Biology subjects

Varadi, A.

Publications and source records attributed to Varadi, A..

3 recordsLinked to original sources

bioRxiv2026

A non-retinoid triazolopyrimidine RBP4 antagonist for the treatment of Stargardt disease

Stargardt disease is a juvenile-onset retinal dystrophy characterized by the buildup of cytotoxic lipofuscin deposits in the retinal pigment epithelium (RPE), leading to photoreceptor degeneration and eventual blindness. Currently, there are no FDA-approved treatments for Stargardt disease. Bisretinoids, byproducts of the visual cycle, are the major cytotoxic components of the lipofuscin deposits, and bisretinoid synthesis relies on the traffic of retinol from the bloodstream to the retina. Selective targeting of the key retinol transporter, Retinol-Binding Protein 4 (RBP4), offers an appealing strategy for halting the buildup of lipofuscin in the RPE and arresting the progression of Stargardt disease. Retinol delivery depends on RBP4 interaction with another serum protein, Transthyretin (TTR). We previously reported several libraries of RBP4 antagonists that effectively blocked the association of the TTR-RBP4-retinol tertiary complex, thereby lowering the overall retinol load in the retina; however, some chemotypes displayed off-target activity that warranted further optimization. Here, we report the pharmacological characterization of AKR-XI-85 and its analogs as promising non-retinoid small-molecule RBP4 antagonists. AKR-XI-85 displayed excellent in vitro and in vivo efficacy and desirable pharmacokinetic properties without any limiting off-target activity. In Abca4-/- mice, chronic dosing of the compound induced a prolonged reduction in serum RBP4 levels and achieved a dramatic, 70 % reduction in the accumulation of A2E, a critical component of toxic lipofuscin. As such, AKR-XI-85 may be an attractive drug candidate for the treatment of Stargardt disease and other lipofuscin-dependent retinopathies.

pharmacology and toxicology↗

bioRxiv2026

SCAMP - an open-source tool for the quantification of calcification in fish larvae

Quantifying skeletal mineralization phenotypes in larval fish is complicated by the natural curvature of the notochord and by sample-to-sample variability in orientation, staining and imaging. Consequently, many studies rely on summary measures such as vertebral counts or total stain intensity. Here we present SCAMP (Spinal Calcification & Mineralization Profiler), an open-source, GUI-based Python tool that computationally straightens the curved notochord of Alizarin Red S-stained fish larvae and generates standardized mineralization profiles along the spinal axis. This approach reduces positional and shape variability, allowing direct, quantitative comparison of calcification patterns within and between experimental cohorts, without requiring programming expertise. We validate SCAMP using a zebrafish model of Pseudoxanthoma elasticum (abcc6aelu15/elu15), recovering genotype-specific differences in the intensity, extent and spatial distribution of ectopic calcification. Using SCAMP, we further show that inorganic pyrophosphate (PPi) supplementation of the medium suppresses ectopic notochord calcification, alters the anterior-posterior distribution of mineralized regions in homozygous mutants, and promotes mineralization at physiological vertebral sites. We also show that methylene blue, a routine antifungal additive in fish medium, reduces baseline calcification, with the most pronounced effects observed in heterozygous controls. SCAMP is freely available and has the potential to be adapted to other fish species used in skeletal and mineralization research.

developmental biology↗

bioRxiv2022

Rapid genotyping of viral samples using Illumina short-read sequencing data

The most important information about microorganisms might be their accurate genome sequence. Using current Next Generation Sequencing methods, sequencing data can be generated at an unprecedented pace. However, we still lack tools for the automated and accurate reference-based genotyping of viral sequencing reads. This paper presents our pipeline designed to reconstruct the dominant consensus genome of viral samples and analyze their within-host variability. We benchmarked our approach on numerous datasets and showed that the consensus genome of samples could be obtained reliably without further manual data curation. Our pipeline can be a valuable tool for fast identifying viral samples. The pipeline is publicly available on the projects github page (https://github.com/laczkol/QVG).

bioinformatics↗