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CHAPTER 6

IDENTIFICATION OF RHEUMATOID ARTHRITIS SIGNIFICANT

TARGET PROTEINS AND THEIR PATHWAYS THROUGH THE

COMBINATORIAL APPROACH (RA-GIM, RA-DTP AND

MICROARRAY DATANALYSIS OF RA)

6.1 INTRODUCTION

The identification and analysis of significant proteins for complex human

disease such as Rheumatoid Arthritis (RA), which is initiated by inflammatory

and autoimmune process is a difficult challenge. The common approach of

focusing a study on just one or a few signature proteins confines the capacity to

distinguish novel genetic effects associated with the disease. In addition, many

susceptibility proteins may exhibit effects that are partially or solely dependent

on interactions with other proteins and/or the environment (Albert et al., 2011).

Even though, there are many advance techniques that have been reported

for diagnosis and treatment of RA, novel signature protein targets are still

required to improve the accuracy of diagnosis and the therapeutic outcomes.

Hence, different approaches can be coupled together to identify signature

molecules for RA that can be viewed effectively. In the previous chapters

(Chapter 3, 4 and 5) the following approaches were carried out to map the

candidate genes/proteins in RA.

Chapter 1: Constructing gene interaction map of RA genes for

identifying candidate targets and their associated pathways by applying

network centralities.

Chapter 2: Constructing Drug’s Target- protein interaction of RA for

identifying candidate proteins and their associated pathways by applying

Q modularity and Traffic Value.

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Chapter 3: Microarray data analysis of macrophages and synovial

fibroblast (SF) of RA for identifying candidate proteins and their

associated pathways.

The candidate proteins for RA demonstrated by the above three

approaches were integrated together by STRING which was studied and refined

further to identify significant proteins and associated pathways in RA. Further,

the network was mapped with the pathophysiology of RA (KEGG: hsa05323)

and focus was given only to the proteins associated with the pathophysiology of

RA. These proteins were again cross checked with their nature of essentiality

(Non-essential proteins would better serve as drug targets because of their lesser

side effects). Hence, the non-essential proteins from the above findings were

completely studied to arrive at significant proteins and pathways which could

serve as a potential drug targets for RA.

6.2 MATERIALS AND METHODS

6.2.1 Materials

Databases

STRING: This database is used to connect all the proteins to each other

as protein protein interactions (PPI) using multiple name option by

implying the seven predictive methods.

KEGG: Kyto Encylopedia of Genes and Genomes was used to

understand the RA pathogenesis pathway reported and for comparing with

new identified significant targets.

Database of Essential Genes: It is used for identification of the proteins

to be essential or nonessential for cell living which can be used as drug

targets.

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Tools and Softwares:

Biointerpreter: It is online tool used for biological annotation and

cellular localization by calculating through P value.

Under STRING, seven predictive methods are used to identify the different

protein protein interactions.

1. Neighborhood: Identical functions for different proteins are identified

considering “Genomic neighbourhood”, which correlates by recognising

similar genomic context in relation to epigenetic marks, physical

interaction with nuclear lamina, etc.

2. Occurrence: Phylogenetic method and metabolic pathways are

considered to identify similar protein networks as these proteins will have

similar function as well.

3. Gene fusion: Evolutionary method of identification considering similar

domains and multi modular structure plays a key role in identifying

similar protein patterns.

4. Co-expression: Co-expression of genes observed as common patterns are

represented as associated proteins.

5. Experiments: Protein-protein interaction databases are included to show

the list of candidate protein interaction

6. Databases: It considers all the curated databases to represent the list of

significant proteins and their interaction clusters.

7. Text mining: Under this method information from the abstracts of

scientific articles are considered to represent protein group similarity.

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6.2.2 Flow Chart

6.2.3 Pooling of candidate proteins

Candidate proteins obtained from Gene Interaction Map (GIM), Drug’s-

targets- protein (RA-DTP) interactome and the microarray expression based data

analysis for macrophages and synovial fibroblast were merged and enriched

Biological validation of significant proteins and their pathways

Biointerpreter

Identification of non-essential proteins

Database of Essential Genes

Common proteins and their interacting proteins obtained

Network comparison with RA pathopysiology

KEGG ID : hsa05323

Merging and enriching the proteins as network model

STRING 9.0 Seven Predictive methods

Pooling of candidate proteins identified by the previous three approaches

GIM (12) RA-DTP (17) RA Microarray Data analysis (11)

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together as network through STRING. Seven predictive methods

(Neighbourhood, text mining, databases, experiments, co-expression, gene fusion

and occurrence) in STRING were used to get the interaction between different

proteins.

6.2.2 Mapping the built network with Rheumatoid Arthritis KEGG

pathway (hsa05323)

The network built by STRING was mapped with RA pathogenesis

pathway reported in KEGG (hsa05323). Only the common proteins and the

interacting ones were selected for the further studies.

6.2.3 Selection of significant proteins by Database of Essential Genes

and its role in Rheumatoid Arthritis pathogenesis

The proteins identified above, were studied through Database of Essential

Genes to identify their essentiality and nonessentially for their indispensable role

in normal cell survival. The nonessential proteins identified by the Database of

Essential Genes were further studied for their involvement in RA pathogeneis

using Biointerpreter.

6.3 RESULTS

Under the first approach of candidate targets identification for RA using

Gene Interaction Map (GIM), out of the 1046 reported proteins for RA 12

proteins were identified as significant through network based studies (Table 6.1),

whereas in second approach of candidate protein identification for RA using

drug’s-targets- protein (RA-DTP) interaction drugs used for RA and their

reported protein- protein interaction were constructed to represent another 17

significant proteins which was identified by betweenness centrality parameters

and biological annotation (Table 6.2). Twelve proteins PTGDS, CTNS, NAGK,

hROAT1, FADD, IRAK1, MAP3K7, RPL7, FOS, KNG1, POMC, HSP90AA1

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were similar from the approach 1 and 2, which were used to map out with the

differentially expressed proteins from RA microarray datasets. Hence, through

the third approach, eleven proteins were reported to be candidate in the

pathophysiology of RA as portrayed in Table 6.3.

Table 6.1: Identification of candidate proteins for Rheumatoid Arthritis

using Gene Interaction Map (GIM)

S.no Symbol Gene/ protein

1. FADD Fas (TNFRSF6)-associated via death domain

2. MAPK8 Mitogen-activated protein kinase 8

3. IRAK1 Interleukin-1 receptor-associated kinase 1

4. MYD88 Myeloid differentiation primary response 88

5. TRAF6 TNF receptor-associated factor 6

6. IKBKB Inhibitor of kappa light polypeptide gene enhancer in B-cells,

kinase beta

7. MAP3K7 Mitogen-activated protein kinase kinase kinase 7

8. REL V-rel reticuloendotheliosis viral oncogene homolog

9. RELB V-rel reticuloendotheliosis viral oncogene homolog B

10. MAP3K14 Mitogen-activated protein kinase 14

11. RPL7 Ribosomal protein L7

12. MAPK3 Mitogen-activated protein kinase 3

Table 6.2: Identification of candidate proteins for Rheumatoid Arthritis

using drug’s- targets- protein interaction

S.No Symbol Gene/ protein

1. FOS FBJ murine osteosarcoma viral oncogene homolog

2. KNG1 Kininogen 1

3. PTGDS Prostaglandin D2 synthase

4. HSP90AA1 Heat shock protein 90kDa alpha, class A member 1

5. REN Renin

6. POMC Proopiomelanocortin

7. FCER1G Fc fragment of IgE, high affinity I, receptor for; gamma

polypeptide

8. IL6 Interleukin 6

9. ICAM1 Intercellular adhesion molecule 1

10. SGK1 Serum/glucocorticoid regulated kinase 1

11. NOS3 Nanos homolog 3

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12. PLA2G4A Phospholipase A2, group IVA (cytosolic, calcium-dependent)

13. RENBP N-acylglucosamine 2-epimerase

14. CTNS Cystinosin, lysosomal cystine transporter

15. NAGK N-acetylglucosamine kinase

16. hROAT1 Ornithine aminotransferase 1

17. OPRK1 Opioid receptor, kappa 1

Table 6.3: Expression based studies (Microarray Data Analysis) for

identification of candidate molecules for Rheumatoid Arthritis

S. No Symbol Gene/ Protein

1. CHIT1 Chitinase 1 (chitotriosidase)

2. FN1 Fibronectin 1

3. AQP9 Aquaporin 9

4. PLA2G7 Phospholipase A2, group VII (platelet-activating factor

acetylhydrolase, plasma)

5. CHI3L1 Chitinase 3-like 1 (cartilage glycoprotein-39)

6. APOE Apolipoprotein E

7. VCAN Versican

8. VEGFA Vascular endothelial growth factor A

9. CD69 Cluster of Differentiation of 69 molecule

10. SPP1 Secreted phosphoprotein 1

11. CASP8 Caspase 8, apoptosis-related cysteine peptidase

These 40 proteins (Appendix II) were enriched as a network using

STRING in which 5 proteins AQP9, RPL7, SGK1, hROAT1/OAT, and CTNS

didn’t showed any interaction as shown in Figure 6.1. However while looking

into the pathophysiology of RA (KEGG: hsa05323), only the presence of three

proteins IL6, VEGF and ICAM1 were obvious (Appendix III). It has been

observed that these 3 proteins are used as drug targets for RA till now. But, as

these proteins are essential proteins, drug targeting poses many side effects

among the patient. A total of 15 proteins were obtained out of which 12 proteins

showed their interactions with the above three proteins.

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Figure 6.1: Enriched network of Rheumatoid Arthritis candidate proteins

through string

Among the 15 proteins 10 were essential proteins and targeting them

might lead to many side effects (Appendix IV), however the remaining 5 non-

essential proteins namely NAGK, CHI3L1, CHIT1, RENBP and SPP1 were

found attractive. During the interaction studies, four proteins were also identified

as a separate network entity, connected to IL6 and VEGF through CHI3L1.

Whereas, SPP1 which is the highest upregulated protein in the whole network

(FC= 96.41) had direct link with VEGF, IL6 and FN1 and indirectly to ICAM1

through CASP8 and FOS (Figure 6.2).

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KEGG Pathway : map05323

FC= 3.79

FC= 59.63

FC= -1.67

FC= 72.81

FC= -1.67

FC= 2.50

FC= -45.60

FC= 2.41

FC= -9.40

FC= 36.75

FC= 57.70

FC= 2.64

FC= 96.41

Figure 6.2: Rheumatoid Arthritis significant proteins and their interactions

Notes: Down regulation, up regulation, FC= Fold Change

6.3.1 Functional study of significant proteins and their pathways

Based on the merged network analysis, 5 proteins namely NAGK,

RENBP, CHIT1, CHI3L1 and SPP1 were proposed to be significant in RA that

can be used as RA drugs targets. These proteins and their associated pathways

were studied in detail.

From the above Figure 6.2, it is evident that RENBP (FC= -1.6, P= 0.002)

and NAGK (FC= 3.6, P= 7.6 E-04) are connected to all other proteins by IL6 and

REN. RENBP and NAGK proteins were linked to Chitinase-3-like protein 1

(CHI3L1) (FC= 72.80, P= 1.92E-06) and Chitonase 1 (CHIT1) (FC= 59.30,

P=4.95E-10). All these four proteins in the cluster were biologically associated

with N-Acetylglucosamine (GlcNAc) metabolism.

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N-Acetylglucosamine (GlcNAc) Metabolism

Hyaluronic Acid

Tissue Remodeling

N-acetylglucosamine Glucuronic acid

CHIT1

NAGK RENBP

N-acetyl-D-glucosamine 6 Phosphate N-acetylmannosamine

N-glycolylneuraminic acid (Neu5Gc) degradation pathway

Amino sugar and Nucleotide sugar metabolism

Osteoclast formation

FC= -1.67 in RA

FC= 3.79 in RA

FC= 59.63 in RA

Figure 6.3: Proposed role of NAGK, RENBP and CHIT1 in N-Acetyl

glucosamine metabolism

Normal synovial fluid contains hyaluronic acid, a polymer of glucuronic

acid and N-acetyl glucosamine joined by alternating beta-1,4 and beta-1,3

glycosidic bonds. GlcNAc on polymerization with glucuronic acid forms

hyaluronic acid and are proposed for treatment of autoimmune diseases as

suggested by Jiang et al., (2011) and Dizon et al., (2011). GlcNAc and

glucuronic acid are the precursors for the hyaluronic acid, a component of

synovial fluid.

High expression of N-acetylglucosamine kinase (NAGK) in RA converts

N-acetyl-glucosamine (GlcNac) to N-acetyl-glucosamine 6-phosphate

(Hinderlich et al., 2000), which then enters into N-acetylglucosamine degration

and N-acetylneuraminate degration through Amino-sugar metabolism. Therefore,

majority of the GlcNAc is compelled to enter in degradation pathway due to

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which there is lack of formation of hyaluronic acid which can further lead to

cartilage destruction and inflammation.

Similarly, high expression of Chitinase 1 (chitotriosidase) (CHIT1) in

RA which break downs the long chain of N-acetyl glucosamine (Pierfrancesco.,

2012) is also additionally involved in osteoclast function, an important step in

bone resorption. This step also depletes GlcNAc to form hyaluronic acid.

Renin binding protein (RENBP) catalyzes the interconversion of N-

acetyl glucosamine to N- acetyl mannosamine as observed by Takahashi et al.

(1999), which shows that it is a GlcNAc 2-epimerase and enters into N-

glycolylneuraminic acid (Neu5Gc) degradation pathway. By binding to renin,

RENBP is found to inhibit the Renin Angiotensin System (RAS), which is the

initiator of inflammation (Knoll et al., 1997). In RA, as RENBP is down

regulated, it clearly spells the presence of increased inflammation in synovial

joint.

Although, Chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1) is

known to be stimulated by macrophages, chondrocytes, neutrophils, and some

tumour cells as observed by Ober et al. (2008). It initiates bone resorption

activity and synovial pannus formation (Tanaka et al., 2014). The high

expression level of the protein (CHI3L1) demonstrates the increase of bone

resorption occurs and due to which bone rebuilding is aggravated. CHI3L1 is

also known to be involved in N-acetyl-glucosamine metabolism, but the clear

mechanism could not be revealed.

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Inflammation and Osteoclastogenesis

Inflammatory Molecule

TH17

RANKLOsteoclastogenesis

IL6

IL23

TGFβ

TNFα

IL17

Joint Destruction

Bone ResorptionSPP1 MMP9

FC= 96.41 in RA

Figure 6.4: Proposed mechanism of SPP1 in initiation of TH17 for bone

degradation

As portrayed in Figure 6.4, in RA, increased synthesis of inflammatory

molecules like IL6, IL23, TGFβ and TNFα stimulates the formation of

pathogenic TH17 cells. This Th17 cells are epigenetically regulated by IL17A

gene (Denhardt et al., 2001). It has been recently observed that Phosphoprotein 1

(SPP1) also known as Osteopontin (OPN) protein which is highly expressed in

bone and proteolytically activated by metalloproteinase 9 (MMP9) has a

remarkable role in increasing the TH17 formation. The whole inflammatory

process occurs when MMP9 cleaves OPN at 166 and 201 residues in vitro, which

results in four fragments. These fragments bind to different cell surface receptors

and induce signalling process which further initiates cell adhension, migration

and cell invasion. High level of SPP1, initiates TH17 which further activates

RANKL, and in turn leads to osteoclastogenesis followed by joint destruction

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and bone resorption activity. Therefore, targeting SPP1 before cleavage step

could control the inflammatory action.

6.4 DISCUSSION

The main goal of the study was to establish significant molecules which

are characteristic for RA and to identify potential diagnostic as well as

therapeutic targets.

Five proteins NAGK, RENBP, CHI3L1, CHIT1 and SPP1 which were

identified as non-essential proteins were involved in three major

categories of function specifically in relation to joints

N-Acetyl Glucosamine metabolism

Fibroblast initiation for pannus formation

Th17 initiation for bone degradation.

These proteins also showed their involvement in Amino sugar and

nucleotide sugar metabolism, ECM-receptor interaction, Toll-like receptor

signalling pathway, and Focal adhesion pathways.

The above findings hypothesize that these five proteins would be the best

possible target proteins in RA and their associated pathways which are

consistent among all the above studies, indicative of its key role in RA

pathogenesis. Further these proteins and their associated pathways can be

explored more specifically for RA and a detail in-vitro and in-vivo study is

required to specifically explore the role of these proteins as diagnostic

markers or drug targets.