Method for identification of condition-associated public antigen receptor sequences

  1. Mikhail V Pogorelyy
  2. Anastasia A Minervina
  3. Dmitriy M Chudakov
  4. Ilgar Z Mamedov
  5. Yuri B Lebedev  Is a corresponding author
  6. Thierry Mora  Is a corresponding author
  7. Aleksandra M Walczak  Is a corresponding author
  1. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Russian Federation
  2. École Normale Supérieure, France

Abstract

Diverse repertoires of hypervariable immunoglobulin receptors (TCR and BCR) recognize antigens in the adaptive immune system. The development of immunoglobulin receptor repertoire sequencing methods makes it possible to perform repertoire-wide disease association studies of antigen receptor sequences. We developed a statistical framework for associating receptors to disease from only a small cohort of patients, with no need for a control cohort. Our method successfully identifies previously validated Cytomegalovirus and type 1 diabetes responsive TCRβ sequences.

Data availability

The following previously published data sets were used

Article and author information

Author details

  1. Mikhail V Pogorelyy

    Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russian Federation
    Competing interests
    No competing interests declared.
  2. Anastasia A Minervina

    Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russian Federation
    Competing interests
    No competing interests declared.
  3. Dmitriy M Chudakov

    Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russian Federation
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0430-790X
  4. Ilgar Z Mamedov

    Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russian Federation
    Competing interests
    No competing interests declared.
  5. Yuri B Lebedev

    Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russian Federation
    For correspondence
    lebedev_yb@mx.ibch.ru
    Competing interests
    No competing interests declared.
  6. Thierry Mora

    Laboratoire de Physique Statistique, École Normale Supérieure, Paris, France
    For correspondence
    tmora@lps.ens.fr
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5456-9361
  7. Aleksandra M Walczak

    Laboratoire de Physique Theorique, École Normale Supérieure, Paris, France
    For correspondence
    awalczak@lpt.ens.fr
    Competing interests
    Aleksandra M Walczak, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2686-5702

Funding

Russian Science Foundation (15-15-00178)

  • Dmitriy M Chudakov
  • Ilgar Z Mamedov
  • Yuri B Lebedev

European Research Council (724208)

  • Aleksandra M Walczak

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, Massachusetts Institute of Technology, United States

Version history

  1. Received: October 24, 2017
  2. Accepted: March 12, 2018
  3. Accepted Manuscript published: March 13, 2018 (version 1)
  4. Version of Record published: March 28, 2018 (version 2)

Copyright

© 2018, Pogorelyy 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

  • 3,021
    views
  • 544
    downloads
  • 52
    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. Mikhail V Pogorelyy
  2. Anastasia A Minervina
  3. Dmitriy M Chudakov
  4. Ilgar Z Mamedov
  5. Yuri B Lebedev
  6. Thierry Mora
  7. Aleksandra M Walczak
(2018)
Method for identification of condition-associated public antigen receptor sequences
eLife 7:e33050.
https://doi.org/10.7554/eLife.33050

Share this article

https://doi.org/10.7554/eLife.33050

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.