Reconstruction and analysis of
human liver-specific metabolic
network based on CNHLPP data
Jing Zhao
Logistical Engineering University
The 6th Chinese Conference of Complex Networks，October 15-18, 2010.
Suzhou, China
OutLine
• Background
• Reconstruction of metabolic networks
• Basic topological features of metabolic
networks for human liver and Homo sapiens
genome
• Functional organization of liver revealed by
topological modules in liver-specific metabolic
network
• Enzyme abundance in topological modules of
liver-specific metabolic network
• Comparison of metabolic network of human
liver with that of the Homo sapiens genome
Function of liver
• producing substances that
break down fats
• converting glucose to
glycogen
• producing urea
• making certain amino acids
• filtering harmful substances
from the blood
• storing vitamins and
minerals
• maintaining a proper level
of glucose in the blood
Network representation of Metabolism: Substrate graph
HLPP: The Human Liver Proteome Project
The first initiative on human tissues/organs launched by the
Human Proteome Organization (HUPO)
Data used in this study
• Data from CNHLPP
Liver metabolic network
6788 distinct proteins (IPI codes) and protein
quantitation data , confidence level 95%
6220 distinct genes
1421 genes encode 721 distinct enzymes
• BiGG database
Human metabolic network
3311 reactions
1555 enzyme-catalyzed reactions
1756 auto-catalytic reactions
Reconstruction of liver metabolic network
CNHLPP data
BiGG
380 enzymes, 1047 enzyme-catalyzed reactions
original core reaction set : 1047 liver enzyme-catalyzed
reactions
initial candidate reaction set: all of the auto-catalytic
reactions
2. Extract all metabolites appearing in core reaction set to get
core metabolite set.
3. Scan the list of candidate reactions for core metabolites.
If all substrates for one reaction can be found in core
metabolite set, add this reaction into core reaction set and
remove it from the candidate set.
4. If step 3 cannot add any more reactions into core reaction
set, stop; else, go to step 2.
1.
Added: 427 auto-catalytic reactions
Basic graph metrics of metabolic networks
Network for human
liver
Metabolic network
Network for H.sapiens
genome
Nodes
1093
1473
Arcs
2209
3361
Density
0.0019
0.0016
Degree distribution
P(k)~k-2.73
P(k)~k-2.67
Average path length
8.5
9.8
Diameter
28
49
Nodes
1026
1407
Arcs
2159
3314
GSC
424 (41.3%)
987 (70.2%)
S
262 (25.5%)
117 (8.32%)
P
187 (18.2%)
272 (19.3%)
IS
153 (14.9%)
31 (2.2%)
Biggest cluster
Bowtie of
biggest cluster
Comparison of the liver metabolic network with its random counterparts
Liver network
Fraction of
Diam
nodes in
eter the biggest
cluster
Nodes
Arcs
Densit
y
Average
path
length
1093
2209
0.0019
8.5
28
0.9387
100 type I random
sub-networks
(same arcs as the liver
network)
Mean
1317
2209
0.0013
10.1
29.7
0.8575
Zscore
-21.83
-
29.03
-3.27
-0.47
5.24
100 type II random
sub-networks
(same nodes as the
liver network)
Mean
1093
1870
0.0016
9.6
30.2
0.8309
Zscore
-
5.25
5.25
-1.25
-0.39
5.09
100 type III random
sub-networks
(same enzymecatalyzed reactions as
the liver network)
Mean
1240
2287
0.0015
8.95
26.3
0.8806
Zscore
-23.2
-6.17
27.2
-1.8
0.67
5.04
Functional organization of liver revealed by
topological modules in liver metabolic network
Core-periphery organization
Main derivative metabolism functions of the topological
modules for human liver-specific metabolic network
Module
Main Function
Main Function
category
Glycan
biosynthesis and
metabolism
Metabolism of
cofactors and
vitamins
Xenobiotics
biodegradation
5
Biosynthesis of chondroitin / heparan sulfate and
keratan sulfate
13
Biosynthesis of N-glycan
12
Degradation of heparan sulfate and N-glycan
14
Degradation of chondroitin sulfate
16
Degradation of keratan sulfate
1
Metabolism of folate and vitamin B6
3
R group synthesis
6
Heme biosynthesis
9
Heme degradation and vitamin A metabolism
11
Tetrahydrobiopterin; Vitamin B12 Metabolism
2
ROS detoxification
5
CYP metabolism
Enzyme abundance in topological modules of liver-specific
metabolic network
P (Q >2.35)=10%; P (Q <0.5)=70%.
Comparison of metabolic network of human liver with that of the
Homo sapiens genome
P-value
k 1
p  1 
i 0
 K  N  K 
 

k 1 
i  n  i 

f (i ) 1  
N
i 0
 
n
10 of the 16 modules ( Module 1,2,3,4,6,8,9,11,12,13) :
P-value < 0.05
Quantitative difference between categories: overlap score
Prototypical overlap score

 
XY
(x, y)  X (x) Y (y)
x X y Y
Normalized overlap score
 XY 
 XY
max( XX , YY )

X ,Y : two categorizations
X(x) ,Y(y) : the fraction of metabolites in category x  X, y Y, respectively
XY(x,y): the joint frequency of x and y, i.e. the fraction of vertices that are
categorized both as x  X and y  Y.
Quantative difference between a feature of the real metabolic
network and its randomized counterparts: Z- score
Z 
f  fr
f r
f : the metric of the feature in the real network
fr
: the mean of the corresponding metric in the randomized ensemble
f r : the standard deviation of the corresponding metric in the randomized
ensemble
v = 0.72; Z =68.5
Acknowledgement
Shanghai Center for Bioinformation and Technology:
Lin Tao, Duanfeng Zhang, Kailin Tang ,Ruixin Zhu , Hong Yu ,Yixue Li,
Zhiwei Cao
Beijing Proteome Research Center:
Chao Geng, Ying Jiang,Fuchu He
Second Military Medical University:
Weidong Zhang
Petter Holme
Eytan Ruppin
Livnat Jerby
Ori Folger
National Natural Science Foundation
of China (10971227, 30900832)
Ministry of Science and Technology
China(2006AA02312, 2009zx10004601, 2010CB833601
Shanghai Municipal Education
Commission (2000236018).
Thanks!