Pig nasal and rectal microbiotas are involved in the antibody response to Glaesserella parasuis

doi:10.21203/rs.3.rs-5108257/v1

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Pig nasal and rectal microbiotas are involved in the antibody response to Glaesserella parasuis

https://doi.org/10.21203/rs.3.rs-5108257/v1

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Background

Vaccination stands as one of the most sustainable and promising strategies to control infectious diseases in animal production. Nevertheless, the causes for antibody response variation among individuals are poorly understood. The animal microbiota has shown to be involved in the correct development and function of the host immunity, including the antibody response. Here, we studied the nasal and rectal microbiota composition and the association with the antibody response against the pathobiont Glaesserella parasuis. We sampled the nasal and rectal microbiota of 24 piglets before vaccination (two farms) and at similar time in an unvaccinated farm (naturally exposed to the pathobiont). Microbiota composition was inferred by v3v4 16S rRNA gene sequencing and bioinformatic analysis. The antibody response to either vaccination or natural exposure to G. parasuis was measured by ELISA and the variation between the levels before and after vaccination (normalized per farm) was used in the analyses.

Results

Piglets with higher antibody responses showed more diverse microbial communities compared to piglets with lower responses. Moreover, we were able to associate swine nasal core microbiota colonizers with higher antibody levels, such as several members from Bacteroidales and Clostridiales orders and genera including Moraxella, Staphylococcus, Fusobacterium and Neisseria. Regarding taxa found in the rectal microbiota, only associations at order level were detected with antibody responses pointing towards a positive role for Clostridialeswhile negative for Enterobacteriales.

Conclusions

Altogether, these results suggest that the microbiota is associated with the antibody response to G. parasuis (and probably to other pathogens) and serves as starting point to understand the factors that contribute to immunization in pigs.

Pig

Swine

Microbiota

Vaccination

Antibody response

Animals live in constant contact with a variety of pathogens, which are controlled by the immune system (1). Antibodies, which target specific pathogens, constitute one of the main effector responses of the adaptative immunity (1). The adaptive immune response can hold memory through antigen-specific memory cells, permitting a more effective response in subsequent encounters with the pathogen (1, 2). One of the best ways to stimulate immunization and memory against pathogens is vaccination (2), which represents an efficacious strategy to control infectious diseases nowadays (3). Vaccines have the potential to reduce disease severity, eliminate pathogens locally and even eradicate them globally (3). In consequence, they also contribute to the reduction in the use of antibiotics, which is particularly needed for minimizing the emergence of multidrug resistant bacteria (4). However, not all individuals exhibit the same level of response to vaccination. Several factors such as maternal immunity, host genetics and environmental factors, among others, can be responsible for vaccination failure (5). To improve the control of infections is essential to deepen the knowledge on why the immune response either after natural exposure to the pathogen or after vaccination, is variable among individuals (4–8), and which are the intrinsic factors involved in a good response (7, 8).

The animal microbiota, the community of microorganisms inhabiting different niches of the animal host, is known to have an important role in the immune system maturation and modulation (7). The microbiota is involved in the local immune response, such as the stimulation of immune cells with bacterial compounds in the intestine or airway mucosae, but also in systemic immune responses via dissemination of microbial products and/or immune signals and cells through the whole organism (7). Moreover, the stated systemic effect can be enhanced by the constant crosstalk of the different microbiotas in an organism (9). In agreement, poor or deficient immune responses in the context of microbiota dysbiosis have been reported (7). The relationship between the microbiota and the immune response is still to unveil, making the study of the microbiota a key point in vaccination efficacy studies (10).

Mounting evidence shows that microbiota composition influences responses to vaccination in humans (7, 10–19), where several studies showed that disrupted patients with disrupted microbiota tend to show a reduced response to vaccination (19) (20).

In the case of swine, vaccination programs are essential to face the strong institutional call towards the reduction in the use of antibiotics in animal farming (21) and are critical to control infectious diseases, such as Glässer’s disease (22), an endemic disease caused by Glaesserella parasuis. G. parasuis is a pathobiont member of the porcine nasal microbiota that colonizes young piglets early after birth (23). Few studies assess the microbiota in relation with vaccine response in pigs. In 2019 and 2020, Munyaka et al. studied the fecal microbiota as a predictor of high and low vaccine response, measured by the levels of specific antibodies against Mycoplasma hyopneumoniae in serum (24, 25). They were able to identify several taxa that discriminated the high vaccine-responders and whose presence was positively correlated with antibody titters. In a different study, Sanglard, et al. showed that the composition of the vaginal microbiota of sows discriminated between high and low antibody-responders against a porcine reproductive and respiratory syndrome virus (PRRSV) vaccine (26).

Here, using Glässer’s disease as a model where antibodies are important for protection, we studied the composition of the nasal and rectal microbiota in pigs and identified taxa associated with different level of antibodies after bacterin vaccination and/or natural exposure with the pathogen.

Samples included in the study

Twenty-four piglets from three Spanish farms (eight per farm) were included in the study. Two of the farms (farms 1 and 2) performed vaccination against Glässer’s disease with a commercial bacterin (HIPRASUIS® GLÄSSER) by injecting 2ml/piglet at 3 and 6 weeks or at 2 and 5 weeks of age, respectively. In addition, in farm 2, sows were also vaccinated with the same vaccine before farrowing and penicillin was administered to the piglets at first day of life. To measure antibody levels, serum samples were taken before the first vaccination and 3 weeks after the second vaccination (9 and 8 weeks of age for farm 1 and 2, respectively). Nasal and rectal microbiota samples were obtained from swabs taken before the first vaccination. Similar times were used for the sampling of non-vaccinated pigs in a third farm (farm 3); i.e. microbiota samples at 1 week and serum samples at 1 and 9 weeks of age. Swabs were stored in 1000 µL DNA/RNA shield (Zymo Research) at 4ºC before further processing. These samples were also used in a previous study assessing gut-associated components of the nasal microbiota (27) and are available at NCBI’s SRA database under BioProject ID PRJNA981084.

Detection of G. parasuis by PCR

The presence of virulent and non-virulent strains of G. parasuis in the nasal samples was confirmed by PCR of the specific vtaA leader sequence, as described in Galofré-Milà, et al. (28), that allows the differentiation of virulent and non-virulent G. parasuis.

Antibody levels

Antibodies against G. parasuis were measured in serum using an in-house ELISA previously described (29). Plates were coated overnight at 4ºC with 250 ng of F4 (a protein fragment from the outer membrane proteins VtaA of G. parasuis) in 50 µl of carbonate-bicarbonate buffer per well. After washing, wells were blocked with 1% casein in phosphate-buffered saline (PBS) with 0.05% Tween 20 (PBS-Tw20). Sera were diluted 1:100 in blocking solution and added to the wells. After 1 h of incubation at 37ºC, wells were washed and incubated with a goat anti-porcine IgG HRP-conjugated antibody (Sigma-Aldrich, Madrid, Spain) diluted 1:10,000. Positive reactions in the ELISA were developed using the 3,3,3,5-tetramethylbenzidine (TMB) substrate (Sigma-Aldrich, Madrid, Spain) and the reactions were stopped with 1 N sulfuric acid. Plates were then read in a Power Wave XS spectrophotometer (Biotech, Winooski, VT, USA) at 450 nm.

A tentative classification into good (higher response) or bad (lower response) responders was performed in each group according to the variation between the initial level of antibodies and the level of antibodies at 8–9 weeks of age (delta antibody value, DAb). The piglets showing variations above the median of the group were considered as good responders and those below the median as bad responders. To deal with farm variability, this classification was done independently within each farm. There were two piglets that were not classified following these criteria. One piglet from farm 2 was considered a bad responder despite showing a DAb above the median, since it showed a clear reduction in the level of antibodies (from 1.197 to 0.605; delta = -0.592). We did not have the initial levels of antibodies for one piglet in farm 3, which was not included in the analyses using the DAb. Nevertheless, it was considered a bad responder according to its final level of antibodies in comparison to the rest of the animals in the farm. Hence, out of the twenty-four piglets, ten were considered to have a good response while fourteen were considered bad responders.

Microbiota sequencing

DNA was extracted from the nasal and rectal swabs following ZymoBIOMICS protocol, with the following modifications. Microbial cell lysis was performed by adding 700 µL of lysis buffer to 350 µL of sample, and DNA was eluted in 50 µL of elution buffer. DNA concentration was measured with a BioDrop DUO (BioDrop Ltd) and stored at -80ºC. A negative sample consisting of DNA/RNA shield alone was included as control.

The library preparation and Illumina sequencing were performed at Servei de Genòmica, Universitat Autònoma de Barcelona. Variable regions 3 and 4 (V3-V4) from 16S rRNA gene were sequenced from genomic libraries prepared using Illumina recommended primers for these variable regions of the gene (fwd 5'TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG, rev 5' GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC) and following Illumina protocol (Illumina pair-end 2X300 bp, MS-102-2003 MiSeq Re285 agent Kit v2, 500 cycle). Sequenced amplicons lengths (expected 460 bp) were checked on a Bioanalyzer DNA 1000 chip (Agilent).

Microbiota bioinformatic analysis

The analysis of the 16S rRNA gene amplicons was performed using Quantitative Insights Into Microbial Ecology (QIIME) 2 software in its 2023.9 version (30). After importing the paired-end raw reads, primers were removed with q2 cutadapt (31), with the option to discard any sequence not containing the primers. Reads were denoised (quality filtered, paired-end merged, chimera cleaned and sorted into Amplicon Sequence Variants or ASVs) with DADA2 (32) used as a qiime2 plugin. Additionally, low quality positions at the 3’ end of the reads were removed. Three extra filtering steps were applied after denoising. The first one, to remove non-prokaryotic sequences with q2 quality control (33) by discarding all ASVs that did not match the Greengenes 13_8 database (34) clustered at 88% identity, after aligning with VSEARCH (35) under permissive parameters (65% identity and 50% query coverage). The second, to discard all sequences classified as Archaea, Chloroplast or Mitochondria. And third, all ASVs present in the negative control sample were removed from the analysis (21 ASVs; as previously done (27)) using ID-based filtering. The phylogenetic tree was built using FastTree (36) after aligning the remaining ASVs with MAFFT (37). Taxonomy was assigned to ASVs using a scikit-learn naïve Bayes classifier (38), previously trained against V3-V4 16S rRNA gene region to increase its accuracy (39) using the same Greengenes 13_8 database (clustered at 99% identity). Functional genes present in the predicted metagenome were inferred with PICRUST2 (40) and mapped to KEGG database (41) modules.

The diversity analysis was done at a common depth of 76,253, corresponding to the lowest sampling depth using q2 diversity. The alpha diversity of the samples was measured with Shannon (42) and Chao1 (43) indexes and the beta diversity with Jaccard (44) and Bray-Curtis (45) dissimilarity indexes (qualitative and quantitative, respectively). The correlation of the vaccine response with alpha diversity indexes was calculated with Spearman correlation coefficient (46) with cor.test function in Stats R package (47) version 4.3.1. Correlation between beta diversity and vaccine response (antibody delta) was computed with Mantel test (999 permutations) included in q2 diversity beta-correlation (48). Correlations between taxa/functional modules and antibody response were inferred using Maaslin2 R package (49) filtering taxa with less than 0.01 abundance and the rest of the parameters set to default, using abundances normalized relatively per sample as input. After Benjamini-Hochberg correction, significance was considered when q < 0.05.

In the discrete comparative analysis (good against bad responders), alpha diversity differences between groups were estimated by pairwise non-parametric t-tests (999 random permutations) using q2 diversity alpha-group significance (50). In the beta diversity discrete analysis, the Principal Coordinate Analysis (PCoA) was computed with q2 diversity core-metrics (51, 52), which was visualized in R with qiime2R package (53). The significance of beta diversity comparisons was tested by PERMANOVA pairwise tests (999 permutations), and the percentage of explanation of the studied variables using Adonis function from Vegan R software package (54), using q2 diversity beta-group-significance (55). For all mentioned tests, P values lower than 0.05 were considered significative.

The healthy swine nasal core microbiota was obtained by filtering all ASVs from the most prevalent genera in healthy pigs as described previously (56). Briefly, all ASVs not classified within the stated genera were filtered out using q2 feature-table filtering options. The prevalence threshold to consider a specific genus as core-microbiota was set to 80%. To find taxa differentially abundant on the remaining nasal core-microbiota between good and bad responders we used Lefse (57) under its default parameters to perform a Linear Discriminant Analysis (LDA). Taxa showing a LDA score > 2 between the two groups were considered as significantly different.

R script language (version 4.2.2) was used in RStudio environment (version 2022.07.0) (58) to process Qiime2 microbiome generated data as well as to generate plots using qiime2r, ggplot2 (59), tidyverse (60) and reshape2 (61) packages.

Statistical modelling

Prior to undertaking statistical analysis, microbiota composition at order level was screened for unlikely or missing values. No data were excluded on this basis. Subsequently, a descriptive statistical analysis was carried out to the nasal and rectal microbiota composition based on the relative abundance of ASVs for both DNA and RNA. We ran two different statistical models of nasal microbiota including a multivariable logistic regression model with the variation (DAb) in vaccine response into good (better response) or bad (worse response) responders as the outcome variable and a multivariable linear regression with the actual value of the DAb in vaccine response as the continuous outcome variable. This has been applied for both microbiota composition based on nasal and rectal samples. Initially, a univariable model was carried out to test the unconditional associations between dependent and various independent variables (relative abundances of members of the nasal microbiota). Only independent variables with P ≤ 0.25 in this initial screening were included in multivariable logistic and linear regression models in accordance with Dohoo et al. (62). To account for the farm variations, an additional model was developed accounting for farm as a random effect for each of nasal and rectal DNA and RNA using generalized mixed models. For each model, the significant independent variables from the univariable analysis were then offered to a multivariable model and a manual backward elimination was implemented, to obtain a final model that exclusively included variables with a P value < 0.05, considered as significant. The P value and the regression coefficient (b) with a 95% confidence interval (95% CI) were reported for each variable. In a similar approach, we developed a model following the same analysis for relative abundances of members of the rectal microbiota. In all statistical analyses, the results were regarded as significant at P ≤ 0.05. All statistical analyses were conducted using the R version 3.3.3 software.

The increase in antibody levels correlated with increase in nasal microbiota diversity

To examine the response to vaccine administration or to natural exposure to G. parasuis, we determined the antibody levels from the piglets before and after vaccination or in equivalent times for those non-vaccinated but naturally exposed to the pathobiont, which was detected by PCR in the nasal samples (Table 1). All piglets showed maternal derived antibodies, although at different levels (farm 2 showed higher maternal antibodies due to sow vaccination). Since the levels of antibodies were diverse at both tested timepoints, the dynamics of the antibody levels (response) were measured using the difference between both values in each animal (delta antibody value, DAb). This value was used to tentatively classify the piglets into good or bad responders, i.e., piglets showing variations above the median of the group or below the median, respectively (see Methods, Supplementary Fig. 1 and Table 1).

Table 1

Level of antibodies against *G. parasuis* (shown as A_450nm from ELISA) and presence of virulent (Vir) and non-virulent (Nvir) *G. parasuis* by PCR.
Farm	Vaccination	Antibodies at 1–3 weeks of age	Antibodies at 8–9 weeks of age	Vir PCR	Nvir PCR	Variation (DAb)*	Response**
Farm 1	Weeks 3 and 6	0.265	0.65	-	+	0.385	Bad
		0.699	0.682	-	+	-0.017	Bad
		0.62	1.55	-	+	0.93	Good
		0.837	1.619	-	+	0.782	Good
		0.859	0.933	+	+	0.074	Bad
		0.54	1.496	-	+	0.956	Good
		1.511	0.959	-	+	-0.552	Bad
		0.757	1.547	-	+	0.79	Good
Farm 2	Weeks 2 and 5	2.083	0.906	-	+	-1.177	Bad
		2.474	0.719	-	+	-1.755	Bad
		2.243	0.835	-	-	-1.408	Bad
		1.927	1.913	+	+	-0.014	Good
		1.197	0.605	-	+	-0.592	Bad
		0.656	1.715	-	+	1.059	Good
		1.061	1.138	-	+	0.077	Good
		2.196	1.069	-	+	-1.127	Bad
Farm 3	Unvaccinated	1.56	0.275	-	+	-1.285	Bad
		0.448	0.623	+	+	0.175	Good
		0.429	0.322	+	+	-0.107	Bad
		1.006	0.433	+	+	-0.573	Bad
		0.363	0.514	+	+	0.151	Good
		0.325	0.682	+	+	0.357	Good
		N.A.	0.42	+	+	N.A.	Bad
		0.461	0.121	-	+	-0.34	Bad

* The variation between the level of antibodies at the two timepoints (DAb).

** A tentative classification into good or bad antibody responders (see Methods).

N.A.: Not available

The association between the nasal microbiota composition and the response to vaccination was initially evaluated in samples from the two vaccinated farms. A strong positive correlation of the alpha diversity estimated through Shannon index was observed with the antibody variation levels, DAb (Spearman Rho = 0.7, P = 0.003, Fig. 1A). A similar but more moderate tendency was observed for Chao1 index (Spearman Rho = 0.48, P = 0.06), indicating that piglets with a more diverse nasal microbiota at vaccination time, responded better to vaccination. Regarding the beta diversity analysis, a weak correlation was detected between the qualitative and quantitative distance matrices and the DAb (Jaccard and Bray-Curtis Spearman Rho = 0.29 and 0.42, respectively; Mantel test P < 0.007), demonstrating no clear correlation between the compositional distances of the nasal microbiota and the response to vaccination.

To examine if the influence of the nasal microbiota was specific for vaccinated animals, we included in the analysis unvaccinated animals with natural exposure to G. parasuis (farm 3), confirmed by 16S sequencing and specific PCR. Although we could not detect a significant correlation between the DAb and the alpha diversity (Shannon index), probably due to the low number of samples, the tendency observed was similar to that detected in vaccinated piglets, i.e. animals with more diverse nasal microbiota showed increased levels of specific antibodies against G. parasuis (Spearman Rho = 0.71, P = 0.088, Fig. 1B). When the three farms were analyzed together, the Shannon index correlated positively with the DAb (Spearman Rho = 0.5, P = 0.015, Fig. 1C), while the Chao1 index showed a weaker tendency (Spearman Rho = 0.38, P = 0.07). These findings support that animals with more diverse nasal microbiotas tend to exhibit higher level of antibodies against G. parasuis even when they are not vaccinated and naturally encounter the pathobiont. Again, the correlation between the beta diversity distance matrix and the antibody variation was also weak under both assessed metrics (Jaccard and Bray-Curtis Spearman Rho = 0.27 and 0.28, respectively; Mantel test P < 0.004).

Nasal microbiota taxa associated with response to G. parasuis

To identify nasal microbiota members associated with the antibody response, we investigated the correlations between the taxa and the DAb in the three farms. First, we examined if the response to G. parasuis correlated with the abundance of this bacterium in the nasal microbiota of the piglets and found no association (Supplementary Fig. 2). When we analyzed the global microbiota composition, 6 orders and 22 genera showed to correlate with the DAb, most of them positively. Clostridiales and Bacteroidales were the most abundant orders identified with positive correlation with the DAb (q < 0.05, Fig. 2). Other orders were also positively correlated, namely Enterobacteriales, Bacillales and Fusobacteriales (q < 0.05, Fig. 2and Supplementary Table 1), while the only order that correlated negatively with the DAb was Pseudomonadales (q = 0.043). Correlations at genus level also followed the same dynamics observed for the corresponding orders (Supplementary Fig. 3 and Supplementary Table 1), such as Prevotella (Bacteroidales) and several members of Clostridiaceae, Lachnospiraaceae, Ruminococcaceae and Veillonellaceae families (belonging to order Clostridiales), which correlated positively with the delta antibody. Other genera with a positive correlation with the antibody variation were Klebsiella, Staphylococcus, Fusobacterium, Pasteurellaceae (uncl.), Escherichia, Moraxella, and Corynebacterium. Within Pseudomonadales, only a divergent Moraxella (originally classified as Enhydrobacter in the used database but confirmed as Moraxella by BLASTn) negatively correlated with DAb (see Supplementary Table 1 for all correlations). To further investigate whether these taxa associations might indicate different functions of the nasal microbiota of animals according with variable antibody response, the association between the inferred metagenome functional modules and the DAb was also evaluated. Three modules were found to be positively associated with DAb (q < 0.05), i.e., iron complex transport system, glycolysis, and simple sugar transport system (Supplementary Table 1).

Some of the previous associations at order level were also identified using multivariable linear regression with the actual value of the antibody variation (DAb) as a continuous outcome variable, including two models with and without farm as a random effect which results are shown on Table 2. Again, the presence of Bacteroidales, Clostridiales, together with Spirochaetales in the nasal mucosa of piglets was positively associated with the antibody response to G. parasuis. Whereas the presence of Pseudomonadales in the nasal mucosa of piglets was negatively associated with the antibody response to G. parasuis.

Table 2

Nasal microbiota members associated with the variation in antibody response (measured as a continuous outcome variable) to *G. parasuis* (P ≤ 0.05) in 23 animals in 3 farms using a mixed effects generalized linear regression model.
Variable	Coefficient Estimate	95% CI	P value*
Pseudomonadales	-1.77	(-4.02) – 0.48	0.016
Bacteroidales	11.71	(-12.99) – 36.41	0.005
Enterobacteriales	18.27	(-26.36) – 62.91	0.845
Clostridiales	5.03	(-6.52) – 16.57	0.028
Erysipelotrichales	-216.01	(-468.99) – 36.98	0.518
Fusobacteriales	89.13	(-31.95) – 210.21	0.272
Spirochaetales	161.34	(-79.71) – 402.39	0.037
Desulfovibrionales	-229.34	(-591.80) – 133.13	0.172
Coriobacteriales	-116.41	(-427.39) – 194.59	0.099
Rhizobiales	-195.29	(-491.74) – 101.16	0.087
*Taxa with P > 0.05 were kept in the final model due to significant confounding effect with other significantly associated taxa

Rectal microbiota diversity also correlated with antibody response

Different microbiotas can crosstalk with the immune system and have a systemic effect. Since previous studies have highlighted the role of the gut microbiota with the antibody response, we aimed to analyze whether similar associations to those detected between the nasal microbiota and the antibody response were also occurring in the rectal microbiota. The alpha diversity of the rectal microbiota of animals from the three farms moderately correlated with the antibody response (Spearman Rho = 0.44 and 0.6; P = 0.036 and 0.003 for Chao1 and Shannon indexes, respectively, supplementary Fig. 4). The beta diversity of the rectal microbiota barely correlated with the DAb (Jaccard and Bray-Curtis Spearman Rho = 0.21; Mantel test P < 0.006). No significant correlations were found between taxa in the rectal samples and the DAb using Maaslin2, except for a Mogibacteriaceae unclassified genus (Clostridiales), positively associated with the DAb (q = 0.021). Nevertheless, four orders were identified as significantly correlating with the delta antibody in both multivariable linear regression models using the DAb as a continuous outcome variable (accounting for farm variations as a random effect and not). The presence of GMD14H09 and Clostridiales in the rectal microbiota of piglets was associated with better antibody response to G. parasuis, whereas the presence of Pasteurellales and Enterobacteriales was negatively associated with the antibody response to G. parasuis (Supplementary Fig. 5 and Table 3). The farm effect was negligible.

Table 3

Rectal microbiota members associated with the variation in antibody response (as a continuous outcome variable) to *G. parasuis* (P ≤ 0.05) in 23 animals in 3 farms using a mixed effects generalized linear regression model.
Variable	Estimate	95% CI	P value
Clostridiales	0.301	(-2.327) – 2.929	0.0075
GMD14H09	76.900	2.731–151.069	0.036
Pasteurellales	-42.895	(-84.076) – (-1.715)	0.036
Enterobacteriales	-4.045	(-7.247) – (-0.842)	0.016

Microbiota associations with piglets classified as good antibody responders

To further investigate and validate the associations with a good antibody response, piglets were tentatively classified according to the DAb. Piglets showing variations above the median of the group were considered good responders, while those below the median were classified as bad responders (see Methods, Supplementary Fig. 1 and Table 1).

Using this classification, a more diverse and richer microbiota was observed in the good responders compared to the bad ones (Shannon and Chao1 indexes, P < 0.05; Fig. 3A). In the beta diversity analysis, no significant differences were detected between good and bad responders. A strong environmental effect caused by the analysis of three different farms together was observed in the clustering of the samples, quantified to be 29% qualitatively and 48% quantitatively (Adonis test R² using Jaccard and Bray-Curtis indexes, respectively, P = 0.001, Fig. 3B). However, the differences between responders were still non-significant when the effect of the farms was used as a nested variable.

With the aim to reduce the effect of the variability among farms and knowing that is frequent to detect transient environmental taxa in nasal microbiota samples, the ASVs from genera not included in the core of the swine nasal microbiota were filtered out (see Methods). Interestingly, the significance of the alpha diversity comparison between good and bad responders increased (Shannon and Chao1 P < 0.009) indicating that differences observed were not caused by transient taxa and truly relied on common swine nasal colonizers. To reveal nasal taxa that were associated with good or bad responders to G. parasuis beyond the farm environmental effect, the filtered microbiota composition from all samples was submitted to linear discriminant analysis (Lefse) at different taxonomic levels (Fig. 4 and Supplementary Fig. 6). In nasal samples, 4 orders and 20 genera from the core of the nasal microbiota were found to significantly discriminate good responders (LDA score > 2), independently of the farm of origin. Among the detected taxa, Clostridiales and Bacteroidales were also found to be associated with good response in the previous analysis using the numeric antibody delta, while Erysipelotrichales was only detected in this discrete analysis (Lefse). Most genera associated with good vaccination response belonged to the above orders, with several genera of the Ruminococcaceae and Lachnospiraceae families (among other Clostridiales), as well as Prevotella (Bacteroidales). Neisseria was also associated with the group with better response. No taxa with a LDA score > 2 were associated with a bad response.

Regarding the rectal microbiota, no differences were detected in alpha, beta diversity or taxonomical composition when good and bad responders were compared.

In this study, we evaluated the swine nasal and rectal microbiota composition according to the response to vaccination against G. parasuis or natural exposure to this bacterium. Despite all animals in this study being healthy and, therefore, no major changes were expected in their microbiota composition, several differences were identified according to the antibody response. To our knowledge, this is the first study that correlates the porcine nasal microbiota composition as a predictor for the response to vaccination providing some notable findings that highlight the relationship between the diversity of the microbiota and the immune response. Moreover, the introduction of an unvaccinated control farm emphasized that these claims could also stand for animals that naturally encounter pathogens.

The initial levels of antibodies were variable between farms, but also within the same farm indicating variable level of maternal immunity at the initial sampling time of the piglets (1–3 weeks of age). Nevertheless, independently of the starting level of antibodies in each farm, piglets exhibited variable dynamics in the levels of antibodies. To analyze the antibody response, and also deal with variability between animals, we decided to measure the response to G. parasuis using the difference between the two time points i.e., after and before vaccination or in equivalent times for the unvaccinated farm (delta antibody value, or DAb).

In agreement with previous reports (8, 13), piglets in this study with more diverse microbiota showed higher antibody response. In the case of nasal samples, these differences were clearer when transient taxa were removed from the analysis (by keeping the healthy core microbiota), highlighting that the major drivers were swine nasal commensals. Moreover, the fact that stronger correlations were observed when alpha diversity was measured with Shannon index rather than Chao1 suggests that not only species richness but also community evenness are important for better immune responses. We found three metabolic modules inferred from the microbiota composition that were positively correlated with the antibody response, that can possibly be related with an increased alpha diversity (these three modules may be associated with more bacterial activity). Several investigations targeting the swine microbiota in different scenarios also reported higher alpha diversity in healthy groups compared to different diseased animals or environments (63–65) and associated it with a better immune status of the animals. The well-known notion that the microbiota participates in the maturation of the immune system (7) also agrees with these results. Contrarily, other studies on gut microbiota did not report any relationship between alpha diversity and response to vaccination (11, 16, 18, 19, 24, 25). This apparent contradiction may be explained by different factors, such as the time when the microbiota was evaluated, the type of vaccine or immune stimulus or the host studied, which all can affect the output of the analyses.

Since our samples came from three different environments (farms), we did not focus the analysis on ASVs, and tried to find associations at higher taxonomic levels (order and genus). We detected several taxa in the nasal cavities of the piglets that positively correlated with a higher increase in the level of antibodies. Among these, taxa frequently found in the gut microbiota (Bacteroidales and Clostridiales) appeared as the most associated. It is not unusual to find these taxa in the swine respiratory tract (27, 66), most are included in the nasal core, and their presence has been recently discussed in a previous study from our group that confirms these bacteria are indeed present in this body site (27). Nevertheless, the role of these gut microbiota-related taxa in the upper respiratory tract is still uncertain (27, 67), and future studies focusing on these microbes may help understanding their connection with the immune response. Interestingly, genera within these two orders appeared in higher abundances in farms without respiratory disease condition compared to farms with Glässer’s disease (63), as well as when compared with farms with polyserositis caused by Mycoplasma hyorhinis (64). Besides these, other taxa frequently found in the swine nasal microbiota, such as Fusobacterium, Staphylococcus, Pasteurellaceae (uncl.), Moraxella and Neisseria (66) were positively associated with the increase of antibodies against G. parasuis. The presence of these taxa may be important for the immune system stimulation and therefore, for the immune response. Moraxella has already been proved as a potential nasal probiotic within a five-bacteria cocktail (68). However, very few nasal colonizers are being considered for the moment as probiotics to enhance the immune response (69), which deserve further investigation. Regarding taxa negatively correlated with the antibody response, only a different group from Moraxella genus negatively correlated with Glaesserella antibody response. In a previous study (63), higher abundances of Moraxellaceae (Moraxella and Enhydrobacter) were found in farms with Glässer’s disease compared to control farms. The variability within the genus Moraxella may explain the different role in health status depending on the species or even the specific strain, as it has been previously observed regarding the virulence of these bacteria (70). Similarly, in gut microbiota, immune stimulation by probiotics is highly influenced by variables like the specific strain, host or environment (also explaining differences across farms in this study) (71).

In the case of rectal samples, fewer taxa were identified as related with the response to vaccination, possibly because this microbiota can be more resilient to exposure to environmental taxa compared to the nasal microbiota. Two gut-microbiota core orders (Clostridiales and Enterobacteriales) (72) were identified as positively and negatively correlated with the antibody response, respectively. Munyaka, et al. showed that in vaccination against Mycoplasma hyopneumoniae, Operational Taxonomic Units (OTUs) from Bacteroidales (mainly Prevotella) found in the fecal microbiota at the moment of vaccination were positively correlated with higher antibody titters (24, 25). OTUs classified within Clostridiales were associated with both high and low responses, while no associations within Enterobacteriales were found. The fact that in these studies samples came from the same environment (farm) and the analyses were performed at OTU level could explain the differences with this study (three farms and associations evaluated at higher taxonomical levels). On the other hand and in agreement with our results, studies on the role of the human microbiota also associated several taxa within Clostridiales with better immune responses (8, 12, 13, 18), possibly through the production of short-chain fatty acids that stimulate the immune system (8, 73). However, other studies found taxa within Clostridiales associated with immune responses below average (19), suggesting differences in the role that taxa within this order may have. Aligned with our findings, higher abundances of Enterobacteriaceae (16) and Enterobacteriales (14) were associated with lower responses to vaccination. In any case, although it is frequent to find bacteria within Clostridiales associated with health, while some Enterobacteriales can be associated with disease (74), both orders contain taxa that contribute metabolically to immune system homeostasis and stimulation (11, 75). Two lower abundance orders (GMD14H09 and Pasteurellales) were also identified in this study as positively and negatively associated with the antibody response, respectively; but their presence and role are unclear in this microbiota. Finally, despite we did not find any associations within Bacteroidetes and Actinobacteria, such as Prevotella and Bifidobacterium, these orders may play a role in immune response in humans and pigs (11, 13–15, 24, 25), and also deserve further study.

At last, a tentative discrete analysis was tried by grouping the animals into good or bad responders, which confirmed the results obtained with the correlation with the level of antibodies regarding the nasal microbiota. In this study, the analysis using the immune response as a numerical value proved a fruitful strategy that may stand as a good alternative to minimize the variability due to uncontrolled factors (such as environmental differences caused by farm management including antimicrobial usage) that may obscure relevant findings.

This study highlights the importance of the diversity of the nasal and rectal microbiotas in the antibody response when facing an antigen (naturally or in vaccination). The associations of several bacterial species with a better response may serve as a starting point to investigate how these taxa stimulate the immune system from the nasal cavity. Additionally, it may help to design targeted interventions to enhance immunization and protection in animals.

Ethics approval and consent to participate

Animal experimentation was performed following proper veterinary practices, in accordance with European (Directive 2010/63/EU) and Spanish (Real Decreto 53/2013) regulation, in compliance with the ARRIVE guidelines (https://arriveguidelines.org/about). Animal sampling in farms was approved by the Ethics Commission in Animal Experimentation of the Generalitat de Catalunya (Protocol number 11213).

Availability of data and materials

The raw sequencing data used in this study can be found at NCBI’s SRA database under BioProject ID PRJNA981084.

Competing interests

The authors declare that HIPRA did not influence the analysis and interpretation of the results.

Funding

This work was supported by the Spanish Ministry of Research and Innovation (project PID2019-106233RB-I00/AEI/10.13039/501100011033 and PID2022-138657OB-I00/AEI/10.13039/501100011033). POG is supported by FPU a (FPU19/02126/AEI/10.13039/501100011033) fellowship from the Spanish Ministry of Science, Innovation and Universities. IRTA-CReSA is also supported by the Centres de Recerca de Catalunya (CERCA) Program from the Generalitat de Catalunya.

Authors' contributions

Designed the study: MS, FCF, VA. Participated in the samplings: MS, EBV. Performed the analyses: POG, YM. Supervised the analyses: FCF, VA. Wrote the manuscript with contributions from all authors: POG. All the authors revised, edited and approved the manuscript.

Acknowledgements

The authors want to acknowledge the technical help of Nuria Aloy and Nuria Galofré from IRTA-CReSA.

Chaplin DD. Overview of the immune response. J Allergy Clin Immunol. 2010;125(2 Suppl 2):S3-S23. doi:10.1016/j.jaci.2009.12.980
Bartlett BL, Pellicane AJ, Tyring SK. Vaccine immunology. Dermatol Ther. 2009;22(2):104-109. doi:10.1111/j.1529-8019.2009.01223.x
Andre FE, Booy R, Bock HL, Clemens J, Datta SK, John TJ, et al. Vaccination greatly reduces disease, disability, death and inequity worldwide. Bull World Health Organ. 2008;86(2):140-6. doi: 10.2471/blt.07.040089.
Lynn DJ, Benson SC, Lynn MA, Pulendran B. Modulation of immune responses to vaccination by the microbiota: implications and potential mechanisms. Nat Rev Immunol. 2022;22(1):33-46. doi:10.1038/s41577-021-00554-7
Roth JA. Mechanistic bases for adverse vaccine reactions and vaccine failures. Adv Vet Med. 1999;41:681-700. doi:10.1016/s0065-3519(99)80053-6
Thacker EL, Thacker BJ, Boettcher TB, Jayappa H. Comparison of antibody production, lymphocyte stimulation, and protection induced by four commercial Mycoplasma hyopneumoniae bacterins. Swine Health and Prod. 1998;6(3):107–112
de Jong SE, Olin A, Pulendran B. The Impact of the Microbiome on Immunity to Vaccination in Humans. Cell Host Microbe. 2020;28(2):169-179. doi:10.1016/j.chom.2020.06.014
Seong H, Choi BK, Han YH, Kim JH, Gim JA, Lim S, et al. Gut microbiota as a potential key to modulating humoral immunogenicity of new platform COVID-19 vaccines. Signal Transduct Target Ther. 2023;8(1):178. doi: 10.1038/s41392-023-01445-0.
Rojas C, Gálvez-Jirón F, De Solminihac J, Padilla C, Cárcamo I, Villalón N, et al. Crosstalk between Body Microbiota and the Regulation of Immunity. J Immunol Res. 2022;2022:6274265. doi: 10.1155/2022/6274265.
Ruck CE, Odumade OA, Smolen KK. Vaccine Interactions With the Infant Microbiome: Do They Define Health and Disease? Front Pediatr. 2020;8:565368. doi: 10.3389/fped.2020.565368.
Harris VC, Armah G, Fuentes S, Korpela KE, Parashar U, Victor JC, et al. Significant Correlation Between the Infant Gut Microbiome and Rotavirus Vaccine Response in Rural Ghana. J Infect Dis. 2017;215(1):34-41. doi: 10.1093/infdis/jiw518.
Harris V, Ali A, Fuentes S, Korpela K, Kazi M, Tate J, et al. Rotavirus vaccine response correlates with the infant gut microbiota composition in Pakistan. Gut Microbes. 2018;9(2):93-101. doi: 10.1080/19490976.2017.1376162.
Eloe-Fadrosh EA, McArthur MA, Seekatz AM, Drabek EF, Rasko DA, Sztein MB, Fraser CM. Impact of oral typhoid vaccination on the human gut microbiota and correlations with s. Typhi-specific immunological responses. PLoS One. 2013;8(4):e62026. doi: 10.1371/journal.pone.0062026.
Huda MN, Lewis Z, Kalanetra KM, Rashid M, Ahmad SM, Raqib R, et al. Stool microbiota and vaccine responses of infants. Pediatrics. 2014;134(2):e362-72. doi: 10.1542/peds.2013-3937.
Huda MN, Ahmad SM, Alam MJ, Khanam A, Kalanetra KM, Taft DH, et al. Bifidobacterium Abundance in Early Infancy and Vaccine Response at 2 Years of Age. Pediatrics. 2019;143(2):e20181489. doi: 10.1542/peds.2018-1489.
Fix J, Chandrashekhar K, Perez J, Bucardo F, Hudgens MG, Yuan L, et al. Association between Gut Microbiome Composition and Rotavirus Vaccine Response among Nicaraguan Infants. Am J Trop Med Hyg. 2020;102(1):213-219. doi: 10.4269/ajtmh.19-0355.
Ng SC, Peng Y, Zhang L, Mok CK, Zhao S, Li A, et al. Gut microbiota composition is associated with SARS-CoV-2 vaccine immunogenicity and adverse events. Gut. 2022;71(6):1106-1116. doi: 10.1136/gutjnl-2021-326563.
Tang B, Tang L, He W, Jiang X, Hu C, Li Y, et al. Correlation of gut microbiota and metabolic functions with the antibody response to the BBIBP-CorV vaccine. Cell Rep Med. 2022;3(10):100752. doi: 10.1016/j.xcrm.2022.100752.
Alexander JL, Mullish BH, Danckert NP, Liu Z, Olbei ML, Saifuddin A, et al. The gut microbiota and metabolome are associated with diminished COVID-19 vaccine-induced antibody responses in immunosuppressed inflammatory bowel disease patients. EBioMedicine. 2023;88:104430. doi: 10.1016/j.ebiom.2022.104430.
Hagan T, Cortese M, Rouphael N, Boudreau C, Linde C, Maddur MS, et al. Antibiotics-Driven Gut Microbiome Perturbation Alters Immunity to Vaccines in Humans. Cell. 2019;178(6):1313-1328.e13. doi: 10.1016/j.cell.2019.08.010.
Guidelines for the Prudent Use of Antimicrobials in Veterinary Medicine. Official Journal of the European Union (2015/C 299/04); Available online: https://ec.europa.eu/health/sites/health/files/antimicrobial_resistance/docs/2015_prudent_use_guidelines_en.pdf.
Costa-Hurtado M, Barba-Vidal E, Maldonado J, Aragon V. Update on Glässer's disease: How to control the disease under restrictive use of antimicrobials. Vet Microbiol. 2020;242:108595. doi:10.1016/j.vetmic.2020.108595
López-Serrano S, Neila-Ibáñez C, Costa-Hurtado M, Mahmmod Y, Martínez-Martínez J, Galindo-Cardiel IJ, et al. Sow Vaccination with a Protein Fragment against Virulent Glaesserella (Haemophilus) parasuis Modulates Immunity Traits in Their Offspring. Vaccines (Basel). 2021;9(5):534. doi: 10.3390/vaccines9050534.
Munyaka PM, Kommadath A, Fouhse J, Wilkinson J, Diether N, Stothard P, et al Characterization of whole blood transcriptome and early-life fecal microbiota in high and low responder pigs before, and after vaccination for Mycoplasma hyopneumoniae. Vaccine. 2019;37(13):1743-1755. doi: 10.1016/j.vaccine.2019.02.016.
Munyaka PM, Blanc F, Estellé J, Lemonnier G, Leplat JJ, Rossignol MN, et al. Discovery of Predictors of Mycoplasma hyopneumoniae Vaccine Response Efficiency in Pigs: 16S rRNA Gene Fecal Microbiota Analysis. Microorganisms. 2020;8(8):1151. doi: 10.3390/microorganisms8081151.
Sanglard LP, Schmitz-Esser S, Gray KA, Linhares DCL, Yeoman CJ, Dekkers JCM, et al. Investigating the relationship between vaginal microbiota and host genetics and their impact on immune response and farrowing traits in commercial gilts. J Anim Breed Genet. 2020;137(1):84-102. doi: 10.1111/jbg.12456.
Obregon-Gutierrez P, Bonillo-Lopez L, Correa-Fiz F, Sibila M, Segalés J, Kochanowski K, Aragon V. Gut-associated microbes are present and active in the pig nasal cavity. Sci Rep. 2024;14(1):8470. doi: 10.1038/s41598-024-58681-9.
Galofré-Milà N, Correa-Fiz F, Lacouture S, Gottschalk M, Strutzberg-Minder K, Bensaid A, et al. A robust PCR for the differentiation of potential virulent strains of Haemophilus parasuis. BMC Vet Res. 2017;13(1):124. doi: 10.1186/s12917-017-1041-4.
López-Serrano S, Mahmmod YS, Christensen D, Ebensen T, Guzmán CA, Rodríguez F, et al. Immune responses following neonatal vaccination with conserved F4 fragment of VtaA proteins from virulent Glaesserella parasuis adjuvanted with CAF®01 or CDA. Vaccine X. 2023;14:100330. doi: 10.1016/j.jvacx.2023.100330.
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852-857. doi: 10.1038/s41587-019-0209-9.
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10–12. doi: 10.14806/ej.17.1.200
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods. 2016;13(7):581–583. doi: 10.1038/nmeth.3869.
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421. doi: 10.1186/1471-2105-10-421.
McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6(3):610-8. doi: 10.1038/ismej.2011.139.
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: A versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. doi: 10.7717/peerj.2584
Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490. doi:10.1371/journal.pone.0009490
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 2013;30(4):772–780. doi: 10.1093/molbev/mst010
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. 2011. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, null (2/1/2011), 2825–2830.
Werner JJ, Koren O, Hugenholtz P, DeSantis TZ, Walters WA, Caporaso JG, et al. Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys. ISME J. 2012;6(1):94-103. doi: 10.1038/ismej.2011.82.
Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38(6):685-688. doi: 10.1038/s41587-020-0548-6.
Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.
Shannon C., Weaver W. The Mathematical Theory of Communication. Bell Syst. Tech. J. 1948;27:379–423. doi: 10.1002/j.1538-7305.1948.tb01338.x.
Eren MI, Chao A, Hwang WH and Colwell RK. Estimating the richness of a population when the maximum number of classes is fixed: A nonparametric solution to an archaeological problem. PLoS One 2012;7(5), e34179. doi.org/10.1371/journal.pone.0034179.
Jaccard, P. Nouvelles Recherches sur la Distribution Florale (Rouge, 1908).
Sørensen TJ. A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and Its Application to Analyses of the Vegetation on Danish Commons. In I kommission hos E. Munksgaard; København, Demark (1948).
Spearman C. The Proof and Measurement of Association between Two Things. AJP. 1904;15 (1): 72–101. doi:10.2307/1412159. JSTOR 1412159.
R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967;27:209–220.
Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol. 2021;17(11):e1009442. doi: 10.1371/journal.pcbi.1009442.
Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc. 1952;47:583–621. doi: 10.1080/01621459.1952.10483441.
Nathan H, Per-Gunnar M, Yoel S and Mark T. An algorithm for the principal component analysis of large data sets. arXiv:1007.5510 (2010).
Legendre P and Legendre L. Numerical Ecology 499. Elsevier; 2012.
Bisanz J. Tutorial: integrating QIIME2 and R for data visualization and analysis using qiime2R (v0.99.6). 2021.
Oksanen J, Simpson GL, Blanchet FG,Kindt R, Legendre P, Minchin PR, et al. Vegan: Community Ecology Package. R Package Version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).
Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46. doi: 10.1111/j.1442-9993.2001.01070.pp.x.
Bonillo-Lopez L, Obregon-Gutierrez P, Huerta E, Correa-Fiz F, Sibila M, Aragon V. Intensive antibiotic treatment of sows with parenteral crystalline ceftiofur and tulathromycin alters the composition of the nasal microbiota of their offspring. Vet Res. 2023;54(1):112. doi: 10.1186/s13567-023-01237-y.
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. doi: 10.1186/gb-2011-12-6-r60.
RStudio|Open Source & Professional Software for Data Science Teams. https://rstudio.com/ (2023).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009). https://doi.org/10.1007/978-0-387-98141-3.
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the tidyverse. J Open Source Softw. 2019;4:1686.
Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21(12), 1–20 (2007).
Dohoo I, Martin W, Stryhn H. 2009. Veterinary Epidemiologic Research; MacPike, SM, Ed.; VER Inc.: Charlottetown, PE, Canada.
Correa-Fiz F, Fraile L, Aragon V. Piglet nasal microbiota at weaning may influence the development of Glässer's disease during the rearing period. BMC Genomics. 2016;17:404. doi: 10.1186/s12864-016-2700-8.
Blanco-Fuertes M, Correa-Fiz F, Fraile L, Sibila M, Aragon V. Altered Nasal Microbiota Composition Associated with Development of Polyserositis by Mycoplasma hyorhinis. Pathogens. 2021;10(5):603. doi: 10.3390/pathogens10050603.
Niederwerder MC, Jaing CJ, Thissen JB, Cino-Ozuna AG, McLoughlin KS, Rowland RR. Microbiome associations in pigs with the best and worst clinical outcomes following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2). Vet Microbiol. 2016;188:1-11. doi: 10.1016/j.vetmic.2016.03.008.
Pirolo M, Espinosa-Gongora C, Bogaert D, Guardabassi L. The porcine respiratory microbiome: recent insights and future challenges. Anim Microbiome. 2021;3(1):9. doi: 10.1186/s42523-020-00070-4.
Pena Cortes LC, LeVeque RM, Funk JA, Marsh TL, Mulks MH. Development of the Tonsil Microbiome in Pigs and Effects of Stress on the Microbiome. Front Vet Sci. 2018;5:220. doi: 10.3389/fvets.2018.00220.
Blanco-Fuertes M, Sibila M, Franzo G, Obregon-Gutierrez P, Illas F, Correa-Fiz F, Aragón V. Ceftiofur treatment of sows results in long-term alterations in the nasal microbiota of the offspring that can be ameliorated by inoculation of nasal colonizers. Anim Microbiome. 2023;5(1):53. doi: 10.1186/s42523-023-00275-3.
Zhang Y, Zhang Y, Liu F, Mao Y, Zhang Y, Zeng H, Ren S, et al. Mechanisms and applications of probiotics in prevention and treatment of swine diseases. Porcine Health Manag. 2023;9(1):5. doi: 10.1186/s40813-022-00295-6.
López-Serrano S, Galofré-Milà N, Costa-Hurtado M, Pérez-de-Rozas AM, Aragon V. Heterogeneity of Moraxella isolates found in the nasal cavities of piglets. BMC Vet Res. 2020;16(1):28. doi: 10.1186/s12917-020-2250-9.
Knecht D, Cholewińska P, Jankowska-Mąkosa A, Czyż K. Development of Swine's Digestive Tract Microbiota and Its Relation to Production Indices-A Review. Animals (Basel). 2020;10(3):527. doi: 10.3390/ani10030527.
Chen C, Zhou Y, Fu H, Xiong X, Fang S, Jiang H, et al. Expanded catalog of microbial genes and metagenome-assembled genomes from the pig gut microbiome. Nat Commun. 2021;12(1):1106. doi: 10.1038/s41467-021-21295-0.
Martin-Gallausiaux C, Marinelli L, Blottière HM, Larraufie P, Lapaque N. SCFA: mechanisms and functional importance in the gut. Proc Nutr Soc. 2021;80(1):37-49. doi: 10.1017/S0029665120006916.
Sun J, Du L, Li X, Zhong H, Ding Y, Liu Z, Ge L. Identification of the core bacteria in rectums of diarrheic and non-diarrheic piglets. Sci Rep. 2019;9(1):18675. doi: 10.1038/s41598-019-55328-y.
Wang J, Zhu N, Su X, Gao Y, Yang R. Gut-Microbiota-Derived Metabolites Maintain Gut and Systemic Immune Homeostasis. Cells. 2023;12(5):793. doi: 10.3390/cells12050793.

No competing interests reported.

suppfigure1.pdf
Supplementary Figure 1: Antibody variation of the samples under study (DAb). The antibody variation is measured as the difference between the level of antibodies after and before vaccination or equivalent times in non-vaccinated piglets) shown per farm. Classification into good (turquoise) or bad (mauve) responders is also provided.
suppfigure2.pdf
Supplementary Figure 2: Correlation between G. parasuis and antibody response. Spearman correlation between G. parasuisrelative abundance (all ASVs collapsed) and antibody response (DAb measured as the difference between the level of antibodies after and before vaccination or equivalent times in non-vaccinated piglets) are shown for nasal microbiota samples from the three farms. The tendency line depicted in the graph (dashed line) was generated using geom_smooth function (ggplot2) using linear model (lm) as the method.
suppfigure3.pdf
Supplementary Figure 3: Correlations with the antibody response and nasal microbiota at genus level. Scatter plots show the relative abundance versus DAb, measured as the difference between the level of antibodies after and before vaccination or equivalent times in non-vaccinated piglets. Only genera found significant with Maaslin2 are shown. Each tendency line depicted in the graphs (dashed lines) was generated using geom_smooth function (ggplot2) using linear model (lm) as the method. Color code corresponds to orders englobing these genera.
suppfigure4.pdf
Supplementary Figure 4: Correlation between rectal microbiota alpha diversity and antibody response. Spearman correlation and the tendency as a linear model (dashed lines) between alpha diversity in the rectal microbiota samples are shown, measured by Chao1 (A) or Shannon (B) indexes, and the antibody response (measured as the difference between the level of antibodies after and before vaccination or equivalent times in non-vaccinated piglets, or DAb). Each tendency line depicted in the graphs (dashed lines) was generated using geom_smooth function (ggplot2) using linear model (lm) as the method.
suppfigure5.pdf
Supplementary Figure 5: Correlations with the antibody response and rectal microbiota at order level. Scatter plots show the relative abundance versus DAb, measured as the difference between the level of antibodies after and before vaccination or equivalent times in non-vaccinated piglets. Only orders found significant with Maaslin2 are shown. Each tendency line depicted in the graphs was generated using geom_smooth function (ggplot2) using linear model (lm) as the method. Spearman correlations values are also depicted in each plot.
suppfigure6.pdf
Supplementary Figure 6: Nasal microbiota taxa discriminating good responders at genus level. Relative abundance of the genera identified as associated with good responders in the nasal microbiota in the discrete analysis using Lefse (Figure 4B) are shown.
supptable1.xlsx

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Pig nasal and rectal microbiotas are involved in the antibody response to Glaesserella parasuis

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Abstract

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Background

Methods

Samples included in the study

Detection of G. parasuis by PCR

Antibody levels

Microbiota sequencing

Microbiota bioinformatic analysis

Statistical modelling

Results

The increase in antibody levels correlated with increase in nasal microbiota diversity

Nasal microbiota taxa associated with response to G. parasuis

Rectal microbiota diversity also correlated with antibody response

Microbiota associations with piglets classified as good antibody responders

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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