Biology subjects

Bleckmann, A.

Publications and source records attributed to Bleckmann, A..

7 recordsLinked to original sources

bioRxiv2026

A GreenGate-compatible vector set for efficient protein purification from E. coli

Recombinant protein purification from E. coli frequently requires screening various affinity tags and variations to optimize yield and purity. However, classical cloning methods are limited in throughput and modularity, which is circumvented by GoldenGate cloning. While GreenGate cloning, a GoldenGate variant, is widely used in plant research, it lacks compatibility with E. coli expression vectors. Here, we introduce a comprehensive, GreenGate-compatible vector toolkit for efficient and versatile assembly of three modules, for N- and C-terminal tagging of a protein of interest into an IPTG-inducible E. coli expression vector. To allow versatility, this toolkit contains diverse affinity tags, with or without HRV3C protease cleavage sites. Moreover, we included plasmids for the homemade low-cost production of this protease. This GreenGate-compatible toolkit allows efficient combinations of different tags and eliminates the need for re-cloning modules between plant and bacterial systems, streamlining the workflow for recombinant protein production, especially in plant research.

Plant Biology↗

bioRxiv2026

Metabolic specialization structures gut bacterial niches and drives colorectal cancer progression

Despite the established association between the gut microbiome and colorectal cancer (CRC), the functional distinction between microbial passengers and drivers of CRC progression remains unresolved. Here, we collected stool, blood, as well as paired tumor, and normal mucosa tissues from seventy-seven CRC patients to characterize the systemic and localized impact of the gut microbiome on early- and late-stage CRC. By deep shotgun metagenomic sequencing, we identified distinct bacterial species and functions residing in tumor versus normal mucosa, highlighting an enrichment of oral-associated bacteria in tumor tissues. Several of these species remained undetected in the stool microbiome analysis. We further combined bacterial culturing with untargeted metabolomics of bacteria enriched in tumor and normal mucosa tissues, revealing distinct clusters of metabolic potential. Functional testing of multiple members from one cluster comprising both tumor- and mucosa-enriched species revealed Leptotrichia wadei as a pro-tumorigenic bacterium in a murine CRC model. Single-nucleus RNA sequencing and in vitro experiments further demonstrated that L. wadei and its secretome induces M2 macrophage polarization to promote tumor growth. Overall, our study shows that metabolic specialization structures microbial colonization niches, while species-specific metabolic outputs identify functional drivers of CRC progression, and uncovers L. wadei as an oncogenic bacterium in CRC.

microbiology↗

bioRxiv2024

Deciphering BRCAness Phenotype in Cancer: A Graph Convolutional Neural Network Approach with Layer-wise Relevance Propagation Analysis

BackgroundCancer variability among patients underscores the need for personalized therapy based on genomic understanding. BRCAness, characterized by vulnerabilities similar to BRCA mutations, particularly in homologous recombination repair, shows potential sensitivity to DNA-damaging agents like PARP inhibitors, highlighting its clinical significance. MethodsWe employed Graph Convolutional Neural Networks (GCNNs) with Layer-wise Relevance Propagation (LRP) to analyze gene expression data from the TCGA Pan-Cancer dataset. The study compared the efficacy of GCNNs against traditional machine learning models and differential gene expression analysis, focusing on their ability to elucidate complex genomic interactions defining BRCAness. ResultsDifferential Gene Expression (DGE) analysis proved limited in capturing the nuances of BRCAness. In contrast, GLRP significantly identified genes related to transcription regulation and cancer processes, emphasizing the phenotypes complexity. Gene Set Enrichment Analysis (GSEA) highlighted crucial pathways like Nuclear Receptors signaling, Cellular Senescence, and ESR-mediated signaling, underscoring their roles in BRCAness and therapeutic potential. ConclusionGLRP outperformed traditional approaches in analyzing BRCAness, providing deep insights into transcriptional and oncogenic processes critical to the BRCAness phenotype. Our findings suggest new directions for developing targeted and personalized cancer treatments, leveraging intricate molecular interactions associated with BRCAness.

bioinformatics↗

bioRxiv2021

The human RNA polymerase I structure reveals an HMG-like transcription factor docking domain specific to metazoans

Transcription of the ribosomal RNA precursor by RNA polymerase (Pol) I is a major determinant of cellular growth and dysregulation is observed in many cancer types. Here, we present the purification of human Pol I from cells carrying a genomic GFP-fusion on the largest subunit allowing the structural and functional analysis of the enzyme across species. In contrast to yeast, human Pol I carries a single-subunit stalk and in vitro transcription indicates a reduced proofreading activity. Determination of the human Pol I cryo-EM reconstruction in a close-to-native state rationalizes the effects of disease-associated mutations and uncovers an additional domain that is built into the sequence of Pol I subunit RPA1. This dock II domain resembles a truncated HMG-box incapable of DNA-binding which may serve as a downstream-transcription factor binding platform in metazoans. Biochemical analysis and ChIP data indicate that Topoisomerase 2a can be recruited to Pol I via the domain and cooperates with the HMG-box domain containing factor UBF. These adaptations of the metazoan Pol I transcription system may allow efficient release of positive DNA supercoils accumulating downstream of the transcription bubble.

biochemistry↗

bioRxiv2020

WNT11 is a novel ligand for ROR2 in human breast cancer

Breast cancer has been associated with activation of the WNT signaling pathway, although the underlying molecular mechanisms are still unclear. Here, we found the WNT receptor ROR2 to be highly expressed in aggressive breast tumors and associated with worse metastasis-free survival. In order to understand the molecular basis of these observations, we overexpressed ROR2 in human breast cancer cell lines, inducing a BRCAness-like phenotype and rendering them resistant to PARP inhibition. High levels of ROR2 were associated with defects in cell morphology and cell-cell-contacts leading to increased tumor invasiveness. Using gene expression analysis we demonstrated an upregulation of several non-canonical WNT ligands in ROR2-overexpressing breast cancer cells, in particular WNT11. Co-immunoprecipitation confirmed that WNT11 is indeed a novel ligand for ROR2 that interacts with its cysteine-rich domain and triggers the invasion-promoting signaling via RHO/ROCK. Knockdown of WNT11 reversed the pro-invasive phenotype and the cellular changes in ROR2-overexpressing cells. Taken together, our studies revealed a novel auto-stimulatory loop in which ROR2 triggers the expression of its own ligand, WNT11, resulting in enhanced tumor invasion associated with breast cancer metastasis.

cancer biology↗

bioRxiv2020

Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

MotivationContemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g. distant metastasis in cancer, for each individual patient. ResultsWe extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset, and then applied the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. As a result this method could be potentially highly useful on interpreting classification results on the individual patient level, as for example in precision medicine approaches or a molecular tumor board. Availabilityhttps://gitlab.gwdg.de/UKEBpublic/graph-lrp https://frankkramer-lab.github.io/MetaRelSubNetVis/ Contacttim.beissbarth@bioinf.med.uni-goettingen.de

bioinformatics↗

bioRxiv2019

Elucidating small RNA pathways in Arabidopsis thaliana egg cells

O_LISmall RNA pathway components and small RNA profiles of flowering plant egg cells are largely unexplored, mainly because they are not easily accessible but deeply buried inside the ovary.\nC_LIO_LIWe describe here the utilization of proliferating callus tissue that adopted transcriptome features of Arabidopsis egg cell as a tool to explore small RNA pathway components and small RNA profiles in egg cells. We furthermore complement our studies with mRNA-Seq data from isolated Arabidopsis egg cells and provide data validation by promoter-reporter studies and whole mount in situ hybridization.\nC_LIO_LISequencing of small RNA libraries demonstrate the predominance of TE-derived siRNAs in the egg cell-related callus. TE-features and expression profiles suggest post-transcriptional silencing of activated Gypsy-like LTR retrotransposons, whereas the majority of class II DNA transposons belonging to Copia, CACTA, hAT-like and Mutator superfamilies are subjected to transcriptional silencing.\nC_LIO_LISmall RNA-seq furthermore led to the identification of differentially expressed known and novel miRNAs whose expression in the egg cell was verified by small RNA whole mount in situ hybridization. Both the strong expression of miRNAs in the egg-cell-adjoining synergids and the secretion of miRNAs into the micropyle suggest hitherto undescribed roles for these accessory cells in intercellular communication with the egg cell and the arriving pollen tube.\nC_LIO_LIIn conclusion, our datasets provide valuable and comprehensive resources to study small RNA pathways and small-RNA-mediated epigenetic reprogramming during egg cell differentiation and the onset of plant embryogenesis.\nC_LI

plant biology↗