computational models of the transcriptional machinery and...

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/


Overview

Transcriptional machinery

The basic ESC switch architecture

Switch properties

An embryonic ground state

Reprogramming

Trophectoderm/endoderm extensions


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


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


Early “single gene” experiments identified OCT4, SOX2 and NANOG as key players




Nature of interactions explored by the dynamics

ONOFF

OFFON

Switch ON => OFF =>


Early “single gene” experiments identified OCT4, SOX2 and NANOG as key players




ONOFF

OFFON

Switch ON => OFF =>

Simple model considerations: Everyone is an activator


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

……..


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


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


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)


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


Lineage specification

Rossant (2006)


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


Lineage specification - trophectoderm

GCNF

Trophectoderm extension

OCT4–SOX2

NANOGCDX2

OCT4 SOX2


Lineage specification -endoderm

GCNF

Endoderm extension

OCT4–SOX2

NANOG

OCT4 SOX2

GATA6


Lineage specification – closing a loop

GCNF

Endoderm extension

OCT4–SOX2

NANOG

OCT4 SOX2

GATA6CDX2Trophectoderm extension


GCNF

Endoderm extension

OCT4–SOX2

NANOG

OCT4 SOX2


Suppress OCT4 -Trophectoderm lineage


GATA6, CDX2, GCNF

OCT4, SOX2, NANOG

SN

Suppress OCT4 -Trophectoderm lineage


GCNF

Endoderm extension

OCT4–SOX2

NANOG

OCT4 SOX2


Overexpress OCT4 - endoderm lineage


Connecting two worlds

Gene expression meets cell division and mobility

1. Trophectoderm formation Oct4/Cdx2

2. Endoderm formation Nanog/Gata-6


t


The early embryonic development

Develop a computational modeling framework for simulating the patterning of the embryo

Mechanics meets biochemistry


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


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


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



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



Cdx2 levels Inner/outer Tracking

Polarity-based model



Position-based model Polarity-based model



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


Blastocoel expansion

The fluid-filled blastocoel is formed after the 32-cell stage

A slowly expanding spherically shaped region


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


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


Towards the endoderm

Differential adhesion only

Directional signal only

Nanog

Gata-6


Towards the endoderm

Nanog

Gata-6

Differential adhesion + directional signal


Efficiency versus adhesion strength


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)


Summary

Patterning mammalian embryonic development

Mechanics is important


Endoderm formation

A simulation modeling framework essential for testing hypothesis

Future work

Implementing signalling and extend mechanics to describe further development


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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computational models of the transcriptional machinery and...

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