Deciphering peripheral nerve myelination by using
Schwann cell expression profiling
Rakesh Nagarajan*, Nam Le*, Heather Mahoney, Toshiyuki Araki, and Jeffrey Milbrandt†
Departments of Pathology and Internal Medicine, Washington University School of Medicine, 660 South Euclid Avenue, Box 8118, St. Louis, MO 63110
Edited by Eric M. Shooter, Stanford University School of Medicine, Stanford, CA, and approved May 6, 2002 (received for review February 8, 2002)
Although mutations in multiple genes are associated with inher-
ited demyelinating neuropathies, the molecular components and
pathways crucial for myelination remain largely unknown. To
approach this question, we performed genome-wide expression
analysis in several paradigms where the status of peripheral nerve
myelination is dynamically changing. Anchor gene correlation
analysis, a form of microarray analysis that integrates functional
information, using correlation-based clustering, with a statistically
rigorous test, the Westfall and Young step-down algorithm, was
applied to this data set. Biological pathways active in myelination,
genes encoding proteins involved in myelin synthesis, and genes
whose mutation results in myelination defects were identified.
Many known genes and previously uncharacterized ESTs not here-
tofore associated with myelination were also identified. One of
these ESTs, MASR (myelin-associated SUR4 protein), encodes a
member of the SUR4 family of fatty acid desaturases, enzymes
involved in elongation of very long chain fatty acids. Its specific
localization in myelinating Schwann cells indicates a crucial role for
MASR in normal myelin lipid synthesis.
insulator that wraps around the axon. In the central nervous
system, myelination is accomplished by oligodendrocytes,
whereas the analogous role is fulfilled by Schwann cells in the
peripheral nervous system. Disruption of myelination in dis-
eases, such as hereditary motor and sensory neuropathies in the
peripheral nervous system and multiple sclerosis in the central
nervous system, are highly prevalent and result in significant
morbidity and mortality (1).
Alterations in several genes including myelin protein zero
(MPZ), peripheral myelin protein (PMP22), connexin32 (Cx32),
and EGR2 have been associated with inherited demyelinating
neuropathies (for review, see ref. 1). However, the molecular
components of myelination remain largely unknown, and a
significant proportion of patients with inherited myelinopathies
do not have mutations in any of the above genes, underscoring
the need to identify additional candidate genes that may be
With the advent of microarray technology, genome-wide
expression analyses are now possible. The predominant types of
microarray experiments include comparisons of predefined sam-
ple groups (e.g., benign vs. tumor) and assessments of expression
over a continuous variable (e.g., a time course). Many of these
studies have used clustering techniques, such as hierarchical
clustering, correlation clustering, and self-organizing maps
(SOMs), to distinguish tumor subtypes via ‘‘expression finger-
prints’’ and to identify functionally related gene clusters (2–5).
The underlying concept derived from these studies is that genes
important in a common process share similar expression profiles.
However, the complexity of mammalian systems makes it diffi-
cult to identify functionally similar genes by cluster analysis.
Furthermore, although several methods have been proposed to
add statistical rigor to analyses of microarray experiments deal-
ing with predefined sample groups (6–8), it is currently unclear
how to assess the statistical significance of techniques such as
SOMs or k-means clustering, which are used to analyze contin-
apid saltatory conduction of electrical signals along the
distance of an axon is facilitated by myelin, a lipid-rich ionic
uous variable experiments. In addition, the analysis of microar-
ray data invariably requires performing multiple comparisons,
which results in a high occurrence rate of false positives (type I
error). A generalized method that can be applied to control
this type I error rate, and that also takes into account the
dependence structure between variables, is the Westfall and
Young step-down algorithm for calculating adjusted P values
by permutation (9).
In this study, we combined the idea of identifying functionally
related genes by their coregulated expression profiles with a
statistical test that accounts for multiple hypotheses. We inte-
grated Westfall and Young step-down algorithm with correla-
tion-based clustering into an algorithm called anchor gene
correlation analysis (AGCA). AGCA was applied to a small
compendium of peripheral nerve expression profiles and en-
abled the identification of genes that are important during
myelination and that may be involved in causing inherited
neuropathies. Additionally, through this approach we discovered
a gene belonging to the SUR4 family, whose members are known
to be involved in very long chain fatty acid (VLCFA) synthesis,
a necessary step in generating sphingomyelin and galactocere-
broside (Gal-C). Thus, we present here a generalized global
approach to studying peripheral nerve biology that has facili-
tated identification of molecular components of biological pro-
cesses and has permitted process-directed gene discovery.
Microarray Analysis. Total RNA was prepared from sciatic nerves
from at least ten C57BL?6 mice at each time point for nerve
crush (days 0, 1, 4, 7, 10, 14, and 28), nerve development (P0 and
P56), or nerve transection (days 0, 14, and 28), as well as for
Egr2lo/lomice and WT littermates. Replicates of the uninjured
P14 and P56 time points were prepared entirely independently
from two separate pools of ten animals each. From the total
RNA, biotinylated cRNA probes were generated, fragmented,
and applied as described (10) to Mouse MU74A (Version 1)
GENECHIP arrays (Affymetrix, Santa Clara, CA). Affymetrix
software was used to filter inaccurately represented probe sets.
The raw chip data are provided as supporting information, which
is published on the PNAS web site, www.pnas.org.
For analysis of the proliferation, cytokine response, and
macrophage infiltration processes, we preprocessed (filtered)
the data for probe sets that are called ‘‘present’’ at 4 (5,931 probe
respectively. We further selected for probe sets that have a
?2-fold increase in expression compared with uninjured nerve.
Similarly, absolute call filtering, fold change filtering, and devi-
ation?mean filter of 0–0.35 for replicate chips, were performed
This paper was submitted directly (Track II) to the PNAS office.
Abbreviations: AGCA, anchor gene correlation analysis; VLCFA, very long chain fatty acid;
MASR, myelin-associated SUR4 protein; ER, endoplasmic reticulum; MGCC, myelin gene
coregulated cluster; RT, reverse transcription; MGAs, myelin gene anchors.
Data deposition: The sequence reported in this paper has been deposited in the GenBank
database [accession no. AF480860 (MASR mRNA)].
*R.N. and N.L. contributed equally to this work.
†To whom reprint requests should be addressed. E-mail: firstname.lastname@example.org.
June 25, 2002 ?
vol. 99 ?
for the myelination process (see Fig. 2), using DCHIP ANALYZER
software (11) and FUNCTION EXPRESS (software written in our
lab). Genes that were ‘‘called’’ absent on all chips by the DCHIP
software and genes whose expression varied by ?0.35 in two
independent microarray experiments conducted on adult nerve
were eliminated. AGCA was performed to find statistically
significant coregulated genes. The correlation coefficient of the
expression profile of an anchor gene and the expression profile
of each of the other probe sets was determined using the Pearson
product moment correlation coefficient (PPMCC) function. To
measure the significance of the correlations, we applied the
(9). Briefly, the t statistic was replaced by the PPMCC and a
one-tailed analysis was performed. One thousand random per-
mutations were performed singly for each anchor or for all
anchors. Adjusted P values were calculated under asymptotic
normality enforcing monotonicity constraints. For analysis of P0
vs. uninjured adult P56 nerve and Egr2lo/lonerves vs. WT (P14)
nerve, probe sets were selected that were called present in the
baseline chips and called ‘‘decreased’’ by ?2 fold change based
on the difference call metric by the Affymetrix analysis software.
Sciatic Nerve Injury and Postnatal Development. All surgical proce-
dures followed National Institutes of Health guidelines for the
care and use of laboratory animals at Washington University.
For each injury time point, ten 8-week-old (P56) male C57BL?6
mice were anesthetized, and the right sciatic nerve was injured
at the hip level by compressing (crush) for 30 sec or cutting
(transection) the nerve and ensuring the ends were not in
contact. The contralateral nerve was exposed, but left uninjured
(control). After the indicated length of time, the animals were
killed for immediate collection of tissues for RNA isolation. For
the developmental paradigm, 60 P0 and 10 P56 animals were
anesthetized, collecting both sciatic nerves for RNA isolation.
Cell Lines, Transfection, and Subcellular Localization. Preparation of
a purified population of cultured Schwann cells has been de-
scribed (12). Transient transfections of CV-1 cells with MASR-
GFP fusion proteins (generated using plasmid EPEHisCsNeo, a
gift from Jane Wu, Department of Pediatrics, Washington
University School of Medicine) were performed in 24-well
plates, using SuperFect (Qiagen) as described by the manufac-
turer. Twenty-four hours after transfection, cells were stained
with 1 mM MitoTracker (Molecular Probes) or 1 mM Lyso-
Tracker (Molecular Probes) for 30 min, washed, and re-
incubated in media. For immunocytochemistry, transfected
CV-1 cells were fixed in ice-cold methanol for 5 min, followed by
ice-cold acetone for 30 s. Anti-calnexin polyclonal antibody
(StressGen Biotechnologies, Victoria, BC, Canada) was used at
1:100 for 1 h, followed by Cy3-conjugated anti-rabbit secondary
antibody (The Jackson Laboratory) at 1:100.
Quantitative Reverse Transcription (RT)-PCR (TaqMan) Analysis of
Gene Expression and Sequence Analysis. Expression levels of genes
were measured by quantitative RT-PCR analysis with 18S rRNA
normalization of samples similarly as described (13). Primer
sequences used for quantitative analysis of each gene are avail-
able on request. Sequence of the mouse MASR cDNA was
obtained by sequencing EST IMAGE clones 2182209 and
2352559, using standard methods and sequence information
available in National Center for Biotechnology Information
Unigene cluster Mm.26171. Human myelin-associated SUR4
protein (MASR) cDNA was found by homology, using sequence
available under GenBank accession no. AK027031.
In Situ Hybridization Analysis. Sense and antisense digoxigenin-
labeled RNA probes for in situ hybridization of MASR were
transcribed from EST IMAGE clone 1400770 after digesting
with NotI or XhoI, respectively. For MPZ probes, nucleotides
into Bluescript II KS (?). In situ hybridization analysis was
performed on fresh-frozen nerve sections as described (14).
Generation of Egr2lo/loMice. Briefly, the same recombination arms
as those used to disrupt Egr2 by Schneider-Maunoury et al. (15)
was used to generate the Egr2lo/lomice. A PGK (phosphoglyc-
erate kinase promoter) neomycin-resistance cassette flanked by
loxP sites was inserted into EGR2’s only intron. Introduction of
this cassette results in severely decreased Egr2 expression and
concomitant hypomyelination of peripheral nerves as deter-
mined by light and electron microscopy.
Anchor Gene Correlation Analysis Identifies Coregulated Genes In-
volved in Multiple Processes. To study the process of peripheral
nerve myelination after injury, microarray time courses were
generated following nerve crush and transection, paradigms in
which the state of myelination is dynamically changing. The
sciatic nerve was chosen for its accessibility and relative homo-
geneity and predominance of Schwann cells. An appropriate
time course following sciatic nerve crush injury was selected,
such that the processes of demyelination, Wallerian degenera-
tion, Schwann cell proliferation, axonal regeneration, and sub-
sequent remyelination could all be monitored. The segments of
nerve distal to the site of injury were collected for microarray
processing at days 0 (uninjured adult P56 nerve), 1, 4, 7, 10, 14,
and 28 post-injury. Unlike the crush paradigm, no axonal
regeneration, and therefore no remyelination, occurs in the
distal stump following transection of a peripheral nerve, result-
ing in a sustained decrease of myelination-specific gene expres-
sion. Thus, distal nerve segments from time points of 14 and 28
days post-transection were also collected.
Because the uninjured adult nerve (P56) serves as a baseline
for comparison, entirely independent replicate samples and
microarray chips were performed at this time point. When these
replicates were compared, 98% of 12,654 probe sets were called
unchanged when using the AFFYMETRIX EXPRESSION SUITE
software (see Fig. 6a, which is published as supporting informa-
tion on the PNAS web site). However, when comparing the
normal nerve chip to that obtained from injured nerve (4 days
post-crush), more than 15% of 12,654 probe sets exhibited
changes in expression from baseline (see Fig. 6b).
Using these expression profiles, we asked whether AGCA
could identify genes involved in biological processes active after
nerve crush. Following nerve injury, Schwann cells dissociate
from the degenerating axon and begin proliferating (16, 17). To
examine this process by using AGCA, Ki-67 antigen, a marker
intimately associated with cell proliferation, was chosen as the
anchor gene. Ki-67 antigen expression rises sharply following
injury, peaks at 4 days post-injury, and then returns to baseline
levels, consistent with prior BrdUrd labeling and Northern blot
studies (17). After a filtering step to select the most informative
probe sets (see Methods) and performing AGCA, we found 19
known genes whose expression profiles correlated significantly
highly associated with proliferation (Fig. 1a, and see Table 1,
which is published as supporting information on the PNAS web
site). However, the gene for proliferating cell nuclear antigen
(PCNA), also a widely accepted marker for proliferation, was not
identified. We reasoned that because proliferation is monitored
by several widely accepted markers, the employment of multiple
anchor genes would provide a more comprehensive identifica-
tion of genes involved in this process. Indeed, by querying for
genes correlating significantly with Ki-67 or PCNA individually
or combined, an additional nine genes were identified, seven of
which are associated with proliferation. Genes in this list in-
Nagarajan et al.
June 25, 2002 ?
vol. 99 ?
no. 13 ?
cluded cyclins, enzymes essential for nucleotide metabolism,
histones, and mitotic motor proteins. Thus, whereas AGCA can
be performed with one anchor gene, the use of multiple anchors
identifies a greater number of genes associated with the process
Following peripheral nerve injury, the inflammatory response
is crucial for Wallerian degeneration and subsequent regener-
ation of the nerve (18). Circulating macrophages must be
recruited into the endoneurium, which requires the release of
inflammatory cytokines. In monitoring the cytokine response,
monocyte chemoattractant protein 1 (MCP-1) and IL-6 were
chosen as anchor genes because they are potent cytokines for
macrophage recruitment and inflammatory initiation, respec-
tively, following nerve injury (19, 20). Consistent with previous
data, MCP-1 and IL-6 are up-regulated immediately after nerve
crush with levels peaking 1 day after injury. To further validate
this profile as representative of the inflammatory cytokine
response, genes whose temporal expression profiles corre-
lated (adjusted P ? 0.05) with MCP-1 and?or IL-6 were
identified using AGCA. We found that several cytokines and
inflammatory-related genes clustered with these anchors (Fig.
1b, and see Table 1). Seven of nine known genes in this group
encode cytokines or inflammatory proteins.
With the release of inflammatory cytokines, macrophages are
subsequently recruited to phagocytose cellular and myelin de-
bris, allowing regrowth of axons through the nerve tubules. The
genes encoding the common macrophage marker CD68 and the
antigen recognized by the macrophage-specific antibody F4?80
(Emr1) were selected as anchors to monitor macrophage infil-
tration. The expression level of these two genes gradually
increases following injury, peaks after 7 days, then declines
toward basal levels, consistent with previous observations of
macrophage infiltration into injured nerve (16, 21). Indeed, 11
of the 14 known genes identified were related to macrophages or
the inflammatory process, including CSF-1 receptor, a well
characterized macrophage marker (Fig. 1b, and see Table 1).
examination of the proliferative and inflammatory responses by
using AGCA confirmed that functionally related genes very
components of the remyelination program, we applied AGCA to
the peripheral nerve crush and transection injury expression
profiles. Because MPZ, PMP22, MBP, MAG, periaxin, proteo-
lipid protein (PLP), and the DM20 variant of PLP have all been
myelin gene anchors (MGAs), were chosen as anchors to identify
other genes involved in the myelination process (1, 22, 23).
In our algorithm, the probe sets selected by the filtering step
(see Methods) were further stratified by taking into account the
magnitude of change in expression level following injury. Using
this magnitude filter, we selected 293 probe sets whose decrease
in expression after nerve crush (day 4) and after nerve transec-
tion (day 14 or day 28) was greater than or equal to the average
decrease of expression of the MGAs after these injuries (Fig. 2).
Finally, application of AGCA identified 98 probe sets, which we
call the myelin gene coregulated cluster (MGCC), whose pattern
of expression following injury was correlated significantly (ad-
justed P ? 0.05) with that of one or all of the MGAs (Fig. 2, and
see Table 2, which is published as supporting information on the
PNAS web site).
To further assess the association of these genes with myeli-
from a developmental perspective. Because myelination
commences shortly after birth in rodents, we would expect
myelination-associated genes, similar to MPZ and MAG for
example, to be expressed at a lower level at age P0, increasing in
expression as myelination ensues postnatally. We found that
approximately 50% of the genes in the MGCC are expressed
?2-fold higher in adult nerve compared with P0 nerve (Fig. 2).
We also examined the expression of MGCC genes in Schwann
cells derived from Egr2lo/lomice whose nerves are hypomyeli-
nated because of severely decreased expression of the key
myelination transcriptional regulator, Egr2 (N.L., R.N., T.A.,
and J.M., unpublished data). This insufficiency of Egr2 activity
diminishes expression of its target genes and the subsequent
induction of myelination. We reasoned that MGCC genes in-
volved in myelination would also be expressed at lower levels in
Egr2lo/lohypomyelinated nerve. Indeed, we found that approxi-
mately 50% of genes in the MGCC were decreased ?2-fold in
hypomyelinated nerve, indicating that it includes many genes
important for myelination by Schwann cells (Fig. 2).
process. The normalized expression of genes identified by AGCA, whose
expression profiles correlate significantly with that of Ki-67 and?or PCNA (a),
as a function of days after nerve crush.
Genes with similar expression profiles participate in a common
myelination. Gene expression profiles following peripheral nerve crush and
transection were established. Using AGCA, 98 probe sets, termed the MGCC,
were identified that correlated significantly (adjusted P ? 0.05) with the
MGAs, genes crucial for myelination (MPZ, MBP, PMP22, periaxin, MAG, PLP,
or DM 20). At each step there was an enrichment for genes whose expression
was decreased in sciatic nerves of age P0 mice and in sciatic nerves of a mouse
model of congenital hypomyelination (Egr2lo/lo). The average expression plot
of all probe sets in the MGCC resembles profiles characteristic of genes
encoding critical myelin proteins during development and following nerve
crush and transection injuries.
Stratification algorithm using AGCA identifies genes involved in
www.pnas.org?cgi?doi?10.1073?pnas.132080999 Nagarajan et al.
have been implicated in myelination and?or are involved in
neuropathies: NDRG1, myelin and lymphocyte protein (MAL),
and protein tyrosine phosphatase-epsilon (PTP-?). Truncation
mutations in NDRG1 have recently been shown to cause hered-
itary motor and sensory neuropathy-Lom, an autosomal reces-
sive form of Charcot–Marie–Tooth (CMT) disease that results
in hypomyelination and demyelination (24). MAL, a proteolipid
protein expressed by both oligodendrocytes and myelinating
glycosphingolipids. Similar to PMP22, overexpression of MAL
results in aberrant myelin formation (25). Finally, PTP-? is a
signaling molecule involved in cell cycle exit and cellular differ-
entiation, and PTP-?-deficient animals exhibit peripheral nerve
hypomyelination at an early postnatal age (26).
of the chromosomal loci linked to inherited neuropathies for
which no genetic defect has been determined. For example,
although the genetic mutations for CMT1A, CMT1B, and
CMT1D have been identified in PMP22, MPZ, and Egr2, re-
spectively, the genetic mutation for CMT1C has not been
assigned. Recent linkage analysis, however, maps CMT1C to
chromosome 16p13.1-p12.3 (27). Intriguingly, one of the MGCC
genes, epithelial membrane protein 2 (EMP2), localizes to this
chromosomal region. Emp2 is a member of the PMP22 family
and has demonstrated similar functional properties to PMP22,
such as regulation of apoptosis (28). Thus, complemented with
disease linkage information, our analysis has identified Emp2 as
a positional candidate for CMT1C.
In inspecting the MGCC, we attempted to identify genes
pertaining to specific biological pathways. Among the genes in
the MGCC were many that encode proteins critical for lipid
metabolism. This is consistent with the fact that cholesterol, as
well as sphingomyelin and Gal-C, which both contain esterified
very long chain fatty acids, are essential constituents of myelin.
Despite this fact, with the exception of HMG CoA reductase,
which is the rate-limiting enzyme in cholesterol biosynthesis and
is coregulated with MPZ during development, very little is
known about the regulation of other lipid synthetic enzymes in
Schwann cells. In our analysis, we found that of the thirteen
cholesterol synthetic enzymes represented on the microarray,
nine were included in the MGCC (Fig. 3). Additionally, ATP-
binding cassette A2 (ABCA2), a member of the ABCA family
of transporters known to traffic cholesterol and phospholipids
(29), as well as oxysterol-binding protein, which traffics choles-
terol and sphingomyelin from the Golgi to the plasma membrane
(30), were members of the MGCC. Thus, we have discovered
that many proteins necessary for lipid metabolism are strictly
coregulated during myelination, including a majority of the
enzymes involved in cholesterol synthesis.
genes with myelination, we examined their expression under
conditions [i.e., adult (P56) vs. newborn (P0) sciatic nerve and
adult sciatic nerve vs. cultured Schwann cells] in which myelin
protein levels vary. Unlike myelinating Schwann cells of adult
peripheral nerve, cultured Schwann cells and newborn Schwann
cells minimally express myelination-associated genes such as
MPZ, PMP22, MAG, and EGR2. Accordingly, MPZ and NDRG1
expression, in contrast to the expression of nonmyelinating
markers, L1 and NCAM, was substantially greater in adult sciatic
nerve relative to cultured Schwann cells (Fig. 4a) or P0 sciatic
nerve (Fig. 4b). Inspection of other genes and ESTs in the
MGCC, including osteoglycin and IL-16, revealed that they were
also expressed at significantly higher levels in adult sciatic nerve
relative to cultured Schwann cells (Fig. 4a) or P0 sciatic nerve
(Fig. 4b), suggesting that they are expressed in myelinating
Identification of a Fatty Acid Desaturase Involved in Myelination.The
MGCC included many ESTs whose expression was coregulated
with genes encoding critical myelin proteins, suggesting they
might also encode proteins important for myelination. To as-
certain a molecular function via homology, several of these ESTs
were sequenced. Analysis of the full-length sequence of one of
the ESTs belonging to Unigene Mm.26171 revealed that it was
a fatty acid desaturase (FAD) similar to the yeast VLCFA-
synthesizing enzymes, SUR4 and FEN1. Mammalian homo-
logues of FEN1 and SUR4 have recently been identified and
designated CIG30, SSC1, and SSC2 (31). Protein alignment of
the full-length mouse and human Mm.26171 protein with Cig30,
Ssc1, and Ssc2 reveals extensive homology (see Fig. 7, which is
published as supporting information on the PNAS web site). For
are identified as genes associated with the myelination process. The choles-
and products, is shown. Enzymes represented on the chip are denoted with
blue font, and those present in the MGCC are designated by an asterisk. A
pseudocolor representation of these genes after nerve crush, transection
(Txn), or during nerve development (Dev) is shown. Full names and respective
3, which is published as supporting information on the PNAS web site.
Multiple genes encoding enzymes required for cholesterol synthesis
adult sciatic nerve vs. cultured Schwann cells (a) and adult vs. newborn sciatic
nerve (b). mRNA levels of NDRG1, osteoglycin, IL-16, and four different ESTs
(denoted by their Unigene ID), identified by AGCA using the MGAs, were
examined in sciatic nerve and cultured Schwann cells by quantitative RT-PCR.
The fold change represents the ratio of expression in sciatic nerve to cultured
Multiple genes identified by AGCA are expressed at higher levels in
Nagarajan et al.
June 25, 2002 ?
vol. 99 ?
no. 13 ?
highly conserved motifs, such as KXXEXXDT, FXHXXHH,
HXXMYXYY, TXXQXXQ, and the consensus sequence
the FAD gene identified in this analysis, MASR.
MASR was highly expressed in peripheral nerve, brain, cere-
suggesting that it has a critical function in myelin-rich tissues (see
Fig. 8a, which is published as supporting information on the
PNAS web site). It is preferentially expressed, however, in the
peripheral nervous system, because levels of MASR mRNA are
70-fold greater in adult sciatic nerve compared with adult brain.
MASR expression is also 17-fold higher in sciatic nerve com-
pared with cultured Schwann cells (Fig. 4a), and its expression
in developing nerve increases 9-fold from P0–P14 (data not
shown), indicating that its expression correlates with peripheral
nervous system myelination. Furthermore, expression analysis of
all four family members by RT-PCR demonstrated that MASR
is the member of this family preferentially expressed in periph-
eral nerve (see Fig. 8b). Because VLCFAs are basic components
of essential myelin lipids, it is likely that MASR is expressed in
myelinating Schwann cells. Indeed, in situ hybridization for
MASR in adult mouse sciatic nerve revealed a characteristic
speckled expression pattern similar to that observed for MPZ
(Fig. 5a) and Egr2 (data not shown), markers of myelinating
Schwann cells, further supporting MASR’s role in peripheral
Synthesis of fatty acids beyond C18, which are called VLCFAs,
is catalyzed by FADs in the endoplasmic reticulum (ER) and
mitochondria. The COOH-terminal sequence of all four SUR4
family members contains a putative ER-retention signal, leading
to the prediction that they are localized to the ER. However,
because this signal is weak in MASR, we examined its subcellular
localization directly. MASR was N- or C-terminally tagged with
enhanced green fluorescent protein (EGFP), and the resulting
fusion protein was expressed in CV-1 cells. EGFP fluorescence
colocalized with MitoTracker, a mitochondrial-specific dye, but
was distinct from either LysoTracker, an endosome?lysosome-
specific dye (data not shown), or immunofluorescence per-
formed against calnexin, an ER-retained protein (Fig. 5b).
MASR’s localization to the mitochondria is consistent with
previous findings that VLCFA synthesis occurs in both mito-
chondria and the ER. In further support of MASR’s activity in
fatty acid elongation, Moon et al. (32) concurrently identified the
MASR gene and named it LCE (long chain fatty acyl elongase).
Through biochemical analyses, they demonstrate the fatty acid
elongation activity of MASR by observing the incorporation of
malonyl-CoA into various fatty acids. Indeed, its expression in
sciatic nerve, homology to fatty acid desaturases, biochemical
analyses, and subcellular localization indicate that MASR has a
critical function in the production of myelin. Supporting this
hypothesis is the observation that, just as MASR expression is
lower in the hypomyelinating nerve of Egr2lo/lomice, the expres-
sion of SSC1 is decreased in the central nervous system of
myelin-deficient mouse mutants, quaking and jimpy (31). As
such, it is possible that mutations in MASR may result in
inherited peripheral neuropathy.
To describe the molecular components involved in myelination
and to identify candidate genes that may be mutated in patients
with inherited neuropathy, we have studied the dynamics of
peripheral nerve myelination by establishing a compendium of
expression profiles. As an alternative to using clustering algo-
rithms, which do not take advantage of the existing wealth of
biological information and lack a statistical basis, we have used
a biologically based and statistically rigorous approach, AGCA,
to identify such candidate genes.
These genes function in diverse pathways, such as lipid me-
tabolism, membrane trafficking, cell adhesion, cell signaling, and
cytoskeletal reorganization, which together orchestrate the my-
elination process by Schwann cells. Concordantly, genes whose
mutation is associated with peripheral neuropathies also fall
within these various functional pathways. Of particular note, we
have been able to identify multiple genes specific not only to
myelin synthesis per se, but to other biological pathways impor-
tant for myelination in general. For example, in addition to
discovering that numerous genes encoding enzymes for lipid
synthesis were strictly coregulated, genes encoding proteins that
function in cytoskeletal reorganization were also identified by
this algorithm. Cytoskeletal reorganization is indeed critical for
myelination because Schwann cells are required to migrate and
wrap around axons. We found that a number of cytoskeletal
structural components, such as myosin I, myosin VIIa, spectrin,
smoothelin, and intermediate filament protein, as well as sig-
naling molecules involved in cytoskeletal reorganization, such as
JIP-1, were coregulated with myelination anchor genes. Intrigu-
ingly, we also identified utrophin, a cytoskeletal-related protein
that binds to F-actin and is part of a Schwann cell-specific
dystrophin–dystroglycan complex that includes periaxin (33).
Mutations in periaxin disrupt this membrane complex and result
in Charcot–Marie–Tooth (CMT) myelinopathies, suggesting
that utrophin mutations could also cause this disease.
The complementation of our expression analysis with chro-
mosomal linkage information should be useful in identifying
candidate genes for inherited neuropathies as we have demon-
nating Schwann cells and is located within mitochondria. (a) In situ hybrid-
ization of MPZ and MASR in sciatic nerve reveals that MASR is expressed
similarly to MPZ, indicating that MASR is specifically expressed in myelinating
Schwann cells. (b) CV-1 cells transiently transfected with an N-terminal EGFP-
fusion of MASR reveals that MASR colocalizes with MitoTracker staining and
is distinct from staining with anti-calnexin, an endoplasmic reticulum marker.
The composite overlay of MASR and MitoTracker or anti-calnexin shows that
MASR is localized in mitochondria (yellow), but not in ER.
The expression pattern of MASR indicates that it functions in myeli-
www.pnas.org?cgi?doi?10.1073?pnas.132080999 Nagarajan et al.
strated with Emp2. Another positional candidate for causing Download full-text
neuropathy based on this idea of complementation is Sirt2,
which is a member of the Sir2 family of NAD-dependent histone
deacetylase enzymes thought to regulate gene silencing, aging,
and the cell cycle (34), is located at 19q13, and overlaps with the
mapping of CMT2B2 at 19q13.3 (35), as well as a severe form of
CMT4 at 19q13.1-q13.3 (36). Finally, AF1q, a transmembrane
protein found to cause acute leukemia when fused with the
protein MLL (mixed lineage leukemia) (37), also overlaps with
the mapping of CMT2B1 at 1q21 (38). As disease linkage
analysis and genomic sequencing information becomes more
refined, the role of global expression profiling complemented
with mapping information will become increasingly useful in un-
derstanding the molecular pathogenesis of disease.
Discovering genes involved in a specific process through
expression analysis is one of the great promises of microarray
technology. Toward this end, we applied a statistically rigorous
correlation clustering algorithm, AGCA, to our microarray data
set of multiple Schwann cell paradigms and discovered MASR,
a VLCFA elongation enzyme involved in myelin lipid synthesis.
We believe that similar analyses of expression profiles will be
increasingly used to associate gene products with particular
We thank Mark Watson, William Shannon, and Aditya Phatak for advice
on bioinformatics. This work was supported by National Institutes of
Health Grant NS4074.
1. Warner, L. E., Garcia, C. A. & Lupski, J. R. (1999) Annu. Rev. Med. 50,
2. Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. (1998) Proc. Natl.
Acad. Sci. USA 95, 14863–14868.
3. Coller, H. A., Grandori, C., Tamayo, P., Colbert, T., Lander, E. S., Eisenman,
R. N. & Golub, T. R. (2000) Proc. Natl. Acad. Sci. USA 97, 3260–3265.
4. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E.,
Lander, E. S. & Golub, T. R. (1999) Proc. Natl. Acad. Sci. USA 96, 2907–2912.
5. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov,
J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999)
Science 286, 531–537.
6. Bhattacharjee, A., Richards, W. G., Staunton, J., Li, C., Monti, S., Vasa, P.,
Ladd, C., Beheshti, J., Bueno, R., Gillette, M., et al. (2001) Proc. Natl. Acad.
Sci. USA 98, 13790–13795.
7. Thomas, J. G., Olson, J. M., Tapscott, S. J. & Zhao, L. P. (2001) Genome Res.
8. Chow, M. L., Moler, E. J. & Mian, I. S. (2001) Physiol. Genomics 5, 99–111.
9. Dudoit, S., Yang, Y. H. Y., Callow, M. & Speed, T. (2000) in University of
California, Berkeley, Department of Statistics Technical Report #578 (Stanford
Univ. School of Medicine, Univ. of California, Berkeley), pp. 1–38.
10. Lipshutz, R. J., Fodor, S. P., Gingeras, T. R. & Lockhart, D. J. (1999) Nat.
Genet. 21, 20–24.
11. Li, C. & Wong, W. H. (2001) Proc. Natl. Acad. Sci. USA 98, 31–36.
L. D. (1999) J. Neurosci. 19, 759–774.
13. Nagarajan, R., Svaren, J., Le, N., Araki, T., Watson, M. & Milbrandt, J. (2001)
Neuron 30, 355–368.
14. Araki, T., Nagarajan, R. & Milbrandt, J. (2001) J. Biol. Chem. 276, 34131–
15. Schneider-Maunoury, S., Topilko, P., Seitanidou, T., Levi, G., Cohen-
Tannoudji, M., Pournin, S., Babinet, C. & Charnay, P. (1993) Cell 75,
16. Fu, S. Y. & Gordon, T. (1997) Mol. Neurobiol. 14, 67–116.
17. Carroll, S. L., Miller, M. L., Frohnert, P. W., Kim, S. S. & Corbett, J. A. (1997)
J. Neurosci. 17, 1642–1659.
18. Bruck, W. (1997) Brain Pathol. 7, 741–752.
19. Toews, A. D., Barrett, C. & Morell, P. (1998) J. Neurosci. Res. 53, 260–267.
20. Wells, M. R., Racis, S. P., Jr., & Vaidya, U. (1992) J. Neuroimmunol. 39,
21. Dailey, A. T., Avellino, A. M., Benthem, L., Silver, J. & Kliot, M. (1998)
J. Neurosci. 18, 6713–6722.
22. Boerkoel, C. F., Takashima, H., Stankiewicz, P., Garcia, C. A., Leber, S. M.,
Rhee-Morris, L. & Lupski, J. R. (2001) Am. J. Hum. Genet. 68, 325–333.
23. Garbern, J. Y., Cambi, F., Tang, X. M., Sima, A. A., Vallat, J. M., Bosch, E. P.,
Lewis, R., Shy, M., Sohi, J., Kraft, G., et al. (1997) Neuron 19, 205–218.
24. Kalaydjieva, L., Gresham, D., Gooding, R., Heather, L., Baas, F., de Jonge, R.,
Blechschmidt, K., Angelicheva, D., Chandler, D., Worsley, P., et al. (2000)
Am. J. Hum. Genet. 67, 47–58.
25. Frank, M., Atanasoski, S., Sancho, S., Magyar, J. P., Rulicke, T., Schwab, M. E.
& Suter, U. (2000) J. Neurochem. 75, 1927–1939.
26. Peretz, A., Gil-Henn, H., Sobko, A., Shinder, V., Attali, B. & Elson, A. (2000)
EMBO J. 19, 4036–4045.
27. Street, V. A., Goldy, J. D., Golden, A. S., Tempel, B. L., Bird, T. D. & Chance,
P. F. (2001) Am. J. Hum. Genet. 70, 244–250.
28. Wang, C. X., Wadehra, M., Fisk, B. C., Goodglick, L. & Braun, J. (2001) Blood
29. Schmitz, G., Kaminski, W. E. & Orso, E. (2000) Curr. Opin. Lipidol. 11,
30. Ridgway, N. D. (2000) Biochim. Biophys. Acta 1484, 129–141.
31. Tvrdik, P., Westerberg, R., Silve, S., Asadi, A., Jakobsson, A., Cannon, B.,
Loison, G. & Jacobsson, A. (2000) J. Cell Biol. 149, 707–718.
32. Moon, Y. A., Shah, N. A., Mohapatra, S., Warrington, J. A. & Horton, J. D.
(2001) J. Biol. Chem. 276, 45358–45366.
33. Sherman, D. L., Fabrizi, C., Gillespie, C. S. & Brophy, P. J. (2001) Neuron 30,
34. Yang, Y. H., Chen, Y. H., Zhang, C. Y., Nimmakayalu, M. A., Ward, D. C. &
Weissman, S. (2000) Genomics 69, 355–369.
35. Leal, A., Morera, B., Del Valle, G., Heuss, D., Kayser, C., Berghoff, M.,
Villegas, R., Hernandez, E., Mendez, M., Hennies, H. C., et al. (2001) Am. J.
Hum. Genet. 68, 269–274.
36. Delague, V., Bareil, C., Tuffery, S., Bouvagnet, P., Chouery, E., Koussa, S.,
Maisonobe, T., Loiselet, J., Megarbane, A. & Claustres, M. (2000) Am. J. Hum.
Genet. 67, 236–243.
37. Tse, W., Zhu, W., Chen, H. S. & Cohen, A. (1995) Blood 85, 650–656.
38. Bouhouche, A., Benomar, A., Birouk, N., Mularoni, A., Meggouh, F., Tassin,
J., Grid, D., Vandenberghe, A., Yahyaoui, M., Chkili, T., et al. (1999) Am. J.
Hum. Genet. 65, 722–727.
Nagarajan et al.
June 25, 2002 ?
vol. 99 ?
no. 13 ?