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/lp/springer-journals/trace-amounts-of-antibiotic-altered-metabolomic-and-microbial-profiles-zPkpVQOFvG
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- Springer Journals
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- Copyright © The Author(s) 2022
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- 2049-1891
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- 10.1186/s40104-022-00703-5
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Abstract
Background: Our previous study has shown that supplementation of trace amounts of antibiotic exacerbated the detrimental effects of enterotoxigenic E. coli (ETEC) infection and delayed the recovery of pigs that may be associated with modified metabolites and metabolic pathways. Therefore, the objective of this study was to explore the impacts of trace levels of antibiotic (carbadox) on host metabolic profiles and colon microbiota of weaned pigs experimentally infected with ETEC F18. Results: The multivariate analysis highlighted a distinct metabolomic profile of serum and colon digesta between trace amounts of antibiotic (TRA; 0.5 mg/kg carbadox) and label-recommended dose antibiotic (REC; 50 mg/kg carbadox) on d 5 post-inoculation (PI). The relative abundance of metabolomic markers of amino acids, carbohydrates, and purine metabolism were significantly differentiated between the TRA and REC groups (q < 0.2). In addition, pigs in REC group had the highest (P < 0.05) relative abundance of Lactobacillaceae and tended to have increased (P < 0.10) relative abundance of Lachnospiraceae in the colon digesta on d 5 PI. On d 11 PI, pigs in REC had greater (P < 0.05) relative abundance of Clostridiaceae compared with other groups, whereas had reduced (P < 0.05) relative abundance of Prevotellaceae than pigs in control group. Conclusions: Trace amounts of antibiotic resulted in differential metabolites and metabolic pathways that may be associated with its slow responses against ETEC F18 infection. The altered gut microbiota profiles by label- recommended dose antibiotic may contribute to the promotion of disease resistance in weaned pigs. Keywords: Carbadox, Colon microbiota, Enterotoxigenic Escherichia coli, Metabolomics, Weaned pigs Background environment, thus, a variety of trace concentrations of Trace amounts of antibiotics are emerging contami- antibiotics have been detected in surface water, waste- nants of environmental concern due to their potential water,soil,air,and dust [2–5]. Exposure to trace risks on non-target organisms and the spread of bac- levels of antibiotics may cause unexpected adverse ef- terial resistance [1]. The excessive and imprudent use fects on the host, such as allergic reactions, disrup- of human and veterinary antibiotics significantly con- tion of digestive system function, and shaping the tributes to a continuous release of antibiotics into the metabolites and microbial community [6, 7]. In par- ticular, exposure to trace amounts of antibiotics at early life can result in the alteration of microbiota * Correspondence: yahliu@ucdavis.edu and metabolic networks, which further accelerate the Department of Animal Science, University of California, Davis, CA 95616, USA Full list of author information is available at the end of the article © The Author(s). 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 2 of 15 possible development of resistant strains [8, 9]. Previ- performance, blood profiles, and immune responses ous studies in mice have shown that subtherapeutic were also reported in previous work [12]. concentrations of penicillin exposure at early life in- terfered with the development of the intestinal im- Sample collections mune system [10] and induced metabolic changes due Sixteen pigs (6 pigs in CON, 4 pigs in TRA, and 6 pigs to altered intestinal microbiota [11]. in REC) were randomly selected and euthanized on d 5 Our recent study has shown that trace amounts of PI near the peak of infection, and the remaining pigs (6 antibiotic in feed exacerbated the detrimental effects of pigs in CON, 5 pigs in TRA, and 7 pigs in REC) were enterotoxigenic E. coli (ETEC) infection by increasing euthanized at the end of the experiment (d 11 PI) that diarrhea and systemic inflammation in weanling pigs was the recovery period of the infection. The selection [12]. The exact mechanisms for the exacerbation of of necropsy time was based on the results of clinical ob- ETEC infection by trace amounts of antibiotic has not servations and immune response parameters that were been determined, but it has been suggested that trace reported in previously published research using the same concentrations of antibiotics can act as signaling mole- ETEC strain and inoculation dose [15, 16]. Before eu- cules to trigger specific bacterial responses [13]. There- thanasia, pigs were anesthetized with a 1-mL mixture of fore, the objective of this study was to explore the 100 mg telazol, 50 mg ketamine, and 50 mg xylazine (2:1: impacts of feeding trace levels of antibiotic on host 1) by intramuscular injection. After anesthesia, intracar- metabolic profiles and colon microbiota of weaned pigs diac injection with 78 mg sodium pentobarbital (Vortech experimentally infected with ETEC F18. Pharmaceuticals, Ltd., Dearborn, MI, USA) per 1 kg of BW was used to euthanize each pig. Blood samples were collected from the jugular vein of all pigs without EDTA Materials and methods to yield serum before ETEC challenge (d 0), and on d 5, Animals, housing, experimental design, and diet and 11 PI. Serum samples were collected by centrifuging The protocol for this study was reviewed and approved approximately 5 mL of whole blood samples at 20 °C at by the Institutional Animal Care and Use Committee at 1500 × g for 15 min and immediately stored at − 80 °C the University of California, Davis (IACUC #19322). until untargeted metabolomics analysis. Colon digesta Samples analyzed here were obtained as described in a were collected from the distal colon of pigs on d 5 and previously published study by Kim et al. [12]. Briefly, 34 11 PI and immediately snap-frozen in liquid nitrogen weanling pigs (crossbred; initial BW: 6.88 ± 1.03 kg) with and stored at − 80 °C for untargeted metabolomics and an equal number of gilts and barrows were randomly microbiome analysis. assigned to one of three treatments in a randomized complete block design with body weight within sex and Untargeted metabolomics analysis litter as the blocks and pig as the experimental unit. The The untargeted metabolomics analysis was performed by 3 dietary treatments included: 1) the complex nursery the NIH West Coast Metabolomics Center using gas basal diet (control; CON), 2) addition of 0.5 mg/kg car- chromatography (Agilent 6890 gas chromatograph con- badox (trace amounts of antibiotic; TRA) to the basal trolled using Leco ChromaTOF software version 2.32, diet, or 3) addition of 50 mg/kg carbadox (label-recom- Agilent, Santa Clara, CA, USA) coupled with time-of- mended dose of antibiotic; REC) to the basal diet. All di- flight mass spectrometry (GC/TOF-MS) (Leco Pegasus ets were formulated to meet pig nutritional IV time-of-flight mass spectrometer controlled using requirements and provided as mash form throughout Leco ChromaTOF software version3 2.32, Leco, Joseph, the experiment. MI, USA). Metabolite extraction was performed follow- After 7 d of adaptation, all pigs were orally inoculated ing procedures described previously [17]. Briefly, frozen with 3 mL of ETEC F18 using a disposable Luer-lock colon digesta samples (approximately 10 mg) were ho- syringe for 3 consecutive days from d 0 post-inoculation mogenized using a Retsch ball mill (Retsch, Newtown, (PI). The ETEC F18 was originally isolated from a field PA, USA) for 30 s at 25 times/s. After homogenization, a disease outbreak by the University of Illinois Veterinary prechilled (− 20 °C) extraction solution (isopropanol/ Diagnostic Lab (isolate number: U.IL-VDL # 05–27,242) acetonitrile/water at the volume ratio 3:3:2, degassed and expresses heat-labile toxin (LT), heat-stable toxin b with liquid nitrogen) was added at a volume of 1 mL/20 (STb), and Shiga-like toxin (SLT-2). The ETEC F18 in- mg of sample. Samples were then vortexed and shaken oculums were provided at 10 colony-forming unit for metabolite extraction. After centrifugation at (CFU) per 3 mL dose in phosphate-buffered saline (PBS). 12,800 × g for 2 min, the supernatant was collected and This dose caused mild diarrhea in the current study as divided into two equal aliquots and concentrated at reported in Kim et al. [12], which is consistent with our room temperature for 4 h in a cold-trap vacuum concen- previous published researches [14–16]. Growth trator (Labconco Centrivap, Kansas City, MO, USA). To Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 3 of 15 separate complex lipids and waxes, the residue was re- control and constructing features. Taxonomic classifica- suspended in 500 μL of 50% aqueous acetonitrile and tion was assigned using the feature-classifier plugin centrifuged at 12,800 × g for 2 min. The resultant super- trained with the SILVA rRNA database 99% operational natant was collected and concentrated in the vacuum taxonomic units (OTU), version 132 [22, 23]. concentrator. Dried sample extracts were derivatized and mixed with internal retention index markers (fatty Data analysis acid methyl esters with the chain length of C8 to C30). The metabolomics data were analyzed using different The samples were injected for GC/TOF analysis, and all modules of a web-based platform, MetaboAnalyst 5.0 samples were analyzed in a single batch. Data acquisition (https://www.metaboanalyst.ca)[24]. Data were filtered by mass spectrometry and mass calibration using FC43 for peaks with detection rates less than 30% of missing (perfluorotributylamine) before starting analysis se- abundances and normalized using logarithmic trans- quences. Metabolite identifications was performed based formation and auto-scaling. Mass univariate analysis was on the two parameters: 1) Retention index window ± performed using one-way ANOVA followed by Tukey’s 2000 U (around ± 2 sec retention time deviation), and 2) post hoc test (adjusted P ≤ 0.05). Fold change analysis Mass spectral similarity plus additional confidence cri- and t-tests were also conducted to determine the fold teria as detailed below (Data analysis). A detailed meth- change and significance of each identified metabolite. odology for data acquisition and metabolite Statistical significance was declared at a false discovery identification described in a previously published article rate (FDR, Benjamini and Hochberg correction; q) q < by Fiehn et al. [17]. 0.2 and fold change > 2.0. Partial least squares discrimin- ant analysis (PLS-DA) was carried out to further identify Gut microbiota in distal Colon discriminative variables (metabolites) among the treat- Bacterial DNA was extracted from digesta samples using ment groups. Pathway analysis and metabolite set en- the Quick-DNA Fecal/Soil Microbe Kit (Zymo Research, richment analysis were performed on identified Irvine, CA, USA) following the manufacturer’s instruc- metabolites that had a Variable Importance in Projection tions. Extracted bacterial DNA was amplified with PCR, (VIP) score > 1. targeting the V4 region of the 16S rRNA gene with Data visualization and statistical analysis for colon primers 515 F (5′- XXXXXXXXGTGTGCCAGCMGCC microbiota were conducted using R (version 3.6.1). Two GCGGTAA-3′) with an 8 bp barcode (X) and Illumina alpha diversity indices, Chao1 and Shannon, were calcu- adapter (GT) and 806 R (5′-GGACTACHVGGGTWTC lated using the phyloseq package. Relative abundance TAAT-3′)[18]. Amplification included thermocycling was calculated using the phyloseq package and visualized conditions of 94 °C for 3 min for denaturation, 35 cycles using the ggplot2 package in R. Relative abundance data of 94 °C for 45 s, 50 °C for 1 min, 72 °C for 1.5 min, and were aggregated at various taxonomical levels. Shapiro- 72 °C for 10 min (final elongation). To reduce PCR bias, Wilk normality test and Bartlett test were used to verify each sample was amplified in triplicate. Each PCR reac- normality and constant variance, respectively, in alpha tion included 2 μL of template DNA, 0.5 μL (10 μmol/L) diversity and relative abundance. Shannon index was an- of barcoded forward primer, 0.5 μL (10 μmol/L) of re- alyzed using ANOVA with the statistical model, includ- verse primer, 12.5 μL of GoTaq 2X Green Master Mix ing sample collection days within treatment as fixed (Promega, Madison, WI, USA), and 9.5 μL of nuclease- effects. Significance in Chao1 index and relative abun- free water. The triplicate PCR products were pooled and dance was observed using Kruskal-Wallis rank-sum test subjectively quantified based on the brightness of the followed by a Conover test for multiple pairwise com- bands on a 2% agarose gel with SYBR safe (Invitrogen parisons using the agricolae package. Beta diversity was Co., Carlsbad, CA, USA). All amplicons were then calculated based on the Bray-Curtis dissimilarity for pooled at equal amounts and further purified using the principal coordinates analysis (PCoA). The homogeneity QIAquick PCR Purification Kit (Qiagen, Hilden, of multivariate dispersions was tested by the vegan pack- Germany). The purified library was submitted to the UC age using the betadisper function, before the adonis Davis Genome Center DNA Technologies Core for 250 function was used to calculate PERMANOVA with 999 bp paired-end sequencing on the Illumina MiSeq plat- replicate permutations. Statistical significance and ten- form (Illumina, Inc. San Diego, CA, USA). dency were considered at P < 0.05 and 0.05 ≤ P < 0.10, The software sabre (https://github.com/najoshi/sabre) respectively. was used to demultiplex and remove barcodes from raw sequences. Sequences were then imported into Quantita- Results tive Insights Into Microbial Ecology 2 (QIIME2; version Metabolite profiles in serum 2018.6) for downstream filtering and bioinformatics ana- A total of 354 (134 identified and 220 unidentified) me- lysis [19, 20]. Plugin q2-dada2 [21] was used for quality tabolites were detected in serum samples. Based on VIP Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 4 of 15 scores and relative abundance, 3 metabolites (fructose, were the most affected metabolic pathways in a compari- mannonic acid, and propyleneglycol) were up-regulated son of TRA with REC (Fig. S3 B, D). by TRA, compared with the pigs in REC on d 0 before ETEC challenge (Table 1). Supplementation of REC Metabolite profiles in distal colon digesta changed the abundances of 6 metabolites (2 up- A total of 398 (178 identified and 220 unidentified) metab- regulated and 4 down-regulated) compared with CON, olites were detected in colon digesta. Based on VIP score while REC changed 16 metabolites (6 up-regulated and and relative abundance, 12 metabolites (9 up-regulated 10 down-regulated) in comparison with TRA on d 5 PI. and 3 down-regulated) were differentiated on d 5 PI, and On d 11 PI, chenodeoxycholic acid was enriched, while one metabolite, inosine, was decreased on d 11 PI in pigs glycerol and inositol-4-monophosphate were reduced in fed with TRA when compared with pigs in the REC group the CON group compared with REC. Pigs in TRA had (Table 2). No differential metabolites were identified when greater chenodeoxycholic acid than pigs in REC, but 5 comparing CON vs. TRA, and CON vs. REC at d 5 and 11 metabolites (p-tolyl glucuronide, glycerol, mannitol, 2- PI. Based on the identified metabolites, a PLS-DA score ketoisocaproic acid, and inositol-4-monophosphate) plot with 95% confidence ranges (shaded areas) showed a were decreased in pigs supplemented with TRA com- clear separation between the TRA and REC groups at both pared with pigs in REC. No differential metabolites were PI time points (Fig. 3). The PLS-DA score plots in a pair- identified when comparing CON vs. TRA throughout wise manner also clearly separated TRA from REC on d 5 the experiment. Based on the identified metabolites, a and 11 PI (Fig. S4). PLS-DA score plot with 95% confidence ranges (shaded Pathway analysis and metabolite set enrichment ana- areas) showed a clear separation between the TRA and lysis were performed on metabolites in colon digesta REC groups throughout the experiment (Fig. 1). To fur- with VIP > 1. Starch and sucrose metabolism, purine me- ther explore the metabolic profile differences among two tabolism, arginine biosynthesis, and arginine and proline dietary treatments, PLS-DA was performed for the fol- metabolism were the most affected metabolic pathways lowing comparisons: (1) TRA vs. REC, and (2) CON vs. when TRA group was compared with the REC group on REC on d 0 before ETEC challenge, d 5 and d 11 PI. d 5 PI (Fig. 4 A, C). Aminoacyl-tRNA biosynthesis, ar- The score plot again distinguished the TRA from the ginine biosynthesis, pentose and glucuronate intercon- REC, and also revealed the metabolic profile differences versions, arginine and proline metabolism, alanine, between CON and REC (Fig. S1). aspartate, and glutamate metabolism, glutathione metab- Pathway analysis and metabolite set enrichment ana- olism, and glyoxylate and dicarboxylate metabolism were lysis were performed on metabolites in serum with VIP > the most affected metabolic pathways on d 11 PI when 1. On d 0 before ETEC challenge, inositol phosphate TRA group was compared with the REC group (Fig. 4 B, metabolism, glyoxylate and discarboxylate metabolism, D). glycine, serine and threonine metabolism, citrate cycle, and ascorbate and aldarate metabolism were the most Microbial profiles in distal colon digesta affected metabolic pathways when comparing CON with A total of 481,102 qualified reads were obtained with REC (Fig. S2A, C). Citrate (TCA) cycle, arginine biosyn- a mean of 15,034 reads per sample. A total of 3561 thesis, and alanine, aspartate, and glutamate metabolism OTUs were identified in the current experiment. No were the most affected metabolic pathways when TRA differences were observed in the alpha diversity of was compared with REC (Fig. S2B, D). On d 5 PI, distal colon content among dietary treatments on d 5 aminoacyl-tRNA biosynthesis, glycine, serine, and threo- and d 11 PI. Both Chao1 and Shannon indices of the nine metabolism, and phenylalanine, tyrosine, and tryp- distal colon content were lower (P <0.05) on d 11 PI tophan biosynthesis were the most affected metabolic than d 5 PI for pigs in the CON group (Fig. S5). Beta pathways when comparing CON vs. REC (Fig. 2 A, C), diversity (Adonis analysis based on the Bray-Curtis while aminoacyl-tRNA biosynthesis, alanine, aspartate, distance) indicated that day (days PI) was a significant and glutamate metabolism, and glycolysis and gluconeo- factor associated with composition distance (R = 0.11, genesis were the most affected metabolic pathways in a P < 0.05; Fig. S6). Compositional differences of the comparison of TRA vs. REC (Fig. 2B, D). On d 11 PI, ar- distal colon microbiota were also observed between ginine biosynthesis, alanine, aspartate and glutamate me- CON vs. REC and TRA vs. REC on d 5 and d 11 PI tabolism, D-glutamine and D-glutamate metabolism, (Pairwise-Adonis, P <0.05; Fig. S6). pyrimidine metabolism, and citrate cycle were the most The dominant phyla in distal colon content were affected metabolic pathways when comparing CON with Firmicutes, Bacteroidetes, Proteobacteria, and Actino- REC (Fig. S3A, C). Arginine biosynthesis, aminoacyl- bacteria, regardless of treatment or sampling day (Fig. tRNA biosynthesis, alanine, aspartate, and glutamate me- S7). Pigs in the TRA or REC group had a lower (P < tabolism, and D-glutamine and D-glutamate metabolism 0.05) relative abundance of Actinobacteria than pigs Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 5 of 15 Table 1 Serum metabolites that differed among the dietary treatment groups 1 2 3 Metabolite Fold change VIP FDR 4 5 TRA vs. REC , d 0 before ETEC challenge Fructose 2.13 1.88 0.108 Mannonic acid 2.21 2.01 0.083 Propyleneglycol 2.38 1.76 0.122 CON vs. REC, d 5 post-inoculation Mannitol 0.23 1.48 0.115 Inosine 0.41 1.63 0.076 Glycerol 2.09 1.79 0.045 Galactonic acid 2.26 1.65 0.076 Propyleneglycol 2.51 1.47 0.119 Shikimic acid 2.64 1.86 0.036 TRA vs. REC, d 5 post-inoculation 2-hydroxyvaleric acid 0.24 2.13 0.001 P-hydroxylphenyllactic acid 0.30 1.10 0.145 Pipecolinic acid 0.38 1.58 0.024 1-methylhydantoin 0.40 1.51 0.031 Histidine 0.45 1.98 0.002 Creatine 0.46 1.51 0.031 Myo-inositol 2.01 1.93 0.002 Guanine 2.03 1.83 0.005 Oleic acid 2.03 1.18 0.114 Montanic acid 2.05 1.57 0.024 Galactonic acid 2.11 1.42 0.046 Hypoxanthine 2.14 1.80 0.006 Glycerol 3.26 1.35 0.067 Propyleneglycol 4.04 2.00 0.002 Shikimic acid 4.47 1.63 0.020 Taurine 5.58 1.27 0.082 CON vs. REC, d 11 post-inoculation Glycerol 0.33 1.74 0.181 Inositol-4-monophosphate 0.48 1.92 0.165 Chenodeoxycholic acid 3.01 1.84 0.171 TRA vs. REC, d 11 post-inoculation P-tolyl glucuronide 0.26 2.10 0.106 Dlycerol 0.30 1.84 0.195 Mannitol 0.30 1.90 0.158 2-ketoisocaproic acid 0.45 1.90 0.158 Inositol-4-monophosphate 0.48 2.08 0.106 Chenodeoxycholic acid 4.67 2.04 0.109 Fold change values less than one indicate that the differential metabolites were reduced in the CON compared to REC or TRA compared to REC, respectively VIP Variable Importance in the projection FDR False discovery rate TRA Trace amounts of antibiotic REC Label-recommended dose of antibiotic CON Control Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 6 of 15 Fig. 1 Partial Least Squares Discriminant Analysis (PLS-DA) 2D score plot of the metabolites in serum showed separated clusters between the TRA and REC groups on d 0 before ETEC challenge (A), d 5 (B) and 11 (C) PI. ● = CON (Control); ● = TRA (Trace amounts of antibiotic); ● = REC (Label- recommended dose of antibiotic). Shaded areas in different colors represent in 95% confidence interval in the CON group on d 11 PI. Within the Firmicutes the REC group had higher (P < 0.05) relative abun- phylum (Fig. 5), pigs in the TRA group had lower dance of Clostridiaceae (17.14% vs. 1.45%) and Strep- (P < 0.05) relative abundance of Lactobacillaceae tococcaceae (10.09% vs. 0.21%), but lower (P < 0.05) (8.91% vs. 21.33%) than pigs in REC on d 5 PI, relative abundance of Lachnospiraceae (20.25% vs. whereas REC had lower (P < 0.05) relative abundance 27.44%) in the distal colon on d 11 PI than on d 5 of Lactobacillaceae (5.82% vs. 23.90% or 27.69%) than PI. Within the Bacteroidetes phylum (Fig. 6), pigs in pigs in the CON or TRA groups on d 11 PI. Pigs in the TRA group had reduced (P <0.05) relative Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 7 of 15 Fig. 2 Significantly changed pathways in serum between the control (CON) and label-recommended dose of antibiotic (REC) groups (A), and trace amounts of antibiotic (TRA) and REC groups (B) on d 5 post-inoculation. The x-axis represents the pathway impact values and the y-axis represents the -log(P) values from the pathway enrichment analysis. Metabolite set enrichment analysis (C, D) shows the metabolic pathways were enriched in CON compared to REC, and TRA compared to REC on d 5 post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1 abundance of Muribaculaceae (0.60% vs. 2.46%) and Discussion Rikenellaceae (0.61% vs. 3.09%) in distal colon on d In-feed antibiotics can influence nutrient metabolism 11 PI than on d 5 PI. On d 11 PI, pigs in the CON and many biological processes in pigs by altering micro- group had higher (P < 0.05) relative abundance of Pre- biota and metabolites [25, 26]. Antimicrobial agent used votellaceae (13.78% vs. 9.32%) in distal colon content, in present study, carbadox, is one of the most common compared with pigs in the REC group. antibiotics and widely used in the U.S. swine industry to Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 8 of 15 Table 2 Colon digesta metabolites that differed among the control enteric diseases and to promote the growth of dietary treatment groups nursery pigs [27]. However, little is known about the im- 1 2 3 Metabolite Fold change VIP FDR pacts of trace amounts of antibiotics on metabolic and 4 5 microbial changes in piglets, especially under disease- TRA vs. REC , d 5 post-inoculation challenged conditions. The present study investigated Octadecanol 0.38 1.82 0.173 the alteration of metabolic pathways in the serum and Nonadecanoic acid 0.39 1.89 0.126 colon digesta by using an untargeted metabolomics ap- Adipic acid 0.40 1.91 0.125 proach when pigs were supplemented with different Pinitol 2.22 1.82 0.173 levels of the antibiotic carbadox. Results from the 3-hydroxy-3-methylglutaric acid 2.57 1.94 0.118 current study highlighted that supplementing label- recommended doses of antibiotics altered metabolomic Proline 2.64 1.92 0.118 markers related to nutrient metabolism in the serum Arabitol 3.42 2.18 0.018 and colon digesta. Moreover, supplementation of differ- Lyxitol 3.92 2.16 0.018 ent levels of antibiotic modified microbial community Dehydroabietic acid 4.27 2.15 0.018 composition and diversity to a different extent in the Propyleneglycol 5.09 1.92 0.118 colon digesta of pigs challenged with ETEC F18. Our Maltotriose 5.18 1.96 0.118 previous research reported that supplementing the label- recommended dose of antibiotic enhanced disease resist- 2-hydroxyvaleric acid 13.35 2.14 0.018 ance and promoted growth, whereas supplementing TRA vs. REC, d 11 post-inoculation trace amounts of antibiotic exacerbated the detrimental Inosine 0.20 1.9793 0.160 effects of ETEC F18 infection on performance and diar- Fold change values less than one indicate that the differential metabolites rhea, and systemic inflammation of weaned pigs [12]. were reduced in the TRA compared to REC VIP Variable Importance in the projection Results from the current study will help us to under- FDR False discovery rate stand the negative impacts of trace amounts of antibiotic TRA Trace amounts of antibiotic on performance and health of young pigs by focusing on REC Label-recommended dose of antibiotic the gut microbiome and their metabolites and the host metabolism. The metabolomics approach exploits high-throughput analytical measurements to identify host and microbiota Fig. 3 Partial Least Squares Discriminant Analysis (PLS-DA) 2D score plot of the metabolites in colon digesta showed separated clusters between the TRA and REC groups on d 5 (A) and 11 (B) post-inoculation. ● = CON (Control); ● = TRA (Trace amounts of antibiotic); ● = REC (Label-recommended dose of antibiotic). Shaded areas in different colors represent in 95% confidence interval Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 9 of 15 AB CD Fig. 4 Significantly changed pathways in colon digesta between the trace amounts of antibiotic (TRA) and label-recommended dose of antibiotic (REC) groups on d 5 (A) and 11 post-inoculation (B). The x-axis represents the pathway impact values and the y-axis represents the -log(P) values from the pathway enrichment analysis. Metabolite set enrichment analysis (C, D) shows the metabolic pathways were enriched in TRA compared to REC group on d 5 and 11 post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1 metabolites and associated biological changes that are af- of antibiotic, especially during the peak infection period fected by internal or external factors to maintain homeo- (d 5 PI). These findings suggest the comparative dose- stasis [28]. In the present study, differences in the response metabolic effects of antibiotics during ETEC metabolic profiles of serum and colon digesta were infection in weaned pigs. found predominately between pigs supplemented with In-feed antibiotics mediate growth enhancement as a trace amounts of antibiotic and label-recommended dose result of improved nutrient utilization in pigs. Growing Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 10 of 15 Fig. 5 Stacked bar plot showing the relative abundance of Firmicutes family in colon digesta of enterotoxigenic E. coli F18 challenged pigs fed diets supplemented with different dose of antibiotic on d 5 and 11 post-inoculation (A). Violin plot showing the relative abundance of individual a-c bacterial phylum (B). Means without a common superscript are different across both time points (Diet × day, P < 0.05). Each least squares mean represents 4 to 7 observations. CON = Control; TRA = Trace amount of antibiotic; REC = Label-recommended dose of antibiotic evidence suggests that the administration of in-feed anti- by increased serum metabolomic markers that are asso- biotics can enhance nutrient digestibility and regulate ciated with amino acid metabolism [25]. Amino acid me- the nutrient metabolism of the host [29]. The bacterio- tabolism is extremely important to support animal static activity of in-feed antibiotics may also impact the growth, maintain homeostasis, and regulate other bio- intestinal microbial metabolites by reducing growth de- logical processes in the host and intestinal microbiota pressing microbiota [30]. It was reported that in-feed an- [31, 32]. In the present study, metabolites related to tibiotics at a subtherapeutic concentration could amino acid metabolism (2-hydroxyvaleric acid, pipecoli- enhance amino acid availability in piglets, as indicated nic acid, histidine, and creatine) were enriched in the Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 11 of 15 Fig. 6 Stacked bar plot showing the relative abundance of Bacteroidetes family in colon digesta of enterotoxigenic E. coli F18 challenged pigs fed diets supplemented with different dose of antibiotic on d 5 and 11 post-inoculation (A). Violin plot showing the relative abundance of a-b individual bacterial phylum (B). Means without a common superscript are different across both time points (Diet × day, P < 0.05). Each least squares mean represents 4 to 7 observations. CON = Control; TRA = Trace amount of antibiotic; REC = Label-recommended dose of antibiotic serum of pigs fed with the label-recommended antibiotic However, 2-hydroxyvaleric acid, a metabolomic marker dose compared with pigs in the trace amounts of anti- of branched-chain amino acid catabolism, was observed biotic group. This was likely due to the reduced peptide to be reduced in the colon digesta of pigs fed with label- catabolism initiated by microbial protease activities when recommended dose antibiotic compared with the trace feeding label-recommended dose of antibiotic [33]. amounts of antibiotic group. These observations are in Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 12 of 15 agreement with a previous research, in which Mu et al. chickens during LPS-induced systemic inflammation. In [25] also reported that increased serum metabolites re- the present animal study, trace amounts of antibiotic ex- lated to amino acid metabolism were concomitant with acerbated the intestinal and systemic inflammatory sta- a decrease in jejunal metabolites associated with amino tus of ETEC F18 challenged pigs [12]. Thus, the acid metabolism in pigs fed with a mixture of antibiotics increased metabolites associated with carbohydrate me- at a growth-promoting dose. Thus, these results suggest tabolism in pigs supplemented with trace amount of an- that the systemic interplay between microbiota and me- tibiotics during the peak of ETEC infection indicates tabolite profiles was promoted by feeding label- that these pigs may utilize more carbohydrates as energy recommended dose of antibiotics. A previous study sources to support their immune responses and recovery using metagenomic analysis also observed that antibi- processes against ETEC F18 instead of growth. otics at subtherapeutic doses reduced the abundance of Interestingly, supplementation of trace amounts of clusters of orthologous groups involved in protein me- antibiotic also enriched serum metabolomic markers of tabolism in the fecal microbiota of pigs [34]. Consistent purine metabolism (hypoxanthine and guanine) during with performance data and clinical signs [12], it is not the peak of ETEC infection. A previous in vitro study re- surprising to observe that trace amount of antibiotics ported that Pasteurella multocida significantly increased had different impacts on serum and colon digesta me- the expression of proteins involved in purine synthesis tabolites that are associated with amino acid metabolism and metabolism, in response to sub-MIC antibiotics, in- when compared with label-recommended dose of anti- cluding amoxicillin, chlortetracycline, and enrofloxacin biotic. Previous in vitro research suggested that E. coli [41]. Ng et al. [42] also demonstrated that extremely low cells stimulated cellular functions and metabolic modifi- concentrations of antibiotics, such as tetracycline and cations of amino acid catabolism upon exposure to the macrolide, upregulated the expression of genes associ- antibiotic ampicillin at below the minimal inhibitory ated with purine metabolism in Streptococcus pneumo- concentrations (sub-MIC) [35]. More specifically, E. coli niae. The metabolites involved in purine metabolism are cells treated with sub-MIC ampicillin resulted in in- often upregulated in the activated immune cells as im- creased amino acid depletion in Luria-Bertani (LB) portant immune signaling molecules [43]. For instance, media due to stress responses, which provided amino previous research reported that mice infected with E. acids as a major energy source for cultured cells. This coli had enriched plasma metabolites that are linked to finding indicates that the alteration of metabolomic the purine metabolic pathway [44]. Likewise, growing markers of amino acid metabolism caused by trace evidence also suggests that trace concentrations of anti- amounts of antibiotic in the current study may be re- biotics may perform as signaling agents and trigger spe- lated to the depletion of amino acids during the host re- cial bacterial responses, such as increased purine sponse to ETEC infection. Subsequently, less amounts of metabolism, following an infection [43, 45], Thus, our amino acids might be available to support the growth of results indicate that purine metabolism might contribute the pigs when they were challenged with ETEC and sup- to the elevated systemic inflammation in pigs fed with plemented with trace amount of antibiotics. trace amounts of antibiotic [12]. Carbohydrate metabolism is essential to support the The composition and diversity of gut microbial com- virulence of pathogenic enterobacteria [36]. It has been munities in pigs are greatly impacted by their age, health reported that the colonization of pathogenic E. coli in status, and nutrient components in feed [46–48]. To test the mouse intestine was supported by the catabolism of the impacts of trace amounts of antibiotic on gut micro- several carbohydrates, including galactose, fucose, man- biota diversity, distal colon contents were collected, and nose, and maltose [37, 38]. In the present study, metabo- 16S rRNA gene sequencing was performed. Consistent lomic markers related to galactose metabolism (glycerol with previously published research, antibiotics-treated at and myo-inositol) and carbohydrate digestion and ab- recommended concentrations clustered separately from sorption (maltotriose) were enriched in the serum or non-treated groups [34, 49], indicating that antibiotics colon digesta from pigs supplemented with trace administration at label-recommended dose altered colon amounts of antibiotic. This finding suggests that trace microbiota composition and diversity. However, there amounts of antibiotic may assist in constitution of an was no clear separation in distal colon microbiota be- ecological niche for ETEC F18 colonization in the intes- tween pigs supplemented with trace amounts of anti- tine of pigs, rather than exhibit its antibacterial activity. biotic and pigs in the control group. Besides the carbohydrate utilization by pathogens to The three most abundant phyla found in the colon colonize, carbohydrate metabolism is also vital for the digesta of pigs in the present study were Firmicutes, Bac- systemic inflammatory response [39]. Baurhoo et al. [40] teroidetes, and Proteobacteria, which was consistent reported that a significant mobilization and catabolism with previously published research [50, 51]. Within the of carbohydrates were observed in the intestine of Firmicutes and Bacteroidetes phyla, the relative Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 13 of 15 abundance of Lachnospiraceae and Lactobacillaceae of microbial composition, which may be highly corre- were enriched in the distal colon of pigs supplemented lated with their enhanced growth performance and dis- with label-recommended dose of antibiotic, but the rela- ease resistance in weaned pigs. The impacted metabolic tive abundance of Lactobacillaceae was reduced in colon pathways and colonic microbial shift may also be closely digesta of trace amounts of antibiotic pigs during the associated with the slow growth and delayed recovery peak infection period. Lachnospiraceae family contain from ETEC infection of weaned pigs supplemented with numerous genera involved in producing butyric acid, trace amounts of antibiotic. Future studies will consider which provides energy for other microbes and host epi- incorporating targeted metabolomics and metagenomics thelial cells and prevents the growth of other microbes to provide more insights into the potential risk of trace in the digestive tract [52, 53]. Moreover, Lactobacilla- amounts of antibiotic on the host response to ETEC in- ceae were reported to be positively correlated with feed fection. The exploration of metabolomic markers and efficiency [54] and nitrogen, energy, cellulose, and hemi- gut microbiome interaction will be important to de- cellulose digestibility in pigs [55]. Although the exact cipher the mechanisms of how trace amounts of anti- mechanism of antimicrobial effects is not yet clear, Lac- biotic negatively impact the health of young animals. tobacillaceae are known for their health-promoting ef- Abbreviations fects and for their ability to inhibit intestinal pathogens FDR: False discovery rate; ETEC: Enterotoxigenic E. coli; MIC: Minimal such as E. coli and Salmonella [56]. Thus, Lachnospira- inhibitory concentrations; OTU: Operational taxonomic units; PI: Post- ceae and Lactobacillaceae have been proposed and in- inoculation; PLS-DA: Partial least squares discriminant analysis; PCoA: Principal coordinates analysis; VIP: Variable importance in the projection vestigated as biomarkers to predict the health status of pigs [57, 58]. Rhouma et al. [59] demonstrated that the Supplementary Information ETEC F4 challenge suppressedthe relative abundanceof The online version contains supplementary material available at https://doi. Lachnospiraceae and Lactobacillaceae in fecal contents of org/10.1186/s40104-022-00703-5. pigs, compared with unchallenged pigs. In addition, Dou et al. [60] also reported that diarrheic pigs, in natural post- Additional file 1: Fig. S1. Partial Least Squares Discriminant Analysis weaning diarrhea, had a lower abundance of Lachnospiraceae (PLS-DA) 2D score plot of the metabolites in serum revealed significant differences on d 0 before E. coli challenge, d 5 and 11 post-inoculation and Lactobacillaceae in feces, compared with healthy pigs. between the TRA and REC groups (A-C) and CON and REC (D-E), respect- Therefore, the modified intestinal microbial environment, in- ively. ● = CON (Control); ● = TRA (Trace amounts of antibiotic); ● = REC cluding the enhanced presence of Lachnospiraceae and Lac- (Label-recommended dose of antibiotic). Shaded areas in different colors represent in 95% confidence interval. Fig. S2. Significantly changed path- tobacillaceae, may contribute to the accelerated recovery ways in serum between the control (CON) and label-recommended dose from ETEC 18 infection in pigs supplemented with label- of antibiotic (REC) groups (A), and trace amounts of antibiotic (TRA) and recommended dose of antibiotics. REC groups (B) on d 0 before E. coli challenge. The x-axis represents the pathway impact values and the y-axis represents the -log(P) values from Previous studies have also reported the contribution of the pathway enrichment analysis. Metabolite set enrichment analysis (C, intestinal microbiota to weight gain in pigs. For example, D) shows the metabolic pathways were enriched in CON compared to Kim et al. [61] observed that Clostridiaceae in the distal REC, and TRA compared to REC on d 0 before E. coli challenge, respect- ively. Both pathway analysis and metabolite set enrichment analysis were gut of pigs were positively correlated with weight gain, performed using identified metabolites with VIP > 1. Fig. S3. Significantly while Unno et al. [62] reported a negative correlation be- changed pathways in serum between the control (CON) and label- tween weight and Prevotellaceae in feces when pigs were recommended dose of antibiotic (REC) groups (A), and trace amounts of antibiotic (TRA) and REC groups (B) on d 11 post-inoculation. The x-axis supplemented with different types of antibiotics. In the represents the pathway impact values and the y-axis represents the present study, pigs fed label-recommended dose anti- -log(P) values from the pathway enrichment analysis. Metabolite set en- biotic had increased relative abundance of Clostridiaceae richment analysis (C, D) shows the metabolic pathways were enriched in CON compared to REC, and TRA compared to REC on d 11 post- but reduced relative abundance of Prevotellaceae com- inoculation, respectively. Both pathway analysis and metabolite set en- pared to pigs in the control group on d 11 PI. These ob- richment analysis were performed using identified metabolites with VIP > servations are consistent with the literature and 1. Fig. S4. Partial Least Squares Discriminant Analysis (PLS-DA) 2D score plot of the metabolites in colon digesta revealed significant differences confirmed the effectiveness of label-recommended dose between the TRA and REC groups on d 5 (A) and 11 (B) post-inoculation. of antibiotic for growth-promoting purposes [12]. ● = TRA (Trace amounts of antibiotic); ● = REC (Label-recommended dose In conclusion, the metabolomics and microbiome ap- of antibiotic). Shaded areas in different colors represent in 95% confi- dence interval. Fig. S5. Alpha diversity as indicated by Chao 1 (A) and proaches in the present study identified the differential Shannon (B) indices in colon digesta of enterotoxigenic E. coli F18 chal- metabolites and their pathways in the serum and distal lenged pigs fed diets supplemented with different dose of antibiotic on a-b colon digesta of ETEC F18 challenged pigs. Compared d 5 and 11 post-inoculation. Means without a common superscript are different across both time points (Diet × day, P < 0.05). Each least squares with label-recommended dose of antibiotic, trace mean represents 4 to 7 observations. CON = Control; TRA = Trace amount amounts of antibiotic oppositely affected metabolomic of antibiotic; REC = Label-recommended dose of antibiotic. Fig. S6. Beta markers related to the metabolisms of amino acids, car- diversity of colon digesta of enterotoxigenic E. coli F18 challenged pigs fed diets supplemented with different dose of antibiotic on d 5 and 11 bohydrates, and purine. Pigs administered label- post-inoculation. Data were analyzed by principal coordinate analysis recommended dose of antibiotic had marked modulation Kim et al. Journal of Animal Science and Biotechnology (2022) 13:59 Page 14 of 15 5. Manzetti S, Ghisi R. The environmental release and fate of antibiotics. Mar (PCoA) based on the Bray-Curtis dissimilarity. Symbols indicate dietary Pollut Bull. 2014;79(1-2):7–15. https://doi.org/10.1016/j.marpolbul.2014.01. treatments and colors indicate different sampling dates. CON = Control; TRA = Trace amount of antibiotic; REC = Label-recommended dose of 6. Sarmah AK, Meyer MT, Boxall ABA. A global perspective on the use, sales, antibiotic. Fig. S7. Stacked bar plot showing the relative abundance of exposure pathways, occurrence, fate and effects of veterinary antibiotics bacterial phyla in colon digesta of enterotoxigenic E. coli F18 challenged (VAs) in the environment. Chemosphere. 2006;65(5):725–59. https://doi.org/1 pigs fed diets supplemented with different dose of antibiotic on d 5 and 0.1016/j.chemosphere.2006.03.026. 11 post-inoculation (A). Violin plot showing the relative abundance of in- a-c 7. Chen J, Ying GG, Deng WJ. Antibiotic residues in food: extraction, analysis, dividual bacterial phylum (B). Means without a common superscript and human health concerns. J Agric Food Chem. 2019;67(27):7569–86. are different across both time points (Diet × day, P < 0.05). Each least https://doi.org/10.1021/acs.jafc.9b01334. squares mean represents 4 to 7 observations. CON = Control; TRA = Trace 8. Nisha AR. Antibiotic residues – a global health hazard. Vet World. 2008;1: amount of antibiotic; REC = Label-recommended dose of antibiotic. 375–7. https://doi.org/10.5455/vetworld.2008.375-377. 9. Davies J, Spiegelman GB, Yim G. The world of subinhibitory antibiotic Acknowledgments concentrations. Curr Opin Microbiol. 2006;9(5):445–53. https://doi.org/10.101 Not applicable. 6/j.mib.2006.08.006. 10. Jin S, Zhao D, Cai C, Song D, Shen J, Xu A, et al. Low-dose penicillin Authors’ contributions exposure in early life decreases Th17 and the susceptibility to DSS colitis in The contributions of the authors were as follows: KK conducted the mice through gut microbiota modification. Sci Rep. 2017;7(1):43662. https:// experiment and wrote the manuscript. CJ and PJ helped to conduct animal doi.org/10.1038/srep43662. trial, part of the laboratory work, data analysis, and helped to revise the 11. Cox LM, Yamanishi S, Sohn J, Alekseyenko AV, Leung JM, Cho I, et al. manuscript. YL was the principal investigator. YL designed the experiment, Altering the intestinal microbiota during a critical developmental window oversaw the development of the study and wrote the last version of the has lasting metabolic consequences. Cell. 2014;158(4):705–21. https://doi. manuscript. The authors declare no conflicts of interest. The authors read org/10.1016/j.cell.2014.05.052. and approved the final manuscript. 12. Kim K, He Y, Jinno C, Kovanda L, Li X, Song M, et al. Trace amounts of antibiotic exacerbated diarrhea and systemic inflammation of weaned pigs Funding infected with a pathogenic Escherichia coli. J Anim Sci. 2021;99(3):1–13. This project was supported by the United States Department of Agriculture https://doi.org/10.1093/jas/skab073. (USDA) National Institute of Food and Agriculture (NIFA), multistate projects 13. Fajardo A, Martínez JL. Antibiotics as signals that trigger specific bacterial W4002 and NC1202. responses. 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Journal
Journal of Animal Science and Biotechnology – Springer Journals
Published: May 9, 2022
Keywords: Carbadox; Colon microbiota; Enterotoxigenic Escherichia coli; Metabolomics; Weaned pigs