Biology subjects

Hillen, H.

Publications and source records attributed to Hillen, H..

3 recordsLinked to original sources

bioRxiv2026

Mechanism of human tRNA 3'CCA maturation

The non-templated addition of the 3CCA end is the final universal step of tRNA maturation. In humans, 3CCA addition on nuclear- (nu-tRNA) and mitochondria-encoded tRNAs (mt-tRNA) is catalyzed by a single CCA-adding enzyme, TRNT1, but its precise mechanism remains unknown. Here, we report structures of TRNT1 trapped at various stages during the 3CCA addition cycle on canonical and non-canonical mt-tRNAs bound to the mitochondrial tRNA maturation platform TRMT10C-SDR5C1. Combined with biochemical data, these structures demonstrate that 3CCA addition proceeds by a continuous polymerization and translocation mechanism, in which the growing RNA primer remodels the TRNT1 catalytic site to define the specificity of non-templated 3CCA addition. Moreover, they reveal a relaxed recognition mode that allows TRNT1 to mature both canonical nu-tRNA and non-canonical mt-tRNA substrates. Finally, biochemical analyses of disease-associated TRNT1 variants provide insights into their molecular pathogenesis. Taken together, these results provide a detailed mechanistic picture of human tRNA-3CCA maturation.

molecular biology↗

bioRxiv2024

Molecular basis of human nuclear and mitochondrial tRNA 3'-processing

Eukaryotic transfer RNA (tRNA) precursors undergo sequential processing steps to become mature tRNAs. In humans, ELAC2 carries out 3-end processing of both nucleus-encoded (nu-tRNAs) and mitochondria-encoded tRNAs (mt-tRNAs). ELAC2 is self-sufficient for processing of nu-tRNAs, but requires TRMT10C and SDR5C1 to process most mt-tRNAs. Here, we show that TRMT10C-SDR5C1 specifically facilitate processing of structurally degenerate mt-tRNAs lacking the canonical elbow. Structures of ELAC2 in complex with TRMT10C, SDR5C1 and two divergent mt-tRNA substrates reveal two distinct mechanisms of pre-tRNA recognition. While canonical nu-tRNAs and mt-tRNAs are recognized by direct ELAC2-RNA interactions, processing of non-canonical mt-tRNAs depends on protein-protein interactions between ELAC2 and TRMT10C. These results provide the molecular basis for tRNA 3-processing in both the nucleus and mitochondria and explain the organelle-specific requirement for additional factors. Moreover, they suggest that TRMT10C-SDR5C1 evolved as a mitochondrial tRNA maturation platform to compensate for the structural erosion of mt-tRNAs in bilaterian animals.

biochemistry↗

bioRxiv2022

Enhancer grammar of liver cell types and hepatocyte zonation states

Cell type identity is encoded by gene regulatory networks (GRN), in which transcription factors (TFs) bind to enhancers to regulate target gene expression. In the mammalian liver, lineage TFs have been characterized for the main cell types, including hepatocytes. Hepatocytes cover a relatively broad cellular state space, as they differ significantly in their metabolic state, and function, depending on their position with respect to the central or portal vein in a liver lobule. It is unclear whether this spatially defined cellular state space, called zonation, is also governed by a well-defined gene regulatory code. To address this challenge, we have mapped enhancer-GRNs across liver cell types at high resolution, using a combination of single cell multiomics, spatial omics, GRN inference, and deep learning. We found that cell state changes in transcription and chromatin accessibility in hepatocytes, liver sinusoidal endothelial cells and hepatic stellate cells depend on zonation. Enhancer-GRN mapping suggests that zonation states in hepatocytes are driven by the repressors Tcf7l1 and Tbx3, that modulate the core hepatocyte GRN, controlled by Hnf4a, Cebpa, Hnf1a, Onecut1 and Foxa1, among others. To investigate how these TFs cooperate with cell type TFs, we performed an in vivo massively parallel reporter assay on 12,000 hepatocyte enhancers and used these data to train a hierarchical deep learning model (called DeepLiver) that exploits both enhancer accessibility and activity. DeepLiver confirms Cebpa, Onecut, Foxa1, Hnf1a and Hnf4a as drivers of enhancer specificity in hepatocytes; Tcf7l1/2 and Tbx3 as regulators of the zonation state; and Hnf4a, Hnf1a, AP-1 and Ets as activators. Finally, taking advantage of in silico mutagenesis predictions from DeepLiver and enhancer assays, we confirmed that the destruction of Tcf7l1/2 or Tbx3 motifs in zonated enhancers abrogates their zonation bias. Our study provides a multi-modal understanding of the regulatory code underlying hepatocyte identity and their zonation state, that can be exploited to engineer enhancers with specific activity levels and zonation patterns.

genomics↗