Biology subjects

Hartung, F.

Publications and source records attributed to Hartung, F..

3 recordsLinked to original sources

bioRxiv2026

Analysis of SpCas9 on- and off-target effects in high efficiency multiplex editing in Arabidopsis

RNA-guided nucleases (RGNs), such as Cas9 from Streptococcus pyogenes (SpCas9), are widely used for plant genome editing. Previous surveys for off-targeting, the modification of unintended targets with similarity to the intended target, indicate high specificity of SpCas9 in plant cells. However, off-targeting has not been assessed for efficiency-optimized editing systems combined with extensive multiplexing, which might increase the likelihood of cleavage at unintended sites. We therefore analyzed Arabidopsis thaliana lines that had been extensively mutagenized using zCas9i and up to 29 gRNAs addressing >45 target sites over several rounds of editing. Genomes were sequenced by short- and long-read technologies, and genome-wide variants were catalogued. Our pipeline for variant calling reliably detected RGN-induced mutations at on-targets. When excluding these on-target modifications, variants were detected in edited lines at frequencies similar to those previously reported for spontaneous mutations. In further analyses, we did not find any evidence for an origin of these variants from RGN activity. Our data are thus consistent with high specificity of SpCas9. In contrast, we detected genomic reorganization events upon editing at two complex loci, RPP1 and RPP7, encompassing multiple homologous genes, and also identified an allele by WGS that had escaped detection by amplicon sequencing. We conclude that, while off-targets may efficiently be avoided by selection of specific gRNAs, on-target modifications may be more extensive than intended, especially at complex loci and/or during multiplexing.

plant biology↗

bioRxiv2020

Amount of fiction reading correlates with higher connectivity between cortical areas for language and mentalizing

Behavioral evidence suggests that engaging with fiction is positively correlated with social abilities. The rationale behind this link is that engaging with fiction and fictional characters may offer a training mode for mentalizing and empathizing with sentient agents in the real world, analogous to a flight simulator for pilots. In this study, we explored the relationship between reading fiction and mentalizing by looking at brain network dynamics in 57 participants who varied on how much fiction they read in their daily lives. The hypothesis was that if reading fiction indeed trains mentalizing, a task that requires mentalizing -Like immersing in a fictional story and engaging with a protagonist-should elicit differences in brain network dynamics depending on how much people read. More specifically, more frequent readers should show increased connectivity within the theory of mind network (ToM) or between the ToM network and other brain networks. While brain activation was measured with fMRI, participants listened to two literary narratives. We computed time-course correlations between brain regions and compared the correlation values from listening to narratives to listening to an auditory baseline condition. The between-region correlations were then related to individual differences measures including the amount of fiction that participants consume in their daily lives. Our results show that there is a linear relationship between how much people read and the functional connectivity in areas known to be involved in language and mentalizing. This adds neurobiological credibility to the fiction influences mentalizing abilities hypothesis as suggested on the basis of conceptual analysis.

scientific communication and education↗

bioRxiv2017

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi

MotivationGenome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction.\n\nResultsHere, we present an extension of the gene prediction tool GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions.\n\nAvailabilityGeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/GeMoMa.\n\nContactjens.keilwagen@julius-kuehn.de

bioinformatics↗