The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases

Abstract

Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts. Here, we present GENDULF (GENetic moDULators identiFication), one of the first methods to facilitate prediction of disease modifiers using healthy and diseased tissue gene expression data. GENDULF is designed for monogenic diseases in which the mechanism is loss of function leading to reduced expression of the mutated gene. When applied to cystic fibrosis, GENDULF successfully identifies multiple, previously established disease modifiers, including EHF , SLC6A14 , and CLCA1 . It is then utilized in spinal muscular atrophy (SMA) and predicts U2AF1 as a modifier whose low expression correlates with higher SMN2 pre‐mRNA exon 7 retention. Indeed, knockdown of U2AF1 in SMA patient‐derived cells leads to increased full‐length SMN2 transcript and SMN protein expression. Taking advantage of the increasing availability of transcriptomic data, GENDULF is a novel addition to existing strategies for prediction of genetic disease modifiers, providing insights into disease pathogenesis and uncovering novel therapeutic targets.

A high-throughput genome-wide RNAi screen identifies modifiers of survival motor neuron protein

“…In this study, knocking down SRSF3 or hnRNP A1 increases full-length SMN expression consistent with previous findings. We also re-identified U2AF1 and PUF60 that have been previously reported to suppress SMN exon 7 inclusion ( Auslander et al, 2020 ; Hastings et al, 2007 ). Together, we found that siRNA targeting almost all the previously confirmed genes that promote SMN exon 7 exclusion is indicative of the robustness of the screening strategy.…”

Single-cell RNA-Sequencing Co-Expression Analysis with CFTR in Lung Tissue

“…Among the CF modifiers, He et al showed in a human nasal and bronchial tissue bulk RNAsequencing study that SLC6A14 is one of the most active co-expressing gene, with respect to both CFTR and other modifiers 15 . Here, we were able to detect significant co-expression with two of the most widely investigated modifier genes in CF, SLC26A9 and SLC6A14 [44][45][46][47][48][49] , with each other and with CFTR in AT2 cells. The expression of these two genes and CFTR are highest in AT2, sAT2, club, differentiating basal and goblet cells compared to the other cell types (Figure S13).…”