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Related Concept Videos

Development of Antibiotic Resistance01:30

Development of Antibiotic Resistance

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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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Antibiotic Selection00:57

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Overview
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Transduction01:16

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Among the three main modes of HGT—transformation, conjugation, and transduction—transduction is unique in that it is mediated by bacteriophages, or bacterial viruses.Transduction occurs in two ways. Generalized transduction occurs during the lytic cycle of a bacteriophage infection. In this process, bacteriophages infect bacterial cells, replicate within them, and ultimately cause cell lysis, releasing newly assembled virions. Occasionally, random fragments of the bacterial genome...
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Defense Against Bacterial Pathogens01:31

Defense Against Bacterial Pathogens

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The human immune system is a complex network of cells, tissues, and organs that work together to defend the body against bacterial infections. It consists of various immune cells, each playing a specific role in the defense mechanism.
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Genomic DNA in Prokaryotes00:46

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The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
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Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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Related Experiment Video

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Testing the Role of Multicopy Plasmids in the Evolution of Antibiotic Resistance
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Testing the Role of Multicopy Plasmids in the Evolution of Antibiotic Resistance

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Evolutionary Trajectories to Antibiotic Resistance.

Diarmaid Hughes1, Dan I Andersson1

  • 1Department of Medical Biochemistry and Microbiology, Uppsala University, 751 23 Uppsala, Sweden;

Annual Review of Microbiology
|July 13, 2017
PubMed
Summary
This summary is machine-generated.

Predicting antibiotic resistance evolution is crucial for extending antibiotic usefulness. However, limited quantitative data on key factors like mutation rates and fitness currently hinders accurate prediction of resistance trajectories.

Keywords:
coselectionepistasismutation ratespopulation bottlenecksrelative fitnessselection pressure

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Area of Science:

  • Microbiology and Evolutionary Biology
  • Pharmacology and Drug Resistance

Background:

  • Antibiotic resistance evolution poses a significant threat to public health.
  • Predicting resistance trajectories is essential for optimizing antibiotic use and efficacy.
  • Current understanding of factors influencing resistance evolution is incomplete.

Purpose of the Study:

  • To identify the critical parameters required for predicting antibiotic resistance evolution.
  • To highlight the limitations in current data that impede accurate prediction models.

Main Methods:

  • Review of factors influencing microbial evolution under antibiotic pressure.
  • Analysis of quantitative data requirements for predictive modeling of resistance.
  • Identification of complex evolutionary processes impacting resistance dynamics.

Main Results:

  • Accurate prediction necessitates data on mutation supply rate, resistance level, mutant fitness, and selective pressure strength.
  • Complex evolutionary factors such as epistasis, coselection, and clonal interference significantly complicate predictions.
  • A severe lack of quantitative data for these parameters currently limits predictive accuracy.

Conclusions:

  • Predicting antibiotic resistance evolution requires comprehensive quantitative data on multiple biological and environmental factors.
  • Further research is needed to gather essential data to develop robust predictive models for antibiotic resistance.
  • Addressing data gaps is critical for improving strategies to combat the growing challenge of antibiotic resistance.