Extracellular vesicles miRNome during subclinical mastitis in dairy cows

Extracellular vesicles miRNome during subclinical mastitis in dairy cows | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Extracellular vesicles miRNome during subclinical mastitis in dairy cows Matteo Cuccato, Sara Divari, Diana Giannuzzi, Riccardo Moretti, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3177629/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Sep, 2024 Read the published version in Veterinary Research → Version 1 posted You are reading this latest preprint version Abstract Bovine mastitis is one of the main inflammatory diseases that can affect the udder during lactation. Somatic cell count and sometimes microbiological tests are routinely adopted during monitoring diagnostics in dairy herds. However, subclinical mastitis is challenging to be identified, reducing the possibilities of early treatments. The main aim of this study was to investigate the miRNome profile of extracellular vesicles isolated in milk as potential biomarkers of subclinical mastitis. Milk samples were collected from a total of 60 dairy cows during routine monitoring tests. Therefore, a smallRNA-sequencing technology was applied to extracellular vesicles of milk samples collected from cows classified according to the somatic cell count, in order to identify differences in the miRNome between mastitic and healthy cows. A total of 1,997 miRNAs were differentially expressed between groups. Among them, 68 miRNAs were obtained with FDR < 0.05, mostly downregulated and with only one upregulated miRNA (i.e., miR -361). Functional analysis revealed that miR -455-3p, miR -503-3p, miR -1301-3p and miR -361-5p were involved in the regulation of several biological processes related to mastitis, including immune system related processes. This study confirmed a strong involvement of extracellular vesicles-derived miRNAs in the regulation of mastitis. Moreover, it provides evidence that miRNA from milk extracellular vesicles can be used to identify biomarkers of mastitis. However, further studies must be conducted to validate those miRNAs, especially for subclinical diagnosis. mastitis bovine dairy cows microRNA extracellular vesicles small RNA-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Bovine mastitis is the principal cause of economic losses in the dairy industry, due to milk production and quality reduction [ 1 ]. In addition, dairy farmers have to deal with increased costs for treatments and the grown herd turnover caused by mastitis outbreaks [ 2 ]. The udder infection is mostly caused by a variety of microorganisms, mainly bacteria ( Staphylococcus spp., Streptococcus spp., and Enterobacteriaceae ), but also yeast belonging to Candida spp. or protozoa of the genus Prototheca [ 3 ]. Clinical classification of mastitis differentiates the disease into clinical and subclinical forms according to the presence or absence of symptoms and signs, such as visibly abnormal milk, swelling, heat, pain and redness of the udder [ 3 ]. Subclinical forms are the main challenge for mastitis control difficulties due to the normal presentation of both the udder and milk, but with increased somatic cell count (SCC) and the presence of bacteria in milk [ 4 ]. According to recent literature on the prevalence of bovine mastitis worldwide, subclinical mastitis is the most prevalent and causes major economic losses [ 5 , 6 ]. Therefore, the early identification of subclinical forms is fundamental for adequate treatments and sustainable dairy herd management. Furthermore, with the Regulation (EU) 2019/6 of the European Parliament about veterinary medicinal products, the European Union introduced strictly limitation of the use of antimicrobials for prophylaxis and metaphylaxis purposes to control the rising and spread of resistance phenomena. In the past, the preventive use of antimicrobials in dairy herds was frequently adopted, especially during the dry period [ 1 , 7 ]. In the new regulatory scenario, the veterinary practitioners working in the dairy industry require alternative solutions for the control of mastitis, including genomic selection for resistance [ 8 ], novel diagnostic and therapeutic tools [ 1 ]. Monitoring of subclinical mastitis is an essential requirement in dairy herd management and consists of SCC measurement and microbiological tests [ 3 ]. Moreover, the SCC parameter is also considered to define milk suitability for human consumption and quality for cheese processing, therefore milk pricing strictly depends on SCC [ 7 ]. An unanimous agreement considers milk with an SCC value higher than 400,000 cells/ml, regardless of the presence of clinical symptoms, as mastitic [ 2 , 9 ]. On the other hand, the milk from a healthy udder has an SCC value lower than 100,000 cells/ml [ 2 , 7 ]. A SCC measurement between those values should be interpreted: it could be typical of subclinical mastitis or a finding related to the recovering processes of mammary gland after infection, when inflammation and pathogens could still be found [ 9 , 10 ]. Nowadays, a SCC value equal to 200,000 cells/mL has been adopted as a threshold to diagnose subclinical mastitis [ 11 ]. However, inflammation of the mammary gland has been also observed at values around 100,000 cells/mL, especially in primiparous cows [ 12 ]. Thus, new biomarkers would help to discriminate cows with subclinical mastitis. In recent years, extracellular vesicles (EVs) have been widely investigated in human and veterinary medicine. EVs are membrane-limited nanoparticles involved in intercellular communication and their presence has been proven in several biological fluids, such as blood, urine, milk and saliva [ 13 – 15 ]. The ability of EVs in regulating cellular and organ processes is due to the presence of different types of cargo inside EVs, such as non-coding RNA, mRNA, DNA, proteins and lipids, which can be delivered to the targeted recipient cell [ 16 ]. Their ability to regulate the cellular communication relies also on the presence of several protein and glycoprotein membrane markers [ 17 ]. In the last decade, EVs have been largely studied and evaluated as innovative biomarkers in human medicine, particularly for cancer and neurodegenerative diseases. In veterinary medicine, microRNAs (miRNAs) role has been particularly investigated in mastitis pathogenesis among domesticated ruminant species (i.e. cow, sheep and goat). miRNAs are an extremely important group of small non-coding RNAs, ranging in length from 20 to 22 nucleotides, that regulate gene expression through mRNA silencing at post-transcriptional level [ 18 ]. Several studies have been conducted in experimentally infected cows and several putative miRNAs differently regulated mainly under Staphylococcus aureus or Escherichia coli infections have been observed [ 19 – 21 ]. Similarly to other infectious diseases, during mastitis miRNAs are involved in the regulation of several pathways, such as pathogen response, arousal and regulation of the inflammatory processes and regulation of the immune response [ 22 ]. The identification of early indicators for rapid and accurate detection of mastitis could lead to earlier and more effective treatment allowing the animal to recover faster, and therefore reducing the associated economic losses. In addition, a better understanding of the molecular regulation of the mammary response to inflammation would allow the identification of such robust indicators. In this context, the main aims of this study were to investigate the role of miRNAs carried by EVs in the regulation of mastitis and to identify putative biomarkers for an early diagnosis of mastitis. 2. Materials and Methods 2.1 Study design and samplecollection As previously described [ 23 ], a total of 120 primiparous Holstein Friesian cows belonging to 10 dairy farms located in the provinces of Cuneo and Turin (Piedmont Region, Northern Italy) were selected. Animals were managed according to the local farm production practices. All manipulations were performed according to the best animal handling and veterinary practices to avoid animal distress. Before sample collection teats were disinfected, and the first milk ejected was collected in a specific container and then discarded. Individual samples were collected in sterile polypropylene tubes (2 aliquots of 50 mL) from all quarters of each cow and immediately stored at 4°C. Before EVs isolation and sequencing, a total of 60 milk samples were randomly selected and further processed for EVs isolation. Milk sampling was conducted during winter (December 2020 – February 2021, n = 52) and summer (June 2021 – September 2021, n = 8) seasons. The resulting dataset was characterized by an average SCC value of 357,220 ± 1,102,894 cells/mL, 138 ± 87.9 (average value ± sd) days in milk (DIM) and 15,8 ± 4.2 kg of milk matter avarage production. Moreover, 17/60 (28.33%) samples were positive to Staphylococcus spp., 2/60 (3.33%) were positive to Streptococcus uberis , while the remaining samples (41/60, 68.33%) had negative bacteriological tests [ 23 ]. Before sequencing, samples were clustered using a SCC threshold level of 200,000 cells/mL, normally considered as a SCC level of subclinical mastitis, in two groups high SCC (group H) and low SCC (group L). 2.3 Statistical analysis The dataset related to the collected milk samples, including parity, DIM, season, farms, milk yield, microbiology and SCC, was fitted to a linear mixed model using the lmer() function from lme4 package [ 24 ] in RStudio (R v.4.1.2, RStudio v. 1.4.1103). Parity and DIM data were set as fixed effects, while time of the sampling (season), farms, milk yield, microbiology and SCC results were set as random effects and the model was then tested using anova() function. 2.4 EVsisolation Milk aliquots were processed with consecutive centrifugations, whose steps were as follows: 3,000 g for 10 min at 4°C to remove milk fat and somatic cells, an additional step of 3,000 g for 10 min at 4°C to remove additional fat and cells residuals, 5,000 g for 30 min at 4°C to remove larger cell debris and finally 10,000 g for 30 min at 4°C to remove smaller cell debris. After these centrifugation steps, the skimmed milk was stored at − 80°C until further analyses. Then, skimmed milk samples were thawed gradually in ice. EVs isolation was performed using a size exclusion chromatography (SEC) system, in detail qEV original 70mm columns (Izon Science, Lyon, France). Before loading milk samples on the SEC column, the volume was reduced using a centrifugation filter tube (AMICON ULTRA-4 50 kDa, Merck Millipore, Burlington, MA, USA) to reach a volume of 500 µL, the maximal loadable capacity of the SEC column. Centrifugations were performed at 4,000 g at 4°C with a variable time from 40 min to 60 min depending on sample viscosity. To avoid protein precipitation at filter level, during centrifugation samples were gently mixed by slow pipetting on ice every 5 min or 10 min pausing the centrifugation. Once reached the 500 µL volume, milk samples were loaded on the SEC columns, which was previously mounted on its automatic fraction collector (qEV AFC system, Izon Science). Following manufacturer's instructions, the first five aliquots (50 µL each) were separately collected in 2 mL tubes corresponding to the most rich and pure EVs fractions. Subsequently, the five EVs fractions (250 µL) were mixed and the volume was reduced with the aforementioned centrifugation filter tube to reach a volume of 50 µL, the minimal useful volume for downstream applications. The protocol was similar to the previously described, excepted the time range of centrifugation was reduced at 15 min to 30 min due to a lower viscosity of samples at this step. Finally, concentrated EVs were stored at -80°C until further analyses. 2.5 Westernblot To validate the EVs isolation method and prove the presence of EVs in the samples, a western blot analysis was conducted. Particularly, two EVs markers were selected as suggested by the MISEV guidelines: TSG101 and CD9 [ 25 ]. Total proteins were extracted from EVs lysate using RIPA buffer supplemented with a protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA). Twenty-five µL of EVs lysate were added with 25 µL of Laemmli buffer (with a 2-mercaptoethanol supplementation just for TSG101) and resolved by 10% and 15% SDS–PAGE for TSG101 and CD9 assessment, respectively. Proteins were blotted to PVDF membranes using the Mini Trans-Blot cell (Bio-Rad, Hercules, CA, USA). The blotted membranes were blocked with a 10% BSA solution (Merck Millipore) for 1 hour at room temperature, followed by an overnight 4°C incubation with the mouse monoclonal anti-TSG101 primary antibody (1:200; sc-136111, Santa Cruz Biotechnology, Dallas, TX, USA) and with the rabbit monoclonal anti-CD9 primary antibody (1:1,000; ab92726, Abcam, Cambridge, UK). The membranes were subsequently incubated with a secondary horseradish peroxidase (HRP)-conjugated anti-goat antibody (1:10,000), developed using Clarity Western ECL Substrate (Bio-Rad) and recorded on CL-XPosure X-ray film (Thermo Fisher Scientific, Waltham, MA, USA). During western blot analysis, a protein extract from TE-1 cells lysate was used as positive control. 2.6 small RNAs extraction Total small RNAs were extracted from EVs samples using Maxwell RSC miRNA Blood kit (Promega, Madison, WI, USA), following manufacturer’s instructions. Then, miRNAs were quantified using a Nanodrop spectrophotometer (Thermo Fisher Scientific) and the Qubit microRNA Assay kit. Finally, small RNAs were analysed using the Agilent small RNA kit for the Bioanalyzer 2100 instrument (Agilent Technologies, Santa Clara, CA, USA). 2.7 smallRNA-sequencing Library preparation for next-generation sequencing was performed using SMARTer smRNA-seq kit for Illumina (Cat. no. #635031, Clontech Laboratories Inc., Kusatsu, Japan) according to the manufacturer’s protocol. Following PCR amplification, purification, and validation, sequencing libraries required size selection performed using SPRIselect beads (Cat. no. #B23318, Beckman Coulter Life Science, Brea, CA, USA). Quality controls were performed on Bioanalyzer 2100 (Agilent Technologies) and Qubit V4 (Thermo Fisher Scientific). Next-generation sequencing was performed on NextSeq500 (Illumina, San Diego, CA, USA) with the reagents kit V2 (75 cycles; Illumina). Samples were processed starting from single-ended 75bp-long sequencing reads. 2.8 Bioinformatic analysis Fastq files were trimmed using cutadapt (cutadapt 3.5 with Python 3.7.7) following the kit manufacturer instructions (parameters: -m 15 -u 3 -a AAAAAAAAAA). Trimmed fastq files were processed using the miRNAseq workflow implemented in docker4seq [ 26 – 28 ]. Briefly, quality control of trimmed reads was performed using FastQC software v. 0.11.9 ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ). Quality of trimmed reads was checked to evaluate the overall distribution of sequenced fragments length. Then, trimmed reads were mapped on bovine miRNA precursors (miRbase 22) using BWA aligner (v. 0.7.12) [ 29 ], and mature 5p and 3p miRNAs were counted with a R script embedded in the docker4seq workflow. Counts filtering, data normalisation, and differential expression analysis were performed in RStudio. We first normalised the miRNAs count matrix with the sequencing depth for each sample by calculating counts per million (CPM). Then, we filtered out genes expressed in less than 10 samples with CPM < 0.5 using the cpm() function from edgeR package (v. 3.36.0) [ 30 ]. miRNAs failing these criteria were removed from the count matrix before the exploration, and differential expression analyses. Once achieved a filtered miRNAs matrix, exploratory analysis of the expressed miRNAs was performed using unsupervised principal component analysis (PCA) and the non-parametric multidimensional similarity (NMDS) analysis with ggplot2 (v 3.3.5) R package [ 31 ]. Differentially expressed (DE) miRNAs analysis was performed pairwise using edgeR (v. 3.36.0) package [ 30 ]. The differential analysis was performed by comparing samples according to their SCC threshold level of 200,000 cells/mL. Counts from expressed genes were first normalised with the calcNormFactors() function [ 32 ]. Then, voom() function from limma R package (v.3.50.0) was used to fit a generalised linear regression model to correct the data with the group as a fixed effect [ 33 ]. The p-values were adjusted for multiple testing using the Benjamini and Hochberg procedure [ 34 ]. Only DE miRNAs with an adjusted P-value < 0.05 were used for the downstream pathway analysis. 2.9 In silico functional analysis Using only DE miRNAs, a predictive functional analysis was performed to evaluate the influence of the DE miRNAs on biological processes and pathway. In silico analyses were performed using human orthologs ( hsa- ), instead of bovine orthologs ( bta- ), since information about miRNAs activity is more detailed and extensive in human than bovine. In this particular step, we used miRWalk 2.0 ( http://mirwalk.umm.uni-heidelberg.de ), OmicsNet 2.0 ( https://www.omicsnet.ca ) and Cytoscape v.3.9.1 with ClueGO plugin v.2.5.9 ( https://cytoscape.org ) softwares. First, the most deposited mature form (-3p or -5p) of DE miRNAs were checked in miRBase database and modified accordingly in the DE miRNA list with the aim of retrieving the most relevant results with the functional analysis. Subsequently, the partially modified DE miRNAs list was used in miRWalk to retrieve all the miRNA-gene interactions. In particular, only validated interactions deposited in miRTarBase database and with biding position data (related to 3UTR, 5UTR and CDS) were selected. In parallel, to identify functional biological categories related to DE miRNAs, Panther Biological Processes analysis was performed using OmicsNet. Then, the categorization of biological processes was manually performed. Biological processes were clustered in two main macro-categories: cell life processes (including cell cycle, regulation of cell cycle, cell proliferation and apoptotic process) and gene expression machinery processes (including regulation of transcription by RNA polymerase II, Transcription DNA-templated, mRNA processing, mRNA splicing via splicesome, and regulation of translation and protein phosphorylation). In addition, target genes involved in the regulation of immunity processes were obtained with ClueGO plugin in Cytoscape using the function GO-ImmuneSystemProcess. These gene lists were filtered by selecting only validated miRNA-gene interactions, previously obtained using miRWalk. The miRNAs-genes network was visualised using Cytoscape, which was used to identify potential networks. 3. Results 3.1 Statistical analysis As previously described, the dataset related to the collected milk samples was fitted to a linear mixed model using the lmer() function from lme4 package in RStudio. Results are showed in Table 1 for each random effects tested in the model using anova() function. 3.2 Western blot To validate the presence of EVs in the samples, a western blot analysis was performed on representative samples, randomly selected from the collected samples. Both EVs markers, TSG101 and CD9, were positive in samples analysed. Specific bands of the two targets are reported in Fig. 1 . 3.3 Annotations and differential analyses In total, 2127 Bos taurus annotated miRNAs (bta-miRNAs) were detected. Using a cut-off value for the SCC parameter of 200,000 cells/ml, a total of 1997 miRNAs were differentially expressed (DE) between group L and group H. Among them, 1267 were upregulated and 730 downregulated including 68 miRNAs with a false discovery rate (FDR) < 0.05. Further investigations were performed using these 68 DE miRNAs. They were mostly downregulated. Intriguingly, only 1 miRNA, bta - miR -361-3p, was upregulated with 2.7-fold change (FC) value. Among the downregulated miRNAs, a total of 15 had a FC < -2. The complete list of DE miRNAs with related FC and p-value data is reported in Additional file 1. 3.3 Functionalanalysis The functional analysis was conducted using human orthologues ( hsa -) instead of bovine ( bta -), since human databases are more complete and more informative regarding miRNA function. The targets for 17 known miRNAs were obtained from OmicsNet with Panther database. In addition, the regulation of immune system processes was investigated using ClueGO plugin in Cytoscape. The main immune processes significantly influenced by the DE miRNAs were mainly related to mediated immunity including immunoglobulin productions and lymphocyte regulation. Therefore, the miRNA-gene networks were displayed using Cytoscape and three separated reactomes were generated considering cell life (Fig. 2 ), gene expression (Fig. 3 ) and immunity processes (Fig. 4 ). Two large nodes of regulation of miRNA-gene interactions involved miR -503-5p and miR -455-3p both cell life and gene expression processes. The third network related to immunity is less complex than the other two. However, 4 main highlighted miRNAs ( miR -455-3p, miR -503-3p, miR -1301-3p and miR -361-5p) were involved in the regulation of immunity. 4. Discussion Bovine mastitis is one of the main challenges that farmers and veterinarians routinely must face in dairy farming. Moreover, the spread of antimicrobial resistance and the recent entering into force of the new European Regulation about the use of veterinary medicinal products amplified the need for the implementation of diagnostic tools to early detect subclinical mastitis, to reduce/avoid antimicrobial treatments. Among the different parameters of mammary gland infection, the increase in the SCC in milk is considered a prognostic value. Therefore, to compare healthy subjects with cows potentially affected by subclinical mastitis, the milk samples were classified according to the SCC measurement, setting the 200,000 cell/ml value as a threshold [ 7 ]. In this study, the miRNome profile of dairy cows affected by subclinical mastitis was investigated and compared to healthy control samples. EVs were obtained from milk samples to better understand the role of miRNAs in the regulation of mastitis and to identify new potential biomarkers of the disease. EVs and miRNAs have been largely studied in human and veterinary medicine for their evaluation as potential biomarkers of different diseases such as cancer, neurodegenerative and infectious diseases. In this study, individual milk samples were collected from a total of 60 Holstein Friesian cows during routinely mastitis screening tests and subjected to EV small RNA-seq analysis to detect miRNome profiles suggestive of the subclinical form of the disease. Indeed, bovine mastitis is able to modify the milk miRNome and several miRNAs have been reported to be differentially expressed during mammary gland inflammation [ 18 ]. The results of the EV miRNome profiling showed statistically significant differences between samples with high SCC level (> 200,000 cell/ml, group H, considered as group with subclinical mastitis) and samples with low SCC level (< 200,000 cell/ml, group L, considered as healthy control group). In particular, 68 miRNAs are DE, mostly downregulated in group H compared to group L. Among these 68 DE miRNAs, 15 miRNAs were downregulated with FC value < -2. Many of these DE miRNAs were specifically related to bovine species and no relevant information could be retrieved from scientific literature. However, three miRNAs can be highlighted: miR -223, miR -124 and miR- 568. The first one, i.e., miR -223, is largely described in literature as regulating the inflammatory response in bovine mastitis [ 35 – 39 ]. All these studies reported an upregulation of miR -223 in response to S. aureus , S. uberis . or S. agalactiae experimental infections. Therefore, miR -223 downregulation in the present study is not consistent with what has been reported in scientific literature. One possible explanation for this controversial result may be related to the different matrixes used for miRNAs extraction. The abovementioned studies investigated miRNAs role during different mastitis models working with mammary gland biopsies [ 35 , 37 , 38 ], cultured Mac-T cells [ 19 ] or blood [ 36 ]. Therefore, an adequate comparison is not really possible. Moreover, in the dataset of this study only two samples were positive to S. uberis and this may not be enough to highlight differences between the groups. Differently, Tzelos and colleagues investigated the expression of miR-223 during mastitis, but a statistically significant different expression was not observed between mastitic and healthy cows [ 40 ]. In addition, in Tzelos’s study miR-223 was investigated both on whole and skim milk samples and not on milk EVs affecting also in this case the possibility of an adequate comparison. However, according to human medicine literature, miR -223 is either expressed almost exclusively or highly enriched in several subsets of white blood cells including T- and B- lymphocytes, neutrophils, mast cells and monocytes [ 41 ]. It is well known that miRNAs that can be observed in milk can have a different origin according to the different cells composing and characterizing the mammary gland during mastitis, i.e. mammary epithelial cells, inflammatory cells, adipocytes, fibroblasts or myoepithelial cells (36). On the other hand, miR -124 and miR -568 were also significantly downregulated in this study and for the first time reported as related to bovine mastitis. According to literature, miR -124 was already described as regulating Schistosoma japonicum and Fasciola gigantica parasitic pathogenesis in both bovine and buffalo [ 42 – 44 ]. Intriguingly, miR -568 inhibitory activity on CD4 + T cells was previously demonstrated suggesting a role in the modulation of lymphocytes activity [ 45 ]. According to the functional analysis, four miRNAs (namely, miR -455, miR -361, miR- 1301, and miR- 503) were mainly involved in the regulation of the biological processes of gene expression, cell life and immune response. As mentioned before, the functional analysis was conducted using human orthologues ( hsa -) and not bovine ones ( bta -). Given this possible bias, the functional results obtained in this study may just be considered as predictive and further studies must be conducted to validate these predictions. However, results can be compared with the scientific literature to contextualise and explain the role of these four miRNAs. MiR- 455 has already been reported in dairy cows subjected to dietary regulation. For example, Webb and colleagues observed a downregulation of miR- 455 in the period after calving in cows fed with a high balanced diet [ 46 ]. Our results are consistent with this study since both reported miR -455 downregulation and its involvement in the regulation of the inflammatory response. Considering human literature, miR- 455 downregulation is associated with the activation of inflammatory pathways in multiple sclerosis [ 47 ]. Moreover, the anti-inflammatory activity of miR- 455 was also described by recent studies reporting an efficient therapeutic role of this miRNA in acute liver injury and cerebral ischemia/reperfusion injury [ 48 , 49 ]. Another interesting result is miR- 361 upregulation, which seems to be involved in the regulation of the biological processes regarding immune response. In bovine, miR- 361 was already reported as downregulated in serum of grazing cows in comparison with housed cattle [ 50 ]. This study may suggest a role of miR- 361 in the regulation of metabolism in cows. In fact, it is well known that mastitis affects the cow metabolism altering lipolysis and milk proteome [ 51 , 52 ]. Therefore, the upregulation of miR -361 in our samples may depend on metabolic factors modified by mastitis. In addition, in humans, miR- 361 is reported as a promising biomarker for tuberculosis diagnosis and it is most likely involved in the regulation of host-pathogen interaction [ 53 , 54 ]. Regarding miR- 1301, its downregulation was firstly reported in mastitis by this study. Luoreng and colleagues reported an upregulation of miR- 1301 in blood of dairy cows experimentally infected by a Staphylococcus aureus strain [ 55 ]. This controversial result may be partially explained by the different biological fluid analysed, milk in the present study and blood in Luoreng study. Furthermore, S. aureus was never identified in the milk samples of this study. It is well-known that etiological agents can differently influence mastitis pathogenesis [ 21 , 55 ], and differences in the milk miRNome between mastitis caused by either Escherichia coli or S. aureus have already been reported [ 56 ]. Therefore, a different regulation of miR- 1301 depending on the specific pathogens could not be excluded. Lastly, miR- 503 represents another important node of regulation according to the functional analysis. Most information regarding miR- 503 are related to human diseases. This miRNA seems to be involved in the pathogenesis of diabetes and lipopolysaccharide injury [ 57 , 58 ], but more intriguingly its downregulation has been reported to be involved in the activation of NF-kB signalling and PPARγ pathway [ 59 , 60 ]. In veterinary medicine, the downregulation of miR -503 was also reported in dog blood mononuclear cells infected by Leishmania infantum , suggesting a role in the modulation of host-pathogen response [ 61 ]. One innovative aspect of this study is the selection of dairy cows naturally affected by mastitis. Studies with an in-field scenario give the opportunity to evaluate pathological conditions taking into account of the actual environmental variability. Moreover, some studies, even if conducted on naturally infected cows, considered a very low sample size [ 62 , 63 ] in comparison with the sample size in this study (60 Holstein Friesian cows). Considering the method applied, another important consideration can be done. In fact, smallRNA-seq allows to investigate the wide and complete panorama of miRNA expression and activity. On the other hand, studies applying qPCR or microarrays can focus only on selected and limited miRNAs and also did not allow the identification of novel bovine miRNAs [ 40 , 64 , 65 ]. Finally, other two studies, respectively from Bagnicka and colleagues and Ozdemir, represent the most comparable studies with the present one [ 66 , 67 ]. Similar methods for smallRNA-seq and a smaller (but still well representative) sample size have been applied. However, the main DE miRNAs reported are different from the present study. The main reason is probably related to the animal selection modality, which analysed only dairy cows positive for coagulase +/- Staphylococci or M. bovis . Instead, the present study focused the selection of cows on SCC and not on bacteriology. Furthermore, as already mentioned, the different biological matrices could influence the results. In fact, it is well known that the origin of miRNAs can affect the miRNA profile. For example, whole milk has a different miRNA profile from skim milk, or somatic cells, or milk fat globules, or mammary gland biopsies [ 68 – 70 ]. 5. Conclusions In conclusion, the results obtained from this study could be a promising starting point in the investigation of miRNAs and EVs regulation of bovine subclinical mastitis. The functional analysis conducted revealed a panel of four miRNAs, promising putative biomarkers of subclinical mastitis: miR -455, miR- 361, miR- 1301 and miR -503. It should be reminded that these results were pointed out by predictive analysis, using human orthologs and therefore their role may not be the same on bovine. To clarify the DE miRNAs results, a future validation using qPCR or ddPCR may be performed on other dairy cows. Furthermore, to define the role of DE miRNAs on the regulation of genes, identified by the functional analysis, and therefore on inflammation, an in vitro luciferase validation test may also be performed. Nevertheless, the poor presence of mastidogen bacteria observed in the dataset may represents a limitation of this study and an additional validation of the obtained results may involve the inclusion of gram negative or Mycoplasma spp. mastitis forms. Further studies must be conducted to identify and validate miRNAs used for mastitis detection, especially subclinical forms. They can be considered to reach an integrative approach between clinical evaluation, SCC, microbiology and miRNAs, which will always be the optimal strategy to early detect bovine mastitis. Declarations Additional data Additional file 1: Table S1, file format .xlsx, DE miRNAs list. In the additional file 1 is reported the complete list of DE miRNAs with related FC and p-value data using the SCC cutoff 200,000 cells/mL for the differential analysis. Availability of data and materials The dataset(s) supporting the conclusions of this article is(are) available in the Genome Sequence Archive (GSA) repository, identified with the code subCRA018425 at https://ngdc.cncb.ac.cn/gsub/submit/biosample/list. Ethics approval and consent to participate Ethical review and approval were not required for the animal study because milk was collected during the routine monitoring of milk quality on farms. Animals were managed according to the local farm-production practices. All manipulations were performed kindly to avoid animal distress. Written informed consent was obtained from the owners for the participation of their animals in this study. Consent for publication Not applicable. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This research was funded by Regione Piemonte (Italy) for the project “Tech4Milk: Tecnologie e soluzioni innovative al servizio della filiera latte piemontese per promuoverne la competitività e sostenibilità” (FESR 2014-2020 – D24I19000980002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions FTC and PS conceived, designed the study, and revised and edited the final version of the manuscript. MC, SD, and AR performed the analyses. DG and MC performed bioinformatic analyses. DG, RM, CL and FTC supervised the bioinformatic analyses. MC, SD, DG and CL performed data curation and statistical analysis. FTC and PS supervised the study. FTC and PS acquired the funding. MC wrote the original draft. SD, DG, RM, CL, PS, and FTC reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version. Acknowledgements The authors are grateful to Dr. Alessia Finotto, Dr. Andrea Salaroglio, Dr. Daniele Giaccone (A.R.A.P.) for their technical assistance with milk sampling and bacteriological analyses. 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PLoS ONE 12:. https://doi.org/10.1371/journal.pone.0177182 Srikok S, Patchanee P, Boonyayatra S, Chuammitri P (2020) Potential role of MicroRNA as a diagnostic tool in the detection of bovine mastitis. Prev Vet Med 182:105101. https://doi.org/10.1016/j.prevetmed.2020.105101 Bagnicka E, Kawecka-Grochocka E, Pawlina-Tyszko K, et al (2021) MicroRNA expression profile in bovine mammary gland parenchyma infected by coagulase-positive or coagulase-negative staphylococci. Vet Res 52:. https://doi.org/10.1186/s13567-021-00912-2 Özdemir S (2020) Identification and comparison of exosomal microRNAs in the milk and colostrum of two different cow breeds. Gene 743:. https://doi.org/10.1016/j.gene.2020.144609 Li Y, Zang H, Zhang X, Huang G (2020) Exosomal Circ-ZNF652 promotes cell proliferation, migration, invasion and glycolysis in hepatocellular carcinoma via MiR-29a-3p/gucd1 axis. Cancer Manag Res 12:7739–7751. https://doi.org/10.2147/CMAR.S259424 Pawlowski K, Lago-Novais D, Bevilacqua C, et al (2020) Different miRNA contents between mammary epithelial cells and milk fat globules: a random or a targeted process? Mol Biol Rep 47:8259–8264. https://doi.org/10.1007/s11033-020-05787-8 Leroux C, Pawlowski K, Billa P-A, et al (2022) Milk fat globules as a source of microRNAs for mastitis detection. Livest Sci 263:104997. https://doi.org/10.1016/J.LIVSCI.2022.104997 Table 1 Table 1. Summary results of ANOVA model tested using lmer() package in RStudio. P-values are reported for each one of the tested random effects. Results were considered statistically significant with p-value 0.05) Farm n.s. (> 0.05) Milk yield n.s. (> 0.05) Microbiology n.s. (> 0.05) SCC n.s. (> 0.05) Supplementary Files Additionalfile1.xlsx Additional file 1: Table S1, file format .xlsx, DE miRNAs list. In the additional file 1 is reported the complete list of DE miRNAs with related FC and p-value data using the SCC cutoff 200,000 cells/mL for the differential analysis. Cite Share Download PDF Status: Published Journal Publication published 19 Sep, 2024 Read the published version in Veterinary Research → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3177629","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":233821288,"identity":"3d6b3e0d-1b46-43a8-a44e-d50f53998e00","order_by":0,"name":"Matteo Cuccato","email":"","orcid":"","institution":"University of Turin: Universita degli Studi di Torino","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Cuccato","suffix":""},{"id":233821289,"identity":"4360b346-3a9d-40fe-81ea-d66afb3f0f39","order_by":1,"name":"Sara Divari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIie3OsQqDMBCA4QuBuFRcz0VfIcXFx9HFPkDBpSCKEEdXS+l7dCx0TZ2FLrq4FRw7NnYpXYxjh/xDSCAfdwAm09/GUB2k6AE+l3w1KbkiONsVhn2hfoxf3YcJ0jCuTzdxsC9h5gCt+iXC5S5AaDFu2lg8bIno5prFOCSARGAAkigiEPlVQ/x6pK+Z+IrsVxHoEjZP8bgidBXh3cjCqEVvK0npngW6x5IUzfJiCe2mNNt40hqmp8gcx6r6aXGxuejnRbX/TSaTyaTrDVSiQKg/ZUceAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6085-0336","institution":"University of Turin: Universita degli Studi di Torino","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Divari","suffix":""},{"id":233821290,"identity":"479d4989-6615-429b-8390-cb86baab3ca8","order_by":2,"name":"Diana Giannuzzi","email":"","orcid":"","institution":"University of Padua: Universita degli Studi di Padova","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Giannuzzi","suffix":""},{"id":233821291,"identity":"ab630719-bcac-4ee9-9568-2fa829ca3216","order_by":3,"name":"Riccardo Moretti","email":"","orcid":"","institution":"University of Turin: Universita degli Studi di Torino","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Riccardo","middleName":"","lastName":"Moretti","suffix":""},{"id":233821292,"identity":"1ec74283-1a43-426d-ad97-60db66e4e052","order_by":4,"name":"Andrea Rinaldi","email":"","orcid":"","institution":"Università della Svizzera Italiana: Universita della Svizzera Italiana","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Rinaldi","suffix":""},{"id":233821293,"identity":"64094c89-a875-4e9b-8f3d-ddf11bca58e5","order_by":5,"name":"Christine Leroux","email":"","orcid":"","institution":"INRAE Centre Clermont-Auvergne-Rhône-Alpes: Institut National de Recherche pour l'Agriculture l'Alimentation et l'Environnement Centre Clermont-Auvergne-Rhone-Alpes","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Leroux","suffix":""},{"id":233821294,"identity":"f06161a8-984a-46bd-887f-b8ffce7cf139","order_by":6,"name":"Paola Sacchi","email":"","orcid":"","institution":"University of Turin: Universita degli Studi di Torino","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Paola","middleName":"","lastName":"Sacchi","suffix":""},{"id":233821295,"identity":"693accf5-8f20-42dd-9313-2288a1821497","order_by":7,"name":"Francesca Tiziana Cannizzo","email":"","orcid":"","institution":"University of Turin: Universita degli Studi di Torino","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"Tiziana","lastName":"Cannizzo","suffix":""}],"badges":[],"createdAt":"2023-07-17 10:51:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3177629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3177629/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13567-024-01367-x","type":"published","date":"2024-09-19T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":43530213,"identity":"2e769531-8dc6-4a9c-93c7-2009d15fed5e","added_by":"auto","created_at":"2023-09-22 13:12:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47032,"visible":true,"origin":"","legend":"\u003cp\u003eWestern blot analysis of EVs samples. Analysed EVs markers were TSG-101 (~ 50 kDa) and CD9 (~ 22 kDa).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3177629/v1/ba07edc87f76292a1ff2ce42.png"},{"id":43531966,"identity":"02a3e042-3c09-4193-b7d9-74421afb4424","added_by":"auto","created_at":"2023-09-22 13:20:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":537414,"visible":true,"origin":"","legend":"\u003cp\u003eNetworks of the differentially abundant miRNAs identified using Cytoscape. The results are related to miRNA-gene interactions involved in cell life processes.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3177629/v1/c4c8bbf03b3f55a5be167ba3.png"},{"id":43530215,"identity":"ed1fa034-8105-4073-bb80-abb8be0f2011","added_by":"auto","created_at":"2023-09-22 13:12:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":937944,"visible":true,"origin":"","legend":"\u003cp\u003eNetworks of the differentially abundant miRNAs identified using Cytoscape. The results are related to miRNA-gene interactions involved in gene expression machinery.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3177629/v1/126d5aa7421539b4496b4321.png"},{"id":43530217,"identity":"dc969a8e-ea85-4ab3-be95-23aacb11dcf3","added_by":"auto","created_at":"2023-09-22 13:12:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":266337,"visible":true,"origin":"","legend":"\u003cp\u003eNetworks of the differentially abundant miRNAs identified using Cytoscape. The results are related to miRNA-gene interactions involved in immune system processes.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3177629/v1/a20fdfddf2f4a2f1b6631e0a.png"},{"id":65104066,"identity":"803c48fa-3985-41d5-8152-660674fa71d8","added_by":"auto","created_at":"2024-09-23 16:11:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1905919,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3177629/v1/c1d1a9d7-4062-4a97-8c4e-676b87e5f89b.pdf"},{"id":43532811,"identity":"e9568f6b-eff7-4f41-a6e7-c238878c509b","added_by":"auto","created_at":"2023-09-22 13:28:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":131084,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Table S1, file format .xlsx, DE miRNAs list. In the additional file 1 is reported the complete list of DE miRNAs with related FC and p-value data using the SCC cutoff 200,000 cells/mL for the differential analysis.\u003c/p\u003e","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3177629/v1/fed83aea77b38e044d72042a.xlsx"}],"financialInterests":"","formattedTitle":"Extracellular vesicles miRNome during subclinical mastitis in dairy cows","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBovine mastitis is the principal cause of economic losses in the dairy industry, due to milk production and quality reduction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition, dairy farmers have to deal with increased costs for treatments and the grown herd turnover caused by mastitis outbreaks [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The udder infection is mostly caused by a variety of microorganisms, mainly bacteria (\u003cem\u003eStaphylococcus\u003c/em\u003e spp., \u003cem\u003eStreptococcus\u003c/em\u003e spp., and \u003cem\u003eEnterobacteriaceae\u003c/em\u003e), but also yeast belonging to \u003cem\u003eCandida\u003c/em\u003e spp. or protozoa of the genus \u003cem\u003ePrototheca\u003c/em\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Clinical classification of mastitis differentiates the disease into clinical and subclinical forms according to the presence or absence of symptoms and signs, such as visibly abnormal milk, swelling, heat, pain and redness of the udder [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Subclinical forms are the main challenge for mastitis control difficulties due to the normal presentation of both the udder and milk, but with increased somatic cell count (SCC) and the presence of bacteria in milk [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to recent literature on the prevalence of bovine mastitis worldwide, subclinical mastitis is the most prevalent and causes major economic losses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, the early identification of subclinical forms is fundamental for adequate treatments and sustainable dairy herd management. Furthermore, with the Regulation (EU) 2019/6 of the European Parliament about veterinary medicinal products, the European Union introduced strictly limitation of the use of antimicrobials for prophylaxis and metaphylaxis purposes to control the rising and spread of resistance phenomena. In the past, the preventive use of antimicrobials in dairy herds was frequently adopted, especially during the dry period [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In the new regulatory scenario, the veterinary practitioners working in the dairy industry require alternative solutions for the control of mastitis, including genomic selection for resistance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], novel diagnostic and therapeutic tools [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Monitoring of subclinical mastitis is an essential requirement in dairy herd management and consists of SCC measurement and microbiological tests [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, the SCC parameter is also considered to define milk suitability for human consumption and quality for cheese processing, therefore milk pricing strictly depends on SCC [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. An unanimous agreement considers milk with an SCC value higher than 400,000 cells/ml, regardless of the presence of clinical symptoms, as mastitic [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. On the other hand, the milk from a healthy udder has an SCC value lower than 100,000 cells/ml [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A SCC measurement between those values should be interpreted: it could be typical of subclinical mastitis or a finding related to the recovering processes of mammary gland after infection, when inflammation and pathogens could still be found [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nowadays, a SCC value equal to 200,000 cells/mL has been adopted as a threshold to diagnose subclinical mastitis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, inflammation of the mammary gland has been also observed at values around 100,000 cells/mL, especially in primiparous cows [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, new biomarkers would help to discriminate cows with subclinical mastitis.\u003c/p\u003e \u003cp\u003eIn recent years, extracellular vesicles (EVs) have been widely investigated in human and veterinary medicine. EVs are membrane-limited nanoparticles involved in intercellular communication and their presence has been proven in several biological fluids, such as blood, urine, milk and saliva [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The ability of EVs in regulating cellular and organ processes is due to the presence of different types of cargo inside EVs, such as non-coding RNA, mRNA, DNA, proteins and lipids, which can be delivered to the targeted recipient cell [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Their ability to regulate the cellular communication relies also on the presence of several protein and glycoprotein membrane markers [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the last decade, EVs have been largely studied and evaluated as innovative biomarkers in human medicine, particularly for cancer and neurodegenerative diseases.\u003c/p\u003e \u003cp\u003eIn veterinary medicine, microRNAs (miRNAs) role has been particularly investigated in mastitis pathogenesis among domesticated ruminant species (i.e. cow, sheep and goat). miRNAs are an extremely important group of small non-coding RNAs, ranging in length from 20 to 22 nucleotides, that regulate gene expression through mRNA silencing at post-transcriptional level [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Several studies have been conducted in experimentally infected cows and several putative miRNAs differently regulated mainly under \u003cem\u003eStaphylococcus aureus\u003c/em\u003e or \u003cem\u003eEscherichia coli\u003c/em\u003e infections have been observed [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly to other infectious diseases, during mastitis miRNAs are involved in the regulation of several pathways, such as pathogen response, arousal and regulation of the inflammatory processes and regulation of the immune response [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The identification of early indicators for rapid and accurate detection of mastitis could lead to earlier and more effective treatment allowing the animal to recover faster, and therefore reducing the associated economic losses. In addition, a better understanding of the molecular regulation of the mammary response to inflammation would allow the identification of such robust indicators. In this context, the main aims of this study were to investigate the role of miRNAs carried by EVs in the regulation of mastitis and to identify putative biomarkers for an early diagnosis of mastitis.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and samplecollection\u003c/h2\u003e \u003cp\u003eAs previously described [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], a total of 120 primiparous Holstein Friesian cows belonging to 10 dairy farms located in the provinces of Cuneo and Turin (Piedmont Region, Northern Italy) were selected. Animals were managed according to the local farm production practices. All manipulations were performed according to the best animal handling and veterinary practices to avoid animal distress. Before sample collection teats were disinfected, and the first milk ejected was collected in a specific container and then discarded. Individual samples were collected in sterile polypropylene tubes (2 aliquots of 50 mL) from all quarters of each cow and immediately stored at 4\u0026deg;C. Before EVs isolation and sequencing, a total of 60 milk samples were randomly selected and further processed for EVs isolation. Milk sampling was conducted during winter (December 2020 \u0026ndash; February 2021, n\u0026thinsp;=\u0026thinsp;52) and summer (June 2021 \u0026ndash; September 2021, n\u0026thinsp;=\u0026thinsp;8) seasons. The resulting dataset was characterized by an average SCC value of 357,220\u0026thinsp;\u0026plusmn;\u0026thinsp;1,102,894 cells/mL, 138\u0026thinsp;\u0026plusmn;\u0026thinsp;87.9 (average value\u0026thinsp;\u0026plusmn;\u0026thinsp;sd) days in milk (DIM) and 15,8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 kg of milk matter avarage production. Moreover, 17/60 (28.33%) samples were positive to \u003cem\u003eStaphylococcus\u003c/em\u003e spp., 2/60 (3.33%) were positive to \u003cem\u003eStreptococcus uberis\u003c/em\u003e, while the remaining samples (41/60, 68.33%) had negative bacteriological tests [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Before sequencing, samples were clustered using a SCC threshold level of 200,000 cells/mL, normally considered as a SCC level of subclinical mastitis, in two groups high SCC (group H) and low SCC (group L).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe dataset related to the collected milk samples, including parity, DIM, season, farms, milk yield, microbiology and SCC, was fitted to a linear mixed model using the \u003cem\u003elmer()\u003c/em\u003e function from lme4 package [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] in RStudio (R v.4.1.2, RStudio v. 1.4.1103). Parity and DIM data were set as fixed effects, while time of the sampling (season), farms, milk yield, microbiology and SCC results were set as random effects and the model was then tested using \u003cem\u003eanova()\u003c/em\u003e function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 EVsisolation\u003c/h2\u003e \u003cp\u003eMilk aliquots were processed with consecutive centrifugations, whose steps were as follows: 3,000 g for 10 min at 4\u0026deg;C to remove milk fat and somatic cells, an additional step of 3,000 g for 10 min at 4\u0026deg;C to remove additional fat and cells residuals, 5,000 g for 30 min at 4\u0026deg;C to remove larger cell debris and finally 10,000 g for 30 min at 4\u0026deg;C to remove smaller cell debris. After these centrifugation steps, the skimmed milk was stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further analyses.\u003c/p\u003e \u003cp\u003eThen, skimmed milk samples were thawed gradually in ice. EVs isolation was performed using a size exclusion chromatography (SEC) system, in detail qEV original 70mm columns (Izon Science, Lyon, France). Before loading milk samples on the SEC column, the volume was reduced using a centrifugation filter tube (AMICON ULTRA-4 50 kDa, Merck Millipore, Burlington, MA, USA) to reach a volume of 500 \u0026micro;L, the maximal loadable capacity of the SEC column. Centrifugations were performed at 4,000 g at 4\u0026deg;C with a variable time from 40 min to 60 min depending on sample viscosity. To avoid protein precipitation at filter level, during centrifugation samples were gently mixed by slow pipetting on ice every 5 min or 10 min pausing the centrifugation. Once reached the 500 \u0026micro;L volume, milk samples were loaded on the SEC columns, which was previously mounted on its automatic fraction collector (qEV AFC system, Izon Science). Following manufacturer's instructions, the first five aliquots (50 \u0026micro;L each) were separately collected in 2 mL tubes corresponding to the most rich and pure EVs fractions. Subsequently, the five EVs fractions (250 \u0026micro;L) were mixed and the volume was reduced with the aforementioned centrifugation filter tube to reach a volume of 50 \u0026micro;L, the minimal useful volume for downstream applications. The protocol was similar to the previously described, excepted the time range of centrifugation was reduced at 15 min to 30 min due to a lower viscosity of samples at this step. Finally, concentrated EVs were stored at -80\u0026deg;C until further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Westernblot\u003c/h2\u003e \u003cp\u003eTo validate the EVs isolation method and prove the presence of EVs in the samples, a western blot analysis was conducted. Particularly, two EVs markers were selected as suggested by the MISEV guidelines: TSG101 and CD9 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Total proteins were extracted from EVs lysate using RIPA buffer supplemented with a protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA). Twenty-five \u0026micro;L of EVs lysate were added with 25 \u0026micro;L of Laemmli buffer (with a 2-mercaptoethanol supplementation just for TSG101) and resolved by 10% and 15% SDS\u0026ndash;PAGE for TSG101 and CD9 assessment, respectively. Proteins were blotted to PVDF membranes using the Mini Trans-Blot cell (Bio-Rad, Hercules, CA, USA). The blotted membranes were blocked with a 10% BSA solution (Merck Millipore) for 1 hour at room temperature, followed by an overnight 4\u0026deg;C incubation with the mouse monoclonal anti-TSG101 primary antibody (1:200; sc-136111, Santa Cruz Biotechnology, Dallas, TX, USA) and with the rabbit monoclonal anti-CD9 primary antibody (1:1,000; ab92726, Abcam, Cambridge, UK). The membranes were subsequently incubated with a secondary horseradish peroxidase (HRP)-conjugated anti-goat antibody (1:10,000), developed using Clarity Western ECL Substrate (Bio-Rad) and recorded on CL-XPosure X-ray film (Thermo Fisher Scientific, Waltham, MA, USA). During western blot analysis, a protein extract from TE-1 cells lysate was used as positive control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6 small RNAs extraction\u003c/h2\u003e \u003cp\u003eTotal small RNAs were extracted from EVs samples using Maxwell RSC miRNA Blood kit (Promega, Madison, WI, USA), following manufacturer\u0026rsquo;s instructions. Then, miRNAs were quantified using a Nanodrop spectrophotometer (Thermo Fisher Scientific) and the Qubit microRNA Assay kit. Finally, small RNAs were analysed using the Agilent small RNA kit for the Bioanalyzer 2100 instrument (Agilent Technologies, Santa Clara, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 smallRNA-sequencing\u003c/h2\u003e \u003cp\u003eLibrary preparation for next-generation sequencing was performed using SMARTer smRNA-seq kit for Illumina (Cat. no. #635031, Clontech Laboratories Inc., Kusatsu, Japan) according to the manufacturer\u0026rsquo;s protocol. Following PCR amplification, purification, and validation, sequencing libraries required size selection performed using SPRIselect beads (Cat. no. #B23318, Beckman Coulter Life Science, Brea, CA, USA). Quality controls were performed on Bioanalyzer 2100 (Agilent Technologies) and Qubit V4 (Thermo Fisher Scientific). Next-generation sequencing was performed on NextSeq500 (Illumina, San Diego, CA, USA) with the reagents kit V2 (75 cycles; Illumina). Samples were processed starting from single-ended 75bp-long sequencing reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Bioinformatic analysis\u003c/h2\u003e \u003cp\u003eFastq files were trimmed using cutadapt (cutadapt 3.5 with Python 3.7.7) following the kit manufacturer instructions (parameters: -m 15 -u 3 -a AAAAAAAAAA). Trimmed fastq files were processed using the miRNAseq workflow implemented in docker4seq [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Briefly, quality control of trimmed reads was performed using FastQC software v. 0.11.9 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Quality of trimmed reads was checked to evaluate the overall distribution of sequenced fragments length. Then, trimmed reads were mapped on bovine miRNA precursors (miRbase 22) using BWA aligner (v. 0.7.12) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and mature 5p and 3p miRNAs were counted with a R script embedded in the docker4seq workflow.\u003c/p\u003e \u003cp\u003eCounts filtering, data normalisation, and differential expression analysis were performed in RStudio. We first normalised the miRNAs count matrix with the sequencing depth for each sample by calculating counts per million (CPM). Then, we filtered out genes expressed in less than 10 samples with CPM\u0026thinsp;\u0026lt;\u0026thinsp;0.5 using the \u003cem\u003ecpm()\u003c/em\u003e function from edgeR package (v. 3.36.0) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. miRNAs failing these criteria were removed from the count matrix before the exploration, and differential expression analyses.\u003c/p\u003e \u003cp\u003eOnce achieved a filtered miRNAs matrix, exploratory analysis of the expressed miRNAs was performed using unsupervised principal component analysis (PCA) and the non-parametric multidimensional similarity (NMDS) analysis with ggplot2 (v 3.3.5) R package [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Differentially expressed (DE) miRNAs analysis was performed pairwise using edgeR (v. 3.36.0) package [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The differential analysis was performed by comparing samples according to their SCC threshold level of 200,000 cells/mL. Counts from expressed genes were first normalised with the \u003cem\u003ecalcNormFactors()\u003c/em\u003e function [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Then, \u003cem\u003evoom()\u003c/em\u003e function from limma R package (v.3.50.0) was used to fit a generalised linear regression model to correct the data with the group as a fixed effect [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The p-values were adjusted for multiple testing using the Benjamini and Hochberg procedure [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Only DE miRNAs with an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used for the downstream pathway analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.9 \u003cem\u003eIn silico\u003c/em\u003e functional analysis\u003c/h2\u003e \u003cp\u003eUsing only DE miRNAs, a predictive functional analysis was performed to evaluate the influence of the DE miRNAs on biological processes and pathway. \u003cem\u003eIn silico\u003c/em\u003e analyses were performed using human orthologs (\u003cem\u003ehsa-\u003c/em\u003e), instead of bovine orthologs (\u003cem\u003ebta-\u003c/em\u003e), since information about miRNAs activity is more detailed and extensive in human than bovine. In this particular step, we used miRWalk 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), OmicsNet 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omicsnet.ca\u003c/span\u003e\u003cspan address=\"https://www.omicsnet.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Cytoscape v.3.9.1 with ClueGO plugin v.2.5.9 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org\u003c/span\u003e\u003cspan address=\"https://cytoscape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) softwares. First, the most deposited mature form (-3p or -5p) of DE miRNAs were checked in miRBase database and modified accordingly in the DE miRNA list with the aim of retrieving the most relevant results with the functional analysis. Subsequently, the partially modified DE miRNAs list was used in miRWalk to retrieve all the miRNA-gene interactions. In particular, only validated interactions deposited in miRTarBase database and with biding position data (related to 3UTR, 5UTR and CDS) were selected. In parallel, to identify functional biological categories related to DE miRNAs, Panther Biological Processes analysis was performed using OmicsNet. Then, the categorization of biological processes was manually performed. Biological processes were clustered in two main macro-categories: cell life processes (including cell cycle, regulation of cell cycle, cell proliferation and apoptotic process) and gene expression machinery processes (including regulation of transcription by RNA polymerase II, Transcription DNA-templated, mRNA processing, mRNA splicing via splicesome, and regulation of translation and protein phosphorylation). In addition, target genes involved in the regulation of immunity processes were obtained with ClueGO plugin in Cytoscape using the function GO-ImmuneSystemProcess. These gene lists were filtered by selecting only validated miRNA-gene interactions, previously obtained using miRWalk. The miRNAs-genes network was visualised using Cytoscape, which was used to identify potential networks.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Statistical analysis\u003c/h2\u003e \u003cp\u003eAs previously described, the dataset related to the collected milk samples was fitted to a linear mixed model using the \u003cem\u003elmer()\u003c/em\u003e function from lme4 package in RStudio. Results are showed in Table\u0026nbsp;1 for each random effects tested in the model using \u003cem\u003eanova()\u003c/em\u003e function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Western blot\u003c/h2\u003e \u003cp\u003eTo validate the presence of EVs in the samples, a western blot analysis was performed on representative samples, randomly selected from the collected samples. Both EVs markers, TSG101 and CD9, were positive in samples analysed. Specific bands of the two targets are reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Annotations and differential analyses\u003c/h2\u003e \u003cp\u003eIn total, 2127 \u003cem\u003eBos taurus\u003c/em\u003e annotated miRNAs (bta-miRNAs) were detected. Using a cut-off value for the SCC parameter of 200,000 cells/ml, a total of 1997 miRNAs were differentially expressed (DE) between group L and group H. Among them, 1267 were upregulated and 730 downregulated including 68 miRNAs with a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Further investigations were performed using these 68 DE miRNAs. They were mostly downregulated. Intriguingly, only 1 miRNA, \u003cem\u003ebta\u003c/em\u003e-\u003cem\u003emiR\u003c/em\u003e-361-3p, was upregulated with 2.7-fold change (FC) value. Among the downregulated miRNAs, a total of 15 had a FC \u0026lt; -2. The complete list of DE miRNAs with related FC and p-value data is reported in Additional file 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Functionalanalysis\u003c/h2\u003e \u003cp\u003eThe functional analysis was conducted using human orthologues (\u003cem\u003ehsa\u003c/em\u003e-) instead of bovine (\u003cem\u003ebta\u003c/em\u003e-), since human databases are more complete and more informative regarding miRNA function. The targets for 17 known miRNAs were obtained from OmicsNet with Panther database. In addition, the regulation of immune system processes was investigated using ClueGO plugin in Cytoscape. The main immune processes significantly influenced by the DE miRNAs were mainly related to mediated immunity including immunoglobulin productions and lymphocyte regulation. Therefore, the miRNA-gene networks were displayed using Cytoscape and three separated reactomes were generated considering cell life (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and immunity processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Two large nodes of regulation of miRNA-gene interactions involved \u003cem\u003emiR\u003c/em\u003e-503-5p and \u003cem\u003emiR\u003c/em\u003e-455-3p both cell life and gene expression processes. The third network related to immunity is less complex than the other two. However, 4 main highlighted miRNAs (\u003cem\u003emiR\u003c/em\u003e-455-3p, \u003cem\u003emiR\u003c/em\u003e-503-3p, \u003cem\u003emiR\u003c/em\u003e-1301-3p and \u003cem\u003emiR\u003c/em\u003e-361-5p) were involved in the regulation of immunity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBovine mastitis is one of the main challenges that farmers and veterinarians routinely must face in dairy farming. Moreover, the spread of antimicrobial resistance and the recent entering into force of the new European Regulation about the use of veterinary medicinal products amplified the need for the implementation of diagnostic tools to early detect subclinical mastitis, to reduce/avoid antimicrobial treatments. Among the different parameters of mammary gland infection, the increase in the SCC in milk is considered a prognostic value. Therefore, to compare healthy subjects with cows potentially affected by subclinical mastitis, the milk samples were classified according to the SCC measurement, setting the 200,000 cell/ml value as a threshold [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this study, the miRNome profile of dairy cows affected by subclinical mastitis was investigated and compared to healthy control samples. EVs were obtained from milk samples to better understand the role of miRNAs in the regulation of mastitis and to identify new potential biomarkers of the disease. EVs and miRNAs have been largely studied in human and veterinary medicine for their evaluation as potential biomarkers of different diseases such as cancer, neurodegenerative and infectious diseases. In this study, individual milk samples were collected from a total of 60 Holstein Friesian cows during routinely mastitis screening tests and subjected to EV small RNA-seq analysis to detect miRNome profiles suggestive of the subclinical form of the disease. Indeed, bovine mastitis is able to modify the milk miRNome and several miRNAs have been reported to be differentially expressed during mammary gland inflammation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of the EV miRNome profiling showed statistically significant differences between samples with high SCC level (\u0026gt;\u0026thinsp;200,000 cell/ml, group H, considered as group with subclinical mastitis) and samples with low SCC level (\u0026lt;\u0026thinsp;200,000 cell/ml, group L, considered as healthy control group). In particular, 68 miRNAs are DE, mostly downregulated in group H compared to group L. Among these 68 DE miRNAs, 15 miRNAs were downregulated with FC value \u0026lt; -2. Many of these DE miRNAs were specifically related to bovine species and no relevant information could be retrieved from scientific literature. However, three miRNAs can be highlighted: \u003cem\u003emiR\u003c/em\u003e-223, \u003cem\u003emiR\u003c/em\u003e-124 and \u003cem\u003emiR-\u003c/em\u003e568. The first one, i.e., \u003cem\u003emiR\u003c/em\u003e-223, is largely described in literature as regulating the inflammatory response in bovine mastitis [\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. All these studies reported an upregulation of \u003cem\u003emiR\u003c/em\u003e-223 in response to \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eS. uberis\u003c/em\u003e. or \u003cem\u003eS. agalactiae\u003c/em\u003e experimental infections. Therefore, \u003cem\u003emiR\u003c/em\u003e-223 downregulation in the present study is not consistent with what has been reported in scientific literature. One possible explanation for this controversial result may be related to the different matrixes used for miRNAs extraction. The abovementioned studies investigated miRNAs role during different mastitis models working with mammary gland biopsies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], cultured Mac-T cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] or blood [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, an adequate comparison is not really possible. Moreover, in the dataset of this study only two samples were positive to \u003cem\u003eS. uberis\u003c/em\u003e and this may not be enough to highlight differences between the groups. Differently, Tzelos and colleagues investigated the expression of miR-223 during mastitis, but a statistically significant different expression was not observed between mastitic and healthy cows [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In addition, in Tzelos\u0026rsquo;s study miR-223 was investigated both on whole and skim milk samples and not on milk EVs affecting also in this case the possibility of an adequate comparison. However, according to human medicine literature, \u003cem\u003emiR\u003c/em\u003e-223 is either expressed almost exclusively or highly enriched in several subsets of white blood cells including T- and B- lymphocytes, neutrophils, mast cells and monocytes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. It is well known that miRNAs that can be observed in milk can have a different origin according to the different cells composing and characterizing the mammary gland during mastitis, i.e. mammary epithelial cells, inflammatory cells, adipocytes, fibroblasts or myoepithelial cells (36). On the other hand, \u003cem\u003emiR\u003c/em\u003e-124 and \u003cem\u003emiR\u003c/em\u003e-568 were also significantly downregulated in this study and for the first time reported as related to bovine mastitis. According to literature, \u003cem\u003emiR\u003c/em\u003e-124 was already described as regulating \u003cem\u003eSchistosoma japonicum\u003c/em\u003e and \u003cem\u003eFasciola gigantica\u003c/em\u003e parasitic pathogenesis in both bovine and buffalo [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Intriguingly, \u003cem\u003emiR\u003c/em\u003e-568 inhibitory activity on CD4\u0026thinsp;+\u0026thinsp;T cells was previously demonstrated suggesting a role in the modulation of lymphocytes activity [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the functional analysis, four miRNAs (namely, \u003cem\u003emiR\u003c/em\u003e-455, \u003cem\u003emiR\u003c/em\u003e-361, \u003cem\u003emiR-\u003c/em\u003e1301, and \u003cem\u003emiR-\u003c/em\u003e503) were mainly involved in the regulation of the biological processes of gene expression, cell life and immune response. As mentioned before, the functional analysis was conducted using human orthologues (\u003cem\u003ehsa\u003c/em\u003e-) and not bovine ones (\u003cem\u003ebta\u003c/em\u003e-). Given this possible bias, the functional results obtained in this study may just be considered as predictive and further studies must be conducted to validate these predictions. However, results can be compared with the scientific literature to contextualise and explain the role of these four miRNAs. \u003cem\u003eMiR-\u003c/em\u003e455 has already been reported in dairy cows subjected to dietary regulation. For example, Webb and colleagues observed a downregulation of \u003cem\u003emiR-\u003c/em\u003e455 in the period after calving in cows fed with a high balanced diet [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Our results are consistent with this study since both reported \u003cem\u003emiR\u003c/em\u003e-455 downregulation and its involvement in the regulation of the inflammatory response. Considering human literature, \u003cem\u003emiR-\u003c/em\u003e455 downregulation is associated with the activation of inflammatory pathways in multiple sclerosis [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, the anti-inflammatory activity of \u003cem\u003emiR-\u003c/em\u003e455 was also described by recent studies reporting an efficient therapeutic role of this miRNA in acute liver injury and cerebral ischemia/reperfusion injury [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Another interesting result is \u003cem\u003emiR-\u003c/em\u003e361 upregulation, which seems to be involved in the regulation of the biological processes regarding immune response. In bovine, \u003cem\u003emiR-\u003c/em\u003e361 was already reported as downregulated in serum of grazing cows in comparison with housed cattle [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This study may suggest a role of \u003cem\u003emiR-\u003c/em\u003e361 in the regulation of metabolism in cows. In fact, it is well known that mastitis affects the cow metabolism altering lipolysis and milk proteome [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Therefore, the upregulation of \u003cem\u003emiR\u003c/em\u003e-361 in our samples may depend on metabolic factors modified by mastitis. In addition, in humans, \u003cem\u003emiR-\u003c/em\u003e361 is reported as a promising biomarker for tuberculosis diagnosis and it is most likely involved in the regulation of host-pathogen interaction [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Regarding \u003cem\u003emiR-\u003c/em\u003e1301, its downregulation was firstly reported in mastitis by this study. Luoreng and colleagues reported an upregulation of \u003cem\u003emiR-\u003c/em\u003e1301 in blood of dairy cows experimentally infected by a \u003cem\u003eStaphylococcus aureus\u003c/em\u003e strain [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This controversial result may be partially explained by the different biological fluid analysed, milk in the present study and blood in Luoreng study. Furthermore, \u003cem\u003eS. aureus\u003c/em\u003e was never identified in the milk samples of this study. It is well-known that etiological agents can differently influence mastitis pathogenesis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and differences in the milk miRNome between mastitis caused by either \u003cem\u003eEscherichia coli\u003c/em\u003e or \u003cem\u003eS. aureus\u003c/em\u003e have already been reported [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Therefore, a different regulation of \u003cem\u003emiR-\u003c/em\u003e1301 depending on the specific pathogens could not be excluded. Lastly, \u003cem\u003emiR-\u003c/em\u003e503 represents another important node of regulation according to the functional analysis. Most information regarding \u003cem\u003emiR-\u003c/em\u003e503 are related to human diseases. This miRNA seems to be involved in the pathogenesis of diabetes and lipopolysaccharide injury [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], but more intriguingly its downregulation has been reported to be involved in the activation of NF-kB signalling and PPARγ pathway [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In veterinary medicine, the downregulation of \u003cem\u003emiR\u003c/em\u003e-503 was also reported in dog blood mononuclear cells infected by \u003cem\u003eLeishmania infantum\u003c/em\u003e, suggesting a role in the modulation of host-pathogen response [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne innovative aspect of this study is the selection of dairy cows naturally affected by mastitis. Studies with an in-field scenario give the opportunity to evaluate pathological conditions taking into account of the actual environmental variability. Moreover, some studies, even if conducted on naturally infected cows, considered a very low sample size [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] in comparison with the sample size in this study (60 Holstein Friesian cows). Considering the method applied, another important consideration can be done. In fact, smallRNA-seq allows to investigate the wide and complete panorama of miRNA expression and activity. On the other hand, studies applying qPCR or microarrays can focus only on selected and limited miRNAs and also did not allow the identification of novel bovine miRNAs [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Finally, other two studies, respectively from Bagnicka and colleagues and Ozdemir, represent the most comparable studies with the present one [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Similar methods for smallRNA-seq and a smaller (but still well representative) sample size have been applied. However, the main DE miRNAs reported are different from the present study. The main reason is probably related to the animal selection modality, which analysed only dairy cows positive for coagulase +/- \u003cem\u003eStaphylococci\u003c/em\u003e or \u003cem\u003eM. bovis\u003c/em\u003e. Instead, the present study focused the selection of cows on SCC and not on bacteriology. Furthermore, as already mentioned, the different biological matrices could influence the results. In fact, it is well known that the origin of miRNAs can affect the miRNA profile. For example, whole milk has a different miRNA profile from skim milk, or somatic cells, or milk fat globules, or mammary gland biopsies [\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, the results obtained from this study could be a promising starting point in the investigation of miRNAs and EVs regulation of bovine subclinical mastitis. The functional analysis conducted revealed a panel of four miRNAs, promising putative biomarkers of subclinical mastitis: \u003cem\u003emiR\u003c/em\u003e-455, \u003cem\u003emiR-\u003c/em\u003e361, \u003cem\u003emiR-\u003c/em\u003e1301 and \u003cem\u003emiR\u003c/em\u003e-503. It should be reminded that these results were pointed out by predictive analysis, using human orthologs and therefore their role may not be the same on bovine. To clarify the DE miRNAs results, a future validation using qPCR or ddPCR may be performed on other dairy cows. Furthermore, to define the role of DE miRNAs on the regulation of genes, identified by the functional analysis, and therefore on inflammation, an \u003cem\u003ein vitro\u003c/em\u003e luciferase validation test may also be performed. Nevertheless, the poor presence of mastidogen bacteria observed in the dataset may represents a limitation of this study and an additional validation of the obtained results may involve the inclusion of gram negative or \u003cem\u003eMycoplasma\u003c/em\u003e spp. mastitis forms. Further studies must be conducted to identify and validate miRNAs used for mastitis detection, especially subclinical forms. They can be considered to reach an integrative approach between clinical evaluation, SCC, microbiology and miRNAs, which will always be the optimal strategy to early detect bovine mastitis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAdditional data\u003c/p\u003e\n\u003cp\u003eAdditional file 1: Table S1, file format .xlsx, DE miRNAs list. In the additional file 1 is reported the complete list of DE miRNAs with related FC and p-value data using the SCC cutoff 200,000 cells/mL for the differential analysis.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe dataset(s) supporting the conclusions of this article is(are) available in the Genome Sequence Archive (GSA) repository, identified with the code subCRA018425 at https://ngdc.cncb.ac.cn/gsub/submit/biosample/list. \u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEthical review and approval were not required for the animal study because milk was collected during the routine monitoring of milk quality on farms. Animals were managed according to the local farm-production practices. All manipulations were performed kindly to avoid animal distress. Written informed consent was obtained from the owners for the participation of their animals in this study.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by Regione Piemonte (Italy) for the project \u0026ldquo;Tech4Milk: Tecnologie e soluzioni innovative al servizio della filiera latte piemontese per promuoverne la competitivit\u0026agrave; e sostenibilit\u0026agrave;\u0026rdquo; (FESR 2014-2020 \u0026ndash; D24I19000980002).\u003c/p\u003e\n\u003cp\u003eThe funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eFTC and PS conceived, designed the study, and revised and edited the final version of the manuscript. MC, SD, and AR performed the analyses. DG and MC performed bioinformatic analyses. DG, RM, CL and FTC supervised the bioinformatic analyses. MC, SD, DG and CL performed data curation and statistical analysis. FTC and PS supervised the study. FTC and PS acquired the funding. MC wrote the original draft. SD, DG, RM, CL, PS, and FTC reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to Dr. Alessia Finotto, Dr. Andrea Salaroglio, Dr. Daniele Giaccone (A.R.A.P.) for their technical assistance with milk sampling and bacteriological analyses. Moreover, a special acknowledgement to all dairy farmers involved in the project TECH4MILK for the availability and support in the project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEl-Sayed A, Kamel M (2021) Bovine mastitis prevention and control in the post-antibiotic era. Trop Anim Health Prod 53:. https://doi.org/10.1007/S11250-021-02680-9\u003c/li\u003e\n\u003cli\u003eRuegg PL (2017) A 100-Year Review: Mastitis detection, management, and prevention. J Dairy Sci 100:10381\u0026ndash;10397. https://doi.org/10.3168/JDS.2017-13023\u003c/li\u003e\n\u003cli\u003eAshraf A, Imran M (2020) Causes, types, etiological agents, prevalence, diagnosis, treatment, prevention, effects on human health and future aspects of bovine mastitis. Anim Health Res Rev 21:36\u0026ndash;49. https://doi.org/10.1017/S1466252319000094\u003c/li\u003e\n\u003cli\u003eAshraf A, Imran M (2018) Diagnosis of bovine mastitis: from laboratory to farm. 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BMC Genomics 15:181. https://doi.org/10.1186/1471-2164-15-181\u003c/li\u003e\n\u003cli\u003eLawless N, Vegh P, O\u0026rsquo;Farrelly C, Lynn DJ (2014) The role of microRNAs in bovine infection and immunity. Front Immunol 5:611. https://doi.org/10.3389/fimmu.2014.00611\u003c/li\u003e\n\u003cli\u003eCuccato M, Divari S, Sacchi P, et al (2022) MALDI-TOF mass spectrometry profiling of bovine skim milk for subclinical mastitis detection. Front Vet Sci 9:1009928. https://doi.org/10.3389/fvets.2022.1009928\u003c/li\u003e\n\u003cli\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67:1\u0026ndash;48. https://doi.org/10.18637/jss.v067.i01\u003c/li\u003e\n\u003cli\u003eTh\u0026eacute;ry C, Witwer KW, Aikawa E, et al (2018) Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. 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Cell Stress Chaperones 23:663\u0026ndash;672. https://doi.org/10.1007/s12192-018-0876-3\u003c/li\u003e\n\u003cli\u003eTzelos T, Ho W, Charmana VI, et al (2022) MiRNAs in milk can be used towards early prediction of mammary gland inflammation in cattle. Sci Rep 12:. https://doi.org/10.1038/S41598-022-09214-9\u003c/li\u003e\n\u003cli\u003eLudwig N, Leidinger P, Becker K, et al (2016) Distribution of miRNA expression across human tissues. Nucleic Acids Res 44:3865\u0026ndash;3877. https://doi.org/10.1093/nar/gkw116\u003c/li\u003e\n\u003cli\u003eGuo X, Guo A (2019) Profiling circulating microRNAs in serum of Fasciola gigantica-infected buffalo. Mol Biochem Parasitol 232:111201. https://doi.org/10.1016/j.molbiopara.2019.111201\u003c/li\u003e\n\u003cli\u003eYu X, Zhai Q, Fu Z, et al (2019) Comparative analysis of microRNA expression profiles of adult Schistosoma japonicum isolated from water buffalo and yellow cattle. Parasit Vectors 12:196. https://doi.org/10.1186/s13071-019-3450-7\u003c/li\u003e\n\u003cli\u003eZhou X, Hong Y, Shang Z, et al (2022) The Potential Role of MicroRNA-124-3p in Growth, Development, and Reproduction of Schistosoma japonicum. Front Cell Infect Microbiol 12:862496. https://doi.org/10.3389/fcimb.2022.862496\u003c/li\u003e\n\u003cli\u003eLie PPY, Cheng CY, Mruk DD (2013) Signalling pathways regulating the blood-testis barrier. Int J Biochem Cell Biol 45:621\u0026ndash;625. https://doi.org/10.1016/j.biocel.2012.12.009\u003c/li\u003e\n\u003cli\u003eWebb LA, Ghaffari MH, Sadri H, et al (2020) Profiling of circulating microRNA and pathway analysis in normal- versus over-conditioned dairy cows during the dry period and early lactation. J Dairy Sci 103:9534\u0026ndash;9547. https://doi.org/10.3168/jds.2020-18283\u003c/li\u003e\n\u003cli\u003eTorabi S, Tamaddon M, Asadolahi M, et al (2019) miR-455-5p downregulation promotes inflammation pathways in the relapse phase of relapsing-remitting multiple sclerosis disease. Immunogenetics 71:87\u0026ndash;95. https://doi.org/10.1007/s00251-018-1087-x\u003c/li\u003e\n\u003cli\u003eShao M, Xu Q, Wu Z, et al (2020) Exosomes derived from human umbilical cord mesenchymal stem cells ameliorate IL-6-induced acute liver injury through miR-455-3p. Stem Cell Res Ther 11:37. https://doi.org/10.1186/s13287-020-1550-0\u003c/li\u003e\n\u003cli\u003eZhang Z, Luo W, Han Y, et al (2022) Effect of microRNA-455-5p (miR-455-5p) on the Expression of the Cytokine Signaling-3 (SOCS3) Gene During Myocardial Infarction. J Biomed Nanotechnol 18:202\u0026ndash;210. https://doi.org/10.1166/jbn.2022.3231\u003c/li\u003e\n\u003cli\u003eMuroya S, Ogasawara H, Hojito M (2015) Grazing Affects Exosomal Circulating MicroRNAs in Cattle. PLOS ONE 10:e0136475. https://doi.org/10.1371/journal.pone.0136475\u003c/li\u003e\n\u003cli\u003eAddis MF, Maffioli EM, Ceciliani F, et al (2020) Influence of subclinical mastitis and intramammary infection by coagulase-negative staphylococci on the cow milk peptidome. J Proteomics 226:. https://doi.org/10.1016/j.jprot.2020.103885\u003c/li\u003e\n\u003cli\u003eGiagu A, Penati M, Traini S, et al (2022) Milk proteins as mastitis markers in dairy ruminants - a systematic review. Vet Res Commun. https://doi.org/10.1007/s11259-022-09901-y\u003c/li\u003e\n\u003cli\u003eQi Y, Cui L, Ge Y, et al (2012) Altered serum microRNAs as biomarkers for the early diagnosis of pulmonary tuberculosis infection. 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J Proteomics 85:89\u0026ndash;98. https://doi.org/10.1016/j.jprot.2013.04.027\u003c/li\u003e\n\u003cli\u003eDe Silva N, Samblas M, Mart\u0026iacute;nez JA, Milagro FI (2018) Effects of exosomes from LPS-activated macrophages on adipocyte gene expression, differentiation, and insulin-dependent glucose uptake. J Physiol Biochem 74:559\u0026ndash;568. https://doi.org/10.1007/s13105-018-0622-4\u003c/li\u003e\n\u003cli\u003eZapała B, Kamińska A, Piwowar M, et al (2023) miRNA Signature of Urine Extracellular Vesicles Shows the Involvement of Inflammatory and Apoptotic Processes in Diabetic Chronic Kidney Disease. Pharm Res 40:817\u0026ndash;832. https://doi.org/10.1007/s11095-023-03481-5\u003c/li\u003e\n\u003cli\u003eLee A, Papangeli I, Park Y, et al (2017) A PPAR\u0026gamma;-dependent miR-424/503-CD40 axis regulates inflammation mediated angiogenesis. Sci Rep 7:2528. https://doi.org/10.1038/s41598-017-02852-4\u003c/li\u003e\n\u003cli\u003eZhou R, Gong A-Y, Chen D, et al (2013) Histone deacetylases and NF-kB signaling coordinate expression of CX3CL1 in epithelial cells in response to microbial challenge by suppressing miR-424 and miR-503. PloS One 8:e65153. https://doi.org/10.1371/journal.pone.0065153\u003c/li\u003e\n\u003cli\u003eSoares MF, Melo LM, Bragato JP, et al (2021) Differential expression of miRNAs in canine peripheral blood mononuclear cells (PBMC) exposed to Leishmania infantum in vitro. Res Vet Sci 134:58\u0026ndash;63. https://doi.org/10.1016/j.rvsc.2020.11.021\u003c/li\u003e\n\u003cli\u003eJu Z, Jiang Q, Liu G, et al (2018) Solexa sequencing and custom microRNA chip reveal repertoire of microRNAs in mammary gland of bovine suffering from natural infectious mastitis. Anim Genet 49:3\u0026ndash;18. https://doi.org/10.1111/age.12628\u003c/li\u003e\n\u003cli\u003eSaenz-De-juano MD, Silvestrelli G, Weber A, et al (2022) Inflammatory Response of Primary Cultured Bovine Mammary Epithelial Cells to Staphylococcus aureus Extracellular Vesicles. Biology 11:. https://doi.org/10.3390/BIOLOGY11030415\u003c/li\u003e\n\u003cli\u003eLai Y-C, Fujikawa T, Maemura T, et al (2017) Inflammation-related microRNA expression level in the bovine milk is affected by mastitis. PLoS ONE 12:. https://doi.org/10.1371/journal.pone.0177182\u003c/li\u003e\n\u003cli\u003eSrikok S, Patchanee P, Boonyayatra S, Chuammitri P (2020) Potential role of MicroRNA as a diagnostic tool in the detection of bovine mastitis. Prev Vet Med 182:105101. https://doi.org/10.1016/j.prevetmed.2020.105101\u003c/li\u003e\n\u003cli\u003eBagnicka E, Kawecka-Grochocka E, Pawlina-Tyszko K, et al (2021) MicroRNA expression profile in bovine mammary gland parenchyma infected by coagulase-positive or coagulase-negative staphylococci. Vet Res 52:. https://doi.org/10.1186/s13567-021-00912-2\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;zdemir S (2020) Identification and comparison of exosomal microRNAs in the milk and colostrum of two different cow breeds. Gene 743:. https://doi.org/10.1016/j.gene.2020.144609\u003c/li\u003e\n\u003cli\u003eLi Y, Zang H, Zhang X, Huang G (2020) Exosomal Circ-ZNF652 promotes cell proliferation, migration, invasion and glycolysis in hepatocellular carcinoma via MiR-29a-3p/gucd1 axis. Cancer Manag Res 12:7739\u0026ndash;7751. https://doi.org/10.2147/CMAR.S259424\u003c/li\u003e\n\u003cli\u003ePawlowski K, Lago-Novais D, Bevilacqua C, et al (2020) Different miRNA contents between mammary epithelial cells and milk fat globules: a random or a targeted process? Mol Biol Rep 47:8259\u0026ndash;8264. https://doi.org/10.1007/s11033-020-05787-8\u003c/li\u003e\n\u003cli\u003eLeroux C, Pawlowski K, Billa P-A, et al (2022) Milk fat globules as a source of microRNAs for mastitis detection. Livest Sci 263:104997. https://doi.org/10.1016/J.LIVSCI.2022.104997\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1. Summary results of ANOVA model tested using lmer() package in RStudio. P-values are reported for each one of the tested random effects. Results were considered statistically significant with p-value \u0026lt; 0.05.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.5945945945946%\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.4054054054054%\"\u003e\n \u003cp\u003eANOVA p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.5945945945946%\"\u003e\n \u003cp\u003eSeason of sampling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.4054054054054%\"\u003e\n \u003cp\u003en.s. (\u0026gt; 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.5945945945946%\"\u003e\n \u003cp\u003eFarm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.4054054054054%\"\u003e\n \u003cp\u003en.s. (\u0026gt; 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.5945945945946%\"\u003e\n \u003cp\u003eMilk yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.4054054054054%\"\u003e\n \u003cp\u003en.s. (\u0026gt; 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.5945945945946%\"\u003e\n \u003cp\u003eMicrobiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.4054054054054%\"\u003e\n \u003cp\u003en.s. (\u0026gt; 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.5945945945946%\"\u003e\n \u003cp\u003eSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.4054054054054%\"\u003e\n \u003cp\u003en.s. (\u0026gt; 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mastitis, bovine, dairy cows, microRNA, extracellular vesicles, small RNA-seq","lastPublishedDoi":"10.21203/rs.3.rs-3177629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3177629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBovine mastitis is one of the main inflammatory diseases that can affect the udder during lactation. Somatic cell count and sometimes microbiological tests are routinely adopted during monitoring diagnostics in dairy herds. However, subclinical mastitis is challenging to be identified, reducing the possibilities of early treatments. The main aim of this study was to investigate the miRNome profile of extracellular vesicles isolated in milk as potential biomarkers of subclinical mastitis. Milk samples were collected from a total of 60 dairy cows during routine monitoring tests. Therefore, a smallRNA-sequencing technology was applied to extracellular vesicles of milk samples collected from cows classified according to the somatic cell count, in order to identify differences in the miRNome between mastitic and healthy cows. A total of 1,997 miRNAs were differentially expressed between groups. Among them, 68 miRNAs were obtained with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, mostly downregulated and with only one upregulated miRNA (i.e., \u003cem\u003emiR\u003c/em\u003e-361). Functional analysis revealed that \u003cem\u003emiR\u003c/em\u003e-455-3p, \u003cem\u003emiR\u003c/em\u003e-503-3p, \u003cem\u003emiR\u003c/em\u003e-1301-3p and \u003cem\u003emiR\u003c/em\u003e-361-5p were involved in the regulation of several biological processes related to mastitis, including immune system related processes. This study confirmed a strong involvement of extracellular vesicles-derived miRNAs in the regulation of mastitis. Moreover, it provides evidence that miRNA from milk extracellular vesicles can be used to identify biomarkers of mastitis. However, further studies must be conducted to validate those miRNAs, especially for subclinical diagnosis.\u003c/p\u003e","manuscriptTitle":"Extracellular vesicles miRNome during subclinical mastitis in dairy cows","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-22 13:12:02","doi":"10.21203/rs.3.rs-3177629/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cfd43cb3-59aa-4a9c-a935-9a6bd2724f0c","owner":[],"postedDate":"September 22nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T16:03:49+00:00","versionOfRecord":{"articleIdentity":"rs-3177629","link":"https://doi.org/10.1186/s13567-024-01367-x","journal":{"identity":"veterinary-research","isVorOnly":false,"title":"Veterinary Research"},"publishedOn":"2024-09-19 15:57:49","publishedOnDateReadable":"September 19th, 2024"},"versionCreatedAt":"2023-09-22 13:12:02","video":"","vorDoi":"10.1186/s13567-024-01367-x","vorDoiUrl":"https://doi.org/10.1186/s13567-024-01367-x","workflowStages":[]},"version":"v1","identity":"rs-3177629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3177629","identity":"rs-3177629","version":["v1"]},"buildId":"qQ7_6M8ijIrYJ9CiyUnPg","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}