Neurons provide a rich setting for studying post-transcriptional control. Here, we investigate the landscape of translational control in neurons and search for mRNA features that explain differences in translational efficiency (TE), considering the interplay between TE, mRNA poly(A)-tail lengths, microRNAs, and neuronal activation. In neurons and brain tissues, TE correlates with tail length, and a few dozen mRNAs appear to undergo cytoplasmic polyadenylation upon light or chemical stimulation. However, the correlation between TE and tail length is modest, explaining <5% of TE variance, and even this modest relationship diminishes when accounting for other mRNA features. Thus, tail length appears to affect TE only minimally. Accordingly, miRNAs, which accelerate deadenylation of their mRNA targets, primarily influence target mRNA levels, with no detectable effect on either steady-state tail lengths or TE. Larger correlates with TE include codon composition and predicted mRNA folding energy. When combined in a model, the identified correlates explain 38%-45% of TE variance. These results provide a framework for considering the relative impact of factors that contribute to translational control in neurons. They indicate that when examined in bulk, translational control in neurons largely resembles that of other types of post-embryonic cells. Thus, detection of more specialized control might require analyses that can distinguish translation occurring in neuronal processes from that occurring in cell bodies.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9074895PMC
http://dx.doi.org/10.1261/rna.079046.121DOI Listing

Publication Analysis

Top Keywords

translational efficiency
8
micrornas neuronal
8
neuronal activation
8
activation neurons
8
tail length
8
interplay translational
4
efficiency polya
4
polya tails
4
tails micrornas
4
neurons provide
4

Similar Publications

Background: Uveal melanoma (UM) is the most common intraocular tumor in adults, arises either de novo from normal choroidal melanocytes (NCMs) or from pre-existing nevi that stem from NCMs and are thought to harbor UM-initiating mutations, most commonly in GNAQ or GNA11. However, there are no commercially available NCM cell lines, nor is there a detailed protocol for developing an oncogene-mutated CM line (MutCM) to study UM development. This study aimed to establish and characterize premalignant CM models from human donor eyes to recapitulate the cell populations at the origin of UM.

View Article and Find Full Text PDF

Background: Chronic hepatitis B virus (HBV) infection is a major risk for development of hepatocellular carcinoma (HCC), a frequent malignancy with a poor survival rate. HBV infection results in significant endoplasmic reticulum (ER) stress and activation of the unfolded protein response (UPR) signaling, a contributing factor to carcinogenesis. As part of the UPR, the ER-associated degradation (ERAD) pathway is responsible for removing the burden of misfolded secretory proteins, to re-establish cellular homeostasis.

View Article and Find Full Text PDF

Making Proteins with Electricity.

Rev Physiol Biochem Pharmacol

January 2025

Institute of Medical Sciences, University of Aberdeen, Aberdeen, Scotland, UK.

Ribosomes use multiple electrical forces to regulate new protein construction, to ensure efficient protein cotranslation, chaperoning, and folding. When these electrical regulatory forces are disrupted as in point charge mutations, specific disease occurs from aberrantly folded proteins. α1 antitrypsin deficiency is perhaps the best-known misfolded protein disease and is covered in some detail.

View Article and Find Full Text PDF

Dysregulation of long non-coding RNAs (lncRNAs) is common in colorectal cancer liver metastasis (CRLM). Emerging evidence links lncRNAs to multiple stages of metastasis from initial migration to colonization of distant organs. In this study we investigated the role of lncRNAs in metabolic reprogramming during CRLM using patient-derived organoid (PDO) models.

View Article and Find Full Text PDF

An in-depth review of AI-powered advancements in cancer drug discovery.

Biochim Biophys Acta Mol Basis Dis

January 2025

AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan; In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan. Electronic address:

The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep learning and advanced data analytics, in accelerating key stages of the drug discovery process: target identification, drug design, clinical trial optimization, and drug response prediction. Cutting-edge tools such as DrugnomeAI and PandaOmics have made substantial contributions to therapeutic target identification, while AI's predictive capabilities are driving personalized treatment strategies.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!