Eric Schadt Ana Griciuc Monica Carson John Hardy Jeremy Miller

In the April 25 Cell, Valur Emilsson at the Icelandic Heart Association and Eric Schadt at Icahn School of Medicine at Mount Sinai, New York, report that they have identified molecular networks that are perturbed in Alzheimer’s disease patients compared to normal, age-matched controls. Several of these networks comprise genes previously linked to AD, including TREM2 and CD33. The scientists also identified a new player, TYROBP, as a master regulator of these molecular modules. Meanwhile, in the April 25 Neuron, researchers led by Rudy Tanzi and Ana Griciuc at Massachusetts General Hospital, Charlestown, report that microglia in the AD brain overproduce CD33, which seems to prevent these cells from binding to and degrading amyloid-β. Together, these findings tighten the link between AD pathology and microglial dysfunction. Join us for an Alzforum Webinar on Thursday, 23 May, 2013 at 12:00 noon, U.S. Eastern time with Eric Schadt and Ana Griciuc, along with Monica Carson, University of California, Riverside; John Hardy, University College London; and Jeremy Miller, Allen Institute for Brain Science, Seattle, Washington. We thank Cell Press for giving Alzforum readers temporary free access to these papers: Zhang B, Gaiteri C, Bodea L-G, Wang Z, McElwee J, Podtelezhnikov AA, Zhang C, Xie T, Tran L, Dobrin R, Fluder E, Clurman B, Melquist S, Narayanan M, Suver C, Shah H, Mahajan M, Gillis T, Mysore J, MacDonald ME, Lamb JR, Bennett DA, Molony C, Stone DJ, Gudnason V, Myers AJ, Schadt EE, Neumann H, Zhu J, Emilsson V. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell. April 25 2013;153:707-720. Read article. Griciuc A, Serrano-Pozo A, Parrado AR, Lesinski AN, Asselin CN, Mullin K, Hooli B, Choi SH, Hyman BT, Tanzi RE. Alzheimer's disease risk gene CD33 inhibits microglial uptake of amyloid β. Neuron. April 25 online. Read article.

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By Tom Fagan

Last year, two groups uncovered mutations in the gene for TREM2 that multiply a person’s risk for Alzheimer’s disease about threefold, making TREM2 the second most important genetic risk factor after ApoE (see ARF related news story). How does TREM2 influence pathogenesis? It encodes a membrane receptor found on different cell types. It may interact with many molecular partners, potentially influencing an untold number of different cellular pathways. The same goes for CD33, an immunoglobulin-like cell-surface receptor of unknown function. While researchers are encouraged that the genetic data point to specific cells or systems, such as microglia or innate immunity, the exact molecular pathways involved in pathogenesis have yet to be worked out. As for the other AD risk genes that emerged from genomewide association studies, including Bin1, CLU, PICALM, CD33, MS4A6A, and MS4A6E (see Alzgene Top Results), some fall into those pathways, but scientists are unsure about their exact role in AD.

How can scientists match genetic variation to function? One approach would be to painstakingly investigate each gene's so-called interactome—that is, look at binding partners and associated pathways and test them for pathogenicity. Alternatively, scientists could correlate known genetic risk factors with processes that are known to have gone awry. Here, systems biology can help. In recent years, researchers have used whole-genome expression data to identify groups of genes, aka transcription modules, that are coordinately expressed and functionally related (see Zhang and Horvath, 2005). This type of network analysis has the potential to uncover, in one experiment, whole pathways that may be perturbed in disease.

Schadt and colleagues have used this approach to identify molecular modules that might contribute to metabolic syndrome (Chen et al., 2008), and others have studied hippocampal networks in Alzheimer's disease similarly (see ARF related news story on Miller et al., 2008). More specifically, this approach can relate genetic hits to biological pathways. For example, researchers have used network analysis to link progranulin mutations to the Wnt signaling pathway in frontotemporal dementia (see ARF related news story).

Now, Schadt and collaborators take a systems approach to uncover dysfunctional expression throughout the whole AD brain. They used network analysis, comparing gene expression patterns in tissue taken from patients and controls, to identify molecular modules that are reconfigured in anatomical regions most devastated in late-onset AD. Network disturbances related to immunity and microglia topped the list. In particular, they predict that the microglial protein TYROBP, a TREM2 binding partner, acts as a master regulator of an innate immunity module that includes other AD GWAS hits MS4A6A, MS4A6E, and CD33. Hematopoietic cells, including neutrophils, macrophages, natural killer cells, and B and T cells make TYROBP. The AD brain seems to overproduce the protein, the scientists report.

AD-specific molecular networks. Image courtesy of Cell Press

How might TYROBP knock molecular networks off balance in AD? A hint comes from Ana Griciuc and Rudy Tanzi. They found elevated expression of CD33 in microglial cells in AD brain samples, and report that overexpression of this protein by microglia in culture impairs their ability to take up and degrade amyloid-β. Consistent with this, they found that in AD brain samples, greater numbers of CD33-positive microglia correlated with higher amyloid burden. Interestingly, carriers of the protective CD33 genetic variant had less of this immunoglobulin in the brain, suggesting that reducing the protein might prove beneficial. In fact, knocking out CD33 in APP/PS1 transgenic mice dramatically reduced amyloid plaques. These findings strengthen the case that TYROBP-regulated pathways, and microglial dysfunction in general, play a role in sporadic AD pathology.

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