380 likes | 460 Views
Generic and specific constraints shaping adaptive gene expression profiles in yeast. Ester Vilaprinyó , Rui Alves, Armindo Salvador, Albert Sorribas. Grup de Bioestadística i Biomatemàtica Dep. Ciències Mèdiques Bàsiques Universitat de Lleida. Coimbra, 2007. Introduction.
E N D
Generic and specific constraints shaping adaptive gene expression profiles in yeast Ester Vilaprinyó, Rui Alves, Armindo Salvador, Albert Sorribas Grup de Bioestadística i Biomatemàtica Dep. Ciències Mèdiques Bàsiques Universitat de Lleida Coimbra, 2007
Introduction • To survive yeast changes its gene expression profile • This allows adaptation of fluxes and concentrations • Environmental conditions change. Cells living in those environments need to adapt to those changes in order to survive environmental stresses (heat shock, osmotic...). Stress
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. Change in gene expression after stress Basal protein abundance Correlation
1/changefold Protein abundance/103 Abundant proteins are inhibited
How to achieve a fast increase in activity? • H2: Low abundance proteins have almost no total activity. To achieve larger relative increases in activity, cell could express proteins of low abundance Change in gene expression after stress Basal protein abundance Correlation
changefold Protein abundance/103 Proteins of low abundance are overexpressed
Are there other ways to design GEP that use resources efficiently? • 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 respond fast and 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. Change in gene expression after stress Protein size (MW or length) Correlation
changefold 1/changefold Protein size (MW) Protein size (MW) Size matters in modulation of gene expression? Size matters in modulation of gene expression H3 Overexpressed genes H4 Repressed genes
Resource usage and quickness of response general constraints for adaptive GEP? Resource usage and quickness of response general constraints for many 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. 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
Glycogen Trehalose Metabolic network & physiological constraints REDUCING POWER New synthesis of sphingolipids in order to change the membrane fluidity C3 NADPH STRUCTURAL INTEGRITY -Avoids aggregation of denatured proteins -Membrane -Acts in synergism with chaperones C2 HIGH ENERGY DEMAND C1 Curto, Sorribas, Cascante (1995) Math. Biosci. 130, 25-50 Voit, Radivovevitch (2000) Bioinformatics 16: 1023-1037
×5 ×2 ×3 ×3 7 × 3 × 5 × ×7 ×3 ×5 Glycogen Trehalose ×3 ×3 ×2 ×3 ×5 Methodology NADPH 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]
Implementation of stress responses Evaluate HS performance Metabolic network Mathematical model Gene expression changes Power Law form Biochemical System Theory (Savageau, 1969) Generalised Mass Action Each GEP has associated a new steady state→ functional changes → HS index of performance Reproduce basal conditions (25ºC)
C1- Synthesis of ATP C2- Synthesis of trehalose C3- Synthesis of NADPH Criteria of performance “Well-known” and studied by experimentalist
% of total GEPs Fold change in gene expression 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 HXT: Hexose transporters GLK: Glucokinase PFK: Phosphofructokinase TDH: Glyceraldhyde 3P dehydrogenase PYK: Pyruvate kinase TPS: Trehalose phosphate syntase G6PDH: Glucose-6-P dehydrogenase
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 50 % cost Criteria of performance “Well-known” and studied by experimentalist • C1- Synthesis of ATP • C2- Synthesis of trehalose • C3- Synthesis of NADPH Well-studied within a system biology perspective No experimental measures are available, so we have chosen as a threshold the value that includes de 50% of all the cases
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 Criteria of performance “Well-known” and studied by experimentalist Well-studied within a system biology perspective
Glycerol production helps in producing NADPH from NADH New synthesis of glycerolipids required Genes are over-expressed C6- Glycerol production 50% Selecting GEPs with the highest glycerol production is synonymous of selecting GEPs with low PYK over-expression Glicerol rate
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 Glycogen Trehalose C7- TPS and PFK 50%
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 C8 C7 C1 C6 C2 C5 C3 C4
% of total GEPs Fold change in gene expression Selected profiles ■% of the change-folds before any selection ■% of the change-folds after selecting by ALL criteria HXT: Hexose transporters GLK: Glucokinase PFK: Phosphofructokinase TDH: Glyceraldhyde 3P dehydrogenase PYK: Piruvate kinase TPS: Trehalose phosphate syntase G6PDH: Glucose-6-P dehydrogenase Fulfill all criteria of HS performance: • SIMULATION: 0.06% of GEPs (4238 ) • All experimental databases
Are the eight criteria of performance specific for heat shock? We analyzed 294 GEPs from microarray experiments under different environmental conditions Only heat shock conditions are selected
factor1 factor2 factor3 factor4 factor2 factor3 factor4 factor1 What happens under other conditions? (Principal Component Analysis) HeatS factor1 Diamide Stationary H2O2 HeatS factor3 factor2 Sporulation Stationary Diamide H2O2
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 • 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 Vilaprinyo, Alves, Sorribas (2006) BMC Bioinformatics 7(1):184
Define Heat Shock performance • Eisen et al. PNAS. 1998 Dec 8;95(25):14863-8. DB1http://genome-www.stanford.edu/clustering • Causton et al. Mol Biol Cell. 2001 Feb;12(2):323-37 DB2http://web.wi.mit.edu/young/environment • Gasch et al. Mol Biol Cell. 2000 Dec;11(12):4241-57 DB3http://WW-genome.stanford.edu/yeast_stress SIMULATIONS 4.637.360 hypothetical gene expression profiles (GEPs)
Selected NonSelected
Validation of the model prediction by comparison to microarray data • A statistical analysis shows that the results are with the allowable error • All microarray gene expression profiles fulfill criteria of performance • Noise of databases is derived from the values of change expression at basal conditions (minute 0) Log2 values