Ethanol production from lignocellulosic biomass holds promise as an alternative fuel. However, industrial stresses, including ethanol stress, limit microbial fermentation and thus prevent cost competitiveness with fossil fuels. To identify novel engineering targets for increased ethanol tolerance, we took advantage of natural diversity in wild Saccharomyces cerevisiae strains. We previously showed that an S288c-derived lab strain cannot acquire higher ethanol tolerance after a mild ethanol pretreatment, which is distinct from other stresses. Here, we measured acquired ethanol tolerance in a large panel of wild strains and show that most strains can acquire higher tolerance after pretreatment. We exploited this major phenotypic difference to address the mechanism of acquired ethanol tolerance, by comparing the global gene expression response to 5% ethanol in S288c and two wild strains. Hundreds of genes showed variation in ethanol-dependent gene expression across strains. Computational analysis identified several transcription factor modules and known coregulated genes as differentially expressed, implicating genetic variation in the ethanol signaling pathway. We used this information to identify genes required for acquisition of ethanol tolerance in wild strains, including new genes and processes not previously linked to ethanol tolerance, and four genes that increase ethanol tolerance when overexpressed. Our approach shows that comparative genomics across natural isolates can quickly identify genes for industrial engineering while expanding our understanding of natural diversity.

Background: The increasing number of transcriptomic datasets has allowed for meta-analyses, which can be valuable due to their increased statistical power. However, meta-analyses can be confounded by so-called “batch effects,” where technical variation across different batches of RNA-seq experiments can clearly produce spurious signals of differential expression and reduce our power to detect true differences. While batch effects can sometimes be accounted for, albeit with caveats, a better strategy is to understand their sources to better avoid them. In this study, we examined the effects of RNA isolation method as a possible source of batch effects in RNA-seq design. Results: Based on the different chemistries of “classic” hot phenol extraction of RNA compared to common commercial RNA isolation kits, we hypothesized that specific mRNAs may be preferentially extracted depending upon method, which could masquerade as differential expression in downstream RNA-seq analyses. We tested this hypothesis using the Saccharomyces cerevisiae heat shock response as a well-validated environmental response. Comparing technical replicates that only differed in RNA isolation method, we found over one thousand transcripts that appeared “differentially” expressed when comparing hot phenol extraction with the two kits. Strikingly, transcripts with higher abundance in the phenol-extracted samples were enriched for membrane proteins, suggesting that indeed the chemistry of hot phenol extraction better solubilizes those species of mRNA. Conclusions: Within a self-contained experimental batch (e.g. control versus treatment), the method of RNA isolation had little effect on the ability to identify differentially expressed transcripts. However, we suggest that researchers performing meta-analyses across different experimental batches strongly consider the RNA isolation methods for each experiment.

Natural variation in gene expression is pervasive within and between species, and it likely explains a significant fraction of phenotypic variation between individuals. Phenotypic variation in acute systemic responses can also be leveraged to reveal physiological differences in how individuals perceive and respond to environmental perturbations. We previously found extensive variation in the transcriptomic response to acute ethanol exposure in two wild isolates and a common laboratory strain of Saccharomyces cerevisiae. Many expression differences persisted across several modules of coregulated genes, implicating trans-acting systemic differences in ethanol sensing and/or response. Here, we conducted expression QTL mapping of the ethanol response in two strain crosses to identify the genetic basis for these differences. To understand systemic differences, we focused on “hotspot” loci that affect many transcripts in trans. Candidate causal regulators contained within hotspots implicate upstream regulators as well as downstream effectors of the ethanol response. Overlap in hotspot targets revealed additive genetic effects of trans-acting loci as well as “epi-hotspots,” in which epistatic interactions between two loci affected the same suites of downstream targets. One epi-hotspot implicated interactions between Mkt1p and proteins linked to translational regulation, prompting us to show that Mkt1p localizes to P bodies upon ethanol stress in a strain-specific manner. Our results provide a glimpse into the genetic architecture underlying natural variation in a stress response and present new details on how yeast respond to ethanol stress.

Lignocellulosic plant material is a viable source of biomass to produce alternative energy including ethanol and other biofuels. However, several factors—including toxic byproducts from biomass pretreatment and poor fermentation of xylose and other pentose sugars—currently limit the efficiency of microbial biofuel production. To begin to understand the genetic basis of desirable traits, we characterized three strains of Saccharomyces cerevisiae with robust growth in a pretreated lignocellulosic hydrolysate or tolerance to stress conditions relevant to industrial biofuel production, through genome and transcriptome sequencing analysis. All stress resistant strains were highly mosaic, suggesting that genetic admixture may contribute to novel allele combinations underlying these phenotypes. Strain-specific gene sets not found in the lab strain were functionally linked to the tolerances of particular strains. Furthermore, genes with signatures of evolutionary selection were enriched for functional categories important for stress resistance and included stress-responsive signaling factors. Comparison of the strains’ transcriptomic responses to heat and ethanol treatment—two stresses relevant to industrial bioethanol production—pointed to physiological processes that were related to particular stress resistance profiles. Many of the genotype-by-environment expression responses occurred at targets of transcription factors with signatures of positive selection, suggesting that these strains have undergone positive selection for stress tolerance. Our results generate new insights into potential mechanisms of tolerance to stresses relevant to biofuel production, including ethanol and heat, present a backdrop for further engineering, and provide glimpses into the natural variation of stress tolerance in wild yeast strains.

Tricarballylate is the causative agent of grass tetany, a ruminant disease characterized by acute magnesium deficiency. Tricarballylate toxicity has been attributed to its ability to chelate magnesium and to inhibit aconitase, a Krebs cycle enzyme. Neither the ruminant nor the normal rumen flora can catabolize tricarballylate to ameliorate its toxic effects. However, the gram-negative enterobacterium Salmonella enterica can use tricarballylate as a carbon and energy source, providing an opportunity to study the genes and enzymes required for tricarballylate catabolism. The tricarballylate utilization ( tcu ) genes are organized into two transcriptional units, i.e., tcuR and tcuABC . Here, we report the initial biochemical analysis of TcuA. TcuA catalyzed the oxidation of tricarballylate to cis -aconitate. The apparent K _m of TcuA for tricarballylate was 3.8 ± 0.4 mM, with a V _max of 7.9 ± 0.3 mM min ⁻¹ , turnover number ( k _cat ) of 6.7 × 10 ⁻² s ⁻¹ , and a catalytic efficiency ( k _cat / K _m ) of 17.8 M ⁻¹ s ⁻¹ . Optimal activity was measured at pH 7.5 and 30°C. The enzyme was inactivated at 45°C. One mole of FAD was present per mole of TcuA. We propose a role for TcuB as an electron shuttle protein responsible for oxidizing FADH ₂ back to FAD in TcuA.

Summary: Although most of the tens of thousands of diatom species are photoautotrophs, a small number of heterotrophic species no longer photosynthesize. We sequenced the genome of a nonphotosynthetic diatom,NitzschiaNitz4, to determine how carbon metabolism was altered in the wake of this trophic shift. NitzschiaNitz4 has retained its plastid and plastid genome, but changes associated with the transition to heterotrophy were cellular‐wide and included losses of photosynthesis‐related genes from the nuclear and plastid genomes, elimination of isoprenoid biosynthesis in the plastid, and remodeling of mitochondrial glycolysis to maximize adenosine triphosphte (ATP) yield. The genome contains a β‐ketoadipate pathway that may allowNitzschiaNitz4 to metabolize lignin‐derived compounds. Diatom plastids lack an oxidative pentose phosphate pathway (oPPP), leaving photosynthesis as the primary source of NADPH to support essential biosynthetic pathways in the plastid and, by extension, limiting available sources of NADPH in nonphotosynthetic plastids. The genome revealed similarities between nonphotosynthetic diatoms and apicomplexan parasites for provisioning NADPH in their plastids and highlighted the ancestral absence of a plastid oPPP as a potentially important constraint on loss of photosynthesis, a hypothesis supported by the higher frequency of transitions to parasitism or heterotrophy in lineages that have a plastid oPPP.