N1-methylation of adenosine (m1A) in ND5 mRNA leads to complex I dysfunction in Alzheimer's disease.

One mechanism of particular interest to regulate mRNA fate post-transcriptionally is mRNA modification. Especially the extent of m1A mRNA methylation is highly discussed due to methodological differences. However, one single m1A site in mitochondrial ND5 mRNA was unanimously reported by different groups. ND5 is a subunit of complex I of the respiratory chain. It is considered essential for the coupling of oxidation and proton transport. Here we demonstrate that this m1A site might be involved in the pathophysiology of Alzheimer's disease (AD). One of the pathological hallmarks of this neurodegenerative disease is mitochondrial dysfunction, mainly induced by Amyloid ß (Aß). Aß mainly disturbs functions of complex I and IV of the respiratory chain. However, the molecular mechanism of complex I dysfunction is still not fully understood. We found enhanced m1A methylation of ND5 mRNA in an AD cell model as well as in AD patients. Formation of this m1A methylation is catalyzed by increased TRMT10C protein levels, leading to translation repression of ND5. As a consequence, here demonstrated for the first time, TRMT10C induced m1A methylation of ND5 mRNA leads to mitochondrial dysfunction. Our findings suggest that this newly identified mechanism might be involved in Aß-induced mitochondrial dysfunction.

Manganese Ions Individually Alter the Reverse Transcription Signature of Modified Ribonucleosides.

Reverse transcription of RNA templates containing modified ribonucleosides transfers modification-related information as misincorporations, arrest or nucleotide skipping events to the newly synthesized cDNA strand. The frequency and proportion of these events, merged from all sequenced cDNAs, yield a so-called RT signature, characteristic for the respective RNA modification and reverse transcriptase (RT). While known for DNA polymerases in so-called error-prone PCR, testing of four different RTs by replacing Mg2+ with Mn2+ in reaction buffer revealed the immense influence of manganese chloride on derived RT signatures, with arrest rates on m1A positions dropping from 82% down to 24%. Additionally, we observed a vast increase in nucleotide skipping events, with single positions rising from 4% to 49%, thus implying an enhanced read-through capability as an effect of Mn2+ on the reverse transcriptase, by promoting nucleotide skipping over synthesis abortion. While modifications such as m1A, m22G, m1G and m3C showed a clear influence of manganese ions on their RT signature, this effect was individual to the polymerase used. In summary, the results imply a supporting effect of Mn2+ on reverse transcription, thus overcoming blockades in the Watson-Crick face of modified ribonucleosides and improving both read-through rate and signal intensity in RT signature analysis.

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures.

Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide resolution across the mapped transcriptome. Further downstream modules include tools for visualization, machine learning, and modification calling. From the machine-learning module, quality assessment parameters are provided to gauge the suitability of the initial dataset for effective machine learning and modification calling. This output is useful to improve the experimental parameters for library preparation and sequencing. In summary, the automation of the bioinformatics workflow allows a faster turnaround of the optimization cycles in modification calling.