Concerted modification of nucleotides at functional centers of the ribosome revealed by single-molecule RNA modification profiling
- University of California, Santa Cruz, United States
- Colegio Santa Francisca Romana, Colombia
- Monta Vista High School, United States
Abstract
Nucleotides in RNA and DNA are chemically modified by numerous enzymes that alter their function. Eukaryotic ribosomal RNA (rRNA) is modified at more than 100 locations, particularly at highly conserved and functionally important nucleotides. During ribosome biogenesis, modifications are added at various stages of assembly. The existence of differently modified classes of ribosomes in normal cells is unknown because no method exists to simultaneously evaluate the modification status at all sites within a single rRNA molecule. Using a combination of yeast genetics and nanopore direct RNA sequencing, we developed a reliable method to track the modification status of single rRNA molecules at 37 sites in 18S rRNA and 73 sites in 25S rRNA. We use our method to characterize patterns of modification heterogeneity and identify concerted modification of nucleotides found near functional centers of the ribosome. Distinct, undermodified subpopulations of rRNAs accumulate upon loss of Dbp3 or Prp43 RNA helicases, suggesting overlapping roles in ribosome biogenesis. Modification profiles are surprisingly resistant to change in response to many genetic and acute environmental conditions that affect translation, ribosome biogenesis, and pre-mRNA splicing. The ability to capture single molecule RNA modification profiles provides new insights into the roles of nucleotide modifications in RNA function.
Data availability
Fastq files from all direct RNA sequencing runs and signalAlign modification calls are publicly available in NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE186634 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE186634). Fast5 and fastq files for all direct RNA sequencing are available in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB48183 (https://www.ebi.ac.uk/ena/browser/view/PRJEB48183). A detailed description of the datasets used and sequenced in this work with their corresponding ENA or GEO IDs can be found in (table supplement 7).Code availabilityDocumentation, install requirements, and analysis scripts for all work specific to this paper can be found at https://github.com/adbailey4/yeast_rrna_modification_detection. SignalAlign v1.0.0 can be found at https://github.com/UCSC-nanopore-cgl/signalAlign and embed_fast5 1.0.0 can be found https://github.com/adbailey4/embed_fast5.
Article and author information
Author details
Andrew D Bailey IV
Shreya Mantripragada
Funding
National Institute of General Medical Sciences (R01 GM040478)
- Manuel Ares Jr
National Human Genome Research Institute (R01 HG010053)
- Manuel Ares Jr
National Human Genome Research Institute (U41HG010972)
- Benedict Paten
National Human Genome Research Institute (R01HG010485)
- Benedict Paten
National Human Genome Research Institute (U01HG010961)
- Benedict Paten
NIH Office of the Director (OT2OD026682)
- Benedict Paten
NIH Office of the Director (OT2OD026682)
- Benedict Paten
National Heart, Lung, and Blood Institute (U01HL137183)
- Benedict Paten
National Human Genome Research Institute (2U41HG007234)
- Benedict Paten
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Bailey et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 3,484
- views
-
- 539
- downloads
-
- 32
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Concerted modification of nucleotides at functional centers of the ribosome revealed by single-molecule RNA modification profiling
eLife 11:e76562.
https://doi.org/10.7554/eLife.76562
Further reading
-
- Cell Biology
Understanding how the brain controls nutrient storage is pivotal. Transient receptor potential (TRP) channels are conserved from insects to humans. They serve in detecting environmental shifts and in acting as internal sensors. Previously, we demonstrated the role of TRPγ in nutrient-sensing behavior (Dhakal et al., 2022). Here, we found that a TRPγ mutant exhibited in Drosophila melanogaster is required for maintaining normal lipid and protein levels. In animals, lipogenesis and lipolysis control lipid levels in response to food availability. Lipids are mostly stored as triacylglycerol in the fat bodies (FBs) of D. melanogaster. Interestingly, trpγ deficient mutants exhibited elevated TAG levels and our genetic data indicated that Dh44 neurons are indispensable for normal lipid storage but not protein storage. The trpγ mutants also exhibited reduced starvation resistance, which was attributed to insufficient lipolysis in the FBs. This could be mitigated by administering lipase or metformin orally, indicating a potential treatment pathway. Gene expression analysis indicated that trpγ knockout downregulated brummer, a key lipolytic gene, resulting in chronic lipolytic deficits in the gut and other fat tissues. The study also highlighted the role of specific proteins, including neuropeptide DH44 and its receptor DH44R2 in lipid regulation. Our findings provide insight into the broader question of how the brain and gut regulate nutrient storage.
-
- Cell Biology
- Immunology and Inflammation
The endothelial blood-brain barrier (BBB) strictly controls immune cell trafficking into the central nervous system (CNS). In neuroinflammatory diseases such as multiple sclerosis, this tight control is, however, disturbed, leading to immune cell infiltration into the CNS. The development of in vitro models of the BBB combined with microfluidic devices has advanced our understanding of the cellular and molecular mechanisms mediating the multistep T-cell extravasation across the BBB. A major bottleneck of these in vitro studies is the absence of a robust and automated pipeline suitable for analyzing and quantifying the sequential interaction steps of different immune cell subsets with the BBB under physiological flow in vitro. Here, we present the under-flow migration tracker (UFMTrack) framework for studying immune cell interactions with endothelial monolayers under physiological flow. We then showcase a pipeline built based on it to study the entire multistep extravasation cascade of immune cells across brain microvascular endothelial cells under physiological flow in vitro. UFMTrack achieves 90% track reconstruction efficiency and allows for scaling due to the reduction of the analysis cost and by eliminating experimenter bias. This allowed for an in-depth analysis of all behavioral regimes involved in the multistep immune cell extravasation cascade. The study summarizes how UFMTrack can be employed to delineate the interactions of CD4+ and CD8+ T cells with the BBB under physiological flow. We also demonstrate its applicability to the other BBB models, showcasing broader applicability of the developed framework to a range of immune cell-endothelial monolayer interaction studies. The UFMTrack framework along with the generated datasets is publicly available in the corresponding repositories.
