Taxonomic and functional microbiota changes in dysenteric colitis produced by Brachyspira hyodysenteriae in pigs

Taxonomic and functional microbiota changes in dysenteric colitis produced by Brachyspira hyodysenteriae in pigs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Taxonomic and functional microbiota changes in dysenteric colitis produced by Brachyspira hyodysenteriae in pigs Lucia Pérez-Pérez, Cristina Galisteo, Juan M. Ortiz Sanjuán, Jose F. Cobo-Díaz, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5979918/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The gut microbiota is essential for maintaining nutritional, physiological and immunological processes, but colonic infections such as swine dysentery, caused by Brachyspira hyodysenteriae ( B. hyo ) disrupt this homeostasis. This study uses shotgun and full-length 16S rRNA sequencing in faeces, colonic contents and mucosa from pigs challenged with B. hyo to provide a high-resolution characterisation of hte taxa, functions and metagenome-assembled genomes (MAGs) of interest, disclose their association with the primary pathogen and how they are affected by the pathological changes of the infection. Results Changes in the microbiota were associated with disease severity. In early infection, no major findings were observed in diversity or abundance analyses, whereas in acute infection, B. hyo load, mucosal neutrophil infiltration, epithelial ulceration and mucosal thickness were clearly associated with changes in microbiota ordination, which were also associated with a decrease in species richness. Changes included a significant increase in Acetivibrio ethanolgignens , Campylobacter hyointestinalis and Roseburia inulinivorans , which, with the exception of C. hyointestinalis , established themselves as part of the core microbiota and shifted the colonic enterotype in acutely infected animals. MAGs analyses revealed that no major virulence genes were detected in the genomes of the species co-interacting with B. hyo in acute infection. Similarly, functional changes were observed only after the onset of clinical signs, with an increase in functions related to inflammation and toxic effects on the colonic epithelium. Conclusions Our study shows that in colitis caused by B. hyo , changes in the microbiota are mainly a consequence of the lesions that occur in the intestine, with no differences observed in early infection. Similarly, the bacterial species that are increased at the onset of clinical signs may promote intestinal inflammation caused by B. hyo infection, but the analysis of their genomes rule out their participation in the primary infection. Swine dysentery Inflammatory disease Metagenomics Shotgun Gut Microbiome Intestine Spirochaetes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background The gastrointestinal tract of mammals is populated by microorganisms, predominantly bacteria, named plainly as microbiota, which are living in a homeostatic symbiosis with the host [1, 2]. This microbiota contributes to physiological processes such as nutrient metabolism, stimulation of immune response or protection against pathogens, thus impacting health and performance, particularly in livestock animals such as pigs [3–6]. The physiological conditions of each segment of the gastrointestinal tract define the microbial density and diversity in each specific location [2]. In this sense, the large intestine environment exhibits certain particularities. Complex saccharides are the main carbon source as most of the nutrients have already been digested and absorbed in the small intestine. These complex compounds are broken down into short chain fatty acids by saccharolytic and butyrate-producing bacteria [7]. Under physiological conditions, the metabolism of surface colonocytes is directed towards oxidative phosphorylation and fatty acid oxidation, resulting in high oxygen consumption which favours the colonisation by obligated anaerobic bacteria [8]. Another characteristic feature of the large intestine is the double mucus layer built by high molecular weight, heavily glycosylated proteins, which offers a permanent habitat to bacteria able to cleave and forage mucins [9]. Thus, in the large intestine, luminal and mucosa-associated microbiota differ [10–12]. The luminal microbiota, composed mainly of members of the families Veillonellaceae , Lachnospiraceae and Oscillospiraceae , is involved in the fermentation of undegraded dietary compounds, leading to products such as butyrate and others short-chain fatty acids, which are mostly absorbed by the host, constituting up to 30% of its energy intake [13–15]. On the other hand, mucosa-associated microbiota such as Pseudomonadaceae , Campylobacteraceae and Helicobacteraceae have the ability to attach to the mucin glycans, interacting directly with the host by regulating the immune system [16, 17]. The aforementioned physiological homeostasis of the large intestine may be disturbed by infectious pathogens which alter the structure and function of the hindgut. Colonic spirochaetosis are intestinal infections produced by different species of the genus Brachyspira in animals and humans. In this sense, Brachyspira hyodysenteriae (hereafter B. hyo ) is probably the most renown of these spirochaetes involved in a severe disease in pigs known as swine dysentery (SD) [18, 19]. This pathogen is able to infect the pig large intestine, eliciting a strong inflammation, necrosis of the epithelium and alteration of the mucin production and mucus layer structure [20, 21]. A number of recent publications have examined the pathogen-microbiota interaction in colonic spirochaetosis, mostly in SD, from different infection perspectives, sequencing and analytical approaches [11, 21–25]. Results from these studies allow to posit that B. hyo infection alters the microbiota and highlight certain taxa, with different level of taxonomic detail, which could side with the primary pathogen. In this study, we address, using a controlled challenge trial, a complete high-resolution characterisation of the microbiota at different stages of the B. hyo infection in targeted samples (intestinal location and infection timing). To this end, by combining shotgun metagenomics and third-generation 16S rRNA long-read sequencing in faecal, colonic and mucosa samples from pigs infected at early and acute SD, we have characterised taxonomic and functional changes prompted by the infection. Furthermore, we have mined targeted species genomes to explore their association to the primary pathogen and link the pathological alterations of the infection with microbiota composition. The overarching objective of this study was to furnish comprehensive data to elucidate the pathogenesis of SD and to contribute to the daunting research in colonic inflammatory diseases. Methods Experimental design and sample collection The experimental design is detailed in Pérez-Pérez et al. (2024) [20] and approved by the University of León Committee on Animal Care and Supply (OEBA-ULE-010-2020). Briefly, 32 seven-week-old crossbred (Landrace x Large-White x Pietrain) female pigs from a SD-free farm were divided into two equal groups in mirrored boxes (16 pigs each) in the biocontention facilities of the University of León (Spain). The pigs in the infection box were orally challenged for three consecutive days with 30 mL of broth containing 5 x 10 8 bacteria/mL of the collection strain B. hyo B-204 (ATCC 31212). Pigs had free-access to water and were fed ad-libitum with a commercial non-medicated pelleted diet for growers. Faecal samples were collected daily from all pigs by digital stimulation and stored at -80°C until DNA extraction. Half of the infected pigs ( n = 8) were euthanised 24 hours after the first q-PCR detection of B. hyo in faeces (named Early_inf). The remaining half of the infected pigs ( n = 8) were euthanised after two consecutive days of severe clinical disease evidenced by mucohaemorrhagic diarrhoea (named Acute_inf). Control pigs were euthanised and necropsied in parallel, one to one, with challenged pigs. During the necropsy, the colon contents were collected through an incision at the apex of the spiral colon and then the apex mucosa was removed and rinsed three times with sterile 1X phosphate-buffered saline (PBS). Both samples were snap-frozen with liquid nitrogen and stored at -80 ºC until processing. Data of relevance obtained in the B. hyo challenge [20], that is B. hyo concentration in faeces, colon content and colon mucosa estimated by q-PCR, ulceration score, neutrophil counts and mucosal thickness were included in this study (Additional file 1). DNA extraction, library preparation and sequencing Faecal samples (32) from 0 days post-inoculation (DPI), eight samples collected from Early_inf group pigs the day before the B. hyo shedding onset and other eight faecal samples from the Acute_inf group collected the day before the development of mucohaemorrhagic diarrhoea were processed. In addition, faeces, colon contents and mucosa were obtained from each necropsied animal (96 samples). Faeces and colon digesta were straight processed for DNA extraction using the QIAamp® PowerFecal® Pro DNA Kit (Qiagen, Germany), according to the manufacturer’s instructions. The frozen apex of the spiral colon was thawed, opened using a scalpel to reveal the mucosa and mucosal scraping was obtained using the blade of a sterile scalpel. Mucosal scrapings were pre-processed with 40 mg lysozyme per gram of sample during 1 hour at 37 ºC and then, DNA was extracted with DNeasy® Blood & Tissue Kit (Qiagen, Germany) according to the manufacturer’s instructions. DNA was quantified using the Qubit BR Assay (Thermo Fisher Scientific, United States) and subsequently stored at -80°C prior to downstream processing. Paired-end sequencing libraries, for whole metagenome sequencing, were prepared from the extracted DNA using the Illumina Nextera XT Library Preparation Kit (Illumina Inc., United States) and sequenced on Illumina NovaSeq 6000 platform (Novogene, United Kingdom) with 150 bp PE approach and 3 Gb/sample, according to the manufacturer’s instructions. Additionally, full-length 16S rRNA gene sequencing was performed for mucosa samples by third-generation sequencing using the Sequel II Sequencing Kit 2.0 (PacBio, United States) on the Sequel II PacBio system (FISABIO, Spain). Details of the sequencing protocol can be seen in Buetas et al. (2024) [26] Briefly, the 27F (AGRGTTYGATYMTGGCTCAG) and 1492R (RGYTACCTTGTTACGACTT) universal primers [27] were used to amplify the full-length 16S rRNA gene from the genomic DNA. Both the forward and reverse 16S rRNA gene primers were tailed with sample specific PacBio barcode sequences to allow for multiplexed sequencing for Multiplexed SMRTbell® Library Preparation and Sequencing protocol (Part Number 101–599–700 Version 04, PACBIO). Processing of raw reads and taxonomic and functional annotation of reads Adapter removal and quality trimming of raw reads was performed using TrimGalore v.0.6.0 with default parameters [28], a wrapper script for Cutadapt v.2.6 [29] and FastQC v.0.11.8 [30]. The human and pig reference genomes, GRCh38 and Scrofa 11.1, respectively, were used to remove contaminant reads using Bowtie2 v.2.3.4.3 [31] with default parameters. Resulting BAM files were processed using SAMtools v.1.9 [32] and converted to FastQ format using BEDTools v.2.27.1 [33]. Filtered reads were taxonomically assigned by MetaPhlAn 4 [34] with vOct22_CHOCOPhlAnSGB_202212 database and functional annotated by SUPER-FOCUS v.0.35 [35] using 90 cluster size database. Full 16S rRNA raw reads were processed using DADA2 v.1.8.0 [36] integrated in dada2 R-package [37] following the authors' tutorial. Firstly, removePrimers R-function was used to remove primer sequences and filterAndTrim was employed to filter reads with minQ = 3, minLen = 1000, maxLen = 16000, maxN = 0, maxEE = 2 paramaters. Amplicon Sequence Variant (ASV) table was obtained by comparing the filtered reads with SILVA database (silva_nr99_v138.1_wSpecies_train_set.fa.gz) [38] by using the assignTaxonomy command in dada2 package [37]. Binning and reconstruction of metagenome assembled genomes (MAGs) Each of the samples were independently subjected to de novo metagenomic assembly through metaSPAdes v.3.13 [39] using default parameters. Filtered reads were mapped against contigs higher than 1000 bp obtained from the same sample using Bowtie2 v.2.3.4.1 [40] with the --verysensitive-local parameter. The jgi_summarize_bam_contig_depths script, from MetaBAT2 v.2.12.1 [41], was used to calculate contigs depth values, recommended for per-sample contig binning based on tetranucleotide frequency, and binning was performed using MetaBAT2 v.2.12.1 and the option -m 1500 [41]. The quality of MAGs was estimated using the lineage_wf workflow of CheckM v.1.1.3 [42], to classify MAGs as high-quality MAGs (> 90% completeness, 50 completeness, < 5% contamination) for further analysis. MAGs taxonomical assignment was done by GTDB-tk [43] and release r220. Phylogenomic analysis was performed by GTDB-tk ( de_novo_wf option) v.2.4.0 tool using data version r220 [43] and based on BAC120 marker set. The topology was inferred with the maximum-likelihood algorithm implemented in FastTree v.2.1.11 [44], considering the Whelan and Goldman model for amino acid evolution [45]. Shimodaira-Hasegawa test [46] checked the robustness of the nodes. Closely related species to the MAGs included in GTDB database version r220 were added to the analysis. Graphical representation was performed using ggtree v.3.12.0 R package [47] and species names were edited with ggtext v.0.1.2 R package [48]. The 89 MAGs were annotated using Bakta v.1.9.3 software (database v.5.1) [49]. AMRFinderPlus v.3.12.80 [50], with database 2024-05-02.2, searched antimicrobial resistance genes as well as virulence factors and stress, heat and biocide resistance in nucleotic sequences. Results with identity value < 90% and alignment < 80% were filtered out. The pangenome of MAGs identified as same species was calculated using SuperPang v.1.3.0 tool [51]. Prodigal v.2.6.3 [52] predicted the proteome and the functional annotation was performed against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database utilizing BlastKOALA online tool [53]. Data was reordered with reshape v.0.8.9 R package [54] in order to plot it using graphical ggplot2 v.3.5.1 [55] and paletteer v.1.6.0 R packages [56]. Statistical analysis Analyses to study differences in microbiota composition and functionality between the variables “Group” (Control, Early_inf and Acute_inf), “Sample_type” (Faeces, Colon_content and Mucosa), “Euthanasia_day” (Before_day_25 and After_day_25), “Shedding_onset” (Before and After) and “Dysentery_onset” (Before and After) were carried out in R v.4.3.3 [57] (Additional file 1). Alpha and beta diversities were calculated from relative abundance data at both species and functional level using vegan v.2.6-4 R package [58] and plots were built using ggplot2 v.3.5.1 R package [55]. Alpha diversity was estimated by Richness, Shannon, Pielou and Simpson evenness indexes. Normality of each diversity index was assessed using Shapiro Wilk’s test from stats v.4.3.3 R package [57]. Alpha diversity indices were compared using one-way ANOVA, followed by pairwise comparisons with the Tukey test ( stats v.4.3.3 R package [57]) when the data were normally distributed; or Kruskal-Wallis followed by pairwise comparison with Wilcoxon test ( ggpubr v.0.6.0 R package [59]) when the data were not normally distributed. P-values were adjusted using Holm adjustment approach. Ordination of samples was performed using Principal Coordinates Analysis (PCoA) and Nonmetric Multidimensional Scaling (NMDS) of previously computed pairwise Bray-Curtis distances amongst samples. The effect of each variable on the ordination was determined using the envfit function from vegan R package [58]. The association between variables under study and species or functional composition were tested using permutational multivariate analysis of variance (PERMANOVA) using the adonis2 ( vegan v.2.6-4 R package [58]) and pairwise.adonis ( pairwiseAdonis v.0.4.1 R package [60]) functions, while intra-group dispersion was determined by estimating distances of each sample to its group centroid, calculated using function betadisper ( vegan v.2.6-4 R package [58]). Differences in dispersion were tested using the same statistical approach described for alpha diversity analysis. The influence of the 15 species and functions with the highest mean abundance, as well as other variables of interest on the ordination was evaluated by linear models fitting on the ordination results using the envfit function [58] and Benjamini-Hochberj (BH) as an adjustment method. To assess the influence of species pattern composition on variation of B. hyo concentration amongst samples, a smooth response curve of variable “[B. hyodysenteriae] (bact./g)” (concentration of bacterium per gram of faeces) was fitted on the ordination results using ordisurf function from vegan v.2.6-4 R package [58]. Using the Bray-Curtis distances of the species abundance, a hierarchical cluster analysis of the samples was performed using the Ward method ( stats v.4.3.3 R package [57]) and the top 25 species and functions with the highest mean abundance among the samples were represented [55]. Analysis of differential abundance at the species and function levels were performed using Linear models for Differential Abundance (LinDA) approach [61], included as function linda in the MicrobiomeStat v.1.2 R package [62]. Species and functions were filtered by excluding those with less than 3 non-zero values. Thereafter, for each “Sample_type”, differential abundance of features was compared across variables “Group”, “Shedding_onset” and “Dysentery_onset”, including “Euthanasia_day” as a co-variate for each model. Significant species and functions were reported with a BH-adjusted P-value < 0.05. The core microbiota for each level of variables “Group”, “Shedding_onset” and “Dysentery_onset” was estimated for each “Sample_type”. Parameters were set using a minimum threshold of 2% abundance in at least 70% of the samples of each group using the phyloseq v.1.46.0 and microbiome v.1.24.0 R packages [63, 64]. Venn diagrams were built using the venn function of the eulerr v.7.0.2 R package [65]. From the species composing the core microbiota with a detection threshold of 0.1% in faeces and colon and 0.01% in mucosa in at least 50% of the samples, community profiles in these three sample types were estimated using Dirichlet multinomial mixtures (DMM) models fitting [66] ( DirichletMultinomial v.1.44.0 R package [67]). Laplace approximation was used to evaluate DMM models fit, and the optimal number of components (clusters) was selected based on the lowest Laplace value. The main species driving differences between each community type were determined by selecting species with a cluster contribution values above the 80th percentile. The results obtained were visualised colouring samples in the ordination plots by cluster group and the differences on clusters were tested using a PERMANOVA analysis [58, 60]. Results Idiosyncrasy of Brachyspira hyodysenteriae infection model impacts on the microbiota global composition and functionality. The time lapse from challenge day until disease onset, either B. hyo detection in faeces or the onset of visual mucohaemorrhagic diarrhoea, ranged from 8 to 36 DPI (Fig. 1 A). This variability in the incubation period among challenged animals was considered both in the study design with paired necropsies of infected animals and non-infected controls and also in data analysis, with the inclusion of a categorical variable which considered the length of the infection in each monitored pig (named “Euthanasia_day”). Statistical analysis of sample ordination using the envfit function revealed that the “Euthanasia_day” variable had a significant influence on the ordination of species and/or microbiota functions regardless the ordination method used (PCoA or NMDS) (Additional file 2). Permutational multivariate analysis of variance confirmed differences among the levels of “Euthanasia_day” (Additional file 3), supported by a significant increase in Shannon index in samples After_day_25, regardless of “Sample_type” and richness index at the Mucosa (P < 0.05) (Additional files 4 and 5). The 25 most abundant species, accounting for 50%-60% of the total relative abundance, were plotted by “Sample_type” and “Euthanasia_day” (Fig. 1 B). Results confirmed that non-infected controls euthanised After_day_25 differed partially in the dominant species (i.e., Streptococcus alactolyticus and Prevotella sp. unclassified) compared to animals included in the group Before_day_25 (i.e., Lactobacillus amylovorus, Limosilactobacillus reuteri ). The 25 most abundant functions accounted for approximately 25% of the relative abundance in Faeces and Colon_content (Additional file 6). Interestingly and in contrast to the result observed in taxa analysis, only minor variations in major functions were observed in both Faeces and Colon_content, regardless of the “Euthanasia_day” variable. The colonic and faecal microbiota changes according to the infection severity Beta-diversity analyses evidenced as well the impact of the infection into the microbiota composition (Additional files 2 and 3). The influence of the infection, measured by the variable “Group”, was maintained in further analysis splitting samples by “Euthanasia_day” or “Sample_type” (Colon_content, Mucosa and Faeces) variables (Additional files 2 and 3). Indeed, differences in beta-diversity between the Acute_inf and any of the other two groups were consistent in Faeces and Colon_content for taxa and functions regardless of the euthanasia time-point. Only at the Mucosa, we observed no taxonomic differences in sample ordination between the acute and early-infection groups (P > 0.05). These changes observed in ordination analyses were associated to a decrease in species richness in Acute_inf group compared to Control and the Early_inf groups both in Faeces and Colon_content (Fig. 1 C and Additional file 7 A), again regardless of the “Euthanasia_day” (Additional files 7 B and C). Functional changes were negligible, just observing a decrease in richness, as well as an increase in the Pielou index in Acute_inf group compared to the other two groups in Colon_content samples. Ward clustering of Bray-Curtis distances amongst samples revealed that sample clustering by the variable of infection “Group” was more evident in samples After_day_25 regardless of the “Sample_type” (Fig. 1 B). It is worth noting the detection of Multidrug Resistance Efflux Pumps function in the Colon_contents of pigs euthanised After_day_25 (Additional file 6). Fine-tuned association of taxa relative abundance and levels of the infection “Group” was achieved by linear modelling analyses and their impact symbolized as vectors fitted in the PCoA (Fig. 2 ). Species vectors defining ordination of samples in Axis 1 were clearly associated to the infection “Group” variable. Indeed, the species defining the PCoA position of the Acute_inf group samples were also pointed out by LinDA analyses (Fig. 3 A and Additional file 8). Thus, we observed a significant increase of B. hyo and Acetivibrio ethanolgignens in Faeces, Colon_content and Mucosa (log 2 FC = 6.21, 10.52, 7.05; P < 0.0001 and log 2 FC = 5.98, 7.89, 9.74; P < 0.0001, respectively). Data analysis also revealed an increase of Campylobacter hyointestinalis in Faeces and Colon_content (log 2 FC = 7.46 and 9.70; P < 0.0001, respectively) and Roseburia inulinivorans in colonic Mucosa samples (log 2 FC = 6.72; P < 0.0001). All these taxa, except C. hyointestinalis , together with different species of the genus Prevotella and Alloprevotella were also identified as unique members of the core microbiota in Acute_inf pigs (Fig. 3 B and C). Early_inf pigs core microbiota was defined by Megasphaera elsdenii and L. reuteri while in non-infected animals, S. alactolyticus was predominant regardless of “Sample_type” (Fig. 3 B) and Butyricicoccus A porcorum as unique specie in Colon_content and Faeces of this group (Fig. 3 C). On the contrary, in the Acute_inf group there was a significant marked decrease in species such as Oliverpabstia intestinalis in Faeces samples (log 2 FC = -4.94; P < 0.0001), Faecousia sp900540635 in Colon_content samples (log 2 FC = -4.96; P < 0.0001) and Ruminococcus gauvreauii or Eubacterium hallii in Mucosa samples (log 2 FC = -4.59 and − 3.47; P < 0.0001). Analysis of functions abundance (Fig. 3 D and Additional file 8) revealed an increase of Alkanesulfonate assimilation or Tolerance to colicin E2 (log 2 FC = 0.88, 3.50; P < 0.001) and decrease of tRNA-dependent amino acid transfers (log 2 FC = -1.76; P < 0.001) in Acute_inf group Faeces compared to Control group. Otherwise, in the Colon_content, we observed an increase in Dot-Icm type IV secretion system (log 2 FC = 3.73; P < 0.0001) and the decrease in Cresol degradation (log 2 FC = -3.74; P < 0.0001). Linear models for differential abundance species or functions between Early_inf and Control groups (Additional file 8 and 9), revealed no major differences. Acute infection defines the microbiota profile beyond changes in specific species We further explored the presence of potential differences in microbial communities among the animals under study using DMM models fitting to find the optimal number of community types in each “Sample_type”. By this clustering analysis, three ( k = 3) distinct clusters were identified in the Mucosa and Colon_content, while two clusters represented the best model fit in the Faeces (Additional file 10). Visualization of these clusters in a NMDS as well as PERMANOVA pairwise tests revealed that microbiota composition was different upon these distinct clusters within the mucosal, colonic and faecal microbial communities (PERMANOVA, P < 0.01) (Figure 4 A and Additional file 11). Interestingly, most of the Colon_content and Mucosa samples from the Acute_inf group were allocated in cluster 3, while pigs from Early_inf and Control groups split in clusters 1 and 2. Cluster 3 enterotype was defined by the contribution of several species from the genus Prevotella in Colon_content and Campylobacter hyointestinalis, Tuzzerella sp. unclassified or Roseburia inulinivorans at the Mucosa (Figure 4 B). Biomarkers of gut microbiota changes during the infection process Further analyses were performed to assess the impact of the infection on the microbiota composition and functionality. Faecal samples collected from animals before the shedding onset and before clinical observation of mucus and haemorrhage, both 24 hours before, were compared with samples from the same animals at the shedding and mucohaemorrhagic diarrhoea onset respectively (“Shedding_onset” Before and After; “Dysentery_onset” Before and After variables). Initial B. hyo shedding varied from 5 x 10 3 bacteria/g to 8 x 10 6 bacteria/g faeces with soft faecal consistency. These observations did not correlate with significant alterations in alpha or beta diversity analyses neither with changes in abundance of taxa nor microbial functions. Limosilactobacillus reuteri and M. elsdenii were the main members of the core microbiota of the monitored animals (Figure 5, Additional file 12, 13 and 14). Core microbiota analyses revealed the loss of Agathobacter sp900549895, Prevotella sp900322035, Prevotella hominis or Prevotella sp002300055 and UBA1436 sp900540405 in the core microbiota when bacterial shedding occurred, and the emergence of CAG-349 sp003539515 (Figure 5 F). In contrast, the shift to mucohaemorrhagic diarrhoea (Figure 6) was associated to significant alterations of alpha diversity indexes with a decrease in the number of bacterial species present in the Faeces (Figure 6 A), fact observed in the core microbiota analyses, which revealed that only four taxa were shared with 1% of relative abundance in 50% of the samples (Figure 6 F) and a notable increase in Prevotella sp900546535, Prevotella sp000434975 and B. hyo , which were detected with an abundance of 2% in 70% of the samples (Figure 6 E). Together with B. hyo (log 2 FC = 9.50, P < 0.01), C. hyointestinalis (log 2 FC = 5.10, P < 0.01), A. ethanolgignens (log 2 FC = 7.00, P < 0.01) and several species of the genus Prevotella altered the ordination of samples after the mucohaemorrhagic diarrhoea onset, also with an increase of distance to the centroid (P < 0.05), which demonstrates a lower similarity of microbiota among acutely infected animals compared to a previous infection stage (Figure 6 B, C and D). This shift in species occurred together with altered functionality reflected by increased abundance of Tricarboxylate transport system and Tolerance to colicin E2 (log 2 FC = 1.59, 4.08; P < 0.01, respectively). Disclosing whether pathobionts or opportunistic commensals are involved in the acute phase of infection. We further analysed species of interest by the information provided by their MAGs (detailed in the MAGs obtained in the study can be further accessed in the Additional file 15). In total, 89 MAGs from A. ethanolgignens (7 MAGs), Alloprevotella sp004552155 (13 MAGs), C. hyointestinalis (4 MAGs), Prevotella copri clade A (2 MAGs), “Prevotella pectinovora” (9 MAGs), Prevotella sp900322035 (38 MAGs), Prevotella sp900546535 (15 MAGs) and R. inulinivorans (1 MAG) were further analysed in their taxonomy and functionality. Detailed information about the aforementioned MAGs is described in Additional file 16. Interestingly, MAGs related to species A. ethanolgignens, C. hyointestinalis and R. inulinivorans were reconstructed from Acute_inf and Early_inf samples, exclusively (Figure 7 A and Additional file 16). First, we observed discrepancies in the taxonomic classification of the specie A. ethanolgignens which in the GTDB database is annotated as “ Velocimicrobium ethanolgignens ” (Figure 7 A). The phylogenomic tree built based on GTDB-tk bacterial markers, BAC120 to establish the phylogenetic position (Additional file 17), revealed that the 7 MAGs identified as A. ethanolgignens shared a node with the type strain of this species, A. ethanolgignens ATCC 33324 T . This cluster showed a closer genomic relationship with the genus Velocimicrobium (family Lachnospiraceae ) than with the other species constituting the genus Acetivibrio (family Oscillospiraceae ) (Additional file 17 A). The four MAGs identified as C. hyointestinalis exhibited a closer relationship with C. hyointestinalis subsp. hyointestinalis CCUG 14169 T than with C. hyointestinalis subsp. lawsonii CHY5 T (Additional file 17 B). The MAGs identified as R. inulinivorans , Alloprevotella sp900546535 and members of the genus Prevotella cluster with the type strain of species of their respective species (Additional file 17 C and D ). We further mined the MAGs to search for putative virulence factors. No major virulence genes were detected in the MAGs of these species. Bacterial functions obtained from the panproteomes identified flagellar assembly genes and lack functions related to lipopolysaccharide biosynthesis in A. ethanolgignens which also harboured the macrolide efflux MFS transporter Mef(A) (identified in a high-quality MAG, 99.1% identity and 99.26% alignment coverage). Other genes detected found with 99.38, 98.12 and 100.00% identity (100.00% alignment coverage) were cfxA , tet(44) and cfr(E) . Flagellar assembly genes were also remarkable in C. hyointestinales and R. inulinivorans , whereas few genes related to motility were found in the other species studied ( Alloprevotella sp004552155, Prevotella copri clade A, “Prevotella pectinovora” , Prevotella sp900322035 and Prevotella sp900546535) (Figure 7 B). Virulence factors and antimicrobial resistance (AMR) genes were predicted for the MAGs under study. The gen nimJ (5’-nitroimidazole reductase) was found in 5 MAGs identified as Prevotella sp900546535 with 91.19% identity and 96.36% alignment coverage. Regarding A. ethanolgignens , one high-quality MAG presented 99.1% identity (99.26% alignment coverage) for macrolide efflux MFS transporter Mef(A). Besides, AMR genes were found with 99.38, 98.12 and 100% identity (100% alignment coverage), respectively, for one medium-quality MAG. Further details in the AMR genes detected in other species can be accessed in the Additional file 15. Discussion Knowledge in Brachyspira infections causing inflammatory disease in the hindgut is scarce compared to other enteric pathogens such as Salmonella spp. in different species or Citrobacter spp. in rodents, yet it is of particular interest to understand pathogen-microbiota interactions in infections specifically targeting the large intestine. Laboratory handling of the fastidious pathogens such as B. hyo [18] and difficulties in achieving successful experimental models, which unusually achieve a 100% of disease incidence [21, 68], are probably the two major obstacles to explore SD. Deliberately increased protein concentration in the diet is used to facilitate SD infection [69, 70] but the dysbiosis created alters the microbiota, biasing potential microbiome analyses. The alternative, a challenge without predisposed animals, increases variability in the incubation period, a hallmark already observed by previous studies [71, 72]. Variability in the incubation period and clinical disease implies changes in microbiota composition in the large intestine, stated in our study by an age-related increase in different species richness and diversity indexes and changes in the dominant microbial species, in consonance with previous studies [4, 73–75]. Thus, in our study, pigs with a short incubation period had an intestinal microbiota dominated by species of the Lactobacillaceae family, which are in highly abundance early after weaning [73, 76],while in animals with longer SD incubation, we observed a change in the dominant species to fibre degrader genera such as Prevotella , which are more efficient in the metabolism of complex carbohydrates present in post-weaning diets [75]. These taxonomic differences were not reflected by functional analyses which showed a ‘functional redundancy’ that helps maintain resilience and stability despite fluctuations [73, 77]. Considering the natural changes of the microbiota at the post-weaning period, at which the SD infection models are performed, our experimental design included paired euthanasia of challenged and control pigs. This approach enabled us to compare accurately infected animals with non-infected counterparts, buffering the biases elicited by physiological changes of the microbiota during this period of life [4, 78]. Ward clustering analysis revealed slight differences in the clustering of acutely infected pigs by the euthanasia time point with animals euthanised after day 25 exhibiting a closer clustering by their microbiota composition compared to animals euthanised before day 25 of infection. With ageing, the microbiota becomes more diverse yet more even [79], as indicated by the Pielou index and the distances to the centroid results across the samples under study. This probably leads to more homogeneous microbiota changes associated to the infection as reflected by this result. The severity of the disease in acute infection contrasts to the moderate shedding and low inflammation elicited during the early infection [20]. This early shedding, although in concentrations below 10 5 bacteria/g of faeces, reveals colonisation and multiplication of B. hyo in the hindgut at this stage. We analysed the impact of this infection stage on the microbiota composition with no major findings. Neither in microbiota composition by diversity analyses, nor by differential abundance or enterotype analyses, we observed differences between pigs euthanised at early infection and non-infected counterparts. The two main members of the core microbiota in this Early_inf group were Megasphaera elsdenii and Limosilactobacillus reuteri , two commensals which do not reveal any particular trait in the infection [80, 81]. The only outstanding result was associated to a loss of Agathobacter sp900549895 and the inclusion of CAG-349 sp003539515 in the core microbiota when the shedding of B. hyo in faeces began. The first microorganism is a beneficial gut commensal [82] while the last has been associated to intestinal disease [83], but this finding seems more circumstantial than B. hyo infection associated. Another study [22] has explored the changes in early infection with several findings. Differences in the design or moment at which samples were collected may explain the divergence observed. The mucohaemorrhagic diarrhoea characteristic of the acute SD is the result of an overstimulation of mucin production and colon epithelial necrosis which expose the capillary to the lumen. Likewise, microscopically strong inflammation in the mucosa is evident, as indicated by significant neutrophil migration and thickening of the colon mucosa [20, 21]. For first time in SD microbiota analyses, we explored the association of these features of SD and their association to the microbiota ordination. Thus, the inclusion of vectors for the variables neutrophil counts, B. hyo concentration and to a lower extent mucosal thickness and ulceration score demonstrated their influence in the PCoA ordination of acutely infected pigs samples. Colon inflammation and colonocyte ulceration prompt the proliferation of undifferentiated cells at the crypts which turns to mucosal hyperplasia (thickness). Together with the inflammation, this process shifts the colonocyte metabolism towards aerobic glycolysis which ends in the alteration of the microbiota-colonocyte homeostasis [8]. As a consequence, species richness in faeces and colon content decreased significantly with the onset of clinical signs [11], fact already reported by other studies with enteropathogens [84], but not in mucosa [21], probably due to the lower species diversity in these samples [85]. Indeed, the change was so abrupt, that colon content and mucosa samples from acutely infected pigs were clustered in a different enterotype. The results emphasize the usefulness of including different targets (colon digesta, faeces and mucosa in our study) to obtain a complete picture of the microbiome changes, considering the particularities of the microbiota in each location. The functional differences identified in the Acute_inf group compared to the Control highlight the role of the microbiota in the disease. Among the altered functions, we observed metabolic routes previously described in inflammatory bowel diseases (i.e., Crohn's disease) [89], such as increased sulphate metabolism that inhibits cytochrome c oxidase catalysing the phosphorylation of ADP to ATP [86–88] or decreased aminoacyl-tRNA synthesis. Furthermore, functions which may help to the primary infection were evidenced. Thus, we observed an increased abundance of processes linked to the fermentation of mucins, that increase the levels of products, such as cresol, which have toxic effects on the colonic epithelium [88]. Also increased antimicrobial production driven by inflammation [90], or the detection of virulence genes such as type IV secretion systems (i.e., associate to genus such as Campylobacter ) [91, 92] are examples of this sort of functions. This window opportunity in acutely infected pigs allowed opportunistic species to increase in abundance. As in previous studies [11, 21], we observed an increase in relative abundance of Acetivibrio ethanolgignens and Campylobacter hyointestinalis in acute SD. Acetivibrio ethanolgignens has been previously noticed in the colon of pigs with clinical SD [21, 93, 94]. We first spotlight by genomic analyses this bacterium. First, we noticed by phylogenomic analysis that both the reference strain A. ethanolgignens ATCC 33324 T and 7 MAGs from this study fit within the family Lachnospiraceae instead of within the family Oscillospiraceae (genus Acetivibrio ) (Additional file 17 A). GTDB database version r220 even renames it as “ Velocimicrobium ethanolgignens ”. However, a complete taxonomic study based on phylogenetic, phylogenomic, phenotypic and chemotaxonomic analysis should be performed to determine whether or not A. ethanolgignens is a member of the genus Velocimicrobium or it constitutes a different genus. We also evaluated its potential role in the disease development. Acetivibrio ethanolgignens has been considered a pro-inflammatory bacterium due to its association to alterations in the immune system [95, 96]. Despite virulence factors were not detected in any of the studied MAGs, it is noteworthy to mention the presence of an oligopeptide ABC transporter (i.e., OppABCDF), identified in the 7 A. ethanolgignens MAGs. Although this oligopeptide permease is involved in different parts of the cell metabolism in prokaryotes, it has also been reported to increase the production of proinflammatory cytokines IL-1β, IL-6 and TNF, inducing the apoptosis of macrophages [97]. Consequently, A. ethanolgignens could be promoting the intestinal inflammation triggered by B. hyo infection, though it does not appear to play a direct role in the primary infection. This idea aligns with the results of co-inoculation experiments of A. ethanolgignens and B. hyo in gnotobiotic pigs performed four decades ago [94]. Our analysis also detailed the taxonomy of Campylobacter species associated to infected animals. The MAGs identified as C. hyointestinalis could not be further typed up to subspecies level. According to the phylogenetic tree, they could be closer to C. hyointestinalis subsp. hyointestinalis (Additional file 17 B) than to C. hyointestinalis subsp. lawsonii but the result is not robust enough as it is based only on 120 bacterial markers (BAC120). Both subspecies are considered emerging zoonotic pathogens. C. hyointestinalis subsp. hyointestinalis has a broader host range (including cattle and humans), whereas C. hyointestinalis subsp. lawsonii is mostly isolated in pigs [98–101]. Previous studies have suggested both subspecies sharing the same niche [98, 102]. Hence, we could conclude that the population of C. hyointestinalis inhabiting the digestive system of pigs infected with B . hyo in the present study have a role in the pathogenesis. On the other hand, tet(44) , cfxA and cfr(E) antimicrobial resistance genes detected in one of the MAGs identified as C. hyointestinalis , as previously reported in this specie [103]. Roseburia inulinivorans and different Prevotella species also exhibited increased relative abundance. The increase of these microorganisms which take part of the commensal intestinal microbiota of pigs [104], may be an artefact but the fact that other studies report also their increase in SD [21] or other inflammatory enteric diseases [105] show how these bacteria take advantage of the new metabolic or mucin context, foraging released mucins or simply migrating from the lumen to the mucosa due to the increase permeability of the mucin layer [9]. Conclusions In conclusion, our study reveals that, the changes observed in the gut microbiota in SD are mainly a consequence of the lesions that occur in the intestine and allow opportunistic species to increase their abundance, localising the changes mainly in the contents of the colon and mucosa where the microbiota exhibited a different enterotype. These opportunistic species could promote the intestinal inflammation caused by B. hyo infection, but according to their genetic background do not seem to participate in the primary infection. No differences were observed due to the establishment and proliferation of B. hyo , as no changes were observed until clinical signs appeared. Our research sheds light on the complex interplay between the pathogen and the gut microbiota that is essential to improve our understanding of the pathogenesis of the disease. Finally, the data offer relevant information to be gathered with other colitis models, including non-infectious colitis in humans. Abbreviations AIC Akaike's Information Criterion AMR Antimicrobial resistance ASV Amplicon Sequence Variant BH Benjamini-Hochberj B. hyo Brachyspira hyodysenteriae DMM Dirichlet multinomial mixtures DPI Days post-inoculation FC Fold Change IL Interleukin KEGG Kyoto Encyclopedia of Genes and Genomes LinDA Linear models for Differential Abundance MAGs Metagenome assembled genomes NMDS Nonmetric Multidimensional Scaling PBS Phosphate-buffered saline PCoA Principal Coordinates Analysis PERMANOVA permutational multivariate analysis of variance SD Swine dysentery TNF Tumor Necrosis Factor. Declarations Ethics approval and consent to participate All procedures were approved by the University of León Committee on Animal Care and Supply (OEBA-ULE-010-2020). Consent for publication Not applicable. Availability of data and materials The metagenomic sequences, MAGs, as well as its associated metadata have been submitted to NCBI Sequence Read Archive (SRA) and will be available under BioProject accession PRJNA1214037: https://www.ncbi.nlm.nih.gov/sra/PRJNA1214037 Competing interests The authors declare that they have no conflicts of interest. Funding This work was performed under the frame of the project SDyMiHO funded by the Spanish Ministerio de Ciencia e Innovación (ref PID2019-110662RB-I00). Authors' contributions HA, AC and PR conceived and supervised the study. LPP, HA, AC and HP performed the challenge and collected the samples. LPP, HA and HP processed the samples. LPP, CG, JMOS and JFCD analysed the data and prepared the figures. LPP, CG, HA, AC and JFCD reviewed the literature and wrote the manuscript. All authors have read and approved the final manuscript. 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Pangenome analysis reveals genetic isolation in Campylobacter hyointestinalis subspecies adapted to different mammalian hosts. Sci Rep. 2021;11:3431. doi:10.1038/s41598-021-82993-9 Oporto B, Hurtado A. Emerging Thermotolerant Campylobacter Species in Healthy Ruminants and Swine. Foodborne Pathog Dis. 2011;8:7:807–813. doi:10.1089/fpd.2010.0803 Harrington CS, On SLW. Extensive 16s rRNA gene sequence diversity in Campylobacter hyointestinalis strains: taxonomic and applied implications. Int J Syst Bacteriol. 1999;49:1171–1175. doi:10.1099/00207713-49-3-1171 On SLW, Bloch B, Holmes B, Hoste B, Vandamme P. Campylobacter hyointestinalis subsp. lawsonii subsp. nov., Isolated from the Porcine Stomach, and an Emended Description of Campylobacter hyointestinalis . Int J Syst Bacteriol. 1995;45:4:767–774. doi:10.1099/00207713-45-4-767. Wilkinson DA, O’Donnell AJ, Akhter RN, Fayaz A, MacK HJ, Rogers LE, et al. Updating the genomic taxonomy and epidemiology of Campylobacter hyointestinalis . Sci Rep. 2018;8:2393. doi:10.1038/s41598-018-20889-x Inglis GD, McAllister TA, Busz HW, Yanke LJ, Morck DW, Olson ME, et al. Effects of Subtherapeutic Administration of Antimicrobial Agents to Beef Cattle on the Prevalence of Antimicrobial Resistance in Campylobacter jejuni and Campylobacter hyointestinalis . Appl Environ Microbiol. 2005;71:7:3872–3881. doi:10.1128/AEM.71.7.3872-3881.2005 Holman DB, Brunelle BW, Trachsel J, Allen HK. Meta-analysis To Define a Core Microbiota in the Swine Gut. mSystems. 2017;2:3:e00004-17. doi:10.1128/msystems.00004-17 Yang Q, Huang X, Zhao S, Sun W, Yan Z, Wang P, et al. Structure and Function of the Fecal Microbiota in Diarrheic Neonatal Piglets. Front Microbiol. 2017;8:502. doi:10.3389/fmicb.2017.00502 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5979918","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428574220,"identity":"5a008b35-2134-4354-aba4-e9aea1007e84","order_by":0,"name":"Lucia Pérez-Pérez","email":"","orcid":"","institution":"Universidad de León, España","correspondingAuthor":false,"prefix":"","firstName":"Lucia","middleName":"","lastName":"Pérez-Pérez","suffix":""},{"id":428574221,"identity":"91504c1a-3d5d-41ae-ba46-e2d55b355da8","order_by":1,"name":"Cristina Galisteo","email":"","orcid":"","institution":"Universidad de León, España","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Galisteo","suffix":""},{"id":428574222,"identity":"18018d6c-22c1-444f-962c-ac5a02318261","order_by":2,"name":"Juan M. Ortiz Sanjuán","email":"","orcid":"","institution":"Teagasc - The Irish Agriculture and Food Development Authority","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"M. Ortiz","lastName":"Sanjuán","suffix":""},{"id":428574224,"identity":"520a8db1-f4c1-46d1-8224-43212db82bd5","order_by":3,"name":"Jose F. Cobo-Díaz","email":"","orcid":"","institution":"Universidad de León, España","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"F.","lastName":"Cobo-Díaz","suffix":""},{"id":428574226,"identity":"8e7c3f32-8b67-4f76-bdc2-6bc486c86ea7","order_by":4,"name":"Hector Puente","email":"","orcid":"","institution":"Universidad de León, España","correspondingAuthor":false,"prefix":"","firstName":"Hector","middleName":"","lastName":"Puente","suffix":""},{"id":428574228,"identity":"f1a1c0b2-4b62-4204-abd0-c27231ac45c3","order_by":5,"name":"Pedro Rubio","email":"","orcid":"","institution":"Universidad de León, España","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Rubio","suffix":""},{"id":428574229,"identity":"e9166005-5a2e-4dce-bf4a-b860bdc13aac","order_by":6,"name":"Ana Carvajal","email":"","orcid":"","institution":"Universidad de León, España","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Carvajal","suffix":""},{"id":428574230,"identity":"48e64d80-4bd5-429f-a195-bc340b50a6dd","order_by":7,"name":"Hector Arguello","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBACAxTegwIGOQYGxsYDBLQwNsB5CQYMxiAB0rQkgnh4tZizNz9/8HEPg7x5/xrDBwkGNukbbjc3HPhQcYeBnx+7TsueY4aNM54xGM658cbYIMEgLXfDnYMNB2ececYgOSMBu8Nu5DA28xxgYJwhccZMIsHgcO6GG4kNh3nbDgOlcPjl/huwFnugFvMfQC3pBiAtf4Fa7M9jd5jBDR6wlsQZ/D1mQO8fTgBrYQTZwoDDYWfSDGfOOCCRPEOCrRjoMCAPqOVgz5nDPBI3cGg5fvjBhw8HbGxn8B/e+OFDhY083430hw9+VByW4+/H7jAokAAiNDN58KmHAByxMApGwSgYBaOAAQA+rWp2eeN6rgAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad de León, España","correspondingAuthor":true,"prefix":"","firstName":"Hector","middleName":"","lastName":"Arguello","suffix":""}],"badges":[],"createdAt":"2025-02-07 09:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5979918/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5979918/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78637193,"identity":"3e88b436-ddbf-42a2-b0de-e6729f399838","added_by":"auto","created_at":"2025-03-17 05:36:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":785705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design, relative species abundance and microbiota alpha-diversity in Faeces, Colon_content and Mucosa. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Experimental design and samplings performed throughout the experiment. (\u003cstrong\u003eB\u003c/strong\u003e) Relative abundance of the main species in Faeces, Colon_content and Mucosa samples from pigs euthanised Before_day_25 and After_day_25. Samples were ordered regarding its specie composition by Ward clustering of the Bray-Curtis distances between samples. The similarity between the different samples was represented by a dendrogram at the bottom of each figure. Species were ranked by abundance, in decreasing order. (\u003cstrong\u003eC\u003c/strong\u003e) Results of richness, Shannon, Pielou and Simpson index at species composition and functional level by “Group” ns P \u0026gt; 0.05, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001, **** P ≤ 0.0001 (Wilcoxon test or ANOVA and Tukey tests, with Holm adjustment). \u003cem\u003eAcetivibrio ethanolgingens\u003c/em\u003e was annotated in the database as\u003cem\u003e Velicimicorbium ethanolgingens\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/2d7b60cf97eb4ba0f6ec3d1e.jpg"},{"id":78636681,"identity":"4a207c21-a34a-4fc0-b58f-1624dfa9540b","added_by":"auto","created_at":"2025-03-17 05:28:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":396583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of microbiota species beta-diversity by “Group” and “Euthanasia_day”. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) PCoA ordination using Bray-Curtis distances at species of the Faeces, (\u003cstrong\u003eB\u003c/strong\u003e) Colon_content and (\u003cstrong\u003eC\u003c/strong\u003e) Mucosal samples. Boxplots indicate the distance to the centroid of each sample per “Group” variable and within each “Group” according to the “Euthanasia_day”. ns P \u0026gt; 0.05, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001 (Wilcoxon test or ANOVA and Tukey tests, with Holm adjustment). Ellipses represent covariance for each “Group” according to the “Euthanasia_day”. The 30 species with the highest mean abundance are represented by blue arrows which pinpoint the sample ordination. Purple arrows pinpoint SD infection variables onto the ordination of samples (by “envfit” model) with a significant BH-adjusted P-value. The length of the arrow represents the strength of influence on the ordination (proportional to the R\u003csup\u003e2\u003c/sup\u003e statistic by the “envfit” model). \u003cem\u003eB. hyo\u003c/em\u003e concentration in Colon_content was represented as a surface area making the size of the samples proportional to the value of the variable. \u003cem\u003eAcetivibrio ethanolgingens\u003c/em\u003e was annotated in the database as\u003cem\u003e Velicimicorbium ethanolgingens\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/af99d6ff4d70e8624f94762a.jpg"},{"id":78636676,"identity":"62c83d9c-6f9c-4079-b561-32f204ce0955","added_by":"auto","created_at":"2025-03-17 05:28:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":741393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of differential abundance of species and functions and core microbiome in Faeces, Colon_content and Mucosa. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Species significantly increased or decreased (P \u0026lt; 0.001, log2FoldChange \u0026gt; 2; BH-adjusted P-value) in the Acute_inf group compared to Control in Faeces, Colon_content and Mucosa. (\u003cstrong\u003eB\u003c/strong\u003e) Heatmap of the core microbiota species of each “Group” in each “Sample_type”. The core microbiota was defined by species with a detection threshold of 1% abundance in at least 50% of the samples from each group. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagram showing the shared and exclusive species of the core microbiota of each “Group” in the different “Sample_type”. (\u003cstrong\u003eD\u003c/strong\u003e) Functions significantly increased or decreased (P \u0026lt; 0.001, log2FoldChange \u0026gt; 1; BH-adjusted P-value) in the Acute_inf group compared to Control in Faeces and Colon_content. \u003cem\u003eAcetivibrio ethanolgingens\u003c/em\u003e was annotated in the database as\u003cem\u003eVelicimicorbium ethanolgingens\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/7780ef838f38105243848a9b.jpg"},{"id":78636673,"identity":"42c0546c-dc5f-4348-adf0-9bf49b6b25ad","added_by":"auto","created_at":"2025-03-17 05:28:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":401138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClusters of the core microbial community in Faeces, Colon_content and Mucosa.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Nonmetric multidimensional scaling (NMDS) ordination of samples of each “Group” within each DMM cluster. (\u003cstrong\u003eB\u003c/strong\u003e) Contribution of the main species of the core microbiota to each cluster.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/ab3e8fd278bca1b1171bd791.jpg"},{"id":78637194,"identity":"666301ac-8f23-4fd4-938b-6231d684455c","added_by":"auto","created_at":"2025-03-17 05:36:20","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":616371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of microbiota Before and After \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBrachyspira hyodysenteriae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e “Shedding_onset”. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Microbiota alpha-diversity. Results of richness, Shannon, Pielou and Simpson index at species composition and functional level of the faecal samples. (\u003cstrong\u003eB\u003c/strong\u003e) Relative abundance of the main species in faecal samples. Species were ranked by abundance in decreasing order. (\u003cstrong\u003eC\u003c/strong\u003e) Microbiota beta-diversity. PCoA ordination using Bray-Curtis distances at species and functions in faecal samples. Ellipses represent covariance for each group according to the “Shedding_onset” and “Euthanasia_day”. Boxplot in the upper right corner of each paned indicate the distance to centroid of each sample by “Shedding_onset”. Arrows indicate the 30 species and functions with the highest mean abundance influencing sample ordination (by “envfit” model) with a significant BH-adjusted P-value. The length of the arrow represents the strength of each specie or function influence on the ordination (proportional to the R\u003csup\u003e2\u003c/sup\u003e statistic by the “envfit” model). ns P \u0026gt; 0.05, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001 (Wilcoxon test or ANOVA and Tukey tests, with Holm adjustment) (\u003cstrong\u003eD\u003c/strong\u003e) Species and functions significantly increased or decreased (P \u0026lt; 0.05, log2FoldChange \u0026gt; 1; BH-adjusted P-value) After \u003cem\u003eB. hyo\u003c/em\u003e “Shedding_onset”. (\u003cstrong\u003eE\u003c/strong\u003e) Heatmap of the core microbiota species of faeces Before and After \u003cem\u003eB. hyo \u003c/em\u003e“Shedding_onset”. (\u003cstrong\u003eF\u003c/strong\u003e) Venn diagram showing the shared and exclusive species of the core microbiota of each group.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/6da0d53599c5f94582cc31d7.jpg"},{"id":78638915,"identity":"c09ce1f3-75eb-41fe-92e6-6b9a0bd647bb","added_by":"auto","created_at":"2025-03-17 06:00:20","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":694980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of microbiota Before and After mucohaemorrhagic diarrhoea onset (“Dysentery_onset”). \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Microbiota alpha-diversity. Results of richness, Shannon, Pielou and Simpson index at species composition and functional level of the faecal samples. (\u003cstrong\u003eB\u003c/strong\u003e) Relative abundance of the main species in faecal samples. Species were ranked by abundance in decreasing order. (\u003cstrong\u003eC\u003c/strong\u003e) Microbiota beta-diversity. PCoA ordination using Bray-Curtis distances at species and functions in faecal samples. Ellipses represent covariance for each group according to the “Dysentery_onset” and “Euthanasia_day”. Boxplot in the upper right corner of each panel indicate the distance to centroid of each sample by “Dysentery_onset”. ns P \u0026gt; 0.05, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001 (Wilcoxon test or ANOVA and Tukey tests, with Holm adjustment). Arrows indicate the 30 species and functions with the highest mean abundance influencing sample ordination (by “envfit” model) with a significant BH-adjusted P-value. The length of the arrow represents the strength of each specie or function influence on the ordination (proportional to the R\u003csup\u003e2\u003c/sup\u003e statistic by the “envfit” model). (\u003cstrong\u003eD\u003c/strong\u003e) Species and functions significantly increased or decreased (P \u0026lt; 0.05 and P \u0026lt; 0.02 respectively, log2FoldChange \u0026gt;\u0026nbsp;1; BH-adjusted P-value) After “Dysentery_onset”. (\u003cstrong\u003eE\u003c/strong\u003e) Heatmap of the core microbiota species of faeces Before and After\u003cem\u003e \u003c/em\u003e“Dysentery_onset”. (\u003cstrong\u003eF\u003c/strong\u003e) Venn diagram showing the shared and exclusive species of the core microbiota of each group. \u003cem\u003eAcetivibrio ethanolgingens\u003c/em\u003e was annotated in the database as\u003cem\u003e Velicimicorbium ethanolgingens\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/bc9535fb1addad6109da7c53.jpg"},{"id":78637197,"identity":"d91a49a8-594f-4fc2-84b1-54d09d5ee605","added_by":"auto","created_at":"2025-03-17 05:36:20","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":600209,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary results of the metagenome assembled genomes (MAGs) in this study. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Medium and high-quality MAGs identified to species level in each “Group” variable.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eB\u003c/strong\u003e) KEGG annotations in pangenomes\u003cstrong\u003e.\u003c/strong\u003e Number of identified KO for the 40 most abundant KEGG modules in the pangenome of MAGs identified as \u003cem\u003eAcetivibrio ethanolgignensis\u003c/em\u003e, \u003cem\u003eCampylobacter hyointestinalis\u003c/em\u003e, \u003cem\u003eAlloprevotella\u003c/em\u003esp004552155, \u003cem\u003ePrevotella copri\u003c/em\u003e clade A, \u003cem\u003e“Prevotella pectinovora”\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003esp900322035, \u003cem\u003ePrevotella\u003c/em\u003e sp900546535 and \u003cem\u003eRoseburia inulinivorans\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/8a9a6174935f12b485e1537d.jpg"},{"id":79341486,"identity":"b14e0fd4-3db7-473c-869a-3fc2bf10a3fd","added_by":"auto","created_at":"2025-03-27 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05:36:20","extension":"csv","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":8258,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile16.csv","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/25235228ede38054a72b91b9.csv"},{"id":78636718,"identity":"3af0ca86-31c1-4d89-ac41-3d6b0fa3db63","added_by":"auto","created_at":"2025-03-17 05:28:21","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":12047491,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile17.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/1ec89e4eb11cbdb12119704d.pdf"},{"id":78636698,"identity":"284479df-ef8e-4f67-b163-26ac8936e5ac","added_by":"auto","created_at":"2025-03-17 05:28:20","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":15901,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfileslegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-5979918/v1/c9f79b09f82d4b2ca5141658.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Taxonomic and functional microbiota changes in dysenteric colitis produced by Brachyspira hyodysenteriae in pigs","fulltext":[{"header":"Background","content":"\u003cp\u003eThe gastrointestinal tract of mammals is populated by microorganisms, predominantly bacteria, named plainly as microbiota, which are living in a homeostatic symbiosis with the host [1, 2]. This microbiota contributes to physiological processes such as nutrient metabolism, stimulation of immune response or protection against pathogens, thus impacting health and performance, particularly in livestock animals such as pigs [3\u0026ndash;6].\u003c/p\u003e \u003cp\u003eThe physiological conditions of each segment of the gastrointestinal tract define the microbial density and diversity in each specific location [2]. In this sense, the large intestine environment exhibits certain particularities. Complex saccharides are the main carbon source as most of the nutrients have already been digested and absorbed in the small intestine. These complex compounds are broken down into short chain fatty acids by saccharolytic and butyrate-producing bacteria [7]. Under physiological conditions, the metabolism of surface colonocytes is directed towards oxidative phosphorylation and fatty acid oxidation, resulting in high oxygen consumption which favours the colonisation by obligated anaerobic bacteria [8]. Another characteristic feature of the large intestine is the double mucus layer built by high molecular weight, heavily glycosylated proteins, which offers a permanent habitat to bacteria able to cleave and forage mucins [9]. Thus, in the large intestine, luminal and mucosa-associated microbiota differ [10\u0026ndash;12]. The luminal microbiota, composed mainly of members of the families \u003cem\u003eVeillonellaceae\u003c/em\u003e, \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eOscillospiraceae\u003c/em\u003e, is involved in the fermentation of undegraded dietary compounds, leading to products such as butyrate and others short-chain fatty acids, which are mostly absorbed by the host, constituting up to 30% of its energy intake [13\u0026ndash;15]. On the other hand, mucosa-associated microbiota such as \u003cem\u003ePseudomonadaceae\u003c/em\u003e, \u003cem\u003eCampylobacteraceae\u003c/em\u003e and \u003cem\u003eHelicobacteraceae\u003c/em\u003e have the ability to attach to the mucin glycans, interacting directly with the host by regulating the immune system [16, 17].\u003c/p\u003e \u003cp\u003eThe aforementioned physiological homeostasis of the large intestine may be disturbed by infectious pathogens which alter the structure and function of the hindgut. Colonic spirochaetosis are intestinal infections produced by different species of the genus \u003cem\u003eBrachyspira\u003c/em\u003e in animals and humans. In this sense, \u003cem\u003eBrachyspira hyodysenteriae\u003c/em\u003e (hereafter \u003cem\u003eB. hyo\u003c/em\u003e) is probably the most renown of these spirochaetes involved in a severe disease in pigs known as swine dysentery (SD) [18, 19]. This pathogen is able to infect the pig large intestine, eliciting a strong inflammation, necrosis of the epithelium and alteration of the mucin production and mucus layer structure [20, 21]. A number of recent publications have examined the pathogen-microbiota interaction in colonic spirochaetosis, mostly in SD, from different infection perspectives, sequencing and analytical approaches [11, 21\u0026ndash;25]. Results from these studies allow to posit that \u003cem\u003eB. hyo\u003c/em\u003e infection alters the microbiota and highlight certain taxa, with different level of taxonomic detail, which could side with the primary pathogen.\u003c/p\u003e \u003cp\u003eIn this study, we address, using a controlled challenge trial, a complete high-resolution characterisation of the microbiota at different stages of the \u003cem\u003eB. hyo\u003c/em\u003e infection in targeted samples (intestinal location and infection timing). To this end, by combining shotgun metagenomics and third-generation \u003cem\u003e16S rRNA\u003c/em\u003e long-read sequencing in faecal, colonic and mucosa samples from pigs infected at early and acute SD, we have characterised taxonomic and functional changes prompted by the infection. Furthermore, we have mined targeted species genomes to explore their association to the primary pathogen and link the pathological alterations of the infection with microbiota composition. The overarching objective of this study was to furnish comprehensive data to elucidate the pathogenesis of SD and to contribute to the daunting research in colonic inflammatory diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design and sample collection\u003c/h2\u003e \u003cp\u003eThe experimental design is detailed in P\u0026eacute;rez-P\u0026eacute;rez et al. (2024) [20] and approved by the University of Le\u0026oacute;n Committee on Animal Care and Supply (OEBA-ULE-010-2020). Briefly, 32 seven-week-old crossbred (Landrace x Large-White x Pietrain) female pigs from a SD-free farm were divided into two equal groups in mirrored boxes (16 pigs each) in the biocontention facilities of the University of Le\u0026oacute;n (Spain). The pigs in the infection box were orally challenged for three consecutive days with 30 mL of broth containing 5 x 10\u003csup\u003e8\u003c/sup\u003e bacteria/mL of the collection strain \u003cem\u003eB. hyo\u003c/em\u003e B-204 (ATCC 31212). Pigs had free-access to water and were fed \u003cem\u003ead-libitum\u003c/em\u003e with a commercial non-medicated pelleted diet for growers. Faecal samples were collected daily from all pigs by digital stimulation and stored at -80\u0026deg;C until DNA extraction. Half of the infected pigs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) were euthanised 24 hours after the first q-PCR detection of \u003cem\u003eB. hyo\u003c/em\u003e in faeces (named Early_inf). The remaining half of the infected pigs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) were euthanised after two consecutive days of severe clinical disease evidenced by mucohaemorrhagic diarrhoea (named Acute_inf). Control pigs were euthanised and necropsied in parallel, one to one, with challenged pigs. During the necropsy, the colon contents were collected through an incision at the apex of the spiral colon and then the apex mucosa was removed and rinsed three times with sterile 1X phosphate-buffered saline (PBS). Both samples were snap-frozen with liquid nitrogen and stored at -80 \u0026ordm;C until processing.\u003c/p\u003e \u003cp\u003eData of relevance obtained in the \u003cem\u003eB. hyo\u003c/em\u003e challenge [20], that is \u003cem\u003eB. hyo\u003c/em\u003e concentration in faeces, colon content and colon mucosa estimated by q-PCR, ulceration score, neutrophil counts and mucosal thickness were included in this study (Additional file 1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction, library preparation and sequencing\u003c/h3\u003e\n\u003cp\u003eFaecal samples (32) from 0 days post-inoculation (DPI), eight samples collected from Early_inf group pigs the day before the \u003cem\u003eB. hyo\u003c/em\u003e shedding onset and other eight faecal samples from the Acute_inf group collected the day before the development of mucohaemorrhagic diarrhoea were processed. In addition, faeces, colon contents and mucosa were obtained from each necropsied animal (96 samples). Faeces and colon digesta were straight processed for DNA extraction using the QIAamp\u0026reg; PowerFecal\u0026reg; Pro DNA Kit (Qiagen, Germany), according to the manufacturer\u0026rsquo;s instructions. The frozen apex of the spiral colon was thawed, opened using a scalpel to reveal the mucosa and mucosal scraping was obtained using the blade of a sterile scalpel. Mucosal scrapings were pre-processed with 40 mg lysozyme per gram of sample during 1 hour at 37 \u0026ordm;C and then, DNA was extracted with DNeasy\u0026reg; Blood \u0026amp; Tissue Kit (Qiagen, Germany) according to the manufacturer\u0026rsquo;s instructions. DNA was quantified using the Qubit BR Assay (Thermo Fisher Scientific, United States) and subsequently stored at -80\u0026deg;C prior to downstream processing. Paired-end sequencing libraries, for whole metagenome sequencing, were prepared from the extracted DNA using the Illumina Nextera XT Library Preparation Kit (Illumina Inc., United States) and sequenced on Illumina NovaSeq 6000 platform (Novogene, United Kingdom) with 150 bp PE approach and 3 Gb/sample, according to the manufacturer\u0026rsquo;s instructions. Additionally, full-length \u003cem\u003e16S rRNA\u003c/em\u003e gene sequencing was performed for mucosa samples by third-generation sequencing using the Sequel II Sequencing Kit 2.0 (PacBio, United States) on the Sequel II PacBio system (FISABIO, Spain). Details of the sequencing protocol can be seen in Buetas et al. (2024) [26] Briefly, the 27F (AGRGTTYGATYMTGGCTCAG) and 1492R (RGYTACCTTGTTACGACTT) universal primers [27] were used to amplify the full-length \u003cem\u003e16S rRNA\u003c/em\u003e gene from the genomic DNA. Both the forward and reverse \u003cem\u003e16S rRNA\u003c/em\u003e gene primers were tailed with sample specific PacBio barcode sequences to allow for multiplexed sequencing for Multiplexed SMRTbell\u0026reg; Library Preparation and Sequencing protocol (Part Number 101\u0026ndash;599\u0026ndash;700 Version 04, PACBIO).\u003c/p\u003e\n\u003ch3\u003eProcessing of raw reads and taxonomic and functional annotation of reads\u003c/h3\u003e\n\u003cp\u003eAdapter removal and quality trimming of raw reads was performed using TrimGalore v.0.6.0 with default parameters [28], a wrapper script for Cutadapt v.2.6 [29] and FastQC v.0.11.8 [30]. The human and pig reference genomes, GRCh38 and Scrofa 11.1, respectively, were used to remove contaminant reads using Bowtie2 v.2.3.4.3 [31] with default parameters. Resulting BAM files were processed using SAMtools v.1.9 [32] and converted to FastQ format using BEDTools v.2.27.1 [33].\u003c/p\u003e \u003cp\u003eFiltered reads were taxonomically assigned by MetaPhlAn 4 [34] with vOct22_CHOCOPhlAnSGB_202212 database and functional annotated by SUPER-FOCUS v.0.35 [35] using 90 cluster size database.\u003c/p\u003e \u003cp\u003eFull \u003cem\u003e16S rRNA\u003c/em\u003e raw reads were processed using DADA2 v.1.8.0 [36] integrated in \u003cem\u003edada2\u003c/em\u003e R-package [37] following the authors' tutorial. Firstly, \u003cem\u003eremovePrimers\u003c/em\u003e R-function was used to remove primer sequences and \u003cem\u003efilterAndTrim\u003c/em\u003e was employed to filter reads with \u003cem\u003eminQ\u0026thinsp;=\u0026thinsp;3, minLen\u0026thinsp;=\u0026thinsp;1000, maxLen\u0026thinsp;=\u0026thinsp;16000, maxN\u0026thinsp;=\u0026thinsp;0, maxEE\u0026thinsp;=\u0026thinsp;2\u003c/em\u003e paramaters. Amplicon Sequence Variant (ASV) table was obtained by comparing the filtered reads with SILVA database (silva_nr99_v138.1_wSpecies_train_set.fa.gz) [38] by using the \u003cem\u003eassignTaxonomy\u003c/em\u003e command in \u003cem\u003edada2\u003c/em\u003e package [37].\u003c/p\u003e\n\u003ch3\u003eBinning and reconstruction of metagenome assembled genomes (MAGs)\u003c/h3\u003e\n\u003cp\u003eEach of the samples were independently subjected to \u003cem\u003ede novo\u003c/em\u003e metagenomic assembly through metaSPAdes v.3.13 [39] using default parameters. Filtered reads were mapped against contigs higher than 1000 bp obtained from the same sample using Bowtie2 v.2.3.4.1 [40] with the \u003cem\u003e--verysensitive-local\u003c/em\u003e parameter. The \u003cem\u003ejgi_summarize_bam_contig_depths\u003c/em\u003e script, from MetaBAT2 v.2.12.1 [41], was used to calculate contigs depth values, recommended for per-sample contig binning based on tetranucleotide frequency, and binning was performed using MetaBAT2 v.2.12.1 and the option \u003cem\u003e-m 1500\u003c/em\u003e [41].\u003c/p\u003e \u003cp\u003eThe quality of MAGs was estimated using the \u003cem\u003elineage_wf\u003c/em\u003e workflow of CheckM v.1.1.3 [42], to classify MAGs as high-quality MAGs (\u0026gt;\u0026thinsp;90% completeness, \u0026lt; 5% contamination) and medium quality MAGs (\u0026gt;\u0026thinsp;50 completeness, \u0026lt; 5% contamination) for further analysis. MAGs taxonomical assignment was done by GTDB-tk [43] and release r220.\u003c/p\u003e \u003cp\u003ePhylogenomic analysis was performed by GTDB-tk (\u003cem\u003ede_novo_wf\u003c/em\u003e option) v.2.4.0 tool using data version r220 [43] and based on BAC120 marker set. The topology was inferred with the maximum-likelihood algorithm implemented in FastTree v.2.1.11 [44], considering the Whelan and Goldman model for amino acid evolution [45]. Shimodaira-Hasegawa test [46] checked the robustness of the nodes. Closely related species to the MAGs included in GTDB database version r220 were added to the analysis. Graphical representation was performed using \u003cem\u003eggtree\u003c/em\u003e v.3.12.0 R package [47] and species names were edited with \u003cem\u003eggtext\u003c/em\u003e v.0.1.2 R package [48].\u003c/p\u003e \u003cp\u003eThe 89 MAGs were annotated using Bakta v.1.9.3 software (database v.5.1) [49]. AMRFinderPlus v.3.12.80 [50], with database 2024-05-02.2, searched antimicrobial resistance genes as well as virulence factors and stress, heat and biocide resistance in nucleotic sequences. Results with identity value\u0026thinsp;\u0026lt;\u0026thinsp;90% and alignment\u0026thinsp;\u0026lt;\u0026thinsp;80% were filtered out.\u003c/p\u003e \u003cp\u003eThe pangenome of MAGs identified as same species was calculated using SuperPang v.1.3.0 tool [51]. Prodigal v.2.6.3 [52] predicted the proteome and the functional annotation was performed against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database utilizing BlastKOALA online tool [53]. Data was reordered with \u003cem\u003ereshape\u003c/em\u003e v.0.8.9 R package [54] in order to plot it using graphical \u003cem\u003eggplot2\u003c/em\u003e v.3.5.1 [55] and \u003cem\u003epaletteer\u003c/em\u003e v.1.6.0 R packages [56].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAnalyses to study differences in microbiota composition and functionality between the variables \u0026ldquo;Group\u0026rdquo; (Control, Early_inf and Acute_inf), \u0026ldquo;Sample_type\u0026rdquo; (Faeces, Colon_content and Mucosa), \u0026ldquo;Euthanasia_day\u0026rdquo; (Before_day_25 and After_day_25), \u0026ldquo;Shedding_onset\u0026rdquo; (Before and After) and \u0026ldquo;Dysentery_onset\u0026rdquo; (Before and After) were carried out in R v.4.3.3 [57] (Additional file 1).\u003c/p\u003e \u003cp\u003eAlpha and beta diversities were calculated from relative abundance data at both species and functional level using \u003cem\u003evegan\u003c/em\u003e v.2.6-4 R package [58] and plots were built using \u003cem\u003eggplot2\u003c/em\u003e v.3.5.1 R package [55]. Alpha diversity was estimated by Richness, Shannon, Pielou and Simpson evenness indexes. Normality of each diversity index was assessed using Shapiro Wilk\u0026rsquo;s test from \u003cem\u003estats\u003c/em\u003e v.4.3.3 R package [57]. Alpha diversity indices were compared using one-way ANOVA, followed by pairwise comparisons with the Tukey test (\u003cem\u003estats\u003c/em\u003e v.4.3.3 R package [57]) when the data were normally distributed; or Kruskal-Wallis followed by pairwise comparison with Wilcoxon test (\u003cem\u003eggpubr\u003c/em\u003e v.0.6.0 R package [59]) when the data were not normally distributed. P-values were adjusted using Holm adjustment approach. Ordination of samples was performed using Principal Coordinates Analysis (PCoA) and Nonmetric Multidimensional Scaling (NMDS) of previously computed pairwise Bray-Curtis distances amongst samples. The effect of each variable on the ordination was determined using the \u003cem\u003eenvfit\u003c/em\u003e function from \u003cem\u003evegan\u003c/em\u003e R package [58]. The association between variables under study and species or functional composition were tested using permutational multivariate analysis of variance (PERMANOVA) using the \u003cem\u003eadonis2\u003c/em\u003e (\u003cem\u003evegan\u003c/em\u003e v.2.6-4 R package [58]) and \u003cem\u003epairwise.adonis\u003c/em\u003e (\u003cem\u003epairwiseAdonis\u003c/em\u003e v.0.4.1 R package [60]) functions, while intra-group dispersion was determined by estimating distances of each sample to its group centroid, calculated using function \u003cem\u003ebetadisper\u003c/em\u003e (\u003cem\u003evegan\u003c/em\u003e v.2.6-4 R package [58]). Differences in dispersion were tested using the same statistical approach described for alpha diversity analysis.\u003c/p\u003e \u003cp\u003eThe influence of the 15 species and functions with the highest mean abundance, as well as other variables of interest on the ordination was evaluated by linear models fitting on the ordination results using the \u003cem\u003eenvfit\u003c/em\u003e function [58] and Benjamini-Hochberj (BH) as an adjustment method. To assess the influence of species pattern composition on variation of \u003cem\u003eB. hyo\u003c/em\u003e concentration amongst samples, a smooth response curve of variable \u0026ldquo;[B. hyodysenteriae] (bact./g)\u0026rdquo; (concentration of bacterium per gram of faeces) was fitted on the ordination results using \u003cem\u003eordisurf\u003c/em\u003e function from \u003cem\u003evegan\u003c/em\u003e v.2.6-4 R package [58].\u003c/p\u003e \u003cp\u003eUsing the Bray-Curtis distances of the species abundance, a hierarchical cluster analysis of the samples was performed using the Ward method (\u003cem\u003estats\u003c/em\u003e v.4.3.3 R package [57]) and the top 25 species and functions with the highest mean abundance among the samples were represented [55]. Analysis of differential abundance at the species and function levels were performed using Linear models for Differential Abundance (LinDA) approach [61], included as function \u003cem\u003elinda\u003c/em\u003e in the \u003cem\u003eMicrobiomeStat\u003c/em\u003e v.1.2 R package [62]. Species and functions were filtered by excluding those with less than 3 non-zero values. Thereafter, for each \u0026ldquo;Sample_type\u0026rdquo;, differential abundance of features was compared across variables \u0026ldquo;Group\u0026rdquo;, \u0026ldquo;Shedding_onset\u0026rdquo; and \u0026ldquo;Dysentery_onset\u0026rdquo;, including \u0026ldquo;Euthanasia_day\u0026rdquo; as a co-variate for each model. Significant species and functions were reported with a BH-adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eThe core microbiota for each level of variables \u0026ldquo;Group\u0026rdquo;, \u0026ldquo;Shedding_onset\u0026rdquo; and \u0026ldquo;Dysentery_onset\u0026rdquo; was estimated for each \u0026ldquo;Sample_type\u0026rdquo;. Parameters were set using a minimum threshold of 2% abundance in at least 70% of the samples of each group using the \u003cem\u003ephyloseq\u003c/em\u003e v.1.46.0 and \u003cem\u003emicrobiome\u003c/em\u003e v.1.24.0 R packages [63, 64]. Venn diagrams were built using the \u003cem\u003evenn\u003c/em\u003e function of the \u003cem\u003eeulerr\u003c/em\u003e v.7.0.2 R package [65]. From the species composing the core microbiota with a detection threshold of 0.1% in faeces and colon and 0.01% in mucosa in at least 50% of the samples, community profiles in these three sample types were estimated using Dirichlet multinomial mixtures (DMM) models fitting [66] (\u003cem\u003eDirichletMultinomial\u003c/em\u003e v.1.44.0 R package [67]). Laplace approximation was used to evaluate DMM models fit, and the optimal number of components (clusters) was selected based on the lowest Laplace value. The main species driving differences between each community type were determined by selecting species with a cluster contribution values above the 80th percentile. The results obtained were visualised colouring samples in the ordination plots by cluster group and the differences on clusters were tested using a PERMANOVA analysis [58, 60].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eIdiosyncrasy of\u003c/b\u003e \u003cb\u003eBrachyspira hyodysenteriae\u003c/b\u003e \u003cb\u003einfection model impacts on the microbiota global composition and functionality.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe time lapse from challenge day until disease onset, either \u003cem\u003eB. hyo\u003c/em\u003e detection in faeces or the onset of visual mucohaemorrhagic diarrhoea, ranged from 8 to 36 DPI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This variability in the incubation period among challenged animals was considered both in the study design with paired necropsies of infected animals and non-infected controls and also in data analysis, with the inclusion of a categorical variable which considered the length of the infection in each monitored pig (named \u0026ldquo;Euthanasia_day\u0026rdquo;).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStatistical analysis of sample ordination using the \u003cem\u003eenvfit\u003c/em\u003e function revealed that the \u0026ldquo;Euthanasia_day\u0026rdquo; variable had a significant influence on the ordination of species and/or microbiota functions regardless the ordination method used (PCoA or NMDS) (Additional file 2). Permutational multivariate analysis of variance confirmed differences among the levels of \u0026ldquo;Euthanasia_day\u0026rdquo; (Additional file 3), supported by a significant increase in Shannon index in samples After_day_25, regardless of \u0026ldquo;Sample_type\u0026rdquo; and richness index at the Mucosa (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Additional files 4 and 5). The 25 most abundant species, accounting for 50%-60% of the total relative abundance, were plotted by \u0026ldquo;Sample_type\u0026rdquo; and \u0026ldquo;Euthanasia_day\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Results confirmed that non-infected controls euthanised After_day_25 differed partially in the dominant species (i.e., \u003cem\u003eStreptococcus alactolyticus\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e sp. unclassified) compared to animals included in the group Before_day_25 (i.e., \u003cem\u003eLactobacillus amylovorus, Limosilactobacillus reuteri\u003c/em\u003e). The 25 most abundant functions accounted for approximately 25% of the relative abundance in Faeces and Colon_content (Additional file 6). Interestingly and in contrast to the result observed in taxa analysis, only minor variations in major functions were observed in both Faeces and Colon_content, regardless of the \u0026ldquo;Euthanasia_day\u0026rdquo; variable.\u003c/p\u003e\n\u003ch3\u003eThe colonic and faecal microbiota changes according to the infection severity\u003c/h3\u003e\n\u003cp\u003eBeta-diversity analyses evidenced as well the impact of the infection into the microbiota composition (Additional files 2 and 3). The influence of the infection, measured by the variable \u0026ldquo;Group\u0026rdquo;, was maintained in further analysis splitting samples by \u0026ldquo;Euthanasia_day\u0026rdquo; or \u0026ldquo;Sample_type\u0026rdquo; (Colon_content, Mucosa and Faeces) variables (Additional files 2 and 3). Indeed, differences in beta-diversity between the Acute_inf and any of the other two groups were consistent in Faeces and Colon_content for taxa and functions regardless of the euthanasia time-point. Only at the Mucosa, we observed no taxonomic differences in sample ordination between the acute and early-infection groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These changes observed in ordination analyses were associated to a decrease in species richness in Acute_inf group compared to Control and the Early_inf groups both in Faeces and Colon_content (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Additional file 7 A), again regardless of the \u0026ldquo;Euthanasia_day\u0026rdquo; (Additional files 7 B and C). Functional changes were negligible, just observing a decrease in richness, as well as an increase in the Pielou index in Acute_inf group compared to the other two groups in Colon_content samples.\u003c/p\u003e \u003cp\u003eWard clustering of Bray-Curtis distances amongst samples revealed that sample clustering by the variable of infection \u0026ldquo;Group\u0026rdquo; was more evident in samples After_day_25 regardless of the \u0026ldquo;Sample_type\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). It is worth noting the detection of Multidrug Resistance Efflux Pumps function in the Colon_contents of pigs euthanised After_day_25 (Additional file 6). Fine-tuned association of taxa relative abundance and levels of the infection \u0026ldquo;Group\u0026rdquo; was achieved by linear modelling analyses and their impact symbolized as vectors fitted in the PCoA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Species vectors defining ordination of samples in Axis 1 were clearly associated to the infection \u0026ldquo;Group\u0026rdquo; variable. Indeed, the species defining the PCoA position of the Acute_inf group samples were also pointed out by LinDA analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Additional file 8). Thus, we observed a significant increase of \u003cem\u003eB. hyo\u003c/em\u003e and \u003cem\u003eAcetivibrio ethanolgignens\u003c/em\u003e in Faeces, Colon_content and Mucosa (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;6.21, 10.52, 7.05; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;5.98, 7.89, 9.74; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, respectively). Data analysis also revealed an increase of \u003cem\u003eCampylobacter hyointestinalis\u003c/em\u003e in Faeces and Colon_content (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;7.46 and 9.70; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, respectively) and \u003cem\u003eRoseburia inulinivorans\u003c/em\u003e in colonic Mucosa samples (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;6.72; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). All these taxa, except \u003cem\u003eC. hyointestinalis\u003c/em\u003e, together with different species of the genus \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eAlloprevotella\u003c/em\u003e were also identified as unique members of the core microbiota in Acute_inf pigs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and C). Early_inf pigs core microbiota was defined by \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e and \u003cem\u003eL. reuteri\u003c/em\u003e while in non-infected animals, \u003cem\u003eS. alactolyticus\u003c/em\u003e was predominant regardless of \u0026ldquo;Sample_type\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and \u003cem\u003eButyricicoccus A porcorum\u003c/em\u003e as unique specie in Colon_content and Faeces of this group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eOn the contrary, in the Acute_inf group there was a significant marked decrease in species such as \u003cem\u003eOliverpabstia intestinalis\u003c/em\u003e in Faeces samples (log\u003csub\u003e2\u003c/sub\u003eFC = -4.94; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), \u003cem\u003eFaecousia\u003c/em\u003e sp900540635 in Colon_content samples (log\u003csub\u003e2\u003c/sub\u003eFC = -4.96; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e or \u003cem\u003eEubacterium hallii\u003c/em\u003e in Mucosa samples (log\u003csub\u003e2\u003c/sub\u003eFC = -4.59 and \u0026minus;\u0026thinsp;3.47; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Analysis of functions abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and Additional file 8) revealed an increase of Alkanesulfonate assimilation or Tolerance to colicin E2 (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;0.88, 3.50; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and decrease of tRNA-dependent amino acid transfers (log\u003csub\u003e2\u003c/sub\u003eFC = -1.76; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in Acute_inf group Faeces compared to Control group. Otherwise, in the Colon_content, we observed an increase in Dot-Icm type IV secretion system (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;3.73; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the decrease in Cresol degradation (log\u003csub\u003e2\u003c/sub\u003eFC = -3.74; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Linear models for differential abundance species or functions between Early_inf and Control groups (Additional file 8 and 9), revealed no major differences.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcute infection defines the microbiota profile beyond changes in specific species\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further explored the presence of potential differences in microbial communities among the animals under study using DMM models fitting to find the optimal number of community types in each \u0026ldquo;Sample_type\u0026rdquo;.\u0026nbsp;By this clustering analysis, three (\u003cem\u003ek\u0026nbsp;\u003c/em\u003e= 3) distinct clusters were identified in the Mucosa and Colon_content, while two clusters represented the best model fit in the Faeces (Additional file 10). Visualization of these clusters in a NMDS as well as PERMANOVA pairwise tests revealed that microbiota composition was different upon these distinct clusters within the mucosal, colonic and faecal microbial communities (PERMANOVA, P \u0026lt; 0.01) (Figure 4 A and Additional file 11). Interestingly, most of the Colon_content and Mucosa samples from the Acute_inf group were allocated in cluster 3, while pigs from Early_inf and Control groups split in clusters 1 and 2. Cluster 3 enterotype was defined by the contribution of several species from the genus \u003cem\u003ePrevotella\u003c/em\u003e in Colon_content and \u003cem\u003eCampylobacter hyointestinalis,\u003c/em\u003e \u003cem\u003eTuzzerella\u0026nbsp;\u003c/em\u003esp. unclassified or \u003cem\u003eRoseburia inulinivorans\u003c/em\u003e at the Mucosa (Figure 4 B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBiomarkers of gut microbiota changes during the infection process\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther analyses were performed to assess the impact of the infection on the microbiota composition and functionality. Faecal samples collected from animals before the shedding onset and before clinical observation of mucus and haemorrhage, both 24 hours before, were compared with samples from the same animals at the shedding and mucohaemorrhagic diarrhoea onset respectively (\u0026ldquo;Shedding_onset\u0026rdquo; Before and After; \u0026ldquo;Dysentery_onset\u0026rdquo; Before and After variables).\u003c/p\u003e\n\u003cp\u003eInitial \u003cem\u003eB. hyo\u003c/em\u003e shedding varied from 5 x 10\u003csup\u003e3\u003c/sup\u003e bacteria/g to 8 x 10\u003csup\u003e6\u003c/sup\u003e bacteria/g faeces with soft faecal consistency. These observations did not correlate with significant alterations in alpha or beta diversity analyses neither with changes in abundance of taxa nor microbial functions.\u003cem\u003e\u0026nbsp;Limosilactobacillus reuteri\u003c/em\u003e and \u003cem\u003eM. elsdenii\u0026nbsp;\u003c/em\u003ewere the main members of the core microbiota of the monitored animals (Figure 5, Additional file 12, 13 and 14). Core microbiota analyses revealed the loss of \u003cem\u003eAgathobacter\u003c/em\u003e sp900549895, \u003cem\u003ePrevotella\u0026nbsp;\u003c/em\u003esp900322035, \u003cem\u003ePrevotella hominis\u003c/em\u003e or \u003cem\u003ePrevotella\u003c/em\u003e sp002300055 and UBA1436 sp900540405 in the core microbiota when bacterial shedding occurred, and the emergence of CAG-349 sp003539515 (Figure 5 F).\u003c/p\u003e\n\u003cp\u003eIn contrast, the shift to mucohaemorrhagic diarrhoea (Figure 6) was associated to significant alterations of alpha diversity indexes with a decrease in the number of bacterial species present in the Faeces (Figure 6 A), fact observed in the core microbiota analyses, which revealed that only four taxa were shared with 1% of relative abundance in 50% of the samples (Figure 6 F) and a notable increase in \u003cem\u003ePrevotella\u003c/em\u003e sp900546535, \u003cem\u003ePrevotella\u003c/em\u003e sp000434975 and \u003cem\u003eB.\u0026nbsp;hyo\u003c/em\u003e, which were detected with an abundance of 2% in 70% of the samples (Figure 6 E).\u0026nbsp;Together with \u003cem\u003eB. hyo\u0026nbsp;\u003c/em\u003e(log\u003csub\u003e2\u003c/sub\u003eFC\u003cem\u003e\u0026nbsp;\u003c/em\u003e= 9.50, P \u0026lt; 0.01), \u003cem\u003eC. hyointestinalis\u0026nbsp;\u003c/em\u003e(log\u003csub\u003e2\u003c/sub\u003eFC = 5.10, P \u0026lt; 0.01), \u003cem\u003eA. ethanolgignens\u0026nbsp;\u003c/em\u003e(log\u003csub\u003e2\u003c/sub\u003eFC = 7.00, P \u0026lt; 0.01)\u003cem\u003e\u0026nbsp;\u003c/em\u003eand several species of the genus \u003cem\u003ePrevotella\u003c/em\u003e altered the ordination of samples after the mucohaemorrhagic diarrhoea onset, also with an increase of distance to the centroid (P \u0026lt; 0.05), which demonstrates a lower similarity of microbiota among acutely infected animals compared to a previous infection stage (Figure 6 B, C and D). This shift in species occurred together with altered functionality reflected by increased abundance of Tricarboxylate transport system and Tolerance to colicin E2 (log\u003csub\u003e2\u003c/sub\u003eFC = 1.59, 4.08; P \u0026lt; 0.01, respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDisclosing whether pathobionts or opportunistic commensals are involved in the acute phase of infection.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further analysed species of interest by the information provided by their MAGs (detailed in the MAGs obtained in the study can be further accessed in the Additional file 15). In total, 89 MAGs from \u003cem\u003eA. \u0026nbsp;ethanolgignens\u0026nbsp;\u003c/em\u003e(7 MAGs), \u003cem\u003eAlloprevotella\u003c/em\u003e sp004552155 (13 MAGs), \u003cem\u003eC. hyointestinalis\u0026nbsp;\u003c/em\u003e(4 MAGs), \u003cem\u003ePrevotella copri\u003c/em\u003e clade A (2 MAGs), \u003cem\u003e\u0026ldquo;Prevotella pectinovora\u0026rdquo;\u003c/em\u003e (9 MAGs), \u003cem\u003ePrevotella\u003c/em\u003e sp900322035 (38 MAGs), \u003cem\u003ePrevotella\u003c/em\u003e sp900546535 (15 MAGs) and \u003cem\u003eR.\u0026nbsp;inulinivorans\u003c/em\u003e (1 MAG) were further analysed in their taxonomy and functionality. Detailed information about the aforementioned MAGs is described in Additional file 16. Interestingly, MAGs related to species \u003cem\u003eA. ethanolgignens, C. hyointestinalis\u0026nbsp;\u003c/em\u003eand \u003cem\u003eR.\u0026nbsp;inulinivorans\u003c/em\u003e were reconstructed from Acute_inf and Early_inf samples, exclusively (Figure 7 A and Additional file 16). First, we observed discrepancies in the taxonomic classification of the specie \u003cem\u003eA. ethanolgignens\u0026nbsp;\u003c/em\u003ewhich in the GTDB database is annotated as \u0026ldquo;\u003cem\u003eVelocimicrobium ethanolgignens\u003c/em\u003e\u0026rdquo; (Figure 7 A). The phylogenomic tree built based on GTDB-tk bacterial markers, BAC120 to establish the phylogenetic position (Additional file 17), revealed that the 7 MAGs identified as \u003cem\u003eA.\u0026nbsp;ethanolgignens\u003c/em\u003e shared a node with the type strain of this species, \u003cem\u003eA. ethanolgignens\u0026nbsp;\u003c/em\u003eATCC 33324\u003csup\u003eT\u003c/sup\u003e. This cluster showed a closer genomic relationship with the genus \u003cem\u003eVelocimicrobium\u003c/em\u003e (family \u003cem\u003eLachnospiraceae\u003c/em\u003e) than with the other species constituting the genus \u003cem\u003eAcetivibrio\u003c/em\u003e (family \u003cem\u003eOscillospiraceae\u003c/em\u003e) (Additional file 17 A). The four MAGs identified as \u003cem\u003eC. hyointestinalis\u003c/em\u003e exhibited a closer relationship with \u003cem\u003eC.\u0026nbsp;hyointestinalis\u003c/em\u003e subsp. \u003cem\u003ehyointestinalis\u003c/em\u003e CCUG 14169\u003csup\u003eT\u003c/sup\u003e than with \u003cem\u003eC.\u0026nbsp;hyointestinalis\u003c/em\u003e subsp. \u003cem\u003elawsonii\u003c/em\u003e CHY5\u003csup\u003eT\u003c/sup\u003e (Additional file 17 B). The MAGs identified as \u003cem\u003eR. inulinivorans\u003c/em\u003e, \u003cem\u003eAlloprevotella\u0026nbsp;\u003c/em\u003esp900546535 and members of the genus \u003cem\u003ePrevotella\u003c/em\u003e cluster with the type strain of species of their respective species (Additional file 17 C and D\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further mined the MAGs to search for putative virulence factors. No major virulence genes were detected in the MAGs of these species. Bacterial functions obtained from the panproteomes identified flagellar assembly genes and lack functions related to lipopolysaccharide biosynthesis in \u003cem\u003eA. ethanolgignens\u0026nbsp;\u003c/em\u003ewhich also harboured the\u003cem\u003e\u0026nbsp;\u003c/em\u003emacrolide efflux MFS transporter Mef(A) (identified in a high-quality MAG, 99.1% identity and 99.26% alignment coverage). Other genes detected found with 99.38, 98.12 and 100.00% identity (100.00% alignment coverage) were \u003cem\u003ecfxA\u003c/em\u003e, \u003cem\u003etet(44)\u003c/em\u003e and \u003cem\u003ecfr(E)\u003c/em\u003e. Flagellar assembly genes were also remarkable in \u003cem\u003eC. hyointestinales\u0026nbsp;\u003c/em\u003eand \u003cem\u003eR. inulinivorans\u003c/em\u003e, whereas few genes related to motility were found in the other species studied (\u003cem\u003eAlloprevotella\u0026nbsp;\u003c/em\u003esp004552155, \u003cem\u003ePrevotella copri\u0026nbsp;\u003c/em\u003eclade A, \u003cem\u003e\u0026ldquo;Prevotella pectinovora\u0026rdquo;\u003c/em\u003e, \u003cem\u003ePrevotella\u0026nbsp;\u003c/em\u003esp900322035 and \u003cem\u003ePrevotella\u0026nbsp;\u003c/em\u003esp900546535) (Figure 7 B).\u003c/p\u003e\n\u003cp\u003eVirulence factors and antimicrobial resistance (AMR) genes were predicted for the MAGs under study. The gen \u003cem\u003enimJ\u003c/em\u003e (5\u0026rsquo;-nitroimidazole reductase) was found in 5 MAGs identified as \u003cem\u003ePrevotella\u003c/em\u003e sp900546535 with 91.19% identity and 96.36% alignment coverage. Regarding \u003cem\u003eA.\u0026nbsp;ethanolgignens\u003c/em\u003e, one high-quality MAG presented 99.1% identity (99.26% alignment coverage) for macrolide efflux MFS transporter Mef(A). Besides, AMR genes were found with 99.38, 98.12 and 100% identity (100% alignment coverage), respectively, for one medium-quality MAG. Further details in the AMR genes detected in other species can be accessed in the Additional file 15.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eKnowledge in \u003cem\u003eBrachyspira\u003c/em\u003e infections causing inflammatory disease in the hindgut is scarce compared to other enteric pathogens such as \u003cem\u003eSalmonella\u003c/em\u003e spp. in different species or \u003cem\u003eCitrobacter\u003c/em\u003e spp. in rodents, yet it is of particular interest to understand pathogen-microbiota interactions in infections specifically targeting the large intestine. Laboratory handling of the fastidious pathogens such as \u003cem\u003eB. hyo\u003c/em\u003e [18] and difficulties in achieving successful experimental models, which unusually achieve a 100% of disease incidence [21, 68], are probably the two major obstacles to explore SD. Deliberately increased protein concentration in the diet is used to facilitate SD infection [69, 70] but the dysbiosis created alters the microbiota, biasing potential microbiome analyses. The alternative, a challenge without predisposed animals, increases variability in the incubation period, a hallmark already observed by previous studies [71, 72]. Variability in the incubation period and clinical disease implies changes in microbiota composition in the large intestine, stated in our study by an age-related increase in different species richness and diversity indexes and changes in the dominant microbial species, in consonance with previous studies [4, 73\u0026ndash;75]. Thus, in our study, pigs with a short incubation period had an intestinal microbiota dominated by species of the \u003cem\u003eLactobacillaceae\u003c/em\u003e family, which are in highly abundance early after weaning [73, 76],while in animals with longer SD incubation, we observed a change in the dominant species to fibre degrader genera such as \u003cem\u003ePrevotella\u003c/em\u003e, which are more efficient in the metabolism of complex carbohydrates present in post-weaning diets [75]. These taxonomic differences were not reflected by functional analyses which showed a \u0026lsquo;functional redundancy\u0026rsquo; that helps maintain resilience and stability despite fluctuations [73, 77].\u003c/p\u003e \u003cp\u003eConsidering the natural changes of the microbiota at the post-weaning period, at which the SD infection models are performed, our experimental design included paired euthanasia of challenged and control pigs. This approach enabled us to compare accurately infected animals with non-infected counterparts, buffering the biases elicited by physiological changes of the microbiota during this period of life [4, 78]. Ward clustering analysis revealed slight differences in the clustering of acutely infected pigs by the euthanasia time point with animals euthanised after day 25 exhibiting a closer clustering by their microbiota composition compared to animals euthanised before day 25 of infection. With ageing, the microbiota becomes more diverse yet more even [79], as indicated by the Pielou index and the distances to the centroid results across the samples under study. This probably leads to more homogeneous microbiota changes associated to the infection as reflected by this result.\u003c/p\u003e \u003cp\u003eThe severity of the disease in acute infection contrasts to the moderate shedding and low inflammation elicited during the early infection [20]. This early shedding, although in concentrations below 10\u003csup\u003e5\u003c/sup\u003e bacteria/g of faeces, reveals colonisation and multiplication of \u003cem\u003eB. hyo\u003c/em\u003e in the hindgut at this stage. We analysed the impact of this infection stage on the microbiota composition with no major findings. Neither in microbiota composition by diversity analyses, nor by differential abundance or enterotype analyses, we observed differences between pigs euthanised at early infection and non-infected counterparts. The two main members of the core microbiota in this Early_inf group were \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e and \u003cem\u003eLimosilactobacillus reuteri\u003c/em\u003e, two commensals which do not reveal any particular trait in the infection [80, 81]. The only outstanding result was associated to a loss of \u003cem\u003eAgathobacter\u003c/em\u003e sp900549895 and the inclusion of CAG-349 sp003539515 in the core microbiota when the shedding of \u003cem\u003eB. hyo\u003c/em\u003e in faeces began. The first microorganism is a beneficial gut commensal [82] while the last has been associated to intestinal disease [83], but this finding seems more circumstantial than \u003cem\u003eB. hyo\u003c/em\u003e infection associated. Another study [22] has explored the changes in early infection with several findings. Differences in the design or moment at which samples were collected may explain the divergence observed.\u003c/p\u003e \u003cp\u003eThe mucohaemorrhagic diarrhoea characteristic of the acute SD is the result of an overstimulation of mucin production and colon epithelial necrosis which expose the capillary to the lumen. Likewise, microscopically strong inflammation in the mucosa is evident, as indicated by significant neutrophil migration and thickening of the colon mucosa [20, 21]. For first time in SD microbiota analyses, we explored the association of these features of SD and their association to the microbiota ordination. Thus, the inclusion of vectors for the variables neutrophil counts, \u003cem\u003eB. hyo\u003c/em\u003e concentration and to a lower extent mucosal thickness and ulceration score demonstrated their influence in the PCoA ordination of acutely infected pigs samples. Colon inflammation and colonocyte ulceration prompt the proliferation of undifferentiated cells at the crypts which turns to mucosal hyperplasia (thickness). Together with the inflammation, this process shifts the colonocyte metabolism towards aerobic glycolysis which ends in the alteration of the microbiota-colonocyte homeostasis [8]. As a consequence, species richness in faeces and colon content decreased significantly with the onset of clinical signs [11], fact already reported by other studies with enteropathogens [84], but not in mucosa [21], probably due to the lower species diversity in these samples [85]. Indeed, the change was so abrupt, that colon content and mucosa samples from acutely infected pigs were clustered in a different enterotype. The results emphasize the usefulness of including different targets (colon digesta, faeces and mucosa in our study) to obtain a complete picture of the microbiome changes, considering the particularities of the microbiota in each location.\u003c/p\u003e \u003cp\u003eThe functional differences identified in the Acute_inf group compared to the Control highlight the role of the microbiota in the disease. Among the altered functions, we observed metabolic routes previously described in inflammatory bowel diseases (i.e., Crohn's disease) [89], such as increased sulphate metabolism that inhibits cytochrome c oxidase catalysing the phosphorylation of ADP to ATP [86\u0026ndash;88] or decreased aminoacyl-tRNA synthesis. Furthermore, functions which may help to the primary infection were evidenced. Thus, we observed an increased abundance of processes linked to the fermentation of mucins, that increase the levels of products, such as cresol, which have toxic effects on the colonic epithelium [88]. Also increased antimicrobial production driven by inflammation [90], or the detection of virulence genes such as type IV secretion systems (i.e., associate to genus such as \u003cem\u003eCampylobacter\u003c/em\u003e) [91, 92] are examples of this sort of functions.\u003c/p\u003e \u003cp\u003eThis window opportunity in acutely infected pigs allowed opportunistic species to increase in abundance. As in previous studies [11, 21], we observed an increase in relative abundance of \u003cem\u003eAcetivibrio ethanolgignens\u003c/em\u003e and \u003cem\u003eCampylobacter hyointestinalis\u003c/em\u003e in acute SD. \u003cem\u003eAcetivibrio ethanolgignens\u003c/em\u003e has been previously noticed in the colon of pigs with clinical SD [21, 93, 94]. We first spotlight by genomic analyses this bacterium. First, we noticed by phylogenomic analysis that both the reference strain \u003cem\u003eA. ethanolgignens\u003c/em\u003e ATCC 33324\u003csup\u003eT\u003c/sup\u003e and 7 MAGs from this study fit within the family \u003cem\u003eLachnospiraceae\u003c/em\u003e instead of within the family \u003cem\u003eOscillospiraceae\u003c/em\u003e (genus \u003cem\u003eAcetivibrio\u003c/em\u003e) (Additional file 17 A). GTDB database version r220 even renames it as \u0026ldquo;\u003cem\u003eVelocimicrobium ethanolgignens\u003c/em\u003e\u0026rdquo;. However, a complete taxonomic study based on phylogenetic, phylogenomic, phenotypic and chemotaxonomic analysis should be performed to determine whether or not \u003cem\u003eA. ethanolgignens\u003c/em\u003e is a member of the genus \u003cem\u003eVelocimicrobium\u003c/em\u003e or it constitutes a different genus. We also evaluated its potential role in the disease development. \u003cem\u003eAcetivibrio ethanolgignens\u003c/em\u003e has been considered a pro-inflammatory bacterium due to its association to alterations in the immune system [95, 96]. Despite virulence factors were not detected in any of the studied MAGs, it is noteworthy to mention the presence of an oligopeptide ABC transporter (i.e., OppABCDF), identified in the 7 \u003cem\u003eA. ethanolgignens\u003c/em\u003e MAGs. Although this oligopeptide permease is involved in different parts of the cell metabolism in prokaryotes, it has also been reported to increase the production of proinflammatory cytokines IL-1β, IL-6 and TNF, inducing the apoptosis of macrophages [97]. Consequently, \u003cem\u003eA. ethanolgignens\u003c/em\u003e could be promoting the intestinal inflammation triggered by \u003cem\u003eB. hyo\u003c/em\u003e infection, though it does not appear to play a direct role in the primary infection. This idea aligns with the results of co-inoculation experiments of \u003cem\u003eA. ethanolgignens\u003c/em\u003e and \u003cem\u003eB. hyo\u003c/em\u003e in gnotobiotic pigs performed four decades ago [94].\u003c/p\u003e \u003cp\u003eOur analysis also detailed the taxonomy of \u003cem\u003eCampylobacter\u003c/em\u003e species associated to infected animals. The MAGs identified as \u003cem\u003eC. hyointestinalis\u003c/em\u003e could not be further typed up to subspecies level. According to the phylogenetic tree, they could be closer to \u003cem\u003eC. hyointestinalis\u003c/em\u003e subsp. \u003cem\u003ehyointestinalis\u003c/em\u003e (Additional file 17 B) than to \u003cem\u003eC. hyointestinalis\u003c/em\u003e subsp. \u003cem\u003elawsonii\u003c/em\u003e but the result is not robust enough as it is based only on 120 bacterial markers (BAC120). Both subspecies are considered emerging zoonotic pathogens. \u003cem\u003eC. hyointestinalis\u003c/em\u003e subsp. \u003cem\u003ehyointestinalis\u003c/em\u003e has a broader host range (including cattle and humans), whereas \u003cem\u003eC. hyointestinalis\u003c/em\u003e subsp. \u003cem\u003elawsonii\u003c/em\u003e is mostly isolated in pigs [98\u0026ndash;101]. Previous studies have suggested both subspecies sharing the same niche [98, 102]. Hence, we could conclude that the population of \u003cem\u003eC. hyointestinalis\u003c/em\u003e inhabiting the digestive system of pigs infected with \u003cem\u003eB\u003c/em\u003e. \u003cem\u003ehyo\u003c/em\u003e in the present study have a role in the pathogenesis. On the other hand, \u003cem\u003etet(44)\u003c/em\u003e, \u003cem\u003ecfxA\u003c/em\u003e and \u003cem\u003ecfr(E)\u003c/em\u003e antimicrobial resistance genes detected in one of the MAGs identified as \u003cem\u003eC. hyointestinalis\u003c/em\u003e, as previously reported in this specie [103].\u003c/p\u003e \u003cp\u003e \u003cem\u003eRoseburia inulinivorans\u003c/em\u003e and different \u003cem\u003ePrevotella\u003c/em\u003e species also exhibited increased relative abundance. The increase of these microorganisms which take part of the commensal intestinal microbiota of pigs [104], may be an artefact but the fact that other studies report also their increase in SD [21] or other inflammatory enteric diseases [105] show how these bacteria take advantage of the new metabolic or mucin context, foraging released mucins or simply migrating from the lumen to the mucosa due to the increase permeability of the mucin layer [9].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our study reveals that, the changes observed in the gut microbiota in SD are mainly a consequence of the lesions that occur in the intestine and allow opportunistic species to increase their abundance, localising the changes mainly in the contents of the colon and mucosa where the microbiota exhibited a different enterotype. These opportunistic species could promote the intestinal inflammation caused by \u003cem\u003eB. hyo\u003c/em\u003e infection, but according to their genetic background do not seem to participate in the primary infection. No differences were observed due to the establishment and proliferation of \u003cem\u003eB. hyo\u003c/em\u003e, as no changes were observed until clinical signs appeared. Our research sheds light on the complex interplay between the pathogen and the gut microbiota that is essential to improve our understanding of the pathogenesis of the disease. Finally, the data offer relevant information to be gathered with other colitis models, including non-infectious colitis in humans.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike's Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntimicrobial resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmplicon Sequence Variant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBenjamini-Hochberj\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eB. hyo\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eBrachyspira hyodysenteriae\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDirichlet multinomial mixtures\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDays post-inoculation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFold Change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLinDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinear models for Differential Abundance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetagenome assembled genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNonmetric Multidimensional Scaling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphate-buffered saline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCoA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Coordinates Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePERMANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epermutational multivariate analysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSwine dysentery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Necrosis Factor.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were approved by the University of Le\u0026oacute;n Committee on Animal Care and Supply (OEBA-ULE-010-2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe metagenomic sequences, MAGs, as well as its associated metadata have been submitted to NCBI Sequence Read Archive (SRA) and will be available under BioProject accession PRJNA1214037: https://www.ncbi.nlm.nih.gov/sra/PRJNA1214037\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was performed under the frame of the project SDyMiHO funded by the Spanish Ministerio de Ciencia e Innovaci\u0026oacute;n (ref\u0026nbsp;PID2019-110662RB-I00).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHA, AC and PR conceived and supervised the study. LPP, HA, AC and HP\u0026nbsp;performed the challenge and collected the samples. LPP, HA and HP processed the samples. LPP, CG, JMOS and JFCD analysed the data and prepared the figures. LPP, CG, HA, AC and JFCD reviewed the literature and wrote the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staff in the Infectious diseases Unit, particularly Diana Molina and the staff at the animal biocontention facilities of the University of Leon for their help and support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLuc\u0026iacute;a P\u0026eacute;rez-P\u0026eacute;rez (PRE2020-093762) and H\u0026eacute;ctor Puente (JDC2023-051122-I) are supported by the Spanish Ministerio de Ciencia, Innovaci\u0026oacute;n y Universidades. Cristina Galisteo is supported by the project LE088P23 from the Junta de Castilla y Le\u0026oacute;n.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLeser TD, M\u0026oslash;lbak L. Better living through microbial action: the benefits of the mammalian gastrointestinal microbiota on the host. Environ Microbiol. 2009;11:9: 2194\u0026ndash;2206. doi:10.1111/j.1462-2920.2009.01941.x\u003c/li\u003e\n\u003cli\u003eLiao SF, Ji F, Fan P, Denryter K. Swine Gastrointestinal Microbiota and the Effects of Dietary Amino Acids on Its Composition and Metabolism. Int J Mol Sci. 2024;25:2:1237. doi:10.3390/ijms25021237\u003c/li\u003e\n\u003cli\u003eMann E, Schmitz-Esser S, Zebeli Q, Wagner M, Ritzmann M, Metzler-Zebeli BU. Mucosa-Associated Bacterial Microbiome of the Gastrointestinal Tract of Weaned Pigs and Dynamics Linked to Dietary Calcium-Phosphorus. 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Animal Nutrition. 2024;19:401\u0026ndash;410. doi:10.1016/j.aninu.2024.05.013\u003c/li\u003e\n\u003cli\u003eKelly J, Daly K, Moran AW, Ryan S, Bravo D, Shirazi-Beechey SP. Composition and diversity of mucosa-associated microbiota along the entire length of the pig gastrointestinal tract; dietary influences. Environ Microbiol. 2017;19:4:1425\u0026ndash;1438. doi:10.1111/1462-2920.13619\u003c/li\u003e\n\u003cli\u003eWalker A, Schmitt-Kopplin P. The role of fecal sulfur metabolome in inflammatory bowel diseases. International Journal of Medical Microbiology. 2021;311:151513. doi:10.1016/j.ijmm.2021.151513\u003c/li\u003e\n\u003cli\u003eLuo W, Zhao M, Dwidar M, Gao Y, Xiang L, Wu X, et al. Microbial assimilatory sulfate reduction-mediated H\u003csub\u003e2\u003c/sub\u003eS: an overlooked role in Crohn\u0026rsquo;s disease development. Microbiome. 2024;12:152. doi:10.1186/s40168-024-01873-2\u003c/li\u003e\n\u003cli\u003eHelm ET, Lin SJ, Gabler NK, Burrough ER. \u003cem\u003eBrachyspira hyodysenteriae\u003c/em\u003e Infection Reduces Digestive Function but Not Intestinal Integrity in Growing Pigs While Disease Onset Can Be Mitigated by Reducing Insoluble Fiber. Front Vet Sci. 2020;7:587926. doi:10.3389/FVETS.2020.587926/BIBTEX\u003c/li\u003e\n\u003cli\u003eWusiman W, Zhang Z, Ding Q, Liu M. The pathophyiological role of aminoacyl-tRNA synthetases in digestive system diseases. Frontiers in Physiology. Frontiers Media S.A.; 2022:13:935576. doi:10.3389/fphys.2022.935576\u003c/li\u003e\n\u003cli\u003eZeng MY, Inohara N, Nu\u0026ntilde;ez G. Mechanisms of inflammation-driven bacterial dysbiosis in the gut. Mucosal Immunol. 2017;10:1:18\u0026ndash;26. doi:10.1038/mi.2016.75\u003c/li\u003e\n\u003cli\u003eGabbert AD, Mydosh JL, Talukdar PK, Gloss LM, McDermott JE, Cooper KK, et al. 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Int J Syst Bacteriol. 1981;31:3:333\u0026ndash;338\u003c/li\u003e\n\u003cli\u003eLi Y, Bao X, Yang F, Tian J, Su W, Yin J, et al. Ornithine \u0026alpha;-Ketoglutarate Alleviates Inflammation \u003cem\u003evia\u003c/em\u003e Regulating Ileal Mucosa Microbiota and Metabolites in Enterotoxigenic \u003cem\u003eEscherichia coli\u003c/em\u003e-Infected Pigs. Front Nutr. 2022;9:862498. doi:10.3389/fnut.2022.862498\u003c/li\u003e\n\u003cli\u003eYuan GH, Zhang Z, Gao XS, Zhu J, Guo WH, Wang L, et al. Gut microbiota-mediated tributyltin-induced metabolic disorder in rats. RSC Adv. 2020;10:71:43619\u0026ndash;43628. doi:10.1039/d0ra07502g\u003c/li\u003e\n\u003cli\u003eYang X, Hu T, Liang J, Xiong Z, Zhao Y, Zhou X, et al. An oligopeptide permease, OppABCD, requires an iron\u0026ndash;sulfur cluster domain for functionality. 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Effects of Subtherapeutic Administration of Antimicrobial Agents to Beef Cattle on the Prevalence of Antimicrobial Resistance in \u003cem\u003eCampylobacter jejuni\u003c/em\u003e and \u003cem\u003eCampylobacter hyointestinalis\u003c/em\u003e. Appl Environ Microbiol. 2005;71:7:3872\u0026ndash;3881. doi:10.1128/AEM.71.7.3872-3881.2005\u003c/li\u003e\n\u003cli\u003eHolman DB, Brunelle BW, Trachsel J, Allen HK. Meta-analysis To Define a Core Microbiota in the Swine Gut. mSystems. 2017;2:3:e00004-17. doi:10.1128/msystems.00004-17\u003c/li\u003e\n\u003cli\u003eYang Q, Huang X, Zhao S, Sun W, Yan Z, Wang P, et al. Structure and Function of the Fecal Microbiota in Diarrheic Neonatal Piglets. Front Microbiol. 2017;8:502. doi:10.3389/fmicb.2017.00502\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Swine dysentery, Inflammatory disease, Metagenomics, Shotgun, Gut, Microbiome, Intestine, Spirochaetes","lastPublishedDoi":"10.21203/rs.3.rs-5979918/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5979918/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe gut microbiota is essential for maintaining nutritional, physiological and immunological processes, but colonic infections such as swine dysentery, caused by \u003cem\u003eBrachyspira hyodysenteriae\u003c/em\u003e (\u003cem\u003eB. hyo\u003c/em\u003e) disrupt this homeostasis. This study uses shotgun and full-length \u003cem\u003e16S rRNA\u003c/em\u003e sequencing in faeces, colonic contents and mucosa from pigs challenged with \u003cem\u003eB. hyo\u003c/em\u003e to provide a high-resolution characterisation of hte taxa, functions and metagenome-assembled genomes (MAGs) of interest, disclose their association with the primary pathogen and how they are affected by the pathological changes of the infection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eChanges in the microbiota were associated with disease severity. In early infection, no major findings were observed in diversity or abundance analyses, whereas in acute infection, \u003cem\u003eB. hyo\u003c/em\u003e load, mucosal neutrophil infiltration, epithelial ulceration and mucosal thickness were clearly associated with changes in microbiota ordination, which were also associated with a decrease in species richness. Changes included a significant increase in \u003cem\u003eAcetivibrio ethanolgignens\u003c/em\u003e, \u003cem\u003eCampylobacter hyointestinalis\u003c/em\u003e and \u003cem\u003eRoseburia inulinivorans\u003c/em\u003e, which, with the exception of \u003cem\u003eC. hyointestinalis\u003c/em\u003e, established themselves as part of the core microbiota and shifted the colonic enterotype in acutely infected animals. MAGs analyses revealed that no major virulence genes were detected in the genomes of the species co-interacting with \u003cem\u003eB. hyo\u003c/em\u003e in acute infection. Similarly, functional changes were observed only after the onset of clinical signs, with an increase in functions related to inflammation and toxic effects on the colonic epithelium.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study shows that in colitis caused by \u003cem\u003eB. hyo\u003c/em\u003e, changes in the microbiota are mainly a consequence of the lesions that occur in the intestine, with no differences observed in early infection. Similarly, the bacterial species that are increased at the onset of clinical signs may promote intestinal inflammation caused by \u003cem\u003eB. hyo\u003c/em\u003e infection, but the analysis of their genomes rule out their participation in the primary infection.\u003c/p\u003e","manuscriptTitle":"Taxonomic and functional microbiota changes in dysenteric colitis produced by Brachyspira hyodysenteriae in pigs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 05:27:33","doi":"10.21203/rs.3.rs-5979918/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7448f952-8056-45b8-94bb-7b3080e054a0","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-27T08:38:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 05:27:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5979918","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5979918","identity":"rs-5979918","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}