PSVI-15 Influences on bacterial diversity of the beef cow uterine microbiome.

Abstract In the beef cattle industry, over $1 billion is lost annually due to reproductive failure during early gestation. Shifts in bacterial communities have been associated with endogenous estradiol concentrations and pregnancy status. However, quantification of factors that influence bacterial diversity of the beef cow uterine microbiome has yet to be evaluated. Our objective was to determine the influence of hormone treatment, day, pregnancy status, and sample location on bacterial diversity of the beef cow uterine microbiome. Crossbred beef cows (n=62) were synchronized utilizing the 7-day CO-Synch + CIDR protocol. Two days post-CIDR removal (day [d] 0), cows were allocated to one of three treatment groups: 1) Estrus Expression (ESTRUS), 2) No Estrus + induced ovulation with gonadotropin releasing hormone (GnRH; GNRH), 3) No Estrus + induced ovulation with GnRH and 0.1 mg estradiol 17β (GNRH+E2). All cows were artificially inseminated on d0 with semen from a single sire. A modified version of the IDEXX Bovine Pregnancy ELISA detected pregnancy associated glycoproteins for pregnancy diagnosis (NONPREG=nonpregnant; PREG=pregnant). Cows were ovariohysterectomized at harvest on d20, d26, and d32. An anti-mesometrial incision was made in each uterine horn (ipsilateral [IPS] and contralateral [CON]) and sterile swabs were rotated along the endometrial mucosa, placed in sterile microcentrifuge tubes, and stored at -80°C. Microbiome analysis targeted the V4 hypervariable region of the 16S rRNA bacterial gene. Sequence quality was assessed utilizing DADA2, and taxon analysis was processed in the Qiime2 pipeline with resulting files exported for diversity analysis using the R package ‘phyloseq’. Alpha diversity was determined with Wilcoxon or Kruskal-Wallis tests, and beta diversity was determined using PERMANOVA. Alpha diversity analysis included observed OTUs and Chao1 index (within sample richness), and Shannon and Simpson indices (within sample richness and evenness). Beta diversity matrices included unweighted UniFrac (between sample bacterial presence or absence) and weighted UniFrac (between sample bacterial presence or absence and abundance). Diversity within samples was different for all alpha diversity matrices compared between days (P≤0.05). Specifically, observed OTUs and Chao1 index indicated greater diversity within samples on d32 compared with d26 (P=0.01). Shannon and Simpson indices indicated greater diversity within samples for both d32 and d20 compared to d26 (P≤0.001). There were differences in diversity between samples for both unweighted and weighted Unifrac matrices compared between days (P < 0.01). Specifically, there was a difference in unweighted Unifrac between d26 and d32 (P < 0.01). However, there were no differences for all alpha and beta diversity matrices between treatments, pregnancy status, and uterine horns (P>0.05). Thus, bacterial diversity of the beef cow uterine microbiome appears to shift by day. Additional studies are needed to elucidate the, as yet, undetermined mechanisms that cause time-dependent alterations in bacterial diversity within the uterine microbiome.