Mahmmod et al. Vet Res (2020) 51:7 https://doi.org/10.1186/s13567-020-0738-8 RESEARCH ARTICLE Open Access Variations in association of nasal microbiota with virulent and non‑virulent strains of Glaesserella (Haemophilus) parasuis in weaning piglets Yasser S. Mahmmod1,2,3* , Florencia Correa‑Fiz1,4 and Virginia Aragon1,4 Abstract Glaesserella (formerly Haemophilus) parasuis causes Glässer’s disease, which results in high economic loss in the swine industry. To understand the polymicrobial interactions of G. parasuis and the nasal microbiota, the statistical associa‑ tion patterns of nasal colonizing bacteria with virulent and non-virulent strains of G. parasuis were studied accounting for the farm management practices as potential risk factors for the occurrence of Glässer’s disease. The nasal micro‑ biota from 51 weaned-piglets from four farms with Glässer’s disease and three farms with no respiratory diseases was previously characterized and included in this study. The presence of virulent and/or non-virulent G. parasuis strains in the nasal cavities was determined in order to establish the potential association with other members of the nasal microbiota. Multivariate logistic and linear regression models were performed among the various members of nasal microbiota and G. parasuis. The multi-site production system and disease presence in the farm were both significantly associated with the presence of G. parasuis virulent strains in the nose of the piglets. Differential bacte‑ rial associations were observed with virulent or non-virulent G. parasuis. Chitinophagaceae, Corynebacteriaceae and Corynebacterium were positively associated with the virulent G. parasuis strains, while Enterobacteriaceae, Peptostrep- tococcaceae, Clostridium XI, and Escherichia/Shigella were negatively associated with virulent G. parasuis. On the other hand, Flavobacteriaceae, Planobacterium, and Phascolarctobacterium were positively associated with the non-virulent G. parasuis strains, while Rikenellaceae, Enterococcaceae, Odoribacter, and Corynebacterium were negatively associated with non-virulent G. parasuis. In conclusion, the nasal microbiota communities showed variations in the association with the G. parasuis strains type. Introduction microorganisms and developing protection against path- The nasal mucosa of piglets is colonized by a wide array ogens [2]. Glaesserella parasuis (formerly Haemophilus of bacteria, the nasal microbiota, many of which are still parasuis [3]) can be found among the microbes widely unknown [1]. The microbiota establishes mutual relation- distributed in swine population, colonizing the upper ship with its host and the resulting cross-talk extends respiratory tract of healthy piglets, particularly the nasal beyond the balance between tolerance to commensal cavity [4, 5]. G. parasuis causes Glässer’s disease, which results in one of the highest economic losses in nursery piglets, but the factors influencing its development are *Correspondence: yasser.mahmmod@irta.cat; yasserpcr@gmail.com; not totally understood [1, 6]. Glässer’s disease is com- yasser@sund.ku.dk monly developed after weaning due to a reduction in 1 IRTA, Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain maternal antibodies, mix of litters, strains variations, Full list of author information is available at the end of the article and other stress factors [7, 8]. Basically, all pig farms © The Author(s) 2020. 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The Creative Commons Public Domain Dedication waiver (http://creat​iveco​mmons​.org/publi​cdoma​in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Mahmmod et al. Vet Res (2020) 51:7 Page 2 of 13 are positive to G. parasuis, but not all farms develop the management practices can influence the nasal microbiota Glasser’s disease symptoms. of pigs and subsequently the nasal methicillin-resistant Recently, through metagenomics techniques it was Staphylococcus aureus carriage. More recently, Correa- revealed how “an equilibrated” microbiota affects poten- Fiz et al. [1] concluded that the nasal microbiota compo- tial pathogens [9, 10]. Metagenomics techniques could sition was associated to the clinical status of the farm of reveal the host-microbe and microbe–microbe interac- origin of the piglets, leading to different susceptibilities to tions toward a predictive, dynamic ecosystem model. invasive infection by G. parasuis. Recent studies on gut microbiota have provided the evi- To the best of our knowledge, no study has investigated dence that the onset of a disease can be the result of a the association between the degree of virulence of G. par- change in the interaction with other microorganisms asuis strains and other members of the nasal microbiota. [11]. Therefore, understanding the polymicrobial interac- We hypothesized that members of the nasal microbiota tions of pathogens, such as G. parasuis, and the microbi- associated with virGp might be different to the ones asso- ota in the nasal cavity is crucial for the following reasons ciated with non-virGp, establishing a differential network (a) colonization is the initial step of the disease process, involving complex interactions. The results of this study (b) colonized piglets serve as reservoirs for bacterial will unravel the interaction of the nasal microbiota with transmission to others in the population and, (c) micro- G. parasuis strains, which will boost our understanding biome structure may have an effect on development of of the development of Glässer’s disease. The objective of Glässer’s disease. this study is to investigate the statistical association pat- Several recent studies uncovered the nasal microbiota terns of nasal microbiome at different taxa levels (fam- composition in pigs and their association with different ily and genus) with the virGp and non-virGp strains, infectious pathogens [1, 12–14]. Strube et al. [14] showed while accounting for the farm management practices as that the porcine nasal mucosa harbored Rothia and a potential risk factors for the occurrence of Glässer’s dis- had higher relative levels of Streptococcus and Morax- ease in weaning piglets. ella, while levels of Aerococcus, Facklamia and Jeotgali- baca were relatively lower. Espinosa-Gongora et al. [13] reported that the nasal microbiota may play a role in Materials and methods the individual predisposition to Staphylococcus aureus Study population and sample collection nasal carriage in pigs. On the contrary, Weese et al. [12] The study population, inclusion criteria and sample col- found no impact of the nasal microbiota on the methi- lection were fully described in Correa-Fiz et al. [1]. cillin-resistant Staphylococcus aureus carriage in pigs. G. Briefly, DNA was extracted from nasal swabs of 51 ran- parasuis is a heterogeneous bacterial pathogen encom- domly selected piglets at 3–4 weeks of age (at wean- passing both virulent and non-virulent strains. Keeping ing). The microbiota composition was determined from that in mind, virulent strains of G. parasuis (virGp) are sequenced amplicons spanning the V3–V4 region of the playing a major role in Glässer’s disease development 16S rRNA gene generated by using the universal prim- in swine populations [15, 16]. However, other research ers described previously by Klindworth et al. [24]. Seven studies demonstrated the role of non-virulent strains swine farms located in Catalonia (Spain) were included in of G. parasuis (non-virGp) as an important colonizer the study (Table 1). Four of these farms (MT, MC, RC, EJ) to the swine nasal mucosa [17–19]. In connection with were having Glässer’s disease outbreaks (number of sam- that, Bello-Ortí et al. [18] reported that non-virGp have pled piglets = 33), while the other three farms had no res- the ability to form robust biofilms in contrast to virulent piratory disease history (number of selected piglets = 18) strains, and biofilm formation might therefore allow the and served as control farms (GM, PT, VL). Production non-virulent strains to colonize and persist in the upper system were a mix of multi-sites systems (n = 38 animals) respiratory tract of pigs. Thus, investigation of the pol- in five farms (GM, PT, EJ, RC and MT) and farrow-to-fin- ymicrobial interactions is essential to understand Gläss- ish systems (n = 13) in the other two farms (MC and VL). er’s disease pathogenesis in a population. Several studies have documented that the type of farm DNA extraction and PCR assay management practices and antibiotic use affect the inci- Total DNA was extracted from the nasal swabs using the dence of swine respiratory diseases, including Glässer’s Nucleospin Blood (Machinery Nagel) kit. Swabs were disease [20–22]. For instance, Hurnik et al. [23] reported resuspended in PBS and suspensions were processed several risk factors in the epidemiology of enzootic pneu- according to manufacturer’s instructions. G. parasuis monia such as feeding methods, housing type, farm strains were detected and differentiated into virulent and size and management practices. As for the nasal micro- non-virulent using the specific PCR assay according to bial composition, Weese et al. [12] revealed that farm Galofré-Milà et al. [25]. Mahmmod et al. Vet Res (2020) 51:7 Page 3 of 13 Table 1 Differentiation of G. parasuis strains into virulent and non-virulent by PCR assay and its frequency by farm, health status and production system type in 51 weaning piglets Item Level Virulent strains Non-virulent strains Total N (%) N (%) N (%) Health status Control (GM, PT, VL) 2 (8) 14 (52) 16 (31) Glässer’s disease (MT, MC, RC, EJ) 22 (92) 13 (48) 35 (69) Production system type Multi-sites (GM, PT, EJ, RC, MT) 24 (100) 24 (89) 48 (94) Farrow to finish (MC, VL) 0 (00) 3 (11) 3 (6) Farm EJ 10 (42) 0 (0) 10 (20) GM 2 (8) 9 (33) 11 (22) MC 0 (00) 0 (00) 0 (00) MT 6 (25) 6 (22) 12 (23) PRAT​ 0 (00) 2 (8) 2 (4) RC 6 (25) 7 (26) 13 (25) VL 0 (00) 3 (11) 3 (6) Total 24 (47) 27 (53) 51 (100) Statistical analysis highly correlated (r > 0.8), only the one with the lower P Data on nasal microbiota composition at the different value in the unconditional associations was retained. A taxonomical levels were available from Correa-Fiz et al. multivariable model analysis including the significant [1]. Prior to undertaking statistical analysis, data were variables from the univariable modelling was built using screened for unlikely or missing values. No data were manual backward elimination procedure by dropping excluded on this basis. Subsequently, a descriptive statis- the least significant variable. The generated final model tical analysis was carried out to the potential risk factors included only variables with P ≤ 0.05. Once the vari- related to piglets’ management (production system and ables to be included in the final model were determined, health status) and nasal microbiota composition at fam- two-way interactions were examined and retained if sig- ily and genus levels based on the relative abundance of nificant. The P value, odds ratio (OR) with a 95% confi- operational taxonomic units (OTUs). dence interval (95% CI), and regression coefficient (b) We ran three different statistical models at family were recorded for each variable. In all statistical analyses, and genus levels of nasal microbiota including a multi- the results were considered to be significant at P ≤ 0.05. variable logistic regression model with the virGp strain The models were constructed using the “lm” and “glm” (present or absent) as the outcome variable and multi- functions for the linear and logistic regressions models, variable logistic regression with non-virGp strain (pre- respectively in R version 3.3.3 [27]. sent or absent) as the outcome variable. The third model was a multivariable linear regression, which was built to investigate the association between nasal microbiota, and Results Glaesserella parasuis strains differentiation metadata with G. parasuis infection, as a continuous out- and distribution come variable. The outcome variable “G. parasuis” was skewed and was, therefore, transformed by taking their Total DNA extracted from the nasal swabs collected natural logarithm. from all the piglets under the study (n = 51) was used to Initially, a univariable model was carried out to test determine the presence and the virulence of the G. par- the unconditional associations between dependent and asuis strains. The proportion of virulent strains was 47% various independent variables of interest including the (24/51), whereas the proportion of non-virulent strains potential farm factors and relative abundances of mem- was 53% (27/51), Table 1. The virGp strains was the bers of the nasal microbiota. Only independent vari- prevalent strain type in the Glässer’s disease group (43%, ables with P ≤ 0.25 in this initial screening were included 22/51) in comparison to the control group. There was in multivariable logistic and linear regression models no difference between the proportion of the non-virGp following the recommended strategy of Dohoo et al. strains in both Glässer’s disease and control groups. In [26]. Before proceeding with building our multivariable respect to the type of production system, the proportion model analysis, we checked for correlations between the of the virGp and non-virGp strains was predominant in retained independent variables. If two variables were the multi-sites production systems (both of them 47%, 24/51) in comparison to farrow-to-finish system, Table 1. Mahmmod et al. Vet Res (2020) 51:7 Page 4 of 13 The farm EJ has the highest proportion of the virGp statistical modelling after excluding 91 and 119 families (20%, 10/51) over the other farms, whereas the farm GM with low relative abundance (cutoff < 0.01), and having showed the highest proportion of the non-virGp (18%, few observations (≤ 10), respectively. The results of uni- 9/51). variable model analyses of virulent strain, non-virulent strain, and relative abundance of G. parasuis, as natural Findings at family level logarithm at family level taxa are shown in Additional In total 284 bacterial families were revealed to colonize file 1. The results of final multivariable model analyses the nasal mucosa of piglets [1]. Out of this number, 52 of virulent strain, non-virulent strain, and G. parasuis families were identified as “others” and were discarded are shown in Tables 2, 3, and 4, respectively. The associa- in this analysis. In total, 22 families were offered to tions between the significantly variables presented in the Table 2 Risk factors and nasal microbiota members (family level) associated with the presence of virulent strains of G. parasuis (P ≤ 0.05) in 51 piglets in 7 Spanish farms using a multivariable logistic regression model Variable Level Estimate OR 95% CI P Production system Farrow to finish Ref Ref Ref 5.5e−07 Multi-site 19.11 1.98 2.5e−46 to NA Health status Control Ref Ref Ref 6.4e−05 Glässer’s disease 2.77 16.03 3.70–113.46 Chitinophagaceae – 250.9 8.8e+108 1.14e+30 to Inf 0.0102 Enterobacteriaceae – −51.7 3.5e−27 1.6e−62 to 1.9e−06 1.75e−05 Corynebacteriaceae – 898.7 Inf 1.89e+135 to Inf 0.00022 Peptostreptococcaceae – −179.1 2.3e−74 1.8e−194 to 1.1e−10 0.017 Ref: reference, Inf: infinity. Table 3 Risk factors and nasal microbiota members (family level) associated with the presence of non-virulent strains of G. parasuis (P ≤ 0.05) in 51 piglets in 7 Spanish farms using a multivariable logistic regression model Variable Level Estimate OR 95% CI P Production system Farrow to finish Ref Ref Ref 0.011 Multi-site −2.75 6.4e-02 3.01e−06 to 1.3e+02 Health status Control Ref Ref Ref 0.0023 Glässer’s disease −6.12 2.2e−03 5.01e−07 to 1.2e−01 Rikenellaceae – −597.77 2.5e−260 0.00 to 5.6e−72 1.6e−06 Flavobacteriaceae – 11.67 1.2e+05 3.3e+01 to 7.3e+11 0.014 Enterococcaceae – −251.52 5.8e−110 2.8e−243 to 2.7e−27 0.0083 Ref: reference. Table 4 Risk factors and nasal microbiota members (family level) associated with relative abundance of G. parasuis, expressed as natural logarithm, (P ≤ 0.05) in 51 piglets in 7 Spanish farms using a multivariable linear regression model Family Level Estimate 95% CI St. error P Production system Farrow to finish Ref Ref Ref 8.5e−08 Multi-site 2.06 1.40–2.71 0.33 Health status Control Ref Ref Ref 0.00514 Glässer’s disease 0.80 0.20–1.40 0.30 Bacteroidaceae – −38.1 (−61.3) to (−14.8) 11.6 4.03e−05 Chitinophagaceae – 27.9 7.5–48.3 10.2 0.032 Streptococcaceae – 13.9 4.9–22.9 4.5 0.004 Mycoplasmataceae – −5.8 (−11.2) to (−0.35) 2.7 0.037 Ref: reference. Mahmmod et al. Vet Res (2020) 51:7 Page 5 of 13 final model and virulent strain, and non-virulent G. par- under the farrow to finish system. Piglets from farms asuis strains at family level are illustrated Figures 1 and 2, with Glässer’s disease had higher odds of having virGp respectively. Illustration of the associations between the strain by 16.03 (95% CI 3.70–113.46) compared to relative abundance of G. parasuis, expressed as natural animals from healthy farms. The presence of Entero- logarithm in 51 piglets and significantly variables pre- bacteriaceae, and Peptostreptococcaceae in the nasal sented in the final model at family level is shown in Addi- mucosa of piglets is associated with a low likelihood tional file 2. of colonization of virGp strain by −51.7 and −179.1, respectively. However, the presence of both Chitin- Microbial association with virulent strain of G. parasuis ophagaceae, and Corynebacteriaceae is associated with Production system, health status, Chitinophagaceae, a higher probability of finding virGp strains by 250.9 Enterobacteriaceae, Corynebacteriaceae, and Peptos- and 898.7, respectively. A significant interaction was treptococcaceae were significantly associated with the present between Enterobacteriaceae and Corynebacte- presence of virGp strains (Table 2). Piglets raised under riaceae indicating that both of them interfere signifi- the multi-sites production system showed a higher risk cantly with colonization of virGp strain. of having virGp strains by 1.98 compared to animals Figure 1 Association of virulent Glaesserella parasuis strains (virGp) and significant bacterial families from the nasal microbiota. Significant variables (P ≤ 0.05) influencing the colonization by virGp in 51 piglets found in the final model: production system (A), health status (B), and nasal microbiota members at family level Chitinophagaceae (C), Enterobacteriaceae (D), Corynebacteriaceae (E) and Peptostreptococcaceae (F). Mahmmod et al. Vet Res (2020) 51:7 Page 6 of 13 Figure 2 Association of non-virulent Glaesserella parasuis strains (non-virGp) and significant bacterial families from the nasal microbiota. Significant variables (P ≤ 0.05) influencing the colonization by non-virGp in 51 piglets found in the final model: production system (A), health status (B), and nasal microbiota members at family level Rikenellaceae (C), Flavobacteriaceae (D) and Enterococcaceae (E). Microbial association with non‑virulent strain of G. parasuis respectively. Whereas presence of Flavobacteriaceae Production system, healthy status, Rikenellaceae, Fla- is associated with a high likelihood of colonization of vobacteriaceae, and Enterococcaceae were significantly non-virGp strain by 11.67. A significant interaction was associated with the non-virGp strain (Table 3). Piglets observed between Rikenellaceae and Enterococcaceae raised under the multi-sites production system showed meaning that both of them interfere significantly with a lower risk of having non-virGp strains by 0.064 colonization of non-virGp strain. (95% CI 3.01e−06 to 1.3e+02) compared to animals under the farrow to finish system. Piglets from farms with Glässer’s disease had lower odds of having non- Microbial associations with G. parasuis virGp strains by 0.0022 (95% CI 5.01e−07 to 1.2e−01) Production system, health status, Bacteroidaceae, Chi- compared to piglets from healthy farms. Presence of tinophagaceae, Streptococcaceae and Mycoplasmataceae Rikenellaceae, and Enterococcaceae in the nasal mucosa were significantly associated with G. parasuis abundance of piglets is associated with a low likelihood of coloni- (Table 4). Piglets raised under the multi-sites produc- zation of non-virGp strain by −597.77 and −251.52, tion system were more likely to be colonized by G. par- asuis by 2.06 (95% CI 1.40–2.71). Piglets from farms with Mahmmod et al. Vet Res (2020) 51:7 Page 7 of 13 Glässer’s disease were more likely to be colonized with G. strain, and non-virulent G. parasuis strains at genus parasuis by 0.80 (95% CI 0.20–1.40). Presence of Bacte- level are illustrated Figures 3 and 4, respectively. Illus- roidaceae, and Mycoplasmataceae in the nasal mucosa of tration of the associations between the relative abun- piglets is significantly associated with a low likelihood of dance of G. parasuis, expressed as natural logarithm in G. parasuis colonization by −38.1 and −5.8, respectively. 51 piglets and significantly variables presented in the Whereas presence of Chitinophagaceae and Streptococ- final model at genus level is shown in Additional file 2. caceae is associated with a high likelihood of colonization of G. parasuis by 27.9 and 13.9. No significant interaction was detected. Microbial associations with virulent strain of G. parasuis Production system, health status, Corynebacterium, Findings at genus level Clostridium XI, and Escherichia/Shigella were signifi- In total, we got 949 bacterial genera colonizing the nasal cantly associated with the virGp strain as depicted in mucosa. Out of this number, 154 genera were identified Table 5. Piglets raised under the multi-sites produc- as others/unassigned/unclassified and were discarded. tion system were more likely to be have a higher risk of In total, 45 genera were used in a valid analysis after virGp strain by 9.8+08 compared to animals under the excluding 251 and 499 genera with low abundance (cut- farrow to finish system. Piglets with Glässer’s disease off ≤ 0.01), and having few observations (≤ 10), respec- were more likely to harbor virGp strain by 4.03+01 tively. The results of univariable model analyses of (95% CI 7.1 to 3.7e+02) compared to non-infected virulent strain, non-virulent strain, and relative abun- animals. Presence of Clostridium XI, and Escherichia/ dance of G. parasuis, as natural logarithm at genus level Shigella in the nasal mucosa of piglets reduce the colo- taxa are shown in Additional file 3. The results of final nization of virGp strain by −204.3 and −295.6, respec- multivariable analyses of virulent, non-virulent strains, tively. Whereas presence of Corynebacterium increases and G. parasuis are presented in Tables 5, 6, and 7, the colonization of virGp strain by 674.01. A significant respectively. The associations between the significantly interaction was present between Corynebacterium and variables presented in the final model and virulent Escherichia/Shigella. Table 5 Risk factors and nasal microbiota members (genus level) associated with the presence of virulent strains of G. parasuis (P ≤ 0.05) in 51 piglets in 7 Spanish farms using a multivariable logistic regression model Genus/variable Level Estimates OR %95 CI P Health status Control Ref Ref Ref 6.48e−05 Glässer’s disease 3.69 4.03+01 7.1 to 3.7e+02 Production system Farrow to finish Ref Ref Ref 5.56e−07 Multi-site 20.70 9.8+08 1.1e−73 to Inf Corynebacterium – 674.01 5.3e+292 7.03e+61 to Inf 0.0013 Clostridium.XI – −204.3 1.9e−89 1.8e−200 to 1.03e−15 5.5e−05 Escherichia.Shigella – −295.6 4.4e−129 0.00 to 3.8e−01 0.0394 Ref: reference, Inf: infinity. Table 6 Risk factors and nasal microbiota members (genus level) associated with the presence of non-virulent strains of G. parasuis (P ≤ 0.05) in 51 piglets in 7 Spanish farms using a multivariable logistic regression model Genus/variable Level Estimates OR %95 CI P Health status Control Ref Ref Ref 0.0073 Glässer’s disease −2.22 0.11 0.015–0.48 Production system Farrow to finish Ref Ref Ref 0.0034 Multi-site 2.34 10.34 2.045–86.57 Corynebacterium – −116.6 2.4-51 6.4e−220 to 1.9e+10 0.0134 Odoribacter – −929.7 0.00 0.00 to 1.1e−187 5.8e−05 Planobacterium – 17.06 2.6e+07 2.2 to 4.5e+27 0.037 Phascolarctobacterium – 518.5 1.6e+225 5.1e+12 to Inf 0.034 Ref: reference, Inf: infinity. Mahmmod et al. Vet Res (2020) 51:7 Page 8 of 13 Table 7 Risk factors and nasal microbiota members (genus level) associated with relative abundance of G. parasuis, expressed as natural logarithm, (P ≤ 0.05) in 51 piglets in 7 Spanish farms using a multivariable linear regression model Genus Level Coff. 95% CI St. Error P Health status Control Ref Ref Ref 0.00514 Glässer’s disease 0.798 0.20–1.40 0.297 Production system Farrow to finish Ref Ref Ref 8.5e−08 Multi-site 2.054 1.40–2.71 0.326 Alloprevotella – 33.1 −34.9 to 101.1 33.7 0.0019 Streptococcus – 8.1 0.45–15.8 3.8 0.0054 Clostridium.XI – 4.9 −27.9 to 37.8 16.3 0.055 Oscillibacter – −82.6 −125.9 to (−39.3) 21.5 6.2e−06 Kingella – 53.5 13.5–93.4 19.8 0.012 Actinobacillus – 27.1 14.3–39.9 6.4 0.0001 Ref: reference. Figure 3 Association of virulent Glaesserella parasuis strains (virGp) and significant bacterial genera from the nasal microbiota. Significant variables (P ≤ 0.05) influencing the colonization by virGp in 51 piglets found in the final model: production system (A), health status (B), and nasal microbiota members at genus level Corynebacterium (C), Clostridium XI (D) and Escherichia.Shigella (E). Mahmmod et al. Vet Res (2020) 51:7 Page 9 of 13 Figure 4 Association of non-virulent Glaesserella parasuis strains (non-virGp) and significant bacterial genera from the nasal microbiota. Significant variables (P ≤ 0.05) influencing the colonization of non-virGp in 51 piglets found in the final model: production system (A), health status (B), and nasal microbiota members at genus level Corynebacterium (C), Odoribacter (D), Planobacterium (E) and Phascolarctobacterium (F). Microbial associations with non‑virulent strain of G. and 518.5. A significant interaction was present between parasuis Odoribacter and Phascolarctobacterium. Production system, health status, Corynebacterium, Odoribacter, Planobacterium and Phascolarctobacte- Microbial association with G. parasuis rium were significantly associated with the non-virulent Production system, health status, Alloprevotella, Strep- strain of G. parasuis (Table 6). Piglets raised under the tococcus, Clostridium XI, Oscillibacter, Kingella, and multi-sites production system have a higher odd of non- Actinobacillus were significantly associated with G. par- virGp strains by 10.34 (95% CI 2.045–86.57) compared asuis (Table 7). Piglets raised under the multi-sites pro- to animals under the farrow to finish system. Piglets with duction system are more susceptible to G. parasuis by Glässer’s disease have lower odds of virGp strain by 0.11 2.054 (95% CI 1.40–2.71). Piglets with Glässer’s disease (95% CI 0.015–0.48) compared to healthy animals. Pres- have a higher odds G. parasuis by 0.80 (95% CI 0.20– ence of Corynebacterium, and Odoribacter in the nasal 1.40). Presence of Oscillibacter in the nasal mucosa of mucosa of piglets is associated with a low likelihood of piglets is significantly associated with a low likelihood colonization of non-virGp strains by −116.6 and −929.7, of colonization of G. parasuis by −82.6. Whereas pres- respectively. Whereas the presence of Planobacterium ence of Alloprevotella, Streptococcus, Clostridium XI, and Phascolarctobacterium is associated with a high like- Kingella, and Actinobacillus is associated with a high lihood of colonization of non-virulent strains by 17.06 likelihood of colonization of G. parasuis by 33.1, 8.1, Mahmmod et al. Vet Res (2020) 51:7 Page 10 of 13 4.9, 53.5, and 27.1. A significant interaction was pre- Microbiota associated with G. parasuis of different sent between Alloprevotella and Oscillibacter, as well as virulence between Streptococcus and Oscillibacter. A strong association pattern was reported between the microbiota with the virGp and non-virGp strains at dif- ferent taxa levels. In that respect it was reported that Discussion some microbial families could nourish the colonization Factors affecting Glässer’s disease epidemiology and presence of the G. parasuis strains, while other dif- Our findings demonstrated that the type of porcine ferent microbial families suppress/compete its coloni- production system has a significant impact on the zation in the environment [1]. One theory that could epidemiology of Glässer’s disease. We found that the explain the reported finding is the antagonistic and syn- multi-site production type increases the prevalence ergistic effects of the microbial populations, which inhab- of the virGp strains and subsequently, increases the iting together under the same environmental conditions. risk of Glässer’s disease in comparison to the one site We found that Streptococcus, Clostridium XI, Kingella, production type (farrow-to-finish). In agreement with and Actinobacillus increases the presence of G. parasuis. that, Rapp-Gabrielson et al. [4] reported that adop- This is in agreement with the findings of previous studies tion of new production systems, particularly multiple [1, 34, 35]. Slifierz et al. [35] found that the most com- site production systems based on a single origin of the mon core OTUs (> 1% relative abundance in ≥ 80% of pigs, together with the emergence of respiratory and pigs) prior to weaning were from Clostridia, while Cor- systemic syndromes have contributed to an increase in rea-Fiz et al. [1] reported a higher relative abundance of prevalence and severity of Glässer’s disease. Plausibly, Mycoplasmataceae, Streptococcus and Haemophilus in this finding could be argued by the stress factors caused swine farms with Glässer’s disease. We found that Myco- by the additional animal handling and transportation. plasmataceae showed a significant reduction in the like- The relocation of the weaning piglets from one environ- lihood of colonization of G. parasuis by −5.8, whereas ment and management conditions to another can cause Streptococcaceae increase the likelihood of colonization a suppression of the animal immune system [28] and of G. parasuis by 13.9. This shows that different bacte- subsequently might favor the development of Gläss- rial species pose different association patterns with G. er’s disease. This is consistent with the conclusion of parasuis. Schmidt et al. [34] demonstrated that the Pep- Nielsen and Danielson [29], who demonstrated that the tostreptococcaceae family was one of the most abundant immune status of the herd is a determinant of the path- members of the Clostridia class colonizing the piglet gut. ogenic outcome of the infection. Nevertheless, the het- The connection between the microbial communities of erogeneity in virulence among G. parasuis strains could gut and the respiratory system has been reported [36]. also be a significant determinant for disease develop- Here, the families Enterobacteriaceae and Peptostrepto- ment [30, 31]. In line with that, Aragon et al. [30] stated coccaceae significantly reduced the likelihood of coloni- that basically all piglets are colonized by G. parasuis zation of virGp strain by −51.7 and −179.1. Additionally, but only specific strains are capable of inducing the previous reports showed that gut of neonates is initially clinical form of Glässer’s disease, sometimes following colonized by members of Enterobacteriaceae and Strep- a perturbation of their host due to distress factors and/ tococcaceae [37, 38]. This interesting finding could shed or co-infections. the light on the role of Peptostreptococcaceae, Enterobac- Piglets from farms with Glässer’s disease have higher teriaceae and Streptococcaceae in the control of Glässer’s odds of being colonized by virGp strains than those disease in piglets. from healthy farms. This finding strongly reflects that Interestingly, those families and genera significantly the virulence of the G. parasuis strains is playing the associated with virGp and non-virGp strains were dif- major role in Glässer’s disease development and epi- ferent from those taxa that were significantly associ- demiology. The reported findings in this study are also ated with Glaesserella genus (as outcome), whose only in agreement with previous studies demonstrating a known species in swine is G. parasuis. These discrepan- marked difference between the epidemiological, molec- cies in the families and genera associated with the two ular and virulence patterns, and clinical manifestations types of strains and G. parasuis are not only important associated with different strains of G. parasuis [3, 15, for understanding Glässer’s disease biology but also for 25, 32, 33]. From the epidemiological and microbial ascertaining our needs for accurate and precise diagno- point of view, we have shown that the identification and sis at strain level. It seems that some microbial popula- differentiation of G. parasuis at the strain level could be tions, from Chitinophagaceae and Corynebacteriaceae used for prognosis of Glässer’s disease in swine farms. families, may facilitate the colonization of virGp strains, meanwhile members from the Flavobacteriaceae family Mahmmod et al. Vet Res (2020) 51:7 Page 11 of 13 could facilitate the colonization of non-virGp strains. from the true population value as a result of random vari- On the other hand, families such as Enterobacteriaceae ation inherent in the sampling process. However, one way and Peptostreptococcaceae may act as inhibitors of the to address this uncertainty in the estimate is by comput- virGp strains, whereas Rikenellaceae, and Enterococ- ing a confidence interval [41], which provides the range caceae could have an inhibitory effect on the non-virGp of values within which the true population value might strains. The variations in the microbiota composition lie as we have reported in our findings. associated with both types of G. parasuis strains could be In terms of the applicability and generalizability of the driven by the physiology of the different bacterial com- obtained findings, the selection criteria for the study munity, which could impact the magnitude of virulent populations should be taken in mind. Particularly, it or non-virulent strains colonization and prevalence, and is well-established that the microbiota composition is subsequently, the piglet’s health status [4]. In a similar dynamically changed and influenced by many contribut- infection study, Espinosa-Gongora et al. [13] reported ing factors, including day of sampling, feeding manage- that nasal microbiome of pigs that are not colonized with ment, breed, age, and health status. Staphylococcus aureus harbors several species/taxa that Gaining knowledge on the polymicrobial interactions are significantly less abundant in carrier pig, suggesting could provide new insights into the disease pathogen- that the nasal microbiota plays a role in the individual esis, as well as novel avenues for prevention and control predisposition to S. aureus nasal carriage in pigs. In the of pathogenic infections. Although biological validation earlier study by Correa-Fiz et al. [1], a marked differ- of our statistical findings is needed, the marked variation ence between the composition and diversity of the nasal in the associations of different microbial taxa inhabit- microbiota between swine farms with different health ing the swine nasal niche with the colonization of virGp status was reported. The authors added that interactions and non-virGp strains points to new possibilities in the between the host and microbial communities of nasal manipulation of the microbiota composition to reduce mucosa may result in selection of the beneficial bacteria, the colonization by this pathogen. which can prevent the colonization by pathogens, and The multi-site production system and disease status of that could be the case for the non-virGp strains. We have the farm were both significantly associated with the pres- elucidated that hypothesis at strain level for G. parasuis ence of virulent strains. Interestingly, we found a wide showing that there are wide variations in the association variation in the association of nasal microbiota commu- of microbial communities with different type of strains. nities with virGp and non-virGp strains in weaning pig- Several genera were found to be associated to strains of lets. The findings of this study boost our understanding G. parasuis depending on their virulence. For instance, of Glässer’s disease development and could be a base for the presence of Escherichia/Shigella reduces the probabil- innovative non-antimicrobial alternatives for Glässer’s ity of colonization by virGp, while the presence of Phas- disease control. colarctobacterium is associated with a high likelihood of colonization by non-virGp. These two genera from the Supplementary information nasal microbiota of piglets have been recently found to be Supplementary information accompanies this paper at https​://doi. associated to a healthier farm status [39] and deserve fur- org/10.1186/s1356​7-020-0738-8. ther investigation on their role in Glässer’s disease. To the best of our knowledge, this is the first study investigat- Additional file 1. The results of univariable model analyses of virulent strains (A), non-virulent strains (B), and relative abundance ing microbial associations with virulent and non-virulent of G. parasuis (C), expressed as natural logarithm at family level strains of G. parasuis. Previous research hypothesized taxa. A Univariable logistic regression for potential risk factors and nasal that the increased abundance and/or prevalence of cer- microbiota at family level associated with the presence of virulent strains of G. parasuis (virGp) infection (P-value ≤ 0.25) in 51 piglets in 7 Spanish tain pathogens in the microbial environment could con- farms. B Univariable logistic regression for potential risk factors and nasal fer a higher risk to develop specific disease [1, 40]. The microbiota at family level associated with the presence of non-virulent reported findings in this study strongly and significantly strains of G. parasuis (non-virGp) infection (P-value ≤ 0.25) in 51 piglets in 7 Spanish farms. C Univariable linear regression for potential risk factors stand with this hypothesis for Glässer’s disease develop- and nasal microbiota at family level associated with relative abundance ment in swine populations. of G. parasuis, expressed as natural logarithm, (P ≤ 0.25) in 51 piglets in 7 One limitation of our study is the sample size of the Spanish farms. study group. In any observational epidemiological stud- Additional file 2. Illustration of the associations between the relative abundance of Glaesserella parasuis, expressed as natural ies, we collect data on a subset of animals (study group) in logarithm in 51 piglets and significantly variables presented in the source study population of interest, which may result the final model including farm management factors (production in a random error bias due to low sample size. However, system and health status) and nasal microbiota members at family (A) and genus (B) taxa. A Plotting the association between the relative Dohoo [41] stated that even if this subset is a true ran- abundance of Glaesserella parasuis, expressed as natural logarithm in 51 dom sample, the obtained estimates will vary somewhat Mahmmod et al. Vet Res (2020) 51:7 Page 12 of 13 3. Dickerman A, Bandara AB, Inzana TJ (2020) Phylogenomic analysis of Hae- piglets and significantly variables (P ≤ 0.05) presented in the final model mophilus parasuis and proposed reclassification to Glaesserella parasuis, including farm management factors (production system and health gen. nov., comb. nov. Int J Syst Evol Microbiol. https​://doi.org/10.1099/ status) and nasal microbiota members at family level (Bacteroidaceae, ijsem​.0.00373​0 Chitinophagaceae, Streptococcaceae, and Mycoplasmataceae). B Plotting 4. Oliveira S, Pijoan C (2004) Haemophilus parasuis: new trends on diagnosis, the association between the relative abundance of Glaesserella parasuis, epidemiology and control. Vet Microbiol 99:1–12 expressed as natural logarithm in 51 piglets and significantly variables 5. Rapp-Gabrielson VJ, Oliveira SR, Pijoan C (2006) Haemophilus parasuis. (P ≤ 0.05) presented in the final model including farm management fac‑ In: Straw BE, Zimmerman JJ, D’Allaire S, Taylor DJ (eds) Diseases of swine. tors (production system and health status) and nasal microbiota members Blackwell Publishing, Hiboken, pp 681–690 at genus level (Alloprevotella, Streptococcus, Clostridium XI, Oscillibacter, 6. Holtkamp D, Rotto H, Garcia R (2007) Economic cost of major health chal‑ Kingella and Actinobacillus). lenges in large US swine production systems. Swine News Newslett 30:4 Additional file 3. The results of univariable model analyses of viru- 7. Costa-Hurtado M, Aragon V (2013) Advances in the quest for virulence lent strain (A), non-virulent strain (B), and relative abundance of G. factors of Haemophilus parasuis. Vet J 198:571–576 parasuis (C), expressed as natural logarithm at genus level taxa. A 8. Pereiraa DA, Dalla Costab FA, Ferronic LB, Moraes CN, Schocken-Iturrino Univariable logistic regression for potential risk factors and nasal microbi‑ RP, Oliveir LG (2017) The challenges with Glässer’s disease in technified ota at genus level associated with the virulent strain of G. parasuis (virGp) pig production. Austral J Vet Sci 49:63–69 infection (P-value ≤ 0.25) in 51 piglets in 7 Spanish farms. B Univariable 9. Bahrndorff S, Alemu T, Alemneh T, Lund Nielsen J (2016) The microbi‑ logistic regression for potential risk factors and nasal microbiota at genus ome of animals: implications for conservation biology. Int J Genomics level associated with the non-virulent strain of G. parasuis (non-virGp) 2016:5304028 infection (P-value ≤ 0.25) in 51 piglets in 7 Spanish farms. C Univariable lin‑ 10. Alves LF, Westmann CA, Lovate GL, de Siqueira GMV, Borelli TC, Guaz‑ ear regression for potential risk factors and nasal microbiota at genus level zaroni ME (2018) Metagenomic approaches for understanding new associated with relative abundance of G. parasuis, expressed as natural concepts in microbial science. Int J Genomics 2018:2312987 logarithm, (P-value ≤ 0.25) in 51 piglets in 7 Spanish farms. 11. Vayssier-Taussat M, Albina E, Citti C, Cosson JF, Jacques MA, Lebrun MH, Le Loir Y, Ogliastro M, Petit MA, Roumagnac P, Candresse T (2014) Shifting the paradigm from pathogens to pathobiome: new concepts in the light Acknowledgements of meta-omics. Front Cell Infect Microbiol 4:29 We acknowledge the logistic support of technicians. We also thank the farm‑ 12. Weese JS, Slifierz M, Jalali M, Friendship R (2014) Evaluation of the nasal ers for their support. microbiota in slaughter-age pigs and the impact on nasal methicillin- resistant Staphylococcus aureus (MRSA) carriage. BMC Vet Res 10:69 Authors’ contributions 13. Espinosa-Gongora C, Larsen N, Schønning K, Fredholm M, Guardabassi L YSM, FC and VA participated in the conception and study design. FC and VA (2016) Differential analysis of the nasal microbiome of pig carriers or non- provided the dataset for YSM. YM carried out the data management, bio- carriers of Staphylococcus aureus. PLoS One 11:e0160331 statistical modelling and drafted the manuscript. YSM, FC and VA participated 14. Strube ML, Hansen JE, Rasmussen S, Pedersen K (2018) A detailed inves‑ in interpretation of the data. FC and VA were involved in the critical review of tigation of the porcine skin and nose microbiome using universal and the manuscript. All authors read and approved the final manuscript. Staphylococcus specific primer. Sci Rep 8:12751 15. Aragon V, Cerdà-Cuéllar M, Fraile L, Mombarg M, Nofrarías M, Olvera A, Funding Sibila M, Solanes D, Segalés J (2010) Correlation between clinico-path‑ This work was supported by the Ministry of Economy and Competitive‑ ological outcome and typing of Haemophilus parasuis field strains. Vet ness (MINECO) of the Spanish Government (grant number AGL2016-77361). Microbiol 142:387–393 YM is funded by a P-SPHERE fellowship from the COFUND-Horizon Marie 16. Aragon V (2013) Exposing serum susceptibility in Haemophilus parasuis. Skłodowska-Curie Actions (grant agreement No 665919). The authors are also Vet J 196:10–11 grateful to the Centres de Recerca de Catalunya (CERCA) programme. 17. Nedbalcova K, Satran P, Jaglic Z, Ondriasova R, Kucerova Z (2006) Hae- mophilus parasuis and Glässer’s disease in pigs: a review. Vet Med-Czech Availability of data 51:168–179 The datasets of the current study are available from the corresponding author 18. Bello-Ortí B, Deslandes V, Tremblay YD, Labrie J, Howell KJ, Tucker AW, on reasonable request. Maskell DJ, Aragon V, Jacques M (2014) Biofilm formation by virulent and non-virulent strains of Haemophilus parasuis. Vet Res 45:104 Competing interests 19. Bello-Ortí B, Costa-Hurtado M, Martinez-Moliner V, Segalés J, Aragon V The authors declare that they have no competing interests. (2014) Time course Haemophilus parasuis infection reveals pathological differences between virulent and non-virulent strains in the respiratory Author details tract. Vet Microbiol 170:430–437 1 20. Jäger HC, McKinley TJ, Wood JL, Pearce GP, Williamson S, Strugnell B, Done IRTA, Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain. 2 Universitat S, Habernoll H, Palzer A, Tucker AW (2012) Factors associated with pleurisy Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Bellaterra, Barcelona, in pigs: a case-control analysis of slaughter pig data for England and Spain. 3 Infectious Diseases, Department of Animal Medicine, Faculty of Vet‑ Wales. PLoS One 7:e29655 erinary Medicine, Zagazig University, Zagazig, Sharkia 44511, Egypt. 4 OIE Col‑ 21. Merialdi G, Dottori M, Bonilauri P, Luppi A, Gozio S, Pozzi P, Spaggiari B, laborating Centre for the Research and Control of Emerging and Re-emerging Martelli P (2012) Survey of pleuritis and pulmonary lesions in pigs at abat‑ Swine Diseases in Europe (IRTA-CReSA), Bellaterra, Barcelona, Spain. toir with a focus on the extent of the condition and herd risk factors. Vet J 193:234–239 Received: 18 October 2019 Accepted: 7 January 2020 22. Colomer MÀ, Margalida A, Fraile L (2019) Improving the management procedures in farms infected with the Porcine Reproductive and Respira‑ tory Syndrome virus using PDP models. Sci Rep 9:9959 23. Hurnik D, Dohoo IR, Bate LA (1994) Types of farm management as risk factors for swine respiratory disease. Prev Vet Med 20:147–157 References 24. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, Glöckner 1. Correa-Fiz F, Fraile L, Aragon V (2016) Piglet nasal microbiota at weaning FO (2013) Evaluation of general 16S ribosomal RNA gene PCR primers for may influence the development of Glässer’s disease during the rearing classical and next-generation sequencing-based diversity studies. Nucleic period. BMC Genomics 17:404 Acids Res 41:e1 2. Thaiss CA, Zmora N, Levy M, Elinav E (2016) The microbiome and innate 25. Galofré-Milà N, Correa-Fiz F, Lacouture S, Gottschalk M, Strutzberg-Minder immunity. Nature 535:65–74 K, Bensaid A, Pina-Pedrero S, Aragon V (2017) A robust PCR for the Mahmmod et al. Vet Res (2020) 51:7 Page 13 of 13 differentiation of potential virulent strains of Haemophilus parasuis. BMC 35. Slifierz MJ, Friendship RM, Weese JS (2015) Longitudinal study of the Vet Res 13:124 early-life fecal and nasal microbiotas of the domestic pig. BMC Microbiol 26. Dohoo I, Martin W, Stryhn H (2009) Veterinary epidemiologic research, ­2nd 15:184–195 edn. VER Inc., Charlottetown 36. Ichinohe T, Pang IK, Kumamoto Y, Peaper DR, Ho JH, Murray TS, Iwasaki 27. Core Team R (2015) R: a language and environment for statistical comput‑ A (2011) Microbiota regulates immune defense against respiratory tract ing. R Foundation for Statistical Computing, Vienna influenza A virus infection. Proc Natl Acad Sci U S A 108:5354–5359 28. Zhang B, Tang C, Liao M, Yue H (2014) Update on the pathogenesis 37. Stewart CS (1997) Microorganisms in hindgut fermentors. In: Mackie RI, of Haemophilus parasuis infection and virulence factors. Vet Microbiol White BA, Isaacson RE (eds) Gastrointestinal microbiology. Chapman & 168:1–7 Hall, New York, pp 142–186 29. Nielsen R, Danielson V (1975) An outbreak of Glässer’s disease. Studies 38. Hong P-Y, Lee BW, Aw M, Shek LPC, Yap GC, Chua KY, Liu WT (2010) Com‑ on etiology, serology and the effect of vaccination. Nordisk Veterinaer‑ parative analysis of fecal microbiota in infants with and without eczema. medicin 27:20–25 PLoS One 5:e9964 30. Aragon V, Segales J, Oliveira S (2012) Glasser’s disease. In: Zimmerm J, 39. Correa-Fiz F, Gonçalves dos Santos JM, Illas F, Aragon V (2019) Antimicro‑ Karrker L, Ramirez A, Schwarz K, Stevenson G (eds) Diseases of swine. bial removal on piglets promotes health and higher bacterial diversity in Wiley, Iowa, pp 760–770 the nasal microbiota. Sci Rep 9:6545 31. Oliveira S, Pijoan C (2004) Computer-based analysis of Haemophilus 40. Wertheim HF, Vos MC, Ott A, van Belkum A, Voss A, Kluytmans JA, van parasuis protein fingerprints. Can J Vet Res 68:71–75 Keulen PH, Vandenbroucke-Grauls CM, Meester MH, Verbrugh HA (2004) 32. Zhang J, Xu C, Guo L, Shen H, Deng X, Ke C, Ke B, Zhang B, Li A, Ren T, Liao Risk and outcome of nosocomial Staphylococcus aureus bacteraemia in M (2012) Prevalence and characterization of genotypic diversity of Hae- nasal carriers versus non-carriers. Lancet 364:703–705 mophilus parasuis isolates from southern China. Can J Vet Res 76:224–229 41. Dohoo IR (2014) Bias—is it a problem, and what should we do? Prev Vet 33. Costa-Hurtado M, Olvera A, Martinez-Moliner V, Galofré-Milà N, Martínez Med 113:331–337 P, Dominguez J, Aragon V (2013) Changes in macrophage phenotype after infection of pigs with Haemophilus parasuis strains of different virulence. Infect Immun 81:2327–2333 Publisher’s Note 34. 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