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Wang, T. R.

Publications and source records attributed to Wang, T. R..

2 recordsLinked to original sources

Phase Behavior of TDP-43 and hnRNP H1: From Soluble and Aggregated States to Liquid-Like Droplets

Cytotoxic inclusions of TAR DNA-binding protein 43 (TDP-43) have been identified in various neurodegenerative diseases. Mediated by its low-complexity C-terminal domain (CTD), TDP-43 can undergo liquid-liquid phase separation (LLPS) and form aggregates and fibrils. These phase transitions are associated with pathological TDP-43 mislocalization and cytoplasmic aggregation. What determines whether and when TDP-43 adopts a liquid droplet state or forms solid aggregates, transiently or irreversibly? A full understanding of the underlying biophysical mechanisms will help explain the cellular toxicity of aberrant forms of TDP-43 and similar proteins, such as some of the large family of RNA-binding proteins. In this study, we generated recombinant full-length TDP-43 and maintained soluble-protein working solutions without the need of a solubility tag. By focusing on conditions that tune the electrostatic characteristics, we induced phase transitions among states of soluble TDP-43, solid aggregates, and liquid droplet phases in an expedient and controlled manner. We also obtained soluble working solutions of TDP-CTD and another crucial splicing factor, hnRNPH1, and investigated their phase behaviors as individual proteins and their interactions between each other during phase transitions. Using this system, we discovered a previously unreported aggregate-to-droplet transition and found evidence that phase separation of low-complexity domain (LCD)-containing proteins likely involves domain matching when they coaggregate out of solution.

molecular biology↗

g.nome, A Transparent Bioinformatics Pipeline that Enables Differential Expression and Alternative Splicing Analysis by Non-Computational Biologists

Reproducibility and accessibility are cardinal principles in the rapidly evolving field of bioinformatics. As the collection of biological data grows, proper use of pipelines to analyze datasets can become a bottleneck restricting efficient analysis. Biologists who collect data and test hypotheses may not have strong computational backgrounds and may not be able to fully understand the underlying strengths and weaknesses of computational approaches or fully exploit their data. Some data may be misunderstood and, perhaps more importantly, critical findings may remain unobserved. High throughput RNA sequencing (RNAseq) has advanced our understanding of transcriptomics across diverse applications. Here we introduce g.nome, a bioinformatics platform that integrates contemporary tools necessary for independent analysis. A user-friendly graphical interface simplifies running jobs and allows simplified analysis of different datasets by non-bioinformaticians. g.nome was used to analyze the consequences of localizing the critical RNAi factor argonaute (AGO) to nuclei of colorectal cancer cell line HCT116. Analysis using the pipeline facilitated the straightforward identification of splicing changes and the prioritization of these splicing changes for validation and further experimental analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/652286v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f0fa7org.highwire.dtl.DTLVardef@ccac5borg.highwire.dtl.DTLVardef@147c331org.highwire.dtl.DTLVardef@5fe66c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗