Characterization of caffeine response regulatory variants in vascular endothelial cells

  1. Carly Boye
  2. Cynthia A Kalita
  3. Anthony S Findley
  4. Adnan Alazizi
  5. Julong Wei
  6. Xiaoquan Wen
  7. Roger Pique-Regi
  8. Francesca Luca  Is a corresponding author
  1. Wayne State University, United States
  2. University of Michigan-Ann Arbor, United States

Abstract

Genetic variants in gene regulatory sequences can modify gene expression and mediate the molecular response to environmental stimuli. In addition, genotype-environment interactions (GxE) contribute to complex traits such as cardiovascular disease. Caffeine is the most widely consumed stimulant and is known to produce a vascular response. To investigate GxE for caffeine, we treated vascular endothelial cells with caffeine and used a massively parallel reporter assay to measure allelic effects on gene regulation for over 43,000 genetic variants. We identified 665 variants with allelic effects on gene regulation and 29 variants that regulate the gene expression response to caffeine (GxE, FDR<10%). When overlapping our GxE results with eQTLs colocalized with CAD and hypertension, we dissected their regulatory mechanisms and showed a modulatory role for caffeine. Our results demonstrate that massively parallel reporter assay is a powerful approach to identify and molecularly characterize GxE in the specific context of caffeine consumption.

Data availability

FASTQ files and read count data are available at the GEO accession number GSE221514

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Carly Boye

    Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Cynthia A Kalita

    Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Anthony S Findley

    Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9922-3076
  4. Adnan Alazizi

    Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Julong Wei

    Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Xiaoquan Wen

    Department of Biostatistics, University of Michigan-Ann Arbor, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Roger Pique-Regi

    Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1262-2275
  8. Francesca Luca

    Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States
    For correspondence
    fluca@wayne.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8252-9052

Funding

National Institute of General Medical Sciences (R01GM109215)

  • Roger Pique-Regi
  • Francesca Luca

National Institute of Environmental Health Sciences (R01ES033634)

  • Xiaoquan Wen
  • Roger Pique-Regi
  • Francesca Luca

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

Reviewing Editor

  1. Stephen CJ Parker, University of Michigan, United States

Version history

  1. Preprint posted: November 24, 2022 (view preprint)
  2. Received: November 30, 2022
  3. Accepted: February 8, 2024
  4. Accepted Manuscript published: February 9, 2024 (version 1)
  5. Version of Record published: February 28, 2024 (version 2)

Copyright

© 2024, Boye 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

  • 364
    views
  • 56
    downloads
  • 2
    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. Carly Boye
  2. Cynthia A Kalita
  3. Anthony S Findley
  4. Adnan Alazizi
  5. Julong Wei
  6. Xiaoquan Wen
  7. Roger Pique-Regi
  8. Francesca Luca
(2024)
Characterization of caffeine response regulatory variants in vascular endothelial cells
eLife 13:e85235.
https://doi.org/10.7554/eLife.85235

Share this article

https://doi.org/10.7554/eLife.85235

Further reading

    1. Genetics and Genomics
    2. Neuroscience
    Yifei Weng, Shiyi Zhou ... Coleen T Murphy
    Research Article

    Cognitive decline is a significant health concern in our aging society. Here, we used the model organism C. elegans to investigate the impact of the IIS/FOXO pathway on age-related cognitive decline. The daf-2 Insulin/IGF-1 receptor mutant exhibits a significant extension of learning and memory span with age compared to wild-type worms, an effect that is dependent on the DAF-16 transcription factor. To identify possible mechanisms by which aging daf-2 mutants maintain learning and memory with age while wild-type worms lose neuronal function, we carried out neuron-specific transcriptomic analysis in aged animals. We observed downregulation of neuronal genes and upregulation of transcriptional regulation genes in aging wild-type neurons. By contrast, IIS/FOXO pathway mutants exhibit distinct neuronal transcriptomic alterations in response to cognitive aging, including upregulation of stress response genes and downregulation of specific insulin signaling genes. We tested the roles of significantly transcriptionally-changed genes in regulating cognitive functions, identifying novel regulators of learning and memory. In addition to other mechanistic insights, a comparison of the aged vs young daf-2 neuronal transcriptome revealed that a new set of potentially neuroprotective genes is upregulated; instead of simply mimicking a young state, daf-2 may enhance neuronal resilience to accumulation of harm and take a more active approach to combat aging. These findings suggest a potential mechanism for regulating cognitive function with age and offer insights into novel therapeutic targets for age-related cognitive decline.

    1. Genetics and Genomics
    Samuel Pattillo Smith, Gregory Darnell ... Lorin Crawford
    Research Article

    LD score regression (LDSC) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. In this work, we present interaction-LD score (i-LDSC) regression: an extension of the original LDSC framework that accounts for interactions between genetic variants. By studying a wide range of generative models in simulations, and by re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals in the UK Biobank and up to 159,095 individuals in BioBank Japan, we show that the inclusion of a cis-interaction score (i.e. interactions between a focal variant and proximal variants) recovers genetic variance that is not captured by LDSC. For each of the 25 traits analyzed in the UK Biobank and BioBank Japan, i-LDSC detects additional variation contributed by genetic interactions. The i-LDSC software and its application to these biobanks represent a step towards resolving further genetic contributions of sources of non-additive genetic effects to complex trait variation.