N. Transcriptome Profiling of Bovine Macrophages Infected by Mycobacterium avium spp. paratuberculosis Depicts Foam Cell and Innate Immune Tolerance Phenotypes. Front Immunol. 2019;10:2874.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  60. 60.

    Knight M, Braverman J, Asfaha K, Gronert K, Stanley S. Lipid droplet formation in Mycobacterium tuberculosis infected macrophages requires IFN-gamma/HIF-1alpha signaling and supports host defense. PLoS Pathog. 2018;14(1):e1006874.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  61. 61.

    Lin J, Zhao D, Wang J, Wang Y, Li H, Yin X, Yang L, Zhou X. Transcriptome changes upon in vitro challenge with Mycobacterium bovis in monocyte-derived macrophages from bovine tuberculosis-infected and healthy cows. Vet Immunol Immunopathol. 2015;163(3–4):146–56.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  62. 62.

    Nalpas NC, Park SD, Magee DA, Taraktsoglou M, Browne JA, Conlon KM, Rue-Albrecht K, Killick KE, Hokamp K, Lohan AJ, et al. Whole-transcriptome, high-throughput RNA sequence analysis of the bovine macrophage response to Mycobacterium bovis infection in vitro. BMC Genomics. 2013;14(1):230.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Lee J, Lee SG, Kim KK, Lim YJ, Choi JA, Cho SN, Park C, Song CH. Characterisation of genes differentially expressed in macrophages by virulent and attenuated Mycobacterium tuberculosis through RNA-Seq analysis. Sci Rep. 2019;9(1):4027.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  64. 64.

    Machugh DE, Taraktsoglou M, Killick KE, Nalpas NC, Browne JA, Park SDE, Hokamp K, Gormley E, Magee DA. Pan-genomic analysis of bovine monocyte-derived macrophage gene expression in response to in vitro infection with Mycobacterium avium subspecies paratuberculosis. Vet Res. 2012;43(1):25.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Lamont EA, Sreevatsan S. Paradigm redux--Mycobacterium avium subspecies paratuberculosis-macrophage interactions show clear variations between bovine and human physiological body temperatures. Microb Pathog. 2010;48(5):143–9.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  66. 66.

    Suarez-Vega A, Gutierrez-Gil B, Klopp C, Tosser-Klopp G, Arranz JJ. Variant discovery in the sheep milk transcriptome using RNA sequencing. BMC Genomics. 2017;18(1):170.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 67.

    Hodges E, Smith AD, Kendall J, Xuan Z, Ravi K, Rooks M, Zhang MQ, Ye K, Bhattacharjee A, Brizuela L, et al. High definition profiling of mammalian DNA methylation by array capture and single molecule bisulfite sequencing. Genome Res. 2009;19(9):1593–605.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Davidson S, Starkey A, MacKenzie A. Evidence of uneven selective pressure on different subsets of the conserved human genome; implications for the significance of intronic and intergenic DNA. BMC Genomics. 2009;10:614.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  69. 69.

    Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet. 2008;40(12):1413–5.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  70. 70.

    Mancuso DJ, Han X, Jenkins CM, Lehman JJ, Sambandam N, Sims HF, Yang J, Yan W, Yang K, Green K, et al. Dramatic accumulation of triglycerides and precipitation of cardiac hemodynamic dysfunction during brief caloric restriction in transgenic myocardium expressing human calcium-independent phospholipase A2gamma. J Biol Chem. 2007;282(12):9216–27.

    CAS  PubMed  Article  Google Scholar 

  71. 71.

    Feng Y, Ren J, Gui Y, Wei W, Shu B, Lu Q, Xue X, Sun X, He W, Yang J, et al. Wnt/beta-catenin-promoted macrophage alternative activation contributes to kidney fibrosis. J Am Soc Nephrol. 2018;29(1):182–93.

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Lee JM, Yang J, Newell P, Singh S, Parwani A, Friedman SL, Nejak-Bowen KN, Monga SP. beta-catenin signaling in hepatocellular cancer: implications in inflammation, fibrosis, and proliferation. Cancer Lett. 2014;343(1):90–7.

    CAS  PubMed  Article  Google Scholar 

  73. 73.

    Katoh M. Multilayered prevention and treatment of chronic inflammation, organ fibrosis and cancer associated with canonical WNT/betacatenin signaling activation (review). Int J Mol Med. 2018;42(2):713–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Vigl B, Aebischer D, Nitschke M, Iolyeva M, Rothlin T, Antsiferova O, Halin C. Tissue inflammation modulates gene expression of lymphatic endothelial cells and dendritic cell migration in a stimulus-dependent manner. Blood. 2011;118(1):205–15.

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Maiorano AM, Lourenco DL, Tsuruta S, Ospina AMT, Stafuzza NB, Masuda Y, Filho AEV, Cyrillo J, Curi RA, Silva J. Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information. PLoS One. 2018;13(8):e0200694.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  76. 76.

    Kolbehdari D, Wang Z, Grant JR, Murdoch B, Prasad A, Xiu Z, Marques E, Stothard P, Moore SS. A whole genome scan to map QTL for milk production traits and somatic cell score in Canadian Holstein bulls. J Anim Breed Genet. 2009;126(3):216–27.

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Kolbehdari D, Wang Z, Grant JR, Murdoch B, Prasad A, Xiu Z, Marques E, Stothard P, Moore SS. A whole-genome scan to map quantitative trait loci for conformation and functional traits in Canadian Holstein bulls. J Dairy Sci. 2008;91(7):2844–56.

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Heuvelink A, Hassan AA, van Weering H, van Engelen E, Bulte M, Akineden O. An intra-laboratory cultural and real-time PCR method comparison and evaluation for the detection of subclinical paratuberculosis in dairy herds. Folia Microbiol (Praha). 2016.

  79. 79.

    Lavers CJ, Dohoo IR, McKenna SL, Keefe GP. Sensitivity and specificity of repeated test results from a commercial milk enzyme-linked immunosorbent assay for detection of Mycobacterium avium subspecies paratuberculosis in dairy cattle. J Am Vet Med Assoc. 2015;246(2):236–44.

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Sonah H, Bastien M, Iquira E, Tardivel A, Legare G, Boyle B, Normandeau E, Laroche J, Larose S, Jean M, et al. An improved genotyping by sequencing (GBS) approach offering increased versatility and efficiency of SNP discovery and genotyping. PLoS One. 2013;8(1):e54603.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Brouard JS, Boyle B, Ibeagha-Awemu EM, Bissonnette N. Low-depth genotyping-by-sequencing (GBS) in a bovine population: strategies to maximize the selection of high quality genotypes and the accuracy of imputation. BMC Genet. 2017;18(1):32.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  82. 82.

    Thirunavukkarasu S, de Silva K, Begg DJ, Whittington RJ, Plain KM. Macrophage polarization in cattle experimentally exposed to Mycobacterium avium subsp. paratuberculosis. Pathog Dis. 2015;73(9):ftv085.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  83. 83.

    Rue-Albrecht K, Magee DA, Killick KE, Nalpas NC, Gordon SV, MacHugh DE. Comparative functional genomics and the bovine macrophage response to strains of the genus. Front Immunol. 2014;5:536.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  84. 84.

    Emam M, Canovas A, Islas-Trejo AD, Fonseca PAS, Medrano JF, Mallard B. Transcriptomic profiles of monocyte-derived macrophages in response to Escherichia coli is associated with the host genetics. Sci Rep. 2020;10(1):271.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Arango-Sabogal JC, Cote G, Pare J, Labrecque O, Roy JP, Buczinski S, Dore E, Fairbrother JH, Bissonnette N, Wellemans V, et al. Detection of Mycobacterium avium subspecies paratuberculosis in tie-stall dairy herds using a standardized environmental sampling technique and targeted pooled samples. Can J Vet Res. 2016;80(3):175–82.

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Stabel JR, Kehrli ME Jr, Reinhardt TA, Nonnecke BJ. Functional assessment of bovine monocytes isolated from peripheral blood. Vet Immunol Immunopathol. 1997;58(2):147–53.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  87. 87.

    Kitani H, Yoshioka M, Takenouchi T, Sato M, Yamanaka N. Isolation and characterization of macrophages from a mixed primary culture of bovine liver cells. Vet Immunol Immunopathol. 2011;140(3–4):341–5.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  88. 88.

    Plain KM, Marsh IB, Waldron AM, Galea F, Whittington AM, Saunders VF, Begg DJ, de Silva K, Purdie AC, Whittington RJ. High-throughput direct fecal PCR assay for detection of Mycobacterium avium subsp. paratuberculosis in sheep and cattle. J Clin Microbiol. 2014;52(3):745–57.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Janagama HK, Jeong K, Kapur V, Coussens P, Sreevatsan S. Cytokine responses of bovine macrophages to diverse clinical Mycobacterium avium subspecies paratuberculosis strains. BMC Microbiol. 2006;6:10.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  90. 90.

    Zhu X, Tu ZJ, Coussens PM, Kapur V, Janagama H, Naser S, Sreevatsan S. Transcriptional analysis of diverse strains Mycobacterium avium subspecies paratuberculosis in primary bovine monocyte derived macrophages. Microbes Infect. 2008;10(12–13):1274–82.

    CAS  PubMed  Article  Google Scholar 

  91. 91.

    Kralik P, Beran V, Pavlik I. Enumeration of Mycobacterium avium subsp. paratuberculosis by quantitative real-time PCR, culture on solid media and optical densitometry. BMC Res Notes. 2012;5:114.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. 92.

    Shin SJ, Han JH, Manning EJ, Collins MT. Rapid and reliable method for quantification of Mycobacterium paratuberculosis by use of the BACTEC MGIT 960 system. J Clin Microbiol. 2007;45(6):1941–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Periasamy S, Tripathi BN, Singh N. Mechanisms of Mycobacterium avium subsp. paratuberculosis induced apoptosis and necrosis in bovine macrophages. Vet Microbiol. 2013;165(3–4):392–401.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  94. 94.

    Dudemaine PL, Thibault C, Alain K, Bissonnette N. Genetic variations in the SPP1 promoter affect gene expression and the level of osteopontin secretion into bovine milk. Anim Genet. 2014;45(5):629–40.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  95. 95.

    Brouard JS, Schenkel F, Marete A, Bissonnette N. The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments. J Anim Sci Biotechnol. 2019;10:44.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  96. 96.

    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97.

    McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Pausch H, MacLeod IM, Fries R, Emmerling R, Bowman PJ, Daetwyler HD, Goddard ME. Evaluation of the accuracy of imputed sequence variant genotypes and their utility for causal variant detection in cattle. Genet Sel Evol. 2017;49(1):24.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  99. 99.

    NCBI: NCBI FTP site. In. ftp://ftp.ncbi.nlm.nih.gov/. Accessed Apr 2018.

  100. 100.

    Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6(2):80–92.

    CAS  Article  Google Scholar 

  101. 101.

    Elsik CG, Unni DR, Diesh CM, Tayal A, Emery ML, Nguyen HN, Hagen DE. Bovine genome database: new tools for gleaning function from the Bos taurus genome. Nucleic Acids Res. 2016;44(D1):D834–9.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the Dairy Producers for allowing access to their animals and the Veterinarians who assisted with animal diagnosis. We acknowledge The Centre for Genetic Improvement of Livestock from University of Guelph for imputation to the BovineHD density reference panel. This research was enabled in part by support provided by Calcul Quebec (www.calculquebec.ca) and Compute Canada (www.computecanada.ca).

Funding

This study was funded by a Collaborative Research and Development grant from the Dairy Cattle Genetics Research and Development (DairyGen) Council of Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) (Project CRDPJ 441719), and Agri-Food and Agriculture Canada (AAFC J000079 and J000075 Projects). In vitro experiments (culture of macrophages), next-generation sequencing, and bioinformatics support (O.A.) were conducted under CRDPJ 441719 project. Validation of the GATK tool and bioinformatics support (J.-S.B.) were performed under the AAFC J000075 project. The animals from 5 and 10 herds participating in the study were monitored and diagnosed periodically during two overlapping periods in the framework of projects J000079 and J000075, respectively.

Author information

Affiliations

Authors

Contributions

The study was designed by NB. OA and JSB performed the bioinformatics analysis. OA performed the pathways analyses. OA and NB drafted the manuscript, Figures, Tables, and Supplementary materials, and AM made substantial contribution to the bioinformatics interpretation. FM and EIA contributed to data acquisition. All authors (OA, JSB, AM, FM, EIA, and NB) provided substantial input in the interpretation of the results, revised, and approved the final manuscript.

Corresponding author

Correspondence to Nathalie Bissonnette.

Ethics declarations

Ethics approval and consent to participate

Animals were owned by the Dairy Producers and a confidentiality agreement has been signed with all Dairy Producers. They were treated according to local management practices and, if required, they were culled following the veterinary practices. They were not euthanized as part of this study. All animal procedures were carried out according to the Canadian Council on Animal Care guidelines for institutional animal use. The ethical approval protocols 424 and 431 were approved by the Comité Institutionnel de Protection des Animaux from the Sherbrooke Research and Development Centre of Agriculture and Agri-Food Canada. The dairy cows selected for the study were from the herds from companion studies (AAFC-J000075 and AAFC-J000079). Enrollment of JD-positive herds was authorized by the owners. The dairy producers of all animals used in this study signed a collaborative agreement allowing the use of their animals.

Consent for publication

Not applicable.

Competing interests

Dr. Eveline Ibeagha-Awemu is a member of the editorial board for the BMC Genomics journal.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Supplementary Figure 1.

Schematic representation of the approach used to generate SNPs for GWAS analysis. The Bovine 50 K DNAchip data were imputed to the BovineHD using a reference panel of 3300 HD animals. After filtering to generate high-confidence set of variants, including RNA-seq, and DNA chip variants, the matched RNA and DNA samples enable verification of RNA SNP calls because they can be compared to the variant present in the respective dataset. Missing variants were imputated using Minimac3.

Additional file 2: Supplementary Figure 2.

Principal component analysis (PCA) of the 50 datasets of DNA variant. The PCA plots of each cow from both JD groups were drawn using the stats R package. All R analyses and graphs were processed in RStudio v0.99.467.

Additional file 3: Supplementary Figure 3.

Visualization of the GWAS results using Quantile-Quantile (Q-Q) plot. GWAS examined hundreds of thousands of DNA variants in 50 cows, testing their statistical association with discrete outcomes, JD case-control study. Q-Q plots display the observed association P-value for all SNPs on the y-axis versus the expected uniform distribution of P-values under the null hypothesis of no association on the x-axis. Strongly associated SNPs will deviate from the diagonal at the upper-right end of the plot, while systematic deviation from the diagonal may indicate problems with the data, such as population stratification or cryptic relatedness.

Additional file 4: Supplementary file 1.

Additional file 5: Supplementary file 2.

Additional file 6: Supplementary file 3.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ariel, O., Brouard, JS., Marete, A. et al. Genome-wide association analysis identified both RNA-seq and DNA variants associated to paratuberculosis in Canadian Holstein cattle ‘in vitro’ experimentally infected macrophages. BMC Genomics 22, 162 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12864-021-07487-4

Download citation

Keywords

  • Mycobacterium avium subspecies paratuberculosis
  • Dairy bovine
  • Macrophage
  • RNA-sequencing
  • Genotyping
  • Genome-wide association studies
  • SNP