Mycobacterial species and their contribution to cholesterol degradation in wastewater treatment plants

Native and non-native freshwater bivalves in the bioremediation of bacterial pollution caused by the disposal of sewage

Treated sewage contains a large diversity of pathogens that can be transmitted to the environment and, directly or indirectly, infect humans through water use (i.e., consumption, bathing, or irrigation). In urban environments, wastewater normally flows into wastewater treatment plants (WWTPs), where it is subjected to different processes in order to eliminate the greatest amount of waste. However, there are inequalities among European countries concerning wastewater management. In this context, we evaluate the potential of freshwater mussels to improve water quality (i.e., reduce bacterial abundance) in rivers receiving primary, secondary, or tertiary sewage-treated effluents. Additionally, because freshwater mussels are declining at a global scale and empty niches are progressively occupied by non-native counterparts, we evaluate if depauperate communities and the Asian clams, Corbicula genus, can provide equivalent ecosystem services (i.e., water quality improvement by biofiltration) formerly provided by diverse native communities. For this, an analysis of the bacterial biodiversity of the samples filtered by the different bivalve communities was carried out. The experimental approach was performed by metabarcoding the 16S rRNA gene using Illumina technologies. According to the results obtained, secondary treatment processes were effective in reducing the bacterial diversity. Furthermore, the waters filtered by the bivalves presented a lower bacterial abundance for certain genera. Biofiltration differs, however, among species, with Corbicula reducing a large number of taxa much more efficiently than native freshwater mussels in both diverse and depauperated communities. These results are likely related to Corbicula being a generalist species in front of native mussels, which may be more selective. Considering it is not possible to eradicate Corbicula from European rivers, its filtering capacity should be considered when managing freshwater ecosystems.

Characterisation of microbial communities and quantification of antibiotic resistance genes in Italian wastewater treatment plants using 16S rRNA sequencing and digital PCR

The spread of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in humans, animals and environment is a growing threat to public health. Wastewater treatment plants (WWTPs) are crucial in mitigating the risk of environmental contamination by effectively removing contaminants before discharge. However, the persistence of ARB and ARGs even after treatment is a challenge for the management of water system. To comprehensively assess antimicrobial resistance dynamics, we conducted a one-year monitoring study in three WWTPs in central Italy, both influents and effluents. We used seasonal sampling to analyze microbial communities by 16S rRNA, as well as to determine the prevalence and behaviour of major ARGs (sul1, tetA, blaTEM, blaOXA-48, blaCTX-M-1 group, blaKPC) and the class 1 Integron (int1). Predominant genera included in order: Arcobacter, Acinetobacter, Flavobacterium, Pseudarcobacter, Bacteroides, Aeromonas, Trichococcus, Cloacibacterium, Pseudomonas and Streptococcus. A higher diversity of bacterial communities was observed in the effluents compared to the influents. Within these communities, we also identified bacteria that may be associated with antibiotic resistance and pose a significant threat to human health. The mean concentrations (in gene copies per liter, gc/L) of ARGs and int1 in untreated wastewater (absolute abundance) were as follows: sul1 (4.1 × 109), tetA (5.2 × 108), blaTEM (1.1 × 108), blaOXA-48 (2.1 × 107), blaCTX-M-1 group (1.1 × 107), blaKPC (9.4 × 105), and int1 (5.5 × 109). The mean values in treated effluents showed reductions ranging from one to three log. However, after normalizing to the 16S rRNA gene (relative abundance), it was observed that in 37.5 % (42/112) of measurements, the relative abundance of ARGs increased in effluents compared to influents. Furthermore, correlations were identified between ARGs and bacterial genera including priority pathogens. This study improves our understanding of the dynamics of ARGs and provides insights to develop more effective strategies to reduce their spread, protecting public health and preserving the future efficacy of antibiotics.

Sensitivity and consistency of long- and short-read metagenomics and epicPCR for the detection of antibiotic resistance genes and their bacterial hosts in wastewater

Wastewater surveillance is a powerful tool to assess the risks associated with antibiotic resistance in communities. One challenge is selecting which analytical tool to deploy to measure risk indicators, such as antibiotic resistance genes (ARGs) and their respective bacterial hosts. Although metagenomics is frequently used for analyzing ARGs, few studies have compared the performance of long-read and short-read metagenomics in identifying which bacteria harbor ARGs in wastewater. Furthermore, for ARG host detection, untargeted metagenomics has not been compared to targeted methods such as epicPCR. Here, we 1) evaluated long-read and short-read metagenomics as well as epicPCR for detecting ARG hosts in wastewater, and 2) investigated the host range of ARGs across the wastewater treatment plant (WWTP) to evaluate host proliferation. Results highlighted long-read revealed a wider range of ARG hosts compared to short-read metagenomics. Nonetheless, the ARG host range detected by long-read metagenomics only represented a subset of the hosts detected by epicPCR. The ARG-host linkages across the influent and effluent of the WWTP were characterized. Results showed the ARG-host phylum linkages were relatively consistent across the WWTP, whereas new ARG-host species linkages appeared in the WWTP effluent. The ARG-host linkages of several clinically relevant species found in the effluent were identified.

Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics

Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins. In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins. With the shortage of pollution source data in developing regions, this suggested approach offers an economical, quick, and accurate solution to locate the origins of water nitrogen pollution using the metagenomic data of microbial communities.

Relay run property of immobilized cholesterol oxidase on magnetic biochar for sterol removal

This study investigated the biocatalytic performance of immobilized cholesterol oxidase (CHOD) on magnetite-based carbon (MBC) for degrading cholesterol. The results showed that MBC-CHOD exhibited higher activity and good affinity towards substrate compared to free enzyme and other immobilized enzymes. Mass spectra analysis revealed that MBC-CHOD damaged the main structure of cholesterol, benefitting the further biological treatment. The study proposes a Fenton process mechanism by which H2O2 is transferred to free radicals such as ·OH under acidic conditions, promoting further substrate degradation. This suggests that MBC-CHOD has a relay run property leading to high degradation of cholesterol. Molecular docking indicates that cholesterol preferentially binds to TYR-28 residue and LYS-138 residue in CHOD through hydrogen bonds. Overall, MBC-CHOD proved to be a promising candidate for efficient and sustainable cholesterol degradation.

T, Tomioka N, Ebie Y, Syutsubo K. Quantitative detection and reduction of potentially pathogenic bacterial groups of Aeromonas, Arcobacter, Klebsiella pneumoniae species complex, and Mycobacterium in wastewater treatment facilities

Water quality parameters influence the abundance of pathogenic bacteria. The genera Aeromonas, Arcobacter, Klebsiella, and Mycobacterium are among the representative pathogenic bacteria identified in wastewater. However, information on the correlations between water quality and the abundance of these bacteria, as well as their reduction rate in existing wastewater treatment facilities (WTFs), is lacking. Hence, this study aimed to determine the abundance and reduction rates of these bacterial groups in WTFs. Sixty-eight samples (34 influent and 34 non-disinfected, treated, effluent samples) were collected from nine WTFs in Japan and Thailand. 16S rRNA gene amplicon sequencing analysis revealed the presence of Aeromonas, Arcobacter, and Mycobacterium in all influent wastewater and treated effluent samples. Quantitative real-time polymerase chain reaction (qPCR) was used to quantify the abundance of Aeromonas, Arcobacter, Klebsiella pneumoniae species complex (KpSC), and Mycobacterium. The geometric mean abundances of Aeromonas, Arcobacter, KpSC, and Mycobacterium in the influent wastewater were 1.2 × 104-2.4 × 105, 1.0 × 105-4.5 × 106, 3.6 × 102-4.3 × 104, and 6.9 × 103-5.5 × 104 cells mL-1, respectively, and their average log reduction values were 0.77-2.57, 1.00-3.06, 1.35-3.11, and -0.67-1.57, respectively. Spearman's rank correlation coefficients indicated significant positive or negative correlations between the abundances of the potentially pathogenic bacterial groups and Escherichia coli as well as water quality parameters, namely, chemical/biochemical oxygen demand, total nitrogen, nitrate-nitrogen, nitrite-nitrogen, ammonium-nitrogen, suspended solids, volatile suspended solids, and oxidation-reduction potential. This study provides valuable information on the development and appropriate management of WTFs to produce safe, hygienic water.

Wastewater surveillance beyond COVID-19: a ranking system for communicable disease testing in the tri-county Detroit area, Michigan, USA

Introduction

Throughout the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has been utilized to monitor the disease in the United States through routine national, statewide, and regional monitoring projects. A significant canon of evidence was produced showing that wastewater surveillance is a credible and effective tool for disease monitoring. Hence, the application of wastewater surveillance can extend beyond monitoring SARS-CoV-2 to encompass a diverse range of emerging diseases. This article proposed a ranking system for prioritizing reportable communicable diseases (CDs) in the Tri-County Detroit Area (TCDA), Michigan, for future wastewater surveillance applications at the Great Lakes Water Authority's Water Reclamation Plant (GLWA's WRP).

Methods

The comprehensive CD wastewater surveillance ranking system (CDWSRank) was developed based on 6 binary and 6 quantitative parameters. The final ranking scores of CDs were computed by summing the multiplication products of weighting factors for each parameter, and then were sorted based on decreasing priority. Disease incidence data from 2014 to 2021 were collected for the TCDA. Disease incidence trends in the TCDA were endowed with higher weights, prioritizing the TCDA over the state of Michigan.

Results

Disparities in incidences of CDs were identified between the TCDA and state of Michigan, indicating epidemiological differences. Among 96 ranked CDs, some top ranked CDs did not present relatively high incidences but were prioritized, suggesting that such CDs require significant attention by wastewater surveillance practitioners, despite their relatively low incidences in the geographic area of interest. Appropriate wastewater sample concentration methods are summarized for the application of wastewater surveillance as per viral, bacterial, parasitic, and fungal pathogens.

Discussion

The CDWSRank system is one of the first of its kind to provide an empirical approach to prioritize CDs for wastewater surveillance, specifically in geographies served by centralized wastewater collection in the area of interest. The CDWSRank system provides a methodological tool and critical information that can help public health officials and policymakers allocate resources. It can be used to prioritize disease surveillance efforts and ensure that public health interventions are targeted at the most potentially urgent threats. The CDWSRank system can be easily adopted to geographical locations beyond the TCDA.

Dioctyl phthalate enhances volatile fatty acids production from sludge anaerobic fermentation: Insights of electron transport and metabolic functions

As one of the most widely used phthalate plasticizers, dioctyl phthalate (DOP) has been detected in wastewater and accumulates in sludge through wastewater treatment, which may adversely affect further sludge treatment. However, the role of DOP on sludge anaerobic fermentation and its mechanism are not yet clear. Therefore, this study focused on the effect of DOP on the volatile fatty acids (VFAs) generation via the anaerobic fermentation of sludge. The results demonstrated that the presence of DOP had a considerable contribution to the generation of VFAs, and the maximum production of VFAs reached 4769 mg COD/L at 500 mg/kg DOP, which was 1.57 folds that of the control. Mechanistic investigation showed that DOP mainly enhanced the hydrolysis, acidification and related enzymes activities of sludge. VFAs-producing microorganisms (e.g., Clostridium and Conexibacter) were also enriched under DOP exposure. Importantly, the presence of DOP increased the electron transfer activity by 26 %, consequently facilitating the organics conversion and fermentation process. Notably, the functional gene expressions involved in substrate metabolism and VFAs biosynthesis were enhanced with DOP, resulting in increased VFAs production from sludge. The results obtained in this study offered a new strategy for the control of pollutants and the recycling of valuable products from sludge.

Molecular surveillance of tuberculosis-causing mycobacteria in wastewater

The surveillance of tuberculosis infections has largely depended on clinical diagnostics and hospitalization data. The advancement in molecular methods creates an opportunity for the adoption of alternative surveillance systems, such as wastewater-based epidemiology. This study presents the use of conventional and advanced polymerase chain reaction techniques (droplet digital PCR) to determine the occurrence and concentration of total mycobacteria and members of the Mycobacterium tuberculosis complex (MTBC) in treated and untreated wastewater. Wastewater samples were taken from three wastewater treatment plants (WWTPs) in the city of Durban, South Africa, known for a high burden of TB/MDR-TB due to HIV infections. All untreated wastewater samples contained total mycobacteria and MTBC at varying percentages per WWTP studied. Other members of the MTBC related to tuberculosis infection in animals, M. bovis and M. caprae were also detected. The highest median concentration detected in untreated wastewater was up to 4.9 (±0.2) Log10 copies/ml for total mycobacteria, 4.0 (±0.85) Log10 copies/ml for MTBC, 3.9 (±0.54) Log10 copies/ml for M. tuberculosis, 2.7 (±0.42) Log10 copies/ml for M. africanum, 4.0 (±0.29) Log10 copies/ml for M. bovis and 4.5 (±0.52) Log10 copies/ml for M. caprae. Lower concentrations were detected in the treated wastewater, with a statistically significant difference (P-value ≤ 0.05) in concentrations observed. The log reduction achieved for these bacteria in the respective WWTPs was not statistically different, indicating that the treatment configuration did not have an impact on their removal. The detection of M. africanum in wastewater from South Africa shows that it is possible that some of the TB infections in the community could be caused by this mycobacterium. This study, therefore, highlights the potential of wastewater-based epidemiology for monitoring tuberculosis infections.

Molecular Methods for Pathogenic Bacteria Detection and Recent Advances in Wastewater Analysis

With increasing concerns about public health and the development of molecular techniques, new detection tools and the combination of existing approaches have increased the abilities of pathogenic bacteria monitoring by exploring new biomarkers, increasing the sensitivity and accuracy of detection, quantification, and analyzing various genes such as functional genes and antimicrobial resistance genes (ARG). Molecular methods are gradually emerging as the most popular detection approach for pathogens, in addition to the conventional culture-based plate enumeration methods. The analysis of pathogens in wastewater and the back-estimation of infections in the community, also known as wastewater-based epidemiology (WBE), is an emerging methodology and has a great potential to supplement current surveillance systems for the monitoring of infectious diseases and the early warning of outbreaks. However, as a complex matrix, wastewater largely challenges the analytical performance of molecular methods. This review synthesized the literature of typical pathogenic bacteria in wastewater, types of biomarkers, molecular methods for bacterial analysis, and their recent advances in wastewater analysis. The advantages and limitation of these molecular methods were evaluated, and their prospects in WBE were discussed to provide insight for future development.

Investigation of the Prevalence of Antibiotic Resistance Genes According to the Wastewater Treatment Scale Using Metagenomic Analysis

Although extensive efforts have been made to investigate the dynamics of the occurrence and abundance of antibiotic resistance genes (ARGs) in wastewater treatment plants (WWTPs), understanding the acquisition of antibiotic resistance based on the WWTP scale and the potential effects on WWTPs is of relatively less interest. In this study, metagenomic analysis was carried out to investigate whether the WWTP scale could be affected by the prevalence and persistence of ARGs and mobile genetic elements (MGEs). As a result, 152 ARG subtypes were identified in small-scale WWTP samples, while 234 ARG subtypes were identified in large-scale WWTP samples. Among the detectable ARGs, multidrug, MLS (macrolide–lincosamide–streptogramin), sulfonamide, and tetracycline resistance genes had the highest abundance, and large and small WWTPs had similar composition characteristics of ARGs. In MGE analysis, plasmids and integrons were 1.5–2.0-fold more abundant in large-scale WWTPs than in small-scale WWTPs. The profile of bacteria at the phylum level showed that Proteobacteria and Actinobacteria were the most dominant bacteria, representing approximately 70% across large- and small-scale WWTPs. Overall, the results of this study elucidate the different abundances and dissemination of ARGs between large- and small-scale WWTPs, which facilitates the development of next-generation engineered wastewater treatment systems.

Viability of a Single-Stage Unsaturated-Saturated Granular Activated Carbon Biofilter for Greywater Treatment

Compared with conventionally collected sewage, source-diverted greywater has a higher potential for on-site treatment and reuse due to its lower contaminant levels and large volume. A new design of granular activated carbon (GAC) biofilters was developed by incorporating unsaturated and saturated zones in a single stage to introduce an efficient, passive, and easy-to-operate technology for greywater on-site treatment at the household scale. The design was customized for its intended application considering various aspects including the reactor’s configuration, packing media, and feeding strategy. With the highest hydraulic and organic loadings of 1.2 m3 m−2 d−1 and 3.5 kg COD m−2 d−1, respectively, and the shortest retention time of 2.4 h, the system maintained an average total chemical oxygen demand removal rate of 94% with almost complete removal of nutrients throughout its 253 days of operation. The system showed a range of reduction efficacy towards five surrogates representing viruses, bacteria, and Cryptosporidium and Giardia (oo)cysts. A well-functioning biofilm was successfully developed, and its mass and activity increased over time with the highest values observed at the top layers. The key microbes within the biofilter were revealed. Feasibility of the proposed technology was investigated, and implications for design and operation were discussed.

Non-Tuberculous Mycobacteria: Molecular and Physiological Bases of Virulence and Adaptation to Ecological Niches

Non-tuberculous mycobacteria (NTM) are paradigmatic colonizers of the total environment, circulating at the interfaces of the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. Their striking adaptive ecology on the interconnection of multiple spheres results from the combination of several biological features related to their exclusive hydrophobic and lipid-rich impermeable cell wall, transcriptional regulation signatures, biofilm phenotype, and symbiosis with protozoa. This unique blend of traits is reviewed in this work, with highlights to the prodigious plasticity and persistence hallmarks of NTM in a wide diversity of environments, from extreme natural milieus to microniches in the human body. Knowledge on the taxonomy, evolution, and functional diversity of NTM is updated, as well as the molecular and physiological bases for environmental adaptation, tolerance to xenobiotics, and infection biology in the human and non-human host. The complex interplay between individual, species-specific and ecological niche traits contributing to NTM resilience across ecosystems are also explored. This work hinges current understandings of NTM, approaching their biology and heterogeneity from several angles and reinforcing the complexity of these microorganisms often associated with a multiplicity of diseases, including pulmonary, soft-tissue, or milliary. In addition to emphasizing the cornerstones of knowledge involving these bacteria, we identify research gaps that need to be addressed, stressing out the need for decision-makers to recognize NTM infection as a public health issue that has to be tackled, especially when considering an increasingly susceptible elderly and immunocompromised population in developed countries, as well as in low- or middle-income countries, where NTM infections are still highly misdiagnosed and neglected.

Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes

BACKGROUND

Microorganisms in activated sludge (AS) play key roles in the wastewater treatment processes. However, their ecological behaviors and differences from microorganisms in other environments have mainly been studied using the 16S rRNA gene that may not truly represent in situ functions.

RESULTS

Here, we present 2045 archaeal and bacterial metagenome-assembled genomes (MAGs) recovered from 1.35 Tb of metagenomic data generated from 114 AS samples of 23 full-scale wastewater treatment plants (WWTPs). We found that the AS MAGs have obvious plant-specific features and that few proteins are shared by different WWTPs, especially for WWTPs located in geographically distant areas. Further, we developed a novel machine learning approach that can distinguish between AS MAGs and MAGs from other environments based on the clusters of orthologous groups of proteins with an accuracy of 96%. With the aid of machine learning, we also identified some functional features (e.g., functions related to aerobic metabolism, nutrient sensing/acquisition, and biofilm formation) that are likely vital for AS bacteria to adapt themselves in wastewater treatment bioreactors.

CONCLUSIONS

Our work reveals that, although the bacterial species in different municipal WWTPs could be different, they may have similar deterministic functional features that allow them to adapt to the AS systems. Also, we provide valuable genome resources and a novel approach for future investigation and better understanding of the microbiome of AS and other ecosystems. Video Abtract.

Bacterial community rather than metals shaping metal resistance genes in water, sediment and biofilm in lakes from arid northwestern China

Lakes in arid northwestern China are valuable freshwater resources that drive socioeconomic development. Environmental pollution can significantly influence the composition of microbial communities and the distribution of functional genes in lakes. This study investigated heavy metal pollution to identify possible correlations with metal resistance genes (MRGs) and bacterial community composition in water, sediment and biofilm samples from Bosten Lake and Ebi Lake in northwestern China. High levels of zinc were detected in all samples. However, the metals detected in the sediment samples of both lakes were determined to be at low risk levels according to an ecological index. The mercury resistance gene subtype merP had the greatest average abundance (4.61 × 10^-3 copies per 16S rRNA) among all the samples, followed by merA and merC. The high abundance of merA in the pelagic zone rather than in benthic sediment suggests that the pelagic microbial community was important in mercury reduction. Proteobacteria were the main phylum found in the microbial communities in all samples. However, microbial communities in most of the water, sediment and biofilm samples had different compositions, indicating that the habitat niche plays an important role in shaping the bacterial communities in lakes. The microbial community, rather than the heavy metals, was the main driver of MRG distribution. The abundances of some bacterial genera involved in the decomposition of organic matter and the terrestrial nitrogen cycle were negatively correlated with heavy metals. This result suggests that metal pollution can adversely affect the biogeochemical processes that occur in lakes.

Comprehensive Comparative Analysis of Cholesterol Catabolic Genes/Proteins in Mycobacterial Species

In dealing with Mycobacterium tuberculosis, the causative agent of the deadliest human disease—tuberculosis (TB)—utilization of cholesterol as a carbon source indicates the possibility of using cholesterol catabolic genes/proteins as novel drug targets. However, studies on cholesterol catabolism in mycobacterial species are scarce, and the number of mycobacterial species utilizing cholesterol as a carbon source is unknown. The availability of a large number of mycobacterial species’ genomic data affords an opportunity to explore and predict mycobacterial species’ ability to utilize cholesterol employing in silico methods. In this study, comprehensive comparative analysis of cholesterol catabolic genes/proteins in 93 mycobacterial species was achieved by deducing a comprehensive cholesterol catabolic pathway, developing a software tool for extracting homologous protein data and using protein structure and functional data. Based on the presence of cholesterol catabolic homologous proteins proven or predicted to be either essential or specifically required for the growth of M. tuberculosis H37Rv on cholesterol, we predict that among 93 mycobacterial species, 51 species will be able to utilize cholesterol as a carbon source. This study’s predictions need further experimental validation and the results should be taken as a source of information on cholesterol catabolism and genes/proteins involved in this process among mycobacterial species.