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Article

Predictive Functional Profiling Reveals Putative Metabolic Capacities of Bacterial Communities in Drinking Water Resources and Distribution Supply in Mega Manila, Philippines

by
Arizaldo E. Castro
* and
Marie Christine M. Obusan
Microbial Ecology of Terrestrial and Aquatic Systems (METAS) Laboratory, Institute of Biology, College of Science, University of the Philippines Diliman, Quezon City 1101, Philippines
*
Author to whom correspondence should be addressed.
Water 2024, 16(16), 2267; https://doi.org/10.3390/w16162267
Submission received: 12 June 2024 / Revised: 2 August 2024 / Accepted: 5 August 2024 / Published: 12 August 2024
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
Browse Figures
Figure 1
<p>Seventeen (17) water sampling sites across Mega Manila, Philippines used in the current study: Laguna Lake Tributary sites (<span class="html-italic">n</span> = 5), deep well sites (<span class="html-italic">n</span> = 2), before treatment plant sites (<span class="html-italic">n</span> = 7), and after treatment plant sites (<span class="html-italic">n</span> = 3). Sampling site map was generated using ArcGIS Pro 3.3.</p> "> Figure 2
<p>Bacterial community taxonomic profiles across all sampling sites.</p> "> Figure 3
<p>Predicted functional profiles of Laguna Lake tributaries and before treatment plant sites with shotgun sequence data.</p> "> Figure 4
<p>(<b>a</b>) Benzoate degradation (BioCyc ID: PWY−283); (<b>b</b>) Dioxin degradation (BioCyc ID: P661−PWY); (<b>c</b>) Styrene degradation (BioCyc ID: PWY−6941); (<b>d</b>) Ammonia oxidation (BioCyc ID: PWY−7082); (<b>e</b>) Sulfate reduction (BioCyc ID: DISSULFRED−PWY). Degradation pathways are adapted from the MetaCyc metabolic pathway database [<a href="https://metacyc.org/" target="_blank">https://metacyc.org/</a>] (accessed on 1 July 2024).</p> "> Figure 4 Cont.
<p>(<b>a</b>) Benzoate degradation (BioCyc ID: PWY−283); (<b>b</b>) Dioxin degradation (BioCyc ID: P661−PWY); (<b>c</b>) Styrene degradation (BioCyc ID: PWY−6941); (<b>d</b>) Ammonia oxidation (BioCyc ID: PWY−7082); (<b>e</b>) Sulfate reduction (BioCyc ID: DISSULFRED−PWY). Degradation pathways are adapted from the MetaCyc metabolic pathway database [<a href="https://metacyc.org/" target="_blank">https://metacyc.org/</a>] (accessed on 1 July 2024).</p> "> Figure 5
<p>Predicted functional profiles of Pasig River, before treatment plant sites, deep wells, and after treatment plant sites.</p> ">
Versions Notes

Abstract

:
Assessing bacterial communities across water resources is crucial for understanding ecological dynamics and improving water quality management. This study examines the functional profiles of bacterial communities in drinking water resources in Mega Manila, Philippines, including Laguna Lake tributaries, pre-treatment plant sites, groundwater sources, and post-treatment plant sites. Using eDNA sequencing, flux balance analysis, and taxonomy-to-phenotype mapping, we identified metabolic pathways involved in nutrient metabolism, pollutant degradation, antibio- tic synthesis, and nutrient cycling. Despite site variations, there are shared metabolic pathways, suggesting the influence of common ecological factors. Site-specific differences in pathways like ascorbate, aldarate, and phenylalanine metabolism indicate localized environmental adaptations. Antibiotic synthesis pathways, such as streptomycin and polyketide sugar unit biosynthesis, were detected across sites. Bacterial communities in raw and pre-treatment water showed potential for pollutant degradation such as for endocrine-disrupting chemicals. High levels of ammonia-oxidizing and sulfate-reducing bacteria in pre- and post-treatment water suggest active nitrogen removal and pH neutralization, indicating a need to reassess existing water treatment approaches. This study underscores the adaptability of bacterial communities to environmental factors, as well as the importance of considering their functional profiles in assessing drinking water quality resources in urban areas.

1. Introduction

Bacterial communities in aquatic environments play crucial roles in nutrient cycling [1,2], pollutant degradation [3,4], and overall ecosystem health. These microbial communities are adapted to specific environmental conditions that allow members to functionally associate with one another. They assume niches within normal physiological conditions or extreme ones [5]. Diverse bacterial groups can be hosted by various aquatic systems, in both natural and built environments. In the context of drinking water supply, freshwater sources include surface water bodies and groundwater (deep wells). Freshwater systems, both natural and built, serve as surface water storage as well as resources for economic activities. If these waterscapes are continuously polluted, the availability of safe freshwater is threatened [6]. Chronic exposure to emerging pollutants such as heavy metals (HMs), endocrine-disrupting chemicals (EDCs), and antimicrobial resistance genes (ARGs) through the consumption of contaminated water compromises human and ecosystem health. Specific microbial communities, i.e., bacterial orders and families, have been reported to be candidate indicators of pollution in drinking water resources [7,8]. In Mega Manila, Philippines, some major water bodies that border the region and serve as raw water sources include the Laguna Lake and the La Mesa Watershed and associated earth dams (Angat, Ipo, and La Mesa Dams).
The diversity of bacterial communities correlates with the ecological dynamics of natural systems. Rare and abundant bacterial communities share biogeographic patterns and contribute to ecosystem functioning [9]. In undisturbed aquatic environments, microbial communities are of interest as they influence nutrient flow and conversion. In polluted and nutrient-rich aquatic environments, more diverse bacterial communities are found such as those composed of Cyanobacteria, Proteobacteria, and Actinobacteria [10] as well as Gammaproteobacteria and Flavobacteria [11]. In the context of drinking water resources and distribution networks, members from Proteobacteria, Bacteroidetes, and Actinobacteria were reported to be found in source water and in pre- and post-filtered water [12]. Post-disinfection processes such as chlorination and chloramination were identified as dynamic factors shaping bacterial communities from water in urban drinking water distribution systems. Correlation between treatment, i.e., chlorination and chloramination, and microbial community composition was previously reported [13]. Identified members of the core population correlated with the presence of chlorine include Cyanobacteria, Methylobacteriaceae, Sphingomonadaceae, Xanthomonadaceae and while those correlated with chloramine include Methylophilaceae, Methylococcaceae, and Pseudomonadaceae. Additionally, it was reported that chlorine disinfection results in homogenous bacterial populations while its absence may support the growth of more biodiverse bacterial communities [14].
In water distribution systems, bacterial communities are affected by several factors, namely water purification [15,16], site-specific properties of distribution system points [17], post-disinfection processes [18], environmental physico-chemical parameters, and nutrient/pollutant loads [19,20,21,22]. A comprehensive understanding of the ecological roles and dynamics of aquatic bacterial communities requires different streams of data such as variables on microbial abundance/function, diversity, interactions, response, and regulation. Molecular techniques that make use of DNA, genomic or environmental, can be used in determining the identity and quantity of specific microbial taxa present in water. PCR-based methods that amplify marker genes or gene regions (e.g., 16S rRNA) are helpful in conducting diversity assessments and overall community profiling of aquatic microbial communities. Other approaches, such as shotgun metagenome sequencing, utilize environmental DNA (eDNA) as a primary data source to be processed for downstream analysis [23].
This study aimed to profile the functional capacities of bacterial communities in various freshwater resources and drinking water distribution supply points in Mega Manila, Philippines. We described the presence of bacterial community phenotypes and metabolic pathways involved in nutrient cycling, nutrient metabolism, pollutant degradation, and antibiotic synthesis using eDNA sequencing, flux balance analysis, and taxonomy-to-phenotype mapping. With its metropolitan focus, this study integrates multiple methodologies to generate baseline data on freshwater and drinking water bacterial communities in an urban Southeast Asian context. By comparing diverse water sources, this study highlights both commonalities and site-specific differences in microbial profiles, revealing potential local environmental adaptations. Overall, we have elucidated the metabolic potentials of bacterial communities in select earth dams, Laguna Lake tributaries, aqueducts, water treatment plants, and groundwater sources of the Mega Manila region in the Philippines as this information relates to the fate of drinking water from source to tap.

2. Materials and Methods

Seventeen (17) sampling sites were included in this study (Figure 1). The sites comprise five Laguna Lake tributaries (LLT; Mangangate River, Biñan River, San Cristobal River, Sapang Malapit Creek, and Pasig River), two deep wells (DW; Bagong Silang Deep well and Rockville Deep well), seven before treatment plant sites (BTP; La Mesa Aqueduct 6, La Mesa Aqueduct 4, Treatment Plant Intake, Treatment Plant Forebay, Ayala Alabang R1, Angat Upstream Dam, and Ipo Dam), and three after treatment plant sites (ATP; After Treatment Plant A, After Treatment Plant B, and After Treatment Plant C). LLT sites are freshwater bodies (four rivers and an urban creek) connected to the Laguna Lake that serves as a raw water abstraction site for Mega Manila. DW sites include two deep wells, one located in Metro Manila and another in Cavite. BTP sites (waterways that are directed to treatment facilities) include two earth dams (Angat Dam and Ipo Dam), two water-conveying aqueducts (La Mesa Aqueducts 6 and 4), and three pre-treatment plant sites. ATP sites (water that underwent treatment plant processing) include one water pumping station, one post-treated water reservoir, and one post-treatment plant site. Water sampling was conducted between May 2021 and February 2022. Sampling was conducted during the rainy season of 2021 and the dry season, transitioning from 2021 to 2022. Water sampling in each site was conducted once; hence, the generalizability of the inferences on bacterial community profiles is only applicable during the period of sampling. All necessary permits from government agencies, water concessionaires, and local government units were secured before sampling.
The measurements of the physical parameters were determined with a multi-parameter meter. The concentrations of heavy metals were determined through different approaches—(i) arsenic was tested in the samples via the 3114 B: Manual Hydride Generation/AAS method; (ii) mercury was tested using inductively coupled plasma-optical emission spectroscopy (ICP-OES); and (iii) lead and cadmium were tested in the samples via 3111-B: Direct Air-Acetylene Flame method. Heavy metal analyses were contracted to a third-party laboratory service provider.
Water samples were collected using 1000 mL sterile glass amber bottles, kept in ice coolers while in transit, and stored at −20 °C in the laboratory until immediate processing. All required pre-processing and processing steps such as replicate pooling, pre-filtration, and membrane filtration were performed within 12 h of collection.

2.1. Extraction of eDNA and Generation of Sequence Data

eDNA extraction from pooled water samples per site was conducted following manufacturer protocol for the DNeasy® PowerWater® Kit (QIAGEN GmbH, Hilden, Germany). Water samples were filtered using nitrocellulose membrane filters with a 0.45 µm pore diameter. Filter membranes were stored at −80 °C until extraction. eDNA extracts were stored at −20 °C until shipment to Macrogen, South Korea for metagenomic sequencing. For shotgun metagenomic sequencing, library preparation was carried out using Nextera XT kit. For metagenome amplicon sequencing, library construction of the 16S rRNA V3-V4 region was carried out using MOPC-purified primers. The forward primer used was Bakt_341F: CCTACGGGNGGCWGCAG while the reverse primer used was Bakt_805R: GACTACHVGGGTATCTAATCC [24]. Sequencing was performed either with a NovaSeq6000 platform with 100 bp paired-end setting or an Illumina MiSeq platform with a 300 bp paired-end setting. Raw sequence data from seventeen sites were deposited to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the following SRA accession numbers: SRR18609916, SRR18497784, SRR18609915, SRR26623576, SRR26623575, SRR18609917, SRR26747321, SRR26678523, SRR26678522, SRR26678525, SRR26678521, SRR26638712, SRR23060440, SRR26699581, SRR26699580, SRR26678524, and SRR23062164.
For post-sequencing data quality control, sequencing adaptors were removed (if present), quality trimming was employed as necessary, and sequencing duplicates was removed as necessary. Pre-processing and quality improvement in sequence data files was performed using fastp (0.23.2) [25]. Quality reports before and after preprocessing were generated using FastQC (0.12.1) [26].

2.2. Assembly-Based and Read-Based Analysis of Shotgun Sequence and Metagenome Amplicon Sequence Data

Metagenome assembly of each shotgun sequence replicate data was conducted using MEGAHIT (1.2.9) [27] via www.usegalaxy.org (accessed on 1 March 2024). High-quality reads were employed for de novo assembly following default parameters of MEGAHIT with the minimum contig length set at 2500 bp. The final metagenomic assembly was evaluated for quality using QUAST (5.2.0) [28] following the metagenome option (metaQUAST).
For sites with shotgun sequence data (n = 6; Biñan River, Mangangate River, San Cristobal River, Sapang Malapit Creek, Treatment Plant Intake, and Treatment Plant Forebay), reads were annotated using mOTUs2 (2.5.1) [29] for the determination of taxonomic profiles using universal, phylogenetic marker gene-based OTUs (mOTUs). Annotation was carried out at the phyla and genera/species levels. Replicate files per site were analyzed individually and relative abundances per identified phylum/genus/species were reported as average values of the replicate files. For samples with metagenome amplicon sequence data (n = 11), taxonomic assignment of sequence reads was carried out using mothur (1.48.0) [30] following an OTU-based approach. Processed sequences were queried against a SILVA reference alignment of the 16S rRNA gene V3 to V4 region. Pre-clustering and chimera removal steps were employed to reduce noise and improve data quality. A taxonomic summary was generated per site. OTUs were defined using a cut-off of 97.0%.
For sites with shotgun sequence data, the Shannon diversity index, inverse Simpson index, and Shannon evenness were computed using the vegan package [31] in RStudio (version 2022.07.2) considering mean relative abundance data for all sites. For samples with metagenome amplicon sequence data, the foregoing diversity indices were computed following standard mothur workflow, specifically by running the summary.single command.
Non-parametric testing was employed to assess the significant differences in bacterial community relative abundances as suggested by the results of normality testing with the Shapiro–Wilk test and generated Q-Q plots. The distribution of bacterial community relative abundances was assessed using Henze–Zirkler’s test for multivariate normality. The test for multivariate normality was performed in RStudio (version 2022.07.2) using the MVN package [32]. A non-parametric comparison of multivariate samples was conducted to test for statistical differences across water resource categories (LLT, BTP, DW, and ATP). The non-parametric comparison was performed in RStudio (version 2022.07.2) using the nonpartest function of the npmv package with the permreps argument set to 1000.

2.3. Functional Profiling of Bacterial Communities

Assembled metagenomes were annotated using RASTtk (1.073) [33]. For each site (n = 6), draft metabolic models per replicate annotated metagenome assemblies were built and later merged for flux balance analysis (FBA) (2.2.1). A mixed-bag community model was employed for FBA. Metabolic profiles were summarized according to KEGG pathways detected after FBA. The percentage proportion of each pathway (refer to Supplementary Materials) was computed using its count against the total number of KEGG pathways detected in the merged community model for each site. Metagenome annotation, metagenome metabolic model build, and community model FBA were all carried out using dedicated apps (Annotate Metagenome Assembly and Re-annotate Metagenome with RASTtk—v1.073, Build Metagenome Metabolic Model v0.1, and Merge Metabolic Models into Community Model) in www.kbase.us (accessed on 25 April 2024).
For samples with metagenome amplicon sequence data, the functional characteristics (refer to Supplementary Materials section) of bacterial communities were predicted through phenotype mapping using the METAGENassist web server [34]. Taxonomic abundance data from mothur along with metadata on samples per resource type were uploaded as input files. Taxonomic-to-phenotypic mapping was carried out to extract information on the abundance of specific phenotypes based on metabolic capacities. Unassigned and unmapped reads were excluded from the analysis. Data filtering using median scores was implemented to detect non-informative variables. Replicates for each sample were normalized by median (replicate vs. replicate). Taxon-to-taxon normalization was implemented using generalized log2 transformation as this approach effectively manages the wide range of abundance levels typical in metagenomic datasets, i.e., some taxa are expected to be extremely abundant while others are sparse.
Flux balance analysis (FBA) is a computational approach used to predict the metabolic capabilities and behavior of microbial communities based on annotated assembled genomes or metagenomes [35,36]. It is particularly useful for assessing bacterial community function, as it enables the exploration of metabolic fluxes through complex biochemical networks. FBA has been widely applied to individual bacterial species and is increasingly being used to model bacterial consortia and communities. For bacterial communities, metabolic networks of individual species are integrated to form a community-level model. There is also phenotype mapping, which is an approach for assessing the functional potential and metabolic capabilities of microbial communities based on overall taxonomic profiles [34]. METAGENassist is a web-based tool designed to facilitate the functional interpretation of metagenomic data, particularly 16S rRNA gene sequence data. It allows for the prediction and visualization of the functional profiles of microbial communities by mapping taxonomic and abundance data to metabolic and ecological functions. Predicting bacterial community functional profiles allows for inferring the roles of bacteria in nutrient cycling processes, such as nitrogen fixation, nitrification, denitrification, carbon sequestration, and pollutant degradation, to name some.

3. Results and Discussion

3.1. Physico-Chemical Quality of Water Samples

The following physico-chemical parameters of water samples were measured: dissolved oxygen (DO), pH, total dissolved solids (TDS), arsenic, lead, cadmium, and mercury (Table 1). The pH values ranged from slightly acidic to neutral, with a pH range of 6.67 to 8.62. For DO, La Mesa Aqueduct 4 reported the highest level at 5.67 ppm, which is higher than river sites, reflecting good water quality in terms of oxygen availability. Sites like Pasig River (0.01 ppm) and Mangangate River (0.14 ppm) have critically low DO levels, indicating severe oxygen depletion. This could be due to organic pollution, high biochemical oxygen demand (BOD), or stagnant water conditions. San Cristobal River (1.53 ppm) and Biñan River (0.85 ppm) have slightly better DO levels but may still be experiencing challenges in maintaining healthy ecosystem dynamics as evidenced by the pollution levels that these sites are known for. High organic pollution from wastewater discharges, industrial effluents, and agricultural runoff can lead to low DO levels. This causes high BOD, consuming oxygen and leading to hypoxic conditions. Very low DO levels can be detrimental to aquatic life and may result in fish death and reduced biodiversity. San Cristobal River (520 mg/L) and Biñan River (306.67 mg/L) stand out with significantly high TDS levels, which might indicate high levels of pollution or natural mineral content in these rivers. After Treatment Plant B (72.67 mg/L) shows moderate levels of TDS, suggesting a higher mineral content compared to most other sites but not excessively high. The rest of the sites have very low TDS levels, indicating good water quality with minimal dissolved solids. High TDS in rivers like San Cristobal and Biñan could be due to industrial discharges, agricultural runoff, or natural mineral deposits. The low and consistent TDS levels of samples from BTP, DW, and ATP may indicate minimal mineral content and effective or optimal filtration and purification processes. Arsenic levels were mostly below the minimum detection limit except for Biñan River, where the concentration was 0.008 mg/L. Lead concentration varied across sites, with San Cristobal River showing the highest level at 1.686 mg/L, and La Mesa Aqueduct 6 the lowest at 0.074 mg/L. Cadmium was detected in modest amounts, with Biñan River and Mangangate River recording 0.012 mg/L and 0.014 mg/L, respectively. La Mesa Aqueduct 6 had cadmium levels below the minimum detection limit. Mercury was found in most of the sites in small quantities, with the highest recorded in the San Cristobal River at 0.002 mg/L.

3.2. Taxonomic Profiles of Bacterial Communities

Proteobacteria, Firmicutes, and Bacteroidetes are the most prevalent communities across all sites (Figure 2), indicating their widespread distribution and metabolic versatility. The four water resource categories (i.e., LLT, DW, BTP, and ATP) showed different community taxonomic signatures. Sites like After Treatment Plant A and Bagong Silang Deep well show high variation in phyla abundance, with some phyla showing nearly complete dominance (e.g., Proteobacteria and Firmicutes) or absence (e.g., Cyanobacteria and Synergistetes). Drinking water distribution supply points such as pump stations are characterized by continuous water flow, which is regulated by water concessionaires in the case of the After Treatment Plant A. The observed community evenness in this site is related to physical processes such as biofilm detachment and suspension of loose deposits [37], probably due to the effect of water pressure management. On the other hand, less even community distribution in the Bagong Silang Deep well suggests that groundwater connectivity may be fragmented within the area as low community diversity results from drier soils that isolate subsurface aquatic habitats [38]. La Mesa Aqueducts 4 and 6, in contrast, showed higher diversity of bacterial communities and even species distribution, which may suggest that these pre-treatment plant water sites maintain conditions that support a variety of communities; however, due to their primary function, i.e., water conveyance, the tendency of dominant communities to be more abundant than the others is affected. The Pasig River and Mangangate River have higher abundances of Proteobacteria compared to other phyla, likely influenced by two factors: (1) urban-related enrichment of diverse organic compounds in these environments; and (2) the inherent metabolic versatility of this bacterial group with some members being able to transition from a phototrophic to a heterotrophic lifestyle in the absence of light. The dominance of Proteobacteria in nutrient rich environments is expected as they play significant roles in nutrient cycling and removal [39]. In contrast, post-treatment plant sites like the After Treatment Plant C show a more balanced distribution of different phyla as indicated by diversity and evenness indices, suggesting conditions favorable for supporting more diverse communities.
Angat Upstream Dam showed the highest community biodiversity among the 17 sampling sites and this finding relates to the role of upstream raw water sites, which is as a seedbank of the microbial communities present in downstream locations [40]. On the other hand, the lower diversity and less even distribution observed in Ipo Dam may be attributed to sediment scouring and passive dispersal of bacterial communities within the earth dam network [40]. Overall, both earth dams show a balanced, even, and biodiverse phyla composition, possibly indicating that these sites experience less disturbance relative to others and are still in pristine condition.
Mangangate River, Biñan River, and Pasig River, which are all tributaries of the Laguna Lake, have lower diversity indices compared to other sites (Table 2). Furthermore, their Shannon evenness values are among the lowest in the group, suggesting uneven species distribution. San Cristobal River, another Laguna Lake tributary, has a slightly higher Shannon index than the Mangangate and Biñan Rivers, indicating higher diversity but same uneven species distribution. Several factors such as fecal pollution [41] and hydrocarbon pollution [42] drive decreases in community diversity of lotic ecosystems such as rivers, and this could be the potential case for the Laguna Lake tributaries. La Mesa Aqueducts 6 and 4, have markedly higher diversity compared to the river tributaries. Particularly, the inverse Simpson index shows much higher diversity at these sites, and their Shannon evenness is considerably higher, indicating a more balanced species distribution compared to those of the rivers. Overall, the computed diversity indices suggest that water supply points that are before treatment plant sites, e.g., La Mesa Aqueducts, maintain higher biodiversity and species evenness compared to the tributaries of Laguna Lake. This could be due to various ecological or anthropogenic factors impacting the tributaries more significantly than the more controlled environments of aqueducts. Sites with lower diversity and evenness indices might be more vulnerable to environmental stressors and could benefit from targeted conservation efforts. The findings suggest that the tributaries could be under more ecological stress or might be impacted by human activities.
Testing for significant differences in terms of bacterial phyla relative abundances revealed that there are no statistically significant differences across all four water resource categories (LLT, BTP, DW, and ATP) as supported by the ANOVA-type test p-value (p = 0.213).

3.3. Predicted Functional Profiles of Bacterial Communities

3.3.1. Metabolic Pathways of Bacterial Communities

A total of 17 sampling sites were profiled for predicting the functional capacities of their bacterial communities. Biñan River, Mangangate River, San Cristobal River, Sapang Malapit Creek, Treatment Plant Intake, and Treatment Plant Forebay were subjected to shotgun sequencing while the remaining 11 other sites were subjected to metagenome amplicon sequencing. Several metabolic pathways were detected from the flux balance analytic models of six sites with shotgun sequence data, e.g., amino sugar and nucleotide sugar metabolisms, arginine (C6H14N4O2) and proline (C5H9NO2) metabolisms, fatty acid biosynthesis, etc. (Figure 3). The detection of these pathways contributes to the understanding of the microbial community activity in these water bodies, reflecting not only environmental health but also the potential biogeochemical processes occurring in these systems. Across the sites, there are notable similarities in the distribution of detected metabolic pathways. Most pathways are present in comparable proportions at each site, suggesting ecological consistency.
Pathways like the arginine and proline metabolisms, purine metabolism, pyrimidine metabolism, pyruvate (C3H3O3−) metabolism, starch-sucrose metabolism, and fatty acid biosynthesis consistently appear to be more prevalent compared to others. Some pathways are totally absent in specific sites: the ascorbate (C6H8O6) and aldarate metabolisms (absent in San Cristobal River and Sapang Malapit Creek), phenylalanine (C9H11NO2) metabolism (absent in Treatment Plant Intake and Forebay), lysine (C6H14N2O2) biosynthesis (absent in San Cristobal River), propanoate metabolism (absent in Treatment Plant Intake), butanoate metabolism (absent in Biñan River, San Cristobal River, Treatment Plant Intake, and Treatment Plant Forebay), and fructose and mannose metabolisms (absent in Biñan River, Treatment Plant Intake, and Treatment Plant Forebay).
Amino sugar and nucleotide sugar metabolisms, related to the synthesis of cell walls [43] and other structural components in microbes, show slight variations across sites but remain relatively consistent, indicating active microbial growth. The arginine and proline metabolisms are notably higher than many other pathways and this indicates a microbial community engaged in active protein metabolism, which could be linked to the nitrogen cycle and metabolism in these aquatic systems.
In terms of site-specific differences, San Cristobal River and Sapang Malapit Creek exhibit unique functional profiles with the complete absence of the ascorbate and aldarate metabolisms in both sites and absence of lysine biosynthesis in San Cristobal River. Ascorbate is a significant compound that interacts with the enzyme superoxide dismutase for the removal of superoxide and reduces the formation of peroxinitrite (ONOO−) [44]. The absence of this pathway suggests that the microbial community is adapted to specific environmental conditions where the ascorbate and aldarate metabolisms are less critical. This could be indicative of low oxidative stress. Microorganisms without this pathway might employ alternative mechanisms to manage oxidative stress or might be more susceptible to it. This finding supports the abundance of anaerobic bacteria in the sites. Another pathway that is completely absent in Treatment Plant Forebay and Treatment Plant Intake is the phenylalanine metabolism. Phenylalanine is an essential amino acid that may fulfill multiple roles in freshwater systems such as a potential nitrogen source [45] and as a precursor to nitrogenous disinfection byproducts. The absence of the metabolic pathway suggests limitations in amino acid availability and in the production of secondary metabolites, which could affect nutrient cycling. Factoring in that the samples from these sites are water indicated for subsequent disinfection, the absence of this metabolic pathway in comparison to functional profiles of Laguna Lake bacterial communities is more of a green flag, as it indicates that bacterial communities residing in pre-treatment plant water are less metabolically active. Lastly, another metabolic pathway that is not found in Treatment Plant Forebay and Treatment Plant Intake is the butanoate metabolism. It is only detected in Mangangate River and Sapang Malapit Creek. The butanoate metabolism refers to the metabolic fate of short-chain fatty acids such as acetate, propionate, and butyrate that are produced by carbohydrate fermentation carried out by bacteria in the vertebrate colon [46]. There appears to be a paucity of information on the butanoate metabolism in the context of aquatic bacterial communities. Interestingly, changes in the butanoate metabolism have been reported in freshwater turtles that are impacted by Per- and polyfluoroalkyl substances (PFAS). The butanoate metabolism is reported to be one of the top altered co-metabolic (host and bacteria) pathways in PFAS-impacted freshwater turtles [47]. In this regard, the presence of the pathway in Mangangate River and Sapang Malapit Creek, which are both heavily impacted water tributaries of the Laguna Lake, may relate to pollution levels at these sites.

3.3.2. Antibiotic Synthesis and Pollutant Degradation Pathways of Bacterial Communities

Although detected as less than 1% of all total metabolic pathways based on annotations of shotgun metagenome assemblies (raw water and pre-treatment water), there are several pathways associated with antibiotic synthesis and degradation of endocrine-disrupting chemicals. These pathways are found to be consistently present in Laguna Lake tributaries and before treatment plant sites. Benzoate degradation (Figure 4a) is a metabolic pathway involving the breakdown of benzoate, a simple aromatic compound. Benzoate can be derived from plant materials and is a common environmental pollutant. It can be metabolized aerobically and anaerobically by various microorganisms [48,49]. In combination with the butanoate (C4H7O2−) metabolism, intermediate byproducts from benzoate degradation, like acetyl-CoA, can feed into the former pathway. The breakdown of benzoate can produce intermediates that are also involved in the breakdown of more complex aromatic compounds like dioxins. Dioxins are a group of chemically related compounds that are persistent environmental pollutants. They are typically degraded through a series of oxidation and reduction reactions facilitated by microbial enzymes [50]. Like dioxin degradation (Figure 4b), the degradation of benzoate can lead to the formation of intermediates such as catechol, which are also involved in the degradation of Polycyclic Aromatic Hydrocarbons (PAHs). PAHs are organic compounds containing multiple fused aromatic rings. They are known environmental pollutants with potential carcinogenic properties [51,52]. In drinking water, detected PAHs with high concentrations include fluoranthene, phenanthrene, pyrene, and anthracene. Microbes that possess enzymatic machinery to degrade PAHs are of varied types such as bacteria, halophilic archaea, fungi, and algae. Degradation of naphthalene, a simple PAH, is a pathway crucial for the bioremediation of PAH-contaminated environments. Multiple Pseudomonas species have been reported to degrade naphthalene and its derivatives in different contaminated settings, including water [53]. Styrene degradation (Figure 4c) involves the breakdown of styrene, a volatile organic compound used in the production of plastics and synthetic rubber. Styrene and its oligomers are known to abound in different environments. The toxicity of this group of chemical pollutants in aquatic systems is predicted to be similar to or potentially more than the toxicity of bisphenol A (BPA) or styrene monomers (SMs) [54]. Microbial degradation of styrene is important for the bioremediation of industrial waste and polluted environments, reducing the impact of this contaminant [55]. Possible sources of styrene may include industrial production (plastics manufacturing and synthetic rubber production), industrial waste streams, breakdown of styrene-based polymers, and leaching from consumer products.
Synthetic metabolic pathways pertinent to antibiotic and metabolite production, which are detected in all sites with shotgun sequence data, occur individually or in combination with related pathways. In multispecies contexts, it is posited that microbial community composition affects the expression of biosynthetic gene clusters and therefore the abundance of associated secondary metabolites [56]. One such pathway is polyketide sugar unit biosynthesis, which involves the formation of sugar moieties that are attached to polyketide compounds. Polyketides are a diverse class of secondary metabolites produced by bacteria, fungi, and plants, with important pharmaceutical attributes, including antibiotic, antifungal, and anticancer properties [57,58].
Combination pathways such as those of biosynthesis of streptomycin with other aminoglycoside antibiotics like butirosin and neomycin, were also detected from all six sites with shotgun sequence data. Streptomycin inhibits protein synthesis by binding to the bacterial ribosome. Butirosin and neomycin are aminoglycoside antibiotics that are also produced by Streptomyces species. In relation to streptomycin synthesis, they share similar biosynthetic pathways involving the assembly of aminocyclitol rings and the attachment of sugar moieties. Similar enzymes and precursors in the biosynthesis pathways of these antibiotics indicate common needs or functional overlap. Another metabolic pathway with a connection to streptomycin biosynthesis is the inositol phosphate metabolism. Inositol phosphates are important signaling molecules involved in various cellular processes, including cell growth, apoptosis, and stress response. Metabolites from the inositol phosphate metabolism can serve as precursors or regulatory molecules in the biosynthesis of streptomycin. Lastly, the biosynthesis of the vancomycin group of antibiotics is found to be present in all sites along with streptomycin and polyketide sugar unit biosynthesis. Vancomycins are a group of glycopeptide antibiotic products that inhibit cell wall synthesis in Gram-positive bacteria. The biosynthesis of vancomycin and other antibiotics involves complex pathways where sugar units play a crucial role in determining antibiotic properties. Raw and pre-treatment water are eutrophic and oligotrophic environments, respectively. Antibiotic production by freshwater bacterial communities is a strategy to create a favorable microenvironment and be able to compete for resources in competitive and hostile conditions.
These notable metabolic pathways highlight the versatility and adaptability of bacterial communities in degrading a wide range of organic compounds, including pollutants as well as the synthesis of complex molecules such as antibiotics. The detection of the aforementioned metabolic pathways underscores the complexity of the microbial metabolism and community dynamics in all of the six sites.

3.3.3. Ammonia Oxidation, Sulfate Reduction, and Contaminant Degradation by Bacterial Communities

Figure 5 summarizes the functional profiles of bacterial communities for all sites (raw water, pre-treatment water, and post-treatment water) with amplicon sequence data that were processed via phenotype mapping. Ammonia is considered a major indicator of water contamination by animal or human waste. In aquatic environments, ammonia concentrations are attributable to waste degradation by bacteria [59]. There is variation in the percentage of ammonia oxidizers across sites, ranging from 7.8% in Pasig River, a Laguna Lake tributary, to 68.1% in La Mesa Aqueduct 6, with other sites showing intermediate levels. Ammonia oxidation (Figure 4d) is variable, likely influenced by environmental factors such as nutrient availability [60], pollution levels, operational conditions at treatment facilities, and quality of the source water fed to treatment [61]. The BTP sites’ high percentages suggest the significant role of nitrification and denitrification in water prior to treatment processes. Nitrogen removal is carried out via nitrification and denitrification. Through nitrification, ammonia is first oxidized to nitrite by ammonia-oxidizing bacteria and archaea. Eventually, nitrite-oxidizing bacteria come into play by converting nitrite to nitrate. The reduction of nitrate to nitrogen gas comes last and is facilitated by heterotrophic denitrifiers. In raw water, like that of Pasig River, nitrification microbes are usually slow growers as the amount of ammonia in raw/source water is low [62]. High levels of ammonia oxidizers, particularly in BTP and ATP sites, highlight the impact of current treatment practices and technologies on bacterial community functions in relation to nitrogen removal. Even after post-treatment, there are still high levels of ammonia-oxidizers in the water samples, suggesting a need to revisit water treatment strategies that target ammonia. Additionally, sulfate reduction is markedly high in La Mesa Aqueduct 6 (60.6%), Bagong Silang Deep well (89.4%), and After Treatment Plant C (54.2%) (Figure 4e). the high rate of sulfate reduction in water bodies indicates stronger anthropogenic impact [63] (e.g., sewage infiltration, fertilizers, synthetic detergents, and industrial wastewater) and in the context of raw and treated water, the activity of sulfate reducers ensures the neutralization of the ambient water’s pH balance.
In terms of amino acid degradation, there is very low to no activity across all sites, with only the After Treatment Plant C showing slight activity (0.1%). This suggests that in all sites with amplicon sequence data, the conditions for microbial populations to carry out amino acid degradation are not favorable. Similarly, acidogenic bacteria are either inactive or under-represented in these samples, as indicated by low levels of organic acid production processes. In terms of biomass degradation, there appears to be a lack of or insignificant activity, which might reflect the absence of required substrates (e.g., plant biomass), or environmental conditions conducive to such processes. On the other hand, the degradation of complex hardly degradable carbohydrates such as chitin and cellulose is detected in all sites. Cellulose degradation is markedly high in Angat Upstream Dam (13.7%), Ayala Alabang R1 (9.7%), Rockville Deep well (10.4%), and After Treatment Plant B (10%). Chitin degradation is markedly high in both La Mesa Aqueducts (23.1% and 15.6%) and in Bagong Silang Deep well (87.5%). Complex carbohydrate degradation in the sampled sites relates well with their corresponding relative abundances for Bacteroidetes, known for their degradation of complex polysaccharides [64]. The polymer-degrading activity of bacterial communities found in water from earth dams and in water to be conveyed for further treatment is expected as these sites are within protected forested areas.
Atrazine metabolism varies across sites, with La Mesa Aqueduct 6 and After Treatment Plant C showing markedly higher percentages (5.7% and 4.1%) compared to other sites. Lower percentages in other sites indicate less potential exposure to the chemical. Atrazine is an herbicide that is widely used and as such, it is commonly detected in groundwater, surface water, and even in drinking water [65]. Furthermore, atrazine has been identified as a potent endocrine-disrupting chemical that is active even at low environmental concentrations [65]. The high levels at the BTP site of La Mesa Aqueduct 6 and the ATP site of After Treatment Plant C compared to other sites suggests exposure to atrazine, likely from agricultural runoff, with the bacterial community having adapted to biodegrade and utilize atrazine as a carbon/energy source. The recalcitrant nature of atrazine in water [66] must be factored into existing drinking water treatment approaches to effectively minimize its concentration prior to water distribution. Degraders of aromatic hydrocarbons and chlorophenol are also detected in all sites. There are two sites with the highest percentages of degraders/degradation activity profiled, i.e., La Mesa Aqueduct 6 and Bagong Silang Deep well. Aromatic hydrocarbons are cyclic, planar, organic compounds made up entirely of hydrogen and carbon atoms. They possess one or more benzene rings and have been widely used as solvents as well as components for the synthesis of certain drugs, dyes, and plastics [67]. Chlorophenol and its derivatives are known as persistent toxicants in the environment. Most of them are employed as intermediates in manufacturing agricultural chemicals, pharmaceuticals, biocides, and dyes [68]. Its solubility in water depends on the isomer type, but generally, it is slightly soluble to soluble in water. Enriched proportions of degraders/degradation activity in La Mesa Aqueduct 6 and Bagong Silang Deep well may indicate higher exposure levels of these sites to the aforementioned contaminants. In the case of Bagong Silang Deep well, its groundwater sources might have accumulated chlorophenols that seeped through the associated soil over time. La Mesa Aqueduct 6, although low in terms of chlorophenol degraders/degradation activity, is still worth monitoring as this is a site intended for raw water conveyance, hence prolonged residence of nutrients and contaminants should be unlikely unless a direct active point source serves as a feeder line of the contaminant into the conveyed water. In the animal body, chlorophenols and their degradation byproducts are known as genotoxic, mutagenic, and carcinogens [68]. Considering their biological significance, indications of their accumulation and persistence in drinking water systems must be treated with utmost urgency. In relation to removing chemical toxicants in water, current technologies that make use of adsorption mechanisms (e.g., activated carbon) or other approaches may not be effective enough to diminish or eliminate the concentrations of these contaminants in drinking water.

4. Conclusions

Utilizing metagenomic-based taxonomic analyses, metabolic modeling, and phenotype mapping, the functional capabilities of bacterial communities in Mega Manila’s drinking water resources and distribution network were inferred. Understanding these functional profiles is essential for assessing potential risks to human and animal health and the effectiveness of current water treatment strategies. Integrating taxonomic profiles with metabolic and ecological functions enables the generalization of insights into the roles of microbial communities in various environments.
Several metabolic pathways were detected, including the amino sugar and nucleotide sugar metabolisms, arginine and proline metabolisms, and fatty acid biosynthesis. Notably, the arginine and proline metabolisms were higher than other pathways, indicating an active protein metabolism linked to the nitrogen cycle in both raw water and pre-treatment water sites. Pathways, like the ascorbate and aldarate metabolism, were absent in specific sites, suggesting adaptations to local environmental conditions, e.g., low oxidative stress. The presence of degradation pathways for the breakdown of benzoate, dioxins, naphthalene, PAHs, and styrene suggests potential exposure of the raw water sites to these chemical contaminants. Antibiotic synthesis pathways, such as streptomycin biosynthesis and polyketide sugar unit biosynthesis, were present in some of the raw water and pre-treatment water sites, highlighting the potential of bacterial communities for producing antibiotics, which could impact microbial community dynamics and resistance profiles. Bacterial groups that are ammonia oxidizers and sulfate reducers were identified in raw water, pre-treatment water, and post-treatment water. Their high percentages in some of the after treatment plant sites (post-treatment water) raise concerns about the efficiency of waste and nutrient removal approaches used in the management of drinking water supply for distribution. Lastly, bacterial communities profiled to metabolize atrazine and chlorophenol, two persistent chemical contaminants in freshwater, were identified in some of the pre-treatment and post-treatment water sites and raise concerns about the sources and factors affecting the residence of these toxicants in water.
The presence of essential metabolic pathways, contaminant/pollutant degradation pathways, antibiotic synthesis pathways, and nutrient cycling pathways underscores the bacterial communities’ ecological roles in varied settings encompassing raw water sourcing to post-treatment. The differences in bacterial community diversity and in the presence or absence of synthetic and degradative pathways suggest that certain sites are under more ecological stress (raw water and ground water sources) and require targeted management and remediation effort.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16162267/s1, Figures S1–S11. Detected metabolic pathways (% of predicted phenotype) in sites with 16S rRNA V3-V4 MAS data based on phenotype mapping with the METAGENassist pipeline; Table S1. Coordinates of 17 sampling sites; Table S2. Detected metabolic pathways through flux balance analytic modeling and their %proportion for sites with shotgun sequence data; Table S3. Detected metabolic pathways (% of predicted phenotype) in sites with 16S rRNA V3-V4 MAS data based on phenotype mapping with the METAGENassist pipeline.

Author Contributions

Conceptualization, A.E.C. and M.C.M.O.; methodology, A.E.C. and M.C.M.O.; data curation, A.E.C.; validation, A.E.C. and M.C.M.O.; formal analysis, A.E.C. and M.C.M.O.; writing—original draft preparation, A.E.C. and M.C.M.O.; writing—review and editing, A.E.C. and M.C.M.O.; funding acquisition, M.C.M.O. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Department of Science and Technology—Philippine Council for Health Research and Development (DOST-PCHRD) through Project 1 (Microbial Communities as Sentinels of Environmental Pollutants in Metro Manila’s Resources) of the Water Assessment of Teratogens and Endocrine Disruptors on Fetal-Maternal Health (Water-FeMaH) research program.

Data Availability Statement

Raw sequence can be accessed using the NCBI SRA accession codes listed in Section 2. The taxonomic and functional profiling datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors acknowledge the following for assistance during sampling and sample processing: Ren Mark Villanueva and Jamaica Ann Caras of the Microbial Ecology of Terrestrial and Aquatic Systems Laboratory-Aquatic Division of the Institute of Biology, College of Science (CS), University of the Philippines Diliman (UPD). The authors also extend their gratitude to Alessandra Nicole Morado of the Marine Mammal Research and Conservation Laboratory of the Institute of Environmental Science and Meteorology, CS, UPD, for assisting in the preparation of the sampling site map.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Seventeen (17) water sampling sites across Mega Manila, Philippines used in the current study: Laguna Lake Tributary sites (n = 5), deep well sites (n = 2), before treatment plant sites (n = 7), and after treatment plant sites (n = 3). Sampling site map was generated using ArcGIS Pro 3.3.
Figure 1. Seventeen (17) water sampling sites across Mega Manila, Philippines used in the current study: Laguna Lake Tributary sites (n = 5), deep well sites (n = 2), before treatment plant sites (n = 7), and after treatment plant sites (n = 3). Sampling site map was generated using ArcGIS Pro 3.3.
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Figure 2. Bacterial community taxonomic profiles across all sampling sites.
Figure 2. Bacterial community taxonomic profiles across all sampling sites.
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Figure 3. Predicted functional profiles of Laguna Lake tributaries and before treatment plant sites with shotgun sequence data.
Figure 3. Predicted functional profiles of Laguna Lake tributaries and before treatment plant sites with shotgun sequence data.
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Figure 4. (a) Benzoate degradation (BioCyc ID: PWY−283); (b) Dioxin degradation (BioCyc ID: P661−PWY); (c) Styrene degradation (BioCyc ID: PWY−6941); (d) Ammonia oxidation (BioCyc ID: PWY−7082); (e) Sulfate reduction (BioCyc ID: DISSULFRED−PWY). Degradation pathways are adapted from the MetaCyc metabolic pathway database [https://metacyc.org/] (accessed on 1 July 2024).
Figure 4. (a) Benzoate degradation (BioCyc ID: PWY−283); (b) Dioxin degradation (BioCyc ID: P661−PWY); (c) Styrene degradation (BioCyc ID: PWY−6941); (d) Ammonia oxidation (BioCyc ID: PWY−7082); (e) Sulfate reduction (BioCyc ID: DISSULFRED−PWY). Degradation pathways are adapted from the MetaCyc metabolic pathway database [https://metacyc.org/] (accessed on 1 July 2024).
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Figure 5. Predicted functional profiles of Pasig River, before treatment plant sites, deep wells, and after treatment plant sites.
Figure 5. Predicted functional profiles of Pasig River, before treatment plant sites, deep wells, and after treatment plant sites.
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Table 1. Physico-chemical parameters of water samples.
Table 1. Physico-chemical parameters of water samples.
Sampling SiteResource TypeDO
[ppm]		pHTDS
[ppt]		Arsenic
[mg/L]		Lead
[mg/L]		Cadmium
[mg/L]		Mercury
[mg/L]
Mangangate RiverLLT0.146.670.300.7340.0140.00018
Biñan RiverLLT0.857.19306.670.0080.220.0120.0017
San Cristobal RiverLLT1.537.0952001.6860.0060.002
La Mesa Aqueduct 6BTP3.987.610.0700.07400.0012
La Mesa Aqueduct 4BTP5.677.480.0700.06800.0013
Treatment Plant IntakeBTP0.248.020.3500.06400.0012
Treatment Plant ForebayBTP0.257.700.3500.02600.0012
After Treatment Plant CATP0.277.090.3600.02400.0014
After Treatment Plant AATP0.238.050.070000
Ayala Alabang R1BTP0.387.010.3500.00100
After Treatment Plant BATP0.257.3672.6700.00100
Sapang Malapit CreekLLT0.417.250.30000
Pasig RiverLLT0.017.450.310000
Bagong Silang Deep wellDW08.620.270000.0004
Bagong Silang Deep wellDW07.530.220000.0007
Angat Upstream DamBTP08.050.070000.0005
Ipo DamBTP07.610.070000.0004
Notes: For heavy metals (arsenic, lead, cadmium, and mercury), sites with values reported as 0 indicate readings that are below the minimum detection limit of the method used); LLT—Laguna Lake tributary, BTP—before treatment plant, DW—deep well, ATP—after treatment plant.
Table 2. Community diversity indices of all sampling sites.
Table 2. Community diversity indices of all sampling sites.
Sampling SiteInverse Simpson (↑)Shannon (↑)Shannon Evenness (↑)
Mangangate River1.0753.9000.432
Biñan River1.1313.6240.402
San Cristobal River1.048 4.254 0.471
La Mesa Aqueduct 614.3723.5400.654
La Mesa Aqueduct 422.0353.7780.651
Treatment Plant Intake1.0184524.9120.829
Treatment Plant Forebay 1.022 4.802 0.810
After Treatment Plant C19.3473.5850.604
After Treatment Plant A1.4320.9150.144
Ayala Alabang R111.1733.2570.585
After Treatment Plant B7.6402.9370.519
Sapang Malapit Creek 1.001 7.295 0.808
Pasig River2.5552.1250.277
Bagong Silang Deep well1.3550.8800.143
Rockville Deep well13.4153.3960.580
Angat Upstream Dam50.1174.2390.728
Ipo Dam11.0092.9540.491
Notes: Upward arrows indicate that higher numerical values mean greater diversity (Inverse Simpson and Shannon indices) or higher species evenness (Shannon Evenness index).
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Castro, A.E.; Obusan, M.C.M. Predictive Functional Profiling Reveals Putative Metabolic Capacities of Bacterial Communities in Drinking Water Resources and Distribution Supply in Mega Manila, Philippines. Water 2024, 16, 2267. https://doi.org/10.3390/w16162267

AMA Style

Castro AE, Obusan MCM. Predictive Functional Profiling Reveals Putative Metabolic Capacities of Bacterial Communities in Drinking Water Resources and Distribution Supply in Mega Manila, Philippines. Water. 2024; 16(16):2267. https://doi.org/10.3390/w16162267

Chicago/Turabian Style

Castro, Arizaldo E., and Marie Christine M. Obusan. 2024. "Predictive Functional Profiling Reveals Putative Metabolic Capacities of Bacterial Communities in Drinking Water Resources and Distribution Supply in Mega Manila, Philippines" Water 16, no. 16: 2267. https://doi.org/10.3390/w16162267

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