Team:IIT-Madras/Design


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Team: IIT-Madras/Design

Design

The rationale:

Introduction:

In synthetic biology and metabolic engineering studying the level of protein expression is very important. This may be phase dependent, inducible or constitutive. There are various factors influencing expression levels of proteins:(Glick et al 1987) [1]

  • The strength of the promoter[2]
  • The efficiency of the RBS[3]
  • Substate cofactor availability[4]
  • The half life of the mRNA[5]
  • The metabolic state of the cell
  • Stability of the foreign protein in the cell
  • The abundance of the specific tRNA/Codon bias[6]
  • The copy number of the gene encoding the protein of interest[7]
  • Interaction of the protein with other proteins in the chassis
  • Presence of an inducer or a signalling molecule for an inducible promoter


In our project, we focused on three factors - Promoters, RBS and Codon bias.

Promoter: It is the region upstream of a gene where RNA polymerase binds and initiates transcription. The binding of RNA polymerase to the promoter is often the rate-limiting step in a bacterial system, as translation and transcription are coupled, unlike in an eukaryotic system where the mRNA undergoes post-transcriptional modifications. Hence the promoter sequence is the major translational regulator of gene expression.

RBS: It is the region just upstream of the coding region where the ribosomal unit binds to initiate translation. The mRNA conformation at RBS is extremely important in bringing both subunits of ribosome together to initiate translation.

Codon bias: The abundance of tRNA varies from species to species. Hence each species has a differing preference a codon for each amino acid as the genetic code is degenerate. The abundance of tRNA levels could also impact the rate of translation. By the process of codon optimization gene expression can be increased.


Promoters (using random and rational approaches): Promoters usually have the construct as shown below.



In region 1 it has typically about 17 Nucleotides and the -35 conserved region is recognized by RNA polymerase which initiates transcription. Following this, Region 2 which is about 17 nucleotides long. -10 follows which is typically a TATA box, a motif that is conserved for promoters. Following this we have a 6 nucleotide long region which we named Region 3 and then a transcription initiation site. This usually starts with A. Transcription usually begins at this site.

The regions flanking the -35 and -10 regions, Region 1,2 and 3 affect the strength of the promoter [8].

For our project we used a T5 promoter [BBa_K592008]. T5 is a constitutive promoter that is not under the influence of any protein. It is known to work in a broad range of microorganisms like E. coli, Acinetobacter baylyi ADP1 etc. [9]. An advantage of creating a T5 promoter-based library is that it might also work in other organisms like E. coli and industrially important chassis like Cornybacterium glutamicum. However, documentation and characterization of these promoters in these other chassis would be required to conclusively prove this. We have characterized these promoters in Acinetobacter baylyi ADP1.

We have classified the promoters we designed based on the method used into two categories:
i)P category: Randomizing the nucleotides flanking the -35 and -10 regions by conserving the GC content percentage wise in individual regions. Nucleotides flanking the -35 and -10 regions were also kept unchanged so as to not alter the interaction of RNA polymerase with the binding sites. Care was taken to prevent the accidental insertion of a biobrick restriction site or an Afl(II) restriction sites.
We designed four promoters from this category. (BBa_K2857003, BBa_K2857004, BBa_K2857005, BBa_K2857006)
ii)Q Category: In this approach, we introduced (in silico) point mutations in the T5 (BBa_K592008) promoter sequence . These promoters were selected based on the percentage of similarity they had with T5 (BBa_K592008) promoter. Care was taken to not create any restriction site or introduce mutations in the -35 and -10 regions. Based on this we selected promoters having 57% 66%, 79%, 85%, and 91% similarity. We selected two promoters showing 79% similarity with different sequences. This method was inspired by Mordaka P. M. et al 2018 [10].
We designed six promotersusing this approach (Q5:BBa_K2857007, Q6:BBa_K2857008, Q70:BBa_K2857009, Q71:BBa_K2857010, Q9:BBa_K2857011, Q8:BBa_K2857012)

Ribosome Binding Site

For each of the 10 promoters, we used Salis lab RBS calculator to calculate RBS specific for each promoter, GFP and Acinetobacter baylyi ADP1.(https://salislab.net/software/) (Salis, H M. “The Ribosome Binding Site Calculator.” Methods in Enzymology., U.S. National Library of Medicine, www.ncbi.nlm.nih.gov/pubmed/21601672.). These RBS have been named as Biobrick BBa_K2857013-BBa_K2857022.
Next, we assembled the RBS with promoter sequences and got the complete DNA sequence synthesized (BioBrick BBa_K2857111-BBa_K2857120).
The same promoters under iGEM RBS are (BBa_K2857101-BBa_K2857110), which also we got synthesized from the IDT and submitted.

PromoterPromoter BioBrick numberCorresponding Salis lab RBS BioBrick numberComplete Assembly(with iGEM RBS)S categoryComplete Assembly (with Salis lab RBS) R category
P1BBa_K2857003BBa_K2857013BBa_K2857101BBa_K2857111
P2BBa_K2857004BBa_K2857014BBa_K2857102BBa_K2857112
P3BBa_K2857005BBa_K2857015BBa_K2857103BBa_K2857113
Q4BBa_K2857006BBa_K2857016BBa_K2857104BBa_K2857114
Q5BBa_K2857007BBa_K2857017BBa_K2857105BBa_K2857115
Q6BBa_K2857008BBa_K2857018BBa_K2857106BBa_K2857116
Q70BBa_K2857009BBa_K2857019BBa_K2857107BBa_K2857117
Q71BBa_K2857010BBa_K2857020BBa_K2857108BBa_K2857118
Q8BBa_K2857012BBa_K2857022BBa_K2857109BBa_K2857119
Q9BBa_K2857011BBa_K2857021BBa_K2857110BBa_K2857120

CUTE, A. baylyi codon usage table and Codon Optimized-GFP:

When we contacted GenScript for sending us codon optimized GFP, they did not have reliable data on codon usage table of Acinetobacter baylyi ADP1. There was one table available on kasuza(https://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=202950) which is based only on two CDS.
We identified this industry based problem that the codon optimizers that they have codon usage table data mainly for standard hosts which are widely used. This is a hindrance in using the unconventional host for one’s studies. So, we created CUTE on the chassidex website. It can be found on CUTE ChassiDex. This online free tool can be used to generate Codon usage of any organism as long as its CDS annotation is available.
We used CUTE to generate Codon usage table data for A. baylyi ADP1 by taking into consideration the CDS annotation available on the NCBI site. This table can be found on the Results page of our wiki. This table is generated by taking into account at least 1194 CDS. We removed putative and hypothetical proteins from the data used to generate the table.
Using this codon usage table, we Codon optimized GFP and mCherry for Acinetobacter baylyi ADP1. We have submitted these Biobrick(BBa_K2857001 GFP and BBa_K2857002 mCherry).

Assembly of promoters and reporter system in pBAV1k vector

iGEM standard vectors pSB1C3 does not replicate in A. baylyi ADP1. From the literature, we found that pBAV1k works in A. baylyi[11]. We amplified pBAV1k reporters less version. For promoter studies, we amplified pBAV1k so as to get promoterless vector backbone where our promoters can be cloned. Similarly, for reporter studies, we amplified pBAV1k such that, we could get reporter-less vector backbone where codon optimized GFP can be placed.
pBAV1k could not be submitted due to the material-transfer agreement but it can be purchased from ADD GENE(https://www.addgene.org/26702/). This is also added as BioBrick (BBa_K1321309). This vector is a high copy, broad host range vector.
GFP was cloned in pBAV1k downstream of the T5 promoter and RBS. To measure the strength of Promoter we conducted fluorometry experiments were carried.
Similarly, Promoters were cloned upstream GFP in pBAV1k. To measure the codon-optimized GFP then fluorometry studies were carried out to find out the expression levels.

References

References

  1. Glick, B.R. & Whitney, G.K. Journal of Industrial Microbiology (1987) 1: 277. https://doi.org/10.1007/BF01569305
  2. Blazeck J., Alper H.S. Promoter engineering: recent advances in controlling transcription at the most fundamental level. Biotechnol. J. 2013;8:46–58. doi: 10.1002/biot.201200120.
  3. Synthetic biology toolbox for controlling gene expression in the cyanobacterium Synechococcus sp. strain PCC 7002. Markley AL, Begemann MB, Clarke RE, Gordon GC, Pfleger BF ACS Synth Biol. 2015 May 15; 4(5):595-603.
  4. (Metabolic pathway balancing and its role in the production of biofuels and chemicals. Jones JA, Toparlak ÖD, Koffas MA Curr Opin Biotechnol. 2015 Jun; 33():52-9.)
  5. Use of expression-enhancing terminators in Saccharomyces cerevisiae to increase mRNA half-life and improve gene expression control for metabolic engineering applications. Curran KA, Karim AS, Gupta A, Alper HS Metab Eng. 2013 Sep; 19():88-97.
  6. Codon usage: nature's roadmap to expression and folding of proteins. Angov E Biotechnol J. 2011 Jun; 6(6):650-9.)
  7. Ajikumar P.K., Xiao W.H., Tyo K.E.J., Wang Y., Simeon F., Leonard E., Mucha O., Heng Phon T., Pfeifer B., Stephanopoulos G. Isoprenoid pathway optimization for Taxol precursor overproduction in Escherichia coli. Science. 2010;330:70–74. doi: 10.1126/science.1191652.
  8. Gilman J, Love J. Synthetic promoter design for new microbial chassis. Biochemical Society Transactions. 2016;44(3):731-737. doi:10.1042/BST20160042
  9. (Hermann Bujard, Reiner Gentz, Michael Lanzer, Dietrich Stueber, Michael Mueller, Ibrahim Ibrahimi, Marie-Therese aeuptle, Bernhard Dobberstein, [26] A T5 promoter-based transcription-translation system for the analysis of proteins in vitro and in vivo, Methods in Enzymology, Academic Press, Volume 155, 1987, Pages 416-433)*
  10. Gilman J, Love J. Synthetic promoter design for new microbial chassis. Biochemical Society Transactions. 2016;44(3):731-737. doi:10.1042/BST20160042
  11. (Murin, Charles Daniel, et al. Applied and Environmental Microbiology, American Society for Microbiology, Jan. 2012, )
  12. Mordaka, Paweł M., and John T. Heap. “Stringency of Synthetic Promoter Sequences in Clostridium Revealed and Circumvented by Tuning Promoter Library Mutation Rates.” ACS Synthetic Biology, vol. 7, no. 2, 2018, pp. 672–681., doi:10.1021/acssynbio.7b00398.
  13. Salis, H M. “The Ribosome Binding Site Calculator.” Methods in Enzymology., U.S. National Library of Medicine, www.ncbi.nlm.nih.gov/pubmed/21601672