DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

Sustainability of the rice-crayfish co-culture aquaculture model: microbiome profiles based on multi-kingdom analyses

While the rice-crayfish culture (RCFP) model, an important aquaculture model in Asia, is generally considered a sustainable model, its sustainability in terms of microbial community profiles has not been evaluated. In this study, multi-kingdom analyses of microbiome profiles (i.e., bacteria, archaea, viruses, and eukaryotes) were performed using environmental (i.e., water and sediment) and animal gut (i.e., crayfish and crab gut) microbial samples from the RCFP and other aquaculture models, including the crab-crayfish co-culture, crayfish culture, and crab culture models, to evaluate the sustainability of the RCFP systematically. Results showed that RCFP samples are enriched with a distinct set of microbes, including Shewanella, Ferroplasma, Leishmania, and Siphoviridae, when compared with other aquaculture models. Additionally, most microbes in the RCFP samples, especially microbes from different kingdoms, were densely and positively connected, which indicates their robustness against environmental stress. Whereas microbes in different aquaculture models demonstrated moderate levels of horizontal gene transfer (HGT) across kingdoms, the RCFP showed relatively lower frequencies of HGT events, especially those involving antibiotic resistance genes. Finally, environmental factors, including pH, oxidation-reduction potential, temperature, and total nitrogen, contributed profoundly to shaping the microbial communities in these aquaculture models. Interestingly, compared with other models, the microbial communities of the RCFP model were less influenced by these environmental factors, which suggests that microbes in the latter have stronger ability to resist environmental stress. The findings collectively reflect the unique multi-kingdom microbial patterns of the RCFP model and suggest that this model is a sustainable model from the perspective of microbiome profiles.

AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data

BACKGROUND

Antimicrobial resistance (AMR) is a global health concern. High-throughput metagenomic sequencing of microbial samples enables profiling of AMR genes through comparison with curated AMR databases. However, the performance of current methods is often hampered by database incompleteness and the presence of homology/homoplasy with other non-AMR genes in sequenced samples.

RESULTS

We present AMR-meta, a database-free and alignment-free approach, based on k-mers, which combines algebraic matrix factorization into metafeatures with regularized regression. Metafeatures capture multi-level gene diversity across the main antibiotic classes. AMR-meta takes in reads from metagenomic shotgun sequencing and outputs predictions about whether those reads contribute to resistance against specific classes of antibiotics. In addition, AMR-meta uses an augmented training strategy that joins an AMR gene database with non-AMR genes (used as negative examples). We compare AMR-meta with AMRPlusPlus, DeepARG, and Meta-MARC, further testing their ensemble via a voting system. In cross-validation, AMR-meta has a median f-score of 0.7 (interquartile range, 0.2-0.9). On semi-synthetic metagenomic data-external test-on average AMR-meta yields a 1.3-fold hit rate increase over existing methods. In terms of run-time, AMR-meta is 3 times faster than DeepARG, 30 times faster than Meta-MARC, and as fast as AMRPlusPlus. Finally, we note that differences in AMR ontologies and observed variance of all tools in classification outputs call for further development on standardization of benchmarking data and protocols.

CONCLUSIONS

AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools.

Linkage and driving mechanisms of antibiotic resistome in surface and ground water: Their responses to land use and seasonal variation

Antibiotic resistance in the environment, mostly mediated by antibiotic resistance genes (ARGs), has posed a threat to ecological and human health. Contamination of surface water and groundwater with ARGs has become a serious environmental concern. However, the distinctions and similarities across ARG profiles, the various ecological processes associated with ARGs, the driving mechanisms for ARG profiles in surface water and groundwater, and how they respond to land use and seasonal variation remain unknown. To tackle these issues, the contamination of ARGs in surface water and groundwater in central China was investigated using metagenomic technology. The results indicated that seasonal changes in ARG abundance and diversity were inconsistent across surface water and groundwater, and that the relationship between ARGs in surface water and groundwater was stronger during the rainy season. Land use had a greater effect on ARGs in surface water than in groundwater and was stronger during the dry season than during the rainy season. More interestingly, the ideal buffer zones with the greatest impact of land use on the ARGs of surface water and groundwater had distinct radii: 1500 m for both dry and rainy seasons in surface water, and 1000 m for dry season and 500 m for rainy season in groundwater. Furthermore, stochastic mechanisms mediated by mobile gene elements (MGEs) contribute significantly more to ARG assemblages than deterministic processes, particularly in groundwater. Furthermore, our results also showed that ARG enrichment in microbial communities was host- dependent, and the risk of ARGs in groundwater was greater both during the rainy season and dry season. In conclusion, the findings have improved our understanding of the relationship and driving mechanisms of ARGs in surface and ground water, as well as their responses to land use and seasonal variation, which may be beneficial for limiting ARG pollution in a watershed with high levels of anthropogenic activity.

Metagenomic insights into surface water microbial communities of a South Asian mangrove ecosystem

Estuaries are one of the most productive ecosystems and their productivity is maintained by resident microbial communities. Recent alterations driven by climate change have further escalated these stressors leading to the propagation of traits such as antibiotic resistance and heavy metal resistance in microbial communities. Surface water samples from eleven stations along the Thakuran and Matla estuaries of the Sundarbans Biosphere Reserve (SBR) of Sundarbans mangrove located in South Asia were sampled in monsoon (June) 2019 to elucidate resident microbial communities based on Nanopore sequencing. Metagenomic analyses revealed the widespread dominance of Proteobacteria across all the stations along with a high abundance of Firmicutes. Other phyla, including Euryarchaeota, Thaumarchaeota, Actinobacteria, Bacteroidetes and Cyanobacteria showed site-specific trends in abundance. Further taxonomic affiliations showed Gammaproteobacteria and Alphaproteobacteria to be dominant classes with high abundances of Bacilli in SBR_Stn58 and SBR_Stn113. Among the eukaryotic communities, the most abundant classes included Prasinophyceae, Saccharyomycetes and Sardariomycetes. Functional annotation showed metabolic activities such as carbohydrate, amino acid, nitrogen and phosphorus metabolisms to be uniformly distributed across all the studied stations. Pathways such as stress response, sulphur metabolism and motility-associated genes appeared in low abundances in SBR. Functional traits such as antibiotic resistance showed overwhelming dominance of genes involved in multidrug resistance along with widespread resistance towards commonly used antibiotics including Tetracycline, glycopeptide and aminoglycoside. Metal resistance genes including arsenic, nickel and copper were found in comparable abundances across the studied stations. The prevalence of ARG and MRG might indicate presence of pollutants and hint toward deteriorating ecosystem health status of Sundarbans mangrove.

Evaluation of Experimental Protocols for Shotgun Whole-Genome Metagenomic Discovery of Antibiotic Resistance Genes

Shotgun metagenomics has enabled the discovery of antibiotic resistance genes (ARGs). Although there have been numerous studies benchmarking the bioinformatics methods for shotgun metagenomic data analysis, there has not yet been a study that systematically evaluates the performance of different experimental protocols on metagenomic species profiling and ARG detection. In this study, we generated 35 whole genome shotgun metagenomic sequencing data sets for five samples (three human stool and two microbial standard) using seven experimental protocols (KAPA or Flex kits at 50ng, 10ng, or 5ng input amounts; XT kit at 1ng input amount). Using this comprehensive resource, we evaluated the seven protocols in terms of robust detection of ARGs and microbial abundance estimation at various sequencing depths. We found that the data generated by the seven protocols are largely similar. The inter-protocol variability is significantly smaller than the variability between samples or sequencing depths. We found that a sequencing depth of more than 30M is suitable for human stool samples. A higher input amount (50ng) is generally favorable for the KAPA and Flex kits. This systematic benchmarking study sheds light on the impact of sequencing depth, experimental protocol, and DNA input amount on ARG detection in human stool samples.

Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes

We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E. coli genes, with 6 of them never associated with antibiotic resistance for any microbe. Iterative link prediction leads to a performance improvement and more findings. The probability of positive findings highly correlates with experimentally validated findings (R2 = 0.94). We also identify 5 homologs in Salmonella enterica that are all validated to confer resistance to antibiotics. This work demonstrates how evidence-driven decisions are a step toward automating knowledge discovery with high confidence and accelerated pace, thereby substituting traditional time-consuming and expensive methods.

Unveiling the occurrence, hosts and mobility potential of antibiotic resistance genes in the deep ocean

As emerging microbial contaminants, antibiotic resistance genes (ARGs) are widely reported in the neritic zone. However, the profiles of ARGs in the deep ocean have not yet been fully resolved. In this study, the distribution, hosts, and mobility potential of ARGs at different water depths in the Western Pacific (WP) were investigated and compared to those in Bohai Sea (BH) waters using environmental parameter measurements, amplicon sequencing, metagenomic assembly and binning approaches. Our results showed that the top eight most abundant known ARG types in WP and BH waters were multidrug (39.85%), peptide (14.98%), aminoglycoside (11.33%), macrolide-lincosamide-streptogramin (MLS, 4.06%), tetracycline (3.74%), beta-lactam (3.12%), fluoroquinolone (1.79%) and rifamycin (1.24%). The ARGs observed in mesopelagic and bathypelagic waters were abundant and diverse as those observed in neritic waters, indicating that deep-sea water could be another environmental reservoir for ARGs. For deep-sea ARGs, members from classes Gammaproteobacteria (70%) and Alphaproteobacteria (21.1%) were the most important potential hosts. In addition, mobile genetic element analysis suggested that the ARG migration potential in dee sea water (> 1000 m) was relatively high. Overall, our findings expanded the understanding of ARGs in deep seawater and provided guidance for ARG pollution control and risk prediction.

Metagenomics-enabled microbial surveillance

Lessons learnt from the COVID-19 pandemic include increased awareness of the potential for zoonoses and emerging infectious diseases that can adversely affect human health. Although emergent viruses are currently in the spotlight, we must not forget the ongoing toll of morbidity and mortality owing to antimicrobial resistance in bacterial pathogens and to vector-borne, foodborne and waterborne diseases. Population growth, planetary change, international travel and medical tourism all contribute to the increasing frequency of infectious disease outbreaks. Surveillance is therefore of crucial importance, but the diversity of microbial pathogens, coupled with resource-intensive methods, compromises our ability to scale-up such efforts. Innovative technologies that are both easy to use and able to simultaneously identify diverse microorganisms (viral, bacterial or fungal) with precision are necessary to enable informed public health decisions. Metagenomics-enabled surveillance methods offer the opportunity to improve detection of both known and yet-to-emerge pathogens.

Machine Learning and Deep Learning Applications in Metagenomic Taxonomy and Functional Annotation

Shotgun sequencing of environmental DNA (i.e., metagenomics) has revolutionized the field of environmental microbiology, allowing the characterization of all microorganisms in a sequencing experiment. To identify the microbes in terms of taxonomy and biological activity, the sequenced reads must necessarily be aligned on known microbial genomes/genes. However, current alignment methods are limited in terms of speed and can produce a significant number of false positives when detecting bacterial species or false negatives in specific cases (virus, plasmids, and gene detection). Moreover, recent advances in metagenomics have enabled the reconstruction of new genomes using de novo binning strategies, but these genomes, not yet fully characterized, are not used in classic approaches, whereas machine and deep learning methods can use them as models. In this article, we attempted to review the different methods and their efficiency to improve the annotation of metagenomic sequences. Deep learning models have reached the performance of the widely used k-mer alignment-based tools, with better accuracy in certain cases; however, they still must demonstrate their robustness across the variety of environmental samples and across the rapid expansion of accessible genomes in databases.

Metagenomic Analysis Reveals A Possible Association Between Respiratory Infection and Periodontitis

Periodontitis is an inflammatory disease that is characterized by progressive destruction of the periodontium and causes tooth loss in adults. Periodontitis is known to be associated with dysbiosis of the oral microflora, which is often linked to various diseases. However, the complexity of plaque microbial communities of periodontitis, antibiotic resistance, and enhanced virulence make this disease difficult to treat. In this study, using metagenomic shotgun sequencing, we investigated the etiology, antibiotic resistance genes (ARGs), and virulence genes (VirGs) of periodontitis. We revealed a significant shift in the composition of oral microbiota as well as several functional pathways that were represented significantly more abundantly in periodontitis patients than in controls. In addition, we observed several positively selected ARGs and VirGs with the Ka/Ks ratio > 1 by analyzing our data and a previous periodontitis dataset, indicating that ARGs and VirGs in oral microbiota may be subjected to positive selection. Moreover, 5 of 12 positively selected ARGs and VirGs in periodontitis patients were found in the genomes of respiratory tract pathogens. Of note, 91.8% of the background VirGs with at least one non-synonymous single-nucleotide polymorphism for natural selection were also from respiratory tract pathogens. These observations suggest a potential association between periodontitis and respiratory infection at the gene level. Our study enriches the knowledge of pathogens and functional pathways as well as the positive selection of antibiotic resistance and pathogen virulence in periodontitis patients, and provides evidence at the gene level for an association between periodontitis and respiratory infection.

Current progress and open challenges for applying deep learning across the biosciences

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.

Metagenomics indicate that public health risk may be higher from flooding following dry versus rainy periods

Urban floodwater could lead to significant risk for public and environmental health from mobilization of microbial pathogens and overflow of wastewater treatment systems. Here, we attempted to assess this risk by obtaining metagenomic profiles of antibiotic resistance genes (ARGs), virulence factors (VFs) and pathogens present in floodwater samples collected in urban Atlanta, GA that were categorized in two distinct groups: floods that occurred after periods of drought and those after regular (seasonal) rain events. Even though no major (known) pathogens were present at the limit of detection of our sequencing effort (~3 Gbp/sample), we observed that floodwaters after drought showed a 2.5-fold higher abundance of both ARGs and VFs compared to floodwater after rainy days. These differences were mainly derived by several novel species of the Pseudomonas genus, which were more dominant in the former versus the latter samples and carried several genes to cope with osmotic stress in addition to ARGs and VFs. These results revealed that there are previously undescribed species that become mobilized after flooding events in the Southeast US urban settings and could represent an increased public health risk, especially after periods of drought, which warrants further attention.

Bacterial diversity and the antimicrobial resistome in the southwestern highlands of Saudi Arabia

Soil is a reservoir of microbial diversity and the most supportive habitat for acquiring and transmitting antimicrobial resistance. Resistance transfer usually occurs from animal to soil and vice versa, and it may ultimately appear in clinical pathogens. In this study, the southwestern highlands of Saudi Arabia were studied to assess the bacterial diversity and antimicrobial resistance that could be affected by the continuous development of tourism in the region. Such effects could have a long-lasting impact on the local environment and community. Culture-dependent, quantitative polymerase chain reaction (qPCR), and shotgun sequencing-based metagenomic approaches were used to evaluate the diversity, functional capabilities, and antimicrobial resistance of bacteria isolated from collected soil samples. Bacterial communities in the southwestern highlands were mainly composed of Proteobacteria, Bacteroidetes, and Actinobacteria. A total of 102 antimicrobial resistance genes (ARGs) and variants were identified in the soil microbiota and were mainly associated with multidrug resistance, followed by macrolide, tetracycline, glycopeptide, bacitracin, and beta-lactam antibiotic resistance. The mechanisms of resistance included efflux, antibiotic target alteration, and antibiotic inactivation. qPCR confirmed the detection of 18 clinically important ARGs. In addition, half of the 49 identified isolates were phenotypically resistant to at least one of the 15 antibiotics tested. Overall, ARGs and indicator genes of anthropogenic activities (human-mitochondrial [hmt] gene and integron-integrase [int1]) were found in relatively lower abundance. Along with a high diversity of bacterial communities, variation was observed in the relative abundance of bacterial taxa among sampling sites in the southwestern highlands of Saudi Arabia.

A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling

The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.

Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing

Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hidden Markov models (Meta-MARC). We use an extensive collection of 585 isolates with available AMR resistance profiles determined by phenotypic tests across nine antibiotic classes. We show how the prediction landscape of AMR classifiers is highly heterogeneous, with balanced accuracy varying from 0.40 to 0.92. Although some algorithms-ResFinder, KARGA and AMRPlusPlus-exhibit overall better balanced accuracy than others, the high per-AMR-class variance and related findings suggest that: (1) all algorithms might be subject to sampling bias both in data repositories used for training and experimental/clinical settings; and (2) a portion of clinical samples might contain uncharacterized AMR genes that the algorithms-mostly trained on known AMR genes-fail to generalize upon. These results lead us to formulate practical advice for software configuration and application, and give suggestions for future study designs to further develop AMR prediction tools from proof-of-concept to bedside.

Ontology-aware neural network: a general framework for pattern mining from microbiome data

With the rapid accumulation of microbiome data around the world, numerous computational bioinformatics methods have been developed for pattern mining from such paramount microbiome data. Current microbiome data mining methods, such as gene and species mining, rely heavily on sequence comparison. Most of these methods, however, have a clear trade-off, particularly, when it comes to big-data analytical efficiency and accuracy. Microbiome entities are usually organized in ontology structures, and pattern mining methods that have considered ontology structures could offer advantages in mining efficiency and accuracy. Here, we have summarized the ontology-aware neural network (ONN) as a novel framework for microbiome data mining. We have discussed the applications of ONN in multiple contexts, including gene mining, species mining and microbial community dynamic pattern mining. We have then highlighted one of the most important characteristics of ONN, namely, novel knowledge discovery, which makes ONN a standout among all microbiome data mining methods. Finally, we have provided several applications to showcase the advantage of ONN over other methods in microbiome data mining. In summary, ONN represents a paradigm shift for pattern mining from microbiome data: from traditional machine learning approach to ontology-aware and model-based approach, which has found its broad application scenarios in microbiome data mining.

The microbiome and resistome of apple fruits alter in the post-harvest period

BACKGROUND

A detailed understanding of antimicrobial resistance trends among all human-related environments is key to combat global health threats. In food science, however, the resistome is still little considered. Here, we studied the apple microbiome and resistome from different cultivars (Royal Gala and Braeburn) and sources (freshly harvested in South Africa and exported apples in Austrian supermarkets) by metagenomic approaches, genome reconstruction and isolate sequencing.

RESULTS

All fruits harbor an indigenous, versatile resistome composed of 132 antimicrobial resistance genes (ARGs) encoding for 19 different antibiotic classes. ARGs are partially of clinical relevance and plasmid-encoded; however, their abundance within the metagenomes is very low (≤ 0.03%). Post-harvest, after intercontinental transport, the apple microbiome and resistome was significantly changed independently of the cultivar. In comparison to fresh apples, the post-harvest microbiome is characterized by higher abundance of Enterobacteriales, and a more diversified pool of ARGs, especially associated with multidrug resistance, as well as quinolone, rifampicin, fosfomycin and aminoglycoside resistance. The association of ARGs with metagenome-assembled genomes (MAGs) suggests resistance interconnectivity within the microbiome. Bacterial isolates of the phyla Gammaproteobacteria, Alphaproteobacteria and Actinobacteria served as representatives actively possessing multidrug resistance and ARGs were confirmed by genome sequencing.

CONCLUSION

Our results revealed intrinsic and potentially acquired antimicrobial resistance in apples and strengthen the argument that all plant microbiomes harbor diverse resistance features. Although the apple resistome appears comparatively inconspicuous, we identified storage and transport as potential risk parameters to distribute AMR globally and highlight the need for surveillance of resistance emergence along complex food chains.

Review and Comparison of Antimicrobial Resistance Gene Databases

As the prevalence of antimicrobial resistance genes is increasing in microbes, we are facing the return of the pre-antibiotic era. Consecutively, the number of studies concerning antibiotic resistance and its spread in the environment is rapidly growing. Next generation sequencing technologies are widespread used in many areas of biological research and antibiotic resistance is no exception. For the rapid annotation of whole genome sequencing and metagenomic results considering antibiotic resistance, several tools and data resources were developed. These databases, however, can differ fundamentally in the number and type of genes and resistance determinants they comprise. Furthermore, the annotation structure and metadata stored in these resources can also contribute to their differences. Several previous reviews were published on the tools and databases of resistance gene annotation; however, to our knowledge, no previous review focused solely and in depth on the differences in the databases. In this review, we compare the most well-known and widely used antibiotic resistance gene databases based on their structure and content. We believe that this knowledge is fundamental for selecting the most appropriate database for a research question and for the development of new tools and resources of resistance gene annotation.

An integrative understanding of the large metabolic shifts induced by antibiotics in critical illness

Antibiotics are commonly used in the Intensive Care Unit (ICU); however, several studies showed that the impact of antibiotics to prevent infection, multi-organ failure, and death in the ICU is less clear than their benefit on course of infection in the absence of organ dysfunction. We characterized here the compositional and metabolic changes of the gut microbiome induced by critical illness and antibiotics in a cohort of 75 individuals in conjunction with 2,180 gut microbiome samples representing 16 different diseases. We revealed an "infection-vulnerable" gut microbiome environment present only in critically ill treated with antibiotics (ICU⁺). Feeding of Caenorhabditis elegans with Bifidobacterium animalis and Lactobacillus crispatus, species that expanded in ICU⁺ patients, revealed a significant negative impact of these microbes on host viability and developmental homeostasis. These results suggest that antibiotic administration can dramatically impact essential functional activities in the gut related to immune responses more than critical illness itself, which might explain in part untoward effects of antibiotics in the critically ill.

The highly diverse Antarctic Peninsula soil microbiota as a source of novel resistance genes

The rise of multiresistant bacterial pathogens is currently one of the most critical threats to global health, encouraging a better understanding of the evolution and spread of antimicrobial resistance. In this regard, the role of the environment as a source of resistance mechanisms remains poorly understood. Moreover, we still know a minimal part of the microbial diversity and resistome present in remote and extreme environments, hosting microbes that evolved to resist harsh conditions and thus a potentially rich source of novel resistance genes. This work demonstrated that the Antarctic Peninsula soils host a remarkable microbial diversity and a widespread presence of autochthonous antibiotic-resistant bacteria and resistance genes. We observed resistance to a wide array of antibiotics among isolates, including Pseudomonas resisting ten or more different compounds, with an overall increased resistance in bacteria from non-intervened areas. In addition, genome analysis of selected isolates showed several genes encoding efflux pumps, as well as a lack of known resistance genes for some of the resisted antibiotics, including colistin, suggesting novel uncharacterized mechanisms. By combining metagenomic approaches based on analyzing raw reads, assembled contigs, and metagenome-assembled genomes, we found hundreds of widely distributed genes potentially conferring resistance to different antibiotics (including an outstanding variety of inactivation enzymes), metals, and biocides, hosted mainly by Polaromonas, Pseudomonas, Streptomyces, Variovorax, and Burkholderia. Furthermore, a proportion of these genes were found inside predicted plasmids and other mobile elements, including a putative OXA-like carbapenemase from Polaromonas harboring conserved key residues and predicted structural features. All this evidence indicates that the Antarctic Peninsula soil microbiota has a broad natural resistome, part of which could be transferred horizontally to pathogenic bacteria, acting as a potential source of novel resistance genes.

Antimicrobial Resistance in Rivers: A Review of the Genes Detected and New Challenges

River ecosystems are very important parts of the water cycle and an excellent habitat, food, and drinking water source for many organisms, including humans. Antibiotics are emerging contaminants which can enter rivers from various sources. Several antibiotics and their related antibiotic resistance genes (ARGs) have been detected in these ecosystems by various research programs and could constitute a substantial problem. The presence of antibiotics and other resistance cofactors can boost the development of ARGs in the chromosomes or mobile genetic elements of natural bacteria in rivers. The ARGs in environmental bacteria can also be transferred to clinically important pathogens. However, antibiotics and their resistance genes are both not currently monitored by national or international authorities responsible for controlling the quality of water bodies. For example, they are not included in the contaminant list in the European Water Framework Directive or in the US list of Water-Quality Benchmarks for Contaminants. Although ARGs are naturally present in the environment, very few studies have focused on non-impacted rivers to assess the background ARG levels in rivers, which could provide some useful indications for future environmental regulation and legislation. The present study reviews the antibiotics and associated ARGs most commonly measured and detected in rivers, including the primary analysis tools used for their assessment. In addition, other factors that could enhance antibiotic resistance, such as the effects of chemical mixtures, the effects of climate change, and the potential effects of the coronavirus disease 2019 pandemic, are discussed. Environ Toxicol Chem 2022;41:687-714. © 2022 SETAC.

Monitoring antibiotic resistance genes in wastewater environments: The challenges of filling a gap in the One-Health cycle

Antibiotic resistance (AR) is a global problem requiring international cooperation and coordinated action. Global monitoring must rely on methods available and comparable across nations to quantify AR occurrence and identify sources and reservoirs, as well as paths of AR dissemination. Numerous analytical tools that are gaining relevance in microbiology, have the potential to be applied to AR research. This review summarizes the state of the art of AR monitoring methods, considering distinct needs, objectives and available resources. Based on the overview of distinct approaches that are used or can be adapted to monitor AR, it is discussed the potential to establish reliable and useful monitoring schemes that can be implemented in distinct contexts. This discussion places the environmental monitoring within the One-Health approach, where two types of risk, dissemination across distinct environmental compartments, and transmission to humans, must be considered. The plethora of methodological approaches to monitor AR and the variable features of the monitored sites challenge the capacity of the scientific community and policy makers to reach a common understanding. However, the dialogue between different methods and the production of action-oriented data is a priority. The review aims to warm up this discussion.

Phyllosphere-associated microbiota in built environment: Do they have the potential to antagonize human pathogens?

INTRODUCTION

The plant microbiota is known to protect its host against invasion by plant pathogens. Recent studies have indicated that the microbiota of indoor plants is transmitted to the local built environment where it might fulfill yet unexplored functions. A better understanding of the interplay of such microbial communities with human pathogens might provide novel cues related to natural inhibition of them.

OBJECTIVE

We studied the plant microbiota of two model indoor plants, Musa acuminata and Chlorophytum comosum, and their effect on human pathogens. The main objective was to identify mechanisms by which the microbiota of indoor plants inhibits human-pathogenic bacteria.

METHODS

Microbial communities and functioning were investigated using a comprehensive set of experiments and methods combining amplicon and shotgun metagenomic analyses with results from interaction assays.

RESULTS

A diverse microbial community was found to be present on Musa and Chlorophytum grown in different indoor environments; the datasets comprised 1066 bacterial, 1261 fungal, and 358 archaeal ASVs. Bacterial communities were specific for each plant species, whereas fungal and archaeal communities were primarily shaped by the built environment. Sphingomonas and Bacillus were found to be prevalent components of a ubiquitous core microbiome in the two model plants; they are well-known for antagonistic activity towards plant pathogens. Interaction assays indicated that they can also antagonize opportunistic human pathogens. Moreover, the native plant microbiomes harbored a broad spectrum of biosynthetic gene clusters, and in parallel, a variety of antimicrobial resistance genes. By conducting comparative metagenomic analyses between plants and abiotic surfaces, we found that the phyllosphere microbiota harbors features that are clearly distinguishable from the surrounding abiotic surfaces.

CONCLUSIONS

Naturally occurring phyllosphere bacteria can potentially act as a protective shield against opportunistic human pathogens. This knowledge and the underlying mechanisms can provide an important basis to establish a healthy microbiome in built environments.

PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning

BACKGROUND

Transcription factors (TFs) are proteins controlling the flow of genetic information by regulating cellular gene expression. A better understanding of TFs in a bacterial community context may open novel revenues for exploring gene regulation in ecosystems where bacteria play a key role. Here we describe PredicTF, a platform supporting the prediction and classification of novel bacterial TF in single species and complex microbial communities. PredicTF is based on a deep learning algorithm.

RESULTS

To train PredicTF, we created a TF database (BacTFDB) by manually curating a total of 11,961 TF distributed in 99 TF families. Five model organisms were used to test the performance and the accuracy of PredicTF. PredicTF was able to identify 24-62% of the known TFs with an average precision of 88% in our five model organisms. We demonstrated PredicTF using pure cultures and a complex microbial community. In these demonstrations, we used (meta)genomes for TF prediction and (meta)transcriptomes for determining the expression of putative TFs.

CONCLUSION

PredicTF demonstrated high accuracy in predicting transcription factors in model organisms. We prepared the pipeline to be easily implemented in studies profiling TFs using (meta)genomes and (meta)transcriptomes. PredicTF is an open-source software available at https://github.com/mdsufz/PredicTF .

Bacterial Genes Encoding Resistance Against Antibiotics and Metals in Well-Maintained Drinking Water Distribution Systems in Finland

Information on the co-occurrence of antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) among bacterial communities in drinking water distribution systems (DWDSs) is scarce. This study characterized ARGs and MRGs in five well-maintained DWDSs in Finland. The studied DWDSs had different raw water sources and treatment methods. Two of the waterworks employed artificially recharged groundwater (ARGW) and used no disinfection in the treatment process. The other three waterworks (two surface and one groundwater source) used UV light and chlorine during the treatment process. Ten bulk water samples (two from each DWDS) were collected, and environmental DNA was extracted and then sequenced using the Illumina HiSeq platform for high-throughput shotgun metagenome sequencing. A total of 430 ARGs were characterized among all samples with the highest diversity of ARGs identified from samples collected from non-disinfected DWDSs. Furthermore, non-disinfected DWDSs contained the highest diversity of bacterial communities. However, samples from DWDSs using disinfectants contained over double the ratio of ARG reads to 16S rRNA gene reads and most of the MRG (namely mercury and arsenic resistance genes). The total reads and types of ARGs conferring genes associated with antibiotic groups namely multidrug resistance, and bacitracin, beta-lactam, and aminoglycoside and mercury resistance genes increased in waterworks treating surface water with disinfection. The findings of this study contribute toward a comprehensive understanding of ARGs and MRGs in DWDSs. The occurrence of bacteria carrying antibiotic or metal resistance genes in drinking water causes direct exposure to people, and thus, more systematic investigation is needed to decipher the potential effect of these resistomes on human health.

Automated extraction of genes associated with antibiotic resistance from the biomedical literature

The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction

Enhanced survival of multi-species biofilms under stress is promoted by low-abundant but antimicrobial-resistant keystone species

Multi-species biofilms are more resistant against stress compared to single-species biofilms. However, the mechanisms underlying this common observation remain elusive. Therefore, we studied biofilm formation of well-known opportunistic pathogens (Acinetobacter baumanii, Enterococcus faecium, Escherichia coli, Staphylococcus haemolyticus and Stenotrophomonas maltophilia) in various approaches. Synergistic effects in their multi-species biofilms were observed. Using metatranscriptomics, changes in the gene expression of the involved members became evident, and provided explanations for the improved survivability under nutrient limitation and exposure to disinfectants. Genes encoding proteins for vitamin B6 synthesis and iron uptake were linked to synergism in the multi-species biofilm under nutrient-limited conditions. Our study indicates that sub-lethal concentrations of an alcohol-based disinfectant enhance biofilm yields in multi-species assemblages. A reduction of the dominant taxa in the multi-species biofilm under disinfectant pressure allowed minor taxa to bloom. The findings underline the importance of minor but antimicrobial-resistant species that serve as "protectors" for the whole assemblage due to upregulation of genes involved in defence mechanisms and biofilm formation. This ultimately results in an increase in the total yield of the multi-species biofilm. We conclude that inter-species interactions may be crucial for the survival of opportunistic pathogens; especially under conditions that are typically found under hospital settings.

MCRL: using a reference library to compress a metagenome into a non-redundant list of sequences, considering viruses as a case study

MOTIVATION

Metagenomes offer a glimpse into the total genomic diversity contained within a sample. Currently, however, there is no straightforward way to obtain a non-redundant list of all putative homologs of a set of reference sequences present in a metagenome.

RESULTS

To address this problem, we developed a novel clustering approach called 'metagenomic clustering by reference library' (MCRL), where a reference library containing a set of reference genes is clustered with respect to an assembled metagenome. According to our proposed approach, reference genes homologous to similar sets of metagenomic sequences, termed 'signatures', are iteratively clustered in a greedy fashion, retaining at each step the reference genes yielding the lowest E values, and terminating when signatures of remaining reference genes have a minimal overlap. The outcome of this computation is a non-redundant list of reference genes homologous to minimally overlapping sets of contigs, representing potential candidates for gene families present in the metagenome. Unlike metagenomic clustering methods, there is no need for contigs to overlap to be associated with a cluster, enabling MCRL to draw on more information encoded in the metagenome when computing tentative gene families. We demonstrate how MCRL can be used to extract candidate viral gene families from an oral metagenome and an oral virome that otherwise could not be determined using standard approaches. We evaluate the sensitivity, accuracy and robustness of our proposed method for the viral case study and compare it with existing analysis approaches.

AVAILABILITY AND IMPLEMENTATION

https://github.com/a-tadmor/MCRL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Advances in Metagenomics and Its Application in Environmental Microorganisms

Metagenomics is a new approach to study microorganisms obtained from a specific environment by functional gene screening or sequencing analysis. Metagenomics studies focus on microbial diversity, community constitute, genetic and evolutionary relationships, functional activities, and interactions and relationships with the environment. Sequencing technologies have evolved from shotgun sequencing to high-throughput, next-generation sequencing (NGS), and third-generation sequencing (TGS). NGS and TGS have shown the advantage of rapid detection of pathogenic microorganisms. With the help of new algorithms, we can better perform the taxonomic profiling and gene prediction of microbial species. Functional metagenomics is helpful to screen new bioactive substances and new functional genes from microorganisms and microbial metabolites. In this article, basic steps, classification, and applications of metagenomics are reviewed.

Is rice-crayfish co-culture a better aquaculture model: From the perspective of antibiotic resistome profiles

Aquaculture ecosystem is a hot-spot for antibiotic resistance genes (ARGs). Rice-crayfish co-culture was considered an eco-friendly aquaculture model and has been widely adopted in China. However, it is unclear whether rice-crayfish co-culture is one of the most eco-friendly models from the perspective of antibiotic resistance profiles. In this study, we evaluated the eco-friendliness of rice-crayfish co-culture, and compared this model with other aquaculture models, from the perspectives of antibiotics and ARG patterns, based on multi-omics and antibiotic profiles. Results showed that the nutrient levels, antibiotic concentrations, dominant microbial genera and ARG patterns in the rice-crayfish co-culture model were profoundly different from the other three aquaculture models (crab only aquaculture model, crayfish only aquaculture model, and crab-crayfish co-culture models). Specifically, the rice-crayfish co-culture model has significantly lower diversity of ARGs and lower potential risks of ARGs when compared to the other aquaculture models. Nutrient and antibiotic concentrations were the important environmental factors for shaping ARG patterns, but compared with environmental factors, the effects of mobile genes and bacteria community on the proliferation and transmission of ARGs were stronger. This study has deepened our understanding of ARGs in freshwater aquaculture ecosystem, and suggested that rice-crayfish co-culture model is a relatively eco-friendly aquaculture model when compared with the other aquaculture models.

Wastewater bypass is a major temporary point-source of antibiotic resistance genes and multi-resistance risk factors in a Swiss river

Untreated combined sewage (bypass) is often discharged by wastewater treatment plants to receiving rivers during stormwater events, where it may contribute to increased levels of antibiotic resistance genes (ARGs) and multi-resistance risk factors (multi-resistant bacteria and multi-resistance genomic determinants (MGDs)) in the receiving water. Other contamination sources, such as soil runoff and resuspended river sediment could also play a role during stormwater events. Here we report on stormwater event-based sampling campaigns to determine temporal dynamics of ARGs and multi-resistance risk factors in bypass, treated effluent, and the receiving river, as well as complimentary data on catchment soils and surface sediments. Both indicator ARGs (qPCR) and resistome (ARG profiles revealed by metagenomics) indicated bypass as the main contributor to the increased levels of ARGs in the river during stormwater events. Furthermore, we showed for the first time that the risk of exposure to bypass-borne multi-resistance risk factors increase under stormwater events and that many of these MGDs were plasmid associated and thus potentially mobile. In addition, elevated resistance risk factors persisted for some time (up to 22 h) in the receiving water after stormwater events, likely due to inputs from distributed overflows in the catchment. This indicates temporal dynamics should be considered when interpreting the risks of exposure to resistance from event-based contamination. We propose that reducing bypass from wastewater treatment plants may be an important intervention option for reducing dissemination of antibiotic resistance.

Shotgun metagenomics of Cheonggukjang, a fermented soybean food of Korea: Community structure, predictive functionalities and amino acids profile

Cheonggukjang is a naturally fermented soybean food of Korea. The present study was aimed to reveal the whole microbial community structure of naturally fermented cheonggukjang along with the prediction of microbial functional profiles by shotgun metagenomic sequence analysis. Metataxonomic profile of cheonggukjang samples showed different domains viz. bacteria (95.83%), virus (2.26%), unclassified (1.84%), eukaryotes (0.05%) and archaea (0.005%). Overall, 44 phyla, 286 families, 722 genera and 1437 species were identified. Firmicutes was the most abundant phylum (98.04%) followed by Proteobacteria (1.49%), Deinococcus-Thermus (0.14%). Bacillus thermoamylovorans was the most abundant species in cheonggukjang followed by Bacillus licheniformis, Bacillus glycinifermentans, Bacillus subtilis, Bacillus paralicheniformis, Bacillus amyloliquifaciens, Brevibacillus borstelensis, Brevibacillus sonorensis Brevibacillus, Acinetobacter, Carnobacterium, Paenibacillus, Cronobacter Enterococcus, Enterobacter, Terriglobus, Psychrobacter and Virgibacillus. A colossal diversity of the genus Bacillus was detected with 150 species. Functional analysis of cheonggukjang metagenome revealed the genes for the synthesis and metabolism of wide range of bioactive compounds including, various essential amino acids, conjugated amino acids, different vitamins, flavonoids, and enzymes. Amino acid profiles obtained from KEGG annotation in cheonggukjang were validated with experimental result of amino acid profiles.

Progressing Antimicrobial Resistance Sensing Technologies across Human, Animal, and Environmental Health Domains

The spread of antimicrobial resistance (AMR) is a rapidly growing threat to humankind on both regional and global scales. As countries worldwide prepare to embrace a One Health approach to AMR management, which is one that recognizes the interconnectivity between human, animal, and environmental health, increasing attention is being paid to identifying and monitoring key contributing factors and critical control points. Presently, AMR sensing technologies have significantly progressed phenotypic antimicrobial susceptibility testing (AST) and genotypic antimicrobial resistance gene (ARG) detection in human healthcare. For effective AMR management, an evolution of innovative sensing technologies is needed for tackling the unique challenges of interconnected AMR across various and different health domains. This review comprehensively discusses the modern state-of-play for innovative commercial and emerging AMR sensing technologies, including sequencing, microfluidic, and miniaturized point-of-need platforms. With a unique view toward the future of One Health, we also provide our perspectives and outlook on the constantly changing landscape of AMR sensing technologies beyond the human health domain.

Phenolic compounds promote the horizontal transfer of antibiotic resistance genes in activated sludge

Phenolic compounds are common organic pollutants in wastewater. During the wastewater treatment process, these compounds may influence the microbial community structure and functions. However, the impact of the phenolic compounds in the wastewater treatment plants on the horizontal transfer of antibiotic resistance genes (ARGs) has not been well assessed. In this study, we investigated the horizontal transfer of ARGs under the stress of phenolic compounds. The results showed that in pure culture bacteria system, p-nitrophenol (PNP), p-aminophenol (PAP) and phenol (PhOH) (10-100 mg/L) can significantly increase the horizontal transfer frequency of ARGs by 2.2-4.6, 3.6-9.4 and 1.9-9.0 fold, respectively. And, the RP4 plasmid transfer from Escherichia coli HB101 (E. coli HB101) to the bacteria in activated sludge increased obviously under the stress of phenolic compounds. Further investigation revealed that the PNP and PhOH at the concentration of 10-100 mg/L increased the production of reactive oxygen species and the permeability of cell membrane in the donor and recipient, which could be the causes of horizontal transfer of RP4 plasmid. In addition, it was also found that PNP, PAP and PhOH stress inhibit the expression of the global regulatory genes korB and trbA in the RP4 plasmid, and increase the expression level of the traF gene, thereby promoting the conjugative transfer of the RP4 plasmid. Taken together, these results improved our understanding of the horizontal transfer of ARGs under the stress of phenolic compounds and provided basic information for management of the systems that treat wastewater containing phenolic compounds.

Bioplastic accumulates antibiotic and metal resistance genes in coastal marine sediments

The oceans are increasingly polluted with plastic debris, and several studies have implicated plastic as a reservoir for antibiotic resistance genes and a potential vector for antibiotic-resistant bacteria. Bioplastic is widely regarded as an environmentally friendly replacement to conventional petroleum-based plastic, but the effects of bioplastic pollution on marine environments remain largely unknown. Here, we present the first evidence that bioplastic accumulates antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) in marine sediments. Biofilms fouling ceramic, polyethylene terephthalate (PET), and polyhydroxyalkanoate (PHA) were investigated by shotgun metagenomic sequencing. Four ARG groups were more abundant in PHA: trimethoprim resistance (TMP), multidrug resistance (MDR), macrolide-lincosamide-streptogramin resistance (MLS), and polymyxin resistance (PMR). One MRG group was more abundant in PHA: multimetal resistance (MMR). The relative abundance of ARGs and MRGs were strongly correlated based on a Mantel test between the Bray-Curtis dissimilarity matrices (R = 0.97, p < 0.05) and a Pearson's analysis (R = 0.96, p < 0.05). ARGs were detected in more than 40% of the 57 metagenome-assembled genomes (MAGs) while MRGs were detected in more than 90% of the MAGs. Further investigation (e.g., culturing, genome sequencing, antibiotic susceptibility testing) revealed that PHA biofilms were colonized by hemolytic Bacillus cereus group bacteria that were resistant to beta-lactams, vancomycin, and bacitracin. Taken together, our findings indicate that bioplastic, like conventional petroleum-based plastic, is a reservoir for resistance genes and a potential vector for antibiotic-resistant bacteria in coastal marine sediments.

A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria

Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.

Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature)

The spread of antibiotic-resistant human bacterial pathogens is a serious threat to modern medicine. Antibiotic susceptibility testing is essential for treatment regimens optimization and preventing dissemination of antibiotic resistance. Therefore, development of antibiotic susceptibility testing methods is a priority challenge of laboratory medicine. The aim of this review is to analyze the capabilities of the bioinformatics tools for bacterial whole genome sequence data processing. The PubMed database, Russian scientific electronic library eLIBRARY, information networks of World health organization and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) were used during the analysis. In this review, the platforms for whole genome sequencing, which are suitable for detection of bacterial genetic resistance determinants, are described. The classic step of genetic resistance determinants searching is an alignment between the query nucleotide/protein sequence and the subject (database) nucleotide/protein sequence, which is performed using the nucleotide and protein sequence databases. The most commonly used databases are Resfinder, CARD, Bacterial Antimicrobial Resistance Reference Gene Database. The results of the resistance determinants searching in genome assemblies is more correct in comparison to results of the searching in contigs. The new resistance genes searching bioinformatics tools, such as neural networks and machine learning, are discussed in the review. After critical appraisal of the current antibiotic resistance databases we designed a protocol for predicting antibiotic resistance using whole genome sequence data. The designed protocol can be used as a basis of the algorithm for qualitative and quantitative antimicrobial susceptibility testing based on whole genome sequence data.

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.

An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction

Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method—heterogeneous graph attention network (‘HGAT–AMR’)—to predict anti-tuberculosis (TB) drug resistance. The HGAT–AMR model is able to accommodate incomplete phenotypic profiles, as well as provide ‘attention scores’ of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is ‘paying attention to’ in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.

Burkholderiaceae and Multidrug Resistance Genes Are Key Players in Resistome Development in a Germfree Soil Model

Assembly of a resistome in parallel with the establishment of a microbial community is not well understood. Germfree models can reveal microbiota interactions and shed light on bacterial colonization and resistance development under antibiotic pressure. In this study, we exposed germfree soil (GS), GS with diluted nontreated soil (DS), and nontreated soil (NS) to various concentrations of tetracycline (TET) in a nongermfree environment for 10 weeks, followed by 2 weeks of exposure to water. High-throughput sequencing was used to profile bacterial communities and antibiotic resistance genes (ARGs) in the soils. The initial bacterial loads were found to shape the profiles of bacterial communities and the resistomes. GS and DS treated with TET and the same soils left untreated had similar profiles, whereas NS showed different profiles. Soils with the same initial bacterial loads had their profiles shifted by TET treatment. Multidrug resistance (MDR) genes were the most abundant ARG types in all soils, with multidrug efflux pump genes being the discriminatory ARGs in GS regardless of different TET treatments and in GS, DS, and NS after TET. Furthermore, MDR genes were significantly enriched by TET treatment. In contrast, tetracycline resistance genes were either absent or low in relative abundance. The family Burkholderiaceae was predominant in all soils (except in NS treated with water) and was positively selected for by TET treatment. Most importantly, Burkholderiaceae were the primary carrier of ARGs, including MDR genes. IMPORTANCE This is the first study to examine how resistomes develop and evolve using GS. GS can be used to study the colonization and establishment of bacterial communities under antibiotic selection. Surprisingly, MDR genes were the main ARGs detected in GS, and TET treatments did not positively select for specific tetracycline resistance genes. Additionally, Burkholderiaceae were the key bacterial hosts for MDR genes in the current GS model under the conditions investigated. These results show that the family Burkholderiaceae underpins the development of resistome and serves as a source of ARGs. The ease of establishment of Burkholderiaceae and MDR genes in soils has serious implications for human health, since these bacteria are versatile and ubiquitous in the environment.

The Human Gut Resistome up to Extreme Longevity

Antibiotic resistance (AR) is indisputably a major health threat which has drawn much attention in recent years. In particular, the gut microbiome has been shown to act as a pool of AR genes, potentially available to be transferred to opportunistic pathogens. Herein, we investigated for the first time changes in the human gut resistome during aging, up to extreme longevity, by analyzing shotgun metagenomics data of fecal samples from a geographically defined cohort of 62 urban individuals, stratified into four age groups: young adults, elderly, centenarians, and semisupercentenarians, i.e., individuals aged up to 109 years. According to our findings, some AR genes are similarly represented in all subjects regardless of age, potentially forming part of the core resistome. Interestingly, aging was found to be associated with a higher burden of some AR genes, including especially proteobacterial genes encoding multidrug efflux pumps. Our results warn of possible health implications and pave the way for further investigations aimed at containing AR accumulation, with the ultimate goal of promoting healthy aging. IMPORTANCE Antibiotic resistance is widespread among different ecosystems, and in humans it plays a key role in shaping the composition of the gut microbiota, enhancing the ecological fitness of certain bacterial populations when exposed to antibiotics. A considerable component of the definition of healthy aging and longevity is associated with the structure of the gut microbiota, and, in this regard, the presence of antibiotic-resistant bacteria is critical to many pathologies that come about with aging. However, the structure of the resistome has not yet been sufficiently elucidated. Here, we show distinct antibiotic resistance assets and specific microbial consortia characterizing the human gut resistome through aging.

Metagenomic and network analysis revealed wide distribution of antibiotic resistance genes in monkey gut microbiota

The emergence and spread of drug-resistant microorganisms that have acquired new resistance mechanisms, leading to antibiotic resistance, continue to threaten the health of humans and animals worldwide. Non-human primates (NHPs), as close living relatives of human beings in the world, have a high degree of genetic and physiological similarity to humans. However, despite its importance, we lack a comprehensive characterization or understanding of the similarities and differences of the antibiotic resistance genes of the gut microbiome carried by non-human primates and humans. In the present study, the diversity and abundance of antibiotic resistance genes carried by the gut microbiota of cynomolgus monkeys (Macaca fascicularis) were investigated by metagenomic analysis. In total, 60 resistance types conferring resistance to 11 categories of antibiotics were identified in the gut microbiome of cynomolgus monkeys. Interestingly, the composition and abundance of ARGs carried by the gut microbiota of cynomolgus monkeys can be significantly affected by dietary changes. Moreover, we found that all ARG types carried by humans are also present in cynomolgus monkeys. The tetracycline resistance gene tet(37) is evolutionarily conserved and highly homologous. Taken together, our study provides a comprehensive overview of the diversity and richness of ARGs in the gut microbiota of cynomolgus monkeys and underlines the potentially crucial role of diet in the gut health of monkeys and humans.

MetaMLP: A Fast Word Embedding Based Classifier to Profile Target Gene Databases in Metagenomic Samples

The functional profile of metagenomic samples enables improved understanding of microbial populations in the environment. Such analysis consists of assigning short sequencing reads to a particular functional category. Normally, manually curated databases are used for functional assignment, and genes are arranged into different classes. Sequence alignment has been widely used to profile metagenomic samples against curated databases. However, this method is time consuming and requires high computational resources. While several alignment-free methods based on k-mer composition have been developed in recent years, they still require large amounts of computer main memory. In this article, MetaMLP (Metagenomics Machine Learning Profiler), a machine learning method that represents sequences as numerical vectors (embeddings) and uses a simple one hidden layer neural network to profile functional categories, is developed. Unlike other methods, MetaMLP enables partial matching by using a reduced alphabet to build sequence embeddings from full and partial k-mers. MetaMLP is able to identify a slightly larger number of reads compared with DIAMOND (one of the fastest sequence alignment methods), as well as to perform accurate predictions with 0.99 precision and 0.99 recall. MetaMLP can process 100M reads in ∼10 minutes on a laptop computer, which is 50 times faster than DIAMOND.

Knowledge gaps in the assessment of antimicrobial resistance in surface waters

The spread of antibiotic resistance in the water environment has been widely described. However, still many knowledge gaps exist regarding the selection pressure from antibiotics, heavy metals and other substances present in surface waters as a result of anthropogenic activities, as well as the extent and impact of this phenomenon on aquatic organisms and humans. In particular, the relationship between environmental concentrations of antibiotics and the acquisition of ARGs by antibiotic-sensitive bacteria as well as the impact of heavy metals and other selective agents on antimicrobial resistance (AMR) need to be defined. Currently, established safety values are based on the effects of antibiotic toxicity neglecting the question of AMR spread. In turn, risk assessment of antibiotics in waterbodies remains a complex question implicating multiple variables and unknowns reinforced by the lack of harmonized protocols and official guidelines. In the present review, we discussed current state-of-the-art and the knowledge gaps related to pressure exerted by antibiotics and heavy metals on aquatic environments and their relationship to the spread of AMR. Along with this latter, we reflected on (i) the risk assessment in surface waters, (ii) selective pressures contributing to its transfer and propagation and (iii) the advantages of metagenomics in investigating AMR. Furthermore, the role of microplastics in co-selection for metal and antibiotic resistance, together with the need for more studies in freshwater are highlighted.

Seizing the moment: now is the time for integrated global surveillance of antimicrobial resistance in wastewater environments

Antimicrobial resistance (AMR) is a growing global health threat that requires coordinated action across One Health sectors (humans, animals, environment) to stem its spread. Environmental surveillance of AMR is largely behind the curve in current One Health surveillance programs, but recent momentum in the establishment of infrastructure for monitoring of the SARS-CoV-2 virus in sewage provides an impetus for analogous AMR monitoring. Simultaneous advances in research have identified striking trends in various AMR measures in wastewater and other impacted environments across global transects. Methodologies for tracking AMR, including metagenomics, are rapidly advancing, but need to be standardized and made modular for access by LMICs, while also developing systems for sample archiving and data sharing. Such efforts will help optimize effective global AMR policy.

Shotgun metagenomic analysis reveals the prevalence of antibiotic resistance genes and mobile genetic elements in full scale hospital wastewater treatment plants

Wastewater treatment plants are considered as hotspots of emerging antimicrobial genes and mobile genetic elements. We used a shotgun metagenomic approach to examine the wide-spectrum profiles of ARGs (antibiotic resistance genes) and MGEs (mobile genetic elements) in activated sludge samples from two different hospital trains at the wastewater treatment plants (WWTPs) in Daegu, South Korea. The influent activated sludge and effluent of two trains (six samples in total) at WWTPs receiving domestic sewage wastewater (SWW) and hospital wastewater (HWW) samples collected at multiple periods were subjected to high throughput 16S rRNA metagenome sequencing for microbial community diversity. Cloacibacterium caeni and Lewinella nigricans were predominant in SWW effluents, while Bacillus subtilis and Staphylococcus epidermidis were predominant in HWW effluents based on the Miseq platform. Totally, 20,011 reads and 28,545 metagenomic sequence reads were assigned to 25 known ARG types in the SWW2 and HWW5 samples, respectively. The higher abundance of ARGs, including multidrug resistance (>53%, MDR), macrolide-lincosamide-streptogramin (>9%, MLS), beta-lactam (>3.3%), bacitracin (>4.4%), and tetracycline (>3.4%), confirmed the use of these antibiotics in human medicine. In total, 190 subtypes belonging to 23 antibiotic classes were detected in both SWW2 and HWW5 samples. RpoB2, MacB, and multidrug (MDR) ABC transporter shared the maximum matched genes in both activated sludge samples. The high abundance of MGEs, such as a gene transfer agent (GTA) (four times higher), transposable elements (1.6 times higher), plasmid related functions (3.8 times higher), and phages (two times higher) in HWW5 than in SWW2, revealed a risk of horizontal gene transfer in HWW. Domestic wastewater from hospital patients also influenced the abundance of ARGs and MGEs in the activated sludge process.

Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning

MOTIVATION

Antimicrobial resistance (AMR) is one of the biggest global problems threatening human and animal health. Rapid and accurate AMR diagnostic methods are thus very urgently needed. However, traditional antimicrobial susceptibility testing (AST) is time-consuming, low throughput and viable only for cultivable bacteria. Machine learning methods may pave the way for automated AMR prediction based on genomic data of the bacteria. However, comparing different machine learning methods for the prediction of AMR based on different encodings and whole-genome sequencing data without previously known knowledge remains to be done.

RESULTS

In this study, we evaluated logistic regression (LR), support vector machine (SVM), random forest (RF) and convolutional neural network (CNN) for the prediction of AMR for the antibiotics ciprofloxacin, cefotaxime, ceftazidime and gentamicin. We could demonstrate that these models can effectively predict AMR with label encoding, one-hot encoding and frequency matrix chaos game representation (FCGR encoding) on whole-genome sequencing data. We trained these models on a large AMR dataset and evaluated them on an independent public dataset. Generally, RFs and CNNs perform better than LR and SVM with AUCs up to 0.96. Furthermore, we were able to identify mutations that are associated with AMR for each antibiotic.

AVAILABILITY AND IMPLEMENTATION

Source code in data preparation and model training are provided at GitHub website (https://github.com/YunxiaoRen/ML-iAMR).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Nanopore-based metagenomics analysis reveals prevalence of mobile antibiotic and heavy metal resistome in wastewater

In-depth studies of the microbiome and mobile resistome profile of different environments is central to understanding the role of the environment in antimicrobial resistance (AMR), which is one of the urgent threats to global public health. In this study, we demonstrated the use of a rapid (and easily portable) sequencing approach coupled with user-friendly bioinformatics tools, the MinION (Oxford Nanopore Technologies), on the evaluation of the microbial as well as mobile metal and antibiotic resistome profile of semi-rural wastewater. A total of 20 unique phyla, 43 classes, 227 genera, and 469 species were identified in samples collected from the Amherst Wastewater Treatment Plant, both from primary and secondary treated wastewater. Alpha diversity indices indicated that primary samples were significantly richer and more microbially diverse than secondary samples. A total of 1041 ARGs, 68 MRGs, and 17 MGEs were detected in this study. There were more classes of AMR genes in primary than secondary wastewater, but in both cases multidrug, beta-lactam and peptide AMR predominated. Of note, OXA β-lactamases, some of which are also carbapenemases, were enriched in secondary samples. Metal resistance genes against arsenic, copper, zinc and molybdenum were the dominant MRGs in the majority of the samples. A larger proportion of resistome genes were located in chromosome-derived sequences except for mobilome genes, which were predominantly located in plasmid-derived sequences. Genetic elements related to transposase were the most common MGEs in all samples. Mobile or MGE/plasmid-associated resistome genes that confer resistance to last resort antimicrobials such as carbapenems and colistin were detected in most samples. Worryingly, several of these potentially transferable genes were found to be carried by clinically-relevant hosts including pathogenic bacterial species in the orders Aeromonadales, Clostridiales, Enterobacterales and Pseudomonadales. This study demonstrated that the MinION can be used as a metagenomics approach to evaluate the microbiome, resistome, and mobilome profile of primary and secondary wastewater.