Biology subjects

Lohmayer, R.

Publications and source records attributed to Lohmayer, R..

3 recordsLinked to original sources

bioRxiv2026

Multifaceted immune resistance landscapes in human oligodendrocytes protect against cytotoxic T cells and are dysregulated in MS brain cell subsets

Multiple sclerosis (MS) is a progressive neuroinflammatory demyelinating disease of the central nervous system (CNS) that remains incurable. Autoreactive myelin-specific T cells contribute to immunopathology by directly targeting and damaging oligodendrocytes in situ. In oligodendrocytes, several immune-modulatory functions have been described that can ameliorate immune damage. However, a systematic discovery of cell-intrinsic mechanisms that protect oligodendrocytes against T cell-derived cytotoxic mechanisms has not been performed. We used human MO3.13 oligodendrocytic cells and human antigen-specific cytotoxic T cells to conduct a high-throughput (HTP) RNAi-based screen with altogether 4155 genes to identify oligodendrocyte-intrinsic immune resistance genes (IRGs). The screen revealed 133 candidate IRGs. Among them, we validated 32, which exerted a strong immuno-protective phenotype. We studied IRG expression landscapes in human brain cell subsets from postmortem brain tissues of MS and control individuals. This revealed clustered expression of IRGs in a cell-type and oligodendrocyte subset-specific manner and differential IRG expression between MS patients and controls in distinct oligodendrocyte subclusters. ChEA3 analysis revealed cell type-specific expression of transcription factors that can drive expression of respective IRGs. Explorative molecular mode of action analyses of five selected IRGs, STK11, KCNH8, ABCA2, SLC1A3 and CHRNA1 revealed that these prevented death receptor-mediated apoptosis induced by T cell-derived cytotoxic molecules. In particular, they controlled TRAIL-induced apoptosis by suppressing JNK1 activation through interfering with several upstream pathways regulating metabolic, potassium, cholesterol, glutamate and acetylcholine homeostasis. In addition, STK11, ABCA2, and CHRNA1 regulated TRAIL-R2 surface expression contributing to increased TRAIL-sensitivity whereas KCNH8 expression in oligodendrocytes inhibited secretion of inflammatory cytokines by cytotoxic T cells. Taken together, we here demonstrate the existence of multiple co-expressed IRGs in human oligodendrocytes that regulate multifaceted mechanisms of T cell resistance and are dysregulated in oligodendrocyte subsets of MS patients.

immunology↗

bioRxiv2024

GGTyper: genotyping complex structural variants using short-read sequencing data

MotivationComplex structural variants are genomic rearrangements that involve multiple segments of DNA. They contribute to human diversity and have been shown to cause Mendelian disease. Nevertheless, our abilities to analyse complex structural variants are very limited. As opposed to deletions and other canonical types of structural variants (SVs), there are no established tools that have explicitly been designed for analysing complex SVs. ResultsHere, we describe a new computational approach that we specifically designed for genotyping complex SVs in short-read sequenced genomes. Given a variant description, our approach computes genotype-specific probability distributions for observing aligned read pairs with a wide range of properties. Subsequently, these distributions can be used to efficiently determine the most likely genotype for any set of aligned read pairs observed in a sequenced genome. In addition, we use these distributions to compute a genotyping difficulty for a given variant, which predicts the amount of data needed to achieve a reliable call. Careful evaluation confirms that our approach outperforms other genotypers by making reliable genotype predictions across both simulated and real data. On up to 7829 human genomes, we achieve high concordance with population-genetic assumptions and expected inheritance patterns. On simulated data, we show that precision correlates well with our prediction of genotyping difficulty. This together with low memory and time requirements makes our approach well-suited for application in biomedical studies involving small to very large numbers of short-read sequenced genomes. AvailabilitySource code is available at https://github.com/kehrlab/Complex-SV-Genotyping. Contactbirte.kehr@klinik.uni-regensburg.de

bioinformatics↗

bioRxiv2022

SpaCeNet: Spatial Cellular Networks from omics data

Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. RNAs or proteins measured in individual cells together with the cells spatial distribution provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. "SpaCeNet" is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). This is achieved by estimating conditional independence relations between captured variables within individual cells and by disentangling these from conditional independence relations between variables of different cells. A python implementation of SpaCeNet is publicly available at https://github.com/sschrod/SpaCeNet.

bioinformatics↗