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Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity
- Source :
- BMC Genomics, Vol 21, Iss 1, Pp 1-16 (2020), Phaneuf, P V, Yurkovich, J T, Heckmann, D, Wu, M, Sandberg, T E, King, Z A, Tan, J, Palsson, B O & Feist, A M 2020, ' Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity ', BMC Genomics, vol. 21, 514 . https://doi.org/10.1186/s12864-020-06920-4, BMC genomics, vol 21, iss 1, BMC Genomics
- Publication Year :
- 2020
- Publisher :
- BMC, 2020.
-
Abstract
- Background Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures. Results Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine. Conclusions The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.
- Subjects :
- lcsh:QH426-470
Bioinformatics
lcsh:Biotechnology
Computational biology
Biology
Proteomics
medicine.disease_cause
Genome
Medical and Health Sciences
03 medical and health sciences
Annotation
Information and Computing Sciences
lcsh:TP248.13-248.65
medicine
Escherichia coli
Genetics
Selection (genetic algorithm)
030304 developmental biology
0303 health sciences
Mutation
030306 microbiology
Methodology Article
Escherichia coli Proteins
Human Genome
Genome project
Biological Sciences
Phenotype
Multiscale genome annotation
lcsh:Genetics
Mutation convergence
Mutation functional analysis
Mutation meta-analysis
DNA microarray
Laboratories
Adaptive laboratory evolution
Biotechnology
Subjects
Details
- Language :
- English
- ISSN :
- 14712164
- Volume :
- 21
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Genomics
- Accession number :
- edsair.doi.dedup.....c75b5d1cf992133af6ebca82ff753185