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Longitudinal description of health-related quality of life and depressive symptoms in polyQ spinocerebellar ataxia patients
Get PDFINTRODUCTION: Due to limited treatment options, managing symptoms has dominated care for Spinocerebellar Ataxia (SCA). Little attention has been given to health-related quality of life (HRQoL) and depressive symptoms experienced by patients across disease duration. OBJECTIVE: To investigate the course of HRQoL and the severity of depressive symptoms in SCA from disease onset to 26 years after onset and identify influencing factors. METHODS: We analyzed data from two longitudinal SCA cohorts, the EUROSCA (European Spinocerebellar Ataxia Registry) and ESMI study (European Spinocerebellar Ataxia Type 3/Machado-Joseph Disease Initiative). Multilevel mixed-effects models were employed to demonstrate the course of HRQoL and depressive symptoms severity to investigate the role of disease progression with disease duration as a predictor of interest, along with time-varying clinical variables and time-fixed covariates. RESULTS: Seven hundred seventy four participants (M(age) = 50.8 ± 13.4; 48.6% female) were included. HRQoL consistently decreased throughout disease duration across all SCA subtypes, but the decline was smallest in SCA6. The decrease in HRQoL was explained by ataxia and depression severity and driven by increasing problems with self-care, usual activities and mobility. Depressive symptoms significantly increased in SCA2 and 3 only, with a trend toward slight improvement in SCA6. CONCLUSIONS: The trend direction of HRQoL and its significant association with the severity of ataxia symptoms align with the literature. The rapid worsening of self-care problems, the differential associations between depression and HRQoL sub-dimensions in different SCA subtypes, and the unexplainable resilience may warrant a deeper look at patient-specific intra- and interpersonal factors
Helmholtz Health task force to strengthen prevention research and its translation globally
No full textEpiproteomic control of the epitranscriptome drives cortical neurogenesis
No full textNeurological conditions are the leading cause of ill health worldwide. Here, we show that the neurodevelopmental disorder-associated ubiquitin ligase UBE3C regulates the cellular composition of the murine cerebral cortex and human brain organoids, with its loss favoring neurogenesis and suppressing glial fate. Using genetic complementation, we demonstrate that disease-associated UBE3C mutations alter its autoubiquitination activity and disrupt cortical lamination. Proteomic profiling of UBE3C-deficient forebrains and organoids identifies Cbll1 as a UBE3C substrate, and we show that the UBE3C-Cbll1 duo drives N(6)-methyladenosine (m6A) mRNA methylation. Hyperactivation of m6A writers in UBE3C-deficient neural progenitors impairs cell cycle exit, a defect reversible in vivo by the METTL3 inhibitor STM2457. Our findings uncover an epiproteomic mechanism controlling m6A-mediated gene expression and define a regulatory axis linking ubiquitin signaling to epitranscriptomic control of neural fate. This work provides a mechanistic framework for understanding neurodevelopmental disorders and highlights potential therapeutic strategies
Selective depletion of cancer cells with extrachromosomal DNA via lentiviral infection
Get PDFExtrachromosomal DNA (ecDNA), a major focal oncogene amplification mode found across cancer, has recently regained attention as an emerging cancer hallmark, with a pervasive presence across cancers. With technical advancements such as high-coverage sequencing and live-cell genome imaging, we can now investigate the behaviors and functions of ecDNA. However, we still lack an understanding of how to eliminate ecDNA. We observed depletion of cells containing ecDNA during lentiviral but not transposon-based transduction while we sought to investigate the mechanism of ecDNA behavior. This discovery may provide critical information on utilizing a lentiviral system in emerging ecDNA research. Additionally, this observation suggests specific sensitivities for cells with ecDNA
Flexynesis: a deep learning toolkit for bulk multi-omics data integration for precision oncology and beyond
Get PDFAccurate decision making in precision oncology depends on integration of multimodal molecular information, for which various deep learning methods have been developed. However, most deep learning-based bulk multi-omics integration methods lack transparency, modularity, deployability, and are limited to narrow tasks. To address these limitations, we introduce Flexynesis, which streamlines data processing, feature selection, hyperparameter tuning, and marker discovery. Users can choose from deep learning architectures or classical supervised machine learning methods with a standardized input interface for single/multi-task training and evaluation for regression, classification, and survival modeling. We showcase the tool’s capability across diverse use-cases in precision oncology. To maximize accessibility, Flexynesis is available on PyPi, Guix, Bioconda, and the Galaxy Server (https://usegalaxy.eu/). This toolset makes deep-learning based bulk multi-omics data integration in clinical/pre-clinical research more accessible to users with or without deep-learning experience. Flexynesis is available at https://github.com/BIMSBbioinfo/flexynesis
Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression
No full textCell type–specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here, we performed single-nucleus multiomics and spatial transcriptomics in up to 32 nondiabetic (ND), autoantibody-positive (AAB(+)), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine subtypes. β, Acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB(+) compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB(+) which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in β cells. Last, single-cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting β cell regulation. Overall, these results revealed drivers of T1D in the pancreas, which form the basis for therapeutic targets for disease prevention
Spatiotemporal dynamics of tumor microenvironment remodeling
Get PDFDuring tumorigenesis, interactions between tumor and stromal cells progressively remodel the tumor microenvironment (TME) towards pro-tumoral functions. Understanding early TME remodeling dynamics is therefore crucial for developing interceptive therapies. However, clinical samples typically provide isolated, late tumorigenesis snapshots. To overcome this limitation, we generated triple-negative breast cancer mice that develop multifocal, asynchronous tumors along a continuous luminal-to-basal transdifferentiation trajectory. Ordering spatial transcriptomes from 100+ ducts along this trajectory reveals the spatiotemporal dynamics of TME remodeling and underlying molecular mechanisms. Cancer-associated myofibroblasts (myCAFs) emerge as key players in advanced tumors, where they orchestrate pro-invasive remodeling of the tumor-stromal interface. myCAFs are conserved in patient-derived xenograft models and steer tumor trajectories towards invasive phenotypes when co-injected with tumor cells in syngeneic mice. Our study shows that temporal ordering of spatially-resolved disease snapshots unravels some of the molecular “forces” that, starting from the cell-of-origin, propel cells/microenvironments along a disease trajectory
Ultra-high-scale cytometry-based cellular interaction mapping
Get PDFCellular interactions are of fundamental importance, orchestrating organismal development, tissue homeostasis and immunity. Recently, powerful methods that use single-cell genomic technologies to dissect physically interacting cells have been developed. However, these approaches are characterized by low cellular throughput, long processing times and high costs and are typically restricted to predefined cell types. Here we introduce Interact-omics, a cytometry-based framework to accurately map cellular landscapes and cellular interactions across all immune cell types at ultra-high resolution and scale. We demonstrate the utility of our approach to study kinetics, mode of action and personalized response prediction of immunotherapies, and organism-wide shifts in cellular composition and cellular interaction dynamics following infection in vivo. Our scalable framework can be applied a posteriori to existing cytometry datasets or incorporated into newly designed cytometry-based studies to map cellular interactions with a broad range of applications from fundamental biology to applied biomedicine