bioRxiv Science⌕ Search

Biology subjects

Bramley, J. C.

Publications and source records attributed to Bramley, J. C..

2 recordsLinked to original sources

Pathogenic Morphological Signatures of Perturbations in Mitochondrial-Related Genes Revealed by Pooled Imaging Assay

Mutations in mitochondrial-related genes underlie numerous neurodegenerative diseases, yet the significance of most variants remains uncertain concerning disease phenotypes. Several thousand genes have been shown to regulate mitochondria in eukaryotic cells, but which of these genes are necessary for proper mitochondrial dynamics? We investigated the degree of morphological disruptions in mitochondrial gene-silenced cells to understand the magnitude of genetic contribution to properly functioning mitochondria and to identify pathogenic variants. We analyzed 5,835 gRNAs in a high dimensional phenotypic dataset produced by the image-based pooled analysis platform Raft-Seq. Using the MFN2-mutant cell phenotype, we identified several genes, including TMEM11, TIMM8A, and three NADH Ubiquinone proteins, as crucial for normal mitochondrial morphology in human U2OS cells. Additionally, we found several missense and UTR variants within the genes SLC25A19 and ATAD3A as drivers of mitochondrial aggregation. By examining multiple features instead of a single readout, this analysis was powered to detect genes which had morphological signatures aligned with MFN2-mutant phenotypes. Reanalysis with anomaly detection revealed other critical genes, including APOOL, MCEE, NIT, PHB, and SLC16A7, which perturb mitochondrial network morphology in a manner divergent from MFN2. These studies offer insights into the molecular basis for mitochondrial dysfunction, setting the stage for new genomic diagnostics and therapeutic discovery.

genetics↗

Mitochondrial Phenotypes Distinguish Pathogenic MFN2 Mutations by Pooled Functional Genomics Screen

Most human genetic variation is classified as VUS - variants of uncertain significance. While advances in genome editing have allowed innovation in pooled screening platforms, many screens deal with relatively simple readouts (viability, fluorescence) and cannot identify the complex cellular phenotypes that underlie most human diseases. In this paper, we present a generalizable functional genomics platform that combines high-content imaging, machine learning, and microraft isolation in a new method termed "Raft-Seq". We highlight the efficacy of our platform by showing its ability to distinguish pathogenic point mutations of the mitochondrial regulator MFN2, even when the cellular phenotype is subtle. We also show that our platform achieves its efficacy using multiple cellular features, which can be configured on-the-fly. Raft-Seq enables a new way to perform pooled screening on sets of mutations in biologically relevant cells, with the ability to physically capture any cell with a perturbed phenotype and expand it clonally, directly from the primary screen. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/434746v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@c774b4org.highwire.dtl.DTLVardef@bee63corg.highwire.dtl.DTLVardef@fd7099org.highwire.dtl.DTLVardef@eb7d87_HPS_FORMAT_FIGEXP M_FIG C_FIG Here, we address the need to evaluate the impact of numerous genetic variants. This manuscript depicts the methods of using machine learning on a biologically relevant phenotype to predict specific point mutations, followed by physically capturing those mutated cells.

genomics↗