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

Abstract

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.

Host-Associated Bacteroides 16S rDNA-Based Markers for Source Tracking of Fecal Pollution in Laguna Lake, Philippines

“…The non-detection of the BoBac marker suggests that cattle feces is unlikely to be a major contributor of contamination at the sampling sites. This is consistent with the recent MST data derived from a library-dependent method (rep-PCR), where only 3.31% (11/332) of the environmental samples were reported to be contaminated with cattle fecal matter [42]. However, our finding differs from that of Abello et al [51], which revealed cattle-associated contamination in the West Bay (Stations I and V) using the heat-labile E. coli toxin gene marker LTIIA.…”

“…This is also reported in the Pilot Ecosystem Account for Laguna de Bay Basin [ 4 ], which stated that 81% of BOD came from domestic wastes. De la Peña et al [ 42 ] also reported that sewage was the most dominant contributor to fecal contamination in Laguna Lake. This could be linked to a lack of sewerage systems in the area, which transport sewage from its source to a treatment and disposal facility [ 41 ].…”

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

“…In addition, E. coli prediction in a watershed, with the aid of the random forest algorithm, was the outcome of dela Pena et al (2021). This result was the combination of applying machine learning algorithms and molecular tools to estimate faecal coliform load, which aligns with the results of our performance metrics of the random forest algorithm after cross-validation [62].…”

Bacterial community profiles of select tributaries of Laguna Lake in the Philippines

“…Key tributaries of interest are those in Metro Manila and Laguna due to urbanization. Heavy metals and antibiotic-resistant bacteria ( 2 ), along with fecal indicators, have been found in the lake ( 3 , 4 ). Therefore, analyzing its bacterial communities is crucial due to their role as pollution indicators.…”