- Research article
- Open Access
Genomic and transcriptomic analyses of the Chinese Maotai-flavored liquor yeast MT1 revealed its unique multi-carbon co-utilization
© Lu et al. 2015
- Received: 16 May 2015
- Accepted: 30 November 2015
- Published: 15 December 2015
Revealing genetic mechanisms behind specific physiological characteristics of Saccharomyces cerevisiae from specific environments is important for industrial applications and requires precise understanding.
Maotai strain MT1 was isolated from the complicated Chinese Maotai-flavored liquor-making environment with extremely high temperatures, and acidic and ethanol stresses. Compared with the type strain S288c, MT1 can tolerate high acidity (pH 2.0), high ethanol levels (16 %) and high temperatures (44 °C). In addition, MT1 can simultaneously utilize various sugars, including glucose, sucrose, galactose, maltose, melibiose, trehalose, raffinose and turanose. Genomic comparisons identified a distinct MT1 genome, 0.5 Mb smaller than that of S288c. There are 145 MT1-specific genes that are not in S288c, including MEL1, MAL63, KHR1, BIO1 and BIO6. A transcriptional comparison indicated that HXT5 and HXT13, which are theoretically repressed by glucose, were no longer inhibited in MT1 and were highly expressed even in a medium containing 70 g/L glucose. Thus, other sugars may be co-utilized with glucose by MT1 without diauxic growth.
Based on a functional genomics analysis, we revealed the genetic basis and evolutionary mechanisms underlying the traits of the Chinese Maotai-flavored yeast MT1. This work provides new insights for the genetic breeding of yeast and also enriches the genetic resources of yeast.
The Chinese liquor-making process is a unique and complicated spontaneous fermentation process under solid state conditions . Chinese liquor is produced by the technique of simultaneous saccharification and fermentation (SSF) using grains, such as sorghum, wheat and barley. The cost-effective SSF combines the enzymatic hydrolysis of starch with the simultaneous fermentation of sugars, resulting in an outpouring carbon source . In addition, Chinese liquor is fermented using combinations of microorganisms, including yeast, bacteria and filamentous fungi . The complex filamentous fungal community produces diversified hydrolases to degrade starch into various sugars, including glucose, galactose, maltose and melibiose . High temperatures (~50 °C) at the starter are generated by the metabolic activities of the microorganisms and the low thermal conductivities of the solid matrices . In addition, there are two other prominent pressures: acidity stress (pH 3.0) and ethanol stress (4.5–5.5 %, v/v) in the alcoholic fermentation stage . Such a severe environment during the SSF process leads to a distinctive community of microorganisms with specific physiological properties and performances , in particular, distinctive heat- and acid-resistant properties .
Although the microorganisms in the Chinese Maotai-flavored liquor have been investigated since the 1960s , there is little information on the functional genomics of the yeast involved in the process. This has inhibited our understanding of the fermentation mechanism and the development of Maotai-flavored liquor. Functional genomics approaches, such as whole-genome sequencing, are powerful tools for the analysis of specific traits on a genomic scale and provide a method of studying cellular physiology from micro-perspective. Since the first complete genome of strain S288c was sequenced in 1996 , it has served as the reference for the Saccharomyces cerevisiae species and has promoted the development of whole-genome approaches . Meanwhile, the genomes of several S. cerevisiae strains have been sequenced and compared with the S288c reference genome, including laboratory strains  and industrial strains, such as sake yeast K7, wine yeasts, beer yeasts and bioethanol yeasts [13–17]. The phenotypic diversity among yeast isolates is significant, and variation is apparent among the surveyed strains at different levels. Ecological factors and geographical locations, as the main reasons for biodiversity, have important effects on the domestication of microbial physiological characteristics . Among the 50 strains with sequenced genomes in the NCBI database, there are only three strains from China . However, strains involved in the SSF process of Chinese liquor are poorly understood. Theoretically, the genomic analysis of strains with different backgrounds should help identify the sequence changes that play important roles in specific physiological characteristics. Comparisons of the publicly available S. cerevisiae genome sequences have revealed the clear signatures [single nucleotide polymorphisms (SNPs), insertions and deletions (Indels), and novel ORFs] in the different strains . Compared with laboratory strains, industrial strains generally show a higher adaptability to specific environments. However, the genetic basis for their improved characteristics is not well understood. Further studies are needed to explore how the genetic variations confer the specific phenotypes. The comprehensive identification of polymorphisms among individuals within a species is essential for studying the genetic basis of phenotypic differences and for elucidating the evolutionary history of the species.
In this study, S. cerevisiae MT1 was isolated from the fermentation environment of Chinese Maotai-flavored liquor. Its physiological characteristics were evaluated in several phenotypic assays. Meanwhile, the MT1 genome was sequenced and compared with the type strain S288c. In addition, we elucidated how genetic variations of MT1 were correlated with specified traits using RNA-seq and revealed the molecular mechanisms related to the adaptation of yeast in Chinese liquor-making. This whole-genome analysis of the S. cerevisiae strain used in Chinese liquor-making will supplement the genomic and phylogenetic knowledge of yeast and provide a guide for the construction of strains with desired traits.
Results and discussion
Carbon source utilization exerts a strong influence on the classification of isolates and may be a useful and functional measure for the characterization and classification of different strains. Unfortunately, there is limited research on the simultaneous fermentation of various carbon sources to produce ethanol. The primary reason is that not many strains can ferment ethanol from various sugars. Presently, there is some research on a strain that could simultaneous utilize glucose and xylose, or arabinose and galactose . MT1s ability to uptake and assimilate sugars was also investigated (Fig. 1b). Among the monosaccharides, glucose could be used for growth and fermentation by both MT1 and S288c, with OD590 and OD730 values exceeding 0.8. When galactose was the sole carbon source, the OD590 and OD730 of MT1 reached 1.23 and 0.98, respectively, while those of S288c were less than 0.3. This indicated that MT1 could uniquely utilize galactose. The other carbon sources that MT1 could uniquely uptake and assimilate were mainly disaccharides, such as melibiose, maltose, trehalose and turhanose. In addition, raffinose, a trisaccharide constituted by galactose, fructose and glucose, as well as methyl-a-D-glucopyranoside, are well utilized by MT1, with OD730 values reaching 1.15 and 0.95, respectively. These results suggested that MT1 was a strain appropriate for multi-carbon fermentation, having the ability to utilize various carbon sources.
MT1 genome structure
Chinese Maotai-flavored liquor yeast MT1 library assembly statistics
Contigs (>200 bp)
Scaffolds (>500 bp)
Min sequence length (bp)
Max sequence length (bp)
Total sequence number
N rate %
Total sequence length (bp)
GC content %
Sequences greater than 1 kb
Essential Saccharomyces cerevisiae genomic information
Genome size (Mb)
GC content (%)
GC content in mRNA region (%)
Coding sequence number
Average coding sequence length (bp)
Total coding sequence length (Mb)
Coding sequence of genome (%)
Among the 5,106 genes of MT1, less than 500 genes displayed similarity levels below 95 % when compared with S288c (Additional file 1: Figure S1). Using an all-vs-all BLASTP and clustering analysis with orthomcl (V2.0.8), 145 MT1-specific genes were identified, 90 % of which had near-perfect-match hits to the nucleotide sequence of S288c, and likely reflect ORF annotation differences, whereas others had nonreciprocal-best-hit homologs in S288c. The functions of these genes fell within stress response (14 genes), carbohydrate metabolic process (seven genes), organic substance transport (five genes), biosynthetic process (24 genes), organic substance metabolic process (42 genes), and others (Fig. 2c). In addition, 695 genes were missing from MT1 compared with S288c. Among them, there were 25 genes and 21 genes in the location of centromere and telomeres, respectively, where assemble errors happen frequently. These genes are Mic17p, VMA9, TAF11, TIR4, TIR2, PRP18, COX16, COX17, SHU1, MNN1, RFA1, PUF4, AVT1, TEN1, GAT3, CMS1, GAT3, SIN3, PFA4, MCD1, PSF1, PDR3, RER1, SGF29, PAU24, PAU12, PAU14, PAU1, PAU18, PAU6, PAU21, PAU22, PAU13, PAU9, PAU3, PAU15, PAU16, PAU20, PAU10, RDS1, HSP32, YPS5, AIF1, BDS1 and ENB1. 60 % of 695 missing genes were hypothetical or uncharacterized ORFs encoding putative proteins of unknown functions as reported in the Saccharomyces Genome Database. The other 40 % of the genes were mainly involved in ion transport, regulation of transcription and translation, DNA and RNA binding and cellular component synthesis, such as membrane components, organelle organization and cellular protein components (Fig. 2d). While there were only about 10 genes listed in S288c as essential, including DAD2, DAD3, DAD4, BRR6, RRP14, ESF2, FAP7, USO1, MCD1 and SPC19. Function of each genes was listed in Additional file 1: Table S5. As indicated by the functional classification of the specific and lost genes in MT1, there is a speculation that MT1 may have a special genomic system. It weakened transcriptional, translational and cytoplasmic synthesis. On the other hand, it strengthened carbohydrate transport and metabolism, energy metabolism and informational processing (especially environmental informational processing) metabolism.
Transcriptomic comparison using RNA-seq
Main differences in the metabolic pathways of the Chinese Maotai-flavored liquor yeast MT1 compared with the Saccharomyces cerevisiae type strain S288c when grown in Sorghum juice medium, pH 6.0
Tricarboxylic acid cycle cycle (ko00020)
Pentose phosphate pathway (ko00030)
Galactose metabolism (ko00052)
Butanoate metabolism (ko00650)
Oxidative phosphorylation (ko00190)
Sulfur metabolism (ko00920)
Carbon fixation pathways (ko00720)
Sulfur metabolism (ko00920)
Purine metabolism (ko00230)
Pyrimidine metabolism (ko00240)
Aminoacyl-tRNA biosynthesis (ko00970)
RNA transport (ko03013)
Ribosome biogenesis in eukaryotes (ko03008)
RNA degradation (ko03018)
Cell growth and death
Cell cycle (ko04110)
Cell cycle – yeast (ko04111)
Meiosis – yeast (ko04113)
Under normal conditions (sorghum juice medium, pH 6.0), there were not only less genes related to transcription and translation in MT1 but they were also expressed at far lower levels than in S288c; however, MT1 could attain a greater biomass than S288c. When the pH was reduced from 6.0 to 2.7, genes with functions related to transcription, replication and repair and, especially, translation in MT1 were strongly up-regulated. Within these, several genes are essential to acid pH resistance, such as CIK1, HSP30, RPS27B, RPL31A, RPL18 and ARO2. There may be two reasons for this strange phenomenon. One is that MT1 requires less proteins and nucleotides for growth than S288c. The other may be that genes involved in transcription, translation, replication and repair in MT1 are more efficient than those in S288c. There is no need to mobilize a large number of related genes under suitable conditions. Once threatened, the cell will increase its investment to survive.
Genetic mechanism of multi-carbon co-utilization
Comparison of the expression levels of HXT genes between the Chinese Maotai-flavored liquor yeast MT1 and the Saccharomyces cerevisiae type strain S288c when grown in sorghum juice medium (70 g/L glucose)
Low-affinity glucose transporter induced by Hxk2p in the presence of glucose and repressed by Rgt1p when glucose is limiting.
1.11 × 10−7
High-affinity glucose transporter induced in low glucose and repressed in high glucose.
2.66 × 10−4
Low affinity glucose transporter induced in low or high glucose conditions.
2.02 × 10−7
Hexose transporter with moderate affinity for glucose induced in the presence of non-fermentable carbon sources and induced by a decrease in growth rate.
1.96 × 10−1
Protein of unknown function similar to hexose transporters, expression is induced in low glucose and repressed in high glucose.
4.64 × 10−1
Putative hexose transporter similar to major facilitator superfamily (MFS) transporters, expression is regulated by transcription factors Pdr1p and Pdr3p.
8.69 × 10−1
Putative hexose transporter, expressed at low levels and expression is repressed by glucose.
6.44 × 10−1
Hexose transporter, induced in the presence of non-fermentable carbon sources, induced in low glucose and repressed in high glucose.
3.34 × 10−7
Protein similar to hexose transporter family members, expression is induced in low glucose and repressed in high glucose
9.70 × 10−1
To verify the multi-carbon co-utilization without glucose repression, which was indicated by the genomic and transcriptomic analyses, cell growth, sugar consumption and ethanol production were compared between MT1 and S288c in media containing mixed sugars. As shown in Fig. 4b, when compared with S288c in 2 % glucose, MT1 grew faster but showed no difference in the glucose consumption and ethanol production. However, in 1 % glucose plus 1 % maltose, MT1 could use maltose to grow and ferment, and consequently its biomass and ethanol yield were twice that of S288c. This was also true in 1 % glucose plus 1 % galactose. As indicated by the dynamic curves of sugar consumption (Fig. 4b), MT1 could utilize maltose and galactose in the early stages of the logarithmic phase without the phenomenon of secondary growth. This was confirmed by the lack of a “lag” period in the growth of MT1 in a medium containing mixed sugars (Fig. 4b). These results indicate that the high expression levels of HXT5 and HXT13 without glucose repression may contribute to the multi-carbon co-utilization.
Other unique genes lead to unique phenotypes
KHR1 is another unique gene of MT1, encoding a heat-resistant killer toxin. This gene is located in a 2-kb fragment inserted into MT1 scaffold 22, which can be mapped to S288c chromosome IX. This ORF has been identified at the same location in the genome of YJM789 . The killer toxin provides MT1 with a stronger survival rate compared with other microorganisms in the Chinese liquor fermentation environment. In addition, two specific BIO genes were found on contig40, resulting in MT1 having biotin prototrophy. The two BIO genes share 100 % identity with BIO1 and BIO6, which encode pimeloyl-CoA synthase and KAPA synthase , respectively. They are also conserved (over 99 %) in several other S. cerevisiae strains, such as Sigma1278b (GenBank: ACVY00000000.1), YJM269 and UC5 (GenBank: AFDD00000000.1). As shown in Additional file 1: Figure S2, BIO1 is located between ESMT1 and MCM10, while BIO6 lies between AXL2 and REV7. Compared with S288c, MT1 could grow in the medium without biotin (data notshown). This confirmed the biotin prototroph of MT1.
The physiological and biochemical characteristic diversity of microbes is very important for industrial applications. Our analysis indicated that MT1 not only had higher tolerances to acidity and ethanol but was also capable of simultaneously producing ethanol from various carbon sources without glucose repression. These abilities could be applied to cellulose ethanol to eliminate the bottleneck during multi-carbon source fermentation. In addition, MT1 is a biotin prototroph, a phenotypic trait with potential in industrial applications. Biotin (vitamin H) is an essential nutrient for all organisms, being a cofactor of many enzymes involved in carboxylation reactions, such as gluconeogenesis, amino acid metabolism, fatty acid biosynthesis and energy metabolism . These physiological and biochemical characteristics place MT1 in a special category of yeast desirable for industry. Revealing the genetic basis of these traits would provide a platform for not only understanding Chinese liquor but also for developing an economically advanced biofuel process. It is necessary to analyze the evolutionary mechanisms of such special phenotypic and physiological characteristics. We assume that these variances may be induced by the raw materials and environmental stresses in the process of Chinese liquor-making. The SSF technique used in making Chinese liquor combines the enzymatic hydrolysis of starch with the simultaneous fermentation of sugars. Diverse hydrolases produced by the complex filamentous fungal community generate various sugars. This results in various carbohydrates that can be utilized by cells to grow and produce ethanol. In addition, Chinese Maotai-flavored liquor is produced by a complicated SSF process in an extremely severe environment, with high temperatures, as well as acidic and ethanol stresses. In the environment, temperatures could reach 50 °C, the pH could be as low as 3.5, and the ethanol concentration could be as high as 5.5 %. Over time, this specific environment has produced specific physiological properties and performances.
Environmental pressure is the external induction force, while the specific genome contains the internal underlying determinants. The genomic structural analysis indicated that MT1 retained a smaller genome than the reference strain S288c and some other industrial strains (Table 1). The genome of MT1 has lost several segments mostly comprised of non-coding protein genes, such as the large missing fragment of 25 kb on Chromosome I (Additional file 1: Figure S3A), which was verified by PCR (Additional file 1: Figure S3B). In S288c, this segment contains only one functional gene, FLO9. MT1 also lost hundreds of genes whose functions fell within transcription, translation, DNA and RNA binding, and cellular component synthesis. In addition, MT1 gained several specific genes, such as MEL1 and MAL63, related to its multi-carbon co-utilization, and KHR1, associated with its competitiveness. These genomic adaptations embody the economy of the MT1 strain: removing some redundant genes, retaining necessary genes for growth and fermentation, and adapting to the complex fermentation environment of Chinese liquor. Consequently, these alterations make MT1 a naturally ascendant strain with a minimal genome, containing special functional genes/clusters . The small genome has a higher economic efficiency. It may have been beneficial to reduce the metabolic redundancy and then improve the metabolic efficiency . Therefore, MT1 has the advantages of a short growth cycle, high production efficiency, high conversion rate, and strain stability in the fermentation process.
Various mechanisms are known to be involved in the adaptive evolution of yeast to the fermentation process, such as gene duplication, polyploidy, chromosomal rearrangements, interspecific hybridization and introgression . On the basis of diverse examples drawn from both mammalian and microbial genetics, Maynard V. Olson proposed the “less is more” hypothesis that states that genetic loss may be an important engine of evolutionary change . Aravind et al.  and Braun et al.  revealed that S. cerevisiae may have lost at least 300 genes, and another 300 or so genes have become highly diverged, since their split from their common ancestor with Saccharomyces pombe. A notable feature of the lost genes is the co-elimination of functionally connected groups of proteins, such as the signalosome and the spliceosome components. The other missing genes are involved in basic cellular processes, translation and ion homeostasis. However, intriguingly, some of the strains in which genes had been disrupted grew better than wild-type strains on a rich medium . The counterintuitive idea that “less is more”, is well-reflected in the evolution of MT1. The gene loss in MT1 has resulted in no obvious disadvantages in growth and fermentation, but provide MT1 the advantages of a short growth cycle and strain stability. Furthermore, a speculative hypothesis is proposed that MT1 gained the genomic space to obtain special functional genes (such as MEL1, MAL63, BIO1, BIO6 and KHR1) by reducing the gene redundancy. This strategy of evolution by gene loss is an important aspect of yeast diversification and may play a major role in their adaptation to the Chinese liquor fermentation ecosystem.
By revealing the genetic basis of specific phenotypes and elucidating the evolutionary history of the Chinese Maotai-flavored liquor yeast MT1, this work enriches our knowledge of S. cerevisiae, especially with regard to minimal genomic research. Furthermore, this functional genomics study of industrial microorganisms could be beneficial in improving breeding strategies to obtain the desired production traits in industrial microorganisms.
Yeast strains and culture conditions
S. cerevisiae S288c and sake yeast Kyokai No. 7 (K7) were obtained from the National Institute of Technology and Evaluation. Diploid strain MT1 was isolated from samples of the Maotai-flavored liquor production distillery in Guizhou, China. Strains from wine, beer or Chinese Rice Wine (Huangjiu) were obtained from China Center of Industrial Culture Collection (CICC). The yeast extract peptone dextrose (YPD) growth medium contained 10 g/L yeast extract, 20 g/L peptone and 20 g/L glucose.
Analysis of yeast tolerance to several stress conditions
Three stress factors that are important features in liquor fermentation were studied: temperature, pH, and ethanol concentration. Except for during the temperature stress test, all cultures were incubated at 30 °C. For each stress factor, several fermentation conditions were tested, including four temperatures (30, 34, 38 and 42 °C), a range of pH levels (2.0 to 5.5) and several ethanol concentrations (0 to 16 %). Yeast was precultured in YPD medium at 30 °C for 24 h. Cells were diluted in sterile water and then was cultured in stress media under static cultivation at 30 °C for 48 h with the same inoculum concentration of 1 × 104 cfu/mL.
Carbon source assimilation experiment
Biolog YT MicroPlates were used to test the ability of MT1 and S288c to utilize or oxidize compounds from a preselected panel of different carbon sources. The test yields a characteristic pattern of purple wells, which constitutes a “Metabolic Fingerprint”. The isolate to be identified is grown on an agar medium and then suspended in sterile water. Then, the cell suspension is inoculated into the YT MicroPlate, at 100 μl per well. All of the wells are colorless when inoculated. During the incubation there is a burst of respiration in the wells, which contain chemicals that can be oxidized, and the cells reduce the tetrazolium dye, forming a purple color. Meanwhile, microbes grow in the wells that contain utilizable carbon sources, leading to a higher turbidity. Negative wells remain colorless, as do the reference wells (A1 and D1) that contain no carbon sources. The OD590 values of lines A–C and the OD730 values of lines D–H were detected after 48 h of cultivation at 30 °C. These values indicated that the carbon source could not be utilized when the corresponding OD590 or OD730 was below 0.2, but could be at values from 0.2 to 0.8, with values >0.8 having the best carbon utilization rates.
Preparation of the sorghum extract medium
To prepare the sorghum extract medium, 2 kg milled sorghum powder was added to 8 L deionized water. The mixture was steamed for 2 h and then saccharified by glucoamylase (5 U/L) at 60 °C for 4 h. The supernatant was collected after being filtered through gauze and centrifuged at 8,000 × g for 15 min. The sugar content was measured on a Leica refractometer (Fisher Scientific, Pittsburg, PA, USA). Apply few solution on the scope then gently close the cover plate. Turn the eyepiece to read the value by adjusting the handwheel. The mixture was then diluted with water to produce a final sugar concentration of 10° Brix (80 ± 5 g/L reducing sugar) before sterilization.
Sequencing and assembly
The MT1 genome was sequenced using the whole-genome shotgun sequencing approach in the Illumina Miseq platform , and four paired-end/mate-paired sequencing libraries were constructed with insert sizes of 450 bp, 700 bp, 3 kb and 8 kb. Raw data were assembled into contigs and scaffolds employing the de novo assembler Newbler  and SSPACE . The assembled sequences were manually checked, and the gaps were closed using the GapCloser program . The sequences of the final contigs were deposited in GenBank under the Whole Genome Shotgun project [GenBank: JRUS00000000].
Gene prediction and annotation
We predicted the MT1 genome using the MAKER pipeline prediction system, combining Augustus , SNAP  and the Glimmer gene prediction software . The functional annotation was based on GO and KEGG  databases. Orthology with the S288c ORFs was evaluated by employing BLASTP similarities.
Sequence comparison and SNPs/Indels analysis
An overall comparison of the MT1 genome sequence with 16 chromosome sequences from S288c was performed using BLASTN. SNPs and Indels based on strain S288c were estimated using the GATK pipeline system and Unified Genotyper, and then they were annotated using Variant Effect Predictor provided by Ensembl.
Construction of the evolutionary tree
First, a family containing 18 yeast genomes was created using the software Orthomcl to obtain the protein sequences with a strict 1:1:1 ratio. Then, the protein sequences were compared using Muscle (version 3.8.31) software with the default parameters. Unreliable sequence alignment loci were removed using Gblock (version 0.91 b). Finally, the phylogenetic tree was constructed by Mega5 software using the neighbor-joining method , Poisson model and uniform rates. We also used the bootstrap method to validate the reliability of the evolutionary tree branches with 1,000 bootstrap replications.
cDNA preparation and transcriptomic analysis using RNA-seq
Genomic DNA was digested using DNase, and total RNA was isolated using the TRIzol reagent. The OligoTex mRNA mini kit (Qiagen) was used to purify poly(A) mRNA from the total RNA samples. Then, the mRNA was fragmented using chemical reagents and high temperatures, and the DNA was synthesized, purified and enriched. The sequencing of the cDNA library products was performed on an Illumina HiSeq 2000 platform. After removing reads containing sequencing adapters and reads of low quality [reads in which the percentage of low quality bases (quality value ≤5) was more than 50 %], the remaining clear reads were aligned to S. cerevisiae S288c or MT1 genes using bowtie2/tophat2. The expression levels were normalized by reads per kilobase of exon region per million mapped reads (RPKM) . The screening of differentially expressed genes and P-value calculations were performed with the method of DESeq . Three biological replicates were performed for each time point for each strain.
This work was supported by the National High Technology Research and Development Program of China (2013AA102108 and 2012AA021301); National Natural Science Foundation of China (31371822 and 31271921); China Postdoctoral Science Foundation (2014 M550265); Priority Academic Program Development of Jiangsu Higher Education Institutions, the 111 Project (No. 111-2-06); and the Jiangsu Province “Collaborative Innovation Center for Advanced Industrial Fermentation” industry development program.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Zhu S, Lu X, Ji K, Guo K, Li Y, Wu C, et al. Characterization of flavor compounds in Chinese liquor Moutai by comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry. Anal Chim Acta. 2007;597(2):340–8.View ArticlePubMedGoogle Scholar
- Ballesteros M, Oliva JM, Negro MJ, Manzanares P, Ballesteros I. Ethanol from lignocellulosic materials by a simultaneous saccharification and fermentation process (SFS) with Kluyveromyces marxianus CECT 10875. Process Biochem. 2004;39(12):1843–8.View ArticleGoogle Scholar
- Li X-R, Ma E-B, Yan L-Z, Meng H, Du X-W, Zhang S-W, et al. Bacterial and fungal diversity in the traditional Chinese liquor fermentation process. Int J Food Microbiol. 2011;146(1):31–7.View ArticlePubMedGoogle Scholar
- Chen B, Wu Q, Xu Y. Filamentous fungal diversity and community structure associated with the solid state fermentation of Chinese Maotai-flavor liquor. Int J Food Microbiol. 2014;179:80–4.View ArticlePubMedGoogle Scholar
- Pandey A. Solid-state fermentation. Biochem Eng J. 2003;13(2):81–4.View ArticleGoogle Scholar
- Xu Y, Ji K Moutai (Maotai): production and sensory properties. Alcoholic beverages: sensory evaluation and consumer research. 2012;315–30.Google Scholar
- Wang C-l, D-j S, G-l G. Microorganisms in Daqu: a starter culture of Chinese Maotai-flavor liquor. World J Microbiol Biotechnol. 2008;24(10):2183–90.View ArticleGoogle Scholar
- Wu Q, Xu Y, Chen L. Diversity of yeast species during fermentative process contributing to Chinese Maotai-flavour liquor making. Lett Appl Microbiol. 2012;55(4):301–7.View ArticlePubMedGoogle Scholar
- Xu Y, Ji K, Piggott J. Moutai (Maotai): production and sensory properties. Alcoholic Beverages: Sensory Evaluation and Consumer Research. 2012;315–30.Google Scholar
- Goffeau A, Barrell B, Bussey H, Davis R, Dujon B, Feldmann H, et al. Life with 6000 genes. Science. 1996;274(5287):546–67.View ArticlePubMedGoogle Scholar
- Kumar A, Snyder M. Emerging technologies in yeast genomics. Nat Rev Genet. 2001;2(4):302–12.View ArticlePubMedGoogle Scholar
- Ralser M, Kuhl H, Ralser M, Werber M, Lehrach H, Breitenbach M, et al. The Saccharomyces cerevisiae W303-K6001 cross-platform genome sequence: insights into ancestry and physiology of a laboratory mutt. Open bio. 2012;2(8):120093.View ArticleGoogle Scholar
- Akao T, Yashiro I, Hosoyama A, Kitagaki H, Horikawa H, Watanabe D, et al. Whole-genome sequencing of sake yeast Saccharomyces cerevisiae Kyokai no. 7. DNA Res. 2011;18(6):423–34.PubMed CentralView ArticlePubMedGoogle Scholar
- Borneman AR, Desany BA, Riches D, Affourtit JP, Forgan AH, Pretorius IS, et al. Whole-genome comparison reveals novel genetic elements that characterize the genome of industrial strains of Saccharomyces cerevisiae. PLoS Genet. 2011;7(2):e1001287.PubMed CentralView ArticlePubMedGoogle Scholar
- Borneman AR, Forgan AH, Pretorius IS, Chambers PJ. Comparative genome analysis of a Saccharomyces cerevisiae wine strain. FEMS Yeast Res. 2008;8(7):1185–95.View ArticlePubMedGoogle Scholar
- Novo M, Bigey F, Beyne E, Galeote V, Gavory F, Mallet S, et al. Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118. Proc Natl Acad Sci U S A. 2009;106(38):16333–8.PubMed CentralView ArticlePubMedGoogle Scholar
- Argueso JL, Carazzolle MF, Mieczkowski PA, Duarte FM, Netto OV, Missawa SK, et al. Genome structure of a Saccharomyces cerevisiae strain widely used in bioethanol production. Genome Res. 2009;19(12):2258–70.PubMed CentralView ArticlePubMedGoogle Scholar
- Liti G, Carter DM, Moses AM, Warringer J, Parts L, James SA, et al. Population genomics of domestic and wild yeasts. Nature. 2009;458(7236):337–41.PubMed CentralView ArticlePubMedGoogle Scholar
- Zheng D-Q, Wang P-M, Chen J, Zhang K, Liu T-Z, Wu X-C, et al. Genome sequencing and genetic breeding of a bioethanol Saccharomyces cerevisiae strain YJS329. BMC Genomics. 2012;13(1):479.PubMed CentralView ArticlePubMedGoogle Scholar
- Dowell RD, Ryan O, Jansen A, Cheung D, Agarwala S, Danford T, et al. Genotype to phenotype: a complex problem. Science. 2010;328(5977):469.PubMed CentralView ArticlePubMedGoogle Scholar
- Jenkins GM, Richards A, Wahl T, Mao C, Obeid L, Hannun Y. Involvement of yeast sphingolipids in the heat stress response of Saccharomyces cerevisiae. J Biol Chem. 1997;272(51):32566–72.View ArticlePubMedGoogle Scholar
- Kawahata M, Masaki K, Fujii T, Iefuji H. Yeast genes involved in response to lactic acid and acetic acid: acidic conditions caused by the organic acids in Saccharomyces cerevisiae cultures induce expression of intracellular metal metabolism genes regulated by Aft1p. FEMS Yeast Res. 2006;6(6):924–36.View ArticlePubMedGoogle Scholar
- Barnett JA. The utilization of sugars by yeasts. Adv Carbohydr Chem Biochem. 1975;32:125–234.View ArticleGoogle Scholar
- Abyzov A, Urban AE, Snyder M, Gerstein M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 2011;21(6):974–84.PubMed CentralView ArticlePubMedGoogle Scholar
- Platt A, Reece RJ. The yeast galactose genetic switch is mediated by the formation of a Gal4p–Gal80p–Gal3p complex. EMBO J. 1998;17(14):4086–91.PubMed CentralView ArticlePubMedGoogle Scholar
- Nijkamp JF, van den Broek M, Datema E, de Kok S, Bosman L, Luttik MA, et al. De novo sequencing, assembly and analysis of the genome of the laboratory strain Saccharomyces cerevisiae CEN.PK113-7D, a model for modern industrial biotechnology. Microb Cell Factories. 2012;11:36.View ArticleGoogle Scholar
- Daran-Lapujade P, Jansen ML, Daran JM, van Gulik W, de Winde JH, Pronk JT. Role of transcriptional regulation in controlling fluxes in central carbon metabolism of Saccharomyces cerevisiae. A chemostat culture study. J Biol Chem. 2004;279(10):9125–38.View ArticlePubMedGoogle Scholar
- Chow TH, Sollitti P, Marmur J. Structure of the multigene family of MAL loci in Saccharomyces. Mol Gen Genet. 1989;217(1):60–9.View ArticlePubMedGoogle Scholar
- Kruckeberg AL. The hexose transporter family of Saccharomyces cerevisiae. Arch Microbiol. 1996;166(5):283–92.View ArticlePubMedGoogle Scholar
- Greatrix BW, van Vuuren HJ. Expression of the HXT13, HXT15 and HXT17 genes in Saccharomyces cerevisiae and stabilization of the HXT1 gene transcript by sugar-induced osmotic stress. Curr Genet. 2006;49(4):205–17.View ArticlePubMedGoogle Scholar
- Wei W, McCusker JH, Hyman RW, Jones T, Ning Y, Cao Z, et al. Genome sequencing and comparative analysis of Saccharomyces cerevisiae strain YJM789. Proc Natl Acad Sci U S A. 2007;104(31):12825–30.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu H, Ito K, Shimoi H. Identification and characterization of a novel biotin biosynthesis gene in Saccharomyces cerevisiae. Appl Environ Microbiol. 2005;71(11):6845–55.PubMed CentralView ArticlePubMedGoogle Scholar
- Ohsugi M, Imanishi Y. Microbiological activity of biotin-vitamers. J Nutr Sci Vitaminol. 1985;31(6):563–72.View ArticlePubMedGoogle Scholar
- Koonin EV. How many genes can make a cell: The minimal-gene-Set concept 1. Annu Rev Genomics Hum Genet. 2000;1(1):99–116.View ArticlePubMedGoogle Scholar
- Murakami K, Tao E, Ito Y, Sugiyama M, Kaneko Y, Harashima S, et al. Large scale deletions in the Saccharomyces cerevisiae genome create strains with altered regulation of carbon metabolism. Appl Microbiol Biotechnol. 2007;75(3):589–97.View ArticlePubMedGoogle Scholar
- Liti G, Louis EJ. Yeast evolution and comparative genomics. Annu Rev Microbiol. 2005;59:135–53.View ArticlePubMedGoogle Scholar
- Olson MV. When less is more: gene loss as an engine of evolutionary change. Am J Hum Genet. 1999;64(1):18–23.PubMed CentralView ArticlePubMedGoogle Scholar
- Aravind L, Watanabe H, Lipman DJ, Koonin EV. Lineage-specific loss and divergence of functionally linked genes in eukaryotes. Proc Natl Acad Sci. 2000;97(21):11319–24.PubMed CentralView ArticlePubMedGoogle Scholar
- Braun EL. Large-scale comparison of fungal sequence information: Mechanisms of innovation in Neurospora crassa and gene loss in saccharomyces cerevisiae. Genome Res. 2000;10(4):416–30.View ArticlePubMedGoogle Scholar
- Goebl MG, Petes TD. Most of the yeast genomic sequences are not essential for cell growth and division. Cell. 1986;46(7):983–92.View ArticlePubMedGoogle Scholar
- Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics. 2012;13:341.PubMed CentralView ArticlePubMedGoogle Scholar
- Nederbragt AJ. On the middle ground between open source and commercial software-the case of the Newbler program. Genome Biol. 2014;15(4):113.PubMed CentralView ArticlePubMedGoogle Scholar
- Boetzer M, Henkel CV, Jansen HJ, Butler D, Pirovano W. Scaffolding pre-assembled contigs using SSPACE. Bioinformatics. 2010;27(4):578–9.View ArticlePubMedGoogle Scholar
- Boetzer M, Pirovano W. Toward almost closed genomes with GapFiller. Genome Biol. 2012;13(6):R56.PubMed CentralView ArticlePubMedGoogle Scholar
- Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003;19 Suppl 2:ii215–25.View ArticlePubMedGoogle Scholar
- Korf I. Gene finding in novel genomes. BMC bioinformatics. 2004;5(1):59.PubMed CentralView ArticlePubMedGoogle Scholar
- Delcher AL, Harmon D, Kasif S, White O, Salzberg SL. Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 1999;27(23):4636–41.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109–114.PubMed CentralView ArticlePubMedGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8.View ArticlePubMedGoogle Scholar
- Anders S, Huber W. Differential expression of RNA-Seq data at the gene level–the DESeq package. Heidelberg: European Molecular Biology Laboratory (EMBL); 2012.Google Scholar
- Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011;28(10):2731–9.PubMed CentralView ArticlePubMedGoogle Scholar