A Comparison Study of Quantitative PCR and Conventional Culture-Based Methods for Microbial Water Quality

doi:10.21203/rs.3.rs-1497117/v1

The goal of this study is to assess the microbial quality of water sources by using conventional culture-dependent microbiological methods and culture-independent molecular-based qPCR application for the monitoring of total bacteria, total coliform, and E. coli as an indicator of fecal contamination. In this experiment, a surface water sample was collected from Gaziantep, Turkey and the detection of microbial load and contamination was performed through gen-specific qPCR, plate count, and MPN quantification techniques. Total bacteria, total coliform, and E. coli levels using qPCR assay were significantly higher than that enumerated by culture-dependent microbiological methods in the water sample. Differences in the water pollution influenced the suitability of molecular method was clarified to the occurring viable but not cultivable (VBNC) bacteria and the measuring of low levels of DNA target by qPCR. The microbial quality of the Gaziantep Alleben water sample predicted by qPCR showed a positive correlation with traditional methods. These results stated that our real-time PCR approach can be suitable to estimate the relative abundances of uncultured and cultured indicator microorganisms affected by the water characteristics. The high level (100%) of resistance observed to clindamycin, erythromycin, penicillin G, tetracycline, and trimethoprim followed cefotaxime (62.5%), ceftazidime (50.0%), streptomycin (50.0%), ciprofloxacin (37.5%), and cefoperazone (25.0%) in a total of 8 strains. The high MAR index (0.33-0.67) of Gr negative strains indicated higher anthropogenic activity such as discharge points of industrial and domestic wastewaters which can contribute to microbiological contamination in surface waters.

Antibiotic Resistance

Methodological approaches

Water quality

qPCR

Microbial contamination of water sources with fecal-derived pathogenic microorganisms (bacteria, virus, protozoa) and human and/or animal feces may lead to serious public and environmental health problems in many countries [1–2]. For this reason, the microbiological safety of waters is essential to protect public health from diseases caused by water-borne pathogens [2].

Microbial techniques have been conventionally applied to observe changes in the microbial quality of surface and drinking water [3]. Culture-dependent methodologies (heterotrophic plate count, membrane filtration, multiple tube fermentation, and most probable number) have been extensively used for routine microbial quality monitoring of drinking and surface water, including the detection of fecal and coliform contamination [3–5].

These culture-based techniques that the time (18–96 h) required for confirmation and verification steps are only showed < 1% of the total microbial diversity of the water samples despite their relative ease of use and low cost [3, 6]. Due to this limitation, culture-independent molecular-based approaches have been recently enabled to obtain the more detailed results of microbial communities [3].

To determine of waterborne bacterial community without the need for a cultivation step, the rapid and very specific three molecular-based methods are here: the immunological, polymerase chain reaction (PCR), and in-situ hybridization (ISH) techniques [7]. Quantitative polymerase chain reaction (qPCR) technique based on quantifying the number of target gene copies of the indicators or pathogens (DNA or RNA) in environmental samples has a high level of specificity and sensitivity without the need for complex cultivation or confirmation step [1, 8–9].

Especially, the coliform bacteria are indicators of biologic pollution in surface water, groundwater, or supply sources of potable water. The presence of Escherichia coli, a member of the coliform group, in water sources, is indicated pollution of human or animal fecal origin [10]. The level of total bacteria, total coliform, and E. coli in water samples have been determined by the absolute number of bacterial quantities with qPCR using a calibration curve generated with standard genomic DNA [8].

This article aims to compare traditional culture-dependent microbiological methods and culture-independent molecular-based qPCR application for the monitoring of total bacteria, total coliform, and Escherichia coli in surface water samples obtained from Gaziantep, Turkey.

Sampling

The geographic coordinate of the sampling site was 37.06589, 37.37848 Gaziantep, Alleben Turkey. The water sample was collected by using a sterile bacteriological sample bottle (250 mL), according to APHA, (1998); WHO, (2006) [11–12]. Following collection, the sample was immediately brought to the laboratory in an icebox and stored at + 4°C in a light-sensitive container until microbiological quality analyses.

Microbial water quality analysis

Culture-Based Techniques

Enumeration of total aerobic bacteria in surface water, 100 µL of the sample was serially diluted and 100 µL of each serial dilution was spread on plate count agar. And plates were incubated for 24–48 h at 22–37°C. After incubation, the number of colonies on agar was calculated and noted as colony-forming units per mL (CFU/mL).

The most probable number method (MPN) illustrated by Prescott et al. (1996) was used to determine the counts of coliform indicator bacteria in the water sample [13]. The sample was inoculated into 9 tubes consisting of 3 tubes for each of three dilution factors (0.1 ml, 1 ml, and 10 ml). The dilutions tubes including Lauryl Sulfate Tryptose Broth (LST, Merck) were incubated at 37°C for 24–48 h for total coliform. Following incubation, each tube containing an inverted Durham tube was visually investigated for bacterial growth and noted gas production (bubbles). After that, 100 µL from each positive LST broth tube was transferred to an EC (Escherichia coli) medium broth tube including a Durham tube and incubated at 44.5°C for 24–48 h for screening fecal coliform. The number of indicator bacteria was calculated by counting the tubes giving positive reactions and comparing them with standard statistical tables.

qPCR

Total bacterial DNA from the filter sample was extracted by using GF-1 Bacterial DNA Extraction Kit (Vivantis) the following manufacturer protocols. The concentration and quality of DNA were detected by using a Nanodrop Multiskan G0 quantification spectrophotometer (Thermo Fisher Scientific, Massachusetts, USA), obtaining ranges from 30–40 µg/µL. Real-time PCR assays (qPCR) were performed to quantify the number of target genes on a LightCycler® Nano Real-Time PCR Instrument. The primers used for gene amplification are presented in Table 1. The calibration curves for qPCR quantification for 16S rRNA bacterial universal gene, uidA E. coli gene, and LacZ coliform gene were plotted using the different dilution series of standard E. coli ATCC 25922 DNA. Standart DNA was isolated by using the GF-1 Bacterial DNA Extraction Kit (Vivantis) process. Each qPCR reaction process contained: 5 µL SYBR Select Master Mix (GoTaq, qPCR Master Mix, 2x), 0.3 µL 10 mM forward primers and reverse primers each, 2 µL DNA template, and 2.4 µL DNAaz free deionized water. qPCR reactions were followed settings: holding at 95°C for 10 min; 40 cycle amplification under conditions of 95°C for 10 s with annealing/extension at (52°C for 16S rRNA bacterial universal gene; 56°C for uidA gene; 50°C for LacZ gene) for 15 s, 72°C for 20 s, finally elongating at 72°C for 7 min. The thermal profile for the melting curve began at 60°C with a gradual increase in temperature (0.1°C/s) to 95°C. A threshold cycle (Ct) value was detected as the cycle number where fluorescence data crossed the threshold line, for all the dilution series of standard DNA. The number of bacterial cells in the surface water sample was estimated from C_t values plotted to the different standard DNA dilutions standard curves.

Table 1

Primers used for detection of total bacteria, total coliform, and *E. coli* in the water sample
Name of gene	The sequence of forward and reverse primers (5'-3')	Target gene	Product size (bp)	References
16srRNA	F: 5′-CCTACGGGAGGCAGCAG-3′ R:5’-CTACCAGGGTATCTAATC C-3′	Universal 16s ribosomal RNA for all bacteria	500	Brosius et al., 1981 [38].
Lac Z3	F: 5′ TTGAAAATGGTCTGCTGCTG 3′ R: 5′ TATTGGCTTCATCCACCACA 3′	β-galactosidase for coliforms	234	Molina et al., 2015 [39].
uidA	F: 5′ TGGTAATTACCGACGAAAACGGC 3′ R: 5′ ACGCGTGGTTACAGTCTTGCG 3′	β-D-glucuronidase for E. coli	162	Bej et al., 1991 [16].

Antibiotic resistance profile

MacConkey agar differentiated them based on lactose fermentation was used for gram-negative bacteria from the water sample. Isolated colonies were identified by applying morphological (Gram staining and cell morphology) and standard microbiological (indole, methyl red, voges proskauer, citrate, and MUG (4-Methylumbelliferyl β-D-Glucuronide) agar test systems) procedures. The isolates were tested for susceptibility to the 15 standard antibiotics by disk diffusion method [14] on Mueller-Hinton agar (Oxoid, UK) plate according to the guidelines and recommendations of Clinical & Laboratory Standards Institute (CLSI).

The following 15 antibiotics that are important for human healthcare system were Cefepime (FEP; 30 µg), Cefoperazone (CEP; 75 µg), Cefotaxime (CTX; 30 µg), Ceftazidime (CAZ; 30 µg), Chloramphenicol (C; 30 µg), Ciprofloxacin (CIP; 5 µg), Clindamycin (DA; 2 µg), Erythromycin (E; 15 µg), Gentamicin (G; 10 µg), Imipenem (IMP; 10 µg), Meropenem (MEM; 10 µg), Penicillin G (P; 10 µg), Streptomycin (S; 10 µg), Tetracycline (TE; 30 µg) and Trimetropim (TR; 5 µg) (Bioanalyse). The susceptiblity of strains was interpreted by the CLSI standard results (Table 1). E. coli ATCC 25922 was used for quality control. Multiple antibiotic resistance (MAR) index of strains was calculated based on the formula declared by Krumperman (1983) [15]. Resistance results were analyzed by using the software SPSS 22.0 for Windows (SPSS Inc., Chicago, IL, USA)

Bacteria Identification

DNA extracted from bacteria by using the Bacterial Genomic DNA Extraction Kit (Hibrigen) process was amplificated by PCR process (initial denaturation at 94^°C for 5 min; denaturation at 94^°C for 35 s, primer annealing at 56^°C for 35 s and extension at 72^°C for 45 s of 45 cycles; final extension at 72^°C for 5 min.) using uidA gene-specific primers [16]. The amplified products were verified by running on agarose gel (1%). The nucleotide sequence of the uidA gene by using both primers was analyzed at the BM-Labosis (Ankara, Turkey). The gene sequence of strain was compared automatically using the Blast software (BLAST), against the sequences of bacteria available in databanks (http://www.ncbi.nlm.nih.gov/). The phylogenetic analysis was constructed using a neighbor-joining algorithm.

In the water sample, the concentrations of culturable and viable bacterial cells were estimated to plate on count agar. As summarized in Table 2, the average (n = 3) plate counts in surface water were 164.5 x 10³ and 89.5 x 10⁴ CFU/mL at 37°C and 22°C, respectively. The probable counts of total coliform (1100 + MPN/100 mL) and viable indicator organism-E. coli (16 MPN/100 mL) were presented in Table 2. These levels were observed to exceed the acceptable levels (0 MPN/100 mL) of surface water according to WHO (2017) and TS 266 (2005) guidelines [17–18].

Table 2

Indicator organism measurements in Alleben water sample using conventional and qPCR methods.
	Conventional Culture-Based Methods	qPCR
Total bacteria	164.5 x 10³ at 37°C/89.5 x 10⁴ CFU/mL at 22°C	125.76 x 10⁴ CFU/mL
Total coliform	1100 + MPN/100 mL	302.21 x 10³ CFU/mL.
E. coli	16 MPN/100 mL	40.189 x 10³ CFU/mL.

Figure 1 shows that the mean bacterial concentration in the environmental DNA sample amplified by different primer sets was calculated based on the plotted standard curves. Accuracy of qPCR performance was defined by % productivity and the R² of standard curves. The high accuracy with an overall R² of 0.9975 and 95.1% productivity for the total bacteria population was revealed. Also, similar results were recorded for total coliform (R² 0.9754 and 87.50%) and E. coli (R² 0.9817 and 94.10%) standard curves. Total bacterial concentration including both the culturable and non-culturable bacteria determined by the average 16S rRNA gene copies in water was 125.76 x 10⁴ CFU/mL. The amounts of the total coliform and E. coli in filtered surface water DNA revealed by qPCR were 302.21 x 10³ and 40.189 x 10³ CFU/mL. qPCR values were correlated with culture-based microbial techniques (r: 0.98, p < 0.01). Discrepancies in the counts of culturable-viable and based on gene copies within two methods could be due to the non-culturable bacteria revealed by qPCR. The standard curve equations are represented in Fig. 3, where y is Ct and x is log10 of CFU/mL.

Eight bacterial isolates recovered on MacConkey plates from a water sample were selected for the further antibiotic susceptible study. There was no clear zone of inhibition on MHA plates including erythromycin, clindamycin, penicillin G, tetracycline, and trimethoprim standard discs across all bacteria. The strains showed susceptibility to the remaining 5 antibiotics (cefepime, chloramphenicol, gentamicin, imipenem, and meropenem) as well as high-frequency resistance against 5 antibiotics (cefoperazone, cefotaxime, ceftazidime, ciprofloxacin, and streptomycin) exhibited ranging from 25 to 62.5% (Table 3.) The MAR index ranged from 0.33–0.67 for Gr negative strains from the Gaziantep water source. Bacterial strain showing the highest MAR index was identified by using the Blast software With 92% sequence similarity, analysis of uidA gene sequence confirms that the identified strain 1360940 A12 is the type strain of Escherichia coli (Fig. 2). The GenBank accession number allotted for uidA β-D-glucuronidase gene sequence of isolate is MZ959605.

Table 3

Percentage of total isolates with antibiotic resistance
	Pattern	Resistance (%)
Clindamycin	R	100 ± 0.0*
Erythromycin	R	100 ± 0.0*
Penicillin G	R	100 ± 0.0*
Tetracycline	R	100 ± 0.0*
Trimethoprim	R	100 ± 0.0*
Cefotaxime	R	62.5 ± 0.25*
Ceftazidime	R	50 ± 0.5*
Streptomycin	R	50 ± 0.45*
Ciprofloxacin	R	37.50.5*
Cefoperazone	R	25 ± 0.5*
Cefepime	S	0 ± 0.0*
Chloramphenicol	S	0 ± 0.0*
Gentamicin	S	0 ± 0.0*
Imipenem	S	0 ± 0.0*
Meropenem	S	0 ± 0.0*
*P < 0.05 compared with the results.

The use of indicator bacteria has a good strategy to characterize microbial contamination (coliform and fecal coliform) of surface water sources [19]. Recently, detection of bacterial pollution in environmental samples has been both used by growth on a culture medium and by the amplification of the specific gene. However, environmental samples have higher levels of chemical and genetic complexity than that tissue or pure cultures. Furthermore, other factors including the type of target organism, the number and diversity of bacteria in the sample, the DNA extraction protocol, and qPCR efficiency have limited molecular experiences [1–2]. For this reason, the amplification of qPCR equals obtained with the target DNA and quantification standards carefully optimized to reach maximum achievable specificity and sensitivity.

Literature researches were defined to comparable results for total bacterial load, fecal and coliform indicator bacteria in the analyses of environmental surface water using culture and qPCR methods [6, 21]. Our approach combined with the culture-based and qPCR for the quantification of indicator bacteria from the surface water sample (Gaziantep, Turkey) was successfully performed. This study resulted in observable PCR products for total bacteria load, total coliform, and E .coli. In results similar to ours, higher cell loads than those of cultivation-based techniques probably due to the presence of dead or viable, but non-culturable, (VBNC) cells were noted by the qPCR method [22–23]. Especially, the higher levels of E. coli, an indicator of fecal contamination, quantified by qPCR assay were reported than those obtained using MPN [24–25]. In our study, qPCR E. coli counts of Gaziantep surface water sample were higher (40.189 x 10³ CFU/mL) than those acquired by traditional plate counts (16 MPN/100 mL). This finding accords with the result of Truchado et al. (2016) who determined that E. coli levels quantified by qPCR assay were higher than that of standard agar plate colony counting methods. Similarly, Ferguson et al. (2012) indicated a better correlation between E. coli detected by molecular approach and the presence of fecal indicator than by culture-based methods [26]. In a study of Buckeye Lake beaches, the qPCR results of 14 samples exceeded the current E. coli Ohio single sample bathing-water standard (235 CFU/100 mL) [27]. Chern et al. (2009) was estimated 7.37 x 10³ CFU E.coli density per 100 mL of marine water from two recreational beaches by qPCR [28]. Vadde et al. (2019) found elevated levels of fecal coliform (> 250 CFU/100 mL) at 15 locations of Tiaoxi River, by microbial source tracking (MST)- qPCR assays [29]. Truchado et al. (2016) compared two E. coli quantification techniques (plate count and qPCR) for irrigation water and fresh produce. E. coli levels using qPCR assay were proved to be significantly higher than that estimated by plate count in all environmental samples [19].

In addition, the qPCR method enables the measuring of low levels of DNA target in environmental samples. Krapf et al. (2016) revealed a lower limit detection of E. coli and E. faecalis/100 mL by qPCR analyses, depending upon the culture condition used in a drinking water sample [21]. In another study, Shrestha and Dorevitch (2019) reported that E. coli DNA targets in only 1% of recreational Chicago area beach waters amplified [30]. For another study, the levels of E. coli contributed to the risk of epidemiological disease in Poland hot water systems were quantitated around 0.00 genomes/mL (less than one genome/mL) by qPCR curve equations [31].

Another important aspect of this manuscript is the determination of antibiotic susceptible profiles in Gr negative bacteria isolated from the Alleben water sample. Although the prevalence of antibiotic-resistant bacteria (ARB) in many freshwater sources of Turkey has been previously demonstrated [32–33], this is the first study that indicated the high frequency of resistance in the surface waters of Alleben, Gaziantep. Similarly, Matyar et al. (2014) high multiple antibiotic resistance indices ranged from 0.2 to 0.81, suggesting exposure to antibiotic contamination in Seyhan Dam Lake and River water samples reported [34]. Icgen and Yılmaz (2014) pointed to the resistance of more than 50% of the Kızılırmak River isolates to a different type of antibiotics [35]. Similar results were obtained by Nakipoglu et al. (2017) who have shown the dissemination of high antibiotic resistance of river water sample isolates[36]. A high percentage of bacterial species isolated from the seawater and sediment samples, Gulluk Bay observed considerable resistance in another study [37].

Bacteriological and ARB findings in the current study are clearly emphasized to discharge household and industrial wastewater systems into the surface water without control. Further, the main source of contamination is the high loading of domestic sewage and solid wastes from surrounding densely populated areas. These anthropogenic factors may affect the metabolic activities in ecosystems and biodiversity of aquatic life. The presence of fecal coliforms in aquatic environments observes the contamination of waters with the fecal material of man or other animals.

Although a load of total bacteria, total coliform, and fecal coliform in many surface water sources of Turkey has been previously reported by plate counting, this is the first study that pointed to the high specificity of culture-independent molecular-based approaches. Our results showed that qPCR is a suitable and fast approach for the detection and enumeration of bacterial contaminants in surface water samples. This preliminary research is a good example that emphasized the need to use molecular techniques as additional methods of standard water analyses to achieve more accurate results in a short detection time.

FUNDING

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

COMPETING INTERESTS

The authors declare no competing financial interests or personal relationships influenced the work reported in this paper

AUTHOR CONTRIBUTIONS

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hatice Aysun Mercimek Takci and Cevher Karaca. The first draft of the manuscript was written by Hatice Aysun Mercimek Takci and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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A Comparison Study of Quantitative PCR and Conventional Culture-Based Methods for Microbial Water Quality

Status:

Version 1

Abstract

Figures

Introduction

Material And Methods

Sampling

Microbial water quality analysis

Culture-Based Techniques

qPCR

Antibiotic resistance profile

Bacteria Identification

Results

Discussion

Conclusion

Declarations

References

Status:

Version 1