Keywords

12.1 Introduction

Antibiotic resistance in bacteria has evolved naturally and long before antibiotics were manufactured in large quantities for human use. Bacteria have evolved a variety of mechanisms to resist antibiotics, including changes in the structure of target proteins, the production of enzymes that break down antibiotics and the efflux of antibiotics out of the cell (Larsson and Flach 2021). The spread of ARGs in the environment is also a major concern, as it can increase the risk of antibiotic resistance in human and animal pathogens, making it more difficult to treat infections (Galera-Laporta and Garcia-Ojalvo 2020; Sun et al. 2020; Reverter et al. 2020). Antibiotic use and consumption have been increasing globally, and projections indicate that this trend is likely to continue. A recent study estimated that, under the scenario of no policy interventions, worldwide antibiotic consumption in 2030 could be as much as 200% higher than the level in 2015 (Klein et al. 2018). Anthropogenic, or human-caused, antibiotic residues in the environment can result in a number of serious environmental problems. Thiamphenicol is one example of an antibiotic that has been shown to have adverse environmental impacts. Research has indicated that thiamphenicol can interfere with nitrate reduction processes in soil, which can result in increased levels of nitrous oxide (N2O) being released into the atmosphere (Yin et al. 2016). The widespread use of antibiotics can put significant selective pressure on microorganisms, leading to the enrichment of ARGs (Wang et al. 2019; Migliorini et al. 2019). Antibiotic resistance is a complex phenomenon that can occur through various mechanisms, namely, enzymatic destruction, efflux pumps, cellular protection and both target defence and antibiotic deactivation (Zhang et al. 2019; Chen et al. 2019; Wilson et al. 2020). Antibiotic resistance can significantly impair the efficacy of antibiotics against pathogens, making it difficult or impossible to treat infections effectively (Pärnänen et al. 2019). Horizontal gene transfer (HGT) is a key mechanism by which antibiotic resistance genes (ARGs) can spread and become more prevalent in the environment. Mobile genetic elements (MGEs) which include transposons, integrons and plasmids are self-contained pieces of DNA that can move from one bacterium to another, often across species boundaries. These MGEs can carry multiple ARGs, providing bacteria with a “toolkit” of resistance mechanisms (Wang et al. 2018; Pallares-Vega et al. 2019; Zhao et al. 2019a). ARGs and antibiotics have frequently been found in rivers (Rodriguez-Mozaz et al. 2015; Singh et al. 2019; Das et al. 2020), lakes (Tang et al. 2015; Yang et al. 2018a), groundwater (Tong et al. 2020; Zainab et al. 2020) and coastal and estuarine environments (Griffin et al. 2019; Zheng et al. 2021). The presence of pollutants, including antibiotics and antibiotic resistance genes (ARGs), in these environments can have negative impacts on the health of estuarine and coastal ecosystems (Ward et al. 2020). The presence of ARGs and antibiotics in coastal and estuarine environments can have far-reaching consequences for biogeochemical cycling, ecological security and human health (Leonard et al. 2015; Zhao et al. 2019b).

Antimicrobial resistance in bacteria is currently the most prevalent type of resistance in microbes and a significant public health issue. The overuse and misuse of antibiotics have led to the evolution of bacteria that are resistant to multiple antibiotics, making it difficult to treat bacterial infections.

12.2 Taxonomic Profiling

Understanding the taxonomic classifications of resistome components is crucial for locating the bacteria that produce a resistome. Taxonomic assignment analysis of resistome elements can provide information about the composition and relative abundance of the microbial community in a sample (Ruppé et al. 2018; Rice et al. 2020). There are two main approaches for identifying bacterial community composition from metagenomic data. The first approach is based on direct analysis of raw sequencing reads and does not require contig assembly. The second approach involves the assembly of contigs from the raw sequencing reads, followed by taxonomic assignment of the contigs (de Abreu et al. 2021). Taxonomic classification of metagenomic data without contig assembly can be a faster and more computationally efficient approach compared to contig assembly (Rodríguez-Brazzarola et al. 2018). The length and quality of sequences are crucial considerations during taxonomy classification analysis. Short reads or low-quality reads may have a higher error rate and may not provide enough information to accurately identify the bacterial taxa present in a sample (Breitwieser et al. 2019; Ye et al. 2019). The length of contigs generated by contig assembly is an advantage for taxonomic classification, as longer contigs provide more information to accurately identify the bacterial taxa present in a sample. Taxonomic classification and contig-based taxonomic classification rely heavily on the use of reference databases (Rodríguez-Brazzarola et al. 2018). Contig assembly can sometimes enable the reconstruction of partial genomes of previously unknown or uncultured bacterial organisms. In contig assembly, short reads from different bacterial taxa can be erroneously assembled together into a single contig, resulting in a chimeric contig. The quality of the assembled contigs and the accuracy of the taxonomic assignments made based on the assembled contigs can strongly influence the interpretation of the microbial community composition and function (Behera et al. 2020a, b, 2021a, b, 2022; Rout et al. 2022).

Furthermore, genome assembly can enable the identification of possible HGT regions, which are genomic regions that have been acquired by a bacterium from another organism through HGT mechanisms such as transduction, transformation or conjugation. In the context of antibiotic resistance genes, the size of gene sequences can have a substantial effect on gene annotation transfer and the investigation of biological mechanisms linked with resistance (ARGs). Taxonomic assignment by contig assembly can be a useful tool for identifying and understanding resistance mechanisms, particularly in the context of studying the structural relationships between microbiota and the resistome. The type of sample being worked with can influence the quality and quantity of DNA/RNA obtained, which can in turn impact the success of the assembly process. The sequences in the datasets are of good quality and long enough to be aligned directly to a reference database. The taxonomic classification can be performed using alignment-based methods, which are computationally less intensive than de novo assembly approaches (Rodríguez-Brazzarola et al. 2018). High-throughput sequencing can be used to study the taxonomic assignments of resistome elements in various environments, including water reservoirs to identifying antibiotic resistance genes (ARGs) in host-pathogen relationships such as hospitals (Chng et al. 2020), water reservoirs (Yu et al. 2020; Ekwanzala et al. 2020), soil (Chen et al. 2017), human faeces (Karkman et al. 2019), livestock wastewater and faeces (Jia et al. 2017), air (Yang et al. 2018b; Li et al. 2021) and biogeochemical and biogeographical processes (Kuang et al. 2016; Liu et al. 2018).

12.3 Functional Study and ARGs Database

The investigation of taxonomic signatures can assist us in gaining a deeper comprehension of the connections that exist among the various members of a microbial diversity. Functional metagenomics is an approach that purposes to identify functions in a microbial community by discovering new enzymes, biosynthetic gene clusters and antibiotic resistance genes (ARGs). Functional annotation typically involves several steps, including gene prediction, annotation transfer and functional assignment (Dong and Strous 2019). Metagenomics is a powerful tool for studying microbial communities and has the potential to provide important insights into microbial ecology, evolution and biotechnology (Zhang et al. 2011). There are numerous databases and methods available for identifying a microbial community’s taxonomic diversity and undertaking functional assessments. These include databases of reference genomes, protein sequences and metabolic pathways, as well as bioinformatic tools for taxonomic classification, functional annotation and pathway analysis. Functional analysis of metagenomic data provides a wealth of information that can be used for various sub-analyses, depending on the sequencing depth and research question. These sub-analyses can include protein-protein interaction, pathway, functional category, gene ontology, protein family and subsystem analysis, among others. Each of these analyses provides different levels of detail and can be used to gain insights into the functional potential and metabolic activities of the microbial community. There are open-source software/applications, like Mothur (Schloss et al. 2009), MEGAN (Huson et al. 2007) and QIIME (Caporaso et al. 2010) for taxonomy and functional analysis. BLAST+ is an important tool in genomic research and is widely used to annotate new genome sequences and to investigate the evolutionary relationships between different species (Altschul et al. 1997). DIAMOND is a powerful tool for annotating genomic data, particularly for the analysis of large datasets such as metagenomic samples. Its speed and accuracy make it an attractive option for researchers working with large amounts of sequence data (Buchfink et al. 2014). USEARCH is a useful tool for bioinformatic analysis, particularly for sequence searches and clustering (Edgar and Bateman 2010). RAPSearch2 is a powerful tool for sequence searching and is well suited for large-scale analyses (Zhao et al. 2012).

The development of new methods and tools for comprehensive metagenomic analyses is an ongoing process, and it is important for researchers to stay up-to-date with the latest advancements in the field in order to conduct the most effective and informative analyses. Resistome databases are crucial resources for understanding antimicrobial resistance and are constantly evolving as new information becomes available (Danko et al. 2021). The use of database for sequence analysis can introduce database bias that can affect the accuracy and relevance of the results, particularly for metagenomic analyses. The genomic surveillance of AMR is crucial for understanding the distribution and spread of resistance genes, as well as for identifying new resistance mechanisms (de Abreu et al. 2021). To support this effort, numerous annotation softwares and databases have been established to facilitate the analysis of antibiotic resistance gene (ARG) content in bacterial genomes or next-generation sequencing (NGS) metagenomic samples. These tools and databases provide valuable resources for researchers and practitioners to identify, annotate and compare ARGs in different bacterial genomes and metagenomes. Some of the most widely used annotation tools and well-known AMR databases are tabulated in Table 12.1.

Table 12.1 Several bioinformatic databases available for studying antibiotic resistance genes (ARGs)
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The Comprehensive Antibiotic Resistance Database (CARD) (Alcock et al. 2020) is unique in that it combines sequence data with bioinformatic tools to aid in the detection and analysis of AMR genes. For example, the database includes curated detection models that can be used to identify AMR genes in sequenced bacterial genomes, as well as tools for visualising and analysing the genomic context of these genes. In addition to resistance genes, CARD also includes information on resistance mutations, which are genetic changes that can confer resistance to antibiotics. Like the resistance genes, the resistance mutations are organised by bacterial species. CARD focuses on delivering high-quality reference material and molecular sequences that are arranged using the Antibiotic Resistance Ontology (ARO), a regulated vocabulary.

ARDB (Liu and Pop 2009) also known as the Antibiotic Resistance Genes Database tracks ARGs. It was first released in 2005 and is maintained by the Antibiotic Resistance Genes Reference Center at the University of Alberta, Canada. It includes information on a wide range of antibiotic resistance genes, including those found in both Gram-positive and Gram- negative bacteria. The database provides detailed annotations for each gene, including information on its function, location and associated resistance mechanisms.

Resfinder (Florensa et al. 2022) is a useful tool for the characterisation and identification of ARGs, and its use can help researchers better understand the distribution and prevalence of resistance genes in bacterial populations and metagenomic datasets. Resfinder also provides information about the sequence similarity and identity of the identified resistance genes, allowing researchers to compare the resistance genes to known reference sequences and assess the potential impact of any genetic variations or mutations. This can be important for understanding the mechanisms of resistance and for predicting the potential effectiveness of different antimicrobial therapies.

ARG-ANNOT (Gupta et al. 2014) is a web-based tool for both complete genomes and draft genomes and can be applied to both Gram-negative and Gram-positive bacteria. The tool provides detailed annotations for each detected AR gene, including information on its function, resistance mechanism and associated resistance phenotype. It also provides information on the genomic context of each gene, including its location on the chromosome and any associated mobile genetic elements.

ARG-database (Yang et al. 2016) ARGs-OAP (Antibiotic Resistance Genes Online Analysis Pipeline) is a bioinformatic tool designed to facilitate the analysis and finding of antibiotic resistance genes (ARGs) in metagenomic data. It includes a large and diverse database of reference sequences for comparison and can be applied to both short-read and long-read sequencing data. By providing a comprehensive and up-to-date database of reference sequences and detailed annotations for each gene, it helps to facilitate research on the molecular mechanisms of antibiotic resistance and on the development of new strategies for combating it.

SARG (Yin et al. 2018) database was designed to help researchers identify ARGs in metagenomic samples, which are complex mixtures of DNA from different microorganisms. SARG v2.0 builds on this concept by incorporating a much larger number of reference sequences for ARGs, which allows for more comprehensive coverage of the resistance gene landscape in different environments. ARGs-OAP v2.0 is designed to help researchers analyse and annotate high-throughput sequencing data for the presence of antibiotic resistance genes (ARGs). The database includes a large number of reference sequences for ARGs, which can be used to identify similar sequences in raw sequencing data using a similarity search strategy.

ARGminer (Arango-Argoty et al. 2020) able to capture a broader range of ARGs and provide more comprehensive information about each gene, including its sequence, function and resistance profile. It looks like the sequences from the different databases that were used to build ARGminer were processed to get rid of duplicates and mark them also with highest similarity from each of the databases. Overall, the use of an ensemble database like ARGminer can be a powerful tool for understanding the genetics and mechanisms of antibiotic resistance, which is a critical public health concern.

NDARO (Feldgarden et al. 2021) is the central hub for scholars to access data related to antimicrobial resistance (AMR) in infective organisms. The purpose of real-time AMR surveillance is that it keeps an up-to-date database of AMR genes and a combination of genetic and antibiotic susceptibility data. The Reference Gene Catalog, which is maintained by the NDARO, is a curated collection of antimicrobial resistance (AMR) genes from various bacterial pathogens. NDARO has expanded its focus beyond just antimicrobial resistance (AMR) genes to include other genetic elements that are important for understanding the biology of clinically important pathogens.

MEGAres (Doster et al. 2020) is an updated version of the MEGARes database, which is a comprehensive resource for studying antimicrobial resistance genes. It contains sequence data for thousands of hand-curated antimicrobial resistance genes, but it also includes additional features that are designed to aid in the analysis of metagenomic sequencing data. A crucial part of preventing AMR is identifying its genetic causes in the fight against this global public health threat. By using MEGARes to analyse metagenomic data, researchers can identify the presence and abundance of AMR genes in various environments and track their distribution and evolution over time.

AMR++ (Bonin et al. 2023) is a powerful and flexible bioinformatic pipeline that is designed to aid in the analysis of antimicrobial resistance genes. In the context of metagenomics, the DNA of a given sample or set of samples is sequenced using high-throughput sequencing technologies. The resulting data can be quite complex, with millions or even billions of short DNA sequences that need to be sorted and analysed. By providing a comprehensive view of the resistome in a given bacterial population, the pipeline can help researchers to better understand the mechanisms and evolution of antibiotic resistance and develop more effective strategies for combatting this important public health. The sources and drivers of AMR in the aquatic environment is crucial for developing effective strategies to mitigate its impact. These strategies may involve improved wastewater treatment processes, responsible antimicrobial use in agriculture and aquaculture, and enhanced surveillance and monitoring programs. Additionally, promoting public awareness and education regarding the proper disposal of pharmaceuticals and reducing unnecessary antimicrobial use are important steps towards combating AMR in the aquatic environment (Fig.12.1).

Fig. 12.1
A schematic represents a flow starting with sediment samples from medical, factory, and pollution, followed by D N A extraction, library preparation, sequencing, bioinformatics data analysis, and ends with antibiotic resistance genes

Antimicrobial resistance genes showing the sources, and drivers of antimicrobial resistance in the aquatic environment

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12.4 Antimicrobial Resistance in Bacteria

Resistome is a term used to describe the total genetic potential of an ecosystem to resist antimicrobial agents (Martínez et al. 2014; Crofts et al. 2017). The resistome of an ecosystem is shaped by a variety of factors, including the presence of anthropogenic stressors, such as the release of antibiotics and other toxic compounds into the environment, and the presence of natural stressors, such as heavy metal ions and other toxic substances (Kraemer et al. 2019). Microorganisms have evolved the ability to detect, interact with and digest tiny compounds that control the antibiotics (Berendonk et al. 2015). The development of novel antibiotics that can avoid resistance can be influenced by knowledge about the evolution of resistance (McGarvey et al. 2012). The genomic era has led to new insights into the biology of bacteria, which could potentially lead to the discovery of new antibiotics, but the process of drug development is complex and time-consuming (Li et al. 2015). Soil microorganisms produce natural antimicrobial compounds, and these have been a rich source of antibiotics used in clinical medicine (Wrighton 2018; Crits-Christoph et al. 2018). Metagenomic mining has revealed that resistance genes have existed in microbial populations long before the modern “antibiotic era” (Yadav and Kapley 2021). The diversity and distribution of ARGs in the environment can help to inform the development of new antibiotic resistance methods, such as using natural chemicals from microbial communities to inhibit the propagation of resistance genes (Berendonk et al. 2015). In this study, antibiotic resistance in environmental bacteria can provide important insights into the natural history of resistance and the mechanisms that underlie the development and spread of resistance in clinical settings (Aminov 2009). The fast growth of antibiotic-resistant infections has revealed our limited understanding of the environmental mechanisms occurring in microbial communities (Waseem et al. 2017). Multi-drug-resistant bacteria (MDR) have evolved the ability to metabolise antimicrobials and can transfer these properties to other bacterial species through horizontal gene transfer (Holt et al. 2015).

12.5 Hotspots for the Spread of Antibiotic Resistance

Antibiotic use in agriculture, veterinary medicine and human medicine can lead to the growth and spread of resistant microorganisms in the environment. Antibiotic-resistant bacteria have been found in various environmental sources, including soil, water and wildlife; there is no direct evidence that these bacteria have existed four million years ago, in caves (Bhullar et al. 2012). ARG bacteria have also been discovered in the gastrointestinal tracts of persons living in distant places who have never been exposed to antibiotics as well as in samples of permafrost that are thousands of years old (Kunhikannan et al. 2021). The use of antibiotics in agriculture and animal husbandry can lead to the selection and spread of ARGs in the gut microbiota of animals (Wichmann et al. 2014; Berendsen et al. 2015). Antibiotic-resistant bacteria can live in close proximity to each other in the soil, which can facilitate the transfer of antibiotic resistance genes through horizontal gene transfer (Christensen et al. 1998). Horizontal gene transfer is a major factor promoting the growth of ARGs in the environment, and it highlights the importance of responsible use of antibiotics to minimise the emergence and spread of antibiotic-resistant bacteria. The use of antibiotics in animals can contribute to the development of antibiotic resistance in humans. When antibiotics are used in animal agriculture, bacteria can become resistant to these drugs and may spread to humans through food or other environmental pathways. This can lead to the rise and spread of ARG infections in humans, which can be difficult to treat with traditional antibiotics. Therefore, reducing the use of antibiotics in animal agriculture and promoting responsible use of antibiotics in human medicine are important strategies for minimising the emergence and spread of antibiotic-resistant infections in humans (Mann et al. 2021). Workers who handle and process meat or work in agriculture may be exposed to bacteria that are resistant to antibiotics, and this can increase the risk of developing antibiotic-resistant infections (Manyi-Loh et al. 2018). It is critical to investigate the various environmental hotspots that contribute to the spread of antibiotic resistance in both pathogenic and non-pathogenic bacteria. Hotspots for antibiotic-resistant bacteria can be found in various environmental sources, with pharmaceutical manufacturing sites, wastewater systems and aquaculture, food and animal production and hospitals (Berendonk et al. 2015).

It is important to find the major drivers that contribute to the development and spread of antimicrobial resistance. Some of the major drivers include the following:

  • Overuse and misuse of antibiotics: Antibiotic-resistant bacteria can arise and spread due to the overuse and improper use of antibiotics in both animals and human beings.

  • Poor infection prevention and control practices: Poor infection control and prevention procedures in healthcare settings can facilitate the spread of antibiotic-resistant bacteria among patients.

  • Inadequate sanitation and hygiene: Inadequate sanitation and hygiene can lead to the spread of antibiotic-resistant bacteria in the environment, particularly in water and soil.

  • Agricultural and animal husbandry practices: The use of antibiotics in agriculture and animal husbandry can lead to the development and spread of ARGs in animals and the environment.

  • Global travel and trade: The global movement of people, animals and goods can facilitate the spread of antibiotic-resistant bacteria across borders.

  • Insufficient funding for research and development: The lack of investment in research and development of new antibiotics and alternative treatments for infectious diseases can limit our ability to effectively treat antibiotic-resistant infections.

Identifying and addressing these drivers is critical to addressing the problem of antimicrobial resistance and preserving the effectiveness of antibiotics for future generations.

12.6 Conclusion

Metagenomics is an effective method for finding and investigating antibiotic resistance pathways utilising both sequence-based and function-based methods. It allows for the comprehensive analysis of complex microbial communities, providing insights into the diversity and distribution of ARGs. Antimicrobial resistance studies are commonly related to other components of the study being conducted, such as investigations of mutations, metabolic pathways, gene expression and infections. These other factors can affect the development and spread of antibiotic resistance, and understanding their interplay with resistance mechanisms is important for developing effective strategies to combat antimicrobial resistance. These studies involve large, complex datasets and require advanced computational and bioinformatic tools for their analysis. Proper data pre-processing, quality control and statistical analysis are essential to ensure the accuracy and reproducibility of results.