Optimal trade-off between boosted tolerance and growth fitness during adaptive evolution of yeast to ethanol shocks.

BACKGROUND: The selection of Saccharomyces cerevisiae strains with higher alcohol tolerance can potentially increase the industrial production of ethanol fuel. However, the design of selection protocols to obtain bioethanol yeasts with higher alcohol tolerance poses the challenge of improving industrial strains that are already robust to high ethanol levels. Furthermore, yeasts subjected to mutagenesis and selection, or laboratory evolution, often present adaptation trade-offs wherein higher stress tolerance is attained at the expense of growth and fermentation performance. Although these undesirable side effects are often associated with acute selection regimes, the utility of using harsh ethanol treatments to obtain robust ethanologenic yeasts still has not been fully investigated. RESULTS: We conducted an adaptive laboratory evolution by challenging four populations (P1-P4) of the Brazilian bioethanol yeast, Saccharomyces cerevisiae PE-2_H4, through 68-82 cycles of 2-h ethanol shocks (19-30% v/v) and outgrowths. Colonies isolated from the final evolved populations (P1c-P4c) were subjected to whole-genome sequencing, revealing mutations in genes enriched for the cAMP/PKA and trehalose degradation pathways. Fitness analyses of the isolated clones P1c-P3c and reverse-engineered strains demonstrated that mutations were primarily selected for cell viability under ethanol stress, at the cost of decreased growth rates in cultures with or without ethanol. Under this selection regime for stress survival, the population P4 evolved a protective snowflake phenotype resulting from BUD3 disruption. Despite marked adaptation trade-offs, the combination of reverse-engineered mutations cyr1A1474T/usv1Δ conferred 5.46% higher fitness than the parental PE-2_H4 for propagation in 8% (v/v) ethanol, with only a 1.07% fitness cost in a culture medium without alcohol. The cyr1A1474T/usv1Δ strain and evolved P1c displayed robust fermentations of sugarcane molasses using cell recycling and sulfuric acid treatments, mimicking Brazilian bioethanol production. CONCLUSIONS: Our study combined genomic, mutational, and fitness analyses to understand the genetic underpinnings of yeast evolution to ethanol shocks. Although fitness analyses revealed that most evolved mutations impose a cost for cell propagation, combination of key mutations cyr1A1474T/usv1Δ endowed yeasts with higher tolerance for growth in the presence of ethanol. Moreover, alleles selected for acute stress survival comprising the P1c genotype conferred stress tolerance and optimal performance under conditions simulating the Brazilian industrial ethanol production.

Physiological and transcriptome analyses of Kluyveromyces marxianus reveal adaptive traits in stress response.

Kluyveromyces marxianus is a non-conventional yeast with outstanding physiological characteristics and a high potential for lignocellulosic ethanol production. However, achieving high ethanol productivity requires overcoming several biotechnological challenges due to the cellular inhibition caused by the inhibitors present in the medium. In this work, K. marxianus SLP1 was adapted to increase its tolerance to a mix of inhibitory compounds using the adaptive laboratory evolution strategy to study the adaptation and stress response mechanisms used by this non-Saccharomyces yeast. The fermentative and physiological parameters demonstrated that the adapted K. marxianus P8 had a better response against the synergistic effects of multiple inhibitors because it reduced the lag phase from 12 to 4 h, increasing the biomass by 40% and improving the volumetric ethanol productivity 16-fold than the parental K. marxianus SLP1. To reveal the effect of adaptation process in P8, transcriptome analysis was carried out; the result showed that the basal gene expression in P8 changed, suggesting the biological capability of K. marxianus to activate the adaptative prediction mechanism. Similarly, we carried out physiologic and transcriptome analyses to reveal the mechanisms involved in the stress response triggered by furfural, the most potent inhibitor in K. marxianus. Stress response studies demonstrated that P8 had a better physiologic response than SLP1, since key genes related to furfural transformation (ALD4 and ALD6) and stress response (STL1) were upregulated. Our study demonstrates the rapid adaptability of K. marxianus to stressful environments, making this yeast a promising candidate to produce lignocellulosic ethanol. KEY POINTS: • K. marxianus was adapted to increase its tolerance to a mix of inhibitory compounds • The basal gene expression of K. marxianus changed after the adaptation process • Adapted K. marxianus showed a better physiological response to stress by inhibitors • Transcriptome analyses revealed key genes involved in the stress response.

How adaptive laboratory evolution can boost yeast tolerance to lignocellulosic hydrolyses.

The yeast Saccharomyces cerevisiae is an excellent candidate for establishing cell factories to convert lignocellulosic biomass into chemicals and fuels. To enable this technology, yeast robustness must be improved to withstand the fermentation inhibitors (e.g., weak organic acids, phenols, and furan aldehydes) resulting from biomass pretreatment and hydrolysis. Here, we discuss how evolution experiments performed in the lab, a method commonly known as adaptive laboratory evolution (ALE), may contribute to lifting yeast tolerance against the inhibitors of lignocellulosic hydrolysates (LCHs). The key is that, through the combination of whole-genome sequencing and reverse engineering, ALE provides a robust platform for discovering and testing adaptive alleles, allowing to explore the genetic underpinnings of yeast responses to LCHs. We review the insights gained from past evolution experiments with S. cerevisiae in LCH inhibitors and propose experimental designs to optimise the discovery of genetic variants adaptive to biomass toxicity. The knowledge gathered through ALE projects is envisaged as a roadmap to engineer superior yeast strains for biomass-based bioprocesses.

Evolutionary and reverse engineering to increase Saccharomyces cerevisiae tolerance to acetic acid, acidic pH, and high temperature.

Saccharomyces cerevisiae scarcely grows on minimal media with acetic acid, acidic pH, and high temperatures. In this study, the adaptive laboratory evolution (ALE), whole-genome analysis, and reverse engineering approaches were used to generate strains tolerant to these conditions. The thermotolerant strain TTY23 and its parental S288C were evolved through 1 year, in increasing concentrations of acetic acid up to 12 g/L, keeping the pH ≤ 4. Of the 18 isolated strains, 9 from each ancestor, we selected the thermo-acid tolerant TAT12, derived from TTY23, and the acid tolerant AT22, derived from S288C. Both grew in minimal media with 12 g/L of acetic acid, pH 4, and 30 °C, and produced ethanol up to 29.25 ± 6 mmol/gDCW/h-neither of the ancestors thrived in these conditions. Furthermore, only the TAT12 grew on 2 g/L of acetic acid, pH 3, and 37 °C, and accumulated 16.5 ± 0.5 mmol/gDCW/h of ethanol. Whole-genome sequencing and transcriptomic analysis of this strain showed changes in the genetic sequence and transcription of key genes involved in the RAS-cAMP-PKA signaling pathway (RAS2, GPA2, and IRA2), the heat shock transcription factor (HSF1), and the positive regulator of replication initiation (SUM1), among others. By reverse engineering, the relevance of the combined mutations in the genes RAS2, HSF1, and SUM1 to the tolerance for acetic acid, low pH, and high temperature was confirmed. Alone, the RAS2 mutation yielded acid tolerance and HSF1 nutation thermotolerance. Increasing the thermo-acidic niche and acetic acid tolerance of S. cerevisiae can contribute to improve economic ethanol production. KEY POINTS: • Thermo-acid tolerant (TAT) yeast strains were generated by adaptive laboratory evolution. • The strain TAT12 thrived on non-native, thermo-acidic harmful conditions. • Mutations in RAS2, HSF1, and SUM1 genes rendered yeast thermo and acid tolerant.

In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products.

Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout's (feature's) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.

Adaptive laboratory evolution of nanocellulose-producing bacterium.

Adaptive laboratory evolution through 12 rounds of culturing experiments of the nanocellulose-producing bacterium Komagataeibacter hansenii ATCC 23769 in a liquid fraction from hydrothermal pretreatment of corn stover resulted in a strain that resists inhibition by phenolics. The original strain generated nanocellulose from glucose in standard Hestrin and Schramm (HS) medium, but not from the glucose in pretreatment liquid. K. hansenii cultured in pretreatment liquid treated with activated charcoal to remove inhibitors also converted glucose to bacterial nanocellulose and used xylose as carbon source for growth. The properties of this cellulose were the same as nanocellulose generated from media specifically formulated for bacterial cellulose formation. However, attempts to directly utilize glucose proved unsuccessful due to the toxic character of the lignin-derived phenolics, and in particular, vanillan and ferulic acid. Adaptive laboratory evolution at increasing concentrations of pretreatment liquid from corn stover in HS medium resulted in a strain of K. hansenii that generated bacterial nanocellulose directly from pretreatment liquids of corn stover. The development of this adapted strain positions pretreatment liquid as a valuable resource since K. hansenii is able to convert and thereby concentrate a dilute form of glucose into an insoluble, readily recovered and value-added product-bacterial nanocellulose.

Growth-Coupled Carotenoids Production Using Adaptive Laboratory Evolution.

Adaptive laboratory evolution is a powerful technique for strain development. However, the target phenotypes using this strategy have been limited by the required coupling of the phenotype-of-interest with fitness or survival, and thus adaptive evolution is generally not used to improve product formation. If the desired product confers a benefit to the host, then adaptive evolution can be an effective approach to improve host productivity. In this book chapter, we describe an effective adaptive laboratory evolution strategy for improving product formation of carotenoids, a class of compounds with antioxidant potential, in the yeast Saccharomyces cerevisiae.