Post on 21-Jan-2016
description
Rui AlvesCiencies Mèdiques Bàsiques
Universitat de Lleidaralves@cmb.udl.es
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.
04/21/23 5
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
04/21/23 9
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.
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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
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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.
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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
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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.
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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
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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
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.
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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?
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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
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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
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'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].
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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
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
With dead end complex
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.
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Analyze higher complexity TCS.
Analyze eukaryotic signal transduction.
Compare both.
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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 (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.
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Focus on dynamics.
Consider changes in protein activity
Analyze other types of stress response.
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Integrative in silico reconstruction of Fe-S biogenesis pathway in yeast.
Design principles of bacterial signal transduction Two Component Systems
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.
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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