Expression in Immune Cells of New Conceptus Signaling Markers Optimizes Prediction of Pregnancy in Beef Cattle

Expression in Immune Cells of New Conceptus Signaling Markers Optimizes Prediction of Pregnancy in Beef Cattle | 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 Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Expression in Immune Cells of New Conceptus Signaling Markers Optimizes Prediction of Pregnancy in Beef Cattle Isabella Rio Feltrin, Gabriela Dalmaso Melo, Pedro Pisani Freitas, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5389974/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 May, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In beef cattle, estrous synchronization aiming a second artificial insemination (AI) requires a reliable estimation of the pregnancy status 20 days (D20) after the first AI. The hypothesis is that the expression of interferon-stimulated genes (ISGs; ISG15, OAS1, RSAD2, and IFI44 ) and cytokines ( IL1β and IL10 ) in mononuclear (PBMC) and polymorphonuclear (PMN) cells is regulated by interferon-τ (IFN-τ) and predicts the pregnancy status. PBMC and PMN were isolated from non-pregnant beef cows (N=9), 10-12 days post-ovulation (D0), and stimulated with 100 ng/mL recombinant ovine (ro) IFN-τ or with pooled uterine flush (UF) from D18 pregnant cows. Both roIFNT and UF stimulated the expression of ISG15, RSAD2, and IFI44 in PBMC and PMN. Expression of IL1β was reduced by UF in both PBMC and PMN. On another experiment, PMN were isolated, and luteal blood perfusion was measured on D20 post-timed-AI in beef females. The accuracy of ISG expression and luteal blood perfusion to predict the pregnancy outcome was determined by ROC curve analysis. All gene combinations were tested, and the best association for increased accuracy (92.7%) and reduction of false-negative results (0.9%, 2/233) was obtained through the combination of the four ISGs ( ISG15, OAS1, RSAD2 , and IFI44 ). The criterion was that if the expression levels of at least one of the four genes were greater than the predefined cutoffs, the animal would be considered pregnant. In conclusion, the expression of ISGs and IL1β was upregulated by roIFNT and UF from pregnancy cows. The combined expression of classical ( ISG15 and OAS1) and non-classical (RSAD2 and IFI44 ) ISGs provided the greatest predictive accuracy of the pregnancy status on D20 in females with active CL by Doppler and is a potential tool to be used in reproductive programs for beef cattle. Biological sciences/Biotechnology Biological sciences/Immunology Biological sciences/Molecular biology ISG immune cells interferon-tau uterine flush pregnancy prediction. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION In cow-calf operations that employ timed-AI (TAI), there is interest in re-insemination of females that did not become pregnant to the first AI [1]. To that end, open females must be identified as soon as possible for a new service. The most common method of pregnancy diagnosis in cattle is transrectal ultrasonography in B-mode, which achieves 100% accuracy between 28-32 days after TAI by the visualization of the viable embryo. Therefore, considering that non-pregnant females return to estrus around 21 days after TAI [2], it is advantageous to determine the pregnancy status ≤ 20 days post-TAI to allow for quick re-insemination. Recently, color-Doppler (Doppler-US) has been used as a tool for early pregnancy diagnosis by the verification of sustained luteal blood perfusion between days 20 and 22 after TAI with an accuracy greater than 90% [3-5]. The advantage of this technique is the sensitivity approaching 100%, which results in very few false-negative diagnoses. However, this method may result in up to 15% false-positive diagnoses in beef, and 40% in dairy cattle [5, 6]. Thus, the development of methods capable of identifying pregnancy by detecting the conceptus or using conceptus-specific markers could reduce false-positive prevalence and improve the accuracy of pregnancy diagnosis methods during the first three weeks after TAI. In domestic ruminants, interferon-τ (IFN-τ) is the main conceptus-derived cytokine responsible for the maternal recognition of pregnancy (MRP) [7]. During MRP, IFN-τ inhibits the endometrial pulsatile release of prostaglandin F 2 α (PGF 2 α ), preventing regression of the corpus luteum (CL). This maintains the synthesis of progesterone (P4) necessary for the continuation of pregnancy [8, 9]. When this sequence of events occurs successfully, pregnancy continues. However, on average, 50% of beef cattle fail to remain pregnant after day 16 after artificial insemination (AI) [10]. For this reason, understanding the mechanisms involved in early pregnancy is essential to mitigate pregnancy loss, and the maternal immune system plays an important role in this period. Studies suggest that conceptus alloantigens alter maternal immune function both locally at the embryonic-maternal junction and systemically in the peripheral blood circulation, to prevent embryonic immune rejection [11, 12]. This is achieved through the modulation of maternal immune cells that direct the balance of cytokines towards the Th2 anti-inflammatory pathway [13]. Immunological tolerance displayed by immune cells can be triggered by several molecules such as hormones, cytokines, and enzymes [14]. Thus, IFN-τ is one of the cytokines responsible for the functional communication between the maternal immune system and the developing embryo in ruminants. The bovine embryo on day 4 of development is already capable of signaling its presence through the modulation of IFN-τ-sensitive genes, regulating the local immune environment in the oviduct [15]. Furthermore, bovine embryos at day 7 communicate with epithelial and immune cells, possibly mediated in part by IFN-τ [16]. IFN-τ is also known to induce the expression of interferon-stimulated genes (ISGs) in the liver, endometrium, luteal cells, and peripheral blood mononuclear (PBMC) and polymorphonuclear (PMN) cells during early pregnancy in cows [3, 16-19]. Genes commonly stimulated by IFN-τ, known as classical ISGs, include ubiquitin-like modifier 15 ( ISG15 ), 2’-5’-oligoadenylate synthetase 1 ( OAS1 ), MX dynamin-like GTPase 1 ( MX1 ) and 2 ( MX2 ). The transcriptional profile of these genes is closely related to the secretion of IFN-τ by the conceptus [3, 16-19]. In cattle, expression of ISGs peaks between days 18 and 20 of pregnancy and returns to basal levels around day 25. Moreover, the expression of mRNA for ISGs in bovine peripheral blood leukocytes is greater in pregnant cows compared to non-pregnant cows on days 18 and 20 after AI. The practical implication is that ISG expression in immune cells may be used for early detection of pregnancy, as well as pregnancy failures [3, 20]. The expression of classical ISGs in peripheral immune cells is a diagnostic method for detecting pregnancy on day 20 in both heifers and cows. However, the accuracy ranged from 62% to 80%, regardless of cell type (PBMC or PMN) [3, 21]. In this context, Rocha et al. [22] used transcriptomic approaches to identify novel genes induced by early pregnancy in PBMCs and PMNs on day 18 post-TAI, beyond the classical ISGs. The general objective of this paper is to provide initial validation of novel candidate genes for the potential use as biomarkers of the pregnancy status of cows. The hypothesis is that the expression of ISGs ( ISG15, OAS1, RSAD2 and IFI44 ) and cytokines ( IL1β and IL10 ) in PBMCs and PMNs is regulated by IFNT and predicts the pregnancy status. The study aimed to: 1) measure the expression of ISG15, RSAD2, IFI44, IL1β and IL10 in PBMCs and PMNs to IFN-τ ( Experiment 1 ) or to uterine flushes (UF) from pregnant cows ( Experiment 2 ); and 2) evaluate the accuracy of RSAD2 and IFI44 expression in PMNs to predict the pregnancy status in cattle ( Experiment 3 ). 2. MATERIAL AND METHODS 2.1 Ethics statement The present study was conducted at the Animal Reproduction Department of the University of São Paulo, in Pirassununga, Brazil. Animal welfare guidelines and handling procedures recommended by the São Paulo State (Brazil) law number 11.977 were strictly followed. That experiment was approved by the Animals Ethics Committee of the School of Veterinary Medicine and Animal Science (CEUA-FMVZ number: 8192280317), and was conducted in accordance with the ARRIVE guidelines. 2.2 Experimental model Initially, to characterize the responsiveness of PBMCs and PMNs to pregnancy factors, these immune cells were isolated from the peripheral blood of Nelore heifers (N=12) and stimulated with 100 ng /mL recombinant ovine interferon-τ (roIFNT, Experiment 1 ) or UF from day 18 of pregnant cows ( Experiment 2 ) in an in vitro culture cell system. Endpoint was the expression of ISGs ( ISG15 , RSAD2, and IFI44 ), pro- ( IL1B ) and anti-inflammatory ( IL10 ) cytokines, measured by quantitative PCR (qPCR). The main purpose was to determine whether the responsiveness of non-classical ISGs ( RSAD2 and IFI44 ) to pregnancy factors resembled that of a known classical ISG ( ISG15 ). Next, based on the responses obtained in the in vitro studies, the expression of ISGs ( ISG15 , OAS1 , RSAD2 and IFI44 ) in PMNs 20 days after TAI was tested for the accuracy in predicting the pregnancy outcomes of females of different parities (i.e., nulliparous, primiparous or pluriparous) compared to ultrasound pregnancy diagnosis (gold standard) on 30 days after TAI ( Experiment 3 ). 2.3 Experimental design of Experiments 1 and 2 Twelve Nelore beef heifers ( Bos taurus indicus ) located at the Animal Reproduction Department of the University of São Paulo (Pirassununga, Brazil), cycling, non-pregnant, with a body condition score between 3 and 4 (on a 1-5 scale) [23] and between 23 and 26 months of age, were maintained on Brachiaria brizantha pastures with free access to water and mineral supplementation. On a random day of the estrous cycle, all animals received 2 mL i.m. of PGF 2 α (500 µg; of sodium cloprostenol; Sincrocio; Ouro Fino Saúde Animal) for estrous synchronization. In the following five days, the females were evaluated daily through ultrasound examinations in B-mode (MyLab Delta Vet Gold; Esaote Healthcare; Italy) to detect ovulation. Ovulations were determined by the disappearance of the pre-ovulatory follicle. Between 10 to 12 days post-ovulation, blood samples (25 mL) were collected from the jugular vein into sodium-heparinized tubes (BD Vacutainer; São Paulo; Brazil) for the isolation of immune cells (Figure 1A). Only animals presenting luteal blood perfusion ≥ 25% (i.e., bearing an active CL) were submitted to blood collection [3]. CL blood perfusion was evaluated by a pulse wave color-Doppler ultrasound instrument (MyLab Delta Vet Gold; Esaote Healthcare; Italy) equipped with a multifrequency linear transducer (3.5–7.5 MHz) in B-mode (RES-A, gain 50%, P 74 mm, X/M, PRS 1) and Doppler-mode (gain 61%, PRF 730 Hz, frequency 6.3 MHz, WF 4, PRS 3, PRC M/2). 2.3.1 Isolation of immune cells from peripheral blood The PBMC and PMN were isolated by density gradient centrifugation using a Ficoll-Paque solution (GE Healthcare, Ref.17144003) [3, 24]. For each cell isolation, whole blood was mixed with an equal volume of PBS in a 50-mL conical tube, and the solution was layered onto 15 mL Ficoll-Paque solution and centrifuged at 1100 g for 30 min at 20°C. After centrifugation, the blood fractions segregated in the following sequence: plasma, buffy coat, and red blood cells together with PMN. The buffy coat was utilized for PBMC isolation, as described by Pugliesi et al. [3] and the last layer containing the granulocytes and red blood cells was utilized for PMN isolation, as described by Jiemtaweeboon et al. [24], with some modifications. The PBMC and PMN were subjected to successive lyses steps with hypertonic solutions to lyse the red blood cells until a clean cell pellet was obtained. At the end of the isolation process, the cell pellet was re-suspended in medium according to treatments assigned by design. The purity of PBMC and PMN was verified by staining freshly isolated samples with the quick panoptic protocol. Samples were considered pure when 95% of the 200 cells counted were mononuclear and polymorphonuclear cells, respectively. In addition, cell viability was assessed pre- and post-culture with Trypan blue (0.4%, Sigma-Aldrich, Ref. T6146) reagent in a Neubauer camera, where only samples that showed viability greater than 85% were used in the study. ( Supplementary Table 1 ). Table 1. Target name, gene number, forward (F) and reverse (R) primer sequence of the genes tested by the qPCR technique Target Name Gene Number Forward primer sequence Reverse primer sequence Reference OAS1 NM_001040606.1 TAGCCTGGAACATCAGGTC TTTGGTCTGGCTGGATTACC Shirasuna, et al. [27] ISG15 NM_174366 GGTATCCGAGCTGAAGCAGTT ACCTCCCTGCTGTCAAGGT Oliveira, et al. [29] RSAD2 NM_001045941.1 TGGTTCCAGAAGTACGGTGAA ACCACGGCCAATAAGGACAT Rocha, et al. [22] IFI44 XM_002686295.6 TCTGCCCATTGCTGAAGGAC CCACATGGACCACATCAGACT Rocha, et al. [22] GAPDH NM_001034034.2 GCCATCAATGACCCCTTCAT TGCCGTGGGTGGAATCA Araújo, et al. [30] ACTB NM_173979.3 GGATGAGGCTCAGAGCAAGAGA TCGTCCCAGTTGGTGACGAT Araújo, et al. [30] PPIA BF230516.1 GCCATGGAGCGCTTTGG CCACAGTCAGCAATGGTGATCT Pugliesi, et al. [3] 2.3.2 Collection of UF on day 18 of the estrous cycle After estrus, Holstein ( Bos taurus taurus; N=10) non-lactating cows were subjected to AI with semen from a single sire (N=3) or remained as non-inseminated controls (N=3). On D18 post-estrus, all females were slaughtered and the reproductive tract (cervix, uterus, and ovaries) was collected, and immediately transported on ice to the laboratory. The uterine horns of each reproductive tract were flushed simultaneously with 20 mL of phosphate-buffer saline (PBS). When a conceptus was present, it was removed from the flush; then, the UF was centrifuged at 300 g for 10 minutes. The supernatant was collected and centrifuged at 2000 g for 10 minutes, and the resulting supernatant at 16,500 g for 30 minutes. All centrifugations were at 4°C. The supernatant from the final centrifugation was stored at -80°C for later use in cell culture experiments. The UF obtained from cows with a conceptus was denominated UF-Conceptus and the UF from non-inseminated cows was used as a control (UF-Control). For cell culture experiments, a UF-Conceptus pool and a UF-Control group pool were built by combining UF from three cows from each group, respectively. 2.3.3 Experiment 1: Stimulation of immune cells with roIFNT Isolated PBMC (N=9 cows; 7 x 10 6 cells/mL) and PMN (N=10 cows; 5 x 10 6 cells/mL) were cultured in 6-well plates in simplicates (Kasvi, Ref. K12-006) in RPMI-1640 medium (Sigma-Aldrich; Ref. 22400071) containing 0.1% FBS (LGC; Ref. 10-bio-500) and Penicillin-Streptomycin (10 µL/mL; Gibco™, Ref. 15140122) in combination with 0 (control) or 100 ng/mL of roIFNT [25] in a humidified atmosphere at 37°C in 5% CO 2 . PMNs were cultured for 3 h while PBMCs were cultured for 24 h. The concentrations of roIFNT used in the present study were determined based previous studies [2, 26, 27], and validated in dose-response study (10, 100, or 1000 ng/mL roIFNT; data not shown). After the incubation, the samples were centrifugated at 700 g for 8 minutes at 25°C. Then, the supernatants were removed and cells were directed to RNA extraction and subsequent gene expression analysis by qPCR. 2.3.4 Experiment 2: Culture of immune cells in UF Isolated PBMC (N=10 cows; 7 x 10 6 cells/mL) and PMN (N=8 cows; 5 x 10 6 cells/mL) were cultured in a 6-well plate in simplicates (Kasvi, Ref. K12-006) in UF-Control or UF-Conceptus containing 0.1% FBS (LGC, Ref. 10-bio-500) in a humidified atmosphere at 37°C in 5% CO 2, according to the methodology described by Rashid et al. [28], with some modifications . PMNs were cultured for 3 h while PBMCs were cultured for 12 h. After the incubation, samples were centrifugated at 700 g for 8 minutes at 25°C, supernatants were removed and cells were directed to RNA extraction and subsequent gene expression analysis by qPCR. 2.4 Experimental design of Experiment 3: accuracy of pregnancy markers in PMN The PMN samples used in this experiment were obtained from a previous study conducted by Dalmaso de Melo et al. [21], where, nulliparous (N=103), primiparous (N=53), and pluriparous (N=91) Nelore ( Bos taurus indicus ) cows were subjected to an estradiol (E2) and P4 based protocol for synchronization of ovulation and TAI (TAI= day 0 [D0]). On D20, the animals were evaluated for CL blood perfusion by color-Doppler ultrasound (MyLab Delta Vet Gold; Esaote Healthcare; Italy) and blood samples (25 mL) were collected from the jugular vein into sodium-heparinized tubes (BD Vacutainer; São Paulo; Brazil) for the isolation of PMN (Figure 1B). PMN were isolated as described in the Experiments 1 and 2 . After isolation, PMN were stored at -80°C for subsequent RNA extraction and gene expression analysis by qPCR. The purity of PMN was checked using the quick panoptic protocol as described previously. Thirty days (D30) after TAI, pregnancy status was verified by the presence of a viable embryo with a heartbeat by B-mode ultrasonography. Ultrasound pregnancy diagnosis on D30 was considered as the gold standard for comparison with the ISG expression and Doppler methods. 2.5 RNA extraction, cDNA synthesis, and quantitative polymerase chain reaction (qPCR) The PBMC and PMN obtained on Experiments 1 and 2 were thawed on ice and the RNA was extracted using PureLink™ RNA Mini Kit (Invitrogen™, Ref. 12183018A). Briefly, the PBMC and PMN pellets were dissolved using the lysis solution and immediately entered the RNA washing procedures as per manufacturer's instructions. For Experiment 3 , the isolated PMN was extracted by a modified protocol using Trizol™ (Thermo Fisher Scientific, Ref. 15596018) reagent associated with the DirectZol-RNA kit (Zymo Research, Ref. R2052), as described in detail by Dalmaso de Melo et al. [21]. Total RNA concentration and purity were measured using a NanoVue™ Plus spectrophotometer (GE Healthcare, UK), and samples with a 260/280 ratio ranging from 1.7 to 2.0 were used for transcript abundance analyses. The isolated RNA from samples in both studies were treated with DNase I (DNase I Amplification Grade; Life Technologies, Ref. 18068015) to avoid genomic DNA contamination, as per the manufacturer’s instructions. Next, the RNA isolated was subjected to reverse transcription using the High-Capacity cDNA Reverse Transcription Kit (Life Technologies, Ref. 4368814), according to the manufacturer’s instructions, and the cDNA of each sample was stored at -20°C until qPCR analysis. Analyses of the relative abundance of transcripts were performed using SYBR Green PCR Master Mix (Life Technologies, Ref. A25742) for amplification reactions in the Step One Plus thermocycler (Applied Biosystems Real-Time PCR System; Life Technologies, Ref. 4376600). The samples were run in triplicate and the maximum CV accepted among the replicates was 0.1. Specific primers for each gene (Table 1) were selected according to previous studies [3, 22, 27, 29, 30]. All newly designed primers were evaluated for sequence specificity using BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). Furthermore, GeNorm software (https://genorm. cmgg. be) was used to select reference genes. Glyceraldehyde-3-Phosphate Dehydrogenase ( GAPDH ) and Actin Beta ( ACTB ) were the most stable genes in PMN, and GAPDH and Ciclofilin ( PPIA ) were the most stable genes on PBMC. We used LinRegPCR software to determine qPCR efficiency and quantification cycle (Cq) values per sample. Quantification was performed after normalization of the target gene expression values by the geometric mean of the endogenous control expression values, as described by Pfaffl [31]. 2.6 Statistical analyses The data were evaluated for detection of outliers using the Dixon test and the significant (P < 0.05) outliers detected were excluded from the analyses. The data that were not normally distributed according to the Shapiro–Wilk test were transformed with normal logarithm, rank, and square root. The abundance of gene transcript for all experiments was analyzed by analysis of variance (ANOVA) using the PROC MIXED procedure of SAS (Version 9.2; SAS Institute). Pearson’s correlation between ISG and cytokines expression was analyzed by the GraphPad Prism software (Version 5.0) for both studies. For the Experiment 1 and 2 , animal was considered as a random effect and the treatments (roIFNT or UF) as fixed effects in the model. Fold change was calculated by the ratio between the gene expression of each sample in the treated group (roIFNT or UF-Conceptus) and the average expression of the control group for each cell type. For the Experiment 3 , animal was considered as a random effect, and the fixed effects of group (pregnant or non-pregnant 30 days after TAI), category (nulliparous, primiparous, or pluriparous), and group-by-category interaction were included in the model. The accuracy of the pregnancy diagnosis methods by ISG expression was calculated by the frequency of false-negative and false-positive observations, negative predictive value, positive predictive value, specificity, and sensitivity, as previously described by Pugliesi et al. [3]. A cutoff value for the expression of each ISG, to distinguish pregnant from non-pregnant animals, was determined from a Receiving Operator Characteristic (ROC) curve that was calculated using GraphPad Prism software. This software was also used to determine the area under the curve (AUC) of the sensitivity by specificity plot for the expression of each gene. The results are reported as arithmetic mean ± SEM. The probability ≤ 0.05 indicated that the effect was significant and between 0.05 > P ≤ 0.10, indicated that the effect approached significance. 3. RESULTS 3.1 Experiments 1 and 2 3.1.1 roIFNT modulates the expression of ISGs and cytokines in cultured PBMCs and PMNs To detect the magnitude of response of PBMC and PMN treated with roIFNT in vitro , the specific immune-related genes including ISGs ( ISG15 , RSAD2 , and IFI44 ), pro-inflammatory ( IL1β ), and anti-inflammatory ( IL10 ) cytokines were analyzed by qPCR. In both PBMC and PMN, the treatment with 100 ng/mL of roIFNT stimulated mRNA expression of ISG15 , RSAD2 , and IFI44 (P < 0.05) compared to the Control group ( Fig. 2 A, B ). When comparing the relative fold change of each gene to the Control group, in PBMC treated with roIFNT, a greater (P < 0.0001) stimulus was observed in the ISG15 and RSAD2 genes than in the IFI44 gene ( Fig. 2 C ) . For PMN, the relative fold change showed a greater (P = 0.05) stimulus in the RSAD2 gene compared to other genes ( Fig. 2 D ). In PBMC, expression of IL1β tended to be less in the IFNT group (P = 0.10); however, IL10 expression was not affected (P = 0.11) ( Fig. 3 A ) . In PMN, the expression of IL1β (P = 0.15) and IL10 (P = 0.85) was not affected by roIFNT treatment ( Fig. 3 B ) . When the relative fold change between the IFNT-treated group and the Control group was calculated, a greater fold change was detected for IL10 (4.1-fold) compared to IL1β (0.7-fold) in PBMC (P = 0.005) ( Fig. 3 C ) . In PMN, the relative fold change did not differ significantly (P = 0.14) between treatments ( Fig. 3 D ) . 3.1.2 UF from pregnant cows modulated the expression of ISGs and IL1β cytokine in cultured PBMCs and PMNs In this experiment, we tested whether UF from pregnant cows induced the expression of ISG and immune genes in PBMC and PMN. The treatment with UF from pregnant cows (UF-Conceptus) induced expression of ISG15 , RSAD2 , and IFI44 (P < 0.05) in PBMC and PMN (Fig. 4 A, B). When comparing the relative fold change, a greater stimulus was observed in the ISG15 and RSAD2 genes than in the IFI44 gene for both, PBMC (P = 0.02) and PMN (P = 0.01) cultured with UF-Conceptus compared to UF-Control (Fig. 4 C, D). Regarding cytokines, a lesser expression of IL1β in PBMC (P = 0.007) and PMN (P = 0.01) was detected in the UF-Conceptus group compared to the UF-Control ( Fig. 5 A, B ) . However, the expression of IL10 in PBMC (P = 0.14) and PMN (P = 0.44) did not differ between the UF-Conceptus and UF-Control groups ( Fig. 5 A, B ) . When the relative fold change of the UF-Conceptus group was compared to the UF-Control group, a greater fold change was detected for IL10 (2.3-fold) compared to IL1β (0.8-fold) in PMN (P = 0.005) ( Fig. 5 D ). In PBMC, the relative fold change did not differ significantly (P = 0.15) between treatments ( Fig. 5 C ) . 3.1.3 UF and roIFNT stimulated co-expression of ISGs in immune cells In PBMC, there were strong positive correlations (r > 0.8) between expression of ISGs ( ISG15 vs RSAD2, ISG15 vs IFI44 , and RSAD2 vs IFI44 ) in cells stimulated with either roIFNT or UF (Table 2 ). There were no significant (P > 0.1) correlations detected between the expression of cytokines ( IL1β vs IL10 ). Table 2 Pearson’s correlation coefficient (r) between the abundance of transcripts in PBMC and PMN culture with recombinant ovine interferon-τ (roIFNT) or uterine flush (UF). Endpoint Between ISGs and Cytokines PBMC IFNT culture UF culture r P r P ISG15 vs RSAD2 0.97 < 0.0001 0.81 < 0.0001 ISG15 vs IFI44 0.94 < 0.0001 0.87 < 0.0001 RSAD2 vs IFI44 0.96 < 0.0001 0.94 < 0.0001 IL1β vs IL10 -0,04 NS 0.15 NS PMN IFNT culture UF culture r P r P ISG15 vs RSAD2 0.96 < 0.0001 0.90 < 0.0001 ISG15 vs IFI44 0.90 < 0.0001 0.84 0.001 RSAD2 vs IFI44 0.95 < 0.0001 0.77 0.006 IL1β vs IL10 0.64 0.01 -0,21 NS Means indicate differences ( P ≤ 0.05) between treatments. NS: non-significant. In PMN, there were strong positive correlations between expression of ISGs in cells stimulated with roIFNT ( ISG15 vs RSAD2, ISG15 vs IFI44 , and RSAD2 vs IFI44) and in cells treated with UF ( ISG15 vs RSAD2 and ISG15 vs IFI44) (Table 2 ). The association between RSAD2 vs IFI44 in PMN treated with UF generated a moderate (0.6 < r < 0.8) positive correlation. For the association between cytokines, a moderate positive correlation (0.6 < r 0.1) correlations were detected. As expected, the strong correlations found between ISGs in both treaments and cell types, demonstrate that these genes are possibly co-modulated by the same activation pathway, such as IFN-τ signaling. For the association between cytokines, the presence of correlations was also expected, since there is a balance between the Th1 ( IL1β ) and Th2 ( IL10 ) immune responses mediated by the factors (cytokines) that these cells produce. However, we only observed a moderate and significant correlation between cytokines in PMN treated with IFNT. To look for potential co-regulation targets, correlation analysis was performed between the expression of ISGs and cytokines. However, no significant correlation was detected (data not shown). 3.2 Experiment 3 3.2.1 Pregnancy stimulated the expression of non-classical ISGs (RSAD2 and IFI44) in PBMCs and PMNs on D20 post-TAI The main effects of group, parity order category, and the group-by-category interaction were significant for the RSAD2 gene (Fig. 6 ). Interpretation of the group-by-category interaction was that RSAD2 expression was similar for pregnant females in all parity categories, whereas in non-pregnant females, expression was lower in pluriparous compared to nulliparous and primiparous females. RSAD2 abundance was 3.2, 4.4, and 8.5-fold greater (P < 0.007) in the pregnant than non-pregnant nulliparous, primiparous and pluriparous females, respectively. The IFI44 abundance was 3.8-fold greater in the pregnant group compared to the non-pregnant (P < 0.0001; Fig. 6 ). A parity category effect (P < 0.0001) indicated that expression of IFI44 was greatest in nulliparous, followed by pluriparous and the lowest in primiparous cows. 3.2.2 Pregnancy stimulated co-expression of ISGs in PMN 20 days post-TAI In nulliparous cows, there was one strong ( ISG15 vs OAS1 ), three moderate ( ISG15 vs RSAD2, OAS1 vs RSAD2 , and RSAD2 vs IFI44 ), and two weak ( ISG15 vs IFI44 and OAS1 vs IFI44 ) correlations observed (Table 3 ). In primiparous and pluriparous cows, the correlations were similar, with one strong ( ISG15 vs OAS1 ), one moderate ( RSAD2 vs IFI44 ), and two weak ( ISG15 vs IFI44 and OAS1 vs IFI44 ) correlations. There were no other significant (P > 0.10) correlations detected. Table 3 Pearson’s correlation coefficient (r) between the abundance of transcripts in PMN on day 20 post-TAI in nulliparous, primiparous, and pluriparous bovine females. Endpoint Between ISGs Nulliparous Primiparous Pluriparous r P r P r P ISG15 vs OAS1 0.81 < 0.0001 0.88 < 0.0001 0.88 < 0.0001 ISG15 vs RSAD2 0.66 < 0.0001 0.20 NS 0.20 NS ISG15 vs IFI44 0.56 < 0.0001 0.44 0.001 0.45 < 0.0001 OAS1 vs RSAD2 0.70 < 0.0001 0.23 NS 0.16 NS OAS1 vs IFI44 0.53 < 0.0001 0.48 0.0003 0.38 0.0003 RSAD2 vs IFI44 0.73 < 0.0001 0.79 < 0.0001 0.73 0.1). 3.2.3 Expression of ISGs in PMNs generated accurate predictitons of pregnancy outcomes The cutoff values of expression for ISG genes ( RSAD2 , IFI44, ISG15 , and OAS1 ) to distinguish between pregnant and non-pregnant cows were established through ROC curve analysis ( Fig. 7 ). Different cutoff values were established for nulliparous ( RSAD2 = 0.92, IFI44 = 0.0086, ISG15 = 1.27 and OAS1 = 0.53), primiparous ( RSAD2 = 0.48, IFI44 = 0.0048, ISG15 = 1.04 and OAS1 = 0.48) and pluriparous cows ( RSAD2 = 0.79, IFI44 = 0.0058, ISG15 = 0.31 and OAS1 = 0.53). In nulliparous heifers, the accuracy for the IFI44 gene was greater when compared to the RSAD2 gene (86% vs 79%, respectively; Table 4 ). In this case, the expression of IFI44 had a lower frequency of false-positives (7/100) and false-negatives (7/100) and, consequently, greater positive (85.7%) and negative (86.3%) predictive values, and sensitivity (85.7%) and specificity (86.3%) when compared to the frequency of false-positives (11/100) and false-negatives (10/100) of RSAD2 . In primiparous females, the accuracy was greater for the RSAD2 when compared to the IFI44 gene (92% vs 84%). The expression of RSAD2 in this category showed the least possible frequency of false-negatives (0/50) and, consequently, a perfect negative predictive value (100%) and sensitivity (100%) when compared to the frequency of false-negatives for IFI44 (4/50). However, the frequency of false-positives (4/50) and, consequently, the specificity (82.6%) was the same for both genes. In pluriparous cows, the accuracy was similar for both ISGs ( RSAD2 = 92.8% vs IFI44 = 91.6%), as well as the frequency of false-negatives, negative predictive value, and sensitivity (Table 4 ). However, the frequency of false positives ( RSAD2 = 1/83 vs IFI44 = 3/83) was lower for RSAD2 , which increased the specificity ( RSAD2 = 97% vs IFI44 = 90.9%) and the positive predictive value ( RSAD2 = 97.8% vs IFI44 = 93.9%) for this gene. Table 4 Number of True-Positive (TP), True-Negative (TN), False-Positive (FP), False Negative (FN), Sensitivity (SENS), Specificity (SPEC), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (ACCU) for determining pregnancy status on D20 post-TAI by RSAD2 and IFI44 in nulliparous, primiparous and pluriparous bovine females. Endpoint Nulliparous Primiparous Pluriparous RSAD2 IFI44 RSAD2 IFI44 RSAD2 IFI44 n 100 100 50 50 83 83 TP (n) 39 42 27 23 45 46 TN (n) 40 44 19 19 32 30 FP (n) 11 7 4 4 1 3 FN (n) 10 7 0 4 5 4 SENS (%) 79.6 85.7 100.0 85.2 90.0 92.0 SPEC (%) 78.4 86.3 82.6 82.6 97.0 90.9 PPV (%) 78.0 85.7 87.1 85.2 97.8 93.9 NPV (%) 80.0 86.3 100.0 82.6 86.5 88.2 ACCU (%) 79.0 86.0 92.0 84.0 92.8 91.6 a Sensitivity (probability that a test result will be positive when the cow is pregnant) = TP/(TP + FN). b Specificity (probability that a test result will be negative when the cow is not pregnant) = TN/(FP + TN). c PPV (probability that the cow is pregnant when the test is positive) = TP/(TP + FP). d NPV (probability that the cow is not pregnant when the test is negative) = TN/(FN + TN). e Accuracy = (TP + TN)/n. 3.2.4 Association of Doppler-US with ISG expression optimized accuracy of pregnancy prediction Considering that there are less than 0.5% false-negatives results when using Doppler ultrasonography, but false-positives are often greater than 10% [ 12 , 17 , 18 ], the combination of Doppler-US and ISG expression was attempted to maximize accuracy of early pregnancy diagnostic in beef females. The approach consisted in applying the cut-off values for ISGs (each individually or combinations) to females with a functional CL (blood perfusion > 25%) on D20. Females that did not have a functional CL on D20 were automatically considered non-pregnant. In nulliparous females, there was a similar accuracy when using combinations of two ( RSAD2/IFI44 , accuracy: 90%) or four ( RSAD2/IFI44/ISG15/OAS1 , accuracy: 91%) ISGs (Table 5 ). The combination of four ISGs yielded less false-negatives (1/100), and consequently, greater negative predictive value (97.7%) and sensitivity (98%) when compared to the combination of two ISGs. However, the combination of two ISGs yielded less false positives (5/100) and, consequently, greater positive predictive value (89.8%) and specificity (84.3%) compared with the combination of four ISGs. In primiparous cows, accuracy was equivalent when using only the RSAD2 gene (accuracy: 98%) or when using the combination of two ISGs ( RSAD2/IFI44 , accuracy: 98%). Moreover, the frequency of false-positives was minimal (1/50), and there were no false-negatives (0/50), resulting in a perfect negative predictive value (100%) and sensitivity (100%) in both cases. In pluriparous cows, the greatest accuracy was obtained using two ISGs ( RSAD2/IFI44 , accuracy: 94%); however, the combination of four ISGs generated the lowest frequency of false-negative (1/83), and consequently, a greater negative predictive value (96.5%) and sensitivity (98%) than when two ISGs were used. Table 5 Number of True-Positive (TP), True-Negative (TN), False-Positive (FP), False-Negative (FN), Sensitivity (SENS), Specificity (SPEC), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (ACCU) for determining pregnancy status on D20 post-TAI by RSAD2 , IFI44, ISG15 , and OAS1 in bovine females with a functional CL. Endpoint Nulliparous Primiparous Pluriparous RSAD2 IFI44 RSAD2/ RSAD2/IFI44/ RSAD2 IFI44 RSAD2/ RSAD2/IFI44/ RSAD2 IFI44 RSAD2/ RSAD2/IFI44/ IFI44 ISG15/OAS1 IFI44 ISG15/OAS1 IFI44 ISG15/OAS1 n 100 100 100 100 50 50 50 50 83 83 83 83 VP (n) 39 42 44 48 27 23 27 27 45 46 48 48 VN (n) 47 47 46 43 22 22 22 21 32 30 30 28 FP (n) 4 4 5 8 1 1 1 2 1 3 3 5 FN (n) 10 7 5 1 0 4 0 0 5 4 2 1 SENS (%) 79.6 85.7 89.8 98.0 100.0 85.2 100.0 100.0 90.0 92.0 96.0 98.0 SPEC (%) 92.2 92.2 90.2 84.3 95.6 95.6 95.7 91.3 97.0 90.9 90.9 84.8 PPV (%) 90.7 91.3 89.8 85.7 96.4 95.8 96.4 93.1 97.8 93.9 94.1 90.7 NPV (%) 82.5 87.0 90.2 97.7 100.0 84.6 100.0 100.0 86.5 88.2 93.7 96.5 ACCU (%) 86.0 89.0 90.0 91.0 98.0 90.0 98.0 96.0 92.8 91.6 94.0 92.8 a Evaluation of RSAD2, IFI44, ISG15 , and OAS1 in females with a functional CL was performed by applying the predefined cutoffs only in females in which CL blood perfusion was > 25% on D20 post-TAI. b The combined use of both genes (RSAD2, IFI44, ISG15 , and OAS1 ) was performed by considering the female as pregnant when the expression levels of at least one gene were greater than the predefined cutoffs. The same approach of applying ISG cut-off values to females with a functional CL on D20 was implemented, regardless of parity category (Table 6 ) . The accuracy was similar between all ISG combinations. However, the combination of four ISGs ( RSAD2/IFI44/ISG15/OAS1 ) generated the lowest frequency of false-negatives (2/233), and consequently, the greatest negative predictive value (97.9%) and sensitivity (98.4%), compared to other combinations. Nevertheless, the frequency of false-positives remains elevated (6.4%; 15/233), even when this combination is employed. Table 6 Number of True-Positive (TP), True-Negative (TN), False-Positive (FP), False Negative (FN), Sensitivity (SENS), Specificity (SPEC), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (ACCU) for determining pregnancy status on D20 post-TAI by RSAD2, IFI44, ISG15 and OAS1 in bovine females with a functional CL. Genes ISG15 OAS1 RSAD2 RSAD2/IFI44/ OAS1 RSAD2 IFI44 RSAD2 IFI44 IFI44 ISG15/OAS1 n 233 233 233 233 233 233 233 TP (n) 108 115 118 122 123 120 124 TN (n) 92 95 95 95 95 98 92 FP (n) 15 12 12 12 12 9 15 FN (n) 18 11 8 4 3 6 2 SENS (%) 85.7 91.3 93.6 96.8 97.6 95.2 98.4 SPEC (%) 86.0 88.8 88.8 88.8 88.8 91.6 86.0 PPV (%) 87.8 90.5 90.8 91.0 91.1 93.0 89.2 NPV (%) 83.6 89.6 92.2 96.0 96.9 93.2 97.9 ACCU (%) 85.8 90.1 91.4 93.1 93.6 93.6 92.7 a Evaluation of ISG15 , OAS1, RSAD2 , and IFI44 in females with a functional CL was performed by applying the predefined cutoffs only in females in which CL blood perfusion was > 25% on D20 post-TAI. b The combined use of both genes ( RSAD2, IFI44, ISG15 , and OAS1 ) was performed by considering the female as pregnant when the expression levels of at least one gene were greater than the predefined cutoffs. 4. DISCUSSION The expression of ISGs in circulating immune cells has been used for pregnancy diagnosis in cattle, as it indirectly signals the presence of the peri-implantation conceptus [ 3 , 32 – 34 ]. However, the accuracy of this method using already known (classical) ISGs did not exceed 80%, regardless of cell type. Consequently, the validation of novel candidate genes for potential use as biomarkers of the pregnancy status could contribute to the improvement of beef and dairy cattle production systems. Here, we reported for the first time the direct effects of pregnancy-related factors using UF from day 18 pregnant cows on the expression of ISGs in PBMC and PMN. We determined that RSAD2 was the most responsive marker of bovine conceptus signaling in PBMC and PMN. Furthermore, we combined ISG expression data with luteal blood perfusion information to optimize the accuracy of early pregnancy outcome prediction. We demonstrated that the association of classical ( ISG15 and OAS1 ) and non-classical ( RSAD2 and IFI44 ) ISGs with the color-Doppler diagnosis is an advanced method to differentiate with high accuracy pregnant and non-pregnant Bos indicus beef heifers and cows on day 20 post-TAI. The expression of ISGs in PBMC and PMN was stimulated in vitro with roIFNT or UF from pregnant cows. To the best of our knowledge, the use of a conceptus-conditioned medium on day 18 of pregnancy has never been attempted previously to investigate the physiological stimulus generated by the conceptus on immune cells. Recent results from our group suggested novel candidate genes for pregnancy prediction based on a transcriptome analysis in PBMC and PMN on day 18 of pregnancy [ 22 ]. Moreover, such novel biomarkers may be more accurate in predicting early pregnancy when compared to classical ISGs [ 3 , 21 ]. Therefore, we selected one classical ( ISG15 ) and two non-classical ( RSAD2 and IFI44 ) ISGs to evaluate gene expression, and subsequent sensitivity and specificity analysis as potential early pregnancy markers. Both for PBMC and PMN, treatment with roIFNT or UF from pregnant cows stimulated the expression of all the ISGs evaluated ( ISG15 , RSAD2 , and IFI44 ). Although expected, it was reassuring that the upregulation of ISGs observed in immune cells harvested from the peripheral blood of pregnant cows could be recapitulated in vitro . This effect was due to the direct effect of the exogenous addition of IFN-τ and likely because of its presence in the UF from pregnant cows. Also, the fold change analysis showed that ISG15 and RSAD2 were the most stimulated ISGs in vitro . These findings confirm previous studies reporting that treatment with increasing doses of recombinant bovine (rbIFNT; 0.1–10 ng/mL) and ovine IFN-τ (roIFNT; 100 ng/mL or 1 µg/mL) induced mRNA expression of classical ( ISG15 and OAS1 ) and non-classical ( IFIT2 , SAMD9 and USP18 ) ISGs in immune cells and bovine endometrial cells in vitro [ 2 , 27 ]. Similarly, Rashid et al. [ 28 ] reported upregulation of ISG15 and OAS1 in PBMC cultured with UF from day 7 pregnant cows. The present results demonstrate that the response of non-classical ISG to IFN-τ stimulus induced a response similar to classical ISG in peripheral blood immune cells. This opened the possibility of using these non-classical ISGs to predict pregnancy in cattle earlier than currently. During early pregnancy establishment, a delicate balance between pro- and anti- inflammatory cytokines are required to promote maternal tolerance towards the semi-allogeneic embryo [ 35 ]. IFN-τ is known to regulate the secretion of bovine granulocyte chemotactic protein 2 in the endometrium, regulating cytokine networks in the uterus of pregnant cows [ 36 ]. However, the direct role of IFN-τ in regulating this cytokine balance throughout early pregnancy is unknown. Here, we investigated the response of pro- ( IL1β ) and anti-inflammatory ( IL10 ) cytokines to stimulation of roIFNT or conceptus-conditioned medium on day 18 of pregnancy. UF from pregnant cows suppressed the expression of the IL1β cytokine in PBMC and PMN but did not affect IL10 expression. Similarly, the effect of roIFNT to reduce IL1β cytokine transcripts in PBMC approached significance. Expression of IL10 in PBMC treated with roIFNT was not changed significantly, but at least numerically, it followed the same direction of upregulation of anti-inflammatory cytokines reported in earlier studies [ 27 , 28 , 37 ]. In addition, the analysis of fold change indicated opposite directions in the expression of IL10 (upregulated) and IL1β (downregulated) in both PMN and PBMC. The findings of the present study corroborate with Rashid et al. [ 28 ] and Fiorenza et al. [ 37 ], that verified downregulation of pro-inflammatory cytokines ( IL1β and TNFα ) and upregulation of anti-inflammatory cytokines ( IL10 and TGFβ1 ) in PBMC and PMN, and bovine uterine epithelial cells stimulated with UF from day 7 of pregnant cows or rbIFNT, respectively. Thus, the analysis of pro- and anti-inflammatory cytokine transcripts in this study suggests that the environment conditioned by the conceptus likely modulated the immunological status of the uterus to accept the semi-allogeneic embryo and induced initially a state of immunological tolerance through the suppression of IL1β , essential for embryo survival and establishment of pregnancy. However, the analysis of transcripts of other pro- and anti-inflammatory cytokines is necessary to confirm these results. Pregnancy diagnosis between days 18 and 20 after AI is possible due to differences in immune cell-ISG expression between pregnant and non-pregnant animals [ 14 ]. In that window, the least overlap of ISG expression levels between pregnant and non-pregnant animals, compared to other time intervals, has been reported. The greater ISG expression in pregnant animals is due to stimulation from conceptus-secreted IFN-τ, which increases throughout the third week of pregnancy, associated with the growth of the trophectoderm [ 38 , 39 ]. Although the most commonly used cell type used for pregnancy diagnosis is the PBMC, the present research provided evidence that the expression of ISGs in both PMNs and PBMCs could be used for early detection of pregnant bovine females. This is because the response to IFN-τ stimulation was similar between PMN and PBMC. Here, we showed that expression of non-classical ISGs ( RSAD2 and IFI44 ) in PMN can predict pregnancy at D20 post-TAI accurately. The PMN samples used in this study were obtained in a prospective study by Dalmaso de Melo et al. [ 21 ]. They determined the accuracy of pregnancy prediction by the abundance of classical ISGs ( ISG15 and OAS1 ). The results presented here extended the assessment of PMNs at D20, indicating a greater abundance of the two non-classical ISGs tested ( RSAD2 and IFI44 ) in pregnant females compared to non-pregnant females. These results are consistent with the expression of classical ISGs in PBMC and whole blood immune cells reported in beef cattle [ 3 , 32 ] and heifers and dairy cows [ 33 , 40 , 41 ]. Furthermore, in non-pregnant females, the abundance of RSAD2 was higher in nulliparous and primiparous than in pluriparous females. For IFI44 , transcript abundance was higher in nulliparous, followed by pluriparous and primiparous cows, regardless of gestational status. The reason for the difference in ISG response between parity categories is unspecified, but could be related to size of the embryo, body size and metabolism, differences in immune function or embryo mortality between different parity order [ 42 , 43 ]. However, the findings of the herein study are adverse, since, the effects of parity order did not follow the same pattern in RSAD2 and IFI44 genes. In this regard, previous studies reported that the expression of the RSAD2 gene in the ovine uterus [ 44 ], and in PBMCs of beef cows [ 45 ] are regulated by P4 concentrations; however, whether this regulation is positive or negative remains contradictory between studies. Based on previous studies [ 3 , 21 , 46 ] the ROC curve was established to determine the efficiency of pregnancy prediction through the expression of the classical ISGs ( ISG15 and OAS1 ) obtained in the study by Dalmaso de Melo et al. [ 21 ]; and non-classical ISGs ( RSAD2 and IFI44 ) obtained in the present study. The ROC curve (Fig. 7 ) showed that all ISGs were considered significant predictors of pregnancy with an accuracy exceeding 70%; and in primiparous and pluriparous cows, RSAD2 and IFI44 were considered the most accurate genes. When RSAD2 expression was compared between parity categories, greater and similar accuracies (92%) were observed in primiparous and pluriparous compared to nulliparous females (Table 4 ). These findings are contrary to those observed by Dalmaso de Melo et al. [ 21 ], in which the accuracy of the ISG15 and OAS1 genes was greater in heifers (81%) compared to cows (72%). Pugliesi et al. [ 3 ] described an accuracy close to 80% when this method was performed on PBMCs on the 20th day of pregnancy in beef cows, but sensitivity ranged from 66 to 78%. Yoshino et al. [ 46 ] reported for ISG expression PMN of dairy cows, accuracies ranging from 57 to 86% between days 20 and 22 of pregnancy. Here, we reported greater predictive accuracy for RSAD2 and IFI44 compared to previous studies that used only classical ISGs, mostly because of the lower frequency of false-positive and false-negative and, consequently greater positive and negative predictive value. Color-Doppler methodology in commercial beef cattle operations for detecting non-pregnant females increased recently [ 47 ]. Although this technology frequently achieves 100% sensitivity, its main limitation is the frequency of false-positive results. Here, we combined this method with the expression of ISGs in PMN, in an attempt to further increase the accuracy of pregnancy prediction. Specifically, the cutoff value of each gene analyzed individually or together ( RSAD2 , IFI44 , RSAD2/IFI44 , or RSAD2/IFI44/ISG15/OAS1 ) was applied only in females with a functional CL on D20 (i.e., females diagnosed as pregnant by the Doppler method) (Table 5 ). Females with a non-functional CL on D20 were automatically classified as non-pregnant. Overall, the combined method increased the accuracy of the diagnosis (e.g., accuracy = 98% using only RSAD2 or RSAD2/IFI44 expression in primiparous cows) due to the reduction in false-positive results, but the false-negative results were still frequent for same associations (1 to 10%). Similarly, Pugliesi et al. [ 3 ] and Dalmaso de Melo et al. [ 21 ] reported an accuracy between 84 to 90% when combining the use of two genes ( OAS1/MX2 or ISG15/OAS1 ) in females with a functional CL on day 20 of pregnancy. Finally, the expression of ISGs in animals with an active CL on D20 regardless of the parity category was evaluated, and all possible combinations of ISGs were performed (Table 6 ). The combination of all four ISGs ( RSAD2/IFI44/ISG15/OAS1) yielded the lowest proportion of false-negative (0.9%; 2/233). The persisting inaccuracy for the prediction of pregnancy status associated with false-negative results may be related to animals in which IFN-τ does not signal enough to stimulate the expression of ISGs. In this context, it is known that the size of the conceptus is directly associated with the amount of IFN-τ released [ 48 ]. On the other hand, the inaccuracy related to false-positive results can occur as an outcome of early embryonic mortality or the induction of ISGs by other types of stimuli. Interferon receptors (IFNAR) are non-selective receptors and can be stimulated by any type 1 interferon, which means that other interferons can bind to it and stimulate the expression of ISGs, as, for example, in viral infections [ 3 , 21 , 41 , 49 ]. Thus, progress in methodology is still needed so that even more accurate and easy-to-apply methods can be developed and routinely used in the reproductive management of beef and dairy females. In summary, we conclude that immune cells respond promptly to IFN-τ and/or conceptus stimulus, which may favor the use of PBMC or PMN in novel methods for the detection of pregnancy in cattle (Fig. 8 ). Furthermore, the presence of the bovine conceptus in the uterine environment possibly induces a state of maternal immune tolerance essential for embryonic survival and the establishment of pregnancy. The greater abundance of RSAD2 and IFI44 in PMN on day 20 post-TAI in pregnant beef heifers and suckled cows allowed a high-accuracy method to detect pregnancy, but false-negative results were not eradicated. In addition, for the first time, our study reports that the association between the expression of classical and non-classical ISGs can be used to obtain a more accurate method of pregnancy prediction in bovine females with functional CL at early pregnancy. Declarations Acknowledgments The authors would like to thank all students from the Molecular Endocrinology Physiology Laboratory (LFEM-FMVZ-USP). Also, we thank Dr. Fuller W. Bazer (Texas A & M University) for gently providing recombinant ovine IFN-τ. We thank the Pirassununga Campus of the University of São Paulo for their structure and animals. We acknowledge the Coordination for the Improvement of Greater Education Personnel (CAPES) for awarding a PhD scholarship. This research was funded by the São Paulo Research Foundation (FAPESP) (grant number 2015/10606-9). Data availability statement All data generated or analyzed during this study are included in this published article [and its supplementary information files]. Conflict of interest The authors declare no conflicts of interest. Author contributions Isabella Rio Feltrin: conceived the study, performed reproductive management and PCR analyses, and wrote the manuscript. Gabriela Dalmaso de Melo: conducted the previous study where the samples used in the present study were obtained. Pedro Pisani Freitas: assisted with reproductive management, collection and processing of samples in the laboratory. Karine Galhego Morelli: assisted with reproductive management, collection and processing of samples in the laboratory. Mario Binelli: assisted with expertise in experimental design, and correction of the manuscript. 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A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29 , e45 (2001). Melo GD, Pinto LMF, Rocha CC, Motta IG, Silva LA, da Silveira JC, Gonella-Diaza AM, Binelli M, Pugliesi G. Type I interferon receptors and interferon-τ-stimulated genes in peripheral blood mononuclear cells and polymorphonuclear leucocytes during early pregnancy in beef heifers. Reprod Fertil Dev . 32 (11), 953-966 (2020). Gifford CA, Racicot K, Clark DS, Austin KJ, Hansen TR, Lucy MC, Davies CJ, Ott TL. Regulation of interferon-stimulated genes in peripheral blood leukocytes in pregnant and bred, nonpregnant dairy cows. J Dairy Sci . 90 (1), 274-80 (2007). Yankey SJ, Hicks BA, Carnahan KG, Assiri AM, Sinor SJ, Kodali K, Stellflug JN, Stellflug JN, Ott TL. Expression of the antiviral protein Mx in peripheral blood mononuclear cells of pregnant and bred, non-pregnant ewes. J Endocrinol . 170 (2), R7-11 (2001). Wegmann TG, Lin H, Guilbert L, Mosmann TR. 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Biol. 19 , 170–177 (2008). Han H, Austin K J, Rempel LA, Hansen TR. Low blood ISG15 mRNA and progesterone levels are predictive of non-pregnant dairy cows. J. Endocrinol. 191 , 505–512 (2006). Green JC, Okamura CS, Poock SE, Lucy MC. Measurement of interferon-tau (IFN-tau) stimulated gene expression in blood leukocytes for pregnancy diagnosis within 18-20d after insemination in dairy cattle. Anim Reprod Sci. 121 (1-2), 24-33 (2010). Berg DK, Van Leeuwen J, Beaumont S, Berg M, Pfeffer P.L. Embryo loss in cattle between days 7 and 16 of pregnancy. Theriogenology . 73 , 250–260 (2010). National Academies of Sciences, Engineering, and Medicine. Nutrient Requirements of Beef Cattle: Eighth Revised Edition . Washington, DC: The National Academies Press. (2016). Gray CA, Abbey CA, Beremand PD, Choi Y, Farmer JL, Adelson DL, Thomas TL, Bazer FW, Spencer TE. Identification of endometrial genes regulated by early pregnancy, progesterone, and interferon tau in the ovine uterus. Biol Reprod. 74 (2) 383-94 (2006). Rocha CC, Martins T, Silva FACC, Sponchiado M, Pohler KG, Binelli M. Viperin (RSAD2) gene expression in peripheral blood mononuclear cells of pregnant crossbred beef cows is altered by Bos indicus genetics. Theriogenology. 209 , 226-233 (2023). Yoshino H, Toji N, Sasaki K, Koshi K, Yamagishi N, Takahashi T, Ishiguro-Oonuma T, Matsuda H, Yamanouchi T, Hashiyada Y, Imai K, Izaike Y, Kizaki K, Hashizume K. A predictive threshold value for the diagnosis of early pregnancy in cows using interferon-stimulated genes in granulocytes. Theriogenology. 107, 188-193 (2018). Pugliesi G, Guimarães da Silva A, Viana JHM, Siqueira LGB. Review: Current status of corpus luteum assessment by Doppler ultrasonography to diagnose non-pregnancy and select embryo recipients in cattle. Animal. 1, 100752 (2023). Spencer TE, Forde N, Lonergan P. Insights into conceptus elongation and establishment of pregnancy in ruminants. Reprod Fertil Dev. 29 (1), 84-100 (2016). Ferraz PA, Filho CASG, Rocha CC, Neto AL, de Andrade Bruni G, Oshiro TSI, Baruselli PS, Lima FS, Pugliesi G. Feasibility and accuracy of using different methods to detect pregnancy by conceptus-stimulated genes in dairy cattle. JDS Commun. 2 (3), 153-158 (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.pdf GraphicalAbstract.jpg Cite Share Download PDF Status: Published Journal Publication published 20 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Jan, 2025 Reviews received at journal 12 Jan, 2025 Reviewers agreed at journal 02 Jan, 2025 Reviews received at journal 10 Dec, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviewers invited by journal 25 Nov, 2024 Editor assigned by journal 25 Nov, 2024 Editor invited by journal 11 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 04 Nov, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-5389974","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":382455951,"identity":"f8509e55-2a99-4c92-a7dc-901f93e58854","order_by":0,"name":"Isabella Rio Feltrin","email":"","orcid":"","institution":"São Paulo State University (UNESP)","correspondingAuthor":false,"prefix":"","firstName":"Isabella","middleName":"Rio","lastName":"Feltrin","suffix":""},{"id":382455952,"identity":"483f7a9d-93ef-41c9-a91d-30bf8f47f2ab","order_by":1,"name":"Gabriela Dalmaso Melo","email":"","orcid":"","institution":"University of Florida (UF)","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"Dalmaso","lastName":"Melo","suffix":""},{"id":382455953,"identity":"abd22413-aa56-478b-b138-97a7a8dcaf0a","order_by":2,"name":"Pedro Pisani Freitas","email":"","orcid":"","institution":"University of São Paulo (USP)","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"Pisani","lastName":"Freitas","suffix":""},{"id":382455954,"identity":"b3bfaa5d-a8eb-4cdb-a49c-b423ecb3b38f","order_by":3,"name":"Karine Galhego Morelli","email":"","orcid":"","institution":"University of São Paulo (USP)","correspondingAuthor":false,"prefix":"","firstName":"Karine","middleName":"Galhego","lastName":"Morelli","suffix":""},{"id":382455955,"identity":"dd700d2b-fc3b-4599-94f3-8033fda56be9","order_by":4,"name":"Mario Binelli","email":"","orcid":"","institution":"University of Florida (UF)","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Binelli","suffix":""},{"id":382455956,"identity":"44588aa2-9c5d-4acf-8a29-1f7d4a193c01","order_by":5,"name":"Claudia Maria Bertan Membrive","email":"","orcid":"","institution":"São Paulo State University (UNESP)","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"Maria Bertan","lastName":"Membrive","suffix":""},{"id":382455957,"identity":"8fb3d60a-fe31-4b70-88fd-122d1d5820b3","order_by":6,"name":"Guilherme Pugliesi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYLCCBwYMDGzsDSDmASK1JIC08BwgSQuIkEggUot8dI/xi4QCG3s+yTfGnwsY7uQT1GJ454yZRYJBWmKbdI6Z9AyGZ5YNBLXMyDEzSDA4nMAG1MLMw3DYgLAtEC3/7dkkzxh/JkqLvESO8YMEgwOMbRI8BtJEaTGQOVYGDOTkxDaetDLpGQbPiLBldvPmDx/+2NnLtx/e/Lmg4g4RttxgYJOAcZgZCGsA2jKDgfkDQssoGAWjYBSMAiwAAIhROMeZncQ/AAAAAElFTkSuQmCC","orcid":"","institution":"University of São Paulo (USP)","correspondingAuthor":true,"prefix":"","firstName":"Guilherme","middleName":"","lastName":"Pugliesi","suffix":""}],"badges":[],"createdAt":"2024-11-04 17:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5389974/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5389974/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-01996-y","type":"published","date":"2025-05-20T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69878214,"identity":"ee5996c9-f181-4e70-8791-e79fb685f4cc","added_by":"auto","created_at":"2024-11-26 08:41:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":546710,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the experimental model. A) In the \u003cem\u003eExp. 1 \u003c/em\u003eand\u003cem\u003e 2\u003c/em\u003e, non-pregnant Nelore heifers (N=12) were submitted to blood sampling collection between D10-D12 post-ovulation (D0=day of ovulation), for the isolation of mononuclear (PBMC) and polymorphonuclear (PMN) cells. Isolated PBMC and PMN were stimulated with roIFNT (100 ng/mL, IFNT group) or uterine flush from day 18 pregnant cows (UF-Conceptus) for 24 h (PBMC) or 3 h (PMN) at 37°C in 5% CO\u003csub\u003e2\u003c/sub\u003e. The groups without treatment [Control or UF from cows on day 18 of the diestrus phase (UF-Control)] served as controls. After the incubation, the cells were directed to RNA extraction, and gene expression of ISGs (\u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eRSAD2,\u003c/em\u003e and \u003cem\u003eIFI44)\u003c/em\u003e was determined by qPCR. B) For \u003cem\u003eExp. 3\u003c/em\u003e, Nelore females (nulliparous, N=103; primiparous, N=53; pluriparous, N=91) were submitted to timed-AI (TAI) on day 0. On D20 post-TAI, PMN was isolated from the peripheral blood of inseminated and non-inseminated cows. After isolation, PMN was directed to RNA extraction, and gene expression of ISGs (\u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eOAS1\u003c/em\u003e, \u003cem\u003eRSAD2,\u003c/em\u003e and \u003cem\u003eIFI44)\u003c/em\u003e was determined by qPCR for the accuracy of pregnancy predictors.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/504f1559e448eb0f367db1cf.png"},{"id":69878215,"identity":"50b121ac-328d-41e7-a2e2-ad36645913ca","added_by":"auto","created_at":"2024-11-26 08:41:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":258988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExperiment 1\u003c/em\u003e. Mean ± SEM for relative expression (reference genes, PBMC: \u003cem\u003eGAPDH/PPIA\u003c/em\u003e; PMN: \u003cem\u003eGAPDH/ACTB\u003c/em\u003e) and fold change of \u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eRSAD2, \u003c/em\u003eand \u003cem\u003eIFI44\u003c/em\u003e genes by qPCR in PBMC (Panel A and C; N=9) cultured for 24 h and PMN (Panel B and D; N=10) cultured for 3 h, and treated (100 ng/mL roIFNT) or untreated (Control) with recombinant ovine interferon-τ (roIFNT). \u003csup\u003e*AB\u003c/sup\u003e An asterisk or different letters above the bar indicates a significant difference (P ≤ 0.05) between the transcripts. \u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/424c0f8f0eec3851eca77ccd.png"},{"id":69874681,"identity":"4a70b437-f09f-4b33-84e5-44d28a51188d","added_by":"auto","created_at":"2024-11-26 08:17:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194462,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExperiment 1.\u003c/em\u003e Mean ± SEM for relative expression (reference genes, PBMC: \u003cem\u003eGAPDH/PPIA\u003c/em\u003e; PMN: \u003cem\u003eGAPDH/ACTB\u003c/em\u003e) and fold change of pro-inflammatory (\u003cem\u003eIL1β\u003c/em\u003e) and anti-inflammatory (\u003cem\u003eIL10\u003c/em\u003e) cytokine genes by qPCR in PBMC (Panel A and C; N=9) cultured for 24 hours and PMN (Panel B and D; N=10) cultured 3 hours, and treated (100 ng/mL roIFNT) or untreated (Control) with recombinant ovine interferon-tau (roIFNT). \u003csup\u003e#\u003c/sup\u003eA hatch tag above the bar indicates a tendency to significance (0.05 \u0026lt; P ≤ 0.1) between the transcripts. \u003csup\u003eAB\u003c/sup\u003e Different letters above the bar indicates a significant difference (P ≤ 0.05) between the transcripts.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/04cd2802deca759edd328bb0.png"},{"id":69875758,"identity":"c27bcdf4-e702-4d1d-b731-1b14fecb3f8d","added_by":"auto","created_at":"2024-11-26 08:25:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":284376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExperiment 2.\u003c/em\u003e Mean ± SEM for relative expression (reference genes, PBMC: \u003cem\u003eGAPDH/PPIA\u003c/em\u003e; PMN: \u003cem\u003eGAPDH/ACTB\u003c/em\u003e) and fold change of \u003cem\u003eISG15, RSAD2,\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e genes by qPCR in PBMC (Panel A and C; N=10) cultured for 12 hours and PMN (Panel B and C; N=8) cultured for 3 hours in UF from day 18 of pregnant cows (UF-Conceptus) or UF from non-pregnant cows (UF-Control). \u003csup\u003e*AB\u003c/sup\u003e An asterisk or different letters above the bar indicates a significant difference (P ≤ 0.05) between the transcripts.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/bd25ffb61da0e1fedbba79f3.png"},{"id":69875763,"identity":"2d72a5de-4676-4aa5-8b6d-e440abe5a1d8","added_by":"auto","created_at":"2024-11-26 08:25:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":196582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExperiment 2.\u003c/em\u003e Mean ± SEM for relative expression (reference genes, PBMC: \u003cem\u003eGAPDH/PPIA\u003c/em\u003e; PMN: \u003cem\u003eGAPDH/ACTB\u003c/em\u003e) and fold change of pro-inflammatory (\u003cem\u003eIL1β\u003c/em\u003e) and anti-inflammatory (\u003cem\u003eIL10\u003c/em\u003e) cytokine genes by qPCR in PBMC (Panel A and C; N=10) cultured for 12 hours and PMN (Panel B and D; N=8) cultured for 3 hours in UF from day 18 of pregnant cows (UF-Conceptus) or UF from non-pregnant cows (UF-Control). \u003csup\u003e*AB\u003c/sup\u003e An asterisk or different letters above the bar indicates a significant difference (P ≤ 0.05) between the transcripts.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/6d34a8fcb8d59cdb2f41b3a3.png"},{"id":69876111,"identity":"54ef8d50-fd7b-441d-8db9-5b95509199ec","added_by":"auto","created_at":"2024-11-26 08:33:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExperiment 3\u003c/em\u003e. Mean ± SEM for relative expression (reference genes, PMN: \u003cem\u003eGAPDH/ACTB\u003c/em\u003e) of \u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44 \u003c/em\u003egenes\u003cem\u003e \u003c/em\u003eby qPCR in PMN from pregnant and non-pregnant nulliparous (N=103), primiparous (N=53), and pluriparous (N=91) bovine females 20 days post-timed-AI (TAI). The main effects of group (G), category (Cat), and interaction group*category (G*Cat) that were significant are shown. \u003csup\u003eXY\u003c/sup\u003e Bars with a different letter indicate a significant difference (P ≤ 0.05) among the parity order in non-pregnant animals. \u003csup\u003eABC \u003c/sup\u003eBars with a different letter indicate a significant difference (P ≤ 0.05) among the parity order, regardless of the pregnancy status.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/1f02ccde1bd49e5b0bf1f995.png"},{"id":69874688,"identity":"e847479c-b384-4f4e-babe-576799a2eb2b","added_by":"auto","created_at":"2024-11-26 08:17:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":781358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExperiment 3.\u003c/em\u003e ROC (Receiver Operating Characteristic) curves of the classical (\u003cem\u003eISG15\u003c/em\u003eand \u003cem\u003eOAS1\u003c/em\u003e) and non-classical (\u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) ISGs on D20 post-timed-AI (TAI) in nulliparous (N=103), primiparous (N=53), and pluriparous (N=91) bovine females. The horizontal and vertical axes represent false positive rate (1 - specificity) and sensitivity, respectively. Non-classical (\u003cem\u003eRSAD2\u003c/em\u003eand \u003cem\u003eIFI44\u003c/em\u003e) ISGs provided the most adequate prediction of pregnancy when compared to classical ISGs (\u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eOAS1\u003c/em\u003e) in primiparous and pluriparous bovine females.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/9467e89bfa4952686042da73.png"},{"id":69874685,"identity":"4fb733a4-1d26-4a3e-a489-3cf53eb22e99","added_by":"auto","created_at":"2024-11-26 08:17:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":490906,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of Results. In the \u003cem\u003e\u003cstrong\u003eExperiments 1 and 2\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e (in vitro\u003c/em\u003e studies), relative expression indicated that the treatments upregulated the ISGs (\u003cem\u003eISG15, RSAD2, \u003c/em\u003eand \u003cem\u003eIFI44\u003c/em\u003e) and downregulated the pro-inflammatory cytokine\u003cem\u003e IL1β \u003c/em\u003ein PBMC and PMN immune cells. According to the \u003cem\u003ein vivo\u003c/em\u003e study \u003cem\u003e\u003cstrong\u003eExperiment 3\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003e(\u003cem\u003ein vivo\u003c/em\u003e study), we propose that an association between the expression of classic (\u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eOAS1\u003c/em\u003e) and non-classic (\u003cem\u003eRSAD2 \u003c/em\u003eand \u003cem\u003eIFI44\u003c/em\u003e) ISGs in females with active CL through Doppler ultrasonography can be used as a high-accurate predictor of pregnancy in cows on day 20 post-timed-AI (TAI). Blue arrows represent downregulation and red arrows represent upregulation of genes.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/ec43e446e8815a4db098bf81.png"},{"id":83460133,"identity":"3558b6e0-cc47-464b-ab5a-45403a0937f1","added_by":"auto","created_at":"2025-05-26 16:10:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4687698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/2bf98cf0-b1a5-40f8-9758-88d89804f881.pdf"},{"id":69875762,"identity":"096baac4-7944-45bd-90c1-3f739d387a73","added_by":"auto","created_at":"2024-11-26 08:25:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":337913,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/822f2d75883ec4accad0428e.pdf"},{"id":69874679,"identity":"1a8c0d6b-be74-47ed-a16e-96dc29865e65","added_by":"auto","created_at":"2024-11-26 08:17:59","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":165543,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5389974/v1/59c46e979ae630b08c81d743.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eExpression in Immune Cells of New Conceptus Signaling Markers Optimizes Prediction of Pregnancy in Beef Cattle\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn cow-calf operations that employ timed-AI (TAI), there is interest in re-insemination of females that did not become pregnant to the first AI [1]. To that end, open females must be identified as soon as possible for a new service. The most common method of pregnancy diagnosis in cattle is transrectal ultrasonography in B-mode, which achieves 100% accuracy between 28-32 days after TAI by the visualization of the viable embryo. Therefore, considering that non-pregnant females return to estrus around 21 days after TAI [2], it is advantageous to determine the pregnancy status ≤ 20 days post-TAI to allow for quick re-insemination. Recently, color-Doppler (Doppler-US) has been used as a tool for early pregnancy diagnosis by the verification of sustained luteal blood perfusion between days 20 and 22 after TAI with an accuracy greater than 90% [3-5]. The advantage of this technique is the sensitivity approaching 100%, which results in very few false-negative diagnoses. However, this method may result in up to 15% false-positive diagnoses in beef, and 40% in dairy cattle [5, 6]. Thus, the development of methods capable of identifying pregnancy by detecting the conceptus or using conceptus-specific markers could reduce false-positive prevalence and improve the accuracy of pregnancy diagnosis methods during the first three weeks after TAI.\u003c/p\u003e\n\u003cp\u003eIn domestic ruminants, interferon-τ (IFN-τ) is the main conceptus-derived cytokine responsible for the maternal recognition of pregnancy (MRP) [7].\u0026nbsp;During MRP, IFN-τ inhibits the endometrial pulsatile release of prostaglandin F\u003csub\u003e2\u003c/sub\u003e\u003csub\u003eα\u003c/sub\u003e (PGF\u003csub\u003e2\u003c/sub\u003e\u003csub\u003eα\u003c/sub\u003e), preventing regression of the corpus luteum (CL). This maintains the synthesis of progesterone (P4) necessary for the continuation of pregnancy [8, 9].\u0026nbsp;When this sequence of events occurs successfully, pregnancy continues. However, on average, 50% of beef cattle fail to remain pregnant after day 16 after artificial insemination (AI) [10].\u0026nbsp;For this reason, understanding the mechanisms involved in early pregnancy is essential to mitigate pregnancy loss, and the maternal immune system plays an important role in this period.\u003c/p\u003e\n\u003cp\u003eStudies suggest that conceptus alloantigens alter maternal immune function both locally at the embryonic-maternal junction and systemically in the peripheral blood circulation, to prevent embryonic immune rejection [11, 12]. This is achieved through the modulation of maternal immune cells that direct the balance of cytokines towards the Th2 anti-inflammatory pathway [13].\u0026nbsp;Immunological tolerance displayed by immune cells can be triggered by several molecules such as hormones, cytokines, and enzymes [14]. Thus, IFN-τ\u0026nbsp;is one of the cytokines responsible for the functional communication between the maternal immune system and the developing embryo in ruminants.\u0026nbsp;The bovine embryo on day 4 of development is already capable of signaling its presence through the modulation of IFN-τ-sensitive genes, regulating the local immune environment in the oviduct [15].\u0026nbsp;Furthermore, bovine embryos at day 7 communicate with epithelial and immune cells, possibly mediated in part by IFN-τ [16].\u003c/p\u003e\n\u003cp\u003eIFN-τ is also known to induce the expression of interferon-stimulated genes (ISGs) in the liver, endometrium, luteal cells, and peripheral blood mononuclear (PBMC) and polymorphonuclear (PMN) cells during early pregnancy in cows [3, 16-19]. Genes commonly stimulated by IFN-τ, known as classical ISGs, include ubiquitin-like modifier 15 (\u003cem\u003eISG15\u003c/em\u003e), 2’-5’-oligoadenylate synthetase 1 (\u003cem\u003eOAS1\u003c/em\u003e), MX dynamin-like GTPase 1 (\u003cem\u003eMX1\u003c/em\u003e) and 2 (\u003cem\u003eMX2\u003c/em\u003e). The transcriptional profile of these genes is closely related to the secretion of IFN-τ by the conceptus [3, 16-19]. In cattle, expression of ISGs peaks between days 18 and 20 of pregnancy and returns to basal levels around day 25. Moreover, the expression of mRNA for ISGs in bovine peripheral blood leukocytes is greater in pregnant cows compared to non-pregnant cows on days 18 and 20 after AI.\u003c/p\u003e\n\u003cp\u003eThe practical implication is that ISG expression in immune cells may be used for early detection of pregnancy, as well as pregnancy failures [3, 20]. The expression of classical ISGs in peripheral immune cells is a diagnostic method for detecting pregnancy on day 20 in both heifers and cows. However,\u0026nbsp;the accuracy ranged from 62% to 80%, regardless of cell type (PBMC or PMN) [3, 21]. In this context, Rocha et al. [22]\u0026nbsp;used transcriptomic approaches to identify novel genes induced by early pregnancy in PBMCs and PMNs on day 18 post-TAI, beyond the classical ISGs. The general objective of this paper is to provide initial validation of novel candidate genes for the potential use as biomarkers of the pregnancy status of cows.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;hypothesis is that the expression of ISGs (\u003cem\u003eISG15, OAS1, RSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) and cytokines (\u003cem\u003eIL1β\u003c/em\u003e and \u003cem\u003eIL10\u003c/em\u003e) in PBMCs and PMNs is regulated by IFNT and predicts the pregnancy status.\u0026nbsp;The study aimed to: 1) measure the expression of \u003cem\u003eISG15, RSAD2, IFI44, IL1β\u003c/em\u003e and \u003cem\u003eIL10\u003c/em\u003e in PBMCs and PMNs to IFN-τ (\u003cstrong\u003e\u003cem\u003eExperiment 1\u003c/em\u003e\u003c/strong\u003e) or to uterine flushes (UF) from pregnant cows (\u003cstrong\u003e\u003cem\u003eExperiment 2\u003c/em\u003e\u003c/strong\u003e); and 2) evaluate the accuracy of \u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e expression in PMNs to predict the pregnancy status in cattle (\u003cstrong\u003e\u003cem\u003eExperiment 3\u003c/em\u003e\u003c/strong\u003e).\u003c/p\u003e"},{"header":"2. MATERIAL AND METHODS","content":"\u003ch3\u003e2.1 Ethics statement\u003c/h3\u003e\n\u003cp\u003eThe present study was conducted at the Animal Reproduction Department of the University of São Paulo, in Pirassununga, Brazil.\u0026nbsp;Animal welfare guidelines and handling procedures recommended by the São Paulo State (Brazil) law number 11.977 were strictly followed. That experiment was approved by the Animals Ethics Committee of the School of Veterinary Medicine and Animal Science (CEUA-FMVZ number: 8192280317), and was conducted in accordance with the ARRIVE guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Experimental model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitially, to characterize the responsiveness of PBMCs and PMNs to pregnancy factors, these immune cells were isolated from the peripheral blood of Nelore heifers (N=12) and stimulated with 100 ng /mL recombinant ovine interferon-τ (roIFNT, \u003cstrong\u003e\u003cem\u003eExperiment 1\u003c/em\u003e\u003c/strong\u003e) or UF from day 18 of pregnant cows (\u003cstrong\u003e\u003cem\u003eExperiment 2\u003c/em\u003e\u003c/strong\u003e) in an \u003cem\u003ein vitro\u003c/em\u003e culture cell system. Endpoint was the expression of ISGs (\u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eRSAD2,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eIFI44\u003c/em\u003e), pro- (\u003cem\u003eIL1B\u003c/em\u003e) and anti-inflammatory (\u003cem\u003eIL10\u003c/em\u003e) cytokines, measured by quantitative PCR (qPCR). The main purpose was to determine whether the responsiveness of non-classical ISGs (\u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) to pregnancy factors resembled that of a known classical ISG (\u003cem\u003eISG15\u003c/em\u003e). Next, based on the responses obtained in the \u003cem\u003ein vitro\u003c/em\u003e studies, the expression of ISGs (\u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eOAS1\u003c/em\u003e, \u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) in PMNs 20 days after TAI was tested for the accuracy in predicting the pregnancy outcomes of females of different parities (i.e., nulliparous, primiparous or pluriparous)\u0026nbsp;compared to ultrasound pregnancy diagnosis (gold standard) on 30 days after TAI (\u003cstrong\u003e\u003cem\u003eExperiment 3\u003c/em\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003e2.3 Experimental design of Experiments 1 and 2\u003c/h3\u003e\n\u003cp\u003eTwelve Nelore beef heifers (\u003cem\u003eBos taurus indicus\u003c/em\u003e) located at the Animal Reproduction Department of the University of São Paulo (Pirassununga, Brazil), cycling, non-pregnant, with a body condition score between 3 and 4 (on a 1-5 scale) [23]\u0026nbsp;and between 23 and 26 months of age, were maintained on \u003cem\u003eBrachiaria brizantha\u003c/em\u003e pastures with free access to water and mineral supplementation. On a random day of the estrous cycle, all animals received 2 mL i.m. of PGF\u003csub\u003e2\u003c/sub\u003e\u003csub\u003eα\u003c/sub\u003e (500 µg; of sodium cloprostenol; Sincrocio; Ouro Fino Saúde Animal) for estrous synchronization. In the following five days, the females were evaluated daily through ultrasound examinations in B-mode (MyLab Delta Vet Gold; Esaote Healthcare; Italy) to detect ovulation. Ovulations were determined by the disappearance of the pre-ovulatory follicle. \u0026nbsp;Between 10 to 12 days post-ovulation, blood samples (25 mL) were collected from the jugular vein into sodium-heparinized tubes (BD Vacutainer; São Paulo; Brazil) for the isolation of immune cells \u003cstrong\u003e(Figure 1A).\u003c/strong\u003e Only animals presenting luteal blood perfusion ≥ 25% (i.e., bearing an active CL) were submitted to blood collection [3]. CL blood perfusion was evaluated by a pulse wave color-Doppler ultrasound instrument (MyLab Delta Vet Gold; Esaote Healthcare; Italy) equipped with a multifrequency linear transducer (3.5–7.5 MHz) in B-mode (RES-A, gain 50%, P 74 mm, X/M, PRS 1) and Doppler-mode (gain 61%, PRF 730 Hz, frequency 6.3 MHz, WF 4, PRS 3, PRC M/2).\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e2.3.1 Isolation of immune cells from peripheral blood\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eThe PBMC and PMN were isolated by density gradient centrifugation using a Ficoll-Paque solution (GE Healthcare,\u0026nbsp;Ref.17144003) [3, 24]. For each cell isolation, whole blood was mixed with an equal volume of PBS in a 50-mL conical tube, and the solution was layered onto 15 mL Ficoll-Paque solution and centrifuged at 1100 g for 30 min at 20°C. After centrifugation, the blood fractions segregated in the following sequence: plasma, buffy coat, and red blood cells together with PMN. The buffy coat was utilized for PBMC isolation, as described by Pugliesi et al. [3]\u0026nbsp;and the last layer containing the granulocytes and red blood cells was utilized for PMN isolation, as described by Jiemtaweeboon et al. [24], with some modifications. The PBMC and PMN were subjected to successive lyses steps with hypertonic solutions to lyse the red blood cells until a clean cell pellet was obtained. At the end of the isolation process, the cell pellet was re-suspended in medium according to treatments assigned by design. The purity of PBMC and PMN was verified by staining freshly isolated samples with the quick panoptic protocol. Samples were considered pure when 95% of the 200 cells counted were mononuclear and polymorphonuclear cells, respectively. In addition, cell viability was assessed pre- and post-culture with Trypan blue (0.4%, Sigma-Aldrich, Ref. T6146) reagent in a Neubauer camera, where only samples that showed viability greater than 85% were used in the study. (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eTarget name, gene number, forward (F) and reverse (R) primer sequence of the genes tested by the qPCR technique\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"926\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6505%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9946%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.479%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward primer sequence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.5868%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReverse primer sequence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6505%;\"\u003e\n \u003cp\u003e\u003cem\u003eOAS1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9946%;\"\u003e\n \u003cp\u003eNM_001040606.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.479%;\"\u003e\n \u003cp\u003eTAGCCTGGAACATCAGGTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.5868%;\"\u003e\n \u003cp\u003eTTTGGTCTGGCTGGATTACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2891%;\"\u003e\n \u003cp\u003eShirasuna, et al. [27]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6505%;\"\u003e\n \u003cp\u003e\u003cem\u003eISG15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9946%;\"\u003e\n \u003cp\u003eNM_174366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.479%;\"\u003e\n \u003cp\u003eGGTATCCGAGCTGAAGCAGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.5868%;\"\u003e\n \u003cp\u003eACCTCCCTGCTGTCAAGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2891%;\"\u003e\n \u003cp\u003eOliveira, et al. [29]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6505%;\"\u003e\n \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9946%;\"\u003e\n \u003cp\u003eNM_001045941.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.479%;\"\u003e\n \u003cp\u003eTGGTTCCAGAAGTACGGTGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5868%;\"\u003e\n \u003cp\u003eACCACGGCCAATAAGGACAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2891%;\"\u003e\n \u003cp\u003eRocha, et al. [22]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6505%;\"\u003e\n \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9946%;\"\u003e\n \u003cp\u003eXM_002686295.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.479%;\"\u003e\n \u003cp\u003eTCTGCCCATTGCTGAAGGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5868%;\"\u003e\n \u003cp\u003eCCACATGGACCACATCAGACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2891%;\"\u003e\n \u003cp\u003eRocha, et al. [22]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6505%;\"\u003e\n \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9946%;\"\u003e\n \u003cp\u003eNM_001034034.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.479%;\"\u003e\n \u003cp\u003eGCCATCAATGACCCCTTCAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.5868%;\"\u003e\n \u003cp\u003eTGCCGTGGGTGGAATCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2891%;\"\u003e\n \u003cp\u003eAra\u0026uacute;jo, et al. [30]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6505%;\"\u003e\n \u003cp\u003e\u003cem\u003eACTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9946%;\"\u003e\n \u003cp\u003eNM_173979.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.479%;\"\u003e\n \u003cp\u003eGGATGAGGCTCAGAGCAAGAGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.5868%;\"\u003e\n \u003cp\u003eTCGTCCCAGTTGGTGACGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2891%;\"\u003e\n \u003cp\u003eAra\u0026uacute;jo, et al. [30]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6505%;\"\u003e\n \u003cp\u003e\u003cem\u003ePPIA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9946%;\"\u003e\n \u003cp\u003eBF230516.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.479%;\"\u003e\n \u003cp\u003eGCCATGGAGCGCTTTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5868%;\"\u003e\n \u003cp\u003eCCACAGTCAGCAATGGTGATCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2891%;\"\u003e\n \u003cp\u003ePugliesi, et al. [3]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cem\u003e2.3.2 Collection of UF on day 18 of the estrous cycle\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eAfter estrus, Holstein (\u003cem\u003eBos taurus taurus;\u0026nbsp;\u003c/em\u003eN=10) non-lactating cows were subjected to AI with semen from a single sire (N=3) or remained as non-inseminated controls (N=3). On D18 post-estrus, all females were slaughtered and the reproductive tract (cervix, uterus, and ovaries) was collected, and immediately transported on ice to the laboratory. The uterine horns of each reproductive tract were flushed simultaneously with 20 mL of phosphate-buffer saline (PBS). When a conceptus was present, it was removed from the flush; then, the UF was centrifuged at 300 g for 10 minutes. The supernatant was collected and centrifuged at 2000 g for 10 minutes, and the resulting supernatant at 16,500 g for 30 minutes. All centrifugations were at 4°C. The supernatant from the final centrifugation was stored at -80°C for later use in cell culture experiments. The UF obtained from cows with a conceptus was denominated UF-Conceptus and the UF from non-inseminated cows was used as a control (UF-Control). For cell culture experiments, a UF-Conceptus pool and a UF-Control group pool were built by combining UF from three cows from each group, respectively.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e2.3.3 Experiment 1: Stimulation of immune cells with roIFNT\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eIsolated PBMC (N=9 cows; 7 x 10\u003csup\u003e6\u0026nbsp;\u003c/sup\u003ecells/mL) and PMN (N=10 cows; 5 x 10\u003csup\u003e6\u0026nbsp;\u003c/sup\u003ecells/mL) were cultured in 6-well plates in simplicates (Kasvi, Ref. K12-006) in RPMI-1640 medium (Sigma-Aldrich; Ref. 22400071) containing 0.1% FBS (LGC; Ref. 10-bio-500) and Penicillin-Streptomycin (10 µL/mL; Gibco™, Ref. 15140122) in combination with 0 (control) or 100 ng/mL of\u0026nbsp;roIFNT [25]\u0026nbsp;in a humidified atmosphere at 37°C in 5% CO\u003csub\u003e2\u003c/sub\u003e. PMNs were cultured for 3 h while PBMCs were cultured for 24 h. The concentrations of roIFNT used in the present study were determined based previous studies [2, 26, 27], and validated in dose-response study (10, 100, or 1000 ng/mL roIFNT; data not shown). After the incubation, the samples were\u0026nbsp;centrifugated\u0026nbsp;at 700 g for 8 minutes at 25°C. Then, the supernatants were removed and cells were directed to RNA extraction and subsequent\u0026nbsp;gene expression analysis by qPCR.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e2.3.4 Experiment 2: Culture of immune cells in UF\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eIsolated PBMC (N=10 cows; 7 x 10\u003csup\u003e6\u0026nbsp;\u003c/sup\u003ecells/mL) and PMN (N=8 cows; 5 x 10\u003csup\u003e6\u0026nbsp;\u003c/sup\u003ecells/mL) were cultured in a 6-well plate in simplicates (Kasvi, Ref. K12-006) in UF-Control or UF-Conceptus containing 0.1% FBS (LGC, Ref. 10-bio-500) in a humidified atmosphere at 37°C in 5% CO\u003csub\u003e2,\u0026nbsp;\u003c/sub\u003eaccording to the methodology described by Rashid et al. [28],\u0026nbsp;with some modifications\u003csub\u003e.\u0026nbsp;\u003c/sub\u003ePMNs were cultured for 3 h while PBMCs were cultured for 12 h. After the incubation, samples were centrifugated at 700 g for 8 minutes at 25°C, supernatants were removed and cells were directed to RNA extraction and subsequent gene expression analysis by qPCR.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.4 Experimental design of Experiment 3: accuracy of pregnancy markers in PMN \u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe PMN samples used in this experiment were obtained from a previous study conducted by Dalmaso de Melo et al. [21], where, nulliparous (N=103), primiparous (N=53), and pluriparous (N=91) Nelore (\u003cem\u003eBos taurus indicus\u003c/em\u003e) cows were subjected to an estradiol (E2) and P4 based protocol for synchronization of ovulation and TAI (TAI= day 0 [D0]). On D20, the animals were evaluated for CL blood perfusion by color-Doppler ultrasound (MyLab Delta Vet Gold; Esaote Healthcare; Italy) and blood samples (25 mL) were collected from the jugular vein into sodium-heparinized tubes (BD Vacutainer; São Paulo; Brazil) for the isolation of PMN \u003cstrong\u003e(Figure 1B).\u003c/strong\u003e PMN were isolated as described in the \u003cem\u003eExperiments 1\u003c/em\u003e and \u003cem\u003e2\u003c/em\u003e. After isolation, PMN were stored at -80°C for subsequent RNA extraction and gene expression analysis by qPCR. The purity of PMN was checked using the quick panoptic protocol as described previously. Thirty days (D30) after TAI, pregnancy status was verified by the presence of a viable embryo with a heartbeat by B-mode ultrasonography.\u0026nbsp;Ultrasound pregnancy diagnosis on D30 was considered as the gold standard for comparison with the ISG expression and Doppler methods.\u003c/p\u003e\n\u003ch3\u003e2.5 RNA extraction, cDNA synthesis, and quantitative polymerase chain \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; reaction (qPCR)\u003c/h3\u003e\n\u003cp\u003eThe PBMC and PMN obtained on \u003cem\u003eExperiments 1\u003c/em\u003e and \u003cem\u003e2\u003c/em\u003e were thawed on ice and the RNA was extracted using PureLink™ RNA Mini Kit (Invitrogen™, Ref. 12183018A). Briefly, the PBMC and PMN pellets were dissolved using the lysis solution and immediately entered the RNA washing procedures as per manufacturer's instructions. For \u003cem\u003eExperiment 3\u003c/em\u003e, the isolated PMN was extracted by a modified protocol using Trizol™ (Thermo Fisher Scientific, Ref.\u0026nbsp;15596018) reagent associated with the DirectZol-RNA kit (Zymo Research, Ref. R2052), as described in detail by Dalmaso de Melo et al. [21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal RNA concentration and purity were measured using a NanoVue™ Plus spectrophotometer (GE Healthcare, UK), and samples with a 260/280 ratio ranging from 1.7 to 2.0 were used for transcript abundance analyses. The isolated RNA from samples in both studies were treated with DNase I (DNase I Amplification Grade; Life Technologies, Ref. 18068015) to avoid genomic DNA contamination, as per the manufacturer’s instructions. Next, the RNA isolated was subjected to reverse transcription using the High-Capacity cDNA Reverse Transcription Kit (Life Technologies, Ref. 4368814), according to the manufacturer’s instructions, and the cDNA of each sample was stored at -20°C until qPCR analysis. Analyses of the relative abundance of transcripts were performed using SYBR Green PCR Master Mix (Life Technologies, Ref. A25742) for amplification reactions in the Step One Plus thermocycler (Applied Biosystems Real-Time PCR System; Life Technologies, Ref.\u0026nbsp;4376600). The samples were run in triplicate and the maximum CV accepted among the replicates was 0.1. Specific primers for each gene \u003cstrong\u003e(Table 1)\u003c/strong\u003e were selected according to previous studies [3, 22, 27, 29, 30]. All newly designed primers were evaluated for sequence specificity using BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). Furthermore, GeNorm software (https://genorm. cmgg. be) was used to select reference genes. Glyceraldehyde-3-Phosphate Dehydrogenase (\u003cem\u003eGAPDH\u003c/em\u003e) and Actin Beta (\u003cem\u003eACTB\u003c/em\u003e) were the most stable genes in PMN, and \u003cem\u003eGAPDH\u003c/em\u003e and Ciclofilin (\u003cem\u003ePPIA\u003c/em\u003e) were the most stable genes on PBMC. We used LinRegPCR software to determine qPCR efficiency and quantification cycle (Cq) values per sample. Quantification was performed after normalization of the target gene expression values by the geometric mean of the endogenous control expression values, as described by Pfaffl [31].\u003c/p\u003e\n\u003ch3\u003e2.6 Statistical analyses\u003c/h3\u003e\n\u003cp\u003eThe data were evaluated for detection of outliers using the Dixon test and the significant (P \u0026lt; 0.05) outliers detected were excluded from the analyses. The data that were not normally distributed according to the Shapiro–Wilk test were transformed with normal logarithm, rank, and square root. The abundance of gene transcript for all experiments was analyzed by analysis of variance (ANOVA) using the PROC MIXED procedure of SAS (Version 9.2; SAS Institute). Pearson’s correlation between ISG and cytokines expression was analyzed by the GraphPad Prism software (Version 5.0) for both studies. For the \u003cem\u003eExperiment 1\u003c/em\u003e and \u003cem\u003e2\u003c/em\u003e, animal was considered as a random effect and the treatments (roIFNT or UF) as fixed effects in the model. Fold change was calculated by the ratio between the gene expression of each sample in the treated group (roIFNT or UF-Conceptus) and the average expression of the control group for each cell type. For the \u003cem\u003eExperiment 3\u003c/em\u003e, animal was considered as a random effect, and the fixed effects of group (pregnant or non-pregnant 30 days after TAI), category (nulliparous, primiparous, or pluriparous), and group-by-category interaction were included in the model. The accuracy of the pregnancy diagnosis methods by ISG expression was calculated by the frequency of false-negative and false-positive observations, negative predictive value, positive predictive value, specificity, and sensitivity, as previously described by Pugliesi et al. [3]. A cutoff value for the expression of each ISG, to distinguish pregnant from non-pregnant animals, was determined from a Receiving Operator Characteristic (ROC) curve that was calculated using GraphPad Prism software. This software was also used to determine the area under the curve (AUC) of the sensitivity by specificity plot for the expression of each gene. The results are reported as arithmetic mean ± SEM. The probability ≤ 0.05 indicated that the effect was significant and between 0.05 \u0026gt; P ≤ 0.10, indicated that the effect approached significance.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Experiments 1 and 2\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 roIFNT modulates the expression of ISGs and cytokines in cultured PBMCs and PMNs\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo detect the magnitude of response of PBMC and PMN treated with roIFNT \u003cem\u003ein vitro\u003c/em\u003e, the specific immune-related genes including ISGs (\u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eRSAD2\u003c/em\u003e, and \u003cem\u003eIFI44\u003c/em\u003e), pro-inflammatory (\u003cem\u003eIL1β\u003c/em\u003e), and anti-inflammatory (\u003cem\u003eIL10\u003c/em\u003e) cytokines were analyzed by qPCR. In both PBMC and PMN, the treatment with 100 ng/mL of roIFNT stimulated mRNA expression of \u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eRSAD2\u003c/em\u003e, and \u003cem\u003eIFI44\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the Control group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B\u003cb\u003e).\u003c/b\u003e When comparing the relative fold change of each gene to the Control group, in PBMC treated with roIFNT, a greater (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) stimulus was observed in the \u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eRSAD2\u003c/em\u003e genes than in the \u003cem\u003eIFI44\u003c/em\u003e gene \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. For PMN, the relative fold change showed a greater (P\u0026thinsp;=\u0026thinsp;0.05) stimulus in the \u003cem\u003eRSAD2\u003c/em\u003e gene compared to other genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn PBMC, expression of \u003cem\u003eIL1β\u003c/em\u003e tended to be less in the IFNT group (P\u0026thinsp;=\u0026thinsp;0.10); however, \u003cem\u003eIL10\u003c/em\u003e expression was not affected (P\u0026thinsp;=\u0026thinsp;0.11) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. In PMN, the expression of \u003cem\u003eIL1β\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.15) and \u003cem\u003eIL10\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.85) was not affected by roIFNT treatment \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. When the relative fold change between the IFNT-treated group and the Control group was calculated, a greater fold change was detected for \u003cem\u003eIL10\u003c/em\u003e (4.1-fold) compared to \u003cem\u003eIL1β\u003c/em\u003e (0.7-fold) in PBMC (P\u0026thinsp;=\u0026thinsp;0.005) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. In PMN, the relative fold change did not differ significantly (P\u0026thinsp;=\u0026thinsp;0.14) between treatments \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.1.2 UF from pregnant cows modulated the expression of ISGs and IL1β cytokine in cultured PBMCs and PMNs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this experiment, we tested whether UF from pregnant cows induced the expression of ISG and immune genes in PBMC and PMN. The treatment with UF from pregnant cows (UF-Conceptus) induced expression of \u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eRSAD2\u003c/em\u003e, and \u003cem\u003eIFI44\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in PBMC and PMN (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). When comparing the relative fold change, a greater stimulus was observed in the \u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eRSAD2\u003c/em\u003e genes than in the \u003cem\u003eIFI44\u003c/em\u003e gene for both, PBMC (P\u0026thinsp;=\u0026thinsp;0.02) and PMN (P\u0026thinsp;=\u0026thinsp;0.01) cultured with UF-Conceptus compared to UF-Control (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRegarding cytokines, a lesser expression of \u003cem\u003eIL1β\u003c/em\u003e in PBMC (P\u0026thinsp;=\u0026thinsp;0.007) and PMN (P\u0026thinsp;=\u0026thinsp;0.01) was detected in the UF-Conceptus group compared to the UF-Control \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e. However, the expression of \u003cem\u003eIL10\u003c/em\u003e in PBMC (P\u0026thinsp;=\u0026thinsp;0.14) and PMN (P\u0026thinsp;=\u0026thinsp;0.44) did not differ between the UF-Conceptus and UF-Control groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e. When the relative fold change of the UF-Conceptus group was compared to the UF-Control group, a greater fold change was detected for \u003cem\u003eIL10\u003c/em\u003e (2.3-fold) compared to \u003cem\u003eIL1β\u003c/em\u003e (0.8-fold) in PMN (P\u0026thinsp;=\u0026thinsp;0.005) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u003cb\u003e).\u003c/b\u003e In PBMC, the relative fold change did not differ significantly (P\u0026thinsp;=\u0026thinsp;0.15) between treatments \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 UF and roIFNT stimulated co-expression of ISGs in immune cells\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn PBMC, there were strong positive correlations (r\u0026thinsp;\u0026gt;\u0026thinsp;0.8) between expression of ISGs (\u003cem\u003eISG15 vs RSAD2, ISG15 vs IFI44\u003c/em\u003e, and \u003cem\u003eRSAD2 vs IFI44\u003c/em\u003e) in cells stimulated with either roIFNT or UF (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There were no significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1) correlations detected between the expression of cytokines (\u003cem\u003eIL1β vs IL10\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson\u0026rsquo;s correlation coefficient (r) between the abundance of transcripts in PBMC and PMN culture with recombinant ovine interferon-τ (roIFNT) or uterine flush (UF).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEndpoint\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eBetween ISGs and Cytokines\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePBMC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIFNT culture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUF culture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eISG15 vs RSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eISG15 vs IFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRSAD2 vs IFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIL1β vs IL10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePMN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIFNT culture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUF culture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eISG15 vs RSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eISG15 vs IFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRSAD2 vs IFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIL1β vs IL10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eMeans indicate differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) between treatments.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNS: non-significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn PMN, there were strong positive correlations between expression of ISGs in cells stimulated with roIFNT (\u003cem\u003eISG15 vs RSAD2, ISG15 vs IFI44\u003c/em\u003e, and \u003cem\u003eRSAD2 vs IFI44)\u003c/em\u003e and in cells treated with UF (\u003cem\u003eISG15 vs RSAD2\u003c/em\u003e and \u003cem\u003eISG15 vs IFI44)\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The association between \u003cem\u003eRSAD2 vs IFI44\u003c/em\u003e in PMN treated with UF generated a moderate (0.6\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.8) positive correlation. For the association between cytokines, a moderate positive correlation (0.6\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.8) was observed for \u003cem\u003eIL1β vs IL10\u003c/em\u003e in cells treated with roIFNT. No other significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1) correlations were detected.\u003c/p\u003e \u003cp\u003eAs expected, the strong correlations found between ISGs in both treaments and cell types, demonstrate that these genes are possibly co-modulated by the same activation pathway, such as IFN-τ signaling. For the association between cytokines, the presence of correlations was also expected, since there is a balance between the Th1 (\u003cem\u003eIL1β\u003c/em\u003e) and Th2 (\u003cem\u003eIL10\u003c/em\u003e) immune responses mediated by the factors (cytokines) that these cells produce. However, we only observed a moderate and significant correlation between cytokines in PMN treated with IFNT. To look for potential co-regulation targets, correlation analysis was performed between the expression of ISGs and cytokines. However, no significant correlation was detected (data not shown).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Experiment 3\u003c/h2\u003e \u003cp\u003e \u003cb\u003e3.2.1 Pregnancy stimulated the expression of non-classical ISGs (RSAD2 and IFI44) in PBMCs and PMNs on D20 post-TAI\u003c/b\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe main effects of group, parity order category, and the group-by-category interaction were significant for the \u003cem\u003eRSAD2\u003c/em\u003e gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Interpretation of the group-by-category interaction was that \u003cem\u003eRSAD2\u003c/em\u003e expression was similar for pregnant females in all parity categories, whereas in non-pregnant females, expression was lower in pluriparous compared to nulliparous and primiparous females. \u003cem\u003eRSAD2\u003c/em\u003e abundance was 3.2, 4.4, and 8.5-fold greater (P\u0026thinsp;\u0026lt;\u0026thinsp;0.007) in the pregnant than non-pregnant nulliparous, primiparous and pluriparous females, respectively. The \u003cem\u003eIFI44\u003c/em\u003e abundance was 3.8-fold greater in the pregnant group compared to the non-pregnant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A parity category effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) indicated that expression of \u003cem\u003eIFI44\u003c/em\u003e was greatest in nulliparous, followed by pluriparous and the lowest in primiparous cows.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e3.2.2 Pregnancy stimulated co-expression of ISGs in PMN\u003c/em\u003e 20 days post-TAI\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn nulliparous cows, there was one strong (\u003cem\u003eISG15 vs OAS1\u003c/em\u003e), three moderate (\u003cem\u003eISG15 vs RSAD2, OAS1 vs RSAD2\u003c/em\u003e, and \u003cem\u003eRSAD2 vs IFI44\u003c/em\u003e), and two weak (\u003cem\u003eISG15 vs IFI44\u003c/em\u003e and \u003cem\u003eOAS1 vs IFI44\u003c/em\u003e) correlations observed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In primiparous and pluriparous cows, the correlations were similar, with one strong (\u003cem\u003eISG15 vs OAS1\u003c/em\u003e), one moderate (\u003cem\u003eRSAD2 vs IFI44\u003c/em\u003e), and two weak (\u003cem\u003eISG15 vs IFI44\u003c/em\u003e and \u003cem\u003eOAS1 vs IFI44\u003c/em\u003e) correlations. There were no other significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.10) correlations detected.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson\u0026rsquo;s correlation coefficient (r) between the abundance of transcripts in PMN on day 20 post-TAI in nulliparous, primiparous, and pluriparous bovine females.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEndpoint\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eBetween ISGs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNulliparous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePrimiparous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePluriparous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eISG15 vs OAS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eISG15 vs RSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eISG15 vs IFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOAS1 vs RSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOAS1 vs IFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRSAD2 vs IFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNS: non-significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Expression of ISGs in PMNs generated accurate predictitons of pregnancy outcomes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe cutoff values of expression for ISG genes (\u003cem\u003eRSAD2\u003c/em\u003e, \u003cem\u003eIFI44, ISG15\u003c/em\u003e, and \u003cem\u003eOAS1\u003c/em\u003e) to distinguish between pregnant and non-pregnant cows were established through ROC curve analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Different cutoff values were established for nulliparous (\u003cem\u003eRSAD2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.92, \u003cem\u003eIFI44\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0086, \u003cem\u003eISG15\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27 and \u003cem\u003eOAS1\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53), primiparous (\u003cem\u003eRSAD2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.48, \u003cem\u003eIFI44\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0048, \u003cem\u003eISG15\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.04 and \u003cem\u003eOAS1\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.48) and pluriparous cows (\u003cem\u003eRSAD2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003eIFI44\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0058, \u003cem\u003eISG15\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31 and \u003cem\u003eOAS1\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53). In nulliparous heifers, the accuracy for the \u003cem\u003eIFI44\u003c/em\u003e gene was greater when compared to the \u003cem\u003eRSAD2\u003c/em\u003e gene (86% \u003cem\u003evs\u003c/em\u003e 79%, respectively; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In this case, the expression of \u003cem\u003eIFI44\u003c/em\u003e had a lower frequency of false-positives (7/100) and false-negatives (7/100) and, consequently, greater positive (85.7%) and negative (86.3%) predictive values, and sensitivity (85.7%) and specificity (86.3%) when compared to the frequency of false-positives (11/100) and false-negatives (10/100) of \u003cem\u003eRSAD2\u003c/em\u003e. In primiparous females, the accuracy was greater for the \u003cem\u003eRSAD2\u003c/em\u003e when compared to the \u003cem\u003eIFI44\u003c/em\u003e gene (92% \u003cem\u003evs\u003c/em\u003e 84%). The expression of \u003cem\u003eRSAD2\u003c/em\u003e in this category showed the least possible frequency of false-negatives (0/50) and, consequently, a perfect negative predictive value (100%) and sensitivity (100%) when compared to the frequency of false-negatives for \u003cem\u003eIFI44\u003c/em\u003e (4/50). However, the frequency of false-positives (4/50) and, consequently, the specificity (82.6%) was the same for both genes. In pluriparous cows, the accuracy was similar for both ISGs (\u003cem\u003eRSAD2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;92.8% \u003cem\u003evs IFI44\u003c/em\u003e\u0026thinsp;=\u0026thinsp;91.6%), as well as the frequency of false-negatives, negative predictive value, and sensitivity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, the frequency of false positives (\u003cem\u003eRSAD2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1/83 \u003cem\u003evs IFI44\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3/83) was lower for \u003cem\u003eRSAD2\u003c/em\u003e, which increased the specificity (\u003cem\u003eRSAD2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;97% \u003cem\u003evs IFI44\u003c/em\u003e\u0026thinsp;=\u0026thinsp;90.9%) and the positive predictive value (\u003cem\u003eRSAD2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;97.8% \u003cem\u003evs IFI44\u003c/em\u003e\u0026thinsp;=\u0026thinsp;93.9%) for this gene.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of True-Positive (TP), True-Negative (TN), False-Positive (FP), False Negative (FN), Sensitivity (SENS), Specificity (SPEC), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (ACCU) for determining pregnancy status on D20 post-TAI by \u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e in nulliparous, primiparous and pluriparous bovine females.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEndpoint\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNulliparous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ePrimiparous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePluriparous\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTN \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFN \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSENS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPEC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCU (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e Sensitivity (probability that a test result will be positive when the cow is pregnant)\u0026thinsp;=\u0026thinsp;TP/(TP\u0026thinsp;+\u0026thinsp;FN).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e Specificity (probability that a test result will be negative when the cow is not pregnant)\u0026thinsp;=\u0026thinsp;TN/(FP\u0026thinsp;+\u0026thinsp;TN).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ec\u003c/sup\u003e PPV (probability that the cow is pregnant when the test is positive)\u0026thinsp;=\u0026thinsp;TP/(TP\u0026thinsp;+\u0026thinsp;FP).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ed\u003c/sup\u003e NPV (probability that the cow is not pregnant when the test is negative)\u0026thinsp;=\u0026thinsp;TN/(FN\u0026thinsp;+\u0026thinsp;TN).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ee\u003c/sup\u003e Accuracy = (TP\u0026thinsp;+\u0026thinsp;TN)/n.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Association of Doppler-US with ISG expression optimized accuracy of pregnancy prediction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eConsidering that there are less than 0.5% false-negatives results when using Doppler ultrasonography, but false-positives are often greater than 10% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], the combination of Doppler-US and ISG expression was attempted to maximize accuracy of early pregnancy diagnostic in beef females. The approach consisted in applying the cut-off values for ISGs (each individually or combinations) to females with a functional CL (blood perfusion\u0026thinsp;\u0026gt;\u0026thinsp;25%) on D20. Females that did not have a functional CL on D20 were automatically considered non-pregnant. In nulliparous females, there was a similar accuracy when using combinations of two (\u003cem\u003eRSAD2/IFI44\u003c/em\u003e, accuracy: 90%) or four (\u003cem\u003eRSAD2/IFI44/ISG15/OAS1\u003c/em\u003e, accuracy: 91%) ISGs (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The combination of four ISGs yielded less false-negatives (1/100), and consequently, greater negative predictive value (97.7%) and sensitivity (98%) when compared to the combination of two ISGs. However, the combination of two ISGs yielded less false positives (5/100) and, consequently, greater positive predictive value (89.8%) and specificity (84.3%) compared with the combination of four ISGs. In primiparous cows, accuracy was equivalent when using only the \u003cem\u003eRSAD2\u003c/em\u003e gene (accuracy: 98%) or when using the combination of two ISGs (\u003cem\u003eRSAD2/IFI44\u003c/em\u003e, accuracy: 98%). Moreover, the frequency of false-positives was minimal (1/50), and there were no false-negatives (0/50), resulting in a perfect negative predictive value (100%) and sensitivity (100%) in both cases. In pluriparous cows, the greatest accuracy was obtained using two ISGs (\u003cem\u003eRSAD2/IFI44\u003c/em\u003e, accuracy: 94%); however, the combination of four ISGs generated the lowest frequency of false-negative (1/83), and consequently, a greater negative predictive value (96.5%) and sensitivity (98%) than when two ISGs were used.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of True-Positive (TP), True-Negative (TN), False-Positive (FP), False-Negative (FN), Sensitivity (SENS), Specificity (SPEC), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (ACCU) for determining pregnancy status on D20 post-TAI by \u003cem\u003eRSAD2\u003c/em\u003e, \u003cem\u003eIFI44, ISG15\u003c/em\u003e, and \u003cem\u003eOAS1\u003c/em\u003e in bovine females with a functional CL.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEndpoint\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eNulliparous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003ePrimiparous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e \u003cp\u003ePluriparous\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRSAD2/\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRSAD2/IFI44/\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eRSAD2/\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eRSAD2/IFI44/\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eRSAD2/\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eRSAD2/IFI44/\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eISG15/OAS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eISG15/OAS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eISG15/OAS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVP \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVN \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFN \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSENS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e92.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e96.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPEC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e93.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e94.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e90.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e86.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e93.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCU (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e92.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e92.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003csup\u003ea\u003c/sup\u003e Evaluation of \u003cem\u003eRSAD2, IFI44, ISG15\u003c/em\u003e, and \u003cem\u003eOAS1\u003c/em\u003e in females with a functional CL was performed by applying the predefined cutoffs only in females in which CL blood perfusion was \u0026gt;\u0026thinsp;25% on D20 post-TAI.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003csup\u003eb\u003c/sup\u003e The combined use of both genes \u003cem\u003e(RSAD2, IFI44, ISG15\u003c/em\u003e, and \u003cem\u003eOAS1\u003c/em\u003e) was performed by considering the female as pregnant when the expression levels of at least one gene were greater than the predefined cutoffs.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe same approach of applying ISG cut-off values to females with a functional CL on D20 was implemented, regardless of parity category (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The accuracy was similar between all ISG combinations. However, the combination of four ISGs (\u003cem\u003eRSAD2/IFI44/ISG15/OAS1\u003c/em\u003e) generated the lowest frequency of false-negatives (2/233), and consequently, the greatest negative predictive value (97.9%) and sensitivity (98.4%), compared to other combinations. Nevertheless, the frequency of false-positives remains elevated (6.4%; 15/233), even when this combination is employed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of True-Positive (TP), True-Negative (TN), False-Positive (FP), False Negative (FN), Sensitivity (SENS), Specificity (SPEC), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (ACCU) for determining pregnancy status on D20 post-TAI by \u003cem\u003eRSAD2, IFI44, ISG15\u003c/em\u003e and \u003cem\u003eOAS1\u003c/em\u003e in bovine females with a functional CL.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGenes\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eISG15\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOAS1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eRSAD2/IFI44/\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOAS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eIFI44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eISG15/OAS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTN \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFN \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSENS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e98.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPEC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e97.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCU (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e92.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003e Evaluation of \u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eOAS1, RSAD2\u003c/em\u003e, and \u003cem\u003eIFI44\u003c/em\u003e in females with a functional CL was performed by applying the predefined cutoffs only in females in which CL blood perfusion was \u0026gt;\u0026thinsp;25% on D20 post-TAI.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eb\u003c/sup\u003e The combined use of both genes (\u003cem\u003eRSAD2, IFI44, ISG15\u003c/em\u003e, and \u003cem\u003eOAS1\u003c/em\u003e) was performed by considering the female as pregnant when the expression levels of at least one gene were greater than the predefined cutoffs.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe expression of ISGs in circulating immune cells has been used for pregnancy diagnosis in cattle, as it indirectly signals the presence of the peri-implantation conceptus [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, the accuracy of this method using already known (classical) ISGs did not exceed 80%, regardless of cell type. Consequently, the validation of novel candidate genes for potential use as biomarkers of the pregnancy status could contribute to the improvement of beef and dairy cattle production systems. Here, we reported for the first time the direct effects of pregnancy-related factors using UF from day 18 pregnant cows on the expression of ISGs in PBMC and PMN. We determined that \u003cem\u003eRSAD2\u003c/em\u003e was the most responsive marker of bovine conceptus signaling in PBMC and PMN. Furthermore, we combined ISG expression data with luteal blood perfusion information to optimize the accuracy of early pregnancy outcome prediction. We demonstrated that the association of classical (\u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eOAS1\u003c/em\u003e) and non-classical (\u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) ISGs with the color-Doppler diagnosis is an advanced method to differentiate with high accuracy pregnant and non-pregnant \u003cem\u003eBos indicus\u003c/em\u003e beef heifers and cows on day 20 post-TAI.\u003c/p\u003e \u003cp\u003eThe expression of ISGs in PBMC and PMN was stimulated \u003cem\u003ein vitro\u003c/em\u003e with roIFNT or UF from pregnant cows. To the best of our knowledge, the use of a conceptus-conditioned medium on day 18 of pregnancy has never been attempted previously to investigate the physiological stimulus generated by the conceptus on immune cells. Recent results from our group suggested novel candidate genes for pregnancy prediction based on a transcriptome analysis in PBMC and PMN on day 18 of pregnancy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, such novel biomarkers may be more accurate in predicting early pregnancy when compared to classical ISGs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, we selected one classical (\u003cem\u003eISG15\u003c/em\u003e) and two non-classical (\u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) ISGs to evaluate gene expression, and subsequent sensitivity and specificity analysis as potential early pregnancy markers. Both for PBMC and PMN, treatment with roIFNT or UF from pregnant cows stimulated the expression of all the ISGs evaluated (\u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eRSAD2\u003c/em\u003e, and \u003cem\u003eIFI44\u003c/em\u003e). Although expected, it was reassuring that the upregulation of ISGs observed in immune cells harvested from the peripheral blood of pregnant cows could be recapitulated \u003cem\u003ein vitro\u003c/em\u003e. This effect was due to the direct effect of the exogenous addition of IFN-τ and likely because of its presence in the UF from pregnant cows. Also, the fold change analysis showed that \u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eRSAD2\u003c/em\u003e were the most stimulated ISGs \u003cem\u003ein vitro\u003c/em\u003e. These findings confirm previous studies reporting that treatment with increasing doses of recombinant bovine (rbIFNT; 0.1\u0026ndash;10 ng/mL) and ovine IFN-τ (roIFNT; 100 ng/mL or 1 \u0026micro;g/mL) induced mRNA expression of classical (\u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eOAS1\u003c/em\u003e) and non-classical (\u003cem\u003eIFIT2\u003c/em\u003e, \u003cem\u003eSAMD9\u003c/em\u003e and \u003cem\u003eUSP18\u003c/em\u003e) ISGs in immune cells and bovine endometrial cells \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Similarly, Rashid et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] reported upregulation of \u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eOAS1\u003c/em\u003e in PBMC cultured with UF from day 7 pregnant cows. The present results demonstrate that the response of non-classical ISG to IFN-τ stimulus induced a response similar to classical ISG in peripheral blood immune cells. This opened the possibility of using these non-classical ISGs to predict pregnancy in cattle earlier than currently.\u003c/p\u003e \u003cp\u003eDuring early pregnancy establishment, a delicate balance between pro- and anti-\u003c/p\u003e \u003cp\u003einflammatory cytokines are required to promote maternal tolerance towards the semi-allogeneic embryo [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. IFN-τ is known to regulate the secretion of bovine granulocyte chemotactic protein 2 in the endometrium, regulating cytokine networks in the uterus of pregnant cows [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, the direct role of IFN-τ in regulating this cytokine balance throughout early pregnancy is unknown. Here, we investigated the response of pro- (\u003cem\u003eIL1β\u003c/em\u003e) and anti-inflammatory (\u003cem\u003eIL10\u003c/em\u003e) cytokines to stimulation of roIFNT or conceptus-conditioned medium on day 18 of pregnancy. UF from pregnant cows suppressed the expression of the \u003cem\u003eIL1β\u003c/em\u003e cytokine in PBMC and PMN but did not affect \u003cem\u003eIL10\u003c/em\u003e expression. Similarly, the effect of roIFNT to reduce \u003cem\u003eIL1β\u003c/em\u003e cytokine transcripts in PBMC approached significance. Expression of \u003cem\u003eIL10\u003c/em\u003e in PBMC treated with roIFNT was not changed significantly, but at least numerically, it followed the same direction of upregulation of anti-inflammatory cytokines reported in earlier studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, the analysis of fold change indicated opposite directions in the expression of \u003cem\u003eIL10\u003c/em\u003e (upregulated) and \u003cem\u003eIL1β\u003c/em\u003e (downregulated) in both PMN and PBMC. The findings of the present study corroborate with Rashid et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and Fiorenza et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], that verified downregulation of pro-inflammatory cytokines (\u003cem\u003eIL1β\u003c/em\u003e and \u003cem\u003eTNFα\u003c/em\u003e) and upregulation of anti-inflammatory cytokines (\u003cem\u003eIL10\u003c/em\u003e and \u003cem\u003eTGFβ1\u003c/em\u003e) in PBMC and PMN, and bovine uterine epithelial cells stimulated with UF from day 7 of pregnant cows or rbIFNT, respectively. Thus, the analysis of pro- and anti-inflammatory cytokine transcripts in this study suggests that the environment conditioned by the conceptus likely modulated the immunological status of the uterus to accept the semi-allogeneic embryo and induced initially a state of immunological tolerance through the suppression of \u003cem\u003eIL1β\u003c/em\u003e, essential for embryo survival and establishment of pregnancy. However, the analysis of transcripts of other pro- and anti-inflammatory cytokines is necessary to confirm these results.\u003c/p\u003e \u003cp\u003ePregnancy diagnosis between days 18 and 20 after AI is possible due to differences in immune cell-ISG expression between pregnant and non-pregnant animals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In that window, the least overlap of ISG expression levels between pregnant and non-pregnant animals, compared to other time intervals, has been reported. The greater ISG expression in pregnant animals is due to stimulation from conceptus-secreted IFN-τ, which increases throughout the third week of pregnancy, associated with the growth of the trophectoderm [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Although the most commonly used cell type used for pregnancy diagnosis is the PBMC, the present research provided evidence that the expression of ISGs in both PMNs and PBMCs could be used for early detection of pregnant bovine females. This is because the response to IFN-τ stimulation was similar between PMN and PBMC. Here, we showed that expression of non-classical ISGs (\u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) in PMN can predict pregnancy at D20 post-TAI accurately. The PMN samples used in this study were obtained in a prospective study by Dalmaso de Melo et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. They determined the accuracy of pregnancy prediction by the abundance of classical ISGs (\u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eOAS1\u003c/em\u003e). The results presented here extended the assessment of PMNs at D20, indicating a greater abundance of the two non-classical ISGs tested (\u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) in pregnant females compared to non-pregnant females. These results are consistent with the expression of classical ISGs in PBMC and whole blood immune cells reported in beef cattle [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and heifers and dairy cows [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, in non-pregnant females, the abundance of \u003cem\u003eRSAD2\u003c/em\u003e was higher in nulliparous and primiparous than in pluriparous females. For \u003cem\u003eIFI44\u003c/em\u003e, transcript abundance was higher in nulliparous, followed by pluriparous and primiparous cows, regardless of gestational status. The reason for the difference in ISG response between parity categories is unspecified, but could be related to size of the embryo, body size and metabolism, differences in immune function or embryo mortality between different parity order [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, the findings of the herein study are adverse, since, the effects of parity order did not follow the same pattern in \u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e genes. In this regard, previous studies reported that the expression of the \u003cem\u003eRSAD2\u003c/em\u003e gene in the ovine uterus [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and in PBMCs of beef cows [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] are regulated by P4 concentrations; however, whether this regulation is positive or negative remains contradictory between studies.\u003c/p\u003e \u003cp\u003eBased on previous studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] the ROC curve was established to determine the efficiency of pregnancy prediction through the expression of the classical ISGs (\u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eOAS1\u003c/em\u003e) obtained in the study by Dalmaso de Melo et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; and non-classical ISGs (\u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) obtained in the present study. The ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) showed that all ISGs were considered significant predictors of pregnancy with an accuracy exceeding 70%; and in primiparous and pluriparous cows, \u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e were considered the most accurate genes. When \u003cem\u003eRSAD2\u003c/em\u003e expression was compared between parity categories, greater and similar accuracies (92%) were observed in primiparous and pluriparous compared to nulliparous females (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings are contrary to those observed by Dalmaso de Melo et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], in which the accuracy of the \u003cem\u003eISG15\u003c/em\u003e and \u003cem\u003eOAS1\u003c/em\u003e genes was greater in heifers (81%) compared to cows (72%). Pugliesi et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] described an accuracy close to 80% when this method was performed on PBMCs on the 20th day of pregnancy in beef cows, but sensitivity ranged from 66 to 78%. Yoshino et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] reported for ISG expression PMN of dairy cows, accuracies ranging from 57 to 86% between days 20 and 22 of pregnancy. Here, we reported greater predictive accuracy for \u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e compared to previous studies that used only classical ISGs, mostly because of the lower frequency of false-positive and false-negative and, consequently greater positive and negative predictive value.\u003c/p\u003e \u003cp\u003eColor-Doppler methodology in commercial beef cattle operations for detecting non-pregnant females increased recently [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Although this technology frequently achieves 100% sensitivity, its main limitation is the frequency of false-positive results. Here, we combined this method with the expression of ISGs in PMN, in an attempt to further increase the accuracy of pregnancy prediction. Specifically, the cutoff value of each gene analyzed individually or together (\u003cem\u003eRSAD2\u003c/em\u003e, \u003cem\u003eIFI44\u003c/em\u003e, \u003cem\u003eRSAD2/IFI44\u003c/em\u003e, or \u003cem\u003eRSAD2/IFI44/ISG15/OAS1\u003c/em\u003e) was applied only in females with a functional CL on D20 (i.e., females diagnosed as pregnant by the Doppler method) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Females with a non-functional CL on D20 were automatically classified as non-pregnant. Overall, the combined method increased the accuracy of the diagnosis (e.g., accuracy\u0026thinsp;=\u0026thinsp;98% using only \u003cem\u003eRSAD2\u003c/em\u003e or \u003cem\u003eRSAD2/IFI44\u003c/em\u003e expression in primiparous cows) due to the reduction in false-positive results, but the false-negative results were still frequent for same associations (1 to 10%). Similarly, Pugliesi et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and Dalmaso de Melo et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] reported an accuracy between 84 to 90% when combining the use of two genes (\u003cem\u003eOAS1/MX2\u003c/em\u003e or \u003cem\u003eISG15/OAS1\u003c/em\u003e) in females with a functional CL on day 20 of pregnancy.\u003c/p\u003e \u003cp\u003eFinally, the expression of ISGs in animals with an active CL on D20 regardless of the parity category was evaluated, and all possible combinations of ISGs were performed (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The combination of all four ISGs (\u003cem\u003eRSAD2/IFI44/ISG15/OAS1)\u003c/em\u003e yielded the lowest proportion of false-negative (0.9%; 2/233). The persisting inaccuracy for the prediction of pregnancy status associated with false-negative results may be related to animals in which IFN-τ does not signal enough to stimulate the expression of ISGs. In this context, it is known that the size of the conceptus is directly associated with the amount of IFN-τ released [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. On the other hand, the inaccuracy related to false-positive results can occur as an outcome of early embryonic mortality or the induction of ISGs by other types of stimuli. Interferon receptors (IFNAR) are non-selective receptors and can be stimulated by any type 1 interferon, which means that other interferons can bind to it and stimulate the expression of ISGs, as, for example, in viral infections [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Thus, progress in methodology is still needed so that even more accurate and easy-to-apply methods can be developed and routinely used in the reproductive management of beef and dairy females.\u003c/p\u003e \u003cp\u003eIn summary, we conclude that immune cells respond promptly to IFN-τ and/or conceptus stimulus, which may favor the use of PBMC or PMN in novel methods for the detection of pregnancy in cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Furthermore, the presence of the bovine conceptus in the uterine environment possibly induces a state of maternal immune tolerance essential for embryonic survival and the establishment of pregnancy. The greater abundance of \u003cem\u003eRSAD2\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e in PMN on day 20 post-TAI in pregnant beef heifers and suckled cows allowed a high-accuracy method to detect pregnancy, but false-negative results were not eradicated. In addition, for the first time, our study reports that the association between the expression of classical and non-classical ISGs can be used to obtain a more accurate method of pregnancy prediction in bovine females with functional CL at early pregnancy.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all students from the Molecular Endocrinology Physiology Laboratory (LFEM-FMVZ-USP). Also, we thank Dr. Fuller W. Bazer (Texas A \u0026amp; M University) for gently providing recombinant ovine IFN-τ. We thank the Pirassununga Campus of the University of São Paulo for their structure and animals. We acknowledge the Coordination for the Improvement of Greater Education Personnel (CAPES) for awarding a PhD scholarship. This research was funded by the São Paulo Research Foundation (FAPESP) (grant number 2015/10606-9).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch3\u003eAuthor contributions\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eIsabella Rio Feltrin:\u003c/strong\u003e conceived the study, performed reproductive management and PCR analyses, and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGabriela Dalmaso de Melo:\u003c/strong\u003e conducted the previous study where the samples used in the present study were obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePedro Pisani Freitas:\u003c/strong\u003e assisted with reproductive management, collection and processing of samples in the laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKarine Galhego Morelli:\u003c/strong\u003e assisted with reproductive management, collection and processing of samples in the laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMario Binelli:\u003c/strong\u003e assisted with expertise in experimental design, and correction of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClaudia Maria Bertan Membrive:\u003c/strong\u003e supervisor and assisted with expertise in experimental design, and correction of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuilherme Pugliesi:\u0026nbsp;\u003c/strong\u003eco-supervisor, provided the project administration, financial support, expertise in experimental design, statistical analysis, and corrected the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSales JNS, Pugliesi G, Carvalho LR, Sim\u0026otilde;es LMS, Lemos LA, Vicente MP, Silva RRR, Baruselli PS. 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Review: Current status of corpus luteum assessment by Doppler ultrasonography to diagnose non-pregnancy and select embryo recipients in cattle. \u003cem\u003eAnimal.\u003c/em\u003e \u003cstrong\u003e1,\u003c/strong\u003e 100752 (2023). \u003c/li\u003e\n\u003cli\u003eSpencer TE, Forde N, Lonergan P. Insights into conceptus elongation and establishment of pregnancy in ruminants. \u003cem\u003eReprod Fertil Dev.\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e(1), 84-100 (2016).\u003c/li\u003e\n\u003cli\u003eFerraz PA, Filho CASG, Rocha CC, Neto AL, de Andrade Bruni G, Oshiro TSI, Baruselli PS, Lima FS, Pugliesi G. Feasibility and accuracy of using different methods to detect pregnancy by conceptus-stimulated genes in dairy cattle. \u003cem\u003eJDS Commun. \u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e(3), 153-158 (2021).\u003c/li\u003e\n\u003c/ol\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ISG, immune cells, interferon-tau, uterine flush, pregnancy prediction. ","lastPublishedDoi":"10.21203/rs.3.rs-5389974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5389974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn beef cattle, estrous synchronization aiming a second artificial insemination (AI) requires a reliable estimation of the pregnancy status 20 days (D20) after the first AI. The hypothesis is that the expression of interferon-stimulated genes (ISGs; \u003cem\u003eISG15, OAS1, RSAD2,\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e) and cytokines (\u003cem\u003eIL1β\u003c/em\u003e and \u003cem\u003eIL10\u003c/em\u003e) in mononuclear (PBMC) and polymorphonuclear (PMN) cells is regulated by interferon-τ (IFN-τ) and predicts the pregnancy status. \u0026nbsp;PBMC and PMN were isolated from non-pregnant beef cows (N=9), 10-12 days post-ovulation (D0), and stimulated with 100 ng/mL recombinant ovine (ro) IFN-τ or with pooled uterine flush (UF) from D18 pregnant cows. Both roIFNT and UF stimulated the expression of \u003cem\u003eISG15, RSAD2,\u003c/em\u003e and \u003cem\u003eIFI44\u003c/em\u003e in PBMC and PMN. Expression of \u003cem\u003eIL1β \u003c/em\u003ewas reduced by UF in both PBMC and PMN. On another experiment, PMN were isolated, and luteal blood perfusion was measured on D20 post-timed-AI in beef females. The accuracy of ISG expression and luteal blood perfusion to predict the pregnancy outcome was determined by ROC curve analysis. All gene combinations were tested, and the best association for increased accuracy (92.7%) and reduction of false-negative results (0.9%, 2/233) was obtained through the combination of the four ISGs (\u003cem\u003eISG15, OAS1, RSAD2\u003c/em\u003e, and \u003cem\u003eIFI44\u003c/em\u003e). The criterion was that if the expression levels of at least one of the four genes were greater than the predefined cutoffs, the animal would be considered pregnant. In conclusion, the expression of ISGs and \u003cem\u003eIL1β\u003c/em\u003e was upregulated by roIFNT and UF from pregnancy cows. The combined expression of classical (\u003cem\u003eISG15 \u003c/em\u003eand\u003cem\u003e OAS1) \u003c/em\u003eand non-classical\u003cem\u003e (RSAD2\u003c/em\u003eand\u003cem\u003e IFI44\u003c/em\u003e) ISGs provided the greatest predictive accuracy of the pregnancy status on D20 in females with active CL by Doppler and is a potential tool to be used in reproductive programs for beef cattle.\u003c/p\u003e","manuscriptTitle":"Expression in Immune Cells of New Conceptus Signaling Markers Optimizes Prediction of Pregnancy in Beef Cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-26 08:17:54","doi":"10.21203/rs.3.rs-5389974/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-21T07:21:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-12T16:41:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161933569895037211094749584179962140659","date":"2025-01-02T18:24:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-10T05:35:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186450478266085836143787660278867341759","date":"2024-11-26T03:05:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-26T00:59:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-26T00:48:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-12T04:28:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-07T12:57:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-11-04T17:13:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24f06c87-a8e8-45ef-b995-a769b668cf38","owner":[],"postedDate":"November 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40762364,"name":"Biological sciences/Biotechnology"},{"id":40762365,"name":"Biological sciences/Immunology"},{"id":40762366,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-05-26T16:04:15+00:00","versionOfRecord":{"articleIdentity":"rs-5389974","link":"https://doi.org/10.1038/s41598-025-01996-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-20 15:58:22","publishedOnDateReadable":"May 20th, 2025"},"versionCreatedAt":"2024-11-26 08:17:54","video":"","vorDoi":"10.1038/s41598-025-01996-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-01996-y","workflowStages":[]},"version":"v1","identity":"rs-5389974","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5389974","identity":"rs-5389974","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}