Bivariate One Strain Many Compounds Designs Expand the Secondary Metabolite Production Space in Corallococcus coralloides.

The scarcely investigated myxobacterium Corallococcus coralloides holds a large genome containing many uncharacterized biosynthetic gene clusters (BGCs) that potentially encode the synthesis of entirely new natural products. Despite its promising genomic potential, suitable cultivation conditions have not yet been found to activate the synthesis of new secondary metabolites (SMs). Finding the right cultivation conditions to activate BGCs in the genome remains a major bottleneck, and its full biosynthetic potential has so far not been determined. We therefore applied a bivariate "one strain many compounds" (OSMAC) approach, using a combination of two elicitor changes at once, for the activation of BGCs and concomitant SM production by C. coralloides. The screening was carried out in Duetz-System 24-well plates, applying univariate and bivariate OSMAC conditions. We combined biotic additives and organic solvents with a complex growth medium for univariate conditions and with minimal medium for bivariate conditions. The success in the activation of BGCs was evaluated by determining the number of new mass features detected in the respective extracts. We found synergistic effects in the bivariate OSMAC designs, evidenced by the detection of completely new mass features in the bivariate OSMAC experiments, which were not detected in the univariate OSMAC designs with only one elicitor. Overall, the bivariate OSMAC screening led to 55 new mass features, which were not detected in the univariate OSMAC design. Molecular networks revealed that these new mass features embody potential novel natural compounds and chemical derivatives like the N-acyl fatty amine N-pentyloctadecanamide and possibly sulfur-containing natural products. Hence, the presence of multiple elicitors in the bivariate OSMAC designs successfully activated the biosynthetic potential in C. coralloides. We propose bivariate OSMAC designs with a complex combination of elicitors as a straightforward strategy to robustly expand the SM space of microorganisms with large genomes.

A targeted metabolomics method for extra- and intracellular metabolite quantification covering the complete monolignol and lignan synthesis pathway.

Microbial synthesis of monolignols and lignans from simple substrates is a promising alternative to plant extraction. Bottlenecks and byproduct formation during heterologous production require targeted metabolomics tools for pathway optimization. In contrast to available fractional methods, we established a comprehensive targeted metabolomics method. It enables the quantification of 17 extra- and intracellular metabolites of the monolignol and lignan pathway, ranging from amino acids to pluviatolide. Several cell disruption methods were compared. Hot water extraction was best suited regarding monolignol and lignan stability as well as extraction efficacy. The method was applied to compare enzymes for alleviating bottlenecks during heterologous monolignol and lignan production in E. coli. Variants of tyrosine ammonia-lyase had a considerable influence on titers of subsequent metabolites. The choice of multicopper oxidase greatly affected the accumulation of lignans. Metabolite titers were monitored during batch fermentation of either monolignol or lignan-producing recombinant E. coli strains, demonstrating the dynamic accumulation of metabolites. The new method enables efficient time-resolved targeted metabolomics of monolignol- and lignan-producing E. coli. It facilitates bottleneck identification and byproduct quantification, making it a valuable tool for further pathway engineering studies. This method will benefit the bioprocess development of biotransformation or fermentation approaches for microbial lignan production.

Triaging of Culture Conditions for Enhanced Secondary Metabolite Diversity from Different Bacteria.

Over the past decade, the one strain many compounds (OSMAC) approach has been established for the activation of biosynthetic gene clusters (BGCs), which mainly encode the enzymes of secondary metabolite (SM) biosynthesis pathways. These BGCs were successfully activated by altering various culture conditions, such as aeration rate, temperature, and nutrient composition. Here, we determined the biosynthetic potential of 43 bacteria using the genome mining tool antiSMASH. Based on the number of BGCs, biological safety, availability of deposited cultures, and literature coverage, we selected five promising candidates: Bacillus amyloliquefaciens DSM7, Corallococcus coralloides DSM2259, Pyxidicoccus fallax HKI727, Rhodococcus jostii DSM44719, and Streptomyces griseochromogenes DSM40499. The bacteria were cultivated under a broad range of OSMAC conditions (nutrient-rich media, minimal media, nutrient-limited media, addition of organic solvents, addition of biotic additives, and type of culture vessel) to fully assess the biosynthetic potential. In particular, we investigated so far scarcely applied OSMAC conditions to enhance the diversity of SMs. We detected the four predicted compounds bacillibactin, desferrioxamine B, myxochelin A, and surfactin. In total, 590 novel mass features were detected in a broad range of investigated OSMAC conditions, which outnumber the predicted gene clusters for all investigated bacteria by far. Interestingly, we detected mass features of the bioactive compounds cyclo-(Tyr-Pro) and nocardamin in extracts of DSM7 and DSM2259. Both compounds were so far not reported for these strains, indicating that our broad OSMAC screening approach was successful. Remarkably, the infrequently applied OSMAC conditions in defined medium with and without nutrient limitation were demonstrated to be very effective for BGC activation and for SM discovery.

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor.

Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single-cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), we transplanted the B. subtilis FBP-binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.

Differential scaling between G1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding yeast.

In the unicellular eukaryote Saccharomyces cerevisiae, Cln3-cyclin-dependent kinase activity enables Start, the irreversible commitment to the cell division cycle. However, the concentration of Cln3 has been paradoxically considered to remain constant during G1, due to the presumed scaling of its production rate with cell size dynamics. Measuring metabolic and biosynthetic activity during cell cycle progression in single cells, we found that cells exhibit pulses in their protein production rate. Rather than scaling with cell size dynamics, these pulses follow the intrinsic metabolic dynamics, peaking around Start. Using a viral-based bicistronic construct and targeted proteomics to measure Cln3 at the single-cell and population levels, we show that the differential scaling between protein production and cell size leads to a temporal increase in Cln3 concentration, and passage through Start. This differential scaling causes Start in both daughter and mother cells across growth conditions. Thus, uncoupling between two fundamental physiological parameters drives cell cycle commitment.

Saccharomyces cerevisiae goes through distinct metabolic phases during its replicative lifespan.

A comprehensive description of the phenotypic changes during cellular aging is key towards unraveling its causal forces. Previously, we mapped age-related changes in the proteome and transcriptome (Janssens et al., 2015). Here, employing the same experimental procedure and model-based inference, we generate a comprehensive account of metabolic changes during the replicative life of Saccharomyces cerevisiae. With age, we found decreasing metabolite levels, decreasing growth and substrate uptake rates accompanied by a switch from aerobic fermentation to respiration, with glycerol and acetate production. The identified metabolic fluxes revealed an increase in redox cofactor turnover, likely to combat increased production of reactive oxygen species. The metabolic changes are possibly a result of the age-associated decrease in surface area per cell volume. With metabolism being an important factor of the cellular phenotype, this work complements our recent mapping of the transcriptomic and proteomic changes towards a holistic description of the cellular phenotype during aging.

Assessment of the interaction between the flux-signaling metabolite fructose-1,6-bisphosphate and the bacterial transcription factors CggR and Cra.

Bacteria regulate cell physiology in response to extra- and intracellular cues. Recent work showed that metabolic fluxes are reported by specific metabolites, whose concentrations correlate with flux through the respective metabolic pathway. An example of a flux-signaling metabolite is fructose-1,6-bisphosphate (FBP). In turn, FBP was proposed to allosterically regulate master regulators of carbon metabolism, Cra in Escherichia coli and CggR in Bacillus subtilis. However, a number of questions on the FBP-mediated regulation of these transcription factors is still open. Here, using thermal shift assays and microscale thermophoresis we demonstrate that FBP does not bind Cra, even at millimolar physiological concentration, and with electrophoretic mobility shift assays we also did not find FBP-mediated impairment of Cra's affinity for its operator site, while fructose-1-phosphate does. Furthermore, we show for the first time that FBP binds CggR within the millimolar physiological concentration range of the metabolite, and decreases DNA-binding activity of this transcription factor. Molecular docking experiments only identified a single FBP binding site CggR. Our results provide the long thought after clarity with regards to regulation of Cra activity in E. coli and reveals that E. coli and B. subtilis use distinct cellular mechanism to transduce glycolytic flux signals into transcriptional regulation.

Calorie restriction does not elicit a robust extension of replicative lifespan in Saccharomyces cerevisiae.

Calorie restriction (CR) is often described as the most robust manner to extend lifespan in a large variety of organisms. Hence, considerable research effort is directed toward understanding the mechanisms underlying CR, especially in the yeast Saccharomyces cerevisiae. However, the effect of CR on lifespan has never been systematically reviewed in this organism. Here, we performed a meta-analysis of replicative lifespan (RLS) data published in more than 40 different papers. Our analysis revealed that there is significant variation in the reported RLS data, which appears to be mainly due to the low number of cells analyzed per experiment. Furthermore, we found that the RLS measured at 2% (wt/vol) glucose in CR experiments is partly biased toward shorter lifespans compared with identical lifespan measurements from other studies. Excluding the 2% (wt/vol) glucose experiments from CR experiments, we determined that the average RLS of the yeast strains BY4741 and BY4742 is 25.9 buds at 2% (wt/vol) glucose and 30.2 buds under CR conditions. RLS measurements with a microfluidic dissection platform produced identical RLS data at 2% (wt/vol) glucose. However, CR conditions did not induce lifespan extension. As we excluded obvious methodological differences, such as temperature and medium, as causes, we conclude that subtle method-specific factors are crucial to induce lifespan extension under CR conditions in S. cerevisiae.

Natural and modified promoters for tailored metabolic engineering of the yeast Saccharomyces cerevisiae.

The ease of highly sophisticated genetic manipulations in the yeast Saccharomyces cerevisiae has initiated numerous initiatives towards development of metabolically engineered strains for novel applications beyond its traditional use in brewing, baking, and wine making. In fact, baker's yeast has become a key cell factory for the production of various bulk and fine chemicals. Successful metabolic engineering requires fine-tuned adjustments of metabolic fluxes and coordination of multiple pathways within the cell. This has mostly been achieved by controlling gene expression at the transcriptional level, i.e., by using promoters with appropriate strengths and regulatory properties. Here we present an overview of natural and modified promoters, which have been used in metabolic pathway engineering of S. cerevisiae. Recent developments in creating promoters with tailor-made properties are also discussed.

Improved linkage analysis of Quantitative Trait Loci using bulk segregants unveils a novel determinant of high ethanol tolerance in yeast.

BACKGROUND: Bulk segregant analysis (BSA) coupled to high throughput sequencing is a powerful method to map genomic regions related with phenotypes of interest. It relies on crossing two parents, one inferior and one superior for a trait of interest. Segregants displaying the trait of the superior parent are pooled, the DNA extracted and sequenced. Genomic regions linked to the trait of interest are identified by searching the pool for overrepresented alleles that normally originate from the superior parent. BSA data analysis is non-trivial due to sequencing, alignment and screening errors. RESULTS: To increase the power of the BSA technology and obtain a better distinction between spuriously and truly linked regions, we developed EXPLoRA (EXtraction of over-rePresented aLleles in BSA), an algorithm for BSA data analysis that explicitly models the dependency between neighboring marker sites by exploiting the properties of linkage disequilibrium through a Hidden Markov Model (HMM). Reanalyzing a BSA dataset for high ethanol tolerance in yeast allowed reliably identifying QTLs linked to this phenotype that could not be identified with statistical significance in the original study. Experimental validation of one of the least pronounced linked regions, by identifying its causative gene VPS70, confirmed the potential of our method. CONCLUSIONS: EXPLoRA has a performance at least as good as the state-of-the-art and it is robust even at low signal to noise ratio's i.e. when the true linkage signal is diluted by sampling, screening errors or when few segregants are available.

An integrated framework for discovery and genotyping of genomic variants from high-throughput sequencing experiments.

Recent advances in high-throughput sequencing (HTS) technologies and computing capacity have produced unprecedented amounts of genomic data that have unraveled the genetics of phenotypic variability in several species. However, operating and integrating current software tools for data analysis still require important investments in highly skilled personnel. Developing accurate, efficient and user-friendly software packages for HTS data analysis will lead to a more rapid discovery of genomic elements relevant to medical, agricultural and industrial applications. We therefore developed Next-Generation Sequencing Eclipse Plug-in (NGSEP), a new software tool for integrated, efficient and user-friendly detection of single nucleotide variants (SNVs), indels and copy number variants (CNVs). NGSEP includes modules for read alignment, sorting, merging, functional annotation of variants, filtering and quality statistics. Analysis of sequencing experiments in yeast, rice and human samples shows that NGSEP has superior accuracy and efficiency, compared with currently available packages for variants detection. We also show that only a comprehensive and accurate identification of repeat regions and CNVs allows researchers to properly separate SNVs from differences between copies of repeat elements. We expect that NGSEP will become a strong support tool to empower the analysis of sequencing data in a wide range of research projects on different species.

Identification of multiple interacting alleles conferring low glycerol and high ethanol yield in Saccharomyces cerevisiae ethanolic fermentation.

BACKGROUND: Genetic engineering of industrial microorganisms often suffers from undesirable side effects on essential functions. Reverse engineering is an alternative strategy to improve multifactorial traits like low glycerol/high ethanol yield in yeast fermentation. Previous rational engineering of this trait always affected essential functions like growth and stress tolerance. We have screened Saccharomyces cerevisiae biodiversity for specific alleles causing lower glycerol/higher ethanol yield, assuming higher compatibility with normal cellular functionality. Previous work identified ssk1E330N…K356N as causative allele in strain CBS6412, which displayed the lowest glycerol/ethanol ratio. RESULTS: We have now identified a unique segregant, 26B, that shows similar low glycerol/high ethanol production as the superior parent, but lacks the ssk1E330N…K356N allele. Using segregants from the backcross of 26B with the inferior parent strain, we applied pooled-segregant whole-genome sequence analysis and identified three minor quantitative trait loci (QTLs) linked to low glycerol/high ethanol production. Within these QTLs, we identified three novel alleles of known regulatory and structural genes of glycerol metabolism, smp1R110Q,P269Q, hot1P107S,H274Y and gpd1L164P as causative genes. All three genes separately caused a significant drop in the glycerol/ethanol production ratio, while gpd1L164P appeared to be epistatically suppressed by other alleles in the superior parent. The order of potency in reducing the glycerol/ethanol ratio of the three alleles was: gpd1L164P > hot1P107S,H274Y ≥ smp1R110Q,P269Q. CONCLUSIONS: Our results show that natural yeast strains harbor multiple specific alleles of genes controlling essential functions, that are apparently compatible with survival in the natural environment. These newly identified alleles can be used as gene tools for engineering industrial yeast strains with multiple subtle changes, minimizing the risk of negatively affecting other essential functions. The gene tools act at the transcriptional, regulatory or structural gene level, distributing the impact over multiple targets and thus further minimizing possible side-effects. In addition, the results suggest polygenic analysis of complex traits as a promising new avenue to identify novel components involved in cellular functions, including those important in industrial applications.

Comparative polygenic analysis of maximal ethanol accumulation capacity and tolerance to high ethanol levels of cell proliferation in yeast.

The yeast Saccharomyces cerevisiae is able to accumulate ≥17% ethanol (v/v) by fermentation in the absence of cell proliferation. The genetic basis of this unique capacity is unknown. Up to now, all research has focused on tolerance of yeast cell proliferation to high ethanol levels. Comparison of maximal ethanol accumulation capacity and ethanol tolerance of cell proliferation in 68 yeast strains showed a poor correlation, but higher ethanol tolerance of cell proliferation clearly increased the likelihood of superior maximal ethanol accumulation capacity. We have applied pooled-segregant whole-genome sequence analysis to identify the polygenic basis of these two complex traits using segregants from a cross of a haploid derivative of the sake strain CBS1585 and the lab strain BY. From a total of 301 segregants, 22 superior segregants accumulating ≥17% ethanol in small-scale fermentations and 32 superior segregants growing in the presence of 18% ethanol, were separately pooled and sequenced. Plotting SNP variant frequency against chromosomal position revealed eleven and eight Quantitative Trait Loci (QTLs) for the two traits, respectively, and showed that the genetic basis of the two traits is partially different. Fine-mapping and Reciprocal Hemizygosity Analysis identified ADE1, URA3, and KIN3, encoding a protein kinase involved in DNA damage repair, as specific causative genes for maximal ethanol accumulation capacity. These genes, as well as the previously identified MKT1 gene, were not linked in this genetic background to tolerance of cell proliferation to high ethanol levels. The superior KIN3 allele contained two SNPs, which are absent in all yeast strains sequenced up to now. This work provides the first insight in the genetic basis of maximal ethanol accumulation capacity in yeast and reveals for the first time the importance of DNA damage repair in yeast ethanol tolerance.

The metabolic costs of improving ethanol yield by reducing glycerol formation capacity under anaerobic conditions in Saccharomyces cerevisiae.

BACKGROUND: Finely regulating the carbon flux through the glycerol pathway by regulating the expression of the rate controlling enzyme, glycerol-3-phosphate dehydrogenase (GPDH), has been a promising approach to redirect carbon from glycerol to ethanol and thereby increasing the ethanol yield in ethanol production. Here, strains engineered in the promoter of GPD1 and deleted in GPD2 were used to investigate the possibility of reducing glycerol production of Saccharomyces cerevisiae without jeopardising its ability to cope with process stress during ethanol production. For this purpose, the mutant strains TEFmut7 and TEFmut2 with different GPD1 residual expression were studied in Very High Ethanol Performance (VHEP) fed-batch process under anaerobic conditions. RESULTS: Both strains showed a drastic reduction of the glycerol yield by 44 and 61% while the ethanol yield improved by 2 and 7% respectively. TEFmut2 strain showing the highest ethanol yield was accompanied by a 28% reduction of the biomass yield. The modulation of the glycerol formation led to profound redox and energetic changes resulting in a reduction of the ATP yield (YATP) and a modulation of the production of organic acids (acetate, pyruvate and succinate). Those metabolic rearrangements resulted in a loss of ethanol and stress tolerance of the mutants, contrarily to what was previously observed under aerobiosis. CONCLUSIONS: This work demonstrates the potential of fine-tuned pathway engineering, particularly when a compromise has to be found between high product yield on one hand and acceptable growth, productivity and stress resistance on the other hand. Previous study showed that, contrarily to anaerobiosis, the resulting gain in ethanol yield was accompanied with no loss of ethanol tolerance under aerobiosis. Moreover those mutants were still able to produce up to 90 gl-1 ethanol in an anaerobic SSF process. Fine tuning metabolic strategy may then open encouraging possibilities for further developing robust strains with improved ethanol yield.

Quantitative trait analysis of yeast biodiversity yields novel gene tools for metabolic engineering.

Engineering of metabolic pathways by genetic modification has been restricted largely to enzyme-encoding structural genes. The product yield of such pathways is a quantitative genetic trait. Out of 52 Saccharomyces cerevisiae strains phenotyped in small-scale fermentations, we identified strain CBS6412 as having unusually low glycerol production and higher ethanol yield as compared to an industrial reference strain. We mapped the QTLs underlying this quantitative trait with pooled-segregant whole-genome sequencing using 20 superior segregants selected from a total of 257. Plots of SNP variant frequency against SNP chromosomal position revealed one major and one minor locus. Downscaling of the major locus and reciprocal hemizygosity analysis identified an allele of SSK1, ssk1(E330N…K356N), expressing a truncated and partially mistranslated protein, as causative gene. The diploid CBS6412 parent was homozygous for ssk1(E330N…K356N). This allele affected growth and volumetric productivity less than the gene deletion. Introduction of the ssk1(E330N…K356N) allele in the industrial reference strain resulted in stronger reduction of the glycerol/ethanol ratio compared to SSK1 deletion and also compromised volumetric productivity and osmotolerance less. Our results show that polygenic analysis of yeast biodiversity can provide superior novel gene tools for metabolic engineering.

Identification of novel causative genes determining the complex trait of high ethanol tolerance in yeast using pooled-segregant whole-genome sequence analysis.

High ethanol tolerance is an exquisite characteristic of the yeast Saccharomyces cerevisiae, which enables this microorganism to dominate in natural and industrial fermentations. Up to now, ethanol tolerance has only been analyzed in laboratory yeast strains with moderate ethanol tolerance. The genetic basis of the much higher ethanol tolerance in natural and industrial yeast strains is unknown. We have applied pooled-segregant whole-genome sequence analysis to map all quantitative trait loci (QTL) determining high ethanol tolerance. We crossed a highly ethanol-tolerant segregant of a Brazilian bioethanol production strain with a laboratory strain with moderate ethanol tolerance. Out of 5974 segregants, we pooled 136 segregants tolerant to at least 16% ethanol and 31 segregants tolerant to at least 17%. Scoring of SNPs using whole-genome sequence analysis of DNA from the two pools and parents revealed three major loci and additional minor loci. The latter were more pronounced or only present in the 17% pool compared to the 16% pool. In the locus with the strongest linkage, we identified three closely located genes affecting ethanol tolerance: MKT1, SWS2, and APJ1, with SWS2 being a negative allele located in between two positive alleles. SWS2 and APJ1 probably contained significant polymorphisms only outside the ORF, and lower expression of APJ1 may be linked to higher ethanol tolerance. This work has identified the first causative genes involved in high ethanol tolerance of yeast. It also reveals the strong potential of pooled-segregant sequence analysis using relatively small numbers of selected segregants for identifying QTL on a genome-wide scale.

Gpd1 and Gpd2 fine-tuning for sustainable reduction of glycerol formation in Saccharomyces cerevisiae.

Gpd1 and Gpd2 are the two isoforms of glycerol 3-phosphate dehydrogenase (GPDH), which is the rate-controlling enzyme of glycerol formation in Saccharomyces cerevisiae. The two isoenzymes play crucial roles in osmoregulation and redox balancing. Past approaches to increase ethanol yield at the cost of reduced glycerol yield have most often been based on deletion of either one or two isogenes (GPD1 and GPD2). While single deletions of GPD1 or GPD2 reduced glycerol formation only slightly, the gpd1Δ gpd2Δ double deletion strain produced zero glycerol but showed an osmosensitive phenotype and abolished anaerobic growth. Our current approach has sought to generate "intermediate" phenotypes by reducing both isoenzyme activities without abolishing them. To this end, the GPD1 promoter was replaced in a gpd2Δ background by two lower-strength TEF1 promoter mutants. In the same manner, the activity of the GPD2 promoter was reduced in a gpd1Δ background. The resulting strains were crossed to obtain different combinations of residual GPD1 and GPD2 expression levels. Among our engineered strains we identified four candidates showing improved ethanol yields compared to the wild type. In contrast to a gpd1Δ gpd2Δ double-deletion strain, these strains were able to completely ferment the sugars under quasi-anaerobic conditions in both minimal medium and during simultaneous saccharification and fermentation (SSF) of liquefied wheat mash (wheat liquefact). This result implies that our strains can tolerate the ethanol concentration at the end of the wheat liquefact SSF (up to 90 g liter(-1)). Moreover, a few of these strains showed no significant reduction in osmotic stress tolerance compared to the wild type.

Quantitative evaluation of yeast's requirement for glycerol formation in very high ethanol performance fed-batch process.

BACKGROUND: Glycerol is the major by-product accounting for up to 5% of the carbon in Saccharomyces cerevisiae ethanolic fermentation. Decreasing glycerol formation may redirect part of the carbon toward ethanol production. However, abolishment of glycerol formation strongly affects yeast's robustness towards different types of stress occurring in an industrial process. In order to assess whether glycerol production can be reduced to a certain extent without jeopardizing growth and stress tolerance, the yeast's capacity to synthesize glycerol was adjusted by fine-tuning the activity of the rate-controlling enzyme glycerol 3-phosphate dehydrogenase (GPDH). Two engineered strains whose specific GPDH activity was significantly reduced by two different degrees were comprehensively characterized in a previously developed Very High Ethanol Performance (VHEP) fed-batch process. RESULTS: The prototrophic strain CEN.PK113-7D was chosen for decreasing glycerol formation capacity. The fine-tuned reduction of specific GPDH activity was achieved by replacing the native GPD1 promoter in the yeast genome by previously generated well-characterized TEF promoter mutant versions in a gpd2Delta background. Two TEF promoter mutant versions were selected for this study, resulting in a residual GPDH activity of 55 and 6%, respectively. The corresponding strains were referred to here as TEFmut7 and TEFmut2. The genetic modifications were accompanied to a strong reduction in glycerol yield on glucose; the level of reduction compared to the wild-type was 61% in TEFmut7 and 88% in TEFmut2. The overall ethanol production yield on glucose was improved from 0.43 g g(-1) in the wild type to 0.44 g g(-1) measured in TEFmut7 and 0.45 g g(-1) in TEFmut2. Although maximal growth rate in the engineered strains was reduced by 20 and 30%, for TEFmut7 and TEFmut2 respectively, strains' ethanol stress robustness was hardly affected; i.e. values for final ethanol concentration (117 +/- 4 g L(-1)), growth-inhibiting ethanol concentration (87 +/- 3 g L(-1)) and volumetric ethanol productivity (2.1 +/- 0.15 g l(-1) h(-1)) measured in wild-type remained virtually unchanged in the engineered strains. CONCLUSIONS: This work demonstrates the power of fine-tuned pathway engineering, particularly when a compromise has to be found between high product yield on one hand and acceptable growth, productivity and stress resistance on the other hand. Under the conditions used in this study (VHEP fed-batch), the two strains with "fine-tuned" GPD1 expression in a gpd2Delta background showed slightly better ethanol yield improvement than previously achieved with the single deletion strains gpd1Delta or gpd2Delta. Although glycerol reduction is known to be even higher in a gpd1Delta gpd2Delta double deletion strain, our strains could much better cope with process stress as reflected by better growth and viability.