Normalized Quantitative PCR Measurements as Predictors for Ethene Formation at Sites Impacted with Chlorinated Ethenes

Abstract

Quantitative PCR (qPCR) targeting Dehalococcoides mccartyi ( Dhc) biomarker genes supports effective management at sites impacted with chlorinated ethenes. To establish correlations between Dhc biomarker gene abundances and ethene formation (i.e., detoxification), 859 groundwater samples representing 62 sites undergoing monitored natural attenuation or enhanced remediation were analyzed. Dhc 16S rRNA genes and the vinyl chloride (VC) reductive dehalogenase genes bvcA and vcrA were detected in 88% and 61% of samples, respectively, from wells with ethene. Dhc 16S rRNA, bvcA, vcrA, and tceA (implicated in cometabolic reductive VC dechlorination) gene abundances all positively correlated with ethene formation. Significantly greater ethene concentrations were observed when Dhc 16S rRNA gene and VC RDase gene abundances exceeded 10⁷ and 10⁶ copies L^-1, respectively, and when Dhc 16S rRNA- and bvcA + vcrA-to-total bacterial 16S rRNA gene ratios exceeded 0.1%. Dhc 16S rRNA gene-to- vcrA/ bvcA ratios near unity also indicated elevated ethene; however, no increased ethene was observed in 19 wells where vcrA and/or bvcA gene copy numbers exceeded Dhc cell numbers 10- to 10 000-fold. Approximately one-third of samples with detectable ethene lacked bvcA, vcrA, and tceA, suggesting that comprehensive understanding of VC detoxification biomarkers has not been achieved. Although the current biomarker suite is incomplete, the data analysis corroborates the value of the available Dhc DNA biomarkers for prognostic and diagnostic groundwater monitoring at sites impacted with chlorinated ethenes.

Figure 1.

Correlations between Dhc biomarker gene abundances and ethene concentrations. Ethene concentrations were significantly greater in samples with (A) Dhc 16S rRNA gene and (B) VC RDase gene abundances greater than 10⁷ copies L⁻¹ and 10⁶ copies L⁻¹, respectively. The Kruskal–Wallis H test, also known as the one-way ANOVA on ranks, was performed with ethene concentration as the dependent variable (y axis) and order of magnitude ranges of log Dhc or log bvcA + vcrA abundance as the independent, categorical factor (x axis). For each category (e.g., log Dhc 16S rRNA gene copies L⁻¹ < 2), the median, mean, and mean ranks of ethene concentrations are shown. Boxes represent the interquartile range. The top and the bottom of each box represent the 25^th and the 75^th percentiles, respectively. Kruskal–Wallis tests [H(k−1,N)] for nonparametric one-way ANOVA indicated significant differences in mean ranks of ethene concentrations with log Dhc abundance [H(7,N = 625) = 141.3, p < 0.05] and log bvc+vcrA abundance [H(7,N = 479) = 83.1, p < 0.05] as the independent variables.

Figure 2.

Dhc biomarker gene abundances normalized to total bacterial 16S rRNA gene abundances inform about ethene formation potential. Ethene concentrations were significantly greater in samples with (A) Dhc 16S rRNA gene and (B) VC RDase gene abundances greater than 1% and 0.1% of the bacterial 16S rRNA gene abundance, respectively. The Kruskal–Wallis H test was performed with ethene concentration as the dependent variable (y axis) and ranges of (A) percent Dhc/bacterial 16S rRNA gene abundance and (B) percent bvc+vcrA /bacterial 16S rRNA gene abundance as the independent, categorical factor (x axis). For each category (e.g., percent Dhc/bacterial 16S rRNA gene abundance >1%), the median, mean, and mean rank of ethene concentration are shown. Boxes represent the interquartile range. The top and the bottom of each box represent the 25^th and the 75^th percentiles, respectively. Kruskal–Wallis tests [H(k−1,N)] for nonparametric one-way ANOVA indicated significant differences in mean ranks of ethene concentrations with percent Dhc/bacterial 16S rRNA gene abundance [H(5,N = 197) = 39.0, p < 0.05] and percent bvc+vcrA/bacterial 16S rRNA gene abundance [H(5,N = 197) = 25.8, p < 0.05] as the independent variables.

Figure 3.

Effect of geochemical conditions on Dhc abundance. (A) Sulfate concentrations were significantly lower in samples with Dhc abundances greater than 10⁵ copies L⁻¹, and (B) Dhc abundances greater than 10⁷ copies L⁻¹ correlated with elevated methane concentrations. The Kruskal–Wallis H test was performed with (A) sulfate concentration and (B) methane concentration as the dependent variables (y axis) and order of magnitude ranges of log Dhc abundance as the independent, categorical factor (x axis). For each category (e.g., log Dhc 16S rRNA gene copies L⁻¹ < 2), the median, mean, and mean rank of ethene concentration are shown. Only samples from sites undergoing biostimulation or bioaugmentation were considered for analysis with sulfate as the dependent variable. Due to the limited number of samples from biostimulation and bioaugmentation sites where methane was reported, all available data were included during analysis with methane as the dependent variable. Kruskal–Wallis tests [H(k−1,N)] for nonparametric one-way ANOVA indicated significant differences in mean ranks of sulfate [H(3,N = 142) = 40.9] and methane [H(3,N = 554) = 69.8] concentrations with log Dhc abundance as the independent variable.

Figure 4.

Correlations between RDase gene and Dhc 16S rRNA gene abundances with detoxification potential. (A) Ratio of RDase to Dhc 16S rRNA gene copies. The solid black line indicates a 1:1 ratio of RDase to Dhc 16S rRNA gene copies. Points above the dashed line are from the 19 groundwater samples where the abundance of RDase genes exceeded Dhc by 10-fold or greater. (B) Ethene concentration as a function of the ratio of the sum of VC RDase gene copies to Dhc 16S rRNA gene copies (bvcA + vcrA/Dhc). The highest ethene concentrations were observed when the ratio of RDase to Dhc 16S rRNA genes was near 1.0. The solid black line indicates that the sum of the RDase genes (i.e., bvcA + vcrA) equals the number of Dhc 16S rRNA genes. The dashed lines outline a 10-fold difference from a 1:1 ratio as an estimated maximum uncertainty associated with target gene quantification.