Kristina Friedland

Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder, highlighting the need to identify novel molecular regulators for effective treatment development. Angiogenin (ANG), a stress-responsive ribonuclease that inhibits apoptosis by generating 5′-tRNA fragments, is a candidate whose expression and regulation in AD is not understood. Here, we investigated ANG expression and regulation using AD cell and animal models, postmortem human brain tissue, and transcriptomic datasets ( n = 645). We found that ANG is dysregulated in AD in a sex-dependent manner, altering downstream levels of 5′-tiRNA ^Gly-GCC . Our analysis revealed female-specific molecular subtypes, absent in males: Subtype 1 featured low ANG levels with increased inflammation and neuronal death; subtype 2 exhibited higher ANG expression and intermediate pathology; subtype 3, marked by the highest ANG levels, showed reduced inflammation, slower cognitive decline, and extended survival. These findings position ANG as a key modulator of neuroinflammation and apoptosis in AD, highlighting its potential as a treatment strategy.

TRNA modifications are critical regulators of RNA stability, decoding fidelity, and cellular stress adaptation, yet their contribution to human neurodegenerative disease remains poorly understood. Beyond their established functions in translational control, emerging evidence shows that RNA modifications influence neurogenesis, neurodevelopment, neuronal function, brain-cell differentiation, and cellular plasticity. Consequently, dysregulation of these molecular processes is increasingly recognized as a mechanistic contributor to neurodegenerative disorders. Alzheimer’s disease (AD), characterized by amyloid pathology, synaptic dysfunction, and progressive neuronal loss, has recently been linked to disturbances in RNA metabolism, suggesting that alterations in the epitranscriptome may represent an underexplored dimension of AD pathophysiology. Here, we systematically profiled the tRNA epitranscriptome across cellular and animal models of AD, as well as in human postmortem brain tissue from non-demented controls and AD patients, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). This method enables highly sensitive quantification of RNA modifications, with limits of detection in the low femtomole range. Across our models, we identified a conserved yet sex-specific remodeling of the tRNA modification landscape in AD. Because therapeutic options and early diagnostic tools for AD remain limited, we leveraged these findings to develop a tRNA-centered RNA-modification score that integrates both nucleobase-specific modification patterns and neuropathological disease severity into a quantitative metric. Together, our findings identify the tRNA epitranscriptome as a unifying molecular sex-specific signature of AD, linking disease pathology and sex to impaired RNA metabolism. This line of research opens a new path toward establishing early biomarkers or diagnostic tools for AD. Graphical

TRNA-derived fragments have emerged as critical regulators in various biological processes, but reliable methods for their quantification remain a challenge due to their small size and extensive RNA modifications. In this study, we present the newly developed Complementary DNA Oligonucleotide Direct In-Gel Quantification (cDINGQ) method for tRF analysis and compare it with traditional radioactive [ ³² P] Northern blotting, non-radioactive approaches, and high-throughput Illumina sequencing under different experimental conditions. The cDINGQ method, utilizing Cy5-labeled hybridization probes, offers high specificity and sensitivity for detecting tRFs with significantly reduced processing time and costs. By applying these techniques to an Alzheimer’s disease (AD) cell model, we demonstrate the reliability of these methods in detecting subtle variations in tRF abundance. Our findings highlight the sensitivity, specificity, and applicability of each method, addressing limitations such as RNA input requirements and probe hybridization conditions. The study further explores the utility of these methods for detecting tRFs in various biological contexts, emphasizing their potential for future research and biomarker discovery in disease-related studies.

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 and an Epi2ME pipeline 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 the epitranscriptome and RNA modification.

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.

The human RNA methyltransferase DNMT2 is thought to be involved in various pathophysiological processes, yet, a major challenge in drug targeting DNMT2 is given by the fact that current SAH-derived inhibitors have poor target selectivity and limited cellular permeability. In this study, we have performed a DNA-encoded library (DEL) screening on DNMT2 yielding five non-SAH-like hit structures, three of which feature a peptidomimetic scaffold. All DEL hits could be validated by orthogonal biophysical and biochemical assays for DNMT2 binding. At the same time, the lead structure did not interact with related methyltransferases from the DNMT and NSUN families highlighting an unmatched DNMT2-targeting selectivity profile. Subsequent crystallographic studies revealed the unique ligand binding mode including an active site loop rearrangement and the formation of a cryptic allosteric binding pocket able to modulate the enzymatic activity by non-covalent DNMT2 dimerization. Based on the crystallographic results, we performed a structure-activity relationship study around the inhibitor lead structure resulting in an optimized DNMT2 inhibitor ( K _D =3.04 µM), which was able to reduce m ⁵ C levels in MOLM-13 tRNA. GRAPHICAL

Methodological developments in biomedical research are currently moving towards single-cell approaches. This allows for a much better spatial and functional characterization of, for example, the deterioration of cells within a tissue in response to noxae. However, subcellular resolution is also essential to elucidate whether observed impairments are driven by an explicit organelle. Here, we use the Single Cellome™ System SS2000 (Yokogawa) to investigate the local effects of Aβ plaque-like deposits (characteristic for Alzheimer’s disease) on mitochondria in the mouse microglial cell line SIM-A9. First, the specificity of subcellular extraction is demonstrated by detecting subcellular staining and RT-qPCR concerning marker genes by comparing nuclear and mitochondrial samples. Oxygen consumption and gene expression is then assessed in cells near and far from peptide deposits. Mostly, all analyses confirm the high specificity and integrity of the sampled material. In addition, impact of the peptide deposits occur concerning spatial distribution of the cells: e.g., oxygen consumption is only reduced in cells close to Aβ deposits but not in proximity to deposits of biologically inactive Aβ (scrambled) or in far distance. Moreover, a distance-related gene expression pattern occurs, demonstrating the local initiation of mitochondrial changes of microglia when approaching toxic peptide deposits.

The synaptic vesicle glycoprotein 2A (SV2A) is a transmembrane protein of synaptic vesicles. It is involved in key functions of neurons, focused on the regulation of neurotransmitter release. Here we report three novel findings suggesting a completely new role of SV2A. First, we demonstrate that SV2A is localized at the outer mitochondrial membrane (OMM) using confocal and super-resolution microscopy. Second, Inactivation of SV2A in our cell and animal models leads to fragmented mitochondria. In addition, SV2A also affects the basal autophagic flux as well as mitophagy. Third, using proteomics analysis we demonstrate that SV2A interacts with the fission factor DRP1 and the autophagy factor ATG9A. Using AlphaFold3 we provide a first glimpse of the molecular interaction between DRP1 and SV2A. Our findings demonstrate that SV2A is not only a vesicular protein but also a mitochondrial protein in the OMM with defined functions regulating mitochondrial morphology and autophagy.

Due to its high modification content tRNAs are notoriously hard to quantify by reverse transcription and RNAseq. Bypassing numerous biases resulting from concatenation of enzymatic treatments, we here report a hybrid approach that harnesses the advantages of hybridization-based and deep sequencing–based approaches. The method renders obsolete any RNAseq related workarounds and correction factors that affect accuracy, sensitivity, and turnaround time. Rather than by reverse transcription, quantitative information on the isoacceptor composition of a tRNA pool is transferred to a cDNA mixture in a single step procedure, thereby omitting all enzymatic conversations except for the subsequent barcoding PCR. As a result, a detailed tRNA composition matrix can be obtained from femtomolar amounts of total tRNA. The method is fast, low in cost, and its bioinformatic data workup surprisingly simple. These properties make the approach amenable to high-throughput investigations including clinical samples, as we have demonstrated by application to a collection of variegated biological questions, each answered with novel findings. These include tRNA pool quantification of polysome-bound tRNA, of tRNA modification knockout strains under stress conditions, and of Alzheimer patients’ brain tissues.