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McFaline-Figueroa JL, Srivatsan S, Hill AJ, Gasperini M, Jackson DL, Saunders L, Domcke S, Regalado SG, Lazarchuck P, Alvarez S, Monnat RJ, Shendure J, Trapnell C. Multiplex single-cell chemical genomics reveals the kinase dependence of the response to targeted therapy. Cell Genom 2024; 4:100487. [PMID: 38278156 PMCID: PMC10879025 DOI: 10.1016/j.xgen.2023.100487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 09/26/2023] [Accepted: 12/15/2023] [Indexed: 01/28/2024]
Abstract
Chemical genetic screens are a powerful tool for exploring how cancer cells' response to drugs is shaped by their mutations, yet they lack a molecular view of the contribution of individual genes to the response to exposure. Here, we present sci-Plex-Gene-by-Environment (sci-Plex-GxE), a platform for combined single-cell genetic and chemical screening at scale. We highlight the advantages of large-scale, unbiased screening by defining the contribution of each of 522 human kinases to the response of glioblastoma to different drugs designed to abrogate signaling from the receptor tyrosine kinase pathway. In total, we probed 14,121 gene-by-environment combinations across 1,052,205 single-cell transcriptomes. We identify an expression signature characteristic of compensatory adaptive signaling regulated in a MEK/MAPK-dependent manner. Further analyses aimed at preventing adaptation revealed promising combination therapies, including dual MEK and CDC7/CDK9 or nuclear factor κB (NF-κB) inhibitors, as potent means of preventing transcriptional adaptation of glioblastoma to targeted therapy.
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Affiliation(s)
- José L McFaline-Figueroa
- Department of Biomedical Engineering, Columbia University, New York, NY, USA; Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Sanjay Srivatsan
- Department of Genome Sciences, University of Washington, Seattle, WA, USA; Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Andrew J Hill
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Molly Gasperini
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Dana L Jackson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lauren Saunders
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Silvia Domcke
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Samuel G Regalado
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Paul Lazarchuck
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Sarai Alvarez
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Raymond J Monnat
- Department of Genome Sciences, University of Washington, Seattle, WA, USA; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA; Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA; Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA; Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA; Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA; Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
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Ng B, Casazza W, Patrick E, Tasaki S, Novakovsky G, Felsky D, Ma Y, Bennett DA, Gaiteri C, De Jager PL, Mostafavi S. Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain. Am J Hum Genet 2019; 105:562-72. [PMID: 31447098 DOI: 10.1016/j.ajhg.2019.07.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/22/2019] [Indexed: 12/20/2022] Open
Abstract
Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a large number of environmental factors at high granularity might not always be feasible. Instead, here we propose extracting cellular embedding of environmental factors from gene expression data by using latent variable (LV) analysis and taking these LVs as environmental proxies in detecting gene-by-environment (GxE) interaction effects on gene expression, i.e., GxE expression quantitative trait loci (eQTLs). Applying this approach to two largest brain eQTL datasets (n = 1,100), we show that LVs and GxE eQTLs in one dataset replicate well in the other dataset. Combining the two samples via meta-analysis, 895 GxE eQTLs are identified. On average, GxE effect explains an additional ∼4% variation in expression of each gene that displays a GxE effect. Ten of these 52 genes are associated with cell-type-specific eQTLs, and the remaining genes are multi-functional. Furthermore, after substituting LVs with expression of transcription factors (TF), we found 91 TF-specific eQTLs, which demonstrates an important use of our brain GxE eQTLs.
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Ogbunugafor CB, Hartl D. A pivot mutation impedes reverse evolution across an adaptive landscape for drug resistance in Plasmodium vivax. Malar J 2016; 15:40. [PMID: 26809718 PMCID: PMC4727274 DOI: 10.1186/s12936-016-1090-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2015] [Accepted: 01/10/2016] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND The study of reverse evolution from resistant to susceptible phenotypes can reveal constraints on biological evolution, a topic for which evolutionary theory has relatively few general principles. The public health catastrophe of antimicrobial resistance in malaria has brought these constraints on evolution into a practical realm, with one proposed solution: withdrawing anti-malarial medication use in high resistance settings, built on the assumption that reverse evolution occurs readily enough that populations of pathogens may revert to their susceptible states. While past studies have suggested limits to reverse evolution, there have been few attempts to properly dissect its mechanistic constraints. METHODS Growth rates were determined from empirical data on the growth and resistance from a set of combinatorially complete set of mutants of a resistance protein (dihydrofolate reductase) in Plasmodium vivax, to construct reverse evolution trajectories. The fitness effects of individual mutations were calculated as a function of drug environment, revealing the magnitude of epistatic interactions between mutations and genetic backgrounds. Evolution across the landscape was simulated in two settings: starting from the population fixed for the quadruple mutant, and from a polymorphic population evenly distributed between double mutants. RESULTS A single mutation of large effect (S117N) serves as a pivot point for evolution to high resistance regions of the landscape. Through epistatic interactions with other mutations, this pivot creates an epistatic ratchet against reverse evolution towards the wild type ancestor, even in environments where the wild type is the most fit of all genotypes. This pivot mutation underlies the directional bias in evolution across the landscape, where evolution towards the ancestor is precluded across all examined drug concentrations from various starting points in the landscape. CONCLUSIONS The presence of pivot mutations can dictate dynamics of evolution across adaptive landscape through epistatic interactions within a protein, leaving a population trapped on local fitness peaks in an adaptive landscape, unable to locate ancestral genotypes. This irreversibility suggests that the structure of an adaptive landscape for a resistance protein should be understood before considering resistance management strategies. This proposed mechanism for constraints on reverse evolution corroborates evidence from the field indicating that phenotypic reversal often occurs via compensatory mutation at sites independent of those associated with the forward evolution of resistance. Because of this, molecular methods that identify resistance patterns via single SNPs in resistance-associated markers might be missing signals for resistance and compensatory mutation throughout the genome. In these settings, whole genome sequencing efforts should be used to identify resistance patterns, and will likely reveal a more complicated genomic signature for resistance and susceptibility, especially in settings where anti-malarial medications have been used intermittently. Lastly, the findings suggest that, given their role in dictating the dynamics of evolution across the landscape, pivot mutations might serve as future targets for therapy.
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Affiliation(s)
- C Brandon Ogbunugafor
- Department of Biology, University of Vermont, Burlington, VT, USA.
- Vermont Complex Systems Center, The University of Vermont, Burlington, VT, USA.
| | - Daniel Hartl
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
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Rava M, Ahmed I, Demenais F, Sanchez M, Tubert-Bitter P, Nadif R. Selection of genes for gene-environment interaction studies: a candidate pathway-based strategy using asthma as an example. Environ Health 2013; 12:56. [PMID: 23822639 PMCID: PMC3708788 DOI: 10.1186/1476-069x-12-56] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2013] [Accepted: 07/02/2013] [Indexed: 06/02/2023]
Abstract
BACKGROUND The identification of gene by environment (GxE) interactions has emerged as a challenging but essential task to fully understand the complex mechanism underlying multifactorial diseases. Until now, GxE interactions have been investigated by candidate approaches examining a small number of genes, or agnostically at the genome wide level. PRESENTATION OF THE HYPOTHESIS In this paper, we propose a gene selection strategy for investigation of gene-environment interactions. This strategy integrates the information on biological processes shared by genes, the canonical pathways to which they belong and the biological knowledge related to the environment in the gene selection process. It relies on both bioinformatics resources and biological expertise. TESTING THE HYPOTHESIS We illustrate our strategy by considering asthma, tobacco smoke as the environmental exposure, and genes sharing the same biological function of "response to oxidative stress". Our filtering strategy leads to a list of 28 pathways involving 182 genes for further GxE investigation. IMPLICATIONS OF THE HYPOTHESIS By integrating the environment into the gene selection process, we expect that our strategy will improve the ability to identify the joint effects and interactions of environmental and genetic factors in disease.
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Affiliation(s)
- Marta Rava
- Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Respiratory and Environmental Epidemiology Team, F-94807, Paris, Villejuif, France
- University Paris-Sud, UMRS 1018, F-94807, Paris, Villejuif, France
| | - Ismaïl Ahmed
- University Paris-Sud, UMRS 1018, F-94807, Paris, Villejuif, France
- Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Biostatistics Team, F-94807, Paris, Villejuif, France
| | - Florence Demenais
- Inserm, U946, F-75010, Paris, France
- Institut Universitaire d’Hématologie, University Paris Diderot, Sorbonne Paris Cité, F-75007, Paris, France
| | - Margaux Sanchez
- Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Respiratory and Environmental Epidemiology Team, F-94807, Paris, Villejuif, France
- University Paris-Sud, UMRS 1018, F-94807, Paris, Villejuif, France
| | - Pascale Tubert-Bitter
- University Paris-Sud, UMRS 1018, F-94807, Paris, Villejuif, France
- Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Biostatistics Team, F-94807, Paris, Villejuif, France
| | - Rachel Nadif
- Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Respiratory and Environmental Epidemiology Team, F-94807, Paris, Villejuif, France
- University Paris-Sud, UMRS 1018, F-94807, Paris, Villejuif, France
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