Skip to main content
SpringerLink
Log in

Reconstruction of genome-scale metabolic models of non-conventional yeasts: current state, challenges, and perspectives

  • Review Paper
  • Published:
Biotechnology and Bioprocess Engineering Aims and scope Submit manuscript

Abstract

Non-conventional yeasts are promising cell factories to produce lipids and oleochemicals, metabolites of industrial interest (e.g., organics acids, esters, and alcohols), and enzymes. They can also use different agro-industrial by-products as substrates within the context of a circular economy. Some of these yeasts can also comprise economic and health burdens as pathogens. Genome-scale metabolic models (GEMs), networks reconstructed based on the genomic and metabolic information of one or more organisms, are great tools to understand metabolic functions and landscapes, as well as propose engineering targets to improve metabolite production or propose novel drug targets. Previous reviews on yeast GEMs have mainly focused on the history and the evaluation of Saccharomyces cerevisiae modeling paradigms or the accessibility and usability of yeast GEMs. However, they did not describe the reconstruction strategies, limitations, validations, challenges, and research gaps of non-conventional yeast GEMs. Herein, we focused on the reconstruction of available non-Saccharomyces GEMs, their validation, underscoring the physiological insights, as well as the identification of both metabolic engineering and drug targets. We also discuss the challenges and knowledge gaps and propose strategies to boost their use and novel reconstructions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Boekhout T, Amend A, El Baidouri F et al (2022) Trends in yeast diversity discovery. Fungal Divers 114:491–537. https://doi.org/10.1007/s13225-021-00494-6

    Article  Google Scholar 

  2. Navarrete C, Martínez JL (2020) Non-conventional yeasts as superior production platforms for sustainable fermentation based bio-manufacturing processes. AIMS Bioeng 7:289–305. https://doi.org/10.3934/bioeng.2020024

    Article  CAS  Google Scholar 

  3. Geijer C, Ledesma-Amaro R, Tomás-Pejó E (2022) Unraveling the potential of non-conventional yeasts in biotechnology. FEMS Yeast Res 22:foz071. https://doi.org/10.1093/femsyr/foab071

    Article  CAS  Google Scholar 

  4. Tezcan EF, Demirtas Y, Cakar ZP et al (2023) Comprehensive genome-scale metabolic model of the human pathogen Cryptococcus neoformans: a platform for understanding pathogen metabolism and identifying new drug targets. Front Bioinform 3:1121409. https://doi.org/10.3389/fbinf.2023.1121409

    Article  PubMed  PubMed Central  Google Scholar 

  5. Viana R, Dias O, Lagoa D et al (2020) Genome-scale metabolic model of the human pathogen Candida albicans: a promising platform for drug target prediction. J Fungi (Basel) 6:171. https://doi.org/10.3390/jof6030171

    Article  CAS  PubMed  Google Scholar 

  6. Liebal UW, Ullmann L, Lieven C et al (2022) Ustilago maydis metabolic characterization and growth quantification with a genome-scale metabolic model. J Fungi (Basel) 8:524. https://doi.org/10.3390/jof8050524

    Article  CAS  PubMed  Google Scholar 

  7. Fang X, Lloyd CJ, Palsson BO (2020) Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat Rev Microbiol 18:731–743. https://doi.org/10.1038/s41579-020-00440-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lu H, Li F, Sánchez BJ et al (2019) A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat Commun 10:3586. https://doi.org/10.1038/s41467-019-11581-3

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121. https://doi.org/10.1038/nprot.2009.203

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Heirendt L, Arreckx S, Pfau T et al (2019) Creation and analysis of biochemical constraint-based models using the COBRA Toolbox vol 3.0. Nat Protoc 14:639–702. https://doi.org/10.1038/s41596-018-0098-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. https://doi.org/10.1038/nbt.1614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Becker SA, Feist AM, Mo ML et al (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2:727–738. https://doi.org/10.1038/nprot.2007.99

    Article  CAS  PubMed  Google Scholar 

  13. Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305. https://doi.org/10.1038/nrmicro2737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lopes H, Rocha I (2017) Genome-scale modeling of yeast: chronology, applications and critical perspectives. FEMS Yeast Res 17:fox050. https://doi.org/10.1093/femsyr/fox050

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chen Y, Li F, Nielsen J (2022) Genome-scale modeling of yeast metabolism: retrospectives and perspectives. FEMS Yeast Res 22:foac003. https://doi.org/10.1093/femsyr/foac003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Domenzain I, Li F, Kerkhoven EJ et al (2021) Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species. FEMS Yeast Res 21:foab002. https://doi.org/10.1093/femsyr/foab002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bernauer L, Radkohl A, Lehmayer LGK et al (2021) Komagataella phaffii as emerging model organism in fundamental research. Front Microbiol 11:607028. https://doi.org/10.3389/fmicb.2020.607028

    Article  PubMed  PubMed Central  Google Scholar 

  18. Sohn SB, Graf AB, Kim TY et al (2010) Genome-scale metabolic model of methylotrophic yeast Pichia pastoris and its use for in silico analysis of heterologous protein production. Biotechnol J 5:705–715. https://doi.org/10.1002/biot.201000078

    Article  CAS  PubMed  Google Scholar 

  19. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30. https://doi.org/10.1093/nar/28.1.27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ren Q, Kang KH, Paulsen IT (2004) TransportDB: a relational database of cellular membrane transport systems. Nucleic Acids Res 32:D284–D288. https://doi.org/10.1093/nar/gkh016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chung BK, Selvarasu S, Andrea C et al (2010) Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement. Microb Cell Fact 9:50. https://doi.org/10.1186/1475-2859-9-50

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hou BK, Kim JS, Jun JH et al (2004) BioSilico: an integrated metabolic database system. Bioinformatics 20:3270–3272. https://doi.org/10.1093/bioinformatics/bth363

    Article  CAS  PubMed  Google Scholar 

  23. Chang A, Jeske L, Ulbrich S et al (2021) BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res 49:D498–D508. https://doi.org/10.1093/nar/gkaa1025

    Article  CAS  PubMed  Google Scholar 

  24. Karp PD, Billington R, Caspi R et al (2019) The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform 20:1085–1093. https://doi.org/10.1093/bib/bbx085

    Article  CAS  PubMed  Google Scholar 

  25. Caspeta L, Shoaie S, Agren R et al (2012) Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials. BMC Syst Biol 6:24. https://doi.org/10.1186/1752-0509-6-24

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Agren R, Liu L, Shoaie S et al (2013) The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput Biol 9:e1002980. https://doi.org/10.1371/journal.pcbi.1002980

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Irani ZA, Kerkhoven EJ, Shojaosadati SA et al (2016) Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins. Biotechnol Bioeng 113:961–969. https://doi.org/10.1002/bit.25863

    Article  CAS  PubMed  Google Scholar 

  28. Choi HS, Lee SY, Kim TY et al (2010) In silico identification of gene amplification targets for improvement of lycopene production. Appl Environ Microbiol 76:3097–3105. https://doi.org/10.1128/AEM.00115-10

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tomàs-Gamisans M, Ferrer P, Albiol J (2016) Integration and validation of the genome-scale metabolic models of Pichia pastoris: a comprehensive update of protein glycosylation pathways, lipid and energy metabolism. PLoS ONE 11:e0148031. https://doi.org/10.1371/journal.pone.0148031

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sauls JT, Buescher JM (2014) Assimilating genome-scale metabolic reconstructions with modelBorgifier. Bioinformatics 30:1036–1038. https://doi.org/10.1093/bioinformatics/btt747

    Article  CAS  PubMed  Google Scholar 

  31. Ye R, Huang M, Lu H et al (2017) Comprehensive reconstruction and evaluation of Pichia pastoris genome-scale metabolic model that accounts for 1243 ORFs. Bioresour Bioprocess 4:22. https://doi.org/10.1186/s40643-017-0152-x

    Article  PubMed  PubMed Central  Google Scholar 

  32. Cankorur-Cetinkaya A, Dikicioglu D, Oliver SG (2017) Metabolic modeling to identify engineering targets for Komagataella phaffii: the effect of biomass composition on gene target identification. Biotechnol Bioeng 114:2605–2615. https://doi.org/10.1002/bit.26380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. King ZA, Lu J, Dräger A et al (2016) BiGG Models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44:D515–D522. https://doi.org/10.1093/nar/gkv1049

    Article  CAS  PubMed  Google Scholar 

  34. UniProt Consortium (2023) UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res 51:D523–D531. https://doi.org/10.1093/nar/gkac1052

    Article  CAS  Google Scholar 

  35. Burgard AP, Vaidyaraman S, Maranas CD (2001) Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments. Biotechnol Prog 17:791–797. https://doi.org/10.1021/bp0100880

    Article  CAS  PubMed  Google Scholar 

  36. Tomàs-Gamisans M, Ferrer P, Albiol J (2018) Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources. Microb Biotechnol 11:224–237. https://doi.org/10.1111/1751-7915.12871

    Article  CAS  PubMed  Google Scholar 

  37. Cavallo E, Charreau H, Cerrutti P et al (2017) Yarrowia lipolytica: a model yeast for citric acid production. FEMS Yeast Res 17:fox084. https://doi.org/10.1093/femsyr/fox084

    Article  CAS  Google Scholar 

  38. Groenewald M, Boekhout T, Neuvéglise C et al (2014) Yarrowia lipolytica: safety assessment of an oleaginous yeast with a great industrial potential. Crit Rev Microbiol 40:187–206. https://doi.org/10.3109/1040841X.2013.770386

    Article  CAS  PubMed  Google Scholar 

  39. Abdel-Mawgoud AM, Markham KA, Palmer CM et al (2018) Metabolic engineering in the host Yarrowia lipolytica. Metab Eng 50:192–208. https://doi.org/10.1016/j.ymben.2018.07.016

    Article  CAS  PubMed  Google Scholar 

  40. Loira N, Dulermo T, Nicaud JM et al (2012) A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica. BMC Syst Biol 6:35. https://doi.org/10.1186/1752-0509-6-35

    Article  PubMed  PubMed Central  Google Scholar 

  41. Loira N, Zhukova A, Sherman DJ (2015) Pantograph: a template-based method for genome-scale metabolic model reconstruction. J Bioinform Comput Biol 13:1550006. https://doi.org/10.1142/S0219720015500067

    Article  PubMed  Google Scholar 

  42. Pan P, Hua Q (2012) Reconstruction and in silico analysis of metabolic network for an oleaginous yeast. Yarrowia lipolytica PLoS One 7:e51535. https://doi.org/10.1371/journal.pone.0051535

    Article  CAS  PubMed  Google Scholar 

  43. Kavšček M, Bhutada G, Madl T et al (2015) Optimization of lipid production with a genome-scale model of Yarrowia lipolytica. BMC Syst Biol 9:72. https://doi.org/10.1186/s12918-015-0217-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mahadevan R, Edwards JS, Doyle FJ 3rd (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83:1331–1340. https://doi.org/10.1016/S0006-3495(02)73903-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kerkhoven EJ, Pomraning KR, Baker SE et al (2016) Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica. NPJ Syst Biol Appl 2:16005. https://doi.org/10.1038/npjsba.2016.5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wei S, Jian X, Chen J et al (2017) Reconstruction of genome-scale metabolic model of Yarrowia lipolytica and its application in overproduction of triacylglycerol. Bioresour Bioprocess 4:51. https://doi.org/10.1186/s40643-017-0180-6

    Article  Google Scholar 

  47. Reitz M, Sacher O, Tarkhov A et al (2004) Enabling the exploration of biochemical pathways. Org Biomol Chem 2:3226–3237. https://doi.org/10.1039/B410949J

    Article  CAS  PubMed  Google Scholar 

  48. Zhang C, Ji B, Mardinoglu A et al (2015) Logical transformation of genome-scale metabolic models for gene level applications and analysis. Bioinformatics 31:2324–2331. https://doi.org/10.1093/bioinformatics/btv134

    Article  CAS  PubMed  Google Scholar 

  49. Gu D, Zhang C, Zhou S et al (2016) IdealKnock: a framework for efficiently identifying knockout strategies leading to targeted overproduction. Comput Biol Chem 61:229–237. https://doi.org/10.1016/j.compbiolchem.2016.02.014

    Article  CAS  PubMed  Google Scholar 

  50. Jian X, Zhou S, Zhang C et al (2016) In silico identification of gene amplification targets based on analysis of production and growth coupling. Biosystems 145:1–8. https://doi.org/10.1016/j.biosystems.2016.05.002

    Article  CAS  PubMed  Google Scholar 

  51. Mishra P, Lee NR, Lakshmanan M et al (2018) Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica. BMC Syst Biol 12:12. https://doi.org/10.1186/s12918-018-0542-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lun DS, Rockwell G, Guido NJ et al (2009) Large-scale identification of genetic design strategies using local search. Mol Syst Biol 5:296. https://doi.org/10.1038/msb.2009.57

    Article  PubMed  PubMed Central  Google Scholar 

  53. Kim M, Yi JS, Lakshmanan M et al (2016) Transcriptomics-based strain optimization tool for designing secondary metabolite overproducing strains of Streptomyces coelicolor. Biotechnol Bioeng 113:651–660. https://doi.org/10.1002/bit.25830

    Article  CAS  PubMed  Google Scholar 

  54. Lakshmanan M, Chung BK, Liu C et al (2013) Cofactor modification analysis: a computational framework to identify cofactor specificity engineering targets for strain improvement. J Bioinform Comput Biol 11:1343006. https://doi.org/10.1142/S0219720013430063

    Article  CAS  PubMed  Google Scholar 

  55. Xu Y, Holic R, Hua Q (2020) Comparison and analysis of published genome-scale metabolic models of Yarrowia lipolytica. Biotechnol Bioprocess Eng 25:53–61. https://doi.org/10.1007/s12257-019-0208-1

    Article  CAS  Google Scholar 

  56. Xu N, Liu L, Zou W et al (2013) Reconstruction and analysis of the genome-scale metabolic network of Candida glabrata. Mol Biosyst 9:205–216. https://doi.org/10.1039/c2mb25311a

    Article  CAS  PubMed  Google Scholar 

  57. Satish Kumar V, Dasika MS, Maranas CD (2007) Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8:212. https://doi.org/10.1186/1471-2105-8-212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Yu CS, Cheng CW, Su WC et al (2014) CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation. PLoS ONE 9:e99368. https://doi.org/10.1371/journal.pone.0099368

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  59. Horton P, Park KJ, Obayashi T et al (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res 35:W585–W587. https://doi.org/10.1093/nar/gkm259

    Article  PubMed  PubMed Central  Google Scholar 

  60. Saier MH, Reddy VS, Moreno-Hagelsieb G et al (2021) The transporter classification database (TCDB): 2021 update. Nucleic Acids Res 49:D461–D467. https://doi.org/10.1093/nar/gkaa1004

    Article  CAS  PubMed  Google Scholar 

  61. Mishra P, Park GY, Lakshmanan M et al (2016) Genome-scale metabolic modeling and in silico analysis of lipid accumulating yeast Candida tropicalis for dicarboxylic acid production. Biotechnol Bioeng 113:1993–2004. https://doi.org/10.1002/bit.25955

    Article  CAS  PubMed  Google Scholar 

  62. Berman J (2012) Candida albicans. Curr Biol 22:R620–R622. https://doi.org/10.1016/j.cub.2012.05.043

    Article  CAS  PubMed  Google Scholar 

  63. Capela J, Lagoa D, Rodrigues R et al (2022) merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models. Nucleic Acids Res 50:6052–6066. https://doi.org/10.1093/nar/gkac459

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Rocha I, Maia P, Evangelista P et al (2010) OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst Biol 4:45. https://doi.org/10.1186/1752-0509-4-45

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Lagoa D, Faria JP, Liu F et al (2022) TranSyT, an innovative framework for identifying transport systems. biorxiv. https://doi.org/10.1101/2021.04.29.441738

    Article  Google Scholar 

  66. Santos ST (2013) Development of computational methods for the determination of biomass composition and evaluation of its impact in genome-scale models predictions. Dissertation, University of Minho

  67. Viana R, Couceiro D, Carreiro T et al (2022) A genome-scale metabolic model for the human pathogen Candida Parapsilosis and early identification of putative novel antifungal drug targets. Genes (Basel) 13:303. https://doi.org/10.3390/genes13020303

    Article  CAS  PubMed  Google Scholar 

  68. Tiukova IA, Prigent S, Nielsen J et al (2019) Genome-scale model of Rhodotorula toruloides metabolism. Biotechnol Bioeng 116:3396–3408. https://doi.org/10.1002/bit.27162

    Article  CAS  PubMed  Google Scholar 

  69. Dinh HV, Suthers PF, Chan SHJ et al (2019) A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data. Metab Eng Commun 9:e00101. https://doi.org/10.1016/j.mec.2019.e00101

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kim J, Coradetti ST, Kim YM et al (2021) Multi-omics driven metabolic network reconstruction and analysis of lignocellulosic carbon utilization in Rhodosporidium toruloides. Front Bioeng Biotechnol 8:612832. https://doi.org/10.3389/fbioe.2020.612832

    Article  PubMed  PubMed Central  Google Scholar 

  71. Wang H, Marcišauskas S, Sánchez BJ et al (2018) RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput Biol 14:e1006541. https://doi.org/10.1371/journal.pcbi

    Article  PubMed  PubMed Central  Google Scholar 

  72. Prigent S, Frioux C, Dittami SM et al (2017) Meneco, a topology-based gap-filling tool applicable to degraded genome-wide metabolic networks. PLoS Comput Biol 13:e1005276. https://doi.org/10.1371/journal.pcbi.1005276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Sánchez BJ, Li F, Kerkhoven EJ et al (2019) SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework. BMC Syst Biol 13:4. https://doi.org/10.1186/s12918-018-0673-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Lieven C, Beber ME, Olivier BG et al (2020) MEMOTE for standardized genome-scale metabolic model testing. Nat Biotechnol 38:272–276. https://doi.org/10.1038/s41587-020-0446-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Arkin AP, Cottingham RW, Henry CS et al (2018) KBase: the united states department of energy systems biology knowledgebase. Nat Biotechnol 36:566–569. https://doi.org/10.1038/nbt.4163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Ranganathan S, Suthers PF, Maranas CD (2010) OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput Biol 6:e1000744. https://doi.org/10.1371/journal.pcbi.1000744

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  77. Grant CE, Bailey TL, Noble WS (2011) FIMO: scanning for occurrences of a given motif. Bioinformatics 27:1017–1018. https://doi.org/10.1093/bioinformatics/btr064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Ebrahim A, Lerman JA, Palsson BO et al (2013) COBRApy: constraints-based reconstruction and analysis for python. BMC Syst Biol 7:74. https://doi.org/10.1186/1752-0509-7-74

    Article  PubMed  PubMed Central  Google Scholar 

  79. Lachance JC, Lloyd CJ, Monk JM et al (2019) BOFdat: generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Comput Biol 15:e1006971. https://doi.org/10.1371/journal.pcbi.1006971

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Balagurunathan B, Jonnalagadda S, Tan L et al (2012) Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis. Microb Cell Fact 11:27. https://doi.org/10.1186/1475-2859-11-27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Hilliard M, Damiani A, He QP et al (2018) Elucidating redox balance shift in Scheffersomyces stipitis’ fermentative metabolism using a modified genome-scale metabolic model. Microb Cell Fact 17:140. https://doi.org/10.1186/s12934-018-0983-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Damiani AL, He QP, Jeffries TW et al (2015) Comprehensive evaluation of two genome-scale metabolic network models for Scheffersomyces stipitis. Biotechnol Bioeng 112:1250–1262. https://doi.org/10.1002/bit.25535

    Article  CAS  PubMed  Google Scholar 

  83. Dias O, Pereira R, Gombert AK et al (2014) iOD907, the first genome-scale metabolic model for the milk yeast Kluyveromyces lactis. Biotechnol J 9:776–790. https://doi.org/10.1002/biot.201300242

    Article  CAS  PubMed  Google Scholar 

  84. Marcišauskas S, Ji B, Nielsen J (2019) Reconstruction and analysis of a Kluyveromyces marxianus genome-scale metabolic model. BMC Bioinformatics 20:551. https://doi.org/10.1186/s12859-019-3134-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Spohner SC, Schaum V, Quitmann H et al (2016) Kluyveromyces lactis: an emerging tool in biotechnology. J Biotechnol 222:104–116. https://doi.org/10.1016/j.jbiotec.2016.02.023

    Article  CAS  PubMed  Google Scholar 

  86. Karim A, Gerliani N, Aïder M (2020) Kluyveromyces marxianus: an emerging yeast cell factory for applications in food and biotechnology. Int J Food Microbiol 333:108818. https://doi.org/10.1016/j.ijfoodmicro.2020.108818

    Article  CAS  PubMed  Google Scholar 

  87. Liebal UW, Fabry BA, Ravikrishnan A et al (2021) Genome-scale model reconstruction of the methylotrophic yeast Ogataea polymorpha. BMC Biotechnol 21:23. https://doi.org/10.1186/s12896-021-00675-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Zorrilla F, Kerkhoven EJ (2022) Reconstruction of genome-scale metabolic model for Hansenula polymorpha using RAVEN. In: Mapelli V, Bettiga M (eds) Yeast metabolic engineering. Methods in Molecular Biology, vol 2513. Humana Press, New York

    Google Scholar 

  89. Sohn SB, Kim TY, Lee JH et al (2012) Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth. BMC Syst Biol 6:49. https://doi.org/10.1186/1752-0509-6-49

    Article  PubMed  PubMed Central  Google Scholar 

  90. Palma M, Sá-Correia I (2019) Physiological genomics of the highly weak-acid-tolerant food spoilage yeasts of Zygosaccharomyces bailii sensu lato. Prog Mol Subcell Biol 58:85–109. https://doi.org/10.1007/978-3-030-13035-0_4

    Article  CAS  PubMed  Google Scholar 

  91. Di Filippo M, Ortiz-Merino RA, Damiani C et al (2018) Genome-scale metabolic reconstruction of the stress-tolerant hybrid yeast Zygosaccharomyces parabailii. bioRxiv. https://doi.org/10.1101/373621v1

    Article  Google Scholar 

  92. Nanda P, Patra P, Das M et al (2020) Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri. Sci Rep 10:16314. https://doi.org/10.1038/s41598-020-73253-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Suthers PF, Dinh HV, Fatma Z et al (2020) Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production. Metab Eng Commun 11:e00148. https://doi.org/10.1016/j.mec.2020.e00148

    Article  PubMed  PubMed Central  Google Scholar 

  94. Almagro Armenteros JJ, Sønderby CK, Sønderby SK et al (2017) DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics 33:3387–3395. https://doi.org/10.1093/bioinformatics/btx431

    Article  CAS  PubMed  Google Scholar 

  95. Seaver SMD, Liu F, Zhang Q et al (2021) The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 49:D575–D588. https://doi.org/10.1093/nar/gkaa746

    Article  CAS  PubMed  Google Scholar 

  96. Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84:647–657. https://doi.org/10.1002/bit.10803

    Article  CAS  PubMed  Google Scholar 

  97. Di Fidio N, Minonne F, Antonetti C et al (2021) Cutaneotrichosporon oleaginosus: a versatile whole-cell biocatalyst for the production of single-cell oil from agro-industrial wastes. Catalysts 11:1291

    Article  Google Scholar 

  98. Pham N, Reijnders M, Suarez-Diez M et al (2021) Genome-scale metabolic modeling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production. Biotechnol Biofuels 14:2. https://doi.org/10.1186/s13068-020-01838-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. de Almeida ELM, Ventorim RZ, de Moura Ferreira MA et al (2022) Papiliotrema laurentii: general features and biotechnological applications. Appl Microbiol Biotechnol 106:6963–6976. https://doi.org/10.1007/s00253-022-12208-2

    Article  CAS  PubMed  Google Scholar 

  100. Ventorim RZ, de Moura Ferreira MA, de Almeida ELM et al (2022) Genome-scale metabolic model of oleaginous yeast Papiliotrema laurentii. Biochem Eng J 180:108353

    Article  CAS  Google Scholar 

  101. Segrè D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99:15112–15117. https://doi.org/10.1073/pnas.232349399

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  102. Kim M, Park BG, Kim EJ et al (2019) In silico identification of metabolic engineering strategies for improved lipid production in Yarrowia lipolytica by genome-scale metabolic modeling. Biotechnol Biofuels 12:187. https://doi.org/10.1186/s13068-019-1518-4

    Article  PubMed  PubMed Central  Google Scholar 

  103. Olicón-Hernández DR, Araiza-Villanueva MG, Pardo JP et al (2019) New insights of Ustilago maydis as yeast model for genetic and biotechnological research: a review. Curr Microbiol 76:917–926. https://doi.org/10.1007/s00284-019-01629-4

    Article  CAS  PubMed  Google Scholar 

  104. Karp PD, Midford PE, Billington R et al (2021) Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology. Brief Bioinform 22:109–126. https://doi.org/10.1093/bib/bbz104

    Article  PubMed  Google Scholar 

  105. Srikanta D, Santiago-Tirado FH, Doering TL (2014) Cryptococcus neoformans: historical curiosity to modern pathogen. Yeast 31:47–60. https://doi.org/10.1002/yea.2997

    Article  CAS  PubMed  Google Scholar 

  106. Rathore SS, Sathiyamoorthy J, Lalitha C et al (2022) A holistic review on Cryptococcus neoformans. Microb Pathog 166:105521. https://doi.org/10.1016/j.micpath.2022.105521

    Article  PubMed  Google Scholar 

  107. Henson MA, Orazi G, Phalak P et al (2019) Metabolic modeling of cystic fibrosis airway communities predicts mechanisms of pathogen dominance. mSystems 4:e00026-19. https://doi.org/10.1128/mSystems.00026-19

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Pitkänen E, Jouhten P, Hou J et al (2014) Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species. PLoS Comput Biol 10:e1003465. https://doi.org/10.1371/journal.pcbi.1003465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Correia K, Mahadevan R (2020) Pan-genome-scale network reconstruction: harnessing phylogenomics increases the quantity and quality of metabolic models. Biotechnol J 15:e1900519. https://doi.org/10.1002/biot.201900519

    Article  CAS  PubMed  Google Scholar 

  110. Lu H, Li F, Yuan L et al (2021) Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. Mol Syst Biol 17:e10427. https://doi.org/10.15252/msb.202110427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Lu H, Kerkhoven EJ, Nielsen J (2022) A pan-draft metabolic model reflects evolutionary diversity across 332 yeast species. Biomolecules 12:1632. https://doi.org/10.3390/biom12111632

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Shen XX, Opulente DA, Kominek J et al (2018) Tempo and mode of genome evolution in the budding yeast subphylum. Cell 175:1533-1545.e20. https://doi.org/10.1016/j.cell.2018.10.023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Domenzain I, Sánchez B, Anton M et al (2022) Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nat Commun 13:3766. https://doi.org/10.1038/s41467-022-31421-1

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the Universidade Federal de Viçosa (UFV), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq Finance Code 140538/2021-6), Fundação de Amparo à Pesquisa de Minas Gerais (FAPEMIG) for their financial support for the research and the scholarship. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

Author information

Authors and Affiliations

  1. Laboratory of Microbial Physiology, Department of Microbiology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil

    Eduardo Luís Menezes de Almeida & Wendel Batista da Silveira

  2. Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96, Gothenburg, Sweden

    Eduard J. Kerkhoven

  3. SciLifeLab, Chalmers University of Technology, 412 96, Gothenburg, Sweden

    Eduard J. Kerkhoven

Authors
  1. Eduardo Luís Menezes de Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eduard J. Kerkhoven
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wendel Batista da Silveira
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

E.L.M. Almeida did conceptualization and writing—original draft. E.J. Kerkhoven done writing—review and editing. W.B. Silveira performed conceptualization, writing—review & editing.

Corresponding author

Correspondence to Wendel Batista da Silveira.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

Neither ethical approval nor informed consent was required for this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Almeida, E.L.M., Kerkhoven, E.J. & da Silveira, W.B. Reconstruction of genome-scale metabolic models of non-conventional yeasts: current state, challenges, and perspectives. Biotechnol Bioproc E 29, 35–67 (2024). https://doi.org/10.1007/s12257-024-00009-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12257-024-00009-5

Keywords

Search

Navigation