Compositional analysis of the tonsil microbiota in relationship to Streptococcus suis disease in nursery pigs in Ontario

A comparative study of the bacterial diversity and composition of nursery piglets' oral fluid, feces, and housing environment

The oral cavity is the portal of entry for many microorganisms that affect swine, and the swine oral fluid has been used as a specimen for the diagnosis of several infectious diseases. The oral microbiota has been shown to play important roles in humans, such as protection against non-indigenous bacteria. In swine, studies that have investigated the microbial composition of the oral cavity of pigs are scarce. This study aimed to characterize the oral fluid microbiota of weaned pigs from five commercial farms in Brazil and compare it to their respective fecal and environmental microbiotas. Bacterial compositions were determined by 16S rRNA gene sequencing and analyzed in R Studio. Oral fluid samples were significantly less diverse (alpha diversity) than pen floor and fecal samples (P < 0.01). Alpha diversity changed among farms in oral fluid and pen floor samples, but no differences were observed in fecal samples. Permutational ANOVA revealed that beta diversity was significantly different among sample types (P = 0.001) and farms (P = 0.001), with separation of sample types (feces, pen floor, and oral fluid) on the principal coordinates analysis. Most counts obtained from oral fluid samples were classified as Firmicutes (80.4%) and Proteobacteria (7.7%). The genera Streptococcus, members of the Pasteurellaceae family, and Veillonella were differentially abundant in oral fluid samples when compared to fecal samples, in which Streptococcus was identified as a core genus that was strongly correlated (SparCC) with other taxa. Firmicutes and Bacteroidota were the most relatively abundant phyla identified in fecal and pen floor samples, and Prevotella_9 was the most classified genus. No differentially abundant taxa were identified when comparing fecal samples and pen floor samples. We concluded that under the conditions of our study, the oral fluid microbiota of weaned piglets is different (beta diversity) and less diverse (alpha diversity) than the fecal and environmental microbiotas. Several differentially abundant taxa were identified in the oral fluid samples, and some have been described as important colonizers of the oral cavity in human microbiome studies. Further understanding of the relationship between the oral fluid microbiota and swine is necessary and would create opportunities for the development of innovative solutions that target the microbiota to improve swine health and production.

Prevalence of Streptococcus suis serotype 2 isolated from pigs: A systematic review and meta-analysis

Background and Aim

Among Streptococcus suis serotypes, S. suis serotype 2 is the most significant serotype that causes serious diseases in pigs and humans worldwide. The present study aimed to estimate the global prevalence of S. suis serotype 2 isolated from pigs, determine its trend, and explore the factors associated with this serotype.

Materials and Methods

We retrieved relevant published studies from PubMed, Scopus, and the Web of Science. The retrieved citations were screened for possible inclusion. Relevant data were then extracted from the included studies. The random-effects model was used for all meta-analyses. A subgroup meta-analysis was used to assess the heterogeneity of the prevalence for four characteristics (continents, sampling organs, reporting unit, and pig's health status). A cumulative meta-analysis was performed to determine the cumulative prevalence over time. Meta-regression analysis was used to determine the trend of pooled prevalence of S. suis serotype 2 over time.

Results

Of 600 articles retrieved, 36 studies comprising a total sample size of 6939 isolates or samples from 16 countries of four continents were included for meta-analysis. The pooled prevalence of S. suis serotype 2 isolated from pigs was 13.6% (95% confidence interval [CI], 10.7%-17.1%), with high heterogeneity among the included studies (Cochran's Q, 431.6; p < 0.001; I2 = 91.9%; Table-1). No statistical significance was observed among subgroups of the four characteristics examined. However, the pooled prevalence of S. suis serotype 2 was as high as 16.0% (95% CI, 12.5%-20.3%; n = 16) in diseased pigs compared with 9.9% (95% CI, 5.6%-17.0%; n = 15) in healthy pigs. The pooled prevalence of S. suis serotype 2 isolated from pigs did not significantly decrease over time [regression coefficient = -0.020 (95% CI, 0.046-0.006, p = 0.139)]. The pooled prevalence of S. suis serotype 2 isolated from pigs fluctuated slightly between 13.2% and 17.8% from 2007 to 2023, although the pooled prevalence gradually decreased from 30.6% in 1987 to over 20% in 2003.

Conclusion

The global prevalence of S. suis serotype 2 isolated from pigs was estimated to be 13.6% (approximately 10% in healthy pigs and around 16% in diseased pigs). S. suis serotype 2 isolated from pigs did not change significantly over time. These results indicate that S. suis serotype 2 remains a problem for the pig industry and poses a threat to human health.

Metabolic insights and background from naturally affected pigs during Streptococcus suis outbreaks

Streptococcus suis (S. suis) is an endemic zoonotic pathogen still lacking adequate prevention in pigs. The present case study looked back to the occurrence and consequences of S. suis outbreaks in our swine research facilities in search of new metabolic and physiological insight. From a series of outbreaks, a dataset was created including 56 pigs sampled during disease detection based on clinical signs. Pigs suspected with S. suis infection were defined as diseased (n = 28) and included pigs defined as neurologically diseased (n = 20) when severe neurological signs (central nervous system dysfunctions, i.e., opisthotonos, ataxia, and generalized tremor) were observed. Another set of 28 pigs included respective pen mates from each case and were defined as control. Representative deaths were confirmed to be caused by S. suis. Tonsillar swabs were collected and analyzed by quantitative polymerase chain reaction (qPCR) for total bacteria, total S. suis, and S. suis serotypes (SS) 2 (and/or 1/2) and 9. Blood and sera were analyzed to quantify blood gases, minerals, and S. suis reactive immunoglobulins against current isolates. Data collected included litter sibling associations, birth and weaning body weight (BW), and average daily gain (ADG) 7 d after the disease detection. In general, the disease increased pH, sO2 and the incidence of alkalosis, but reduced pCO2, glucose, Ca, P, Mg, K, and Na in blood/serum compared to control. The SS2 (and/or SS1/2) prevalence was significantly (P < 0.05) increased in neurologically diseased pigs and its relative abundance tended (P < 0.10) to increase in tonsils. In contrast, the relative abundance of total S. suis was lower (P > 0.05) in diseased pigs than control pigs. Levels of S. suis reactive IgG2 were lower, but IgM were higher (P < 0.03) in neurologically affected pigs compared to control. Furthermore, there was an increased proportion of sibling pigs that were diseased compared to control. In conclusion, our results evidence that naturally affected pigs were associated to average performing pigs without any predisease trait to highlight but a sow/litter effect. Besides, neurologically affected pigs had increased S. suis (SS2 and/or 1/2) prevalence and relative abundance, a respiratory alkalosis profile, and mineral loss.

Microbiome depiction through user-adapted bioinformatic pipelines and parameters

Introduction. The role of the microbiome in health and disease continues to be increasingly recognized. However, there is significant variability in the bioinformatic protocols for analysing genomic data. This, in part, has impeded the potential incorporation of microbiomics into the clinical setting and has challenged interstudy reproducibility. In microbial compositional analysis, there is a growing recognition for the need to move away from a one-size-fits-all approach to data processing.Gap Statement. Few evidence-based recommendations exist for setting parameters of programs that infer microbiota community profiles despite these parameters significantly impacting the accuracy of taxonomic inference.Aim. To compare three commonly used programs (DADA2, QIIME2, and mothur) and optimize them into four user-adapted pipelines for processing paired-end amplicon reads. We aim to increase the accuracy of compositional inference and help standardize microbiomic protocol.Methods. Two key parameters were isolated across four pipelines: filtering sequence reads based on a whole-number error threshold (maxEE) and truncating read ends based on a quality score threshold (QTrim). Closeness of sample inference was then evaluated using a mock community of known composition.Results. We observed that raw genomic data lost were proportionate to how stringently parameters were set. Exactly how much data were lost varied by pipeline. Accuracy of sample inference correlated with increased sequence read retention. Falsely detected taxa and unaccounted for microbial constituents were unique to pipeline and parameter. Implementation of optimized parameter values led to better approximation of the known mock community.Conclusions. Microbial compositions generated based on the 16S rRNA marker gene should be interpreted with caution. To improve microbial community profiling, bioinformatic protocols must be user-adapted. Analysis should be performed with consideration for the select target amplicon, pipelines and parameters used, and taxa of interest.

Risk factors associated with Streptococcus suis cases on pig farms in Spain

BACKGROUND

Streptococcus suis can cause meningitis, polyarthritis and acute death in piglets. However, the risk factors associated with S. suis infection remain incompletely understood. Therefore, a longitudinal study was carried out, in which six batches from two Spanish pig farms with S. suis problems were repeatedly examined to determine possible risk factors.

METHODS

A prospective case-control study was conducted, and potential risk factors were evaluated using mixed-effects logistic regression models. The explanatory variables included: (a) concomitant pathogens; (b) biomarkers associated with stress, inflammation and oxidative status; (c) farm environmental factors; and (d) parity and S. suis presence in sows. Three models were built to study the effect of these variables, including two to assess the risk factors involved in the subsequent development of disease.

RESULTS

Risk factors for S. suis-associated disease included porcine reproductive and respiratory syndrome virus co-infection at weaning (odds ratio [OR] = 6.69), sow parity (OR = 0.71), haptoglobin level before weaning (OR = 1.01), relative humidity (OR = 1.11) and temperature (OR = 0.13).

LIMITATIONS

Laboratory diagnosis was done at the batch level, with individual diagnosis based on clinical signs only.

CONCLUSIONS

This study confirms the multifactorial nature of S. suis-associated disease, with both environmental factors and factors related to the host involved in disease development. Controlling these factors may, therefore, help prevent the appearance of disease.

Environmental and maternal factors shaping tonsillar microbiota development in piglets

Background

The palatine tonsils are part of the mucosal immune system and stimulate immune responses through M cell uptake sampling of antigens and bacteria in the tonsillar crypts. Little is known about the development of the tonsillar microbiota and the factors determining the establishment and proliferation of disease-associated bacteria such as Streptococcus suis. In this study, we assessed tonsillar microbiota development in piglets during the first 5 weeks of life and identified the relative importance of maternal and environmental farm parameters influencing the tonsillar microbiota at different ages. Additionally, we studied the effect sow vaccination with a bacterin against S. suis on microbiota development and S. suis colonisation in their offspring.

Results

Amplicon sequencing of the 16S rRNA gene V3-V4 region revealed that a diverse tonsillar microbiota is established shortly after birth, which then gradually changes during the first 5 weeks of life without a large impact of weaning on composition or diversity. We found a strong litter effect, with siblings sharing a more similar microbiota compared to non-sibling piglets. Co-housing in rooms, within which litters were housed in separate pens, also had a large impact on microbiota composition. Sow parity and prepartum S. suis bacterin vaccination of sows had weaker but significant associations with microbiota composition, impacting on the abundance of Streptococcus species before and after weaning. Sex and birthweight had limited impact on the tonsillar microbiota, and none of the measured factors had consistent associations with microbiota diversity.

Conclusions

The piglet tonsillar microbiota is established shortly after birth. While microbiota development is associated with both environmental and maternal parameters, weaning has limited impact on microbiota composition. Intramuscular vaccination of sows pre-partum had a significant effect on the tonsillar microbiota composition of their piglets. These findings provide new insights into the mechanisms shaping the tonsillar microbiota.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-022-02625-8.