Abstract

Molecular mimicry is an evolutionary strategy adopted by viruses to exploit the host cellular machinery. We report that SARS-CoV-2 has evolved a unique S1/S2 cleavage site, absent in any previous coronavirus sequenced, resulting in striking mimicry of an identical FURIN-cleavable peptide on the human epithelial sodium channel α-subunit (ENaC-α). Genetic alteration of ENaC-α causes aldosterone dysregulation in patients, highlighting that the FURIN site is critical for activation of ENaC. Single cell RNA-seq from 65 studies shows significant overlap between expression of ENaC-α and the viral receptor ACE2 in cell types linked to the cardiovascular-renal-pulmonary pathophysiology of COVID-19. Triangulating this cellular characterization with cleavage signatures of 178 proteases highlights proteolytic degeneracy wired into the SARS-CoV-2 lifecycle. Evolution of SARS-CoV-2 into a global pandemic may be driven in part by its targeted mimicry of ENaC-α, a protein critical for the homeostasis of airway surface liquid, whose misregulation is associated with respiratory conditions.

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. Praveen Anand

    R&D, nference, Bangalore, India
    Competing interests
    Praveen Anand, The author is an employee of nference..
  2. Arjun Puranik

    Data Science, nference, San Francisco, United States
    Competing interests
    Arjun Puranik, The author is an employee of nference..
  3. Murali Aravamudan

    R&D, nference, Cambridge, United States
    Competing interests
    Murali Aravamudan, The author is an employee of Nference..
  4. AJ Venkatakrishnan

    R&D, nference, Cambridge, United States
    For correspondence
    aj@nference.net
    Competing interests
    AJ Venkatakrishnan, Author is an employee of nference.
  5. Venky Soundararajan

    R&D, nference, Cambridge, United States
    For correspondence
    venky@nference.net
    Competing interests
    Venky Soundararajan, The author is an employee of nference..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7434-9211

Funding

The authors declare that there was no external funding for this work.

Reviewing Editor

  1. Gian Paolo Rossi, University of Padova, Italy

Version history

  1. Received: May 5, 2020
  2. Accepted: May 25, 2020
  3. Accepted Manuscript published: May 26, 2020 (version 1)
  4. Version of Record published: July 8, 2020 (version 2)

Copyright

© 2020, Anand 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

  • 20,310
    Page views
  • 2,398
    Downloads
  • 92
    Citations

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

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. Praveen Anand
  2. Arjun Puranik
  3. Murali Aravamudan
  4. AJ Venkatakrishnan
  5. Venky Soundararajan
(2020)
SARS-CoV-2 strategically mimics proteolytic activation of human ENaC
eLife 9:e58603.
https://doi.org/10.7554/eLife.58603

Share this article

https://doi.org/10.7554/eLife.58603

Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Thomas Grandits, Christoph M Augustin ... Alexander Jung
    Research Article

    Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

    1. Computational and Systems Biology
    2. Neuroscience
    Domingos Leite de Castro, Miguel Aroso ... Paulo Aguiar
    Research Article Updated

    Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson’s disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.