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Antibiotic Resistance, Microbial

Antibiotic Resistance, Microbial: A critical issue in public health, this term describes the ability of microorganisms, such as bacteria, to resist the effects of antibiotics.
This resistance can develop through genetic changes or the acquisition of resistance genes, making infections more difficult to treat.
Understanding the mechanisms and prevalence of antibiotic resistance is vital for developing effective strategies to combat this growing threat to global health.
The PubCompare.ai platform can help streamline your research on this important topic, allowing you to easily locate and compare protocols from literature, preprints, and patents, while leveraging AI-driven analysis to identify the best approaches and products.
Optimze your research and make data-driven decisions with this powerful tool.

Most cited protocols related to «Antibiotic Resistance, Microbial»

1
Rapid Detection of Integrative and Conjugative Elements in Bacterial Genomes
We have developed a tool, called ICEfinder, available online and as a standalone version for the rapid detection of ICEs and IMEs in bacterial genome sequences. ICEfinder employs a method we called ‘Pattern-based hit co-localization’ (see the Supplementary Methods) that detects the signature sequences of the recombination modules and conjugation modules based on their profile HMMs (19 (link)) (Supplementary Table S2, S3 and Figure S4). It also searches for the oriT region using the approach proposed by oriTfinder (18 (link)). It then co-localizes, filters and groups the corresponding genes. At last, those elements carrying an integrase gene, a relaxase gene and T4SS gene clusters (12 (link),20 (link)) are considered as T4SS-type ICEs, while those without T4SS but with integrase, replication and the AICE translocation-related proteins are thought to be putative AICEs. Those without T4SS but with integrase and relaxase are tagged as putative IMEs. ICEfinder also tries to detect some particular IMEs with integrase and an oriT but no relaxase. ICEfinder employs ARAGORN (21 (link)) with the default parameters to identify the 3′ termini of the tRNA/tmRNA genes as the putative ICE insertion sites. It also uses Vmatch (http://vmatch.de/) with the default options to detect the directed repeats as the tRNA-distal boundaries. The acquired antibiotic resistance genes and virulence factors are also identified by NCBI BLASTp (22 (link)) with the cut-off of Ha-value of 0.64 (12 (link)).
The ICEfinder online tool allows users to submit a GenBank file containing a nucleotide sequence and its annotation as a query. A FASTA format file of a raw nucleotide sequence is also accepted, which is annotated using our gene annotation tool CDSeasy (12 (link)) and is then used as the input for the following ICE detection. ICEfinder uses the CGView circular genome visualization tool (23 (link)) to display the distribution of the predicted T4SS-type ICEs, IMEs and AICEs in the query bacterial genome. In addition, the ICEfinder has a comparison module (Supplementary Figure S5) that allows performing the alignment between the identified ICE loci against the ICEberg-archived ICEs using MultiGeneBlast (24 (link)).
Liu M., Li X., Xie Y., Bi D., Sun J., Li J., Tai C., Deng Z, & Ou H.Y. (2018). ICEberg 2.0: an updated database of bacterial integrative and conjugative elements. Nucleic Acids Research, 47(Database issue), D660-D665.
Publication 2018
Antibiotic Resistance, Microbial Base Sequence DNA Replication Gene Annotation Gene Clusters Genes Genome Genome, Bacterial Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Ice Icebergs Integrase Proteins Recombination, Genetic tmRNA Transfer RNA Translocation, Chromosomal Virulence Factors
2
Comprehensive FGR Sequence Annotation Pipeline
Sequences in the FGR are selected from GenBank in a five-step process. First the files for the bacterial, plant, environmental divisions and Whole Genome Shotgun (WGS) data for the current GenBank release are downloaded. Then the release files are converted to the BioSeq XML format using the asn2all (ftp://ftp.ncbi.nih.gov/ncbi-asn1) tool available from the GenBank ftp site. Next the coding sequence (CDS) annotations are read from the BioSeq files. However, since the gene and product annotations on CDS regions are free text fields and not always reliable, we do not map CDS to gene families in the FGR directly via the annotations. Instead, every gene family contained in the FGR has an associated HMM. These HMMs were built using HMMER3 from seed sequences selected as representatives by researchers interested in the particular gene family and from selected gene families obtained from Pfam. HMMER3 is used to scan the protein translation of every CDS record and default significance cutoffs are used to filter insignificant hits. Each translated GenBank CDS is scored with each HMM, and those with a significant hit to one or more FGR gene family's HMM are recorded in the FGR database. In addition to the protein sequence, the nucleotide sequence, bibliographic reference (if present), protein and nucleotide accession numbers, organism name, and description are also stored in the FGR database. When a new version of a record is released from GenBank, the existing version in the FGR database is replaced.
A Java Native Interface (JNI) wrapper around the HMMER3 scanning pipeline was developed in order to tightly integrate HMMER3 with the FGR release pipeline. The JNI wrapper is available as part of the RDP Alignment Tools package (Table 2). The JNI wrapper trades higher memory usage for faster running time by storing all HMMs in memory instead of rereading models for every query sequence.
The FGR currently contains 77 gene families organized into seven categories: Antibiotic resistance, Biodegradation, Biogeochemical Cycles, Metal Cycling, Phylogenetic Markers, Plant Pathogenicity, and “Other” for gene families not in the listed categories. FGR is intended to tap community efforts to expand its database. New gene families are added with each release and researchers are invited to work with the RDP to get new gene families incorporated into the FGR.
Fish J.A., Chai B., Wang Q., Sun Y., Brown C.T., Tiedje J.M, & Cole J.R. (2013). FunGene: the functional gene pipeline and repository. Frontiers in Microbiology, 4, 291.
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Publication 2013
Amino Acid Sequence Antibiotic Resistance, Microbial Bacteria Environmental Biodegradation Genes Genome Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Memory Metals Nucleotides Open Reading Frames Pathogenicity Plants Proteins
3
Profiling Antibiotic Resistance in Gut Microbiome
We applied ShortBRED to profile antibiotic resistance (AR) in the human gut microbiome. We first produced a set of new AR marker sequences by applying ShortBRED-Identify to a combination of (i) a curated version of the ARDB which we obtained by deleting sequences no longer stored at NCBI and (ii) a set of known antibiotic resistance genes obtained from resistant bacterial libraries. We then used ShortBRED-Quantify to profile the relative abundance of corresponding AR protein families across 552 gut metagenomes: 82 from U.S. adults sampled during the Human Microbiome Project (HMP) [10 (link)], 363 from Chinese adults with and without diabetes [11 (link)], and 107 individuals from Malawi, Venezuela, and the U.S. [12 (link)]. We used the first-visit samples from multi-visit HMP subjects to avoid redundancy. For 454-based samples characterized by sub-optimal sequencing depth, we mapped reads to full-length centroid sequences to avoid compromising sensitivity.
Kaminski J., Gibson M.K., Franzosa E.A., Segata N., Dantas G, & Huttenhower C. (2015). High-Specificity Targeted Functional Profiling in Microbial Communities with ShortBRED. PLoS Computational Biology, 11(12), e1004557.
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Publication 2015
Adult Antibiotic Resistance, Microbial Bacteria Chinese Diabetes Mellitus Gastrointestinal Microbiome Genes, vif Homo sapiens Human Microbiome Hypersensitivity Metagenome Proteins
4
ORFeome v3.1: Improved Gateway Cloning

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Publication 2007
Antibiotic Resistance, Microbial Clone Cells Cloning Vectors Codon, Terminator DNA-Directed DNA Polymerase Escherichia coli Nucleotides Oligonucleotide Primers Open Reading Frames Plasmids Protein C Spectinomycin Tissue Donors
5
Construction and Complementation of B. thetaiotaomicron Mutants

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Publication 2008
Alleles Antibiotic Resistance, Microbial Bacteroides Cloning Vectors Codon, Initiator Gene Deletion Genes Genome Hybrids Mutagenesis, Insertional Oligonucleotide Primers Plasmids Reading Frames Real-Time Polymerase Chain Reaction Serine-Specific tRNA Strains

Most recents protocols related to «Antibiotic Resistance, Microbial»

1
Marker-Free Bacillus subtilis Strain Engineering
Not available on PMC !

Example 1

This example describes the generation of a marker-free B. subtilis strain expressing allulose epimerase. Briefly, in a first step, a B. subtilis strain was transformed with a cassette encoding the BMCGD1 epimerase and including an antibiotic resistance marker. This cassette recombined into the Bacillus chromosome and knocked out 8 kb of DNA, including a large sporulation gene cluster and the lysine biosynthesis gene lysA. In a second step, a second cassette was recombined into the B. subtilis chromosome, restoring the lysA gene and removing DNA encoding the antibiotic resistance. E. coli strain 39 A10 from the Keio collection was used to passage plasmid DNA prior to transformation of B. subtilis. The relevant phenotype is a deficiency in the DNA methylase HsdM in an otherwise wild-type K-12 strain of E. coli.

In detail, a cassette of 5120 bp (SEQ ID NO:1; synthetic DNA from IDT, Coralville, Iowa) was synthesized and cloned into a standard ampicillin resistant pIDT vector. The synthetic piece encoded 700 bp upstream of lysA on the B. subtilis chromosome, the antibiotic marker cat (651 bp), the DNA-binding protein lad (1083 bp), and the allulose epimerase (894 bp), and included 700 bp of homology in dacF. This vector was transformed into E. coli strain 39 A10 (Baba et al., 2006), and plasmid DNA was prepared and transformed into B. subtilis strains 1A751 and 1A976.

Transformants were selected on LB supplemented with chloramphenicol. The replicon for pIDT is functional in E. coli but does not work in Gram positive bacteria such as B. subtilis. The colonies that arose therefore represented an integration event into the chromosome. In strain 1A751, the colony morphology on the plates was used to distinguish between single and double recombination events. The double recombination event would knock out genes required for sporulation, whereas the single recombination would not. After three days on LB plates, colonies capable of sporulation were brown and opaque; sporulation-deficient colonies were more translucent.

B. subtilis strain 1A976 with the allulose epimerase cassette is auxotrophic for histidine and lysine and can achieve very high transformation efficiency upon xylose induction. A 1925 bp synthetic DNA (SEQ ID NO:2) was amplified by primers (SEQ ID NO:3, SEQ ID NO:4) and Taq polymerase (Promega). This PCR product encoded the lysA gene that was deleted by the dropping in the epimerase cassette and 500 bp of homology to lad. A successful double recombination event of this DNA should result in colonies that are prototrophic for lysine and sensitive to chloramphenicol; i.e., the entire cat gene should be lost.

Transformants were selected on Davis minimal media supplemented with histidine. Colonies that arose were characterized by PCR and streaking onto LB with and without chloramphenicol. Strains that amplified the introduced DNA and that were chloramphenicol sensitive were further characterized, and their chromosomal DNA was extracted.

Strain 1A751 containing the chloramphenicol resistant allulose was transformed with this chromosomal DNA and selected on Davis minimal media supplemented with histidine. Transformants were streaked onto LB with and without chloramphenicol and characterized enzymatically as described below.

US11859228B2. Epimerase enzymes and their use (2024-01-02). Archer Daniels Midland Company [US]. Inventors: William Schroeder [US], Padmesh Venkitasubramanian [US], Sridhar Govindarajan [US], Mark Welch [US].
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Patent 2024
Ampicillin Anabolism Antibiotic Resistance, Microbial Antibiotics Bacillus Bacillus subtilis Chloramphenicol Chromosomes Cloning Vectors DNA, A-Form DNA-Binding Proteins Epimerases Escherichia coli Gene Clusters Gene Knockout Techniques Genes Gram-Positive Bacteria Histidine Lysine Methyltransferase Oligonucleotide Primers Phenotype Plasmids psicose Recombination, Genetic Replicon Strains Taq Polymerase Xylose
2
Multiplex PCR for Campylobacter Virulence
All Campylobacter strains were examined using the uniplex PCR technique for VirB11, ciaB and iam virulence genes, which facilitate the invasion of Campylobacter inside host cells. Additionally, tetA and BlaOXA-61 antibiotic resistance genes for tetracyclins and extended-spectrum β-lactamases, respectively, were detected in all Campylobacter strains. In Supplementary Table 1, the primer sequence, cycle conditions, and predicted amplicon size are shown. Both PCR and electrophoresis were carried out as previously mentioned. Saline served as the negative control, and C. jejuni ATCC 33,560 and C. coli ATCC 33,559 served as the positive controls. 
Tawakol M.M., Nabil N.M., Samir A., M. H.H., Yonis A.E., Shahein M.A, & Elsayed M.M. (2023). The potential role of migratory birds in the transmission of pathogenic Campylobacter species to broiler chickens in broiler poultry farms and live bird markets. BMC Microbiology, 23, 66.
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Publication 2023
Antibiotic Resistance, Microbial Campylobacter Cells Electrophoresis Genes Oligonucleotide Primers Saline Solution Strains Tetracycline Trientine Virulence
3
Comparative Transcriptome Analysis of Antibiotic Resistance
Monocultures expressing the wildtype and all mutants for a given antibiotic resistance gene were grown on the same day along with an uninduced wild-type control. Samples for RNA-seq were prepared as described previously (Mehlhoff et al. 2020 (link)). The resulting double-stranded cDNA was pooled and submitted for Illumina TruSeq (2 × 75 bp) at the Single Cell and Transcriptomics Core facility at Johns Hopkins University.
Reads were inspected with FastQC (Andrews et al. 2018 (link)) to check per base sequence quality. We indexed the NEB 5-alpha F’Iq genome (NCBI Reference Sequence: NZ_CP053607.1) after having removed genes that appeared in both the episome and chromosomal DNA. Such genes had their counts otherwise overwritten as verified by mapping to the NEB 5-alpha genome (NCBI Reference Sequence: NZ_CP017100.1). We mapped our paired end reads to the indexed NEB 5-alpha F’Iq transcriptome using STAR (Dobin et al. 2013 (link)). featureCounts (Liao et al. 2014 (link)) was used to quantify and tabulate transcript abundance while edgeR (Robinson et al. 2009 (link)) was used to tabulate the normalized counts per million.
Mehlhoff J.D, & Ostermeier M. (2023). Genes Vary Greatly in Their Propensity for Collateral Fitness Effects of Mutations. Molecular Biology and Evolution, 40(3), msad038.
Publication 2023
Antibiotic Resistance, Microbial Base Sequence Cells Chromosomes DNA, Complementary Episomes Gene Expression Profiling Genes Genome RNA-Seq Transcriptome
4
Constructing and Evaluating Antibiotic Resistance Mutants
We constructed a total of 34 mutants across the three genes consisting of 12 CAT-I mutants, 13 NDM-1 mutants, and 9 aadB mutants. We used inverse PCR to introduce the mutations. We also used inverse PCR to construct a control plasmid, pSKunk1-ΔGene, which had the coding region of the studied antibiotic resistance genes deleted.
For the C26D and C26S mutants in NDM-1, we found that an IS4-like element ISVsa5 family transposase insertion would occur within the NDM-1 gene during the six hours of induced monoculture growth (supplementary Text, Supplementary Material online). We made two synonymous mutations within the 5′-GCTGAGC-3′ insertion site that fully overlapped codons 23 and 24 to reduce transposase insertion and get an accurate measure of the collateral fitness effects for the C26D and C26S mutations. The new sequence was 5′-GTTATCA-3′. Inverse PCR was used to introduce these synonymous mutations. All mutant plasmids were transformed into NEB 5-alpha LacIq electrocompetent cells.
Mehlhoff J.D, & Ostermeier M. (2023). Genes Vary Greatly in Their Propensity for Collateral Fitness Effects of Mutations. Molecular Biology and Evolution, 40(3), msad038.
Publication 2023
Antibiotic Resistance, Microbial Chloramphenicol O-Acetyltransferase Codon Genes Inverse PCR Mutation Pancreatic alpha Cells Plasmids Silent Mutation Transposase
5
Illumina MiSeq Sequencing and Fitness Analysis
Output files from Illumina MiSeq were first run through FastQC (Andrews et al. 2018 (link)) to check read quality. The paired-end reads were merged using PEAR (Stamatakis et al. 2014 (link)) set to a minimum assembly length of 150 base pairs reads allowing for high quality scores at both ends of the sequence. Adapters were trimmed from the ends of the antibiotic resistance genes' coding sequence using Trimmomatic (Bolger et al. 2014 (link)). Enrich2 (Rubin et al. 2017 ) was used to count the frequency of each allele for use in calculating selection coefficients and associated statistical measures. We set Enrich2 to filter out any reads containing bases with a quality score below 20, bases marked as N, or mutations at more than one codon.
Fitness of an allele (wi) was calculated from the enrichment of the synonyms of the wild-type gene ( εwt ), the enrichment of allele i ( εi ) and the fold increase in the number of cells during the growth competition experiment (r) as described by Equation 1. We utilize the frequency of wildtype synonymous alleles as the reference instead of the frequency of wildtype because wildtype synonyms occurred more frequently in the library and wildtype sequencing counts are more prone to being affected by the artifact of PCR template jumping during the preparation of barcoded amplicons for deep-sequencing. Detailed derivations of the following equations (Equations 1–6) can be found in our previous work (Mehlhoff et al. 2020 (link)).
We calculate the variance in the fitness as
where the frequency of allele (fi) is calculated from counts of that allele (ci) and the total sequencing counts (cT).
From the variance in fitness, we calculated a 99% confidence interval. Additionally, we calculated a P-value using a 2-tailed test. Details of the Z-score and P-value equations are available in Mehlhoff et al. (2020) (link).
We estimated the number of false positives that would be included at P < 0.01 and P < 0.001 significance in order to correct for multiple testing (Storey and Tibshirani 2003 (link)) in our DMS datasets as described previously (Mehlhoff et al. 2020 (link)). For TEM-1, we estimated that our data would contain approximately 55.0 false positives on average at P < 0.01 significance and an estimated 5.6 false positives on average at P < 0.001 significance for a single replica (Mehlhoff et al. 2020 (link)). Those values are 44.1 and 4.3 (CAT-I), 52.8 and 5.3 (NDM-1), and 33.8 and 3.4 (aadB) at P < 0.01 and P < 0.001 significance, respectively. We chose to report the frequency of mutations having fitness effects that met the P-value criteria in both replica experiments to limit the occurrence of false positives.
Mehlhoff J.D, & Ostermeier M. (2023). Genes Vary Greatly in Their Propensity for Collateral Fitness Effects of Mutations. Molecular Biology and Evolution, 40(3), msad038.
Publication 2023
Alleles Antibiotic Resistance, Microbial AT-001 Chloramphenicol O-Acetyltransferase Codon DNA Library Genes Mutation Open Reading Frames Pears

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1
Ciprofloxacin by Thermo Fisher Scientific
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Ciprofloxacin is a synthetic antibiotic that belongs to the fluoroquinolone class. It is a broad-spectrum antimicrobial agent effective against a variety of Gram-positive and Gram-negative bacteria.
2
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Etest is a quantitative antimicrobial susceptibility testing (AST) method developed by bioMérieux. It provides minimum inhibitory concentration (MIC) values for specific antimicrobial agents. Etest utilizes a predefined antimicrobial gradient on a plastic strip to determine the MIC of a tested microorganism.
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The MiSeq platform is a benchtop sequencing system designed for targeted, amplicon-based sequencing applications. The system uses Illumina's proprietary sequencing-by-synthesis technology to generate sequencing data. The MiSeq platform is capable of generating up to 15 gigabases of sequencing data per run.
4
Ampicillin by Thermo Fisher Scientific
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Ampicillin is an antibiotic that is commonly used in microbiology and molecular biology laboratories. It is a broad-spectrum penicillin-type antibiotic that inhibits the synthesis of bacterial cell walls, effectively killing or preventing the growth of susceptible bacteria.
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More about "Antibiotic Resistance, Microbial"

Antibiotic Resistance, Microbial is a critical public health issue describing the ability of microbes, like bacteria, to resist the effects of antibiotics.
This resistance can develop through genetic changes or the acquisition of resistance genes, making infections more difficult to treat.
Understanding the mechanisms and prevalence of antimicrobial resistance is vital for developing effective strategies to combat this growing global health threat.
Key subtopics related to antibiotic resistance include: - Antimicrobial resistance: Synonymous with antibiotic resistance, this term encompasses the ability of microbes to withstand the effects of antibacterial, antiviral, antifungal, and antiparasitic agents. - Antimicrobial susceptibility testing: Techniques like the Etest, Vitek 2 system, and Mueller-Hinton agar are used to determine the sensitivity of microbes to specific antimicrobial agents. - Genetic mechanisms of resistance: Bacteria can acquire resistance genes through mutation or the transfer of genetic material, enabling them to survive and proliferate in the presence of antibiotics. - Multidrug resistance: Some microbes have developed resistance to multiple classes of antibiotics, making infections extremely difficult to treat. - Stewardship and prevention: Strategies to combat antimicrobial resistance include responsible antibiotic prescribing, infection control measures, and the development of new antimicrobial agents.
The PubCompare.ai platform can help streamline your research on this important topic, allowing you to easily locate and compare protocols from literature, preprints, and patents, while leveraging AI-driven analysis to identify the best approaches and products.
Optimize your research and make data-driven decisions with this powerful tool.