Computational models of the transcriptional machinery and...
Transcript of Computational models of the transcriptional machinery and...
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Computational Biology Lund University
Computational models of the transcriptional machinery and spatial patterning of the early mammalian embryo
Systems Biology of Stem Cells, Irvine, CAMay 24-25, 2010
Carsten PetersonComputational Biology & Biological Physics
Lund University, Swedenhttp://cbbp.thep.lu.se/
Lund Stem Cell Center, Lund University, Sweden
Cdx2 Oct4 Sox2 Nanog Gata-6
http://cbbp.thep.lu.se/
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Computational Biology Lund University
Overview
Transcriptional machinery
The basic ESC switch architecture
Switch properties
An embryonic ground state
Reprogramming
Trophectoderm/endoderm extensions
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Computational Biology Lund University
Overview
Spatial patterning
Geometrical constraints in the embryo
Mechanics and biochemistry
Trophectoderm formation
Endoderm formation
Transcriptional machinery
The basic ESC switch architecture
Switch properties
An embryonic ground state
Reprogramming
Trophectoderm/endoderm extensions
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Computational Biology Lund University
Early “single gene” experiments identified OCT4, SOX2 and NANOG as key players Niwa (2000); Chambers (2003)
Followed by ChIP-chip experiments for their binding sites and microarray profiling for further insights
Boyer (2005); Loh (2006); Ivanova (2006)
A core structure emerges:
Core transcriptional network of embryonic stem cells
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Computational Biology Lund University
Early “single gene” experiments identified OCT4, SOX2 and NANOG as key players
Followed by ChIP-chip experiments for their binding sites and microarray profiling for further insights
A core structure emerges:
Core transcriptional network of embryonic stem cells
Nature of interactions explored by the dynamics
ONOFF
OFFON
Switch ON => OFF =>
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Computational Biology Lund University
Early “single gene” experiments identified OCT4, SOX2 and NANOG as key players
Followed by ChIP-chip experiments for their binding sites and microarray profiling for further insights
A core structure emerges:
Core transcriptional network of embryonic stem cells
ONOFF
OFFON
Switch ON => OFF =>
Simple model considerations: Everyone is an activator
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Deterministic rate equations
Solve iteratively the Shea-Ackers rate equations:
Concentrations: [A] = External signal [O] = OCT4 [S] = SOX2 [N] = NANOG [OS] = OCT4/SOX2 complex
OS Binds first Then recruits NANOG
……..
…….. Similar structure
……..
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Turning the switch “ON”
ONOFF
NANOG
Signal A+
OCT4-SOX2
ONOFF
Wnt + ..
How does the Oct4-Sox2-Nanog system respond to external activating signals, e.g. Wnt?
The switch is very robust against parameter variations
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Turning the switch “OFF”
OCT4-SOX2
NANOG
Signal B-
ON
ON
OFF
OFF
p53
How does the Oct4-Sox2-Nanog system respond to external repressive signals, e.g. p53?
The switch is very robust against parameter variations
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An ESC “ground state”
Signal A+
By over-expressing NANOG one obtains an irreversible switch.
Once ON, the stem cell can continue to self-renew in the absence of external factors – a “ground state” Ying (2008)
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Reprogramming
Expressing Oct4/Sox2 when OFF turns the switch ON
- Mechanism: Oct4 recruits Nanog to turn on the switch
- Only expressing Nanog is not sufficient
The other known reprogramming factors Klf4 and c-Myc are not part of our simplified network, but …
Takahashi, 2006
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Lineage specification
Rossant (2006)
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Lineage specification – the extensions
OCT4
OCT4
TARG
ET
TARG
ET
Global expression profiling of Oct4manipulated ES cells combined with (ChIP) assays => Genes show both activation and repression depending on Oct4 expression levels
Matoba (2006)
OCT4 Trophectoderm
Endoderm
OCT4
ES
T E
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Lineage specification - trophectoderm
GCNF
Trophectoderm extension
OCT4–SOX2
NANOGCDX2
OCT4 SOX2
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Lineage specification -endoderm
GCNF
Endoderm extension
OCT4–SOX2
NANOG
OCT4 SOX2
GATA6
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Lineage specification – closing a loop
GCNF
Endoderm extension
OCT4–SOX2
NANOG
OCT4 SOX2
GATA6CDX2Trophectoderm extension
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GCNF
Endoderm extension
OCT4–SOX2
NANOG
OCT4 SOX2
GATA6CDX2Trophectoderm extension
Suppress OCT4 -Trophectoderm lineage
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GATA6, CDX2, GCNF
OCT4, SOX2, NANOG
SN
Suppress OCT4 -Trophectoderm lineage
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GCNF
Endoderm extension
OCT4–SOX2
NANOG
OCT4 SOX2
GATA6CDX2Trophectoderm extension
Overexpress OCT4 - endoderm lineage
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Connecting two worlds
Gene expression meets cell division and mobility
1. Trophectoderm formation Oct4/Cdx2
2. Endoderm formation Nanog/Gata-6
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t
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The early embryonic development
Develop a computational modeling framework for simulating the patterning of the embryo
Mechanics meets biochemistry
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Mechanistic model of embryogenesis
Blastomers are incompressible ellipsoids
Elastic response is lumped into principal axes
Measure deformation in cell overlap
Elastic, adhesion and drag forces
Total force = Felastic + Fadhesion + Fdrag
Overdamped mechanics (no acceleration)
• Felastic
• Fadhesion
Attracts nearby cells in proportion to overlap area
Tangential drag force proportional to relative velocities and overlap area
• Fdrag
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Mechanistic model of embryogenesis
Blastomers are incompressible ellipsoids
Elastic response is lumped into principal axes
Measure deformation in cell overlap
Elastic, adhesion and drag forces
Total force = Felastic + Fadhesion + Fdrag
Overdamped mechanics (no acceleration)
• Felastic
• Fadhesion
Attracts nearby cells in proportion to overlap area
Tangential drag force proportional to relative velocities and overlap area
• Fdrag
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Cell division
• Cell cycle times sampled from experimental distribution
Daughters share parental cell volume
• Selection of division plane is either
- random or directional (with respect to pellucid zone)
• Partition rules for cell content
- Equal partition
- Asymmetric partition
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Ready to go
Each cell has a set of internal data
- concentration of proteins, cell cycle length, etc.
Different “cell species” can have different division/growth rules,
interaction parameters
Neighborhood determined by Voronoi diagram relation
Track cell lineages, protein concentration, elastic energy, etc.
Analyze statistics of different cell lineages - explore hypothesis
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Trophectoderm formation
Current conceptional models
Position-based model (inside-outside): Inner or outer position of a cell dictates its Cdx2 level
Polarity-based model: Outer cells, which are known to be polarized, polarize Cdx2 as well
Asymmetric divisions for cells with high/low Cdx2 content
Model the alternatives with mechanics and a simplified biochemical network with Cdx2 and Oct4 only
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Trophectoderm formation
Cdx2 levels Inner/outer Tracking
Polarity-based model
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Trophectoderm formation
Position-based model Polarity-based model
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Trophectoderm formation
Computational model outcome:
Position-based model (inside-outside) or polarity-based model?
Both models give rise to the desired pattern
However, the inside-outside model is more robust
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Blastocoel expansion
The fluid-filled blastocoel is formed after the 32-cell stage
A slowly expanding spherically shaped region
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Endoderm formation
After the blastocoel is formed:
Nanog and Gata-6 cells randomly distributed
Problem: How do these separate (cluster) in a directional manner?
Proposed mechanisms: Differential adhesion and directional signaling
Model the system and evaluate the impact from such mechanisms
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Differential adhesion
For moving cells; randomly, through homing signals or cell divisions,adhesion properties could be important
We explore such effects in endoderm formation by assigning different adhesion and cross-adhesion strengths
With Nanog/Nanog > Gata-6/Gata-6 > Nanog/Gata-6, the two cell populations segregate
However, a homing signal from the blastocoil surface is needed for robust endoderm formation
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Towards the endoderm
Differential adhesion only
Directional signal only
Nanog
Gata-6
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Towards the endoderm
Nanog
Gata-6
Differential adhesion + directional signal
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Efficiency versus adhesion strength
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Summary
The ESC switch including lineage sub-switches
Core Oct4/Sox2/Nanog model (2006) still captures essentials Bistability Reprogramming with Oct4/Sox2 “ground state” with no external signals
The Cdx2 and Gata-6 “plug-ins” handles trophectoderm and endoderm formation
Ongoing and future work (in progress; Chickarmane, Olariu)
Epigenetics and more componentsNoise – transcriptional versus epigenetic
Benefit from novel data; e.g on Nanog knockdowns (Lu, 2010)
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Summary
Patterning mammalian embryonic development
Mechanics is important
Trophectoderm formation
Endoderm formation
A simulation modeling framework essential for testing hypothesis
Future work
Implementing signalling and extend mechanics to describe further development
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Key collaborators & publications
Vijay Chickarmane Biology Division, Caltech
V. Chickarmane et al, Transcriptional dynamics of the embryonic stem cell switch, PloS Comp Bio e123 (2006)
V. Chickarmane et al, A computational model for understanding stem cell, trophectoderm and endoderm lineage determination, PloS One e3470 (2008)
P. Krupinski et al, Simulating the mammalian blastocyst – how biochemical and mechanical interactions pattern the embryo, submitted (2010)
Pawel Krupinski Computational Biology, Lund
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