Myg1 exonuclease couples the nuclear and mitochondrial translational programs through RNA processing.

Semi-autonomous functioning of mitochondria in eukaryotic cell necessitates coordination with nucleus. Several RNA species fine-tune mitochondrial processes by synchronizing with the nuclear program, however the involved components remain enigmatic. In this study, we identify a widely conserved dually localized protein Myg1, and establish its role as a 3'-5' RNA exonuclease. We employ mouse melanoma cells, and knockout of the Myg1 ortholog in Saccharomyces cerevisiae with complementation using human Myg1 to decipher the conserved role of Myg1 in selective RNA processing. Localization of Myg1 to nucleolus and mitochondrial matrix was studied through imaging and confirmed by sub-cellular fractionation studies. We developed Silexoseqencing, a methodology to map the RNAse trail at single-nucleotide resolution, and identified in situ cleavage by Myg1 on specific transcripts in the two organelles. In nucleolus, Myg1 processes pre-ribosomal RNA involved in ribosome assembly and alters cytoplasmic translation. In mitochondrial matrix, Myg1 processes 3'-termini of the mito-ribosomal and messenger RNAs and controls translation of mitochondrial proteins. We provide a molecular link to the possible involvement of Myg1 in chronic depigmenting disorder vitiligo. Our study identifies a key component involved in regulating spatially segregated organellar RNA processing and establishes the evolutionarily conserved ribonuclease as a coordinator of nucleo-mitochondrial crosstalk.

Synthesizer: Expediting synthesis studies from context-free data with information retrieval techniques.

Scientists have unprecedented access to a wide variety of high-quality datasets. These datasets, which are often independently curated, commonly use unstructured spreadsheets to store their data. Standardized annotations are essential to perform synthesis studies across investigators, but are often not used in practice. Therefore, accurately combining records in spreadsheets from differing studies requires tedious and error-prone human curation. These efforts result in a significant time and cost barrier to synthesis research. We propose an information retrieval inspired algorithm, Synthesize, that merges unstructured data automatically based on both column labels and values. Application of the Synthesize algorithm to cancer and ecological datasets had high accuracy (on the order of 85-100%). We further implement Synthesize in an open source web application, Synthesizer (https://github.com/lisagandy/synthesizer). The software accepts input as spreadsheets in comma separated value (CSV) format, visualizes the merged data, and outputs the results as a new spreadsheet. Synthesizer includes an easy to use graphical user interface, which enables the user to finish combining data and obtain perfect accuracy. Future work will allow detection of units to automatically merge continuous data and application of the algorithm to other data formats, including databases.