Variations in the progranulin gene affect global
gene expression in frontotemporal lobar
Alice S. Chen-Plotkin1,2,3, Felix Geser1,2, Joshua B. Plotkin5, Chris M. Clark3,4,6,
Linda K. Kwong1,2, Wuxing Yuan1,2, Murray Grossman3, Vivianna M. Van Deerlin1,2,
John Q. Trojanowski1,2and Virginia M.-Y. Lee1,2,?
1Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine,2Institute on
Aging,3Department of Neurology and4Alzheimer’s Disease Center, University of Pennsylvania School of Medicine,
Philadelphia, PA 19104, USA,5Department of Biology and6Center of Excellence for Research on Neurodegenerative
Diseases, University of Pennsylvania, Philadelphia, PA 19104, USA
Received October 23, 2007; Revised and Accepted January 17, 2008
Frontotemporal lobar degeneration is a fatal neurodegenerative disease that results in progressive decline in
behavior, executive function and sometimes language. Disease mechanisms remain poorly understood.
Recently, however, the DNA- and RNA-binding protein TDP-43 has been identified as the major protein pre-
sent in the hallmark inclusion bodies of frontotemporal lobar degeneration with ubiquitinated inclusions
(FTLD-U), suggesting a role for transcriptional dysregulation in FTLD-U pathophysiology. Using the
Affymetrix U133A microarray platform, we profiled global gene expression in both histopathologically
affected and unaffected areas of human FTLD-U brains. We then characterized differential gene expression
with biological pathway analyses, cluster and principal component analyses, and subgroup analyses
based on brain region and progranulin (GRN) gene status. Comparing 17 FTLD-U brains to 11 controls, we
identified 414 upregulated and 210 downregulated genes in frontal cortex (P-value < 0.001). Moreover, cluster
and principal component analyses revealed that samples with mutations or possibly pathogenic variations in
the GRN gene (GRN1, 7/17) had an expression signature that was distinct from both normal controls and
FTLD-U samples lacking GRN gene variations (GRN-, 10/17). Within the subgroup of GRN1 FTLD-U, we
found >1300 dysregulated genes in frontal cortex (P-value < 0.001), many participating in pathways uniquely
dysregulated in the GRN1 cases. Our findings demonstrate a distinct molecular phenotype for GRN1 FTLD-
U, not readily apparent on clinical or histopathological examination, suggesting distinct pathophysiological
mechanisms for GRN1 and GRN- subtypes of FTLD-U. In addition, these data from a large number of human
brains provide a valuable resource for future testing of disease hypotheses.
Frontotemporal lobar degeneration (FTLD) is the second most
common cause of dementia in individuals below the age of 65
years (1), with devastating effects on patients and families.
Several clinical classification schemes for FTLD exist (2–4),
but in general individuals suffer progressive atrophy of their
frontal and temporal lobes, with corresponding deficits in the
domains of behavior, social and executive function, and/or
language. Pathologically, FTLD can be divided into those
cases which have tau-positive inclusions (FTLD-tau) and
those which are tau- and alpha-synuclein-negative but show
ubiquitinated inclusions (FTLD-U), with rare cases exhibiting
neither tau-positive nor ubiquitin-positive inclusions (5,6).
?To whom correspondence should be addressed to: Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory
Medicine, University of Pennsylvania School of Medicine, 3rd Floor Maloney, 3600 Spruce Street Philadelphia, PA 19104, USA.
Tel: þ1 2156624474; Fax: þ1 2153495909; Email: firstname.lastname@example.org
# The Author 2008. Published by Oxford University Press. All rights reserved.
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Human Molecular Genetics, 2008, Vol. 17, No. 10
Advance Access published on January 25, 2008
FTLD-U is the most commonly seen pathology (7), and
recent advances have led to a greater understanding of
FTLD-U pathophysiology at a protein and gene level. In
2006, two groups demonstrated that mutations in the gene pro-
granulin (GRN) were associated with many cases of FTLD-U
(8,9). Most of the GRN mutations found thus far result in pre-
mature termination (8–10), and protein haploinsuffiency has
been suggested as a mechanism of disease (8–11). The major
protein in the hallmark ubiquitinated inclusions of FTLD-U
was subsequently identified as TAR DNA binding protein 43
(TDP-43), encoded by the gene TARDBP (12). Very recently,
progranulin depletion has been shown to increase caspase-
dependent cleavage of TDP-43 in an in vitro model, offering
a possible mechanism to link these two disease-associated
abnormalities (13). Strikingly, TDP-43 appears to be the
major protein within the pathological inclusions of frontotem-
poral dementia with motor neuron disease (FTD-MND) and
sporadic amyotrophic lateral sclerosis (ALS) as well (12,14–
17), lending support to the view that these diseases are part of
a clinicopathological spectrum (18).
With the advent of TDP-43 immunostaining, different histo-
pathological subtypes of FTLD-U are increasingly being
recognized (6,17,19). Within these subtypes, GRN mutations
are found mainly, possibly exclusively, among a subtype
(Sampathu Type 3) characterized by numerous neuronal cyto-
plasmic inclusions, short dystrophic neurites and a variable
number of neuronal intranuclear inclusions in the superficial
cortical layers (19). However, while GRN mutations are
found in Type 3 cases, not all Type 3 cases carry GRN
mutations (6,17). Clinically, GRN mutation cases present het-
erogeneously within the FTLD spectrum (10,20,21).
Although the function of the disease-associated protein
TDP-43 is largely unknown, several lines of evidence point
to a role in transcriptional regulation. First, TDP-43 is
known to bind DNA (22,23), having first been identified in
its role as a protein that binds to HIV transactive response
DNA (24). Second, TDP-43 is known to bind RNA, regulating
through this interaction the splicing of the cystic fibrosis trans-
membrane conductance regulator (25) and other genes (26).
Finally, physiologic expression of TDP-43 appears to be
nuclear (12,27,28), where it colocalizes with other nuclear
bodies believed to function in transcription and splicing
(27), and TDP-43 binds a number of heterogeneous ribo-
nucleoproteins with well-known splicing activities (28).
The confluence of permissive technology and what is
known about the biology of this disease sets the stage for a
genome-wide analysis of gene expression in FTLD-U. In
this study, we used microarray technology to evaluate the
global gene expression of multiple regions of human
FTLD-U brain, in comparison with neurologically normal con-
trols. Our aims were (i) to identify dysregulated genes and
pathways in this disease, (ii) to compare gene expression
changes in neuropathologically affected (frontal cortex, hippo-
campus) and unaffected (cerebellum) areas of brain and (iii) to
compare gene expression changes in FTLD-U cases with
(GRNþ) and without (GRN2) progranulin gene alterations.
Wefound that mRNA
FTLD-U. Moreover, GRNþ
expression signature that was distinct from both GRN- cases
and controls, with over 1300 genes dysregulated in frontal
cortex. Gene Ontology (GO) and Kyoto Encyclopedia of
Genes and Genomes (KEGG) pathway analyses implicated
some biological pathways as common to both GRNþ and
GRN2 cases (e.g. MAPK signaling), while others (e.g.
TGF-beta signaling, cell cycle regulation) were unique to
GRNþ cases. Taken together, our findings reveal major tran-
scriptional changes in FTLD-U as a whole, as well as a pre-
viously unrecognized molecular signature for GRNþ FTLD-U.
cases had a global gene
are extensive in
Identification of genes differentially expressed
in FTLD-U cases compared to controls
We examined global gene expression in 17 human FTLD-U
brains and 11 age- and sex-matched controls (Table 1). Ana-
lyses included tissue dissected from the frontal cortex and hip-
pocampus (regions showing significant histopathology in
disease) and cerebellum (region with little histopathology in
disease). We compared FTLD-U cases with controls, treating
each brain region separately and controlling for age and
gender. FTLD-U frontal cortex exhibited a greater number
of differentially expressed transcripts relative to controls,
while FTLD-U cerebellum had many fewer differentially
Table 1. Human brain samples analyzed by microarray
ControlAll FTLD-U GRNþ FTLD-UGRN2 FTLD-U Total
Median Age (IQR)
Median PMI (IQR)
PMI, postmortem interval; IQR, interquartile range (25th–75th percentile).
Age, sex and PMI were not significantly different between cases and controls by a Mann–Whitney test. GRNþ FTLD-U includes all cases with progranulin gene
abnormalities (mutations and variants), while GRN2 FTLD-U denotes cases without progranulin gene abnormalities.
1350Human Molecular Genetics, 2008, Vol. 17, No. 10
expressed genes, consistent with patterns of histopathology.
Because of a paucity of hippocampal control samples, we
could not make a statistically meaningful comparison
between disease and control among the hippocampal samples.
In FTLD-U frontal cortex (16 FTLD-U, 8 controls), 624
probe sets representing 536 genes demonstrated significant
differential expression at a P-value of 0.001 (Table 2). Of
these, 414 probe sets were increased in disease, while 210
were decreased in disease. In FTLD-U cerebellum (10
FTLD-U, 7 controls), in contrast, only 70 probe sets represent-
ing 75 genes demonstrated significant differential expression
at a P-value of 0.001. (Some probes recognized more than
one gene.) To correct for multiple hypothesis testing, false dis-
covery rates (FDR) were calculated for each comparison
(Table 2). Of the 624 probe sets differentially expressed in
FTLD-U frontal cortex, only 4% are expected to be false posi-
tives. In addition, to focus on genes likely to have a more sig-
nificant biological effect, we imposed a minimum fold-change
cutoff of two to identify genes with greater magnitudes of dys-
regulated expression. Applying this fold-change cutoff still
resulted in our finding 185 probe sets representing 159 genes
dysregulated in FTLD-U frontal cortex (Table 2). Of these,
142 transcripts were increased in disease, and 43 transcripts
were decreasedin disease
Table S1); the top 20 transcripts showing differential
expression are summarized in Table 3.
To further evaluate the overall structure of transcriptional
variation, we performed a cluster analysis of gene expression
levels. Using Spearman correlation coefficients, we clustered
all microarray samples across all regions and disease states
in an unbiased fashion to create a hierarchical tree. With one
exception, all 17 cerebellar samples regardless of disease
status fell into one branch, demonstrating that these samples
show different global expression from other brain regions
(Fig. 1). Samples from frontal cortex and hippocampus,
however, were admixed, despite the fact that cytoarchitecture
in these regions is different.
If transcriptional dysregulation is a key feature of FTLD-U,
one might expect more changes in gene expression in this
disease than in others because of the putative nuclear tran-
scriptional functions of TDP-43. We therefore compared
the results of our study with microarray studies of other
neurodegenerative diseases. Comparisons made across micro-
array datasets are hampered, however, by the fact that differ-
ent platforms, statistical conditions
conditions are used, leading to differences in statistical
power and noise. To control for some of these conditions,
we compared our study only with those studies using postmor-
tem human cortical samples, with sample sizes comparable
to or larger than our own frontal cortex dataset (16 disease,
8 control). We chose three representative comparison
studies, one evaluating 22 disease and 9 control hippocampal
samples in Alzheimer’s Disease (AD, 29) one evaluating 15
disease and 5 control frontal cortex samples in sporadic
Creutzfeld-Jacob Disease (sCJD, 30) and one evaluating 11
disease and 9 control motor cortex samples in sporadic ALS
(31). In each case, we re-evaluated our dataset using the stat-
istical conditions used in the comparison study, to obtain the
number of genes that would be deemed to be significantly
changed in a comparable analysis (Supplementary Material,
Table S2). As shown in Figure 2, many more gene changes
were found in the present FTLD-U study than in studies of
AD or ALS; the number of dysregulated genes in FTLD-U
was comparable to that seen in cases of sCJD.
In summary, many genes are differentially expressed in
FTLD-U cases compared to controls. In addition, regional
patterns of gene expression changes are consistent with pat-
terns of histopathology, with frontal cortex being much
more affected than cerebellum. Finally, cerebellar gene
expression carries a signature distinct from that of frontal
cortex or hippocampus.
Effect of progranulin gene status on global
Genetic screening of our FTLD-U cases revealed 7 cases con-
taining progranulin gene abnormalities (GRNþ). Of these,
four possessed truncation mutations previously associated
with disease and believed to be pathogenic, while the remain-
ing three cases exhibited new variants of unknown signifi-
cance (VUS, Table 4). Preliminary studies suggest that at
least some of these VUS are pathogenic, and this avenue of
investigation is currently ongoing. Using Spearman correlation
coefficients, we clustered all frontal cortex and hippocampal
Table 2. mRNA changes in human FTLD-U brain—by brain region and progranulin status
Samples Overall probesIncreased in disease Decreased in disease FDR (%)
FTLD-U versus normal
GRNþ FTLD-U versus Normal
GRN- FTLD-U versus Normal
16 versus 8
10 versus 7
6 versus 8
4 versus 7
10 versus 8
6 versus 7
Numbers reflect probe sets with differential expression (overall probes), increased expression in FTLD-U (increased in disease) or decreased expression
in FTLD-U (decreased in disease) at P , 0.001. The total number meeting this statistical threshold is shown, while the number of probes meeting both a
P , 0.001 criterion and fold change .2 criterion appears in parentheses.
FDR, false discovery rate. Determined by class permutation analysis (similar results were obtained with calculations based on nominal P-value). GRNþ
FTLD-U¼FTLD-U cases with progranulin gene abnormalities. GRN2 FTLD-U¼FTLD-U cases without progranulin gene abnormalities.
Human Molecular Genetics, 2008, Vol. 17, No. 101351
samples into a hierarchical dendrogram to look for any
naturally arising subsets of samples. We found that almost
all GRNþ samples (9/11), regardless of region of origin,
clustered together into one branch without interspersion of
GRN2 cases of FTLD-U or control samples (Fig. 3A). This
clustering result was robust to statistical methodology, seen
in dendrograms produced using Pearson correlation coeffi-
cients as well.
Principal component analysis was performed as a second
methodology for looking in an unbiased manner at overall
data structure (Fig. 3B). Within histopathologically affected
regions of brain, GRNþ FTLD-U cases again grouped
together, defined by a low score along the first principal com-
ponent (X-axis) and a high score along the second principal
Based on the observation from unbiased cluster and
principal component analyses that GRNþ FTLD-U cases
have a molecular expression signature which is distinct from
GRN2 FTLD-U cases and control subjects, we performed sub-
group analyses of our gene expression data. Comparing 6
GRNþ frontal cortex samples to 8 normal frontal cortex
samples, we found 1311 probe sets representing 1131 genes
with differential expression at a P-value of 0.001, with an
FDR of 1.9% (Table 2). Of these, 579 probe sets were increased
in disease, and 732 were decreased in disease. After imposing
a minimum fold change cutoff of 2, 772 probe sets represent-
ing 657 genes were still found to be differentially expressed
at a P-value of 0.001. Of these, 284 probe sets were increased
in disease, and 488 were decreased in disease (Supplementary
Material, Table S3); the top 20 transcripts showing differential
Table 3. Top 20 transcripts showing differential expression in FTLD-U
Genes showing greatest increases in expression
Inhibitor of DNA binding 4
ATP-binding cassette, sub-family A (ABC1), member 8
Kruppel-like factor 4 (gut)
Extracellular matrix protein 2
Aquaporin 1 (Colton blood group)
Solute carrier family 14 (urea transporter)
Complement component 4A
Complement component 4A
Aquaporin 1 (Colton blood group)
Ankyrin repeat domain 25
Growth arrest-specific 1
Solute carrier family 7
Collagen, type IV, alpha 5 (Alport syndrome)
Angiotensin II receptor-like 1
Genes showing greatest decreases in expression
Neuronal pentraxin II
Early growth response 4
Early growth response 3
Heparan sulfate (glucosamine) 3-O-sulfotransferase 2
Synaptic vesicle glycoprotein 2C
5-hydroxytryptamine (serotonin) receptor 2A
Gremlin 2, Cysteine knot superfamily
Activity-reg cytoskeleton-associated protein
5-hydroxytryptamine (serotonin) receptor 2A
VGF nerve growth factor inducible
Early growth response 1
Early growth response 1
Discs, large (Drosophila) homolog-associated protein 2
Protein phosphatase 3 (formerly 2B), regulatory subunit B, 19 kDa, alpha isoform
GDNF family receptor alpha 2
Protein tyrosine phosphatase, receptor type, N
Glutamate receptor, metabotropic 7
P21 (CDKN1A)-activated kinase 3
Sixteen FTLD-U cases were compared to 8 normal controls on Affymetrix U133A microarrays. A statistical cutoff of P , 0.001 was used to identify
genes with differential expression, which were then ranked based on fold change (FC, disease/normal). The 20 genes with largest increases (top) and
decreases (bottom) in expression, identified by GeneSymbol and name, are shown for frontal cortex samples.
1352 Human Molecular Genetics, 2008, Vol. 17, No. 10
expression are summarized in Table 5. In contrast, comparing
10 GRN- frontal cortex samples to 8 normal frontal cortex
samples, we found only 154 probe sets representing 147 genes
showing differential expression at a P-value of 0.001 (FDR
13.8%, Table 2). In fact, when GRNþ and GRN2 frontal
cortex samples were compared directly, more genes were
foundtobedifferentially expressed(208 probesetsrepresenting
214 genes—several probes recognize more than one gene)
between these molecular subtypes of disease than between the
GRN2 cases and normals (Supplementary Material, Table S4).
The finding that there are many more dysregulated genes
among the GRNþ cases is striking given the fact that the
smaller number of GRNþ cases offers less statistical power.
The large number of dysregulated genes common to this
subset of cases corroborates evidence from our cluster and
principal component analyses that the GRNþ cases have a dis-
tinct expression signature.
As with the full dataset, cerebellar samples exhibited many
fewer dysregulated genes (Table 2).
Quantitative real-time QRT–PCR validation of gene
changes detected on microarrays
To corroborate gene changes detected on our microarrays,
we performed QRT–PCR for a subset of upregulated and
downregulated genes. For all genes tested, microarray
and QRT–PCR results were consistent in both GRNþ and
GRN2 FTLD-U cases (Fig. 4). We used ß-actin as our refer-
ence gene for normalizing gene expression levels based on
microarray data showing stable expression; comparable
results were obtained using cyclophilin A as an alternate refer-
ence gene (data not shown). In general, relative to QRT–PCR,
microarray results tended to underestimate the magnitude of
gene changes. This has been observed in other studies as
well (30,32), and likely reflects the greater amount of noise
around microarray measurements, with their subsequent need
Biological pathway analysis
Using the Database for Annotation, Visualization, and Inte-
grated Discovery (DAVID) database mining tool (Materials
and Methods), we identified biological process categories sig-
nificantly over-represented in GRNþ and GRN2 cases of
FTLD-U. We queried both the GO database and the KEGG
database using all probe sets showing differential expression
in frontal cortex between disease and normal at P , 0.001.
We analyzed GRNþ and GRN- FTLD-U cases separately
because of our observation that GRNþ cases have a distinct
signature and because the strong effect of the GRNþ cases
tended to drive overall findings within a mixed FTLD-U
The top GO and KEGG biological process categories
showing over-representation in genes dysregulated in GRN-
FTLD-U cases (P , 0.05) are shown in Table 6. Genes
involved in the MAPK signaling pathway, which has been
implicated in a number of neurodegenerative diseases (33–
35), are downregulated, as are genes involved in ion transport
and cell localization. Genes involved in lipid metabolism, on
the other hand, appear to be upregulated in GRN2 FTLD-U,
as has been shown for AD and HD as well (29,35).
Many more biological pathways were over-represented
in GRNþ FTLD-U (Table 7). Of these, the majority of the
biological process categories were made up of genes
downregulated in disease. These included all three categories
Figure 2. Number of dysregulated genes identified in human cortex microar-
ray studies of various neurodegenerative diseases. Data from present study
(FTLD-U) was re-analyzed using the same statistical conditions as each com-
parison study (29–31). See Supplementary Material, Table S3 for details of
other studies. The black portion of each bar represents the number of upregu-
lated genes in disease compared to control, while the white portion represents
the number of downregulated genes in disease compared to control. Many
more gene changes were found in the present FTLD-U study than in studies
of AD or ALS.
Figure 1. Cluster analysis of all samples shows a distinct expression signature
for cerebellum. Hierarchical dendrogram produced by clustering of expression
levels for 56 brain samples (columns) and 22 277 transcripts (rows), using
Spearman correlation coefficients. With one exception, all 17 cerebellar
samples (red) regardless of disease status fell into one branch (bold), while
samples from frontal cortex (blue) and hippocampus (yellow) were
admixed. Disease status for each sample is shown as well, with orange denot-
ing normal cases, purple denoting FTLD-U cases without progranulin gene
abnormalities (GRN2 FTLD-U) and teal denoting FTLD-U cases with progra-
nulin gene abnormalities (GRNþ FTLD-U). Heatmap tiles show standardized
expression levels of individual genes with red denoting high expression, grey
denoting medium expression and blue denoting low expression levels.
Human Molecular Genetics, 2008, Vol. 17, No. 10 1353
port, localization), as well as many pathways which are unique
to GRNþ FTLD-U. Among the downregulated pathways
unique to GRNþ FTLD-U were pathways identified in other
neurodegenerative disease microarray studies—synaptic trans-
mission, calcium signaling, neurotransmitter secretion, micro-
tubule-based movement (29,35)—as well as pathways such as
axon guidance identified in proteomics studies of neurodegen-
We found eight biological processes categories upregulated
in GRNþ FTLD-U (Table 7). These did not include lipid
metabolism, which was the only biological pathway upregu-
lated in the GRN2 cases. As with the downregulated path-
ways, many biological processes found in this study had
been identified in previous studies of neurodegenerative
disease. These include signal transduction (including small
GTPase-mediated signal transduction) and focal adhesion,
identified to be dysregulated in HD (35) and AD (29), respect-
ively, as well as regulation of actin cytoskeleton, implicated in
a proteomics study of multiple neurodegenerative diseases
(34). In addition, we found several specific biological path-
ways upregulated in this subset which may be unique to
GRNþ FTLD-U; they have not been identified in previous
microarray-based neurodegenerative disease studies or in our
GRN2 FTLD-U cases. These include the TGF-ß signaling
pathway and regulation of the cell cycle.
Global mRNA expression profiling with the Affymetrix
U133A microarray platform interrogated over 22 000 tran-
scripts and detected a large number of differentially expressed
genes in FTLD-U postmortem frontal cortex samples. The vast
majority of these dysregulated genes were not found in cer-
ebellum samples. This aspect of our analysis echoes previous
histopathological studies, which have shown significant path-
ology, both by traditional methods and by TDP-43 staining,
in frontal cortex but not in cerebellum (6). With respect to
GRN status, however, this molecular phenotyping approach
revealed differences which are not captured clinically or histo-
A distinct expression signature for GRN1 FTLD-U
By cluster analysis, principal component analysis and biologi-
cal pathway analysis, we found that GRNþ FTLD-U cases had
a distinct expression signature. While there have been sugges-
tions of clinical and pathological differences in some FTLD-U
cases with GRN mutations (36,38,39), the overall heterogen-
eity of presentations makes it difficult or nearly impossible
to make distinctions on clinical or histopathological grounds
alone (10,20,21). The ability to detect distinct molecular phe-
notypes, however, may be important in identifying patient
groups more responsive to targeted therapies.
It is important to note that cluster and principal component
analyses revealing the distinct expression signature for GRNþ
FTLD-U cases were performed in an unbiased manner; we did
not pre-specify subgroups but rather evaluated the overall
pattern of similarity in global gene expression among all
samples. The fact that the GRN truncation mutants (four
cases) formed a group with the three cases carrying GRN
gene VUS was somewhat surprising. This finding has impli-
cations for the pathogenicity of these variants, which are
under investigation now.
Notably, 2 of the 11 GRNþ samples were more distant in
global gene expression from the other 9 samples (Fig. 3B,
labeled arrows). Of these, one (Fig. 3A, arrow) was particu-
larly distant and remained so regardless of clustering method-
ology. This frontal cortex sample came from a case bearing the
truncation mutation R493X. The corresponding hippocampal
sample from this case did cluster with the other GRNþ
cases. On reviewing this case, we noted significant alpha-
synuclein pathology in addition to TDP-43 pathology in the
frontal cortex sample, which may explain the status of this
case as an outlier. The R493X mutation has previously been
found in multiple FTLD-U families (10). Frontotemporal
dementia and primary progressive aphasia have been the
associated clinical diagnoses; pathologically, cases bearing
this mutation have been characterized by ubiquitin-positive
intranuclear inclusions, consistent with a Sampathu Type 3
classification (10,19). Coexisting alpha-synuclein pathology
has not been previously described; thus, along with the
cases bearing GRN gene VUS, this case is also being inves-
tigated further. The other sample clustering separately from
Table 4. Cases bearing progranulin gene alterations
Case cDNAProtein Truncation/variantReference
IVS708þ1-þ4 del GTGA
Silent variant near splice site
Intronic variant at splice site
7Missense variant New
FTLD-U cases bearing progranulin gene abnormalities (GRNþ FTLD-U) included 4 cases with truncation mutations previously associated with disease
and believed to be pathogenic, as well as 3 cases with new variants of unknown significance (VUS) that are being investigated further.
aCase 6 possesses three separate abnormalities within the progranulin gene. In each case, cDNA numbering is based on GenBank NM_002087.2, and predicted
protein numbering is based on GenPept NP_0020278.1.
cDNA, complementary DNA; New, new variant which has not been previously reported.
1354Human Molecular Genetics, 2008, Vol. 17, No. 10
the majority of the GRNþ FTLD-U cases is a frontal cortex
sample also bearing a truncation mutation (R418X). This
sample, however, appeared more related to the other GRNþ
FTLD-U samples, with distance decreasing with the use
of a parametric correlation coefficient. The corresponding
hippocampal sample from this case fell into the main GRNþ
We considered various possibilities to explain our finding of
a distinct GRNþ FTLD-U expression signature. First, this
finding could be the spurious result of different treatment
Figure 3. Cluster analysis and principal components analysis of samples from histopathologically affected brain regions show a distinct expression signature for
GRNþ FTLD-U. (A) Hierarchical dendrogram produced by clustering of expression levels for 39 brain samples from frontal cortex or hippocampus (columns)
and 22 277 transcripts (rows), using Spearman correlation coefficients. With two exceptions, all 11 GRNþ FTLD-U samples (yellow) fell into one branch (bold),
while samples from GRN2 FLTD-U (red) and normal controls (blue) were admixed. Brain region for each sample is shown as well, with orange denoting frontal
cortex samples and teal denoting hippocampal samples. Heatmap tiles show standardized expression levels of individual genes with red denoting high expression,
grey denoting medium expression and blue denoting low expression levels. (B) Principal components analysis of frontal and hippocampal samples using 22 277
transcripts also revealed a distinct expression signature for GRNþ FTLD-U. GRNþ FTLD-U samples (yellow) were characterized by a low score along the first
principal component (X-axis) and a high score along the second principal component (Y-axis), separating them from GRN2 FTLD-U samples (red) and normal
samples (blue). Arrows indicate the two samples that were more distant in global gene expression by cluster analysis. Within GRNþ FTLD-U cases, samples
bearing truncation mutations (squares) and samples bearing variants of unknown significance (triangles) were admixed. R493X¼predicted protein change for
indicated truncation mutant. R418X¼predicted protein change for indicated truncation mutant.
Human Molecular Genetics, 2008, Vol. 17, No. 101355
conditions for these samples. However, the 56 brain samples
used in this study were collected in random order, with the
operator blinded to disease and gene status. Processing of all
samples after collection was performed on two consecutive
days, using the same batch of microarray platforms, and the
GRNþ samples were divided between the 2 days. Second,
this finding could be the result of differing RNA quality in
this subset. However, review of RNA quality parameters for
these selected cases did not reveal any obvious differences
from the rest of the samples. Third, while all cases in this
study bearing GRN gene abnormalities exhibited Sampathu
Type 3 histopathology (19), not all of the GRN2 FTLD-U
cases were Type 3, raising the possibility that the non-Type
3 cases drove this apparent difference in global gene
expression. We therefore re-analyzed our data excluding the
four GRN2 FTLD-U cases that were not Sampathu Type
3. Excluding these cases did not materially change results
(Supplementary Material, Fig. S1). Thus, we believe that the
distinct global expression signature observed for GRNþ
FTLD-U cases in this study carries biological significance.
Comparison to previous FTLD and ALS gene
While our study corroborated the findings of a previous FTLD
gene expression study (40) that synapse-related genes are
downregulated and cytoskeleton-associated genes are upregu-
lated, both our experimental design and overall findings exhib-
ited several important differences from the previous study.
Because we compared affected and unaffected brain regions
and also cases with and without GRN mutations, we were
able to detect significant differences in expression levels in
Table 5. Top 20 transcripts showing differential expression in GRNþ FTLD-U
Genes showing greatest increases in expression
ATP-binding cassette, sub-family A (ABC1), member 8
Kruppel-like factor 4 (gut)
Chromosome 11 open reading frame 43
Inhibitor of DNA binding 4
Extracellular matrix protein 2
ZIC family member 1
Ecotropic viral integration site 2B
Growth arrest-specific 1
Leukocyte immunoglobulin-like receptor, subfamily B; lysozyme (renal amyloidosis)
Ankyrin repeat domain 25
Collagen, type I, alpha 2
Complement component 4A
Lysosomal-associated membrane protein 2
Genes showing greatest decreases in expression
Neurofilament, light polypeptide 68 kDa
Proprotein convertase subtilisin/kexin type 1
ATPase, NAþ/Kþ transporting, a3 polypeptide
Regulator of G-protein signaling 4
Polo-like kinase 2 (Drosophila)
Early growth response 3
Regulator of G-protein signaling 4
Tachykinin, precursor 1 (substance K)
Cyclic AMP-regulated phosphoprotein, 21 kDa
GABA A receptor, alpha 1
Cell division cycle 42 (GTP binding protein, 25 kDa)
Secretogranin II (chromogranin C)
Neurofilament, light polypeptide 68 kDa
Sodium channel, voltage-gated, type III, beta
Six FTLD-U cases with progranulin gene abnormalities (GRNþ FTLD-U) were compared to 8 normal controls on Affymetrix U133A microarrays. A
statistical cutoff of P , 0.001 was used to identify genes with differential expression, which were then ranked based on fold change (FC, disease/
normal). The 20 genes with largest increases (top) and decreases (bottom) in expression, identified by GeneSymbol and name, are shown for frontal
1356 Human Molecular Genetics, 2008, Vol. 17, No. 10
frontal cortex versus cerebellum, and in GRNþ cases versus
GRN- cases relative to each other and controls. In addition,
compared to the previous study, we found many more genes,
involving many more biological pathways, to be dysregulated
in both our FTLD-U overall group and in our GRNþ subset
(Supplementary Material, Table S2). Fluorescent signal was
present for TARDBP and GRN gene expression in all of our
samples, which was not the case in the previous study (40).
Finally, we found approximately equal numbers of up- and
down-regulated genes for FTLD-U overall, as well as our
GRNþ and GRN- subsets. In contrast, the previous study
found almost all of the genes showing significant differential
expression to be downregulated in disease (40).
The differing results of these two studies could reflect the
fact that our study included many more samples, with
greater pathological homogeneity, both of which increase stat-
In addition, we compared our results to a study of gene
expression changes in sporadic ALS motor cortex (31) and
found many more genes with differential expression (Fig. 2).
In sporadic ALS, TDP-43-positive inclusions have been
found in lower motor neurons (16) as well as motor cortex
(37,41). These regional differences in TDP-43 histopathology
and gene expression changes in FTLD-U and ALS are worthy
of further examination.
Genes implicated in previous neurodegenerative
Several of the individual genes found to be dysregulated in our
study have been implicated in previous studies of neurodegen-
erative disease. The gene found to be most downregulated in
our GRNþ FTLD-U frontal cortex samples was the 68 kD
neurofilament (NF) light polypeptide (NEFL), which has
been associated with motor neuron disease on pathological,
mouse model and human genetic grounds. Pathologically,
aberrant NF accumulation has long been considered a hall-
mark of ALS (42). In addition, mice overexpressing NEFL
Figure 4. Validation of microarray results by quantitative reverse transcription real-time PCR (QRT–PCR). Relative expression levels of genes are indicated by
the fold changes in expression level for FTLD-U samples with progranulin gene abnormalities (GRNþ), and FTLD-U samples without progranulin gene abnorm-
alities (GRN2). For QRT–PCR, average fold changes and standard deviations of three cases in each group are shown. PCR reactions were performed in dupli-
cate. GLRB, glycine receptor beta subunit; GRM5, metabotropic glutamate receptor 5; HDAC1, histone deacetylase 1; HSPA2, heat shock protein 2 (70 kD);
HTR2A, serotonin receptor 2A; ID4, inhibitor of DNA binding 4; NOTCH2, Notch homolog (Drosophila) 2.
Table 6. Dysregulated biological processes in GRN2 FTLD-U
P-value Biological process subgroups
Biological processes enriched in upregulated genes
GO Lipid metabolism group2.39E202 Cellular lipid metabolism, lipid metabolism, lipid biosynthesis, membrane
Biological processes enriched in downregulated genes
KEGGMAPK signaling pathway
GO Transport group
3.26E202 Di-, tri-valent inorganic cation transport, calcium ion transport, metal ion
transport, cation transport, transport, ion transport
Establishment of localization, transport, localizationGO Localization group4.78E202
Frontal cortex samples from 10 FTLD-U cases without progranulin gene abnormalities (GRN2 FTLD-U) were compared to 8 normal controls on
Affymetrix U133A microarrays to identify genes with differential expression at P , 0.001. Biological pathway analysis using the KEGG and GO
databases was then performed as described in Materials and Methods, and biological processes over-represented (P , 0.05) in genes upregulated in
disease (top) and downregulated in disease (bottom) are shown. Biological process categories from the GO database were combined into functionally
related groups, for which the median P-value of the subgroups is shown.
Human Molecular Genetics, 2008, Vol. 17, No. 101357
(43) and mice carrying a point mutation in NEFL (44) show
selective dysfunction and degeneration of spinal motor
neurons. More recently, it has been argued that TDP-43 acts
physiologically as a stabilizer of NEFL mRNA, with loss of
this normal function involved in the pathology of FTLD-U
(45). Because NFs are abundant structural components of
neurons, the apparent loss of NF mRNA from postmortem
samples of brain or spinal cord might be attributable simply
to neuronal loss. Arguing against this, however, is the fact
that we found NEFL mRNA expression to be downregulated
only in our GRNþ cases. The GRN2 FTLD-U cases, which
have as much neuronal loss, did not show any differences in
levels of NEFL mRNA expression.
GRN mutations are associated with FTLD-U, but we did not
find dysregulated GRN mRNA expression in either our GRNþ
or GRN2 subsets. We considered several possible expla-
nations. First, this result could be an artifact of combining
GRN truncation mutants (four cases) with cases containing
GRN gene VUS (three cases). We therefore evaluated these
two categories separately but were still unable to find a statisti-
cally significant difference in GRN gene expression between
either group and normal controls. Second, although total
mRNA and protein levels of progranulin have been shown
to be decreased in FTLD-U cases with GRN mutations (8,9),
most of these studies have focused on mRNA isolated from
lymphoblastoid cells rather than brain. In addition, sequencing
of cDNA from brain and lymphoblastoid cells has demon-
strated preferential expression of the normal allele (8,9), but
this does not exclude the possibility that there is a compensa-
tory increase in expression of the normal allele in the affected
organ (brain), resulting in little difference in total mRNA
levels. Third, in one case of documented decreased total
mRNA expression in brain (46), GRN mRNA levels were
decreased by ?20% in frontal cortex and ?10% in cerebel-
lum; by microarray standards, such changes are relatively
small and may have been below the detection level of our
experimental design. Finally, the P-values for differential
expression for the three GRN probe sets on our microarray
platform were 0.006, 0.048 and 0.005. In an effort to detect
the most robust gene changes, we may have set our statistical
threshold too stringently (at P , 0.001) to identify this gene as
significantly dysregulated. Of note, we are systematically eval-
uating progranulin transcript levels in different regions of
human brain with QRT–PCR now and will report these find-
TDP-43, the major protein found in the characteristic
inclusions of FTLD-U and ALS, has been the subject of
intense study recently. Initial genetic studies of TDP-43,
encoded by the gene TARDBP, have not revealed obvious
mutations within FTLD-U patients (47); mRNA expression
Table 7. Dysregulated biological processes in GRNþ FTLD-U
Database Biological Process
P-valueBiological process subgroups
Biological processes enriched in upregulated genes
KEGG Focal adhesion
KEGG TGF-beta signaling
KEGGRegulation of actin cytoskeleton
KEGG ECM-receptor interaction
GONegative regulation of biological
GO Cell communication group
GO Cell cycle group
2.16E203 Negative regulation of cellular process, negative regulation of cellular physiological process,
negative regulation of biological process, negative regulation of physiological process
Intracellular signaling cascade, signal transduction, cell communication
Negative regulation of progression through cell cycle, regulation of progression through cell
cycle, regulation of cell cycle
Regulation of Ras protein signal transduction, Ras protein signal transduction, regulation of
small GTPase mediated signal transduction
GOSmall GTPase mediated signal
Biological processes enriched in downregulated genes
KEGG Calcium signaling pathway
KEGG Axon guidance
KEGG MAPK signaling pathway
GOOrganismal physiological process/
synaptic transmission group
GO Localization group
7.24E208Transmission of nerve impulse, synaptic transmission, cell–cell signaling,
neurophysiological process, organismal physiological process
Establishment of localization, localization, transport
Neurotransmitter secretion, regulated secretory pathway, regulation of neurotransmitter
levels, synaptic vesicle transport, vesicle-mediated transport, secretion, secretory pathway,
synaptic vessel exocytosis, exocytosis
Metal ion transport, cation transport, calcium ion transport, ion transport, monovalent
inorganic cation transport, di-,tri-valent inorganic cation transport, potassium ion transport
Microtubule-based movement, protein polymerization, cytoskeleton organization and
biogenesis, cytoskeleton-dependent intracellular transport, microtubule-based process
GOIon transport group1.60E204
Frontal cortex samples from 6 FTLD-U cases with progranulin gene abnormalities (GRNþ FTLD-U) were compared to 8 normal controls on Affymetrix
U133A microarrays to identify genes with differential expression at P , 0.001. Biological pathway analysis using the KEGG and GO databases was then
performed as described in Materials and Methods, and biological processes over-represented in genes upregulated in disease (top, P , 0.05) and
downregulated in disease (bottom, P , 0.02) are shown. Biological Process categories from the GO database were combined into functionally related
groups, for which the median P-value of the subgroups is shown.
1358 Human Molecular Genetics, 2008, Vol. 17, No. 10
studies are lacking. In our microarray analysis, TARDBP
mRNA levels were not significantly changed among either
GRNþ or GRN2 cases, compared to controls. There was a
suggestion (Fig. 4) of slightly higher mRNA levels in the
three GRNþ cases selected for QRT–PCR testing, but this
observation remains preliminary.
Novel biological pathways in GRN1 FTLD-U
The present study of RNA expression profiles in human
FTLD-U brain provides evidence for the commonality of
some biological pathways in multiple neurodegenerative dis-
eases. This accords with previous work implicating mechan-
isms such as oxidative stress (48), mitochondrial dysfunction
(49), aberrant protein folding and accumulation (50), synaptic
dysfunction (51) and others in several neurodegenerative dis-
eases. Indeed, some have argued that neurodegenerative dis-
orders are linked not just by final common pathways of cell
dysfunction and death but by an actual network of interacting
proteins in which abnormalities in one or a few proteins may
cause dysfunction of the entire network (52,53). One recent
study constructed an interacting proteomic map, with nodes
representing various proteins implicated in AD, PD, ALS,
HD, dentatorubropallidoluysian atrophy and prion disease
(34); this map was then used to identify KEGG biological
pathways enriched in disease-associated proteins. Of the 8
KEGG pathways identified in this manner, 3 (axon guidance,
focal adhesion, regulation of actin cytoskeleton) were ident-
ified in our study as well, with an additional 2 (adherens junc-
tion, P ¼ 0.07, Wnt signaling pathway, P ¼ 0.08) falling just
short of our P-value cutoff for significance.
Although these common pathways are important in under-
standing neurodegeneration as a whole, biological pathways
specific to a particular disease may be key to developing tar-
geted therapies. To that end, our study identified two biologi-
cal pathways which may be specific to GRNþ FTLD-U. We
found the categories of TGF-ß signaling and the cell cycle
to be significantly enriched in genes upregulated in GRNþ
FTLD-U. These pathways did not emerge from human brain
gene expression studies of HD (35) or AD (29), general pro-
teomics studies of neurodegeneration (34) or from analyses
of our GRN2 FTLD-U subset of cases.
Limitations of postmortem brain studies
One limitation of studies using postmortem tissue is that
observed changes in gene expression may reflect either
primary events in pathogenesis or secondary responses such
as inflammation, cell death and neuronal loss. In addition,
we used a regional sampling approach which by definition
involves sampling of mixed populations of glial and neuronal
cells. We chose this approach to minimize the amount of RNA
degradation, which is greater with microdissection techniques.
However, in doing so, we considered the possibility that
primary pathophysiological changes might be masked by sec-
ondary changes of less biological importance.
Two lines of evidence from our present study support the
idea that most of the genes and pathways identified have bio-
logical relevance. First, we found a distinct molecular pheno-
type for the GRNþ cases, which is unlikely to be due simply
to non-specific inflammation and cell death, since the GRN2
cases were clinically and histopathologically similar and
handled the same way. Similarly, the presence of a distinct
GRNþ FTLD-U expression signature made it unlikely that
our findings are simply the result of neuronal cell loss,
causing a more ‘glial’ signature in diseased brains, since
amounts of neuronal cell loss. Second, many microarray
studies have found genes involved in stress or immune
response and apoptosis to be upregulated in their datasets
(29,31), raising the issue of whether microarrays on postmor-
tem tissue simply reflect the end-stage events of vulnerable
cells dying. While categories of response to stress and apopto-
sis did appear in our biological pathway analysis, they were
much less significantly enriched and did not meet our stringent
statistical criterion for inclusion.
Conclusions and implications for future studies
We used a global mRNA expression profiling approach to
characterize human postmortem FTLD-U brain. Our study
was large, with 56 brain samples, and designed to evaluate
the effect of brain region and progranulin gene status. We
found many dysregulated genes within the regions of the
brain affected histopathologically in disease, but not in areas
which are relatively histopathologically spared. In addition,
we found a distinct molecular phenotype for GRNþ
FTLD-U, with associated biological pathways which may be
specific for this subtype of disease. This finding has impli-
cations for future therapeutic strategies.
Although our study did not focus exclusively on one histo-
pathological subtype of FTLD-U, Sampathu Type 3 FTLD-U
cases (19) comprised the majority of our disease samples.
We emphasized this type in order to create a balanced com-
parison between GRNþ and GRN2 cases, since GRN
mutations are found primarily within Type 3 cases. Future
studies investigating whether the gene changes seen extend
to all histopathological subtypes of FTLD-U, and to
FTD-MND and ALS, would be a valuable addition to the
data presented here.
Finally, the rationale for performing an RNA expression
analysis in FTLD-U came from the observation that a DNA-
and RNA-binding protein, TDP-43, may be involved in patho-
expression in brain regions characterized by TDP-43 pathology
lends support to the idea that gene expression changes seen in
‘hits’ for further analysis. If transcriptional dysregulation is a
key disease mechanism in FTLD-U, these changes may be
central to its pathophysiology.
MATERIALS AND METHODS
Human brain samples
Seventeen FTLD-U cases and 11 neurologically normal con-
trols were sampled from the University of Pennsylvania
Center for Neurodegenerative Disease Research Brain Bank
(Table 1). Informed consent was obtained for postmortem
studies. Age, sex and postmortem interval (PMI) to autopsy
Human Molecular Genetics, 2008, Vol. 17, No. 10 1359
were not significantly different between cases and controls.
All FTLD-U cases were reviewed by a board-certified neuro-
pathologist. In order to facilitate comparison of GRNþ and
GRN2 cases, the majority of the FTLD-U cases used (13/
17) met criteria for FTLD-U Type 3 according to Sampathu
(19). All FTLD-U cases were evaluated for abnormalities in
the progranulin gene, as previously described (54). Among
the FTLD-U cases, seven had progranulin gene alterations
(GRNþ), with four cases carrying known pathogenic
mutations and three cases carrying VUS (Table 4). Prelimi-
nary evidence indicates that at least two of the VUS may be
pathogenic by affecting mRNA splicing. Studies of these var-
iants are in progress. All seven GRNþ cases met criteria for
FTLD-U Type 3. Control brains had no evidence of neurologi-
cal disease either clinically or neuropathologically. For each
brain used, a 50–100 mg sample of frozen tissue was collected
from frontal cortex grey matter anterior to the genu of the
corpus callosum, cerebellum and hippocampus (where avail-
able), while keeping the brain on dry ice. Samples were
either processed immediately or stored at 2808C.
RNA extraction and hybridization
Frozen tissue samples were homogenized for 30–60 s using a
PowerGen 35 (Fisher Scientific). RNA was isolated using
TRIzol (Invitrogen) and RNeasy Mini columns (Qiagen),
according to standard manufacturer protocols. All subsequent
steps were conducted as described in the Affymetrix Gene-
Chip Expression Analysis Technical Manual (Affymetrix).
Briefly, 3 mg of total RNA from each sample were used to
prepare biotinylated fragmented cRNA with products from
Affymetrix according to manufacturer protocols. Micro-
arrays (Affymetrix Human Genome U133A) assessing
expression of 22 277 transcripts were hybridized for 16 h at
458C. Chips were washed and stained, and hybridization
signals were collected with a GeneChip 3000 7G scanner
Microarray quality control
Measures were taken both prior to microarray hybridization
and after hybridization to ensure quality control. After RNA
isolation, RNA purity and integrity were assessed by spectro-
photometric measurement of 260/280 nm OD ratios and by
capillary electrophoresis on an Agilent 2100 Bioanalyzer.
All samples used for further analysis had a 260/280 ratio of
.1.9, with sharp ribosomal RNA peaks (Supplementary
Material, Fig. S2), virtually undetectable low molecular
weight RNA, and RIN numbers .8 (55). After microarray
hybridization, 30/50ratios of GAPDH were calculated, and
only chips with ratios ,3 were retained. Finally, we used a
quality assessment algorithm based on weights and residuals
from robust regression models of gene expression to identify
outlier chips (56), which were excluded from analysis. Four
samples were eliminated from analysis after all quality
control measures; of these, two were identified by all three
methods, implying that initial RNA quality may be the most
Microarray data analysis
Statistical analyses of gene expression were performed using
open source R software packages available from http://www.
bioconductor.org (57). Fluorescence intensity measures of
gene expression were normalized and quantified by robust
multi-array analysis (58), using the affy package (59). To
identify transcripts differentially expressed between disease
(all FTLD-U, GRNþ subset, GRN2 subset) and controls for
each brain region, we used the limma package (60), which
adopts a Bayesian approach. Our model corrected for gender
and age. FDR were calculated based on nominal P-values as
described (35) and by using a class permutation test to
adjust for co-expressed genes. R-scripts for these analyses
are available on request. In addition, principal components
analysis and hierarchical cluster analysis using Spearman
and Pearson correlation coefficients was performed using
Partek software, version 6.3 Copyright # 2007 (Partek, Inc.,
St Louis, MO, USA).
Biological pathway analysis
We used the DAVID 2007 program available at http://david.
abcc.ncifcrf.gov/ (61) to assign differentially expressed
genes to biological pathways and processes annotated in the
GO and KEGG databases, available at http://www.geneontol-
ogy.org (62) and http://www.genome.jp/kegg/ (63), respect-
overrepresentation of differentially expressed genes within
each biological pathway represented in the GO and KEGG
databases. In addition, because of the hierarchical organization
of GO categories, simple lists tend to result in similar, redun-
dant categories. DAVID curates these GO categories into
related groups based on this hierarchical structure, allowing
for a more biologically meaningful analysis. We retained
those pathways with a P-value of 0.05 or less (or, in the
case of GO categories, functional groups with a median
P-value of 0.05 or less) as statistically significant.
QRT–PCR was performed using the Applied Biosystems
7900HT Fast Real-Time PCR system. Briefly, total RNA iso-
lated from postmortem brain samples was treated with DNAse
(DNA-free kit; Ambion) according to manufacturer protocols,
and 1.75 mg of DNAse-treated RNA was used to make single-
stranded cDNA (Superscript III; Invitrogen) according to the
manufacturer’s protocols. One microliter of the resulting
cDNA then underwent further PCR cycles, using the
Taqman Gene Expression System (Applied Biosystems).
PCR conditions used were 958C for 10 min, followed by 40
cycles of denaturing at 958C for 15 s and annealing/extension
at 608C for 1 min, in a 20 ml reaction volume. Detailed infor-
mation on primer sets used is given in Supplementary
Material, Table S5. The delta–delta method (64) was used
for relative quantification of gene expression. We used
ß-actin and cyclophilin A as our reference genes. For QRT–
PCR validation of microarray results, three frontal cortex
samples from each group were selected to represent the con-
trols, GRNþ cases and GRN2 cases (9 total samples).
1360Human Molecular Genetics, 2008, Vol. 17, No. 10
Supplementary Material is available at HMG Online.
We are grateful to the Penn Microarray Facility and Dr Don
Baldwin for advice and technical assistance with array
samples. We thank Dr Bruce Miller for recruiting patients
who were studied. Finally, we thank our patients and their
families whose generosity has made this work possible.
Conflict of Interest statement: None declared.
These studies were supported by grants from the National
Institutes of Health (NIH, AG17586, AG10124, AG15116,
AG023501, AG19724 and NS44266). A.S.C.-P. is a fellow
of the NINDS Morris K. Udall Parkinson’s Disease Research
Center of Excellence and the NIH Research Fellowship
Training Program for Clinicians in Translational Research in
Neurobiology of Disease. J.B.P. is supported by a Burroughs
Wellcome Fund Career Award at the Scientific Interface.
V.M.-Y.L. is the John H. Ware, 3rd Professor of Alzheimer’s
disease research. J.Q.T. is the William Maul Measey-Truman
G. Schnabel, Jr Professor of Geriatric Medicine and
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