The Lyme Disease agent co-opts adiponectin receptor-mediated signaling in its arthropod vector

  1. Xiaotian Tang  Is a corresponding author
  2. Yongguo Cao
  3. Gunjan Arora
  4. Jesse Hwang
  5. Andaleeb Sajid
  6. Courtney L Brown
  7. Sameet Mehta
  8. Alejandro Marín-López
  9. Yu-Min Chuang
  10. Ming-Jie Wu
  11. Hongwei Ma
  12. Utpal Pal
  13. Sukanya Narasimhan
  14. Erol Fikrig  Is a corresponding author
  1. Yale University, United States
  2. Yale University School of Medicine, United States
  3. University of Maryland, United States

Abstract

Adiponectin-mediated pathways contribute to mammalian homeostasis; however, little is known about adiponectin and adiponectin receptor signaling in arthropods. In this study, we demonstrate that Ixodes scapularis ticks have an adiponectin receptor-like protein (ISARL) but lack adiponectin - suggesting activation by alternative pathways. ISARL expression is significantly upregulated in the tick gut after Borrelia burgdorferi infection suggesting that ISARL-signaling may be co-opted by the Lyme disease agent. Consistent with this, RNA interference (RNAi)-mediated silencing of ISARL significantly reduced the B. burgdorferi burden in the tick. RNA-seq-based transcriptomics and RNAi assays demonstrate that ISARL-mediated phospholipid metabolism by phosphatidylserine synthase I is associated with B. burgdorferi survival. Furthermore, the tick complement C1q-like protein 3 interacts with ISARL, and B. burgdorferi facilitates this process. This study identifies a new tick metabolic pathway that is connected to the life cycle of the Lyme disease spirochete.

Data availability

The RNA-seq data are available in the Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information under the accession number: GSE169293.

The following data sets were generated

Article and author information

Author details

  1. Xiaotian Tang

    Yale University, New Haven, United States
    For correspondence
    xiaotian.tang@yale.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0171-9354
  2. Yongguo Cao

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9533-7516
  3. Gunjan Arora

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Jesse Hwang

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Andaleeb Sajid

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Courtney L Brown

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7423-3331
  7. Sameet Mehta

    Yale Center for Genome Analysis Bioinformatics, Yale University School of Medicine, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Alejandro Marín-López

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Yu-Min Chuang

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2241-5541
  10. Ming-Jie Wu

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Hongwei Ma

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Utpal Pal

    Department of Veterinary Medicine, University of Maryland, College Park, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Sukanya Narasimhan

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Erol Fikrig

    Yale University, New Haven, United States
    For correspondence
    erol.fikrig@yale.edu
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (AI126033)

  • Erol Fikrig

National Institutes of Health (AI138949)

  • Erol Fikrig

Steven and Alexandra Cohen Foundation

  • Erol Fikrig

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

Ethics

Animal experimentation: Animal care and housing were performed according to the Guide for the Care and Use of laboratory Animals of National Institutes of Health, USA. All protocols in this study were approved by the Yale University Institutional Animal Care and Use Committee (YUIACUC) (approval number 2018-07941).

Copyright

© 2021, Tang 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. Xiaotian Tang
  2. Yongguo Cao
  3. Gunjan Arora
  4. Jesse Hwang
  5. Andaleeb Sajid
  6. Courtney L Brown
  7. Sameet Mehta
  8. Alejandro Marín-López
  9. Yu-Min Chuang
  10. Ming-Jie Wu
  11. Hongwei Ma
  12. Utpal Pal
  13. Sukanya Narasimhan
  14. Erol Fikrig
(2021)
The Lyme Disease agent co-opts adiponectin receptor-mediated signaling in its arthropod vector
eLife 10:e72568.
https://doi.org/10.7554/eLife.72568

Share this article

https://doi.org/10.7554/eLife.72568

Further reading

    1. Medicine
    2. Microbiology and Infectious Disease
    Berit Siedentop, Viacheslav N Kachalov ... Sebastian Bonhoeffer
    Research Article

    Background:

    Under which conditions antibiotic combination therapy decelerates rather than accelerates resistance evolution is not well understood. We examined the effect of combining antibiotics on within-patient resistance development across various bacterial pathogens and antibiotics.

    Methods:

    We searched CENTRAL, EMBASE, and PubMed for (quasi)-randomised controlled trials (RCTs) published from database inception to 24 November 2022. Trials comparing antibiotic treatments with different numbers of antibiotics were included. Patients were considered to have acquired resistance if, at the follow-up culture, a resistant bacterium (as defined by the study authors) was detected that had not been present in the baseline culture. We combined results using a random effects model and performed meta-regression and stratified analyses. The trials’ risk of bias was assessed with the Cochrane tool.

    Results:

    42 trials were eligible and 29, including 5054 patients, qualified for statistical analysis. In most trials, resistance development was not the primary outcome and studies lacked power. The combined odds ratio for the acquisition of resistance comparing the group with the higher number of antibiotics with the comparison group was 1.23 (95% CI 0.68–2.25), with substantial between-study heterogeneity (I2=77%). We identified tentative evidence for potential beneficial or detrimental effects of antibiotic combination therapy for specific pathogens or medical conditions.

    Conclusions:

    The evidence for combining a higher number of antibiotics compared to fewer from RCTs is scarce and overall compatible with both benefit or harm. Trials powered to detect differences in resistance development or well-designed observational studies are required to clarify the impact of combination therapy on resistance.

    Funding:

    Support from the Swiss National Science Foundation (grant 310030B_176401 (SB, BS, CW), grant 32FP30-174281 (ME), grant 324730_207957 (RDK)) and from the National Institute of Allergy and Infectious Diseases (NIAID, cooperative agreement AI069924 (ME)) is gratefully acknowledged.