High-throughput sequencing as a tool for monitoring prokaryote communities in a wastewater treatment plant

Graph-based deep learning for predictions on changes in microbiomes and biogas production in anaerobic digestion systems

Anaerobic digestion (AD), which relies on a complex microbial consortium for efficient biogas generation, is a promising avenue for renewable energy production and organic waste treatment. However, understanding and optimising AD processes are challenging because of the intricate interactions within microbial communities and the impact of volatile fatty acids (VFAs) on biogas production. To address these challenges, this study proposes the application of graph convolutional networks (GCNs) to comprehensively model AD processes. GCN models were developed to predict microbial dynamics and biogas production by integrating network analyses of high-throughput sequencing data and VFA inhibition effects. The models were trained based on the responses of anaerobic digesters to organic loading rate shock, starvation, and bioaugmentation for 281 d under various feeding conditions. Shifts in microbial community composition during AD stages and feeding conditions were successfully identified using next-generation sequencing tools. Graph topological features indicated a significant coupling between VFAs and microbial families, and the hydrogenotrophic archaeal families were most frequently connected to other families or residual acids. The GCN accurately predicted microbial abundances and gas production rates, achieving a mean squared error of 0.11 and 0.01 and a coefficient of determination of 0.72 and 0.87 for the testing dataset. These results provide valuable insights into the effects of starvation and bioaugmentation on the microbiome by utilising GCNs to model anaerobic treatment processes, predict microbial dynamics, and assess reactor productivity. Our study suggests a new modelling framework for understanding and improving AD systems by considering microbial interaction networks in relation to chemical parameter information at relevant operating scales.

Bioremediation of trichloroethylene-contaminated groundwater using green carbon-releasing substrate with pH control capability

In this research, a sustainable substrate, termed green and long-lasting substrate (GLS), featuring a blend of emulsified substrate (ES) and modified rice husk ash (m-RHA) was devised. The primary objective was to facilitate the bioremediation of groundwater contaminated with trichloroethylene (TCE) using innovative GLS for slow carbon release and pH control. The GLS was concocted by homogenizing a mixture of soybean oil, surfactants (Simple Green™ and soya lecithin), and m-RHA, ensuring a gradual release of carbon sources. The hydrothermal synthesis was applied for the production of m-RHA production. The analyses demonstrate that m-RHA were uniform sphere-shape granules with diameters in micro-scale ranges. Results from the microcosm study show that approximately 83% of TCE could be removed (initial TCE concentration = 7.6 mg/L) with GLS supplement after 60 days of operation. Compared to other substrates without RHA addition, higher TCE removal efficiency was obtained, and higher Dehalococcoides sp. (DHC) population and hydA gene (hydrogen-producing gene) copy number were also detected in microcosms with GLS addition. Higher hydrogen concentrations enhanced the DHC growth, which corresponded to the increased DHC populations. The addition of the GLS could provide alkalinity at the initial stage to neutralize the acidified groundwater caused by the produced organic acids after substrate biodegradation, which was advantageous to DHC growth and TCE dechlorination. The addition of m-RHA reached an increased TCE removal efficiency, which was due to the fact that the m-RHA had the zeolite-like structure with a higher surface area and lower granular diameter, and thus, it resulted in a more effective initial adsorption effect. Therefore, a significant amount of TCE could be adsorbed onto the surface of m-RHA, which caused a rapid TCE removal through adsorption. The carbon substrates released from m-RHA could then enhance the subsequent dechlorination. The developed GLS is an environmentally-friendly and green substrate.

Propidium monoazide - polymerase chain reaction reveals viable microbial community shifts in anaerobic membrane bioreactors treating domestic sewage at low temperature

An anaerobic membrane bioreactor (AnMBR) treated domestic sewage at 15 °C under different hydraulic retention time (HRT) conditions (6, 12, 16, and 24 h). Propidium monoazide (PMA)-PCR excluded microorganisms without intact cell membranes, focusing on the viable microbial community in anaerobic digestion. The results showed that the 6-hour HRT had poor treatment performance: low chemical oxygen demand removal efficiency (below 80%) and high mean trans-membrane pressure and flux (15 kPa and 9.4 L/(m2 h)). Comparatively, PMA-PCR combined with next-generation sequencing improved the identification of microbial changes compared to conventional 16S rRNA gene sequencing. HRT influenced microorganisms in the hydrolysis and acid-production stages, including carbohydrate-degrading bacteria such as Bifidobacterium and Prevotella 1. Remarkably, a comparison with an AnMBR at 25 °C showed Proteobacteria to be the main cause of membrane fouling in the low-temperature AnMBR, with most operational taxonomic units negatively correlated with HRT and solids retention time.

Chlorine disinfection modifies the microbiome, resistome and mobilome of hospital wastewater - A nanopore long-read metagenomic approach

The aim of the present study was to analyze changes in the microbiome, resistome, and mobilome of hospital wastewater (HWW) induced by disinfection with chlorine compounds. Changes in bacterial communities and specific antibiotic resistance genes (ARGs) in HWW were determined with the use of a nanopore long-read metagenomic approach. The main hosts of ARGs in HWW were identified, and the mobility of resistance mechanisms was analyzed. Special attention was paid to the prevalence of critical-priority pathogens in the HWW microbiome, which pose the greatest threat to human health. The results of this study indicate that chlorine disinfection of HWW can induce significant changes in the structure of the total bacterial population and antibiotic resistant bacteria (ARB) communities, and that it can modify the resistome and mobilome of HWW. Disinfection favored the selection of ARGs, decreased their prevalence in HWW, while increasing their diversity. The mobility of the HWW resistome increased after disinfection. Disinfection led to the emergence of new drug resistance mechanisms in previously sensitive bacterial taxa. In conclusion, this study demonstrated that HWW disinfected with low (sublethal) concentrations of free chlorine significantly contributes to the mobility and transfer of drug resistance mechanisms (including critical mechanisms) between bacteria (including pathogens).