An Integrative Tissue-network Approach to Identify and Test Human Disease Genes

Overview

Journal Nat Biotechnol

Specialty Biotechnology

Date 2018 Oct 23

PMID 30346941

Citations 30

Authors

Victoria Yao

Rachel Kaletsky

William Keyes

Danielle E Mor

Aaron K Wong

Salman Sohrabi

Coleen T Murphy

Olga G Troyanskaya

Affiliations

Soon will be listed here.

Abstract

Effective discovery of causal disease genes must overcome the statistical challenges of quantitative genetics studies and the practical limitations of human biology experiments. Here we developed diseaseQUEST, an integrative approach that combines data from human genome-wide disease studies with in silico network models of tissue- and cell-type-specific function in model organisms to prioritize candidates within functionally conserved processes and pathways. We used diseaseQUEST to predict candidate genes for 25 different diseases and traits, including cancer, longevity, and neurodegenerative diseases. Focusing on Parkinson's disease (PD), a diseaseQUEST-directed Caenhorhabditis elegans behavioral screen identified several candidate genes, which we experimentally verified and found to be associated with age-dependent motility defects mirroring PD clinical symptoms. Furthermore, knockdown of the top candidate gene, bcat-1, encoding a branched chain amino acid transferase, caused spasm-like 'curling' and neurodegeneration in C. elegans, paralleling decreased BCAT1 expression in PD patient brains. diseaseQUEST is modular and generalizable to other model organisms and human diseases of interest.

Citing Articles

Behavioral screening reveals a conserved residue in Y-Box RNA-binding protein required for associative learning and memory in C. elegans.

Hayden A, Brandel K, Pietryk E, Merlau P, Vijayakumar P, Leptich E PLoS Genet. 2024; 20(10):e1011443.

PMID: 39423228 PMC: 11524487. DOI: 10.1371/journal.pgen.1011443.

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.

Argov C, Shneyour A, Jubran J, Sabag E, Mansbach A, Sepunaru Y Mol Syst Biol. 2024; 20(11):1187-1206.

PMID: 39285047 PMC: 11535248. DOI: 10.1038/s44320-024-00061-6.

Computational strategies for cross-species knowledge transfer and translational biomedicine.

Yuan H, Mancuso C, Johnson K, Braasch I, Krishnan A ArXiv. 2024; .

PMID: 39184546 PMC: 11343225.

Metabolic reprogramming of the inflammatory response in the nervous system: the crossover between inflammation and metabolism.

Amo-Aparicio J, Dinarello C, Lopez-Vales R Neural Regen Res. 2024; 19(10):2189-2201.

PMID: 38488552 PMC: 11034585. DOI: 10.4103/1673-5374.391330.

An atlas of cell-type-specific interactome networks across 44 human tumor types.

Li Z, Liu G, Yang X, Shu M, Jin W, Tong Y Genome Med. 2024; 16(1):30.

PMID: 38347596 PMC: 10860273. DOI: 10.1186/s13073-024-01303-w.

References

Greene C, Krishnan A, Wong A, Ricciotti E, Zelaya R, Himmelstein D . Understanding multicellular function and disease with human tissue-specific networks. Nat Genet. 2015; 47(6):569-76. PMC: 4828725. DOI: 10.1038/ng.3259. View

Mathelier A, Zhao X, Zhang A, Parcy F, Worsley-Hunt R, Arenillas D . JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. 2013; 42(Database issue):D142-7. PMC: 3965086. DOI: 10.1093/nar/gkt997. View

Huttenhower C, Schroeder M, Chikina M, Troyanskaya O . The Sleipnir library for computational functional genomics. Bioinformatics. 2008; 24(13):1559-61. PMC: 2718674. DOI: 10.1093/bioinformatics/btn237. View

Wagey R, Pelech S, Duronio V, Krieger C . Phosphatidylinositol 3-kinase: increased activity and protein level in amyotrophic lateral sclerosis. J Neurochem. 1998; 71(2):716-22. DOI: 10.1046/j.1471-4159.1998.71020716.x. View

Myers C, Barrett D, Hibbs M, Huttenhower C, Troyanskaya O . Finding function: evaluation methods for functional genomic data. BMC Genomics. 2006; 7:187. PMC: 1560386. DOI: 10.1186/1471-2164-7-187. View

Barrett T, Wilhite S, Ledoux P, Evangelista C, Kim I, Tomashevsky M . NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2012; 41(Database issue):D991-5. PMC: 3531084. DOI: 10.1093/nar/gks1193. View

Newgard C, An J, Bain J, Muehlbauer M, Stevens R, Lien L . A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009; 9(4):311-26. PMC: 3640280. DOI: 10.1016/j.cmet.2009.02.002. View

Christophersen I, Rienstra M, Roselli C, Yin X, Geelhoed B, Barnard J . Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation. Nat Genet. 2017; 49(6):946-952. PMC: 5585859. DOI: 10.1038/ng.3843. View

Chikina M, Huttenhower C, Murphy C, Troyanskaya O . Global prediction of tissue-specific gene expression and context-dependent gene networks in Caenorhabditis elegans. PLoS Comput Biol. 2009; 5(6):e1000417. PMC: 2692103. DOI: 10.1371/journal.pcbi.1000417. View

10.

Harris T, Baran J, Bieri T, Cabunoc A, Chan J, Chen W . WormBase 2014: new views of curated biology. Nucleic Acids Res. 2013; 42(Database issue):D789-93. PMC: 3965043. DOI: 10.1093/nar/gkt1063. View

11.

Pendse J, Ramachandran P, Na J, Narisu N, Fink J, Cagan R . A Drosophila functional evaluation of candidates from human genome-wide association studies of type 2 diabetes and related metabolic traits identifies tissue-specific roles for dHHEX. BMC Genomics. 2013; 14:136. PMC: 3608171. DOI: 10.1186/1471-2164-14-136. View

12.

Mansfeld J, Urban N, Priebe S, Groth M, Frahm C, Hartmann N . Branched-chain amino acid catabolism is a conserved regulator of physiological ageing. Nat Commun. 2015; 6:10043. PMC: 4686672. DOI: 10.1038/ncomms10043. View

13.

Guan Y, Ackert-Bicknell C, Kell B, Troyanskaya O, Hibbs M . Functional genomics complements quantitative genetics in identifying disease-gene associations. PLoS Comput Biol. 2010; 6(11):e1000991. PMC: 2978695. DOI: 10.1371/journal.pcbi.1000991. View

14.

Lynch C, Adams S . Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol. 2014; 10(12):723-36. PMC: 4424797. DOI: 10.1038/nrendo.2014.171. View

15.

Kenyon C . The genetics of ageing. Nature. 2010; 464(7288):504-12. DOI: 10.1038/nature08980. View

16.

Maeda I, Kohara Y, Yamamoto M, Sugimoto A . Large-scale analysis of gene function in Caenorhabditis elegans by high-throughput RNAi. Curr Biol. 2001; 11(3):171-6. DOI: 10.1016/s0960-9822(01)00052-5. View

17.

Takata A, Matsumoto N, Kato T . Genome-wide identification of splicing QTLs in the human brain and their enrichment among schizophrenia-associated loci. Nat Commun. 2017; 8:14519. PMC: 5333373. DOI: 10.1038/ncomms14519. View

18.

Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R . Missing value estimation methods for DNA microarrays. Bioinformatics. 2001; 17(6):520-5. DOI: 10.1093/bioinformatics/17.6.520. View

19.

Lambert J, Ibrahim-Verbaas C, Harold D, Naj A, Sims R, Bellenguez C . Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013; 45(12):1452-8. PMC: 3896259. DOI: 10.1038/ng.2802. View

20.

Park C, Wong A, Greene C, Rowland J, Guan Y, Bongo L . Functional knowledge transfer for high-accuracy prediction of under-studied biological processes. PLoS Comput Biol. 2013; 9(3):e1002957. PMC: 3597527. DOI: 10.1371/journal.pcbi.1002957. View