generic and specific constraints shaping adaptive gene expression profiles in yeast
Post on 07-Jan-2016
28 Views
Preview:
DESCRIPTION
TRANSCRIPT
Generic and specific constraints shaping adaptive gene
expression profiles in yeast
Ester Vilaprinyó, Rui Alves, Armindo Salvador, Albert Sorribas
Coimbra, 2007 Grup de Bioestadística i BiomatemàticaDep. Ciències Mèdiques BàsiquesUniversitat de Lleida
Introduction Environmental conditions change. Cells living
in those environments need to adapt to those changes in order to survive environmental stresses (heat shock, osmotic...).
Stress
To survive yeast changes its gene expression profile
This allows adaptation of fluxes and concentrations
Introduction
In principle different quantitative and qualitative gene expression profiles (GEP) could produce the same physiological adaptation
However, what has been observed is that GEPs are specific for each type of stress
Constraints to the changes in gene expression
Adaptation is multiobjective. Gene expression profiles (GEPs) must
induce expression of genes whose proteins are needed for the response SPECIFIC CONSTRAINTS
There may be constraints that are common to most stress conditions? GENERAL CONTRAINTS?
Goals
Can we identify general and specific constraints that shape an adaptive gene expression profile (GEP) of yeast under stress conditions?
If so, can we use them to characterize the quantitative changes (design principles) required for a given response?
Outline
Identification of a general type of constraints to GEP design
Identification of specific constraints for heat shock & Quantitative design of GEPs in heat shock response
What is common to all stress responses?
To adapt quickly cells need to synthesize proteins quickly and using as few resources as possible.
Globally, changes in gene expression correlate well with changes in protein levels.
Proteins are the most expensive of macromolecules.
Synthesis of new metabolites is expensive but stress specific.
Therefore a general selective pressure in stress response to adapt quickly and at low cost could shape the regulation of expression for the different genes in the GEP
How to save resources in protein synthesis?
H1: If proteins are abundant in the basal state, the cell is spending energy synthesizing them and keeping them at high level. Because their activity is already abundant, to save energy cells could inhibit expression of abundant proteins.
Basalprotein
abundance
Change ingene
expression after stress
Correlation
Abundant proteins are inhibited
Protein abundance/103
1/changefold
H2: Low abundance proteins have almost no total activity. To achieve larger relative increases in activity, cell could express proteins of low abundance
Basalprotein
abundance
Change ingene
expression after stress
Correlation
How to achieve a fast increase in activity?
Proteins of low abundance are overexpressed
Protein abundance/103
changefold
H3: If in addition to downregulation of abundant proteins, the cell downregulates genes that code for large proteins, it will save more energy.
Are there other ways to design GEP that use resources efficiently?
H4: Upregulation of genes that code for small proteins. This will produce new proteins quicker and at lower cost than if upregulated proteins where larger.
Protein size (MW or length)
Change ingene
expression after stress
Correlation
Are there other ways to design GEP that respond fast and use
resources efficiently?
Size matters in modulation of gene expression?
Protein size (MW)
changefold
Repressed genes
Overexpressed genes
Protein size (MW)
1/changefold
Size matters in modulation of gene expression
H3
H4
Resource usage and quickness of response general constraints
for adaptive GEP?
H1: To save energy cells should inhibit proteins that are abundant
H2: To achieve larger relative increases in activity, cell should express proteins of low abundance
H3: Downregulation of genes that code for large proteins.
H4: Upregulation of genes that code for small proteins.
Resource usage and quickness of response general constraints
for many adaptive GEP
The hypotheses are consistent with these selective pressures in the design of adaptive GEPs
Outline
Identification of a general constraint to GEP
Identification of specific constraints for heat shock & Quantitative design of GEPs in heat shock response
Heat shock response
Well characterized physiologically Previous work (Voit & Radivovevitch) Enough information to identify
contraints Enough information for mathematical
modelling of the relevant reactions
Metabolic network & physiological constraints
Glycogen Trehalose
NADPH
HIGH ENERGY DEMANDC1
STRUCTURAL INTEGRITY-Avoids aggregation of denatured proteins-Membrane -Acts in synergism with chaperonesC2
REDUCING POWERNew synthesis of sphingolipids in order to change the membrane fluidityC3
Curto, Sorribas, Cascante (1995) Math. Biosci. 130, 25-50 Voit, Radivovevitch (2000) Bioinformatics 16: 1023-1037
Glycogen Trehalose
Methodology
×5
×5
×5
5 ×
HXT GLK PFK TDH PYK TPS G6PDH
hip1 5 1 1 1 5 1 5
HXT GLK PFK TDH PYK TPS G6PDH
hip1 5 1 1 1 5 5 5hip2 3 3 3 3 3 3 3
HXT GLK PFK TDH PYK TPS G6PDH
hip1 5 1 1 1 5 5 5hip2 3 3 3 3 3 3 3
hip3 2 1 1 1 2 7 7
×2
×7
×2
7 ×
SIMULATIONS To explain why expression of particular genes is changed, we scanned the gene expression space and translated that procedure into different gene expression profiles (GEP)
Consider a set of possible values for each enzyme.Explore all possible combinations.Total: 4.637.360 hypothetical GEPs
GLK, TPS [ 1, 2.5, 4, ..., 14.5, 16, 17.5, 19]
HXT [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
G6PDH [1, 2, 3, 4, 5, 6, 7, 8]
PFK, TDH, PYK [ 0.25, 0.33, 0.5, 1, 2, 3, 4]
HXT
GLK PFK TDH PYKTPS G6PDH
hip1 5 1 1 1 5 5 5hip2 3 3 3 3 3 3 3hip3 2 1 1 1 2 7 7......
...
.........
...
.........
...
.........
...
...
...
.........
...
.........
...
.........
...
.........
hip4637360
×3
×3
×3
3 ×
×3
×3
×3
NADPH
Implementation of stress responses
Metabolic network
Mathematical model
Power Law form Biochemical System Theory
(Savageau, 1969)
16 25 2712 21
25 27 32 35 38 62 611 72 71521
83 84 85 812
32 35 38 43 45 49 414
43 45 49
.
1 1 2 6 2 1 5 7
.
2 2 1 5 7 3 2 5 8 6 2 11 7 2 15
.8 3 4 5 12
3 3 2 5 8 4 3 5 9 14
.
4 4 3 5 9 14
2
2
f f ff f
f f f f f f f f ff
f f f ff f f f f f f
f f f
X X X X X X
X X X X X X X X X X X
X X X XX X X X X X X X
X X X X X
53 54 55 510414
43 45 49 53 54 55 510 25 27 32 35 38 62 611 95 913414 21
5 3 4 5 10
.
5 4 3 5 9 14 5 3 4 5 10 2 1 5 7 3 2 5 8 6 2 11 9 5 132
f f f ff
f f f f f f f f f f f f f f f ff f
X X X X
X X X X X X X X X X X X X X X X X X X
Each GEP has associated a new steady state→ functional changes → HS index of performance
Reproduce basal conditions (25ºC)
Generalised Mass Action
Gene expression changes
Evaluate HS performance
Criteria of performance
C1- Synthesis of ATP C2- Synthesis of trehalose C3- Synthesis of NADPH
“Well-known” and studied by experimentalist
C1-C3 Production of trehalose, ATP, and NADPH
If we only consider the criteria concerning an increase of fluxes selects a wide set of possible GEPs (27.8 %, 1.290.454)
The enzymes involved directly in the synthesis should be over-expressed.
In many cases, flux increase involve large metabolite accumulation, which is an undesirable situation in terms of appropriate response
■ % of the change-folds before any selection ■ % of the change-folds after selecting by C1-C3
Fold change in gene expression
% o
f to
tal G
EP
s
HXT: Hexose transporters
GLK: Glucokinase
PFK: Phosphofructokinase
TDH: Glyceraldhyde 3P dehydrogenase
PYK: Pyruvate kinase
TPS: Trehalose phosphate syntase
G6PDH: Glucose-6-P dehydrogenase
Criteria of performance
C4- Accumulation of intermediates: High fluxes with high metabolite concentrations are considered a sub-optimal adaptation Reactivity Cell solubility Metabolic waste
C5- Cost of changing gene expression: GEPs that allow adaptation with minimal changes in gene expression are favoured Adaptation should be economic Minimize protein burden
“Well-known” and studied by experimentalist
Well-studied within a system biology perspective
C1- Synthesis of ATP C2- Synthesis of trehalose C3- Synthesis of NADPH
abs ln mRNA change foldCost cost
50 %
No experimental measures are available, so we have chosen as a threshold the value that includes de 50% of all the cases
Criteria of performance
C1- Synthesis of ATP C2- Synthesis of trehalose C3- Synthesis of NADPH
C4- Accumulation of intermediates C5- Cost of changing gene expression
C6- Glycerol production C7- TPS and PFK over-expression C8- F16P levels should be maintained
“Well-known” and studied by experimentalist
Well-studied within a system biology perspective
C6- Glycerol production
Glycerol production helps in producing NADPH from NADH
New synthesis of glycerolipids required
Genes are over-expressed
Glicerol rate
50%Selecting GEPs with the highest glycerol production is synonymous of selecting GEPs with low PYK over-expression
C7- TPS and PFK
TPS is directly related with vtrehalose PFK is inversely related with vtrehalose If PFK is over-expressed, then TPS
should also be over-expressed, which compromises C5 (cost)
Sensitivity analysis shows that the system is highly sensible to change PFK
/ trehaloseTPS PFK v
50%
Glycogen Trehalose
F16P is required for glycerol synthesis
F16P feed-forward effect to the lower part of the glycolysis PYK velocity is increased in vitro by as much as
20 by F16P and hexose phosphates in their physiological concentration ranges
This enzyme modulation facilitates the flow of material and avoids accumulation of intermediates
C8- F16P levels should be maintained
Results based on all previous criteria
Values for Criteria Percentage of GEPs selected
using each criteria
Absolute values Ratio to basal
values Individual Accumulated
C1 VATPa > 180.6 3 45.13e
C2 VTREa > 0.03 25 60.95e
C3 VNADPHa > 3.54 2 85.86e 27.83
C4 GLCb < 0.04 1.2 86.40f G6Pb < 20.22 20 76.04f F16Pb < 22.86 2.5 51.91f PEPb < 0.01 1.2 65.44f ATPb < 6.77 6 89.32f 2.40
C5 Costc < 12.06 12.06 50 0.59 C6 VGlycerol
a > 0.39 0.22 50 0.25 C7 d < 28.10 0.391 50 0.16 C8 F16Pb > 8.64 0.95 61.93 0.06
C1
C2C3 C4
C5
C6C7
C8
Selected profiles
HXT: Hexose transporters
GLK: Glucokinase
PFK: Phosphofructokinase
TDH: Glyceraldhyde 3P dehydrogenase
PYK: Piruvate kinase
TPS: Trehalose phosphate syntase
G6PDH: Glucose-6-P dehydrogenase
■ % of the change-folds before any selection ■ % of the change-folds after selecting by ALL criteria
Fold change in gene expression
% o
f to
tal G
EP
s
Fulfill all criteria of HS performance:• SIMULATION: 0.06% of GEPs (4238 ) • All experimental databases
Eisen et al. at 10 min (BD1 10’) Causton et al. at 15’ (BD2 15’) Gasch et al. at 10’ (DB3 10’) Gasch et al. at 15’ (DB3 15’) Gasch et al. at 20’ (DB3 20’)
Are the eight criteria of performance specific for heat
shock?
We analyzed 294 GEPs from microarray experiments under different environmental conditions
C1 C2 C3 C4 C5 C6 C7 C8
Alkali H202 Diamide
...
HeatShock
Only heat shock conditions are selected
What happens under other conditions? (Principal Component Analysis)
Stationary
HeatS
H2O2
Diamide
Stationary
HeatS
H2O2
Diamide
Sporulation
factor1 factor2 factor3 factor4
factor1
factor2
factor3
factor4
factor2
factor1
factor3
Summary
Identification of general constraints in GEP
Identification of a set of constraints that are specific for heat shock
Identification of the quantitative design of the heat shock GEP
Support by experimental evidence Specificity of the set of constraints
Acknowledgments
FCT
Ramon y Cajal Program MCyT
FUP program MCyT
What next?
Dynamic patterns Define performance criteria based on dynamics Obtain precise measurements of the dynamic
gene expression changes Consider additional metabolic processes Measure in situ levels of metabolites and
fluxes Evaluate the energy and redox status of
the cell Seek for specific constrains that explain
differences and shared behaviors with other stress responses
Interpretation
Vilaprinyo, Alves, Sorribas (2006) BMC Bioinformatics 7(1):184
To generate an appropriate HS response some enzymes seems to have a restricted range of allowable variation. High sensitivity towards these enzymes can explain this result Enzymes (genes) that show no changes may be very important to
understand adaptive responses Fine tuning of fluxes and metabolite levels should be achieved
through coordinated changes in several enzyme levels. The experimental GEPs are situated within the predicted
ranges Our analysis helps identifying the more appropriate GEPs. Also,
we can explain why most of the hypothetical GEPs are inappropriate for HS response.
The considered criteria can be seen as constrains for heat shock performance
Eisen et al. PNAS. 1998 Dec 8;95(25):14863-8. DB1
http://genome-www.stanford.edu/clustering
Causton et al. Mol Biol Cell. 2001 Feb;12(2):323-37 DB2 http://web.wi.mit.edu/young/environment
Gasch et al. Mol Biol Cell. 2000 Dec;11(12):4241-57 DB3 http://WW-genome.stanford.edu/yeast_stress
Conceptual model
Mathematical model
Reproduce basal conditions 25ºC
Calculate new steady states (37º)
SIMULATION OF GEPs
Select which fulfill the criteria of performance
CASE 1CASE 2………
etc
MICROARRAY (3DB)
Conceptual model
Mathematical model
Reproduce basal conditions 25ºC
Calculate new steady states (37º)
SIMULATION OF GEPs
Select which fulfill the criteria of performance
CASE 1CASE 2………
etc
MICROARRAY (3DB)
Define Heat Shock performance
SIMULATIONS
4.637.360 hypothetical gene expression profiles (GEPs)
C4d96.%C4e99.%C5100.%C651.%C77.8%C875.%C12.7%C25.8%C317.%C4a76.%C4b99.%C4c94.%
Selected NonSelected
Heat Heat
Heat
Heat
Diamide
Diamide
AlkaliststYPD
ststYPDspo
Diauxic
DTTDiamide
NaCl
Sorbitol
ststYPD
mutNdepl
CsourceCold Alkali
AAstarv
H2O2
C1 C2
C7 C3
Heat Heat
Heat
Heat
Diamide
Diamide
AlkaliststYPD
ststYPDspo
Diauxic
DTTDiamide
NaCl
Sorbitol
ststYPD
mutNdepl
CsourceCold Alkali
AAstarv
H2O2
C1 C2
C7 C3
Validation of the model prediction by comparison to microarray data
1.540.91-0.04-1.06-1.47160
1.10.61-0.2-0.97-1.3280
1.550.82-0.2-1.43-2.0640
2.180.87-0.1-1.89-2.6420
2.961.23-0.2-1.56-2.5610
1.120.52-0.1-0.56-0.760
0.990.950.50.050.01
QuantilesMinute
DB1
1.540.91-0.04-1.06-1.47160
1.10.61-0.2-0.97-1.3280
1.550.82-0.2-1.43-2.0640
2.180.87-0.1-1.89-2.6420
2.961.23-0.2-1.56-2.5610
1.120.52-0.1-0.56-0.760
0.990.950.50.050.01
QuantilesMinute
DB1
2.481.720.58-0.46-1.05120
2.431.740.55-0.59-1.260
2.411.820.71-0.35-0.9145
2.791.950.7-0.39-0.9830
3.011.850.39-1.02-1.7215
0.650.510-0.81-1.210
0.990.950.50.050.01
QuantilesMinute
DB2
2.481.720.58-0.46-1.05120
2.431.740.55-0.59-1.260
2.411.820.71-0.35-0.9145
2.791.950.7-0.39-0.9830
3.011.850.39-1.02-1.7215
0.650.510-0.81-1.210
0.990.950.50.050.01
QuantilesMinute
DB2
1.680.84-0.04-0.86-1.3680
1.70.83-0.04-0.89-1.3460
2.611.34-0.1-1.6-2.2540
3.611.9-0.1-2.06-3.1830
3.791.99-0.1-2.32-3.8420
4.022.1-0.1-2.4-4.3215
3.431.63-0.3-2.32-3.6910
3.011.2-0.2-1.18-2.065
1.290.960.03-1.18-2.470
0.990.950.50.050.01
QuantilesMinute
DB3
1.680.84-0.04-0.86-1.3680
1.70.83-0.04-0.89-1.3460
2.611.34-0.1-1.6-2.2540
3.611.9-0.1-2.06-3.1830
3.791.99-0.1-2.32-3.8420
4.022.1-0.1-2.4-4.3215
3.431.63-0.3-2.32-3.6910
3.011.2-0.2-1.18-2.065
1.290.960.03-1.18-2.470
0.990.950.50.050.01
QuantilesMinute
DB3
Noise of databases is derived from the values of change expression at basal conditions (minute 0)
Log2 values
A statistical analysis shows that the results are with the allowable error
All microarray gene expression profiles fulfill criteria of performance
top related