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Assessment of cecal microbiota modulation from piglet dietary supplementation with copper



Swine production expanded in the last decades. Efforts have been made to improve meat production and to understand its relationship to pig gut microbiota. Copper (Cu) is a usual supplement to growth performance in animal production. Here, two performance studies were conducted to investigate the effects of three different sources of Cu on the microbiota of piglets. A total of 256 weaned piglets were randomly allocated into 4 treatments (10 replicates per treatment of 4 piglets per pen in Trial 1 and 8 replicates of 3 piglets per pen in Trial 2). Treatments included a control group (fed 10 mg/kg of Cu from CuSO4), a group fed at 160 mg/kg of Copper (II) sulfate (CuSO4) or tri-basic copper chloride (TBCC), and a group fed with Cu methionine hydroxy analogue chelated (Cu-MHAC) at 150, 80, and 50 mg/kg in Phases 1 (24–35 d), 2 (36–49 d), and 3 (50–70 d), respectively. At 70 d, the cecum luminal contents from one pig per pen were collected and polled for 16 S rRNA sequencing (V3/V4 regions). Parameters were analyzed in a completely randomized block design, in which each experiment was considered as a block.


A total of 1337 Operational Taxonomic Units (OTUs) were identified. Dominance and Simpson ecological metrics were statistically different between control and treated groups (P < 0.10) showing that different Cu sources altered the gut microbiota composition with the proliferation of some bacteria that improve gut health. A high abundance of Prevotella was observed in all treatments while other genera were enriched and differentially modulated, according to the Cu source and dosage. The supplementation with Cu-MHAC can modify a group of bacteria involved in feed efficiency (FE) and short chain fatty acids (SCFA) production (Clostridium XIVa, Desulfovibrio, and Megasphera). These bacteria are also important players in the activation of ghrelin and growth hormones that were previously reported to correlate with Cu-MHAC supplementation.


These results indicated that some genera seem to be directly affected by the Cu source offered to the animals. TBCC and Cu-MHAC (even in low doses) can promote healthy modifications in the gut bacterial composition, being a promising source of supplementation for piglets.


The microbiota in the mammalian gastrointestinal tract (GIT) has about 1014 bacteria [1]. Species interact and contribute to the metabolism in processes such as energy acquisition from food [2]. The cecum is one of the most microorganisms diverse to the gastrointestinal segments. The high prevalence of fermentative microorganisms is supported by the meaningful function of the large intestine (cecum and colon), in absorbing short-chain fatty acids (SCFA), in addition to vitamin K, B7, potassium, and sodium [3]. The large intestine contains gut-associated lymphoid tissues (GALT) that are important sites for intestinal effector lymphocyte generation, thus contributing to adaptive immune responses [4].

The complex gut microbiota influences intestinal homeostasis and immunologic process, for example, the anti-inflammatory role [5]. Some factors such as age, diet, and drugs are known to be microbiota composition modifiers [6]. Several GIT microbiomes have been studied to elucidate their role in the modulation of gut health, such as human [7, 8], bovine [9], chicken [10, 11], and swine [12, 13]. Swine is the second most widely eaten meat worldwide, after poultry [14], so efforts to improve production are essential to develop the industries and increase the availability of this food to the population. In the piglet post-weaning transition, GIT microbiota changes [15] can increase the incidence of diarrheal infection [16, 17]. To prevent diseases and improve feed efficiency, antibiotics are still widely used. However, antibiotics are banned in several countries. Supplements could be used as an alternative, but studies comparing the benefits and synergistic and adverse effects of alternative supplements must be thoroughly studied [16, 18].

The feed supplements usually administered are prebiotics, plant extracts, and minerals, among others [19, 20]. Trace minerals are required for animal development while presenting antimicrobial properties when used in doses higher than the nutritional requirements [21]. For instance, zinc (Zn) is fundamental to biological processes in mammals [21], and its supplementation as zinc oxide (ZnO) has decreased diarrhea cases, improving piglet growth [19, 20]. Copper (Cu) is crucial to many metalloenzymes, cellular protection against oxidative stress and metabolic reactions [19], and it has been used for its potential antibiotic properties since ancient times [22]. The uptake of Cu seemed to be regulated by different transporters and transport mechanisms and depends on the source with which it is associated [23]. A study on supplementation with tribasic copper chloride (TBCC) and copper sulfate (CuSO4) showed that both improved performance in weaning pigs [24]. However, evidence indicates that TBCC has fewer adverse effects on animals than CuSO4 [19].

Despite the benefits, some undesirable effects are associated with supra-nutritional levels of Cu (125 to 250 mg/kg) depending on the source (i.e., antagonisms, vitamin oxidation, high excretion, presence of contaminants, etc.). The organic source Copper methionine hydroxy analogue chelated (Cu-MHAC) has been demonstrated to be more bioavailable, so lower concentrations are needed for similar growth promoter effects [25]. In addition, piglets fed lower levels of Cu through Cu-MHAC had an improvement in general performance than when fed CuSO4 and TBCC. The results also suggested an increase in mRNA expression of ghrelin and serum growth hormone (GH) levels in the animals [26]. To add to the knowledge on the mode of action of Cu sources, this study investigated the cecal microbiota composition of piglets after dietary supplementation with Cu-MHAC, CuSO4, or TBCC.


Animal and sample collection

The experimental procedures followed Gonzalez-Esquerra et al. [26]. Two trials of equal design were conducted sequentially in the same open-side barn piglet facility with slatted-floor pens. The barn was not cleaned before the trials to simulate common adverse conditions. A total of 256 commercially acquired Agroceres PIC piglets weaned at 24 ± 2 d were used. Trial 1 (summer) included 160 piglets (80 barrows and 80 guilts) weighted 5,43 ± 0,90 kg and Trial 2 (spring) included 96 piglets (48 barrows and 48 f) weighted 4,73 ± 0,95 kg. Pigs were allocated to four treatments in a completely randomized block design with ten replicates per treatment. We used four pigs per replicate in Trial 1 and three pigs per replicate in Trial 2 in four treatments. All groups were fed corn, soybean meal, and dairy by products based diets and submitted to dietary treatments from 24 to 70 days as follows: (i) the Control received 10 mg/kg of Cu from copper sulfate, (ii) the second group was supplemented with 160 mg/kg Cu from CuSO4, (iii) the third group supplemented with 150 (from 24 to 35 d), 80 (36 to 49 d) and 50 mg/kg Cu (50 to 70 d) from Cu-MHAC, and (iv) the fourth group supplemented with 160 mg/kg Cu from TBCC. Zinc oxide was included during the first 2 phases post-weaning at 2,200 and 1,500 mg/kg as commonly used in piglets in Brazil. Feed and water were provided ad libitum throughout the entire experimental period and the diets contained antibiotics (halquinol at 200 g/ton in all phases and amoxicillin at 255 g/ton in Phase 2 only). At 70 d of age, animals were sacrificed and the cecum luminal contents from one pig per pen (72 animals) were collected, snap-frozen in liquid nitrogen, and stored at -80 ºC until analyses.

DNA extraction and 16 S rRNA sequencing

The genomic DNA purification, quantification, sequencing, read processing, and phylogenetic analyses were performed as previously described (9). Briefly, genomic DNA from each sample was purified using QIAamp Fast DNA Stool Mini Kit (QIAGEN, Hilden, Germany) following the manufacturer. Then, DNA quality was evaluated by agarose gel electrophoresis and quantified using the NanoVue Plus spectrophotometer (GE Healthcare, Marlborough, USA). After quantification, all samples were diluted at 50 ng/µL. Four pools per treatment (two pools per trial) were produced using the same volume (5 µL) of 4 samples for Trial 1 and 3 samples for Trial 2. The pooled samples from the cecum were used to amplify approximately 460 bp of the 16 S ribosomal RNA by PCR using specific primers V3 and V4 (Klindworth et al., 2013). PCR products were used to build the metagenomics library for sequencing using MiSeq Reagent kit v3 (600 cycles) (Illumina Inc., San Diego, California, USA). The sequencing of partial 16 S ribosomal RNA was performed by next-generation sequencing using the Illumina MiSeq platform that produced thousands of 300 bp paired-end reads (2 × 300 bp) for each library.

Bioinformatics analysis

The reads from each pool were analyzed on USEARCH (version 10.0.240) [27]. The pair of reads were merged with a minimum of 200 bp and filtered by the quality and unique abundance. This data was used for the rarefaction curve, alpha diversity calculation, and Venn diagram ( We tested a total of eleven alpha metrics regarding diversity (Richness, Chao 1, Shannon, Jost, and Jost 1) and evenness (Simpson, Dominance, Equitability, Robbins, Berger, and Parker). The phylogenetic categories found were compared to the Ribosomal Data Project (RDP) classifier [28] with 0.80 to a cutoff on the USEARCH.

Statistical analyses

Data were analyzed in a completely randomized block design using PROC GLM of SAS 9.3 (SAS Inc., NC, 2011) with each experiment used as a block. Differences among means were compared by Tukey’s least significant difference. Orthogonal contrasts were used to test relevant comparisons which included the effect of Cu-MHAC vs. feeding inorganic Cu (CuSO4 and TBCC), CuSO4 vs. TBCC, and nutritional vs. supra-nutritional Cu levels (Control vs. Others). Differences among the groups were considered significant when p-value < 0.05 while p-values ranging from 0.05 to 0.10 was denoted as a statistical trend since they were also biologically relevant, as previously demonstrated [29]. Analysis of variance (ANOVA) was used to determine if the means of the treatments were different. Non-parametric statistics were applied to the data that did not meet the assumptions of the statistical model using the Freedman test. The hierarchical cluster of the heatmap made with pvclust (version 2.0.0) [30] aimed at grouping treatments and OTUs, testing with 10,000 interactions.

Data availability

16 S sequencing data for the control and all treatments have been submitted to the NCBI under BioProject ID: PRJNA798269 and in the following link:

Results and discussion

Treatments with different copper sources and levels were related to the modification of the microbial composition in piglets

We sequenced 16 pooled samples from the cecum of 256 animals (four replicates of each treatment: Control, Cu-MHAC, CuSO4, and TBCC). Pooled samples were used to reduce individual variabilities focusing on the modifications produced by the treatments. Our analyses revealed that 1,858,722 high-quality sequences were obtained and distributed in 1,337 Operational Taxonomic Units (OTUs) (Table S1). Although the number of reads and OTUs was different among samples, the rarefaction plots (Fig. S1a) of the variables (OTUs and reads), and (Fig. S1b) of the Chao1 (alpha diversity metric), with the data subsampled (± 10,000 per sample), showed that all curves were rarefied, so the collected data were representative of the sampled diversity. Beta diversity (Bray-Curtis dissimilarity) analysis indicated that the diversity of the samples collected in the same trial was more similar, considering the bacterial community for each one, as observed in Principal component analysis (PCA) in Fig. S1c. The impact of the treatment can be considered lower, considering this beta diversity metric.

Eleven alpha diversity metrics analyzed were divided into richness and equitability categories. Table 1 shows the mean of each metric by treatment, the coefficient of variation of the mean (CVmean), and the statistics of the mean. The uniformity and equitability index measured by Buzas-Gibson, Robbins, and equitability metrics showed a significant statistical difference between the two trials (used as blocks). The gut environment has great importance on the microbiota composition and microbial metabolism. Sometimes, a small change in environmental factors such as temperature or pH could lead to drastic alterations [31]. Alterations could be related to the season in which that trial was conducted. Considering the effect of treatment, the mean of treatments of Dominance and Simpson diversity metrics tended to indicate differences (P < 0.10) in the analysis of variance (ANOVA), pointing to an effect of high levels of Cu on the evenness of the microorganisms in the gut (as seen by contrasting Control vs. Others). The decrease in Simpson and increase in Dominance diversity indexes indicate that Cu-MHAC tended to increase the microorganism diversity compared to TBCC (P < 0.10 by contrast) (Table 1).

Table 1 Alpha diversity metrics means by treatment, ANOVA, and orthogonal contrasts analysis

Treatments with different copper sources regulate piglets’ gut microbiota

A total of 946 OTUs were shared among treatments, while some OTUs were exclusive to one treatment (Fig. 1A). The Control treatment presented 23 exclusive OTUs, CuSO4 presented 20, Cu-MHAC presented 36, and TBCC presented 29. Although some of these OTUs were from the Firmicutes phylum, they were represented by different OTUs related to a specific treatment, indicating that they may represent different species of this phylum. Firmicutes is abundant in swine gut microbiota [32, 33], comprising the class Clostridia that embraces strict anaerobe genera as Clostridium, Ruminococcus, Dorea, and Eubacterium. These members can be associated with gut homeostasis, immune system, or recognized pathogens on a global scale, like Clostridium difficile [34,35,36,37].

The genus Lysobacter (OTU 1,263), the order Clostridiales (OTU 1,247), and the family Ruminococcaceae (OTU 1,247) were only found in the Control group. Several species of Lysobacter are associated with antibiotic sources and with cooper resistance [38], while Ruminococcaceae has been shown as adjuvants to immune checkpoint inhibitors and negatively associated with the presence of endotoxin [39]. Species from the Clostridiales order were reported to attenuate inflammation and allergic diseases. In addition to the OTU 1,247 found exclusively in the control group, we found a specific genus known as Clostridium cluster XIVa. This genus plays an important role in intestinal homeostasis, and it was increased with TBCC treatment, as later discussed (Table 2).

Cloacibacterium (OTU 266) was found exclusively in animals treated with Cu-MHAC. Cloacibacterium has four recognized species [40] and it was statistically more abundant in unreactive (“health”) ileocecal lymph nodes, in comparison to pathologically changed nodes of slaughtered pigs [29], suggesting a benefit for swine health.

The complete analysis of the OTUs revealed the ensemble of 18 phyla. The four prevalent phyla in all treatments were Bacteroidetes, Firmicutes, Proteobacteria, and Spirochaetes. The clustering using the Euclidean distance showed the clusterization between CuSO4 and TBCC and another cluster between Cu-MHAC and control samples (Fig. 1B, Table S2, and Fig. S2), as also found in a study that analyzed the cecal microbiota of piglets after antibiotic supplementation [41]. Bacteroidetes is a component of several microbiomes and the phylum is dominant in post-weaning piglet feces [15]. Their members are associated with the degradation of polysaccharides and proteins [42] contributing to gut homeostasis and health [43]. Bacteroidetes together with Firmicutes are related to obesity in humans [44, 45] and could be involved in the weight gain in swine.

Cu-MHAC showed (numerically) the lowest mean of Proteobacteria and Spirochaetes (approximately − 1.4 and − 2.1-fold compared to the other treatments), while TBCC showed the highest mean of both phyla (P > 0.05) (Table S2). A predominance of Proteobacteria has been reported in the wastewater of a farm environment, and its proportion was correlated to several disorders, including dysbiosis and inflammatory bowel disease (IBD), suggesting a close relationship between these bacteria and inflammation [46,47,48,49,50]. Spirochaetes includes a large group of motile bacteria with four clinically important genera: Treponema, Borrelia, Leptospira, and Brachyspira. They are disease agents for syphilis and Lyme disease. Bacteria included in this phylum are common in pigs and adult chickens with colitis/typhlitis, diarrhea, poor growth rates, and weight loss [51, 52].

Euryarchaeota abundance was high in the Control group than in copper-treated animals (P = 0.0442 by orthogonal contrast) and numerically lower in Cu-MHAC treated animals (P = 0.0812). This phylum is one of the most prevalent in the gut of swine and, together with Proteobacteria and Fusobacteria, are known as harmful to the intestine [53, 54]. The occurrence of members from Synergistetes was also decreased in the Cu-MHAC treatment. The abundance of this phylum was decreased with the elevation of Cu2+ and Zn2+ concentrations in swine wastewater and it was apparently involved in crude fiber (CF) digestibility [55, 56].

The cluster of the twenty most prevalent genera together with the “Unassigned” bacteria formed 2 distinct groups: a cluster of Cu-MHAC and Control, and another of CuSO4 and TBCC. (Fig. 1C). These most prevalent genera have been reported in several studies showing implications for modification of metabolic sources or biological activities [41, 57, 58] (Table S3). Among them, Prevotella prevailed numerically (mean = 32.8%; P > 0.10) (Table 2), especially in Cu-MHAC. This is in agreement with previous studies that showed a high abundance of the genus in the pig colon [59] and cecum [60]. Prevotella includes species that can be associated with (i) inflammatory features [61], (ii) high dietary consumption of carbohydrates [62] and fibers [63], (iii) increasing glycogen storage and protection against glucose intolerance [64], and (iv) more prevalent in healthy than diarrheic fecal samples of pigs [65, 66]. Therefore, the prevalence of this genus in the cecal microbiota can offer advantages to the health of piglets. In addition, in a recent study of our group, we showed that Cu-MHAC supplementation is accompanied by a significant increase of ghrelin mRNA in pigs [26]. The increase in ghrelin likely a stimulator of GH secretion thus acting as a positive factor in the performance of pigs and increasing weight gain [26]. Consistently, Queipo-Ortuño et al. found a relationship between gut microbiota and appetite-regulating hormones that associates ghrelin levels with an increase in the Prevotella genus in rats’ gut [67]. Considering that the piglets employed in this study had been also studied by Gonzalez-Esquerra [26], our results strongly supports the same relationship observed in rats. The increase of ghrelin and Prevotella phyla observed in the Cu-MHAC treatment was also important for a better feed conversion ratio (FCR) in our pigs. Additionally, the Cu-MHAC treatment also showed a numerical enrichment (Fig. 1C) in Oscillibacter (mean = 2.53%; P = 0.1450) which is a genus that has a recognized role in anti-inflammatory metabolites production [68].

Using 16 S rRNA sequencing, we were unable to differentiate between the Escherichia and Shigella genera since their V3-V4 sequences are identical. Although a previous study reported a decrease in Escherichia coli persistence after piglets received CuSO4 (175 mg kg-1) [69]. Our results showed a different pattern, since the category in which Escherichia and Shigella were grouped increased in all Cu treatments as shown by orthogonal contrast (P = 0.0245). Interestingly, the highest mean (mean = 3.8026) was recorded in the TBCC group (Table 2). Thus, because the 16 S analysis bias, this enrichment could be caused by an increase in Shigella or other Escherichia species that are commonly related to enteric diseases [70, 71]. Nonetheless, further studies are needed to clarify this point.

CuSO4 showed numerical enrichment in (i) Helicobacter (mean = 0.1985; P = 0.1110) (Fig. 1C) includes pathogenic members for animals and humans [72] causing colitis [47]; (ii) Roseburia (mean = 3.0873; P = 0.2500) that correlates with anti-inflammatory properties due to SCFAs production [73]; and (iii) Succinivibrio (mean = 5.9400; P = 0.2242), which produces succinate [74] and is a potential fiber-degrader [75]. Meanwhile, the Control had increased (Table 2) (i) Megasphaera (mean = 1.6950; P = 0.0686), which produces amino acids and vitamins [76], and (ii) Paracteroides (mean = 2.7010) and Bacteroides (mean = 1.5775; P = 0.2582), both bacteriocin producers that can protect the gut against exogenous microorganisms [77].

Fig. 1
figure 1

Taxonomic distribution of treatments with different copper supplementations. Venn diagram of intersections and exclusive numbers of OTUs from the Control, CuSO4, Cu-MHAC, and TBCC treatments. The identified OTUs are in at least two samples (A). Phylum composition relative abundance by treatment (B). Heatmap showing the twenty most prevalent genera and the “Unassigned” bacteria by treatment. The color gradient indicates the lowest (blue) and highest (red) abundances. The taxonomic levels (domain, phylum, class, order, and family) were indicated in Table S2. The clustering was made using the Euclidean distance and complete linkage method in the web tool ClustVis [78], and analyzed by pvclust Fig. S2 [30] (C)

The administration of different copper sources influenced the specific modulation of Clostridium XIVa, Desulfovibrio, and Megasphera

The 20 most prevalent genera were compared among Cu treated and control groups, taking into account the trial and treatment. The results showed that nine bacterial groups (Prevotella, Treponema, Clostridium, Desulfovibrio, Megasphera, Streptococcus, Roseburia, Acidominococcus, and Escherichia/Shigella) were differentially regulated (P < 0.05) between the two different trials. This indicates an impact of the environment on the microbiota since trials were done in different seasons. However, Clostridium XIVa, Desulfovibrio, and Megasphera had a regulation by treatment or source of Cu. Desulfovibrio increased when higher levels of Cu were used (P < 0.05 by contrast). In addition, CuSO4, Cu-MHAC, and TBCC increased Desulfovibrio prevalence compared to the Control (P < 0.001). An increment in the cluster Clostridium XIVa was observed only when the organic source or TBCC was used instead of CuSO4 (P < 0.05). For this cluster of bacteria, high levels of CuSO4 seem to have a role in the reduction of these microorganisms, since their abundance was lower and not different from the Control (Table 2). Megasphera (P = 0.0686) abundance was reduced by supra-nutritional levels of Cu (P < 0.05 by contrast).

Table 2 Comparison of the relative abundance means among the top 20 genera by treatment

Desulfovibrio strains play an important role in the growth performance and health improvement of piglets during the early-weaned stage (Table S4). An increase in this group of bacteria was associated with a reduction in gut colonization of pathogens and an increase in energy conversion [79]. Additionally, Desulfovibrio was involved in the removal of the excess hydrogen generated by the microbiota during digestion, and this withdrawal has also been associated with the improvement of continuous SCFA production that was inhibited by an excess of hydrogen [80].

Clostridium XIVa, also known as Clostridium coccoides group, is a group of microorganisms that help the host to use nutrients that cannot be properly digested. These bacteria are also known as good SCFAs producers, playing an important role in intestinal homeostasis [81]. Shi et al. in observed an increase in these bacteria in the group of piglets supplemented with early food introduction (milk) that had an increase in SCFA production. SCFAs could contribute to the decrease of pro-inflammatory cytokines reducing the proliferation of pathogens [82] and contributing to the health of piglets (Table S4).

The homeostasis of the gut is a combination of several factors as the production of metabolites from both the host and its microbiota as well as the interaction of these metabolites between them. SCFAs have been reported to affect appetite regulation and energy homeostasis [83] and the gut microbiota-derived acetate was described as stimulating ghrelin secretion [84]. Gonzalez-Esquerra et al. [26] showed that the individuals used in this study and treated with Cu-MHAC had an increase in weight gain and the expression of mRNA for Ghrelin when compared to TBCC treatment. Prevotella is another genus involved in SCFA production, which was numerically increased in the group treated with Cu-MHAC. Finally, Megasphaera decreased after the Cu-MHAC treatment. These bacteria were usually found with an increment in the intestines of pigs in response to Lactobacillus and were correlated with intestinal disorders or immune responses in pigs. Additionally, they could be increased by the mycotoxins Deoxynivalenol (DON) and zearalenone (ZEN) which are frequently increased in the gut by ingestion of contaminated maize and grain cereals. Altogether, these results showed that different sources of copper in the pig diet promoted the proliferation of different genera of bacteria and that the interaction of the microbiota and the host could stimulate several genetic factors involved in gut health that could also modulate feed efficiency (FE), an important variable to swine production (Table S4).


Different copper sources can modulate the cecal microbiota even of healthy piglets fed with antibiotics as growth promoters. Dominance and Simpson ecological indexes usually differed, showing a prevalence of specific genera according to treatment. Some groups of bacteria seem to be directly affected by the source of copper that was offered to the animals. Overall, Cu-MHAC can be a beneficial supplement even in low doses, since it seems to affect the diversity of bacteria, mainly Desulfovibrio and Clostridium_XIVa that are involved in the improvement of gut health and FE and SCFAs production (Table S4). The increase in SCFAs, especially acetate, could be involved in the modulation of ghrelin and growth hormone expression on top of improving performance as previously reported by our group [26]. A graphical abstract summarizing the methods and main results is shown in Fig S3.

Data availability

All data and material are available in the manuscript and as supplementary.




CuSO4 :

Copper (II) sulfate


tri-basic copper chloride


Cu methionine hydroxy analogue chelated


Operational Taxonomic Units


short chain fatty acids


feed efficiency


gastrointestinal tract




zinc oxide






feed efficiency


Analysis of variance

CVmean :

coefficient of variation of the mean


  1. Kim HB, Isaacson RE. The pig gut microbial diversity: Understanding the pig gut microbial ecology through the next generation high throughput sequencing. Vet Microbiol [Internet]. Elsevier B.V.; 2015;177:242–51. Available from:

  2. Tremaroli V, Bäckhed F. Functional interactions between the gut microbiota and host metabolism. Nature. 2012;489:242–9.

    Article  CAS  PubMed  Google Scholar 

  3. Lærke HN. & M. S. Hedemann. The digestive system of the pig. In: Bach Knudsen, Knud Erik, Kjeldsen, Niels Jørgen, Poulsen, Hanne Damgaard, Jensen BB, editor. Nutr Physiol pigs - Online Publ [Internet]. 2012. p. 27. Available from:

  4. Jørgensen PB, Eriksen LL, Fenton TM, Bailey M, Agace WW, Mörbe UM. The porcine large intestine contains developmentally distinct submucosal lymphoid clusters and mucosal isolated lymphoid follicles. Dev Comp Immunol [Internet]. Pergamon; 2022 [cited 2023 Jan 27];131:104375. Available from:

  5. Lazar V, Ditu LM, Pircalabioru GG, Gheorghe I, Curutiu C, Holban AM, et al. Aspects of gut microbiota and immune system interactions in infectious diseases, immunopathology, and cancer. Front. Immunol. Frontiers Media S.A.; 2018. p. 1830.

  6. Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, et al. Host-gut microbiota metabolic interactions. Sci (80-). 2012;336:1262–7.

    Article  CAS  Google Scholar 

  7. Davenport ER, Mizrahi-Man O, Michelini K, Barreiro LB, Ober C, Gilad Y. Seasonal variation in human gut microbiome composition. PLoS One Public Library of Science. 2014;9:e9070731.

    Google Scholar 

  8. Almeida A, Mitchell AL, Boland M, Forster SC, Gloor GB, Tarkowska A, et al. A new genomic blueprint of the human gut microbiota. Nat Nat Publishing Group. 2019;568:499–504.

    CAS  Google Scholar 

  9. Myer PR. Bovine genome-microbiome interactions: Metagenomic Frontier for the selection of efficient Productivity in cattle Systems. mSystems. Volume 4. American Society for Microbiology; 2019.

  10. Ding J, Dai R, Yang L, He C, Xu K, Liu S, et al. Inheritance and establishment of gut microbiota in chickens. Front Microbiol Frontiers Media S A. 2017;8:1–11.

    Google Scholar 

  11. Hayashi RM, Lourenço MC, Kraieski AL, Araujo RB, Gonzalez-Esquerra R, Leonardecz E, et al. Effect of feeding bacillus subtilis spores to broilers challenged with Salmonella enterica serovar Heidelberg brazilian strain UFPR1 on performance, immune response, and gut health. Front Vet Sci Frontiers Media S A. 2018;5:1–12.

    Google Scholar 

  12. Wang X, Tsai T, Deng F, Wei X, Chai J, Knapp J et al. Longitudinal investigation of the swine gut microbiome from birth to market reveals stage and growth performance associated bacteria. Microbiome. Springer Science and Business Media LLC; 2019;7:109.

  13. Chen L, Xu Y, Chen X, Fang C, Zhao L, Chen F. The maturing development of gut microbiota in commercial piglets during the weaning transition. Front Microbiol Frontiers Media S A. 2017;8:1688.

    Article  Google Scholar 

  14. OECD-FAO Agricultural OECD. ; 2022 [cited 2023 Jan 26]. Available from:

  15. Alain B, Pajarillo E, Chae J-P, Balolong P, Bum Kim M, Kang H. D-K. Assessment of fecal bacterial diversity among healthy piglets during the weaning transition. J Gen Appl Microbiol [Internet]. 2014 [cited 2019 Jul 23];60:140–6. Available from:

  16. Gresse R, Chaucheyras-Durand F, Van de Fleury MA, Forano E, Blanquet-Diot S. Gut Microbiota Dysbiosis in Postweaning Piglets: Understanding the Keys to Health. Trends Microbiol [Internet]. Elsevier; 2017 [cited 2019 Sep 23];25:851–73. Available from:

  17. Pluske JR, Turpin DL, Kim J-C, Gastro. 1. Pluske JR, Turpin DL, Kim J-C. 2018. Gastrointestinal tract (gut) health in the young pig. Anim Nutr (Zhongguo xu mu shou yi xue hui) 4:187–196.intestinal tract (gut) health in the young pig. Anim Nutr (Zhongguo xu mu shou yi xue hui) [Internet]. KeAi Publishing; 2018 [cited 2019 Sep 5];4:187–96. Available from:

  18. Looft T, Johnson TA, Allen HK, Bayles DO, Alt DP, Stedtfeld RD, et al. In-feed antibiotic effects on the swine intestinal microbiome. Proc Natl Acad Sci. 2012;109:1691–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Liu Y, Espinosa CD, Abelilla JJ, Casas GA, Lagos LV, Lee SA et al. Non-antibiotic feed additives in diets for pigs: A review. Anim Nutr [Internet]. Elsevier; 2018 [cited 2019 Aug 14];4:113–25. Available from:

  20. Debski B. Supplementation of pigs diet with zinc and copper as alternative to conventional antimicrobials. Pol J Vet Sci. 2016;19:917–24.

    Article  CAS  PubMed  Google Scholar 

  21. Shannon MC, Hill GM. Trace mineral supplementation for the intestinal health of young monogastric animals. Front Vet Sci Frontiers Media S A. 2019;6:73.

    Article  Google Scholar 

  22. Grass G, Rensing C, Solioz M. Metallic Copper as an Antimicrobial Surface. Appl Environ Microbiol [Internet]. 2011;77:1541–7. Available from:

  23. Wen Y, Li R, Piao X, Lin G, He P. Different copper sources and levels affect growth performance, copper content, carcass characteristics, intestinal microorganism and metabolism of finishing pigs. Anim Nutr Elsevier. 2022;8:321–30.

    Article  CAS  Google Scholar 

  24. Cromwell GL, Lindemann MD, Monegue HJ, Hall DD, Orr DE. Tribasic copper chloride and copper sulfate as copper sources for weanling pigs. J Anim Sci [Internet]. American Society of Animal Science; 1998 [cited 2019 Nov 14];76:118. Available from:

  25. Zhao J, Allee G, Gerlemann G, Ma L, Gracia MI, Parker D, et al. Effects of a chelated copper as growth promoter on performance and carcass traits in pigs. Asian-Australasian J Anim Sci Asian-Australasian Association of Animal Production Societies. 2014;27:965–73.

    CAS  Google Scholar 

  26. Gonzalez-Esquerra R, Araujo RB, Haese D, Kill JL, Cunha AF, Monzani PS et al. Effect of dietary copper sources on performance, gastric ghrelin-RNA expression, and growth hormone concentrations in serum in piglets. J Anim Sci. Oxford University Press (OUP); 2019;97:4242–7.

  27. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.

    Article  CAS  PubMed  Google Scholar 

  28. Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, et al. The ribosomal database project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 2009;37:141–5.

    Article  Google Scholar 

  29. Mann E, Dzieciol M, Metzler-Zebeli BU, Wagner M, Schmitz-Esser S. Microbiomes of unreactive and pathologically altered ileocecal lymph nodes of slaughter pigs. Appl Environ Microbiol American Society for Microbiology (ASM). 2014;80:193–203.

    Article  CAS  Google Scholar 

  30. Suzuki R, Shimodaira H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics. 2006;22:1540–2.

    Article  CAS  PubMed  Google Scholar 

  31. Hasan N, Yang H. Factors affecting the composition of the gut microbiota, and its modulation. PeerJ [Internet]. PeerJ Inc.; 2019 [cited 2021 Jun 23];2019:e7502. Available from:

  32. Isaacson R, Kim HB. The intestinal microbiome of the pig. Anim Heal Res Rev [Internet]. Cambridge University Press; 2012 [cited 2019 Jul 18];13:100–9. Available from:

  33. Crespo-Piazuelo D, Migura-Garcia L, Estellé J, Criado-Mesas L, Revilla M, Castelló A et al. Association between the pig genome and its gut microbiota composition. Sci Rep [Internet]. Nature Publishing Group; 2019 [cited 2019 Jul 18];9:8791. Available from:

  34. Lopetuso LR, Scaldaferri F, Petito V, Gasbarrini A. Commensal Clostridia: leading players in the maintenance of gut homeostasis. Gut Pathog. BioMed Central; 2013. p. 23.

  35. Schubert AM, Rogers MAM, Ring C, Mogle J, Petrosino JP, Young VB et al. Microbiome data distinguish patients with clostridium difficile infection and non-c. Difficile-associated diarrhea from healthy controls. MBio [Internet]. 2014;5:e01021-14. Available from:

  36. n Den Abbeele P, Belzer C, Goossens M, Kleerebezem M, De Vos WM, Thas O, et al. Butyrate-producing Clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model. ISME J Nature Publishing Group. 2013;7:949–61.

    Google Scholar 

  37. Livanos AE, Snider EJ, Whittier S, Chong DH, Wang TC, Abrams JA et al. Rapid gastrointestinal loss of Clostridial Clusters IV and XIVa in the ICU associates with an expansion of gut pathogens. Wilson BA, editor. PLoS One [Internet]. Public Library of Science; 2018 [cited 2020 Mar 10];13:e0200322. Available from:

  38. Han Y, Wang Y, Yu Y, Chen H, Shen Y, Du L. Indole-induced reversion of intrinsic multiantibiotic resistance in Lysobacter enzymogenes. Appl Environ Microbiol American Society for Microbiology. 2017;83:e00995–17.

    CAS  Google Scholar 

  39. Faintuch J, Faintuch JJ. Precision medicine: the microbiome and metabolome. Microbiome Metabolome diagnosis, Ther other Strateg Appl. Elsevier; 2019. pp. 435–49.

  40. Parte AC. LPSN - list of prokaryotic names with standing in nomenclature (, 20 years on. Int J Syst Evol Microbiol Microbiology Society. 2018;68:1825–9.

    Article  Google Scholar 

  41. Looft T, Allen HK, Cantarel BL, Levine UY, Bayles DO, Alt DP, et al. Bacteria, phages and pigs: the effects of in-feed antibiotics on the microbiome at different gut locations. ISME J Nature Publishing Group. 2014;8:1566–76.

    CAS  Google Scholar 

  42. Thomas F, Hehemann JH, Rebuffet E, Czjzek M, Michel G. Environmental and gut Bacteroidetes: the food connection. Front Microbiol. Front Res Foundation. 2011;2:93.

    Google Scholar 

  43. Gibiino G, Lopetuso LR, Scaldaferri F, Rizzatti G, Binda C, Gasbarrini A. Exploring Bacteroidetes: metabolic key points and immunological tricks of our gut commensals. Dig. Liver dis. Elsevier B.V.; 2018. pp. 635–9.

  44. Stephens RW, Arhire L, Covasa M. Gut Microbiota: From Microorganisms to Metabolic Organ Influencing Obesity. Obesity [Internet]. 2018 [cited 2019 Nov 8];26:801–9. Available from:

  45. Koliada A, Syzenko G, Moseiko V, Budovska L, Puchkov K, Perederiy V et al. Association between body mass index and Firmicutes/Bacteroidetes ratio in an adult Ukrainian population. BMC Microbiol [Internet]. 2017 [cited 2020 Jan 10];17:120. Available from:

  46. Shin NR, Whon TW, Bae JW, Proteobacteria. Microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. Elsevier Ltd; 2015. pp. 496–503.

  47. Mukhopadhya I, Hansen R, El-Omar EM, Hold GL. IBD-what role do Proteobacteria play? Nat Rev Gastroenterol Hepatol [Internet]. 2012 [cited 2020 May 13];9:219–30. Available from:

  48. Liu AC, Chou CY, Chen LL, Kuo CH. Bacterial community dynamics in a swine wastewater anaerobic reactor revealed by 16S rDNA sequence analysis. J Biotechnol Elsevier. 2015;194:124–31.

    Article  CAS  Google Scholar 

  49. Arroyo P, Ansola G, Blanco I, Molleda P, Calabuig EDL, Sáenz De Miera LE. Comparative analysis of the composition of bacterial communities from two constructed wetlands for municipal and swine wastewater treatment. J Water Health IWA Publishing. 2010;8:147–57.

    Article  CAS  Google Scholar 

  50. Duda RM, da Silva Vantini J, Martins LS, de Mello Varani A, Lemos MVF, Ferro MIT, et al. A balanced microbiota efficiently produces methane in a novel high-rate horizontal anaerobic reactor for the treatment of swine wastewater. Bioresour Technol Elsevier Ltd. 2015;197:152–60.

    Article  CAS  Google Scholar 

  51. Gupta RS, Mahmood S, Adeolu M. A phylogenomic and molecular signature based approach for characterization of the phylum spirochaetes and its major clades: proposal for a taxonomic revision of the phylum. Front Microbiol Frontiers Media S A. 2013;4:217.

    Google Scholar 

  52. Hampson DJ. The spirochete brachyspira pilosicoli, enteric pathogen of animals and humans. Clin Microbiol Rev American Society for Microbiology. 2018;31:e00087–17.

    Google Scholar 

  53. Han GG, Lee JY, Jin GD, Park J, Choi YH, Kang SK, et al. Tracing of the fecal microbiota of commercial pigs at five growth stages from birth to shipment. Sci Rep. 2018;8:6012.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Li Y, Wang P, Yin J, Jin S, Su W, Tian J et al. Effects of ornithine α-ketoglutarate on growth performance and gut microbiota in a chronic oxidative stress pig model induced by d-galactose. Food Funct [Internet]. Royal Society of Chemistry; 2020 [cited 2021 Jul 5]. p. 472–82. Available from:

  55. Zeng Z, Zheng P, Kang D, Li Y, Li W, Xu D et al. The removal of copper and zinc from swine wastewater by anaerobic biological-chemical process: Performance and mechanism. J Hazard Mater [Internet]. J Hazard Mater; 2021 [cited 2021 Sep 13];401:123767. Available from:

  56. Niu Q, Li P, Hao S, Zhang Y, Kim SW, Li H, et al. Dynamic distribution of the gut microbiota and the relationship with apparent crude fiber digestibility and growth stages in pigs. Sci Rep Nature Publishing Group. 2015;5:9938.

    CAS  Google Scholar 

  57. Holman DB, Brunelle BW, Trachsel J, Allen HK. Meta-analysis To Define a Core Microbiota in the Swine Gut. mSystems [Internet]. 2017;2:e00004-17. Available from:

  58. Zhao W, Wang Y, Liu S, Huang J, Zhai Z, He C, et al. The dynamic distribution of porcine microbiota across different ages and gastrointestinal tract segments. PLoS ONE. 2015;10:1–13.

    Google Scholar 

  59. Crespo-Piazuelo D, Estellé J, Revilla M, Criado-Mesas L, Ramayo-Caldas Y, Óvilo C, et al. Characterization of bacterial microbiota compositions along the intestinal tract in pigs and their interactions and functions. Sci Rep. 2018;8:12727.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Leser TD, Amenuvor JZ, Jensen TK, Lindecrona RH, Boye M, Moøller K. Culture-independent analysis of gut bacteria: the pig gastrointestinal tract microbiota revisited. Appl Environ Microbiol. 2002;68:673–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Larsen JM. The immune response to Prevotella bacteria in chronic inflammatory disease. Immunol Blackwell Publishing Ltd. 2017;151:363–74.

    CAS  Google Scholar 

  62. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen Y-Y, Keilbaugh SA et al. Linking long-term dietary patterns with gut microbial enterotypes. Science [Internet]. NIH Public Access; 2011 [cited 2019 Oct 29];334:105–8. Available from:

  63. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci U S A. 2010;107:14691–6.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Kovatcheva-Datchary P, Nilsson A, Akrami R, Lee YS, De Vadder F, Arora T, et al. Dietary Fiber-Induced improvement in glucose metabolism is Associated with increased abundance of Prevotella. Cell Metab Cell Press. 2015;22:971–82.

    Article  CAS  Google Scholar 

  65. Koh HW, Kim MS, Lee JS, Kim H, Park SJ. Changes in the swine gut microbiota in response to porcine epidemic diarrhea infection. Microbes Environ Japanese Society of Microbial Ecology. 2015;30:284–7.

    Google Scholar 

  66. Dou S, Gadonna-Widehem P, Rome V, Hamoudi D, Rhazi L, Lakhal L et al. Characterisation of Early-Life Fecal Microbiota in Susceptible and Healthy Pigs to Post-Weaning Diarrhoea. Loh G, editor. PLoS One [Internet]. 2017 [cited 2020 Jan 13];12:e0169851. Available from:

  67. Queipo-Ortuño MI, Seoane LM, Murri M, Pardo M, Gomez-Zumaquero JM, Cardona F, et al. Gut microbiota composition in male rat models under different nutritional status and physical activity and its association with serum leptin and ghrelin levels. PLoS One [Internet]. 2013;8:e65465. Available from:

    Article  PubMed  Google Scholar 

  68. Li J, Sung CYJ, Lee N, Ni Y, Pihlajamäki J, Panagiotou G, et al. Probiotics modulated gut microbiota suppresses hepatocellular carcinoma growth in mice. Proc Natl Acad Sci U S A National Academy of Sciences. 2016;113:E1306–15.

    CAS  Google Scholar 

  69. Højberg O, Canibe N, Poulsen HD, Hedemann MS, Jensen BB. Influence of dietary zinc oxide and copper sulfate on the gastrointestinal ecosystem in newly weaned piglets. Appl Environ Microbiol. 2005;71:2267–77.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Khalil IA, Troeger C, Blacker BF, Rao PC, Brown A, Atherly DE, et al. Morbidity and mortality due to shigella and enterotoxigenic Escherichia coli diarrhoea: the global burden of Disease Study 1990–2016. Lancet Infect Dis Lancet Publishing Group. 2018;18:1229–40.

    Article  Google Scholar 

  71. Dos Reis RS, Horn F. Enteropathogenic Escherichia coli, Samonella, Shigella and Yersinia: Cellular aspects of host-bacteria interactions in enteric diseases. Gut Pathog [Internet]. BioMed Central Ltd.; 2014 [cited 2020 Feb 26];2:8. Available from:

  72. Mladenova-Hristova I, Grekova O, Patel A. Zoonotic potential of Helicobacter spp. J Microbiol Immunol Infect Elsevier Ltd. 2017;50:265–9.

    Article  Google Scholar 

  73. Tamanai-Shacoori Z, Smida I, Bousarghin L, Loreal O, Meuric V, Fong SB, et al. Roseburia spp.: a marker of health? Future Microbiol. Future Med Ltd. 2017;12:157–70.

    CAS  Google Scholar 

  74. Pope PB, Smith W, Denman SE, Tringe SG, Barry K, Hugenholtz P et al. Isolation of Succinivibrionaceae implicated in low methane emissions from Tammar wallabies. Science (80-) [Internet]. 2011 [cited 2020 Jan 13];333:646–8. Available from:

  75. Tandon D, Haque MM, Shaikh RS, Dubey SPS et al. AK, A snapshot of gut microbiota of an adult urban population from Western region of India. Arora PK, editor. PLoS One [Internet]. 2018 [cited 2019 Nov 9];13:e0195643. Available from:

  76. Shetty SA, Marathe NP, Lanjekar V, Ranade D, Shouche YS. Comparative Genome Analysis of Megasphaera sp. Reveals Niche Specialization and Its Potential Role in the Human Gut. Heimesaat MM, editor. PLoS One [Internet]. Public Library of Science; 2013 [cited 2020 Jun 7];8:e79353. Available from:

  77. Nakano V, Ignacio A, Fernandes MR, Fukugaiti MH, Avila-campos MJ. Intestinal Bacteroides and Parabacteroides species producing antagonistic substances. Curr Trends Microbiol [Internet]. 2013 [cited 2020 Jan 14];1:1–4. Available from:

  78. Metsalu T, Vilo J, ClustVis. A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res.Oxford University Press; 2015;43:pp. W566–70.

  79. Xu J, Li Y, Yang Z, Li C, Liang H, Wu Z et al. Yeast probiotics shape the gut microbiome and improve the health of early-weaned piglets.Front Microbiol [Internet]. Frontiers MediaS.A.; 2018 [cited 2021 Jun 23];9:2011. Available from:

  80. Karnachuk OV, Rusanov II, Panova IA, Grigoriev MA, Zyusman VS, Latygolets EA et al. Microbial sulfate reduction by Desulfovibrio is an important source of hydrogen sulfide from a large swine finishing facility. Sci Rep [Internet]. Nature Publishing Group; 2021 [cited 2021 Jun 23];11:10720. Available from:

  81. Guo P, Zhang K, Ma X, He P. Clostridium species as probiotics: potentials and challenges. J Anim Sci Biotechnol [Internet]. Springer Science and Business, Media LLC. ; 2020 [cited 2021 Jun 23];11:1–10. Available from:

  82. Shi C, Zhu Y, Niu Q, Wang J, Wang J, Zhu W. The Changes of Colonic Bacterial Composition and Bacterial Metabolism Induced by an Early Food Introduction in a Neonatal Porcine Model. Curr Microbiol [Internet]. Springer New York LLC; 2018 [cited 2021 Jun 23];75:745–51. Available from:

  83. Byrne CS, Chambers ES, Morrison DJ, Frost G. The role of short chain fatty acids in appetite regulation and energy homeostasis. Int J Obes [Internet]. Nature Publishing Group; 2015 [cited 2021 Jun 23];39:1331–8. Available from:

  84. Perry RJ, Peng L, Barry NA, Cline GW, Zhang D, Cardone RL et al. Acetate mediates a microbiome-brain-β-cell axis to promote metabolic syndrome. Nature [Internet]. Nature Publishing Group; 2016 [cited 2021 Jun 23];534:213–7. Available from:

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This study was funded by Novus International, Inc., Indaiatuba, Sao Paulo, Brazil with grants for all experiments procedures. This study was partially financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001 (Scholarships to Ana Paganin and Graziela Rezende).

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RBA, RGE, and AFC designed the study; DH and JLK did the animal trials; RBA and RGE collected pig samples; PSM, ACLP, and GSR conducted the laboratory experiments; ACLP, MFC, GSR, and CCMF conducted the bioinformatics analysis; ACLP, CCMF, and CGL conducted statistical analyses, ACF, RBA and ACLP wrote the manuscript with the input from all authors. All authors read and approved the final manuscript.

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Correspondence to Anderson Ferreira da Cunha.

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The trials were conducted at Instituto Federal do Espírito Santo and all the procedures were approved by the Ethics Committee on Animal Use of Centro de Tecnologia Animal (number CEUA_047_20213).

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Authors RBA and RGE were employed by Novus International, Inc. Authors DH and JLK were employed by Centro de Tecnologia Animal Ltda. The remaining authors declare no conflict of interest.

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Paganin, A.C.L., Monzani, P.S., Carazzolle, M.F. et al. Assessment of cecal microbiota modulation from piglet dietary supplementation with copper. BMC Microbiol 23, 92 (2023).

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