Bovine microRNomics: Implications during oocyte maturation and pathophysiology of endometrium

MicroRNAs which are known for posttranscriptional gene regulation are evidenced for their essential role during animal development and disease. In this study, identification and expression profiling of bovine miRNAs during oocyte maturation are scrutinized using heterologous approach, while miRNA regulated novel molecular signature underlying bovine subclinical endometritis was dissected using an integrative approach. Primarily, identification and expression profiling of microRNAs during bovine oocyte maturation was investigated using miRCURYTM locked nucleic acids (LNA) array (Exiqon, Vedbaek, Denmark) microarray that consist of 454 capture probes for human, mouse and rat miRNAs. The result revealed differential expression of 59 miRNAs, of which 31 and 28 miRNAs were found to be preferentially expressed in immature and matured oocytes, respectively. Here, 32 new bovine orthologous miRNAs were identified using a heterologous approach. Furthermore, the preliminary attempt to dissect the specific function of miR-99a and miR-100 in invitro cumulus cell showed that both miRNAs down regulate bovine tribbles homologue 2 (TRB2). On the other hand, genome wide RT2 miRNA PCR array consisting of 354 well characterized human miRNA primers was used to analyze miRNA expression in the uterine cytobrush samples taken from cows with subclinical endometritis and healthy. The result showed the aberrant expression of 23 miRNAs in cows with subclinical endometritis as compared to the healthy ones. Interestingly, the Ingenuity Pathway Analysis (IPA) for high ranking target genes of aberrantly expressed miRNAs identified gene networks, canonical pathways and biological functions that converged to array of signaling pathways and cellular activities inherent to the endometrium during estrous cycle and pregnancy. Furthermore, the luciferase assay data substantiated the primary information from bioinformatic prediction and enabled us to deduce a convincing link between the aberrantly expressed miRNAs and target genes. Taken together, identification and dynamic expression pattern of certain class of miRNAs during bovine oocyte maturation suggests their potential involvement in early embryo development; where as, aberrant expression of uterine miRNAs in animals with subclinical endometritis potentially interfere with the tight uterine gene regulation.Bovines microRNomics: Bedeutung während der Eizellreifung und die Pathophysiologie des Endometriums MikroRNAs (miRNAs), die bereits für die Regulierung von posttranskriptionalen Genen bekannt sind, konnte eine essentielle Rolle für die Entwicklung von Tieren und Krankheiten nachgewiesen werden. In dieser Studie wurden bovine miRNAs während der Eizellenreifung identifiziert und mittels verschiedener Methoden das Expressionsprofil untersucht. miRNAs, die eine neuartigen molekulare Signatur auf Grundlage einer subklinischen Endometritis zeigten wurden mit Hilfe eines integrativen Untersuchungsansatzes erforscht. Zuerst wurde die Identifikation und die Erstellung von Expressionsprofilen von miRNAs während der bovinen Eizellenreifung mittels des „miRCURYTM locked nucleic acids (LNA) array“ (Exiqon, Vedbeak, Denmark) durchgeführt. Dieser Microarray besteht aus 454 bekannten Sonden von Mensch, Maus und Ratten miRNAs. Die Ergebnisse zeigten 59 unterschiedlich exprimierte miRNAs, von denen 31 hauptsächlich in unreifen und 28 in reifen Eizellen überexprimiert wurden. Hierbei wurden mit verschiedenen Ansätzen 32 neue orthologe bovine miRNAs identifiziert. Des Weiteren wurden erste Versuche durchgeführt, um die spezifischen Funktionen von miR-99a und miR-100 innerhalb eines „in-vitro“ Kumuluszellen Modells zu untersuchen. Die Ergebnisse zeigten, dass miR-99a und miR-100 die Expression des bovinen tribbles homologue 2 Gens (TRB2) runter regulieren. Zum Anderen wurde mittels eines genomweitem RT2 miRNA PCR Arrays, bestehend aus 354 gut beschriebenen humanen miRNA Primern, an uterinen Cytobrush-Proben von Kühen die entweder an subklinischer Endometritis erkrankt oder gesund waren, Expressionsanalysen durchgeführt. Das Ergebnis dieses Versuches zeigte abweichende Expressionen von 23 miRNAs in Geweben von Kühen mit subklinischer Endometritis im Vergleich zu den gesunden Tieren. Interessanterweise konvergieren die mittels der Igenuity Pathway Analyse(IPA), identifizierten Gennetzwerke, bekannten Pathways sowie biologische Funktionen mit den Signalwegen und zellulären Aktivitäten innerhalb des Endometriums während des Östruszyklus und der Trächtigkeit. Des Weiteren bestätigte der Luziferase Assay die vorangegangenen Informationen der bioinformatischen Auswertung und ermöglichte uns einen schlüssigen Zusammenhang zwischen der veränderten miRNA Expression und den Zielgenen abzuleiten. Zusammengefasst, zeigt die Identifikation sowie das dynamischen Expressionsmuster der bestimmten Klasse von miRNAs während der bovinen Eizellreifung ihren potentiellen Einfluss auf die frühe Embryonalentwicklung; wohingegen die unterschiedliche Expression der uterinen miRNAs bei Tieren mit subklinischer Endometritis möglicherweise die uterine Genregulation beeinträchtigt

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679-0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm

Effects of Incorporating Dry Matter Intake and Residual Feed Intake into a Selection Index for Dairy Cattle Using Deterministic Modeling.

The inclusion of feed efficiency in the breeding goal for dairy cattle has been discussed for many years. The effects of incorporating feed efficiency into a selection index were assessed by indirect selection (dry matter intake) and direct selection (residual feed intake) using deterministic modeling. Both traits were investigated in three ways: (1) restricting the trait genetic gain to zero, (2) applying negative selection pressure, and (3) applying positive selection pressure. Changes in response to selection from economic and genetic gain perspectives were used to evaluate the impact of including feed efficiency with direct or indirect selection in an index. Improving feed efficiency through direct selection on residual feed intake was the best scenario analyzed, with the highest overall economic response including favorable responses to selection for production and feed efficiency. Over time, the response to selection is cumulative, with the potential for animals to reduce consumption by 0.16 kg to 2.7 kg of dry matter per day while maintaining production. As the selection pressure increased on residual feed intake, the response to selection for production, health, and fertility traits and body condition score became increasingly less favorable. This work provides insight into the potential long-term effects of selecting for feed efficiency as residual feed intake

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms.

peer reviewedKnowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points

The Resilient Dairy Genome Project - a general overview of methods and objectives related to feed efficiency and methane emissions.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries, i.e., Australia [AUS], Canada [CAN], Denmark [DNK], Germany [DEU], Spain [ESP], Switzerland [CHE], and United States of America [USA] contribute with genotypes and phenotypes including DMI and CH4. However, combining data is challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis

Identification of serum metabolites associated with the risk of metritis in transition dairy cows

In this study, we aimed to identify metabolite signatures that characterize metritis prior to, during, and after the disease incidence. Blood samples were collected from 100 Holstein cows at 5 time points before and after parturition. Six cows that developed metritis and 20 controls were selected for metabolomics analysis in a nested case-control study. Twenty nine serum metabolites were quantified using gas chromatography - mass spectroscopy. Results showed that similar panels of metabolites differentiated pre-metritic and CON cows at -8 and -4 wks prepartum. The top most important metabolites that differentiated the two groups of cows at -8 wks prepartum were oxalate, ornithine, pyroglutamic acid, D-mannose, glutamic acid and at -4 wks prepartum were ornithine, pyroglutamic acid, D-mannose, glutamic acid, phosphoric acid, suggesting their potential use as risk biomarkers for metritis. Area under the curve with values of 1.0 and 0.969 at -8 and -4 wks, respectively, indicated that those panels of metabolites have a very high sensitivity and specificity to be used as risk biomarkers for metritis. Overall, results showed that specific serum metabolite signatures can be used to screen cows for susceptibility to metritis during the dry off period and to better understand the etio-pathobiology of the disease.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Mice Treated Subcutaneously with Mouse LPS-Converted PrPres or LPS Alone Showed Brain Gene Expression Profiles Characteristic of Prion Disease

Previously, we showed that bacterial lipopolysaccharide (LPS) converts mouse PrPC protein to a beta-rich isoform (moPrPres) resistant to proteinase K. In this study, we aimed to test if the LPS-converted PrPres is infectious and alters the expression of genes related to prion pathology in brains of terminally sick mice. Ninety female FVB/N mice at 5 weeks of age were randomly assigned to 6 groups treated subcutaneously (sc) for 6 weeks either with: (1) Saline (CTR); (2) LPS from Escherichia coli 0111:B4 (LPS), (3) one-time sc administration of de novo generated mouse recombinant prion protein (moPrP; 29-232) rich in beta-sheet by incubation with LPS (moPrPres), (4) LPS plus one-time sc injection of moPrPres, (5) one-time sc injection of brain homogenate from Rocky Mountain Lab (RLM) scrapie strain, and (6) LPS plus one-time sc injection of RML. Results showed that all treatments altered the expression of various genes related to prion disease and neuroinflammation starting at 11 weeks post-infection and more profoundly at the terminal stage. In conclusion, sc administration of de novo generated moPrPres, LPS, and a combination of moPrPres with LPS were able to alter the expression of multiple genes typical of prion pathology and inflammation