RNA-sequencing of PC3M prostate cancer cells depleted of the RNA binding protein HNRNPA2B1
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE198261 | GEO | 2025/03/08
REPOSITORIES: GEO
Similar Datasets
Project description:RNA-sequencing of PC3M prostate cancer cells depleted of the RNA binding protein HNRNPA2B1
| PRJNA814296 | ENA
Project description: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 both RNA mislocalization and transport 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 HNRNPA2B1’s 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.
2024-08-20 | GSE275212 | GEO
Project description:To identify HNRNPA2B1 binding sites on endogenous nuclear RNAs, we performed HITS-CLIP for endogenous HNRNPA2B1 and RNA-seq to analyze the nuclear RNA under either METTL3 or HNRNPA2B1 depletion. Wild type MDA-MB-231 cells were subjected to the HITS-CLIP procedure on immunoprecipitated HNRNPA2B1 associated RNA obtained from the nuclear fraction (Licatalosi D, et al. 2008, Nature 456:464-U22). For RNA-seq, nuclear RNA was extracted from MDA-MB-231 or Hela cells knocked down for METTL3 or HNRNPA2B1.
2015-06-20 | E-GEOD-70061 | biostudies-arrayexpress
Project description:Extracellular vesicles (EVs) are cell-secreted membranous particles contributing to intercellular communication. Coding and non-coding RNAs are widely detected as EV cargo, and RNAbinding proteins (RBPs), such as hnRNPA2B1, have been circumstantially implicated in EV-RNA sorting mechanisms. However, the contribution of competitive RBP-RNA interactions responsible for RNA-sorting outcomes is still unclear, especially for predicting the EV-RNA content. We designed a reverse proteomic analysis exploiting the EV-RNA to identify intracellular protein binders in vitro and used cells expressing a recombinant hnRNPA2B1 to normalize competitive interactions. Interestingly, we prioritized heterogeneous nuclear ribonucleoproteins in networks including RAB proteins and recognizing purine-rich RNA sequences representing a subset of previously identified EXO motifs. A screening campaign using a full-length human hnRNPA2B1 protein and a synthetic purine-rich RNA probe brought to small molecule inhibitors orthogonally validated through biochemical and cell-based approaches. Selected drugs effectively interfered with a post-transcriptional layer impacting secreted EV-RNAs, reducing the vesicular pro-inflammatory miR-221 while counteracting the hnRNPA2B1- or TDP43Q331K-dependent paracrine activation of NF-kB in EV recipient cells. These results demonstrate the relevance of post-transcriptional mechanisms for EV-RNA sorting and the possibility of predicting the EV-RNA quality for developing innovative strategies targeting discrete paracrine functions.
2025-02-24 | PXD052658 | Pride
Project description:RNA-binding proteins (RBPs) are essential for skeletal muscle regeneration and RBP dysfunction causes muscle degeneration and neuromuscular disease (NMD). How the timing of RBP function governs the complex cell fate decisions during muscle regeneration is poorly understood. Here, single cell analysis of skeletal muscle regeneration reveals the timing of NMD-associated RBPs expression in muscle progenitors, including a massive upregulation of Hnrnpa2b1 (A2b1). A2b1 promotes muscle progenitor plasticity by regulating the splicing of RNAs expressed at specific times during the trajectory of muscle differentiation. Using RBP-RNA engagement scoring and machine learning, we accurately predict NMD-associated RBP functionality in directing myogenesis. Together our analysis reveals how A2b1 regulates myogenic plasticity and provides a broadly applicable single cell methodology for examining how RBPs influence complex cell fate trajectories.
2020-06-16 | GSE152467 | GEO
Project description:Unfolded protein response (UPR) is a central stress response pathway in normal cells that is hijacked by tumor cells for their survival. However, how activation of UPR in cancer cells shapes the tumor microenvironment (TME) remain largely unexplored. Here, we investigated the role of IRE1α-XBP1s on modulation of TME dynamics in prostate cancer (PCa).
2024-08-28 | PXD044287 | Pride
Project description:RNA-binding proteins (RBPs) are crucial factors of post-transcriptional gene regulation and their modes of action are intensely investigated. At the center of attention are RNA motifs that guide where RBPs bind. However, sequence motifs recognized by RBPs are typically a poor predictor of RBP-RNA interactions in vivo. It is hence believed that many RBPs recognize RNAs as complexes, to increase specificity and regulatory potential. To probe the potential for RBP–RBP complex formation, we assembled a library of 978 mammalian RBPs and used rec-Y2H screening to detect direct interactions between RBPs, sampling >1M possible interactions. We discovered 1994 new interactions and demonstrate that our interaction screening discovers RBP pairs that bind RNAs adjacently. We further find that the mRNA binding region preferences of an RBP can deviate, depending on its adjacently binding interaction partner. Finally, we reveal novel RBP–RBP interaction networks among major RNA processing steps and show that RBP mutations observed in cancer rewire spliceosomal interaction networks.
2021-09-01 | E-MTAB-9612 | biostudies-arrayexpress
Project description:To identify HNRNPA2B1 binding sites on endogenous nuclear RNAs, we performed HITS-CLIP for endogenous HNRNPA2B1 and RNA-seq to analyze the nuclear RNA under either METTL3 or HNRNPA2B1 depletion.
2015-06-20 | GSE70061 | GEO
Project description:A data-entrained computational model for
testing the regulatory logic of the vertebrate unfolded protein
response
This model is described in the article:
A data-entrained
computational model for testing the regulatory logic of the
vertebrate unfolded protein response.
Diedrichs DR, Gomez JA, Huang CS,
Rutkowski DT, Curtu R.
Mol. Biol. Cell 2018 Apr; :
mbcE17090565
Abstract:
The vertebrate unfolded protein response (UPR) is
characterized by multiple interacting nodes among its three
pathways, yet the logic underlying this regulatory complexity
is unclear. To begin to address this issue, we created a
computational model of the vertebrate UPR that was entrained
upon and then validated against experimental data. As part of
this validation, the model successfully predicted the
phenotypes of cells with lesions in UPR signaling, including a
surprising and previously unreported differential role for the
eIF2? phosphatase GADD34 in exacerbating severe stress but
ameliorating mild stress. We then used the model to test the
functional importance of a feed-forward circuit within the
PERK/CHOP axis, and of cross-regulatory control of BiP and CHOP
expression. We found that the wiring structure of the UPR
appears to balance the ability of the response to remain
sensitive to ER stress yet also to be rapidly deactivated by
improved protein folding conditions. This model should serve as
a valuable resource for further exploring the regulatory logic
of the UPR.
This model is hosted on
BioModels Database
and identified by:
MODEL1803300000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000703 | BioModels
Project description:Erguler2013 - Unfolded protein stress response
The model investigates the mechanism by which UPR (unfolded protein response) outcome switches between survival and death.
This model is described in the article:
A mathematical model of the unfolded protein stress response reveals the decision mechanism for recovery, adaptation and apoptosis.
Erguler K, Pieri M, Deltas C.
BMC Syst Biol. 2013 Feb 21;7(1):16.
Abstract:
BACKGROUND: The unfolded protein response (UPR) is a major signalling cascade acting in the quality control ofprotein folding in the endoplasmic reticulum (ER). The cascade is known to play an accessory rolein a range of genetic and environmental disorders including neurodegenerative and cardiovasculardiseases, diabetes and kidney diseases. The three major receptors of the ER stress involved withthe UPR, i.e. IRE1a, PERK and ATF6, signal through a complex web of pathways to convey anappropriate response. The emerging behaviour ranges from adaptive to maladaptive depending on theseverity of unfolded protein accumulation in the ER; however, the decision mechanism for the switchand its timing have so far been poorly understood.
RESULTS:
Here, we propose a mechanism by which the UPR outcome switches between survival and death.We compose a mathematical model integrating the three signalling branches, and perform a comprehensivebifurcation analysis to investigate possible responses to stimuli. The analysis reveals threedistinct states of behaviour, low, high and intermediate activity, associated with stress adaptation, tolerance,and the initiation of apoptosis. The decision to adapt or destruct can, therefore, be understoodas a dynamic process where the balance between the stress and the folding capacity of the ER playsa pivotal role in managing the delivery of the most appropriate response. The model demonstratesfor the first time that the UPR is capable of generating oscillations in translation attenuation and theapoptotic signals, and this is supplemented with a Bayesian sensitivity analysis identifying a set ofparameters controlling this behaviour.
CONCLUSIONS:
This work contributes largely to the understanding of one of the most ubiquitous signalling pathwaysinvolved in protein folding quality control in the metazoan ER. The insights gained have direct consequenceson the management of many UPR-related diseases, revealing, in addition, an extended listof candidate disease modifiers. Demonstration of stress adaptation sheds light to how preconditioningmight be beneficial in manifesting the UPR outcome to prevent untimely apoptosis, and paves the wayto novel approaches for the treatment of many UPR-related conditions.
In the paper, PERKA refers to the amount of phosphorylated PERK monomer. However, it refers to the active complex in the model. The complex with the model parameterization is formed of 4 monomers (n=4). So, the value of PERKA should be multiplied by 4, in order to generate the figures in the paper (eg. Figure 12).
An additional parameter (tmr=10)) is used in the model. This parameter is not mentioned in the paper. The model values of kf(=10) and kr(=1) are not consistent with that of the paper (kf=100, kr=10, in the paper). However, this is corrected by the introduction of "tmr" in the model, which is multiplied with kf and kr to get the resulting values.
The term "tmr" was missing in the kinetic laws of the reactions reu7 and reu8, in the original model. This has been corrected as per the author's request.
This model is hosted on BioModels Database
and identified
by: MODEL1302180000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to the public
domain worldwide. Please refer to CC0 Public Domain
Dedication
for more information.
2024-09-02 | BIOMD0000000446 | BioModels