Iron-regulated proteome and transcriptome of Neisseria meningitidis

1
Iron-regulatedproteome and transcriptomeof
Neisseria meningitidis

• M. BASLER, I. LINHARTOVÁ, P. HALADA,J. NOVOTNÁ,
  S. BEZOUŠKOVÁ, R. OSICKA,J. WEISER, J. VOHRADSKÝ
  and P. ŠEBO
• Institute of Microbiology of the Czech Academy of
  Sciences, Prague

2
IRON HOMEOSTASIS

• Iron is essential to virtually all organisms, but
  poses problems of toxicity and poor solubility

3
Basic principles of iron homeostasis

• There are essentially 5 strategies used by
  bacteria in the management of iron
• High-affinity iron transport enabling iron to be
  scavenged, in various forms, from the
  surroundings.
• Deposition of intracellular iron stores to
  provide a source of iron that can be drawn upon
  when external supplies are limited.
• Employment of redox stress resistance systems
  (e.g. degradation of iron-induced reactive oxygen
  species and repair of redox stress-induced
  damage).
• Control of iron consumption by down-regulating
  the expression of iron-containing proteins under
  iron-restricted conditions.
• An over-arching iron-responsive regulatory system
  that co-ordinates the expression of the above
  iron homeostatic machinery according to iron
  availability.

4
Mechanism of Fur regulation
However, recently also iron-responsive activation
of gene transcription was discovered
iron-responsive repression of gene transcription
ON
OFF
NADH dehydrogenase subunits
NADH dehydrogenase subunits
Andrews FEMS Microbiology Reviews 27 (2003)
Delany Mol Microbiol 52 (2004)
5
Gene expression in N. meningitidisunder 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

6
Neisseria meningitidis
Obligate human commensal gram-negative bacterium
colonizing the nasopharynx of about 10 of
healthy subjects.
Risk factors upper respiratory infection,
immunodeficiency, age Risk groupsmilitary
recruits, refugees, contacts of patients
Treatment (7 to 14 days)intravenous penicillin
or cephalosporins, chloramphenicolVaccinepurif
ied polysaccharidesserogroups A, C, Y and W-135
7
Neisseria meningitidis life cycle Iron
availability in the human host
lactoferrin
2 µM iron
transferrin hemoglobin
ferritin
8
Experimental design iron starvation
9
Iron regulatedPROTEOME

• I. LINHARTOVÁ, P. HALADA,J. NOVOTNÁ, S.
  BEZOUŠKOVÁ, J. VOHRADSKÝ

10
Fe(NO3)3 Desferal
Image and data analysis
Mass Spectrometry
11
theor. 788 proteins
theor. 962 proteins
4
7
6
11
pI
pI
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 MS64 unique
proteins in DF set27 unique proteins in Fe set
12
Iron regulatedTRANSCRIPTOME

• M. BASLER, I. LINHARTOVÁ

13
Fe(NO3)3 Desferal
Chip
Target PCR products
Cy3
Cy5

Probe
Data mining and visualization
Hybridization
Image processing
14
N. meningitidis whole genome slide (Eurogentec) -
2194 ORFs
3 biological experiments 8 whole genome slides 62
genes up-regulated in DF64 genes up-regulated in
Fe
15
sebo_at_biomed.cas.cz basler_at_biomed.cas.cz
16
DATA ANALYSIS

• scanning, image analysis, quality control,
  background subtraction, normalization, data mining

17
Microarray Data Flow
Scanner
Printer
.tiff Image File
Image Analysis
Raw Gene Expression Data
Gene Annotation
Normalization / Filtering
Normalized Data with Gene Annotation
Expression Analysis
Interpretation of Analysis Results
18
Scanning
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Image analysisquality controlbackground
subtraction

• SpotFinder
• www.tigr.org

22
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

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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
26
NormalizationData mining, filtering

• MIDAS
• www.tigr.org
• R
• www.r-project.org

27
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!

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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 lt 0.01
• Expression ratio gt 1.7x
• Clustering
• KEGG GENES Database
• PubMed

31
Finding Significant Genes by t-test
Distribution of intensity ratios for each gene
Not significant p-val gt 0.01
Average ratio is same
Significant p-val lt 0.01
32
RESULTS
33
Complementarity of proteome and
transcriptome199 genes regulated by iron
91 genes found inproteome
126 genes found in transcriptome
114 genes up-regulated in low iron85 genes
up-regulated in high iron
34
Identification of iron-activated and repressed
Fur-dependent 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.

35
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

36
191 genes found by Siena group 40 not on EGT
chip, 4 more than once
85 genes found inproteome 1 not similar to NmA
or NmB
117 genes found in transcriptome
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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!

39
Some what came out
40
IRON HOMEOSTASIS

• Iron is essential to virtually all organisms, but
  poses problems of toxicity and poor solubility

41
Basic principles of iron homeostasis

• There are essentially 5 strategies used by
  bacteria in the management of iron
• High-affinity iron transport enabling iron to be
  scavenged, in various forms, from the
  surroundings.
• Deposition of intracellular iron stores to
  provide a source of iron that can be drawn upon
  when external supplies are limited.
• Employment of redox stress resistance systems
  (e.g. degradation of iron-induced reactive oxygen
  species and repair of redox stress-induced
  damage).
• Control of iron consumption by down-regulating
  the expression of iron-containing proteins under
  iron-restricted conditions.
• An over-arching iron-responsive regulatory system
  that co-ordinates the expression of the above
  iron homeostatic machinery according to iron
  availability.

42
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

43
Iron sources in the human host
lactoferrin
2 µM iron
transferrin hemoglobin
ferritin
44
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.

45
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!
46
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?
47
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?
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II.REGULATORY SYSTEMS

• An over-arching iron-responsive regulatory system
  thatco-ordinates the expression of the iron
  homeostatic machinery according to iron
  availability is the Fur system

49
Mechanism of Fur regulation
However, recently also iron-responsive activation
of gene transcription was discovered
iron-responsive repression of gene transcription
ON
OFF
NADH dehydrogenase subunits
NADH dehydrogenase subunits
Andrews FEMS Microbiology Reviews 27 (2003)
Delany Mol Microbiol 52 (2004)
50
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
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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?

52
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
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III.IRON STORAGE

• Deposition of intracellular iron in stores offers
  a source of iron that can be used when external
  supplies are limited

54
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
  ironferritin - only iron and not heme
• In presence of iron
• bfrA - up-regulated more than 11 timesbfrB -
  up-regulated nearly 8 times
• In presence of desferal
• putative ferredoxin - up-regulated 2.4 times

55
Structures of iron storage proteins from E. coli
Bfr 500 kDa, 2000-3000 iron atoms/24-mer
Dps 250 kDa, 500 iron atoms/12-mer
Andrews FEMS Microbiology Reviews 27 (2003)
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IV.IRON CONSUMPTION

• Control of iron consumption by down-regulating
  the expression of iron-containing proteins under
  iron-restricted conditions

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CITRATE CYCLE
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The overlap of proteome and transcriptome data
shows thatFumC 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 isoenzymefumarate
  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
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PROTEOSYNTHESIS
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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
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HYPOTHETICAL PROTEINS
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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
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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
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SUMMARY
66
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

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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

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Acknowledgments

• Irena Linhartová
• Petr Halada
• Jana Novotná
• Silvia Bezoušková
• Jirí Vohradský
• Radim Osicka
• Jaroslav Weiser
• Peter Šebo

• Sponsors
• AV CR
• MBÚ AV CR
• HHMI