The Evolution of Earth

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• The Evolution of Earths Core Metabolism
• (Frozen Metabolic Accidents)
• Paul G. Falkowski
• Institute of Marine and Coastal Science
• and Dept. Of Earth and Planetary Sciences
• Rutgers University, New Brunswick, NJ
• Gordon Conference June 13,2010

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Basic Premises/Hypothesis

1. In the first ca. 2.5 Ga of Earths history,
   nature invested heavily in RD from which a
   core set of metabolic machines that evolved.
2. There are approximately 1500 core metabolic genes
   that make the world go around
3. This period of metabolic innovation is
   characterized by machinery that has been retained
   virtually without change to the present time
   (frozen metabolic accidents).
4. All of the key metabolic processes were developed
   in prokaryotes

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• 5. These metabolic sequences are coupled on local
  and planetary scales to facilitate an electron
  market between C, N, O, and S.
• 6. Most of the metabolic sequences were rapidly
  appropriated by a large number of groups of
  microbes and some (not all) subsequently were
  subsumed into eukaryotic lineages via primary and
  secondary symbioses.
• 7. The eukaryotes derived secondary metabolic
  adapations during the 2nd half of Earths history
  the era of metabolic adaptation, but did
  not invent any new fundamental process.

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• 8. However, the dispersal of the core metabolic
  processes to large numbers of widely differing
  taxa helped to ensure their continuity
  (resiliance).
• 9. All these metabolic sequences are observable
  in the modern world but many are extremely
  inefficient.
• 10. Despite these inefficiencies, alternatives
  have not been selected. Why not?

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Falkowski, Fenchel and Delong, Science, 2008
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Three examples of frozen metabolic accidents

• Carbon fixation (C)- Rubisco
• Nitrogen fixation (N) - Nitrogenase
• Oxygen evolution (O)- The reaction center of
  Photosystem II

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Example 1 Carbon Fixation and the evolution of
RuBisCO
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RUBisCO
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• Rubisco arose from a methionine salavge pathway
  long before it was appropriated for use in the
  Calvin-Benson cycle.
• The enzyme is catalytically challenged, and can
  barely figure out what its substrate looks like
  (blind and slow).
• In an oxygen rich world Rubisco is notoriously
  inefficient (dumb).
• However, there is very little selection pressure
  on Rubisco active sites. Why not?

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Remove the selection pressure

• Cells can make a lot of Rubisco but dont
  reinvent the technology (hire lots of dumb,
  blind, slow workers), or
• They developed a secondary set of adaptations
  that removed or reduce the selection pressure
  e.g., the Carbon Concentrating Mechanism

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Carbon concentrating mechanism
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Example 2 Nitrogenase

• A detour into the rise of oxygen the coupling
  between C,N and O cycles on Earth
• 2N2 4H 3CH2O ? 4NH4 3CO2
• A 6 electron transfer reaction

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Nitrogenase
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The Biological Nitrogen Cycle
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1G20 -- Nitrogenase MoFe protein only
Fe8S7
Fe7MoS9
Fe8S7
Fe7MoS9
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Digression

• Evolution of core structural motifs
• The paradox of structure/sequence divergence

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• Science. 1966 Apr 15152(3720)363-366.
• Evolution of the Structure of Ferredoxin Based on
  Living Relics of Primitive Amino Acid Sequences.
• Eck RV, Dayhoff MO.
• The structure of present-day ferredoxin, with its
  simple, inorganic active site and its functions
  basic to photon-energy utilization, suggests the
  incorporation of its prototype into metabolism
  very early during biochemical evolution, even
  before complex proteins and the complete modern
  genetic code existed. Ferredoxin has evolved by
  doubling a shorter protein, which may have
  contained only eight of the simplest amino acids.
  This shorter ancestor in turn developed from a
  repeating sequence of the amino acids alanine,
  aspartic acid or proline, serine, and glycine. We
  explain the persistence of living relics of this
  primordial structure by invoking a conservative
  principle in evolutionary biochemistry The
  processes of natural selection severely inhibit
  any change a well-adapted system on which several
  other essential components depend.

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1FXR Ferredoxin I from Desulfovibrio Africanus

• Ferredoxin protein with Fe4S4 cluster

• Image Courtesy Dr. Vikas Nanda (UMDNJ)

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1FXR Ferredoxin I from Desulfovibrio africanus

• Fe4-S4 cluster in ferredoxin tightly held by four
  cystein groups via thiolate bonds

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Beta carbon distribution (385 structures)

• 385 Structures from Protein Data Bank

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22 High Potential Iron Proteins (amide)
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21 Fe Protein of Nitrogenase (amide)
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HiPIP and Fe Nitrogenase

• N

Fe Nitrogenase
HPiP
Number of Cysteine peaks. Signature of different
environment for different redox potential?
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High potential protein vs Fe protein of
Nitrogenase (contd)
Fe Nitrogenase
HPiP
Signature of different environment for different
redox potential?
-number of CYS peaks -Hydrophilic group contrast
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• The basic FeS binding
• 30 of all FeS clusters are bound to a
• C XX C XX(X) C motif with a final C in a
  variable position further along the protein.
• The most common X residues are neutral aas
  (especially A, L and I).
• These motifs are virtually all chiral!

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Chronically Crippled Nitrogenase
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Example 3 The oxygen evolving complex and the
evolution of Photosystem II
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Example 3 The oxygen evolving complex and the
evolution of Photosystem II
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PSII type Reaction Center
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The Mn cluster in PS II
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• All oxygenic photosythetic organisms share common
  centers.
• The protein, D1, in photosystem II was inherited
  from purple sulfur bacteria.
• In all oxygenic organisms this protein is damaged
  and replaced (not simply repaired) every 30 min
  during the day.
• Despite this inefficiency, there is almost no
  change in the primary sequence of this protein
  for the past 3 Ga (86 identity at the aa level)
• Lesson learned If it works, keep using the old
  technology. Just pay the costs and fix the
  machinery.

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Hypothesis The core metabolic machines are
usually multimeric protein complexes that bind
prostetic groups. The tempo of evolution of the
core complexes is constrained by protein-protein
interactions. The Rubic cube paradox Dont
mess with it unless you know how to get the thing
back to the original configuration.
Why doesnt this core metabolic machinery improve
with age?
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Photosynthetic gene clusters in cyanobacteria
1 kb
Synechocystis sp. PCC6803
EFLJ
C
K
J
E
G
I
A
D
F
A
B
B
D
psb
ndh
ndh
ndh
psa
crt
Anabaena sp. PCC7120
EFLJ
E
G
I
A
A
B
F
D
C
K
J
B
D
ndh
psa
psb
ndh
ndh
crt
Thermosynechococcus elongatus BP-1
EFLJ
E
G
I
A
A
B
D
F
B
D
ndh
psa
ndh
psb
crt
Synechococcus sp. WH8102
JLFE
E
G
I
A
B
A
ndh
ndh
psb
psb
psa
ndh
Prochlorococcus marinus MED4
EFLJ
D
F
E
G
I
A
C
K
J
B
A
C
D
ndh
ndh
ndh
psa
psb
psb
Prochlorococcus marinus MIT9313
EFLJ
C
K
J
D
F
E
G
I
A
ndh
psb
ndh
ndh
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Protein-protein interactions in linked
photosystems revealed by the co-evolutionary
analysis. Red lines represent predicted
interactions with coefficient values better than
0.8. Also shown is a network of protein-protein
interactions in the ATPase complex. The pattern
of protein-protein interactions strongly suggests
co-evolution of photosynthetic genes driven by
electron transport and redox state of the primary
photochemistry. Black arrows, electron transfer
blue arrows, proton transfer.
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So how can such apparently inefficient machinery
be both robust and resilient?

• Spread the risk (The Microsoft approach)
• Select secondary adaptive features

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Euglenophyta Euglena
Chlorophyta
Bacillariophyta Odontella
Chlorella
psbM petA petD petL psaI rne rpl19 rpoA
cysA cysT ftsW infA minD ndhA-I ndhK
accD ccsA cemA chlB chlL chlN clpP
Nephroselmis
ftsW ndhA-I ndhK
Cryptophyta Guillardia
cemA cpeB ftrB ilvB ilvH infB minD pbsA psaK rne t
sf
psaM
Rhodophyta
Mesostigma
Porphyra
rpl22
Cyanidium
thiG bas1
accD
Secondary endosymbiosis
bas1 cpeB infB minD pbsA psbX rps20
hisH minD
ndhJ odpB rpl33 rps15 rps16
Streptophyta
Glauco- cystophyta Cyanophora
chlN cpcA cpcB cpcG dfr glnB gltB hisH infC nblA
accA accB accD apcA apcB apcD apcE apcF argB carA
ntcA odpA odpB petJ preA rpl28 trpA trpG trxA
Marchantia
rps16
rpl21
cysA, cysT rpl21, ndhA-K
chlB chlL cpeA dsbD fabH fdx moeB
pgmA rpl9 rps1 syfB syh upp
Pinus
cysA, cysT, rpl21 chlB,L,N, psaM
atpI cemA minD odpB
rne rpl23 rpl32
Nicotiana
accD
acpP apcA apcB apcD apcE apcF atpD atpG cpcA cpcB
dnaK groEL hisH petF petM preA psaE psaF

psbV psbW psbX rbcR rpl1 rpl3 rpl6 rpl11
rpl18 rpl28 rpl34 rpl35 rps5 rps6 rps10 rps13 rps
17 rps20 secY trpG
Oryza
chlI ftsW minD odpB rne rpl5 rpl12 rpl19 rps9 tuf
A
Zea
clpP ftsW psbM rbcLg
Secondary endosymbiosis
glnB gltB ilvB ilvH infB infC moeB nblA ntcA odpA
pbsA petJ pgmA psaD psaK psaL rbcLr
rbcSr rpl4 rpl9 rpl13 rpl24 rpl27 rpl29 rpl31 rps
1 secA syfB syh thiG trpA trxA tsf upp
cystA cystT infA ndhA-K rps15
accA accB argB bas1 carA clpC cpcG cpeA cpeB dna
B dfr dsbD fabH fdx ftrB ftsH
bioY crtE groES hemA mntA mntB nadA rbcSg
gt 90 of genes lost
Primary endosymbiosis
Ancestral photosynthetic prokaryote
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PS genes retained in chloroplasts are very highly
conserved
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How does this inform us about N limitation in the
ocean or other aquatic ecosystems?

• Reflection of elemental N/P ratios from organic
  matter in the soluble inorganic pool of fixed N
  and P.
• Is the Redfield paradigm for the ocean a
  coincidence or a true biological feedback?

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Variations in NP
Analyzed deep water DIN, DIP, and O2 measurements
from 104 observational data sets for 33 water
bodies Water bodies ranged from ocean basins to
freshwater lakes, from 109 km2 to 0.075
km2 Seawater NP averages 15-16 Freshwater NP
range from 0.005 (Lake Lugano (Barbieri and
Simona, 2001)) to 8700 (Lake Superior (Sterner et
al., 2007)). Restricted basin NP range from 20
(Med and Red Seas) to 1 (Caspian (Sapozhnikov et
al., 2007))
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Distribution of NP Data
Canonical Redfield ratio is anomalous
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NP vs. O2
NP linearly correlated to O2 when O2 is lt
100?M Loss of N linked to denitrification under
low O2
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Variance in NP vs. Basin Area
More variability in NP for small basins Larger
basins less volatile with respect to variations
in nutrient input, productivity, etc.
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What did this exercise reveal?
Deep water NP ratios are linearly correlated to
O2 when O2 is less than 100?M This correlation
breaks down at O2gt100?M NP ratios are generally
higher, but no simple link This may be due to
anthropogenic influences (e.g. N loading) There
is less variability in NP ratios amongst larger
basins (not a surprise) Small basins more
affected by changes in input, productivity and
seasonal cycles NP in the soluble pools is not
simply controlled by organic matter
remineralization, but also by REDOX state of the
water body
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What about on a molecular level?

• Assume protein NrRNA P 161
• In 1 ribosome, there are 5732 P, so then there
  are 83792 protein N per ribosome
• Assume 1.4 N/aa, so then 59851 aa/ribosome
• If that protein turns over every day, then
  translation rate per
• ribosome 0.69 aa/s
• This is gt10 of the average ribosomal translation
  capacity
• Tentative conclusion - about 90 of the time,
  ribosomes in the ocean are idling - because
  they are waiting for a charged tRNA - N
  limitation on a cellular/global level

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Resiliency on a Global Scale

• There is 10 Gg of nitrogenase in the oceans.
• There is 10,000 x more Rubisco
• What limits N fixation over geological time? Is
  Fe really the ultimate limiting nutrient? the
  untestested hypothesis.

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Conclusions

• On a planetary scale, the key metabolic pathways
  that sustain life are all based on old,
  inefficient technologies that have been widely
  dispersed.
• Evolutionary history suggests that selection for
  body plans and secondary adaptive strategies has
  permitted the continuation of the core machinery
  without need for de novo invention of metabolism.
  
• Some pathways (e.g., N2fixation) appear to be
  less functionally redundant (more vulnerable
  but also more effective in regulating
  biogeochemical processes) than others (e.g.,
  photosynthesis).

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Conclusions Continued

• Small changes in efficiency in one pathway can
  alter planetary chemistry. These changes are
  primarily regulated at the POST-TRANSLATIONAL
  LEVEL and appear to be driven by the presence of
  MOLECULAR OXYGEN.
• The feedback on RuBisCO primarily affects
  terrestrial ecosystems while the feedback on
  nitrogenase primarily affects aquatic ecosystems.
• Despite the inefficiencies, the old technologies
  work under many different environmental
  conditions and appear to have co-evolved into a
  network of very strong feedbacks on Earths
  metabolic cycles (robust and resilient) .