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Diurnal oscillations of amino acids dynamically associate with microbiota and resistome in the colon of pigs

Animal Microbiome volume 7, Article number: 26 (2025) Cite this article

Abstract

Background

Nutrients are one of the key determinants of gut microbiota variation. However, the intricate associations between the amino acid (AA) profile and the dynamic fluctuations in the gut microbiota and resistome remain incompletely elucidated. Herein, we investigated the temporal dynamics of AA profile and gut microbiota in the colon of pigs over a 24-hour period, and further explored the dynamic interrelationships among AA profile, microbiota, and resistome using metagenomics and metabolomics approaches.

Results

JTK_circle analysis revealed that both the AA profile and the gut microbiota exhibited rhythmic fluctuations. With respect to the feed intake, all AAs except L-homoserine (PAdj = 0.553) demonstrated significant fluctuations. Over 50% of Lactobacillaceae, Ruminococcaceae, Clostridiaceae, and Eubacteriaceae species reached their peaks during T15 ∼ T21 when 50% of Lachnospiraceae species experienced a trough. The eLSA results showed that most AAs positively correlated with Prevotellaceae species but negatively correlated with Lactobacillaceae and Lachnospiraceae species. Moreover, most of the AAs negatively correlated with the mobile genetic elements Tn916 and istA group but positively correlated with plasmids. Further partial least squares structural equation model analysis indicated that AAs affected the antibiotic resistance gene dynamics through mobile genetic elements and the gut microbiota.

Conclusions

Taken together, the AA profile and the gut microbiota exhibit robust fluctuations over a day. The AA profile can affect the gut microbiota and resistome in a direct or indirect manner. These findings may provide new insights into a potential strategy for manipulating the gut microbiota and resistome.

Background

Since the 1950s, the inclusion of antibiotics in the feed of food animals has been widely adopted as a means to enhance growth and mitigate the risk of infection by pathogenic microorganisms. Notably, the utilization of antibiotics in livestock contributes to over 70% of global total consumption in 2010 [1]. However, the abuse of antibiotics in animal feed can lead to antibiotic-resistant microorganisms and the presence of antibiotic residues in animal products [2]. This gradual emergence of resistant microorganisms and antibiotic resistance genes (ARGs) has resulted in significant food safety concerns and environmental risks. Increasing evidence indicates that the gastrointestinal tract of livestock serves as the main reservoir for resistant microorganisms and ARGs [3, 4]. Therefore, comprehending the variations and determinants of ARGs within the gastrointestinal tracts of food animals is critically important for mitigating the dissemination of ARGs in human environments.

Studies conducted on mice have demonstrated diurnal oscillation in both the functionality and abundance of the gut microbiota throughout a day [5,6,7,8,9], the natural fluctuations of these gut microbes play vital roles in preserving normal host physiology, immune function, and metabolism [8, 10,11,12]. Convincing evidence has established a correlation between the rhythmicity of the gut microbiota and obesity, type 2 diabetes, and cardiovascular diseases [11, 13]. The abundance of ARGs is closely related to the microbial abundance and community composition. Our previous research found that the abundance of ARGs in the gastrointestinal tract exhibits significant diurnal variation with the gut microbiota [14]. While various factors can influence the configuration and function of the intestinal microbiota, dietary intake, particularly nutrients therein, serves as the primary driver of the intestinal microbial ecosystem [15, 16]. Additionally, a robust connection was established between the presence of ARGs in the colon and the levels of colonic ammonia nitrogen [14]. Ammonia nitrogen is the primary product of AA metabolism by microorganisms. Therefore, these findings may imply a potential association between AAs and ARGs within the intestinal environment.

AAs are indispensable nutrients for the gut microbiota and its host. Protein feed (such as soybean meal) is widely utilized in swine diets due to its abundant AAs and optimal AA balance. However, escalating protein feed costs and the potential environmental pollution resulting from nutrient excess have prompted a shift toward reduced protein and AA-supplemented diets in livestock production, particularly in pig farming [17]. There is substantial evidence from in vitro and in vivo research indicating the involvement of the gut microbiota in the metabolism of AAs and their derivatives within the small and large intestines [18,19,20]. Notably, the concentration, composition, proportion, and equilibrium of specific AAs (such as L-glutamine, leucine, and valine) and their derivatives (such as N-acetyl cysteine) may selectively shape the configure of the microbial community [21,22,23,24]. However, the dynamic interplay among AAs, gut microbes and the resistome is not yet understood. Consequently, the primary aim of the current study was to elucidate the dynamic interactions among AAs, gut microbiota and the resistome in the colon of a pig model under ad libitum conditions.

Methods

Animals, housing, and sampling

A total of 8 growing crossbred barrows (Duroc × Landrace × Large White) with an average bodyweight of 57.03 ± 1.78 kg were utilized. Pigs were surgically fitted with a medical polyethylene T-cannula (internal diameter = 15 mm, length = 82 mm, wings = 100 mm) in the proximal colon (approximately 10 cm from the ileocaecal junction in the first colonic coil), as previously described [25, 26]. After surgery, ceftriaxone sodium was administered to the pigs via subcutaneous injection for 7 days to prevent bacterial infection. Once all the pigs had fully recovered and returned to normal physical condition, further observations were made. All pigs were given free access to commercial growing pellet feed (Additional file 1) and water throughout the experiment. The experiment lasted for 9 days. At 6:00 am on the 8th day, colonic digesta samples were collected from the T cannula every 3 h for 24 consecutive hours; these samples were marked as T06 (6:00 am), T09 (9:00 am), T12 (12:00 pm), T15 (3:00 pm), T18 (6:00 pm), T21 (9:00 pm), T24 (12:00 am), T27 (3:00 am), and T30 (6:00 am). Samples were stored under − 80℃ for microbiome, resistome, AAs and metabolites analysis.

DNA extraction, metagenomics sequencing, and metagenomics data processing

Total DNA from these samples was extracted using the cetyltrimethylammonium bromide method following previous methods [19, 27]. Briefly, colonic digesta samples were initially dissolved in a saline solution to eliminate large particulate impurities. Subsequently, the samples were resuspended in cetyltrimethylammonium bromide solution and subjected to vigorous agitation to disrupt microbial cell walls and release intracellular DNA. RNAse was then added to the mixture for incubation to remove RNA contaminants. Following this, a phenol: chloroform: isoamyl alcohol (25:24:1) solution was employed for repeated extractions, performed three to five times, to thoroughly isolate the DNA sample. The extracted DNA was then placed in 90% ice-cold ethanol at -20 °C overnight and subsequently precipitated using 70% ethanol. Finally, the DNA quality was tested using a NanoDrop spectrophotometer (Thermo, Wilmington, DE, USA).

The details of the metagenomic shotgun sequencing process and quality control are described in our previous article [27]. The determination of the taxonomy of clean reads for each sample was finished by Kraken2 with default confidence score threshold [28]. The abundance of each taxon was determined by Bracken (https://ccb.jhu.edu/software/bracken/). The relative abundance of each species was calculated for bacteria and eukaryotes. The annotation of ARGs and Mobile genetic elements (MGEs) was described in our previously published study [14].

LC-MS metabolome and targeted metabolomics of AAs

All procedures and parameters used in metabolome and targeted metabolomics analyses of AAs have been described in our previously published work [29]. All metabolome analyses were finished on a liquid chromatography-mass spectrometry platform (Thermo, Ultimate 3000LC, Q Exactive) equipped with a chromatographic column of Hyper Gold C18 (100 mm × 2.1 mm × 1.9 μm). DL-o-Chlorophenylalanine was used for quality control. The extraction of metabolites and all AAs were accomplished by Compound Discoverer software (Thermo, version: 3.0). Metabolites and AAs for which 30% of values missing were excluded and further replaced by the minimum of the corresponding dataset. Features in the QC group with a standard deviation over 30% were also removed from the dataset. The peak intensity underwent logarithmic transformation for further analysis.

Statistical analysis

Diurnal fluctuation of the AA profile and the gut microbiota were calculated by JTK_circle analysis according to the instructions for a period of 24 h and interval of 3 h [7, 8, 30]. The permutation-based P (PAdj) and amplitude (AMP) were calculated. Features with PAdj < 0.05 were considered to have diurnal fluctuations. Cluster analyses of the microbiome and AAs were based on Fuzzy C-Means Clustering algorithm and finished by an R package named Mfuzz [31]. The dynamic interactions between AA profile, the gut microbiome, the ARGs, and the MGEs were calculated with an extended local similarity analysis (eLSA) which designed to capture dynamic correlations between time series data of microbes and environmental factors [32]. Raw data were subsequently subjected to F-transform and Z-score normalization before the calculation of local similarity coefficients (LS). A permutation test was used to assess the statistical significance of P-values [33]. Time delay (D) between a pairwise was also given. Only a pairwise with LS > 0.70 and P < 0.05 were selected for further analysis. The daily difference in the microbiota between light phase (T09, T12, T15, Z18) and dark phase (T06, T21, T24, T27, and T30) was tested by a two-tailed t-test based on STAMP (version 2.1.3). P < 0.05 represents a significant difference. PLS-SEM was accomplished by R package PLS-SEM [34]. Data were visualized using Cytoscape (version 3.7.1) and R (version 4.0.4). Principal coordinate analysis (PCoA) of AA profile and the gut microbiome was finished via an online website [35].

Results

Dynamic fluctuations of the colonic microbiota

A total of 6,796,902,028 reads were generated via shotgun metagenome sequencing with 151,042,267 ± 2446487.48 (mean ± standard error of the mean) reads per sample (Additional file 2). After filtering the host genes and performing quality control, a total of 6,498,723,288 clean reads were obtained with 144416073.1 ± 2417188.85 clean reads per sample. Then a total of 15,115,793 contigs (335907 ± 11948 per sample) were obtained followed by de novo assembly.

These clean reads were further annotated with the NR database. After BLAST alignment against the NR database, these contigs were mainly annotated as bacteria (99.68 ± 0.0064%), eukaryotes (0.23 ± 0.0070%), and the others (Additional file 3). Using JTK_circle, a nonparameter analysis (Fig. 1A), the relative abundances of bacteria (PAdj = 4.09 × 10− 3) and eukaryotes (PAdj = 2.23 × 10− 3) significantly fluctuated throughout 24 h. Interestingly, bacteria were greater at dark phase than in light phase in abundance (P = 0.003, Fig. 1B). In contrast, that of eukaryota was higher in light phase (P = 0.039, Fig. 1B).

The microbial communities of bacteria and eukaryotes of different sampling timepoints exhibited obvious differences according to the PCoA model (Fig. 1C). For bacteria, the dominant phyla were Firmicutes (80.64 ± 1.47%), Bacteroidetes (15.61 ± 1.40%), Proteobacteria (1.03 ± 0.057%), and Actinobacteria (0.96 ± 0.046%) (Additional file 3). Notably, all of these parameters fluctuated significantly (PAdj < 0.05). Of note, the average cumulative relative abundance of these cycling phyla was over 98%. Among the top 10 most dominant genera (Fig. 1D), the relative abundances of Lactobacillus (PAdj = 1.89 × 10− 3), Prevotella (PAdj = 3.88 × 10− 6), Bacteroides (PAdj = 3.42 × 10− 5), Clostridium (PAdj = 0.021), Roseburia (PAdj = 3.35 × 10− 3), Butyricicoccus (PAdj = 1.77 × 10− 5), and Subdoligranulu (PAdj = 0.021) significantly fluctuated. At the species level, 19.29% of the annotated species exhibited significant fluctuations (PAdj = 0.05) in relative abundance (Fig. 1E, F). Strikingly, the cumulative relative abundance of these cyclical species reached 58.86% (Fig. 1F). Among the top dominant species, the relative abundances of Lactobacillus johnsonii (PAdj = 6.26 × 10− 3), Lactobacillus reuteri (PAdj = 1.74 × 10− 3), Prevotella copri (PAdj = 3.38 × 10− 7), Butyricicoccus porcorum (PAdj = 5.56 × 10− 6), Ruminococcaceae bacterium (PAdj = 2.85 × 10− 3), Subdoligranulum sp. (PAdj = 9.11 × 10− 3), and Gemmiger formicilis (PAdj = 6.76 × 10− 3) exhibited significant oscillations (Fig. 1E).

For eukaryotes, Fgnui (27.42 ± 1.01%), Metazoa (41.02 ± 0.85%), and Viridiplantae (26.45 ± 0.88%), accounted for the largest proportion at the kingdom level (Additional file 3). Remarkably, all of them exhibited significantly oscillations. At the phylum level, except for Mucoromycota, the other 8 phyla Nematoda (PAdj = 2.89 × 10− 4), Streptophyta (PAdj = 7.95 × 10− 5), Ascomycota (PAdj = 3.49 × 10− 4), Chytridiomycota (PAdj = 5.56 × 10− 6), Chordata (PAdj = 7.29 × 10− 3), Preaxostyla (PAdj = 0.021), Parabasalia (PAdj = 1.74 × 10− 3), and Evosea (PAdj = 0.011) exhibited significant rhythmicity. In addition, 15.22% of the annotated species in eukaryotes underwent rhythmic oscillations (Fig. 1E, F). The accumulated relative abundance of which contributed 39.74% to the total number of eukaryotes (Fig. 1F).

Fig. 1
figure 1

Dynamic oscillations of gut microbiota in the colonic digesta of growing pigs. A Dynamic fluctuations of the gut microbiota at the domain level. B Diurnal differences in the gut microbiota at the domain level. C Principal coordinate analysis (PCoA) plots of bacterial and eukaryotic communities from different sampling timepoints within a day. D Dynamic changes in the relative abundance of the top 10 bacterial genera and eukaryotic phyla. E Scatter plot depicting bacteria and eukaryotes with diurnal fluctuations at the species level. F Pie chart showing the percentages of cyclical bacteria and eukaryotes, and corresponding cumulative abundances at the species level

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Fig. 2
figure 2

Cluster analysis of the microbiota with diurnal fluctuations in colonic digesta of pigs

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Interestingly, rhythmic bacterial species peaking at different times within a day were mainly clustered into 7 classes (Fig. 2). Over 50% of the bacterial community in C1 is comprised of the Lachnospiraceae, exhibiting a peak at time point T12 and a trough at T21. Approximately 90% of the bacterial population in C3 is classified under the Acidaminococcaceae. C4 represents the group with the highest bacterial abundance, reaching a peak at T09 and declining between T18 and T21, with approximately 80% of its bacterial constituents belonging to the Prevotellaceae. Furthermore, more than 95% of the bacteria in C7 are members of the Lactobacillaceae (Fig. 2). Over 50% of Lachnospiraceae species were clustered into class 3 which had a trough during T15 ∼ T21, over 50% of Bacteroidaceae and Prevotellaceae were clustered into class 4, which had a peak during T06 ∼ T12; and over 50% of Erysipelotrichaceae were clustered into class 5, which had a peak during T12 ∼ T15. Over 50% of Clostridiaceae, Eubacteriaceae, Lactobacillaceae, and Ruminococcaceae were clustered into class 5, whose abundance peaked mainly during T15 ∼ T21 (Additional file 4).

Dynamic fluctuations in the AA profile of the colonic digesta

Data from untargeted metabolomics revealed that the metabolite profiles (including AAs, sugars, and fatty acids) in the digesta underwent rhythmic fluctuations over a day (Additional file 5). The metabolome explained the variation in microbial composition to a certain extent (Additional file 6). AA profile explained 13% of the variation in bacterial community composition, whereas organic acid profile explained 5% of the variation in eukaryotic community composition (Additional file 5, Additional file 6).

Therefore, we further determined AA profile in the colonic digesta by the targeted metabolomics. These results indicated that AA profile exhibited robust fluctuations within the day in the PCoA model. The T09, T12, T15, and T18 samples were distinguished from the T21, T24, and T27 samples (Fig. 3A). All 35 identified AAs and their derivatives except L-homoserine (PAdj = 0.553) exhibited significant fluctuations (Fig. 3B). The most rhythmic AAs were citrulline (PAdj = 5.39 × 10− 10) and L-methionine (PAdj = 9.62 × 10− 10). Furthermore, most of these AAs were positively correlated with feed intakes (Table 1A). These rhythmic AAs peaked at different times within a day and were mainly clustered into 5 classes (Fig. 4A). The branched-chain AAs L-valine (PAdj = 7.88 × 10− 4), L-leucine (PAdj = 1.89 × 10− 4) and L-isoleucine (PAdj = 1.85 × 10− 4) were clustered into class 2, which peaked at T06 ∼ T12 (Fig. 4A). The concentrations of L-valine (P = 0.011), L-leucine (P = 4.06 × 10− 5) and L-isoleucine (PAdj = 3.02 × 10− 5) were higher in dark phase than during light phase (Fig. 4B). The aromatic AAs L-tryptophan (PAdj = 2.26 × 10− 3), L-tyrosine (PAdj = 4.50 × 10− 6) and L-phenylalanine (PAdj = 8.61 × 10− 7) were clustered into class 4 which were peaked at T12 ∼ T18 (Fig. 4A). The most rhythmic AA citrulline (PAdj = 5.39 × 10− 10) and L-methionine (PAdj = 9.62 × 10− 10) were clustered into class 5 which were peaked at T09 ∼ T15 (Fig. 4A). The concentrations of citrulline (P = 1.13 × 10− 3) and L-methionine (P = 0.034) were higher in light phase than during dark phase (Fig. 4B).

Fig. 3
figure 3

Dynamic fluctuations in AAs and their correlation with feed intake. A Principal coordinates analysis (PCoA) plots of AAs from different sampling timepoints within a day. B Scatter plot depicting AAs with diurnal fluctuations

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Table 1 Dynamic correlation network between AAs and the feed intake
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Fig. 4
figure 4

Cluster analysis and diurnal differences in the AA profile. A Cluster analysis of the AAs in the colonic digesta of pigs. B Diurnal differences in the AAs

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AAs dynamics affected the fluctuation of the gut microbes and the resistome

Based on a dynamic-correlation model eLSA, general relationships among the AA profile, the gut microbes, and the resistome were established. For the gut microbiota, most of these AAs positively correlated with Prevotellaceae species whereas negatively correlated with Lactobacillaceae and Lachnospiraceae species (Fig. 5A). For instance (Fig. 5B), citrulline (LS = 0.93, PAdj = 0.001, D = 2), 4-hydroxyproline (LS = 0.91, PAdj = 0.001, D = 3), and L-alanine (LS = 0.87, PAdj = 0.006, D = 2) were positively correlated with Prevotella copri whereas L-methionine (LS = -0.92, PAdj = 0.004, D = 0), and 4-hydroxyproline (LS = -0.86, PAdj = 0.012, D = 0) were negatively correlated with Lactobacillus reuteri, L-citrulline (LS = -0.91, PAdj = 0.005, D = -1), L-histidine (LS = -0.94, PAdj = 0.001, D = -1), and 4-hydroxyproline (LS = -0.89, PAdj = 0.007, D = 0) were negatively correlated with Roseburia sp. AM51-8.

The diurnal fluctuation in MGEs and ARGs were fully described in our work [14]. For the MGEs, AA profile negatively correlated with the Tn916 and istA group whereas positively correlated with plasmid (Fig. 6A). For instance (Fig. 6B), citrulline were positively correlated with rep14 (LS = 0.90, PAdj = 0.011, D = 1) and IncQ1 (LS = 0.83, PAdj = 0.021, D = 0). L-canisurine, an intermediate product of phenylalanine metabolism, were negatively correlated with Tn916-orf16 (LS = -0.97, PAdj < 0.001, D = 0), Tn916-orf18 (LS = -0.97, PAdj < 0.001, D = 0), and Tn916-orf20 (LS = -0.97, PAdj < 0.001, D = 0). For the ARGs, AAs had tightly correlations with ARGs belonging to major facilitator superfamily (MFS) antibiotic efflux pump family, tetracycline-resistant ribosomal protection protein, OXA beta-lactamase, and tetracycline-resistant ribosomal protection protein (Additional file 6). For example, citrulline were positively correlated with tet32 (LS = -0.89, PAdj = 0.007, D = -1) whereas negatively correlated with tetQ (LS = 0.90, PAdj = 0.004, D = -1) and tetB(P) (LS = 0.90, PAdj = 0.005, D = 1). L-canisurine were positively correlated with tetO (LS = 0.90, PAdj = 0.011, D = 1) and tetB(P) (LS = 0.93, PAdj = 0.002, D = 2) whereas negatively correlated with tetW (LS = -0.91, PAdj = 0.003, D = 0) and tetM (LS = -0.97, PAdj < 0.001, D = 0). Methionine was positively correlated with tetQ (LS = 0.93, PAdj < 0.001, D = -2) and tetB(P) (LS = 0.97, PAdj < 0.001, D = 1) whereas negatively correlated with tetM (LS = -0.95, PAdj < 0.001, D = -1) (Additional file 7).

Furthermore, using the partial least squares structural equation model (PLS-SEM, Fig. 7), we found that AA profile directly affected the microbial α-diversity (R2 = 0.12), β-diversity (R2 = 0.60) and MGEs (R2 = 0.60). AA profile indirectly affected ARGs by affecting MGEs (R2 = 0.35) and microbial β-diversity (R2 = 0.35).

Fig. 5
figure 5

Extended local similarity (eLSA) correlations between AAs and gut microbiota at the species level. A Correlation network established by extended local similarity (eLSA) method. The thickness of the edges indicates the local similarity coefficient (a thicker edge represents a higher local similarity coefficient). Edge in red color represents a positive correlation whereas blue edge represents a negative correlation. 1-Methyl-L-Histidine = 1MH, 4-Hydroxyproline = HYP, Citrulline = Cit, Creatine = Cre, DL-homocysteine = Hcy, L-Alanine = Ala, L-Arginine = Arg, L-Aspartic Acid = Asp, L-Canisurine = Csu, L-Cysteine = Cys, L-glutamic acid = Glu, L-Glycine = Gly, L-Histidine = His, L-Homoproline = Hpr, L-Homoserine = Hse, L-Isoleucine = Ile, L-Leucine = Leu, L-Lysine = Lys, L-Methionine = Met, L-O-phosphoserine = P-Ser, L-Phenylalanine = Phe, L-Proline = Pro, L-Serine = Ser, L-Threonine = Thr, L-Tryptophan = Trp, L-Tyrosine = Tyr, L-Valine = Val, L-white = Whi, N,N-dimethylglycine = DMG, N-Acetyl-L-glutamic acid = NAG, N-Acetyl-β-Alanine = NAA, Nα-Acetyl-L-Lysine = NAL, Ornithine = Orn, Pyroglutamic acid = pGlu, S-(5 ‘-adenosine) -L-homocysteine = Hcy. B Dynamic correlation between AA and specific microbes. Red line represents an AA whereas blue line represents a microbe

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Fig. 6
figure 6

Correlations between AA profile and the MEGs. A Correlation network established by extended local similarity (eLSA) method. The thickness of the edges indicates the local similarity coefficient (a thicker edge represents a higher local similarity coefficient). Edge in red color represents a positive correlation whereas blue edge represents a negative correlation. 1-Methyl-L-Histidine = 1MH, 4-Hydroxyproline = HYP, Citrulline = Cit, Creatine = Cre, DL-homocysteine = Hcy, L-Alanine = Ala, L-Arginine = Arg, L-Aspartic Acid = Asp, L-Canisurine = Csu, L-Cysteine = Cys, L-glutamic acid = Glu, L-Glycine = Gly, L-Histidine = His, L-Homoproline = Hpr, L-Homoserine = Hse, L-Isoleucine = Ile, L-Leucine = Leu, L-Lysine = Lys, L-Methionine = Met, L-O-phosphoserine = P-Ser, L-Phenylalanine = Phe, L-Proline = Pro, L-Serine = Ser, L-Threonine = Thr, L-Tryptophan = Trp, L-Tyrosine = Tyr, L-Valine = Val, L-white = Whi, N,N-dimethylglycine = DMG, N-Acetyl-L-glutamic acid = NAG, N-Acetyl-β-Alanine = NAA, Nα-Acetyl-L-Lysine = NAL, Ornithine = Orn, Pyroglutamic acid = pGlu, S-(5 ‘-adenosine) -L-homocysteine = Hcy. B Dynamic correlation between AA and specific MEGs. Red line represents an AA whereas blue line represents a MEG

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Fig. 7
figure 7

Partial least squares structural equation model depicting the relationships between AA, microbiota, and the resistome. ***p < 0.001, **p < 0.01, *p < 0.05. Edge in red color represents a positive correlation whereas blue edge represents a negative correlation. AAs = Amino acids and their derivatives; ARGs = antibiotic resistance genes; MGEs = mobile genetic elements

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Discussion

Chronobiology is an increasingly popular discipline of biology that aims to study cyclic phenomena and underlying mechanisms in living organisms including both prokaryotes and eukaryotes. Recently, a relatively mature theory was generally developed based on studies concerning biorhythm in eukaryotes [20, 36]. However, much less is known about the details of the diurnal rhythmicity of gut microbiome. Increasing evidence has shown that the gut microbiota in both human and mice significantly oscillates at both compositional and functional levels [5,6,7,8,9]. The diurnal fluctuations in microbial activity can subsequently affect the global regulation of the host’s circadian transcriptional, epigenetic, and metabolite oscillations. Moreover, the arrhythmicity homeostasis in the gut microbiome not only abolishes the normal chromatin and transcriptional oscillations but also induces genome-wide de novo oscillations in both the intestine and liver of the host [7]. These changes further affect the diurnal fluctuations of host physiology and susceptibility to disease. Convincing evidence has established a correlation between the rhythmicity of the gut microbiota and obesity, type 2 diabetes, and cardiovascular diseases [11, 13]. More specifically, the relative abundance of more than 15% of detected OTUs showed significant fluctuations, which contributed to approximately 60% of the microbiota composition [8]. A similar percentage of 15.2% cyclical OTUs was reported in humans [9]. In pigs, 11.22% of microbes at the ASV level were identified [27]. Herein, a higher percentage (19.29%) of cyclical species was identified. However, a more higher percentage of 23.38% cyclical OTUs exhibited rhythmic patterns in epithelial adherence community samples from mice [7]. These inconsistencies may be caused by microbial variation across hosts and between individuals, sequencing methods, algorithm for rhythmicity, sample sampling locations, and sampling interval. However, consistent with previous study [8], a similar percentage (58.86%) of the cumulative relative abundance of these cyclical species was found. According to previous reports, the 2 most dominant phyla, Firmicutes and Bacteroidetes, underwent significant oscillation at the phylum level [11].

In the last century, the widespread adoption of incorporating sub-therapeutic levels of antibiotics into livestock and poultry feed to improve production efficiency has raised concerns of escalating bacterial resistance and the presence of antibiotic residues [37, 38]. The presence of ARGs inhabiting the gastrointestinal tract of farm animals was key contributor to environmental contamination through the dissemination of these genes via animal excreta [39]. This phenomenon poses a dual threat to both animal health and productivity, as well as potential risks to public health. Consequently, the mitigation of resistance gene transmission in livestock production has become a pressing issue that warrants urgent action. Our previous study revealed a notable association between intestinal nutritional substrates, gut microbiota, and ARGs [14, 40]. Herein, we explored the crosstalk among AAs, the gut microbiota, and ARGs. We found that the AA profile in the colonic digesta had extensive correlations with the ARGs therein. AAs play crucial roles in maintaining the crosstalk of host and intestinal microbiome. Gut microbes affect the metabolism of AAs in the gut [41, 42]. Whereas, the level and composition of AAs in turn determine the configure and function of gut microbiota to a certain extent [43, 44].

Herein, we found that AAs had tightly correlations with ARGs belonging to major facilitator superfamily (MFS) antibiotic efflux pump family, tetracycline-resistant ribosomal protection protein, OXA beta-lactamase, and tetracycline-resistant ribosomal protection protein. This study employed PLS-SEM to demonstrate that the influence of AAs on ARGs may be mediated through MGEs. The mechanism underlying the dissemination of ARGs within microbial communities remains incompletely elucidated. However, substantial research indicates that MGEs play a significant role in expediting the transmission of these ARGs. MGEs mainly including integrons, insertion sequences, transposons and plasmids, are important for capturing, accumulating, and disseminating ARGs [45]. These elements, such as transposons and plasmids, enhance horizontal gene transfer among bacteria, thereby promoting the swift propagation of antibiotic resistance traits both within and across species. For example, Tn916-like genetic elements are recognized for imparting multidrug resistance by transferring resistance genes via a conjugative mechanism, which entails excision from donor cells and subsequent integration into recipient genomes [46]. Specific AAs can influence microbial growth rates, community diversity, and the expression of resistance mechanisms [38]. Supplementation with methionine (0.2%) decreased ARGs in abundance in the gut, highlighting the intricate interplay between dietary factors, microbial metabolism, and antibiotic resistance [47]. Lysine acetylation exerts a dual regulatory role in Escherichia coli by negatively impacting bacterial metabolism modulating antibiotic-resistance and influencing bacterial motility [48]. In particular, our study identified a significant association between citrulline and the prevalence of ARGs. Consistent with these findings, the addition of citrulline and glutamine enhanced the Tricarboxylic Acid cycle, leading to increased production of NADH within the bacteria and an elevated proton-motive force. This, in turn, facilitated the entry of Apramycin into the bacterial cells, thereby effectively eliminating the drug-resistant bacteria [49]. These studies suggest that changing the diet of livestock could help reduce the spread of ARGs. Nevertheless, we need more work to explain how AAs affect these processes and to develop effective interventions.

Conclusions

In summary, we estimated dynamic interplay among AAs, the gut microbiome, and the resistome. These findings suggest that dietary AA composition could potentially influence the abundance of ARGs through affecting the gut microbiota and MGEs. These findings may provide new insights into a novel avenue for mitigating the spread of antibiotic resistance.

Data availability

The datasets generated and/or analyzed during the current study are available in the NCBI Sequence Read Archive (SRA) repository, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA843783/.

Abbreviations

AAs:

Amino acids

ARGs:

Antibiotic resistance genes

MGEs:

Mobile genetic elements

PCoA:

Principal coordinate analysis

PLS-SEM:

Partial least squares structural equation model

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Acknowledgements

The authors would like to thank Prof. Weiyun Zhu for kindly advise for this study.

Funding

This work was supported by grants from National Key R&D Program of China (2023YFD1301304) and National Natural Science Foundation of China (32072688).

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Authors and Affiliations

  1. Laboratory of Gastrointestinal Microbiology, Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China

    Hongyu Wang, Yue Li, Ni Feng, Dongfang Wang & Yong Su

  2. Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China

    Jinwei You & Xiaobo Feng

  3. College of Animal Science, Anhui Science and Technology University, Chuzhou, China

    Hongyu Wang

Authors
  1. Hongyu Wang
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Contributions

YS and XF conceptualised and designed the study. HW, YL, JY, NF and DW carried out the experiments. HW, YL and JY contributed to the data curation and software. HW, YL and JY performed the data analyses and wrote the manuscript. YS and XF edited manuscript draft. All the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yong Su or Xiaobo Feng.

Ethics declarations

Ethics approval and consent to participate

The study was conducted in accordance with the guidelines set forth by the Animal Care and Use Committee of Nanjing Agricultural University (SYXK2019-0066). All animal care procedures employed in the study adhered to the Experimental Animal Care and Use Guidelines of China (EACUGC2018-01).

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Not applicable.

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The authors declare no competing interests.

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Wang, H., Li, Y., You, J. et al. Diurnal oscillations of amino acids dynamically associate with microbiota and resistome in the colon of pigs. anim microbiome 7, 26 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s42523-025-00393-0

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s42523-025-00393-0

Keywords

Animal Microbiome

ISSN: 2524-4671