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Antibiotic Resistance Protocols

Overview of attention for book
Cover of 'Antibiotic Resistance Protocols'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Methods for Measuring the Production of Quorum Sensing Signal Molecules
  3. Altmetric Badge
    Chapter 2 Construction and Use of Staphylococcus aureus Strains to Study Within-Host Infection Dynamics
  4. Altmetric Badge
    Chapter 3 Method for Detecting and Studying Genome-Wide Mutations in Single Living Cells in Real Time
  5. Altmetric Badge
    Chapter 4 Detecting Phenotypically Resistant Mycobacterium tuberculosis Using Wavelength Modulated Raman Spectroscopy
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    Chapter 5 A Flow Cytometry Method for Assessing M. tuberculosis Responses to Antibiotics
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    Chapter 6 Application of Continuous Culture for Assessing Antibiotic Activity Against Mycobacterium tuberculosis
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    Chapter 7 Real-Time Digital Bright Field Technology for Rapid Antibiotic Susceptibility Testing
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    Chapter 8 Enhanced Methodologies for Detecting Phenotypic Resistance in Mycobacteria
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    Chapter 9 Methods to Determine Mutational Trajectories After Experimental Evolution of Antibiotic Resistance
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    Chapter 10 Selection of ESBL-Producing E. coli in a Mouse Intestinal Colonization Model
  12. Altmetric Badge
    Chapter 11 Transcriptional Profiling Mycobacterium tuberculosis from Patient Sputa
  13. Altmetric Badge
    Chapter 12 Direct in Gel Genomic Detection of Antibiotic Resistance Genes in S1 Pulsed Field Electrophoresis Gels
  14. Altmetric Badge
    Chapter 13 Using RT qPCR for Quantifying Mycobacteria marinum from In Vitro and In Vivo Samples
  15. Altmetric Badge
    Chapter 14 Use of Larval Zebrafish Model to Study Within-Host Infection Dynamics
  16. Altmetric Badge
    Chapter 15 A Method to Evaluate Persistent Mycobacterium tuberculosis In Vitro and in the Cornell Mouse Model of Tuberculosis
Attention for Chapter 3: Method for Detecting and Studying Genome-Wide Mutations in Single Living Cells in Real Time
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

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Chapter title
Method for Detecting and Studying Genome-Wide Mutations in Single Living Cells in Real Time
Chapter number 3
Book title
Antibiotic Resistance Protocols
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7638-6_3
Pubmed ID
Book ISBNs
978-1-4939-7636-2, 978-1-4939-7638-6
Authors

Marina Elez, Lydia Robert, Ivan Matic, Elez, Marina, Robert, Lydia, Matic, Ivan

Abstract

DNA sequencing and fluctuation test have been choice methods for studying DNA mutations for decades. Although invaluable tools allowing many important discoveries on mutations, they are both highly influenced by fitness effects of mutations, and therefore suffer several limits. Fluctuation test is for example limited to mutations that produce an identifiable phenotype, which is the minority of all generated mutations. Genome-wide extrapolations using this method are therefore difficult. DNA sequencing detects almost all DNA mutations in population of cells. However, the obtained population mutation spectrum is biased because of the fitness effects of newly generated mutations. For example, mutations that affect fitness strongly and negatively are underrepresented, while those with a strong positive effect are overrepresented. Single-cell genome sequencing can solve this problem. However, sufficient amount of DNA for this approach is obtained by massive whole-genome amplification, which produces many artifacts.We describe the first direct method for monitoring genome-wide mutations in living cells independently of their effect on fitness. This method is based on the following three facts. First, DNA replication errors are the major source of DNA mutations. Second, these errors are the target for an evolutionarily conserved mismatch repair (MMR) system, which repairs the vast majority of replication errors. Third, we recently showed that the fluorescently labeled MMR protein MutL forms fluorescent foci on unrepaired replication errors. If not repaired, the new round of DNA synthesis fixes these errors in the genome permanently, i.e., they become mutations. Therefore, visualizing foci of the fluorescently tagged MutL in individual living cells allows detecting mutations as they appear, before the expression of the phenotype.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 17%
Researcher 1 17%
Student > Postgraduate 1 17%
Unknown 3 50%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 33%
Agricultural and Biological Sciences 1 17%
Physics and Astronomy 1 17%
Unknown 2 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 April 2022.
All research outputs
#13,547,803
of 23,578,176 outputs
Outputs from Methods in molecular biology
#3,577
of 13,339 outputs
Outputs of similar age
#214,391
of 444,765 outputs
Outputs of similar age from Methods in molecular biology
#321
of 1,480 outputs
Altmetric has tracked 23,578,176 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,339 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 444,765 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 1,480 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.