T cell receptor repertoires of mice and humans are clustered in similarity networks around conserved public CDR3 sequences

  1. Asaf Madi
  2. Asaf Poran
  3. Eric Shifrut
  4. Shlomit Reich-Zeliger
  5. Erez Greenstein
  6. Irena Zaretsky
  7. Tomer Arnon
  8. Francois Van Laethem
  9. Alfred Singer
  10. Jinghua Lu
  11. Peter D Sun
  12. Irun R Cohen
  13. Nir Friedman  Is a corresponding author
  1. Weizmann Institute of Science, Israel
  2. Weizmann Institute, Israel
  3. National Cancer Institute, United States
  4. National Institute of Allergy and Infectious Diseases, United States

Abstract

Diversity of T cell receptor (TCR) repertoires, generated by somatic DNA rearrangements, is central to immune system function. However, the level of sequence similarity of TCR repertoires within and between species has not been characterized. Using network analysis of high-throughput TCR sequencing data, we found that abundant CDR3-TCRβ sequences were clustered within networks generated by sequence similarity. We discovered a substantial number of public CDR3-TCRβ segments that were identical in mice and humans. These conserved public sequences were central within TCR sequence-similarity networks. Annotated TCR sequences, previously associated with self-specificities such as autoimmunity and cancer, were linked to network clusters. Mechanistically, CDR3 networks were promoted by MHC-mediated selection, and were reduced following immunization, immune checkpoint blockade or aging. Our findings provide a new view of T cell repertoire organization and physiology, and suggest that the immune system distributes its TCR sequences unevenly, attending to specific foci of reactivity.

Data availability

The following previously published data sets were used
    1. Nir Friedman
    (2015) Young mice TCR repertoire
    Publicly available at NCBI Sequence Read Archive (accession no: SRP042610).

Article and author information

Author details

  1. Asaf Madi

    Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3441-3228
  2. Asaf Poran

    Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
    Competing interests
    The authors declare that no competing interests exist.
  3. Eric Shifrut

    Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
    Competing interests
    The authors declare that no competing interests exist.
  4. Shlomit Reich-Zeliger

    Department of Immunology, Weizmann Institute, Rehovot, Israel
    Competing interests
    The authors declare that no competing interests exist.
  5. Erez Greenstein

    Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
    Competing interests
    The authors declare that no competing interests exist.
  6. Irena Zaretsky

    Department of Immunology, Weizmann Institute, Rehovot, Israel
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4161-4677
  7. Tomer Arnon

    Department of Immunology, Weizmann Institute, Rehovot, Israel
    Competing interests
    The authors declare that no competing interests exist.
  8. Francois Van Laethem

    Experimental Immunology Branch, National Cancer Institute, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Alfred Singer

    Experimental Immunology Branch, National Cancer Institute, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Jinghua Lu

    Structural Immunology Section, National Institute of Allergy and Infectious Diseases, Rockville, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Peter D Sun

    Structural Immunology Section, National Institute of Allergy and Infectious Diseases, Rockville, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Irun R Cohen

    Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
    Competing interests
    The authors declare that no competing interests exist.
  13. Nir Friedman

    Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
    For correspondence
    nir.friedman@weizmann.ac.il
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9678-3550

Funding

Minerva Foundation

  • Nir Friedman

Federal German Ministry for Education and Research

  • Nir Friedman

I-CORE

  • Nir Friedman

Israel Science Foundation

  • Nir Friedman

M.D. Moross Institute for Cancer Reseach

  • Asaf Madi

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

Reviewing Editor

  1. Arup K Chakraborty, Ragon Institute of MGH, MIT and Harvard, United States

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#24110116-2) of the Weizmann Institute of Science. The protocol was approved by the Committee on the Ethics of Animal Experiments of the Weizmann Institute of Science. Every effort was made to minimize suffering.

Version history

  1. Received: October 10, 2016
  2. Accepted: July 14, 2017
  3. Accepted Manuscript published: July 21, 2017 (version 1)
  4. Version of Record published: August 11, 2017 (version 2)

Copyright

© 2017, Madi 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

  • 6,743
    views
  • 1,113
    downloads
  • 130
    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. Asaf Madi
  2. Asaf Poran
  3. Eric Shifrut
  4. Shlomit Reich-Zeliger
  5. Erez Greenstein
  6. Irena Zaretsky
  7. Tomer Arnon
  8. Francois Van Laethem
  9. Alfred Singer
  10. Jinghua Lu
  11. Peter D Sun
  12. Irun R Cohen
  13. Nir Friedman
(2017)
T cell receptor repertoires of mice and humans are clustered in similarity networks around conserved public CDR3 sequences
eLife 6:e22057.
https://doi.org/10.7554/eLife.22057

Share this article

https://doi.org/10.7554/eLife.22057

Further reading

    1. Computational and Systems Biology
    2. Medicine
    Seo-Gyeong Bae, Guo Nan Yin ... Jihwan Park
    Research Article

    Erectile dysfunction (ED) affects a significant proportion of men aged 40–70 and is caused by cavernous tissue dysfunction. Presently, the most common treatment for ED is phosphodiesterase 5 inhibitors; however, this is less effective in patients with severe vascular disease such as diabetic ED. Therefore, there is a need for development of new treatment, which requires a better understanding of the cavernous microenvironment and cell-cell communications under diabetic condition. Pericytes are vital in penile erection; however, their dysfunction due to diabetes remains unclear. In this study, we performed single-cell RNA sequencing to understand the cellular landscape of cavernous tissues and cell type-specific transcriptional changes in diabetic ED. We found a decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in diabetic fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. Moreover, the newly identified pericyte-specific marker, Limb Bud-Heart (Lbh), in mouse and human cavernous tissues, clearly distinguishing pericytes from smooth muscle cells. Cell-cell interaction analysis revealed that pericytes are involved in angiogenesis, adhesion, and migration by communicating with other cell types in the corpus cavernosum; however, these interactions were highly reduced under diabetic conditions. Lbh expression is low in diabetic pericytes, and overexpression of LBH prevents erectile function by regulating neurovascular regeneration. Furthermore, the LBH-interacting proteins (Crystallin Alpha B and Vimentin) were identified in mouse cavernous pericytes through LC-MS/MS analysis, indicating that their interactions were critical for maintaining pericyte function. Thus, our study reveals novel targets and insights into the pathogenesis of ED in patients with diabetes.

    1. Computational and Systems Biology
    Rebecca A Deek, Siyuan Ma ... Hongzhe Li
    Review Article

    Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.