Author Correction: Impaired cAMP-PKA-CREB1 signalling drives mitochondrial dysfunction in skeletal muscle during cancer cachexia.
Explore the source record for details and available documents.
Published Erratum↗
Biomedical subjects
Vittorio Sartorelli
Publications and source records attributed to Vittorio Sartorelli.
The DND1-NANOS3 complex shapes the primordial germ cell transcriptome via a heptanucleotide sequence in mRNA 3' UTRs.
The RNA-binding proteins DND1 and NANOS3 are essential for primordial germ cell survival1-5. Their co-immunoprecipitation and overlapping loss-of-function phenotypes suggest joint function6-8, yet how they co-regulate target mRNAs remains unclear. Here, we developed Tandem PAR-CLIP and identified a DND1-NANOS3 ribonucleoprotein that specifically recognizes an AUGAAUU heptanucleotide on target mRNAs, termed the NANOS3-dependent DND1 Recognition Element (N3-DRE). mRNAs containing 3'-UTR N3-DREs are aberrantly upregulated in DND1- or NANOS3-deficient germ cells and encode key cell-cycle and epigenome regulators, such as CDK1. Genome editing showed that the N3-DRE is essential for Cdk1 repression in mouse PGCs in vivo. A 1.7-Å crystal structure of the ternary complex of DND1, NANOS3, and CDK1-N3-DRE RNA revealed a continuous RNA-binding surface that confers high-affinity, sequence-specific recognition. Together, these findings define the molecular and functional basis of N3-DRE-mediated mRNA regulation in germ cell development. Moreover, we provide a paradigm of two RNA-binding proteins with low (DND1) or no (NANOS3) intrinsic sequence-specificity, jointly building a high-information-content RNA sequence motif that is different from the sum of their individual preferences. Because RNA-binding protein specificities are typically studied individually9-13, rather than in the context of ribonucleoproteins, this type of "two-factor authorization" may be an underappreciated mechanism to protect posttranscriptional gene regulatory networks from aberrant expression of an individual ribonucleoprotein component.
Journal Article↗
A deep learning framework for denoising and ordering scRNA-seq data using adversarial autoencoder with dynamic batching.
Single-cell RNA sequencing (scRNA-seq) provides high resolution of cell-to-cell variation in gene expression and offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, technical challenges such as low capture rates and dropout events can introduce noise in data analysis. Here, we present a deep learning framework, called the dynamic batching adversarial autoencoder (DB-AAE), for denoising scRNA-seq datasets. First, we describe steps to set up the computing environment, training, and tuning. Then, we depict the visualization of the denoising results. For complete details on the use and execution of this protocol, please refer to Ko et al.1.
Deep Learning↗