Skip to content


Biotechnology for Biofuels

Open Access

Whole-genome de novo sequencing, combined with RNA-Seq analysis, reveals unique genome and physiological features of the amylolytic yeast Saccharomycopsis fibuligera and its interspecies hybrid

  • Jin Ho Choo1,
  • Chang Pyo Hong2,
  • Jae Yun Lim3,
  • Jeong-Ah Seo4,
  • Young-Suk Kim5,
  • Dong Wook Lee1,
  • Sin-Gi Park2,
  • Gir Won Lee2,
  • Emily Carroll4,
  • Yin-Won Lee3 and
  • Hyun Ah Kang1Email author
Contributed equally
Biotechnology for Biofuels20169:246

Received: 27 July 2016

Accepted: 22 October 2016

Published: 11 November 2016



Genomic studies on fungal species with hydrolytic activity have gained increased attention due to their great biotechnological potential for biomass-based biofuel production. The amylolytic yeast Saccharomycopsis fibuligera has served as a good source of enzymes and genes involved in saccharification. Despite its long history of use in food fermentation and bioethanol production, very little is known about the basic physiology and genomic features of S. fibuligera.


We performed whole-genome (WG) de novo sequencing and complete assembly of S. fibuligera KJJ81 and KPH12, two isolates from wheat-based Nuruk in Korea. Intriguingly, the KJJ81 genome (~38 Mb) was revealed as a hybrid between the KPH12 genome (~18 Mb) and another unidentified genome sharing 88.1% nucleotide identity with the KPH12 genome. The seven chromosome pairs of KJJ81 subgenomes exhibit highly conserved synteny, indicating a very recent hybridization event. The phylogeny inferred from WG comparisons showed an early divergence of S. fibuligera before the separation of the CTG and Saccharomycetaceae clades in the subphylum Saccharomycotina. Reconstructed carbon and sulfur metabolic pathways, coupled with RNA-Seq analysis, suggested a marginal Crabtree effect under high glucose and activation of sulfur metabolism toward methionine biosynthesis under sulfur limitation in this yeast. Notably, the lack of sulfate assimilation genes in the S. fibuligera genome reflects a unique phenotype for Saccharomycopsis clades as natural sulfur auxotrophs. Extended gene families, including novel genes involved in saccharification and proteolysis, were identified. Moreover, comparative genome analysis of S. fibuligera ATCC 36309, an isolate from chalky rye bread in Germany, revealed that an interchromosomal translocation occurred in the KPH12 genome before the generation of the KJJ81 hybrid genome.


The completely sequenced S. fibuligera genome with high-quality annotation and RNA-Seq analysis establishes an important foundation for functional inference of S. fibuligera in the degradation of fermentation mash. The gene inventory facilitates the discovery of new genes applicable to the production of novel valuable enzymes and chemicals. Moreover, as the first gapless genome assembly in the genus Saccharomycopsis including members with desirable traits for bioconversion, the unique genomic features of S. fibuligera and its hybrid will provide in-depth insights into fungal genome dynamics as evolutionary adaptation.


Amylolytic yeasts Saccharomycopsis fibuligera GenomeHybridRNA-Seq


Genomic studies of the fungal species with hydrolytic activity have gained increased attention due to their great biotechnological potential in current and future biofuel production based on biomass [1]. Saccharomycopsis fibuligera (synonymous with Endomyces fibuligera), a member of the subphylum Saccharomycotina of the phylum Ascomycota of the Fungi kingdom, is found worldwide as the major amylolytic yeast utilized in indigenous food fermentation using rice and cassava. As a dimorphous yeast, S. fibuligera propagates by forming abundantly branched septate hyphae along with typical budding yeast-like cells [2]. This yeast has been considered one of the best producers of amylolytic enzymes among ascomycetous yeast species [3], since its capacity to perform starch hydrolysis was first reported by Wickerham et al. [4]. It is commonly found as a dominant yeast species in traditional Asian alcoholic starters for rice wine production, such as ‘Daqu’ in China [5], ‘Ragi’ in Indonesia [6], ‘Loogpang’ in Thailand [7], ‘Banh Men’ in Vietnam [8], and ‘Nuruk’ in Korea [9]. In addition, S. fibuligera was cultivated alone on starchy waste in Czechoslovakia or in mixed culture with Candida utilis on potato-processing wastes in Sweden to produce single-cell protein, which was used for protein supplementation in animal feeds [10]. Moreover, this yeast has been isolated as one of spoilage fungi causing chalk mold defects, which are commonly seen on the dark bread that is popular in continental Europe and the UK. It produces visible growth on the bread surface, exhibits a white and chalky appearance, and can spoil bread within a few days [11, 12].

Saccharomycopsis fibuligera has recently received increasing attention, as it produces trehalose, amylases, acid protease, and β-glucosidase, which have many applications in the food, fermentation, biofuel, and pharmaceutical industries [1315]. The major role of S. fibuligera in the production process for traditional rice wine involves the conversion of starch into sugars, which can then be fermented into ethanol and organic acids [16]. It is noteworthy that some glucoamylases produced by S. fibuligera can digest native starch, which improves the degradation of starch from raw materials (i.e., barley and pea) in ‘Daqu’ [13]. To exploit the strong amylolytic activity of S. fibuligera in utilizing starch as an inexpensive carbon source, synergistic co-cultures of S. fibuligera with a good ethanol producer, such as Saccharomyces cerevisiae or Zymomonas mobilis, have been grown to produce ethanol [17, 18]. Using cassava starch, S. fibuligera has been cultivated to produce amylolytic enzymes at industrial production levels [19]. Based on this high hydrolytic activity, S. fibuligera has recently been recognized as a potential industrial host for the bioremediation of agricultural waste [20]. Saccharomycopsis fibuligera has also served as a good donor of genes that are involved in saccharification to engineer S. cerevisiae for the development of a new yeast strain that can directly produce ethanol from biomass without the need for a separate saccharification process [21]. For example, the S. fibuligera BGL1 gene, encoding β-glucosidase 1, has been heterologously expressed in S. cerevisiae to construct cellobiose-growing and fermenting strains [2224]. Several studies aimed at overexpressing S. fibuligera β-glucosidase in heterologous hosts have been also conducted to provide the recombinant enzyme as supplement in cellulase mixtures to enhance the saccharification of cellulose [25, 26]. Although the production of lignocellulosic ethanol is expected to increase rapidly as advanced biofuel in the renewable fuel industry, starch is still the most commonly used feedstock for the production of conventional biofuel ( A recombinant yeast strain for the consolidated bioprocessing of starch biomass to ethanol, which can carry out raw starch hydrolysis and fermentation without any pretreatment of commercial enzyme addition, was recently developed by introducing the S. fibuligera α-amylase (SFA1) and Thermomyces lanuginosus glucoamylase (TLG1) genes into the industrial S. cerevisiae strains [27]. In addition to the bioconversion of starchy biomass to useful bioproducts, such as biofuels and trehalose, the high amylolytic activity of S. fibuligera can be usefully exploited in the production of other starch derivatives as well, including corn syrup, detergents, paper, textiles, and adhesive [28].

Despite the great potential of S. fibuligera as an industrial strain with a long history of use in various biotechnological applications and industrial fermentation, very little is known about the basic physiology and genomic features of S. fibuligera. In the present study, we conducted whole-genome (WG) de novo sequencing of two S. fibuligera isolates from Nuruk, a starter culture for the traditional rice wine Makgeolli, in Korea and performed a complete genome assembly with high-quality annotation, which was validated by RNA-Seq analysis. Furthermore, genome sequencing of another S. fibuligera isolate from chalky rye bread in Germany was performed and compared with the genomes of the Nuruk isolates. Our data revealed the unique genome features of S. fibuligera and its interspecies hybrid, which was generated by a very recent hybridization event. The completely sequenced and assembled S. fibuligera genomes with high-quality annotation, presented here, establish an important foundation for functional inference of this yeast and offer an interesting snapshot of the genomic evolutionary events that occurred after (inter)specific hybridization. Moreover, as the first gapless genome assembly reported in the genus Saccharomycopsis with desirable traits for bioconversion, it will serve as a reference genome to elucidate biological peculiarities specific to this yeast and its related lineage, facilitating the discovery of new genes applicable to the production of novel valuable enzymes and chemicals.


Isolation and growth characteristics of S. fibuligera KJJ81 and KPH12 from Nuruk

Several fungal species were isolated from wheat-based Nuruk samples collected at various provinces in Korea. Among them, two yeast isolates, one from Jeju (KJJ81) and one from Pohang (KPH12), were chosen for further study based on their high saccharification activity. By analyzing the 5.8S ribosomal DNA (rDNA) sequences flanked by internal transcribed spacer (ITS) regions 1 and 2, both yeast isolates were identified as S. fibuligera and were thus named S. fibuligera KJJ81 and KPH12, respectively. The two yeast isolates S. fibuligera KJJ81 and KPH12 grow exclusively as filamentous forms with a minor fraction of budding yeast cells, and the hyphae of KJJ81 appear to be thicker than that of KPH12 (Fig. 1a).
Fig. 1

The morphology and genome organization of S. fibuligera KPH12 and KJJ81. a Filamentous growth of S. fibuligera KPH12 and KJJ81 with a minor fraction of budding yeast cells cultivated on YPD plates. b Diagram depicting the genome landscapes of KPH12 (left) and KJJ81 (right), with sequence coverage and similarity values. c Synteny analysis between the KPH12 and KJJ81 genomic sequences. WG dot plots of the two genomes were generated using SyMAP [82]. The red boxes indicate an interchromosomal translocation and the green box indicates a deletion. d Structure of rDNA repeats in the S. fibuligera genomes. ETS1, 5′-ETS (external transcribed spacer); ETS2, 3′-ETS; NTS, nontranscribed spacer; ITS, internal transcribed spacers; FRA1, an ORF encoding a putative Xaa-Pro aminopeptidase; TNT1-94, an ORF homologous to the retrovirus-related Pol polyprotein gene from Nicotiana tabacum transposon; PLB1, an ORF encoding lysophospholipase 1

The transition from yeast to hyphal growth was monitored by inoculation of the yeast-type cells of S. fibuligera KJJ81 and KPH12, which were enriched via filtration, into liquid YPD medium. With increased cultivation time, single yeast cells exhibited dimorphic growth, with a mixture of multipolar budding yeast cells, which are short ovals or long branched ovals, and septate mycelia. Nuclear Hoechst staining and calcofluor white staining for the septum and cell wall (Additional file 1: Figure S1, top) suggested that these hyphae-like structures were mostly pseudohyphae, which are actually chains of budded yeast cells that did not separate after duplication. Interestingly, we observed that yeast-like growth could be induced under carbon-limited culture conditions or in the presence of antimycin A, which blocks electron transport (Additional file 1: Figure S1, bottom). These observations suggest that the morphological shift (from yeast to hyphae and vice versa) of S. fibuligera is dependent on environmental conditions, such as nutrient- and energy-limited conditions, as has been observed for other dimorphic fungal species [29].

Whole-genome de novo sequencing and assembly of S. fibuligera KJJ81 and KPH12

The WG de novo assemblies of S. fibuligera KJJ81 and KPH12 were produced from long-read single-molecule real-time and Illumina sequence data (Additional file 2: Tables S1–S4, Additional file 3: Figure S2). The final assembled KPH12 and KJJ81 genomes consisted of 7 and 14 linear scaffolds with N50 at 1.9 and 3 Mb, totaling 19.7 and 38.6 Mb, respectively (Table 1). The average sequence depth was 98.3× for KPH12 and 51× for KJJ81, respectively, with ≥99.97% genome coverage, indicating high accuracy of the assemblies (Additional file 4: Figure S3). In addition, TruSeq synthetic long-read (TSLR) assemblies for the two genomes supported the high concordance of the WG de novo assemblies with 96% coverage in the genome fraction. The accuracy of the scaffold order and the orientation of the KPH12 genome were further validated by optical mapping data [30]. The seven scaffolds of the KPH12 genome were anchored with 97.83% coverage to the 19 contigs assembled by BioNano optical mapping (Additional file 5: Figure S4). The optical mapping data showed the presence of one large deletion region of ~0.5 Mb in the largest scaffold assigned as chromosome 1 (chromosomes 1–7 in decreasing size from ~4.9 to ~1.3 Mb). The gap region on chromosome 1 was identified as the rDNA repeat locus with ~50 copies, estimated based on the size of the deletion region and the size of a single rDNA unit (9314 bp). The results strongly supported the notion that the seven scaffolds for the KPH12 genome were correctly assembled at the chromosome level (Fig. 1b, left).
Table 1

Summary of the S. fibuligera KPH12 and KJJ81 nuclear genome assembly and annotation




Scaffolds (no.)



Total length (bp)a



GC content (%)



Protein-coding gene models (no.)



 Unique gene models (no.)



 Genes with isoforms (no.)



 Supported by RNA-Seq (no.)



 Annotated (no.)



Average gene length (bp)



Total length of gene models (Mb)




 No. of exons



 No. of average exons per gene



 Average exon length (bp)




 No. of introns



 No. of average introns per gene



 Average intron length (bp)



Non-coding RNAs (no.)







Average non-coding RNA length (bp)







Transposable element fragments (no.)




 DNA transposons



Mitochondrial genome

 Total length (bp)



 GC content (%)



aTotal length of scaffolds after extension of the telomere region

Quite intriguingly, comparative analysis of the KPH12 and KJJ81 genomic sequences revealed the co-linearity of two KJJ81 homologous scaffolds to each of the KPH12 pseudo-chromosomes (Fig. 1b, left). The 14 scaffolds of KJJ81 were classified into two subgenomes, designated A (an average similarity between KPH12 and A of 99.1%; totaling 19.7 Mb for seven scaffolds) and B (an average similarity between KPH12 and B of 88.6%; totaling 18.8 Mb for seven scaffolds) on the basis of sequence similarity between the two genomes. The similarity between two subgenomes of KJJ81was 89%. This result suggests that S. fibuligera KJJ81 is an adapted species containing seven chromosome pairs generated by hybridization between a progenitor of KPH12 and a divergent subspecies with approximately 88.6% nucleotide identity at the genome level.

Annotation and synteny analysis of the S. fibuligera KJJ81 and KPH12 genomes

Employing ab initio and evidence-driven gene prediction methodology, we predicted a non-redundant set of 6155 protein-coding genes in the KPH12 genome, with an average length of 1.7 kb, an average exon length of 1.4 kb, and an average of 1.2 exons per gene. For the KJJ81 genome, 12,135 protein-coding genes were predicted with similar contents compared with those of KPH12 (Table 1). In the two genomes, the transcriptome (RNA-Seq) data generated in this study supported 98.7% of predicted gene models, and 89% of predicted genes had homology with gene models in the UniProt (77%), NCBI non-redundant (NR) (84%), and InterPro (85%) databases. Functional annotations were tentatively assigned to 64% of the genes (Additional file 2: Table S5).

Synteny analysis between the seven chromosomes of the KPH12 genome and KJJ81 subgenome A revealed that the chromosomes possess almost identical gene order. In contrast, a few noticeable alterations in genomic context, such as a reciprocal translocation between chromosomes 3 and 5 and a large deletion of the rDNA cluster encoding ribosomal RNA on chromosome 1, were observed in the synteny analysis between the KPH12 genome and KJJ81 subgenome B (Fig. 1c, left). Similarly, the seven chromosome pairs in subgenomes A and B of the KJJ81 hybrid genome completely preserved the conserved synteny, with the exception of the interchromosomal translocation between chromosomes 3 and 5 and the deletion of the rDNA cluster on chromosome 1 (Fig. 1c, right). Such a high level of synteny between the two subgenomes of KJJ81 strongly suggests that the hybrid event between the progenitor of KPH12 and its closely related subspecies took place very recently.

Complete sequences of the mitochondrial genomes (i.e., mtDNA) of KPH12 (67,427 bp) and KJJ81 (67,516 bp) were also determined (Table 1). Both S. fibuligera mtDNAs were almost identical at the nucleic acid sequence level, with a G-C content of 24.93%, except for one structural difference in the 5′ portion of one unidentified open reading frame (orf856 in KPH12 versus orf1057 in KJJ81) in the inverted repeat regions (Additional file 6: Figure S5a, b). The mtDNA contained at least 20 protein-coding genes (Additional file 2: Table S6), including the standard repertoire of yeast mtDNAs [31], along with 37 tRNA genes and 1 small subunit (rrnS)- and 2 large subunit (rrnL)-ribosomal RNA genes. Saccharomycopsis fibuligera mtDNAs contain genes encoding seven subunits of NADH dehydrogenase (nad1–6 and nad4L), which are not present in Saccharomycetaceae species, including the traditional yeast S. cerevisiae [32].

Unique features of the S. fibuligera KJJ81 hybrid and KPH12 genomes

Ribosomal DNA clusters

The arrangement of the rRNA genes in S. fibuligera was revealed as similar to that in S. cerevisiae (Fig. 1d), in which a single unit consists of two transcribed regions, the 35S precursor rRNA- and 5S rRNA-coding regions, and two nontranscribed spacers (NTSs), NTS1 and NTS2. Both the KPH12 genome and KJJ81 subgenome A had rDNA loci consisting of ~50 copies of a repeating unit of 9314 bp on chromosome 1 (Fig. 1d, top panel). In contrast, KJJ81 subgenome B exhibited the deletion of the rDNA clusters, leaving only a single-copy rDNA unit of 9288 bp (Fig. 1d, lower panel). It is notable that there were three copies of retrotransposon elements, showing homology to tobacco TNT1–94, downstream of 18S rDNA in subgenome B of KJJ81 (Fig. 1d, lower panel). It can be speculated that the unilateral loss of rDNA clusters in subgenome B of KJJ81 might be mediated by transposon activity.

Putative centromere locations and telomere sequences

Members of the Saccharomycetaceae clade possess point centromeres with three characteristic conserved regions, whereas GC-poor troughs containing retrotransposon clusters have been suggested to mark the locations of centromeres in some yeast species [33]. Although the nucleotide GC composition in the S. fibuligera chromosomes was relatively uniform, without extended AT-rich regions (Additional file 7: Figure S6, black line), we observed a unique island of highly repeated and degenerate sequences of long terminal repeat (LTR) retrotransposons, belonging to the Ty1/Copia group, on each chromosome of KPH12 and KJJ81 (Additional file 7: Figure S6, red line). The clusters of direct and inverted repeats of LTR elements were identified at the same position on each chromosome pair, mostly in the middle of each S. fibuligera chromosome, with the exception of chromosome 5, suggesting that these retrotransposon clusters likely correspond to the centromere. The transcriptional profiles clearly showed a sharp and pronounced drop in the RNA-Seq signal strength with the minimum values at the predicted centromere regions in the plots (Additional file 7: Figure S6, green line). Thus, S. fibuligera centromeres are likely unique for each chromosome and marked by clusters of LTR sequences.

Sequence analysis of the extended end fragments of each S. fibuligera chromosome revealed the presence of “GGGTGGTGTAA” as the telomeric repeat sequences. It is noteworthy that the telomeric repeats of both S. fibuligera isolates contained ‘TGGTGT’, the most conserved motif observed among the various budding yeast species [34], but exhibited differences in the sequences flanking the conserved motif. We also observed the repeated sequences in slightly different patterns in the subtelomeric regions of S. fibuligera chromosomes (Additional file 8: Figure S7). The front, middle, and end blocks of the subtelomeric regions in subgenome B possessed repeat sequences that were distinct from those observed in subgenome A, reflecting subtelomeric polymorphisms between the two subgenome lineages. The presence of typical telomere and subtelomeric repeat sequences in each of the chromosomes strongly supports a complete and high-fidelity de novo genome assembly of S. fibuligera KPH12 and its hybrid isolate KJJ81.

Mating type locus and pheromone genes

The S. fibuligera KJJ81 and KPH12 genomes have two MAT loci, MAT a and MATα, which exhibited very similar organization to those of methylotrophic yeast (Additional file 9: Figure S8a). The MAT a locus, containing MAT a1 and a2, was localized 170 kb away from the centromere of chromosome 4; the other MATα locus, containing MATα1 and α2, was present near the MAT a locus. The SLA2, which is a conserved gene found adjacent to the MAT loci in many yeasts and fungi [35], is also located beside the MAT a locus in S. fibuligera. However, the S. fibuligera MAT loci were not located in close proximity to the centromere or to the telomere and do not possess invertible sequences, which are observed in the methylotroph genomes [36], indicating that a different but as yet uncharacterized mechanism of switching and silencing is required for S. fibuligera.

Interestingly, the putative α-pheromone-encoding MFα genes of S. fibuligera were present on the other arm of chromosome 4, on which the MAT loci were clustered. It is quite noteworthy that the MFα genes in KJJ81 subgenomes A and B were quite distinctive, encoding two different precursor pheromone peptides with variations in spacer sequences and generating identical mature α-pheromone peptides, but with different copy numbers. The MFα1 gene (726-bp ORF) in KJJ81 subgenome B is expected to encode an α-factor precursor and will generate nine copies of the 11-amino acid mature peptide with the sequence “WAGVAPNQPIF” after proteolytic processing by the Kex2 protease and the Ste13 dipeptidyl aminopeptidase A. In contrast, the gene product of MFα2 (306-bp ORF) in KJJ81 subgenome A and the KPH12 genome is expected to generate only three copies of the mature α-peptide. The presence of different MFα genes in each of subgenomes A and B was clearly validated by PCR analysis using common forward and reverse primers designed based on the ORF sequences of MFα1 and MFα2 (Additional file 9: Figure S8b).

By searching for small encoded peptides with a terminal CAAX motif and an isoprenylation site, features that are conserved in the mature bioactive a-factors of several yeast species [37], we identified two putative S. fibuligera MFA1 and MFA2 genes encoding two types of mature a-factors with one amino acid substitution at their C-termini, which were tandem clustered on chromosome 3 in KJJ8 subgenome A and the KPH12 genome. In contrast, KJJ81 subgenome B possessed only the MFA1 gene with duplicate copies, thus producing only one type of a-factor (Additional file 9: Figure S8c). The a-factor of subgenome B demonstrated differences in two amino acid residues compared with those of subgenome A and the KPH12 genome. Such differences observed in the amino acid sequences and copy numbers of mating type pheromones between KJJ81 and KPH12 might affect mating efficiency and the cell cycle, which need to be investigated by performing more systematic experiments.

Phylogenetic position of S. fibuligera based on genome comparison

With the expectation that WG comparisons will provide far greater phylogenetic signals than the present five-gene dataset [38], we performed a phylogenetic analysis of S. fibuligera based on WG comparisons (Fig. 2a). Reciprocal pairwise comparison of the KPH12 and KJJ81 gene models with 12 WG sequenced fungal species of the phylum Ascomycota resulted in the identification of 55 orthologous genes that are commonly conserved in all of the analyzed species (Additional file 10). A phylogeny inferred on the basis of these orthology data positioned S. fibuligera as an early divergent of the subphylum Saccharomycotina, which was separated from a common ancestor much earlier before the divergence of Saccharomycetaceae and the CTG clades (Fig. 2a). Additionally, S. fibuligera was estimated to have diverged from S. cerevisiae approximately 164.5–194.1 Myr ago by calibrating the divergence times among S. cerevisiae, Aspergillus oryzae, and Schizosaccharomyces pombe using Molecular Evolutionary Genetics Analysis software MEGA6 [39]. The S. fibuligera genome rarely shares detectable syntenic gene blocks with any of these ascomycete fungal species, indicating its unique genome organization that was adapted after early divergence from a common ancestor of Saccharomycotina.
Fig. 2

Phylogenetic and evolutionary analysis of S. fibuligera genomes in the phylum Ascomycota. a Phylogenetic tree analysis of KPH12 and KJJ81 based on genome sequence. A phylogenetic tree was constructed using MEGA6 [39] with other members of the phylum Ascomycota, which is composed of three major subphyla, Pezizzomycotina, Saccharomycotina, and Taphrinomycotina. A total of 55 orthologous genes were selected from among 12 ascomycete fungal species, including A. gossypii, K. lactis, C. glabrata, S. cerevisiae, C. lusitaniae, C. tropicalis, D. hansenii, Y. lipolytica, A. fumigatus, A. oryzae, and S. pombe. WGD whole-genome duplication event that occurred in a Saccharomyces species ancestor. b Expansion and contraction of protein families among the ascomycete fungal species, analyzed using the Pfam protein families database [84]

Comparative gene inventories of S. fibuligera

The shared and unique protein families among fungal genomes are expected to be related to the histories and lifestyles of the compared fungal species. Therefore, we analyzed the contracted and extended Pfam domains in S. fibuligera relative to those of fungal species belonging to the phylum Ascomycota (Fig. 2b). Overall, the abundance of Pfam domains represented in S. fibuligera appeared to be quite unique in that several domains, including haloacid dehalogenase-like hydrolase, eukaryotic aspartyl protease, the subtilase family, peptidase inhibitor I9, velvet factor, helix-loop-helix DNA-binding domain, and glycolipid-2-α-mannosyltransferase, were particularly abundant in S. fibuligera. It is quite noteworthy that there was a significant overrepresentation of redundant gene families involved in protein degradation, such as eukaryotic aspartyl protease, the subtilase family, and peptidase inhibitor I9, in the S. fibuligera genomes. The expanded family of subtilins in S. fibuligera KPH12 consisted of 12 PRB1 genes encoding vacuolar proteinase B, which is involved in the maturation of vacuolar aspartyl protease Pep4, four KEX1 genes encoding a protease with a carboxypeptidase B-like function involved in killer toxin and α-factor precursor processing [40], and two RRT12 genes encoding a protease with a role in spore wall assembly [41]. Most of these genes also contained the peptidase inhibitor I9 domain, which might be associated with the regulation of their own or other serine protease activities.

The velvet family of regulatory proteins plays a key role in coordinating development and secondary metabolism in response to light in several filamentous fungi carrying multiple light sensors that respond to different light wavelengths [42]. Such an extension of the velvet domain gene family, consisting of 12 genes encoding putative transcription factors, suggests that S. fibuligera might have evolved unique transcriptional regulation networks distinguishable from other yeast species. The significant expansions of the haloacid dehalogenase-like hydrolase (26 genes in KPH12) and the glycolipid 2-α-mannosyltransferase (12 genes in KPH12) gene families also suggest the peculiar physiological activity of S. fibuligera, which requires further study to be elucidated. Moreover, in part, the abundant patterns of some domains, including the aldo/keto reductase family, zinc-binding dehydrogenase, the alcohol dehydrogenase GroES-like domain, FAD-dependent oxidoreductase, and the major facilitator superfamily, were rather similar to those of Aspergillus species. This characteristic catalog for overrepresented and/or underrepresented protein domains would provide an important foundation for comparative biology and functional inference in S. fibuligera, elucidating biological peculiarities specific to S. fibuligera and its relative lineage.

Reconstruction of the S. fibuligera C- and S-metabolic pathways with transcriptome profiles

C-metabolic pathway

Sugar metabolism provides an essential source of energy and metabolites for most organisms. The availability of the complete genome sequence of S. fibuligera allowed the identification of genes involved in glucose assimilation, including glycolysis, glyoxylate, the oxidative pentose phosphate pathway, the tricarboxylic acid cycle (TCA), and ethanol production (Fig. 3a). Transcript levels of genes involved in central carbon metabolism under high (2%, D2) and low glucose (0.1%, D0.1) were analyzed by RNA-Seq (Fig. 3b; Additional file 11). In contrast to S. cerevisiae, known as Crabtree-positive yeasts, which are subject to the glucose-mediated repression of respiration at high glucose levels [43], S. fibuligera did not show significant differences in the mRNA expression levels of the genes involved in the TCA cycle and cytochrome components under two culture conditions: 2% (D2) vs. 0.1% glucose (D0.1). In contrast, the genes encoding gluconeogenesis enzymes, including phosphoenolpyruvate carboxykinase (PCK1, step 31) and fructose-1,6-bisphosphatase (FBP1, step 32), showed apparently increased expression under 0.1% glucose compared with that under 2% glucose (Fig. 3b). These findings strongly indicate that S. fibuligera is highly likely to be a Crabtree-negative yeast, in which respiration capacity is not subject to glucose repression.
Fig. 3

Reconstruction of the S. fibuligera carbon metabolic pathway mapped with transcriptome data. a In silico reconstructed carbon metabolic pathways of S. fibuligera based on annotated genome information. b Differential gene expression profiles of the C-metabolic pathway under low-glucose condition. D0.1, YPD containing 0.1% glucose; D2, YPD containing 2% glucose. The number in parentheses indicates the number of paralogs. Gene symbols, annotations, and expression abundance data are included in Additional file 11. The steps showing differential expression with a ≥1.5 fold change (the cutoff set at P < 0.05 in duplicated samples) under low-glucose condition (D0.1 versus D2) are indicated in red (induced) or blue (repressed) in the reconstructed C-metabolic pathway

It is interesting to note that S. fibuligera possesses only the gene encoding glucose kinase (GLK1) and no genes encoding hexose kinases (HXK1 and HXK2), which are required to initiate glucose assimilation and glucose phosphorylation (step 1). In contrast, S. cerevisiae possesses all three of these kinase genes, among which HXK2 is reported to be mainly involved in glucose repression [43]. Moreover, S. cerevisiae possesses more than 20 HXT genes encoding hexose transporters, whereas S. fibuligera has only one homolog of HXT5, encoding a moderate-affinity glucose transporter, and four homologs for HGT1, encoding a high-affinity glucose transporter that is mostly found in Crabtree-negative yeasts and filamentous fungi [44]. In contrast, no low-affinity hexose transporter was predicted in the genome of S. fibuligera. It can be speculated that the lower numbers of genes involved in glucose transport and glucose phosphorylation for glycolysis might partly reflect the lesser degree of glucose repression in S. fibuligera. Along with the presence of the mitochondrial genes encoding NADH dehydrogenase in S. fibuligera, these observations explain that S. fibuligera is less efficient at ethanol fermentation than the good ethanol producer S. cerevisiae.

The marginal Crabtree effect was confirmed in S. fibuligera through the comparative analysis of glucose consumption and ethanol production under shake-flask cultivation with different glucose concentrations (Additional file 12: Figure S9). When cultivated in YP broth containing 0.1% glucose, ethanol production was not detected in either S. cerevisiae or S. fibuligera (Additional file 16: Figure S12a). However, in the presence of 2% glucose, ethanol accumulation with a maximum level of ~9 g/L was observed in S. cerevisiae, whereas ethanol was not detectable in S. fibuligera isolates KJJ81 and KPH12 (Additional file 16: Figure S12b). At much higher glucose concentrations, such as under cultivation in YP broth containing 10% glucose, S. cerevisiae was shown to efficiently convert glucose to ethanol, with up to ~46 g/L ethanol accumulating in the culture supernatant. In contrast, S. fibuligera KJJ81 and KPH12 accumulated ~15 and ~8 g/L ethanol, respectively, as the highest production levels (Additional file 16: Figure S12c). Compared with the Crabtree-positive yeast S. cerevisiae, S. fibuligera showed much less glucose consumption and ethanol production, while supporting cell growth comparably. This trend became stronger at high glucose concentrations. Interestingly, the S. fibuligera isolates KJJ81 and KPH12 showed different capacities of glucose consumption and ethanol fermentation, reflecting the differential gene expression profiles observed through RNA-Seq analysis (Fig. 3b).

S-metabolic pathway

Cellular requirements for sulfur can be fulfilled by the uptake of sulfur-containing amino acids, or by the assimilation of inorganic sulfur into organic compounds [45]. It is notable that all known species of the Saccharomycopsis clade are reported to be deficient in sulfate uptake and require supplementation with one of a variety of organic sulfur sources [46]. Intriguingly, our genome data revealed the presence of ORFs encoding uncharacterized sulfate transporters and a sulfite pump, but the absence of ORFs showing homology to other essential genes for sulfate assimilation, such as MET3 (sulfate adenylyltransferase), MET14 (adenylyl-sulfate kinase), MET16 (phosphoadenylyl-sulfate reductase), and MET5/ME10 (sulfite reductase alpha/beta), in the genomes of S. fibuligera KJJ81 and KPH12 (Fig. 4a).
Fig. 4

Reconstruction of the S. fibuligera sulfur assimilation pathway mapped with transcriptome data. a In silico reconstructed sulfur metabolic pathways of S. fibuligera based on annotated genome information. b Differential gene expression profiles of sulfur metabolic pathways under sulfur-limited condition. B, B medium containing 2% glucose; D2, YPD containing 2% glucose. The number in parentheses indicates the number of paralogs. Gene symbols, annotations, and expression abundance data are included in Additional file 11. The steps showing differential expression with a ≥1.5-fold change (the cutoff set at P < 0.05 in duplicated samples) under sulfur-limited condition (B versus D2) are indicated in red (induced) or blue (repressed) in the reconstructed S-metabolic pathway

In yeast and fungal species, cysteine biosynthesis from sulfide can be divided into two pathways. In one pathway, sulfide is condensed with O-acetylserine to generate cysteine in a process catalyzed by cysteine synthase (OAS pathway). In the other pathway, sulfide is condensed with O-acetylhomoserine to generate homocysteine (OAH pathway), which can be converted to cystathionine and then to cysteine via a transsulfuration pathway [47, 48]. In contrast to S. cerevisiae, which lacks the OAS pathway, but similar to filamentous fungal species employing both pathways for cysteine biosynthesis, S. fibuligera has genes encoding a direct pathway to cysteine in addition to a transsulfuration pathway from homocysteine. The transcriptome profile analysis revealed that the sulfur pathway toward methionine biosynthesis, such as those genes encoding methionine transporters (steps 16 and 17) and the interconversion of cysteine to methionine pathways (steps 7 and 8) and the OAH pathway (steps 1, 2, and 3), was obviously activated when S. fibuligera was cultivated on sulfur-limited B medium compared with cultivation on rich YPD medium (Fig. 4b; Additional file 11). In contrast, the transsulfuration pathway for the biosynthesis of cysteine from homocysteine (steps 6 and 9) is dramatically depressed under sulfur-limited conditions. This finding indicates the presence of a regulatory system in S. fibuligera to activate and turn off the sulfur assimilation pathway toward the biosynthesis of methionine, depending on the presence of readily usable sulfur compounds (Additional file 11).

Extracellular enzymes of S. fibuligera with biotechnological applications

Cellulose degradation enzymes

The enzymatic degradation of cellulose, the most abundant polymer on earth, includes the joint action of exoglucanases or cellobiohydrolases, endoglucanases, and β-glucosidases (BGL). The hydrolytic enzymes have attracted intensive research interest due to their use in lignocellulosic biomass decomposition for the production of biofuels and high-value chemicals. Analysis of the S. fibuligera genome revealed that this yeast retains several genes encoding hydrolytic enzymes, which are found in the majority of cellulolytic fungi (Fig. 5a). There were four or five copies of the genes encoding BGL, which acts mainly on cellobiose, in the S. fibuligera genome. Comparison of the S. fibuligera KJJ81 and KPH12 genomes unveiled quite interesting differences in the composition of the BGL family: the gene encoding β-glucosidase 1 (sfBGL1, P22506.1) is present in all three genomes, whereas the gene encoding sfBGL2 (sfBGL2, P22507.1) is absent in subgenome A of S. fibuligera KJJ81 and the KPH12 genome. A novel gene encoding a protein with considerable homology to SfBGL1 (55% identity), designated sfBGL3, and two additional genes encoding homologs of S. pombe putative β-glucosidase, designated sfBGL4, were discovered in all genomes of S. fibuligera. The catalytic activities of those novel β-glucosidases must be investigated and compared with BGL1 and BGL2.
Fig. 5

Expression profiles of S. fibuligera genes associated with cellulose, starch, and cell wall degradation. Differential gene expression profiles of genes in the KJJ81 and PH12 genomes that are associated with the degradation of cellulose (a), starch (b), and the cell wall (c). D2, YP containing 2% glucose; D0.1, YP containing 0.1% glucose; B, B medium containing 2% glucose. The number in parentheses indicates the number of paralogs. Gene symbols, annotations, and expression abundance data are included in Additional file 13

Furthermore, three S. fibuligera homologs to the thermophilic fungus T. reesei and Sporotrichum thermophile Cel61A proteins, formerly known as GH61 proteins with β1,4 endoglucanase activity, were annotated. Recent studies have reported that T. reesei and S. thermophile Cel61A are actually copper-dependent polysaccharide monooxygenases (PMOs), which constitute a novel class of enzymes that catalyze the O2-dependent oxidative cleavage of recalcitrant polysaccharides [49]. PMOs are of increased biotechnological interest because they boost the efficiency of common cellulases, resulting in increased hydrolysis yields while reducing the protein loading needed for plant polysaccharide degradation. Another interesting observation is the presence of a homolog of A. nidulans abfC, encoding a probable α-l-arabinofuranosidase C involved in the degradation of hemicellulose polymers with arabinosyl resides, in the S. fibuligera KJJ81 and KPH12 genomes. The expression of this S. fibuligera abfC homolog is highly induced under glucose-limited and sulfur-limited conditions (Additional file 13). Retention of a subset of cellulose degradation genes in S. fibuligera is quite noteworthy, in that most yeast species belonging to the Saccharomycotina subphylum have lost the gene sets involved in cellulose degradation. Biochemical characterization is required to define their substrate specificity and the biological functions of putative S. fibuligera Cel61A and abfC proteins.

Amylolytic enzymes for starch degradation

Starch is a practical substrate for the production of yeast cells and their fermentation products on a large scale due to its low price and the easy availability of raw material in most regions of the world. S. fibuligera can degrade starch with great efficacy because it expresses glucoamylase, catalyzing the release of glucose from the non-reducing ends of starch molecules, in addition to α-amylase, catalyzing the cleavage of 1,4-α-glycosidic bonds in starch. Only one gene encoding a secretable α-amylase (ALP1), whose deduced sequence was previously reported to be highly homologous with that of α-amylase from A. oryzae [50], was found in the S. fibuligera KJJ81 and KPH12 genomes (Fig. 5b). The GLA1 and GLU1 genes, encoding two types of glucoamylases, Gla and Glu, reported in S. fibuligera KZ and HUT7212, respectively [51], and an ORF, homologous to the C. albicans glucoamylase gene (GAM1), were identified in our S. fibuligera genomes. However, the GLA1 gene appeared to be lost in S. fibuligera KJJ81 subgenome B (Additional file 13). Moreover, the GLM1 gene (EMBL accession no. AJ311587) encoding the raw starch digestion glucoamylase Glm in S. fibuligera IFO 0111 [52] was not identified in the genomes of S. fibuligera KJJ81 and KPH12, indicating that S. fibuligera IFO 0111 might be unique in the repertories of the amylolytic enzyme complex. Interestingly, the expression of these amylolytic enzyme genes, except for GLA1, was observed to be highly induced under low-glucose and sulfur-limitation conditions.

Cell wall degradation enzymes

Another notable observation in the S. fibuligera genome was the presence of redundant genes related to the degradation of β-glucan, the major component of the fungal cell wall and grains. The major cell wall polysaccharide in the endosperm of cereals is a linear β-1,3(4)-d-glucan (β-glucan), which accounts for up to 5.5% of the dry weight of grains [53]. There were multiple genes, annotated as glucan 1,3-β-glucosidase, endo-1,3(4)-β-glucanase, 1,3-β-glucanase/transglucosidase, and secreted β-glucosidase, which are involved in cell wall maintenance and cytokinesis (Fig. 5c; Additional file 13). In particular, β-1,3(4)-β-glucanases (EC; lichenase), which strictly cleave the β-1,4-glycosidic linkage adjacent to a 3-O-substituted glucose residue in mixed linked β-glucans, are important biotechnological aids in the brewing and animal feed stuff industries [54].

Acidic proteases

As indicated in Fig. 2, which shows the contracted and extended Pfam domains in the S. fibuligera genome, the aspartic protease family genes were observed to be particularly enriched in S. fibuligera compared with A. oryzae and S. cerevisiae (Fig. 6a). Notably, the genome of S. fibuligera KPH12 contained more than 37 ORFs encoding putative acid proteases, 31 of which encoded extracellular secretory acid proteases (Additional file 13). The predicted secretory acidic proteases carry a hydrophobic amino-terminal segment as a secretion signal sequence and contain a catalytic active domain surrounding the two active-site aspartate residues, which demonstrate significant homologies to the aspartyl protease family. The secretory acid proteases, with optimal pH values in the acidic range (pH 3–4), play important roles in hydrolyzing proteins in the fermentation mash to liberate amino acids or peptides under the acidic conditions of fermentation on mash [55].
Fig. 6

Analysis of putative protease genes in S. fibuligera. a Pie charts of the putative protease family of A. oryzae, S cerevisiae, and S. fibuligera KPH12. Families of proteolytic enzymes are grouped according to the peptidase database MEROPS ( A aspartic, C cysteine, G glutamic, M metallo, N asparagine, P mixed, S serine, T threonine, U unknown proteases. The numbers of genes assigned to each of the families are shown. b Phylogenic tree of secretory proteases in KJJ81 subgenome A. Saccharomyces cerevisiae and putative S. fibuligera yapsins carrying the GPI motif are shown in green. A. oryzae acidic protease (AopepA, XP_001824175.1), S. cerevisiae vacuolar acidic protease PEP4 (ScPEP4, CAA97859.1), S. cerevisiae yapsin proteases, ScYPS1 (KZV09366.1), ScYPS2 (KZV12382.1), ScYPS3 (KZV09368.1), ScYPS6 (KZV10664.1), and ScYPS7 (KZV12588.1) are included for comparison. c Transcriptional analysis of the S. fibuligera genes encoding secreted acidic proteases. D2, YP containing 2% glucose; D0.1, YP containing 0.1% glucose; B, B medium containing 2% glucose. The number in parentheses indicates the number of paralogs. Gene symbols, annotations, and expression abundance data are included in Additional file 13

The first identified PEP1 gene, encoding a secretory acid protease in S. fibuligera A11 (synonymous to APG, accession number E01179), does not contain a glycosylphosphatidylinositol (GPI) anchor motif [56]. While 24 genes among a total of 31 S. fibuligera genes encoding secretory acidic protease were grouped as PEP1 paralogs, 7 genes were analyzed and found to encode secretory proteases containing the GPI anchor motif at their C-termini, as reported in the S. cerevisiae yapsin and Candida albicans sap proteases, which are mostly localized at the cell surface [57]. Therefore, genes encoding GIP-anchored yeast aspartyl protease members are annotated as paralogs of yapsin-like proteases (Fig. 6b, indicated in green). Phylogenetic tree analysis suggested that the PEP1 family members might have diverged from yapsin-like proteases via loss of the GPI anchor motif. It appeared that the expression of some PEP1 family members was highly induced under low-glucose and sulfur-limited conditions, whereas the expression of yapsin-like proteases was mostly constitutive, without significant changes at the transcription level (Fig. 6c; Additional file 13).

Comparative genome analysis of an S. fibuligera isolate from chalky rye bread in Germany

Genome sequencing provides the most complete understanding of the genome structure of an organism and allows for the most in-depth comparisons to be made between related species and even between different strains of one species that have been isolated from varied environmental conditions. Therefore, we undertook WG de novo assembly of S. fibuligera ATCC 36309, an isolate from chalky rye bread in Germany, from long-read SMRT and Illumina sequence data. A total of seven scaffolds were assembled on the basis of KPH12 assembly data, estimated to be approximately 19.6 Mb in length (Additional file 2: Table S7). Ab initio gene prediction using extrinsic evidence extracted from KPH12 and KJJ81 revealed a total of 6121 gene models in the ATCC 36309 genome (Additional file 14: Figure S10). Initial nucleotide alignments of the ATCC 36309, KPH12, and KJJ81 genomes revealed that the ATCC 36309 genome is highly identical to the KPH12 genome, with 97.97% sequence identity. The identities of the ATCC 36309 genome in relation to subgenome A and subgenome B of KJJ81 were 97.85 and 89.97%, respectively, reflecting the fact that the ATCC 36309 genome is closely related to the KPH12 genome (Fig. 7a). The sequence of the ATCC 36309 rDNA unit was almost identical to that of KPH12 rDNA, with the exception of a deletion of three copies of the repeated sequence “TTAGCGAAAAAAAC” in the ETS2 region downstream of 25S. The positions of the putative centromere of ATCC 36309 and the telomere structures of ATCC 36309 were also highly identical to those observed in the KPH12 genome (Additional file 15: Figure S11). S. fibuligera ATCC 36309 possessed the α-pheromone-encoding MFα2 (306-bp ORF), but not the other MFα1 gene, as observed in the KPH12 genome and KJJ81 subgenome A (Additional file 9: Figure S8b).
Fig. 7

Comparative analysis of the S. fibuligera ATCC 36309, and KPH12 and KJJ81 genomes. a Synteny analysis between the two genomic sequences (top) with information on sequence coverage and similarity of the S. fibuligera genomes (bottom). WG dot plots of the two genomes were generated using SyMAP. The red boxes indicate an interchromosomal translocation and the green box indicates a deletion. b Synteny blocks between the two genomic sequences were visualized through chromosome painting in SynChro [85]. c Hybrid formation between two S. fibuligera subspecies with subgenomes A and B, respectively. Two subspecies were separated from the last common ancestor of S. fibuligera by various genetic changes. In the subgenome A lineage, S. fibuligera KPH12 diverged from S. fibuligera ATCC 36309 with additional genetic changes, such as the loss of BGL2 and reciprocal translocation between chromosomes 3 and 5. Hybridization of S. fibuligera KPH12 and a parent with subgenome B, followed by loss of the rDNA cluster in subgenome B, might generate the S. fibuligera KJJ81 hybrid carrying seven chromosome pairs. Loss of some non-essential genes in the subgenome B lineage could occur after a hybridization event (asterisk)

Quite interestingly, despite such high identity between the ATCC 36309 and KPH12 genomes, the synteny analysis of the ATCC 3609 and the KPH12 genomes revealed the presence of reciprocal translocation between chromosomes 3 and 5 (Fig. 7a). An interchromosomal translocation was also detected in the synteny analysis between the ATCC 36309 genome and KJJ81 subgenome A, but not between the ATCC 36309 genome and KJJ81 subgenome B. These observations indicated that while the original synteny was preserved without major rearrangement in the ATCC 36309 genome and KJJ81 subgenome B, a reciprocal translocation occurred between chromosomes 3 and 5 in the KPH12 genome (Fig. 7b). Thus, it is highly likely that the reciprocal translocation between chromosomes 3 and 5 occurred in the KPH12 genome before the KJJ81 genome was generated via hybrid formation.

There were several other interesting differences observed in the ATCC 36309 genome compared with the KPH12 and KJJ81 genomes. One notable difference was the sizes of chromosomes 6 and 7. In the ATCC 36309 genome, chromosome 7 was slightly larger than chromosome 6, a finding that was reversed in the other S. fibuligera genomes (Additional file 2: Table S7). A notable difference between the ATCC 36309 and KPH12 genomes was the deletion of a ~20-kb fragment containing five genes encoding SAP6, abfC, HGT1, RTA1 and YRF1-2 at the subtelomeric region of chromosome 3 in the ATCC 36309 genome (Additional file 16: Figure S12). The ATCC 36309 genome preserved the sfBGL2 gene, which is present in subgenome B of S. fibuligera KJJ81, but absent in subgenome A of KJJ81 and the KPH12 genome (Additional file 2: Table S9). These findings indicate that the common ancestor of S. fibuligera contained the BGL2 gene, but that it was lost in the KPH12 lineage. In addition, the loss of ~250 paralogous genes in the synteny relationship between the ATCC 36309 and KPH12 genomes reflects the fact that sequence divergence occurred during growth adaptation to different environments, such as Nuruk in Korea and chalky rye bread in Germany.


The widespread WG sequencing of yeast species in the last decade has provided new insights into the biodiversity, population structure, phylogeography, and evolutionary history of fungal populations [58]. However, most genomics studies have been conducted in S. cerevisiae and closely related yeast species, and high-quality whole genomes for other yeast species, such as members of the early-diverging Saccharomycotina, are therefore quite scarce. In the present study, we performed the WG de novo sequencing and complete assembly of two S. fibuligera isolates from wheat-based Nuruk in Korea, S. fibuligera KPH12 and KJJ81, and an isolate from chalky rye bread in Germany, S. fibuligera ATCC 36309. Comparative genomic analysis, based on the completely assembled S. fibuligera genomes from telomere to telomere, revealed the unique genome structure and evolutionary history of S. fibuligera and its interspecies hybrid. The S. fibuligera KJJ81 hybrid genome is composed of subgenomes A and B, which are derived from two progenitors, S. fibuligera KPH12 and a closely related (sub)species. The origin of the parent strain for subgenome B in the S. fibuligera KJJ81 hybrid isolate remains unknown, due to the lack of close sequence similarity among existing yeast species other than S. fibuligera.

The sequence divergence observed between both subgenomes A and B (10.84% at the nucleotide level between syntenic regions) was equivalent to the divergence described between the genomes of S. cerevisiae and S. paradoxus, two distinct species of the genus Saccharomyces [59] (Additional file 17: Figure S13). A plausible hypothesis for the formation of this architecture is believed to involve the hybridization of two genomes differing by approximately 10% via inter(sub)species mating. The highly conserved synteny over the whole genome in the S. fibuligera KJJ81 hybrid genome strongly indicates that a recent hybridization event occurred. Analysis of the synteny relationships of each pair of genes between subgenomes A and B of KJJ81 revealed that several subgenome A-specific single-copy genes were also present in both the KPH12 and ATCC 36309 genomes (Additional file 2: Table S8), indicating that these genes might have been lost from subgenome B. Due to the lack of information on S. fibuligera isolates belonging to the subgenome B lineage, whether the gene loss observed in subgenome B had occurred after or before the hybridization event cannot be determined. In contrast, a few genes, such as COX7, BGL2, COX17, LSB5, and SATL1, were present as intact ORFs in KJJ81 subgenome B, but present as truncated or altered spliced forms in both KJJ81 subgenome A and the KPH12 genome, indicating that inactivation of these genes had occurred in the subgenome A lineage before hybridization (Additional file 2: Table S9). The essentiality of rDNA for cell growth suggests a relatively recent loss of these rDNA clusters in the hybrid isolate KJJ81. Therefore, the S. fibuligera KJJ81 hybrid genome reflects the very early stage of genome stabilization, which preserves most of the synteny relationship between two subgenomes, but involves recent unilateral loss of the rDNA cluster and a few genes in subgenome B (Fig. 7c). The hybrid genome of S. fibuligera KJJ81 offers an interesting snapshot of the genomic evolutionary events that occurred after inter(sub)specific hybridization.

When we compared the growth phenotypes of the S. fibuligera KJJ81, KPH12, and ATCC 36309, the two isolates from Nuruk showed higher thermotolerance than the isolate from chalky rye bread in Germany. Moreover, the hybrid KJJ81 appeared more adapted to higher temperatures (Additional file 18: Figure S14a) than KPH12, thus exhibiting a higher survival potential during Nuruk fermentation, which is typically conducted at a continuous temperature range of 30–45 °C [60]. Therefore, such temperature variation during Nuruk fermentation might generate certain selective pressures on particular communities with higher thermotolerance over others. In addition, KJJ81 exhibited more adaptive growth in the presence of inorganic sulfur as the only S source compared to KPH12 (Additional file 18: Figure S14b), indicating that some advantageous properties were generated by hybrid formation.

The S. fibuligera genome with high-quality annotation establishes an important foundation for making functional inference of S. fibuligera in the digestion of fermentation mash. In analyzing the genomes of KJJ81 and KPH12, we unexpectedly discovered numerous genes for extracellular hydrolytic enzymes, such as amylase, β-glucosidase, cellulase, and acidic protease, involved in saccharification and proteolysis (Additional file 13). During cultivation on Nuruk or bread, S. fibuligera grows on the surface of whey or barley, where amino acids and sugars are initially deficient. The need for S. fibuligera to effectively obtain access to external carbon and nitrogen sources thorough the degradation of proteins and starches appears consistent with the observed expansion of hydrolytic enzymes in the S. fibuligera genome, strongly indicating that S. fibuligera is highly saccharolytic and proteolytic, resulting in its dominant appearance as a major yeast species in Asian traditional alcoholic starters made of various grains. It is noteworthy that the expression patterns of these extracellular hydrolytic enzymes generally appeared to be induced under nutrient-limited conditions, such as cultivation in low-glucose or B-minimal medium (Figs. 5, 6). The S. fibuligera genes that have been newly discovered through genome research in this study are expected to be applicable to the production of novel valuable enzymes and chemicals. To obtain information on the substrate specificity and functional features of the new S. fibuligera enzymes, secretory expression and purification of these enzymes as recombinant proteins in a heterologous host system are currently underway for subsequent biochemical characterization, particularly focusing on those enzymes associated with polysaccharide degradation including novel β-glucosidases, PMOs, and abfC.

Intensive research efforts have recently been made in searching for and genome sequencing of microorganisms that secrete hydrolytic enzymes with high potential for applications in sectors such as the food, detergent, laundry, textile, baking, and biofuel industries [61, 62]. The distinctive genomic inventory of S. fibuligera, enriched in genes for extracellular hydrolytic enzymes, further supports the high potential of this amylolytic yeast to serve as an economical host for the production of industrial enzymes and bioproducts from renewable resources. However, our RNA-Seq analysis showed that the production of these enzymes is subject to catabolite repression at transcription levels by glucose or other available nutrients. Therefore, to more efficiently exploit S. fibuligera as a cell factory for bioconversion, it is necessary to elucidate the gene regulatory networks underlying its metabolism with industrial potential and to systematically modulate the expression of relevant genes, such as transcription factor genes, in this yeast. The complete WG sequence of S. fibuligera with high-quality annotation will facilitate the development of new tools for genetic manipulation and allow the application of systems biology approaches to the identification of metabolic targets in engineering S. fibuligera strains for the production of biofuels and chemicals based on bioconversion of starchy or cellulosic biomass.

The genus Saccharomycopsis, in the phylum Ascomycota, was initially described by Schiönning (1903) with only the species Saccharomycopsis capsularis, but it has currently been expanded to at least 17 species [63]. As the first gapless genome assembly in the genus Saccharomycopsis, the genome information obtained for S. fibuligera and its hybrid in the present study is also expected to provide a useful basis for the elucidation and documentation of evolutionary consequences within fungal populations. Phylogenetic position analysis based on our comparative analysis of S. fibuligera genome indicated the Saccharomycopsis clade as an early divergent of the subphylum Saccharomycotina. Interestingly, the majority of the species of the genus Saccharomycopsis are predacious yeasts that are able to penetrate and kill various yeast prey [46]. Some members of the genus Saccharomycopsis have drawn increasing attention due to exhibiting unique physiological characteristics that are useful for various biotechnological applications, such as Saccharomycopsis fermentation filtrate as a component of cosmetics [64] and high hydrolytic activity for the bioremediation of agricultural waste [20]. The S. fibuligera genome will serve as a useful basis for comparative genomics studies to investigate functional peculiarities specific to this yeast and its relative lineage within the Saccharomycopsis clade.


The completely sequenced S. fibuligera genome with high-quality annotation and the RNA-Seq analysis, presented in this report, will greatly appeal to researchers with a broad spectrum of interests in both basic and applied areas of biological research. It establishes an important foundation for functional inference of S. fibuligera in the degradation of fermentation mash. The unique gene inventory of S. fibuligera provides insights into novel physiological activities with potent biotechnical applications, which requires further research to be elucidated. As a high-quality reference genome, it will elucidate biological peculiarities specific to this yeast and its relative lineage, facilitating various omics analyses to investigate metabolic pathways with biotechnological potential and their regulatory networks under culture conditions relevant to industrial processes. This information will also allow the implementation of new genetic manipulation platforms for S. fibuligera, applicable to the development of engineered yeast strains for simultaneous saccharification/fermentation processes, along with improved starter strains tailored for the production of rice wine with desired flavor profiles or an improved nutraceutical composition. Moreover, as the first report of a gapless WG assembled within the Saccharomycopsis genus with a high bioremediation capacity, the genomic information obtained for S. fibuligera and its interspecies hybrid strain provides an important foundation for comparative biological analyses, representing a useful basis for the delineation and documentation of evolutionary consequences and divergence in fungal populations.


Strains, media, and cultivation conditions

Saccharomycopsis fibuligera KJJ81 and KPH12 strains were isolated from Nuruk samples collected from Jeju (KJJ81) and Pohang (KPH12), respectively. The 5.8S rDNA sequences flanked by ITS regions 1 and 2 [65] were amplified using primers ITS-1 (5′-TCC GTA GGT GAA CCT GCGG-3′) and ITS-4 (5′-TCC TCC GCT TAT TGA TAT GC-3′) and were analyzed using ISHAM ITS DB ( Saccharomycopsis fibuligera ATCC 36309 (KCTC 7806, NRRL Y-2388), an isolate from chalky rye bread in Germany, was used as a type strain of S. fibuligera. Yeast cells were cultured using YP (1% yeast extract, 2% Bacto peptone) medium containing 2% glucose (D2) or 0.1% glucose (D0.1). For sulfur assimilation analysis, solid sulfur-free B medium (synthetic medium with 2% glucose without any sulfur source) [47] was made with 1% agarose instead of agar.

Genome sequencing and assembly

For WG sequencing of S. fibuligera KPH12 and KJJ81, long, short, and long-mated pair reads were produced using PacBio RS II, TruSeq Synthetic Long Reads, and Illumina HiSeq 2500 sequencing technologies (Additional file 2: Table S1). WG de novo assembly for KPH12 and KJJ81 was performed via a hybrid approach (Additional file 1: Figure S1, Additional file 2: Table S2). The quality of the assemblies was assessed by aligning short-insert reads to the two assemblies (Additional file 2: Table S3). Quality assessment of genome assemblies was performed by comparing between the resulting scaffolds and the TSLR assembly using QUAST [66] (Additional file 2: Table S4). Moreover, the mapping rates of short reads to the scaffolds and their insert size distribution were examined. For WG de novo assembly of ATCC 36309, long-read SMRT and Illumina (500-bp short-insert reads) sequence data were produced, and scaffolds were assembled based on KPH12 assembly data using the above-mentioned method.

BioNano physical mapping

A DNA plug containing the S. fibuligera KPH12 genome was prepared using the CHEF Yeast Genomic DNA Plug Kit (Bio-Rad) with slightly modified conditions, treated with the nicking enzyme Nt.BspQI (New England BioLabs, NEB), labeled using a fluorescent nucleotide analog with the IrysPrep Reagent Kit (BioNano Genomics, Inc.), and then loaded onto the nanochannel array of a BioNano Genomics IrysChip via DNA electrophoresis.

RNA-Seq analysis

For RNA-Seq analysis, yeast cells cultivated overnight were initially inoculated at an OD600 (optical density at 600 nm)  of 0.2 in YP medium containing 2%, 0.1% glucose, 2% starch, and B medium and were grown to early logarithmic phase (OD600  =  0.5) at 37 °C. Yeast cells were collected by filtration using a 10-µm polyvinylidene difluoride membrane. Total RNA was isolated via a physical cell breaking method [67], and RNA-Seq was then performed with an Illumina HiSeq 2500 instrument. RNA-Seq libraries were prepared using a TruSeq RNA Sample Prep Kit (Illumina, Inc., San Diego, CA, USA). After qPCR validation using a KAPA library quantification kit (KAPA Biosystems, South Africa), libraries were subjected to paired-end sequencing with a 100-bp read length using an Illumina HiSeq 2500 platform, with ≥38 million reads per library. For the validation of genome annotation based on RNA-Seq data, clean reads with average quality scores of more than Q30 for all libraries were aligned to the KPH12 and KJJ81 genomes using the STAR spliced read aligner [68] with a set of gene model annotations. Total counts of read fragments aligned to protein-coding genes were obtained using an HTSeq [69] and were used as a basis for the quantification of gene expression. Differential expression analysis between sample groups of interest (i.e., D0.1 vs. D2, B vs. D2) was performed using the TCC package [70].

Genome annotation

Gene models were predicted by combining evidence from transcriptome and protein sequence alignments with ab initio prediction on the basis of repeat-masked genome sequences. GeneMark-ET [71] was used to perform iterative training and to generate initial gene structures with RNA-Seq data information. AUGUSTUS [72] was further used to perform de novo prediction with gene models trained by GeneMark-ET, with exon–intron boundary information predicted by transcriptome and protein sequence alignments. TopHat [73] was used for RNA-Seq alignment, and Exonerate [74] was used for protein sequence alignment with subphylum Saccharomycotina sequences. In addition, homologous genes between KPH12 and KJJ81 were searched using TBLASTN [75], with an E-cutoff value of 1E-10, and MCScanX [76] to identify missing genes through de novo prediction. For functional annotation, genes were searched against the UniProt, NCBI non-redundant (NR), and relative fungi RefSeq (i.e., S. cerevisiae, C. glabrata, A. oryzae, and S. pombe) databases using BLASTP [75] with an E-cutoff value of 1E-10. Protein domains were also searched using InterProScan [77]. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations were performed using Blast2GO [78].

The tRNA genes were identified with tRNAscan-SE [79] using default parameters. The rRNAs, collected from the NCBI NT database, were compared with the genomes using BLASTN [75]. Other non-coding RNAs, including miRNA and snRNA, were identified using INFERNAL [80] via comparison with the Rfam. Repetitive DNA sequences, including retrotransposons, DNA transposons, microsatellites, and other repeats, were screened using RepeatMasker [81], which was developed for de novo repeat family identification and modeling. The repeat-masked scaffolds were used for gene prediction as described above. Mitochondrial genome annotation was performed using MFannot ( with the correction of ORF structures. Synteny blocks between two genomes (i.e., KPH12 vs. KJJ81, KJJ81 subgenome A vs. KJJ81 subgenome B, KPH12 vs. ATCC 36309, and KJJ81 vs. ATCC 36309) were identified with SyMAP [82].

Phylogeny and gene family analysis

For the construction of a genome-based phylogeny, protein sequences of KPH12 were searched against those of 12 species, including A. gossypii, K. lactis, C. glabrata, S. cerevisiae, C. lusitaniae, C. tropicalis, D. hansenii, Y. lipolytica, A. fumigatus, A. oryzae, and S. pombe, which were downloaded from Ensembl Fungi ( The search was performed using reciprocal BLAST, a common computational method to predict orthologous genes. The highest-scoring genes were taken with the following: (1) ≥50% sequence identity and (2) 10% deviation in compared sequence length. A total of 55 orthologous genes (Additional file 10) were finally selected from among 12 species, and a single concatenating sequence per species was built. The resulting sequences were multiple-aligned using ClustalW [83]. A phylogenetic tree was constructed using MEGA6 [39] via the neighbor-joining method, with bootstrap values for 1000 replicates. Orthologous gene families were assigned to the 12 species against the Pfam database using HMMPfam.

Accession numbers

The assembled sequences for the S. fibuligera KJJ81 and KPH12 chromosomal genomes were deposited in the GenBank database under the accession nos. CP012809–CP012822 and CP012823–CP012829, respectively (nuclear genome). The assembled genome sequence for S. fibuligera ATCC 36309 was deposited in the GenBank database under the accession nos. CP015978–CP015984 (nuclear genome). The raw RNA-Seq data were deposited in the BioSample database of NCBI. Accession nos. SAMN05180633, SAMN05180634 (KJJ81 on B medium); SAMN05180635, SAMN05180636 (KJJ81 on YP containing glucose 0.1%); SAMN05180637, SAMN05180638 (KJJ81 on YP containing glucose 2%); SAMN05180639, SAMN05180640 (KPH12 on B medium); SAMN05180641, SAMN05180642 (KPH12 on YP containing glucose 0.1%); SAMN05180637, SAMN05180638 (KPH12 on YP containing glucose 2%).




internal transcribed spacer


whole genome


TruSeq synthethic long reads


ribosomal DNA


mitochondrial genome


nontranscribed spacers


long terminal repeat


tricarboxylic acid cycle




glucose 6-phosphate


fructose 6-phosphate


fructose 1,6-bisphosphate


dihydroxyacetone phosphate


glyceraldehyde 3-phosphate












coenzyme A




glycerol 3-phosphate


oxaloacetic acid




trehalose 6-phosphate










xylulose 5-phosphate








O-acetyl homoserine












O-acetyl serine








Authors’ contributions

JHC performed the WG sequencing and optical mapping, and drafted the manuscript. CPH carried out the genome assembly and annotation, participated in RNA-Seq analysis, and drafted the manuscript. JYL participated in the sequence alignment and performed the bioinformatics analysis of centromeres and telomeres. JAS conceived the study and participated in the isolation of yeast strains. YSK participated in characterizing yeast strains. DWL participated in RNA-Seq analysis and growth analysis. SGP performed the bioinformatics analysis of the mtDNA. GWL participated in the genome-based phylogenetic analysis. EC participated in the identification and characterization of yeast strains. YWL conceived the study and participated in the data analysis. HAK conceived the study, participated in its design and coordination, and drafted the manuscript. All authors read and approved the final manuscript.


The authors are thankful to Dr. J. F. Kim for his valuable comments on the manuscript.

Competing interests

The authors declare that they have no competing interests.

Availability of supporting data

All data generated or analyzed during this study are included in this published article and its supplementary information files.


This work was supported by Grant No. 914007-4 (Strategic Initiative for Microbiomes in Agriculture and Food) from the Ministry of Agriculture, Food and Rural Affairs, Republic of Korea.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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 ( applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

Department of Life Science, Chung-Ang University, Seoul, South Korea
Theragen Bio Institute, TheragenEtex, Suwon, South Korea
Department of Agricultural Biotechnology, Seoul National University, Seoul, South Korea
School of Systems Biomedical Science, Soongsil University, Seoul, South Korea
Department of Food Science and Engineering, Ewha Womans University, Seoul, South Korea


  1. Grigoriev I, Cullen D, Hibbett D, Goodwin S, Jeffries T, Kubicek C, et al. Fueling the future with fungal genomics. Mycology. 2011;2:192–209.Google Scholar
  2. Kurtzman CP, Smith MT. Saccharomycopsis schiönning (1903). The yeasts, a taxonomic study. 5th ed. Amsterdam: Elsevier Science; 2011. p. 751–63.Google Scholar
  3. Demot R, Andries K, Verachtert H. Comparative study of starch degradation and amylase production by ascomycetous yeast species. Syst Appl Microbiol. 1984;5:106–18.View ArticleGoogle Scholar
  4. Wickerham LJ, Lockwood LB, Pettijohn OG, Ward GE. Starch hydrolysis and fermentation by the yeast Endomycopsis fibuliger. J Bacteriol. 1944;48:413–27.Google Scholar
  5. Zheng XW, Yan Z, Han BZ, Zwietering MH, Samson RA, Boekhout T, et al. Complex microbiota of a Chinese “Fen” liquor fermentation starter (Fen-Daqu), revealed by culture-dependent and culture-independent methods. Food Microbiol. 2012;31:293–300.View ArticleGoogle Scholar
  6. Hesseltine CW, Rogers R, Winarno FG. Microbiological studies on amylolytic oriental fermentation starters. Mycopathologia. 1988;101:141–55.View ArticleGoogle Scholar
  7. Sukhumavasi J, Kato K, Harada T. Glucoamylase of a strain of Endomycopsis fibuligera isolated from mould bran (Loog Pang) of Thailand. J Ferment Technol. 1975;53:559–65.Google Scholar
  8. Thanh VN, Mai LT, Tuan DA. Microbial diversity of traditional Vietnamese alcohol fermentation starters (banh men) as determined by PCR-mediated DGGE. Int J Food Microbiol. 2008;128:268–73.View ArticleGoogle Scholar
  9. Bal J, Yun SH, Song HY, Yeo SH, Kim JH, Kim JM, et al. Mycoflora dynamics analysis of Korean traditional wheat-based nuruk. J Microbiol. 2014;52:1025–9.View ArticleGoogle Scholar
  10. Kurtzman C, Fell J. The yeasts: a taxonomic study. Fourth revised and enlarged edition. Amsterdam: Elsevier Science; 2000. p. 1–525.Google Scholar
  11. Deschuyffeleer N, Audenaert K, Samapundo S, Ameye S, Eeckhout M, Devlieghere F. Identification and characterization of yeasts causing chalk mould defects on par-baked bread. Food Microbiol. 2011;28:1019–27.View ArticleGoogle Scholar
  12. Burgain A, Bensoussan M, Dantigny P. Validation of a predictive model for the growth of chalk yeasts on bread. Int J Food Microbiol. 2015;204:47–54.View ArticleGoogle Scholar
  13. Chi ZM, Chi Z, Liu GL, Wang F, Ju L, Zhang T. Saccharomycopsis fibuligera and its applications in biotechnology. Biotechnol Adv. 2009;27:423–31.View ArticleGoogle Scholar
  14. Yu XJ, Madzak C, Li HJ, Chi ZM, Li J. Surface display of acid protease on the cells of Yarrowia lipolytica for milk clotting. Appl Microbiol Biotechnol. 2010;87:669–77.View ArticleGoogle Scholar
  15. Wang DS, Zhao SF, Zhao MX, Li J, Chi ZM. Trehalose accumulation from cassava starch and release by a highly thermosensitive and permeable mutant of Saccharomycopsis fibuligera. J Ind Microbiol Biotechnol. 2011;38:1545–52.View ArticleGoogle Scholar
  16. Limtong S, Sintara S, Suwannarit P, Lotong N. Yeast diversity in traditional Thai fermentation starter (Loog-pang). Kasetsart J. 2002;36:149–58.Google Scholar
  17. Abouzied MM, Reddy CA. Fermentation of starch to ethanol by a complementary mixture of an amylolytic yeast and Saccharomyces cerevisiae. Biotechnol Lett. 1987;9:59–62.View ArticleGoogle Scholar
  18. Reddy OVS, Basappa SC. Direct fermentation of cassava starch to ethanol by mixed cultures of Endomycopsis fibuligera and Zymomonas mobilis: synergism and limitations. Biotechnol Lett. 1996;18:1315–8.View ArticleGoogle Scholar
  19. González CF, Farina JI, de Figueroa LIC. Optimized amylolytic enzymes production in Saccharomycopsis fibuligera DSM-70554—an approach to efficient cassava starch utilization. Enzym Microb Technol. 2008;42:272–7.View ArticleGoogle Scholar
  20. Kurtzman CP, Mateo RQ, Kolecka A, Theelen B, Robert V, Boekhout T. Advances in yeast systematics and phylogeny and their use as predictors of biotechnologically important metabolic pathways. FEMS Yeast Res. 2015;15:fov050.View ArticleGoogle Scholar
  21. Knox AM, du Preez JC, Kilian SG. Starch fermentation characteristics of Saccharomyces cerevisiae strains transformed with amylase genes from Lipomyces kononenkoae and Saccharomycopsis fibuligera. Enzym Microb Technol. 2004;34:453–60.View ArticleGoogle Scholar
  22. van Rooyen R, Hahn-Hagerdal B, La Grange DC, van Zyl WH. Construction of cellobiose-growing and fermenting Saccharomyces cerevisiae strains. J Biotechnol. 2005;120:284–95.View ArticleGoogle Scholar
  23. Guo ZP, Zhang L, Ding ZY, Gu ZH, Shi GY. Development of an industrial ethanol-producing yeast strain for efficient utilization of cellobiose. Enzym Microb Technol. 2011;49:105–12.View ArticleGoogle Scholar
  24. Gurgu L, Lafraya A, Polaina J, Marin-Navarro J. Fermentation of cellobiose to ethanol by industrial Saccharomyces strains carrying the β-glucosidase gene (BGL1) from Saccharomycopsis fibuligera. Bioresour Technol. 2011;102:5229–36.View ArticleGoogle Scholar
  25. Ma YY, Liu XW, Yin YC, Zou C, Wang WC, Zou SL, et al. Expression optimization and biochemical properties of two glycosyl hydrolase family 3 beta-glucosidases. J Biotechnol. 2015;206:79–88.View ArticleGoogle Scholar
  26. Van Zyl J, Den Haan R, Van Zyl W. Overexpression of native Saccharomyces cerevisiae ER-to-Golgi SNARE genes increased heterologous cellulase secretion. Appl Microbiol Biotechnol. 2016;100:505–18.View ArticleGoogle Scholar
  27. Favaro L, Viktor MJ, Rose SH, Viljoen-Bloom M, van Zyl WH, Basaglia M, et al. Consolidated bioprocessing of starchy substrates into ethanol by industrial Saccharomyces cerevisiae strains secreting fungal amylases. Biotechnol Bioeng. 2015;112:1751–60.View ArticleGoogle Scholar
  28. Sun HY, Zhao PJ, Ge XY, Xia YJ, Hao ZK, Liu JW, et al. Recent advances in microbial raw starch degrading enzymes. Appl Biochem Biotechnol. 2010;160:988–1003.View ArticleGoogle Scholar
  29. Orlowski M. Mucor dimorphism. Microbiol Rev. 1991;55:234–58.Google Scholar
  30. Cao H, Hastie AR, Cao D, Lam ET, Sun Y, Huang H, et al. Rapid detection of structural variation in a human genome using nanochannel-based genome mapping technology. Gigascience. 2014;3:34.View ArticleGoogle Scholar
  31. Freel KC, Friedrich A, Schacherer J. Mitochondrial genome evolution in yeasts: an all-encompassing view. FEMS Yeast Res. 2015;15:fov023.View ArticleGoogle Scholar
  32. Dujon B. Yeast evolutionary genomics. Nat Rev Genet. 2010;11:512–24.View ArticleGoogle Scholar
  33. Lynch DB, Logue ME, Butler G, Wolfe KH. Chromosomal G + C content evolution in yeasts: systematic interspecies differences, and GC-poor troughs at centromeres. Genome Biol Evol. 2010;2:572–83.View ArticleGoogle Scholar
  34. Zakian VA. Telomeres: the beginnings and ends of eukaryotic chromosomes. Exp Cell Res. 2012;318:1456–60.View ArticleGoogle Scholar
  35. Gordon JL, Armisen D, Proux-Wera E, OhEigeartaigh SS, Byrne KP, Wolfe KH. Evolutionary erosion of yeast sex chromosomes by mating-type switching accidents. Proc Natl Acad Sci USA. 2011;108:20024–9.View ArticleGoogle Scholar
  36. Hanson SJ, Byrne KP, Wolfe KH. Mating-type switching by chromosomal inversion in methylotrophic yeasts suggests an origin for the three-locus Saccharomyces cerevisiae system. Proc Natl Acad Sci USA. 2014;111:E4851–8.View ArticleGoogle Scholar
  37. Dignard D, El-Naggar AL, Logue ME, Butler G, Whiteway M. Identification and characterization of MFA1, the gene encoding Candida albicans a-factor pheromone. Eukaryot Cell. 2007;6:487–94.View ArticleGoogle Scholar
  38. Kurtzman CP, Robnett CJ. Saitoella coloradoensis sp. nov., a new species of the Ascomycota, subphylum Taphrinomycotina. Antonie Van Leeuwenhoek. 2012;101:795–802.View ArticleGoogle Scholar
  39. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.View ArticleGoogle Scholar
  40. Dmochowska A, Dignard D, Henning D, Thomas DY, Bussey H. Yeast KEX1 gene encodes a putative protease with a carboxypeptidase B-like function involved in killer toxin and α-factor precursor processing. Cell. 1987;50:573–84.View ArticleGoogle Scholar
  41. Suda Y, Rodriguez RK, Coluccio AE, Neiman AM. A screen for spore wall permeability mutants identifies a secreted protease required for proper spore wall assembly. PLoS ONE. 2009;4:e7184.View ArticleGoogle Scholar
  42. Ahmed YL, Gerke J, Park HS, Bayram O, Neumann P, Ni M, et al. The velvet family of fungal regulators contains a DNA-binding domain structurally similar to NF-κB. PLoS Biol. 2013;11:e1001750.View ArticleGoogle Scholar
  43. Gancedo JM. Carbon catabolite repression in yeast. Eur J Biochem. 1992;206:297–313.View ArticleGoogle Scholar
  44. Billard P, Menart S, Blaisonneau J, BolotinFukuhara M, Fukuhara H, WesolowskiLouvel M. Glucose uptake in Kluyveromyces lactis: role of the HGT1 gene in glucose transport. J Bacteriol. 1996;178:5860–6.View ArticleGoogle Scholar
  45. Saito K. Sulfur assimilatory metabolism. The long and smelling road. Plant Physiol. 2004;136:2443–50.View ArticleGoogle Scholar
  46. Lachance MA, Pupovac-Velikonja A, Natarajan S, Schlag-Edler B. Nutrition and phylogeny of predacious yeasts. Can J Microbiol. 2000;46:495–505.View ArticleGoogle Scholar
  47. Cherest H, Surdin-Kerjan Y. Genetic analysis of a new mutation conferring cysteine auxotrophy in Saccharomyces cerevisiae: updating of the sulfur metabolism pathway. Genetics. 1992;130:51–8.Google Scholar
  48. Marzluf GA. Molecular genetics of sulfur assimilation in filamentous fungi and yeast. Annu Rev Microbiol. 1997;51:73–96.View ArticleGoogle Scholar
  49. Dimarogona M, Topakas E, Christakopoulos P. Recalcitrant polysaccharide degradation by novel oxidative biocatalysts. Appl Microbiol Biotechnol. 2013;97:8455–65.View ArticleGoogle Scholar
  50. Itoh T, Yamashita I, Fukui S. Nucleotide sequence of the α-amylase gene (ALP1) in the yeast Saccharomycopsis fibuligera. FEBS Lett. 1987;219:339–42.View ArticleGoogle Scholar
  51. Natalia D, Vidilaseris K, Satrimafitrah P, Purkan WTI, Permentier H, Fibriansah G, et al. Biochemical characterization of a glucoamylase from Saccharomycopsis fibuligera R64. Biologia. 2011;66:27–32.View ArticleGoogle Scholar
  52. Hostinová E, Solovicova A, Dvorsky R, Gasperik J. Molecular cloning and 3D structure prediction of the first raw-starch-degrading glucoamylase without a separate starch-binding domain. Arch Biochem Biophys. 2003;411:189–95.View ArticleGoogle Scholar
  53. McCleary BV, Shameer I, Glennieholmes M. Measurement of (1→3), (1→4)-β-d-glucan. Methods Enzymol. 1988;160:545–51.View ArticleGoogle Scholar
  54. Celestino KR, Cunha RB, Felix CR. Characterization of a β-glucanase produced by Rhizopus microsporus var. microsporus, and its potential for application in the brewing industry. BMC Biochem. 2006;7:23.View ArticleGoogle Scholar
  55. Kitano H, Kataoka K, Furukawa K, Hara S. Specific expression and temperature-dependent expression of the acid protease-encoding gene (pepA) in Aspergillus oryzae in solid-state culture (Rice-Koji). J Biosci Bioeng. 2002;93:563–7.View ArticleGoogle Scholar
  56. Hirata D, Fukui S, Yamashita I. Nucleotide sequence of the secretable acid protease gene PEP1 in the yeast Saccharomycopsis fibuligera. Agric Biol Chem. 1988;52:2647–9.Google Scholar
  57. Albrecht A, Felk A, Pichova I, Naglik JR, Schaller M, de Groot P, et al. Glycosylphosphatidylinositol-anchored proteases of Candida albicans target proteins necessary for both cellular processes and host-pathogen interactions. J Biol Chem. 2006;281:688–94.View ArticleGoogle Scholar
  58. Nakao Y, Kanamori T, Itoh T, Kodama Y, Rainieri S, Nakamura N, et al. Genome sequence of the lager brewing yeast, an interspecies hybrid. DNA Res. 2009;16:115–29.View ArticleGoogle Scholar
  59. Cliften PF, Hillier LW, Fulton L, Graves T, Miner T, Gish WR, et al. Surveying Saccharomyces genomes to identify functional elements by comparative DNA sequence analysis. Genome Res. 2001;11:1175–86.View ArticleGoogle Scholar
  60. Yang S, Lee J, Kwak J, Kim K, Seo M, Lee YW. Fungi associated with the traditional starter cultures used for rice wine in Korea. J Korean Soc Appl Biol Chem. 2011;54:933–43.View ArticleGoogle Scholar
  61. Tanimura A, Kikukawa M, Yamaguchi S, Kishino S, Ogawa J, Shima J. Direct ethanol production from starch using a natural isolate, Scheffersomyces shehatae: toward consolidated bioprocessing. Sci Rep. 2015;5:9593.View ArticleGoogle Scholar
  62. Gupta VK, Steindorff AS, de Paula RG, Silva-Rocha R, Mach-Aigner AR, Mach RL, et al. The post-genomic era of Trichoderma reesei: what’s next? Trends Biotechnol. 2016. doi:10.1016/j.tibtech.2016.06.003.Google Scholar
  63. Jacques N, Louis-Mondesir C, Coton M, Coton E, Casaregola S. Two novel Saccharomycopsis species isolated from black olive brines and a tropical plant. Description of Saccharomycopsis olivae f. a., sp. nov. and Saccharomycopsis guyanensis f. a., sp. nov. Reassignment of Candida amapae to Saccharomycopsis amapae f. a., comb. nov., Candida lassenensis to Saccharomycopsis lassenensis f. a., comb. nov. and Arthroascus babjevae to Saccharomycopsis babjevae f. a., comb. nov. Int J Syst Evol Microbiol. 2014;64(Pt 6):2169–75.View ArticleGoogle Scholar
  64. Tsai HH, Chen YC, Lee WR, Hu CH, Hakozaki T, Yoshii T, et al. Inhibition of inflammatory nitric oxide production and epidermis damages by Saccharomycopsis ferment filtrate. J Dermatol Sci. 2006;42:249–57.View ArticleGoogle Scholar
  65. White TJ, Bruns TD, Lee S, Taylor JW. PCR protocols: a guide to methods and applications. Acad Press. 1990;18:315–22.Google Scholar
  66. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.View ArticleGoogle Scholar
  67. Chen L, Zhong HY, Kuang JF, Li JG, Lu WJ, Chen JY. Validation of reference genes for RT-qPCR studies of gene expression in banana fruit under different experimental conditions. Planta. 2011;234:377–90.View ArticleGoogle Scholar
  68. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.View ArticleGoogle Scholar
  69. Anders S, Pyl PT, Huber W. HTSeq-a python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.View ArticleGoogle Scholar
  70. Sun JQ, Nishiyama T, Shimizu K, Kadota K. TCC: an R package for comparing tag count data with robust normalization strategies. BMC Bioinform. 2013;14:219.View ArticleGoogle Scholar
  71. Lomsadze A, Burns PD, Borodovsky M. Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm. Nucleic Acids Res. 2014;42:e119.View ArticleGoogle Scholar
  72. Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008;24:637–44.View ArticleGoogle Scholar
  73. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.View ArticleGoogle Scholar
  74. Slater GS, Birney E. Automated generation of heuristics for biological sequence comparison. BMC Bioinform. 2005;6:31.View ArticleGoogle Scholar
  75. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.View ArticleGoogle Scholar
  76. Wang YP, Tang HB, DeBarry JD, Tan X, Li JP, Wang XY, et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012;40:e49.View ArticleGoogle Scholar
  77. Quevillon E, Silventoinen V, Pillai S, Harte N, Mulder N, Apweiler R, et al. InterProScan: protein domains identifier. Nucleic Acids Res. 2005;33:W116–20.View ArticleGoogle Scholar
  78. Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21:3674–6.View ArticleGoogle Scholar
  79. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25:955–64.View ArticleGoogle Scholar
  80. Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013;29:2933–5.View ArticleGoogle Scholar
  81. Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinform. 2004;25:4.10.1–14.Google Scholar
  82. Soderlund C, Bomhoff M, Nelson WM. SyMAP v3.4: a turnkey synteny system with application to plant genomes. Nucleic Acids Res. 2011;39:e68.View ArticleGoogle Scholar
  83. Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;22:4673–80.View ArticleGoogle Scholar
  84. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–30.View ArticleGoogle Scholar
  85. Drillon G, Carbone A, Fischer G. SynChro: a fast and easy tool to reconstruct and visualize synteny blocks along eukaryotic chromosomes. PLoS ONE. 2014;9:e92621.View ArticleGoogle Scholar


© The Author(s) 2016