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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,
iv
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
10
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.
11
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.
12
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)
13
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.
16
(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
17
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.
19
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
20
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.
22
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.
60
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.
64
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
65
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
66
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).
67
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
69
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.
70
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
71
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
72
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.,
73
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.
75
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
76
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
77
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
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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