Skip to main content

Advertisement

Log in

Exploring the dark genome: implications for precision medicine

  • Published:
Mammalian Genome Aims and scope Submit manuscript

Abstract

The increase in the number of both patients and healthcare practitioners who grew up using the Internet and computers (so-called “digital natives”) is likely to impact the practice of precision medicine, and requires novel platforms for data integration and mining, as well as contextualized information retrieval. The “Illuminating the Druggable Genome Knowledge Management Center” (IDG KMC) quantifies data availability from a wide range of chemical, biological, and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the “dark genome”), and their potential contribution to specific pathologies. Using the “Target Importance and Novelty Explorer” (TIN-X) highlights the role of LRRC10 (a dark gene) in dilated cardiomyopathy. Combining mouse and human phenotype data leads to increased strength of evidence, which is discussed for four additional dark genes: SLX4IP and its role in glucose metabolism, the role of HSF2BP in coronary artery disease, the involvement of ELFN1 in attention-deficit hyperactivity disorder and the role of VPS13D in mouse neural tube development and its confirmed role in childhood onset movement disorders. The workflow and tools described here are aimed at guiding further experimental research, particularly within the context of precision medicine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abbott WM, Damschroder MM, Lowe DC (2014) Current approaches to fine mapping of antigen-antibody interactions. Immunology 142(4):526–535

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Abifadel M, Varret M, Rabès J-P, Allard D, Ouguerram K, Devillers M, Cruaud C et al (2003) Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet 34(2):154–156

    Article  CAS  PubMed  Google Scholar 

  • Amberger J, Bocchini CA, Scott AF, Hamosh A (2009) McKusick’s Online mendelian inheritance in man (OMIM). Nucleic Acids Res 37:793–796

    Article  CAS  Google Scholar 

  • Anding AL, Wang C, Chang T-K, Sliter DA, Powers CM, Hofmann K, Youle RJ, Baehrecke EH (2018) Vps13D encodes a ubiquitin-binding protein that is required for the regulation of mitochondrial size and clearance. Curr Biol 28(2):287–295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ashburner M, Ball CA, Blake JA, Botstein D, Butler JH, Cherry M, Davis AP et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25–29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Berger KM, Schneck PA (2019) National and transnational security implications of asymmetric access to and use of biological data. Front Bioeng Biotechnol 7(February):21

    Article  PubMed  PubMed Central  Google Scholar 

  • Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bezemer T, de Groot MC, Blasse E, Ten Berg MJ, Kappen TH, Bredenoord AL, van Solinge WW, Hoefer IE, Haitjema S (2019) A human(e) factor in clinical decision support systems. J Med Internet Res 21(3):e11732

    Article  PubMed  PubMed Central  Google Scholar 

  • Cannon DC, Yang JJ, Mathias SL, Ursu O, Mani S, Waller A, Schürer SC et al (2017) TIN-X: target importance and novelty explorer. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx200

    Article  PubMed  PubMed Central  Google Scholar 

  • Clementi N, Mancini N, Castelli M, Clementi M, Burioni R (2013) Characterization of epitopes recognized by monoclonal antibodies: experimental Approaches supported by freely accessible bioinformatic tools. Drug Discov Today 18(9–10):464–471

    Article  CAS  PubMed  Google Scholar 

  • Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9):793–795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dolan J, Mitchell KJ (2013) Mutation of Elfn1 in mice causes seizures and hyperactivity. PLoS ONE 8(11):e80491

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Edwards AM, Isserlin R, Bader GD, Frye SV, Willson TM, Frank HY (2011) Too many roads not taken. Nature 470(7333):163–165

    Article  CAS  PubMed  Google Scholar 

  • Gaulton A, H A, Nowotka AM, Bento P, Chambers J, Mendez D, Mutowo P et al (2017) The ChEMBL database in 2017. Nucleic Acids Res 45(D1):D945–D954

    Article  CAS  PubMed  Google Scholar 

  • Gauthier J, Meijer IA, Lessel D, Mencacci NE, Krainc D, Hempel M, Tsiakas K et al (2018) Recessive mutations in > VPS13D cause childhood onset movement disorders. Ann Neurol 83(6):1089–1095

    Article  CAS  PubMed  Google Scholar 

  • Hajduk PJ, Huth JR, Tse C (2005) Predicting protein druggability. Drug Discov Today 10(23–24):1675–1682

    Article  CAS  PubMed  Google Scholar 

  • Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1(9):727–730

    Article  CAS  PubMed  Google Scholar 

  • Kandoi G, Acencio ML, Lemke N (2015) Prediction of druggable proteins using machine learning and systems biology: a mini-review. Front Physiol 6(December):366

    PubMed  PubMed Central  Google Scholar 

  • Kibbe WA, Arze C, Felix V, Mitraka E, Bolton E, Fu G, Mungall CJ et al (2015) Disease ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 43:1071–1078

    Article  CAS  Google Scholar 

  • Kiermer V (2008) Antibodypedia. Nat Methods 5(10):860–861

    Article  CAS  Google Scholar 

  • Knowles J, Gromo Gianni (2003) Target Selection in drug discovery. Nat Rev Drug Discov 2(1):63–69

    Article  CAS  PubMed  Google Scholar 

  • Koscielny G, Yaikhom G, Iyer V, Meehan TF, Morgan H, Atienza-Herrero J et al (2014) The international mouse phenotyping consortium web portal, a unified point of access for knockout mice and related phenotyping data. Nucleic Acids Res 42:802–809

    Article  CAS  Google Scholar 

  • Koscielny G, An P, Carvalho-Silva D, Cham JA, Fumis L, Gasparyan R, Hasan S et al (2017) Open targets: a platform for therapeutic target identification and validation. Nucleic Acids Res 45(D1):D985–D994

    Article  CAS  PubMed  Google Scholar 

  • Lenat DB, Feigenbaum EA (1991) On the thresholds of knowledge. Artif Intell 47:185–250

    Article  Google Scholar 

  • Lin Y, M S, Küçük-McGinty H, Turner JP, Vidovic D, Forlin M, Koleti A et al (2017) Drug target ontology to classify and integrate drug discovery data. J Biomed Semant 8(1):50

    Article  Google Scholar 

  • Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23(1–3):3–25

    Article  CAS  Google Scholar 

  • MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, Junkins H et al (2017) The new NHGRI-EBI catalog of published genome-Wide association studies (GWAS Catalog). Nucleic Acids Res 45(D1):D896–D901

    Article  CAS  PubMed  Google Scholar 

  • McMurry JA, Köhler S, Washington NL, Balhoff JP, Borromeo C, Brush M, Carbon S et al (2016) Navigating the phenotype frontier: the monarch initiative. Genetics 203(4):1491–1495

    Article  PubMed  PubMed Central  Google Scholar 

  • Mould DR, Meibohm B (2016) Drug development of therapeutic monoclonal antibodies. BioDrugs 30(4):275–293

    Article  CAS  PubMed  Google Scholar 

  • National Research Council, Division on Earth and Life Studies, Board on Life Sciences, and Committee on A Framework for Developing a New Taxonomy of Disease (2012) Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. National Academies Press, Washington DC

    Google Scholar 

  • Nguyen D-T, Mathias S, Bologa C, Brunak S, Fernandez N, Gaulton A, Hersey A et al (2017) Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Res 45(D1):D995–D1002

    Article  CAS  PubMed  Google Scholar 

  • Nooren IMA, Thornton JM (2003) Diversity of protein–protein interactions. EMBO J 22(14):3486–3492

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Oprea TI, Bologa CG, Brunak S, Campbell A, Gan GN, Gaulton A, Gomez SM et al (2018a) Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 17(5):377

    Article  CAS  PubMed  Google Scholar 

  • Oprea TI, Jan L, Johnson GL, Roth BL, Ma’ayan A A, Schürer S, Shoichet BK, Sklar LA, McManus MT (2018b) Far away from the lamppost. PLoS Biol 16(12):e3000067

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, Vasileiadou A, Arvanitidis C, Jensen LJ (2013) The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text. PLoS ONE 8(6):e65390

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pandey AK, Lu L, Wang X, Homayouni R, Williams RW (2014) Functionally enigmatic genes: a case study of the brain ignorome. PLoS ONE 9(2):e88889

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Perlman RL (2016) Mouse models of human disease: an evolutionary perspective. Evol Med Public Health 2016(1):170–176

    PubMed  PubMed Central  Google Scholar 

  • Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ (2015) DISEASES: text Mining and data integration of disease-gene associations. Methods 74(March):83–89

    Article  CAS  PubMed  Google Scholar 

  • Poirier S, Mayer G, Benjannet S, Bergeron E, Marcinkiewicz J, Nassoury N, Mayer H, Nimpf J, Prat A, Seidah NG (2008) The proprotein convertase PCSK9 induces the degradation of low density lipoprotein receptor (LDLR) and its closest family members VLDLR and ApoER2. J Biol Chem 283(4):2363–2372

    Article  CAS  PubMed  Google Scholar 

  • Prosperi M, Min JS, Bian J, Modave F (2018) Big data hurdles in precision medicine and precision public health. BMC Med Inf Decis Mak 18(1):139

    Article  Google Scholar 

  • Rader DJ, Cohen J, Hobbs HH (2003) Monogenic hypercholesterolemia: new insights in pathogenesis and treatment. J Clin Investig 111(12):1795–1803

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rath A, Olry A, Dhombres F, Brandt MM, Urbero B, Ayme S (2012) Representation of rare diseases in health information systems: the orphanet approach to serve a wide range of end users. Hum Mutat 33(5):803–808

    Article  PubMed  Google Scholar 

  • Robinson PN, Mungall CJ, Haendel M (2015) Capturing phenotypes for precision medicine. Cold Spring Harb Mol Case Stud 1(1):a000372

    Article  PubMed  PubMed Central  Google Scholar 

  • Rodgers G, Austin C, Anderson J, Pawlyk A, Colvis C, Margolis R, Baker J (2018) Glimmers in illuminating the druggable genome. Nat Rev Drug Discov 17(5):301–302

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, Ma’ayan A (2016) The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database. https://doi.org/10.1093/database/baw100

    Article  PubMed  PubMed Central  Google Scholar 

  • Rye K-A, Barter PJ (2014) Cardioprotective functions of HDLs. J Lipid Res 55(2):168–179

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Santos R, Ursu O, Gaulton A, Bento AP, Donadi RS, Bologa CG, Karlsson A et al (2017) A comprehensive map of molecular drug targets. Nat Rev Drug Discov 16(1):19–34

    Article  CAS  PubMed  Google Scholar 

  • Seneviratne MG, Kahn MG, Hernandez-Boussard T (2019) Merging heterogeneous clinical data to enable knowledge discovery. Pac Symp Biocomput 24:439–443

    PubMed  PubMed Central  Google Scholar 

  • Seong E, Insolera R, Dulovic M, Kamsteeg E-J, Trinh J, Brüggemann N, Sandford E et al (2018) Mutations in VPS13D lead to a new recessive ataxia with spasticity and mitochondrial defects. Ann Neurol 83(6):1075–1088

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Southam L, Gilly A, Süveges D, Farmaki A-E, Schwartzentruber J, Tachmazidou I, Matchan A et al (2017) Whole genome sequencing and imputation in isolated populations identify genetic associations with medically-relevant complex traits. Nat Commun 8(May):15606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Southan C, Sharman JL, Benson HE, Faccenda E, Pawson AJ, Alexander SPH, Buneman OP et al (2016) The IUPHAR/BPS guide to pharmacology in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res 44(D1):D1054–D1068

    Article  CAS  PubMed  Google Scholar 

  • Stoeger T, Gerlach M, Morimoto RI, Amaral LAN (2018) Large-scale investigation of the reasons why potentially important genes are ignored. PLoS Biol 16(9):e2006643

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Suntharalingam G, Perry MR, Ward S, Brett SJ, Castello-Cortes A, Brunner MD, Panoskaltsis N (2006) Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N Engl J Med 355(10):1018–1028

    Article  CAS  PubMed  Google Scholar 

  • Surade S, Blundell TL (2012) Structural biology and drug discovery of difficult targets: the limits of ligandability. Chem Biol 19(1):42–50

    Article  CAS  PubMed  Google Scholar 

  • Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M et al (2019) STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47(D1):D607–D613

    Article  CAS  PubMed  Google Scholar 

  • Target importance and novelty explorer (TIN-X) (2014) TIN-X. http://newdrugtargets.org/. Accessed 14 Dec 2014

  • Tomioka NH, Yasuda H, Miyamoto H, Hatayama M, Morimura N, Matsumoto Y, Suzuki T et al (2014) Elfn1 recruits presynaptic mGluR7 in trans and its loss results in seizures. Nat Commun 5(July):4501

    Article  CAS  PubMed  Google Scholar 

  • UniProt Consortium (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:204–212

    Article  CAS  Google Scholar 

  • Ursu O, Holmes J, Knockel J, Bologa CG, Yang JJ, Mathias SL, Nelson SJ, Oprea TI (2017) DrugCentral: online drug compendium. Nucleic Acids Res 45(D1):D932–D939

    Article  CAS  PubMed  Google Scholar 

  • Ursu O, Glick M, Oprea T (2019a) Novel drug targets in 2018. Nat Rev Drug Discov. https://doi.org/10.1038/d41573-019-00052-5

    Article  PubMed  Google Scholar 

  • Ursu O, Holmes J, Bologa CG, Yang JJ, Mathias SL, Stathias V, Nguyen D-T, Schürer S, Oprea T (2019b) DrugCentral 2018: an update. Nucleic Acids Res 47(D1):D963–D970

    Article  CAS  PubMed  Google Scholar 

  • van der Harst P, Verweij N (2018) Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ Res 122(3):433–443

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Velayos-Baeza A, Vettori A, Copley RR, Dobson-Stone C, Monaco AP (2004) Analysis of the human VPS13 gene family. Genomics 84(3):536–549

    Article  CAS  PubMed  Google Scholar 

  • Watkins, X, Garcia LJ, Pundir S, Martin MJ, UniProt Consortium (2017) ProtVista: visualization of protein sequence annotations. Bioinformatics 33(13):2040–2041

    Article  CAS  Google Scholar 

  • Woon MT, Long PA, Reilly L, Evans JM, Keefe AM, Lea MR, Beglinger CJ et al (2018) Pediatric dilated cardiomyopathy-associated LRRC10 (Leucine-rich repeat-containing 10) variant reveals LRRC10 as an auxiliary subunit of cardiac L-type Ca2 + channels. J Am Heart Assoc 7(3):1–10. https://doi.org/10.1161/JAHA.117.006428

    Article  CAS  Google Scholar 

  • Wu Fan, Ma Cong, Tan Cheemeng (2016) Network motifs modulate druggability of cellular targets. Sci Rep 6(November):36626

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by NIH Grants U54CA189205, U24CA224370 (for IDG KMC), and U24TR002278 (for IDG RDOC).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tudor I. Oprea.

Ethics declarations

Conflict of interest

Dr. Oprea was a former full-time employee at AstraZeneca (1996–2002). He has received honoraria, or consulted for, Abbott, AstraZeneca, Chiron, Genentech, Infinity Pharmaceuticals, Merz Pharmaceuticals, Merck Darmstadt, Mitsubishi Tanabe, Novartis, Ono Pharmaceuticals, Pfizer, Roche, Sanofi, and Wyeth. His spouse was a full-time employee of AstraZeneca (2002–2014) and is a full-time employee of Genentech Inc.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Electronic supplementary material 1 (XLSX 199 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oprea, T.I. Exploring the dark genome: implications for precision medicine. Mamm Genome 30, 192–200 (2019). https://doi.org/10.1007/s00335-019-09809-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00335-019-09809-0

Navigation