Biology subjects

Günther, S.

Publications and source records attributed to Günther, S..

8 recordsLinked to original sources

bioRxiv2026

Single-Cell Mapping of tRNA Expression Dynamics Across Human Hematopoiesis

How transfer RNA (tRNA) expression changes as hematopoietic stem cells are specified into human blood cell types remains unknown. tRNAs translate the 64-codon genetic code into the 21-amino acid protein code, and their relative abundance can influence proteomic output. Here, we introduce simultaneous single-cell tRNA and mRNA sequencing (sc-STM-seq), a scalable, high-throughput approach for profiling tRNAs alongside mRNA transcriptomes in individual cells. Applying sc-STM-seq to human bone marrow, we map tRNA expression and splicing across hematopoietic differentiation trajectories and identify tRNAs associated with stemness, differentiation, and specific cell lineages. Our study provides an atlas of the hematopoietic tRNA landscape and establishes tRNA expression as a previously underappreciated layer of cellular heterogeneity during human blood cell development.

cell biology↗

bioRxiv2026

Emergence of mpox in Guinea: sporadic clade IIa cases and a clade IIb upsurge

BackgroundMpox virus (MPXV) has caused recurrent outbreaks in West Africa. However, Guinea had not previously reported laboratory-confirmed cases or MPXV genomic data. MethodsSuspected cases were identified in the NZerekore region as well as in the Conakry (Sept 2024- Dec 2025) and confirmed by real-time PCR in regional and central laboratories, respectively. Whole-genome sequencing using nanopore technology was performed in-country, followed by phylogenetic and time-scaled evolutionary analyses. FindingsThe first mpox case was clinically diagnosed in September 2024 in the NZerekore region and laboratory-confirmed by the prefectural laboratory in Gueckedou. In total, seven cases were confirmed in Forest Guinea, of which five complete or almost complete MPXV genomes were recovered. All belonged to MPXV clade IIa. Genomic divergence, ancestral dating, and low APOBEC3-associated mutation frequencies were consistent with multiple independent zoonotic spillover events. In June 2025, one of the first mpox cases of an unfolding outbreak was confirmed in Conakry. Whole genome sequencing revealed MPXV clade IIb lineage G.1. By December 2025, the number of laboratory-confirmed mpox cases nationwide increased to 2,151. A total of nine outbreak strains were sequenced, all belonging to Clade IIb. The genomes clustered with contemporaneous genomes from Sierra Leone and showed high APOBEC3-associated mutation frequencies, suggesting sustained human-to-human transmission in the region. InterpretationThese data demonstrate simultaneous circulation of MPXV clade IIa and IIb strains in Guinea, likely resulting from zoonotic spillover and human-to-human transmission, respectively. Decentralised diagnostics and in-country sequencing facilitated rapid case confirmation and genomic surveillance, highlighting the importance of these critical capacities for outbreak preparedness and response. FundingGerman Federal Ministry of Health and the German Center for Infection Research (DZIF).

genomics↗

bioRxiv2024

Long non-coding RNAs direct the SWI/SNF complex to cell-specific enhancers

The coordination of chromatin remodeling is essential for DNA accessibility and gene expression control1. The highly conserved and ubiquitously expressed SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex plays a central role in cell type- and context-dependent gene expression2. Despite the absence of a defined DNA recognition motif, SWI/SNF binds lineage specific enhancers genome-wide where it actively maintains open chromatin state2-5. It does so while retaining the ability to respond dynamically to cellular signals4. However, the mechanisms that guide SWI/SNF to specific genomic targets have remained elusive. Here we demonstrate that trans-acting long non-coding RNAs (lncRNAs) direct the SWI/SNF complex to cell type-specific enhancers. SWI/SNF preferentially binds lncRNAs and these predominantly bind DNA targets in trans. Together they localize to enhancers, many of which are cell type-specific. Knockdown of SWI/SNF- and enhancer-bound lncRNAs causes the genome-wide redistribution of SWI/SNF away from enhancers and a concomitant differential expression of spatially connected target genes. These lncRNA-SWI/SNF-enhancer networks support an enhancer hub model of SWI/SNF genomic targeting. Our findings reveal a competitive recruitment of SWI/SNF by lncRNAs which provide a specific and dynamic layer of control in chromatin accessibility and gene expression.

molecular biology↗

bioRxiv2022

Aging-regulated TUG1 is dispensable for endothelial cell function

The evolutionary conserved Taurine Upregulated Gene 1 (TUG1) is a ubiquitously expressed gene that is one of the highest expressed genes in human and rodent endothelial cells (ECs). We here show that TUG1 expression decreases significantly in aging mouse carotid artery ECs and human ECs in vitro, indicating a potential role in the aging endothelial vasculature system. We therefore investigated if, and how, TUG1 might function in aging ECs, but despite extensive phenotyping found no alterations in basal EC proliferation, apoptosis, barrier function, migration, mitochondrial function, or monocyte adhesion upon TUG1 silencing in vitro. TUG1 knockdown did slightly and significantly decrease cumulative sprout length upon vascular endothelial growth factor A stimulation in human umbilical vein endothelial cells (HUVECs), though TUG1-silenced HUVECs displayed no transcriptome-wide mRNA expression changes explaining this effect. Further, ectopic expression of the highly conserved and recently discovered 153 amino acid protein translated from certain TUG1 transcript isoforms did not alter angiogenic sprouting in vitro. Our data show that, despite a high expression and strong evolutionary conservation of both the TUG1 locus and the protein sequence it encodes, TUG1 does not seem to play a major role in basic endothelial cell function.

cell biology↗

bioRxiv2021

Towards the prediction of non-peptidic epitopes

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69-0.96 depending on the molecule cluster.

bioinformatics↗

bioRxiv2021

Molecular basis of selective cytokine signaling inhibition by antibodies targeting a shared receptor

Interleukin-1 (IL-1) family cytokines are potent mediators of inflammation, acting to coordinate local and systemic immune responses to a wide range of stimuli. Aberrant signaling by IL-1 family cytokine members, however, is linked to myriad inflammatory syndromes, autoimmune conditions and cancers. As such, blocking the inflammatory signals inherent to IL-1 family signaling is an established and expanding therapeutic strategy. While several FDA-approved IL-1 inhibitors exists, including an Fc fusion protein, a neutralizing antibody, and an antagonist cytokine, none specifically targets the co-receptor IL-1 receptor accessory protein (IL-1RAcP). Most IL-1 family cytokines form productive signaling complexes by binding first to their cognate receptors - IL-1RI for IL-1 and IL-1{beta}; ST2 for IL-33; and IL-36R for IL-36, IL-36{beta} and IL-36{gamma} - after which they recruit the shared secondary receptor IL-1RAcP to form a ternary cytokine/receptor/co-receptor complex. Recently, IL-1RAcP was identified as a biomarker for both AML and CML. IL-1RAcP has also been implicated in tumor progression in solid tumors and an anti-IL1RAP antibody (nadunolimab, CAN04) is in phase II clinical studies in pancreatic cancer and non-small cell lung cancer (NCT03267316). As IL-1RAcP is common to all of the abovementioned IL-1 family cytokines, targeting this co-receptor raises the possibility of selective signaling inhibition for different IL-1 family cytokines. Indeed, previous studies of IL-1{beta} and IL-33 signaling complexes have revealed that these cytokines employ distinct mechanisms of IL-1RAcP recruitment even though their overall cytokine/receptor/co-receptor complexes are structurally similar. Here, using functional, biophysical, and structural analyses, we show that antibodies specific for IL-1RAcP can differentially block signaling by IL-1 family cytokines depending on the distinct IL-1RAcP epitopes that they engage. Our results indicate that targeting a shared cytokine receptor is a viable therapeutic strategy for selective cytokine signaling inhibition.

immunology↗

bioRxiv2020

Catalytic cleavage of HEAT and subsequent covalent binding of the tetralone moiety by the SARS-CoV-2 main protease

Here we present the crystal structure of SARS-CoV-2 main protease (Mpro) covalently bound to 2-methyl-1-tetralone. This complex was obtained by co-crystallization of Mpro with HEAT (2-(((4-hydroxyphenethyl)amino)methyl)-3,4-dihydronaphthalen-1(2H)-one) in the framework of a large X-ray crystallographic screening project of Mpro against a drug repurposing library, consisting of 5632 approved drugs or compounds in clinical phase trials. Further investigations showed that HEAT is cleaved by Mpro in an E1cB-like reaction mechanism into 2-methylene-1-tetralone and tyramine. The catalytic Cys145 subsequently binds covalently in a Michael addition to the methylene carbon atom of 2-methylene-1-tetralone. According to this postulated model HEAT is acting in a pro-drug-like fashion. It is metabolized by Mpro, followed by covalent binding of one metabolite to the active site. The structure of the covalent adduct elucidated in this study opens up a new path for developing non-peptidic inhibitors.

biophysics↗

bioRxiv2019

Automated recognition of functional compound-protein relationships in literature

MotivationMuch effort has been invested in the identification of protein-protein interactions using text mining and machine learning methods. The extraction of functional relationships between chemical compounds and proteins from literature has received much less attention, and no ready-to-use open-source software is so far available for this task.\n\nMethodWe created a new benchmark dataset of 2,753 sentences from abstracts containing annotations of proteins, small molecules, and their relationships. Two kernel methods were applied to classify these relationships as functional or non-functional, named shallow linguistic and all-paths graph kernel. Furthermore, the benefit of interaction verbs in sentences was evaluated.\n\nResultsThe cross-validation of the all-paths graph kernel (AUC value: 84.2%, F1 score: 81.8%) shows slightly better results than the shallow linguistic kernel (AUC value: 81.6%, F1 score: 79.7%) on our benchmark dataset. Both models achieve state-of-the-art performance in the research area of relation extraction. Furthermore, the combination of shallow linguistic and all-paths graph kernel could further increase the overall performance. We used each of the two kernels to identify functional relationships in all PubMed abstracts (28 million) and provide the results, including recorded processing time.\n\nAvailabilityThe software for the tested kernels, the benchmark, the processed 28 million PubMed abstracts, all evaluation scripts, as well as the scripts for processing the complete PubMed database are freely available at https://github.com/KerstenDoering/CPI-Pipeline.\n\nAuthor summaryText mining aims at organizing large sets of unstructured text data to provide efficient information extraction. Particularly in the area of drug discovery, the knowledge about small molecules and their interactions with proteins is of crucial importance to understand the drug effects on cells, tissues, and organisms. This data is normally hidden in written articles, which are published in journals with a focus on life sciences. In this publication, we show how text mining methods can be used to extract data about functional interactions between small molecules and proteins from texts. We created a new dataset with annotated sentences of scientific abstracts for the purpose of training two diverse machine learning methods (kernels), and successfully classified compound-protein pairs as functional and non-functional relations, i.e. no interactions. Our newly developed benchmark dataset and the pipeline for information extraction are freely available for download. Furthermore, we show that the software can be easily up-scaled to process large datasets by applying the approach to 28 million abstracts.

bioinformatics↗