The analysis of pathogenic genetic variations has always been an important challenge in the field of human genetics. Single nucleotide mutations in genes related to Mendelian genetic diseases should be the easiest type of mutation to study in human genetics. However, for many disease genes, the clinical importance of at least half of single nucleotide mutations has not been determined, seriously affecting the diagnostic efficiency of genetic diseases, especially rare genetic diseases. Deep mutational screening (DMS) refers to a research method that generates large-scale gene variations through saturation mutagenesis and then studies genetic variations in cell lines through high-throughput methods. This method can greatly accelerate the diagnosis of genetic diseases and deepen our understanding of the clinical importance of single nucleotide mutations. However, the cost and complexity of current deep mutation screening methods hinder their widespread application in clinical research.

On September 25, 2024, Monkol Lek's team from Yale University and Dr. Kaiyue Ma from Shanghai Jiao Tong University published a research article titled Saturation Mutaagenesis Re inforced Functional Assays (SMuRF) for Disease related Genes online in the journal Cell. Researchers have established a deep mutation screening workflow for studying disease genes called "saturation mutagenesis assisted functional method" - SMuRF. This workflow includes a novel and low-cost saturation mutagenesis method, a new sensitive high-throughput flow cytometer functional method, and multiple independent validation functional methods, which can achieve rapid and efficient deep mutation screening of multiple disease genes. In addition, to validate and demonstrate the effectiveness of SMuRF, researchers applied SMuRF to score all possible single nucleotide mutations in the two α - anti muscular atrophy related glycoprotein disease genes FKRP and LARGE1. The results show that the establishment and application of SMuRF provide possibilities for studying functional mutations in various rare diseases.

Figure 1: Schematic diagram of SMuRF biotechnology development and application

Saturated genome (base) editing (SGE/SBE) is currently one of the most widely used saturation mutagenesis methods, but its application cost is high and it is limited by the editing efficiency differences of the CRISPR system, resulting in unpredictable differences in the coverage of mutations at different sites. Therefore, in clinical research, SGE and SBE are difficult to be widely applied in disease research laboratories lacking CRISPR technology experience.

Unlike SGE/SBE, PALS (programmed allelic series) is a method that can generate high coverage saturation mutations without preference (PMID: 25559584). Researchers have developed a PALS-C method based on PALS, which also has no preference, high coverage, low cost, and simple operation. In addition, based on the development of molecular biology biotechnology, researchers focus on exploring the impact of mutations on cellular functional changes, optimizing and improving a flow cytometer functional detection method, which efficiently enhances the sensitivity of DMS detection.

Figure 2: Technical details of SMuRF workflow

Meanwhile, based on the development and optimization of the above-mentioned biotechnology methods, researchers used SMuRF to conduct functional studies on the genetic mutations of FKRP and LARGE1. After comparing with the human genetic variation database ClinVar and the largest human genetic variation database gnomAD, researchers confirmed that the SMuRF score is consistent with the clinical classification and frequency of variations reported in the database. Subsequently, the researchers found that the SMuRF score could better reflect the severity of FKRP related diseases by integrating 8 patient cohorts. The researchers also compared the functional scores of SMuRF and artificial intelligence mutation prediction tools represented by AlphaMissine, and proposed that SMuRF has the potential to further enhance the predictive ability of related artificial intelligence tools. Subsequently, the researchers projected SMuRF scores onto the protein structures of FKRP and LARGE1, revealing important disease-related regions. Finally, adhering to the principle that "independent validation experiments should be an important part of the deep mutation screening workflow," the researchers used high-throughput methods based on the LASV virus invasion mechanism and immunofluorescence staining validation methods to validate α - DG glycosylation and SMuRF scoring in muscle cell lines.

In summary, SMuRF provides a simple and cost-effective saturation mutagenesis method that, when combined with high-throughput functional screening techniques, can effectively solve the problem of clinically unclear mutations. SMuRF has shown great potential in predicting disease severity, analyzing key structural regions, and providing training datasets for computational prediction models.

Dr. Ma Kaiyue from the Bio-X Research Institute of Shanghai Jiao Tong University (formerly a doctoral student at Yale University) and Professor Monkol Lek from Yale University are co corresponding authors of the paper. Dr. Ma Kaiyue is the sole first author of this article.