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In silico metabolic reconstruction of Fe-S cluster biogenesis in yeast
Rui Alves
Ciencies Mediques Basiques
Universitat de Lleida
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Introduction
Novel pathways are being discovered with genome sequencing
Well known proteins are shown to be involved in some of the pathway but information about how the pathway structure is formed in unknown.
Finding these circuits & pathways is an important problem
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Objective of the research line
Develop coherent 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
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Fe-S clusters
Iron-Sulfur Clusters are coordinated ions that participate in electron transfer
Protein Cysteine
Protein Cysteine
Fe FeS
S
e- e-
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What is known about FeSC biogenesis
About 15 different mitochondrial proteins are known to be involved in yeast
The assembly process is ill-understood
All 15 proteins have one thing in common
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Phenotype of FeSC machinery deletion mutants
WT
Fe Level
WT
FeSC Dependent Protein Activity
Fe Accumulates FeSC dependent protein activity is impaired
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FeSC biogenesis in a nutshell
Fe S
Scaffold Scaffold
FeSCSynthesis
Transfer
RepairHolo-P
Damaged FeSC
Apo-PHolo-P
FeSC
Scaffold Scaffold
(S)
(T)
(R)
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The proteins and their function
Understanding the role of seven proteins in S. cerevisiae. FeS cluster biogenesis:
Grx5, Arh1-Yah1, Ssq1-Jac1-Mge1, Nfs1
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Grx5 is involved in FeSC biogenesis in S. cerevisiae
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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Predicting partners for Grx5: the protocol
•Combine literature analysis, phylogenetic analysis of fully sequenced genomes and in silico protein docking to predict the most likely targets of Grx5
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•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
iHOP literature network reconstruction
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Similarly, if Grx5 is absent in all genomes in which protein B is present there is a likelihood that they perform the same function!
Grx5 has highest CIs with the scaffold proteins
Using phylogenetic profiles to predict protein interactions
Sequence (Grx5) script
Database of profiles for each protein in each organism
Database of proteins in fully sequenced genomes
Protein id Grx5
Target Genome
Homologue in Genome 1?
Homologue in Genome 2?
…
Grx5
B
C
…
Y
N
Y
…
N
Y
N
…
…
…
…
…
Grx5 B
00i/number of genomes<1C
1j/number of genomes
Grx5 1
C 0.9
… …
B 0.11
… …Proteins (Grx5 and C) that are present and absent in the same set of genomes are likely to be involved in the same process and therefore interact
2
Calculate coincidence index
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Low level study of docking interactions in silico
…SSQIE……SSQEE…
Sequence of known structure
OPTIMIZE
DOCK
THREAD
Homologue sequence for structure prediction
Scaffold proteins
Nfs1
(Cys Desulfurase)
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Possible partners of Grx5 in FeSC biogenesis
Grx5
Nfs1
Isa2
Isa1
Isu1 Bibliography
Docking
Phylogeny+ Docking
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FeSC biogenesis in a nutshell
Fe S
Scaffold Scaffold
FeSCSynthesis
Transfer
RepairHolo-P
Damaged FeSC
Apo-PHolo-P
FeSC
Scaffold Scaffold
(S)
(T)
(R)
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Possible roles of Grx5 in FeSC biogenesis:regulation of glutathionylation
HSGlutathione
Grx5
S-SGPP
S
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Possible roles of Grx5 in FeSC Biogenesis:Recovery of dead-end complex
Grx5
SHScaffold HS Nfs1
SScaffold S Nfs1
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Studying the effect of Grx5: The modeling
Nfs1-SSG Nfs1
Grx5,…
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
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Studying the effect of Grx5: the protocol
•Create models for alternative networks
•Normalize equations and scan parameters
•Compare simulations with known systemic behavior to validate or invalidate alternatives
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Model reproduces effect of gene deletion on protein activity if Grx5 recovers Nfs1 activity
Recovering Nfs1 and Scaffold
FeSC Dependent Protein Activity
Not recovering Nfs1 and Scaffold
Belli et al. MBC 13:1109
1000s of simulations
1
0.1
0.50.5
WT
1
0.1
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Model reproduces effect of gene deletion on protein activity if Grx5 recovers Nfs1 activity
Fe Levels
WT Recovering Nfs1 and Scaffold
Not recovering Nfs1and Scaffold
Belli et al. MBC 13:1109
1 1
1000s of simulations
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Grx5 is predicted to dock facing the Nfs1 active center
Alternative Grx5 Binding solutionsAlternative
Grx5 Binding solution
Nfs1 dimer
Active center Nfs1 Cys residue
Active center Grx5 Cys residue
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Conclusions:
Grx5 modulates Nfs1 and Scaffold activity/Interactions
Possible Modes of action for Grx5
9Reproducing experimental phenotype?
No 6
Yes 3 Nfs1-Scaffold
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Predictions for Grx5:
Grx5 modulates Cysteine Desulfurase (Nfs1) and scaffold activity and maybe the interaction between both
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Grx5 interacts with scaffold in two-hybrid assay
Negative Controls Grx5 Scaffold
Positive Control
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Arh1-Yah1 proteins are involved in FeSC biogenesis in S. cerevisiae
Arh1-Yah1 Ferredoxin Reductase – Ferredoxin
These proteins supply/drain electrons from other processes
What is their role in FeSC biogenesis?
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Possible partners of Arh1-Yah1 in FeSC biogenesis
Isu2
Arh1
Isa2
Isa1
Docking + Phylogeny
Bibliography + Docking + Phylogeny
Yah1Yfh1
Nfu1
Isu1
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Possible modes of Arh1-Yah1 action
Fe S
Scaffold Scaffold
FeSCSynthesis
Transfer
RepairHolo-P
Damaged FeSC
Apo-PHolo-P
FeSC
Scaffold Scaffold
Arh1/Yah1
Arh1/Yah1
Arh1/Yah1
(S)
(T)
(R)
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Model reproduces gene deletion effect on FeSC dependent activity if Arh1-Yah1 act on
ST
T RS
R
RST RT
S
ST
FeS
C D
epen
dent
Pro
tein
Act
ivit
y
Li et al. JBC 276:1503Lange et al. PNAS 97:1050
1000s of simulations
WT
1
0.2
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Model reproduces Fe accumulation upon gene deletion if Arh1-Yah1 act on ST
Fe Levels
Li et al. JBC 276:1503Lange et al. PNAS 97:1050
R RT
RST
S T
RS
S
ST
1000s of simulations
WT
1
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Conclusions for Arh1-Yah1:
Arh1-Yah1 act on ST steps of FeSC biogenesis
Possible Modes of Arh1-Yah1 action
7Reproducing experimental phenotype?
No 5
Yes 2S
ST
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Ssq1-Jac1-Mge1 proteins are involved in FeSC biogenesis in S. cerevisiae
Ssq1-Jac1-Mge1 Chaperone – Co-Chaperone – Nucleotide Release
Factor These proteins help fold-stabilize other proteins
It has been suggested that they help stabilize the FeSC in the scaffold for transfer.
Is this role necessary to justify the phenotypes?
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Possible Modes of Chaperone Action
Fe S
Scaffold Scaffold
FeSCSynthesis
Transfer
RepairHolo-P
Damaged FeSC
Apo-PHolo-P
FeSC
Scaffold Scaffold
Ssq1/Jac1/Mge1
(S)
(T)
(R)
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Model reproduces gene deletion effect on FeSC dependent activity if Arh1-Yah1 act on
3 steps
Stability Fold
FeS
C D
epen
dent
Pro
tein
Act
ivit
y
1000s of simulations
WT
1
0.2
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Model reproduces Fe accumulation upon gene deletion if chaperones act on stability
Fe Levels
FoldStability
1000s of simulations
WT
1
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Conclusions:
Scaffolds need to act only on folding in FeSC biogenesis
Possible Modes of Ssq1-Jac1-Mge1 action
3Reproducing experimental phenotype?
No 1
Yes 2Fold
Stability/Fold
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Nfs1 is involved in FeSC biogenesis in S. cerevisiae
Nfs1 Cisteine Desulfurase
This protein provides sulfur for the biogenesis
It has been shown in vitro that it is able to repair FeSC in situ and bypass the biogenesis pathway
Is this role important?
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Possible modes of Nfs1 action
Fe S
Scaffold Scaffold
FeSCSynthesis
Transfer
RepairHolo-P
Damaged FeSC
Apo-PHolo-P
FeSC
Scaffold Scaffold
Nfs1
Nfs1
(S)
(T)
(R)
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Model reproduces gene deletion effect on FeSC dependent activity if Nfs1 acts on SR
R S
RS
FeSC Dependent Protein Activity
1000s of simulations
WT
1
0.2
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Model reproduces Fe accumulation upon gene deletion if Nfs1 acts on S
Fe Levels
R S
RS
1000s of simulations
WT
1
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Conclusions:
Nfs1 needs only to act on synthesis but can also act on repair of FeSC
Possible Modes of Nfs1 action
3Reproducing experimental phenotype?
No 1
Yes 2 S
SR
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Summary
Arh1-Yah1 acts on Synthesis and transfer of FeSC
Grx5 modulates Cysteine Desulfurase (Nfs1) activity and maybe Scaffold activity
Ssq1-Jac1-Yah1 act on folding FeSC proteins
Nfs1 acts on in situ synthesis of clusters
Yfh1 does not modulate Fe import into the mitochondria
Alves et. al. 2004 Proteins 57:481Vilella et. al. 2004 Comp. Func. Genomics 5:328
Alves et. al. 2004 Proteins 56:354
Alves & Sorribas 2007 BMC Systems Biology
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PS: The reconstruction method
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Acknowledgments
Albert Sorribas Enric Herrero Armindo Salvador
FCT Spanish Government NIH (Mike Savageau)
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Possible partners of Nfs1 in FeSC biogenesis
Nfs1
Isa2
Isa1
Docking
Docking + Phylogeny
Bibliography + Docking + Phylogeny
Isu1
Nfu1
Isu2
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Metabolic Reconstruction: FeSC biogenesisThe view from here
Test the predictions
Extend the analysis to a variety of bacteria (Preliminary results for E. coli and Buchnera)
Create an interactive database/server to implement the methodology and apply it to other systems
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Process of interest
Determining gene+proteins involved in process
Refining using automated literature analysis
Refining using phylogenetic and “omics” data
Protein Structure
(PDB/ Models)
In silico Protein Docking
Predict networks
Two Hybrid Screens
Predict networks
Co-evolution analysis
Predict networks
Omics data analysis
Predict networks
Baeysian network/human curation for alternative network structures
Automated creation of mathematical models
Analysis of model behavior
No model is validated by existing data
Some models are validated by comparison with existing data
Design new experiments to distinguish between alternatives
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Reconstructing Metabolism and Investigating Design Principles in Molecular Biology II
Rui Alves
Ciencies Mediques Basiques
Universitat de Lleida
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Outline Metabolic Network Reconstruction
Iron-Sulfur Cluster Biogenesis Pathway in S. cerevisiae.
Pathway Evolution Amino acid biosynthetic pathways protein
composition
Design Principles Regulatory Design in Networks
Two Component Systems• Mono-functionality vs. Bi-functionality of Sensor Proteins
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Studying an organism
…ACTG…
>Dna
MAACTG…
>DNA Pol
MTC…
Stress
Measure Response
Find signatures for physiological dynamics in
genomic data
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Stress Regulation Some genes are expressed specifically in response
to a stimulus.
For such genes, the amino acid composition of the proteins can be biased to facilitate their own synthesis and the physiological response.
This creates a signature of the response in the protein composition
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Cognate Bias Cognate Bias
Carbon & Sulfur fixing enzymes have a lower content of amino acids with sidechains containing a lot of carbon or sulfur atoms (Baudouin-Cornu et al. Science 293:297)
Amino acid biosynthetic enzymes have a lower content of their cognate amino acid (Alves & Savageau 2005 Mol. Microbiol. 56:1017-34)
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Regulation of amino acid biosynthetic pathways gene expression
aa rich medium t
Switch to AA poor medium
No cognate aa in biosynthetic proteins
Lots of cognate aa in biosynthetic proteins
aa levels
protein levels
aa poor medium
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Effect of relative content of cognate amino acid on protein synthesis
Low cognate aa content in biosynthetic pathway aa
levelsProtein Synthesis
tHigh cognate aa content in biosynthetic pathway
aa levels
Protein Synthesis
t
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The Low Cognate Bias Hypothesis
Thus, we hypothesize that evolution would lead to the selection of amino-acid biosynthetic enzymes that have a relatively low content of their cognate amino acid. We call this the “cognate-bias hypothesis”.
Test Cases: E. coli S. typhimurium B. subtilis
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Calculating the bias
Protein Sequences
GeneBank
Specific genome
KEGG
Metacyc
Literature
aa-biosynthesis proteins
Proteome
Growing cells aa composition
Control Groups:
Calculate aa composition
Rank w/respect to proteome
Av.0.5
1
0Low
High
P(aa)
…
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There is low cognate bias in aa biosynthesis
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Positive correlation between relative cognate aa composition and specific activity of proteins
Specific activity
Specific activity
Protein copies
Protein copies
aa synthesis
aa synthesis
P<0.003
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Negative correlation between relative cognate aa composition and number of proteins in pathway
Average aa content
+
++
+++
++++
1 2 3 4
P<0.003 (except Bs)
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Higher bias is due to functional requirements
Active Center
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Exceptions to the cognate bias hypothesis
Functional Reasons Active Centers (Phe, Tyr) Dimerization Domains (Asp)
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Low cognate bias is present in organisms with the full complement of pathways
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Environmental effects in cognate bias
The relative abundance of amino acid should influence the pressure to keep a low cognate amino acid content in biosynthetic pathways
E. coli (or S. typhimurium) and B. subtilis live in different environments
Amino acid availability is different
Is there a signal for these differences?
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Differences in amino acid composition of distinct habitats
Low amounts Ala, Asp, Gly, Ser, Thr
High amounts Arg, Glu, Lys, Trp, Tyr
Low amounts Arg, Glu, Lys, Trp Tyr
Intermediate amounts
Ala, Asp, Thr
High amounts Gly, Ser
Savageau 1983 Am. Naturalist. 122:732
Soil Gut
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Positive correlation between environmental levels of aa and cognate bias
Rank of Environmental aa
Rank of Cognate Bias
Presence of aa in environment decreases pressure for low cognate bias
P<0.004
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Environmental availability influences cognate bias
Thus the effect of amino acid availability in the environment and the consequent regulation and physiological dynamics of gene expression does leaves a signature in the cognate bias of the different pathways.
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Conclusions:
The low cognate bias hypothesis is supported by: Analysis of protein composition Analysis of protein specific activity Analysis of pathway length
Some factors that overcome selective pressure for low cognate bias Functional requirements for specific cognate residues Environmental factors
Alves & Savageau 2005 Mol. Microbiol. in press
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Amino acid composition: the view from here
Extend the work for other bacteria and attempt to create organism/environment biosignatures
Analyze ribosomal proteins amino acid bias, amino acid transport proteins bias and catabolic protein bias
Analyze influence of oxydizability on selection of surface amino acids in proteins
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Question Given that environment and bias are related, and
that aa production is mostly biotic, can different broad environments be identified by their fractional amino acid composition?
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Environmental amino acid data
From the literature : Soil
10 different soil types, different times of the year Internal
Intestine of different animals Oceans, Rivers, lakes
13 sites with determinations all over the world and all year sampling
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Looking for amino acid signaturesAla Cys Asp …
Pacific 0.15 0 0.07 …
Artic
Wilderness
.
.
.
0.14 0 0.08 …
Principal component analysis using the covariance matrix
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Environments are well separated with three principal components
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Questions Can different broad environments be identified by
their fractional amino acid composition?YES!
Similarly, can groups of organisms be classified using such a broad feature as their fractional amino acid composition?
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Obtaining the biological data
Identify environment
KEGGTake genomes
Get protein sequences
Estimate the relative
composition of each cell type
KEGG
Literature
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Different proteins contribute differently
Proteins have different levels of expression
Highly expressed protein will contribute more for the relative amino acid composition
Karlin (late 90s) proposed that highly expressed proteins can be identified by their codon bias
Rybossomal proteins are on average highly expressed
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Using Codon Bias to weight gene expression
Calculate average codon frequency for rybosomal proteins
(CUAA1,…,CUAA20)
Calculate codon frequency for each protein
(CUAA1Pi,…,CUAA20Pi)
Normalize
Weight number of amino acids in protein j by Normalized DPj
63 2
, ,Pri=1
1,
2,
=
/ 1
1 /
k i Ribosome i oteink
k k
k k
CU CU
d Exp Max in genome Exp Exp Max in genome
d Max in genome
Do Principal Components Analysis
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Segregation follows phylogeny
Archaea
Eukaryotes
Bacteria
Archaea
Eukaryotes
Gram PositiveGram Negative
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Environmental segregation in Bacteria
Sea
Soil
Water
Parasite
Versatile
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Environmental segregation seen in power spectrum
Gram PositiveGram
NegativeArchaea
Eukaryotes
SoilSea
WaterParasiteVersatil
e
Fou
rier
Tra
nsfo
rmF
ouri
er T
rans
form
Protein Length Protein Length
Relative content in proteins Relative content in proteins
Alanine Alanine
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Questions Can different broad environments be identified by their
fractional amino acid composition? YES!
Similarly, can broad groups of organism be identified using such a broad feature as their fractional amino acid composition? To some extend yes. Broad phylogenetic grouping is
captured; environmental grouping is captured with less certainty, dependent on environment + phylogenetic group.
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Final Question Environments can be identified by their aa
composition The cognate environment of a cell can be predicted
to some extent based on the cell’s amino acid content
Is there a correlation between the fractional amino acid composition of an environment and that of genomes from that environment?
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Looking for co-adaptation
1, 1 1, 1 20, 1 20, 1, ,..., ,O Env O EnvAA AA AA AA
Calculate correlation coefficients
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Final Question There is significant correlation between amino acid
content of cells and that of environment Signal is not strong enough to show adaptation to
specific environmental niches
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Summary There are environmental and cellular signatures for
the amino acid composition There is correlation between the two types of
signatures but not enough signal for this correlation to be predictive.
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Acknowledgments
Mike Savageau Mike Sternberg Albert Sorribas Enric Herrero Armindo Salvador
PGDBM JNICT FCT Spanish Government Portuguese Government NIH (Mike Savageau) DOD (ONR) (Mike Savageau)
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Metabolic Reconstruction: the view from here (I)
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The “Putting it together” Section
Continue FeSC work
Use Know how to reconstruct TCS network in M. xanthus
Analyze more TCS designs
Analyze aa biosyntesis and enzyme networks evolution
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Grx5 interacts with Scaffold in Two-Hybrid assay
Two-hybrid analysis of the interaction between Grx5 and Scaffold. Numbers over bars indicate the beta-galactosidase activity (Miller units) in cultures of S. cerevisiae cells co-transformed with plasmids pGBT9 and pACT2 vectors alone, or derivatives expressing the respective Gal4 fusion proteins with Grx5, Scaffold, and two proteins known to interact. Results are the mean of three independent experiments.
Negative Controls Grx5 Scaffold
Positive Control
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ATP binding domain important in functionality of sensor
Alves & Savageau 2003 Mol. Microbiol. 48:25
…SSQIE……SSQ-E…
Sensor with known structureSensor sequence for structure prediction
100s of structure predictions
Predicted to be Bifunctional Sensor
Predicted to be Monofunctional Sensor
Differences in ATP lid