This PowerPoint helps students to consider the concept of infinity.
Conferencia Narendra Maheshri
1. Cell-to-cell variability: origins,
consequences, applications
Narendra Maheshri
Assistant Professor
Department of Chemical Engineering, MIT
Tecnologico de Monterey, Queretaro
Oct 5, 2010
2. Fluorescent Reporters
Spying on Gene Expression Dynamics
RA XFP
DEGRADATION
Expression Rate
mRNA
K
[A], Activator
BD Biosciences
Steady-state Input/Output Response
3. Probing gene regulation dynamics
from Bottom-up
Bar-Joseph et al, Nat Biot 2003
With synthetic networks 3
4. Single cell analysis is required to distinguish
homogeneous and heterogeneous responses
Activator
GFP
Expression
Average
Activator
Activator
Activator
Expression Expression
7. Variability in siRNA efficacy in T-Cells
Toriello N M et al. PNAS 2008;105:20173-20178
8. Variability in Nanog levels affects differentiation
potential in embryonic stem cells
Able to differentiate
Glauche et al PLoS ONE 2010
Kalmar et al PLoS Biol 2009
9. • What’s the source of variability?
• Can we control/exploit it?
• Need to know variability when designing
bioprocesses.
10. A „tale of two switches‟
analog signal
digital response
driven by trans-encoded fluctuations in transcription factor
multiple
analog inputs
FLO11 Promoter
Homo- or hetero-
geneous response
driven by cis-encoded fluctuations in promoter state
11. What are the role of fluctuations in gene expression?
A probabilistic description is necessary to describe the outcome of
reactions involving small numbers of chemical species.
K=1
N=1 N=2 N=3
Mean [ ] = 50% of total Reactions within cells can involve molecular
species that number in the 10-100’s
Stdev / Mean ~ N-0.5
14. mRNA number distribution in single cells
suggests bursty gene transcription
• m
Red – mRNA
Green –
protein/cell
Blue - nucleus
Frequency
mRNA per cell
14
15. Txnal bursting dominates in eukaryotes
λ μM μP
γ δM δP
λ μM μP
<P> = --------- = burst freq * burst size
γ δM δP
HIGH burst frequency LOW burst frequency
LOW burst size HIGH burst size
16. Transcriptional bursting is ubiquitous
Yeast Bacteria Mammalian cells
(Golding et al., Cell (Raj et al., PLoS Biol.
2005) 2006)
16
17. Noisy expression with positive feedback can lead to
an all-or-none response
BURSTY
To and Maheshri
Science 2010 expression
18. Hallmarks of noise-induced bimodality
are wide-spread
~ 10%
Lee et al, Science 2002 Zhang et al, NAR 2006
Belle et al, PNAS 2006 Kosugi et al, PNAS 2009
19. Hallmarks of noise-induced bimodality
are wide-spread
Gene Host Direct Activator # of TF High
positive half-life Binding sites expression
feedback variability
ComK B. subtilis 15 min 4 mRNA
Downstream
PDR3 S. cerevisiae 51 min 2
readout PDR5
REB1 S. cerevisiae 12 min 3 mRNA
ELT-2 C. elegans N/A Multiple mRNA
ftz D. melanogaster 7-40 min 6 Protein
Nanog Mammals 90 min N/A Protein
Ets-l Mammals 70-80 min 3 Protein
c-Jun Mammals 150 min 2 N/A
20. What if promoter switching was slow?
OFF/SILENCED ON
Promoter is ON 50% of the time:
Fast switching Slow switching
21. A “Sticky” Phenotype
The FLO gene family are yeast ADHESIN proteins that promote
hydrophobic cell-cell and cell-matrix interactions.
From Verstrepen et.al Mol.
Microb. 2006
22. Evidence for Variation in FLO Gene
Expression
Intragenic Repeats in ORF
• Repeats grow and contract due to replication slippage
• More repeats lead to greater adhesion
• Repeats present in both fungal and non-fungal microbes
Verstrepen et al 2005
Slow Epigenetic Switching
• Cells switch from a transcriptionally active to silent state
• Combinatorial explosion of phenotypes if different adhesins
switch independently
Ploidy Regulation Halme et al 2004
• Higher ploidy leads to lower expression
4N 2N N Galitski et al
1999
23. Does FLO11 expression occur independently at each
allele in a diploid cell? Independence results in
additional variation in gene expression
FLO11pr
YFP
FLO11pr
CFP
Slow Chromatin Dynamics
SILENT COMPETENT ON
26. Multiple FLO11 genes switch equivalently
and independently in the same cell
~0.3 / gen ~0.7 / gen
Octavio et al PLoS Genet 2009
27. A two-state model can correctly infer transition
rates from a static distributions
Octavio et al PLoS Genet 2009
28. l/d
g/d
Two-state l m d
Model silent open protein
g
dx
= -δx + μ f(t) Steady state: beta distribution
dt (Raj et al 2006)
Variability from promoter state fluctuations ONLY (f(t) switches from 0 to 1)
29. What rate(s) do trans-regulators of FLO11 affect?
Stress,
nutritional
signals
Ras2p cAMP
cAMP
Kss1p Msn1p pKA pathway
MAPK Hda1p
pathway
Mss11p Flo8p Sfl1p
Ste12p Tec1p Phd1p
FLO11
~ 3.4 kb
Gagiano, M. et al. (1999) Mol. Microbiol. 31:103-116.
Halme, A. et al (2004) Cell 116:405-415. Bardwell et al. (1998) Genes & Dev 12:2887-2898.
Borneman, A.R. et al (2006) Genes & Dev. 20:435-448. Pan,X. and Heitman,J. (2002) Mol Cell Biol 22(12):3981-3993.
31. Sfl1p has dual role as repressor, and critical level of
Sfl1p is needed for silencing
1. Conventional repression
RNA pol complex
transcription
Sfl1p
2. Repression by silencing (critical level of Sfl1p required for this function)
Histone
transcription
deacetylase
complex
Sfl1p
32. Activators fall into 3 classes
CLASS I: Flo8p: CLASS II:
Cannot challenge Weak stabilization/ Challenge the silent
the silent state destabilization of state by stabilizing
competent state the competent state
33. Synthetic activator (rtTA) can recapitulate all
3 classes depending on placement of (tetO)
binding site in the FLO11 promoter
-0.5 0
FLO11
sites:
Phd1
Ste12
Tec1
1 tetO
CLASS CLASS
-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5
I 0
II
ICR1 FLO11
-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0
34. Different input combinations map to a wide
range of population-level heterogeneity
Octavio et al PLoS Genet 2009
35. Role of fluctuations in EPA adhesin gene
expression in C. glabrata virulence
• Is there combinatorial diversity at the EPA genes in C. glabrata?
• What controls / can we control the extent of that diversity?
• Does the extent of diversity correlate with virulence (potentially
in a mouse model)?
36. Generating Phenotypic Diversity: Random sampling
of SETS of Genes/Pathways
N genes which turn “ON” and
“OFF” independently
2N unique expression states in 1 strain.
37. Gracias!
Lab
T.L. To
Tek Hyung Lee
C.J. Zopf
Bradley Neisner
Shawn Finney-Manchester
Katie Quinn
Nick Wren
Leah Octavio (w/ G. Fink)
Huayu Din (UROP)
Collaborators mRNA FISH: Arjun Raj (UPenn)
Kevin Verstrepen (KU Leuven) Alexander Van Oudenaarden (MIT)
Gerry Fink (MIT)
Eran Segal (Weissman)
![Fluorescent Reporters
Spying on Gene Expression Dynamics
RA XFP
DEGRADATION
Expression Rate
mRNA
K
[A], Activator
BD Biosciences
Steady-state Input/Output Response](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)