The document discusses obtaining estimations of cell metabolic activities from gene expression data using summarized metabolic pathway models. It describes normalizing gene expression data and using an algorithm to estimate metabolic module activities for two conditions, identifying differentially active modules using statistical tests. Metabolic module activities are correlated with protein activation status and metabolite abundance changes, associated with cancer types and treatment responses, and predictive of cell survival. A web server called Metabolizer is introduced that performs various metabolic pathway analyses on gene expression data.

1. Joaquín Dopazo
Clinical Bioinformatics Area,
Fundación Progreso y Salud,
Functional Genomics Node, (INB-ELIXIR-es),
Bioinformatics in Rare Diseases (BiER-CIBERER),
Sevilla, Spain.
Differential metabolic activity and
discovery of therapeutic targets using
summarized metabolic pathway models
http://www.clinbioinfosspa.es
http://www. babelomics.org
@xdopazo, @ClinicalBioinfo
3rd Disease Maps Community Meeting.
Paris, 21-22 June 2018

2. Our aim: obtaining estimations on cell
metabolic activities from affordable gene
expression data
Cases / controls
Normalizedgenes
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
…….
…….
…….
Cases / controls
Activity
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
…….
…….
…….
We seek for a simple transformation of raw data (normalization) and an
algorithm that result in accurate estimations of metabolic module
activities.
It should be possible to include mutational profiles in the algorithm (either
by integrating them with transcriptomic data or assuming gene
expression taken from databases)
Metabolic
modulesCases / controls
Rawdata
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
…….
…….
…….

3. On maps and and navigation
A map
describes
connections
between
elements
An influence map
describes origin and
destinations that define
processes

4. KEGG Metabolic modules summarize
the human metabolism
A total of 95 modules that represent
building blocks of metabolic pathways
were used, that comprise a total of
446 reactions and 553 genes. The
pathway modules were downloaded
form the KEGG MODULE web page
(http://www.genome.jp/kegg/module.html)

5. Modeling metabolic modules
Where ni is the activity of the current node n, A is the total
number of edges arriving to the node that account for the flux
of metabolites produced in other nodes with activity values na.
Differential metabolic activity: two
conditions are compared by means of a
Wilcoxon test (FDR adjusted across
modules)
Parts of the map
are collapsed in
the influence
map because
transcriptomic
profiles are
uninformative
for using them

6. Protein activation status is
correlated with module activity
Cancer types
Metabolic module Description End molecule Protein BLCA BRCA COAD GBM HNSC KIRC LGG LUSC PRAD READ STAD THCA UCEC
M00082_1 Fatty acid biosynthesis, initiation
C05744:
Acetoacetyl-[acp] ACACA 0,46 0,45 0,28 0,44 0,51 0,2 0,48 0,43 0,51 0,41 0,35 0,11 0,23
M00082_2 Fatty acid biosynthesis, initiation
C05744:
Acetoacetyl-[acp] ACACA 0,43 0,42 0,23 0,36 0,47 0,16 0,49 0,34 0,41 0,33 0,22 0,08 0,22
M00004
Pentose phosphate pathway
(Pentose phosphate cycle)
Cycle: Pentose
phosphate
pathway G6PD 0,27 NA NA NA NA NA 0,15 NA NS NA 0,26 0,24 NA
M00006
Pentose phosphate pathway,
oxidative phase, glucose 6P =>
ribulose 5P
C00199: D-
Ribulose 5-
phosphate G6PD 0,33 NA NA NA NA NA NS NA NS NA 0,26 NS NA
M00001
Glycolysis (Embden-Meyerhof
pathway), glucose => pyruvate C00022: Pyruvate GAPDH 0,37 NA NA NA NA NA 0,21 NA 0,17 NA 0,31 0,18 NA
M00002
Glycolysis, core module involving
three-carbon compounds C00022: Pyruvate GAPDH 0,37 NA NA NA NA NA 0,2 NA 0,26 NA 0,3 0,16 NA
M00003
Gluconeogenesis, oxaloacetate
=> fructose-6P
C05345: beta-D-
Fructose 6-
phosphate GAPDH NS NA NA NA NA NA 0,16 NA 0,16 NA 0,14 NS NA
M00009
Citrate cycle (TCA cycle, Krebs
cycle)
Cycle: Citrate
cycle (TCA cycle,
Krebs cycle) IDH3A NS NA NA NA NA NA NA NA NA NA NA NA NA
M00010
Citrate cycle, first carbon
oxidation, oxaloacetate => 2-
oxoglutarate
C00026: 2-
Oxoglutarate IDH3A NS NA NA NA NA NA NA NA NA NA NA NA NA
M00131
Inositol phosphate metabolism,
Ins(1,3,4,5)P4 => Ins(1,3,4)P3 =>
myo-inositol
C00137: myo-
Inositol INPP4B 0,44 0,25 0,13 0,39 0,39 NS 0,53 NS NS 0,39 0,24 0,16 0,34
*Numbers are Spearman's rank correlation coefficients; NS, not significant (p > 0.05); NA, not available
Correlations between RPPA values and module activity predictions

7. Correspondence between
module activity and change in
metabolite abundance
Module Cancer type First metabolite
Intermediate
metabolite
End
metabolite
Prediction
M00035_1
C00073
C00019 /
C00021 /
C00155
C02291
BRCA 1,9 NA / 2.7 / NA 7,8 Up Correct
KIRC 0,5 0.9 / 1.7 / 0.6 NA Down NA
M00100
C00319 C06124 C00346
BRCA 11,1 NA 17,3 Up Correct
KIRC 2,2 NA 0,8 Down Correct
M00135
C00134
C02714 /
C05936 /
C02946
C00334
BRCA 12,9 NA / NA / NA 1,2 Down Correct
KIRC 3,6 NA / NA / NA 0,3 Down Correct

8. In a large meta-analysis of 9424 samples of
14 cancer types, metabolic modules capture
differential metabolic activity

9. Metabolic module activity is associated
with response to treatments
ModuleID Description/Name End Molecule FDR adj. p-value Log-fold change
M00020 Serine biosynthesis, glycerate-3P => serine C00065: L-Serine 3,22E-02 6,53E-01
M00027 GABA (gamma-Aminobutyrate) shunt C00042: Succinate 2,52E-02 3,99E-01
M00035_1 Methionine degradation C02291: L-Cystathionine 4,70E-02 1,61E-01
M00048 Inosine monophosphate biosynthesis, PRPP + glutamine => IMP C00130: IMP 1,26E-02 1,87E+00
M00049 Adenine ribonucleotide biosynthesis, IMP => ADP,ATP C00002: ATP 3,22E-02 3,46E-01
M00050 Guanine ribonucleotide biosynthesis IMP => GDP,GTP C00044: GTP 1,30E-02 2,49E-01
M00052 Pyrimidine ribonucleotide biosynthesis, UMP => UDP/UTP,CDP/CTP C00112: CDP 3,22E-02 4,69E-01
M00078 Heparan sulfate degradation G02632: A glycan product of heparan sulfate degradation 1,26E-02 -1,23E+00
M00079_1 Keratan sulfate degradation G01391: A glycan product of keratan sulfate degradation 1,26E-02 -3,43E-01
M00089 Triacylglycerol biosynthesis C00422: Triacylglycerol 2,00E-02 -2,02E-01
M00118 Glutathione biosynthesis, glutamate => glutathione C00051: Glutathione 1,26E-02 3,34E-01
M00128 Ubiquinone biosynthesis, eukaryotes, 4-hydroxybenzoate => ubiquinone C00399: Ubiquinone 2,65E-02 9,72E-01
M00135 GABA biosynthesis, eukaryotes, putrescine => GABA C00334: 4-Aminobutanoate 2,30E-02 -4,52E-01
Metabolic modules significantly associated with response to letrozole in breast cancer
Module Description/name End molecule p-value Log-fold change
M00042
Catecholamine biosynthesis, tyrosine => dopamine =>
noradrenaline => adrenaline
C00788: L-Adrenaline 4,0E-02 1,16
Metabolic modules significantly associated with response to neoadjuvant Herceptin in
breast cancer

10. Metabolic modules also capture cell
functionality associated to cancer prognostic
High activity of Guanine ribonucleotide biosynthesis and Pyrimidine
ribonucleotide biosynthesis modules is associated to low survival.
These modules are target of Mercaptopurine and Gemcitabine.
The mechanism of action of these drugs involves inhibition of DNA synthesis
and that leads to cell death

11. Model validation (I)
The activity of some modules is correlated with cell survival
Increasedsurvival
Increased module activity
Survival data from Achilles cell line KOs (Broad Institute) can be
compared to the change in module activities predicted by the model
Essential modules: once found, other ways of deactivating these modules
can be find, opening the door to knowledge-based target discovery
Onco-module Tumor suppressor module

12. Model validation (2)
Deactivation of tumor
suppressor modules
Deactivation in onco-modules
Prostate cell line Skin cell line
1) Prediction of other gene targets, whose inhibition (modeled KO) deactivate
tumor suppressor or onco-modules
2) Validation of the real KO effects with Achilles II (Tsherniak A, et al. 2017, Cell
170: 564-576): 48 of the 77 predictions (62% validation rate), covering 24 of
the 28 modules predicted to affect cell viability

13. The metabolizer web server
http://metabolizer.babelomics.org/
Input: gene expression matrix
Analyses:
• Differential metabolic activity
• Case control
• Correlation
• KnockOut.
• Tests the effect of a KO
in the metabolic profile
• Auto KO: in a
case/control search for
the KO that makes
cases’ the metabolic
activity profile as close
as possible to controls
(phenotype reversion)
• Prediction: Builds a class
predictor (RF or SVM) based
on metabolic profiles

14. An example of KO prediction
Pyrimidine degradation pathway was predicted to be an onco-module in gastric
cancer cell lines. Predicted genes that switch the pathway off are DPYD, DPYS
(confirmed in Achilles) and UPB1 (not present in Achilles)

15. Confirmation of gene essentiality from
predicted metabolic pathway essentiality
UPB1 encodes an enzyme (β-ureidopropionase) that catalyzes the last step in the
pyrimidine degradation pathway, required for epithelial-mesenchymal transition
Pyrimidine degradation pathway was predicted to be an onco-module in gastric
cancer cell lines. Predicted genes that switch the pathway off are DPYD, DPYS
(confirmed in Achilles) and UPB1

16. Clinical Bioinformatics Area
Fundación Progreso y Salud, Sevilla, Spain, and…
...the INB-ELIXIR-ES, National Institute of Bioinformatics
and the BiER (CIBERER Network of Centers for Research in Rare Diseases)
@xdopazo
@ClinicalBioinfo
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