The improvement of novel sequencing technologies is quickly transforming the scientific investigation and the therapeutic treatments of rare genetic diseases, which research is hampered by the limited cohort of diagnosed patients and by the difficulty of evaluating the effect of Variants of Unknown Significance (VUS). In silico predictors represent the gold standard of clinical bodies for interpreting mutations identified through sequencing approaches. Unfortunately, many of them are classified as VUS, leading to wrong diagnoses and inadequate treatment of patients. To address this point, MITEseq (Mutagenesis by Integrated TilEs) is a novel saturation mutagenesis technique that allows us to quickly test thousands of potentially pathogenic protein variants in a single high throughput biological assay. As proof of principle, we decided to apply it to mutagenize two regions of P63 (a portion of the DNA Binding Domain and the SAM domain), a transcription factor that, besides an oncogenic function, is mainly a regulator of skin development. To follow the biological activity of each generated variant, we set up a very efficient conversion strategy from fibroblasts to keratinocytes-like-cells (through P63-KLF4 induction). Using the specific keratinocytes membrane antigen ITGβ4, we separated the converted and non-converted cells, studied the enrichment of each variant in the different populations and ranked them according to their pathogenic effect. To expand such method to any disease-driving gene, we developed a scRNAseq-based platform. Through this approach, we could associate each cell (i.e., each variant) to a specific physiological or pathological activity based on a specific transcriptional signature. This approach could represent a milestone in the field of genetic disorders, providing the scientific community with a robust (and easy-to-use) functional tool to explore the molecular bases of rare diseases.

A PERTURB-SEQ SATURATION MUTAGENESIS APPROACH TO DISSECT THE MOLECULAR BASES OF GENETIC DISEASES

VACCARO, LORENZO
2022

Abstract

The improvement of novel sequencing technologies is quickly transforming the scientific investigation and the therapeutic treatments of rare genetic diseases, which research is hampered by the limited cohort of diagnosed patients and by the difficulty of evaluating the effect of Variants of Unknown Significance (VUS). In silico predictors represent the gold standard of clinical bodies for interpreting mutations identified through sequencing approaches. Unfortunately, many of them are classified as VUS, leading to wrong diagnoses and inadequate treatment of patients. To address this point, MITEseq (Mutagenesis by Integrated TilEs) is a novel saturation mutagenesis technique that allows us to quickly test thousands of potentially pathogenic protein variants in a single high throughput biological assay. As proof of principle, we decided to apply it to mutagenize two regions of P63 (a portion of the DNA Binding Domain and the SAM domain), a transcription factor that, besides an oncogenic function, is mainly a regulator of skin development. To follow the biological activity of each generated variant, we set up a very efficient conversion strategy from fibroblasts to keratinocytes-like-cells (through P63-KLF4 induction). Using the specific keratinocytes membrane antigen ITGβ4, we separated the converted and non-converted cells, studied the enrichment of each variant in the different populations and ranked them according to their pathogenic effect. To expand such method to any disease-driving gene, we developed a scRNAseq-based platform. Through this approach, we could associate each cell (i.e., each variant) to a specific physiological or pathological activity based on a specific transcriptional signature. This approach could represent a milestone in the field of genetic disorders, providing the scientific community with a robust (and easy-to-use) functional tool to explore the molecular bases of rare diseases.
6-dic-2022
Inglese
Functional genomics; Saturation mutagenesis; RNAseq; Genetic diseases; Human genetics; P63
Università degli Studi di Milano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/173073
Il codice NBN di questa tesi è URN:NBN:IT:UNIMI-173073