Synthetic Multicellular Bacterium

SMB: Synthetic Multicellular Bacterium

Introduction

Design & models

Experimental validation of the design

Applications & Perspectives: E. colight

Conclusions

Why a Synthetic Multicellular Bacterium

Multicellularity as a backbone for complex synthetic biology

Tool for metabolic engineering:decoupling growth and transgene expression

Studying fundamental aspects of multicellularity

Decoupling functions in complex systemsThe germline & soma solution

Differentiation

Tradeoff between growth & transgene expression

Partial dissociation between growth& transgene expression

Towards a Synthetic Multicellular Bacterium

Feeding

DifferentiationE coli

Differentiation

Feeding

Germline

Soma

Reproduction Reproduction

Tu

rns

ON

Basin of attraction Exponential growth

Proof of Feasibility

growth

Stability and fixed point analysisPopulation collapsesPopulation size remains constantPopulation growth is exponential

There are sets of parameters for which exponential growth exist

differentiation deathG = GermlineS = Soma

Design of DevicesDifferentiation device

T

Irreversible recombination

loxscar Y

Feeding deviceDifferentiation In a dapA strain

(+)

lox71 lox66ftsK dapATT

Combining both devices

cre

Differentiation control

chromosomelox66 Ylox71 X

dapA

Choices

No simple bypass or reversion Overproduction and excretionSurvival in DAP starvationNo growth in LBDAP sensitive expression mechanism

dapA Subtilis

Peptidoglycan and lysine pathways Feedback insensitive

Auxotrophy Metabolite: Essential gene:

Different Soma / Germline phenotypesLongevityLittle impact on metabolismGenetic isolation

ftsK Cellular division

Concept & Implementation

DAP feeding

Differentiation

loxSc

ftsK

loxScar

gfp T

No replication origin

Somatic cell

dapAftsKlox71 gfp T T lox66

Germline cell

cre

Differentiation control

DAP starvation >> RECOMBINATION >> Differentiation

dapA

cre

T

Models overviewFour approaches to answer four questions

Qualitative models

Quantitative models

How does differentiation induces feeding? How do spatial organization and distribution evolve?

How sensitive is the system to noise?

How robust and tunable is the system?

Cellular automaton

Multi-agents based system

Gillespie based simulationKinetic model

Spatial Simulation

An Agent Based Model Mechanical model

• Masses/springs system• Delaunay triangulation neighborhood

Biological model• Differentiation• DAP production/consumption/diffusion• Cells volume growth

Coupling both modelsCells volume growth modifies the

mechanical constraints and neighborhood

Simulations reproduce the 3 formerly predicted behaviors

Exponential growth

Stability

Red = GermlineGreen = Soma

Differentiation rate ++

Differentiation rate +

Biochemical Kinetic model

Quantitative analysis on an ODE model Molecular levelMean concentration values on the population

Outcome of the simulation:

•Range of valid parameters

Optimization and Robustness

•Critical parameters:

• DAP excretion

• Differentiation rate

S

GG+S

Time

Po

p.

siz

e

Exploring the impact of recombination Frequency through simulation

Differentiation by Recombination : Influence of Frequency

There is an optimum differentiation rate for growth

Gro

wth

rat

e

Differentiation rate

lox KnR lox

36% recombination rate per generation

Experimental analysis of recombination frequency

Growth onkanamycin

NO growthOn kan

lox

cre

pBAD

Time

cre

pBAD

C.F

.U /

ML

Differentiation through Recombination:Influence of frequency

Tradeoff between:

Maximize growth

Decrease germline generation time

Increase germline proportion

Increase DAP concentration

Increase differentiation rate

Decrease differentiation rate

Trade off

germline generation time / germline proportion

G

G G S

Differentiationdivision

G G S

G G S

50% recombination per generation stability

Differentiation through Recombination: Introducing Feedback

crerbs

dapAp

DAP

pBAD

crerbs

ara +

E coli

Differentiation

Feeding

Germline

Soma

Reproduction Reproduction

Tu

rns

ON

Tunable constant differentiationConditional differentiation

Inh

ibits

Pop

ula

tion

siz

e

SomaGermline

Soma with retrocontrolGermline with retrocontrol

Soma withRetrocontrol

Soma

Germline with retrocontrol

Germline

time

Differentiation through Recombination: Introducing Feedback

Retrocontrol can increase robustness

100µ

M

0µM

Differentiation through Recombination: Introducing feedback

DAP Concentration

Mea

n F

luo

resc

ence

(A

U)

rfprbs

dapA promoter

DAP?

dapA promoter can be used in the SMB to provide retro-control on differentiation

dapA strain on limited DAP concentration

Range of limiting growth [DAP] = range of dapAp activation = 0-100µM

Coculture

Experimental validation of DAP choice

DAP feeding supports survival

dapA- cell

dapA- cell

Prototrophcell

DAP?

Survival

dapA deletant + trp deletant strain coculture

1E+0

1E+1

1E+2

1E+3

1E+4

1E+5

1E+6

0 2 4 6 8 10

Time (h)

CFU

/ml

Coculture w ith TG1

dapA- LB

Coculture and survival

Construction ProcessChromosomal insertion

lox71 gfp T

ftsKIn a dapA strain

DapAlox66

loxSc

ftsK

loxScar

gfp T

No replication origin

Somatic cell

dapA subtilisftsKlox71 gfp T T lox66

Germline cell

Cre

dapAp

DAP starvation >> RECOMBINATION >> Differentiation

dapA subtilis

Cre

T

cre

Differentiation control

Perspectives & ApplicationsSMB as a tool for biological engineering.

Differentiation

DAP feeding

Tradeoff between growth & transgene expression

Partial dissociation between growth& transgene expression

E. colight: potential application of the SMB as a “metabolic plant”

Triglyceride inclusion

Free fatty acid

DGATAcyl-coA

Triglyceride

Ph

osp

ho

lipid

Diacylglycerol

Triglycerides synthesis only in Soma Soma isolation through differentiation induction Ingestion to absorb the fatty acids as you eat

Differentiation

DAP feeding

Eat fat don’t get fat

E. colight: experimental proof

IPTG +

DGAT +

DGAT -

- sodium oleate + sodium oleate (2mM)

IPTG - IPTG -IPTG + IPTG +

• Cloning of DGAT of acinetobacter ADP1 under pLac control

• Specific triglycerides coloration: Nile Red

E.colight makes triglycerides!

A new synthetic organism !!

Computational proof of principleExperimental & computational analysis orienting the design processConstruction of the SMB genetic cassettes19 New Biobricks added & characterized in the registryInserting a transcription factor in both Somatic in germline cassettes enables full modularity of our deviceFull traceability of molecular biology work and full wiki documentation

Achievements

Acknowledgements

Who did what ?Wet Lab: David Bikard Thomas Landrain David Puyraimond Eimad Shotar

Modeling team: Gilles Vieira Aurélien Rizk

Modeling tools Biocham MGS

Interface Wet/Dry: David Guegan Nicolas Chiaruttini

Logistics: Thomas Clozel Thomas Landrain

Instructors and advisors: Samuel Botanni Franck delaplace Francois Kepes Ariel Lindner Vincent Schächter Antoine Spicher Alfonso Jaramillo

DAP

All

ost

eric

co

ntr

ol

dapA

Lysine

Dap

A

Gen

etic

fee

db

ackpeptidoglycan