Integrating uterine microbiome and metabolome to advance the understanding of the uterine environment in dairy cows with metritis

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Casaro, J. G. Prim, T. D. Gonzalez, F. Cunha, R. S. Bisinotto, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3897972/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Metritis is a prevalent uterine disease that affects the welfare, fertility, and survival of dairy cows. The uterine microbiome from cows that develop metritis and those that remain healthy do not differ from calving until 2 days postpartum, after which there is a dysbiosis of the uterine microbiome characterized by a shift towards opportunistic pathogens such as Fusobacteriota and Bacteroidota. Whether these opportunistic pathogens proliferate and overtake the uterine commensals could be determined by the type of substrates present in the uterus. The objective of this study was to integrate uterine microbiome and metabolome data to advance the understanding of the uterine environment in dairy cows that develop metritis. Holstein cows (n = 104) had uterine fluid collected at calving and at the day of metritis diagnosis. Cows with metritis (n = 52) were paired with cows without metritis (n = 52) based on days after calving. First, the uterine microbiome and metabolome were evaluated individually, and then integrated using network analyses. Results The uterine microbiome did not differ at calving but differed on the day of metritis diagnosis between cows with and without metritis. The uterine metabolome differed both at calving and on the day of metritis diagnosis between cows that did and did not develop metritis. Omics integration was performed between 6 significant bacteria genera and 153 significant metabolites on the day of metritis diagnosis. Integration was not performed at calving because there were no significant differences in the uterine microbiome. A total of 3 bacteria genera (i.e. Fusobacterium, Porphyromonas , and Bacteroides ) were strongly correlated with 49 metabolites on the day of metritis diagnosis. Seven of the significant metabolites at calving were among the 49 metabolites strongly correlated with opportunistic pathogenic bacteria on the day of metritis diagnosis. The main metabolites have been associated with attenuation of biofilm formation by commensal bacteria, opportunistic pathogenic bacteria overgrowth, tissue damage and inflammation, immune evasion, and immune dysregulation. Conclusions The data integration presented herein helps advance the understanding of the uterine environment in dairy cows with metritis. The identified metabolites may provide a competitive advantage to the main uterine pathogens Fusobacterium, Porphyromonas and Bacteroides , and may be promising targets for future interventions aiming to reduce opportunistic pathogenic bacteria growth in the uterus. microbiome metabolome multi-omics metritis uterine disease dairy cows Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Metritis affects around 25% of Holstein cows shortly after calving [ 1 ], impacting production, reproduction, culling, and welfare [ 2 – 4 ]. Metritis is characterized by a dysbiosis of the uterine microbiome in which opportunistic pathogenic bacteria such as Fusobacterium , Porphyromonas , and Bacteroides overtake the uterine commensals [ 5 – 7 ]. Interestingly, the uterine microbiome from cows that develop metritis and those that remain healthy do not differ from calving until 2 days postpartum [ 6 ]. Therefore, whether opportunistic pathogenic bacteria proliferate and overtake the uterine commensals could be determined by the type of substrates present inside the uterus shortly after calving. For instance, greater abundance of lactic acid has been identified in the uterus of cows with metritis when compared with cows without metritis [ 8 ]. Lactic acid is one of the main substrates for Fusobacterium necrophorum growth [ 9 ]. Lactic acid utilizing bacteria such as Fusobacterium necrophorum can also metabolize the amino acid tryptophan into indole and its derivatives, which can serve as bacterial signaling molecules, effectively regulating virulence, biofilm formation, motility, and sporulation, thereby inhibiting the growth of commensal bacteria [ 10 , 11 ]. The indole derivative, indole-3-acetate has been shown to be greater in the uterus from cows that develop metritis when compared with cows without metritis [ 8 ]. Therefore, it is possible that the uterine metabolome may promote the growth of opportunistic pathogenic bacteria, which consequently may inhibit the growth of uterine commensals leading to uterine dysbiosis and metritis development. Although previous research focused on the difference in uterine microbiome [ 5 , 6 ] or metabolome [ 8 ] between cows that developed metritis and cows that did not, little is known about the microbiome-metabolome interactions in the uterus of cows that develop metritis. Therefore, the hypothesis of the current study was that specific uterine metabolites are associated with specific bacteria genera involved in the development of metritis. Hence, the objectives of the study were to compare the uterine microbiome and metabolome at calving and on the day of metritis diagnosis between cows that developed metritis and cows that did not and integrate both omics datasets to advance the understanding of the uterine environment in dairy cows with metritis. Methods This case-control observational study was conducted at the University of Florida Dairy Unit from September 2019 to March 2020. The cows used for this study were a subset of cows used in a previous study [ 12 ]. Analyses of uterine microbiome [ 6 ] and serum [ 8 , 13 ] metabolome using principal component analysis, principal coordinate analysis (PCoA), and partial least square discriminant analysis (PLS-DA) encompassing 12 to 24 cows per group were able to depict statistical differences; therefore, the inclusion of a larger number of cows per group (n = 52) was expected to ensure sufficient power for the characterization of changes in the uterine microbiome and metabolome associated with metritis in the current study. Cows, Housing, and Feeding A total of 128 Holstein cows, consisting of 71 primigravid and 57 multigravid cows, were included in the study which started at 260 days of gestation and ended at 13 ± 1 days after calving. Throughout the study period, primigravid and multigravid cows were housed in separate naturally ventilated barns with sand-bedded free-stalls. In the prepartum phase, multigravid cows were provided with a total mixed ration (TMR) twice daily formulated to either meet or exceed the nutritional requirements recommended for dry Holstein cows weighing 680 kg [ 14 ]. After calving, multiparous cows were fed a TMR formulated to meet or exceed the nutrient requirements for lactating Holstein cows weighing 680 kg and producing 45 kg of 3.5% fat-corrected milk [ 14 ] twice daily. Nulliparous cows were housed in a separate free-stall barn with individual feeding gates (Calan Broadbent Feeding System, American Calan Inc., Northwood, NH) starting at 241 d of gestation and were fed a TMR once daily. After parturition, primiparous cows were relocated to a postpartum pen, also equipped with individual feeding gates, and each cow was assigned to a specific gate until reaching 100 days after calving. Throughout the postpartum period, all cows were milked twice daily at 0600 and 1800 hours. The rolling herd average milk yield was approximately 11,000 kg during the course of the study. Case Definition and Diagnosis Metritis was diagnosed by examination of the uterine discharge with a Metricheck device (Metricheck, Simcro, New Zealand) at 3 ± 1, 7 ± 1, 10 ± 1 and 13 ± 1 days after calving using a 5-point scale as previously described [ 5 ]: 1 = not fetid normal lochia, viscous, clear, red, or brown; 2 = cloudy, pink, red, or brown mucoid discharge with flecks of pus; 3 = not fetid, pink red or brown mucopurulent discharge with < 50% pus; 4 = not fetid, pink, red or brown purulent discharge with ≥ 50% pus; 5 = fetid red-brownish, watery discharge. Cows with a discharge score ≤ 4 were classified as healthy and cows with a score of 5 in at least one examination were classified as having metritis. Incidences of mastitis, digestive problems, respiratory disease, and antimicrobial treatments in the first 35 days after calving were also recorded for individual cows, and cows with any of these diseases, cows submitted to antimicrobial treatment before metritis diagnosis, and cows diagnosed with metritis after 10 days after calving were excluded from the study. A total of 13 cows were excluded. Four cows were excluded because they were treated with antimicrobials before metritis diagnosis. Three cows were excluded because of death. One cow was excluded because of uterine torsion and one cow was excluded because of peritonitis. Four cows were excluded because they were diagnosed with metritis at 13 ± 1 days after calving; therefore, could not be paired to a healthy counterpart. A total of 52 cows with metritis paired with 52 cows without metritis were used for bioinformatic and statistical analyses. Uterine Fluid Collection All cows had uterine fluid collected at calving (first 24 hours after calving), and at diagnosis of metritis. Briefly, the cow’s cervix was stabilized by rectal palpation, the vulva was rinsed with alcohol 70% (vol/vol) and dried with paper towels. Subsequently, a single-use plastic round-tip pipette (UterFlush pipettes, Van Beek) was introduced into the vagina at a 45° angle and manipulated through the cervix. A total of 50 mL of sterile saline solution (0.9% sodium chloride irrigation, Baxter) was infused into the uterine lumen using a 60-mL syringe (Covidien) attached to the end of the pipette. Uterine contents were homogenized, retrieved into the same 60-mL syringe, and transferred to a sterile 15-mL conical tube (VWR). After collection, tubes were placed on ice and transported to the laboratory within 2 hours. Once in the laboratory, uterine fluid samples were aliquoted into 2-mL microcentrifuge tubes (Eppendorf) and stored at -80 o C until essayed. Microbiome Analysis One frozen uterine fluid aliquot was submitted to FERA Diagnostics and Biologicals Corporate in College Station, Texas for microbiome analysis. Samples were analyzed by technicians blinded to study groups. DNA extraction was performed using a Mag-Bind Universal Pathogen 96 Kit (Omega Bio-Tek, Norcross, GA) in accordance with manufacturer instructions. The 16S rRNA gene was amplified by PCR. Amplification of the V4 hypervariable region of the bacterial/archaeal 16S rRNA gene was performed as previously described [ 15 ] using the Illumina MiSeq platform (Illumina Inc.). Description of PCR and thermocycler conditions are available in https://earthmicrobiome.org/protocols-and-standards/16s/ . After DNA amplification, electrophoresis using 1.2% (wt/vol) agarose gels stained with 0.5 mg/mL ethidium bromide was used to verify amplicon presence and size. DNA purification was carried out using magnetic beads Mag-Bind TotalPure NGS (Omega Bio-Tek, Norcross, GA) in accordance with manufacturer instructions. Samples were standardized to the same concentration and pooled into a run for library preparation and sequencing, which was performed using the MiSeq Reagent Kit v2 (300 cycles) on the MiSeq platform (Illumina Inc.). Non-biological nucleotides were removed, and raw sequenced amplicons were analyzed using the DADA2 package of RStudio Version 2023.06.1 + 524 (RStudio, PBC, Boston, MA) following the DADA2 Pipeline Tutorial ( https://benjjneb.github.io/dada2/tutorial.html ). After filtering and trimming, the amplicon sequence variant (ASV) table was constructed. Then, chimeric reads were removed, and the number of reads were standardized to the median read number of all the samples. Taxonomy was assigned to ASV using the Greengenes database ( http://greengenes.lbl.gov ). Total Bacteria 16S rRNA Gene Quantification The total bacterial 16S rRNA gene quantification was carried out using the Femto™ Bacterial Quantification Kit (Zymo Research Corp, Irvine, CA) according to the manufacturer's instructions. First, DNA extracts were diluted to 1:10 prior to quantification. Briefly, 18 µL of the kit’s master mix was added to each well with 2 µL of each sample. The PCR cycling condition consisted of 95°C for 10 minutes for initial denaturation, 40 cycles of 95°C for 30 seconds (denaturation), 50°C for 30 seconds (annealing), and 72°C for 1 minute (extension), followed by a final extension of 72°C for 7 minutes. The amount of DNA in each sample was calculated based on the standard curve. Data for total 16S rRNA are described as nanograms of 16S rRNA per mL. All samples were run in duplicate. Intra-assay coefficient of variation for plates 1 to 8 were 1.01, 0.35, 1.34, 0.45, 2.80, 0.40, 0.28, and 0.61%, respectively. The inter-assay coefficient of variation was 0.91%. Estimated bacterial counts were calculated multiplying the total bacterial 16S rRNA by the relative abundance of each bacterial genus. Logarithms to the base 10 conversions of the raw values were then determined. Metabolome Analysis The second frozen uterine fluid aliquot was submitted to the University of California West Coast Metabolomics Center in Davis, CA for metabolome analysis. Samples were analyzed by technicians blinded to study groups using untargeted gas chromatography with time-of-flight mass spectrometry in a single batch as previously described [ 16 , 17 ]. The carrier selected was helium gas, and a column comprised of 95% dimethyl/5diphenyl polysiloxanesne was used. The column flow rate was set at 1 mL/minute, and the initial oven temperature was set at 50°C followed by a 20°C increase per min up to a final temperature of 330°C, which was held constant for a period of 5 minutes. Injection temperature was set to begin at 50°C followed by a 12°C increase per second up to 250°C. Retention of primary metabolites was evaluated using default settings from ChromaTOF v. 2.32 and quantification was reported as peak height. Each metabolite was identified based on its mass and charge relationship. Metabolites were annotated using PubChem, Kyoto Encyclopedia of Genes and Genomes, and Human Metabolome Database. Of the 873 detected metabolites, a total of 253 metabolites were annotated, and 620 were unknown (Supplemental Table S1 ). Statistical Analyses Differences in metabolites and bacteria genera associated with metritis were analyzed at each timepoint separately using RStudio Version 2023.06.1 + 524 (RStudio, PBC, Boston, MA). For microbiome analysis, alpha diversity was evaluated calculating Shannon and Simpson indexes using the estimate_richness function of the phyloseq package. The effect of metritis was analyzed on each index using the Wilcoxon test from the stats package. To assess beta diversity, permutational analyses of variance (PERMANOVA) were performed based on Bray-Curtis distances with 9,999 permutations using the Adonis2 function of the vegan package. The models included the effects of metritis (metritis vs. no metritis), parity (primiparous vs. multiparous), and their interaction. To visualize the differences in bacteria genera associated with metritis, PCoA with Bray-Curtis distances were performed using the ordinate function of the phyloseq package. The significance and effect size of individual bacteria genera were investigated as both relative abundance and estimated counts by performing Wilcoxon tests with Bonferroni corrections followed by Linear discriminant analysis Effect Size (LEfSe) using the lefser function of the lefser package. For metabolome analysis, metabolites were first log-transformed and auto-scaled. To analyze differences in the plasma metabolome between metritis and parity on each timepoint, PERMANOVA were performed based on Euclidean distances with 9,999 permutations using the Adonis2 function of the vegan package. The models included the effects of metritis (metritis vs. no metritis), parity (primiparous vs. multiparous), and their interaction. To visualize the differences in metabolites associated with metritis, PLS-DA were performed using the splsda function of the mixOmics package. When an effect of metritis was observed (PERMANOVA P ≤ 0.05), the significance and effect size of individual metabolites were investigated by performing Wilcoxon tests with Bonferroni corrections followed by LEfSe using the lefser function of the lefser package. Microbiome and metabolome data integration was performed between estimated microbial counts and metabolites with a P ≤ 0.05 to the Bonferroni corrected Wilcoxon tests using the Data Integration Analysis and Biomarker discovery using Latent variable approaches for Omics studies (DIABLO) function of the mixOmics package [ 18 ]. Briefly, DIABLO is a multivariate integrative classification method created to identify correlated or co-expressed variables from heterogeneous datasets. Herein, the N-integration supervised Sparse PLS-DA approach for variable selection was performed to identify latent structures composed of strongly correlated variables across both datasets [ 19 , 20 ]. Firstly, optimal number of components and variables were selected with the perf function of the mixOmics package using cross-validation with 10 folds and 100 repetitions. Then, the block.splsda function was performed on the selected variables to assess the correlation between the bacteria genera and metabolites able to differentiate among cows with and without metritis as previously described [ 21 ]. Lastly, the network function from the mixOmics package was used to export the correlation networks, which were then edited for presentation purposes using Metscape 2 [ 22 ] within the CytoScape 3.8 platform. Because there were no differences in the uterine microbiome at calving, omics integration was only performed at diagnosis. Statistical significance was considered at P ≤ 0.05. Results Microbiome Measures of alpha (Supplemental Figure S1 A, S1B) and beta (Fig. 1 A, 1 B, 1 C) diversity at calving did not differ (P > 0.05) between cows that did and did not develop metritis. Cows that developed metritis had greater Shannon ( P < 0.01; Supplemental Figure S1 C) and Simpson ( P < 0.01; Supplemental Figure S1 D) indexes at the day of metritis diagnosis compared with cows that did not develop metritis. The PCoA combined with PERMANOVA showed an effect ( P < 0.01) of metritis on the uterine microbiome structure at the day of metritis diagnosis (Fig. 1 D). Cows that developed metritis had greater ( P < 0.05) relative abundance of Porphyromonas , Bacteroides , Parvimonas , Peptostreptococcus , and Peptoniphilus than cows that did not develop metritis at the day of metritis diagnosis (Fig. 1 E). Cows that developed metritis had greater ( P < 0.05) estimated absolute counts of Fusobacterium, Porphyromonas , Bacteroides , Parvimonas , Peptostreptococcus , and Peptoniphilus than cows that did not develop metritis at the day of metritis diagnosis (Fig. 1 F). Metabolome The PLS-DA combined with PERMANOVA showed that metritis had an effect ( P = 0.01) on the uterine metabolome structure at calving (Fig. 2 A). Evaluation of individual metabolites revealed that 29 metabolites differed ( P < 0.05) between cows that did and did not develop metritis (Supplemental Table S2 ). The 10 most important annotated metabolites driving the difference between cows that developed or did not develop metritis were erythritol, 2-deoxypentitol, creatinine, citramalic acid, lactamide, isothreonic acid, pantothenic acid, threitol, 3-hydroxy-3-methylglutaric acid, and ribitol (Fig. 2 B). There was an effect ( P < 0.01) of metritis on the uterine metabolome structure at the day of metritis diagnosis (Fig. 2 C). Evaluation of individual metabolites revealed that 152 metabolites differed ( P < 0.05) between cows that did and did not develop metritis at the day of metritis diagnosis (Supplemental Table S2 ). The 10 most important annotated metabolites driving the difference were 3-(4-hydroxyphenyl) propionic acid, hydrocinnamic acid, phenylacetic acid, 5-aminovaleric acid, 4-hydroxyphenylacetic acid, 2-deoxytetronic acid, pipecolic acid, 2,6-diaminopimelic acid, ile-ile, and piperidone (Fig. 2 D). Uterine Integration Because no differences were observed in the uterine microbiome at calving, uterine microbiome-metabolome integration was not performed. Inclusion of the 6 microbial genera and 152 metabolites identified as statistically different between cows with and without metritis on the day of metritis diagnosis were fed into the sparce PLS-DA model resulting in the selection of 3 genera ( Fusobacterium, Porphyromonas , and Bacteroides ) and 120 metabolites as variables for the latent structures. Of the 120 metabolites, 49 had a correlation coefficient ( M ) greater than 0.5 or lesser than − 0.5 with at least one of the 3 microbial genera (Fig. 3 ). The top 10 metabolites with the greatest median correlation coefficients against the 3 bacteria genera were phenylacetic acid ( M = 0.70), 4-hydroxyphenylacetic acid ( M = 0.69), O-acetylserine ( M = 0.66), pipecolic acid ( M = 0.66), tyrosol ( M = 0.65), 3-(4-hydroxyphenyl) propionic acid ( M = 0.65), hydrocinnamic acid ( M = 0.65), 5-aminovaleric acid ( M = 0.64), 2,6-diaminopimelic acid ( M = 0.64), and 2-deoxytetronic acid ( M = 0.63). All of these, except for 4-hydroxyphenylacetic acid, O-acetylserine, and tyrosol were also among the top 10 most important annotated metabolites driving the difference in the uterine metabolome between cows with and without metritis. Although not among the top 10 metabolites, Arachidonic acid ( M = -0.61), nicotinamide ( M = -0.58), and citric acid ( M = -0.57) had the strongest negative correlation with the 3 bacteria genera. The complete correlation matrix can be found in Supplemental Table S3 . Discussion Herein, uterine microbiome and metabolome at calving and at metritis diagnosis were compared between cows that did and did not develop metritis. Then, differential bacteria genera and metabolites were used to assess their intra-omics correlation aiming to advance our understanding of the uterine environment in dairy cows with metritis. Because no differences in the microbiome were found at calving, data integration was not performed at calving. At the day of metritis diagnosis, the top 10 metabolites with the greatest median correlation coefficients against Fusobacterium, Porphyromonas , and Bacteroides are discussed. All of these, except for 4-hydroxyphenylacetic acid, O-acetylserine, and tyrosol were also among the top 10 most important annotated metabolites driving the difference in the uterine metabolome between cows with and without metritis. Furthermore, although not within the top 10, the 3 metabolites with the greatest negative correlation coefficients against these bacteria are discussed. Lastly, differentially abundant metabolites at calving that were also differentially abundant at metritis diagnosis are discussed. Within the 10 most important metabolites correlated with the most important bacteria at metritis diagnosis, phenylacetic acid and 4-hydroxyphenylacetic acid derive from microbial fermentation of aromatic amino acids, a process particularly prevalent among Bacteroides [ 23 , 24 ]. Phenylacetic acid has been described to disrupt quorum sensing and attenuate biofilm formation by Pseudomona aeruginosa [ 25 ] and inhibit growth of Staphylococcus aureus and Escherichia coli [ 26 ]. Furthermore, Bacteroides has been shown to produce phenylacetic acid and 4-hydroxyphenylacetic acid from aromatic amino acids in the absence of glucose. Conversely, bacteria previously associated with a healthy uterus, such as Firmicutes [ 6 ] need glucose to ferment aromatic amino acids [ 23 ]. Herein, glucose was lower in the uterine fluid of cows with metritis; therefore, it is possible that the growth and proliferation ability of Bacteroides and other bacteria associated with metritis may be in part mediated by their ability to proliferate in an environment where glucose is lacking. Furthermore, the byproducts of aromatic amino acid metabolism may help opportunistic pathogenic bacteria thrive over uterine commensals. O-acetylserine is a key intermediate metabolite for de-novo cysteine production by microbes. The synthesis of cysteine is a two-step process, catalyzed by two enzymes, serine acetyltransferase (CysE) catalyzes the first step, producing O-acetylserine. O-acetylserine sulfhydrylase combines O-acetylserine with a sulfur source for the synthesis of cysteine [ 27 ]. The deletion of CysE in Salmonella typhimurium renders it incapable of synthesizing O-acetylserine, leading to less cysteine, and therefore, lesser glutathione production, increasing its susceptibility to oxidative stress [ 28 ]. The greater abundance of O-acetylserine in cows with metritis together with the strong positive correlation with opportunistic pathogenic bacteria suggests that these bacteria may produce O-acetylserine as a defense mechanism against the reactive oxygen species produced by immune cells. On the other hand, biofilm formation by Escherichia coli and Providencia stuartii was reduced when O-acetylserine was added to the culture media [ 29 ], indicating that O-acetylserine may expose bacteria to the immune system by decreasing biofilm formation. It is plausible, therefore, that opportunistic pathogenic bacteria like Fusobacterium , Porphyromonas , and Bacteroides have a greater production of O-acetylserine compared with uterine commensals, which would protect them against reactive oxygen species in an environment with decreased protection from biofilm. Pipecolic acid is a cyclic amino acid product of lysine degradation by microbes [ 30 ]. Bacteria subjected to hyperosmotic stress degrade lysine, leading to an increase in pipecolic acid production, which is believed to contribute to salt tolerance [ 31 ]. The uterus of a dairy cow with metritis can be considered a challenging environment given the strong immune response against pathogens [ 32 ]. It is not clear why pipecolic acid is increased in cows with metritis but its strong positive correlation with opportunistic pathogenic bacteria suggests that the production of pipecolic acid may be a response mechanism to inflammation in the uterus. Tyrosol is a phenolic compound derived from microbial fermentation of tyrosine [ 33 ] that has been shown to reduce biofilm formation by gram-positive bacteria [ 34 – 36 ] and ATP production by Escherichia coli [ 37 ]. Independent free-floating bacterial cells, not protected by biofilm, are more susceptible to nutrient deprivation and phagocytosis [ 38 ]. Fusobacterium is one of the few bacteria able to produce phenols from tyrosine [ 39 ]; therefore, it is possible that Fusobacterium produces tyrosol to impair the growth of competing bacteria, thereby promoting its own proliferation and dominance in the microbial ecosystem. Hydrocinnamic acid and its metabolite 3-(4-hydroxyphenyl) propionic acid are two bacterial metabolites typically associated with bacterial fermentation of plant cell wall components [ 40 ]. In humans, hydrocinnamic acids are associated with intestinal protective effects by modulating intestinal immunity [ 41 , 42 ], downregulating the activation of TLR- 4/NF-κβ [ 43 ], and by increasing the Bacteroidota/Bacillota ratio [ 44 ]. Although in humans hydrocinnamic acids are associated with a healthy intestinal environment, in the context of the bovine uterus, a greater abundance of Bacteroidota is associated with uterine disease [ 6 , 7 , 45 ]. Downregulation of the immune response against uterine pathogens may predispose dairy cows to metritis development. We have previously shown that cows with metritis had a reduction in monocyte and T-helper activation in the peripheral blood [ 46 ], and plasma metabolomic changes associated with a reduction in leukocyte activation on the day of metritis diagnosis [ 12 ]. We hypothesize that the greater levels of hydrocinnamic acids, potentially produced by opportunistic pathogenic bacteria, may be contributing to the immune dysregulation observed in the peripheral blood of cows with metritis. 5-aminovaleric acid is another byproduct of lysine metabolism [ 47 ]. Elevated levels of urinary 5-aminovaleric acid have been associated with intestinal inflammation [ 48 ]. Lin et al. (2010) proposed that higher levels of 5-aminovaleric may be caused by inflammation-induced tissue damage, which increases the levels of lysine for intestinal microbiota and the release of 5-aminovaleric acid [ 48 ]. Cows with metritis have extensive endometrial damage and inflammation [ 32 ] which may provide lysine for bacterial degradation, increasing 5-aminovaleric acid levels in the uterine fluid of cows that developed metritis. 2,6-Diaminopimelic acid is a bacterial cell wall peptidoglycan component found in gram-negative bacteria [ 49 ]; therefore, an increase in 2,6-diaminopimelic acid reflects the greater Fusobacterium, Porphyromonas , and Bacteroides abundance in the uterus of cows with metritis. 2-deoxytetronic acid is a sugar acid derivative that acts as an appetite suppressant in rats [ 50 ] has been associated with subclinical ketosis in dairy cows [ 51 ]. The role of 2-deoxytetronic acid in the crosstalk between microbes and metabolites in the uterus of cows that develop metritis is unclear and deserves further investigation. Although not within the top 10 metabolites with the greatest absolute correlation with opportunistic pathogenic bacteria, it is also important to mention the metabolites with the greatest negative correlation with opportunistic pathogenic bacteria because these could potentially be used to reduce the abundance of these bacteria in the uterus. Arachidonic acid is a polyunsaturated fatty acid commonly known as a rate-limiting precursor to the synthesis of biologically active eicosanoic acids [ 52 ]. Since the intracellular availability of arachidonic acid is a bottleneck in the biosynthesis of eicosanoic acids [ 53 ], immune cells can uptake extracellular arachidonic acid to aid in the production of eicosanoic acids [ 53 , 54 ]. Therefore, it is plausible that arachidonic acid is being taken up by immune cells to mount an inflammatory response against the opportunistic pathogenic bacteria. Nicotinamide, a form of vitamin B3, is used by bacteria to form nicotinamide adenine dinucleotide (NAD + ), releasing nicotinic acid to the extracellular space [ 55 ]. Bacteria use NAD + as a co-factor for several enzymes involved in ATP production and bacterial survival [ 56 ]. Interestingly, not only nicotinamide had a strong negative correlation with opportunistic pathogenic bacteria genera, but also nicotinic acid had a strong positive correlation with opportunistic pathogenic bacteria ( M = 0.50), suggesting that opportunistic pathogenic bacteria may use nicotinamide as a precursor for NAD + , boosting microbial growth and survival. Furthermore, given that citric acid is an important substrate for energy production by bacteria [ 57 ], the strong negative correlation between citric acid and nicotinamide with opportunistic pathogenic bacteria indicates that these substrates may be used for their growth. From the 49 metabolites with a strong correlation with Fusobacterium, Porphyromonas , and Bacteroides on the day of metritis diagnosis only 7 also differed at calving. Interestingly, of those 7 metabolites, erythritol, xylitol, myo -inositol, creatinine, and lactamide were greater at calving but lesser at the day of diagnosis indicating that these metabolites may serve as substrate for opportunistic pathogenic bacteria growth. Erythritol and xylitol are sugar alcohols used as substrates for energy production [ 58 , 59 ]. myo -Inositol is a six-carbon polyol involved in energy metabolism [ 60 ] that can be utilized by bacteria as a carbon and energy source for growth and proliferation [ 61 ]. The greater abundance of erythritol, xylitol, and myo -inositol at calving together with their lesser abundance at the day of metritis diagnosis in cows that developed metritis indicates that opportunistic pathogenic bacteria may be using these metabolites as substrates for growth. Creatinine is a byproduct of muscle metabolism that when exposed to mouse macrophages, leads to a sharp reduction in TLR-2, -3, -4, and − 7 transcript levels [ 62 ]. Toll-like receptors are involved in sensing the presence of bacterial antigens prior to their phagocytosis and killing [ 62 ]. It is possible that the greater levels of creatinine at calving in cows that developed metritis may be contributing to a poorer immune response against opportunistic pathogenic bacteria, leading to their overgrowth. Lactamide is an acyl amide derivative from the amidation of lactic acid [ 63 ]; however, lactamide metabolism and role in bacterial proliferation is not clear and deserves further investigation. Conclusions This observational study provides insights into the uterine microbiome and metabolome at calving and on the day of metritis diagnosis in cows that developed metritis. Furthermore, this is the first-time uterine crosstalk between opportunistic pathogenic bacteria and metabolites on the day of metritis diagnosis has been explored. Altogether, from the 49 metabolites with a strong correlation with opportunistic pathogenic bacteria on the day of metritis diagnosis, the 17 metabolites discussed herein have been described as part of processes associated with attenuation of biofilm formation by commensal bacteria, opportunistic pathogenic bacteria overgrowth, tissue damage and inflammation, immune evasion, and immune dysregulation. The data integration presented herein helps advance the understanding of the uterine environment in dairy cows with metritis. Furthermore, the metabolites described herein may be promising targets for future interventions aiming to reduce opportunistic pathogenic bacteria growth in the uterus, and therefore, reducing the incidence of metritis. Abbreviations PCoA: Principal coordinate analysis PLS-DA: Partial least square – discriminant analysis TMR: Total mixed ration ASV: Amplicon sequence variant PERMANOVA: Permutational analysis of variance LEfSe: Linear discriminant analysis effect size DIABLO : Data integration analysis and biomarker discovery using latent variable approaches for omics studies LDA: Linear discriminant analysis score CysE: Serine acetyltransferase NAD + : Nicotamide adenine dinucleotide Declarations Ethics approval and consent to participate All procedures involving cows were approved by the Institutional Animal Care and Use Committee of the University of Florida; protocol number 201910623. Consent for publication Not applicable. Availability of data and material The raw sequence data generated during this study are available in the NCBI repository under BioProject OR883023 (www.ncbi.nlm.nih.gov/nuccore/OR883023) - OR883397 (www.ncbi.nlm.nih.gov/nuccore/OR883397). The metabolomics dataset analyzed during the current study is available in the NIH Common Fund’s National Metabolomics Data Repository website, the Metabolomics Workbench repository [64] under Study ID ST002994, http://dx.doi.org/10.21228/M8S425. The Metabolomics Workbench is supported by NIH grant U2C-DK119886 and OT2-OD030544 grants. Competing interests The authors declare that they have no competing interests. Funding This work was supported by U.S. Department of Agriculture Grant # 2019-67015-29836, Accession No: 1019435. Authors' contributions JEPS, CDN, SJJ, RCB, JPD, and KNG initiated the study design and provided scientific input during manuscript writing. RSB and RCC provided scientific input during manuscript writing. SC, JGP, and TDG collected the on-farm data. SC and FC performed the laboratory work. SC performed the statistical analyses. KNG and FC provided their scientific input during statistical analyses. SC and KNG wrote the manuscript. All authors edited and approved the manuscript. Acknowledgements We would like to thank the general manager, Mr. Eric Williams, and the staff of the University of Florida Dairy Unit for allowing the use of their animals and facilities. References Pinedo P, Santos JEP, Chebel RC, Galvão KN, Schuenemann GM, Bicalho RC, et al. Early-lactation diseases and fertility in 2 seasons of calving across US dairy herds. J Dairy Sci. 2020;103:10560–76. Figueiredo CC, Merenda VR, de Oliveira EB, Lima FS, Chebel RC, Galvão KN, et al. Failure of clinical cure in dairy cows treated for metritis is associated with reduced productive and reproductive performance. J Dairy Sci. 2021;104:7056–70. Barragan AA, Piñeiro JM, Schuenemann GM, Rajala-Schultz PJ, Sanders DE, Lakritz J, et al. Assessment of daily activity patterns and biomarkers of pain, inflammation, and stress in lactating dairy cows diagnosed with clinical metritis. J Dairy Sci. 2018;101:8248–58. 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Xylitol: A Review on Bioproduction, Application, Health Benefits, and Related Safety Issues. Crit Rev Food Sci Nutr. 2015;55:1514–28. Tu‐sekine B, Kim SF. The Inositol Phosphate System—A Coordinator of Metabolic Adaptability. Int J Mol Sci. 2022;23. Krings E, Krumbach K, Bathe B, Kelle R, Wendisch VF, Sahm H, et al. Characterization of myo-inositol utilization by Corynebacterium glutamicum: The stimulon, identification of transporters, and influence on L-lysine formation. J Bacteriol. 2006;188:8054–61. Leland KM, McDonald TL, Drescher KM. Effect of creatine, creatinine, and creatine ethyl ester on TLR expression in macrophages. Int Immunopharmacol. 2011;11:1341–7. Geistlinger K, Schmidt JDR, Beitz E. Human monocarboxylate transporters accept and relay protons via the bound substrate for selectivity and activity at physiological pH. PNAS Nexus. 2023;2:1–8. Sud M, Fahy E, Cotter D, Azam K, Vadivelu I, Burant C, et al. Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 2016;44:D463–70. Additional Declarations No competing interests reported. Supplementary Files SupplementalFigureS1.docx SupplementalTableS1.xlsx SupplementalTableS2.xlsx SupplementalTableS3.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Mar, 2024 Reviews received at journal 10 Feb, 2024 Reviewers agreed at journal 09 Feb, 2024 Reviewers invited by journal 09 Feb, 2024 Editor assigned by journal 08 Feb, 2024 Submission checks completed at journal 27 Jan, 2024 First submitted to journal 25 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3897972","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269548803,"identity":"ed88caf6-af2f-4d91-b917-85807b1ff722","order_by":0,"name":"S. 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The uterine microbiome was identified by amplification of the V4 hypervariable region of the bacterial/archaeal 16S rRNA. Panel A and D: results from principal coordinate analyses with Bray-Curtis distances at calving and at metritis diagnosis, respectively. Percentages within each principal component (PCo) correspond to the percentage of variation explained by the component. The ellipses correspond to 95% confidence intervals. P-values correspond to permutational analysis of variance (PERMANOVA) based on Bray-Curtis distances with 9,999 permutations including the effect of metritis (MET vs. NoMet), parity (multiparous vs. primiparous) and their interaction; Panel B, C, E, F: individual bacteria genera comparison as relative abundance (B, E) and as estimated counts (C, F) at calving and at metritis diagnosis, respectively, between cows that developed metritis and cows that did not develop metritis. Bacteria genera with less than 1% relative abundance were grouped together as “Other”. Significance was tested using Wilcoxon tests with Bonferroni corrections for multiple testing. Effect size was tested using linear discriminant analysis effect size (LEfSe). Circles represent median and lines crossing circles horizontally represent the interquartile range. Asterisks correspond to adjusted P \u0026lt; 0.05. Estimated bacterial counts were calculated multiplying the total bacterial 16S rRNA by the relative abundance of each bacteria genera. Figures were created using the ggplot2 package of Rstudio Version 2023.06.1+524 (RStudio, PBC, Boston, MA).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3897972/v1/bd747da3f788f7ed4fa3fc76.png"},{"id":50380565,"identity":"f2a16ebd-53bc-4f0c-86f6-fe8c6ae740df","added_by":"auto","created_at":"2024-01-30 16:50:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":254623,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of uterine metabolome at calving (A, B) and on the day of metritis diagnosis (7±2 days after calving; C, D) between dairy cows that developed metritis (MET; orange; n = 52) and dairy cows that did not develop metritis (NoMET; blue; n = 52). The uterine metabolome was identified by gas chromatography time-of-flight mass spectrometry. Panel A and C: results from partial least squares - discriminant analysis (PLS-DA) at calving and at metritis diagnosis, respectively. Percentages within each principal component (PCo) correspond to the percentage of variation explained by the component. The ellipses correspond to 95% confidence intervals. P-values correspond to permutational analysis of variance (PERMANOVA) based on Euclidean distances with 9,999 permutations including the effect of metritis (MET vs. NoMet), parity (multiparous vs. primiparous) and their interaction; Panel B and D: top 10 metabolites sorted by effect size resulting from individual metabolite comparisons at calving and at metritis diagnosis, respectively, between cows that developed metritis and cows that did not develop metritis. Significance was tested using Wilcoxon tests with Bonferroni corrections for multiple testing. Effect size was tested using linear discriminant analysis effect size (LEfSe). Circles represent median and lines crossing circles horizontally represent the interquartile range. Asterisks correspond to adjusted P \u0026lt; 0.05. 3-3.glutaric acid, 3-hydroxy-3-methylglutaric acid; 3-4.propionic acid, 3-(4-hydroxyphenyl) propionic acid; 4h.phenylacetic acid, 4-hydroxyphenylacetic acid. Figures were created using the ggplot2 package of Rstudio Version 2023.06.1+524 (RStudio, PBC, Boston, MA).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3897972/v1/40f191869e8a9649c69bd01c.png"},{"id":50380238,"identity":"ea2b5445-21aa-421a-8fa6-be56e0f392c7","added_by":"auto","created_at":"2024-01-30 16:42:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":273821,"visible":true,"origin":"","legend":"\u003cp\u003eCompound network between bacteria genera and metabolites with the best discriminatory ability between dairy cows that developed metritis (n = 52) and dairy cows that did not develop metritis (n = 52) on the day of metritis diagnosis (7±2 days after calving) according to the sparce partial least squares - discriminant analysis (sPLS-DA). Compound network analysis was performed between bacteria genera and metabolites with a correlation coefficient (M) \u0026gt; 0.5 or \u0026lt; -0.5 using the Data Integration Analysis and Biomarker discovery using Latent variable approaches for Omics studies (DIABLO) function of the mixOmics package. Metscape 2 within the CytoScape 3.8 platform was used to edit the figure for easier visualization. Green hexagons correspond to metabolites that differed between dairy cows that developed metritis and dairy cows that did not develop metritis, while blue hexagons correspond to bacteria genera that differed between dairy cows that developed metritis and dairy cows that did not develop metritis. Large hexagons represent metabolites or bacteria genera with greater abundance, while small hexagons represent metabolites with lower abundance in the uterine fluid of dairy cows that developed metritis when compared with dairy cows that did not develop metritis. Black lines correspond to positive correlation coefficients while orange lines correspond to negative correlation coefficients.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3897972/v1/d46631ced95101b84d238922.png"},{"id":50380236,"identity":"7fd76810-01eb-44f2-a018-3d6a193462ce","added_by":"auto","created_at":"2024-01-30 16:42:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":630389,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5. Illustration of the possible role of metabolites in the uterine crosstalk with opportunistic pathogenic bacteria on the day of metritis diagnosis. Altogether, from the 49 metabolites with a strong correlation with opportunistic pathogenic bacteria on the day of metritis diagnosis, the 17 metabolites illustrated herein have been described as part of processes associated with attenuation of biofilm formation by commensal bacteria, opportunistic pathogenic bacterial overgrowth, tissue damage and inflammation, immune evasion, and immune dysregulation. Down arrows indicate reduction, and up arrows indicate increase in cows with metritis. NAD\u003csup\u003e+\u003c/sup\u003e, nicotinamide adenine dinucleotide; HP, hydrophenyl; ROS, reactive oxygen species; TLR, toll-like receptor; NFκβ, nuclear factor kappa beta. Figure created with Biorender.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3897972/v1/37f670c9f4d305007ed41f0e.png"},{"id":50380836,"identity":"b0432779-f1a0-4624-8cff-7787040c816b","added_by":"auto","created_at":"2024-01-30 16:58:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1913130,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3897972/v1/0b54e5fb-7c94-47b8-9259-0adb1e6cca5c.pdf"},{"id":50380240,"identity":"9dfbd59b-87a7-4a76-b765-d215b56535ee","added_by":"auto","created_at":"2024-01-30 16:42:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":142857,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3897972/v1/4fed0956b4a741714557104c.docx"},{"id":50380242,"identity":"dec57725-589e-496a-a1df-c54b6c90fa5f","added_by":"auto","created_at":"2024-01-30 16:42:45","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":187929,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3897972/v1/a2c38e74346e41c92b089dba.xlsx"},{"id":50380237,"identity":"0a33758f-1d0c-4559-9266-dbbc0d9fb64a","added_by":"auto","created_at":"2024-01-30 16:42:45","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":23402,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3897972/v1/25fb3234481e7b48d5d4c964.xlsx"},{"id":50380241,"identity":"fea0031e-77d6-4ef5-b42d-f138b9810511","added_by":"auto","created_at":"2024-01-30 16:42:45","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15415,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3897972/v1/45e67a05598c0a5d70a786e5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating uterine microbiome and metabolome to advance the understanding of the uterine environment in dairy cows with metritis","fulltext":[{"header":"Background","content":"\u003cp\u003eMetritis affects around 25% of Holstein cows shortly after calving [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], impacting production, reproduction, culling, and welfare [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Metritis is characterized by a dysbiosis of the uterine microbiome in which opportunistic pathogenic bacteria such as \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003ePorphyromonas\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e overtake the uterine commensals [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Interestingly, the uterine microbiome from cows that develop metritis and those that remain healthy do not differ from calving until 2 days postpartum [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, whether opportunistic pathogenic bacteria proliferate and overtake the uterine commensals could be determined by the type of substrates present inside the uterus shortly after calving. For instance, greater abundance of lactic acid has been identified in the uterus of cows with metritis when compared with cows without metritis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Lactic acid is one of the main substrates for \u003cem\u003eFusobacterium necrophorum\u003c/em\u003e growth [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Lactic acid utilizing bacteria such as \u003cem\u003eFusobacterium necrophorum\u003c/em\u003e can also metabolize the amino acid tryptophan into indole and its derivatives, which can serve as bacterial signaling molecules, effectively regulating virulence, biofilm formation, motility, and sporulation, thereby inhibiting the growth of commensal bacteria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The indole derivative, indole-3-acetate has been shown to be greater in the uterus from cows that develop metritis when compared with cows without metritis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, it is possible that the uterine metabolome may promote the growth of opportunistic pathogenic bacteria, which consequently may inhibit the growth of uterine commensals leading to uterine dysbiosis and metritis development.\u003c/p\u003e \u003cp\u003eAlthough previous research focused on the difference in uterine microbiome [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] or metabolome [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] between cows that developed metritis and cows that did not, little is known about the microbiome-metabolome interactions in the uterus of cows that develop metritis. Therefore, the hypothesis of the current study was that specific uterine metabolites are associated with specific bacteria genera involved in the development of metritis. Hence, the objectives of the study were to compare the uterine microbiome and metabolome at calving and on the day of metritis diagnosis between cows that developed metritis and cows that did not and integrate both omics datasets to advance the understanding of the uterine environment in dairy cows with metritis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis case-control observational study was conducted at the University of Florida Dairy Unit from September 2019 to March 2020.\u003c/p\u003e \u003cp\u003eThe cows used for this study were a subset of cows used in a previous study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Analyses of uterine microbiome [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and serum [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] metabolome using principal component analysis, principal coordinate analysis (PCoA), and partial least square discriminant analysis (PLS-DA) encompassing 12 to 24 cows per group were able to depict statistical differences; therefore, the inclusion of a larger number of cows per group (n\u0026thinsp;=\u0026thinsp;52) was expected to ensure sufficient power for the characterization of changes in the uterine microbiome and metabolome associated with metritis in the current study.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCows, Housing, and Feeding\u003c/h2\u003e \u003cp\u003eA total of 128 Holstein cows, consisting of 71 primigravid and 57 multigravid cows, were included in the study which started at 260 days of gestation and ended at 13\u0026thinsp;\u0026plusmn;\u0026thinsp;1 days after calving. Throughout the study period, primigravid and multigravid cows were housed in separate naturally ventilated barns with sand-bedded free-stalls. In the prepartum phase, multigravid cows were provided with a total mixed ration (TMR) twice daily formulated to either meet or exceed the nutritional requirements recommended for dry Holstein cows weighing 680 kg [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. After calving, multiparous cows were fed a TMR formulated to meet or exceed the nutrient requirements for lactating Holstein cows weighing 680 kg and producing 45 kg of 3.5% fat-corrected milk [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] twice daily. Nulliparous cows were housed in a separate free-stall barn with individual feeding gates (Calan Broadbent Feeding System, American Calan Inc., Northwood, NH) starting at 241 d of gestation and were fed a TMR once daily. After parturition, primiparous cows were relocated to a postpartum pen, also equipped with individual feeding gates, and each cow was assigned to a specific gate until reaching 100 days after calving. Throughout the postpartum period, all cows were milked twice daily at 0600 and 1800 hours. The rolling herd average milk yield was approximately 11,000 kg during the course of the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCase Definition and Diagnosis\u003c/h2\u003e \u003cp\u003eMetritis was diagnosed by examination of the uterine discharge with a Metricheck device (Metricheck, Simcro, New Zealand) at 3\u0026thinsp;\u0026plusmn;\u0026thinsp;1, 7\u0026thinsp;\u0026plusmn;\u0026thinsp;1, 10\u0026thinsp;\u0026plusmn;\u0026thinsp;1 and 13\u0026thinsp;\u0026plusmn;\u0026thinsp;1 days after calving using a 5-point scale as previously described [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]: 1\u0026thinsp;=\u0026thinsp;not fetid normal lochia, viscous, clear, red, or brown; 2\u0026thinsp;=\u0026thinsp;cloudy, pink, red, or brown mucoid discharge with flecks of pus; 3\u0026thinsp;=\u0026thinsp;not fetid, pink red or brown mucopurulent discharge with \u0026lt;\u0026thinsp;50% pus; 4\u0026thinsp;=\u0026thinsp;not fetid, pink, red or brown purulent discharge with \u0026ge;\u0026thinsp;50% pus; 5\u0026thinsp;=\u0026thinsp;fetid red-brownish, watery discharge. Cows with a discharge score\u0026thinsp;\u0026le;\u0026thinsp;4 were classified as healthy and cows with a score of 5 in at least one examination were classified as having metritis.\u003c/p\u003e \u003cp\u003eIncidences of mastitis, digestive problems, respiratory disease, and antimicrobial treatments in the first 35 days after calving were also recorded for individual cows, and cows with any of these diseases, cows submitted to antimicrobial treatment before metritis diagnosis, and cows diagnosed with metritis after 10 days after calving were excluded from the study. A total of 13 cows were excluded. Four cows were excluded because they were treated with antimicrobials before metritis diagnosis. Three cows were excluded because of death. One cow was excluded because of uterine torsion and one cow was excluded because of peritonitis. Four cows were excluded because they were diagnosed with metritis at 13\u0026thinsp;\u0026plusmn;\u0026thinsp;1 days after calving; therefore, could not be paired to a healthy counterpart. A total of 52 cows with metritis paired with 52 cows without metritis were used for bioinformatic and statistical analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eUterine Fluid Collection\u003c/h2\u003e \u003cp\u003eAll cows had uterine fluid collected at calving (first 24 hours after calving), and at diagnosis of metritis. Briefly, the cow\u0026rsquo;s cervix was stabilized by rectal palpation, the vulva was rinsed with alcohol 70% (vol/vol) and dried with paper towels. Subsequently, a single-use plastic round-tip pipette (UterFlush pipettes, Van Beek) was introduced into the vagina at a 45\u0026deg; angle and manipulated through the cervix. A total of 50 mL of sterile saline solution (0.9% sodium chloride irrigation, Baxter) was infused into the uterine lumen using a 60-mL syringe (Covidien) attached to the end of the pipette. Uterine contents were homogenized, retrieved into the same 60-mL syringe, and transferred to a sterile 15-mL conical tube (VWR). After collection, tubes were placed on ice and transported to the laboratory within 2 hours. Once in the laboratory, uterine fluid samples were aliquoted into 2-mL microcentrifuge tubes (Eppendorf) and stored at -80 \u003csup\u003eo\u003c/sup\u003eC until essayed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiome Analysis\u003c/h2\u003e \u003cp\u003eOne frozen uterine fluid aliquot was submitted to FERA Diagnostics and Biologicals Corporate in College Station, Texas for microbiome analysis. Samples were analyzed by technicians blinded to study groups. DNA extraction was performed using a Mag-Bind Universal Pathogen 96 Kit (Omega Bio-Tek, Norcross, GA) in accordance with manufacturer instructions. The 16S rRNA gene was amplified by PCR. Amplification of the V4 hypervariable region of the bacterial/archaeal 16S rRNA gene was performed as previously described [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] using the Illumina MiSeq platform (Illumina Inc.). Description of PCR and thermocycler conditions are available in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthmicrobiome.org/protocols-and-standards/16s/\u003c/span\u003e\u003cspan address=\"https://earthmicrobiome.org/protocols-and-standards/16s/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. After DNA amplification, electrophoresis using 1.2% (wt/vol) agarose gels stained with 0.5 mg/mL ethidium bromide was used to verify amplicon presence and size. DNA purification was carried out using magnetic beads Mag-Bind TotalPure NGS (Omega Bio-Tek, Norcross, GA) in accordance with manufacturer instructions. Samples were standardized to the same concentration and pooled into a run for library preparation and sequencing, which was performed using the MiSeq Reagent Kit v2 (300 cycles) on the MiSeq platform (Illumina Inc.).\u003c/p\u003e \u003cp\u003eNon-biological nucleotides were removed, and raw sequenced amplicons were analyzed using the DADA2 package of RStudio Version 2023.06.1\u0026thinsp;+\u0026thinsp;524 (RStudio, PBC, Boston, MA) following the DADA2 Pipeline Tutorial (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://benjjneb.github.io/dada2/tutorial.html\u003c/span\u003e\u003cspan address=\"https://benjjneb.github.io/dada2/tutorial.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After filtering and trimming, the amplicon sequence variant (ASV) table was constructed. Then, chimeric reads were removed, and the number of reads were standardized to the median read number of all the samples. Taxonomy was assigned to ASV using the Greengenes database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://greengenes.lbl.gov\u003c/span\u003e\u003cspan address=\"http://greengenes.lbl.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTotal Bacteria 16S rRNA Gene Quantification\u003c/h2\u003e \u003cp\u003eThe total bacterial 16S rRNA gene quantification was carried out using the Femto\u0026trade; Bacterial Quantification Kit (Zymo Research Corp, Irvine, CA) according to the manufacturer's instructions. First, DNA extracts were diluted to 1:10 prior to quantification. Briefly, 18 \u0026micro;L of the kit\u0026rsquo;s master mix was added to each well with 2 \u0026micro;L of each sample. The PCR cycling condition consisted of 95\u0026deg;C for 10 minutes for initial denaturation, 40 cycles of 95\u0026deg;C for 30 seconds (denaturation), 50\u0026deg;C for 30 seconds (annealing), and 72\u0026deg;C for 1 minute (extension), followed by a final extension of 72\u0026deg;C for 7 minutes. The amount of DNA in each sample was calculated based on the standard curve. Data for total 16S rRNA are described as nanograms of 16S rRNA per mL. All samples were run in duplicate. Intra-assay coefficient of variation for plates 1 to 8 were 1.01, 0.35, 1.34, 0.45, 2.80, 0.40, 0.28, and 0.61%, respectively. The inter-assay coefficient of variation was 0.91%. Estimated bacterial counts were calculated multiplying the total bacterial 16S rRNA by the relative abundance of each bacterial genus. Logarithms to the base 10 conversions of the raw values were then determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMetabolome Analysis\u003c/h2\u003e \u003cp\u003eThe second frozen uterine fluid aliquot was submitted to the University of California West Coast Metabolomics Center in Davis, CA for metabolome analysis. Samples were analyzed by technicians blinded to study groups using untargeted gas chromatography with time-of-flight mass spectrometry in a single batch as previously described [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The carrier selected was helium gas, and a column comprised of 95% dimethyl/5diphenyl polysiloxanesne was used. The column flow rate was set at 1 mL/minute, and the initial oven temperature was set at 50\u0026deg;C followed by a 20\u0026deg;C increase per min up to a final temperature of 330\u0026deg;C, which was held constant for a period of 5 minutes. Injection temperature was set to begin at 50\u0026deg;C followed by a 12\u0026deg;C increase per second up to 250\u0026deg;C. Retention of primary metabolites was evaluated using default settings from ChromaTOF v. 2.32 and quantification was reported as peak height. Each metabolite was identified based on its mass and charge relationship. Metabolites were annotated using PubChem, Kyoto Encyclopedia of Genes and Genomes, and Human Metabolome Database. Of the 873 detected metabolites, a total of 253 metabolites were annotated, and 620 were unknown (Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eDifferences in metabolites and bacteria genera associated with metritis were analyzed at each timepoint separately using RStudio Version 2023.06.1\u0026thinsp;+\u0026thinsp;524 (RStudio, PBC, Boston, MA).\u003c/p\u003e \u003cp\u003eFor microbiome analysis, alpha diversity was evaluated calculating Shannon and Simpson indexes using the estimate_richness function of the phyloseq package. The effect of metritis was analyzed on each index using the Wilcoxon test from the stats package. To assess beta diversity, permutational analyses of variance (PERMANOVA) were performed based on Bray-Curtis distances with 9,999 permutations using the Adonis2 function of the vegan package. The models included the effects of metritis (metritis vs. no metritis), parity (primiparous vs. multiparous), and their interaction. To visualize the differences in bacteria genera associated with metritis, PCoA with Bray-Curtis distances were performed using the ordinate function of the phyloseq package. The significance and effect size of individual bacteria genera were investigated as both relative abundance and estimated counts by performing Wilcoxon tests with Bonferroni corrections followed by Linear discriminant analysis Effect Size (LEfSe) using the lefser function of the lefser package.\u003c/p\u003e \u003cp\u003eFor metabolome analysis, metabolites were first log-transformed and auto-scaled. To analyze differences in the plasma metabolome between metritis and parity on each timepoint, PERMANOVA were performed based on Euclidean distances with 9,999 permutations using the Adonis2 function of the vegan package. The models included the effects of metritis (metritis vs. no metritis), parity (primiparous vs. multiparous), and their interaction. To visualize the differences in metabolites associated with metritis, PLS-DA were performed using the splsda function of the mixOmics package. When an effect of metritis was observed (PERMANOVA \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05), the significance and effect size of individual metabolites were investigated by performing Wilcoxon tests with Bonferroni corrections followed by LEfSe using the lefser function of the lefser package.\u003c/p\u003e \u003cp\u003eMicrobiome and metabolome data integration was performed between estimated microbial counts and metabolites with a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 to the Bonferroni corrected Wilcoxon tests using the Data Integration Analysis and Biomarker discovery using Latent variable approaches for Omics studies (DIABLO) function of the mixOmics package [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Briefly, DIABLO is a multivariate integrative classification method created to identify correlated or co-expressed variables from heterogeneous datasets. Herein, the N-integration supervised Sparse PLS-DA approach for variable selection was performed to identify latent structures composed of strongly correlated variables across both datasets [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Firstly, optimal number of components and variables were selected with the perf function of the mixOmics package using cross-validation with 10 folds and 100 repetitions. Then, the block.splsda function was performed on the selected variables to assess the correlation between the bacteria genera and metabolites able to differentiate among cows with and without metritis as previously described [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Lastly, the network function from the mixOmics package was used to export the correlation networks, which were then edited for presentation purposes using Metscape 2 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] within the CytoScape 3.8 platform. Because there were no differences in the uterine microbiome at calving, omics integration was only performed at diagnosis. Statistical significance was considered at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eMicrobiome\u003c/h2\u003e\n\u003cp\u003eMeasures of alpha (Supplemental Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eA, S1B) and beta (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC) diversity at calving did not differ (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between cows that did and did not develop metritis.\u003c/p\u003e\n\u003cp\u003eCows that developed metritis had greater Shannon (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Supplemental Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eC) and Simpson (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Supplemental Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eD) indexes at the day of metritis diagnosis compared with cows that did not develop metritis. The PCoA combined with PERMANOVA showed an effect (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of metritis on the uterine microbiome structure at the day of metritis diagnosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Cows that developed metritis had greater (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) relative abundance of \u003cem\u003ePorphyromonas\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eParvimonas\u003c/em\u003e, \u003cem\u003ePeptostreptococcus\u003c/em\u003e, and \u003cem\u003ePeptoniphilus\u003c/em\u003e than cows that did not develop metritis at the day of metritis diagnosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). Cows that developed metritis had greater (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) estimated absolute counts of \u003cem\u003eFusobacterium, Porphyromonas\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eParvimonas\u003c/em\u003e, \u003cem\u003ePeptostreptococcus\u003c/em\u003e, and \u003cem\u003ePeptoniphilus\u003c/em\u003e than cows that did not develop metritis at the day of metritis diagnosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eMetabolome\u003c/h2\u003e\n\u003cp\u003eThe PLS-DA combined with PERMANOVA showed that metritis had an effect (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) on the uterine metabolome structure at calving (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Evaluation of individual metabolites revealed that 29 metabolites differed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between cows that did and did not develop metritis (Supplemental Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). The 10 most important annotated metabolites driving the difference between cows that developed or did not develop metritis were erythritol, 2-deoxypentitol, creatinine, citramalic acid, lactamide, isothreonic acid, pantothenic acid, threitol, 3-hydroxy-3-methylglutaric acid, and ribitol (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eThere was an effect (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of metritis on the uterine metabolome structure at the day of metritis diagnosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Evaluation of individual metabolites revealed that 152 metabolites differed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between cows that did and did not develop metritis at the day of metritis diagnosis (Supplemental Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). The 10 most important annotated metabolites driving the difference were 3-(4-hydroxyphenyl) propionic acid, hydrocinnamic acid, phenylacetic acid, 5-aminovaleric acid, 4-hydroxyphenylacetic acid, 2-deoxytetronic acid, pipecolic acid, 2,6-diaminopimelic acid, ile-ile, and piperidone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eUterine Integration\u003c/h2\u003e\n\u003cp\u003eBecause no differences were observed in the uterine microbiome at calving, uterine microbiome-metabolome integration was not performed.\u003c/p\u003e\n\u003cp\u003eInclusion of the 6 microbial genera and 152 metabolites identified as statistically different between cows with and without metritis on the day of metritis diagnosis were fed into the sparce PLS-DA model resulting in the selection of 3 genera (\u003cem\u003eFusobacterium, Porphyromonas\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e) and 120 metabolites as variables for the latent structures. Of the 120 metabolites, 49 had a correlation coefficient (\u003cem\u003eM\u003c/em\u003e) greater than 0.5 or lesser than \u0026minus;\u0026thinsp;0.5 with at least one of the 3 microbial genera (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The top 10 metabolites with the greatest median correlation coefficients against the 3 bacteria genera were phenylacetic acid (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70), 4-hydroxyphenylacetic acid (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.69), O-acetylserine (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66), pipecolic acid (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66), tyrosol (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65), 3-(4-hydroxyphenyl) propionic acid (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65), hydrocinnamic acid (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65), 5-aminovaleric acid (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.64), 2,6-diaminopimelic acid (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.64), and 2-deoxytetronic acid (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.63). All of these, except for 4-hydroxyphenylacetic acid, O-acetylserine, and tyrosol were also among the top 10 most important annotated metabolites driving the difference in the uterine metabolome between cows with and without metritis. Although not among the top 10 metabolites, Arachidonic acid (\u003cem\u003eM\u003c/em\u003e = -0.61), nicotinamide (\u003cem\u003eM\u003c/em\u003e = -0.58), and citric acid (\u003cem\u003eM\u003c/em\u003e = -0.57) had the strongest negative correlation with the 3 bacteria genera. The complete correlation matrix can be found in Supplemental Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHerein, uterine microbiome and metabolome at calving and at metritis diagnosis were compared between cows that did and did not develop metritis. Then, differential bacteria genera and metabolites were used to assess their intra-omics correlation aiming to advance our understanding of the uterine environment in dairy cows with metritis. Because no differences in the microbiome were found at calving, data integration was not performed at calving. At the day of metritis diagnosis, the top 10 metabolites with the greatest median correlation coefficients against \u003cem\u003eFusobacterium, Porphyromonas\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e are discussed. All of these, except for 4-hydroxyphenylacetic acid, O-acetylserine, and tyrosol were also among the top 10 most important annotated metabolites driving the difference in the uterine metabolome between cows with and without metritis. Furthermore, although not within the top 10, the 3 metabolites with the greatest negative correlation coefficients against these bacteria are discussed. Lastly, differentially abundant metabolites at calving that were also differentially abundant at metritis diagnosis are discussed.\u003c/p\u003e \u003cp\u003eWithin the 10 most important metabolites correlated with the most important bacteria at metritis diagnosis, phenylacetic acid and 4-hydroxyphenylacetic acid derive from microbial fermentation of aromatic amino acids, a process particularly prevalent among \u003cem\u003eBacteroides\u003c/em\u003e [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Phenylacetic acid has been described to disrupt quorum sensing and attenuate biofilm formation by \u003cem\u003ePseudomona aeruginosa\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and inhibit growth of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, \u003cem\u003eBacteroides\u003c/em\u003e has been shown to produce phenylacetic acid and 4-hydroxyphenylacetic acid from aromatic amino acids in the absence of glucose. Conversely, bacteria previously associated with a healthy uterus, such as \u003cem\u003eFirmicutes\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] need glucose to ferment aromatic amino acids [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Herein, glucose was lower in the uterine fluid of cows with metritis; therefore, it is possible that the growth and proliferation ability of \u003cem\u003eBacteroides\u003c/em\u003e and other bacteria associated with metritis may be in part mediated by their ability to proliferate in an environment where glucose is lacking. Furthermore, the byproducts of aromatic amino acid metabolism may help opportunistic pathogenic bacteria thrive over uterine commensals.\u003c/p\u003e \u003cp\u003eO-acetylserine is a key intermediate metabolite for de-novo cysteine production by microbes. The synthesis of cysteine is a two-step process, catalyzed by two enzymes, serine acetyltransferase (CysE) catalyzes the first step, producing O-acetylserine. O-acetylserine sulfhydrylase combines O-acetylserine with a sulfur source for the synthesis of cysteine [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The deletion of CysE in \u003cem\u003eSalmonella typhimurium\u003c/em\u003e renders it incapable of synthesizing O-acetylserine, leading to less cysteine, and therefore, lesser glutathione production, increasing its susceptibility to oxidative stress [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The greater abundance of O-acetylserine in cows with metritis together with the strong positive correlation with opportunistic pathogenic bacteria suggests that these bacteria may produce O-acetylserine as a defense mechanism against the reactive oxygen species produced by immune cells. On the other hand, biofilm formation by \u003cem\u003eEscherichia coli and Providencia stuartii\u003c/em\u003e was reduced when O-acetylserine was added to the culture media [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], indicating that O-acetylserine may expose bacteria to the immune system by decreasing biofilm formation. It is plausible, therefore, that opportunistic pathogenic bacteria like \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003ePorphyromonas\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e have a greater production of O-acetylserine compared with uterine commensals, which would protect them against reactive oxygen species in an environment with decreased protection from biofilm.\u003c/p\u003e \u003cp\u003ePipecolic acid is a cyclic amino acid product of lysine degradation by microbes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Bacteria subjected to hyperosmotic stress degrade lysine, leading to an increase in pipecolic acid production, which is believed to contribute to salt tolerance [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The uterus of a dairy cow with metritis can be considered a challenging environment given the strong immune response against pathogens [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. It is not clear why pipecolic acid is increased in cows with metritis but its strong positive correlation with opportunistic pathogenic bacteria suggests that the production of pipecolic acid may be a response mechanism to inflammation in the uterus.\u003c/p\u003e \u003cp\u003eTyrosol is a phenolic compound derived from microbial fermentation of tyrosine [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] that has been shown to reduce biofilm formation by gram-positive bacteria [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and ATP production by \u003cem\u003eEscherichia coli\u003c/em\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Independent free-floating bacterial cells, not protected by biofilm, are more susceptible to nutrient deprivation and phagocytosis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. \u003cem\u003eFusobacterium\u003c/em\u003e is one of the few bacteria able to produce phenols from tyrosine [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; therefore, it is possible that \u003cem\u003eFusobacterium\u003c/em\u003e produces tyrosol to impair the growth of competing bacteria, thereby promoting its own proliferation and dominance in the microbial ecosystem.\u003c/p\u003e \u003cp\u003eHydrocinnamic acid and its metabolite 3-(4-hydroxyphenyl) propionic acid are two bacterial metabolites typically associated with bacterial fermentation of plant cell wall components [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In humans, hydrocinnamic acids are associated with intestinal protective effects by modulating intestinal immunity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], downregulating the activation of TLR- 4/NF-κβ [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and by increasing the Bacteroidota/Bacillota ratio [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Although in humans hydrocinnamic acids are associated with a healthy intestinal environment, in the context of the bovine uterus, a greater abundance of Bacteroidota is associated with uterine disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Downregulation of the immune response against uterine pathogens may predispose dairy cows to metritis development. We have previously shown that cows with metritis had a reduction in monocyte and T-helper activation in the peripheral blood [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and plasma metabolomic changes associated with a reduction in leukocyte activation on the day of metritis diagnosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We hypothesize that the greater levels of hydrocinnamic acids, potentially produced by opportunistic pathogenic bacteria, may be contributing to the immune dysregulation observed in the peripheral blood of cows with metritis.\u003c/p\u003e \u003cp\u003e5-aminovaleric acid is another byproduct of lysine metabolism [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Elevated levels of urinary 5-aminovaleric acid have been associated with intestinal inflammation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Lin et al. (2010) proposed that higher levels of 5-aminovaleric may be caused by inflammation-induced tissue damage, which increases the levels of lysine for intestinal microbiota and the release of 5-aminovaleric acid [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Cows with metritis have extensive endometrial damage and inflammation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] which may provide lysine for bacterial degradation, increasing 5-aminovaleric acid levels in the uterine fluid of cows that developed metritis.\u003c/p\u003e \u003cp\u003e2,6-Diaminopimelic acid is a bacterial cell wall peptidoglycan component found in gram-negative bacteria [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]; therefore, an increase in 2,6-diaminopimelic acid reflects the greater \u003cem\u003eFusobacterium, Porphyromonas\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e abundance in the uterus of cows with metritis.\u003c/p\u003e \u003cp\u003e2-deoxytetronic acid is a sugar acid derivative that acts as an appetite suppressant in rats [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] has been associated with subclinical ketosis in dairy cows [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The role of 2-deoxytetronic acid in the crosstalk between microbes and metabolites in the uterus of cows that develop metritis is unclear and deserves further investigation.\u003c/p\u003e \u003cp\u003eAlthough not within the top 10 metabolites with the greatest absolute correlation with opportunistic pathogenic bacteria, it is also important to mention the metabolites with the greatest negative correlation with opportunistic pathogenic bacteria because these could potentially be used to reduce the abundance of these bacteria in the uterus. Arachidonic acid is a polyunsaturated fatty acid commonly known as a rate-limiting precursor to the synthesis of biologically active eicosanoic acids [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Since the intracellular availability of arachidonic acid is a bottleneck in the biosynthesis of eicosanoic acids [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], immune cells can uptake extracellular arachidonic acid to aid in the production of eicosanoic acids [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Therefore, it is plausible that arachidonic acid is being taken up by immune cells to mount an inflammatory response against the opportunistic pathogenic bacteria.\u003c/p\u003e \u003cp\u003eNicotinamide, a form of vitamin B3, is used by bacteria to form nicotinamide adenine dinucleotide (NAD\u003csup\u003e+\u003c/sup\u003e), releasing nicotinic acid to the extracellular space [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Bacteria use NAD\u003csup\u003e+\u003c/sup\u003e as a co-factor for several enzymes involved in ATP production and bacterial survival [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Interestingly, not only nicotinamide had a strong negative correlation with opportunistic pathogenic bacteria genera, but also nicotinic acid had a strong positive correlation with opportunistic pathogenic bacteria (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50), suggesting that opportunistic pathogenic bacteria may use nicotinamide as a precursor for NAD\u003csup\u003e+\u003c/sup\u003e, boosting microbial growth and survival. Furthermore, given that citric acid is an important substrate for energy production by bacteria [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], the strong negative correlation between citric acid and nicotinamide with opportunistic pathogenic bacteria indicates that these substrates may be used for their growth.\u003c/p\u003e \u003cp\u003eFrom the 49 metabolites with a strong correlation with \u003cem\u003eFusobacterium, Porphyromonas\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e on the day of metritis diagnosis only 7 also differed at calving. Interestingly, of those 7 metabolites, erythritol, xylitol, \u003cem\u003emyo\u003c/em\u003e-inositol, creatinine, and lactamide were greater at calving but lesser at the day of diagnosis indicating that these metabolites may serve as substrate for opportunistic pathogenic bacteria growth. Erythritol and xylitol are sugar alcohols used as substrates for energy production [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. \u003cem\u003emyo\u003c/em\u003e-Inositol is a six-carbon polyol involved in energy metabolism [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] that can be utilized by bacteria as a carbon and energy source for growth and proliferation [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The greater abundance of erythritol, xylitol, and \u003cem\u003emyo\u003c/em\u003e-inositol at calving together with their lesser abundance at the day of metritis diagnosis in cows that developed metritis indicates that opportunistic pathogenic bacteria may be using these metabolites as substrates for growth. Creatinine is a byproduct of muscle metabolism that when exposed to mouse macrophages, leads to a sharp reduction in TLR-2, -3, -4, and \u0026minus;\u0026thinsp;7 transcript levels [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Toll-like receptors are involved in sensing the presence of bacterial antigens prior to their phagocytosis and killing [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. It is possible that the greater levels of creatinine at calving in cows that developed metritis may be contributing to a poorer immune response against opportunistic pathogenic bacteria, leading to their overgrowth. Lactamide is an acyl amide derivative from the amidation of lactic acid [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]; however, lactamide metabolism and role in bacterial proliferation is not clear and deserves further investigation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis observational study provides insights into the uterine microbiome and metabolome at calving and on the day of metritis diagnosis in cows that developed metritis. Furthermore, this is the first-time uterine crosstalk between opportunistic pathogenic bacteria and metabolites on the day of metritis diagnosis has been explored. Altogether, from the 49 metabolites with a strong correlation with opportunistic pathogenic bacteria on the day of metritis diagnosis, the 17 metabolites discussed herein have been described as part of processes associated with attenuation of biofilm formation by commensal bacteria, opportunistic pathogenic bacteria overgrowth, tissue damage and inflammation, immune evasion, and immune dysregulation. The data integration presented herein helps advance the understanding of the uterine environment in dairy cows with metritis. Furthermore, the metabolites described herein may be promising targets for future interventions aiming to reduce opportunistic pathogenic bacteria growth in the uterus, and therefore, reducing the incidence of metritis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003ePCoA:\u003c/strong\u003e Principal coordinate analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePLS-DA:\u003c/strong\u003e Partial least square \u0026ndash; discriminant analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTMR:\u003c/strong\u003e Total mixed ration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eASV:\u003c/strong\u003e Amplicon sequence variant\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePERMANOVA:\u003c/strong\u003e Permutational analysis of variance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLEfSe:\u003c/strong\u003e Linear discriminant analysis effect size\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDIABLO\u003c/strong\u003e: Data integration analysis and biomarker discovery using latent variable approaches for omics studies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDA:\u003c/strong\u003e Linear discriminant analysis score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCysE:\u003c/strong\u003e Serine acetyltransferase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNAD\u003csup\u003e+\u003c/sup\u003e:\u003c/strong\u003e Nicotamide adenine dinucleotide\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involving cows were approved by the Institutional Animal Care and Use Committee of the University of Florida; protocol number 201910623.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequence data generated during this study are available in the NCBI repository under BioProject OR883023 (www.ncbi.nlm.nih.gov/nuccore/OR883023) - OR883397 (www.ncbi.nlm.nih.gov/nuccore/OR883397).\u003c/p\u003e\n\u003cp\u003eThe metabolomics dataset analyzed during the current study is available in the NIH Common Fund\u0026rsquo;s National Metabolomics Data Repository website, the Metabolomics Workbench repository [64] under Study ID ST002994, http://dx.doi.org/10.21228/M8S425. The Metabolomics Workbench is supported by NIH grant U2C-DK119886 and OT2-OD030544 grants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by U.S. Department of Agriculture Grant # 2019-67015-29836, Accession No: 1019435.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJEPS, CDN, SJJ, RCB, JPD, and KNG initiated the study design and provided scientific input during manuscript writing. RSB and RCC provided scientific input during manuscript writing. SC, JGP, and TDG collected the on-farm data. SC and FC performed the laboratory work. SC performed the statistical analyses. KNG and FC provided their scientific input during statistical analyses. SC and KNG wrote the manuscript. All authors edited and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the general manager, Mr. Eric Williams, and the staff of the University of Florida Dairy Unit for allowing the use of their animals and facilities.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePinedo P, Santos JEP, Chebel RC, Galv\u0026atilde;o KN, Schuenemann GM, Bicalho RC, et al. Early-lactation diseases and fertility in 2 seasons of calving across US dairy herds. J Dairy Sci. 2020;103:10560\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eFigueiredo CC, Merenda VR, de Oliveira EB, Lima FS, Chebel RC, Galv\u0026atilde;o KN, et al. Failure of clinical cure in dairy cows treated for metritis is associated with reduced productive and reproductive performance. 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PLoS One. 2012;7:e50876.\u003c/li\u003e\n\u003cli\u003eUr-Rehman S, Mushtaq Z, Zahoor T, Jamil A, Murtaza MA. Xylitol: A Review on Bioproduction, Application, Health Benefits, and Related Safety Issues. Crit Rev Food Sci Nutr. 2015;55:1514\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eTu‐sekine B, Kim SF. The Inositol Phosphate System\u0026mdash;A Coordinator of Metabolic Adaptability. Int J Mol Sci. 2022;23.\u003c/li\u003e\n\u003cli\u003eKrings E, Krumbach K, Bathe B, Kelle R, Wendisch VF, Sahm H, et al. Characterization of myo-inositol utilization by Corynebacterium glutamicum: The stimulon, identification of transporters, and influence on L-lysine formation. J Bacteriol. 2006;188:8054\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eLeland KM, McDonald TL, Drescher KM. Effect of creatine, creatinine, and creatine ethyl ester on TLR expression in macrophages. Int Immunopharmacol. 2011;11:1341\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eGeistlinger K, Schmidt JDR, Beitz E. 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Nucleic Acids Res. 2016;44:D463\u0026ndash;70.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"microbiome, metabolome, multi-omics, metritis, uterine disease, dairy cows","lastPublishedDoi":"10.21203/rs.3.rs-3897972/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3897972/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetritis is a prevalent uterine disease that affects the welfare, fertility, and survival of dairy cows. The uterine microbiome from cows that develop metritis and those that remain healthy do not differ from calving until 2 days postpartum, after which there is a dysbiosis of the uterine microbiome characterized by a shift towards opportunistic pathogens such as Fusobacteriota and Bacteroidota. Whether these opportunistic pathogens proliferate and overtake the uterine commensals could be determined by the type of substrates present in the uterus. The objective of this study was to integrate uterine microbiome and metabolome data to advance the understanding of the uterine environment in dairy cows that develop metritis. Holstein cows (n\u0026thinsp;=\u0026thinsp;104) had uterine fluid collected at calving and at the day of metritis diagnosis. Cows with metritis (n\u0026thinsp;=\u0026thinsp;52) were paired with cows without metritis (n\u0026thinsp;=\u0026thinsp;52) based on days after calving. First, the uterine microbiome and metabolome were evaluated individually, and then integrated using network analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe uterine microbiome did not differ at calving but differed on the day of metritis diagnosis between cows with and without metritis. The uterine metabolome differed both at calving and on the day of metritis diagnosis between cows that did and did not develop metritis. Omics integration was performed between 6 significant bacteria genera and 153 significant metabolites on the day of metritis diagnosis. Integration was not performed at calving because there were no significant differences in the uterine microbiome. A total of 3 bacteria genera (i.e. \u003cem\u003eFusobacterium, Porphyromonas\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e) were strongly correlated with 49 metabolites on the day of metritis diagnosis. Seven of the significant metabolites at calving were among the 49 metabolites strongly correlated with opportunistic pathogenic bacteria on the day of metritis diagnosis. The main metabolites have been associated with attenuation of biofilm formation by commensal bacteria, opportunistic pathogenic bacteria overgrowth, tissue damage and inflammation, immune evasion, and immune dysregulation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe data integration presented herein helps advance the understanding of the uterine environment in dairy cows with metritis. The identified metabolites may provide a competitive advantage to the main uterine pathogens \u003cem\u003eFusobacterium, Porphyromonas and Bacteroides\u003c/em\u003e, and may be promising targets for future interventions aiming to reduce opportunistic pathogenic bacteria growth in the uterus.\u003c/p\u003e","manuscriptTitle":"Integrating uterine microbiome and metabolome to advance the understanding of the uterine environment in dairy cows with metritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 16:42:40","doi":"10.21203/rs.3.rs-3897972/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-13T15:21:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-10T09:14:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"f28cdb90-db12-4ace-840d-0545c7ee5075","date":"2024-02-09T13:26:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-09T12:13:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-09T00:28:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-27T05:06:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Microbiome","date":"2024-01-25T17:44:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"52f43679-25d4-4d89-9dd6-44a990a1f17d","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-02T12:44:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 16:42:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3897972","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3897972","identity":"rs-3897972","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}