Taxonomic classification at phylum, genus and OTU level was done through the RDP database classifier using the standalone RDP classifier version 2.6 (Chapter
2.10).
The abundances of specific taxa at phylum and genus level were log transformed and tested in a linear mixed model (LMM) to determine if changes in abundance could indicate a change in health status. Sampling week (to accommodate for potential temporal trends) was fitted as a fixed effect and participant ID as a random effect using lme4 package (version 1.1.9) from R (version 3.1.2). Species richness at OTU level was also tested in a LMM against sample status (healthy and unhealthy) to determine if unhealthy samples had reduced species richness overall.
Local contributions to beta diversity (Vegan, version 2.4.0) in R (version 3.1.2) was performed to show dysbiosis of unhealthy communities as described in Legendre & De Cáceres, 2013. This method involves measuring beta diversity to show the variation in species composition by generating a single number estimate of beta diversity in the different health groups. This was done by using Hellinger transformation to compute the total sum of squares of the species composition from which the local contributions to beta diversity could be derived generating the total beta diversity. This generates values known as local contributions to beta diversity (LCBD) where a large LCBD value indicates samples that have different species composition. This was performed at OTU level producing a timeline of samples for each participant displaying the P values for the unhealthy samples.
The abundance changes in community composition between the different health groups were tested by identifying the most significant OTUs present in regards to health status using the DESeq2 package (version 1.24.0) (Love et al., 2014) in R (version 3.1.2). This was determined from a negative binomial GLM to model the abundance data (OTU frequencies) and empirical Bayes to shrink OTU-wise dispersions to identify OTUs that have log-fold changes between different
conditions. The cut off value was P < 0.01 with P adjusted values being used for multiple comparisons (Chapter 2.11.2).
Alpha diversity at OTU level was investigated to determine the possible associations between health status and oropharyngeal community structure. For alpha diversity analysis, samples were rarefied using rarefaction to the minimum number of reads (5118). The diversity indices calculated for healthy and unhealthy samples were species richness, Shannon H index and Simpson index (Chapter 2.11.2). Significant differences between the different health groups were measured using aov() from Vegan (version 2.4.0) taking into account repeated sampling from participants to calculate pair-wise ANOVA generating P values which were displayed on top of alpha diversity figures.
4.3 Results
4.3.1 Comparison of the healthy and unhealthy oropharynx
microbiome from non-smokers
4.3.1.1 Initial exploration of the unhealthy oropharynx microbiome
The taxonomic profiling of unhealthy samples from the oropharynx of non- smoking participants identified with RDP classifier revealed 5 to 9 phyla (median = 8), 20 to 70 genera (median = 38) and 100 to 300 assignments at OTU level (median = 182) (Table 4.1). This was broadly similar to the taxonomic profiling of healthy samples (5 to 10 phyla (median = 9), 20 to 70 genera (median = 45) and 140 to 340 OTUs (median = 218), but showing a reduction in OTUs in unhealthy samples as determined in a linear mixed model (t value = -5.693, P <
Table 4.1 – The total number of unhealthy samples from non-smokers and the
range in taxa numbers received from each participant. Participants HB, HR and HT are excluded due to having no unhealthy samples.
4.3.1.2 Community composition of the unhealthy oropharynx microbiome
The five main phyla (in terms of abundance) found in the healthy samples (Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria and Proteobacteria) were also present in unhealthy samples (cold and viral samples) and antibiotic treated samples (Appendix 8) but the abundances between the different health conditions were different. There was a decrease in Firmicutes in 9 participants (n=12) and an increase in Proteobacteria in 9 participants (n=12) when going from a healthy to cold state (Figure 4.1A) whereas the viral group had a decrease in Firmicutes in 3 participants (n=4) and an increase in Proteobacteria in 2 participants (n=4) (Figure 4.1B). Antibiotic treated samples on the other hand showed similar abundances of Firmicutes in comparison to the healthy samples, but 2 participants (n=3) did show an increase in the phylum
Actinobacteria (Figure 4.1C). Overall, when looking at the healthy and unhealthy
group there was a significant increase in the abundance of the phylum
Proteobacteria (P = 0.002) and a significant decrease in Bacteroidetes (P = 0.05)
in unhealthy samples (Table 4.2). No differences between the healthy and unhealthy samples were found in the abundances of Firmicutes, Actinobacteria or Fusobacteria.
Participant Total
samples Phylum Min - Genus OTU
Max Median Min -Max Median Min - Max Median
HA 2 8-8 N/A 27-47 N/A 109-245 N/A
HC 2 7-9 N/A 57-65 N/A 145-294 N/A
HD 1 9 N/A 60 N/A 272 N/A
HE 1 7 N/A 28 N/A 113 N/A
HF 5 5-7 6 29-51 35 111-241 149
HG 1 8 N/A 42 N/A 214 N/A
HI 6 7 7-8 30-47 41 144-230 186
HJ 4 6-8 7 29-44 37 125-198 173
HL 1 6 N/A 40 N/A 163 N/A
HM 3 6-8 7 35-44 43 158-206 180
HN 1 8 N/A 44 N/A 198 N/A
HO 1 8 N/A 35 N/A 130 N/A
HQ 1 7 N/A 30 N/A 116 N/A
HS 1 8 N/A 38 N/A 170 N/A
Figure 4.1A – Box plot showing the most abundant phyla (n=9) (the rest pooled in the category ‘Others’) and the median abundance in each participant in healthy and cold samples.
Figure 4.1B – Box plot showing the most abundant phyla (n=9) (the rest pooled in the category ‘Others’) and the median abundance in each participant in healthy and viral positive samples.
Figure 4.1C – Box plot showing the most abundant phyla (n=9) (the rest pooled in the category ‘Others’) and the median abundance in each participant in healthy and antibiotic treated samples.
Table 4.2 – Summary of the parameter estimates of the linear mixed model
(LMM) investigating the abundances (response variable) of the five most abundant phyla in unhealthy samples compared to healthy samples (reference category). Significant P values are shown in bold.
Unhealthy samples
Estimate Std. Error df t value P value
Firmicutes 0.028704 0.211088 313 0.136 0.891925
Proteobacteria 0.9015 0.293 310.4 3.077 0.0023
Bacteroidetes -0.552464 0.281813 307.71 -1.96 0.0509
Actinobacteria -0.089296 0.274397 313 -0.325 0.745
Fusobacteria -0.420947 0.292411 311 -1.44 0.151
At genus level, the most abundant genera in both healthy and unhealthy samples (cold and viral) and antibiotic treated samples were Streptococcus, Prevotella and Veillonella, but there were marked increases in specific genera such as
Pseudomonas in comparison to the healthy samples (Appendix 9). When
comparing the healthy and cold samples (Figure 4.2A) there was a decrease in abundances of Streptococcus and Prevotella in 8 and 9 participants respectively (n=12). The viral group had 3 participants where the abundance of Streptococcus decreased (n=4) and an increase in Neisseria and Haemophilus was observed in 1 and 3 participants respectively (n=4) (Figure 4.2B). For samples from subjects that had undergone antibiotic treatment (Figure 4.2C), 2 participants showed decreases in Streptococcus (n=3) and 2 participants had increases in the genus
Actinomyces (n=2). When looking at the healthy and unhealthy samples overall,
there was a significant decrease in the abundance of Prevotella in unhealthy samples (P = 0.012) with no significant difference in the abundance of
Streptococcus and Veillonella (P = 0.808 & P = 0.385) (Table 4.3). Therefore,
unhealthy communities had the same genera present, but the communities differed by having different abundances of genera.
Figure 4.2A– Box plot showing the most abundant genera (n=10) (the rest pooled in the category ‘Others’) and the median abundance in each participant in
Figure 4.2B– Box plot showing the most abundant genera (n=10) (the rest pooled in the category ‘Others’) and the median abundance in each participant in
Figure 4.2C– Box plot showing the most abundant genera (n=10) (the rest pooled in the category ‘Others’) and the median abundance in each participant in healthy and antibiotic treated samples.
Table 4.3 – Summary of the parameter estimates of the linear mixed model
(LMM) investigating the abundances (response variable) of the five most abundant genera in unhealthy samples compared to healthy samples (reference category). Significant P values are shown in bold.
Unhealthy samples
Estimate Std. Error df t value P value
Streptococcus -0.05 0.21 311.00 -0.24 0.81
Prevotella -0.77 0.31 312.00 -2.53 0.012
Veillonella -0.25 0.29 311.00 -0.87 0.39
Serratia 0.23 0.38 313 0.59 0.55
Pseudomonas 0.23 0.38 311 4.75 <0.001
OTUs that were the most abundant in healthy samples were also present in the unhealthy samples (cold and viral - Figure 4.3A & B) and antibiotic treated samples (Figure 4.3C) but at differing abundances in participants. For example, participant HM showed an increase in Streptococcus salivarius in the cold samples, whereas participant HJ’s cold samples showed a decrease in
Streptococcus salivarius. Therefore healthy and unhealthy samples had differing
Figure 4.3A – Box plot showing the most abundant operational taxonomic units (OTUs) (n=10) with the rest pooled in the category ‘Others’ and the median abundance in each participant in healthy and cold samples.
Figure 4.3B – Box plot showing the most abundant operational taxonomic units (OTUs) (n=10) with the rest pooled in the category ‘Others’ and the median abundance in each participant in healthy and viral positive samples.
Figure 4.3C – Box plot showing the most abundant operational taxonomic units (OTUs) (n=10) with the rest pooled in the category ‘Others’ and the median abundance in each participant in healthy and antibiotic treated samples.