The Journal of Neuroscience For Peer Review...

Early Gene Expression During Natural Spinal Cord Regeneration in the Salamander Ambystoma mexicanum

Journal: Journal of Neuroscience

Manuscript ID: JN-RM-2849-06

Manuscript Type: Regular Manuscript

Manuscript Section: Development Plasticity Repair

Date Submitted by the Author:

05-Jul-2006

Complete List of Authors: Monaghan, James; University of Kentucky, Biology & SCoBIRC Walker, John; University of Kentucky, Biology & SCoBIRC Page, Robert; University of Kentucky, Biology & SCoBIRC Putta, Srikrishna; University of Kentucky, Biology & SCoBIRC Beachy, Christopher; Minot State University, Biology Voss, Stephen; University of Kentucky, Biology & SCoBIRC

Keywords:salamander, microarray, Regeneration, In situ hybridization, Spinal cord, axolotl, ependymal, real time PCR

Themes & Topics:c. Regeneration: CNS < 7. Transplantation and Regeneration < Theme A: Development

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The Journal of NeuroscienceFor Peer Review Only

1

Section: Development/Plasticity/Repair Senior Editor: Dr. Pat Levitt

Title: Early Gene Expression During Natural Spinal Cord Regeneration in the Salamander

Ambystoma mexicanum

James R. Monaghan1, John A. Walker1, Robert Page1, Srikrishna Putta1, Christopher K. Beachy2,

and S. Randal Voss1

1Department of Biology & Spinal Cord and Brain Injury Research Center, University of

Kentucky, Lexington, KY 40506

2Department of Biology, Minot State University, Minot SD 58707

Corresponding Author: S. Randal Voss

Department of Biology & Spinal Cord and Brain Injury Research Center, University of

Kentucky, Lexington, KY 40506 ([email protected])

Figures: 3

Tables: 6

Pages: 49

Keywords (8): Salamander, microarray, regeneration, in situ hybridization, spinal cord,

axolotl, ependymal, real time PCR

Acknowledgments: The project described was supported by the Kentucky Spinal Cord Injury

Research Trust and Grant Number 5-R24-RR016344-06 from the National Center for Research

Resources (NCRR), a component of the National Institutes of Health (NIH).

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mailto:[email protected]

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Abstract

In contrast to mammals, salamanders have a remarkable ability to regenerate their spinal cord

and recover full movement and function after tail amputation. To identify genes that may be

associated with the greater regenerative ability of salamanders, we designed a custom

oligonucleotide microarray and profiled early gene expression during natural spinal cord

regeneration in Ambystoma mexicanum. We sampled tissue at five early time points after tail

amputation and identified genes that registered significant changes in mRNA abundance during

the first seven days of regeneration. A list of 1,036 statistically significant genes was identified.

Additional statistical and fold change criteria were applied to identify a smaller list of 360 genes

that were used to describe predominant expression patterns and gene functions. Real-time RT-

PCR was used to validate gene expression and in situ hybridization was used to localize mRNAs

of ten genes to specific tissues and cell populations of the spinal cord. Most gene expression

profiles consisted of a single, significant deflection from baseline followed by relatively constant

mRNA abundance. Our results show that a robust and diverse injury response is activated in

concert with extra-cellular matrix remodeling mechanisms during the early acute phase of natural

spinal cord regeneration. We also report gene expression similarities and differences between our

study and studies that have profiled gene expression after spinal cord injury in rat. Our study

illustrates the utility of a salamander model for identifying genes and gene functions that may

enhance regenerative ability in mammals.

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Introduction

Salamanders have a remarkable ability to regenerate complex body parts including the limb, tail,

lens, and central nervous system (CNS). Although salamander regeneration has been studied for

several hundred years (Spallanzani, 1768; Müller, 1864), molecular-level studies have been

limited to a relatively few important transcription factors and signaling molecules that are highly

conserved among vertebrates, and in some cases metazoans (e.g. Schnapp et al., 2005;

Christensen et al., 2002; Carlson et al., 2001; Caubit et al., 1997; Torok et al., 1999). Broader

assessments of gene expression during salamander regeneration may identify mechanisms that

can be exploited to enhance regenerative ability in humans.

Salamanders regenerate their spinal cords and regain full movement and function after

tail amputation. Within a few hours of amputation, injury responses are initiated to increase cell

survival and transform the tissue-damaged environment into one that is permissive for repair and

subsequent regeneration. It is possible that the unrivaled regenerative ability of salamanders is

due in part to this early injury response phase of regeneration, but very little is known about early

response genes and associated biological processes. Most attention has been directed to

understand cellular and developmental changes during the dramatic and conspicuous de-

differentiation and re-patterning phases of regeneration. During de-differentiation, cells of

mesodermal origin (muscle, dermal fibroblasts, and cartilage) re-enter the cell cycle and

proliferate to form a mass called the blastema (Hay and Fischman, 1961). Blastemal cells

subsequently re-differentiate into mesodermal tissues but apparently do not contribute to the

regenerating spinal cord. Epithelial cells (ependymoglia) of the ependymal lining that surrounds

the central canal of the spinal cord re-form neural tissues of the regenerating spinal cord

(Nordlander and Singer, 1978; Egar and Singer, 1972). The signals that initiate and maintain the

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proliferative response of ependymoglia are largely unknown, however recent studies implicate

some of the same highly conserved genes that are known to regulate the proliferation and

differentiation of neural stem cells among vertebrates (O’Hara and Chernoff, 1994; Zhang et al.,

2000, 2002; Schnapp et al., 2005; Chernoff et al., 2003). This suggests that some aspects of

salamander spinal cord regeneration may be shared with organisms that have little or no potential

for neural regeneration. Analyses of gene expression in salamanders may point to key similarities

and differences that are associated with regenerative ability (Alvarez-Buylla et al., 2000;

Stevenson and Yoon, 1981; Takahashi et al., 2003; Zhang et al., 2003).

We designed a custom Affymetrix GeneChip® and performed the first microarray

analysis of spinal cord regeneration in the Mexican axolotl (Ambystoma mexicanum). We

sampled regenerating spinal cord tissue at five early time points after amputation and identified

differentially expressed genes and temporal patterns of gene expression. We compared our lists

of significantly regulated genes to lists that have been similarly compiled from microarray

studies of spinal cord injury in rat. Our results highlight genes and gene expression patterns that

are associated with the salamander’s natural ability to regenerate spinal cord.

Materials and Methods

Animals, tissue collection, and RNA isolation

The handling and surgical manipulation of all salamanders was carried out according to Animal

Care and Use guidelines at the University of Kentucky (IACUC #00609L2003). The caudal 1/3

of the tail was amputated from 225 Mexican axolotl sibs (avg. 6.2 cm snout-vent length) from an

inbred strain generated by the Voss laboratory. Spinal cord tissue was collected approximately

1.0 mm rostral to the injury plane at 0, 1, 3, 5, and 7 days post amputation. An average of 1.7 µg

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total RNA was extracted from pools of nine tissues for each of five replicates that were collected

at each time point. Labeled probes were produced for the 25 RNA samples and each was

hybridized to a separate Affymetrix GeneChip® (Santa Clara, CA). Probe labeling, GeneChip®

hybridization, and scanning were performed by a single staff member of the University of

Kentucky Microarray Core Facility.

Development of a microarray platform

A custom Ambystoma Affymetrix GeneChip® was designed from curated expressed sequence tag

(EST) assemblies for A. mexicanum and A. t. tigrinum (http://salamander.uky.edu/ESTdb; Smith

et al., 2005). These ESTs are enriched for genes expressed in neural and regenerating tissues

(Putta et al. 2004). The array contains 4,844 total probe sets, 254 of which are controls or

replicate probe sets. Of the remaining 4,590 probe sets, all but 188 correspond to unique A.

mexicanum contigs, of which 2,960 are significantly identical in nucleotide composition (e-7;

BLASTX) to a human sequence in the non-redundant, RefSeq protein database. Significant

salamander-human blast hits were considered gene orthologs in our analyses and we assumed

that salamander-human orthologs have similar gene functions or ontologies.

Assessment of GeneChip® precision

The repeatability of probe set estimates of hybridization intensity was evaluated between

microarray GeneChip®s. We examined the correlation of hybridization intensities across all

probe sets among GeneChip®s that were replicated for each regeneration time point (average

across all replicates, r = 0.994; range r = 0.983-0.998). These results demonstrate that we were

able to obtain a high level of repeatability. With respect to general performance measures, a high

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http://salamander.uky.edu/ESTdb

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percentage (84.7%) of probe sets scored for presence using MAS 5.0; this likely reflects the

biased selection of regeneration associated genes and high quality contigs for probe set design.

Robust Multiarray Averaging (RMA) showed that average background intensity (61.5) and noise

(4.22) were low, and mean and median hybridization intensities were typical of Affymetrix

GeneChip® performance (1,772.8 and 420, respectively). Raw data files can be obtained at

http://www.ambystoma.org and http://www.ncbi.nlm.nih.gov/geo/.

Quality Control and Low Level Analyses.

We used the Bioconductor package affy (www.bioconductor.org) that is available for the

statistical programming environment R (www.r-project.org) to perform a variety of quality

control and preprocessing procedures at the individual probe level (Bolstad et al., 2005a). These

procedures included: (1) generating matrices of M versus A plots for all replicate chips (n = 5

chips for five sampling times), (2) investigating measures of central tendency, measures of

dispersion, and the distributions of all 25 chips via boxplots and histograms, (3) viewing images

of the log2 (intensity) values for each chip to check for spatial artifacts, and (4) viewing an RNA

degradation plot (Bolstad et al., 2005b) that allows for visualization of the 3’ RNA labeling bias

across all chips simultaneously. In addition, we used ArrayAssist Lite software (Stratagene, La

Jolla, CA) to assess several quality control measures that are recommended by Affymetrix

(GeneChip® Expression Analysis: Data Analysis Fundamentals, 2002, www.affymetrix.com)

such as average background, scale factors, and percent present. Next, we processed our data

similarly to the methods of Choe et al. (2005) to determine a probe set intensity value. Briefly,

our processing method consisted of using the MAS 5.0 background correction algorithm, the

quantiles algorithm for probe-level normalization, the MAS 5.0 algorithm for perfect match

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http://www.affymetrix.com/

http://www.r-project.org/

http://www.bioconductor.org

http://www.ncbi.nlm.nih.gov/geo/

http://www.ambystoma.org

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(PM)/mismatch (MM) correction, the median polish algorithm for expression summary

generation, and a loess normalization at the probe set level using the GoldenSpike package for R

(Choe et al., 2005; www.ccr.buffalo.edu/halfon/spike/index.html).

Detection of differentially expressed genes and data filtration.

Microarray platforms may not accurately or precisely quantify genes with low intensity values

(Choe et al., 2005; Draghici et al., 2006). Because low intensity genes contribute to the multiple

testing problem that is inherent to all microarray studies, we filtered probe sets whose mean

expression values across all chips (n = 25 per gene) were smaller than or equal to the mean of the

lowest quartiles (25th percentiles) across all chips (n = 25, mean = 6.44, SD = 0.09; data

presented on a log2 scale). Upon performing this filtration step, 3,641 probe sets were available

for significance testing. Probe sets were tested for differential expression via a one-way fixed

effect linear model (intensity = day sampled) using the Fs test of Cui et al. (2005) and

J/MAANOVA software (http://www.jax.org/staff/churchill/labsite/software/anova/index.html).

Initially, we adjusted for multiple testing by setting the false discovery rate (FDR) to 0.01 using

the step-up algorithm of Benjamini and Hochberg (1995). As is shown in Figure 1a, upon

performing this FDR correction, 2771 probe sets of the 3,641 probe sets tested (76.11%) were

selected as differentially expressed. We then took a more conservative approach to our first pass

at selecting differentially expressed genes by setting the family-wise error rate (FWER) to 0.01.

Upon adjusting the FWER to 0.01, 1,273 of the 3,641 genes tested (34.96%) were selected as

differentially expressed (Figure 1b). In order to identify a smaller subset of probe sets to focus

our attention, we prioritized probe sets that were selected as differentially expressed (with the

FWER adjusted to 0.01) that exhibited: (1) a 2-fold (raw intensity scale) change at any time point

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http://www.jax.org/staff/Churchill/labsite/software/Jmaanova/index.html

http://www.ccr.buffalo.edu/halfon/spike/index.html

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relative to day zero and (2) Fs values that were in the upper 50% of these 1,273 genes (Fs >

28.36). Upon performing this final filtering step, 376 probe sets were identified. Three pairs of

probe sets were designed for the same contigs and yielded nearly identical hybridization

intensities. Thus, these intensities were averaged. Contigs that correspond to the same human

gene were combined, yielding a final short list of 360 unique genes (Supplemental Table 1).

Candidate gene lists may differ when different preprocessing algorithms are used to

identify statistically significant genes from oligonucleotide microarrays (Millenaar et al., 2006).

To address this concern, we compared the 376 candidate probe set list above to a 646 probe set

list that was generated using only the robust multi-array average (RMA) algorithm (Irizarry et

al., 2003), One-way analysis of variance (FDR = 0.01), and a 2-fold change (raw intensity scale)

criterion. Only 11 of the 376 candidate probe sets (2.9%) were unique, indicating that our

methodology for identifying candidate genes is largely concordant with other statistical

approaches.

Identification of gene expression patterns

We used the following criteria to define temporal gene expression patterns for the 360 genes that

met statistical and fold-level criteria. For each gene we assigned a score to qualify the mRNA

abundance at each post-amputation sample time (d1, d3, d5, and d7). A gene received a score of

non-significant (N) for each sample time that mRNA abundance was < 2 fold deviant of the day

0 estimate. We refer to the day 0 estimate as the baseline estimate of mRNA abundance. A gene

received a score of up-regulated (U) or down-regulated (D) for the first post-amputation sample

time that mRNA abundance deviated by > 2 fold from baseline. For subsequent sample times,

each gene received one of three possible scores: constant, up, or down. A score of constant (C)

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was assigned if the fold level estimate was < 2 fold deviant of the previous U or D estimate (C

was never assigned after N), and > 2-fold deviant from baseline. A score of U or D was assigned

if mRNA abundance deviated again by > 2 fold. Using this scoring system, a gene received a

score of U, D, or N for day 1, and U, D, N, or C for each of the three subsequent time points

(Table 1). To annotate genes, we used multiple databases (GO, KEGG, IHOP, OMIM, etc.) and

searched the literature for information about the expression and functions of each gene that we

identified as significant in our study. We biased our annotations to emphasize possible gene

functions that have been described in regeneration and spinal cord injury research fields.

Identification of genes expressed differently between salamander regeneration and rat spinal

cord injury

A bioinformatics approach was used to identify gene orthologs that are expressed similarly or

differently after salamander tail amputation versus rat spinal cord injury. We used current (as of

May 2006) human Entrez Gene ID’s that were assigned to each annotated probe set on the

Ambystoma chip to identify all presumptive salamander orthologs on RatU34A, B, and C

GeneChip®s. To accomplish this cross-referencing task, we used Resourcerer (Tsai et al., 2001),

a database that allows orthologous genes to be identified among species-specific microarray

resources. This yielded a list of 1,036 probe sets between the Ambystoma and RatU34

GeneChip®s that presumably correspond to 662 unique, orthologous genes. We compared the

expression pattern of each of these genes using results from this study and published studies that

profiled gene expression after spinal cord injury in rat, using RatU34 GeneChip®s (Song et al.,

2001; Carmel et al., 2001; De Biase et al., 2005; Aimone et al., 2004; n = 1,571). For each gene,

we qualified gene expression as either significantly up, significantly down, or non-significant.

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We used statistical and fold-level criteria (FWER < 0.01; > 2-fold change) to score 522

salamander genes for these criteria. If a gene was reported as significantly regulated in the rat

studies, we recorded it as such; otherwise we recorded it as non-significant.

Quantitative Real-Time RT-PCR

Ten genes from the microarray experiment were selected for validation by real-time RT-PCR.

These genes yielded a broad range of relative fold change estimates by microarray analysis and

included both possible directions of differential expression. These genes also exhibited a range of

hybridization intensity values; for example, the average intensity value of hes1 ranked among the

bottom 37% of all probe sets while galectin 1 ranked among the top 95%. For each gene, three

reactions were performed using independent RNA samples from the microarray analysis. A

BioRad iScript cDNA synthesis kit (Hercules, CA) was used to synthesize cDNA templates

(poly-T and random hexamer priming). Primers were designed using Primer3 to amplify DNA

fragments from the same gene regions that were used to design corresponding GeneChip® probe

sets (Table 2; Rozen and Skaletsky, 2000). Reactions included cDNA that was synthesized from

10ng total RNA, 300nM primers, and iQ SYBR-Green real-time PCR mix (BioRad). The

reactions were performed on a BioRad I Cycler real-time RT-PCR system. PCR efficiencies

were calculated for each primer pair with a dilution series of 0, 0.1ng, 1ng, 5ng, 10ng, 20ng, and

40ng. The three replicates were normalized against a gene that showed no significant gene

expression change in the microarray experiment (glyceraldehyde-3-phosphate dehydrogenase,

MC01187). PCR efficiencies for each primer were incorporated into the relative fold change

calculations according to the Pfaffl method (Pfaffl, 2001). Student’s t-tests were performed using

the three normalized biological replicates for day 3 and day 0 samples.

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http://jura.wi.mit.edu/rozen

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In situ hybridization

Digoxigenin (DIG)-labeled RNA probe production and in situ hybridization (ISH) were

performed as described by Hirota et al. (1992) with minor modifications. RNA probes were

synthesized by in vitro transcription using 300-350 base pair PCR products as template. PCR

primers were designed from the same gene region as corresponding GeneChip® probe sets and

included SP6 or T3 RNA polymerase promoters appended to the 5’ ends (Table 2). PCR

products were cleaned using a Qiagen PCR purification column before performing in vitro

transcription. In vitro transcription efficiency was evaluated by electrophoresis of DIG-probes on

1% agarose gels and visualized using ethidium bromide. Axolotl tissues were collected three

days after tail amputation and fixed at 4ºC in 1x PBS, 4% paraformaldehyde overnight. Bone

was decalcified by incubating the tissue in 500 mM EDTA (pH8.0), 1xPBS for at least two days.

Tissue was cryoprotected overnight in 30% sucrose and embedded in O.C.T. medium (Fisher

Sci., Pittsburgh, PA). Sections were cut to 16µm using a Microm 500HM cryostat, adhered to

Superfrost Plus microscopy slides (Fisher Sci.), and stored at -80ºC. Sections were taken within

2.5 mm from the end of the regenerating spinal cord. Hybridization, washing, and colorimetric

detection with NBT/BCIP were performed on a Tecan Genesis Workstation 200 liquid handling

robot with a Genepaint® hybridization station (Zurich, Switzerland). Sections were mounted in

Permount (Fisher Sci.) for microscopy using an Olympus AX70 microscope and image

acquisition using Magnafire 2.1 software.

Genes were classified as expressed in a particular cell population according to the locality

and morphology of the cell within multiple tissue sections. Ependymoglia were identified as cells

lining the central canal with radial morphology and processes extending outward toward the pia

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mater. Neurons were identified as large, round cells within the grey matter of the spinal cord.

Cells located in the white matter were grouped as a single category although this is likely a

heterogeneous population of oligodendrocytes, astrocytes, endogenous microglia, and infiltrating

leukocytes. Cells outside the spinal cord resembling blastemal cells or leukocytes were also

classified as a single category (Table 6).

Results

Identification of differentially expressed genes and gene expression patterns

We identified 360 probe sets as detecting significantly different mRNA abundances between day

0 and another time point (Day 1-7), using statistical and fold change thresholds (ANOVA p <

0.01; FWER of 0.01; Fs > 28.36; > 2-fold). More than half of these probe sets (n = 210)

correspond to salamander sequences (genes) that show high sequence identify to a presumptive

human protein-coding locus; the remainder correspond to anonymous EST contigs. In

comparison to day 0 (baseline) mRNA levels, most genes exhibited significantly different

mRNA abundances at two or more post-amputation time points. This temporal variability did not

yield an extensive list of gene expression patterns. Although a total of 100 different gene

expression patterns were possible under our scoring system, only 32 different patterns were

observed and over 85% of all genes were classified into 10 categories (Table 1; Table 3). Eight

of the top ten categories identified groups of genes in which mRNA abundance increased or

decreased at a particular time point, and afterward the level remained constant through day 7.

Transcript levels for a few genes did increase or decrease by > 2 fold among post-amputation

time points, however only three genes (UNND) yielded a temporal expression profile that

deviated significantly from baseline in both up- and down-regulated directions during the seven

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day period. Thus, the majority of the gene expression profiles that we examined consisted of a

single, significant deflection from base line levels followed by relatively constant mRNA

abundance. It is likely that many of the uniquely expressed genes at day 7 are regulated at later

time points because only 43 of the 360 genes exhibited transcript levels at day 7 that

approximated baseline. Clearly, we only sampled the initial phases of a continuous gene

expression program that extends beyond day 7. However, our experiment does precisely sample

discrete phases of gene expression variability during this temporal process (Figure 2). Below, we

describe major gene expression patterns in greater detail. We also highlight some of the genes

and gene functions that were found in each of the major gene expression categories. Finally, we

compare the expression of salamander genes to presumptive rat orthologs that have been

examined in microarray studies of spinal cord injury.

Gene expression patterns

Overall, a greater number of genes were up-regulated above baseline during the 7-day period (n

= 235). The majority (n = 134) were significantly up-regulated at the first sample time after

amputation (day 1) and half of these genes (UCCC: n = 64) registered constant mRNA

abundances above baseline at all subsequent post-amputation sample times (d3, d5, and d7). A

similar pattern of expression was observed for a small group of genes (UUCC: n = 12) that

maintained above baseline mRNA abundances after successive 2-fold increases at day 1 and 3.

The remainder of the day 1 up-regulated genes showed decreasing mRNA abundances at later

sample points. Some of these genes yielded mRNA abundances at day 3 (UCNN: n = 5) or day 5

(UCCN, UDNN: n = 5) that approximated day 0 levels, while others remained above baseline

(UDCC, UCDC, UNND, UDDC: n = 22). Up-regulated genes that were significantly down-

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regulated at later sample times were down-regulated below baseline level in only three cases

(UNND). The early group of up-regulated genes suggests that a diversity of regulatory pathways

and biological processes are activated within the first 24 hours after tail amputation. In addition

to genes that presumably function in wounding, stress, inflammation, and immunity, this group

includes genes that function in tissue remodeling, apoptosis, ion transport, cell-cell interactions,

cell migration, vitamin B economy, lipid metabolism, and cytoskeleton dynamics (Table 3).

Several different regulatory networks are implicated directly or indirectly among these day 1 up-

regulated responses, including MAPK, WNT, v-MYC, TNF, v-YES, RAS, and TGF-beta.

Other groups of genes were up-regulated for the first time at day 3, 5, and 7 (n = 119).

The majority of the day 3 and 5 genes maintained high, constant mRNA levels at subsequent

time points (NUCC: n = 24, NNUC: n = 44). Gene functions that were observed among day 1

up-regulated genes were also represented among day 3-7 up-regulated genes. However, the

distribution of genes among these functional categories was very different. In particular, fewer

injury response genes and a greater number of extracellular matrix (ECM) and cytoskeleton-

associated genes were observed compared to day 1 up-regulated genes. Also, a greater number of

cell cycle related genes were observed, especially at day 5 (NNUC; n = 14). In addition to genes

that regulate cell cycle entry, progression, and apoptosis, this later group includes genes that

presumably function in DNA replication, metabolism, chromatin assembly, and cytokinesis.

These results suggest that the regeneration gene expression program transitions during the first

seven days from an injury responsive phase to one that is defined primarily by the up-regulation

of genes that function in mitosis. Throughout both injury response and cell proliferation phases,

genes that function in tissue remodeling are significantly regulated.

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Relative to the total number of up-regulated genes, a much smaller number of genes (n =

126) were down-regulated significantly below baseline levels during regeneration. In contrast to

the up-regulated gene set, very few of these genes were down-regulated at day 1 (DCCC, DCNN,

DNNN, DCDC = 12). Also, the magnitude of the fold-level changes was generally lower than

those measured for significantly up-regulated genes (mean of maximum up-regulated fold

changes = 6.61; down-regulated = -3.07). The largest number of down-regulated genes was

observed at day 3 (n = 45) and this was followed by additional groups of down-regulated genes

at day 5 (n = 29) and day 7 (n = 35). In general, many genes with neural related functions were

down-regulated, including those that function in ion transport, glutamate metabolism, glutamate

binding, neuroprotection, neurotransmission, neurogenesis, and lipid metabolism. Some of the

same functional categories that were observed among up-regulated genes were also observed

among down-regulated genes, including apoptosis, cytoskeleton, ECM, signal transduction, and

heat shock (Table 3). The overall pattern indicates that fewer genes are down-regulated during

the first seven days of regeneration, and down-regulated genes show significantly lower mRNA

abundances at day 3, after the early up-regulation of genes at day 1.

Some gene expression patterns were more complicated than linear, directional responses,

involving changes in mRNA abundance that fluctuated both above and below the baseline. For

example, the mRNA abundances of 12 genes (UDCC) increased significantly at day 1, decreased

significantly at day 3 (but still above baseline), and then remained constant through day 7. A

similar pattern was observed for eight genes (UCDC) that increased significantly at day 1 and

decreased significantly at day 5. Some of these and other genes (NUCN, UDDC, NDCD,

UUCD) with complex expression patterns may function in the regulation of biological processes

during regeneration. These include genes that function in ECM remodeling (matrix

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metalloproteinase (mmp) 1, mmp13, tissue inhibitor of metalloproteinase 1), coagulation (tissue

factor pathway inhibitor 2), vitamin B transport (intrinsic factor, transcobalamin 1), cell

proliferation (v-Ha-ras viral oncogene, hypothetical protein FLJ20303), transcriptional

regulation (jun-b proto-oncogene), and cell signaling (latent TGF beta binding protein;

chromosome 8 orf 4; secreted frizzled-related protein 2 [sfrp2]).

Gene expression after spinal cord injury: salamander verses rat

To identify similarities and differences between the salamander and mammalian spinal cord

injury response, we compared our gene expression results to published results from the rat spinal

cord microarray literature. Specifically, we compared the expression of 662 presumptive rat –

salamander orthologous genes that are represented on both the Ambystoma and rat Affymetrix

GeneChip®s. The resulting list of gene orthologs represents an unbiased sampling of ~24,000

transcripts on the rat chips and 4,590 transcripts on the salamander chip. Although the majority

of gene orthologs were not significantly regulated (n = 553), we identified many similar and

dissimilar gene expression responses between these organisms (Table 4). Eleven genes are up-

regulated in both species, with no common genes down-regulated. There were 46 and 41

uniquely up-regulated genes in the salamander and rat, respectively. Among the 46 uniquely up-

regulated salamander genes, we identified ten that are either up-regulated during tissue

regeneration or are known to enhance tissue regeneration in other organisms. Furthermore, eight

cell cycle genes, a necessary process for true tissue regeneration, are present within these unique

salamander genes including cell division cycle 2, kinesin family member 11, and mitotic arrest

deficient-like 1.

Real-time RT-PCR

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Using real-time RT-PCR, we estimated fold change between day 0 and day 3 for ten genes from

the microarray experiment (Table 5). All of the transcripts that met statistical and fold level

criteria from the microarray analysis registered significant differences in mRNA abundance by

real-time RT-PCR (6/10). Overall, we were able to verify nine of the ten gene changes in the

correct direction with close agreement in most cases. We only failed to replicate the microarray

estimate for sox3, which was not significant by real-time RT-PCR and registered such a low fold

change that it was excluded from the short list of microarray gene candidates. Thus, for all genes

that met our stringent statistical and fold level criteria, and three genes that did not, real-time RT-

PCR validated microarray estimates of gene expression with very good precision.

Spatial analysis of mRNAs using in situ hybridization

Ten genes that were significantly regulated during spinal cord regeneration were examined

further by ISH. We probed tissues that were collected three days after tail amputation to localize

expression among cell types that were within 2.5 mm from the end of the regenerating spinal

cord. This spatial range included degenerating, regenerating, and nearly intact tissue, thus

allowing analysis of normal and early-regenerating tissue all within the same animal. Diverse

patterns of spatial expression were observed with hybridization found in the ependymoglia,

neurons, and white matter, as well as among presumptive blastema cells outside the spinal cord

(Table 6; Figure 3). Four of the ten transcripts assayed were expressed primarily in

ependymoglia including annexin A1, mmp9, cytokeratin 18 (ck18), and fibroblast growth factor

binding protein 1. Sfrp2 and thioredoxin were expressed in multiple cell types. Four genes were

highly expressed within inflammatory-like cells in the white matter including apoliprotein E

(apoE), galectin 1, galectin 3, and ferritin heavy polypeptide 1 (Table 6; Figure 3). These results

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show that the general increase or decrease in mRNA abundance as determined from microarray

analysis can be replicated and localized to specific cell populations and tissues of the spinal cord

using ISH.

Discussion

We built a custom Affymetrix GeneChip® and profiled gene expression during the early phases

of natural spinal cord regeneration in a salamander model (Ambystoma mexicanum). Our results

show that regeneration involves significant changes in mRNA abundance for many genes that

are represented on the chip. The overall list of 1,273 genes that met a very stringent statistical

criterion is available as a new resource for regeneration and spinal cord injury research fields

(www.ambystoma.org). The large number of genes on this list, which were identified using a

custom microarray with enriched gene content, shows that thousands of genes are significantly

regulated during the first few days of natural spinal cord regeneration. We used additional

statistical and fold change criteria to sample a smaller sub-group of candidate genes to describe

gene expression patterns and biological functions. The presumptive functions of this smaller list

of genes suggest the operation of many biological processes that change temporarily during

spinal cord regeneration. Below we discuss up- and down-regulated genes and gene functions

that may be important in the regenerative response. In particular, we compare our gene

expression results to several studies that have examined rat spinal cord injuries using

microarrays.

Up-regulated gene responses

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19

Similar gene expression changes are often observed after tissue injury, regardless of the type of

injury or specific tissue type examined. With respect to CNS tissue injury in mammals, an early

acute phase is characterized in part by the expression of transcription factors and immune

response genes (Bareyre and Schwab, 2003). Our study identified several genes that change by

day 1 in salamander spinal cord that are also expressed during the mammalian CNS acute injury

response (Vazquez-Chona et al., 2005). These include jun-B proto-oncogene, interferon

regulatory factor 1, heme oxygenase 1, and apoE. Overall, many of our day 1 and day 3 up-

regulated genes encode proteins that participate in immune response functions, including

lymphocyte, platelet and monocyte activation, macrophage differentiation and migration, cell

adhesion, thrombosis, coagulation, inflammation, oxidative and metabolic stress, and apoptosis.

In addition to immune response genes, we also observed the up-regulation of genes that function

in transport and binding of vitamin B and lipids, and ECM remodeling. While processes like

vitamin B homeostasis have received little attention in regeneration and injury fields (Bauer,

1998), the association of lipid turnover and MMP activity is well documented to be associated

with regeneration (Vance et al., 2000; Poirier, 1994; Yang et al., 1999; Vinarsky et al., 2005). In

A. mexicanum, MMP2, MMP9, and MMP1 activity is associated with proliferating ependymal

cells after 2-3 weeks of regeneration (Chernoff, 2000). Our study shows that mmps 1, 3, 9, 13,

27, and tissue inhibitor of metalloproteinase 1 are all highly up-regulated by 24 hours after

injury, which is the first association of mmps 13 and 27 with regeneration in urodeles. Overall,

our results show that a robust and diverse injury response is activated in concert with ECM

remodeling mechanisms during the acute phase of natural spinal cord regeneration.

The majority of the early-activated genes were up-regulated throughout the first seven

days, extending into a subsequent phase of cell cycle-related gene expression at day 5. The

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accumulation of mRNAs that increase during the first seven days of regeneration suggests a

temporal change toward biological processes that are associated with cell division. Many genes

up-regulated at day 5 (NNUC) are associated with mitotic cell cycle regulation including four

genes involved in the G2/M transition and six associated with mitosis (Table 3). These gene

expression changes maybe associated with the early proliferation of blastema and ependymal cell

populations, which are known to expand after the first week of regeneration (Lo et al., 1993;

Zhang et al., 2003). Cell cycle-related genes are also up-regulated early after rat spinal cord

injury, but the functions of these genes are associated primarily with S-phase and DNA repair

and expressed in damaged or apoptotic neurons, not proliferating cells (Di Giovanni et al., 2003).

Thus, within a few days after spinal cord injury, cell-cycle gene expression is biased towards cell

death pathways in mammals but cell survival and proliferation pathways in salamanders.

Down-regulated gene responses

In comparison to up-regulated genes, there were fewer down-regulated genes and most showed

gradual changes over time. Multiple genes were down-regulated whose products are associated

with neural functions, including axon guidance, ion transport, glutamate metabolism,

neuroprotection, and neurotransmitter signaling. Changes in neural-related gene expression

patterns may reflect the damage or loss of neural cell types verses the survival, infiltration, and

proliferation of other cell types. This explanation has been advanced to explain the down-

regulation of genes after mammalian spinal cord injury, where there can be extensive tissue

damage and cell loss (Profyris et al., 2004; Bareyre and Schwab, 2003). Indeed, even in the

regenerating salamander, there is local spinal cord tissue loss after injury (Zhang et al., 2003;

Stensaas, 1983). Thus, in both mammals and salamanders, many of the down-regulated gene

Page 20 of 61



21

expression patterns may reflect the variability and stochastic nature of cell survival at the injury

site. However, we did observe significant down-regulation of several genes that are associated

with glutamate metabolism and transport, that are up-regulated after CNS injury in mammals.

This suggests the possibility that some genes are actively and uniquely repressed during

salamander regeneration.

Identification of genes expressed differently between salamander regeneration and rat

spinal cord injury

We compared genes that changed during early salamander spinal cord regeneration to gene lists

that were compiled from microarray studies of spinal cord injury in rats. We acknowledge that

this comparison is potentially confounded by several sources of variance including experimental,

technical, statistical, tissue, and organismal differences. However, as we described above, some

of the same genes that are expressed early after mammalian CNS injury are also up-regulated

during spinal cord regeneration in salamander. If similar gene expression programs underlie

homologous tissues, then comparisons of homologous tissues among distantly related organisms

may filter conserved gene expression responses and help identify uniquely regulated genes.

Some of the uniquely regulated genes from salamander are associated with regeneration in other

organisms and tissues including amphibian limb regeneration (ck18, Corcoran and Ferretti, 1997;

msx1, Beck et al., 2003; msx2, Koshiba et al., 1998; Carlson et al., 1998; and mmp9, Yang et al.,

1999), fish tailfin regeneration (ck18 and periostin, Padhi et al., 2004), and annelid epimorphic

regeneration (phosphoribosylaminoimidazole carboxylase, Myohara et al., 2006). Several other

up-regulated genes are associated with mammalian liver regeneration, including follistatin

(Borgnon et al., 2005), cystathionase (Teshigawara et al., 1995), laminin alpha 1 (Kikkawa et al.,

Page 21 of 61



22

2005), transglutaminase 1 (Ohtake et al., 2006), and uncoupling protein 2 (Horimoto et al.,

2004). Up-regulation of the same gene orthologs across multiple regeneration paradigms

suggests that regeneration is definable across taxa and tissues by distinct gene expression

patterns. Further studies are needed to determine if a conserved group of genes function in

molecular pathways that are required for regeneration.

Molecules that regulate morphogenic signaling

Morphogenic molecules such as sonic hedgehog (Shh), bone morphogenic proteins (BMPs),

WNT factors, and fibroblast growth factors (FGFs) have been associated with regeneration

because they establish positional identity, control cell proliferation, and regulate cell fate during

development (Vergara et al., 2005; Whitehead et al., 2005; Zhang et al., 2000; Schnapp et al.,

2005; Beck et al., 2003; Niemann, 2006). In this study, we identified changed genes participating

in BMP, WNT, and FGF signaling. Follistatin, a protein that regulates dorsal-ventral patterning

of the developing vertebrate nervous system through BMP inhibition, is up-regulated during the

first week of regeneration (Table 5; NUCC; Lee and Jessel, 1999). mRNAs for sfrp2, a secreted

WNT antagonist that blocks ligand binding to frizzled receptors (Kawano and Kypta, 2003), is

highly up-regulated in both the early regenerating spinal cord and tail (Table 5; UUCD).

Furthermore, wnt5A demonstrates a 3.86 fold increase in expression (UCCC), suggesting a

network of pro- and negative WNT signaling during the regeneration. Lastly, fibroblast growth

factor binding protein 1, a secreted molecule that sequesters FGF ligands from the ECM (Tassi

et al., 2001), is expressed in ependymoglia and neurons of the uninjured spinal cord (Table 6)

and down-regulated 6.42 fold at 24 hours (DCCC). These large gene expression changes suggest

that BMP, WNT, and FGF signaling pathways are all altered during early spinal cord

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regeneration. Further studies with each of these molecules and their corresponding binding

substrates will be needed in order to assess their possible roles during regeneration.

Conclusion

The salamander’s unique ability to regenerate complex body parts has long been recognized as

an important model in developmental biology, however salamanders have received relatively

little attention from researchers of mammalian spinal cord injury. Our study shows that genomic

and bioinformatics resources are now available to associate gene expression changes with

cellular and molecular aspects of natural spinal cord regeneration. The emerging salamander

perspective on regeneration promises to extend existing research paradigms and may suggest

novel therapies for CNS injury in humans.

Acknowledgements

The project described was supported by the Kentucky Spinal Cord Injury Research Trust and

Grant Number 5-R24-RR016344-06 from the National Center for Research Resources (NCRR),

a component of the National Institutes of Health (NIH). Its contents are solely the responsibility

of the authors and do not necessarily represent the official views of NCRR or NIH.

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Table 1. Distribution of gene expression patterns for 360 significantly regulated

salamander genes during spinal cord regeneration

Pattern N Description

UCCC 64 up D1, above baseline D1-7

NNUC 44 baseline D1-3, up D5, above baseline D5-7

NDCC 42 baseline D1, down D3, below baseline D3-7

NNND 35 baseline D1-5, down D7

NNNU 28 baseline D1-5, up D7

NNDC 27 baseline D1-D3, down D5, below baseline D5-7

NUCC 24 baseline D1, up D3, above baseline D3-7

UNNN 20 up D1, baseline D3-D7

UUCC 12 up D1 and D3, above baseline D1-7

UDCC 11 up D1, down D3, above baseline D1-7

UCDC 8 up D1, down D3, above baseline D1-7

DCCC 6 down D1, below baseline to D1-7

UCNN 5 up D1, above baseline D1-3, baseline D5-7

UCCN 4 up D1, above baseline D1-5, baseline D7

DCNN 4 down D1, below baseline D1-3, baseline D5-D7

UNND 3 up D1, baseline D3-D5, below baseline D7

UNNU 3 up D1 and D7, baseline D3-D5

NUCN 3 baseline D1, up D3, above baseline D5, baseline D7

NUNN 2 baseline D1, up D3, baseline D5-7

NNUU 2 baseline D1-D3, up D5 and D7

UNUC 2 up D1 and D5, baseline D3, above baseline D5-7

DNNN 1 down D1, baseline D3-D7

NDCD 1 baseline D1, down D3 and D5, below baseline D3-7

NNDN 1 baseline D1-D3, down D5, baseline D7

NNDD 1 baseline D1-D3, down D5 and D7

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NDNN 1 baseline D1, down D3, baseline D5-7

NDCN 1 baseline D1, down D3, below baseline D5, baseline D7

UUCD 1 up D1 and D3, down D7, above baseline D1-7

UDNN 1 up D1, down D3, above baseline D1-3, baseline D5-7

UDDC 1 up D1, down D3 and D5, above baseline D7

NUCU 1 baseline D1, up D3 and D7, above baseline D3-7

DCDC 1 down D1 and D5, below baseline D1-7

The 32 gene expression patterns are described in materials and methods. N = total number of

genes in each category.

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Table 2. Primer sequences for Q-RT-PCR and in situ hybridization analysis

Sal ID Forward Primer Reverse Primer

MC03237 5’-GGCAAACTGCCTCTCCTT-3’ 5’-CCTGTGGTTTTCCCATGA-3’

MC00341 5’-TTTCTGGACAGCCACTGC-3’ 5’-TTTCTGGACAGCCACTGC-3’

MC01765 5’-CCTGATGGGGATCATCG-3’ 5’-GTGCAGCCGGTACTTGTC-3’

MC03278 5’-TGCATCAAAGCCAAGTCC-3’ 5’-CCTCGGTTCACCTTGAAA-3’

MC01583 5’-ATCCCGGAGAACAAGAGC-3’ 5’-CATCCTTGAGCCAGAGCA-3’

MC02459 5’-CAACGAGTGCATGAACGA-3’ 5’-GCCAGACAGGTGGCCTA-3’

MC01067 5’-TGAGACCAATGCCTTTGC-3’ 5’- GAGCCCCAGAAGCAGAGT-3’

MC01275 5’-GAGGCCAGAAAACCCAGA-3’ 5’-CCGGTTTGGAAATTTCATC-3’

MC01620 5’-TGGCCTGACCAGTAACGA-3’ 5’-AAGTCCCATTCAGCACCA-3’

MC02501 5’-TCCATCCATGTCCTCTGC-3’ 5’-CTGTTTGCGATTGCATGA-3’

MC01187 5’-CCAGGCGGCAGGTCAAGTCAAC-3’ 5’-GTCGGCAAGGTCATCCCAGAGC-3’

in situ hybridization primers

MC02287 5’-GCGCACGATGTCTTTCTGTA-3’ 5’-GCGGTGGTACTCCAACTCAT-3’

MC00365 5’- CGGCTTAGCCAGAAAATGAG-3’ 5’- GTGGTTTCAGCAAAGCCAAT-3’

MC00145 5’- GGCAAGATCATGGCTGAAAT-3’ 5’- CAAGCCGCAGTATCATGTTG -3’

MC02339 5’- TTTGGGAGCGAAGAAGAAAA-3’ 5’- GAACGCTTGGTCTGGTAAGC-3’

MC01583 5’-AAATTCCAATGCAAGTTGGG-3’ 5’-ACGCCGTTCAGCTTGTAGAT-3’

MC01706 5’-ACAACTCAGCCTGGACCATC-3’ 5’- GTCTTTCACCCATTGCAGGT-3’

MC00018 5’-ACATGGAGGACACCAAAAGC-3’ 5’-AGCTCTGGAGTTCAGCTGGT-3’

MC01275 5’-TATTAAGGGCCACGTTCCAG-3’ 5’-TATGGAAGCCCTCCACAGAC-3’

MC01182 5’-GTGCTGCAGGATGTCAAGAA-3’ 5’-GCTGGTGTCTTCCTCTCTGG-3’

MC01277 5’- AATCCAGCCACATCCTTCAC-3’ 5’- GGCGCACCACATACACATAC-3’

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Forward ISH primers were appended with an SP6 RNA polymerase promoter and reverse

primers with a T3 promoter at the 5’ end (MC02287 and MC01277 RNA polymerase promoters

are appended to the opposite primers). RNA polymerase promoter sequences are SP6: 5’-

ATTTAGGTGACACTATAGAAGAG-3’ and T3: 5’-AATTAACCCTCACTAAAGGGAGA-3’.

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Table 3. Gene symbols and functions for 360 changed salamander genes during the first

week of spinal cord regeneration

Symbol Function Symbol Function

UCCC = 64 Unk; n = 30 PAICS purine metabolism

MYO1B actin binding CTPS pyrimidine biosynthesis

TAGLN actin binding SSB mRNA processing

ADFP lipid metabolism EXOSC2 rRNA processing

TNFAIP8 anti-apoptotic RANBP1 signal transduction

ETHE1 anti-apoptotic TPRT ubiquinone biosynthesis

LGALS1 apoptosis C6ORF115 unknown

GADD45G apoptosis CCDC58 unknown

GADD45B apoptosis NNDC = 27 Unk; n = 8

EFHD2 calcium binding CDKN1C negative cell proliferation

AGC1 unknown MLF1 cell differentiation

MYC cell cycle KRT6L cytoskeleton

CTSL cysteine protease ABLIM1 cytoskeleton

CTSK cysteine protease GLUD1 glutamate catabolism

TUBB6 cytoskeleton ATP1B3 ion transport

FBP1 glycolysis FXYD3 ion transport

B3GNT5 glycosylation PRKAG2 lipid metabolism

TGFB1 growth factor COX4I2 metabolism

TYROBP immune response BHMT metabolism

SLC11A1 immune response PCBD metabolism

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MPEG1 immune response CKMT1A metabolism

CXCR4 immune response SERPINI1 neurogenesis

LGALS3BP immune response ABAT neurotransmission

CYBB immune response TM4SF2 protein biosynthesis

ANKRD1 injury response GNB5 signal transduction

FTH1 iron homeostasis POLR2L transcription

ATP6V0D1 proton transport C6ORF110 unknown

C1ORF33 ribosomal FAM79A unknown

MAP2K3 signal transduction NUCC = 24 Unk; n = 6

MMP9 tissue remodeling TAGLN actin binding

MMP1 tissue remodeling ASAH1 apoptosis

LYN tyrosine kinase UHRF1 cell cycle; S

TMEM49 unknown CDK4 cell cycle; G1/S

FLJ2262 unknown KIAA0101 cell cycle; S

WNT5A WNT signaling PPGB cellular transport

NNUC = 44 Unk; n = 14 LGMN cysteine protease

GLRX antioxidant CSTB protease inhibitor

LGALS3 carbohydrate binding RRM2 DNA metabolism

KIF11 cell cycle; M ANXA1 inflammation

CCNB3 cell cycle; G2/M THBS2 ECM component

PLK1 cell cycle; G1/S: G2/M GLB1 metabolism

RFC2 cell cycle; S ATP6V0E proton transport

PCNA cell cycle; S RPL31 ribosomal

MCM7 cell cycle; S APOE lipid metabolism

STK6 cell cycle; M METTL2 ubiquinone biosynthesis

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CCNA2 cell cycle; G1/S: G2/M CCDC43 unknown

CDC2 cell cycle; G1/S: G2/M ANGPTL2 growth factor

CHEK1 cell cycle; DNA damage UNNN = 20 Unk; n = 7

MAD2L1 cell cycle; M BYSL cell adhesion

CDC20 cell cycle; M GLN3 cell cycle; G1/S

AURKB cell cycle; M DUSP1 heat shock

CDCA8 cell cycle; M IRF1 immune response

KRT18 cytoskeleton HSPA5 injury response

CALD1 cytoskeleton SLC30A1 ion transport

FEN1 DNA metabolism AGXT2L1 metabolism

SLBP mRNA processing DKC1 ribosomal

CKAP4 inflammation RPS6KA1 signal transduction

KPNA2 intracellular transport PUS1 tRNA processing

VRK1 kinase FLJ36031 unknown

COL12A1 ECM component TGM1 injury response

UCP2 neuroprotection IFIH1 immune response

CTSK protease UUCC = 12 Unk; n = 8

OLFML2B signal transduction F13A1 wound healing

CNIH4 unknown MARCO immune response

CCDC82 unknown FABP2 lipid metabolism

RPL38 ribosomal GPNMB negative cell proliferation

NDCC = 42 Unk; n = 22 UDCC = 10 Unk; n = 6

KRT7 cytoskeleton TFPI2 coagulation

KIF21A cytoskeleton MMP1 tissue remodeling

SLC1A2 glutamate transport GIF vitamin B transport

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SLC1A3 glutamate transport C8ORF4 WNT signaling

HSPA8 heat shock UCDC = 8 Unk; n = 3

TTR hormone transport HMOX1 heat shock

SLC12A2 ion transport MMP13 tissue remodeling

KCTD3 ion transport MMP1 tissue remodeling

FDPS lipid metabolism TIMP1 tissue remodeling

FAAH lipid metabolism JUNB transcription

COL8A1 ECM component DCCC = 6 Unk; n = 5

FBN2 ECM component CYP2A13 metabolism

GSTM4 metabolism UCCN = 4

GRM3 neurotransmission LTB4DH antioxidant

PADI3 protein metabolism TXNDC2 antioxidant

APCDD1 signal transduction TXN antioxidant

TJP1 signal transduction BTBD3 protein binding

APC signal transduction DCNN = 4 Unk; n = 2

MAPRE3 structural protein RGMA axon guidance

CRYAB structural protein FHL1 protein binding

NNND = 35 Unk; n = 17 UCNN = 5

PDCD4 apoptosis CES1 neuroprotection

APP apoptosis USP2 protein breakdown

CCNI cell cycle RAP2B signal transduction

SPTAN1 cytoskeleton SAT polyamine homeostasis

SPTBN1 cytoskeleton CD63 signal transduction

PYGM glycogen metabolism UNNU = 3 Unk; n = 2

FABP7 lipid metabolism NOL5A ribosomal

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CHPT1 lipid metabolism NUCN = 3

SLC25A4 mitochondrial transport HRAS cell proliferation

GPM6B neurogenesis NSUN2 cell proliferation

AHNAK neurogenesis LTBP1 TGFbeta signalling

GSTM1 neuroprotection UNND = 3 Unk; n = 3

GSTP1 neuroprotection NNUU = 2

GABARAPL2 neurotransmission COL11A1 ECM component

PBP protease inhibitor POSTN skeletal development

CALCA signal transduction NUNN = 2 Unk; n = 2

TRAPPC6B transport UNUC = 2 Unk = 2

C11ORF74 unknown UDDC = 1

NNNU = 28 Unk; n = 7 TCN1 vitamin B transport

KIFC1 cell cycle; M NDCD = 1

CHC1 cell cycle; G2/M AGR2 cell survival

RPA2 cell cycle; DNA damage NUCU = 1

MCM6 cell cycle; S FLJ1472 unknown

CTH cysteine synthesis UUCD = 1

CALD1 cytoskeleton SFRP2 WNT signaling

NASP histone transport UDNN = 1

COL2A1 ECM component IL8RB immune response

LAMA1 ECM component DCDC = 1

DAG1 ECM component KRT5 cytoskeleton

P2RY2 neuronal differentiation NDCC; DNNN; NDCN; NDNN; NNDD; NNDN

NUP107 nuclear transport Unk; n = 1

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ANP32E phosphotase inhibitor

Each column contains highlighted categories that describes gene expression patterns on days 1,

3, 5, and 7 days post amputation compared to basal gene expression (day 0). Gene symbols are

found under each category.

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Table 4. Gene list for changed genes found on both the Ambystoma and rat U34 Affymetrix

GeneChip®s

Sal ID Symbol Sal ID Symbol Sal ID Symbol

Up sal. and rat; n=11 MC04538 VKORC1L1 MC00251 AK1

MC02474 LGALS3BP MC01709 TYROBP MC05114 C11orf74

MC01211 HMOX1 MC05477 OLFML2B MC00841 FHL1

MC01277 LGALS3 MC07609 MPS1 MC01620 SOX2

MC01006 ATP1B2 MC03420 ANKRD1 Up rat, N/C sal.; n=41

MC01230 IRF1 MC02287 MMP9 MC04731 HNRPD

MC03756 NIP7 MC02182 ATP6V0D1 MC04672 SEC13L1

MC01354 GPNMB gi|40809686 MSX2 MC01058 CBR1

TC01086 TIMP1 MC02833 POSTN gi|5199142 LDHA

MC03344 CNIH4 MC00043 F13A1 MC01272 LDHB

MC04256 C8ORF4 D. rat, N/C sal.; n=18 MC03456 C9ORF10

MC00018 APOE MC01716 UBE2G1 MC01484 PSMD8

Up sal, N/C rat; n=46 MC02022 RNF14 MC04823 ELOF1

MC00197 TGM1 MC02955 TMED2 MC02100 HNRPAB

MC04407 SLC30A1 NP_002133 HOXA9 MC00811 EIF4A1

MC00663 ARG2 MC00031 COL1A2 MC01697 TPT1

MC02499 SGK MC01720 UBE2L3 MC00919 HSPB1

MC03162 LTB4DH MC03866 LOC51255 MC00523 RPL28

MC00337 LTBP1 MC00030 COL1A1 MC04974 HTRA3

MC01521 RAC2 MC01092 COL2A1 MC03977 NOLA2

MC00770 DKC1 MC00212 COL5A2 MC04373 RPL36A

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TC00995 RPA2 MC05202 YWHAZ MC03396 CORO1C

MC02792 NOL5A MC00423 IGF1R MC03842 HSPC152

MC03278 FST MC01940 ATIC MC02234 GSTO1

MC01728 UCP2 MC00862 GSTA1 MC00196 TGFBI

MC01639 SSB MC04981 FKBP1A MC01786 DUSP11

gi|1139526 MSX1 MC03940 OTUB1 MC05214 ANXA11

MC01194 GLRX MC01364 OCLN MC03882 FKBP11

MC04782 WDR75 MC04214 DDIT4 MC04970 LRRC42

MC02823 PAICS D. rat, up sal; n=1 MC01722 UBE2N

MC01405 PIM1 MC02177 RAB11A MC00545 RPS3

MC02564 MPHOSPH10 D. sal., N/C rat; n=21 MC02237 EIF4E2

MC01526 RANBP1 MC00837 FAAH MC05309 EMP3

MC04306 NUP107 MC00399 CYP2A13 MC01868 TRADD

MC00145 KRT18 MC01099 COL8A1 MC01933 ANXA7

MC00535 RPL38 MC02628 FXYD3 MC01392 PGD

MC00222 GLB1 MC00345 ADH1A MC01095 COL4A1

MC01541 RGS10 MC02307 SERPINI1 MC00332 GPX1

MC02123 KIF11 TC00019 APC MC00274 HEXB

MC01119 CTH MC03555 MAST3 MC01711 RPS27A

MC02344 GIF MC01037 BHMT MC03904 CHRAC1

MC02468 LAMA1 MC00741 CSRP3 MC03898 SMN1,SMN2

MC01297 MAD2L1 MC01503 PTPRR MC02876 HSPA8

MC02065 DAG1 MC04857 COX4I2 gi|49473435 NPY

MC04570 CRELD2 MC04494 MLF1 MC02061 CSPG3

MC01751 VRK1 MC01631 SPTBN1 MC00282 TP53

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MC02308 PLK1 TC00539 FABP7 Up rat, D. sal.; n=1

MC01068 CDC2 MC05220 GSTM5 TC00219 APP

MC00594 RRM2 MC04424 MID1IP1

Genes were determined to be up-regulated, down-regulated, or not changed (N/C) according to

the criteria set by each rat microarray study.

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46

Table 5. Comparison of microarray versus real-time RT-PCR estimates of fold change for

ten genes that were quantified on day 0 and day 3

Sal ID Gene Name Microarray Real Time

MC01620 SOX2 -1.7 -1.86**

MC02459 HES1 -2.15 -1.35

MC02501 SOX3 -1.92 1.14

MC03278 FST 3.09 3.45**

MC01765 CXCR4 3.17* 4.19**

MC01067 CD63 2.17* 4.77**

MC00341 TGFB1 3.69* 5.43**

MC01275 LGALS1 2.55* 10.5**

MC01583 SFRP2 17.95* 23.8**

MC03237 AGC1 56.84* 42.9**

*In the list of 360 genes that met statistical and fold level criteria.

** Significant fold change difference between day 0 and day 3 according to real-time RT-PCR

(student’s unpaired t-test, P < 0.05).

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47

Table 6. In situ hybridization results using tails collected three days after injury

Spatial expression pattern

Sal ID Gene Name Neurons Ependymoglia White matter Outside SC

MC00365 ANXA1 ↑ + ++ + +

MC02287 MMP9 ↑ + +++ + ++

MC00145 CK18 ↑ ++ +++ 0 0

MC02339 FGFBP1 ↓ ++ +++ 0 0

MC01583 SFRP2 ↑ ++ ++ ++ +++

MC01706 TXN ↑ ++ ++ 0 0

MC00018 APOE ↑ 0 0 +++ +

MC01275 LGALS1 ↑ + + +++ +++

MC01182 FTH1 ↑ + + +++ +++

MC01277 LGALS3 ↑ + 0 ++ +++

Gene expression was qualitatively ranked as 0, +, ++, or +++ according to the intensity of stain

and number of cells expressing the transcript. Arrows indicate if the transcript was up-regulated

or down-regulated after injury.

Figure 1. Volcano plots showing the number of genes selected as differentially expressed with

the FDR set to 0.01 (a) and the FWER set to 0.01 (b). Genes selected by each of these respective

criteria are gray and non-selected genes are black.

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48

Figure 2. Two-dimensional plot of a principal component analysis (PCA) showing the

relatedness of each GeneChip®. JMP statistical software was used to perform PCA on 25

GeneChip®s. A PCC matrix was made for 25 Genechip®s using intensity values for 376 changed

genes. Principal component 1 (PC1; 80.35% of the variation; eigenvalue 20.09) is displayed on

the x-axis and Principal component 2 (PC2; 13.54% of the variation; eigenvalue 3.34) is

displayed on the y-axis. The cumulative variation accounted for by PC1 and PC2 is 93.89%.

Twenty-five principal components account for 100% of the variation in the dataset. Five

biological replicate chips used for each of the five time points are enclosed by an oval to

illustrate their close proximity. ♦: Day 0, n = 5; ■: Day 1, n = 5; ▲: Day 3, n = 5; ●: Day 5, n =

5; ▬: Day 7, n = 5.

Figure 3. In situ hybridization of axolotl spinal cord using RNA probes that correspond to

significantly regulated genes from the microarray analysis. Rows show three representative

cross-sections of tissue samples collected three days after tail amputation. Photos in column 1

show a more caudal section than photos in column 2. Anti-sense probes are represented in

columns 1 and 2, and sense control probes in column 3. (A) Mmp9 expression can be seen in the

ependymoglia cell population (arrowhead) lining the central canal. Other cells found outside the

spinal cord are also positive for Mmp9 expression (arrow and data not shown). (B) Mmp9 is

expressed in few cells caudal to the injured spinal cord. Strong expression can be seen in a single

cell (arrowhead). (D) ck18 mRNA is strongly expressed exclusively in ependymoglial cells near

the end of the regenerating spinal cord. (E) ck18 is expressed in both ependymoglial cells and

subpopulations of neurons rostral to the injury. (G) Cross-section just rostral to the injury plane

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49

shows thioredoxin mRNA expression in both ependymoglia and some neurons. (H) Rostrally,

thioredoxin is expressed more exclusively in cells resembling neurons rather than ependymoglia.

(J) Many cells in the white matter are expressing apoE with little or no expression in neurons or

ependymoglia. Cells are often closely associated with the ependymoglia (arrowhead). Several

positive cells have processes (arrows; Fig J and K) resembling microglia or other phagocytic

cells. (K) Rostrally, few cells are expressing ApoE (arrow). Bar = 100 µm.

Supplemental Table 1. Genes and associated functions for the list of 360 significantly regulated

genes that met all statistical and fold level criteria. Average fold level changes between Day 0

and each of the post-amputation time points (Day1, 3, 5, or 7). Empty rows represent probe sets

that have another probe set designed for the same gene. One set of intensity values is represented

for probe set pair.

Page 49 of 61



Volcano plots showing the number of genes selected as differentially expressed with the FDR set to 0.01 (a) and the FWER set to 0.01 (b). Genes selected by each of these

respective criteria are gray and non-selected genes are black.

Page 50 of 61



Two-dimensional plot of a principal component analysis (PCA) showing the relatedness of each GeneChip®. JMP statistical software was used to perform PCA on 25 GeneChip®s. A PCC matrix was made for 25 Genechip®s using intensity values for 376 changed genes. Principal component 1 (PC1; 80.35% of the variation; eigenvalue 20.09) is displayed on the x-axis and Principal component 2 (PC2; 13.54% of the variation; eigenvalue 3.34) is

displayed on the y-axis. The cumulative variation accounted for by PC1 and PC2 is 93.89%. Twenty-five principal components account for 100% of the variation in the

dataset. Five biological replicate chips used for each of the five time points are enclosed by an oval to illustrate their close proximity. ♦: Day 0, n = 5; ■: Day 1, n = 5; ▲: Day 3, n =

5; ●: Day 5, n = 5; ▬: Day 7, n = 5.

Page 51 of 61



In situ hybridization of axolotl spinal cord using RNA probes that correspond to significantly regulated genes from the microarray analysis. Rows show three

representative cross-sections of tissue samples collected three days after tail amputation. Photos in column 1 show a more caudal section than photos in column 2. Anti-sense

probes are represented in columns 1 and 2, and sense control probes in column 3. (A) Mmp9 expression can be seen in the ependymoglia cell population (arrowhead) lining the

central canal. Other cells found outside the spinal cord are also positive for Mmp9 expression (arrow and data not shown). (B) Mmp9 is expressed in few cells caudal to the injured spinal cord. Strong expression can be seen in a single cell (arrowhead). (D) ck18

mRNA is strongly expressed exclusively in ependymoglial cells near the end of the regenerating spinal cord. (E) ck18 is expressed in both ependymoglial cells and

subpopulations of neurons rostral to the injury. (G) Cross-section just rostral to the injury plane shows thioredoxin mRNA expression in both ependymoglia and some

neurons. (H) Rostrally, thioredoxin is expressed more exclusively in cells resembling neurons rather than ependymoglia. (J) Many cells in the white matter are expressing

Page 52 of 61



apoE with little or no expression in neurons or ependymoglia. Cells are often closely associated with the ependymoglia (arrowhead). Several positive cells have processes

(arrows; Fig J and K) resembling microglia or other phagocytic cells. (K) Rostrally, few cells are expressing ApoE (arrow). Bar = 100 µm.

Page 53 of 61



Pattern/ID Gene Name Function Day1 Day3 Day5 Day7

UCCC = 64

MC02410 myosin IB actin binding 2.47 4.37 4.19 4.03MC01655 Transgelin actin binding 2.15 3.67 5.39 10.18MC00632 adipose differentiation-related protein lipid metabolism 19.99 11.86 7.4 4.04MC03405 TNF, alpha-induced protein 8 anti-apoptosis 2.76 2.64 2.23 2.18MC03384 ethylmalonic encephalopathy 1 anti-apoptotic 3.67 3.15 3.11 2.77MC01275 galectin 1 apoptosis 2.01 2.55 2.58 2.57MC02911 growth arrest and DNA-damage-inducible, gamma apoptosis 7.03 5.98 4.06 2.34MC03641 growth arrest and DNA-damage-inducible, beta apoptosis 11.59 6.42 4.24 3.73MC04572 EF hand domain containing 2 calcium binding protein 2.81 2.65 2.22 1.98MC03237 c-type lectin unknown 34.29 56.84 47.01 31.02MC01330 v-myc viral oncogene cell cycle 5.33 4.41 2.34 2.02MC05219

TC03644

TC00185 cathepsin K cysteine protease 2.58 3.21 2.57 2.16MC04847 tubulin beta cytoskeleton 4.7 4.67 3.59 3.35MC00259 fructose-1,6-bisphosphatase 1 glycolysis 4.95 4.49 3.02 2.82MC04771 UDP-GlcNAc glycosylation 5.19 2.63 3.06 3.39MC00341 transforming growth factor, beta 1 growth factor 2.65 3.69 3.37 2.94MC01709 TYRO protein tyrosine kinase binding protein immune response 2.93 3.79 3.32 3.27MC00330 solute carrier family 11, member 1 immune response 3.93 7.46 6.57 5.14MC07609 macrophage expressed gene 1 immune response 3.47 6.91 5.92 4.61MC01765 chemokine (C-X-C motif) receptor 4 immune response 3.59 3.17 3.02 3.03MC02474 galectin 3 binding protein immune response 2.94 5.12 4.4 3.81MC00218

TC00186

MC03420 ankyrin repeat domain 1 (cardiac muscle) injury response 6.78 5.13 5.42 5.65MC01182 ferritin, heavy polypeptide 1 iron homeostasis 3.44 4.88 4.56 4.8MC02182 ATPase, H+ transporting, V0 subunit d isoform 1 proton transport 3.07 5.74 5.73 5.97MC03786 chromosome 1 open reading frame 33 ribosomal 2.82 3.07 2.29 2.56MC05180 mitogen-activated protein kinase kinase 3 signal transduction 2.08 3.17 3.4 2.62MC02287 matrix metalloproteinase 9 tissue remodeling 7.44 5.58 5.29 5.83MC01311 matrix metalloproteinase 1 tissue remodeling 13.1 6.45 6.24 6.97TC00829 v-yes-1 viral related oncogene homolog tyrosine kinase 3.43 3.5 2.71 2.6MC04702 vacuole membrane protein 1 unknown 8.46 7.71 8.2 7.23MC10230 similar to CG12279-PA unknown 2.21 3.3 5.13 5.74MC04619 hypothetical protein FLJ22662 unknown 30.97 41.64 30.2 23.85gi|62426 wingless-type MMTV, member 5A WNT signaling 2.37 3.86 4.55 4.71MC05999 unknown unknown 12.23 8.36 4.4 2.46MC10145 unknown unknown 33.58 20.9 22.76 21.76MC09114 unknown unknown 9.48 6.61 5.18 3.33MC01847 unknown unknown 9.12 4.62 3.93 4.56MC10221 unknown unknown 5.26 7.55 6.17 4.14MC08530 unknown unknown 2.5 4.57 6.18 6.4MC10258 unknown unknown 7.18 4.48 4.94 4.68MC10136 unknown unknown 4.34 7.18 7.99 7.51MC07184 unknown unknown 6.69 5.5 3.07 2.46MC09299 unknown unknown 7.46 11.28 11.7 6.91MC00640 unknown unknown 5.89 4.66 3.91 2.98MC10333 unknown unknown 9.5 6.16 5.29 4.93

8.65 7.41

cathepsin L cysteine protease

cytochrome b-245, beta polypeptide immune response 11.11 11.7

3.84 5.95 3.26 2.6

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MC08352 unknown unknown 14.29 5.98 5.65 5.77MC06325 unknown unknown 2.1 3.42 4.09 3.39MC09544 unknown unknown 2.5 3.35 3.27 3.2MC09298 unknown unknown 7.09 7.71 9.38 6MC09381 unknown unknown 3.54 2.81 2.96 3.05MC07259 unknown unknown 2.78 4.86 4.37 3.64MC09443 unknown unknown 4.74 4.92 5.26 5.64MC09474 unknown unknown 4.25 4.3 4.41 5.29MC10140 unknown unknown 3.9 3.52 2.95 2.3MC09930 unknown unknown 3.61 3.07 2.77 2.27MC07617 unknown unknown 3.46 4.54 4.08 3.91MC10140 unknown unknown 3.9 3.52 2.95 2.3MC07874 unknown unknown 6.22 6.41 5.98 6.11MC10062 unknown unknown 4.4 4.74 5.09 3.89MC08348 unknown unknown 3.21 2.1 1.92 2MC05353 unknown unknown 2.04 3.43 2.71 2.26MC10093 unknown unknown 6.42 3.25 2.59 2.42

NNUC = 44

MC01194 glutaredoxin antioxidant 1.49 1.97 2.05 2.06MC01277 galectin 3 carbohydrate binding 1.42 1.97 2.77 3.32MC02123 kinesin family member 11 cell cycle; M -1.22 1.08 2.17 2.31MC04914 -1.2 -1 2.23 2.91MC04954 -1.17 -1.01 2.29 2.76MC02308 polo-like kinase 1 (Drosophila) cell cycle; G1/S:G2/M -1.2 1.38 2.27 2.57MC05457 replication factor C 2 cell cycle; S 1.15 1.74 2.33 2.8MC05488 proliferating cell nuclear antigen cell cycle; S -1.07 1.95 2.18 2.18MC02615 minichromosome maintenance deficient 7 cell cycle; S 1.05 1.68 2.02 2.07MC01795 serine/threonine kinase 6 cell cycle; M -1.08 1.58 2.96 3.51MC00687 cyclin A2 cell cycle; G1/S: G2/M -1.08 1.29 2.42 2.65MC01068 cell division cycle 2 cell cycle; G1/S: G2/M -1.21 1.44 2.58 2.61MC00703 CHK1 checkpoint homolog cell cycle; DNA damage -1.26 1.71 2.14 2.24MC01297 MAD2 mitotic arrest deficient-like 1 cell cycle; M -1.21 1.81 2.18 2.46MC00694 cell division cycle 20 homolog cell cycle; M -1.18 1.4 2.53 3.25MC02001

MC02003

MC04043 cell division cycle associated 8 cell cycle; M -1.18 1.07 2.01 2.41MC00145 cytokeratin 18 cytoskeleton component 1.63 1.92 2.08 2.19MC04928 caldesmon 1 cytoskeleton component 1.02 1.52 2.24 3.14MC01972 flap structure-specific endonuclease 1 DNA metabolism -1 1.9 2.47 2.92MC02851

MC02852

MC02963 cytoskeleton-associated protein 4 inflammatory response -1.12 1.97 3.5 5.93MC01242 karyopherin alpha 2 intracellular transport 1.68 1.79 2.27 2.58MC01751 vaccinia related kinase 1 kinase 1.02 1.3 2.21 2.56MC05045 collagen, type XII, alpha 1 ECM component 1.24 1.79 2.66 4.47MC01728 uncoupling protein 2 neuroprotection 1.44 1.87 2.05 2.01MC00215 cathepsin K protease -1.52 1.75 3.6 6.26MC05477 olfactomedin-like 2B signal transduction -1.27 1.06 2.36 3.36MC03344 HSPC163 protein signal transduction 1.59 1.99 2.09 2.36MC04608 hypothetical protein FLJ23518 unknown 1.19 1.81 2.88 3.22MC00535 ribosomal protein L38 ribosomal 1.54 1.97 2.08 2.2

2.12 2.47stem-loop (histone) binding protein histone mRNA processing -1.03 1.83

-1.1 1.19 2.16 2.44

cyclin B3 cell cycle; G2/M

aurora kinase B cell cycle; M

Page 55 of 61



MC04760 unknown unknown -1.48 1.09 2.65 3.51MC10004 unknown unknown 1.07 1.37 2.14 2.96MC09962 unknown unknown 1.11 1.34 2.12 2.72MC06887 unknown unknown -1.15 1.46 2.63 3.19MC00214 unknown unknown -1.03 1.63 3.94 6.6MC08928 unknown unknown 1.3 1.8 3.53 5.1MC07152 unknown unknown 1.01 1.48 2.15 2.17MC06483 unknown unknown -1.07 1.53 2.19 3.01MC09462 unknown unknown 1.19 1.9 2.05 2.28MC05554 unknown unknown 1.53 1.82 2.82 3.33MC08286 unknown unknown 1.77 1.83 2.28 2.4MC09310 unknown unknown -1.15 1.61 2.13 2.35MC07493 unknown unknown -1.07 1.33 2.15 2.57MC06189 unknown unknown 1.3 1.45 2.17 3.04

NDCC = 42

TC01675 keratin 7 cytoskeletal element -1.83 -2.19 -4.89 -9.13MC03935 kinesin family member 21A cytoskeleton component -1.56 -2.27 -2.46 -3.16

gi|2655016 solute carrier family 1, member 2 glutamate transport -1.61 -5.22 -5.98 -7.32 gi|2655014 solute carrier family 1, member 3 glutamate transport -1.8 -2.71 -2.71 -3.02MC02877 heat shock 70 kDa protein 8 heat shock 1.6 -2.16 -2.05 -2.52MC00207 transthyretin hormone transport -1.12 -2.11 -2.43 -2.77MC00599

TC00434

MC03761 potassium channel KCTD3 ion transport -1.42 -2.41 -3.16 -4.47MC01168 farnesyl diphosphate synthase lipid metabolism -1.27 -2.98 -3.01 -2.4MC00837 fatty acid amide hydrolase lipid metabolism -1.44 -2.14 -2.63 -4.47MC01099 collagen, type VIII, alpha 1 ECM component -1.41 -4.34 -4.66 -4.34MC00050 fibrillin 2 ECM component -1.64 -2.45 -2.86 -2.44MC00415 glutathione S-transferase M4 metabolism -1.39 -2.01 -2.27 -2.93MC00413 glutamate receptor, metabotropic 3 neurotransmission -1.61 -3.28 -2.91 -3.34MC03121 peptidyl arginine deiminase, type III protein metabolism -1.27 -2.04 -3.18 -4.82MC05295 adenomatosis polyposis coli down-reg 1 signal transduction -1.67 -2.67 -2.45 -2.33MC01681 tight junction protein 1 signal transduction -1.34 -2.2 -2.26 -2.36TC00019 adenomatosis polyposis coli signal transduction -1.4 -2.12 -2.24 -2.97MC03199 microtubule-associated protein MAPRE3 structural protein -1.61 -2.01 -2.89 -4.48MC01113 crystallin, alpha B structural protein -1.26 -2.9 -3.37 -4.5MC07514 unknown unknown -1.18 -2.01 -2.02 -2.22MC10135 unknown unknown -1.95 -2.94 -2.55 -2.91MC09228 unknown unknown 1.68 -2.04 -2.14 -2.58MC08604 unknown unknown -1.39 -2.67 -2.71 -2.82MC10167 unknown unknown -1.38 -2.14 -2.43 -2.99MC07622 unknown unknown -1.45 -2.2 -2.85 -3.83MC07141 unknown unknown -1.24 -2.12 -2.79 -4MC09886 unknown unknown -1.51 -3.05 -3.95 -5.79MC07029 unknown unknown -1.74 -3.61 -3.41 -4.55MC07816 unknown unknown -1.45 -2.05 -2.52 -3.42MC05592 unknown unknown -1.88 -2.58 -3.54 -4.59MC10052 unknown unknown -1.41 -2.01 -2.25 -2.67MC05593 unknown unknown -1.64 -2.17 -2.67 -3.61MC08388 unknown unknown -1.22 -2.12 -1.94 -2.28MC07961 unknown unknown -1.3 -2.49 -2.44 -2.66

-2.91 -3.7solute carrier family 12, member 2 ion transport -1.63 -2.07

Page 56 of 61



MC07497 unknown unknown -1.31 -2.86 -3.37 -3.47MC07444 unknown unknown -1.32 -2.09 -1.91 -2.07MC09586 unknown unknown -1.36 -2.09 -2.15 -2.77MC10106 unknown unknown -1.42 -2.04 -2.68 -3.42MC10355 unknown unknown -1.29 -2.01 -2.08 -3.2MC09589 unknown unknown -1.57 -2.61 -3.09 -3.65MC07565 unknown unknown -1.89 -2.13 -1.92 -2.35

NNND = 35

MC03428 programmed cell death 4 apoptosis 1.07 -1.8 -1.85 -2.13TC00219 amyloid beta (A4) precursor protein apoptosis -1.38 -1.41 -1.78 -2.24MC02974 cyclin I cell cycle -1.24 -1.6 -1.93 -2.25MC01630 spectrin, alpha, non-erythrocytic 1 cytoskeleton component -1.34 -1.72 -1.91 -2.38MC01631

MC01632

MC02490 phosphorylase, glycogen glycogen metabolism -1.39 -1.53 -1.96 -2.43TC00539 fatty acid binding protein 7, brain lipid metabolism -1.24 -1.37 -1.68 -2.34MC04289 choline phosphotransferase 1 lipid metabolism -1.17 -1.97 -1.81 -2.16MC00987 solute carrier family 25, member 4 mitochondrial transport -1.39 -1.36 -1.67 -2.1MC02382 glycoprotein M6B neurogenesis -1.34 -1.75 -1.85 -2.13MC10102 AHNAK nucleoprotein (desmoyokin) neurogenesis -1.19 -1.66 -1.85 -2.03MC00326

MC00327

MC00419 glutathione S-transferase pi neuroprotection -1.29 -1.74 -1.92 -2.52MC03106 GABA receptor-associated GABARAPL2 neurotransmission -1.36 -1.67 -1.91 -2.54MC01369 prostatic binding protein protease inhibitor -1.31 -1.38 -1.57 -2.03MC01046 calcitonin-related polypeptide, alpha signal transduction 1.15 -1.15 -1.63 -2.84MC05420 trafficking protein particle complex 6B transport -1.41 -1.66 -1.91 -2.18MC05114 hypothetical protein BC009561 unknown -1.26 -1.67 -1.78 -2.06MC09850 unknown unknown -1.3 -1.71 -1.92 -2.86MC09217 unknown unknown -1.21 -1.38 -1.67 -2.39MC07208 unknown unknown -1.21 -1.59 -1.65 -2.11MC07303 unknown unknown 1.06 -1.24 -1.67 -2.11MC06742 unknown unknown 1.04 -1.55 -1.69 -2.09MC10155 unknown unknown 1.11 -1.43 -1.89 -2.09MC09850 unknown unknown -1.3 -1.71 -1.92 -2.86MC07685 unknown unknown -1.26 -1.69 -1.85 -2.34MC07838 unknown unknown -1.56 -1.91 -1.89 -2.41MC09395 unknown unknown -1.22 -1.78 -1.91 -2.28MC09898 unknown unknown 1.1 -1.45 -1.71 -2.16MC09918 unknown unknown 1.21 -1.45 -1.63 -2MC10156 unknown unknown -1.17 -1.57 -1.7 -2.15MC09554 unknown unknown -1.56 -1.77 -1.95 -2.17MC09092 unknown unknown -1.24 -1.31 -1.58 -2.19MC07421 unknown unknown -1.32 -1.69 -1.78 -2.22MC10165 unknown unknown -1.28 -1.45 -1.66 -2.07

NNNU = 28

MC02466 kinesin family member C1 cell cycle; M -1.83 -1.08 1.8 2.03MC00700 chromosome condensation 1 cell cycle; G2/M 1.16 1.67 1.82 2.29TC00995 replication protein A2, 32kDa cell cycle; DNA damage -1.06 1.28 1.74 2MC02613 minichromosome maintenance deficient 6 cell cycle; S -1.05 1.64 1.99 2.14

-1.73 -2.18

spectrin, beta, non-erythrocytic 1 cytoskeleton component

glutathione S-transferase M1 neuroprotection -1.02 -1.58

-1.38 -1.75 -1.92 -2.65

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MC01119 cystathionase cysteine synthesis -1.02 1.95 1.86 2.36MC02037 caldesmon 1 cytoskeleton -1.26 1.51 1.98 2.95MC05347 nuclear autoantigenic sperm protein histone transport 1.31 1.62 1.93 2.03MC01093 collagen, type II, alpha 1 ECM component -1.18 -1.99 1.05 2.56MC02468 laminin, alpha 1 ECM component -1.3 1.09 1.73 2.43MC02066 dystroglycan 1 ECM component -1.31 1.29 1.77 2.44MC01368 purinergic receptor P2Y neuronal differentiation -1.25 1.22 1.75 2.32MC04306 nucleoporin nuclear transport 1.02 1.61 1.75 2.14MC04694 acidic nuclear phosphoprotein ANP32E phosphotase inhibitor 1.09 1.47 1.65 2.16MC02823

MC02824

MC01129 CTP synthase pyrimidine biosynthesis 1 1.56 1.77 2.03MC01639 Sjogren syndrome antigen B RNA processing 1.4 1.96 1.94 2.03MC03377 exosome component 2 rRNA processing 1.28 1.82 1.83 2.09MC01526 RAN binding protein 1 signal transduction 1.36 1.82 1.78 2.09MC03393 trans-prenyltransferase ubiquinone biosynthesis 1.26 1.87 1.77 2.28MC05825 chromosome 6 orf 115 unknown 1.39 1.81 1.94 2.23MC07600 hypothetical LOC131unknown76 unknown 1.31 1.6 1.77 2.08MC05568 unknown unknown -1.29 1.15 1.89 2.67MC09570 unknown unknown -1.14 1.1 1.87 2.58MC07466 unknown unknown 1.58 1.68 1.81 2MC10295 unknown unknown -1.11 1.04 1.64 2.26MC10029 unknown unknown -1.33 1.1 1.53 2.19MC09210 unknown unknown 1.49 1.42 1.85 3.05MC05927 unknown unknown 1.87 1.93 1.95 2.18

NNDC = 27

MC00028 cyclin-dependent kinase inhibitor 1C negative cell proliferation -1.45 -1.72 -2.23 -3.29MC04494 myeloid leukemia factor 1 cell differentiation -1.4 -1.72 -2.09 -2.45MC00232 keratin 6L cytoskeleton component -1.12 -1.6 -2.53 -5.03MC02916 actin binding LIM protein 1 cytoskeleton component -1.37 -1.88 -2.1 -2.66MC02377 glutamate dehydrogenase 1 glutamate catabolism -1.52 -1.64 -2.01 -2.64MC01008 ATPase, Na+/K+ transporting, beta 3 ion transport -1.39 -1.66 -2.01 -2.25MC02628 FXYD ion transport regulator 3 ion transport -1.26 -1.66 -2.04 -3.85MC03790 AMP-activated protein kinase, gamma 2 lipid metabolism -1.56 -1.8 -2.03 -2.54MC04857 cytochrome c oxidase COX4I2 metabolism -1.29 -1.87 -2.14 -2.62MC01037 betaine-homocysteine methyltransferase metabolism -1.55 -1.65 -2.2 -2.8MC00170 6-pyruvoyl-tetrahydropterin synthase metabolism -1.48 -1.87 -2.23 -2.65MC04363 creatine kinase, mitochondrial 1A metabolism -1.62 -1.56 -2.15 -2.74MC02307 serine proteinase inhibitor SERPINI1 neurogenesis -1.58 -1.73 -2.29 -3.21MC00344 4-aminobutyrate aminotransferase neurotransmission -1.6 -1.98 -2.82 -4MC02165 transmembrane 4 superfamily member 2 protein biosynthesis -1.53 -1.87 -2.54 -3.6

gi|14029843 guanine nucleotide binding GNB5 signal transduction -1.21 -1.99 -2.11 -2.26MC04397 polymerase (RNA) II polypeptide L transcription -1.31 -1.9 -2.16 -3.18MC06347 chromosome 6 orf 110 unknown -1.33 -1.7 -2.32 -2.98MC05509 hypothetical protein LOC127262 unknown -1.42 -1.95 -2.29 -2.63MC07786 unknown unknown -1.26 -1.6 -2.85 -4.95MC08476 unknown unknown -1.41 -1.94 -2.03 -2.52MC08958 unknown unknown -1.32 -1.85 -2.19 -2.59MC08418 unknown unknown -1.42 -1.98 -2.26 -3.12TC07363 unknown unknown -1.3 -1.55 -2.11 -2.26MC09779 unknown unknown -1.5 -1.69 -2.11 -2.64

1.86 2.14phosphoribosylaminoimidazole carboxylase purine metabolism 1.07 1.63

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MC10283 unknown unknown -1.44 -1.64 -2.04 -2.52MC09671 unknown unknown -1.3 -1.46 -2.06 -2.82

NUCC = 24

TC01065 transgelin actin binding 1.99 2.66 3.47 5.7MC02029 N-acylsphingosine amidohydrolase 1 apoptosis 1.58 2.69 2.77 2.44MC03249 ubiquitin-like 1 cell cycle; S -1.23 2.71 2.49 2.71MC06890 cyclin-dependent kinase 4 cell cycle; G1/S 1.17 2.28 2.18 2.66MC03476 KIAA0101 gene product cell cycle; S -1.33 2.86 3.24 3.56MC00182 protective protein for beta-galactosidase cellular transport 1.4 3.31 2.6 2.31MC02488

MC02489

MC00038 cystatin B (stefin B) protease inhibitor 1.71 2.49 2.54 2.4MC00594 ribonucleotide reductase M2 polypeptide DNA metabolism -1.42 2.27 2.29 2.62MC00365 annexin A1 inflammatory response 1.67 2.1 2.09 2.08MC01673 thrombospondin 2 ECM component 1.65 2.43 2.26 3.35MC00222 galactosidase, beta 1 metabolism 1.42 2.79 2.91 2.22MC01923 ATPase, H+ transporting, lysosomal 9kDa proton transport 1.7 2.25 2.15 2.27MC00526 ribosomal protein L31 ribosomal 1.44 2.11 2.43 2.88MC00018 apolipoprotein E lipid metabolism 1.32 3.76 4.27 3.7MC04130 methyltransferase like 2 ubiquinone biosynthesis 1.34 2.03 1.83 2MC05155 hypothetical protein FLJ31795 unknown 1.6 2.04 1.84 2.21MC03138 angiopoietin-like 2 vascular growth factor 1.77 5.5 4.97 5.68MC10381 unknown unknown 1.26 2.18 2.34 2.2MC07325 unknown unknown 1.43 3.67 3.08 2.19MC02207 unknown unknown 1.71 2.52 2.18 2.27MC10141 unknown unknown 1.91 3.36 3.43 3.33MC10204 unknown unknown 1.36 2.59 2.22 2.27MC09797 unknown unknown 1.68 1.81 1.62 1.88

UNNN = 20

MC01947 bystin-like cell adhesion 2.45 1.9 1.48 1.44MC03408 nucleostemin cell cycle; G1/S 2.19 1.81 1.6 1.65MC02084 dual specificity phosphatase 1 heat shock 4.09 1.94 1.57 1.71MC01230

MC01231

MC02404 heat shock 70 kDa protein 5 injury response 3.89 1.88 1.8 1.93MC04407 solute carrier family 30, member 1 ion transport 2.06 1.36 1.15 -1.02MC04719 glyoxylate aminotransferase 2-like 1 metabolism 3.95 1.23 1.17 1.26MC00770 dyskeratosis congenita 1, dyskerin ribosomal 2.52 1.81 1.8 1.99MC01559 ribosomal protein S6 kinase signal transduction 2.52 1.71 1.14 1.17MC04660 pseudouridylate synthase 1 tRNA processing 2.82 1.81 1.66 1.8MC05410 hypothetical protein FLJ36031 unknown 2.37 1.4 1.79 1.87MC00197 transglutaminase 1 injury response 2.24 1.04 -1.34 -1.99MC04487 melanoma differentiation associated 5 immune response 2.98 1.05 1.09 1.11MC05886 unknown unknown 2.13 1.25 1.12 1.07MC07124 unknown unknown 2.46 1.48 1.57 1.58MC09976 unknown unknown 2.28 1.62 1.37 1.17MC09774 unknown unknown 4.36 1.1 1.23 1.28MC09902 unknown unknown 2.07 1.65 1.45 1.5MC07587 unknown unknown 3.21 1.15 1.07 1.24MC09869 unknown unknown 2.3 1.26 1.47 1.42

interferon regulatory factor 1 immune response 3.82 1.62

3.08 2.69

1.31 1.22

legumain protease 1.45 3.45

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UUCC = 12

MC00043 coagulation factor XIII, A1 polypeptide wound healing 5.25 11.56 10.03 9.58MC02942 macrophage receptor with collagenous structure immune response 3.49 12.45 11.39 7.7MC00047 fatty acid binding protein 2, intestinal lipid metabolism 6.54 21.27 21.75 15.3MC01354

MC01355

MC10310 unknown unknown 9.86 24.32 33.76 31.76MC07205 unknown unknown 9.51 29.58 36.73 35.52MC10047 unknown unknown 3.01 6.8 6.62 4.3MC09973 unknown unknown 2.48 5.14 4.36 3.36MC09209 unknown unknown 2.08 6.18 5.7 3.43MC09990 unknown unknown 3.8 11.74 15.51 13.67TC02494 unknown unknown 2.38 7.37 5.29 3.65MC03347 unknown unknown 2.4 6.44 6.58 9.98

UDCC = 10

MC02853 tissue factor pathway inhibitor 2 coagulation 4.62 1.89 1.98 2.29TC00840 matrix metalloproteinase 1 tissue remodeling 18.97 5.6 5.4 5.76MC02344 intrinsic factor vitamin B transport 14.23 5.01 2.83 2.43MC04256 chromosome 8 orf 4 WNT signaling 14.21 3.99 2.32 2.49MC07780 unknown unknown 63.75 26.12 23.1 21.7MC09473 unknown unknown 21.19 5.79 3.33 3.21MC10351 unknown unknown 9.08 3.36 2.54 2.18MC09159 unknown unknown 14.23 4.11 2.62 2.12MC09527 unknown unknown 16.37 6.79 8.32 5.85MC07978 unknown unknown 6.88 2.21 1.94 2.17

UCDC = 8

MC01211 heme oxygenase 1 heat shock 18.02 19.45 6.88 6.84TC00846 matrix metalloproteinase 13 tissue remodeling 18.49 32.2 10.16 5.76MC01312 matrix metalloproteinase 1 tissue remodeling 18.53 27.54 11.47 6.97TC01086 tissue inhibitor of metalloproteinase 1 tissue remodeling 29.63 16.72 5 2.71MC01239 jun-B proto-oncogene transcription 24.14 12.15 10.8 11.45MC09943 unknown unknown 18.07 9.67 3.55 2.08MC07614 unknown unknown 12.92 8.72 2.98 2.51MC09763 unknown unknown 49.69 43.99 16.7 17.47

DCCC = 6

MC00399 cytochrome P450 (CYP2A13) metabolism -2 -2.03 -3.7 -4.35MC06562 unknown unknown -2.03 -3.92 -3.6 -3.9MC05696 unknown unknown -3.95 -2.91 -2.23 -2.7MC09604 unknown unknown -2.25 -2.9 -2.78 -2.89MC08782 unknown unknown -2.06 -2.67 -2.74 -2.53MC07692 unknown unknown -2.52 -2.15 -2.33 -3.03

UCCN = 4

MC03162 leukotriene B4 12-hydroxydehydrogenase antioxidant 2.66 2.74 2.03 1.7MC04794 thioredoxin domain containing 2 antioxidant 2.53 2.41 2.16 1.94MC01706 thioredoxin antioxidant 2.33 2.34 2.09 1.64MC04937 BTB (POZ) domain containing 3 protein binding 3.96 2.54 2.09 1.69

26.95 22.3glycoprotein nmb negative cell proliferation 3.48 17.61

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DCNN = 4

MC04283 RGM domain family, member A axon guidance -2.29 -2.37 -1.92 -1.93MC00841 four and a half LIM domains 1 protein binding -2.04 -2.33 -1.8 -1.59MC06550 unknown unknown -2.1 -2.31 -1.42 -1.47MC09691 unknown unknown -3.17 -2.2 -1.33 -1.21

UCNN = 5

MC01890 carboxylesterase 1 neuroprotection 3.29 2.97 1.55 1.15MC05339

MC05340

TC00979 RAP2B, member of RAS oncogene family signal transduction 2.43 2.23 1.5 1.2MC01571 spermidine N1-acetyltransferase polyamine homeostasis 2.75 2.03 1.72 1.55MC01067 CD63 antigen (melanoma 1 antigen) signal transduction 2.16 2.17 1.84 1.61

UNNU = 3

MC02792 nucleolar protein 5A ribosomal 2.15 1.51 1.79 2MC07460 unknown 2.03 1.47 1.97 2.38MC09319 unknown 3.68 1.67 1.92 2.16

NUCN = 3

TC00879 v-Ha-ras viral oncogene cell proliferation 1.65 2.55 2.05 1.97MC03953 hypothetical protein FLJ20303 cell proliferation -1.08 2.09 2.12 1.89MC00337 latent TGF beta binding protein 1 TGFbeta signalling 1.72 2.84 2.21 1.7

UNND = 3

MC05564 unknown unknown 2.61 1.3 -1.66 -2.17MC05563 unknown unknown 2.75 1.37 -1.8 -2.16MC07520 unknown unknown 2.27 -1.05 -1.4 -2.14

NNUU = 2

MC05041 collagen, type XI, alpha 1 ECM component -1.01 -1.22 2.13 4.38MC02833 periostin, osteoblast specific factor skeletal development -1.04 1.64 4.05 9.56

NUNN = 2

MC10107 unknown unknown -1.04 2.39 1.86 1.75MC09645 unknown unknown 1.4 2.3 1.96 1.71

UNUC = 2

MC09345 unknown unknown 5.11 1.91 3.03 4.08MC06118 unknown unknown 2.13 1.56 2.08 2.48

UDDC = 1

MC00601 transcobalamin I vitamin B transport 19.97 8.71 3.89 3

NDCD = 1

MC02800 anterior gradient 2 homolog cell survival 1.01 -2.57 -4.34 -9.82

NDCC = 1

MC09373 unknown unknown -1.16 -2.7 -2.97 -4.17

1.6 1.39ubiquitin specific protease 2 protein breakdown 3.45 2.14

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NUCU = 1

MC05482 hypothetical protein FLJ14712 unknown 1.15 2.16 4.11 8.65

UUCD = 1

MC01583 secreted frizzled-related protein 2 WNT signaling 8.16 17.95 17.42 7.55

UDNN = 1

MC00339 interleukin 8 receptor, beta immune response 12.11 2.66 1.22 1.38

DCDC = 1

TC00198 keratin 4 cytoskeleton component -2.01 -2.1 -5.21 -9.37

DNNN = 1


NDCN = 1

MC07147 unknown unknown -1.08 -2.96 -2 -1.82

NDNN = 1


NNDD = 1


NNDN = 1


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