Plasma metabolomic profiles differ at the time of artificial insemination based on pregnancy outcome, in Bos taurus beef heifers

Phillips, Kaitlyn; Read, Casey C; Kriese-Anderson, L. A.; Rodning, Soren P.; Brandebourg, Terry D.; Biase, Fernando H.; Marks, M. L.; Elmore, J. Bradford; Stanford, Michelle; Dyce, Paul W.

doi:10.1038/s41598-018-31605-0

Cited by 17 publications

(19 citation statements)

References 47 publications

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“…Among them, kynurenine, which was detected in comparatively lower levels in plasma of repeat breeder cows, was identified as the metabolite with the most highly variable concentration. Essentially, similar with the present study, Phillips et al [5] also found that kynurenine was present in significantly lower levels in plasma of infertile heifers compared to levels detected in samples from fertile heifers. In addition, reduced levels of 2-aminobutyric acid were recognized as candidate biomarkers for poor reproductive performance from the results of the present and previous study [5].…”

Section: Discussionsupporting

confidence: 92%

“…Essentially, similar with the present study, Phillips et al [5] also found that kynurenine was present in significantly lower levels in plasma of infertile heifers compared to levels detected in samples from fertile heifers. In addition, reduced levels of 2-aminobutyric acid were recognized as candidate biomarkers for poor reproductive performance from the results of the present and previous study [5]. These findings suggest that levels of kynurenine and 2-aminobutyric acid may predict fertility problems including both infertility and repeat breeder syndrome in cattle.…”

Section: Discussionsupporting

confidence: 92%

“…Metabolomics involves the quantitative measurement of a global set of low molecular weight metabolites in a biological fluid [ 5 ]. Metabolomics data represent a downstream of outcome of systems biology that has drawn significant interest for its capacity to unravel essential biological processes [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…As such, findings from metabolomics studies may help to identify potential biomarkers of given phenotypes related to health and diseases, including pregnancy and pregnancy-associated dysfunctions [ 7 ]. Within cattle populations, metabolomics analysis has been utilized to provide assessments of the quality of embryos and oocytes to identify interactions between mother and embryo, and to determine differences in fertility and infertility using plasma, uterine luminal fluid, and seminal plasma [ 5 , 6 , 8 , 9 ]. However, currently, there are no published studies that have reported metabolomics-based differences between healthy and repeat breeder cows.…”

Section: Introductionmentioning

confidence: 99%

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Metabolomic profiles of plasma and uterine luminal fluids from healthy and repeat breeder Holstein cows

et al. 2021

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Background Repeat breeding is a critical reproductive disorder in cattle. The problem of repeat breeder cattle remains largely unmanageable due to a lack of informative biomarkers. Here, we utilized metabolomic profiling in an attempt to identify metabolites in the blood plasma and uterine luminal fluids. We collected blood and uterine fluid from repeat breeder and healthy cows on day 7 of the estrous cycle. Results Metabolomic analysis identified 17 plasma metabolites detected at concentrations that distinguished between the two groups, including decreased various bile acids among the repeat breeders. However, no metabolites that varied significantly were detected in the uterine luminal fluids between two groups. Among the plasma samples, kynurenine was identified as undergoing the most significant variation. Kynurenine is a metabolite produced from tryptophan via the actions of indoleamine 2,3-dioxygenase (IDO). As IDO is key for maternal immune tolerance and induced in response to interferon tau (IFNT, ruminant maternal recognition of pregnancy factor), we examined the responsiveness to IFNT on peripheral blood mononuclear cells (PBMC) isolated from healthy and repeat breeder cows. The mRNA expression of IFNT-response makers (ISG15 and MX2) were significantly increased by IFNT treatment in a dose-dependent manner in both groups. Although treatment with IFNT promoted the expression of IDO in PBMCs from both groups, it did so at a substantially reduced rate among the repeat breeder cows, suggesting that decreased levels of kynurenine may relate to the reduced IDO expression in repeat breeder cows. Conclusions These findings provide valuable information towards the identification of critical biomarkers for repeat breeding syndrome in cattle.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Metabolomic profiles of plasma and uterine luminal fluids from healthy and repeat breeder Holstein cows

et al. 2021

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“…This is because milk fat, protein, and lactose percentages are predicted from MIR spectra (De Marchi et al, 2014). Moreover, it also implies that MIR spectra contain other information related to the fertility status of the animal, which might be further elucidated using metabolomics (Phillips et al, 2018), proteomics (Koh et al, 2018), or genome-wide association studies (Wang and Bovenhuis, 2018;Benedet et al, 2019). In terms of a practical application, these results mean that MIR was of primary importance in prediction of fertility of dairy cows.…”

Section: Resultsmentioning

confidence: 98%

Predicting the likelihood of conception to first insemination of dairy cows using milk mid-infrared spectroscopy

Pryce

2020

Journal of Dairy Science

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The objective of this study was to examine the ability of milk mid-infrared (MIR) spectroscopy and other onfarm data, such as milk yield, milk composition, stage of lactation, calving age, days in milk at insemination, and somatic cell count, to identify cows that were most or least likely to conceive to first insemination. A total of 16,628 spectral and milk production records of 7,040 cows from 29 commercial dairy herds across 3 Australian states were used. Three models, comprising different explanatory variables, were tested. Model 1 included features that are readily available on farms participating in milk recording, such as milk yield, milk composition, somatic cell count, days from calving to insemination, and calving season. Days in milk and age at calving were incorporated into model 1 to form model 2. In model 3, MIR was added to model 2, but to avoid double counting, milk composition traits of model 2 were removed. The models were first trained on extreme data [i.e., including cows that (1) conceived to first insemination and (2) cows with no conception event recorded and with only 1 insemination]. Then, the models were validated in a fresh data set with all cows regardless of conception outcomes present to test for their ability to identify cows that conceived or did not conceive to first insemination. To do this, we ranked the predicted probability of all cows in the validation set and then selected the top and bottom records in varying proportions from 5 to 40% (i.e., where the model predicted the highest versus lowest likelihood of conception to first insemination, respectively) and compared with the actual values. The model's performance was evaluated through herd-year by herd-year external validation and measured as the proportion of selected records being correct. The results show that when more cows are selected (i.e., descending confidence), the accuracy of the models was reduced, and selecting the 10% of cows with the highest confidence of predictions produces optimal accuracy. Irrespective of the proportions, none of the models could predict cows that conceived to first insemination, with an accuracy around 0.48. When attempting to predict the bottom 10% of cows, which had the least likelihood of conception to first insemination, model 1 had prediction accuracy around 0.64. Compared with model 1, the addition of days in milk and calving age (model 2) resulted in a negligible improvement in prediction accuracy (0.01 to 0.03). Model 3 had the highest prediction accuracy (0.76), which implies that in the models tested, MIR is of primary importance in the prediction of fertility of dairy cows. In conclusion, this study indicates that MIR and other milk recording data could be used to identify cows with potential difficulty in getting pregnant to first insemination with promising accuracy.

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Untargeted metabolomic analysis of heterotrophic Euglena gracilis cells in response to the organic carbon source

Farjallah,

Guéguen

2023

Algal Research

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Plasma metabolomic profiles differ at the time of artificial insemination based on pregnancy outcome, in Bos taurus beef heifers

Cited by 17 publications

References 47 publications

Metabolomic profiles of plasma and uterine luminal fluids from healthy and repeat breeder Holstein cows

Metabolomic profiles of plasma and uterine luminal fluids from healthy and repeat breeder Holstein cows

Predicting the likelihood of conception to first insemination of dairy cows using milk mid-infrared spectroscopy

Untargeted metabolomic analysis of heterotrophic Euglena gracilis cells in response to the organic carbon source

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