Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity

Assemblies of huntingtin (HTT) fragments with expanded polyglutamine (polyQ) tracts are a pathological hallmark of Huntington's disease (HD). The molecular mechanisms by which these structures are formed and cause neuronal dysfunction and toxicity are poorly understood. Here, we utilized available gene expression data sets of selected brain regions of HD patients and controls for systematic interaction network filtering in order to predict disease-relevant, brain region-specific HTT interaction partners. Starting from a large protein-protein interaction (PPI) data set, a step-by-step computational filtering strategy facilitated the generation of a focused PPI network that directly or indirectly connects 13 proteins potentially dysregulated in HD with the disease protein HTT. This network enabled the discovery of the neuron-specific protein CRMP1 that targets aggregation-prone, N-terminal HTT fragments and suppresses their spontaneous self-assembly into proteotoxic structures in various models of HD. Experimental validation indicates that our network filtering procedure provides a simple but powerful strategy to identify disease-relevant proteins that influence misfolding and aggregation of polyQ disease proteins.DFG [SFB740, 740/2-11, SFB618, 618/3-09, SFB/TRR43 A7]; BMBF(NGFN-Plus) [01GS08169-73, 01GS08150, 01GS08108]; HDSA Coalition for the Cure; EU (EuroSpin) [Health-F2-2009-241498, HEALTH-F2-2009-242167]; Helmholtz Association (MSBN, HelMA) [HA-215]; FCT [IF/00881/2013]info:eu-repo/semantics/publishedVersio

Interactionsnetzwerke und gestörte zellulare Funktionen in Schizophrenie

Schizophrenia (SCZ) is a devastating psychiatric disease with a worldwide prevalence of approximately 1%. It is therefore one of the leading causes of public health burden. Twin studies suggest that the impact of heritable factors causing SCZ is very high. Thus, in the recent past huge efforts were made to identify the genetic factors responsible for the disease. Many genes and genomic variations have already been associated with schizophrenia, but the interplay of these genes, as well as the precise mechanism of how they are involved in the development of schizophrenia is still not fully understood. To get a deeper insight into the role of SCZ associated genes I used protein-protein interaction analyses combined with bioinformatical methods. My goal was to answer three mayor questions: The first question was, if schizophrenia associated proteins form clusters within protein-protein interaction networks and how these clusters are involved in functional processes. For that reason, a propagation-based algorithm was invented that identified five clusters with high potential for SCZ relevance. The two highest scoring clusters represented known synaptic complexes and were validated with LuTHy assays. The second question was, if there is a potential SCZ relevance of a set of 39 protein coding candidate genes of a small exome sequencing study and if their importance could be prioritized. Therefore, a protein-protein interaction network was created, using the HIPPIE database, including all medium high confident interactions of these genes. In a next step the density of SCZ associated proteins within the created network were compared to all HIPPIE proteins, not already included in the created network and their connectivity to SCZ related proteins. Chi-squared tests revealed indeed a significant enrichment of schizophrenia associated proteins within the created candidate protein-protein interaction network. In order to rank candidate genes, the browser based ToppNet tool was used. The third question should shed light on the functional role of ZNF804A. This protein had repeatably been associated with schizophrenia before, but its functional role remained unclear. By following the hypothesis “guilt by association”, a proteome scaled Y2H screen was preformed and 18 new ZNF804A interacting proteins had been been identified with functional enrichment for RNA binding, the circadian clock and inflammation pathways. By using DULIP and LuTHy assays, 67% of identified ZNF804A interactions were validated. The functional implications of ZNF804A with the most promising interaction partner STAT2 were further analyzed. STAT2 is a key protein of the intracellular interferon response and ZNF804A was identified to co-translocate with STAT2 into the nucleus upon interferon induction. Overexpression, as well as CRISPR/Cas9 induced knock down of ZNF804A indicated a potential modulating role of ZNF804A in STAT2 mediated interferon response. The results of my work help to better understand the role of SCZ related genes and their interplay. Additionally, my studies demonstrate that protein-protein interaction analyses are able to gather information on different levels and are a key tool set to reveal the molecular implications of genes associated with schizophrenia

SNPs in the coding region of the metastasis-inducing gene MACC1 and clinical outcome in colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Colorectal cancer is one of the main cancers in the Western world. About 90% of the deaths arise from formation of distant metastasis. The expression of the newly identified gene metastasis associated in colon cancer 1 (MACC1) is a prognostic indicator for colon cancer metastasis. Here, we analyzed for the first time the impact of single nucleotide polymorphisms (SNPs) in the coding region of MACC1 for clinical outcome of colorectal cancer patients. Additionally, we screened met proto-oncogene (Met), the transcriptional target gene of MACC1, for mutations.</p> <p>Methods</p> <p>We sequenced the coding exons of MACC1 in 154 colorectal tumors (stages I, II and III) and the crucial exons of Met in 60 colorectal tumors (stages I, II and III). We analyzed the association of MACC1 polymorphisms with clinical data, including metachronous metastasis, UICC stages, tumor invasion, lymph node metastasis and patients’ survival (n = 154, stages I, II and III). Furthermore, we performed biological assays in order to evaluate the functional impact of MACC1 SNPs on the motility of colorectal cancer cells.</p> <p>Results</p> <p>We genotyped three MACC1 SNPs in the coding region. Thirteen % of the tumors had the genotype cg (rs4721888, L31V), 48% a ct genotype (rs975263, S515L) and 84% a gc or cc genotype (rs3735615, R804T). We found no association of these SNPs with clinicopathological parameters or with patients’ survival, when analyzing the entire patients’ cohort. An increased risk for a shorter metastasis-free survival of patients with a ct genotype (rs975263) was observed in younger colon cancer patients with stage I or II (P = 0.041, n = 18). In cell culture, MACC1 SNPs did not affect MACC1-induced cell motility and proliferation.</p> <p>Conclusion</p> <p>In summary, the identification of coding MACC1 SNPs in primary colorectal tumors does not improve the prediction for metastasis formation or for patients’ survival compared to MACC1 expression analysis alone. The ct genotype (rs975263) might be associated with a reduced survival for younger colon cancer patients in early stages. However, further studies with larger sample sizes are needed.</p

mHTT Seeding Activity:A Marker of Disease Progression and Neurotoxicity in Models of Huntington's Disease

Self-propagating, amyloidogenic mutant huntingtin (mHTT) aggregates may drive progression of Huntington's disease (HD). Here, we report the development of a FRET-based mHTT aggregate seeding (FRASE) assay that enables the quantification of mHTT seeding activity (HSA) in complex biosamples from HD patients and disease models. Application of the FRASE assay revealed HSA in brain homogenates of presymptomatic HD transgenic and knockin mice and its progressive increase with phenotypic changes, suggesting that HSA quantitatively tracks disease progression. Biochemical investigations of mouse brain homogenates demonstrated that small, rather than large, mHTT structures are responsible for the HSA measured in FRASE assays. Finally, we assessed the neurotoxicity of mHTT seeds in an inducible Drosophila model transgenic for HTTex1. We found a strong correlation between the HSA measured in adult neurons and the increased mortality of transgenic HD flies, indicating that FRASE assays detect disease-relevant, neurotoxic, mHTT structures with severe phenotypic consequences in vivo. Ast et al. present the development of a FRET-based aggregate seeding (FRASE) assay that facilitates the detection and quantification of mHTT seeding activity (HSA) in complex biosamples. They show that HSA is detectable in brains of presymptomatic Huntington's disease (HD) mice and correlates with disease progression and neurotoxicity in HD transgenic flies