Abstract
Heat stress (HS) is an impactful condition in ruminants that negatively affects their physiological and rumen microbial composition. However, a fundamental understanding of metabolomic and metataxonomic mechanisms in goats under HS conditions is lacking. Here, we analyzed the rumen metabolomics, metataxonomics, and serum metabolomics of goats (n = 10, body weight: 41.08 ± 1.83 kg) under optimum temperature period (OTP) (HS-free, temperature humidity index (THI): 57.13 ± 3.98) and high temperature period (HTP) (HS-exposed, THI: 80.27 ± 1.22) conditions, to identify changes in key metabolites and the rumen microbiome induced by HS. Compared to the OTP and HTP conditions, metabolomic analysis revealed significant changes in rumen metabolites related to energy and amino acid metabolism, with HTP goats showing potential rumen metabolic biomarkers, such as butyrate, isopropanol, phenylacetate, and 2-oxoisocaproate (P < 0.001). Serum analysis revealed significant changes in energy metabolism and immune response, with HTP goats showing potential metabolic biomarkers, including acetate, betaine, glucuronate, and kynurenine (P < 0.05). Metataxonomic analysis revealed that HS affected the alpha diversity measurements, including the Chao1 estimate (P < 0.05) and evenness (P < 0.05) between OTP and HTP groups. Through the metabolic association of the rumen microbiome with the metabolome, we found that Fibrobacter and Ruminococcus were enriched in HTP and positively correlated with ruminal microbial metabolites, such as acetate. In addition, Prevotellaceae UCG-003, which was denoted as the keynote genus in the HTP, co-occurred with acetate-producing bacteria such as Quinella and Ruminococcus. Furthermore, we identified that Oscillospiraceae UCG-002, an enriched bacterial genus in HTP, showed a positive correlation with functional features, such as biotin and sulfur metabolism. Our study provided fundamental insights into how HS affected the physiology and rumen microbial compositions of goats and how both microbiome and host-dependent mechanisms contributed to these changes. These findings could potentially suggest strategies for mitigating the adverse effects of HS, including changes in the microbial population and energy metabolism in goats.
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Introduction
The heat stress (HS) environmental condition for ruminants is defined as the temperature humidity index (THI)1, which is a combined function of the ambient temperature and relative humidity. A THI value between 70 and 74 indicates a potential HS environment for ruminants2. The increased release of greenhouse gases from various sources, including agriculture and livestock, has resulted in global warming and increased the HS exposure time-period for ruminants3. HS severely affects ruminants, because of its negative effects on feed intake4, growth performance, reproduction5, and product quality6,7. In addition, HS-exposed ruminants also experience down regulation of immune responses and frequently suffer from metabolic diseases caused by unusual concentrations of certain metabolites8,9,10.
Among the methods for phenotyping organisms, metabolomics is a more comprehensive concept than genomics, proteomics, and/or transcriptomics11. Additionally, it is crucial in systemic biology for determining metabolic diseases, finding potential biomarkers, and identifying novel metabolic pathways in clinical studies12. Proton nuclear magnetic resonance1H-NMR) spectroscopy is one of the most widely metabolomic platforms, because it requires minimal sample preparation; while gas and liquid chromatography-mass spectrometry platforms are more sensitive, the sample-preserving capability and highly reproducible quantifications of1H-NMR serve to offset its lower sensitivity11,13. Numerous studies have investigated metabolite changes in biological samples of heat-stressed ruminants14,15,16,17. However, these have focused on large ruminants (dairy or beef Holstein, Jersey, Angus, etc.), and those on small ruminants (dairy or beef goats, sheep, etc.) are lacking. Goats and sheep play important roles in the economies of millions of people earning their livelihoods by rearing these animals under different weather conditions worldwide18,19. In Korea, goats are the second most important source of meat after Hanwoo cattle (Korean native cattle; Bos taurus coreanae). Therefore, research on the prevention and diagnosis of HS in goats is essential.
Metataxonomics allows a comprehensive analysis of the complex and diverse microbial communities that reside in the rumen and are essential for metabolism and overall health. The structure and function of the rumen microbial community are affected by numerous physical and chemical factors, including diet, feeding programs, animal phenotypes, and environmental factors such as HS3,20. HS can alter the microbial composition in the rumen, leading to physiological changes and consequently those associated with productivity21. Recent research demonstrated that HS directly affects the composition and function of rumen microbial communities. However, these studies did not integrate the rumen and host metabolomes, which is crucial for a comprehensive understanding of rumen physiology22,23. Through examining the linkages between these metabolomes, the metabolic processes that occur in the rumen and how they are influenced by HS can be understood. Therefore, integrating rumen and host metabolomic data is necessary for completely understanding the effects of HS on ruminal physiology.
We hypothesized that HS would alter both the rumen microbiome composition and metabolic profiles, leading to changes in host metabolism in Korean native goats (Capra hircus coreanace). Therefore, this study aimed to perform metabolomics studies using1H-NMR spectroscopy for rumen fluid and serum and metataxonomic studies using 16S rRNA gene amplicon sequencing for rumen bacteria to understand the differential biological responses in Korean native goats under optimum temperature period (OTP) and high temperature period (HTP) conditions. This integrative analysis provides an in-depth understanding of the complex metabolomics, microbiomes, and their potential interactions in heat-stressed goats. Moreover, we suggest strategies to ameliorate under the HS, including changes in the microbial population and energy metabolic disorders in goats.
Results
Multivariate statistical analyses
To characterize the variations in the rumen fluid and serum metabolic profiles of the OTP and HTP conditions, PCA and PLS-DA were conducted (Fig. 1). In the rumen fluid, PCA score plot revealed difference, which were separated in two conditions as THI changed (PC 1: 20.9% and PC 2: 7.9%) (Fig. 1A). In addition, the serum metabolites (PC 1: 25.6% and PC 2: 8.6%) were clearly separated in two periods as THI changed (Fig. 1A). These results showed that expression levels of rumen fluid and serum metabolites were obviously different under OTP and HTP conditions. In the PLS-DA score plots were clearly separated into two conditions as the THI changed from OTP to HTP, indicating changes in the metabolites of the two biological samples (rumen fluid, component 1: 20.6% and component 2: 6.8%; serum, component 1: 26.4% and component 2: 5.5%) (Fig. 1B). Notably, as shown in the PLS-DA score plot, we determined less variation in the serum metabolites obtained during HTP than those obtained during OTP (Fig. 1B).
Principal components analysis (PCA) score plot and partial least square-discriminant analysis (PLS-DA) score of rumen fluid and serum of Korean native goats under optimum temperature period (OTP) and high temperature period (HTP) conditions. (A) PCA and (B) PLS-DA were performed for rumen fluid and serum from Korean native goats under OTP and HTP conditions. On the score plot, each point represents individual samples, with blue dot representing the OTP (n = 10) and red triangle representing the HTP (n = 10). The PCA score plot, x-axis and y-axis represent the variance associated with PC 1 and 2, respectively. The PLS-DA score plot shaded ellipses represent the 95% confidence interval estimated from the score. The x-axis and y-axis represent the variance associated with component 1 and 2 respectively.
Identification of metabolites showing differential abundance between OTP and HTP conditions
Differentially abundant metabolites in the rumen fluid and serum samples were investigated based on their comparative intensities under the OTP and HTP conditions. The metabolomic profiles from rumen fluid and serum were substantially clustered together under OTP and HTP conditions (Fig. 2 and Tables S1-S2). In the rumen fluid, 2-oxoisocaproate, butyrate, valine, leucine, and propionate levels were significantly (P < 0.05) higher in the OTP than in the HTP. In contrast, acetate, isopropanol, benzoate, urea, and dimethyl sulfone level were significantly (P < 0.05) higher in the HTP than in the OTP (Fig. 2A and Table S1). In the serum, lactate, trimethylamine N-oxide, acetate, glucose, and urea levels were significantly (P < 0.05) higher in the OTP than in the HTP. In contrast, glucuronate, formate, 3-hydroxyphenylacetate, glycine, and tyrosine levels were significantly (P < 0.05) higher in the HTP than in the OTP (Fig. 2B and Table S2). The ammonia nitrogen concentration was 7.28 mg/dL in the OTP and 6.90 mg/dL in the HTP, with no significant difference (P > 0.05) between the two conditions (data not shown). As shown in Fig. 3, signature metabolites in rumen fluid and serum samples with VIP scores > 1.5 were identified and ranked using the PLS-DA model. In the rumen fluid, 2-oxoisocaproate (VIP score:2.66), valine (2.663), leucine (2.36), isobutyrate (2.33), and methylsuccinate (2.26) had the highest VIP scores in the OTP than in the HTP (Fig. 3A). In contrast, isopropanol (2.37), benzoate (1.79), and dimethyl sulfone (1.75) exhibited the highest VIP scores in the HTP than in the OTP (Fig. 3A). In the serum, all metabolites had higher VIP scores in the HTP than in the OTP, including galactarate (2.11), glucuronate (2.07), syringate (1.94), glycylproline (1.78), and isoeugenol (1.77) (Fig. 3B). The differential rumen fluid and serum metabolites identified in these analyses could candidate potential biomarkers indicating HS in goats.
Heatmap analysis for differential metabolites in rumen fluid (A) and serum (B) of Korean native goats under optimum temperature period (OTP) and high temperature period (HTP) conditions. Heatmap analysis of differential metabolites obtained by Student’s t-test model analysis (False discovery rate P < 0.05) of Korean native goat’s rumen fluid and serum (n = 10). The red and blue colors in the plot describe high and low intensities, and the value ranges from − 3 to + 3. RF = rumen fluid; SR = serum.
Variable importance in projection (VIP) scores for differential metabolites in rumen fluid (A) and serum (B) of Korean native goat’s under optimum temperature period (OTP) and high temperature period (HTP) conditions (n = 10). The selected metabolites were those with VIP score > 1.5. Heat map with red or blue boxes on the right indicates high and low abundance ratio, respectively, of the corresponding rumen fluid and serum metabolites in OTP and HTP conditions. The VIP score was based on the partial least square-discriminant analysis model.
Metabolic pathway analysis of the metabolomes
Metabolic pathways were identified using significantly different metabolites observed in rumen fluid and serum under OTP and HTP conditions (Fig. 4 and Tables S3-S4). There were eight pathways with impact values higher than 0.1, which is the cut-off value for relevance associated with rumen fluid, including phenylalanine, tyrosine and tryptophan biosynthesis (impact value: 0.50); phenylalanine metabolism (0.36); alanine, aspartate, and glutamate metabolism (0.31); glycine, serine, and threonine metabolism (0.30); and cysteine and methionine metabolism (0.15) (Fig. 4A and Table S3).
Metabolic pathway mapping significantly differs in rumen fluid (A) and serum (B) (n = 10) metabolites under optimum temperature period and high temperature period conditions. The pathway analysis was performed using Metaboanalyst 5.0 program. The x-axis and y-axis represent pathway impact value and -log 10 P value, respectively. The results are presented graphically as a bubble plot. The darker color and larger size represent higher P value from enrichment analysis and greater impact from pathway topology analysis, respectively. Metabolic pathway names: A = phenylalanine, tyrosine and tryptophan biosynthesis; B = phenylalanine metabolism; C = alanine, aspartate and glutamate metabolism; D = glycine, serine and threonine metabolism; E = cysteine and methionine metabolism; F = glyoxylate and dicarboxylate metabolism; G = citrate cycle (TCA cycle); H = arginine and proline metabolism; I = glutathione metabolism; J = pentose and glucuronate interconversions; K = ascorbate and aldarate metabolism; L = biotin metabolism; M = starch and sucrose metabolism; N = histidine metabolism; O = tryptophan metabolism; P = tyrosine metabolism.
In the serum samples, 13 pathways with impact values higher than 0.1 were observed, including phenylalanine, tyrosine, and tryptophan biosynthesis (0.50); glutathione metabolism (0.34); glycine, serine, and threonine metabolism (0.34); pentose and glucuronate interconversions (0.30); and ascorbate and aldarate metabolism (0.25) (Fig. 4B and Table S4). Results of the enrichment and impact pathways of rumen fluid and serum identified several common metabolic pathways for numerous combinations of the two metabolomes. Five common metabolic pathways were identified: phenylalanine, tyrosine, and tryptophan biosynthesis; alanine, aspartate, and glutamate metabolism; and glycine, serine, and threonine metabolism.
Composition of rumen prokaryotic communities
In total, 1,907,001 sequences were obtained from 16S rRNA gene amplicon sequencing analysis of the two groups. After quality filtering using QIIME 2 (Q score > 25), 772,199 quality-controlled sequences were generated with an average of 38,610 ± 10,232 (mean ± SD) sequences per sample. The sequencing depth for the analysis of rumen microbiota was deemed sufficient, as Good’s coverage values were greater than 99.9% for all samples (data not shown). The alpha diversity measurements and Chao1 estimates (P < 0.05) were significantly higher in the rumen fluid samples of the HTP, whereas evenness (P < 0.05) was lower than that in the OTP. The Shannon and Simpson indices did not differ between the OTP and HTP conditions (Fig. 5A). PCoA plot constructed based on the Weighted UniFrac distance (P < 0.05) and Unweighted UniFrac distance (P < 0.001) revealed that the HTP rumen microbiota clustered separately from that of the OTP (Fig. 5B).
(A) Alpha- and (B) Beta- diversity of Korean native goats under optimal temperature period (OTP) and high temperature period (HTP) conditions (n = 10). Alpha diversity was analyzed using a Student’s t-test. Beta-diversity was calculated based on Weighted and Unweighted UniFrac distances using Principal Coordinates Analysis (PCoA) plots. Statistical analysis was performed using PERMANOVA. (C) The relative abundance of the rumen bacteria phyla and top 10 genera present in both groups.
Venn diagrams were used to compare the bacterial phyla, families, and genera (both classified and unclassified at the genus level) detected in the OTP and HTP conditions, revealing both shared and exclusively detected taxa (Fig. S1). At the phylum level, 17 of the 18 detected phyla were shared between OTP and HTP conditions. The phylum of bacteria that were unclassified was exclusively found in the HTP. Of the 64 detected family levels, 56 were shared, whereas 2 and 6 were exclusively found in the OTP and HTP conditions, respectively. At the genus level, 103 of the 119 detected genera were shared between the OTP and HTP conditions, and only 7 and 9 genera were exclusively detected in the OTP and HTP conditions, respectively.
At the phylum level, Bacteroidota (54.1% vs. 53.0%, FDR = 0.774), Firmicutes (30.6% vs. 27.9%, FDR = 0.506), Proteobacteria (4.33% vs. 7.50%, FDR = 0.396), and Verrucomicrobiota (2.56% vs. 3.28%, FDR = 0.396) were the enriched bacteria under the OTP and HTP conditions (Fig. 5C and Table S5). At the genus level, Prevotella (24.4% vs. 23.7%, FDR = 0.903), Rikenellaceae RC9 gut group (12.6% vs. 14.1%, FDR = 0.594), Quinella (6.29% vs. 4.92%, FDR = 0.645), and Bacteroidales RF16 group (5.15% vs. 3.17%, FDR = 0.587) were the enriched genera under the OTP and HTP conditions (Fig. 5C and Table S6).
Identification of rumen microbiota showing differential abundance between OTP and HTP conditions
We identified the differentially abundant microbial phyla and genera between the OTP and HTP conditions using LEfSe analysis (LDA > 2.0, P < 0.05) (Fig. 6). At the phylum level, Desulfobacterota was enriched in the OTP, whereas Fibrobacterota was enriched in the HTP. At the genus level, 10 genera were enriched in the OTP: Papillibacter, Prevotellaceae NK3B31, Muribaculaceae, [Ruminococcus] gauvreauii, Lachnospiraceae ND3007, Lachnospiraceae NK3A20, Lachnospiraceae XPB1014, Desulfovibrio, Butyrivibrio, and F082. In contrast, four genera were enriched in the HTP: Fibrobacter, Anaeroplsma, Ruminococcus, and Oscillospiraceae UCG-002.
Rumen bacterial compositional profiles of Korean native goats under optimum temperature period (OTP) and high temperature period (HTP) conditions (n = 10). (A) Phylum (B) Cladogram and (C) LDA effect size of significantly different taxa identified in the rumen microbiota data sets of goats based on the cut-off LDA > 2.0 and P < 0.05. Only major classified taxa (each representing ≥ 0.1% in at least one of the dietary treatments) were visualized.
Rumen microbial interactions identified using co-occurrence networks
We used genera accounting for ≥ 0.1% average relative abundance in at least one of the OTP and HTP groups for the co-occurrence network analysis. It revealed 52 and 7 significant interactions under the OTP and HTP conditions, respectively, from among the 148 overall edges exclusively found in the rumen microbiota (Table S7 and Fig. S2). Based on two centrality measurements (authority and eigen centrality), Paillibacter and Prevotellaceae UCG-003 were the keystone genera under the OTP and HTP conditions, respectively. In the OTP, Papillibacter co-occurred with five genera: Bacteroidales BS11 gut group, Succiniclasticum, Anaerovorax, UCG-010, and Muribaculaceae; whereas they were mutually exclusive with five genera; Flexilinea, Oscillospira, Hungateiclostridiaceae UCG-012, Clostridia vadinBB60 group, and Methanobrevibacter. In the HTP, Prevotellaceae UCG-003 co-occurred with four genera; Quinella, Ruminococcus, and Hungateiclostridiaceae UCG-012; whereas negative interactions were observed with three genera; UG Lachnospiraceae, Oscillospiraceae NK4A214, and Anaerovibrio.
Functional changes in rumen microbiota
To predict the functional biomarkers of HS in goats, we performed PICURSt2 analysis. Seven different reference databases were used to identify the predicted functional features of rumen microbiota; however, no significant differences were observed between the OTP and HTP conditions (Table S8). We identified differentially abundant KEGG pathways and modules between the OTP and HTP conditions using LEfSe analysis (LDA > 2.0, P < 0.05) (Fig. 7). A total of 11 major KEGG pathways were identified between the two conditions. Among these, nine pathways were enriched in OTP, whereas two pathways were enriched in HTP. The enriched metabolisms in the OTP were as follows: nitrotoluene degradation (ko00633), quorum sensing (ko02024), porphyrin (ko00860), nitrogen (ko00910), methane (ko00680), butanoate (ko00650), glycoxylate and dicarboxylate metabolism (ko00630), arginine and proline (ko00330) metabolisms, and microbial metabolism in diverse environment (ko01120) were enriched. In contrast, biotin (ko00780) and sulfur (ko00920) metabolisms were enriched in HTP.
Prediction of differentially abundant KEGG pathways and modules in Korean native goats under optimum temperature period (OTP) and high temperature period (HTP) conditions (n = 10). The LDA effect size of significantly different (A) KEGG pathways and (B) modules identified in the rumen microbiota data sets of goats based on the cut-off (LDA > 2.0 and P < 0.05). Only major classified taxa (each representing ≥ 0.1% in at least one of the dietary treatments) were visualized. Only strong correlation (|r| ≥ 0.5, P < 0.05) were shown on the heat map. LEfSe linear discriminant analysis effect size, LDA linear discriminant analysis, KEGG kyoto encyclopedia of genes and genomes, UCG uncultured genus-level group, UG unclassified genus.
Furthermore, to identify the linkages between the KEGG pathways and rumen microbiota, we performed Spearman’s rank correlation analysis (Fig. S3). The relative abundance of Fibrobacter positively correlated with biotin metabolism (ko00780) and negatively correlated with Butyrivibrio. Notably, most genera were correlated with methane metabolism (ko00680), which was negatively correlated with Anaeroplasma and Oscillospiraceae UCG-002, and positively correlated with Lachnospiraceae, [Ruminococcus] gauvreauii, F082, Desulfovibrio, Butyrivbrio, Prevotellaceae NK3B31, and Methanobrevibacter.
Regarding the metabolism of cofactors and vitamins, 4 out of 10 were enriched in OTP (cobalamin biosynthesis; M00122, M00924, and M00925; molybdenum cofactor biosynthesis; M00880), while 6 out of 10 were enriched in the HTP (heme biosynthesis; M00121, M00926, and M00868; NAD biosynthesis; M00912; pimeloyl-ACP biosynthesis; M00572; and coenzyme A biosynthesis; M00120). Regarding the energy metabolism, two out of three were enriched in the OTP (incomplete reductive citrate cycle; M00620 and reductive citrate cycle; M00173), whereas one out of three was enriched in the HTP (NADH: quinone oxidoreductase; M00144). Xenobiotics biodegradation (phenylacetate degradation; M00878), signature modules (beta-Lactam resistance; M00627), and carbohydrate (CHO) metabolism (citrate cycle; M00009) were only enriched in the OTP, while nucleotide (De novo purine biosynthesis; M00048) and amino acid (AA) (cysteine biosynthesis; M00021) metabolism, lipid (fatty acid biosynthesis; M00083) metabolisms, and biosynthesis of other secondary metabolites (aurachin biosynthesis; M00848) were only enriched in the HTP.
Microbe-metabolites interactions associated with HS
A total of 149 metabolites were identified in the rumen metabolomes, and subjected to Spearman’s rank correlation analysis to select HS associated metabolites. Based on the correlation analysis results, a total of 35 rumen metabolites were considered to be HS associated metabolites (|r| ≥ 0.5, P ≤ 0.5) and used for predicting HS using the RF model. For each metabolite, the mean decrease accuracy score was calculated to evaluate its contribution to the model’s predictive accuracy. This score represents the average reduction in classification accuracy when the given metabolite is excluded from the predictors, providing a direct measure of its importance. Based on metabolome results, four metabolites, butyrate, isopropanol, phenylacetate, and 2-oxoisocaproate, were selected using the RF model with a mean decrease accuracy > 3 (Fig. 8). The constructed model had an AUC of 0.930 to 1, indicating a high level of accuracy in predicting HS. Multi-omic biplots depicting microbe-metabolite interactions under OTP and HTP conditions are shown in Fig. 8B,C, respectively. Additionally, heatmaps were generated to visualize the inferred conditional probabilities (> 1) of specific metabolites, revealing distinct interaction patterns between microbes and metabolites under the OTP and HTP conditions.
Prediction of microbe and metabolite co-occurrences in Korean native goats under optimum temperature period (OTP) and high temperature period (HTP) conditions (n = 10). (A) Receiver operating characteristic (ROC) curve and confusion matrix for the random forest model using the four selected metabolites (shown in blue) with mean decrease accuracy > 3. Biplot drawn from the microbe‑metabolite vectors (mmvec) conditional probabilities estimated for the dataset of (B) OTP and (C) HTP conditions. Axes correspond to principal components from the singular value decomposition of the microbe-metabolite conditional probabilities estimated using mmvec. Microbes are represented by arrows and metabolites by dots. Heatmaps display the inferred conditional probabilities for various metabolites given the presence of specific microbial taxa in the rumen of goats under (B) OTP and (C) HTP conditions.
Relationship between rumen metabolome and microbiome, and serum metabolome
Spearman’s rank correlation analysis was performed to identify the linkages between rumen metabolites (FDR < 0.05) and microbiota (LDA > 2.0, P < 0.05) (Fig. 9A). A total of 35 metabolites were strongly correlated (|r| ≥ 0.5, P ≤ 0.5). The relative abundance of Fibrobacter was positively correlated with acetate concentration and negatively correlated with the relative abundance of Desulfovibrio, Lachnospiraceae XPB1014, Muribaculaceae, F082, Butyrivibrio, and Papillibacter. Isopropanol concentration was positively correlated with the relative abundances of Fibrobacter, Anaeroplasma, and Oscillospiraceae UCG-002. Butyrate concentration was positively correlated with the relative abundance of Lachnospiraceae ND3007, Lachnospiraceae NK3A20, Desulfovibrio, and Prevotellaceae NK3B31 and negatively correlated with the relative abundance of Ruminococcus. The concentration of phenylacetate was negatively correlated with the relative abundance of Oscillospiraceae UCG-002, Ruminococcus, and Fibrobacter and positively correlated with the relative abundance of [Ruminococcus] gauvreauii, Lachnospiraceae NK3A20, Desulfovibrio, Lachnospiraceae XPB1014, Lachnospiraceae ND3007, and Desulfovibrio.
Interactions between rumen metataxonome, metabolome and serum metabolome under optimum temperature period and high temperature period conditions (n = 10). (A) Spearman’s rank correlations between rumen microbiota and rumen metabolites. (B) Spearman’s rank correlations between rumen microbiota and serum metabolites. Only strong (correlation coefficient |r| ≥ 0.5) and significant (P < 0.05) correlations were selected to be displayed. Color intensity represent the magnitude correlation. Blue = positive correlation; red = negative correlation.
The results of Spearman’s rank correlation analysis between serum metabolites (FDR < 0.05) and rumen microbiota revealed that 35 metabolites had strong correlation (|r| ≥ 0.5, P ≤ 0.5) (Fig. 9B). Acetate concentration was negatively correlated with the relative abundance of Fibrobacter, Anaeroplasma, Oscillospiraceae UCG-002, and Ruminococcus and positively correlated with the relative abundance of [Ruminococcus] gauvreauii, Lachnospiraceae NK3A20, Desulfovibrio, Lachnospiraceae XPB1014, Lachnospiraceae ND3007, Muribaculaceae, and Butyrivibrio. The kynurenine concentration was negatively correlated with the relative abundance of Desulfovibrio and [Ruminococcus] gauvreauii.
Discussion
We performed a comprehensive analysis of the rumen metataxonomic, along with rumen and serum metabolomics, to gain insight regarding the complex interactions between the rumen microbiome and host metabolome, which plays a critical role in regulating physiological responses in HS goats. Using this integrated approach, we identified and quantified the relative effects of rumen microbial composition, function, and metabolites on the observed variations due to HS.
Amino acids metabolites, which are essential for rumen function and animal performance24,25 are principally obtained from protein and microprotein degradation by the rumen microbiota26. In the present study, AA metabolites were found to be significantly affected by HS. AA metabolites (leucine, glycine, methionine, creatine) (P < 0.05) and 2-oxoisocaproate (P < 0.0001, VIP score: 2.66) concentrations were significantly higher in OTP than in HTP. These AA are known to enhance metabolism, antioxidant status, and immunity27,28,29, while 2-oxoisocaproate, a metabolic intermediate of leucine30, is related with amino-acetyl-tRNA biosynthesis31. The significant difference in amino-acetyl-tRNA biosynthesis (P < 0.0001) between conditions further suggests that HS substantially affects ruminal AA metabolism.
Under HS, decreased feed intake leads to lower blood glucose concentrations, potentially affecting energy metabolism and liver function32,33,34. When glucose supply is insufficient, increased lipolysis leads to higher production of ketone bodies including acetone35,36. In the rumen, acetone is converted to isopropanol37,38, suggesting that isopropanol could serve as an indirect biomarker of HS. In this study, isopropanol concentration was significantly higher (P < 0.0001 and VIP score: 2.37) in HTP than in OTP, while serum glucose was higher (P < 0.05) in OTP. Additionally, glycolysis and gluconeogenesis showed significant differences (P < 0.01) between conditions. Due to nutrient scarcity under HS, AA metabolism increases in the liver, resulting in elevated blood AA concentrations39,40. Consistent with previous studies in HS cows and steers that showed increased levels of glucogenic amino acids40,41,42, we found significantly higher concentrations of glutamine, methionine, tyrosine (P < 0.05), and glycine (P < 0.01) in HTP. Several AA metabolic pathways, particularly phenylalanine, tyrosine and tryptophan biosynthesis (P < 0.01, impact value: 0.5), were significantly altered. The acetate concentration was significantly higher (P < 0.05) in OTP than in HTP, which is notable as acetate has been associated with anti-inflammatory effects43 and serves as an important lipogenic substrate in ruminants44.
Kynurenine which has immunomodulatory properties43 was significantly higher (P < 0.01) in HTP than in OTP. As kynurenine is related with inflammatory responses and energy balance45,46, its increased concentration could be related to HS. In our study, kynurenine showed a negative correlation with the abundance of Desulfovibrio and [Ruminococcus] gauvreauii group in OTP, and tryptophan metabolism was significantly different (P < 0.001, impact value: 0.15) between conditions. Similarly, betaine and glutathione, which have anti-inflammatory properties47, showed significantly higher concentrations (P < 0.01) in HTP than in OTP, consistent with previous findings in HS cows14. Glutathione metabolism also showed significant differences (P < 0.01, impact value: 0.34) between conditions. Given that HS adversely affects immunological functions in ruminants48, these metabolites such as kynurenine, betaine, and glutathione could serve as potential serum biomarkers for HS.
Regarding rumen microbiota, we found differences in the Chao1 estimate and evenness between the OTP and HTP conditions, indicating that HS affected ruminal microbial composition. This is consistent with the results of a previous study on Holstein cows, except for evenness3. However, our results contradicted those of who reported that alpha diversity was not affected by HS in chamber settings21. The differences in the effects of HS on ruminal microbial composition among different studies may be due to several factors, including exposure duration or intensity21, animal breed49, age50, and gender50. Additionally, changes in rumen and serum metabolomes under HS conditions, such as alterations in amino acid metabolism and increased stress-related metabolites, provided important insights into the physiological responses of goats to HS.
Biotin metabolism, which is essential nutrient for both rumen microbes and the host and a cofactor for various enzymes required for AA, CHO, and fatty acids metabolisms51. Most rumen fibrolytic bacteria require biotin for growth52, to improve rumen fiber fermentation53. This is consistent with our finding that Fibrobacter is positively correlated with biotin metabolism. Moreover, serum biotin concentration was positively correlated with Oscillospiraceae UCG-002, which was more abundant in the HTP. This genus is associated with the activation of energy metabolism such as glycolysis in the rumen54. Cows with high milk yield have abundant biotin metabolism, suggesting a critical role of biotin in milk production22. Although our study did not focus on host production, it is possible that the role of biotin in energy metabolism could be particularly important during HS, which causes the animals to lend more energy for thermoregulation and other stress-related responses.
Here, we identified certain co-occurring and mutually exclusive relationships between genera exclusively detected in the OTP and HTP conditions. Notably, ruminal microbiota of the OTP exhibited significantly higher levels of co-occurrence or mutual exclusion relationships compared to the HTP, with a six-fold difference (52 vs. 9). In HTP, Prevotellaceae UCG-003 was mutually exclusive with butyrate-producing bacteria such as UG Lachnospiraceae55 and Anaerovibrio56, whereas it co-occurred with acetate-producing bacteria, such as Quinella57 and Ruminococcus58. The findings of our study differ from those of who reported mutual exclusion between Prevotella and Ruminococcus21. However, it is important to note that the experimental conditions of these two studies differed. In contrast to our study, Zhong et al.21 did not consider the effects of thermal conditions on co-occurrence relationships, which may have influenced the observed patterns of bacterial interactions. However, another study that considered the effects of thermal conditions on the co-occurrence relationships showed that OTP in dairy cows have a mutually exclusive relationship with Prevotella and Ruminococcus3. These conflicts among prior reports and our findings may be explained by the fact that ruminal microbiota may differ among animal species under HS. Nevertheless, our results identified the co-occurrence and mutual exclusion relationships exclusively detected for OTP and HTP conditions in the ruminal microbiota of goats. These findings suggested that HS can alter the response of these genera of microbiota in goats, highlighting the importance of considering various animal species in studies regarding the effects of HS on the ruminal microbiota.
In the present study, we found that HS resulted in the differential enrichment of acetate and butyrate in the rumen, suggesting a shift in the metabolic pathways of ruminal microbes. HS-induced reduction in feed intake has been reported to cause changes in VFAs production and energy requirements59,60 with changes in rumen microbial abundance being a main factor affecting rumen fermentation characteristics61. While previous studies reported decreased acetate concentrations during HS in ruminants62,63, we found significantly higher acetate concentration (P < 0.0001) in HTP than in OTP. This aligns with findings in HS buffaloes, where acetate increased due to microbial adaptation to HS63, and in certain goat breeds (Osmanabadi and Malabari) where increased acetate concentration was attributed to differences in rumen microbe abundance and feed digestibility64,65. Our results suggest that acetate increased in HTP goats to compensate for HS-induced energy deficiency.
In the HTP, acetate-producing bacteria, particularly Fibrobacter and Ruminococcus, were enriched. Fibrobacter, which breaks down plant-based cellulose to produce acetate66, was highly enriched in the rumen microbial community of HTP despite higher Chao1 estimates and lower evenness. This enrichment was supported by its positive correlation with ruminal acetate concentration. While this increase in Fibrobacter contradicted previous findings in HS goats21, but aligned with observations in HS dairy cows20. In our study, it is possible that the Fibrobacter had stronger heat resistance than the other ruminal bacteria as suggested by Kim et al.20.
Previous studies have reported increased butyrate concentrations in ruminants exposed to HS. However, in the present study, butyrate concentrations were significantly lower (P < 0.0001) in the HTP than in the OTP. Pragna et al. (2018)49 similarly reported that the concentration of butyrate in the three goat breeds significantly decreased under HS. Butyrate is important factor in the postnatal development of ruminal epithelium67, which is responsible for many important physiological functions including absorption, transport, and short-chain fatty acid metabolism68. While the genus Butyrivibrio was enriched in OTP without correlation to butyrate concentration, it showed the highest probability of co-occurrence with butyrate in HTP. Although the HTP had lower butyrate concentrations, the role of Butyrivibrio in butyrate production may be more important under HTP. Regarding phenylacetate metabolism, which promotes cellulose degradation and growth of rumen microbiota including Ruminococcus sp69,70,71, we found significantly higher phenylacetate concentrations (P < 0.001) in OTP than in HTP. Phenylacetate showed a positive correlation with the [Ruminococcus] gauvreauii group in OTP, and the concentration of 3-phenylpropionate also tended to be higher (0.05 ≤ P ≤ 0.01) in OTP. The observed co-occurrence probability between microbiota and phenylacetate in HTP suggests that HS affects these metabolic relationships. Further research is needed to understand the underlying mechanisms of these HS-induced changes in the ruminal microbiome.
Most genera that showed significantly higher abundance in the OTP belonged to the Lachnospiraceae family, indicating that two of its main genera, Butyrivibrio and Lachnospiraceae, are present in the rumen and are involved in butyrate and acetate synthesis55. In the present study, Lachnospiraceae ND3007 group and Butyrivibrio were enriched > 2-fold in the OTP, and the Lachnospiraceae NK3A20 group showed > 5-fold enrichment. These genera were positively correlated with butyrate concentrations in the rumen fluid, suggesting that they genera play an essential role in butyrate biosynthesis. Additionally, Papillibacter is involved in butyrate production72 and was enriched in OTP, even though no correlation was observed with butyrate concentration in the present study. Desulfovibrio, which serves as a butyrate-oxidizing bacterium in the rumen, was also positively correlated with butyrate concentration and was enriched in the OTP. The enrichment of butyrate-producing or butyrate-oxidizing bacteria in under the OTP suggested that they may have played a role in the increased butanoate metabolism observed during this period. Additionally, the higher butyrate concentration observed under the OTP may be associated with the enrichment of the butanoate metabolism pathway. Therefore, it is possible that the observed enrichment of these bacteria under the OTP contributed to the overall increase in butyrate production, potentially through the butanoate metabolism pathway.
Conclusions
In conclusion, our study investigated the changes in metabolic and rumen microbial populations in Korean native goats under HS. We observed differential expression of metabolites under OTP and HTP conditions. We found that several metabolites (butyrate, isopropanol, phenylacetate, and 2-oxoisocaproate in the rumen fluid and acetate, betaine, glucuronate, and kynurenine in the serum) were significantly differently altered between the two periods, and hence could potentially be used as HS biomarkers in goats. Furthermore, our analysis of rumen and serum metabolomes highlight the importance of considering these factors to comprehensively understand the effects of HS on rumen microbial composition. Specifically, we observed that the main acetate-producing bacteria in the rumen, such as Fibrobacter and Ruminococcus, were enriched in the HTP, whereas butyrate-producing bacteria such as the families Lachnospiraceae, Butyrivibrio, and Papillibacter were enriched in the OTP. The observed enrichment was consistent with the concentrations of the rumen metabolites. To the best our knowledge, this is the first study to use multi-omics tools for investigating the physiological responses of goats to HS, thus providing novel into changes in microbial diversity. By identifying changes in the concentrations of acetate and butyrate in the rumen, our study provided evidence regarding the physiological responses of goats to HS. These findings contribute to a more comprehensive understanding of the effects of HS on animal health, productivity and may have implications for developing strategies for mitigating the adverse effects of HS in goats.
Materials and methods
Animal ethic statement
All methods and experimental protocols were carried out in accordance with the guidelines and regulations of the Gyeongsang National University Institutional Animal Care and Use Committee (GNU-IACUC) and approved under protocol number GNU-210,705-E0063. The experiments were carried out at the Gyeongsang National University Animal Breeding Farm from May 1 to August 11 in Jinju, Gyeongsangnamdo, Republic of Korea (35.12.35° N,128.08.21° E). All animal studies followed the ARRIVE guidelines (https://arriveguidelines.org). The goats used in this study were not sacrificed and were returned to their normal housing and management conditions at the conclusion of the experiment.
Experimental design, animals and diet
A total of 10 Korean native goats [Capra hircus coreanae, 41.08 ± 1.83 kg (mean ± standard deviation), male] were used in the study. Goats were fed a diet composed of tall fescue hay and a commercial concentrate in a 50:50 ratio to meet nutrient requirements, as per National Research Council recommendations73. Diet was provided twice daily in two equal meals at 0800 h and 1600 h. To encourage forage consumption before concentrate intake, tall fescue hay was offered first, followed by the concentrate mix. Although feed intake was not specifically measured, all animals were observed during feeding to ensure consistent access to their assigned diet. The study was conducted over two distinct 10-day periods, corresponding to different temperature-humidity index (THI) conditions: optimal temperature period (OTP, May 1–10) and high temperature period (HTP, August 2–11). This resulted in a total study duration of 20 days, with the goats exposed to each period once. The THI was calculated using temperature and humidity data collected every hour with a temperature and humidity meter (Testo 174 H Mini data logger; West Chester, PA, USA). The THI equation used was as follows73.
The average THI values for OTP and HTP were 57.13 ± 3.98 and 80.27 ± 1.22, respectively. Rumen fluid and serum samples were collected at the end of each period for comparative analysis to assess the effects of HS on goats.
Feed sampling and analyses
Dried feed samples (tall fescue and concentrate) were ground through a 1 mm sieve using a Wiley Mill (Arthur Thomas CO., Philadelphia, PA) and submitted to Cumberland Valley Analytical Services Inc. (Waynesboro, PA) to be analyzed by wet chemistry methods for dry matter (DM, AOAC International, 2000; method 930.15), crude protein (CP, AOAC International, 2000; method 990.03), ether extract (EE, AOAC International, 2006; method 2003.05), ash (AOAC International, 2000; method 942.05), minerals (AOAC international, 2000; method 985.01), amylase-treated neutral detergent fiber [aNDF74), , acid detergent fiber (ADF, AOAC international, 2000; method 973.18), neutral detergent insoluble crude protein (NDICP, Leco FP-528 N Combustion Analyzer), acid detergent insoluble crude protein (ADICP, Leco FP-528 N Combustion Analyzer), lignin, and starch. Non-fiber carbohydrates (NFC) were calculated according to the equation; NFC = 100 – [(CP – NDICP) + EE + ash + NDF]. Net energy for maintenance was calculated using the OARDC Summative Energy Equation. The chemical composition of the tall fescue and commercial concentrate are presented in Table 1.
Rumen fluid and blood sampling analyses
Rumen fluid contents were collected with oral stomach tubing (length of 150 cm and a diameter of 0.8 cm) from each animal at before morning feeding. Briefly, samples of rumen fluid contents were collected by inserting an oral stomach tube to a depth of about 20 cm, so that the probe head could reach the central rumen fluid. To minimize contamination from the saliva, the first 20 mL of each rumen fluid sample was discarded. (n = 10)75. The samples were centrifuged at 806 × g at 4 ℃ for 15 min to remove feed particles, and the supernatant was stored at − 80 ℃ for1H-NMR spectroscopy analysis and filtered rumen fluid (5 mL) was centrifuged at 20,000 × g at 4 ℃ for 15 min, the supernatant was discarded, and the pellet was stored at − 80 ℃ for microbial analysis, respectively.
On d 20 of each sampling period before the morning feeding, blood from the jugular neck vein was collected in a serum-separating tube (BD Vacutainer, SST™ II advance, Becton Dickinson Co., Franklin Lakes, NJ, USA) from goats. The blood samples were centrifuged at 1006 × g at 4 ℃ for 15 min, and the serum was stored at -80 ℃ until 1H-NMR spectroscopy analysis.
Samples preparation and1H-NMR spectroscopy analysis
The rumen fluid sample was centrifuged at 12,902 × g at 4 ℃ for 10 min, and 300 µL of the supernatant was collected. A standard buffer solution (2,2,3,3-d(4)-3-(trimethylsilyl) propionic acid [TSP] sodium salt) was added to 300 µL of the supernatant in deuterium oxide (D2O) solvent/standard buffer solution (300 µL). The supernatants (600 µL) were transferred to 5 mm NMR tubes for1H-NMR spectroscopy anaslysis76,77. We prepared saline buffer (concentration of 0.9% wt/vol) by applying NaCl in 100% D2O solvent. The stored serum samples were centrifuged at 14,000 × g at 4 ℃ for 10 min. The supernatant (200 µL) was added to 400 µL of saline buffer in 5 mm NMR tube for1H-NMR spectroscopy analysis77,78.
The spectra of rumen fluid and serum were obtained son a SPE-800 MHz NMR-MS Spectrometer (Bruker BioSpin AG, Fällanden, Switzerland) equipped with a 5 mm triple-resonance inverse cryoprobe with Z-gradients (Bruker BioSpin Co., Billerica, Massachusetts, USA). The pulse sequence used for the rumen fluid and serum were a Carr-Purcell-Meiboom-Gill pulse sequence. We collected 64,000 data points with 128 transients, and spectral width of 16,025.641 Hz, a relaxation delay of 4.0 s, and an acquisition time of 2.0 s79.
Metabolites identification, quantification and statistical analyses
Metabolite identification and quantification were carried out by importing the analyzed spectral data into the Chenomx NMR suite 8.4 software (ChenomxInc, Edmonton, Alberta, Canada). The baseline and phase were matched for comparison between samples using the Chenomx processor. The spectral width was δ 0.2 to 10.0 mg/kg and was referenced to the TSP signal at 0.0 mg/kg. Qualitative and quantitative metabolite analyses were performed using the Livestock Metabolome Database (http://www.lmdb.ca), Bovine Metabolome Database (http://www.bmdb.ca), and the Chenomx profiler. Statistical analyses of the metabolite data were performed using MetaboAnalyst 5.0 (http://www.metaboanalyst.ca). To perform a standard cross-sectional two-periods study, we compared the periods of OTP and HTP conditions. The resulting data were processed by normalization-selected methods, followed by sample normalization via normalization to constant sum, data transformation via log normalization, and data scaling via pareto scaling80. The rumen fluid and serum metabolite data with 50% of samples under the identification limit or with at least 50% of values missing were eliminated from the analysis. The metabolites that were missing positive in the original data. The univariate Student’s t-test was used to quantify differences between in the metabolite profiles of the rumen fluid and serum under the OTP and HTP conditions. P values were corrected for false discovery rate (FDR) and P < 0.05 and 0.05 ≤ P ≤ 0.01 were considered as significant and tendency effects, respectively. Principal components analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were used as multivariate data analysis techniques to generate a classification model and provide quantitative information for discriminating rumen fluid and serum metabolites. The different groups of rumen fluid and serum metabolites from OTP and HTP conditions were determined based on a statistically significant threshold of variable importance in projection (VIP) scores. Metabolites with VIP scores higher than 1.5 were obtained using the PLS-DA model. Metabolic pathway analysis was performed using the Bos taurus pathway library [Kyoto Encyclopedia of Genes and Genomes (KEGG), http://www.kegg.com]. Significantly different metabolic pathways in the rumen fluid and serum metabolites of the other study animals were statistically analyzed and determined using MetaboAnalyst 5.0, which is based on the database source by KEGG.
Metataxonomic sequencing and data processing
Total DNA of rumen fluid (1.8 mL) was extracted using the RBB + C method81. The quality and quantity of extracted DNA were evaluated using a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). To amplify the V3-V4 region from the 16S rRNA gene (primer set: forward: 5′-CCTACGGGNGGCWGCAG-3′, reverse, 5′- GACTACHVGGGTATCTAATCC-3′) were used. 16S rRNA gene amplicon was sequenced using Illumina MiSeq platform (San Diego, CA, USA) at Macrogen, Inc. (Seoul, Korea). Fastq files obtained from MiSeq paired-end sequencing data were analyzed using QIIME2 (version 2021.11)82. Briefly, after demultiplexing the sequences, the barcode and primer sequences were removed using Cutadapt83. Then, the DADA2 plugin was used to denoise the forward and reverse reads with quality filtering (Q-score > 25) and merged, which was followed by chimera removal84. Taxonomic classification was performed using the naïve Bayes taxonomy classifier manually-trained on Silva (SSU138) 16S rRNA gene database (clustered at 99% similarity; 341 F/805R region)85. Unassigned, mitochondria, and chloroplast were excluded before downstream analysis. To reduce the sampling heterogeneity, the ASV table was rarefied to the same reads per sample (20,080 ASVs) with 1,000 times using the ‘q2-repeat-rarefy’ plugin from QIIME286. Microbial diversity was evaluated within samples (alpha diversity) or between samples (beta diversity) on rarefied ASVs table. Alpha diversity was evaluated using richness (Chao1 estimate), Evenness, Simpson’s index, and Shannon’s index. Beta diversity was evaluated using phylogenetic distance of Weighted UniFrac and Unweighted UniFrac. Prediction of metabolic functions (KEGG modules and pathways) from the rumen microbiota was performed using the PICRUSt2 tool (v.2.4.1)87.
Bioinformatics and statistical analysis
Spearman’s rank correlation coefficient (|r| ≥ 0.5, P ≤ 0.5) between microbes at the genus level and metabolites in host serum and rumen fluid, were identified using PROC CORR procedure SAS 9.4. The correlation heatmap was generated using the R software “pheatmap” package. To understand relationship among the taxonomic groups of the major genera (relative abundance ≥ 0.1%) in the OTP and HTP conditions, the co-occurrence network analysis was generated using the ‘FastSpar’88 which use SparCC algorithm89. Comparison of each exclusive networks were accomplished by use of Co-expression Differential Network Analysis (CoDiNA)90. To define network statistics, we used the built-in plugins in Gephi (v. 0.9.2)91 to calculate measurements of centrality (i.e., eigenvector centrality and authority). The “randomForest (RF)” package in R was used for the RF analysis92. The rumen metabolites were used as inputs in the RF model. For each metabolite, a mean decrease accuracy score was calculated based on the increase in error caused by removing that metabolite from the predictors. This score reflects the importance of metabolites in the model. The best predictive model was identified based on the maximum area under the curve (AUC), using the “pROC package” in R93. To minimize potential overfitting, we applied a 10-fold cross-validation approach using the “trainControl” package in R94.
To predict the probabilities of co-occurrence between microbial genera and metabolites in host rumen fluid, we employed microbe-metabolite vectors (mmvec’s) neural network-based approach, which infers the nature of interactions across omics datasets95. The interactions between microbes and metabolites were ranked and visualized through the standard dimensionality reduction interface that is implemented as a plugin in QIIME2 (Version 2021.2)82.
Data normality was analyzed using Shapiro-Wilk test in SAS 9.4 (SAS Institute Inc., NC, USA). Normally distributed data were further analyzed using t-test. For abnormally distributed data, a non-parametric Wilcoxon rank-sum test, and P values were corrected for a false discovery rate using the Benjamini-Hochberg method, with FDR-corrected P < 0.05 being considered significant. The resulting distance matrices served as inputs for principal coordinates analysis (PCoA) and significance of sample clustering was analyzed by permutational multivariate analysis of variance (PERMANOVA) with 9,999 permutations. The differential relative abundances of the rumen microbiota and its predicted metabolic categories were analyzed via linear discriminant analysis effect size (LEfSe) analysis using Galaxy web application96. The normalized ASV counts in each sample were used as the input for the LEfSe analysis. Linear discriminant analysis uses a nonparametric factorial Kruskal Wallis and Wilcoxon rank sum test followed by a linear discriminate analysis to estimate the effect size of each taxon. A significance level of P < 0.05 and effect size threshold of 2 were applied in the trial to identify the biomarker taxa. Statistical significance was set to P < 0.05, and a tendency of difference was declared at 0.05 ≤ P ≤ 0.10.
Data availability
All data presented in this study are available on request from the corresponding authors. The rumen metagenome sequences were deposited into NCBI Sequence Read Archive (SRA) under the accession number PRJNA931831 (https://www.ncbi.nlm.nih.gov/bioproject/ PRJNA931831).
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Acknowledgements
This research was supported by a grant from the National Research Foundation (NRF) of Republic of Korea, funded by the Korean Government (grant number: NRF- 2021R1A6A3A010874521261382116530102).
Funding
This research was supported by a grant from the National Research Foundation (NRF) of Republic of Korea, funded by the Korean Government (grant number: NRF- 2021R1A6A3A010874521261382116530102).
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Author contributionsJun Sik Eom: Conceptualization, Methodology, Software, Writing-Original Draft, Project administration, Funding acquisition. Youyoung Choi: Conceptualization, Methodology, Software, Writing-Original Draft, Visualization. Shin Ja Lee: Supervision, Software. Hyun Sang Kim: Methodology, Data curation, Software. Seong Uk Jo: Methodology, Investigation. Dongryeoul Bae: Methodology, Supervision. Lim Dong-Hyun: Methodology. Eun Tae Kim: Software, Methodology. Sang Bum Kim: Validation, Resources. Sung Sill Lee: Conceptualization, Validation, Resources, Project administration, Funding acquisition.
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All animal experiments were performed in accordance with the guidelines (protocol number: GNU-210705-E0063) of the Institutional Animal Care and Use Committee (GNU-IACUC). The experiments were carried out at the Gyeongsang National University Animal Breeding Farm from May 1 to August 11 in Jinju, Gyeongsangnam-do, Republic of Korea (35.12.35 °N, 128.08.21 °E).
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Eom, J.S., Choi, Y., Lee, S.J. et al. Integrated analysis of rumen metabolomics and metataxonomics to understand changes in metabolic and microbial community in Korean native goats under heat stress. Sci Rep 14, 31416 (2024). https://doi.org/10.1038/s41598-024-83017-y
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DOI: https://doi.org/10.1038/s41598-024-83017-y
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