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. 2019 Feb 13;9:1945. doi: 10.1038/s41598-018-38365-x

Characterization of circulating miRNA signature in water buffaloes (Bubalus bubalis) during Brucella abortus infection and evaluation as potential biomarkers for non-invasive diagnosis in vaginal fluid

Cristina Lecchi 1,✉, Carlotta Catozzi 1, Valentina Zamarian 1, Gaia Poggi 4, Giorgia Borriello 2, Alessandra Martucciello 2,3, Domenico Vecchio 2,3, Esterina DeCarlo 2,3, Giorgio Galiero 2, Fabrizio Ceciliani 1
PMCID: PMC6374377  PMID: 30760784

Abstract

Brucellosis is an infectious disease caused by bacteria from the Brucella genus that can be transmitted to humans through contact with infected animals or contaminated animal products. Brucellosis also causes financial losses in animal production. Ruminants are highly susceptible to brucellosis, and the causative agent water buffaloes (Bubalus bubalis) is Brucella abortus. Circulating microRNAs (miRNAs) are cropping up as promising biomarkers for several infectious diseases. The goals of this study were to characterize the serum miRNA signature associated with brucellosis in water buffaloes and investigate the miRNAs’ potential use as biomarkers in vaginal fluids. Next Generation Sequencing was used to assess miRNA expression profiles in Brucella-positive and Brucella-negative blood sera; dysregulated miRNAs in blood serum and vaginal fluids were validated using RT-qPCR. ROC curves were generated to evaluate the diagnostic value of miRNAs for Brucella. GO and KEGG pathway enrichment analyses were exploited to investigate the biological functions of dysregulated miRNAs. The results showed that 20 miRNAs were modulated, of which, 12 were upregulated and 8 were downregulated. These findings were corroborated by RT-qPCR, and ROC curves indicated that the miRNAs can serve as potential biomarkers for Brucella. GO and KEGG pathway analyses pointed out that some of these miRNAs are related to immune response and apoptosis. These results provided an overview of miRNA expression profiles and highlighted potential biomarkers for Brucella infection in water buffaloes. We also demonstrated the potential of vaginal fluids in studies involving microRNA detection. Further functional and mechanistic studies of these miRNAs may improve our understanding of the biological processes involved in Brucella infection and host immune response.

Introduction

Brucellosis is one of the most important zoonotic diseases in ruminants. The aetiological agent is Brucella, a gram-negative, facultative, intracellular pathogen1,2. The favoured reproductive niche of Brucella is the intracellular milieu of macrophages, dendritic cells and placental trophoblasts3. Brucellosis is a zoonosis of considerable importance to public health with more than 500,000 new human infections being estimated annually. Brucellosis also causes financial losses in animal production4,5. Routine screening and animal vaccinations have led to Brucellosis disappearing in western regions, although it remains endemic in developing regions such as the Middle East, Asia, Africa and South America and in some areas of Italy6. Brucella is transmitted to humans by consuming raw milk or after direct contact with infected animals. The infectious course of brucellosis is divided into three phases, each marked by distinct bacteriological, clinical and pathological profiles: (i) onset of infection; (ii) the acute phase during which clinical, haematological and pathological symptoms are first observed; and (iii) the chronic phase, characterized by intermittent clinical symptoms and evident pathological signs2. Ruminants are highly susceptible to brucellosis; small and large ruminants are preferentially infected by B. melitensis and B. abortus, respectively. In most areas examined thus far, namely, South America7,8, Pakistan9, Italy10 and Africa11, the main causative agent in water buffaloes is B. abortus biovar 1. In pregnant females, the bacterium invades the placenta, and subsequently the foetus, prompting abortion mainly during the last third of the pregnancy12,13. Nonpregnant animals, still shedding the bacteria through secretions, may be asymptomatic with no evident clinical or pathological signs14. Brucella infections must be diagnosed early to control disease spreading. B. abortus and B. melitensis ruminant brucellosis are diagnosed based on bacteriological and immunological tests, the latter being routinely used in control, eradication and surveillance programmes15,16. Serological tests are used to initially diagnose brucellosis, but the results can be negative, even when the bacterium is present, particularly during the early disease phases. Thoroughly understanding Brucella biology and identifying novel biomarkers are essential for diagnosis and prophylaxis protocols. MicroRNAs (miRNAs) are small noncoding RNA that regulate gene expression posttranscriptionally. They play pivotal roles in cellular homeostasis, and their expression is dysregulated during stress conditions, disorders and diseases17. MicroRNA are involved in pathogen-host interactions18 and are stable in body fluids, from which they can be easily extracted19. Consequently, miRNAs are promising biomarkers for diagnosing several diseases and stress disorders in both humans20,21 and animals22–24. Changes in miRNA expression patterns have been observed in association with infectious diseases25–27 and as reactions to specific stresses such as thermal stress28. Brucella has also been shown to modulate in vitro expression of miRNAs involved in host immune responses29–31.

Brucella infection reduces fertility by inducing abortion as well as suppurative placentitis32. Since no information has been reported on circulating miRNAs during Brucella infection in water buffaloes (Bubalus bubalis), the present study aimed to (a) assess miRNA expression profiles in the blood sera of water buffaloes infected by B. abortus; (b) extract and measure miRNA expressions in vaginal fluid during B. abortus infection; (c) determine whether miRNAs can be used as biomarkers to assess brucellosis; and (d) integrate miRNAs to their target genes and relative biological processes.

Results

Identifying differentially expressed serum microRNAs during B. abortus infection by miRNA sequencing

Serum miRNAs were sequenced to determine the differential miRNA profiles of B. abortus-infected and healthy buffaloes. A total of 469 miRNAs were identified, of which, 20 showed significantly altered expression in seropositive animals compared with seronegative animals. In seropositive animals, the expressions of 12 miRNAs were upregulated 1.8- to 3.7-fold, while 8 miRNAs were downregulated 1.8- to 6.7-fold (P ≤ 0.05) (Table 1).

Table 1.

Differentially expressed miRNAs in the serum of seropositive buffaloes compared with seronegative buffaloes by sequencing.

MiRNA Fold Change P Val
UP let-7f 2.6 0.003
UP miR-126-5p 2.1 0.044
UP let-7i 1.8 0.07
UP miR-138 5.7 0.022
UP miR-143 2.4 0.045
UP miR-146b 3.7 0.033
UP miR-151-3p 1.9 0.01
UP miR-191 2.1 0.014
UP miR-215 2.5 0.007
UP miR-381 5.3 0.037
UP miR-92a 1.9 0.021
UP miR-92b 2.1 0.019
DOWN miR-133a −6.7 0.003
DOWN miR-127 −3.2 0.044
DOWN miR-150 −6.7 0.001
DOWN miR-221 −2.7 0.017
DOWN miR-30e-5p −1.8 0.034
DOWN miR-30d −2.1 0.012
DOWN miR-320a −2.5 0.045
DOWN mir-339b −2.5 0.045
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Validating microRNA sequencing results and quantifying differentially expressed miRNAs in blood serum and vaginal fluid samples

qPCR validation was performed on the 30 sequenced samples and a separate independent cohort of 30 buffaloes. To validate the sequencing results, 12 differentially expressed (DE)-miRNAs were selected, and their relative abundance was quantified using RT-qPCR. MiRNA levels were normalized to that of cel-miR-39, an artificial spike-in which was used as an internal control. The selected miRNA targets were detected in all blood serum and vaginal fluid samples. RT-qPCR results for the blood serum demonstrated that the levels of four miRNAs (miR-320a, miR-133a, miR-92a, and miR-221) were significantly downregulated in seropositive buffaloes (Fig. 1). An analysis of 12 selected miRNAs from the vaginal fluid demonstrated that 10 miRNAs (miR-let-7i, miR-150, miR-320a, miR-191, miR-let-7f, miR-339b, miR-30e, miR-151, miR-126-5p, and miR-92a) were significantly differentially expressed between Brucella-positive and negative buffaloes. Figure 2 presents an overview of these results. DE-miRNA levels were unaffected by the animals’ oestrus phases (linear regression, P > 0.05).

Figure 1.

Figure 1

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Box plots of DE-miRNAs in the blood serum. Significance was declared at *P < 0.05, **P < 0.01 and ***P < 0.001. Black lines inside the boxes mark the medians. Whiskers indicate variability outside the upper and lower quartiles.

Figure 2.

Figure 2

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Box plots of the DE-miRNAs in the vaginal fluid. Significance was declared at *P < 0.05, **P < 0.01 and ***P < 0.001. Black lines inside the boxes mark the medians. Whiskers indicate variability outside the upper and lower quartiles.

Assessing the diagnostic value of DE-miRNAs during B. abortus infection

To analyse the diagnostic value of DE-miRNAs in the blood serum and vaginal fluid, ROC curves were analysed, and the associated area under the curve (AUC) was used to confirm the diagnostic potency of each miRNA. The ROC was analysed as previously reported22. Table 2 summarizes the diagnostic performance of each DE-miRNA and shows combinations of some DE-miRNAs. The AUC was fair for blood serum miR-320a and miR-92a and poor for blood serum miR-133a and miR-221 (Supplemental Material 1). The AUC was excellent for vaginal fluid miR-151 and miR-30e, with calculations of 0.957 and 0.931, respectively; good for miR-let-7f, miR-339b, miR-150 and miR-191 (AUC ≥ 0,799); fair for miR-let-7i, miR-92a and miR-320a; and poor for miR-126-5p (Fig. 3). To test potential collinearity, a Spearman correlational analysis was performed on the vaginal fluid miRNAs with excellent and good AUC values, suggesting that relative concentrations of miR-151, miR-339b, miR-150, miR-191, and miR-30e are positively correlated with each other (data not shown).

Table 2.

Sensitivity, specificity, and area under the curve (AUC) for DE-miRNAs in the blood serum and vaginal fluid. MiRNAs combined in the vaginal fluid include miR-let-7f, miR-151, miR-30e, miR-191, miR-150 and miR-339b.

MiRNA AUC 95% CI P Value Cut-Off Sensitivity Specificity
BLOOD SERUM miR-320a 0.736 0.603–0-844 0.0005 1.33 84 65.62
miR-92a 0.749 0.616–0.854 0.0001 0.82 80 65.62
miR-133a 0.693 0.556–0.808 0.0069 1.08 76 56.25
miR-221 0.661 0.524–0.781 0.026 1.25 72 56.25
miR-92a, miR-320a 0.753 0.620–0.857 0.0001 0.90 80 56.25
VAGINAL FLUID miR-126-5p 0.67 0.491–0.819 0.0142 0.04 42.86 92.86
miR-92a 0.76 0.589–0.886 0.0011 0.099 86.36 50
miR-320 0.727 0.554–0.862 0.014 11.12 86.36 64.29
miR-let7f 0.88 0.728–0.964 <0.0001 25.13 95.45 64.29
miR-let7i 0.799 0.632–0.913 0.0007 7.57 95.45 64.29
miR-151 0.957 0.843–0.996 <0.0001 2.0936 100 75
miR-30e 0.931 0.806–0.986 <0.0001 2.133 95.24 70
miR-339 0.89 0.753–0.966 <0.0001 1.85617 95.25 70
miR-150 0.826 0.676–0.926 <0.0001 3.31 95.24 55
miR-191 0.898 0.763–0.970 <0.0001 2.09 95.24 70
miRNAs combination 0.88 0.742–0.959 <0.0001 0.023 94.45 85
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Figure 3.

Figure 3

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Receiver-operator characteristic (ROC) curve analysis of DE-miRNAs in the vaginal fluid. AUC, area under the curve; CI, confidence interval.

Discriminant analysis was applied to further investigate the potential for improving diagnostic performance by analysing multiple DE-miRNAs. Statistical analysis was performed examining the weighted average relative quantification (RQ) values of the miRNAs with an AUC > 0.7 for serum blood (miR-320a and miR-92a) and AUC > 0.8 for vaginal fluid (miR-let-7f, miR-151, miR-30e, miR-191, miR-150 and miR-339b) (Fig. 4). Median expression levels in the blood sera were 0.649 (range, 0.229 to 1.639) and 0.542 (range, 0.188 to 1.889) and in the vaginal fluid 11.62 (range, −6.6 to 40.36) and −6.16 (range, −67.6 to 73.8) in Brucella-positive and Brucella-negative buffaloes, respectively (Fig. 4A,C). The predicted probability of being discriminated as positive from the logit model based on the two blood sera [logit = (0.6045 × expression level of miR-320a) + (0.4045 × expression level of miR-92a)] or the six vaginal fluid miRNAs [logit = (0.936 × expression level of let-7f) + (4.183 × expression level of miR-151) + (−13.777 × expression level of miR-339b) + (−21.946 × expression level of miR-30e) + (5.372 × expression level of miR-150) + (3.784 × expression level of miR-191)] was used to construct the ROC curves (Fig. 4C,D). The AUC for the combined blood serum miRNAs was 0.753 (95% CI 0.620–0.857) with a cut-off value of 0.90 and 80% sensitivity and 56.25% specificity. The AUC for the combined vaginal fluid miRNAs was 0.88 (95% CI 0.742–0.959) with a cut-off value of 0.023 and 95.45% sensitivity and 85% specificity. Clustering patterns were further visualized using multidimensional scaling plots (MDS) (Fig. 5). These plots generate distances between samples corresponding to the biological coefficient of variation between miRNAs in each sample. The MDS plots comparing seropositive and seronegative buffaloes showed distinct groupings of infected and control animals in both the blood serum and vaginal fluid samples, highlighting a clear miRNA expression distinction by infection type.

Figure 4.

Figure 4

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Average expression of the DE-miRNAs in the blood serum and vaginal fluid. Weighted average relative quantification (RQ) values of DE-miRNAs in the blood serum (A) and vaginal fluid (C). ROC curve analysis, constructed using the logit model, for DE-miRNAs in the blood serum (B) and vaginal fluid (D). AUC, area under the curve; CI, confidence interval. Black lines mark the medians. ***P < 0.0001.

Figure 5.

Figure 5

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Multidimensional scaling plots comparing miRNA expression levels in the (A) blood serum and (B) vaginal fluids of Brucella-positive and negative buffaloes. For comparison to the seronegative buffaloes, seropositive buffaloes are grouped together.

miRNA target prediction and pathway enrichment

Predicted significant DE-miRNA targets were computationally fetched from the TargetScan database, and genes with cumulative weighted context++ scores (CWCS) < −0.4 were further analysed. mRNA was enriched using DAVID bioinformatics to explore the function and pathogenesis of these candidate biomarkers. Gene Ontology (GO) analysis was performed using DAVID at three levels: Molecular Function (MF), Cellular Component (CC) and Biological Process (BP). Figure 6A illustrates the top 10 items that were significantly enriched by target genes for each of the above GO levels. The enriched GO terms in MF mainly included protein binding and kinase activities. The CC items in which the predicted targets were involved were related to proteins involved in structure (nucleus and cytoplasm) and function (signal transduction and replication, such as G-protein complexes and transcription factors, respectively). Most GO BP items converged on regulating proliferation-apoptosis processes. KEGG pathway analysis was carried out on the whole targets of miRNA biomarkers using DAVID. Since altered immune responses are believed to contribute to the bacteria’s ability to hide and survive in the host, mRNA targets encoding for genes involved in immune pathways were enriched. The list of immune-related targets included 78 genes that were employed in further analyses. The top 15 significantly enriched KEGG pathways are outlined in Fig. 6B, with chemokine signalling, transcriptional misregulation, Chagas disease and FoxO signalling being the top pathways.

Figure 6.

Figure 6

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Target prediction and pathway enrichment. (A) GO annotation of genes regulated by identified DE-miRNAs. The target genes were annotated by DAVID at three levels: molecular function, cellular component and biological process. The top 10 significantly enriched items are shown. (B) Pathway enrichment analysis for genes regulated by DE-miRNAs. Genes regulated by DE-miRNAs were retrieved and enriched in KEGG using DAVID. The P value was negative 10-base log transformed. The top 15 enriched KEGG pathways are reported.

Discussion

Although serum circulating miRNAs are some of the most promising clinical diagnostic or therapeutic biomarkers for diagnosing various diseases and stress disorders in both humans33 and animals22,23, their diagnostic potential in veterinary medicine remains to be fully explored.

To the best of the authors’ knowledge, the present study is the first to address the relationship between Brucella infection and miRNAs in dairy water buffaloes. We adopted a multistep approach, using miRNomics as a first step to identifying differentially expressed miRNAs in blood serum, then validated them in blood serum and vaginal fluid, which was shown to be a reliable source of microRNA for the first time. We selected vaginal fluid as a potential miRNA source because the reproductive system is the preferential target organ for Brucella infection. We found that (I) the levels of four miRNAs were significantly downregulated in the blood serum of seropositive buffaloes; (II) the levels of 10 miRNAs were significantly higher in the vaginal fluids of seropositive than in seronegative animals; (III) diagnostic accuracy for B. abortus was excellent (0.90 < AUC < 1) for two miRNAs (miR-151 and miR-30e) and good (0.80 < AUC < 0.90) for four miRNAs (miR-let-7f, miR-339b, miR-150 and miR-191); and (IV) the diagnostic accuracy of the combination of six differentially expressed miRNAs was good (AUC = 0.88) with 95.45% sensitivity and 85% specificity. We also demonstrated that miRNAs can be efficiently extracted from vaginal fluids.

Sequencing results provided a list of differentially abundant miRNAs but validation with RT-qPCR demonstrated that the differential expression rate in the serum between Brucella-seropositive and seronegative animals was not statistically significant. Therefore, the results suggested that miRNA expression levels were similar in the blood sera of seropositive and seronegative water buffaloes. Nevertheless, technical bias inherent in sequencing technologies may generate distorted results. A substantial distortion between miRNA levels in NGS data and true miRNA abundancy may occur using Illumina sequencing technology34. Because vaginal fluids may express the same miRNAs as in the serum during B. abortus infection35, these miRNAs were quantified, and the results are promising.

In addition to well-established tests, such as serological and microbiological tests, a molecular approach was used to broadly assess Brucella spp. infections36–38. MiRNAs regulate gene expression in many pathophysiological pathways, including those involved in microbial infection. Changes in host miRNA expression occur following infection with exclusively extracellular (Helicobacter pylori) or intracellular (Salmonella enterica) gram-negative bacteria, as well as in response to gram-positive bacteria (Listeria monocytogenes) and other pathogens (Mycobacterium and Francisella species)17,39. The infected cells modulate miRNAs, suppressing host immune responses and promoting microbial pathogenesis. For example, let-7 family miRNAs, which were upregulated in the present study, are involved in modulating two major cytokines, IL6 and IL10, which have opposite effects on the immune response: specifically IL6 promotes, whereas IL10 dampens the proinflammatory programme. The miR-150 family (both miR150 and miR-151 were downregulated in the present study) is involved in lymphocyte development and regulation; a recent study identified that serum circulating miR-150 is a general lymphocyte activation sensor and may serve as a biomarker for human lymphocyte activation in healthy and diseased conditions40. The miR-30 family, to which miR-30e belongs and which was downregulated in the present study, is involved in modulating host SUMOylation by downregulating UBC9, a protein involved in T cell development41. The results reported herein suggest that B. abortus may influence the immune responses of infected buffaloes by modulating the production and exocytosis of miRNAs that influence both innate and adaptive immune responses, potentially acting on phagocytic cells and lymphocytes as previously suggested for cattle42. Accurate screening tests are important for the success of brucellosis eradication programmes. This study suggests that vaginal DE-miRNA levels accurately differentiated B. abortus-infected buffaloes from controls. Many miRNAs aberrantly expressed mRNAs have been identified between Brucella-infected and noninfected buffaloes, and the relative expressions of DE-miRNAs, such as miR-151 and miR-30e, were used to discriminate seropositive vs seronegative animals. These miRNAs had high sensitivity (>94%) up to 100% for miR-151, but low specificity, which reached 85% when some of these were combined. These data showed that miRNAs collected from vaginal fluids may be suitable for use in screening because they can solve many problems that arise from other tests14,15. The sensitivity and specificity of the immunological tests used to routinely diagnose brucellosis in cattle have been summarized by several authors16,43,44 who believe that more than one test should be applied to serologically diagnose brucellosis in the field. The main issue concerns test sensitivity, which normally does not exceed 90%, whereas specificity can reach 100%. Quantifying a panel of miRNAs in the vaginal fluid increases sensitivity to 95% with an adequate 85% specificity. The authors believe that applying this miRNA panel may help to identify infected animals, particularly in extensive breeding systems where brucellosis is endemic or transmission is relatively low. Combining immunological and molecular tests may enable an ideal balance between specificity and sensitivity. Our results suggest that noninvasive molecular biomarkers may help to more accurately assess and monitor Brucella infections.

Gene Ontology analysis demonstrated that DE-miRNAs regulated mRNA coding for proteins involved in several molecular functions, cellular components and biological processes involved in Brucella pathogenesis. For example, these DE-miRNAs may target genes coding for G-proteins, plasma membranes and membrane rafts, which are essential for Brucella endocytosis, highlighted at the CC and MF levels. The pivotal role of these mechanisms was elucidated by Rossetti and colleagues45, who demonstrated that pathogens attaching to the cell surface induce a zipper-like mechanism for internalization, and binding activates the GTPases, promoting cytoskeleton reorganization and thus host cell membrane rearrangement. Inside cells, these pathogens reside in the Brucella-containing vacuole (BCV) and influence two other processes potentially targeted by DE-miRNAs: the inflammatory reaction, which is modulated by T4SS (type IV secretion system)46, and changes in host cell transcription processes47. PI3K (phosphatidylinositol 3-kinase) activities, highlighted by both GO and KEGG analysis, are involved in Brucella uptake48 and modulating host cell proinflammatory responses. PI3K promotes binding in TIR-containing proteins and subsequent TLR signalling activation. Sengupta and colleagues49 demonstrated that Brucella expressed the TIR-containing protein, Btp1, which competes with MyD88 and interferes with the TLR4/TLR2 cascade, dendritic cell maturation, and proinflammatory cytokine production. KEGG analysis identified genes and pathways related to cell apoptosis: proliferation and chemokine signalling. B. abortus infection does not lead to cell death50, and B. suis inhibits apoptosis induced by chemical stimuli in human macrophages51. The mechanisms underlying apoptosis inhibition, include modulating genes from the BCL2 pathway51, with both pro- and anti-apoptotic activities. The DE-miRNAs may modulate apoptosis-related genes, promoting cell survival by targeting prosurvival BCL2 proteins, such as BCL2 (B-cell lymphoma 2) and MCL1 (ML1 myeloid cell leukaemia 1), and prodeath proteins, such as BAK1 (Bcl-2 homologous antagonist/killed 1) and PUMA (p53-upregulated modulator of apoptosis). Apoptosis is an important innate immune mechanism that eliminates pathogen-infected cells; the possibility that Brucella may modulate host miRNAs regulating apoptotic signalling pathways might partially explain Brucella’s ability to promote propagation and evade host defences.

In conclusion, the present study identified for the first time DE-miRNAs in the blood serum and vaginal fluid of water buffaloes affected by Brucella abortus compared with healthy animals. These miRNAs were involved in regulating the transcriptions of genes related to the molecular pathogenesis of brucellosis. Moreover, we found that miR-let-7f, miR-151, miR-30e, miR-191, miR-150 and miR-339b extracted from vaginal fluids are potentially useful biomarkers of Brucella infection. Although this study provided new and important insights into brucellosis pathogenesis, further experiments involving more animals are required to validate the miRNAs’ potential use in diagnosing brucellosis. A better grasp of the mechanisms regulated by these miRNAs will be important for developing interventions such as designed therapy and vaccines.

Materials and Methods

Ethical statement

Samples were collected during routine disease testing for the national brucellosis eradication programme. The study design was approved by Campania Region DGR No. 352/2013 and DRD No. 603 dated 11/03/2014. All experiments were performed in accordance with the relevant guidelines and regulations.

Animal husbandry, sample collection and oestrus phase classification

Blood and vaginal fluid samples were collected from 60 buffaloes (Bubalus bubalis) raised on 3 farms in the Caserta area in June and July 2016 during routine national brucellosis prophylaxis programme screening. Animals were housed in an open yard breeding system. Blood was collected by tail venepuncture in serum and Monovette EDTA tubes (Sarstedt Company, Nümbrecht, Germany) and centrifuged at 800 × g for 15 min. Serum and plasma were stored at −80 °C. To collect the vestibulovaginal fluid swabs, the buffaloes were restrained and a sterile swab (sterile swab on a wooden stick with a cotton tip in a polypropylene test tube (12 × 150 mm), FL Medical s.r.l. Unipersonale Padova, Italy) was introduced into the cranial vagina. The swab was exposed to the vestibule-vaginal fluid, pulled inside the pipette and stored at −80 °C.

Oestrous cycle stage was determined by progesterone (P4)52–54 and oestradiol (E2)55 plasma concentrations using radioimmune assays (RIA).

Serological and bacteriological analysis

Serology for brucellosis was performed using the Rose-Bengal and complement fixation tests on all samples, as recommended by the World Organization for Animal Health (OIE) Manual of Diagnostic Tests and Vaccines for Terrestrial Animals 2017 (version adopted in May 2016)56. Microbiological analysis was performed as previously reported by O’Grady and colleagues57.

MiRNA extraction, library preparation, and sequencing

Total RNA was extracted using the miRNeasy Serum/Plasma Kit (Qiagen, catalogue number 217184, Milan, Italy). Serum was thawed on ice and centrifuged at 3000 × g for 5 min at 4 °C. An aliquot of 200 μl per sample was transferred to a new tube, and RNA was extracted using miRNeasy Serum/Plasma Kits (Qiagen, catalogue number 217184, Milano, Italy) in accordance with the manufacturer’s instructions. Animals were divided into six pools of 5 by the presence or absence of Brucella infection, and the pools were sequenced.

Libraries were prepared using TruSeq SmallRNA Sample Prep kits (Illumina, San Diego, CA, USA) following the manufacturer’s instructions. Both RNA samples and final libraries were quantified using the Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA) and quality tested by the Agilent 2100 Bioanalyzer RNA Nano assay (Agilent Technologies, Santa Clara, CA, USA). Libraries were then processed with Illumina cBot for cluster generation on flowcells, following the manufacturer’s instructions and sequenced on single-end mode at the multiplexing level requested on HiSeq. 2500 (Illumina, San Diego, CA). The CASAVA 1.8.2 version of the Illumina pipeline was used to process raw data for both format conversion and demultiplexing58. The reads obtained were mapped to the Bos taurus database because the complete Bubalus bubalis genome is lacking.

Validation by qPCR

Total RNA was extracted using the miRNeasy Serum/Plasma Kit (Qiagen, catalogue number 217184, Milano, Italy) as reported above. One millilitre of Qiazol (Qiagen) was added to an aliquot of 200 μl per sample. After incubation at room temperature for 5 min, 3.75 μl (25 fmol final concentration) of the exogenous synthetic spike-in control Caenorhabditis elegans miRNA cel-miR-39 (Qiagen, catalogue number 219610) was spiked into samples at the beginning of the extraction procedure. Reverse transcription was performed in 15 μl volume reactions using the TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems, catalogue number 4366596, Monza, Italy) using miRNA-specific stem-loop RT primers. The mix reactions contained 1.5 µl 10× miRNA RT buffer, 1 µl MultiScribe reverse transcriptase (50 U/µl), 0.30 µl 100 mM dNTP mix, 0.19 µl RNase Inhibitor (20 U/µl), 6 μl of custom RT primer pool and 3.01 μl of nuclease-free water. The custom RT primer pool was prepared by combining 10 µl of each 5× RT primer in a final volume of 1000 µl; the final concentration of each primer in the RT primer pool was 0.05× each. Three μl of serum RNA was added to each RT reaction. Each RT reaction mixture was incubated on ice for 5 min, 16 °C for 30 min, 42 °C for 30 min and 85 °C for 5 min.

To validate the sequencing results, the qPCR experiments were designed following MIQE guidelines59. Small RNA TaqMan assays were performed per the manufacturer’s instructions using the selected primer/probe assays (ThermoFisher Scientific, Monza, Italy) reported in Table 3. Quantitative reactions were performed in duplicate in scaled-down (12 μl) reaction volumes using 6 μl TaqMan 2X Universal Master Mix II (ThermoFisher Scientific, Monza, Italy, catalogue number 4440044), 0.6 μl miRNA specific TaqMan Assay 20× and 1μl of the RT product per reaction on the Eco Real-Time PCR detection system (Illumina, Milan, Italy). The standard cycling programme was 50 °C for 2 min, 95 °C for 10 min and 40 cycles at 95 °C for 15 sec and 60 °C for 60 sec. Data were normalized relative to the cel-miR-39 expression. MiRNA expression levels are presented as fold changes normalized to cel-miR-39 expression using the 2−ΔΔCq formula. The significant miRNA targets were determined using the TargetScan database (http://www.targetscan.org/vert_71/), functional mRNA were enriched using DAVID bioinformatics resources (https://david.ncifcrf.gov/), and biological pathways in KEGG were examined for enrichment (http://www.genome.jp/kegg/).

Table 3.

List of TaqMan probes (ThermoFisher Scientific, Monza, Italy) and assay IDs.

miRNA Assay ID
cel-miR-39-3p 000200
hsa-let-7i 002221
hsa-miR-320a 002277
hsa-miR-92a 000431
hsa-miR-126-5p 000451
hsa-let-7f 000382
hsa-miR-151-3p 002254
hsa-miR-30e-5p 007791_mat
hsa-miR-150 006586_mat
hsa-miR-339b 241893_mat
hsa-miR-191 002299
mmu-miR-221 001134
hsa-miR-133a 002246
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Statistical analysis

Statistical analysis was performed using XLStat for Windows (Addinsoft, New York, U.S.A.) and MedCalc 14.0 (MedCalc Software bvba, Ostend, Belgium). Statistical significance was accepted at P < 0.05. Data were tested for normality and homogeneity of variance using the Kolmogorov-Smirnov and Levene tests, respectively. Because the data were not normally distributed, nonparametric statistical tests were applied. The Kruskal-Wallis test was used to assess differences in miRNA concentrations. P values were adjusted using the Bonferroni correction. Linear regression was used to investigate relationships between DE-miRNAs and oestrus phases. Receiver operating characteristic (ROC) analysis was performed to determine the diagnostic accuracy of targets that statistically differed between Brucella-positive and negative animals. The diagnostic values were calculated for miRNAs that showed significant differential expression in the buffalo blood. Distance matrices were processed by multidimensional scaling (MDS) to obtain a dimensionally reduced map of the miRNA coordinates. Linear regression was used to investigate relationships between miRNAs and the buffaloes’ ages. Spearman’s Rho test was performed to evaluate whether the expression levels of the various miRNAs were correlated.

Supplementary information

Supplementary figure 1 (491.4KB, pdf)

Author Contributions

G.G., G.B. and C.L. designed the study. E.D.C. and F.C. guided the experimental performance. A.M. and D.V. collected the samples. C.L., C.C., V.Z. and G.P. performed the laboratory experiments and bioinformatic data analysis. C.L. wrote the main manuscript. All authors critically read and approved the manuscript.

Competing Interests

The authors declare no competing interests.

Footnotes

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Supplementary information accompanies this paper at 10.1038/s41598-018-38365-x.

References

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