Feed additives such as 3-nitrooxypropanol (3-NOP) can reduce a dairy cow's enteric methane (CH4) emissions. Our study aimed to examine whether there is genetic variation in the cow's response in reduction of CH4 emission after receiving 3-NOP feed additives. Data were available from a project in which the CH4-mitigating effect of 3-NOP (Bovaer, DSM-Firmenich) was tested for Norwegian dairy cows. The one-year trial took place in a commercial dairy herd and included 79 Norwegian Red cows. The cows' CH4 emissions were measured by a GreenFeed unit. A total of 14,166 daily CH4 measures were analyzed, and the overall mean (SD) was 387 (96) g of CH4 per cow per day. Cows were divided into 2 groups, where 54 cows received between 1.2 and 1.5 g of 3-NOP per day and 25 cows were in a control group without any feed additives. The trait of interest, CH4-response, was defined as the cow's change in CH4 after introducing 3-NOP in the diet compared with the base level CH4 in periods without 3-NOP. The base level period consisted of 3 wk in September of 2023 and 4 wk in January and February of 2024. The trait daily CH4 (g/d) was analyzed with a linear animal repeatability model with fixed effects of 3-NOP group, parity, lactation week, and test day, and random animal genetic and permanent environment effects. Fixed effect solutions from this model were used to compute yield deviations for CH4 (YD_CH4), a corrected phenotype (i.e., daily CH4 corrected for effects of parity, lactation stage, and test day). For cows fed 3-NOP, we calculated their individual base level CH4 as the average YD_CH4 from the periods without 3-NOP. The trait CH4-response was then calculated as YD_CH4 minus base level CH4. The new CH4-response trait had a total of 7,293 daily records from 42 cows and was analyzed with a linear animal repeatability model with the fixed effect of test day and random additive genetic effect of cow. Although this is a small dataset and results should be interpreted with caution, the estimated heritability of 0.15, with an SE of 0.03, suggests that genetic variation in response in the reduction of CH4 emission after receiving 3-NOP feed additives exists for Norwegian Red dairy cows.

The increased uptake of sensor technologies and precision farming tools for the dairy cattle sector is enabling real-time monitoring of animal health, welfare, and productivity. These digital advancements provide high-frequency, objective, and large-scale phenotypic data for breeding purposes. This review explores the potential of sensor-derived data to improve genetic and genomic evaluations in dairy cattle and outlines key challenges, opportunities, and approaches associated with their implementation. While these data streams have great potential for genetic evaluations, their integration into national and international breeding programs remains limited due to fragmentation across sensor brands, lack of standardization, and challenges related to data accessibility, data access and portability rights, business interests, and governance. A crucial aspect of leveraging digital technologies in dairy cattle breeding is data harmonization and integration. We highlight the importance of establishing standardized data collection and data sharing protocols, implementing robust quality control and data cleaning methodologies, as well as defining novel sensor-based traits and estimating their genetic background. In this context, we compiled heritability estimates for novel traits derived from data recorded by sensors and other technologies in dairy cattle populations. The development of phenomics in breeding programs, which involves integrating multisource data-including sensor-based, genomic, and management information-will be key to accelerating genetic progress, especially for traits related to animal welfare, health, resilience, and efficiency. This review presents a roadmap for the effective use of sensor-derived data in genetic evaluations, advocating for centralized data infrastructures, transparent data-sharing agreements, and the role of different stakeholders from academia and industry, including organizations such as the International Committee on Animal Recording (ICAR) in establishing global standards and guidelines. By addressing these challenges, dairy breeding programs can fully harness precision dairy farming technologies to enhance production and environmental efficiency, improve animal health and welfare, and drive sustainable genetic advancements in the dairy cattle sector.

With the integration of genomic selection in the cattle artificial insemination (AI) industry, bulls are selected for their semen production capacity and fertility at a younger age than previously. Norwegian Red bull calves selected as candidates to become future AI bulls based on their genomic breeding value are kept in a performance testing station from around the age of 3–12 months, allowing for sample collection and analysis of different parameters during their pre- and peripubertal period. Insulin-like factor 3 (INSL3) is a small peptide hormone specifically secreted by the mature Leydig cells of the testes. In the foetus, it induces the first phase of testicular descent and is considered to reflect Leydig cell development during puberty; it could therefore be an interesting early indicator of future semen production capacity. The main objective of our study was to evaluate the relationship between INSL3, scrotal circumference (SC), and semen characteristics. This is the first time INSL3 was measured in the Norwegian Red population. We collected blood samples for analysis of INSL3 from 142 Norwegian Red bulls at the performance testing station and measured their SC on the same day. Altogether, measurements were made at four time points: upon arrival at the performance testing station (quarantine (Q): 2–5 months) and later at approximately 6, 9 and 12 months of age. Information on season and place of birth were made available from the database of the breeding company Geno, together with data on semen characteristics from the test station and the AI station. The median SCs for age groups Q, 6, 9, and 12 were 15, 21.5, 29, and 34 cm, respectively. INSL3 was shown to be positively correlated with SC (R = 0.4) but not with any of the semen characteristics. Similarly, we found no correlation between SC and sperm characteristics from data on ejaculates analysed at the performance testing station and AI station. The mean sperm volume for the 31 selected bulls with at least 10 ejaculates produced in the AI station increased from 2.3 ml at the performance testing station to 6.4 ml at the AI station. The corresponding increase in mean sperm concentration was from 497 million/ml to 1 049 million/ml. We conclude that INSL3 exhibits high inter-individual variability in the Norwegian Red bull population, which cannot be explained by the parameters measured in this study. At present, INSL3 cannot be used as a biomarker of sperm production in this breed.

Council on Dairy Cattle Breeding, Bowie, MD, United States, Department of Animal Sciences, University of Wisconsin, Madison, WI, United States, College of Animal Science and Technology, China Agricultural University, Beijing, China, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China, Animal Genomics and Improvement Laboratory, USDA, Agricultural Research Service, Beltsville, MD, United States, Department of Animal Sciences, Purdue University, West Lafayette, IN, United States, Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Aas, Norway, Department of Animal Science, Washington State University, Pullman, WA, United States

The use of genomic selection significantly reduces the age of dairy bulls entering semen production compared to progeny testing. The study aimed to identify early indicators that could be used for screening bulls during their performance testing period and could give us insight into their future semen production performance, acceptance for the AI station, and prediction of their future fertility. The study population consisted of 142 young Norwegian Red bulls enrolled at the performance test station, followed until we received semen production data, semen doses, and, subsequently, non-return rates (NR56) from the AI station. A range of semen quality parameters were measured with computer-assisted sperm analysis and flow cytometry from ejaculates collected from 65 bulls (9-13 months). The population morphometry of normal spermatozoa was examined, showing that Norwegian Red bulls at 10 months of age have homogenous sperm morphometry. Norwegian Red bulls could be separated into 3 clusters according to their sperm's reaction patterns to stress test and cryopreservation. Results of semi-automated morphology assessment of young Norwegian Red bulls showed that 42% of bulls rejected for the AI station and 18% of bulls accepted had ejaculates with abnormal morphology scores. For the youngest age group at 10 months, the mean (SD) proportion of spermatozoa with normal morphology was 77.5% (10.6). Using novel interpretation of sperm stress test combined with sperm morphology analysis and consecutive cryopreservation at a young age allowed identification of the candidate's sperm quality status. This could help breeding companies introduce young bulls earlier to the AI stations.

Genomic selection in cattle breeding has gradually allowed younger bulls to be recruited for semen production. In this study, sperm quality parameters, seminal plasma and sperm metabolites, semen production capacity and fertility in young Norwegian Red bulls were analysed. For in vitro analyses of sperm quality and metabolites, ejaculates were collected from the same 25 bulls at both 14 and 17 months of age. Semen production and fertility data were collected for all Norwegian Red bulls in production from December 2017 throughout 2019. Bull fertility was measured as 56 days non-return rate (NR56), for both age groups. In both fresh and frozen-thawed semen samples, the proportion of hyperactive spermatozoa, average path velocity, curvilinear velocity and amplitude of lateral head displacement were higher in samples collected at 17 months of age compared to 14 months (P<0.05). In addition, several amino acids including arginine, glutamine, cysteine and proline, were affected by age (P<0.05). The concentrations of K and Ba increased significantly (P<0.05) with age in both seminal plasma and sperm cells. Sperm concentration and volume of the ejaculate increased significantly with increasing age, while the percentage of discarded batches decreased. The bulls' NR56 decreased significantly with increasing age (75% vs. 74%). However, the difference of 1% is unlikely to be of biological importance. In conclusion, even small age differences in young bulls may significantly affect several sperm quality parameters, metabolite levels and semen production traits.

Cow activity measurements are widely used in herd management tools for estrus detection. The aim of this study was to examine whether a new trait based on activity measures can be useful for genetic evaluation of cow fertility in Norwegian Red dairy cattle. Data from 284 herds with Lely milking robot were collected. Daily measurements of cow activity level were available from Lely activity tags and included data from 13,224 lactations of 8,139 Norwegian Red cows. We analyzed daily activity and included records from 10 to 150 d in milk for cows with at least 50 records. The trait analyzed was interval from calving to first high activity (CFHA). In total 87% of the cows had at least one episode of high activity recorded. The mean (standard deviation) of CFHA was 42 (28) days. A linear animal repeatability model with fixed effects of month-year of calving, age-parity, and herd, and random animal and permanent environment effects was used for estimation of variance components. The trait CFHA showed significant genetic variation with a heritability of 0.05 (0.01). This trait reflects the cow's ability to return to estrus cycle and show estrus after calving, which are important aspects of cow fertility.

The aim of this study was to estimate heritability of methane emissions for Norwegian Red dairy cows. Methane measured by GreenFeed was available from 12 commercial herds. Data was collected from February to October 2021 and the final dataset had a total of 62,057 records from 252 cows. Methane emission from individual visits was analysed. The average methane emission was 441 g/day. A linear animal repeatability model with fixed effects of parity and lactation week, and random effects of herd-testday, animal, and permanent environment was used to estimate variance components. The estimated heritability was 0.22 and repeatability 0.36.

Subclinical endometritis (SCE) is highly prevalent in dairy cows, causing negative effects on reproductive outcomes and the producer economy. Genetic selection for animals with better resilience against uterine disease should be prioritized due to both sustainability and animal welfare. Therefore, the aim of the present study was to estimate the heritability of SCE in the Norwegian Red (NR) population. Moreover, future perspectives of the condition as a fertility phenotype for breeding are discussed. A total of 1,642 NR cows were sampled for SCE at the time of artificial insemination, using cytotape. The percentage of polymorphonuclear cells (PMN) in each sample was established by cytology, through the counting of 300 PMN and epithelial cells. The mean percentage of PMN was 5%. Different trait definitions were examined, and SCE was defined as binary traits, based on the following cut-off levels of PMN: Cyto0 = PMN >0, Cyto3 = PMN >3%, Cyto5 = PMN >5%, Cyto10 = PMN >10%, and Cyto20 = PMN >20%. The mean ranged from 0.07 (Cyto20) to 0.59 (Cyto0). We also analyzed PMN as a continuous variable using percent PMN. Information on the animals and herds was obtained from the Norwegian Dairy Herd Recording System. The pedigree of cows with data included a total of 24,066 animals. A linear animal model was used to estimate the heritability. The only trait definition that had an estimated genetic variance larger than the standard error was Cyto5, with an estimated heritability of 0.04. For all other definitions, the genetic variance was not significantly different from zero. A cut-off level of 5% PMN has been established as a general threshold for the definition of SCE in earlier literature. The standard errors of the estimated variance components were relatively large, and results should be interpreted with caution. However, the current study indicates that SCE is heritable at a similar level to that of clinical endometritis and metritis, and has potential as a future fertility phenotype to be used for breeding purposes. A more feasible method to diagnose SCE is needed to establish larger data sets.

In this study, the complexity of chromatin integrity was investigated in frozen-thawed semen samples from 37 sires with contrasting fertility, expressed as 56-day non-return rates (NR56). Protamine deficiency, thiols, and disulfide bonds were assessed and compared with previously published data for DNA fragmentation index (DFI) and high DNA stainability (HDS). In addition, in vitro embryo development and sperm DNA methylation were assessed using semen samples from 16 of these bulls. The percentages of DFI and HDS were negatively associated with NR56 and cleavage rate and positively associated with sperm protamine deficiency (p < 0.05). Significant differences in cleavage and blastocyst rates were observed between bulls of high and low NR56. However, once fertilization occurred, further development into blastocysts was not associated with NR56. The differential methylation analysis showed that spermatozoa from bulls of low NR56 were hypermethylated compared to bulls of high NR56. Pathway analysis showed that genes annotated to differentially methylated cytosines could participate in different biological pathways and have important biological roles related to bull fertility. In conclusion, sperm cells from Norwegian Red bulls of inferior fertility have less compact chromatin structure, higher levels of DNA damage, and are hypermethylated compared with bulls of superior fertility.

Response variables that are scored as counts, for example, number of mastitis cases in dairy cattle, often arise in quantitative genetic analysis. When the number of zeros exceeds the amount expected such as under the Poisson density, the zero-inflated Poisson (ZIP) model is more appropri- ate. In using the ZIP model in animal breeding studies, it is necessary to accommodate genetic and environmental covariances. For that, this study proposes to model the mixture and Poisson parameters hierarchically, each as a function of two random effects, representing the genetic and environ- mental sources of variability, respectively. The genetic random effects are allowed to be correlated, leading to a correlation within and between clusters. The environmental effects are introduced by independent residual terms, ac- counting for overdispersion above that caused by extra-zeros. In addition, an inter correlation structure between random genetic effects affecting mix- ture and Poisson parameters is used to infer pleiotropy, an expression of the extent to which these parameters are influenced by common genes. The methods described here are illustrated with data on number of mastitis cases from Norwegian Red cows. Bayesian analysis yields posterior distributions useful for studying environmental and genetic variability, as well as genetic correlation.

An autosomal genome scan for quantitative trait loci (QTL) affecting twinning rate was carried out in the Norwegian Cattle population. Suggestive QTL were detected on Chromosomes (Chr) 5, 7, 12, and 23. Among these, the QTL positions on both Chr 5 and Chr 23 are strongly supported by literature in the field. Our results also confirm previous mapping of a QTL for twinning to Chr 7, but definitely suggest a different location of the QTL on this chromosome. The most convincing QTL peak was observed for a region in the middle part of Chr 5 close to the insulin-like growth factor 1 (IGF1) gene. Since IGF1 plays an important role in the regulation of folliculogenesis, a mutation search was performed by sequencing more than 3.5 kb of the gene in actual families. The sequencing revealed three polymorphisms in noncoding regions of the gene that will be important in fine structure mapping and characterization of the QTL.

The aim was to investigate whether subjectively scored milking speed, temperament, and leakage are genetically the same trait when measured in different milking systems. Data were provided by the Norwegian Dairy Herd Recording System and included a total of 260,731 first-parity Norwegian Red cows calving between January 2009 and February 2019 and milked either in a traditional milking system (milking parlor or pipeline) or by an automatic milking system (AMS). Genetic parameters were estimated and lower heritabilities and less genetic variation were found for the 3 traits when measured in AMS herds. The heritability of temperament, leakage, and milking speed were 0.05, 0.04, and 0.22, respectively, with data from AMS herds; and 0.09, 0.14, and 0.27, respectively, with data from cows milked in traditional milking systems. The genetic correlations between temperament and leakage (-0.19), between milking speed and leakage (-0.88), and between milking speed and temperament (0.30) in AMS were slightly stronger than between the corresponding traits assessed in other milking systems (-0.15, -0.82, and 0.16, respectively). The genetic correlations between traits across milking systems were strong: 0.98, 0.96, and 0.86 for milking speed, leakage, and temperament, respectively. Strong correlations indicate that the traits were almost genetically similar despite being scored in different milking systems. The rank correlations among estimated sire breeding values were strong: 0.98 and 0.99 for milking speed and leakage, with little or no reranking of bull performance across milking systems. Temperament had the lowest genetic correlation (0.86) and rank correlation (0.91) across milking systems. These data suggest that AMS farmers evaluate temperament slightly differently from farmers using other milking systems or that different aspects of temperament are important for farmers with AMS.

Abstract Background Oestrous synchronisation of cattle has been widely applied to accomplish simultaneous ovulation in animals and facilitate timed artificial insemination. The main aim of this study was to investigate the ovarian follicular growth and ovulatory response to oestrus and ovulation synchronisation in Norwegian Red heifers and cows. Oestrous cycles in 34 heifers and 10 cows from 4 herds were synchronised with two PGF2α analogue treatments 11 days apart, followed by GnRH analogue treatment for induction of ovulation. Thereafter, the ovaries were examined by ultrasonography at 3 h intervals until ovulation. Results The luteolytic effect of the PGF2α analogue was verified in 9 of 10 cows by progesterone contents in milk. Maximum physical activity of the cows occurred on average 69 h after PGF2α analogue treatment. An ovulatory response was recorded in 95.5% (42/44) of the animals. A significant difference in follicle size at ovulation was found between 2 of the herds. Animals with medium sized and large follicles and heifers aged > 16 months ovulated earlier than other animals. Conclusions The applied sequence of treatments in the study was shown to be effective in synchronizing and inducing ovulation within a relatively narrow time interval in the Norwegian Red heifers and cows, consistent with findings in other cattle breeds.

An extended lifespan of spermatozoa following artificial insemination (AI) can make the timing of insemination less critical, as previously demonstrated with immobilized spermatozoa that are gradually released from an alginate gel. The purpose was to examine the in vivo dissolution of SpermVital (SV) alginate gel over time by endoscopy and secondly to assess spermatozoa quality after incubation of the gel. In vivo endoscopy showed SV gel in the uterus 3, 6, 20 and 24 hr after AI, demonstrating the potential release of spermatozoa to the uterus during this period. In utero ex vivo incubation of the semen demonstrated that high motility and viability of sperm cells was sustained following overnight incubation.

The current study aimed to investigate new udder health traits based on data from automatic milking systems (AMS) for use in routine genetic evaluations. Data were from 77 commercial herds; out of these, 24 had equipment for measuring online cell count (OCC), whereas all had data on electrical conductivity (EC). A total of 4,714 Norwegian Red dairy cows and 2,363,928 milkings were included in the genetic analyses. Electrical conductivity was available on quarter level for each milking, whereas OCC was measured per milking. The AMS traits analyzed were log-transformed online cell count (lnOCC), maximum conductivity (ECmax), mean conductivity (ECmean), elevated mastitis risk (EMR), and log-transformed EMR (lnEMR). In addition, lactation mean somatic cell score (LSCS) was collected from the Norwegian dairy herd recording system. Elevated mastitis risk expresses the probability of a cow having mastitis and was calculated from smoothed lnOCC values according to individual trend and level of the OCC curve. The udder health traits from AMS were analyzed as repeated milkings from 30 to 320 DIM, and LSCS as repeated parities. In addition, both ECmax and lnOCC were analyzed as multiple traits by splitting the lactation into 5 periods. (Co)variance components were estimated from bivariate mixed linear animal models, and investigated traits showed genetic variation. Estimated heritabilities of ECmean, ECmax, and lnEMR were 0.35, 0.23, and 0.12, respectively, whereas EMR and lnOCC both showed heritabilities of 0.09. Heritability varied between periods of lactation, from 0.04 to 0.13 for lnOCC and from 0.12 to 0.27 for ECmax, although standard errors of certain periods were large. Genetic correlations among the AMS traits ranged from 0 to 0.99. The genetic correlations between EC-based traits and OCC-based traits in AMS were 0. Genetic correlations with LSCS were favorable, ranging from 0.37 to 0.80 (±0.11-0.22). The strongest correlation (0.80 ± 0.13) was found between LSCS and lnEMR. Results question the value of ECmax and ECmean as indicators of udder health in genetic evaluations and suggest OCC to be more valuable in this manner. This study demonstrates a potential of using AMS data as additional information on udder health for genetic evaluations, although further investigation is recommended before these traits can be implemented.

In the dairy breeding industry, prediction of bull fertility in artificial insemination (AI) is important for efficient and economically sustainable production. However, it is challenging to identify bulls with superior fertility applying conventional in vitro sperm assays. In the present study, sperm functionality was investigated to identify a multivariate model that could predict fertility. Two groups of young Norwegian Red bulls were selected, one with inferior fertility (18 bulls) and one with superior fertility (19 bulls) based on non-return rate after 56 days (NR56). Frozen-thawed semen doses were analysed for sperm chromatin integrity, viability, acrosome integrity, motility, and ATP content. A targeted approach was used to study intracellular concentrations of amino acids and trace elements in viable sperm cells. Significant differences between the two groups of bulls were observed, both for sperm functional attributes and intracellular concentrations of metabolites. Pearson correlation analyses indicated a negative relationship between NR56 and chromatin integrity parameters, DNA fragmentation index (DFI) and high DNA stainability (HDS). Several motility parameters correlated positively with NR56. The concentrations of cysteine and glutamic acid in sperm cells correlated negatively with NR56, while the concentrations of aspartic acid, leucine and serine showed a positive NR56-correlation. The sperm intracellular concentrations of the trace elements Fe, Al and Zn, correlated negatively with NR56. Correlations were observed between several sperm parameters and metabolites. Stepwise multiple regression analysis indicated that the best predictor of NR56 was a model containing %DFI, together with the intracellular sperm concentration of aspartic acid, Fe and Zn. This model explained 59% of the variability in NR56.

The aim of this study was to estimate genetic parameters and genetic trends for male fertility in Norwegian Red bulls. We analyzed data on semen characteristics traits collected at the performance test station of young bulls from 1994 to 2016, in an andrology test used to ensure acceptable semen quality before being selected as an artificial insemination bull. Traits included were volume, concentration, and motility (percentage of moving sperm cells) in fresh samples and after storing for 24 and 48 h, and sperm defects. The data consisted of 14,972 ejaculates from 3,927 young (11-15 mo) Norwegian Red bulls. Genetic parameters were estimated using bivariate linear animal models that included age in months, group-year, and collection-group (main effect of the interaction between ejaculate number and interval between collections) as fixed effects, and test-day and additive genetic and permanent environment effect of the bull as random effects. Considerable genetic coefficients of variation were found for concentration and volume, with lower values for motility. Estimated heritabilities ranged from 0.02 and 0.03 (for sperm defects and motility in fresh samples) to 0.14 (volume and concentration measured on a continuous scale). All estimated genetic correlations were favorable, but the genetic correlations between volume and concentration and volume and sperm defects were not significantly different from zero. The genetic correlations between concentration and motility traits ranged from 0.53 to 0.83, and those between volume and the motility traits were between 0.24 and 0.57. All traits showed a slightly unfavorable genetic trend. Our results indicate that selection of bulls with better sperm quality is possible.

The aim of this study was to identify genes associated with chronic subclinical mastitis (SCM) in Norwegian Red (NR) cattle. Twelve SCM traits defined based on fixed threshold for test-day somatic cell count (SCC) were, together with lactation-average somatic cell score (LSCS) used for association and pathway enrichment analyses. A GWAS was performed on 3795 genotyped NR bulls with 777K SNP data and phenotypic information from 7 300 847 test-day SCC observations from 3 543 764 cows. At 5% chromosome-wide significance level 36 unique SNP were detected to be associated with one or more of the traits. These SNPs were analysed for linked genes using genomic positions of topologically associated domains (TAD). For the SCM traits with SCC >50 000 and >100 000 cells/ml on two test-days in a row and LSCS, the same top significant genes were identified - checkpoint clamp loader component (RAD17) and cyclin B1 (CCNB1). The SCM traits with SCC >250 000, 300 000, 350 000 or 400 000 cells/ml on two test-days in a row and D400 (number of days before the first case with SCC >400 000 cells/ml) displayed similar top significant genes: acyl-CoA thioesterase 2 and 4 (ACOT2; ACOT4). For the traits SCM200_3 (SCC >200 000 cells/ml on three test-days in a row) and SCM150, SCM200 (SCC >150 000; 200 000 cells/ml on two test-days in a row) a group of chemokine (C-X-C motif) ligand genes and the Fos proto-oncogene, AP-1 transcription factor subunit (FOS) gene, were identified. Further functional studies of these identified candidate genes are necessary to clarify their actual role in development of chronic SCM in NR cattle.

Macrophages are key cells of innate immune response and serve as the first line of defense against bacteria. Transcription profiling of bacteria-infected macrophages could provide important insights on the pathogenicity and host defense mechanisms during infection. We have examined transcription profiles of bovine monocyte-derived macrophages (bMDMs) isolated from the blood of 12 animals and infected in vitro with two strains of Streptococcus agalactiae. Illumina sequencing of RNA from 36 bMDMs cultures exposed in vitro to either one of two sequence types of S. agalactiae (ST103 or ST12) for 6 h and unchallenged controls was performed. Analyses of over 1,656 million high-quality paired-end sequence reads revealed 5,936 and 6,443 differentially expressed genes (p < 0.05) in bMDMs infected with ST103 and ST12, respectively, versus unchallenged controls. Moreover, 588 genes differentially expressed between bMDMs infected with ST103 versus ST12 were identified. Ingenuity pathway analysis of the differentially up-regulated genes in the bMDMs infected with ST103 revealed significant enrichment for granulocyte adhesion and diapedesis, while significant enrichment for the phagosome formation pathway was found among down-regulated genes. Moreover, Ingenuity pathway analysis of the differentially up-regulated genes in the bMDMs infected with ST12 showed significant enrichment for type 1/type 2 T helper cell activation, while the complement activation pathway was overrepresented in the down-regulated genes. Our study identified pathogen-induced regulation of key genes and pathways involved in the immune response of macrophages against infection but also likely involved in bacterial evasion of the host immune system. These results may contribute to better understanding of the mechanisms underlying subclinical infection such as bovine streptococcal mastitis.