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How industrial bacterial cultures can be kept stable over time.

Trudy M WassenaarK Zimmermann
Published in: Letters in applied microbiology (2020)
The tremendous variation that exists between bacterial species illustrates the power of evolution, which is the continuous process of mutation and selection over time. Even within a bacterial species, individual members can harbour an impressive degree of genetic variation, depending on the species. The question then arises how similar the offspring of a given bacterial cell over time is, and how long it takes before differences are noticeable? Here we show that on the one hand one can expect random mutations to arise, as a result of various mechanisms. On the other hand, there are forces at play that keep the offspring of a cell genetically relatively constant, unless there is selection for a particular characteristic. The most common mechanisms behind mutations that can appear in a bacterial population are briefly introduced. Next, it is explained why nevertheless such mutations are rarely observed, as long as single colonies are randomly selected, unless selective pressures apply. Since quality control of industrial bacterial cultures is likely to depend heavily on genome sequencing in the near future, the accuracy of whole-genomic sequencing technologies is also discussed. It can be concluded that the bacteriologists who started picking single colonies from agar plates more than hundred years ago were unknowingly ingeneous, as their practice maintains a bacterial culture stable over time. SIGNIFICANCE AND IMPACT OF STUDY: The questions addressed here are relevant for industries that depend on live bacteria for (manufacturing of) their products, as they have to guard their bacterial cultures that remain unchanged over time. The explanation why randomly selection of single colonies keeps a population stable can be of use in bacteriology courses. The limitations of whole-genome sequencing are relevant to legislators to avoid overinterpretation of those data.
Keyphrases
  • single cell
  • stem cells
  • gene expression
  • primary care
  • quality control
  • type diabetes
  • skeletal muscle
  • cell therapy
  • deep learning
  • copy number
  • neural network