rui alves ciencies mèdiques bàsiques universitat de lleida [email protected]

Rui AlvesCiencies Mèdiques Bàsiques

Universitat de [email protected]

http://web.udl.es/usuaris/pg193845/Courses/Other%20Seminars/

04/21/23 2

Integrative in silico reconstruction of Fe-S biogenesis pathway in yeast.

Design principles of bacterial signal transduction Two Component Systems.

Quantitative design of Gene Expression Profiles in yeast stress response.

Understanding pathway assembly and function is fundamental to the understanding of how a cell works.

In annotated genomes, network of cellular pathways is “known”. Mapping orthologues onto known maps (KEGG, BIOCYC, etc.). However, regulatory topology is organism specific.

Nevertheless, reconstructing the topology of new pathways can not be done by mapping. No maps available. How to reconstruct?

04/21/23 3

Traditionally, identification & reconstruction of a pathway/circuit would entail painstaking, mostly blind, experimental work.

Currently, availability of “omics” data provides information to facilitate this task.

Computational Biology and Bioinformatics. Integrate information, predict systemic behavior and rank

hypothesis for experimental testing Facilitates a better understanding of how cellular systems

work.

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Develop and apply coherent yet flexible framework where different computational methods and data sets are integrated to predict the connectivity of biological pathways & circuits. Today: focus on the biology

and the reconstruction of FeS cluster biogenesis in yeast S. cerevisiae.

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Iron-Sulfur Clusters are coordinated ions that participate in electron transfer.

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Protein Cysteine

Protein Cysteine

Fe FeS

S

e- e-

About 15 different mitochondrial proteins are known to be involved in yeast.

The assembly process is ill-understood.

It is unclear how most of the proteins assemble as a pathway and how the activity of this pathway is regulated.

All 15 proteins have one thing in common.

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WT

Fe Level

WT

FeSC Dependent Protein Activity

Fe Accumulates

FeSC dependent protein activity is impaired

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Fe S

Scaffold Scaffold

FeSCSynthesis

Transfer

RepairHolo-P

Damaged FeSC

Apo-PHolo-P

FeSC

Scaffold Scaffold

(S)

(T)

(R)

Grx5Isa1Isa2Isu1Isu2Nfu1Atm1Nfs1Arh1Yah1Yfh1Isd11Ssq1Jac1Mge1

04/21/23 10

Process of

interest

1. Bibliometric analysis

Identify Genes

involved in process

2. Phylogenetic analysis

Identify additional

Genes involved in

process

Get protein structures

(PDB, models)

Genes with

similar co-evolution profiles

List of reported

Two-hybrid

interactions

List of predicted

interactions

2. Interrogate 2H databases

3. In silico protein docking

Human curation

Expert Knowledge

Derive alternative

network structures

Create mathematical models for

each alternative

network

No Valid Model 4. Simulation

and comparison to experimental

results

Validated

models

Falsified models

New Simulation experiments

Literature co-occurence of genes can be taken as a signal that they are functionaly related and maybe interact physically.

iHOP performes this type of analysis automatically.

04/21/23 11

Proteins that are present and absent in the same set of genomes are likely to be involved in the same process and therefore interact.

Target Genome

Orthologue in Genome 1?

Orthologue in Genome 2?

…

Grx5BC…

YNY…

NYN…

…………

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Sequence (Grx5)

Protein id Grx5

Calculate coincidence

index.

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…SSQIE……SSQEE…

Sequence with known structure.

OPTIMIZE

DOCK

THREAD

Homologue sequence for structure prediction.

Differential scores for docking to

different targets.

Nfs1-SSG Nfs1

Grx5,…

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11 1 21 5 1... ...

hg gd NfsNfs Grx Nfs

dt

g<0 inhibits flux.

g=0 no influence on flux.

g>0 activates flux.

Use approximate formalism:•Power Law Formalism•No need for detailed mechanism.•Semi quantitative estimation of many parameter values.

Create models for alternative networks.

Normalize equations and scan parameters to see what happens when a gene is deleted from the model.

Compare simulations with known systemic behavior to validate or invalidate alternatives.

04/21/23 15

Glutaredoxin: Mediates glutathionylation state of Cys residues. May mediate protein-protein disulfide bridge

reduction (Belli et al. 2002, Tamarit et al. 2003, JBC).

FeSC coordinate (mostly) with Cys residues.

Is Grx5 regulation of Cys reduction state in any specific protein(s) involved in FeSC biogenesis sufficient for phenotype?

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Grx5

Nfs1

Isa2

Isa1

Isu1Bibliography

Docking

Phylogeny+ Docking

Scaffolds

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Recovering Nfs1 and Scaffold

FeSC Dependent Protein Activity

Not recovering Nfs1 and Scaffold

Belli et al. 2002 MBC 13:1109

10000s of simulations

1

0.1

0.50.5

WT

1

0.1

04/21/23 19

Fe Levels

WT Recovering Nfs1 and Scaffold

Not recovering Nfs1and Scaffold

Belli et al. 2002 MBC 13:1109

1 1

10000s of simulations

Grx5 modulates Nfs1 and Scaffold activity/Interactions.

04/21/23 20

Possible Modes of action for

Grx5

9Reproducing experimental phenotype?

No 6

Yes 3 Nfs1-Scaffold

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Negative Controls

Grx5 Scaffold

Positive Control

04/21/23 22

Alves et. al. 2004 Proteins 57:481

Vilella et. al. 2004 Comp. Func. Genomics 5:328

Alves et. al. 2004 Proteins 56:354

Alves & Sorribas 2007 BMC Systems Biology 1:10

Prediction Verified?

Grx5 modulates Nfs1 and Scaffold activity/Interactions

Detected interaction with scaffolds

Arh1-Yah1 act on S or ST Yes [PNAS 97:1050; JBC 276:1503]

Arh1-Yah1 interaction same as in mammals

No reported experiment

Yfh1 acts on S, T, or ST Yes [Science 305:242; EMBO Rep 4:906; JBC 281:12227; FEBS Lett

557:215]

Yfh1 storage of Fe not important for its role in biogenesis

Yes [EMBO Rep 5:1096]

Nfs1 acts in S, not necessarily in R No reported experiment

Chaperones act on Folding, Stability

Yes for Folding [JBC 281:7801]

Alves et. al. 2008 Current Bioinformatics, accepted

Create a FLEXIBLE tool for other researchers. Automation of text search 75% done;

Phylogenetic profiling 75% done, Protein interactions 75% done, Automation of structural modeling & docking 0%.

Data sets very noise, human curation required & very important in the forseeable future.

04/21/23 23

04/21/23 24

Process of

interest

1. Bibliometric analysis

Identify Genes

involved in process

2. Phylogenetic analysis

Identify additional

Genes involved in

process

Get protein structures

(PDB, models)

Genes with

similar co-evolution profiles

List of reported

Two-hybrid

interactions

List of predicted

interactions

2. Interrogate 2H databases

3. In silico protein docking

Human curation

Expert Knowledge

Derive alternative

network structures

Create mathematical models for

each alternative

network

No Valid Model Simulation and

comparison to experimental

results

Validated

models

Falsified models

New Simulation experiments

Add Genomics,

Proteomics, Metabolomics,

Fluxomics

Fe-S Human, chimp, coli, subtilis, xanthus, albicans.

Signal transduction reconstruction in xanthus.

04/21/23 25

Fe-S biogenesis pathways shows variations in the different organisms we are analyzing (coli, human, chimp, xanthus, subtilis).

Set of proteins not always the same, surely regulation will also be different.

Why differences? Random thing, that is it. There are functional advantages to the

alternative designs, this causes selection of different alternatives under different conditions and accounts for maintenance of the designs.

04/21/23 26

04/21/23 27


Design principles of bacterial signal transduction Two Component Systems

Quantitative design of Gene Expression Profiles in yeast stress response

Previous work in gene circuits, signal transduction & metabolic pathways suggests that often the differences are relevant to the functionality of the system.

Understanding the selection and maintenance of these differences can helps us in discovering design principles for the system of interest.

04/21/23 28

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S

S*

R*

R

Q1 Q2

Monofunctional Sensor Bifunctional Sensor

S

S*

R*

R

Q1 Q2

Is bifunctionality relevant for the function of the TCS?

04/21/23 30

1 – Identify functional criteria that have physiological relevance.

2 – Create Mathematical models for the alternatives S-system has analytical steady state solutionAnalytical solutions → General features of the

model that are independent of parameter values.

3 – Compare the behavior of the two models with respect to the functional criteria defined in 1.

Comparison must be made appropriately, using Mathematically Controlled Comparisons. [Alves &

Savageau Bioinformatics 16:534; 786]

X3

X1

X2

X4

X5 X6

3/ 1/

4 / 2 /

dX dt dX dt

dX dt dX dt

13 15 11 141 11/ 3 5 1 4g g h hdX dt X X X X

21 26 242

2222 / 21 6 4g g g hdX dt X X X X

04/21/23 31

Monofunctional Sensor

X3

X1

X2

X4

X5 X6

3/ 1/

4 / 2 /

dX dt dX dt

dX dt dX dt

13 15 11 121 11/ 3 5 1 2g g h hdX dt X X X X

21 26 224 22

2322 / 1 6 4 2 3g h hg g XdX dt X X X X

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Bifunctional Sensor

31 34 32 33 363 3 1 4 3 2 3 6

...

...

g g h h hX X X X X X

04/21/23 33

'34 32 33 363 3 4 3 2 3 6

...

'

...

g h h hX X X X X

AM

Q

AB

Q

AB

AM

Q

1

04/21/23 34

0 2.5 5 0 1.5 3 Primary Signal Secondary Signal

1

0.5

0

2

1

0

Rati

o o

f sig

nal

am

plifi

cati

on

Bifunctional design lowers X6 signal amplification. prefered when cross-talk is undesirable.

(EnvZ)

Monofunctional design elevates X6 signal amplification. prefered when cross-talk is desirable. (CheA)

04/21/23 35

Bifunctionality appears to be relevant for the function of the TCS.

Alves & Savageau 2003 Mol. Microbiology 48: 25

Bacterial signal transduction systems can have graded responses.

They can also have switch-like responses [Igoshin et al. 2007 Mol Microbiol. 61:165].

04/21/23 36

Signal

Resp

on

se

Are there specific topological elements in a TCS Module that allow switch-like behavior?

X3

X1

X2

X4

X5 X6

04/21/23 37

X7

[Dead end complex]

Independent Phosphatase

7 alternative topologies

Monofunctional Bifunctional

No dead end complex

No dead end complex

With dead end complex


No independent phosphatase

Independent phosphatase

Independent phosphatase & dead end complex

Signal

R

R-P

04/21/23 38

X3

X1

X2

X4

X5 X6

04/21/23 39

X7

[Dead end complex]

Independent Phosphatase

Topologies allowing for switching behavior

Bifunctional Module

Independent phosphatase & dead end complex

Monofunctional Module


04/21/23 40

Signal Intensity Signal Intensity Signal Intensity

Par

amet

er V

alue

s

Igoshin, Alves & Savageau 2008, Mol Microbiol, accepted

In TCS we found that:

Bifunctionality vs. Monofunctionality may be selected based on the requirements for cross talk.

Wiring of the circuit (dead end complex and flux channel for the dephosphorylation of the RR, independent of the sensor) constraint dynamic behavior (switch vs. graded).

This does not ensure that switch like behavior will be found but:

Points to where to look for it. Helps in design of artificial TCS with switch-like behavior.

04/21/23 41

Analyze higher complexity TCS.

Analyze eukaryotic signal transduction.

Compare both.

04/21/23 42

04/21/23 43



Quantitative design of Gene Expression Profiles (GEP) in yeast stress response

The wiring of the network (topological design principles) constrains the possible range of dynamic responses for the network.

This response in principle has evolved to ensure survival under specific conditions (fine tuning).

Given the functional requirements for a specific cellular response it should be possible to explain the quantitative aspects of the response

Analysis of gene expression changes in heat shock response to test this hypothesis

04/21/23 44

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1 – Identify functional criteria that have physiological relevance.

2 – Create mathematical model describing main aspects of the metabolic adaptation during the response.

3 – Decide range of allowable variation for gene expression & do large scale scanning of gene expression.

4 – Map gene expression onto model.

5 – Calculate how different GEP perform according to the functionality criteria.

04/21/23

C1- ATP synthesis. C2- Threalose synthesis. C3- NADPH synthesis.

C4- Low accumulation of intermediates. C5- Burden of change.

C6- Glycerol production. C7- Specific relationship in changes of activity between

certain enzymes that are important to create an appropriate metabolic response.

C8- Maintenance of F16P levels to keep a high glycolytic flux.

46

04/21/23 47

Glycogen Trehalose

NADPH

HXT: Hexose transporters

GLK: Glucokinase

PFK: Phosphofructokinase

TDH: Glyceraldhyde 3P dehydrogenase

PYK: Pyruvate kinase

TPS: Trehalose phosphate syntase

G6PDH: Glucose-6-P dehydrogenase

Curto et al. 1995 Math. Biosci. 130: 25 Voit, Radivoyevitch 2000 Bioinformatics 16: 1023

Glycogen Trehalose

04/21/23 48

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 5

hip2 3 3 3 3 3 3 3

hip3 2 1 1 1 2 7 7......

...

.........

...

.........

...

.........

...

...

...

.........

...

.........

...

.........

...

.........

hip4637360

NADPH

04/21/23 49

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

04/21/23

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

Fullfil all criteria:■ SIMULATION: 0.06% GEPs (4238)■ 3 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’)

50Vilaprinyo et al. 2006 BMC Bioinformatics. 7: 184

04/21/23 51

Principal component analysis

C1

C2

C3

C4

C5

C6

C7

C8

Alcalino H202

Diamida

...

HS

Group of criteria is specific, individual criteria are promiscuous.

Vilaprinyo et al. in preparation

Identification of a set of constraints that are specific for heat shock response.

Identification of the quantitative design of the heat shock GEP.

04/21/23 52

Focus on dynamics.

Consider changes in protein activity

Analyze other types of stress response.

04/21/23 53

04/21/23 54



Operational principles of Gene Expression Profiles (GEP) in yeast stress response

Integrative computational & systems biology can greatly assist in guiding experimental endeavours that aim at reconstructing intact systems and understanding their behavior.

It can also help answering questions that are hard to address experimentally.

04/21/23 55

Enric HerreroFelip VillelaAlbert Sorribas Ester VilaprinyoOleg Igoshin Mike SavageauArmindo Salvador

04/21/23 56

PGDBM (PORTUGAL)

JNICT (PORTUGAL)

FCT (PORTUGAL)

MCyT (SPAIN)

NIH (USA)

DOD (ONR) (USA)

Network ReconstructionTwo Component

SystemsGene Expression

Analysis

04/21/23 57

Alternative Grx5 Binding solutions

Alternative Grx5 Binding solution Nfs1 dimer

Active center Nfs1 Cys residue

Active center Grx5 Cys residue

04/21/23 58

Fe S

Scaffold Scaffold

FeSCSynthesis

Transfer

RepairHolo-P

Damaged FeSC

Apo-PHolo-P

FeSC

Scaffold Scaffold

(S)

(T)

(R)

04/21/23 59

HSGlutathione

Grx5

S-SGPP

S

04/21/23 60

Grx5

SHScaffold HS Nfs1

SScaffold S Nfs1

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

04/21/23

Metabolic network

Mathematical Model

Each GEP creates a new metabolic state (37ºC) → functional changes → different performance indices.

Generalised Mass ActionPower-law form

Changes in GEP

Evaluate adaptive response(8 criteria functional effectiveness)

61

Recurrent qualitative or quantitative rules that are observed in similar types of systems as a solution to a given functional problem.

Topological design principles are recurrent rules in the wiring of the network that are observed under similar functional requirements.

04/21/23 62

04/21/23 63

1 – Create Mathematical models for alternative designs

Bifunctional Design.with/without independent phosphatase.

with/without dephosphorylated dead end complex between both proteins.

Monofunctional Design. with/without independent phosphatase.

with/without dephosphorylated dead end complex between both proteins.2 – Use parameter values from realistic system (EnvZ, but similar to other TCS). 3 – Compare the behavior of the alternatives using Mathematically Controlled Comparisons.

Response to environmental stress leads to changes in the GEP (Gene Expression Profiles) of yeast.

This leads to changes in protein activity and in metabolic fluxes and concentrations → Adaptation.

Given that the data suggest that there are specific strategies selected in heat shock response, can we establish the quantitative design principles for gene expression in heat shock response?

04/21/23 64

rui alves ciencies mèdiques bàsiques universitat de lleida [email protected]

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