microRNAs modulate immunological and inflammatory responses in Holstein cattle naturally infected with Mycobacterium avium subsp. paratuberculosis

doi:10.21203/rs.3.rs-3462813/v1

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microRNAs modulate immunological and inflammatory responses in Holstein cattle naturally infected with Mycobacterium avium subsp. paratuberculosis

https://doi.org/10.21203/rs.3.rs-3462813/v1

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published 02 Jan, 2024

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MicroRNAs (miRNAs) regulate the post-transcriptional expression of genes by binding to their target mRNAs. In this study, whole miRNA sequencing was used to compare the expression of miRNAs in ileocecal valve (ICV) and peripheral blood (PB) samples of cows with focal or diffuse paratuberculosis (PTB)-associated lesions in gut tissues versus (vs) control cows without lesions. Among the eight miRNAs differentially expressed in the PB samples from cows with diffuse lesions vs controls, three (miR-19a, miR-144, miR32) were also down-regulated in cows with diffuse vs focal lesions. In the ICV samples, we identified a total of 4, 5, and 18 miRNAs differentially expressed in cows with focal lesions vs controls, diffuse lesions vs controls, and diffuse vs focal lesions, respectively. The expression changes observed in five of the differentially expressed microRNAs (miR-19a, miR-144, miR-2425-3p, miR-139, miR-101) were validated using RT-qPCR. Next, mRNA target prediction was performed for each differentially expressed miRNA. A functional analysis using the predicted gene targets revealed a significant enrichment of the RNA polymerase and MAPK signaling pathways in the comparison of cows with focal vs no lesions and with diffuse vs focal lesions, respectively. The identified miRNAs could be used for the development of novel diagnostic and therapeutical tools for PTB control.

Biological sciences/Biological techniques

Biological sciences/Biological techniques/Gene expression analysis

Paratuberculosis

gene expression regulation

microRNAs

RNA-Seq

pro-inflammatory immune responses

Bovine paratuberculosis (PTB), often known as Johne’s disease, is a chronic granulomatous enteritis that affects ruminants worldwide. This disease is caused by Mycobacterium avium subsp. paratuberculosis (MAP) and is mainly characterized by a decrease in milk production and weight loss. Global economic losses from PTB cases are estimated to exceed US1.5 billion dollars per year¹, with US198.42 million dollars in the United States and US364.31 million dollars in Europe² primarily due to decreased milk production, increased management costs, and premature culling. Indeed, bovine PTB is considered endemic in the United States and Europe with more than 50% of herds being ELISA positive for anti-MAP antibodies³. In addition, scientific evidence links MAP to human inflammatory bowel disease (IBD), autoimmune diseases, as well as colorectal cancer and Alzheimer´s disease^4–6. This potential threat to human health has stimulated interest in this disease and in the development of more sensitive diagnostic and control methods.

Transmission of MAP usually occurs early in the life of the animal by ingestion of MAP-contaminated feces or milk. MAP crosses the intestinal barrier via interaction with M and epithelial cells^7–9 and can survive within subepithelial macrophages by inhibiting apoptosis and phagosome acidification, as well as by preventing antigens´ presentation to the immune system¹⁰. There are different stages of MAP infection (silent, subclinical, clinical, and advanced clinical) each with distinct immunological and pathological characteristics^11,12. Th1 immune responses, in particular interferon-gamma (IFNɣ) and tumoral necrosis factor (TNFα production, are characteristics of silent infections, occur very early after infection, and can effectively contain MAP infections. Around 10–15% of the infected animals eventually enter the subclinical phase of the infection where the Th1 responses gradually decline. This blockade of macrophage defenses allows MAP to survive and persist within infected host macrophages during the long-lasting subclinical stage of the infection. As the infection progresses, a shift from a T-helper Th17 or regulatory T-cells (Treg) cytoprotective response to an uncontrolled release of pro-inflammatory mediators occurs. Granulomas containing MAP have an impact on the intestinal mucosa and over time, they cause serious damage to the ileum and jejunum, diarrhea, progressive wasting, reduced milk yield, and the eventual death of the infected animal^13,14. In the most advanced clinical stages of the infection, an ineffective Th2 humoral response arises and is indicative of MAP bacilli escaping granulomas and disseminating to various tissues and organs.

Current MAP control strategies generally rely on identifying and culling infected animals. The most common diagnostic tests are ELISA for the detection of anti-MAP antibodies and real-time PCR for the detection of MAP DNA in fecal samples. However, the “gold standard” test for the ante-mortem diagnosis of PTB is the bacteriological culture of feces¹⁵. Fecal culture is a time-consuming method due to the slow growth rate of MAP, and its sensitivity varies depending on the stage of infection (between 39.0 %and 92.0 %^16,17. The identification of infected animals during the subclinical stage of the infection is still a challenge. New methods for diagnosing early infections, including digital polymerase chain reaction, metagenomic next-generation sequencing, and the detection of specific host biomarkers, showed promise^18–21, but have not yet been fully implemented or require further validation.

RNA sequencing (RNA-Seq) is a high-throughput sequencing technique that provides data on the transcriptome of a cell or tissue. This technology has enormous potential because it enables the study of disease pathogenesis and can be used to identify biomarkers for the development of novel diagnostic tools, drugs, or vaccines²². RNA-Seq can provide biological information on how an animal is responding to an infection and be a source of novel biomarkers for early infection, disease progression, and therapy^23,24. In the context of PTB, we previously identified host mRNAs differentially expressed in ileocecal valve (ICV) and peripheral blood (PB) samples of MAP-infected animals with distinct lesions in gut tissues ²⁵. Non-coding RNAs (ncRNAs) are involved in numerous pathological processes and have attracted increasing attention. MiRNAs are highly conserved small ncRNAs (18 to 25 nucleotides long) that regulate mRNA expression by binding to the 3’-untranslated region of their target mRNAs²⁶. This binding results in either mRNA cleavage or protein translational repression. In contrast, miRNA binding to the 5’-untranslated regions (5’UTRs), exons, and even DNA elements may enhance translation and transcription²⁷. Because miRNAs tend to associate with proteins and/or travel in serum and plasma within vesicles, extracellular miRNAs are more resistant to degradation than other RNAs or proteins and can, therefore, be measured with a high sensitivity²⁸. The first studies showing miRNA expression in bovine tissues were conducted in 2007^29,30. Since then, 793 mature miRNAs that are encoded on all 30 chromosomes, have been identified in the Bos taurus genome. These miRNAs make up about 25 % of the 3825 ncRNA predicted in the genome by Ensembl³¹. Previous studies have found that certain miRNAs are differentially expressed in cattle infected with MAP versus (vs) uninfected cows, implying that certain miRNA profiles correlated with natural MAP infection³². It is therefore possible that different stages of MAP infection might have distinct miRNA signatures. Some of the previous studies used experimentally challenged cattle that might have a different miRNA profile compared with naturally infected animals^33–35 and, therefore, the results of these studies must be validated in field studies and in animals naturally infected with MAP.

In the current study, we used small RNA sequencing (sRNA-Seq) to sequence the small RNAs (sRNAs) of PB and ICV from 14 naturally infected Holstein cattle with distinct histopathological lesions in gut tissues. We compared the miRNA profiles of cows with focal and diffuse lesions vs cows without lesions and cows with focal vs diffuse lesions to identify differentially expressed miRNAs. We next performed a target prediction analysis using mRNA target databases and algorithms and built miRNA-mRNA pairs with significant negative correlations using mRNA expression data from the same animals. Using the predicted target genes, we finally performed a functional analysis to determine which pathways the differentially expressed miRNAs were engaged in. The goal was to ascertain whether the differences in miRNA profiles would be reflected in the mRNA data set and explain differential disease outcomes. A schematic representation of the workflow is presented in Fig. 1.

Ethical statement. All methods were carried out in accordance with relevant guidelines and regulations. The study is reported in accordance with ARRIVE guidelines (https://arriveguidelines.org). The Animal Ethics Committee of the Servicio Regional de Investigation y Desarrollo Agroalimentario (SERIDA) approved the procedures on the animals included in this study. All procedures were authorized by the Regional Consejería de Agroganadería y Recursos Autóctonos of the Principality of Asturias (approval code PROAE 29/2015 and PROAE 66/2019) and were carried out in accordance with the European Guidelines for the Care and Use of Animals for Research Purposes (2012/63/EU). PB, gut tissues, and fecal samples were collected by trained personnel and in accordance with good veterinary practices.

Animals and PTB infectious status. The fourteen Holstein cows animals included in this study belonged to a single farm in Asturias (Spain). The infectious status of these animals was determined by histopathological analysis of gut tissues, ELISA for the detection of anti-MAP antibodies, and fecal and gut tissues PCR and bacteriological culture as previously described^25,36. The average age of the animals without lesions and with multifocal and diffuse lesions was 5.45, 5.09, and 4.38 years old, respectively.

RNA extraction, RNA-Seq library preparation, sequencing, and differential expression analysis. Total RNA isolation, RNA-Seq library preparation, and sequencing were previously performed²⁵. Briefly, PB samples were collected from the coccygeal vein of all the cows after slaughter in PAXgene® Blood RNA tubes (2.5 ml) (Qiagen, Hilden, Germany) and total RNA was extracted using the PAXgene® Blood RNA kit according to the manufacturer’s instructions (Qiagen). For tissue samples, 200–250 mg of ICV of the animals were collected and immediately submerged in 2 ml of RNA later (Sigma, St. Louis, MO). Next, RNA purification was performed using the RNeasy Mini Kit according to the manufacturer’s instructions (Qiagen). RNA quality was measured using a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, US). All samples had an RNA integrity value of 7 or greater. Approximately 250 ng of RNA were used for each RNA-Seq library creation using the Illumina NEBNext® Ultra Directional RNA Library preparation kit and following the manufacturer’s instructions (Illumina Inc, CA, US). All RNA-Seq libraries were quantified using a Qubit® Fluorometer and doubled-stranded DNA High Sensitivity Assay Kit (Invitrogen, Madrid, Spain). The quality of the libraries was assessed using an Agilent Bioanalyzer and Agilent high-sensitivity DNA chip to confirm that the insert sizes were 200–250 bp. RNA libraries were single-end sequenced using an Illumina Nextseq500 in a 1 x 75 format at the Genomic Unit of the Scientific Park of Madrid, Spain. The raw reads were filtered by length (>75bp) and by the percentage of ambiguous bases (<10%) using FastQC v0.11.9³⁷ and had PCR primers and Illumina adapters trimmed with Trim Galore³⁸. The fastq reads (NCBI Gene Expression Omnibus database; Acc. Number GSE137395) were mapped against the most recent Bos taurus reference genome sequence (ARS-UCD1.2) using the Spliced Transcripts Alignment to a Reference aligner (STAR 2.5.3a)³⁹. The alignments files (.bam) from STAR were used to generate a table of counts for each gene using the FeatureCounts function from Rsubread 2.12.0⁴⁰. Gene counts were then normalized with the mean-of-ratios method of DESeq2 1.38.0⁴¹. In the current study, DESeq2 was also used for the differential gene expression analysis. An mRNA was differentially expressed if its false discovery rate (FDR)-adjusted p-value was lower than 0.05.

sRNA-Seq library preparation and sequencing.

For each sample, 150 ng of total RNA was used to prepare a sRNA-Seq library using the NEB-Next Small RNA Sample Preparation following the manufacturer´s protocol (New England Biolabs, Ipswich, MA, US). The kit has been developed to capture any short RNAs (17-150 nucleotides) including short ribosomal RNAs (rRNAs) if present in the sample. Each of the generating libraries was run through a mini-PROTEAN TBE Precast gel 5% (BioRAD, Madrid, Spain), and fragments from 140 to 325 bp of the library were excised from the gel and purified. A unique pool containing all the purified libraries was prepared and sequenced in 101 bp single-read in a NovaSeq 6000 sequencer (Illumina) at the Genomic Unit of the Scientific Park of Madrid, Spain.

miRNAs differential expression analysis. The Illumina bcl2fastq2 conversion Software v2.20 was used to demultiplex sequencing data and convert base call (bcl) files into fastq files. Quality control of the raw reads was performed using FastQC v0.11.9³⁷. Trimmed reads were analyzed using sRNAtoolbox, which consists of independent tools for sRNA analysis^42,43. sRNAbench, a tool from sRNAtoolbox, was used to filter and align the trimmed reads to the Bos taurus genome (ARS-UCD1.2). Reads with less than 15 bp and with a mean phred score below 20 were filtered out of the analysis, and the maximum number of allowed mismatches in the mapping process was 1. miRbase 22.1 database was used to annotate the miRNAs, and other ncRNAs were annotated using RNAcentral 20.0 and Ensembl release 104 databases. For the normalization and differential expression analysis of the miRNAs, the alignments obtained from sRNAbench were fed directly to sRNAde, another web-based tool from sRNAtoolbox⁴². sRNAde uses different algorithms (DESeq2 2.12 and EdgeR 1.38.0) to test for differential expression. A miRNA was differentially expressed if its FDR-adjusted p-value based on Benjamini and Hochberg testing correction⁴⁴ was lower than 0.05 in the comparisons: with focal lesions vs controls, with diffuse lesions vs controls, or with diffuse vs focal lesions.

Reverse transcription quantitative PCR (RT-qPCR) for RNA-Seq validation. Changes in the expression of five of the differentially expressed miRNAs were validated by RT-qPCR. For specific and highly sensitive RT-qPCR detection of a mature miRNA, 10ng of total RNA was reverse transcribed into cDNA using the miRCURY LNA RT kit (Qiagen) according to the manufacturer´s instructions. During the cDNA synthesis, a poly(A) tail was added to the mature miRNA template, and the cDNA was then synthesized using a poly(T) primer with a 3’ degenerate anchor and a 5’ universal tag. For the RT reactions, a total volume of 10 µl (2x MiRCURY RT Enzyme mix, reaction buffer, target RNA, and RNase-free water) was incubated for 60 minutes at 42ºC to perform the RT and 5 minutes at 95ºC to inactivate the reverse transcriptase. RT control reactions without the enzyme mix were included. The resultant cDNAs (1:40 dilution) were amplified on a LightCycler® 480 thermal cycler using the miRCURY SYBR® Green PCR Kit (Qiagen) in the presence of miRNA-specific and locked nucleic acid (LNA)-enhanced forward and reverse primers (Qiagen). PCR conditions were: 95ºC for 2 minutes (heat activation), followed by 40 cycles of 95ºC for 5 seconds (denaturation) and 56ºC for 30 seconds (annealing and extension), and a final melting curve. Appropriate controls (no template) were included. RT-qPCR experiments were performed by duplicate and in each experiment three replicates were run for each combination of primers and samples.

To obtain Ct values, the 2nd derivative max method of the LightCycler® 480 Software was used. Using the results of the samples from cows without lesions or with focal lesions as controls, fold changes in expression were calculated using the 2^–ΔΔCt method. Normalization was performed using the U6 snRNA and 5S rRNA endogenous reference genes. Two comparisons were made: samples of cows with diffuse lesions vs controls and with diffuse vs focal lesions. The results were expressed as fold changes and were standardized by log₂ transformation to be comparable to the sRNA-Seq differential expression results. To test if the differences between the groups were statistically significant, an unpaired t-test was run using R⁴⁵. The Pearson correlation coefficient between the sRNA-Seq and RT-qPCR quantitative results was calculated using R. Differences and correlations were considered statistically significant if the p-value was ≤ 0.05.

miRNA-gene target prediction. The putative target mRNAs for each miRNA were predicted using the tool mRNAconsTarget from sRNAtoolbox with the miRanda⁴⁶ and PITA⁴⁷ algorithms with default parameters. In parallel, the prediction database TargetScan^48,49 was used to predict mRNA targets with a score ≥ 0.2. To reduce potential false positive targets, only targets predicted by the three algorithms were considered for further analysis. Next, the correlations between the miRNAs and predicted mRNA expression levels were tested with the Spearman and Pearson correlation tests using R⁴⁵. mRNAs with a negative correlation (R ≤ 0 and p-value ≤ 0.05) with respect to their related miRNA using both correlation tests were considered as putatively affected by the miRNA expression.

Functional analysis. Using the predicted gene targets for each comparison, an enrichment analysis of gene ontology (GO) processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed using ClusterProfiler 4.0⁵⁰ and String 12.0⁵¹. An FDR-adjusted p-value lower than 0.05 was considered significant. The function of the candidate genes was searched in GeneCards by searching their gene symbol.

sRNA-seq data. The animals included in the study were tested by histopathological analysis of gut tissues, acid-fast stain (Zielh-Neelsen), specific antibody response against MAP measured by ELISA, fecal and tissue qPCR, and bacteriological culture as previously described ^25,36. The infectious status of the 14 animals included in the study are presented in Table 1. The four control animals had no histopathological lesions in gut tissues and had a negative result for ELISA, fecal and tissue bacteriological culture, and qPCR. All the infected animals had focal or diffuse lesions in gut tissues. The five animals with focal lesions in gut tissues had a positive tissue qPCR, none of them had heavy bacterial load (> 50 cfu/gr) in feces and tissues, and none of them was culled due to PTB-associated clinical signs. Only one of the animals with focal lesions was fecal PCR positive. All the animals with diffuse lesions had positive ZN and ELISA results, positive fecal and tissue qPCR results, and positive bacteriological culture from gut tissues. Four of the five animals with diffuse lesions had heavy bacterial load in gut tissues.

sRNA-Seq libraries were prepared from total RNA extracted from PB and ICV samples of the 14 animals which were classified into three groups according to the results of the histopathological analysis: animals with focal and diffuse lesions and animals without lesions in gut tissues and regional lymph nodes (controls). An average of 40,954,530 and 36,762,469 raw reads per library were obtained after sequencing the PB and ICV samples, respectively. After the quality control, an average of 35.8 million reads per library (92.4 %) passed the filtering process and were mapped with sRNAbench to the Bos taurus reference genome (ARS-UCD1.2). The alignment of the filtered reads resulted in 34.2 million mapped reads (94.6 %) per library. Assembled reads were annotated using miRBase v22 to identify mapped known miRNAs. Of the mapped reads, 11.6 million reads (32.8 %) per library mapped to known miRNAs.

miRNA differential expression analysis. A total of 420 and 358 mature miRNAs identified in the PB and ICV samples were analysed for differential gene expression with the EdgeR and DESeq2 algorithms. In general, there were more miRNAs with decreased than increased expression levels in all the groups (Tables 2 and 3). As expected, the number of differentially expressed miRNAs was larger in the ICV than in the PB samples. None of the miRNAs was differentially expressed in both the PB and ICV samples and, therefore, our results showed PB and ICV-specific miRNA expression. A list of the differentially expressed miRNAs identified with the EdgeR and/or DESeq2 algorithms in the PB samples of the infected animals is presented in Table 2. In the comparison of focal vs controls, we did not identify any dysregulated miRNA. In the PB samples from animals with diffuse lesions vs controls, the consensus of the two algorithms revealed five miRNAs; two upregulated (bta-miR-2425-3p and bta-miR-139) and three downregulated (bta-miR-27a-5p, bta-miR-144, and bta-miR-181a). In this comparison, DESeq2 was able to identify an additional bta-miR-32 (fold = -2.62) and the Edge2 identified the bta-miR-27a-5p (fold = -3.68) and bta-miR-181a (fold = -1.86). The bta-miR-2425-3p was the most upregulated miRNA in our study with a fold of 5.76. Among the eight miRNAs differentially expressed in PB samples from cows with diffuse lesions vs controls, three downregulated miRNAs (bta-miR-19a, bta-miR-144, bta-miR-32) were also found downregulated in PB samples of cows with diffuse vs focal lesions which indicated their association with clinical stages of the disease.

The list of the differentially expressed miRNAs identified in the ICV samples with the EdgeR and DESeq2 algorithms is presented in Table 3. The bta-miR-2478 (fold= 3.23) was the most highly upregulated miRNA in the tested ICV samples. In the ICV samples, we identified a total of 4, 5, and 18 miRNAs differentially expressed in cows with focal lesions vs controls, diffuse lesions vs controls, and diffuse vs focal lesions, respectively. In the comparison of focal lesions vs controls, DESeq2 was able to identify two upregulated (bta-miR-2478 and bta-miR-150) and two downregulated (bta-miR-23a and bta-miR-23b-3p) miRNAs which could be potential biomarkers of early infection. In the ICV samples of cows with diffuse lesions, both DESeq2 and EdgeR identified two downregulated miRNAs (bta-miR-135b and bta-miR-433) when compared with control cows. In this comparison, DESeq2 was able to identify two additional downregulated miRNAs (bta-miR-146a and bta-miR-99a-5p) and EdgeR identified the bta-miR-215 (fold = 2.60). Some of the miRNAs that were differentially expressed in this comparison (bta-miR-433, bta-miR-146a, bta-miR-99a-5p) were also dysregulated in the comparison of diffuse vs focal lesions suggesting a dysregulation of these miRNAs only in more advanced stages of MAP infection. Two miRNAs (bta-miR-23a and bta-miR-23b-3p) were downregulated in the comparisons focal lesions vs controls and diffuse vs focal lesions which suggests that they could considered as biomarkers of early infection and disease progression.

Validation of the differentially expressed miRNAs in PB samples by RT-qPCR. The expression changes observed in five of the differentially expressed microRNAs (miR-19a, miR-144, miR-2425-3p, miR-139, miR-101) were validated by RT-qPCR using total RNA extracted from PB samples of the animals included in the study (Figure 2). The selected miRNAs were all identified with both DESeq2 and EdgeR algorithms. The bta-miR-2425-3p and bta-miR-139 were selected because they were the only miRNAs significantly upregulated in PB samples. The bta-miR-19a and bta-miR-144 were selected because they were found downregulated in all the tested comparisons. The results of the qRT-PCR are presented in Figure 2 and were expressed as fold changes and standardized by log₂transformation to be comparable to the miRNA-Seq differential expression results analyzed with DESeq2. Although the normalization of the RT-qPCR results was performed using the expression of the U6 RNA as the endogenous reference gene in Figure 2, similar results were obtained when the 5S gene was used (data not shown). The RT-qPCR results showed a statistically significant upregulation of the bta-miR-2425-3p in the comparison with diffuse lesions vs controls (p-value= 0.010). In the comparison of diffuse vs focal lesions, a statistically significant downregulation of the bta-miR-19a was observed (p-value= 0.002). A similar trend (upregulation or downregulation) was observed in the results obtained with both RNA-Seq and RT-qPCR methods. Indeed, the Pearson correlation coefficient calculated for the miRNA-Seq and RT-qPCR data was equal to 0.978 (p-value= 0.003).

miRNA-gene target prediction. Potential target mRNAs of the differentially expressed miRNAs were predicted using three different algorithms (miRanda, PiTA, TargetScan). All the consensus mRNA targets were then investigated for negative Spearman and Pearson correlations with corresponding miRNA using mRNA sequencing data from the same animals. The number of differentially expressed miRNAs and their predicted mRNA targets for each comparison is presented in Table 4. In the PB samples, 35 and 32 mRNA targets were identified in the comparisons: diffuse lesions vs controls and diffuse vs focal lesions, respectively. In the ICV samples; 33, 2, and 198 mRNA targets were identified in the comparisons focal lesions vs controls, diffuse lesions vs controls, and diffuse vs focal lesions, respectively.

The heatmaps representing the changes in the expression of the differentially expressed miRNAs in the different comparisons along with the changes in the expression of their target mRNAs are presenting in Figures 3 and 4. As seen in Figures 3A and 3B, 32 mRNA targets identified in the two comparisons of PB samples (diffuse lesions vs controls and diffuse vs focal lesions) were upregulated and controlled by only two downregulated miRNAs, bta-miR-144 and bta-miR-19a. In the ICV samples, 33 mRNA targets were controlled by two upregulated (bta-miR-150 and bta-miR-2478) and two downregulated (bta-miR-23a and bta-miR-23b-3p) miRNAs in the comparison focal lesions vs controls (Figure 3C). In the comparison of cows with diffuse lesion vs controls, the upregulation of bta-miR-215 (fold= 2.60) resulted in the downregulation of the mRNAs encoding the G Protein-Coupled Receptor 22 (GPR22) and the BMX Non-Receptor Tyrosine Kinase (BMX). Finally, 198 mRNAs were found dysregulated and controlled by 16 DE miRNAs identified in the comparison of diffuse vs focal lesions (Figure 4).

Functional enrichment analysis. Functional analysis of all the predicted gene targets was performed by enrichment analysis of GO processes and KEGG pathways (Table 5). Using the predicted gene targets identified in the PB samples, no GOs or pathways could be identified. Using ClusterProfiler, two KEGG pathways, the RNA polymerase (bta:03020) and the mitogen-activated protein kinase (MAPK) signaling pathway (bta:04010) were found enriched in the ICV samples of cows with focal lesions vs controls and with diffuse vs focal lesions, respectively. In the comparison of focal lesions vs controls, the RNA Polymerase I Subunit E (POLR1E) was positively regulated by bta-miR-23a and the RNA Polymerase III Subunit G (POLR3G) was negatively regulated by the bta-miR-2478, the most highly upregulated miRNA in the tested ICV samples (fold= 3.23). In the comparison of diffuse vs focal lesions, downregulation of bta-miR-214, bta-miR-23a, bta-miR-99a-5p, bta-let-7c, bta-miR-204, bta-let-7e caused upregulation of 11 genes belonging to the MAPK signaling pathway. In this comparison, the regulation of immune system processes (GO:0002682) and the nucleolus cellular component (GO:0002682) were significantly enriched with 29 and 22 enriched genes in each pathway, respectively. In addition, several KEGG pathways related to the induction of cytokines and inflammatory immune response (WP997) were enriched including the MAPK signaling pathway, pathways in cancer, Kaposi sarcoma-associated herpesvirus infection, Hepatitis C, Measles, Epstein-Barr virus infection, pancreatic cancer, P53 signaling pathway, lysosome, PI3K-Akt, and JAK-STAT signaling pathways.

Previous studies analyzed the differential expression of miRNAs in Holstein cattle experimentally infected with MAP^33,34. A comparison of the serum miRNome of MAP-challenged IFNɣ responders to unchallenged controls six months after infection did not identify significant differences in miRNA expression³³. Similarly, Shaughnessy et al. did not detect differentially expressed miRNAs at either the early 6 months or late (43, 46, and 49 months) intervals across seropositive and seronegative animals³⁴. Using naturally infected cattle previous studies compared the miRNA profiles of cattle positive and negative for MAP antibodies by ELISA³² or compared miRNA profiles according to the stage of MAP infection defined by fecal shedding, ELISA, and clinical signs⁵². In cattle, however, signs of infection such as the appearance of focal lesions in gut tissues can be detected before fecal shedding and ELISA, and therefore, examining the transcriptome of animals according to the histopathological lesions facilitates the identification of animals in the subclinical stage of the infection when the amount of MAP and antibodies can be undetected with current pre-mortem diagnostic methods. In the present study, the absence or presence of PTB-associated lesions (focal or diffuse) was used to define the severity of MAP infection and to compare the miRNA profiles of animals in early and more advanced stages of the infection vs uninfected cattle without lesions in gut tissues. To our knowledge only one study compared miRNA expression in the serum of cattle classified according to histopathological data, serology, and MAP bacterial culture into 4 disease groups: control, mild, moderate, and severe⁵³. Instead of using RNA-Seq, Gupta et al used NanoString technology (Seattle, WA, US) to detect bovine miRNAs using probe sets specific for human miRNAs. Careful should be taken in generalizing human results to cattle as the miRNome and targets of miRNAs can be organism-specific. Indeed, none of the miRNAs detected by Gupta et al., was detected in the current study.

Most previous studies analyzed circulating miRNA profiles in serum samples. In our study, however, miRNA profiles were analyzed in two target tissues, PB and ICV samples, from naturally infected Holstein cattle with distinct histopathological lesions in gut tissues. This is relevant because by analyzing miRNA expression changes in ICV samples of animals with PTB-associated lesions with distinct severity, we were able to distinguish between the different stages of MAP infection. To our knowledge, our study is the first to analyze miRNAs profiles in tissue samples of MAP-infected cattle. This might explain why most of the miRNAs identified in our study had not been associated with MAP infection in cattle before. Such a low number of common findings and a high number of novel miRNAs can also be due to the use of different bioinformatics tools and databases between studies, diagnostic methods for evaluating disease outcomes, and by using naturally infected cattle instead of experimental infections.

Our analysis showed that specific miRNAs were dysregulated in cows with different PTB-associated lesions. Specifically, in PB samples, we identified eight miRNAs in the comparison of cows diffuse lesions vs controls and three in the comparison of cows with diffuse vs focal lesions. Importantly, the miRNA-Seq analysis's results and the RT-qPCR results were correlated. Among the eight miRNAs differentially expressed in PB samples from cows with diffuse lesions vs controls, three (bta-miR-19a, bta-miR-144, bta-miR-32) were also found downregulated in PB samples of cows with diffuse vs focal lesions which suggest their strong association with advanced stage of the infection. In agreement with these findings, the bta-miR-32 was also found downregulated in a previous study where the miRNA profiles of Holstein cattle positive and negative for MAP antibodies were compared ³².

In the ICV samples, we identified a total of 4, 5, and 18 miRNAs differentially expressed in cows with focal lesions vs controls, diffuse lesions vs controls, and diffuse vs focal lesions, respectively. This suggests that the miRNA expression in ICV changes more as MAP infection progresses. In the comparison of cows with focal lesions vs controls, DESeq2 was able to identify two upregulated (bta-miR-2478 and bta-miR-150) and two downregulated (bta-miR-23a and bta-iR-23b-3p) miRNAs which could be considered potential biomarkers of early infection. Some of the differentially expressed miRNAs in the comparison diffuse lesions vs controls (bta-miR-433, bta-miR-146a, bta-miR-99a-5p) were also dysregulated in the comparison diffuse vs focal lesions suggesting a strong dysregulation of these miRNAs in advanced stages of MAP infection. The bta-miR-146a with a conserved human ortholog acts as an inhibitor of the pro-inflammatory immune response⁵⁴. In our study, upregulation of miR-146a negatively regulates the TNF Receptor Associated Factor 6 (TRAF6) and to control the pro-inflammatory immune response⁵⁵. Two miRNAs (bta-miR-23a and bta-miR-23b-3p) were downregulated in both comparisons, focal lesions vs controls and diffuse vs focal lesions, which suggests that they could be considered as biomarkers of early infection and disease progression. Further studies with a larger number of animals will be required to establish the suitability of these miRNAs as biomarkers.

We also integrated miRNA and mRNA expression data to ascertain whether the differences in miRNA profiles would be reflected in the mRNA data set. The 32 common mRNA targets identified in the two comparisons of PB samples were upregulated in all the comparisons and controlled by only two downregulated miRNAs, bta-miR-144 and bta-miR-19a. In the ICV samples; 33, 2, and 198 mRNA targets were identified in the comparisons focal lesions vs controls, diffuse lesions vs controls, and diffuse vs focal lesions, respectively. Functional analysis of all the predicted gene targets allows identifying two enriched KEGG pathways, the RNA polymerase (bta:03020) and the mitogen-activated protein kinase (MAPK) signaling pathway (bta:04010), in the ICV samples of cows with focal lesions vs controls and with diffuse vs focal lesions, respectively.

A more thorough examination of the gene targets in each enriched pathway. allowed us to obtain a better understating of the regulation of the innate and inflammatory responses by miRNAs (Figure 5). In the comparison of cows with focal lesions vs controls (Figure 5A), the RNA Polymerase I Subunit E (POLR1E) was positively regulated by bta-miR-23a, and the RNA Polymerase III Subunit G (POLR3G) was negatively regulated by the bta-miR-2478, the most highly upregulated miRNA in the tested ICV samples (fold = 3.23). POLR3G is part of the RNA polymerase III. POLR3G is involved either in the recruitment and stabilization of the subcomplex within RNA polymerase III, or in stimulating catalytic functions of other subunits during initiation. The RNA polymerase III acts as a nuclear and cytosolic non-self dsDNA sensor, is involved in the positive regulation of innate immune responses and Type I IFN and Nuclear factor (NF-κβ) activation through the RIG1 pathway and plays a key role in sensing and limiting infection by intracellular bacteria and DNA viruses. The POLR3G downregulation might be responsible for the blockade of the innate immune responses generally observed upon MAP infection. This blockage might allow MAP to persist and establish infection within macrophages during the long-term subclinical phase of the infection. The downregulation of POLR3G might be a way for MAP to prolong its survival within infected macrophages by decreasing DNA-driven innate immune responses.

In the comparison of cows with diffuse lesions vs controls (Figure 5B), the upregulation of bta-miR-215 (fold = 2.60) resulted in the downregulation of the mRNAs encoding the G Protein-Coupled Receptor 22 (GPR22) and the BMX Non-Receptor Tyrosine Kinase (BMX). BMX is expressed in hematopoietic cells of the myeloid lineage, granulocytes and monocytes, and has a significant role in cytokine signaling and inflammation⁵⁶. More specifically, BMX is required for the phosphorylation and activation of the Myeloid Differentiation Primary Response protein (MyD88), Mal, and transforming grow factor-β activated kinase (TAK1) leading to the activation of the NF-кβ and mitogen-activated protein kinase (MAPK) signaling pathways. Impaired production of BMX might result in a control of type I IFN and pro-inflammatory cytokines production. Interestingly, a recent study showed that the downregulation of miR-27a in MAP-infected macrophages significantly inhibited pro-inflammatory responses in MAP infected macrophages by targeting the expression of TAK1 binding proteins 2 and 3 (TAB2/3), important components of the MAPK signaling pathway⁵⁷. In the comparison of cows with diffuse lesions vs controls, we observed that the upregulation of bta-miR-146a was negatively associated with the expression of TRAF6 which in normal conditions would ensure a check-in of the pro-inflammatory signalling of NF-κβ and MAPK.

In the comparison of cows with diffuse vs focal lesions (Figure 5C), we observed that the downregulation of bta-miR-214, bta-miR-23a, bta-miR-99a-5p, bta-let-7c, bta-miR-204, bta-let-7e was associated with upregulation of 12 genes belonging to the MAPK signaling pathway. The MAPK signaling pathway plays a significant role in the induction of pro-inflammatory responses that occur in MAP-infected cattle in advanced stages of the infection⁵⁸. Among the 12 gene targets controlled by the differentially expressed miRNAs identified in the comparison diffuse vs focal lesions we found the Tumor Necrosis Factor Receptor Type 1-associated DEATH Domain Protein (TRADD), Rac Family Small GTPase 2 (RAC2), and Colony Stimulating Factor 1 (CSF1). TRADD mediates programmed cell death, RAC2 is involved in the generation of reactive oxygen species, and CSF1 promotes the release of pro-inflammatory chemokines, and thereby plays an important role in the stimulation of inflammatory processes. In our study, TRADD, RAC2 and CSF1 expression regulation was associated with the bta-mir-214. Besides the MAPK signaling pathway, the software String revealed that the target genes identified in the comparison diffuse vs focal lesions were associated with the regulation of the immune system process (GO:0002682) and with several pathways related to the induction of cytokines and inflammatory immune response (WP997) in several diseases including cancer (bta05200), Kaposi sarcoma-associated herpesvirus infection (bta05167), Hepatitis C (bta05167), Measles (bta05162), Epstein-Barr virus infection (bta05169), pancreatic cancer (bta05212). Signaling pathways associated with inflammation such as the P53 signaling pathway (bta04115), lysosome (bta04142), PI3K-Akt (bta04151), and JAK-STAT (bta04630) were also found significantly enriched.

Through modulation of the expression of miRNAs, MAP infection modifies host gene expression and modulates the innate and inflammatory responses in distinct stages of the infection. In early stages of the infection, the bta-miR-247 downregulates POLR3G, an activator of the RNA polymerase III that functions as a non-self dsDNA sensor and is implicated in the activation of DNA-driven innate immune responses. The POLR3G downregulation might be responsible at least in part for the blockade of the innate immune responses that allows MAP persistence within infected macrophages during the long-term subclinical phase of the infection. As infection progresses, MAP grows within granulomas but the upregulation of bta-miR-215 and bta-miRNA-146a controls an excessive pro-inflammatory response by targeting BMX and TRAF6, respectively. In advanced stages of the infection, however, the dysregulation of bta-miR-214, bta-miR-23a, bta-miR-99a-5p, bta-let-7c, bta-miR-204, bta-let-7e upregulates several genes of the MAPK signaling pathway causing the activation of a strong pro-inflammatory response commonly observed in the clinical stages of the infection. Aside from the utility of the miRNAs identified in the current study as biomarkers, modifying or targeting the expression of specific miRNAs might serve as a novel strategy for PTB control.

Acknowledgements

Financial support for this study was provided by grants (RTA2014-00009; RTI2018-094192, PID2021-122197OR) funded by the National Institute for Agricultural Research (INIA), MCIN/AEI/10.13039/ 501100011033, and by FEDER, “Una manera de hacer Europa”. Funds were also provided by the Gobierno del Principado de Asturias Regional funds PCTI 2021–2023 (GRUPIN: IDI2021-000102) co-funded by FEDER. MC, CBV, and GBB have been awarded fellowships from INIA and MCIN/AEI/10.13039/501100011033 and “FSE Invierte en tu futuro”; grants FPI2016-00041, CPD2016-0142, and PRE2019-090562, respectively. Special thanks are due to the dairy producer and veterinarians for their collaboration with sample collection. We would like to thank the i2basque Research and Academic Network for computational support. We are grateful to Kyle P. Hearn for the careful editing of the manuscript.

Author contribution

M.A.H. coordinated the study. R.C. and C.B.V. participated in sample collection and characterization of the disease status of the animals included in the RNA-Seq and sRNA-Seq. R.T. and R.R. collaborated in the RNA-Seq and sRNA-Seq libraries preparation and sequencing. S.O. helped with the RT-qPCR experiments. M.C. and G.B.B. performed the RNA-Seq analysis. G.B.B. performed the sRNA-Seq analysis, miRNAs targets prediction, functional analysis, drafted the manuscript, and prepared all tables and figures. G.B.B., RC, and M.A.H. collaborated on the interpretation and discussion of the results. M.A.H. performed data analysis and reviewed and edited the final manuscript. All the authors carefully read, reviewed, and approved the final manuscript.

Competing interests

The author(s) declare no competing interests.

Data availability

RNA-Seq data have been deposited in the NCBI Gene Expression Omnibus (GEO) database under the accession number (GSE137395). The datasets generated during the current study are available from the corresponding author on reasonable request.

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Table 1

Results of the histopathological analysis, ZN stain, ELISA, PCR, and bacteriological culture from the 14 animals included in the study.
Animal	Histopathological analysis			ELISA	Fecal	Fecal	Tissue	Tissue
ID	Microscopic	Macroscopic	ZN		PCR	Culture	PCR	Culture
1	Negative	No	Negative	Negative	Negative	Negative	Negative	Negative
2	Negative	No	Negative	Negative	Negative	Negative	Negative	Negative
3	Negative	No	Negative	Negative	Negative	Negative	Negative	Negative
15	Negative	No	Negative	Negative	Negative	Negative	Negative	Negative
4	Focal	No	Negative	Negative	Negative	Negative	Positive	Negative
5	Focal	No	Negative	Negative	Negative	Negative	Positive	Low
6	Focal	No	Negative	Negative	Negative	Negative	Positive	Negative
7	Focal	No	Negative	Negative	Negative	Negative	Positive	Negative
8	Focal	No	Negative	Positive	Positive	Negative	Positive	Medium
10	Diffuse	Yes	Positive	Positive	Positive	Negative	Positive	Heavy
11	Diffuse	Yes	Positive	Positive	Positive	Negative	Positive	Heavy
12	Diffuse	Yes	Positive	Positive	Positive	Heavy	Positive	Heavy
13	Diffuse	Yes	Positive	Positive	Positive	Negative	Positive	Low
14	Diffuse	Yes	Positive	Positive	Positive	Negative	Positive	Heavy
Bacterial load was classified as low (< 10 CFU), medium (between 10 to 50 CFU) or heavy (> 50 CFU).
N, Ziehl-Neelsen; CFU, Colony Forming Units.

Table 2

List of the differentially expressed miRNAs identified with the EdgeR and DESeq2 algorithms in the peripheral blood samples of animals with focal lesions vs controls and animals with diffuse vs focal lesions.
Comparison	Algorithm	miRNA name	Fold Change (log₂)	Adjusted p-value
Diffuse lesions vs controls	DESeq2	bta-miR-2425-3p	5.760949308	0.000901
		bta-miR-144	-3.614670202	0.002987
		bta-miR-19a	-2.643658463	0.011826
		bta-miR-32	-2.628257174	0.049391
		bta-miR-139	1.841533295	0.049391
		bta-miR-101	-2.375232972	0.049391
	EdgeR	bta-miR-144	-3.523181922	0.000791
		bta-miR-27a-5p	-3.686888682	0.003369
		bta-miR-2425-3p	5.698593367	0.005617
		bta-miR-19a	-2.5239869	0.011452
		bta-miR-101	-2.218090784	0.036952
		bta-miR-139	1.914828963	0.036952
		bta-miR-181a	-1.860477015	0.036952
Diffuse vs Focal lesions	DESeq2	bta-miR-19a	-3.071981051	2.96E-05
		bta-miR-144	-3.798502613	2.96E-05
		bta-miR-32	-2.958950883	0.001641
	EdgeR	bta-miR-144	-3.91750149	0.001931
		bta-miR-19a	-3.182992869	0.001931
		bta-miR-32	-3.05395378	0.003893

Table 3

List of the differentially expressed miRNAs identified with the EdgeR and DESeq2 algorithms in ileocecal valve samples of infected animals with focal or diffuse lesions vs controls and with diffuse vs focal lesions.
Comparison	Algorithm	miRNA	Fold Change (log₂)	Adjusted p-value
Focal lesions vs controls	DESeq2	bta-miR-150	1.72663	0.03206
		bta-miR-23b-3p	-1.11804	0.04662
		bta-miR-2478	3.23118	0.04662
		bta-miR-23a	-0.92494	0.04662
Diffuse lesions vs controls	DESeq2	bta-miR-146a	1.21000	0.00001
		bta-miR-433	-2.81867	0.01386
		bta-miR-99a-5p	-0.80058	0.01608
		bta-miR-135b	-4.45335	0.01608
	EdgeR	bta-miR-135b	-5.50608	0.01547
		bta-miR-215	2.60383	0.01547
		bta-miR-433	-2.76018	0.01547
Diffuse vs Focal lesions	DESeq2	bta-miR-204	-2.38038	0.00173
		bta-miR-146b	2.16842	0.00917
		bta-miR-146a	1.91842	0.00969
		bta-miR-99a-5p	-1.68395	0.00969
		bta-miR-10174-3p	-1.83497	0.00969
		bta-miR-154c	-1.29954	0.00969
		bta-miR-433	-3.24259	0.00969
		bta-miR-23b-3p	-1.19803	0.01058
		bta-let-7e	-1.59002	0.01184
		bta-miR-214	-0.89678	0.02824
		bta-miR-23a	-0.88296	0.03594
		bta-miR-382	-1.66611	0.04663
		bta-miR-145	-1.92401	0.04663
		bta-let-7c	-1.73719	0.04663
		bta-miR-30a-5p	-1.21416	0.04663
		bta-miR-27b	-0.80365	0.04663
		bta-miR-10167-3p	2.54170	0.04663
		bta-miR-411a	-1.02399	0.04663

Table 4

Number (Nº) of differentially expressed (DE) miRNAs in peripheral blood (PB) and ileocecal valve (ICV) for each comparison and their predicted mRNAs targets identified with PiTA, miRanda, and TargetScan algorithms and with a negative Spearman and Pearson correlation.
Sample	Comparison	Nº of DE miRNAs	No of DE miRNAs with targets	Nº of targets Pearson correlation	Nº of targets Spearman correlation	Nº of total targets
PB	Focal lesions vs controls	0	0	0	0	0
	Diffuse lesions vs controls	8	4	11	32	35
	Diffuse vs Focal lesions	3	2	10	30	32
ICV	Focal lesion vs controls	4	4	28	19	33
	Diffuse lesions vs controls	5	1	0	2	2
	Diffuse vs Focal lesions	18	16	152	171	198

Table 5

Gene ontologies (GOs) and canonical pathways associated with the predicted gene targets for the differentially expressed miRNAs in each comparison.
Comparison	Analysis	GO/Pathway	Description	Gene Ratio	p- adjusted	Gene ID
Focal lesions vs controls	Cluster profiler	bta03020	RNA polymerase	2/15	0.03291	POLR1E,POLR3G
Diffuse vs Focal lesions	Cluster profiler	bta04010	MAPK signaling pathway	12/105	0.02923	TRADD,MAPKAPK2, MAP4K1,RASGRP3,TP53,TRAF6,ANGPT1,CSF1,RAC2, CACNB4, IGF2,PDGFB
	String	GO:0002682	Regulation of immune system process	29/1158	0.0094	CACNB4,NLRC5,CXCL14, SIRPA,PLCL2,RAC2, CYP19A1,IL13,TRAF6,IL7, RBCK1,PIK3AP1,CD200, CNN2,EPHB6,TNFSF14,C2,NCKAP1L,CD84,ARG2, CSF1,EGR3,IL12B,NR1H3, STK10,IGF2,STAT1,CDK6, MAPKAPK2
		GO:0005730	Nucleolus	22/832	0.0138	ZNRF2,TP53,LIN28A, POLR2F,VRK3,SPC24,TOP1,NFIB,CDKN2AIPNL,ZMAT3,SRP19,CCDC86,CDCA8, SDHAF2,DCTN3,MRPL40, MYCN,CCDC137,NFKBIE, EDF1,STAT1,DDX46
		bta04010	MAPK signaling pathway	12/267	0.0027	TP53,CACNB4,RAC2, TRADD,ANGPT1,TRAF6, PDGFB,CSF1,RASGRP3, IGF2,MAPKAPK2,MAP4K1
		bta05200	Pathways in cancer	16/488	0.0027	TP53,TPM3,RAC2,IL13, TRAF6,IL7,CCND1,GNGT2, PDGFB,IL12B,RASGRP3, IGF2,STAT1,CDK6,FZD3, WNT4
		bta05167	Kaposi sarcoma-associated herpesvirus infection	9/175	0.0046	TP53,ICAM1,TRADD, CCND1,GNGT2,PDGFB, STAT1,CDK6,MAPKAPK2
		bta05160	Hepatitis C	8/150	0.0072	TP53,TRADD,TRAF6, CCND1,CLDN11,NR1H3, STAT1,CDK6
		bta05162	Measles	7/136	0.0197	TP53,TRADD,TRAF6, CCND1,IL12B,STAT1,CDK6
		bta05169	Epstein-Barr virus infection	8/197	0.0283	TP53,ICAM1,TRADD,TRAF6,CCND1,NFKBIE,STAT1, CDK6
		bta05212	Pancreatic cancer	5/69	0.0283	TP53,RAC2,CCND1,STAT1, CDK6
		bta04115	p53 signaling pathway	5/74	0.0289	TP53,RRM2,ZMAT3,CCND1,CDK6
		bta04142	Lysosome	6/119	0.0308	CLTA,NAGPA,ASAH1,CTSA, PSAP,CTSW
		bta04151	PI3K-Akt signaling pathway	10/332	0.0308	TP53,ANGPT1,IL7,CCND1, PIK3AP1,GNGT2,PDGFB, CSF1,IGF2,CDK6
		bta04630	JAK-STAT signaling pathway	7/163	0.0308	IL13,IL7,CCND1,IL21R, PDGFB,IL12B,STAT1

No competing interests reported.

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microRNAs modulate immunological and inflammatory responses in Holstein cattle naturally infected with Mycobacterium avium subsp. paratuberculosis

Status:

Journal Publication

Version 1

Abstract

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Introduction

Materials and methods

Results

Discussion

Conclusions

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Additional Declarations

Status:

Journal Publication

Version 1