A unique metabolic gene cluster regulates lactose and galactose metabolism in the yeast Candida intermedia
ABSTRACT
Lactose assimilation is a relatively rare trait in yeasts, and Kluyveromyces yeast species have long served as model organisms for studying lactose metabolism. Meanwhile, the metabolic strategies of most other lactose-assimilating yeasts remain unknown. In this work, we have elucidated the genetic determinants of the superior lactose-growing yeast Candida intermedia. Through genomic and transcriptomic analyses, we identified three interdependent gene clusters responsible for the metabolism of lactose and its hydrolysis product galactose: the conserved LAC cluster (LAC12, LAC4) for lactose uptake and hydrolysis, the conserved GAL cluster (GAL1, GAL7, and GAL10) for galactose catabolism through the Leloir pathway, and a “GALLAC” cluster containing the transcriptional activator gene LAC9, second copies of GAL1 and GAL10, and a XYL1 gene encoding an aldose reductase involved in carbon overflow metabolism. Bioinformatic analysis suggests that the GALLAC cluster is unique to C. intermedia and has evolved through gene duplication and divergence, and deletion mutant phenotyping proved that the cluster is indispensable for C. intermedia’s growth on lactose and galactose. We also show that the regulatory network in C. intermedia, governed by Lac9 and Gal1 from the GALLAC cluster, differs significantly from the galactose and lactose regulons in Saccharomyces cerevisiae, Kluyveromyces lactis, and Candida albicans. Moreover, although lactose and galactose metabolism are closely linked in C. intermedia, our results also point to important regulatory differences.
IMPORTANCE
This study paves the way to a better understanding of lactose and galactose metabolism in the non-conventional yeast C. intermedia. Notably, the unique GALLAC cluster represents a new, interesting example of metabolic network rewiring and likely helps to explain how C. intermedia has evolved into an efficient lactose-assimilating yeast. With the Leloir pathway of budding yeasts acting like a model system for understanding the function, evolution, and regulation of eukaryotic metabolism, this work provides new evolutionary insights into yeast metabolic pathways and regulatory networks. In extension, the results will facilitate future development and use of C. intermedia as a cell-factory for conversion of lactose-rich whey into value-added products.
INTRODUCTION
Assimilation of lactose is a rather uncommon characteristic among microorganisms, including yeasts. Growth screening of 332 genome-sequenced yeasts from the Ascomycota phylum showed that only 24 (<10%) species could grow on lactose, and these lactose utilizers are scattered throughout the phylogenetic tree (1). The “dairy yeasts” from the Kluyveromyces genus, including Kluyveromyces lactis and Kluyveromyces marxianus, have been carefully characterized (2–5), whereas most other lactose-metabolizing yeast species remain largely understudied. Elucidating the mechanisms behind their lactose metabolism can help shed light on how eukaryotic metabolic pathways and the associated regulatory networks have evolved. Moreover, it can enable the development of new yeast species as cell factories for conversion of lactose in the abundant industrial side stream cheese whey into a range of different products (6).
Lactose, a disaccharide of D-glucose and D-galactose connected through a β-1,4-glycosidic linkage, is hydrolysed by lactase enzymes, typically β-galactosidases. Several different enzyme families encode lactases, which can be found intracellularly or extracellularly. In Kluyveromyces yeasts, lactose is transported across the plasma membrane by a LAC12-encoded lactose permease and hydrolyzed intracellularly by a LAC4-encoded β-galactosidase (5). The lactose-derived glucose and galactose moieties are further catabolized through glycolysis and the Leloir pathway, respectively. The Leloir pathway is carried out by Gal1, Gal7, and Gal10 and starts by conversion of β-D-galactose into α-D-galactose by the mutarotase domain of Gal10 (aldose-1-epimerase). Gal1 (galactokinase) then phosphorylates α-D-galactose into α-D-galactose-1-phosphate, whereafter Gal7 (galactose-1-phosphate uridylyl transferase) transfers uridine diphosphate (UDP) from UDP-α-D-glucose-1-phosphate to α-D-galactose-1-phosphate (7). The epimerase (UDP-galactose-4-epimerase) domain of Gal10 catalyzes the final step, where UDP-α-D-galactose-1-phosphate is converted to UDP-α-D-glucose-1-phosphate (8–10). In parallel to the Leloir pathway, some yeasts and filamentous fungi have an alternative galactose catabolic pathway called the oxidoreductive pathway, where galactose is first converted into galactitol through the action of an aldose reductase (9, 10).
Comparative genomic studies have revealed that the GAL1, 7, and 10 genes often form a “GAL cluster” in yeast genomes (11). Similarly, LAC4 and LAC12 form a “LAC cluster” in, for example, K. marxianus and K. lactis (5, 11). Such metabolic gene clusters are particularly prevalent for pathways involved in sugar and nutrient acquisition and synthesis of vitamins and secondary metabolites (12). Like bacterial operons, the eukaryotic cluster genes are co-regulated in response to environmental changes, allowing the microorganism to rapidly adapt to environmental cues and avoiding deleterious recombination events and high concentrations of local protein products. For example, co-regulation of the GAL genes prevents accumulation of the toxic intermediate galactose-1-phosphate (11, 13).
In Saccharomyces cerevisiae, the three proteins ScGal4, ScGal80, and ScGal3 are responsible for galactose regulation. In the absence of galactose, the transcriptional activation domain of ScGal4 is bound to the inhibitor ScGal80. In the presence of galactose, ScGal3 relieves ScGal4 from ScGal80 in a galactose- and ATP-dependent manner, resulting in the induction of the GAL structural genes (14–17). Like for S. cerevisiae, the K. lactis GAL regulatory system relies on relieving KlLac9 (ortholog of ScGal4) from KlGal80 inhibition. However, K. lactis lacks Gal3 and instead uses a bifunctional galactokinase KlGal1 to induce both galactose and lactose genes (18). Similar to K. lactis, Candida albicans lacks Gal3 but possesses a CaGal1 with both enzymatic and regulatory functions, but in this yeast, the GAL gene expression is controlled by transcription factors CaRtg1/CaRtg3 (19) and/or CaRep1/CaCga1 (14, 20). Such transcriptional rewiring is common among yeasts, which calls for coupling of comparative genomics with detailed mutant phenotyping and transcriptional analysis to decipher how regulation occurs in individual species.
While galactose and lactose metabolism in S. cerevisiae and K. lactis has long served as a model system for understanding the function, evolution, and regulation of eukaryotic metabolic pathways, the corresponding knowledge in other yeasts is scarce. One such understudied species is Candida intermedia, a haploid yeast belonging to the Metschnikowia family in the CUG-Ser1 clade (1), which has previously received attention as a fast-growing yeast on xylose (21–26). C. intermedia is one of very few yeasts in the Metschnikowia family that can grow on lactose (1), and it has been used for cheese whey bioremediation in the past (27). Our previous works on the in-house isolated C. intermedia strain CBS 141442 in terms of genomics, transcriptomics, and physiology (24, 28, 29) and genetic toolbox development (30) provide a stable platform for exploration of the genetic determinants of lactose metabolism in this yeast. In the present study, we show that C. intermedia possesses a unique “GALLAC” cluster, in addition to the conserved GAL and LAC clusters, which proved to be essential for growth on lactose and highly important for growth on galactose. Characterization of the individual GALLAC cluster genes revealed differentiation in their functionality, enabling the yeast to regulate the expression of galactose and lactose genes differently. This cluster represents a new, interesting example of metabolic network rewiring in yeast and sheds light on how C. intermedia has evolved into an efficient lactose-assimilating yeast.
RESULTS
C. intermedia is among the top five lactose growers out of 332 sequenced ascomycetous yeasts
As a start, we wanted to assess the capacity of C. intermedia to grow on lactose compared with other yeasts. We cultured the 24 out of 332 ascomycetous species that have previously scored positive for lactose growth (1), as well as C. intermedia strains CBS 572 (type strain), CBS 141442, and PYCC 4715 (previously characterized for utilization of xylose) (1, 25). The yeasts displayed different growth patterns in lag phase, doubling time, and final biomass (Fig. 1; Fig. S1). When ranked based on lowest doubling time, K. lactis and K. marxianus were the fastest growers on lactose, closely followed by C. intermedia strains PYCC 4715 and CBS 141442, Debaryomyces subglobusus, and Blastobotrys muscicola (Fig. 1; Fig. S1). Species such as Kluyveromyces aestuarii, Millerozyma acaciae, and Lipomyces mesembris showed poor or no growth under the conditions tested, while others had very long lag phases. Thus, under the assessed conditions, our results establish Candida intermedia as one of the top five fastest lactose-growing species out of the 24 ascomycetous yeasts tested.
Fig 1
Genomic and transcriptomic analyses identify three gene clusters involved in lactose and galactose assimilation
To identify the genetic determinants for lactose metabolism in C. intermedia CBS 141442, we searched the genome for orthologs of known genes involved in the uptake and conversion of lactose and its tightly coupled hydrolysis-product, galactose. We found several genes encoding expected transcription factor orthologs including LAC9, GAL4, RTG1, RTG3, REP1, and CGA1 that have been associated with lactose and galactose metabolism in K. lactis (31), S. cerevisiae (32), and C. albicans (19, 20). In accordance with previous reports for yeasts belonging to the genus Candida (8), we did not find orthologs of GAL80, strongly suggesting that C. intermedia does not possess the Gal3-Gal80-Gal4 regulon.
Moreover, the genome of C. intermedia contains the conserved GAL cluster including GAL1, 7, and 10 genes as well as an ORF-X gene encoding a putative glucose-4,6-dehydratase, similar to GAL clusters in Candida/Schizosaccharomyces strains (8, 11) (Fig. 2A). We also identified the conserved LAC cluster containing the β-galactosidase gene LAC4 and lactose permease gene LAC12 (2, 3, 5), which correlates well with C. intermedia predominantly displaying intracellular β-galactosidase activity (data not shown). To our surprise, C. intermedia also possesses a third cluster, hereafter referred to as the GALLAC cluster, containing a putative transcriptional regulator gene LAC9 (LAC9_2) next to a second copy of the GAL1 gene (GAL1_2), followed by one of the three xylose/aldose reductase genes (XYL1_2) previously characterized in C. intermedia (28), and lastly, a second copy of GAL10 (GAL10_2). Interestingly, the GAL10_2 gene is shorter than GAL10 in the GAL cluster and seems to encode only the epimerase domain, similar to GAL10 orthologs in Schizosaccharomyces species and filamentous fungi (8).
Fig 2
Next, we performed transcriptome analysis using RNA-seq technology on the CBS 141442 strain cultivated in media containing 2% of either lactose, galactose, or glucose (Fig. 2B). Compared with glucose, we identified 516 genes with over twofold differential expression in galactose: 58 downregulated and 458 upregulated. In lactose, 423 genes were differentially expressed: 56 downregulated and 367 upregulated. Notably, 299 genes were >twofold differentially expressed in both galactose and lactose, and the vast majority of the genes were regulated in the same direction, suggesting a strong regulatory and metabolic interconnection between the two carbon sources (Table S1). All genes in the LAC and GAL clusters and the GAL10_2 and XYL1_2 genes in the GALLAC cluster were among the 20 highest upregulated genes in both galactose and lactose (Table S1). Also GAL1_2 was greater than twofold upregulated on both of these carbon sources, while the LAC9_2 gene was constitutively expressed in all three carbon sources (Fig. 2A). Among the most highly expressed genes, we also identified several additional LAC12 paralogs encoding putative disaccharide transporters, HGT1_2 and MAL11 that likely encode galactose and maltose transporters, and BGLS_2 predicted to encode a β-glucosidase (Table S1). These gene products may have functions during growth on galactose and lactose, or they may simply be derepressed in the absence of glucose.
The GALLAC cluster is essential for growth on lactose and unique to C. intermedia
To decipher the importance of the three clusters for galactose and lactose metabolism in C. intermedia, we deleted the clusters one by one using the split-marker technique previously developed for this yeast (30). The cluster deletion mutants (lacΔ, galΔ, and gallacΔ) grew almost as well as the wild-type (WT) strain in minimal media containing glucose (Fig. 3A). As expected, galΔ failed to grow on galactose, which can be attributed to the complete shut-down of the Leloir pathway, whereas the lacΔ grew like the WT. Interestingly, no growth was observed for the gallacΔ in galactose during the first 90 h, whereafter it started to grow slowly (Fig. 3B). With lactose as a carbon source, both lacΔ and gallacΔ completely failed to grow, whereas galΔ started to grow slowly after approx. 100 h (Fig. 3C). Neither of the slow-growing mutants reached the same biomass as the WT during a total of 200 h of cultivation. It is important to note that the late growth observed could be due to suppressing mutations in other parts of the genome or complementation by enzymes expressed from paralogous genes. Nevertheless, our results show that the GALLAC cluster is essential for growth on lactose and at least highly important for growth on galactose.
Fig 3
To the best of our knowledge, the existence of a GALLAC-like cluster and its interdependence with the GAL and LAC clusters have never been reported before. This, along with the severe growth defects of gallacΔ, encouraged us to determine the origin and prevalence of the cluster in other yeasts. First, we performed a comparative genomic analysis among the data set of 332 genome-sequenced ascomycetous yeasts (1), determining which yeasts contain the GAL, LAC, and GALLAC cluster orthologs and determining the genes’ respective genomic location. Although GAL1 and GAL10 were found clustered together as parts of the conserved GAL clusters in 150/332 species, C. intermedia was the only species where these genes also clustered with LAC9 and XYL1 genes (Table S2). Importantly, the GALLAC cluster was found in the genomes of all five C. intermedia strains assessed (CBS 572, CBS 141442, PYCC 4715, NRRL Y-981, and P5906) (Table S3). Next, to decipher the evolutionary events that led to the formation of the GALLAC cluster, we generated phylogenetic trees for each individual gene product of the cluster (exemplified in Fig. 3D; additional trees are shown in Fig. S2 through S4). Our analysis revealed that although the amino acid identities between the paralogs in C. intermedia are relatively low (56% for Gal1 and Gal1_2, 72% for Gal110 and Gal10_2, 49% for Lac9_2 and Lac9, and 66% and 62% for Xyl1_2 compared with Xyl1 and Xyl1_3, respectively); the identities between the paralogs are still higher than for most orthologs in other species. Combined, these results strongly suggest that the unique GALLAC cluster has evolved within C. intermedia through gene duplication and divergence (Fig. 3E).
Deletion of individual genes in the GAL and GALLAC clusters reveals importance of Lac9_2 and Gal1_2 for galactose and lactose metabolism
To elucidate the physiological function of genes situated in the GALLAC cluster and to better understand the interdependence between the clusters, we deleted individual genes in both the GALLAC and GAL clusters. The mutant phenotypes were compared with WT and complete cluster deletions regarding growth, consumption of sugars, and production of metabolites in defined media containing either 2% galactose or lactose.
With galactose as carbon source, deletion of LAC9_2 located in the GALLAC cluster resulted in an extended lag phase accompanied by galactitol production, indicating that this putative transcription factor is involved in the regulation of galactose metabolism (Fig. 4A). In contrast, deletion of the other genes in the GALLAC cluster did not result in severe growth defects. For mutants deleted of individual GAL cluster genes and grown in galactose, we saw the expected severe growth defects for gal1Δ, gal7Δ, and gal10Δ (Fig. 4A). However, gal10Δ repeatedly displayed some growth after a very long lag phase of approx. 250 h (Fig. S5), which could suggest that Gal10_2 from the GALLAC cluster can partly complement the deletion of GAL10 from the GAL cluster.
Fig 4
With lactose as carbon source, lac9_2Δ displayed a delay in the onset of growth similar to what was observed with galactose, while gal10_2Δ and xyl1_2Δ grew like the WT (Fig. 4B). However, in contrast to growth on galactose, deletion of GAL1_2 alone abolished growth and resembled the deletion of the whole GALLAC cluster, indicating an important function for the encoded protein in lactose metabolism and a clear phenotypic difference between the two carbon sources. On the contrary, deletion of GAL1 from the GAL cluster did not fully abolish growth on lactose, but growth was slower and accompanied with substantial accumulation of galactitol (3.4 g/L from a total of 10.8 g lactose consumed), suggesting that most of the lactose-derived galactose is catabolized through the action of an aldose reductase (such as Xyl1_2), rather than through the putative galactokinase Gal1_2 in this mutant. Also, gal10Δ grew slowly but with no measurable accumulation of galactose or galactitol, again suggesting that the GAL10_2 in the GALLAC cluster can partly complement this deletion. Deletion of the only copy of the GAL7 gene encoding for galactose-1-phosphate uridylyltransferase resulted in complete growth inhibition on lactose (as for galactose), and we speculate that the severe growth phenotype is due to the accumulation of toxic intermediate galactose-1-phosphate as seen in S. cerevisiae in previous studies (13).
Since no single deletion resembled the growth defect observed for gallacΔ on galactose, we hypothesized that two or more genes must be deleted for the same phenotype to occur. Because construction of double mutants in C. intermedia is very time-consuming, we prioritized creating a lac9_2Δgal1_2Δ double deletion mutant, as deletion of LAC9_2 extended the lag phase in both carbon sources and deletion of GAL1_2 led to a severe growth defect in lactose. Indeed, the resulting strain displayed a growth defect in galactose strikingly similar to that of the complete GALLAC cluster mutant (Fig. 4C). Although we cannot rule out that other double mutants in the GALLAC cluster would also exhibit severe growth phenotypes, we can conclude that Lac9_2 and Gal1_2 have important functions during both galactose and lactose growth.
Lac9 binding motifs are found in promoters in the GALLAC cluster genes and regulates their expression in galactose and lactose
To better understand the putative role of Lac9_2 as a transcriptional regulator, we performed Multiple Em for Motif Elicitation (MEME; version 5.5.43) (33) analysis to identify conserved transcription factor binding motifs in gene promoters in the three clusters. The analysis revealed Lac9 (Gal4) binding motifs (P value = 8.66 × 10−3) in the promoters of GAL1_2, XYL1_2, and GAL10_2 in the GALLAC cluster (Fig. 5A), but not in the promoters in the GAL and LAC clusters. These results confirm the bioinformatic analysis of the 332 ascomycetous yeast recently published, showing that C. intermedia and many other CUG-Ser1 clade yeasts lack Lac9/Gal4 binding sites in the GAL clusters (11). Besides Lac9/Gal4, we also found binding sites for other transcription factors, including Mig1 that is known to repress GAL genes in S. cerevisiae (34) (Fig. 5A).
Fig 5
To determine if the deletion of LAC9_2 affects the expression of GALLAC cluster genes, we grew WT and lac9_2Δ in glucose, galactose, and lactose. Samples were taken during the lag phase and early and late logarithmic phases for the two strains, and quantitative PCR (qPCR) was performed to measure the expression of GAL1_2, GAL10_2, and XYL1_2. While both strains exhibited low expression of all three genes in glucose, the WT strain showed significant upregulation of all three genes in galactose and lactose, although GAL1_2 was not upregulated to the same extent as the other two genes (in line with the RNA-seq results). In contrast, the lac9_2Δ strain had markedly lower expression of all three genes throughout growth in galactose and lactose, indicating a clear role of Lac9_2 as a regulator of the GALLAC cluster genes (Fig. 5B).
Besides LAC9_2 in the GALLAC cluster, our comparative genomic analysis also identified a second, non-clustered LAC9 gene (Fig. 3) as well as GAL4 gene. All three proteins have predicted Gal4-like DNA-binding domains, but they differ substantially in protein sequence identity (45% identity between Lac9_2 and Lac9, while Gal4 is only 18% and 19% identical to Lac9 and Lac9_2, respectively). Deletion mutants of LAC9 and GAL4 did not display growth defects on lactose or galactose (Fig. S6). Although we cannot exclude the possibility of functional redundancy between the Lac9 paralogs, our results indicate that the GALLAC cluster-derived Lac9_2 is the most important transcriptional regulator for galactose and lactose metabolism in C. intermedia and that Lac9_2 likely exerts its regulatory role by directly binding to the promoters and controlling the expression of the GALLAC cluster genes.
Gal1_2 is required for the induction of LAC cluster genes in C. intermedia
Our deletion mutant phenotyping results suggest that Gal1 and Gal1_2 have at least partly different physiological functions in C. intermedia (Fig. 4). As the two GAL1 genes are highly upregulated on both galactose and lactose in the WT strain (Fig. 2), we speculated that the encoded proteins must differ in their activities as galactokinases or regulators. To this end, we expressed both proteins in S. cerevisiae BY4741 gal1Δ, which successfully rescued the mutant’s growth defect on galactose (Fig. 6A). This experiment demonstrates that both proteins have galactokinase activity, at least when expressed in S. cerevisiae. We also compared the predicted structures of Gal1 and Gal1_2 using AlphaFold2 (35, 36), observing that even though the amino acid sequence identity between the two proteins is as low as 56%, the protein structures are very similar to each other (rmsd 0.490 Å; Fig. 6B) as well as to the experimentally solved structure of ScGal142 (rmsd 0.778 and 0.758 Å for Gal1 and Gal1_2, respectively). Additionally, we observed that the amino acids interacting with galactose in ScGal1 (PDB ID: 2aj4) are identical to those in the CiGal1 proteins, apart from Asn213 in ScGal1 (Asn205 in CiGal1), which interacts with the O2 hydroxyl group, which in CiGal1_2 is instead a serine residue (Ser199). The active site clefts of all enzymes are only big enough to accommodate monosaccharides like galactose. Thus, it is highly unlikely that they bind to other, larger substrates such as lactose (Fig. 6B). In S. cerevisiae, the regulator ScGal3 is similar in structure to the galactokinase ScGal1 but has lost its galactokinase activity due to an addition of two extra amino acids (Ser-Ala dipeptide) (37). However, no such structural changes were seen for CiGal1 or CiGal1_2 that could help us predict regulatory functions.
Fig 6
We examined the role of Gal1 and Gal1_2 as regulators of lactose metabolism by performing β-galactosidase assays with C. intermedia strains gal1Δ, gal1_2Δ, and the WT (positive control) and lacΔ (negative control). Our RNA-seq data showed that in the WT, LAC4 is expressed during growth in both galactose and lactose (Fig. 2). Thus, we assessed the lactase activity during growth in both these sugars to include at least one condition where all strains could grow. Lactase activity was readily detected in the WT during growth in both galactose and lactose. In gal1Δ cells that do not grow in galactose, we only observed lactase activity during lactose growth. In contrast, gal1_2Δ that only grows in galactose displayed no lactase activity during growth in this carbon source, similar to lacΔ where the lactase gene LAC4 has been deleted (Fig. 6C). These results suggest that Gal1_2 is essential for induction of lactase activity.
Moreover, qPCR analysis of the WT and gal1_2Δ revealed that LAC4 expression in lactose was diminished in gal1_2Δ as compared with the WT. Conversely, GAL1 expression was higher in the mutant compared with the WT during the first 28 h of cultivation and was then markedly downregulated at 40 h, likely due to the lack of growth for this mutant in lactose (Fig. 6D). This indicates that regulation of LAC4 is exerted on the transcriptional level and fortifies the growth phenotyping results where we saw a clear difference in growth on galactose (+) and lactose (−) for gal1_2Δ. Overall, these results firmly establish a difference in function between Gal1 and Gal1_2, where a lack of Gal1_2 diminishes lactase activity while a lack of Gal1 does not, and further indicate important differences in the regulation of lactose and galactose metabolism and growth.
DISCUSSION
In this work, we have investigated how galactose and lactose are metabolized in the non-conventional yeast C. intermedia and shed light on the genetic determinants behind this trait. Interestingly, we found that the genome of C. intermedia contains not only the conserved GAL and LAC clusters but also a unique GALLAC cluster that has evolved through gene duplication and divergence. Our results show that galactose metabolism is impaired in both galΔ and gallacΔ strains, while lactose metabolism is impaired in all three cluster deletion strains. As the GAL cluster encodes the structural genes in the Leloir pathway, it is logical that deletion of this cluster effectively suppresses galactose metabolism. In the gallacΔ strain, the GAL cluster remains intact, but the strain failed to grow, indicating that both clusters are needed for galactose and lactose growth and thus demonstrating their interdependence. By combining results from comparative genomics, transcriptomics analysis, deletion mutant phenotyping, and metabolite profiling, we have started to unravel parts of the regulatory networks of the three clusters and can show that the GALLAC cluster plays a vital role in regulating both galactose and lactose metabolism in this yeast. With the Leloir pathway of budding yeasts acting like a model system for understanding the function, evolution, and regulation of eukaryotic metabolic pathways, this work adds interesting new pieces to the puzzle.
Our results show that C. intermedia grows relatively fast on lactose, and strains of this species have been isolated several times from lactose-rich niches including fermentation products like white-brined cheese (38) and cheese whey (39). In these lactose-rich environments, survival likely necessitates a genetic makeup that can help outcompete rivaling microorganisms. Is the GALLAC cluster facilitating the fast lactose growth observed for C. intermedia, and if so, how? This is currently unresolved, but the genes within the cluster and the mutant phenotyping results provide some clues. First, the GALLAC cluster seems to have important regulatory functions, which can help finetune metabolic fluxes and growth. We demonstrate that the cluster-encoded transcription factor Lac9_2 is important for onset of galactose and lactose growth, as deletion of LAC9_2 leads to increased lag phases on both carbon sources. However, as lac9_2Δ cells eventually grow, Lac9_2 cannot be solely responsible for expression of the metabolic genes. Moreover, Lac9 binding motifs were only found in the promoters of GALLAC genes, suggesting that other transcriptional activators are responsible for induction of the GAL and LAC cluster genes.
In addition to Lac9_2, Gal1_2 from the GALLAC cluster seems to be an important regulator of galactose and lactose growth. The bioinformatic analysis strongly suggests that GAL1_2 in C. intermedia formed through gene duplication and divergence from the GAL1 gene in the GAL cluster. Our results also show that Gal1_2 is essential for LAC4 transcription and, in extension, lactase activity and lactose growth, whereas deletion of GAL1_2 alone did not abolish GAL1 expression and galactose growth. Although we have not provided evidence for a direct regulatory role of Gal1_2, these results suggest that the original Gal1 has maintained the function as the main galactokinase, while Gal1_2 has taken on the role as an important regulator. This evolutionary trajectory mirrors the path taken by Gal1 and Gal3 in S. cerevisiae (37), but with a crucial distinction: the Gal1 proteins in C. intermedia have evolved in response to both lactose and galactose. On galactose, impairing growth required the deletion of both GAL1_2 and LAC9_2, whereas the deletion of GAL1_2 alone was sufficient to impair growth on lactose. This suggests that the yeast senses and regulates the expression of genes for galactose and lactose metabolism somewhat differently. Since Gal1_2 does not have a DNA binding capacity, we hypothesize that Gal1_2 binds galactose and thereafter activates unknown transcription factor(s) that ultimately bind and induce expression from the LAC and GAL clusters (Fig. 7). It should be noted that the GAL cluster likely also has a regulatory role, as indicated by the fact that the galΔ strain grows poorly on lactose, even though it possesses an intact LAC cluster and a functional glycolysis pathway for glucose catabolism. Although many details are still to be elucidated, it is clear that C. intermedia has developed a way of regulating its galactose and lactose metabolism that differs from other yeast species studied to date, including the Gal3-Gal80-Gal4 regulon in S. cerevisiae (40), the Gal1-Gal80-Lac9 equivalent in K. lactis (31), and the Rep1-Cga1 regulatory complex in C. albicans (20) (Fig. 7). Future research will include identifying these unknown transcription factors and fully elucidating the roles of Lac9_2 and Gal1_2 in sensing, signaling, and regulating the cellular response to changes in the nutritional environment.
Fig 7
Another interesting feature of the GALLAC cluster is the XYL1_2 gene encoding an aldose reductase. Although no galactitol or other intermediates of an oxidoreductive pathway were detected in the WT under the growth conditions assessed, several of the constructed mutants (in particular, galΔ and gal1Δ) accumulate galactitol upon growth on lactose. In S. cerevisiae, galactitol functions as an overflow metabolite ensuring that cells avoid accumulation of galactose-1-phosphate, a known toxic intermediate of the Leloir pathway in the cell (13, 41), and it is reasonable to assume that the same is true for C. intermedia. Moreover, it is interesting to note that aldose reductases can directly convert β-D-galactose, the hydrolysis product of lactose, whereas galactokinase requires β-D-galactose conversion into α-D-galactose before it can be metabolized via the Leloir pathway. We speculate that induction of an aldose reductase gene in tandem with the LAC and GAL genes in response to lactose (and galactose) can be an efficient way to quickly metabolize these sugars, providing a growth advantage in competitive lactose-rich environments.
In addition to the basic scientific questions that can be answered by studying evolution and sugar metabolism in lactose-growing yeast species, these yeasts can also be used as cell factories in industrial biotechnology processes. Here, a better understanding of the underlying genetics for this trait enables metabolic engineering to optimize the conversion of lactose-rich whey into value-added products. The dairy yeasts K. lactis and K. marxianus have been developed and used for whey-based production of ethanol (42), recombinant proteins (43), and bulk chemicals such as ethyl acetate (44), while exploration of new lactose-metabolizing yeasts allows for additional product diversification. With lactose as substrate, a carbon-partition strategy can be used for bioproduction, where the glucose moiety is converted into energy and yeast biomass and the galactose moiety is steered into production of the wanted metabolite, or vice versa (45). Through this strategy, the non-conventional yeast C. intermedia can also be explored to produce various growth-coupled metabolites, including galactitol and derivatives thereof.
In conclusion, our work on the non-conventional, lactose-metabolizing yeast C. intermedia has paved the way toward a better understanding of the galactose and lactose metabolism in this relatively understudied species. To the best of our knowledge, we show for the first time that gene duplication and divergence resulted in the formation of a unique GALLAC cluster and its essential role in galactose and lactose metabolism in this yeast, providing new insights of how organisms can evolve metabolic pathways and regulatory networks. In addition, the proven ability of C. intermedia to grow relatively well on lactose establishes this yeast as an interesting lactose-assimilating species also for future industrial applications.
MATERIALS AND METHODS
Culture conditions and molecular techniques
For amplification of plasmids, E. coli was grown on LB medium (1% tryptone, 1% NaCl, and 0.5% yeast extract) containing ampicillin (100 µg/mL) for plasmid selection.
C. intermedia CBS 141442 was grown in YPD medium (1% yeast extract, 2% bactopeptone, and 2% glucose) prior to yeast transformation using the split marker technique as described previously (30). Using this technique, deletion cassettes were constructed as two partially overlapping fragments, each containing half of the selection marker fused to either upstream or downstream sequences of the target gene. Deletion fragments were transformed using electroporation (Bio-Rad Micropulse electroporator) (30). After transformation, cells were plated on YPD agar containing 200 µg/mL nourseothricin to select for integration and expression of the CaNAT1 selection marker.
Colony PCR was used to identify transformants with correct gene deletions, where single colonies were resuspended in 50 µL dH2O using a sterile toothpick and then heated to 90°C for 10 min. After cooling to 12°C, 2 µL of each suspension was used as a template for PCR using PHIRE II polymerase (Thermo Fisher Scientific, USA). For each mutant, three PCR primers were used, where the first primer was designed to hybridize to the genome outside the flanking region, the second to the marker gene and the third to the targeted gene (negative control). Since replicative plasmids and overexpression cassettes are missing for C. intermedia, we have not been able to perform complementation analyses to ensure that the targeted deletions are linked to the phenotypes we observe. Instead, three individual transformants for each deleted gene have been carefully tested, to ensure that they exhibit the same growth phenotype. To construct the double gene deletion mutant (lac9_2, the split marker method was used twice in the same strain background, first employing the split CaNAT1 selection marker as described above and then a split KanMX selection marker PCR amplified from the plasmid pTO149_RFP_CauNEO developed for Candida auris) (46). Correctly assembled and genome-integrated KanMX markers gave rise to C. intermedia transformants resistant to the antibiotic Geneticin (200 µg/mL).
For complementation tests in S. cerevisiae, C. intermedia GAL1 and GAL1_2 genes were synthesized and cloned in a vector backbone (pESC-URA; GenScript Biotech, New Jersey, USA). Codon CTG were adjusted to alternate codon prior to optimization of the complete gene for expression in S. cerevisiae using the GenSmart Codon Optimization tool (GenScript Biotech, New Jersey, USA). S. cerevisiae BY4741 GAL1 knockouts used for complementation experiment were grown on YP media with 2% glucose and transformed with above-mentioned plasmids using LiAc/PEG heat-shock method (47). Transformants were selected on agar plates with YNB -uracil and 2% glucose, restreaked, and then tested for growth in liquid YNB-URA media with 2% galactose in GrowthProfiler at 30°C and 250 rpm. S. cerevisiae BY4741 gal1Δ transformed with p426 (empty vector with URA3 as selection marker) was used as negative control.
Growth experiments
Growth profiler
To follow growth over time for C. intermedia CBS 141442 and the other yeasts characterized in this work, strains were precultured at 30°C, 180 rpm overnight in synthetic defined minimal Verduyn media (48) containing 2% glucose (wt/vol). Precultured cells were then inoculated in 250 µL minimal media supplemented with 20 g/L carbon source to a starting OD600 = 0.1. All yeast strains were grown in biological triplicates in a 96-well plate setup in a GrowthProfiler 960 (Enzyscreen, Netherlands). “Green Values” (GV) measured by the GrowthProfiler correspond to growth based on pixel counts, and GV changes were recorded every 30 min for the duration of the experiment and at 30°C and 150 rpm.
Cell growth quantifier (CGQ)
Growth characterization was also performed in shake flasks using CGQ (Scientific Bioprocessing, Germany) (49). Wild-type and mutant strains were precultured at 30°C, 200 rpm overnight in synthetic defined minimal Verduyn media containing 2% glucose (wt/vol), followed by inoculation of 25 mL of minimal medium supplemented with 2% carbon source in 100-mL shake flasks to a starting OD600 = 0.1. Growth was quantified as “Scatter values” by the CGQ system (49). Scatter values were recorded for 10 days at 30°C and 200 rpm for each strain growth in biological triplicates, and sampling was performed for sugar and polyol analyses.
Lactase activity assay
β-Galactosidase activity was determined using the Yeast β-Galactosidase Assay Kit (Thermo Fisher Scientific, USA) following the manufacturer’s instructions. Cells were harvested at different timepoints during growth and tested for lactase activity. A working solution was prepared by mixing equal amounts of 2× β-Galactosidase Assay Buffer [containing ortho-nitrophenyl-β-galactoside (ONPG)] and Yeast Protein Extraction Reagent. The reaction was initiated by mixing 100 μL of working solution with 100 μL cell culture and incubated for 30 min at 37°C in a thermomixer. After 30 min, cell mix was centrifuged at 5,000 rpm for 3 min and the supernatant was analyzed for lactase activity by measuring o-nitrophenol release from ONPG at 420 nm in a microplate reader (FLU-Ostar Omega-BMG LabTech, Ortenberg, Germany).
Determination of sugar and polyol concentrations
Sugar and galactitol concentrations were measured using a Dionex high-performance liquid chromatography system equipped with an RID-10A Refractive Index Detector and an Aminex HPX-87H Carbohydrate Analysis Column (Bio-Rad Laboratories). Analysis was performed with the column at 80°C and 5 mM H2SO4 as mobile phase at a constant flow rate of 0.8 mL/min. Culture samples were pelleted prior to analysis, following which the supernatant was passed through a 0.22-µm polyether sulfone syringe filter. Chromatogram peaks were identified and integrated using the Chromeleon v6.8 (Dionex) software and quantified against prepared analytical standards.
Comparative genomics and evolutionary mapping
We established the blast database for 332 yeast species based on the work of Shen et al. (1). Then, we used tblastn [with threshold pident (percentage of identical matches) > 40% and qcovs (Query Coverage Per Subject) > 60%] to get gene hits for each specific gene in three clusters against 332 yeast species. Based on the generated data, we further mapped gene hits from species to clade levels.
To investigate the evolution of genes in the GALLAC cluster, a comprehensive pipeline based on the work of Goncalves and colleagues was developed (50). For each candidate gene in the GALLAC cluster, BLASTP was run against the NCBI non-redundant (nr) protein sequence database and homologs were selected according to the top 300 BLAST hits to each query sequence. These homologs were aligned with MAFFT v7.310 (51) using default settings for multiple sequence alignment. Poorly aligned regions were removed with trimAl (52) using the “-automated1” option. Subsequently, phylogenetic trees were built using IQ-TREE v1.6.12 (53) with 1,000 ultrafast bootstrapping replicates (54). Each tree was rooted at the midpoint using a customized script combining R packages ape v5.4-1 and phangorn v2.5.5. Finally, the resulting phylogenies were visualized using iTol v5 (55).
Transcription factor binding motif analysis
To determine the binding motifs of transcription factors in promoter regions of the GAL, LAC, and GALLAC cluster, MEME (version 5.5.43) promoter binding motif analysis was used. Promoter regions of all genes from the three clusters were added as query sequences with the following constraints: maximum number of motifs = 5, maximum length of motif = 25 bases, any number of motif repetitions (-anr), and background model = 0—order model of sequences. Motif(s) derived from this analysis were then fed as input to Tomtom (56) (version 5.5.4) to compare with Yeastract (57) database.
RNA sequencing
Transcriptomics using RNA sequencing was performed as previously described (28). In brief, C. intermedia CBS 141442 was grown in controlled stirred 1-L bioreactor vessels (DASGIP, Eppendorf, Hamburg, Germany) containing 500 mL synthetic defined minimal Verduyn media with 2% glucose, galactose, or lactose. Reactor conditions were maintained as follows: Temp = 30°C; pH = 5.5 (maintained with 2M Potassium Hydroxide); Aeration = 1 Vessel Volume per Minute; stirring = 300 rpm.
RNA extraction
For RNA extraction, samples (10 mL) were collected when the dissolved oxygen of the culture was 35%–40% (vol/vol). After washing the cells, the pellets were immediately frozen using liquid nitrogen. Frozen pellets were stored at −80°C until extraction. The frozen pellets were thawed in 500 µL of TRIzol (Ambion—Foster City, CA, USA) and thoroughly resuspended. Then, cells were lysed in 2-mL tubes with Lysing Matrix C (MP Biomedical, Santa Ana, CA, USA) in a FastPrep FP120 (Savant, Carlsbad, CA, USA) for five cycles, at intensity 5.5 for 30 s. Tubes were cooled on ice for a minute between cycles and resuspended once again in 500 µL of TRIzol and vortexed thoroughly. After incubation at room temperature for 5 min, tubes were centrifuged for 10 min at 12,000 rpm and 4°C. Chloroform was added to the collected supernatants (200 µL of chloroform per mL of supernatant) and vortexed vigorously for 30 s. After centrifugation for 15 min at 12,000 rpm, 4°C, the top clear aqueous phase was collected and transferred to a new RNase-free tube, to which an equal amount of absolute ethanol was slowly added while mixing. Each sample was loaded into an RNeasy column (RNeasy Mini Kit, Qiagen—Hilden, Germany), and further steps followed the protocol of the manufacturer. The RNA was eluted with RNase-free water, and samples were stored at −80°C until use.
Data analysis
RNA samples were analyzed in a TapeStation (Agilent, Santa Clara, CA, USA), and only samples with RNA integrity number above 8 were used for library preparation. Sequencing using the HiSeq 2500 system (Illumina Inc.—San Diego, CA, USA), with paired-end 125-bp read length, and v4 sequencing chemistry was followed by quality control of read data using the software FastQC version 0.11.5 (58). Software Star version 2.5.2b (59) was used to map reads to the reference genome. Gene counts were normalized with weighted trimmed mean of M values using the calcNormFactor function from the package edgeR (60), and Limma package (61) was used to transform and make data suitable for linear modeling. The estimated P values were corrected for multiple testing with the Benjamini-Hochberg procedure, and genes were considered significant if the adjusted P values were lower than 0.05. The raw counts were filtered such that genes with CPM > 3.84 in at least 12% (5/43) of the samples were retained. The R function “varianceStabilizingTransformation()” from R package “DESeq2” (62) was used to convert raw counts to variance-stabilized counts. Expression data for C. intermedia on galactose and lactose were normalized using glucose as control condition.
Gene expression analysis using qPCR
Primers used for mRNA quantification using qPCR are listed in Table S4. Primers were designed using Primer3 (https://primer3.ut.ee/) and were checked for efficiency. Only primers having efficiency rates between 90% and 110% were used for qPCR. Cultures with a starting OD600 = 1 were grown at 30°C and 200 rpm in 100-mL shake flasks containing 25–100 mL synthetic defined minimal Verduyn media containing either 2% glucose (control), galactose, or lactose as carbon source. Cells were harvested for each strain at lag, early log, and late log phases, taking three biological replicates. Harvested cells were pelleted by centrifugation at 4°C for 5 min at 5,000 rpm and washed twice by resuspending in ice-cold sterile dH2O water and centrifugation. Cell pellet was snap frozen using liquid nitrogen and stored at −80°C for cDNA synthesis. RNA extraction was performed as described for RNA sequencing above. cDNA synthesis and real-time qPCR analysis were performed using a Maxima H Minus First Strand cDNA Synthesis Kit (Thermo Fisher) and Maxima SYBR Green/Fluorescein qPCR Master Mix (2×) (Thermo Fisher), according to the manufacturer’s instruction. Fold change was calculated using the delta-delta Ct method (2-ΔΔCt) with expression values in glucose as control condition and CiACT1 as the reference gene for normalization.
Statistical analysis
Statistical analysis for qPCR data were performed using R software, function t.test for two-tailed t-test. Statistical significance was established at P < 0.05 and marked by *P < 0.05, **P < 0.01, and ***P < 0.001.
ACKNOWLEDGMENTS
We thank Peter Dahl from the Department of Chemistry & Molecular Biology, Gothenburg University, for providing S. cerevisiae BY4741 and BY4742 knockout strains and Dr. Xiang Jiao from the Department of Life Sciences, Chalmers University of Technology, for providing the plasmid p426. We also thank ARS culture collection (NRRL) for providing us with different lactose-metabolizing strains upon request.
This research was funded by Formas, grant number 2017-01417. The AlphaFold2 structure predictions were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725.
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Information & Contributors
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Applied and Environmental Microbiology
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eLocator: e01135-24
Editor: Sophie Roussel, Anses, Maisons-Alfort Laboratory for Food Safety, Maisons-Alfort, France
PubMed: 39240082
Copyright
© 2024 Peri et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.
History
Received: 9 June 2024
Accepted: 19 August 2024
Published online: 6 September 2024
Keywords
Data Availability
The RNA-Seq data sets are available in the ENA with the accession number E-MTAB-6670.
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Sophie Roussel
Editor
Anses, Maisons-Alfort Laboratory for Food Safety, Maisons-Alfort, France
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