A New Transcription and Performance Interpretation of J.S. Bach\u27s Chromatic Fantasy BWV 903 for Unaccompanied Clarinet

A new transcription of Bach’s Chromatic Fantasy is presented to offset limitations of previous transcriptions by other editors. Certain shortcomings of the clarinet are addressed which add to the difficulty of creating an effective transcription for performance: the inability to sustain more than one note at a time, phrase length limited by breath capacity, and a limited pitch range. The clarinet, however, offers qualities not available to the keyboard that can serve to mitigate these shortcomings: voice-like legato to perform sweeping scalar and arpeggiated gestures, the increased ability to sustain melodic lines, use of dynamics to emphasize phrase shapes and highlight background melodies, and the ability to perform large leaps easily. A unique realization of the arpeggiated section takes advantage of the clarinet’s distinctive registers and references early treatises for an authentic wind instrument approach. A linear analysis, prepared by the author, serves as a basis for making decisions on phrase and dynamic placement. A comparison of various keyboard editions and available analyses provides essential performance practice references in order to produce as authentic a representation of the work as possible. Unique to this transcription is the graphic layout of the single staff, providing a synthesis between standard clarinet notation and the keyboard grand staff. By altering stem directions, and by grouping notes with broken beams and slurs, this version gives the clarinetist a clearer means of visualizing and interpreting the structural content of the original work. The complete transcription is included as an appendix

Comparative Analysis of Serine/Arginine-Rich Proteins across 27 Eukaryotes: Insights into Sub-Family Classification and Extent of Alternative Splicing

Alternative splicing (AS) of pre-mRNA is a fundamental molecular process that generates diversity in the transcriptome and proteome of eukaryotic organisms. SR proteins, a family of splicing regulators with one or two RNA recognition motifs (RRMs) at the N-terminus and an arg/ser-rich domain at the C-terminus, function in both constitutive and alternative splicing. We identified SR proteins in 27 eukaryotic species, which include plants, animals, fungi and “basal” eukaryotes that lie outside of these lineages. Using RNA recognition motifs (RRMs) as a phylogenetic marker, we classified 272 SR genes into robust sub-families. The SR gene family can be split into five major groupings, which can be further separated into 11 distinct sub-families. Most flowering plants have double or nearly double the number of SR genes found in vertebrates. The majority of plant SR genes are under purifying selection. Moreover, in all paralogous SR genes in Arabidopsis, rice, soybean and maize, one of the two paralogs is preferentially expressed throughout plant development. We also assessed the extent of AS in SR genes based on a splice graph approach (http://combi.cs.colostate.edu/as/gmap_SRgenes). AS of SR genes is a widespread phenomenon throughout multiple lineages, with alternative 3′ or 5′ splicing events being the most prominent type of event. However, plant-enriched sub-families have 57%–88% of their SR genes experiencing some type of AS compared to the 40%–54% seen in other sub-families. The SR gene family is pervasive throughout multiple eukaryotic lineages, conserved in sequence and domain organization, but differs in gene number across lineages with an abundance of SR genes in flowering plants. The higher number of alternatively spliced SR genes in plants emphasizes the importance of AS in generating splice variants in these organisms

Transcriptome Analysis of the Model Protozoan, Tetrahymena thermophila, Using Deep RNA Sequencing

Background: The ciliated protozoan Tetrahymena thermophila is a well-studied single-celled eukaryote model organism for cellular and molecular biology. However, the lack of extensive T. thermophila cDNA libraries or a large expressed sequence tag (EST) database limited the quality of the original genome annotation. Methodology/Principal Findings: This RNA-seq study describes the first deep sequencing analysis of the T. thermophila transcriptome during the three major stages of the life cycle: growth, starvation and conjugation. Uniquely mapped reads covered more than 96 % of the 24,725 predicted gene models in the somatic genome. More than 1,000 new transcribed regions were identified. The great dynamic range of RNA-seq allowed detection of a nearly six order-of-magnitude range of measurable gene expression orchestrated by this cell. RNA-seq also allowed the first prediction of transcript untranslated regions (UTRs) and an updated (larger) size estimate of the T. thermophila transcriptome: 57 Mb, or about 55 % of the somatic genome. Our study identified nearly 1,500 alternative splicing (AS) events distributed over 5.2 % of T. thermophila genes. This percentage represents a two order-of-magnitude increase over previous EST-based estimates in Tetrahymena. Evidence of stage-specific regulation of alternative splicing was also obtained. Finally, our study allowed us to completely confirm about 26.8 % of the genes originally predicted by the gene finder, to correct coding sequence boundaries an

Suppression of MAPK11 or HIPK3 reduces mutant Huntingtin levels in Huntington's disease models.

Most neurodegenerative disorders are associated with accumulation of disease-relevant proteins. Among them, Huntington disease (HD) is of particular interest because of its monogenetic nature. HD is mainly caused by cytotoxicity of the defective protein encoded by the mutant Huntingtin gene (HTT). Thus, lowering mutant HTT protein (mHTT) levels would be a promising treatment strategy for HD. Here we report two kinases HIPK3 and MAPK11 as positive modulators of mHTT levels both in cells and in vivo. Both kinases regulate mHTT via their kinase activities, suggesting that inhibiting these kinases may have therapeutic values. Interestingly, their effects on HTT levels are mHTT-dependent, providing a feedback mechanism in which mHTT enhances its own level thus contributing to mHTT accumulation and disease progression. Importantly, knockout of MAPK11 significantly rescues disease-relevant behavioral phenotypes in a knockin HD mouse model. Collectively, our data reveal new therapeutic entry points for HD and target-discovery approaches for similar diseases

Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.National Science Foundation (U.S.) (DB1-0821391)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM089903)National Institutes of Health (U.S.) (P30-ES002109