![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)
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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
- 5. Genetically identical cells can possess various degrees of heterogeneity Fluorescence Microscopy
- 6. Variability in Single-Cell Secretion Love et al B&B 2010
- 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
- 12. Measuring & Partitioning Noise in Gene Expression Dual-reporter assay Intrinsic Noise Extrinsic Noise
- 13. Single Molecule mRNA FISH Red – mRNA Green – protein/cell Blue - nucleus 13
- 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
- 24. GREEN : YFP RED : CFP
- 25. GREEN : YFP RED : CFP
- 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.
- 30. Strategy: titrate selected regulators, infer rates Activators: Flo8p, Msn1p, Mss11p, Tec1p, Ste12p, Phd1p Repressor: Sfl1p + doxycycline PFLO11 CFP PFLO11 YFP
- 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)