Biology subjects

Kranz, J.

Publications and source records attributed to Kranz, J..

6 recordsLinked to original sources

bioRxiv2026

Polyploid cardiomyocytes define disease-specific transcriptional states in the mammalian heart

The adult mammalian heart has a limited regenerative capacity. Following injury, cardiomyocytes undergo a hypertrophic response accompanied by polyploidization, which has been described as a barrier to proliferation and regeneration of the heart1,2. However, the unique molecular programs of polyploidy, or genome multiplied cardiomyocytes, and their influence on the disease-related myocardial remodelling process remains unclear. Here, we integrate single-nuclei and high-resolution spatial multi-omics across human, rat, and mouse hearts to define novel cardiac cell states and their tissue niches in ischemic and non-ischemic heart disease. Computational analysis across scales allowed us to generate detailed networks of the cardiac tissue remodelling process as well as tissue and sub-cellular environments uniquely enriched in polyploid cardiomyocytes or their diploid origins. We identify a conserved, dichotomous transcriptional program distinguishing diploid from polyploid cardiomyocytes. Polyploid cardiomyocytes demonstrated rewired metabolic and chromatin-remodeling transcriptional programs and recapitulate the gene signature of immature human fetal cardiomyocytes. Notably, we observe that polyploid cardiomyocytes--rather than the general myocyte population--are the primary sites of enrichment for major heart-failure drug targets, including the mineralocorticoid, {beta}1-adrenergic, and glucagon-like peptide-1 receptors. Based on our cross-species dataset we further identified TNIK, a Wnt-pathway regulator expressed in polyploid cardiomyocytes across species, as a potential therapeutic target and demonstrate that pharmacological TNIK inhibition improves cardiac function after myocardial infarction in rats. Together, this species-spanning, disease-resolved study redefines cardiomyocyte heterogeneity in heart disease and suggests a therapeutic path to heart failure treatment by targeting polyploid cardiomyocytes.

genomics↗

bioRxiv2025

High-throughput engineering and modification of non-ribosomal peptide synthetases based on Golden Gate assembly

Non-ribosomal peptide synthetases (NRPS) are multimodular enzymes that produce complex peptides with diverse biological activities, potentially being used as clinical drugs. However, the pharmaceutical applications of such natural peptides often require further derivatisation and modification of the peptide backbone, mainly performed by chemical synthesis. A sustainable alternative resembles the in vivo engineering of NRPS to change and modify the enzyme properties rationally and, thus, the produced products. The novel NRPS engineering concept, the eXchange Unit Thiolation domain (XUT), allows the efficient modular assembly of different natural NRPS fragments to form hybrid NRPS that produce defined peptides. In this study, we describe a Golden Gate assembly (GGA) method for efficient high-throughput generation of novel and engineered NRPS libraries utilising the XUT concept. This method was applied to generate over 100 novel NRPS with the possibility of changing starter, elongation, and termination modules, respectively. Additionally, we applied this method for targeted modification of the xenoamicin biosynthetic gene cluster (BGC) XabABCD from Xenorhabdus doucetiae, resulting in the generation of 25 novel xenoamicin derivatives. Graphical AbstractA Golden Gate assembly (GGA) method was developed for the efficient assembly of natural and engineered non-ribosomal peptide synthetases (NRPS). This method has enabled the creation of NRPS libraries to generate novel peptides in high-throughput as well as the targeted derivatisation of natural products (NP). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/650154v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@1161805org.highwire.dtl.DTLVardef@1832a43org.highwire.dtl.DTLVardef@4bc6b8org.highwire.dtl.DTLVardef@e3908d_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2021

Influence of condensation domains on activity and specificity of adenylation domains

Many clinically used natural products are produced by non-ribosomal peptide synthetases (NRPSs), which due to their modular nature should be accessible to modification and engineering approaches. While the adenylation domain (A) plays the key role in substrate recognition and activation, the condensation domain (C) which is responsible for substrate linkage and stereochemical filtering recently became the subject of debate - with its attributed role as a "gatekeeper" being called into question. Since we have thoroughly investigated different combinations of C-A didomains in a series of in vitro, in vivo, and in situ experiments suggesting an important role to the C-A interface for the activity and specificity of the downstream A domain and not the C domain as such, we would like to contribute to this discussion. The role of the C-A interface, termed extended gatekeeping, due to structural features of the C domains, can also be transferred to other NRPSs by engineering, was finally investigated and characterised in an in silico approach on 30 wild-type and recombinant C-A interfaces. With these data, we not only would like to offer a new perspective on the specificity of C domains, but also to revise our previously established NRPS engineering and construction rules.

bioengineering↗

bioRxiv2021

Dissecting CD8+ T cell pathology of severe SARS-CoV-2 infection by single-cell epitope mapping

The current COVID-19 pandemic represents a global challenge. A better understanding of the immune response against SARS-CoV-2 is key to unveil the differences in disease severity and to develop future vaccines targeting novel SARS-CoV-2 variants. Feature barcode technology combined with CITE-seq antibodies and DNA-barcoded peptide-MHC I Dextramer reagents enabled us to identify relevant SARS-CoV-2-derived epitopes and compare epitope-specific CD8+ T cell populations between mild and severe COVID-19. We identified a strong CD8+ T cell response against an S protein-derived epitope. CD8+ effector cells in severe COVID-19 displayed hyperactivation, T cell exhaustion and were missing characteristics of long-lived memory T cells. We identify A*0101 WTAGAAAYY as an immunogenic CD8+ T cell epitope with the ability to drive clonal expansion. We provide an in-depth characterization of the CD8+ T cell-mediated response to SARS-CoV-2 infection which will be relevant for the development of molecular and targeted therapies and potential adjustments of vaccination strategies.

immunology↗

bioRxiv2020

Deep learning-based molecular morphometrics for kidney biopsies

Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific cellular morphometrics in human kidney biopsies, which combines indirect immunofluorescence imaging with U-Net-based architectures for image-to-image translation and dual segmentation tasks, achieving human-level accuracy. In the kidney, podocyte loss represents a hallmark of glomerular injury and can be estimated in diagnostic biopsies. Thus, we profiled over 27,000 podocytes from 110 human samples, including patients with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA-GN), an immune-mediated disease with aggressive glomerular damage and irreversible loss of kidney function. Previously unknown morphometric signatures of podocyte depletion were identified in patients with ANCA-GN, which allowed patient classification and showed potential for risk stratification in combination with routine clinical tools. Together, our approach enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology. SummaryDeep learning enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology.

pathology↗

bioRxiv2020

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Diverse tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from nine renal cell carcinoma patients. We used COSMOS to generate novel hypotheses such as the impact of Androgen Receptor on nucleoside metabolism and the influence of the JAK-STAT pathway on propionyl coenzyme A production. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/057893v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@37d2d7org.highwire.dtl.DTLVardef@f333d4org.highwire.dtl.DTLVardef@cfcac3org.highwire.dtl.DTLVardef@90318_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗