Abstract

The COVID-19 pandemic demands assimilation of all biomedical knowledge to decode mechanisms of pathogenesis. Despite the recent renaissance in neural networks, a platform for the real-time synthesis of the exponentially growing biomedical literature and deep omics insights is unavailable. Here, we present the nferX platform for dynamic inference from 45 quadrillion+ possible conceptual associations from unstructured text and triangulation with insights from Single Cell RNA-sequencing, bulk RNAseq and proteomics from diverse tissue types. A hypothesis-free profiling of ACE2 suggests tongue keratinocytes, olfactory epithelial cells, airway club cells and respiratory ciliated cells as potential reservoirs of the SARS-CoV-2 receptor. We find the gut as the putative hotspot of COVID-19, where a maturation correlated transcriptional signature is shared in small intestine enterocytes among coronavirus receptors(ACE2, DPP4, ANPEP). A holistic data science platform triangulating insights from structured and unstructured data holds potential for accelerating the generation of impactful biological insights and hypotheses.

Data availability

All data used in this manuscript were obtained from published and freely available sources online. A complete list of these can be found in Supplementary File 1.

Article and author information

Author details

  1. AJ Venkatakrishnan

    R&D, nference, Cambridge, United States
    Competing interests
    AJ Venkatakrishnan, AJ Venkatakrishnan is affiliated to nference. The author has financial interests in nference..
  2. Arjun Puranik

    Data Science, nference, San Francisco, United States
    Competing interests
    Arjun Puranik, Arjun Puranik is affiliated to nference. The author has financial interests in nference..
  3. Akash Anand

    Data Science, nference, Bangalore, India
    Competing interests
    Akash Anand, Akash Anand is affiliated to nference. The author has financial interests in nference..
  4. David Zemmour

    R&D, nference, Cambridge, United States
    Competing interests
    David Zemmour, David Zemmour is affiliated to nference. The author has no financial interests to declare..
  5. Xiang Yao

    R&D Data Sciences, Janssen, San Diego, United States
    Competing interests
    Xiang Yao, Xiang Yao is affiliated to Janssen. The author has no financial interests to declare..
  6. Xiaoying Wu

    R&D Data Sciences, Janssen, Spring House, United States
    Competing interests
    Xiaoying Wu, Xiaoying Wu is affiliated to Janssen. The author has no financial interests to declare..
  7. Ramakrishna Chilaka

    Engineering, nference, Bangalore, India
    Competing interests
    Ramakrishna Chilaka, Ramakrishna Chilaka is affiliated to nference. The author has financial interests in nference..
  8. Dariusz K Murakowski

    R&D, nference, Cambridge, United States
    Competing interests
    Dariusz K Murakowski, Dariusz Murakowski is affiliated to nference. The author has financial interests in nference..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9920-4980
  9. Kristopher Standish

    R&D Data Sciences, Janssen, San Diego, United States
    Competing interests
    Kristopher Standish, Kristopher Standish is affiliated to Janssen. The author has no financial interests to declare..
  10. Bharathwaj Raghunathan

    Data Sciences, nference, Toronto, Canada
    Competing interests
    Bharathwaj Raghunathan, Bharathwaj Raghunathan is affiliated to nference. The author has financial interests in nference..
  11. Tyler Wagner

    R&D, nference, Cambridge, United States
    Competing interests
    Tyler Wagner, Tyler Wagner is affiliated to nference. The author has financial interests in nference..
  12. Enrique Garcia-Rivera

    R&D, nference, Cambridge, United States
    Competing interests
    Enrique Garcia-Rivera, Enrique Garcia-Rivera is affiliated to nference. The author has financial interests in nference..
  13. Hugo Solomon

    R&D, nference, Cambridge, United States
    Competing interests
    Hugo Solomon, Hugo Solomon is affiliated to nference. The author has financial interests to declare..
  14. Abhinav Garg

    Engineering, nference, Bangalore, India
    Competing interests
    Abhinav Garg, Abinav Garg is affiliated to nference. The author has financial interests in nference..
  15. Rakesh Barve

    Data Sciences, nference, Bangalore, India
    Competing interests
    Rakesh Barve, Rakesh Barve is affiliated to nference. The author has financial interests in nference..
  16. Anuli Anyanwu-Ofili

    R&D Strategy & Operations, Janssen, Spring House, United States
    Competing interests
    Anuli Anyanwu-Ofili, Anuli Anyanwu-Ofili is affiliated to Janssen. The author has no financial interests to declare..
  17. Najat Khan

    R&D Data Sciences, R&D Strategy & Operations, Janssen, Spring House, United States
    Competing interests
    Najat Khan, Najat Khan is affiliated to Janssen. The author has no financial interests to declare..
  18. Venky Soundararajan

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

Funding

No external funding was received for this work.

Reviewing Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Version history

  1. Received: April 18, 2020
  2. Accepted: May 27, 2020
  3. Accepted Manuscript published: May 28, 2020 (version 1)
  4. Version of Record published: July 20, 2020 (version 2)

Copyright

© 2020, Venkatakrishnan 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

  • 4,083
    views
  • 522
    downloads
  • 45
    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. AJ Venkatakrishnan
  2. Arjun Puranik
  3. Akash Anand
  4. David Zemmour
  5. Xiang Yao
  6. Xiaoying Wu
  7. Ramakrishna Chilaka
  8. Dariusz K Murakowski
  9. Kristopher Standish
  10. Bharathwaj Raghunathan
  11. Tyler Wagner
  12. Enrique Garcia-Rivera
  13. Hugo Solomon
  14. Abhinav Garg
  15. Rakesh Barve
  16. Anuli Anyanwu-Ofili
  17. Najat Khan
  18. Venky Soundararajan
(2020)
Knowledge synthesis of 100 million biomedical documents augments the deep expression profiling of coronavirus receptors
eLife 9:e58040.
https://doi.org/10.7554/eLife.58040

Share this article

https://doi.org/10.7554/eLife.58040

Further reading

    1. Epidemiology and Global Health
    2. Medicine
    3. Microbiology and Infectious Disease
    Edited by Diane M Harper et al.
    Collection

    eLife has published the following articles on SARS-CoV-2 and COVID-19.

    1. Medicine
    2. Neuroscience
    Yunlu Xue, Yimin Zhou, Constance L Cepko
    Research Advance

    Retinitis pigmentosa (RP) is an inherited retinal disease in which there is a loss of cone-mediated daylight vision. As there are >100 disease genes, our goal is to preserve cone vision in a disease gene-agnostic manner. Previously we showed that overexpressing TXNIP, an α-arrestin protein, prolonged cone vision in RP mouse models, using an AAV to express it only in cones. Here, we expressed different alleles of Txnip in the retinal pigmented epithelium (RPE), a support layer for cones. Our goal was to learn more of TXNIP’s structure-function relationships for cone survival, as well as determine the optimal cell type expression pattern for cone survival. The C-terminal half of TXNIP was found to be sufficient to remove GLUT1 from the cell surface, and improved RP cone survival, when expressed in the RPE, but not in cones. Knock-down of HSP90AB1, a TXNIP-interactor which regulates metabolism, improved the survival of cones alone and was additive for cone survival when combined with TXNIP. From these and other results, it is likely that TXNIP interacts with several proteins in the RPE to indirectly support cone survival, with some of these interactions different from those that lead to cone survival when expressed only in cones.