Sequencing methods categorize the position of nucleic acid bases (adenine, gua-nine, cytosine, and thymine) of gene or genome partially or completely. It has been used in microbiology as a culture-independent tool for identifying the wide varie-ties of microbes (Shendure et al. 2017). Sanger sequencing is one of the first technol-ogies to be developed for sequencing (Sanger et al. 1977). This technology is highly accurate (99.99%), but it is useful only for short sequence reads. The currently avail-able version of the Sanger sequencer can sequence 96 samples simultaneously on a single sequencing run (Shendure et al. 2017). Shotgun sequencing is the next availa-ble sequencing technology. In it, long DNA sequences are randomly broken into smaller pieces and are amplified with the PCR method. Sequencing is then done as in Sanger sequencing. Then each sequence fragments are assembled with an algo-rithm into the long original DNA sequence. This technology can be used for se-quencing an entire genome (Shokralla et al. 2012).
Next-generation sequencing (NGS) was developed in 2005 (Margulies et al. 2005). The first version of NGS was based on 454 pyrosequencing. NGS methods can sequence a high number of samples simultaneously on a single sequence run (Shokralla et al. 2012). Most currently available NGS technologies are based on the sequencing of PCR amplicons, such as Illumina, Roche 545, Ion Torrent sequencing, and pyrosequencing platforms. These PCR based methods can sequence less than 800 bases. Illumina MiSeq is one of the most widely used technologies and has a capacity of sequencing up to 300-600 bases (Illumina 2020). Still, the technology is continuously being updated, so the sequencing of longer bases can be available in the future. Further, Pacific Biosciences System is a single-molecule sequencing-based technology that operates without PCR amplification (Shokralla et al. 2012).
This method can sequence the longer sequences (> 1500 bases) in less than an hour (Shokralla et al. 2012).
NGS of the 16S rRNA gene is one of the most widely used methods for prokary-otic community analysis, mainly for bacterial diversity and taxonomic composition analysis. It can also be used for seeking potential health-related bacteria and to pre-dict the enzymatic function of bacterial communities (Ye and Zhang 2011, Langil-le et al. 2013, Koo et al. 2017, Wu et al. 2019, Jin et al. 2018). The NGS method of the 16S rRNA gene was demonstrated to be more sensitive and identified more bacteria per sample than the traditional culture-based method (Gupta et al. 2019). This method enumerates the read counts (both absolute and relative) on different taxo-nomic levels and evaluates the diversity of the microbial community. In this meth-od, DNA from the study sample is extracted, a specific hypervariable region of the
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16S rRNA gene is amplified, sequenced, and then the base position is identified (Gupta et al. 2019). Based on the base position of the sequence, the microbial com-munity composition is identified on different taxonomic levels with publicly avail-able databases, such as SILVA, RDP, NCBI or Greengenes (Bonk et al. 2018). The method allows for parallel sequencing of hundreds of samples simultaneously and such obtained reads are analyzed with bioinformatics tools. The major shortcom-ings of the method are that the result is affected by various sequencing factors, such as variable regions used for sequencing, sequencing platforms, and sequence analy-sis pipelines (Gupta et al. 2019). Further, on the lower taxonomic level, mainly on the species-level, the method may not be equally effective for identifying microbes.
For example, the V4 region of two species, S. aureus and S. epidermidis, were 100%
identical (Gupta et al. 2019). Besides, most of the taxonomic annotations with am-plicon sequencing approach results often unidentified species or genera, largely because these have not been cultured, identified, and reported earlier in the data-base (Gupta et al. 2019). However, the available datadata-bases are gradually expanding to better identify microbial communities to desired taxonomic levels in natural eco-systems.
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4 BATHING WATER QUALITY REGULATIONS
The World Health Organization (WHO) has published guidelines for the safe use of bathing water over the years (WHO 2003, WHO 2009). The guideline has recommended for using iENT for both inland and coastal water, uses a 95%
percentile value of 20 bathing water samples analysed in bathing season of a year for making a decision about the level of faecal pollution (Table 4). It has provisions for risk estimation, based on both FIB enumeration and sanitary surveys.
The European BWD (EC 2006), US recreational water quality criteria (USEPA 2012), and Canadian standard (2012) are major global standards related to bathing and recreational water quality aiming at protecting the health of bathers (Table 4).
All standards specify the acceptable counts of FIB, majorly enterococci and E. coli.
However, each standard has specified its own acceptable enumeration methods for FIB (Table 4).
The EU legislation requires the use of ISO methods or equivalent methods for FIB enumeration (EC 2006). EU member states may use the alternative method, but they must prove the alternative method is as reliable as that of the reference meth-od (EC 2006). It means that the enumeration of target FIB from the alternative method should not significantly differ from the enumeration from the reference method. The comparison of quantitative microbial procedures is specified on the ISO 17994, 2004 procedure that has been recently revised (ISO 17994 2014). In the EU countries, the health protection authorities calculate percentile values of FIB enumerated values of four years.
Historically, EU countries began monitoring bathing waters in 1976, by measur-ing the total coliform, faecal coliform, faecal streptococci, salmonella, and enterovi-rus (Table 4). In 2002, EU modified the quality standard and began to use iENT and E. coli as FIB for the monitoring of microbial bathing water quality (Table 4). In 2006, the new BWD came into force (EC 2006). It continued measuring iENT and E.
coli as FIB. It has provisions of classification (excellent, good, sufficient, or poor) and requires health protection authorities to make a profile of each bathing site, based on the pollution level and source of contamination. The provision of classifications offers choices for bathers to visit a bathing site that has the best available hygienic quality. This provision may encourage management authorities of local bathing sites to reach an even better classification than the current one. The provision for the profiling of bathing sites is important, as the source of contamination deter-mines the level of human health risk.
The major aim of the European BWD (EC 2006) is to protect the health of bath-ers. Further, it aims to preserve, protect and improve the quality of the environment and to protect human health by complementing Directive 2000/60/EC. The national authorities implementing EUBWD collect sufficient data to produce information on the general status of bathing water and report the data to the EU regulatory body to
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infer how good the management practices related to wastewater treatments are (Oliver et al. 2014).
The directive (EC 2006) has some shortcomings. For example, it does not have a provision for assessing the microbiological quality of beach substrates, such as coastal sand and vegetation. The microbial quality of such substrates can have pub-lic health imppub-lications (Abdelzaher et al. 2010, Whitman et al. 2014). Further, the current FIB standards were developed, based on epidemiological studies conducted in limited geographical locations, majorly in the United Kingdom (Fleisher et al.
1996). However, the land use pattern and sanitary practices of different countries can vary. A certain etiological agent found in a certain location may not be so com-mon in the next location (Ferguson et al. 2008, Soller et al. 2010). The EU standard does not have a provision of measuring viral indicators and, the hygienic quality evaluation could benefit even more if supplemented by using MST and molecular markers of FIB. Further, the calculation of the percentile value of FIB gives high weight to the standard deviation of data. Although the high standard deviation of FIB may indicate a higher chance of fluctuation of high FIB counts than the data with lower standard deviation, sometimes, the high percentile values may come even on lower FIB counts, due to high standard deviation. As an EU member coun-try, Finland began the surveillance of BW according to the rules of the BWD during the bathing season of 2008.
The USEPA 1986 criteria started the provision of measuring E. coli in freshwater and enterococci in marine sites (Table 4). Later, USEPA (2012) validated using gene copies of enterococci enumerated with the qPCR method, in addition to the E. coli and enterococci enumerated with the culture method. In the US and Canada, the use of the mEI agar method or equivalent method is recommended for enumerating ENT. The US calculates both the geometrical mean (GM) or percentile value, whereas Canada uses only the GM.
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Table 4. Historical development of bathing and recreational water standards, regulations, guidelines, and indicators on freshwater and marine bathing sites. iEnt- intestinal entero- cocci, ENT- enterococci, GC=gene copies, STV=single time value, CCE = Calibrator Cell Equivalents, per= percentile, GM = geometric mean, AFRI = Acute febrile respiratory illness, GI = gastroenteritis, NEEAR =National Epidemiological and Environmental Assessment of Recreational Water. Parameters with “-” were not available. (EC 1976, USEPA 1986, WHO 2003, EC 2006, Canada Standard 2012, USEPA 2012, Zhanget al. 2019). Regulation or guideline IndicatorWater typeFIB value (CFU or MPN/100ml) MeasureIllness rate for swimmers SymptomsSampling frequency EU 2006iEntFresh200* (Excellent), 400* (Good), 330** (Sufficient)*95 per, **90 perAFRI: Excellent 1%, Good 2.5%, GI: Excellent 3%, Good 5%AFRI, GI illness~ 4 /bathing season EU 2006iEntMarine100* (Excellent), 200* (Good), 185** (Sufficient)*95 per, **90 perAFRI: Excellent 1%, Good 2.5%, GI: Excellent 3%, Good 5%AFRI, GI illness~ 4 /bathing season EU 2006E. coli Fresh500* (Excellent), 1000* (Good), 900** (Sufficient)*95 per, **90 perAFRI: Excellent 1%, Good 2.5%, GI: Excellent 3%, Good 5%AFRI, GI illness~ 4 /bathing season EU 2006E. coliMarine250* (Excellent), 500* (Good), 500** (Sufficient)*95 per, **90 perAFRI: Excellent 1%, Good 2.5%, GI: Excellent 3%, Good 5%AFRI, GI illness~ 4 /bathing season EU 1976, EC COM 2002 iEntFresh/Marine 200/100- - - - EU 1976, EC COM 2002 E. coliFresh/Marine500/250- - - - EU 1976Total coliformMarine500 / 10,00080 per/ 95 per- - - EU 1976Faecal coliform Marine100 / 2000 80 per/ 95 per- - - EU 1976Faecal streptococci Marine<10090 per- - - EU 1976SalmonellaMarineZeroǂ- - - - EU 1976Enteroviruses MarineZeroǂǂ - - - - USEPA 2012ENT Fresh30/ 110 STVGM/ STV 32/1000NEEAR GI illness ~ 5/30 days USEPA 2012E. coli Fresh30/ 110 STVGM/ STV32/1000NEEAR GI illness ~ 5/30 days USEPA 2012ENT Marine35/ 130 STVGM/ STV 36/1000NEEAR GI illness ~ 5/30 days USEPA 2012ENT Fresh/Marine130 90 per- NEEAR GI illness ~ 5/30 days USEPA 2012ENT Fresh/Marine70 75 per- NEEAR GI illness ~ 5/30 days USEPA 2012ENT qPCR (GC)Fresh/Marine470 CCEmedian- NEEAR GI illness ~ 5/30 days USEPA 2012ENT qPCR (GC)Fresh/Marine1000 CCE75- NEEAR GI illness ~ 5/30 days USEPA 2012ENT qPCR (GC)Fresh/Marine2000 CCE90- NEEAR GI illness ~ 5/30 days USEPA 1986ENT Fresh/Marine35- - - ~ 5/30 days USEPA 1986E. coli Fresh126 - - - ~ 5/30 days Canada Standard 2012ENT Fresh/Marine35; 70 GM/ STV - - ~ 5/30 days Canada Standard 2012E. coli Fresh/Marine200; 400GM/ STV - - ~ 5/30 days WHO 2003EntFresh/Marine500 95 per10% GI illness riskGI illness~ 20/bathing season Note: ǂ per litre, ǂǂ PFU per 10 litres.
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5 AIMS OF THE STUDY
The overall objective of this doctoral study was to provide tools and knowledge for monitoring the bathing water quality so that the health of the bathers can be pro-tected in a better way than before. The main goal was to test the applicability of various microbial water quality monitoring tools for the monitoring of bathing wa-ter quality. The study had the following specific objectives and hypotheses:
1. To compare the performance of the Colilert-18 method against the Minia-turized MPN method for E. coli enumeration during the monitoring of bathing water quality in Finland.
Hypothesis: The alternative Colilert-18 method gives equivalent E. coli count re-sults, compared to the reference Miniaturized MPN method in Finnish bathing water.
2. To evaluate the categorical performance characteristics of the ISO 7899-2 method for intestinal enterococci enumeration at bathing sites in Finland.
Hypothesis: The performance of the ISO 7899-2 method is sufficient for intestinal enterococci enumeration in Finnish bathing water.
3. To investigate the dynamics of intestinal enterococci, V. cholerae and enteric virus counts in coastal bathing water, beach sediments, and vegetation.
Hypothesis: Myriophyllum sibiricum and beach sediments affect the survival of en-terococci, V. cholerae and enteric viruses in coastal bathing water.
4. To identify the bacterial diversity dynamics in the Finnish surface water, due to spatial and seasonal change.
Hypothesis: Amplicon based high-throughput sequencing reveals the bacterial community structure in Finnish surface water.
In addition to those objectives, this study aimed to test the applicability of qPCR methods for FIB enumeration as a rapid tool and MST for identification of the con-tamination source, and the amplicon-based high-throughput sequencing method for bacterial community analysis of Finnish surface water. These novel tools can be effective for answering the pressing questions related to the impacts of environ-mental loading on bathing water microbiome, pathogenicity, and human health risks. This study explored the limitations of the current microbial quality monitor-ing technique of bathmonitor-ing water, which can be useful for improvmonitor-ing the technique for future use. The evaluation of the ecological relation between faecal microbes and coastal water, sediment, and vegetation was part of the objectives. Further, the study aimed to reveal the bacterial community structure in Finnish surface water, from the point sources of contaminations to the drinking water production process (DWPP).
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6 MATERIALS AND METHODS
The study materials consisted mainly of water samples, bacterial strains, and envi-ronmental nucleic acids. The methods were the microbiological analysis of FIB (E.
coli and enterococci), Enterococcus speciation, detection of human pathogens (no-rovirus, adenovirus, Campylobacter ssp. and Vibrio cholerae), a set of quantitative PCR markers (common FIB and host-specific source identifiers) and microbial community analysis, using the 16S rRNA gene amplicon approach.