Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Characterization of the bacterial fecal microbiota composition of pigs preceding the clinical signs of swine dysentery

  • Jéssica A. Barbosa,

    Roles Data curation, Investigation, Methodology, Validation, Visualization, Writing – original draft

    Affiliations Animal Science Department, Federal University of Lavras, Lavras, Minas Gerais, Brazil, Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada

    ⨯
  • Juan C. P. Aguirre,

    Roles Investigation, Methodology

    Affiliation Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada

    ⨯
  • Roman Nosach,

    Roles Investigation, Methodology

    Affiliation Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada

    ⨯
  • John C. S. Harding,

    Roles Methodology

    Affiliation Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada

    ⨯
  • Vinícius S. Cantarelli,

    Roles Funding acquisition, Writing – review & editing

    Affiliation Animal Science Department, Federal University of Lavras, Lavras, Minas Gerais, Brazil

    ⨯
  • Matheus de O. Costa

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    matheus.costa@usask.ca

    Affiliations Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada, Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University. Utrecht, the Netherlands

    ⨯

Abstract

Swine dysentery (SD) is a worldwide production-limiting disease of growing-finishing pigs in commercial farms. The importance of the large intestinal microbiota in the swine dysentery pathogenesis has been established, but not well characterized. The objective of this study was to characterize the fecal bacterial microbiota of pigs immediately prior to developing clinical signs of swine dysentery. A total of 60 fecal samples were collected from 15 pigs with SD. Sampling times included a time point prior to SD (d0, n=15), 2 days before mucohaemorrhagic diarrhea was observed (d-2SD, n=15), 1 day before mucohaemorrhagic diarrhea was observed (d-1SD, n=15), and the day when pigs developed mucohemorragic diarrhea (MHD, n=15). Sequencing of cpn60 amplicons was used to profile the microbiome, and analyses were performed on QIIME2. Increased Chao1 index in d-1SD and MHD samples when compared to the d0 was the only change observed in alpha diversity. No differences between sampling times on beta diversity (Bray-Curtis dissimilarity) were found. Although a small sample size was investigated, differential abundance analysis revealed that Alistipes dispar and Parabacteroides gordonii were increased in MHD fecal samples when compared to d-2SD and d-1SD. It is suggested that these taxa may play a role in the pathogenesis of SD, which is known to require the presence of Brachyspira spp. and an anaerobe for severe disease development.

Citation: Barbosa JA, Aguirre JCP, Nosach R, Harding JCS, Cantarelli VS, Costa MdO (2023) Characterization of the bacterial fecal microbiota composition of pigs preceding the clinical signs of swine dysentery. PLoS ONE 18(11): e0294273. https://doi.org/10.1371/journal.pone.0294273

Editor: Gianmarco Ferrara, University of Naples Federico II: Universita degli Studi di Napoli Federico II, ITALY

Received: July 1, 2023; Accepted: October 27, 2023; Published: November 10, 2023

Copyright: © 2023 Barbosa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Minimal anonymized dataset is publicly available online from the OSF repository (https://doi.org/10.17605/OSF.IO/8TSX6).

Funding: MOC - Natural Sciences and Engineering Research Council of Canada - NSERC Discovery - RGPIN-2020-06353. https://www.nserc-crsng.gc.ca/index_eng.asp The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Background

Swine dysentery (SD) is a production limiting enteric disease affecting grower-finisher pigs worldwide. The anaerobic tolerant spirochete Brachyspira hyodysenteriae was the etiological agent of SD [1], which is characterized by mucohaemorrhagic diarrhea and fibrinonecrotic colitis [1, 2]. Recently, B. suanatina [3] and B. hampsonii [4] were also recognized as agents of SD.

A complex interaction between the large intestine microbiota and B. hyodysenteriae has been demonstrated. Studies based on culture have shown that B. hyodysenteriae requires the presence of other bacteria for severe SD expression [5–9]. Several studies provided evidence that gnotobiotic pigs inoculated with B. hyodysenteriae alone did not develop mucohaemorrhagic diarrhea [5, 10]. Colitis and mucohaemorrhagic diarrhea was reported in gnotobiotic pigs co-inoculated with B. hyodysenteriae and Fusobacterium necrophorum or Bacteroides vulgatus [5]. Mucoid diarrhea was found in pigs co-inoculated with B. hyodysenteriae, F. necrophorum, B. vulgatus, a Clostridium species, or Listeria denitrificans, but not when the spirochete was the only inoculum [9]. Recently, high-throughput sequencing of microbial barcode genes, such as 16S rRNA or cpn60, has been applied to study SD. Colonic contents and mucosal scrapings from pigs inoculated with B. hyodysenteriae or B. hampsonii and clinical signs of SD had lower species richness than their uninoculated counterparts [11]. Lower Bacteroidetes:Firmicutes ratio was linked to mucohaemorrhagic diarrhea following inoculation of pigs with B. hampsonii compared with sham-inoculated control or inoculated pigs that did not develop clinical disease [12]. Campylobacter spp., Mogibacterium spp., Brachyspira spp., and Desulfovibrio spp. were found in higher numbers in mucosal scrapings of pigs that developed SD, whereas Bifidobacterium spp. and Lactobacillus spp. were significantly more abundant in pigs without SD [11].

It has also been suggested that diet may influence the colonic microbiome and theincidence of SD. However, results have been contradictory [13–16]. Highly fermentable soluble fiber has been associated with a protective effect against SD [17–19]. These diets promoted the growth of lactic acid and butyric acid-producing bacteria, such as Bifidobacterium spp., Megasphaera spp., and Faecalibacterium spp. [16, 20, 21]. Conversely, poorly fermentable insoluble fiber has been linked to increased odds of pigs developing SD [22]. This observation was combined with augmented loads of anaerobes such as Shuttleworthia spp., Ruminococcus torques, and Mogibacterium spp., which may play a synergistic role with B. hyodysenteriae in inducing SD [21].

It is well established that the colonic microbial community changes following the development of SD, in comparison to pigs in a clinically healthy state [9, 11, 12, 16]. However, little is known regarding the changes preceding the clinical signs of SD. We hypothesize that the fecal microbiome is disturbed 24 to 48 hours before clinical SD is observed. Thus, the goal of this study was to characterize the bacterial fecal microbiome composition of pigs immediately prior to developing clinical signs of swine dysentery, during the disease incubation period.

Methods

Animal trial and samples

Barrows (n=15), age 9 to 10 weeks-old, were obtained from a PRRSV, Mycoplasma hyopneumoniae negative, high-health herd farm with no gastrointestinal clinical signs and no history or previous laboratory diagnosis of SD. Animals were housed and allowed to acclimate in a BSL-2 animal care facility for 7 days prior to inoculation. An unmedicated, commercial starter diet fed ad libitum was used [23] (S1 Table). Fecal samples were collected from contact pigs that were exposed to diseased seeder pigs experimentally inoculated intragastrically thrice over 72 hours with 100 mL of a 24h broth culture of B. hyodysenteriae strain G44 (total dose 3.72 x 1011 genome equivalents/mL). Contact pigs were sampled one day after contact with seeder pigs (d0, n=15); 2 days before mucohaemorrhagic diarrhea was observed (d-2SD, n=15); 1 day before mucohaemorrhagic diarrhea was observed (d-1SD, n=15); and the day when pigs developed mucohemorragic diarrhea (MHD, n=15), totaling 60 samples. A summary of the samples used from this trial is shown on S1 Table. All fecal samples were collected by digital stimulation and stored at -80°C until processing for analysis. The development of swine dysentery was confirmed by associating clinical signs, a positive fecal B. hyodysenteriae culture and gross necropsy lesions.

DNA extraction, cpn60 amplification and sequencing

For each sample, total DNA was extracted from 200 mg of feces using a commercial kit (MagMax DNA Ultra v2.0; Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA) on a KingFisher Flex platform (Thermo Fisher Scientific, Waltham, MA, USA). Amplification and indexing of the cpn60 universal target barcode region were performed as previously described [24]. Briefly, the cpn60 gene was amplified using a primer mix comprised of 100 μM from each of the following primers: M279 Forward (5’ – GAIIIIGCIGGIGAYGGIACIACIAC – 3’), M280 Reverse (5’ – YKIYKITCICCRAAICCIGGIGC– 3’), M1612 Forward (5’ – GAIIIIGCIGGYGACGGYACSACSAC– 3’), and M1613 Reverse (5’ – CGRCGRTCRCCGAAGCCSGGIGCCTT– 3’). Primers were mixed in a 1:3 molar ratio of M279:M280 (3 μL each), and M1612:M1613 (9 μL each) and diluted in 276 μL of ultrapure water for a total volume of 300 μL. PCR reactions had a total reaction volume of 50 μL, for 2 μL of DNA template. The master mix was prepared using 38.1 μL ultrapure water, 5 μL of 10x PCR buffer, 2.5 μL of MgCI2 (50mM), 0.4 μL of Platinum Taq Polymerase (Invitrogen, Thermo Fisher Scientific, USA), 1 μL of dNTP mix (10 mM; Invitrogen, Thermo Fisher Scientific, USA) and 1 μL of the primer cocktail described above. Reactions were incubated at 95°C for initial denaturation for 5 minutes, followed by 40 cycles of denaturation at 95°C for 30 seconds, annealing at 60°C for 30 seconds, extension at 72°C for 30 seconds, and a final extension at 72°C for 2 minutes. Cpn60 amplicons were purified using NucleoMag NGS beads (Macherey-Nagel Inc., Germany). Indexing PCR for library preparation was performed using a Nextera XT primers library preparation kit (Illumina Inc., San Diego, CA, USA) according to the manufacturer’s protocol. Indexed amplicons were size‐selected using NucleoMag NGS beads. Indexed amplicons were quantified, normalized, and diluted to 10pM libraries containing 5% PhiX DNA, Sequencing was carried out on an Illumina MiSeq (Illumina Inc., San Diego, CA, USA) platform using a 500-cycle reagent kit v2 (401 R1, 101 R2, Illumina Inc., San Diego, CA, USA).

Sequencing data analysis

Following sequencing, on-rig quality control procedures were executed. Raw data was processed by initially removing sequencing and amplification primers using Cutadapt [25], and low quality or short length and technical sequences were trimmed with Trimmomatic [26]. Filtered sequence reads were imported to Quantitative Insights Into Microbial Ecology 2 (QIIME2) [27], and variant calling was carried out using DADA2, truncating at 150 bp from the 5′ end [28]. Reads were mapped to the nonredundant version of cpnDB (cpnDB_nr) using watered BLAST [29]. Downstream analysis used only amplicon sequence variants (ASV) with >55% sequence similarity to a cpnDB_nr match. An feature table was generated and analyzed using a web-based platform for high-throughput sequencing data statistical analyses [30]. For further statistical analysis and visualization, the ASV table with taxa and metadata file were uploaded to the MicrobiomeAnalyst tool (Xia Lab, McGill University, Quebec, Canada; available at: https://www.microbiomeanalyst.ca) [31]. At the data filtering step, a low count filter was used to filter all ASV features with <4 counts in at least 20% prevalence, and 10% minimum variance among samples, leaving 169 ASV [30]. Shapiro-Wilk test was used to evaluate the normality of data. The alpha diversity indices (Chao1 and Shannon’s index) were calculated on raw data and comparisons were performed using ANOVA followed by post-hoc Tukey test. Beta-diversity differences between groups were analyzed by permutational multivariate analysis of variance (PERMANOVA) using Bray-Curtis dissimilarity index. Principal coordinate analysis (PCoA) was used to visualize the beta diversity data (MicrobiomeAnalyst). Differential abundance analysis at the phylum and ASV levels between sampling days was performed using DESeq2 [32], and the adjusted p-value<0.05 was used to report the significance (R version 4.2.1, RStudio, Boston, MA, USA, Love et al., 2014).

Results

Following quality control steps, sequencing resulted in 2,639,963 high-quality reads (average 44,745 per sample, ranging from 4,755 to 86,947). One sample, 387_MHD (rep 8), was removed from the analysis due to an extremely low number of reads generated (159). A total of 589 ASV were detected, 420 had ≥ 2 reads total and were kept for downstream analyses.

Fecal microbial community composition

Proportional taxa abundance data at the phylum and family level are shown in Fig 1A and 1B, respectively. The top 3 most abundant phyla were firmicutes, terrabacteria-group and bacteroidetes. The top 3 most abundant family were Bacteroidales, Clostridiales and Ruminococcaceae.

thumbnail
Download:
Fig 1. Stacked bar charts representing proportional abundance of bacterial taxa one day after contact with seeder pigs (d0, n=15); 2 days pre-SD (d-2SD, n=15); 1 day pre-SD (d-1SD, n=15); and the day mucohaemorrhagic diarrhea was observed for the first time (MHD, n =14).

(A) Depicts data at the phylum level, and (B) at the family level.

https://doi.org/10.1371/journal.pone.0294273.g001

No significant differences in alpha diversity indexes (Chao1, P = 0.056, Shannon’s index, P = 0.248) were observed between days at the phylum level (Fig 2A, 2B). At the genus level, Chao1 index was increased at d-1SD (P = 0.042) and MHD (P = 0.001) when compared to d0 samples (Fig 2C), and no significant differences in Shannon’s index were observed among days (Fig 2D; P =0.270). No changes in beta diversity were observed at the phylum level. At the genus level, four samples clustered separately from the others significantly affecting the data distribution, but an even distribution of high and low ranks within and between days was observed as evidenced by the small R2 value identified (R2 = 0.091684, P = 0.003, Fig 3).

thumbnail
Download:
Fig 2. Alpha-diversity metrics from fecal samples collected after contact with seeder pigs (d0, n=15); 2 days pre-SD (d-2SD, n=15); 1 day pre-SD (d-1SD, n=15); and the day mucohaemorrhagic diarrhea was observed for the first time (MHD, n =14).

A: Chao1 richness at the phylum level; B: Shannon’s diversity index at the phylum level; 2C: Chao1 richness index at the genus level; D: Shannon’s diversity index at the genus level. Boxes shows interquartile ranges, whiskers depict the minimum and maximum values.

https://doi.org/10.1371/journal.pone.0294273.g002

thumbnail
Download:
Fig 3. Beta diversity (Bray-Curtis dissimilarity data) at genus level based on principal coordinates analysis (PCoA) of fecal samples from pigs after contact with seeder pigs (d0, n=15); 2 days pre-SD (d-2SD, n=15); 1 day pre-SD (d-1SD, n=15); and the day mucohaemorrhagic diarrhea was observed for the first time (MHD, n =14).

(R-squared: 0.091684; p-value: 0.003).

https://doi.org/10.1371/journal.pone.0294273.g003

Differential abundance analysis

Reads associated with actinobacteria and spirochetes significantly differed between days (Fig 4A and 4B). ASVs with more than 10.000 total read counts and differentially abundant between d-2SD vs. MHD, d-1SD vs. MHD and d0 vs. all other days are presented in Tables 1 and 2 and S2 Table, respectively.

thumbnail
Download:
Fig 4. Phyla significantly enriched or depleted in fecal samples after contact with seeder pigs (d0, n=14); 2 days pre-SD (d-2SD, n=15); 1 day pre-SD (d-1SD, n=15); and the day mucohaemorrhagic diarrhea was observed for the first time (MHD, n =14).

Boxplot represents the sum of reads associated with a given phylum at a given sampling time, and whiskers depict the minimum and maximum number of reads.

https://doi.org/10.1371/journal.pone.0294273.g004

thumbnail
Download:
Table 1. Amplicon sequence variants (ASV) significantly different abundance between d-2SD and MHD.

https://doi.org/10.1371/journal.pone.0294273.t001

thumbnail
Download:
Table 2. Amplicon sequence variant (ASV) with significantly different abundance between d-1SD and MHD.

https://doi.org/10.1371/journal.pone.0294273.t002

Discussion

This study characterized the fecal microbiota of pigs on the two days immediately prior to the development of swine dysentery clinical signs. Alpha and beta diversity were not significantly on the 2 days prior the observation of mucohaemorrhagic diarrhea. However, differential abundance analysis revealed ASVs significantly affected prior to the observation of clinical SD. This work is an intermediary step towards the complete understanding of SD pathogenesis, and the role of the microbiome in this mechanism.

A high abundance of Eubacterium brachy and a low abundance of Parabacteroides gordonii was observed in d-2SD, when compared to MHD samples. The Eubacterium genera is known to be present in the healthy mammalian intestinal microbiota, as demonstrated in mice, humans and pigs [33–35]. In pigs, P. gordonii was found in feces of healthy animals and suggested to have an effect on growth performance [36–38]. Oral administration of P. distasonis membrane fractions relieved intestinal inflammation in mice with acute and chronic colitis induced by dextran sodium sulfate [39]. In the same study, the authors observed a reduction in TNF-a production by macrophages in vitro [39]. Relative abundance of P. distasonis was found inversely associated with colonic production of IL-1β [40]. However, members of this fairly new genus (established in 2006) have been associated with a dichotomous role in intestinal and systemic disease [41]. It remains unclear how P. gordonii may have contributed to the pathogenesis of SD, and further studies are suggested to try and elucidate its potential relationships with Brachyspira spp. Eubacterium spp. is suggested to benefit the host largely due to its production of butyrate [42–44]. Eubacterium brachy, a Gram-positive strict anaerobe, was frequently isolated from patients with periodontitis and pleuropulmonary infection [45, 46]. Mouse models of colitis using dextran sodium sulfate revealed that E. limosum and its metabolites were associated with reduced clinical scores through increased butyrate levels. In T84 colonocyte cells, this effect is mediated by reduced IL-6 and TLR4 expression [47]. Additionally, lower abundance of Eubacterium spp. has been reported in patients with ulcerative colitis or Crohn’s disease, when compared to healthy patients [48, 49]. Previous work showing the association between a higher abundance of Eubacterium spp. and attenuated colitis corroborates our findings, as this genera was found depleted in MHD samples. In contrast, Parabacteroides spp. is found in low amounts in the human gut microbiota [50]. In pigs, it is suggested to be part of the intestinal microbiota of healthy animals [36–38]. Humans with IBD have decreased abundance of Parabacteroides, when compared to healthy patients [51, 52]. Bacteria of this genera were found to produce short chain fatty acids (SCFA) [53], regulate immunity in multiple sclerosis through IL-10 induction [54], and relieve intestinal inflammation in mice with acute and chronic colitis by reducing the levels of pro-inflammatory cytokines [39]. Alistipes dispar was also found depleted on d-1SD and d-2SD, when compared to MHD day. Alistipes spp. are enriched in human fecal samples from patients with colorectal cancer [55, 56], and other non-intestinal disorders, such as depression and atherosclerotic cardiovascular disease [57, 58]. It is suggested to thrive in the inflamed colon of IL-10-/- knocked out mice, being sufficient to induce colitis and tumorigenesis through IL-6–STAT3 signaling [59]. In contrast, decreased abundance of Alistipes spp. has been associated with protective effects in IBD patients and ulcerative colitis in mice [60, 61]. Interestingly, when A. finegoldii was administered together with Bacteroides eggerthii, a colitis-predisposing bacterium, it attenuated the severity of dextran sulfate sodium (DSS)-induced colitis in mice depleted of intestinal microbiota [60]. We speculate both anaerobes, Parabacteroides spp. and Alistipes dispar, increased abundance at MHD day is either linked to their opportunistic profile or they truly are part of the ancillary microbiota required for the severe expression of SD, as previously shown to be required [5–7, 9, 11].

Actinobacteria read counts decreased from d0 samples to MHD. This has not been reported before, although differences in analytical methods between this study and previous research may explain such finding [11, 12]. Interestingly, a significant increase in Actinobacteria abundance and decreased incidence of clinical SD was observed in B. hyodysenteriae-inoculated pigs consuming a highly fermentable fiber, when compared to pigs fed a low fermentable fiber [21]. Several studies have found a decline in Actinobacteria abundance during different gastrointestinal disorders, such as acute hemorrhagic diarrhea in dogs [62], new neonatal porcine diarrhea [63], and post-weaning diarrhea in piglets [64]. Although this phylum has been found in higher abundance in healthy intestinal samples when compared to diseased ones, to clarify the exact role of actinobacteria in SD requires further studies.

Higher Chao1 index was identified on d-1SD, when compared to the control d0 samples. Differing from our findings, Burrough et al. [11] found a low Chao1 diversity index in colonic contents and mucosal scrapings of B. hyodysenteriae or B. hampsonii inoculated pigs, when compared to uninoculated controls. No changes in alpha diversity were observed in the fecal microbiota of pigs inoculated with B. hampsonii [12], E. coli F18+ [65, 66], or S. Typhimurium [67], when compared to matching controls. When compared to other alpha diversity indices, Chao1 index is considered sensitive to rare taxa [68, 69]. Given the inherent limitations of high-throughput sequencing, changes in ASVs with low abundance could be a simple result of the technique used to generate the data, or the bioinformatic algorithms used.

Beta-diversity analysis at the genus level revealed a single cluster with all samples intertwined. A previous study found no significant differences in beta-diversity fecal samples of pigs prior to inoculation and at the onset of mucohaemorrhagic diarrhea [12]. However, differences in beta-diversity between the luminal content and mucosal scrapings were observed in pigs with and without SD [11]. It is known that the mucosal, luminal and fecal microbiomes are compositionally different [70], which may have limited our findings. As a limitation of our study, the design used relied on ante-mortem samples. Unless surgical intervention was performed, which could confound the development of clinical signs as well as the gut microbiome, sampling the luminal or mucosal microbiome of pigs on the days prior to SD is rather challenging.

While we recognize that a small sample size was used in this study, our investigation revealed that the fecal microbiota changed in the days prior to the development of clinical SD. We suggest that the anaerobes A. dispar and P. gordonii may play a role in contributing to the development of SD. This knowledge may be employed for the future development of preventative tools that may not target the agents of SD, but still prevent clinical disease. Further investigation on their specific role may help clarify the importance of other microbes in SD. In addition, studies associating the fecal metabolome to the microbiota taxonomic composition may shed a light on the microbiota role in SD pathogenesis.

Supporting information

S1 Table. Description of swine dysentery custom diet.

https://doi.org/10.1371/journal.pone.0294273.s001

(PDF)

S2 Table. Description of fecal samples used in this study.

https://doi.org/10.1371/journal.pone.0294273.s002

(PDF)

S3 Table. Amplicon sequence variants (ASV) with significant differential abundance between d0 and all other days.

https://doi.org/10.1371/journal.pone.0294273.s003

(PDF)

Acknowledgments

The authors are grateful to Champika Fernando and Scott do Santos for technical assistance and helpful discussions on data analysis.

References

  1. 1. Taylor D.J., Alexander T.J., The Production of dysentery in swine by feeding cultures containing a spirochaete, Br. Vet. J. 127 (1971) lviii–lxi. pmid:5167827
  2. 2. Burrough E.R., Swine Dysentery: etiopathogenesis and diagnosis of a reemerging disease, Vet. Pathol. 54 (2017) 22–31. https://doi.org/10.1177/0300985816653795.
  3. 3. Råsbäck T., Jansson D.S., Johansson K.E., Fellström C., A novel enteropathogenic, strongly haemolytic spirochaete isolated from pig and mallard, provisionally designated “Brachyspira suanatina” sp. nov, Environ. Microbiol. 9 (2007) 983–991. https://doi.org/10.1111/j.1462-2920.2006.01220.x.
  4. 4. Rubin J.E., Costa M.O., Hill J.E., Kittrell H.E., Fernando C., Huang Y., et al., Reproduction of mucohaemorrhagic diarrhea and colitis indistinguishable from swine dysentery following experimental inoculation with “Brachyspira hampsonii" strain 30446, PLoS One. 8 (2013) e57146. pmid:23460829
  5. 5. Harris D., Alexander T., Whipp S., Swine dysentery: studies of gnotobiotic pigs inoculated with Treponema hyodysenteriae, Bacteroides vulgatus, and Fusobacterium necrophorum., J. Am. Vet. Med. Assoc. 172 (1978) 468–471. pmid:624669
  6. 6. Meyer R.C., Simon J., Byerly C.S., The etiology of Swine Dysentery: III. The role of selected gram-negative obligate anaerobes, Vet. Pathol. 12 (1975) 46–54. https://doi.org/10.1177/030098587501200107.
  7. 7. Meyer R.C., Simon J., Byerly C.S., The etiology of swine dysentery: II.Effect of a known microbial flora, weaning and diet on disease production in gnotobiotic and conventional swine, Vet. Pathol. 11 (1974) 527–534. https://doi.org/10.1177/030098587401100607.
  8. 8. Wannemuehler M.J., Dorn A., Inability of Brachyspira hyodysenteriae to induce mucosal inflammation and disease in Germfree mice, Gastroenterology. 124 (2003) A324. https://doi.org/10.1016/s0016-5085(03)81634-7.
  9. 9. Whipp S.C., Robinson I.M., Harris D.L., Glock R.D., Matthews P.J., et al., Pathogenic synergism between Treponema hyodysenteriae and other selected anaerobes in gnotobiotic pigs, Infect. Immun. 26 (1979) 1042–1047. pmid:528047
  10. 10. Meyer R.C., Simon J., Byerly C.S., The etiology of swine dysentery.II. Effect of a known microbial flora, weaning and diet on disease production in gnotobiotic and conventional swine, Vet. Pathol. 11 (1974) 515–526. https://doi.org/10.1177/030098587401100606.
  11. 11. Burrough E.R., Arruda B.L., Plummer P.J., Comparison of the luminal and mucosa-associated microbiota in the colon of pigs with and without swine dysentery, Front. Vet. Sci. 4 (2017) 139. https://doi.org/10.3389/fvets.2017.00139.
  12. 12. Costa M.O., Chaban B., Harding J.C.S., Hill J.E., Characterization of the fecal microbiota of pigs before and after inoculation with “Brachyspira hampsonii,” PLoS One. 9 (2014) e106399. pmid:25166307
  13. 13. Kirkwood R.N., Huang S.X., McFall M., Aherne F.X., Dietary factors do not influence the clinical expression of swine dysentery, J. Swine Heal. Prod. 8 (2000) 73–76.
  14. 14. Lindecrona R.H., Jensen T.K., Jensen B.B., Leser T.D., Jiufeng W., Møller K., The influence of diet on the development of swine dysentery upon experimental infection, Anim. Sci. 76 (2003) 81–87. https://doi.org/10.1017/s1357729800053340.
  15. 15. Il Lee G., Hedemann M.S., Jensen B.B., Knudsen K.E.B., Influence of infection with Brachyspira hyodysenteriae on clinical expression, growth performance, and digestibility in growing pigs fed diets varying in type and level of fiber, J. Anim. Sci. 100 (2022) skac066. pmid:35255495
  16. 16. Leser T.D., Lindecrona R.H., Jensen T.K., Jensen B.B., Moller K., Changes in bacterial community structure in the colon of pigs fed different experimental diets and after infection with Brachyspira hyodysenteriae, Appl. Environ. Microbiol. 66 (2000) 3290–3296. https://doi.org/10.1128/aem.66.8.3290-3296.2000.
  17. 17. Hansen C.F., Hernández A., Mansfield J., Hidalgo Á., La T., Phillips N.D., et al., A high dietary concentration of inulin is necessary to reduce the incidence of swine dysentery in pigs experimentally challenged with Brachyspira hyodysenteriae, Br. J. Nutr. 106 (2011) 1506–1513. https://doi.org/10.1017/s000711451100208x.
  18. 18. Hansen C.F., Phillips N.D., La T., Hernandez A., Mansfield J., Kim J.C., et al., Diets containing inulin but not lupins help to prevent swine dysentery in experimentally challenged pigs, J. Anim. Sci. 88 (2010) 3327–3336. https://doi.org/10.2527/jas.2009-2719.
  19. 19. Hampson D.J., Robertson I.D., La T., Oxberry S.L., Pethick D.W., Influences of diet and vaccination on colonisation of pigs by the intestinal spirochaete Brachyspira (Serpulina) pilosicoli, Vet. Microbiol. 73 (2000) 75–84. https://doi.org/10.1016/s0378-1135(99)00200-x.
  20. 20. Mølbak L., Thomsen L.E., Jensen T.K., Bach Knudsen K.E., Boye M., Increased amount of Bifidobacterium thermacidophilum and Megasphaera elsdenii in the colonic microbiota of pigs fed a swine dysentery preventive diet containing chicory roots and sweet lupine., J. Appl. Microbiol. 103 (2007) 1853–1867. pmid:17953596
  21. 21. Helm E.T., Gabler N.K., Burrough E.R.,. Highly fermentable fiber alters fecal microbiota and mitigates swine dysentery induced by Brachyspira hyodysenteriae, Animals11 (2021) 1–20. https://doi.org/10.3390/ani11020396.
  22. 22. Pluske J.R., Durmic Z., Pethick D.W., Mullan B.P., Hampson D.J., Confirmation of the role of rapidly fermentable carbohydrates in the expression of swine dysentery in pigs after experimental infection, J. Nutr. 128 (1998) 1737–1744. pmid:9772144
  23. 23. Parra-Aguirre J.C., Nosach R., Fernando C., Hill J.E., Harding J.C.S., Improving the consistency of experimental swine dysentery inoculation strategies, Vet. Res. 54 (2023) 1–17. https://doi.org/10.1186/S13567-023-01180.
  24. 24. Fernando C., Hill J.E., cpn60 metagenomic amplicon library preparation for the Illumina Miseq platform, (2021). https://doi.org/10.21203/rs.3.pex-1438/v1.
  25. 25. Martin M., Cutadapt removes adapter sequences from high-throughput sequencing reads, EMBnet.Journal. 17 (2011) 10–12. https://doi.org/10.14806/ej.17.1.200.
  26. 26. Bolger A.M., Lohse M., Usadel B., Trimmomatic: a flexible trimmer for Illumina sequence data, Bioinformatics. 30 (2014) 2114–2120. pmid:24695404
  27. 27. Bolyen E., Rideout J.R., Dillon M.R., Bokulich N.A., Abnet C.C., Al-Ghalith G.A., et al., Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2, Nat. Biotechnol. 2019 378. 37 (2019) 852–857. https://doi.org/10.1038/s41587-019-0209-9.
  28. 28. Callahan B.J., McMurdie P.J., Rosen M.J., Han A.W., Johnson A.J.A., et al., DADA2: High-resolution sample inference from Illumina amplicon data, Nat. Methods 2016 137. 13 (2016) 581–583. https://doi.org/10.1038/nmeth.3869.
  29. 29. Schellenberg J., Links M.G., Hill J.E., Dumonceaux T.J., Peters G.A., Tyler S., et al., Pyrosequencing of the chaperonin-60 universal target as a tool for determining microbial community composition, Appl. Environ. Microbiol. 75 (2009) 2889–2898. https://doi.org/10.1093/nar/gkx295.
  30. 30. Chong J., Liu P., Zhou G., Xia J., Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data, Nat. Protoc. 2020 153. 15 (2020) 799–821. https://doi.org/10.1038/s41596-019-0264-1.
  31. 31. Dhariwal A., Chong J., Habib S., King I.L., Agellon L.B., Xia J., MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data, Nucleic Acids Res. 45 (2017) W180–W188. pmid:28449106
  32. 32. Love M.I., Huber W., Anders S., Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol. 15 (2014) 1–21. pmid:25516281
  33. 33. Harris M.A., Reddy C.A., Carter G.R., Anaerobic bacteria from the large intestine of mice, Appl. Environ. Microbiol. 31 (1976) 907–912. pmid:938042
  34. 34. Turpin W., Espin-Garcia O., Xu W., Silverberg M.S., Kevans D., Smith M.I., et al., Association of host genome with intestinal microbial composition in a large healthy cohort, Nat. Genet. 2016 4811. 48 (2016) 1413–1417. https://doi.org/10.1038/ng.3693.
  35. 35. Leser T.D., Amenuvor J.Z., Jensen T.K., Lindecrona R.H., Boye M., Moøller K., Culture-independent analysis of gut bacteria: The pig gastrointestinal tract microbiota revisited, Appl. Environ. Microbiol. 68 (2002) 673–690. https://doi.org/10.1128/aem.68.2.673-690.2002.
  36. 36. Bak J.E., Oh B.S., Ryu S.W., Yu S.Y., Choi W.J., Kim J.S., et al., Parabacteroides faecalis sp. nov. isolated from swine faeces, Curr. Microbiol. 80 (2023) 1–7. pmid:36725751
  37. 37. Han G.G., Lee J.Y., Jin G.D., Park J., Choi Y.H., Kang S.K., et al, Tracing of the fecal microbiota of commercial pigs at five growth stages from birth to shipment, Sci. Rep. 8 (2018) 1–9. https://doi.org/10.1038/s41598-018-24508-7.
  38. 38. Li X.J., Wang M., Xue Y., Duan D., Li C., Han X., et al., Identification of microflora related to growth performance in pigs based on 16S rRNA sequence analyses, AMB Express. 10 (2020) 1–13. pmid:33123840
  39. 39. Kverka M., Zakostelska Z., Klimesova K., Sokol D., Hudcovic T., Hrncir T., et al., Oral administration of Parabacteroides distasonis antigens attenuates experimental murine colitis through modulation of immunity and microbiota composition, Clin. Exp. Immunol. 163 (2011) 250. https://doi.org/10.1111/j.1365-2249.2010.04286.x.
  40. 40. Pfalzer A.C., Nesbeth P.D.C., Parnell L.D., Iyer L.K., Liu Z., Kane A. V., et al., Diet- and genetically-induced obesity differentially affect the fecal microbiome and metabolome in Apc1638N mice, PLoS One. 10 (2015) e0135758. pmid:26284788
  41. 41. Ezeji J.C., Sarikonda D.K., Hopperton A., Erkkila H.L., Cohen D.E., Martinez S.P., et al., Parabacteroides distasonis: intriguing aerotolerant gut anaerobe with emerging antimicrobial resistance and pathogenic and probiotic roles in human health, Gut Microbes. 13 (2021). pmid:34196581
  42. 42. Sato T., Matsumoto K., Okumura T., Yokoi W., Naito E., Yoshida Y., et al., Isolation of lactate-utilizing butyrate-producing bacteria from human feces and in vivo administration of Anaerostipes caccae strain L2 and galacto-oligosaccharides in a rat model, FEMS Microbiol. Ecol. 66 (2008) 528–536. https://doi.org/10.1111/j.1574-6941.2008.00528.x.
  43. 43. Levine U.Y., Looft T., Allen H.K., Stanton T.B., Butyrate-producing bacteria, including mucin degraders, from the swine intestinal tract, Appl. Environ. Microbiol. 79 (2013) 3879–3881. https://doi.org/10.1128/aem.00589-13.
  44. 44. Duncan S.H., Louis P., Flint H.J., Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product, Appl. Environ. Microbiol. 70 (2004) 5810–5817. https://doi.org/10.1128/aem.70.10.5810-5817.2004.
  45. 45. Hill G.B., Ayers O.M., Kohan A.P., Characteristics and sites of infection of Eubacterium nodatum, Eubacterium timidum, Eubacterium brachy, and other asaccharolytic eubacteria, J. Clin. Microbiol. 25 (1987) 1540–1545. pmid:3624445
  46. 46. Rochford J.C., Pleuropulmonary infection associated with Eubacterium brachy, a new species of Eubacterium, J. Clin. Microbiol. 12 (1980) 722–723. https://doi.org/10.1128/jcm.12.5.722-723.1980.
  47. 47. Kanauchi O., Fukuda M., Matsumoto Y., Ishii S., Ozawa T., Shimizu M., et al., Eubacterium limosum ameliorates experimental colitis and metabolite of microbe attenuates colonic inflammatory action with increase of mucosal integrity, World J. Gastroenterol. 12 (2006) 1071. https://doi.org/10.3748/wjg.v12.i7.1071.
  48. 48. Hirano A., Umeno J., Okamoto Y., Shibata H., Ogura Y., Moriyama T., et al., Comparison of the microbial community structure between inflamed and non-inflamed sites in patients with ulcerative colitis, J. Gastroenterol. Hepatol. 33 (2018) 1590–1597. https://doi.org/10.1111/jgh.14129.
  49. 49. Takahashi K., Nishida A., Fujimoto T., Fujii M., Shioya M., Imaeda H., et al., Reduced abundance of butyrate-producing bacteria species in the fecal microbial community in Crohn’s disease, Digestion. 93 (2016) 59–65. pmid:26789999
  50. 50. Nishijima S., Suda W., Oshima K., Kim S.W., Hirose Y., Morita H., et al., The gut microbiome of healthy Japanese and its microbial and functional uniqueness, DNA Res. 23 (2016) 125–133. pmid:26951067
  51. 51. Walker A.W., Sanderson J.D., Churcher C., Parkes G.C., Hudspith B.N., Rayment N., et al., High-throughput clone library analysis of the mucosa-associated microbiota reveals dysbiosis and differences between inflamed and non-inflamed regions of the intestine in inflammatory bowel disease, BMC Microbiol. 11 (2011) 7. pmid:21219646
  52. 52. Zitomersky N.L., Atkinson B.J., Franklin S.W., Mitchell P.D., Snapper S.B., Comstock L.E., et al., Characterization of adherent Bacteroidales from intestinal biopsies of children and young adults with inflammatory bowel disease, PLoS One. 8 (2013) 63686. pmid:23776434
  53. 53. Ahmed S., Busetti A., Fotiadou P., Vincy Jose N., Reid S., Georgieva M., et al., In vitro characterization of gut microbiota-derived bacterial strains with neuroprotective properties, Front. Cell. Neurosci. 13 (2019) 402. https://doi.org/10.3389/fncel.2019.00402.
  54. 54. Cekanaviciute E., Yoo B.B., Runia T.F., Debelius J.W., Singh S., Nelson C.A., et al., Gut bacteria from multiple sclerosis patients modulate human T cells and exacerbate symptoms in mouse models, Proc. Natl. Acad. Sci. U. S. A. 114 (2017) 10713–10718. pmid:28893978
  55. 55. Baxter N.T., Zackular J.P., Chen G.Y., Schloss P.D., Structure of the gut microbiome following colonization with human feces determines colonic tumor burden, Microbiome. 2 (2014) 1–11. https://doi.org/10.1186/2049-2618-2-20.
  56. 56. Dai Z., Coker O.O., Nakatsu G., Wu W.K.K., Zhao L., Chen Z., et al., Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers, Microbiome. 6 (2018) 70. pmid:29642940
  57. 57. Naseribafrouei A., Hestad K., Avershina E., Sekelja M., Linløkken A., Wilson R., et al., Correlation between the human fecal microbiota and depression, Neurogastroenterol. Motil. 26 (2014) 1155–1162. pmid:24888394
  58. 58. Jie Z., Xia H., Zhong S.L., Feng Q., Li S., Liang S., et al., The gut microbiome in atherosclerotic cardiovascular disease, Nat. Commun. 2017 81. 8 (2017) 1–12. https://doi.org/10.1038/s41467-017-00900-1.
  59. 59. Moschen A.R., Gerner R.R., Wang J., Klepsch V., Adolph T.E., Reider S.J., et al., Lipocalin 2 protects from inflammation and tumorigenesis associated with gut microbiota alterations, Cell Host Microbe. 19 (2016) 455–469. pmid:27078067
  60. 60. Dziarski R., Park S.Y., Kashyap D.R., Dowd S.E., Gupta D., Pglyrp-regulated gut microflora Prevotella falsenii, Parabacteroides distasonis and Bacteroides eggerthii enhance and Alistipes finegoldii attenuates colitis in mice, PLoS One. 11 (2016) e0146162. pmid:26727498
  61. 61. Frank D.N., St A.L. Amand R.A. Feldman E.C. Boedeker N. Harpaz N.R. Pace, Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases, Proc. Natl. Acad. Sci. U. S. A. 104 (2007) 13780–13785. https://doi.org/10.1073/pnas.0706625104.
  62. 62. Suchodolski J.S., Markel M.E., Garcia-Mazcorro J.F., Unterer S., Heilmann R.M., Dowd S.E., et al., The fecal microbiome in dogs with acute diarrhea and idiopathic inflammatory bowel disease, PLoS One. 7 (2012) e51907. pmid:23300577
  63. 63. Hermann-Bank M.L., Skovgaard K., Stockmarr A., Strube M.L., Larsen N., Kongsted H., et al., Characterization of the bacterial gut microbiota of piglets suffering from new neonatal porcine diarrhea, BMC Vet. Res. 11 (2015). https://doi.org/10.1186/s12917-015-0419-4.
  64. 64. Li Q., Peng X., Burrough E.R., Sahin O., Gould S.A., Gabler N.K., et al., Dietary soluble and insoluble fiber with or without enzymes altered the intestinal microbiota in weaned pigs challenged with Enterotoxigenic E. coli F18, Front. Microbiol. 11 (2020) 1110. pmid:32536908
  65. 65. Duarte M.E., Kim S.W., Significance of mucosa associated microbiota and its impacts on intestinal health of pigs challenged with F18+E. coli, Pathogens. 11 (2022) 589. https://doi.org/10.3390/pathogens11050589.
  66. 66. Pollock J., Hutchings M.R., Hutchings K.E.K., Gally D.L., Houdijk J.G.M., Changes in the ileal, but not fecal, microbiome in response to increased dietary protein level and enterotoxigenic Escherichia coli exposure in pigs, Appl. Environ. Microbiol. 85 (2019). https://doi.org/10.1128/aem.01252-19.
  67. 67. Argüello H., Estellé J., Zaldívar-López S., Jiménez-Marín Á., Carvajal A., López-Bascón M.A., et al., Early Salmonella Typhimurium infection in pigs disrupts microbiome composition and functionality principally at the ileum mucosa, Sci. Rep. 8 (2018) 1–12. https://doi.org/10.1038/s41598-018-26083-3.
  68. 68. Chao A., Nonparametric estimation of the number of classes in a population, Scand. J. Stat. (1984).
  69. 69. Kim B.R., Shin J., Guevarra R.B., Lee J.H., Kim D.W., Seol K.H., et al., Deciphering diversity indices for a better understanding of microbial communities, J. Microbiol. Biotechnol. 27 (2017) 2089–2093. pmid:29032640
  70. 70. 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. 10 (2015). pmid:25688558