The objective of this study was to evaluate the progression of the uterine microbiota from calving until establishment of metritis. Uterine swabs (n= 72) collected at 0, 2, and 6 ± 2 days postpartum (dpp) from 12 metritic and 12 healthy cows were used for metagenomic sequencing of the 16S rRNA gene on the Illumina MiSeq platform. A heat map showed that uterine microbiota was established at calving. The microbiota changed rapidly from 0 to 6 ± 2 dpp, with a decrease in the abundance ofProteobacteriaand an increase in the abundance ofBacteroidetesandFusobacteria, which were dominant in metritic cows. Uterine microbiota composition was shared; however, metritic and healthy cows could be discriminated using relative abundance of bacterial genera at 0, 2, and 6 ± 2 dpp.Bacteroideswas the main genus associated with metritis because it was the only genus that showed significantly greater abundance in cows with metritis. As the abundance ofBacteroidesorganisms increased, the uterine discharge score, a measure of uterine health, worsened.Fusobacteriumwas also an important genus associated with metritis becauseFusobacteriumabundance increased asBacteroidesabundance increased and the uterine discharge score worsened as the abundance increased. The correlation with uterine discharge score and the correlation withBacteroidesorFusobacteriumshowed that other bacteria, such asHelcoccocus,Filifactor, andPorphyromonas, were also associated with metritis. There were also bacteria associated with uterine health, such as “CandidatusBlochmannia,”Escherichia,Sneathia, andPedobacter.

Until 2010, our knowledge of the uterine microbiome in cows that developed uterine disease relied almost exclusively on culture-dependent studies and mostly included cows with clinical endometritis (i.e., with purulent uterine discharge). Those studies consistently found a strong positive correlation between Trueperella pyogenes and clinical endometritis, whereas other pathogens such as Escherichia coli, Fusobacterium necrophorum, Prevotella melaninogenica, and Bacteroides spp. were also commonly cocultured. In contrast, Streptococcus spp., Staphylococcus spp., and Bacillus spp. were usually isolated from healthy cows. Starting in 2010, culture-independent studies using PCR explored the microbiome of cows with metritis and clinical endometritis, and observed that E. coli was a pioneer pathogen that predisposed cows to infection with F. necrophorum, which was strongly associated with metritis, and to infection with T. pyogenes, which was strongly associated with clinical endometritis. Starting in 2011, culture-independent studies using metagenomic sequencing expanded our knowledge of the uterine microbiome. It has been shown that cows have bacteria in the uterus even before calving, they have an established uterine microbiome within 20 min of calving, and that the microbiome structure is identical between cows that develop metritis and healthy cows until 2 d postpartum, after which the bacterial structure of cows that developed metritis deviates in favor of greater relative abundance of Bacteroidetes and Fusobacteria and lesser relative abundance of Proteobacteria and Tenericutes. The shift in the uterine microbiome in cows that develop metritis is characterized by a loss of heterogeneity and a decrease in bacterial richness. At the genus level, Bacteroides, Porphyromonas, and Fusobacterium have the strongest association with metritis. At the species level, we observed that Bacteroides pyogenes, Porphyromonas levii, and Helcococcus ovis were potential emerging uterine pathogens. Finally, we have shown that the hematogenous route is a viable route of uterine infection with uterine pathogens. Herein, we propose that metritis is associated with a dysbiosis of the uterine microbiota characterized by decreased richness, and an increase in Bacteroidetes and Fusobacteria, particularly Bacteroides, Porphyromonas, and Fusobacterium.

Metritis is an inflammatory disease of the uterus caused by bacterial infection, particularly Bacteroides, Porphyromonas, and Fusobacterium. Bacteria from the environment, feces, or vagina are believed to be the only sources of uterine contamination. Blood seeps into the uterus after calving; therefore, we hypothesized that blood could also be a seeding source of uterine bacteria. Herein, we compared bacterial communities from blood, feces, and uterine samples from the same cows at 0 and 2 days postpartum using deep sequencing and qPCR. The vaginal microbiome 7 days before calving was also compared. There was a unique structure of bacterial communities by sample type. Principal coordinate analysis revealed two distinct clusters for blood and feces, whereas vaginal and uterine bacterial communities were more scattered, indicating greater variability. Cluster analysis indicated that uterine bacterial communities were more similar to fecal bacterial communities than vaginal and blood bacterial communities. Nonetheless, there were core genera shared by all blood, feces, vaginal, and uterine samples. Major uterine pathogens such as Bacteroides, Porphyromonas, and Fusobacterium were part of the core genera in blood, feces, and vagina. Other uterine pathogens such as Prevotella and Helcococcus were not part of the core genera in vaginal samples. In addition, uterine pathogens showed a strong and significant interaction with each other in the network of blood microbiota, but not in feces or vagina. These microbial interactions in blood may be an important component of disease etiology. The copy number of total bacteria in blood and uterus was correlated; the same did not occur in other sites. Bacteroides heparinolyticus was more abundant in the uterus on day 0, and both B. heparinolyticus and Fusobacterium necrophorum were more abundant in the uterus than in the blood and feces on day 2. This indicates that B. heparinolyticus has a tropism for the uterus, whereas both pathogens thrive in the uterine environment early postpartum. Blood harbored a unique microbiome that contained the main uterine pathogens such as Bacteroides, Porphyromonas, and Fusobacterium. The presence of these pathogens in blood shortly after calving shows the feasibility of hematogenous spread of uterine pathogens in cows.

The objective of this study was to compare the economic outcome of reproductive programs using estrus detection (ED), timed artificial insemination (TAI), or a combination of both (TAI-ED) using a stochastic dynamic Monte-Carlo simulation model. Programs evaluated were (1) ED only; (2) TAI: Presynch-Ovsynch for first AI, and Ovsynch for resynchronization of open cows at 32 d after AI; (3) TAI-ED: Presynch-Ovsynch for first AI, but cows underwent ED and AI after first AI, and cows diagnosed open 32 d after AI were resynchronized using Ovsynch. Evaluated were the effect of ED rate (40 vs. 60%; ED40 or ED60), accuracy of estrus detection (85 vs. 95%), compliance with the timed AI protocol (85 vs. 95%), and milk price ($0.33 vs. 0.44/kg). Conception rate to first service was set at 33.9% and then decreased by 2.6% for every subsequent service. Abortion was set at 11.3%. Cows were not AI after 366 d in milk, and open cows were culled after 450 d in milk. Culled cows were immediately replaced. Herd size was maintained at 1,000 cows, and the model accounted for all incomes and costs. Simulation was performed until steady state was reached (3,000 d), and then average daily values for the subsequent 2,000 d were used to calculate profit/cow per year. Net daily value was calculated by subtracting the costs (replacement, feeding, breeding, and other costs) from the daily income (milk sales, cow sales, and calf sales). The ED40 models resulted in greater profits than the TAI-85 model but lower profits than the TAI-95 model. Both ED60 models resulted in greater profits than the TAI-95 model. Combining TAI and ED increased profits within each level of accuracy or compliance. Adding TAI to ED would increase overall profit/cow per year by $46.8 to $74.7 with 40% ED, and by $8.9 to $30.5 with 60% ED. Adding ED to TAI would increase profit/cow per year by $64.2 to $99.4 with 85% compliance and by $31.8 to $59.7 with 95% compliance. Although combining TAI and ED increased profits within each level of accuracy or compliance, when evaluated separately, ED60 with 95% accuracy or TAI with 95% compliance were as profitable as or more profitable than TAI-ED with low ED, accuracy, or compliance. Therefore, producers can improve their profits by combining TAI and ED as reproductive management; however, if a herd can achieve high ED with high accuracy or have high compliance with injections, using only ED or TAI might be more profitable than trying to do both.

The objective of this study was to estimate the cost of metritis in dairy herds. Data from 11,733 dairy cows from 16 different farms located in 4 different regions of the United States were compiled for up to 305 d in milk, and 11,581 cows (2,907 with and 8,674 without metritis) were used for this study. Metritis was defined as fetid, watery, red-brownish vaginal discharge that occurs ≤21 d in milk. Continuous outcomes such as 305-d milk production, milk sales ($/cow), cow sales ($/cow), metritis treatment costs ($/cow), replacement costs ($/cow), reproduction costs ($/cow), feeding costs ($/cow), and gross profit per cow ($/cow) were analyzed using mixed effect models using the MIXED procedure of SAS (SAS Institute Inc., Cary, NC). Gross profit was also compared using the Kruskal-Wallis test. Dichotomous outcomes such as pregnant and culling by 305 d in milk were analyzed using the GLIMMIX procedure of SAS. Time to pregnancy and culling were analyzed using the PHREG procedure of SAS. Models included the fixed effects of metritis, parity, and the interaction between metritis and parity, and farm as the random effect. Variables were considered significant when P ≤ 0.05. Metritis cost was calculated by subtracting the gross profit of cows with metritis from the gross profit of cows without metritis. A stochastic analysis was performed with 10,000 iterations using the observed results from each group. Milk yield and proportion of cows pregnant were lesser for cows with metritis than for cows without metritis, whereas the proportion of cows leaving the herd was greater for cows with metritis than for cows without metritis. Milk sales, feeding costs, residual cow value, and gross profit were lesser for cows with metritis than for cows without metritis. Cow sales and replacement costs were greater for cows with metritis than for cows without metritis. The mean cost of metritis from the study herds was $511 and the median was $398. The stochastic analysis showed that the mean cost of a case of metritis was $513, with 95% of the scenarios ranging from $240 to $884, and that milk price, treatment cost, replacement cost, and feed cost explained 59%, 19%, 12%, and 7%, respectively, of the total variation in cash flow differences. In conclusion, metritis caused large economic losses to dairy herds by decreasing milk production, reproduction, and survival in the herd.