Abstract
The past decades have witnessed a paradigm shift from the traditional
drug discovery shaped around the idea of “one target, one disease” to
polypharmacology (multiple targets, one disease). Given the lack of
clear-cut boundaries across disease (endo)phenotypes and genetic
heterogeneity across patients, a natural extension to the current
polypharmacology paradigm is to target common biological pathways
involved in diseases via endopharmacology (multiple targets, multiple
diseases). In this study, we present proximal pathway enrichment
analysis (PxEA) for pinpointing drugs that target common disease
pathways towards network endopharmacology. PxEA uses the topology
information of the network of interactions between disease genes,
pathway genes, drug targets and other proteins to rank drugs by their
interactome-based proximity to pathways shared across multiple
diseases, providing unprecedented drug repurposing opportunities. Using
PxEA, we show that many drugs indicated for autoimmune disorders are
not necessarily specific to the condition of interest, but rather
target the common biological pathways across these diseases. Finally,
we provide high scoring drug repurposing candidates that can target
common mechanisms involved in type 2 diabetes and Alzheimer’s disease,
two conditions that have recently gained attention due to the increased
comorbidity among patients.
Keywords: drug repurposing, proximal pathway enrichment analysis,
network endopharmacology, systems medicine, comorbidity, autoimmune
disorders, Alzheimer’s disease, type 2 diabetes
1. Introduction
Following Paul Ehrlich’s more-than-a-century-old proposition on magic
bullets (one drug, one target, one disease), the drug discovery
pipeline traditionally pursues a handful of leads identified in vitro
based on their potential to bind to target(s) known to modulate the
disease [[38]1]. The success of the selected lead in the consequent
clinical validation process relies on the prediction of a drug’s effect
in vivo. Although it is often more desirable to tinker the cellular
network by targeting multiple proteins [[39]2], this is hard to achieve
in practice due to the interactions of the compound and its targets
with other proteins and metabolites. As a result, the characterization
of drug effect has been a daunting task, yielding high pre-clinical
attrition rates for novel compounds [[40]3,[41]4].
The high attrition rates can be attributed to the immense response
heterogeneity across patients, likely stemming from a polygenic nature
of most complex diseases. Consequently, researchers have turned their
attention to polypharmacology, where novel therapies aim to alter
multiple targets involved in the pathway cross-talk pertinent to the
disease pathology, rather than single proteins [[42]5,[43]6]. This has
given rise to network-based approaches that predict the effects of
individual drugs [[44]7] as well as drug combinations [[45]8], allowing
for the repositioning of compounds for novel indications.
Over the past years, reusing existing drugs for conditions different
from their intended indications has emerged as a cost effective
alternative to traditional drug discovery. Various drug repurposing
methods aim to mimic the most likely therapeutic and safety outcomes of
candidate compounds based on similarities between compounds and
diseases characterized by high-throughput omics data
[[46]9,[47]10,[48]11]. Most studies so far, however, have focused on
repurposing drugs for a single condition of interest, failing to
recognize the cellular, genetic and ontological complexity inherent to
human diseases [[49]12,[50]13]. In reality, pathway cross-talk plays an
important role in modulating the pathophysiology of diseases [[51]14]
and most comorbid diseases are interconnected to each other in the
interactome through proteins belonging to similar pathways
[[52]15,[53]16,[54]17,[55]18,[56]19]. The pathway cross-talk is
especially relevant for autoimmune disorders, which have been shown to
share several biological functions involved in immune and inflammatory
responses [[57]20,[58]21]. Autoimmune disorders affect around 15% of
the population in the USA [[59]22] and co-occur in the same patient
more often than expected (i.e., comorbid) [[60]23]. Recent evidence
suggests that endophenotypes—shared intermediate
pathophenotypes—[[61]24], such as inflammasome, thrombosome, and
fibrosome play essential roles in the progression of not only
autoimmune disorders but also many other diseases [[62]25].
Here, we propose a novel drug repurposing approach, Proximal pathway
Enrichment Analysis (PxEA), to specifically target intertangled
biological pathways involved in the common pathology of complex
diseases. We first identify pathways proximal to disease genes across
various autoimmune disorders. Then we use PxEA to investigate whether
the drugs promiscuously used in these disorders target specifically the
pathways associated with one disease or the pathways shared across the
diseases. We find several examples of anti-inflammatory drugs where the
pathways proximal to the drug targets in the interactome correspond to
the pathways shared between two autoimmune disorders. The observed lack
of specificity among these drugs points to the existence of immune
system related endophenotypes, motivating us to explore shared disease
mechanisms for repurposing drugs. We demonstrate that PxEA is a
powerful computational strategy for targeting multiple pathologies
involving common biological pathways, such as type 2 diabetes (T2D) and
Alzheimer’s disease (AD). Based on these findings, we argue that PxEA
paves the way for simultaneously targeting endophenotypes that manifest
across various diseases, a concept which we refer to as
endopharmacology.
2. Results
2.1. Pathway Proximity Captures the Similarities between Autoimmune Disorders
Conventionally, functional enrichment analysis relies on the
significance of the overlap between a set of genes belonging to a
condition of interest and a list of genes involved in known biological
processes (pathways). Using known pathway genes, one can identify
pathways associated with the disease via a statistical test (e.g.,
Fisher’s exact test for the overlap between genes or z-score comparing
the observed number of common genes to the number of genes one would
have in common if genes were randomly sampled from the data set). We
start with the observation that such an approach (hereafter referred as
to conventional approach) often misses key biological processes
involved in the disease due to the limited overlap between the disease
and pathway genes. To show that this is the case, we focus on nine
autoimmune disorders for which we obtain genes associated with the
disease in the literature and we calculate p-values based on the
overlap between these genes and the pathway genes for each of the 674
pathways in the Reactome database (Fisher’s exact test, one-sided
[MATH: p≤0.05
:MATH]
). Intriguingly, [63]Table 1 demonstrates that this conventional
approach yields less than ten pathways that are significantly enriched
in five out of nine diseases, potentially underestimating the molecular
underpinning of these diseases.
Table 1.
Number of pathways enriched across nine autoimmune disorders based on
the overlap between the pathway and disease genes (one-sided
[MATH: p≤0.05
:MATH]
, assessed by a Fisher’s exact test) and the proximity of the pathway
genes to the disease genes in the interactome (
[MATH:
z≤−2
:MATH]
, see Methods for details).
Disease # of Pathways
Overlap Proximity
celiac disease 7 143
Crohn’s disease 5 116
diabetes mellitus, insulin-dependent 16 121
Graves’ disease 3 92
lupus erythematosus, systemic 17 98
multiple sclerosis 12 138
psoriasis 5 50
rheumatoid arthritis 55 17
ulcerative colitis 6 138
[64]Open in a new tab
Alternatively, the shortest distance between genes in the interactome
can be used to find pathways closer than random expectation to a given
set of genes [[65]7,[66]26], augmenting substantially the number of
pathways relevant to the disease pathology. Using network-based
proximity [[67]7], we define the pathway span of a disease as the set
of pathways significantly proximal to the disease (
[MATH:
z≤−2
:MATH]
, see Methods). We show that the number of pathways involved in
diseases increases substantially when proximity is used ([68]Table 1).
To show the biological relevance of the identified pathways using
interactome-based proximity, we check how well these pathways can
highlight genetic and phenotypic relationships between nine autoimmune
disorders. First, to serve as a background model, we build a disease
network for the autoimmune disorders (diseasome) using the genes and
symptoms shared between these diseases as well as the comorbidity
information extracted from medical insurance claim records (see
Methods). The autoimmune diseasome ([69]Figure 1a) is extremely
connected, covering 33 out of 36 potential links between nine diseases
(with average degree
[MATH: <k> :MATH]
= 7.3 and clustering coefficient
[MATH:
CC=0.93
:MATH]
). The three missing links are those between ulcerative colitis and
rheumatoid arthritis, ulcerative colitis and Graves’ disease, and
Graves’ disease and type 1 diabetes. On the other hand, several
diseases such as celiac disease, Crohn’s disease, systemic lupus
erythematosus, and multiple sclerosis are connected to each other with
multiple evidence types in the autoimmune diseasome based on genetic
(shared genes) and phenotypic (shared symptoms and comorbidity)
similarities, emphasizing the shared pathological components underlying
these diseases.
Figure 1.
[70]Figure 1
[71]Open in a new tab
Genetic, phenotypic and functional overlap across autoimmune disorders.
Disease relationships (links) based on (a) shared genes (gray solid
lines), shared symptoms (orange dashed lines) and comorbidity (blue
sinusoidal lines); (b) shared pathways (gray solid lines) using common
disease and pathway genes, (c) shared pathways (gray solid lines) using
the proximity of the pathway genes to the diseases genes in the
interactome.
We compare the autoimmune diseasome generated using shared genes,
common symptoms and comorbidity, to the disease network in which the
disease-disease connections are identified using the pathways they
share. We identify the pathways enriched in the diseases using both the
conventional and proximity approaches mentioned above and check whether
the number of common pathways between two diseases is significant
(two-tailed Fisher’s exact test,
[MATH: p<0.05
:MATH]
). The disease network based on pathways shared across diseases using
the overlap between the pathway and disease genes is markedly sparser
than the original diseasome, containing 17 links ([72]Figure 1b). None
of the diseases share pathways with psoriasis and among the connections
supported by multiple evidence in the original diseasome, the links
between Crohn’s disease and celiac disease as well as Crohn’s disease
and systemic lupus erythematosus are missing. On the contrary, the
disease network based on shared pathways using proximity of the pathway
genes to the disease genes consists of 34 links, where the only
unconnected disease pairs are Crohn’s disease and Graves’ disease and
type 1 diabetes and psoriasis, suggesting that it captures the
connectedness of the original diseasome better than the conventional
approach.
We next turn our attention to the shared pathways across diseases
identified by both conventional and proximity based approaches and
observe that most common pathways involve biological processes relevant
to the immune system endophenotypes. In particular, we see that
inflammasome-related pathways, such as signaling of cytokines
(interferon gamma, interleukins like IL6, IL7) and lymphocytes (ZAP70,
PD1, TCR, among others) are overrepresented. While conventional
enrichment finds that most of these pathways are shared among only 4–5
diseases, proximity based enrichment points to the commonality of these
pathways among almost all the diseases. Furthermore, the proximity
based enrichment uncovers the involvement of additional interleukin
(IL2, IL3, IL5) and lymphocyte (BCR) molecules ubiquitously in
autoimmune disorders. These findings suggest that proximity-based
pathway enrichment identifies biological processes relevant to the
diseases, highlighting the common etiology across autoimmune disorders.
2.2. Diseases Targeted by the Same Drugs Exhibit Functional Similarities
Having observed that pathway proximity to diseases in the interactome
captures the underlying biological mechanisms across diseases, we seek
to investigate the potential implications of the connections between
diseases for drug discovery. We hypothesize that a drug indicated for
several autoimmune disorders would exert its effect by targeting the
shared biological pathways across these diseases. To test this, we use
25 drugs that are indicated for two or more of the autoimmune disorders
in Hetionet [[73]27] and split disease pairs into two groups: (i)
diseases for which a common drug exists and (ii) diseases for which no
drugs are shared. We then count the number of pathways in common
between two diseases for each pair in the two groups using pathway
enrichment based on both the gene overlap and proximity in the
interactome. We find that the diseases targeted by the same drugs tend
to involve an elevated number of common pathways compared to the
disease pairs that do not have any drug in common ([74]Figure 2). The
average number of pathways shared among diseases that are targeted by
the same drug is 3.4 and 38 using overlap and proximity based
enrichment, respectively, whereas, the remaining disease pairs share 2
and 31 pathways on average using the two enrichment approaches. We note
that due to the relatively small sample size and potentially incomplete
drug indication information, we interpret the elevated number of
pathways as a trend rather than a general rule across all diseases (
[MATH: p=0.043
:MATH]
and
[MATH: p=0.066
:MATH]
, assessed by one-tailed Mann-Whitney U test, for the overlap and
proximity based approaches, respectively). Nevertheless, taken together
with the high overall pathway level commonalities observed in the
autoimmune disorders mentioned in the previous section, this result
suggests that the drugs used for multiple indications are likely to
target common pathways involved in these diseases.
Figure 2.
[75]Figure 2
[76]Open in a new tab
Number of shared pathways across disease pairs that are targeted by the
same drug compared to the rest of the pairs. The pathway enrichment is
calculated using (a) gene overlap and (b) proximity of genes in the
interactome. The number of disease pairs in each group is given in the
parenthesis below the group label in the x-axis.
2.3. Proximal Pathway Enrichment Analysis Reveals Drugs Targeting the
Autoimmune Endophenotypes
The results indicating that the drugs used for multiple autoimmune
disorders potentially target common pathways raise the following
question: “Can pathway level commonalities between diseases be
leveraged to quantify the impact of a given drug on these diseases?” To
this end, we propose PxEA, a novel method for Proximal pathway
Enrichment Analysis that scores the likelihood of a set of pathways
(e.g., targeted by a drug) to be represented among another set of
pathways (e.g., disease pathways) based on the proximity of the pathway
genes in the interactome. As opposed to the Gene Set Enrichment
Analysis (GSEA) [[77]28] which uses gene sets and the ranking of genes
based on differential expression, PxEA uses pathway sets and the
ranking of pathways based on proximity in the interactome. PxEA scores
a drug based on whether or not the pathways targeted by the drug are
proximal to a pathway set of interest, such as pathways shared across
different diseases. For a given drug and a pair of diseases, we first
identify the pathways in the pathway span of both of the diseases, then
we rank the pathways with respect to the proximity of the drug targets
to the pathway genes and finally we calculate a running sum statistics
corresponding to the enrichment score between the drug and the disease
pair ([78]Figure 3, see Methods for details).
Figure 3.
[79]Figure 3
[80]Open in a new tab
Schematic overview of proximal pathway enrichment analysis (PxEA). PxEA
scores a drug with respect to its potential to target the pathways
shared between two diseases. For a given drug and two diseases of
interest, PxEA first identifies the common pathways between the two
diseases and then uses the proximity-based ranking of the pathways
(i.e., average distance in the interactome to the nearest pathway gene,
normalized with respect to a background distribution of expected
scores) to assign a score to the drug and the disease pair.
We employ PxEA to score 25 drugs indicated for at least two of the
seven autoimmune disorders (there were no common drugs for celiac and
Graves’ diseases). For each disease, we first run PxEA using the
pathways proximal to the disease and the proximity of the drugs used
for that disease to these pathways. We then run PxEA for each disease
pair, using the pathways proximal to both of the diseases in the pair
and the drugs commonly used for the two diseases. We notice that
several drugs indicated for multiple conditions score higher using
common pathways between two diseases than using the pathways of the
disease they are indicated for ([81]Figure 4). This is not surprising
considering that many of the drugs used for autoimmune disorders target
common immune and inflammatory processes. For instance, sildenafil, a
drug used for the treatment of erectile dysfunction and to relieve the
symptoms of pulmonary arterial hypertension, is reported by Hetionet to
show palliative effect on type 1 diabetes and multiple sclerosis.
Actually, sildenafil is not specific to any of these two conditions and
targets a number of the 57 pathways in common between type 1 diabetes
and multiple sclerosis including but not limited to pathways mentioned
in [82]Table 2, such as “IL-3, 5 and GM CSF signaling” (
[MATH:
z=−1.6
:MATH]
), “regulation of signaling by CBL” (
[MATH:
z=−1.1
:MATH]
), “regulation of KIT signaling” (
[MATH:
z=−1.0
:MATH]
), “IL receptor SHC signaling” (
[MATH:
z=−1.0
:MATH]
), and “growth hormone receptor signaling” (
[MATH:
z=−1.0
:MATH]
).
Figure 4.
[83]Figure 4
[84]Open in a new tab
PxEA scores of drugs used in autoimmune disorders. (a) Disease-disease
heatmap, in which for each disease pair, the common pathways proximal
to the two diseases are used to run PxEA. Note that the diagonal
contains the PxEA scores obtained when the proximal pathways for only
that disease are used. The hue of the color scales with the PxEA score.
(b) Drug-disease heatmap, in which the PxEA is run using the pathways
proximal to the pathways of the disease in the column for the drugs in
the rows (25 drugs that are used at least in two diseases). The last
two columns show the median and maximum values of the PxEA scores
obtained for the drug among all disease pairs the drug is indicated
for.
Table 2.
Pathways shared by autoimmune disorders based on the overlap and
proximity of genes (only pathways that appear most commonly across
diseases are shown).
Pathway # of Shared Diseases
Overlap Proximity
interferon gamma signaling 5 8
costimulation by the CD28 family 5 7
cytokine signaling in immune system 5 7
translocation of ZAP-70 to immunological synapse 5 6
phosphorylation of CD3 and TCR zeta chains 5 6
PD1 signaling 5 4
IL-6 signaling 4 8
generation of second messenger molecules 4 6
TCR signaling 4 6
signaling by ILs 3 9
immune system 3 7
downstream TCR signaling 3 7
interferon signaling 3 7
adaptive immune system 3 3
regulation of KIT signaling 2 7
IL-7 signaling 2 6
CTLA4 inhibitory signaling 2 5
chemokine receptors bind chemokines 2 3
extrinsic pathway for apoptosis 2 3
MHC class II antigen presentation 2 2
IL receptor SHC signaling - 9
IL-3, 5 and GM CSF signaling - 9
signaling by the B cell receptor BCR - 8
regulation of IFNG signaling - 8
growth hormone receptor signaling - 8
IL-2 signaling - 8
regulation of signaling by CBL - 8
[85]Open in a new tab
Similarly, prednisone, a synthetic anti-inflammatory glucocorticoid
agent that is indicated for six of the autoimmune disorders, is
assigned a higher PxEA score using the pathways shared by Crohn’s
disease and systemic lupus erythematosus compared to using the pathways
involved only in Crohn’s disease, systemic lupus erythematosus,
multiple sclerosis, psoriasis, rheumatoid arthritis, or ulcerative
colitis. Thus, prednisone does not specifically target any of the six
autoimmune disorders but rather acts on the endophenotypes that
manifest across these diseases. We observe a similar trend in
meloxicam, an anti-inflammatory drug that shows analgesic and
antipyretic effects by inhibiting prostaglandin synthesis. Consistent
with its known mechanism of action, meloxicam is proximal to
“cholesterol biosynthesis” (
[MATH:
z=−3.5
:MATH]
), “fatty acid, triacylglycerol, and ketone body metabolism” (
[MATH:
z=−2.0
:MATH]
), and “prostanoid ligand receptors” (
[MATH:
z=−1.7
:MATH]
) pathways in the interactome. While meloxicam is originally indicated
for rheumatoid arthritis and systemic lupus erythematosus, the higher
PxEA score when common arthritis and lupus pathways are used suggests
that it targets common inflammatory processes in these two diseases.
2.4. Targeting the Common Pathology of Type 2 Diabetes and Alzheimer’s
Disease
T2D and AD, two diseases highly prevalent to an ageing society, are
known to exhibit increased comorbidity [[86]29,[87]30]. Recently,
repurposing anti-diabetic agents to prevent insulin resistance in AD
has gained substantial attention due to the therapeutic potential it
offers [[88]31]. Indeed, the pathway spans of T2D and AD cover 170 and
82 pathways, respectively, 35 of which are shared between two diseases,
linking significantly the two diseases at the pathway level (Fisher’s
exact test, two-sided
[MATH:
p=2.2×10
−4 :MATH]
).
We use PxEA to score 1466 drugs from DrugBank using the 35 pathways
involved in the common pathology of T2D and AD. When we look at the
drugs ranked on the top of the list ([89]Table 3), we spot orlistat, a
drug indicated for obesity and T2D in Hetionet. Interestingly, existing
studies also suggest a role for this drug in the treatment of AD
[[90]32]. Orlistat targets extracellular communication
(Ras-Raf-MEK-ERK, NOTCH, and GM-CSF/IL-3/IL-5 signaling) and lipid
metabolism pathways ([91]Figure 5). Several of the proteins in the
pathways pertinent to the common T2D-AD pathology, such as APOA1,
PSEN2, PNLIP, LPL, and IGHG1 are either Orlistat’s targets themselves
or are in the close vicinity of the targets. The next top scoring drugs
are chenodeoxycholic and obeticholic acid, biliar acids that are in
clinical trials for T2D ([92]NCT01666223) and are argued to modulate
cognitive changes in AD [[93]33].
Table 3.
Top ten drug repurposing opportunities to target common T2D and AD
pathology, where the drugs that target the same proteins according to
DrugBank are grouped together in the same row and the Anatomical
Therapeutic Chemical (ATC) classification and indication information
within the same group is marked with the first letter of the drug in
the parenthesis (if applicable).
Drug ATC Hetionet Indication DrugBank Indication PxEA Score
Adjusted p-Value
orlistat A08 obesity, type 2 diabetes obesity 94.07
[MATH: <0.0001 :MATH]
obeticholic acid, chenodeoxycholic acid A05 primary biliary cirrhosis
(C) liver disease (O), primary biliary cholangitis (O), gallbladders
(C) 74.06 <0.0001
esmolol, practolol C07 hypertension (E) atrial fibrillation (E),
noncompensatory sinus tachycardia (E), cardiac arrhythmias (P) 70.55
<0.0001
clenbuterol R03 - asthma 70.44 <0.0001
erythrityl tetranitrate C01 - angina 70.32 <0.0001
fenoterol, arbutamine, bupranolol R03 (F),
G02 (F)
C01 (A),
C07 (B) - asthma (F), coronary
artery disease (A),
hypertension (B),
tachycardia (B),
glaucoma (B) 68.97 <0.0001
dalfampridine N07 multiple sclerosis multiple sclerosis 68.44 <0.0001
magnesium sulfate D11, V04, A06, B05, A12 - eclampsia, acute nephritis,
acute hypomagnesemia, uterine tetany 68.27 <0.0001
roflumilast, crisaborole R03 (R) chronic obstructive pulmonary disease
(R) chronic obstructive
pulmonary disease (R),
dermatitis (C),
psoriasis (C) 66.33 <0.0001
montelukast R03 chronic obstructive pulmonary disease, asthma, allergic
rhinitis asthma 65.94 <0.0001
[94]Open in a new tab
Figure 5.
[95]Figure 5
[96]Open in a new tab
Orlistat from PxEA perspective. The subnetwork shows how the targets of
Orlistat are connected to the nearest pathway protein for the pathways
shared between T2D and AD. For clarity, only the pathways that are
proximal to the drug are shown. Blue rectangles represent pathways,
circles represent drug targets (orange) or proteins on the shortest
path to the nearest pathway gene (gray). Blue dashed lines denote
pathway membership, solid lines are protein interactions. The
interactions between the drug and its targets are shown in dashed
orange lines and the interactions between the drug targets and their
neighbors are highlighted with solid orange lines.
It is noteworthy that the top scoring drugs belong to a diverse set of
Anatomical Therapeutic Chemical (ATC) classes, covering alimentary
tract and metabolism drugs (A05, A06, A08, A12), blood substitutes
(B05), dermatologicals (D11) as well as cardiovascular (C01, C07),
genito-urinary (G02), nervous (N07), and respiratory (R03) system
drugs. The diversity of the ATC classes of top scoring drugs indicates
that PxEA is not biased towards any particular ATC class. We also
calculate the significance of the PxEA scores by permuting the ranking
of the pathways. We find that the adjusted p-values (corrected for
multiple hypothesis testing using Benjamini–Hochberg procedure) for the
top candidates are all below
[MATH:
1×10−4<
/mn> :MATH]
, the minimum possible value (due to the 10,000 permutations used in
the calculation).
3. Discussion
The past decades have witnessed a substantial increase in human life
expectancy owing to major breakthroughs in translational medicine. Yet,
the increase on average age and changes in life style, have given rise
to a spectra of problems challenging human health like cancer,
neurodegenerative disorders and diabetes. These diseases do not only
limit the life expectancy but also induce a high burden on public
healthcare costs. In the US alone, more than 20 and 5 million people
have been affected by T2D and AD, respectively, ranking these diseases
among the most prevalent health problems [[97]29].
Mainly characterized by hyperglycemia due to resistance to insulin, the
disease mechanism of T2D involves a combination of multiple genetic and
dietary factors. On the other hand, AD is relatively less understood
and several hypotheses have been proposed for its cause: reduced
synthesis of neurotransmitter acetylcholine, accumulation of amyloid
beta plaques and/or tau protein abnormalities, giving rise to
neurofibrillary tangles. Accordingly, most available treatments in AD
are palliative (treating symptoms rather than the cause). Given the
comorbidity between T2D and AD [[98]29,[99]30] several studies have
recently suggested repurposing diabetes drugs for AD [[100]31].
However, to our knowledge, currently there is no systematic method that
can pinpoint drugs that could be useful to target common disease
pathology such as the one between T2D and AD.
In this study, we first show that diseases that share drugs also tend
to share biological pathways and hypothesize that these pathways can be
targeted to exploit novel drug repurposing opportunities. We introduce
PxEA, a method based on (i) pathways that are proximal to diseases and
(ii) the ranking of the pathways targeted by a drug using the topology
information encoded in the human interactome. We show that PxEA picks
up whether drugs target specifically the pathways associated with a
disease or common pathways shared across various conditions. We observe
that many anti-inflammatory drugs are not specific to the condition
they are used for and likely to target pathways involved in the
autoimmune endophenotypes.
To further explore shared disease mechanisms for repurposing drugs, we
use PxEA and rank drugs for their therapeutic potential in targeting
the common disease pathology between T2D and AD. We identify orlistat,
a semisynthetic derivative of lipstatin that inhibits lipase—a
pancreatic enzyme that breaks down fat—as the top repurposing
candidate. Orlistat inhibits hydrolysis of triglycerides, which in
turn, reduces the absorption of monoaclglycerides and free fatty acids
[[101]34]. Recent evidence indicates that perturbations in unsaturated
fatty acid metabolism are tightly coupled to neuritic plaque and
neurofibrillary tangle formation in AD patients [[102]35]. Thus,
orlistat might help slowing down the plaque and tangle formation due to
its effect on the fatty acid metabolism. Targeting of fatty acid
metabolism for improving the cognitive performance presents a novel
therapeutic approach and is further supported by experiments in mouse
models [[103]36].
PxEA can suggest rather counter-intuitive repositioning opportunities
such as the use of clenbuterol, an asthmatic drug, in the treatment of
metabolic and neurodegenerative diseases such as T2D and AD. In fact,
the potential use of clenbuterol in these diseases is not too far
fetched: it enhances cognitive performance in aging rats and monkeys
[[104]37], improves memory deficit in mice [[105]38], and reduces the
insulin resistance in obese rats [[106]39]. On the flip side, while
PxEA provides a cellular network based perspective to recommend drugs,
it does not take into account dosage-related effects of drugs,
potential adverse events, or the genetic background of the patients.
For instance, practolol, a beta-adrenergic antagonist that stands out
among the T2D-AD candidates, has been withdrawn from the market due to
its high toxicity, limiting its potential therapeutic use in the
clinical setting. Despite the limitations of PxEA, such as the
incompleteness in the drug target, disease and pathway genes, lack of
consideration of dosage-related effects or genetic heterogeneity, we
believe PxEA is the first step towards achieving endopharmacology, that
is, targeting endophenotypes involved across multiple diseases.
4. Materials and Methods
4.1. Protein Interaction Data and Interactome-Based Proximity
To define a global map of interactions between human proteins, we
obtained the physical protein interaction data from a previous study
that integrated various publicly available resources [[107]16]. We
downloaded the supplementary data accompanying the article to generate
the human protein interaction network (interactome) containing data
from MINT [[108]40], BioGRID [[109]41], HPRD [[110]42], KEGG [[111]43],
BIGG [[112]44], CORUM [[113]45], and PhosphoSitePlus [[114]46]. We used
the largest connected component of the interactome in our analyses,
which covered 141,150 interactions between 13,329 proteins (represented
by ENTREZ gene ids).
Network-based proximity is a graph theoretic approach that incorporates
the interactions of a set of genes (i.e., disease genes or drug
targets) with other proteins in the human interactome and contextual
information as to where the genes involved in pathways reside with
respect to the original set of genes [[115]7]. To quantify
interactome-based proximity between two gene sets (such as drug
targets, pathway genes or disease genes), we used the average shortest
path length from the first set to the nearest protein in the second set
following the definition in the original study [[116]7]. Accordingly,
the proximity from nodes S to nodes T in a network
[MATH:
G(V,E) :MATH]
, is defined as
[MATH:
d(S,T)=1∥S
∥∑u∈S<
/mi>minv∈Td(u,v) :MATH]
where
[MATH:
d(u,v) :MATH]
is the shortest path length between nodes u and v in G. We then
calculated a z-score based on the distribution of the average shortest
path lengths across random gene sets
[MATH:
Srandom :MATH]
and
[MATH:
Trandom :MATH]
(
[MATH:
drandom(S,T)=d(
Srandom
,Tra
ndom)
:MATH]
) as follows:
[MATH:
z(S,T)=d(S,T)−μ
drandom(S,T
)σd
random
(S,T)<
/mrow> :MATH]
where
[MATH:
μdra<
mi>ndom(S,T)
:MATH]
and
[MATH:
σdra<
mi>ndom(T,S)
:MATH]
are the mean and the standard deviation of the
[MATH:
drandom(S,T) :MATH]
, respectively, obtained using 1000 realizations of random sampling of
gene sets that match the original sets in size and degree. We refer to
the pathways that are significantly proximal (
[MATH:
z≤−2
:MATH]
) to a disease as the pathway span of the disease throughout text.
Note that, instead of average shortest path distances, one can also use
random-walk based distances to calculate proximity between gene sets
[[117]26]. However, random walks in the networks are inherently biased
towards high-degree nodes [[118]47,[119]48] and require additional
statistical adjustment [[120]26,[121]48]. Sampling based on size and
degree matched gene sets has been shown to be robust against
data-incompleteness in the interactome and in the known pathway
annotations [[122]7,[123]48].
To investigate the effect of noise in the pathway data, following the
procedure proposed in [[124]49], we created a synthetic pathway data
set, in which we defined pathways using a certain percentage k of known
disease genes in T2D and AD (k = 10, 25, 50, 75, 90). Hence, for each
value of k, we created 10 groups of genes, containing a random sampling
of k% of the T2D-associated genes. We repeated the procedure using the
AD-associated genes, yielding 100 gold standard pathways (10 for each
disease across 5 different values of k) that were subsets of the known
disease genes. For each gold standard pathway, we then generated so
called control pathway, that is, randomly selected group of genes in
the interactome that match the size of the gold standard pathway under
consideration. Next, we assessed the shortest path distance based
proximity between the gold standard pathways and the disease genes
(proximity of the gold standard T2D pathways to the T2D disease genes
and of the gold standard AD pathways to the AD disease genes) and
compared it to the proximity of the control pathways to the same
disease genes. We also calculated the proximity using random walk
scores as proposed in a previous study [[125]50]. We used the random
walk implementation in GUILD software package [[126]51] with the
default parameters. As one would expect, the gold standard pathways
were significantly more proximal (
[MATH:
z≤−2
:MATH]
) to the disease genes than the control pathways using both proximity
calculation approaches ([127]Figure 6). On the other hand, the shortest
path distance based proximity distinguished better the overlap between
the gold standard pathway genes and the disease genes by providing
lower values than the random walk based proximity as the noise in the
pathway information decreased (higher values of k in the gold
pathways).
Figure 6.
[128]Figure 6
[129]Open in a new tab
Effect of noise in the pathway data on the random walk and shortest
path based proximity calculation. To assess the robustness of the
interactome-based proximity in regards to noise in the pathway data, we
generated synthetic gold standard pathways containing a certain
proportion (k%) of the known disease genes in T2D and AD (see text for
details). We compared the proximity between these gold standard
pathways and the disease genes to the proximity between the control
pathways (random groups of gene in the interactome) and the disease
genes. The proximity values using random walk and the shortest path for
increasing k values are shown for the control and gold standard
pathways.
4.2. Disease-Gene, Drug and Pathway Information
We compiled genes associated with nine autoimmune disorders listed in
[130]Table 4 using disease-gene annotations from DisGeNET [[131]52]. We
downloaded curated disease-gene associations from DisGeNET that
contained infromation from UniProt [[132]53], ClinVar [[133]54],
Orphanet [[134]55], GWAS Catalog [[135]56] and CTD [[136]57]. To ensure
that the disease-gene associations were of high confidence, we kept
only the associations that were also provided in a previous large-scale
analysis of human diseases [[137]16].
Table 4.
Disease-gene associations for the nine autoimmune disorders used in
this study.
Disease # of Genes Genes
celiac disease 11 IL21 CCR4 HLA-DQA1 BACH2 RUNX3 ICOSLG SH2B3 CTLA4
MYO9B ZMIZ1 ETS1
Crohn’s disease 19 DNMT3A IL12B IRGM IL10 CCL2 FUT2 SMAD3 TYK2 ATG16L1
BACH2
IL2RA NKX2-3 PTPN2 NOD2 TAGAP MST1 DENND1B IL23R ERAP2
diabetes mellitus, insulin-dependent 18 IL10 GLIS3 HLA-DQA1 HLA-DRB1
PTPN22 SLC29A3 INS BACH2 CLEC16A
PAX4 HLA-DQB1 IL2RA CD69 IL27 HNF1A CTSH SH2B3 C1QTNF6
Graves’ disease 4 RNASET2 CTLA4 FCRL3 TSHR
lupus erythematosus, systemic 29 IKZF1 CFB RASGRP3 PDCD1 RASGRP1 DNASE1
HLA-DRB1 PTPN22 ETS1 TNIP1
FCGR2B TNFSF4 IRF5 C2 PRDM1 PXK TLR5 TREX1 TNFAIP3 SLC15A4 PHRF1
HLA-DQA1 STAT4 ITGAX ITGAM BLK C4A BANK1 CR2
multiple sclerosis 15 CD58 CD6 IRF8 HLA-DQB1 CBLB HLA-DRA KIF1B IL2RA
TNFSF14 VCAM1 IL7R HLA-DRB1 CD24 TNFRSF1A PTPRC
psoriasis 15 IL12B TNIP1 LCE3D IL13 IL23R TYK2 HLA-DQB1 HLA-C FBXL19
ERAP1 TRAF3IP2 TNFAIP3 TNF REL NOS2
rheumatoid arthritis 23 MIF CD40 ANKRD55 HLA-DRB1 PTPN22 RBPJ IL2RA
AFF3 CCL21 REL SLC22A4 CCR6
IRF5 SPRED2 CTLA4 PADI4 TNFAIP3 NFKBIL1 HLA-DQA2 STAT4 IL6 BLK TRAF1
ulcerative colitis 24 IL12B JAK2 ICOSLG IL1R2 LSP1 CXCR2 IL10 IL7R
CXCR1 DAP NKX2-3 CARD9 GNA12
IRF5 PRDM1 HNF4A CCNY SLC26A3 FCGR2A IL23R IL17REL MST1 TNFSF15 CDH3
[138]Open in a new tab
We retrieved drug target information from DrugBank for 1489 drugs in
the version 5.0.6 of the database [[139]58], 1466 of which had at least
a target in the interactome. UniProt ids from DrugBank were mapped to
ENTREZ gene ids using UniProt id mapping file (retrieved on October
2017). We used drug indication information from Hetionet (compound
treats or palliates disease edges) that compiled data from publicly
available resources [[140]27]. We focused on 78 drugs that were
indicated for nine autoimmune disorders above. We created a subset of
drugs used for two or more of the autoimmune disorders, yielding 25
drugs across seven conditions (there were no indications for celiac
disease, and the two drugs used for Graves’ disease were not used in
any other disease).
The ENTREZ gene ids of the proteins involved in biological pathways
were taken from the version 5.0 of MSigDB curated gene sets [[141]59].
In our analysis, we used 674 Reactome [[142]60] pathways and the genes
associated with these pathways in the MSigDB.
4.3. Genetic, Phenotypic and Functional Relationships across Diseases
To identify relationships across disease pairs (autoimmune diseasome),
we used the similarities between diseases in terms of the genes and
symptoms they share. We assessed the significance of the overlap
between genes (or symptoms) associated with two diseases using Fisher’s
exact test. An alpha value of 0.05 was set to deem the connections
significant (two-sided test
[MATH: p≤0.05
:MATH]
). The disease symptom information was taken from a previous study
based on text mining of PubMed abstracts [[143]61]. In this study, the
number of times a symptom appears in a PubMed abstract was adjusted by
the frequency of the symptom in the whole corpus using time
frequency-inverse document frequency approach (TF-IDF). To ensure that
the disease-symptom associations are of high quality, we considered
associations with TF-IDF score higher than 3.5 as suggested in the
original study.
Comorbidity relationships across diseases were inferred using data from
medical insurance claims, where we assessed whether two diseases
occurred more often in the same patient compared to the rest using the
relative risk score [[144]62]. Relative risk score relies on the
relative occurrence frequencies of diseases across patients, adjusting
for the prevalence of the diseases. We mapped the ICD9 codes to MeSH
identifiers using the annotations provided by Disease Ontology
[[145]63] and we considered the disease pairs with a relative risk
score higher than 1 as potential commorbidity links.
To identify pathways enriched in diseases, we used the significance (i)
of the overlap between the pathway and disease genes assessed by a
one-tailed Fisher’s exact test and (ii) of the proximity between the
pathway and disease genes in the interactome. We considered the
pathways that had
[MATH: p≤0.05
:MATH]
and
[MATH:
z≤−2
:MATH]
, respectively, as the pathways that were enriched in a given disease
using the two approaches. The pathway information was taken from
Reactome and the proximity was calculated as explained above.
4.4. PxEA: Proximal Pathway Enrichment Analysis
Toward the goal of pathway level characterization of the common
pathology of diseases and to evaluate the therapeutic potential of
drugs based on their impact on the common pathways, we developed
Proximal pathway Enrichment Analysis (PxEA), a novel method that scores
drugs based on the proximity of drug targets to pathway genes in the
interactome. PxEA uses a GSEA-like running sum score [[146]28], where
the pathways are ranked with respect to the proximity of drug targets
to the pathways and each pathway is evaluated to see whether or not it
appears among the pathways of interest (e.g., common pathways between
two diseases). Given D, the pathways ranked with respect to their
proximity to drug targets,
[MATH: pi :MATH]
, the pathway in consideration within D, and C, the set of pathways of
interest, the running score is defined as follows [[147]64]:
[MATH:
ES(D,C)=∑pi∈DXi :MATH]
where,
[MATH:
Xi={
|D
|−|C||C|,ifpi∈<
mi>C−|C
||D|−|C|,other<
/mi>wise :MATH]
To calculate p-values for the case study, we repeat the procedure above
10,000 times, shuffling randomly D to calculate the expected enrichment
score
[MATH:
ES(Drandom
,C) :MATH]
. We then calculate the p-value for the enrichment using
[MATH:
P=|E
S(D,C)<ES(D
random,C)|10,000 :MATH]
The p-values were corrected for multiple hypothesis testing using
Benjamini-Hochberg procedure [[148]65].
4.5. Implementation Details and Code Availability
We used the toolbox Python package for running PxEA, available at
github.com/emreg00/toolbox. The proximity was calculated using networkx
package that implements Dijkstra’s shortest path algorithm. The
statistical tests were conducted in R ([149]www.R-project.org) and
Python ([150]www.python.org). The network visualizations were generated
using Cytoscape [[151]66] and the plots were drawn using either Seaborn
python package [[152]67] or ggplot2 R package [[153]68].
Abbreviations
The following abbreviations are used in this manuscript:
AD Alzheimer’s disease
ATC Anatomical Therapeutic Chemical
GSEA Gene set enrichment analysis
PxEA Proximal pathway enrichment analysis
T2D Type 2 diabetes
TF-IDF Time frequency-inverse document frequency approach
[154]Open in a new tab
Author Contributions
Conceptualization, E.G.; Methodology, E.G.; Software, J.A.-P., J.P.,
E.G.; Validation, J.A.-P., J.P. and E.G.; Formal Analysis, J.A.-P. and
E.G.; Investigation, J.A.-P., J.P. and E.G.; Resources, F.S., L.I.F.,
H.H.H.W.S., B.O. and E.G.; Data Curation, J.A.-P. and J.P.;
Writing—Original Draft Preparation, J.A.-P. and E.G.; Writing—Review &
Editing, J.A.-P., J.P., J.M., F.S., L.I.F, H.H.H.W.S., B.O. and E.G.;
Visualization, J.A.-P., E.G.; Supervision, J.M., F.S., L.I.F.,
H.H.H.W.S., B.O., E.G.; Project Administration, E.G.; Funding
Acquisition, J.M., F.S., L.I.F., H.H.H.W.S., B.O. and E.G.
Funding
The authors received funding from the Innovative Medicines Initiative 2
Joint Undertaking under grant agreement No. 116030. This Joint
Undertaking receives support from the European Union’s Horizon 2020
research and innovation programme and EFPIA. The authors also received
support from EU H2020 Programme 2014–2020 under grant agreement No.
676559 (Elixir-Excelerate). E.G. was supported by EU-cofunded Beatriu
de Pinós incoming fellowship from the Agency for Management of
University and Research Grants (AGAUR) of Government of Catalunya and
L.I.F. received support from ISCIII-FEDER (CPII16/00026). H.H.H.W.S.
has received funding from from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 777111
(Repotrial). The Research Programme on Biomedical Informatics (GRIB) is
a member of the Spanish National Bioinformatics Institute (INB),
PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I+D+i
2013–2016, funded by ISCIII and FEDER. The DCEXS is a “Unidad de
Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370).
Conflicts of Interest
E.G. has received compensation from Scipher Medicine for scientific
consulting. The founding sponsors had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the
writing of the manuscript, and in the decision to publish the results.
References