Impact of hypoxia on gene expression patterns by the human pathogen, Vibrio vulnificus, and bacterial community composition in a North Carolina estuary

Abstract

Estuarine environments are continuously being shaped by both natural and anthropogenic sources which directly/indirectly influence the organisms that inhabit these important niches on both individual and community levels. Human infections caused by pathogenic Vibrio species are continuing to rise, and factors associated with global climate change have been suggested to be impacting their abundance and geographical range. Along with temperature, hypoxia has also increased dramatically in the last 40 years, which has led to persistent dead zones worldwide in areas where these infections are increasing. Thus, utilizing membrane diffusion chambers, we investigated the impact of in situ hypoxia on the gene expression of one such bacterium, Vibrio vulnificus, which is an inhabitant of these vulnerable areas worldwide. By coupling these data with multiple abiotic factors, we were able to demonstrate that genes involved in numerous functions, including those involved in virulence, environmental persistence, and stressosome production, were negatively correlated with dissolved oxygen. Furthermore, comparing 16S ribosomal RNA, we found similar overall community compositions during both hypoxia and normoxia. However, unweighted beta diversity analyses revealed that although certain classes of bacteria dominate in both low- and high-oxygen environments, there is the potential for quantitative shifts in lower abundant species, which may be important for effective risk assessment in areas that are becoming increasingly more hypoxic. This study emphasizes the importance of investigating hypoxia as a trigger for gene expression changes by marine Vibrio species and highlights the need for more in depth community analyses during estuarine hypoxia.

Keywords: Vibrio; Vibrio vulnificus; bacterial communities; dead zone; gene expression; hypoxia.

Figure 1

(a) Temporal environmental parameters as measured every 30 min for three days and (b) normalized change in DO, temperature, salinity, and pH over time. Normalized change was calculated by calculating change from one sampling event to the next followed by subtracting the mean from each measured change and dividing by its SD; values used fell within ±1.5 h around high‐ and low‐DO sampling times. One‐way ANOVA revealed a significant (p < 0.0001) difference in DO normalized change when compared to all other parameters.

Figure 2

Relative temporal expression of representative genes involved in (a) CPS/EPS production, (b) attachment and motility, (c) global gene regulation, (d) putative virulence factor production, (e) stressosome formation, and (f) metabolic function. Temporal fold change represents expression at corresponding x axis time relative to previous sample. Error bars represent SD of three biological replicates (n = 3), comprising two technical replicates averaged for strain CMCP6. Grey shaded areas indicate hypoxic sampling events. Symbols (#/$/) represent values that were not statistically different from the previous time point (one‐way ANOVA, p > 0.05). Arrows indicate that expression was outside the limit of detection at T = 0 in which case the fold changes were set to an arbitrary 100‐fold increase.

Figure 3

Heat map of correlations between environmental conditions and gene expression by V. vulnificus. The correlation coefficients are indicated by color and range from −1 (red, negatively correlated) to 1 (blue, positively correlated) with 0 (purple) indicating no correlation between variables. Environmental values used were taken within ±1.5 h of the high‐ and low‐DO sampling times and were averaged at each sampling event. Averages of 2 biological replicates with 3 technical replicates each were used for expression values. Asterisks represent significant correlations (p < 0.05) between environmental parameters and gene expression (Pearson correlation).

Figure 4

(a) Relative abundance of 16S rRNA sample libraries, (b) Bray‐Curtis measure of dissimilarity, and (c and d) beta diversity PCoA plots of bacterial communities collected at Hoop Pole Creek over a 3 day sampling event. Abundances represent the top 20 OTUs present in the samples. Bray‐Curtis represents relative abundances of all OTUs at one time point compared to the next time point; 0 = perfect similarity and 1 = complete dissimilarity. Beta diversity of both weighted (Figure 4c) and unweighted (Figure 4d) UniFrac distances is shown with colors corresponding to sampling date and DO profile.