Lessons learned from the Genome-scale metabolic reconstruction

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The Eli and Edythe L. Broad Institute

A Collaboration of Massachusetts Institute of Technology, Harvard University and affiliated Hospitals, and Whitehead Institut
e f
or Biomedical
Research

Lessons learned from the Genome
-
scale metabolic reconstruction and
curation

of

Neurospora

crassa

Jeremy
Zucker

Jonathan
Dreyfuss

Heather Hood

James
Galagan



Capture Metabolic Knowledge

Pathway
-
tools/
BioCyc

KEGG


Reactions


Interactions


Literature


Visualizing ‘
omics

Data

Provide a visually intuitive, metabolic framework for interpreting
large ‘
omics

datasets

in
silico

Predictions

Algorithmically

Interpret Expression Data in a Metabolic Context?

Example: Plasmodium

Validation



KO Phenotype Predictions


90% Accuracy



External Metabolite Changes


70% Accuracy


New Predictions



40 Enzymatic drug targets



Experimental validation of novel target

Eflux
*

*
Colijn
, C., A.
Brandes
, J.
Zucker
, et al. (2009).
PLoS

Comput

Biol

Modeling in the
Neurospora

PO1

Clock

Visualization

and Analysis

Profiling

RNA
-
Seq

ChIP
-
Seq

Interpretation

of Expression Profiling and Regulatory
Network Data in a Metabolic Context


Inform Experiments

BUILDING THE MODEL

Manual reconstruction protocol

Nature Protocols
, Vol. 5, No. 1. (07 January 2010), pp. 93
-
121.

Automated Model SEED
reconstruction pipeline

Nature biotechnology, Vol. 28, No. 9. (29 September 2010), pp. 977
-
982

Genome sequence to metabolic model

Pathways

Literature

Nutrient

media

(
Vogels
)

NeurosporaCyc

Elements

Metadata

Complexes

Reactions

Transporters

Biomass
composition

EFICAz2 predicts enzymes



Decision

tree

Databases

HMMs

FDR

SVM

9934 protein

sequences

1993 enzymes

1770 reactions

BMC Bioinformatics 2009, 10:107

Protein Complex editor

182 reactions with
isozymes

or complexes

31 complexes
experimentally
validated through
literature search



2
-
oxoisovalerate alpha subunit



2
-
oxoisovalerate beta subunit






fatty acid
synthase

beta

subunit
dehydratase



fatty acid
synthase

alpha

subunit
reductase






Identify multiple genes
of reaction

Allow curator to validate
potential complexes

2
-
oxoisovalerate
complex

Present all possible
combinations of
complexes

Fatty acid
synthase

complex



Transport inference parser (TIP)

9934 free
-
text

Protein annotations

176 transporters assigned
to

97 transport reactions



MFS glucose transporter



ATP
synthase






sucrose transporter





Filter proteins for
transporters

Infer
multimeric

complex

Infer substrate

Infer energy
-
coupling
mechanism



Bioinformatics (2008) 24 (13): i259
-
i267.


Pathologic predicts pathways

1770 enzyme
-

catalyzed

reactions

265 Pathways





X = #
rxns

in
metacyc

pwy

Y = #
rxns

with enzyme
evidence

Z = #unique
rxns

in
pwy

P(X|Y|Z) =
prob

of
pwy

in
Neurospora

Science 293:2040
-
4, 2001.



Literature
curation

validates predictions



1212 citations
associated with

307 pathways

31 complexes

168 genes




Neurospora

Cellular overview

NEUROSPORACYC

New feature on Broad website

NeurosporaCyc

Cellular overview

NeurosporaCyc

cellular overview

Googlemaps
-
like
zoomable

interface

Highlight genes on overview

Highlight genes on overview

Highlight genes on overview

NeurosporaCyc

Omics

Viewer

Omics

data mapped onto metabolism

Omics

data mapped onto metabolism

Omics

data mapped onto metabolism

Omics

data mapped onto Genome

Omics

data mapped onto Genome

Omics

data mapped onto Genome

DEBUGGING THE BUG

The problem with EC numbers

Reaction

class

Number of reactions
neurospora

(
metacyc
)

Balanced normal reactions

993 (4585)


Generic reactions

198 (688)

Protein modification reactions:

82 (469)

Reactions with
instanceless

classes:

80 (228)

Generic redox reactions

36 (212)

Polymeric reactions

24 (91)

Polymerization pathway reactions

11 (17)

Generic Reactions

3.6.1.42 instance of 3.6.1.6?

Protein Modification reactions

Reactions with
instanceless

classes

Solution: Instantiate classes

Generic Redox reactions

Polymeric reactions

Polymerization Pathway reactions

Solution: Instantiate polymerization
steps


POLYMER
-
INST
-
Fatty
-
Acids
-
C16 + coenzyme A +
ATP
-
> POLYMER
-
INST
-
Saturated
-
Fatty
-
Acyl
-
CoA
-
C16 +
diphosphate

+ AMP + H+


POLYMER
-
INST
-
Fatty
-
Acids
-
C14 + coenzyme A +
ATP
-
> POLYMER
-
INST
-
Saturated
-
Fatty
-
Acyl
-
CoA
-
C14 +
diphosphate

+ AMP + H
+





POLYMER
-
INST
-
Fatty
-
Acids
-
C0 + coenzyme A +
ATP
-
> POLYMER
-
INST
-
Saturated
-
Fatty
-
Acyl
-
CoA
-
C0 +
diphosphate

+ AMP + H+


What happens when the metabolic
network is infeasible?



Add a “reaction” with the smallest number of
reactants and products that results in a
feasible model





minimize
card
(
r
)





subject to





Sv

+ r = 0





l ≤ v ≤ u


Fast Automated Reconstruction of
Metabolism


Input:


EFICAz probabilities for each reaction


Biomass components


Experimental growth / no growth phenotypes in different
nutrient conditions


Gene essentiality


Manual curation of pathways


Output:


Metabolic network of
MetaCyc

reactions maximally
consistent with input


VALIDATING THE MODEL WITH IN
SILICO

KNOCKOUT PREDICTIONS

Neurospora

phenotypes for validation



Neurospora

e
-
Compendium



29 Mutants essential on
minimal
media


Non
-
essential
on supplemental media



PO1 Phenotype Collection


79 non
-
essential KOs under minimal media


Additional
phenotypes are observed.

Used FBA with
Neurospora

model to simulate gene
knockouts in minimal medium

Neurospora

phenotype prediction results

Predicted

Essential

Non
-
Essential

Observed

Essential

22 (TN)

7 (FP)

Non
-
Essential

14 (FN)

65 (TP)

Precision


TP/

(TP+FP)

90%

Recall

TP/

(TP+FN)

82%

Specificity

TN/

(TP+FP)

76%

Accuracy

(TP+TN)/

(TP+TN+FP+FN)

81%

Comparison of model organisms under
minimal media

Yeast (iND750)
1

E.Coli

(iAF1260)
2

Neurospora

Viable

Predicted/

Observed

439/455=96%

993/1022=97%

65/79=82%

Essential Predicted/

Observed

35/109=32%

159/238=67%

22/29=76%

Overall accuracy

84%

91%

81%

[1] Genome Res. 2004. 14: 1298
-
1309

[2] Molecular Systems Biology 2007 3:121

MODELING THE EFFECT OF OXYGEN
LIMITATION ON XYLOSE FERMENTATION

Biofuels

from
Neurospora
?


Growing interest for obtaining
biofuels

from fungi


Neurospora

crassa

has more
cellulytic

enzymes than
Trichoderma

reesei


N.
crassa

can degrade cellulose and
hemicellulose

to ethanol [Rao83]


Simultaneous
saccharification

and
fermentation means that
N.
crassa

is a
possible candidate for consolidated
bioprocessing



Xylose

Ethanol

Effects of Oxygen limitation on
Xylose

fermentation in
Neurospora

crassa

Zhang,

Z
.
,

Qu
,

Y
.
,

Zhang,

X
.
,

Lin,

J
.
,

March

2008
.

Effects

of

oxygen

limitation

on

xylose

fermentation,

intracellular

metabolites,

and

key

enzymes

of

Neurospora

crassa

as
3
.
1602
.

Applied

biochemistry

and

biotechnology

145

(
1
-
3
),

39
-
51
.

Xylose

Pyruvate

TCA

Ethanol

Respiration

Fermentation

Glycolysis

0
10
20
30
40
50
60
70
0
2
4
6
8
10
12
14
Ethanol production vs Oxygen level

Oxygen level (
mmol
/L*
g
)

Ethanol conversion (%)

Low O
2

Intermediate O
2

High O
2

Pentose phosphate

Aerobic respiration

Fermentation

TCA Cycle

Model of
Xylose


Fermentation

Xylose

Oxygen

Ethanol

ATP

Two

paths from
xylose

to
xylitol

Pentose phosphate

Aerobic respiration

Fermentation

TCA Cycle

Oxygen=5

ATP=16.3

NADPH

Regeneration

NADPH

&

NAD
+

Utilization

High

Oxygen

NAD
+


Regeneration

Pentose phosphate

Aerobic respiration

Fermentation

TCA Cycle

Ethanol

Low

Oxygen

Oxygen=0

Pentose phosphate

Aerobic respiration

Fermentation

TCA Cycle

Ethanol

Intermediate

Oxygen


Optimal

Ethanol

NADPH

&

NAD

Utilization

Oxygen=0.5

ATP=2.8

NAD

Regeneration

NADPH

Regeneration

All O
2

used to
regenerate
NAD used in
first step

Pentose phosphate

Aerobic respiration

Fermentation

TCA Cycle

Ethanol

Intermediate

Oxygen


Optimal

Ethanol

NADPH

&

NAD

Utilization

Oxygen=0.5

ATP=2.8

NAD

Regeneration

NADPH

Regeneration

All O
2

used to
regenerate
NAD used in
first step

Bottleneck

Pyruvate

decarboxylase

Improve NADH

enzyme

USING E
-
FLUX TO PREDICT DRUG
TARGETS BY INTEGRATING EXPRESSION
DATA WITH
FBA

E
-
Flux explanation

Application of E
-
flux to TB

Next Steps


Annotation: use phenotype predictions to
improve model


NeurosporaCyc
: Use E
-
flux to interpret the
effect of clock genetic regulatory program on
metabolism.


Validation: add additional phenotypes


Acknowledgements

Neurospora

P01 Project

Heather Hood

Jonathan
Dreyfuss

James
Galagan

SRI

Peter Karp

Mario
Latendresse

Markus
Krumenacker

Ingrid
Kesseler

Tomer

Altman

Suzanne Paley

Ron
Caspi

Mike Travers

Fast Automated Reconstruction of Metabolism
(FARM)

Gene

Calls

(Broad)

Protein

Complex

prediction


Transport

predictor

(TIP)

Pathway

prediction

(Pathologic)

Enzyme

prediction

(
EFICAz
)


Literature

curation

(CAP)

Nutrient

media

(
Vogels
)

NeurosporaCyc

C

Fast Automated Reconstruction of Metabolism (FARM)

846 Reactions

640 Metabolites

564 Genes



EFICAz

predictions



Pathway predictions



Nutrient conditions



Biomass composition



Protein complexes



Transport