Quantitative detection of selenate-reducing bacteria by real-time PCR targeting the selenate reductase gene
Introduction
A variety of natural, agricultural, and industrial processes can cause serious selenium- (Se) environmental contamination; among which the main toxic forms of Se are soluble selenate (SeO42−) and selenite (SeO32−) [18], [31]. Soluble forms of Se impose serious health concerns because of their toxicities to organisms [12]. Hence, regulations in the United States have set up a maximum contaminant level (MCL) for drinking water of 50 μg-Se/L [46]). Bacterial strains capable of respiratory selenate reduction, a process that produces insoluble selenium (Se0), have been isolated from different environmental sources [8], [11], [21], [34], [35], [26]. Though these selenate-reducing bacteria (SeRB) have been studied for decades, their population dynamics in the environment remain unknown.
Estimating the abundance and growth of functional bacteria helps to assess accurately the potential of biological remediation and further optimize current treatment processes. Real-time qPCR was invented in 1993 [17] and has been applied to quantitatively detect functional bacterial groups by their specific genes (functional genes or conserved partial 16S rRNA gene). For instance and after as first reported by Braker et al. [5], the nirK and nirS gene were intensively applied to target denitrifying bacteria (DB) in many different types of environmental samples [2], [50]. Nozawa-Inoue et al. [30] developed a very accurate assay using pcrA gene to quantitatively analyze the abundance of perchlorate-reducing bacteria (PRB). The abundances of methanogens and Dehalococcoides have successfully been detected by qPCR as well [53], [10]. Nevertheless and to our knowledge, scientific evidence is missing on the quantification of SeRB by qPCR.
Part of the problem is the frequent association of selenate reduction with denitrification process. Based on previous studies, selenate reduction has often been suggested to be a side reaction of the respiratory nitrate reductases. Both the membrane-bound (Nar) and periplasmic (Nap) nitrate reductases from Paracoccus denitrificans and Paracoccus pantotrophus were found to have selenate reductase activity [37], [3]. However, researchers also found that nitrate reductases are poor reducers of selenate and may not contribute significantly to global selenate reduction [37], [3], [7]. Although common in practice [37], tracking denitrifying bacteria as surrogate for the abundance of selenate reducers is not accurate enough. Thus, designing specific primers to target SeRB is necessary.
In the process of biological reduction of SeO42−, bacteria respire SeO42− as a terminal electron acceptor to SeO32− by selenate reductase (SerABC) [41] or nitrate reductase (Nar or Nap) [15], [37]. SeO32− is then reduced to Se0 by one of the following pathways: (1) periplasmic nitrite reductase (Nir) [11], or (2) hydrogenase I [52], and (3) through non-enzymatic processes. This last pathway refers to gram-negative bacteria having glutathione, which can react with SeO32− to form triselensulfide [13] to be further reduced to Se0 [40].
Two types of selenate reductase have been reported: periplasmic selenate reductase and membrane-bound selenate reductase [48], [29]. While the membrane-bound selenate reductase has a broad substrate specificity [43], the periplasmic selenate reductase shows a restricted substrate specificity and does not reduce nitrate, chlorate, or sulphate [41]. The selenate reductases belong to the molybdenum dependent reductase (molybdoreductase) family [19]. The alignment of the periplasmic selenate reductase (SerA) with the periplasmic (NapA) and membrane-bound (NarG) nitrate reductases suggests that SerA is more closely related to NarG than to NapA.
Based on the limited information of the serA gene, the objective of this study was to design a real-time quantitative PCR (qPCR) assay to quantitatively detect periplasmic serA enzyme-containing selenate-reducing bacteria (SeRB) in environmental samples. To date, this is the first report of qPCR assay developed to detect SeRB. We analyzed the partial serA sequences for SeRB and DB isolates, as well for selenate-, and nitrate-reducing membrane biofilm reactor (MBfR) biofilms. Developing such an assay improves the specificity and accuracy to detect SeRB instead of using nitrate reductases as surrogates.
Section snippets
Primer design
To identify conserved regions, deduced SerA protein sequences from Dechloromonas sp. A34 (GeneBank accession ACV70151) and Thauera selenatis (Q9S1H0) were aligned using Clustal W [44]. Although the serA from Dechloromonas is a putative selenate reductase, this strain is reported capable to reduce selenate. To identify unique SerA sequence regions, Fig. 1 shows SerA protein sequences from the strains above mentioned along with several molybdoenzyme sequences from the dimethyl sulfoxide (DMSO)
Sensitivity of the designed primers
We tested the sensitivity of the serA designed primers with three SeRB strains, three DB strains and the selenate-reducing MBfR biofilm samples. The SeRBs are: Thauera. selenatis (ATCC, accession number: 55363 [26], Thauera. sp (lab culture isolated from Selenate-reducing culture), and Pseudomonas. stutzeri [24]. The DBs are: D. agitate (ATCC, accession number: 700666 [1], Marinobacter. pelagius sp. nov [51], lab culture isolated from seawater samples), and Pseudomonas sp. (lab isolated from
Comparison between nirS and serA primer set
NirS is the gene for cd1 nitrite reductase, which catalyzes the reduction of nitrite to nitric oxide in DB. This enzyme contains one heme c as the electron entry site and one heme d1 as component of the catalytic center [49]. Ward [47] firstly reported the use of nirS gene-based PCR primer set to identify denitrifiers. Later in 2006, Kandeler et al. reported that nirS gene-based PCR primer set was applied successfully in environmental samples collected from a glacier foreland. NirS gene has also
Ethical statement
This article does not contain any studies with human participants or animals performed by any of the authors.
Acknowledgments
Authors greatly thank the “National Natural Science Foundation of China (Grant No. 21107091, 21377109, 21577123)”, “National High Technology Research and Development Program of China (2013AA06A205)”, the “Fundamental Research Funds for the Central Universities (2014FZA6008)” and “Public Welfare Project of the Science and Technology Department of Zhejiang Province (2015C33016)” for their financial support.
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These authors contributed equally to this work.