Master of Regulation Tandem Promoters & dCas9-based - - PowerPoint PPT Presentation

Master of Regulation

Tandem Promoters & dCas9-based Multi-level Promoters

2013 iGEM EM of Wuhan University

Proper Promoter is crucial to ideal performance of Genes

Just like Power Source to Equipments

375V Tesla S 25000V HighSpeed rail 4V iPhone

Brainstorming

VS

Expression at any-amount, any-where Expression Control in non-model species is hard

Algea Flower Fungus

Multi-level Regulator Brainstorming

Multi-level Regulator

Sliding Scribing Base of Rheostat

Resistance Multilevel Resistance

Brainstorming

The Base Sliding Scribing Modeling Human Practice

Tandem Promoters

Employ limited promoters to reach various expression levels

Tandem Promoters - The Base

P1:J23102 P3:J23116 P2:J23106 E X S P RFP

Promoter1 Promoter2

Employ limited promoters to reach various expression levels

Tandem Promoters - The Base

E X S P RFP

BBa_K1081002

P1:J23102

Promoter1

P1:J23102 P3:J23116 P2:J23106

Promoter2

P1:J23102 P2:J23106 P2:J23106 P1:J23102 P2:J23106 P3:J23116 P3:J23116 P1:J23102 P2:J23106

*

(* One of Our Seven Biobricks)

Employ limited promoters to reach various expression levels

Tandem Promoters - The Base

BBa_K1081002 BBa_K1081003 BBa_K1081004 BBa_K1081005 BBa_K1081006 BBa_K1081007 BBa_K1081008

Expression Assay

• Broader Strength Range
• Diverse Combinations

Seven New Biobricks (From K1081002 to K1081008) Tandem Promoters with Higher Strength Threshold More Levels for Potential Regulations

Primary Achievements

The Base Sliding Scribing Modeling Human Practice

Cas9-based Regulation

dCas9 - Sliding Scribing

Tandem Promoter Multilevel Promoter Sequence-specific Targeting

dCas9 - Sliding Scribing

CRISPR System

• Stable System
• Simple
• Convenient

• Repress RNAP binding & Initiation

dCas9

RNA Pol

dCas9 - Sliding Scribing

dCas9 Construction & Expression

One of Our Biobricks: BBa_K1081000

Multi-level Regulation

Multi-level Regulation Result 1

J23106-116 +dCas9 J23106-116 +dCas9+gRNA

(Repress J23106)

J23106-116 +dCas9+gRNA

(Repress J23116)

Multi-level Regulation Result 2

J23106-102 +dCas9 J23106-102 +dCas9+gRNA (Repress J23106)

α subunit

RNA Pol

Transcription Activated RNAP Recruited

Improve Regulation by aCas9

More levels

aCas9 Construction

Applications of dCas9: Multi-level regulator Constructed an aCas9 plasmid

Primary Achievements

P5 P1 P2 P3 P4 Sliding Scribing

More Regulatory Sites Changes of Number, Type and Order

Future Work

The Base Sliding Scribing Modeling Human Practice

Design your own Multilevel Promoter

Modeling

Design Your Own Multilevel Promoter (MP)

1.Determine the required expression levels 2.Design the tandem-repeat promoter(TRP) 3.Design the targeting sequence and gRNA

0.1 0.2 0.3 0.4 0.5 0.6

Pr Prom

• moter Str

trenght ht

• 1. Expression Level of MP

dCas9 gRNA1 Level1: No inhibition Level2: Inhibit one sub-promoter Level3: Inhibit both sub-promoter no gRNA gRNA2

• 35
• 35

Level 1 P1-P2 + dCas9 Level 2 P1-P2 + dCas9 + gRNA(Rep.P1) Single Promoter P2 + dCas9 Level 3 P1-P2 + dCas9 + gRNA(Rep.P2)

90% inhibition

P1: J23106 P2: J23116

Example. Target TRP Before Regulation Level1: 0.06 Total: 0.6 Level2: 0.33 sub: 0.3 Level3: 0.6

Modeling

Design Your Own Multilevel Promoter (MP)

1.Determine the required expression levels 2.Design the tandem-repeat promoter(TRP) 3.Design the targeting sequence and gRNA

Promoter number (data from [18]) Normalize strength

' 1 (1 )

n j i i

Strength p n   



error less than 10%

Compared with published data Compared with

• ur data

2.Tandem-repeat Promoter Model

The Derivation of the model Kinetic part

[ ] [ ] [ ] [ ] [ ] [ ] d mRNA RP mRNA dt d protein v mRNA k protein dt      

• 1. Transcription-Translation analysis

[ ] [ ] v Strength RP RP k     

• 2. Time scale seperation of Transcription initiation and RNAP binding

3 1 2 1 3

k K k c i

• k

k

DNA RNAP RP RP RP DNA RNA protein



              

1 slow

K K

DNA RNAP RP protein     

2.Tandem-repeat Promoter Model

So pi is propotional to [RP]

The Derivation of the model Thermodynamic part

• 3. RNAP binding Boltzmann equilibrium probability analysis

2 2

! ! ( 2) ( 1)( 1) ( 2)!( 2)! !( )! 1 ! ( 2) ( 1) ( ) ( 1)!( 1)!

ij tot i j

N N p Z P Z N P P NP P N P P N P N p p N P P NP Z P P N P                    

• 4. RNAP binding probablity to tandem promoter

1 ; 1

n i i tot i i

q p p q    

• 5. Tandem promoter strength adjustment

[1 (1 )]

n j i i

u Strength p n V    



1 (1 )

n j tot i i

p p n   



2.Tandem-repeat Promoter Model

Modeling

Design Your Own Multilevel Promoter (MP)

1.Determine the required expression levels 2.Design the tandem-repeat promoter(TRP) 3.Design the targeting sequence and gRNA

3.Cas9 Off-target Model

Requirement of regulation: Simplicity & Orthogonality

d/aCas9 gRNA Target site Potential off-target site in genome

• If possible, choose a target that has at least 4bp

difference with its most similar sequence.

• Otherwise, employ our model to find out a relatively

better choice.

• 1. All target can be divided to two groups.

The energy fuction △G'=F() may be a sigmoid function, result in insensitive to energy change at two extremes.

Sequence Single Mismatch tolerance G/C Ref.

TCATGCTGTTTCATATGATC low 7 [4] AACTTTCAGTTTAGCGGUCU low 8 [3] TGTGAAGAGCTTCACTGAGT low 9 [1] GATGCCGTTCTTCTGCTTGT low 10 [8] AGTCCTCATCTCCCTCAAGC low 10 [1] GAGATGATCGCCCCTTCTTC low 11 [2] CTCCCTCAAGCAGGCCCCGC low 15 [1]

• Ave. G/C 10.0

GCAGATGTAGTGTTTCCACA medium 9 [1] GGTGGTGCAGATGAACTTCA high 10 [8] GGGGCCACTAGGGACAGGAT high 13 [2] GTCCCCTCCACCCCACAGTG high 14 [2] GGGCACGGGCAGCTTGCCGG high 16 [8]

• Ave. G/C 12.4

3.Cas9 Off-target Model

2.The binding energy between gRNA and DNA determine the targeting

• efficiency. Different position on gRNA has different weight

(importance). Calculate △G(i) according to NN nearest neighbor model ATCG.............CCGG (G) gRNA TGGC.............GCCC (A) potential off-target DNA AT terminal+ GG + CG +....... CC + CC + GG terminal TG terminal+ CT + GC +....... GC + GC + CC terminal : : : : : :

△G(1), △G(2), △G(3) △G(17),△G(18),△G(19)

' ( ) ( [ (1), (2), (3)...., (19)] )

T

G F a b F G G G G b             

3.Cas9 Off-target Model

• 2. △G(i) determines the targetting efficiency

in the mismatch sensitive case

Our result based on DNA thermodaynamic model and data from [1] The data of Single- nucleotide specificity of Cas9 from [7]

3.Cas9 Off-target Model

• 3. Derive the kinetic functions of Cas9 binding.

Model prediction vs. data from [3] and [4]

3.Cas9 Off-target Model

2 2 1 1

2 1 1 1 2 2 2 1

[measurement ] [ ] [ ] [measurement ] [ ] [ ]

G G G RT d RT G d RT

K TF E S e e TF E S K e

       

    

w

[ ] [ ] [ ] [ ] / = [ ] [ ] [ ] [ ]

w r

G RT b dw G br dw dr dr RT

p E E E K E e p E K E K E K E e

   

       

[0.21, 0.25, 0.30, 0.39, 0.36, 0.32, 0.35, 0.39, 1.04, 1.19, 1.20, 1.05, 1.22, 2.80, 1.83, 1.92, 2.30, 2.36, 2.09 ]  

• 4. Kinectic analysis show expression time of Cas9 is also

crucial for off-target control in editing.

* * 1 2

1 [ ] [ ][1 ( )( )] [ ] [ ] ;

a b

k t k t b a a b cat cat a b M M

C A k e k e k k k E k E k k K K

 

      

1 1 1 1

,

G cat RT M a

k k k K K e k k

    

   

3.Cas9 Off-target Model

Boundary conditions were set as [A0]=1.0, [B0]=[C0]=0, ka=0.2 min-1,kb=0.1 min-1 for blue line; And [A0]=1.0, [B0]=[C0]=0, ka=0.1 min-1,kb=0.05 min-1 for red line.

Reversible binding Irreversible enzymatic reaction

Cas9+DNA Cas9-DNA Double strand break DNA Cas9     

Modeling Conclusion

• Produce designed expression level
• Switch between serveal expression levels
• Explore the best output in a systematic way

Promoter 1 N20(1) Promoter 2 N20(2) Promoter 3

…

The Base Sliding Scribing Modeling Human Practice

iGEM Popularization

Forum in Wuhan Display during the Science Festival Communication with CAU team Communication with USTC team Lectures for Bio students Lectures for Chem students Communication with 2013-HUST & HZAU

To iGEM-2013

Shenzhen_BGIC_ATCG

Helping Others

Constructed a functional dCas9 plasmid for their project

• Kenji Adzuma
• Yancheng Liu
• Prof. David Liu and Vikram Pattanayak
• Kuanwei Sheng, Jiawei Hang, Wenxiong Zhou, Boxiang Wang
• Prof.Fenyong Liu and Prof. Xiangdong Chen
• Yao Yang, Zhongqiao Lin and Qiaolin He
• Tengfei Ma, Shimeng Liu, Yun Huang, Liangliang Ji, Wenjia Gu
• Gen Xiao
• Prof. Zhixiong Xie, Prof. Xiangdong Gao, Prof.Yu Chen
• College of Life Sciences, WHU

Acknowledgements

Thank Y You!

• 1. Pattanayak, Vikram, et al. "High-throughput profiling of off-target DNA cleavage reveals RNA-

programmed Cas9 nuclease specificity." Nature biotechnology (2013).

• 2. Mali, Prashant, et al. "CAS9 transcriptional activators for target specificity screening and paired

nickases for cooperative genome engineering." Nature biotechnology 31.9 (2013): 833-838. 3.Qi, Lei S., et al. "Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression." Cell 152.5 (2013): 1173-1183. 4.Bikard, David, et al. "Programmable repression and activation of bacterial gene expression using an engineered CRISPR-Cas system."Nucleic Acids Research (2013). 5.SantaLucia Jr, John, and Donald Hicks. "The thermodynamics of DNA structural motifs." Annu. Rev.

• Biophys. Biomol. Struct. 33 (2004): 415-440.
• 6. Mathews, David H., et al. "Expanded sequence dependence of thermodynamic parameters improves

prediction of RNA secondary structure." Journal of molecular biology 288.5 (1999): 911-940.

• 7. Fu, Yanfang, et al. "High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human

cells." Nature biotechnology 31.9 (2013): 822-826. 8.Hsu, Patrick D., et al. "DNA targeting specificity of RNA-guided Cas9 nucleases." Nature biotechnology 31.9 (2013): 827-832.

• 9. Alon, Uri. Introduction to Systems Biology: And the Design Principles of Biological Networks. Vol. 10.

CRC press, 2007. Page 6.

• 10. Buchler, Nicolas E., Ulrich Gerland, and Terence Hwa. "Nonlinear protein degradation and the

function of genetic circuits." Proceedings of the National Academy of Sciences of the United States of America 102.27 (2005): 9559-9564.

References

• 11. Jinek, Martin, et al. "A programmable dual-RNA–guided DNA endonuclease in adaptive bacterial

immunity." Science 337.6096 (2012): 816-821.

• 12. Halford, Stephen E., Nicola P. Johnson, and John Grinsted. "The reactions of the EcoRi and other

restriction endonucleases." Biochemistry. J 179 (1979): 353-365.

• 13. Halford, Stephen E., Nicola P. Johnson, and John Grinsted. "The EcoRI restriction endonuclease with

bacteriophage lambda DNA. Kinetic studies."Biochemistry. J 191 (1980): 581-592.

• 14. Fogg, Jonathan M., et al. "Yeast resolving enzyme CCE1 makes sequential cleavages in DNA junctions

within the lifetime of the complex." Biochemistry 39.14 (2000): 4082-4089.

• 15. Fogg, Jonathan M., and David MJ Lilley. "Ensuring productive resolution by the junction-resolving

enzyme RuvC: large enhancement of the second-strand cleavage rate." Biochemistry 39.51 (2000): 16125- 16134.

• 16. Alberts, Bruce. Molecular biology of the cell (4th edition). Garland Science, (2000): 191-234
• 17. Gutfreund, Herbert. Enzymes: physical principles. London: Wiley-interscience, 1972.
• 18. Li, Mingji, et al. "A strategy of gene overexpression based on tandem repetitive promoters in

Escherichia coli." Microb Cell Fact 11 (2012): 19.

• 19. Bintu, Lacramioara, et al. "Transcriptional regulation by the numbers: models." Current opinion in

genetics & development 15.2 (2005): 116-124.

• 20. Buc, Henri, and William R. McClure. "Kinetics of open complex formation between Escherichia coli RNA

polymerase and the lac UV5 promoter. Evidence for a sequential mechanism involving three steps." Biochemistry24.11 (1985): 2712-2723.

• 21. DeHaseth, Pieter L., and John D. Helmann. "Open complex formation by Escherichia coli RNA

polymerase: the mechanism of polymerase‐induced strand separation of double helical DNA." Molecular microbiology 16.5 (1995): 817-824.

References

Expression level of MP

90% inhibition

Cas9 Off-target Model Limits

• 1. Inherit inaccuracy by employing "free DNA thermodaynamic

model" to mimic "protein influenced DNA-RNA binding"

• 2. Unable to calculate "b" as the unavailable of enzymatic data.
• 3. Unable to predict the variation in the "platform" area of

binding energy.

However,

They said "it is difficult to define simple rules for gRNA design based

• n the results of the four studies" --Dana Carroll, Nature Biotechnology, 2013

And we provided our insights

Factor influence mRNA translation， given same RBS 1 3 2

1.Egbert, Robert G., and Eric Klavins. "Fine-tuning gene networks using simple sequence repeats." Proceedings of the National Academy of Sciences 109.42 (2012): 16817-16822. 2.Na, Dokyun, Sunjae Lee, and Doheon Lee. "Mathematical modeling of translation initiation for the estimation of its efficiency to computationally design mRNA sequences with desired expression levels in prokaryotes." BMC systems biology 4.1 (2010): 71. 3.Nishizaki, Tomoko, et al. "Metabolic engineering of carotenoid biosynthesis in Escherichia coli by ordered gene assembly in Bacillus subtilis." Applied and environmental microbiology 73.4 (2007): 1355-1361.

changing the operon order of GGPP synthase and taxadiene synthase affect taxadiene synthase expression by 20% (GGPP synthase plus its RBS is ~1kb)

Promoter RBS GGPP RBS taxadiene syn Promoter RBS taxadiene syn RBS GGPP

1kb 20%

Why not IPTG, etc.

• Just two platform stage, Noise
• Non-model organism that IPTG may not work
• tranditional Chinese medicine, fungi, algea...

Strength [gRNA] No gRNA gRNA 1 gRNA 2

Novel aCas9 Platform

• Gal11 interacts with Gal4-1/-2 & VP16
• CI repressor interacts with CI repressor

Normalized gRNA Assembly

• Three cycles of Overlap PCR

TALE-Based Multi-Level Regulator?

Repressor

Wei, Chuanxian, et al. "TALEN or Cas9--rapid, efficient and specific choices for genomic modifications." Journal of Genetics and Genomics (2013).

TALE-Based Multi-Level Regulator?

Explore the Best Outputin a Systematic Way

V.G. Yadav et al. The future of metabolic engineering and synthetic biology:Towards a

• systematicpractice. Metabolic Engineering 14 (2012) 233–241