Insights into Antibiotic Resistance Through Metagenomic Approaches

Robert Schmieder; Robert Edwards

Future Microbiol. 2012;7(1):73-89.

Abstract and Introduction

Abstract

The consequences of bacterial infections have been curtailed by the introduction of a wide range of antibiotics. However, infections continue to be a leading cause of mortality, in part due to the evolution and acquisition of antibiotic-resistance genes. Antibiotic misuse and overprescription have created a driving force influencing the selection of resistance. Despite the problem of antibiotic resistance in infectious bacteria, little is known about the diversity, distribution and origins of resistance genes, especially for the unculturable majority of environmental bacteria. Functional and sequence-based metagenomics have been used for the discovery of novel resistance determinants and the improved understanding of antibiotic-resistance mechanisms in clinical and natural environments. This review discusses recent findings and future challenges in the study of antibiotic resistance through metagenomic approaches.

Introduction

Infectious diseases are the second-leading cause of death globally and the most significant cause of death in children.^[1,2] Antibiotics represent one of the largest therapeutic categories used in the treatment of infectious diseases caused by bacteria, but the successful use of any therapeutic agent is compromised by the potential development of tolerance or resistance to that compound from the time it is first employed. In clinical environments, pathogenic and commensal bacteria are challenged with high concentrations of antibiotics and bacteria have become resistant to most of the antibiotics developed.^[3] Within the hospital setting, resistance pathogens often emerge within a few years after a new antibiotic is introduced (Figure 1). The spread of resistant bacteria and resistance genes depends on different factors but the major pressure is antibiotic usage.^[4] Nosocomial (hospital-linked) infections result in approximately 100,000 deaths and cost more than US$25 billion per year in the USA alone. Worldwide, it is estimated that 5–10% of patients entering hospitals develop an infection as a result of their stay.^[101] Despite the problem of antibiotic resistance in infectious bacteria, little is known regarding the diversity, distribution and origins of resistance genes, especially for the unculturable majority of environmental bacteria.

(Enlarge Image)

Figure 1.

Antibiotic resistance evolution showing the rapid development of resistance for several classes of antibiotics. The bars mark the time from the introduction of an antibiotic to the clinic until the first clinical case of resistance to that antibiotic was reported. The high number of available antibiotics during the 1950s and 1960s attributed to the longer times until the first resistance case was reported (e.g., for nalidixic acid). If the time frames for the same antibiotic differed between the data sources, the shorter time frame was used.
Data taken from [94–97].

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Next Section

Table 1. Summary of resistance genes identified through functional metagenomics.
Environment	Number of clones screened (total library size)	Number of resistant clones	Resistant to (predicted mechanisms)	Ref.
Soil (plano silt loam)	1,158,000 (4.1 Gb)	9 1	Aminoglycoside (inactivation) Tetracycline (efflux)	[20]
Soil (apple orchard)	446,000 (13 Gb)	3 1 9	Aminoglycoside (inactivation) Tetracycline (efflux) β-lactam (inactivation)	[23]
Soil (Alaskan)	714,000 (12.4 Gb)	14^†	β-lactam (inactivation)	[22]
Soil (Alaskan)	-^‡ (13.2 Gb)	1	Amphenicol (efflux)	[59]
Soil (loamy)	550,000 (3.6 Gb)	2 3 2 4	Aminoglycoside (inactivation) β-lactam (inactivation) Amphenicol (efflux) Antifolate (inactivation)	[63]
Activated sludge	- (1.8 Gb) 1161 (1.2 Mb)	1 1 7 0^§	Aminoglycoside (inactivation) β-lactam (inactivation) Amphenicol (inactivation) -	[69]
Activated sludge	96,000 (3.2 Gb)	3^¶	Glycopeptide (inactivation)	[25]
Human (fecal, saliva)	-^# (9.3 Gb)	356 2408 12 1330 191	Aminoglycoside Amino acid derivative Amphenicol β-lactam Tetracycline	[48]
Human (plaque, saliva)	450^†† (-)	18	Tetracycline	[24]
Human (plaque, saliva)	1860^†† (-)	14 32 58	Aminoglycoside β-lactam Tetracycline	[51]
Insect (midgut disect)	38,500 (0.3 Gb)	2 3^‡‡ 1^‡‡	Macrolide (efflux) β-lactam (inactivation) Amphenicol	[29]
Organic pig (fecal)	9000 (0.1 Gb)	10	Tetracycline	[87]

^†Resistance genes could only be identified in 13 clones.
^‡Library from [22] used.
^§Phage metagenomic library.
^¶Two of three resistant clones were assumed to be from the same region due to identical sequences.
^#Insert length between 1000 and 3000 bp.
^††Insert length between 800 and 3000 bp.
^‡‡One clone was resistant to all three antibiotic classes in a secondary screen.

Box 1.
The generic term 'antibiotic' is used to denote any class of organic molecules that inhibits or kills microbes by specific interactions with bacterial targets, without any consideration of the source of the particular compound or class The term 'antibiotic resistome' was proposed for the collection of all antibiotic resistance genes in microorganisms, from both pathogenic and nonpathogenic bacteria 'Metagenome' describes the genetic potential of a community 'Sequence-based metagenomics' involves extracting and random sequencing of DNA directly from the environment without the need for culturing 'Functional metagenomics' involves cloning and heterologous expression of environmental DNA in a surrogate host with coupled activity-based screening The term 'microbiome' has been suggested by Nobel laureate Joshua Lederberg to describe the collective genome of our indigenous microbes 'Metatranscriptomics' is a culture-independent method by which the collective RNA transcript of an entire community of microorganisms is analyzed

Sidebar

Executive Summary

Mechanisms of Antibiotic Resistance

Resistance to antibiotics can be caused by four general mechanisms (inactivation, alteration of the target, circumvention of the target pathway or efflux of the antibiotic) and bacteria can develop resistance by mutating existing genes, or by acquiring new genes from other strains or species.

Detection of Antibiotic Resistance

Antibiotic resistance is a highly selectable phenotype and can be detected using growth inhibition assays; however, culture-based approaches can take days to weeks for slow-growing bacteria and only work for a fraction of microbes.
Antibiotic resistance genes were typically isolated by cloning from cultured bacteria or by PCR amplification. Those methods missed potential antibiotic resistance reservoirs because of uncultivable bacteria or dependence on known resistance genes.
Metagenomics overcomes the limitations of culture-dependent techniques and is a powerful tool to identify novel resistance genes and to describe the presence or absence of genes or genetic variations responsible for antibiotic resistance.

Culture-independent Study of Resistance Through Metagenomics

Functional metagenomics involves cloning and expression of DNA in a surrogate host with coupled activity-based screening.
Sequence-based metagenomics involves extracting and random sequencing of DNA directly from the environment. The metagenomic sequences are then compared to known sequences to identify resistance genes and/or mutations.

Antibiotic Resistance in Human-associated Microbes

It is estimated that there are ten-times as many microbes in and on any given human as there are cells in that person's body and the majority of those have not yet been cultured. Large-scale sequencing projects such as the HMP and MetaHIT have been started as part of the International Human Microbiome Consortium to investigate the human microbiota.
Antibiotics may have long-term consequences on the human microbiota, which cannot fully recover after repeated antibiotic perturbation, and might cause a shift to a different, but stable community.
Our oral and fecal microbiota are distinctly different, both in the organisms present and the resistance mechanisms in those organisms.

Antibiotic Resistance in Soil Microbial Communities

There are an estimated 10 ⁹ microorganisms per gram of soil and less than 1% of those microbes are readily culturable by current methods. The high density of antibiotic-producing bacteria makes soil a reservoir for antibiotic resistance.
Metagenomic studies of soil environments identified novel and known genes that mediate resistance to different classes of antibiotics, including soil from remote locations with no known exposure to antibiotic drugs. The resistance proteins had low similarity to previously published sequences suggesting that soil microorganisms harbor more genetic diversity than previously assumed.

Antibiotic Resistance Associated With Wastewater Treatment Plants

Wastewater treatment plants are interfaces between different environments and provide an opportunity for resistance to mix between pathogens, opportunistic pathogens, and environmental bacteria. The sludge products are increasingly used to fertilize agricultural crops, dispersing unknown amounts of resistance genes and antibiotics.
Activated sludge showed evidence for a high diversity of resistance genes and allowed the identification of novel resistance genes.
The survival of antibiotic-resistance genes through the sewage treatment process and in the aquatic environment merits further study.

Antibiotic Resistance in Marine Microbial Communities

Antibiotic-resistant bacteria have been found widely in aquatic environments and resistance genes found in clinical human pathogens have been identified in marine microbes.

Animal- & Agriculture-related Resistance

Antibiotics are extensively used for animal farming and for agricultural purposes and may have effects on the selection of resistance.
Residues from farms can contain antibiotic resistance genes that may impact environmental microbiota.

Ongoing Challenges for Detecting Antibiotic Resistance in Environmental Samples

The results of functional metagenomics are dependent upon each gene's ability to be expressed in surrogate hosts. The limitations of Escherichia coli -based screening most likely resulted in an underestimate of the frequency of resistance determinants in environmental samples. In addition, a range of media types, antibiotic concentrations and incubation methods make it difficult to compare results between environments or laboratories.
The biggest challenge with sequence-based metagenomics is the large number of sequences that show no significant similarity as sequence-based approaches are generally limited to only identify things we already know. High-throughput sequencing technologies generate data that currently challenge data storage, management and processing, demanding access to supercomputing resources and expertise in bioinformatics.
Sequence-based identification of a resistance gene requires separate functional confirmation; however, with the advancement of mRNA extraction from environmental samples, metatranscriptomics may become be the main method for the detection of functional resistance genes.

Conclusion

Functional and sequence-based metagenomics have been used for the discovery of novel resistance determinants and the improved understanding of antibiotic-resistance mechanisms.
Sequence-based metagenomic data has the ability to combine antibiotic resistance gene abundance data with community composition and metabolic pathway information to provide a more complex profile of specific samples.
The study of the environmental resistance reservoir using metagenomic approaches could provide an early warning system for future clinically relevant antibiotic resistance mechanisms.