Human cytomegalovirus interactome analysis identifies degradation hubs, domain associations and viral protein functions

  1. Luis V Nobre
  2. Katie Nightingale
  3. Benjamin J Ravenhill
  4. Robin Antrobus
  5. Lior Soday
  6. Jenna Nichols
  7. James A Davies
  8. Sepehr Seirafian
  9. Eddie CY Wang
  10. Andrew J Davison
  11. Gavin WG Wilkinson
  12. Richard J Stanton
  13. Edward L Huttlin
  14. Michael P Weekes  Is a corresponding author
  1. University of Cambridge, United Kingdom
  2. MRC-University of Glasgow Centre for Virus Research, United Kingdom
  3. Cardiff University School of Medicine, United Kingdom
  4. Harvard Medical School, United States

Abstract

Human cytomegalovirus (HCMV) extensively modulates host cells, downregulating >900 human proteins during viral replication and degrading ≥133 proteins shortly after infection. The mechanism of degradation of most host proteins remains unresolved, and the functions of many viral proteins are incompletely characterised. We performed a mass spectrometry-based interactome analysis of 169 tagged, stably-expressed canonical strain Merlin HCMV proteins, and two non-canonical HCMV proteins, in infected cells. This identified a network of >3,400 virus-host and >150 virus-virus protein interactions, providing insights into functions for multiple viral genes. Domain analysis predicted binding of the viral UL25 protein to SH3 domains of NCK Adaptor Protein-1. Viral interacting proteins were identified for 31/133 degraded host targets. Finally, the uncharacterised, non-canonical ORFL147C protein was found to interact with elements of the mRNA splicing machinery, and a mutational study suggested its importance in viral replication. The interactome data will be important for future studies of herpesvirus infection.

Data availability

All data analysed during this study are included in the manuscript and supporting files. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://www.proteomexchange.org/) via the PRIDE (Vizcaino et al., 2016) partner repository with the dataset identifier PXD014845.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Luis V Nobre

    Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Katie Nightingale

    Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Benjamin J Ravenhill

    Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Robin Antrobus

    Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Lior Soday

    Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Jenna Nichols

    MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. James A Davies

    Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3569-4500
  8. Sepehr Seirafian

    Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. Eddie CY Wang

    Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  10. Andrew J Davison

    MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  11. Gavin WG Wilkinson

    Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5623-0126
  12. Richard J Stanton

    Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6799-1182
  13. Edward L Huttlin

    Department of Cell Biology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Michael P Weekes

    Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    mpw1001@cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3196-5545

Funding

Wellcome (108070/Z/15/Z)

  • Michael P Weekes

Medical Research Council (MR/L018373/1)

  • Eddie CY Wang
  • Gavin WG Wilkinson
  • Richard J Stanton

Medical Research Council (MR/P001602/1)

  • Eddie CY Wang
  • Gavin WG Wilkinson
  • Richard J Stanton

Wellcome (WT090323MA)

  • Eddie CY Wang
  • Gavin WG Wilkinson
  • Richard J Stanton

Medical Research Council (MC_UU_12014/3)

  • Andrew J Davison

National Institutes of Health (U24 HG006673)

  • Edward L Huttlin

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

Reviewing Editor

  1. Piet Maes, KU Leuven, Rega Institute for Medical Research, Belgium

Version history

  1. Received: July 3, 2019
  2. Accepted: December 24, 2019
  3. Accepted Manuscript published: December 24, 2019 (version 1)
  4. Version of Record published: January 14, 2020 (version 2)

Copyright

© 2019, Nobre 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

  • 5,890
    views
  • 777
    downloads
  • 78
    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. Luis V Nobre
  2. Katie Nightingale
  3. Benjamin J Ravenhill
  4. Robin Antrobus
  5. Lior Soday
  6. Jenna Nichols
  7. James A Davies
  8. Sepehr Seirafian
  9. Eddie CY Wang
  10. Andrew J Davison
  11. Gavin WG Wilkinson
  12. Richard J Stanton
  13. Edward L Huttlin
  14. Michael P Weekes
(2019)
Human cytomegalovirus interactome analysis identifies degradation hubs, domain associations and viral protein functions
eLife 8:e49894.
https://doi.org/10.7554/eLife.49894

Share this article

https://doi.org/10.7554/eLife.49894

Further reading

    1. Computational and Systems Biology
    2. Medicine
    Seo-Gyeong Bae, Guo Nan Yin ... Jihwan Park
    Research Article

    Erectile dysfunction (ED) affects a significant proportion of men aged 40–70 and is caused by cavernous tissue dysfunction. Presently, the most common treatment for ED is phosphodiesterase 5 inhibitors; however, this is less effective in patients with severe vascular disease such as diabetic ED. Therefore, there is a need for development of new treatment, which requires a better understanding of the cavernous microenvironment and cell-cell communications under diabetic condition. Pericytes are vital in penile erection; however, their dysfunction due to diabetes remains unclear. In this study, we performed single-cell RNA sequencing to understand the cellular landscape of cavernous tissues and cell type-specific transcriptional changes in diabetic ED. We found a decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in diabetic fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. Moreover, the newly identified pericyte-specific marker, Limb Bud-Heart (Lbh), in mouse and human cavernous tissues, clearly distinguishing pericytes from smooth muscle cells. Cell-cell interaction analysis revealed that pericytes are involved in angiogenesis, adhesion, and migration by communicating with other cell types in the corpus cavernosum; however, these interactions were highly reduced under diabetic conditions. Lbh expression is low in diabetic pericytes, and overexpression of LBH prevents erectile function by regulating neurovascular regeneration. Furthermore, the LBH-interacting proteins (Crystallin Alpha B and Vimentin) were identified in mouse cavernous pericytes through LC-MS/MS analysis, indicating that their interactions were critical for maintaining pericyte function. Thus, our study reveals novel targets and insights into the pathogenesis of ED in patients with diabetes.

    1. Computational and Systems Biology
    Rebecca A Deek, Siyuan Ma ... Hongzhe Li
    Review Article

    Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.