Comparative transcriptome profiling of amyloid precursor protein family members in the adult cortex
- Dorothee Aydin†1,
- Mikhail A Filippov†1,
- Jakob-Andreas Tschäpe1,
- Norbert Gretz2,
- Marco Prinz3,
- Roland Eils1, 4,
- Benedikt Brors4 and
- Ulrike C Müller1Email author
© Aydin et al; licensee BioMed Central Ltd. 2011
Received: 22 December 2010
Accepted: 24 March 2011
Published: 24 March 2011
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.
Despite its key role in Alzheimer's disease (AD) pathogenesis, the physiological functions of the β-amyloid precursor protein (APP) and its close homologue, the β-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.
Previously, we showed that knockout (KO) mice deficient 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  and APP-/-APLP1-/-APLP2-/- triple mutants  die shortly after birth, likely due to defects of neuromuscular transmission . Neither APP-/- nor APLP2-/- mice display obvious defects of central nervous system (CNS) morphology, yet APP-/- mice revealed reduced body weight and defects in spatial learning associated with impaired synaptic plasticity including long-term potentiation (LTP) . However, the molecular mechanisms underlying these defects have remained unclear.
Processing of APP gives rise to several fragments including besides neurotoxic Aβ the α-secretase-generated soluble APPsα fragment that is neuroprotective and involved in synaptic plasticity [27, 28]. To delineate its specific functions, we previously generated APPsα knockin (APPα/α) mice by inserting via gene targeting a stop codon into the endogenous APP locus right after the α-secretase cleavage site . Thus, APPα/α knockin mice express only secreted APPsα 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 physiological 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 signaling 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 closer look at the role of different APP fragments, in particular APPsα. Therefore, we compared the transcriptome of APPα/α mice both to WT (WT/APPα/α) and APP-/- mice (APPα/α/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), APPα/α (n = 3), APLP2-/- (n = 3), APLP2(R1)-/- (n = 3) (Figure 1c). WT, APP-/-, APPα/α, 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 proteolytic fragments derived from it (APPsα, Aβ, APPsβ, αCTF, βCTF and AICD), whereas APPα/α mice express APPsα but lack full length APP and all other fragments.
Raw data was processed according to the RMA procedure [29, 30]. We validated the microarray data by clustering the processed raw data based on all available App/Aplp2 probe sets. As expected, all samples grouped according to their genotypes: WT, APP-/-, APPα/α, and APLP2-/- samples were clearly separated (Additional file 1).
Differential gene expression in mice lacking APP family members
Overview over differentially expressed genes (R6 animals)
A total of 359 genes (274 up- and 85 down-regulated) were differentially expressed in WT/APP-/-. The comparison WT/APLP2-/- led to 1242 differentially expressed genes (1142 up- and 100 down-regulated). For the comparison WT/APPα/α, we observed 447 significantly regulated genes (250 up- and 197 down-regulated). In contrast, we only observed 29 significant genes in the comparison APPα/α/APP-/- (all of them were up-regulated). Based on the total number of significant 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/APPα/α 15 genes, and in APPα/α/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) alterations in gene expression. This is consistent with previous studies  and likely due to the complex nature of cortical tissue consisting of a multitude of neuronal and glial subpopulations. In APP-/- and APPα/α animals, no compensatory up-regulation of Aplp1 and Aplp2 at the mRNA level was observed. Likewise, no up-regulation of App and Aplp1 was observed in APLP2-/- animals thus confirming previous Western blot results .
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 . Within DAVID, we did Functional Annotation Clustering using Gene Ontology terms (biological processes) and pathway databases (Biocarta, Reactome, Panther, KEGG) to gain an overview about the nature of genes and potential shared functional pathways.
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 neurogenesis in the hippocampus is impaired  which has been mainly attributed to Aβ-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 members in this cellular process, possibly via AICD/ALID signaling or via more indirect mechanisms. Shared functional clusters were also found for WT/APP-/- and WT/APPα/α, namely neurogenesis and negative regulation of protein kinase activity which may indicate that phenotypes 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 Aβ serves as a central trigger for AD pathogenesis, 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 comprising 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 AlzGene 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
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 overexpression in cell lines may not necessarily reflect a role of AICD for endogenous gene expression. In line with this study, we previously found no impact on Kai1, Gsk3a, Gsk3b, App, and Nep mRNA expression when treating different cell lines with the γ-secretase inhibitor DAPT or when assessing endogenous gene expression in AICD-deficient model systems . 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 . On the other hand, one might expect an inverse regulation of target genes upon AICD deficiency as opposed to overexpression. A genome-wide microarray-based approach to detect AICD target genes used an inducible FE65/AICD cell line . Here, no change in Kai1 and Gsk3b mRNA expression was detected. Similarly, Waldron et al.  found no alteration in mRNA expression of Kai1, Bace1, Egfr, Tip60, and p53 in AICD-enriched FE65-tranfected cells. Moreover, transcriptome analysis in AICD transgenic mouse brain revealed no apparent difference between transgenic animals and littermate controls  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 cannot 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 previously analyzed the expression of a subset of target genes in APP-/-APLP2-/- embryonic brain and fibroblasts . As APLP1 is not expressed in fibroblasts, APP-/-APLP2-/- fibroblasts (compared to APP retransfected 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 . A global assessment 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  reported a reduction of Vglut2 mRNA and VGLUT2 protein expression in glutamatergic neurons obtained by retinoic 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
It is noteworthy that in APP-overexpressing transgenic mice Hspa5 had previously been found to be up-regulated  suggesting an inverse transcriptional regulation 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 hippocampus, as evidenced by increased proliferation of neuronal progenitors in p21-/- mice . Given the Cdkn1a/p21 down-regulation we found here, one might thus expect APP-/- (or APLP2-/-) mice to show dysregulated 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) . Depletion of APPsα by infusion of APP-binding antibodies or as a consequence of pharmacological inhibition of APPsα production reduced the number of neuronal progenitor cells in the SVZ . Thus neurogenesis might be under complex control of APP-mediated signaling pathways, both by membrane-anchored APP and secreted APP isoforms.
Role of APP domains for transcriptome changes
In addition, we raised the question whether the constitutive expression of APPsα would lead to changes in gene expression, as recently reported for APPsβ . We found a small percentage (8.5%) of probe sets in the intersection of APPα/α/APP-/- and WT/APPα/α (Figure 6c). To investigate 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 APPα/α from WT and APP-/- cortices. This points to a small subpopulation of genes that are actually regulated by the constant production of APPsα in the absence of APP full length and all other fragments.
Arc mRNA accumulates in activated synapses, modulates AMPAR trafficking and is critically involved in memory consolidation and LTP . Both FOS, best known for its binding to the Jun/AP-1 transcription factor 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 intriguing 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
Overview over differentially expressed genes: impact of genetic background
To study the impact of genetic background more closely, 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 independent 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 expectation, 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 differentially expressed genes, we created a Venn diagram from the pairwise comparisons WT(R6)/APLP2(R6)-/-, WT(R6)/APLP2(R1)-/-, and APLP2(R6)-/-/APLP2(R1)-/- (Figure 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 separated (Figure 8d). Probe sets in this intersection represent 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.
Here, we determined the effect of APP-/-, APPα/α and 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 transcriptional 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 neuronal differentiation, neurogenesis and transcriptional regulation. This common genetic profile points towards shared physiological functions in these pathways. When comparing APPsα knockin mice and APP-/- mice we 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 constant, particularly if gene expression changes are rather subtle to reliably correlate affected pathways (and physiological 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 laboratories. 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 members 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 .
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).
APP-/-, APLP2-/-, APPα/α animals were previously described [28, 57, 58]. All animals were kept under specific 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 homozygosity. 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 spectrophotometer 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 GeneChip Mouse Genome 430 2.0 Arrays (Affymetrix, Santa Clara). Chips were washed and scanned on the Affymetrix Complete GeneChip® Instrument System generating digitized image data files.
If not stated otherwise, data analysis and processing was carried out within the statistical computing environment R, version 2.8.0, using Bioconductor, BioC Release 2.4 . Raw data was processed with the RMA algorithm (Robust Multiarray Average) developed by Irizarry et al.  and normalized using quantile normalization .
Hierarchical clustering was carried out using Euclidean distances to calculate the distances between the genes and between the sample groups. Calculated distances 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) . As a cut-off value for significance, we set the false discovery rate (FDR) to 5.33% (WT/APP-/-), 4.96% (WT/APLP2-/-), 4.5% (WT/APPα/α), 4.79% (APPα/α/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 identifiers. 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 certain probe set, the probe set was not counted.
For group testing (GO terms, pathways) DAVID bioinformatics resources was used . 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.
Quantitative real-time PCR (qPCR)
Total RNA was prepared using High Capacity cDNA kit based on random hexamer primer method (Applied Biosystems). For each qPCR reaction 20ng of total RNA were reverse transcribed into cDNA. qPCR was performed using FAM™-MGB dye labeled TaqMan® Gene Expression Assays (Applied Biosystems) for Bace1 (assay Mm00478664_m1), Kai1 (assay Mm00492061_m1), Egfr (assay Mm01187858_m1), Gsk3b (assay Mm00444911_m1), p53 (assay Mm01731290_g1), Tip60 (assay Mm00724374_m1), Vglut2 (assay Mm00499876_m1), Hspa5 (assay Mm00517690_g1), Cdkn1a/p21 (assay Mm01303209_m1), Arc (assay Mm00479619_g1), Fos (assay Mm00487426_g1), Egr2 (assay Mm00456650_m1), Dio2 (assay Mm00515664_m1), Ccl21 (assay Mm03646971_gH) and beta-Actin as an internal standard (assay 4352933E). Quantification of qPCR results were evaluated by the 2-ΔΔCT method and normalized to wild-type animals. Significance was calculated using unpaired Student's t-test (*, p < 0.05; **, p < 0.01; ***, p < 0.001).
CCL21 ELISA measurements
Brain homogenates for ELISA were generated as described before . 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 (Sartorius, 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 concentration 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.
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.
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