Comprehensive re-analysis of hairpin small RNAs in fungi reveals loci with conserved links

  1. Nathan R Johnson
  2. Luis F Larrondo
  3. José M Álvarez  Is a corresponding author
  4. Elena A Vidal  Is a corresponding author
  1. Universidad Mayor, Chile
  2. Pontificia Universidad Católica de Chile, Chile
  3. Universidad Andrés Bello, Chile

Abstract

RNA interference is an ancient mechanism with many regulatory roles in eukaryotic genomes, with small RNAs acting as their functional element. While there is a wide array of classes of small-RNA-producing loci, those resulting from stem-loop structures (hairpins) have received profuse attention. Such is the case of microRNAs (miRNAs), which have distinct roles in plants and animals. Fungi also produce small RNAs, and several publications have identified miRNAs and miRNA-like (mi/milRNA) hairpin RNAs in diverse fungal species using deep sequencing technologies. Despite this relevant source of information, relatively little is known about mi/milRNA-like features in fungi, mostly due to a lack of established criteria for their annotation. To systematically assess mi/miRNA-like characteristics and annotation confidence, we searched for publications describing mi/milRNA loci and re-assessed the annotations for 41 fungal species. We extracted and normalized the annotation data for 1,727 reported mi/milRNA-like loci and determined their abundance profiles, concluding that less than half of the reported loci passed basic standards used for hairpin RNA discovery. We found that fungal mi/milRNA are generally more similar in size to animal miRNAs and were frequently associated with protein-coding genes. The compiled genomic analyses identified 25 mi/milRNA loci conserved in multiple species. Our pipeline allowed us to build a general hierarchy of locus quality, identifying more than 150 loci with high-quality annotations. We provide a centralized annotation of identified mi/milRNA hairpin RNAs in fungi which will serve as a resource for future research and advance in understanding the characteristics and functions of mi/milRNAs in fungal organisms.

Data availability

Sequencing data used in this work is available in public repositories, with publication details provided in Table S1 and all data accessions provided in Table S3. Results of abundance profiling are found in File S1 and summarized in Table S4.

The following previously published data sets were used

Article and author information

Author details

  1. Nathan R Johnson

    Facultad de Ciencias, Universidad Mayor, Santiago, Chile
    Competing interests
    No competing interests declared.
  2. Luis F Larrondo

    Molecular Genetics and Microbiology department, Pontificia Universidad Católica de Chile, Santiago, Chile
    Competing interests
    Luis F Larrondo, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8832-7109
  3. José M Álvarez

    Facultad de Ciencias, Universidad Andrés Bello, Santiago, Chile
    For correspondence
    jose.alvarez.h@unab.cl
    Competing interests
    No competing interests declared.
  4. Elena A Vidal

    Facultad de Ciencias, Universidad Mayor, Santiago, Chile
    For correspondence
    elena.vidal@umayor.cl
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8208-7327

Funding

Fondo Nacional de Desarrollo Científico y Tecnológico (11220727)

  • Nathan R Johnson

Instituto Milenio de Biologia Integrativa (ICN17_022)

  • Nathan R Johnson
  • Luis F Larrondo
  • Elena A Vidal

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Johnson 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

  • 1,230
    views
  • 159
    downloads
  • 5
    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)

  1. Nathan R Johnson
  2. Luis F Larrondo
  3. José M Álvarez
  4. Elena A Vidal
(2022)
Comprehensive re-analysis of hairpin small RNAs in fungi reveals loci with conserved links
eLife 11:e83691.
https://doi.org/10.7554/eLife.83691

Share this article

https://doi.org/10.7554/eLife.83691

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Eric V Strobl, Eric Gamazon
    Research Article

    Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics
    Steven Henikoff, David L Levens
    Insight

    A new method for mapping torsion provides insights into the ways that the genome responds to the torsion generated by RNA polymerase II.