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Scheper, W.

Publications and source records attributed to Scheper, W..

2 recordsLinked to original sources

Neuronal lipofuscinosis caused by Kufs disease/CLN4 DNAJC5 mutations but not by a CSPα/DNAJC5 deficiency

Kufs disease/CLN4 is an autosomal dominant neurodegenerative disorder that affects young adults, caused by mutations in the DNAJC5 gene that encodes the synaptic vesicle co-chaperone Cysteine String Protein (CSP/DNAJC5). The Leu115Arg and Leu116{Delta} mutations in humans are known to independently cause the disease, although the underlying mechanisms are unknown. To investigate the disease mechanisms in vivo, we generated three independent mouse lines overexpressing different versions of CSP/DNAJC5 under the neuron-specific Thy1 promoter: wild-type (WT), Leu115Arg, and Leu116{Delta}. Mice expressing mutant CSP/DNAJC5 are viable and do not show any significant increase in morbidity or mortality. However, we observed the presence of pathological lipofuscinosis in the mutants, indicated by autofluorescent punctate structures labeled with antibodies against ATP synthase subunit C, which were absent in the WT transgenic line. Additionally, transmission electron microscopy revealed intracellular structures resembling granular osmiophilic deposits (GRODs), observed in Kufs disease patients, in the mutants but not in non-transgenic controls or the WT transgenic mice. Notably, conventional, or conditional knockout mice lacking CSP/DNAJC5 did not exhibit any signs of increased lipofuscinosis or GRODs. Our novel mouse models thus provide a valuable tool to investigate the molecular mechanisms underlying Kufs disease/CLN4. We conclude that DNAJC5 mutations cause neuronal lipofuscinosis through a cell-autonomous gain of a novel but pathological function of CSP/DNAJC5.

neuroscience↗

STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data

The prediction of peptide-MHC (pMHC) recognition by {beta} T-cell receptors (TCRs) remains a major biomedical challenge. Here, we develop STAPLER (Shared TCR And Peptide Language bidirectional Encoder Representations from transformers), a transformer language model that uses a joint TCR{beta}- peptide input to allow the learning of patterns within and between TCR{beta} and peptide sequences that encode recognition. First, we demonstrate how data leakage during negative data generation can confound performance estimates of neural network-based models in predicting TCR - pMHC specificity. We then demonstrate that, because of its pre-training and fine-tuning masked language modeling tasks, STAPLER outperforms both neural network-based and distance-based ML models in predicting the recognition of known antigens in an independent dataset, in particular for antigens for which little related data is available. Based on this ability to efficiently learn from limited labeled TCR- peptide data, STAPLER is well-suited to utilize growing TCR - pMHC datasets to achieve accurate prediction of TCR - pMHC specificity.

bioinformatics↗