Advertisement
Journal of Investigative Dermatology Home

Making New Connections—Chromosome Conformation Capture for Identification of Disease-Associated Target Genes

  • Matthew T. Patrick
    Affiliations
    Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA
    Search for articles by this author
  • Lam C. Tsoi
    Affiliations
    Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA

    Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA

    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
    Search for articles by this author
  • Johann E. Gudjonsson
    Correspondence
    Correspondence: Johann E. Gudjonsson, Department of Dermatology, University of Michigan Medical School, 1910 Taubman Medical Center, 1500 East Medical Center Drive, Ann Arbor, Michigan, USA.
    Affiliations
    Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA
    Search for articles by this author
      Most susceptibility loci for complex dermatologic diseases fall within non-coding regions, making assignment of function difficult. Chromosome conformation capture addresses this problem by using the spatial organization of chromatin to identify the genes these loci interact with, often across long distances. Furthermore, as gene regulation is tissue-/cell-dependent, studying relevant cell populations is critical to reveal the molecular mechanisms involved.
      • 90% of genome-wide association study loci fall within non-coding regions, suggesting they have a regulatory function
      • Chromosome confirmation capture assesses the physical interaction of chromatin in 3-dimensions and together with chromatin immunoprecipitation can help identify interactions between disease-associated loci and target genes
      • By incorporating cell-/tissue-type level information, it is possible to more effectively predict target genes for genome-wide association loci

       The importance of knowing which genes are affected by genome-wide association study loci

      Genome-wide association studies (GWAS) have identified large numbers of genetic loci for complex diseases, including many for dermatologic conditions (
      • Tsoi L.C.
      • Patrick M.T.
      • Elder J.T.
      Research techniques made simple: using genome-wide association studies to understand complex cutaneous disorders.
      ). However, 90% of these signals fall within non-coding regions of the genome (
      • Jeng M.Y.
      • Mumbach M.R.
      • Granja J.M.
      • Satpathy A.T.
      • Chang H.Y.
      • Chang A.L.S.
      Enhancer connectome nominates target genes of inherited risk variants from inflammatory skin disorders.
      ), and they do not to alter the functions of proteins by changing their amino acid sequence. Instead, more recent studies have identified enrichment of disease-associated loci in regulatory elements (
      • Farh K.K.
      • Marson A.
      • Zhu J.
      • Kleinewietfeld M.
      • Housley W.J.
      • Beik S.
      • et al.
      Genetic and epigenetic fine mapping of causal autoimmune disease variants.
      ) that likely affect the expression of nearby genes. In order to translate GWAS findings into biological insights, it is necessary to identify the genes affected by GWAS loci. This has not been a trivial task. Progress in this direction will increase our understanding of the biological mechanisms involved, inform subsequent functional experiments, and suggest new targets for therapeutic development.
      DNA has a 3-dimensional structure, allowing enhancers to be brought close to promoters that they regulate through long-range looping interactions. Thus, the gene nearest to a susceptibility locus is not necessarily the most relevant target. Furthermore, some loci have multiple targets. For example, the major histocompatibility complex (MHC) region is important for immune responses, as it encodes multiple HLAs that regulate the immune system, and genetic variations within the MHC have the largest effect sizes in many complex inflammatory skin conditions.

       Predicting target genes for GWAS loci in non-coding regions

      The paper by
      • Jeng M.Y.
      • Mumbach M.R.
      • Granja J.M.
      • Satpathy A.T.
      • Chang H.Y.
      • Chang A.L.S.
      Enhancer connectome nominates target genes of inherited risk variants from inflammatory skin disorders.
      aimed to address these issues and identify probable genes for different susceptibility regions through the use of a chromosome conformation capture approach, which assesses the spatial structure of DNA by evaluating interactions between genetic loci and their putative gene targets in various dermatologic conditions.
      The chromosome conformation capture approach was first described in 2002 (
      • Dekker J.
      • Rippe K.
      • Dekker M.
      • Kleckner N.
      Capturing chromosome conformation.
      ) using specific primers for PCR to detect interactions between single pairs of loci. This required the candidate gene and causal marker to be known in advance. Subsequent techniques, such as chromosome confirmation capture-on-chip and Hi-C (Figure 1), utilized microarray or high-throughput sequencing technologies and assessed millions of potential interactions without the need for a priori knowledge (
      • de Wit E.
      • de Laat W.
      A decade of 3C technologies: insights into nuclear organization.
      ). Hi-C looks for interactions across the whole genome (i.e., between any two pairs of loci), whereas chromosome confirmation capture-on-chip starts with a specific locus and checks for interactions with other loci, thus enabling higher resolution than Hi-C. Chromosome confirmation capture carbon copy focuses the interaction detection to a specific region, but has lower coverage and is unsuitable for genome-wide interactions. More recent approaches (such as chromatin interaction analysis with paired-end tag) incorporate data from chromatin immunoprecipitation (ChIP) to filter interactions down to a specific protein factor.
      Figure thumbnail gr1
      Figure 1A summary of the various confirmation capture techniques and their methodologies. The method used by
      • Jeng M.Y.
      • Mumbach M.R.
      • Granja J.M.
      • Satpathy A.T.
      • Chang H.Y.
      • Chang A.L.S.
      Enhancer connectome nominates target genes of inherited risk variants from inflammatory skin disorders.
      uses Hi-C in combination of chromatin immunoprecipitation. The term one vs all refers to a definition of a single viewpoint, and the genome is screened for sequences that contact this selected site. The other terms are variation on this depending on the number of viewpoints being assessed and contact sites in the genome that are being assessed. 3C, chromosome conformation capture; 4C, chromosome confirmation capture-on-chip; 5C, chromosome confirmation capture carbon copy; ChIA-PET, chromatin interaction analysis with paired-end tag; ChIP, chromatin immunoprecipitation.
      Reproduced from
      • Li G.
      • Cai L.
      • Chang H.
      • Hong P.
      • Zhou Q.
      • Kulakova E.V.
      • et al.
      Chromatin interaction analysis with paired-end tag (ChIA-PET) sequencing technology and application.
      , with permission. Copyright Cold Spring Harbor Laboratory Press.
      In the paper by
      • Jeng M.Y.
      • Mumbach M.R.
      • Granja J.M.
      • Satpathy A.T.
      • Chang H.Y.
      • Chang A.L.S.
      Enhancer connectome nominates target genes of inherited risk variants from inflammatory skin disorders.
      , the authors used a protein-directed conformation capture approach that they recently described, called HiChIP (
      • Mumbach M.R.
      • Rubin A.J.
      • Flynn R.A.
      • Dai C.
      • Khavari P.A.
      • Greenleaf W.J.
      • et al.
      HiChIP: efficient and sensitive analysis of protein-directed genome architecture.
      ). Instead of performing ChIP first and ligating the resulting fragments (chromatin interaction analysis with paired-end tag), HiChIP starts by performing in situ Hi-C and then applies ChIP to the contact library. As a result, HiChIP requires fewer input cells and can produce more informative reads. For this study, the authors utilized the connectome map constructed by HiChiP in T cells and the active enhancer mark H3K27ac to investigate enhancer-promoter interactions for cutaneous disease-associated susceptibility loci. The authors applied their approach to infer gene targets for more than 600 unique disease-associated markers extracted for 21 cutaneous conditions. Significant findings include the identification of three zinc-finger transcription factors from systemic lupus erythematosus loci that were biased towards different subsets of T cells: IKZF1 showed strong bias toward CD4+ naïve cells, whereas IKZF2 was biased toward T regulatory cells and IKZF3 was biased toward T helper type 17 cells. More than one third of interactions identified by this study for inflammatory conditions occurred within the MHC, but in addition to the MHC region, consistent involvement of the JAK-STAT signaling pathway was observed. JAK-STAT signaling is downstream of multiple different cytokine pathways (
      • Hirahara K.
      • Schwartz D.
      • Gadina M.
      • Kanno Y.
      • O'Shea J.J.
      Targeting cytokine signaling in autoimmunity: back to the future and beyond.
      ), and JAK inhibitors are currently in clinical trials in a number of immune-mediated diseases, including psoriasis, atopic dermatitis, alopecia areata, and vitiligo (
      • Damsky W.
      • King B.A.
      JAK inhibitors in dermatology: the promise of a new drug class.
      ,
      • Villarino A.V.
      • Kanno Y.
      • O'Shea J.J.
      Mechanisms and consequences of Jak-STAT signaling in the immune system.
      ).
      To validate their findings,
      • Jeng M.Y.
      • Mumbach M.R.
      • Granja J.M.
      • Satpathy A.T.
      • Chang H.Y.
      • Chang A.L.S.
      Enhancer connectome nominates target genes of inherited risk variants from inflammatory skin disorders.
      used expression quantitative trait loci analysis, which correlates the alleles of specific variants to the expression of particular genes. Using data from GTEx, a large-scale effort with more than 17,000 transcriptomes across 49 tissue types (
      eGTEx Project
      Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease.
      ),
      • Jeng M.Y.
      • Mumbach M.R.
      • Granja J.M.
      • Satpathy A.T.
      • Chang H.Y.
      • Chang A.L.S.
      Enhancer connectome nominates target genes of inherited risk variants from inflammatory skin disorders.
      identified a significant expression quantitative trait loci (rs3129763 in the MHC region) for HLA-DQB1, confirming the prediction made by their approach, even though the marker lies closer to the promoters of two other genes (HLA-DQA1 and HLA-DRB1). This marker also showed high chromatin accessibility, according to H3K27ac and ATAC signals.

       Some potential caveats for gene target identification

      Identifying relevant genes for GWAS loci requires predictions about the relationships between genetic variants and the genes they regulate.
      • Jeng M.Y.
      • Mumbach M.R.
      • Granja J.M.
      • Satpathy A.T.
      • Chang H.Y.
      • Chang A.L.S.
      Enhancer connectome nominates target genes of inherited risk variants from inflammatory skin disorders.
      use in situ Hi-C and then ChIP to identify putative target genes of GWAS loci of various complex skin conditions. Using this approach in T cells, they were able to reveal far more interactions with genes for GWAS loci in inflammatory diseases (e.g., lupus) than for diseases (e.g., keloids) that do not involve the immune system. This suggests their approach is able to achieve useful cell-type specificity, and
      • Jeng M.Y.
      • Mumbach M.R.
      • Granja J.M.
      • Satpathy A.T.
      • Chang H.Y.
      • Chang A.L.S.
      Enhancer connectome nominates target genes of inherited risk variants from inflammatory skin disorders.
      took advantage of this to identify systemic lupus erythematosus loci that showed bias towards different subsets of T cells. However, it is worth noting that many of the diseases used in this study involve primarily stromal skin cells, such as keratinocytes (i.e., basal cell carcinoma) or fibroblasts (i.e., keloid), and for many of the inflammatory diseases studied (e.g., psoriasis) keratinocytes play a role in pathogenesis. As gene regulation operates in a tissue- and cell-specific manner, extending this approach to stromal cell subsets would be likely to provide an additional wealth of data and increase our understanding of the specific target genes involved.
      It should also be noted that, without further functional experiments, it is difficult to know for certain how a marker will affect gene expression, and ultimately the pathogenesis of a particular disease. The effect is also dependent on other factors, such as male/female sex and the environment. However, this study represents a big step forward and will help lead the way toward a more complete understanding of how non-coding genetic variants promote and shape their associated diseases.

      Conflict of Interest

      JEG serves on advisory boards for Novartis, MiRagen, AbbVie, and has received research support from AbbVie, SunPharma, and Genentech. The remaining authors state no conflicts of interest.

      References

        • Damsky W.
        • King B.A.
        JAK inhibitors in dermatology: the promise of a new drug class.
        J Am Acad Dermatol. 2017; 76: 736-744
        • de Wit E.
        • de Laat W.
        A decade of 3C technologies: insights into nuclear organization.
        Genes Dev. 2012; 26: 11-24
        • Dekker J.
        • Rippe K.
        • Dekker M.
        • Kleckner N.
        Capturing chromosome conformation.
        Science. 2002; 295: 1306-1311
        • Farh K.K.
        • Marson A.
        • Zhu J.
        • Kleinewietfeld M.
        • Housley W.J.
        • Beik S.
        • et al.
        Genetic and epigenetic fine mapping of causal autoimmune disease variants.
        Nature. 2015; 518: 337-343
        • eGTEx Project
        Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease.
        Nat Genet. 2017; 49: 1664-1670
        • Hirahara K.
        • Schwartz D.
        • Gadina M.
        • Kanno Y.
        • O'Shea J.J.
        Targeting cytokine signaling in autoimmunity: back to the future and beyond.
        Curr Opin Immunol. 2016; 43: 89-97
        • Jeng M.Y.
        • Mumbach M.R.
        • Granja J.M.
        • Satpathy A.T.
        • Chang H.Y.
        • Chang A.L.S.
        Enhancer connectome nominates target genes of inherited risk variants from inflammatory skin disorders.
        J Invest Dermatol. 2019; 139: 605-614
        • Li G.
        • Cai L.
        • Chang H.
        • Hong P.
        • Zhou Q.
        • Kulakova E.V.
        • et al.
        Chromatin interaction analysis with paired-end tag (ChIA-PET) sequencing technology and application.
        BMC Genomics. 2014; 15: S11
        • Mumbach M.R.
        • Rubin A.J.
        • Flynn R.A.
        • Dai C.
        • Khavari P.A.
        • Greenleaf W.J.
        • et al.
        HiChIP: efficient and sensitive analysis of protein-directed genome architecture.
        Nat Methods. 2016; 13: 919-922
        • Tsoi L.C.
        • Patrick M.T.
        • Elder J.T.
        Research techniques made simple: using genome-wide association studies to understand complex cutaneous disorders.
        J Invest Dermatol. 2018; 138: e23-e29
        • Villarino A.V.
        • Kanno Y.
        • O'Shea J.J.
        Mechanisms and consequences of Jak-STAT signaling in the immune system.
        Nat Immunol. 2017; 18: 374-384

      Linked Article

      • Enhancer Connectome Nominates Target Genes of Inherited Risk Variants from Inflammatory Skin Disorders
        Journal of Investigative DermatologyVol. 139Issue 3
        • Preview
          The vast majority of polymorphisms for human dermatologic diseases fall in noncoding DNA regions, leading to difficulty interpreting their functional significance. Recent work using chromosome conformation capture technology in combination with chromatin immunoprecipitation (ChIP) has provided a systematic means of linking noncoding variants in active enhancer loci to putative gene targets. Here, we apply H3K27ac HiChIP high-resolution contact maps, generated from primary human T-cell subsets (CD4+ naïve, T helper type 17, and regulatory T cells), to 21 dermatologic conditions associated with single nucleotide polymorphisms from 106 genome-wide association studies.
        • Full-Text
        • PDF
        Open Access