As compared to the strongly restricted genetic and epigenetic ...

Joachim Størling and Regine Bergholdt
, Hagedorn Research Institute, Niels Steensensvej 1,
DK
-
2820 Gentofte
, Denmark. E
-
mail:
jstq@hagedorn.dk
, rber@hagedorn.dk

Chapter 21

Predictive protein networks and i
dentification of drugable targets in the
beta
-
cell


Joachim Størling and Regine Bergholdt


Abstract

A prerequisite for designing good drugs that perform through clinical
development with the final goal to treat h
uman diseases is a detailed
understanding of the mechanisms underlying disease. This is particularly true for
complex diseases such as diabetes. It has become increasingly clear that
complex traits or phenotypes are the result of

an
interplay between
envir
onment
al factors

and numerous genes and proteins

that jointly

affect

the
functionality of

biological systems
.
Since
interactions between proteins in
networks and pathways make up
biological systems, it is essential that we learn
more about how networks and

pathways are influenced by environment
al factors

and genetic variation, and how such influence
s

cause disease
.
In this chapter
,

we will discuss recent data,
advancement

and ideas on how more valid drugable
targets to treat diabetes may be predicted by the

application of
bioinformatics
and
systems biology.

Keywords
: beta
-
cells, diabetes etiology, drug targets, GWAS
,
phenotype
description,

protein

networks
,

systems biology


21.1
The need for new ways of identifying drugable targets

Tens of billions of Euros
and dollars are spent each year by the pharmaceutical
industry on the
development of

new drugs to treat human diseases. However,
21
-
2

drug discovery is an extremely expensive and risky business, and d
espite the

enormous investment in
drug discovery
, the rate of

failure of drug candidates in
clinical development is dreadfully high.
O
ne explanation for this is that the
strongly restricted genetic and epigenetic backgrounds and environm
ental
settings of simple animal
-

and
in vitro

cell systems
used
to model

human
d
isease

and preclinical drug testing, differs greatly from the genetically,
environmentally and epigenetically much more heterogeneous nature of the
human population.

Another explanation is that
drug discovery

traditionally has
been aiming at designing drug
s against targets considered to affect simple
biological systems

or
signalling pathways
, and such an approach represents an
exceedingly simplistic view of the mechanisms underlying complex human
diseases

[1]
.
An improvement of the success rate of drug
s

in clinical
development will require

new approaches
to
pinpoint more valid drug target
candidates for preclinical testing
.
Obvi
ously, a

prerequisite for this will be a
n

improved

understanding

of
disease
mechanisms
and increased

insight into the
complex biological systems in tissue
s

and

cells in a heterogeneous human
population
. This is
the
true challenge and
entail
s

innovative
way
s of studying
disease and disease model systems and highlight
s

the need for
systems biology

and
bioinformatics

approaches.


21.2 How can drug target identification be optimized?

I
mproved prediction of valid drug targets will require increased insight into
the
specific biological and molecular systems in tissues and cells that are responsible
for causing disease.
Most human diseases
,

including type 1 and 2 diabetes which
are the result of complete or relative destruction and dysfunction of the
beta
21
-
3

cells,
ar
e caused by a complex interplay between
environment and

genes
.

The
interaction between environmental factors and the
genetic background

of an
individual affects
susceptibility

to disease and progression of disease.

Also
the

response to
drug treatment

is de
termined by the
individual
´
s specific
environmental and genetic settings.

Different genes
contributing to a specific
phenotype
may

encode proteins
involved in the

same biological system

or in its
regulation
. Therefore,
causal

genes in complex diseases
can
be expected to
affect the functionality of the same protein
networks

and
pathways
.

If we can
improve
the
predict
ion
, identif
ication

and functional validat
ion

and
characteriz
ation of

networks

involved in disease

in carefully selected model
systems and
in
hu
mans,
we will have a greatly increased likelihood of choosing
the most reliable

drugable target
s

for
drug development
. This will
increase

the
chance of the drug to
endure

clinical development
.


Current drugs to treat
type 2
diabetes

work by

increasing

beta
-
cell

insulin
secretion
, decrease the amount of glucose released from the liver, increase the
sensitivity of cells to insulin, decrease the absorption of carbohydrates from the
intestine, and slow emptying of the stomach to delay the presentation of
carboh
ydrates for digestion and absorption in the small intestine. Drugs
increasing insulin output by the
beta
-
cells

have

been widely used to treat type 2
diabetes and represent the existing group of diabetes dr
ugs directly targeting
the beta
-
cells.
These m
edica
tions belong to a class of drugs called
sulfonylureas
,
which

increase insulin secretion by

inhibiting ATP
-
regulated K
+

channels

leading
to

plasma membrane depolarization
and

influx of Ca
2+

that triggers insulin
-
containing vesicles to fuse with the plasma m
embrane and release insulin.
21
-
4

Sulfonylureas are ineffective where there is absolute deficiency of insulin
production as in
type 1 diabetes
.
Development of

novel drugs targeting the
beta
-
cell may represent
new

ways of increasing insulin secretion in typ
e 2
d
iabetes and/or preserving beta
-
cell mass

and insulin
secretory

capacity in type
1
diabetes.


H
ow do we obtain a better knowledge of the pathological mechanisms i.e. which
prote
in
networks

and

pathways

that lie behind disease
, and

what kind of data
can be e
xploited for this purpose?
Much knowledge about disease mechanisms
and pathologies is
to a large

extent
based on data from animal models
and

cell
systems. However, tra
nslation of results from animal

and
in vitro

experiments

to
humans

is often
difficult
due

to the fact that the environmental and genetic
settings of model systems
are
much too simple.

Therefore,
drug targets

should
preferentially be identified from a platform of human data.

“
Integrative
genomics
” is an emerging, promising field to tackle compl
ex disease. It provides
increased knowledge about functional mechanisms underlying disease and
thereby an approach to increase our understanding of disease pathogenesis.
Disease associated
networks

today are, however, based on incomplete data, we
have not
yet characterized rare variation or
copy number variation
, we do not
know enough about non
-
coding RNAs,

alternative splicing,

genetic isoforms,
heterogeneity among populations, as well as dynam
ics in molecular systems.
Most
biological systems

are character
ized by consid
erable
redundancy

and
therefore

the analysis of genes
and

proteins in the context of their networks will
provide the most important functional and quantitative information.
Networks

should

be seen as a framework of how to explore the context
in which a given
21
-
5

gene operate
s

and to causally associate
networks

with physiological

states
associated with disease.

This will lead to
a more comprehensive
understanding
and
view of disease
as compared to examination of

individual
components

of the
network
. Integrating data like DNA variations, gene expression data, DNA
-
protein binding and
protein
-
protein interactions

and molecular phenotypic data
may construct more comprehensive
networks

and thereby improve
understanding of the molecular processes underlyi
ng disease.


21.2.1
GWAS and systems biology

That diabetes has a strong genetic component is underlined by the fact that

the
concordance

rate

for both type 1 and 2 diabetes
is up to ~70% in monozygotic
twins
[2, 3]
.

Genetic variation may influence protein networks and thus cellular
function at several different levels. C
hanges in amino acid sequence, alterations
in protein expression or modification in enzymatic activity etc. can be the result
of
genetic variation
. S
uch changes to proteins
can cause perturbations

of
the
functionality of
protein networks. Depending on the degree of disturbances of
network function, this can lead to
cellular malfunctioning
, changes in phenotype,
and ultimately to disease. However, genet
ic variation may account for different
levels of risk for disease in different individuals, suggesting that integrative
methods for gene discovery are necessary.

With the advent in recent years of
huge amounts of data from
genome
-
wide association studies

(
GWAS
),
transcriptomics

and proteomics

experiments
etc., now increasing

focus is on
interactions between DNA, RNA and proteins and whole system physiology, as
well as integration of large
-
scale, high through
-
put molecular and physiological
data with clinica
l data.

Genome
-
wide association studies

in complex diseases are
21
-
6

producing an unprecedented amount of genetic data.

However, identifying the
individual
genes

can be difficult because each only contributes weakly to the
pathology.

Alternatively, identificati
on of entire cellular systems involved in a
particular disease could be attempted. Such a strategy should be feasible in
many different complex diseases since most genes exert their function as
members of molecular
networks

where groups of proteins contrib
uting to
disease
may

be expected to
affect

the same
biological

pa
thways.

Experimental
evidence
for this

is supported by the finding that the expression of genes
which
are
all involved in oxidative phosphorylation is coordinately downregulated in
human diab
etic
muscle
[4]
.

Analysis of an entire disease
-
related
biological
system might provide insight
i
n
to the molecular etiology of the disease that
would not emerge from isolated functional studies of single genes.

It is clear
that
results of e.g.
GWAS

do

not

themselves directly identify clinical useful drug
targets, but by integrati
ng
GWAS

data
with
othe
r
types

of data and more refined
phenotyping, this may
well
be possible.


Genetic
disease loci

for
diabetes

typically only confers modest disease risk and
only for very few are the causal gene
s

known. Even replicated disease
associations do not provide clu
es about the functional roles of a given candidate
gene. A genetic association is not enough
for drug development strategies.

T
here is no doubt that addition
al functional support is needed such as

evaluating
potential causal genes in the broader biological

context in which they operate.
The most likely causal candidate gene for an association may or may not be
genes in closest proximity of the associated
single nucleotide polymorphism

(
SNP
)
. However, a combination of such knowledge with an evaluation of the

21
-
7

biological function of the genes, e.g. in
expressional profiling

studies under
disease relevant conditions

and

in

functional studies
, may provide insight into
the mechanistic nature of complex traits beyond what human genetic association
studies can do al
one. Use of molecular traits can enhance the interpretation of
GWAS

results by putting them into a broader biological context and ultimately
elucidate the
networks

definin
g disease associated processes.


21.2.2 Moving from genomes to networks

If genetic da
ta are integrated with
networks

of physically

and functionally

interacting proteins
, this is likely to increase the probability of identifying
positional candidate disease genes and proteins

(Fig. 21.1).


FIGURE 21.1. INSERT COLOR VERSION HERE.


LEGEND:

Fi
gure 21.1. Mapping of genetic loci onto a human interaction network. The
creation of networks based on protein
-
protein interactions of proteins encoded
by genes in genetic regions associated to disease allows identification of
“disease” networks, i.e. netw
orks that are enriched for proteins encoded by
genes in these regions.


M
any
disease
-
associated
genes
are known
today, now the challenging task is to
understand how they affect disease risk and how to select key proteins for
drug
development
.
As mentioned,

diabetes

involve
s

multiple interacting genetic
determinants
, repres
ent
ing

functional relationships between genes, in which the
21
-
8

phenotypic effect

of one gene
may be

modified by another. However,
new
strategies for detecting sets of marker
loci
, which are l
inked to multiple
interacting disease genes are in demand.
Data mining

methods have been used
to evaluate
genetic interactions

[5]
, and the importance of predicted
genetic
in
teractions

was
in this report
supported by comprehensive, high
-
confidence
protein
-
protein interaction networks

of the corresponding regions. This allowed
identification of candidate genes of likel
y functional significance in
type 1
diabetes
, representing a

suggestion of genetic
epistasis

in a multi
-
factorial
disease supported by protein
network

analysis with implications for functionality
[5, 6]
. Another approach for selecting candidate genes of functional importance

is
transcriptional profiling
. Intermediate between DNA variations and variation in
phenotype are variation in gene expression, protein expression, protein state
and metabolite levels. Such intermediates are believed to respond to variations
in DNA and the
n
potentially
lead to changes in phenotype

and

disease

state
.

Following identification of genes there is a huge demand for functional
genomics. The number of identified
susceptibility genes

may continue to grow,
and the elucidation of their function in th
e pathogenesis of diseases, will be
important for understanding their molecular pathogenesis. Approaches used will
vary according to the function of the genes, but
may

include expression studies
and generation of transgenic and knockout animal models. Wher
eas the genome
is rather static, interaction
networks

are more dynamic and dependent on the
biological
context. They might be active only under certain conditions, in certain
cell types or stages of development. Ideally, all conditions and cell types shoul
d
be tested to capture this presumed variability.

21
-
9

For prioritization of positional candidate genes in genetic association or
linkage

intervals the use of functional interaction
networks

(
interactomes
) may be a
valuable method. If intervals obtained for a d
isease are queried for functional
interactions with each other and related to phenotype information for the
disease, this holds promise for selection of putative disease genes for further
investigation
[7, 8]
. Such
studies have the potential of identifying new,
previously unrecognized components of disease mechanisms, as well as of pin
-
pointing the most important protein complexes involved. Furthermore, many
diseases have overlapping clinical manifestations/sub
-
pheno
types and it could
be speculated that this may be represented by genetic variation in the same
functional pathways
. The existence of so called disease sub
-
networks has been
suggested. It was demonstrated that proteins encoded by genes mutated in one
inheri
ted genetic disorder, were likely to interact with proteins known to cause
similar disorders, presumably by sharing common underlying biochemical
mechanisms
[7]
. The feasibility of constructing such functional human gene
networks has been demonstrated and applied to positional candidate gene
identification
[9]
. It was shown that obvious candidate genes are not a
lways
involved, and that taking an
unbiased

approach in finding candidate genes, e.g.
by using
functional networks

may result in new testable hypotheses
[9]
.




21.2.3 Moving from networks to phenotype
s

A systematic, large
-
scale analysis of human p
rotein complexes comprising gene
products implicated in many different categories of human disease
s

has been
used to create a
“
phenome
-
interactome

network
”
[8]
. This was the first study to
explain disease phenotypes by genome
-
wide mapping of genetic loci onto
a
21
-
10

human interaction
network
. This strategy was expanded to include epistasis and
statistical methods for evaluating the significance of
deduced

networks
[5]
.
Protein interact
ion
networks

were by this method used to examine whether gene
products from interacting genetic regions could also be shown to interact in
biological
pathways
. Support for physical interactions at the protein level for all
the predicted genetic interaction
s were
suggested

[5]
, representing a novel
exploration of integrative genomics. The resulting
networks

point directly to
novel candidates visualized in context of their inter
action
network
, potentially
providing even further biological insight.

Another study evaluated changes at the
proteome level after
exposure of pancreatic

insulin
-
producing

cells
to
pro
-
inflammatory cytokines

resembling the inflammatory milieu surrounding t
he
islets in type 1 diabetes.

That study
demonstrated a large protein interaction
network containing many of the differentially expressed proteins
[10]
. Despite
use of different species and model systems and unknown dynamic differences in
the
transcriptome

and proteome
,

a si
gnificant overlap existed between genes
pinpointed in this study
[10]

and in other studies
[5, 6]
, providing evidence that
common networks and pathways can be identified using different model systems
and underlines the power of integr
ating protein
-
protein
interaction data with
genetic data and expression profiling.


Major histocompatibility complex

(
MHC
)

fine mapping data has
been
analyzed by
the same approach to characterize the
MHC

susceptibility

interactome
[11]
.
This
approach allowed identification of functionally important genes and gene
-
gene
interactions independent of the genetic
linkage disequilibrium

that characterizes
the
MHC

regi
on, as protein
-
protein interactions are unlikely to depend on linkage
21
-
11

disequilibrium between the genes encoding the proteins.

Approaches like these
may be valuable in prioritizing candidate genes in linkage regions or from
disease associated regions, in wh
ich the disease gene(s) are not known.
I
nformation on

whether genes from the different loci observed, do interact at a
functional level are potentially interesting. Obviously, the input information is
crucial for the success of such an approach. Studies wi
ll be biased by absence of
complete functional information in databases of the majority of genes, and also
interaction databases are far from complete. However, hypotheses generated
with existing knowledge may be of value, and genes, that would otherwise n
ot
have been predicted to be involved in the disease in question, might be
identified this way. Data amounts in databases are rapidly increasing. This
include increased knowledge regarding genes, proteins, interactions among
them, methods integrating high
throughput genomic and proteomic approaches,
as well as text mining methods extracting functional relationships from the
literature.

Candidate genes

involved in putative interaction networks should be further
examined not only at the single gene level, bu
t also in the context of the
networks of which they form an integral part.
mRNA

expression levels for each
gene can be evaluated e.g. under different relevant conditions. Genes with
differential regulation are believed to be most important. This approach h
as
been used recently evaluating predicted interaction networks in
type 1 diabetes

[6]
. Differential regulation of several genes was demonstrated, e.g. after
cytokine

expo
sure of human pancreatic islets, supporting the prediction of the
interaction
network

as a whole as a risk factor
. In addition, enrichment of type 1
diabetes

associated
SNPs

in the individual interaction
networks

were measured
21
-
12

to evaluate evidence of signi
ficant association at
network

level. This method
provided additional
support, in an independent data
set, that some of the
interaction
networks

could be involved in
type 1 diabetes

[6]
.



21.2.3
Future directions

Systems biology

approaches complement more classical analyses of the genetics
of complex diseases and may shed light on the underlying biological
pathways

and help us understand the complex interplay between multipl
e factors
contributing to disease pathogenesis.

Combining
GWAS
,
protein networks
,
molecular biology

studies, and phenotype data in searching for functional
candidates for observed
genetic associations

has been shown to be a feasible
approach
[5, 8]
.
Characterization of phenotypic effects of
SNPs

on gene
expression or on protein function or interaction will provide a more efficient
approach to the identification of risk variants and will provide insights into
possible

mechanisms whereby these variants modify disease risk. Focusing on
interplay between many components in modules or systems may demonstrate
how defects in such modules can lead to human disease.

Such an understanding
is likely to be helpful in defining new

key targets for prediction, prevention and
improved therapeutic responsiveness.

Elucidation of
networks and
signaling
pathways associated with disease and examination of the effects of
combi
nations of experimental changes and
variations are important in d
rug
discovery, and a prerequisite in translation of results into clinically useful
predictors of disease and
drug targets
.

Interaction networks can identify sub
-
networks corresponding to functional units in the
biological system
. Sub
-
networks associated wi
th disease may link
molecular biology

to physiology and
21
-
13

thereby to clinically relevant issues, and the aim is that predictive gene
networks

can lead directly to discovery of
drug targets

and
biomarkers

of
disease.


For identifying
drug targets

it is necess
ary to understand how the causal genes
function and act in their biological context. Identified genes from a
GWAS

may
not be chemically suitable as
drug target
s
.

However, proteins

in the same
signaling pathway

may
constitute more rational and better

drug t
argets
. Disease
associated
genetic loci

and intermediate molecular phenotypes that are
connected with these loci and cause disease are obvious starting points to
uncover the drivers of disease. It is important to evaluate pertubations of
networks

and
pathw
ays

with the potential to thereby identify key steps or nodes
that drive diseases, and which may act as targets for
therapeutic intervention.

To develop disease therapies by targeting a given gene it is necessary to know if
activation, inhibition or partia
l activation leads to disease
[12]
. We can now
begin to u
nderstand the context in which a gene operates and thereby suggest
the
best possible points of therapeutic intervention

[12]
.


FIGURE 21.2. INSERT
BLACK/WHITE

VERSION HERE.


LEGEND:

Figure
2
1.2
. Strategy for
drug target identification
.

Genome
-
wide association
scan data alone or integrated with transcriptomics
-
,
proteomics
-
, or epigentics
data etc. are used as “input” data.

P
rotein
-
protein interaction data and the
application of bioinformatics and systems biology allow in silico generation of
21
-
14

networks. Text mining analysis of these networks for enrichment of prote
ins
with association to disease phenotype leads to a score and ranking of each
network.
This will end up in a list of potential candidate proteins whose
functional relevance can be tested in model systems using e.g. RNA
interference. From the outcome of th
e functional studies, the most promising
drugable targets are selected for drug development. Seen as a whole, this
method

will from a platform of thousands of data
, step by step narrow down the
number of candidate proteins ultimately resulting in identific
ation of

a few
numbers of plausible drug targets.


Systems biology

approaches to develop drugs to treat human diseases is of high
interest and with the high cost of developing novel therapies, improved ways of
selecting
valid drug target candidates

are ext
remely important. Novel and highly
interdisciplinary
systems biology

approaches are likely to identify
networks from
which the most rational target can be selected
.
We are still far from a
comprehensive understanding of the molecular pathogenesis of multi
-
factorial
diseases. This makes it difficult to identify optimal strategies for intervention and
treatment.

The recent success of
GWAS

and the prospects for combining
genetics with high
-
throughput genomics, as well as general advances in genome
informatics,

genotyping technology, statistical methodology and large clinical
materials are sources of optimism for the future.


References:


21
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