Laboratory evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance
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59 BASIC BIOLOGICAL SCIENCES↗
Engineering topics
Szubin, Richard
Publications and source records attributed to Szubin, Richard.
Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility
In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available.
59 BASIC BIOLOGICAL SCIENCES↗
Generation of ionic liquid tolerant Pseudomonas putida KT2440 strains via adaptive laboratory evolution
Although the use of ionic liquids (ILs) for the pretreatment of lignocellulosic biomass has been limited due to high costs, recent efforts to develop low-cost protic ILs show promise for achieving cost-effectiveness for biorefineries. However, an additional challenge remains in that ILs present in biomass hydrolysates are toxic to most microbial hosts, resulting in poor growth phenotypes. To address this issue, we applied an adaptive laboratory evolution (ALE) approach for tolerizing Pseudomonas putida KT2440, an industrially relevant bacterial host, to two low-cost ILs (triethanolammonium acetate [TEOH][OAc] and triethylammonium hydrogen sulfate [TEA][HS]). After continuous cultivations with gradually increased IL levels, we obtained evolved strains showing significant improvements in their growth performance under high concentrations of the ILs (maximum 4% [TEOH][OAc] and 8% [TEA][HS], in w/v) at which the wild-type strain cannot grow. Sequencing of evolved strains revealed multiple regions where mutations were associated with improved performance in minimal media conditions (relA, gacS, oprB/PP_1446, fleQ, tktA, and uvrY/PP_4100) and in IL-specific conditions (PP_5350, PP_4929/emrE, oprD, and PP_5324). We further validated the causality of the PP_5350 and emrE genes for improved IL tolerance via reverse engineering and transcriptomic analysis. Here, a common mutation in the PP_5350 gene, encoding a RpiR family transcriptional regulator, was shown to significantly upregulate the glyoxylate cycle for efficient acetate catabolism. In addition, it was suggested that the emrE gene encodes an efflux pump which can export [TEA][HS]. Finally, the cultivation of two of the best performing evolved strains with IL-treated biomass hydrolysates demonstrated their considerable potential to be used as platform strains. Taken as a whole, this work provides strains for utilization of IL-treated biomass and a mechanistic understanding that could be further leveraged to develop efficient microbial bioprocesses.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗