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Antibiotic Resistance Genes (ARGs) Analysis Solution

  • Service Overview
  • Service Specifications
  • FAQs
  • Demo
  • Customer Case
Service Overview

The advent of superbugs has brought the issue of antibiotic resistance to the forefront of scientific discourse. Undoubtedly, antibiotic resistance remains a paramount threat to global health, food security, and socio-economic development. The emergence and worldwide proliferation of novel resistance mechanisms jeopardize our capacity to manage common infectious diseases effectively. In response, CD Genomics has developed comprehensive solutions for the analysis of antibiotic resistance genes. These solutions cater to the diverse needs of researchers, offering both qualitative and quantitative assessments. Moreover, they are adept at identifying mutation loci in antibiotic resistance genes with exceptional accuracy.

Our Advantages:
  • We focus on drug-resistance genes and components that are closely related to the generation and transmission of drug-resistance genes.
  • With technologies such as qPCR, WGS, and gene chips, we can determine microbial antibiotic resistance.
  • The application of multiple technologies helps discover as many antibiotic resistance mechanisms as possible and promotes the study of bacterial resistance mechanisms.
  • The rich technology platform enables rapid, high-throughput, and simultaneous detection of multiple antibiotic resistance genes.
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What Are the Antibiotic Resistance Genes

Antibiotic resistance genes (ARGs) are distinct segments of DNA that confer the ability to bacteria to survive the inhibitory or lethal effects of antibiotics. These genes are predominantly located on mobile genetic elements such as plasmids, transposons, and integrons, which promote horizontal gene transfer (HGT) among varied bacterial species. This molecular process significantly enhances the dissemination of resistance traits within and across bacterial populations, posing critical challenges to global health. A case in point is the plasmid-borne blaNDM-1 gene, notable for its association with resistance to carbapenems, a vital class of antibiotics. The identification of blaNDM-1 in diverse bacterial species across the globe underscores the pervasive threat posed by ARGs.

Antibiotic Resistance Genes in Plasmids

Plasmids play a pivotal role in the propagation of ARGs due to their capacity for autonomous replication, independent of chromosomal DNA. These extrachromosomal elements can carry multiple resistance genes and facilitate their rapid dissemination via conjugation, a process whereby two bacteria establish a direct physical connection to exchange genetic material. For instance, the pBDB1 plasmid in Escherichia coli harbors a suite of resistance genes, contributing to the swift emergence of multi-drug-resistant strains. The ubiquity of such plasmids in both pathogenic and non-pathogenic bacteria highlights the imperative of monitoring plasmid-mediated resistance, as it adds considerable complexity to the landscape of antibiotic resistance.

How do You Identify Antibiotic Resistance Genes

The identification of ARGs necessitates the use of integrated molecular and bioinformatics approaches. Conventional methods such as polymerase chain reaction (PCR) assays specifically target and amplify distinct gene sequences, while next-generation sequencing (NGS) provides a comprehensive overview of the resistome within a given sample. The analysis of sequencing data relies extensively on bioinformatics tools, which enable researchers to accurately annotate resistance genes and decipher their genetic contexts. Tools like MEGA and BLAST are instrumental in identifying and characterizing ARGs from environmental samples, thereby enhancing our understanding of resistance mechanisms and their implications on ecological balances.

Overview of Antibiotic Resistance Genes Databases

ARG databases play a crucial role in consolidating information on resistance mechanisms, facilitating research and strategies to address antimicrobial resistance. The following table summarizes key databases that support this essential work.

Database Description
ARDB A curated database that annotates genes and resistance types, with links to external resources. No updates since 2009; data consolidated into CARD.
ARG-ANNOT A curated database of AMR reference sequences and SNPs, valuable for research.
CARD A comprehensive database covering resistance genes and mechanisms, managed by the Antibiotic Resistance Ontology (ARO).
CBMAR Provides molecular and biochemical information on the β-lactamase family.
MvirDB Integrates data on toxins, virulence factors, and ARGs relevant to biodefense.
NCBI BioProject PRJNA313047 Organizes AMR gene sequences with a focus on resistance characteristics.
PATRIC A system annotating complete pathogen genomes, based on ARDB and CARD, with AMR metadata.
Resfams A database of protein families and hidden Markov models (HMMs) identifying antibiotic resistance functions.
ResFinder Focuses on horizontally acquired AMR genes, aiding in novel resistance identification.
SARG Integrates information on ARGs, subtypes, and reference sequences from ARDB and CARD.

Applications of Antibiotic Resistance Genes Analysis

  • Public Health Insights: ARG analysis offers critical insights into the prevalence and dissemination of resistance genes, thereby supporting strategies to control potential outbreaks.
  • Agricultural Management: Monitoring ARGs in livestock facilitates prudent antibiotic usage, thereby mitigating the risk of resistance gene transmission to other bacterial populations.
  • Environmental Monitoring: Detecting ARGs in various environments helps elucidate the impact of anthropogenic activities on their distribution, guiding initiatives to manage pharmaceutical pollution.
  • Research Advancements: The comprehensive analysis of ARGs fosters the discovery of novel resistance mechanisms, contributing to the development of innovative antimicrobial solutions.
  • Epidemiological Research: ARG data are indispensable for tracking the spread of resistance genes across different environments and host species, thereby refining intervention methodologies.
  • Risk Assessment: Evaluating the potential impacts of ARGs on both ecosystems and public health is critical for devising informed and effective responses.
Service Specifications

Introduction to Our Antibiotic Resistance Genes Analysis Solutions

At CD Genomics, we are acutely aware of the urgent need for effective methodologies to detect antibiotic-resistant bacteria. Our suite of solutions is meticulously designed to support large-scale surveillance, elucidate mechanisms of drug resistance, and aid in controlling the dissemination of antibiotic-resistant strains. Leveraging cutting-edge technologies including Sanger sequencing, NGS, and long-read sequencing, we provide comprehensive services tailored to address diverse research demands.

  • PCR-based Microbial Antibiotic Resistance Gene Analysis: Our PCR-based platforms utilize real-time PCR techniques for the rapid and accurate detection of ARGs. Our offered assays, including SYBR Green I-based and TaqMan-based qPCR, deliver high specificity and reproducibility, making them indispensable tools in ARG quantification.
  • SYBR Green I-based qPCR Assay for ARGs: Utilizing SYBR Green I dye for real-time PCR amplification monitoring, this assay is particularly efficient in detecting multiple ARGs within a single reaction. Its high sensitivity renders it suitable for low-abundance target detection.
  • TaqMan-based qPCR Assay for ARGs: The TaqMan assay employs sequence-specific probes to enhance sensitivity and specificity, allowing for precise ARG quantification. This method excels in detecting specific ARGs within complex sample matrices.
  • PASS-based Microbial ARGs Analysis: Our PASS (PCR Amplification and Sequencing Strategy) approach integrates amplification with sequencing, ensuring robust and accurate identification and characterization of ARGs across a variety of microbial populations. This method is integral for comprehensive ARG analysis in diverse microbial communities.
  • DNA Microarray-based ARGs Analysis: Harnessing the power of DNA chip technology, our high-throughput microarray solutions enable the simultaneous detection of multiple antibiotic resistance genes. This technology facilitates efficient screening across various bacterial strains, contributing to a holistic understanding of ARG distribution.
  • WGS for Antibiotic Resistance Genes: Our whole genome sequencing services support extensive and high-throughput detection and monitoring of bacterial drug resistance. By analyzing complete genomes, we provide in-depth insights into the genetic foundations of resistance and track its evolution and spread.
  • Metagenomic Resistance Gene Sequencing Service: Our metagenomic sequencing platform offers both qualitative and quantitative analyses of antibiotic resistance genes in environmental samples. This comprehensive approach enables detailed profiling of microbial communities, identifying their resistance capabilities and ecological impacts.

Antibiotic Resistance Gene Analysis Workflow

The Workflow of Antibiotic Resistance Genes (ARGs) Analysis Solution.

What We Offer

Some of the antibiotic resistance genes that we can target are listed in the table, but this is not all of our capabilities. We can develop a solution for you that exactly meets your needs.

Gene Classification Mechanism
catA1, catB3, cfr, etc (flor)/(chlor)/(am)phenicol deactivate
cmlA1, cmx(A), floR, etc efflux
qnr, etc -
aac, aacA/aphD, aacC, aacC1, aacC2, aacC4, aadA, aadD, aadE, aph, aph6ia, aphA1(akakanR), spcN-01, spcN-02, str, strA, strB, etc Aminoglycosides deactivate
tetA, tetB, tetC, tetD, tetE, tetG, tetH, tetJ, tetK, tetL, tetPA, tet, tetV, etc Tetracyclines efflux
tet(32), tet(36), tetM, tetO, tetW, tetPB, tetS, tetT, tetQ, etc protection
tet(34), tet(37), tetU-01, tetX, tet(35), etc -
GES, KPC, IMP-1, NDM-1(C), blaOXA-48, etc Carbapenems -
vanA, vanB, vanC, vanG, vanHB, vanHD, vanRA, vanRB, vanRC, vanRD, vanSA, vanSB, vanSC, vanTC, vanTE, vanTG, vanWB, vanWG, vanXA, vanXB, vanXD, vanYB, vanYD, etc Vancomycin protection
acrA, adeA, acrF, ceoA, cmeA, cmr, marR, mdetl1, mdtE/yhiU, mepA, mexA, mexD, mexE, mexF, mtrC, mtrD, oprD, oprJ, pmrA, qac, qacA, qacA/qacB, qacH, rarD, sdeB, tolC, ttgB, yceE/mdtG, yceL/mdtH, yidY/mdtL, ttgA, emrD, etc Multidrug efflux
ampC/blaDHA, ampC, bla1, blaCMY, blaCTX, blaGES, bla-L1, blaMOX/blaCMY, blaOCH, blaOKP, blaOXA1/blaOXA30, blaOXY, blaPAO, blaPER, blaPSE, blaROB, blaSFO, blaSHV-01, blaTEM, blaTLA, blaVEB, blaVIM, blaZ, cepA, cfiA, cfxA, cphA, fox5, NDM1, ampC, etc Beta_Lactamas
Beta_Lactamas		 deactivate
mecA, pbp, pbp2x, Pbp5, penA, etc protection
ereB, lnuA, lnuB, lnuC, mphA, mphB, mphC, vatB, vatC, vatE, vgb, vgbB, etc MLSB deactivate
carB, ImrA, matA/mel, mdtA, mefA, msrC, oleC, vgaA, vgbB, msrA, etc efflux
erm, ermA, ermA/ermTR, ermB, ermC, ermF, ermJ/ermD, ermK, ermT, ermX, ermY, etc protection
dfrA, folA, etc Sulfonamides deactivate
sul, etc protection
IS613, tnpA, Tp614, etc MGEs transposase
int, etc integrase

Bioinformatics Analysis

Our bioinformatics analysis encompasses several critical components to ensure thorough examination of antibiotic resistance genes:

  • Initial Data Processing
  • Gene Annotation
  • Resistance Gene Profiling
  • Statistical Evaluations

Sample Requirement

Types of Samples:

  • DNA
  • cDNA
  • RNA
  • mRNA
  • Tissue sample
  • Environmental samples
  • For PCR-based assays, a minimum of 50 µL of isolated DNA is preferred.
  • For DNA microarray and whole genome sequencing, at least 1 µg of high-quality genomic DNA is recommended.
  • DNA should be free of contaminants and have a purity ratio (A260/A280) between 1.8 and 2.0.

Note: If you wish to obtain more accurate and detailed information regarding sample requirements, please feel free to contact us directly.

Deliverables

  • Comprehensive Bioinformatics Report
  • Quality-Control Dashboard
  • Statistical Data Summary
  • Visualization Outputs
  • Annotated Gene Lists
  • Your Designated Bioinformatics Report
FAQs

Antibiotic Resistance Genes Analysis FAQ

Demo

Demo

Partial results of our antibiotic resistance genes analysis service are shown below:

Distribution histogram illustrating the presence of resistance genes.

Distribution Histogram of Resistance Genes

Circos diagram depicting the distribution of resistance genes.

Circos Diagram of Resistance Gene Distribution

Two-dimensional PCoA plot representing the variation of resistance genes.

Two-Dimensional PCoA Plot of Resistance Genes

Adonis/PERMANOVA analysis comparing resistance gene groups.

Adonis/PERMANOVA Analysis of Resistance Gene Groups

LEfSe analysis highlighting the differences among resistance gene groups.

LEfSe Analysis of Resistance Gene Groups

Boxplot showing the differential expression of resistance genes.

Boxplot of Differential Resistance Genes

Customer Case

Customer Case

Customer Case

Metagenomic analysis reveals the shared and distinct features of the soil resistome across tundra, temperate prairie, and tropical ecosystems
Journal: Microbiome
Impact factor: 14.650
Published: 2021

Find out more

Backgrounds

Plant and soil ecosystems serve as critical reservoirs for antibiotic resistance genes (ARGs), which may pose significant environmental and public health risks. This study explores the diversity and abundance of ARGs within soil DNA samples collected from three native ecosystems: the Alaskan tundra, the Midwestern prairie, and the Amazon rainforest. Furthermore, it assesses the impact of converting these pristine environments to agricultural and pastoral land, thereby contributing valuable insights into the dynamics of environmental resistomes and their broader ecological implications.

Methods

Sample preparation:

  • Soil samples
  • Converted land samples
  • DNA extraction

Method:

Data Analysis:

  • Identification of ARGs
  • Classification of ARG hosts in de novo assembly
  • Soil bacterial community
  • Statistical analyses

Results

Diversity and Abundance: The investigation revealed a significant diversity and abundance of antibiotic resistance genes across multiple ecosystems, with a total of 242 ARG subtypes identified. Among these, multidrug resistance and efflux pumps emerged as the predominant ARG classes.

Fig 1. Composition of ARGs and regulatory genes across 26 soil metagenomes. (Qian et al., 2021)Fig 1. Composition of ARGs and regulator genes in 26 soil metagenomes.

Regulatory Genes: The analysis consistently identified ten regulatory genes, which accounted for 13-35% of the total resistome abundance. The most prevalent regulatory genes included arlR, cpxR, ompR, vanR, and vanS.

Fig 2. Diversity and abundance of ARGs in soils from three distinct ecosystems. (Qian et al., 2021)Fig 2. Diversity and abundance and of ARGs among soils of three ecosystems.

Shared ARGs: Across all soil samples, fifty-five non-regulatory ARGs were commonly detected, constituting over 81% of the non-regulatory resistome's abundance. The primary hosts of these ARGs were identified as members of the Proteobacteria, Firmicutes, and Actinobacteria phyla.

Fig 3. Network representation of identified hosts of ARGs at the phylum level, with different colors indicating various classes of ARGs. (Qian et al., 2021)Fig 3. Network showing identified hosts of ARGs at phylum level. Different colors represent different classes of ARGs

Human Pathogens: Upon evaluating twelve clinically significant ARGs at the sequence level, it was found that these ARGs were distinct from those found in human pathogens. Notably, marked differences in bacterial community structures predominantly influenced the resistome profiles.

Conclusions

The study underscores the existence of a core set of ARGs shared across varied soil ecosystems. It provides an in-depth categorization of the hosts of these ARGs, quantifies resistance classes, and evaluates the environmental impact on soil resistomes. The evidence indicates that the ARGs identified in soil ecosystems are significantly different from those associated with human pathogens, suggesting a low risk of direct transfer. These insights are crucial for advancing our understanding of the environmental resistome and its potential implications for public health.

Reference

  1. Qian X, Gunturu S, Guo J, et al. Metagenomic analysis reveals the shared and distinct features of the soil resistome across tundra, temperate prairie, and tropical ecosystems. Microbiome. 2021 May 14;9(1):108.
microbioseq
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