Biology subjects

Menzel, S.

Publications and source records attributed to Menzel, S..

4 recordsLinked to original sources

bioRxiv2026

Cyclic uremia without kidney injury drives cardiovascular disease with rapid onset of immunosenescence and heart failure

End-stage kidney disease (ESKD) is a major driver of cardiovascular disease (CVD) and requires dialysis treatment leading to a unique metabolic phenotype with cyclic transitions between accumulation and clearance of uremic toxins. Although ESKD patients display the highest cardiovascular mortality among CKD patients, the effect of cyclic uremia on cardiovascular disease remains subject to discussion. Existing mouse models rely on induction of kidney injury and do not resolve the interconnected pathophysiological effects of metabolic, hypertensive and endocrine renal failure. They are therefore unable to distinguish uremia-driven effects from well-established drivers of cardiovascular disease such as hypertension. Here, we established a simple, reproducible, and sex-inclusive mouse model of uremia in absence of kidney injury or hypertension leveraging a bistable vesico-peritoneal shunt (VPS). Strikingly, we identify cyclic uremia as an independent driver of ESKD-induced CVD, that induces heart failure with preserved ejection fraction (HFpEF), vascular inflammation and immunosenescence. As such, the VPS represents the first preclinical model that enables investigation of cyclic uremia as a driver of cardiovascular disease.

systems biology↗

bioRxiv2025

Development of AI-designed protein binders for detection and targeting of cancer cell surface proteins

Artificial intelligence (AI)-based protein design opens new avenues for the rapid generation of new research tools and therapeutics, but experimental validation lags behind the computational design throughput. Here, we present a scalable workflow for the discovery and validation of AI-designed protein binders. Leveraging the RFdiffusion protein design pipeline with a custom filter for stable alpha-helical bundle folds, we construct libraries of thousands of AI-binders against cancer-associated surface proteins. Mammalian cell-surface and phage display screening yield multiple high-affinity PD-L1 binders but fewer hits for CD276 (B7-H3) and VTCN1 (B7-H4), reflecting the target-dependent efficiency of RFdiffusion in generating high-quality designs. Using our experimentally validated AI-designed binder libraries, we benchmark freely available structure prediction models. We find that interface predicted template modelling (ipTM) scores by Chai-1 with ESM embedding correlate well with experimental success and even predict deleterious effects of binding interface mutations. To demonstrate the versatility of AI-binders as research tools, we deploy them in CAR-T cells and also assemble them with fluorophore-labeled streptavidin into tetravalent quattrobinders, which achieve antibody-comparable staining of endogenous PD-L1 by flow cytometry. With high production yields and accessible structural models, AI-designed quattrobinders are versatile and cost-effective research tools amenable to community-driven validation and optimization.

bioengineering↗

bioRxiv2021

COPILOT: a Containerised wOrkflow for Processing ILlumina genOtyping daTa

BackgroundThe Illumina genotyping microarrays generate data in image format, which is processed by the platform-specific software GenomeStudio, followed by an array of complex bioinformatics analyses. This process can be time-consuming, lead to reproducibility errors, and be a daunting task for novice bioinformaticians. ResultsHere we introduce the COPILOT (Containerised wOrkflow for Processing ILlumina genOtyping daTa) protocol, which provides an in-depth and clear guide to process raw Illumina genotype data in GenomeStudio, followed by a containerised workflow to automate an array of complex bioinformatics analyses involved in a GWAS quality control (QC). The COPILOT protocol was applied to two independent cohorts consisting of 2791 and 479 samples genotyped on the Infinium Global Screening (GSA) array with Multi-disease (MD) drop-in (~750,000 markers) and the Infinium H3Africa consortium array (~2,200,000 markers) respectively. Following the COPILOT protocol, an average sample quality improvement of 1.24% was observed across sample call rates, with notable improvement for low-quality samples. For example, from the 3270 samples processed, 141 samples had an initial sample call rate below 98%, averaging 96.6% (95% CI 95.6-97.7%), which is considered below the acceptable sample call rate threshold for a typical GWAS analysis. However, following the COPILOT protocol, all 141 samples had a call rate above 98% after QC and averaged 99.6% (95% CI 99.5-99.7%). In addition, the COPILOT pipeline automatically identified potential data issues, including gender discrepancies, heterozygosity outliers, related individuals, and population outliers through ancestry estimation. ConclusionsThe COPILOT protocol makes processing Illumina genotyping data transparent, effortless and reproducible. The container is deployable on multiple platforms, improves data quality, and the end product is analysis-ready PLINK formatted data, with a comprehensive and interactive summary report to guide the user for further data analyses.

genomics↗

bioRxiv2019

scOpen: chromatin-accessibility estimation of single-cell ATAC data

A major drawback of single cell ATAC (scATAC) is its sparsity, i.e. open chromatin regions with no reads due to loss of DNA material during the scATAC-seq protocol. We propose scOpen, a computational method for imputing and quantifying the open chromatin status of regulatory regions from sparse scATAC-seq experiments. We show that scOpen improves crucial down-stream analysis steps of scATAC-seq data as clustering, visualisation, cis-regulatory DNA interactions and delineation of regulatory features. We demonstrate the power of scOpen to dissect regulatory changes in the development of fibrosis in the kidney. This identified a novel role of Runx1 and target genes by promoting fibroblast to myofibroblast differentiation driving kidney fibrosis.

bioinformatics↗