Chernoff, Yury O.

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Yeast models for prion and amyloid diseases

2009-06-15 , Chernoff, Yury O.

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Gene identification in novel eukaryotic genomes by self-training algorithm

2005 , Lomsadze, Alexandre , Ter-Hovhannisyan, Vardges , Chernoff, Yury O. , Borodovsky, Mark

Finding new protein-coding genes is one of the most important goals of eukaryotic genome sequencing projects. However, genomic organization of novel eukaryotic genomes is diverse and ab initio gene finding tools tuned up for previously studied species are rarely suitable for efficacious gene hunting inDNA sequences of a new genome. Gene identification methods based on cDNA and expressed sequence tag (EST) mapping to genomic DNA or those using alignments to closely related genomes rely either on existence of abundant cDNA and EST data and/ or availability on reference genomes. Conventional statistical ab initio methods require large training sets of validated genes for estimating gene model parameters. In practice, neither one of these types of data may be available in sufficient amount until rather late stages of the novel genome sequencing. Nevertheless, we have shown that gene finding in eukaryotic genomes could be carried out in parallel with statistical models estimation directly from yet anonymous genomic DNA. The suggested method of parallelization of gene prediction with the model parameters estimation follows the path of the iterative Viterbi training. Rounds of genomic sequence labeling into coding and non-coding regions are followed by the rounds of model parameters estimation. Several dynamically changing restrictions on the possible range of model parameters are added to filter out fluctuations in the initial steps of the algorithm that could redirect the iteration process away from the biologically relevant point in parameter space. Tests on well-studied eukaryotic genomes have shown that the new method performs comparably or better than conventional methods where the supervised model training precedes the gene prediction step. Several novel genomes have been analyzed and biologically interesting findings are discussed. Thus, a self-training algorithm that had been assumed feasible only for prokaryotic genomes has now been developed for ab initio eukaryotic gene identification.

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Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training

2008-12 , Ter-Hovhannisyan,Vardges , Lomsadze, Alexandre , Chernoff, Yury O. , Borodovsky, Mark

We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects

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Huntingtin toxicity in yeast model depends on polyglutamine aggregation mediated by a prion-like protein Rnq1

2002-06-10 , Meriin, Anatoli B. , Zhang, Xiaoqian , He, Xiangwei , Newnam, Gary P. , Chernoff, Yury O. , Sherman, Michael Y.

The cause of Huntington’s disease is expansion of polyglutamine (polyQ) domain in huntingtin, which makes this protein both neurotoxic and aggregation prone. Here we developed the first yeast model, which establishes a direct link between aggregation of expanded polyQ domain and its cytotoxicity. Our data indicated that deficiencies in molecular chaperones Sis1 and Hsp104 inhibited seeding of polyQ aggregates, whereas ssa1 , ssa2 , and ydj1–151 mutations inhibited expansion of aggregates. The latter three mutants strongly suppressed the polyQ toxicity. Spontaneous mutants with suppressed aggregation appeared with high frequency, and in all of them the toxicity was relieved. Aggregation defects in these mutants and in T sis1–85 were not complemented in the cross to the hsp104 mutant, demonstrating an unusual type of inheritance. Since Hsp104 is required for prion maintenance in yeast, this suggested a role for prions in polyQ aggregation and toxicity. We screened a set of deletions of nonessential genes coding for known prions and related proteins and found that deletion of the RNQ1 gene specifically suppressed aggregation and toxicity of polyQ. Curing of the prion form of Rnq1 from wild-type cells dramatically suppressed both aggregation and toxicity of polyQ. We concluded that aggregation of polyQ is critical for its toxicity and that Rnq1 in its prion conformation plays an essential role in polyQ aggregation leading to the toxicity.

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Phenotypic detection of mouse PrP aggregation in yeast

2008-03-31 , Chernoff, Yury O.