Biology subjects

Feist, A. M.

Publications and source records attributed to Feist, A. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Improving acetate metabolism of Pseudomonas putida KT2440 by evolutionary and rational engineering

Acetate is a promising carbon source for microbial biotechnology as it can be produced sustainably from lignocellulosic biomass or C1 gases. Since acetate is directly activated to acetyl-CoA, it is especially suitable for producing acetyl-CoA-derived products, showcased here with the production of 3-(3-hydroxyalkanoyloxy) alkanoic acids (HAAs). P. putida KT2440 can natively metabolize acetate, but the weak acid has also inhibitory effects on microbial growth. We present an in-depth study on the physiology of P. putida KT2440 using acetate as carbon and energy source and evaluate acetate as feedstock for the biosynthesis of HAAs. Initially, a rational engineering approach to overexpress acetyl-CoA synthetase for acetate activation resulted in an improved growth rate of 16% and reduced lag phase by six hours. To further increase the performance of P. putida KT2440 on acetate, adaptive laboratory evolution was performed. This resulted in an improvement in the growth rate from 0.4 h-1 to 0.6 h-1 and enabled growth on up to 12.5 g L-1 acetate with a shortened lag phase compared to the wild type. Whole-genome sequencing revealed mutations in proteins involved in gene expression regulation and signal transduction. This evolutionary engineering approach informed the deletions of gacS and crc, which resulted in a reduction in the lag phase from seven hours to one hour and an improvement of the growth rate by 25 %, matching the growth properties of the evolved clones. Using the evolved strains for the production of HAAs resulted in faster biomass and product formation with product titers reaching up to 94 % of that of the wild type. In conclusion, we identified mechanisms in the acetate metabolism of P. putida KT2440 and improved the growth performance of the strain by rational and evolutionary engineering, demonstrating the potential of the promising, but challenging 3rd generation feedstock acetate.

microbiology↗

bioRxiv2026

Spore-Based Biocomposite Thermoplastic Polyesters with Enhanced Toughness and Programmable Disintegration

Thermoplastic polyesters are widely used in commodity and high-performance applications due to their tunable and exceptional properties, versatile performance, and increasing relevance in sustainable materials. Integrating biological functionality into these polymers offers a promising route to enhance performance and end-of-life behavior beyond what conventional additives can achieve. Here, we report the generalization of an embedded spore-based engineered living material concept to three representative thermoplastic polyesters; polycaprolactone (PCL), polylactic acid (PLA), and poly(butylene adipate-co-terephthalate) (PBAT). Heat-shock-tolerized Bacillus subtilis spores were compounded with each polyester as a living biofiller via hot melt extrusion. The resulting biocomposite polyesters retained high spore viability (>90%) after extrusion and exhibited improved mechanical performance (up to 41% toughness improvement compared to neat polymers). End-of-life behavior was evaluated in a microbially-limited composting environment, where spore-containing PCL exhibited nearly complete disintegration within five months, corresponding to a [~]7-fold increase in degradation kinetics relative to neat PCL. Finally, 3D printing of biocomposite PCL was demonstrated through fused deposition modeling and direct ink writing methodologies. Together, this work demonstrated the successful extension of spore-based engineered living materials from thermoplastic polyurethane to multiple thermoplastic polyesters. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/707801v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@4eaafaorg.highwire.dtl.DTLVardef@bb17c2org.highwire.dtl.DTLVardef@114e4ceorg.highwire.dtl.DTLVardef@b9bd0c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

Simultaneous optimization of lignocellulosic sugar catabolism via systematic laboratory evolution in dynamic conditions

Efficient co-utilization of hexose and pentose sugars from lignocellulose is essential for microbial production of bio-based chemicals, yet engineered non-native catabolic pathways can be suboptimal and evolutionarily unstable in complex resource environments. We used a Pseudomonas putida strain, previously engineered to catabolize xylose and arabinose to examine how resource abundance, temporal availability, and sub-culturing criteria shape evolutionary outcomes. Using an automated adaptive laboratory evolution (ALE) platform, we evolved the strain under static conditions with single selection pressures and dynamic regimes that imposed selection pressures on multiple sugars. These environments drove divergence between catabolic specialists and generalists. While selection regimes with weak or absent selection for xylose frequently resulted in loss of xylose catabolism, evolution under carbon-limited, mixed-sugar environments promoted stable retention and coordinated optimization of multiple catabolic pathways, increasing total sugar consumption in mixed-sugar conditions. Genomic, proteomic, and biochemical analyses showed that evolutionary stability was determined by pathway-specific fitness costs, leading to either pathway loss or cost-reducing refinement, depending on selection strength. An isolated generalist clone also exhibited improved indigoidine production from mixed sugars when compared to the parental strain. Together, these findings link resource dynamics to fitness landscapes that determine catabolic specialization, generalization, evolutionary trade-offs, and applicability to bioconversion.

bioengineering↗

bioRxiv2025

Multi-strain Analysis of Pseudomonas putida Reveals the Metabolic and Genetic Diversity of the Species.

Pseudomonas putida is a gram-negative bacterial species increasingly utilized in biotechnology due to its robust growth, ability to degrade aromatic compounds, solvent tolerance, and genetic tractability. In this study, we report a comprehensive multi-strain analysis of 164 P. putida strains. We performed whole-genome sequencing and hybrid assembly for 40 strains, contributing a [~]8% increase to the available genomic data for P. putida. Furthermore, high-throughput phenotypic profiling using the Biolog phenotype microarray system for 24 strains on 190 unique carbon sources, along with 15 aromatic compounds not present on Biolog plates, yielded 4,920 unique strain-phenotype measurements. These data were leveraged to curate GEMs for 24 representative strains, including a refined model for strain KT2440, which comprised 1,480 genes and 2,191 metabolites, achieving a prediction accuracy of 91.2% in carbon utilization. Systematic comparison of genomes and GEMs revealed both conserved core pathways and significant allelic and functional divergence across strains, highlighting strain-specific variation in aromatic degradation. While pathways for protocatechuate and phenylacetate degradation were widely conserved, metabolic capabilities for compounds such as ferulate, phenol, and cresols varied markedly, suggesting adaptation to distinct ecological niches. Alleleome analysis of enzymes such as PcaI and PcaJ revealed distinct, functionally similar clades, indicating possible convergent evolution or horizontal gene transfer. These results provide computable resources and models for selecting P. putida strains with desired traits for biomanufacturing and bioremediation and offer insights into the evolution and phylogeny of the P. putida species.

bioengineering↗

bioRxiv2025

Evolved microbial diversity enables combinatoric biosensing in complex environments

Whole-cell biosensors (WCBs) offer rapid, cost-effective monitoring of environmental contamination and human disease. Current WCB efforts to optimize detection of single target analytes under laboratory conditions have achieved vastly improved performance, setting the stage for WCB deployment in complex environments. We propose a framework that leverages the cross-specificity of single-target WCBs to quantify multiple targets using supervised machine learning. Specifically, we engineer six sensors for heavy metal contaminants in laboratory E. coli. We then evolve the strain to generate five chassis with improved growth in seawater conditions. We transform the chassis with the sensors, creating a set of 30 variants. The variant dynamic responses are characterized with microfluidics, revealing significant diversity. Leveraging this diversity, we construct a consortium to combinatorically quantify multiple analytes with machine learning, outperforming single-target biosensors in over 90% of our test samples. These results form a generalizable framework that facilitates WCB translation toward settings beyond the laboratory.

bioengineering↗

bioRxiv2025

Aerobicity stimulon in Escherichia coli revealed using multi-scale computational systems biology of adapted respiratory variants

Energy homeostasis facilitated by the interplay of substrate-level and oxidative phosphorylation is crucial for bacterial adaptation to diverse substrates and environments. To investigate how bioenergetic systems optimize under restrictive conditions, we evolved ETS variants with distinct proton-pumping efficiencies (1, 2, 3, or 4 proton(s) per electron) on succinate and glycerol. These substrates impose unique metabolic constraints: succinate requires complete gluconeogenesis, while glycerol supports mixed glycolytic and gluconeogenic fluxes. Multi-scale computational analysis of the strains revealed (a) Growth optimization across carbon substrates for multiple ETS variants, (b) A conserved aerobicity stimulon comprising seven independently regulated gene groups that are co-regulated with increasing aerobic capacities, (c) Proteome reallocation linked to aerobicity, validated using genome-scale metabolism and expression modeling, and (d) Carbon source-specific compensatory mutations in succinate transporters and regulatory elements. These findings define the aerobicity stimulon and establish a unifying framework for understanding bacterial respiratory flexibility, demonstrating how transcriptional networks and metabolic systems integrate to achieve energy homeostasis and bioenergetic resilience.

systems biology↗

bioRxiv2025

Evolution guided tolerance engineering of Pseudomonas putida KT2440 for production of the sustainable aviation fuel precursor isoprenol

Isoprenol (3-methyl-3-buten-1-ol) is a sustainable aviation fuel precursor and commodity chemical which can be microbially synthesized from renewable carbon streams. Its production has been demonstrated in Pseudomonas putida KT2440 but its titers, rates, and yields have yet to reach commercially viable levels, potentially due to its inherent toxicity. We hypothesized that utilization of Tolerization Adaptive Laboratory Evolution (TALE) would generate P. putida hosts more tolerant to isoprenol and suitable for enhanced production phenotypes. Here, we performed a comprehensive TALE campaign using three strains, the wild-type and two strains lacking subsets of known isoprenol catabolism and transport functions in quadruplicate independently evolved lineages. Several evolved clones from each starting strain displayed robust growth (up to 0.2 h-1) at 8 g/L of isoprenol, where starting strains could not grow. Whole genome resequencing of the 12 independent strain lineages identified convergent mutations. Reverse engineering four commonly mutated regions individually (gnuR, ttgB-PP_1394, PP_3024-PP_5558, PP_1695) each resulted in a partial recovery of the tolerance phenotypes observed in the evolved strains. Additionally, a proteomics-guided deletion of the master motility regulator, fleQ, in an evolved clone alleviated the tolerance vs. production trade-off, restoring isoprenol titers and consumption to levels observed in the starting strains. Collectively, this work demonstrated that an integrated strategy of laboratory evolution and rational engineering was effective to develop robust biofuel production hosts with minimized product toxicity.

microbiology↗

bioRxiv2024

Growth-coupled microbial biosynthesis of the animal pigment xanthommatin

The mining of genomes across life has unearthed a bounty of biosynthetic potential to diverse molecules key to a biobased future. While the heterologous expression of metabolic pathways has achieved broad success, most approaches suffer a similar fate in low initial production levels that require extensive, resource-heavy iterative strain engineering refinement. Herein we introduce a growth-coupled biosynthetic (GrowBio) strategy that irrevocably connects microbial growth with specialized compound production. We demonstrate the plug-and-play versatility of GrowBio in the production of the structurally complex animal biopigment xanthommatin, a color-changing ommochrome with material and cosmetic potential. Xanthommatin biosynthesis directly fuels growth of a newly designed Pseudomonas putida 5,10-methylenetetrahydrofolate auxotroph (PUMA). Aided by genome-scale metabolic modeling, PUMA was designed and built to be controlled by endogenous formate co-produced as a coupled biosynthetic byproduct in the multistep conversion of tryptophan to xanthommatin. Adaptive laboratory evolution was utilized to streamline xanthommatins gram-scale bioproduction via growth rate selection, establishing GrowBio as a promising biotechnological approach for establishing and optimizing the microbial production of value-added molecules.

synthetic biology↗

bioRxiv2024

Metabolic growth coupling strategies for in vivo enzyme selection systems

Whole-cell biocatalysis facilitates the production of a wide range of industrially and pharmaceutically relevant molecules from sustainable feedstocks such as plastic wastes, carbon dioxide, lignocellulose, or plant-based sugar sources. The identification and use of efficient enzymes in the applied biocatalyst is key to establishing economically feasible production processes. The generation and selection of favorable enzyme variants in adaptive laboratory evolution experiments using growth as a selection criterion is facilitated by tightly coupling enzyme catalytic activity to microbial metabolic activity. Here, we present a computational workflow to design strains that have a severe, growth-limiting metabolic chokepoint through a shared class of enzymes. The resulting chassis cell, termed enzyme selection system (ESS), is a platform for growth-coupling any enzyme from the respective enzyme class, thus offering cross-pathway application for enzyme engineering purposes. By applying the constraint-based modeling workflow, a publicly accessible database of 25,505 potential and experimentally tractable ESS designs was built for Escherichia coli and a broad range of production pathways with biotechnological relevance. Model-based analysis of the generated design database reveals a general design principle that the target enzyme activity is linked to overall microbial metabolic activity, not just the synthesis of one biomass precursor. Furthermore, the use of currently available genome-reduced strains or one preeminent carbon source does not significantly enable or improve growth-coupling of target enzymes. Most importantly, observed trade-offs between the predicted viability of ESSs, the design-inflicted metabolic perturbations, and the coupling strength suggest that a suboptimal coupling has benefits regarding the experimental implementation of ESSs and growth-coupling in general. Overall, the computed design database, which is accessible through https://biosustain.github.io/ESS-Designs/, and its analysis lay the foundation for generating valuable in vivo ESSs for a range of biotechnological applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/613188v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@15d5a10org.highwire.dtl.DTLVardef@1c4067aorg.highwire.dtl.DTLVardef@856ce9org.highwire.dtl.DTLVardef@1b14cdb_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG HighlightsO_LIEnzyme selection system designs link enzyme activities to global metabolic processes. C_LIO_LIEnzymes can be categorized based on growth-couplable reaction stoichiometries. C_LIO_LIA database of strain designs for building enzyme selection systems was created. C_LIO_LIEnzyme selection systems enable optimization of a range product synthesis pathways. C_LIO_LIApplication recommendations: weakly coupled enzyme activities can have practical advantages. C_LI

systems biology↗

bioRxiv2024

Laboratory evolution reveals transcriptional mechanisms underlying thermal adaptation of Escherichia coli

Adaptive laboratory evolution (ALE) is able to generate microbial strains which exhibit extreme phenotypes, revealing fundamental biological adaptation mechanisms. Here, we use ALE to evolve Escherichia coli strains that grow at temperatures as high as 45.3{degrees}C, a temperature lethal to wild type cells. The strains adopted a hypermutator phenotype and employed multiple systems-level adaptations that made global analysis of the DNA mutations difficult. Given the challenge at the genomic level, we were motivated to uncover high temperature tolerance adaptation mechanisms at the transcriptomic level. We employed independently modulated gene set (iModulon) analysis to reveal five transcriptional mechanisms underlying growth at high temperatures. These mechanisms were connected to acquired mutations, changes in transcriptome composition, sensory inputs, phenotypes, and protein structures. They are: (i) downregulation of general stress responses while upregulating the specific heat stress responses; (ii) upregulation of flagellar basal bodies without upregulating motility, and upregulation fimbriae; (iii) shift toward anaerobic metabolism, (iv) shift in regulation of iron uptake away from siderophore production, and (v) upregulation of yjfIJKL, a novel heat tolerance operon which we characterized using AlphaFold. iModulons associated with these five mechanisms explain nearly half of all variance in the gene expression in the adapted strains. These thermotolerance strategies reveal that optimal coordination of known stress responses and metabolism can be achieved with a small number of regulatory mutations, and may suggest a new role for large protein export systems. ALE with transcriptomic characterization is a productive approach for elucidating and interpreting adaptation to otherwise lethal stresses.

systems biology↗

bioRxiv2023

Biocomposite Thermoplastic Polyurethanes Containing Evolved Bacterial Spores as Living Fillers to Facilitate Polymer Disintegration

The field of engineered living materials (ELMs) seeks to pair living organisms with synthetic materials to generate biocomposite materials with augmented function since living systems can provide highly-programmable and complex behavior. ELMs have typically been fabricated using techniques in benign aqueous environments, limiting their application. In this work, biocomposite fabrication was demonstrated in which spores from polymer-degrading bacteria were incorporated into a thermoplastic polyurethane (TPU) using high-temperature melt processing. Bacteria were engineered using adaptive laboratory evolution to improve their heat tolerance to ensure nearly complete cell survivability during manufacturing at 135 {degrees}C. Furthermore, the overall tensile properties of spore-filled TPUs were substantially improved, resulting in a significant improvement in toughness. The biocomposites facilitated disintegration in compost in the absence of a microbe-rich environment. Finally, spores retained a programmed function, expressing green fluorescent protein. This research provides a scalable method to fabricate advanced biocomposite materials in industrially-compatible processes.

bioengineering↗

bioRxiv2023

Using the E. coli Alleleome in Strain Design

Leveraging observed variants in strain design is a promising technique for creating strains with specific properties. Adaptive laboratory evolution (ALE) experiments generate variants that enhance fitness under specific conditions and can contribute to application-specific strain designs. Further, the wild-type (WT) coding alleleome of an organism, the complete set of its genes WT alleles, can provide an additional amount and diversity of variants not yet accessible from the aggregation of ALE experiment results. This study used both an ALE mutation database (3093 genomes) and a large set of WT genomes (12,661 genomes) to explore the sequence solution space of genes involved in tolerance to 10 conditions of industrial importance. To accomplish this, ALE variants for 22 genes previously identified as potentially important for industrial chemical tolerance were collected and supplemented with all available variants from the WT coding alleleome. A total of 4879 variants were reintroduced and used in 10 selection experiments. Both ALE and WT contributed highly enriched variants, where the enrichment and benefits depended on the conditions, genes, and gene product regions. The results also revealed that variants not originating from the initial experiment could potentially confer substantially greater benefits. Additionally, ALE and WT variants rarely overlapped on AA positions, but their clustering did coincide with where highly enriched variants were ultimately located. For genes primarily hosting potential gain-of-function variations, substitutions predicted to have a conservative impact frequently outperformed more radical substitutions. Case studies demonstrated that maximizing the amount of variants enabled easier identification of variant trends, which in turn can be used to better understand areas and characteristics of genes that can be feasibly varied, representing what could be thought of as a genome design variable. The combination of ALE and WT variants is a promising approach for use in future projects to better constrain and ultimately achieve practical coverage in the exploration of feasible sequence solution space. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/558058v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1a8e843org.highwire.dtl.DTLVardef@28a134org.highwire.dtl.DTLVardef@16f6849org.highwire.dtl.DTLVardef@127b43c_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2022

Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress tolerance mechanisms: 1) modification of transport, 2) activation of ROS stress responses, 3) use of ROS-sensitive iron regulation, 4) motility, 5) broad transcriptional reallocation toward growth, and 6) metabolic rewiring to decrease NADH production. This work thus reveals the genome-scale systems biology of ROS tolerance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/521246v1_ufig1.gif" ALT="Figure 1"> View larger version (83K): org.highwire.dtl.DTLVardef@187f455org.highwire.dtl.DTLVardef@ba0798org.highwire.dtl.DTLVardef@148b175org.highwire.dtl.DTLVardef@17a8f8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Machine-learning from Pseudomonas putida transcriptomes reveals its transcriptional regulatory network

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1,993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.

systems biology↗

bioRxiv2020

Environmental conditions dictate differential evolution of vancomycin resistance in Staphylococcus aureus

While microbiological resistance to vancomycin in Staphylococcus aureus is rare, clinical vancomycin treatment failures are common, and methicillin-resistant S. aureus (MRSA) strains isolated from patients after prolonged vancomycin treatment failure remain susceptible. Adaptive laboratory evolution was utilized to uncover mutational mechanisms associated with MRSA vancomycin resistance in a bacteriological medium used in clinical susceptibility testing and a physiological medium. Sequencing of resistant clones revealed shared and media-specific mutational outcomes, with an overlap in cell wall regulons (walKRyycHI, vraSRT). Evolved strains displayed similar genetic and phenotypic traits to resistant clinical isolates. Importantly, resistant phenotypes that developed in physiological media did not translate into resistance in bacteriological media. Further, a bacteriological media-specific mechanism for vancomycin resistance enabled by a mutated mprF was confirmed. This study bridges the gap of understanding between clinical and microbiological vancomycin resistance in S. aureus and expands the number of allelic variants that result in vancomycin resistance phenotypes.

microbiology↗

bioRxiv2019

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they are often noisy, non-physiological, inconsistent, and labor-intensive to obtain.\n\nWe use a data-driven approach to estimate in vivo kcats using metabolic specialist E. coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo kcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of noisy and inconsistent parameterizations of cellular models.

systems biology↗

bioRxiv2019

Experimental evolution reveals the genetic basis and systems biology of superoxide stress tolerance

Bacterial response to oxidative stress is of fundamental importance. Oxidative stresses are endogenous, such as reactive oxidative species (ROS) production during respiration, or exogenous in industrial biotechnology, due to culture conditions or product toxicity. The immune system inflicts strong ROS stress on invading pathogens. In this study we make use of Adaptive Laboratory Evolution (ALE) to generate two independent lineages of Escherichia coli with increased tolerance to superoxide stress by up to 500% compared to wild type. We found: 1) that the use of ALE reveals the genetic basis for and systems biology of ROS tolerance, 2) that there are only 6 and 7 mutations, respectively, in each lineage, five of which reproducibly occurred in the same genes (iron-sulfur cluster regulator iscR, putative iron-sulfur repair protein ygfZ, pyruvate dehydrogenase subunit E aceE, succinate dehydrogenase sucA, and glutamine tRNA glnX), and 3) that the transcriptome of the strain lineages exhibits two different routes of tolerance: the direct mitigation and repair of ROS damage and the up-regulation of cell motility and swarming genes mediated through phosphate starvation, which has been linked to biofilm formation and aggregation. These two transcriptomic responses can be interpreted as flight and fight phenotypes.\n\nImportanceBacteria encounter oxidative stress from multiple sources. During pathogenic infections, our bodys immune system releases ROS as a form of antimicrobial defense whilst bacteria used in industrial biotechnology are frequently exposed to genetic modifications and culture conditions which induce oxidative stress. In order to get around the bodys defences, pathogens have developed various adaptations to tolerate high levels of ROS, and these adaptive mechanisms are not always well understood. At the same time, there is a need to improve oxidative stress tolerance for industrially relevant strains in order to increase robustness and productivity. In this study we generate two strains of superoxide tolerant Escherichia coli and identify several adaptive mechanisms. These findings can be directly applied to improve production strain fitness in an industrial setting. They also provide insight into potential virulence factors in other pathogens, highlighting potential targets for antimicrobial compounds.

systems biology↗

bioRxiv2019

Adaptive laboratory evolution of Escherichia coli under acid stress

The ability of Escherichia coli to tolerate acid stress is important for its survival and colonization in the human digestive tract. Here, we performed adaptive laboratory evolution of the laboratory strain E. coli K-12 MG1655 at pH 5.5 in glucose minimal medium. By 800 generations, six independent populations under evolution reached 18.0% higher growth rates than their starting strain at pH 5.5, while maintaining comparable growth rates to the starting strain at pH 7. We characterized the evolved strains to find that: (1) whole genome sequencing of isolated clones from each evolved population revealed mutations in rpoC appearing in 5 of 6 sequenced clones; (2) gene expression profiles revealed different strategies to mitigate acid stress, that are related to amino acid metabolism and energy production and conversion. Thus, a combination of adaptive laboratory evolution, genome resequencing, and expression profiling reveals, on a genome-scale, the strategies that E. coli deploys to mitigate acid stress.

microbiology↗