An expanded toolkit for gene tagging based on MiMIC and scarless CRISPR tagging in Drosophila
Abstract
We generated two new genetic tools to efficiently tag genes in Drosophila. The first, Double Header (DH) utilizes intronic MiMIC/CRIMIC insertions to generate artificial exons for GFP mediated protein trapping or T2A-GAL4 gene trapping in vivo based on Cre recombinase to avoid embryo injections. DH significantly increases integration efficiency compared to previous strategies and faithfully reports the expression pattern of genes and proteins. The second technique targets genes lacking coding introns using a two-step cassette exchange. First, we replace the endogenous gene with an excisable compact dominant marker using CRISPR making a null allele. Second, the insertion is replaced with a protein::tag cassette. This sequential manipulation allows the generation of numerous tagged alleles or insertion of other DNA fragments that facilitates multiple downstream applications. Both techniques allow precise gene manipulation and facilitate detection of gene expression, protein localization and assessment of protein function, as well as numerous other applications.
Data availability
All data generated or analyzed during this study are included in the manuscript and supporting files. The whole genome sequencing files are available on Zenodo (https://zenodo.org/record/1341241).
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An expanded toolkit for gene tagging based on MiMIC and scarless CRISPR tagging in Drosophila - sequence filesPublicly available on Zenodo under a Creative Commons Attribution 4.0 License.
Article and author information
Author details
Funding
National Institutes of Health (R01GM067858)
- Hugo J Bellen
Howard Hughes Medical Institute
- Hugo J Bellen
Robert A. and Renee E. Belfer Family Foundation
- Hugo J Bellen
Huffington Foundation
- Hugo J Bellen
National Institutes of Health (Training grant T32NS043124)
- David Li-Kroeger
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Yukiko M Yamashita, University of Michigan, United States
Publication history
- Received: May 28, 2018
- Accepted: July 25, 2018
- Accepted Manuscript published: August 9, 2018 (version 1)
- Version of Record published: August 16, 2018 (version 2)
Copyright
© 2018, Li-Kroeger et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Computational and Systems Biology
- Genetics and Genomics
Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.
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- Genetics and Genomics
- Medicine
Background: Causality between plasma triglyceride (TG) levels and atherosclerotic cardiovascular disease (ASCVD) risk remains controversial despite more than four decades of study and two recent landmark trials, STRENGTH and REDUCE-IT. Further unclear is the association between TG levels and non-atherosclerotic diseases across organ systems.
Methods: Here, we conducted a phenome-wide, two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW) regression to systematically infer the causal effects of plasma TG levels on 2,600 disease traits in the European ancestry population of UK Biobank. For replication, we externally tested 221 nominally significant associations (p < 0.05) in an independent cohort from FinnGen. To account for potential horizontal pleiotropy and the influence of invalid instrumental variables, we performed sensitivity analyses using MR-Egger regression, weighted median estimator, and MR-PRESSO. Finally, we used multivariable MR controlling for correlated lipid fractions to distinguish the independent effect of plasma TG levels.
Results: Our results identified 7 disease traits reaching Bonferroni-corrected significance in both the discovery (p < 1.92 × 10-5) and replication analyses (p < 2.26 × 10-4), suggesting a causal relationship between plasma TG levels and ASCVDs, including coronary artery disease (OR 1.33, 95% CI 1.24-1.43, p = 2.47 × 10-13). We also identified 12 disease traits that were Bonferroni-significant in the discovery or replication analysis and at least nominally significant in the other analysis (p < 0.05), identifying plasma TG levels as a novel potential risk factor for 9 non-ASCVD diseases, including uterine leiomyoma (OR 1.19, 95% CI 1.10-1.29, p = 1.17 × 10-5).
Conclusions: Taking a phenome-wide, two-sample MR approach, we identified causal associations between plasma TG levels and 19 disease traits across organ systems. Our findings suggest unrealized drug repurposing opportunities or adverse effects related to approved and emerging TG-lowering agents, as well as mechanistic insights for future studies.
Funding: RD is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (R35-GM124836) and the National Heart, Lung, and Blood Institute of the NIH (R01-HL139865 and R01-HL155915).