Identification of Candidate Genes for Neuropathic Pain at the

Pain1 Locus on Mouse Chromosome 15

by

Tina Elahipanah

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Department of Physiology

University of Toronto

© Copyright by Tina Elahipanah 2010

ii

Identification of Candidate Genes for Neuropathic Pain at the Pain1

Locus on Mouse Chromosome 15

Tina Elahipanah

Master of Science

Department of Physiology

University of Toronto

2010

Abstract

Sciatic and saphenous neurectomy produces in mice a neuropathic pain-like behaviour

(‗autotomy‘). A/J mice express higher autotomy levels than C57BL6/J mice. A previous study

used autotomy data for these strains and their 23 recombinant daughter inbred lines of the AXB-

BXA set, to map a quantitative trait locus (QTL) for autotomy on chromosome 15. Since then, this

QTL, named Pain1, was replicated twice. Since all three studies used a low-resolution genetic map

based on microsatellites its confidence length precluded identification of candidate gene(s). To

overcome this problem, I used a higher resolution SNP-based genetic map and refined Pain1‘s

peak location, identifying in it 80 candidate genes. But only Lynx1, Arc and Sharpin had sequence

mismatches between A/J and C57BL6/J, known neural functions, and contrasting expression levels

in DRGs and spinal cord of intact, sham-operated, and neurectomized mice of these lines. Meeting

these criteria made them our best candidate genes for autotomy.

iii

AKNOWLEDGEMENTS

I would like to express my deepest gratitude to all who have made it possible for me to do the

research presented in this thesis:

My Supervisor, Prof. Ze‘ev Seltzer for his guidance, insight, patience and encouragement

throughout the entire research process.

The members of my Advisory Committee and my Defense Committee, Prof. Jonathan

Dostrovsky, Prof. Barry Sessle, Prof. Siew-Ging Gong, Prof. Michael Salter and

Dr. Limor Avivi-Arber for their advice and constructive questions.

This thesis is dedicated to:

My wonderful children Ryan and Aiden for being my motivation and for bringing magic into

my life,

My husband, Arash J-Chitsazi for his ongoing support,

My loving parents, Ashraf and Ahmad for being there for me whenever I needed them,

My sister, Ava who is always available to answer my questions,

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TABLE OF CONTENTS

Abstract ............................................................................................................................... ii

Aknowledgements.............................................................................................................. iii

Table of Contents ............................................................................................................... iv

List of Tables ................................................................................................................... viii

List of Figures .................................................................................................................... ix

List of Abbreviations ......................................................................................................... xi

1. Introduction 1

1.1 Pain Terms .....................................................................................................................1

1.1.1 Types of Pain ..............................................................................................................1

1.2 Characteristics of Human Neuropathic Pain ..................................................................3

1.3 Mechanisms Underlying Peripheral Neuropathic Pain ..................................................3

1.3.1 Injury Discharge..............................................................................................3

1.3.2 Ectopic Impulse Generation ............................................................................4

1.3.3 Ectopic Transduction ......................................................................................4

1.3.4 Peripheral and Central Sensitization ...............................................................4

1.3.5 Sympathetically-Maintained Pain ...................................................................6

1.3.6 Disinhibition ...................................................................................................6

1.3.7 Structural Changes in the Termination Zone of Primary Afferents ................7

1.3.8 Neuro-Immune Interactions ............................................................................8

1.39 Microglia ..........................................................................................................8

1.4 Chronic/Neuropathic Pain Behavioural Models in Animals .........................................9

1.4.1 The Neuroma Model/Autotomy ......................................................................9

v

1.5 Genetics of Pain ...........................................................................................................10

1.6 Recombinant Inbred Mice ............................................................................................12

1.7 Pain1 ............................................................................................................................14

1.8 Hypotheses ...................................................................................................................15

1.9 Aims of the Study ........................................................................................................15

1.10 Rationale ....................................................................................................................16

1.11 Summary and Conclusion ..........................................................................................17

2.0 Methods 19

2.1 In-silico Remapping of Pain1 ......................................................................................19

2.1.1 Line Distribution Pattern (LDP) .................................................................. 19

2.1.2 Interval Mapping ...........................................................................................19

2.1.3 Software Options and Switches ....................................................................20

2.1.3.1 Permutation Test ............................................................................20

2.1.3.2 Bootstrap Test ................................................................................21

2.1.3.3 Haplotype Analysis ........................................................................21

2.1.3.4 Additive Effect ...............................................................................22

2.1.3.5 Gene Track .....................................................................................21

2.1.3.6 Variant Browser .............................................................................22

2.1.4 Heritability (h2) ............................................................................................22

2.1.5 Number of Effective Genetic Loci (EGL) ....................................................22

2.1.6 Correlation Analysis .....................................................................................23

2.2. Microarray Gene Expression Profiling of 26 Candidate Genes ..................................23

2.2.1 Animal Experiments .....................................................................................23

vi

2.2.2. Surgery .........................................................................................................23

2.2.3 Phenotyping ..................................................................................................24

2.2.4 Perfusion .......................................................................................................24

2.2.5 Tissue Extraction ..........................................................................................25

2.2.6 Group Selection for Expression Profiling .....................................................25

2.2.7 RNA Extraction ............................................................................................26

2.2.8 Gene Expression Protocol .............................................................................27

3.0 Results 29

3.1 Remapping Pain1 on Mouse Chr 15 ............................................................................29

3.1.1 General Methodological Considerations .......................................................31

3.1.2 Remapping INC_2 ........................................................................................32

3.1.3 Mapping INC_1, INC_3 and INC_5 .............................................................34

3.1.4 Mapping AOD_1, AOD_3, and AOD_5 ......................................................38

3.1.5 Mapping AOD_AS_D36 ..............................................................................41

3.1.6 Correlation of Autotomy with other Traits ...................................................51

3.1.7 Heritability (h2) and Number of Effective Genetic Loci (EGL) ..................52

3.2 Identifying Candidate Autotomy Gene(s) in Pain1 .....................................................52

3.3 Gene Expression .........................................................................................................58

4.0 Discussion 62

4.1 Remapping Pain1.........................................................................................................62

4.2 Eleven Candidate Autotomy Genes in Pain1 ..............................................................66

4.2.1 Lynx1 ............................................................................................................66

4.2.2 Ly6c ...............................................................................................................68

vii

4.2.3 Ly6d...............................................................................................................69

4.2.4 Ly6i ...............................................................................................................69

4.2.5 Ly6k ...............................................................................................................70

4.2.6 Arc .................................................................................................................70

4.2.7 Plec1 .............................................................................................................72

4.2.8 Sharpin ..........................................................................................................74

4.2.9 2010109I03RIK .............................................................................................75

4.2.10 9030619P08RIK ..........................................................................................75

4.2.11 Zfp707 .........................................................................................................76

4.3 Limitations of the Study...............................................................................................78

4.4 Clinical Applications ...................................................................................................79

4.5 Summary ......................................................................................................................80

5.0 References ....................................................................................................................82

Appendix 1 .........................................................................................................................96

viii

LIST OF TABLES

Table 1: High priority candidate genes for human neuropathic pain .................................11

Table 2: QTLs for pain related traits.................................................................................13

Table 3: Correlation coefficients and significance level of the correlations between the

autotomy traits ...................................................................................................................30

Table 4: Position and significance level of Pain1 for all autotomy traits ..........................43

Table 5: Possible contribution of Pain 1 and Pain3 to INC_3 for each line .....................50

Table 6: Correlation table values of autotomy INC_3 ......................................................51

Table 7: List and description of genes located at the peak of Pain1 and position ............53

Table 8: Sequence mismatches in exons of genes in the significant peak of Pain1 ..........55

Table 9: Sequence mismatches in 5‘ UTR for genes in the significant peak of Pain1 ......56

Table 10: Sequence mismatches in 3‘ UTR for genes in the significant peak of Pain1 ....57

Table 11: Degree of fold change in the expression level of 11 genes with sequence

mismatches in the significant peak of Pain1 .....................................................................61

ix

LIST OF FIGURES

Figure 1: The production of recombinant inbred lines by sibling mating ...................... 12

Figure 2: LDP of the INC_2 ........................................................................................... 31

Figure 3: Whole genome map for INC_2 ...................................................................... 33

Figure 4: Interval physical map of chromosome 15 for INC_2 ..................................... 34

Figure 5: LDP of INC_1 ................................................................................................. 36

Figure 6: LDP of INC_3 ................................................................................................. 36

Figure 7: LDP of INC_5 ................................................................................................. 36

Figure 8: Interval physical map of chromosome 15 for INC_1 ...................................... 37

Figure 9: Interval physical map of chromosome 15 for INC_3 ...................................... 37

Figure 10: Interval physical map of chromosome 15 for INC_5 .................................... 37

Figure 11: LDP of AOD_1 ............................................................................................. 38

Figure 12: Interval map of chromosome 15 for AOD_1 ................................................ 39

Figure 13: LDP of AOD_3 ............................................................................................. 39

Figure 14: Interval map of chromosome 15 for AOD_3 ................................................ 40

Figure 15: LDP of AOD_5 ............................................................................................. 40

Figure 16: Interval map of chromosome 15 for AOD_5 ................................................ 41

Figure 17: LDP of the average autotomy scores on day 36 PO (AS_D36) ................... 41

Figure 18: Interval map of chromosome 15 for AS_D36 ............................................... 42

Figure 19: Chr 15 from 64 – 91.5 Mb ............................................................................. 44

Figure 20: INC_3 Interval map of Pain1 (excluding BXA13 and AXB13/14) .............. 46

Figure 21: Whole genome interval map of INC_3 (including data for all lines) ............ 47

x

Figure 22: Interval map of INC_3 for chromosome 14 (including data for all lines) .... 48

Figure 23: Magnified interval map of INC_3 for chromosome 14 ................................ 48

Figure 24: Histograms showing the effect of carrying the A and B genotypes in Pain1

and Pain3 for INC_3 for each RI line ............................................................................. 49

Figure 25: Bioanalyzer results for DRGs ........................................................................ 59

Figure 26: Photomicrograph of the Agilent 4X44 microarray chip ................................ 60

Figure 27A-C: Position of Pain1on mouse chromosome 15 .......................................... 63

Figure 28: Expression levels of Lynx1 in neural and other tested tissues ....................... 66

Figure 29: Expression levels of Arc in neural and other tested tissues .......................... 71

Figure 30: Expression levels of Plec1 in neural and other tested tissues ....................... 73

Figure 31: Pain1 orthologous regions on human chromosomes ................................... 77

xi

LIST OFABBREVIATIONS

A: A/J

A-beta: Afferent fibres having a large diameter axon and a myelin sheath, receptive to low

threshold stimuli

A-delta: Afferent fibres having a small diameter axon, a myelin sheath, and receptive to thermal

and high-threshold stimuli

AD: Denervated A mice

AI: Intact A mice

AOD_1: Line average onset day of autotomy scores 1

AOD_3: Line average onset day of autotomy scores 3

AOD_5: Line average onset day of autotomy scores 5

ANOVA: Analysis of Variance

AS: Sham operated A mice

AS_D36: Average autotomy score on the last day of the experiment (day 36)

AMPA: alpha amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid

ATP: Adenosine triphosphate

AXB/BXA: AXB/BXA Recombinant Inbred Mice Strains

B: C57BL/6J

BD: Denervated B mice

BDNF: Brain-derived neurotrophic factor

BI: Intact B mice

xii

Base pair (Bp): Two DNA bases complementary to one another (A and T or G and C) that join

the complementary strands of DNA to form the double helix characteristic of DNA

BS: Sham-operated B mice

Centi Morgan( cM): A unit of measure of genetic recombination frequency. One cM is equal to

a 1% chance that a marker at one genetic locus will be separated from a marker at another locus

due to crossing over in a single generation

chr: chromosome

CNS: Central Nervous System

CRPS: Complex regional pain syndrome

DEPC: Diethylpyrocarbonate

DRG: dorsal root ganglion

EGL: Number of Effective Genetic Loci

F1: First generation

F2: Second generation

FC: Fold change

HA: High Autotomy

IASP: The International Association for the Study of Pain

INC_1: Percent of mice expressing autotomy incidence of score 1 or more at the end of the

follow up period (day 36 PO)

INC_2: Percent of mice expressing autotomy incidence of score 2 or more at the end of the

follow up period (day 36 PO)

INC_3: Percent of mice expressing autotomy incidence of score 3 or more at the end of the

follow up period (day 36 PO)

xiii

INC_5: Percent of mice expressing autotomy incidence of score 5 or more at the end of the

follow up period (day 36 PO)

Indels: Insertion deletion

In-silico: Mapping genes using computer analysis

LA: Low Autotomy

LDP: Line distribution pattern

LOD: Logarithm of the odds ratio

LRS: Likelihood ratio statistic

nAChRs: Nicotinic acetylcholine receptors

Mb: Mega base pairs

NMDA: N-methyl D-aspartate

P1, P2: Female parent, Male parent

PNS: Peripheral Nervous System

PO: Postoperatively

RI: Recombinant Inbred

RVM: Rostroventromedial medulla

SCI: Spinal cord injury

SNP: Single Nucleotide Polymorphism

STR: Short tandem repeat

TRPV1: Transient receptor potential cation channel subfamily V, member 1 protein

QTL: Quantitative trait locus

TrkB: Tyrosine kinase type 2

VARE: Variance due to environment

xiv

VARG: Variance due to genetics

1

1. Introduction

The International Association for the Study of Pain (IASP) has defined pain as ―an unpleasant

sensory and emotional experience associated with actual or potential tissue damage, or described

in terms of such damage. Pain is always unpleasant and therefore it is an emotional experience‖

(http://www.iasp-pain.org).

1.1 Pain Terms A list of pain terms as defined by The International Association for the Study of

Pain is shown below (Adapted from Merskey and Bogduk, 1994).

Allodynia: Pain due to a stimulus that does not normally provoke pain

Analgesia: Absence of pain in response to stimulation that would normally be painful

Hyperalgesia: An increased response to a stimulus that is normally painful

Hyperesthesia: Increased sensitivity to stimulation, excluding the special senses

Hyperpathia: A painful syndrome characterized by an abnormally painful reaction to a

stimulus, especially a repetitive stimulus, as well as an increased threshold

Hypoalgesia: Diminished pain in response to a normally painful stimulus

Hypoesthesia: Decreased sensitivity to stimulation, excluding the special senses

Paraesthesia: A sensation of tingling, pricking, or numbness

Dysesthesia: An unpleasant abnormal sensation, whether spontaneous or evoked

1.1.1 Types of Pain

Pain is classified broadly into two entities, acute and chronic. For pain to be chronic, it needs to

be present for at least 3 months after the inciting event (Mersky and Bogduk, 1994). Nociceptive

Pain is mediated by high-threshold unmyelinated C-fibres or thinly myelinated A-delta primary

sensory neurons that feed input to nociceptive pathways in the central nervous system (CNS)

(Woolf and Ma 2007). This pain plays an important role in protecting us against tissue injury, by

warning us of an injury or impending tissue damage. Inflammatory Pain is a type of pain that

occurs as part of an inflammation caused by tissue injury, exposure to UV, toxins, immune cells

infiltration, bacteria and other pathogens. Inflammatory pain serves an important role by helping

2

to heal and repairing the injured body part. Neuropathic Pain as identified by the IASP is a pain

―initiated or caused by a lesion or dysfunction in the nervous system‖ (Merskey and Bogduk,

1994; Haanpää and Treede, 2010)

Neuropathic pain involves two key concepts:

1) Inappropriate impulse activity in nociceptive fibres (injured and uninjured).

2) Sensory processing changes in the central nervous system caused by these abnormalities

(Meyer et al., 2006).

Neuropathic pain can be caused by peripheral or central injuries:

Neuropathic pain is known as a peripheral neuropathic pain when the origin of the lesion is

primarily in the peripheral nervous system (PNS). This can be caused by injury or dysfunction in

a peripheral nerve, the dorsal root ganglia (or trigeminal ganglion) or the dorsal (or trigeminal)

root(s). Mechanical trauma, metabolic disorders, neurotoxic chemicals including drugs,

chemotherapy, surgery, radiation, nerve compression, inflammation, infection or tumour

invasion, may cause such peripheral injury (Dworkin et al., 2003).

Neuropathic pain is known as central neuropathic pain when the origin of the lesion is primarily

in the central nervous system (CNS). This can be caused when there is damage or injury in the

spinal cord or brain. Most common examples are spinal cord injury, stroke, and multiple

sclerosis (Ducreux et al., 2006).

Neuropathic pain can be acute or temporary or it can be chronic; persisting long after all possible

healing of the damaged tissues has occurred (Dworkin, 2002). It can have a delayed onset after

the initial nerve injury and it may spread beyond the cutaneous distribution of the injured nerves,

suggesting an important role for the CNS. But there are also other peripheral mechanisms that

may mediate spread of pain (Mannion et al., 1996; Devor and Seltzer, 1999). Mechanical and

thermal allodynia following spinal cord injury (SCI) may also be due to development of central

sensitization of dorsal horn neurons (Christensen and Hulsebosch, 1997). The underlying

molecular mechanisms of neuropathic pain are not entirely elucidated, and therefore, current

analgesic treatment is insufficient in many cases.

3

1.2 Characteristics of Human Neuropathic Pain

Neuropathic pain may have several manifestations in humans, as described by Wang et al., 2003

and others.

Spontaneous pain: pain not activated by an external stimulus, posture or movement.

Allodynia: pain evoked by a stimulus, which is not normally noxious.

Hyperalgesia: exaggerated pain to a stimulus that is normally noxious.

Duration: neuropathic pain may last many months or longer sometimes even for life.

Delayed onset: pain that starts weeks, months and even years after the inciting event.

Quality of pain: burning, stabbing, shooting, electric shock, piercing, etc. The McGill Pain

Questionnaire (Melzack, 1975; Melzack and Katz, 2001) is a tool that helps classifying the types

of pain a patient has and its intensity.

Distribution: pain may spread beyond the cutaneous distribution of an injured nerve. Pain may

even appear contralaterally in ‗mirror image‘ sites.

Phantom pain: In patients with an amputated limb, but also following mastectomy and removal

of teeth, eyes, womb and testicles, some individuals complain of pain in the missing body part.

This pain sometimes mimics the original pain that would have been felt in that body part, had it

not been removed. Phantom limb pain may gradually ‗telescope‘ into the distal end of the

residual limb (the stump). Nerve-end neuroma and dorsal root ganglia (and trigeminal ganglion)

ectopic inputs (see below), as well as abnormal processing of such inputs in the CNS, may

underlie the appearance of phantom pain (Sherman, 1997; Flor et al., 2006).

1.3 Mechanisms Underlying Peripheral Neuropathic Pain

1.3.1 Injury Discharge

When sensory fibres are damaged, a discharge of impulses is emitted immediately after the

injury, which can last up to a few minutes in some fibres, and many hours in others (‗Injury

Discharge‘) (Wall et al., 1974). Injury-induced discharge plays a role in triggering the 'autotomy'

behaviour in rats, i.e., self-mutilation of the denervated body parts (Wall et al., 1979). Blocking

the injury discharge by a local anaesthetic significantly delays the time of autotomy onset and

suppresses the severity of this behaviour compared to control rats receiving saline (Seltzer et al.,

1991). In a follow up study the authors used HA ( high autotomy) and LA (low autotomy) rats,

i.e., two lines of rats genetically selected from a common stock strain to express high autotomy

4

or low autotomy levels following the same hindpaw denervation procedure. Blocking injury

discharge in HA rats just prior to the denervation procedure, prevented autotomy. Also, artificial

prolongation of the injury discharge in LA rats, just prior to neurectomy, increased autotomy

levels (Cohn and Seltzer, 1991). Also, Seltzer et al., (1990) showed that a single intrathecal

injection of NMDA receptor blockers at the lumbar enlargement of the spinal cord, just prior to

neurectomy, significantly reduced autotomy levels, while blocking glycinergic inhibition with a

single strychnine intrathecal injection significantly increased autotomy levels. Their study

showed that injury discharge, in spite of its short duration compared to the duration of chronic

pain, may play an important role in triggering the cascade of mechanisms that are involved in the

development of neuropathic pain.

1.3.2 Ectopic Impulse Generation

Peripheral nerve section causes Wallerian degeneration of the distal segments of the axons,

separated from their soma. Some primary afferents and their cell bodies in the dorsal root ganglia

degenerate but those axons in the proximal stump which have survived the injury attempt to

regenerate by sprouting from the parent axons. In cases where their way is blocked or prevented

(e.g., by amputation of the limb), a neuroma is formed at the nerve end. The neuroma comprises

entangled sprouts of sensory, motor and sympathetic efferents. Afferents caught in the neuroma

may develop ectopic impulses. In addition to the sensory input from the neuroma, the cell bodies

of the injured afferents in dorsal root ganglia (DRG) may also develop ectopic firing (Wall and

Gutnick, 1974; Wall and Devor, 1983; Amir et al., 2005). Moreover the neighboring uninjured

nociceptors (Ali et al., 1999).

1.3.3 Ectopic Transduction

Injured sensory neurons entangled in a nerve end neuroma may develop decreased threshold to

mechanical (tapping on the injured nerve) or/and thermal, and endogenous chemicals (Wall and

Devor, 1983). Such stimuli may cause aggravation of existing neuropathic pain.

1.3.4 Peripheral and Central Sensitization

Following lesions to the somatosensory nervous system, adaptive and maladaptive changes may

occur within the nociceptive system (i.e., neuroplasticity). Maladaptive plasticity manifested as

5

exaggerated response to noxious (hyperalgesia) and innocuous (allodynia) stimuli may be result

of sensitization mechanisms within the PNS (peripheral sensitization) or the CNS (central

sensitization). The quality and degree of this maladaptive plasticity depend on several factors

including ectopic impulse generation, enhancement of excitatory synaptic transmission,

disinhibition, activation of glia cells, neuro-immune interactions, changes in membrane

excitability in nociceptors and their central terminals in the spinal and trigeminal dorsal horns,

changes in transmitter synthesis, release, transport and degradation, and abundance of their

receptors, postsynaptic signaling in the PNS and CNS and gender, age and genetic

polymorphisms (Campbell and Meyer, 2006; Costigan et al., 2009).

Peripheral sensitization is manifested as decrease in activation threshold and/or an increased

response of nociceptors transferring input from peripheral targets such as skin, muscle, joints and

the viscera to the CNS (spinal cord and brainstem). One example for this sensitization is

increased sensitivity of the skin following sunburn, leading to hyperalgesia and allodynia

(Gustorff et al., 2004; Harrison et al., 2004). This hypersensivity can be caused by changes in

key proteins and ion channels (known as transduction proteins) that determine the excitability of

the nociceptor terminal (Woolf and Salter, 2000; Salter, 2005).These changes occur in two

levels;

1) Changes to existing proteins (post-translational processing).

2) Changes to the production of proteins (altered gene expression in the cell body of the sensory

neurons in the dorsal root ganglion).

One example that includes both these changes is the TRPV1 (transient receptor potential cation

channel subfamily V, member 1) protein, an ion channel that responds to heat stimuli. Activation

of kinases takes minutes (changes in post-translational processing) but changes in protein levels

take a day or so (changes in gene expression).

Central sensitization is due to an increase in the excitability of neurons within the central

nervous system. Following nerve injury, the burst of activity of nociceptive and non-nociceptive

afferents (injury discharge), as well as other signals of injured tissues that may include cytokines

and chemokine, alter the strength of the synaptic connections between primary afferents and

projection neurons of the dorsal horn in the spinal cord and trigeminal system in a way that

6

normal inputs from the periphery produce abnormal response in the CNS resulting in allodynia

and hyperalgesia. Noxious-stimulus-evoked inputs are transmitted in the CNS through excitatory

glutamatergic synapses, e.g., alpha amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid

(AMPA), kainate and N-methyl-D-aspartate (NMDA) subtypes of ionotropic glutamate

receptors. Following nerve injury, nociceptive signaling is enhanced at these glutamatergic

synapses "central sensitization," which is the key element in pain hypersensitivity (Salter, 2005).

Activation of microglia and astrocytes also take part in central sensitization (see below). Finally

it has been suggested that enhanced and persistent nociceptive signaling following peripheral

nerve injury may result in up-regulation of the NMDA receptors in the rostroventromedial

medulla (RVM), activating descending facilitation from the RVM and further contributing to

central sensitization (Porreca et al, 2002).

1.3.5 Sympathetically-Maintained Pain

Following nerve injury α–adrenergic receptors are upregulated in the soma of nociceptive

afferents. These receptors are transported downstream in the axons to be assembled in their

terminals in the neuroma. Norepinephrine released from the sympathetic terminals in the

neuroma may bind to α–adrenergic receptors on nociceptive neuroma afferents, causing

spontaneous pain in response to sympathetic activity. Complex Regional Pain Syndrome (CRPS,

formerly named Reflex Sympathetic Dystrophy and Causalgia) is an example in which some of

the patients experience sympathetically-maintained pain and pain that is aggravated by stress,

fear, and other causes to sympathetic activity, as well as ongoing pain, touch-evoked pain,

abnormal regulation of blood flow, sweating, and trophic changes (Baron et al., 1999; Baron,

2006). There are also other forms of peripheral sensitization that occur during inflammation.

1.3.6 Disinhibition

Modulatory descending pathways originating in several supraspinal regions including the

anterior cingulate gyrus of the cortex, amygdala and hypothalamus, are relayed to the spinal cord

and medullary dorsal horns through various brain stem nuclei such as the periaqueductal gray

and rostroventral medulla. These descending pathways can inhibit or facilitate the sensory

processing of inputs in ascending pain pathways (Pertovaara and Almeida, 2006; Wei et al.,

2008). The neurotransmitters mediating these effects include transmitters such as norepinephrine,

7

5- hydroxytryptamine (serotonin), endogenous opioids and others. Spinal and trigeminal dorsal

horn interneurons express receptors for these neurotransmitters. After nerve injury, the inhibitory

and facilitatory systems undergo changes that manifest in two forms: (i) reduced inhibition

(disinhibition) and/or increased facilitation. Following peripheral nerve injury, the injury

discharge as well as sustained ectopic activity of primary sensory afferents can cause glutamate-

mediated excitotoxicity, which in turn results in the death of some of the dorsal horn neurons

(Dubner and Ruda, 1992; Ji et al., 2006; Scholz et al., 2005; Wei et al., 2010). After peripheral

nerve injury many primary afferents die as well, however the role of injury discharge in their

death is not fully known. Small-diameter dorsal horn neurons show a greater loss, and since

many of these are inhibitory interneurons in substantia gelatinosa, their absence may underlie the

recorded disinhibition of projection pain neurons (Okamoto et al., 2001). Loss of spinal dorsal

horn inhibitory interneurons may contribute to the persistence of neuropathic pain. Another

mechanism of disinhibition is related to reduction in the tonic noradrenergic inhibition that

normally acts on alpha 2 adrenoceptors on dorsal horn neurons (Rahman et al., 2008). Also, not

only expression of mu opioid receptors on primary afferent terminals in the CNS is decreased,

but also, dorsal horn neurons become less sensitive to inhibitory effect of mu opioid agonists

(Kohno et al., 2005). Moreover, there is a loss of pre- and postsynaptic GABAergic inhibition in

the spinal cord (Scholz et al., 2005). Furthermore, following nerve injury activation of some

GABAa receptors no longer lead to hyperpolarization as normally, but instead, they induce

depolarization (Coull et al., 2005).

1.3.7 Structural Changes in the Termination Zone of Primary Afferents

Large myelinated A-beta fibres normally terminate in the deeper laminae of the spinal and

trigeminal dorsal horns (laminae III-VI), whereas thinly myelinated A-delta fibres and

unmyelinated C- nociceptive fibres terminate in the superficial laminae (I and II) and in deeper

lamina (IV-VI). Following peripheral nerve injury, A-beta fibres sprout dorsally into superficial

lamina II fibre (Woolf and Salter, 2000; Kohama et al., 2000; Soares et al., 2002, 2007; Okamoto

et al., 2001). This may explain how low-threshold Aß fibres input may activate nociceptive

pathways in the CNS, resulting in allodynia to light touch, warm and cold stimuli.

8

1.3.8 Neuro-Immune Interactions

Following nerve injury, immune responses in the injured nerve, associated DRGs and trigeminal

ganglion, spinal and trigeminal dorsal horns, and brain contribute to pain hypersensitivity

(Scholz and Woolf, 2007). Following a nerve injury, macrophages in the PNS identify the

cellular debris that result from Wallerian degeneration and tissue damage, and clear the tissue as

part of the regenerative and tissue healing process. The M1 subset of macrophages produces high

levels of oxidative metabolites and proinflammatory cytokines causing collateral damage to

healthy tissue, therefore promoting inflammation (Kigerl et al., 2009). During this process they

present surface antigens that activate T-lymphocytes, to produce and release cytokines and

chemokines that activate and sensitize neurons, Schwann cells, DRG and trigeminal satellite

cells, activate microglia and astrocytes as well as CNS neurons (Dublin and Hanani, 2007; Zhang

et al., 2007; Thacker et al., 2009; Ren, 2010).

1.3.9 Microglia

Microglia has several functions. One of the functions is being the macrophages of the CNS. They

release glio-transmitters and many immune and inflammatory modulators that contribute to the

induction and maintenance of neuropathic pain by altering neuronal function and maintaining

central sensitization (Scholz and Woolf, 2007). Astroglia also take part in this process (Okada et

al., 2009; Ren, 2009; Ji et al., 2006; Wei et al., 2008).

Microglia-neuron signaling is achieved by several messengers, one of which is via release of

brain-derived neurotrophic factor (BDNF) from microglia. This signaling pathway may play a

critical role in the pathogenesis of pain hypersensitivity caused by injury to peripheral nerves

(Beggs and Salter, 2010). P2X4 purinergic receptors are ionotropic ATP receptors on microglia

that respond to the release of extracellular ATP following peripheral nerve injury. The activation

of these receptors causes microglia to release BDNF. BDNF is the signal from the microglia that

activates TrkB (tyrosine kinase B) receptors on lamina I neurons which increase the intracellular

chloride levels in the dorsal horn neurons causing disinhibition of nociceptive dorsal horn

neurons (Beggs and Salter, 2010). Another study has shown that continuous (for seven days

following surgery) intrathecal infusion of minocycline (a microglial inhibitor) can prevent the

development of persistent mechanical allodynia and thermal hyperalgesia induced by spinal

9

nerve ligation (Lin et al., 2007). Following injury to the peripheral nerve not only the number of

spinal microglia increases (Beggs and Salter, 2007) but there is also an up-regulation of the

P2X4 receptors on the microglia (Trang et al., 2006). In addition to this disinhibition, the

excitatory synaptic transmission in the dorsal horn is also enhanced following peripheral nerve

injury (Coull et al., 2005; Beggs and Salter, 2010). These changes occur not only after complete

nerve section, but also after partial injuries such as in a ‗near miss‘ injury that forms a partial

neuroma or a neuroma ‗in-continuity‘. Moreover, certain diseases such as diabetes mellitus may

destroy primary afferents along peripheral nerves, thereby producing multiple micro neuromas

along such nerves, each presenting the same pathophysiological mechanisms as a total section or

crush.

1.4 Chronic/ Neuropathic Pain Behavioural Models in Animals

Since understanding the mechanisms of neuropathic pain necessitates invasive experimentation

by way of producing injuries to the CNS and PNS in living subjects, the availability of clinically

relevant animal pain models is crucial to accomplish this goal (Zeltzer and Seltzer, 1974; Seltzer

1985; Wang et al. 2003; Mogil, 2009).

1.4.1 The Neuroma Model/Autotomy

This model has been used in my study. It is a model of spontaneous neuropathic pain. Total

transection of the sciatic and saphenous nerves causes total denervation of the hindpaw and the

growth of a neuroma at each nerve end. Following this procedure, some rodents belonging to

certain strains and selection lines express an abnormal self-mutilation behaviour known as

‗Autotomy‘. This peripheral nerve injury and the behaviour associated with it are termed the

Neuroma Model (Wall et al., 1979). Autotomy consists of licking, scratching and biting of the

denervated hindpaw and coincides with spontaneous ectopic discharges from the afferent fibers

in such neuromas (reviewed by Devor and Seltzer, 1999). Since drugs that suppress neuropathic

pain in humans also reduce the extent of autotomy, this behaviour is perceived to be an attempt

of the animal to relieve itself from spontaneous pain referred to the hindpaw. Catecholamine re-

uptake blockers, GABAa agonists (Seltzer et al., 1989) and elevation of plasma corticosteroids,

(Seltzer et al., 1987) which are known to decrease human neuropathic pain also decrease the

level of autotomy. This suggests that the Neuroma Model may be a good model to study human

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neuropathic pain. Based on the extent of this self-mutilation behaviour and its postoperative

kinetics, autotomy can be scored, such that a higher score represents increased levels of

spontaneous pain (Wall et al., 1979). In this scoring scale, score of 1 point was assigned for the

injury of one or more nails and an additional score was given to each half toe, to a maximum of

11 points. This model has been used to show that chronic pain levels are strain-specific,

indicating that genetic determinants control this behaviour (Devor and Raber, 1990; Defrin et al.,

1996; Seltzer et al., 2001; Devor et al 2005; Nissenbaum et al., 2010).

1.5 Genetics of pain

Like any behavioural trait, pain behaviours as well as pain syndromes are complex heritable

traits, determined by a combination of partly identified genetic polymorphisms interacting with

environmental parameters (Mogil and Seltzer, 2004). Acute pain tests suggest that estimated

heritability of pain sensitivity in healthy humans is 20%–60% (Nielsen et al., 2008; Norbury et

al., 2007).

Two general strategies are available for human genetic studies: identifying rare mutations having

large effects on the investigated trait that produce distinct genetic diseases, or studying common

genetic variants having smaller effects on the studied trait and that can be identified when

studying large patient cohorts (Belfer et al., 2004).

In animals, a study using 11 inbred mice strains tested with 22 different measures of nociception

and neuropathic pain-like sensory abnormalities, revealed heritability between 30% and 76%

(Mogil et al., 1999a,b; Lariviere and Mogil, 2010). In respect with the Neuroma Model, there is

strong evidence that a single genetic locus has a major effect on the variability in autotomy levels

both in rats and mice (Devor and Raber, 1990; Seltzer et al., 2001; Nissenbaum et al., 2005). HA

and LA rat lines were selected from a common Sabra rat strain, such that all offspring of HA

parents show high pain-related scores, and all offspring of LA parents show low scores of

autotomy following the same hindpaw denervation procedure. However, all offspring of HA/LA

intercrossed animals (F1) show LA phenotype, suggesting that the HA trait is recessive to LA.

When F1 animals were backcrossed to parental HA and LA mates, an autosomal Mendelian (i.e.,

single gene) mode of inheritance was evident (Devor and Raber, 1990; Nissenbaum et al., 2005),

but the identity of this gene is still unknown.

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Table 1: High priority candidate genes for human neuropathic pain (Belfer et al., 2004). This

table shows the name of the gene , location of the genetic mutation that is implicated in the pain

trait and whether the mutation causes an amino acid change in the protein it encodes.

(1) PAIN: strength of evidence supporting involvement of the gene in pain processing, (2)

FREQ: frequency of the specific variant, and (3) FUNCTION: likelihood that the polymorphism

alters function. Each polymorphism is assigned zero to three points in each of these three

categories, with a maximum score of 9.

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Since then, many genes have been studied in association with neuropathic pain. Recently,

Cacng2, a gene implicated in autotomy in mice was also found to have a role in neuropathic pain

in humans, supporting the clinical relevance of the Neuroma Model (Nissenbaum et al., 2010).

Table 1 shows a list of some of the high priority candidate genes that have been studied until

2004 (Belfer et al., 2004).

A more current Table that focuses only on genes for ion channels was published recently (Cregg

et al., 2010).

1.6 Recombinant inbred Mice

As shown in Figure 1, Recombinant inbred lines (RI) are produced by crossing two inbred strains

(P1 and P2), followed by crossing the resultant F1 generation, and then followed by 20 or more

consecutive generations of sibling mating to produce inbred daughter recombinant lines (Bailey,

1971; Taylor, 1978). Each line inherited a unique ‗mosaic‘ of chromosomal segments from the

P1 and P2 parents. Mice belonging to each line are homozygous, that is they are genetically

identical at each chromosomal region.

Figure 1: The production of recombinant inbred lines by sibling mating (Browman, 2005)

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Table 2: QTLs for mouse pain-related traits (Mogil and Max, 2006). The Table shows the

mapping population (i.e., the strains which were used for mapping QTLs), the chromosome

harbouring the QTL, its peak location (in cM), the LOD score associated with the peak, which

candidate gene is favoured at the peak, and the reference of the study.

All individuals belonging to each RI line are genetically identical, hence, the contributions from

the two parental inbred lines to the genome of every individual is exactly the same; therefore, if

needed, one mouse can be replaced by another of the same line. RI lines obviously include mice

14

of both sexes, enabling the study of gender effects in the expression of a trait. RI lines are

isogenic (Bailey, 1971), suggesting any variance is due to either environment or error in method.

Thus, being genetically identical is highly valuable for studying gene-environment interaction.

On this basis, when the parental strains express a contrast in the phenotype, RI lines can be used

to map quantitative trait loci (QTLs) by identifying chromosomal regions harbouring genotypes

for which a linkage is found between a certain genotype and phenotype in a genome-wide,

unbiased manner. Table 2 shows QTLs for pain-related traits that were studied in recombinant

inbred lines and other genetic assays such as genotyping pain-phenotyped F2 generation rodents.

1.7 Pain1

Seltzer et al., (2001) used the neuroma model in RI lines to confirm that autotomy, spontaneous

chronic pain-like behaviour in this model, are strain-specific in mice as well, indicating that

genetic determinants control autotomy levels. First, these authors found that the levels of

autotomy expressed by denervated males and females of the inbred lines A/J (‗A‘) and C57BL6/J

(‗B‘) are highly contrasting, such that A mice express high levels and B mice express low levels

of autotomy following the same procedure of total denervation of the hindpaw. This contrast

justified their using of the already available 23 different AXB-BXA RI mice, that were first

produced by Muriel Nesbitt by crossing the A and B strains in the mid- and late-1970s, and first

used by Skamene et al., (1984) and Peleg and Nesbitt (1984). By taking advantage of an

available genetic map for these recombinant mice lines, first produced by Sampson et al., (1998)

using a crude panel of ~400 microsatellite markers, Seltzer et al., (2001) mapped a QTL for

autotomy on chromosome (‗chr‘) 15 and named it Pain1. As shown in Figure 27A (see

Discussion) the peak position of this QTL was at marker D15Mit28 (located at 34.29 cM,

74,745,784 – 74,745,947 Bp), flanked on one side by the microsatellite marker D15Mit156

(located at 32.19 cM, 71,155,976 -71,156,119 Bp) and on the other side by the marker Ly6a

(located at 34.29 cM, 74,825,307-74,828,318 Bp), spanning 2.1 cM, 3.7 Mb. The cM location of

these markers (especially the latter) has since changed when more accurate mapping information

became available for these and other markers. In 2005 this finding was replicated by Devor et al.,

who genotyped chr 15 using a few microsatellite markers in hundreds of offspring that were

phenotyped for autotomy following the same denervation procedure as used by Seltzer et al.,

(2001). Devor et al., (2005) used the F2 progeny of a cross of two mice lines other than the A

15

and B lines (C58/J, expressing low autotomy, and C3H/HeJ that express high autotomy levels)

and replicated the presence of Pain1 on chr 15, at a location very close to the peak of Pain1 in

Seltzer et al.,‘s locus. As shown in Figure 27B (taken from Devor et al., 2005), Pain1 in their

map peaks at marker D15Mit68 (36.28cM; 76,740,612 Bp), flanked on one side by marker

D15Mit156 (32.19 cM, 71,155,976 -71156119 Bp) and D15Mit105 (33.42cM; 72,331,040-

72,331,161 Bp), 1.23 cM, 1.175 Mb away from Seltzer et al.,‘s. Thus, while confirming the

existence of an autotomy QTL on chr 15, their study further refined the interval length of Pain1.

Both studies resulted in interval lengths that precluded identifying the causative gene by

sequencing or studying the regulation of its expression by autotomy levels.

1.8 Hypotheses

(1) There is an autotomy gene harboured in the confidence interval of Pain1. To identify

it, one would need to further refine the Pain1 map using phenotypic data of the AXB-

BXA recombinant inbred mice strains.

(2) The contrasting levels of autotomy in the A and B strains are caused by a mismatch in

the sequence, or insertions or deletions (‗indels‘) in coding regions of the autotomy

gene(s) in Pain1. This mismatching sequence can be identified by comparing

sequences in candidate genes in these strains.

(3) This mismatch manifests in a difference in the expression of these genes in the DRG

and/or the spinal cord of intact A and B strains or following hindpaw denervation by

sciatic and saphenous transection, but not following sham operation.

1.9 Aims of the Study

The aims of my study were to address these hypotheses by:

(1) Refining the position of Pain1 on chr 15 and its peak using a new SNP-based map for

the AXB-BXA RI set that became available in 2006, as well as by mapping additional

autotomy traits that were not used by Seltzer et al., (2001) when originally mapping

Pain1. These additional traits include incidence of different autotomy scores and

average onset day of these scores for each RI and parental lines. Studying additional

autotomy traits could help us refine the map of Pain1 or could help us find other

QTL(s) that interact epistatically with Pain1.

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(2) Listing the candidate autotomy genes located within the significant confidence length

of Pain1.

(3) Prioritizing these candidate autotomy genes by:

a. Identifying genes that have sequence mismatches between A and B mice in

the coding and regulatory regions.

b. Carrying out gene expression profiling of intact and denervated A and B mice,

in the:

i. Relevant lumbosacral DRGs associated with the injured nerves.

ii. Relevant lumbosacral spinal cord segments on the ipsilateral side to

the injury.

c. Searching the literature for genes shortlisted by steps (i) and (ii) and

highlighting those having known function in pain or other relevant neural,

inflammatory or immune functions.

d. Genes found to have: (i) sequence mismatches between A and B, (ii)

contrasting experession levels between these parental lines, (iii) supporting

evidence from the literature for a functional role in pain mechanisms, will be

prioritized.

1.10 Rationale: The original map of Pain1 was too long to be sequenced; therefore, identifying

autotomy gene(s) in this QTL could not be done at that time. In addition, due to the limited

resolution conferred by the usage of sparsely apart microsatellite markers in the genetic mapping

tool used by Seltzer et al., (2001), even the peak location of Pain1 was not certain. The original

map used by Seltzer et al. to map Pain1 was based on 400 microsatellite markers, of which 17

were on chromosome 15. But even the replication study of Devor et al., (2005) could not further

refine this map since they also used few microsatellite markers, in fact fewer markers (N=9) than

used by Seltzer et al., (2001). Since the confidence length of the Pain1 interval was too long,

testing so many candidate genes was not practical. In order to determine the candidate genes in

Pain1, another step was needed to further refine the confidence length of Pain1. In my study,

this became possible by using a new genetic map of the AXB-BXA RI set that became available

in 2006 (Shiffman et al., 2006). This map is based on 7,696 single nucleotide polymorphisms

(SNPs), evenly spaced across the mouse genome. This map was implemented as a part of the

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web-based QTL mapping software for these RI lines (http://www.genenetwork.org). The new

map has nearly 20 fold more SNPs for chr 15 than the number of STRs used by Seltzer et al.,

(2001) and each SNP can detect linkage to a causative locus that is spaced up to 10kbp apart.

This considerably higher resolution marker panel offered the possibility that remapping QTLs for

autotomy traits in chromosome 15 could significantly refine the interval length of Pain1 where

gene(s) for autotomy are located, and perhaps even identify the candidate pain gene(s) at this

QTL.

1.11 Summary and Conclusion:

We experience pain in daily life, and it is crucial to our survival. Under pathological conditions

pain changes characteristics and persists without serving the purpose of alarming of potential

tissue damage or promoting healing. Neuropathic pain is caused by a nerve injury in the

peripheral or central nervous system. Symptoms of neuropathic pain may include allodynia,

hyperalgesia, sensory deficits (hypoesthesia and hypoalgesia) and spontaneous pain. The

mechanisms of neuropathic pain and especially the genetic contribution are not fully known and

current pharmacologic treatments are insufficient in most cases. Chronic pain is a major health

problem, affecting about one-fifth of the population, causing much suffering, significantly

eroding the quality of life and incapacitating affected individuals. Identification of genes for

neuropathic pain is still in its infancy and much more work needs to be done. Pain is a

multidimensional experience, involving sensory discriminative, emotive/aversive and cognitive

evaluative aspects. Each of these aspects is controlled by genetic and epigenetic factors. Animal

studies using recombinant inbred lines provide us with a method to begin the process of

identifying candidate pain genes by mapping quantitative trait loci on mouse chromosomes

which harbour such genes. Various animal models have been produced to study neuropathic pain

conditions in humans. One of these, the Neuroma Model, is used as a model to study phantom

pain in human limb amputees and women post-mastectomy, and in patients with anaesthesia

dolorosa and plexus avulsion. Previous studies have shown that a QTL on mouse chromosome

15 harbours gene(s) associated with autotomy (behaviour related to neuropathic pain quantified

in the Neuroma Model). My main goal was to refine this chromosomal interval by in-silico

mapping and combine it with expression profiling of candidate autotomy genes. It is possible

that identified autotomy genes in mice, may also play a role in neuropathic pain in humans,

18

which could lead to a better understanding of the molecular pathways underlying these pain

syndromes, and to the development of better treatments for neuropathic pain in humans.

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2. Methods

2.1 In-silico remapping of Pain1

All phenotypic data used in this study to map QTLs for autotomy are from the trait data

collected by Seltzer et al., for their 2001 pain study. This included the data published by these

authors in that paper as well as many other traits which are unpublished to date.

In order to confirm the existence of the Pain1 QTL and refine its peak position, I used the

WebQTL software to remap Pain1. WebQTL is a website that combines databases of various

murine traits and gene browsers with software for interval mapping of QTLs, as well as for

correlating a trait under investigation with other reported traits that use the same RI set, linked to

PubMed indexed journals for direct access to the original publications in which such traits are

reported. In addition, queries of unpublished phenotypes can be submitted to WebQTL by

investigators, resulting in immediately accessible interval maps, with links to genes reported for

intervals of interest. The WebQTL databases include browsable neuroanatomical,

pharmacological, and behavioural traits. WebQTL also includes updated and well-curated

genotypic data for five sets of mouse RI lines, including the AXB-BXA that I used in this study.

Mapping functions include identifying QTLs for a trait under investigation and additional

functions (Wang et al., 2003; http://www.webqtl.org/). In order to remap Pain1, I entered the

trait data for each of the 23 recombinant inbred lines, as well as the parental A and B strains (I

used data from only male mice in my experiments), and implemented the software to generate a

report including the following outputs:

2.1.1 Line Distribution Pattern (LDP): A list of trait values for each RI line, in ascending order

from lowest to highest, and histograms of original data and data converted to z-scores. Where

applicable, these values were line averages and standard error bars, and in other cases non-

parametric data was also used.

2.1.2 Interval Mapping: This map is based on the availability of genotypic data for all RI lines.

When constructing the mapping software, informative SNPs and microsatellite markers were

selected based on their ability to identify at each marker locus whether the origin is from parent

A or B. For each marker locus a statistical correlation is performed to estimate the goodness of

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fit between the observed genotype and trait levels for each line, and the expected link between a

parental genotype on that marker locus and the parental phenotype (Williams, 2005).

While the available map is based on thousands of SNPs, it is still not exhaustive

enough. Therefore, the software computes inferred probabilities for chromosomal intervals in

regions not yet genotyped, by estimating the genotype from the closest flanking markers. This

results in interval maps that show markers as sharp inflection points and intervals in between as

smooth curves.

2.1.3 Software options and switches:

2.1.3.1 Permutation Test:

This test is used to evaluate the significance of an identified QTL by randomly

reassigning trait values and genotypes for the lines used in the analysis. The permutated datasets

have the same values of phenotypes and the same genotypes but in a different order. The

significance of the linkage between permutated genotype/phenotype datasets is computed for a

thousand such permutations of the data. Significance of the linkage for each marker is

determined by comparing the statistical results of the original dataset with those of the

permutated datasets. If the LRS (likelihood ratio statistic) value for the un-permutated dataset is

larger than 95% of the permutated datasets the significance level is define as p =0.05 for a whole

genome significance threshold. I used this test in my analyses. LRS is a measurement of linkage

between differences in autotomy levels and differences in the SNP genotypes at a certain marker

locus. Likelihood ratio describes the relative probability of two different options to explain

observed differences in autotomy across the RI lines, the first option is that a certain genotype is

linked with different autotomy levels in the RI lines, and the second option is that there is no

linkage between the genotype and phenotype. The probabilities of these options are computed for

every marker and the logarithm of the odds ratio (LOD score) is used to assess the significance

of the linkage for that locus marker. If option 1 is 1000 times more probable than option 2, then

the LOD score is 3 (=log1000:1). LOD scores can be converted to LRS values by multiplying by

4.61. Values of LRS that correspond to a genome-wide p-value of 0.05 are considered

significant. This threshold corresponds to a probability of 5% of falsely rejecting the possibility

of no linkage anywhere in the genome. In addition to significant thresholds, the software

calculates the very permissive ‗suggestive threshold‘ representing an LRS value that corresponds

21

to a genome-wide p-value of 0.63 (corresponding to a probability of 63% of falsely rejecting the

option that there is no linkage at that marker locus). Inclusion of the suggestive threshold is to

draw the attention of investigators to loci that may be worth a follow-up, as was done in my

analysis.

2.1.3.2 Bootstrap Test: WebQTL uses this test to evaluate approximate confidence limits of

QTL peaks by generating 1000 resampling of the original dataset in a way that the number of

data points is always kept at 25 (i.e., 23 RI lines plus the 2 parental lines), the values of some

lines are represented more than once while others are omitted. The LRS is recomputed for each

such resampling round and the location of the locus with the largest LRS score is recorded.

These loci are stacked on top of each other to create a histogram (in yellow bars) that is

superimposed on the same genetic map to show the loci showing the highest percent out of 1,000

bootstrap resamples in which they were linked with the highest LRS. The higher the % the more

confident one can be that the precise location of the observed peak of the QTL is accurate. I used

this test in my analyses.

2.1.3.3 Haplotype Analysis: A graphic display made for each chromosome separately and for

every line in the scan. Each horizontal bar (comprising red, green and blue bars) shows the

haplotype structure for one scanned line or parental strain. The bar for each parental strain is in

one colour throughout the horizontal bar (green for A and red for B). Some of the 23 RI lines

may also show one colour throughout the horizontal bar (green if they inherited this segment

from the A parent and red if from B). Other RI lines may have interlacing intervals inherited

from the A parental strain in green horizontal segments, and segments inherited from the B

parental strain in red. Between the green and red segments are segments in blue (to denote

unknown parental origin). The horizontal bars for all 25 lines and parental strains that were used

in my scans are stacked on top of each other and ordered by incremental ascending trait levels,

facilitating comparisons of the haplotypic structure across all RI panel. This feature enabled me

to identify RI lines whose haplotypes at the peak of the QTLs did not match their phenotypic

level, justifying excluding them from the interval analysis for that QTL, thereby refining peak

location, the values of the LRS and the confidence level as determined by the bootstrap test.

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2.1.3.4 Additive Effect: This software option maps in red and green lines the additive effect,

estimating the change in the phenotype level that would result from substituting the allele (SNP)

originating from one parent with the SNP of the other parent. I used this test in my analysis.

2.1.3.5 Gene Track: This feature lists known genes in the chromosomal region of interest. I

used this feature in my analysis.

2.1.3.6 Variant Browser: This tool shows mismatching sequences at a SNP level between

selected strains (in my case – A and B) across an interval of interest. In addition, the browser

indicates whether a mismatching sequence is located in exons, introns, 3‘UTR or 5‘UTR

(regulatory regions) or intergenic regions, splice variants and indels.

2.1.4 Heritability (h2): The ‗narrow-sense heritability‘ (h

2) levels of a few autotomy traits were

carried out. h2 refers to the proportion of the variation in a trait across the RI lines and their

parental strains that is attributable to genetic variation among individuals in the population (i.e.,

the RI panel; Hegmann and Possidente, 1981). Heritability can also be considered the proportion

of the trait variance that is attributable to genetic control. It is specific to a particular collection of

strains and for a given set of environmental conditions in which the traits were studied. An

estimate of h2 can be obtained by comparing the between-line variance to the total trait variance.

Being inbred, all mice in each line of the AXB-BXA panel are isogenic (i.e., genetically

identical), therefore, between-line variance provides an approximate measure of the additive

genetic variation (VARG), whereas within-line variance represents the non-genetic,

environmental variability (VARE). Thus, h2 = 0.5VARG / (0.5VARG+VARE). Since RI lines are

all homozygous and the heterozygotes are missing, a factor of 0.5 is applied to adjust for the 2X

overestimation in the additive genetic variance among inbred strains (Chesler et al., 2004). I

applied this method to a few autotomy traits.

2.1.5 Number of Effective Genetic Loci (EGL): The apparent number of effective genetic loci

(‗EGLs‘) controlling each trait can also be estimated from the phenotypic data collected in this

project. The significance in knowing the number of EGLs is in providing us an estimate of how

many QTLs having a major effect size should we expect to identify. Assuming that each EGL is

23

unlinked and exerts equal and additive effects on the trait variance (with no epistatic interactions

– which is clearly not the case in our trait where we found interactions between Pain1 and a QTL

on chr 14, see Results), then EGL = [(highest strain mean) – (lowest strain mean)] 2

/(4 x 0.5 x

VARG). As described above, the genetic variance of RI lines needs to be corrected by a factor of

0.5 (Hegmann and Possidente, 1981).

2.1.6 Correlation Analysis: Spearman correlation coefficients and significance of the

correlation between autotomy traits were computed implementing SPSS (ver. 16.0).

2.2. Microarray Gene Expression Profiling of 26 Candidate Genes

The in-silico experiments (see Results) confirmed the existence of Pain1 on chr 15, enabling me

to determine the position of the peak of Pain1, and listing 80 genes in the confidence length of

the new QTL location, and to further shortlist 26 candidate genes out of the 80 candidates having

sequence mismatches between the A and B strains. In order to narrow down this list even more, I

performed a microarray expression profiling experiment on two neural structures of the A and B

parental strains. This was part of a larger study that was carried out in my lab, which used

expression microarrays to screen the whole genome.

2.2.1 Animal Experiments: were performed on 44 male A/J and 44 male C57BL/6J mice, 8-9

weeks old. Experimental procedures were approved by the Institutional Animal Care and Use

Committee of University of Toronto, and followed the standards of humane treatment of

laboratory animals set out by the International Association for the Study of Pain (IASP),

National Institute of Health (NIH) and the province of Ontario. Mice were maintained under

standard colony conditions, four caged together. Dry food pellets and water were available ad

libitum. The day: night cycle was 12 h: 12 h (7:00 lights on; 19:00 off); temperature was

maintained at 22–26oC.

2.2.2. Surgery: Surgery to produce the Neuroma Model (Wall et al., 1979) was done by Dr. Shi-

Hong Zhang (a colleague in my lab) a few months before I joined the lab. Animals (only male

subjects) were anaesthetized with halothane 4% for induction and 2% for maintenance of

anesthesia. Surgery was performed under aseptic precautions; the sciatic nerve was exposed low

24

in the popliteal fossa through an incision in the lateral thigh. Near the point at which the common

sciatic nerve separates into its 3 tributary branches serving the lower leg: the tibial, common

peroneal and sural nerves, the sciatic was tightly ligated with a 5–0 silk thread and cut about 1

mm distal to the ligature. About 5 mm of the distal nerve stump was excised to further impede

regeneration. The incision was closed in layers using 4–0 silk and stainless steel wound clips

which were removed on day 10 postoperatively. The saphenous nerve was then exposed on the

same side through a skin incision on the medial thigh, ligated in a 5–0 silk thread, and cut with 5

mm of the distal stump excised. The skin was closed with clips. Surgery was performed on the

left side of 24 A and 24 B mice resulting in total denervation of the hindpaw in these two groups

(Denervated Groups). Sham operation included only cutting and sewing back the muscles and

stapling the skin with michel metal clips at the exact same anatomical region as in the denervated

mice, but keeping the nerve intact. This was performed on the left side of 12 A and 12 B mice.

Eight intact animals from each strain were used as a control group for each strain. After surgery

the mice were placed in a thermostatic chamber (heated to 30oC) to recover from the anaesthesia.

Then, the animals were returned to their cages in their original cage groupings, four from the

same strain caged together.

2.2.3 Phenotyping: Dr. Shi-Hong Zhang also scored the autotomy levels (Wall et al. 1979),

using the following system. Within the first few days after surgery, the completeness of the

denervation procedure was verified by pinching the foot and toes in awake mice, to confirm that

the stimulus elicited no nocifensive withdrawal response. Scoring of autotomy levels was done

on a daily basis using the following system: a single point was assigned for loss of one or more

toe nails, and an additional point was tallied for injury or removal of the distal or proximal half

of each digit, for a total possible score of 11. Following the University of Toronto ethics

protocol, as soon as the animals reached an autotomy score of 11 they were euthanized promptly

by deep anaesthesia, then perfused with RNAlater and tissues of interest (i.e., ipsilateral dorsal

root ganglia and spinal cord) were extracted. All other animals were sacrificed on day 14

postoperatively and their autotomy score on day 14 was used as their final score.

2.2.4 Perfusion: All animals were deeply anaesthetized by Dr. Shi-Hong Zhang in the following

manner. The animals were first deeply anesthetized with an intraperitoneal injection of Urethane

25

(1.5 mg/kg). The thoracic cavity was then opened and the ascending aorta was canulated with a

25-gauge blunt tip needle and clamped by a hemostat to the heart. The right atrium was cut.

Perfusion–fixation was performed manually using two sterile 30cc syringes connected to a 3-way

stopcock. First, the vascular tree was flushed with approximately 10 cc of diethyl pyrocarbonate

(DEPC)-treated normal saline. DEPC treatment was used to limit the possibility of exogenous

RNAase from entering vascular beds. RNAlater (25 cc; Ambion, Austin, TX) was then injected

into the vascular system to fix the neural tissues and stabilize RNA by perfusion. The manual

pressure was gauged by the flow of the efflux of fluid from the right atrium. Progressively

increasing manual force was required during the process of injecting RNAlater into the vascular

tree (LeDoux et al., 2006).

2.2.5 Tissue Extraction: Under direct vision using a surgical microscope at 25X magnification

and dribbling RNAlater during the entire procedure, spinal cord (L3-L6) and DRGs (L3-L6) of the

mice were dissected and immediately transferred to tubes containing RNAlater. RNAlater is a

stabilization reagent which immediately stabilizes RNA in tissue samples for further gene

expression profiling. For intact animals, both left and right DRGs (L3-L6) and spinal cord were

dissected and used for RNA extraction. For sham and denervated animals, only DRGs from the

left operated side and the left half of the spinal cord were used for RNA extraction, in order to

maximize the chances of recording changes in gene expression regulated by the denervation.

Extracted tissues were stored in RNAlater and stored in –20°C for future RNA extraction.

Samples stored in RNAlater solution at –20°C preserve the integrity of RNA for extended

periods (LeDoux et al., 2006).

2.2.5 Group Selection for Expression Profiling: For RNA extraction I randomly selected from

the perfused mice 8 intact mice per strain, 12 sham operated mice/strain and 24 denervated

mice/strain. As expected, intact and sham groups of both strains, as well as denervated B mice,

showed no autotomy. The denervated A mice showed high autotomy scores of 8-11, moderate

score of 3-7, or low/no autotomy scores of 0-2; 56% of the denervated A mice showed moderate-

to-high scores and 44% showed no-or-low scores of autotomy.

26

2.2.7 RNA Extraction: The following part of the study was done with another graduate student

in the lab (Merav Yarkoni-Abitbul). While I used these samples for expression profiling of a

select group of candidate genes on Pain1, she used the same samples for a whole genome gene

expression profiling. DRGs and spinal cord tissues were disrupted and lysed with GITC-

containing buffer, buffer RLT (Qiagen) using the disposable blue polypropylene pellet pestle

(Sigma). The samples were homogenized using a syringe and a needle (20-gauge). High-

molecular weight DNA was sheared by passing the lysate though the needle at least 20 times or

until a homogeneous lysate was achieved. Since the RNeasy Micro Kit (Qiagen) is designed for

purification of up to 45 μg RNA from small cell and tissue samples, I used this kit for the

extraction of RNA from the DRGs. Briefly, ethanol was added to the samples to adjust binding

conditions, and then applied to RNeasy MinElute Spin Columns for adsorption of RNA to

membrane. Contaminants were removed with simple wash steps, and RNA was eluted from the

column with RNase-free water. The spinal cord was homogenized in QIAzol (Qiagen) using a

rotor/stator type tissue homogenizer (Tissue-Tearor; Biospec Products). For the extraction of

RNA from the spinal cord, I used the RNeasy mini kit (Qiagen). Briefly, Chloroform was added

to the samples to separate the phases of the lysate, and then ethanol was added to the aqueous

phase to adjust binding conditions. Samples were applied to RNeasy Spin Columns for

adsorption of RNA to membrane. Contaminants were removed with simple wash spins of buffers

RW1 and RPE, and RNA was eluted from column with RNase-free water. RNA purity and

concentration were confirmed from OD 260/280 readings on a dual beam UV

spectrophotometer, then by running the samples on 1% agarose gel. RNA integrity was then

determined by capillary electrophoresis using the RNA 6000 Nano LabChip and the Agilent

Bioanalyzer 2100. RNA samples were then stored at -80°C for further analysis.

Separate microarrays included total RNA from individual animals, that is, one array included

material from the ipsilateral L3-L6 DRGs and another array for the same animal‘s ipsilateral L3-

L6 spinal cord. No pooling of material from more than one animal was used per microarray, 5

animals were included for each group (5 biological replicates per group in the form of ‗one array

per one mouse‘). The average (±sem) of these five replicates was considered the expression level

for that group. Thus, compared groups for the B strain were: intact, sham operated, and

denervated (all of which expressed no/low autotomy levels), and for the A strain I compared

intact, sham operated, and 5 denervated mice expressing high levels (60 arrays altogether, 30 for

27

the DRGs and 30 for the spinal cord). Note that the selection of DRGs and spinal cord as source

tissues for gene expression analysis was based on the fact that DRGs and spinal cord are the two

earlier ―stations‖ of neurotransmission and processing of pain input.

2.2.8 Gene Expression Protocol: The 60 RNA samples (30 DRGs and 30 spinal cords) were

hybridized to the microarrays, by having the complementary RNA (cRNA) amplified. Labeled

cRNA was synthesized using double-stranded cDNA (Quick Amp Labeling protocol, Agilent):

first and second strand cDNA was synthesized from 200 ng (or more) total RNA, using an

MMLV-RT oligo dT promoter primer containing a T7 RNA polymerase promoter site (Agilent

Quick Amp Kit, Two-Color); the cRNA was synthesized and labeled with Cy5-CTP or Cy3-CTP

NPTs by in vitro transcription using T7 promoter-coupled double stranded cDNA as template.

The amplified cRNA product was purified using an RNeasy mini-spin column (Qiagen) with RPE

buffer. The yield of the in vitro transcription reaction was determined by product absorbance at

260 nm, measured using the NanoDrop ND-1000 UV-VIS Spectrophotometer (Agilent, version

3.2.1). This part of the experiment was outsourced to the University Health Network Microarray

Centre. Following labeling samples were hybridized to the two-color Agilent 4x44K gene chip

using the Agilent Gene Expression Hybridization Kit (65ºC, 17 hrs, 10 rpm). Arrays were washed

and scanned immediately to minimize the impact of environmental oxidants on signal intensity

(GenePix 4000B scanner; Costigan et al., 2002). The fluorescence intensity in each spot was

scanned and translated into a numerical value conveyed in a tabulated form. Photographed

microarrays were extracted to images using Agilent Feature Extraction Software, producing

output files of the ‗*tiff‘ format, as archives. Analysis of the expression levels of the 26 candidate

genes was done in my lab using the Partek Genomic Suite software version 6.4, by David

Tichauer, a biostatistician. The normalized log10 ratio of the red signal (assayed gene) / green

signal (reference mouse gene) was used for statistical analysis. The Partek software normalizes

the numerical values of the scanned spots, taking into consideration the day the scans were done,

since day-to-day variations in the hybridization as well as the operation of the scanner may affect

the intensity of the scanned probes. Each array included control probes expected to result in

known scanned intensity. Comparing these spots across scanning days facilitated this

normalization.

28

In the Data Analysis, I used ANOVA followed by Tukey‘s post hoc test to compare group

averages (intact, sham-operated, and denervated A and B strains, for a total of 6 group

comparisons); each group had data from 5 mice. No correction of the alpha level was done to

compensate for multiple tests. A value of P=0.05 was considered the threshold of significance for

all tests. Note that in some genetic studies data is presented with or without a correction of the

alpha level. In the current study, I also provided both data, i.e., with and without a Bonferroni

correction.

29

3. Results:

3.1 Remapping Pain1 on Mouse chr 15

To test the possibility that the new genetic map for AXB-BXA would refine the position of

Pain1, I first used the original autotomy values used by Seltzer et al. (2001) and re-mapped this

trait to chromosome 15 using the new genetic map for these mice lines. In the original study, the

phenotype that was selected for mapping was the percent of mice from each line that showed an

incidence of autotomy score of 2 or more (INC_2; Appendix 1). However, in addition to this

phenotype I also mapped six additional phenotypes of autotomy that were not included in the

original paper on Pain1 or were studied elsewhere before. These included the autotomy

incidence at the end of the follow up period (day 36 postoperative) of scores 1, 3, and 5 (INC_1,

INC_3, and INC_5, respectively). The rationale for trying incrementing levels of incidence of

autotomy is that it is possible that the gene(s) in Pain1 have an increasing importance on

controlling higher levels of autotomy. In addition, I also tested the line average onset day of

autotomy scores 1, 3, and 5 (AOD_1, AOD_3, and AOD_5, respectively). The reason for

including these traits is that it is possible that variability in the onset day of autotomy may be

encoded by different genes than those controlling the variability in the incidence of autotomy,

and that therefore; Pain1 might play a more important role in the temporal aspects of autotomy.

In such a case, the map of Pain1 for the onset of autotomy may unravel a higher, narrower, and

more significant peak. The last trait I tested was the average autotomy score on the last day of

the experiment (AS_D36; Appendix 1). Table 3 shows the Spearman correlation coefficients and

significance of the correlation between these traits. The Table shows that all traits are

significantly and highly correlated. INC_1 and AOD_1 yielded smaller coefficients than the

corresponding traits for higher autotomy scores. All autotomy incidence traits were reciprocally

correlated with all autotomy onset traits.

Interval mapping was done for the whole genome, and then, a specific focus was made to

one of the chromosomes. Likewise, during the interval mapping of the different autotomy traits

for all chromosomes, it became evident that in addition to chromosome 15 there are QTLs on

other chromosomes such as 14, 13, and 7, which most likely harbour genes associated with

autotomy, however, my main focus remained chromosome 15 and Pain1. The interest in

identifying genes in other QTLs for autotomy became the subject matter of other colleagues in

30

my lab, however, to better understand the epistatic interaction between Pain1 and other genetic

loci, I also made reference to QTLs on chromosome 14 (see Results below).

Table 3: Correlation coefficients and significance level of the correlations between the autotomy

traits. This table shows that autotomy traits are highly correlated and may be considered as one

trait. Autotomy phenotypes include AOD1, AOD2 and AOD3 representing average onset day of

autotomy scores 1, 2 and 3. INC_1, INC_2, INC_3, INC_5 represent percent of mice expressing

autotomy scores of at least 1, of at least 2, of at least 3 or of at least 5 respectively. AS_36

represents average autotomy score on day 36 for each line

INC_1 INC_2 INC_3 INC_5 AOD_1 AOD_2 AOD_3 AOD_5

Corr. Coeff.

INC_2 0.58

Sign. 0.0023

Corr. Coeff.

INC_3 0.54 0.98

Sign. 0.005 0.000

Corr. Coeff.

INC_5 0.53 0.95 0.98

Sign. 0.007 0.000 0.000

Corr. Coeff.

AOD_1 -0.94 -0.73 -0.67 -0.64

Sign. 0.000 0.000 0.000 0.001

Corr. Coeff.

AOD_2 -0.55 -0.87 -0.88 -0.86 0.65

Sign. 0.004 0.000 0.000 0.000 0.000

Corr. Coeff.

AOD_3 -0.55 -0.97 -0.99 -0.99 0.67 0.88

Sign. 0.005 0.000 0.000 0.000 0.000 0.000

Corr. Coeff.

AOD_5 -0.52 -0.94 -0.97 -0.99 0.63 0.86 0.99

Sign. 0.008 0.000 0.000 0.000 0.001 0.000 0.000

Corr. Coeff.

AS_d36 0.58 0.95 0.96 0.98 -0.69 -0.83 -0.98 -0.99

Sign. 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000

31

3.1.1 General Methodological Considerations:

The optimal LDP for mapping QTLs is one that has the following characteristics:

(i) The parental lines show a significant contrast in the phenotype. A good example meeting this

criterion is shown in Figure 2 for INC_2. This is not a surprising finding, however, since this

contrast is the basis of my project

Figure 2 LDP of INC_2. The X axis shows the name of the RI lines and the Y axis shows INC_2

(Percent of mice expressing autotomy score of at least 2).The names of the lines are abbreviated

(e.g., ‗BXA2‘ denotes an RI line produced by crossing a B male with an A female. The number 2

is a serial number of this line. In this histogram and similar ones below ‗BXA2‘ is abbreviated as

‗B02‘, and other lines are abbreviated similarly). The coloured triangles denote the parental lines,

red for B, and green for A.

RI lines + Parental lines

(ii) The autotmy values of the other RI lines (between the parental lines) produce an LDP that

makes full use of the dynamic range of the measured phenotype. For example, in the trait

‗average autotomy score on the last day of the experiment‘ (AS_D36; Figure 17), some lines

(BXA-2 and AXB-18) expressed no autotomy at all (i.e., AS_D36 = 0) whereas BXA-13

expressed the highest possible score of 11. The ratio between the line having the maximal value

and that of the lines showing the minimal value cannot be calculated since the latter had a value

of 0. But if arbitrarily assigned the value of AS_D36 = 0.1, the ratio is 110.

(iii) The autotmy values of the other RI lines in the panel gradually increase from the minimal to

the maximal score. The more diversity in the panel, the higher informative capacity it has for

% m

ice w

ith

sco

res ≥

2

32

QTL mapping. The diagonal dashed line depicted in Figure 1 illustrates this criterion; the closer

to this diagonal, the better. However, as seen in Figure 2, and in other traits depicted below, all

lines (other than the extremes) exhibit considerably lower (as in Figure 2) or higher values (e.g.,

Figure 13) than the diagonal, indicating dominant effects for low autotomy.

The ―better‖ the LDP dataset - the sharper the peak on the QTL map. As described above,

a better LDP is such that many lines contribute to the mapping capacity of a specific QTL.

Having an LDP where many lines show saturation values (either low or high) leaves fewer lines

with gradual phenotypic levels to cover the dynamic range of the measured trait. In such a case,

elimination of some informative lines (i.e., those showing non-saturated values) during the re-

sampling runs (i.e., the bootstraps) might have a critical effect on the interval mapping outcome,

resulting in a chromosomal region where the QTL map is ‗smeared‘ over a large region, with a

low regional peak.

3.1.2 Remapping INC_2: First I used the new genetic SNP-based map to replicate the map of

Pain1 on chromosome 15 by using the same data produced by Seltzer et al. in 2001. Figure 2

shows the LDP for the trait ‗% mice expressing autotomy score ≥2 on day 36‘ (INC_2), using the

same values that were used by Seltzer et al. (2001), with a good contrast between the parental

lines, but not a good gradual increase across the RI lines, with autotomy incidence of scores ≥2

being lower than the diagonal for all lines except BXA-13. The ratio between the value of the

line having the maximal value (BXA-13=100%) and that of the lines showing the minimal value

(0%) cannot be calculated (due to division by zero), but if the latter are assigned a value of 0.1,

the ratio is 1000. This LDP was fed to the interval mapping software, resulting in the following

map for the whole genome (Figure 3). This is done 1000 times, each time keeping track of the

location of those marker loci that yielded the highest LRS scores. These loci are then

accumulated to produce the height of the yellow bars in a histogram form for the best loci where

peak scores were found. The units of this histogram (shown on the right side axis) is the % out of

1000 reiterative maps, such that, for example, a frequency of 16.7% (the highest peak in Figure

3) means that in 167 out of 1000 interval reiterations there was a peak score at this location on

chromosome 15.

The suggestive QTLs on chromosome 14 were not published before, and more work is

needed to substantiate their link with autotomy. Focusing at a higher magnification on

33

chromosome 15, in the region of Pain1, Figure 4 shows a map only for chromosome 15, with

two QTLs, a smaller one near the end of the chromosome and the bigger one at the same position

as Pain1. This result replicates the findings of Seltzer et al., (2001) and Darvasi et al., (2005).

Figure 3: Whole genome map for INC_2 showing 3 suggestive QTLs, 2 on chromosome 14, and

1 on chromosome 15. LRS is the Likelihood Ratio Statistic. The plots in red and green show the

additive effect, estimating the change in the phenotype level that would result from substituting

the allele (SNP) originating from one parent with the SNP

of the other parent. The histogram in yellow ) is superimposed on the genetic map to show the

loci showing the highest percent out of 1,000 bootstrap resamples in which they were linked with

the highest LRS. 3

Suggestive

Chr Number

Significant

34

Figure 4: Physical map of QTLs on chromosome 15 for INC_2 showing a suggestive QTL at the

region of Pain1. The X axis shows the physical location of the QTL on mouse chr 15 in

megabases. The Y axis shows the Likelihood Statistic Ratio (LRS) which is the significance of

the linkage between the QTL and the phenotype. The two horizontal lines in pink and blue

indicate the significant and suggestive level of linkage, respectively. The green and red plots

indicate the parental genotypic contribution to the trait.

The green line in Figure 4 indicates that A/J alleles are associated with increasing

incidence of autotomy scores ≥2. The position of the new peak for Pain1 is between SNP

rs8259436 (located at 74,677,202 bp) and SNP CEL-15_75758067 (located at 75,276,094 bp).

This is very close to the original Pain1 peak position that was at marker D15Mit28 (located at

34.29 cM, 74,745,784 – 74,745,947 bp), flanked on one side by the microsatellite marker

D15Mit156 (located at 32.19 cM, 71,155,976 -71,156,119 Bp) and on the other side by the

marker Ly6a (located at 34.29 cM, 74,825,307-74,828,318 Bp), spanning 2.1 cM, 3.7 Mb.

3.1.3 Mapping INC_1, INC_3 and INC_5: While reaffirming the existence of an autotomy

QTL in the Pain1 region using the new marker panel, the new map was still far from confining

its confidence length to justify sequencing it in an attempt to identify the gene(s) controlling the

trait. In order to further refine this QTL, and assess whether Pain1 also controls other autotomy

traits, I proceeded with mapping chromosome 15 for other traits related to the incidence of

Suggestive

Significant

35

autotomy. Figures 5-7 show LDP for INC_1, INC_3, and INC_5. The X axis shows the name of

the RI lines and the Y axis shows percent of mice expressing autotomy score of at least 1, 3 and

5 respectively. As shown in these figures, compared to the LDP for INC_1, fewer lines expressed

autotomy scores ≥3 and even fewer had scores ≥5, resulting in LDPs that became more and more

stumped towards an incidence level of 0 for the incidence of higher and higher autotomy scores.

These Figures also show that the rank order of the A and B parental lines amongst the RI lines

changed dramatically from showing no contrast between the parental lines in INC_1, located

juxtaposed in its LDP, to a considerable contrast between these lines in the INC-2, INC_3 and

INC_5 LDPs, that results in positioning them further from each other in their corresponding

LDPs, with a maximum in INC_2. The ratio between the value of the line having the maximal

value (BXA13) and that of the lines showing the minimal value cannot be calculated since the

latter had INC=0%, but if arbitrarily assigning them a value of 0.1, the ratio is 1000.

36

Figures 5-7: Line distribution pattern (LDP) of INC_1, INC_3, and INC_5.

Figure 5: LDP of INC_1.

Figure 6: LDP of INC_3.

Figure 7: LDP of INC_5.

RI + Parental lines

% m

ice w

ith

sco

res ≥

1

% m

ice w

ith

sco

res ≥

5

% m

ice w

ith

sco

res ≥

3

R I + Parental lines

37

These LDPs were then fed to the interval mapping software, resulting in the maps in

Figures 8-10. The map in Figure 8 shows no significant or suggestive QTLs, possibly due to lack

of contrast between the parental lines for INC_1.

Figure 8: Interval physical map of chromosome 15 for INC_1.

Figure 9: Interval physical map of chromosome 15 for INC_3.

Figure 10: Interval physical map of chromosome 15 for INC_5.

Suggestive

Suggestive

Suggestive

38

The maps for INC_3 (Figure 9) and INC_5 (Figure 10) shows Pain1 at just below the

―suggestive‖ level, but since it is located in the same coordinates as the QTL on INC_2, one is

allowed to take it as a confirmation of the latter QTL.

A possible interpretation of no peak on chr 15 for INC_1 (Figure 8) is that autotomy

score of 1 (i.e., the removal of 2 nails) corresponding to an incidence of scores lower than 2, is

not related to pain but to the neglect of an anesthetic body part that got injured accidentally

because of lack of sensory inputs. As a result, the insensate nails get trapped and injured in a

manner that is not related to the intentional attempt of the animal to remove the nails because of

disagreeable sensations referred to the nails.

3.1.4 Mapping AOD_1, AOD_3, and AOD_5: Next, I mapped QTLs for the average line onset

day of specific scores of autotomy. Figure 11 shows the LDP of the Average Onset Day of

Autotomy Score 1 for the 23 RI lines and the parental lines. There is high variability across the

lines and a good gradient for mapping QTLs, BXA-2 and AXB-18 showed no autotomy till the

very last day of experiment (day 36), whereas other lines (such as BXA-13, BXA-11) started

expressing autotomy behaviour very early following the denervation. The ratio between the

maximal and minimal AOD_1 values for these extreme lines was 8. But the difference between

the A and B strains was not significant.

Figure 11: LDP of AOD_1

RI and Parental lines

RI + Parental lines

Avera

ge O

ns

et

Day o

f sc

ore

1

39

The LDP in Figure 11 shows values gradually incrementing very close to the diagonal,

connecting the minimal possible value of AOD_1=0 and maximal possible value of AOD_1=36,

indicating that this trait is a polygenic trait, but these data cannot suggest whether it is a

dominant or recessive phenotype for early or late autotomy onsets. As previously mentioned, an

optimal LDP should show a significant contrast between the parental lines which is not the case

for this trait (AOD_1), suggesting that it might not be optimal for interval mapping of QTLs.

Indeed, the map of chromosome 15 in Figure 12 shows no significant or suggestive QTLs for

this trait, concluding that a genetic locus at Pain1 is not controlling the variation across the lines

in the onset of autotomy score 1.

Figure 12: Interval map of chromosome 15 for AOD_1.

In contrast to AOD_1, Figures 13 and 14 show the LDP for AOD_3 and the QTL map for

chromosome 15, harbouring a nearly suggestive QTL on chromosome 15 for AOD_3.

Figure 13: LDP of AOD_3.

Suggestive

Avera

ge O

ns

et

Day o

f sc

ore

3

RI + Parental lines

40

Figure 14: Interval map of chromosome 15 for AOD_3.

This suggests that the genetic locus in Pain1 controls both the incidence and onset day of

autotomy. Figures 15 and 16 show the LDP and physical map for AOD_5, with considerably less

variability across the lines, since the lines on the right side of the LDP reached saturation for this

trait, limiting the contribution of this trait to further mapping.

Figure 15: LDP of AOD_5.

Suggestive

Avera

ge O

ns

et

Day o

f sc

ore

5

RI + Parental lines

41

Figure 16: Interval map of chromosome 15 for AOD_5.

The AOD maps show that the genotypes at Pain1 contributing to this QTL (marked by

the red line) originated from the B line, in contrast to the line contributing to the INC_2, INC_3,

and INC_5, where the contributing genotype was the A line. These traits are set at diametrically

opposing directions that are reciprocal to each other, such that low autotomy scores and a

delayed onset are associated with higher AOD values, yet with low INC values. This explains

the reversal in genetic contribution to the QTL.

3.1.5 Mapping AOD_AS_D36:

Figure 17: LDP of the average (±SEM bars) autotomy scores on day 36 PO (AS_D36) for the 23

AXB-BXA lines and their parental lines A and B, showing a good contrast between the two

parental lines (filled triangles), yet saturated at the low AS_D36 values. The exponential trend

line shows a deviation from the diagonal and dominance of the low autotomy trait.

Suggestive

42

Figure 18 shows the interval map of this trait, with a low and insignificant QTL at Pain1 region.

Figure 18: Interval map of chromosome 15 for AS_D36.

Now that all autotomy traits have been mapped, Table 4 summarizes the data

characterizing the position of Pain1 on chromosome 15, including the peak location (in Mb), and

the interval length (in Mb) for all mapped traits. The Table also provides the LRS values

associated with this QTL, as well as the relevant data from previous maps of Pain1. As seen in

the Table, neither mapping the additional autotomy traits nor using the detailed SNP-based

marker panel, helped in refining the confidence length for Pain1, and reached only a suggestive

level of significance.

43

Table 4: Position and significance level of Pain1 for all autotomy traits.

SOURCE

Autotomy trait Location (Mb) Significance level

TYPE Score Downstream

end Peak

Upstream

end

Confidence

lengthb

LRSa P

Elahipanah

et al.

(this study)

Incidence of

certain

scores on

day 36 PO

INC_1 NAc NA NA NA NA NA

INC_2 57.7 74.6 88.0 NA 9.0 Less than

suggestived

INC_3 57.7 74.6 88.0 NA 10.0 Less than

suggestive

INC_5 63.1 74.6 88.0 NA 7.5 Less than

suggestive

Average

onset day

of a certain

score

AOD_1 NA NA NA NA NA Less than

suggestive

AOD_3 57.7 74.6 88.0 NA 9.5 Less than

suggestive

AOD_5 63.1 74.6 88.0 NA 7.0 Less than

suggestive

Average

score

on day 36

PO

AS_36 NA NA NA NA NA Less than

suggestive

Average 59.5 74.6 88.0 NA NA

+/- SEM 1.1 0 0

NA

Seltzer et al.

2001 Incidence INC_2 55.1 74.7 89.0 34.0 Χ

2=18 p=0.0003

Darvasi et al.

2005

Scores 0-1 vs 9-11 on

d35PO ~20cM ~38cM ~70cM ~50cM LOD=3.1 (LRS=14.3)

a LRS values <7.0 were considered too low and confidence length and therefore, peak position for this QTL were not recorded.

b Confidence intervals could not be determined for the QTLs only gaining a suggestive level of significance.

cNA = not applicable.

dSuggestive at P=0.63.

44

Figure 19: Chr. 15 from 64 – 91.5 Mb, an interval including Pain1 for INC_3 (Panel B) and

haplotypic structure for Pain1 (Panel A). This map includes BXA13, and shows a peak that

reaches the suggestive level. The deflections of the blue line in Panel B correspond to the

haplotypic structure in Panel A. Black arrows connect certain parts of the haplotypes in Panel A

of the RI lines that help understanding how the QTL map in Panel B was constructed.

At the right in Panel A we have the name of the RI lines. The horizontal lines in green and red

and blue indicate the parental origin of chromosomal segments for each RI line. Green shows the

parental origin is A, red shows the parental origon is B and blue shows the parental origin is

unknown.

Panel A

Panel B

Suggestive Suggestive

45

Next, I considered the haplotypic structure of the Pain1 QTL region, by using the INC_3

phenotype, which was linked with the highest LRS amongst all autotomy traits. As mentioned

above, the high autotomy trait is ―driven‖ by a genotype at Pain1 inherited from the A parent

(Figure 19). Thus, if Pain1 is the only genetic locus driving the incidence of autotomy scores 3

and higher, I expect that only lines carrying the A genotypes at the peak of Pain1 should express

high autotomy. Figure 19 shows in Panel B the Pain1 region. The inset Table in the right side of

panel A lists the RI lines and shows the % INC_3 for each line. Those boxed in the solid line

showed high trait levels (INC_3 ranging from 16.7% - 100%) whereas those in the dashed line

have low trait levels (ranging from 0 – 12.5%). Underneath these haplotypes are the name and

position of the SNPs with which the map was constructed. This haplotypic structure shows that

contrary to our initial expectation, two lines, BXA13 and AXB13/14 (red arrows in the inset

Table) carry the B parent genotype on Pain1 (shown as red bars) but present high levels of

INC_3 (100% and 33% , respectively). In addition, BXA13 has the shortest average onset day of

all RI lines (day 6.86±1.56 PO; data not shown in this Figure). These values should be

compatible with carrying the A genotypes and phenotypes, which drive the high autotomy trait.

However, as seen in this Figure, BXA13 inherited the B genotype in the Pain1 QTL (with an

exception of a small interval upstream Pain1 between 87.5 and 90.5 Mb which does not

correspond to the peak of Pain1). This suggests that genotypes in Pain1 do not control autotomy

expression in BXA13. Moreover, including these two lines in the interval mapping of Pain1 is

likely to introduce noise that interferes with mapping this QTL. Indeed, Figure 20 shows that

excluding the data for these lines from the LDP for INC_3 more than doubled the LRS size,

increasing the significance level from just barely ―suggestive‖ significance level (at an LRS=9.5)

to a robustly significance level of 22.7 (LOD=4.9), that surpassed the threshold of significance of

16.6 by 6.1 LRS units. The yellow bars of the bootstrap histogram show a convincing peak of

69.7%.

46

Figure 20: INC_3 Interval map of Pain1 (excluding data for BXA13 and AXB13/14).

This highlights a number of findings: 1) The highly significant LRS of the peak on Pain1

strongly confirms the existence of gene(s) on the Pain1 QTL having a major effect on autotomy

levels. 2) Omission of the data of BXA13 and AXB13/14 increased the significance and refined

the peak, showing that the region between 73.25Mb and 76.32Mb reached the significant level of

LRS. (3) This procedure substantiates the possibility that Pain1 is not the only locus that controls

this phenotype.

Based on these results, I remapped all other autotomy traits (in addition to INC_3),

without the data of BXA13 and AXB13/14. All maps showed significant peaks at Pain1 that

were considerably higher than when these lines were included in the interval mapping (data not

shown). Since INC_3 showed the highest peak on Pain1 I continued the analyses with this

phenotypic dataset.

Since Pain1 did not explain the autotomy of BXA13 and AXB13/14, I considered the

possibility that one of the QTLs on chromosome 14 (or both of them), might better elucidate the

control of autotomy for these two lines by way of their epistatic interaction with Pain1. Figures

21 and 22 show the map for INC_3, including these 2 lines, for the whole genome (Figure 21)

and just for chromosome 14 (Figure 22), with a significant QTL on chromosome 14 spanning

Significant

Suggestive

47

from 99.15 – 99.75Mb. Since the name Pain2 is already occupied by a QTL reported for

autotomy in the rat on chromosome 2 (Nissenbaum et al., 2005), I named this QTL Pain3.

Figure 21: Whole genome interval map of INC_3 (including data for all lines).

Since RI lines showing high autotomy inherit this trait from parent A, they should either

inherit it from Pain1 or Pain3, or both, or from other loci on the genome. In the case of BXA13

and AXB13/14, the explanation for their high autotomy incidence may lie in Pain3. Next, just as

I excluded BXA13 and AXB13/14 from the map of Pain1, inspection of the haplotypic structure

of Pain3 showed 3 lines (AXB5, AXB6, and BXA14) that carry the B genotype (which is linked

with low incidence of autotomy yet these mice show high incidence of autotomy) and BXA25

which carries the A genotype (which is linked with high autotomy yet they showed low

autotomy). When excluding these 4 lines from the interval map of Pain3 the LRS shows a

dramatic increase from the barely significant LRS of 17.2 (when all lines were included, see

Figure 22) to the highly significant level of 31.2 (compared to the significance threshold of 16.5;

Figure 23) spanning from 99.15 Mb – 99.75Mb. The peak of the QTL is determined by the

haplotypic structure of a key line – AXB1 that carries at the peak the B genotypes.

Significant

Suggestive

Pain3

48

Figure 22: Interval map of INC_3 for chromosome 14 (including data for all lines).

Figure 23: Magnified interval map of INC_3 for chromosome 14 (Panel B) showing the haplotype structure for all lines excluding the

outliers for Pain3 (i.e. AXB5, AXB6, BXA14 and BXA25; Panel A).

Line (%)

Panel A

Panel B

Suggestive

Significant

Significant

Suggestive

49

Figure 24: Histograms showing the effect of carrying the A and B genotypes in Pain1 and

Pain3 for INC_3 for each RI line. Based on the expectation that the A genotype is linked with

high levels of INC_3 (light yellow box) and B genotype linked with low INC_3 levels (grey

box), some lines do not meet this expectation (red arrows). The INC_3 level of parent B is 8.3%.

The RI line having the next higher level of INC_3 is 12.5% of BXA11, which like parent B

carries the B genotype both on Pain1 and Pain3. This would suggest that an INC_3 = 12.5% is

to be regarded as ―low Incidence‖, like that of the B parent. The RI line having the next higher

level of INC_3 is AXB1, at 16.7%. Since, it is linked with an A genotype on Pain1 and a B

genotype for Pain3, this level of INC_3 was selected as just suprathreshold for an incidence of

autotomy. The red dashed line in these histograms represent this cutoff (i.e., INC_3=16.7%).

Table 5 summarizes the possible contributions of the genotypes on Pain 1 and Pain3 to explain

the levels of INC_3 for each RI line, highlighting two important rules. If we presume that all the

variance across the RI lines and the parental strains in the levels of INC_3 is attributed only to

genotypes at the Pain1 and Pain3 QTLs, then: 1) For a line to express low INC_3 level both

Pain1 and Pain3 should carry the B genotype. As seen in the Table, this is true for all lines

except line BXA25 that carries the A genotype on Pain3 and B genotype on Pain1 yet it has low

levels of INC_3. 2) For a line to express high levels of INC_3 it is sufficient to have at least

50

Pain1 or Pain3 carry the A genotype. Thus, carrying the A genotype on any of these QTLs has a

dominant effect on the expression of INC_3.

Table 5: Possible contribution of Pain 1 and Pain3 to INC_3 for each line.

RI Line

Phenotype Parental origin of the genotype at the QTL QTLs explaining INC_3

levels (INC_3) Pain3 (CHR-14) Pain1 (CHR-15)

AXB4 Low 0 B B Pain3 / Pain1 AXB8 Low 0 B B Pain3/Pain1 AXB10 Low 0 B B Pain3 / Pain1 AXB12 Low 0 B B Pain3 / Pain1 AXB15 Low 0 B Unknown, (Predicting B) Pain3 / Pain1 AXB18/19/20 Low 0 B Unknown, (Predicting B) Pain3 / Pain1 BXA1 Low 0 B B Pain3/Pain1 BXA2 Low 0 B B Pain3/Pain1 BXA4 Low 0 B B Pain3/Pain1 BXA7 Low 0 B B Pain3/Pain1 BXA12 Low 0 B B Pain3 / Pain1 BXA24 Low 0 B B Pain3 / Pain1 BXA25 Low 0 A (outlier for Pain3) B Pain1 (but Pain3 not) B Low 8.3 B B Pain3 / Pain1 BXA11 Low 12.5 B B Pain3 / Pain1 AXB1 High 16.7 B A Pain1 BXA14 High 25 B A Pain1 AXB13/14 High 33.3 A B Pain3 AXB5 High 37.5 B Unknown, (Predicting A) Pain1 AXB24 High 37.5 A A Pain3 / Pain1 AXB6 High 44.4 B A Pain1 AXB2 High 55.6 A A Pain3 / Pain1 A High 58.3 A A Pain3 / Pain1 BXA8/17 High 100 A A Pain3 / Pain1 BXA13 High 100 A B Pain3

For some of the lines shown in Figures 23 (AXB1, AXB5, AXB18/19/20), the haplotype

structure at the peak of the QTLs is depicted in blue, because the genotype at Pain1 is unknown,

and it could be either A or B. However, based on the two rules stipulated above I predict that for

AXB1 and AXB18/19/20 (having a low level of INC_3) the genotype of Pain1 should be B, and

for AXB5 (having a high level of INC_3) the genotype of Pain1 should be A. Nevertheless,

based on these two rules we still cannot explain the low autotomy of BXA25 (INC_3=0) where

Pain3 is an A genotype but if abiding rule 1 it should have carried the B genotype. This

51

discrepancy may suggest that for this line other loci may suppress the effect of carrying the A

genotype on Pain3. Indeed, chr 14 (e.g., Figure 3), and other chromosomes, harbours additional

smaller QTLs, which may have a minor effect in most RI lines, yet a major effect in BXA25.

3.1.6 Correlation of Autotomy with Other Traits

The AXB-BXA RI panel has been used by many investigators to study 169 traits other

than pain and autotomy. A significant correlation between the rank orders of two traits across

this panel could suggest shared genetic control. WebQTL contains an algorithm correlating a

trait under investigation (INC_3 in our case) and all other traits that ever used this panel. Our

panel included 25 lines (23 RIs and 2 parental strains), but for many of the other traits the

number of studied lines is smaller. The software only calculates the correlation for cases of N>5

lines). The resulting output values include the coefficient of correlation (r, ranging from -1 to 1),

p-value for the correlation, and r2 which ranges from 0-1 or 0-100% and explains the proportion

of the variance in one of the traits by the variance in the other trait. Table 6 shows a list of traits

that are significantly correlated with INC_3 at P<0.05.

Table 6: Correlation coefficients between various traits and autotomy INC_3 (including data on

BXA13 and AXB13/14), including source of the data used for the correlation and number of

shared strains.

Phenotype Authors Year Corr. Coeff. N p Value

Insulin sensitivity Surwit et al. 1991 -0.93 6 0.0042

Lung protein kinase C (PKC)-alpha Dwyer-Nield LD, et al. 2000 -0.62 18 0.0053

Nonsyndromic cleft lip Juriloff DM, et al. 2001 0.80 8 0.014

Production of cardiac myosin autoantibodies in Coxsackievirus B3-infected lines

Traystman MD, et al. 1991 0.61 14 0.018

Resistance/susceptibility to Listeria monocytes post Listeria infection in liver

Gervais F, et al. 1984 0.68 10 0.029

Mortality rate within 30 days of inoculation with 2X104 PFU MHV

Dindzans VJ, et al. 1986 -0.52 17 0.030

Proliferation of CFU 72 h after Listeria monocytogenes

Stevenson MM, et al. 1984 0.65 10 0.039

52

The traits listed in Table 6 seem to involve inflammatory and immune functions that may

be shared with autotomy. Many genes have a pleiotropic role whereby their product functions in

more than one system, metabolic pathway, cell type or tissue. Thus, it is possible that gene(s) in

Pain1 associated with autotomy, may have comparable roles in other tissues.

3.1.7 Heritability (h2) and Number of Effective Genetic Loci (EGL)

The next analysis was to calculate the ‗narrow-sense heritability‘ (h2) levels of some of

the autotomy traits. We calculated this parameter for AOD_3 and AS_D36. Using the equation

h2=0.5VARG/(0.5VARG+VARE) I found that h

2AOD_3 = 0.42 and for h

2AS_D36 = 0.35.

Calculations I made for the same autotomy traits showed that EGLAOD_3 = 7.92 and for

EGLAS_D36 = 6.1. This suggests that in addition to the gene(s) in Pain1 one should expect to find

a few more genes in other QTLs.

3.2 Identifying Candidate Autotomy Gene(s) in Pain1

In order to identify the candidate gene(s) in Pain1, I browsed WebQTL for the known genes in

the refined location of the significant confidence length of Pain1. Table 7 shows a list and

description of 80 annotated and hypothetical genes from 73.0 - 76.30 Mb and their location

(start, in Mb). As mentioned in the Introduction, my hypothesis was that the contrasting levels of

autotomy in the A and B strains is caused by a mismatch in the sequence or indels in coding and

regulatory regions of causative genes, that manifests in certain gene expression levels in the

DRG and/or the spinal cord of intact and/or denervated mice. Therefore, to shortlist the candidate

genes, in Table 7, I found no indels in any exon of these genes, however, Table 8 shows the ID

of 21 SNPs in 9 out of these 80 genes that have mismatching SNPs in coding regions, some with

a missense or a silent mutation, but none in splice sites. In addition, Table 9 shows the ID and

position of 41 SNPs in the 5‘UTR of 16 genes in this region (some are the same genes as in

Table 8), and Table 10 shows the same data for 70 SNPs found in the 3‘UTR regions in 14 of the

80 genes (again, some are the genes in Tables 8,9). In total, these SNPs are located in the

following 26 candidate genes: Ly6a, Ly6d, Ly6c1, Ly6c2, Ly6e, Ly6i, Ly6k, Lynx1, Rhpn1,

Plec1, Sharpin, Bop1, Ptp4a3, Bai1, Arc, Tsta3, Zfp623, Zfp707, Gpaa, Gm628, Naprt1, Eppk1,

BC024139, 2010109I03Rik, 9030619P08Rik, and 4930572J05Rik.

53

Table 7: List and description of genes located at the peak of Pain1, and position (start, in Mb).

N Gene Symbol Position (start, Mb)

Gene description

1 Ptk2 73.035534 PTK2 protein tyrosine kinase 2 2 LOC497255 73.253740 hypothetical LOC497255 3 Dennd3 73.342989 DENN/MADD domain containing 3 4 EG386506 73.411678 predicted gene, EG386506 5 Slc45a4 73.411986 solute carrier family 45, member 4

6 1700010B13Rik 73.476281 RIKEN cDNA 1700010B13 gene 7 Gpr20 73.525036 G protein-coupled receptor 20 8 Ptp4a3 73.578841 protein tyrosine phosphatase 4a3 9 Gm628 73.617365 gene model 628, (NCBI) 10 Bai1 74.346625 brain-specific angiogenesis inhibitor 1 11 1700016M24Rik 74.436458 RIKEN cDNA 1700016M24 gene

12 Arc 74.499512 activity regulated cytoskeletal-associated protein 13 Jrk 74.534992 jerky 14 4933427E11Rik 74.539780 RIKEN cDNA 4933427E11 gene 15 Psca 74.545268 prostate stem cell antigen 16 4930572J05Rik 74.551663 RIKEN cDNA 4930572J05 gene 17 Slurp1 74.557073 Slurp1 secreted Ly6/Plaur domain containing 1

18 Lypd2 74.562681 Ly6/Plaur domain containing 2 19 2300005B03Rik 74.573268 RIKEN cDNA 2300005B03 gene 20 Lynx1 74.578285 Ly6/neurotoxin 1 21 Ly6d 74.592485 Ly6d lymphocyte antigen 6 complex, locus D 22 D730001G18Rik 74.598204 RIKEN cDNA D730001G18 gene 23 Ly6k 74.627303 lymphocyte antigen 6 complex, locus K

24 Gml 74.643886 GPI anchored molecule like protein 25 Hemt1 74.649505 Hemt1 hematopoietic cell transcript 1 26 LOC382202 74.663200 similar to Cytochrome P450 11B1 27 Cyp11b1 74.665324 cytochrome P450, family 11 subfamily b, polypeptide 1 28 Cyp11b2 74.681439 cytochrome P450, family 11 subfamily b, polypeptide 2 29 2010109I03Rik 74.708765 RIKEN cDNA 2010109I03 gene

30 Ly6e 74.786082 lymphocyte antigen 6 complex, locus E 31 Ly6i 74.810331 lymphocyte antigen 6 complex, locus i 32 Ly6a 74.825306 lymphocyte antigen 6 complex, locus a 33 Ly6c1 74.875444 lymphocyte antigen 6 complex, locus c1 34 Ly6c2 74.938590 lymphocyte antigen 6 complex, locus c2 35 I830127L07Rik 74.961806 RIKEN cDNA I830127L07 gene

36 BC025446 75.047025 cDNA sequence BC025446 37 Ly6f 75.098850 lymphocyte antigen 6 complex, locus f 38 9030619P08Rik 75.258035 RIKEN cDNA 9030619P08 gene 39 Ly6h 75.395174 lymphocyte antigen 6 complex, locus h 40 Gpihbp1 75.427087 GPI-anchored HDL-binding protein 1 41 Zfp41 75.447114 zinc finger protein 41

42 2810039B14Rik 75.472558 RIKEN cDNA 2810039B14 gene 43 Top1mt 75.487462 DNA topoisomerase 1, mitochondrial 44 Rhpn1 75.534823 rhophilin, Rho GTPase binding protein 1 45 Mafa 75.577272 Mafa v-maf musculoaponeurotic fibrosarcoma oncogene family, protein A 46 Zc3h3 75.584876 zinc finger CCCH type containing 3 47 Gsdmd 75.692768 gasdermin D

48 Naprt1 75.721393 nicotinate phosphoribosyltransferase domain containing 1

54

49 Eef1d 75.725229 eukaryotic translation elongation factor 1 delta 50 Tigd5 75.740164 tigger transposable element derived 5

51 Pycrl 75.746892 pyrroline-5-carboxylate reductase-like 52 Tsta3 75.755112 tissue specific transplantation antigen P35B 53 Zfp623 75.771381 zinc finger protein 623 54 Zfp707 75.799614 zinc finger protein 707 55 2410075B13Rik 75.811106 RIKEN cDNA 2410075B13 gene 56 Mapk15 75.824198 mitogen-activated protein kinase 15

57 AA409316 75.831532 family with sequence similarity 83, member H 58 K230010J24Rik 75.840317 RIKEN cDNA K230010J24 gene 59 4933407E14Rik 75.867075 RIKEN cDNA 4933407E14 gene 60 Scrib 75.877615 scribbled homolog (Drosophila) 61 Puf60 75.900613 poly-U binding splicing factor 60 62 Nrbp2 75.916023 nuclear receptor binding protein 2

63 Eppk1 75.931917 epiplakin 1 64 BC024139 75.949949 cDNA sequence BC024139 65 Plec1 76.001405 plectin 1 66 Grina 76.077236 glutamate receptor, ionotropic, N-methyl D-aspartate-associated protein 1 67 Spatc1 76.098518 spermatogenesis and centriole associated 1 68 4930551A22Rik 76.124696 RIKEN cDNA 4930551A22 gene

69 Oplah 76.127032 5-oxoprolinase (ATP-hydrolysing) 70 Exosc4 76.157826 exosome component 4 71 Gpaa1 76.161723 GPI anchor attachment protein 1 72 Cyc1 76.173952 cytochrome c-1 73 Sharpin 76.177469 SHANK-associated RH domain interacting protein 74 Maf1 76.181735 MAF1 homolog (S. cerevisiae)

75 Brp16 76.199327 brain protein 16 76 Tssk5 76.202387 testis-specific serine kinase 5 77 D330001F17Rik 76.263337 RIKEN cDNA D330001F17 gene 78 Bop1 76.283425 block of proliferation 1 79 Scx 76.287867 scleraxis 80 Hsf1 76.307874 heat shock factor 1

55

Table 8: Sequence mismatches in exons of genes in the significant peak of Pain1.

N ID Mb Domain Gene Function Alleles Source Strains

A C57BL6/J

1 rs31764112 74.71021 Exon 1

2010109I03Rik

C/T Celera T C

2 rs32206043 74.71031 Silent G/A Perl/NIEHS A G

3 rs32400297 74.7112 Exon 2 C/T Celera T C

4 rs32100474 74.71212 Exon 3 G/A Celera A G

5 NES15641900 74.7888 Exon 3

Ly6e

G/A Perl Impute A G

6 rs13469391 74.78901 Exon 4 Silent G/A Celera A G

7 mCV22970245 74.78946 Exon 4 T/C Celera C T

8 NES15641907 74.78946 Exon 4 T/C Perl Impute C T

9 rs31834161 74.81343 Exon 3

Ly6i

Silent C/G Celera G C

10 rs31638661 74.81347 Exon 3 Missense G/T Perl Impute T G

11 rs13482650 74.81368 Exon 3 C/T Celera T C

12 NES15641602 74.81375 Exon 3 G/C Perl Impute G C

13 rs32279213 74.82591 Exon 4 Ly6a

Missense T/C Celera C T

14 NES15641462 74.82689 Exon 3 T/G Perl Impute T G

15 NES15641468 74.828 Exon 2 C/A Perl Impute C A

16 NES16972767 74.93899 Exon 1 Ly6c2

T/C Perl Impute C T

17 NES16972770 74.93912 T/C Perl Impute C T

18 rs32220845 75.54372 Exon 3 Rhpn1 Missense G/A Celera G A

19 NES16968061 76.00412 Exon 32 Plec1 T/C Perl Impute T C

20 rs13459188 76.1833 Exon 4 Sharpin Missense G/T Perl/NIEHS T G

21 rs31966277 76.30528 Exon 2 Bop1 Missense A/G Celera G A

56

Table 9: Sequence mismatches in 5‘ UTR for genes in the significant peak of Pain1.

N ID Mb Gene Alleles Source Strains

A C57BL6/J

1 NES15628020 73.587683

Ptp4a3

A/C Perl Impute A A 2 NES15628022 73.587879 T/C Perl Impute T T 3 mCV22353313 73.587950 C/T Celera C C 4 MRS10330574 73.588130 T/C CITG C

5 MRS10330575 73.588227 T/C CITG T 6 NES15627730 73.588456 T/A Perl Impute T T 7 NES15627732 73.588476 T/C Perl Impute T T 8 NES15627734 73.588555 C/T Perl Impute C C 9 NES15627736 73.588702 T/C Perl Impute T T

10 NES15627723 73.588950 G/A Perl Impute G G

11 NES15639947 73.617509 Gm628

C/G Perl Impute C C 12 NES15639948 73.617712 C/T Perl Impute C C 13 NES15639949 73.617736 G/A Perl Impute G G 14 MRS10330618 73.617863

Gm628

G/A CITG G 15 NES15639950 73.618040 G/T Perl Impute G G 16 NES15639951 73.618068 G/A Perl Impute G G

17 rs4230807 74.419547 Bai1

A/- GNF1 A A 18 rs4230808 74.419748 C/- GNF1 C C 19 rs4230809 74.419817 T/C GNF1 T T 20 rs37868596 74.502946 Arc T/C Perl/NIEHS T T 21 rs32436202 74.551677 4930572J05Rik A/T Celera A A 22 rs37309405 74.582368 Lynx1 C/T Perl/NIEHS C C

23 NES15644359 74.630383 Ly6k G/A Perl Impute G G 24 rs31793657 74.938614

Ly6C2

A/G Perl Impute G A 25 NES16972763 74.938688 G/C Perl Impute G G 26 rs31587854 74.938726 G/C Celera C G 27 rs3090551 74.938748 A/C Perl Impute C A 28 rs32354455 74.938878 A/C Perl Impute C A

29 NES15638479 75.262201 9030619P08Rik

T/C Perl Impute T 30 rs32099107 75.262227 C/T Celera T C 31 rs39265817 75.724761 Naprt1

G/A Perl/NIEHS G G

32 rs36933174 75.724794 C/T Perl/NIEHS C C

33 mCV24098429 75.724848 Naprt1 A/A Celera A

34 rs38277574 75.759471 Tsta3 G/T Perl/NIEHS G G 35 rs13470832 75.771567

Zfp623

G/T Perl/NIEHS G G 36 rs36612941 75.777575 G/A Perl/NIEHS G G 37 rs36436610 75.777622 A/G Perl/NIEHS A A

38 NES16968963 75.949544 Eppk1 G/C Perl Impute G G 39 NES16968767 75.956769 BC024139 A/G Perl Impute A A 40 rs13462857 76.182026 Sharpin G/C Celera C G 41 NES17023670 76.307675 Bop1 C/T Perl Impute C C

57

Table 10: Sequence mismatches in 3‘ UTR for genes in the significant peak of Pain1.

N ID Mb Gene Alleles Source Strains

A C57BL6/J

1 rs31925887 73.581881 Ptp4a3

T/G Celera T T 2 MRS10330569 73.582147 G/T CITG T

3 rs31763794 73.582153 T/G Celera T T 4 rs37624099 74.359419 Bai1 T/C Perl/NIEHS T T 5 rs37489790 74.499713

Arc

A/T Perl/NIEHS T A 6 rs38383308 74.499720 T/A Perl/NIEHS T T 7 rs38811147 74.499790 T/C Perl/NIEHS T T 8 rs31680847 74.499996 T/C Celera T T

9 rs37092904 74.500144 C/T Perl/NIEHS C C 10 rs32355848 74.500147 A/T Perl/NIEHS A A 11 mCV23228155 74.500256 C/T Celera C C 12 mCV23228148 74.500274

Arc

C/C Celera C C 13 NES14564416 74.500323 A/G Perl Impute A A 14 mCV23228146 74.500343 -

a/G Celera - G

15 mCV23228139 74.500345 -/G Celera - G 16 mCV23228138 74.500347 -/T Celera - T 17 rs32438587 74.500415 C/T Celera C C 18 rs32406346 74.500645 G/A Celera G G 19 rs31904341 74.501084 C/T Celera C C 20 rs32212690 74.501155 T/C Perl/NIEHS T T

21 rs32043445 74.501261 T/C Celera T T 22 rs31725818 74.501279 A/G Celera A A 23 rs32166703 74.501315 C/T Perl/NIEHS C C 24 rs32256859 74.501402 C/T Celera C C 25 rs36621612 74.578542

Lynx1

T/C Perl/NIEHS T T 26 rs31910398 74.578570 T/C Celera T T

27 rs37045884 74.578680 T/G Perl/NIEHS T T 28 rs32113452 74.578698 C/T Perl/NIEHS C C 29 rs36652267 74.578966 C/T Perl/NIEHS C C 30 rs36687818 74.579269 C/T Perl/NIEHS C C 31 rs36344281 74.579312 A/G Perl/NIEHS A A 32 rs36482276 74.579354 T/A Perl/NIEHS T T

33 rs36426657 74.579610 G/A Perl/NIEHS G G 34 rs38539767 74.579692 T/C Perl/NIEHS T T 35 rs32525467 74.579736 T/C Perl/NIEHS T T 36 rs32523426 74.579823

Lynx1

T/C Perl/NIEHS T T 37 rs32040879 74.579978 C/T Celera C C 38 rs37841220 74.580772 A/C Perl/NIEHS A A

39 rs36294085 74.580874 C/T Perl/NIEHS C C 40 rs37166720 74.580924 G/A Perl/NIEHS G G 41 rs38338917 74.580985 G/A Perl/NIEHS G G 42 rs38201107 74.581046 T/A Perl/NIEHS T T 43 rs37238525 74.581061 G/A Perl/NIEHS G G 44 rs39255510 74.581106 G/T Perl/NIEHS G G

45 rs37330404 74.581158 C/T Perl/NIEHS C C 46 rs36617176 74.581208 C/A Perl/NIEHS C C 47 rs32343423 74.581574 G/A Celera G G

58

48 rs36883137 74.581659 C/T Perl/NIEHS C C 49 rs32189877 74.592639 Ly6d

A/G Celera A A

50 rs32199611 74.592769 A/G Celera A A 51 NES15641480 74.825547 Ly6a A/G Perl Impute G G 52 NES15641112 74.875564 Ly6c1 A/G Perl Impute A A 53 NES15638637 75.258259

9030619P08Rik

G/A Perl Impute G G 54 NES15638638 75.258340 G/A Perl Impute G G 55 NES15638639 75.258384 G/A Perl Impute G G

56 NES15638640 75.258520 A/G Perl Impute A A 57 NES15638641 75.258535 C/A Perl Impute C C 58 NES15638642 75.258542 G/A Perl Impute G G 59 rs13464656 75.755353 Tsta3 T/C Perl/NIEHS T T 60 rs38861253 75.779195

Zfp623

C/T Perl/NIEHS C C 61 rs36895213 75.779255 A/G Perl/NIEHS A A

62 rs36623990 75.779692 T/A Perl/NIEHS T T 63 rs38451313 75.779754 C/T Perl/NIEHS C C 64 rs38141395 75.779772 A/G Perl/NIEHS A A 65 NES16970823 75.799668 Zfp707

C/T Perl Impute C C

66 NES16970824 75.799813 A/G Perl Impute A A 67 NES16968057 76.002333 Plec1 A/G Perl Impute A A

68 rs13465489 76.165277 Gpaa1

C/T Perl/NIEHS T C 69 NES16966127 76.165286 G/A Perl Impute G G 70 NES17026044 76.184419 Sharpin G/A Perl Impute G G

a could not be determine

3.3 Gene Expression:

In order to further shortlist the 26 genes with mismatching sequence I compared their expression

levels in the DRG and spinal cord of A and B intact, sham-operated and denervated mice

(N=5/group, one array per mouse; 30 arrays per neural structure). RNA extracted from these

tissues and samples was run on 1% agarose gel to be checked for integrity. Samples of mice that

did not show intensely enough bands were replaced by RNA samples of other mice of the same

autotomy phenotype level. RNA integrity was then confirmed using the Agilent Bioanalyzer

2100 for all 30 DRGs and 30 spinal cord samples. Fig. 25 shows an example output chart for

DRGs of mouse number AJ26 with a RIN (RNA Integrity Number) value of 8.8. The range of

RIN values we got for all 60 samples was 8.3-10.0 (for DRGs), and 8.0-9.2 (for spinal cord

samples). Since, values of RIN ≥ 8.0 are acceptable for expression profiling with microarrays,

we submitted these samples to the facility that carried out the microarray assay.

59

Figure 25: Bioanalyzer results for DRGs from a typical mouse (number AJ26), showing the

RNA concentration and integrity (RIN).

Figure 26 shows an example of an array of the DRG of another typical mouse

(denervated; number AJ11 with a phenotype of high autotomy). The red rectangle highlights a

region in higher magnification, showing probes in which the expression intensity of the reference

probes was higher than that of the sample mouse (in green), probes showing higher expression

levels of the sample mouse relative to the reference levels (in red), and in yellow are the

superimposed red and green spots which correspond to probes whose expression intensity was

not different between the assayed sample and reference probes. The fluorescence intensity in

each spot was scanned and translated into a numerical value conveyed to us in a tabulated form.

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Figure 26: Photomicrograph of the Agilent 4X44 microarray chip for RNA extracted from

DRGs (of mouse number AJ11) showing the relative hybridization intensities of 44,000 probes.

We then received the output data files, and analyzed them, assisted by our lab‘s

biostatistician (David Tichauer), using the Partek (St. Louis, Missouri, USA) software package

(Genomic Suite, Ver. 6.4). Next, I isolated from the whole genome dataset the normalized data

(see Methods for the normalization process) for the 26 genes in Pain1. Table 11 shows the genes

with significant fold change (FC) in their expression level. AI and BI designate naïve (intact)

mice of these strains; AS and BS mark mice that had sham operation and AD and BD are the

respective abbreviations for denervated A and B mice, harvested on days 8-14. For every

denervated mouse sacrificed on a certain postoperative day we sacrificed a sham operated mouse

of the same strain to control against differences in the survival time postoperatively. Fold

changes were calculated as a ratio of group averages. The significance of the FC was calculated

by using ANOVA. Only significant p-values are shown.

Table 11 shows 11 genes (Lynx1, Ly6d, Ly6c, Ly6i, Ly6k, Arc, Plec1, Sharpin, Zfp707,

2010109I03Rik, and 9030619P08Rik) selected from the 26 candidate genes, based on significant

difference in expression levels either in intact A vs. B mice, or denervation mice, in the spinal

cord and/or the DRGs. Table 11 also shows the abundance of constitutive expression levels of

the shortlisted 11 genes in various structures in the nervous system, as derived from the literature

(http://biogps.gnf.org), as well as prioritization of these genes as autotomy candidate genes (see

Discussion for rationale and implications). We included in this Table, genes whose significance

level of p<0.05 was not corrected by a Bonferroni adjustment of the alpha level.

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Table 11: Fold change in the expression level of 11 genes with sequence mismatches in the significant peak of Pain1 as well as

significant differences in constitutive and/or postoperative fold changes in expression levels compared within- and across-strains. Red font

highlights values that will not remain statistically significant if submitted to a Bonferroni correction of the alpha level. The rest, in bold

black font, highlights values that will survive such correction and remain significant.

Tissue Gene Name Constitutive CNS+PNS expression

Sequence mismatches A Intact vs. B Intact

AD vs. AS BD vs. BS AD/AS vs. BD/BS Priority as an autotomy gene

d N mismatches

b–type

c p-value FC p-value FC p-value FC p-value FC

Spinal

cord

Arc ++++ 20-3', 1-5' 0.039 -1.24 0.022 -1.27 0.037 -1.26 MH

Ly6d + 2-3' 0.041 -2.01 L

2010109I03Rik + 4-E 0.054 -1.60 0.0012 2.44 M

Ly6c-1 + 2-E,1-3',5-5' 0.063 1.47 M

Ly6c-2 + 2-E,1-3',5-5' 0.018 1.50 M

9030619P08Rik + 6-3',2-5' 0.014 1.39 0.011 -1.43 L

Zfp707 + 2-3' 0.044 -1.27 L

Plec1-2 +++ 1-E,1-3' 0.014 2.26 M

DRG

Arc ++++ 20-3', 1-5' 0.034 -1.23 0.017 1.26 0.00031 1.53 MH

Lynx1 +++++ 24-3',1-5' 0.043 1.30 0.024 -1.38 0.001 1.52 H

Ly6k + 1-5‘ 0.044 -1.82 L

Ly6i + 4-M,S 0.0022 1.81 ML

Ly6c-1 + 2-E,1-3',5-5' 0.0045 1.77 M

Plec1-5 +++ 1-E,1-3' 0.001 -2.29 M

Sharpin + 1-M,1-3',1-5' 0.006 -1.19 0.0012 -1.23 MH

a from http://biogps.gnf.org

b The number of sequence mismatches between A and B mice

c E = exons; M=Missense; S= silent; 3‘ = 3‘UTR; 5‘ = 5‘UTR

d L = Low; ML= Medium-Low; M = Medium; MH = Medium-High; H = High. Assigning these priority levels to the genes was based

on weighting the constitutive expression levels in the nervous system (as published in http://biogps.gnf.org), the significance level of

constitutive and fold changes found in this study, type and number of sequence mismatches, as well as known biological function related

to pain.

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4. Discussion

4.1 Remapping Pain1:

Three previous studies had shown a significant link between Pain1 and autotomy. Since

they used a crude panel of microsatellite markers it was impossible to map exactly where the

peak of Pain1 is. The original position of Pain1 on mouse chr 15 was mapped by Seltzer et al.

(2001) using 15 microsatellite markers in male mice. Their peak was at marker D15Mit28

(located at 34.29cM, 74,742,609Bp (Figure 27A). Devor et al. replicated this study twice, once

in 2005 (Devor et al., 2005) and again in 2007 (Devor et al., 2007). In both replications they used

a different genetic approach than that used by Seltzer et al., i.e., genotyping several hundred

autotomy-phenotyped F2 male and female offspring mice of a cross between the inbred strains

C3H/HebJ and C58/J, using 9 microsatellite markers. Despite the genetic differences between the

strains used by Devor et al. and Seltzer et al., there was one important similarity between these

maps, and one dissimilarity. As shown in Figure 27C, according to Devor et al. (2005), there are

two QTLs on chr 15 linked to autotomy levels, a broader one located between D15Mit138 (at

15.6cM, 39,861,164Bp) and D15Mit88 (at 25.7cM, 61,184,980Bp), and a narrower QTL

peaking at marker D15Mit68 (located at 36.28cM, 76,740,612Bp), which is only ~2 Mb

upstream of the original position of Pain1 according to Seltzer et al., a difference acceptable

considering the low number of markers used in both studies especially that both studies had used

the ‗crude‘ microsatellite markers for the mapping. However, in the second mapping attempt of

Pain1 by Devor et al. (2007), as shown in Figure 27B, they claimed to have observed a gender

effect associated with this locus, and consequently produced two new separate maps, one for

each gender. The map for male was similar in shape to the map reported in 2005 for males and

females, with the same two QTLs peaking at exactly the same loci as in the 2005 map. However,

the map for females was very different than the previous map, NOT showing Pain1, suggesting

that Pain1 is a gender specific QTL (only for male). Instead, the females show only one broad

QTL peaking at marker D15Mit277 (located at 30.11 cM, 68,603,159 Bp). The latter QTL,

located 6.17cM or 8.14Mb away from Pain1, must be a different QTL that cannot be explained

by the small number of microsatellite markers that they and Seltzer et al. used, since a few of

their markers were positioned in a location that could have detected the presence of a peak in

Pain1, if there was one for females. This large females‘ QTL downstream of Pain1 was not

detected by Seltzer et al. and by the present study since both used a male dataset only.

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Figure 27A-C: Position of Pain1 (highlighted quadrangle) on mouse chromosome 15 according

to: A. Seltzer et al. (2001) using only male data. B. Devor et al. (2007) for males and females

separately. C. Devor et al. (2005) for males and females together.

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The presence of this new female QTL and a male QTL in Pain1 that was still too broad to be

sequenced or analyzed for gene expression, necessitated remapping Pain1 which I undertook as

the first aim of my study. A major advantage was the newly available genetic map for AXB-

BXA that is based on many thousands of SNPs, hundreds of which are located on chr 15. I also

used a number of autotomy traits in male mice that were not previously studied by Seltzer et al.

or others since that publication. The QTL downstream of Pain1, if replicated, maybe related to

the genetic contrast between the C3H/HebJ and C58/J strains that Devor et al. used, and not

between the A/J and C57BL6/J strains that were used by us.

To summarize my results for QTL mapping, using the original phenotypic data of INC_2 and the

new SNP-based genetic map of AXB-BXA, I was able to replicate the existence of a QTL on chr

15 that exactly corresponded to the original peak of Pain1 (as shown in Figure 27A) as reported

by Seltzer et al. (2001). Pain1 is also linked to other autotomy traits that were never mapped

before, including INC_3, INC_5, AOD 2, AOD_3, AOD_5 and AS_D36. This may not be

surprising since I also found that all these traits are highly correlated (Table 3). Such a

correlation between the temporal and severity aspects of autotomy was never noted before for

mice. Analyzing the haplotypic structure of the AXB-BXA lines at Pain1, vis-à-vis the ancestral

origin of chromosomal segments inherited from the parental strains A and B, I was able to

explain the autotomy levels (for INC_3) for most RI lines except two (BXA13 and AXB13/14).

Since I found that Pain1 was not a causative QTL for BXA13 and AXB13/14, this raised the

possibility that QTLs other than Pain1 may control the levels of autotomy in these two lines.

Indeed, whole genome interval mapping using the INC_3 trait revealed the existence of more

than one QTL on chromosome 14, one of which was highly significant (which I named Pain3)

and another one (or perhaps two additional ones) that were only at a suggestive level. Like

Pain1, the haplotypic structure of Pain3 explains trait levels in most lines, but unlike Pain1, the

haplotype structure on Pain3 could also explain the phenotypes of BXA13 and AXB13/14, by

showing that inheriting the A genotype in Pain3 is linked with a higher incidence of autotomy

scores 3. The only exception is the line BXA25, where QTLs other than Pain1 and Pain3 explain

the low trait levels for this line.

The genetic model these results offer suggests that inheriting at least one A genotype on Pain1

or Pain3 is necessary (but not sufficient only in the case of BXA25) to express high levels of

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autotomy. Moreover, inheriting the B genotype on both Pain1 and Pain3 is necessary and

sufficient for expression of low incidence of autotomy in all lines.

Remapping Pain1 in the AXB-BXA inbred mice was based on sequence differences in the RI

lines. This process limited the significant peak of Pain1 to 73.25 to 76.35 Mb. Based on the

notion that any gene relevant to this trait must show a sequence mismatch between the A and B

strains (since the genotype difference between A and B was the basis of finding Pain1 by Seltzer

et al., in 2001), I was able to identify 26 candidate genes (from the 80 genes harboured in this

region) that showed such sequence mismatch. Next, I and another student in the lab conducted a

whole genome microarray expression study comparing intact A to B mice and denervated versus

sham operated mice of the two strains, both in the DRG and spinal cord. Limiting myself to the

26 genes in the region of Pain1 having sequence mismatch, I found that 11 of these genes had a

significant constitutive difference in the expression level in naïve/intact mice of these two

strains, and/or significant fold changes post-denervation surgery. However, since no correction

was made for multiple comparisons, some of these findings may be false positive. Therefore, to

further prioritize these genes, I selected them based on their known biological function in

processing pain, neuropathic pain, or other neural functions, also their role in human psychiatric

and neurological diseases. Moreover, I browsed genetic databases to determine which of these

genes is expressed constitutively in the nervous system, or in tissues that are known to affect

neural functions related to pain. The following section discusses all the data I found for each of

these 11 genes. Note that the selection of the candidate genes with the highest priority was based

on the following factors:

(i) A gene could show a difference in the constitutive expression levels between the intact A and

intact B parental strains, either in the spinal cord or DRG or both. The basis for this criterion is

the possibility that the constitutive expression level of an autotomy gene may affect the duration

and/or frequency of the injury discharge at the time of nerve injury. Many studies have shown

that injury discharge is an important trigger of chronic pain following nerve injury.

(ii) A gene could show a difference in expression levels when comparing the post-denervation

versus sham-operated groups between the A and B parental strains either in the spinal cord, or

DRG, or both. The basis for this criterion is that an autotomy gene is associated with denervation

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and not sham operation which is consistent with our data showing that none of the sham operated

A and B expressed autotomy.

(iii) An autotomy gene is likely to show a significant constitutive expression level in the CNS

and/or PNS (i.e., DRGs) and other tissues such as the immune system.

The following section discusses the role of each criterion for the 11 candidate genes.

4.2 Eleven Candidate Autotomy Genes in Pain1:

4.2.1 Lynx1: This gene encodes for Neurotoxin1, a small 11 kDa protein that shows a homology

with alpha-Bungarotoxin and with the Ly-6 family (see below, hence its name Ly-nx1), which

are a related group of proteins that are found on the surface of mouse lymphocytes and elsewhere

(Gumley et al. 1995). What distinguishes Lynx1, and makes it of unusual interest to our study, is

that in the nervous system it is expressed as glycophosphoinositol-linked cell surface proteins

(Dessaud et al., 2006), and associated with nicotinic acetylcholine receptors (nAChRs) that affect

a wide array of biological processes, including learning, memory, attention, addiction and pain

(see below). As reported previously, Figure 28 shows that Lynx1 has high constitutive abundance

in the DRG and spinal cord, as well as the cerebellum, hippocampus, cortex, and other structures.

Figure 28: Expression levels of Lynx1 in neural and other tested tissues

Printout from http://biogps.gnf.org/#goto=genereport&id=66004. Vertical purple lines denote

(from left to right) the median expression level in all tested body tissues, followed by 3X, 10X

and 30X the median levels. Lynx1 modulates

nAChR function in vitro by altering agonist

sensitivity and desensitization, slowing their

kinetics (Miwa et al., 2006) as well as

reducing single channel conductance

(Dessaud et al., 2006; Miwa et al., 2006). Loss of Lynx1 in knockout mice was associated with a

10-fold decrease in the EC50 for nicotine, decreasing receptor desensitization, thereby elevating

intracellular Ca+2

levels in response to acetylcholine, and enhancing synaptic efficacy (Miwa et

al., 2006). In the short term, Lynx1 knockout mice show enhanced synaptic efficiency (thus,

enhanced learning and memory), but in the long term they show a decrease in cholinergic

signaling, and in some contexts also vulnerability to glutamatergic toxicity (Miwa et al., 2006).

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As for pain networks, activation of presynaptic nAChRs enhances inhibitory synaptic

transmission in superficial and deep dorsal horn layers (Kiyosawa et al., 2001; Fucile, 2002;

Takeda et al., 2003; Genzen et al., 2005; Takeda et al 2007). These studies show that Lynx1, via

its effect on nAChRs, may have an important role in inhibition of synaptic activity in the dorsal

horn. In the brain, nAChRs have a similar role, such that their activation enhances GABAergic

synaptic transmission on periaqueductal gray neurons (Nakamura and Jang, 2010). Indeed, Lynx1

is highly expressed in the brain. nAChR agonists are currently a target in development of

analgesics for persistent pain (Conell-Price, 2008; Gao et al., 2010). Thus, Lynx1, by way of

desensitizing nAChRs to the effects of agonists, including endogenous acetylcholine, could play

a role in the pain network. Higher levels of Lynx1 expressed postoperatively could be associated

with increased neuropathic pain and autotomy levels, which is consistent with our data, as

follows.

Constitutive levels in my results: The expression level of Lynx1 was not significantly contrasting

in the spinal cord of intact A and B mice. But in the DRGs, intact B mice have lower constitutive

levels compared to A mice (yet only significant before a correction for multiple comparisons was

made), suggesting that lower levels may be protective against the induction of autotomy by way

of restraining the effect of injury discharge on the CNS.

Postoperative levels: In the DRGs (but not spinal cord) of denervated B (but not A) mice, I

found that the expression level of Lynx1 is significantly down-regulated compared to sham-

operated mice. This suggests that reduced levels of Lynx1 are associated with lower autotomy.

Compatible with this suggestion, the DRGs levels in BD/BS mice are significantly lower than in

AD/AS mice, supporting the protective role that lower Lynx1 levels may have have against the

maintained drive for autotomy.

Sequence mismatches: The 25 known sequence mismatches between A and B mice are in the

regulatory regions of this gene, 24 of which are in the 3‘ UTR, suggesting that if Lynx1 is an

autotomy gene, its control of autotomy levels is by way of its mRNA expression levels.

In conclusion, this gene is a high priority candidate gene for autotomy. To further identify

whether Lynx1 is an autotomy gene one could produce the Neuroma Model in knockout A mice

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or in missense treated A mice, compare the autotomy levels with the A wildtype or vehicle

treated, and if levels of autotomy in the KO/treated mice are significantly lower than this would

support the gene‘s candidacy.

4.2.2 Ly6c: This gene (like Ly6d, Ly6i, and Ly6k that were also shortlisted as candidate genes in

my data, see below) encodes the 6c member of the low-molecular weight lymphocyte antigen 6

complex family. Each member of this family encodes a specific antigen (locus C, in the case of

Ly6c), that serves as a cell surface glycoprotein. It is bound to intracellular phosphatidyl inositol,

suggesting it has a role in signal transduction (Stroncek et al., 2004). However, the expression of

Ly6c is relatively very low in the nervous system compared to some other tissues. The expression

on specific leucocyte subpopulations in peripheral lymphoid tissues suggests an association

between the regulation of Ly-6 expression and the development and homeostasis of the immune

system. Recently, Ly6c was implicated in the production of encephalitis by way of triggering the

transformation of inflammatory monocytes (that carry this antigen) to microglia, when

challenged by West Nile virus (Getts et al., 2008; Graeber and Streit, 2010). Thus, the function

of Ly6c in neuropathic pain may be related to its possible post-denervation role, by perhaps

transforming monocytes to microglia that enter the CNS, aggregate in pain pathways and interact

with pain pathways to drive the mice to autotomize.

Constitutive levels in my study: The expression levels of the two probes (Ly6c1 and Ly6c2)

were not significantly contrasting in the spinal cord of intact A and B mice. But in the DRG,

intact B mice had significant lower constitutive levels compared to A mice, suggesting that lower

levels may be protective against the induction of autotomy.

Postoperative levels: The postoperative expression level differences for Ly6c2 are not significant

after the correction for multiple comparisons is made; however, before the correction there is a

significant contrast. Only in the spinal cord (but not in the DRG) the expression level of this gene

(Ly6c1 and Lyc2) is significantly higher in AD/AS vs. BD/BS. Since, there were no significant

fold changes in denervated A and B (AD, BD) versus their respective sham groups (AS, BS), it is

possible that an insignificant upregulation in A combined with an insignificant down-regulation

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in B resulted in the significant fold change in the expression levels of AD/AS vs. BD/BS. Thus,

Ly6c may be associated with autotomy.

Sequence mismatches: As shown in Table 11, there are sequence mismatches between the A and

B strains in exons and regulatory regions.

So in conclusion, this gene is a candidate gene for autotomy.

4.2.3 Ly6d: This gene encodes the D antigen (E48) and is abundantly found in B cells and

lymphoid tissues but the expression of Ly6d is relatively very low in the nervous system

compared to some other tissues.

Constitutive levels in my study: The difference in constitutive expression levels of this gene is

not significant after the correction for multiple comparisons is made. Before the correction there

is a significant contrast. The expression level of Ly6d in the spinal cord (but not DRG) of intact

B mice is significantly higher compared to A mice, suggesting that higher levels are protective

against autotomy.

Postoperative levels: No significant contrasts in the fold change were observed for this gene in

the spinal cord or DRG.

Sequence mismatches: As shown in Table 11, the sequence mismatches between A and B strains

are in the regulatory regions.

In conclusion, this gene remains a low priority candidate gene for autotomy.

4.2.4 Ly6i: This gene encodes the I antigen on lymphocytes. It is almost non-existent in the

nervous system.

Constitutive levels in my study: The expression level of Ly6i in the DRG (but not spinal cord) of

intact A mice is significantly higher compared to B mice, suggesting that lower levels are

protective against the induction of autotomy.

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Postoperative levels: No significant contrasts in the fold change were observed for this gene in

the spinal cord or DRG.

Sequence mismatches: There are 4 missense and silent sequence mismatches between A and B

strains; therefore, it is possible that a structural difference in the gene product may contribute to

the contrasting autotomy in these strains.

In conclusion, this gene remains a low priority candidate gene for autotomy.

4.2.5 Ly6k: This gene encodes the locus K antigen on lymphocytes. The expression of Ly6K is

relatively very low in the nervous system compared to some other tissues.

Constitutive levels in my study: The difference in constitutive expression levels of this gene is

not significant after the correction for multiple comparisons is made. Before the correction there

was a significant contrast. The expression level of Ly6k in the DRG (but not spinal cord) of intact

A mice is lower compared to B mice at a marginally significant level, suggesting that higher

levels may be protective against the induction of autotomy.

Postoperative levels: No significant contrasts in the fold change were observed for this gene in

the spinal cord or DRG.

Sequence mismatches: There is a single sequence mismatch between the A and B strains in the

5‘UTR region; therefore, the constitutive levels of expression of Ly6k may contribute to the

contrasting autotomy levels in these strains.

In conclusion, this gene is most likely not to be considered a candidate gene for autotomy.

4.2.6 Arc: This immediate-early gene (IEG) encodes the Activity-Regulated Cytoskeleton-

Associated protein. Its product plays a role in plasticity, learning and memory. Arc is highly

abundant in the brain (Figure 29) but with only minimal levels in the spinal cord and DRG

(http://biogps.gnf.org/#goto=genereport&id=11838). Arc knockout mice show high GluR1

subunit expression levels, increased miniature excitatory postsynaptic currents (mEPSCs), and

deficiencies in long-term memory (Plath et al., 2006). Recent studies have found that following

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application of formalin or after induction of chronic inflammatory pain, pain behavior in

Arc/Arg3.1 KO mice was not significantly different compared to the wild type (Hossaini et al,

2010), however it is still possible that this gene plays a role in other pain phenotypes such as

autotomy.

Figure 29: Expression level of Arc in neural and other tested tissues

Printout from http://biogps.gnf.org/#goto=genereport&id=11838. Vertical purple lines denote

(from left to right) the median expression level of Arc in all tested body tissues, followed by lines

designating 3X, 10X and 30X the median levels.

Constitutive levels: The difference in constitutive expression levels of this gene is not significant

after the correction for multiple comparisons is made. However, before the correction there was a

significant contrast. Intact A mice have lower constitutive expression levels compared to B mice

in the DRG and spinal cord. This suggests that higher levels of ARC in B mice in these tissues,

at the time of nerve injury, could protect them against the induction of autotomy.

Postoperative levels: Denervation changes the expression pattern of Arc. In the DRGs it

increased postoperatively both in A and B mice (but this finding is not significant after

correction for multiple comparison). But since there was no significant difference between

AD/AS vs. BD/BS mice we conclude that the postoperative expression levels of Arc do not

explain the contrast in autotomy in these strains. In the spinal cord, however, the expression

levels in denervated A (but not B) mice was down-regulated significantly compared to the sham-

operated mice, and AD/AS was lower than BD/BS (before correction for multiple comparison).

Thus, having lower levels of ARC may be associated with autotomy in denervated A mice. This

finding is compatible with the published data that nociceptive stimulation induces expression of

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Arc in encephalin containing neurons in the spinal cord suggesting an anti-nociceptive role for

Arc (Hossaini et al, 2010). Intrathecal injection of brain derived neurotrophic factor (BDNF) also

induced expression of Arc (Hossaini et al, 2010). Other studies have showed that knockout mice

show higher GluR1 subunit expression levels compared to the wild type, and increased miniature

excitatory postsynaptic currents (Plath et al., 2006). To further validate whether Arc is a

candidate autotomy gene one could produce the Neuroma Model in Arc KO B mice, to determine

if the autotomy level contrasts significantly when compared to wildtype B mice, which normally

express no/low autotomy. If Arc knockout B mice show high levels of autotomy one could

conclude that Arc is linked with autotomy.

Sequence mismatches: As seen in Table 11 there are 21 sequence mismatches between A and B

mice, all of which are in the regulatory regions, suggesting that if this is an autotomy gene the

molecular mechanism by which it regulates autotomy levels may be via the abundance of ARC

and not by its structure-function relationship.

Since ARC is considerably more abundant in the brain than DRG and spinal cord, regulation of

Arc levels in the brain may play a considerably more important role in autotomy, compared to

the DRGs and spinal cord. If correct, my expectation is that intact and/or post-denervation A

mice would have lower levels of ARC in the brain compared to B mice, which can be tested.

In conclusion, this gene is a high priority candidate gene for autotomy.

4.2.7 Plec1: This giant gene encodes the protein Plectin1 (hemidesmosomal protein 1) that is

found in nearly all mammalian cells, acting as a link between the three main components of the

cytoskeleton: actin microfilaments, microtubules and intermediate filaments. In addition, Plectin

links the cytoskeleton to junctions found in the plasma membrane that structurally connect

different cells. Therefore, Plectin may be involved in maintaining the mechanical integrity and

viscoelastic properties of neural tissues. Plectin-immunoreactive cells include astrocytes (in their

end feet abutting to the blood vessels), and on the pial surface and endothelial cells lining brain

capillaries, suggesting a possible role in the blood-brain barrier (Lie, 1998). Homozygous

deletion mutations of Plec1 were found in patients with epidermolysis bullosa simplex and also

associated with late-onset muscular dystrophy (Gache et al., 1996; Pfendner et al., 2005), and in

neocortical and hippocampal tissue of patients who had undergone epilepsy surgery (Lee et al.,

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2007). As shown in Figure 30, expression of Plec1 is considerably high in the DRGs but

relatively low throughout the CNS. Like CNS astroglia, Satellite cells in the DRGs express glial

fibrillary acidic protein, suggesting that satellite cells may be the source of Plec1 expression in

the DRGs.

Figure 30: Expression level of Plec1 in neural and other tested tissues - Printout from

http://biogps.gnf.org/#goto=genereport&id=5339 .

Vertical purple lines denote (from left to right) the

median expression level of Plec1 in all tested body

tissues, followed by lines designating 3X, 10X and the

30X the median levels.

Constitutive levels in my study: Out of the 5 probes in the expression array, only one (Plec1-2;

Table 11) showed a significant contrast in the spinal cord (but not in the DRGs), while another

probe (Plec1-5; Table 11) showed a significant contrast in the DRGs (but not in the spinal cord).

Intact A and B (both in the spinal cord and DRGs) show a significant difference in the

constitutive levels of this gene. But while in the spinal cord A have higher levels than B, in the

DRGs the opposite contrast was seen, i.e., A mice have lower constitutive levels than B.

Postoperative levels: No significant fold changes were found in the spinal cord or DRG of

denervated A or B mice compared to the sham operated mice, and also not when comparing

AD/AS vs. BD/BS mice.

Sequence mismatches: The sequence mismatches between the A and B strains are in exons and

in regulatory regions, suggesting that not only the quantity of the gene product (i.e., expression

levels) but also quality of the gene product (e.g., structure/function of the protein) may be

different in A and B strains. Nevertheless, the reported abundance of Plec1 in DRGs and the

contrast I found in the constitutive levels of A and B in the DRGs versus the spinal cord suggest

that contrasting constitutive levels similarly affect autotomy to produce high autotomy in A and

low in B mice. So Plec1 may be implicated in the induction of autotomy but not driving it

postoperatively. In conclusion, this gene remains a candidate gene for autotomy.

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4.2.8 Sharpin: This gene encodes a scaffolding protein named Sharpin (Shank-associated RH

domain interacting protein) that is expressed in many tissues including the CNS, spinal cord and

DRGs. Its relevance to neuropathic pain is that Sharpin co-localizes with Shank1 in the

postsynaptic density (PSD) of excitatory synapses in the brain and spinal cord (Lim et al., 2001).

In fact, clustering of Shank1 in the PSD of rat dorsal horn neurons a few hours after a partial

sciatic nerve injury (in the CCI model) was associated with acute heat allodynia and reduced

weight bearing on the denervated side (Miletic et al., 2005). This could implicate Sharpin in the

induction of autotomy. However, our following data do not support this suggestion.

Constitutive levels: No contrast was found in the constitutive levels of this gene in the spinal

cord or DRGs of intact A and B mice. Thus, this gene may not be relevant to triggering

autotomy.

Postoperative levels: In the DRGs (but not the spinal cord) we found a small but significant

down-regulation of Sharpin expression levels in A (but not B) mice postoperatively, compared to

sham-operated mice. This down-regulation was also noted in the negative fold change in the

lower levels of AD/AS versus BD/BS.

The fact that Sharpin is part of the PSD would have made it a good candidate as an autotomy

gene if there was a fold change in the spinal cord however my finding shows that it is

significantly regulated in the DRGs but not in the spinal cord suggesting its role in the DRG may

be unrelated to its postsynaptic role.

Since a Sharpin knockout mouse is not available, in order to further identify whether Sharpin is a

candidate autotomy gene one could produce the Neuroma Model in a disease model of Sharpin

in C57BL/Ka mice with a spontaneous mutation (Intragenic deletion causing a premature stop

codon) (Hogen-Esch et al., 1993), determine the autotomy level and compare it with the wildtype

level , if the two levels of autotomy are significantly different, then one may conclude that this

gene is linked with autotomy.

Sequence mismatches: Sequence mismatches between A and B strains are a missense in an exon

as well as in regulatory regions.

In conclusion, this gene remains a candidate gene for autotomy.

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4.2.9 2010109I03RIK: This gene encodes an unknown protein with minimal expression levels in

the nervous system.

Constitutive levels: The difference in constitutive expression levels of this gene is not significant

after correction for multiple comparisons. Before the correction there was a significant contrast

between A and B mice in the spinal cord. Since A mice had lower levels than B mice, this gene

may be associated with protection of B mice from the induction of autotomy.

Postoperative levels: No significant differences were found in the DRGs or spinal cord when

comparing AD vs. AS or BD vs. BS. However, when comparing AD/AS to BD/BS we noted a

highly significant fold change of 2.44, suggesting that an insignificant up-regulation in AD vs.

AS, coupled with an insignificant down-regulation in BD vs. BS may have caused the significant

contrast in autotomy levels in AD/AS vs. BD/BS mice. Thus, denervation may trigger regulation

of this gene in two opposite directions in the A and B mice.

Sequence mismatches: The sequence mismatches between A and B strains are all in exons,

therefore, the effect on autotomy may be related to a difference in structural/functional properties

for this gene‘s product.

In conclusion, this gene remains a candidate gene for autotomy.

4.2.10 9030619P08RIK: This gene encodes an unknown protein with minimal expression levels

in the nervous system.

Constitutive levels: No contrast was found in the constitutive levels of this gene in the spinal

cord or DRGs of intact A and B mice. Thus, this gene may not be relevant to triggering

autotomy.

Postoperative levels: Our data show that the expression of this gene is not significant after the

correction for multiple comparisons is made. Before the correction significant up-regulation was

found only in the spinal cord (but not the DRGs) of denervated B (but not A) mice, compared to

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sham-operated mice. This up-regulation is also manifested in a significant increased fold change

in BD/BS vs. AD/AS. Thus, this gene may protect the B strain against autotomy.

Sequence mismatches: The 2 sequence mismatches between A and B strains are in the regulatory

regions.

In conclusion, this gene remains a candidate gene for autotomy.

4.2.11 Zfp707: This gene encodes the zinc finger protein 707, expressed in all tissues but has

relatively low expression in the nervous system.

Constitutive levels: No contrast was found in the constitutive levels of this gene in the spinal

cord or DRG of intact A and B mice. Thus, this gene may not be relevant to triggering autotomy.

Postoperative levels: The postoperative expression levels of this gene are not significantly

different after the correction for multiple comparisons is made (but before the correction it was

significant different in the spinal cord when comparing AD/AS to BD/BS).

Sequence mismatches: The sequence mismatch between A and B is in the regulatory region.

In conclusion, this gene seems to be a weak candidate gene for autotomy.

Other candidate genes have previously been studied in the region where the location of

Pain1 has been considered in previous mapping attempts, including Cacng2 (Nissenbaum et al,

2010; abstracts on KCTD17 and CSF2RB). Several supporting lines of evidence were included

in mouse and human studies, however, since none of these genes is located in the new location of

Pain1, this study did not include them in the analysis. Table 11 also shows for each of these 11

genes a priority score ranking their relevance to autotomy by categorizing them into 5 levels

ranging from Low to High. Assigning these priority levels to the genes was based on (1)

published constitutive expression levels in the nervous system

(http://biogps.gnf.org/?#goto=genereport&id=18810), (2) the significance level of constitutive

and fold changes found in this study, (3) the type and number of sequence mismatches between

the A and B strains, as well as (4) known biological function related to pain. The following three

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genes were scored High (Lynx1) or Medium-High (Arc and Sharpin) priority. The following

suggestions for future studies may help identify which one of these is the best candidate

autotomy gene in Pain1.

1. Carry out a comparison of the sequence mismatches only for these genes for males (Seltzer et

al., 2001; Devor et al., 2005, 2007) of other mice lines whose autotomy levels are known,

including C3H/HeB, SM/J, and Balb/cJ, (expressing high autotomy), DBA/2J, IL/nJ, and

129/SvJ, (expressing moderate levels of autotomy), and RIIIS/J, C58/J, and AKR/J (expressing

low levels of autotomy).

2. Determine autotomy levels in the Neuroma Model for the Lynx1 (Miwa et al., 2006) and Arc

(Plath et al., 2006) available knockout mice and also for the Sharpin mutated mouse.

3. Functional Assay - measuring functionally active proteins encoded by these three candidates

in neural structures in intact, sham-operated and denervated mice, e.g., by ELISA, using

antibodies against the proteins of interest or by using other methods.

4. Finally, since there is a common ancestry among mammalian species, and pain mechanisms

maybe conserved across mice and humans, the same candidate gene may be involved in

neuropathic pain in humans. Pain1 on mouse chr 15 is orthologous to two human chromosomes:

chr 8 and chr 22 (Figure 31). However, these 3 candidate genes are located on human chr 8.

Figure 31: Pain1 orthologous regions on human chromosomes; Pain1 on mouse chr 15 (interval

marked by the B/W checkerboard, followed by an interval

marked with longitudinal B/W stripes) maps partly to

human chr 22 (marked by B/W checkerboard) and partly

to human chr 8 (longitudinal B/W stripes). The peak of

Pain1 on mouse chr 15 (marked by the red rectangle)

corresponds to human chr 8 (marked by the red line).

Based on the exclusive availability in our lab of more than

4,000 DNA samples of pain patients and their matching

controls, including women post-mastectomy and men and

women post-amputation of a limb, we could genotype

tagging SNPs in extreme case/controls and study whether

there is a significant association between these genotypes

78

and neuropathic pain levels. In future studies, a candidate gene showing significant results could

be sequenced in a subgroup of cases/controls to identify causal SNPs.

During the period I have been working on my thesis Darvasi et al. (2010) have been working

towards identifying the gene in Pain1, based on a number of experiments different from mine,

they were able to localize this QTL to an interval spanning from 75.0 to 79.50 Mb, which only

partially overlaps with the map location that I was able to map, spanning from 73.25Mb and

76.32Mb. Based on several criteria they believe that Cacng2, a gene near their newly mapped

peak, is the autotomy gene in Pain1 (Nissenbaum et al., 2010). The location of this gene is chr.

15:77822178-77950458 bp, which is outside the peak that I was able to map. In fact, our lab

(including me) have contributed to this paper (Nissenbaum et al., 2010) genotypic data on two

human cohorts, one on 220 human amputees (with or without phantom limb pain) and another

one on 549 women postmastectomy (with or without postmastectomy chronic pain, PMPS). No

significant association was found for the limb amputation cohort, whereas marginal significance

was found for the PMPS cohort. Additional supporting evidence was provided from gene

expression data, and functional data using the Stargazer mouse (a natural mutant at this gene).

My data suggest that other genes in the peak I located may be perhaps better candidate genes and

they should be tested as well before concluding that Cacng2 is indeed the only likely autotomy

gene in Pain1.

4.3. Limitations of the study

4.3.1. This study is based on the assumption that the Neuroma Model is a model for neuropathic

pain and that autotomy behaviour is a response of the animal to ectopic inputs from the nerve-

end neuromas and the associated DRGs, which when reaching the brain this input is translated to

a sensation of pain referred to the denervated limb. Since the Neuroma Model can only be used

in rodents, it is impossible to know whether it truly corresponds to the feeling of spontaneous

pain or is a response to other disagreeable sensations like paresthesia and dysethesia. However,

since treatments that enhance autotomy, also enhance the expression of pain it would be

reasonable to link the two together (Coderre and Melzack, 1986). Moreover, finding that Cacng2

is both an autotomy gene and a gene for neuropathic pain in women post-mastectomy supports

the clinical relevance of this model.

79

4.3.2. When selecting candidate genes based on sequence mismatch between the parental lines, I

only looked at the exons and regulatory regions; 5‘UTR and 3‘UTR, and not at intronic and

intergenic (between genes) sequence mismatches. In spite of the unknown role of the intergenic

non-coding regions, the fact that some of them are highly conserved and show a high degree of

similarity between human and mouse DNA compared to the protein-coding genes (Kryukov et

al., 2005) suggests that intergenic non-coding regions may play an important role in genetic

control of complex traits, which is not known as of yet. Future work could screen these

intergenic and intronic regions.

4.3.3. Selection of the 11 genes from the previous list of 26 genes was based on the significant

difference in expression levels between A and B parental lines, however it is also possible that a

sequence mismatch would change the quality of the gene product (protein) affecting its receptor

binding efficiency rather than the quantity of the gene product (levels of the mRNA) that I

measured in my study.

4.3.4. Selection of the 3 finalist genes from the previous list of 11 genes was based on the

significant difference in expression levels between A and B parental lines in the spinal cord

and/or DRGs. However, it is possible that changes supraspinally or in the neuromas might have

been more relevant since the gene for autotomy operates exclusively there and not in the

structures I studied here.

4.4. Clinical Application

Autotomy (self-mutilation behaviour) is rare in humans, however several studies have reported

cases where patients with neuropathic pain have expressed self-mutilation behaviour. One such

study reports on cases of self-mutilation in young children following brachial plexus birth injury

(McCann et al., 2004), another study describes four cases of compulsive self-injurious behaviour

in patients with central nervous system (CNS) lesions. This behaviour targets the painful part of

the body which is usually analgesic or hypoalgesic very much like autotomy of the anaesthetic

hindpaw in the Neuroma Model (Mailis, 1996). These human cases of autotomy are rare. It is

possible that this is not a typical human form of expression of neuropathic pain, perhaps because

80

humans have a better understanding of the detrimental aspects of self-mutilation. Rodents may

be more ‗oral‘ animals, treating their body (‗grooming‘), and objects in space, using their mouth,

including treating a painful limb (licking, biting, etc). On the other hand, humans are ‗manual‘

mammals, using hands to achieve the same goals.

The autotomy behavior is used as a model or a surrogate to an expression of pain such as

anesthesia dolorosa, brachial plexus avulsion and phantom limb pain following amputation of a

limb or removal of an organ. Finding genes for autotomy in rodents and neural pathways where

their product is expressed, can lead to identifying targets for pharmacological interventions for

human neuropathic pain. Therefore, my results may lead to the identification of a target for such

treatment.

4.5. SUMMARY

In this study, by using the original phenotypic neuropathic pain data from Seltzer et al.

(2001), as well as adding a number of additional autotomy phenotypes that were never studied

before, I was able to replicate the location of Pain1 as a QTL on chr 15 that is associated with

neuropathic pain-like behaviour. I then remapped the peak of this QTL to a new position that is a

few Mb away from its original location, and then determined the significant confidence length of

Pain1. I also used this data to estimate the heritability level (h2) of a number of autotomy traits

and showed that they range from 0.35 to 0.42. This is lower than reported values but those were

not corrected for homozygocity. After such correction my data fits well with reported values. I

also estimated that the number of effective loci (EGL) controlling these traits is <8, suggesting

that autotomy is controlled oligogenically. I then reconstructed the haplotypic structure of Pain1,

a step that indicated the existence of other autotomy QTLs including 2 on chr 14, one of which

has significant effects on autotomy, which I named Pain3. For some RI lines Pain3 plays the

major role in autotomy whereas for other lines it is Pain1 and for others – both loci. I suggested

that the level of autotomy a strain of mice or an RI line expresses is the combined effect of

several genes and that the interaction may be due to complex epistatic effects. The refined map

of Pain1 enabled me to identify 80 candidate autotomy genes on Pain1, of which only 26

showed sequence mismatches between A and B strains. Eleven of the 26 genes had significant

differences in the constitutive and/or post-denervation fold changes in the expression levels in A

versus B mice. When further considering the following additional data for each of these 11

81

genes, I was able to prioritize the candidacy of these genes for being an autotomy gene by

considering: (i) the type and number of sequence mismatches in these 11 genes between A and B

mice, (ii) published data on the expression levels of these 11 genes in various neural structures,

(iii) their known biological function related to pain or other neural functions, and (iv) the

expression data from my study. Based on this selection process I shortlisted the following three

genes: Lynx1, Arc and Sharpin, as my best candidates. Cacng2 has been identified as a possible

candidate pain gene in Pain1. However, further research is needed to select from these 4

candidates an autotomy gene. I offered a number of experiments that could be done to

accomplish this goal. To my understanding, before testing the three genes I identified as the most

likely candidates, the final identification of Cacng2 as the autotomy gene in this chromosomal

interval cannot be made. Finding a gene for autotomy may have clinical applications in treating

pain patients.

82

References

Ali Z, Ringkamp M, Hartke TV, Chien HF, Flavahan NA, Campbell JN, Meyer RA. Uninjured

C-fibre nociceptors develop spontaneous activity and alpha-adrenergic sensitivity following

L6 spinal nerve ligation in the monkey. J Neurophysiol 1999;81:455-66.

Amir R, Kocsis JD, Devor M. Multiple interacting sites of ectopic spike electrogenesis in

primary sensory neurons. J Neurosci 2005;25:2576-85.

Bailey DW. Recombinant-inbred strains. An aid to finding identity, linkage, and function of

histocompatibility and other genes. Transpl 1971;11:325-7.

Baron R, Levine JD, Fields HL. Causalgia and reflex sympathetic dystrophy: does the

sympathetic nervous system contribute to the generation of pain? Muscle Nerve 1999;

22:678-95.

Baron R. Complex regional pain syndromes. In: McMahon SB, Koltzenberg M, editors. Wall

and Melzack's Textbook of Pain, 5th edition. Amsterdam: Elsevier; 2006. p. 1011-27.

Beggs S, Salter MW. Stereological and somatotopic analysis of the spinal microglial response to

peripheral nerve injury Brain, Behav, Immun 2007; 21: 624-33.

Beggs S, Salter MW, Microglia–neuronal signaling in neuropathic pain hypersensitivity. Curr

Opin Neurobiol 2010; 20:271-3.

Belfer I, Wu T, Kingman A, Krishnaraju RK, Goldman D, Max MB. Candidate gene studies

of human pain mechanisms: Methods for optimizing choice of polymorphisms and sample

size. Anesthesiology 2004;100:1562-72.

Browman KW. The Genomes of recombinant inbred lines. Genetics 2005;169:1133-6.

83

Campbell JN, Meyer RA. Mechanisms of neuropathic pain. Neuron 2006;52:77-92.

Christensen M D, Hulsebosch CE. Chronic central pain after spinal cord injury. J Neurotrauma

1997;14:517-37.

Coderre TJ, Melzack R. Procedures which increase acute pain sensitivity also increase autotomy.

Exp Neurol. 1986;92:713-22.

Cohn S, Seltzer Z. Inherited propensity for neuropathic pain is mediated by sensitivity to injury

discharge. Neuroreport 1991;2:647-50.

Costigan M, Scholz J, Woolf CJ. Neuropathic Pain: A maladaptive response of the nervous

system to damage. Annu Rev Neurosci 2009;32:1–32.

Coull JA, Beggs S, Boudreau D, Boivin D, Tsuda M, Inoue K, Gravel C, Salter MW, De

Koninck Y. BDNF from microglia causes the shift in neuronal anion gradient underlying

neuropathic pain. Nature 2005;438:1017-21.

Cregg R, Momin A, Rugiero F, Wood JN, Zhao J. Pain channelopathies. J Physiol. 2010; 588.:

1897–1904

D'Andrea R J, Harrison-Findik D, Butcher C M, Finnie J, Blumbergs P, Bartley P, McCormack

M, Jones K, Rowland R, Gonda TJ, Vadas MA. Dysregulated hematopoiesis and a

progressive neurological disorder induced by expression of an activated form of the human

common beta chain in transgenic mice. J Clin Invest 1998;102:1951-1960

Defrin R, Zeitoun I, Urca G. Strain differences in autotomy levels in mice: relation to spinal

excitability. Brain Res 1996;711:241-4.

Dessaud E, Salaün D, Gayet O, Chabbert M, deLapeyrière O. Identification of lynx2, a novel

member of the ly-6/neurotoxin superfamily, expressed in neuronal subpopulations during

mouse development. Mol Cell Neurosci 2006;31:232-242.

84

Devor M, Raber P. Heritability of symptoms in an experimental model of neuropathic pain. Pain

1990;42:51-67.

Devor M, Seltzer Z. Pathophysiology of damaged nerves in relation to chronic pain. In:

Textbook of pain (1999), Ed 4 (Wall PD, Melzack R), pp 129–164. London: Churchill

Livingstone.

Devor M, Gilad A, Arbilly M, Yakir B, Raber P, Pisanté A, Darvasi A. Pain1: a neuropathic pain

QTL on mouse chromosome 15 in a C3HxC58 backcross. Pain 2005;116:289-93.

Devor M, Canho S. Raber P. Heritability of symptoms in the Neuroma Model of neuropathic

pain Replication and complementation analysis. Pain 2005;116:294–301.

Devor M, Gilad A, Arbilly M, Nissenbaum J, Yakir B, Raber P, Minert A, Pisanté A, Darvasi A.

Sex-specific variability and a 'cage effect' independently mask a neuropathic pain

quantitative trait locus detected in a whole genome scan. Eur J Neurosci 2007;26:681-8.

Dublin P and Hanani M. Satellite glial cells in sensory ganglia: their possible contribution to

inflammatory pain. Brain Behav Immun 2007;21:592-98.

Dubner R, Ruda MA. Activity-dependent neuronal plasticity following tissue injury and

inflammation. Trends Neurosci 1992;15:96-103.

Ducreux D, Attal N, Parker F, Bouhassira D. Mechanisms of central neuropathic pain: a

combined psychophysical and fMRI study in syringomyelia. Brain 2006;129:963-76.

Dworkin R H, An Overview of neuropathic pain: syndromes, symptoms, signs, and several

mechanisms. Clin J Pain 2002;18:343–9

Dworkin RH, Backonja M, Rowbotham MC, Allen RR, Argoff CR, Bennett GJ, Bushnell MC,

Farrar JT, Galer BS, Haythornthwaite JA, Hewitt DJ, Loeser JD, Max MB, Saltarelli M,

Schmader KE, Stein C, Thompson D, Turk DC, Wallace MS, Watkins LR, Weinstein SM.

85

Advances in neuropathic pain: diagnosis, mechanisms, and treatment recommendations.

Arch Neurol 2003;60:1524-34.

H Flor, L Nikolajsen, T S Jensen. Phantom limb pain: a case of maladaptive CNS plasticity? Nat

Rev Neurosci 2006; 7: 873-881

Frost FS, Mukkamala S, Covington E. Self-inflicted finger injury in individuals with spinal cord

injury: an analysis of 5 cases J Spinal Cord Med 2008; 31, Number 1

Fucile S, Lax P, Eusebi F: Nicotine modulates the spontaneous synaptic activity in cultured

embryonic rat spinal cord interneurons. J Neurosci Res 2002;67:329-36.

Gache Y, Chavanas S, Lacour J P, Wiche G, Owaribe K, Meneguzzi G, Ortonne JP. Defective

expression of plectin/HD1 in epidermolysis bullosa simplex with muscular dystrophy. J.

Clin Invest 1996;97:2289–98.

Gao B, Hierl M, Clarkin K, Juan T, Nguyen H, Valk M, Deng H, Guo W, Lehto SG, Matson D,

McDermott JS, Knop J, Gaida K, Cao L, Waldon D, Albrecht BK, Boezio AA, Copeland

KW, Harmange JC, Springer SK, Malmberg AB, McDonough SI. Pharmacological effects

of nonselective and subtype-selective nicotinic acetylcholine receptor agonists in animal

models of persistent pain. Pain 2010;149:33-49.

Genzen JR, McGehee DS: Nicotinic modulation of GABAergic synaptic transmission in the

spinal cord dorsal horn. Brain Res 2005;1031:229-237.

Getts DR, Terry RL, Getts MT, Muller M, Rana S, Shrestha B, Radford J, Van Rooijen N,

Campbell IL, King NJ. Ly6c? inflammatory monocytes are microglial precursors recruited

in a pathogenic manner in West Nile virus encephalitis. J Exp Med 2008;205:2319–37.

Graeber MB, Streit W J. Microglia: biology and pathology Acta Neuropathol 2010;119:89–105.

86

Gumley TP, McKenzie IFC, Sandrin MS. Tissue expression, structure and function of the murine

Ly-6 family of molecules. Immunol. Cell Biol 1995a;73:277–96.

Gumley TP, McKenzie IFC, Sandrin MS. Sequence and structure of the mouse ThB gene.

Immunogenetics 1995b;42:221–24.

Gustorff B, Anzenhofer S, Sycha T, Lehr S, Kress HG. The sunburn pain model: the stability of

primary and secondary hyperalgesia over 10 hours in a crossover setting. Anesth Analg

2004;98:173–7

Harrison GI, Young AR, Mcmahon SB. Ultraviolet radiation-induced inflammation as a model

for cutaneous hyperalgesia. Journal of Investigative Dermatology 2004;122:183–9

Hegmann JP, Possidente B, Estimating genetic correlations from inbred strains. Behav Genet

1981;11:103-14.

HogenEsch H; Gijbels MJ; Offerman E; van Hooft J; van Bekkum DW; Zurcher C. A

spontaneous mutation characterized by chronic proliferative dermatitis in C57BL mice. Am

J Pathol 1993; 143:972-82

Hong D, Conell-Price J, Cheng S, Flood P. Transdermal nicotine patch for postoperative pain

management: a pilot dose-ranging study. Anesth Analg 2008;107:1005-10.

Hossaini M, Jongen JL M, Biesheuvel K, Kuhl D, Holstege JC. Nociceptive stimulation induces

expression of Arc/Arg3.1 in the spinal cord with a preference for neurons containing

encephalin. Mol Pain 2010; 6:43.

Hu P, Bembrick AL, Keay KA, McLachlan EM. Immune cell involvement in dorsal root ganglia

and spinal cord after chronic constriction or transection of the rat sciatic nerve. Brain Behav

Immun 2007; 21:599-616.

87

Ji R, Kawasaki Y, Zhuang Z, Wen Y, Decosterd I. Possible role of spinal astrocytes in

maintaining chronic pain sensitization: review of current evidence with focus on bFGF/JNK

pathway. Neuron Glia Biol 2006;2:259–69.

Kigerl K A, Gensel J C, Ankeny DP, Alexander JK, Donnelly DJ, Popovich PG. Identification of

Two distinct macrophage subsets with divergent effects causing either neurotoxicity or

regeneration in the injured mouse spinal cord. J Neurosci 2009;29:13435-44.

Kiyosawa A, Katsurabayashi S, Akaike N, Pang ZP. Nicotine facilitates glycine release in the rat

spinal dorsal horn. J Physiol 2001;536:101-10.

Kohama I, Ishikawa K, Kocsis JD. Synaptic reorganization in the substantia gelatinosa after

peripheral nerve neuroma formation: aberrant innervation of lamina II neurons by Abeta

afferents. J Neurosci 2000;20:1538–49.

Kohno T, Ji RR, Ito N, Allchorne AJ, Befort K, Karchewski LA, Woolf CJ. Peripheral

axonal injury results in reduced opioid receptor action pre- and post-synaptically in the spinal

cord. Pain 2005; 117:77–87

Kryukov GV, Schmidt S, Sunyaev S. Small fitness effect of mutations in highly conserved non-

coding regions. Human Mol Gen 2005;14: 2221-9

Lariviere WR, Mogil JS. The genetics of pain and analgesia in laboratory animals. Methods Mol

Biol 2010;617:261-78.

LeDoux MS, Xu L, Xiao J, Ferrell B, Menkes DL, Homayouni R. Murine central and peripheral

nervous system transcriptomes: comparative gene expression. Brain Res 2006;1107:24-41.

Lee T, Mane S, Eid T, Zhao H, Lin A, Guan Z, Kim JH, J Schweitzer, King-Stevens D, Weber P,

Spencer SS, Spencer DD, de Lanerolle NC. Gene Expression in Temporal Lobe Epilepsy is

Consistent with Increased Release of Glutamate by Astrocytes. Mol Med 2007;13:1–13.

88

Lie AA, Schröder R, Blümcke I, Magin TM, Wiestler OD, Elger C E. Plectin in the human

central nervous system: predominant expression at pia/glia and endothelia/glia interfaces.

Acta Neuropathol 1998;96:215-221.

Lim S, Sala C, Yoon J, Park S, Kuroda S, Sheng M, Kim E. Sharpin, a novel postsynaptic

density protein that directly interacts with the shank family of proteins. Mol Cell Neurosci

2001;17:385-97.

Lin CS, Tsaur ML, Chen CC, Wang TY, Lin CF, Lai YL, Hsu TC, Pan YY, Yang CH, Cheng

JK. Chronic intrathecal infusion of minocycline prevents the development of spinal-nerve

ligation-induced pain in rats. Reg Anesth Pain Med. 2007;32:209–16.

Mailis A. Compulsive targeted self-injurious behavior in humans with neuropathic pain: A

counterpart of animal autotomy? Four case reports and literature review. Pain 1996;64:569-

78

Manly KF, Wang J, Williams RW. Weighting by heritability for detection of quantitative trait

loci with microarray estimates of gene expression. Genome Biol 2005;6:R27.

Mannion RJ, Doubell TP, Coggeshall RE, Woolf CJ. Collateral Sprouting of uninjured primary

afferent A-Fibres into the superficial dorsal horn of the adult rat spinal cord after topical

capsaicin treatment to the sciatic nerve. J Neurosci 1996;16:5189–95.

McCann ME, Waters P, Goumnerova LC, Berde C. Self-mutilation in young children following

brachial plexus birth injury. Pain 2004;110:123–9

Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain

1975;1:277–99.

89

Melzack R, Katz J. The McGill Pain Questionnaire: Appraisal and current status. In Turk DC,

Melzack R. (Eds). Handbook of Pain Measurement, 2nd edition, New York: Guilford Press,

2001;35–52.

Merskey H, Bogduk N. IASP 1994. Task force on taxonomy. In: Classification of Chronic Pain:

Description of Chronic Pain Syndromes and definition of Pain Terms. IASP Task Force on

Taxonomy 1994. Merskey H, Bogduk N (Eds). IASP Press Seattle, WA, USA. 210.

Haanpää M, Treede RD . Diagnosis and classification of neuropathic pain. IASP, Pain: Clinical

Updates. 18; 7, September 2010.

Meyer RA, Ringkamp M, Campbell JN, Raja SN. Peripheral mechanisms of cutaneous

nociception. In Wall' and Melzalck's Textbook of Pain. ed. Mcmahon SB & Koltzenburg M,

2006 pp. 27. Elsevier Churchill Livingstone

Miletic G, Miyabe T, Gebhardt KJ, Miletic V. Levels of Homer1b/c and Shank1a in the post-

synaptic density of spinal dorsal horn neurons are associated with neuropathic pain in rats.

Neurosci Lett 2005;386:189-93.

Miwa JM, Stevens TR, King SL, Caldarone BJ, Ibanez-Tallon I, Xiao C, Fitzsimonds R M,

Pavlides C, Lester H A, Picciotto M R, Heintz N. The prototoxin lynx1 acts on nicotinic

acetylcholine receptors to balance neuronal activity and survival in vivo. Neuron 2006;51:

587–600.

Mogil JS, Wilson SG, Bon K, Lee SE, Chung K, Raber P, Pieper JO, Hain HS, Belknap JK,

Hubert L, Elmer GI, Chung JM, Devor M. Heritability of nociception I: responses of 11

inbred mouse strains on 12 measures of nociception. Pain 1999a;80:67-82.

90

Mogil JS, Wilson SG, Bon K, Lee SE, Chung K, Raber P, Pieper JO, Hain HS, Belknap JK,

Hubert L, Elmer GI, Chung JM, Devor M. Heritability of nociception. II. Types of

nociception revealed by genetic correlation analysis. Pain 1999;80:83-93.

Mogil JS, Seltzer Z, Devor M. Gene-environment interactions affecting pain phenotype. In:

Mogil JS, editor. The genetics of pain. Seattle (WA). IASP Press 2004;257–82.

Mogil JS, Max MB. The genetics of pain. In Koltzenburg, M. and McMahon, S.B. (Eds.) Wall

and Melzack's Textbook of Pain, 5th ed. Philadelphia: Elsevier/Churchill Livingstone,

2006;159-174.

Mogil J. Animal models of pain: progress and challenges. Nat Rev Neurosci 2009;10:283–294.

Nakamura M, Jang IS. Presynaptic nicotinic acetylcholine receptors enhance GABAergic

synaptic transmission in rat periaqueductal gray neurons. Eur J Pharmacol 2010;640:178-84.

Nielsen CS, Stubhaug A, Price DD, Vassend O, Czajkowski N, Harris JR. Individual differences

in pain sensitivity: genetic and environmental contributions. Pain 2007;136:21-29.

Nissenbaum J, Shpigler H, Pisanté A, delCanho S, Minert A, Seltzer Z, Devor M, Darvasi A.

pain2: A neuropathic pain QTL identified on rat chromosome 2, Pain 2008;135:92-97.

Nissenbaum J, Devor M, Seltzer Z, Gebauer M, Michaelis M, Tal M, Dorfman R, Abitbul-

Yarkoni M, Lu Y, Elahipanah T, DelCanho S, Minert A, Fried K, Persson A, Shpigler H,

Shabo E, Yakir B, Pisante A, Darvasi A. Susceptibility to chronic pain following nerve

injury is genetically affected by CACNG2. Genome Res 2010 ;20:1180-90.

Norbury TA, MacGregor AJ, Urwin J, Spector TD, McMahon SB. Heritability of responses to

painful stimuli in women: a classical twin study. Brain 2007;130:3041–49.

Okada-Ogawa A, Suzuki I, Sessle BJ, Chiang CY, Salter MW, Dostrovsky JO, Tsuboi Y, Kondo

M, Kitagawa J, Kobayashi A, Noma N, Imamura Y, Iwata K. Astroglia in medullary dorsal

91

horn (trigeminal spinal subnucleus caudalis) are involved in trigeminal neuropathic pain

mechanisms. J Neurosci 2009;29:11161-71.

Okamoto M, Baba H, Goldstein PA, Higashi H, Shimoji K, Yoshimura M. Functional

reorganization of sensory pathways in the rat spinal dorsal horn following peripheral nerve

injury. J Physiol 2001;532:241-50.

Peleg L, Nesbitt MN. Genetic control of thymus size in inbred mice. J Hered 1984;75:126-130.

Pertovaara A, Almeida A. Pain, Endogenous Pain Modulation: Descending Inhibitory Systems.

In Cervero F, Jensen TS (Eds.) Handbook of Clinical Neurology. Pain vol. 81. Elsevier,

Amsterdam 2006.

Pfendner E, Rouan F, Uitto J. Progress in epidermolysis bullosa: the phenotypic spectrum of

plectin mutations. Exp. Dermatol 2005;14 :241–9.

Plath N, Ohana O, Dammermann B, Errington ML, Schmitz D, Gross C, Mao X, Engelsberg A,

Mahike C, Welzi H, Kobalz U, Stawrakakis A, Fernandez E, Walteriet R, Bick-Sander A,

Therstappen E, Cooke SF, Blanquet V, Wurst W, Salmen B, Bosl MR, Lipp HP, Grant

SGN, Bliss TVP, Wolfer DP, Kuhl D. Arc/Arg3.1 is essential for the consolidation of

synaptic plasticity and memories. Neuron 2006;52:437-444.

Porreca F, Ossipov MH, GF Gebhart. Chronic pain and medullary descending facilitation.

Trends Neurosci 2002; 25:319-25

Rahman W, D‘Mello R, Dickenson AH. Peripheral nerve injury-induced changes in spinal

alpha(2)-adrenoceptor-mediated modulation of mechanically evoked dorsal horn neuronal

responses. J Pain. 2008;9:350–9

Ren K. Emerging role of astroglia in pain hypersensitivity. Jpn Dent Sci Rev 2010;46:86-92.

92

Salter M W. Cellular neuroplasticity mechanisms mediating pain persistence. J Orofac Pain

2004;18:318-24

Salter M W. Cellular Signalling pathways of spinal pain neuroplasticity as targets for analgesic

development. Curr Top Med Chem 2005;5:557-67.

Sampson SB, Higgins DC, Elliot RW, Taylor BA, Lueders KK, Koza RA, Paigen B. An edited

linkage map for the AXB and BXA recombinant inbred mouse strains. Mamm Genome

1998;9:688-94.

Scholz J, Broom DC, Youn DH, Mills CD, Kohno T, Suter MR, KA Moore, Decosterd I,

Coggeshall RE, CJ Woolf. Blocking Caspase activity prevents transsynaptic neuronal

apoptosis and the loss of inhibition in lamina II of the dorsal horn after peripheral nerve

injury. J Neurosci 2005; 25:7317–23.

Scholz J, Woolf CJ. The neuropathic pain triad: neurons, immune cells and glia. Nat. Neurosci

2007;10:1361–68.

Seltzer Z, Dubner R, Shir Y. A novel behavioural model of neuropathic pain disorders produced

in rats by partial sciatic nerve injury. Pain 1990;43:205-18.

Seltzer Z, Cohn S, Ginzburg R, Beilin B. Modulation of neuropathic pain behaviour in rats by

spinal disinhibition and NMDA receptor blockade of injury discharge. Pain 1991;45:69-75.

Seltzer Z, Beilin BZ, Ginzburg R, Paran Y, Shimko T. The role of injury discharge in the

induction of neuropathic pain behaviour in rats. Pain 1991;46:327-36.

Seltzer Z, Beilin BZ, Ginzburg R, Paran Y, Shimko T. The role of injury discharge in the

induction of neuropathic pain behaviour in rats. Pain 1991;46:327-36.

Seltzer Z, Wu T, Max MB, Diehl SR. Mapping a gene for neuropathic pain-related behaviour

following peripheral neurectomy in the mouse. Pain 2001;93:101-6

93

Seltzer Z, Herzberg R, Rozin M, Adziashvili L. Further evidence correlating autotomy with

chronic pain in peripherally deafferented rats: postoperative alterations in adrenocortical

activity. Neuroscience 1987;22:S321.

Seltzer Z, Tal M, Sharav Y. Suppression of autotomy following peripheral nerve injury in rats

by amitriptylline, diazepam and saline. Pain 1989;37:245-52.

Sherman RA. Phantom pain. In: Sherman RA, Devor M, Heermann-Do K, editors. New York.

Plenum Press. 1997:1-52.

Shifman S, Bell J T, Copley R R, Taylor MS, Williams RW. A high-resolution single nucleotide

polymorphism genetic map of the mouse genome. PLoS Biol 2006;4:e395.

Skamene E, James SL, Meltzer MS, Nesbitt MN. Genetic control of macrophage activation for

killing of extracellular targets. J Leukocyte Biol 1984;35:65-69.

Soares S, Barnat M, Salim C, von Boxberg Y, Ravaille-Veron M, Nothias F. Extensive structural

remodeling of the injured spinal cord revealed by phosphorylated MAP1B in sprouting

axons and degenerating neurons. Eur. J. Neurosci 2007;26:1446–61

Soares S, von Boxberg Y, Lombard MC, Ravaille-Veron M, Fischer I, Eyer J, Nothias F.

Phosphorylated MAP1B is induced in central sprouting of primary afferents in response to

peripheral injury but not in response to rhizotomy. Eur J Neurosci 2002;16:593–606.

Stroncek D F, Caruccio L, Bettinotti M. CD177: A member of the Ly-6 gene superfamily

involved with neutrophil proliferation and polycythemia vera. J Transl Med 2004; 2:8.

Takeda D, Nakatsuka T, Papke R, Gu JG: Modulation of inhibitory synaptic activity by a non-

α4β2, non-α7 subtype of nicotinic receptors in the substantia gelatinosa of adult rat spinal

cord. Pain 2003;101:13-23.

94

Takeda D, Nakatsuka T, Gu JG, Yoshida M. The activation of nicotinic acetylcholine receptors

enhances the inhibitory synaptic transmission in the deep dorsal horn neurons of the adult rat

spinal cord. Mol Pain 2007;3:26.

Taylor BA. Recombinant inbred strains: Use in gene mapping. In: Morse III HC (ed), Origins of

Inbred Mice. Academic Press, NY. 1978;423-38.

Thacker MA, Clark AK, Bishop T, Grist J, Yip PK, Moon LD, Thompson SW, Marchand F,

McMahon SB. CCL2 is a key mediator of microglia activation in neuropathic pain states.

Eur J Pain 2009;13:263-72.

Trang T, Beggs S, Salter M W. Purinoceptors in microglia and neuropathic pain. Eur J Physiol

2006; 452: 645–52

Wall PD, Waxman S, Basbaum AI. Ongoing activity in peripheral nerve: Injury discharge. Exp

Neurol 1974;45:576-89.

Wall PD, Gutnick M. Properties of afferent nerve impulses originating from a neuroma. Nature

1974;248:740-43.

Wall PD, Devor M, Inbal R, Scadding JW, Schonfeld D, Seltzer Z, Tomkiewicz MM. Autotomy

following peripheral nerve lesions: experimental anaesthesia dolorosa. Pain 1979;7:103-11.

Wall P D, Devor M. Sensory afferent impulses originate from dorsal root ganglia as well as from

the periphery in normal and nerve injured rats. Pain 1983;17:321-39.

Wang LX, Wang ZJ. Animal and cellular models of chronic pain. Adv Drug Deliv Rev

2003;55:949–65.

Wang J, Williams RW, Manly KF. WebQTL: web-based complex trait analysis.

Neuroinformatics 2003;1:299-308.

95

Wei F, Guo W, Zou S, Ren K, Dubner R. Supraspinal glial-neuronal interactions contribute to

descending pain facilitation. J Neurosci 2008;28:10482-95.

Wei F, Dubner R, Zou S, Ren K, Bai G, Wei D, Guo W. Molecular depletion of descending

serotonin unmasks its novel facilitatory role in the development of persistent pain. J

Neurosci 2010; 23;30:8624-36

Woolf CJ, Salter MW. Neuronal Plasticity: Increasing the Gain in Pain. Science 2000;288:1765

– 68.

Woolf CJ, Ma Q. Nociceptors—noxious stimulus detectors. Neuron 2007; 55:353–64.

Zhang J, Shi XQ, Echeverry S, Mogil JS, De Koninck Y, Rivest S. Expression of CCR2 in both

resident and bone marrow-derived microglia plays a critical role in neuropathic pain. J

Neurosci 2007;27:12396-406.

96

Appendix 1 – Original autotomy data (Seltzer et al., 2001) for 23 Recombinant inbred lines

and parental lines A/J and C57BL/6J. Autotomy phenotypes include A1, A2 and A3

representing average onset day of autotomy scores 1, 2 and 3 for each line. INC_1, INC_2,

INC_3, INC_5 represent percent of mice expressing autotomy scores of 1, 2, 3 or 5 respectively.

AS_36 represents average autotomy socre on day 36 for each line

Traits

/ Lines N

A1 A3 A5 INC_1 INC_2 INC_3 INC_5

AS_d36

AVG SEM AVG SEM AVG SEM AVG A_sem

C57BL/6J 12 22.50 3.90 34.70 1.30 34.70 1.30 66.70 8.30 8.30 8.30 0.80 0.20

A/J 11 16.40 4.21 21.10 4.00 21.10 4.00 66.70 58.30 58.30 58.30 6.70 1.60

AXB1 6 28.00 5.06 32.00 4.00 36.00 0 33.33 16.67 16.67 0 0.67 0.49

AXB2 9 11.00 4.74 20.33 5.14 24.33 5.01 77.78 55.56 55.56 44.44 4.44 1.45

AXB4 8 31.88 4.13 36.00 0 36.00 0 25.00 0 0 0 0.25 0.16

AXB5 8 8.25 2.18 24.38 5.71 25.50 5.38 100 37.50 37.50 37.50 3.63 1.40

AXB6 9 7.00 3.04 24.33 4.76 28.67 3.81 100 55.56 44.44 33.33 3.89 1.22

AXB8 8 20.63 4.59 36.00 0 36.00 0 75.00 0 0 0 0.75 0.16

AXB10 9 26.00 5.07 36.00 0 36.00 0 33.33 22.22 0 0 0.56 0.29

AXB12 9 18.67 4.52 36.00 0 36.00 0 77.78 0 0 0 0.78 0.15

AXB13/14 9 18.00 5.70 32.33 2.64 33.67 2.33 55.56 33.33 33.33 11.11 1.89 0.84

AXB15 7 30 4.68 36.00 0 36.00 0 28.57 0 0 0 0.29 0.18

AXB18/19/20 9 36.00 0 36.00 0 36.00 0 0 0 0 0 0 0

AXB24 8 20.25 5.99 24.75 5.52 25.13 5.32 50 37.50 37.50 37.50 4.25 1.98

BXA1 7 15.43 4.16 36.00 0 36.00 0 85.71 0 0 0 0.86 0.14

BXA2 8 36.00 0 36.00 0 36.00 0 0 0 0 0 0 0

BXA4 8 17.00 4.71 36.00 0 36.00 0 75.00 12.50 0 0 0.88 0.23

BXA7 7 15.14 5.09 36.00 0 36.00 0 57.14 0 0 0 0.57 0.20

BXA8/17 6 3.50 0.50 11.00 4.75 12.50 5.45 100 100 100 100 7.83 1.11

BXA11 8 5.25 0.94 33.38 2.63 36.00 0 100 25.00 12.50 0 1.38 0.26

BXA12 8 33.00 1.50 36.00 0 36.00 0 37.50 0 0 0 0.38 0.18

BXA13 7 5.14 0.86 6.86 1.57 6.86 1.57 100 100 100 100 11.00 0

BXA14 8 7.50 2.60 28.50 4.94 28.88 4.67 100 37.50 25.00 25.00 3.63 1.61

BXA24 8 26.63 3.18 36.00 0 36.00 0 62.50 0 0 0 0.63 0.18

BXA25 8 30.75 3.62 36.00 0 36.00 0 25.00 0 0 0 0.25 0.16