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.

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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  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

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

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    Persisting HIV reservoir viruses in resting CD4 T cells and other cellular subsets are a barrier to cure efforts. Early antiretroviral therapy (ART) enables post-treatment viral control in some cases, but mechanisms remain unclear. We hypothesised that ART initiated before peak viremia impacts HIV-1 subtype C reservoirs. We studied 35 women at high risk of infection from Durban, South Africa, identified with hyperacute HIV by twice-weekly HIV-RNA testing. Participants included 11 starting ART at a median of 456 (297–1203) days post-onset of viremia (DPOV) and 24 at 1 (1–3) DPOV. Peripheral blood mononuclear cells (PBMCs) were used to measured total HIV-1 DNA by droplet digital PCR (ddPCR) and sequence viral reservoir genomes by full-length proviral sequencing (FLIP-seq). ART during hyperacute infection blunted peak viremia (p<0.0001), but contemporaneous total HIV-1 DNA did not differ (p=0.104). Over 1 year, a decline of total HIV-1 DNA was observed in early treated persons (p=0.0004), but not late treated. Among 697 viral genome sequences, the proviral genetic landscape differed between untreated, late treated, and early treated groups. Intact genomes after 1 year were higher in untreated (31%) versus late treated (14%) and early treated (0%). Treatment in both late and early infection caused more rapid decay of intact (13% and 51% per month) versus defective (2% and 35%) viral genomes. However, intact genomes persisted 1 year post chronic treatment but were undetectable with early ART. Early ART also reduced phylogenetic diversity of intact genomes and limited cytotoxic T lymphocyte immune escape variants in the reservoir. Overall, ART initiated in hyperacute HIV-1 subtype C infection did not impact reservoir seeding but was associated with rapid intact viral genome decay, reduced genetic complexity, and limited immune escape, which may accelerate reservoir clearance in combination with other interventional strategies.