Metataxonomic and metabolomic profiling revealed Pinus koraiensis cone essential oil reduced methane emission through affecting ruminal microbial interactions and host-microbial metabolism

Abstract

Background: Pinus koraiensis cone essential oil (PEO) contains functional compounds such as monoterpene hydrocarbons, and the administration of PEO reduced methane (CH₄) emissions during growing phase of goats. However, the mode of action of PEO driven CH₄ reduction is not known, especially how the administration of PEO can affect rumen microbiota and host metabolism in goats during the fattening phase. This study aimed to elucidate the potential microbial and host responses PEO supplementation in goats using metataxonomics (prokaryotes and protozoa) and metabolomics (rumen fluid and serum).

Results: Ten fattening Korean native goats were divided into two dietary groups: control (CON; basal diet without additives) and PEO (basal diet + 1.5 g/d of PEO) with a 2 × 2 crossover design and the treatment lasted for 11 weeks. Administration of PEO reduced CH₄ concentrations in the exhaled gas from eructation by 12.0-13.6% (P < 0.05). Although the microbial composition of prokaryotes (bacteria and archaea) and protozoa in the rumen was not altered after PEO administration. MaAsLin2 analysis revealed that the abundance of Selenomonas, Christensenellaceae R-7 group, and Anaerovibrio were enriched in the rumen of PEO supplemented goats (Q < 0.1). Co-occurrence network analysis revealed that Lachnospiraceae AC2044 group and Anaerovibrio were the keystone taxa in the CON and PEO groups, respectively. Methane metabolism (P < 0.05) was enriched in the CON group, whereas metabolism of sulfur (P < 0.001) and propionate (P < 0.1) were enriched in the PEO group based on microbial predicted functions. After PEO administration, the abundance of 11 rumen and 4 serum metabolites increased, whereas that of 25 rumen and 14 serum metabolites decreased (P < 0.1). Random forest analysis identified eight ruminal metabolites that were altered after PEO administration, among which four were associated with propionate production, with predictive accuracy ranging from 0.75 to 0.88. Additionally, we found that serum sarcosine (serum metabolite) was positively correlated with CH₄ emission parameters and abundance of Methanobrevibacter in the rumen (|r|≥ 0.5, P < 0.05).

Conclusions: This study revealed that PEO administration reduced CH₄ emission from of fattening goats with altered microbial interactions and metabolites in the rumen and host. Importantly, PEO administration affected utilizes various mechanisms such as formate, sulfur, methylated amines metabolism, and propionate production, collectively leading to CH₄ reduction. The knowledge is important for future management strategies to maintain animal production and health while mitigate CH₄ emission.

Keywords: Enteric methane emission; Essential oil; Goat; Metabolomics; Metataxonomics.

Fig. 1

Principal coordinate analysis (PCoA) of the ruminal microbiota of A bacteria and archaea and B protozoa based on the matrices of Weighted UniFrac distance and Unweighted UniFrac distance. CON, without PEO; PEO, Pinus koraiensis cone essential oil

Fig. 2

Compositional profiles of ruminal microbiota in goats, including A bacteria and archaea, B protozoa, and C venn diagrams showing the genera of rumen microbes shared between and unique to the CON and PEO group. D horizontal barplots showing the genera associated with the PEO group, compared to the CON group, as detected by MaAsLin2. Genera with Benjamini–Hochberg false discovery rate-adjusted Q < 0.1 were considered statistically significant for bacteria and archaea. Relative abundance of major phyla and genera (relative abundance ≥ 0.1% in more than 50% animals) for all individuals. CON, without PEO; PEO, Pinus koraiensis cone essential oil; Coef, coefficient; Q-value, P value corrected by the Benjamini–Hochberg method; MaAsLin2; microbiome multivariable association with linear models

Fig. 3

Exclusive co-occurrence and mutual exclusion microbial network in A CON and B PEO oral administration. The node color represents bacteria (white) and keystone genus (skyblue). The keystone genus is selected based on authority and eigenvector centrality measurements within each exclusive network. The edge color represents co-occurrence (blue) or mutual exclusive (red) interactions. The thickness of the edges is adjusted based on the absolute value of the correlation coefficients of each interaction. Only genera accounting for ≥ 0.1% average relative abundance in at least one of the treatments were used. CON, without PEO; PEO, Pinus koraiensis cone essential oil

Fig. 4

Predicted prokaryotic functions (CowPI database) detected using LEfSe (LDA > 2.0, P < 0.05) in the ruminal microbiota of CON and PEO groups. Only the functional parameters accounting for ≥ 0.1% average relative abundance in at least one of the treatments were statistically analyzed by LEfSe. CON, without PEO; PEO, Pinus koraiensis cone essential oil; LDA: linear discriminant analysis; LEfSe: linear discriminant analysis effect size. *P < 0.1, **P < 0.05, ***P < 0.001

Fig. 5

Differential abundance of enzymes involved in A methane and B sulfur metabolism in the CON and PEO groups. Enzymes involved in these metabolic modules are shown in yellow. The blue text represents enzymes enriched in the PEO group, while the red text indicates enzymes enriched or tending to be enriched in the CON group. Inside the navy rectangles are the rumen microbiota that play an important role in the pathway. *P < 0.1, **P < 0.05, ***P < 0.01. Metabolic modules include: M00567: methanogenesis, carbon dioxide to methane, M00563: methanogenesis, methylamine/dimethylamine/trimethylamine to methane, M00357: methanogenesis, acetate to methane, M00356: methanogenesis, methanol to methane, M00596: dissimilatory sulfate reduction, sulfate to hydrogen sulfide (H₂S)

Fig. 6

A Classification of measured metabolites according to chemical class in rumen fluid. B partial least square discriminant analysis (PLS-DA) score plot of rumen fluid. C abundance of carbohydrates, D abundance of propionate precursors and propionate, E abundance of choline, trimethylamine, and formate, I concentration of total VFA, molar proportions of individual VFAs, and AP ratio. H metabolic pathway mapping of common quantified metabolites in the rumen fluid. Selected metabolites met the criteria of P < 0.1 and VIP score ≥ 1.5. CON, without PEO; PEO, Pinus koraiensis cone essential oil; VFA, volatile fatty acid; Others, sum of valerate, isovalerate and isobutyrate; AP, acetate to propionate; VIP, variable importance in projection. *P < 0.1, **P < 0.05

Fig. 7

A Classification of measured metabolites according to chemical class in serum using ¹H-NMR. B partial least square discriminant analysis (PLS-DA) score plot of serum. C abundance of lipids and D amino acids. E metabolic pathway mapping of common quantified metabolites in the serum. F abundance of serum metabolites and liver enzymes using UV spectroscopy and colorimetry method. Selected metabolites obtained from ¹H-NMR met the criteria of P < 0.1 and VIP score ≥ 1.5. CON, without PEO; PEO, Pinus koraiensis cone essential oil; ALT/SGPT, alanine transaminase/serum glutamic pyruvate transaminase; AST/SGOT, aspartate aminotransferase/serum glutamic oxaloacetic transaminase; BUN: blood urea nitrogen. *P < 0.1, **P < 0.05

Fig. 8

Correlation of A rumen and B serum metabolites, and C animal performance parameters with the relative abundance of major bacterial and archaeal and protozoal (green) genera (occupying over 0.1% average relative abundance in at least one of the treatments). Correlation analyses were conducted using Spearman’s rank correlation. Only strong correlation coefficients (|r|≥ 0.6) and significant (P < 0.05) correlations were selected to be shown on the plot. BW, body weight; CH₄, methane; DMI, dry matter intake; DDMI, digestible dry matter intake; NH₃-N, ammonia nitrogen; Others, valerate, isovalerate, and isobutyrate; AP, acetate to propionate

Fig. 9

Prediction of microbe and metabolite co-occurrences in Korean native goats between CON and PEO group. A receiver operating characteristic (ROC) curve and confusion matrix for the random forest model using the eight selected metabolites (shown in navy) with mean decrease accuracy > 2. Biplot drawn from the microbe‑ metabolite vectors (mmvec) co-occurrence probabilities estimated for the dataset of B CON and C PEO groups. Axes correspond to principal components from the singular value decomposition of the microbe-metabolite co-occurrence probabilities estimated using mmvec. Microbes are represented by arrows and metabolites by dots. Heatmaps display the inferred co-occurrence probabilities for various metabolites given the presence of specific microbial taxa in the rumen of goats under B CON and C PEO groups. Colors indicate genera of bacteria (black) and protozoa (green)