Machine learning predicts the impact of antibiotic properties on the composition and functioning of bacterial community in aquatic habitats

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.

Machine learning reveals the selection pressure exerted by nonantibiotic pharmaceuticals at environmentally relevant concentrations on antibiotic resistance genotypes

The emergence and increasing prevalence of antibiotic resistance pose a global public risk for human health, and nonantimicrobial pharmaceuticals play an important role in this process. Herein, five nonantimicrobial pharmaceuticals, including acetaminophen (ACT), clofibric acid (CA), carbamazepine (CBZ), caffeine (CF) and nicotine (NCT), tetracycline-resistant strains, five ARGs (sul1, sul2, tetG, tetM and tetW) and one integrase gene (intI1), were detected in 101 wastewater samples during two typical sewage treatment processes including anaerobic-oxic (A/O) and biological aerated filter (BAF) in Harbin, China. The impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on both the resistance genotypes and resistance phenotypes were explored. The results showed that a significant impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on tetracycline resistance genes encoding ribosomal protection proteins (RPPs) was found, while no changes in antibiotic phenotypes, such as minimal inhibitory concentrations (MICs), were observed. Machine learning was applied to further sort out the contribution of nonantibiotic pharmaceuticals at environmentally relevant concentrations to different ARG subtypes. The highest contribution and correlation were found at concentrations of 1400-1800 ng/L for NCT, 900-1500 ng/L for ACT and 7000-10,000 ng/L for CF for tetracycline resistance genes encoding RPPs, while no significant correlation was found between the target compounds and ARGs when their concentrations were lower than 500 ng/L for NCT, 100 ng/L for ACT and 1000 ng/L for CF, which were higher than the concentrations detected in effluent samples. Therefore, the removal of nonantibiotic pharmaceuticals in WWTPs can reduce their selection pressure for resistance genes in wastewater.

Machine learning modeling for the prediction of phosphorus and nitrogen removal efficiency and screening of crucial microorganisms in wastewater treatment plants

The effectiveness of wastewater treatment plants (WWTPs) is largely determined by the microbial community structure in their activated sludge (AS). Interactions among microbial communities in AS systems and their indirect effects on water quality changes are crucial for WWTP performance. However, there is currently no quantitative method to evaluate the contribution of microorganisms to the operating efficiency of WWTPs. Traditional assessments of WWTP performance are limited by experimental conditions, methods, and other factors, resulting in increased costs and experimental pollutants. Therefore, an effective method is needed to predict WWTP efficiency based on AS community structure and quantitatively evaluate the contribution of microorganisms in the AS system. This study evaluated and compared microbial communities and water quality changes from WWTPs worldwide by meta-analysis of published high-throughput sequencing data. Six machine learning (ML) models were utilized to predict the efficiency of phosphorus and nitrogen removal in WWTPs; among them, XGBoost showed the highest prediction accuracy. Cross-entropy was used to screen the crucial microorganisms related to phosphorus and nitrogen removal efficiency, and the modeling confirmed the reasonableness of the results. Thirteen genera with nitrogen and phosphorus cycling pathways obtained from the screening were considered highly appropriate for the simultaneous removal of phosphorus and nitrogen. The results showed that the microbes Haliangium, Vicinamibacteraceae, Tolumonas, and SWB02 are potentially crucial for phosphorus and nitrogen removal, as they may be involved in the process of phosphorus and nitrogen removal in sewage treatment plants. Overall, these findings have deepened our understanding of the relationship between microbial community structure and performance of WWTPs, indicating that microbial data should play a critical role in the future design of sewage treatment plants. The ML model of this study can efficiently screen crucial microbes associated with WWTP system performance, and it is promising for the discovery of potential microbial metabolic pathways.

Long-distance responses of ginger to soil sulfamethoxazole and chromium: Growth, co-occurrence with antibiotic resistance genes, and consumption risk

The coexistence of antibiotics and heavy metals in agroecosystems is nonnegligible, which permits the promotion of antibiotic resistance genes (ARGs) in crops, thus posing a potential threat to humans along the food chain. In this study, we investigated the bottom-up (rhizosphere→rhizome→root→leaf) long-distance responses and bio-enrichment characteristics of ginger to different sulfamethoxazole (SMX) and chromium (Cr) contamination patterns. The results showed that ginger root systems adapted to SMX- and/or Cr-stress by increasing humic-like exudates, which may help to maintain the rhizosphere indigenous bacterial phyla (i.e., Proteobacteria, Chloroflexi, Acidobacteria and Actinobacteria). The root activity, leaf photosynthesis and fluorescence, and antioxidant enzymes (SOD, POD, CAT) of ginger were significantly decreased under high-dose Cr and SMX co-contamination, while a "hormesis effect" was observed under single low-dose SMX contamination. For example, CS100 (co-contamination of 100 mg/L SMX and 100 mg/L Cr) caused the most severe inhibition to leaf photosynthetic function by reducing photochemical efficiency (reflected on PAR-ETR, φPSII and qP). Meanwhile, CS100 induced the highest ROS production, in which H2O2 and O2·- increased by 328.82% and 238.00% compared with CK (the blank control without contamination). Moreover, co-selective stress by Cr and SMX induced the increase of ARG bacterial hosts and bacterial phenotypes containing mobile elements, contributing to the high detected abundance of target ARGs (sul1, sul2) up to 10-2∼10-1 copies/16S rRNA in rhizomes intended for consumption.

Impact of antibiotics on microbial community in aquatic environment and biodegradation mechanism: a review and bibliometric analysis

Antibiotic residues in aquatic environments pose a potential hazard, and microbes, which play important roles in aquatic ecosystems, are vulnerable to the impacts of antibiotics. This study aimed to analyze the research progress, trends, and hot topics of the impact of antibiotics on microbial community and biodegradation mechanism using bibliometric analysis. An in-depth analysis of the publication characteristics of 6143 articles published between 1990 and 2021 revealed that the number of articles published increased exponentially. The research sites have been mainly concentrated in the Yamuna River, Pearl River, Lake Taihu, Lake Michigan, Danjiangkou Reservoir, etc., illustrating that research around the world is not even. Antibiotics could change the diversity, structure, and ecological functions of bacterial communities, stimulate a widespread abundance of antibiotic-resistant bacteria and antibiotic-resistant genes, and increase the diversity of eukaryotes, thus triggering the shift of food web structure to predatory and pathogenic. Latent Dirichlet allocation theme model analysis showed three clusters, and the research hotspots mainly included the effect of antibiotics on the denitrification process, microplastics combined with antibiotics, and methods for removing antibiotics. Furthermore, the mechanisms of microbe-mediated antibiotic degradation were unraveled, and importantly, we provided bottlenecks and future research perspectives on antibiotics and microbial diversity research.

Geographic patterns of microbial traits of river basins in China

River microbiotas contribute to critical geochemical processes and ecological functions of rivers but are sensitive to variations of environmental drivers. Understanding the geographic pattern of river microbial traits in biogeochemical processes can provide important insights into river health. Many studies have characterized river microbial traits in specific situations, but the geographic patterns of these traits and environmental drivers at a large scale are unknown. We reanalyzed 4505 raw 16S rRNA sequences samples for microbiota from river basins in China. The results indicated differences in the diversity, composition, and structure of microbiotas across diverse river basins. Microbial diversity and functional potential in the river basins decreased over time in northern China and increased in southern China due to niche differentiation, e.g., the Yangtze River basin was the healthiest ecosystem. River microbiotas were mainly involved in the cycling of carbon and nitrogen in the river ecosystems and participated in potential organic metabolic functions. Anthropogenic pollutants discharge was the most critical environmental driver for the microbial traits, e.g., antibiotic discharge, followed by climate change. The prediction by machine-learning models indicated that the continuous discharge of antibiotics and climate change led to high ecological risks for the rivers. Our study provides guidelines for improving the health of river ecosystems and for the formulation of strategies to restore the rivers.

Shaping effects of rice, wheat, maize, and soybean seedlings on their rhizosphere microbial community

The rhizosphere microbiome plays critical roles in plant growth and is an important interface for resource exchange between plants and the soil environment. Crops at various growing stages, especially the seedling stage, have strong shaping effects on the rhizosphere microbial community, and such community reconstruction will positively feed back to the plant growth. In the present study, we analyzed the variations of bacterial and fungal communities in the rhizosphere of four crop species: rice, soybean, maize, and wheat during successive cultivations (three repeats for the seedling stages) using 16S rRNA gene and internal transcribed spacer (ITS) high-throughput sequencing. We found that the relative abundances of specific microorganisms decreased after different cultivation times, e.g., Sphingomonas, Pseudomonas, Rhodanobacter, and Caulobacter, which have been reported as plant-growth beneficial bacteria. The relative abundances of potential plant pathogenic fungi Myrothecium and Ascochyta increased with the successive cultivation times. The co-occurrence network analysis showed that the bacterial and fungal communities under maize were much more stable than those under rice, soybean, and wheat. The present study explored the characteristics of bacteria and fungi in crop seedling rhizosphere and indicated that the characteristics of indigenous soil flora might determine the plant growth status. Further study will focus on the use of the critical microorganisms to control the growth and yield of specific crops.

Effects of erythromycin and roxithromycin on river periphyton: Structure, functions and metabolic pathways

Macrolides have been frequently detected in the surface waters worldwide, posing a threat to the aquatic microbes. Several studies have evaluated the ecotoxicological effects of macrolides on single algal and bacterial strains. However, without considering the species interaction in the aquatic microbial community, these results cannot be extrapolated to the field. Thus, the present study aimed to evaluate the effects of two macrolides (erythromycin and roxithromycin) on the structure, photosynthetic process, carbon utilization capacity, and the antibiotic metabolic pathways in river periphyton. The colonized periphyton was exposed to the graded concentration (0 μg/L (control), 0.5 μg/L (low), 5 μg/L (medium), 50 μg/L (high)) of ERY and ROX, respectively, for 7 days. Herein, high levels of ERY and ROX altered the community composition by reducing the relative abundance of Chlorophyta in the eukaryotic community. Also, the Shannon and Simpson diversity indexes of prokaryotes were reduced, although similar effects were seldomly detected in the low and medium groups. In contrast to the unchanged carbon utilization capacity, the PSII reaction center involved in the periphytic photosynthesis was significantly inhibited by macrolides at high levels. In addition, both antibiotics had been degraded by periphyton, with the removal rate of 51.63-66.87% and 41.85-48.27% for ERY and ROX, respectively, wherein the side chain and ring cleavage were the main degradation pathways. Overall, this study provides an insight into the structural and functional toxicity and degradation processes of macrolides in river periphyton.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Response of microbial antibiotic resistance to pesticides: An emerging health threat

The spread of microbial antibiotic resistance has seriously threatened public health globally. Non-antibiotic stressors have significantly contributed to the evolution of bacterial antibiotic resistance. Although numerous studies have been conducted on the potential risk of pesticide pollution for bacterial antibiotic resistance, a systematic review of these concerns is still lacking. In the present study, we elaborate the mechanism underlying the effects of pesticides on bacterial antibiotic resistance acquisition as well as the propagation of antimicrobial resistance. Pesticide stress enhanced the acquisition of antibiotic resistance in bacteria via various mechanisms, including the activation of efflux pumps, inhibition of outer membrane pores for resistance to antibiotics, and gene mutation induction. Horizontal gene transfer is a major mechanism whereby pesticides influence the transmission of antibiotic resistance genes (ARGs) in bacteria. Pesticides promoted the conjugation transfer of ARGs by increasing cell membrane permeability and increased the proportion of bacterial mobile gene elements, which facilitate the spread of ARGs. This review can improve our understanding regarding the pesticide-induced generation and spread of ARGs and antibiotic resistant bacteria. Moreover, it can be applied to reduce the ecological risks of ARGs in the future.