The increasing interest in microbial resistance genes has become a major topic in microbiological and environmental research. These genes are now widely studied for their role in microbial ecology and genetic diversity. 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.
Antibiotic resistance genes (ARGs) are specific segments of DNA that enable bacteria to survive exposure to antibiotics that would normally inhibit or kill them. These genes are often found on mobile genetic elements such as plasmids, transposons, and integrons, which facilitate horizontal gene transfer (HGT) between different bacterial species. This mechanism significantly accelerates the spread of resistance traits within microbial communities, creating serious challenges for public health worldwide. For example, certain plasmid-associated resistance genes have been found to confer resistance to critical antibiotics, and their detection in diverse bacterial strains globally illustrates the far-reaching impact and persistence of ARGs.
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, specific plasmids in common bacterial strains have been found to contain multiple resistance determinants that can contribute to rapid multidrug tolerance development.
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.
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. |
At CD Genomics, we are acutely aware of the urgent need for effective methodologies to detect antibiotic-resistant bacteria. Our solutions are designed to support environmental and microbial research by characterizing resistance gene profiles and their distribution. Leveraging cutting-edge technologies including Sanger sequencing, NGS, and long-read sequencing, we provide comprehensive services tailored to address diverse research demands.
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 | critical antimicrobial classes | - |
vanA, vanB, vanC, vanG, vanHB, vanHD, vanRA, vanRB, vanRC, vanRD, vanSA, vanSB, vanSC, vanTC, vanTE, vanTG, vanWB, vanWG, vanXA, vanXB, vanXD, vanYB, vanYD, etc | enzyme targets in resistance studies | 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 | 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 |
Our bioinformatics analysis encompasses several critical components to ensure thorough examination of antibiotic resistance genes:
Types of Samples:
Note: If you wish to obtain more accurate and detailed information regarding sample requirements, please feel free to contact us directly.
Partial results of our antibiotic resistance genes analysis service are shown below:
Distribution Histogram of Resistance Genes
Circos Diagram of Resistance Gene Distribution
Two-Dimensional PCoA Plot of Resistance Genes
Adonis/PERMANOVA Analysis of Resistance Gene Groups
LEfSe Analysis of Resistance Gene Groups
Boxplot of Differential Resistance Genes
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
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:
Method:
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 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 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 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
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