A multi-omics study on quantifying antimicrobial resistance in European freshwater lakes

Microbial, holobiont, and Tree of Life eDNA/eRNA for enhanced ecological assessment

Microbial environmental DNA and RNA (collectively 'eNA') originate from a diverse and abundant array of microbes present in environmental samples. These eNA signals, largely representing whole organisms, serve as a powerful complement to signals derived from fragments or remnants of larger organisms. Integrating microbial data into the toolbox of ecosystem assessments and biotic indices therefore has the potential to transform how we use eNA data to understand biodiversity dynamics and ecosystem functions, and to inform the next generation of environmental monitoring. Incorporating holobiont and Tree of Life approaches into eNA analyses offers further holistic insight into the range of ecological interactions between microbes and other organisms, paving the way for advancing our understanding of, and ultimately manipulating ecosystem properties pertinent to environmental management, conservation, wildlife health, and food production.

Urban wastewater disinfection by iron chelates mediated solar photo-Fenton: Effects on seven pathogens and antibiotic resistance transfer potential

The effects of solar photo-Fenton (SPF) process mediated by the iron chelate Fe3+ imminodisuccinic acid (Fe:IDS) on both the inactivation of seven relevant pathogens and the potential for antibiotic resistance transfer (degradation of antibiotic resistance genes (ARGs) and after treatment regrowth), in real secondary treated urban wastewater, were investigated for the first time. A comparison with results obtained by sunlight/H2O2 process and Fe3+ ethylenediaminedisuccinic acid (Fe:EDDS) SPF was also carried out. ARGs were quantified by polymerase chain reaction (PCR) in samples before and after (3 h) the treatment. The persistence of the selected pathogens and ARGs was also evaluated in regrowth tests (72 h) under environmentally mimicking conditions. Fe:IDS SPF resulted to be more effective (from 1.4 log removal for Staphylococcus spp. to 4.3 log removal for Escherichia coli) than Fe:EDDS SPF (from 0.8 log removal for Pseudomonas aeruginosa to 2.0 log removal for Total coliphages) and sunlight/H2O2 (from 1.2 log removal for Clostridium perfringens to 3.3 log removal for E. coli) processes for the seven pathogens investigated. Potential pathogens regrowth was also severely affected, as no substantial regrowth was observed, both in presence and absence of catalase. A similar trend was observed for ARGs removal too (until 0.001 fold change expression for qnrS after 3 h). However, a poor effect and a slight increase in fold change was observed after treatment especially for gyrA, mefA and intl1. Overall, the effect of the investigated processes on ARGs was found to be ARG dependent. Noteworthy, coliphages can regrow after sunlight/H2O2 treatment unlike SPF processes, increasing the risk of antibiotic resistance transfer by transduction mechanism. In conclusion, Fe:IDS SPF is an attractive solution for tertiary treatment of urban wastewater in small wastewater treatment plants as it can provide effective disinfection and a higher protection against antibiotic resistance transfer than the other investigated processes.

Differences in pathogenic community assembly processes and their interactions with bacterial communities in river and lake ecosystems

Pathogenic bacterial infections caused by water quality degradation are one of the most widespread environmental problems. Clarifying the structure of pathogens and their assembly mechanisms in lake ecosystems is vital to prevent the infestation of waterborne pathogens and maintain human health. However, the composition and assembly mechanisms of pathogenic bacterial communities in river and lake ecosystems are still poorly understood. In this study, we collected 17 water and 17 sediment samples from Lake Chaohu and its 11 inflow rivers. Sequencing of 16S rRNA genes was used to study bacterial pathogen communities. The results of the study showed that there was a significant difference (P < 0.05) in the composition of the pathogen community between riverine and lake habitats. Acinetobacter (36.49%) was the dominant bacterium in the river, whereas Flavobacterium (21.6%) was the most abundant bacterium in the lake. Deterministic processes (i.e., environmental filtering and species interaction) drove the assembly of pathogenic bacterial communities in the lake habitat, while stochastic processes shaped river pathogenic bacterial communities. Spearman correlation analysis showed that the α-diversity of bacterial communities was linearly and negatively linked to the relative abundance of pathogens. Having a higher bacterial community diversity had a suppressive effect on pathogen abundance. In addition, co-occurrence network analysis showed that bacterial communities were tightly linked to pathogenic bacteria. Pseudomonas aeruginosa and Salmonella enterica were identified as keystone species in an inflow water sampling network (W_FR), reducing the complexity of the network. These results provide a reference for assessments of water quality safety and pathogenic bacteria posing risks to human health in large freshwater lakes.

Current Status of Omics in Biological Quality Elements for Freshwater Biomonitoring

Freshwater ecosystems have been experiencing various forms of threats, mainly since the last century. The severity of this adverse scenario presents unprecedented challenges to human health, water supply, agriculture, forestry, ecological systems, and biodiversity, among other areas. Despite the progress made in various biomonitoring techniques tailored to specific countries and biotic communities, significant constraints exist, particularly in assessing and quantifying biodiversity and its interplay with detrimental factors. Incorporating modern techniques into biomonitoring methodologies presents a challenging topic with multiple perspectives and assertions. This review aims to present a comprehensive overview of the contemporary advancements in freshwater biomonitoring, specifically by utilizing omics methodologies such as genomics, metagenomics, transcriptomics, proteomics, metabolomics, and multi-omics. The present study aims to elucidate the rationale behind the imperative need for modernization in this field. This will be achieved by presenting case studies, examining the diverse range of organisms that have been studied, and evaluating the potential benefits and drawbacks associated with the utilization of these methodologies. The utilization of advanced high-throughput bioinformatics techniques represents a sophisticated approach that necessitates a significant departure from the conventional practices of contemporary freshwater biomonitoring. The significant contributions of omics techniques in the context of biological quality elements (BQEs) and their interpretations in ecological problems are crucial for biomonitoring programs. Such contributions are primarily attributed to the previously overlooked identification of interactions between different levels of biological organization and their responses, isolated and combined, to specific critical conditions.

Unsupervised encoding selection through ensemble pruning for biomedical classification

BACKGROUND

Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever-growing amount of annotated peptides, researchers leverage artificial intelligence to circumvent the cumbersome, wet-lab-based identification and automate the detection of promising candidates. However, the prediction of a peptide's function is not limited to antimicrobial efficiency. To date, multiple studies successfully classified additional properties, e.g., antiviral or cell-penetrating effects. In this light, ensemble classifiers are employed aiming to further improve the prediction. Although we recently presented a workflow to significantly diminish the initial encoding choice, an entire unsupervised encoding selection, considering various machine learning models, is still lacking.

RESULTS

We developed a workflow, automatically selecting encodings and generating classifier ensembles by employing sophisticated pruning methods. We observed that the Pareto frontier pruning is a good method to create encoding ensembles for the datasets at hand. In addition, encodings combined with the Decision Tree classifier as the base model are often superior. However, our results also demonstrate that none of the ensemble building techniques is outstanding for all datasets.

CONCLUSION

The workflow conducts multiple pruning methods to evaluate ensemble classifiers composed from a wide range of peptide encodings and base models. Consequently, researchers can use the workflow for unsupervised encoding selection and ensemble creation. Ultimately, the extensible workflow can be used as a plugin for the PEPTIDE REACToR, further establishing it as a versatile tool in the domain.

Genome-resolved insight into the reservoir of antibiotic resistance genes in aquatic microbial community

Aquatic microbial communities are an important reservoir of antibiotic resistance genes (ARGs). However, distribution and diversity of different ARG categories in environmental microbes with different ecological strategies is not yet well studied. Despite the potential exposure of the southern part of the Caspian Sea to the release of antibiotics, little is known about its natural resistome profile. We used a combination of Hidden Markov model (HMM), homology alignment and a deep learning approach for comprehensive screening of the diversity and distribution of ARGs in the Caspian Sea metagenomes at genome resolution. Detected ARGs were classified into five antibiotic resistance categories including prevention of access to target (44%), modification/protection of targets (30%), direct modification of antibiotics (22%), stress resistance (3%), and metal resistance (1%). The 102 detected ARG containing metagenome-assembled genomes of the Caspian Sea were dominated by representatives of Acidimicrobiia, Gammaproteobacteria, and Actinobacteria classes. Comparative analysis revealed that the highly abundant, oligotrophic, and genome streamlined representatives of taxa Acidimicrobiia and Actinobacteria modify the antibiotic target via mutation to develop antibiotic resistance rather than carrying extra resistance genes. Our results help with understanding how the encoded resistance categories of each genome are aligned with its ecological strategies.