Y-RNA activates RIG-I gain-of-function mutants in the kidney, causing lethal autoimmune diseases including lupus nephritis.

Understanding how cells turn genes on and off is one of biology's most enduring mysteries. Now, a new technology developed by chemist Brian Liau and his collaborators at Harvard offers an unprecedented window into this activity, an advance that has the potential to transform our understanding of the basic science as well as the treatment of genetic diseases.

Li et al. show that the chromatin-associated long noncoding RNA Pvt1 is a negative regulator of Myc transcription by increasing local RNA concentration and limiting bursting from the Myc promoter. This mechanism serves to restrain Myc levels and restrict its proliferative and oncogenic activities.

Genome-wide association studies (GWAS) identify numerous disease-linked genetic variants at noncoding genomic loci, yet therapeutic progress is hampered by the challenge of deciphering the regulatory roles of these loci in tissue-specific contexts. Single-cell multimodal assays that simultaneously profile chromatin accessibility and gene expression could predict tissue-specific causal links between noncoding loci and the genes they affect. However, current computational strategies either neglect the causal relationship between chromatin accessibility and transcription or lack variant-level precision, aggregating data across genomic ranges due to data sparsity. To address this, we introduce GrID-Net, a graph neural network approach that generalizes Granger causal inference to detect new causal locus–gene associations in graph-structured systems such as single-cell trajectories. Inspired by the principles of optical parallax, which reveals object depth from static snapshots, we hypothesi

ABSTRACT Given that the SARS-CoV-2 virus, and the COVID-19 pandemic, constitutes a major environmental challenge faced by billions of people worldwide, we investigated whether paternal pre-conceptual SARS-CoV-2 infection has impacts on sperm RNA conten...