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Computational identification of adaptive mutants using the VERT system.

Winkler J, Kao KC - J Biol Eng (2012)

Bottom Line: Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events.The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking.Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, Texas A&M University, College Station, TX, USA. kao.katy@mail.che.tamu.edu.

ABSTRACT

Unlabelled: :

Background: Evolutionary dynamics of microbial organisms can now be visualized using the Visualizing Evolution in Real Time (VERT) system, in which several isogenic strains expressing different fluorescent proteins compete during adaptive evolution and are tracked using fluorescent cell sorting to construct a population history over time. Mutations conferring enhanced growth rates can be detected by observing changes in the fluorescent population proportions.

Results: Using data obtained from several VERT experiments, we construct a hidden Markov-derived model to detect these adaptive events in VERT experiments without external intervention beyond initial training. Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events. A method to determine the optimal time point to isolate adaptive mutants is also introduced.

Conclusions: The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking. Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool.

No MeSH data available.


Sampling Example. Following the identification of adaptive events, estimates of optimal sampling points as described in the text are then computed to further assist in mutant isolation.
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Figure 4: Sampling Example. Following the identification of adaptive events, estimates of optimal sampling points as described in the text are then computed to further assist in mutant isolation.

Mentions: Now that adaptive events have been identified, adaptive mutants must be isolated from the chemostat population. Preserved population samples stored at -80°C may be regrown in the selective media, plated, and analyzed to determine which clonal isolate contains the adaptive mutation. Since any sample can potentially contain the mutant of interest, an additional tool based on the emission sequence generated by the statistical classifier and the hidden state data from the PSM was developed to guide sampling efforts so that the sample with the highest proportion of the adaptive mutant is identified. Firstly, the endpoints of each contiguous series of adaptive events ("A" states) are identified using the PSM output. Then, for each distinct adaptive event the emission sequence for that subpopulation is examined until a "N" symbol (statistically significant negative slope) is found at point i. The sampling suggestion is then set to i-1 as that time point likely contains the largest proportion of the mutant. Applying this procedure to this chemostat yields the sampling predictions highlighted in dark blue in Figure 4. The identified sampling points are either immediately adjacent to each adaptive expansion (if followed shortly by another expansion in a different subpopulation) or in the case of the final, high fitness yellow mutant, some distance away from the calculated adaptive event endpoint. The latter estimate arises from the fact that the yellow subpopulation essentially overran the chemostat environment, so that the optimum sampling point coincided with the final population measurement. Quantitative PCR measurement of allele frequency in each population supports this sampling scheme [13]. Altogether, these sampling suggestions provide a useful and accurate tool for the experimentalist to optimize their VERT experiment and minimize unnecessary mutant isolation.


Computational identification of adaptive mutants using the VERT system.

Winkler J, Kao KC - J Biol Eng (2012)

Sampling Example. Following the identification of adaptive events, estimates of optimal sampling points as described in the text are then computed to further assist in mutant isolation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351376&req=5

Figure 4: Sampling Example. Following the identification of adaptive events, estimates of optimal sampling points as described in the text are then computed to further assist in mutant isolation.
Mentions: Now that adaptive events have been identified, adaptive mutants must be isolated from the chemostat population. Preserved population samples stored at -80°C may be regrown in the selective media, plated, and analyzed to determine which clonal isolate contains the adaptive mutation. Since any sample can potentially contain the mutant of interest, an additional tool based on the emission sequence generated by the statistical classifier and the hidden state data from the PSM was developed to guide sampling efforts so that the sample with the highest proportion of the adaptive mutant is identified. Firstly, the endpoints of each contiguous series of adaptive events ("A" states) are identified using the PSM output. Then, for each distinct adaptive event the emission sequence for that subpopulation is examined until a "N" symbol (statistically significant negative slope) is found at point i. The sampling suggestion is then set to i-1 as that time point likely contains the largest proportion of the mutant. Applying this procedure to this chemostat yields the sampling predictions highlighted in dark blue in Figure 4. The identified sampling points are either immediately adjacent to each adaptive expansion (if followed shortly by another expansion in a different subpopulation) or in the case of the final, high fitness yellow mutant, some distance away from the calculated adaptive event endpoint. The latter estimate arises from the fact that the yellow subpopulation essentially overran the chemostat environment, so that the optimum sampling point coincided with the final population measurement. Quantitative PCR measurement of allele frequency in each population supports this sampling scheme [13]. Altogether, these sampling suggestions provide a useful and accurate tool for the experimentalist to optimize their VERT experiment and minimize unnecessary mutant isolation.

Bottom Line: Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events.The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking.Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, Texas A&M University, College Station, TX, USA. kao.katy@mail.che.tamu.edu.

ABSTRACT

Unlabelled: :

Background: Evolutionary dynamics of microbial organisms can now be visualized using the Visualizing Evolution in Real Time (VERT) system, in which several isogenic strains expressing different fluorescent proteins compete during adaptive evolution and are tracked using fluorescent cell sorting to construct a population history over time. Mutations conferring enhanced growth rates can be detected by observing changes in the fluorescent population proportions.

Results: Using data obtained from several VERT experiments, we construct a hidden Markov-derived model to detect these adaptive events in VERT experiments without external intervention beyond initial training. Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events. A method to determine the optimal time point to isolate adaptive mutants is also introduced.

Conclusions: The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking. Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool.

No MeSH data available.