Identification of constraints that shape the gene expression response of yeast to stress
-
Upload
fletcher-oneill -
Category
Documents
-
view
28 -
download
8
description
Transcript of Identification of constraints that shape the gene expression response of yeast to stress
Identification of constraints that shape the gene expression response of yeast to stress
Ester Vilaprinyó, Rui Alves, Albert Sorribas
Munich, ICMSB 2006 Grup de Bioestadística i BiomatemàticaDep. Ciències Mèdiques BàsiquesUniversitat de Lleida
Motivations and Goals Environmental stresses (heat shock,
osmotic...) trigger gene expression changes in yeast
ADAPTATION:There is a redistribution of fluxes and metabolite concentrations (physiology).
This can be achieved by different strategies. Only some of them have been selected. ANTECEDENTS: Voit & Radivovevitch
Seek the constraints that shape the gene expression profile (GEP) of yeast to stress conditions
Metabolic network
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
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)
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. No clear conclusion can be reached.
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 the cost Sensitivity analysis shows that the system is
highly sensible to change PFK
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
/ trehaloseTPS PFK v
50%
C8- F16P levels should be maintained
Glycogen Trehalose
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
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’)
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
Are the eight criteria of performance specific of 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
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
Albert Sorribas
JaumeMarch
JavierTrujillano
MontseRué
RuiAlves
Aknowledgments
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