van Roon-Mom, W. M. C. | Search bioRxiv

bioRxiv2025

Determining off-target effects of splice-switching antisense oligonucleotides using short read RNAseq in neuronally differentiated human induced pluripotent stem cells

Antisense oligonucleotides (AONs) are small pieces of chemically modified DNA or RNA that bind to RNA in a sequence-specific manner based on Watson-Crick base-pairing. Splice-switching AONs are designed to modulate pre-mRNA splicing, thereby for instance restoring protein expression or modifying the eventual protein to restore its function or reduce its toxicity. Given the current lack of in silico methods that adequately predict off-target splicing events, assessment of off-target effects of AONs in human cells using RNAseq could be a promising approach. The identification and prioritization of potential off-target effects for validation and further investigation into the biological relevance would contribute to the development of safe and effective AONs. In this study, we used three different splice-switching AONs targeting three different human transcripts to study their transcriptome-wide, hybridization-dependent off-target effects with short read RNAseq. Using the computational tools rMATS and Whippet, we identified differential splicing events of which only a minority could be explained by hybridization, illustrating the difficulty of predicting off-target effects based on sequence homology. The main splicing events could all be validated with RT-PCR. Furthermore, from the three AONs studied, one AON induced considerably more changes in gene expression and splicing compared to the two other AONs assessed, which was confirmed in a validation experiment. Our study demonstrates that the interpretation of short read RNAseq data to determine off-target effects is challenging. Nonetheless, we showed that valuable results can be obtained as it allows the comparison of toxicity between different AONs within an experiment and identification of AON-specific off-target profiles.

genetics↗

bioRxiv2018

Peripheral mitochondrial function correlates with clinical severity in idiopathic Parkinson’s disease

BackgroundParkinson disease is an intractable disorder with heterogeneous clinical presentation that may reflect different underlying pathogenic mechanisms. Surrogate indicators of pathogenic processes correlating with clinical measures may assist in better patients stratification. Mitochondrial function - which is impaired in and central to PD pathogenesis - may represent one of such surrogate indicators.\n\nMethodsMitochondrial function was assessed by respirometry experiment in fibroblasts derived from idiopathic patients (n=47) in normal conditions and in experimental settings that do not permit glycolysis and therefore force energy production through mitochondrial function. Respiratory parameters and clinical measures were correlated with bivariate analysis. Machine learning based classification and regression trees were used to classify patients on the basis of biochemical and clinical measures. Effects of mitochondrial respiration on alpha-synuclein stress was assessed monitoring the protein phosphorylation in permitting versus restrictive glycolysis conditions.\n\nResultsBioenergetics properties in peripheral fibroblasts correlate with clinical measures in idiopathic patients and correlation is stronger with predominantly non-dopaminergic signs. Bioenergetics analysis under metabolic stress, in which energy is produced solely by mitochondria, shows that patients fibroblasts can augment respiration, therefore indicating that mitochondrial defects are reversible. Forcing energy production through mitochondria, however, favors alpha-synuclein stress in different cellular experimental systems. Machine learning-based classification identified different groups of patients in which increasing disease severity parallels higher mitochondrial respiration.\n\nConclusionSuppression of mitochondrial activity in Parkinson disease may be an adaptive strategy to cope with concomitant pathogenic factors. Moreover, mitochondrial measures are potential biomarkers to follow disease progression.

neuroscience↗