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Yap CX, Vo DD, Heffel MG, Bhattacharya A, Wen C, Yang Y, Kemper KE, Zeng J, Zheng Z, Zhu Z, Hannon E, Vellame DS, Franklin A, Caggiano C, Wamsley B, Geschwind DH, Zaitlen N, Gusev A, Pasaniuc B, Mill J, Luo C, Gandal MJ. Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics. Sci Adv 2024; 10:eadn7655. [PMID: 38781333 PMCID: PMC11114225 DOI: 10.1126/sciadv.adn7655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/14/2024] [Indexed: 05/25/2024]
Abstract
Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.
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Affiliation(s)
- Chloe X. Yap
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel D. Vo
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew G. Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cindy Wen
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yuanhao Yang
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- The National Centre for Register-based Research, Aarhus University, Denmark
| | - Eilis Hannon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Dorothea Seiler Vellame
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Alice Franklin
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Christa Caggiano
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Brie Wamsley
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel H. Geschwind
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Michael J. Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Chen PB, Chen R, LaPierre N, Chen Z, Mefford J, Marcus E, Heffel MG, Soto DC, Ernst J, Luo C, Flint J. Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior. Cell Genom 2024; 4:100545. [PMID: 38697120 PMCID: PMC11099346 DOI: 10.1016/j.xgen.2024.100545] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/23/2024] [Accepted: 04/04/2024] [Indexed: 05/04/2024]
Abstract
Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.
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Affiliation(s)
- Patrick B Chen
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Rachel Chen
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nathan LaPierre
- Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zeyuan Chen
- Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joel Mefford
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Emilie Marcus
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Matthew G Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniela C Soto
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jason Ernst
- Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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Zhou J, Luo C, Liu H, Heffel MG, Straub RE, Kleinman JE, Hyde TM, Ecker JR, Weinberger DR, Han S. scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia. bioRxiv 2024:2024.01.25.577200. [PMID: 38328094 PMCID: PMC10849713 DOI: 10.1101/2024.01.25.577200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies.
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Affiliation(s)
- Jiyun Zhou
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21287, USA
| | - Chongyuan Luo
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA
| | - Matthew G. Heffel
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Richard E. Straub
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21287, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21287, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21287, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21287, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21287, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Chen PB, Chen R, LaPierre N, Chen Z, Mefford J, Marcus E, Heffel MG, Soto DC, Ernst J, Luo C, Flint J. Complementation testing identifies causal genes at quantitative trait loci underlying fear related behavior. bioRxiv 2024:2024.01.03.574060. [PMID: 38260483 PMCID: PMC10802323 DOI: 10.1101/2024.01.03.574060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Knowing the genes involved in quantitative traits provides a critical entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. Here we address a key step towards that goal by deploying a test that directly queries whether a gene mediates the effect of a quantitative trait locus (QTL). To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six QTLs, and identified six genes. Four genes, Lsamp, Ptprd, Nptx2 and Sh3gl, have known roles in synapse function; the fifth gene, Psip1, is a transcriptional co-activator not previously implicated in behavior; the sixth is a long non-coding RNA 4933413L06Rik with no known function. Single nucleus transcriptomic and epigenetic analyses implicated excitatory neurons as likely mediating the genetic effects. Surprisingly, variation in transcriptome and epigenetic modalities between inbred strains occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results open a bottleneck in using genetic mapping of QTLs to find novel biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.
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Flint J, Heffel MG, Chen Z, Mefford J, Marcus E, Chen PB, Ernst J, Luo C. Single-cell methylation analysis of brain tissue prioritizes mutations that alter transcription. Cell Genom 2023; 3:100454. [PMID: 38116123 PMCID: PMC10726494 DOI: 10.1016/j.xgen.2023.100454] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/08/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023]
Abstract
Relating genetic variants to behavior remains a fundamental challenge. To assess the utility of DNA methylation marks in discovering causative variants, we examined their relationship to genetic variation by generating single-nucleus methylomes from the hippocampus of eight inbred mouse strains. At CpG sequence densities under 40 CpG/Kb, cells compensate for loss of methylated sites by methylating additional sites to maintain methylation levels. At higher CpG sequence densities, the exact location of a methylated site becomes more important, suggesting that variants affecting methylation will have a greater effect when occurring in higher CpG densities than in lower. We found this to be true for a variant's effect on transcript abundance, indicating that candidate variants can be prioritized based on CpG sequence density. Our findings imply that DNA methylation influences the likelihood that mutations occur at specific sites in the genome, supporting the view that the distribution of mutations is not random.
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Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Matthew G. Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Zeyuan Chen
- Department of Computer Science, Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Joel Mefford
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Emilie Marcus
- Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Patrick B. Chen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Jason Ernst
- Department of Computer Science, Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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Jiménez-Marín B, Rakijas JB, Tyagi A, Pandey A, Hanschen ER, Anderson J, Heffel MG, Platt TG, Olson BJSC. Gene loss during a transition to multicellularity. Sci Rep 2023; 13:5268. [PMID: 37002250 PMCID: PMC10066295 DOI: 10.1038/s41598-023-29742-2] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 02/09/2023] [Indexed: 04/03/2023] Open
Abstract
Multicellular evolution is a major transition associated with momentous diversification of multiple lineages and increased developmental complexity. The volvocine algae comprise a valuable system for the study of this transition, as they span from unicellular to undifferentiated and differentiated multicellular morphologies despite their genomes being similar, suggesting multicellular evolution requires few genetic changes to undergo dramatic shifts in developmental complexity. Here, the evolutionary dynamics of six volvocine genomes were examined, where a gradual loss of genes was observed in parallel to the co-option of a few key genes. Protein complexes in the six species exhibited novel interactions, suggesting that gene loss could play a role in evolutionary novelty. This finding was supported by gene network modeling, where gene loss outpaces gene gain in generating novel stable network states. These results suggest gene loss, in addition to gene gain and co-option, may be important for the evolution developmental complexity.
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Affiliation(s)
- Berenice Jiménez-Marín
- Division of Biology, Kansas State University, Manhattan, KS, 66506, USA
- Interdepartmental Genetics Graduate Program, Kansas State University, Manhattan, KS, 66506, USA
| | - Jessica B Rakijas
- Division of Biology, Kansas State University, Manhattan, KS, 66506, USA
| | - Antariksh Tyagi
- Division of Biology, Kansas State University, Manhattan, KS, 66506, USA
| | - Aakash Pandey
- Division of Biology, Kansas State University, Manhattan, KS, 66506, USA
| | | | - Jaden Anderson
- Division of Biology, Kansas State University, Manhattan, KS, 66506, USA
| | - Matthew G Heffel
- Division of Biology, Kansas State University, Manhattan, KS, 66506, USA
- Interdepartmental Genetics Graduate Program, Kansas State University, Manhattan, KS, 66506, USA
| | - Thomas G Platt
- Division of Biology, Kansas State University, Manhattan, KS, 66506, USA
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Abstract
There is a critical need for further research into methods to control biological populations. Numerous challenges to agriculture, ecological systems, and human health could be mitigated by the targeted reduction and management of key species (e.g. pests, parasites, and vectors for pathogens). The discovery and adaptation of the CRISPR/Cas editing platform co-opted from bacteria has provided a mechanism for a means to alter an entire population. A CRISPR-based gene drive system can allow for the forced propagation of a genetic element that bypasses Mendelian inheritance which can be used to bias sex determination, install exogenous information, or remove endogenous DNA within an entire species. Laboratory studies have demonstrated the potency by which gene drives can operate within insects and other organisms. However, continued research and eventual application face serious opposition regarding issues of policy, biosafety, effectiveness, and reversal. Previous mathematical work has suggested the use of modified gene drive designs that are limited in spread such as daisy chain or underdominance drives. However, no system has yet been proposed that allows for an inducible reversal mechanism without requiring the introduction of additional individuals. Here, we study gene drive effectiveness, fitness, and inducible drive systems that could respond to external stimuli expanding from a previous frequency-based population model. We find that programmed modification during gene drive propagation could serve as a potent safeguard to either slow or completely reverse drive systems and allow for a return to the original wild-type population.
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Affiliation(s)
- Matthew G Heffel
- Division of Biology, 116 Ackert Hall, Kansas State University, Manhattan, KS 66506, USA
| | - Gregory C Finnigan
- Department of Biochemistry and Molecular Biophysics, 141 Chalmers Hall, Kansas State University, Manhattan, KS 66506, USA.
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