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Singhal P, Verma SS, Ritchie MD. Gene Interactions in Human Disease Studies-Evidence Is Mounting. Annu Rev Biomed Data Sci 2023; 6:377-395. [PMID: 37196359 DOI: 10.1146/annurev-biodatasci-102022-120818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.
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Affiliation(s)
- Pankhuri Singhal
- Genetics and Epigenetics Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Shefali Setia Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
- Penn Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Sadee W, Hartmann K, Seweryn M, Pietrzak M, Handelman SK, Rempala GA. Missing heritability of common diseases and treatments outside the protein-coding exome. Hum Genet 2014; 133:1199-215. [PMID: 25107510 PMCID: PMC4169001 DOI: 10.1007/s00439-014-1476-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Accepted: 07/23/2014] [Indexed: 02/07/2023]
Abstract
Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the 'missing heritability'. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an 'off-well' state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common 'normal' variants remain hidden in the noise. Second, epistasis (dynamic gene-gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene-gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.
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Affiliation(s)
- Wolfgang Sadee
- Department of Pharmacology, Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, 5184A Graves Hall, 333 West 10th Avenue, Columbus, OH, 43210, USA,
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Shestov AA, Barker B, Gu Z, Locasale JW. Computational approaches for understanding energy metabolism. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2013; 5:733-50. [PMID: 23897661 PMCID: PMC3906216 DOI: 10.1002/wsbm.1238] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
There has been a surge of interest in understanding the regulation of metabolic networks involved in disease in recent years. Quantitative models are increasingly being used to interrogate the metabolic pathways that are contained within this complex disease biology. At the core of this effort is the mathematical modeling of central carbon metabolism involving glycolysis and the citric acid cycle (referred to as energy metabolism). Here, we discuss several approaches used to quantitatively model metabolic pathways relating to energy metabolism and discuss their formalisms, successes, and limitations.
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Affiliation(s)
| | - Brandon Barker
- Division of Nutritional Sciences, Cornell University, Ithaca NY 14850
- Tri-Institutional Field of Computational Biology and Medicine, Cornell University, Ithaca NY 14850
| | - Zhenglong Gu
- Division of Nutritional Sciences, Cornell University, Ithaca NY 14850
- Tri-Institutional Field of Computational Biology and Medicine, Cornell University, Ithaca NY 14850
| | - Jason W Locasale
- Division of Nutritional Sciences, Cornell University, Ithaca NY 14850
- Tri-Institutional Field of Computational Biology and Medicine, Cornell University, Ithaca NY 14850
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Locasale JW. The consequences of enhanced cell-autonomous glucose metabolism. Trends Endocrinol Metab 2012; 23:545-51. [PMID: 22920571 DOI: 10.1016/j.tem.2012.07.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2012] [Revised: 07/09/2012] [Accepted: 07/16/2012] [Indexed: 01/19/2023]
Abstract
The intake and metabolism of carbohydrates for the generation of energy and biomass is evolutionarily conserved, down to the most primitive of cells. Although a basal rate of glucose metabolism occurs in all cells, the processing rates of glucose can become dramatically enhanced when cells acquire malignant properties, or remain undifferentiated. This article investigates the consequences of how increased glucose metabolism affects cellular physiology by altering the physicochemical properties of the whole metabolic network. As a result, enhanced lactate production in the presence of oxygen (the Warburg effect) is required, and metabolism is consequently reconfigured, through multiple mechanisms, to confer numerous physiological and possibly regulatory properties to cells.
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Affiliation(s)
- Jason W Locasale
- Division of Nutritional Sciences, Cornell University, Ithaca, NY 14850, USA.
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Facchetti G, Zampieri M, Altafini C. Predicting and characterizing selective multiple drug treatments for metabolic diseases and cancer. BMC SYSTEMS BIOLOGY 2012; 6:115. [PMID: 22932283 PMCID: PMC3744170 DOI: 10.1186/1752-0509-6-115] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2012] [Accepted: 08/13/2012] [Indexed: 12/13/2022]
Abstract
BACKGROUND In the field of drug discovery, assessing the potential of multidrug therapies is a difficult task because of the combinatorial complexity (both theoretical and experimental) and because of the requirements on the selectivity of the therapy. To cope with this problem, we have developed a novel method for the systematic in silico investigation of synergistic effects of currently available drugs on genome-scale metabolic networks. RESULTS The algorithm finds the optimal combination of drugs which guarantees the inhibition of an objective function, while minimizing the side effect on the other cellular processes. Two different applications are considered: finding drug synergisms for human metabolic diseases (like diabetes, obesity and hypertension) and finding antitumoral drug combinations with minimal side effect on the normal human cell. The results we obtain are consistent with some of the available therapeutic indications and predict new multiple drug treatments. A cluster analysis on all possible interactions among the currently available drugs indicates a limited variety on the metabolic targets for the approved drugs. CONCLUSION The in silico prediction of drug synergisms can represent an important tool for the repurposing of drugs in a realistic perspective which considers also the selectivity of the therapy. Moreover, for a more profitable exploitation of drug-drug interactions, we have shown that also experimental drugs which have a different mechanism of action can be reconsider as potential ingredients of new multicompound therapeutic indications. Needless to say the clues provided by a computational study like ours need in any case to be thoroughly evaluated experimentally.
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Affiliation(s)
- Giuseppe Facchetti
- Statistical and Biological Physics Department, SISSA (International School for Advanced Studies), Via Bonomea 265 - 34136, Trieste, Italy
| | - Mattia Zampieri
- Institute of Molecular Systems Biology, ETH (Eidgenoessische Technische Hochschule), Wolfgang Pauli Str. 16 - 8093, Zurich, Switzerland
| | - Claudio Altafini
- Functional Analysis DepartmentSISSA (International School for Advanced Studies), , Via Bonomea 265 - 34136, Trieste, Italy
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Pang X, Wang Z, Yap JS, Wang J, Zhu J, Bo W, Lv Y, Xu F, Zhou T, Peng S, Shen D, Wu R. A statistical procedure to map high-order epistasis for complex traits. Brief Bioinform 2012; 14:302-14. [PMID: 22723459 DOI: 10.1093/bib/bbs027] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Genetic interactions or epistasis have been thought to play a pivotal role in shaping the formation, development and evolution of life. Previous work focused on lower-order interactions between a pair of genes, but it is obviously inadequate to explain a complex network of genetic interactions and pathways. We review and assess a statistical model for characterizing high-order epistasis among more than two genes or quantitative trait loci (QTLs) that control a complex trait. The model includes a series of start-of-the-art standard procedures for estimating and testing the nature and magnitude of QTL interactions. Results from simulation studies and real data analysis warrant the statistical properties of the model and its usefulness in practice. High-order epistatic mapping will provide a routine procedure for charting a detailed picture of the genetic regulation mechanisms underlying the phenotypic variation of complex traits.
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Jacobs C, Segrè D. Organization Principles in Genetic Interaction Networks. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2012; 751:53-78. [DOI: 10.1007/978-1-4614-3567-9_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Richard G, Belta C, Julius AA, Amar S. Controlling the outcome of the Toll-like receptor signaling pathways. PLoS One 2012; 7:e31341. [PMID: 22363624 PMCID: PMC3282698 DOI: 10.1371/journal.pone.0031341] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2011] [Accepted: 01/06/2012] [Indexed: 11/30/2022] Open
Abstract
The Toll-Like Receptors (TLRs) are proteins involved in the immune system that increase cytokine levels when triggered. While cytokines coordinate the response to infection, they appear to be detrimental to the host when reaching too high levels. Several studies have shown that the deletion of specific TLRs was beneficial for the host, as cytokine levels were decreased consequently. It is not clear, however, how targeting other components of the TLR pathways can improve the responses to infections. We applied the concept of Minimal Cut Sets (MCS) to the ihsTLR v1.0 model of the TLR pathways to determine sets of reactions whose knockouts disrupt these pathways. We decomposed the TLR network into 34 modules and determined signatures for each MCS, i.e. the list of targeted modules. We uncovered 2,669 MCS organized in 68 signatures. Very few MCS targeted directly the TLRs, indicating that they may not be efficient targets for controlling these pathways. We mapped the species of the TLR network to genes in human and mouse, and determined more than 10,000 Essential Gene Sets (EGS). Each EGS provides genes whose deletion suppresses the network's outputs.
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Affiliation(s)
- Guilhem Richard
- Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America
| | - Calin Belta
- Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America
| | - A. Agung Julius
- Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, New York, United States of America
| | - Salomon Amar
- Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America
- Center for Anti-Inflammatory Therapeutics, Goldman School of Dental Medicine, Boston University, Boston, Massachusetts, United States of America
- * E-mail:
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Bordbar A, Palsson BO. Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Intern Med 2012; 271:131-41. [PMID: 22142339 PMCID: PMC3243107 DOI: 10.1111/j.1365-2796.2011.02494.x] [Citation(s) in RCA: 77] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Metabolism plays a key role in many major human diseases. Generation of high-throughput omics data has ushered in a new era of systems biology. Genome-scale metabolic network reconstructions provide a platform to interpret omics data in a biochemically meaningful manner. The release of the global human metabolic network, Recon 1, in 2007 has enabled new systems biology approaches to study human physiology, pathology and pharmacology. There are currently more than 20 publications that utilize Recon 1, including studies of cancer, diabetes, host-pathogen interactions, heritable metabolic disorders and off-target drug binding effects. In this mini-review, we focus on the reconstruction of the global human metabolic network and four classes of its application. We show that computational simulations for numerous pathologies have yielded clinically relevant results, many corroborated by existing or newly generated experimental data.
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Affiliation(s)
- A Bordbar
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
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Imielinski M, Belta C. Deep epistasis in human metabolism. CHAOS (WOODBURY, N.Y.) 2010; 20:026104. [PMID: 20590333 PMCID: PMC2909311 DOI: 10.1063/1.3456056] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2010] [Accepted: 06/01/2010] [Indexed: 05/08/2023]
Abstract
We extend and apply a method that we have developed for deriving high-order epistatic relationships in large biochemical networks to a published genome-scale model of human metabolism. In our analysis we compute 33,328 reaction sets whose knockout synergistically disables one or more of 43 important metabolic functions. We also design minimal knockouts that remove flux through fumarase, an enzyme that has previously been shown to play an important role in human cancer. Most of these knockout sets employ more than eight mutually buffering reactions, spanning multiple cellular compartments and metabolic subsystems. These reaction sets suggest that human metabolic pathways possess a striking degree of parallelism, inducing "deep" epistasis between diversely annotated genes. Our results prompt specific chemical and genetic perturbation follow-up experiments that could be used to query in vivo pathway redundancy. They also suggest directions for future statistical studies of epistasis in genetic variation data sets.
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Affiliation(s)
- Marcin Imielinski
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA.
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Segrè D, Marx CJ. Introduction to focus issue: genetic interactions. CHAOS (WOODBURY, N.Y.) 2010; 20:026101. [PMID: 20590330 PMCID: PMC2909308 DOI: 10.1063/1.3456057] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2010] [Indexed: 05/29/2023]
Abstract
The perturbation of a gene in an organism's genome often causes changes in the organism's observable properties or phenotypes. It is not obvious a priori whether the simultaneous perturbation of two genes produces a phenotypic change that is easily predictable from the changes caused by individual perturbations. In fact, this is often not the case: the nonlinearity and interdependence between genetic variants in determining phenotypes, also known as epistasis, is a prevalent phenomenon in biological systems. This focus issue presents recent developments in the study of epistasis and genetic interactions, emphasizing the broad implications of this phenomenon in evolutionary biology, functional genomics, and human diseases.
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Affiliation(s)
- Daniel Segrè
- Department of Biology, Boston University, Boston, Massachusetts 02215, USA
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