One of the driving questions in Alzheimer’s research has been its nature—is AD simply an extension of normal aging, or is it a disease unto itself? If it is a disease unto itself, what changes over time in the brain to make the aged tissue so much more vulnerable to attack? Using high-level bioinformatic approaches, Miller, Oldham, and Geschwind take a closer look at the interplay between aging and AD using transcriptional profiles from previously published data sets.

On a technical scale, this work is extremely thorough and careful. It is a terrific example of a well-reasoned meta-analysis in its truest form, paying attention to statistical probabilities at each stage of the analyses (e.g., probability of overlap between the two studies based on the discovery power in either). Rather than simply comparing lists of genes from two studies, the authors stripped the information from each study down to its most raw form (at least as raw as they could get it, i.e., CEL files from Affymetrix array scans), and used a consistent probe level algorithm across both studies to create...  Read more

One of the driving questions in Alzheimer’s research has been its nature—is AD simply an extension of normal aging, or is it a disease unto itself? If it is a disease unto itself, what changes over time in the brain to make the aged tissue so much more vulnerable to attack? Using high-level bioinformatic approaches, Miller, Oldham, and Geschwind take a closer look at the interplay between aging and AD using transcriptional profiles from previously published data sets.

On a technical scale, this work is extremely thorough and careful. It is a terrific example of a well-reasoned meta-analysis in its truest form, paying attention to statistical probabilities at each stage of the analyses (e.g., probability of overlap between the two studies based on the discovery power in either). Rather than simply comparing lists of genes from two studies, the authors stripped the information from each study down to its most raw form (at least as raw as they could get it, i.e., CEL files from Affymetrix array scans), and used a consistent probe level algorithm across both studies to create new data sets that are more comparable.

Using the innovative WGCNA approach to clustering, the authors established modules of genes based on similar behavior across disease states or aging. Within these modules, the authors established connectivity among genes and identified “hub” genes that appeared to be most often linked to other genes within the module. Among these, two genes stood out: VDAC1 in the mitochondrial module of AD, and YWHAZ, a fairly uncharacterized, but extremely abundant gene product in brain in a module that stubbornly refused to reveal a functional association. Further, well-known PSEN1 was found to be related to myelinating processes in a “guilt-by-association” module of myelin-related gene products. The authors found that two of the modules from the AD data set (synaptic and mitochondrial) were related to a single module from the aging study that contained both functional categories.

The implication here is that aging and AD do share some common processes. However, the question remains as to whether AD represents a frankly different process than aging. That’s because there are both intriguing agreements (discussed in the paper), and disagreements (for instance, that the mitochondrial and synaptic genes split into two distinct modules in AD, but resided in one aging module) between the two studies compared here.

It is remarkable that these relationships were seen even despite the confounds on a technical level of different labs, and times, as well as the biological confounds of different tissue type (hippocampal CA1 versus forebrain cortex). It would be interesting to know, in future studies using this approach, what the level of agreement would be between two studies attempting to examine the same disease state—such as AD—in different brain regions. Our own observations are that changes are more consistent at the functional group level as opposed to the per gene level.

Other interesting further work might include determining the consequences of various cutoff decisions in the course of implementing the algorithm, e.g., number of presence calls, variability for inclusion, lack of connectivity for exclusion, should number of connections for determining hub genes be adjusted for number of genes in module, etc. Also interesting would be to determine “anti-modules,” functional groups that remain static across groups.

View all comments by Eric Blalock

This interesting and technically innovative paper addresses the still enigmatic physiological function of APP. The study sheds new light on APP by comparing its “interactome” (i.e., the sum of its interactions with other proteins) with those of the APP paralogs, the APP-like proteins 1 and 2.

To achieve the goal of catching as many as possible interacting proteins in the act, a new way of cross-linking is employed. This method, termed "time-controlled transcardiac perfusion cross-linking," basically glues all protein complexes together while the animal still lives. This is advantageous as it reduces the risk of mapping artifactual interactions, which often occur after tissue is extracted, and it should better enable the identification of transient and low-affinity interactions. After immuno-affinity purification of the cross-linked protein complexes, the authors employ isobaric tagging and quantitative mass spec to map differences in the complexes formed by the three paralogous proteins.

This strategy poses a twofold advantage. First, non-specific interactions with...  Read more

This interesting and technically innovative paper addresses the still enigmatic physiological function of APP. The study sheds new light on APP by comparing its “interactome” (i.e., the sum of its interactions with other proteins) with those of the APP paralogs, the APP-like proteins 1 and 2.

To achieve the goal of catching as many as possible interacting proteins in the act, a new way of cross-linking is employed. This method, termed "time-controlled transcardiac perfusion cross-linking," basically glues all protein complexes together while the animal still lives. This is advantageous as it reduces the risk of mapping artifactual interactions, which often occur after tissue is extracted, and it should better enable the identification of transient and low-affinity interactions. After immuno-affinity purification of the cross-linked protein complexes, the authors employ isobaric tagging and quantitative mass spec to map differences in the complexes formed by the three paralogous proteins.

This strategy poses a twofold advantage. First, non-specific interactions with other proteins—a notorious problem for membrane proteins—are identified as they will likely be the same for a group of proteins with similar structural make-up. Second, any interactors identified that are specific for APP may yield clues to why APP but not its cousins can go awry in Alzheimer disease.

Indeed, the results obtained here are interesting. Firstly, it is always great to find interactions previously observed with other approaches, and a number of known APP binding proteins are identified. This includes cystatin C which has been proposed as a genetic marker for AD (see Alzgene). Secondly, a number of novel interacting proteins are identified. An intriguing case is LINGO-1, a neuronal transmembrane protein. The authors go on to demonstrate that siRNA-mediated reduction of LINGO-1 in APPsw-cells caused a reduction in Aβ production, while LINGO-1 overexpression caused increased β-secretase cleavage of APP. In summary, the combination of techniques used here carries a lot of promise for the elucidation of membrane-protein complexes, which should contribute to a better understanding of their role in disease processes and as drug targets.

View all comments by Gerard Drewes

Bai et al. use a novel approach for identifying APP family member-candidate interacting proteins. For identification of interacting proteins, the investigators used transcardiac perfusion cross-linking, followed by immunoprecipitation of the cross-linked complexes from mouse brain with APP family member-specific antibodies and LC/MS/MS analysis of recovered tryptic peptides. Validation of the putative interaction of APP with one of the identified proteins, LINGO-1, a transmembrane protein that binds p75, could only be demonstrated in immune complexes recovered from cross-linked mouse brain proteins, suggesting that this interaction is weak, transient, or mediated by an intermediate protein. However, manipulations of LINGO-1 protein levels in HEK293 APPSwe cells using siRNA or overexpression produced opposite effects on levels of APP proteolytic fragments, suggesting that this interaction is physiologically relevant for APP processing.

The data in this study confirm the previously identified interaction of APP family members with each other, as well as the interaction of APP...  Read more

Bai et al. use a novel approach for identifying APP family member-candidate interacting proteins. For identification of interacting proteins, the investigators used transcardiac perfusion cross-linking, followed by immunoprecipitation of the cross-linked complexes from mouse brain with APP family member-specific antibodies and LC/MS/MS analysis of recovered tryptic peptides. Validation of the putative interaction of APP with one of the identified proteins, LINGO-1, a transmembrane protein that binds p75, could only be demonstrated in immune complexes recovered from cross-linked mouse brain proteins, suggesting that this interaction is weak, transient, or mediated by an intermediate protein. However, manipulations of LINGO-1 protein levels in HEK293 APPSwe cells using siRNA or overexpression produced opposite effects on levels of APP proteolytic fragments, suggesting that this interaction is physiologically relevant for APP processing.

The data in this study confirm the previously identified interaction of APP family members with each other, as well as the interaction of APP with the membrane-associated proteins: calsyntenins (alcadeins) and PrP; the ER proteins BIP and calnexin, and the secreted proteins F-spondin and Cystatin C. One advantage of this approach is the identification of proteins that are located in the membrane or in the vicinity of membranes, including the plasma membrane. However, a striking absence of previously identified APP C-terminal binding proteins was observed using this experimental approach. In the case of the FE65 proteins, this cannot be attributed to lack of importance of this protein family for APP biology, since the FE65/FE65L1 double knockout mice display a neuronal positioning defect in the developing cortex that is remarkably similar to the APP triple knockout mice (Guénette et al., 2006; Herms et al., 2004).

Given that APLP2 and APLP1 were found in APP immunoprecipitates, it is unfortunate that no proteins common to all three APP family member immune complexes were identified. Such proteins would be good candidates for studies aimed at elucidating the molecular mechanisms underlying the phenotypes observed in APP/APLP1/APLP2 triple knockout mice. Nevertheless, it remains possible that disruptions of APP family member interactions with proteins identified by Bai et al. contribute to the type II lissencephaly-like phenotype observed in the APP family member knockout mice. In this respect, SPARC-L1, an extracellular protein known to localize to the surface of radial glial fibers in the outer layers of the developing cortex, is particularly interesting. That is because it was recently shown to block adhesion of migrating neurons to radial glia, thereby terminating radial glia-mediated neuronal migration (Gongidi et al., 2004). Continued adhesion of neurons to radial glia in mice deficient for the APP family members may lead to neuronal overmigration and heterotopia formation, similar to what is observed in the developing cortex of APP/APLP1/APLP2 triple knockout mice (Herms et al., 2004).

Finally, the identification of calsyntenins (alcadeins) as APP cross-linked proteins in the absence of X11L (X11β or Mint-2) is interesting, given that alcadein α1 (Alcα1) was identified as an interactor for X11L in a two-hybrid screen (Araki et al., 2003). Alcadein was initially proposed to form an APP-X11L-Alcα1 tripartite complex because all three proteins could be recovered from brain membrane fractions in immune complexes isolated with an APP C-terminal antibody.

However, the binding domain within X11L for alcadein was identified as the same domain that mediates binding to APP, the PTB domain of X11L; thus X11L cannot act as an adapter protein in the formation of this complex. The data provided by Bai et al. suggest that the tripartite complex is formed by the interaction of the N-terminal domains of APP and alcadein with binding of either alcadein or the APP C-terminus to X11L. Furthermore, this may allow for complex formation of secreted APP or alcadein with X11L-bound, membrane-associated alcadein or APP, respectively (Araki et al., 2004).

Unique APP and APLP-interacting proteins have been identified using different experimental strategies. Therefore, it seems reasonable to consider all candidate interactors when thinking of the APP/APLP interactome, and how it may shed light on our understanding of the physiological functions of APP and/or the APLPs through their protein-protein interactions.

View all comments by Suzanne Guenette