Metagenomics as a Tool To Monitor Reclaimed-Water Quality

A metagenome-derived artificial intelligence modeling framework advances the predictive diagnosis and interpretation of petroleum-polluted groundwater

Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution.

Pathogen profile of Baiyangdian Lake sediments using metagenomic analysis and their correlation with environmental factors

Increasing concerns about public health and safety after covid-19 have raised pathogen studies, especially in aquatic environments. However, the extent to how different location and human activities affect geographic occurrence and distribution of pathogens in response to agricultural pollution, boat tourism disturbances and municipal wastewater inflow in a degraded lake remains unclear. Since the surrounding residents depend on the lake for their livelihood, understanding the pathogens reserved in lake sediment and the regulation possibility by environmental factors are challenges with far-reaching significance. Results showed that 187 pathogens were concurrently shared by the nine sediment samples, with Salmonella enterica and Pseudomonas aeruginosa being the most abundant. The similar composition of the pathogens suggests that lake sediment may act as reservoirs of generalist pathogens which may pose infection risk to a wide range of host species. Of the four virulence factors (VFs) types analyzed, offensive VFs were dominant (>46 % on average) in all samples, with dominant subtypes including adherence, secretion systems and toxins. Notably, the lake sediments under the impact of agricultural use (g1) showed significantly higher diversity and abundance of pathogen species and VFs than those under the impact of boat tourism (g2) and/or municipal wastewater inflow with reed marshes filtration (g3). From the co-occurrence networks, pathogens and pesticides, aggregate fractions, EC, pH, phosphatase have strong correlations. Strong positive correlations between pathogens and diazinon in g1 and ppDDT in g2 and g3 suggest higher pesticide-pathogen co-exposure risk. These findings highlight the need to explore pathogen - environmental factor interaction mechanisms in the human-impacted water environments where the control of pathogen invasion by environmental factors may accessible.

Functional traits and health implications of the global household drinking-water microbiome retrieved using an integrative genome-centric approach

The biological safety of drinking water plays a crucial role in public health protection. However, research on the drinking water microbiome remains in its infancy, especially little is known about the potentially pathogenic bacteria in and functional characteristics of the microbiome in household tap water that people are directly exposed to. In this study, we used a genomic-centric approach to construct a genetic catalogue of the drinking water microbiome by analysing 116 metagenomic datasets of household tap water worldwide, spanning nine countries/regions on five continents. We reconstructed 859 high-quality metagenome-assembled genomes (MAGs) spanning 27 bacterial and 2 archaeal phyla, and found that the core MAGs belonging to the phylum Proteobacteria encoded the highest metabolic functional diversity of the 33 key complete metabolic modules. In particular, we found that two core MAGs of Brevibacillus and Methylomona encoded genes for methane metabolism, which may support the growth of heterotrophic organisms observed in the oligotrophic ecosystem. Four MAGs of complete ammonia oxidation (comammox) Nitrospira were identified and functional metabolic analysis suggested these may enable mixotrophic growth and encode genes for reactive oxygen stress defence and arsenite reduction that could aid survival in the environment of oligotrophic drinking water systems. Four MAGs were annotated as potentially pathogenic bacteria (PPB) and thus represented a possible public health concern. They belonged to the genera Acinetobacter (n = 3) and Mycobacterium (n = 1), with a total relative abundance of 1.06 % in all samples. The genomes of PPB A. junii and A. ursingii were discovered to contain antibiotic resistance genes and mobile genetic elements that could contribute to antimicrobial dissemination in drinking water. Further network analysis suggested that symbiotic microbes which support the growth of pathogenic bacteria can be targets for future surveillance and removal.

Investigation of bacterial and fungal population structure on environmental surfaces of three medical institutions during the COVID-19 pandemic

Objectives

To evaluate the population structure of environmental bacteria and fungi in three different types of medical institutions and the potential risks due to antibiotic resistance during the coronavirus disease 2019 (COVID-19) pandemic.

Methods

One hundred twenty-six environmental surface samples were collected from three medical institutions during the COVID-19 pandemic. A total of 6,093 and 13,514 representative sequences of 16S and ITS ribosomal RNA (rRNA) were obtained by amplicon sequencing analysis. The functional prediction was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool based on the Greengenes database and the FAPROTAX database.

Results

On environmental surfaces in three medical institutions during the COVID-19 pandemic, Firmicutes (51.6%) and Bacteroidetes (25%) were the dominant bacteria, while Ascomycota (39.4%) and Basidiomycota (14.2%) were the dominant fungi. A number of potential bacterial and fungal pathogens were successfully identified by the metagenomic approach. Furthermore, compared with the bacterial results, the fungi showed a generally closer Bray Curtis distance between samples. The overall ratio of Gram-negative bacteria to Gram-positive bacteria was about 3:7. The proportion of stress-tolerant bacteria in medical institutions A, B and C reached 88.9, 93.0 and 93.8%, respectively. Anaerobic bacteria accounted for 39.6% in outdoor environments, 77.7% in public areas, 87.9% in inpatient areas and 79.6% in restricted areas. Finally, the β-Lactam resistance pathway and polymyxin resistance pathway were revealed through functional prediction.

Conclusion

We described the microbial population structure changes in three different types of medical institutions using the metagenomic approach during the COVID-19 pandemic. We found that the disinfection measures performed by three healthcare facilities may be effective on the “ESKAPE” pathogens, but less effective on fungal pathogens. Moreover, emphasis should be given to the prevention and control of β-lactam and polymyxin antibiotics resistance bacteria during the COVID-19 pandemic.

Toward shotgun metagenomic approaches for microbial source tracking sewage spills based on laboratory mesocosms

Little is known about the genomic diversity of the microbial communities associated with raw municipal wastewater (sewage), including whether microbial populations specific to sewage exist and how such populations could be used to improve source attribution and apportioning in contaminated waters. Herein, we used the influent of three wastewater treatment plants in Atlanta, Georgia (USA) to perturb laboratory freshwater mesocosms, simulating sewage contamination events, and followed these mesocosms with shotgun metagenomics over a 7-day observational period. We describe 15 abundant non-redundant bacterial metagenome-assembled genomes (MAGs) ubiquitous within all sewage inocula yet absent from the unperturbed freshwater control at our analytical limit of detection. Tracking the dynamics of the populations represented by these MAGs revealed varied decay kinetics, depending on (inferred) phenotypes, e.g., anaerobes decayed faster than aerobes under the well-aerated incubation conditions. Notably, a portion of these populations showed decay patterns similar to those of common markers, Enterococcus and HF183. Despite the apparent decay of these populations, the abundance of β-lactamase encoding genes remained high throughout incubation relative to the control. Lastly, we constructed genomic libraries representing several different fecal sources and outline a bioinformatic approach which leverages these libraries for identifying and apportioning contamination signal among multiple probable sources using shotgun metagenomic data.

Comparison of ultrafiltration and iron chloride flocculation in the preparation of aquatic viromes from contrasting sample types

Viral metagenomes (viromes) are a valuable untargeted tool for studying viral diversity and the central roles viruses play in host disease, ecology, and evolution. Establishing effective methods to concentrate and purify viral genomes prior to sequencing is essential for high quality viromes. Using virus spike-and-recovery experiments, we stepwise compared two common approaches for virus concentration, ultrafiltration and iron chloride flocculation, across diverse matrices: wastewater influent, wastewater secondary effluent, river water, and seawater. Viral DNA was purified by removing cellular DNA via chloroform cell lysis, filtration, and enzymatic degradation of extra-viral DNA. We found that viral genomes were concentrated 1-2 orders of magnitude more with ultrafiltration than iron chloride flocculation for all matrices and resulted in higher quality DNA suitable for amplification-free and long-read sequencing. Given its widespread use and utility as an inexpensive field method for virome sampling, we nonetheless sought to optimize iron flocculation. We found viruses were best concentrated in seawater with five-fold higher iron concentrations than the standard used, inhibition of DNase activity reduced purification effectiveness, and five-fold more iron was needed to flocculate viruses from freshwater than seawater—critical knowledge for those seeking to apply this broadly used method to freshwater virome samples. Overall, our results demonstrated that ultrafiltration and purification performed better than iron chloride flocculation and purification in the tested matrices. Given that the method performance depended on the solids content and salinity of the samples, we suggest spike-and-recovery experiments be applied when concentrating and purifying sample types that diverge from those tested here.

Genome-Resolved Metagenomics and Antibiotic Resistance Genes Analysis in Reclaimed Water Distribution Systems

Water reuse is increasingly pursued to alleviate global water scarcity. However, the wastewater treatment process does not achieve full removal of biological contaminants from wastewater, hence microorganisms and their genetic elements can be disseminated into the reclaimed water distribution systems (RWDS). In this study, reclaimed water samples are investigated via metagenomics to assess their bacterial diversity, metagenome-assembled genomes (MAGs) and antibiotic resistance genes (ARGs) at both point of entry (POE) and point of use (POU) in 3 RWDS. The number of shared bacterial orders identified by metagenome was higher at the POE than POU among the three sites, indicating that specific conditions in RWDS can cause further differentiation in the microbial communities at the end of the distribution system. Two bacterial orders, namely Rhizobiales and Sphingomonadales, had high replication rates in two of the examined RWDS (i.e., site A and B), and were present in higher relative abundance in POU than at POE. In addition, MAG and ARG relative abundance exhibited a strong correlation (R2 = 0.58) in POU, indicating that bacteria present in POU may have a high incidence of ARG. Specifically, resistance genes associated with efflux pump mechanisms (e.g., adeF and qacH) increased in its relative abundance from POU to POE at two of the RWDS (i.e., site A and B). When correlated with the water quality data that suggests a significantly lower dissolved organic carbon (DOC) concentration at site D than the other two RWDS, the metagenomic data suggest that low DOC is needed to maintain the biological stability of reclaimed water along the distribution network.