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Functional Studies of Genetic Variants Associated with Human Diseases in Notch Signaling-Related Genes Using Drosophila

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Notch Signaling Research

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2472))

Abstract

Rare variants in the many genes related to Notch signaling cause diverse Mendelian diseases that affect myriad organ systems. In addition, genome- and exome-wide association studies have linked common and rare variants in Notch-related genes to common diseases and phenotypic traits. Moreover, somatic mutations in these genes have been observed in many types of cancer, some of which are classified as oncogenic and others as tumor suppressive. While functional characterization of some of these variants has been performed through experimental studies, the number of “variants of unknown significance” identified in patients with diverse conditions keeps increasing as high-throughput sequencing technologies become more commonly used in the clinic. Furthermore, as disease gene discovery efforts identify rare variants in human genes that have yet to be linked to a disease, the demand for functional characterization of variants in these “genes of unknown significance” continues to increase. In this chapter, we describe a workflow to functionally characterize a rare variant in a Notch signaling related gene that was found to be associated with late-onset Alzheimer’s disease. This pipeline involves informatic analysis of the variant of interest using diverse human and model organism databases, followed by in vivo experiments in the fruit fly Drosophila melanogaster. The protocol described here can be used to study variants that affect amino acids that are not conserved between human and fly. By “humanizing” the almondex gene in Drosophila with mutant alleles and heterologous genomic rescue constructs, a missense variant in TM2D3 (TM2 Domain Containing 3) was shown to be functionally damaging. This, and similar approaches, greatly facilitate functional interpretations of genetic variants in the human genome and propel personalized medicine.

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Acknowledgments

J.L.S. received support from the National Institutes of Health (NIH) training grant T32GM008307. S.Y. received support from the NIH (R01AG071557), Alzheimer’s Association (NIRH-15-364099), and Nancy Chang PhD, Award for Research Excellence from Baylor College of Medicine related to the project discussed in this article. We thank J. Michael Harnish for providing useful feedback.

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Authors and Affiliations

  1. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Sheng-An Yang, Jose L. Salazar & Shinya Yamamoto

  2. Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA

    Sheng-An Yang, Jose L. Salazar, David Li-Kroeger & Shinya Yamamoto

  3. Department of Neurology, Baylor College of Medicine, Houston, TX, USA

    David Li-Kroeger

  4. Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA

    Shinya Yamamoto

  5. Development, Disease Models and Therapeutics Graduate Program, Baylor College of Medicine, Houston, TX, USA

    Shinya Yamamoto

Authors
  1. Sheng-An Yang
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  2. Jose L. Salazar
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  3. David Li-Kroeger
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  4. Shinya Yamamoto
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Corresponding authors

Correspondence to David Li-Kroeger or Shinya Yamamoto .

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Editors and Affiliations

  1. Department of Biology, Georgia Southern University, Statesboro, GA, USA

    Dongyu Jia

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Yang, SA., Salazar, J.L., Li-Kroeger, D., Yamamoto, S. (2022). Functional Studies of Genetic Variants Associated with Human Diseases in Notch Signaling-Related Genes Using Drosophila. In: Jia, D. (eds) Notch Signaling Research. Methods in Molecular Biology, vol 2472. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2201-8_19

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  • DOI: https://doi.org/10.1007/978-1-0716-2201-8_19

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2200-1

  • Online ISBN: 978-1-0716-2201-8

  • eBook Packages: Springer Protocols

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