Biofuels have emerged as alternatives to fossil-based liquid fuels due to their renewable and carbon-neutral nature. Generally, bioethanol is produced from food-crops including sugarcane and maize, which causes grain shortage. Meanwhile, crops as a sustainable source would take up a huge amount of arable land; for instance, 300% of the total US area would be needed to cultivate maize to meet the energy demands of the US alone (Usmani et al., 2021). Lignocellulosic biomass from agricultural practices, forests, and marginal lands is believed to be a potential resource for future fuel supply (Mehmood et al., 2017a). Unfortunately, biological conversion of lignocellulose faces difficulties mainly due to the stubbornness alleviated by lignin and hemicellulose, often requiring physicochemical pretreatments to break the recalcitrance of the biomass (Pant et al., 2022). Various inhibitory compounds derived from the pretreatment belong to four different groups, namely, organic acids (e.g., acetic acid), phenols (e.g., phenol), aldehydes (e.g., furfural), and ketones; these chemicals and their toxicity cannot be decomposed during enzymatic hydrolysis and the fermentation stage (Liu, 2011; Sjulander and Kikas, 2020). Unlike simple substrate (glucose) and product inhibition (ethanol) in VHG (very high gravity) fermentation, multiple inhibitory effects of the lignocellulosic hydrolysate pose remarkable problems to the ethanol production efficiency of brewing yeast, which also cannot be alleviated by using traditional methods of bioreaction engineering (Melendez et al., 2022).

Apart from toxic byproducts, higher ethanol titers and elevated temperatures also exert stress on the Saccharomyces cerevisiae, that is, the workhorse to produce cellulosic bioethanol, which significantly compromises productivity and yield. If achieved, high concentration of ethanol does not only reduce the energy consumption for ethanol recovery by distillation but also reduces stillage discharge (Liu et al., 2019). However, high-concentration ethanol inhibits yeast cells, resulting in paused fermentation (Stanley et al., 2010; Vamvakas and Kapolos, 2020). On the other hand, elevated temperature facilitates the control of contamination, but S. cerevisiae cannot usually tolerate more than 34°C (Cripwell et al., 2020). Consequently, a chilled water system is needed for almost all fuel ethanol plants, since regular cooling of water from the cooling tower with a temperature only 2–3°C lower than the environmental temperature cannot cool down the fermenters in summer when the temperature reaches as high as 35°C, which always increases the cost of production. Apparently, it is believed that thermo-tolerant strains can address this issue.

The adaptation-based response of S. cerevisiae to the above stresses that adversely influence growth and metabolism can be sorted into ethanol-, inhibitor-, oxidative-/thermal-tolerance, and intriguing joint cellular protections including membrane integrity, organelle modifications, and so forth (Li et al., 2022). Rapid advancements in “OMICs” and metabolic engineering techniques have paved a way to elucidate the underlying mechanisms with characteristic phenotypes under the stressful conditions followed by targeted pathway engineering to develop stress-tolerant strains for biofuel production (Jayakody and Jin, 2021; Mehmood et al., 2021). Hence, in this review, the recent progress on efficient ethanol production made through OMICs-based studies including comparative genomics or multi-OMICs to identify the molecular mechanism of stress tolerance is discussed. Second, the up/down regulation of key genes from bioethanol strains depending on specific-conditioned inhibitions is overviewed. Moreover, some fine-tuning approaches for tolerance improvement of recombinants, as well as inevitable challenges or striving directions of lignocellulosic bioethanol production, are subsequently pointed out.

The 2G (de novo or re-sequencing) and third-generation (single molecule sequencing) DNA sequencing have been applied to reveal genetic traits of yeasts. The whole genome of model S. cerevisiae S288c was sequenced (Engel et al., 2013). Since then, comprehensive investigation of comparative genomics was performed to study numerous structural variations, genomic loci, and nucleotide mutations for assigning diversity of S. cerevisiae strains. Compared with S288c, SNPs (single nucleotide polymorphisms), Indels (insertions/deletions), CNV (copy number variation), and unique ORFs (open reading frames) from genomics of special niche isolated strains were identified, for instance, the wild strain YJM789, containing 60,000 SNPs, 6,000 indels, and several unique ORFs that might have been acquired through gene transfer (Wei et al., 2007) as well as functional gene conversions from meiotic recombination (Sun et al., 2019). Another example is Kyokai No. 7 (K7) in Japanese sake brewery, a closely related strain to the S288c, but sub-telomeric polymorphisms and large inversions were found. Besides, it was pinpointed that two DNA segments located on the chromosome numbers I and VII of S288c were replaced by the Ty elements of K7 (Akao et al., 2011); whereafter, chromosomal recombination defect of haploids of K7 was examined (Shimoi et al., 2019).

Comparison of genome-scale variations among species is being used in confirming the crucial independent domestication or artificial selection that took place during the evolutionary history. The sequenced-genome of interspecific hybrid and wine-making strain VIN7 showed several SNPs and chromosomal rearrangements between the diploid S. cerevisiae and the haploid S. kudriavzevii. A distance tree, based on SNP variations, was constructed among VIN7 and 24 other S. cerevisiae strains which demonstrated that it had a European origin (Borneman et al., 2012). Genome assemblies of six commercial S. cerevisiae strains containing four wine (Lalvin QA23, AWRI796, Vin13, and VL3) and two brewing strains (Fosters O and Fosters B) with S. cerevisiae strains (S288c, YJM789, JAY291, RM11-1a, and EC1118) have revealed clear signature sequences which implied that each industrial strain has signature genetic variations comprising SNPs, large-scale Indels, and putative novel genes between them (Borneman et al., 2011). The genome variability of 16 yeast strains, of both laboratory and commercial origins, where sub-telomeric instability, differences of Ty element insertion regions, copy number changes, or depletion were listed, was studied (Carreto et al., 2008). Likewise, a survey of a collection of 63 diverse strains from diverse ecological niches manifested nearly two million SNPs and the occurrence of 3,985 deletions of a length >200 bp (Schacherer et al., 2009). An investigation of 83 strains confirmed interspecific hybridization and pervasive CNV in wild and industrial strains (Dunn et al., 2012). The genomes of 93 strains, collected from different geographical sites, indicated that most of the genetic or phenotypic variations were quantitative (Strope et al., 2015). These studies pointed to the promising prospect of studying genome-scale change to elucidate the origin of novel genetic elements.

Phenotypic impact of genomic variations provides the evidence for enabling strains to endure different stresses, so the resistance potential of the strains can be harnessed through inferring the genetic basis of tolerance. Genomic features of the yeast PE-2 derived diploid (JAY270) and haploid (JAY291) strains uncovered higher zygosity and polymorphism (∼2 SNPs/kb) in the JAY270 between homologous chromosomes, and chromosomal rearrangements were limited to the peripheral sites of chromosomes, which may illustrate adaptation of two strains to ethanol, thermal, and oxidative stresses (Argueso et al., 2009). The CNVs of important industrial strains, BG-1, CAT-1, PE-2, SA-1, and VR-1, which are being used to produce fuel ethanol from sugarcane in Brazil, were characterized, and substantial augmentations of the SNO and SNZ genes were discovered, which exhibited the adaptive competence to biomass utilization in an industrial environment (Stambuk et al., 2009). A similar event was also discovered in the Brazilian strain JAY291 derived mutant which could tolerate 16–17% ethanol. It was observed that three closely located genes, namely, MKT1, SWS2, and APJ1, were responsible for affecting ethanol tolerance. Mkt1 was recruited to form a complex that may mediate posttranscriptional regulation of HO endonuclease gene. Sws2 is a mitochondrial ribosomal protein that is essential for respiratory growth. Apj1 is the chaperone of Hsp40 with a role in SUMO-mediated protein degradation (Swinnen et al., 2012). The genome of S. cerevisiae CAT-1 strain contained ∼36,000 homozygous and ∼30,000 heterozygous SNPs, several duplications, and deletions, particularly at the telomeric regions, whereas some genes like IRA1 and IRA2, encoding GTPase-activating proteins, were associated with bioethanol production (Babrzadeh et al., 2012). Alterations in gene expression involved central carbon metabolism, antioxidation activities, and membrane conformations that might be responsible for multiple stress tolerance of bioethanol-producing and tolerant strains YJS329 and ZK2. ELO1 (encoding a fatty acid elongase) overexpression improved acetic acid tolerance of both strains, and introduction of ZNF1 (Zinc cluster transcription factor) into ZK2 increased ethanol, H₂O₂, and acetic acid tolerance (Zheng et al., 2013). An isolate (S3-110) capable of tolerating higher ethanol titer under the VHG condition was obtained through whole genome shuffling of an industrial strain ZTW1. The strain could tolerate 55°C and oxidative and ethanol stresses, which was attributed to the CNVs of the stress-responsive genes (Zheng et al., 2014). In particular, the ZTW1 was confirmed as an aneuploidy strain; its changed DNA dosage, such as CNVs in chromosomal segments or CNVs of functional genes, might have improved its fermentation rate and tolerance to various stresses (Zhang et al., 2016).

A set of stress-responsive genes in different metabolic pathways were selected by genome-wide scale analysis of yeast in the laboratory and industry that annotated novel functions for regulatory and protein-coding genes. For BY4743 strain, 331 genes were found to confer hypersensitivity to oxidative stress (3 mM H₂O₂). Among these genes, 71 genes were essential, which were associated with nucleolus, rRNA, and tRNA synthesis (Okada et al., 2014). For BY4741 strain, 650 genes were speculated to be involved in acetic acid stress (70–110 mM, pH 4.5), which mediated cellular processes including carbohydrate metabolism, pH homeostasis, cell wall assembly, biogenesis of vacuoles, and mitochondria. More than 75% of those genes were first certified for acetic acid tolerance (Mira et al., 2010a). Pereira et al. (2011) reported 8 genes linked with concurrent resistance to stress imposed by higher concentrations of ethanol, glucose, and acetic acid; meanwhile, 11 genes were associated with lignocellulosic-derived stress tolerance. In addition, BUD31 and HPR1 were found to have a key role in ethanol yield and fermentation rate. PHO85, VRP1, and YGL024w were indispensable for maximal ethanol production. In a subsequent analysis, genes responsible for vitamin metabolism, peroxisomal and mitochondrial functions, and biogenesis of ribosomes and microtubules were identified in wheat straw hydrolysate resistance (Pereira et al., 2011; Pereira et al., 2014). Besides, genome sequencing of the natural yeast strain MUCL28177 with quantitative trait loci (QTL) mapping led to the discovery that several new QTLs are responsible for heat stress and MKT1, PRP42, and SMD2 as causative genes suggests RNA processing is crucial in thermotolerance (Yang et al., 2013). PRP42 and SMD2 encoding U1 snRNP protein and subunit of U2-type pre-spliceosome complex, respectively.

Due to a mixture of lignocellulose-derived hydrolysates, for existing multiple stresses, five genes, namely, ERG2 (encoding C-8 sterol isomerase in ergosterol biosynthesis), PRS3 (encoding pyrophosphate synthetase), RAV1 (encoding subunit of RAVE complex), RPB4 (encoding RNA polymerase II subunit), and VMA8 (encoding subunit of the domain of V-ATPase), which were demonstrated as the tolerant genes, contributed to maintaining the cell viability and maximal fermentation rate during fermentation of wheat straw hydrolysates (Pereira et al., 2014). Ubp7, Art5, Nrg1 (stress response transcriptional repressors), and Gdh1 (glutamate dehydrogenase) of strain R57 evolved via genome shuffling were selected as biological determinants for tolerance of spent sulfite liquor, which provided certain targets for strain breeding (Pinel et al., 2015). The industrial strain ISO12, which is tolerant to inhibitors at 37°C, unfolded several SNPs and Indels in its parental strain ethanol Red. Besides, CYC3 (encoding cytochrome C heme lyase), MTL1 (encoding putative plasma membrane sensor), and FLO genes (encoding flocculating proteins) have explained positive selection in thermal and inhibitor stresses (Wallace-Salinas et al., 2015). Therefore, composited stress-related genes must be screened for hydrolysate fermentation to achieve desired properties that are highly resistant to compound inhibitors.

Furthermore, S. cerevisiae NCIM3186 (Goud and Ulaganathan, 2015), NCIM3107 (Ulaganathan et al., 2015), IR-2 (Sahara et al., 2014), BG-1 (Coutouné et al., 2017), and BT0510 (Costa et al., 2021) are being utilized in production of bioethanol from various feedstocks, such as sweet sorghum, sugarcane, or the wild-type FMY097 (Nagamatsu et al., 2019) and Pf-1 (Kanamasa et al., 2019) that isolated at special habitats. These strains have been genome-sequenced, which will conceivably yield fresh genetic insights into genomics diversity and evolutionary adaptation.

As a prerequisite, there is an urgent demand for screening and sequencing of newly separated and industrial strains, which could resolve significative variations in genetics and correlated mutations on evolution through comparative genome analysis. Fundamental work on data mining of genomics and function validation on stress tolerance still needs to be done and evaluated.

Multi-OMICs other than genomics, including transcriptomics, proteomics, and metabolomics, have been implemented to explore mechanisms of stress tolerance (Amer and Baidoo, 2021). Similarly, microarray analysis and gene deletion libraries, at a genome-wide, transcriptional, and translational level, can interpret the functional elements responding to characteristic stresses. Here, representative OMICs studies of S. cerevisiae tolerance in the past decades are summarized in Table 1.

Keeping in view its applications, transcriptomics or RNA sequencing unveils the relationship of phenotypes at variable transcriptional levels and detects hot spots of gene engineering that help develop strains for industry (Negi et al., 2022). Although market price of protein-seq is more expensive than that of RNA-seq, mass spectrometry technologies have highlighted the great potential to precisely profile the proteome, to map protein–protein interactions, and to distinguish the protein modifications (Lenz and Dihazi, 2016). Based on optimal experimental design, metabolomics has been used in bioprocessing of ethanol production to account for differential metabolite profiles caused by various feedstocks (Abdelnur et al., 2014) and inhibitors (Chen Z. et al., 2016). There are two catalogs of metabolome: 1) untargeted metabolomics, which represents the characterization of simultaneous qualitative and quantitative analysis of a large number of metabolites, and 2) targeted metabolomics, which includes quantitative data on a pre-defined set of compounds, like lipidome (Sévin et al., 2015). Yeast lipidome is critical in balancing fatty acid metabolism, maintaining the plasma membrane integrity, and adapting to ambient surroundings, including sphingolipids, glycerophospholipids, and so on, which, thus, immediately provides insights into explicating adjustments of metabolic reaction for both the intra- and extra-cellular stimuli (Yuan et al., 2011; Klose et al., 2012). Taking lipidomics analyses of a furfural-, a phenol-, and an acetic acid–tolerant strain as an example, their metabolomes were discriminated by phosphatidylcholines, phosphatidylinositols, and phosphatidic acids, individually, when compared to their initial strain (Xia and Yuan, 2009).

It has been demonstrated over the last decades that considerable data can be quickly collected by multi-OMICs sequencing, illustrative of the tolerant phenotypes under characteristic stresses that seem to become much easier, but it is critical to center on a set of key data and then analyze the real reason underlying the intrinsic mechanism. The complexity of OMICs-derived interpretation lies not only in the regulations of tolerance such as synergy and antagonism but in the superimposed effects of stimulation or suppression from inhibitors generated by different materials and pretreatment methods on yeast cells. Primarily, faced with toxicities of ethanol and hydrolysates and co-existing pressures of thermal and oxidative stress, the molecular evidence reflects a paradox that the tolerating performance of strains engineered for multi-substrate conversion that is directly affected before ethanol production; these events should be concerned, as shown in Figure 1.

The S. cerevisiae possesses a prominent merit: ethanol fermentation is almost simultaneously coupled with its powerful cell growth. Starch-based fermentation brings about high ethanol yield, often accompanied by alcohol tolerance of cells; when compared to the fermentation with lignocellulosic hydrolysates, the first dilemma is posed by typical inhibitors that give rise to gravely suppressed cell growth. Therefore, taking aim at efficient ethanol production and effective resistance to inhibitory factors, the preferable strategies of strain improvement should be balanced and controlled. Arguably, identified genes participating in the glycolysis might be assigned to take charge of tolerance enhancement, as shown in Figure 2 (information obtained from Saccharomyces Genome Database; Hasunuma et al., 2014b), if it is possible to be accomplished expectantly by genetic modification. Nevertheless, other strategies on strain engineering are still required to widen substrate utilization, to achieve higher ethanol titer, and to confer tolerance to inhibitors by inverse metabolic engineering. With the advent of technology and the assist of OMICs tools, it should be emphasized that attention to tolerance should not be minimized so that promoted specific growth rate and cell reproductive capacity and improved fermentation performance will be subsequently achieved.

For a feasible production of lignocellulose ethanol, technical challenges lie in various inhibitors derived from physiochemial pretreatments of agricultural and forestry wastes, followed by detoxification processes that also unavoidably remove the dissolved sugars, thereby decreasing the sugar yield and content. The mixture of fermentable and non-fermentable saccharides enables the fermenting strain to be less efficient in substrate utilization and ethanol yield; nevertheless, the robustness of the metabolically engineered strain is not simply realized.

Moreover, S. cerevisiae cannot tolerate multiple stress factors, some of which are inescapable due to the harsh production process and lead to the adverse impact on the specific growth rate and ethanol productivity. Accordingly, mutual enhancement of tolerance and fermentation performance is a not-so-trivial pursuit, which severely affects the fermentation efficiency on an industrial scale and brings discouraged economic costs.

The challenges mentioned are plotted and shown in Figures 1, 3; future research directions are recommended in S. cerevisiae strains for highly efficient and sustainable 2G fuel-ethanol production and beyond as follows:

1) Multi-omics sequencing and data mining should be widely applied, and deep-level investigations are needed for potentially important yeasts in commercial use. It is demonstrated that joint study of static genomics and another dynamic-omics indeed contributes to satisfying the demand of identification or modulation from genotype to phenotype and vice versa, but owing to complexity, the related work is rarely reported in the ethanol fermentation industry and is still an exquisite subject that attracts research interest.

As a vigorous cell factory, S. cerevisiae may support the reliable supply of transportation fuel, which has exhibited a credible bioconversion efficiency of mixed sugars- and grain-substrates to cost-effective production of ethanol. Combined with obvious advantages of lignocellulose utilization, lignocellulosic feedstock is environment-friendly, abundantly available, and low-cost for green bio-refinery. Despite this, bottlenecks remained; the limitations are not merely profitable ones encountered by the pilot-scale and large-scale fermentation; the application prospect of transformation of hemicelluloses of lignocellulose to ethanol is worthy of more attention.

During past decades, a lot of progress has been made on tolerance research of S. cerevisiae (Mitsui and Yamada, 2021). Especially, the multi-omics analysis of transcriptome, proteome, and metabolome, linked with comparative yeast genomics that focusing on ethanol tolerance, inhibitions of hydrolysates, oxidative, and thermal stresses, was concluded. These studies revealed the microbial genetic background or adaption mechanisms and provided targeted DNA modifications; meanwhile, profiling characteristics of physiological reaction and cell metabolism in response to condition-specific environments offered the beneficial experience for continuing research in this field. S. cerevisiae strains aimed at broadening the substrate spectrum and effectively integrating conversion processes have been developed.