1. Genetics and Genomics

Human genetic analyses of organelles highlight the nucleus in age-related trait heritability

  1. Rahul Gupta  Is a corresponding author
  2. Konrad J Karczewski
  3. Daniel Howrigan
  4. Benjamin M Neale  Is a corresponding author
  5. Vamsi K Mootha MD  Is a corresponding author
  1. Howard Hughes Medical Institute, Massachusetts General Hospital, United States
  2. Broad Institute of MIT and Harvard, United States
  3. Massachusetts General Hospital, United States
https://doi.org/10.7554/eLife.68610
Share this article
Cite this article
  1. Rahul Gupta
  2. Konrad J Karczewski
  3. Daniel Howrigan
  4. Benjamin M Neale
  5. Vamsi K Mootha MD
(2021)
Human genetic analyses of organelles highlight the nucleus in age-related trait heritability
eLife 10:e68610.
https://doi.org/10.7554/eLife.68610
  2. Download BibTeX
  3. Download .RIS

Abstract

Most age-related human diseases are accompanied by a decline in cellular organelle integrity, including impaired lysosomal proteostasis and defective mitochondrial oxidative phosphorylation. An open question, however, is the degree to which inherited variation in or near genes encoding each organelle contributes to age-related disease pathogenesis. Here, we evaluate if genetic loci encoding organelle proteomes confer greater-than-expected age-related disease risk. As mitochondrial dysfunction is a 'hallmark' of aging, we begin by assessing nuclear and mitochondrial DNA loci near genes encoding the mitochondrial proteome and surprisingly observe a lack of enrichment across 24 age-related traits. Within nine other organelles, we find no enrichment with one exception: the nucleus, where enrichment emanates from nuclear transcription factors. In agreement, we find that genes encoding several organelles tend to be 'haplosufficient', while we observe strong purifying selection against heterozygous protein-truncating variants impacting the nucleus. Our work identifies common variation near transcription factors as having outsize influence on age-related trait risk, motivating future efforts to determine if and how this inherited variation then contributes to observed age-related organelle deterioration.

Data availability

Heritability point estimates and standard errors for age-related traits are listed in Supplementary File 1. Genetic and phenotypic correlation point estimates and standard errors/p-values plotted in Figure 1B are available in Figure 1-Source data 1. Summary statistics from mtDNA-GWAS (plotted in Figure 2 and Figure 2-Figure supplement 9) are available in Source data 2. All gene-based enrichment analysis p-values and point estimates are available in Source data 1 and Source data 3. Period prevalence data for diseases in the UK can be obtained from Kuan et al. 2019. Gene-sets can be found using COMPARTMENTS (https://compartments.jensenlab.org), MitoCarta 2.0 (https://www.broadinstitute.org/files/shared/metabolism/mitocarta/human.mitocarta2.0.html), Lambert et al. 2018 (DOI: 10.1016/j.cell.2018.01.029), Frazier et al. 2019 (DOI: 10.1074/jbc.R117.809194), Finucane et al. 2018 (https://alkesgroup.broadinstitute.org/LDSCORE/), Kapopoulou et al. 2015 (DOI: 10.1111/evo.12819), and the Macarthur laboratory (https://github.com/macarthur-lab/gene_lists). Gene age estimates were obtained from Litman, Stein 2019 (DOI: 10.1053/j.seminoncol.2018.11.002). GWAS catalog annotations can be obtained from: https://www.ebi.ac.uk/gwas. Heritability estimates across UKB can be obtained at: https://nealelab.github.io/UKBB_ldsc/. UKB summary statistics can be obtained from Neale lab GWAS round 2: https://github.com/Nealelab/UK_Biobank_GWAS. Annotations for the Baseline v1.1 and BaselineLD v2.2 models as well as other relevant reference data, including the 1000G EUR reference panel, can be obtained from https://alkesgroup.broadinstitute.org/LDSCORE/. eQTL and expression data in human tissues can be obtained from GTEx (https://www.gtexportal.org). Constraint estimates can be found via gnomAD: https://gnomad.broadinstitute.org. See citations for publicly available GWAS meta-analysis summary statistics (28,29,51,52,30-37).

The following previously published data sets were used

Article and author information

Author details

  1. Rahul Gupta

    Department of Molecular Biology, Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, United States
    For correspondence
    rahul@broadinstitute.org
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8263-2455

  2. Konrad J Karczewski

    Broad Institute of MIT and Harvard, Cambridge, United States
    Competing interests
    Konrad J Karczewski, is a consultant for Vor Biopharma..

  3. Daniel Howrigan

    Analytic & Translational Genetics Unit, Massachusetts General Hospital, Boston, United States
    Competing interests
    No competing interests declared.

  4. Benjamin M Neale

    Broad Institute of MIT and Harvard, Cambridge, United States
    For correspondence
    bneale@broadinstitute.org
    Competing interests
    Benjamin M Neale, BMN is a member of the scientific advisory board at Deep Genomics and RBNC Therapeutics. BMN is a consultant for Camp4 Therapeutics, Takeda Pharmaceutical and Biogen..

  5. Vamsi K Mootha MD

    Molecular Biology, Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, United States
    For correspondence
    vamsi@hms.harvard.edu
    Competing interests
    Vamsi K Mootha, is an advisor to and receives compensation or equity from Janssen Pharmaceuticals, 5am Ventures, and Raze Therapeutics..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9924-642X

Funding

National Institutes of Health (T32AG000222)

  • Rahul Gupta

National Institutes of Health (R35GM122455)

  • Vamsi K Mootha MD

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

Copyright

© 2021, Gupta 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

  • 2,543
    views
  • 255
    downloads
  • 9
    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. Rahul Gupta
  2. Konrad J Karczewski
  3. Daniel Howrigan
  4. Benjamin M Neale
  5. Vamsi K Mootha MD
(2021)
Human genetic analyses of organelles highlight the nucleus in age-related trait heritability
eLife 10:e68610.
https://doi.org/10.7554/eLife.68610

Share this article

https://doi.org/10.7554/eLife.68610

Categories and tags

Research organism

Further reading

    1. Genetics and Genomics
    2. Immunology and Inflammation

    ACK1 and BRK non-receptor tyrosine kinase deficiencies are associated with familial systemic lupus and involved in efferocytosis

    Stephanie Guillet, Tomi Lazarov ... Frédéric Geissmann
    Research Article

    Systemic lupus erythematosus (SLE) is an autoimmune disease, the pathophysiology and genetic basis of which are incompletely understood. Using a forward genetic screen in multiplex families with SLE, we identified an association between SLE and compound heterozygous deleterious variants in the non-receptor tyrosine kinases (NRTKs) ACK1 and BRK. Experimental blockade of ACK1 or BRK increased circulating autoantibodies in vivo in mice and exacerbated glomerular IgG deposits in an SLE mouse model. Mechanistically, NRTKs regulate activation, migration, and proliferation of immune cells. We found that the patients’ ACK1 and BRK variants impair efferocytosis, the MERTK-mediated anti-inflammatory response to apoptotic cells, in human induced pluripotent stem cell (hiPSC)-derived macrophages, which may contribute to SLE pathogenesis. Overall, our data suggest that ACK1 and BRK deficiencies are associated with human SLE and impair efferocytosis in macrophages.

    1. Genetics and Genomics
    2. Microbiology and Infectious Disease

    Genetic stability of Mycobacterium smegmatis under the stress of first-line antitubercular agents

    Dániel Molnár, Éva Viola Surányi ... Judit Toth
    Research Article

    The sustained success of Mycobacterium tuberculosis as a pathogen arises from its ability to persist within macrophages for extended periods and its limited responsiveness to antibiotics. Furthermore, the high incidence of resistance to the few available antituberculosis drugs is a significant concern, especially since the driving forces of the emergence of drug resistance are not clear. Drug-resistant strains of Mycobacterium tuberculosis can emerge through de novo mutations, however, mycobacterial mutation rates are low. To unravel the effects of antibiotic pressure on genome stability, we determined the genetic variability, phenotypic tolerance, DNA repair system activation, and dNTP pool upon treatment with current antibiotics using Mycobacterium smegmatis. Whole-genome sequencing revealed no significant increase in mutation rates after prolonged exposure to first-line antibiotics. However, the phenotypic fluctuation assay indicated rapid adaptation to antibiotics mediated by non-genetic factors. The upregulation of DNA repair genes, measured using qPCR, suggests that genomic integrity may be maintained through the activation of specific DNA repair pathways. Our results, indicating that antibiotic exposure does not result in de novo adaptive mutagenesis under laboratory conditions, do not lend support to the model suggesting antibiotic resistance development through drug pressure-induced microevolution.

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Functional characteristics and computational model of abundant hyperactive loci in the human genome

    Sanjarbek Hudaiberdiev, Ivan Ovcharenko
    Research Article

    Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.