Antimicrobial Resistance

What is antibiotic resistance?

Antibiotic resistance is a genetic trait that enables bacteria to survive in the presence of antibiotics that would normally kill or inhibit their growth. Antibiotic resistance is a growing problem worldwide, as bacteria have evolved and adapted to survive exposure to many different types of antibiotics that are commonly used to treat bacterial infections.

Antibiotic resistance is a serious public health problem, as it can make bacterial infections more difficult to treat and increase the risk of complications and mortality. To address this problem, researchers are working to develop new antibiotics and alternative treatments, as well as to promote more judicious use of antibiotics to reduce the selective pressure for the emergence and spread of antibiotic-resistant bacteria.

Perhaps you have heard of penicillin. Did you know that there are several varieties of penicillin that have different mechanisms and coverages? Pathogens that have been exposed to this antibiotic and survived to tell the tale have developed resistance (e.g. using β-lactamase enzymes) and this can be passed on and shared with other pathogens.

How does this apply to genomics?

Genomics plays a critical role in the study of antibiotic resistance, as it provides tools and techniques for identifying, characterizing, and tracking the spread of resistance genes and mutations in bacterial populations.

One application of genomics in the study of antibiotic resistance is through the use of whole-genome sequencing (WGS), which allows researchers to sequence and analyze the entire genetic material of bacterial strains. WGS can be used to identify specific genes and mutations that confer antibiotic resistance, as well as to compare the genetic relatedness and diversity of different bacterial strains, which can help to track the spread of resistance.

In addition, genomics has enabled the development of new diagnostic and surveillance tools for monitoring antibiotic resistance. For example, PCR-based assays can be used to detect specific resistance genes in clinical samples, which can help guide treatment decisions and prevent the spread of resistant strains.

What insights does genomics provide?

Genomics also provides insights into the evolutionary dynamics of antibiotic resistance, such as the selective pressures that drive the emergence and spread of resistance, the mechanisms by which resistance genes are acquired and transferred between bacterial strains, and the potential for the evolution of new forms of resistance in response to selective pressures. This knowledge can inform the development of strategies for preventing and controlling antibiotic resistance.









Methicillin and other β-lactams


Vancomycin and other glycopeptides











Table of antibiotic-resistance genes

Antibiotic resistance gene references used by EzBioCloud Clinical Metagenomics

In the EzBioCloud Clinical Metagenomics, we use the catalogue of antibiotic resistance genes that we curated from the Ez-Mx genome database as the reference panel for antibiotic resistance gene detection. The Ez-Mx genome database consists of all prokaryotic genome assemblies provided in the NCBI GenBank plus a bunch of WGS projects conducted in-house at CJ Bioscience. We annotated the antibiotic resistance genes on all genome entries using the NCBI’s AMRFinderPlus, and combined the CDS nucleotide sequences of the annotated antibiotic resistance genes – keeping the resistance property attributes annotated by the AMRFinderPlus attached to the CDS sequences. The pooled CDS sequences were de-replicated at 99% identity, using MMseqs2, and indexed into the reference Bowtie2 index for read mapping. The “taxonomic association” provided for the detected antibiotic resistance gene alleles in the Clinical Metagenomics reports comes from the original catalogue (before de-replication) of resistance gene CDSs.

Detection of antibiotic resistance gene alleles from metagenome reads

In the EzBioCloud Clinical Metagenomics, we first align the metagenome reads against the above-described de-replicated reference antibiotic resistance gene alleles. Because of the high similarity shared among the reference alleles, it is very common to see that many reads align equally well against multiple references. Without treating these chaos, the resulting profiles would become noisy. We take the winner-takes-all approach to reduce the noise. Briefly, we define the groups of references that are co-aligned by bunch of reads, and then pick a single winner reference allele per each group of references based on the coverage breadth of alignment. All reads that were mapped to any of the group member references are reallocated to the winner subsequently, leading to a few hits with strong coverage rather than many hits with similarly poor coverages.

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