14 February 2008. Fundamental knowledge gaps in Alzheimer disease research include a clear grasp of the normal role of the amyloid precursor protein (APP), and the molecular basis for the increased risk for AD as people age. Two recent papers took a systems approach to deciphering both. In the February 6 Journal of Neuroscience, Daniel Geschwind and colleagues at the University of California, Los Angeles, describe a weighted analysis to uncover significant differences in gene expression between Alzheimer disease and control samples, and also between normal samples at different ages. Their methodology uncovers genes and gene networks that may be central to normal aging and to AD pathophysiology. Researchers led by Gerold Schmitt-Ulms at the University of Toronto, Canada, took a slightly different approach to identify gene products that may be important in AD biology. They used in vivo cross-linking to identify APP binding partners. Their paper appears in this month’s Molecular and Cellular Proteomics and identifies more than 30 new potential partners for APP.
Geschwind and colleagues used a technique called weighted gene coexpression network analysis (WGCNA) to re-examine microarray analysis of transcript levels in the CA1 region of the hippocampus in AD patients (see Blalock et al., 2004 and ARF related news story) and also in the frontal lobes of normal people between ages 26 and 106 (see Lu et al., 2004 and ARF related news story) . The WGCNA approach arranges genes into transcriptional modules, where genes in each module have expression patterns that are more similar to each other than to the patterns of genes in other modules (see Zhang and Horvath, 2005). The approach, therefore, links together genes that may be functionally related.
First author Jeremy Miller and colleagues first applied the approach to the AD sample set, which consisted of eight controls, six incipient, eight moderate, and six severe AD cases—all age-matched. The analysis identified 12 modules, which were characterized for function according to gene database annotations. Ten of the 12 modules represented functional categories that are associated with AD pathology, including synaptic transmission, mitochondrial function, extracellular transport, and neuronal transmission. Within each module the researchers also identified several hub genes, defined as those with 15 or more interactions with other genes in the module. These included mitochondrial membrane proteins involved in ion transport (VDAC1, VDAC3, ATP5F1), a voltage-gated calcium channel subunit (CACNB2), a glycine receptor subunit (GLRB), and some unknown genes (FLJ14346 and LOC152719).
From the frontal lobe dataset, the researchers identified three modules, two with genes that mostly increased in expression with age, and one in which gene expression predominantly decreased with age. To see if any gene changes are common between AD and aging, Miller and colleagues compared the AD and aging modules. Interestingly, they found that there was statistically significant overlap between genes in the three aging modules and genes in the three modules from the AD sample set. Two of these module pairs also had overlapping functional gene categories, and eight genes were identified as hub genes in both analyses. The most interesting hub gene was, perhaps, Cdk5, which has been implicated in both tau and Aβ pathology (see ARF related news story).
In these analyses, the researchers built the networks starting with the 5,000 most variable transcripts irrespective of their relationship to disease status. Focusing on genes known to be involved in AD yielded a slightly different picture. This local network analysis showed that presenilin 1 is a hub gene in the AD sample set, but not in the aging set. “Although this effect may reflect differences in brain regions, microarrays, or AD progression, a changed role for PSEN1 in the AD network is consistent with its established role in the disease,” write the authors. Also of interest is the fact that in both AD and aging samples, presenilin expression highly correlates with expression of genes that regulate myelination or are related to oligodendrocytes. “These results provide a new set of evidence supporting the hypothesis that demyelination and oligodendrocyte dysfunction may play a role in AD progression,” write the authors.
It remains to be seen whether the gene networks and hubs identified in this study represent cause or effect. “In either case, hubs of modules correlated with AD progression play key roles in processes disrupted in AD, and understanding the function of such genes may lead to a better understanding of AD progression,” write the authors. A prime example is the YWHAZ gene coding for 14.3.3 γ, a member of a protein family that is involved in cell signaling, cell cycle regulation, and cytoskeletal structure. This protein family has been linked to a variety of neurodegenerative disorders, including Huntington’s and prion diseases.
The APP interactome identified by Schmitt-Ulms and colleagues in Toronto was obtained in an entirely different way. First authors Yu Bai, Kelly Markham, and colleagues used in vivo time-controlled transcardiac perfusion cross-linking in mice, a method that pumps controlled amounts of formaldehyde through the circulatory system and results in limited protein cross-linking. After the perfusion, the researchers isolated APP complexes by immunoprecipitation with APP antibodies. They then digested the proteins and identified the resulting peptides by mass spectroscopy.
To distinguish proteins that bind specifically to APP and not to APLP1 and APLP2 homologs, Bai and colleagues used a variety of different antibodies. For APP they used one antibody that recognizes the intracellular C-terminal end and one that binds the extracellular domain flanked by α- and β-secretase sites. The latter does not recognize APLP1 and 2. The researchers also used two antibodies specific for APLP1 and 2. They identified three types of interactors: those that are non-specific, those involving a single APP family member, and those that bind to more than one family member. The last group was small, containing, in addition to the APP proteins themselves, only one other protein, the RasGAP-activating-like protein 1.
The researchers identified a total of 33 proteins that bound exclusively to APP. Twelve were pulled down by both antibodies. The C-terminal antibody pulled down an additional 10 proteins, most of them cytoplasmic. The antibody directed against the extracellular epitope also pulled down 10 additional proteins, mostly extracellular. The 12 proteins pulled down by both antibodies are either membrane proteins or reside in the ER. Overall, the pattern of hits suggests that the strategy identifies physiologically relevant interactions rather than non-specific cross-links. But to confirm the specificity, the researchers turned to an internal control system for quantifying interactomes. Called iTRAQ, for isobaric Tags for Relative and Absolute Quantitation, the system relies on chemical tags of the same mass to serve as tracers in four separate experiments. This allowed the researchers correct data for the relative abundance of peptides in individual samples. The data obtained using the iTRAQ analysis was in “excellent agreement with data obtained following analysis of individual IPs and as such strongly argue that mere sampling bias was not the underlying cause for differences seen above in interactome data of APP and family members,” write the authors.
Some proteins in the interactome were conspicuous by their absence. α-, β- and γ-secretases did not show up, for example. But the authors note that “short-lived interactions of catalytic nature (e.g., proteolytic enzymes) commonly escape detection following chemical cross-linking.” The interactome did include F-spondin, a potential ligand for APP (see ARF related news story); the remaining proteins were potential novel APP partners. The authors chose to focus on LINGO-1, a type 1 transmembrane protein that has been linked to myelination and axon regeneration, to test the validity of the interactome. Antisense probes showed that LINGO-1 and APP colocalize in the brain, predominantly in the CA1 and CA3 region of the hippocampus, and cell culture experiments suggest a functional interaction between the two proteins. Knocking down LINGO-1 in HEK293 cells expressing APP with the Swedish mutation reduced β-secretase cleavage and Aβ production by about 30 percent and increased α-secretase cleavage by about 70 percent. In contrast, overexpression of LINGO-1 had the opposite effects, increasing Aβ production.
While this example serves to show that the interactome has some validity, the authors recognize that intensive validation will be required to appreciate the roles of the proteins in this network. “It is hoped that further investigations of this network will reveal an ‘Achilles’ heel’ in the molecular biology of APP that can be exploited for diagnostic or therapeutic purposes,” write the authors. The same may be said for the networks uncovered by Geschwind and colleagues.—Tom Fagan.
Miller JA, Oldham MC, Geschwind DH. A systems level analysis of transcriptional changes in Alzheimer’s disease and normal aging. J. Neurosci. 2008. Feb 06;28:1410-1420. Abstract
Bai Y, Markham K, Chen F, Weerasekera R, Watts J, Horne P, Wakutani Y, Bagshaw R, Mathews PM, Fraser PE, Westaway D, St George-Hyslop P, Schmitt-Ulms G. The in vivo brain interactome of the amyloid precursor protein. Mol Cell Proteomics. 2008 Jan 1;7(1):15-34. Abstract