On the basis of the signed Contract, the Institute of Molecular Genetics and Genetic Engineering submits to the holder VIS Health d.o.o. and the Innovation Fund a report on the activities carried out for the period 01.04.2023-30.06.2023.
In the past quarter, after sampling and isolating DNA from dogs in this study, DNA was sent for metagenomic sequencing of 16S rRNA regions to the Novogene sequencing center (Cambridge, UK). Metagenomic sequencing of the 16S rRNA region determines the total composition of bacteria in the analyzed sample with precise identification to the level of genus. After creating the library necessary for sequencing, it was found that a total of 22 DNA samples (from pretreatment) and 22 DNA samples from the treatment group had a satisfactory amount and purity of DNA. The sequencing process itself was performed on the NovaSeq platform, reading the V3-V4 hypervariable region and a depth of 100,000 sequences per sample.
After the metagenomic sequence, we processed the resulting raw sequences using a bioinformatics program for analyzing 16S rRNA sequences qiime2 version 2023.2 (https://docs.qiime2.org/2023.2/) in the command line of the Linux operating system. Sequence filtering, chime removal, primer, and sequencing artifacts were done using the dada2 tool. Taxonomic identification of bacteria in dog samples was carried out using silva database version 138 (https://www.arb-silva.de/) on previously trained V3-V4 primers with 99% accuracy by sequencing amplicon sequence variant (ASV).
The results obtained using the qiime2 software package were then imported into R studio version 4.2.3. All subsequent analyses were done in this programming language, which is very suitable for statistical analysis in bioinformatics. After removing sequences belonging to the domain of archaea and unidentified sequences, a total of 18,697 ASV (Figure 1) was obtained. Then a filtering of sequences was done whose representation is very low and which could represent artifacts.
Filtration was done by adjusting 2 parameters – prevalence with a threshold of 3% and sequence representation – 10 reads. After the filtration, a total of 7,964 ASV was obtained in a total of 4,824,229 sequence readings.
Based on the analysis of the distribution of ASW between two analyzed groups of oral microbiota of dogs, it was found that out of a total of 7964 ASV (Figure 2), the number of common amplicon variants was 4221, while the number of unique variants was higher in the treatment group (2267 ASV) compared to pretreatment (1456 ASV). This can be explained by the fact that in the case of accumulation of dental plaques or biofilms (pretreatment), there is an overgrowth of bacteria that are adapted for life in biofilm. Due to competition for limited resources in this polymicrobial community, bacteria that have the ability to produce fakora virulence in this case take precedence and thus disable greater diversity of microbiota composition, which could be the reason for the reduced number of ASVs in the pretreatment group of oral microbites of dogs analyzed in this study.
The next step in this study was to analyze the composition of the dog's microbiota at different taxonomic levels. It was found that the microbiota of the analyzed dogs can be classified into 46 different taxonomic categories at the filum level, 116 at the level of the class, 210 on the new order, 410 on the new family, 839 at the genus level, while 583 different categories were identified at the species level.
At the filum level, the most common taxa were Proteoobacteria, Bacteroidetes, Firmicutes, Actinobacteria and Epsilonbacteraeota , which together account for about 90% of all taxa (Figure 3). These taxonomic units were the most prevalent both in pretreatment and in the treatment group. These are, based on literature data, the most common filums in dental microbites of dogs (Ruparellet al., 2020; Kačírová et al., 2021). The largest differences in percentage representation between the 2 analyzed sample groups were observed in the case of Bacteroidetes (30.7% pretreatment, 19.2% treatment). This is important to note because a significant number of pathogenic bacteria belong to this filum. At the genus level, the most represented taxa in both analyzed groups were Porphyromonas, Campylobacter, Neisseria, Pasteurella and Pseudomonas (Figure 4).
Porphyromonas is generally the most common genus in dental plaques of dogs but is also associated with the onset of inflammatory diseases such as periodontitis and gingivitis in dogs (Kačírová et al., 2021). In the case of the two groups analyzed in this study, the percentage of this taxon was significantly reduced in treatment (12%) compared to pretreatment (22%). Also, a significant change was observed in the relative prevalence of Actinomyces, Staphylococcus and Capnocytophaga (reduction in the treatment group), bacteria that correlate with poorer dental status in dogs. A significant change was observed in the case of relative representation of the genus Pseudomonas (4.5% pretreatment, 0.38% treatment). Members of this genus are characterized by their ability to form biofilms, which can later cause the formation of inflammatory processes. Also, the presence of this bacterial taxon is correlated with poorer health status, because its overgrowth prevents the development of the microbiota necessary to maintain the normal health status of the host.
A further step in this analysis was the analysis of the diversity of the microbiota within the groups themselves (alpha diversity) and the analysis of the diversity of the microbiota between the groups themselves (beta diversity) with the application of adequate statistical tests. For this purpose, both quantitative and qualitative parameters were taken into account, in order to obtain a better insight into the composition of the microbiota between the analyzed groups (pretreatment, treatment, use of different brushes, measurement of the gingival index).
In the study of alpha diversity between analyzed groups of dog samples, three parameters were followed: Observed ASV (qualitative measure of microbiota diversity of the analyzed community), Shannon index (quantitative measure of microbiota diversity of the analyzed community) and Inverse Simpson index (quantitative measure of diversity and uniformity of microbiota of the analyzed community). In order to investigate the difference in alpha diversity in the analysis of pretreatment vs treatment, a paired t-test was used to interpret the obtained values from the aspect of statistical significance (Figure 5). The probability values p < 0.05 or less are considered statistically significant and are denoted by the following symbols: ** for p < 0.005, * for p < 0.05. Also, in the event that the returned values did not have a normal distribution, a logarithmic transformation (log InvSimpson, Figure 5) was applied. In all three tested parameters (Observed ASV, Shannon index, Inverse Simpson index) it was found that group treatment had higher values, with statistical significance for Shannon (p=0.022*) and Inverse Simpson index (p=0.002**). When it comes to the second analyzed group – dog brushes, values were monitored between 3 subgroups (no - without using a brush; control - ordinary brush or placebo; smart - application of smart pet toothbrush). For this purpose, a statistical test of repeated measurements of variance analysis – ANOVA was used. Statistically significant differences in diversity were reestablished for the Shannon (p=0.046*) and The Inverse Simpson Index (p=0.007**). The next step was to examine which subgroups among themselves showed statistical significance. By applying the bonferoni statistical post-hoc test to obtained ANOVA values, it was found that the differences in the Shannon and Inverse Simpson indexes stem from statistical significance between the no-smart subgroups for Shannon (p=0.043*) and Inverse Simpson (p=0.011*), while the remaining 2 groups no-control and control-smart showed no statistical significance. In order to confirm the results obtained by analyzing alpha diversity, two statistical models were applied: linear mixed model (LMM) and information theory model (LMM). Information theory model selection. The application of these models once again confirmed that statistically significant differences in alpha diversity occur in the case of treatment (pretreatment vs treatment group) and in the application of smart pet toothbrushes (toothbrush group).
When testing beta diversity between analyzed groups of dog samples, three parameters or 3 distance diversity were followed: Weighed Unifrac (a quantitative measure of community diversity that includes phylogenetic relationships between the analyzed characteristics), Unweighed Unifrac (a qualitative measure of community diversity that includes phylogenetic relationships between the analyzed characteristics) and Aitchison (based on logarithmically transformed Euclidean distance and suitable for composite data). For the purpose of testing beta diversity, models were made that included the analyzed parameters: pretreatment vs treatment, brush, gingival index (gi), plaque index (pi), calculus index (ci) and race. Pretreatment vs treatment, brush and gingival index (gi) were singled out as the most important parameters, and as such they were examined in more detail (Figure 6). For this purpose, perman's statistical method based on the adonis2 function was applied, with 9999 repetitions. Basic coordinate analysis (eng. Principal coordinates analysis (PCoA) was used to visualize Weighed and Unweighed Unifrac, while the basic component analysis (PCoA) was used to visualize Weighed and Unweighed Unifrac. Principal component analysis (PCA) used to interpret Aitchison distance. Based on the obtained results (Figure 6), it was found that statistically significant changes in beta diversity in the case of pretreatment vs treatment group occur in Unweighed Unifrac (p=0.040*) and Aitchison distances (p=0.006**), while for Weighed Unifrac a limit value was obtained (p=0.056).
In the case of toothbrushes, the analyzed groups (Figure 6, Figure 7) showed statistical significance for all three analyzed distances of beta diversity Weighed Unifrac (p=0.008**), Unweighed Unifrac (p=0.019*) and Aitchison (p=0.004**). In order to investigate the existence of statistical significance in beta diversity between the analyzed subgroups (no, control, smart) by comparing their pairs, the statistical method pairwise adonis was applied. Results were obtained showing that only in the case of comparison of pairs no vs smart there is a statistically significant difference in beta diversity for all three analyzed distances - Weighed Unifrac (p=0.017*), Unweighed Unifrac (p=0.031*) and Aitchison (p=0.009**).
Then, in the case of testing the change of beta diversity by analyzing the ginginval index (Figure 6, Figure 8), the analyzed groups showed statistical significance for all three analyzed distances of beta diversity Weighed Unifrac (p=0.020*), Unweighed Unifrac (p=0.024*) and Aitchison (p=0.009**).
When the difference in beta diversity between different subgroups (no, moderate, high) was examined, it was found that only in the case of Weighed Unifrac distance there is statistical significance when comparing moderate vs no (p=0.016*) and high vs no gingival index (p=0.022*).
- Based on the results obtained in this study, it was found that a greater diversity of oral microbiota of dogs is possessed by dogs after treatment using toothbrushes.
- By comparing the composition of the microbiota at different taxonomic levels, a decrease in the percentage of bacteria causing inflammatory diseases was observed in favor of the treatment group.
- An analysis of alpha diversity found that the treatment and application of smart pet toothbrushes have statistically significant values for the Shannon and Inverse Simpson indices.
- These results were supported by the use of statistical models - a linear mixed model and a model of information theory.
- The treatment with a smart pet toothbrush is statistically significant correlation with all three analyzed beta distances. Also, a statistically significant change in the values of the gingival index after treatment was observed.
1. Ruparell, A., Inui, T., Staunton, R. et al. The canine oral microbiome: variation in bacterial populations across different niches. BMC Microbiol 20, 42 (2020).
2. Kačírová, J., Maďari, A., Mucha, R. et al. Study of microbiocenosis of canine dental biofilms. Sci Rep 11, 19776 (2021).