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Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity

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Summary of a great paper by Yeger-Lotem et al, on their new algorithm ResponseNet which can be used to combine data from mRNA profiling and genetic library screens to devise protein/gene-interaction …

Summary of a great paper by Yeger-Lotem et al, on their new algorithm ResponseNet which can be used to combine data from mRNA profiling and genetic library screens to devise protein/gene-interaction networks. They used alpha-synuclein overexpression data as a working example.

http://www.nature.com/ng/journal/v41/n3/abs/ng.337.html

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  • 1. Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity Yeger-Lotem, Riva, Su, Gitler, Cashikar, King, Auluck, Geddie, Valastyan, Karger, Lindquist, Fraenkel Nature Genetics 2009, 41:3.
  • 2. Introduction
    • Study of cellular responses to pertubations increasingly depends on high-throughput techniques i.e. mRNA profiling and genetic library screens.
    • But, both approaches have limitations (see next slide).
    • ResponseNet : an algorithm that connects both approaches, identifies molecular interactions between genes/proteins, and allows construction of cellular response pathways.
    • In this study ResponseNet is applied to a yeast model of alpha-synuclein overexpression and toxicity, highlighting several response pathways and interaction networks.
  • 3.
    • mRNA profiling : Provides insight into transcriptional changes following pertubations, but doesn‘t elucidate the pathway needed to achieve said differential expression. Results enriched for metabolic processes.
    • Genetic screening : Identifies genetic ‚hits‘ (genes that are functionally relevant to response) by overexpression/knockdown/etc, but functional relationship often unclear and some genes are missed. Genetic hits typically involved in regulation e.g. transcription, signal transduction.
    • Both approaches are biased towards detecting certain genes/proteins and show little overlap in their findings e.g. well-studied DNA damage response pathway .
  • 4. Comparing genetic hits and differentially expressed genes
  • 5. Hypothesis : „Some of the genetic hits, which are enriched for response regulators, will be connected via regulatory pathways to the differentially expressed genes, which are the output of such pathways, via components of the response that are missing from the experimental data.“
  • 6. ResponseNet algorithm for identifying response networks
    • : Network model of yeast interactome made of 5622 proteins and 5510 genes with 57955 gene/protein or protein/protein interactions.
    • : Protein/protein interactions are the usual. If a genetic hit is to be connected to a differentially expressed gene, it has to be linked by a transcriptional regulator.
    • : Interactions weighted by the flow algorithm: How strong is the experimental evidence? Are the proteins part of one pathway? Are there undetected intermediary proteins easing the flow from „source node“ to „sink node“?
  • 7. Graphical representation of the relation between genetic and transcriptional profiling data corresponding to a specific perturbation.
  • 8. Network examples:
    • ‚ Traditional‘ hairball network connecting 193 nodes & 778 edges of STE5 deletion strain.
    • ResponseNet network connecting the same data but weighted & with intermediary nodes.
    • ResponseNet subnetwork of DNA damage response pathway; only highly ranked nodes.
  • 9. Validation of the ResponseNet algorithm
    • Combined data from screening of genes associated with Ste5 (scaffold protein that coordinates MAP kinase cascade activated by pheromone) and differentially expressed genes in a strain lacking Ste5. Found proteins in the pheromone response pathway.
    • Combining results of two screens for vital genes and differentially expressed genes following treatment with Methyl Methanesulfonate (MMS) revealed a network of 258 proteins (166 of these intermediate) and 361 connections, including highly ranked proteins involved in response to DNA damage, DNA repair, signal transduction and transcription (validated by years of ‚traditional‘ research).
  • 10. Cellular response to DNA damaging MMS
  • 11. Mapping the cellular responses to alpha-synuclein toxicity
    • Expression of human alpha-synuclein in yeast results in similar deficits to those observed in mammals.
    • Screened results of 5500 overexpression yeast strains, identifying 55 suppressors and 22 enhancers of alpha-syn toxicity incl. vesicle-trafficking genes, kinases/phosphatases, ubiquitin-related proteins, transcriptional regulators, manganese transporters, trehalose-biosynthesis genes
    • One particular hit was ATP13A2 (PARK9) , see also Gitler et al (same issue) who did work in yeast and C. elegans .
    • mRNA profiling highlighted genes with oxidoreductase activity (up), ribosomal genes (down), mitochondrial proteins (down).
    • As exprected, only four genes overlapped.
  • 12. Results of genetic screen of alpha-synuclein overexpressing yeast strains
  • 13. Results using ResponseNet
    • Linkage of 34 genetic hits and 166 differentially expressed genes via 106 intermediary proteins.
    • Major cellular pathways: cell cycle regulation, ubiquitin-dependent protein degradation, vesicle-trafficking.
  • 14. Results using ResponseNet II
  • 15. Results using ResponseNet III…
  • 16. Nitrosative Stress : Only genetic hit found was Fzf1, but it‘s connected to 4 upregulated transcripts, including PDI. : PDI (protein disulfide isomerase) protects from neurotoxicity resulting from ER stress and protein misfolding. : PDI is S-nitrosylated in PD. : Also, alpha-synuclein overexpression increased S-nitrosylation, even though yeast was thought not to produce NO until recently.
  • 17. Heat Shock : While heat-shock genes were absent from the list of genetic suppressors, ResponseNet predicted involvement of Hsp90 and Hsf1. : Connections provide an explanation for the toxicity-suppressing effects of overexpressing Gip2, which is upstream of Hsf1.
  • 18. Mevalonate-ergosterol biosynthesis pathway : Pathway targeted by cholesterol-lowering statins, responsible for sterol, farnesyl (for vesicle trafficking), and ubiquinone synthesis. : Hrd1 (regulator of statin target) and Hap1 (transcriptional regulator) both highly ranked. : Lovastatin (HMG-CoA reductase inhibitor) further reduced the growth of strains overexpressing alpha-synuclein, but not WT or strains expressing Huntingtin. Patients appear to have lower low-density lipoprotein (LDL) levels than spouses, and in a study of Japanese men, low LDL preceded PD.
  • 19. The target of rapamycin (TOR) pathway : Tor1 and Tor2 proteins as well as some TFs have been predicted by ResponseNet. :TOR-inhibitor rapamycin enhances alpha-synuclein toxicity, which is strange since other studies suggested that rapamycin treatment might be beneficial in PD (could be due to the big range of processes associated with TOR activation).
  • 20. Discussion
    • ResponseNet allows for uncovering genetic modifiers & differentially expressed targets which would otherwise be missed by either approach alone, and can predict unknown intermediary proteins.
    • Flow algorithm enables weighting of results without imposing a priori restrictions on the output (like maximum pathway length).
    • Overexpression screen identified many genetic hits that were previously implicated with neuropathology/PD but not directly linked to alpha-synuclein.
    • ResponseNet provided functional context for said genetic hits identified e.g. the mevalonate-ergosterol pathway which has recently been linked to PD and could alter downstream pathways associated with mitochondrial dysfunction and vesicle trafficking.
    • ResponseNet allows for meaningful integration of high-throughput genetic and transcriptional data, thereby increasing the usefulness of these increasingly common assays.
  • 21. Disclaimer
    • Yes, the images were shamelessly stolen from the paper. If you – the author of people from Nature – don‘t like that, please tell me and I‘ll put everything done. Just don‘t sue me please, I‘m a lowly PhD student.