Altered ruminal microbiome tryptophan metabolism and their derived 3-indoleacetic acid inhibit ruminal inflammation in subacute ruminal acidosis goats

All the experimental designs and protocols used in the present study were approved by the Institutional Animal Care and Use Committee (IACUC) of Northwest A&F University (Shaanxi, China; approval number: NWAFU-DK-2022155).

Forty-seven healthy, multiparous female dairy goats (2 to 3 years, ~ 40 kg) with ruminal fistulas were used. Eleven dairy goats, were fed a basal diet (30% concentrate, DM basis, named as LGW diet) (Table S1). The other 36 dairy goats were subsequently fed a high-concentrate diet supplemented with whole corn (70% concentrate, DM basis, named as HGW diet and referred to as the low-rumen-degradable-starch diet) (Table S1). A total of 2 kg of fresh TMR experimental diet (under restricted feeding condition ensuring that all dairy goats consumed their entire ration daily without any leftover feed) was fed to each goat twice daily at 08:00 and 17:00. On the 28th and 35th days, the pH of the ruminal fluid was measured for 14 h with a mobile pH meter (HI 9024 C; RI, USA). The HGW feeding goats (n = 8), whose pH was lower than 5.8 for more than 3 h, were determined to be high-grain-whole-corn SARA (HGW-SARA) goats. The remaining dairy goats, selecting 5 of them to keep feeding HGW diet, the others were further fed a high-rumen-degradable-starch diet with crushed corn for another 28 days (70% concentrate, DM basis, named the HGC diet) (Table S1). Similarly, on the 56th and 63rd days of the entire trial period, the ruminal pH of all the goats was determined. When fed the HGC diet, dairy goats (n = 7), whose pH was lower than 5.8 for more than 3 h, were determined to be high-grain-crushed-corn SARA (HGC-SARA) goats.

Another eight healthy dairy goats with rumen fistulas were fed a basal diet (30% concentrate, DM basis) (Table S1). On the slaughter days, the ruminal fluid of the 3 donor HGW-SARA goats was collected and then transplanted to another 3 healthy dairy goats with ruminal fistulas after the ruminal fluid content was removed. The detailed steps were performed in accordance with the methods of Zhou [19]. This group of recipient goats was subsequently named the CON + SARA group, and another 5 healthy dairy goats were named the CON group. After transplantation, the goats in the CON and CON + SARA groups were further fed a normal concentration of feed (50% concentrate, DM basis) for 2 weeks (Table S2). These 8 goats were subsequently slaughtered for sample collection.

The 24 dairy goats were fed the same high-grain diet with a concentrate:forage ratio of 6:4 (Table) and divided into high rumen-degradable-starch control (HRDS-C) and high rumen-degradable-starch with 3-indoleacetic acid (HRDS-IA) groups. The twelve goats in HRDS-IA were fed 2.5 g/day 3-indoleacetic acid (Sigma‒Aldrich, China), which was mixed into the concentrates. The experimental period lasted 6 weeks, and the rumen fluid and plasma samples were collected at 7:00, 9:00, 11:00, 17:00, and 19:00 on the 42nd day. S3

Before slaughter, 20 mL of blood was collected from the jugular vein and anticoagulated with heparin sodium. After slaughtering, 200 mL of rumen fluid was collected. Furthermore, approximately 1 cm² of epithelial tissue from the dorsal rumen at a similar position was collected from each goat. The other epithelial tissues of the rumen were collected in 2 × 2 cm² pieces and fixed in optimum cutting temperature (OCT) compound.

RMT from goats to mice was performed in accordance with our previously published methods [20]. On slaughter days, the ruminal fluid of the slaughtered HGW-SARA goats (n = 5) and LGW-CON goats (n = 5) was collected, placed inside a sterile and anaerobic collection tube, and then centrifuged at 6000 × g for 15 min. The precipitate without the supernatant was resuspended in 1 × PBS, and then, the resulting suspensions were transferred directly to the recipient mice. Ten male Kunming (KM) mice underwent a 10-day adaptation period and were then treated with antibiotics for 3 weeks dissolved in drinking water and for 3 days via gavage (Fig. S1 and Table S4). After a 24-h antibiotic-free period, the mice were infused by intragastric gavage with 0.3 mL of mixed rumen fluid derived from LGW-CON goats (called CON) or HGW-SARA goats (called CON + SARA) for 3 days. After being fed for 10 days, all the mice were sacrificed and weighed, and colonic tissues were collected for further RNA extraction and inflammation-related gene expression detection.

The rumen fluid was centrifuged at 13,000 × g for 10 min. We analyzed the concentration of VFAs in the rumen fluid and plasma and the concentration of lactate in the rumen fluid, which were separated and quantified with an Agilent 7820 GC system, as previously described [21, 22]. The ruminal NH₃-N levels were measured via the phenol‒sodium hypochlorite colorimetric method. Lipopolysaccharide (LPS) was quantified via a Limulus amoebocyte lysate (LAL) assay, and the level of lipopolysaccharide binding protein (LBP) was determined via an indirect competitive enzyme-linked immunosorbent assay [23, 24]. The levels of the rumen and plasma inflammation-related proteins IL-1β, TLR-4, LBP, IL-6, IL-10, TNF-α, and IL-17 were measured with commercial ELISA kits (COIBO BIO, Shanghai, China).

Rumen tissues were fixed in 4% paraformaldehyde, sectioned (4 μm), and stained with H&E. The immunofluorescence measurements were conducted as previously described [25]. Immunofluorescence was performed using anti-IL-17A antibodies, with nuclei stained by DAPI. Images were analyzed via ImageJ.

Total RNA extraction was performed via TRIzol reagent (Carlsbad, USA), and the RNA was reverse transcribed into cDNA following the standard protocol provided with the PrimeScript® RT Kit (Takara, China). Quantitative real-time polymerase chain reaction (qRT‒PCR) assays were executed with SYBR® Premix Pro TaqTM II (Takara, China) on a real-time fluorescence quantitative thermal cycler (Light Cycler 9,603,030,973). The specific primer pairs utilized for the target genes are listed in Table S5. Gene expression levels were analyzed via the 2^−ΔΔCt method for relative quantification [26].

Total metagenomic DNA was extracted from the rumen fluid samples via the E.Z.N.A.® Soil DNA Kit (Omega Biotek, Norcross, GA, USA), with repeated bead beating and after concentration, purification and integrity verification, the DNA was fragmented to ~ 400 bp. After bridge PCR amplification, metagenomic sequencing was performed via an Illumina NovaSeq/HiSeq Xten (Illumina, USA) sequencing platform.

Paired-end Illumina reads underwent adaptor trimming and filtering of low-quality sequences utilizing fastp [27] (version 0.20.0) and Trimmomatic (version 0.39). The reads were aligned to the reference genome of Capra hircus via BWA [28, 29] (version 0.7.9a), and assembled with MEGAHIT [30] (version 1.1.2). Contigs reaching or exceeding 300 bp in length were retained as the final assembly output. Open reading frames (ORFs) were inferred from assembled contigs utilizing MetaGene. ORFs extending to or beyond 100 base pairs were extracted and translated into amino acid sequences to compile a nonredundant gene catalogue via CD-HIT [31] (version 4.6.1 Postquality control reads were mapped to the nonredundant gene catalogue with 95% identity via SOAPaligner (version 2.21), evaluating quantification of gene abundance within individual samples. The total gene abundance of each sample was normalized.

For taxonomic classification, representative sequences of the nonredundant gene catalogue were aligned to the NCBI NR database via Diamond [32] (version 0.8.35). Functional annotations via the Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out with Diamond [32] against the KEGG database. Carbohydrate-active enzymes (CAZymes) were annotated by subjecting the catalogue to hmmscan analysis against the CAZy database. The relative contribution of species to the microbial function were calculated using the method described by Zhang et al. [33].

The metagenomic assembled contigs were obtained. The contig sequences ≥ 1000 bp were filtered via the case tools Metabat [34] (Version 2.12.1), CONCOCT [35] (Version 0.5.0), and Maxbin [36] (Version 2.2.5) to conduct single-sample binning. The bins of different software programs were merged via DAS_Tool [37] Version 1.1.0), and the bins were regenerated. RefineM (Version 0.0.24) was used to purify bins and obtain bins that were redefined as metagenome-assembled genomes (MAGs). All binned MAGs were clustered together, and dRep (Version 2.2.9) was used to cluster the bins to remove duplicate MAGs under the thresholds of ANI ≥ 99% and genome overlap ≥ 10%. In accordance with the CheckM [38] (Version 1.0.12) quality evaluation standard, the nonredundant MAGs were selected as having medium quality (completeness ≥ 50% and contamination < 10%) and were further analyzed. Taxonomic assignment was performed via GTDB-tk [39].

Rumen fluid metabolites and a pooled quality control (QC) sample were analyzed by liquid chromatography‒mass spectrometry (LC‒MS). Differentially abundant metabolites were summarized by PLS-DA (VIP ≥ 1, fold change ≥ 2 or ≤ 0.5, q < 0.05) and mapped to their biochemical pathways through metabolic enrichment and pathway analysis via the KEGG database.

Total RNA was extracted from the tissue via TRIzol® Reagent according to the manufacturer’s instructions (Invitrogen) The construction of transcriptome libraries and sequencing methods refer to Liu et al. [40]. The raw paired-end reads were trimmed and quality controlled by SeqPrep and Sickle with default parameters. The clean reads were subsequently separately aligned to the reference genome via HISAT2 [41]. The mapped reads of each sample were assembled via StringTie via a reference-based approach [42].

The expression level of each transcript was calculated according to the transcripts per million reads (TPM) method. RSEM [43] was used to quantify gene abundances. Essentially, differential expression analysis was performed via DESeq2 [44], and DEGs with |log2FC|> 1 and a P value ≤ 0.05 were considered to be significantly DEGs. KEGG functional enrichment analyses were performed via KOBAS with threshold as BH-corrected P values ≤ 0.05. Furthermore, the gene expression of differential KEGG pathways was further identified via gene set enrichment analysis (GSEA) [45].

The specific method was described in a previous study [46]. Briefly, the sample was incubated on ice, filtered, and centrifuged. The isolated nuclei were resuspended, filtered and counted. A final concentration of 1000 nuclei per microliter was used for loading on a 10 × channel. The gel beads-in-emulsion (GEM) mixture was exposed to cell lysis buffer, and polyadenylated RNA molecules were retrieved for reverse transcription to cDNA. The library was constructed, quantified and then sequenced to obtain 150 bp paired-end reads.

The reads were processed via the Cell Ranger workflow. Clean reads were aligned to Capra_hircus ARS1.2 via the STAR algorithm [47]. A gene‒barcode matrix was then imported into the Seurat R toolkit for quality control. The clusters are visualized on a 2D map produced with t-distributed stochastic neighbor embedding (t-SNE) [48]. The cell cluster identity was assigned by manual annotation on the basis of the expression of known marker genes. DEG (differentially expressed gene) identification was performed with |log2FC|> 0.25 and a Q value < = 0.05 via the function FindMarkers in Seurat via a likelihood ratio test. KEGG enrichment analysis was performed to identify which DEGs were significantly enriched in KEGG pathways at a Bonferroni-corrected P value ≤ 0.05.

To identify DEGs between two different samples or clusters, the function FindMarkers in Seurat was used, and a likelihood ratio test was performed. Essentially, DEGs with |log2FC|> 0.25 and a Q value < = 0.05 were considered significantly differentially expressed genes.

The statistical analyses were conducted in IBM SPSS Statistics 27 via the mean ± SEM and one-way ANOVA followed by the least significant difference (LSD) test. For data that were not normally distributed, the Kruskal‒Wallis H test was used to compare the differences. *P < 0.05, **P < 0.01, and ***P < 0.001 indicate significance and were visualized with GraphPad Prism 9.5. The correlation analysis was performed in R studio with the R package ComplexHeatmap. The structural equation model was built via the R package lavaan, with data dimension reduction handled via the vegan package [49]. Data normalization was performed with R’s scale function. The variance‒covariance matrix was estimated via maximum likelihood, and model fit was assessed via the P value, comparative fit index (CFI), goodness-of-fit index (GFI), and degrees of freedom (DF).

On days 56 and 63 of the trial period, the pH level in the rumen of goats in the HGW-SARA and HGC-SARA groups remained below 5.8 for more than 6 h on each of those days (Fig. S1). This exceeded the critical threshold for diagnosing SARA (Fig. 1A). Compared with those in the LGW-CON group, the concentrations of ruminal total VFAs, acetate, propionate, and NH₃-N were significantly greater in goats from the HGW-SARA and HGC-SARA groups (Fig. 1B, 1C), but the ruminal butyrate and lactate concentrations were significantly increased only in the HGC-SARA group, whereas the ruminal LPS concentration was significantly increased only in the HGW-SARA group (Fig. 1B, 1C). Furthermore, significantly greater gene expression of TLR-4 and IL-1β was detected in the HGW-SARA groups than in the LGW-CON and HGC-SARA groups (Fig. 1D), whereas the stratum corneum thickness of the rumen epithelium was significantly greater in the HGC-SARA groups than in the LGC-CON group (Fig. 1E, 1F).

In addition to the epithelial injury and inflammation in these 2 SARA groups, systemic inflammation may have occurred, which can be identified from the significantly increased serum LPS, LPS binding protein (LBP), TNF-α, IL-1β, and IL-6 concentrations in the HGW-SARA and HGC-SARA groups compared with those in the LGW-CON group (Fig. 1G). Owing to the effects of restricted feeding, the final body weights of all the goats slightly increased but not significantly differed among the 3 groups, and only the body weight gain of the CON group was significantly lower than that of the HGC-SARA group (Table S6). Further association analysis indicated that ruminal fermentation indices were negatively associated with the ruminal pH and that the ruminal pH was further negatively associated with the ruminal LPS concentration. Furthermore, ruminal LPS was positively associated with the serum LPS concentration and was positively associated with pro-inflammatory factors (Fig. 1H).

The ruminal epithelial transcriptomes revealed that more infection- and inflammation-related pathways were enriched in the HGW-SARA group than in the LGW-CON group but not in the HGC-SARA group. Ten coenriched pathways related to HGW-SARA vs. LGW-CON and HGC-SARA vs LGW-CON were identified (Fig. S2A–C). Among them, only the gene expression patterns of the influenza A and IL-17 signalling pathways were almost opposite in these 2 comparisons (Fig. S2D and S2E). GSEA revealed that the expression of genes involved in the IL-17 signalling pathway was increased in the HGW-SARA group but decreased in the HGC-SARA group (Fig. S2D–S2F). The expression of the proinflammatory chemokine CCL20 and the proinflammatory cytokine PTGS2 was increased in the HGW-SARA group, but the expression of the proinflammatory genes IL-17A, IL-17B, IL-17F, and FOS was decreased in the HGC-SARA group (Fig. S3A and S3B).

Endothelial cells, epithelial cells, fibroblasts, macrophages, mesenchymal cells, neurons, smooth muscle cells, stem cells, Th17 cells and regulatory T (Treg) cells were identified in the rumen epithelial samples (Fig. 2A). Among these cells, a comparative assessment of the cellular composition in the three groups revealed an increased proportion of Th17 cells, specifically in HGW-SARA goats (7.02%), a marked increase over LGW-CON (3.15%), and HGC-SARA (2.40%) (Fig. 2B). Given that IL-17A serves as an established biomarker for Th17 cells, immunofluorescence assays were carried out to map their distribution and quantify their expression levels (Fig. 2C, 2D).

The Shannon index of the HGC-SARA group was greater than that of the LGW-CON group (Fig. 3A; P = 0.059). β diversity analysis of the identified species revealed a significantly altered microbiome between the SARA goats (HGW-SARA and HGC-SARA) and the healthy goats (LGW-CON) (Fig. 3B). The SARA goats presented increased Ruminococcus and decreased Prevotella (Fig. 3C). Furthermore, LEfSe analysis revealed that the abundances of Bacteroides bacterium, Bacteroidales bacterium WCE2004, Bacteroidetes bacterium HGW Bacteroidetes 20, Prevotella sp ne3005, Prevotella ruminicola, Prevotella sp tf2 5 and others, from the genera Prevotella and Bacteroidales, were decreased in SARA goats (Fig. 3D). And the abundance of species from the Ruminococcus were significantly increased in the SARA groups (Fig. 3D). Compared with those in the LGW-CON group, the microbial carbohydrate metabolism abilities were significantly weakened in the SARA goats, especially the relative abundances of cellulose and hemicellulose enzymes, such as GH26, GH51, GH10, GH92, and GH130 (FDR value < 0.05, LDA value > 3), whereas the lysozymes and LPS synthesis enzymes, such as GH24, GH25 and GT8, significantly increased (Fig. S4A–4B).

Correlation analysis indicated that the rumen microbiota might be involved in inducing rumen epithelial inflammation when SARA occurred in dairy goats. Of these, the Ruminococcus sp. AF.12–5, and Mitsuokella multacida which upregulated in SARA occurrence groups, were significantly positively correlated with the serum IL-1β, LBP, IL-6, and LPS contents (Fig. 3E). The RDA results indicated that the ruminal IL-1β and LPS concentrations; and the serum LPS, LBP, IL-1B, and TNF-α concentrations were significantly positively related to the ruminal microbiome composition of the 2 SARA groups (Fig. 3F).

Integrated transcriptomics and snRNAseq revealed significantly downregulated expression of IL-17 signalling pathway genes such as PTGS2, CCL20, and IL-17A (P < 0.05) and reduced Th17 cell differentiation activity in the HGC-SARA group compared with the HGW-SARA group (P < 0.05) (Fig. S5A–S5B). Concurrently, in the HGC-SARA group, the relative abundances of those species from the Selenomonas and Bifidobacterium genera were significantly greater than those in the goats from the HGW-SARA group (LDA > 2, P < 0.05) (Fig. 4A). Metabolomics uncovered 146 differentially abundant metabolites between the HGW-SARA and HGC-SARA groups, with tryptophan metabolism being a key enriched pathway (P < 0.05) (Table S7 and Fig. 4B). Notably, five of ten tryptophan-derived metabolites, such as 3-indoleacetic acid (IAA) and 5-hydroxyindoleacetic acid (5-HIAA), were upregulated in HGC-SARA (P < 0.05) (Fig. 4C). The species and functional contribution analysis indicated that the two main species contributing to tryptophan metabolism were Bifidobacterium merycicum (3.85% for HGW-SARA and 8.81% for HGC-SARA) and Selenomonas sp. DSM 106892 (96.14% for HGW-SARA and 91.18% for HGC-SARA) (Fig. 4D). Microbial enzyme analysis highlighted the increased expression of tryptophan-metabolizing genes such as tnaA, ALDH, IGPS, KMO and kynB in HGC-SARA (Fig. 4E).

SEM analysis revealed that bacteria from the genera Selenomonas and Bifidobacterium that were upregulated in the HGC-SARA group could modulate the tryptophan derivatives IAA and 5-HIAA to significantly negatively regulate the number of Th17 cells in the ruminal epithelium, subsequently affecting the secretion of inflammatory cytokines and chemokines (Fig. 4F). We compared tryptophan metabolism genes in the rumen epithelium and identified a total of 52 genes, but they did not show significant differences between the HGW-SARA and HGC-SARA groups (Fig. S6). Taken together, these data indicated that the differences in Th17 cell differentiation and IL-17 signaling pathways caused by differences in tryptophan metabolism only depend on microbial metabolism.

To identify the microbial role, the ruminal contents of HGW-SARA goats were transplanted into another 3 healthy goats. Compared with those in the CON group, which did not receive RMT, the pH level in the rumen of goats in the CON + SARA group remained below 5.8 for more than 3 h (Fig. 6A). Furthermore, after RMT, the relative expression of the ruminal epithelial genes IL-1β and TLR-4 in receiving goats was significantly greater than that in the LGW-CON group (Fig. 6B, 6C). The serum LPS, LBP, IL-1β, and TNF-α concentrations were also significantly greater in the CON + SARA group than in the LGW-CON group (Fig. 6D–H). Moreover, the RMT mouse model was also further used to detect the role of the rumen microbiome in the occurrence of gastrointestinal epithelial inflammation, which was also associated with increased mRNA expression of proinflammatory factors (Fig. 6I).

Compared with those in HRDS-C dairy goats, the concentrations of isobutyrate and isovalerate as well as the proportion of isobutyrate in the plasma significantly increased, whereas the proportions of isobutyrate significantly decreased in the rumens of HRDS-IA goats (Fig. 6J–M). Furthermore, the milk yield was significantly greater in HRDS-IA goats than in HRDS-C goats on the 42nd day (Fig. 6N). The measurement of cytokines in the plasma revealed that the level of IL-17 significantly decreased and that of IL-10 significantly increased in HRDS-IA goats (Fig. 6O, 6P).

All the experimental designs and protocols used in the present study were approved by the Institutional Animal Care and Use Committee (IACUC) of Northwest A&F University (Shaanxi, China; approval number: NWAFU-DK-2022155).

Not applicable.

The authors declare no competing interests.

All the data generated or analysed for this study are included in this paper and the supplementary material. The metagenome data, RNA-seq data, metabolomics data and single-nucleus RNA-seq data were deposited into the China National Genebank (https://db.cngb.org/cnsa/↗) under accession number CNP0006226.