ENVIRONMENTAL MICROBIOLOGY (I001762)
Prevalence of Escherichia coli in surface waters of
Southeast Asian cities
Kenneth Widmer, Nguyen Thi Van Ha, Soydoa Vinitnantharat, Suthipong Sthiannopkao, Setiawan Wangsaatmaja,
Maria Angela Novi Prasetiati, Nguyen Cong Thanh, Kasame Thepnoo, Arief Dhany Sutadian, Huynh Thi Thanh
Thao, Deby Fapyane, Vibol San, Pierangeli Vital, and Hor-Gil Hur
World J Microbiol Biotechnol (2013) 29: 2115 - 2124
Mohammed A. M. Herzallah - Student ID (01303232)
Ridwan M. Rifai - Student ID (01302922)
1st Master of Environmental Sanitation
Faculty Bioscience Engineering
University of Ghent
This term paper is going to present a discussion about the research article “Prevalence of
Escherichia coli in surface waters of Southeast Asian cities”. The article was written and
studied by a big team of professors and scientist from Asia and the names of these team are
Kenneth Widmer, Nguyen Thi Van Ha, Soydoa Vinitnantharat, Suthipong Sthiannopkao,
Setiawan Wangsaatmaja, Maria Angela Novi Prasetiati, Nguyen Cong Thanh, Kasame Thepnoo,
Arief Dhany Sutadian, Huynh Thi Thanh Thao, Deby Fapyane, Vibol San, Pierangeli Vital, and
Hor-Gil Hur. The article was written in September 2012 and it was accepted and published in
May 2013 in “Springer Science + Business Media Dordrecht”.
The study cases were collected from the rivers which fed into large urban areas within
Vietnam, Indonesia, Cambodia, and Thailand and to Selected isolates were further characterized
using PCR to detect the presence of specific virulence genes of Escherichia coli. Fecal
contamination in water sources is a key issue in evaluating urban water quality and
understanding the potential risk to urban populations and for mammals.
Public health are pathogenic E. coli, of which several types can induce diarrheagenic
infections in humans, with some capable of causing more serious infections such as hemorrhagic
colitis. Determining potential risk to public health is problematic, however quantitative microbial
risk assessment is a tool that policy makers can utilize to better manage fecal contaminated
The focus of this study was to enumerate E. coli in urban surface waters within Southeast
Asian countries, and further characterize isolates as either, enteroinvasive E. coli (EIEC), Shiga
toxin-producing E. coli (STEC), enterotoxigenic E. coli (ETEC), enteropahogenic E. coli
(EPEC), or enterohemorrhagic E. coli (EHEC). Further, enumeration data would be compared to
determine if seasonal or urban land-use would have an impact on relative numbers of E. coli in
these surface waters.
Material and Methods
The design of the study was take water samples collected from four main rivers; the
Citarum River in Bandung (West Java, Indonesia), the lower Chao Phraya in Bangkok
(Thailand), the Saigon river in Ho Chi Minh City (Vietnam), and the Tonle Sap-Bassac in
Phnom Penh (Cambodia). Urban metropolitan sampling sites were selected based primarily on
their proximity to high density population areas or close association with industrial activity. As
comparative sampling sites, more rural locations where surface waters were used for irrigation,
aquaculture, or were fed agricultural runoff were also sampled.
The team works on approximately 10 – 20 sites and it takes around 2 months work for
both dry and wet season. For both sample sites in Thailand and Vietnam, it was taken two
sampling event but in Indonesia and Cambodia it was only one sampling event. As a total of
samples it was 157 samples from all four countries.
The way of sampling was Approximately 100 ml grab samples of surface water were
collected into sterilized polypropylene bottles at 30 cm depths from the center of the river
channels. and then all the samples were saved and kept under 10ºC , then the samples were
sequentially filtered through sterile, 0.45 lm, 47 mm filters (Pall Korea Ltd., Seoul, Korea) in 10,
1, and 0.1 ml volumes. If the sample volume was under 10 ml, it was mixed with 10 ml sterile DI
water to ensure an even sample distribution over the filter surface.
E. coli DNA extraction and PCR
As well for E. coli study the scientific groups were used bacterial cultures to detect and
study it; the study used several types of cultures E. coli NCCP 10004 (ETEC), E. coli NCCP
13719 (EIEC), and isolate of E. coli O157:H7.
PCR reactions were run as multiplex or single reactions. Each reaction was prepared
using 2 ll template and primers at concentrations of 0.5 lM for AL65/125 (ETEC), 0.25 lM for
primer sets LTL/R (ETEC), ipa III/IV (EIEC), 0.25 lM of primer sets stx1F/R and stx2F/R
(STEC), eaeAF/R (EPEC), and hlyAF/R (EHEC). DI water was added to bring each reaction
volume up to 20 ll. In addition to the samples, non-template controls (DI water) and 1 ll template
DNA extracted from the control strains were used as negative and positive controls. Primers used
summarized on Table 1 as follows.
Table 1. PCR primers sets emploted in this study
PCR was run based on Toma et al. (2003) to amplify clone in order gene fragment can be read in
electrophoresis stage. Exception only for primers sets AL65/125 which had annealing
temperature of 58oC, not 57oC as the others. PCR was done by 30 cycles. Further, profile of E.
coli multiplex PCR can be constructed as in Figure 1.
Figure 1. PCR profile from denaturation to cool down stage with 30 cycles of annealing and elongation
PCR products then analyzed wit gel electrophoresis to detect certain gene fragment
associated with virulence key of each strain of E. coli. Presence of est and/or elt gene indicated
ETEC, hylA gene indicated EHEC, stx1 and/or stx2 gene indicated STEC, ipaH gene indicated
EIEC, and eaeA gene indicated EPEC strain. Positive electrophoresis isolates then were
recultivated on EMB agar. Isolates presence then extracted and purified based on protocol by
PCR Purification Kit, ELPIS-Biotech, Korea and analyzed using 3730xl DNA Analyzer.
Sequences obtained then identified using BLAST.
E. coli enumeration
E. coli enumeration resulted in slightly different value for each river during wet and dry
season from analysis of 68 and 89 samples, respectively, except for Lowest Chao Praya where
amount of E. coli in dry wet season roughly had decreased three times. For all countries, mean of
wet season counted as log 2.76 cfu/100mL and mean of dry season counted as log 4.27
cfu/100mL. Roughly amount of E. coli 1.5 times lower in wet season compared to dry season
and based on statistic calculation, mean of wet season were significantly lower (p = 0.001) than
mean of dry season.
From total 157 water samples, it was obtained mean of E. coli as log 3.61 cfu/100mL (±
0.14 s.e.) with lowest value was generated from Saigon (2.66 cfu/100mL) and highest value was
generated from Citarum (4.63 cfu/100mL) for both wet and dry season. For individual river,
Tonle Sap-Bassac (log 3.05) and Saigon (log 2.86) being approximately 1 log lower than
Citarum (log 4.58) and Lower Chao Praya (log 3.94). Data summarized on Table 2.
Table 2. Table of E. coli counts based on season and general water quality characteristics (mean ± standard error)
Physico-chemical parameters were found had variations between each river. Temperature
and pH were found no significant difference but not applicable for turbidity and total dissolved
solid (TDS). Turbidity of Tonle Sap-Bassac (106.5 ± 18.2) was known four times higher than
Lower Chao Praya (26.7 ± 4.0) and two times bigger than Saigon. TDS of Lower Chao Praya
(2877.7 ± 924.25) was very huge compared to other rivers which means that Lowest Chao Praya
had large amount of both inorganic and organic material.
Samples were taken based on land use classification (urban, rural, or combination) to
determine whether any association between urban activity to runoff compared to agricultural
activity in rural area or combination of activities. Mean of E. coli (cfu/100mL) for these areas
resulted had value of log 1.7, log 3.2, log 3.8, log 3.9, and log 4.1 for water treatment sites,
agricultural/rural areas, industrial sites, mixed land use locations, and urban areas, respectively.
Analysis was conducted to compare sample from agricultural/rural areas (41 samples) to either
urban (77 samples), industrial (21 samples), and mixed land use locations (6 samples).
Comparison did not performed to samples from water treatment sites due to low observed counts
and limited collected samples. Statistical analysis generated result stated that there were
significant difference observed between agricultural/rural areas and both urban areas (p = 0.001)
and industrial sites (p = 0.022). Statistical difference was no observed between mean of E. coli in
agricultural/rural areas to mixed land use locations. Data can be observed in Table 3.
Table 3. Mean E. coli counts based on land use and season (mean ± standard error)
Observed pathogenic E. coli types
Presence of virulence gene in PCR product identified in 22 out of 564 isolates processed
(22%). To obtain more accurate data, these results were followed by sequencing and BLAST
analysis. From 157 water samples, all colonies which had such similar pathogenic profiles were
isolated and processed further without any consideration to distinguish between sampling
location and sampling time. Because there was probability several colonies with same
pathogenic profile (detected by PCR analysis) from same sampling location and collection dates
were clones, these isolates were not ensued further for data analysis.
Table 4 shows that STEC pathogenic type were found dominant in river samples (n = 9)
followed by EPEC strain (n = 7) and ETEC strain (n = 6). From 7 EPEC isolates, 3 of them
identified had both eaeA and stx1 pathogenic gene which is normally present in STEC strain
(Aidar-Ugrinovich et al., 2007). But since intimin gene eaeA is main identity of EPEC, those
were considered as EPEC strain. Neither EHEC nor EIEC strain identified since there were not
gene ipaH and hylA recognized. Neither gene stx2 nor est found also in this experiment. Based
on different land uses, pathogenic E. coli were found as many as 12 isolates in urban areas and
11 isolates in agricultural/rural areas. Both Lower Chao Praya and Saigon River were known
possess these pathogenic bacteria.
Table 4. Observed E. coli virulence genes and pathogen types based on land use
US-EPA (2012) stated that threshold of standard recreational water is log 2.61
cfu/100mL. From data obtained, it was known that no river had this threshold. It means that
every river studied in this research had no appropriateness to become recreational niche,
moreover for daily consumption. If it considered about difference between wet and dry season,
there was a close acceptable value of mean E. coli in the river for wet season which was log 2.76
cfu/100mL but no for dry season (log 4.27cfu/100mL). From Table 2 also it can be examined
that most urban water source exceeded this proposed threshold value. It was assumed that
regional water treatment possess capability to purify water since they had mean of E. coli as log
Seasonal difference of mean E. coli recognized in this research (lower value at wet
season) expected because even heavy precipitation might made run-off higher and contamination
from domestic sewage increase into the river, high precipitation also caused in dilution effect of
bacteria in surface water (Isobe et al., 2004). It has to be noted that amount of E. coli in urban
areas significantly higher that amount of E. coli in agricultural/rural areas. It was assumed that
this condition was instigated due to high fecal contamination load into the water body from
urban people population (Yen-Phi et al., 2010; Giri et al., 2005; Kido et al., 2009).
Pathogenic E. coli were identified as 4% of total isolates which many of them possess
virulence gene which associate with Shiga-toxin producing E. coli (STEC). This strain of E. coli
associated with E. coli O157:H7 which is known as the most pathogenic E. coli ever. Actually
STEC is normally found in animal excrement (Nataro and Kaper, 1998) so that rivers might be
contaminated with livestock feces. Diarrheagenic ETEC and EPEC strain known as common
pathogen E. coli in Southeast Asia (Huang et al., 2012; Richie et al., 1997) so that detection of
these two strains in river water might not indisputable.
Even common E. coli has low pathogenicity to human (Kothary and Babu, 2001),
continuously exposure due to ingestion or direct contact between human and bacteria can emerge
such public health problem in the future. As E. coli recognized as indicator microorganism, E.
coli might be associated with other more pathogen bacteria such as Salmonella Typhii and Vibrio
cholerae. This can lead to a serious disease if there is no restoration in public and private
sanitation because E. coli and its associated bacteria are coming from feces contaminations
which get into water body. Thus, to minimize risk to pathogen exposure, people should aware to
their sanitation and do not utilize water which do not possess appropriate condition to be use as
daily consumption, agriculture, or recreational purposes.
This research demonstrated that rivers in Southeast Asia had exceeded amount of E. coli
suitable for daily use. Based on different land uses, E. coli more likely had higher number in
urban areas compared to rural/agricultural areas because urban domestic activity impact. Further,
sanitation of urban population should be inflated since it was identified that 4% of total isolates
recognized as pathogenic strain which close to STEC, ETEC, or EPEC. Hence, this research
highlighted the importance of monitor and treatment of urban waters of Southeast Asia countries
because unmonitored water that use in daily activity can lead into emerging health problems in
Remarks and Further Calculations
Pathogenic E. coli are classified into several group based on their invasive mechanism.
Nataro and Kaper (1998), Kothary and Babu (2001), Madigan et al. (2009), and Todar (2012)
categorized E. coli into five groups which are Entero-toxigenic strain (ETEC),
Enterohemorrhagic strain (EHEC), Enteropathogenic strain (ETEC), Enteroinvasive strain
(EIEC), and Enteroaggregative strain (EAEC). ETEC secretes labile and stabile toxins which
similar to cholera toxin (CTx), EHEC causes lyses of red blood cell by producing Shigella toxin
(STx), EPEC attacks ileum and colon epithelial cells by EPEC adherence factor EAF, EIEC
destroys cell which its infected, and EAEC has ability to adhere to tissues with making aggregate
and then secretes EASTx which will lead to death of tissues.
Since only amount of ETEC, EIEC, EPEC, EHEC, and STEC assessed in this research,
there must be any lack data of whole infection of E. coli because EAEC not included in count.
Results of this study might have a complete picture to relative risk of population in Southeast
Asia countries that are using surface water if EAEC was included in analysis. STEC strain has an
ambiguous means since in this research; STEC was differed from other strains by Shigella toxin
producing mechanism. But in fact, EHEC also produce shigella toxin (Nataro and Kaper, 1998;
Todar, 2012) which is called verotoxin (Madigan et al., 2009). Moreover, EIEC has similar
Shigella toxin in its pathogenicity mechanism but research further must be performed (Nataro
and Kaper, 1998). Gene detected for STEC were stx1 and stx2 which can be found also in EHEC
strain. It would make any different perception. Probably, different between STEC and EHEC is
region of others virulence genes where EHEC strain possess hlyA, rfbO111, and rfbO157 in its
plasmid but STEC strain does not. As an advice, for detection of STEC, alternative virulence
genes such as eae, ehxA, and saa will be more preferable (Paton and Paton, 2002). Another
ambiguous statement that is EPEC, by this research, has gene eaeA as key identification. But in
opposite, Paton and Paton (1998) stated that Shigella-toxic producing E. coli has virulence gene
eaeA also. Hence, it can be assumed that might be STEC that was analyzed in this research
included in EHEC or EIEC or EPEC in other references.
Another remark is equality of sample amount and treatment in advance procedure. As
described in paper, numbers of samples were different for each river and season. This can lead to
inequality of data distribution and might have inaccurate statistical analyses. More data will
provide more accurate result but each parameter should be in equal amount. Pathogenic E. coli
were identified only from Thailand (Lower Chao Praya) and Vietnam (Saigon). This will make
an incomplete conclusion because there were no pathogenecity analyses for isolated obtained
from Indonesia (Citarum) and Cambodia (Tonle Sap-Bassac). Complete research should have
same treatment between batches so that conclusion obtained can be accounted for science.
Further calculation can be performed as quantitative risk assessment of E. coli. This
assessment can describe probability someone become sick if get contact (particularly ingestion)
with pathogenic bacteria in such amount in certain time. Method of assessment was explained by
Rose and Gerba (1991) as Monte-Carlo modeling. This Monte-Carlo model comply equation
explained by Boon et al. (2013) as follows.
P = Probability to become sick (P = 1 means 100% chance)
N = number of propagules taken in the body
α,β = species specific constants (β >> 1, α << β)
Assumed in this research all E. coli are pathogenic strain producing Shigella toxin (E. coli
O157:H7). Then α value for this bacteria is 0.267 and β value is 5.435 (Cassin et al., 1998).
Supposed two persons ingest 1mL and 100mL of surface water a day in each river. Probability of
become sick for daily ingestion (P) and annual ingestion (PA) can be seen in Table 5.
Table 5. Probability of getting sick when ingest river water daily and annually
Tonle Sap- Bassac, KH
Lower Chao Praya, TH
Tonle Sap- Bassac, KH
Lower Chao Praya, TH
Sickness will more have a preference occur for persons consume Citarum water because Citarum
water has the highest E. coli contained. On daily consumptions, all rivers provide chance to
getting sick lower than 1 but if this consumption continues as annual, people will get sick by
100% chance. From previous equation, minimum number of bacteria can be generated by this
equation. Besides, this probability can be explained by graph of effect concentration (either ID10
or ID50). Cassin et al. (1998) constructed this graph for pathogenic E. coli as in Figure 1.
Figure 1. Beta-Binomial dose–response model – Uncertainty in average probability of illness vs. ingested dose of E.
From graph, it can be identified that ID50 for this strain E. coli lay on ± log 3.5 cfu or about 3162
propagules. Then for each 100 ml of river water, Tonle Sap-Bassac and Saigon have less than
50% probability to make people who consumed the water get illness. In other hand, Lower Chao
Praya and Citarum water give probability of getting illness for more than 50%.
Aidar-Ugrinovich L, Blanco L, Blanco M, Blanco JE, Leomil L, Dahbi G, Mora A, Onuma DL,
Silveira WD, Pestana de Castro AF. 2007. Serotypes, virulence genes, and intimin types
of Shiga toxin-producing Escherichia coli (STEC) and enteropathogenic E. coli (EPEC)
isolated from calves in Sao Paulo, Brazil. Int J Food Microbiol 115(3): 297 – 306.
Boon N, Verstraete W, Auvinen H. 2013. Environmental Microbiology: Theory Lecture Notes.
Ghent: Ghent University.
Cassin MH, Lammerding AM, Todd ECD, Ross W, McColl RS. 1998. Quantitative risk
assessment for Escherichia coli O157:H7 in ground beef hamburgers. International
Journal of Food Microbiology 41: 21 – 44.
Giri RR, Takeuchi J, Ozaki H. 2005. Influence of night soil contamination on activated sludge
microbial communities in Bangkok, Thailand. Ecol Eng 25(4): 395 – 404.
Huang SW, Hsu BM, Su YJ, Ji DD, Lin WC, Chen JL, Shih FC, Kao PM, Chiu YC. 2012.
Occurrence of diarrheagenic Escherichia coli genes in raw water of water treatment
plants. Environ Sci Pollut Res 19(7): 2776 – 2783.
Isobe KO, Tarao M, Chiem NH, Minh LY, Takada H. 2004. Effect of environmental factors on
the relationship between concentrations of coprostanol and fecal indicator bacteria in
tropical (Mekong Delta) and temperate (Tokyo) freshwaters. Appl Environ Microbiol
70(2): 814 – 821.
Kido M, Yustiawati Y, Syawal M, Sulastri S, Hosokawa T, Tanaka S, Saito T, Iwakuma T,
Kurasaki M. 2009. Comparison of general water quality of rivers in Indonesia and Japan.
Environ Monit Assess 156(1): 317-329.
Kothary MH, Babu US. 2001. Infective dose of foodborne pathogens in volunteers: a review. J
Food Staf 21(1): 49 – 68.
Madigan MT, Martinko JM, Dunlap PV, Clark DP. 2009. Brock Biology of Microorganism 12th
edition. San Francisco: Pearson Benjamin Cummings.
Nataro JP, Kaper JB. 1998. Diarrheagenic Escherichia coli. Clin Microbiol Rev 11(1): 142 –
Paton AW, Paton JC. 1998. Detection and characterization of shiga toxigenic Escherichia coli by
using multiplex PCR assays for stx1, stx2, eaeA, enterohemorrhagic E. coli hylA, rfbO111,
and rfbO157. J Clin Microbiol 36(2): 598 – 602.
Paton AW, Paton JC. 2002. Direct Detection and Characterization of Shiga Toxigenic
Escherichia coli by multiplex PCR for stx1, stx2, eae, ehxA, and saa. J Clin Microbiol
40(1): 271 – 274.
Richie E, Punjabi NH, Corwin A, Lesmana M, Rogayah I, Lebron C, Echeverria P, Simanjuntak
CH. 1997. Enterotoxigenic Escherichia coli diarrhea among young children in Jakarta,
Indonesia. Am J Trop Med Hyg 57(1): 85 – 90.
Rose JB, Gerba CP. 1991. Use of risk assessment for the development of microbial standards.
Water Scence & Technology 24(2): 29 – 34.
Todar K. 2012. Pathogenic E.coli (page 4). http://textbookofbacteriology.net/e.coli_4.html.
Accessed 7 Dec 2013.
Toma C, Lu Y, Higa N, Nakasone N, Chinen I, Baschkier A, Rivas M, Iwanaga M. 2003.
Multiplex PCR assay for identification of human darrheagenic Escherichia coli. J Clin
Microbiol 41(6): 2669 – 2671.
US-EPA. 2012. Recreational water quality criteria. Office of Water. EPA 820-F-12-058.
Yen-Phi VT, Rechenburg A, Vinneras B, Clemens J, Kistemann T. 2010. Pathogens in septage in
Vietnam. Sci Total Environ 408(9): 2050 – 2053.