DNA fingerprinting using BOX-A1R and (GTG)5 primers identify spatial variations of fecal contamination along Pasig River, Philippines

Pasig River is one of the most economically important rivers in Metro Manila, Philippines. It traverses some of the region's major cities, and because of its strategic location, it is utilized as a means of transportation, as a source of water for domestic and industrial uses, and for recreational purposes. However, due to population growth, industrialization, and land use, the river's water quality is deteriorating. Wastes that pollute the river pose health risks to the people that benefit from it. To prevent the river's further degradation, it is essential to identify the origin of contamination. In this study, the sources of fecal contamination in Pasig River were identified using BOX-A1R and (GTG)₅ primers in the DNA fingerprinting of Escherichia coli isolated from the river. Results showed the dominance of human contamination (percent composition = 65.55%), followed by agricultural sources (percent composition = 23.48%), and the lowest was from sewage (percent composition = 10.98%). The results of this research can help in evaluating public health risks and can be used as a scientific basis for policymaking and implementation for the rehabilitation and improvement of Pasig River.

Microbial source tracking of fecal contamination in Laguna Lake, Philippines using the library-dependent method, rep-PCR

Laguna Lake is an economically important resource in the Philippines, with reports of declining water quality due to fecal pollution. Currently, monitoring methods rely on counting fecal indicator bacteria, which does not supply information on potential sources of contamination. In this study, we predicted sources of Escherichia coli in lake stations and tributaries by establishing a fecal source library composed of rep-PCR DNA fingerprints of human, cattle, swine, poultry, and sewage samples (n = 1,408). We also evaluated three statistical methods for predicting fecal contamination sources in surface waters. Random forest (RF) outperformed k-nearest neighbors and discriminant analysis of principal components in terms of average rates of correct classification in two- (84.85%), three- (82.45%), and five-way (74.77%) categorical splits. Overall, RF exhibited the most balanced prediction, which is crucial for disproportionate libraries. Source tracking of environmental isolates (n = 332) revealed the dominance of sewage (47.59%) followed by human sources (29.22%), poultry (12.65%), swine (7.23%), and cattle (3.31%) using RF. This study demonstrates the promising utility of a library-dependent method in augmenting current monitoring systems for source attribution of fecal contamination in Laguna Lake. This is also the first known report of microbial source tracking using rep-PCR conducted in surface waters of the Laguna Lake watershed.

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

Laguna Lake is the largest inland freshwater body in the Philippines. Although it is classified to be usable for agricultural and recreational purposes by the country's Department of Environment and Natural Resources (DENR), studies looking at lake ecology revealed severe fecal contamination which contributes to the deterioration of water quality. Determining the sources of fecal contamination is necessary for lake protection and management. This study utilized a library-independent method of microbial source tracking (LIM-MST) to identify sources of fecal contamination in selected Laguna Lake stations and tributaries. Genetic markers of the host-associated Escherichia coli, heat-labile toxin (LTIIA) and heat-stable II (STII), were used to identify cattle and swine fecal contaminations, respectively. Meanwhile, human mitochondrial DNA (mtDNA) was used to identify human fecal contamination. Results identified the presence of agricultural and human fecal contamination in Laguna Lake Stations 1 and 5, Mangangate River, and Alabang River. The selected sites are known to be surrounded by residential and industrial complexes, and most of their discharges find their way into the lake. The identification of the specific sources of fecal contamination will guide management practices that aim to regulate the discharges in order to improve the water quality of Laguna Lake.

Selecting rep-PCR markers to source track fecal contamination in Laguna Lake, Philippines

Fecal contamination is one of the factors causing deterioration of Laguna Lake. Although total coliform levels are constantly monitored, no protocol is in place to identify their origin. This can be addressed using the library-dependent microbial source tracking (MST) method, repetitive element sequence-based polymerase chain reaction (rep-PCR) fingerprinting. Serving as a prerequisite in developing the host-origin library, we assessed the discriminatory power of three fingerprinting primers, namely BOX-A1R, (GTG)₅, and REP1R-1/2-1. Fingerprint profiles were obtained from 290 thermotolerant Escherichia coli isolated from sewage waters and fecal samples of cows, chickens, and pigs from regions surrounding the lake. Band patterns were converted into binary profiles and were classified using the discriminant analysis of principal components. Results show that: (1) REP1R-1/2-1 has a low genotyping success rate and information content; (2) increasing the library size led to more precise estimates of library accuracy; and (3) combining fingerprint profiles from BOX-A1R and (GTG)₅ revealed the best discrimination (average rate of correct classification (ARCC) = 0.82 ± 0.06) in a two-way categorical split; while (4) no significant difference was found between the combined profiles (0.74 ± 0.15) and using solely BOX-A1R (0.76 ± 0.09) in a four-way split. Testing the library by identifying known isolates from a separate dataset has shown that a two-way classification performed better (ARCC = 0.66) than a four-way split (ARCC = 0.29). The library can be developed further by adding more representative isolates per host source. Nevertheless, our results have shown that combining profiles from BOX-A1R and (GTG)₅ is recommended in developing the MST library for Laguna Lake.