Cell-Cell Communications Ron Weiss Department of Electrical - PDF document

Cell-Cell Communications Ron Weiss Department of Electrical Engineering Princeton University Computing Beyond Silicon Summer School, Caltech, Summer 2002 Programming Cell Communities Diffusing signal E. coli proteins Program cells to perform various tasks using: • Intra-cellular circuits – Digital & analog components • Inter-cellular communication – Control outgoing signals, process incoming signals 1

Intercellular Communications • Certain inducers useful for communications: 1. A cell produces inducer 2. Inducer diffuses outside the cell 3. Inducer enters another cell 4. Inducer interacts with repressor/activator � change signal main metabolism (1) (2) (3) (4) The Intercellular AND Gate inactive active activator activator RNA P inducer no transcription transcription RNA P promoter operator gene promoter operator gene • Inducers can activate activators: -L-Homoserine lacton) � luxR – VAI (3-N-oxohexanoyl • Use as a logical AND gate: Activator Inducer Output 0 0 0 Activator 0 1 0 Output 1 0 0 Inducer 1 1 1 2

Communications Simulator � Cells sit on 3D agar grid � Model genetic networks in cells (ODE, stochastic) � ODE diffusion model with reflective boundaries cells A i-1,j,k A i,j-1,k A i,j,k A i,j+1,k agar A i+1,j,k 3D diffusion: d A i,j,k / d t = k diff (A i,j-1,k + A i,j+1,k + A i-1,j,k + A i+1,j,k + A i,j,k+1 + A i,j,k-1 - 6A i,j-1,k ) Two Cell Simulation 3

Eupryma scolopes Light organ Quorum Sensing • Cell density dependent gene expression Example: Vibrio fischeri [density dependent bioluminscence] (Light) (Light) Luciferase Luciferase LuxI hv LuxR hv P luxR luxI luxC luxD luxA luxB luxE luxG P Regulatory Genes Structural Genes The lux Operon LuxI metabolism � autoinducer (VAI) 4

The lux box Low and High Cell Densities Acyl-HSL O O O O O O O O O N N O O N H O H O O O H O O O O O N N H O O O O H O O O O O O N Low Cell Density Low Cell Density High Cell Density High Cell Density H O O O O O O O N H O O O N N H O H O N N H O H O O O O O O O O O O O O LuxR O LuxR O O N N H O H O O N N H O H O N H O (Light) (Light) Luciferase Luciferase hv LuxR hv LuxI LuxR LuxI (+) P P luxR luxI luxC luxD luxA luxB luxE luxG luxR luxI luxC luxD luxA luxB luxE luxG P P symbiotic, 10 10 cells/liter free living, 10 cells/liter <0.8 photons/second/cell 800 photons/second/cell 5

P. Aeruginosa P. Aeruginosa • Two autoinducer systems regulate virulence/biofilm formation • Secrete virulence factors when population high enough to overcome host defenses 6

Sources for a Library of Signals N-acyl-L-Homoserine Lactone Autoinducers in Bacteria Species Relation to Host Regulate Production of I Gene R Gene Vibrio fischeri marine symbiont Bioluminescence luxI luxR Vibrio harveyi marine symbiont Bioluminescence luxL,M luxN,P,Q Pseudomonas aeruginosa Human pathogen Virulence factors lasI lasR Rhamnolipids rhlI rhlR Yersinia enterocolitica Human pathogen ? yenI yenR Violaceumproduction Chromobacterium violaceum Human pathogen Hemolysin cviI cviR Exoprotease Enterobacter agglomerans Human pathogen ? eagI ? Agrobacterium tumefaciens Plant pathogen Ti plasmid conjugation traI traR Virulence factors Erwinia caratovora Plant pathogen expI expR Carbapenem production Erwinia stewartii Plant pathogen Extracellular Capsule esaI esaR Rhizobium leguminosarum Plant symbiont Rhizome interactions rhiI rhiR Pseudomonas aureofaciens Plant beneficial Phenazine production phzI phzR Cell-Cell Communication Circuits Sender cells Receiver cells VAI VAI + tetR P(tet) aTc luxI luxR GFP(LVA) P(Ltet-O1) Lux P(L) Lux P(R) Sender cells Receiver cells LuxR 0 GFP tetR 0 luxI ◊ VAI aTc VAI aTc pLuxI-Tet-8 pRCV-3 7

Time-Series Response to Signal 2500 r o l n t c o i v e s i t 2000 p o pRCV-3 + pUC19 Fluorescence 1500 pRCV3 + pSND-1 10X VAI extract pRCV-3 1000 pRCV-3 + pRW-LPR-2 g n l l i n a g pRCV-3 + pTK-1 AI s i c t e i r d 500 negative controls 0 0:00 0:30 1:00 1:30 2:00 Time (hrs) Fluorescence response of receiver (pRCV-3) Characterizing the Receiver Response of receiver to different levels of VAI extract 8

Controlling the Sender’s Signal Strength 75,000 Fluorescence 50,000 Receiver LuxTet4B9 RCV Only 25,000 0 2 Null 20 200 10x AI 2,000 20,000 200,000 aTc (ng / ml) Dose response of receiver cells to aTc induction of senders 0.1mm senders receivers overlay 9

20 µm receivers senders overlay 10

Bi-Directional Communication [Karig, Weiss] Construct A Construct B IPTG C4HSL lacI rhlI P(lacIq) P(lac) rhlR luxI hcred P L (rhl) qsc luxR gfp 3OC6HSL P L (lux) P R (lux) • Explore substrate properties – Crosstalk – Time scale/delay – Signal strength • Create constructs useful in later systems Demonstrating rhlI Communications senders receivers 11

Testing Crosstalk Does 3OC6HSL bind RhlR to activate transcription? Signal Processing / Analog Circuits 12

Detecting Chemical Gradients signal O O O O O N N O O H H O O H N O analyte O O O O N O O O N H O H O H O N O O O source O H N O O O O O O N O O O N H O H O N H O O O O N O H O O O N H O reporter rings GF P Analyte source detection [HSL] Circuit Components O O O N H O O O O N O O H O O O O O O N O H O O N H O O O H N O O N O O H O O N H O P(X) O O Z L u xR O L u xR N H O P(R) P(Z) luxR X Y GFP P(lux) P(Y) W P(W) Z Components: 1. Acyl-HSL detect 2. Low threshold GFP 3. High threshold 4. Negating combiner [HSL] 13

Acyl-HSL Detection O O O N H O O O O N O O H O O O O O O N O H O N O H O O O N H O O N O O H O O N H O P(X) O O Z L u xR O L u xR N H O P(R) P(Z) luxR X Y GFP P(lux) P(Y) W P(W) Z X � low threshold Y � high threshold Low Threshold Detection O O O H N O O O O N O O H O O O O O O N O H O O N H O O O N H O O N O O H O O N H O P(X) O O Z 1 L u xR O L u xR N H O P(R) P(Z) luxR X Y GFP P(lux) P(Y) W P(W) Z 2 14

High Threshold Detection O O O N H O O O O N O O H O O O O O O N O H O N O H O O O N H O O N O O H O O N H O P(X) O O Z 1 L u xR O L u xR N H O P(R) P(Z) luxR X Y GFP P(lux) P(Y) W P(W) Z 2 Protein Z Determines Range O O O H N O O O O N O O H O O O O O O N O H O O N H O O O N H O O N O O H O O N H O P(X) O O Z 1 L u xR O L u xR N H O P(R) P(Z) luxR X Y GFP P(lux) P(Y) W P(W) Z 2 15

Negating Combiner O O O N H O O O O N O O H O O O O O O N O H O N O H O O O N H O O N O O H O O N H O P(X) O O Z 1 L u xR O L u xR N H O P(R) P(Z) luxR X Y GFP P(lux) P(Y) W P(W) Z 2 Engineering Circuit Characteristics � HSL-mid: the midpoint where GFP has the highest concentration � HSL-width: the range where GFP is above 0.3uM HSL-mid 0.3 HSL-width 16

Tuning the Range: Repressor/Operator Affinities 3 1.5 2.5 2 hsl-width hsl-mid 1.5 1 1 0.5 0 0.5 4 4 3 0 3 0 1 2 1 2 2 2 1 1 3 3 0 4 0 k-bind-X2-P-X 4 k-bind-Y2-P-Y k-bind-X2-P-X k-bind-Y2-P-Y range width range mid-point versus versus X & Y repressor efficiencies X & Y repressor efficiencies rep/op affinity increases � transfer-curve shifts left Tuning the Range: Ribosome Binding Sites 4 2 3 1.5 hsl-width hsl-mid 2 1 1 0 0.5 1 1 0.1 0.1 0.8 0.8 0.15 0.15 0.2 0.2 0.6 0.6 0.25 0.25 0.3 0.3 0.4 0.4 0.35 0.35 kxlate-X kxlate-X kxlate-Y kxlate-Y range width range mid-point versus versus X & Y RBS efficiencies X & Y RBS efficiencies RBS efficiency increases � transfer-curve shifts left 17

HSL Detection Sender cells Receiver cells VAI VAI + tetR P(tet) aTc luxI luxR GFP(LVA) P(Ltet-O1) Lux P(L) Lux P(R) Low Threshold Component lacI lacI cI P(tet) λ P(R) YFP [high] 0 CFP YFP IPTG P(tet) λ P(R) (Off) P(lac) IPTG measure TC cI CFP P(lac) #2 #2: mutate operator #1 RBS #1: modify RBS Weiss & Basu, NSC 2002 18

Genetic Circuit for High Threshold pCMB-2/pCMB-100 10000 1000 Fluorescence 100 10 pCMB-2/pCMB-100 1 0.1 1 10 100 aTc Concentration Level P(bla) tetR P(lac) cI aTc λ P(tet) lacI CFP YFP P(R) Circuit Design Principles • Separation of low threshold and high threshold – RBS efficiency of X must be higher than that of Y – Binding affinity of X to its respective promoter has to be higher than that of Y • Constants associated with Y have more impact on range- width and range-midpoint – Y passes through an additional gain stage • Leakiness and sensitivity of lux promoter determines the lower bound of detection of acyl-HSL 19

Amorphous Computing Programming Cell Aggregates • Amorphous Computing: “How does one engineer prespecified, coherent behavior from the cooperation of vast numbers of unreliable parts that are interconnected in unknown, irregular, and time-varying ways.” • An aggregate of cells is an example of an amorphous computing substrate 20