Back

The RNA binding protein HNRNPA2B1 regulates RNA abundance and motor protein activity in neurites

Lo, J.; Vaeth, K. F.; Bhardwaj, G.; Mukherjee, N.; Russ, H. A.; Moore, J.; Taliaferro, M.

2024-08-26 molecular biology Community evaluation
10.1101/2024.08.26.609768 bioRxiv
Show abstract

RNA molecules are localized to subcellular regions through interactions between localization-regulatory cis-elements and trans-acting RNA binding proteins (RBPs). However, the identities of RNAs whose localization is regulated by a specific RBP as well as the impacts of that RNA localization on cell function have generally remained unknown. Here, we demonstrate that the RBP HNRNPA2B1 acts to keep specific RNAs out of neuronal projections. Using subcellular fractionation, high-throughput sequencing, and single molecule RNA FISH, we find that hundreds of RNAs demonstrate markedly increased abundance in neurites in HNRNPA2B1 knockout cells. These RNAs often encode motor proteins and are enriched for known HNRNPA2B1 binding sites and motifs in their 3' UTRs. The speed and processivity of microtubule-based transport is impaired in these cells, specifically in their neurites. HNRNPA2B1 point mutations that increase its cytoplasmic abundance relative to wildtype lead to stronger suppression of RNA mislocalization defects than seen with wildtype HNRNPA2B1. We further find that the subcellular localizations of HNRNPA2B1 target RNAs are sensitive to perturbations of RNA decay machinery, suggesting that it is HNRNPA2B1s known role in regulating RNA stability that may explain these observations. These findings establish HNRNPA2B1 as a negative regulator of neurite RNA abundance and link the subcellular activities of motor proteins with the subcellular abundance of the RNAs that encode them.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.