Download Microarray data analysis - Laboratory of Molecular Biology of

Iron-regulated
proteome and transcriptome
of Neisseria meningitidis
M. BASLER, I. LINHARTOVÁ, P. HALADA,
J. NOVOTNÁ, S. BEZOUŠKOVÁ, R. OSIČKA,
J. WEISER, J. VOHRADSKÝ and P. ŠEBO
Institute of Microbiology of the Czech Academy of Sciences, Prague
IRON HOMEOSTASIS
Iron is essential to virtually all organisms, but
poses problems of toxicity and poor solubility
Basic principles of iron homeostasis
• There are essentially 5 strategies used by bacteria in the
management of iron:
1) High-affinity iron transport enabling iron to be scavenged, in
various forms, from the surroundings.
2) Deposition of intracellular iron stores to provide a source of iron
that can be drawn upon when external supplies are limited.
3) Employment of redox stress resistance systems (e.g. degradation
of iron-induced reactive oxygen species and repair of redox stressinduced damage).
4) Control of iron consumption by down-regulating the expression of
iron-containing proteins under iron-restricted conditions.
5) An over-arching iron-responsive regulatory system that coordinates the expression of the above iron homeostatic machinery
according to iron availability.
Mechanism of Fur regulation
However,
recentlyrepression
also iron-responsive
activation
iron-responsive
of gene
transcriptionof
gene transcription was discovered
High iron
ON
NADH dehydrogenase
subunits
Andrews – FEMS Microbiology Reviews 27 (2003); Delany – Mol Microbiol 52 (2004)
Low iron
OFF
NADH dehydrogenase
subunits
Gene expression in N. meningitidis
under iron starvation
• In human body more than 99,9% of iron is bound to
transport (transferrin, lactoferrin) and storage proteins
(ferritin, heme-containing compounds)
• For invasion and proliferation bacteria need to induce
specific pathways capable of scavenging iron from the host
• Low iron concentration tells the pathogen it is inside the
host
• Several Neisseria virulence genes are iron-regulated
Neisseria meningitidis
Obligate human commensal
gram-negative bacterium
colonizing the nasopharynx of
about 10% of healthy subjects.
Risk factors:
upper respiratory infection,
immunodeficiency, age
Treatment (7 to 14 days):
intravenous penicillin or
cephalosporins, chloramphenicol
Risk groups:
military recruits, refugees,
contacts of patients
Vaccine:
purified polysaccharides
serogroups A, C, Y and W-135
Neisseria meningitidis – life cycle
Iron availability in the human host
lactoferrin
2 µM iron
ferritin
transferrin
hemoglobin
Experimental design – iron starvation
Proteins
2-D + MS
RNA
7 µM Fe(NO3)3
10 h
O/N
microarray
Proteins
RPMI
2h
2-D + MS
100 µM Desferal
10 h
RNA
microarray
Iron regulated
PROTEOME
I. LINHARTOVÁ, P. HALADA,
J. NOVOTNÁ, S. BEZOUŠKOVÁ, J. VOHRADSKÝ
+ Fe(NO3)3
+ Desferal
Image and data
analysis
Mass
Spectrometry
theor. 788 proteins
4
theor. 962 proteins
pI
7
6
pI
11
100
100
kDa
kDa
5
15
DF set – 6 gels
Fe set – 7 gels
362 protein spots analyzed
46 spots in DF set
31 spots in Fe set
DF set – 8 gels
Fe set – 10 gels
238 protein spots analyzed
67 spots in DF set
11 spots in Fe set
114 spots were identified by MS
64 unique proteins in DF set
27 unique proteins in Fe set
Iron regulated
TRANSCRIPTOME
M. BASLER, I. LINHARTOVÁ
+ Fe(NO3)3
+ Desferal
Chip
Target: PCR
products
Cy5
Cy3
+
Probe
Data mining and
Hybridization
Image processing
visualization
N. meningitidis whole genome slide
(Eurogentec) - 2194 ORFs
3 biological experiments
8 whole genome slides
62 genes up-regulated in DF
64 genes up-regulated in Fe
[email protected]
[email protected]
DATA ANALYSIS
scanning, image analysis, quality
control, background subtraction,
normalization, data mining
Microarray Data Flow
Printer
Scanner
.tiff Image File
Image
Analysis
Raw Gene Expression
Data
Gene Annotation
AGED
Others…
MAD
Database
Normalization /
Filtering
Normalized Data with
Gene Annotation
Database
Database
Expression
Analysis
Interpretation of
Analysis Results
Scanning
Image analysis
quality control
background subtraction
SpotFinder
www.tigr.org
Basic Steps from Image to Table
1. Image File Loading
2. Construct or Apply an Overlay Grid
3. Computations
Find Spot Boundary and Area
Intensity Calculation
Background Calculation and Correction
4. Quality Control
5. Text File Output
Applying an Overlay Grid
• What does it accomplish?
–The grid cells set a boundary for the spot
finding algorithms.
–The grid cells also define an area for
background correction.
Area inside contour
is used for spot
intensity calculation
Area outside contour is
used for local background
calculation
Reported “Intensity” = Integral – BKG * A
Normalization
Data mining, filtering
MIDAS
www.tigr.org
R
www.r-project.org
Why is normalization important?
• There are many sources of experimental variation:
During preparation – mRNA extraction, labeling
During manufacture of array – amount of spotted DNA
During hybridization – amount of sample applied, amount of target hybridized
After hybridization – optical measurements, label intensity, scanner
• Proper normalization is needed before ratios from
different chips are compared!
Intensity vs. expression ratio
slide #6
6
4
2
0
-2
Expression ratio
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4.5
5.0
5.5
6.0
6.5
Mean of Log10 intensities for both channels
-0.05
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-0.07
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0.0 0.2 0.4 0.6 0.8 1.0
-0.02
Density
0.1
Histogram of expression ratios
normalized data - slide #6
5146 spots
-3
-2
-1
0
1
2
3
Data mining
• Visualization and control (R)
• Filtering (MS Excel, R)
One sample t-test
• mean of Log2 ratios for all replicates
• mean is not equal to 0
• p-val < 0.01
Expression ratio > 1.7x
• Clustering
• KEGG GENES Database
• PubMed
Finding Significant Genes by t-test
Distribution of intensity ratios for each gene
Not significant
p-val > 0.01
Average ratio is same
Significant
p-val < 0.01
RESULTS
Complementarity of
proteome and transcriptome
199 genes regulated by iron
91 genes found in
126 genes found in
proteome
transcriptome
73
18
108
114 genes up-regulated in low iron
85 genes up-regulated in high iron
Identification of iron-activated and repressed Furdependent genes by transcriptome analysis of
Neisseria meningitidis group B
Grifantini et al., PNAS, August 5, 2003
• After iron addition to an iron-depleted bacterial culture 153
genes were up-regulated and 80 were down-regulated
• Only 50% of the iron-regulated genes were found to
contain Fur-binding consensus sequences in their
promoter regions.
• Different growth conditions. N. meningitidis MC58 cultures
were grown in chemically defined medium with 12.5 µM
desferal (iron-depleted) for 3 h. After this adaptation to iron
starvation, half of the culture was supplemented with 100 µM
ferric nitrate, and growth continued for a 5-h period.
Overlap of PNAS and our data
• PNAS data are for N. m. B
• NMB to NMA conversion table
blastall -p blastp -d Nm_Z2491 -b1 -m8 -i
MC58.txt -o NmB_in_NmA.txt
191 genes found by Siena group
+ 40 not on EGT chip, + 4 more than once
145
5 (2)
62
15
1
24
77
85 genes found in
117 genes found in
proteome
transcriptome
+ 1 not similar to NmA or NmB
Correlation between our data and PNAS data
39 genes
PNAS data (Log2 expression ratio)
3
R2 = 0.7568
2
1
0
-4
-3
-2
-1
0
1
-1
-2
-3
-4
Our data (Log2 expression ratio)
2
3
4
Conclusions for combined results
• There is more iron-regulated genes than
expected! Up to about 300.
• In a single type of experiment we and the
Siena group found 10x more genes
regulated by iron concentration than before
the entire scientific community in 40 years!
Some what came out …
IRON HOMEOSTASIS
Iron is essential to virtually all organisms, but
poses problems of toxicity and poor solubility
Basic principles of iron homeostasis
• There are essentially 5 strategies used by bacteria in the
management of iron:
1) High-affinity iron transport enabling iron to be scavenged, in
various forms, from the surroundings.
2) Deposition of intracellular iron stores to provide a source of iron
that can be drawn upon when external supplies are limited.
3) Employment of redox stress resistance systems (e.g. degradation
of iron-induced reactive oxygen species and repair of redox stressinduced damage).
4) Control of iron consumption by down-regulating the expression of
iron-containing proteins under iron-restricted conditions.
5) An over-arching iron-responsive regulatory system that coordinates the expression of the above iron homeostatic machinery
according to iron availability.
I.
TRANSPORT OF IRON
High-affinity iron transport systems allowing acquisition in
various forms from the environment
are vital to all commensal and pathogenic bacteria
Iron sources in the human host
lactoferrin
2 µM iron
ferritin
transferrin
hemoglobin
Iron acquisition mechanisms
• Siderophore mediated
N. meningitidis utilize heterologous siderophores
• Receptor mediated
Transferrin and lactoferrin receptors
Hemoglobin receptor
Haptoglobin-hemoglobin receptor
• Siderophores and hemophores are taken into the
cell whole.
• Host carrier proteins are not transported into the
cell. Iron and heme must be stripped away prior to
transport.
Iron acquisition system is up-regulated in low iron
4x - LbpA
5x - LbpB
7x
5x
3x
3x
5x
4x
These results validate the experimental procedure!
Proteins up-regulated in low iron
Method
Reg
Protein Name
Arrays
3.13
possible periplasmic protein
Arrays
6.50
putative integral membrane protein
Arrays
2.55
putative integral membrane protein
Arrays
1.91
putative membrane protein
Arrays
3.24
putative lipoprotein
Arrays
5.50
putative periplasmic protein
Arrays
5.24
putative periplasmic protein
Proteome
2.35
putative periplasmic protein
Arrays
1.87
putative periplasmic hypothetical protein
Other iron acquisition system?
Basic periplasmic proteins up in low iron
Protein name
Reg
MW
pI
putative periplasmic protein
-5.50
16427.6
11.0
putative periplasmic protein
-5.24
31673.7
9.9
hypothetical protein NMA1073
-3.14
19533.4
10.9
major ferric iron binding protein
-2.79
35841.9
10.2
Other periplasmic transporters?
II.
REGULATORY SYSTEMS
An over-arching iron-responsive regulatory system that
co-ordinates the expression of the iron homeostatic machinery
according to iron availability is the Fur system
Mechanism of Fur regulation
However,
recentlyrepression
also iron-responsive
activation
iron-responsive
of gene
transcriptionof
gene transcription was discovered
High iron
ON
NADH dehydrogenase
subunits
Andrews – FEMS Microbiology Reviews 27 (2003); Delany – Mol Microbiol 52 (2004)
Low iron
OFF
NADH dehydrogenase
subunits
Transcriptional regulators possibly
involved regulation of iron homeostasis
Iron can regulate gene expression in a Fur-independent
manner for approx. 50 % of the up/down regulated genes.
Method
+/-
Reg
Protein Name
Both
DF
2.15
ferric uptake regulation protein
Arrays
DF
2.68
putative transcriptional regulator
Arrays
DF
2.02
putative transcriptional regulator
Proteome
DF
only
DNA-binding response regulator
Proteome
DF
only
Integration host factor alpha-subunit (IHF-alpha)
Arrays
Fe
2.28
AsnC-family transcriptional regulator
Arrays
Fe
2.53
putative transcriptional regulator
Arrays
Fe
1.93
putative ATP-dependent RNA helicase
Arrays
Fe
1.79
ribonuclease PH
Grifantini – PNAS, 2003; V. Scarlato (2003, J Bact) – Fur is autoregulated in Neisseria meningitidis
Transcriptional regulators possibly
involved regulation of iron homeostasis
• The generally accepted concept that iron
homeostasis in bacteria is regulated by Fur
may be an oversimplification.
• Is there a hierarchy of iron-dependent
regulation by a cascade of transcriptional
activators and/or repressors?
Positive regulation by Fur in E. coli
A small non-coding RNA (RyhB) acts as a Fur repressed negative regulator
of genes induced in presence of iron in E. coli.
Masse – PNAS, 2002
III.
IRON STORAGE
Deposition of intracellular iron in stores offers a source of iron
that can be used when external supplies are limited
Proteins involved in iron storage
• Free iron in presence of oxygen can form free
radicals which are toxic to the cell.
• Storage of iron in nontoxic form is very important!
• Two types of iron storage proteins have been
identified in bacteria:
bacterioferritin - heme iron and nonheme iron
ferritin - only iron and not heme
• In presence of iron
bfrA - up-regulated more than 11 times
bfrB - up-regulated nearly 8 times
• In presence of desferal
putative ferredoxin - up-regulated 2.4 times
Structures of iron storage proteins from E. coli
Bfr
Dps
500 kDa, 2000-3000 iron atoms/24-mer
250 kDa, 500 iron atoms/12-mer
Andrews – FEMS Microbiology Reviews 27 (2003)
IV.
IRON CONSUMPTION
Control of iron consumption by down-regulating the
expression of iron-containing proteins under ironrestricted conditions
CITRATE CYCLE
Fe
D
F
The overlap of proteome and transcriptome data shows that
FumC substitutes for FumA during iron starvation
• In presence of iron
– Neisseria express iron containing (Fe-S)
fumarate hydratase class I (FumA)
– up-regulated almost 2 times on level of RNA and FumA
protein was found only in Fe set of gels.
• In presence of desferal
– Neisseria express “iron free” isoenzyme
fumarate hydratase class II (FumC)
– up-regulated almost 4 times on level of RNA and FumC
protein was found only in DF set of gels.
Park – Journal of bacteriology, 1995
PROTEOSYNTHESIS
Proteins up-regulated in iron
Method
Reg
Protein Name
Arrays
1.73
30S ribosomal protein S18
Arrays
1.83
30S ribosomal protein S6
Arrays
1.80
50S ribosomal protein L27
Arrays
1.90
50S ribosomal protein L31
Arrays
1.82
putative additional 50S ribosomal protein L31
Proteome
only
50S ribosomal protein L4
Proteome
2.06
50S ribosomal protein L9
Proteome
2.13
elongation factor G (EF-G)
Proteome
only
hypothetical protein NMA1094*
Proteome
only
translation elongation factor Tu
*Protein NMA1094 was annotated by TIGR as ribosomal 5S rRNA E-loop binding protein Ctc/L25/TL5
HYPOTHETICAL PROTEINS
Hypothetical proteins up in low iron
Method
Reg
Protein Name
Proteome
only
conserved hypothetical protein
Proteome
only
hypothetical protein NMA1013
Arrays
7.89
hypothetical protein NMA0957
Arrays
6.00
hypothetical protein NMA0963
Arrays
5.55
hypothetical protein NMA1078
Arrays
3.39
hypothetical protein NMA1076
Arrays
3.14
hypothetical protein NMA1073
Arrays
2.92
hypothetical protein
Arrays
2.30
hypothetical protein
Proteome
2.19
conserved hypothetical protein
Arrays
2.10
hypothetical protein
Arrays
2.02
hypothetical protein NMA0482
Arrays
1.97
hypothetical protein NMA1070
Arrays
1.89
hypothetical protein
Arrays
1.89
hypothetical protein
Arrays
1.89
hypothetical protein NMA0401
Arrays
1.88
hypothetical protein NMA1220
Arrays
1.75
hypothetical protein NMA1067
Arrays
1.75
hypothetical protein NMA1071
Arrays
1.74
hypothetical protein NMA0565
Arrays
1.74
hypothetical protein NMA0737
Arrays
1.74
hypothetical protein NMA1484
Arrays
1.73
hypothetical protein NMA1072
Arrays
1.71
hypothetical protein NMA0787
Hypothetical proteins up in high iron
Method
Reg
Protein Name
Proteome
only
conserved hypothetical protein
Proteome
only
conserved hypothetical protein
Proteome
only
hypothetical protein NMA1013
Proteome
only
hypothetical protein NMA1094
Arrays
3.25
hypothetical protein NMA0004
Arrays
3.20
hypothetical protein
Arrays
2.96
hypothetical protein NMA0013
Arrays
2.70
hypothetical protein
Arrays
2.08
hypothetical protein NMA0003
Arrays
1.90
hypothetical periplasmic protein
Arrays
1.87
outer membrane protein
Arrays
1.84
hypothetical protein
Arrays
1.81
hypothetical protein
Arrays
1.78
putative periplasmic binding protein
Arrays
1.74
putative periplasmic protein
SUMMARY
Genes up-regulated at low-iron conditions
114 genes
• Transport and binding
proteins
transferrin and lactoferrin
binding proteins
TonB protein
siderophore receptor
ferric binding protein
ABC transporter
• Virulence factors
pilins
opaD
• Transcriptional regulators
ferric uptake regulation
protein
integration host factor (IHF)
hypothetical DNA binding
proteins
putative regulators
• 15 putative membrane and
periplasmic proteins
• 30 hypothetical proteins
Genes up-regulated at high-iron conditions
85 genes
• Iron storage
bacterioferritins
• Energy metabolism
electron transport
• cytochromes
• NADH dehydrogenase
TCA cycle
• fumarate hydratase
• aconitate hydratase
• citrate synthase
• Protein synthesis
ribosomal proteins
translation and elongation
factors
• Transcriptional regulators
AsnC-family transcriptional
regulator
DNA binding proteins
putative regulators
ribonuclease
• 15 hypothetical proteins
Acknowledgments
Irena Linhartová
Petr Halada
Jana Novotná
Silvia Bezoušková
Jiří Vohradský
Radim Osička
Jaroslav Weiser
Peter Šebo
Sponsors:
AV ČR
MBÚ AV ČR
HHMI