Comunicación

The APP gene represents a strong positional and functional candidate for AD risk on many levels. Linkage studies have provided strong evidence for a susceptibility loci potentially predisposing to LOAD in the vicinity of the APP gene on chromosome 21 (Myers, Wavrant De-Vrieze et al. 2002; Olson, Goddard et al. 2002; Blacker, Bertram et al. 2003). APP is the parent protein from which Aβ is derived and coding mutations in APP or duplications of the gene have been associated with EOAD (Goate, Chartier-Harlin et al. 1991). Furthermore, mutations in other familial AD genes, such as PSEN1, lead to altered APP metabolism (Pastor and Goate 2004) and APP locus duplication in trisomy 21 can result in elevated levels of circulating Aβ peptide (Schupf, Patel et al. 2001). Additionally, complete or partial trisomy of chromosome 21 leads to Down Syndrome including AD pathology, however only when the APP gene is present in three copies (Rumble, Retallack et al. 1989; Prasher, Farrer et al. 1998). All this evidence suggests that factors such as genetic polymorphisms, that impact upon APP processing, may exacerbate the risk for AD. In this study we have attempted to comprehensively investigate the genetic role of APP in AD by genotyping 39 SNPs covering the complete genetic region of the APP gene in a large case- control cohort. The initial single-marker, haplotype and sliding-window analyses revealed association signals within two marker peak regions (SNP # 14 – 25 and # 33 – 37) spanning from intron 3 to 8 and intron 1, respectively. A selection of SNPs underwent replication in a second large independent case-control series, including a pooled analysis, as well as an independent sib-pairs cohort. The replication analyses confirmed signals in SNPs # 16, 17, 20, 21, 23, 25, 35 and 36 and therefore their implication to Alzheimer´s disease.

Munich cohort, which was confirmed when tested for association with clinical measures. Identification of genetic factors, which induce an early onset of disease are of special interest. The associated allele of SNP # 9 was coupled with an approx. 2 year later age of onset; however it was also associated with a lower score on the MMSE, suggesting a more rapid decline in cognition in carriers of the rs1783016-G allele.

SNPs from the peak regions underwent further analyses in brain samples to ascertain the functional implications of polymorphisms across the APP gene on the transcriptional level (gene expression), the translational level (Western Blot) and the post-translational level (ELISA for Aβ isoforms). Effects on APP expression was apparent in one SNP, (# 25, rs1041420) located in intron 3. In the case of SNP # 25 the risk allele (T) was associated with elevated APP mRNA levels in the brain; however no significant difference was observed with respects to altered APP protein levels or soluble Aβ levels in the same samples. These findings suggest that the rs1041420-T risk allele might contribute to the development of AD due to altered amounts of APP substrate and not due to altered APP processing, as seen in APP gene duplication (Schupf, Patel et al. 2001) and trisomy21 (Rumble, Retallack et al. 1989; Prasher, Farrer et al. 1998).

Although none of the samples tested on the Western blot showed significant differences, an association can not be ruled out. The applied assay may not have enough sensitivity to detect existing density distinctions. Nevertheless, the Western blot approach confirmed the clear elevated presence of Aβ in brain samples from affected subjects in comparison to healthy subjects, where no Aβ was present (Hardy and Selkoe 2002).

Another observation during functional analysis was made for SNPs # 35 and # 36 (rs6516727 and rs2830099), where total Aβ levels were lower in carriers of the risk alleles in the brain samples, but no significant associations were observed for APP mRNA levels. The observed Aβ levels seem to be counterintuitive, since one would expect higher Aβ levels in carriers of the risk allele. This observation may be explained by the fact that only soluble (unbound) Aβ can be measured by the applied ELISA and the majority of present Aβ42 may already have formed plaques in the affected brains. Moreover, an association in the same direction as for total Aβ was made for Aβ40. This also suggests, that the protective allele favors the formation of the soluble Aβ40, which does not form neuritic plaques (Verdile, Fuller et al. 2004). Therefore one might nevertheless argue, that the risk alleles (rs6516727-T and rs2830099-G) are rather associated with altered processing than higher substrate levels, since no affect on the gene expression was observed. Moreover, the small number of brain

in the Munich and Swedish case-control cohorts, another plausible explanation why the results seem not to fit perfectly. In another approach, SNP # 36 revealed a significant association of homozygosity of the major allele (G) with elevated CSF Aβ42 levels in a set of 85 cerebrospinal fluid (CSF) samples from AD patients, fitting to the lower Aβ levels that were found in carriers of this allele in brain samples (Bouwman, Schoonenboom et al. 2008).

Overall, the applied functional approach was well-chosen and is applicable for our experiments, since it can investigate the effect of the SNP variants on different stages (transcriptional, translational and processing level). Therefore, the implication of the present allele on altered APP expression or altered processing can be discovered. The fact, that the ELISA applied in brain samples can only detect soluble forms of Aβ combined with the small sample sizes, suggest that further study is required for confirmation of the results, in particularly the measurement of both soluble and insoluble fractions of Aβ42 in larger sample sizes. Moreover, all performed experiments in brain samples need to be repeated in a greater collective, since a too small sample size harbors the risk of introducing a bias due to random allele occurence or technical problems, which may not be representative of the real population structure.

From the data analysis performed in the scope of this study, it is assumed, that common variations within the APP gene contribute to the development of AD, due to increased APP expression or plaque formation.

There has previously been only one major study of polymorphisms across the entire APP gene (Nowotny, Simcock et al. 2007). Unlike the current study this prior study found no evidence for association with LOAD for either single SNPs or haplotypes in a non-stratified case-control cohort and only a weak evidence of association in an ApoE ε4 positive subset. The current study overlapped the prior study in seven instances and in all but one case the same association signal was observed in the non-stratified cohort. In six of these SNPs (SNP # 14, 19, 21, 28 and 29 in the present study) no association was observed in both studies. However, in two cases, SNP # 17 (rs2830012) and SNP # 26 (rs2830046), association results differed in the two studies. For SNP # 26 (rs2830046), a weak association signal was observed for the study of Nowotny, when stratified by ApoE genotype (present in ε4 carriers only), whereas no such observation was made in our Munich collective. The other exception was observed for SNP # 17 (rs2830012), which was present with a weak association signal in

the same regions. A conservation of LD patterns across different European samples had been observed by Mueller and co-workers (Mueller, Lohmussaar et al. 2005). The collective used by Nowotny was no European sample, but a Caucasian sample from the Washington University Hospital. Therefore, our observation suggests a conservative LD structure in the region of the APP gene on chromosome 21q21 not only throughout different European samples, but also throughout Caucasian samples in general.

The lack of association signals observed in the prior study was suggested to be possibly a result of selection criteria excluding individuals most likely to carry a LOAD risk factor in the APP gene, in particularly a recent history of a stroke. However, this exclusion criterion was also applied during the recruitment process of this study. A second caveat of the prior study was that it was designed to test whether common variations in APP explain any risk for AD and thus it could not rule out the possibility that multiple rare variants in APP may be responsible, as suggested by another study (Theuns, Brouwers et al. 2006). Likewise, this current study cannot rule out additional rare variants being associated with AD; however, the association signals that were observed, were located in SNPs with a minor allele frequency between 20 to 30%.

In summary, this study is one of the two largest and most comprehensive analyses of the APP gene, as a risk factor for sporadic AD, reported to date. In contrast to the other study (Nowotny, Simcock et al. 2007) we observed evidence for an association with sporadic AD, which was replicated in additional sample sets. Additionally, we observed evidence to suggest that these SNPs have an impact on cognitive measures and both APP expression and processing. The applied functional approach seems to be adequate for the research of functional implications, although replication in a larger cohort of brain samples is of utmost importance to thoroughly investigate involvement of the analyzed SNPs. A sample size of 20 does not give the statistical power to draw final conclusions, but only can give hints or show tendencies. Unfortunately, since brain samples from a sufficient number of AD patients and healthy controls are very difficult to obtain, this approach could not be undertaken during this study.

6.3.2 Summary and Discussion of Results for Genes Involved in APP Processing –