Effects of biochar addition on the fate of ciprofloxacin and its associated antibiotic tolerance in an activated sludge microbiome

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.

Side effects of the addition of an adsorbent for the nitrification performance of a microbiome in the treatment of an antibiotic mixture

This work determined the effect of biochar (BC) as an adsorbent on the nitrifying microbiome in regulating the removal, transformation, fate, toxicity, and potential environmental consequences of an antibiotic mixture containing oxytetracycline (OTC) and sulfamethoxazole (SMX). Despite the beneficial role of BC as reported in the literature, the present study revealed side effects for the nitrifying microbiome and its functioning arising from the presence of BC. Long-term monitoring revealed severe disruption to nitratation via the inhibition of both nitrite oxidizers (e.g., Nitrospira defluvii) and potential comammox species (e.g., Ca. Nitrospira nitrificans). Byproducts (BPs) more toxic than the parent compounds were found to persist at a high relative abundance, particularly in the presence of BC. Quantitative structure-activity relationship modeling determined that the physicochemical properties of the toxic BPs significantly differed from those of OTC and SMX. The results suggested that the BPs tended to mobilize and accumulate on the surface of the solids in the system (i.e., the BC and biofilm), disrupting the nitrifiers growing at the interface. Collectively, this study provides novel insights, demonstrating that the addition of adsorbents to biological systems may not necessarily be beneficial; rather, they may generate side effects for specific bacteria that have important ecosystem functions.

Environmental consequences of transformation products from an antibiotic mixture and their mitigation in a wastewater microbiome using an HCl-modified adsorbent

This study enhanced our understanding of antibiotic mixtures' occurrence, transformation, toxicity, and ecological risks. The role of acid-modified biochar (BC) in treating antibiotic residues was explored, shedding light on how BC influences the fate, mobility, and environmental impact of antibiotics and transformation products (TPs) in an activated sludge (AS) microbiome. A mixture of oxytetracycline and sulfamethoxazole was found to synergistically (or additively) inhibit cell growth of AS and disrupt the microbiome structure, species richness/diversity, and function. The formation of TPs with potentially higher toxicity and persistence than the original compounds was identified, explaining the microbiome disruption. Agricultural waste-derived BC was optimized for contaminant adsorption, leading to a reduction in toxicity when added to AS by sequestering TPs on its surface. This work highlighted adsorbents as a practical engineering strategy for mitigating liquid-phase contaminants' toxicological consequences, proactively controlling the fate and effects of antibiotics and TPs.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

Activated sludge microbiome with H2O2-modified biochar enhances the treatment resilience and detoxification of oxytetracycline and its toxic byproducts

The widespread presence of oxytetracycline (OTC) in aquatic ecosystems poses both health risks and ecological concerns. The present study revealed the beneficial role of hydrogen peroxide (H2O2)-pretreated biochar (BC) derived from agricultural hardwood waste in an activated sludge (AS) bioprocess. The BC addition significantly enhanced the removal and detoxification of OTC and its byproducts. BC was initially modified using H2O2 to improve its OTC adsorption. Two AS reactors were then established, one with H2O2-modified BC and one without, and both were exposed to OTC. The BC-added reactor exhibited significantly higher OTC removal rates during both the start-up (0.97 d-1) and steady-state (0.98 d-1) phases than the reactor without BC (0.54 d-1 and 0.83 d-1, respectively). Two novel transformation pathways for OTC were proposed, with four byproducts originating from OTC identified, some of which were found to be more toxic than OTC itself. The BC-added reactor had significantly higher system functioning in terms of its heterotrophic activity and the reduction of the toxicity of OTC and its byproducts, as illustrated by structure-based toxicity simulations, antimicrobial susceptibility experiments, analytical chemistry, and bioinformatics analysis. Bioinformatics revealed two novel bacterial populations closely related to the known OTC-degrader Pandoraea. The ecophysiology and selective enrichment of these populations suggested their role in the enzymatic breakdown and detoxification of OTC (e.g., via demethylation and hydrogenation). Overall, the present study highlighted the beneficial role of H2O2-modified BC in combination with the AS microbiome in terms of enhancing treatment performance and resilience, reducing the toxicological disruption to biodiversity, and detoxifying micropollutants.

Unveiling the drives behind tetracycline adsorption capacity with biochar through machine learning

This study aimed to develop a robust predictive model for tetracycline (TC) adsorption onto biochar (BC) by employing machine learning techniques to investigate the underlying driving factors. Four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN), were used to model the adsorption of TC on BC using the data from 295 adsorption experiments. The analysis revealed that the RF model had the highest predictive accuracy (R² = 0.9625) compared to ANN (R² = 0.9410), GBDT (R² = 0.9152), and XGBoost (R² = 0.9592) models. This study revealed that BC with a specific surface area (S (BET)) exceeding 380 cm³·g^-1 and particle sizes ranging between 2.5 and 14.0 nm displayed the greatest efficiency in TC adsorption. The TC-to-BC ratio was identified as the most influential factor affecting adsorption efficiency, with a weight of 0.595. The concentration gradient between the adsorbate and adsorbent was demonstrated to be the principal driving force behind TC adsorption by BC. A predictive model was successfully developed to estimate the sorption performance of various types of BC for TC based on their properties, thereby facilitating the selection of appropriate BC for TC wastewater treatment.

Effect of antibiotic cocktail exposure on functional disturbance of nitrifying microbiome

The present study quantitatively determined the degree and type of functional disturbance in the nitrifying microbiome caused by exposure to a single oxytetracycline (OTC) and a two-antibiotic mixture containing OTC and sulfamethoxazole (SMX). While the single antibiotic had a pulsed disturbance on nitritation that was recoverable within three weeks, the antibiotic mixture caused a more significant pulsed disturbance on nitritation and a potential press disturbance on nitratation that was not recoverable for over five months. Bioinformatic analysis revealed significant perturbations for both canonical nitrite-oxidizing (Nitrospira defluvii) and potential complete ammonium-oxidizing (Ca. Nitrospira nitrificans) populations that were strongly associated with the press perturbation on nitratation. In addition to this functional disturbance, the antibiotic mixture reduced the biosorption of OTC and altered its biotransformation pathways, resulting in different transformation products compared with those produced when OTC was treated as a single antibiotic. Collectively, this work elucidated how the antibiotic mixture can affect the degree, type, and duration of the functional disturbance on nitrifying microbiome and offer new insights into the environmental consequences of antibiotic residues (e.g., their fate, transformation, and ecotoxicity) when present as an antibiotic mixture rather than single antibiotics.

Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant

Membrane bioreactor (MBR) systems are one of the most widely used wastewater treatment processes for various municipal and industrial waste streams. The present study aimed to advance the understanding of ecologically important keystone taxa that play an important role in full-scale MBR systems. A machine-learning (ML) modeling framework based on microbiome data was developed to successfully predict, with an average accuracy of >91.6%, the operational characteristics of three representative full-scale wastewater systems: an MBR, a conventional activated sludge system, and a sequencing batch reactor. ML-based feature-importance analysis identified Ferruginibacter as a keystone organism in the MBR system. The phylogeny and known ecophysiology of members of Ferruginibacter supported their role in metabolizing complex organic polymers (e.g., extracellular polymeric substances) in MBR systems characterized by high concentrations of mixed liquor suspended solids and a high solid retention time. ML regression modeling also revealed temporal patterns of Ferruginibacter in response to water temperature. ML modeling was thus successfully employed in the present study to investigate complex/non-linear relationships between keystone taxa and environmental conditions that cannot be detected using conventional approaches. Overall, our microbiome-data-enabled ML modeling approach represents a methodological advance for identifying keystone taxa and their complex ecological interactions, which has implications for the sustainable and predictive management of MBR systems.