Publications by authors named "I I Minia"
Article Synopsis
- The study investigates how mRNA molecules move and are processed in different parts of mouse embryonic stem cells, measuring their rates of production, stability, and degradation.
- The researchers used a combination of techniques, including RNA labeling and sequencing, to analyze over 9,000 genes and found that many mature mRNAs have long lifetimes in the nucleus, suggesting that nuclear retention limits their overall flow.
- Additionally, the study reveals that mRNAs are regulated differently based on their location, with unique stability patterns for those associated with membranes and in the cytoplasm, providing insights into gene expression regulation across cellular compartments.
RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time‐dependent kinetic rates during dynamic processes. Here, we present SLAM‐Drop‐seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol‐fixed cells with droplet‐based sequencing to detect newly synthesized and preexisting mRNAs in single cells.
View Article and Find Full Text PDFCoronaviruses modify their single-stranded RNA genome with a methylated cap during replication to mimic the eukaryotic mRNAs. The capping process is initiated by several nonstructural proteins (nsp) encoded in the viral genome. The methylation is performed by two methyltransferases, nsp14 and nsp16, while nsp10 acts as a co-factor to both.
View Article and Find Full Text PDFProtein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions.
View Article and Find Full Text PDFProtein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions.
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