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Cooling redistributed endotoxin across different biofluids via modulating the ruminal microbiota and metabolome without altering quorum sensing signal levels in heat-stressed beef bulls

Animal Microbiome volume 7, Article number: 38 (2025) Cite this article

Abstract

Background

Cooling is one of the most common and economical methods to ameliorate heat stress (HS), and it has been discovered to alter the lipopolysaccharide (LPS) endotoxin level in ruminants. However, whether the endotoxin variation induced by cooling relates to the quorum sensing (QS) within the ruminal microflora remains unknown. The current study was consequently performed to examine whether cooling could influence the endotoxin distribution across different biofluids, ruminal microbiota, and ruminal metabolisms through affecting the QS of rumen microorganisms in beef cattle exposed to HS. Thirty-two Simmental bulls were used as experimental animals and randomly assigned to either the control (CON) group, or the mechanical ventilation and water spray (MVWS) treatment. The temperature-humidity index (THI) was recorded throughout this trial, and samples of the rumen liquid, blood, and urine were collected.

Results

Cooling significantly lowered (P < 0.05) the temperature-humidity index (THI), ruminal endotoxin, and endotoxin concentration and excretion in urine, and significantly raised endotoxin level in blood (P < 0.05), but did not change the ruminal concentrations of QS signals including 3-OXO-C6-HSL and the AI-2 (P > 0.05). The linear discriminant analysis effect size (LEfSe) analysis revealed that Prevotellaceae, Rikenellaceae, Monoglobales and their affiliated members, as well as other bacterial taxa were significantly differently (P < 0.05) enriched between the two treatments. The Tax4Fun2 prediction suggested that QS function was upregulated in MVWS compared to CON. The metabolomic analysis indicated that cooling altered the ruminal metabolism profile and downregulated the pathways of lysine degradation, phenylalanine, tyrosine and tryptophan biosynthesis, and ubiquinone and other terpenoid-quinone biosynthesis. The significant (P < 0.05) correlations of the differential bacteria and metabolites with endotoxin and QS molecules were also demonstrated through Spearman analysis.

Conclusions

Based on the results of this trial, it could be speculated that the cooling reshaped the endotoxin distribution across different biofluids through manipulating ruminal microbiota and metabolome, which might involve the participation of QS. Further investigations are warranted to disclose and verify the mechanisms for those correlations found in this study.

Background

The climate warming has been posing an increasingly stern challenge to global ruminant husbandry [1], since the ruminants are susceptible to the heat stress (HS) caused by the deteriorations of multiple environmental factors including the ambient temperature, relative humidity, thermal radiation, and air movement [2]. It has been commonly acknowledged that, as a critical environmental stressor, HS would impair both the production and reproduction performances of domestic ruminants [3, 4]. In particular, Wang et al. [5] reported the rise in the flux of bacterial lipopolysaccharide (LPS) endotoxin across the portal-drained and mesenteric-drained viscera under HS condition, which could successively lead to the expression of pro-inflammatory cytokines and the systematic inflammation, as HS injures the barrier integrity of gastro-intestinal tract of ruminants [6, 7].

A few studies including our precedent investigations have revealed that cooling (a major method of thermal climate management) [8], as well as the nutritional interventions such as raising dietary cation–anion difference [6] and supplementing L-theanine [9] both altered endotoxin concentrations in different biofluids (i.e., rumen fluid, blood, milk, and urine) and attenuated relevant inflammatory responses in dairy cows subjected to HS. Nevertheless, although mitigation strategies targeting HS and their effects have been intensively investigated in dairy production system, information on the solutions for ameliorating HS of beef cattle is somewhat lacking [10].

Quorum sensing (QS) is a universal cell-to-cell communication mode also possessed by the ruminal microbiome [11, 12], and its regulatory role in the LPS endotoxin production of some typical gram-negative bacteria has been verified [13]. However, whether the HS mitigation strategies that have been found to modulate the endotoxin level in the rumen fluid could influence QS within the ruminal bacterial populations is still uncertain. Therefore, the present study was performed to explore the impacts of cooling by mechanical ventilation and water spray on the endotoxin contents across multiple biofluids (i.e., rumen fluid, blood, and urine) and the QS signaling molecules, together with the reactions of the ruminal bacterial microflora and ruminal metabolisms in beef cattle.

Methods

Animals, management, and diet

Regarding all the relevant animal procedures, this study was conducted with the supervision and approvement from the Animal Care Committee (approval number: 20220626), College of Animal Science and Technology, Hunan Agricultural University, Changsha, China. The current trial was carried out from the July to August of 2022 at a beef cattle farm in Huaihua, China. Thirty-two healthy Simmental bulls averaging 420 ± 47 kg (initial mean ± SD) of body weight were used as the experimental animal following a completely randomized design. All the bulls were randomly assigned to either the control (CON) group or the mechanical ventilation and water spray (MVWS) treatment. Cattle of these two treatments were separately housed in two tie-stall barns, and the MVWS barn was equipped with fans and sprinklers. More specifically, a total of 6 fans (motor speed: 710 rpm) were uniformly set on the both sides of the MVWS barn, with the height at 2.5 m, an interval of 6 m, and an angle at 60° to the horizontal. A sum of 52 sprinklers (each water flow: 0.02–0.05 L/min) were evenly installed at the height of 1.7 m and the interval of 1.5 m. The fans and sprinklers worked constantly from 08:00 to 20:00 on each day, and automatically paused if the temperature in the barn dropped below 25 °C. Bulls from the two treatments were fed the same basal total mixed ration (TMR) diet (Table 1) ad libitum twice (06:00 and 18:00) per day, and provided with free access to fresh water. The whole experiment consisted of 15 days of adjustment and 30 days of data and sample collection.

Table 1 Constituents and nutritional composition of the basal TMR ration
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Data and sample collection

Throughout the 30 days for collecting data and samples, the temperature-humidity index (THI) value was calculated and recorded thrice (08:00, 14:00, and 20:00) daily as previously described [9], using 12 thermo-hygrometers (9013, Deli Group, Ningbo, China) equably placed in the two barns at the height of 1.5 m. Cattle were considered as being exposed to HS when the THI rose above the threshold of 72 [14]. The collection and preparation of samples from the rumen fluid, blood, and urine was performed using sterilized tools on the day 30 and 45, in accordance to the operations detailedly illustrated in our precedent report [6]. Additionally, the rumen liquid sampled 2 h before 4 h after morning feeding were equally mixed. For the full-length 16S rRNA gene sequencing and metabolomic analysis, six bulls from each treatment were randomly chosen and their rumen fluid samples were thence used. All the above samples were preserved at − 20 °C until the subsequent analysis. The data for production performance, rumen fermentation characteristics, and physiological indexes of the bulls in this experiment has been published elsewhere [15].

Chemical and biochemical analysis

The nutritional contents of the basal TMR diet were analyzed as priorly depicted [16]. For the measurement of LPS endotoxin in the rumen liquid, blood, and urine, the chromogenic endpoint Tachypleus Amebocyte Lysate assay kit (EC80545S, Chinese Horseshoe Crab Reagent Manufactory Co. Ltd, Xiamen, China) was employed referring to our previously introduced procedures [6, 9]. The urine volume of each cattle was estimated by employing urinary creatinine as the marker [17, 18], and it was further used to assess the daily discharge of endotoxin via the urine.

The signaling molecule autoinducer-2 (AI-2) of QS was detected on a 1260 high performance liquid chromatography equipped with a fluorescence detector (HPLC-FD) system (Agilent, Santa Clara, USA) using a ZORBAX Eclipse XDB-C18 column (250 mm × 4.6 mm, 5 μm) (Agilent, Santa Clara, USA), by following the general approaches developed by Song et al. [19]. As to the QS signals of acyl-homoserine lactones (AHLs), the quantification was conducted on the ExionLC 30A-QTRAP 5500 ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) system (AB SCIEX, Framingham, USA) with an ACQUITY BEH C18 column (2.1 × 100 mm) (Waters, Milford, USA), as depicted in previous study [20].

Full-length 16S rRNA gene sequencing analysis

The genomic DNA was firstly extracted from the rumen fluids by adopting the phenol-free bead-beating method introduced by Yu and Morrison [21], and was subsequently used to amplify the full-length 16S rRNA genes through the universal primers 27F (50-AGRGTTTGATYNTGGCTCAG-30) and 1492R (50-TASGGHTACCTTGTTASGACTT-30) with barcodes. The amplicon sequencing was performed on the PacBio Sequel II platform (Pacific Biosciences, Menlo Park, United States) with single-end reads generated. The raw sequencing data pretreatment consisted of the circular consensus sequencing (CCS) reads recognition, CCS reads quality filtering, and chimera sequence removal was conducted by referring to approaches depicted in our precedent studies [22]. The operational taxonomic unit (OTU) taxonomic annotation was carried out using the SILVA database (release 138) with a confidence threshold at 80%, and OTUs abundance information was normalized using a standard of sequence number corresponding to the sample with the fewest sequences. The analysis for Alpha diversity and Beta diversity were performed through the QIIME (version 1.9.1) and R software (version 3.5.0), and Tax4Fun2 (version 1.1.5) function prediction were consecutively operated complying with the methods adopted in our prior investigations [16, 22, 23]. All the raw sequences generated in this study were submitted to the sequence read archive (SRA) of the NCBI database under the accession number PRJNA1066970.

Metabolomics analysis

The metabolomic analysis for the rumen liquid samples was fulfilled through the successive processes of the metabolites isolation, UHPLC-MS analysis, raw data treatment, metabolites classification, and metabolomics data analysis, by adopting our previous approaches [24]. Metabolites respectively identified under the positive and negative polarity mode were combined, and the principal components analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were performed following the internal standard normalization and data logarithmic conversion. Annotation for the metabolic pathways and relevant enrichment analysis were accomplished using the MetaboAnalyst (version 5.0) by referring to the Kyoto encyclopedia of genes and genomes (KEGG) database (release 110.1) [26].

Statistical and data analysis

To evaluate the effects of cooling on the endotoxin concentration and excretion, QS signaling molecules, and Alpha diversity indices, the corresponding data were analyzed using SPSS statistics (version 23.0, IBM Corporation, Armonk, United States) through the unpaired t-test, whilst the THI index data recorded during this trial were analyzed by adopting the GLMM model. Statistical difference was considered as significant, highly or extremely significant at the adjusted P < 0.05, < 0.01 or < 0.001, respectively. The linear discriminant analysis effect size (LEfSe) was used to contrast the relative abundances of microbial taxa between the two treatments, and significant differences were declared with a linear discriminant analysis (LDA) score > 3 and adjusted P < 0.05. The differential predicted functions and differential metabolites between treatments were examined through the rank-sum Wilcoxon test, and differential metabolites were further annotated as significantly different with the variable importance in projection (VIP) value > 1 and adjusted P < 0.05. The Spearman correlation analysis was employed to check the interconnections between endotoxin, QS signals, bacterial taxa (the overall 18 phyla, the top 30 genera and species), and significantly differential metabolites. The model-based integration of metabolite observations and species abundances 2 (MIMOSA2, version 2.1.0) [25] analysis was performed by combining the data of 16S rRNA gene sequencing and metabolites annotation in the present investigation.

Results

THI, endotoxin, and QS signals

Throughout the entire period of data and sample collection, even though the most majority of the average daily THI indexes recorded in both of the two barns exceeded the threshold at 72, the averaged THI value in MVWS was significantly lower than that in the CON (P < 0.05) (Fig. 1A). Besides, cooling highly significantly or significantly decreased the LPS endotoxin densities in the rumen fluid (P < 0.01) and urine (P < 0.01), as well as the urinary excretion of endotoxin (P < 0.05) (Fig. 1B–E). Nonetheless, the endotoxin concentration in the blood of MVWS bulls was extremely higher than their counterparts in the CON (P < 0.001). The 3-OXO-C6-HSL and the AI-2 were the only two QS signaling molecules detected in the rumen fluid of this trial, and significant difference was observed in none of these two molecules between the two treatments (P > 0.05) (Fig. 1F–G).

Fig. 1
figure 1

Effects of cooling on the THI index, endotoxin, and QS signaling molecules. *: significant (P < 0.05); **: highly significant (P < 0.01); ***: extremely significant (P < 0.001). A Averaged daily THI values in the two treatment barns throughout the period for data and sample collection, B LPS endotoxin concentration in the rumen fluid. C LPS endotoxin concentration in the blood, D LPS endotoxin concentration in the urine, E estimated LPS endotoxin excretion through the urine, F 3-OXO-C6-HSL belonging to the AHLs. G AI-2

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Ruminal bacterial community

In the current trial, cooling was found to exert profound influences on the entire ruminal bacterial microbiota. It was firstly revealed that none of the Alpha diversity indexes for the ruminal bacterial microbiota in the heat-stressed bulls was influenced by cooling (P > 0.05) (Fig. 2A–D, Table S1), and no treatment-dependent clustering of the bacterial populations was presented through the either the unweighted Unifrac-based or the weighted Unifrac-based Principal coordinate analysis (PCoA) analysis (Fig. 2E–F). According to the LEfSe diagram showing the significantly differential taxa (Fig. 2G–H, Tables S2, S3, S4), the bacterial family Prevotellaceae and its sequentially subordinate genera of Prevotella and Prevotellaceae UCG-001, as well as species of uncultured_rumen_bacterium_g_Prevotella and uncultured_rumen_bacterium_g_Prevotellaceae_UCG-001 were significantly enriched in the CON (P < 0.05), whilst the uncultured_rumen_bacterium_g_Sphaerochaeta, unclassified_g_Lachnospiraceae_UCG-006, unidentified_rumen_bacterium_RFN85, and unclassified_g_Acetitomaculum were also identified with significantly higher abundances in CON compared to MVWS (P < 0.05). In comparison, the species of uncultured_bacterium_g_Rikenellaceae_RC9_gut_group and uncultured_rumen_bacterium_g_Rikenellaceae_RC9_gut_group, the genera Rikenellaceae RC9_gut_group, and their upper family Rikenellaceae were successively significantly enriched in MVWS (P < 0.05). Besides, the relative abundances of Monoglobales, Monoglobaceae, and Monoglobus were significantly increased in MVWS compared with the CON (P < 0.05), from the order to the genus level. Moreover, the order no_rank_c_Clostridia, the genus UCG-004, and the species unclassified_g_Christensenellaceae_R-7_group were also discovered with significantly more quantities in MVWS (P < 0.05).

Fig. 2
figure 2

Effects of cooling on Alpha diversity indices, Beta diversity, and bacterial taxa relative abundances of the ruminal bacterial microbiota. A Ace index, B chao index, C Shannon index, D Simpson index, E principal coordinate analysis (PCoA) profile based on the unweighted Unifrac matrix, F PCoA profile based on the weighted Unifrac matrix, G Cladogram displaying significantly enriched bacterial taxa (LDA score > 3.0 and P < 0.05) from the phylum to the species level through the LEfSe analysis. Red: taxa abundant in CON; Blue: taxa abundant in MVWS, H LEfSe bar chat displaying the significantly differential taxa between treatments. The LDA scores represented the difference in relative abundance with exponent fold change of 10 across treatments

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The Spearman analysis suggested that the phylum WPS-2 was significantly positively (P < 0.05) related to the endotoxin level in blood and 3-OXO-C6-HSL in rumen liquid, and Planctomycetota was significantly positively (P < 0.05) connected to the AI-2 (Fig. 3A). At the genus level, a significantly positive relation (P < 0.05) between the norank_f_Bacteroidales_BS11_gut_group and the endotoxin excretion in urine was marked, while the significantly negative and positive relationship (P < 0.05) between Anaeroplasma and endotoxin in blood and its discharge through urine were observed, respectively (Fig. 3B). The norank_f_Lachnospiraceae was found significantly positively (P < 0.05) related to blood endotoxin concentration, and Eubacterium_hallii_group was significantly positively (P < 0.05) connected to the urinary endotoxin level. A significantly negative connection between UCG-002 and 3-OXO-C6-HSL, as well as the significantly positive relationships between AI-2 and bacterial phyla including Sphaerochaeta, norank_f_p-2534-18B5_gut_group, Acetitomaculum, and p-1088-a5_gut_group were exhibited. In terms of the bacterial species, the Ruminococcus_sp._g_Ruminococcus and uncultured_rumen_bacterium_g_Eubacterium_hallii_group were both significantly positively (P < 0.05) related to the urinary endotoxin, whilst uncultured_rumen_bacterium_g_Anaerovorax was significantly negatively (P < 0.05) connected to the endotoxin content in urine (Fig. 3C). The molecule 3-OXO-C6-HSL was significantly negatively (P < 0.05) correlated to uncultured_rumen_bacterium_g_UCG-002. As to the AI-2, its significantly positive (P < 0.05) correlations with uncultured_rumen_bacterium_g_norank_f_p-2534-18B5_gut_group and unclassified_g_Lachnospiraceae_NK3A20_group, and significantly negative (P < 0.05) connections with uncultured_rumen_bacterium_g_Prevotellaceae_UCG-003 and unclassified_g_Prevotella were illustrated, respectively.

Fig. 3
figure 3

Correlations between endotoxin, QS signals, and bacterial taxa, and the Tax4Fun2 prediction for the ruminal bacterial populations. A Correlations between endotoxin, QS signals, and bacterial taxa at the phylum level. Red: positive; Blue: negative, B correlations between endotoxin, QS signals, and bacterial taxa at the genus level, C correlations between endotoxin, QS signals, and bacterial taxa at the species level, D Wilcoxon test for the predicted potential functions for the ruminal bacterial microflora between treatments based on the Tax4Fun2 analysis

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As was illustrated through the Tax4Fun2 prediction with high accuracy, a sum of 19 potential functions of the ruminal bacterial microbiome were classified as significantly differentially expressed between the CON and MVWS (Fig. 3D). The relative abundances of bacteria participating in metabolic pathways, riboflavin metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and other 10 functions in the CON were significantly higher (P < 0.05) than those in MVWS. By contrast, the QS, bacterial chemotaxis, flagellar assembly, histidine metabolism, C5-branched dibasic acid metabolism, and salmonella infection were predicted to be significantly upregulated (P < 0.05) in the rumen bacteria of the MVWS cattle.

Ruminal metabolome

The effects of cooling on the ruminal metabolisms of the heat stressed bulls were demonstrated through the metabolomic analysis of this study. Although no separation in the metabolic profiles between the two treatments was depicted in the PCA chart (Fig. 4A), the OPLS-DA score diagram suggested a clear treatment-dependent clustering of the metabolite profile of rumen fluid (Fig. 4C), with its model validity confirmed by the permutation test (Fig. 4B). It was further revealed that 31 metabolites amongst the 710 totally detected in this trial (Table S5) were classified as significantly different (P < 0.05) in the comparison of MVWS to CON, within which the 2-naphthalenesulfonic acid, cholic acid, and other 6 metabolites were upregulated, while aminomethyl fluorescein, vanilloylglycine, and other 19 metabolites were downregulated (Fig. 4D, Table S6).

Fig. 4
figure 4

Effects of cooling on the profile of the ruminal metabolites, and the correlations between endotoxin, QS signals, differential metabolites, and bacterial species. A Principal component analysis (PCA) plot, B permutation test plot of the orthogonal projections to latent structures-discriminant analysis (OPLS-DA), C OPLS-DA score scatter plot, D variable importance in projection (VIP) bubble plot for the differential metabolites, E correlations between endotoxin, QS signals, and the differential metabolites, F model-based integration of metabolite observations and species abundances 2 (MIMOSA2) analysis illustrating the potential correlations between the bacterial species and the differential metabolites

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A considerable portion of the differential metabolites decreased in MVWS, e.g., the N-acetylproline, caproic acid, and 4-hydroxybenzoic acid, were discovered being significantly negatively (P < 0.05) connected to the blood endotoxin (Fig. 4E), whereas the significantly positive (P < 0.05) correlations between some of these downregulated metabolites and the endotoxin in rumen fluid and its discharge through urine were noticed. In addition, gingerol, 4-hydroxybenzoic acid, and salicylic acid were significantly positively (P < 0.05) related to 3-OXO-C6-HSL, and these metabolites were all reduced in MVWS. Furthermore, the increased palmitoylcarnitine and the decreased DL-2-aminooctanoic acid in MVWS were found to be significantly negatively and positively (P < 0.05) interrelated to AI-2, respectively.

As was portrayed through the MIMOSA2 analysis based on the combination of 16S rRNA gene sequencing and metabolomics analysis (Fig. 4F), the species Lactobacillus mucosae_LM1 positively contributed to the increment of cholic acid in MVWS, while the negative connections between cholic acid and Lactobacillus amylovorus, Eubacterium cellulosolvens_6, Eubacterium limosum, and Bifidobacterium longum were also shown.

Based on the KEGG enrichment analysis, a total of 10 metabolic pathways were identified as differential pathways between the two treatments (Fig. 5A, Table S7). It was further manifested by the differential abundance score plot that amongst the above 10 differential pathways, the lysine degradation, phenylalanine, tyrosine and tryptophan biosynthesis, and ubiquinone and other terpenoid-quinone biosynthesis were marked as extremely significantly (P < 0.001), highly significantly (P < 0.01), and significantly (P < 0.05) downregulated in MVWS compared with CON, respectively (Fig. 5B). Besides, the significant (P < 0.05) upregulation of bile secretion and choline metabolism in cancer in MVWS was also observed. By referring to the reference map of the KEGG database, the significantly different pathways of phenylalanine, tyrosine and tryptophan biosynthesis, and ubiquinone and other terpenoid-quinone biosynthesis were further integrated, with the involving differential metabolites exhibited alongside (Fig. 5C).

Fig. 5
figure 5

Effects of cooling on the ruminal metabolomic pathways. A Bubble plot for the KEGG enrichment analysis of the differential metabolomic pathways between treatments. The size of the bubble represents the number of the enriched differential metabolites for each pathway, B differential abundance score plot for the differential metabolomic pathways in the comparison of MVWS to CON. The size of the bubble represents the number of the enriched differential metabolites for each pathway, C integrated significantly differential metabolic pathways between treatments. The down arrow indicates that the metabolite was significantly downregulated (P < 0.05) in the comparison of MVWS to CON

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Discussion

The present study was designed to investigate whether cooling, the primary anti-HS strategy, could change the endotoxin distribution via influencing the bacterial QS function as well as the overall ruminal bacterial microbiome and metabolome. It was firstly observed that, despite the fact that the overwhelming majority of the daily THI values of the both two barns were beyond the mild HS threshold at 72 [14], the averaged THI of the CON barn exceeded the threshold of high HS at 78 [27], whilst the averaged THI of the MVWS barn fell below that value. This discrepancy indicated that the bulls receiving cooling were exposed to less severe HS throughout this trial, testifying the apparent mitigatory effects of cooling as one of the most practicable and efficient strategies against HS [28]. Unlike the L-theanine supplementation at 16 g/d that raised the LPS endotoxin level in the rumen fluid of heat-stressed dairy cows in our prior investigation [9], the decrement of the ruminal endotoxin by cooling was noticed in the present experiment, which implied that the endotoxin production of the ruminal gram-negative bacteria could be reduced. However, it was also surprisingly noted that the blood endotoxin content in MVWS were higher than the CON, which was yet in agreement with the previous finding that cooling increased endotoxin level in the blood of dairy cows under HS [8]. Further in this study, both the concentration and elimination of endotoxin in the urine declined in response to cooling, which might partially lead to the above-mentioned increasing accumulation of endotoxin in the peripheral blood. This speculation requires deeper investigations to be examined.

In the current trial, cooling seemed to trigger the variations in the abundances of several bacterial taxa at different levels without altering the QS signals, as well as the Alpha and Beta diversity of the bacterial community in rumen liquid. It was noteworthy that the family Prevotellaceae and its subordinates (i.e., Prevotella, Prevotellaceae_UCG-001, uncultured_rumen_bacterium_g_Prevotella, and uncultured_rumen_bacterium_g_Prevotellaceae_UCG-001) were reduced in MVWS, when compared with the CON. These Prevotellaceae members have been found to contribute to less weight gain of goat, mice, and human [29,30,31]. In fact, the bulls treated with cooling of this experiment tended to show greater average daily gain (ADG) [15], which was consistent with the reductions of the above Prevotellaceae-related taxa in MVWS group. As a negative correlation between the genus Lachnospiraceae UCG-006 and the butyric acid concentration has been discovered [32], thence the decline of ruminal butyrate in the CON of this trial [15] might partially result from the enrichment of unclassified_g_Lachnospiraceae_UCG-006. Moreover, the enrichment of unclassified_g_Acetitomaculum in the CON could also explained the ADG that tended to decrease in the same group [15] to some extent, since the ruminal Acetitomaculum spp. has been priorly found negatively interconnected with the ADG of sheep [33].

Qiu et al. [34] discovered that the Rikenellaceae RC9_gut_group affliated to the Rikenellaceae family was positively correlated to the rumen pH, and the ratio of acetate to propionate, which was further verified by the augment of Rikenellaceae and its subordinate taxa (Rikenellaceae RC9_gut_group, uncultured_rumen_bacterium_g_Rikenellaceae_RC9_gut_group, and uncultured_bacterium_g_Rikenellaceae_RC9_gut_group) along with the rises in ruminal pH and the acetate-to-propionate ratio [15] presented in the MVWS of this experiment. Besides, it was also reported that Rikenellaceae RC9_gut_group and Monoglobus were associated with the rumen epithelial morphology in different manners [35, 36]. Therefore, the exact effects of the simultaneously increments in these two genera observed in the MVWS of this study on the rumen epithelium barrier function and the corresponding translocation of ruminal endotoxin needed to be further elaborated, considering the possibly impaired barrier function of gastro-intestine caused by intensive cooling [37]. The Christensenellaceae R-7_group is known as a probiotic butyrate-producer that basically decomposes fiber and protein, and it could improve rumen development and immune function [38, 39]. The enrichment of unclassified_g_Christensenellaceae_R-7_group noted might thereby partially result in the increases of ruminal butyric acid, immunoglobulin (Ig) A, and IgG in MVWS [15], yet this assumption necessitates further verification.

The bacterial phylum WPS-2 has been occasionally found at relatively minor proportions in the rumen microbiome, with its role in ruminal ecosystem remaining undefined [40, 41]. In this study, the positive correlations between WPS-2, blood endotoxin, and 3-OXO-C6-HSL were noticeable, suggesting that more researches are warranted to disclose the explicit functions of WPS-2 concerning QS and endotoxin translocation. The suppressive effect of Anaeroplasma on the cellulolysis by a few ruminal fungi and bacteria, as well as its stimulative impact on the propionate production have been priorly marked [42]. In this trial, the negative and positive correlation of Anaeroplasma to the blood endotoxin and urinary endotoxin excretion were respectively shown, implying that Anaeroplasma might participate in the endotoxin elimination through urination. Similarly, Eubacterium_hallii_group is considered to contribute to the ruminal butyrate production and rumen development [43], and it was also positively connected to the urinary endotoxin level, along with its affiliated uncultured_rumen_bacterium_g_Eubacterium_hallii_group. Moreover, the negative correlation between the relative abundance of Lachnospiraceae family in fecal microbiota and serum endotoxin has been previously discovered in hepatitis B virus-related liver cirrhosis patients [44], but it was noticed that the genus norank_f_Lachnospiraceae positively correlated to the blood endotoxin in this study. This contradiction could stem from the disparities in host and ambience, or the indeterminacy of norank_f_Lachnospiraceae, and requires more investigations to be interpreted. Mogibacterium is regarded as a pathogenic anaerobe associated with the injured gut health, which could probably facilitate the endotoxin transfer into peripheral blood [45, 46]. In this trial, the negative association between uncultured_rumen_bacterium_g_Mogibacterium_group belonging to Mogibacterium and endotoxin discharge by urination was displayed, further suggesting the potential involvement of this species in endotoxemia.

In terms of the QS signaling molecules, the positive connection of Planctomycetota to AI-2 was observed, which was similar to the concurrence of increasing abundance in Planctomycetota and enhancement in QS function discovered in surrounding river sediments of a Cu-polymetallic deposit [47]. Furthermore, it was remarkable that both Sphaerochaeta and Acetitomaculum were positively related to the AI-2 molecule, and the abundance reductions of their affiliated uncultured_rumen_bacterium_g_ Sphaerochaeta and unclassified_g_Acetitomaculum were also exhibited in MVWS of this study. The relatively low average proportions (< 0.5%) of these two varying species could to some extent serve as a possible explanation for the unchanged AI-2 concentration between treatments. In the current investigation, the positive interrelationship between AI-2 and the unclassified_g_Lachnospiraceae_NK3A20_group was partially supported by previous finding that some ruminal Lachnospiraceae species could take part in QS [48]. Conversely, the negative interconnection between AI-2 and unclassified_g_Prevotella of this trial was inconsistent with precedent reports that the eminent genera Prevotella carries abundant AI-2-related genes and plays a critical role in QS amongst the ruminal microbiota [11, 12], necessitating more researches to clarify this dissimilarity.

As demonstrated by the Tax4Fun2 analysis, QS within the rumen bacterial community was augmented by cooling, being contradictory to the aforementioned unaffectedness of 3-OXO-C6-HSL and AI-2 in rumen fluid. In fact, the production and secretion of QS signaling molecules are more of instant and density-dependent activities during a cascade of reactions of the microbial community to ambient stimulations [49], thence it becomes rather challenging to timely detect the subtle variations of QS molecules within the diverse and complex rumen ecosystem upon environmental stresses or changes in vivo [50]. Besides, the limitations of the metagenomic analysis in interpreting the actual activities of the ruminal microflora could also result in the above-discussed contradiction to some extent [22, 51]. Furthermore, since the bacterial chemotaxis and flagellar assembly can help to boost the nutrient utilization of ruminal bacteria and thereby improve the feed efficiency of the host [52], the upregulation of these two pathways might also lead to the increasing tendency of ADG in the bulls treated with cooling [15]. It was noteworthy that the biosynthesis of lipoarabinomannan (LAM), a primary type of the LPS endotoxin of Mycobacterium tuberculosis and other mycobacteria [53], was decreased in the MVWS group, which was supported by the above-described abatement of the ruminal endotoxin in the same treatment.

According to the OPLS-DA plot of this study, a clear separation between the samples of the two treatments was portrayed, indicating that cooling reshaped the metabolomic pattern of the rumen liquid in bulls exposed to HS. The subsequent Spearman analysis showed the negative associations between blood endotoxin and several distinct metabolites, as well as the positive interrelationships between ruminal endotoxin, urinary endotoxin excretion, and a few different metabolites. It was prominent that all of these endotoxin-relevant chemicals were downregulated in the MVWS group, possibly leading to the changes of endotoxin across different biofluids by cooling in this study, and these metabolites could be selected as potential biomarkers for preventing and diagnosing the endotoxemia in ruminants [54]. Moreover, a negative relation between 3-OXO-C6-HSL and gingerol was manifested in this study, which was in agreement with precedent investigations that confirmed the inhibitory effect of gingerol on QS and QS-mediated biofilm formation as a structural analog of AHL [55, 56]. As a consequence, the decrement of gingerol in MVWS might somewhat promote the AHL-regulated QS activity despite the unchanged QS signals, which further supported the result of Tax4Fun2 and the difficulty in accurately detecting QS shifts [49, 50]. The salicylic acid has also been classified as a QS inhibitor which hinders the QS-related and virulent gene expressions of Pseudomonas aeruginosa [57, 58], the influences of its reduction thereby would probably be similar to that of gingerol. In contrast, the 4-hydroxybenzoic acid is employed by several bacteria as a QS signal in manipulating physiological functions and virulence factors [59], its negative association with 3-OXO-C6-HSL revealed in this trial needs more researches to be verified. Additionally, the simultaneous correlations of 4-hydroxybenzoic acid and salicylic acid with endotoxin were marked, implying the participation of QS in the endotoxin variations of this experiment. The palmitoylcarnitine and DL-2-aminooctanoic acid that were found to be correlated to AI-2 might could serve as possible regulators targeting the QS within ruminal microbiota.

In the current research, the interconnections between cholic acid and a few bacterial species from the genera of Lactobacillus, Eubacterium, and Bifidobacterium were depicted through the MIMOSA2 analysis. Kurdi et al. [60] observed the accumulations of cholic acid by Lactobacillus members at different levels, however in this study, the cholic acid amount was negatively and positively related to the Lactobacillus mucosae_LM1 and Lactobacillus amylovorus, respectively. In comparison, it has been reported that members of Eubacterium can convert cholic acid into secondary bile acids through 7a-dehydroxylation [61], which was consistent with the negative interrelationships of Eubacterium cellulosolvens_6 and Eubacterium limosum with cholic acid. It can be hence inferred that the rise of cholic acid in MVWS could result from the interactions within these relevant species.

Zhou et al. [62] confirmed that both 3-hydroxybenzoic acid and 4-hydroxybenzoic acid are essential for the production of ubiquinone by a rice bacterial pathogen Xanthomonas oryzae pv. oryzae. This finding could explain the downregulation of ubiquinone and other terpenoid-quinone biosynthesis in the MVWS of the present experiment, since 3-hydroxybenzoic acid and 4-hydroxybenzoic acid were both decreased in the ruminal fluid of cooling-treated bulls. As exhibited in the diagram of assembled differential pathways, the phenylalanine, tyrosine, and tryptophan biosynthesis that was also reduced in MVWS closely connected to the ubiquinone and other terpenoid-quinone biosynthesis. Phenylalanine is the substrate for the synthesis of catecholamine, an interkingdom signal between the animal and microflora that has been found to increase in response to HS [63,64,65], whilst tryptophan can act as the precursor of some bioactive compounds involving inflammation [66]. Therefore, the incline of phenylalanine, tyrosine, and tryptophan biosynthesis could to some extent represent the mitigation of HS in the MVWS treatment, and contribute to the decreases of proinflammatory cytokines in the bulls of this experiment [15]. Zhen et al. [67] observed the downregulation of QS together with the upregulation of lysine degradation, and ubiquinone and other terpenoid-quinone biosynthesis in the ileal of hens treated with yeast β-glucan. In the current trial, it was revealed by Tax4Fun2 that QS was upregulated in MVWS, while the metabolomics showed that lysine degradation, and ubiquinone and other terpenoid-quinone biosynthesis were both decreased, suggesting a potentially negative correlations between these two pathways and QS yet requiring deeper verifications. By contrast, the upregulation of bile secretion in MVWS was noted, which could help to boost the digestion and utilization of lipids by the bulls receiving cooling [60].

Conclusion

As was demonstrated by the present investigation, it could be concluded that cooling not only lowered the degree of HS, but also alleviated the harms of HS by redistributing endotoxins across different biofluids in beef bulls. The alterations of endotoxin across different biofluids by cooling involved the manipulations of ruminal microflora and metabolism, as a series of differential bacterial taxa, metabolites, and metabolic pathways were discovered to be associated with endotoxin and QS, despite that the levels of 3-OXO-C6-HSL and AI-2 were unchanged (Fig. 6). Future studies are necessitated to elucidate and testify mechanisms for the mediation of QS in the generation, translocation, and excretion of endotoxin in cattle subjected to HS.

Fig. 6
figure 6

Overview summary of the present study

Full size image

Availability of data and materials

All the raw sequences obtained during the sequencing in this study were available at the NCBI database (https://www.ncbi.nlm.nih.gov/bioproject/) with the accession number PRJNA1066970.

Abbreviations

AI-2:

Autoinducer-2

CON:

Control

HPLC-FD:

Liquid chromatography equipped with a fluorescence detector

HS:

Heat stress

KEGG:

Kyoto encyclopedia of genes and genomes

LDA:

Linear discriminant analysis

LEfSe:

Linear discriminant analysis effect size

LPS:

Lipopolysaccharide

MVWS:

Mechanical ventilation and water spray

OPLS-DA:

Orthogonal projections to latent structures-discriminant analysis

PCA:

Principal components analysis

QS:

Quorum sensing

SRA:

Sequence read archive

THI:

Temperature-humidity index

TMR:

Total mixed ration

UHPLC-MS:

Ultra-high performance liquid chromatography-mass spectrometry

VIP:

Variable importance in projection

References

  1. Liu W, Zhou J, Ma Y, Chen S, Luo Y. Unequal impact of climate warming on meat yields of global cattle farming. Commun Earth Environ. 2024;5:65.

    Article  Google Scholar 

  2. Bohmanova J, Misztal I, Cole JB. Temperature-humidity indices as indicators of milk production losses due to heat stress. J Dairy Sci. 2007;90:1947–56.

    Article  CAS  PubMed  Google Scholar 

  3. Herbut P, Angrecka S, Godyń D, Hoffmann G. The physiological and productivity effects of heat stress in cattle – a review. Anna Anim Sci. 2019;19:579–93.

    Article  Google Scholar 

  4. Negrón-Pérez VM, Fausnacht DW, Rhoads ML. Invited review: Management strategies capable of improving the reproductive performance of heat-stressed dairy cattle. J Dairy Sci. 2019;102:10695–710.

    Article  PubMed  Google Scholar 

  5. Wang L, Xue B, Wang K, Li S, Li Z. Effect of heat stress on endotoxin flux across mesenteric-drained and portal-drained viscera of dairy goat: Effect of heat stress on endotoxin absorption. J Anim Physiol Anim Nutr. 2011;95:468–77.

    Article  CAS  Google Scholar 

  6. Wang Z, Li XY, Yu YN, Yang LY, Zhang PH, He JH, et al. Enhancing dietary cation-anion difference reshaped the distribution of endotoxin across different biofluids and influenced inflammatory response in dairy cows exposed to heat stress. Anim Feed Sci Technol. 2020;262: 114444.

    Article  CAS  Google Scholar 

  7. Zou B, Long F, Xue F, Chen C, Zhang X, Qu M, et al. Protective effects of niacin on rumen epithelial cell barrier integrity in heat-stressed beef cattle. Animals. 2024;14:313.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Gao ST, Guo ZT, Baumgard LH, Ma L, Bu DP. Cooling ameliorates decreased milk protein metrics in heat-stressed lactating Holstein cows. J Dairy Sci. 2021;104:12139–52.

    Article  CAS  PubMed  Google Scholar 

  9. Wang Z, Zhang L, Li Z, Yu Y, Yang L, Zhang P, et al. Alterations of endotoxin distribution across different biofluids and relevant inflammatory responses by supplementing L-theanine in dairy cows during heat stress. Anim Nutr. 2021;7:1253–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Edwards-Callaway LN, Cramer MC, Cadaret CN, Bigler EJ, Engle TE, Wagner JJ, et al. Impacts of shade on cattle well-being in the beef supply chain. J Anim Sci. 2021;99:skaa375.

    Article  PubMed  Google Scholar 

  11. Liu X, Liu Q, Sun S, Sun H, Wang Y, Shen X, et al. Exploring AI-2-mediated interspecies communications within rumen microbial communities. Microbiome. 2022;10:167.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Won M-Y, Oyama LB, Courtney SJ, Creevey CJ, Huws SA. Can rumen bacteria communicate to each other? Microbiome. 2020;8:23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ganesh PS, Vishnupriya S, Vadivelu J, Mariappan V, Vellasamy KM, Shankar EM. Intracellular survival and innate immune evasion of Burkholderia cepacia: improved understanding of quorum sensing-controlled virulence factors, biofilm, and inhibitors. Microbiol Immunol. 2020;64:87–98.

    Article  CAS  PubMed  Google Scholar 

  14. Reith RR, Sieck RL, Grijalva PC, Swanson RM, Fuller AM, Diaz DE, et al. Transcriptome analyses indicate that heat stress-induced inflammation in white adipose tissue and oxidative stress in skeletal muscle is partially moderated by zilpaterol supplementation in beef cattle. J Anim Sci. 2022;100:skac019.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wu Q, Wan F, Liu L, Cheng A, Lan X, Wang Z, et al. Effects of mechanical ventilation and water spraying on growth performance, apparent nutrient digestibility, rumen fermentation parameters and serum indexes of heat-stressed beef cattle. Chin J Anim Nutr. 2024;36:4388–99 (in Chinese).

    CAS  Google Scholar 

  16. Wang Z, Zhao Y, Lan X, He J, Wan F, Shen W, et al. Tannic acid supplementation in the diet of Holstein bulls: Impacts on production performance, physiological and immunological characteristics, and ruminal microbiota. Front Nutr. 2022;9:1066074.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Chen XB, Gomes MJ. Estimation of microbial protein supply to sheep and cattle based on urinary excretion of purine derivatives—an overview of technical details. International Feed Resources Unit. Rowett Research Institute; 1992.

  18. Valadares RFD, Broderick GA, Filho SCV, Clayton MK. Effect of replacing alfalfa silage with high moisture corn on ruminal protein synthesis estimated from excretion of total purine derivatives. J Dairy Sci. 1999;82:2686–96.

    Article  CAS  PubMed  Google Scholar 

  19. Song X-N, Qiu H-B, Xiao X, Cheng Y-Y, Li W-W, Sheng G-P, et al. Determination of autoinducer-2 in biological samples by high-performance liquid chromatography with fluorescence detection using pre-column derivatization. J Chromatogr A. 2014;1361:162–8.

    Article  CAS  PubMed  Google Scholar 

  20. Doberva M, Stien D, Sorres J, Hue N, Sanchez-Ferandin S, Eparvier V, et al. Large Diversity and Original Structures of Acyl-Homoserine Lactones in Strain MOLA 401, a Marine Rhodobacteraceae Bacterium. Front Microbiol. 2017;8:1152.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yu Z, Morrison M. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques. 2004;36:808–12.

    Article  CAS  PubMed  Google Scholar 

  22. Wang Z, Yu Y, Li X, Xiao H, Zhang P, Shen W, et al. Fermented soybean meal replacement in the diet of lactating holstein dairy cows: modulated rumen fermentation and ruminal microflora. Front Microbiol. 2021;12: 625857.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wemheuer F, Taylor JA, Daniel R, Johnston E, Meinicke P, Thomas T, et al. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environ Microbiome. 2020;15:11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wang Z, Yu Y, Shen W, Tan Z, Tang S, Yao H, et al. Metabolomics analysis across multiple biofluids reveals the metabolic responses of lactating holstein dairy cows to fermented soybean meal replacement. Front Vet Sci. 2022;9: 812373.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Noecker C, Eng A, Muller E, Borenstein E. MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data. Bioinformatics. 2022;38:1615–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M, et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021;49:W388–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Armstrong DV. Heat stress interaction with shade and cooling. J Dairy Sci. 1994;77:2044–50.

    Article  CAS  PubMed  Google Scholar 

  28. West JW. Effects of heat-stress on production in dairy cattle. J Dairy Sci. 2003;86:2131–44.

    Article  CAS  PubMed  Google Scholar 

  29. Hjorth MF, Christensen L, Larsen TM, Roager HM, Krych L, Kot W, et al. Pretreatment Prevotella-to-Bacteroides ratio and salivary amylase gene copy number as prognostic markers for dietary weight loss. Am J Clin Nutr. 2020;111:1079–86.

    Article  PubMed  Google Scholar 

  30. Rodríguez-Daza M-C, Roquim M, Dudonné S, Pilon G, Levy E, Marette A, et al. Berry polyphenols and fibers modulate distinct microbial metabolic functions and gut microbiota enterotype-like clustering in obese mice. Front Microbiol. 2020;11:2032.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wang D, Tang G, Wang Y, Yu J, Chen L, Chen J, et al. Rumen bacterial cluster identification and its influence on rumen metabolites and growth performance of young goats. Anim Nutr. 2023;15:34–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li H, Liu F, Lu J, Shi J, Guan J, Yan F, et al. Probiotic mixture of lactobacillus plantarum strains improves lipid metabolism and gut microbiota structure in high fat diet-fed mice. Front Microbiol. 2020;11:512.

    Article  PubMed  PubMed Central  Google Scholar 

  33. McLoughlin S, Spillane C, Claffey N, Smith PE, O’Rourke T, Diskin MG, et al. Rumen microbiome composition is altered in sheep divergent in feed efficiency. Front Microbiol. 2020;11:1981.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Qiu X, Qin X, Chen L, Chen Z, Hao R, Zhang S, et al. Serum Biochemical parameters, rumen fermentation, and rumen bacterial communities are partly driven by the breed and sex of cattle when fed high-grain diet. Microorganisms. 2022;10:323.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Huang C, Ge F, Yao X, Guo X, Bao P, Ma X, et al. Microbiome and metabolomics reveal the effects of different feeding systems on the growth and ruminal development of yaks. Front Microbiol. 2021;12: 682989.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zhen Y, Xi Z, Nasr SM, He F, Han M, Yin J, et al. Multi-omics reveals the impact of exogenous short-chain fatty acid infusion on rumen homeostasis: insights into crosstalk between the microbiome and the epithelium in a goat model. Microbiol Spectr. 2023;11:e05343-e5422.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Johnson JS, Sapkota A, Lay DC. Rapid cooling after acute hyperthermia alters intestinal morphology and increases the systemic inflammatory response in pigs. J Appl Physiol. 2016;120:1249–59.

    Article  CAS  PubMed  Google Scholar 

  38. Conte G, Dimauro C, Daghio M, Serra A, Mannelli F, McAmmond BM, et al. Exploring the relationship between bacterial genera and lipid metabolism in bovine rumen. Animal. 2022;16:100520.

    Article  CAS  PubMed  Google Scholar 

  39. Wang X, Guo C, Xu X, Zhang L, Li S, Dai D, et al. Effect of alkaline mineral complex buffer supplementation on rumen fermentation, rumen microbiota and rumen epithelial transcriptome of newborn calves. Fermentation. 2023;9:973.

    Article  CAS  Google Scholar 

  40. Granja-Salcedo YT, Ramirez-Uscategui RA, Machado EG, Duarte Messana J, Takeshi Kishi L, Lino Dias AV, et al. Studies on bacterial community composition are affected by the time and storage method of the rumen content. PLoS ONE. 2017;12: e0176701.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Tian X-Z, Li J-X, Luo Q-Y, Zhou D, Long Q-M, Wang X, et al. Effects of purple corn anthocyanin on blood biochemical indexes, ruminal fluid fermentation, and rumen microbiota in goats. Front Vet Sci. 2021;8: 715710.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Rey J, Díaz de Otálora X, Atxaerandio R, Mandaluniz N, García-Rodríguez A, González-Recio O, et al. Effect of chitosan on ruminal fermentation and microbial communities, methane emissions, and productive performance of dairy cattle. Animals. 2023;13:2861.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Wang H, Wu F, Guan T, Zhu Y, Yu Z, Zhang D, et al. Chopping roughage length improved rumen development of weaned calves as revealed by rumen fermentation and bacterial community. Animals. 2020;10:2149.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Deng Y-D, Peng X, Zhao R-R, Ma C, Li J, Yao L. The intestinal microbial community dissimilarity in hepatitis B virus-related liver cirrhosis patients with and without at alcohol consumption. Gut Pathogens. 2019;11:1–13.

    Article  Google Scholar 

  45. O’Hara E, Kelly A, McCabe MS, Kenny DA, Guan LL, Waters SM. Effect of a butyrate-fortified milk replacer on gastrointestinal microbiota and products of fermentation in artificially reared dairy calves at weaning. Sci Rep. 2018;8:14901.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Shang Q, Liu S, Liu H, Mahfuz S, Piao X. Maternal supplementation with a combination of wheat bran and sugar beet pulp during late gestation and lactation improves growth and intestinal functions in piglets. Food Funct. 2021;2021(12):7329–42.

    Article  Google Scholar 

  47. Gao W, Liu P, Ye Z, Zhou J, Wang X, Huang X, et al. Divergent prokaryotic microbial assembly, co-existence patterns and functions in surrounding river sediments of a Cu-polymetallic deposit in Tibet. Sci Total Environ. 2022;851: 158192.

    Article  CAS  PubMed  Google Scholar 

  48. Xie Y, Sun H, Xue M, Liu J. Metagenomics reveals differences in microbial composition and metabolic functions in the rumen of dairy cows with different residual feed intake. Anim microbiome. 2022;4:19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhao K, Li Y, Yue B, Wu M. Genes as early responders regulate quorum-sensing and control bacterial cooperation in pseudomonas aeruginosa. PLoS ONE. 2014;9:e101887.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ran T, Zhou C, Xu L, Geng M, Tan Z, Tang S, et al. Initial detection of the quorum sensing autoinducer activity in the rumen of goats in vivo and in vitro. J Integr Agric. 2016;15:2343–52.

    Article  Google Scholar 

  51. Morton JT, Aksenov AA, Nothias LF, Foulds JR, Quinn RA, Badri MH, et al. Learning representations of microbe–metabolite interactions. Nat Methods [Internet]. 2019 [cited 2019 Nov 25]. Available from: http://www.nature.com/articles/s41592-019-0616-3

  52. Xue M, Xie Y, Zang X, Zhong Y, Ma X, Sun H, et al. Deciphering functional groups of rumen microbiome and their underlying potentially causal relationships in shaping host traits. iMeta. 2024;3:e225.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Gao J, Liao G, Wang L, Guo Z. Synthesis of a Miniature Lipoarabinomannan. Org Lett. 2014;16:988–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Kiafar P, Chalmeh A, Pourjafar M. Effects of nitroglycerin and flunixin meglumine on circulating inflammatory, cardiovascular and hepatorenal biomarkers of ovine experimental endotoxemia model. Small Rumin Res. 2022;207: 106615.

    Article  Google Scholar 

  55. Choi H, Ham S-Y, Cha E, Shin Y, Kim H-S, Bang JK, et al. Structure-activity relationships of 6- and 8-gingerol analogs as anti-biofilm agents. J Med Chem. 2017;60:9821–37.

    Article  CAS  PubMed  Google Scholar 

  56. Parmar P. The rise of gingerol as anti-QS molecule_ Darkest episode in the LuxR-mediated bioluminescence saga. Bioorg Chem. 2020;99:103823.

    Article  CAS  PubMed  Google Scholar 

  57. Ahmed SAKS, Rudden M, Smyth TJ, Dooley JSG, Marchant R, Banat IM. Natural quorum sensing inhibitors effectively downregulate gene expression of Pseudomonas aeruginosa virulence factors. Appl Microbiol Biotechnol. 2019;103:3521–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Yang L, Rybtke MT, Jakobsen TH, Hentzer M, Bjarnsholt T, Givskov M, et al. Computer-aided identification of recognized drugs as pseudomonas aeruginosa quorum-sensing inhibitors. Antimicrob Agents Chemother. 2009;53:2432–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wang M, Zeng J, Zhu Y, Chen X, Guo Q, Tan H, et al. A 4-hydroxybenzoic acid-mediated signaling system controls the physiology and virulence of Shigella sonnei. Microbiol Spectr. 2023;11:e04835-e4922.

    PubMed  PubMed Central  Google Scholar 

  60. Kurdi P, Veen HWV, Tanaka H, Mierau I, Konings WN, Tannock GW, et al. Cholic acid is accumulated spontaneously, driven by membrane ΔpH, in many Lactobacilli. J Bacteriol. 2000;182:6525–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Doerner KC, Takamine F, LaVoie CP, Mallonee DH, Hylemon PB. Assessment of fecal bacteria with bile acid 7 alpha-dehydroxylating activity for the presence of bai-like genes. Appl Environ Microbiol. 1997;63:1185–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zhou L, Huang T-W, Wang J-Y, Sun S, Chen G, Poplawsky A, et al. The rice bacterial pathogen Xanthomonas oryzae pv. oryzae produces 3-hydroxybenzoic acid and 4-hydroxybenzoic acid via XanB2 for use in xanthomonadin, ubiquinone, and exopolysaccharide biosynthesis. MPMI. 2013;26:1239–48.

    Article  CAS  PubMed  Google Scholar 

  63. Dasgupta R, Saha I, Ray PP, Maity A, Pradhan D, Sarkar HP, et al. Arecoline plays dual role on adrenal function and glucose-glycogen homeostasis under thermal stress in mice. Archiv Physiol Biochem. 2020;126:214–24.

    Article  CAS  Google Scholar 

  64. Lyte JM, Lyte M, Daniels KM, Oluwagbenga EM, Fraley GS. Catecholamine concentrations in duck eggs are impacted by hen exposure to heat stress. Front Physiol. 2023;14:1122414.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Waisbren SE, Noel K, Fahrbach K, Cella C, Frame D, Dorenbaum A, et al. Phenylalanine blood levels and clinical outcomes in phenylketonuria: a systematic literature review and meta-analysis. Mol Genet Metab. 2007;92:63–70.

    Article  CAS  PubMed  Google Scholar 

  66. Garcia-Lino AM, Gomez-Gomez A, Garcia-Mateos D, de la Fuente A, Alvarez AI, Pozo OJ, et al. Analysis of the interaction between tryptophan-related compounds and ATP-binding cassette transporter G2 (ABCG2) using targeted metabolomics. Food Chem. 2021;344: 128665.

    Article  CAS  PubMed  Google Scholar 

  67. Zhen W, Liu Y, Shao Y, Ma Y, Wu Y, Guo F, et al. Yeast β-Glucan altered intestinal microbiome and metabolome in older hens. Front Microbiol. 2021;12: 766878.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the assistance from technicians of the Institute of Subtropical Agriculture, Chinese Academy of Sciences (Changsha, China), and the Majorbio Biomed. Tech. Co., Ltd. (Shanghai, China).

Funding

The present work was financially supported by the National Natural Science Foundation of China (Grant No. 32302764), Hunan Provincial Natural Science Foundation (Grant No. 2024JJ5179), Hunan Provincial Education Department (Grant No. 22A0168), Science and Technology Department of Xinjiang Uygur Autonomous Region (Grant No. 2022A02001-1), Hunan Provincial Science and Technology Department (Grant No. 2021RC4060), and Hunan Herbivores Industry Technological System.

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  1. Yuelushan Laboratory, College of Animal Science and Technology, Hunan Agricultural University, Changsha, Hunan, 410128, People’s Republic of China

    Zuo Wang, Qingyang Wu, Weijun Shen, Fachun Wan & Jianhua He

  2. College of Veterinary Medicine, Hunan Agricultural University, Changsha, Hunan, 410128, People’s Republic of China

    Lei Liu

  3. Key Laboratory of Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, Hunan, 410125, People’s Republic of China

    Shaoxun Tang & Zhiliang Tan

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Contributions

ZW and WS conceived the ideas and designed the research. ZW, QW, and WS conducted the research. ZW and QW analyzed the data. ZW wrote and revised the manuscript. ZW, WS, FW, JH, LL, ST, and ZT supervised the study. All authors contributed to this study and read and approved the final manuscript.

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Correspondence to Weijun Shen.

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All procedures involving animals in this experiment were reviewed and approved by the Animal Care Committee (approval number: 20220626), College of Animal Science and Technology, Hunan Agricultural University, Changsha, China.

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Wang, Z., Wu, Q., Shen, W. et al. Cooling redistributed endotoxin across different biofluids via modulating the ruminal microbiota and metabolome without altering quorum sensing signal levels in heat-stressed beef bulls. anim microbiome 7, 38 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s42523-025-00400-4

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s42523-025-00400-4

Keywords

Animal Microbiome

ISSN: 2524-4671