Mapping the common gene networks that underlie related diseases

Sara Brin Rosenthal, Sarah N. Wright, Sophie Liu, Christopher Churas, Daisy Chilin-Fuentes, Chi-Hua Chen, Kathleen M. Fisch, Dexter Pratt, Jason F. Kreisberg, Trey Ideker

Published: 2023-01-17 DOI: 10.1038/s41596-022-00797-1

gene networks

disease colocalization

network propagation

community detection

shared etiology

AI 解读

The GFP thermal shift assay for screening ligand and lipid interactions to solute carrier transporters

Co-fractionation–mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Simultaneously measuring the methylation of parent and daughter strands of replicated DNA at the single-molecule level by Hammer-seq

The Nodewalk assay to quantitate chromatin fiber interactomes in very small cell populations

Nondestructive production of exosomes loaded with ultrathin palladium nanosheets for targeted bio-orthogonal catalysis

An optimized culture system for efficient derivation of porcine expanded potential stem cells from preimplantation embryos and by reprogramming somatic cells

Engineering SARS-CoV-2 using a reverse genetic system

cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA

Growing and making nano- and microcrystals

Composable microfluidic spinning platforms for facile production of biomimetic perfusable hydrogel microtubes

查看全部

Sections

Figures

References

A longstanding goal of biomedicine is to understand how alterations in molecular and cellular networks give rise to the spectrum of human diseases. For diseases with shared etiology, understanding the common causes allows for improved diagnosis of each di
Introduction
Materials
Procedure
Timing
Troubleshooting
Anticipated results
Conclusion
- References
- References
References
ACKNOWLEDGMENTS
ACKNOWLEDGMENTS

Tam, V. et al. Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 20, 467–484 (2019).
Cirulli, E. T. & Goldstein, D. B. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet. 11, 415–425 (2010).
Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat. Rev. Genet. 20, 631–656 (2019).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Cowen, L., Ideker, T., Raphael, B. J. & Sharan, R. Network propagation: a universal amplifier of genetic associations. Nat. Rev. Genet. 18, 551–562 (2017).
Vanunu, O., Magger, O., Ruppin, E., Shlomi, T. & Sharan, R. Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 6, e1000641 (2010).
Hofree, M., Shen, J. P., Carter, H., Gross, A. & Ideker, T. Network-based stratification of tumor mutations. Nat. Methods 10, 1108–1115 (2013).
Leiserson, M. D. M. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114 (2015).
Rosenthal, S. B. et al. A convergent molecular network underlying autism and congenital heart disease. Cell Syst. https://doi.org/10.1016/j.cels.2021.07.009 (2021).
Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).
Paull, E. O. et al. Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE). Bioinformatics 29, 2757–2764 (2013).
Jia, P. & Zhao, Z. VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data. PLoS Comput. Biol. 10, e1003460 (2014).
Ruffalo, M., Koyutürk, M. & Sharan, R. Network-based integration of disparate omic data to identify ‘silent players’ in cancer. PLOS Comput. Biol. 11, e1004595 (2015).
Tuncbag, N. et al. Network-based interpretation of diverse high-throughput datasets through the omics integrator software package. PLOS Comput. Biol. 12, e1004879 (2016).
Erten, S., Bebek, G., Ewing, R. M. & Koyutürk, M. DADA: Degree-aware algorithms for network-based disease gene prioritization. BioData Min. 4, 19 (2011).
Zheng, F. et al. HiDeF: identifying persistent structures in multiscale ‘omics data. Genome Biol. 22 (2021).
Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584.e23 (2020).
Eppig, J. T. et al. Mouse genome informatics (MGI): resources for mining mouse genetic, genomic, and biological data in support of primary and translational research. Methods Mol. Biol. 1488, 47–73 (2017).
Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2018).
Breitkreutz, B.-J. et al. The BioGRID Interaction Database: 2008 update. Nucleic Acids Res. 36, D637–D640 (2008).
Lee, I., Blom, U. M., Wang, P. I., Shim, J. E. & Marcotte, E. M. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 21, 1109–1121 (2011).
Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).
Hermjakob, H. IntAct: an open source molecular interaction database. Nucleic Acids Res. 32, 452D–455D (2004).
Huang, J. K. et al. Systematic evaluation of molecular networks for discovery of disease genes. Cell Syst. 6, 484–495.e5 (2018).
Singhal, A. et al. Multiscale community detection in Cytoscape. PloS Comput. Biol. 16, e1008239 (2020).
Simon, H. A. The architecture of complexity. Proc. Am. Philos. Soc. 106, 467–482 (1962).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Pratt, D. et al. NDEx, the network data exchange. Cell Syst. 1, 302–305 (2015).
Jin, S. C. et al. Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands. Nat. Genet. 49, 1593–1601 (2017).
Zaidi, S. & Brueckner, M. Genetics and genomics of congenital heart disease. Circ. Res. 120, 923–940 (2017).
Lasalle, J. M. Autism genes keep turning up chromatin. OA Autism 1, 14 (2013).
Ackerman, M. J. The long QT syndrome: ion channel diseases of the heart. Mayo Clin. Proc. 73, 250–269 (1998).
Colbert, C. M. & Pan, E. Ion channel properties underlying axonal action potential initiation in pyramidal neurons. Nat. Neurosci. 5, 533–538 (2002).
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Hesdorffer, D. C. Comorbidity between neurological illness and psychiatric disorders. CNS Spectr. 21, 230–238 (2016).
Willsey, A. J. et al. The Psychiatric Cell Map Initiative: a convergent systems biological approach to illuminating key molecular pathways in neuropsychiatric disorders. Cell 174, 505–520 (2018).
Ramnath, D. et al. Hepatic expression profiling identifies steatosis-independent and steatosis-driven advanced fibrosis genes. JCI Insight 3, e120274 (2018).

Mapping the common gene networks that underlie related diseases

Supplementary information

推荐阅读