Phenotypic analysis of various Clostridioides difficile ribotypes reveals consistency among core processes

Abstract

Clostridioides difficile infections (CDI) cause almost 300,000 hospitalizations per year of which ~15-30% are the result of recurring infections. The prevalence and persistence of CDI in hospital settings has resulted in an extensive collection of C. difficile clinical isolates and their classification, typically by ribotype. While much of the current literature focuses on one or two prominent ribotypes (e.g., RT027), recent years have seen several other ribotypes dominate the clinical landscape (e.g., RT106 and RT078). Some ribotypes are associated with severe disease and / or increased recurrence rates, but why are certain ribotypes more prominent or harmful than others remains unknown. Because C. difficile has a large, open pan-genome, this observed relationship between ribotype and clinical outcome could be a result of the genetic diversity of C. difficile. Thus, we hypothesize that core biological processes of C. difficile are conserved across ribotypes / clades. We tested this hypothesis by observing the growth kinetics, sporulation, germination, bile acid sensitivity, bile salt hydrolase activity, and surface motility of fifteen strains belonging to various ribotypes spanning each known C. difficile clade. In viewing these phenotypes across each strain, we see that core phenotypes (growth, germination, sporulation, and resistance to bile salt toxicity) are remarkably consistent across clades / ribotypes. This suggests that variations observed in the clinical setting may be due to unidentified factors in the accessory genome or due to unknown host-factors.

Importance: C. difficile infections impact thousands of individuals every year many of whom experience recurring infections. Clinical studies have reported an unexplained correlation between some clades / ribotypes of C. difficile and disease severity / recurrence. Here, we demonstrate that C. difficile strains across the major clades / ribotypes are consistent in their core phenotypes. This suggests that such phenotypes are not responsible for variations in disease severity / recurrence and are ideal targets for the development of therapeutics meant to treat C. difficile related infections.

Figure 1:. Phylogeny of strains used in this study

The neighbor joining phylogeny generated for the strains in this study created from LCB 145, a 1,418,215 bp segment of the MAUVE alignment representing approximately 25% of any given genome in the study. The phylogeny was created using the Geneious Tree Builder application in the Geneious Prime software using the Tamura-Nei genetic distance model. Strains are grouped by their respective ribotypes / clades with the scale bar representing the number of substitutions per 1000 bp.

Figure 2:. Growth of strains in rich medium

OD₆₀₀ measurements for Clade 1 (A), Clade 2 (B), Clade 3 (C), Clade 4 (D), and Clade 5 (E) strains were taken every 30 minutes over the course of 8 hours. This same data is shown in (F) grouped by ribotype / clade. Data from the most linear portion of the growth curve was used to calculate doubling time presented by strain (G) and by ribotype / clade (H). Data points represent the average from independent biological triplicates with error bars representing the standard error of the mean. For (G), Šidák’s multiple comparisons test was used, while a Tukey’s multiple comparisons test was used for (H). No statistically significant differences between strains were found.

Figure 3:. Growth of strains in minimal medium

Strains were grown in CDMM supplemented with either glucose (A), xylose (D), fructose (G), or trehalose (J). OD₆₀₀ measurements for each strain were taken every 3 minutes for 22 hours. Data from the most linear portion of the growth curve was used to calculate generation times for growth in CDMM supplemented with glucose (B,C), xylose (E,F), fructose (H,I), or trehalose (K,L). Data points represent the average from independent biological triplicates with error bars representing the standard error of the mean. For (B, E, H, and K), Šidák’s multiple comparisons test was used, while a Tukey’s multiple comparisons test was used for (C,F,I, and L). Asterisks indicate p-values of * = < 0.5, ** = < 0.02, *** = < 0.01, and **** = < 0.0001.

Figure 4:. Spore production by strains over 48 hours

The number of spores produced on BHIS over 48 hours for Clade 1 (A), Clade 2 (B), Clade 3 (C), Clade 4 (D), and Clade 5 (E) reported on a log₁₀ scale. This same data is grouped by clade in (F). Data points represent the average from independent biological triplicates with error bars representing the standard error of the mean. For (A – E), Šidák’s multiple comparisons test was used, while a Tukey’s multiple comparisons test was used for (F). Asterisks indicate a p-value of ** = < 0.02.

Figure 5:. Strain sensitivity to germinants

Germination assays for each strain were performed in the presence of various concentrations of glycine (A,B), TA (C,D), or TA+CDCA (E,F). Germinant sensitivity was calculated using the maximum slope for each condition plotted against (co)-germinant concentration. The data fitted to a linear relationship by taking the inverse of the slope vs. concentration plot and from this Ki/EC₅₀ was calculated with EC₅₀ equaling the concentration of germinant which produces the half maximum germination rate. The efficiency of the competitive inhibitor was calculated using the following equation ${\mathrm{K}}_{\mathrm{i}}=\left[\mathrm{i}\mathrm{n}\mathrm{h}\mathrm{i}\mathrm{b}\mathrm{i}\mathrm{t}\mathrm{o}\mathrm{r}\right]/\left(\right({\mathrm{K}}_{\mathrm{C}\mathrm{D}\mathrm{C}\mathrm{A}}/{\mathrm{K}}_{\mathrm{T}\mathrm{A}})-1)$ (55, 56). Data points represent the average from independent biological triplicates with error bars representing the standard error of the mean. For (A, C, and E), Šidák’s multiple comparisons test was used, while a Tukey’s multiple comparisons test was used for (B, D, and F). Asterisks indicate p-values of * = < 0.5, ** = < 0.02, *** = < 0.01, and **** = < 0.0001.

Figure 6:. Bile salt sensitivity of strains

Stationary-phase cultures of each strain were inoculated into BHIS of the indicated pH and concentration of CA (A,B), CDCA (C,D), and DCA (E,F). MICs were assessed by the presence/absence of growth after ~18 hours. Data represents the average from independent biological triplicates

Figure 7:. Bile salt hydrolase activity

Each strain was grown in the presence of 1 mM TA and incubated for 24 hours. The bile salts present in each culture following incubation were identified / quantified by reverse-phase high performance liquid chromatography (HPLC). Percent deconjugation was calculated using the following formula: $\mathrm{\%}\mathrm{d}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{j}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}=\mathrm{C}\mathrm{A}/(\mathrm{T}\mathrm{A}+\mathrm{C}\mathrm{A})$. Data points represent the average from independent biological triplicates with error bars representing the standard error of the mean.