Comparative transcriptome profiling of amyloid precursor protein family members in the adult cortex.

Abstract

The β-amyloid precursor protein (APP) and the related β-amyloid precursor-like proteins (APLPs) undergo complex proteolytic processing giving rise to several fragments. Whereas it is well established that Aβ accumulation is a central trigger for Alzheimer's disease, the physiological role of APP family members and their diverse proteolytic products is still largely unknown. The secreted APPsα ectodomain has been shown to be involved in neuroprotection and synaptic plasticity. The γ-secretase-generated APP intracellular domain (AICD) functions as a transcriptional regulator in heterologous reporter assays although its role for endogenous gene regulation has remained controversial.
To gain further insight into the molecular changes associated with knockout phenotypes and to elucidate the physiological functions of APP family members including their proposed role as transcriptional regulators, we performed DNA microarray transcriptome profiling of prefrontal cortex of adult wild-type (WT), APP knockout (APP-/-), APLP2 knockout (APLP2-/-) and APPsα knockin mice (APPα/α) expressing solely the secreted APPsα ectodomain. Biological pathways affected by the lack of APP family members included neurogenesis, transcription, and kinase activity. Comparative analysis of transcriptome changes between mutant and wild-type mice, followed by qPCR validation, identified co-regulated gene sets. Interestingly, these included heat shock proteins and plasticity-related genes that were both down-regulated in knockout cortices. In contrast, we failed to detect significant differences in expression of previously proposed AICD target genes including Bace1, Kai1, Gsk3b, p53, Tip60, and Vglut2. Only Egfr was slightly up-regulated in APLP2-/- mice. Comparison of APP-/- and APPα/α with wild-type mice revealed a high proportion of co-regulated genes indicating an important role of the C-terminus for cellular signaling. Finally, comparison of APLP2-/- on different genetic backgrounds revealed that background-related transcriptome changes may dominate over changes due to the knockout of a single gene.
Shared transcriptome profiles corroborated closely related physiological functions of APP family members in the adult central nervous system. As expression of proposed AICD target genes was not altered in adult cortex, this may indicate that these genes are not affected by lack of APP under resting conditions or only in a small subset of cells.

Full text

Page 1

RESEARCH ARTICLEOpen Access
Comparative transcriptome profiling of amyloid
precursor protein family members in the adult
cortex
Dorothee Aydin1†, Mikhail A Filippov1†, Jakob-Andreas Tschäpe1, Norbert Gretz2, Marco Prinz3, Roland Eils1,4,
Benedikt Brors4and Ulrike C Müller1*
Abstract
Background: The b-amyloid precursor protein (APP) and the related b-amyloid precursor-like proteins (APLPs)
undergo complex proteolytic processing giving rise to several fragments. Whereas it is well established that Ab
accumulation is a central trigger for Alzheimer’s disease, the physiological role of APP family members and their
diverse proteolytic products is still largely unknown. The secreted APPsa ectodomain has been shown to be
involved in neuroprotection and synaptic plasticity. The g-secretase-generated APP intracellular domain (AICD)
functions as a transcriptional regulator in heterologous reporter assays although its role for endogenous gene
regulation has remained controversial.
Results: To gain further insight into the molecular changes associated with knockout phenotypes and to elucidate
the physiological functions of APP family members including their proposed role as transcriptional regulators, we
performed DNA microarray transcriptome profiling of prefrontal cortex of adult wild-type (WT), APP knockout
(APP-/-), APLP2 knockout (APLP2-/-) and APPsa knockin mice (APPa/a) expressing solely the secreted APPsa
ectodomain. Biological pathways affected by the lack of APP family members included neurogenesis, transcription,
and kinase activity. Comparative analysis of transcriptome changes between mutant and wild-type mice, followed
by qPCR validation, identified co-regulated gene sets. Interestingly, these included heat shock proteins and
plasticity-related genes that were both down-regulated in knockout cortices. In contrast, we failed to detect
significant differences in expression of previously proposed AICD target genes including Bace1, Kai1, Gsk3b, p53,
Tip60, and Vglut2. Only Egfr was slightly up-regulated in APLP2-/-mice. Comparison of APP-/-and APPa/awith wild-
type mice revealed a high proportion of co-regulated genes indicating an important role of the C-terminus for
cellular signaling. Finally, comparison of APLP2-/-on different genetic backgrounds revealed that background-
related transcriptome changes may dominate over changes due to the knockout of a single gene.
Conclusion: Shared transcriptome profiles corroborated closely related physiological functions of APP family
members in the adult central nervous system. As expression of proposed AICD target genes was not altered in
adult cortex, this may indicate that these genes are not affected by lack of APP under resting conditions or only in
a small subset of cells.
* Correspondence: u.mueller@urz.uni-hd.de
† Contributed equally
1Department of Bioinformatics and Functional Genomics, Institute of
Pharmacy and Molecular Biotechnology, Heidelberg University, Im
Neuenheimer Feld 364, D-69120 Heidelberg, Germany
Full list of author information is available at the end of the article
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
© 2011 Aydin et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

Page 2

Background
Despite its key role in Alzheimer’s disease (AD) patho-
genesis, the physiological functions of the b-amyloid
precursor protein (APP) and its close homologue, the b-
amyloid precursor-like protein 2 (APLP2), are still
poorly understood. This is due to two major problems
complicating the in vivo analysis. i) APP is subject to
complex proteolytical processing and ii) APP is part of a
gene family with partially overlapping functions.
APP is a type I transmembrane protein, and proces-
sing (see Figure 1a) is initiated either by a-secretase
cleavage within the Ab region, or by b-secretase (BACE)
cleavage at the N-terminus of Ab, leading to the secre-
tion of large soluble ectodomains, termed APPsa and
APPsb respectively. Subsequent g-secretase processing
of the C-terminal fragments (bCTF, or aCTF) results in
the production of secreted Ab, p3 and the APP intracel-
lular domain (AICD). Both APLPs are similarly pro-
cessed by the same secretases. It is evident that APP/
APLPs are highly complex molecules, that may exert
important functions as unprocessed cell surface mole-
cules (APP-FL) as well as functions mediated by their
diverse proteolytic fragments. APP processing is highly
reminiscent to that of Notch with g-secretase-mediated
release of the Notch intracellular domain (NICD) trig-
gering the translocation of NICD to the nucleus. This
results in transcriptional regulation of defined target
genes involved in e.g. neuronal differentiation. Thus, a
similar functional role for AICD (and the related intra-
cellular fragments of APLPs, termed ALID1 and ALID2)
as transcriptional regulator has been proposed [1].
Indeed, AICD has been shown to translocate to the
-/-
APP
?/?
APP
WT
-/-
APLP2(R1)
-/-
APLP2
???
APP
NH2
COOH
?
?/?
APP
NH2
Endogenous
APP promoter
Exon 1 Exon 2 Exon 16
Exon 18 3’-UTR
“STOP”
QK L
COOH
???
??
??
APP
NH2
COOH
APPs?
APPs?
?CTF
?CTF
A?
AICDAICD p3
(a)
(b)(c)
Figure 1 Overview over study design. (a) Overview of APP processing. APP is first cleaved by either a-secretase or b-secretase, thereby
shedding soluble APPsa or APPsb, respectively. The membrane-bound C-terminal fragments (CTFs) are then cleaved by g-secretase: aCTF gives
rise to p3 and AICD whereas bCTF is cleaved to Ab and AICD. (b) In APPa/aknock-in mice, a stop codon was introduced behind the a-secretase
cleavage site by homologous recombination into the endogenous APP locus. Note that no full length APP or any other fragment can be
generated from the APPsa knockin locus. (c) Overview of genotypes used for microarray analysis. APLP2(R1)-/-had been backcrossed for one
generation to C57BL/6 whereas WT, APP-/-, APPa/a, APLP2-/-had been backcrossed for six generations. Six pairwise comparisons were performed:
WT versus APP-/-, WT versus APPa/a, WT versus APLP2-/-, APPa/aversus APP-/-, WT versus APLP2(R1)-/-, and APLP2-/-versus APLP2(R1)-/-. The arrow
indicates reference and tested group of each comparison.
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 2 of 17

Page 3

nucleus and can form a complex with the adaptor FE65
and the histone acetyltransferase TIP60. This complex can
induce the transcription of artificial reporter constructs in
transfected cells [2,3]. Likewise, APLP1 and APLP2 are
subject to g-secretase processing and can stimulate the
expression of heterologous reporter constructs in an
FE65-dependent manner [4]. Additional complexity comes
from recent studies indicating that APP (and both APLPs)
can form tripartite complexes with the adaptor protein
MINT3 and the transcriptional co-activators TAZ and
YAP. When overexpressed in HEK293 cells, this complex
functions in GAL4 reporter assays [5,6]. To date, several
putative AICD target genes have been identified (mainly
using overexpression approaches) including Kai1 [7],
Gsk3b [8,9], Nep [10], Egfr [11], p53 [12], Lrp [13], Tip60,
Bace1, App itself [14] as well as genes involved in cytoske-
letal dynamics [15]. However, the validity of these pro-
posed targets, in particular regarding the question of
whether they also constitute endogenous AICD/ALID tar-
get genes, has remained controversial [16-22]. Interest-
ingly, in several recent studies, increased production of
AICD either in transfected cells or in transgenic animals
did not lead to a consistent up-regulation of previously
proposed target genes [15,20,22].
Previously, we showed that knockout (KO) mice defi-
cient in a single family member such as APP (or one of
the APLPs) are viable [23,24] whereas combined APP-/-
APLP2-/-or APLP1-/-APLP2-/-double KO mice [24] and
APP-/-APLP1-/-APLP2-/-triple mutants [25] die shortly
after birth, likely due to defects of neuromuscular trans-
mission [26]. Neither APP-/-nor APLP2-/-mice display
obvious defects of central nervous system (CNS) mor-
phology, yet APP-/-mice revealed reduced body weight
and defects in spatial learning associated with impaired
synaptic plasticity including long-term potentiation
(LTP) [26]. However, the molecular mechanisms under-
lying these defects have remained unclear.
Processing of APP gives rise to several fragments
including besides neurotoxic Ab the a-secretase-gener-
ated soluble APPsa fragment that is neuroprotective
and involved in synaptic plasticity [27,28]. To delineate
its specific functions, we previously generated APPsa
knockin (APPa/a) mice by inserting via gene targeting a
stop codon into the endogenous APP locus right after
the a-secretase cleavage site [28]. Thus, APPa/aknockin
mice express only secreted APPsa from the endogenous
APP promoter (Figure 1b).
Here, we employed a rational unbiased approach and
investigated transcriptional changes arising due to the
lack of APP family members in the adult cortex of
knockout mice to gain further insight into the physiolo-
gical and signaling functions of APP family members.
This includes transcriptome changes that may arise due
to a lack of direct AICD/ALID-mediated transcriptional
regulation as well as changes resulting from indirect sig-
naling events mediated by transmembrane APP/APLP
isoforms. First, we analyzed transcriptome changes due
to the complete absence of APP or APLP2 (including all
their proteolytic fragments) by conducting the pairwise
comparisons of WT versus APP-/-(WT/APP-/-) and WT
versus APLP2-/-(WT/APLP2-/-). Second, we had a clo-
ser look at the role of different APP fragments, in parti-
cular APPsa. Therefore, we compared the transcriptome
of APPa/amice both to WT (WT/APPa/a) and APP-/-
mice (APPa/a/APP-/-), respectively. Third, we addressed
the influence of the genetic background by comparing
knockout animals of mixed 129 × C57BL/6 genetic
background (APLP2(R1)-/-) to those backcrossed to
C57BL/6 for 6 generations.
Results and Discussion
We subjected prefrontal cortices of adult male mice (24 -
28 weeks of age) of the following groups to transcriptome
analysis: WT (n = 3), APP-/-(n = 3), APPa/a(n = 3),
APLP2-/-(n = 3), APLP2(R1)-/-(n = 3) (Figure 1c). WT,
APP-/-, APPa/a, APLP2-/-had been backcrossed for six
generations to C57BL/6 mice. APLP2(R1)-/-mice harbor
the identical knockout allele as APLP2-/-but were only
backcrossed once. Note that APP-/-mice lack membrane-
anchored full length APP (APP-FL) as well as all proteoly-
tic fragments derived from it (APPsa, Ab, APPsb, aCTF,
bCTF and AICD), whereas APPa/amice express APPsa
but lack full length APP and all other fragments.
Raw data was processed according to the RMA proce-
dure [29,30]. We validated the microarray data by cluster-
ing the processed raw data based on all available App/
Aplp2 probe sets. As expected, all samples grouped accord-
ing to their genotypes: WT, APP-/-, APPa/a, and APLP2-/-
samples were clearly separated (Additional file 1).
Differential gene expression in mice lacking APP family
members
First, we wanted to address the question of which
impact each genotype has on transcription by searching
for genes that show differential expression in the differ-
ent comparisons. To identify significantly up- or down-
regulated genes, we performed a Significance Analysis of
Microarrays (SAM) with a False Discovery Rate (FDR)
of app. 5% (see Table 1 and Additional file 2).
A total of 359 genes (274 up- and 85 down-regulated)
were differentially expressed in WT/APP-/-. The com-
parison WT/APLP2-/-
led to 1242 differentially
expressed genes (1142 up- and 100 down-regulated).
For the comparison WT/APPa/a, we observed 447 sig-
nificantly regulated genes (250 up- and 197 down-regu-
lated). In contrast, we only observed 29 significant genes
in the comparison APPa/a/APP-/-(all of them were up-
regulated). Based on the total number of significant
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 3 of 17

Page 4

differentially expressed genes in these comparisons, the
APLP2 knockout has the highest impact on gene
expression.
To get an idea how many differentially expressed
genes with high fold changes are within each list, we
introduced a fold change criterion and determined the
number of genes that differ by at least 2-fold (Table 1).
In the comparisons WT/APP-/-11 genes, in WT/
APLP2-/-11 genes, in WT/APPa/a15 genes, and in
APPa/a/APP-/-2 genes passed this criterion. This shows
that the majority of significant differentially expressed
genes show only small to moderate (up to 2-fold) altera-
tions in gene expression. This is consistent with pre-
vious studies [31] and likely due to the complex nature
of cortical tissue consisting of a multitude of neuronal
and glial subpopulations. In APP-/-and APPa/aanimals,
no compensatory up-regulation of Aplp1 and Aplp2 at
the mRNA level was observed. Likewise, no up-regula-
tion of App and Aplp1 was observed in APLP2-/-ani-
mals thus confirming previous Western blot results [24].
Analysis of biological pathways affected in APP/APLP
knockout mice
Subsequently, we analyzed the list of significant genes
from all comparisons using DAVID bioinformatics
resources [32]. Within DAVID, we did Functional
Annotation Clustering using Gene Ontology terms (bio-
logical processes) and pathway databases (Biocarta,
Reactome, Panther, KEGG) to gain an overview about
the nature of genes and potential shared functional
pathways.
We found 24 enriched clusters in the comparison
WT/APP-/-, 29 in WT/APPa/a, and 35 in WT/APLP2-/-
(Additional file 3). Due to the low number of significant
genes in APPa/a/APP-/-, no gene set enrichment could
be assessed. Interestingly, several of these enriched clus-
ters were shared between the different pairwise compari-
sons including regulation of neurogenesis, transcription
and kinase activity (Figure 2, Additional file 3).
The finding that lack of either APP or APLP2 affects
expression of genes involved in neurogenesis confirms
and further extends previous studies that implicated
APP in neuronal progenitor regulation [33-35]. In APP-
overexpressing transgenic mouse models, adult neuro-
genesis in the hippocampus is impaired [36] which has
been mainly attributed to Ab-mediated toxic effects.
Regulation of transcription was identified as another
shared cluster between WT/APP-/-and WT/APLP2-/-
pointing towards similar functions of APP family mem-
bers in this cellular process, possibly via AICD/ALID
signaling or via more indirect mechanisms. Shared func-
tional clusters were also found for WT/APP-/-and WT/
APPa/a, namely neurogenesis and negative regulation of
protein kinase activity which may indicate that pheno-
types of APP-KO mice, e.g. defects in synaptic plasticity,
arise due to alterations in the phosphorylation state of
yet to be identified target proteins.
Although Ab serves as a central trigger for AD patho-
genesis, the physiological role of APP and the question of
whether a loss of its functions contributes to AD are still
unclear. We therefore investigated a possible enrichment
of genes previously linked to Alzheimer’s disease in our
dataset. Comparing the 359 genes differentially expressed
in WT/APP-/-with the AlzGene dataset (currently com-
prising 662 genes), we identified 14 genes, namely Abcg4,
Ache, Aldh2, Arsb, Bcl2, Bdnf, Crh, Egr2, Fos, Gstz1,
Hspa1a, Hspa1b, Hspa5, Ppp1r3c. Next, we assessed
whether this number of 14 identified genes represents a
significant enrichment of AD-related genes in the WT/
APP-/-dataset. To this end, we randomly drew 100 gene
sets of the same size (n = 359) from the pool of genes
covered by the array and checked them against the Alz-
Gene set. We found an average of 9 genes per randomly
drawn gene set and used this as reference for Fisher’s
exact test. However, no significant enrichment of genes
from the AlzGene dataset was present in WT/APP-/-.
Proposed AICD target genes show only a minor or no
significant differential expression in APP- and APLP2-
deficient cortex
Several genes have been proposed to be directly regu-
lated at the transcriptional/promoter level by an AICD/
FE65 transcriptionally active complex including Bace1
[14], Kai1 [7], Egfr [11], Gsk3b [8,9], p53 [12], Tip60
[14], and Vglut2 [37]. By array analysis we detected in
APLP2-/-mice for Vglut2 and Gsk3b a small yet signifi-
cant up-regulation of 1.27-fold and 1.2-fold, respectively,
compared to wild-type animals (Additional File 2). In all
other genotypes, including APP-/-, expression was, how-
ever, not significantly altered. To further validate these
results we conducted a qPCR analysis of these proposed
target genes (Figure 3). This way, we detected a 1.6-fold
Table 1 Overview over differentially expressed genes (R6
animals)
total updown
FC≥2no FCCFC≥2no FCCFC≥2no FCC
WT/APP-/-
WT/APLP2-/-
WT/APPa/a
APPa/a/APP-/-
11
359
2
274
9
85
11
1242
6
1142
5
100
15
447
2
250
13
197
2
29
2
29
0
0
The number of significant genes is displayed for all relevant comparisons if at
least one probe set identifier of the gene meets the respective criteria. Note
that probe sets for App and Aplp2 are not part of the indicated numbers.
Numbers indicate significant genes either with fold change criterion (FC≥2,
highlighted in bold) or without fold change criterion (no FCC). Note that all
animals had been backcrossed for 6 generations to C57BL/6 (R6).
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 4 of 17

Page 5

increase of Egfr mRNA expression solely in APLP2-/-
animals, compared to wild type controls (p < 0.05). Of
note, qPCR analysis of APP-/-, APPa/aand APLP2-/-cor-
tex failed to detect significant expression differences of
all other tested candidate genes, including Vglut2 and
Gsk3b.
What might be the reasons that proposed target genes
have proven difficult to confirm in follow-up studies
including work reported here? A major reason may be
the difference in experimental systems used as overex-
pression in cell lines may not necessarily reflect a role
of AICD for endogenous gene expression. In line with
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
enrichment score
regulation of neurogenesis
positive regulation of transcription
regulation of neuron projection development
negative regulation of kinase activity
negative regulation of transcription
regulation of protein kinase activity
negative regulation of protein kinase activity
regulation of phosphorylation
regulation of neurogenesis
neurofilament cytoskeleton organization
RNA splicing, via transesterification reactions
regulation of neurogenesis
negative regulation of apoptosis
negative regulation of transcription
cerebellar Purkinje cell differentiation
?/?
WT/APP
WT/APLP2
-/-
WT/APP
-/-
Figure 2 Functional annotation clustering. The five most enriched clusters in the comparisons WT/APP-/-, WT/APPa/a, WT/APLP2-/-including
their respective enrichment score are shown. The name of one gene group out of each cluster was taken to represent the complete cluster.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Egfr
Bace1 Tip60p53Vglut2
Kai1
Gsk3b
relative mRNA expression
*
WT
APP
APP
APLP2
?/?
-/-
-/-
Figure 3 qPCR analysis of indicated target genes. mRNA expression was measured and displayed relative to wild-type level set as one. Values
represent means ± SEM of 3 mice/genotype. (Student’s t-test: *, p-value < 0.05).
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 5 of 17

Page 6

this study, we previously found no impact on Kai1,
Gsk3a, Gsk3b, App, and Nep mRNA expression when
treating different cell lines with the g-secretase inhibi-
tor DAPT or when assessing endogenous gene expres-
sion in AICD-deficient model systems [16]. In
addition, as shown by the same study, clonal variability
of immortalized fibroblast lines may lead to variable
gene expression irrespective of either APP-/-or APP+/+
genotype [16]. On the other hand, one might expect
an inverse regulation of target genes upon AICD defi-
ciency as opposed to overexpression. A genome-wide
microarray-based approach to detect AICD target
genes used an inducible FE65/AICD cell line [15].
Here, no change in Kai1 and Gsk3b mRNA expression
was detected. Similarly, Waldron et al. [22] found no
alteration in mRNA expression of Kai1, Bace1, Egfr,
Tip60, and p53 in AICD-enriched FE65-tranfected
cells. Moreover, transcriptome analysis in AICD trans-
genic mouse brain revealed no apparent difference
between transgenic animals and littermate controls
[20] and qPCR analysis of proposed target genes,
including those studied here, failed to detect significant
changes in mRNA expression. Overall, our results are
highly consistent with these studies. Although our
study clearly indicates that AICD or ALID2 are on
their own not essential transcriptional regulators of
tested target genes in adult prefrontal cortex, we can-
not exclude at present that other APP family members
(including APLP1) may at least partially compensate
for a single gene deficiency. Due to the lethality of
combined mutants shortly after birth we had pre-
viously analyzed the expression of a subset of target
genes in APP-/-APLP2-/-embryonic brain and fibro-
blasts [16]. As APLP1 is not expressed in fibroblasts,
APP-/-APLP2-/-fibroblasts (compared to APP retrans-
fected cells) provide a cellular model in which all APP
family members are lacking. However, neither Nep nor
Gsk3b expression was significantly affected in either
embryonic brain or fibroblasts [16]. A global assess-
ment of transcriptome changes in adult brain lacking
multiple APP family members (a tissue more relevant
for AD) will await the generation of viable conditional
mutants. Considering the complexity of cortical tissue,
it is still possible that gene expression differences
occurring only in distinct cell types may remain below
the detection limit of our analysis. In line with this
hypothesis, Schrenk-Siemens et al [37] reported a
reduction of Vglut2 mRNA and VGLUT2 protein
expression in glutamatergic neurons obtained by reti-
noic acid differentiation of APP-/-APLP2-/-embryonic
stem cells whereas in this study no difference was
detectable in cortical tissue. It remains to be seen
whether regulation of other target genes might also be
cell type-specific.
Genes co-regulated due to the lack of either APP or
APLP2
As there is genetic [24,25] and cell biological evidence
(such as a shared set of protein interaction partners
[38]) that APP family members serve related physiologi-
cal functions, we searched for genes that are, compared
to wild-type, differentially regulated in more than one
genotype (Figure 4a). To this end, we created a Venn
diagram of the pairwise comparisons WT/APP-/-and
WT/APLP2-/-. We found 213 probe set identifiers
(Additional file 4) representing 181 known genes that
are differentially expressed in both cases (Figure 4b) and
regulated in the same direction as shown by cluster ana-
lysis (Figure 4c). Furthermore, we analyzed how many of
these genes were in addition significantly regulated in
WT/APPa/a. Out of 181 genes co-regulated by lack of
either APP or APLP2, 97 were also found in the com-
parison WT/APPa/aand regulated in the same direction
(Figure 4d, Additional file 4). Thus, functional similari-
ties of APP family members are also reflected at the
transcriptional level by co-regulated gene sets in the
respective loss-of-function mutants. To investigate this
more closely we further examined two of these genes by
qPCR analysis: heat shock protein 5 (Hspa5) and cyclin-
dependent kinase inhibitor 1A (Cdkn1a).
Hspa5 attracted our attention for two reasons.
Amongst co-regulated genes we found a consistent
down-regulation of four heat-shock proteins including
besides Hspa5, Hspa1b, Hspb1, and Hsph1. HSPA5 (also
known as GRP78) is an ER chaperone involved in the
ER stress response and had previously been shown to
interact with APP and modulate Ab production [39]. In
addition, GRP78 was recently identified as a gene that
may counteract the proliferative effect of secreted APPs
in tumor models [40]. As functional annotation cluster-
ing identified neurogenesis as a pathway affected in all
genotypes compared to wild-type, it was interesting to
find Cdkn1a, also known as p21, amongst these co-regu-
lated genes. Based on array analysis, both Cdkn1a and
Hspa5 were down-regulated by about two-fold in all
three mutant genotypes compared to WT (Figure 5a).
Employing qPCR we confirmed these results and found
again a significant down-regulation of about the same
magnitude (Figure 5b).
It is noteworthy that in APP-overexpressing transgenic
mice Hspa5 had previously been found to be up-regu-
lated [41] suggesting an inverse transcriptional regula-
tion as a consequence of either loss or gain of APP-
dependent signaling. The CDK inhibitor p21 has been
shown to restrict adult neurogenesis in the hippocam-
pus, as evidenced by increased proliferation of neuronal
progenitors in p21-/-mice [42]. Given the Cdkn1a/p21
down-regulation we found here, one might thus expect
APP-/-(or APLP2-/-) mice to show dysregulated
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 6 of 17

Page 7

neurogenesis. On the other hand, we had previously
shown that endogenous APPs and APLP2s play a crucial
role as growth factors for neuronal stem cells in the
adult subventricular zone (SVZ) [35]. Depletion of
APPsa by infusion of APP-binding antibodies or as a
consequence of pharmacological inhibition of APPsa
production reduced the number of neuronal progenitor
cells in the SVZ [35]. Thus neurogenesis might be
under complex control of APP-mediated signaling path-
ways, both by membrane-anchored APP and secreted
APP isoforms.
Role of APP domains for transcriptome changes
Next, we compared the transcriptome of APP-/-mice to
that of APPa/aanimals (Figure 6a). Specifically, we
wanted to answer the question of whether the transcrip-
tome of APPa/amice would be more similar to that of
WT mice (which would indicate that APP-FL and/or
the APP C-terminus is of minor importance), or would
rather resemble that of APP-/-cortices (which would
indicate an important role of APP-FL and/or APP C-
terminal fragments for signaling). As APPsa is sufficient
to rescue the learning impairment and LTP defect of
-/-
APP
?/?
APP
WT
-/-
APLP2(R1)
-/-
APLP2
-/-
W
P
T/A P
?/?
W
P
T/A P
-
-/
WT/APLP2
-2
-1
0
1
2
-/-
APLP2 -1
-/-
APLP2 -2
-/-
APLP2 -3
WT-2
WT-1
WT-3
-/-
APP -2
-/-
APP -1
-/-
APP -3
-/-
WT/APLP2
-/-
WT/APP
(b)(a)
(c)
1061178181
color key: normalized expression value
-2
-1
0
1
2
-/-
APLP2 -1
-/-
APLP2 -2
-/-
APLP2 -3
WT-2
WT-1
WT-3
?/?
APP -1
?/?
APP -2
?/?
APP -3
-/-
APP -2
-/-
APP -1
-/-
APP -3
(d)
color key: normalized expression value
Figure 4 Co-regulation of genes in WT/APP-/-and WT/APLP2-/-. (a) Overview of the analyzed comparisons. (b) Venn diagram of the two
comparisons WT/APP-/-and WT/APLP2-/-based on gene lists obtained by significance analysis. The number of significant genes is indicated in
the respective segments. Note that 181 genes are co-regulated by the lack of either APP or APLP2. (c) Heatmap of the 213 identifiers
corresponding to the set of 181 genes that are co-regulated in both pairwise comparisons. All identifiers show differential expression in the
same direction. (d) Heatmap of 122 identifiers corresponding to the set of 97 genes that are found in both pairwise comparisons as well as in
WT/APPa/a. The values of the heatmaps (c,d) are normalized expression values with red and blue color representing the number of standard
standard deviations above or below the mean expression for each probe set, respectively.
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 7 of 17

Page 8

APP-/-mice [28], we initially inferred that this would also
apply for the transcriptome of APPa/amice. However, in
the significance analysis, APPa/a/APP-/-was the compari-
son with the lowest number of significant genes whereas
the number of significant differentially expressed genes
for WT/APP-/-and WT/APPa/awas at least 10 times
higher (see Table 1) indicating a close resemblance of
APPa/aand APP-/-samples. Next, we generated a Venn
diagram to gain an overview on the absolute number of
significant genes found in each intersection (Figure 6b,
Additional file 5). A high proportion of genes significant
in WT/APPa/awas also significant in WT/APP-/-leading
to a subset of 169 co-regulated genes. Second, to get a
more quantitative understanding on this part of the data-
set, we calculated the percentage of probe sets that were
co-regulated between the different pairwise comparisons.
To this end, we ranked all probe sets according to their
absolute significance score and used the most significant
200 probe sets of each of these pairwise comparisons to
generate the Venn diagram (Figure 6c). WT/APP-/-and
WT/APPa/ahave 40% of the 200 probe sets in common
whereas WT/APP-/-and APPa/a/APP-/-share only 7 out
of 200 probe sets (Figure 6c). Taken together these
results suggest that with regard to transcriptional
changes APPa/ais functionally very similar to a complete
APP knockout. These data may indicate an important
role of full length APP and/or APP C-terminal fragments
for direct or indirect signaling resulting in transcriptome
changes.
In addition, we raised the question whether the constitu-
tive expression of APPsa would lead to changes in gene
expression, as recently reported for APPsb [43]. We found
a small percentage (8.5%) of probe sets in the intersection
of APPa/a/APP-/-and WT/APPa/a(Figure 6c). To investi-
gate this finding further, we analyzed the 6 significant
genes that are found within this group (Figure 6b) and
used their corresponding 8 probe sets for a cluster analysis
(Figure 6d). Hierarchical clustering results in a clear
separation of APPa/afrom WT and APP-/-cortices. This
points to a small subpopulation of genes that are actually
regulated by the constant production of APPsa in the
absence of APP full length and all other fragments.
Next, we took a closer look at genes co-regulated by
the complete absence of APP or APP-FL as identified in
the intersection of WT/APP-/-and WT/APPa/a(Figure
6b). Interestingly, this gene set comprises several synap-
tic plasticity-related genes including the immediate early
response factors Arc (activity-regulated cytoskeleton-
associated protein), Fos (FBJ osteosarcoma oncogene),
Egr2 (early growth response 2), and Dio2 (deiodinase,
iodothyronine, type II), a key enzyme in the biosynthesis
of the nuclear hormone Triiodothyronine (T3). Valida-
tion of these genes by qPCR (Figure 7) consistently
identified an about 1.5- to 2-fold down-regulation in all
genotypes and thus confirmed gene expression changes
identified by array analysis. In case of Dio2, down-regu-
lation in APLP2-/-now reached significance level when
using qPCR analysis as compared to array values.
1416064_a_at 1427464_s_at
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
relative mRNA expression
1421679_a_at 1424638_at
Hspa5
Cdkn1a
*
*
***
*
*
***
*
*
***
*
*
*
WT
APP
?/?
-/-
APP
APLP2
-/-
Hspa5Cdkn1a
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
relative mRNA expression
****
*
**
*
(a)(b)
ArrayqPCR
Figure 5 Relative mRNA expression of selected genes co-regulated in all comparisons. (a) Relative mRNA expression obtained by array
analysis. Values of indicated probe set identifiers were compared to WT levels. Values represent relative expression value ± relative standard
deviation. Differences were tested for significance by SAM (*, q-value < 0.05; **, q-value < 0.01; ***, q-value < 0.001). (b) The corresponding
mRNA expression was measured by qPCR and displayed relative to wild-type level set as one. Values represent means ± SEM of 3 animals per
group. (Student’s t-test: *, p-value < 0.05; **, p-value < 0.01; ***, p-value < 0.001).
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 8 of 17

Page 9

Arc mRNA accumulates in activated synapses, modu-
lates AMPAR trafficking and is critically involved in
memory consolidation and LTP [44]. Both FOS, best
known for its binding to the Jun/AP-1 transcription fac-
tor complex, and the Zn2+-finger transcription factor
EGR2/KROX-20 are induced during neuronal activity
[45,46] and play an important role in learning and
memory as well as LTP [46-48]. As APP-/-mice show
an age-dependent deficit in spatial learning associated
with impaired long-term potentiation (LTP), it was intri-
guing that we found a down-regulation of genes
previously implicated in synaptic plasticity although
further studies are needed to establish a causal link.
Influence of genetic background on gene transcription
Genetic background is known to profoundly influence the
occurrence, penetrance and severity of transgenic and
knockout phenotypes, e.g. with regard to behavior or Ab
deposition [49,50]. For one of our mutants, APLP2-/-, we
had kept animals that had been backcrossed only once to
C57BL/6 (designated APLP2(R1)-/-), whereas all other
mutants had been backcrossed for six generations
-/-
APP
?/?
APP
WT
-/-
APLP2(R1)
-/-
APLP2
?/?
-/-
APP /APP
/-
-
WT/APP
?/?
WT/APP
(a)
(c)
?/?
-/-
APP /APP
-/-
WT/APP
?/?
WT/APP
88% 3.5%56%
0.5%
8%40%
51.5%
(b)
?/?
-/-
APP /APP
-/-
WT/APP
?/?
WT/APP
19 3186
1
6 169
271
-1.5
-1
0
1
1.5
WT-2
WT-1
WT-3
?/?
APP -1
?/?
APP -2
?/?
APP -3
-/-
APP -2
-/-
APP -1
-/-
APP -3
(d)
0.5
-0.5
color key: normalized expression value
Figure 6 Analysis of common genetic profiles in WT/APP-/-, WT/APPa/aand APPa/a/APP-/-. (a) Overview of the analyzed comparisons. (b)
Venn diagram with SAM-based gene lists of the three comparisons WT/APP-/-, WT/APPa/aand APPa/a/APP-/-. The numbers indicate the absolute
number of differentially expressed genes in the respective comparison. (c) Percentage of probe set identifiers co-regulated in two or three
pairwise comparisons. Values are based on the 200 most significantly differentially expressed identifiers for each pairwise comparison. Note that
40% of probe sets are found in the overlap between WT/APP-/-and WT/APPa/a. (d) Heatmap of the probe set identifiers corresponding to the
set of 6 genes (b) in the intersection of WT/APPa/a, APPa/a/APP-/-. The values of the heatmap are normalized expression values with red and
blue color representing the number of standard standard deviations above or below the mean expression for each probe set, respectively.
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 9 of 17

Page 10

(designated R6). This allowed us to investigate the impact
of genetic background on transcriptome changes versus
changes that arise as a consequence of APLP2 gene defi-
ciency (Figure 8a). To make this more clear for the reader,
we will for the remaining study use the designation R6
and R1 for all subsequent comparisons (please note how-
ever that in figures 1234567 all animals were also of R6
genetic background).
The comparison APLP2(R6)-/-/APLP2(R1)-/-, i.e.
between animals of the same genotype but with different
genetic background, led to 144 significant differentially
expressed genes of which 50 were up- and 94 were
down-regulated (Table 2, Additional file 6). For the
comparison WT(R6)/APLP2(R1)-/-(animals with both
different genotype and different genetic background) we
found a total of 242 genes, 157 up-, 85 down-regulated
Arc Egr2FosDio2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
relative mRNA expression
**
**
*
*
*
***
**
**
(b)
1418687_at
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
e
r
relativ mRNA exp ession
1418937_at 1418938_at 1426081_a_at 1427682_a_at 1427683_at
Arc
Dio2Egr2
WT
APP
APP
APLP2
?/?
-/-
-/-
1423100_at
Fos
(a)
Array
qPCR
***
***
*
*
***
***
***
***
***
*
****
*
Figure 7 Relative mRNA expression of selected genes co-regulated in WT/APP-/-and WT/APPa/a. (a) Relative mRNA expression level
obtained by array analysis. Values of indicated probe set identifiers/genes were compared to WT levels. Values represent relative expression
value ± relative standard deviation. Differences were tested for significance by SAM (*, q-value < 0.05; **, q-value < 0.01; ***, q-value < 0.001). (b)
The corresponding mRNA expression was measured by qPCR and displayed relative to wild-type level set as one. Values represent means ± SEM
of 3 animals per group. (Student’s t-test: *, p-value < 0.05; **, p-value < 0.01; ***, p-value < 0.001).
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 10 of 17

Page 11

(Table 2, Additional file 6). The sizes of these two sets are
more than 5-fold smaller than the 1242 genes found for
WT(R6)/APLP2(R6)-/-(Table 2, Additional file 6). How-
ever, genetic background-related probe sets differentially
regulated between APLP2(R6)-/-versus APLP2(R1)-/-
have very high significance scores due to high gene
expression changes (Additional file 6). This considerable
influence of genetic background is best reflected in the
corresponding volcano plot (Figure 9) yielding, when
compared to WT(R6)/APLP2(R6)-/-(Figure 9b), a much
larger number of probe sets with both high significance
score (arbitrarily set to 6) and high fold change in the
comparison APLP2(R6)-/-/APLP2(R1)-/-(Figure 9a). Of
note, only three probe sets matching these criteria were
-/-
APP(R6)
?/?
APP(R6)
WT(R6)
WT(R6)/APLP2(R6)
-/-
APLP2(R1)
-/-
APLP2(R6)
-/-
WT(R6)/APLP2(R1)
-/-
APLP2(R6) /APLP2(R1)
-/-
-/-
-/-
WT(R6)/APLP2(R6)
-/-
WT(R6)/APLP2(R1)
-/--/-
APLP2(R6) /APLP2(R1)
(b)(a)
1160 50 109
5
2778
34
(c)
-/-
WT(R6)/APLP2(R6)
-/-
WT(R6)/APLP2(R1)
-/- -/-
APLP2(R6) /APLP2(R1)
87%3%49.5%
0%
10%47.5%
42.5%
-1.0
0.0
1.0
1.5
-/-
APLP2(R6) -1
-/-
APLP2(R6) -2
-/-
APLP2(R6) -3
WT(R6)-2
WT(R6)-1
WT(R6)-3
(d)
color key: normalized expression value
0.5
-0.5
-1.5
-/-
APLP2(R1) -1
-/-
APLP2(R1) -2
-/-
APLP2(R1) -3
Figure 8 Co-regulation of genes in WT(R6)/APLP2(R6)-/-, WT(R6)/APLP2(R1)-/-, APLP2(R6)-/-/APLP2(R1)-/-. (a) Overview of the analyzed
comparisons. (b) Percentage of probe set identifiers co-regulated in two or three pairwise comparisons. Values are based on the 200 most
significantly differentially expressed identifiers for each pairwise comparison. (c) Venn diagram with SAM-based gene lists of the three
comparisons WT(R6)/APLP2(R6)-/-, WT(R6)/APLP2(R1)-/-and APLP2(R6)-/-/APLP2(R1)-/-. (d) Cluster analysis of probe sets corresponding to the set of
27 genes from (c) found exclusively in the comparisons WT(R6)/APLP2(R6)-/-and APLP2(R6)-/-/APLP2(R1)-/-. The values of the heatmap are
normalized expression values with red and blue color representing the number of standard standard deviations above or below the mean
expression for each probe set, respectively.
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 11 of 17

Page 12

found for the comparison WT(R6)/APLP2(R6)-/-(Figure
9b). Similarly, this also holds true for any of the other
pairwise comparisons for which genetic background was
kept constant (data not shown).
To study the impact of genetic background more clo-
sely, we calculated the percentages of overlap from the
most significant 200 probe sets for each of the three
comparisons (Figure 8b). If transcriptome changes arise
primarily as a consequence of APLP2 deficiency inde-
pendent of genetic background, we would expect a high
number of co-regulated probe sets in the comparisons
WT(R6)/APLP2(R6)-/-and WT(R6)/APLP2(R1)-/-. The
percentage of probe sets found in this intersection is,
however, surprisingly small (3%). Contrary to our expec-
tation, the highest overlap of probe sets (47.5%) is found
in the intersection of WT(R6)/APLP2(R1)-/-and APLP2
(R6)-/-/APLP2(R1)-/-(Figure 8b).
To gain an overview on absolute number of differen-
tially expressed genes, we created a Venn diagram from
the pairwise comparisons WT(R6)/APLP2(R6)-/-, WT
(R6)/APLP2(R1)-/-, and APLP2(R6)-/-/APLP2(R1)-/-(Fig-
ure 8c, Additional file 7). Interestingly, we found a set
of 27 significant genes in the intersection of WT(R6)/
APLP2(R6)-/-and APLP2(R6)-/-/APLP2(R1)-/-(Figure
8c). The corresponding cluster analysis shows that
APLP2(R1)-/-samples cluster together with WT(R6)
samples while APLP2(R6)-/-samples were clearly sepa-
rated (Figure 8d). Probe sets in this intersection repre-
sent genes that are differentially expressed due to
APLP2 deficiency but only in combination with an R6
background. In summary, these findings clearly indicate
that genetic background may dominate transcriptome
changes and needs to be carefully controlled to establish
a clear link between phenotypes and altered genotype.
Eventually, we were interested to identify genes that
are highly differentially expressed in our knockout mod-
els compared to wild-type. Ccl21 (chemokine (C-C
motif) ligand 21) was the gene with the highest fold
change in combination with the highest significance
score and exclusively up-regulated in APLP2-/-cortices
on R6 genetic background (Figure 10a), that is preferred
in behavioral studies and to which we had therefore
backcrossed our mutants. In the periphery, CCL21
serves as ligand for CCR7 that is expressed by various
cells of the immune systems and is involved in lympho-
cytes homing (reviewed in [51]). Recent studies show
that CCL21 is also expressed in the CNS by endangered
neurons to activate microglia via CXCR3 [52,53]. CCL21
is transported in vesicles along the axon to presynaptic
structures and thereby constitutes a mediator of directed
neuron-microglia signaling and remote microglia activa-
tion [54]. As microglia activation is associated with AD
pathogenesis [55] and is also frequently observed as a
general indicator of brain damage, we investigated
CCL21 expression in more detail. Moreover, we sought
to validate the genetic background dependence of
Table 2 Overview over differentially expressed genes:
impact of genetic background
totalup down
FC≥2 no
FCC
FC≥2no
FCC
FC≥2no
FCC
WT(R6)/APLP2(R6)-/-
WT(R6)/APLP2(R1)-/-
APLP2(R6)-/-/APLP2
(R1)-/-
11
1242
6
1142
5
100
35
242
19
157
16
85
40
144
19
50
21
94
The number of significant genes is displayed for all relevant comparisons if at
least one probe set identifier of the gene meets the respective criteria. Note
that probe sets for App and Aplp2 are not part of the indicated numbers.
Numbers indicate significant genes either with fold change criterion (FC≥2,
highlighted in bold) or without fold change criterion (no FCC). R6:
backcrossed to C57BL/6 for 6 generations. R1: backcrossed to C57BL/6 for 1
generation. Note that the comparison WT(R6)/APLP2(R6)-/-is identical to WT/
APLP2-/-.
Figure 9 Volcano plot of APLP2(R6)-/-/APLP2(R1)-/-and WT(R6)/APLP2(R6)-/-. The absolute value of each probe set identifier’s significance
score was plotted against the corresponding log2-transformed fold change of (a) APLP2(R6)-/-/APLP2(R1)-/-and (b) WT(R6)/APLP2(R6)-/-. The red
line was set arbitrarily to a score value of 6 to highlight the difference between the two Volcano plots. (APLP2-specific data points were
removed prior to plotting).
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 12 of 17

Page 13

CCL21 expression. Using qPCR analysis, we confirmed
the significant up-regulation of Ccl21 mRNA yielding a
fold change in qPCR (33-fold up-regulation) that was
even more pronounced compared to array analysis (4-
fold up-regulation) (Figure 10b). Importantly and consis-
tent with our array analysis, no up-regulation of Ccl21
mRNA expression was found in APLP2(R1)-/-samples.
This indicates that i) other loci distinct from APLP2 are
involved in Ccl21 transcriptional regulation and that ii)
these loci give rise to allelic variants that differ function-
ally between the R6 and R1 genetic background.
Encouraged by the high increase of Ccl21 mRNA
expression, we determined CCL21 protein expression by
ELISA in cortical tissue of APLP2(R6)-/-animals and
wild-type controls. CCL21 protein expression was signif-
icantly increased by about 1.7-fold (Figure 11a). As this
magnitude of up-regulation was lower than expected
from qPCR (Figure 10b), we also measured mRNA levels
of Ccl21 in the same brain samples used for the ELISA
measurement and reconfirmed the high up-regulation of
about 33-fold (Figure 11b). This difference in differential
mRNA (33-fold) and protein (1.7-fold) expression sug-
gests that additional posttranslational mechanisms
(including e.g. protein stability and turnover) limit
CCL21 expression. In line with the moderate induction
of CCL21 protein expression we did not detect an
increase in gliosis in APLP2-deficient brains using
immunohistochemistry (data not shown).
Conclusions
Here, we determined the effect of APP-/-, APPa/aand
APLP2-/-genotypes on gene expression in the adult
murine cortex. We found large sets of differentially
expressed genes, however, fold changes were in most
cases only small to moderate. Previously proposed
AICD target genes were not convincingly affected by
lack of either APP or APLP2 (and thus lack of AICD
and ALID) in the complex cortical tissue of adult brain.
This may either indicate that the role of AICD in tran-
scriptional regulation has been overestimated or that
gene expression changes occur only in a distinct subset
of cells that is below the detection level of our analysis.
Remarkably, we found the largest set of differentially
expressed genes in APLP2-/-brain, although so far no
apparent morphological or other phenotypic changes
had been reported for APLP2-KO mice. A substantial
proportion of genes were identified as co-regulated by
lack of APP or APLP2, notably in pathways such as neu-
ronal differentiation, neurogenesis and transcriptional
regulation. This common genetic profile points towards
shared physiological functions in these pathways. When
comparing APPsa knockin mice and APP-/-mice we
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
r
e
R A expr ss
elativ m N
e
ion
0
1
2
30
35
40
WT(R6)
?/?
APP(R6)
APP(R6)
-/-
-/-
APLP2(R6)
APLP2(R1)
-/-
r
e
R A expr ss
elativ m N
e
ion
n.s.
n.s.
n.s.
***
n.s.
n.s.
n.s.
***
(a)(b)
Array qPCR
Figure 10 Relative mRNA expression of Ccl21. (a) Array-based
analysis of Ccl21 mRNA expression normalized to wild-type level.
Values represent relative expression value ± relative standard
deviation. Significance was tested by SAM (*, q-value < 0.05; **, q-
value < 0.01; ***, q-value < 0.001). (b) qPCR analysis of Ccl21 mRNA
expression displayed relative to wild-type level set as one. Values
represent means ± SEM. (Student’s t-test,*, p-value < 0.05; **, p-
value < 0.01; ***, p-value < 0.001).
0
50
100
150
200
250
300
350
0
5
10
15
20
25
30
35
40
relative mRNA expression
CCL21 concentration (pg/mg tissue)
(b)(a)
**
***
WT(R6)
-/-
APLP2(R6)
ELISAqPCR
Figure 11 Analysis of CCL21 protein and Ccl21 mRNA
expression. (a) CCL21 protein expression was determined by ELISA
in either WT(R6) (n = 3) or APLP2(R6)-/-(n = 4) brain tissue from one
hemisphere comprising cortex, hippocampus and olfactory bulb. (b)
qPCR analysis of Ccl21 mRNA expression using brain tissue from the
contralateral side of the same animals (identical brain regions as in
(a). (Student’s t-test;*, p-value < 0.05; **, p-value < 0.01; ***, p-value
< 0.001).
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 13 of 17

Page 14

observed a close resemblance of the two genotypes
pointing towards a crucial role of the APP C-terminus
for transcriptome changes. Interestingly, we could
demonstrate that several synaptic plasticity-related genes
found in this gene set are considerably down-regulated
which further substantiates the importance of APP
family members in this regard.
Finally, we addressed the role of genetic background for
transcriptome changes. Here, we report that the presence
of different WT-alleles can lead to profound changes in
gene expression that are even higher in magnitude than
those resulting from the knockout of a single gene such as
APLP2. Thus, it is crucial to keep genetic background con-
stant, particularly if gene expression changes are rather
subtle to reliably correlate affected pathways (and physio-
logical functions inferred from them) with a knockout
phenotype. In many studies regarding AICD signaling this
issue has not been addressed which may at least partially
explain the conflicting results reported by different labora-
tories. Here, we identified the chemokine Ccl21 as a gene
that is highly up-regulated in APLP2-/-cortex, but only in
conjunction with C57BL/6-specific background alleles.
Moreover, our study corroborates that APP family mem-
bers are not only structurally related but also serve related
physiological functions. It will therefore be of high interest
to analyze phenotypic and gene expression changes in
adult APP/APLP2 double or APP/APLP1/APLP2 triple
deficient brain, once viable conditional combined mutants
become available that are currently generated by crossing
mice with floxed APP and APLP2 alleles with transgenic
tissue-specific Cre mice [56].
Methods
Data
Raw and processed data discussed in this publication
have been deposited in the NCBI’s Gene Expression
Omnibus database (GEO) and are accessible through
GEO Series accession number GSE25926 (http://www.
ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25926).
Animals
APP-/-, APLP2-/-, APPa/aanimals were previously
described [28,57,58]. All animals were kept under speci-
fic pathogen free housing conditions (SPF unit) and in
compliance with the regulations of the German animal
protection law. For transcriptome analysis, animals had
been backcrossed to C57BL/6 wild-type animals for 6
generations (R6) before they were interbred to homo-
zygosity. All animals were adult males (24-28 weeks)
and not challenged with any cognitive or stress tasks.
RNA preparation and microarray data generation
Animals were sacrificed by cervical dislocation. Mouse
brains were dissected and stored in RNAlater (Qiagen)
at -20°C. Subsequently, the prefrontal cortex was cut
out and used for total RNA preparation (RNAeasy kit,
Qiagen). Quality of RNA was assessed with a spectro-
photometer and Bioanalyzer (Agilent). 1 μg of total
RNA was used for cDNA preparation (Oligo(dT)
method, Invitrogen). Subsequent cRNA was prepared
with Affymetrix One-Cycle Target Labeling and Control
Reagent kit (Affymetrix Inc., Santa Clara, California,
USA). The biotinylated cRNA was hybridized onto Gen-
eChip Mouse Genome 430 2.0 Arrays (Affymetrix, Santa
Clara). Chips were washed and scanned on the Affyme-
trix Complete GeneChip®Instrument System generating
digitized image data files.
Statistical analysis
If not stated otherwise, data analysis and processing
was carried out within the statistical computing envir-
onment R, version 2.8.0, using Bioconductor, BioC
Release 2.4 [59]. Raw data was processed with the
RMA algorithm (Robust Multiarray Average) developed
by Irizarry et al. [29] and normalized using quantile
normalization [30].
Hierarchical clustering was carried out using Eucli-
dean distances to calculate the distances between the
genes and between the sample groups. Calculated dis-
tances were clustered by complete linkage clustering.
Expression values for each probe set were normalized to
zero mean and unit variance. The values shown thus
represent the number of standard deviations above or
below the mean expression for each gene. Calculated
expression differences for each probe set can be found
in the respective additional file.
Significant differentially expressed probe sets between
two groups were detected by a Significance Analysis of
Microarrays (SAM) [60]. As a cut-off value for signifi-
cance, we set the false discovery rate (FDR) to 5.33%
(WT/APP-/-), 4.96% (WT/APLP2-/-), 4.5% (WT/APPa/a),
4.79% (APPa/a/APP-/-), 5.02% (WT(R6)/APLP2(R1)-/-),
and 5.11% (APLP2(R6)-/-/APLP2(R1)-/-).
For counting significant differentially expressed genes,
probe set identifiers were mapped to Entrez Gene iden-
tifiers. If at least one probe set was significant in the
SAM, the gene was regarded to be significant as well. If
no gene information (Entrez ID) was available for a cer-
tain probe set, the probe set was not counted.
For group testing (GO terms, pathways) DAVID bioin-
formatics resources was used [61]. Gene symbols from
each list were taken as input, and redundant entries
were discarded. The following gene sets were included
into the analysis: GOTERM_BP_FAT (Gene Ontology),
Biocarta (Pathways), KEGG_PATHWAY, PANTHER_-
PATHWAY, REACTOME_PATHWAY. Functional
annotation clustering was carried out using the highest
classification stringency.
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 14 of 17

Page 15

Quantitative real-time PCR (qPCR)
Total RNA was prepared using High Capacity cDNA kit
based on random hexamer primer method (Applied Bio-
systems). For each qPCR reaction 20ng of total RNA
were reverse transcribed into cDNA. qPCR was per-
formed using FAM™-MGB dye labeled TaqMan®Gene
Expression Assays (Applied Biosystems) for Bace1 (assay
Mm00478664_m1), Kai1 (assay Mm00492061_m1), Egfr
(assayMm01187858_m1),
Mm00444911_m1), p53 (assay Mm01731290_g1), Tip60
(assay Mm00724374_m1),
Mm00499876_m1), Hspa5 (assay Mm00517690_g1),
Cdkn1a/p21 (assay Mm01303209_m1), Arc (assay
Mm00479619_g1), Fos (assay Mm00487426_g1), Egr2
(assayMm00456650_m1),
Mm00515664_m1), Ccl21 (assay Mm03646971_gH) and
beta-Actin as an internal standard (assay 4352933E).
Quantification of qPCR results were evaluated by the 2-
ΔΔCTmethod and normalized to wild-type animals. Sig-
nificance was calculated using unpaired Student’s t-test
(*, p < 0.05; **, p < 0.01; ***, p < 0.001).
Gsk3b (assay
Vglut2 (assay
Dio2(assay
CCL21 ELISA measurements
Brain homogenates for ELISA were generated as
described before [62]. Briefly brains were homogenized
in lysis buffer (100mM phosphate, pH 7.4, 1mM EDTA,
supplemented with complete protease inhibitor cocktail
(Roche, Germany) using a PotterS homogenizer (Sartor-
ius, Germany), followed by centrifugation at 1,500 ×g
for 10 min. Supernatants were directly used for ELISA
determinations.
ELISA measurements were performed using a mouse
CCL21/6Ckine kit (R & D Systems Inc., MN) according
to the manufacturer’s protocol with slight modifications.
Briefly, the standard curve was performed in a concen-
tration range of 0 - 5000 pg/ml, the antibodies were
used in the dilutions suggested by the protocol, except
for the HRP-streptavidine conjugate, which was diluted
1:100. As a substrate one-step TMB-ELISA (Thermo
Scientific, IL) was used.
Additional material
Additional file 1: Heatmap of the processed dataset. The heatmap
shows the clustering of the processed data by App/Aplp2-specific probe
sets. App and Aplp2 probe sets were taken from the ENSEMBL database
and remapped onto the modified respective genomic loci of APP-/-,
APPa/a, and APLP2-/-animals. Only probe sets that map to exonic
sequences or UTRs were chosen for hierarchical cluster analysis. The first
three probe sets correspond to App probe sets whereas the last five are
Aplp2-specific. The values of the heatmap are normalized expression
values with red and blue color representing the number of standard
standard deviations above or below the mean expression for each probe
set, respectively.
Additional file 2: Lists of significant probe sets (R6 animals)
resulting from the pairwise significance analyses. for each significant
Affymetrix probe set identifier, information about the gene (ENTREZ gene
ID, gene symbol, gene name) and the output from the SAM (score,
numerator, denominator, fold change, q-value) are displayed. Data was
ranked by the absolute test score. WT/APP-/-(= WTAPP), WT/APLP2-/-(=
WTAPLP2), WT/APPa/a(= WTAPPsa), APPa/a/APP-/-(= APPsaAPP).
Additional file 3: Result from the GO term and pathway analysis
using DAVID for WT/APP-/-, WT/APPa/a, WT/APLP2-/-. Annotation
clusters are displayed with their respective enrichment score. For each
annotation cluster, the gene groups (GO terms, pathways) that belong to
the cluster are listed including test statistics and genes in the gene
group that were significant in the respective pairwise comparison (WT/
APP-/-(= WTAPP), WT/APPa/a(WTAPPsa), WT/APLP2-/-(= WTAPLP2)).
Additional file 4: Lists of probe sets in the intersections of WT/
APP-/-, WT/APLP2-/-, and WT/APPa/a. for each significant Affymetrix
probe set identifier, information about the gene (ENTREZ gene ID, gene
symbol, gene name), the output from the SAM (score, numerator,
denominator, fold change, q-value), and the respective pairwise
comparison are displayed for the intersection of WT/APP-/-and WT/
APLP2-/-(= WTAPP & WTAPLP2) as well as the intersection of WT/APP-/-
and WT/APLP2-/-and WT/APPa/a(= WTAPP & WTAPLP2 & WTAPPsa).
Data was ranked by gene symbols.
Additional file 5: Lists of probe set in the intersections of WT/APP-/-,
WT/APPa/a, APPa/a/APP-/-. for each significant Affymetrix probe set
identifier, information about the gene (ENTREZ gene ID, gene symbol,
gene name), the output from the SAM (score, numerator, denominator,
fold change, q-value), and the respective pairwise comparison are
displayed for the intersection of WT/APP-/-and WT/APPa/a(= WTAPP &
WTAPPsa) as well as WT/APP-/-and APPa/a/APP-/-(= WTAPP & APPsaAPP)
and WT/APPa/aand APPa/a/APP-/-(= WTAPPsa & APPsaAPP). Data was
ranked by gene symbols.
Additional file 6: Lists of significant probe sets of animals with
mixed genetic background compared to backcrossed animals.
Results from the pairwise significance analyses: WT(R6)/APLP2(R1)-/-(= WT
(R6)APLP2(R1)), APLP2(R6)-/-/APLP2(R1)-/-(= APLP2(R6)APLP2(R1)). For each
significant Affymetrix probe set identifier, information about the gene
(ENTREZ gene ID, gene symbol, gene name) and the output from the
SAM (score, numerator, denominator, fold change, q-value) are displayed.
Data was ranked by the absolute test score.
Additional file 7: Lists of probe set in the intersections of WT(R6)/
APLP2(R6)-/-, WT(R6)/APLP2(R1)-/-, APLP2(R6)-/-/APLP2(R1)-/-. for each
significant Affymetrix probe set identifier, information about the gene
(ENTREZ gene ID, gene symbol, gene name) and the output from the
SAM (score, numerator, denominator, fold change, q-value), and the
respective pairwise comparison are displayed for the intersection of WT
(R6)/APLP2(R1)-/-and APLP2(R6)-/-/APLP2(R1)-/-(= WTAPLP2(R1) & R6R1),
WT(R6)/APLP2(R6)-/-and APLP2(R6)-/-/APLP2(R1)-/-(= WTAPLP2 & R6R1),
and WT/APLP2-/-and WT/APLP2(R1)-/-(= WTAPLP2 & WTAPLP2(R1)). Data
was ranked by gene symbols.
Acknowledgements
We thank J. Gobbert and M. Saile for excellent technical assistance. This
work was supported by grants from the NGFNplus, Deutsche
Forschungsgemeinschaft (SFB488-D18, MU1457/8-1 and MU1457/9-1) and
the Hans and Ilse Breuer foundation to UCM.
Author details
1Department of Bioinformatics and Functional Genomics, Institute of
Pharmacy and Molecular Biotechnology, Heidelberg University, Im
Neuenheimer Feld 364, D-69120 Heidelberg, Germany.2Medical Research
Center, Medical Faculty Mannheim, Heidelberg University, D-68167
Mannheim, Germany.3Institute for Neuropathology, University Hospital
Freiburg, Freiburg, Germany.4Department of Theoretical Bioinformatics,
German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany.
Authors’ contributions
All authors read and approved the final manuscript. DA performed the
bioinformatics analysis and drafted the manuscript. MAF harvested the
Aydin et al. BMC Genomics 2011, 12:160
http://www.biomedcentral.com/1471-2164/12/160
Page 15 of 17