Abstract
Promoters are indispensable elements of a standardized parts collection for synthetic biology. Regulated promoters of a wide variety of well-defined induction ratios and expression strengths are highly interesting for many applications. Exemplarily, we discuss the application of published genome scale transcriptomics data for the primary selection of methanol inducible promoters of the yeast Pichia pastoris (Komagataella sp.). Such a promoter collection can serve as an excellent toolbox for cell and metabolic engineering, and for gene expression to produce heterologous proteins.
Keywords: Pichia pastoris, Komagataella, Promoter, Induction, Synthetic biology, Protein production
Background
A major task of synthetic biology is the provision of standardized elements for rapid assembly of predictable recombinant gene expression cassettes [1, 2]. These elements include vectors, selection markers, and most importantly collections of regulatory elements like promoters, transcription terminators, secretory leaders and other signal sequences. Ideally, collections of these parts are cataloged in standardized, easy to assemble formats like BioBrick [3]. Promoters are indispensable parts for synthetic biology approaches [4] and are needed for different expression strength in order to balance the expression levels in a synthetic pathway [5]. There are a plethora of studies which characterize, e.g. constitutive promoters of different strength for Escherichia coli [6], Aspergillus niger [7] or Pichia pastoris [8]. Depending on the application it might be necessary to tightly control the promoter activity. Especially regulated promoters are often strictly host specific, so that they need to be identified, characterized and standardized for the host species of interest, as shown e.g. for E. coli [9].
Methanol regulated promoters
Methylotrophic yeasts such as P. pastoris (syn. Komagataella sp.) have gained great interest as production hosts for recombinant proteins [10] and more recently also as platform for metabolite production [2]. Both applications require promoter collections of different strength for metabolic and cell engineering to enable and enhance productivity. Promoter libraries were developed based on mutating transcription factor binding sites [11], or by random mutagenesis [8]. Strong constitutive and regulated promoters were identified by transcriptomics studies [12, 13]. Delic et al. [14] described a collection of native regulated promoters of different strength with the main aim of providing repressible promoters for gene knockdown studies. Synthetic core promoters represent a source for transcriptional initiators at different strength, however with the loss of regulatory features [1, 15].
A specific feature of methylotrophic yeasts is the carbon source dependent regulation of the genes involved in methanol metabolism. Recently we have redefined the methanol assimilation pathway of P. pastoris [16], a finding that was initially based on the identification of all genes that are upregulated on methanol as a substrate. These include hitherto unknown genes, controlled by promoters of a wide range of expression strength on methanol (Table 1). Beside different expression levels upon induction by methanol, these promoters feature a wide variety of induction degrees, defined as the ratio of expression levels in the induced state (presence of methanol) vs. the non-induced state (cells grown on glucose or glycerol). Some of these promoters are even deregulated on substrate limit without addition of methanol, illustrating a variety of regulation patterns which can be summarized by correlating the genes according to the similarity of their regulatory behavior in a plethora of different growth conditions, such as different carbon sources [17] or different growth rates, featuring different degrees of substrate limitation [18]. Thus they are allowing controllable expression of genes depending on the needs or growth conditions of the host cells.
Table 1.
Ranked expression level (methanol)a | Short name | ORF nameb | Co-regulation: 1 = with A/D/F; 2 = with A; 3 = with D/F; 4 = up at glucose limitc | Methanol inductiond |
---|---|---|---|---|
1 | DAS1 | PP7435_Chr3-0352 | 1;4 | Strong |
2 | AOX2 | PP7435_Chr4-0863 | 2;4 | Strong |
3 | AOX1 | PP7435_Chr4-0130 | 1;4 | Strong |
4 | DAS2 | PP7435_Chr3-0350 | 3;4 | Strong |
5 | FDH1 | PP7435_Chr3-0238 | 1;4 | Strong |
6 | PMP20 | PP7435_Chr1-1351 | Strong | |
7 | THI11 | PP7435_Chr4-0952 | Weak | |
8 | FLD | PP7435_Chr3-0140 | 3 | Intermediate |
9 | FBA1-2 | PP7435_Chr1-0639 | 1 | Strong |
10 | SHB17 | PP7435_Chr2-0185 | 3 | Intermediate |
11 | FGH1 | PP7435_Chr3-0312 | 1 | Intermediate |
12 | DAK2 | PP7435_Chr3-0343 | 3 | Intermediate |
13 | CTA1 | PP7435_Chr2-0137 | 3 | Weak |
14 | PMP47 | PP7435_Chr3-1139 | 1 | Strong |
15 | MPP1 | PP7435_Chr3-0349 | 3 | Weak |
16 | FBP1 | PP7435_Chr3-0309 | 3 | Weak |
17 | PIM1-2 | PP7435_Chr1-0484 | 2 | Weak |
18 | PAS_chr1-1_0037 | PP7435_Chr1-0336 | 1 | Strong |
19 | PAS_chr3_1071 | PP7435_Chr3-0094 | 1 | Strong |
20 | PEX11 | PP7435_Chr2-0790 | 3;4 | Intermediate |
21 | PEX13 | PP7435_Chr2-0217 | 1 | Weak |
22 | PAS_chr1-1_0343 | PAS_Chr1-1_0343 | 4 | Intermediate |
23 | PEX12 | PP7435_Chr4-0200 | 1 | Weak |
24 | INP1 | PP7435_Chr4-0597 | 3 | Weak |
25 | PEX6 | PP7435_Chr1-0900 | 1 | Weak |
26 | PEX17 | PP7435_Chr4-0347 | 1 | Weak |
27 | ATG37 | PP7435_Chr4-0369 | 1 | Weak |
28 | TAL1-2 | PP7435_Chr2-0358 | 1 | Intermediate |
29 | PEX5 | PP7435_Chr2-0195 | 3 | Intermediate |
30 | PEX2 | PP7435_Chr3-1201 | 3 | Weak |
31 | PAS_chr3_1020 | PP7435_Chr3-0149 | 3 | Strong |
32 | PEX1 | PP7435_Chr3-0122 | 1 | Weak |
33 | PEX26 | PP7435_Chr4-0482 | 1 | Weak |
34 | PEX10 | PP7435_Chr1-1379 | 3 | Weak |
35 | PEX14 | PP7435_Chr4-0157 | 3 | Weak |
36 | PAS_chr3_0408 | PP7435_Chr3-0805 | Intermediate | |
37 | ARO7 | PP7435_Chr4-0965 | 3 | Weak |
38 | PEX8 | PP7435_Chr1-1134 | 1 | Weak |
39 | PAS_chr1-4_0459 | PP7435_Chr1-1255 | 1 | Intermediate |
40 | FAD1 | PP7435_Chr1-0246 | Intermediate | |
41 | YLR177 W | PP7435_Chr1-0659 | 3 | Intermediate |
42 | PEX11C | PP7435_Chr1-1331 | 3 | Weak |
43 | ACS2 | PP7435_Chr3-0810 | Weak | |
44 | PAS_chr3_0439 | PAS_chr3_0439 | 2 | Intermediate |
45 | RKI1-2 | PP7435_Chr4-0797 | 3 | Intermediate |
aRelative gene expression levels were derived from signal intensities on DNA microarrays at methanol induction [16, 17] and ordered from highest to lowest
bORF names derived from published P. pastoris genome sequences [19, 20]
cThe gene correlation was calculated using transcriptomic datasets comprising 29 different conditions. The log2 fold change data was used to look for co-regulations in this data set. The data was processed via the DeGNServer to calculate Spearman´s rank correlation using a CLR-based Network and an association cut-off value of 3.8 [21]. Co-regulation was analyzed with three genes involved in methanol utilization: AOX1 (A), DAS1 (D), FBA1-2 (F). Up at glucose limit means that expression is deregulated in glucose limited culture conditions without methanol (data from [12])
dInduction on methanol was classified based on the transcriptional regulation patterns obtained by [16, 17] by comparing expression levels of cells grown on methanol to cells grown on glucose or glycerol
Conclusions
Genome scale transcriptomic studies are a valuable source of information on native promoters and have been successfully used to identify promoters of different strength and desired regulatory behavior. Well defined promoters are core elements of synthetic biology part collections. The collection of P. pastoris promoters presented here, and others analyzed in the cited references can serve as a basis for setting up a P. pastoris promoter collection. Promoters with different regulatory strength are crucial elements of toolboxes for cell and metabolic engineering. In addition, they can be directly employed for gene expression to produce heterologous proteins or metabolites in yeasts.
Authors’ contributions
All authors contributed equally to this commentary. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Contributor Information
Brigitte Gasser, Email: brigitte.gasser@boku.ac.at.
Matthias G. Steiger, Email: matthias.steiger@boku.ac.at
Diethard Mattanovich, Phone: +43-1-47654-6569, Email: diethard.mattanovich@boku.ac.at.
References
- 1.Redden H, Alper HS. The development and characterization of synthetic minimal yeast promoters. Nat Commun. 2015;6:7810. doi: 10.1038/ncomms8810. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Zhang X, Liu J, Yu X, Wang F, Yi L, Li Z, Liu Y, Ma L. High-level expression of human arginase I in Pichia pastoris and its immobilization on chitosan to produce L-ornithine. BMC Biotechnol. 2015;15:66. doi: 10.1186/s12896-015-0184-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Røkke G, Korvald E, Pahr J, Oyås O, Lale R. BioBrick assembly standards and techniques and associated software tools. Methods Mol Biol. 2014;1116:1–24. doi: 10.1007/978-1-62703-764-8_1. [DOI] [PubMed] [Google Scholar]
- 4.Yadav VG, De Mey M, Lim CG, Ajikumar PK, Stephanopoulos G. The future of metabolic engineering and synthetic biology: towards a systematic practice. Metab Eng. 2012;14:233–241. doi: 10.1016/j.ymben.2012.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Keasling JD. Manufacturing molecules through metabolic engineering. Science. 2010;330:1355–1358. doi: 10.1126/science.1193990. [DOI] [PubMed] [Google Scholar]
- 6.Kelly JR, Rubin AJ, Davis JH, Ajo-Franklin CM, Cumbers J, Czar MJ, de Mora K, Glieberman AL, Monie DD, Endy D. Measuring the activity of BioBrick promoters using an in vivo reference standard. J Biol Eng. 2009;3:4. doi: 10.1186/1754-1611-3-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Blumhoff M, Steiger MG, Marx H, Mattanovich D, Sauer M. Six novel constitutive promoters for metabolic engineering of Aspergillus niger. Appl Microbiol Biotechnol. 2013;97:259–267. doi: 10.1007/s00253-012-4207-9. [DOI] [PubMed] [Google Scholar]
- 8.Qin X, Qian J, Yao G, Zhuang Y, Zhang S, Chu J. GAP promoter library for fine-tuning of gene expression in Pichia pastoris. Appl Environ Microbiol. 2011;77:3600–3608. doi: 10.1128/AEM.02843-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Balzer S, Kucharova V, Megerle J, Lale R, Brautaset T, Valla S. A comparative analysis of the properties of regulated promoter systems commonly used for recombinant gene expression in Escherichia coli. Microb Cell Fact. 2013;12:26. doi: 10.1186/1475-2859-12-26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Gasser B, Prielhofer R, Marx H, Maurer M, Nocon J, Steiger M, Puxbaum V, Sauer M, Mattanovich D. Pichia pastoris: protein production host and model organism for biomedical research. Future Microbiol. 2013;8:191–208. doi: 10.2217/fmb.12.133. [DOI] [PubMed] [Google Scholar]
- 11.Hartner F, Ruth C, Langenegger D, Johnson S, Hyka P, Lin-Cereghino G, Lin-Cereghino J, Kovar K, Cregg J, Glieder A. Promoter library designed for fine-tuned gene expression in Pichia pastoris. Nucleic Acids Res. 2008;36:e76. doi: 10.1093/nar/gkn369. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Prielhofer R, Maurer M, Klein J, Wenger J, Kiziak C, Gasser B, Mattanovich D. Induction without methanol: novel regulated promoters enable high-level expression in Pichia pastoris. Microb Cell Fact. 2013;12:5. doi: 10.1186/1475-2859-12-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Stadlmayr G, Mecklenbräuker A, Rothmüller M, Maurer M, Sauer M, Mattanovich D, Gasser B. Identification and characterisation of novel Pichia pastoris promoters for heterologous protein production. J Biotechnol. 2010;150:519–529. doi: 10.1016/j.jbiotec.2010.09.957. [DOI] [PubMed] [Google Scholar]
- 14.Delic M, Mattanovich D, Gasser B. Repressible promoters—a novel tool to generate conditional mutants in Pichia pastoris. Microb Cell Fact. 2013;12:6. doi: 10.1186/1475-2859-12-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Vogl T, Ruth C, Pitzer J, Kickenweiz T, Glieder A. Synthetic core promoters for Pichia pastoris. ACS Synth Biol. 2014;3:188–191. doi: 10.1021/sb400091p. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Russmayer H, Buchetics M, Gruber C, Valli M, Grillitsch K, Modarres G, Guerrasio R, Klavins K, Neubauer S, Drexler H, et al. Systems-level organization of yeast methylotrophic lifestyle. BMC Biol. 2015;13:80. doi: 10.1186/s12915-015-0186-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Prielhofer R, Cartwright SP, Graf AB, Valli M, Bill RM, Mattanovich D, Gasser B. Pichia pastoris regulates its gene-specific response to different carbon sources at the transcriptional, rather than the translational, level. BMC Genomics. 2015;16:167. doi: 10.1186/s12864-015-1393-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Rebnegger C, Graf AB, Valli M, Steiger MG, Gasser B, Maurer M, Mattanovich D. In Pichia pastoris, growth rate regulates protein synthesis and secretion, mating and stress response. Biotechnol J. 2014;9:511–525. doi: 10.1002/biot.201300334. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Küberl A, Schneider J, Thallinger GG, Anderl I, Wibberg D, Hajek T, Jaenicke S, Brinkrolf K, Goesmann A, Szczepanowski R, et al. High-quality genome sequence of Pichia pastoris CBS7435. J Biotechnol. 2011;154:312–320. doi: 10.1016/j.jbiotec.2011.04.014. [DOI] [PubMed] [Google Scholar]
- 20.Mattanovich D, Graf A, Stadlmann J, Dragosits M, Redl A, Maurer M, Kleinheinz M, Sauer M, Altmann F, Gasser B. Genome, secretome and glucose transport highlight unique features of the protein production host Pichia pastoris. Microb Cell Fact. 2009;8:29. doi: 10.1186/1475-2859-8-29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Li J, Wei H, Zhao PX. DeGNServer: deciphering genome-scale gene networks through high performance reverse engineering analysis. Biomed Res Int. 2013;2013:856325. doi: 10.1155/2013/856325. [DOI] [PMC free article] [PubMed] [Google Scholar]