i

Interferon Lambda and Its Receptor: Identification of

Potential Transcription Factors Involved In Interferon

Lambda Receptor

(IFN-λRα) Expression

By

Hashaam Akhtar

(2010-NUST-TfrPhD-V&I-77)

Atta-Ur-Rahman School of Applied Biosciences

National University of Science & Technology

Islamabad, Pakistan

2016

ii

Interferon Lambda and Its Receptor: Identification of

Potential Transcription Factors Involved In Interferon

Lambda Receptor

(IFN-λRα) Expression

By

Hashaam Akhtar

(2010-NUST-TfrPhD-V&I-77)

A thesis submitted in partial fulfillment of the requirement for the degree of

Doctor of Philosophy

In

Virology and Immunology

Atta-Ur-Rahman School of Applied Biosciences

National University of Science & Technology

Islamabad, Pakistan

iii

iv

Dedicated

To

My Mom

&

My Dad

MOHAMMAD AKHTAR IMRAN

(May His Soul Rest In Peace)

v

Table of Contents ACKNOWLEDGEMENTS ......................................................................................................... viii

LIST OF ABBREVIATIONS ...................................................................................................... viii

LIST OF TABLES ....................................................................................................................... xiii

LIST OF FIGURES ..................................................................................................................... xiv

ABSTRACT ............................................................................................................................... xviii

Chapter 1

INTRODUCTION .......................................................................................................................... 1

Chapter 2

REVIEW OF LITERATURE ....................................................................................................... 12

2.1 Classification of Type III Interferons and Their Receptor .................................................. 13

2.1.1. Type III Interferon ...................................................................................................... 13

2.1.2. Interleukin-10 (IL-10) Family ..................................................................................... 15

2.2 Type III Interferons Verses Type I Interferons ................................................................... 16

2.3Interferons and Their Class IICytokine Receptors ............................................................... 17

2.4 Gene Location of Interferon Lambda and Its Receptor (IFN-λ) ......................................... 19

2.5 Alternative Splicing of IFN-λRα ........................................................................................ 22

2.6 ADecade of Type IIIInterferon ...................................................................................... 24

2.8 Expression of IFN-λRαand its Pathway .............................................................................. 27

2.9 Type III Interferons as Modulator of Immune Response and Mutiny of IFN-λ4 ............... 34

2.10.1 Phylogenetic Tree of Type III Interferons ................................................................. 37

2.10.2 Polymorphism in IFN-λ1, IFN-λ2 and IFN-λ3 .......................................................... 40

2.10.3 The Influence of IFN-λ3 Polymorphism on Type III Interferon Biology ................. 42

2.10.4 The Impact of IL-28B/IFN-λ3 Polymorphism on HCV Infection ............................. 42

2.10.5 Combined IL-28B/IFN-λ3 Polymorphisms ............................................................... 43

2.10.6 Polymorphism In IFN-λ4 gene .................................................................................. 44

2.10.7 Polymorphism In IFN-λRα/IL-28Rα Gene ................................................................ 44

Chapter 3

MATERIALS AND METHODS .................................................................................................. 46

3.1. Expression of IFN-λRα in Monocytes and Type I and Type IIMacrophages.................... 46

3.1.1 Sample Collection ........................................................................................................ 46

vi

3.1.2 PBMC Isolation, Freezing and Thawing ..................................................................... 46

3.1.3 In vitroGeneration of Monocytes-Derived Human Macrophages Using M-CSF

Induction ............................................................................................................................... 47

3.1.4 Standard Protocol for Generation of GM-CSF Differentiated Macrophages .............. 48

3.1.5 Optimized Protocol for in vitroGeneration of M-CSF Macrophages

Methodological Considerations ............................................................................................ 49

3.1.6 Interferon Treatment Assay ......................................................................................... 50

3.1.7 Cell Stimulations, RNA Extraction, cDNA Synthesis, and Real-time

Quantitative PCR .................................................................................................................. 51

3.1.8 Data analysis ................................................................................................................ 51

3.2 Expression of IFN-λRα with Altered Signal Peptide .......................................................... 51

3.2.1 In-vitro and In-silico Analysis of Signal Peptide ......................................................... 51

3.2.2 Expression of Mut-IFNλR1 in HEK-293 Cells ........................................................... 52

3.3 In-silico studies on transcription factors involved in expression of IFN-λRα .................... 53

3.3.1. Computational Aanalysis ............................................................................................ 54

3.4 Functional analysis of IFN-λ4 ............................................................................................ 57

3.4.1. Interferon Treatment Assay & IFN Treatment ........................................................... 57

3.4.2. Cell Stimulations, RNA Extraction, cDNA Synthesis, and Real-Time

Quantitative PCR .................................................................................................................. 58

3.4.3. Data Analysis .............................................................................................................. 58

Chapter 4

RESULTS ..................................................................................................................................... 59

4.1 Expression of IFN-λRα in Monocytes and Type 1 and Type 2 Macrophages.................... 59

4.2 Amplification of various splice variants of IFNλR1(SV1, SV2,SV3) ................................ 62

4.3 Expression of IL-28Rα with altered signal peptide ............................................................ 66

4.4 Computational Analysis in Predicting the Transcription Factors Involved in

Expression of IFN-λRα (IL-28Rα) (NM_170743) ................................................................... 69

4.4.1 Promoter 2.0 Prediction Results .................................................................................. 69

4.5 Interaction of IL-28Rα with Various Cytokines and Transcription Factors ....................... 81

4.6 Functional analysis of IFN-λ4. ........................................................................................... 83

vii

Chapter 5

DISCUSSION ...................................................................................................................................

CONCLUSION ........................................................................................................................... 101

RECOMMENDATION .............................................................................................................. 103

REFERENCES ........................................................................................................................... 104

Annexure

Annexure no. 1 ........................................................................................................................ 133

Annexure No. 2 ....................................................................................................................... 133

Supplementary Table: Oligonucleotides used in this study. (referred in material and

methods) .............................................................................................................................. 133

Annexure No. 3 ....................................................................................................................... 134

Annexure No. 4 ....................................................................................................................... 143

TESS ................................................................................................................................... 143

Annexure no. 5 ........................................................................................................................ 146

AliBaba2.1 .......................................................................................................................... 146

AliBaba2.1 predicts the following sites in your sequence .................................................. 146

Sequence seq_75 ................................................................................................................. 146

Sequence seq_76 ................................................................................................................. 157

Sequence seq_77 ................................................................................................................. 161

Annexure no. 6 ........................................................................................................................ 163

Transfac (gene-regulation.com) .......................................................................................... 163

Annexure No. 7 ....................................................................................................................... 166

Transfac............................................................................................................................... 166

viii

ACKNOWLEDGEMENTS

I fully realize the blessings upon me by the most gracious and divine force of the universe

that enabled me, and gave me sense and insight to accomplish this scientific assignment

objectively and successfully.

First and foremost, I owe my profound thanks to Principal ASAB, Asst Prof. Dr.

Peter John and my supervisor, Dr. Hajra Sadia whose motivations, valuable discussion

and personal interest enabled me to complete this tedious work. Heartfelt gratitude to

Associate Prof. Dr. Rune Harmann, Department of Molecular Biology and Genetics,

Åarhus University, Denmark for his kind and sincere help, and for providing me the

necessary research facilities during my research.

I feel great pleasure in expressing my ineffable to Dr. Najam Us Sahar Sadaf

Zaidi, Dr. Sobia Manzoor, Dr. Sadia Andleeb, Dr. Mohammad Yameen, Dr. Ole J.

Hamming, Sanne E. Jørgensen, Dr. Hans Henrik Gad, Ewa Terczyn´ska-Dyla and

Susanne Vends for constant support during this work. I extend my deepest gratitude to all

faculty of the ASAB, all batch mates, my lab associates and my friends for their

invaluable guidance, encouragement, co-operation and advises in the research which led

to this thesis.

Thanks to financial support provided during research work by Higher Education

Commission, Pakistan.

I wish to articulate my profound appreciation to my loving parents especially my

Dad (R.I.P) and my siblings, whose guidance and love from cradle of childhood to the

prime of my life enabled me to lead a thriving life. With the constant care and support of

my Mom, my journey towards success was made a lot easier, would not have been

possible without you Mom.

Hashaam Akhtar

ix

LIST OF ABBREVIATIONS

APC Antigen presenting cells

bp Base pair

BSA Bovine serum albumin

cDNA Complementary deoxyribonucleic acid

CDS's Coding reading frames

CFSE Carboxy fluorescein diacetate succinimidyl ester

CMV Cytomegalovirus

CRF2-12 Cytokine receptor family 2 member 12

DC Dendritic cells

DENV Dengue virus

dNTP Deoxyribonucleoside triphosphate

DMSO Dimethyl sulfoxide

E. coli Eschericia coli

EDTA Ethylenediaminetetraacetic acid

EMCV Encephalomyocarditis virus

ERK Extracellular signal-regulated kinases

FBS Foetal bovine serum

FCS Foetal calf serum

GAF complex IFN-g-activation factor complex

GC3s Third synonymous codon positions

GM-CSF Granulocyte macrophage colony stimulating factor

HAART Highly Active Antiretroviral Therapy

x

HCV Hepatitis C Virus

HS Human Serum

HSV Herpes Simplex Virus

IAV Influenza A Virus

IDT Integrated DNA Technologies

IFIT Interferon-Induced Protein With Tetratricopeptide Repeats

IFN Interferon

IFN-α Interferon Alpha

IFN-γ Interferon Gamma

IFN-λ Interferon Lambda

IFN-λR Interferon Lambda Receptor

IRF Interferon Regulatory Factor

IL-10 Interleukin 10

IL-10Rβ IL-10 ReceptorBeta

IL-28 Interleukine 28

IL-28Rα Interleukine 28 ReceptorAlpha

IL-29 Interleukine 29

IPTG Isopropyl-βD Thiogalactopyranoside

IRF Interferon Regulatory Factor

ISGs Interferon Stimulated Genes

ISGF3 Interferon Stimulated Gene Factor 3

Jak Janus Kinase

JNK c-Jun N Terminal Kinases

xi

kDa Kilo Dalton

KSHV Kaposi’s Sarcoma Associated Virus

LPS Lipopolysaccharide

Μg Microgram

MAPK Mitogen Activated Protein Kinase

Mg Milli Gram

M-CSF Macrophage Colony Stimulating Factor

MHC Major Histocompatibility Complex

mM Millimolar

MxA Interferon Induced GTP-Binding Protein A

NCBI National Center for Biotechnology Information

NFκB Nuclear Factor Kappa Beta

NF-Y Nuclear Factor Y

PAGE Polyacrylamide Gel Electrophoresis

PBMC Peripheral Blood Mononuclear Cell

PBS Phosphate Buffered Saline

pDC Plasmacytoid Dendritic Cells

PKR Protein Kinase R

poly(I:C) Polyinosinic:polycytidylic Acid

Rpm Revolutions Per Minute

RPMI Roswell Park Memorial Institute Medium

RSCU Relative Synonymous Codon Usage

RT Reverse Transcription

xii

SDS Sodium Dodecyl Sulfate,

SH2 Src Homology 2

SOCS Suppressor of Cytokine Signaling

Src Sarcoma

STAT Signal Transducers and Activators of Transcription

TAM Tumour Associated Macrophage

TBE Tris Borate EDTA

TBS Tris Buffered Saline

TF Transcription Factor

TFBS Transcription Factor Binding Site

TLRs Toll Like Receptors

TYK Tyrosine Kinase

Tyr343 Active Site Tyrosine 343

U Units

Xgal 5-Bromo-4-Chloro-3-Indolyl- Beta-D-Galactopyranoside

xiii

LIST OF TABLES

Table no. Title Page No.

Table 2.1 Classification of Interferons 15

Table 2.2 A decade of type III IFNs: discoveries research and results 25

Table 2.3 Summary of the impact of different IL28B polymorphisms on HCV

infection in several conditions (SVR: sustained virological

response; RVR: rapid virological response) (Bellanti et al., 2012).

41

Table 3.1 Various computational softwares are available online for prediction

of TFBS.

56

Table 4.1 Various scoring parameters of the signal peptides of IFN-λR1 and

IFN_αR1 attained through computational analysis.

68

Table 4.2 Summary table from the results obtained using F-Match searches

showing important Transcription factors as reported.

72

Table 4.3 Some of the notable transcription factors in IL-28Rα gene in Homo

sapiens selected from a set of 164 segments (complete in

supplementary data) as potential binding sites reported by

AliBaba2.1.

73

Table 4.4 Selected results from TESS software shows various transcription

factor binding sites in IL-28Rα gene in Homo sapiens.

79

Table 4.5 Summary table of the results obtained using Gene regulation

Biobase TRANSFAC suite, reported here are TF’s chosen based on

supporting statistical threshold values.

79

Table 4.6 Summary table of the results obtained using Genomatix software

suite reported here are TF’s chosen based on supporting statistical

threshold values.

80

xiv

LIST OF FIGURES

Figure No. Title Page

No.

Figure 1.1 Global Prevalence of Chronic Hepatitis C Infection in 2013. 2

Figure 1.2 Prevalence of Chronic Hepatitis C Infection in Pakistan in 2012. 4

Figure 1.3 Various receptor complexes with their ligands (Renauld, 2003). 8

Figure 1.4 Phylogenetic tree for correlating fish and human IFNs. 10

Figure 2.1 Phylogenetic tree of class II cytokines and receptor. 18

Figure 2.2 Gene location of IFN-λRα on chromosome 1. 21

Figure 2.3 Splice variants and the expression pattern of IFN-λRα. 23

Figure 2.4 Genome and mRNA transcribed. 24

Figure 2.5 IFN-λRα gene expression in normal human tissues (normalized

intensities).

28

Figure 2.6 Protein Expression in different cell types: IFN-λRα Protein expression

data from MOPED, PaxDb and MAXQB.

29

Figure 2.7 The main pathway of type I and type III interferon induced gene

expression.

31

Figure 2.8 Phylogenetic tree using the actual or predicted DNA sequences of

spliced interferon lambda genes or pseudogenes in human, mouse, dog

and guinea pig (Fox et al., 2009).

38

Figure 2.9 Gene Comparison Tree of Homo sapiens with other animals with

reference to IL-28RA (Flicek et al., 2011, Hubbard et al., 2002).

39

Figure 2.10 In silico results shows that IFN-λRα (rs 10903035) allele is a

significant predictor of measuring IFN-λRα expression levels with

respect to the TF binding opportunities and playing its relevant roles

(Flicek et al., 2011).

45

Figure 4.1 Quantitative PCR for IFN-λR1 in monocytes, type II and type II

macrophages, which show that the expression of receptor is higher in

59

xv

both types of macrophages as compared to monocytes. Arrow head

shows the closest value from the three repeats.

Figure 4.2 Quantitative PCR for OASL in monocytes, type I and type II

macrophages.

60

Figure 4.3 Quantitative PCR for measuring the expression of IFN-λR1 in type 1

macrophage of day 5.

61

Figure 4.4 Quantitative PCR for measuring the expression of OASL during the 5

days of differentiation of monocyte to type I macrophages, in which

IFNλR1was expressed. They were induced continuously with various

interferons with GM-CSF until day 5 and then lysed to collect the

RNA. Arrow head shows the closest value from the three repeats.

61

Figure 4.5 Quantitative PCR for measuring primer efficacy for various primer

pairs. from left to right.

62

Figure 4.6 Quantitative PCR with primer set 2 of splice variant 1 and the

maximum expression was seen in the macrophages, which are well

known responsive cells to type III interferons. Other cell are non-

expressive of IFNλR1, hence show no expression of IFNλR1.

63

Figure 4.7 Different primer sets used to amplify splice variant 1, splice variant 2

and splice variant 3.

64

Figure 4.8 Splice variant 3 is not a completely functional receptor and is quite

similar to splice variant 1.

65

Figure 4.9 Next generation sequencing data performed on IFN-λR1 gene shows

that splice variant 1, 3 and 4 are more likely to be expressed in various

situations as compared to splice variant 2, 5 and 6. Splice variant 1 of

IFN-λR1 is the fully functional receptor od type III interferons.

66

Figure 4.10 C-scores, S-scores and Y-scores of the signal peptides of IFN-λR1 and

IFN-αR1 attained through SignalP.

67

Figure 4.11 Image through confocal microscopy showing expression of mutated

IFN-λR1 in HEK 293 cells.

68

xvi

Figure 4.12 Luciferase assay performed to measure the strength of signal peptide.

Wild type IL-28RA receptor showed its maximum expression in HEK

293 cells, whereas HA tagged wild type and mutated IL-28RA

showed more or less similar expression pattern and efficiency.

69

Figure 4.13 Results from MATCH (gene-regulation.com) showing various

transcription factor binding sites with their sequences, positions, core

matches and the matrix matches in IL-28Rα gene of Homo sapiens.

71

Figure 4.14 Graphical representation from the results obtained using MatInspector

from Genomatix software suite of various transcription factor binding

sites in multiple sequences of IL-28Rα gene in Homo sapiens.

74

Figure 4.15 Results from MatInspector shows various transcription factor binding

sites in IL-28Rα gene of various species, which have an influence in

the evolution of this receptor. It has compared the gene in Homo

sapiens, rhesus monkey, chimpanzee, mouse, rat, rabbit, horse, cow,

pig, dog (vertebrates).

75

Figure 4.16 Selected results from SNPInspector displaying SNPs located in coding

exons which influence the protein sequence.

76

Figure 4.17 Figure showing highlights from the results obtained using Genomatix’

Overrepresented TF families tool. Z-score shows the distance of our

sequence from the population mean in units of population standard

deviation.

77

Figure 4.18 The graph above displays the most relevant transcription factors up to

10 in 20kb upstream and 10kb downstream of gene IL28Ra as per

reported by Qiagen.

78

Figure 4.19 Intensity of interaction of IFN-λRα (IL28Rα) with other transcription

factors and cytokines shown in a networking style by using various

colorful lines representing limit of interaction and confidence in that

interactions.

82

Figure 4.20 HepG2 cells were treated with IFNa (1000 U/ml), IFNl3 (10 ng/ml) or

IFNl4 (10 ng/ml). After 4 h, the level of the interferon-induced genes,

83

xvii

IFIT1, MX1 and OASL, was quantified by qPCR, four independent

experiments are shown, mean and s.e.m. are plotted

Figure 5.1 Monocytes differentiate into various immune cells on exposure to

various cytokines and chemokines. Mostly they are differentiated to

either DCs or Macrophages upon exposure to GM-CSF/IL4 or GM-

CSF alone representatively.

85

Figure 5.2 Key elements describing the expression of IL-28Rα, the receptor

protein gets expressed with the combined effect of a series of

transcription regulatory elements as highlighted by our study via

various computational tools.

98

xviii

ABSTRACT

Type III interferons signals through a combination of two heterodimer receptors (IFN-

λRα and IL-10Rβ) and activates the anti-viral pathways like ISRE, GAS driven

transcriptions or STAT independent actions. All type III IFNs were preferred over type I

IFNs as a candidate for antiviral regimen since their discovery, because of the selective

expression of their private receptor chain i.e. IFN-λRα, which lessens the side effects

during their therapies. We investigated the expression pattern of IFN-λRα in

macrophages, their precursor cells i.e. monocytes, HEK293 and HepG2 cells through

various techniques and showed that the expression of IFN-λRα neither depends on the

strength of its signal peptide, nor on the balance maintained by the expression of its splice

variants, rather on the epigenetics (transcription factors) involved. We furthermore

investigated the SNPs involved in the TFBS of those TFs and found that NFYA is the

most important of all TFs predicted and literature shows that SNPs involved in its TFBS

affect the outcome of IFN-α therapy. We further studied the behavior of IFN-λ4 protein

in macrophages and HepG2 cells and elucidated its ability to activate ISGs comparable to

IFN-λ3 and identified the receptor complex in its mode of action. Our study shows that

although SNPs identified in IL-28B are found to be an important factor in predicting the

outcomes of IFN-α therapies, but we cannot neglect the importance of the naturally

occurring SNPs in its receptor too, which can also result in resistant IFN-α treatments for

HCV patients, moreover controversies regarding the natural expression of IFN-λ4 and

pessimistic effects on HCV treatment should be reconsidered as we have shown its potent

antiviral behavior in vitro.

1

Chapter 1

INTRODUCTION

Hepatitis C virus (HCV) is an enveloped, positive sense, single stranded RNA

virus, which is 55-65 nm in size (Kato, 2000). It belongs to Flaviviridae family of viruses

and causes hepatitis C infections in humans (Bradley, 1992; Lau, 1994;Mizokami et al.,

1994). HCV infections are among the major global health problems with approximately

80 million viremia infections and about 300 million carriers (Mathur et al., 2001, Kutcher

et al., 2001, Solomon et al., 2015, Lazarus et al., 2014, Spina et al., 2014, Cullen et al.,

2014, Suryaprasad et al., 2014, Jackson et al., 2014, Saleha et al., 2014, Gower et al.,

2014). Sixty to eighty percent of the carriers progresses to chronic liver diseases (Kao et

al., 2002). Chronic infections caused by HCV results in over 350,000 deaths annually and

are the leading cause of hepatocellular carcinomas, fibrosis and liver cirrhosis (Averhoff

et al., 2012)(Purow and Jacobson, 2003, Mathur et al., 2001, Kutcher et al., 2001).

HCV is endemic in South Asia, affecting about 10% of the population (Lehman

and Wilson, 2009, Mohd Hanafiah et al., 2013, Karoney and Siika, 2013, Sievert et al.,

2011, Rantala and van de Laar, 2008, 2011, Guerra et al., 2012, Nguyen and Nguyen,

2013, Pellicano and Fagoonee, 2012,). According to WHO, the prevalence of HCV in

Pakistan is around 3 to 14% and it falls in the high endemic zone of the world (Figure. 1)

(Muhammad Umar, APRIL-JUNE 2012). One out of every twelve individuals is at a risk

of hepatitis C and it is also a wide spread viral diseases across the globe (Figure. 2)

(Nwokediuko, 2010).

2

Figure 1.1: Global Prevalence of Chronic Hepatitis C Infection in 2013. Black shaded parts are

the least prevalent parts for HCV, whereas dark gray parts of the world map falls in 3-6 %

prevalent sections. Pakistan falls among 6-9% prevalent countries and therefore shaded as light

gray in the global prevalence map.(Lehman and Wilson, 2009, Mohd Hanafiah et al., 2013,

Karoney and Siika, 2013, Sievert et al., 2011, Rantala and van de Laar, 2008, 2011, Guerra et

al., 2012, Nguyen and Nguyen, 2013, Pellicano and Fagoonee, 2012, Touzet et al., 2000, Yan et

al., 2012, Youssef et al., 2012, Ziglam et al., 2012, Ankur et al., 2012, Katabuka et al., 2012,

Kung et al., 2012, Nagmoti et al., 2012, Stewart et al., 2012, Sood et al., 2012b, Jayavelu and

Sambandan, 2012, Palmateer et al., 2013, Naeem et al., 2012, Zhong et al., 2012,

Babamahmoodi et al., 2012, Viet et al., 2012, Hartleb et al., 2012, Zermiani et al., 2012, Cazein

et al., 2012, Samimi-Rad et al., 2012, Abdelwahab et al., 2012, Vickerman et al., 2012, Harris et

al., 2012, Sater et al., 2001, Barnett et al., 2001).

In some recent studies conducted in different cities of Pakistan, the prevalence of

HCV was 6.5% in Hafizabad (Punjab) and 5.5% in Kech (Balochistan) (Fig. 1.2),

whereas it has also been comparably found across the border with India (Ali et al., 2005,

3

Luby et al., 1997, Aziz et al., 2013, Ahmed et al., 2012, Basu et al., 2013, Sood et al.,

2012b, Sarma et al., 2012, Sood et al., 2012a).

HCV is a blood-borne viruses that spread via contact with body fluids during

unsafe sex practices, blood transfusions, child birth and sharing of blades or needles

(Khuwaja et al., 2002, Kane et al., 1999). HCV in co-infection with HBV is also

common and can be associated with life threatening complications like liver cirrhosis

and hepatocellular carcinomas (HCC) (Alberti et al., 1995, Raimondo and Saitta, 2008).

HCC is the fifth most cancer around the world, it is one of the top three leading death

causing cancers and the second most common cancer in males (Bosch et al., 2004).

Over 8.6 million individuals are infected with hepatitis C virus (HCV) in Pakistan

and with this much account, Pakistan stands at second position (4.8 % of population)

after Egypt (22% of population) in rating by World health organization (WHO) (WHO,

2012). Pakistan lacks awareness programs to educate people, that how to stop or at least

slow down its transmission within population. The primary modes of transmission

highlighted in Pakistan are reuse or misuse of syringes, barbers, dentists and unsafe sex.

Pakistan comprises mainly of rural areas and it has been reported that one out of five can

be infected with any viral disease and literacy rate is too low there, which creates a gap

between housewives and health practitioners; if husband gets infected due to any

unknown reasons, his life partner will have maximum chances to get involved in vertical

and horizontal transmissions of viral and bacterial diseases (Khuwaja A, 2014). Another

important fact involved in the high prevalence of HCV in these countries is the

inadequate treatment against HCV. Chronic infections with HCV are a leading cause of

4

liver failure, fibrosis, cirrhosis, HCC and insulin resistance, it is therefore the major

indicator of liver transplantation and costs over US $ 50,000/case.

Figure 1.2: Prevalence of Chronic Hepatitis C Infection in Pakistan in 2012. Punjab being the

highly populated province has the highest prevalence rate too, whereas the KPK and northern

parts of Pakistan are not much studied with reference to the prevalence of HCV and seems to

have low prevalence in literature (Naeem MA, 2008, Rizwan Hashim, 2005, Afridi et al., 2013,

Lavanchy, 2009, Idrees and Riazuddin, 2008, Tariq et al., 1999, Khan et al., 2000, Butt et al.,

2008, Muhammad Umar, 2012, Chaudhary I A 2007, 2010).

The gold standard of care for HCV patients was interferon alpha (IFB-α) and

Ribavirin until December 2013, when Sofosbuvir and Simepriver was added to improve

5

the triple therapy against HCV (Oriol Gutierrez, March 12, 2014 ). Pegylated interferon

is the drug of choice in most of the cases in this triple therapy. Few antiviral drugs like

telaprevir or boceprevir were also approved against HCV, but are not considered now a

days due to their intense adverse effects and low patient compliance (Oriol Gutierrez,

March 12, 2014 ).

Interferons are basically a group of proteins, characterized as cytokines and were

first discovered in 1957 by Isaacs and Lindenmann (Isaacs and Lindenmann, 1957).

Interferons were biologically active proteins and were named so, for their ability to

‘interfere’ with the replication of viruses and exhibiting resistance towards the spread of

viral infections in chick cells (Thibault and Utz, 2003). IFNs proved themselves as an

antiviral drug and showed their therapeutic potential against respiratory viral infections

and since then, they have established themselves clinically as antiviral, antitumor and

antineoplastic agents against number of disorders.

IFNs are classified into three distinct groups after the discovery of their isoforms,

based on their amino acid sequences and interaction with specific receptors. In

vertebrates, the primary antiviral defense mechanism is type I IFNs, which are part of

innate immunity and evolutionary history has resulted in the development of at least

eight distinct subfamilies: IFN-alpha (IFN-α), IFN-beta (IFN-β), IFN-epsilon (IFN-ε),

IFN-kappa (IFN-κ), IFN-omega (IFN-ω), IFN-delta (IFN-δ), IFN-zeta (IFN-ζ) (limitin),

IFN-tau (IFN-τ) and IFN-sigma (IFN-σ)(Pestka et al., 2004). Majority of them are found

in humans, where IFN α dominates with its 13 subtypes (Pestka et al., 2004). Some of

the type I interferons are found in mammals other than humans, like IFN σ in pigs, and

IFN ζ (limitin) in mice and IFN τ in ruminants during early stages of pregnancy (Leaman

6

and Roberts, 1992, Lefevre and Boulay, 1993, Oritani and Tomiyama, 2004, Oritani and

Kanakura, 2005).

Type II interferons contains interferon gamma (IFN-γ) only, where as a new

group of interferons called the interferon lambdas (IFN-λ) have recently been discovered

and are grouped into type III interferons (Kotenko et al., 2003, Sheppard et al., 2003).

They include interferon-lambda 1, -2 and -3 (IL-29, IL-28A and IL-28B) until 2014,

when another breakthrough in this field took place and Prokunina and his group fellows

discovered another member of type III interferons and named it as IFN-λ4(Prokunina-

Olsson et al., 2013).

In mammals number of IFN genes are variable but the number of functional genes

identified that encode type I IFNs are 17 non-allelic genes in humans only. All lack

introns and cluster on chromosome 9. Of the type I IFNs, there are 13 IFN-α whereas

there is only one type of IFN-β, IFN-ω, IFN-ε or IFN-κ. But all are mostly non-

glycosylated proteins of 165-plus amino acids, share homologies that range from 30–

85% within a species. Essentially all have relatively high specific potencies. There are

four interferon lambda genes clustered on human chromosome 19 and most importantly

as compared to type I IFN genes which lack introns, the four type III IFNs have several

exons. Six for IFN-λ1 (IL-29), thirteen exons for IFN-λ2 (IL-28A), whereas five each for

IFN-λ3 (IL-28B) and IFN-λ4.

IFN-λ4 has been found to be inactivated in a large human population due to

frameshift mutation and extensive genetic study of this gene have revealed that the

existence of this novel genes (IFNL4) ss469415590, TT or ▲G, that is; people with ▲G

allele show lesser chances of HCV clearance and usually have a negative treatment

7

outcome, whereas people with TT allele leads to a frameshift mutation and inactivates

IFN-λ4(Prokunina-Olsson et al., 2013). Thus, disorder in this gene is beneficial for

countering HCV infection through interferon alpha therapy.

Another interesting feature of IFN-λ4 protein is that it is equally functional as an

antiviral protein as its other family members, which has created a mystery for scientists

all over the world now a days (Hamming et al., 2013).IFN-λ4 activates all the antiviral

pathways, which are involved in the mechanism of action of IFN-λ1, -2 and -3, but still it

has a negative impact on the treatment of HCV. The structure of IFN-λ4 has not been

fully understood either as the protein of IFN-λ4has not been crystalized yet. Hamming et

al has also shown that IFN-λ4 functions through the same heterodimer receptor that is

common and unique for type III interferons (Hamming et al., 2013).

Like type I IFNs, type III IFNs functions through a heterodimeric receptor

consisting of a private chain for type III interferons i.e Interferon lambda receptor 1

(CRF2/12, IFNLR, IL-28R1), and a shared chain, which is common among many

interleukins (IL10Rβ) (Sheppard et al., 2003, Gad et al., 2009, Commins et al., 2008).

IFNs are produced by Retinoic acid–inducible gene (RIG)–I–like receptors (RLRs) after

the stimulation induced by viruses, microbial products or chemicals (Uze and Monneron,

2007). Exposure of IL-28R1 receptor complex by IFN-λs activates various defensive

pathways through intracellular signaling cascades and stimulates the expression of a

number of overlapping and unique genes involved in the antiviral, anti-inflammatory and

immune responses(Uze and Monneron, 2007). The treatment and pathogenesis of

infectious diseases, allergies and autoimmunity has become advanced with the increasing

8

knowledge about the evolutions and improvements of innate immune system (Akira et

al., 2006).

IL-28R belongs to the class II cytokines receptors that are known to induce gene

expression in targeted cells through The Janus kinase/signal transducers and activators of

transcription (JAK/STAT) pathway (Kotenko et al., 2003). As discussed before, IFN-λs

binds to a heterodimer of IL-28Rα and IL-10Rβ, where they induce their responsive

cascade of reactions, which includes kinases and phosphatases for phosphorylation and

dephosphorylation of various proteins respectively (Darnell et al., 1994). Most class II

cytokine receptors function in the form of heterodimers as shown in the figure

1.3(Renauld, 2003).

Figure 1.3:Various receptor complexes with their ligands(Renauld, 2003). Heterodimeric

receptor complexes are most common among this class of cytokines and they activates their

pathways after attaching themselves to the specific receptor complexes. Most of them shares

same receptor complexes and obeying this rule, type III IFNs also falls in the same category.

9

Many members of the class II cytokines have been under research for quite some

time now and what makes them interesting is their sequence homology, creating doubts

about the gene ancestry. Until the establishment of 3D structures of Zebra fish

interferons (IFN-ϕ1 and IFN-ϕ2), it was believed that these IFNs were orthologs of

mammalian type III IFNs, but after solving crystal structure of IFN-ϕ1 and IFN-ϕ2, they

can be classified into type I IFN family revealing that both antiviral IFN systems have

arisen twice during vertebrate evolution (Renauld, 2003, Hamming et al., 2011). It is

their common genomic organization and structural similarities that points to a common

ancestry. Figure 1.4 shows the work done on figuring out the evolution of cytokines of

this class (Renauld, 2003, Hamming et al., 2011). Distant receptors for IFN-ϕ1 and IFN-

ϕ2 are present in Zebra fish with customary long and short chain combinations(Aggad et

al., 2009) .

IL-28R, a foremost member of class II cytokines, is primarily present on the

epithelial cells, B cells, certain macrophages and hepatocytes (Liu et al., 2011a). An

interesting fact that has recently been put forth is that these receptors appear to be

present on monocyte derived macrophages however; they are neither present on the

precursor monocytes nor on monocytes driven dendritic cells (Liu et al., 2011a). It is for

this reason that IFN λ plays a role in inducing toll like receptor (TLR) induced cytokine

production in monocyte derived macrophages only (Liu et al., 2011a, Liu et al., 2011b).

10

Figure 1.4: Phylogenetic tree for correlating fish and human IFNs. The phylogenetic tree

derived from structure-guided multiple alignments confirms that both groups of fish IFNs are

evolutionarily closer to type I than to type III tetrapod IFNs (Hamming et al., 2011).

To explore this difference, it is important to study the development and

differentiation process of both these cells. The haematopoitic stem cells (HSC) in the

bone marrow give rise to lymphoid and myeloid lineages of immune cells (Vodyanik et

al., 2005). The monocytes arise from the myeloid lineage and afterwards give rise to

macrophages as well as dendritic cells (Vodyanik et al., 2005).

So far three splice variants have been reported in literature with splice variant 1

being the fully functional and membrane associated receptor chain (memIFN-λR1)

11

(Witte et al., 2009, Sheppard et al., 2003, Dumoutier et al., 2003). The second type of

splice variant has neither been detected as a protein nor its expression has been

measured ever but according to NCBI, it lacks the sequence of the first part of exon VII,

which is the part of its intracellular domain (Witte et al., 2009, Sheppard et al., 2003,

Dumoutier et al., 2003). Splice variant 3 is also called as the soluble IFN-λR (sIFN-λR1)

and it lacks the sequence on exon VI. It lacks 244 aa as compared to the fully functional

protein, which deprive it from having the transmembrane and intracellular region of the

functional protein. This deficiency makes it a secreted single chain receptor, which is

capable of attaching the IFN-λ protein and has been detected by scientists (Witte et al.,

2009, Sheppard et al., 2003,Dumoutier et al., 2003).

In viral infections, innate immune responses are initiated when viruses or their

genetic material are recognized by cellular pattern recognition receptors such as TLRs or

RIG-1/ MDA-5 leading to the activation of several transcription factor systems and their

receptors gene expression (Takeuchi and Akira, 2008, Prescott et al., 2007).

The hypothesis was dependent on a finding regarding the expression of IL-28R1

in monocyte-derived macrophages, whereas IL-28R1 is absent in both the human

primary monocytes and monocytes-derived DCs (Liu et al., 2011a).In this study, we

proposed a model for IL-28R gene regulation, where we propose that the expression of

IL-28R depends on specific transcriptional regulating factors that activate the gene

expression independently via specially separated promoter elements. The post

transcriptional regulation of this IL-28R1 was also experimentally verified during this

study.

12

Chapter 2

REVIEW OF LITERATURE

Cytokines are a large group of glycoproteins, involved in various immune

responses and IFNs belong to the same class because of their pro-immunity activities

against foreign bodies or self-defense (Isaacs and Lindenmann, 1957, Lindenmann et al.,

1957, Pestka, 2007). IFNs are divided into three groups and their division is based on the

receptors they bind and act upon. All three types of IFNs bind to three different

heterodimeric receptor complex to initiate their corresponding antiviral pathways (Li et

al., 2009).

Type III interferons are naturally produced by immune cells of many animals in

response to any viral or bacterial attack or induction of certain chemicals (Kotenko et al.,

2003). They are also produced in human bodies in conditions of certain cancers or

autoimmunity (Coccia et al., 2004). Although a wide range of immune cells co-induce

type III IFNs with IFN-β in the presence of dsRNAs or lipopolysaccharides (LPS) but

their mode of actions vary from each other, since type I IFNs initiate the antiviral or anti-

tumor responses through their own pair of receptors and type III IFNs initiate the same

process through a heterodimeric receptor consisting of a private chain called IL-28Rα and

a common β chain of IL-10 called IL-10Rβ (Kotenko et al., 2003, Coccia et al., 2004,

Sheppard et al., 2003).

Type III IFNs initiate the immune response through their class II cytokine

receptors, which initiates several intracellular pathways, resulting in inhibition of viral

replication within the host cells through three main pathways, which includes JAK/STAT

pathway, STAT independent pathway and GAS pathway (Kotenko et al., 2003, Sheppard

13

et al., 2003). These pathways then help the immune system in activation of natural killer

cells and macrophages thereby increasing the antigen presentation to lymphocytes and

establish immunity in host cells from viral infections(Uze and Monneron, 2007, Kotenko

et al., 2003).

2.1 Classification of Type III Interferons and Their Receptor

2.1.1. Type III Interferon

Interferons were named after their property of interfering with viral replication in

cells. They were classified into two types until 2003, when two scientists simultaneously

discovered a new group of interleukins, which included three cytokines and all of them

showed similar characteristics like other interferons. This newly discovered group was

included in the category of interferons as a new group called ‘type III interferons’

(Kotenko et al., 2003, Sheppard et al., 2003).

Type I IFNs are produced by leukocytes or fibroblasts and they initiate

cytotoxicity in NK cells, whereas type II IFNs are produced by T-cells or NK cells and

have a potent role in activating macrophages, thus once they were called as macrophage

activating factors (Dean and Virelizier, 1983, Strander, 1969, Clark and Hirtenstein,

1981). Type III IFNs now include four cytokines and are mostly produced by DCs and

macrophages (Prokunina-Olsson et al., 2013, Liu et al., 2011a). They also show antiviral

and anti-proliferative properties. This classification is based on the receptor complexes

through which they initiate the functional signaling cascades, which sometime intersects

each other.

Cytokines play an important role in virtually all types of immunity and stages of

inflammation. Any single cytokine activation usually triggers a cascade of responses,

14

which may involve several other cytokines, playing as co-activators to perform a

complete job (Lindenmann et al., 1957, Strander, 1969).

Classifying and understanding the role of an individual cytokine is challenging

because individual play can be dependent and vary with the cellular source, targets,

diseases and stages of the immune response (Commins et al., 2008). Another

distinguished feature that makes the cytokines of IL-10 family more complicated is that,

they can have both proinflammatory and anti-inflammatory potentials, and play

complementary roles in various immunological conditions (Sheppard et al., 2003). These

intricate connections among the cytokines of IL-10 family which is highly pleiotropic

make the precise classification difficult (Commins et al., 2008). They share similar

genetic make-up, receptor complexes, even the signaling cascades they initiate can

intersect each other at various points (Commins et al., 2008).

Proteins of type III IFNs have sequence homology among themselves and with

other members of IL-10 family, so they were included in IL-10 family but due to their

function and genomic structure, they were classified as interferons (Table 2.1)(Gad et al.,

2009).

15

Table 2.1 Classification of Interferons

Type I Interferons Type II Interferons Type III Interferons

Based on the type of receptor through which they signal

IFN-alpha (IFN-α):

α-1, α-2, α-4, α-5, α-6, α-7, α-8, α-

10, α-13, α-14, α-16, α-17, α-21

IFN-Gamma (IFN-γ) IFN-Lambda1 (IFN-λ1)/IL-29

IFN-Lambda2 (IFN-λ2)/IL-28A

IFN-Lambda3 (IFN-λ3)/IL-28B

IFN-Lambda4 (IFN-λ4)

IFN-beta (IFN-β)

IFN-epsilon (IFN-ε)

IFN-kappa (IFN-κ)

IFN-omega (IFN-ω)

IFN-delta (IFN-δ)

IFN-zeta (IFN- ζ)

IFN-tau (IFN-τ)

IFN-sigma (IFN-σ)

RECEPTORS

IFNAR1/IFNAR2 IFNGR1/IFNGR2 IFN-λRα/IL-10R2

*(IFN-δ = only found in pig)(IFN-τ = only found in ruminants)(IFN –ζ =only found in mice)

2.1.2. Interleukin-10 (IL-10) Family

IL-29 (IFN-λ1), IL-28A (IFN-λ2), IL-28B (IFN-λ3) and IFN-λ4 are included in a

new cytokine family and named as type III IFNs. They are functionally similar to type I

IFNs and exhibit anti proliferative, antitumor and antiviral activities, but if their genetic

sequence is compared, they resemble more with IL-10 family (Sheppard et al., 2003, Gad

et al., 2009). Furthermore studies have revealed the similar hetero dimeric form of

receptor complexes, which includes the beta chain of IL-10 receptor (this IL-10Rβ is

being shared among various cytokines included in IL-10 family like IL22 and IL26)

(Commins et al., 2008, Gad et al., 2009). The reserved chain of type III receptor is also

located on the same chromosome (1p36.11) where the gene of IL-22 receptor is located,

which also belongs to the IL-10 family.

16

2.2 Type III Interferons Verses Type I Interferons

Type III IFNs induces antiviral activity through the same intracellular signaling

pathways as type I IFNs and attains consequently similar anti-viral effects. IFN-λ4 is also

a newly discovered member of Type III IFNs and is generated by the immune system in

response to viral infection (Hamming et al., 2013). The receptor for type III IFNs,

however, is distinct from the receptor used by IFN-α and is present on fewer cell types

within the tissues of the body that are important sites of viral infection, including

hepatocytes. IFN-λRα is expressed at low levels on hematopoietic cells compared to the

IFN-αR and this restricted receptor distribution pattern has the potential to result in a

more favorable safety profile for type III IFNs than for type I IFNs with less side effects

(Dumoutier et al., 2004, Liu et al., 2011a). A unique feature of type III IFNs is that IFN-

λs shifts immature DCs toward a program characterized by the ability to produce Foxp3-

expressing CD4+CD25+ regulatory T cells. Results from a study performed with a mixed

lymphocyte reaction demonstrate that IFN-λ treated DCs induce IL-2–dependent

proliferation of CD4+CD25+Foxp3+ innate regulatory T cells. Hence responsibility being

performed as IFN-λs is able to generate tolerance-inducing DCs(Mennechet and Uze,

2006). As discussed before, IFN-λ exhibits antiviral activities and inhibits hepatitis B and

C virus replication, this observation is constructive for another antiviral treatment

because IFN-λ might be an alternative to IFN-α for HCV infected patients displaying

resistance to IFN-α, especially if their resistance is just because of type I IFN

dysfunction(Robek et al., 2005, Naka et al., 2005).

17

2.3Interferons and Their Class II Cytokine Receptors

Type III IFNs bind to a set of heterodimeric class II cytokine receptor complex;

consisting of an orphan class II receptor chain (IFN-λRα) and a shared IL-10Rβ

(Sheppard et al., 2003). Class II cytokine receptors were originally named for the

homology in their sequences of extracellular domains and their ligands sharing similar

structures (Renauld, 2003). Most of the class II cytokines hold therapeutic potentials

(Commins et al., 2008). Class II cytokine receptors are also involved in certain crucial

polymorphism and have been clinically proven as important markers for treatment or

cause of certain diseases and disorders (Renauld, 2003, Chae et al., 2006). All class II

cytokines show relatively weak sequence homologies but it is believed that they have

evolved from a common ancestor, as the genes encoding them are clustered on four

different loci of mouse and human genome (Renauld, 2003).

The aligned sequences of the human class II cytokines and the extracellular

domain of their receptors are shown in figure 2.1. The class II cytokine binding domain is

duplicated in the IFN-αR1 chain, the sequence of this receptor has been split into amino

and carboxyl for the first and second receptor domains, respectively. The genomic

localization of the corresponding gene in humans and the amino-acid identity with IL-10

for the cytokines, and with the extracellular domain of IL-10R are also shown in the

figure 2.1. (Renauld, 2003).

18

Figure 2.1: Phylogenetic tree of class II cytokines and receptor. A) Alignment of class II

cytokines in human. B) Alignment of extracellular domains of the receptors of class II cytokines

(Renauld, 2003).

In relation to the discovery of these genetically and structurally similar class II

cytokine ligands, a family of interdependent heterodimeric receptors were discovered,

which usually share one of the receptor chains in their complexes and bind with a distant

ligand of class II cytokines. These receptors share fairly similar structures and

extracellular domains. They are different from class I cytokine receptors on the basis of

conserved cysteine sequences and the absence of Trp-Ser-Xaa-Trp-Ser motif. They are

unique because of two type III fibronectin domains in their extracellular structure

(Renauld, 2003).

19

Class II cytokine receptor family concludes on twelve members, of which ten

contain a transmembrane protein, which provides the high affinity binding site after

heterodimerization for class II cytokines (Renauld, 2003). The intracellular portions

(cytoplasmic domain) also show some variety and that’s why these receptors can easily

be classified as short or long chain receptors. Most of the heterodimeric complexes share

common chain out of the two involved, for example IL-10Rβ chain is the most shared

one and it completes the complex with either IL-10Rα for IL-10 or with IL-22R, to attract

IL-22, or IL-28Rα/ IFN-λRα to bind with any of the four type III IFNs (Renauld, 2003,

Sheppard et al., 2003, Hamming et al., 2013). Although these receptor complexes contain

partial similarities, still they show highly conserved ligand binding affinities and diversity

in initiating signaling cascades afterwards (Lewerenz et al., 1998, Renauld, 2003). The

interactions involved in the class II cytokines receptors are shown in the following figure:

2.4 Gene Location of Interferon Lambda and Its Receptor (IFN-λ)

Geneticists in 2003 discovered the genes of three type III IFNs to be clustered on

chromosome 19 (q 13. 13 region) and named them as IL-28a, IL-28b and IL-29 (IFN-λ1,

IFN-λ2 and IFN-λ3) but later in the start of 2013, another gene was discovered located in

between these genes and was named IFN-λ4 because of its similar actions and structure

(Hamming et al., 2013, Prokunina-Olsson et al., 2013, Sheppard et al., 2003). Type III

IFNs were included in IL-10 family instead of making another group of IFNs, as they are

more related to IL-10 family in genome structure but their protein structure and actions

had more resemblance with type I IFNs (Gad et al., 2009). Similar to IL-10 family genes

having multiple introns, type III IFN’s genes also consist of several introns, six in IFN-λ1

gene, thirteen in IFN-λ2 gene and five each in IFN-λ3 and IFN-λ4, however type I IFNs

20

do not have introns within the genes (Sheppard et al., 2003). All four members of type III

IFN family display self-homology, as IFN-λ3 is identical to both IFN-λ2 and IFN-λ4,

whereas IFN-λ1 has 81% homology with IFN-λ2, IFN-λ3 and IFN-λ4 (Sheppard et al.,

2003, Hamming et al., 2013). The cysteine pattern and amphipathic profiles of type III

IFNs genes are conserved among themselves thereby making them functionally similar

after binding with the heterodimer receptor (Gad et al., 2009, Hamming, 2010, Hamming

et al., 2013). The heterodimer receptor has a private chain of a novel member of class II

cytokine receptor family (IL-28Rα/ IFN-λRα) and a common chain of IL-10 family’s

receptor (IL-10Rβ), which is believed as a perfect match and show stronger binding

affinity (Sheppard et al., 2003).

The gene for IFN-λRα is located on chromosome 1 (1p36.11) and has highest

sequence homology of 23% with IL-22Rα2 (Sheppard et al., 2003) (figure 2.2). Three

splice variants of IFN-λRα have been reported in literature but in-vivo expression of

splice variant 2 is still not experimentally proven up to satisfactory levels (Liu et al.,

2011a, Witte et al., 2009b). 5’ UTR and the signal peptide of IFN-λRα is encoded by the

first exon, whereas the transmembrane structure is encoded by the sixth exon of this gene

(Kotenko et al., 2003, Sheppard et al., 2003). The heterodimer receptor contains IL-

10Rβ, which is the shorter intracellular moiety (79 aa) and located at 21q22.11, near the

receptors of type I and type II IFNs (Wolk and Sabat, 2006, Kotenko et al., 2003,

Sheppard et al., 2003). IFN-λRα on the other hand constructs the longer intracellular

moiety of the receptor with 271 aa and contains three tyrosine residues, which are

phosphorylated during signal transductions (Kotenko et al., 2003, Sheppard et al., 2003).

Tyr343 and Tyr517 of IFN-λRα are involved in STAT2 activation by type III IFNs and

21

they independently activate anti-viral and anti- proliferative actions of type III IFNs

(Dumoutier et al., 2004). Mutations resulting in the conversion of these two tyrosines

manifest itself by complete annihilation of their antiviral and anti-proliferative character

(Dumoutier et al., 2004).

Figure 2.2: Gene location of IFN-λRα on chromosome 1. IFN-λRα is present on chromosome 1

(1p36.11) in close proximity to the receptor of IL-22 (Flicek et al., 2011, Hubbard et al., 2002).

IFN-λRα genes are also duplicated in various species similar to receptors of type I

IFNs, e.g in murine IFN-λRα is located on chromosome 4D3 whereas IL-10Rβ is present

on 16C4 (Lasfar et al., 2006). IFN-λRα chain of mouse is approximately 67% similar to

its Homo sapiens counterpart and contains three additional tyrosine residues (Lasfar et

al., 2006). Both of these receptors contain four putative N-linked glycosylation sites in

their extracellular domains (Kotenko et al., 2003, Sheppard et al., 2003).

Usually a single copy of IFN-λ is present in birds like chicken and they inhibit the

viral replication quite effectively (Reuter et al., 2014), whereas in mouse the first exon of

IFN-λ1 gene contains a stop codon, but IFN-λ2 and IFN-λ3 genes and proteins are

homologous to human IFN-λs. IFN-λ4 has not been identified in mouse yet but it has

22

been predicted in many species like Macaca fascicularis, Myotis brandtii , Sus scrofa,

Myotis lucifugus, Leptonychotes, Pteropus alecto, Felis catus and Bos mutus. However,

it has been sequenced in Macaca fascicularis, Papio anubis and Pan troglodytes(Bartlett

et al., 2004, Yao et al., 2014, Hermant et al., 2014, Pruitt et al., 2003, Feolo et al., 2000,

Pruitt and Maglott, 2001).

2.5 Alternative Splicing of IFN-λRα

Alternative splicing is a highly regulated process (Ruan et al., 1999, Wang et al.,

2002), which results in the formation of multiple proteins from a single coding gene . The

large gene of IFN-λRα shows various transcription points using bioinformatics tools,

which may involve up to 9 exons through alternative splicing as shown in the following

figure 2.3 & 2.4.

23

Figure 2.3: Splice variants and the expression pattern of IFN-λRα. Altogether there are 5 splice

variants of IFN-λRα found in silico studies but theoretically first three are known to be expressed

naturally (Hubbard et al., 2002, Flicek et al., 2011).

To date literature reveals that only three splice variants of IFNλR1 exist and out

of them only two have been amplified and measured (SV1 and SV3) (Witte et al., 2009a).

According to Witte et al. the mRNA encoding full length IL28Rα can have two

alternatively spliced variants; 29 aa in the inter cytoplasmic tail is missing, which makes

it incapable of passing signals inside the cell and other one being a secreted version,

which contains ectodomain of the receptor only (Witte et al., 2009a). According to in

silico studies performed, a total of 7 splice variants are possible including 5 which are

24

more likely to be translated, though none except the three described earlier have been

reported yet (Rebhan et al., 1997, Rebhan and Prilusky, 1997, Sheppard et al., 2003).

Figure 2.4: Genome and mRNA transcribed: The mRNA encoding IFN-λRα show various splice

variants in human cells(Lee et al., 2007).

2.6 A Decade of Type III Interferon

All four type III IFNs were said to be included in IL-28 family on the basis of their

gene locations but according to the latest classification (Table 2.2), they are named as

interferon lambdas only (Kotenko et al., 2003, Hamming et al., 2013).

25

Table 2.2 A decade of type III IFNs: discoveries research and results

Year Event References

2003 Discovery of type III IFN and their receptor (Kotenko et al., 2003)

2004 Role of tyrosine in IL-28Rα/ IFN-λRα in antiviral and anti-

proliferative activities

(Dumoutier et al., 2004)

2005 Antiviral activity of IFN-λ hepatitis B and C virus replication. (Robek et al., 2005)

2005 interaction of SOCS and ISGs produced by IFN- λ (Brand et al., 2005)

2006 Polymorphism in IL-28Rα/ IFN-λRα and allergic rhinitis (Chae et al., 2006)

2006 Activity of IFN-λs against respiratory syncytial virus (Chi et al., 2006)

2006 Anti-tumor activity of IFN-λs in mouse (Lasfar et al., 2006)

2006 Role of deficient type III IFN-λ production in asthma exacerbations (Contoli et al., 2006)

2006 IFN-λs and production of FOXP3-expressing suppressor T cells (Mennechet and Uze, 2006)

2007 IFN-λ s modulates the Th1/Th2 response (Jordan et al., 2007)

2007 Role of IFN-λ in hantavirus-infected patients (Stoltz et al., 2007)

2008 Role of IFN-λs in innate immunity of mice against influenza A (Mordstein et al., 2008)

2008 Identification of IFN-λs in birds (Karpala et al., 2008)

2009 IFN-λs can inhibits HIV type 1 infection of macrophages (Hou et al., 2009)

2009 Prediction of IFN- λ4 pseudogene (Fox et al., 2009)

2009 Expression of IFN-λ in human neuronal cells (Zhou et al., 2009)

2009 Structural study of IFN-λs (Gad et al., 2009)

2009 Role of IFN-λs against West Nile virus (Ma et al., 2009)

2009 Clinical trials of IFN-λ against hepatitis C (Miller et al., 2009)

2009 Polymorphism in IL28B is associated with HCV treatment failure (Thomas et al., 2009)

2010 IFN-λ induces apoptosis in oesophageal carcinoma cells and show

anti-tumor effects

(Li et al., 2010)

2010 IFN-λ show viral resistance in epithelial cells of the respiratory and

gastrointestinal tracts

(Mordstein et al., 2010)

2011 Role of IFN-λ in allergic asthma (Edwards and Johnston, 2011)

2011 Interferon lambda inhibits herpes simplex virus type I infection of

human astrocytes and neurons

(Li et al., 2011)

2012 Overexpression of IFN-λ through codon usage bias (Akhtar et al., 2013)

2013 Discovery of IFN-λ4 (Prokunina-Olsson et al., 2013)

2013 Purification of IFN-λ4 and identification of its receptors (Hamming et al., 2013)

2013 IFN-λ antagonizes the antiviral activity of interferon-alpha in vitro (Bordi et al., 2013)

2014 Interferon lambda 1 expression in cervical cells differs between (Cannella et al., 2014)

26

low-risk and high-risk human papillomavirus-positive women

2014 Functional Characterization of Canine Interferon-Lambda (Fan et al., 2014)

2014 Interferon lambda 4 polymorphism effect on outcome of telaprevir,

pegylated interferon, ribavirin combination therapy for hepatitis C

(Nagaoki et al., 2014)

2.7 Synthesis and Regulation of Type III Interferons

IFN-λs are part of adaptive immune system and are synthesized by the antigen

presenting cells (APC), such as DC and macrophages, but they need a stimulation

through any TLR agonist (Coccia et al., 2004, Siren et al., 2005). Type III IFNs are

produced during viral infections or stimulation with LPS or polyinosinic: polycytidylic

acid (poly (I:C)) by plasmacytoid dendritic cells (pDC) like type I IFNs (Coccia et al.,

2004, Siren et al., 2005). If macrophages are stimulated with TLR3 or TLR4 agonists or

with IFN-λ itself, they increase the production of IFN-λ. Viruses usually activate

interferon regulatory factor 3 (IRF3), IRF7 or nuclear factor kappa B (NFκB), in response

to which, immune cells produce IFNs (Coccia et al., 2004, Siren et al., 2005).

Phosphorylated IRF3 and IRF7 collectively bind to the promoter region of IFN-λ1 to

initiate its transcription; whereas IRF7 alone is required for the production of IFN-λ2 and

-λ3 (Onoguchi et al., 2007, Osterlund et al., 2005). Essential transcription factors

involved in the transcription of IFN-λ4 are not identified yet however, it is anticipated

that it follows the same route of activation as mentioned above (Hamming et al., 2013).

IFN-λ4 is believed to be the negative role player in treatment therapies using IFN-α,

ribavirin and telaprevir but the key mechanism governing the phenomenon are not yet

elucidated (Prokunina-Olsson et al., 2013, Nagaoki et al., 2014). Activated NFκβ also

plays vital role in the activation of type III IFNs after its translocation into the nucleus

(Levy et al., 2011, Crotta et al., 2013).

27

The transcriptions factors involved in the regulation of type III interferons are

related to the efficacy of type I interferons against different diseases in one way or

another, this concept of polymorphism is discussed under the heading of polymorphism

in type III IFNs later on (2.11 polymorphism in type III IFNS). Gene regulation of these

cytokines is interconnected and is not completely known yet, they show different pattern

of cytokine and receptors expression in different cells, e.g. IFN-λ has low expressions in

CNS as compared to IFN-α after Theiler’s virus or La Crosse virus infections, whereas

IFN-λ competes in expression in hepatic cells (Caroline Sommereyns., 2008).

2.8 Expression of IFN-λRαand its Pathway

All four type III IFNs interact via a cell surface receptor complex (a heterodimer

of IFN-λRα and IL10Rβ) (Kotenko et al., 2003, Sheppard et al., 2003). The orphan chain

of this receptor complex is unique for all four type III IFNs, whereas the second partner

of the complex i.e IL-10Rβ is being shared among IL-10, IL-22 and IL-26 (Sheppard et

al., 2003, Commins et al., 2008). Nomenclature of IFN-λRα consists of three alternative

names CRF2-12, IL28R and LICR (Kotenko et al., 2003, Sheppard et al., 2003,

Dumoutier et al., 2004,Commins et al., 2008). In comparison with the receptors of type I

IFNs (IFNαR1 and IFNαR2), IFN-λRα is expressed in selective cells, whereas IL-10Rβ is

ubiquitously expressed. Dimerization of two receptor chains is essential for type III IFNs

to initiate their actions, making IFN-λs targeted as a therapeutic agent, through selective

expression of IFN-λRα. Type III IFNs are under clinical trials and have shown promising

results as an antiviral drug. IFN-λs have shown few adverse effects as compared to IFN-

α, which is sometimes not recommended or discontinued due to low patient compliance.

Expression of IFN-λRα genes in normal human tissues is shown in figure 2.5.

28

Figure 2.5:IFN-λRα gene expression in normal human tissues (normalized intensities).IFN-λRα

gene expression differes in various organs and the efficaicy of IFN-λ depends upon the

expression of IFN-λRα protein(Rebhan et al., 1998, Rebhan and Prilusky, 1997,Rebhan et al.,

1997) .

29

The gene of IFN-λRα is located on chromosome 1 in human genome, in close

proximity to IL-22R gene and is transcribed in many cell types, but the protein

expression is limited to few cells types only (Witte et al., 2009a). The mature and fully

functional receptor consists of a cytoplasmic domain of 223 amino acids linked with a

transmembrane domain and pops-up with a reasonable extracellular domain of 200 aa

(Witte et al., 2009a). IFN-λRα is not widely produced by various cells but is expressed

by epithelial cells and human colonic tissues naturally (Witte et al., 2009a). Figure 2.6

shows protein expression in different cell types.

Figure 2.6:Protein Expression in different cell types: IFN-λRα Protein expression data from

MOPED, PaxDb and MAXQB.

30

An interesting fact that has recently been put forth is that these receptors appear to be

present on monocyte derived macrophages however; they are neither present on the

precursor monocytes nor monocytes driven dendritic cells (Liu et al., 2011a). It can be

the reason that IFN-λs plays a role in inducing TLR induced cytokine production in

monocyte derived macrophages only (Liu et al., 2011a, Liu et al., 2011b). Expression of

IFN-λR1 mRNA in various human tissues, cell populations of the immune system and the

skin is variable and the efficacy of IFN-λ protein depends upon the expression pattern of

IFN-λRα protein(Witte et al., 2009a).

IFN-λRα gene consists of seven exons and the position of cysteine is conserved,

which enables classification of this receptor as of class two cytokines (Commins et al.,

2008). Alternative splicing of this gene produces three splice variants, which has already

been discussed under heading 2.5.Signaling pathways initiated by type III IFNs through

IFN-λRα may vary from the pathways activated by type I IFNs, but gene array studies

have proven that the induced genes are similar in both cases (Zhou et al., 2007a, Marcello

et al., 2006, Doyle et al., 2006). A comparison of the signaling cascades of all three types

of IFNs using literature shows that type I and type III IFNs induces a limited number of

genes, which includes a complex of transcription factors known as interferon stimulated

gene factor 3 (ISGF3), comprising of STAT 1, STAT 2 and IRF9 (Zhou et al., 2007b).

Although using different receptors of same class, they show redundancy in various

outcomes (Figure 2.7). The activation of kinases leads to phosphorylation of STAT1 and

STAT2 that form a STAT1-STAT2 heterodimer after the receptor-ligand complex

formations. The dimer of STAT1.STAT2 binds to IRF9 forming the ISGF3 complex that

31

migrates to the nucleus where it binds to ISRE elements thus facilitating the transcription

of ISGs (Li et al., 2009).

Intracellular domain of CRF-2 is the binding site for members of the Janus Kinase

(JAK) family. JAK1 is involved in signaling of type III IFNs and binds to the IFN-λRα ,

whereas TYK2 binds to the IL-10R2 to initiate further signaling waves. After the binding

of type III IFNs with its receptor complex, JAK1 and TYK2 becomes activated and

cross-phosphorylate each other (Sheppard et al., 2003, Dumoutier et al., 2004,Zhou et al.,

2007b).

Figure 2.7: The main pathway of type I and type III interferon induced gene expression. Binding

of IFN-α to the type I interferon receptor as well as IFN-λ binding to the type III interferon

receptor complex allows the JAK kinases JAK1 and TYK2 to cross phosphorylate one another.

(Li et al., 2009).

32

The next step of signaling involves the phosphorylation of the three tyrosine

residues of IFN-λRα, which are present on the intracellular portion of the receptor

(Tyr343, Tyr406, and Tyr517) (Dumoutier et al., 2004). Out of the three tyrosine

residues, Tyr343 and Tyr517 seems to an essential element of antiviral signaling cascade,

because they create a docking site for Src Homology 2 (SH2) domain of STAT2

(Dumoutier et al., 2004).

Although IFN-λRα is unique to type III IFNs but it activates the signaling

pathways by phosphorylation of STAT, as performed by few other CRF2. It

phosphorylates the C-terminal tyrosine residue of STAT protein, which then catches

activated JAK1 and TYK2. This combination then constructs docking sites for the SH2

domain of STAT proteins. STAT proteins then form either a homodimer or a

heterodimers accordingly (Chen et al., 1998, Dumoutier et al., 2004). IFN-λRα passes the

signal to IRF9, STAT 1 and STAT 2 to form a complex called ISGF3, but it also

activates STAT 3 and 5 sideways. It is well known fact that a single receptor complex

can activate various pathways but it requires a dominating track too, similarly in case of

IFN-λRα, ISGF3 complex is an ultimate response of IFN-λ but STAT2 phosphorylation

plays a key role in IFN-λ signaling (Zhou et al., 2007b, Gad et al., 2009). Some scientists

say that STAT3 is not activated during IFN-λ signaling but Novak et al. has proved that

STAT3 plays a role and is up-regulated during IFN-λ treatment in myeloma B cells

(Diegelmann et al., 2010, Novak et al., 2008). The exact role STAT3 and STAT5 is not

known yet but their IFN-λRα dependent activation has early been shown in BW5147

cells (Dumoutier et al., 2004, Hamming, 2010). Studies are being conducted currently

33

aiming to see if this can influence post-transcriptional antiviral gene expression (Ding et

al., 2014).

ISGF3 complex attaches to the cis element ISRE in the promoter region of the

targeted genes after its translocation into the nucleus and activates the transcription of the

interferon stimulated genes (ISGs) (Dumoutier et al., 2004). IFN-λRα is also involved in

GAS driven pathways through STAT proteins but type II IFN when binds to its receptor,

it activates the phosphorylation of STAT1, which makes a homodimer and forms a GAF

complex (Miller et al., 2009, Lasfar et al., 2006,Dumoutier et al., 2004). This GAF

complex then translocates into the nucleus and binds with the GAS element, to initiate

IFN-γ induced responses (Li et al., 2009).

Type I IFNs also induce the homodimerization of STAT1 proteins, which then

moves inside the nucleus, but both type I and type III IFNs show oblique behaviors. They

trigger mitogen activated protein kinases (MAPK), which includes p38, JNK, and ERK

kinases (Brand et al., 2005, Zhou et al., 2007b). Type I and III IFNs acts on discrete

receptors but show redundancy in inducing ISGs (Kotenko et al., 2003, Sheppard et al.,

2003,Dumoutier et al., 2004)(Figure 2.7). They are easily be classified in the same class

of antiviral cytokines as they show antiviral behavior (Sheppard et al., 2003). IFN-λ4 also

acts through the same receptor and activates the same pathways as are induced by its

group fellows (Hamming et al., 2013). ISGs initiate various biological actions, such as

inhibition of viral replication, MHC expression, and apoptosis etc. (Akhtar, 2013b).

Type 1 IFNs are essential for a complete antiviral response, as IFNαR gene

knock-out experiments have shown that IFN-λs cannot maintain an effective antiviral

response alone, whereas IL-28αR knockouts has minor effects on overall pharmacology

34

of interferons (Asokan et al., 2006, Onoguchi et al., 2007). However, TLR3 and TLR9

agonist treatment of mice with receptors knock-out effectively reduces the viral load

(Onoguchi et al., 2007, Asokan et al., 2006). These studies have proved that the mode of

action of IFN-λ is through IFN-λRα only and they use JAK-STAT pathway to control

viral load and cellular proliferation.

2.9 Type III Interferons as Modulator of Immune Response and Mutiny

of IFN-λ4

Immune cells like T cells, B cells, macrophages and DCs are the essential part of

immune system. All types of these cells perform different roles but have the same

progenitors. Changes occur at every single step of hematopoiesis and cells differentiate

into dissimilar cells because of certain important markers, chemokines, cytokines or any

other stimulants, which initiate the differentiation of those cells. Similarly, studies have

proven that IFN-λRαis primarily present on the epithelial cells, B cells, hepatocytes and

certain macrophages but not on monocytes (Liu et al., 2011a). They have shown that it is

present on monocyte derived macrophages but neither on the precursor monocytes nor

monocytes driven dendritic cells(Liu et al., 2011a). Efficacy of IFNλs in induction of

TLR induced cytokines is dominating in monocyte derived macrophages only (Liu et al.,

2011a, Liu et al., 2011b).

Type III IFNs can modulate innate as well as adaptive immune responses by

inducing MHC class I, which increases the antigen presentation by infected cells and

immune cells stimulate adaptive responses after recognizing them (Mennechet and Uze,

2006). IFN-λRα is not expressed on leukocytes but during differentiation, pDCs start

expressing IFN-λRα and respond to type III IFNs. Type III stimulated DCs migrate to

35

lymphatic nodes and spleen and induce Foxp3+CD25+CD4+T-cell proliferation

(Mennechet and Uze, 2006). These DCs are rich in CCR7, MHC class I and MHC class

II expression but low in CD80 and CD40 markers. Although regulatory T-cells suppress

T-cell proliferation but insertion with type III IFNs expressing plasmids in murine models

has led to down regulation of Foxp3+CD25+CD4+T-cells in the spleen (Morrow et al.,

2009, Fontenot et al., 2003). These linages of immunology under type III IFNs need to be

studied in detail to answer various questions of differences within its four members or its

phylogenetic (Nagaoki et al., 2014, Ding et al., 2014, Prokunina-Olsson et al., 2013,

Hamming et al., 2013). Type III IFNs disturb the Th1/Th2 balance by increasing the

quantity of Th1 cells if naïve T cells are treated with them, because of the inhibition of

Th2 development, in reaction to reduced production of IL-13 and the role of type II IFNs

in elevating Th1 cell numbers (Jordan et al., 2007, Dai et al., 2009).

All type III IFNs including the disreputable IFN-λ4 acts via same receptor and

show similar antiviral responses including the up-regulation of MHC class I receptors and

boosting ThI responses (Hamming et al., 2013). This enhances the immunity of the host,

as the infected cells present the antigens to immune cells to attack the viruses in response

and control the conditions (Pestka, 2007, Hamming et al., 2013). These properties of type

III IFNs have made them a strong candidate for treatment of hepatitis C infections and

pharmaceutical companies are hoping to bring a major change in this therapeutic world

(Riva et al., 2014, Galmozzi et al., 2014, Sui et al., 2014).

2.10 Polymorphism in Type III IFNs

The concept of polymorphism emerged in the last century and is applied when

more than one morph or form exists in a same population of species (Ford, 1957). All

36

species should have the same environment or habitat and belong to a panmictic

population in order to be classified as polymorphs (Ford et al., 1957, Ford, 1957,Ford,

1966). There is also a common type of genetic variation among people, when a single

nucleotide gets deleted, inserted or exchanged with any other nucleotide between two

genomes ( for example, replacement of Guanine (G) nucleotide with the thymine (T)

nucleotide); commonly known as single nucleotide polymorphism frequently called SNPs

(pronounced “snips”).

SNPs occur approximately once in every three hundred nucleotides throughout

human DNA, which is calculated to be around 10000000 SNPs throughout human

genome (Gu et al., 1998, Chauvet, 1998). In the modern era, these variations of single

nucleotides found in DNA sequences of various genes are used as biological markers to

help researchers to locate mutations in genes associated with certain disorders or diseases

(Hohjoh et al., 1999, Yamada et al., 1998). Locating SNPs within genes or its regulating

regions is more related to the disorders as they directly affect the gene’s function (Gu et

al., 1998, Chauvet, 1998).

In over 10 million SNPs present in human genome, most of them have no effect

on health or normal functions of the body, but few of them have impact on the normal

gene regulation or functions, which gets disturb due to the altered gene sequence and are

important in the study of human health (Suzuki et al., 2004, Hijikata et al., 2000).

Scientist has found SNPs that may help in prediction of individual’s response to type I

IFNs therapy against HCV (Suzuki et al., 2004, Hijikata et al., 2000). These SNPs can

have synergetic effect on IFN therapy or can develop IFN resistances in certain cases,

which can depend on type or therapeutical formulation of IFNs (Ramos et al., 2012,

37

Yuan et al., 2012, Kawaoka et al., 2012, Suzuki et al., 2004, Hijikata et al., 2000).

Inheritance of certain diseases can also be tracked among families through identification

of certain SNPs (Veal et al., 2002). Studies are being conducted now days to identify or

build associations between SNPs and the complex diseases such as cancer, diabetes or

heart diseases.

2.10.1 Phylogenetic Tree of Type III Interferons

Comparing sequences of different proteins to construct a functional relationship

initiated the need of bioinformatics in this field too. It has moved up to the evolutionary

relationships after the algorithms comparing huge DNA sequences and large volume of

proteins between organisms in seconds (Altschul et al., 1997). The complexities of the

genome has spread towards the study of the non-coding regions and have included

transposable elements, chromosome rearrangements, genetic variation, transcription

factor binding sites, comparative genomics and much more. Evolutionary relationships

between proteins or more specifically between genes have been made possible due to the

study of the non-coding sequences (Fox et al., 2009). The protein structure of type III

IFNs couldn’t be predicted precisely using computational techniques until Gad and his

co-workers came up with the structure of IFN-λ (Gad et al., 2009).

IL-10 family and first two types of IFNs are helical cytokines and have

comparable three-dimensional folds, although their functions are difficult to be judged on

basis of sequences alone and these similarities with later-on differences has opened the

ways towards evolutionary studies. Mammalian genome sequence analysis has reviled

that it is almost certain to have nine copies of IFN-λ genes in a genome (Fox et al., 2009).

38

Type III IFNs show very little similarities if we BLAST it with other helical

cytokines, as around 5 % homology is shown between type III IFNs and type II IFNs,

12% with type I and 15% with the members of IL-10 family (Korf and Gish, 2000, Fox et

al., 2009). But if we compare intra-family homologies, it goes up to 96% between IFN-

λs. The gene of type III IFNs contains five conserved exons but an additional exon is also

there at 5’ end for IFN-λ2 and IFN-λ3.

The gene organization of human and mouse is quite similar in mouse and humans

and the orthology review says that they have been conserved for around 100 million years

(Lasfar et al., 2011).

Figure 2.8: Phylogenetic tree using the actual or predicted DNA sequences of spliced interferon

lambda genes or pseudogenes in human, mouse, dog and guinea pig (Fox et al., 2009).

The phylogenetic tree of human, mice, dog and guinea pig genes in figure 2.8

&2.9 shows that this gene has risen from a single interferon lambda gene in an ancestral

39

specie (Fox et al., 2009). IFN-λR1 gene on the other hand is located in close proximity

with the IL-22R gene on human chromosome 1 (Kotenko et al., 2003, Sheppard et al.,

2003) and have few splice variants in humans too. Figure shows the phylogenetic tree of

the gene sequences of human, chimpanzee, Gorilla, Orangutan, Gibbon, Macaque,

Marmoset, Tarsier and mouse. Connections show that all of them also came from a single

progenitor. The linkages have taken few sidewise turns with the passage of time for birds,

Platypus and Marsupials.

Figure 2.9: Gene Comparison Tree of Homo sapiens with other animals with reference to IL-

28RA/ IFN-λRα(Flicek et al., 2011, Hubbard et al., 2002).

40

2.10.2 Polymorphism in IFN-λ1, IFN-λ2 and IFN-λ3

Type III IFNs are a recently discovered group of IFNs as compared to type I IFNs

and after the discovery of HCV in 1989, type I IFNs became the treatment of choice

against HCV (Lavanchy, 2011). Chronic hepatitis C shows heterogeneity in treatment

outcomes and clinical presentations. This heterogeneous behavior can be related to the

host or viral factors, such as genotypes, which has clearly been shown to have a

connection with the disease progression (Akhtar, 2013a, Alberti et al., 2005).

After the discovery of type III IFNs, the sustained virological response (SVR) has

been associated with the SNPs present in IFN-λ3 gene, if the patient is treated with

pegylated interferon (PEG-IFN) plus ribavirin (RBV). Genotype 1 of HCV give the SVR

rate to be around 50% as compared to 70+ % in other genotypes f HCV (Akhtar, 2013a).

It is widely acknowledged now days about patient to undergo some latest genotype tests

before starting the antiviral therapy, which includes genotyping of virus and the patient.

SNPs, viral load, stage of liver disease and genotyping strongly predicts the outcomes of

antiviral therapy (Kau et al., 2008). Recently it has been reported that SNPs linked to

IFN-λ3 or IL-28B are a strong predictors of the outcome of PEG-IFN/RBV therapy in

patients infected with genotype 1 HCV, which has been studied through genome-wide

association study (GWAS) (Tanaka et al., 2009, Suppiah et al., 2009). Literature is being

rapidly increasing daily related to the significance of polymorphism in type III IFNs and

viral clearance in hepatitis C. The significance of IL-28B polymorphism has made the

genotype testing of IL-28B gene a part of the HCV treatment regimen. Physicians now

want to predict the outcome of the PEG-IFN/RBV therapy in HCV infection before they

set the dose and time frame of the treatment to attain SVR.

41

Table 2.3: Impact of different IL28B polymorphisms on HCV infection in several conditions

(SVR:sustained virological response; RVR: rapid virological response) (Bellanti et al., 2012).

Polymorphism Genotype Impact on HCV Impact on HCV/HIV Impact on liver graft

reinfection

rs12979860 C/C

(i)   Higher SVR (genotypes

1, 2, 3, 4) (Ge et al., 2009,

Clark et al., 2012, Mangia

et al., 2010, Pineda et al.,

2010, Sarrazin et al., 2011)

(ii)   Spontaneous clearance

(Ge et al., 2009)

(iii) Early viral kinetics

(Asselah et al., 2012, Chu et

al., 2012)

(i)    Higher

SVR(Nattermann et

al., 2011)

(ii)   No influence on

acute HCV infection

(Rembeck et al.,

2012b)

(iii) Higher all-cause

mortality (Clausen et

al., 2012, Parczewski

et al., 2012)

(i)  Natural course and

treatment outcome

dependent on donor

rather than recipient

genetic background

(Prokunina-Olsson et al.,

2013)

(ii) Higher frequency of

posttransplant diabetes

mellitus (Tillmann et al.,

2010)

rs8099917

T/G or

T/T

(i)  Treatment failure

(genotypes 1, 2, 4) (Knapp

et al., 2011, Rauch et al.,

2010)

(ii) Increased viral clearance

and virological response in

Taiwanese patients (Lange

et al., 2011)

 

(i)  SVR in patients with

recurrent HCV infection

(Veldt et al., 2012)

G/G  

(i)  High prevalence

(Kawaoka et al., 2011)

(ii) Treatment failure

(genotype 1) (Hsu et

al., 2011)

 

Combined C/C and

/T

(i)    Spontaneous clearance

(Chen et al., 2012)

(ii)   Early viral kinetics

(Fukuhara et al., 2010,

Aizawa et al., 2012, van den

Berg et al., 2011)

(iii) RVR but not SVR

(genotype 3) (Aparicio et

al., 2010)

(i) Early viral kinetics

(genotypes 1, 4)(van

den Berg et al., 2011)

42

2.10.3 The Influence of IFN-λ3 Polymorphism on Type III Interferon Biology

IL-28B/IFN-λ3 is a potent inducer of innate immune response against viruses and

it signals through the conventional heterodimeric receptor complex, whose expression is

distinct if compared to the other receptors of its class (Dumoutier et al., 2004, Commins

et al., 2008). Furthermore, it has also been reported that IL-28B/IFN-λ3 have potentials

to induce helper T-cell type 1 biased adaptive cellular immune responses, which is

helpful in maintaining the immune response initiated by the other immune cells of the

body (Morrow et al., 2009). IL-28B/IFN-λ3 also has an effect on antigen-specific CD8+

T-cell function with respect to cytotoxicity, as it amplifies the CD8+ T-cell killing

characteristics (Honda et al., 2010).

IL-28B/IFN-λ3 has some genetic variations associated with expression of

downstream genes involved in either boosting immune response or expression of some

receptors, which have influenced the outcomes for diseases like HCV in IFN-α therapy.

In 2009 and 2010, many studies were reported, showing association between sustain

virological responses (SVR) and SNP located near to IL-28B/IFN-λ3 gene (rs12980275

and rs8099917), which had effects on the outcomes of PEG-IFN/RBV combination

therapy in HCV infected patients and were also associated with the expression of ISG

(Honda et al., 2010, Urban et al., 2010)(Thomas et al., 2009, Tanaka et al., 2009,

Suppiah et al., 2009).

2.10.4 The Impact of IL-28B/IFN-λ3 Polymorphism on HCV Infection

rs12979860 is the SNP on 19q13, which is associated with the SVR of Hepatitis C

infected patients, who are infected with genotype 1 of HCV. Researchers have identified

43

three possible genotypes; C/C, it is associated with higher rates of SVR, higher appetite,

good sleep and more energy, as compared to T/T (Ge et al., 2009). These SNPs have

different outputs in different genotypes of HCV, as the C allele appears to have a positive

effect on early viral kinetics in genotype 1 but is associated with liver tissue damage in

HCV genotype 3 (Rembeck et al., 2012a). rs8099917 (T/G or T/T) is another much

studied SNP and found to be associated with the progression of chronic Hepatitis and IFN

resistance, but genotype 3 of HCV is not much studied with reference to this

polymorphism (Kawaoka et al., 2011, Rauch et al., 2010). T/T genotype shows better

SVR then T/G in Asian patients as compared with western patients so have shown

increased viral clearance in certain studies (Hsu et al., 2011).

2.10.5 Combined IL-28B/IFN-λ3 Polymorphisms

rs12979860 (C/C) and rs8099917 (T/T) were found to be auspicious and

promising in spontaneous HCV clearance, but some variants of IL-28B/IFN-λ3 were also

associated with resistance in IFN therapy and may show slower fibrosis progression in

genotypes other than 1 of HCV (van den Berg et al., 2011, Bochud et al., 2012). If we

talk about Pakistan, genotype 3 is very prevalent here and concerning genotype 3 infected

patients, IL-28B/IFN-λ3 polymorphisms are associated with rapid viral response (RVR)

but not SVR or early viral response (EVR) to PEG-IFN therapy (Moghaddam et al.,

2011). In patients infected with non-3 genotypes of HCV, studies have shown that IL-

28B/IFN-λ3 polymorphism is strongly associated with EVR during PEG-IFN/RBV

therapy of chronic HCV infection (Bochud et al., 2012, Arends et al., 2011,Rivero-Juarez

et al., 2012). In EVR, blocking the production of virions or their release can be crucial

44

and evidences claims that polymorphism of IL-28B/IFN-λ3 may have a vital role in these

processes (Dahari and Perelson, 2007).

2.10.6 Polymorphism In IFN-λ4 gene

IFN-λ4 has been found to be inactivated in a large human population due to

frameshift mutation and extensive genetic study of this gene have revealed that the

existence of this novel genes (IFNL4) ss469415590, TT or ▲G, that is; people with ▲G

allele show lesser chances of HCV clearance and usually have a negative treatment

outcome, whereas people with TT allele leads to a frameshift mutation and inactivates

IFN-λ4(Prokunina-Olsson et al., 2013). Thus, disorder in this gene is beneficial for

countering HCV infection through interferon alpha therapy.

2.10.7 Polymorphism In IFN-λRα/IL-28Rα Gene

SNP at IL28Rα (rs10903035 G) is crucial in IFN-α treatment and this creates a

site for a transcription factor (TF) to bind and that TF is NF-Y, which is recently found to

be involved in the expression of IFN-λRα in different cell lines (Ding et al., 2014) (figure

2.10). Like the importance of IL-28B/IFN-λ3 polymorphism in predicting the outcome of

the IFN therapy, IFN-λRα polymorphism is also studied to be associated with HCV

treatment failures specially in HIV co-infected patients (Jimenez-Sousa et al., 2014, Jin et

al., 2014,). Studies have been conducted all over the world to come up with a

combination of SNPs beneficial for the treatment of HCV, as once we know the friendly

polymorphs, we can easily set up a standard therapy module for various population again

various genotypes. Chinese have also come up with the polymorphism in IFN-λRα (rs

45

10903035), where the A allele was over taking the G allele in a persistent infected group

of patients (Cui et al., 2011)

Figure 2.10: In silico results shows that IFN-λRα (rs 10903035) allele is a significant predictor

of measuring IFN-λRα expression levels with respect to the TF binding opportunities and playing

its relevant roles (Flicek et al., 2011).

Future studies are needed to establish the role of IL-28B/IFN-λ3 genotype using

direct antivirals, which rapidly reduce the viral load and may therefore lower the

influence of IL-28B/IFN-λ3 genotyping in predicting SVR. Further functional studies of

IFN-λs and the significant SNPs should be investigated to improve the positive predictive

value using the point mutation analysis of the targeted polymorphisms. For applying a

practical tailor-made therapy, it is also necessary to reveal the cause of exceptional cases

that do not follow the IL-28B/IFN-λ3 genotyping.

46

Chapter 3

MATERIALS AND METHODS

3.1. Expression of IFN-λRα in Monocytes and Type I and Type II

Macrophages

3.1.1 Sample Collection

Blood samples from 2 healthy blood donors were collected from blood bank,

Skejby, Aarhus University Hospital Denmark. Blood Bags were kept in cold conditions

until they reached the laboratory. Hospital confirmed the healthy conditions of all the

donors used in this study. Isolation of monocytes and their differentiation was performed

at Department of Biomedicine, Aarhus University, Aarhus, Denmark.

3.1.2 PBMC Isolation, Freezing and Thawing

Heparinised buffy coats from healthy donors (provided by the blood bank,

Skejby, Aarhus University Hospital Denmark) were diluted 1:4 in 0.9 % NaCl at Room

temperature. A volume of 11.5 mL Ficoll-Paque PLUS (GE Healthcare, Uppsala,

Sweden) was slowly over layered on 14mL diluted blood. Density gradient centrifugation

was performed in two steps in order to remove thrombocytes i) centrifugation at 180xg at

room temperature for 20 minutes without break, ii) 8 mL of the upper layer plasma was

removed and the second centrifugation was performed at 380xg for 20 minutes with

breaks. Peripheral blood mononuclear cells (PBMCs) were then harvested from the

interphase. The isolated PBMCs were washed 3 times in cold PBS containing 1mM

EDTA at 300xg for 10 minutes. PBMCs were resuspended in cold PBS with 0.5% (v/v)

BSA and counted in methyl violet acetic acid by a Bürker-Türk counting chamber.

47

If PBMCs were required to be stored, an amount of 10-60 million PBMCs were

resuspended in 1 mL ice-cold freezing medium (RPMI 1640 (provided from the in-house

core facility for reagents) with 20% (v/v) heat inactivated foetal calf serum (Gibco,

Paisley, UK) and 10% DMSO). The ampules were immediately transferred to a cold

freezing container and placed at -80 0C overnight. For long term storage, cells were

moved to -134 0C the following day.

Cells were carried on dry ice, thawed in a 37 0C water bath and immediately

transferred to 9 mL thawing medium (PBS with 0.5% BSA and 20 % (v/v FCS). Cell

suspensions were centrifuged at 200xg for 10 minutes at room temperature. Supernatants

were removed and cell pellets were resuspended in incubation media (RPMI 1640 with

1% L-glutamine (provided from the in-house core Facility for reagents), and 10% (v/v)

FCS. Before using the cells for experiments, they were counted in trypan blue using a

Bürker-Türk counting chamber to check for cell number and viability. Trypan blue can

pass through the membrane of dead cells; thus coloring dead cells blue and leaving viable

cells unstained.

3.1.3 In vitro Generation of Monocytes-Derived Human Macrophages Using M-CSF

Induction

When culturing the cells in six-well plates, 150 million PBMCs were seeded per

plate, while culturing the cells in T-25 culture flasks, the amount of PBMCs seeded was

50-60 million per flask; the proper amount of PBMCs seeded was defined as the amount

that could give a confluent monolayer of monocytes after removal of lymphocytes.

PBMCs were incubated at 37 0C for 90 minutes in incubation medium with 10% FCS.

48

After incubation, cells were washed twice in PBS in order to remove non-adherent

lymphocytes.

The remaining adherent monocytes were cultured in complete culture media

(RPMI 1640 with 1% L-glutamine, 1% penicillin+streptomycin (provided from the in-

house core facility for reagents), and 10% heat inactivated human serum (Invitrogen, AB

human serum) or FCS). Monocytes were differentiated in the presence of 20 ng/mL M-

CSF (recombinant human M-CSF expressed in E-coli, Sigma-Aldrich, catalogue N0 M

6518). The concentrations of M-CSF used for in vitro differentiation of macrophages

were different among published studies, ranging from 10 ng/mL to 100 ng/mL (Inaba et

al., 1993, Romo et al., 2011). In this study, the proper concentration of M-CSF for

differentiation of macrophages had been determined by titration (performed by master

student Jacob Vemb Hansen). Cells were cultured in a 370C incubator with a CO2 level of

5% and 75-85% humidity.

At day two, a great part of the cells had detached from the polystyrene surface.

Thus, the medium was not changed, but twice the existing amount of complete culture

media was added to the culture, and the cells were re-stimulated with 20 ng/mL M-CSF.

From day Six to eight (depended on the morphology of the cells), macrophages were

harvested by scraping using a rubber policeman (Weischenfeldt and Porse, 2008).

Macrophages were stained with CD1a (dendritic cell surface marker) to test for

contaminating dendritic cells (Annexure 1).

3.1.4 Standard Protocol for Generation of GM-CSF Differentiated Macrophages

The protocol for generation of GM-CSF differentiated macrophages was similar

to the standard protocol for generation of M-CSF differentiated macrophages, except for

49

the concentration of GM-CSF (100 ng/mL human recombinant GM-CSF, Leukine

sargramostim, Berlex ÅKH) added to the culture instead of M-CSF. As for M-CSF,

different concentrations of GM-CSF had been used among published studies.

In this study the same concentration of GM-CSF was used as selected by Romo et al. in

2011(Romo et al., 2011).

3.1.5 Optimized Protocol for in vitro Generation of M-CSF Macrophages

Methodological Considerations

An optimization of the existing standard protocol for in vitro generation of M-

CSF macrophages was needed based on the finding that the standard protocol gave an

unstable yield of macrophages, and mostly a very low yield.

This could be explained partly by the low degree of monocyte adherence at the

beginning of the culture, and partly by the harvesting method by scraping that resulted in

a high amount of dead cells.

Therefore, we aimed to optimize the existing protocol to give a stable and high

yield of viable macrophages, with focus on monocyte adherence and harvesting method.

3.1.5.1 PBMC Isolation and Their Differentiation into Macrophages

The amount of PBMCs seeded was the same as in the standard protocol. PBMCs

were incubated at 37oC for 90 minutes in incubation medium with 10% human serum.

FCS had been replaced with human serum during incubation time based on the

observation that human serum strongly enhanced the adherence of monocytes to the

culture well or flask. After incubation cells were washed twice in 37oC RPMI 1640 to

remove non-adherent lymphocytes. The cells were washed with RPMI1640 instead of

PBS (as in the standard protocol) to avoid disturbing of the existing milieu in the culture.

50

Monocytes were cultured in complete culture media with 20 ng\mL M-CSF and 10%

human serum.

At day two of culture cells were re-stimulated with 20 ng\mL M-CSF and twice

the existing amount of complete culture media was added. Cells had already gained

macrophage morphology at day five (assessed by light microscope), and assumed to be

ready for analysis. Cells were then harvested by using EDTA\PBS combined with

scraping.

For harvesting, macrophages were incubated in cold PBS with 5 mM EDTA at 4

oC for 20 minutes and vigorously pipetted in cold PBS with 0.05 % BSA. The remaining

adherent cells were gently removed with rubber policeman. Cells were then centrifuged

at 200 x g for 10 minutes at 4 oC and resuspended in cold PBS with 0.05% BSA, ready

for antibody staining.

3.1.6 Interferon Treatment Assay

Monocytes (60,000 in 100 uL RPMI 1640) were seeded, incubated for 72 h with

M-CSF and GM-CSF in RPMI 1640, different IFNs mixed with RPMI 1640 and RPMI

alone. Cells were collected on day 0, day 3 and day 5, when they changed their

morphologies. Supernatants were removed after and cells were washed with PBS and

EDTA. RPMI 1640 and exact concentrations of IFN-λ3 (10 ng\mL), IFN-λ4 (10 ng\mL)

and IFN-α (1000 U\mL)(Hamming et al., 2013) was poured into respective wells

according to time span.

51

3.1.7 Cell Stimulations, RNA Extraction, cDNA Synthesis, and Real-time

Quantitative PCR

Cells were incubated for 0 h, 72 h and 120 h. Lyzed and collected afterwards

using RNA extraction kit (Omega) according to manufacturer’s instructions. cDNA

synthesis, and analysis by real-time quantitative PCR were performed with standard

protocols (Hamming et al., 2013, Stevens et al., 2008, Hardick et al., 2003). Primer

sequences are listed in Table (Annexure no 2).

3.1.8 Data analysis

The crossing points of the amplification curves were determined by using the

second derivate method on Roche LightCycler software. The data obtained from the

Light Cycler was normalized using the mathematical model described by Pfaffl (Pfaffl,

2001) and the PCR efficiency for each primer pair was calculated from the slope of the

standard curve. Unless otherwise stated, the experiments were performed in triplicates,

three independent populations of cells were treated as indicated and RNA harvested. For

each RNA sample one Q-PCR was performed for each individual experiment. The

expression of the gene of interest was normalized to the expression of the GAPDH. For

the untreated control, the mean of the triplicates were used to calculate fold induction for

the other samples. Hereafter, the mean and standard deviation was plotted on the graph.

3.2 Expression of IFN-λRα with Altered Signal Peptide

3.2.1 In-vitro and In-silico Analysis of Signal Peptide

Signal peptides of interferon lambda receptor and interferon alpha receptors were

analyzed through SignalP 4.1 (Petersen et al., 2011) and then used following primers to

mutate BamH1 site and attach HA-Tag

52

5’- TACCCATACGATGTTCCAGATTACGCT-3’,

5’-GCATTACATGGCCAGGGGATCCTCTGCAGATATCC-3’

5’-GGATATCTGCAGAGGATCCCCTGGCCATGTAATGC-3’

We furthermore used IFN-αR1 signal peptide sequence as sense primer and IFN-

λR1 -HA-Tag reverse as anti-sense primer to exchange the signal peptides of IFN-λR1

with the signal peptide of IFN-αR1. Sequencing of our construct confirmed exchanging

success.

Sequence of the signal peptide of IFN_αR1 is

ATGCTTTTGAGCCAGAATGCCTTCATCTTCAGATCACTTAATTTGGTTCTCATG

GTGTATATCAGCCTCGTGTTTGGT and for the signal peptide of IFN-λR1 is

ATGGCGGGGCCCGAGCGCTGGGGCCCCCTGCTCCTGTGCCTGCTGCAGGCCG

CTCCAGGG

3.2.2 Expression of Mut-IFNλR1 in HEK-293 Cells

3.2.2.1 Imaging of Receptors Through Confocal Microscopy

1-2 x 105 HEK 293 cells were seeded in a 6 well plate in plain DMEM and 10 %

FBS and coverslip was placed over them. On day 2, required concentrations of mutated

construct, wild type construct and empty vector with lipofectamine 2000 (invitrogen)

were made and used for transfection with the help of user’s manual. Immunofluorescence

was performed on day three after washing the cells twice with PBS at room temperature

(RT); cells were fixed with 4 % formaldehyde. Washed again twice with PBS and

permeabalised the cells with 0.1% Triton x100 (in PBS) for 90 sec (RT). Cells were

53

washed again and blocked with coverslip by adding 1% BSA (in PBS) and incubated for

10-30 min (RT). 1x HA (Abcam), 1x calreticulin and 1x Giatin were prepared in PBS. 1x

HA (Abcam) was incubated for 1 hour (RT) on coverslip after placing it on a parafilm.

Washed with PBS and incubated again for an hour in dark with 20 μl of secondary

antibodies 2x goat (invitrogen) + 2x rabbit (invitrogen). Added DAPI without washing

and incubated for 3 minutes. Washed twice with PBS and coverslip was mounted using

prolong gold on microscope slides. Store microscope slides with coverslip in the dark or

take images on confocal microscope.

3.2.2.2 Luciferase Assay

4 x 105 HEK 293 cells were seeded in a 24 well plate and transfection with

lipofectamine of positive control, negative control, tagged wild type and tagged mutant

was performed on day two. A dual-luciferase reporter assay was performed according to

the author’s instruction and previously described protocol on day three (Bruce A. Sherf,

1996, Hamming et al., 2013) (Dual-Luciferase Reporter Assay System, Promega).

3.3 In-silico studies on transcription factors involved in expression of

IFN-λRα

Gene sequence of IFN-λRα gene was obtained through Genbank (NCBI) with the

accession no. NM_170743.3.Various computational tools were used in our present study

to predict transcription factors (TF) and transcription factor binding sites (TFBS)

involved in the selective expression of IFNλRα. Some of the noticeable softwares are

discussed as follows with some basic introduction.

54

3.3.1. Computational Analysis

3.3.1.1 Gene-Regulation\BIOBASE

(http://www.biobase-international.com/product/transcription-factor-binding-sites)

‘Transfac professional’ software was initially used, provided by Biobase to

predict the TFs involved in the expression of IFN-λRα, as Transfac provides the most

comprehensive assortment of TFs, majority of which are usually experimentally proven

through chromatin immunoprecipitation (ChIP) or many labs trust on its authenticity in

carrying on their further experiments. The options in prediction of TFBS given by

TRANSFAC® are as follows

a. Match: It is prediction software, which uses library of weight matrix from

TRANSFAC® Public 6.0 to predict TFBS in DNA sequences.

b. F-match: Statistically over-represented TFBS are compared with control sets in

this program and it assumes the binominal distribution of TFBS frequency. F-

Match uses the Match algorithm with the same library of positional weight

matrices from TRANSFAC®6.0

c. Patch: This program uses the set of binding sites from TRANSFAC® Public 6.0

and is pattern-based software for prediction of TFBS in DNA sequences.

d. P-match: it is a multi-targeted program, which unlike match, uses the

combination of pattern matching and weight matrix approaches from

TRANSFAC® Public 6.0 to predict TFBS in DNA sequences, in addition to this,

it also include the side alignments associated with the matrices.

e. AliBaba2: AliBaba2 also uses the binding sites from TRANSFAC® Public for

the prediction of TFBS in an unknown DNA sequence.

55

3.3.1.2. Genomatrix

Genomatix Software GmbH is a German company with many biological

computational softwares and two of them which we used in the present study are (Table

3.1)

a. MatInspector: it is used for finding physical TF binding sites (TFBS), this tool

uses the library of matrix descriptions for TFBS to identify matches in DNA

sequences. The filters and the quality of selection is quite reliable as compared to

other softwares. It has been in practice since 2005 and has been cited in many

publications (Quandt et al., 1995, Cartharius et al., 2005).

Key features of MatInspector includes various vital and helpful outputs like TFBS

grouping as matrix families, graphical representation of results, promotor finding

and the overlapping involved, lastly the TFBS predictions and ChIP seq data.

b. DiAlign TF: it displays TFBS with help of MatInspector with in a multiple

alignment. It shows its results in color boxes with the most realistic ones to be on

top and is very user friendly.

c. ElDorado: this genome annotation is based on genome references of 33 various

organisms and is used in various ways as it contains much more data as compared

to other softwares like it contains transcripts with exon\intron structure, UTRs,

CDS and protein sequences, it contains CAGE tags, SNPs that effect transcription

factors, promoter regions and regulatory elements e.t.c.

56

Table 3.1: Various computational softwares are available online for prediction of TFBS. Some of

them are enlisted in the following table with some description and the URL (van Helden et al.,

1998, van Helden et al., 2000)

Program Operating principle Technical data URL

MatInspector

(Genomatix

software)

The program MatInd

constructs a description

for a consensus (e.g. of a

transcription factor

binding site) which

consists of

>a nucleotide

distribution matrix,

>the conservation of

each position within the

matrix represented by an

array of values termed

consensus index vector

(Ci-vector).

MatInspector is a software tool that

utilizes a large library of matrix

descriptions for transcription factor

binding sites to locate matches in DNA

sequences. MatInspector is almost as fast

as a search for IUPAC strings but has

been shown to produce superior results. It

assigns a quality rating to matches and

thus allows quality-based filtering and

selection of matches.

http://www.genom

atix.de/online_help

/help_matinspector

/matinspector_help

.html

TESS

(Transcription

Element Search

software)

TESS is a web tool for predicting

transcription factor binding sites in DNA

sequences. It can identify binding sites

using site or consensus strings and

positional weight matrices from the

TRANSFAC, JASPAR, IMD, and our

CBIL-GibbsMat database. You can use

TESS to search a few of your own

sequences or for user-defined

CRMs genome-wide near genes

throughout genomes of interest.

http://www.cbil.up

enn.edu/cgi-

bin/tess/tess

Ingenuity

Pathway

IPA is software that helps researchers

model, analyze, and understand the

http://www.ingenui

ty.com/products/IP

57

Systems:

complex biological and chemical systems

at the core of life science research. IPA

has been broadly adopted by the life

science research community and is cited

in thousands of peer-reviewed journal

articles

A/Free-Trial-

Software.html

Gene Go GenGo is a leading provider of data

mining and analysis solutions in systems

biology. These data mining tools are

databases help to capture and define the

underlying biology behind different types

of high-throughput experimental data and

understand the effects of small molecule

drug compounds in human tissue. GeneGo

provides system biology solutions for a

full range of applications in life science

research and drug development, from pre-

clinical discovery and NCE applications

to clinical trials, covering all aspects of

biology and chemistry.

http://www.genego

.com/

3.4 Functional analysis of IFN-λ4

3.4.1. Interferon Treatment Assay & IFN Treatment

Human carcinoma hepatocytes (HepG2) maintained in Dulbecco’s modified

Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 100

U/mL penicillin and 100 μg/mL streptomycin (All from Invitrogen), were seeded at 2

x105 cells/well in 12 wells, incubated for 24 h. Supernatants were removed after 24 h, and

cells were washed with PBS. DMEM with antibiotics, 10% FBS and exact concentrations

58

of IFN-λ3 (10 ng\mL), IFN-λ4 (10 ng\mL) and IFN-α (1000 U\mL) {Chemicon}) was

poured into respective wells.

3.4.2. Cell Stimulations, RNA Extraction, cDNA Synthesis, and Real-Time

Quantitative PCR

Cells were incubated for 4 h, then lyzed and collected using RNA extraction kit

(Omega) according to manufacturer’s instructions. cDNA synthesis, and analysis by real-

time quantitative PCR were performed as previously described (Melchjorsen et al., 2009).

Primer sequences are listed in Table (Annexure 2).

3.4.3. Data Analysis

The crossing points of the amplification curves were determined by using the

second derivate method on Roche LightCycler software 3.5. The data obtained from the

Light Cycler was normalized using the mathematical model described by Pfaffl (Pfaffl,

2001) and the PCR efficiency for each primer pair was calculated from the slope of the

standard curve. Unless otherwise stated, the experiments were performed in

quadruplicates, four independent populations of cells were treated as indicated and RNA

harvested. For each RNA sample one Q-PCR was performed for each individual

experiment. The expression of the gene of interest was normalized to the expression of

the GAPDH. For the untreated control, the median of the quadruplicates were used to

calculate fold induction for the other samples. Hereafter, the mean and standard deviation

was plotted on the graph.

59

Chapter 4

RESULTS

4.1 Expression of IFN-λRα in Monocytes and Type 1 and Type 2

Macrophages

IFN-λRα is expressed on type I and type II macrophages but not on their

precursor cell i.e. monocytes, as shown in the Figure 4.1. Mature macrophages show

higher expression as compared to the immature macrophages.

Figure 4.1: Quantitative PCR for IFN-λR1 in monocytes, type II and type II macrophages,

which show that the expression of receptor is higher in both types of macrophages as compared to

monocytes. Arrow head shows the closest value from the three repeats.

IFN-λRα is a fully functional receptor in macrophages and it stimulates the

interferon stimulating genes (ISG) if induced with type III interferons. The expression of

0

1

2

3

4

5

6

Mo MØ2 (Day 3) MØ1 (Day 3) MØ2 (Day 5) MØ1 (Day 5) nontreated

Mo : MonocytesMØ2 : M-CSF Treated

60

OASL (encoding 2'-5'-oligoadenylate synthetase-like) after induction with type III IFN is

shown in figure 4.2, as OASL is specific to type III IFNs only.

Figure 4.2: Quantitative PCR for OASL in monocytes, type I and type II macrophages after

10ng IFN-λ3 induction for 4 hours, which confirm the expression of IFN-λR1 and its functional

behavior in both types of macrophages as compared to monocytes. Arrow head shows the closest

value from the three repeats.

Expression of IFN-λR1 is sensitive to the concentration of type I or type III IFNs

present in the proximity. It is increased or decreased with the low and high concentrations

of various IFNs, as shown in the figure 4.3.Interferon lambda 4 (IFN-λ4) is a newly

discovered type III interferon and it activates the antiviral pathways by stimulating the

ISGs in macrophages as shown in the figure 4.4. IFN-λ3 is well known for its antiviral

activities now but type I interferons fails to stimulate OASL gene, even at higher

concentrations.

0

2000

4000

6000

8000

10000

12000

14000

Mo MØ2 (Day 3) MØ1 (Day 3) MØ2 (Day 5) MØ1 (Day 5) nontreated

Fold

In

crea

se (

2-d

dC

t )

Mo : MonocytesMØ2 : M-CSF Treated

61

Figure 4.3: Quantitative PCR for measuring the expression of IFN-λR1 in type 1 macrophage of

day 5. They were induced continuously with various interferons with GM-CSF until day 5 and

then lysed to collect the RNA. Arrow head shows the closest value from the three repeats.

Figure 4.4: Quantitative PCR for measuring the expression of OASL during the 5 days of

differentiation of monocyte to type I macrophages, in which IFNλR1was expressed. They were

induced continuously with various interferons with GM-CSF until day 5 and then lysed to collect

the RNA. Arrow head shows the closest value from the three repeats.

0

0.5

1

1.5

2

2.5

3

IFN-λ3 (10ng) IFN-λ4 (10ng) IFN-α 100U IFN-α 1000U untreated

Fold

Incr

ease

(2

-dd

Ct )

0

10

20

30

40

50

60

70

80

IFN-λ3 (10ng) IFN-λ4 (10ng) IFN-α 100U IFN-α 1000U untreated

Fo

ld I

ncre

ase

(2

-dd

Ct )

62

4.2 Amplification of various splice variants of IFNλR1(SV1, SV2,SV3)

Various primer sets (as described in the material and method section) were used

to amplify three splice variants of IFNλR1 from different cell lines and cell types. Results

for splice variant 1 with forward 2 and reverse 2 primers are shown as follows (figure

4.5):

Figure 4.5: from left to right: 100bp ladder, no template control, amplification of splice variant-1

of IL-28Rα from HepG2 cells, amplification of splice variant-1 of IL-28Rα from plasmids of

SV1, SV2 and SV3, amplification of splice variant-1 of IL-28Rα from monocytes and

macrophages. Expression is too low in monocytes and negative in NTC, SV2 plasmid and SV3

plasmid, whereas HepG2, HEK293, HT1080 cells and macrophages showed notable expressions

after 35 cycles. Required band size was 139bp.

63

Splice variant 1 of IFN-λR1 was amplified through qPCR using set 2 of primers

and results showed that splice variant 1 is only expressed in macrophages out of the four

selected types of cells. Result of qualitative PCR is shown in figure 4.6.

Figure 4.6: qPCR done with primer set 2 of splice variant 1 and the maximum expression was

seen in the macrophages, which are well known responsive cells to type III interferons. Other cell

are non-expressive of IFNλR1, hence show no expression of IFNλR1.

Various primer sets were used to amplify all three splice variants of IFN-λR1

separately but due to their close sequence homology, specific primers were unable to pick

one splice variant at a time for splice variant 2 and 3. Results are shown in the figure 4.7

and 4.8. Various primer sets for splice variant 3 and 2 were designed and were used to

amplify these splice variants. Results of amplifications are shown in the figure 4.7 and

4.8, where forward 3 and reverse 3 for splice variant 3 was calculated to give a product of

80 bps for SV3, but it was not specific to SV3 only. Similarly forward 3 of SV3 were

tried with reverse 2 of SV3 but it was also nonspecific for SV3. Details of these primer

pairs are given in annexure 3 and results of different combinations are shown in the

following figure 4.7.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

HT1080 Monocyte Macrophages HEK293 nontreated

Fold

Incr

ease

(2

-dd

Ct )

64

Figure 4.7: Different primer sets used to amplify splice variant 1, splice variant 2 and splice

variant 3. Primer set 1 for splice variant 3 were not specific against splice variant 3 and amplified

all three splice variant types as shown in the figure. Primer set 2 for splice variant 3 was also not

specific against SV-3 only and amplified splice variant 1 also. Primer sets against splice variant 1

and splice variant 2 were also not specific and amplified undesired bands of other splice variants

too.

65

Figure 4.8: Splice variant 3 is not a completely functional receptor and is quite similar to splice

variant 1. We designed various primer sets to amplify splice variant 3 alone but all 5 sets were not

specific against SV-3 alone. Desired band size and primer sets combinations are mention in the

figure above.

The next generation sequencing data performed on IFN-λR1 gene by our

colleague in

Germany reveals that splice variant 1, 3 and 4 out of 6 have more chances of expressing

as shown in the figure 4.9 below.

66

Figure 4.9: Next generation sequencing data performed on IFN-λR1 gene shows that splice

variant 1, 3 and 4 are more likely to be expressed in various situations as compared to splice

variant 2, 5 and 6. Splice variant 1 of IFN-λR1 is the fully functional receptor of type III

interferons.

4.3 Expression of IL-28Rα with altered signal peptide

The results through the computational analysis show that the mean S- score for

the signal peptide of IFN-λR1 and IFN-αR1 is 0.513 and 0.821 respectively. Figure 4.10

shows that the C-scores for IFN-λR1 and IFN-αR1 were same with a small difference in

its position.

67

Figure 4.10: C-scores, S-scores and Y-scores of the signal peptides of IFN-λRα and IFN-αR1

attained through SignalP.

Table 4.1 shows the differences in different parameters measured and proves

that the signal peptide of IFN-αR1 is much stronger than the signal peptide of IFN-

λR1.Expression of mutated IFN-λR1 was observed through confocal microscopy, which

can be seen in figure 4.11.

68

Table 4.1: Various scoring parameters of the signal peptides of IFN-λRα and IFN-αR1

attained through computational analysis.

IFN-λRα

Measure

Position

Value

IFN-αR1

Measure

Position

Value

max. C 25 0.139 max. C 28 0.139

max. Y 25 0.268 max. Y 18 0.313

max. S 18 0.681 max. S 11 0.934

mean S 1-24 0.513 mean S 1-17 0.821

D 1-24 0.400 D 1-17 0.587

SP NO SP YES Cleavage site

between pos.

17 and 18:

ATC-GC

D 0.400 D 0.587

D-cutoff 0.450 D-cutoff 0.450

HEK 293 cells were stained through Dapi staining after the expression of the

receptors and were prepared for confocal microscopy. Images were taken and seen under

confocal microscope. Blue color represents the depi staining, whereas green color

represents the expression of the receptor.

Figure 4.11: Image through confocal microscopy. Expression of mutated IFN-λR1 in HEK 293

cells is shown.

69

Functional analysis of the mutated and wild type IFN-λR1 was measured

through luciferase assay, which shows almost similar expression of tagged mutated and

wild type receptors. Results can be seen in figure 4.12.

Figure 4.12: Luciferase assay performed to measure the strength of signal peptide. Wild type IL-

28RA receptor showed its maximum expression in HEK 293 cells, whereas HA tagged wild type

and mutated IL-28RA showed more or less similar expression pattern and efficiency.

4.4 Computational Analysis in Predicting the Transcription Factors

Involved in Expression of IFN-λRα (IL-28Rα) (NM_170743)

4.4.1 Promoter 2.0 Prediction Results

Computational softwares were used to predict the promoter region of the IFN-λRα

gene. human gene sequences were selected for this task and the gene selected is as

follows.

0

5

10

15

20

25

Wild Type IL-28RA Wild Type IL-28RA +

HA tag

Mutated signal

peptide + HA tag

no receptor

Fir

efl

y/R

eni

lla

Rat

io

70

4.4.1.1 Input Sequence

Gene sequence of IFN-λRα gene was obtained through Genbank (NCBI) with the

accession no. NM_170743.3 and was used to predict the promoter sequence.

4.4.1.2 Predicted Transcription Start Sites

Promoter predictions for IL28Rα eukaryotic sequence with score cutoff 0.80

(transcription start shown in larger font):

Promoter predictions for sequence:

Start End Score Promoter Sequence

1789 1839 0.99

TAGGCTGAGCTATAAGAGGGGTGGACACAGGGTGGGCTGAGGTCAGAGGT

3142 3192 0.93 GCGCCATGGGGCTATAGGAGCCTCCCACTTTCACCAGAGCAGCCTCACTG

4512 4562 0.91 CTAAACTGTTTAAATAAAGAGCTCTATTTTTAAAGAAAAAAGGTACAATT

Sequence, 4563 nucleotides

Position Score Likelihood

2200 1.102 Highly likely prediction

3200 0.713 Marginal prediction

3900 0.550 Marginal prediction

After using all these softwares, few dependable hits of TFs were shortlisted,

which included many of those, which are already part of JAK-STAT pathway or many of

71

those TFs, which are identified in nearly all essentials pathways involved in the

differentiation of cells. Literature review has also helped us in showing some nearest hits

like AP-2, c-JUN, STAT-1 or LyF-1. Tables 4.2, 4.3 and 4.6 show the summary of all the

predicted TFs as per reported by using various computational tools and many of them are

similar as predicted before in literature.

4.4.1.3 Gene-Regulation\BIOBASE

Different options of predicting transcription factors are available in Gene-regulation

software network and few of them were selected to predict transcription factors in the

expression pattern of IFN-λRα gene.

a) Match(Gene-regulation.com)

Match from Gene-regulation software house was use to predict the transcription

factors involved in the expression of IFN-λRα gene. The results are shown as follows in

figure 4.13.

Figure 4.13: Results from MATCH (gene-regulation.com) showing various transcription factor

binding sites with their sequences, positions, core matches and the matrix matches in IL-28Rα

gene of Homo sapiens. Matrix match scores are describing the score of the complete matrix

72

match (more important values, ranges between 0-1) and the core similarity is the score of the

highest conserved positions of a matrix match. Both thresholds have to be reached for a matrix

match. c-Rel, ElK-1, c-Ets-1(p54), STATx, AP-1, NF-kappaB, NKx2-5, and v-Myb are reported

with matrix similarity values of 0.987, 0.990, 0.993, 1.0, 0.976,1.0, 1.0, 0.971 respectively as the

TFs with greater confidence.

b) F-MATCH

F-Match is another software available at the house of gene-regulation softwares. It

was used to predict the transcription factors responsible for the expression of IFN-λRα

gene. results have been summarized in the table 4.2. These results were obtained from

analyzing our sequence of length: 4563 having total number of sequences: 1 & Total

number of sites: 267 Number of sequences with sites: 1, Frequency of sites: 0.05851 and

Average number of sites per sequence: 267.00 against the background human genome

version: human/hg38.

Table 4.2: Summary table from the results obtained using F-Match searches showing important

Transcription factors as reported.

Recued Transcription Factors through F-Match

STAT1 STAT

3

ELF4 BR-

C Z2

ZIC3 Mbp1p HNF-I

α

FPM31

5

PLAG T3Rβ YLR27

8C

GABPα ER81 ATF3 ISL2 COE1 ETS1 C-Rel Tcf1D

EC2

ELF1 Sin3

A

STATB

1

Zic2 STAT

5B

Arnt Zic3 P50

(REL-

P65)

NF-

Kappa

B (P50)

Dde

box

c-Myb Zic1 CTC

F

CPBP

TF3C-β Stb5p Myoge

nin/NF

-1

STR

E

Eve FOXN1 RXR-α CHES

1L

FOXN

4

Ybr2

39c

Prrx2

73

c) AliBaba2.1

AliBaba2.1 was used from gene-regulation software to highlight the most closely

related transcription factors, complete results have been shown in the annexure 4, where

the match TFBS are highlighted with each transcription factor and summary of all three

sub software used from gene-regulation software hub have been summarized in table

4.5.

Table 4.3: Some of the notable transcription factors in IL-28Rα gene in Homo sapiens

selected from a set of 164 segments (complete in Annexure 4) as potential binding sites

reported by AliBaba2.1.

Recued Transcription Factors through AliBaba2.1

Sp1 NF-1 MyoD YY1 Myf-3 NF-1

GATA1 NF-kappaB C/EBPβ REB1 REV-Erbα RSRC4

GBF1/2 sox2 ICSBP EBP-1 NF-EM5 ISGF-3

4.4.1.4 Genomatix Software Suite

Genomatix consists of various softwares, out of which, few were used to predict the

transcription factors and the single nucleotide polymorphism involved at those TFBS.

The solutions offered through Genomatix are as follows:

a) MatInspector

The transcription factors predicted through the MetInspector are always alligned

throug there positions in the sequence and the figure below shows the pattern of the

TFBS and the binding of those TF. IFN-λRα gene was selected through the software to

predict the TFBS. MatInspector uses a large library available of described matrixes to

predict the TFBS and the results are as follows (Figure 4.14):

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Figure 4.14: Graphical representation from the results obtained using MatInspector from

Genomatix software suite of various transcription factor binding sites in multiple sequences of

IL-28Rα gene in Homo sapiens. By examining the detailed MatInspector summary table some

TFs (complete supplementary data) were found to be the preferred Matrix family with p-values

lesser than 0.5 out of the 127 obtained matrix families as shown in summary table 4 at the end.

b) DiAlign TF:

DiAlign through Genomatix uses the sequences of various species and color them

separately for each predicted TF.

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Figure 4.15: Results from MatInspector shows various transcription factor binding sites in IL-

28Rα gene of various species, which have an influence in the evolution of this receptor. It has

compared the gene in Homo sapiens, rhesus monkey, chimpanzee, mouse, rat, rabbit, horse, cow,

pig, dog (vertebrates). KLFS, NOLF, CAAT, MYOD, PLAG as common TFs located in the

aligned regions in consensus within 7 organisms (70%) of their sequences.

c) SNPInspector: Identify TF sites affected by SNPs:

Most likely single nucleotide polymorphism sites were predicted through the

SNPInspector software. Resulys at various sites are shown as follows:

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Figure 4.16: Selected results from SNPInspector displaying SNPs located in coding exons which

influence the protein sequence. These variant calls and reported factor family NFY are duly

supported by literature as well. These results were obtained using NM_170743 (1 sequence, 4563

bp) found on chromosome 1 of Homo sapiens, NCBI build 37, ElDorado 08-2011 Extracted

region: NC_000001 between 24472207 and 24522206 (50000 bp) as analysis parameters. Probes

displayed are from chip Human Genome U133 Plus 2.0. Column 1-6 report, Position, dbSNP,

Transcription factor binding sites lost due to SNP. Transcription factor binding sites generated

due to SNP, Allele Information, Start-Stop sites, Strand (+/-), Core similarity and Matrix

Similarity respectively.

d) Overrepresented transcription factor binding sites or modules:

Genomatix gives a collective option of to summarize the overrepresented

transcription factors and the results are as follows:

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Figure 4.17: Figure showing highlights from the results obtained using Genomatix’

Overrepresented TF families tool. Z-score shows the distance of our sequence from the

population mean in units of population standard deviation. Promoter association shows how many

TF Families known to occur more than twice as often in promoters as in genomic sequence.

Default sorting: by column "Z-Score (genome)". The results reveal CTCF, GLIF, PLAG, NOLF,

NFKB, AP2F, WHNF, SP1F, ZFHX, ZF02, NGRE, NRSF, NRF1, SAL2, PRDM, MAZF, STAF,

HESF, KLFS, ZF07, MTEN, CP2F, ESRR, BEDF, HIFF, MYBL, XBBF, TF2D, ZF01, PURA,

TELO, INSM, HAML, ZF5F, CSEN, OAZF, MYOD, HICF, NACA, RXRF, SNAI, CDEF as

over represented TF families with Z-score (number of standard deviations an observation or

datum is above the mean) values greater than 2.5.

4.4.1.5 Qiagen

Qiagen software hub was also used to predict the TF and the results are as follows (figure

4.18):

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Figure 4.18: The graph above displays the most relevant transcription factors up to 10 in

20kb upstream and 10kb downstream of gene IL28Ra as per reported by Qiagen. Results

from Qiagen shows various transcription factor binding sites in IL-28Rα gene of Homo

sapiens, where STAT 1, 3 and 5 have various TFBS, in addition to this Rel A ,c-Fos, c-

Jun, Lyf-1 NF-KappaB and others with significant sequence complementarity for binding

at various transcription factor binding sites.

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4.4.1.6 TESS

TESS software was used predict the TF in IFN-λRα gene and Sp1, MIG1 , FSADR1

,LGC, LVC like TFs were found as over-represented transcription factors with significant

statistical values passing the threshold filters and providing confidence in our results.

Table 4.4: Selected results from TESS software shows various transcription factor

binding sites in IL-28Rα gene in Homo sapiens.

Recued Transcription Factors through TESS

Sp1 LBP1 MIG1 T-Ag FACB TEF CAC TEF LVc

FACB GR NFY UCRF-L GCF ER-

alpha

ADR1 ADR1

Summary of TFs predicted through the gene-regulation/Biobase are summarized as

follows (Table 4.5):

Table 4.5: Summary table of the results obtained using Gene regulation Biobase

TRANSFAC suite, reported here are TF’s chosen based on supporting statistical

threshold values. A)Results from Match suggest few transcription factors which are

overrepresented in the software. B) Results from F-Match showing likely transcription

factors in expressing the gene. C) Transcription factors predicted through AliBaba 2.1 on

high prediction scores.

Gene Regulation Biobase

A. Match B. F-Match C. AliBaba

COMP1 STATx Sp1

c-Rel USF YY1

CHOP-C/EBPalpha AML-1a Myf-3

Elk-1 SRY NF-kappaB1

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c-Ets-1(p54) ZID AP-2alphaA

STATx CBF-A C/EBPalpha

Pax-4 c-Rel MyoD

AP-1 NF-kappa B

c-Rel Nkx-2

ZID Lyf-1

NF-kappaB

Nkx2-5

Pax-4

v-Myb

NF-1

Summary table of the results obtained using Genomatix software suite reported here

(Table 4.6) are TF’s chosen based on supporting statistical threshold values.

Table 4.6: Summary of Genomatix suite.A) Results from MatInspector suggest few

transcription factors which are overrepresented in the software. B) DiAlign shows the

transcription factor binding sites of the most likely transcription factors. C) SNPInspector

predicts the transcription factor binding sites of the most important transcription factors

in expression of the gene which have a different effect if there is a single nucleotide

polymorphism. D) Summary of the results obtained from the softwares under

Genomatixs, which are selected through their repetitive behaviors.

Genomatix Software Suite

A. MatInspector

MTEN TF2B TF2D XCPE AARF ABDB AHRR AIRE AP1F AP2F ZF03 SORY

ARID ATBF BCDF BCL6 BEDF BRAC

BRN5 BRNF BTBF CART ZF07 SPZ1

CDEF CDXF CEBP CHOP CHRE CIZF CREB CTCF DEAF DLXF ZF08 SRFF

DMRT DMTF E2FF EBOX EGRF ESRR FKHD FXRE GFI1 GLIF ZF13 STAF

GRHL HAML HDBP HDBP HDGF HESF HICF HMTB HNF1 HNF6 ZF35 STAT

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HNFP HOMF HOXC HOXH HZIP IKRS INSM IRFF KLFS LTSM ZF57 STEM

MAZF MEF2 MOKF MYOD MYT1 MZF1 NDPK NF1F NFKB NKX1 ZF5F TAIP

NKX6 NOLF NRF1 NRSF OSRF P53F PARF PAX3 PAX5 PAX9 ZFHX WHNF

PAXH PDX1 PLAG PRDF PURA RORA RXRF SATB SF1F SMAD ZBED YY1F

B. DiAlign TF C. SNPInspector

KLFS rs80179676 (NFY.01)

rs10903032 (NRF.01)

rs10903034 (NYMC.01)

rs10903035 (NFY.03)

rs11249002 (TCFAP2B.01)

rs11249006 (MYBL.01)

rs72648600 (NFY.03)

NOLF

CAAT,

MYOD

PLAG

TSS

D. Overrepresented TFBS

CTCF GLIF PLAG NOLF NFKB AP2F WHNF SP1F ZFHX ZF02

NGRE NRSF NRF1 SAL2 PRDM MAZF STAF HESF KLFS ZF07

MTEN CP2F ESRR BEDF HIFF MYBL XBBF TF2D ZF01 PURA

TELO INSM HAML ZF5F CSEN OAZF MYOD HICF NACA

4.5 Interaction of IL-28Rα with Various Cytokines and Transcription

Factors

String 9.05 was used to see the interaction of IFN-λRα (IL-28Rα) with other

cytokines and TFs. The relationship of various cytokines and chemokines with the

protein of interest was divided into different categories on the basis of the evidences

found in either literature of in vivo studies.

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Figure 4.19: Intensity of interaction of IFN-λRα (IL28Rα) with other transcription

factors and cytokines shown in a networking style by using various colorful lines

representing limit of interaction and confidence in that interactions.

confidence evidence actions interactive advanced more less

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4.6 Functional analysis of IFN-λ4.

IFN-λ4 is included in the type III IFNs section but its mode of action was still

unknown, so we performed various experiments to confirm the pathways it activates to

initiate its antiviral or pro-viral activities. Experiments were started by measuring the

expression pattern of IFN-β, ISG56, Mx and OASL.

Figure 4.20: HepG2 cells were treated with IFNa (1000 U/ml), IFNl3 (10 ng/ml) or IFNl4 (10

ng/ml). After 4 h, the level of the interferon-induced genes, IFIT1, MX1 and OASL, was

quantified by qPCR, four independent experiments are shown, mean and s.e.m. are plotted

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Chapter 5

DISCUSSION

Interferons (IFNs) are naturally produced in the human body through immune

cells and have potent role in boosting over all immunity by inducing resistance in viral

replication, up regulating MHC class I expression, inhibit cell proliferation and attracting

or inducing maturation of immune cells like DCs or NK cells (Akhtar, 2013). IFNs are

known for producing a strong and durable antiviral response against various viruses and

cancers (Isaacs and Lindenmann, 1957,Kotenko et al., 2003,Sheppard et al., 2003). Due

to its reliable antiviral qualities, it was artificially produced through cloning and was

tested as an anti-viral drug synthetically produced in labs and tested on animals (Isaacs

and Lindenmann, 1957). It has still not let down scientists after so many years and

mutations in various viruses, as it is effective in its pegylated form against few resistant

viral genotypes (Zeuzem et al., 2000, Fried et al., 2002). DCs, NK cells and macrophages

are among the front line immune cells that either respond to IFNs or produce IFNs to play

their part against foreign invaders (Weiler and Von Bulow, 1987, Ito et al.,

1988,Goodbourn et al., 2000). Hematopoiesis is a complex mechanism inside animal

body and one of its component is macrophages, which are differentiated from monocytes

and are they differentiate in various types to play diversified roles, according to their

environment and requirements (Yin et al., 2012). Environment of every cytokine or

immune cell may vary with time or situation.

Out of many vital chemokines, GM-CSF and M-CSF play an important role in

monocyte differentiation to M1 and M2a respectively (Wang et al., 1994, Lemaire et al.,

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1996). Results of the current study depicts that monocytes do not express IFN-λRα,

whereas when they differentiate into M1 and M2a with the exposure of GM-CSF and M-

CSF, they start expressing IFN-λRα from day 3 (Figure 4.1). The expressed IFN-λRα is

tested for its function with the exposure to various type III IFNs and they respond to the

IFN-λ3, which was measured through the fold increase of OASL gene through Q-PCR in

the functional analysis experiment (Figure 4.2).

Figure 5.1: Monocytes differentiate into various immune cells on exposure to various cytokines

and chemokines. Mostly they are differentiated to either DCs or Macrophages upon exposure to

GM-CSF/IL4 or GM-CSF alone representatively.

Monocytes do no respond to type III IFNs, as they do not express IFN-λRα (Liu

et al., 2011), which makes them deprived of building an antiviral response on induction

with type III IFNs and show divesting behavior in OASL expression when measured

through qPCR. Nevertheless the IFN-λRαexpressed by M1 and M2a gave good response

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when induced with IFN-λ3 and the fold increase on OASL gene measured was really

high in both M1 and M2a (in vitro).

M1 macrophages were induced with type I and type III IFNs until day 5 of its

differentiation and on day 5, they were lysed to collect RNA to proceed with the

quantitative PCR of IFN-λRα in M1. Results show that IFN-λRα was up regulated when

they were induced with type III IFNs during their differentiation process, whereas it had

no effect with induction of type I IFN, but it addition to this, IFN-λRα was down

regulated when the concentration of type I IFN was increased. By increasing the

concentration of type III IFNs by 10 times had no negative results on IFN-λRα receptor

(Figure 4.3).

Over-expression of the private receptor chain of type III IFNs was reconfirmed

for its availability to entertain type III IFNs and its functionality. Expression of OASL

gene was also measured through qPCR after the five days of differentiation of monocyte

to type I macrophages, in which IFN-λRα was expressed. They were induced

continuously with various interferons with GM-CSF until day 5 and then lysed to collect

the RNA. IFN-λ3 and IFN-λ4 were found equally efficient in activating OASL

expression, which means that IFN-λ4 is a competent and effective antiviral protein like

other type III IFNs (Figure 4.4). The levels of OASL were increased nearly 100 times,

when induced with type III IFNs as compared to type I IFNs or untreated samples. Type

III IFNs show redundancy in building an antiviral response but the underline signaling

pathways differ from type I IFNs. IFN-λ4 also uses the same receptor as other three type

III IFNs and helps the expression of its private receptor chain to amplify its antiviral

impact (Hamming et al., 2013).

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IFN-λRα mRNA can have three splice variants of in human beings as shown in

the literature, which includes a complete and mature splice variant, named as splice

variant 1, the second splice variant lacks the first part of exon VII, resulting in a 29

amino acid omission within the intracellular domain, which makes it nonfunctional in

transmitting the signals and initiating signaling cascades (Dumoutier et al., 2004,

Sheppard et al., 2003). The third splice variant lacks transmembrane domain containing

region (exon VI), which results in a frame shift mutation ending up with a stop codon

early on (Dumoutier et al., 2004, Sheppard et al., 2003). The third splice variant is

believed to be the soluble receptor, which can hold up the type III IFN proteins and

generate hindrance in their antiviral functions (Witte et al., 2009). Primers were designed

for all three splice variants and tried to amplify them in various cell lines and cell types.

SV1 in HT1080 was amplified, macrophages, HepG2 and HEK293 and its expression

were minimal in monocytes, which represent and verify our previous results too (figure

4.5, figure 4.6).

Help of computational biological software was also taken and predicted the most

likely splice variants of IFN-λRα (figure 5.2), five splice variants were predicted, out of

which, only three had potential to get fully mature. The conclusive results showed that

only splice variant 1 was successfully expressed and quantified which is the fully mature

splice variant of IFN-λRα (figure 4.7, figure 4.8).

Different primer sets used to amplify splice variant 1, splice variant 2 and splice

variant 3. Primer set 1 for splice variant 3 were not specific against splice variant 3 and

amplified all three splice variant types as shown in the figure. Primer set 2 for splice

variant 3 was also not specific against SV-3 only and amplified splice variant 1 also.

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Primer sets against splice variant 1 and splice variant 2 were also not specific and

amplified undesired bands of other splice variants too (Figure 4.7).

Splice variant 3 is not a completely functional receptor and is quite similar to

splice variant 1. We designed various primer sets to amplify splice variant 3 alone but all

5 sets were not specific against sv-3 alone. Desired band size and primer sets

combinations are mention in the figure above (figure 4.8).

According to the results of the next generation sequencing performed, splice

variant 1 is the most prominent splice variant, whereas SV3 and SV4 shows some

competency of expressing but still nothing can be said about their expression pattern

(figure 4.9).

The pathways initiated through the receptor of type I and type III IFNs are similar

and both act as the initiators of the antiviral activities performed with the binding of their

respective analog (figure 2.9). They may have similar roles to play but they differ a lot

from each other in many perspectives. The biggest difference is the legend and the

receptor itself, as the type I interferons includes the IFN-α, IFN-β and IFN-τ etc. with the

binding affinity with IFNαR1 and IFNαR2, whereas the type III interferons include IFN-

λ1, IFN-λ2, IFN-λ3 and IFN-λ4 with the binding affinity with a heterodimeric receptor,

made by the combination of one private chain of IFN-λs called the IFN-λRα and the other

shared chain is from IL-10 group called the IL-10Rβ. This heterdimeric combination of

receptor and the structure of IFN-λ proteins has made it in between the IL-10 family and

the interferons (Gad et al., 2009).

Expression of the private IFN-λR chain is also uneven in various cells types and

organs, which makes it site specific for its actions and a better choice against hepatitis C

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virus as compared to type I IFNs, as its expression is high in the HCV reservoir i.e liver.

We computationally compared the signal peptide of IFN-λRα with the signal peptide of

IFNαR1 and found IFN-λRα to be weak at some points (Figure 4.10)

C-score is a raw cleavage site score of any protein and it distinguishes cleavage

site of the signal peptide from everything else. The red line in figure 4.10 shows that the

cleavage site for IFN-λRα is at position 25, whereas the cleavage site for IFNαR1 is at

position 28. This score is not very crucial in evaluating the strength of any signal peptide,

though its position plays an important role. The maximum C value was 1.39 for both of

the compared proteins.

S-score defines the signal peptide score and it distinguishes the positions within

signal peptides from positions in mature part of the proteins and from proteins without

signal peptides. It is an imperative measurement of any protein and the results shows a

clear difference within the scoring of both proteins; the mean S-score of IFNαR1 is 0.821

with the highest peak of 0.934, whereas the mean S-score of IFN-λRα is 0.513 with the

elevation up to 0.681, as shown in table 4.1. This scoring clearly states a marked

difference within the strength of the signal peptides of both proteins.

Y-score is actually a combination of geometric average of C-score and the slope

of the S-score. It is a better predicting tool to get cleavage site as compared to C-score

alone. Maximum Y-score of IFN-λRα was found to be 0.268 at position 25 and it was

0.313 for IFNαR1 at position 18 (table 4.1). C-score can have multiple peaks in one

sequence but the cleavage site is always one, so Y-score comes up with the best option by

distinguishing between the peaks of C-score by selecting the one where the slope of the

S-score is sudden or steep. D-score is the discrimination score and it is the average of

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mean s-score and maximum Y-score reached. D-score is vital in differentiating signal

peptide from mature protein and all scores are either negative or near 0.1 on average if

the protein is a non-secretory protein (Petersen et al., 2011).

Signal peptides of IFN-λRα were also exchanged with the signal peptide of IFN-

αR and named it as the mut-IFN-λRα. Both the wild type IFN-λRα and mut-IFN-λRα

were expressed in HEK 293 cells and were checked for their expression and

functionality. HEK 293 cells do not express IFN-λRα naturally but following an assay

developed in our lab (Hamming et al., 2013), we expressed these receptors in HEK 293

cells and verified its expression in a confocal microscope (figure 4.11). Both wild type

IFN-λRα and mut IFN-λRα were able to reach the cell membrane and express

themselves. Their functional and quantitative assay was performed through Dual-

Luciferase Reporter Assay System (Promega) (figure 4.12). Both HA-tagged wild type

IFN-λRα and mut IFN-λRα were able to show similar results and have proved that the

selective expression of IFN-λRα is independent of the quality of its signal peptide, as

Ding et al., has recently found that expression of IFN-λRα is dependent on histone

deacetylase (HDAC) and few transcription factors (TF) like NF-Y and E2F (Ding et al.,

2014). Infect they were able to express IFN-λRα in U-87 cells, which are naturally a non-

responder cell lines with reference to IFN-λs (Ding et al., 2014). Expression of IFN-λRα

was also claimed to be depending on TFs in 2010 by Yang et. al., in an in silico study but

none of their claimed TF has yet been confirmed in wet lab (Yang et al., 2010).

Another hint from macrophages (Mø1 and Mø2) and dendritic cells (pDC) was

also predicted, as they share common progenitor; monocytes (Liu et al., 2011).

Monocytes lack IFN-λRα, but when they differentiate into Mø1 and Mø2, they express

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IFN-λRα and respond to IFN-λRα, whereas when they differentiate into pDC, they don’t

express IFN-λRα. However, the debates are still there to resolve the common precursor

for both types of cells, because there are some pathways that up or/and down regulate

their signaling receptors (Newman et al., 1980).

Various computational tools were applied in present study to predict transcription

factors (TF) and transcription factor binding sites (TFBS) involved in the selective

expression of IFN-λRα. Some of the noticeable softwares results are discussed as

follows.

‘Transfac professional’ software was used initially, provided by Biobase to

predict the TFs involved in the expression of IL-28Rα, as Transfac provides the most

comprehensive assortment of TFs, majority of which are usually experimentally proven

through chromatin immunoprecipitation (ChIP) or many labs trust on its authenticity in

carrying on their further experiments. The options in prediction of TFBS given by

TRANSFAC® are as follows.

a. Match:

It is prediction software, which uses library of weight matrix from TRANSFAC®

Public 6.0 to predict TFBS in DNA sequences. Transcription factor binding sites with

their sequences, positions, core matches and the matrix matches in IL-28Rα gene of

Homo sapiens were predicted using this software and Matrix match scores are describing

the score of the complete matrix match (more important values, ranges between 0-1) and

the core similarity is the score of the highest conserved positions of a matrix match. Both

thresholds have to be reached for a matrix match. c-Rel, ElK-1, c-Ets-1(p54), STATx,

AP-1, NF-kappaB, NKx2-5, and v-Myb are reported with matrix similarity values of

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0.987, 0.990, 0.993, 1.0, 0.976,1.0, 1.0, 0.971 respectively as the TFs with greater

confidence (figure 4.13)

b. F-match:

Statistically over-represented TFBS are compared with control sets in this

program and it assumes the binominal distribution of TFBS frequency. F-Match uses the

Match algorithm with the same library of positional weight matrices from

TRANSFAC®6.0. results were obtained from analyzing our sequence of length: 4563

having total number of sequences: 1 & Total number of sites: 267 Number of sequences

with sites: 1, Frequency of sites: 0.05851 and Average number of sites per sequence:

267.00 against the background human genome version: human/hg38 (table 4.2 and 4.5)

c. AliBaba2:

It uses the binding sites from TRANSFAC® Public for the prediction of TFBS in

an unknown DNA sequence. Results from this software included some notable

transcription factors like GATA1, C/EBPbeta, REB1, NF-EM5 and NF-kappaB. These

were highly repeated sequences with highest scores in prediction criteria of the software,

which depicts that these were most expected to get attached on those TFBS and expresses

the desired gene.

Genomatix Software GmbH is a German company with many biological computational

softwares and two of them which we used in the present study are

a. MatInspector:

It is used for finding physical TF binding sites (TFBS), this tool uses the library of

matrix descriptions for TFBS to identify matches in DNA sequences. The filters and the

quality of selection are quite reliable as compared to other softwares. It has been in

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practice since 2005 and has been cited in many publications (Quandt et al., 1995,

Cartharius et al., 2005). Figure 4.14 shows the predicted transcription factors using this

software, which are marked with different colors at various positions according to their

binding sites predicted. The transcription factor binding sites along the 600 bps are

included in the results shown. Key features of MatInspector includes various vital and

helpful outputs like TFBS grouping as matrix families, graphical representation of

results, promoter finding and the overlapping involved, lastly the TFBS predictions and

ChIP seq data.

b. DiAlign TF:

It is a (DNA or protein) alignment program under the umbrella of genomatix that

comparison the whole segments of sequences instead of comparison of single

nucleic/amino acids and depends on those authenticated results. It displays TFBS with

help of MatInspector with in a multiple alignment. It shows its results in color boxes with

the most realistic ones to be on top and is very user friendly. Figure 4.15 shows multiple

alignments from various animals, choosing the common sites of interest, which are more

likely to play role in the expression of this gene. the results are colored according to their

scores in the prediction criteria and KLFS, NOFL, CAAT, MYOD and PLAG are some

common transcription factors predicted through this software.

c. SNPInspector:

Single nucleotide polymorphisms (SNPs) is analyzed in this software to see the

potential effects on the expression or repression of those genes and proteins. TFBS are

either generated or deleted due to the SNPs and we studied the same deletions and

generations of sites for our predicted transcription factors. Figure 4.16 shows various

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sites for NFY transcription factor, which has either been deleted or generated due to SNP.

These results were obtained using NM_170743 (1 sequence, 4563 bp) found on

chromosome 1 of Homo sapiens, NCBI build 37, ElDorado 08-2011 Extracted region:

NC_000001 between 24472207 and 24522206 (50000 bp) as analysis parameters. Probes

displayed are from chip Human Genome U133 Plus 2.0. Column 1-6 report, Position,

dbSNP, Transcription factor binding sites lost due to SNP. Transcription factor binding

sites generated due to SNP, Allele Information, Start-Stop sites, Strand (+/-), Core

similarity and Matrix Similarity respectively.

Results are also summarized in table 4.6, which highlights all of the overrepresented TS

generated through this software.

After using all these softwares, we were able to short list few dependable hits of

TFs, which included many of those, which are already part of JAK-STAT pathway or

many of those TFs, which are identified in nearly all essentials pathways involved in the

differentiation of cells. Literature review has also helped us in showing some nearest hits

like AP-2, c-JUN, NFYA, MYODSTAT-1 or LyF-1(Yang et al., 2010). Figure 4.17

shows all of the over presented transcription factor binding sites for IFN-λRα gene

overrepresented TF families tool. Z-score shows the distance of our sequence from the

population mean in units of population standard deviation. Promoter association shows

how many TF Families known to occur more than twice as often in promoters as in

genomic sequence. Default sorting: by column "Z-Score (genome)". The results reveal

CTCF, GLIF, PLAG, NOLF, NFKB, AP2F, WHNF, SP1F, ZFHX, ZF02, NGRE, NRSF,

NRF1, SAL2, PRDM, MAZF, STAF, HESF, KLFS, ZF07, MTEN, CP2F, ESRR, BEDF,

HIFF, MYBL, XBBF, TF2D, ZF01, PURA, TELO, INSM, HAML, ZF5F, CSEN,

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OAZF, MYOD, HICF, NACA, RXRF, SNAI, CDEF as over represented TF families

with Z-score (number of standard deviations an observation or datum is above the mean)

values greater than 2.5.

In the start of 2014, Ding et al. found relationship between the expression of IFN-

λRα and a transcription factor called NF-YA, which was also found in our research as

CBFA2 (Ding et al., 2014). This transcription factors work when DNMT and histone

deacetylase (HDAC) are blocked naturally in IFN-λRα expressing cell lines or artificially

by 5azadC or MS-275 in non- IFN-λRα expressing cell lines. Ding et al., also showed the

role of NFY-A in the expression of IFN-λRα and this finding can be of great importance

in the success of interferon therapy against the resistance or relapses (Ding et al., 2014).

Our results obtained show agreement with reference to the discussed study and found

CBFA2 as a close finding, this finding lead us to SNPInspector and ElDorado, which

enhanced our finding on the SNPs involved in the TFBS, which may affect the bindings

of TF.

SNPInspector and DiAlign uses genome references of various organisms and use

them in various ways as it contains enough data to predict the SNP sites. they compare

transcripts with exon\intron structure, UTRs, the SNPs that effect transcription factors,

promoter regions and regulatory elements, CDS and protein sequences etc. SNPInspector

is a powerful tool and results of SNPInspector can be used in various prospects. We used

SNPInspector to shortlist the SNPs, which may affect the binding of vital TFs, which are

shown in figure 4.16 and table 4.6c.

The SNPs of type III IFNs are clinically proven important to play a role in the

treatment outcome of IFN-α therapy against hepatitis C, yet the mechanism behind this

96

relationship is not fully understood. Few SNP in IFN-λRα have also been identified and

claimed to be responsible in playing combined roles with the identified SNPs of IL-28B

in affecting the treatment outcomes but the relationships are still unknown. A

computational linkage between the expressions of IFN-λRα with respect to the SNPs was

also found which involved in outcomes of IFN-α therapy (table 4.6c).

In the previous results, it was shown that inducing IFN-α or IFN-λ on monocyte

differentiation to macrophages had effected the expression of IFN-λRα, whereas the

higher concentration of IFN-α can also down regulate the expression of IFN-λRα even in

hepatocytes (figure 4.3). Computational software (SNPInspector and DiAlign) was used

to predict the important SNPs in IFN-λRα. Out of many SNPs found through the

computational analysis, the selection criteria was narrowed down to those, which were

important in the binding of various important transcription factors or involves (creates or

vanishes) the transcription binding sites. Out of those sites, the sites involving the

transcription factor NFY and highlighted four dbSNPs i.e. rs10903035, rs80179676,

rs72648600 and rs6698365.rs10903035 was found to be the only one out of those four

SNPs identified, which had been studied before and was linked with the outcomes of

HCV infections in Chinese population (Cui et al., 2011), where the AA genotype had a

significant increased risk of persistent HCV infection. G A mutation makes a new site

for the transcription factor NFY, which has been proven to play an important role in the

expression of IFN-λRα, as NFYA is easily accessible in IFN-λRα expressive cells as

compared to the non-expressive cells and HDAC mediated closed chromatin

conformation are the main silencing mechanism of IFN-λRα expression in IFN-λ

unresponsive cells (Ding et al., 2014) (table 4.6c). Excessive expression of IFN-λRα can

97

be the reason behind resistance of interferon in non-responsive hepatitis patients. In

addition to this, the AA genotype of rs10903035 has been found to be associated with the

insulin resistance in HIV\HCV co-infected patients, as it seems to have effects in the

glucose homeostasis (Jimenez-Sousa et al., 2014).

Duong el al. has shown in his article that the IFN-λ3 genotype was associated

with the expression level of IFN-λRα but not with differential expression of IFN-λ

(Duong et al., 2014). This finding may also be the missing link between the IFN-λ3

genotypes and the non-responders of IFN-α. Expression of IFN-λRα is tissue specific and

the epigenetic reprogramming of the IFN-λRα gene is dependent on the binding of TFs at

the particular sites. There is a possibility that the IFN-λ3 genotype may also contains

some SNP that effect the expression of IFN-λRα as we have seen rs10903035 and the

binding site for NFY-A. Figure 5.2 shows the summary of the findings of the predicted

TFBS and the SNPs involved at those sites with reference to NFY-A.

All type III IFNs signals through IFN-λRα and IL10R2 but the receptor of the

newly discovered IFNλ4 was unknown. IFNλ4 was believed to be inactive in majority of

population or play a negative role in the therapy of patients suffering from HCV (O'Brien

et al., 2014, Prokunina-Olsson et al., 2013). Extensive genomic studies of area around

IFNL3gene have helped us in discovering another gene of the same family, which has

been named as IFNL4. IFNL4 gene is naturally activated or inactivated in many patients

as it depends on a dinucleotide variant (ss469415590, TT or DG). There is a frame shift

mutation within this gene when the allele is TT, which results in inactivation of this gen,

whereas the DG allele leads to a fully functional IFNL4 gene (Prokunina-Olsson et al.,

2013).

98

Figure 5.2: Key elements describing the expression of IL-28Rα, the receptor protein gets

expressed with the combined effect of a series of transcription regulatory elements as highlighted

by our study via various computational tools. NFY-A as per supported by our results from

SNPInspector are; family of TFs shown to play significant role in the expression (reported as

various SNPs in a series of literature as well).Alongside are the results from DiAlign TF

suggesting the binding sites for a series of differentially expressed TFs that are in consensus to 7

of the notable vertebrate species.

Expression of IFNλ4 is believed to play a negative role in the treatment of HCV

through IFN-α therapy but it was not proved experimentally before we expressed and

purified IFNλ4 in Rune Hartmann’s lab (Ole Hamming’s work) (Hamming et al., 2013).

After the purification and proper refolding of IFNλ4 protein, it was tested for its activity.

Recombinant IFNλ4 was tested in HL-116, which were stably transfected with the private

chain of type III IFN heterodimer receptor (IFN-λRα) and a luciferase reporter regulated

under IFI6 promoter (Uze and Monneron, 2007). The activity of recombinant IFNλ4 was

99

comparable with IFNλ3 as it activates the IFI6 promoter up to the satisfactory levels

(Hamming et al., 2013).

Furthermore, the activity of IFNλ4 in HepG2 cells were also verified (figure

4.20), as they naturally express IFN-λRα and are responsive to type III IFNs (Dumoutier

et al., 2004, Sheppard et al., 2003). HepG2 cells was induced with various concentrations

of IFNα2, IFNλ3 and IFNλ4 and lysed them with different time intervals e.g 4 hrs, 6 hrs,

8 hrs and 16 hrs. After lysis, the RNA was collected and after measuring the

concentration on nano drop, it was converted it to cDNA with the help of cDNA kit.

After 1\10 dilution of the cDNA, we measured the induction of the interferon-stimulated

genes (ISGs) MX1, IFIT1 and OASL through qPCR (Figure 4.20). ISGs were stimulated

by all three interferons and in addition to this, results show that the induction of ISGs by

IFNλ3 and IFNλ4 were indistinguishable at certain time intervals. All three genes were

induced by all three IFNs used and IFNλ4 showed its efficacy and potent behavior for the

first time in literature (Hamming et al., 2013).

The ISGs was measured as IFNs perform their antiviral duties through these genes

and if IFNλ4 is a real type III IFN, it should be inducing ISGs as other family members.

The gene concentration of IFIT1 (ISG56) was measured as it is upregulated after type III

IFN induction and as quoted in literature, IFNλ4 also induces IFIT1 like IFNλ3, though

not at same level, but at a comparable levels. IFNα2 induces IFIT1 in high concentrations

i.e 1000U, but falls to the base points as the concentration is reduced to 100U. IFNλ3

showed its maximum efficacy at 4 hours interval and displayed similar pattern with

respect to its concentration levels on various time intervals.

100

Mx is another prominent ISG, which is induced by type III IFNs. Both IFNλ3 and

IFNλ4 showed their competency in upregulation of Mx gene after their inductions at

various time intervals. For the first time IFNλ4 showed identical behavior to IFNλ3 at all-

time intervals and concentrations. IFNα2, as known previously is a poor inducer of Mx as

compared to type III IFNs and showed less capability in inducing Mx gene. Type III IFNs

up regulate various ISG genes to initiate antiviral activities and OASL is one of them.

Inductions shown and calculated by both IFNλ3 and IFNλ4 were nearly homogeneous

and type I IFN i.e IFNα2 was quite far from the activity shown by both type III IFNs.

Hence the activity pattern and the potent behavior shown by IFNλ4 was

comparable with that of IFNλ3 and the previous negative statements about the expression

of newly discovered IFNλ4 stays controversial as we have shown its ability to induce

ISGs in HepG2 cells.

In our results, antiviral activity of IFNλ4 is shown and in addition to this,

HEK293 cells was used to study the receptor complex used for these inductions of

IFNλ4. HEK293 cells respond poorly to type III IFNs as they express low levels of IFN-

λRα naturally (Meager and Das, 2005). An assay at Rune Hartmann’s lab was developed,

in which we transfected HEK293 cells with an IFN-λRα expressing plasmid or used

siRNA to knock down IL-10Rβ to disturb the balance of this heterodimer combination of

receptors (Hamming et al., 2013). Results have shown that IFNλ4 also uses the same

receptor complex, which is used by its other family members. Moreover, it activates

similar and equivalent antiviral pathways, as are activated by other known type III IFNs

(Hamming et al., 2013).

101

CONCLUSION

Type III interferons are an interesting new addition to the interferon family, they

undoubtedly induce a response in cells which is highly similar to that of type I IFN.

Recently a new addition in this family has been reported and has been named as IFN-λ4.

We have shown in our recent publication that IFN-λ4 also signals through the same

heterodimer receptor i.e. (IFN-λRα and IL-10Rβ). In this study, we extended the previous

findings regarding IFN-λRα, that the selective expression of this type III IFN private

receptor makes their pharmacodynamics different than other IFNs. Selective expression

of IFN-λRα is beneficial as it makes type III IFNs targeted to the certain cell types.

Monocytes actually do not express IFN-λRα, but when they differentiate to macrophages,

they express and respond to type III IFNs including the newly discovered IFN-λ4, which

was shown for the first time in this study. Nevertheless, IFN-λ4 showed comparable

results in activating anti-viral pathways in macrophages and HepG2 cells. Expression of

IFN-λRα was also studied through various methodologies, including bioinformatics,

which we used to measure the strength of signal peptides of IFN-λRα and IFN-αR. In

current study, we experimentally interchanged the signal peptides of both studied

receptors but concluded that the expression is independent of the strength of signal

peptides. Further the proposed splice variant of IFN-λRα was measured, which are

believed to be present naturally and the soluble splice variant is believed to keep a

balance with the fully functional IFN-λRα, but still didn’t find any connection. Moreover,

the study was enhanced to the prediction of transcription factor binding sites and

transcription factors through various computational techniques, meanwhile in 2014, there

was another finding by another group that NF-YA plays a vital role in expression of IFN-

102

λRα, which has some similarity with our results and our results investigated the single

nucleotide polymorphism at the vital TFBS, which ended up with some interesting

findings like the SNP rs10903035 was found to be important as TFBS, specially for NF-

YA and it has previously been studied as the important marker for the failure of IFN-α

therapy.

Type III IFNs are a promising drug candidate for use against various viral

infections, particularly as the early observation of a very favorable side effect profile is

confirmed by full scale clinical trials. However, its receptor is not yet deeply exploited

and there is need to take these finding together, because actions of type III IFNs are

dependent on expression of IFN-λRα and it has been seen in our study that even the SNPs

involved in the expression pattern of IFN-λRα can affect the outcomes of the antiviral

therapies.

103

RECOMMENDATION

This is first study of showing the pharmacological action of IFN-λ4 in

macrophages and HepG2 cells. The results have shown comparable potent behaviors of

both IFN-λ3 and IFN-λ4, studied through the expression or activation of ISGs and

measured with the help of qPCR. IFNs are potent antiviral drug of choice for HCV.

Hepatitis C virus is the major pathological agent responsible for liver diseases

worldwide and it is the causative agent of chronic hepatitis that leads to hepatic steotosis,

fibrosis, cirrhosis, hepatocellular carcinoma (HCC) and ultimately liver failure.

Expression of IFN-λ4 is supposed to be a negative factor in IFN-α therapy, as scientists

had previously seen its expression in resistant cases, but according to our results, IFN-λ4

is a functional antiviral protein, which has the potential to activate antiviral pathways like

its other family members. Our studies also recommends that the role of IFN-λ4 in failures

of IFN-α therapy should be reconsidered, as it has shown its devotion with its family and

shown reliable positive results.

Type III IFNs functions through the heterodimer receptor complex (IFN-λRα and

IL-10Rβ), our group have confirmed the binding of IFN-λ4 with the same receptor

complex, furthermore we have studied the expression behavior of IFN-λRα and

concluded that its expression neither dependent of the strength of its signal peptide, nor

on the balance maintained by its splice variants, but on the transcription factors involved

in its expression. We have identified few TFBS, which encompass certain SNPs that

show trepidation in failure of IFN-α therapy. We recommend that SNPs of IFN-λRα

should also be considered as the SNP testing of IL-28B is almost included in the

pretreatment testing phase of HCV treatment.

104

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Annexure

Annexure no. 1

Annexure No. 2

Supplementary Table: Oligonucleotides used in this study. (referred in material and

methods)

Forward Reverse

GAPDH CGACCACTTTGTCAAGCTCA GGTGGTCCAGGGGTCTTACT

Mx1 ACCTACAGCTGGCTCCTGAA CGGCTAACGGATAAGCAGAG

ISG56 CCTCCTTGGGTTCGTCTACA GGCTGATATCTGGGTGCCTA

OASL AGAGACTTCCTGAAGCAGCG GAGCTCCAGGGCATACTGAG

IFNλR1

(NM_170743.3)

TAGTAATTGCCGCAGGGGGT GTGTGTCCAGAAAAGTCCAGGG

134

Annexure No. 3 Short listed transcription factors after MetInspector:

Seq. name Locus

Id

Gene

sym.

Family Matrix Opt.

thresh

old

Start

pos.

End

pos.

Strand Matri

x sim.

Core

sim.

Sequence

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AP

2F

V$TCF

AP2A.0

1

0.93 7 21 + 0.952 0.938 gatccccgG

GGGcat

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AP

2F

V$TCF

AP2A.0

1

0.93 8 22 - 0.946 0.893 aatgccccC

GGGgat

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AB

DB

V$HO

XC9.01

0.83 13 29 + 0.894 1.0 cgggggcaT

TAAaggga

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

NF

V$TST

1.01

0.9 15 33 + 0.913 1.0 ggggcATT

Aaagggaatc

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HO

MF

V$HHE

X.01

0.95 16 34 - 0.955 1.0 gcgattccctt

TAATgccc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$GKL

F.02

0.96 16 32 + 0.981 1.0 gggcattAA

AGggaatc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AH

RR

V$AHR

ARNT.

02

0.77 23 47 + 0.814 1.0 aaagggaatc

GCGTgtgta

aggcgc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$MO

KF

V$MO

K2.02

0.98 37 57 - 0.986 1.0 gctgagctcc

gcgCCTTa

cac

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F.

03

0.85 39 55 + 0.883 1.0 gtaagGCG

Cggagctca

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$TF2

B

O$BRE

.01

0.97 43 49 - 1.0 1.0 ccgCGCC

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$MT

EN

O$HM

TE.01

0.88 64 84 - 0.892 0.761 ggATCCga

gcgcgtttctg

ag

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

1_DP1.

01

0.81 76 92 - 0.832 1.0 cattGGCG

ggatccgag

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$YY

1F

V$REX

1.01

0.87 78 98 - 0.891 1.0 caatgCCA

Ttggcgggat

ccg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$RB_

E2F1_

DP1.01

0.71 79 95 + 0.726 0.796 ggatcCCG

Ccaatggca

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$RX

RF

V$VDR

_RXR.0

6

0.75 118 142 - 0.755 0.812 ggcaggatta

gggagAGT

Tcaaggc

GXP_260584

(IL28RA/hu

GXL_2

18381

IL28

RA

V$MY

T1

V$MY

T1L.01

0.92 119 131 - 0.945 1.0 ggagAGTT

caagg

135

man)

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CA

RT

V$PHO

X2.01

0.87 128 148 + 0.871 1.0 ctcccTAAT

cctgccaaaat

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$YY

1F

V$YY2

.01

0.96 134 154 - 0.97 1.0 acgggCCA

Ttttggcagg

att

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HN

F1

V$HNF

1.03

0.8 178 194 + 0.895 1.0 gGTTAttga

tcatcagc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HN

F1

V$HNF

1.02

0.77 179 195 - 0.791 0.762 ggcTGATg

atcaataac

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$DE

AF

V$NU

DR.01

0.73 191 209 + 0.733 0.778 cagCCGGtt

tcttcccctc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF

HX

V$ARE

B6.04

0.98 192 204 + 0.991 1.0 agccgGTT

Tcttc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF3

5

V$ZNF

35.01

0.96 197 209 - 0.965 1.0 gaggggAA

GAaac

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PU

RA

V$PUR

ALPHA

.01

0.97 198 210 - 0.971 1.0 ggAGGGg

aagaaa

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$CKR

OX.01

0.88 201 217 - 0.897 1.0 gggcagGG

GAggggaa

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$KKL

F.01

0.91 201 217 - 0.953 1.0 gggcaGGG

Gaggggaag

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$RX

RF

V$VDR

_RXR.0

6

0.75 204 228 - 0.753 0.812 gcacggggg

aagggcAG

GGgagggg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

4_DP2.

01

0.76 243 259 - 0.799 0.776 aattcCCGG

aaaaaaaa

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ST

AT

V$STA

T3.02

0.94 243 261 - 0.965 1.0 tcaaTTCCc

ggaaaaaaaa

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BC

L6

V$BCL

6.04

0.88 244 260 - 0.896 1.0 caaTTCCc

ggaaaaaaa

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ST

AT

V$STA

T.01

0.87 245 263 + 1.0 1.0 ttttttccgGG

AAttgagt

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BC

L6

V$BCL

6.04

0.88 246 262 + 0.887 1.0 tttTTCCgg

gaattgag

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PA

RF

V$HLF

.01

0.84 253 269 + 0.877 1.0 gggaattgaG

TAAaaca

136

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CE

BP

V$CEB

PA.01

0.94 254 268 + 0.95 0.917 ggaattgaGT

AAaac

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$IRF

F

V$ISG

F3G.01

0.82 259 279 + 0.873 0.762 tgagtaaaac

AAAActaa

gtg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$IRF

F

V$IRF4

.03

0.87 298 318 - 0.927 1.0 ccccgcgctc

GAAActcg

ccc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F.

02

0.84 301 317 - 0.849 1.0 cccgcgctcG

AAActcg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$MT

EN

O$DM

TE.01

0.77 303 323 + 0.79 1.0 agtttcgAG

CGcggggac

cgg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$MT

EN

O$HM

TE.01

0.88 311 331 - 0.945 1.0 ggAGCGc

gccggtcccc

gcgc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HD

BP

V$HDB

P1_2.01

0.84 314 332 + 0.857 1.0 cggggaCC

GGcgcgctc

cc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PA

X5

V$PAX

5.02

0.73 315 343 - 0.779 1.0 gaggggggg

gggggAGC

Gcgccggtcc

cc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

2.01

0.85 317 333 - 0.893 1.0 ggggaGCG

Cgccggtcc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

2.01

0.85 318 334 + 0.912 1.0 gaccgGCG

Cgctccccc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF5

F

V$ZF5.

01

0.95 318 332 - 0.953 1.0 gggagcGC

GCcggtc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF5

F

V$ZF5.

02

0.83 319 333 + 0.889 1.0 accggCGC

Gctcccc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF5

F

V$ZF5.

01

0.95 320 334 - 0.96 1.0 gggggaGC

GCgccgg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$KKL

F.01

0.91 322 338 - 0.949 1.0 gggggGGG

Gagcgcgcc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$KKL

F.01

0.91 325 341 - 0.947 1.0 gggggGGG

Ggggagcgc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$WT1

.01

0.92 326 342 - 0.943 0.837 aggggggG

GGGggagc

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$KKL

F.01

0.91 327 343 - 0.958 1.0 gagggGGG

Ggggggagc

137

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$WT1

.01

0.92 329 345 - 0.945 0.837 gggagggG

GGGggggg

a

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$KKL

F.01

0.91 330 346 - 0.966 1.0 ggggaGGG

Ggggggggg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$WT1

.01

0.92 331 347 - 0.93 0.837 gggggagG

GGGggggg

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PU

RA

V$PUR

ALPHA

.01

0.97 332 344 - 0.993 1.0 ggAGGGg

gggggg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$WT1

.01

0.92 333 349 - 0.945 0.837 cggggggA

GGGggggg

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$CKR

OX.01

0.88 335 351 - 1.0 1.0 cgcgggGG

GAgggggg

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

4.01

0.96 347 363 + 0.978 1.0 ccgcgGCG

Gggctgtcc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$KLF

6.01

0.92 348 364 + 0.934 1.0 cgcggcGG

GGctgtccc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PA

X5

V$PAX

5.03

0.8 405 433 + 0.847 0.842 ccgcgGCT

Ctgcgggcca

ttggctgccga

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$YY

1F

V$YY2

.02

0.82 415 435 + 0.823 1.0 gcgggCCA

Ttggctgccg

act

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AH

RR

V$AHR

ARNT.

02

0.77 426 450 + 0.775 1.0 gctgccgact

GCGTcacc

tgcccgc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CR

EB

V$ATF

.01

0.9 427 447 - 0.909 1.0 ggcaggTG

ACgcagtcg

gcag

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CR

EB

V$CRE

B.02

0.89 430 450 - 0.947 1.0 gcgggcagg

TGACgcag

tcgg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$WH

NF

V$WH

N.01

0.95 432 442 - 0.956 1.0 gtgACGCa

gtc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

AC

V$EO

MES.02

0.88 433 453 - 0.898 1.0 accgcgggca

GGTGacgc

agt

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$MY

OD

V$E47.

01

0.92 435 451 - 0.926 1.0 cgcggGCA

Ggtgacgca

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

4.01

0.96 466 482 + 0.985 1.0 gggagGCG

Ggaggcggg

GXP_260584

(IL28RA/hu

GXL_2

18381

IL28

RA

V$EG

RF

V$CKR

OX.01

0.88 467 483 + 0.917 1.0 ggaggcGG

GAggcggg

138

man) a

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

4.01

0.96 473 489 + 0.985 1.0 gggagGCG

Ggaggcggg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$CKR

OX.01

0.88 474 490 + 0.917 1.0 ggaggcGG

GAggcggg

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

4.01

0.96 480 496 + 0.981 1.0 gggagGCG

Gggacctgg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PU

RA

V$PUR

ALPHA

.01

0.97 481 493 + 0.981 1.0 ggAGGCg

gggacc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$KLF

7.01

0.92 495 511 + 0.933 1.0 gggcccgG

GCGgggac

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$WH

NF

V$WH

N.01

0.95 506 516 + 0.95 1.0 gggACGCc

gcg

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$MT

EN

O$DM

TE.01

0.77 533 553 - 0.81 1.0 gggccccAG

CGctcgggc

ccc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$MT

EN

O$DM

TE.01

0.77 534 554 + 0.874 1.0 gggcccgA

GCGctggg

gcccc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF

HX

V$ARE

B6.01

0.93 580 592 - 0.932 1.0 cccttACCT

ggag

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

3.01

0.85 587 603 + 0.879 1.0 taaggGCG

Cggggccgc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EB

OX

V$MY

CMAX.

03

0.91 590 602 - 0.919 1.0 cggcccCG

CGccc

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$TF2

B

O$BRE

.01

0.97 591 597 - 1.0 1.0 ccgCGCC

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$CKR

OX.01

0.88 598 614 + 0.973 1.0 ggccgcGG

GAgggagg

g

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$CKR

OX.01

0.88 602 618 + 0.977 1.0 gcgggaGG

GAggggga

a

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$WT1

.01

0.92 604 620 + 0.929 0.837 gggagggA

GGGggaag

a

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PU

RA

V$PUR

ALPHA

.01

0.97 605 617 + 0.973 1.0 ggAGGGa

ggggga

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$RX

RF

V$VDR

_RXR.0

1

0.85 605 629 + 0.868 1.0 ggagggagg

gggaaGAG

Ggctcccc

139

GXP_260584

(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF3

5

V$ZNF

35.01

0.96 611 623 + 0.975 1.0 agggggAA

GAggg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

NF

V$BRN

3.03

0.83 8 26 - 0.863 1.0 aaaatatTA

ATggttgttg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HO

MF

V$BAR

X2.01

0.95 8 26 - 0.984 1.0 aaaatatTA

ATggttgttg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

NF

V$BRN

3.02

0.89 11 29 + 0.933 1.0 caaccatTA

ATattttggt

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CA

RT

V$XVE

NT2.01

0.82 16 36 + 0.83 0.75 attaaTATTt

tggtaattatt

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CA

RT

V$S8.0

1

0.97 19 39 - 0.992 1.0 aataaTAAT

taccaaaatatt

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PA

RF

V$TEF.

01

0.85 19 35 + 0.85 1.0 aatattttgGT

AAttat

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

NF

V$BRN

2.04

0.82 22 40 + 0.857 1.0 attttggTAA

Ttattattc

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HO

MF

V$BSX

.01

0.95 22 40 + 0.983 1.0 attttggtAA

TTattattc

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

NF

V$BRN

2.04

0.82 23 41 - 0.866 1.0 ggaataaTA

ATtaccaaaa

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HO

MF

V$BSX

.01

0.95 23 41 - 0.975 1.0 ggaataatA

ATTaccaaa

a

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CA

RT

V$S8.0

1

0.97 24 44 + 0.992 1.0 tttggTAAT

tattattccaaa

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HN

F1

V$HNF

1.02

0.77 24 40 - 0.786 1.0 gaaTAATa

attaccaaa

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HN

F1

V$HNF

1.02

0.77 26 42 + 0.849 1.0 tggTAATta

ttattcca

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

NF

V$BRN

2.01

0.86 34 52 + 0.868 0.967 atTATTcca

aatatctttc

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BC

L6

V$BCL

6.03

0.8 45 61 - 0.813 0.992 gtatgcaTA

GAaagata

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BC

L6

V$BCL

6.02

0.77 109 125 + 0.79 0.771 tttttttTTGA

aactga

GXP_148784

5(IL28RA/hu

GXL_2

18381

IL28

RA

V$CE

BP

V$CEB

PA.01

0.94 110 124 + 0.94 0.972 ttttttttGAA

Actg

140

man)

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$IRF

F

V$ISG

F3G.01

0.82 120 140 - 0.851 1.0 gcgacagagt

GAAActca

gtt

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F

4_DP1.

01

0.84 153 169 - 0.841 1.0 cgagatCGC

Gccactgc

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$E2F

F

V$E2F.

03

0.85 154 170 + 0.871 1.0 cagtgGCG

Cgatctcgg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PU

RA

V$PUR

ALPHA

.01

0.97 179 191 - 0.986 1.0 ggAGGCg

gaggtt

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$MY

T1

V$MY

T1L.01

0.92 225 237 + 0.922 0.909 tgatAGCTg

ggat

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$MY

OD

V$MY

OGENI

N.01

0.91 234 250 + 0.913 1.0 ggattACA

Ggtgaatgc

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF

HX

V$ARE

B6.02

0.97 237 249 - 0.989 1.0 cattCACCt

gtaa

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$MT

EN

O$HM

TE.01

0.88 246 266 - 0.897 0.961 tcAGCCgg

gcgtggtggc

att

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EB

OX

V$ATF

6.01

0.93 250 262 + 0.946 1.0 ccaCCACg

cccgg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$KLF

7.01

0.92 250 266 - 0.943 1.0 tcagccgGG

CGtggtgg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CR

EB

V$CJU

N_ATF

2.01

0.99 322 342 + 0.995 1.0 aactccTGA

Cctcaggcga

tc

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$RX

RF

V$RAR

_RXR.0

1

0.78 324 348 - 0.806 1.0 cgggtggatc

gcctgAGG

Tcaggag

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AP

2F

V$AP2.

02

0.92 327 341 - 0.941 1.0 atcGCCTg

aggtcag

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$KL

FS

V$EKL

F.02

0.93 346 362 - 0.934 1.0 ttgggagGG

TGaggcgg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$EG

RF

V$CKR

OX.01

0.88 350 366 - 0.885 1.0 cactttGGG

Agggtgag

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AH

RR

V$NXF

_ARNT

.01

0.9 369 393 + 0.904 1.0 gggattacag

gCGTGagc

caccgcg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$MT

EN

O$HM

TE.01

0.88 382 402 - 0.958 0.961 gtAGCCgg

gcgcggtggc

tca

141

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF5

F

V$ZF5.

03

0.84 387 401 + 0.876 1.0 cacCGCGc

ccggcta

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

O$TF2

B

O$BRE

.01

0.97 389 395 + 1.0 1.0 ccgCGCC

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HD

BP

V$HDB

P1_2.01

0.84 389 407 + 0.867 1.0 ccgcgcCC

GGctacaca

ca

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

NF

V$BRN

2.01

0.86 404 422 - 0.944 1.0 ccCATTaa

aaaagtgtgtg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

NF

V$BRN

4.01

0.89 405 423 + 0.9 1.0 acacactttttT

AATgggc

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HO

MF

V$HHE

X.01

0.95 405 423 + 0.979 1.0 acacactttttT

AATgggc

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$YY

1F

V$YY2

.02

0.82 406 426 - 0.825 1.0 taggcCCA

Ttaaaaaagt

gtg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$HO

MF

V$BAR

X2.01

0.95 409 427 + 0.951 1.0 actttttTAA

Tgggcctat

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AB

DB

V$HO

XC13.0

1

0.91 410 426 - 0.923 1.0 taggcccatT

AAAaaag

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$IRF

F

V$IRF3

.01

0.85 436 456 - 0.952 1.0 ctgagaaaca

GAAAagc

gagt

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF

HX

V$ARE

B6.04

0.98 443 455 + 0.994 1.0 tttctGTTTc

tca

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CE

BP

V$CEB

P.02

0.92 455 469 + 0.948 1.0 agtgtgttGC

AAaca

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

AC

V$EO

MES.02

0.88 461 481 - 0.894 1.0 tcgacaccga

GGTGtttgc

aa

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$BR

AC

V$BRA

CH.01

0.66 464 484 + 0.726 0.75 caaacacctC

GGTgtcgat

ac

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$IRF

F

V$IRF6

.01

0.89 469 489 - 0.908 0.809 ggtgtgtatcG

ACAccgag

gt

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$DE

AF

V$NU

DR.01

0.73 489 507 + 0.794 1.0 catTCGGc

aacgtcctcct

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AB

DB

V$HO

XC13.0

2

0.83 499 515 + 0.88 1.0 cgtcctccTA

AAgggcc

GXP_148784

5(IL28RA/hu

GXL_2

18381

IL28

RA

V$PA

X5

V$PAX

5.02

0.73 504 532 - 0.738 0.789 gacgcgcaat

attaTGCGg

142

man) ccctttagga

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$PA

RF

V$DBP

.01

0.84 510 526 - 0.859 1.0 caataTTAT

gcggccct

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ZF3

5

V$ZNF

35.01

0.96 519 531 - 0.964 1.0 acgcgcAA

TAtta

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$WH

NF

V$WH

N.01

0.95 524 534 - 0.954 1.0 acgACGCg

caa

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AH

RR

V$AHR

.01

0.78 526 550 + 0.797 1.0 gcgcgtcgtg

GCGTgtgc

cttactg

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$CR

EB

V$ATF

6.02

0.85 528 548 + 0.85 0.75 gcgtcgtGG

CGtgtgcctt

ac

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$MO

KF

V$MO

K2.02

0.98 530 550 + 0.984 1.0 gtcgtggcgtg

tgCCTTact

g

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$ST

AT

V$STA

T6.01

0.84 540 558 - 0.846 1.0 tagcTTCCc

agtaaggcac

GXP_148784

5(IL28RA/hu

man)

GXL_2

18381

IL28

RA

V$AP

2F

V$AP2.

02

0.92 579 593 + 0.942 1.0 acaGCCTtc

ggggtc

143

Annexure No. 4

TESS

Column Headings in Tabular Results

Beg Start of the site in the query sequence. Numbered from 1

Sns Sense of the site: N - normal, R - reverse complement

Len Length of the site

Sequence Matching portion of the query sequence colored or cased to indicate

mismatches

La Log-likelihood score, higher is better.

La/ La / Len, higher is better, maximum is 2.0.

Lq La / L_M, where L_M is the maximum La possible for the site model,

higher is better, best is 1.0

Ld L_M - La, 0 is best, higher is worse.

L Pv Approximate p-value for La score

Sc Core similarity as reported at TRANSFAC site

Sm Matrix similarity as reported at TRANSFAC site

S Pv Approximate p-value for Sm score

P-v Poisson-model p-value

Model Which site strings or weight matrix was used to pick this site

Factor Which factor(s) does the model represent

144

Hit Sense and Strength Coloring Scheme

Color Strand Secondary Threshold

= + Above

= - Above

- + Below

- - Below

This color scheme is used to color the lines which indicate binding site matches. It is used in both the Java applet and the HTML annotated sequence displays.

Syndrome Coloring Scheme

Color Deficit Meaning

black 0.1 or

less

best or

perfect

match

blue 1.0 or

less pretty

good match

red others mismatch

This color scheme is used to color individual bases in the 'Sequence' column of the tabulated section. The goal is to indicate which positions of the site were good matches and which not.

Color Database

(section)

IMD

TRANSFAC

matrix

CBIL matrices

TRANSFAC site

TRANSFAC/CBIL

string-matrix

This color

scheme is

used to

indicate what

database was

the source of

the model for

a binding site

hit. In

principle this

is indicated

by the first

letter of the

weight

matrix of site

string

accession

number.

145

146

Annexure no. 5

AliBaba2.1

164 segments in complete file identified as potential binding sites

AliBaba2.1 predicts the following sites in your sequence

Sequence seq_75

=======================================================================

===========

seq( 0.. 59)

gggcggggacgccgcggcaggaaggccatggcggggcccgagcgctggggccccctgctc

Segments:

2.3.1.0 12 21 ====Sp1===

2.3.1.0 24 37 ======Sp1=====

2.3.1.0 30 39 ====Sp1===

2.3.1.0 49 59

=====Sp1===

=======================================================================

===========

seq( 60.. 119)

ctgtgcctgctgcaggccgctccagggaggccccgtctggcccctccccagaatgtgacg

Segments:

2.3.1.0 81 90 ====YY1===

2.3.1.0 87 96 ====Sp1===

2.3.1.0 96 108 ======Sp1====

=======================================================================

===========

seq( 120.. 179)

ctgctctcccagaacttcagcgtgtacctgacatggctcccagggcttggcaacccccag

Segments:

2.3.1.0154 163====Sp1===

=======================================================================

===========

seq( 180.. 239)

gatgtgacctattttgtggcctatcagagctctcccacccgtagacggtggcgcgaagtg

Segments:

2.2.1.1199 208===GATA-1=

2.3.1.0210 219====Sp1===

4.4.1.0219 228=====E2===

=======================================================================

===========

seq( 240.. 299)

gaagagtgtgcgggaaccaaggagctgctatgttctatgatgtgcctgaagaaacaggac

Segments:

=======================================================================

===========

seq( 300.. 359)

ctgtacaacaagttcaagggacgcgtgcggacggtttctcccagctccaagtccccctgg

Segments:

4.1.1.0332 341=====Dl===

147

2.3.1.0357 366===

=======================================================================

===========

seq( 360.. 419)

gtggagtccgaatacctggattacctttttgaagtggagccggccccacctgtcctggtg

Segments:

2.3.1.0357 366=Sp1===

2.3.1.0399 411======Sp1====

=======================================================================

===========

seq( 420.. 479)

ctcacccagacggaggagatcctgagtgccaatgccacgtaccagctgcccccctgcatg

Segments:

1.1.5.3450 459====GBF1==

1.1.5.2453 462====GBF2==

2.3.1.0464 477======Sp1=====

2.3.1.0479 488=

=======================================================================

===========

seq( 480.. 539)

cccccactggatctgaagtatgaggtggcattctggaaggagggggccggaaacaagacc

Segments:

2.3.1.0479 488===Sp1===

2.3.1.0515 529=======Sp1=====

2.3.1.0521 530====Sp1===

2.3.1.0521 531=====Sp1===

=======================================================================

===========

seq( 540.. 599)

ctatttccagtcactccccatggccagccagtccagatcactctccagccagctgccagc

Segments:

1.3.1.2555 564====USF===

1.2.2.0587 596===Myf-3==

9.9.539593 602====NF-

=======================================================================

===========

seq( 600.. 659)

gaacaccactgcctcagtgccagaaccatctacacgttcagtgtcccgaaatacagcaag

Segments:

9.9.539593 6021==

=======================================================================

===========

seq( 660.. 719)

ttctctaagcccacctgcttcttgctggaggtcccagaagccaactgggctttcctggtg

Segments:

1.2.1.0670 679=====Da===

4.1.1.0705 714=NF-kappa=

=======================================================================

===========

seq( 720.. 779)

ctgccatcgcttctgatactgctgttagtaattgccgcagggggtgtgatctggaagacc

Segments:

2.3.1.0756 767=====Sp1====

=======================================================================

===========

seq( 780.. 839)

ctcatggggaacccctggtttcagcgggcaaagatgccacgggccctggacttttctgga

148

Segments:

1.6.1.0783 792=AP-2alph=

2.1.2.10805 814====COUP==

2.3.1.0819 828====Sp1===

2.3.2.3839 848=

4.6.1.0839 848=

9.9.1840839 848=

9.9.1841839 848=

9.9.1842839 848=

=======================================================================

===========

seq( 840.. 899)

cacacacaccctgtggcaacctttcagcccagcagaccagagtccgtgaatgacttgttc

Segments:

2.3.2.3 839 848 WT1_I_-K=

4.6.1.0 839 848 ==Sox-2==

9.9.1840 839 848 ==WT1_I==

9.9.1841 839 848 =WT1-del2

9.9.1842 839 848 WT1_I-de=

=======================================================================

===========

seq( 900.. 959)

ctctgtccccaaaaggaactgaccagaggggtcaggccgacgcctcgagtcagggcccca

Segments:

3.5.3.0 912 921 ===NF-EM5=

2.1.2.10925 934====COUP==

2.1.2.3927 936=REV-ErbA=

2.3.1.0947 956====Sp1===

2.3.1.0953 962====Sp1

=======================================================================

===========

seq( 960.. 1019)

gccacccaacagacaagatggaagaaggaccttgcagaggacgaagaggaggaggatgag

Segments:

2.3.1.0953 962===

2.3.1.0 1003 1012

====Sp1===

2.3.1.0 1006 1015

====Sp1===

=======================================================================

===========

seq( 1020.. 1079)

gaggacacagaagatggcgtcagcttccagccctacattgaaccaccttctttcctgggg

Segments:

1.1.2.0 1034 1043 ====CREB==

1.1.3.0 1034 1043 =C/EBPalp=

=======================================================================

===========

seq( 1080.. 1139)

caagagcaccaggctccagggcactcggaggctggtggggtggactcagggaggcccagg

Segments:

2.3.1.0 1113 1125 ======Sp1====

2.3.1.0 1125 1134

====YY1===

=======================================================================

===========

149

seq( 1140.. 1199)

gctcctctggtcccaagcgaaggctcctctgcttgggattcttcagacagaagctgggcc

Segments:

2.3.1.0 1192 1201

====Sp1=

=======================================================================

===========

seq( 1200.. 1259)

agcactgtggactcctcctgggacagggctgggtcctctggctatttggctgagaagggg

Segments:

2.3.1.0 1192 1201 ==

2.3.1.0 1209 1218 ====Sp1===

2.3.1.0 1225 1234 ====Sp1===

2.3.1.0 1252 1265

======Sp

2.3.1.0 1258 1267

==

=======================================================================

===========

seq( 1260.. 1319)

ccaggccaagggccgggtggggatgggcaccaagaatctctcccaccacctgaattctcc

Segments:

2.3.1.0 1252 1265 1=====

2.3.1.0 1258 1267 ==YY1===

2.3.1.0 1269 1281 ======Sp1====

2.3.1.0 1278 1287 ====Sp1===

1.2.2.0 1302 1311

====MyoD==

1.1.3.0 1312 1321

=C/EBPbe

=======================================================================

===========

seq( 1320.. 1379)

aaggactcgggtttcctggaagagctcccagaagataacctctcctcctgggccacctgg

Segments:

1.1.3.0 1312 1321 ta

3.4.1.0 1344 1353 =HSF1_(lo=

=======================================================================

===========

seq( 1380.. 1439)

ggcaccttaccaccggagccgaatctggtccctgggggacccccagtttctcttcagaca

Segments:

3.5.3.0 1424 1433

===ICSBP==

9.9.428 1424 1433

===ISGF-3=

=======================================================================

===========

seq( 1440.. 1499)

ctgaccttctgctgggaaagcagccctgaggaggaagaggaggcgagggaatcagaaatt

Segments:

2.3.1.0 1473 1487 =======Sp1=====

2.3.1.0 1479 1488 ====Sp1===

=======================================================================

===========

seq( 1500.. 1559)

gaggacagcgatgcgggcagctggggggctgagagcacccagaggaccgaggacaggggc

150

Segments:

2.3.1.0 1522 1532 =====Sp1===

2.3.1.0 1523 1532 ====Sp1===

2.3.1.0 1550 1564

=======Sp1

=======================================================================

===========

seq( 1560.. 1619)

cggacattggggcattacatggccaggtgagctgtcccccgacatcccaccgaatctgat

Segments:

2.3.1.0 1550 1564 =====

9.9.539 1570 1579 ====NF-1==

=======================================================================

===========

seq( 1620.. 1679)

gctgctgctgcctttgcaaggactactgggcttcccaagaaactcaagagcctccgtacc

Segments:

=======================================================================

===========

seq( 1680.. 1739)

tcccctgggcggcggaggggcattgcacttccgggaagtccacctagcggctgtttgcct

Segments:

2.3.1.0 1680 1693 ======Sp1=====

2.3.1.0 1687 1700 ======Sp1=====

3.1.2.2 1701 1710 ===Oct-1==

4.1.1.0 1711 1720 =NF-kappa=

9.9.590 1711 1720 =NF-kappaB

=======================================================================

===========

seq( 1740.. 1799)

gtcgggctgagcaacaagatgcccctccctcctgtgacccgccctctttaggctgagcta

Segments:

2.3.1.0 1741 1750 ====Sp1===

2.3.1.0 1759 1771 ======Sp1====

2.1.2.3 1768 1777 =REV-ErbA=

2.1.1.4 1771 1780 =====ER===

2.3.1.0 1775 1786 =====Sp1====

=======================================================================

===========

seq( 1800.. 1859)

taagaggggtggacacagggtgggctgaggtcagaggttggtggggtgtcatcaccccca

Segments:

2.3.1.0 1803 1812 ====Sp1===

2.3.1.0 1816 1828 ======Sp1====

2.1.2.3 1823 1832 ====T3R===

9.9.853 1823 1832 =T3R-beta1

2.1.2.2 1824 1835 ===RXR-beta=

1.1.1.6 1825 1834 =ATF-3del=

2.3.3.0 1826 1835 =CPE_bind=

2.3.1.0 1837 1846 ====Sp1===

=======================================================================

===========

seq( 1860.. 1919)

ttgtccctagggtgacaggccagggggaaaaattatccccggacaacatgaaacaggtga

Segments:

2.1.2.1 1869 1878 =RAR-alph=

2.3.1.0 1877 1886 ====Sp1===

151

3.5.3.0 1906 1915

===ICSBP==

2.1.2.3 1915 1924

====T

2.3.3.0 1916 1925

=CPE

2.1.2.2 1918 1927

==

=======================================================================

===========

seq( 1920.. 1979)

ggtcaggtcactgcggacatcaagggcggacaccaccaaggggccctctggaacttgaga

Segments:

2.1.2.3 1915 1924 3R===

2.3.3.0 1916 1925 _bind=

2.1.2.2 1918 1927 RXR-beta

2.1.1.4 1923 1932 =====ER===

=======================================================================

===========

seq( 1980.. 2039)

ccactggaggcacacctgctatacctcatgcctttcccagcagccactgaactcccccat

Segments:

=======================================================================

===========

seq( 2040.. 2099)

cccagggctcagcctcctgattcatgggtcccctagttaggcccagataaaaatccagtt

Segments:

2.3.1.0 2047 2056 ====Sp1===

=======================================================================

===========

seq( 2100.. 2159)

ggctgagggttttggatgggaagggaagggtggctgtcctcaaatcctggtctttggagt

Segments:

9.9.29 2154 2163

====AP

=======================================================================

===========

seq( 2160.. 2219)

catggcactgtacggttttagtgtcagacagaccggggttcaaatcccagctctgctgtt

Segments:

9.9.29 2154 2163 -1==

=======================================================================

===========

seq( 2220.. 2279)

cactggttgtatgatcttggggaagacatcttccttctctgcctcggcttcctcatctgc

Segments:

3.5.2.0 2268 2277

====PU.1==

2.2.1.1 2269 2278

===GATA-1=

=======================================================================

===========

seq( 2280.. 2339)

agctacgcctgggtgtggtgagggttctaggggatctcagatgtgtgtagcacggagcct

Segments:

2.3.1.0 2289 2298 ====Sp1===

152

=======================================================================

===========

seq( 2340.. 2399)

gctgtgtcctgggtgctctctacgtggtggccggtagaattctccatctatccaggctcc

Segments:

2.3.1.0 2395 2404

====S

=======================================================================

===========

seq( 2400.. 2459)

aggagacccctgggcatctcccacctgtggcccctaaacccagagtgactgagagcactt

Segments:

2.3.1.0 2395 2404 p1===

9.9.29 2443 2452

====AP-1==

=======================================================================

===========

seq( 2460.. 2519)

aacattcagcttgtctcatccccagtctacctccttccttctaccctcactgcctcccag

Segments:

2.3.1.0 2510 2519

====Sp1===

=======================================================================

===========

seq( 2520.. 2579)

tcaggagagtgagctctcagaagccagagccccacccaaggggaccctggtctctccgcc

Segments:

2.3.1.0 2548 2557 ====Sp1===

2.3.1.0 2573 2582

====Sp1

=======================================================================

===========

seq( 2580.. 2639)

ttcacctagcaatgggaaccctgcttcccaggggaggaaccaactgctccaccttctagg

Segments:

2.3.1.0 2573 2582 ===

1.2.8.0 2605 2614 ===Olf-1==

2.3.1.0 2610 2619 ====Sp1===

=======================================================================

===========

seq( 2640.. 2699)

gacccagtttgttggagtaggacagtaacatggcaggaatcggacttctgggcctgtaat

Segments:

=======================================================================

===========

seq( 2700.. 2759)

cccagtttggatggcacgttagactcttggttgaccgttgtggtccttagaagtcccatt

Segments:

9.9.539 2706 2715 ====NF-1==

=======================================================================

===========

seq( 2760.. 2819)

ctcccttccagttatgagaaaccaatgccttctagattcaggtgactatccttacctggg

Segments:

2.3.1.0 2788 2797 ====Sp1===

2.3.1.0 2814 2823

====Sp

153

=======================================================================

===========

seq( 2820.. 2879)

ggtgctgatgcatcctcagttaacctacacccacctgaatatagatgagcgtagctgagt

Segments:

2.3.1.0 2814 2823 1===

3.5.1.2 2879 2888

=

=======================================================================

===========

seq( 2880.. 2939)

tttcacccgtaggaccgaagtgttttgtggtggagtatctgaacaaccttggctctgtgg

Segments:

3.5.1.2 2879 2888 ===REB1==

=======================================================================

===========

seq( 2940.. 2999)

ccattcaacctgccaggactaacatttctggatttgtgaagaagggatcttcaaagccat

Segments:

=======================================================================

===========

seq( 3000.. 3059)

tgaacccacagagctgtgttgctttaaagccaccacaagggtacagcattaaatggcaga

Segments:

=======================================================================

===========

seq( 3060.. 3119)

actggaaaagcttcttagggcatctcatccagggattctcaaaccatgtcccccagaggc

Segments:

=======================================================================

===========

seq( 3120.. 3179)

cttgggctgcagttgcagggggcgccatggggctataggagcctcccactttcaccagag

Segments:

9.9.539 3122 3131 ====NF-1==

2.3.1.0 3134 3143 ====Sp1===

=======================================================================

===========

seq( 3180.. 3239)

cagcctcactgtgccctgattcacacactgtggctttccacgtgaggttttgtttagagg

Segments:

2.3.2.2 3211 3220 ====Odd===

=======================================================================

===========

seq( 3240.. 3299)

gatccactactcaagaaaaagttagcaaaccactccttttgttgcaaaggagctgaggtc

Segments:

1.1.3.0 3288 3297

=C/EBPalp=

2.1.2.2 3291 3300

==RXR-bet

9.9.853 3291 3300

=T3R-beta

2.1.2.3 3292 3301

====T3R=

2.1.2.10 3296 3305

====

154

=======================================================================

===========

seq( 3300.. 3359)

aagggtggcaaaggcacttgtccaaggtcgcccagcagtgctgctctgatgacttgtgca

Segments:

2.1.2.2 3291 3300 a

9.9.853 3291 3300 1

2.1.2.3 3292 3301 ==

2.1.2.10 3296 3305 COUP==

1.1.3.0 3315 3324 =C/EBPdel=

9.9.29 3344 3353

====AP-1==

2.3.1.0 3357 3366

===

=======================================================================

===========

seq( 3360.. 3419)

catccccaagggtaagagcttcgatctctgcacagccgggccaacctctgaccccttgtc

Segments:

2.3.1.0 3357 3366 =Sp1===

2.1.2.10 3405 3414

====COUP==

=======================================================================

===========

seq( 3420.. 3479)

catgtcagtaaaatatgaaggtcacagccaggatttctaagggtcaggaggccttcaccg

Segments:

2.3.1.0 3459 3468 ====Sp1===

=======================================================================

===========

seq( 3480.. 3539)

ctgctggggcacacacacacacatgcatacacacatacgacacacacctgtgtctcccca

Segments:

2.3.2.3 3489 3498 =WT1_I_-K=

4.6.1.0 3489 3498 ===Sox-2==

9.9.1840 3489 3498 ===WT1_I==

9.9.1841 3489 3498 ==WT1-del2

9.9.1842 3489 3498 =WT1_I-de=

1.2.1.0 3524 3533

=====E1===

1.2.2.0 3524 3533

====MyoD==

1.6.1.0 3531 3540

====AP-2=

=======================================================================

===========

seq( 3540.. 3599)

ggggttttccctgcagtgaggcttgtccagatgattgagcccaggagaggaagaacaaac

Segments:

1.6.1.0 3531 3540 =

9.9.590 3540 3549 =NF-kappaB

4.1.1.0 3541 3550 =====Dl===

9.9.592 3541 3550 =NF-kappa=

3.3.2.0 3591 3600

===HNF-3=

1.1.3.0 3593 3602

=C/EBPa

155

=======================================================================

===========

seq( 3600.. 3659)

aaactacggagctggggagggctgtggcttggggccagctcccagggaaattcccagacc

Segments:

3.3.2.0 3591 3600 =

1.1.3.0 3593 3602 lp=

2.3.1.0 3613 3622 ====Sp1===

1.1.3.0 3617 3626 =C/EBPbeta

1.6.1.0 3625 3634 =AP-2alph=

9.9.213 3644 3653

===EBP-1==

4.1.1.0 3644 3654 ==NF-

kappaB

9.9.590 3645 3654 =NF-

kappaB

=======================================================================

===========

seq( 3660.. 3719)

tgtaccgatgttctctctggcaccagccgagctgcttcgtggaggtaacttcaaaaaagt

Segments:

=======================================================================

===========

seq( 3720.. 3779)

aaaagctatcatcagcatcatcttagacttgtatgaaataaccactccgtttctattctt

Segments:

=======================================================================

===========

seq( 3780.. 3839)

aaaccttaccatttttgttttgttttgtttttttgagtcggagttttgttctttttgcct

Segments:

3.3.2.0 3791 3800 ===HNF-3==

1.1.3.0 3794 3803 =C/EBPalp=

2.3.2.2 3805 3814 =====Hb===

=======================================================================

===========

seq( 3840.. 3899)

aggctggagtgcagtggtacaatctcggctcactgcaacctccacctcccgggttcaagt

Segments:

2.3.1.0 3879 3888 ====Sp1===

=======================================================================

===========

seq( 3900.. 3959)

gattctcctgcctcagcctcccaagtagctgggattacaggcacccgccaccacacctgg

Segments:

1.2.1.0 3903 3912 =====E1===

2.3.1.0 3904 3913 ====Sp1===

2.3.1.0 3911 3920 ====Sp1===

2.3.1.0 3942 3956

=======Sp1=====

=======================================================================

===========

seq( 3960.. 4019)

ctaatttttttgtatttttagtagagacggggtttcaccatgttggccaggctggtctcg

Segments:

4.5.1.0 3967 3976 ====TBP===

156

9.9.539 4000 4009 ====NF-

1==

=======================================================================

===========

seq( 4020.. 4079)

aactcctgacctcaggtgatccgcccgcctcggcctcccaaagtgctgggattacaggcg

Segments:

2.1.2.2 4024 4033 ==RXR-beta

2.3.3.0 4025 4034 =CPE_bind=

2.3.1.0 4039 4051 ======Sp1====

2.3.2.3 4041 4050 ====WT1===

2.3.1.0 4048 4057 ====Sp1===

=======================================================================

===========

seq( 4080.. 4139)

tgagccaccgcgcccagccaaaccttactatttttttaaagaattttttccagagtttaa

Segments:

2.3.1.0 4084 4098 =======Sp1=====

9.9.539 4093 4102 ====NF-1==

1.1.3.0 4122 4131

=C/EBPalp=

=======================================================================

===========

seq( 4140.. 4199)

tttctgacatagcttaagttttccagtaactctaaactccatctcctttatcgtcattaa

Segments:

=======================================================================

===========

seq( 4200.. 4259)

gtcattcacaaaaagccaggagaagcatttggaaagggcatgataatcagtataataatt

Segments:

3.1.2.2 4239 4248 ===Oct-1==

3.1.1.2 4251 4260

====Antp=

2.2.1.1 4257 4266

===

=======================================================================

===========

seq( 4260.. 4319)

tgccttgtgtggtcagcacttaactgtttacaaagccctttcacgtgcacagcaggtggg

Segments:

3.1.1.2 4251 4260 =

2.2.1.1 4257 4266 GATA-1=

1.2.1.0 4309 4318

=====E1===

1.2.2.0 4309 4318

====MyoD==

2.3.1.0 4318 4327

==

=======================================================================

===========

seq( 4320.. 4379)

aactgcgcggtgtgggctgggcctgcgctggaagcatatcccgtgaaaagtgttagtgcc

Segments:

2.3.1.0 4318 4327 ==Sp1===

2.3.1.0 4329 4341 ======Sp1====

157

=======================================================================

===========

seq( 4380.. 4439)

ttaggtgaaagcaacatgtatccctttagactactaacggtatatgttgttcttatgtat

Segments:

=======================================================================

===========

seq( 4440.. 4499)

ttgtatttatttctattttttctatgtttatgtcatatttaaacgatatcctactgcttg

Segments:

1.1.3.0 4443 4452 =C/EBPalp=

=======================================================================

===========

seq( 4500.. 4559)

ttggtattaccctaaactgtttaaataaagagctctatttttaaagaaaaaaggtacaat

Segments:

4.3.1.1 4534 4543 ===RSRFC4=

=======================================================================

===========

seq( 4560.. 4619) tga

Segments:

164 segments in this sequence identified as potential binding sites

164 segments in complete file identified as potential binding sites

Sequence seq_76

Class Factor Start Stop

2.3.1.0 Sp1 12 21

2.3.1.0 Sp1 24 37

2.3.1.0 Sp1 30 39

2.3.1.0 Sp1 49 59

2.3.1.0 YY1 81 90

2.3.1.0 Sp1 87 96

2.3.1.0 Sp1 96 108

2.3.1.0 Sp1 154 163

2.2.1.1 GATA-1 199 208

2.3.1.0 Sp1 210 219

4.4.1.0 E2 219 228

4.1.1.0 Dl 332 341

2.3.1.0 Sp1 357 366

2.3.1.0 Sp1 399 411

1.1.5.3 GBF1 450 459

1.1.5.2 GBF2 453 462

2.3.1.0 Sp1 464 477

2.3.1.0 Sp1 479 488

2.3.1.0 Sp1 515 529

2.3.1.0 Sp1 521 530

158

2.3.1.0 Sp1 521 531

1.3.1.2 USF 555 564

1.2.2.0 Myf-3 587 596

9.9.539 NF-1 593 602

1.2.1.0 Da 670 679

4.1.1.0 NF-kappaB1 705 714

2.3.1.0 Sp1 756 767

1.6.1.0 AP-2alphaA 783 792

2.1.2.10 COUP 805 814

2.3.1.0 Sp1 819 828

2.3.2.3 WT1_I_-KTS 839 848

4.6.1.0 Sox-2 839 848

9.9.1840 WT1_I 839 848

9.9.1841 WT1-del2 839 848

9.9.1842 WT1_I-del2 839 848

3.5.3.0 NF-EM5 912 921

2.1.2.10 COUP 925 934

2.1.2.3 REV-ErbAalpha 927 936

2.3.1.0 Sp1 947 956

2.3.1.0 Sp1 953 962

2.3.1.0 Sp1 1003 1012

2.3.1.0 Sp1 1006 1015

1.1.2.0 CREB 1034 1043

1.1.3.0 C/EBPalpha(p20)

1034

1043

2.3.1.0 Sp1 1113 1125

2.3.1.0 YY1 1125 1134

2.3.1.0 Sp1 1192 1201

2.3.1.0 Sp1 1209 1218

2.3.1.0 Sp1 1225 1234

2.3.1.0 Sp1 1252 1265

2.3.1.0 YY1 1258 1267

2.3.1.0 Sp1 1269 1281

2.3.1.0 Sp1 1278 1287

1.2.2.0 MyoD 1302 1311

1.1.3.0 C/EBPbeta 1312 1321

3.4.1.0 HSF1_(long) 1344 1353

3.5.3.0 ICSBP 1424 1433

9.9.428 ISGF-3 1424 1433

2.3.1.0 Sp1 1473 1487

2.3.1.0 Sp1 1479 1488

2.3.1.0 Sp1 1522 1532

2.3.1.0 Sp1 1523 1532

2.3.1.0 Sp1 1550 1564

9.9.539 NF-1 1570 1579

2.3.1.0 Sp1 1680 1693

159

2.3.1.0 Sp1 1687 1700

3.1.2.2 Oct-1 1701 1710

4.1.1.0 NF-kappaB1 1711 1720

9.9.590 NF-kappaB 1711 1720

2.3.1.0 Sp1 1741 1750

2.3.1.0 Sp1 1759 1771

2.1.2.3 REV-ErbAalpha 1768 1777

2.1.1.4 ER 1771 1780

2.3.1.0 Sp1 1775 1786

2.3.1.0 Sp1 1803 1812

2.3.1.0 Sp1 1816 1828

2.1.2.3 T3R 1823 1832

9.9.853 T3R-beta1 1823 1832

2.1.2.2 RXR-beta 1824 1835

1.1.1.6 ATF-3deltaZIP 1825 1834

2.3.3.0 CPE_binding_pro

1826

1835

2.3.1.0 Sp1 1837 1846

2.1.2.1 RAR-alpha1 1869 1878

2.3.1.0 Sp1 1877 1886

3.5.3.0 ICSBP 1906 1915

2.1.2.3 T3R 1915 1924

2.3.3.0 CPE_binding_pro

1916

1925

2.1.2.2 RXR-beta 1918 1927

2.1.1.4 ER 1923 1932

2.3.1.0 Sp1 2047 2056

9.9.29 AP-1 2154 2163

3.5.2.0 PU.1 2268 2277

2.2.1.1 GATA-1 2269 2278

2.3.1.0 Sp1 2289 2298

2.3.1.0 Sp1 2395 2404

9.9.29 AP-1 2443 2452

2.3.1.0 Sp1 2510 2519

2.3.1.0 Sp1 2548 2557

2.3.1.0 Sp1 2573 2582

1.2.8.0 Olf-1 2605 2614

2.3.1.0 Sp1 2610 2619

9.9.539 NF-1 2706 2715

2.3.1.0 Sp1 2788 2797

2.3.1.0 Sp1 2814 2823

3.5.1.2 REB1 2879 2888

9.9.539 NF-1 3122 3131

2.3.1.0 Sp1 3134 3143

2.3.2.2 Odd 3211 3220

1.1.3.0 C/EBPalpha 3288 3297

160

2.1.2.2 RXR-beta 3291 3300

9.9.853 T3R-beta1 3291 3300

2.1.2.3 T3R 3292 3301

2.1.2.10 COUP 3296 3305

1.1.3.0 C/EBPdelta 3315 3324

9.9.29 AP-1 3344 3353

2.3.1.0 Sp1 3357 3366

2.1.2.10 COUP 3405 3414

2.3.1.0 Sp1 3459 3468

2.3.2.3 WT1_I_-KTS 3489 3498

4.6.1.0 Sox-2 3489 3498

9.9.1840 WT1_I 3489 3498

9.9.1841 WT1-del2 3489 3498

9.9.1842 WT1_I-del2 3489 3498

1.2.1.0 E1 3524 3533

1.2.2.0 MyoD 3524 3533

1.6.1.0 AP-2 3531 3540

9.9.590 NF-kappaB 3540 3549

4.1.1.0 Dl 3541 3550

9.9.592 NF-kappaB(-like

3541

3550

3.3.2.0 HNF-3 3591 3600

1.1.3.0 C/EBPalpha 3593 3602

2.3.1.0 Sp1 3613 3622

1.1.3.0 C/EBPbeta 3617 3626

1.6.1.0 AP-2alphaA 3625 3634

9.9.213 EBP-1 3644 3653

4.1.1.0 NF-kappaB 3644 3654

9.9.590 NF-kappaB 3645 3654

3.3.2.0 HNF-3 3791 3800

1.1.3.0 C/EBPalpha 3794 3803

2.3.2.2 Hb 3805 3814

2.3.1.0 Sp1 3879 3888

1.2.1.0 E1 3903 3912

2.3.1.0 Sp1 3904 3913

2.3.1.0 Sp1 3911 3920

2.3.1.0 Sp1 3942 3956

4.5.1.0 TBP 3967 3976

9.9.539 NF-1 4000 4009

2.1.2.2 RXR-beta 4024 4033

2.3.3.0 CPE_binding_pro

4025

4034

2.3.1.0 Sp1 4039 4051

2.3.2.3 WT1 4041 4050

2.3.1.0 Sp1 4048 4057

2.3.1.0 Sp1 4084 4098

161

9.9.539 NF-1 4093 4102

1.1.3.0 C/EBPalpha(p20)

4122

4131

3.1.2.2 Oct-1 4239 4248

3.1.1.2 Antp 4251 4260

2.2.1.1 GATA-1 4257 4266

1.2.1.0 E1 4309 4318

1.2.2.0 MyoD 4309 4318

2.3.1.0 Sp1 4318 4327

2.3.1.0 Sp1 4329 4341

1.1.3.0 C/EBPalpha 4443 4452

4.3.1.1 RSRFC4 4534 4543

Number of sites found: 164

CDS only Sequence seq_77

Class Factor Start Stop

2.3.1.0 Sp1 22 32

2.3.1.0 YY1 54 63

2.3.1.0 Sp1 60 69

2.3.1.0 Sp1 69 81

2.3.1.0 Sp1 127 136

2.2.1.1 GATA-1 172 181

2.3.1.0 Sp1 183 192

4.4.1.0 E2 192 201

4.1.1.0 Dl 305 314

2.3.1.0 Sp1 330 339

2.3.1.0 Sp1 372 384

1.1.5.3 GBF1 423 432

1.1.5.2 GBF2 426 435

2.3.1.0 Sp1 437 450

2.3.1.0 Sp1 452 461

2.3.1.0 Sp1 488 502

2.3.1.0 Sp1 494 503

2.3.1.0 Sp1 524 533

1.3.1.2 USF 528 537

1.2.2.0 Myf-3 560 569

9.9.539 NF-1 566 575

1.2.1.0 Da 643 652

4.1.1.0 NF-kappaB1 678 687

2.3.1.0 Sp1 729 740

162

1.6.1.0 AP-2alphaA 756 765

2.1.2.10 COUP 778 787

2.3.1.0 Sp1 792 801

2.3.2.3 WT1_I_-KTS 812 821

4.6.1.0 Sox-2 812 821

9.9.1840 WT1_I 812 821

9.9.1841 WT1-del2 812 821

9.9.1842 WT1_I-del2 812 821

3.5.3.0 NF-EM5 885 894

2.1.2.10 COUP 898 907

2.1.2.3 REV-ErbAalpha 900 909

2.3.1.0 Sp1 920 929

2.3.1.0 Sp1 926 935

2.3.1.0 Sp1 976 985

1.1.2.0 CREB 1007 1016

1.1.3.0 C/EBPalpha(p20) 1007 1016

2.3.1.0 Sp1 1016 1025

2.3.1.0 Sp1 1086 1098

2.3.1.0 YY1 1098 1107

2.3.1.0 Sp1 1165 1174

2.3.1.0 Sp1 1182 1191

2.3.1.0 Sp1 1198 1207

2.3.1.0 Sp1 1225 1238

2.3.1.0 YY1 1231 1240

2.3.1.0 Sp1 1242 1254

2.3.1.0 Sp1 1251 1260

1.2.2.0 MyoD 1275 1284

1.1.3.0 C/EBPbeta 1285 1294

3.4.1.0 HSF1_(long) 1317 1326

3.5.3.0 ICSBP 1397 1406

9.9.428 ISGF-3 1397 1406

2.3.1.0 Sp1 1446 1460

2.3.1.0 Sp1 1452 1461

2.3.1.0 Sp1 1496 1505

2.3.1.0 Sp1 1523 1537

9.9.539 NF-1 1543 1552

Number of sites found: 60

163

Annexure no. 6

Transfac (gene-regulation.com)

matrix position core matrix sequence (always the factor nameidentifier

(strand) match match (+)-strand is shown)

V$MYOGNF1_01

myogenin

NF-1

95 (-)

0.811

0.775

gtctggcccctcCCCAGaatgtgacgctg

V$COMP1_01

COMP1

188 (-)

0.822

0.800

cctattttgtggcCTATCagagct

V$CREL_01

c-Rel

706 (+)

1.000

0.987

tgggcTTTCC

V$CHOP_01

CHOP-C/EBPalpha

1698 (-)

1.000

0.988

gggcaTTGCActt

V$ELK1_02

Elk-1

1705 (-)

1.000

0.990

gcacTTCCGggaag

V$CETS1P54_01

c-Ets-1(p54)

1706 (-)

1.000

0.993

cactTCCGGg

V$STAT_01

STATx

1709 (-)

1.000

1.000

ttccGGGAA

V$PAX4_01

Pax-4

1935 (+)

0.986

0.840

ggacaTCAAGggcggacacca

V$AP1_Q4

AP-1

2444 (+)

1.000

0.990

agTGACTgaga

V$PAX4_01

Pax-4

3294 (+)

0.986

0.906

tgaggTCAAGggtggcaaagg

V$CREL_01

c-Rel

3541 (+)

1.000

0.999

ggggtTTTCC

V$ZID_01

ZID

3570 (-)

1.000

0.983

gatgattGAGCCc

V$NFKB_Q6

NF-kappaB

3644 (-)

1.000

0.976

agggaaaTTCCCag

V$NFKAPPAB65_01

NF-kappaB

3646 (-)

1.000

1.000

GGAAAttccc

(p65)

V$CREL_01

c-Rel

3646 (-)

1.000

0.990

GGAAAttccc

V$NFKAPPAB_01

NF-kappaB

3646 (-)

1.000

1.000

ggaaaTTCCC

V$NKX25_01

Nkx2-5

3895 (+)

1.000

1.000

tcAAGTG

V$PAX4_01

Pax-4

4014 (-)

0.979

0.878

ggtctcgaactCCTGAcctca

V$VMYB_01

v-Myb

4413 (+)

1.000

0.971

actAACGGta

164

Total sequences length=4563

Total number of sites found=19

Frequency of sites per nucleotide=0.004164

1

<-------------------------...V$MYOGNF1_01(0.78)

../etc/usr/hashaamakhtar/tmp_5357/5357_default1.

GGGCGGGGACGCCGCGGCAGGAAGGCCATGGCGGGGCCCGAGCGCTGGGGCCCCCTGCTCCTGTGCCTGCT 120

1---V$MYOGNF1_01(0.78) <-------------------

----V$COMP1_01(0.80)

GCAGGCCGCTCCAGGGAGGCCCCGTCTGGCCCCTCCCCAGAATGTGACGCTGCTCTCCCAGAACTTCAGCGTGTACCTGACATGGCTCC

CAGGGCTTGGCAACCCCCAGGATGTGACCTA 240

TTTTGTGGCCTATCAGAGCTCTCCCACCCGTAGACGGTGGCGCGAAGTGGAAGAGTGTGCGGGAACCAAGGAGCTGCTATGTTCTATGA

TGTGCCTGAAGAAACAGGACCTGTACAACAA 360

GTTCAAGGGACGCGTGCGGACGGTTTCTCCCAGCTCCAAGTCCCCCTGGGTGGAGTCCGAATACCTGGATTACCTTTTTGAAGTGGAGC

CGGCCCCACCTGTCCTGGTGCTCACCCAGAC 480

GGAGGAGATCCTGAGTGCCAATGCCACGTACCAGCTGCCCCCCTGCATGCCCCCACTGGATCTGAAGTATGAGGTGGCATTCTGGAAGG

AGGGGGCCGGAAACAAGACCCTATTTCCAGT 600

1

--------->V$CREL_01(0.99)

CACTCCCCATGGCCAGCCAGTCCAGATCACTCTCCAGCCAGCTGCCAGCGAACACCACTGCCTCAGTGCCAGAACCATCTACACGTTCA

GTGTCCCGAAATACAGCAAGTTCTCTAAGCC 720

CACCTGCTTCTTGCTGGAGGTCCCAGAAGCCAACTGGGCTTTCCTGGTGCTGCCATCGCTTCTGATACTGCTGTTAGTAATTGCCGCAG

GGGGTGTGATCTGGAAGACCCTCATGGGGAA 840

CCCCTGGTTTCAGCGGGCAAAGATGCCACGGGCCCTGGACTTTTCTGGACACACACACCCTGTGGCAACCTTTCAGCCCAGCAGACCAG

AGTCCGTGAATGACTTGTTCCTCTGTCCCCA 960

AAAGGAACTGACCAGAGGGGTCAGGCCGACGCCTCGAGTCAGGGCCCCAGCCACCCAACAGACAAGATGGAAGAAGGACCTTGCAGAGG

ACGAAGAGGAGGAGGATGAGGAGGACACAGA 1080

AGATGGCGTCAGCTTCCAGCCCTACATTGAACCACCTTCTTTCCTGGGGCAAGAGCACCAGGCTCCAGGGCACTCGGAGGCTGGTGGGG

TGGACTCAGGGAGGCCCAGGGCTCCTCTGGT 1200

CCCAAGCGAAGGCTCCTCTGCTTGGGATTCTTCAGACAGAAGCTGGGCCAGCACTGTGGACTCCTCCTGGGACAGGGCTGGGTCCTCTG

GCTATTTGGCTGAGAAGGGGCCAGGCCAAGG 1320

GCCGGGTGGGGATGGGCACCAAGAATCTCTCCCACCACCTGAATTCTCCAAGGACTCGGGTTTCCTGGAAGAGCTCCCAGAAGATAACC

TCTCCTCCTGGGCCACCTGGGGCACCTTACC 1440

ACCGGAGCCGAATCTGGTCCCTGGGGGACCCCCAGTTTCTCTTCAGACACTGACCTTCTGCTGGGAAAGCAGCCCTGAGGAGGAAGAGG

AGGCGAGGGAATCAGAAATTGAGGACAGCGA 1560

TGCGGGCAGCTGGGGGGCTGAGAGCACCCAGAGGACCGAGGACAGGGGCCGGACATTGGGGCATTACATGGCCAGGTGAGCTGTCCCCC

GACATCCCACCGAATCTGATGCTGCTGCTGC 1680

1 <------------V$CHOP_01(0.99)

2 <-------------V$ELK1_02(0.99)

3 <---------V$CETS1P54_01(0.99)

4 <--------V$STAT_01(1.00)

CTTTGCAAGGACTACTGGGCTTCCCAAGAAACTCAAGAGCCTCCGTACCTCCCCTGGGCGGCGGAGGGGCATTGCACTTCCGGGAAGTC

CACCTAGCGGCTGTTTGCCTGTCGGGCTGAG 1800

CAACAAGATGCCCCTCCCTCCTGTGACCCGCCCTCTTTAGGCTGAGCTATAAGAGGGGTGGACACAGGGTGGGCTGAGGTCAGAGGTTG

GTGGGGTGTCATCACCCCCATTGTCCCTAGG 1920

1 -------------------->V$PAX4_01(0.84)

GTGACAGGCCAGGGGGAAAAATTATCCCCGGACAACATGAAACAGGTGAGGTCAGGTCACTGCGGACATCAAGGGCGGACACCACCAAG

GGGCCCTCTGGAACTTGAGACCACTGGAGGC 2040

165

ACACCTGCTATACCTCATGCCTTTCCCAGCAGCCACTGAACTCCCCCATCCCAGGGCTCAGCCTCCTGATTCATGGGTCCCCTAGTTAG

GCCCAGATAAAAATCCAGTTGGCTGAGGGTT 2160

TTGGATGGGAAGGGAAGGGTGGCTGTCCTCAAATCCTGGTCTTTGGAGTCATGGCACTGTACGGTTTTAGTGTCAGACAGACCGGGGTT

CAAATCCCAGCTCTGCTGTTCACTGGTTGTA 2280

TGATCTTGGGGAAGACATCTTCCTTCTCTGCCTCGGCTTCCTCATCTGCAGCTACGCCTGGGTGTGGTGAGGGTTCTAGGGGATCTCAG

ATGTGTGTAGCACGGAGCCTGCTGTGTCCTG 2400

1 ---------->V$AP1_Q4(0.99)

GGTGCTCTCTACGTGGTGGCCGGTAGAATTCTCCATCTATCCAGGCTCCAGGAGACCCCTGGGCATCTCCCACCTGTGGCCCCTAAACC

CAGAGTGACTGAGAGCACTTAACATTCAGCT 2520

TGTCTCATCCCCAGTCTACCTCCTTCCTTCTACCCTCACTGCCTCCCAGTCAGGAGAGTGAGCTCTCAGAAGCCAGAGCCCCACCCAAG

GGGACCCTGGTCTCTCCGCCTTCACCTAGCA 2640

ATGGGAACCCTGCTTCCCAGGGGAGGAACCAACTGCTCCACCTTCTAGGGACCCAGTTTGTTGGAGTAGGACAGTAACATGGCAGGAAT

CGGACTTCTGGGCCTGTAATCCCAGTTTGGA 2760

TGGCACGTTAGACTCTTGGTTGACCGTTGTGGTCCTTAGAAGTCCCATTCTCCCTTCCAGTTATGAGAAACCAATGCCTTCTAGATTCA

GGTGACTATCCTTACCTGGGGGTGCTGATGC 2880

ATCCTCAGTTAACCTACACCCACCTGAATATAGATGAGCGTAGCTGAGTTTTCACCCGTAGGACCGAAGTGTTTTGTGGTGGAGTATCT

GAACAACCTTGGCTCTGTGGCCATTCAACCT 3000

GCCAGGACTAACATTTCTGGATTTGTGAAGAAGGGATCTTCAAAGCCATTGAACCCACAGAGCTGTGTTGCTTTAAAGCCACCACAAGG

GTACAGCATTAAATGGCAGAACTGGAAAAGC 3120

TTCTTAGGGCATCTCATCCAGGGATTCTCAAACCATGTCCCCCAGAGGCCTTGGGCTGCAGTTGCAGGGGGCGCCATGGGGCTATAGGA

GCCTCCCACTTTCACCAGAGCAGCCTCACTG 3240

1 --------------------

>V$PAX4_01(0.91)

TGCCCTGATTCACACACTGTGGCTTTCCACGTGAGGTTTTGTTTAGAGGGATCCACTACTCAAGAAAAAGTTAGCAAACCACTCCTTTT

GTTGCAAAGGAGCTGAGGTCAAGGGTGGCAA 3360

AGGCACTTGTCCAAGGTCGCCCAGCAGTGCTGCTCTGATGACTTGTGCACATCCCCAAGGGTAAGAGCTTCGATCTCTGCACAGCCGGG

CCAACCTCTGACCCCTTGTCCATGTCAGTAA 3480

1 --------->V$CREL_01(1.00)

<------------V$ZID_01(0.98)

AATATGAAGGTCACAGCCAGGATTTCTAAGGGTCAGGAGGCCTTCACCGCTGCTGGGGCACACACACACACATGCATACACACATACGA

CACACACCTGTGTCTCCCCAGGGGTTTTCCC 3600

1 <-------------V$NFKB_Q6(0.98)

2 <---------V$NFKAPPAB65_01(1.00)

3 <---------V$CREL_01(0.99)

4 <---------V$NFKAPPAB_01(1.00)

TGCAGTGAGGCTTGTCCAGATGATTGAGCCCAGGAGAGGAAGAACAAACAAACTACGGAGCTGGGGAGGGCTGTGGCTTGGGGCCAGCT

CCCAGGGAAATTCCCAGACCTGTACCGATGT 3720

TCTCTCTGGCACCAGCCGAGCTGCTTCGTGGAGGTAACTTCAAAAAAGTAAAAGCTATCATCAGCATCATCTTAGACTTGTATGAAATA

ACCACTCCGTTTCTATTCTTAAACCTTACCA 3840

1 ------>V$NKX25_01(1.00)

TTTTTGTTTTGTTTTGTTTTTTTGAGTCGGAGTTTTGTTCTTTTTGCCTAGGCTGGAGTGCAGTGGTACAATCTCGGCTCACTGCAACC

TCCACCTCCCGGGTTCAAGTGATTCTCCTGC 3960

1 <--------------------

V$PAX4_01(0.88)

CTCAGCCTCCCAAGTAGCTGGGATTACAGGCACCCGCCACCACACCTGGCTAATTTTTTTGTATTTTTAGTAGAGACGGGGTTTCACCA

TGTTGGCCAGGCTGGTCTCGAACTCCTGACC 4080

TCAGGTGATCCGCCCGCCTCGGCCTCCCAAAGTGCTGGGATTACAGGCGTGAGCCACCGCGCCCAGCCAAACCTTACTATTTTTTTAAA

GAATTTTTTCCAGAGTTTAATTTCTGACATA 4200

GCTTAAGTTTTCCAGTAACTCTAAACTCCATCTCCTTTATCGTCATTAAGTCATTCACAAAAAGCCAGGAGAAGCATTTGGAAAGGGCA

TGATAATCAGTATAATAATTTGCCTTGTGTG 4320

1

--------->V$VMYB_01(0.97)

166

GTCAGCACTTAACTGTTTACAAAGCCCTTTCACGTGCACAGCAGGTGGGAACTGCGCGGTGTGGGCTGGGCCTGCGCTGGAAGCATATC

CCGTGAAAAGTGTTAGTGCCTTAGGTGAAAG 4440

CAACATGTATCCCTTTAGACTACTAACGGTATATGTTGTTCTTATGTATTTGTATTTATTTCTATTTTTTCTATGTTTATGTCATATTT

AAACGATATCCTACTGCTTGTTGGTATTACC 4560

CTAAACTGTTTAAATAAAGAGCTCTATTTTTAAAGAAAAAAGGTACAATTGA1

GCTGGGCCTGCGCTGGAAGCATATCCCGTGAAAAGTGTTAGTGCCTTAGGTGAAAGCAACATGTAT 4680

CCCTTTAGACTACTAACGGTATATGTTGTTCTTATGTATTTGTATTTATTTCTATTTTTTCTATGTTTATGTCATATTTAAACGATATC

CTACTGCTTGTTGGTATTACCCTAAACTGTT 4800

TAAATAAAGAGCTCTATTTTTAAAGAAAAAAGGTACAATTGA1

4844

Annexure No. 7

Transfac

** TFSEARCH ver.1.3 ** (c)1995 Yutaka Akiyama (Kyoto Univ.)

This simple routine searches highly correlated sequence fragments

versus TFMATRIX transcription factor binding site profile database

by E.Wingender, R.Knueppel, P.Dietze, H.Karas (GBF-Braunschweig).

&lt;Warning> Scoring scheme is so straightforward in this version.

score = 100.0 * ('weighted sum' - min) / (max - min)

The score does not properly reflect statistical

significance!

Database: TRANSFAC MATRIX TABLE, Rel.3.3 06-01-1998

Query: IL28R (4563 bases)

Taxonomy: Vertebrate

Threshold: 95.0 point

TFMATRIX entries with High-scoring:

1 GGGCGGGGAC GCCGCGGCAG GAAGGCCATG GCGGGGCCCG AGCGCTGGGG entry

score

51 CCCCCTGCTC CTGTGCCTGC TGCAGGCCGC TCCAGGGAGG CCCCGTCTGG entry

score

651 ATACAGCAAG TTCTCTAAGC CCACCTGCTT CTTGCTGGAG GTCCCAGAAG entry

score

701 CCAACTGGGC TTTCCTGGTG CTGCCATCGC TTCTGATACT GCTGTTAGTA entry

score

----------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00053>M00053</A> c-Rel

95.9

751 ATTGCCGCAG GGGGTGTGAT CTGGAAGACC CTCATGGGGA ACCCCTGGTT entry

score

167

1251 TGAGAAGGGG CCAGGCCAAG GGCCGGGTGG GGATGGGCAC CAAGAATCTC entry

score

--------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00083>M00083</A> MZF1

98.3

1301 TCCCACCACC TGAATTCTCC AAGGACTCGG GTTTCCTGGA AGAGCTCCCA entry

score

1701 CATTGCACTT CCGGGAAGTC CACCTAGCGG CTGTTTGCCT GTCGGGCTGA entry

score

&lt;---------<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00223>M00223</A> STATx

100.0

1751 GCAACAAGAT GCCCCTCCCT CCTGTGACCC GCCCTCTTTA GGCTGAGCTA entry

score

2251 TTCCTTCTCT GCCTCGGCTT CCTCATCTGC AGCTACGCCT GGGTGTGGTG entry

score

-----><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00271>M00271</A> AML-1a

100.0

2301 AGGGTTCTAG GGGATCTCAG ATGTGTGTAG CACGGAGCCT GCTGTGTCCT entry

score

2451 GAGAGCACTT AACATTCAGC TTGTCTCATC CCCAGTCTAC CTCCTTCCTT entry

score

&lt;-------<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00240>M00240</A> Nkx-2.

97.7

2501 CTACCCTCAC TGCCTCCCAG TCAGGAGAGT GAGCTCTCAG AAGCCAGAGC entry

score

-- <A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00072>M00072</A> CP2

95.8

2551 CCCACCCAAG GGGACCCTGG TCTCTCCGCC TTCACCTAGC AATGGGAACC entry

score

--------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00072>M00072</A> CP2

95.8

2601 CTGCTTCCCA GGGGAGGAAC CAACTGCTCC ACCTTCTAGG GACCCAGTTT entry

score

2651 GTTGGAGTAG GACAGTAACA TGGCAGGAAT CGGACTTCTG GGCCTGTAAT entry

score

2701 CCCAGTTTGG ATGGCACGTT AGACTCTTGG TTGACCGTTG TGGTCCTTAG entry

score

------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00271>M00271</A> AML-1a

100.0

168

2751 AAGTCCCATT CTCCCTTCCA GTTATGAGAA ACCAATGCCT TCTAGATTCA entry

score

&lt;---<A HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00073>M00073</A>

deltaE 95.2

2801 GGTGACTATC CTTACCTGGG GGTGCTGATG CATCCTCAGT TAACCTACAC entry

score

-- <A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00073>M00073</A> deltaE

96.7

------- <A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00073>M00073</A> deltaE

95.2

2851 CCACCTGAAT ATAGATGAGC GTAGCTGAGT TTTCACCCGT AGGACCGAAG entry

score

--------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00073>M00073</A> deltaE

96.7

2901 TGTTTTGTGG TGGAGTATCT GAACAACCTT GGCTCTGTGG CCATTCAACC entry

score

------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00271>M00271</A> AML-1a

100.0

2951 TGCCAGGACT AACATTTCTG GATTTGTGAA GAAGGGATCT TCAAAGCCAT entry

score

3001 TGAACCCACA GAGCTGTGTT GCTTTAAAGC CACCACAAGG GTACAGCATT entry

score

&lt;-----<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00271>M00271</A> AML-1a

100.0

3051 AAATGGCAGA ACTGGAAAAG CTTCTTAGGG CATCTCATCC AGGGATTCTC entry

score

3201 TCACACACTG TGGCTTTCCA CGTGAGGTTT TGTTTAGAGG GATCCACTAC entry

score

&lt;-------<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00148>M00148</A> SRY

100.0

--------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00217>M00217</A> USF

97.9

3251 TCAAGAAAAA GTTAGCAAAC CACTCCTTTT GTTGCAAAGG AGCTGAGGTC entry

score

3501 ACATGCATAC ACACATACGA CACACACCTG TGTCTCCCCA GGGGTTTTCC entry

score

---------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00053>M00053</A> c-Rel

97.5

169

3551 CTGCAGTGAG GCTTGTCCAG ATGATTGAGC CCAGGAGAGG AAGAACAAAC entry

score

---- <A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00148>M00148</A> SRY

100.0

&lt;--------------<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00085>M00085</A> ZID

95.2

3601 AAACTACGGA GCTGGGGAGG GCTGTGGCTT GGGGCCAGCT CCCAGGGAAA entry

score

--><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00148>M00148</A> SRY

100.0

&lt;----<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00054>M00054</A> NF-kap

100.0

&lt;----<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00052>M00052</A> NF-kap

100.0

&lt;----<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00053>M00053</A> c-Rel

96.7

3651 TTCCCAGACC TGTACCGATG TTCTCTCTGG CACCAGCCGA GCTGCTTCGT entry

score

----- <A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00054>M00054</A> NF-kap

100.0

----- <A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00052>M00052</A> NF-kap

100.0

----- <A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00053>M00053</A> c-Rel

96.7

3701 GGAGGTAACT TCAAAAAAGT AAAAGCTATC ATCAGCATCA TCTTAGACTT entry

score

3751 GTATGAAATA ACCACTCCGT TTCTATTCTT AAACCTTACC ATTTTTGTTT entry

score

&lt;------<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00148>M00148</A> SRY

100.0

&lt;-<A HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00148>M00148</A>

SRY 100.0

3801 TGTTTTGTTT TTTTGAGTCG GAGTTTTGTT CTTTTTGCCT AGGCTGGAGT entry

score

----- <A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00148>M00148</A> SRY

100.0

&lt;------<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00148>M00148</A> SRY

100.0

170

3851 GCAGTGGTAC AATCTCGGCT CACTGCAACC TCCACCTCCC GGGTTCAAGT entry

score

------ <A HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00240>M00240</A>

Nkx-2. 100.0

3901 GATTCTCCTG CCTCAGCCTC CCAAGTAGCT GGGATTACAG GCACCCGCCA entry

score

><A HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00240>M00240</A> Nkx-

2. 100.0

&lt; <A HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00271>M00271</A>

AML-1a 100.0

3951 CCACACCTGG CTAATTTTTT TGTATTTTTA GTAGAGACGG GGTTTCACCA entry

score

----- <A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00271>M00271</A> AML-1a

100.0

4001 TGTTGGCCAG GCTGGTCTCG AACTCCTGAC CTCAGGTGAT CCGCCCGCCT entry

score

4051 CGGCCTCCCA AAGTGCTGGG ATTACAGGCG TGAGCCACCG CGCCCAGCCA entry

score

&lt;---------<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00141>M00141</A> Lyf-1

100.0

4101 AACCTTACTA TTTTTTTAAA GAATTTTTTC CAGAGTTTAA TTTCTGACAT entry

score

4251 TATAATAATT TGCCTTGTGT GGTCAGCACT TAACTGTTTA CAAAGCCCTT entry

score

------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00271>M00271</A> AML-1a

100.0

&lt;-------<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00240>M00240</A> Nkx-2.

97.7

4301 TCACGTGCAC AGCAGGTGGG AACTGCGCGG TGTGGGCTGG GCCTGCGCTG entry

score

-------><A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00217>M00217</A> USF

97.6

4351 GAAGCATATC CCGTGAAAAG TGTTAGTGCC TTAGGTGAAA GCAACATGTA entry

score

&lt;-----------<A

HREF=http://www.cbrc.jp/htbin/bget_tfmatrix?M00073>M00073</A> deltaE

95.2

4401 TCCCTTTAGA CTACTAACGG TATATGTTGT TCTTATGTAT TTGTATTTAT entry

score

4451 TTCTATTTTT TCTATGTTTA TGTCATATTT AAACGATATC CTACTGCTTG entry

score

171

4501 TTGGTATTAC CCTAAACTGT TTAAATAAAG AGCTCTATTT TTAAAGAAAA entry

score

4551 AAGGTACAAT TGA entry

score

Total 29 high-scoring sites found.

Max score: 100.0 point, Min score: 95.2 point