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Simple Summary
Neuronal plasticity refers to the brain’s ability to adapt in response to activity-dependent changes. This process, among others, allows the brain to acquire memory or to compensate for a neurocognitive deficit. We analyzed adult FTSJ1-deficient mice in order to gain insight into the role of FTSJ1 in neuronal plasticity. These mice displayed alterations in the hippocampus (a brain structure that is involved in memory and learning, among other functions) e.g., in the form of changes in dendritic spines. Changes in dendritic spines are considered to represent a morphological hallmark of altered neuronal plasticity, and thus FTSJ1 deficiency might have a direct effect upon the capacity of the brain to adapt to plastic changes. Long-term potentiation (LTP) is an electrophysiological correlate of neuronal plasticity, and is related to learning and to processes attributed to memory. Here we show that LTP in FTSJ1-deficient mice is reduced, hinting at disturbed neuronal plasticity. These findings suggest that FTSJ1 deficiency has an impact on neuronal plasticity not only morphologically but also on the physiological level.
Abstract
The role of the tRNA methyltransferase FTSJ1 in the brain is largely unknown. We analyzed whether FTSJ1-deficient mice (KO) displayed altered neuronal plasticity. We explored open field behavior (10 KO mice (aged 22–25 weeks)) and 11 age-matched control littermates (WT) and examined mean layer thickness (7 KO; 6 WT) and dendritic spines (5 KO; 5 WT) in the hippocampal area CA1 and the dentate gyrus. Furthermore, long-term potentiation (LTP) within area CA1 was investigated (5 KO; 5 WT), and mass spectrometry (MS) using CA1 tissue (2 each) was performed. Compared to controls, KO mice showed a significant reduction in the mean thickness of apical CA1 layers. Dendritic spine densities were also altered in KO mice. Stable LTP could be induced in the CA1 area of KO mice and remained stable at for at least 1 h, although at a lower level as compared to WTs, while MS data indicated differential abundance of several proteins, which play a role in neuronal plasticity. FTSJ1 has an impact on neuronal plasticity in the murine hippocampal area CA1 at the morphological and physiological levels, which, in conjunction with comparable changes in other cortical areas, might accumulate in disturbed learning and memory functions.
Non-coding RNA (ncRNA) classes take over important housekeeping and regulatory functions and are quite heterogeneous in terms of length, sequence conservation and secondary structure. High-throughput sequencing reveals that the expressed novel ncRNAs and their classification are important to understand cell regulation and identify potential diagnostic and therapeutic biomarkers. To improve the classification of ncRNAs, we investigated different approaches of utilizing primary sequences and secondary structures as well as the late integration of both using machine learning models, including different neural network architectures. As input, we used the newest version of RNAcentral, focusing on six ncRNA classes, including lncRNA, rRNA, tRNA, miRNA, snRNA and snoRNA. The late integration of graph-encoded structural features and primary sequences in our MncR classifier achieved an overall accuracy of >97%, which could not be increased by more fine-grained subclassification. In comparison to the actual best-performing tool ncRDense, we had a minimal increase of 0.5% in all four overlapping ncRNA classes on a similar test set of sequences. In summary, MncR is not only more accurate than current ncRNA prediction tools but also allows the prediction of long ncRNA classes (lncRNAs, certain rRNAs) up to 12.000 nts and is trained on a more diverse ncRNA dataset retrieved from RNAcentral.