Biology subjects

Gerber, S.

Publications and source records attributed to Gerber, S..

At least 19 recordsLinked to original sources

bioRxiv2026

DModE: An end-to-end framework for Differential Modification and Expression Analysis of Nanopore direct RNA sequencing data

SummaryNanopore direct RNA sequencing (DRS) enables simultaneous quantification of transcript abundance and RNA modifications from native RNA molecules, providing a unique opportunity to study transcriptional and epitranscriptomic regulation within a single experiment. However, comprehensive analysis of DRS data remains challenging, as existing workflows typically focus on individual processing steps and often require manual integration of multiple software packages for expression analysis, modification detection, statistical testing, and visualization. Furthermore, integrated differential expression and differential RNA modification analysis at both gene and isoform resolution remains poorly supported by current workflows. Here, we present DModE (Differential Modification and Expression Analysis), an end-to-end framework for integrated analysis of Nanopore DRS data. DModE combines an Epi2ME-compatible Nextflow preprocessing workflow with a dedicated Python package for downstream statistical analysis, visualization, and reporting. The framework supports differential gene and isoform expression analysis, differential RNA modification analysis at genome and transcript level, metagene profiling, exploratory epitranscriptomic analyses, and integrated assessment of relationships between expression and modification dynamics. Results are automatically summarized in interactive HTML reports, facilitating reproducible and accessible data interpretation. By integrating transcriptomic and epitranscriptomic analyses within a single framework, DModE substantially simplifies comprehensive DRS data analysis and lowers the barrier for studying RNA modification biology using Nanopore sequencing. Availability and implementationThe DModE Preprocessing pipeline is available on GitHub (https://github.com/johannesmiedema/DModE-preprocessing). The Python package can be installed via pip and is also available on GitHub (https://github.com/johannesmiedema/dmode).

bioinformatics↗

bioRxiv2026

16p11.2 Copy Number Variation Alters Genome Architecture and Transcriptional Regulation During Neurodevelopment

Microdeletions and microduplications in 16p11.2 are responsible for a spectrum of neurodevelopmental disorders (NDDs) with partially reciprocal and overlapping symptoms. However, the clinical variability in 16p11.2 microduplication patients is significantly greater than that in patients carrying a microdeletion. Here, we use iPSCs derived from members of a family carrying a 16p11.2 microduplication and model neurodevelopment through in vitro differentiation into neural progenitor cells (NPCs) and neurons. The analysis is complemented by reanalysis of publicly available data from 16p11.2 microdeletion patients. Transcriptome analysis revealed MAPK3-centered hubs of upregulated genes in the microduplication-carrying and downregulated in the microdeletion-carrying cells, indicating that MAPK3 is a central driver of 16p11.2 Copy Number Variation (CNV) pathology. While genes within the 16p11.2 region showed about a two-fold reduction in expression in cells carrying the microdeletion, their expression levels in microduplication-derived NPCs and neurons, but not in iPSCs, were elevated to a degree much higher than expected. This observation was accompanied by a substantial number of dysregulated genes unrelated to the genes in the critical region or their interaction networks. To further investigate whether altered chromatin organization may accompany these transcriptional changes, we generated Hi-C data from patient and control iPSCs and NPCs. This revealed increased chromatin contacts within the duplicated 16p11.2 region in patient-derived iPSCs, while genome-wide compartment analysis showed that increased compartments preferentially co-occurred with upregulated differentially expressed genes, particularly in NPCs. Together, these findings suggest that 16p11.2 microduplication may influence gene expression through both local dosage-dependent mechanisms and broader, differentiation-associated changes in chromatin organization. Our data support a model in which CNV-associated genome architecture changes may modulate transcriptional dysregulation and contribute to the variable neurodevelopmental phenotypes associated with 16p11.2 rearrangements.

pathology↗

bioRxiv2026

Benchmarking ambient RNA removal across droplet and well-plate platforms reveals artificial count generation as a critical failure mode of scAR and CellClear

BackgroundAmbient RNA contamination is a pervasive artifact of single-cell and single-nucleus RNA sequencing (sxRNA-seq), yet no consensus exists on which computational removal tool performs best across experimental platforms. ResultsWe present a systematic benchmark of six tools: CellBender, DecontX, SoupX, scCDC, scAR, and CellClear - evaluated across six human-mouse cell line mixing (hgmm) datasets (1k-20k cells) providing partial ground truth, two droplet-based complex tissue datasets (PBMC scRNA-seq; prefrontal cortex snRNA-seq), and a well-plate-based dataset (BD Rhapsody WBC). Using inter-species counts as partial ground truth, we quantify sensitivity, specificity, precision, and removal consistency per tool. We further apply a count-integrity criterion quantifying gene-cell positions where corrected values exceed raw counts. This reveals that scAR and CellClear do not merely denoise but fundamentally restructure count matrices: CellClear replaces >93% of counts with values derived from matrix factorization, while scAR generates spurious cell types absent from uncorrected data, including three spurious coarse cell types in the BD Rhapsody dataset and up to eight novel cell types in the prefrontal cortex. CellBender and SoupX exhibit reliable contamination removal with minimal count distortion. DecontX and scCDC are the only tools operable on non-droplet platforms without raw count matrix access. Runtime benchmarking at atlas scale (up to 172,000 nuclei) further demonstrates that CellClear fails to scale. ConclusionsCount matrix integrity, not removal sensitivity alone, must be a primary criterion when selecting ambient RNA correction tools. We provide platform-specific recommendations and a decision framework to guide tool selection across experimental contexts.

bioinformatics↗

bioRxiv2026

Correcting Preprocessing Bias in Sparse Chromatin Contact Data Enables Physically Interpretable Reconstruction of Genome Architecture

DNA is the largest biopolymer in nature, and chromatin contact maps are widely interpreted as quantitative readouts of its three-dimensional organization. However, the validity of such interpretations critically depends on how these maps are processed. Here, we identify a previously overlooked but fundamental source of bias in chromatin contact data analysis. We demonstrate that a widely adopted preprocessing convention, namely whole-matrix percentile clipping, systematically distorts sparse contact maps by collapsing their dynamic range. This effect is strongest in near-diagonal interactions, precisely the regime encoding chromatin domains and looping structures, thereby compromising quantitative interpretation while preserving superficial structural features. We show that this distortion represents a sparsity-dependent failure mode of current preprocessing standards and affects the comparability of datasets and computational methods across technologies and sequencing depths. To address this, we introduce a statistically consistent preprocessing framework based on nonzero-percentile clipping and log-space normalization, which preserves the intrinsic dynamic range of observed contacts. Building on this foundation, we present CCUT, a modular deep learning framework for chromatin contact map reconstruction. Under corrected preprocessing, reconstructed maps recover domain organization, contact decay, and scaling behavior consistent with polymer physics. Importantly, we demonstrate quantitative agreement between reconstructed maps and simulated contact patterns derived from a kinetic Monte Carlo loop extrusion model, enabling direct comparison between experimental data and physical models. Together, our results establish preprocessing as a decisive determinant of the physical interpretability of chromatin contact maps and provide a principled framework for robust and comparable analysis across chromatin conformation capture technologies.

biophysics↗

bioRxiv2025

Redefining human pre-rRNA processing at single nucleotide resolution using long read Nanopore sequencing

Ribosome biogenesis requires the synthesis and processing of precursor rRNAs (pre-rRNAs) into mature rRNAs. Traditional methods like northern blotting and metabolic labeling offer limited resolution. We present NanoRibolyzer, a nanopore-based, long-read sequencing approach that enables ab initio identification and quantification of rRNA precursors. Using both supervised and unsupervised mapping, it detects known and novel pre-rRNA species and defines cleavage sites at single-nucleotide resolution. A simple cell fractionation protocol provides spatial separation of nuclear and cytoplasmic pre-rRNAs. Targeted knockdowns quantify intermediate accumulations, revealing condition-specific processing fingerprints with biomarker potential. Pseudouridine mapping shows that the primary 47S transcript is extensively modified, while aberrant products are not. With its high resolution and unique mapping strategy, NanoRibolyzer offers new insights into rRNA processing and modification, enhancing our understanding of ribosome biogenesis.

cell biology↗

bioRxiv2025

ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing

RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanosine, and N1-methyladenosine) using direct RNA sequencing. Alongside ModiDeC, we provide an extensive database of in vitro-transcribed and synthetic sequences generated with both the new RNA004 chemistry and the old RNA002 kit. We show that RNA modifications can be accurately recognized and distinguished across different sequence motifs using synthetic data as well as in HEK293T cells and human blood samples. ModiDeC comes with a graphical user interface that allows easy customization and adaptation to specific research questions, such as learning and classifying additional RNA modifications and further sequence motifs. The reproducibility across samples, together with the low rate of false positives, underscores the potential of ModiDeC as a powerful tool for advancing the analysis of epitranscriptomes and RNA modification.

bioinformatics↗

bioRxiv2024

Enhancing Detection of Polygenic Adaptation: A Comparative Study of Machine Learning and Statistical Approaches Using Simulated Evolve-and-Resequence Data

Detecting signals of polygenic adaptation remains a significant challenge in population genomics, as traditional methods often struggle to identify the associated subtle, multi-locus allele-frequency shifts. Here, we introduced and tested several novel approaches combining machine learning techniques with traditional statistical tests to detect polygenic adaptation patterns in time-series of allele frequency changes from whole genome data. We implemented a Naive Bayesian Classifier (NBC) and One-Class Support Vector Machines (OCSVM), and compared their performance against the classical Fishers Exact Test (FET). Furthermore, we combined machine learning and statistical models (OCSVM-FET and NBC-FET), resulting in 5 competing approaches. Using a simulated data set based on empirical evolve-and-resequencing Chironomus riparius genomic data, we evaluated methods across evolutionary scenarios, varying in generations, selection strength and numbers of loci under selection. Our results demonstrate that the combined OCSVM-FET approach consistently outperformed competing methods, achieving the lowest false positive rate, highest area under the curve, and high accuracy. The performance peak aligned with what we term the late dynamic phase of adaptation--the period after initial selection has occurred but before fixation--highlighting the methods sensitivity to ongoing selective processes and thus its value for experimental approaches. Furthermore, we emphasize the critical role of parameter tuning, balancing biological assumptions with methodological rigor. Our approach offers a powerful tool for detecting polygenic adaptation from time series, e.g. pool sequencing data from evolve-and-resequence experiments.

genetics↗

bioRxiv2024

Premature upregulation of miR-92a's target RBFOX2 hijacks PTBP splicing and impairs cortical neuronal differentiation.

Alternative splicing is a crucial component of neuronal differentiation, yet the mechanisms that regulate splicing transitions during embryonic brain development remain incompletely understood. Here, we identify a post-transcriptional mechanism that times the expression of the splicing factor Rbfox2 during neurogenesis. RBFOX2 is normally expressed at low levels in neural progenitor cells (NPCs) and becomes upregulated in newborn neurons where it promotes neuronal differentiation. Unexpectedly, premature expression of Rbfox2 in NPCs of the embryonic mouse neocortex blocked their differentiation into neurons rather than promoting it. Genome-wide analysis revealed widespread alternative splicing changes enriched for NDD genes and associated with a hybrid NPC- and neuron-like splicing pattern that significantly deviates from the normal splicing developmental trajectory. Remarkably, premature Rbfox2 expression induced the inclusion of validated target exons that are otherwise repressed by PTBP2 pointing to an antagonistic splicing relationship. Integrative scRNA-seq analysis confirmed a negatively correlated expression between these two RNA-binding proteins (RBP) along differentiation pseudotime. Strikingly, we identified the NPC-specific miRNA 92a-3p as a regulator of the Rbfox2 expression switch: expression of miR-92a reduced RBFOX2 levels and reversed splicing patterns of target genes in vitro, while silencing miR-92a in vivo increased RBFOX2 expression in the embryonic cortex. Together, these findings reveal a previously unrecognized miRNA-RBP regulatory axis that ensures the proper timing of NPC-to-neuron splicing transitions in the developing cortex and provide new insights into splicing dysregulation as a contributing factor to the emergence of neurodevelopmental disorders. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=152 HEIGHT=200 SRC="FIGDIR/small/614071v3_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@16248caorg.highwire.dtl.DTLVardef@198dd0dorg.highwire.dtl.DTLVardef@d8887aorg.highwire.dtl.DTLVardef@1e8419e_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical Abstract Schematic representation of the proposed splicing regulation for the transition of undifferentiated NPCs to neurons in the developing cerebral cortex.

genomics↗

bioRxiv2024

Deep Learning Reaction Network: a machine learning framework for modeling time resolved data

Model-based analysis is essential for extracting information about chemical reaction kinetics in full detail from time-resolved data sets. Such analysis combines experimental hypotheses of the process with mathematical models related to the systems physical mechanisms. This combination can provide a concise description of complex system dynamics and extrapolate kinetic model parameters, such as kinetic pathways, time constants, and species amplitudes. However, the process leading to the final kinetic model requires several intermediate steps in which different assumptions and models are tested, even using different experimental data sets. This approach requires considerable experience in modeling and data comprehension, as poor decisions at any stage of time-resolved data analysis (such as time-resolved spectra and agarose gel electrophoresis) can lead to an incorrect or incomplete kinetic model, resulting in inaccurate model parameters and amplitudes. The Deep Learning Reaction Network (DLRN) can rapidly provide a kinetic reaction network, time constants, and amplitude for the system, with comparable performance and, in part, even better than a classical fitting analysis. Additionally, DLRN works in scenarios in which the initial state is a non-emitting dark state and for multiple timescales. The utility of DLRN is also shown for more than one 2D system, as it performed well for both spectral and time-resolved agarose gel electrophoresis data.

biophysics↗

bioRxiv2024

Direct RNA sequencing (RNA004) allows for improved transcriptome assessment and near real-time tracking of methylation for medical applications

Direct RNA sequencing (DRS) is a nanopore-based technique for analyzing RNA in its native form, promising breakthroughs in diagnostics and biomarker development. Coupled to RNA002 sequencing chemistry, its clinical implementation has been challenging due to low throughput, low accuracy, and lack of large-scale RNA-modification models. In this study, we evaluate the improvements achieved by pairing the latest RNA004 chemistry with novel modified-base-calling models for pseudouridine and N6-methyladenosine using diverse RNA samples from cell lines, synthetic oligos, and human blood. Finally, we present the first clinical application of DRS by confirming the loss of RNA methylation in a patient carrying truncating mutations in the methyltransferase METTL5. Conclusively, the combined use of RNA004 chemistry with the base-calling models significantly improved the throughput, accuracy, and site-specific detection of modifications. From this perspective, we offer an outlook on the potential suitability of DRS for use in routine diagnostics, the first comprehensive benchmark of human peripheral blood and quality assessments of RNA therapeutics.

bioinformatics↗

bioRxiv2024

ClearFinder: a Python GUI for annotating cells in cleared mouse brain

BackgroundTissue clearing combined with light-sheet microscopy is gaining popularity among neuroscientists interested in unbiased assessment of their samples in 3D volume. However, the analysis of such data remains a challenge. ClearMap and CellFinder are tools for analyzing neuronal activity maps in an intact volume of cleared mouse brains. However, these tools lack a user interface, restricting accessibility primarily to scientists proficient in advanced Python programming. The application presented here aims to bridge this gap and make data analysis accessible to a wider scientific community. ResultsWe developed an easy-to-adopt graphical user interface for cell quantification and group analysis of whole-cleared adult mouse brains. Fundamental statistical analysis, such as PCA and box plots, and additional visualization features allow for quick data evaluation and quality checks. Furthermore, we report significant differences in total cell counts between CellFinder and ClearMap when cross-analyzing the same samples, underscoring the need for optimizing reproducibility within the field. ConclusionsOur easily accessible tool allows more researchers to implement the methodology, troubleshoot arising issues, and develop quality checks, benchmarking, and standardized analysis pipelines for cell detection and region annotation in whole volumes of cleared brains.

bioinformatics↗

bioRxiv2024

Neuroimage Denoiser for removing noise from transient fluorescent signals in functional imaging.

We developed Neuroimage Denoiser, a novel U-Net-based model that effectively removes noise from microscopic recordings of transient local fluorescent signals. The model makes the denoising process independent of the recording frequency and the kinetics of the sensor used. The framework is easy to use for denoising and training and has minimal hardware requirements, thus, making it accessible for an average laboratory to create a custom version specific to their experimental setup. Neuroimage Denoiser significantly enhances the quality of functional microscopy recordings by effectively removing noise, thereby facilitating a more accurate and reliable analysis of neural activity. Highlights- Neuroimage Denoiser is a deep learning framework to remove noise from functional microscopic recordings, particularly trained and tested for glutamate imaging - Neuroimage Denoiser balances the removal of noise while preserving the amplitude of responses - Neuroimage Denoiser operates without re-training for different sensors (when the localization is similar) and recording frequencies MotivationAccurate measurements of neuronal activity through functional imaging are critical in understanding mechanisms of synaptic plasticity and learning concerning changes in the molecular composition of single synapses. Traditional denoising methods, such as Gaussian or Median filters, indiscriminately smooth entire recordings, reducing temporal and spatial resolutions considerably. Existing frameworks are not suited to remove noise from glutamate recordings due to the fast dynamics of the sensor. Therefore, a specialized tool for the challenges imposed by glutamate recordings, i.e. faster dynamics, and synaptic localization, is needed.

bioinformatics↗

bioRxiv2024

Direct CCUT: A Versatile and Standardized Framework to Train 3C Deep Restoration Models

Chromatin Capture Experiments such as Hi-C and Micro-C have become popular methods for genome architecture exploration. Recently, also a protocol for long read sequencing, Pore-C, was introduced, allowing the characterization of three-dimensional chromatin structures using Oxford Nanopore Sequencing Technology. Here, we present a framework that focuses on the efficient reconstruction of low-resolution Pore-C data but can also process all other 3C data, such as Hi-C and Micro-C matrices, using models that can be trained on a consumer GPU. Furthermore, we integrate building blocks of popular super-resolution methods such as SWIN-Transformer or residual-in-residual-blocks to modify or build customized networks on the fly. Pre-built models were trained and evaluated on multiple publicly available gold-standard Micro-C and Pore-C datasets, allowing for fine-scale structure prediction. Our work aims to overcome the drawback of high sequencing costs to construct high resolution contact matrices, as well as the problem of mapping low-coverage libraries to high-resolution structures in the genome. Although there have been major breakthroughs regarding NGS-based methods for the reconstruction of high-resolution chromatin interaction matrices from low-resolution data, for data obtained by long-read sequencing, there is currently no solution to reconstruct missing and sparse information and to improve the quality. AvailabilityThe tool is available at (https://github.com/stasys-hub/CCUT)

bioinformatics↗

bioRxiv2024

Cortexa - a comprehensive resource for studying gene expression and alternative splicing in the murine brain.

MotivationGene expression and alternative splicing are strictly regulated processes that shape brain development and determine the cellular identity of differentiated neural cell populations. Despite the availability of multiple valuable datasets, many functional implications, especially those related to alternative splicing, remain poorly understood. Moreover, neuroscientists working primarily experimentally often lack the bioinformatics expertise required to process alternative splicing data and produce meaningful and interpretable results. Notably, re-analyzing publicly available datasets and integrating them with in-house data can provide substantial novel insights. However, such analyses necessitate devel-oping harmonized data handling and processing pipelines which in turn requires considerable computational resources and in-depth bioinformatics expertise. ResultsHere, we present Cortexa - a comprehensive web-portal that incorporates RNA-sequencing datasets from the mouse cerebral cortex (longitudinal or cell-specific) and the hippocampus. Cortexa facilitates understandable visualization of the expression and alternative splicing patterns of individual genes. Our platform also provides SplicePCA - a tool that allows users to integrate their alternative splicing dataset and compare it to cell-specific or developmental neocortical splicing patterns. All gene expression and alternative splicing data have been processed in a standardized manner and they can also be downloaded for further in-depth down-stream analysis. AvailabilityThe data portal is available at https://cortexa-rna.com/ Contacthristo.todorov@uni-mainz.de.

genomics↗

bioRxiv2024

Real-time transcriptomic profiling in distinct experimental conditions

Nanopore technology offers real-time sequencing opportunities, providing rapid access to sequenced data and allowing researchers to manage the sequencing process efficiently, resulting in cost-effective strategies. Here, we present focused case studies demonstrating the versatility of real-time transcriptomics analysis in rapid quality control for long-read RNA-seq. We illustrate its utility through four experimental setups: 1) transcriptome profiling of distinct human cellular populations, 2) identification of experimentally enriched transcripts, 3) transcriptional analysis of cells under heat shock conditions and 4) identification of experimentally manipulated genes (knockout and overexpression) in several yeast strains. We show how to perform multiple layers of quality control as soon as sequencing has started, addressing both the quality of the experimental and sequencing traits. Real-time quality control measures assess sample/condition variability and determine the number of identified genes per sample/condition. Furthermore, real-time differential gene/transcript expression analysis can be conducted at various time points post-sequencing initiation (PSI), revealing dynamic changes in gene/transcript expression between two conditions. Using real-time analysis, which occurs in parallel to the sequencing run, we identified differentially expressed genes/transcripts as early as 1hr PSI. These changes were consistently observed throughout the entire sequencing process. We discuss the new possibilities offered by real-time data analysis, which have the potential to serve as a valuable tool for rapid and cost-effective quality checks in specific experimental settings and can be potentially integrated into clinical applications in the future.

bioinformatics↗

bioRxiv2023

Stage-specific expression patterns and co-targeting relationships among miRNAs in the developing mouse cerebral cortex

microRNAs are particularly important during brain development, however, the composition and temporal dynamics of miRNA regulatory networks are not sufficiently characterized. Here, we performed small RNA sequencing of mouse embryonic cortical samples at E14, E17, and P0 as well as in neural progenitor cells differentiated in vitro into neurons. Using co-expression network analysis, we detected clusters of miRNAs that were co-regulated at distinct developmental stages. miRNAs such as miR-92a/b acted as hubs during early, and miR-124 and miR-137 during late neurogenesis. Notably, validated targets of P0 hub miRNAs were enriched for down-regulated genes related to stem cell proliferation, negative regulation of neuronal differentiation and RNA splicing, among others, suggesting that miRNAs are particularly important for modulating transcriptional programs of crucial factors that guide the switch to neuronal differentiation. As most genes contain binding sites for more than one miRNA, we furthermore constructed a co-targeting network where numerous miRNAs shared more targets than expected by chance. Using luciferase reporter assays, we demonstrated that simultaneous binding of miRNA pairs to neurodevelopmentally relevant genes exerted an enhanced transcriptional silencing effect compared to single miRNAs. Taken together, our study provides a comprehensive resource of miRNA longitudinal expression changes during corticogenesis. Furthermore, we highlight several potential mechanisms through which miRNA regulatory networks can shape embryonic brain development.

developmental biology↗

bioRxiv2023

Freezing responses during prolonged threat memory retrieval reflect trait-like anxiety endophenotypes in female and male inbred mice

Trait anxiety is a major risk factor for stress-induced and anxiety disorders in humans. However, animal models accounting for the inter-individual variability in stress vulnerability are largely lacking. Moreover, the pervasive bias of using mostly male animals in preclinical studies poorly reflects the increased prevalence of psychiatric disorders in women. Using the threat imminence continuum theory, we designed and validated an auditory aversive conditioning-based pipeline in both female and male mice. We operationalized trait anxiety by harnessing the naturally occurring variability of defensive freezing responses combined with a model-based clustering strategy. While sustained freezing during prolonged retrieval sessions was identified as an anxiety-endophenotype biomarker in both sexes, females were consistently associated with an increased freezing response RNA-sequencing of CeA, BLA, ACC and BNST revealed massive differences in phasic and sustained responders transcriptomes, correlating with transcriptomic signatures of psychiatric disorders, particularly PTSD. Moreover, we detected significant alterations in the excitation/inhibition balance of principal neurons in the lateral amygdala. These findings provide compelling evidence that trait anxiety in inbred mice can be leveraged to develop translationally relevant preclinical models to investigate mechanisms of stress susceptibility in a sex-specific manner. We like to think we have constrained an organism to the task we have set. In practice, our paradigms are constrained by the way the organisms respond McNaughton & Corr, 2004

neuroscience↗

bioRxiv2023

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 Alzheimers disease (AD). One of the pathological hallmarks of this neurodegenerative disease is mitochondrial dysfunction, mainly induced by Amyloid {beta} (A{beta}). A{beta} 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 A{beta}-induced mitochondrial dysfunction.

neuroscience↗