https://orcid.org/0009-0007-4020-9916
Figures
Abstract
Modern high producing dairy cows are still affected by poor fertility and disease, despite improvements achieved through genetic selection programs. Additional biomarkers of health and performance traits in cattle could enhance animal welfare and profitability by allowing farmers to cull animals before problems occur. We performed pilot investigations of plasma microRNA (miRNA) profiles during early life as potential biomarkers associated with future performance in dairy cows. The latter included survival to two years of age, age at first calving, yield of milk, fat and protein, mastitis and lameness traits, conception rate, number of services per conception, and calving interval. Using qPCR, we obtained longitudinal measurements and ratios involving nine miRNAs (miR-126-3p, miR-127, miR-142-5p, miR-154b, miR-27b, miR-30c-5p, miR-34a, miR-363, miR-425-3p) in plasma samples from three age groups: calves (<1 month), heifers (14–23 months), and first lactation cows (29–35 months). Changes in miR-126-3p from calf to first lactation cow were associated with first lactation milk yield and second lactation milk somatic cell count (an udder health indicator). Moreover, the miR-127 to miR-30c-5p ratio in cows was associated with milk fat and protein yield in the first two lactations, whereas miR-142-5p levels and several miRNA ratios involving this miRNA, were associated with second calving interval (a cow fertility trait). Our results identified novel early life biomarkers that warrant further investigation to determine whether they may predict dairy cattle performance.
Citation: MacLeay M, Donadeu FX, Banos G (2025) Dairy cow performance is associated with longitudinal microRNA profiles. PLoS One 20(8): e0328765. https://doi.org/10.1371/journal.pone.0328765
Editor: Amod Kumar, National Bureau of Animal Genetic Resources, INDIA
Received: July 12, 2024; Accepted: July 7, 2025; Published: August 1, 2025
Copyright: © 2025 MacLeay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: MM received a University of Edinburgh Principal's Career Development PhD Scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
High producing dairy cows present higher incidence of both poor fertility and disease. There is an antagonistic relationship of milk yield with animal health and fertility, partly due to negative energy balance in early lactation, when nutrients and reserves are allocated to elevated milk production. Animals with greater negative energy balance and loss of body condition take longer to return to oestrus after calving and have lower conception rates [1,2]. Adding relevant fertility and health traits to genetic selection programs has helped, but progress is slow and there is room for further improvement [3].
Gates (2013) reported that less than 7% of dairy heifers calve by the goal age of 24 months, and calving interval averaged 428 days instead of the one-year goal [4]. This has knock-on effects on later cow performance and farm profitability. First calving at around two years of age is associated with better subsequent fertility and milk production compared to later or, in fact, earlier ages [5,6]. Moreover, a significant proportion of dairy cows are culled, primarily due to disease and poor fertility [7]. For example, in the UK, 20% of dairy heifers are culled before the first lactation [8], whereas in the USA, an average annual cull rate of cows of 38% has been reported [9].
Disease has a major impact on cow welfare, survival, fertility, and production. Mastitis leads to reduced milk production, lower conception and pregnancy rates, higher pregnancy losses, and involuntary culling, in addition to further profit loss because milk is discarded after treatment [10]. Lameness also diminishes milk production, affects nutrition by reducing time spent feeding, and reduces fertility [11]. Other postpartum diseases including endometritis and disorders relating to nutrition, such as ketosis and hypocalcaemia, may delay post-partum return to oestrus and subsequent conception [12,13]. Reducing disease risk in dairy cows would improve cow health and welfare. Accurate prediction of future performance would also improve farm profitability [9], and existing culling decision support tools would benefit from more detailed input [14].
There is a wealth of promising research on the utility of microRNAs (miRNAs) as biomarkers of animal disease, fertility, milk production, and survival. Circulating miRNAs are ideal biomarkers; they can be exported from cells and show enhanced stability in circulation due to association with lipids and exosomes [15]. Mastitis and other infections in cattle are reportedly associated with distinct miRNA profiles in body fluids [16,17]. Such profiles also vary during the oestrus cycle [18], pregnancy [19,20] and lactation [21,22] in cows. miRNAs have also been associated with puberty onset in chicken and rats [23,24], though in cattle there are very few similar reports [25]. Studies of milk content revealed differentially expressed miRNAs in animals with varying milk fat [26] and casein content [27]. miRNAs are integral in growth and developmental processes, and circulating miRNA levels vary with age [28,29]. Use of miRNAs as risk biomarkers for later outcomes including cardiovascular disease, metabolic syndrome, and even lifespan has been proposed in humans [30–32].
In the present study, we hypothesized that variation in miRNA profiles during early life may be associated with future performance and survival in dairy cattle. Our previous work used longitudinal measurements of plasma miRNA to investigate how miRNAs change with age and whether they are associated with early life performance, such as calf health and growth [33]. Here, we further investigated the shared functions and correlations among the previously studied miRNAs and assess associations between miRNA expression profiles (single miRNAs, miRNA combinations, and longitudinal expression changes) in young animals and later cow performance traits, including milk production, fertility, health, and survival.
Materials and methods
Ethics approval
All animal procedures were performed with approval from The Roslin Institute (University of Edinburgh) Animal Welfare and Ethical Review Board and following the UK Animals (Scientific Procedures) Act, 1986. This study was conducted and reported in accordance with ARRIVE guidelines. Consent to access animal facilities was granted by the University of Edinburgh.
Experimental animals and sample collection
Blood plasma samples were available from cows born at Langhill Farm (University of Edinburgh, Scotland) during the winter of 2017/18 (n = 119) and 2018/19 (n = 108). By design, cows at this farm calve over the winter months each year, thus the winter of calving was used as a cohort factor.
Samples were collected at three timepoints on each animal corresponding to three distinct age groups: calves ≤1 month of age (n = 227), heifers aged 14–23 months (n = 209), and first lactation cows aged 29–35 months (n = 98). Samples were transported on ice from the farm to the lab, and plasma was separated the same day. To isolate the plasma, whole blood was centrifuged at 1900xg at 4°C for 10 minutes. The supernatant was centrifuged again at 1900xg at 4°C for 10 minutes. The supernatant was removed and stored at −80°C until use. Haemolysis in blood plasma samples causes release of intracellular miRNAs, thus can alter levels of several miRNAs and influence quantification accuracy. To account for this, samples were tested for haemolysis based on absorbance at 414nm as measured on a Nanodrop 1000 spectrophotometer, which was shown to be one of the more sensitive methods, after miRNA ratio [34]. In the present study, samples with readings over 0.2 were considered significantly haemolysed and removed from further study, as recommended by Kirschner and colleagues [35]. All identified readings above this threshold were also outliers (>2 standard deviations above the mean).
Quantification of circulating miRNAs
The miRNAs of interest had been identified in a previous study using PCR array profiling of animals that varied in different early life performance traits [33]. Briefly, Generalized Linear Mixed Models and t-tests were used to identify miRNAs in the PCR arrays that were significantly associated with early life growth, fertility, and health. Nine candidate miRNAs were chosen for further analysis as these displayed statistically significant associations with multiple early life performance traits, and were also considered in the present study: miR-126-3p, miR-127, miR-142-5p, miR-154b, miR-27b, miR-30c, miR-34a, miR-363, and miR-425-3p. These were quantified in plasma from 91 animals at all age groups (calf, heifer, cow). RNA extraction and RT-qPCR was performed [33,36] using miRCURY LNA system (Qiagen) and a Stratagene Mx3000P qPCR machine (Agilent, USA). The mean expression of two miRNAs, bta-miR-20a and bta-miR-106a, was used to normalize all qPCR data, as in our previous study [33].
Relationships among miRNAs
We explored interdependencies between the nine studied miRNAs to inform the number of independent groups to account for when adjusting p-values for multiple testing in subsequent analyses. As functions for these miRNAs have not been experimentally validated in cattle, they cannot reliably inform the number of independent groups to consider. Therefore, we explored the relationships among these miRNAs in several ways to determine the most appropriate number of independent tests, as well as provide additional insight into their expression as a group: pairwise correlations among the miRNAs and clustering analyses. Spearman pairwise correlations between miRNA levels across all age groups were calculated using R v4.2.2. A multiple factor analysis (MFA) was performed followed by K means clustering to group miRNAs based on their levels in each of the three age groups. To this effect, normalized miRNA levels were adjusted for batch differences (different RNA extraction and RT-qPCR runs) by adding the residuals from a model of batch on each miRNA to the overall mean of that miRNA. Subsequently, miRNA measurements were binary log-transformed to ensure a normal distribution [37]. There were missing values because some sampled animals had been culled or died before all samples could be collected or their samples were of poor quality, mainly due to haemolysis, PCR failure or >1.5Ct difference between replicates. Therefore, multiple imputation was performed using the missMDA package in R and the function, imputeMFA(). The FactoMineR package was used for MFA and the resulting coordinates were used for K means clustering. Four clusters were chosen by visual inspection. To determine whether the miRNAs cluster based on the putative gene pathways they target, miRPath v3.0 was used. This software was used previously to identify the gene pathways targeted by the human orthologues of the miRNAs based on Tarbase v7.0 [33]. The software performs hierarchical clustering of the miRNAs and produces a dendrogram (‘Targeted pathways clusters’ option) [38,39]. Bta-miR-154b is ruminant specific so was not included in this analysis.
Ratios between all possible pairs of miRNAs, irrespective of order, were also calculated for further investigation.
Hierarchical clustering of animals based on miRNA levels
Principal Component Analysis and agglomerative hierarchical clustering of animals with average linkage were performed on batch-adjusted log-transformed miRNA levels, as measured across all age groups. These analyses were repeated separately for each of the nine studied miRNAs using the HCPC() function from FactoMineR in R. The number of clusters was determined automatically.
Associations between miRNA profiles and cow performance traits
Each cow had records on multiple performance traits, which included milk production (first and second lactation yield of milk, fat, and protein), fertility (conception rate [CR], number of services per conception [S/C], age at first calving [AFC], calving interval), health (incidence of mastitis, incidence of lameness, median lactation milk somatic cell count [SCC]), and survival to two years of age.
The miRNA profiles considered for association with cow performance included measurements of single miRNA levels in each of the three age groups (calf, heifer, cow); ratios between all miRNA pairs within age group; the cluster the animal belonged to for a single miRNA, as identified using hierarchical clustering; and fold change in levels of each miRNA between two age groups.
Univariate general(ised) linear mixed models were applied to each cow trait, separately, including each miRNA profile measure as a fixed term. The other terms in the models were determined by backwards selection to include factors with a significant effect (P < 0.05) on each cow trait. All models included the cohort of calving (or birth, for AFC and survival to two years) effect. Additional terms included length of lactation (days) for milk production traits; the calendar month of calving for S/C, CR, and calving interval; calendar month of birth for AFC; the age of calving (days) for CR and calving interval; and the number of episodes of infection in the first 12 months of life for survival to two years. The animal effect including pedigree was fitted as a random effect in all analyses. The miRNA measurement effect estimated with these analyses would then be reflective of the association of the respective miRNA, ratio, cluster, or fold change with performance adjusted for all other effects in the model.
Prior to the statistical analysis, SCC records were log-transformed to ensure normal distribution. Linear models were then applied to all milk production traits, SCC, calving interval, and AFC. A Poisson function was applied to S/C. Binomial functions were applied to CR, incidence of mastitis, incidence of lameness, and survival to two years of age.
The nominal statistical significance level (P = 0.05) was corrected for multiple testing using the Bonferroni method, considering 16 independent tests. This number was based on the relationships among miRNAs previously described, where the K means clustering and the miRPath dendrogram revealed four distinct miRNA groups; and four groups of cow trait functions related to milk production, fertility, health, and survival, respectively.
Results
Relationships among miRNAs
Pairwise correlations between all nine miRNAs revealed a total of 23 significant correlations, with respective coefficients ranging from 0.26 to 0.88 (Table 1). The miRNA with the fewest statistically significant (P < 0.05) correlations was miR-27b, which was positively correlated with miR-34a (r = 0.42) and miR-126-3p (r = 0.42). The miRNA with the most significant correlations was miR-126-3p, which was correlated with all other candidate miRNAs. This correlation was positive with miR-27b, miR-30c-5p, miR-34a, miR-142-5p, and miR-425-3p, and negative with miR-127, miR-154b, and miR-363.
Results from the multiple factor analysis (MFA; Fig 1) suggested that heifer and cow miRNA levels were more similar to each other than to the calf miRNA levels, since the former had higher contributions to the first principal component (36.5% and 36.8%, respectively) and the calf levels had the highest contribution to the second principal component (59.7%). This reflects the greater changes in miRNA expression levels between calves and heifers than between heifers and cows, as previously reported [28,33]. miRNAs grouped in four clusters, one containing five miRNAs and another containing two (miR-27b and miR-126-3p), whilst miR-127 and miR-142-5p each formed their own cluster.
This shows the first two dimensions (with proportion of variance explained) and each miRNA coloured by K means cluster.
The dendrogram generated by miRPath analysis of the studied miRNAs is shown in Fig 2. The gene pathways targeted by these miRNAs was also previously reported [33]. Only eight of the bovine miRNAs had human counterparts, and among these, there were three clusters. Since miR-154b is ruminant-specific and could not be included in these three clusters, there were four groups of miRNAs to consider as independent during the ensuing downstream analyses.
Hierarchical clustering of animals based on miRNA levels
Fig 3 shows the biplots from the Principal Component Analysis of animals, shaded by cluster, for each miRNA. The size of clusters varied; for many miRNAs (e.g., miR-30c-5p, miR-34a, miR-363) there was an outlier that formed its own cluster. Clusters of only 3–4 animals were common. Fig 4 shows how the miRNA levels change with age for each cluster. Usually, the miRNA levels in different clusters changed in the same direction, but the magnitude of change varied. However, for some miRNAs, the direction of change with age was different between clusters. For example, for miR-126-3p and miR-27b, some clusters showed a steady increase in levels over time while others showed a peak at the heifer stage. Similarly, for miR-127 and miR-363, most clusters showed a decrease in miRNA levels over time, but there was one cluster that showed a subsequent increase at the cow stage. For miR-425-3p, some clusters showed a decrease from calf to heifer while others showed an increase.
Associations between miRNA profiles and cow performance traits
The miRNA profile measurements considered here were 1) expression levels in calves, heifers, and cows, 2) pairwise miRNA ratios in each of the age groups, 3) fold change from one age group to another, and 4) a categorical variable for the cluster an animal belongs to for each miRNA, as described in the previous section. The effect of these miRNA variables on multiple cow performance traits was assessed.
The associations that remained statistically significant (P < 0.05) or had borderline statistical trends (0.05 < P < 0.1) after adjusting for multiple testing are listed in Table 2. Corresponding plots for the significant associations (P < 0.05) can be seen in S1 File. Most significant associations involved miRNA ratios and pertained to milk production traits and calving interval. Among individual miRNAs, there was a significant positive association between miR-142-5p measured at the cow age group and second calving interval (from second to third calving of the cow). Levels of miR-126-3p in calves was associated with elevated SCC in first lactation, implying higher (sub)clinical mastitis incidence, whilst miR-30c-5p in calves was associated with decreased milk yield in second lactation. Calf to cow fold change in miR-126-3p was significantly associated with lower milk yield across the first two lactations. Animal cluster based on this miRNA was significantly associated with log median SCC in the second lactation. Cluster 1 had cows with the lowest median SCC, alongside the lowest calf miR-126-3p levels and the greatest cow miR-126-3p levels. Cluster 3 had the highest median SCC, with the highest heifer miR-126-3p levels and intermediate calf and cow levels. No significant associations were identified for S/C, CR, mastitis, lameness, survival to two years of age, or AFC.
Discussion
This study aimed to identify novel associations between plasma profiles of nine miRNAs and cow performance traits related to milk production, fertility, health, and survival. We also investigated how these miRNAs may interact with each other, using correlation estimates, multiple factor analysis, and putative gene pathways targeted. The results deepened our understanding of how expression of these miRNAs changes throughout early life in dairy cattle and demonstrated for the first time that miRNA ratios and longitudinal miRNA profiles are associated with cow performance.
Multiple miRNA ratios were associated with cow traits of interest, suggesting a potential synergistic role in performance traits. miRNA ratios have not been widely studied outside cancer research in humans [40]. In the present study, most associations were between the second calving interval, a cow fertility trait manifested during her second lactation, and miRNA ratios measured prior to this, so these miRNAs have potential to predict later life performance. For example, two ratios including miR-142-5p (miR-142-5p: miR-27b and miR-142-5p: miR-363) were unfavourably associated with calving interval, implying delays in cow conception after her second calving. Individual levels of miR-142-5p were also adversely associated with the same trait. This miRNA is expressed in white blood cells [41,42] as well as lymph nodes and spleen [33]. Multiple studies have suggested miR-142-5p as a candidate early mastitis biomarker [43–45] in cattle. Lipopolysaccharide stimulation of bovine endometrial stromal cells caused upregulation of miR-142-5p, suggesting involvement with endometritis [46]. Mastitis, endometritis, and other postpartum infections may influence conception rate and, consequently, calving interval [10,12]. High levels of miR-142-5p may indicate infection that lengthens post-partum return to ovulation and oestrus and lowers chances of conception. This miRNA has also been associated with milk fat metabolism in goats [47] and cattle [48]. Therefore, altered metabolism may be at play, perhaps influenced by the presence of disease, and may affect calving interval [49].
Ratios involving miR-363 were associated with health and fertility traits. Similarly to miR-142-5p, miR-363 is found in the bovine spleen and lymph node [33], and has been linked with inflammation [50]. This miRNA is reportedly downregulated in bovine serum during Johne’s disease episodes [51], and has been linked with reduced endothelial cell inflammation [52,53]. This supports certain associations with udder inflammation found in the present study, namely, those of cow ratios miR-363: miR-127 and miR-154b: miR-363 with lower and higher second lactation median SCC, respectively. Milk SCC levels are a widely accepted indicator of udder infection including both clinical and subclinical mastitis. However, the present study did not identify any significant miRNA associations with clinical mastitis, probably due to very few clinical episodes that are routinely recorded in the herd. Studying a larger sample and additional clinical disease data would be of interest in this regard.
There was a significant association between second calving interval and the ratios miR-126-3p: miR-27b and miR-425-3p: miR-27b. miR-425-3p has been linked to endometritis in one previous study in cows [54], which is a condition that may lead to reduced conception rates and longer calving intervals. miR-27b has multiple roles with the potential to influence fertility and is broadly conserved among mammals. This is one of the most highly expressed miRNAs in many bovine tissues [19,44,55,56] and was differentially expressed in one previous study on mastitis in cattle [57]. Additional studies have linked this miRNA to adipogenesis in humans [58], mice [59], and sheep [60]. miR-27b is also reportedly involved in milk fat synthesis in goat mammary cells [61]. This miRNA may therefore be involved in metabolic disorders, which can impact calving interval as discussed above.
The ratio miR-127: miR-30c-5p was negatively associated with milk production traits in the first lactation and combined first and second lactations. Moreover, we saw a statistical trend between calf levels of miR-30c-5p and second lactation milk yield. In humans, miR-30c-5p has been primarily linked to lipid metabolism and adipogenesis [62,63]. There are fewer studies in livestock, but this miRNA is reportedly differentially expressed in beef cattle with high and low intramuscular fat [64], and is upregulated during adipogenesis of chicken adipocytes [65], suggesting a conserved role in fat deposition. miR-127 is highly expressed in mesenchymal stem cells and may affect cell differentiation into myotubes and adipocytes, including in mouse [66,67] and pig, respectively [68]. As a widely conserved miRNA, it is likely to be involved in fat and protein deposition in cattle. It has also been associated with disease, such as bovine lameness and mastitis [28], which could influence milk yield. It is interesting that both miRNAs are involved in adipogenesis and that the relative proportion of the two miRNAs was associated with milk fat and protein yield in the present study. This could be due to the negative association of adipogenesis with miR-127 [68] and positive association with miR-30c-5p [63,65]. This implies greater combined than individual effects of the two miRNAs on cow performance.
The ratio of miR-154b: miR-127 was positively associated with second lactation protein yield. There is scarce literature on these miRNAs in cattle, which does not always suggest involvement in milk protein. Wang et al. [69] showed that miR-154b expression in mammary tissues of cows was associated with milk fat and protein content. This miRNA requires further research in cattle to elucidate its function and potential role in milk protein yield.
Our results demonstrated that the change in miRNA levels over time may be of greater significance to cattle performance traits than single miRNA measurements. The fold change in miR-126-3p from young calf to first lactation cow was significantly associated with milk yield across the first two lactations of the cow. Previous research has linked miR-126-3p with lipid production and deposition, which may be related to milk production. Specifically, miR-126-3p was associated with lipid synthesis in mouse mammary epithelia [70], and was differentially expressed in the mammary glands of lactating goats undergoing food deprivation [71] and in the skeletal muscle of beef cattle of differing fatty acid composition [72]. Additionally, this miRNA has been associated with tissue angiogenesis [73]. Mammary tissue becomes more vascularized during pregnancy and lactation, followed by involution [74], and cows with higher mammary blood flow had higher milk yield [75]. Hence, the effects of miR-126-3p on vascular function may contribute to mammary tissue development during calf growth and/or when milk production is initiated during cow pregnancy and early lactation. However, the role of miR-126-3p depends on context; for example, this miRNA boosts differentiation of embryonic stem cells into endothelial cells but reduces proliferation in mature endothelial cells [73]. Therefore, further experiments investigating the role of this miRNA in different mammary-derived cell types and at different stages of animal life would provide new insights.
We performed cluster analysis of animals based on their miRNA levels, revealing differences between individuals in the trajectory of miRNA expression over time. For miR-126-3p, these clusters were significantly associated with second lactation SCC, a (sub)clinical mastitis indicator. In previous studies, the opposite strand (miR-126-5p) was reportedly downregulated in the mammary gland during mastitis episodes [76]. miR-126-3p is also elevated during pregnancy and lactation in mouse mammary epithelia and cow plasma [70,77,78], and is differentially expressed in the bovine corpus luteum during the oestrus cycle [79]. These previous studies identified expression changes in miR-126-3p during pregnancy, lactation, and infection, but here we identified associations of longitudinal measurements of miR-126-3p with milk production and udder inflammation. Additionally, a statistical trend was identified between calf levels of miR-126-3p and first lactation SCC. This could occur if this miRNA is involved in the development of mammary tissue and immunity, with possible effects on milk yield and infection risk, respectively, later in life. Further studies should investigate the role of miR-126-3p in postnatal and pubertal development in cattle.
In all the above analyses, we implemented a Bonferroni multiple testing correction based on 16 independent tests. This number was derived from four miRNA groups emanating from the miRNA relationships analyses and four cow performance trait groups corresponding to milk production, health, fertility and survival. Admittedly, this correction may have been stringent, as all miRNAs are eventually intercorrelated and all cow performance traits investigated are in some way related to each other. Therefore, we believe that results that remained significant after correction are true reflections of the associations identified.
In summary, building on previous data on early life performance biomarkers in cattle, we used longitudinal measurements in cows to report associations between the expression of several miRNAs, ratios, fold changes, and clusters with milk yield and composition, udder health, and fertility in the first two lactations of dairy cows. These promising associations suggest that these miRNAs warrant validation in further studies as a potential source of predictive biomarkers.
Further research should aim at better defining the functions of these miRNAs in cattle and quantifying their value as predictors of lifetime performance. For example, quantifying these miRNAs in a large sample size and performing cross-validation analyses with bootstrapping would investigate their predictive capacity. Further study is required to. elucidate the physiological bases proposed for the associations found in this study. Examples based on the above results could include studying the associations between miR-126-3p levels and mammary vascularisation or the effect of miR-126-3p, miR-30c-5p, or miR-127 on mammary epithelial cell function, as they were related to milk production traits. Studying correlations between miR-142-5p levels and postpartum metabolic changes would be warranted due to its association with calving interval.
Supporting information
S1 File. Figures S1-S13 demonstrating associations between miRNA measurements and cow performance traits that remained significant (P < 0.05) after adjusting for multiple testing.
https://doi.org/10.1371/journal.pone.0328765.s001
(PDF)
Acknowledgments
We are very thankful to staff at Langhill Farm, particularly Mr. Wilson Lee, as well as to Dr. Tim Connelly, Dr. Thomas Tzelos, Dr. Enguang Rong and Dr. Seungmee Lee for assistance with sample collection. For the purpose of open access, the authors have applied a CC-BY public copyright license to any Author Accepted Manuscript version arising from this submission.
References
- 1. Roche JR, Macdonald KA, Burke CR, Lee JM, Berry DP. Associations among body condition score, body weight, and reproductive performance in seasonal-calving dairy cattle. J Dairy Sci. 2007;90(1):376–91. pmid:17183106
- 2. Buckley F, O’Sullivan K, Mee JF, Evans RD, Dillon P. Relationships among milk yield, body condition, cow weight, and reproduction in spring-calved Holstein-Friesians. J Dairy Sci. 2003;86(7):2308–19.
- 3.
Miglior F, Chesnais J, Van Doormaal BJ, Food G, Canada A, Network CD, et al. Genetic improvement: a major component of increased dairy farm profitability. In: 38th International Committee for Animal Recording. 2012. p. 1–13.
- 4. Gates MC. Evaluating the reproductive performance of British beef and dairy herds using national cattle movement records. Vet Rec. 2013;173(20):499. pmid:23926171
- 5. Cooke JS, Cheng Z, Bourne NE, Wathes DC. Association between growth rates, age at first calving and subsequent fertility, milk production and survival in Holstein-Friesian heifers. Open J Anim Sci. 2013;3(1):1–12.
- 6. Ettema JF, Santos JEP. Impact of age at calving on lactation, reproduction, health, and income in first-parity Holsteins on commercial farms. J Dairy Sci. 2004;87(8):2730–42.
- 7. Wathes DC, Brickell JS, Bourne NE, Swali A, Cheng Z. Factors influencing heifer survival and fertility on commercial dairy farms. Animal. 2008;2(8):1135–43. pmid:22443725
- 8.
Macrae A, Esslemont R. Dairy cattle herd health the prevalence and cost of important endemic diseases and fertility in dairy herds in the UK. Bovine medicine. 2015. p. 325–37.
- 9. De Vries A, Marcondes MI. Review: Overview of factors affecting productive lifespan of dairy cows. Animal. 2020;14(S1):s155–64. pmid:32024570
- 10. Kumar N, Manimaran A, Kumaresan A, Jeyakumar S, Sreela L, Mooventhan P, et al. Mastitis effects on reproductive performance in dairy cattle: a review. Trop Anim Health Prod. 2017;49(4):663–73. pmid:28283873
- 11. Huxley JN. Impact of lameness and claw lesions in cows on health and production. Livest Sci. 2013;156(1–3):64–70.
- 12. Dubuc J, Denis-Robichaud J. A dairy herd-level study of postpartum diseases and their association with reproductive performance and culling. J Dairy Sci. 2017;100(4):3068–78. pmid:28161186
- 13.
Parkinson TJ. Infertility in the cow due to functional and management deficiencies. In: Veterinary Reproduction & Obstetrics. W.B. Saunders; 2018. p. 361–407.
- 14. Sorge US, Kelton DF, Lissemore KD, Sears W, Fetrow J. Evaluation of the dairy comp 305 module “cow value” in two Ontario dairy herds. J Dairy Sci. 2007;90(12):5784–97.
- 15. Ge Q, Zhou Y, Lu J, Bai Y, Xie X, Lu Z. miRNA in plasma exosome is stable under different storage conditions. Molecules. 2014;19(2):1568–75. pmid:24473213
- 16.
Oyelami FO, Usman T, Suravajhala P, Ali N, Do DN. Emerging roles of noncoding RNAs in Bovine Mastitis diseases. Vol. 11, Pathogens. Multidisciplinary Digital Publishing Institute; 2022. p. 1009.
- 17. Fatima A, Morris DG. MicroRNAs in domestic livestock. Physiol Genomics. 2013;45(16):685–96.
- 18. Ioannidis J, Donadeu FX. Circulating microRNA Profiles during the Bovine Oestrous Cycle. PLoS One. 2016;11(6):e0158160. pmid:27340826
- 19. Ioannidis J, Donadeu FX. Circulating miRNA signatures of early pregnancy in cattle. BMC Genomics. 2016;17(1):184.
- 20. Tzelos T, Lee S, Pegg A, Donadeu FX. Association between blood miR-26a levels following artificial insemination, and pregnancy outcome in dairy cattle. PLoS One. 2023;18(8):e0289342. pmid:37566616
- 21. Li Z, Liu H, Jin X, Lo L, Liu J. Expression profiles of microRNAs from lactating and non-lactating bovine mammary glands and identification of miRNA related to lactation. BMC Genomics. 2012;13:731. pmid:23270386
- 22. Wang M, Moisá S, Khan MJ, Wang J, Bu D, Loor JJ. MicroRNA expression patterns in the bovine mammary gland are affected by stage of lactation. J Dairy Sci. 2012;95(11):6529–35.
- 23. Han W, Zhu Y, Su Y, Li G, Qu L, Zhang H, et al. High-Throughput sequencing reveals circulating miRNAs as potential biomarkers for measuring puberty onset in chicken (Gallus gallus). PLoS One. 2016;11(5):154958.
- 24. Sangiao-Alvarellos S, Manfredi-Lozano M, Ruiz-Pino F, Navarro VM, Sánchez-Garrido MA, Leon S, et al. Changes in hypothalamic expression of the Lin28/let-7 system and related microRNAs during postnatal maturation and after experimental manipulations of puberty. Endocrinology. 2013;154(2):942–55. pmid:23291449
- 25. Fernández ME, Prando A, Rogberg-Muñoz A, Peral-García P, Baldo A, Giovambattista G. Association of a region of bovine chromosome 1 (BTA1) with age at puberty in Angus bulls. Reprod Fertil Dev. 2016;28(10):1618–21.
- 26. Xia L, Zhao Z, Yu X, Lu C, Jiang P, Yu H. Integrative analysis of miRNAs and mRNAs revealed regulation of lipid metabolism in dairy cattle. Funct Integr Genomics. 2021;21(3–4):393–404.
- 27. Zhang X, Wang Y, Wang M, Zhou G, Chen L, Ding L, et al. Arginine supply impacts the expression of candidate microRNA controlling milk casein yield in Bovine mammary tissue. Animals (Basel). 2020;10(5):797. pmid:32380753
- 28. Ioannidis J, Sánchez-Molano E, Psifidi A, Donadeu FX, Banos G. Association of plasma microRNA expression with age, genetic background and functional traits in dairy cattle. Sci Rep. 2018;8(1):12955. pmid:30154501
- 29. Tewari RS, Ala U, Accornero P, Baratta M, Miretti S. Circulating skeletal muscle related microRNAs profile in Piedmontese cattle during different age. Sci Rep. 2021;11(1):15815. pmid:34349188
- 30. de Gonzalo-Calvo D, Martínez-Camblor P, Bär C, Duarte K, Girerd N, Fellström B, et al. Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics. 2020;10(19):8665–76. pmid:32754270
- 31. Huan T, Chen G, Liu C, Bhattacharya A, Rong J, Chen BH, et al. Age-associated microRNA expression in human peripheral blood is associated with all-cause mortality and age-related traits. Aging Cell. 2018;17(1):e12687. pmid:29044988
- 32. Kinser HE, Pincus Z. MicroRNAs as modulators of longevity and the aging process. Hum Genet. 2020;139(3):291–308. pmid:31297598
- 33. MacLeay M, Banos G, Donadeu FX. Association of plasma miRNAs with early life performance and aging in dairy cattle. PLoS One. 2023;18(7):e0288343. pmid:37428722
- 34. Shah JS, Soon PS, Marsh DJ. Comparison of methodologies to detect low levels of hemolysis in serum for accurate assessment of serum microRNAs. PLoS One. 2016;11(4):e0153200. pmid:27054342
- 35. Kirschner MB, Edelman JJB, Kao SC-H, Vallely MP, van Zandwijk N, Reid G. The impact of hemolysis on cell-free microRNA biomarkers. Front Genet. 2013;4:94. pmid:23745127
- 36.
Ioannidis J, Risse J, Donadeu FX. Profiling of MicroRNAs in the biofluids of livestock species. In: Methods in Molecular Biology. Humana Press Inc.; 2018. p. 65–77.
- 37.
Hellemans J, Vandesompele J. qPCR data analysis–unlocking the secret to successful results. In: PCR troubleshooting and optimization: The essential guide. Caister Academic Press; 2011. p. 139–50.
- 38. Vlachos IS, Zagganas K, Paraskevopoulou MD, Georgakilas G, Karagkouni D, Vergoulis T, et al. DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res. 2015;43(W1):W460-6. pmid:25977294
- 39. Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M, Maragkakis M, et al. DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res. 2012;40(Web Server issue):W498-504. pmid:22649059
- 40. Fang R, Zhu Y, Hu L, Khadka VS, Ai J, Zou H. Plasma microRNA pair panels as novel biomarkers for detection of early stage breast cancer. Front Physiol. 2019;10(JAN):369711.
- 41. Sun H-Z, Chen Y, Guan LL. MicroRNA expression profiles across blood and different tissues in cattle. Sci Data. 2019;6:190013. pmid:30747916
- 42. Wu H, Neilson JR, Kumar P, Manocha M, Shankar P, Sharp PA, et al. miRNA profiling of naïve, effector and memory CD8 T cells. PLoS One. 2007;2(10):e1020. pmid:17925868
- 43. Lu J, Gu B, Lu W, Liu J, Lu J. miR-142-5p regulates lipopolysaccharide-induced bovine epithelial cell proliferation and apoptosis via targeting BAG5. Exp Ther Med. 2021;22(6):1425. pmid:34707706
- 44. Sun J, Aswath K, Schroeder SG, Lippolis JD, Reinhardt TA, Sonstegard TS. MicroRNA expression profiles of bovine milk exosomes in response to Staphylococcus aureus infection. BMC Genomics. 2015;16:806. pmid:26475455
- 45. Tzelos T, Ho W, Charmana VI, Lee S, Donadeu FX. MiRNAs in milk can be used towards early prediction of mammary gland inflammation in cattle. Sci Rep. 2022;12(1):5131. pmid:35332227
- 46. Yan C, Lv H, Peng Z, Yang D, Shen P, Yu J, et al. Analysis of miRNA expression changes in bovine endometrial stromal cells treated with lipopolysaccharide. Theriogenology. 2021;167:85–93. pmid:33784501
- 47. Sui M, Wang Z, Xi D, Wang H. miR-142-5P regulates triglyceride by targeting CTNNB1 in goat mammary epithelial cells. Reprod Domest Anim. 2020;55(5):613–23. pmid:32083783
- 48. Mobuchon L, Le Guillou S, Marthey S, Laubier J, Laloë D, Bes S, et al. Sunflower oil supplementation affects the expression of miR-20a-5p and miR-142-5p in the lactating bovine mammary gland. PLoS One. 2017;12(12):e0185511. pmid:29281677
- 49. Rojas Canadas E, Herlihy MM, Kenneally J, Grant J, Kearney F, Lonergan P, et al. Associations between postpartum phenotypes, cow factors, genetic traits, and reproductive performance in seasonal-calving, pasture-based lactating dairy cows. J Dairy Sci. 2020;103(1):1016–30. pmid:31759601
- 50. Kästle M, Bartel S, Geillinger-Kästle K, Irmler M, Beckers J, Ryffel B. MicroRNA cluster 106a~363 is involved in T helper 17 cell differentiation. Immunology. 2017;152(3):402–13.
- 51. Choi S-W, Kim S, Park H-T, Park H-E, Choi J-S, Yoo HS. MicroRNA profiling in bovine serum according to the stage of Mycobacterium avium subsp. paratuberculosis infection. PLoS One. 2021;16(11):e0259539. pmid:34735546
- 52. Zhou T, Li S, Yang L, Xiang D. microRNA-363-3p reduces endothelial cell inflammatory responses in coronary heart disease via inactivation of the NOX4-dependent p38 MAPK axis. Aging (Albany NY). 2021;13(8):11061–82. pmid:33744854
- 53. Zhou T, Li S, Yang L, Xiang D. Correction for: microRNA-363-3p reduces endothelial cell inflammatory responses in coronary heart disease via inactivation of the NOX4-dependent p38 MAPK axis. Aging (Albany NY). 2021;2022(3):11061–82.
- 54. Pereira G, Charpigny G, Guo Y, Silva E, Silva MF, Ye T. Characterization of circulating microRNA profiles of postpartum dairy cows with persistent subclinical endometritis. J Dairy Sci. 2023;106(12):9704–17.
- 55. Jin W, Ibeagha-Awemu EM, Liang G, Beaudoin F, Zhao X, Guan LL. Transcriptome microRNA profiling of bovine mammary epithelial cells challenged with Escherichia coli or Staphylococcus aureus bacteria reveals pathogen directed microRNA expression profiles. BMC Genomics. 2014;15:181. pmid:24606609
- 56. Salilew-Wondim D, Ahmad I, Gebremedhn S, Sahadevan S, Hossain MDM, Rings F, et al. The expression pattern of microRNAs in granulosa cells of subordinate and dominant follicles during the early luteal phase of the bovine estrous cycle. PLoS One. 2014;9(9):e106795. pmid:25192015
- 57. Ngo S, Moloney S, Li X, McNaughton L, A P, Sheppard MA. Distinct microRNA signatures for mastitis measured in milk following natural exposure in dairy herds. Performances Cold-Set Bind Food Hydrocolloids, Commer Meat Bind Phys Chem Charact Tilapia Fish Balls. 2017;1(1):1–8.
- 58. Hu X, Tang J, Hu X, Bao P, Pan J, Chen Z, et al. MiR-27b impairs adipocyte differentiation of human adipose tissue-derived mesenchymal stem cells by targeting LPL. Cell Physiol Biochem. 2018;47(2):545–55. pmid:29794473
- 59. Lin Q, Gao Z, Alarcon RM, Ye J, Yun Z. A role of miR-27 in the regulation of adipogenesis. FEBS J. 2009;276(8):2348–58. pmid:19348006
- 60. Li B, Huang X, Yang C, Ge T, Zhao L, Zhang X, et al. miR-27a Regulates sheep adipocyte differentiation by targeting CPT1B gene. Animals (Basel). 2021;12(1):28. pmid:35011132
- 61. Lin X, Luo J, Zhang L, Zhu J. MicroRNAs synergistically regulate milk fat synthesis in mammary gland epithelial cells of dairy goats. Gene Expr. 2013;16(1):1–13. pmid:24397207
- 62. Irani S, Hussain MM. Role of microRNA-30c in lipid metabolism, adipogenesis, cardiac remodeling and cancer. Curr Opin Lipidol. 2015;26(2):139–46.
- 63. Karbiener M, Neuhold C, Opriessnig P, Prokesch A, Bogner-Strauss JG, Scheideler M. MicroRNA-30c promotes human adipocyte differentiation and co-represses PAI-1 and ALK2. RNA Biology. 2011;8(5):850–60.
- 64. Mir BA, Reyer H, Komolka K, Ponsuksili S, Kühn C, Maak S. Differentially expressed MiRNA-gene targets related to intramuscular fat in musculus longissimus dorsi of charolais × holstein f2-crossbred bulls. Genes (Basel). 2020;11(6):1–12.
- 65. Huang HY, Zhao GP, Liu RR, Li SF, Zhao ZH, Li QH. Expression profiles of novel genes and microRNAs involved in lipid deposition in chicken’s adipocyte. Ital J Anim Sci. 2018;17(3):593–8.
- 66. Li J, Wang G, Jiang J, Fu L, Zhou P, Ren H. The microRNA-127-3p directly targeting Vamp2 in C2C12 myoblasts. Anim Cells Syst (Seoul). 2018;22(5):299–304. pmid:30460111
- 67. Li J, Wang G, Jiang J, Zhang L, Zhou P, Ren H. MicroRNA-127-3p regulates myoblast proliferation by targeting Sept7. Biotechnol Lett. 2020;42(9):1633–44. pmid:32382971
- 68. Gao Y, Wang Y, Chen X, Peng Y, Chen F, He Y, et al. MiR-127 attenuates adipogenesis by targeting MAPK4 and HOXC6 in porcine adipocytes. J Cell Physiol. 2019;234(12):21838–50. pmid:31206669
- 69. Wang X, Zhang L, Jin J, Xia A, Wang C, Cui Y, et al. Comparative transcriptome analysis to investigate the potential role of miRNAs in milk protein/fat quality. Sci Rep. 2018;8(1):6250. pmid:29674689
- 70. Chu M, Zhao Y, Feng Y, Zhang H, Liu J, Cheng M, et al. MicroRNA-126 participates in lipid metabolism in mammary epithelial cells. Mol Cell Endocrinol. 2017;454:77–86. pmid:28599789
- 71. Mobuchon L, Marthey S, Le Guillou S, Laloë D, Le Provost F, Leroux C. Food deprivation affects the miRNome in the lactating goat mammary gland. PLoS One. 2015;10(10):e0140111. pmid:26473604
- 72. de Oliveira PSN, Coutinho LL, Cesar ASM, Diniz WJ da S, de Souza MM, Andrade BG, et al. Co-expression networks reveal potential regulatory roles of miRNAs in fatty acid composition of Nelore cattle. Front Genet. 2019;10:651. pmid:31354792
- 73. Chistiakov DA, Orekhov AN, Bobryshev YV. The role of miR-126 in embryonic angiogenesis, adult vascular homeostasis, and vascular repair and its alterations in atherosclerotic disease. J Mol Cell Cardiol. 2016;97:47–55. pmid:27180261
- 74. Ryman VE, Packiriswamy N, Sordillo LM. Role of endothelial cells in bovine mammary gland health and disease. Anim Health Res Rev. 2015;16(2):135–49. pmid:26303748
- 75. Berger H, Lietzau M, Tichy A, Herzog K. Investigations of mammary and uterine blood flow in relation to milk yield, postpartum disease, and pregnancy result in dairy cows. Theriogenology. 2016;86(8):1906–12. pmid:27405919
- 76. Pu J, Li R, Zhang C, Chen D, Liao X, Zhu Y, et al. Expression profiles of miRNAs from bovine mammary glands in response to Streptococcus agalactiae-induced mastitis. J Dairy Res. 2017;84(3):300–8. pmid:28831974
- 77. Cui W, Li Q, Feng L, Ding W. MiR-126-3p regulates progesterone receptors and involves development and lactation of mouse mammary gland. Mol Cell Biochem. 2011;355(1–2):17–25. pmid:21526342
- 78. Lim H j o o, Kim HJ, Lee JH, Lim DH, Son JK, Kim E t a e. Identification of plasma miRNA biomarkers for pregnancy detection in dairy cattle. J Anim Reprod Biotechnol. 2021;36(1):35–44.
- 79. Dai L, Xu J, Liu S, Ma T, Zhu Y, Xu F, et al. Characterization of miR-126-3p and its target talin2 in the bovine corpus luteum during the oestrus cycle. Reprod Domest Anim. 2014;49(6):913–9. pmid:25244588