Library-independent source tracking of fecal contamination in selected stations and tributaries of Laguna Lake, Philippines

Microbial source tracking (MST) emerged in the late 20th century with the aim of identifying the sources of fecal contamination in the environment, especially in water sources (Stoeckel & Harwood 2007). It was developed mainly to address the different health risks of microbial contamination in water sources. In view of this, Guan & Holley (2003) reviewed the movements of different fecal contaminants from water sources to fresh produce in relation to disease outbreaks.

Several MST studies investigated different fecal indicator bacteria (FIB) and their possible sources (Khatib et al. 2002; Stoeckel & Harwood 2007; Harwood et al. 2014; Schriewer et al. 2015). MST consists of two methods: library-dependent method (LDM) and library-independent method (LIM). The former uses culture-based techniques that focus more on the identification and correlation of FIB with their specific sources in the environment. Escherichia coli and Enterococcus sp. are the common FIB used in LDM (Stoeckel & Harwood 2007). Through recent advancements in molecular biology, the use of gene markers in tracking the source of contamination has become useful. As a result, LIM became more focused on the development of markers specific to the host sources, thus eliminating the use of culture-based techniques (Harwood et al. 2014).

Several studies emphasized the importance of determining the sources of microbial contamination. Schriewer et al. (2015) examined the presence of Bacteriodales genetic markers in India through quantitative polymerase chain reaction (qPCR) and found that there was prevalent human fecal contamination which could pose a major public health risk. Another study found that LTIIa and STII toxin genes of E. coli can be associated with fecal contamination of cows and water buffalo, and pigs, respectively (Khatib et al. 2002, 2003). In addition, Krolik et al. (2014) investigated different public drinking well waters in Ontario, Canada. The use of human and bovine markers revealed a higher degree of human fecal contamination compared with bovine contamination. Also, it is now known that human mitochondrial DNA (mtDNA) is being used as one of the most promising biomarkers of MST to identify human fecal contamination. Studies on human mtDNA were based on the idea that humans shed cellular materials, including mitochondria, in their feces (He et al. 2016).

In the Philippines, there is generally a lack of scientific information on the use of MST in identifying sources of microbial contamination. One of the preliminary studies involving MST in the country was by Labrador et al. (2020), in which the best marker combination for the LDM of MST in Laguna Lake was identified. So far, most of the studies focused on the identification and quantification of FIB present in an environment. Garcia et al. (2015) detected the fecal contamination in irrigation water and checked the microbial quality of vegetables in selected urban farms in Metro Manila. Moe et al. (1991) reported bacterial indicators of drinking water quality and identified risks for waterborne diarrheal diseases in the Philippines.

At present, one of the major environmental concerns in the Philippines is the regulation and rehabilitation of the different bodies of water, one of which is Laguna Lake. The lake is situated east of Metro Manila between the provinces of Laguna (south) and Rizal (north). It plays an important role as a site of aquaculture for fish production, a source of hydroelectric power and water for drinking and irrigation, and a place for recreation (Rivera et al. 2002). However, this important water resource has been greatly affected by developmental pressures like population growth, rapid industrialization, and resource allocation. Considering the economic and cultural importance of the lake, the Laguna Lake Development Authority (LLDA), the sole government agency that has jurisdiction over its management, is regularly monitoring the lake's water quality. However, the agency's water quality monitoring is only focused on the enumeration of total fecal coliforms for microbial quality measurement of biological oxygen demand, dissolved oxygen, and other physico-chemical parameters. Given the need to maintain the water quality of the lake, it is important to identify the specific sources of fecal contamination. There are different sets of gene markers that can be used for this purpose. For example, the LTIIa gene and STII genes of E. coli are used to confirm cow and pig fecal contamination (Khatib et al. 2002, 2003), respectively, while the human mtDNA is used to confirm human fecal contamination (Caldwell & Levine 2009).

The general objective of this study was to identify the different sources of fecal contamination in selected stations of Laguna Lake. Specifically, it aimed to identify the sources of fecal contamination using the LIM (1) through the detection of host-associated (cattle and swine) markers in E. coli isolates and (2) through direct screening of water samples for human mitochondrial genes to determine possible sources of human fecal contamination. LIM screening was done to speed up the process of source tracking and to avoid sampling bias in building up a host-origin library. The results of this study may help the concerned agencies to properly regulate wastewater discharges to Laguna Lake.

Water samples were collected from two sites within Laguna Lake (Station 1 and Station 5) and two rivers that feed into the lake, Mangangate River and Alabang River (Figure 1).

Water samples (500 mL) were collected in duplicates using sterile wide-mouth Nalgene^® bottles. Sampling was done once during the months of May, July, September, and November 2018. There was no sample collected from Alabang River in November 2018 due to its closure to give way to construction-related activities. The water samples were transported in coolers for immediate processing in the laboratory.

For the detection and enumeration of E. coli in the samples, 10-fold serial dilutions up to 10⁻⁵ were performed on all water samples using 0.9% saline solution as the diluent. These were filtered through the GN-6 Metricel membrane filter (47 mm diameter, 0.45 μM pore size; Pall Corp., USA), and the last three dilutions (10⁻³ to 10⁻⁵) were plated on modified membrane thermotolerant E. coli (mTEC) agar. The plates were incubated at 35–37 °C for 2 h and then at 44.5 °C for 18–24 h. The growth of purple to deep violet colonies after incubation was considered presumptive E. coli. These colonies were streaked on eosin methylene blue (EMB) agar and then incubated again overnight at 35–37 °C. Colonies exhibiting green metallic sheen were identified as E. coli.

The molecular identification of E. coli isolates was confirmed through PCR. DNA extraction of the positive E. coli cultures from the EMB agar was done using a boil-lysis method (De Medici et al. 2003). The primer pairs were adapted from Takahashi et al. (2009). These primers target the uidA gene of the organism and can detect even the pathogenic E. coli 0157:H7. The 20 μL of PCR mixture consisted of Promega GoTaq^® Green PCR Mastermix, 0.10 μM of forward and reverse primers, nuclease-free water, and 2 μL of DNA template, where each set of samples was assayed in duplicate. Negative, positive, and non-template controls were included in each run. PCR thermocycling conditions were adapted from the study of Vital et al. (2014), with an initial denaturation of 98 °C for 2 min, followed by 35 cycles at 98 °C for 30 s, 63 °C for 1 min, and 72 °C for 1 min and a final extension of 72 °C for 5 min.

All confirmed E. coli isolates were screened for the presence of the heat-labile toxin IIA (LTIIa) gene (Khatib et al. 2002) and the heat-stable II (STII) gene (Khatib et al. 2003) as biomarkers for cattle and swine fecal contaminations, respectively. The 20 μL of PCR mixture consisted of Promega GoTaq^® Green PCR Mastermix, 0.10 μM of forward and reverse primers, nuclease-free water, and 5 μL of DNA template, where each set of samples was assayed in duplicates. Moreover, the PCR conditions for LTIIa and STII genes were as follows: 95 °C for 1 min, followed by 30 cycles at 95 °C for 30 s, 61 °C for 30 s (LTIIa gene) or 47 °C for 30 s (STII gene), 72 °C for 30 s, and a final extension of 6 min at 72 °C (Khatib et al. 2002, 2003). DNA extracted from the fecal material of cattle and swine served as positive controls in the PCR run.

The presence of LTIIa- and STII-positive E. coli was computed using the formula: total number of LTIIa- or STII-positive E. coli divided by the total number of E. coli isolates multiplied by 100.

For the detection of human fecal contamination, human mtDNA was used as a biomarker. Water samples, with a volume of 1 L each, were collected from sampling sites and were filtered using the GN-6 Metricel membrane filter (47 mm diameter, 0.45 μM pore size; Pall Corp., USA). The filter was then directly subjected to DNA extraction using the PureLink™ Microbiome DNA Purification Kit, with a yield volume of 50 μL DNA sample. Afterwards, qPCR analysis was performed with the reaction mixture consisting of 20 μL of PowerUp SYBR Green^® PCR Mastermix, 0.10 μM of forward and reverse primers, nuclease-free water, and 2 μL of DNA template. Primer sequences and amplification protocols were adopted from Caldwell & Levine (2009). Human fecal genomic DNA served as positive control. Table 1 shows the various primers utilized in this study.

Amplification curves were observed in all samples, and DNA copy number was calculated to determine the amount of human fecal contamination present in each sampling site. DNA copy number was calculated by the machine using the absolute quantification assay of qPCR where the concentration of the unknown samples is tested against the standard curve of the known concentration of positive control (i.e., human fecal DNA). Comparing the threshold cycle values of the unknown samples to this standard curve allows the quantification of the target DNA template. DNA copy number is calculated using the amount of DNA in nanogram multiplied by Avogadro's constant, divided by the length of DNA, a conversion factor of (1 × 10⁹), and the average mass of 1 bp of dsDNA which is 650 g. (This is based on the premise that the average weight of a single base pair is 650 Da. Hence, an average mass of 1 bp dsDNA is 650 g.) The value of the DNA copy number may infer the extent of human fecal contamination in the sample. All DNA copy number values were then log-transformed to provide normality and homoscedasticity.

The protocol for handling human samples was reviewed and approved by the University of the Philippines Manila Research Ethics Board (UPMREB-2018-356-01).

A total of 424 E. coli were isolated from water samples obtained from four sampling stations in Laguna Lake. The greatest number of isolates was recovered from Mangangate River (n = 191), followed by Alabang River (n = 90), Laguna Lake Station 1 (n = 81), and Laguna Lake Station 5 (n = 62).

LTIIa and STII genes, which are markers for cattle and swine, respectively, were detected from E. coli isolated from different sampling sites (Table 2). As shown in Figure 2, there is 93.72% detection of LTIIa gene in isolates from Mangangate, 53.33% in Alabang, 95.06% in Lake Station 1, and 93.55% in Lake Station 5. On the other hand, lower detection of STII genes was observed. Among the E. coli isolates from Mangangate, there is only 2.09% with STII, while only 23.33% in Alabang isolates. For the lake stations, only 4.94% of samples from Station 1 have STII, while only 3.23% in Station 5. Few of the isolates also did not show the presence of either one of each gene (Alabang = 23.33%, Mangangate = 4.19%, and Station 5 = 3.22%).

A total of 15 environmental DNA samples were obtained from each selected site. Among the sampling sites, Alabang and Laguna Lake Station 1 have the highest DNA copy number (Figure 3). The DNA copy number values indicate the presence of human NADH mitochondrial gene in the environmental sites.

Among all sampling stations, Mangangate River has the highest number of E. coli isolated. This information concurs with the data of LLDA on fecal coliform count of the lake stations and river tributaries (http://www.llda.gov.ph/). Alabang River has the second most number of E. coli species. This river is not part of the major tributaries of Laguna Lake; however, it merges downstream with Mangangate River, thus contributing to the source of contamination in the lake. Low levels of E. coli detected in lake stations also concur with the fecal coliform data which is less than 100 MPN/100 mL (http://www.llda.gov.ph/).

The PCR-based assay demonstrated the utility of heat-labile toxin IIa (LTIIa) and heat-stable toxin II (STII) from enterotoxigenic E. coli as biomarkers for cattle and swine fecal contaminations, respectively. Based on the results, fecal contamination in the sampling sites was mostly of cattle origin as indicated by the higher detection of the LTIIa gene compared with the STII gene among all sampling stations. The detection of these genes indicates that contamination in the water samples may be coming from cows and pigs even though Mangangate and Alabang are located in highly urbanized areas. Since E. coli are natural inhabitants of the intestines of animals, it is possible that their presence is associated with agricultural and industrial effluents such as those coming from livestock backyard farms, cattle and swine abattoirs, and meat manufacturing companies that find their way to the river systems.

The E. coli LTIIa and STII toxin genes are both useful biomarkers for MST since they are source-specific. They are sensitive and geographically widespread. Sequence analyses of LTIIa and STII showed that the two markers were 100% specific to cattle and pig, respectively (Harwood et al. 2014). This explains why there was no concurrent detection of genes in one E. coli isolate. Either LTIIa or STII gene is present in one isolate, confirming the utility of these gene markers for specifically indicating cattle or swine fecal contamination.

One of mtDNA's major advantages among others is that it is geographically stable. The host mtDNA is not easily influenced by diet, compared with FIB, which are part of the host's gut microbiota (Graves et al. 2007). Several studies proved the effectiveness of mtDNA in MST. In the study of Caldwell et al. (2007), multiplex real-time PCR amplifying fecal mtDNA exhibited the identification of human mtDNA with no cross-reaction between human and non-human sources. Although mtDNA may not be as abundant in the environment as other microbial DNA markers like the Bacteriodales 16S rRNA gene marker, this marker is a good candidate for the identification of human fecal contamination in the environment. Another study showed the application of mtDNA in source tracking in shellfish harvesting waters wherein mtDNA was used to detect human fecal contamination from single fecal sources in experimental microcosms and shellfish samples (Baker-Austin et al. 2010).

In this study, human mtDNA was successfully detected from all target sampling sites through the qPCR assay. The highest DNA copy number was detected in Alabang River and Laguna Lake Station 1 (Figure 3). Overall, this finding indicates that there are sources of human fecal contamination in all sites. Both Alabang and Mangangate Rivers traverse directly along residential areas and industries which may directly discharge their wastes into the river. Moreover, the increased prevalence of human mtDNA in the lake stations can be due to various factors such as the presence of fishermen in the area and the other river tributaries that discharge their waste in the lake. The lake stations are being surrounded by various household infrastructures, farms, and small-scale agriculture and manufacturing industries. Unregulated waste discharged into these rivers may have eventually found its way into the lake, thus the detection of mtDNA in all sampling sites. This is alarming since the lake water is utilized for irrigation and cultivation of small backyard farms for vegetables and fishes as well as a source of potable water. Though there are considerable advantages in using mtDNA as a source tracking tool, it is also important to note that mtDNA could also come from other non-fecal sources such as from human skin and other tissues of the body. It was reported that healthy human blood samples containing mononuclear cells have around 1,000 mtDNA copies/cell (Gourlain et al. 2003) and normal endometrial cells have around 158–2625 mtDNA copies/cell (Wang et al. 2005). Hence, there is an ongoing debate on this marker and other fecal indicator organisms based on host relevance, viability, and source reliability.

Different studies were conducted to check for the ecological status of the lake. One study, that of Sia Su & Cervantes (2008), pointed out that the lake is currently in a disturbed state and this is supported by pieces of evidence, indicating that the pollution present in the lake affects the growth of agricultural animals. As a result, lower harvests in the open waters ensue because the food chain is adversely affected by pollution and perennially high water turbidity and sedimentation rate in the lake.

At present, Laguna Lake is continuously meeting the needs of numerous sectors of society particularly as a source of freshwater supply. Hence, a study that identifies the sources of contamination would help in addressing the problem of pollution in this specific body of water. With this, local government units, as well as the larger governing body, may include in their policies different ways to regulate various discharges in the lake and its tributaries.

The use of two library-independent MST platforms, one focusing on the microbial species as a biomarker (i.e., LTIIa gene and STII gene of E. coli species) and the other using human mtDNA biomarker NADH gene, revealed that fecal contamination in Laguna Lake comes from agricultural (cow and pig) and human sources. The biomarkers used are host-specific and do not require large amounts of water samples for detection, making them good markers for MST. The findings of this study can be used as a basis for drafting regulations in managing the land use of areas surrounding the lake and in designing water treatment strategies.

We thank the Laguna Lake Development Authority (LLDA) for the technical support. This study was supported by a research grant from the Philippine Council for Industry, Energy, and Emerging Technology Research and Development (PCIEERD) of the Department of Science and Technology.

The authors declare that they have no conflicts of interest.

All relevant data are included in the paper or its Supplementary Information.