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Djokic, M.

Publications and source records attributed to Djokic, M..

2 recordsLinked to original sources

Cellular context defines brain-specific functions of genes associated with neurodevelopmental disorders

Differential gene expression rarely explains why certain genes, e.g. associated with tissue-specific manifestations of human disease, exert tissue-specific functions. To understand how gene expression and molecular context act in concert to drive tissue-specific gene function, we focus on Neurodevelopmental disorders (NDDs), for which underlying disease mechanisms are still poorly understood. We developed a computational framework that harmonizes and integrates spatiotemporal transcriptome datasets enabling us to systematically quantify preferential gene expression across tissues and developmental stages. Applying this pipeline to approximately 1,000 NDD-associated genes, we find that only about 10% show brain-preferential expression, with distinct gene clusters instead exhibiting significant upregulation in non-brain tissues. Investigation of these clusters reveals regulatory mechanisms that govern mitochondrial function to meet neurons' high energetic demands, which are mechanistically distinct from those employed by other energy-intensive tissues such as heart and skeletal muscle. Furthermore, by integrating tissue-agnostic protein-protein interaction data with our expression resource, we identify putative brain-specific interaction partners for 22 uniformly expressed NDD-associated genes. We experimentally validate these interactions for QRICH1 and demonstrate that disease-associated mutations disrupt them, providing a molecular hypothesis for how QRICH1 acquires brain-specific function and how its disruption might cause Ververi-Brady syndrome. Together, our findings highlight the importance of molecular context for brain-specific gene function in the context of neurodevelopmental disease. We provide a framework for dissecting molecular mechanisms of tissue-specific Mendelian-like disorders and for exploring how biological processes are adapted to tissue-specific needs.

bioinformatics↗

Variant characterization in the intrinsically disordered human proteome

Variant effect prediction remains a key challenge to resolve in precision medicine. Sophisticated computational models that exploit sequence conservation and structure are increasingly successful in the characterization of missense variants in folded protein regions. However, 37% of all annotated missense variants reside in 25% of the proteome that is intrinsically disordered, lacking positional sequence conservation and stable structures. To significantly advance variant effect prediction in disordered protein regions, we combined sequence pattern searches with AlphaFold and experiments to structurally annotate 1,300 protein-protein interactions with interfaces mediated by short disordered motifs binding to folded domains in partner proteins. These interfaces were selected based on their overlap with uncertain missense variants enabling reliable prediction of deleterious effects of 1,187 uncertain variants in disordered protein regions. Extensive experimental efforts validate predicted interfaces and deleterious variant effects that were predicted as benign by AlphaMissense. This study demonstrates how critical structural information on protein interaction interfaces is for variant effect prediction especially in disordered protein regions and provides a clear avenue towards its system-wide implementation.

systems biology↗