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 Biomatemàtica Dep. Ciències Mèdiques Bàsiques Universitat 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. Basal protein abundance Change in gene expression after stress Correlation

Abundant proteins are inhibited Protein abundance/10 3 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 Basal protein abundance Change in gene expression after stress Correlation How to achieve a fast increase in activity?

Proteins of low abundance are overexpressed Protein abundance/10 3 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 in gene 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 DEMAND C1 STRUCTURAL INTEGRITY -Avoids aggregation of denatured proteins -Membrane -Acts in synergism with chaperones C2 REDUCING POWER New synthesis of sphingolipids in order to change the membrane fluidity C3  Curto, Sorribas, Cascante (1995) Math. Biosci. 130,  Voit, Radivovevitch (2000) Bioinformatics 16:

Glycogen Trehalose Methodology ×5×5 ×5×5 ×5×5 5 × HXTGLKPFKTDHPYKTPSG6PDH hip HXTGLKPFKTDHPYKTPSG6PDH hip hip HXTGLKPFKTDHPYKTPSG6PDH hip hip hip ×2×2 ×7×7 ×2×2 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: 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] HXTGLKPFKTDHPYKTPSG6PDH hip hip hip hip ×3×3 ×3×3 ×3×3 3 × ×3×3 ×3×3 ×3×3 NADPH

Implementation of stress responses Metabolic network Mathematical model Power Law form Biochemical System Theory (Savageau, 1969) 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 %, )  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 % of total GEPs 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 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 v trehalose  PFK is inversely related with v trehalose  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  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 C1 C2 C3 C4 C5 C6 C7 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 % of total GEPs 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 C1C2C3C4C5C6C7C8 Alkali   H202H202   Diamide  ... HeatShock  Only heat shock conditions are selected

What happens under other conditions? (Principal Component Analysis) Stationary HeatS H2O2H2O2 Diamide Stationary HeatS H2O2H2O2 Diamide Sporulation factor1factor2factor3factor4 factor1 factor2factor3factor4 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 Dec 8;95(25): DB1  Causton et al. Mol Biol Cell Feb;12(2): DB2  Gasch et al. Mol Biol Cell Dec;11(12): DB3 Define Heat Shock performance SIMULATIONS hypothetical gene expression profiles (GEPs)

 Selected  NonSelected

Validation of the model prediction by comparison to microarray data  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