With the help of simple model organisms, two research groups have come up with dozens of genes that could be tied to pathological events in two major neurodegenerative diseases. The screens identified genes that modulate amyloid-β (Aβ) toxicity in Drosophila, or the formation of α-synuclein inclusions in C. elegans. The experiments nominated multiple candidate genes as players in the neurotoxic processes involved in Alzheimer disease and Parkinson disease, respectively, and should stimulate a wealth of additional studies in other organisms and other models.

In the first study, Mary Konsolaki and colleagues at Rutgers in Piscataway, New Jersey, worked with collaborators at the Novartis Institutes for Biomedical Research in Cambridge, Massachusetts, to test the effects of 1,963 different insertional mutations in a fly model of Aβ toxicity (Cao et al., 2008). In the model, expression of a secreted Aβ42 peptide in the eye produces photoreceptor cell death, resulting in a visible rough eye phenotype. Of the mutants tested, 23 either exacerbated or ameliorated that phenotype, according to the results published February 2 in the online edition of the journal Genetics.

First author Weihuan Cao and coworkers report that the modifiers included genes involved in familiar pathways, such as the secretory and vesicular pathways (e.g., homologs of carboxypeptidase D and the γ subunit of the AP3 adaptor), and one involved in cholesterol homeostasis (a homolog of the human metabolic regulator AMP kinase).

The investigation turned up some new pathways, too. One mutant that suppressed Aβ toxicity affected two genes, a homolog of the human autophagy gene Atg1 and a component of the Sin3A chromatin-remodeling complex. A study of additional mutations in the latter gene, called SAP130, and related genes, revealed that loss of function of SAP130 enhanced the rough eye phenotype, as did mutating Sin3A or either of two histone deacetylases found in the Sin3A complex (HDAC1 or HDAC4). The results implicate chromatin remodeling in Aβ toxicity. Five other genes involved in transcriptional regulation were also identified in the screen.

In most cases, the mutations did not change total Aβ levels, or only increased them modestly by 20-30 percent. Some mutations did result in a significant increase or decrease in soluble Aβ, but did not always produce the expected enhancement or suppression of the phenotype. Interestingly, loss-of-function mutations in Sin3A, the HDAC1 homolog Rpd3, HDAC4 or SAP130, resulted in a significant accumulation of soluble Aβ, suggesting that the Sin3A complex might regulate the turnover of Aβ. Consistent with this idea, recent work suggested that another HDAC is an autophagy-dependent genetic modifier of neurodegeneration in flies (Pandey et al., 2007).

Konsolaki and coworkers looked for genes that might play a general role in neurodegeneration. They did that by looking for effects by the Aβ-modifying mutations on the rough-eye phenotype when that phenotype was elicited by two other pathologic proteins, tau or a polyglutamine-expanded fragment of the huntingtin protein. Half of the mutations tested modified the tau phenotype, while 75 percent modified huntingtin toxicity, but the effects of any one mutant were not always consistent between the two models. Five mutants tested suppressed rough eye in all three models, including the one in the γ subunit of the AP3 adaptor, and the Atg1/SAP130 insertion.

In total, the results offer up pathways involved in trafficking, autophagy, and possibly gene transcription as potentially interesting avenues for further work. The caution to the work, of course, is the question of how closely the handling of a secreted Aβ peptide, and its effects on cells in the fly model, resembles what happens in human disease.

The second study uses a worm model to study how cells deal with α-synuclein, a protein that accumulates in intraneuronal inclusions in Parkinson disease. Ellen Nollen and colleagues at the University of Groningen in the Netherlands took advantage of see-through C. elegans worms and a yellow fluorescent protein-labeled synuclein protein to monitor inclusion formation in the body wall muscle cells of living animals.

Published in today’s PLoS Genetics, their results show that as the worms age, they develop inclusions resembling those seen in PD. First author Tjakko van Ham then used a genome-wide RNA knockdown approach to identify 80 genes that affected inclusion formation. Genes involved in protein quality control and vesicle trafficking in the ER/Golgi showed up in the screen more often than would be expected by chance. The genetic link to aging came from the observation that knocking down several aging-associated genes, including the worm homolog of SIRT1 and lagr-1, a sphingolipid synthase, increased inclusion formation.

“Our list of suppressors of age-dependent inclusion formation reveals a clear link between α-synuclein inclusion formation and cellular aging, most probably via and endomembrane-related mechanism,” the authors conclude.

A caveat to the work remains that it involved inclusion formation in muscle cells, not neurons. One reason for this was that muscle expression made for a visual assay that could be performed in living worms. In addition, RNAi knockdown by feeding is more efficient in muscle cells than neurons, the authors say. However, as the authors point out, the assay did not pick up two modifiers of synuclein toxicity that were recently identified in a screen for neuronal toxicity in Drosophila, namely the G protein-coupled receptor kinase 2 (GRK2) and Hsp70 (see ARF related news story and Chen and Feany, 2005). In exploring this discrepancy, the authors found knocking down GRK2 in worms resulted in fewer inclusions, which is the opposite of the phenotype they were screening for, and the opposite of the phenotype seen in flies. Further work is needed to reconcile the worm and fly results related to inclusion formation and toxicity. On the other hand, the worm screen did find SIRT1 to enhance inclusion formation, a result that jibes with the finding that in Drosophila neurons, SIRT2 promotes inclusion formation and prevents α-synuclein toxicity (see ARF related news story).

Nollen and colleagues also wondered if they would find common pathways for protein aggregation in different neurodegenerative diseases. In the case of protein folding, their results indicate the answer is no. The investigators found little overlap in the gene lists for the α-synuclein study and their own previous screen for aggregation of a polyglutamine-expanded protein (Nollen et al., 2004). That experiment identified many proteasomal genes and chaperones. In fact, the two screens turned up only one gene in common, which suggests distinct cellular processes are at play in the aggregation of the two proteins.—Pat McCaffrey.

References:
Cao W, Song HJ, Gangi T, Kelkar A, Antani I, Garza D, Konsolaki M. Identification of novel genes that modify phenotypes induced by Alzheimer's beta amyloid overexpression in Drosophila. Genetics. 2008 Feb 3; [Epub ahead of print] Abstract

Van Ham TJ, Thijssen KL, Breitling R, Hofstra RMW, Plasterk RHA, Nollen EAA. C. elegans model identifies genetic modifiers of alpha-synuclein inclusion formation during aging. PLoS Genetics. 2008 March 21; 4(3):e100027. Abstract

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  1. These papers report using invertebrate models of human neurodegenerative disease to identify genes that play a role in the cellular toxicity associated with aggregation-prone, disease-relevant proteins. The studies focus on different diseases and use different model organisms, but have the same underlying rationale: use of these models allows “unbiased” screening for relevant genes without any a priori assumptions about the mechanism of disease protein aggregation or toxicity. In the Van Ham et al. study, the nematode worm C. elegans was used to identify genes that influence the aggregation of α-synuclein, believed to play a causal role in Parkinson disease. Cao et al. used transgenic Drosophila expressing the human β amyloid peptide (Aβ), linked to Alzheimer disease, to identify genes that modulate Aβ toxicity. Both studies demonstrate the ability of this approach to uncover unexpected interacting genes, as well as the difficulty of making sense of the genes identified.

    The study of Van Ham et al. follows an approach previously used by this group to identify genes involved in the aggregation of a polyglutamine-repeat reporter protein (relevant to polyglutamine-repeat associated diseases such as Huntington’s). Approximately 16,000 C. elegans genes were individually knocked down by RNA interference, and the ability of these gene knockdowns to increase the aggregation of an α-synuclein::GFP fusion reporter protein was assayed. Inhibited expression of 80 genes (~0.5 percent of the total) was found to reproducibly increase the aggregation of the α-synuclein reporter. In my view, the most surprising result is the large range of function of the genes identified, including roles in chemosensation, gene expression, and intracellular protein trafficking. The second most surprising finding is that there is essentially no overlap between genes that influence α-synuclein aggregation and genes that affect polyglutamine-repeat protein aggregation. This result strongly argues that all aggregating proteins are not created equal, at least in the context of this model system. One thing not assayed in this study is a direct measurement of α-synuclein toxicity, so this study cannot address the important question of whether α-synuclein aggregates are directly toxic or represent a defense mechanism against α-synuclein toxicity.

    The Cao et al. study employs flies expressing (human) β amyloid peptide in their eyes, which leads to visible abnormalities in the fly eye. Using this model, they screened ~2,000 existing mutations for modulation of this abnormal eye phenotype, leading to the identification of 23 mutations (~1 percent of total mutations tested). As in the C. elegans study, the mutated genes have a large range of functions, and do not fall into an obvious single biological pathway (additionally, none of these genes appears to be homologous to the genes identified in the Van Ham study). Unlike the worm study described above, this group found that approximately half of the identified mutations affected the phenotype of model flies expressing a toxic form of huntingtin.

    Both of these well-done studies have undertaken extensive, high-throughput screens and identified intriguing collections of genes with potential relevance to Parkinson and Alzheimer disease. What have we learned? Perhaps the most obvious lesson is that much more work will need to be done to sort out what these findings mean and how they can be applied to human disease. Although both studies seek to connect identified genes to the extensive neurodegeneration literature, the importance of these connections is hard to determine. This is due to what I call the “six degrees of separation” problem in biology—the observation that, given the complexity and extensive knowledge base in well-studied biological fields (e.g., neurodegeneration), it is not difficult to make rational connections between even a random list of genes and previously published studies. (This problem hinders interpretation of “hit lists” from gene expression studies in particular.) This skepticism does not weaken the importance of these studies; it just reinforces the notion that there is now some really important biology that needs to be done to understand how the identified genes influence the aggregation or toxicity of disease proteins. In particular, it will be interesting to know which of the modifier genes act cell-autonomously (e.g., the chemotaxis genes identified in the C. elegans study are likely expressed only in neurons—how do they affect α-synuclein aggregation in muscle cells?), and whether identified genes can be ordered in a pathway (e.g., what is the effect of combining a suppressor of Aβ toxicity with an enhancer?). I believe that there is a high probability that some of the genes identified in these studies are telling us important things about neurodegenerative disease—the challenge now is identifying which of these genes are the truly informative ones.

References

News Citations

  1. Molecular Chaperones Can Ameliorate Neuronal Loss in Drosophila PD Model
  2. Parkinson Disease—Potential Targets, Therapies

Paper Citations

  1. . Identification of novel genes that modify phenotypes induced by Alzheimer's beta-amyloid overexpression in Drosophila. Genetics. 2008 Mar;178(3):1457-71. PubMed.
  2. . HDAC6 at the intersection of autophagy, the ubiquitin-proteasome system and neurodegeneration. Autophagy. 2007 Nov-Dec;3(6):643-5. PubMed.
  3. . Alpha-synuclein phosphorylation controls neurotoxicity and inclusion formation in a Drosophila model of Parkinson disease. Nat Neurosci. 2005 May;8(5):657-63. PubMed.
  4. . Genome-wide RNA interference screen identifies previously undescribed regulators of polyglutamine aggregation. Proc Natl Acad Sci U S A. 2004 Apr 27;101(17):6403-8. PubMed.
  5. . C. elegans model identifies genetic modifiers of alpha-synuclein inclusion formation during aging. PLoS Genet. 2008 Mar;4(3):e1000027. PubMed.

Further Reading

Papers

  1. . C. elegans model identifies genetic modifiers of alpha-synuclein inclusion formation during aging. PLoS Genet. 2008 Mar;4(3):e1000027. PubMed.
  2. . Genetic modifiers of tauopathy in Drosophila. Genetics. 2003 Nov;165(3):1233-42. PubMed.
  3. . Identification of genes that modify ataxin-1-induced neurodegeneration. Nature. 2000 Nov 2;408(6808):101-6. PubMed.
  4. . Molecular pathways that influence human tau-induced pathology in Caenorhabditis elegans. Hum Mol Genet. 2006 May 1;15(9):1483-96. PubMed.
  5. . Identification of novel genes that modify phenotypes induced by Alzheimer's beta-amyloid overexpression in Drosophila. Genetics. 2008 Mar;178(3):1457-71. PubMed.

Primary Papers

  1. . C. elegans model identifies genetic modifiers of alpha-synuclein inclusion formation during aging. PLoS Genet. 2008 Mar;4(3):e1000027. PubMed.
  2. . Identification of novel genes that modify phenotypes induced by Alzheimer's beta-amyloid overexpression in Drosophila. Genetics. 2008 Mar;178(3):1457-71. PubMed.