1. Genetics and Genomics
  2. Medicine

A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes

  1. Xiaojing Chu
  2. Anna WM Janssen
  3. Hans Koenen
  4. Linzhung Chang
  5. Xuehui He
  6. Irma Joosten
  7. Rinke Stienstra
  8. Yunus Kuijpers
  9. Cisca Wijmenga
  10. Cheng-Jian Xu
  11. Mihai G Netea
  12. Cees J Tack  Is a corresponding author
  13. Yang Li  Is a corresponding author
  1. University Medical Center Groningen, Netherlands
  2. Radboud University Nijmegen Medical Centre, Netherlands
  3. Helmholtz Centre for Infection Research, Germany
  4. University of Groningen, Netherlands
https://doi.org/10.7554/eLife.73709
Share this article
Cite this article
  1. Xiaojing Chu
  2. Anna WM Janssen
  3. Hans Koenen
  4. Linzhung Chang
  5. Xuehui He
  6. Irma Joosten
  7. Rinke Stienstra
  8. Yunus Kuijpers
  9. Cisca Wijmenga
  10. Cheng-Jian Xu
  11. Mihai G Netea
  12. Cees J Tack
  13. Yang Li
(2022)
A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes
eLife 11:e73709.
https://doi.org/10.7554/eLife.73709
  2. Download BibTeX
  3. Download .RIS

Abstract

Background: The large inter-individual variability in immune-cell cell composition and function determines immune responses in general and susceptibility to immune-mediated diseases in particular. While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown.

Methods: Blood samples were collected from 243 patients with T1D of Dutch descent. We applied genetic association analysis on > 200 immune cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls.

Results: Genetic variants that determine susceptibility to T1D significantly affect T cell composition. Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation. Genome-wide quantitative trait loci (QTL) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation.

Conclusion: This study provides new insights into the genetic factors that affect immunological responses in T1D.

Funding: This work was supported by an ERC starting grant (no. 948207) and a Radboud University Medical Centre Hypatia grant (2018) to Y.L. and an ERC advanced grant (no. 833247) and a Spinoza grant of the Netherlands Association for Scientific Research to M.G.N. C.T received funding from the Perspectief Biomarker Development Center Research Programme, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). AJ was funded by a grant from the European Foundation for the Study of Diabetes (EFSD/AZ Macrovascular Programme 2015). X.C was supported by the China Scholarship Council (201706040081).

Data availability

All the raw data on immune phenotypes and summary statistics generated directly from genetic data needed to precisely reproduce published results are deposited in Dryad (https://doi.org/10.5061/dryad.4f4qrfjd0). Custom scripts for generating summary statistics and all results are deposited in GitHub (https://github.com/Chuxj/Gf_of_ip_in_T1D). Individual genetic data and other privacy-sensitive individual information are not publicly available because they contain information that could compromise research participant privacy. For data access, please contact Prof. Cees Tack (Cees.Tack@radboudumc.nl). This original data is available for qualified researchers, i.e. senior investigators employed or legitimately affiliated with an academic, non-profit or government institution who have a track record in the field. We would ask the researcher to sign a data access agreement that needs to be signed by applicants and legal representatives of their universities. In addition, we would require a research proposal, to ensure that 'Applications for access to Data must be Specific, Measurable, Attainable, Resourced and Timely.' The applicant must implement the proposed research within the designed time frame and the data must be deleted after finishing the proposal.

The following data sets were generated

Article and author information

Author details

  1. Xiaojing Chu

    Department of Genetics, University Medical Center Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  2. Anna WM Janssen

    Department of Internal Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  3. Hans Koenen

    Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  4. Linzhung Chang

    Department of Genetics, University Medical Center Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  5. Xuehui He

    Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  6. Irma Joosten

    Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  7. Rinke Stienstra

    Department of Internal Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

  8. Yunus Kuijpers

    Department of Computational Biology for Individualised Infection Medicine, Helmholtz Centre for Infection Research, Hannover, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5075-3970

  9. Cisca Wijmenga

    Department of Genetics, University of Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5635-1614

  10. Cheng-Jian Xu

    Department of Computational Biology for Individualised Infection Medicine, Helmholtz Centre for Infection Research, Hannover, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1586-4672

  11. Mihai G Netea

    Department of Internal Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2421-6052

  12. Cees J Tack

    Department of Internal Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    For correspondence
    Cees.Tack@radboudumc.nl
    Competing interests
    The authors declare that no competing interests exist.

  13. Yang Li

    Department of Computational Biology for Individualised Infection Medicine, Helmholtz Centre for Infection Research, Hannover, Germany
    For correspondence
    yang.li@helmholtz-hzi.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4022-7341

Funding

ERC Starting grant (948207)

  • Yang Li

Radboud Universitair Medisch Centrum (Hypatia Grant 2018)

  • Yang Li

ERC advanced grant (833247)

  • Mihai G Netea

the Netherlands Association of Scientific Reasearch (Spinoza grant)

  • Mihai G Netea

the Netherlands Organisation for Scientific Research (Perspectief Biomarker Development Center Research Programme)

  • Cees J Tack

European Foundation for the Study of Diabetes (AZ Macrovascular Programme 2015)

  • Anna WM Janssen

China Scholarship Council (201706040081)

  • Anna WM Janssen

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

Ethics

Human subjects: The 500FG-DM study was approved by the ethical committee of Radboud University Nijmegen (NL-number: 54214.091.15). Experiments were conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from all participants.

Copyright

© 2022, Chu 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

  • 2,392
    views
  • 455
    downloads
  • 5
    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. Xiaojing Chu
  2. Anna WM Janssen
  3. Hans Koenen
  4. Linzhung Chang
  5. Xuehui He
  6. Irma Joosten
  7. Rinke Stienstra
  8. Yunus Kuijpers
  9. Cisca Wijmenga
  10. Cheng-Jian Xu
  11. Mihai G Netea
  12. Cees J Tack
  13. Yang Li
(2022)
A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes
eLife 11:e73709.
https://doi.org/10.7554/eLife.73709

Share this article

https://doi.org/10.7554/eLife.73709

Categories and tags

Research organism

Further reading

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics

    Deep sequencing of yeast and mouse tRNAs and tRNA fragments using OTTR

    Hans Tobias Gustafsson, Lucas Ferguson ... Oliver J Rando
    Research Article

    Among the major classes of RNAs in the cell, tRNAs remain the most difficult to characterize via deep sequencing approaches, as tRNA structure and nucleotide modifications can each interfere with cDNA synthesis by commonly-used reverse transcriptases (RTs). Here, we benchmark a recently-developed RNA cloning protocol, termed Ordered Two-Template Relay (OTTR), to characterize intact tRNAs and tRNA fragments in budding yeast and in mouse tissues. We show that OTTR successfully captures both full-length tRNAs and tRNA fragments in budding yeast and in mouse reproductive tissues without any prior enzymatic treatment, and that tRNA cloning efficiency can be further enhanced via AlkB-mediated demethylation of modified nucleotides. As with other recent tRNA cloning protocols, we find that a subset of nucleotide modifications leave misincorporation signatures in OTTR datasets, enabling their detection without any additional protocol steps. Focusing on tRNA cleavage products, we compare OTTR with several standard small RNA-Seq protocols, finding that OTTR provides the most accurate picture of tRNA fragment levels by comparison to "ground truth" Northern blots. Applying this protocol to mature mouse spermatozoa, our data dramatically alter our understanding of the small RNA cargo of mature mammalian sperm, revealing a far more complex population of tRNA fragments - including both 5′ and 3′ tRNA halves derived from the majority of tRNAs – than previously appreciated. Taken together, our data confirm the superior performance of OTTR to commercial protocols in analysis of tRNA fragments, and force a reappraisal of potential epigenetic functions of the sperm small RNA payload.

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics

    Major nuclear locales define nuclear genome organization and function beyond A and B compartments

    Omid Gholamalamdari, Tom van Schaik ... Andrew S Belmont
    Research Article

    Models of nuclear genome organization often propose a binary division into active versus inactive compartments yet typically overlook nuclear bodies. Here, we integrated analysis of sequencing and image-based data to compare genome organization in four human cell types relative to three different nuclear locales: the nuclear lamina, nuclear speckles, and nucleoli. Although gene expression correlates mostly with nuclear speckle proximity, DNA replication timing correlates with proximity to multiple nuclear locales. Speckle attachment regions emerge as DNA replication initiation zones whose replication timing and gene composition vary with their attachment frequency. Most facultative LADs retain a partially repressed state as iLADs, despite their positioning in the nuclear interior. Knock out of two lamina proteins, Lamin A and LBR, causes a shift of H3K9me3-enriched LADs from lamina to nucleolus, and a reciprocal relocation of H3K27me3-enriched partially repressed iLADs from nucleolus to lamina. Thus, these partially repressed iLADs appear to compete with LADs for nuclear lamina attachment with consequences for replication timing. The nuclear organization in adherent cells is polarized with nuclear bodies and genomic regions segregating both radially and relative to the equatorial plane. Together, our results underscore the importance of considering genome organization relative to nuclear locales for a more complete understanding of the spatial and functional organization of the human genome.

    1. Cell Biology
    2. Genetics and Genomics

    Pyruvate and related energetic metabolites modulate resilience against high genetic risk for glaucoma

    Keva Li, Nicholas Tolman ... UK Biobank Eye and Vision Consortium
    Research Article

    A glaucoma polygenic risk score (PRS) can effectively identify disease risk, but some individuals with high PRS do not develop glaucoma. Factors contributing to this resilience remain unclear. Using 4,658 glaucoma cases and 113,040 controls in a cross-sectional study of the UK Biobank, we investigated whether plasma metabolites enhanced glaucoma prediction and if a metabolomic signature of resilience in high-genetic-risk individuals existed. Logistic regression models incorporating 168 NMR-based metabolites into PRS-based glaucoma assessments were developed, with multiple comparison corrections applied. While metabolites weakly predicted glaucoma (Area Under the Curve = 0.579), they offered marginal prediction improvement in PRS-only-based models (p=0.004). We identified a metabolomic signature associated with resilience in the top glaucoma PRS decile, with elevated glycolysis-related metabolites—lactate (p=8.8E-12), pyruvate (p=1.9E-10), and citrate (p=0.02)—linked to reduced glaucoma prevalence. These metabolites combined significantly modified the PRS-glaucoma relationship (Pinteraction = 0.011). Higher total resilience metabolite levels within the highest PRS quartile corresponded to lower glaucoma prevalence (Odds Ratiohighest vs. lowest total resilience metabolite quartile=0.71, 95% Confidence Interval = 0.64–0.80). As pyruvate is a foundational metabolite linking glycolysis to tricarboxylic acid cycle metabolism and ATP generation, we pursued experimental validation for this putative resilience biomarker in a human-relevant Mus musculus glaucoma model. Dietary pyruvate mitigated elevated intraocular pressure (p=0.002) and optic nerve damage (p<0.0003) in Lmx1bV265D mice. These findings highlight the protective role of pyruvate-related metabolism against glaucoma and suggest potential avenues for therapeutic intervention.