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Knowledge synthesis of 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

  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  Is a corresponding author
  1. nference, United States
  2. nference, India
  3. Janssen, United States
  4. nference, Canada
Research Article
  • Cited 32
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Cite this article as: eLife 2020;9:e58040 doi: 10.7554/eLife.58040

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

Publication 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.

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Research organism

    1. Part of

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    1. Genetics and Genomics
    2. Medicine

    SMA-miRs: miR-181a-5p, -324-5p, -451a are overexpressed in spinal muscular atrophy skeletal muscle and serum samples

    Emanuela Abiusi et al.
    Research Article

    Background:

    Spinal muscular atrophy (SMA) is a neuromuscular disorder characterized by the degeneration of the second motor-neuron. The phenotype ranges from very severe to very mild forms. All patients have the homozygous loss of the SMN1 gene and a variable number of SMN2 (generally two-to-four copies), inversely related with the severity. The amazing results of the available treatments have made compelling the need of prognostic biomarkers to predict the progression trajectories of patients. Beside the SMN2 products, few other biomarkers have been evaluated so far, including some miRs.

    Methods:

    We performed whole miRNome analysis of muscle samples of patients and controls (14 biopsies and 9 cultures). The levels of muscle differentially expressed miRs were evaluated in serum samples (51 patients and 37 controls) and integrated with SMN2 copies, SMN2-full length transcript levels in blood and age (SMA-score).

    Results:

    Over 100 miRs were differentially expressed in SMA muscle; three of them (HSA-miR-181a-5p, -324-5p, -451a; SMA-miRs) were significantly up-regulated in serum of patients. The severity predicted by the SMA-score was related with that of the clinical classification at a correlation coefficient of 0.87 (p<10-5).

    Conclusions:

    miRNome analyses suggest the primary involvement of skeletal muscle in SMA pathogenesis; the SMA-miRs are likely actively released in the blood flow, even if their function and target cells require to be elucidated. The accuracy of the SMA-score needs to be verified in replicative studies: if confirmed, its use could be crucial for the routine prognostic assessment, also in pre-symptomatic patients.

    Funding:

    Telethon Italia (grant # GGP12116).