Assessing the potential of mutational strategies to elicit new phenotypes in industrial strains

Abstract

Industrial strains have been traditionally improved by rational approaches and combinatorial methods involving mutagenesis and selection. Recently, other methods have emerged, such as the use of artificial transcription factors and engineering of the native ones. As methods for generating genetic diversity continue to proliferate, the need for quantifying phenotypic diversity and, hence, assessing the potential of various genetic libraries for strain improvement becomes more pronounced. Here, we present a metric based on the quantification of phenotypic diversity, using Lactobacillus plantarum as a model organism. We found that phenotypic diversity can be introduced by mutagenesis of the principal factor, that this diversity can be modulated by tuning the sequence diversity, and that this method compares favorably with commonly used protocols for chemical mutagenesis. The results of the diversity metric here developed also correlated well with the probability of finding improved mutants in the different libraries, as determined by recursive screening under stress. In addition, we subjected our libraries to lactic and inorganic acids and found strains with improved growth in both conditions, with a concomitant increase in lactate productivity.divergence ͉ phenotypic diversity ͉ strain improvement ͉ stress tolerance ͉ transcriptional engineering P roduction of chemicals and fuels from renewable sources has become a major focus of the biotechnology industry as petroleum sources become scarcer and riskier to secure (1). Metabolic engineering has emerged as the enabling technology for designing entire biochemical pathways, both heterologous and native to the engineered strain (2). By using mostly rational approaches for pathway design and construction, this field has met with remarkable success in manufacturing commercial products (3-5). A major remaining challenge is engineering environmental tolerance phenotypes to allow robust growth and production under adverse industrial conditions (6, 7). In general, these phenotypes are hard to engineer, because stress conditions elicit multigenic responses (8) coordinated at the transcriptomic and proteomic levels (9, 10).Robust industrial strains have been traditionally obtained through adaptation and selection (11,12). Because natural adaptation is time-and resource-intensive, mutagens are used to introduce diversity in the population and accelerate the process (13). Alternatively, and in recognition of the more direct mapping between transcriptome and phenotype, efforts in strain improvement have focused on direct manipulation of the transcript profile (14 -19). Global transcription machinery engineering (gTME) has been successfully applied to introduce transcriptional-level modifications that are transferable between strains. Examples include random mutagenesis of the TATA-binding protein of Saccharomyces cerevisiae (14) and of the principal -factor of Escherichia coli (15). In addition to artificial transcription factors and gTME, other methods for phenotypic alteration ...

Mutagenesis of the Bacterial RNA Polymerase Alpha Subunit for Improvement of Complex Phenotypes

“…4) compared to ϳ40% in the case of rpoD as measured by the same method (48). Despite these results, we do not believe that this constitutes sufficient evidence for favoring one target or the other; instead, we have argued that a better measure of the usefulness of a library is related to the a priori probability of finding improved mutants (22), not to whether a particular target delivers a phenotype of interest or not. However, we do believe that mutagenesis of the alpha subunit may complement the use of sigma factors because it is permanently bound to the RNAP holoenzyme and has been shown to interact with most, if not all promoters (38), circumventing the fact that some transcriptional states may be hard to access by D in certain conditions (19,20).…”

“…Second, since the alpha and sigma units regulate promoter preferences by different mechanisms, combining these mutants within the same strain could result in synergistic transcriptional responses unachievable by either subunit tested separately. Although such combinations significantly increase the number of possible libraries, the use of a previously developed phenotypic diversity metric (22) could aid in designing better ones, e.g., by increasing or decreasing the mutagenesis rate in these expanded libraries.…”

“…It is clear from these results that significant phenotypic diversity was generated through the introduction of the rpoA library. Because phenotypic diversity is often linked to a greater probability of finding a desirable strain within a library (22), this observation serves as a qualitative indication that rpoA is a good target for transcriptional engineering. The rpoA-HA mutant (Fig.…”

“…The sigma-less RNAP is capable of all steps of transcription except initiation, which requires binding to one of the seven sigma factors (6). Because of their direct role in promoter recognition and binding, sigma factors have generally been regarded as interesting targets for mutagenesis (1,22). However, although sigma factors are often recognized as the primary determinants of promoter specificity, the alpha subunit of RNAP has also been implicated in promoter recognition, interacting not only with DNA but also with many activator and repressor proteins mainly through its carboxy-terminal domain (C-terminal one-third of the alpha subunit, denoted ␣CTD) (6, 9, 13, 37).…”

“…Because there are several ways in which cells regulate the process of transcription, we were interested in finding new targets for transcriptional engineering. A good target, when mutated, should generate richly diverse populations of strains from which new phenotypes can be selected (22).…”