Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.

Greater blood concentrations of nonesterified fatty acids (NEFA) and lesser blood concentrations of glucose are indicative of the normal process of nutrient partitioning that occurs in early postpartum dairy cows. The objective was to determine the relationship between blood NEFA and glucose concentrations and subsequent conception at first insemination in postpartum dairy cows. Holstein (n=148) and Guernsey (n=8) dairy cows were blood sampled at approximately d 10, 7, and 3 prepartum, on the day of calving and 3, 7, 14, and 21 d postpartum for measurement of NEFA and glucose concentrations. Serum and plasma were harvested and used for measurement of NEFA and glucose concentrations, respectively. Cows were given a presynchronization treatment (2 injections of PGF(2α) 14 d apart) with the second PGF(2α) injection occurring 14 d before the initiation of the timed AI (TAI) protocol. Blood for determination of progesterone concentrations was collected at each presynchronization injection and at the initiation of the TAI protocol that was used for first insemination (74±7 d postpartum). Cows were considered noncycling if serum progesterone concentrations at the 2 presynchronization PGF(2α) injections (d 37 and 51±7 postpartum) and at the initiation of the TAI protocol (d 65±7 postpartum) were ≤1 ng/mL, and there was no indication of ovulation or presence of a corpus luteum by ultrasound examination at the initiation of the TAI protocol. Pregnancy was determined at 33 d and again at 61 d after first insemination by using ultrasound. Across all days, serum NEFA and plasma glucose concentrations were not different between cows that ovulated before the initiation of the TAI program (cycling) compared with those that did not ovulate (noncycling). Serum NEFA concentrations, however, were less and plasma glucose concentrations were greater during the early postpartum period for cows that subsequently became pregnant at first insemination compared with those that failed to become pregnant. Logistic regressions were used to predict the probability of pregnancy based on NEFA and glucose concentrations from individual days. The prediction with the greatest likelihood ratio was for d 3 postpartum NEFA and glucose concentrations. Nutritional status during the early postpartum period (within 1 wk after calving), as indicated by blood NEFA and glucose concentrations, may affect subsequent fertility by a mechanism that is independent from interval to first ovulation.

By mapping translated metagenomic reads to a microbial metabolic network, we show that ruminal ecosystems that are rather dissimilar in their taxonomy can be considerably more similar at the metabolic network level. Using a new network bi-partition approach for linking the microbial network to a bovine metabolic network, we observe that these ruminal metabolic networks exhibit properties consistent with distinct metabolic communities producing similar outputs from common inputs. For instance, the closer in network space that a microbial reaction is to a reaction found in the host, the lower will be the variability of its enzyme copy number across hosts. Similarly, these microbial enzymes that are nearby to host nodes are also higher in copy number than are more distant enzymes. Collectively, these results demonstrate a widely expected pattern that, to our knowledge, has not been explicitly demonstrated in microbial communities: namely that there can exist different community metabolic networks that have the same metabolic inputs and outputs but differ in their internal structure.

Genetic and environmental variances and covariances and associated genetic parameters were estimated for weaning weight, asymptotic mature weight, and repeated mature weights. Data consisted of a set of weight measurements of 3,044 Angus cows born between 1976 and 1990. Mature weight was predicted by individually fitting Brody growth curves (asymptotic weight) and by using weights repeatedly measured after 4 yr of age. Variance and covariance components for mature weight were estimated by REML from a single-trait animal model with asymptotic weight, a two-trait animal model with asymptotic and weaning weight, and a two-trait animal model with repeated weights and weaning weight. Weaning and cow contemporary groups were defined as fixed effects. Random effects for weaning weight included direct genetic, maternal genetic, and permanent environmental effects; and for mature weight, direct genetic and repeated measurements (if in the model). Heritability estimates for weaning weight were similar for both two-trait models (.53 and .59). Estimates of heritability for mature weight were .44, .52, and .53 for the single-trait model with asymptotic weight, two-trait model with asymptotic weight, and two-trait model with repeated measures weights, respectively. The estimate of the genetic correlation between mature and weaning weight was higher for the repeated measures model (.85 vs. .63). A lower heritability estimate for mature weight from the single-trait model was likely due to postweaning culling. Therefore, a genetic evaluation of mature weight from field data should include a trait recorded earlier in life that is less subjected to selective data reporting.

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