Biology subjects

Mader, K.

Publications and source records attributed to Mader, K..

4 recordsLinked to original sources

bioRxiv2026

Correction of multiple splicing mutations associated with CFTR exon 18 using a single exon-specific U1 snRNA

Splice site mutations represent a major class of pathogenic mutations in many diseases, as these changes disrupt normal splicing leading to gene expression changes. Cystic fibrosis (CF) results from mutations to the cystic fibrosis transmembrane conductance regulator (CFTR) gene that encodes an essential ion channel. Approximately 13% of the over 2,100 known CFTR mutations disrupt 3 or 5 splice sites and are predicted to cause splicing defects. Because each splicing mutation is rare, developing individualized therapies to treat each one is financially challenging. Exon specific U1 snRNA (ExSpeU1) targets the non-conserved intronic region downstream the 5 splice site (ss) to rescue exon skipping. Because this approach is exon-rather than mutation-specific, a single agent can potentially rescue multiple mutations. In this study, we have developed a platform to systematically classify all patient variants associated with an exon that are predicted to affect splicing and then determine their rescue potential using ExSpeU1. Here we report the results of these studies. Our minigene reporter study shows that 7 of 10 exon 18 variants resulted in exon skipping. Four mutations at the 3 and 5 ss were rescued at least partially using a single ExSpeU1. Using a luciferase reporter, we observe that the splicing rescue is reflected at the protein level. Lastly, we demonstrate exon-targeting ExSpeU1s can also rescue 3 and 5 ss mutations. Overall, this study exemplifies the power of our platform to screen and rescue multiple patient-derived splicing mutations using a single agent.

genetics↗

bioRxiv2025

Simultaneous single-cell CRISPR, RNA, and ATAC-seq enables multiomic CRISPR screens to identify gene regulatory relationships

The ability to identify gene functions and interactions in specific cellular contexts has been greatly enabled by functional genomics technologies. CRISPR-based genetic screens have proven invaluable in elucidating gene function in mammalian cells. Single-cell functional genomics methods, such as Perturb-seq and Spear-ATAC, have made it possible to achieve high-throughput mapping of the functional effects of gene perturbations by profiling transcriptomes and DNA accessibility, respectively. Combining single-cell chromatin accessibility and transcriptomic data via multiomic approaches has facilitated the discovery of novel cis and gene regulatory interactions. However, pseudobulk readouts from cell populations can often cloud the interpretation of results due to a heterogeneous response from cells receiving the same genetic perturbation, which could be mitigated by using transcriptional profiles of single cells to subset the ATAC-seq data. Existing methods to capture CRISPR guide RNAs to simultaneously assess the impact of genetic perturbations on RNA and ATAC profiles require either cloning of gRNA libraries in specialized vectors or implementing complex protocols with multiple rounds of barcoding. Here, we introduce CAT-ATAC, a technique that adds CRISPR gRNA capture to the existing 10X Genomics Multiome assay, generating paired transcriptome, chromatin accessibility and perturbation identity data from the same individual cells. We demonstrate up to 77% guide capture efficiency for both arrayed and pooled delivery of lentiviral gRNAs in induced pluripotent stem cells (iPSCs) and cancer cell lines. This capability allows us to construct gene regulatory networks (GRNs) in cells under drug and genetic perturbations. By applying CAT-ATAC, we were able to identify a GRN associated with dasatinib resistance, indirectly activated by the HIC2 gene. Using loss of function experiments, we further validated that the gene, ZFPM2, a component of the predicted GRN, also contributes to dasatinib resistance. CAT-ATAC can thus be used to generate high-content multidimensional genotype-phenotype maps to reveal novel gene and cellular interactions and functions.

genomics↗

bioRxiv2025

Network-aware self-supervised learning enables high-content phenotypic screening for genetic modifiers of neuronal activity dynamics

High-throughput phenotypic screening has historically relied on manually selected features, limiting our ability to capture complex cellular processes, particularly neuronal activity dynamics. While recent advances in self-supervised learning have revolutionized the ability to study cellular morphology and transcriptomics, dynamic cellular processes have remained challenging to phenotypically profile. To address this limitation, we developed Plexus, a self-supervised model specifically designed to capture and quantify network-level neuronal activity dynamics. Unlike existing phenotyping tools that focus on static readouts, Plexus leverages a network-level cell encoding method, which enables it to efficiently encode dynamic neuronal activity data into rich representational embeddings. In turn, Plexus achieves state of the art performance in detecting phenotypic changes in neuronal activity. We validated Plexus using a comprehensive GCaMP6m simulation framework and demonstrated its enhanced ability to classify distinct neuronal activity phenotypes compared to traditional signal-processing approaches. To enable practical application, we integrated Plexus with a scalable experimental system utilizing human iPSC-derived neurons equipped with the GCaMP6m calcium indicator and CRISPR interference machinery. This integrated platform successfully identified nearly seventeen times as many distinct phenotypic changes in response to genetic perturbations compared to conventional signal processing methods, as demonstrated in a 52-gene CRISPRi screen across multiple iPSC lines. Using this framework, we identified potential genetic modifiers of aberrant neuronal activity in frontotemporal dementia, illustrating its utility for understanding complex neurological disorders.

bioinformatics↗

bioRxiv2024

Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney function

Kidney disease is highly heritable; however, the causal genetic variants, the cell types in which these variants function, and the molecular mechanisms underlying kidney disease remain largely unknown. To identify genetic loci affecting kidney function, we performed a GWAS using multiple kidney function biomarkers and identified 462 loci. To begin to investigate how these loci affect kidney function, we generated single-cell chromatin accessibility (scATAC-seq) maps of the human kidney and identified candidate cis-regulatory elements (cCREs) for kidney podocytes, tubule epithelial cells, and kidney endothelial, stromal, and immune cells. Kidney tubule epithelial cCREs explained 58% of kidney function SNP-heritability and kidney podocyte cCREs explained an additional 6.5% of SNP-heritability. In contrast, little kidney function heritability was explained by kidney endothelial, stromal, or immune cell-specific cCREs. Through functionally informed fine-mapping, we identified putative causal kidney function variants and their corresponding cCREs. Using kidney scATAC-seq data, we created a deep learning model (which we named ChromKid) to predict kidney cell type-specific chromatin accessibility from sequence. ChromKid and allele specific kidney scATAC-seq revealed that many fine-mapped kidney function variants locally change chromatin accessibility in tubule epithelial cells. Enhancer assays confirmed that fine-mapped kidney function variants alter tubule epithelial regulatory element function. To map the genes which these regulatory elements control, we used CRISPR interference (CRISPRi) to target these regulatory elements in tubule epithelial cells and assessed changes in gene expression. CRISPRi of enhancers harboring kidney function variants regulated NDRG1 and RBPMS expression. Thus, inherited differences in tubule epithelial NDRG1 and RBPMS expression may predispose to kidney disease in humans. We conclude that genetic variants affecting tubule epithelial regulatory element function account for most SNP-heritability of human kidney function. This work provides an experimental approach to identify the variants, regulatory elements, and genes involved in polygenic disease.

genetics↗