Circulating cell-free mature microRNAs and their target gene prediction in bovine metritis

Abstract

Uterine infections in dairy cows are common after calving, reduce fertility and cause substantial economic losses. Conventional diagnosis (based on clinical signs) and treatment can be challenging. Serum microRNA (miRNA) profiles serve as non-invasive biomarkers in several pathological conditions including inflammatory diseases. The objective was to identify differentially expressed serum miRNAs in cows with metritis and normal uterus (four cows per group), integrate miRNAs to their target genes, and categorize target genes for biological processes involved in bacterial infection and inflammatory responses. Out of 84 bovine-specific, prioritized miRNAs analyzed, 30 were differentially expressed between metritis and normal cows (p ≤ 0.05, fold regulation ≥2 magnitudes). Bta-miR-15b, bta-miR-17-3p, bta-miR-16b, bta-miR-148a, bta-miR-26b, bta-miR-101 and bta-miR-29b were highly up-regulated whereas bta-miR-148b, bta-miR-199a-3p, bta-miR-122, bta-miR-200b and bta-miR-10a were highly down-regulated in cows with metritis compared to cows with normal uterus. Highly scored target genes of up-regulated and down-regulated miRNAs were categorized for various biological processes, including biological regulation, cellular process, developmental process, metabolic process, localization, multicellular organismal process, response to stimulus, immune system process, cellular components organization, apoptotic process, biological adhesion, developmental process, and locomotion that are critical to combat bacterial infections and provoke inflammatory responses.

Circulating cell-free mature microRNAs and their target gene prediction in bovine metritis

OPEN

Vanmathy Kasimanickam & John Kastelic

Uterine infections in dairy cows are common after calving, reduce fertility and cause substantial economic losses. Conventional diagnosis (based on clinical signs) and treatment can be challenging. in cows with metritis and normal uterus (four cows per group), integrate miRNAs to their target genes, between metritis and normal cows (p

were highly down-regulated in cows with metritis compared to cows with normal uterus. Highly scored target genes of up-regulated and down-regulated miRNAs were categorized for various biological processes, including biological regulation, cellular process, developmental process, metabolic process, localization, multicellular organismal process, response to stimulus, immune system process, cellular components organization, apoptotic process, biological adhesion, developmental process, and

Uterine diseases are prevalent in dairy cows, cause poor reproductive performance, reduced milk yield, and substantial economic losses13. Uterine diseases can be classied as puerperal metritis, clinical metritis, clinical endometritis and subclinical endometritis46. Although uterine diseases are oen diagnosed and classied by systemic signs, nature of uterine discharge, uterine cytology and systemic illness, diagnosis can be challenging46.

Consequently, gene expression and protein production due to uterine inammation in postpartum dairy cows have been studied710. Gene expression of key inammatory cytokines such as tumor necrosis factor- (TNF-), interleukin (IL)-1 and IL-6 in blood monocytes varied between post-partum cows with metritis versus normal uterus11. Endometrial gene expression of mucin-1 (MUC-1) and various cytokines [Toll-like receptor (TLR) 4, IL-1, IL-8, TNF-, insulin-like growth factor-1 (IGF-1), and IGF-binding protein-2 (IGF-BP-2)] diered in cows with uterine inammation compared to unaected post-partum cows12. Although several studies have considered genetic components of uterine inammation in dairy cattle1113, few investigations have elucidated epigenetic changes such as altered expression of regulatory RNAs and their subsequent integration with coding genes that participate in bovine metritis14,15.

Epigenetics denotes heritable changes in gene expression that are not involved with the coding sequence modications. These epigenetic alterations are manifested by DNA methylation, chromatin remodeling, histone modications and small non-coding RNAs16. Mammalian microRNAs (miRNA) are small (20 to 25 nucleotides) evolutionarily conserved non-coding RNAs. They are transcribed by RNA polymerase II enzyme in the nucleus as a long primary transcript (pri-miRNA) which may contain more than one mature miRNAs. Subsequently, pri-miRNAs are processed by RNase III enzyme (e.g., Drosha-DGCR8 complex) to form pre-miRNAs, which are exported to the cytosol by exportin 5. In the cytoplasm, Dicer processes pre-miRNAs to mature miRNAs. Subsequently, one of the strands is incorporated with RNA-induced silencing complex (RISC) where it is directed to its target mRNAs17,18. The MiRNAs are involved in both post-transcriptional gene regulation (causes

Veterinary Clinical Sciences Department & Center for Reproductive Biology, College of Veterinary Medicine, Department of Production Animal Health, Faculty of

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translational suppression) and direct degradation of mRNAs19. To date, 793 bovine mature miRNAs have been identied (http://www.mirbase.org) although their role in pathogenesis of disease is not well documented.

Means and mechanisms that regulate expression of inammatory mediators and terminate their activities are important to understand pathogenesis of uterine inammatory diseases due to various infectious agents. A broad range of regulatory roles of miRNAs in infectious and inammatory diseases in humans have been investigated. Perturbations of miRNAs at tissue expression level and in peripheral circulation have been demonstrated. Induced miR-155 in macrophages potentiated the immune response against Salmonella typhimurium in mice vaccinated against this bacterium20. Furthermore, there were increased levels of intra-renal and urinary miR-155 and miR-146a in human IgA associated inammatory nephropathy21; therefore, various miRNAs levels in body uids might serve as non-invasive biomarkers in inammatory diseases.

Regulatory roles of miRNAs in autoimmune diseases including diabetes, atopic dermatitis, Sjogrens syndrome and inammatory bowel disease have also been recognized2226. Furthermore, regulatory functions of microRNAs in normal uterine physiological status and in pathological disorders (e.g., endometriosis, dysfunctional uterine bleeding and endometrial cancer) in humans have been addressed27. Potential regulatory role of miRNAs in development and progression of bovine subclinical endometritis has been investigated by studying expression of miRNAs in uterine endometrial samples28,29. The objective of this study was to identify dierentially expressed serum miRNAs in cows with metritis or a normal uterus (four cows in each group), integrate miRNAs to their target genes, and categorize target genes for biological processes involved in bacterial infection and inammatory responses.

Materials and Methods

Ethics statement. This study was performed in strict accordance with the ethics, standard operating procedure, handling of animals, collection of biomaterials, and use of biouids for research. The protocol was approved by the institutional animal care and use committee of the Washington State University (Protocol Number: 04070-001).

Cows and sample collection. Cows with metritis (n = 4) of similar severity and cows with no apparent uterine abnormality (n=4) within 1 wk aer calving were included. Aected cows had reddish brown fetid uterine discharge and elevated rectal temperature (>39.5C), whereas normal cows had clear or no uterine discharge and <18% neutrophils on uterine cytology at 21 d post-partum. Blood was collected by coccygeal venipuncture from these postpartum cows at the time of diagnosis and used to investigate circulating miRNAs.

Silica spray-coated red-top 10mL serum tubes (BD Biosciences, San Jose, CA, USA) were used to collect blood. Samples were allowed to clot for 15min at room temperature and then put on ice in a cooler and transported to the laboratory within 2h aer collection. Tubes were centrifuged (1000g for 10min) in a refrigerated centrifuge and serum was apportioned into 0.5-mL aliquots and stored at 80C until further processing.

Small RNA isolation and reverse transcription. Small RNAs were puried using a miRNeasy serum/ plasma kit (Qiagen, Valencia, CA, USA). The miRNeasy serum/plasma kit includes phenol/guanidine-based lysis of samples and silica membrane column-based isolation of small RNAs. The kit was designed to isolate cell-free small RNAs. Frozen serum samples were thawed at room temperature, 150L of serum was pipetted into a 1.5mL eppendorf tube, and 750 L of QIAzol reagent was added, mixed by repeated pipetting and incubated at room temperature for 5 min. Following incubation, 3.5 L miRNeasy serum/plasma spike-in-controls (lyophilized C. elegans miR-39 miRNA mimic) at 1.6 108 copies/L was added. Then, 150 L of chloroform was added and the mixture was shaken vigorously for 15s, incubated at room temperature for 23min, and then centrifuged at 12,000g for 15min at 4C. The upper portion of the aqueous phase was transferred to a new 1.5mL eppendorf tube without contaminating the interphase. Approximately 1.5 volumes of 100% ethanol was added to the aqueous contents and thoroughly mixed by pipetting. Approximately half of the mix was transferred into an RNeasy MinElute spin column in a 2mL collection tube and centrifuged at 12,000g for 15s at room temperature. The ow-through was discarded and this step was repeated with the rest of the sample. The RNA was bound to the membrane. Subsequently, membrane with attached RNAs was washed by 700 L of buer RWT and 500 L of buer RPE with two centrifugation steps. Then, the RNeasy MinElute column in a new 2mL collection tube was dried for 5 min by a full speed centrifugation step (14,000 g) and the ow-through and collection tube were discarded. Subsequently, small RNAs were eluted in a new 1.5mL collection tube in 24L RNase-free water by a centrifugation step (14,000g for 1min).

The RNA was reverse transcribed using miScript II RT kit (Qiagen, Valencia, CA, USA). A HiSpec buer (5) was used to prepare cDNA for mature miRNA proling. RNase-free water, 10x miScript Nucleics mix and 5x HiSpec buer were thawed at room temperature. All contents were mixed by icking the tubes, spun them briey to collect residues and kept on ice. A reverse transcription reaction was prepared by adding 4 L of HiSpec buer, 2L of Nucleics mix, 2L of miScript reverse transcriptase enzyme and 12L of the template RNA. The reaction was gently mixed and briey spun. The mix was incubated at 37C for 60min. Then, reverse transcriptase enzyme was inactivated by incubation at 95C for 5min and the content was placed on ice. Subsequently, the cDNA was diluted in nuclease free water (nal volume, 110 L) and stored at 20C.

MiScript miRNA PCR array technology was used to identify circulating cell-free mature miRNA in bovine serum. Since this expression proling consists of SYBR green-based real-time PCR, single miRNA gene validation experiments were not considered necessary. A bovine miRBase proler plate 1 (Table1, Qiagen, Valencia, CA, USA) was used. The array plate consisted of specic primers to identify 84 highly prioritized bovine mature miRNAs from the most current miRNA genome, as annotated in

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1 2 3 4 5 6 7 8 9 10 11 12

A bta-let-7f bta

miR-101

btamiR-103

bta-miR-125a

bta-miR-125b

bta-miR-126-3p

btamiR-128

btamiR-145

bta-miR-148a

bta-miR-151-3p

bta-miR-151-5p

bta-miR-16b

B bta-miR-

181a

bta-miR-18a

bta-miR-18b

bta-miR-

199a-5p

btamiR-205

bta-miR-20a

bta-miR-21-5p

btamiR-221

btamiR-222

bta-miR-26a

bta-miR-26b

bta-miR-27a-3p

C bta-miR-

27b

bta-miR-29a

bta-miR-30b-5p

bta-miR-

30d bta-miR-31

bta-miR-320a

bta-miR-34b

btamiR-484

btamiR-499

bta-miR-99a-5p

bta-let-

7a-5p bta-let-7d

D bta-let-7g bta-let-7i bta-miR-

106a

btamiR-107

bta-miR-10a

bta-miR-10b

btamiR-122

bta-miR-124a

btamiR-127

btamiR-132

btamiR-138

btamiR-139

E bta-

miR-140

bta-miR-142-3p

bta-miR-142-5p

bta-miR-148b

btamiR-150

bta-miR-15b

bta-miR-17-3p

bta-miR-17-5p

bta-miR-181b

bta-miR-181c

btamiR-186

btamiR-191

F bta-

miR-192

bta-miR-

193a-3p

bta-miR-

193a-5p

bta-miR-

199a-3p

bta-miR-199b

bta-miR-200a

bta-miR-200b

bta-miR-200c

bta-miR-20b

btamiR-210

bta-miR-21-3p

btamiR-214

G bta-

miR-215

btamiR-218

bta-miR-22-5p

bta-miR-23a

bta-miR-23b-3p

bta-miR-

24-3p bta-miR-25

bta-miR-29b

bta-miR-29c

bta-miR-30a-5p

bta-miR-30c

bta-miR-30e-5p

H cel-miR-

39-3p

cel-miR-

39-3p SNORD42B SNORD69 SNORD61 SNORD68 SNORD96A RNU6-6P miRTC miRTC PPC PPC

Table 1. Cow miRBase proler plate 1, consisting of primers for 84 target miRNAs and primers for control genes.

miRBase V20 (www.miRBase.org). Controls included were cel-miR-39-3p, SNORD42B, SNORD69, SNORD61, SNORD68, SNORD96A, RNU6-2, miRTC and PPC.

MiScript SYBR Green PCR Kit (Qiagen, Valencia, CA, USA) which contains miScript Universal Primer (reverse primer) and QuantiTect SYBR Green PCR Master Mix, was used to prepare real-time PCR reactions. The reaction volume (2750L) was prepared using 1375L of Master Mix, 275L of Universal Primer, 1000L of RNase-free water and 100L of template cDNA, with 25L of reaction volume added to each well (96-well plates). Amplication was programmed in a StepOne Plus instrument (Applied Biosystems Inc., Carlsbad, CA, USA). Cycling conditions for real-time PCR included an initial activation step at 95C for 15min to activate HotStar Taq DNA polymerase and 40 cycles of denaturation (at 94C for 15s), annealing (at 55C for 30s) and extension (at 70C for 30s). The ROX passive reference dye was designed to normalize uorescent reporter signal and baseline and threshold were set automatically for all real-time PCR runs. Fluorescence data were collected at the holding stage of the extension step. Specicity and identity were veried by melting curve analyses. Threshold cycles values (CT) were exported as an Excel le for analyses.

Eighty-four high-priority bovine mature miRNAs were selected from the miRNA genome to analyze. Reverse transcription and positive controls were chosen to ensure efficiency of the array, reagents and instrument. Raw CT in Excel version (19972003 (.XLS le format)) was uploaded to the data analysis center (http://www.qiagen.com). Data quality control was examined to assess amplication reproducibility and reverse transcription efficiency, and to detect any other contamination in amplied samples. The CT values of samples were calibrated to the CT values of cel-miR-39-3p. Global CT mean of expressed

miRNAs was chosen to normalize the target circulating miRNAs. The distribution of CT values and raw data average in both groups were reviewed. Average CT, 2^CT, fold change, P-value and fold regulation were calculated in the web-based program and P-values were included in subsequent graphical analyses. Average CT values were converted into linear 2^CT values and P-values were calculated with a Students t-test.

Computational prediction of potential mRNAs targets. Target genes were predicted using miRDB (http://mirdb.org/miRDB/) for dierentially expressed miRNAs in metritis, and top ranked predicted genes were run through a PANTHER classication system18 to identify associated biological processes in response to uterine infection and inammation.

Results and Discussion

On miRNA quantitative proling, 30 miRNAs were dierentially expressed (p0.05; Fold Regulation 2 magnitude) in serum samples of cows with metritis compared to normal cows, out of 84 bovine specic miRNAs investigated (Figs1 and 2). Highly up-regulated miRNAs were bta-miR-15b, bta-miR-17-3p, bta-miR-16b, bta-miR-148a, bta-miR-26b, bta-miR-101, bta-miR-29b, bta-miR-27b and bta-miR-215 (10 magnitude of Fold Regulation) whereas bta-miR-148b, bta-miR-199a-3p, bta-miR-122, bta-miR-200b and bta-miR-10a (10 magnitude of Fold Regulation) were highly down-regulated in cows with metritis compared to control cows (Table2). Response of host cells to microbial infection, and immune activation and inammatory process by host cells in metritis might have caused dysregulation of miRNAs in the present study. In mice30, miR-15b was highly up-regulated in whole blood upon intra-peritoneal injection of lipopolysaccharide, a potent mediator of gram negative bacteria implicated in sepsis and inammation.

Lipopolysaccharide complex associated with gram-negative pathogens (e.g., Escherichia coli) in cows with metritis may have contributed to up-regulation of bta-miR-15b in the current study. Escherichia coli is one among the main types of bacteria causing uterine inammation, and the uterine pathology is largely caused with the bacterial endotoxin lipopolysaccharide. The highly expressed bta-miR-15b may have down-regulated its target genes that are necessary for the physiological events associated with the normal uterine involution. In that regard, bovine-specic miRNAs were dierentially expressed in normal mammary tissue challenged with Staphylococcus

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Figure 1. Volcano plot: Metritis versus control group. Boundary=1.5; p<0.05; Undetermined miRNAs in both groups were removed.

Figure 2. Clustergram of target miRNAs in metritis (Group 1) and control groups. p<0.05; Undetermined miRNAs in both groups were removed.

aureus31 and dierentially expressed miRNAs were bacteria-specic. Bta-miR-184, miR-24-3p, miR-148, miR-486 and let-7a-5p were unique to E. coli, whereas bta-miR-2339, miR-499, miR-23a and miR-99b were specic to S. aureus in an in-vitro study with bovine mammary epithelial cells32. In the present study, upregulation of bta-miR-148a may have been due to E. coli infection associated with metritis. Post-partum involution completes the reproductive cycle aer pregnancy and calving by returning the uterus to its normal non-pregnant state so that the cow can come into estrus again. This uterine involution involves substantial uterine tissue reorganization via the activation of matrix metalloproteases, extracellular matrix degradation and cellular autophagy/apoptosis. This is driven by several cellular processes and multitude of gene expression. The E. coli infection may have down-regulated the genes necessary for the uterine involution through up-regulation of bta-miR-148a. In addition, some of the predicted target genes for the bta-miR-148 are involved in apoptosis and matrix degradation. MiR-101 regulated the innate immune response of macrophages challenged with lipopolysaccharide via its target gene MAPKP-133. Serum levels of miR-16, miR-17, miR-20a, miR-20b, miR-26a, and miR-26b were up-regulated in an experimental sepsis condition induced by cecal ligation and puncture in mice34, whereas over-expression of miR-21, miR-29b and miR-148a occurred in systemic lupus erythematosus35,36. Dierential expression of these miRNAs would have been an outcome of immune-mediated inammatory responses in systemic lupus erythematosus, and in the present study, up-regulation of bta-miR-148a may be the result of uterine inammatory processes in response to post-partum microbial infections. This would have been aected the normal uterine involution by repressing the genes involved in matrix degradation, cell autophagy and cell apoptosis.

Interestingly, in the current study, serum bta-miR-148b level was lower, in contrast to the higher serum level of bta-miR-148a in cows with metritis, although they belong to the same miRNA gene family. This might be plausible, since top-ranked target genes, based on miRDB total target score for both miRNAs were dierent, suggesting

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miRNA ID Fold Regulation p-value

bta-miR-15b 84.3353 0.001637

bta-miR-17-3p 73.9288 0.000000

bta-miR-16b 55.0650 0.003596

bta-miR-148a 51.7348 0.004212

bta-miR-26b 41.2998 0.000002

bta-miR-101 32.4032 0.000001

bta-miR-29b 16.7149 0.002533

bta-miR-27b 11.2595 0.000150

bta-miR-215 11.0660 0.000049

bta-miR-18b 9.1139 0.000814

bta-miR-22-5p 7.7978 0.015375

bta-miR-218 5.5139 0.037113

bta-miR-145 4.7176 0.000207

bta-miR-106a 2.3103 0.008010

bta-miR-139 2.1039 0.011376

bta-miR-142-5p 2.0821 0.024574

bta-miR-148b 31.7115 0.006327

bta-miR-199a-3p 30.4197 0.001416

bta-miR-122 24.7943 0.000000

bta-miR-200b 24.2000 0.000762

bta-miR-10a 18.4677 0.000000

bta-miR-17-5p 4.7963 0.000768

bta-let-7g -4.2337 0.001229

bta-miR-214 3.3448 0.025404

bta-miR-31 3.1864 0.000027

bta-miR-205 2.9627 0.000000

bta-let-7d 2.7263 0.036396

bta-miR-192 2.4656 0.000352

bta-miR-30b-5p 2.1539 0.043542

bta-let-7a-5p 2.0236 0.045190

Table 2. Fold regulation of serum miRNAs in cows with metritis compared to normal cows. Of 84 bovine-specic well-characterized miRNAs investigated, 16 were greater (p0.05; fold 2) and 14 were lower (p0.05; fold 2) in serum of cows with metritis.

Foldregulation Targeted gene

hsa-miR-15b-5p 84.3353 ZFHX4, SYNJ1, CDCA4, NUP50, PAPPA, LUZP1, SLC13A3, UNC80, MTMR3, PTPN4, PHF19,

RECK, N4BP1, PPM1A, ZMAT3, SLC9A6, AKT3, BTRC, IPO7, SCN8A

hsa-miR-17-3p 73.9288 KAT7, TSHZ3, MAML3, HNRNPA3, LMLN, RAP2A, FAM168B, KIAA1804, VEZF1, EPHA6,

TGFBR1, TRIM59, KIAA0232, SESTD1, ZFHX4, EBF1, SLC40A1, RAB21, CNOT2, ARID2

hsa-miR-16-5p 55.065 ZFHX4, SYNJ1, SLC9A6, IPO7, CDCA4, NUP50, PAPPA, LUZP1, SLC13A3, UNC80, MTMR3,

PTPN4, PHF19, ZBTB44, RECK, N4BP1, VEGFA, PPM1A, ZMAT3, RNF144B

hsa-miR-148a-3p 51.7348 SOS2, LDLR, RPS6KA5, B4GALT6, CDK19, ABCB7, SZRD1, ZFYVE26, MXD1, NPTN, USP33,

GPATCH8, SIK1, GLRX5, B4GALT5, TIMM23, ADAM22, DDX6, STARD13, INO80

hsa-miR-26b-5p 41.2998 SLC2A13, SLC7A11, FAM98A, SLC45A4, ZDHHC6, PITPNC1, STRADB, RNF6, ZNF608, USP9X,

MAPK6, MARK1, CLASP2, SLC25A16, CILP, NAB1, EPB41L3, ADM, ULK2, CIPC

hsa-miR-101-3p 32.4032 MPPE1, MOB4, CACNB2, TNPO1, STC1, ABHD17C, FLRT3, MYCN, TSHZ3, LCOR, C3orf58,

SOCS5, ZFP36L2, FZD6, REV3L, FZD4, RORA, TMEM65, ZNF654, FGA

hsa-miR-29b-3p 16.7149 COL3A1, ERCC6, NFIA, TET3, DGKH, BRWD3, FBN1, TET1, ATAD2B, RNF19A, VEGFA,

DNMT3A, FBXW9, ELN, COL5A3, HMCN1, TMEM183A, GRIP1, ROBO1, NAV3

hsa-miR-27b-3p 11.2595 EYA4, ST6GALNAC3, AFF4, GSPT1, GPAM, TNPO1, AKIRIN1, DCUN1D4, GAB1, GXYLT1,

MIER3, CCNK, ABHD17C, TMCC1, FOXA3, TRIM23, ZDHHC17, UNKL, SLC7A11, PPARG

hsa-miR-215-5p 11.066 EREG, DYRK3, LPAR4, ZEB2, MSN, ARFGEF1, CCNT2, PDP1, GPR22, DICER1, ANAPC10, LIMS1,

WDR44, RPAP2, FRMD4B, ARL2BP, STX7, CNGB3, BHLHE22, FGD5

hsa-miR-18b-5p 9.1139 NEDD9, RORA, BBX, MAP7D1, INADL, PHF19, ZBTB47, CDK19, ERI1, DICER1, HIF1A, CTGF,

GIGYF1, PHC3, GLRB, NCOA1, TMEM170B, CREBL2, ZNF367, TRIOBP

Table 3. Top ranked (based on total target score of miRDB) targeted genes for up-regulated miRNAs.

microRNA ID

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Foldregulation Target genes

hsa-miR-148b-5p 31.712 DLST, ZC3H14, ETS1, BACH2, UBE3A, C6orf62, SSBP2, TCEAL1, KCNIP1, ELOVL2, FMO3,

SHANK2, UBL3, CDK19, ARHGAP31, RGS2, ASF1A, ZBTB44, PGM3, CDCP2

hsa-miR-199a-3p 30.42 ETNK1, CELSR2, ADAMTSL3, KLHL3, ACVR2A, LRP2, BCAR3, SERPINE2, NOVA1, MAP3K4,

FAM110C, KIAA0319L, RB1, ZHX1, KDM5A, PSD2, LIN28B, LLGL2, ITGA3, CHMP5

hsa-miR-122-5p 24.794 HNRNPU, CLIC5, CLIC4, PIP4K2A, CPEB1, FKBP5, SCN3B, CD40LG, FOXP2, ANKRD13C,

P4HA1, COX10, PAPOLA, RABL6, BAI2, C20orf112, TOR3A, LAMC1, SLC52A2, FUNDC2

hsa-miR-200b-3p 24.002 TCEB1, TRIM33, LHFP, PTPN21, ARHGAP6, VASH2, HIPK3, NR5A2, ZEB2, WASF3, ZEB1, RECK,

SLIT2, AP1S2, ERRFI1, AFF3, CCNJ, MAP4K5, SESN1, ELL2

hsa-miR-10a-5p 18.468 BDNF, KCNA6, RORA, TFAP2C, CSMD1, ZNF367, SON, PATL1, NR6A1, KLHL29, TRIM2,

ARHGEF12, RBMS3, GALNT1, RPRD1A, KLF11, LCA5, SLC24A4, BBX, CLCC1

hsa-miR-17-5p 4.7963 ZNF800, ARID4B, ADARB1, PTPN4, PKD2, GAB1, SLC40A1, ZNFX1, FBXL5, EPHA4, PDCD1LG2,

EPHA5, FGD4, EZH1, STK17B, ARID4A, GPR137C, USP3, SALL1, MASTL

hsa-let-7g-5p 4.2337 SMARCAD1, LIN28B, FAM178A, GATM, LRIG3, GNPTAB, DNA2, TMEM2, BZW1, ADAMTS8,

ADRB2, C8orf58, TTLL4, TMPRSS2, IGDCC3, NME6, HIC2, SCN4B, DMD, ZFYVE26

hsa-miR-214-3p 3.3448 NAA15, SEC24C, ATP2A3, UHMK1, MED19, RNF169, FBXO32, AKAP13, SARM1, FNDC5,

TBL1XR1, PIK3CB, HR, SPIRE1, SNX12, PTER, RCSD1, EZH1, TGOLN2, RALGAPB

hsa-miR-31-5p 3.1864 RSBN1, IDE, PIK3C2A, SEPHS1, PDZD2, TBXA2R, HIAT1, LBH, RFWD3, PRKCE, SH2D1A,

SH2D1A, GXYLT1, GCH1, CCAR2, EGLN3, LATS2, CAMK2D, IGFBP7, MAPKAPK2

hsa-miR-205-5p 2.9627 CCNJ, CLDN11, CDK19, PTPRM, ZFYVE16, CDK14, C11orf86, FOXF1, PLCB1, SLC19A2, SORBS1,

BTBD3, EZR, KANSL3, HS3ST1, CALCRL, MLLT4, TAPT1, GPM6A, EVA1C

Table 4. Top ranked (based on total target score of miRDB) targeted genes for down-regulated miRNAs.

Figure 3. Integrated genes for up-regulated miRNAs in metritis. Go biological process; number of genes run into the gene list analysis in the PANTHER classication system was 200; number of genes hit was 172; total number of process hit was 347.

molecular functions and biological processes can diverge. Infection with L. monocytogenes signicantly reduced expression of intestinal miR-200b37, whereas in the present study, bta-miR-200b was down-regulated, consistent with involvement of a bacterial pathogen or an inammatory outcome of a comparable process in bovine metritis. Also, miR-200b was inversely correlated to transforming growth factor-beta 1(TGF-1) and TGF- family involves in preparation of pregnancy and in vascular remodeling. Therefore, repressed miR-200b may have activated the TGF- signaling and may have caused the uterine inammation and hindered the normal involution. MiR-122 has been considered liver-specic and associated with hepatitis C virus infection38. However, in the current study, the serum level of bta-miR-122 was highly divergent between cows with metritis and control cows, emphasizing the necessity of future exploration of its function with regards to metritis. Interestingly, miR-199a maintained uterine quiescence by suppressing COX-2, enhancing contractile prostaglandins, and mediating progesterone and estrogen eects during pregnancy and labor in humans39, whereas a lower level of serum bta-miR-199a in the current study may have contributed to clearance of infectious materials and inammatory products from the bovine uterus. Regulatory T-cells control immune responses, and miR-10a was reported to be a key mediator of Treg in pathogen-mediated inammation40, whereas serum bta-miR-10a was lower in cows with metritis in the current study, suggesting a critical role in the biological process of uterine inammation.

In the current study, target genes were identied (miRDB28) for highly dierentiated miRNAs between metritis and control cows. Up-regulated miRNAs in metritis were integrated to several genes; top ranking genes were

microRNA ID

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Figure 4. Integrated genes for down-regulated miRNAs in metritis. Go biological process; number of genes run into the gene list analysis in the PANTHER classication system was 200; number of genes hit was 196; total number of process hit was 386.

ZFHX4, SYNJ1, CDCA4, KAT7, TSHZ3, MAML3, ZFHX4, SLC9A6, and IPO7, whereas top-ranking genes with down-regulated miRNAs were DLST, ZC3H14, ETS1, ETNK1, CELSR2, ADAMTSL3, HNRNPU, CLIC5, and CLIC4 (Tables3 and 4). On the analysis of 200 targeted genes of the 10 up-regulated miRNAs, and 200 integrated genes of the down-regulated miRNAs, using the PANTHER classication system, genes were categorized into several biological processes, including biological regulation, cellular process, developmental process, metabolic process, localization, multicellular organismal process, response to stimulus, immune system process, cellular components organization, apoptotic process, biological adhesion, developmental process, and locomotion (Figs3 and 4). Several of these biological processes are critical in responses to uterine infection, for inammatory progression, and for clearing infectious materials and inammatory products following the course of metritis.

Conclusion

In conclusion, there were highly discriminated serum miRNAs identied between cows with metritis and normal cows. Of 84 bovine-specic prioritized miRNAs investigated, 16 and 14 were present in higher and lower levels, respectively, in cows with metritis. Hundreds of integrated genes were identied for these up-regulated and down-regulated miRNAs, and top-ranked genes based on total target scores were categorized into biological processes critical for responding to uterine infection, mediating uterine inammatory process, and for clearance of infectious agents and inammatory products.

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Acknowledgements

Authors gratefully acknowledge nancial support from the College of Veterinary Medicine, Washington State University.

Author Contributions

V.K. designed the study, performed the experiment, analyzed the data and draed the manuscript. J.K. analyzed the data and revised the manuscript.

Additional Information

Competing nancial interests: The authors declare no competing nancial interests.

How to cite this article: Kasimanickam, V. and Kastelic, J. Circulating cell-free mature microRNAs and their target gene prediction in bovine metritis. Sci. Rep. 6, 29509; doi: 10.1038/srep29509 (2016).

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