DOI QR코드

DOI QR Code

Draft Genome Assembly and Annotation for Cutaneotrichosporon dermatis NICC30027, an Oleaginous Yeast Capable of Simultaneous Glucose and Xylose Assimilation

  • Wang, Laiyou (School of Biological and Chemical Engineering, Nanyang Institute of Technology) ;
  • Guo, Shuxian (School of Biological and Chemical Engineering, Nanyang Institute of Technology) ;
  • Zeng, Bo (School of Biological and Chemical Engineering, Nanyang Institute of Technology) ;
  • Wang, Shanshan (School of Biological and Chemical Engineering, Nanyang Institute of Technology) ;
  • Chen, Yan (School of Biological and Chemical Engineering, Nanyang Institute of Technology) ;
  • Cheng, Shuang (School of Biological and Chemical Engineering, Nanyang Institute of Technology) ;
  • Liu, Bingbing (School of Biological and Chemical Engineering, Nanyang Institute of Technology) ;
  • Wang, Chunyan (School of Biological and Chemical Engineering, Nanyang Institute of Technology) ;
  • Wang, Yu (College of Biological Science and Engineering, Jiangxi Agricultural University) ;
  • Meng, Qingshan (State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University)
  • Received : 2021.11.03
  • Accepted : 2022.02.02
  • Published : 2022.02.28

Abstract

The identification of oleaginous yeast species capable of simultaneously utilizing xylose and glucose as substrates to generate value-added biological products is an area of key economic interest. We have previously demonstrated that the Cutaneotrichosporon dermatis NICC30027 yeast strain is capable of simultaneously assimilating both xylose and glucose, resulting in considerable lipid accumulation. However, as no high-quality genome sequencing data or associated annotations for this strain are available at present, it remains challenging to study the metabolic mechanisms underlying this phenotype. Herein, we report a 39,305,439 bp draft genome assembly for C. dermatis NICC30027 comprised of 37 scaffolds, with 60.15% GC content. Within this genome, we identified 524 tRNAs, 142 sRNAs, 53 miRNAs, 28 snRNAs, and eight rRNA clusters. Moreover, repeat sequences totaling 1,032,129 bp in length were identified (2.63% of the genome), as were 14,238 unigenes that were 1,789.35 bp in length on average (64.82% of the genome). The NCBI non-redundant protein sequences (NR) database was employed to successfully annotate 11,795 of these unigenes, while 3,621 and 11,902 were annotated with the Swiss-Prot and TrEMBL databases, respectively. Unigenes were additionally subjected to pathway enrichment analyses using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cluster of Orthologous Groups of proteins (COG), Clusters of orthologous groups for eukaryotic complete genomes (KOG), and Non-supervised Orthologous Groups (eggNOG) databases. Together, these results provide a foundation for future studies aimed at clarifying the mechanistic basis for the ability of C. dermatis NICC30027 to simultaneously utilize glucose and xylose to synthesize lipids.

Keywords

Acknowledgement

This work was supported by Basic and Frontier Research Project of Nanyang city (JCQY010), Interdisciplinary Research Project of Nanyang Institute of Technology (JC20191205), Opening Foundation of Henan Key Laboratory of Industrial Microbial Resources and Fermentation Technology (HIMFT20200101 and HIMFT20200204), National Natural Science Foundation of China (31800001), Key Scientific Research Project of Colleges and Universities in Henan Province (20A180019) and Key Technologies R&D Program of Nanyang city (KJGG079).

References

  1. Roy S, Dikshit PK, Sherpa KC, et al. Recent nanobiotechnological advancements in lignocellulosic biomass valorization: a review. J Environ Manage. 2021;297:113422. https://doi.org/10.1016/j.jenvman.2021.113422
  2. Kumar D, Singh B, Korstad J, et al. Utilization of lignocellulosic biomass by oleaginous yeast and bacteria for production of biodiesel and renewable diesel. Renewable Sustainable Energy Rev. 2017;73:654-671. https://doi.org/10.1016/j.rser.2017.01.022
  3. Akhlisah ZN, Yunus R, Abidin ZZ, et al. Pretreatment methods for an effective conversion of oil palm biomass into sugars and high-value chemicals. Biomass Bioenergy. 2021;144:105901. https://doi.org/10.1016/j.biombioe.2020.105901
  4. Abdel-Rahman MA, Tashiro Y, Sonomoto K, et al. Lactic acid production from lignocellulose-derived sugars using lactic acid bacteria: overview and limits. J Biotechnol. 2011;156(4):286-301. https://doi.org/10.1016/j.jbiotec.2011.06.017
  5. Hoang AT, Nizetic S, Ong HC, et al. Insight into the recent advances of microwave pretreatment technologies for the conversion of lignocellulosic biomass into sustainable biofuel. Chemosphere. 2021;281:130878. https://doi.org/10.1016/j.chemosphere.2021.130878
  6. Awasthi MK, Sarsaiya S, Patel A, et al. Refining biomass residues for sustainable energy and bioproducts: an assessment of technology, its importance, and strategic applications in circular bioeconomy. Renewable Sustainable Energy Rev. 2020;127:109876. https://doi.org/10.1016/j.rser.2020.109876
  7. Bhatia SK, Kim S-H, Yoon J-J, et al. Current status and strategies for second generation biofuel production using microbial systems. Energy Convers Manage. 2017;148:1142-1156. https://doi.org/10.1016/j.enconman.2017.06.073
  8. Tanimura A, Takashima M, Sugita T, et al. Lipid production through simultaneous utilization of glucose, xylose, and L-arabinose by Pseudozyma hubeiensis: a comparative screening study. AMB Express. 2016;6(1):58. https://doi.org/10.1186/s13568-016-0236-6
  9. Brandenburg J, Blomqvist J, Pickova J, et al. Lipid production from hemicellulose with Lipomyces starkeyi in a pH regulated fed-batch cultivation. Yeast. 2016;33(8):451-462. https://doi.org/10.1002/yea.3160
  10. Tanadul O-U-M, Noochanong W, Jirakranwong P, et al. EMS-induced mutation followed by quizalofop-screening increased lipid productivity in chlorella sp. Bioprocess Biosyst Eng. 2018;41(5):613-619. https://doi.org/10.1007/s00449-018-1896-1
  11. Sarkar P, Goswami G, Mukherjee M, et al. Heterologous expression of xylose specific transporter improves xylose utilization by recombinant Zymomonas mobilis strain in presence of glucose. Process Biochem. 2021;102:190-198. https://doi.org/10.1016/j.procbio.2021.01.006
  12. Sun T, Yu Y, Wang K, et al. Engineering Yarrowia lipolytica to produce fuels and chemicals from xylose: a review. Bioresour Technol. 2021;337:125484. https://doi.org/10.1016/j.biortech.2021.125484
  13. Hu C, Wu S, Wang Q, et al. Simultaneous utilization of glucose and xylose for lipid production by Trichosporon cutaneum. Biotechnol Biofuels. 2011;4(1):25., https://doi.org/10.1186/1754-6834-4-25
  14. Tanimura A, Sugita T, Endoh R, et al. Lipid production via simultaneous conversion of glucose and xylose by a novel yeast, Cystobasidium iriomotense. PLOS One. 2018;13(9):e0202164. https://doi.org/10.1371/journal.pone.0202164
  15. Wang L, Wang D, Zhang Z, et al. Comparative glucose and xylose coutilization efficiencies of soil-isolated yeast strains identify Cutaneotrichosporon dermatis as a potential producer of lipid. ACS Omega. 2020;5(37):23596-23603. https://doi.org/10.1021/acsomega.0c02089
  16. Gadanho M, Sampaio JP. Occurrence and diversity of yeasts in the mid-atlantic ridge hydrothermal fields near the Azores Archipelago. Microb Ecol. 2005;50(3):408-417. https://doi.org/10.1007/s00248-005-0195-y
  17. Liu X-Z, Wang Q-M, Goker M, et al. Towards an integrated phylogenetic classification of the tremellomycetes. Stud Mycol. 2015;81:85-147. https://doi.org/10.1016/j.simyco.2015.12.001
  18. do Espirito Santo EPT, Monteiro RC, da Costa ARF, et al. Molecular identification, genotyping, phenotyping, and antifungal susceptibilities of medically important Trichosporon, Apiotrichum, and Cutaneotrichosporon species. Mycopathologia. 2019;185:307-317.
  19. Pagani DM, Heidrich D, Paulino GVB, et al. Susceptibility to antifungal agents and enzymatic activity of Candida haemulonii and Cutaneotrichosporon dermatis isolated from soft corals on the Brazilian reefs. Arch Microbiol. 2016;198(10):963-971. https://doi.org/10.1007/s00203-016-1254-0
  20. Shu J, Ning P, Guo T, et al. First report of leaf spot caused by Colletotrichum fructicola and C. siamense on Zizyphus mauritiana in Guangxi, China. Plant Dis. 2020;104(12):3256-3256.
  21. Chai AL, Zhao Q, Li XJ, et al. First report of cercospora leaf spot caused by Cercospora cf. flagellaris on okra in China. Plant Dis. 2021;105(7):2018. https://doi.org/10.1094/PDIS-10-20-2155-PDN
  22. Sarnecka AK, Nawrat D, Piwowar M, et al. DNA extraction from FFPE tissue samples - a comparison of three procedures. Contemp Oncol. 2019;23(1):52-58. https://doi.org/10.5114/wo.2019.83875
  23. Das P, Pandey P, Harishankar A, et al. A high yield DNA extraction method for medically important candida species: a comparison of manual versus QIAcube-based automated system. Indian J Med Microbiol. 2016;34(4):533-535. https://doi.org/10.4103/0255-0857.195360
  24. Malentacchi F, Ciniselli CM, Pazzagli M, et al. Influence of pre-analytical procedures on genomic DNA integrity in blood samples: the SPIDIA experience. Clin Chim Acta. 2015;440:205-210. https://doi.org/10.1016/j.cca.2014.12.004
  25. Nasiri H, Forouzandeh M, Rasaee MJ, et al. Modified salting-out method: high-yield, high-quality genomic DNA extraction from whole blood using laundry detergent. J Clin Lab Anal. 2005;19(6):229-232. https://doi.org/10.1002/jcla.20083
  26. Lang J, Zhu R, Sun X, et al. Evaluation of the MGISEQ-2000 sequencing platform for Illumina target capture sequencing libraries. Front Genet. 2021;12:730519. https://doi.org/10.3389/fgene.2021.730519
  27. Kong N, Ng W, Thao K, et al. Automation of PacBio SMRTbell NGS library preparation for bacterial genome sequencing. Stand Genomic Sci. 2017;12:27. https://doi.org/10.1186/s40793-017-0239-1
  28. Zhang L-L, Huang W, Zhang Y-Y, et al. Genomic and transcriptomic study for screening genes involved in the limonene biotransformation of Penicillium digitatum DSM 62840. Front Microbiol. 2020;11:744. https://doi.org/10.3389/fmicb.2020.00744
  29. Bickhart DM, McClure JC, Schnabel RD, et al. Symposium review: advances in sequencing technology herald a new frontier in cattle genomics and genome-enabled selection. J Dairy Sci. 2020;103(6):5278-5290. https://doi.org/10.3168/jds.2019-17693
  30. Liu L, Li Y, Li S, et al. Comparison of next-generation sequencing systems. J Biomed Biotechnol. 2012;2012:251364. https://doi.org/10.1155/2012/251364
  31. Yoshinaga Y, Daum C, He G, et al. Genome sequencing. Methods Mol Biol. 2018;1775:37-52. https://doi.org/10.1007/978-1-4939-7804-5_4
  32. Takashima M, Sriswasdi S, Manabe R-I, et al. A trichosporonales genome tree based on 27 haploid and three evolutionarily conserved 'natural' hybrid genomes. Yeast. 2018;35(1):99-111. https://doi.org/10.1002/yea.3284
  33. Close D, Ojumu J. Draft genome sequence of the oleaginous yeast Cryptococcus curvatus ATCC 20509. Genome Announc. 2016;4(6):e01235-16.
  34. Sun S, Coelho MA, Heitman J, et al. Convergent evolution of linked mating-type loci in basidiomycete fungi. PLOS Genet. 2019;15(9):e1008365. https://doi.org/10.1371/journal.pgen.1008365
  35. Hofmeyer T, Hackenschmidt S, Nadler F, et al. Draft genome sequence of Cutaneotrichosporon curvatus DSM 101032 (formerly Cryptococcus curvatus), an oleaginous yeast producing polyunsaturated fatty acids. Genome Announc. 2016;4(3):e00362-16.
  36. Alva A, Sabido-Ramos A, Escalante A, et al. New insights into transport capability of sugars and its impact on growth from novel mutants of Escherichia coli. Appl Microbiol Biotechnol. 2020;104(4):1463-1479. https://doi.org/10.1007/s00253-019-10335-x
  37. Sharma NK, Behera S, Arora R, et al. Xylose transport in yeast for lignocellulosic ethanol production: current status. J Biosci Bioeng. 2018;125(3):259-267. https://doi.org/10.1016/j.jbiosc.2017.10.006
  38. Vasylyshyn R, Kurylenko O, Ruchala J, et al. Engineering of sugar transporters for improvement of xylose utilization during high-temperature alcoholic fermentation in Ogataea polymorpha yeast. Microb Cell Fact. 2020;19(1):96. https://doi.org/10.1186/s12934-020-01354-9
  39. Zhang B, Zhang J, Wang D, et al. Simultaneous fermentation of glucose and xylose at elevated temperatures co-produces ethanol and xylitol through overexpression of a xylose-specific transporter in engineered Kluyveromyces marxianus. Bioresour Technol. 2016;216:227-237. https://doi.org/10.1016/j.biortech.2016.05.068
  40. Runquist D, Fonseca C, Radstrom P, et al. Expression of the Gxf1 transporter from Candida intermedia improves fermentation performance in recombinant xylose-utilizing Saccharomyces cerevisiae. Appl Microbiol Biotechnol. 2009;82(1):123-130. https://doi.org/10.1007/s00253-008-1773-y
  41. Young E, Poucher A, Comer A, et al. Functional survey for heterologous sugar transport proteins, using Saccharomyces cerevisiae as a host. Appl Environ Microbiol. 2011;77(10):3311-3319. https://doi.org/10.1128/AEM.02651-10
  42. Wang X, Goh E-B, Beller HR, et al. Engineering E. coli for simultaneous glucose-xylose utilization during methyl ketone production. Microb Cell Fact. 2018;17(1):12. https://doi.org/10.1186/s12934-018-0862-6
  43. Hua Y, Wang J, Zhu Y, et al. Release of glucose repression on xylose utilization in Kluyveromyces marxianus to enhance glucose-xylose co-utilization and xylitol production from corncob hydrolysate. Microb Cell Fact. 2019;18(1):24. https://doi.org/10.1186/s12934-019-1068-2
  44. Abe K, Uchida K. Correlation between depression of catabolite control of xylose metabolism and a defect in the phosphoenolpyruvate: mannose phosphotransferase system in Pediococcus halophilus. J Bacteriol. 1989;171(4):1793-1800. https://doi.org/10.1128/jb.171.4.1793-1800.1989
  45. Khunnonkwao P, Jantama SS, Kanchanatawee S, et al. Re-engineering Escherichia coli KJ122 to enhance the utilization of xylose and xylose/glucose mixture for efficient succinate production in mineral salt medium. Appl Microbiol Biotechnol. 2018;102(1):127-141. https://doi.org/10.1007/s00253-017-8580-2
  46. Ranade S, Zhang Y, Kaplan M, et al. Metabolic engineering and comparative performance studies of Synechocystis sp. PCC 6803 strains for effective utilization of xylose. Front Microbiol. 2015;6:1484. https://doi.org/10.3389/fmicb.2015.01484
  47. Chen L, Zhang Z, Hoshino A, et al. NADPH production by the oxidative pentose-phosphate pathway supports folate metabolism. Nat Metab. 2019;1:404-415. https://doi.org/10.1038/s42255-019-0043-x
  48. Minard KI, McAlister-Henn L. Sources of NADPH in yeast vary with carbon source. J Biol Chem. 2005;280(48):39890-39896. https://doi.org/10.1074/jbc.M509461200
  49. Choi JW, Da Silva NA. Improving polyketide and fatty acid synthesis by engineering of the yeast acetyl-CoA carboxylase. J Biotechnol. 2014;187:56-59. https://doi.org/10.1016/j.jbiotec.2014.07.430
  50. Montoya AM, Luna-Rodriguez CE, Bonifaz A, et al. Physiological characterization and molecular identification of some rare yeast species causing onychomycosis. J Mycol Med. 2021;31(2):101121. https://doi.org/10.1016/j.mycmed.2021.101121
  51. Gallagher MD, Chen-Plotkin AS. The Post-GWAS era: from association to function. Am J Hum Genet. 2018;102(5):717-730. https://doi.org/10.1016/j.ajhg.2018.04.002
  52. Price MN, Arkin AP. Curated BLAST for genomes. mSystems. 2019;4(2):e00072-19.
  53. Pace J, Youens-Clark K, Freeman C, et al. PuMA: a papillomavirus genome annotation tool. Virus Evol. 2020;6(2):veaa068. https://doi.org/10.1093/ve/veaa068
  54. Magrini V, Gao X, Rosa BA, et al. Improving eukaryotic genome annotation using single molecule mRNA sequencing. BMC Genomics. 2018;19(1):172. https://doi.org/10.1186/s12864-018-4555-7
  55. Watson JD, Laskowski RA, Thornton JM, et al. Predicting protein function from sequence and structural data. Curr Opin Struct Biol. 2005;15(3):275-284. https://doi.org/10.1016/j.sbi.2005.04.003
  56. Skov LK, Mirza O, Henriksen A, et al. Amylosucrase, a glucan-synthesizing enzyme from the alpha-amylase family. J Biol Chem. 2001;276(27):25273-25278. https://doi.org/10.1074/jbc.M010998200
  57. Dietmann S,PJ, Notredame C, Heger A, et al. A fully automatic evolutionary classification of protein folds: dali domain dictionary version 3. Nucleic Acids Res. 2001;29(1):55-57. https://doi.org/10.1093/nar/29.1.55
  58. Krissinel E, Henrick K. Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions. Acta Crystallogr D Biol Crystallogr. 2004;60(12):2256-2268. https://doi.org/10.1107/S0907444904026460
  59. Harrison A, Pearl F, Sillitoe I, et al. Recognizing the fold of a protein structure. Bioinformatics. 2003;19(14):1748-1759. https://doi.org/10.1093/bioinformatics/btg240
  60. Binkowski TA, Freeman P, Liang J, et al. pvSOAR: detecting similar surface patterns of pocket and void surfaces of amino acid residues on proteins. Nucleic Acids Res. 2004;32:W555-8. https://doi.org/10.1093/nar/gkh390
  61. Ra L. SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph. 1995;13(5):323-330. https://doi.org/10.1016/0263-7855(95)00073-9
  62. Glaser F, Morris RJ, Najmanovich RJ, et al. A method for localizing ligand binding pockets in protein structures. Proteins. 2006;62(2):479-488. https://doi.org/10.1002/prot.20769
  63. Innis CA, Anand AP, Sowdhamini R, et al. Prediction of functional sites in proteins using conserved functional group analysis. J Mol Biol. 2004;337(4):1053-1068. https://doi.org/10.1016/j.jmb.2004.01.053
  64. Wilkins A, Erdin S, Lua R, et al. Evolutionary trace for prediction and redesign of protein functional sites. Methods Mol Biol. 2012;819:29-42. https://doi.org/10.1007/978-1-61779-465-0_3
  65. Pillai-Kastoori L, Schutz-Geschwender AR, Harford JA, et al. A systematic approach to quantitative western blot analysis. Anal Biochem. 2020;593:113608. https://doi.org/10.1016/j.ab.2020.113608
  66. Atout S, Shurrab S, Loveridge C, et al. Evaluation of the suitability of RNAscope as a technique to measure gene expression in clinical diagnostics: a systematic review. Mol Diagn Ther. 2022;26(1):19-37., https://doi.org/10.1007/s40291-021-00570-2
  67. Aslam B, Basit M, Nisar MA, et al. Proteomics: technologies and their applications. J Chromatogr Sci. 2017;55(2):182-196. https://doi.org/10.1093/chromsci/bmw167
  68. Pappireddi N, Martin L, Wuhr M, et al. A review on quantitative multiplexed proteomics. Chembiochem. 2019;20(10):1210-1224. https://doi.org/10.1002/cbic.201800650