Puberty classifications in beef heifers are moderately to highly heritable and associated with candidate genes related to cyclicity and timing of puberty.

Introduction: Pubertal attainment is critical to reproductive longevity in heifers. Previously, four heifer pubertal classifications were identified according to attainment of blood plasma progesterone concentrations > 1 ng/ml: 1) Early; 2) Typical; 3) Start-Stop; and 4) Non-Cycling. Early and Typical heifers initiated and maintained cyclicity, Start-Stop started and then stopped cyclicity and Non-Cycling never initiated cyclicity. Start-Stop heifers segregated into Start-Stop-Discontinuous (SSD) or Start-Stop-Start (SSS), with SSD having similar phenotypes to Non-Cycling and SSS to Typical heifers. We hypothesized that these pubertal classifications are heritable, and loci associated with pubertal classifications could be identified by genome wide association studies (GWAS). Methods: Heifers (n = 532; 2017 - 2022) genotyped on the Illumina Bovine SNP50 v2 or GGP Bovine 100K SNP panels were used for variant component estimation and GWAS. Heritability was estimated using a univariate Bayesian animal model. Results: When considering pubertal classifications: Early, Typical, SSS, SSD, and Non-Cycling, pubertal class was moderately heritable (0.38 ± 0.08). However, when heifers who initiated and maintained cyclicity were compared to those that did not cycle (Early+Typical vs. SSD+Non-Cycling) heritability was greater (0.59 ± 0.19). A GWAS did not identify single nucleotide polymorphisms (SNPs) significantly associated with pubertal classifications, indicating puberty is a polygenic trait. A candidate gene approach was used, which fitted SNPs within or nearby a set of 71 candidate genes previously associated with puberty, PCOS, cyclicity, regulation of hormone secretion, signal transduction, and methylation. Eight genes/regions were associated with pubertal classifications, and twenty-two genes/regions were associated with whether puberty was attained during the trial. Additionally, whole genome sequencing (WGS) data on 33 heifers were aligned to the reference genome (ARS-UCD1.2) to identify variants in FSHR, a gene critical to pubertal attainment. Fisher's exact test determined if FSHR SNPs segregated by pubertal classification. Two FSHR SNPs that were not on the bovine SNP panel were selected for additional genotyping and analysis, and one was associated with pubertal classifications and whether they cycled during the trial. Discussion: In summary, these pubertal classifications are moderately to highly heritable and polygenic. Consequently, genomic tools to inform selection/management of replacement heifers would be useful if informed by SNPs associated with cyclicity and early pubertal attainment.

Metabolic trajectories of diabetic ketoacidosis onset described by breath analysis.

Purpose: This feasibility study aimed to investigate the use of exhaled breath analysis to capture and quantify relative changes of metabolites during resolution of acute diabetic ketoacidosis under insulin and rehydration therapy. Methods: Breath analysis was conducted on 30 patients of which 5 with DKA. They inflated Nalophan bags, and their metabolic content was subsequently interrogated by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS). Results: SESI-HRMS analysis showed that acetone, pyruvate, and acetoacetate, which are well known to be altered in DKA, were readily detectable in breath of participants with DKA. In addition, a total of 665 mass spectral features were found to significantly correlate with base excess and prompt metabolic trajectories toward an in-control state as they progress toward homeostasis. Conclusion: This study provides proof-of-principle for using exhaled breath analysis in a real ICU setting for DKA monitoring. This non-invasive new technology provides new insights and a more comprehensive overview of the effect of insulin and rehydration during DKA treatment.

Characterization of the Ruminal Microbiome of Water Buffaloes (Bubalus bubalis) Kept in Different Ecosystems in the Eastern Amazon.

Increasing the efficiency of rumen fermentation is one of the main ways to maximize the production of ruminants. It is therefore important to understand the ruminal microbiome, as well as environmental influences on that community. However, there are no studies that describe the ruminal microbiota in buffaloes in the Amazon. The objective of this study was to characterize the rumen microbiome of the water buffalo (Bubalus bubalis) in the eastern Amazon in the dry and rainy seasons in three grazing ecosystems: Baixo Amazonas (BA), Continente do Pará (CP), Ilha do Marajó (IM), and in a confinement system: Tomé-Açu (TA). Seventy-one crossbred male buffaloes (Murrah × Mediterranean) were used, aged between 24 and 36 months, with an average weight of 432 kg in the rainy season and 409 kg in the dry season, and fed on native or cultivated pastures. In the confinement system, the feed consisted of sorghum silage, soybean meal, wet sorghum premix, and commercial feed. Samples of the diet from each ecosystem were collected for bromatological analysis. The collections of ruminal content were carried out in slaughterhouses, with the rumen completely emptied and homogenized, the solid and liquid fractions separated, and the ruminal pH measured. DNA was extracted from the rumen samples, then sequenced using Restriction Enzyme Reduced Representation Sequencing. The taxonomic composition was largely similar between ecosystems. All 61 genera in the reference database were recognized, including members of the domains Bacteria and Archaea. The abundance of 23 bacterial genera differed significantly (p < 0.01) between the Tomé-Açu confinement and other ecosystems. Bacillus, Ruminococcus, and Bacteroides had lower abundance in samples from the Tomé-Açu system. Among the Archaea, the genus Methanomicrobium was less abundant in Tomé-Açu, while Methanosarcina was more abundant. There was a difference caused by all evaluated factors, but the diet (available or offered) was what most influenced the ruminal microbiota.