Discovery of Novel Antibiotic Resistance Determinants in Forest and Grassland Soil Metagenomes

An in-depth evaluation of metagenomic classifiers for soil microbiomes

BACKGROUND

Recent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.

RESULTS

In this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.

CONCLUSION

This study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

Chemical attributes, bacterial community, and antibiotic resistance genes are affected by intensive use of soil in agro-ecosystems of the Atlantic Forest, Southeastern Brazil

Soil is one of the largest reservoirs of microbial diversity in nature. Although soil management is vital for agricultural purposes, intensive practices can have a significant impact on fertility, microbial community, and resistome. Thus, the aim of this study was to evaluate the effects of an intensive soil management system on the chemical attributes, composition and structure of prevalent bacterial communities, and presence and abundance of antimicrobial resistance genes (ARGs). The chemical characterization, bacterial diversity and relative abundance of ARGs were evaluated in soils from areas of intensive vegetable cultivation and forests. Results indicate that levels of nutrients and heavy metals were higher in soil samples from cultivated areas. Similarly, greater enrichment and diversity of bacterial genera was detected in agricultural areas. Of the 18 target ARGs evaluated, seven were detected in studied soils. The oprD gene exhibited the highest abundance among the studied genes and was the only one that showed a significantly different prevalence between areas. The oprD gene was identified only from soil of the cultivated areas. The blaSFO, erm(36), oprD and van genes, in addition to the pH, showed greater correlation with in soil of cultivated areas, which in turn exhibited higher contents of nutrients. Thus, in addition to changes in chemical attributes and in the microbial community of the soil, intensive agricultural cultivation systems cause a modification of its resistome, reinforcing the importance of the study of antimicrobial resistance in a One Health approach.

Exploiting genomics for antimicrobial resistance surveillance at One Health interfaces

The intersection of human, animal, and ecosystem health at One Health interfaces is recognised as being of key importance in the evolution and spread of antimicrobial resistance (AMR) and represents an important, and yet rarely realised opportunity to undertake vital AMR surveillance. A working group of international experts in pathogen genomics, AMR, and One Health convened to take part in a workshop series and online consultation focused on the opportunities and challenges facing genomic AMR surveillance in a range of settings. Here we outline the working group's discussion of the potential utility, advantages of, and barriers to, the implementation of genomic AMR surveillance at One Health interfaces and propose a series of recommendations for addressing these challenges. Embedding AMR surveillance at One Health interfaces will require the development of clear beneficial use cases, especially in low-income and middle-income countries. Evidence of directionality, risks to human and animal health, and potential trade implications were also identified by the working group as key issues. Addressing these challenges will be vital to enable genomic surveillance technology to reach its full potential for assessing the risk of transmission of AMR between the environment, animals, and humans at One Health interfaces.

Molecular mechanism of plasmid-borne resistance to sulfonamide antibiotics

The sulfonamides (sulfas) are the oldest class of antibacterial drugs and inhibit the bacterial dihydropteroate synthase (DHPS, encoded by folP), through chemical mimicry of its co-substrate p-aminobenzoic acid (pABA). Resistance to sulfa drugs is mediated either by mutations in folP or acquisition of sul genes, which code for sulfa-insensitive, divergent DHPS enzymes. While the molecular basis of resistance through folP mutations is well understood, the mechanisms mediating sul-based resistance have not been investigated in detail. Here, we determine crystal structures of the most common Sul enzyme types (Sul1, Sul2 and Sul3) in multiple ligand-bound states, revealing a substantial reorganization of their pABA-interaction region relative to the corresponding region of DHPS. We use biochemical and biophysical assays, mutational analysis, and in trans complementation of E. coli ΔfolP to show that a Phe-Gly sequence enables the Sul enzymes to discriminate against sulfas while retaining pABA binding and is necessary for broad resistance to sulfonamides. Experimental evolution of E. coli results in a strain harboring a sulfa-resistant DHPS variant that carries a Phe-Gly insertion in its active site, recapitulating this molecular mechanism. We also show that Sul enzymes possess increased active site conformational dynamics relative to DHPS, which could contribute to substrate discrimination. Our results reveal the molecular foundation for Sul-mediated drug resistance and facilitate the potential development of new sulfas less prone to resistance.

ResFinderFG v2.0: a database of antibiotic resistance genes obtained by functional metagenomics

Metagenomics can be used to monitor the spread of antibiotic resistance genes (ARGs). ARGs found in databases such as ResFinder and CARD primarily originate from culturable and pathogenic bacteria, while ARGs from non-culturable and non-pathogenic bacteria remain understudied. Functional metagenomics is based on phenotypic gene selection and can identify ARGs from non-culturable bacteria with a potentially low identity shared with known ARGs. In 2016, the ResFinderFG v1.0 database was created to collect ARGs from functional metagenomics studies. Here, we present the second version of the database, ResFinderFG v2.0, which is available on the Center of Genomic Epidemiology web server (https://cge.food.dtu.dk/services/ResFinderFG/). It comprises 3913 ARGs identified by functional metagenomics from 50 carefully curated datasets. We assessed its potential to detect ARGs in comparison to other popular databases in gut, soil and water (marine + freshwater) Global Microbial Gene Catalogues (https://gmgc.embl.de). ResFinderFG v2.0 allowed for the detection of ARGs that were not detected using other databases. These included ARGs conferring resistance to beta-lactams, cycline, phenicol, glycopeptide/cycloserine and trimethoprim/sulfonamide. Thus, ResFinderFG v2.0 can be used to identify ARGs differing from those found in conventional databases and therefore improve the description of resistomes.

Identification of diverse antibiotic resistant bacteria in agricultural soil with H218O stable isotope probing combined with high-throughput sequencing

BACKGROUND

We aimed to identify bacteria able to grow in the presence of several antibiotics including the ultra-broad-spectrum antibiotic meropenem in a British agricultural soil by combining DNA stable isotope probing (SIP) with high throughput sequencing. Soil was incubated with cefotaxime, meropenem, ciprofloxacin and trimethoprim in 18O-water. Metagenomes and the V4 region of the 16S rRNA gene from the labelled "heavy" and the unlabelled "light" SIP fractions were sequenced.

RESULTS

An increase of the 16S rRNA copy numbers in the "heavy" fractions of the treatments with 18O-water compared with their controls was detected. The treatments resulted in differences in the community composition of bacteria. Members of the phyla Acidobacteriota (formally Acidobacteria) were highly abundant after two days of incubation with antibiotics. Pseudomonadota (formally Proteobacteria) including Stenotrophomonas were prominent after four days of incubation. Furthermore, a metagenome-assembled genome (MAG-1) from the genus Stenotrophomonas (90.7% complete) was retrieved from the heavy fraction. Finally, 11 antimicrobial resistance genes (ARGs) were identified in the unbinned-assembled heavy fractions, and 10 ARGs were identified in MAG-1. In comparison, only two ARGs from the unbinned-assembled light fractions were identified.

CONCLUSIONS

The results indicate that both non-pathogenic soil-dwelling bacteria as well as potential clinical pathogens are present in this agricultural soil and several ARGs were identified from the labelled communities, but it is still unclear if horizontal gene transfer between these groups can occur.

Addressing antibiotic resistance: computational answers to a biological problem?

The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.

Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes

BACKGROUND

Bacterial communities in humans, animals, and the external environment maintain a large collection of antibiotic resistance genes (ARGs). However, few of these ARGs are well-characterized and thus established in existing resistance gene databases. In contrast, the remaining latent ARGs are typically unknown and overlooked in most sequencing-based studies. Our view of the resistome and its diversity is therefore incomplete, which hampers our ability to assess risk for promotion and spread of yet undiscovered resistance determinants.

RESULTS

A reference database consisting of both established and latent ARGs (ARGs not present in current resistance gene repositories) was created. By analyzing more than 10,000 metagenomic samples, we showed that latent ARGs were more abundant and diverse than established ARGs in all studied environments, including the human- and animal-associated microbiomes. The pan-resistomes, i.e., all ARGs present in an environment, were heavily dominated by latent ARGs. In comparison, the core-resistome, i.e., ARGs that were commonly encountered, comprised both latent and established ARGs. We identified several latent ARGs shared between environments and/or present in human pathogens. Context analysis of these genes showed that they were located on mobile genetic elements, including conjugative elements. We, furthermore, identified that wastewater microbiomes had a surprisingly large pan- and core-resistome, which makes it a potentially high-risk environment for the mobilization and promotion of latent ARGs.

CONCLUSIONS

Our results show that latent ARGs are ubiquitously present in all environments and constitute a diverse reservoir from which new resistance determinants can be recruited to pathogens. Several latent ARGs already had high mobile potential and were present in human pathogens, suggesting that they may constitute emerging threats to human health. We conclude that the full resistome-including both latent and established ARGs-needs to be considered to properly assess the risks associated with antibiotic selection pressures. Video Abstract.

Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings

The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment - including animals, plants, soil, and water - are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.

Plasmid-Mediated Transfer of Antibiotic Resistance Genes in Soil

Due to selective pressure from the widespread use of antibiotics, antibiotic resistance genes (ARGs) are found in human hosts, plants, and animals and virtually all natural environments. Their migration and transmission in different environmental media are often more harmful than antibiotics themselves. ARGs mainly move between different microorganisms through a variety of mobile genetic elements (MGEs), such as plasmids and phages. The soil environment is regarded as the most microbially active biosphere on the Earth’s surface and is closely related to human activities. With the increase in human activity, soils are becoming increasingly contaminated with antibiotics and ARGs. Soil plasmids play an important role in this process. This paper reviews the current scenario of plasmid-mediated migration and transmission of ARGs in natural environments and under different antibiotic selection pressures, summarizes the current methods of plasmid extraction and analysis, and briefly introduces the mechanism of plasmid splice transfer using the F factor as an example. However, as the global spread of drug-resistant bacteria has increased and the knowledge of MGEs improves, the contribution of soil plasmids to resistance gene transmission needs to be further investigated. The prevalence of multidrug-resistant bacteria has also made the effective prevention of the transmission of resistance genes through the plasmid-bacteria pathway a major research priority.

Grassland ecology system: A critical reservoir and dissemination medium of antibiotic resistance in Xilingol Pasture, Inner Mongolia

Antibiotic resistance is a major threat to human health. It is necessary to explore all the potential sources and comprehend the pathways that antibiotic resistance genes (ARGs) are transmitted. In this study, by applying high-throughput quantitative PCR and high-throughput sequencing, ARGs and microbial community structure were determined, to understand the reservoirs and spread of ARGs in the Xilingol grassland system. A total of 151,140 and 138 different ARGs were observed in manure, soil, and water samples, respectively. Only 12 ARGs were shared in all environmental and animal manure samples. Multidrug defense system, such as efflux pump, was the most dominant factor in manure and soil samples, followed by antibiotic deactivation processes. These genes coffering resistance to major classes of antibiotics including β_Lactamase (bla_SFO, fox5, bla_CTX-M-04, bla_OXY), vancomycin (vanC-03, vanXD), MLSB (vatE-01, mphA-01), aminoglycoside (aadA2-01), Multidrug (oprJ) and others (oprD, qacEdelta1-02), except sulfonamide and tetracycline. The 12 ARGs were significantly enriched in water samples compared to manure and soil samples (p < 0.01) and demonstrated that the water environment was an important transmission source of ARGs in the grassland. The highest enrichment was up to 324.5-fold. Moreover, the 12 shared ARGs were positively correlated with the mobile genetic elements (p < 0.01). The nonrandom co-occurrence network patterns between ARGs and microbial community suggested that a total of three bacterial phyla were viewed as the potential ARGs hosts. These findings indicate that ARGs were highly enriched in water samples, demonstrating that the water environment was a critical source and sink of ARGs in the grassland system. It may illuminate the mechanism stressing the effects of human activity on the occurrence and transmission of ARGs in the grassland system.

ARG-SHINE: improve antibiotic resistance class prediction by integrating sequence homology, functional information and deep convolutional neural network

Antibiotic resistance in bacteria limits the effect of corresponding antibiotics, and the classification of antibiotic resistance genes (ARGs) is important for the treatment of bacterial infections and for understanding the dynamics of microbial communities. Although several methods have been developed to classify ARGs, none of them work well when the ARGs diverge from those in the reference ARG databases. We develop a novel method, ARG-SHINE, for ARG classification. ARG-SHINE utilizes state-of-the-art learning to rank machine learning approach to ensemble three component methods with different features, including sequence homology, protein domain/family/motif and raw amino acid sequences for the deep convolutional neural network. Compared with other methods, ARG-SHINE achieves better performance on two benchmark datasets in terms of accuracy, macro-average f1-score and weighted-average f1-score. ARG-SHINE is used to classify newly discovered ARGs through functional screening and achieves high prediction accuracy. ARG-SHINE is freely available at https://github.com/ziyewang/ARG_SHINE.

Comparative microbial antibiotic resistome between urban and deep forest environments

A paradoxical result of using antibiotics to eradicate microbial pathogens is the emergence of a vast number of resistant microbes in various environments. The concern that environmental microbes will inevitably become resistant to virtually every clinically usable antibiotics has been exacerbated by the spread of these resistance genes across different environments and the emergence of multidrug resistant phenotypes. Here, we provide metagenomic insights into the microbiomes and resistomes of 16 soil samples collected from hospitals, residential areas, and forest parks in the megacity of Beijing and deep forests in the Yunnan province. Using Illumina HiSeq sequencing, we investigated the microbial diversity within the metagenomic shotgun reads and identified 486 antibiotic-resistant genes (ARGs) classified into 30 types from these samples, among which multidrug resistance genes were the most abundant. Our results present an important reference and direct comparison of microbial antibiotic resistomes of soil samples from a megacity and deep forests and extend our understanding of the spread of ARGs in modern urban and natural environments.

Dynamics of Diversity and Abundance of Sulfonamide Resistant Bacteria in a Silt Loam Soil Fertilized by Compost

Although composting is effective in deactivating antibiotic substances in manure, the influence of compost fertilization on the occurrence and dissemination of antibiotic resistance in arable soils remains to be controversial. Herein, the abundance and diversity of two sulfonamide resistance genes (sul1 and sul2) in soil fertilized by compost spiked with two concentrations of sulfadiazine (1 and 10 mg kg⁻¹) were studied intensively by qPCR and high throughput sequencing based on a two-month microcosm experiment. The concentration of sulfadiazine decreased rapidly after spiking from 25% at Day 1 to less than 2.7% at Day 60. Relative abundance of both sul1 and sul2 were significantly higher in soil amended with compost than the non-amended control at Day 1 and slightly decreased with incubation time except for sul2 in the S10 treatment. Soil bacterial communities were transiently shifted by compost fertilization regardless of the presence of sulfadiazine. Relative abundance of genera in three hubs positively interlinked with sul1 and sul2 were significantly higher in compost treated soil than the control at Day 1, 7 and 21, but not at Day 60. High throughput sequencing analyses revealed that most detected (>67% in relative abundance) sul1 and sul2 genotypes sharing >99% similarity with those found in gammaproteobacterial pathogens frequently were commonly present in compost and soil. These results indicated that compost fertilization might increase the abundance rather than diversity of sulfadiazine-resistant populations in soil, which may be facilitated by the presence of sulfadiazine.

Novel Soil-Derived Beta-Lactam, Chloramphenicol, Fosfomycin and Trimethoprim Resistance Genes Revealed by Functional Metagenomics

Antibiotic resistance genes (ARGs) in soil are considered to represent one of the largest environmental resistomes on our planet. As these genes can potentially be disseminated among microorganisms via horizontal gene transfer (HGT) and in some cases are acquired by clinical pathogens, knowledge about their diversity, mobility and encoded resistance spectra gained increasing public attention. This knowledge offers opportunities with respect to improved risk prediction and development of strategies to tackle antibiotic resistance, and might help to direct the design of novel antibiotics, before further resistances reach hospital settings or the animal sector. Here, metagenomic libraries, which comprise genes of cultivated microorganisms, but, importantly, also those carried by the uncultured microbial majority, were screened for novel ARGs from forest and grassland soils. We detected three new beta-lactam, a so far unknown chloramphenicol, a novel fosfomycin, as well as three previously undiscovered trimethoprim resistance genes. These ARGs were derived from phylogenetically diverse soil bacteria and predicted to encode antibiotic inactivation, antibiotic efflux, or alternative variants of target enzymes. Moreover, deduced gene products show a minimum identity of ~21% to reference database entries and confer high-level resistance. This highlights the vast potential of functional metagenomics for the discovery of novel ARGs from soil ecosystems.

Gentamicin Adsorption onto Soil Particles Prevents Overall Short-Term Effects on the Soil Microbiome and Resistome

Antibiotics used in agriculture may reach the environment and stimulate the development and dissemination of antibiotic resistance in the soil microbiome. However, the scope of this phenomenon and the link to soil properties needs to be elucidated. This study compared the short-term effects of a range of gentamicin concentrations on the microbiome and resistome of bacterial enrichments and microcosms of an agricultural soil using a metagenomic approach. Gentamicin impact on bacterial biomass was roughly estimated by the number of 16SrRNA gene copies. In addition, the soil microbiome and resistome response to gentamicin pollution was evaluated by 16SrRNA gene and metagenomic sequencing, respectively. Finally, gentamicin bioavailability in soil was determined. While gentamicin pollution at the scale of µg/g strongly influenced the bacterial communities in soil enrichments, concentrations up to 1 mg/g were strongly adsorbed onto soil particles and did not cause significant changes in the microbiome and resistome of soil microcosms. This study demonstrates the differences between the response of bacterial communities to antibiotic pollution in enriched media and in their environmental matrix, and exposes the limitations of culture-based studies in antibiotic-resistance surveillance. Furthermore, establishing links between the effects of antibiotic pollution and soil properties is needed.

Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance

A multitude of factors, such as drug misuse, lack of strong regulatory measures, improper sewage disposal, and low-quality medicine and medications, have been attributed to the emergence of drug resistant microbes. The emergence and outbreaks of multidrug resistance to last-line antibiotics has become quite common. This is further fueled by the slow rate of drug development and the lack of effective resistome surveillance systems. In this review, we provide insights into the recent advances made in computational approaches for the surveillance of antibiotic resistomes, as well as experimental formulation of combinatorial drugs. We explore the multiple roles of antibiotics in nature and the current status of combinatorial and adjuvant-based antibiotic treatments with nanoparticles, phytochemical, and other non-antibiotics based on synergetic effects. Furthermore, advancements in machine learning algorithms could also be applied to combat the spread of antibiotic resistance. Development of resistance to new antibiotics is quite rapid. Hence, we review the recent literature on discoveries of novel antibiotic resistant genes though shotgun and expression-based metagenomics. To decelerate the spread of antibiotic resistant genes, surveillance of the resistome is of utmost importance. Therefore, we discuss integrative applications of whole-genome sequencing and metagenomics together with machine learning models as a means for state-of-the-art surveillance of the antibiotic resistome. We further explore the interactions and negative effects between antibiotics and microbiomes upon drug administration.

Country Income Is Only One of the Tiles: The Global Journey of Antimicrobial Resistance among Humans, Animals, and Environment

Antimicrobial resistance (AMR) is one of the most complex global health challenges today: decades of overuse and misuse in human medicine, animal health, agriculture, and dispersion into the environment have produced the dire consequence of infections to become progressively untreatable. Infection control and prevention (IPC) procedures, the reduction of overuse, and the misuse of antimicrobials in human and veterinary medicine are the cornerstones required to prevent the spreading of resistant bacteria. Purified drinking water and strongly improved sanitation even in remote areas would prevent the pollution from inadequate treatment of industrial, residential, and farm waste, as all these situations are expanding the resistome in the environment. The One Health concept addresses the interconnected relationships between human, animal, and environmental health as a whole: several countries and international agencies have now included a One Health Approach within their action plans to address AMR. Improved antimicrobial usage, coupled with regulation and policy, as well as integrated surveillance, infection control and prevention, along with antimicrobial stewardship, sanitation, and animal husbandry should all be integrated parts of any new action plan targeted to tackle AMR on the Earth. Since AMR is found in bacteria from humans, animals, and in the environment, we briefly summarize herein the current concepts of One Health as a global challenge to enable the continued use of antibiotics.

Unravelling the antibiotic and heavy metal resistome of a chronically polluted soil

The antibiotic and heavy metal resistome of a chronically polluted soil (3S) obtained from an automobile workshop in Ilorin, Kwara State, Nigeria was deciphered via functional annotation of putative ORFs (open reading frames). Functional annotation of antibiotic and heavy metal resistance genes in 3S metagenome was conducted using the Comprehensive Antibiotic Resistance Database (CARD), Antibiotic Resistance Gene-annotation (ARG-ANNOT) and Antibacterial Biocide and Metal Resistance Gene Database (BacMet). Annotation revealed detection of resistance genes for 15 antibiotic classes with the preponderance of beta lactamases, mobilized colistin resistance determinant (mcr), glycopepetide and tetracycline resistance genes, the OqxBgb and OqxA RND-type multidrug efflux pumps, among others. The dominance of resistance genes for antibiotics effective against members of the Enterobacteriaceae indicate possible contamination with faecal materials. Annotation of heavy metal resistance genes revealed diverse resistance genes responsible for the uptake, transport, detoxification, efflux and regulation of copper, zinc, cadmium, nickel, chromium, cobalt, mercury, arsenic, iron, molybdenum and several others. Majority of the antibiotic and heavy metal resistance genes detected in this study are borne on mobile genetic elements, which facilitate their spread and dissemination in the polluted soil. The presence of the heavy metal resistance genes is strongly believed to play a major role in the proliferation of antibiotic resistance genes. This study has established that soil is a huge repertoire of antibiotic and heavy metal resistome and due to the intricate link between human, animals and the soil environment, it may be a major contributor to the proliferation of multidrug-resistant clinical pathogens.

Fungicides enhanced the abundance of antibiotic resistance genes in greenhouse soil

Long-term substantial application of fungicides in greenhouse cultivation led to residual pollution in soils and then altered soil microbial community. However, it is unclear whether residual fungicides could affect the diversity and abundance of antibiotic resistance genes (ARGs) in greenhouse soils. Here, the dissipation of fungicides and its impact on the abundance of ARGs were determined using shotgun metagenomic sequencing in the greenhouse and mountain soils under laboratory conditions. Our results showed the greenhouse soils harbored more diverse and abundant ARGs than the mountain soils. The application of carbendazim, azoxystrobin, and chlorothalonil could increase the abundance of total ARGs in the greenhouse soils, especially for those dominant ARG subtypes including sul2, sul1, aadA, tet(L), tetA(G), and tetX2. The abundant ARGs were significantly correlated with mobile genetic elements (MGEs, e.g. intI1and R485) in the greenhouse soils but no significant relationship in the mountain soils. Meanwhile, the co-occurrence patterns of ARGs and MGEs, e.g., sul2 and R485, sul1 and transposase, were further verified via the genetic arrangement of genes on the metagenome-assembled contigs in the greenhouse soils. Additionally, host tracking analysis indicated that ARGs were mainly carried by enterobacteria in the greenhouse soils but actinomyces in the mountain soils. These findings confirmed that some fungicides might serve as the co-selectors of ARGs and elevated their abundance via MGEs-mediated horizontal gene transfer in the greenhouse soils.