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Leveraging genetic background effects in Saccharomyces cerevisiae to improve lignocellulosic hydrolysate tolerance

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english
पत्रिका:
Applied and Environmental Microbiology
DOI:
10.1128/AEM.01603-16
Date:
July, 2016
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AEM Accepted Manuscript Posted Online 22 July 2016
Appl. Environ. Microbiol. doi:10.1128/AEM.01603-16
Copyright © 2016, American Society for Microbiology. All Rights Reserved.

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Leveraging genetic background effects in Saccharomyces cerevisiae to improve

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lignocellulosic hydrolysate tolerance

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Maria Sardi1,2, Nikolay Rovinskiy1,*, Yaoping Zhang1, Audrey P. Gasch1, 3 #
1 Great Lakes Bioenergy Research Center, University of Wisconsin-Madison,
Madison WI, 53706
2 Microbiology Training Program, University of Wisconsin-Madison, Madison WI,
53706
3 Laboratory of Genetics, University of Wisconsin-Madison, Madison WI, 53706
* Current address: DNAStar, Madison WI

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Leveraging genetic backgrounds to improve tolerance

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Corresponding author: Audrey Gasch, agasch@wisc.edu

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University of Wisconsin-Madison, 425 G Henry Mall, Room 3426, Madison, WI 53706

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ABSTRACT

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A major obstacle to sustainable lignocellulosic biofuel production is microbe inhibition

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by the combinatorial stresses in pretreated plant hydrolysate. Chemical biomass

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pretreatment releases a suite of toxins that interact with other stressors, including high

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osmolarity and temperature, which together can have poorly understood synergistic

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effects on cells. Improving tolerance in industrial strains has been hindered, in part

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because mechanisms of tolerance reported in the literature often fail to recapitulate in

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other strain backgrounds. Here, we explored and then exploited variation in stress

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tolerance, toxin-induced transcriptomic responses, and fitness effects of gene over-

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expression in different yeast strains to identify genes and processes linked to tolerance

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of hydrolysate stressors. Using six different Saccharomyces cerevisiae strains that

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together maximized phenotypic and genetic diversity, first we explored transcriptomic

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differences between resi; stant and sensitive strains to implicate common and strain-

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specific responses. This comparative analysis implicated primary cellular targets of

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hydrolysate toxins, secondary effects of defective defense strategies, and mechanisms

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of tolerance. Dissecting the responses to individual hydrolysate components across

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strains pointed to synergistic interactions between osmolarity, pH, hydrolysate toxins,

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and nutrient composition. By characterizing the effects of high-copy gene over-

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expression in three different strains, we revealed the breadth of background-specific

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effects of gene-fitness contributions in synthetic hydrolysate. Our approach identified

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new genes for engineering improved stress tolerance in diverse strains while

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illuminating the effects of genetic background on molecular mechanisms.

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IMPORTANCE

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Recent studies on natural variation within Saccharomyces cerevisiae have uncovered

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substantial phenotypic diversity. Here, we take advantage of this diversity, using it as a

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tool to infer the effects of combinatorial stress found in lignocellulosic hydrolysate. By

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comparing sensitive and tolerant strains, we implicated primary cellular targets of

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hydrolysate toxins and elucidated cells’ physiological state when exposed to this stress.

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We also explored strain-specific effects of gene overexpression to further implicate

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strain-specific responses to hydrolysate stresses and to identify genes that improve

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hydrolysate tolerance independent of strain background. This study underscores the

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importance of studying multiple strains to understand the effects of hydrolysate stress

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and provides a method to find genes that improve tolerance across strain backgrounds.

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INTRODUCTION

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Lignocellulosic plant material is a sustainable and renewable source of biomass for

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bioenergy and biochemical production. Plant cellulose and hemicellulose harbor

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significant concentrations of sugars that can be used to produce desired compounds

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through microbial fermentation. In recent years, several technologies have been

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developed to hydrolyze plant biomass in order to release monomeric sugars (1, 2). For

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most types of chemical pretreatment, the resulting hydrolysate contains high sugar

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concentrations, and thus high osmolarity, and also toxic compounds such as weak acids,

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furans, and phenolics that are generated as a byproduct of chemical hydrolysis. These

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hydrolysate toxins (HTs) are known to inhibit microbial growth and fermentation;

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however, the mechanisms of stress tolerance remain unclear for many of these

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compounds (3-5). Because removal of these inhibitors from the hydrolysate is

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expensive (6), a focus is to utilize inhibitor-tolerant microorganisms to produce biofuels

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and chemicals from plant biomass in an economically viable way.

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One strategy is to generate hydrolysate-tolerant microbes by engineering stress

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tolerance based on the mechanism of toxin action. Most studies elucidating inhibitory

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mechanisms have focused on individual toxins applied in isolation to single or a few

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strain backgrounds. Weak acids such as acetic, formic, and levulinic acids can cross

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membranes when protonated at low pH, whereupon they dissociate to decrease

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cytosolic pH (7) and consequently stimulate plasma membrane ATPases that consume

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ATP to pump protons out of the cell (8, 9). Furans such as 5-hydroxymethyl furfural

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(HMF) and furfural are also common inhibitors in hydrolysate, formed by the

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degradation of xylose and glucose, respectively (10). Furan derivatives inhibit alcohol

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dehydrogenase (ADH), pyruvate dehydrogenase (PDH) and aldehyde dehydrogenase

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(ALDH) enzymes (11) while producing reactive oxygen species that broadly damage

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membranes, DNA, proteins, and cellular structures (12). Cells respond by reducing

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furans to less inhibitory compounds at the expense of NAD(P)+ reduction, thereby

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limiting cell division and biofuel production (13, 14). Among other inhibitors, phenolics

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are the most diverse and the least well understood. These compounds are formed

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during lignin breakdown, and thus their concentrations and identities mainly depend on

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the source of plant biomass (4, 15). Phenolic compounds exert considerable inhibitory

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effects by causing the loss of membrane integrity (16, 17), decreasing cellular ATP (18,

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19), causing oxidative damage (17), inhibiting de novo nucleotide biosynthesis (20) and

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inhibiting translation (21). While the effects of individual toxins are becoming clear in

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some cases, the compounded effects of multiple toxins in hydrolysate are poorly

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understood (22, 23). Compounded stress is especially important to consider, since

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microbes encounter multiple inhibitors at the same time during industrial fermentation of

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lignocellulosic hydrolysates.

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Many studies have characterized the response of Saccharomyces cerevisiae

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and other organisms to plant hydrolysate, in an effort to identify engineering strategies

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to improve hydrolysate tolerance in industrial strains (22, 24-27). However, the impact

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of these strategies is often limited, since mechanisms identified in one strain frequently

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fail to produce similar results when ported to other strain backgrounds (28-31).

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Furthermore, many studies elucidate the mechanisms of toxin inhibition in lab-

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domesticated strains, which poorly represent the stress-tolerance potential of the

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species (32-34). The degree and breadth of background-specific effects is recognized in

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a practical sense but poorly quantified, and thus this represents a major hurdle for

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rational engineering (35, 36).

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Here, we leveraged genetic background effects across distinct lineages of

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Saccharomyces cerevisiae to explore strain-specific responses to a synthetic mimic of

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ammonia fiber expansion (AFEX)-pretreated corn stover (ACSH) (23, 37). Using a

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synthetic hydrolysate allowed us to dissect the transcriptional response to the base-

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media composition, toxin cocktail, pH, and their combination, across multiple strain

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backgrounds. Comparing transcriptomic and fitness responses in strains with different

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levels of HT resistance provided key insights into toxins’ primary cellular targets,

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synergistic effects among hydrolysate toxins, and common as well as strain-specific

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mechanisms of toxin defense. We found striking differences in the fitness contributions

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of gene over-expression to HT tolerance. Comparing across strain backgrounds

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revealed genes that increased HT tolerance independent of strain lineage. Together,

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our work quantifies the impact of genetic background on toxin tolerance while

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implicating mechanisms and genes important for improved hydrolysate tolerance

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independent of strain background.

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MATERIALS AND METHODS

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Strains and growth condition

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Strains and phenotypes are listed in Supplementary Table 1. The SynH media

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mimics ACSH with 90 g glucan/L loading and was prepared as in Serate et al. (2015)

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except that all concentrations were increased 1.5-fold to emulate a higher glucan

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loading (Supplementary Table 2). Gene knockouts were generated by homologous

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recombination of the KAN-MX cassette into the locus of interest in a haploid version of

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YPS128 and verified by diagnostic PCR. Unless otherwise indicated, cultures were

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grown with vigorous shaking at 30oC. Where indicated, media was supplemented with

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iron (II) sulfate heptahydrate (Sigma-Aldrich, St. Louis, MO). Over-expression

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experiments were performed using the molecular barcoded yeast (MoBY 2.0) ORF

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library (38), growing cells in Synthetic Complete medium (SC) with high sugar

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concentrations and no ammonium to support G418 selection (39) (1.7 g/L YNB w/o

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ammonia sulfate and amino acids, 1 g/L monosodium glutamic acid, 2 g/L amino acid

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drop-out lacking leucine, 48 µg/L leucine, 90 g/L dextrose, 45 g/L xylose) along with the

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toxin cocktail (Supplementary Table S4).

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Phenotyping

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10 µl of thawed frozen stock of cells was used to inoculate a 96-well plate (NUNC,

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Thermo Scientific, Rockford, IL) containing 190 µl of YPD media. Plates were sealed

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with breathable tape (AeraSeal, Sigma-Aldrich, St. Louis, MO), covered with a lid and

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incubated at 30oC while shaking for 24 h, after which 10 µl of saturated cultures were

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used to inoculate 190 µl of YPD and grown to log phase for 6 h. Growth phenotyping

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was performed after inoculating 10 µl of the log phase culture into 190 µl of SynH or

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SynH -HTs, and growing without shaking in Tecan M200 Pro microplate reader (Tecan

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Systems, Inc., San Jose, CA) maintaining an interior chamber temperature of 30oC.

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Anaerobic phenotyping was performed similarly using a Tecan F500 inside an

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anaerobic chamber. The average of six optical density at 600 nm (OD600)

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measurements distributed from across the well was taken every 30 minutes for 48 hours.

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Growth rates were calculated using the program GrowthRates (40). An HT resistance

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score was taken as the average of two biological-replicate growth rate measurements

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from SynH versus the average growth rates in SynH -HTs, in both aerobic and

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anaerobic conditions.

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RNA-Seq library construction and sequencing

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Strains were grown in biological duplicate on different days to mid-log phase for seven

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generations in YPD and then shifted to YPD, SynH -HTs, or SynH medium for at least

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three generations to log phase (OD600 ~0.5) and collected by centrifugation. RNA was

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extracted by hot phenol lysis (41). Total RNA was DNAse-treated at 37oC for 30 min

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with TURBO DNase (Life Technologies, Carlesbad, CA), followed by RNA precipitation

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at -20oC in 2.5M LiCl for 30 min. rRNA depletion and library generation was via the

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TruSeq® Stranded Total RNA Sample Preparation Guide (Rev.C) using the Illumina

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TruSeq® Stranded Total RNA (Human/Mouse/Rat) kit (Illumina Inc., San Diego,

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California, USA) with minor modifications, using Agencourt RNAClean XP beads

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(Beckman Coulter, Indianapolis IN, USA), SuperScript II Reverse Transcriptase

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(Invitrogen, Carlsbad, California, USA) as described in the Illumina kit. Adapter ligated

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DNA was amplified in a Linker Mediated PCR reaction (LM-PCR) for 12 cycles using

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PhusionTM DNA Polymerase and Illumina's PE genomic DNA primer set and then

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purified by paramagnetic beads. Libraries were standardized to 2 μM. Cluster

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generation was performed using standard Cluster Kits (v3) and the Illumina Cluster

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Station. Single-end 100bp reads were generated using standard SBS chemistry (v3) on

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an Illumina HiSeq2500 sequencer. Raw data was deposited in NIH SRA database

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under project number SRP069207.

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RNA-Seq read processing and analyses

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Reads were processed with Trimmomatic (42) and mapped to reference genome

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S288C (NC_001133, version 64 (43)) using Bowtie2 (44) with default settings. HTseq

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version 5.5 (45) was used to calculate read counts for each gene. Differential

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expression analysis was performed using the program edgeR v.3.8.6 (46) using a

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general linearized model with strain background and media type as factors and pairing

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replicate samples. Benjamini and Hochberg correction (47) was used to estimate FDR.

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Sequences were normalized using the reads per kilobase per million mapped reads

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(RPKM) method. Hierarchical clustering analysis was performed using the program

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Cluster 3.0 (48) and visualized with the program Java Treeview

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(https://www.princeton.edu/~abarysh/treeview/) (49). Where noted, expression of each

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gene was normalized to the mean expression level for that gene across all strains.

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Functional enrichment analysis was performed using FunSpec (50, 51) or using a

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Hypergeometric test using four different datasets previously defined (52). All P values

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cited are Bonferroni-corrected, unless otherwise noted.

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NAD+/NADH measurement

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Total NAD and NAD+/NADH were measured in biological triplicate using Quantification

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Colorimetric Kit (BioVision, Milpitas, CA) following the recommended protocol. Briefly,

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strains were grown in SynH and SynH -HTs for at least three doublings, and collected

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while in log phase (OD600 ~ 0.5). NAD and NADH levels were calculated as outlined

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by the kit, and NAD+ was inferred from the other two measurements.

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Correlations between expression and toxin tolerance

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We first identified 2,777 genes with significant expression differences compared to the

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mean expression for that gene (FDR < 0.01). We then averaged the replicate RPKM

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expression values for each strain and used Python statistical functions (SciPy.org) to

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calculate the Pearson correlation between each gene’s expression pattern and the HT

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resistance scores across strains. Genes whose expression correlated with resistance

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were chosen based on p < 0.05.

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High throughput gene overexpression fitness effects
Competition experiments were performed similar to that previously described (53,
54). Briefly, a molecular barcoded yeast ORF library (MoBY-ORF 2.0) (38, 55) was

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introduced into three different strains, by transforming cells with a pool library of the

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MoBY 2.0 collection containing 4,282 barcoded high-copy plasmids, each expressing a

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different yeast gene. Transformation efficiency was determined by platting serial

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dilutions onto YPD agar + G418-containing plates. Transformations with more than

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30,000 colonies were pooled together to generate glycerol stocks used for further

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experiments. For competition experiments, cells were grown in SC containing

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monosodium glutamate as a nitrogen source and high sugar mimicking SynH (9%

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glucose, 4.5% xylose) plus HT cocktail and 200 mg/L of G418 (see Media) for 5, 10,

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and 15 generations, while maintaining cells in log phase. This medium was used

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instead of SynH since G418 selection required for plasmid maintenance does not

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function in the presence of ammonium. DNA was extracted using QIAprep Spin

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Miniprep kit (Qiagen, Hilden, Germany) after cell pellet pretreatment with 1 µl of R-

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Zymolyase (Zymo Research, Irvine, CA) and 100 µl of glass beads, with vortexing for 5

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minutes. Plasmid barcodes were amplified with multiplex primers containing Illumina

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adapters. Barcodes of two replicates were sequenced using an Illumina HiSeq2500

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Rapid Run platform. Differential abundance and significance of plasmids were

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determined using edgeR (46), using a linear model for each strain, identifying genes

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that provided a significant different fitness contribution to media +HTs compared to the

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starting pool before selection over time (5, 10, and 15 doublings).

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RESULTS

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Wide range of HT tolerances across Saccharomyces cerevisiae strains

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We began by investigating the response of diverse S. cerevisiae strains to

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lignocellulosic hydrolysate, by phenotyping growth rates of 79 strains grown in base

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medium with and without toxins. The strains collection included industrial strains as

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well as natural isolates from a variety of niches and geographical locations, together

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representing five of the defined genetic lineages in S. cerevisiae (i.e. Malaysian, West

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African, North America, vineyard/European, and sake/Asian strains) (34). The group

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included strains domesticated to ferment wine, beer, and sake, strains used to produce

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biofuel, and wild strains isolated from trees and spoiled fruits (Supplementary Table

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S1). Strains were grown in a synthetic hydrolysate mimic of ACSH called SynH (see

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Methods). A phenotypic score representing resistance to hydrolysate toxins (HTs) was

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calculated for each strain as the relative growth rate in complete SynH, which contains

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the full cocktail of HTs, versus in the hydrolysate mimic without the toxin cocktail (SynH

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-HTs), in both aerobic and anaerobic conditions. Resistance to HTs was highly

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correlated regardless of oxygen availability (R2 = 0.9) (Figure 1A); thus, we focused on

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aerobic conditions for simplicity.

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We found a wide distribution of toxin-resistance phenotypes, revealing that HT

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tolerance is a complex trait in yeast (Figure 1B, Supplementary Table S1).

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Interestingly, the differences in phenotype could be partly explained by lineage-specific

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differences. We grouped strains based on previously defined genetic lineages (34, 56-

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58). Strains of the sake/Asian lineage are the most sensitive to HTs, while Malaysian

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strains display the highest resistance (Figure 1C). Strains of the vineyard/European

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lineage, along with mosaic strains that show admixture from different lineages, showed

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the widest phenotypic distribution. The lineage-specific effects are consistent with

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several other studies that showed lineage-associated traits across strains (32-34, 58,

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59). To quantify the impact of genetic background effects in S. cerevisiae, we chose

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six strains that would maximize the phenotypic and genetic diversity for further analysis.

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Five of the strains came from clean lineages: fermentation strain NCYC361 of the

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vineyard/European lineage, sake-producing strain K11, West African strain NCYC3290

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isolated from bili wine, North American oak-tree isolate YPS128, and Malaysian strain

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UWO.SO5.22-7. We gave preference to homozygous strains and strains that were

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amenable to genetic transformation when choosing particular strains. We also

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included one mosaic strain Y7568, isolated from a rotten papaya, which had

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particularly high HT tolerance (Figure 1D). Both the vineyard/European and sake

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strains grow slower in rich lab medium than the rest of the strains (~90 min versus ~70

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min, doubling time); however, their growth is comparable to well-studied lab strains

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((60) and data not shown). The phenotypes of these six strains represented the

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distribution seen for all strains in the collection (Figure 1B, D).

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Variation in the transcriptome response to lab medium implicates strain-specific

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states

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To explore basal transcriptome differences, we started by profiling transcriptome

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variation across the six strains growing in rich, non-stress laboratory medium (YPD),

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through RNA-sequencing in biological duplicate (see Methods). We found 4,523 genes

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whose expression was significantly different (false discovery rate, FDR < 1%)

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(Supplementary Dataset S1) in one or more strains compared to the mean of all strains,

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representing a remarkable 72% of all genes. Of these genes, 2,214 had at least a two-

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fold expression difference in one or more strains compared to the mean expression

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level for that gene across all six strains (Supplementary Dataset S1).

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Hierarchical clustering of mean-centered transcript levels (see Methods)

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revealed that many of the differentially expressed genes were specific to the HT-

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sensitive strain NCYC361, in which 1,200 genes were differentially expressed

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compared to the mean of all strains (Supplementary Figure S1, Supplementary Dataset

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S1). Expression at 858 of these genes was similarly skewed in the HT-sensitive sake

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strain K11. These genes primarily displayed higher expression in these strains and

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were enriched for genes involved in detoxification (Bonferroni-corrected P = 3.2e-8,

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hypergeometric test), for targets of the transcription factor Gln3 that responds to

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nitrogen limitation (P = 0.001), and for thiamine genes (P = 0.0004). However, unlike

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any other isolate, NCYC361 showed induction of the environmental stress response

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(ESR) (61) even in the absence of added stress (P < 9.83e-121). We noticed that this

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strain has higher expression of genes involved in iron homeostasis (P = 1.2e-11) but

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lower expression of genes involved in the electron transport chain, amino acid

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biosynthesis, and lipid biosynthesis, compared to the mean of all strains (P = 4.71e-13,

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2.386e-10, 8.963e-10, respectively) (Supplementary Dataset S1). This transcriptional

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response can be a signature of iron starvation (62), suggesting that NCYC361 may

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have a defect in iron uptake/metabolism in YPD. Indeed, we found that iron

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supplementation to YPD partially alleviated the slow growth of this strain specifically

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(Supplementary Figure S2). This result showcases our power to implicate

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physiological responses (and strategies to augment them) based solely on gene

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expression differences (see Discussion).
Because the unusual response of NCYC361 may provide a biased view of the

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breadth of background effects, we removed this strain from the analysis and identified

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genes that were differentially expressed in each of the remaining strains compared to

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the mean expression across strains. We found 3,323 genes with significant expression

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differences in one or more strains compared to the mean (Supplementary Dataset S1);

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1,036 of these displayed at least 2-fold differences from the mean. Genes differentially

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expressed across strains were enriched for genes linked to thiamine, sterol, and amino

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acid biosynthesis among others functional responses (Supplementary Dataset S1).

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Transcriptome responses to SynH with and without HTs implicate common and

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strain-specific toxin responses

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To investigate how genetically distinct isolates experience the stress found in

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lignocellulosic hydrolysate, we investigated strains' transcriptome changes while

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growing in SynH compared to YPD lab medium. NCYC361 was removed from the

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analysis due to its aberrant response even in lab medium. A linear model was used to

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identify genes differentially expressed in each strain, in each media condition, and in a

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manner affected by a strain-by-media ("Gene by Environment") interaction (see

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Methods). We identified 2,073 genes that were differentially expressed in response to

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SynH compared to YPD (FDR < 0.01): 1,884 genes were differentially expressed

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regardless of the strain, while 740 genes showed a strain-by-media interaction (Figure

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2, Supplementary Dataset S2).

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Among the common responses to SynH were activation of the ESR (as defined

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in Gasch et al.), repression of ergosterol biosynthetic genes (P = 0.023), and induction

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of targets of transcription factor Sko1 that responds to osmotic stress (as defined in

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Chasman & Ho et al., P = 0.01). Expression of genes involved in aerobic respiration (P

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= 7.41e-10) were increased in most strains, most highly in the HT-sensitive strain K11

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and least strongly in the most resistance Malaysian strain. We also observed strong

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induction of genes involved in sulfate assimilation (P = 2.46e-7) as well as a broader

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set of genes regulated by the transcription factor Gcn4, which is activated by amino

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acid starvation (P = 1.18e-6). Higher expression of Gcn4 targets raised the possibility

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that strains experienced amino acid starvation in SynH compared to rich YPD medium,

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especially given the stark differences in media composition (Supplementary Dataset

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S2).

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One key advantage of synthetic hydrolysate is that the effects of nutrient

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availability and HTs can be dissected, by omitting toxins from the recipe. We therefore

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profiled transcriptional changes provoked by SynH without toxins (SynH -HTs), to

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distinguish the stress responses specific to the base SynH -HT medium and responses

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unique to the toxin cocktail. We analyzed the response to SynH -HTs compared to

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YPD in the five strains and found 970 genes differentially expressed regardless of the

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strain and 394 genes with strain-by-media interactions (FDR < 0.01) (Supplementary

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Dataset S3).

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This analysis distinguished several of responses to SynH that are primarily due

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to the base medium composition separate from the toxins, and responses common to

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most or all strains. We identified clusters of genes enriched for specific functional

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categories. Co-regulated genes enriched for Sko1 targets were induced by SynH -HTs,

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consistent with the high osmolarity of the base medium (Figure 3A), while genes

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involved in ergosterol biosynthesis were repressed in response to SynH -HT medium

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(Figure 3B). However, both of these responses were exacerbated in a statistically

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significant manner in the presence of HTs and in several strains. This pattern was also

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true for genes linked to sulfate metabolism (Figure 3C), which were induced by SynH -

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HTs but expressed even higher in SynH with HTs (Supplementary Dataset S4). Thus,

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several responses to the base medium were amplified by the presence of toxins,

354

suggesting complex interactions (see Discussion).

355

Other responses were specific to the presence of the toxins. For example,

356

genes involved in aerobic respiration were generally expressed more specifically in

357

response to toxins (Figure 3D). Surprisingly, amino-acid biosynthetic genes regulated

358

by the Gcn4 transcription factor were induced specifically in response to toxins and not

359

in response to the base SynH -HTs medium in most strains (Figure 3E)

360

(Supplementary Dataset S4). Thus induction of amino acid biosynthetic genes is not

361

due to lower amino acid concentrations in SynH but a direct response to the toxins.

362

Addition of amino acids, either as pools or individually, to the SynH medium did not

363

alleviate growth inhibition (data not shown). Deleting GCN4 in HT-resistant strain

364

YPS128 significantly reduced the growth of that strain independent of HTs

365

(Supplemental Figure S3). Thus, the HT-dependent induction of Gcn4 targets may

366

reflect an indirect response to toxins, perhaps the accumulation of uncharged tRNAs

367

(63).

368

In the course of testing the effect of amino acids, we found that strains were

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346

more sensitive to HTs at lower pH. This synergistic response is known for weak acids,

370

which are significantly more toxic at low pH because protonated acids diffuse readily

371

into the cell (7). To test the pH effects on other compounds, we divided the HT

372

cocktail into three groups consisting of the amides, weak acids, or aldehydes

373

(Supplementary Table S2) and tested their inhibitory effect at pH 4.5, 5.0, or 5.5 in HT-

374

sensitive K11 and HT-resistant YPS128. We found that low pH exacerbated the

375

effects of all three HT classes (Figure 4), particularly for the HT-sensitive K11 strain

376

(Figure 4A). In contrast, increasing pH above the normal pH of SynH improved

377

tolerance to weak acids and to aldehydes, but not to amides. The pH effect was

378

strongest when cells were exposed to the complete HT cocktail, which showed the

379

greatest synergistic interaction with low pH, especially in HT-tolerant strain YPS128

380

(Figure 4B). Thus, pH has a potent synergistic interaction with all three classes of HTs.

381
382

Genes responding specifically to HTs implicate diverse defense strategies

383

To explicitly identify gene expression changes to HTs, we compared the

384

transcriptome response to SynH directly to the response to SynH –HTs across the six

385

strains. This identified 226 genes that were differentially expressed in one more strains,

386

specifically in response to the toxin cocktail. From those genes, 149 were differentially

387

expressed independent of the strain, while 119 genes were influenced by strain-by-

388

media interaction (FDR < 0.01) (Supplementary Dataset S5).

389

Among the induced genes were targets of the oxidant-induced transcription

390

factors Yap1 (P = 1.5e-14) and Skn7 (P = 7.4e-5) (Supplementary Dataset S5),

391

consistent with HT-induced redox stress induced. Many of the other induced genes

17

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369

include those that are induced in response to a broad array of stresses (61). These

393

included genes encoding heat shock proteins and genes responding to high osmolarity,

394

cell wall integrity, DNA damage response, organic solvent stress, and Msn2 regulation

395

(Supplementary Dataset S5). The induced gene set also included several genes

396

implicated in the reduction of HTs into less toxic compounds, including aldehyde

397

reductases and dehydrogenases, aryl alcohol dehydrogenases known to be involved in

398

oxidative stress response (64), an alpha-keto amide reductase most likely responding

399

to the toxic amides in the HTs cocktail, and plasma membrane transporters involved in

400

toxin transport among others (Supplementary Table S3).

401

Interestingly, genes related to thiamine metabolism were enriched (P =

402

0.000123) in the set of HT-responsive genes with lower expression in several strains,

403

including those involved in biosynthesis (THI2, THI6, THI20, THI21) and uptake (THI7,

404

THI73) (Supplementary Dataset S5). Thiamine is important for sugar fermentation (65)

405

and defense against oxidative and osmotic stress in S. cerevisiae (66), thus reduced

406

expression was unexpected. Expression of thiamine genes can respond to NAD+

407

levels (67), since NAD+ is a precursor for de novo thiamine production (68), and thus

408

we suspected fluctuations in NAD+/NADH levels during HTs detoxification. We

409

quantified NAD+/NADH in the absence and presence of HTs. Strikingly, we found that

410

total NAD+ plus NADH (NAD+/H) levels (Figure 5A) as well as the NAD+/NADH ratio

411

(Figure 5B) increased in the presence of HTs. This was true for both the resistant

412

strain YPS128 and the sensitive K11 strain, although the effect was greater in K11.

413

Interestingly, genes involved in de novo biosynthesis of NAD+ were also induced by

414

the presence of HTs (Supplementary Dataset S5). Together, these results suggest

18

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392

415

that cells increase levels of NAD+/H in the presence of toxins, perhaps reflecting a

416

defect in NADH regeneration during the course of detoxification (see Discussion).

418

Identifying genes whose expression correlates with HT resistance across strains

419

We were especially interested in exploiting the physiological differences between

420

resistant and sensitive strains to find novel genes and mechanisms that could increase

421

SynH tolerance. We therefore identified genes whose expression level was correlated

422

with toxin tolerance (see Methods). This identified 253 genes whose expression was

423

negatively correlated with HT resistance (Figure 6A) and 32 genes whose transcript

424

abundance was positively correlated with HT resistance (Figure 6B) (Supplemental

425

Dataset S6). The genes whose transcript abundance was negatively correlated with

426

HT resistance – meaning that they were expressed proportionately higher as HT

427

tolerance decreased – suggested cellular targets of the toxins. Although enrichment

428

did not pass stringent bonferroni correction, 21% of these genes encode proteins

429

localized in the mitochondria (uncorrected p = 0.0006). This group included other

430

genes involved in cell wall organization, fatty acid metabolic process, DNA repair,

431

protein folding, and genes involved in NAD biosynthesis (Supplementary Dataset S6).

432

The stronger expression response in HT-sensitive strains suggests that cells

433

experiencing stronger HT stress may struggle more to maintain critical processes.

434

Consistent with this notion, sensitive cells generally showed higher expression of

435

genes induced in the Environmental Stress Response than tolerant cells

436

(Supplementary Figure S4). In contrast, genes whose expression was positively

437

correlated with HT resistance were enriched for translation (uncorrected p = 1.4e-7),

19

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417

suggesting that resistant cells may be growing better under these conditions. Other

439

genes whose expression was positively correlated with HT resistance were involved in

440

fatty acid elongation (uncorrected p = 6.8e-5), amino acid transmembrane transport

441

(uncorrected p = 0.005), and degradation of arginine (uncorrected p = 2.2e-5)

442

(Supplementary Dataset S6).

443
444
445

Fitness effects of gene over-expression are influenced by genetic background
We were particularly interested in identifying and testing genes whose over-

446

expression improved HT tolerance. To do this, we measured changes to cellular

447

fitness due to high-copy expression of each of 4,282 genes, using ‘bar-seq’ analysis of

448

a high-copy gene library expressed in three different strains (YPS128, NCYC3290, and

449

K11) growing in media with HTs (see Methods).

450

The effects of gene over-expression were significantly influenced by genetic

451

background (Supplementary Figure S5) (Supplementary Dataset S7). Of all genes that

452

increased fitness in SynH in any strain, only 32% (28 genes) were common in all three

453

strains. These were weakly enriched for genes annotated in mRNA localization

454

(uncorrected P = 0.001) and cellular carbohydrate metabolic process (uncorrected P =

455

0.002) (Figure 7A). Somewhat surprisingly, the tolerant strain YPS128 showed fitness

456

increases in response to the greatest number of overexpressed genes (which together

457

had weak enrichment for genes encoding membrane proteins, uncorrected P = 0.006),

458

while the sensitive sake strain was influenced by only a single strain-specific gene

459

(DBP2, whose functions involved mRNA decay and rRNA processing (69, 70),

460

although we cannot exclude that these trends are not influenced by differences in

20

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438

statistical power (see Methods). The West African strain uniquely benefited from over-

462

expression of genes linked to DNA replication and chromatin modulation

463

(Supplementary Figure S5 and Supplementary Dataset S7). Interestingly, the sensitive

464

sake strain showed a fitness defect in response to over-expression of a large number

465

of genes (Figure 7B), with weak enrichment for GTPase activator activity (un-corrected

466

P = 0.0003), SNARE binding (un-corrected P = 0.0005), and ubiquitin-protein ligase

467

activity (un-corrected P = 0.0005). The extensive differences in fitness contributions

468

depending on strain background highlights that strategies for engineering tolerance to

469

a complex stress such as the ones found in SynH may require strain-specific strategies.

470

Interestingly, there was no statistically significant overlap in the high-copy genes that

471

contributed fitness benefits to one or more strains and genes that showed significant

472

expression differences across strains, since only two out of 28 commonly beneficial

473

genes displayed a significant expression change specifically in response to HTs.

474

We confirmed the library results by measuring fitness of cells expressing

475

individual plasmids, compared to the empty-vector control (Figure 7B). (We note that

476

this assay is different from the competitive library experiment, in which each gene’s

477

fitness contribution is effectively normalized to the average of all plasmids (see

478

Methods)). We chose three genes identified in all strains (MET14, THI20, ERG26), two

479

genes identified in two of the strains (MDJ1, identified in YPS128 and NCYC3290, and

480

TPK2, identified in YPS128 and K11), one gene (NUP53) specific to the tolerant strain

481

YPS128, and a control gene (PBI1) whose expression was not predicted to change

482

fitness. Among the six tested genes, three (MET14, ERG26, and MDJ1) significantly

483

improved growth in at least two strains. The most striking was MDJ1, involved in

21

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461

protein folding/refolding in the mitochondrial matrix (71), which improved growth 118%

485

in NCYC3290 and 28% in the already-tolerant YPS128. Most of the genes did not

486

provide a strong benefit over the empty vector in K11; however, the strong negative

487

impact of thousands of genes in the library suggests that this strain has a competing

488

fitness deficit due to protein over-expression.

489
490

Discussion

491

In real industrial fermentations, multiple distinct stresses can have compounded

492

effects that produce unique challenges for cells. How these different stressors interact

493

with one another can be difficult to discern, especially in real hydrolysates that can vary

494

extensively from batch-to-batch and according to the biomass type and source (72, 73).

495

Furthermore, the response can be quite different depending on the genetic background

496

of the strain. These distinctions present challenges from an industrial standpoint,

497

especially in terms of identifying general mechanisms to improve tolerance to industrial

498

stresses.

499

Our strategy to leverage genetic variation, both to understand stressors in

500

lignocellulosic hydrolysate and to identify high-impact genes for directed engineering,

501

presents a useful strategy to tackle these hurdles. Responses common to all strains

502

implicated the imposing stresses in SynH, including osmotic stress from the high sugar

503

concentrations, oxidative stress produced by several HTs (74, 75), and redox

504

imbalance, perhaps due to HT detoxification (76, 77). In contrast, responses that were

505

graded with HT sensitivity implicate downstream cellular targets at greatest risk. For

506

example, sensitive strains displayed stronger expression changes at genes involved in

22

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484

cell wall organization, fatty acid metabolism, DNA repair, protein folding, suggesting

508

that the cell wall, membranes, the genome and proteome are primary targets of the

509

reactants in the HT cocktail (12, 17, 77, 78). The more sensitive strains also had

510

stronger induction of genes involved in energy generation, suggesting a tax on the

511

energy balance (24, 76, 79). Our results also implicate a variety of defense strategies,

512

including toxin reduction, redox defense, and drug efflux and detoxification. Several of

513

these strategies require NADH (80-85), which likely contributes to the observed

514

increase in NAD+/H and the NAD+/NADH ratio across strains (a response also seen in

515

Escherichia coli growing in SynH (24)).

516

Comparing strain responses under different situations also revealed new

517

insights into synergistic stress interactions. HT sensitivity was exacerbated at low pH,

518

expanding the know synergy between pH and weak acids (7) to interactions with other

519

HTs and in particular the full HT cocktail. Several expression responses to SynH –HT

520

were exacerbated by the addition of HTs. In some cases, dual stressors may

521

exacerbate a single cellular challenge. For example, the amplified induction of sulfur

522

biosynthesis genes when HTs are added to the base medium may be a response to

523

NADPH depletion, since both HT detoxification and sulfur assimilation consume

524

NADPH (13, 14, 86). Notably, sulfate genes are also induced in bacteria growing in

525

the presence of furfural (86) and in SynH (24). In other cases, the synergy may

526

emerge because the defense strategy against one stress renders cells more sensitive

527

to a second stressor. For example, cells growing in high-osmolarity SynH -HTs

528

induced expression of osmo-induced Sko1 targets (87) and decreased expression of

529

ergosterol biosynthesis genes. Decreased ergosterol is a physiologically adaptation to

23

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507

osmotic stress that may help to decrease membrane fluidity (88-90). However,

531

reduced ergosterol is associated with lower resistance to vanillin (91); thus, altered

532

ergosterol content could produce antagonistic effects on tolerance to osmolarity versus

533

HTs. It is particularly interesting that genes related to two of these interactions –

534

adenylylsulfate kinase MET14 required for sulfur assimilation and ERG26 involved in

535

ergosterol synthesis – improve HT tolerance in the context of high sugar

536

concentrations, in all three strains tested.

537

It is well known in industry that engineering strategies are strain specific (35,

538

92); yet most investigations fail to consider this when identifying new engineering

539

targets and instead focus on a single, often laboratory, strain. Our approach to

540

examine multiple strains that together maximize genetic and phenotypic diversity not

541

only implicated genes with background-independent benefits, but also uncovered the

542

breadth of responses in the species. Over half the mRNAs in the transcriptome varied

543

in abundance across strains, in one or more conditions. In several cases, we were

544

able to predict and validate cellular phenotypes based on transcriptomic differences

545

(see Figure S1 and 5), demonstrating how far knowledge of yeast gene functions has

546

progressed in terms of predictive power. But in other ways, our results highlight the

547

limitations in understanding the interaction between genotype and phenotype. This is

548

particularly true in the case of high-copy gene expression, whose differential effects

549

suggest that background effects will be the norm rather than the exception. Our work

550

sets the stage for more detailed mapping of phenotypic variation across strain

551

backgrounds.

552

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530

553
554
Funding Information:

556

This work was supported by the DOE Great Lakes Bioenergy Research Center (DOE

557

Office of Science BER DE-FC02-07ER64494). M.Sardi is supported by the National

558

Science Foundation Graduate Research Fellowship Program under grant number

559

DGE-1256259.

560
561

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both nonsense-mediated mRNA decay and rRNA processing. Mol Cell Biol

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products formed during ammonia fiber expansion (AFEX) and dilute acid based

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pretreatments. Bioresour Technol 101.

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Switchgrass (Panicum virgatum, L.) Hemicelluloses. ACS Sustainable Chemistry

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to weak acids present in lignocellulosic hydrolysate. FEMS Yeast Res 14:1234-

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detoxification of lignocellulose hydrolysates. Appl Microbiol Biotechnol 90:809-

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Gorsich SW, Dien BS, Nichols NN, Slininger PJ, Liu ZL, Skory CD. 2006.

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Tolerance to furfural-induced stress is associated with pentose phosphate

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pathway genes ZWF1, GND1, RPE1, and TKL1 in Saccharomyces cerevisiae.

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Appl Microbiol Biotechnol 71:339-349.

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lag phase uncover YAP1, PDR1, PDR3, RPN4, and HSF1 as key regulatory

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genes in genomic adaptation to the lignocellulose derived inhibitor HMF for

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Saccharomyces cerevisiae. BMC Genomics 11:660.

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811

and adaptation of ethanologenic Saccharomyces cerevisiae to furfural, a

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lignocellulosic inhibitory compound. Appl Environ Microbiol 75:3765-3776.

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detoxifies lignocellulosic biomass conversion inhibitors by reprogrammed

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pathways. Mol Genet Genomics 282:233-244.

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Adaptive response of yeasts to furfural and 5-hydroxymethylfurfural and new

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chemical evidence for HMF conversion to 2,5-bis-hydroxymethylfuran. J Ind

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Microbiol Biotechnol 31:345-352.
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Nilsson A, Gorwa-Grauslund MF, Hahn-Hagerdal B, Liden G. 2005. Cofactor

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dependence in furan reduction by Saccharomyces cerevisiae in fermentation of

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acid-hydrolyzed lignocellulose. Appl Environ Microbiol 71:7866-7871.

823

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Grauslund MF, Liden G. 2006. A 5-hydroxymethyl furfural reducing enzyme

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encoded by the Saccharomyces cerevisiae ADH6 gene conveys HMF tolerance.

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Yeast 23:455-464.

827

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Liu ZL, Moon J. 2009. A novel NADPH-dependent aldehyde reductase gene

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from Saccharomyces cerevisiae NRRL Y-12632 involved in the detoxification of

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aldehyde inhibitors derived from lignocellulosic biomass conversion. Gene

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831

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Almeida JR, Roder A, Modig T, Laadan B, Liden G, Gorwa-Grauslund MF.

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and its implications on product distribution in Saccharomyces cerevisiae. Appl

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Microbiol Biotechnol 78:939-945.

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Liu ZL, Moon J, Andersh BJ, Slininger PJ, Weber S. 2008. Multiple gene-

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mediated NAD(P)H-dependent aldehyde reduction is a mechanism of in situ

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detoxification of furfural and 5-hydroxymethylfurfural by Saccharomyces

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cerevisiae. Appl Microbiol Biotechnol 81:743-753.

839
840

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Miller EN, Jarboe LR, Turner PC, Pharkya P, Yomano LP, York SW, Nunn D,
Shanmugam KT, Ingram LO. 2009. Furfural inhibits growth by limiting sulfur

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820

841

assimilation in ethanologenic Escherichia coli strain LY180. Appl Environ

842

Microbiol 75:6132-6141.
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Regulation of the Sko1 transcriptional repressor by the Hog1 MAP kinase in

845

response to osmotic stress. EMBO J 20:1123-1133.

846

88.

Montanes FM, Pascual-Ahuir A, Proft M. 2011. Repression of ergosterol

847

biosynthesis is essential for stress resistance and is mediated by the Hog1 MAP

848

kinase and the Mot3 and Rox1 transcription factors. Mol Microbiol 79:1008-1023.

849

89.

Rodriguez-Vargas S, Sanchez-Garcia A, Martinez-Rivas JM, Prieto JA,

850

Randez-Gil F. 2007. Fluidization of membrane lipids enhances the tolerance of

851

Saccharomyces cerevisiae to freezing and salt stress. Appl Environ Microbiol

852

73:110-116.

853

90.

Abe F, Hiraki T. 2009. Mechanistic role of ergosterol in membrane rigidity and

854

cycloheximide resistance in Saccharomyces cerevisiae. Biochim Biophys Acta

855

1788:743-752.

856

91.

Endo A, Nakamura T, Shima J. 2009. Involvement of ergosterol in tolerance to

857

vanillin, a potential inhibitor of bioethanol fermentation, in Saccharomyces

858

cerevisiae. FEMS Microbiol Lett 299:95-99.

859

92.

Kasavi C, Finore I, Lama L, Nicolaus B, Oliver SG, Toksoy Oner E, Kirdar B.

860

2012. Evaluation of industrial Saccharomyces cerevisiae strains for ethanol

861

production from biomass. Biomass and Bioenergy 45:230-238.

862
863

38

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843

864
865
FIGURE LEGENDS:

867
868

Figure 1. Strain-specific differences in hydrolysate tolerance (HT). HT resistance

869

scores were calculated as outlined in Materials and Methods for 79 strains. (A) HT

870

scores measured in aerobic and anaerobic conditions are highly correlated. (B) The

871

distribution of aerobic HT scores across all strains, where each score represents the

872

average of two biological duplicates for each strain. (C) The distribution of HT scores

873

for each of six lineages: sake (n = 5), West African (WA) (n = 3), North American (NA)

874

(n = 5), Malaysian (MA) (n = 3), Vineyard/European (V/E) (n = 18), and Mosaic (MOS)

875

(n = 45). (D) The average and standard deviation of HT resistance scores for each of

876

six strains chosen for further analysis (n = 3).

877
878

Figure 2. Expression responses to SynH versus rich lab media. Shown are 2,073

879

differentially expressed genes identified by the linear model, as expressed in strain K11

880

(Sake), NCYC3290 (WA), Y7568 (MOS), YPS128 (NA), and UWO.SO5.22-7 (MA).

881

Each row represents expression of a given gene and each column represents each of

882

two biological replicates for each strain. Yellow indicates higher expression in the

883

denoted strain growing in SynH versus YPD and blue represents lower expression in

884

SynH compared to YPD, with fold-change according to the key. The data were

885

organized by hierarchical clustering. Functional enrichments were assessed for each

886

cluster, and those that passed a bonferroni corrected p < 0.01 included ergosterol

39

Downloaded from http://aem.asm.org/ on July 22, 2016 by UNIV OF CALIF SAN DIEGO

866

biosynthesis (A), protein synthesis genes normally repressed in the ESR (B), aerobic

888

respiration (C), Gcn4 gene targets (D), sulfate assimilation (E), genes normally induced

889

in the ESR along with Sko1 targets (F), and ribosome biogenesis genes normally

890

repressed in the ESR (G).

891
892

Figure 3. Expression differences for key groups of genes. Transcriptome

893

differences across strains and media for specific gene clusters. Each histogram

894

represents the average expression level (Log2 RPKM values, see Methods) of

895

specified genes as measured in two biological replicates for strain K11 (sake),

896

NCYC3290 (WA), Y7568 (MOS), YPS128 (NA), and UWO.SO5.22-7 (MA).

897

Gene clusters were selected based on hierarchical clustering of the various datasets.

898

(A) 27 genes enriched for Sko1 targets, (B) 317 genes enriched for ergosterol

899

biosynthesis genes, (C) 27 genes enriched for sulfate assimilation genes, (D) 236

900

genes involved in aerobic respiration, (E) 50 genes enriched for targets of Gcn4. An

901

asterisk indicates a significant difference in expression for that gene group in SynH -

902

HTs versus YPD, and a circle indicates significant differences in expression in SynH

903

versus SynH -HTs (p < 0.05, T-test across all genes in each group).

904
905

Figure 4. Low pH exacerbates the effects of all HT classes. Growth rate was

906

calculated for cells growing in SynH and SynH -HTs at pH 4.5, 5.0, and 5.5 in (A) HT-

907

sensitive strain K11 (Sake) and (B) HT-resistant strain YPS128 (NA). The average and

908

standard deviation of growth rates measured in four biological replicates is shown.

909

Statistically significant differences for each HT group at pH 4.5 versus 5.0 are shown

40

Downloaded from http://aem.asm.org/ on July 22, 2016 by UNIV OF CALIF SAN DIEGO

887

910

with an asterisk, and differences between pH 5.0 versus 5.5 are indicated with a

911

diamond (p<0.01, T-test)

913

Figure 5. NAD levels change in response to HT exposure. (A) The average and

914

standard deviation of total NAD+/H and (B) the ratio of NAD+ to NADH are shown for

915

HT-sensitive K11 (Sake) and HT-resistant YPS128 (NA). Data represent the average

916

of biological triplicates and asterisks indicate statistical differences between SynH and

917

SynH –HTs (* p < 0.05, ** p < 0.01, Ttest).

918
919

Figure 6. Identifying expression differences that correlate with HT resistance.

920

Boxplots showing the distribution of relative transcript abundances (measured in each

921

strain and compared to the mean expression of that gene across all strains). Shown

922

are (A) 253 genes whose transcript abundance are negatively correlated and (B) 32

923

genes whose abundance is positively correlated with strain resistant scores. Strains

924

are organized according to least (left) to highest (right) resistance.

925
926

Figure 7. Gene overexpression affects HT tolerance. (A, B) The number of genes

927

whose over-expression affected strain fitness (FDR < 0.01, see Methods) is shown.

928

(A) Genes that increased and (B) decreased fitness in YPS128 (NA), NCYC3290 (WA)

929

or K11 are represented in the Venn diagram. (C) Final cell density after 24h growth of

930

denoted strains and over-expression constructs for cells growing in synthetic complete

931

medium with high sugar content and HTs. Measurements represent the average and

41

Downloaded from http://aem.asm.org/ on July 22, 2016 by UNIV OF CALIF SAN DIEGO

912

932

standard deviation of biological triplicates. Asterisks indicate statistical differences

933

between Empty vector and gene overexpression (* p < 0.01, Ttest).

Downloaded from http://aem.asm.org/ on July 22, 2016 by UNIV OF CALIF SAN DIEGO

42

(B)
2

HT Relative Growth Rate

R = 0.90

100
80
60
40
20
0
0

20

40

60

80

100

HT Relative Growth Rate - Anaerobic

Strains

PS

8
56
Y7

05
_

22

7_

2

28

90

S1

YP

V/E MOS

O

MA

W

NA

Lineages

U

WA

YC
32

Sake

N
C

20

1

40

K1
1

60

80
70
60
50
40
30
20
10
0
36

80

YC

HT Relative Growth Rate

(D)

N
C

(C)
HT Relative Growth Rate

90
80
70
60
50
40
30
20
10
0

Downloaded from http://aem.asm.org/ on July 22, 2016 by UNIV OF CALIF SAN DIEGO

HT Relative Growth Rate - Aerobic

(A)

Sake

WA

MOS

NA

MA
A
B

C

G
>4-fold
lower
expression in
SynH

>4-fold
higher
expression in
SynH

Downloaded from http://aem.asm.org/ on July 22, 2016 by UNIV OF CALIF SAN DIEGO

D
E
F

Ave. Log2 RPKM
GCN4 gene targets cluster

Ave. Log2 RPKM
Sulfate assimilation cluster
6

0

6

5

0

*

5

Sake

5

4

Sake

4

Sake

*

WA

*

WA

WA

*

MOS

*

MOS

MOS

*

NA

*
*

NA

NA

*

4

3

2

1

(C)

7

*

3

2

1

0
MA

*

3

2

(B)

MA

8

MA

7

6

5

0

6

5

*

4
3

2

1

(D)

Sake

*

4

Sake

SynH

*
*

WA
MOS

*
*

WA

YPD

SynH -HTs
MOS

*
*

NA
MA

*

3

NA

*

2

1

0

(E)
MA

6

*
*-HTs vs. YPD p < 0.05

SynH vs. -HTs p < 0.05

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1

Ave. Log2 RPKM
Ergosterol biosynthesis cluster

7

Ave. Log2 RPKM
Aerobic respiration cluster

Ave. Log2 RPKM
SKO1 gene targets cluster

(A)

(A)

(B)
K11 - Sake

pH 4.5

*

*

*

*

0.002

*

*

0.003

*

*

pH 5.0

*
*

pH 5.5

0.002

*

0.001

4.5 vs. 5.0
p < 0.01

0.001
5.0 vs. 5.5
p < 0.01

es

+H
Ts

es

+a

m
id

s

yd

id
ld

eh

+a
c
+a

es

+H
Ts

id

es
yd
eh

+a
m

ds
ci
ld
+a

-H
Ts

+a

-H
Ts

0

0

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Growth Rate

YPS128 - NA

0.004

0.003

(B)

(A)

*

NAD+/NADH

1200
800
400
0

16

*

1600

**

**

SynH -HTs

12

SynH
8

* p value < 0.05
4

** p value < 0.01
K11

YPS128

0

K11

YPS128

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Total NAD, pmol

2000

(B)

(A)

Positively correlated
Relative transcript abundance

4
2
0
-2
-4
-6

3
2
1
0
-1
-2

-8
Sake

V/E

WA

NA

MOS

MA

Sake

V/E

WA

NA

MOS

MA

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Relative transcript abundance

Negatively correlated

Empty Vector
PBI1 - Control
Met14
Mdj1
Erg26
Nup53
Thi20
Tpk2
Empty Vector
PBI1 - Control
Met14
Mdj1
Erg26
Nup53
Thi20
Tpk2

NA
36

WA8

2

WA
28

10

K11

C.

10

***
*
***

8

6

4

*

2

0

NA
WA
K11

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Empty Vector
PBI1 - Control
Met14
Mdj1
Erg26
Nup53
Thi20
Tpk2

OD 600 After 24 hours

A.
B.

NA
86
104

9
1,757

1

K11