samples), Σx (sum of the total number of counts in the OTU table).
Body mass index was calculated for each participant as weight in kilograms divided by height in meters-squared (kg/m2). Obesity was defined as a BMI greater than 30, overweight as a BMI from 25-29.9, and healthy weight as BMI of 18.5-24.9.
Inferential analysis
Statistics were generated using R. Spearman’s rank-order correlation was used for the majority of the statistical modeling due to the non-normal nature of the OTUs after normalization
25
in the small sample size. Spearman correlation with each phylum level OTU was tested for BMI, IgG ELISA, HAI, and T cell flow cytometry data. Spearman correlation with phylum-level Shannon alpha diversity and richness was tested for BMI, IgG ELISA, HAI, and T cell flow cytometry data. For the BMI comparison with OTU, the data was subset by gender and race to address confounding. For comparisons done between gender and binary obesity and Shannon alpha diversity, a Wilcoxon rank sum test was used to generate a p-value. A significance level of α = 0.05 was used for these analyses. The Benjamini and Hochberg (BH) correction was used to generate false discovery rate (FDR) – adjusted p values as well, due to the large number of comparison conducted. In analyzing the adjusted p values, the significance level was increased to α = 0.1, because it is standard to increase the false discovery rate to 10% after FDR-adjustment. Bray Curtis Principle Coordinates Analysis (PCoA) were also run to analyze clustering based on body weight status and immune measures. Principle component analysis (PCA) was conducted as a second measure of clustering. PERMANOVA was not conducted due to a lack of clustering in the PCA.
Results
Interpretation of Results: A Benjamini Hochberg (BH) correction was conducted to mitigate the false discovery rate as mentioned previously. This was done on all Spearman correlation tests (rank-order) which means that the new significance level is 0.1 instead of 0.05. R graphics outputs contain the p value before the correction, and the corrected p values can be found in the plot captions. The BH correction was NOT done on on the Wilcoxon rank-sum tests due to the very small number of hypotheses tested using this method and the significance level remains at 0.05.
26
Body Mass Index Correlated with One Phylum Level OTU. The number of Firmicutes
OTUs was significantly correlated with body mass index (p = 0.004247, R = -0.76) as seen in Figure 2. This was contrary to the hypothesis that Firmicutes would increase in abundance as BMI increased. There was no observed change in the abundance of Bacteroidetes which was contrary to the hypothesis that Bacteroidetes OTUs would decrease with increased BMI. No other phyla were significantly correlated with BMI. After subsetting the data by gender and race to account for multiple predictors, it was determined that Caucasian participants had the highest amount of correlation in this comparison (p = 0.02519, R = -0.93), and the only significant correlation of all 4 subsets (African American p = 0.2109, R = -0.71), (male p = 0.1172, R = -0.82), (female p = 0.3334, R = -0.60). This method of accounting for the interaction between BMI and gender was stringent at this small sample size. Graphical correlation plots for the subset by race are found in Figure 3. Graphical correlation plots for the subset by gender are found in Figure 4.
Figure 2. Correlation plot between Firmicutes OTU (y axis) and body mass index (x axis). Higher BMI is correlated with a decrease in Firmicutes OTU found in the gut microbiota (p = 0.004247).
27
Figure 3. Correlation plot between Firmicutes OTU (y axis) and body mass index (x axis). African American participants (top) and Caucasian participants (bottom). Higher BMI is correlated with a decrease in Firmicutes OTU found in the gut microbiota in the Caucasian subset only (p = 0.02519). P – values pictured are unadjusted for FDR and were not used in this analysis. For the African American subset, p = 0.2109.
28
Figure 4. Correlation plot between Firmicutes OTU (y axis) and body mass index (x axis). Female participants (top) and male participants (bottom). P – values pictured are unadjusted for FDR and were not used in this analysis. For the male subset, p = 0.1172 and for the female subset p = 0.3334.
29
BMI Correlated with Microbial Diversity. Normalized Shannon alpha diversity was correlated negatively with BMI according to Spearman’s correlation (p = 0.0127, R = -0.59) such that Shannon alpha diversity decreases and BMI increases. Shannon alpha diversity is a measure of the intra-individual diversity of microbiota and accounts for differences in abundance and evenness. The normalized Shannon alpha diversity also differed significantly between obese and non-obese individuals according to a Wilcoxon Rank-sum test (p = 0.0320). There was no observed change, however, in richness (the sum of all OTUs in an individual) which is an additional measure of diversity.
30
Figure 5. Correlation plot (top) between Shannon alpha diversity and BMI (p = 0.0127). Wilcoxon rank sum (bottom) test comparing Shannon alpha diversity between obese and non-obese individuals (p = 0.0320). Both show diminishing Shannon alpha diversity of gut microbial flora with increased body mass.
B Cell Measures of Vaccine Response Correlated with BMI. BMI correlated positively
and significantly with post-vaccination total IgG (p = 0.01493, R = 0.67), total IgG pre:post vaccination ratio (p = 0.008669, R = 0.72), and total IgG pre - post difference (p = 0.008669, R = 0.71). Correlation was also significant and positive between BMI and the post-vaccination HAI viral titer (p = 0.008669, R = 0.72). No other measures of IgG or HAI titers were significantly correlated with BMI. In all of these measures, as BMI increased so did the magnitude of the B cell response. See Figure 6.
32
Figure 6. (From top down): Correlation plots of BMI with post-vaccination total IgG (p = 0.01493), pre:post vaccination total IgG ratio (p = 0.008669), pre – post vaccination total IgG difference (p = 0.008669), and post-vaccination HAI titer (p = 0.008669).
33
Firmicutes and Bacteroidetes OTUs differ with Gender. There were significant differences between Firmicutes (0.0811) and Bacteroidetes (0.03850) with gender as the binary stratification criteria. Males had lower abundance of Firmicutes and higher abundance of Bacteroidetes than females in the study population. No other microbial phyla had significant differences between genders.
Figure 7. Comparison of Firmicutes (top) and Bacteroidetes (bottom) phylum OTUs between gender. Male participants had a lower mean abundance of Firmicutes (p = 0.02811). Female participants had a lower abundance of Bacteroidetes (p = 0.03850).
Some OTUs Correlated with B Cell Measures. This is a notable finding, particularly in the case of the phylum Firmicutes. Firmicutes, which was found to correlate significantly with BMI, was also significantly correlated with pre : post vaccination total IgG ratio (p = 0.01723, R = -0.72), pre – post vaccination total IgG difference (p = 0.01723, R = -0.70), and post vaccination HAI titer (p = 0.03632, R = -0.64). This is notable because it represents a phylum level link between body mass, the gut microbiota, and the immune response to the TIV and the influenza
34
virus. Firmicutes also showed promising association with post vaccination total IgG, post vaccination IgG1, and pre vaccination HAI titer, but none of these were significant (p = 0.1114, p = 0.1646, and p = 0.1896, respectively at α = 0.1). Significant correlation visualizations for Firmicutes and the B Cell parameters can be found in Figure 8. Another correlation of note was between the phylum Actinobacteria and pre vaccination IgG2 (p = 0.05444, R = -0.68). Though this is not a function of the 2014-2015 vaccine, it is also a one-year post 2013-2014 TIV immunization measure within the cohort. See Figure 8 for the Actinobacteria correlation plot. For both Firmicutes and Actinobacteria, as the OTU increased the B cell response decreased. No other microbial phyla were significantly correlated with measures of the B cell response to the TIV.
36
Figure 8. (From top down) Correlation plots between Firmicutes OTU and pre : post vaccination total IgG ratio (p = 0.01723), pre – post vaccination total IgG difference (p = 0.01723), and post vaccination HAI titer (p = 0.03632). Correlation plot between Actinobacteria and pre vaccination IgG2 (p = 0.05444).
37
B Cell Response not Correlated with Diversity. Neither Shannon alpha diversity nor richness at the phylum level were associated significantly with any of the IgG or HAI titer measures of the B cell response to the TIV or influenza virus.
T Cell response not Correlated with BMI. None of the T cell parameters designated in the functional panel gating strategy were significantly correlated with BMI in the study population.
Some OTUs Correlated with T Cell Response. While the phylum Firmicutes, which is correlated with BMI and B cell response was not significantly correlated with the T cell response as measure by flow cytometry, there were three other microbial phyla that were significantly correlated with the T cell response in the study population. Phylum Actinobacteria was significantly positively correlated with the number of CD8+ cytotoxic T cells that expressed neither of the functional cytokines IFN-γ (interferon gamma) nor GrB (granzyme B) upon stimulation with influenza virus (p > 0.0001, R = 1), which are needed in response to influenza. Additionally, the phylum Patescibacteria was significantly negatively correlated with the number of CD4+ helper T cells in unstimulated PBMCs (p = 0.04905, R = -0.96). Lastly, phylum Verrucomicrobia was significantly correlated with the frequency of CD4+ CD28+ helper T cells expressing CD69 upon activation with influenza virus (p = 0.0870, R = 0.95).
39
Figure 9. Correlation plot between Actinobacteria (top) and the count of IFN-γ, GrB double- negative CD8+ cytotoxic T cells upon activation with influenza virus (p > 0.0001). Correlation plot between Patescibacteria (middle) and count of CD4+ helper T cells in unstimulated PBMCs (p = 0.04905). Correlation plot between Verrucomicrobia (bottom) and frequency of CD4+ CD28+ influenza activated helper T cells expressing CD69 (p = 0.0870).
T Cell Response Correlated with Diversity.While none of the T cell parameters designated in the functional panel gating strategy were significantly correlated with Shannon alpha diversity in the study population, there were two parameters that were significantly correlated with richness. Richness (the sum of the OTUs within each individual) was significantly correlated with the frequency of CD4+ CD28+ helper T cells expressing CD69 (p = 0.04390, R = 0.95) upon activation with influenza virus. Richness was also significantly correlated with the number of unstimulated CD8+ cytotoxic helper T cells that expressed IFN-γ but not GrB (p = 0.09154, R = 0.93). As richness increases, both of these parameters increase. See Figure 10.
40
Figure 10. Correlation plot between richness and (top) frequency of CD4+ CD28+ helper T cells expressing CD69 (p = 0.04390). Correlation plot between richness and (bottom) count of unstimulated CD8+ cytotoxic helper T cells that expressed IFN-γ but not GrB (p = 0.09154).
41
OTUs did not Demonstrate Significant Clustering by Demographic, Body Mass, T Cell, or B Cell Metadata.In both PCA (principal component analysis) of variation and PCoA (principal coordinate analysis) of distances, we did not find any significant clustering or separation based on the OTUs or the metadata. In the PCA analysis, while we did observe tighter clustering in non- obese individuals compared to obese individuals, it was not possible to prove that there was a difference in the clustering behavior since the lean cluster was entirely contained within the obese cluster at every combination of dimensional analysis we conducted. This result is intuitive because obesity contains more gastrointestinal disease phenotypes than being healthy weight. See Figure 11 and 12 for PCA plots. As seen in Figure 11, there was no improvement in clustering when examining multiple dimensions. Therefore, a PERMANOVA would not provide substantial information about clustering behavior between individuals in the study. We analyzed all dimensions for gender and BMI (Figure 12) as well and did not find any evidence of significant clustering between individuals. In Figure 13 we generated a Biplot using the PCA to create a visualization of which phyla contributed more to which individuals in the study. In Figure 14 we generated a factor map as a visualization of which phyla contributed the most to the total variance explained in the PCA. See Figure 15 for PCoA plots. When analyzing across multiple dimensions we had similar results in the PCoA as we did in the PCA.
44
Figure 11. (From top down): PCA plots visualizing dimensions 1 through 4: dimension 1 and 2 (top), dimension 1 and 3, dimension 2 and 3, dimension 1 and 4, dimension 2 and 4, and dimension 3 and 4 (bottom). No significant clustering occurred. Coloring by obese, lean, and overweight as designated by BMI.
45
Figure 12. PCA Plots coloring by gender (top) and BMI gradient (bottom). When analyzing multiple dimensions, there was no improvement in clustering by gender or BMI.
46
Figure 13. Biplot where individuals are plotted as points (e.g. S11) and the phyla are plotted as factors on the biplot to show which phyla contribute more to which individuals.
Figure 14. Factor map colored by contribution score for each phylum. A phylum that is closer to the correlation circle with a higher contribution score accounts for more of the variance explained in the PCA.
48
Figure 15. PCoA plot coloring by obese vs non-obese individuals (top), gender (middle), and median-stratified post-vaccination total IgG (bottom). None of the PCoA analyses yielded significant clustering after analyzing all dimensions.
Discussion
The tests in my analysis are designed to generate future hypotheses. In the bioinformatics analysis I conducted above, all tests are associative and therefore cannot prove mechanistic causation of any kind. This study offers the first reports of BMI – associated gut microbiota differences being associated with the humoral response to the influenza vaccination, which is promising for future studies.
BMI and the Immune Response to the TIV:
BMI correlated significantly with post-vaccination total IgG (p = 0.01493), total IgG pre:post vaccination ratio (p = 0.008669), and total IgG pre - post difference (p = 0.008669). Correlation was also significant between BMI and the post-vaccination HAI viral titer (p = 0.008669). This positive correlation is counterintuitive because IgG and HAI titer responses post
49
vaccination are measures of protection against the influenza virus. It is not true the obese individuals are more protected against the flu. Previous research in the Beck lab has shown B cell functionality to drop off significantly between one month and one-year post vaccination in obese individuals compared to healthy weight individuals. This means that obese individuals cannot sustain an influenza antibody response that lasts as long as healthy-weight individuals do after the TIV. Those with a diminished antibody response are more susceptible to infection and severe outcomes of infection.
Studies in Beck Lab have shown the adaptive immune system is impaired in response to influenza vaccination and infection. Despite vaccination, obese individuals are two times as likely to have influenza or influenza like illness.2 Further investigation identified T cells in obese individuals as the potential cause of impaired immune response. T cells from obese individuals express lower levels of activation markers CD69 and CD28 and lower levels of functional markers IFN-γ and Granzyme B.
None of the T cell parameters designated in the functional panel in this study were significantly correlated with BMI in the study population. This also contrasts previous research done in our lab which found notable impairment of T cell functional and metabolic function. This discrepancy is likely due to the small sample size of this investigation which could have made this study underpowered to detect smaller differences in flow cytometry data. Overall, my data suggest that the investigation should continue into obesity-related impairments of B cells and plasma cells. BMI and the Gut Microbiota:
BMI was found to correlate only one phylum level OTU in the gut microbiota: Firmicutes. Firmicutes OTUs were significantly correlated with body mass index (p = 0.004247). In Figure 2
50
it is clear that amount of Firmicutes diminishes with increasing BMI. This contrasted with the hypothesis that the amount of Firmicutes would increase with BMI. It also contrasted the hypothesis that Bacteroidetes would diminish significantly with increasing BMI.
Normalized Shannon alpha diversity was correlated negatively with BMI according to Spearman’s correlation (p = 0.0127) such that Shannon alpha diversity decreases and BMI increases. Shannon alpha diversity is a measure of the intra-individual diversity of microbiota and accounts for differences in abundance and evenness. The normalized Shannon alpha diversity also differed significantly between obese and non-obese individuals according to a Wilcoxon Rank- sum test (p = 0.320). This contrasted the previous finding that obesity is associated with lower Shannon alpha diversity.
These results appear to further support the idea that according to the standard analytical and sequencing methods, there is an extreme lack of consistency in findings of association between BMI and any measures of the gut microbiota. This phenomenon could indicate a need for the development of smarter bioinformatics tools and more affordable and precise approaches to sequencing the gut microbiome. However, it could also indicate simply that obesity is highly variable in humans, and therefore has effects on the gut microbiota that are diverse in nature.
Richness (the sum of the OTUs within each individual) was significantly correlated with the frequency of CD4+ CD28+ helper T cells expressing CD69 (p = 0.04390) upon activation with influenza virus. Richness was also significantly correlated with the number of unstimulated CD8+ cytotoxic helper T cells that expressed IFN-γ but not GrB (p = 0.09154). As richness increases, both of these parameters increase. This means that Th cells and CTLs were more activated in individuals who have a more “rich” gut microbiota. The cumulative number of OTU’s is not a perfect measure of the amount of organisms in one’s gut microbiota, it is simply a measure of how
51
many recurring 97% matched 16S rRNA sequences were observed. However, this result could imply that in individuals with higher richness, more bacterial fragments and products could be permeating the gut, which could be caused by multiple potential factors. This would then result increased inflammation by causing visceral immune cells to secrete more pro-inflammatory cytokines. Richness was not found to be a function of BMI in this analysis however, but this should also be a direction for future research.
The Gut Microbiota and the Immune Response to TIV:
It is hypothesized that high fat diets cause increased intestinal permeability and increased translocation of bacterial components and their products which increases inflammation in the visceral adipose tissue.23 However, the mechanistic connections between specific gut microbiota and the immune components that are affected by the gut are poorly understood.39
The most notable finding of this study is in the case of the phylum Firmicutes. Firmicutes, which was found to correlate significantly with BMI, was also significantly correlated with pre : post vaccination total IgG ratio (p = 0.01723), pre – post vaccination total IgG difference (p = 0.01723), and post vaccination HAI titer (p = 0.03632). This is significant because it represents a phylum level link between body mass, the gut microbiota, and the immune response to the TIV and the influenza virus. Firmicutes also showed promising association with post vaccination total IgG, post vaccination IgG1, and pre vaccination HAI titer, that might be significant with a more adequately powered investigation. Another correlation of note was between the phylum Actinobacteria and pre vaccination IgG2 (p = 0.05444). Though the pre-vaccination IgG ELISA is not a result of the 2014-2015 vaccine, it is a measure of one-year post 2013-2014 TIV immunization response due to annual vaccination in our cohort. As Actinobacteria increased the B cell response decreased.
52
In the relationship between BMI, the gut microbiota at the phylum level, and the humoral antibody response to the trivalent influenza vaccination, it is true that three factors are significantly correlated with each other. This data shows that there is a need for future investigation into the causal directionality of this interaction. This could be achieved by the use of a gnotobiotic mouse model where fecal samples from lean and obese individuals are transplanted and the mouse is analyzed for its immune response to influenza virus or the TIV immunization.
While the phylum Firmicutes, which is correlated with BMI and B cell response was not