32 research outputs found
Susceptibility and permissivity of zebrafish (Danio rerio) larvae to cypriniviruses
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A fish herpesvirus highlights functional diversities among Zα domains related to phase separation induction and A-to-Z conversion
Get PDFZalpha (Zα) domains bind to left-handed Z-DNA and Z-RNA. The Zα domain protein family includes cellular (ADAR1, ZBP1 and PKZ) and viral (vaccinia virus E3 and cyprinid herpesvirus 3 (CyHV-3) ORF112) proteins. We studied CyHV-3 ORF112, which contains an intrinsically disordered region and a Zα domain. Genome editing of CyHV-3 indicated that the expression of only the Zα domain of ORF112 was sufficient for normal viral replication in cell culture and virulence in carp. In contrast, its deletion was lethal for the virus. These observations revealed the potential of the CyHV-3 model as a unique platform to compare the exchangeability of Zα domains expressed alone in living cells. Attempts to rescue the ORF112 deletion by a broad spectrum of cellular, viral, and artificial Zα domains showed that only those expressing Z-binding activity, the capacity to induce liquid-liquid phase separation (LLPS), and A-to-Z conversion, could rescue viral replication. For the first time, this study reports the ability of some Zα domains to induce LLPS and supports the biological relevance of dsRNA A-to-Z conversion mediated by Zα domains. This study expands the functional diversity of Zα domains and stimulates new hypotheses concerning the mechanisms of action of proteins containing Zα domains
Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells
No full textInternational audienceGlioblastoma cell ability to adapt their functioning to microenvironment changes is a source of the extensive intra-tumor heterogeneity characteristic of this devastating malignant brain tumor. A systemic view of the metabolic pathways underlying glioblastoma cell functioning states is lacking. We analyzed public single cell RNA-sequencing data from glioblastoma surgical resections, which offer the closest available view of tumor cell heterogeneity as encountered at the time of patients' diagnosis. Unsupervised analyses revealed that information dispersed throughout the cell transcript repertoires encoded the identity of each tumor and masked information related to cell functioning states. Data reduction based on an experimentally-defined signature of transcription factors overcame this hurdle. It allowed cell grouping according to their tumorigenic potential, regardless of their tumor of origin. The approach relevance was validated using independent datasets of glioblastoma cell and tissue transcriptomes, patient-derived cell lines and orthotopic xenografts. Overexpression of genes coding for amino acid and lipid metabolism enzymes involved in anti-oxidative, energetic and cell membrane processes characterized cells with high tumorigenic potential. Modeling of their expression network highlighted the very long chain polyunsaturated fatty acid synthesis pathway at the core of the network. Expression of its most downstream enzymatic component, ELOVL2, was associated with worsened patient survival, and required for cell tumorigenic properties in vivo. Our results demonstrate the power of signature-driven analyses of single cell transcriptomes to obtain an integrated view of metabolic pathways at play within the heterogeneous cell landscape of patient tumors
