Please cite as: Zamanian-Azodi M, Mortazavi-Tabatabaei SA, Mansouri V, Vafaee R. Metabolite- protein interaction (MPI) network

analysis of obsessive- compulsive disorder (OCD) from reported metabolites. Arvand J Health Med Sci 2016;1(2):112-120.

Arvand Journal of Health & Medical Sciences

©2016 Abadan University of Medical sciences

Metabolite- protein interaction (MPI) network analysis of obsessive-

compulsive disorder (OCD) from reported metabolites

Mona Zamanian-Azodi1, Seyed Abdolreza Mortazavi-Tabatabaei2, Vahid Mansouri1, Reza Vafaee2 1 Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Reprint or Correspondence: Seyed Abdolreza Mortazavi-Tabatabaei; PhD Proteomics Research Center, Shahid Beheshti

University of Medical Sciences, Tehran, Iran

@ rtaba@yahoo.com.

ABSTRACT

Obsessive-Compulsive Disorder (OCD) is a lifetime mental condition with the prevalence of 1-3% in general population.

Molecular evaluation of OCD is prominent to understand the complex basis of underlying mechanisms. Here, by the

application of MPI construction of significant changed metabolites in OCD patients, it is tried to obtain a preliminarily

insight of related molecular interactions. These interactions are fundamental for cell processes and drug design. For

this purpose, a number of ten OCD related metabolites were obtained from literature, by the use of PubMed and

Google Scholar Resources. Then, the ten candidate metabolites were examined by the application of STITCH 4.0 Online

Resource, for investigating the ten significant linked proteins with our query metabolites and the related BPs. Top ten

scored interacting proteins with the query metabolites were retrieved and the action types were identified. Some of

the protein partners were previously detected as potential polymorphisms in OCD etiology. The Regulation of hormone

secretion and regulation of phosphorylation are the most significant (corrected p value< 0.05) related biological

process of the ten investigated metabolites in the whole network. The result suggests that, insulin may be influenced

significantly in OCD profile as it is significantly regulated by the six of our metabolites in the whole network. In fact,

either metabolites or proteins changes may influence each other levels and promotes OCD risk. Thus, further

systematic analysis is suggested for confirmation and introduction of new biomarkers.

Keywords: Obsessive-Compulsive Disorder (OCD), Molecular Origin, Metabolite-Protein Interaction (MPI)

Network.

Received: 12 March 2016 Accepted: 9 May 2016

Introduction

Obsessive-compulsive disorder is deliberating

mental condition that is typical with obsessive

thoughts and compulsive behavior (1). It has a

lifetime prevalence of 1-2.5% in the general

population (2). It has also recognized by WHO, as

the leading mental disability that mostly people of

the age of 15 to 44 are affected by this disorder

(3). This complex illness is a combination of multi-

factorial interactions. The interaction of these

factors can lead to manifestation of different

subtypes with different kinds of comorbidities (1).

The related disorders (comorbidity) are

ORIGINAL ARTICLE

Zamanian-Azodi M. et al 113

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commonly tourette syndrome, chronic hair

pulling ,trichotillomania, and anxiety (4) that

made OCD as one of the prominent complex

disorders for treatment approaches (5). Many

different related polymorphisms have been

identified in OCD risk with small effect. These

polymorphisms have been introduced by clinical

and neuroimaging investigation. However, the

functional level of molecular origin and the

complex interaction between these elements has

been remained elusive (6). In addition, according

to one study, gene expression level does not

significantly associated to the reported OCD

polymorphisms (7). On the other hand, many

metabolites reported to be linked to OCD

pathogenesis. Some of these metabolites are also

important in other brain diseases. The changes in

metabolites’ levels may indicate the disease

state(8). In another way, specific kinds of

metabolites may be recognized as potential

biomarkers for disease determinations (9). These

metabolites interact with other metabolites and

proteins. Interaction between molecules can lead

to specific condition of a disease (10). The analysis

of molecular interaction on the bases on PPI and

MPI is important to understand pathological

process in the disorder. Here, the MPI analysis of

the reported OCD metabolites has been examined

to facilitate understanding OCD underling

mechanisms.

Materials and Methods

Metabolites involved in OCD risk were

obtained by the application of PubMed Database

and Google Scholar Motor engine search. To

conduct a systematic literature review, keywords

used for searching in databases include

“Obsessive-Compulsive Disorder”, “Metabolites”,

and “Metabolome”. The compound’s identifiers

were extracted from KEGG Database

(http://www.genome.jp/kegg-bin/get_htext) for

each metabolite. The IDs were then applied for

network building and functional annotation

enrichments. In a way that, ten metabolites were

searched through STITCH 4.0 Resource (search

tool for interacting chemicals), available

at http://stitch.embl.de, in which predicted

interactions of proteins and chemicals can be

retrieved. The linkage between proteins and

chemicals are extracted from experiments,

databases and the literature (11). Confidence

score is determined for interactions between

elements. The network of each individual

metabolite and the whole ten metabolites were

constructed. The action view of the networks is

helpful to understand the modes of it with

defined scores. The annotation study of whole

network provides another level of understanding

of disease mechanism. For this purpose,

enrichment analysis of network related to

biological processes is also studied by STITCH 4.0

Resource. The statistical significance is

determined by corrected p value< 0.05. The

correction method for this evaluation is

bonferroni test.

Results

OCD related metabolites were investigated

through literature. A number of ten metabolites

were assigned to OCD risk. The function of

metabolites and their disease annotations are

tabulated in table1.

The network view of ten metabolites retrieved

individually from STITCH Source is represented in

figure 1. The whole MPI network composed of

interacting proteins with our ten investigated

metabolites (See figure 2).

Biological process enrichment of the OCD ten

metabolites by the use of STITCH 4.0 is depicted

in table2.

114 Metabolite- protein interaction (MPI) network analysis of obsessive- compulsive disorder (OCD)

Arvand J Health Med Sci 2016;1(2):112-120

Discussion

Metabolite-protein interactions are the

essential parts of system biology. The

interactions may be as allosteric regulation or

enzymatic function (32). These biochemical

pathways are run by those connections, and are

also important for pharmaceutical interventions

(32). Cellular functions and drug impact on

cells can be studied by analyzing MPI networks

(11). In OCD molecular profile, there are many

important metabolites as shown in table1.

These metabolites are also fundamental in

other brain diseases. Furthermore, to provide

insight into the connectivity and topology

of these related metabolites of OCD, network

map of MPI is evaluated. As indicated in

figure1, the investigated metabolites interact

with other proteins that some of them express

higher connectivity. Top ten related proteins for

each individual metabolite are included in each

network. Cholesterol as one of the important

reduced elements in the serum of OCD patients

has a noteworthy connection to apolipoprotein

A-I (APOA1) in its network. Apolipoprotein A-I

participates in the reverse transport of

cholesterol from tissues to the liver regulation.

APOA1, beside activation role has other

functions including inhibition and catalysis.

APOA1 is reported to have decreased level in

the serum of schizophrenia patients (36). It may

also have some roles in cholesterol reduction

levels in OCD patients. Proteomic analysis of

serum of OCD patients can be helpful in this

regard. In N-acetyl aspartate interaction

topology, the only binding formed between N-

acetyl aspartate and caspase1 (CASP1). Vitamin

B12, folate, and homocysteine play important

roles in production of serotonin and other

monoamine neurotransmitters. These

metabolites contributes in carbon

Table1. The list of identified metabolites from literature (PubMed and Google Scholar)

Metabolite Name KEGG Compound ID Function in Brain Other Brain Diseases

Cholesterol(12) C00187 Membrane permeability (12) Schizophrenia (13),Alzheimer’s Disease(14)

N-acetyl aspartate(15) C6H9NO5

C01042 Neuronal protein synthesis regulation, Myelin production Neurotransmitters ‘ production (16)

Schizophrenia(17),Bipolar(18) Parkinson's disease(19)

Vitamin B12(20) (cobalamin)

C05776 Neurotransmitter production(20)

Schizophrenia, Autism(21)

Folate (vitamin B9)(20) C00504 Neurotransmitter production(20)

Depressive disorder(22), Autsim(23), Bipolar(24)

Homocysteine(20) CE1401 Neurotransmitter production(20)

Depressive disorder(22), Autsim(25), Bipolar(24)

Creatine(15) C00300 Energy supplier(26) ADHD (27),Bipolar, Schizophrenia(28)

Glucose(29) C00293 Energy supplier(29) Schizophrenia , Autism(30), Bipolar(31)

Glutamate(32) C00025 Neurotransmitter(32) Addiction(33),Schizophrenia Dopamine(34) C03758 Neurotransmitter(34) Schizophrenia Serotonin (5-HT)(35) C00780 Neurotransmitter(35) Schizophrenia

Zamanian-Azodi M. et al 115

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Figure 1. Action view of MPI networks of ten investigated metabolites. Different edge colors indicate the interaction type. The connections are determined by confidence score. The top ten interacting proteins with the query metabolites are determined in each individual network.

116 Metabolite- protein interaction (MPI) network analysis of obsessive- compulsive disorder (OCD)

Arvand J Health Med Sci 2016;1(2):112-120

transfer metabolism (methylation), which is a

part of neurotransmitter production (20). Low

levels of vitamin B12 and B9 and high levels of

homocsteine in serum are linked to abnormal

cognitive tasks, decline and dementia (20). These

changes are also reported for OCD risk (20).

Folate is highly connected to dihydrofolate

reductase (DHFR) with the score of 0.998. DHFR

role is binding, inhibition and catalysis of folate.

Vitamin B12 tightly connected to gastric intrinsic

factor (vitamin B synthesis), (GIF), which activates

absorption of the essential vitamin cobalamin

(Cbl) in the colon (37). Homocysteine binding to 5-

methyltetrahydrofolate-homocysteine

methyltransferase (MTR) is the most significant

interaction in its network. MTR catalyzes the

transfer of a methyl group from methyl-

cobalamin to homocysteine. Low levels of folate

Figure 2. The metabolite-protein interaction network of ten metabolites using STITCH 4.0. Different

colors of edges indicate functional properties.

Zamanian-Azodi M. et al 117

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is reported to cause homocysteine accumulation

(38). One of the important proteins in creatine

network is creatine kinase that catalysis the

conversion of creatine. Phosphocreatine is used

as an energy supplier of brain. Another essential

interacting agent with creatine is solute carrier

family 6 (SLC6A8), which is required for creatine

transportation to brain. Increased creatine level

of medial prefrontal cortex is reported in OCD

patients (15).

Table 2. Two top ranked biological processes of OCD

ten related metabolites (corrected p value<0.05), the

terms are ordered based on statistical significance.

Term Number of

Genes

P value

bonferroni

Regulation of

hormone secretion

5 2.21E-04

Regulation of

phosphorylation

7 4.61E-04

Glucose as another essential element that its

metabolisms changes in OCD state has strong

reaction with insulin (INS) in the network.

Dopamine as vital neurotransmitter in brain has

many connections. As indicated in its network,

proteins including dopamine transporters and

receptors are essential parts of its network. DRD1,

DRD2, DRD3 and SLC6A3 as receptors and

transporter respectively, were previously

reported to be involved in OCD risk with different

types of polymorphisms. Furthermore,

polymorphisms of other related proteins

including catechol-O-methyltransferase (COMT)

and monoamine oxidase A (MAOA) are also

showed to have contributions in OCD profile.

There are other highly interacting proteins

(MAOB, TAAR1 and SST) in dopaminerigc system

that may be involved in OCD risk. In glutamate

network profile, glutamate receptor (GRIN) and

solute carrier (SLCA) are one of the previously

recognized polymorphism in OCD development.

The modes of actions between GRIN and

glutamate are divers including activation, binding,

catalysis and reaction. In addition, glutamate

receptor (GRM5) activated by glutamate, is the

most significant related functional partner.

Serotonin is another vital neurotransmitter in

brain mood regulation (35). Examining ten most

related interacting proteins show that serotonin

receptors (HTR family) have direct interactions

and possesses high relation scores. Solute carrier

(SLC6A4) also contributes for serotonin

transportation. All these loci have been verified to

have associations with OCD. This fact implies on

the importance of these interactions that level

changes of either metabolites or proteins may

induce pathogenesis processes of OCD. In other

word, expression changes of proteins or

metabolites levels may due to imbalance the

metabolome profile and protein regulation,

respectively. In addition, some of the interactive

proteins to the designated metabolites were

previously investigated in the genetic level with

different kinds of polymorphisms for OCD (35). In

this regard, this finding can justify the fact that,

these polymorph genes may have presented in

the functional level as their interacted

metabolites demonstrated significant changes in

OCD psychopathology. However, systematic

molecular investigation is required for validation

of this claim. Moreover, examining interacting

elements in the whole metabolic network of the

ten metabolites provides a better understanding

of OCD underlying mechanisms. Based on figure2,

GCK, DRD2, APOA1,GRM5, HK1, INS,

HTR1A,HTR2C, GALM and LDLCQ3 indicate high

confidence of interactions with the investigated

metabolites. Network topology of the ten

metabolites show that some of these molecules

are in close relations. Serotonin and dopamine

118 Metabolite- protein interaction (MPI) network analysis of obsessive- compulsive disorder (OCD)

Arvand J Health Med Sci 2016;1(2):112-120

have significant interaction with combined score

of 0.995. This evidence suggests the reaction link

between these two metabolites. Glucose binds to

glutamate and previous findings confirmed that

glucose metabolism is relevant to glutamate

concentration changes in brain (39).

Hypermetabolism of glucose in some parts of OCD

patients’ brain may intricate to this

psychopathology process. Insulin is regulated by

many of the metabolites including glucose

(inhibition and activation), serotonin (inhibition

and activation), dopamine (inhibition and

expression), homocysteine (inhibition),

cholesterol (inhibition and activation), glutamate

(activation). In this regard, regulation of insulin

secretion may be disrupted widely in OCD

patients. On the other hand, enrichment analysis

of the whole network map (table2), identifies two

most statistical significant biological processes

that are regulation of hormone secretion and

regulation of phosphorylation that may play

important role in OCD development.

Conclusion

In conclude, for better understanding OCD

associated mechanisms, evaluation of other

related partners in MPI network of OCD is

recommended by adding more agents (lower

confidence score) to the studied networks.

However, application of systems biology approach

including metabolic as well as proteomic

investigations is a requirement for clarifying the

complex nature of OCD.

Acknowledgement

This research is derived from the Ph.D. thesis of

Miss. Mona Zamanian-Azodi.

Conflict of Interest

The authors declare no conflict of interest.

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