1. Chromosomes and Gene Expression
Download icon

DDK/Hsk1 phosphorylates and targets fission yeast histone deacetylase Hst4 for degradation to stabilize stalled DNA replication forks

  1. Shalini Aricthota
  2. Devyani Haldar  Is a corresponding author
  1. Centre for DNA Fingerprinting and Diagnostics, India
Research Article
  • Cited 0
  • Views 268
  • Annotations
Cite this article as: eLife 2021;10:e70787 doi: 10.7554/eLife.70787

Abstract

In eukaryotes, paused replication forks are prone to collapse, which leads to genomic instability, a hallmark of cancer. Dbf4 Dependent Kinase (DDK)/Hsk1Cdc7 is a conserved replication initiator kinase with conflicting roles in replication stress response. Here, we show that fission yeast DDK/Hsk1 phosphorylates sirtuin, Hst4 upon replication stress at C-terminal serine residues. Phosphorylation of Hst4 by DDK marks it for degradation via the ubiquitin ligase SCFpof3. Phosphorylation defective hst4 mutant (4SA-hst4) displays defective recovery from replication stress, faulty fork restart, slow S-phase progression and decreased viability. The highly conserved Fork Protection Complex (FPC) stabilizes stalled replication forks. We found that the recruitment of FPC components, Swi1 and Mcl1 to the chromatin is compromised in the 4SA-hst4 mutant, although whole cell levels increased. These defects are dependent upon H3K56ac and independent of intra S-phase checkpoint activation. Finally, we show conservation of H3K56ac dependent regulation of Timeless, Tipin and And-1 in human cells. We propose that degradation of Hst4 via DDK increases H3K56ac, changing the chromatin state in the vicinity of stalled forks facilitating recruitment and function of FPC. Overall, this study identified a crucial role of DDK and FPC in the regulation of replication stress response with implications in cancer therapeutics.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

Article and author information

Author details

  1. Shalini Aricthota

    Chromatin Biology and Epigenetics, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India
    Competing interests
    The authors declare that no competing interests exist.
  2. Devyani Haldar

    Chromatin Biology and Epigenetics, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India
    For correspondence
    devyani@cdfd.org.in
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1445-1374

Funding

Science and Engineering Research Board (Grant EMR/2016/003933)

  • Devyani Haldar

University Grants Commission (Ref No. 23/06/2013i(EU-V))

  • Shalini Aricthota

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

Reviewing Editor

  1. Jerry L Workman, Stowers Institute for Medical Research, United States

Publication history

  1. Received: May 31, 2021
  2. Accepted: October 1, 2021
  3. Accepted Manuscript published: October 5, 2021 (version 1)

Copyright

© 2021, Aricthota & Haldar

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

  • 268
    Page views
  • 70
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Chromosomes and Gene Expression
    2. Computational and Systems Biology
    Zelin Liu et al.
    Tools and Resources

    Circular RNAs (circRNAs) act through multiple mechanisms via their sequence features to fine-tune gene expression networks. Due to overlapping sequences with linear cognates, identifying internal sequences of circRNAs remains a challenge, which hinders a comprehensive understanding of circRNA functions and mechanisms. Here, based on rolling circular reverse transcription (RCRT) and nanopore sequencing, we developed circFL-seq, a full-length circRNA sequencing method, to profile circRNA at the isoform level. With a customized computational pipeline to directly identify full-length sequences from rolling circular reads, we reconstructed 77,606 high-quality circRNAs from seven human cell lines and two human tissues. circFL-seq benefits from rolling circles and long-read sequencing, and the results showed more than tenfold enrichment of circRNA reads and advantages for both detection and quantification at the isoform level compared to those for short-read RNA sequencing. The concordance of the RT-qPCR and circFL-seq results for the identification of differential alternative splicing suggested wide application prospects for functional studies of internal variants in circRNAs. Moreover, the detection of fusion circRNAs at the omics scale may further expand the application of circFL-seq. Together, the accurate identification and quantification of full-length circRNAs make circFL-seq a potential tool for large-scale screening of functional circRNAs.

    1. Chromosomes and Gene Expression
    2. Computational and Systems Biology
    Lucy Ham et al.
    Research Article

    Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges for inferring dynamics, as well as causes of cell-to-cell variability. In particular, we typically cannot separate dynamic variability from within cells ('intrinsic noise') from variability across the population ('extrinsic noise'). Here we make this non-identifiability mathematically precise, allowing us to identify new experimental set-ups that can assist in resolving this non-identifiability. We show that multiple generic reporters from the same biochemical pathways (e.g. mRNA and protein) can infer magnitudes of intrinsic and extrinsic transcriptional noise, identifying sources of heterogeneity. Stochastic simulations support our theory, and demonstrate that 'pathway-reporters' compare favourably to the well-known, but often difficult to implement, dual-reporter method.