Combined modelling of mRNA decay dynamics and single-molecule imaging in the <i>Drosophila</i> embryo uncovers a role for P-bodies in 5′ to 3′ degradation

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Abstract

Regulation of mRNA degradation is critical for a diverse array of cellular processes and developmental cell fate decisions. Many methods for determining mRNA half-lives rely on transcriptional inhibition or metabolic labelling. Here, we use a non-invasive method for estimating half-lives for hundreds of mRNAs in the early Drosophila embryo. This approach uses the intronic and exonic reads from a total RNA-seq time series and Gaussian process regression to model the dynamics of premature and mature mRNAs. We show how regulation of mRNA stability is used to establish a range of mature mRNA dynamics during embryogenesis, despite shared transcription profiles. Using single-molecule imaging, we provide evidence that, for the mRNAs tested, there is a correlation between short half-life and mRNA association with P-bodies. Moreover, we detect an enrichment of mRNA 3′ ends in P-bodies in the early embryo, consistent with 5′ to 3′ degradation occurring in P-bodies for at least a subset of mRNAs. We discuss our findings in relation to recently published data suggesting that the primary function of P-bodies in other biological contexts is mRNA storage.

Abstract

Regulation of mRNA degradation is critical for a diverse array of cellular processes and developmental cell fate decisions. This study uses modelling of total RNA-seq time series data across early Drosophila embryogenesis to provide estimates of mRNA half-lives; single-molecule imaging reveals that unstable mRNAs are more enriched in P-bodies, as are 3′ mRNA fragments.

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Nicolas Bray, Harold Pimentel, Páll Melsted … (2016)

We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.

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GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

Eran Eden, Roy Navon, Israel Steinfeld … (2009)

Background Since the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results. Results GOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets. This is particularly useful in many typical cases where genomic data may be naturally represented as a ranked list of genes (e.g. by level of expression or of differential expression). GOrilla employs a flexible threshold statistical approach to discover GO terms that are significantly enriched at the top of a ranked gene list. Building on a complete theoretical characterization of the underlying distribution, called mHG, GOrilla computes an exact p-value for the observed enrichment, taking threshold multiple testing into account without the need for simulations. This enables rigorous statistical analysis of thousand of genes and thousands of GO terms in order of seconds. The output of the enrichment analysis is visualized as a hierarchical structure, providing a clear view of the relations between enriched GO terms. Conclusion GOrilla is an efficient GO analysis tool with unique features that make a useful addition to the existing repertoire of GO enrichment tools. GOrilla's unique features and advantages over other threshold free enrichment tools include rigorous statistics, fast running time and an effective graphical representation. GOrilla is publicly available at:

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Imaging individual mRNA molecules using multiple singly labeled probes

Arjun Raj, Patrick van den Bogaard, Scott A. Rifkin … (2008)

We describe a method for imaging individual mRNA molecules in fixed cells by probing each mRNA species with 48 or more short, singly labeled oligonucleotide probes. This makes each mRNA molecule visible as a computationally identifiable fluorescent spot via fluorescence microscopy. We demonstrate simultaneous detection of three mRNA species in single cells and mRNA detection in yeast, nematodes, fruit fly wing discs, mammalian cell lines and neurons.

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Author and article information

Contributors

Lauren Forbes Beadle: Role: ConceptualizationRole: InvestigationRole: Writing – original draftRole: Writing – review & editing

Jennifer C. Love: Role: ConceptualizationRole: InvestigationRole: Writing – original draftRole: Writing – review & editing

Yuliya Shapovalova: Role: ConceptualizationRole: InvestigationRole: SoftwareRole: Writing – original draftRole: Writing – review & editing

Artem Artemev: Role: Software

Magnus Rattray:

ORCID: https://orcid.org/0000-0001-8196-5565

Role: ConceptualizationRole: Funding acquisitionRole: Writing – original draftRole: Writing – review & editing

Hilary L. Ashe:

ORCID: https://orcid.org/0000-0002-8379-7830

Role: ConceptualizationRole: Funding acquisitionRole: Writing – original draftRole: Writing – review & editing

Simon Bullock: Role: Academic Editor

Journal

Journal ID (nlm-ta): PLoS Biol

Journal ID (iso-abbrev): PLoS Biol

Journal ID (publisher-id): plos

Title: PLOS Biology

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Print): 1544-9173

ISSN (Electronic): 1545-7885

Publication date (Electronic): 17 January 2023

Publication date Collection: January 2023

Publication date PMC-release: 17 January 2023

Volume: 21

Issue: 1

Electronic Location Identifier: e3001956

Affiliations

[1 ] Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom

[2 ] Department of Computing, Imperial College London, London, United Kingdom

MRC Laboratory of Molecular Biology, UNITED KINGDOM

Author notes

The authors have declared that no competing interests exist.

[¤]

Current address: Institute for Computing and Information Sciences, Radboud University, Nijmegen, the Netherlands

* E-mail: magnus.rattray@ 123456manchester.ac.uk (MR); hilary.ashe@ 123456manchester.ac.uk (HLA)

Author information

Magnus Rattray https://orcid.org/0000-0001-8196-5565

Hilary L. Ashe https://orcid.org/0000-0002-8379-7830

Article

Publisher ID: PBIOLOGY-D-22-00853

DOI: 10.1371/journal.pbio.3001956

PMC ID: 9882958

PubMed ID: 36649329

SO-VID: 9ecd1b89-a893-42ce-96d2-7655480da5f5

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 13 April 2022

Date accepted : 13 December 2022