Biology subjects

Zupanic, A.

Publications and source records attributed to Zupanic, A..

6 recordsLinked to original sources

bioRxiv2026

Synovial fibroblast niche shapes the efficacy - safety dynamics of JAK inhibition in rheumatoid arthritis

Synovial fibroblasts (SF) drive joint pathology in rheumatoid arthritis (RA). Difficult-to-treat RA frequently exhibits a fibroblast-rich synovial pathotype, enriched in DKK3{square} and CD34{square} SF, highlighting a critical therapeutic gap. Through multicohort transcriptomic analysis of synovial tissues and mechanistic in vitro studies, we identified SF as principal targets of Janus kinase (JAK) inhibition in RA. We demonstrated that JAK inhibitors (JAKi) can target multiple core aspects of fibroblast pathobiology - therapeutic refractoriness, cartilage destruction, and inflammation - offering a mechanistic rationale for JAKi superiority in difficult-to-treat RA. JAK1 was the dominantly expressed JAK across synovial pathotypes and SF subsets, including DKK3{square} and CD34{square} populations. A STAT1-interferon type I gene program was enriched in matrix-destructive PRG4{square} SF, consistent with JAKi efficacy in erosive RA. In contrast, canonical IL-6 signaling predominated in IL6-expressing inflammatory CXCL12high and HLA-DR+ SF, and was reproduced in cytokine-stimulated cultured SF, underscoring the autocrine nature of synovial IL-6 signaling. These data inferred a heightened JAKi sensitivity of PRG4{square}, CXCL12high, and HLA-DR+ SF subsets, informing precision therapeutic strategies. We uncovered a strong synergy between TNF and IL-6 trans-signaling, profoundly amplifying fibroblast inflammation. In high and synergistic cytokine milieu, STAT1/3 phosphorylation and IL-6 secretion persisted in SF despite tofacitinib treatment, revealing tofacitinibs functional ceiling. This could explain reduced tofacitinib efficacy and adherence in patients with high baseline arthritis activity. Finally, inflamed SF partially uncoupled STAT3 activation from sustained JAK1 phosphorylation, limiting inflammatory output. Similar uncoupling in tofacitinib-treated SF, likely drove rapid STAT1/3 reactivation following tofacitinib washout. These data aligned with JAKi withdrawal complications and clinical recommendations for gradual JAKi tapering. Collectively, our study identifies SF as key cellular targets of JAK inhibition and delineates cytokine- and drug-driven mechanisms that may constrain the efficacy and safety profiles of JAKi in RA.

molecular biology↗

bioRxiv2026

BoolDog: integrated Boolean and semi-quantitative network modelling in Python

SummaryBoolean and logic-based modeling approaches are well suited for the analysis of complex biological systems, particularly when detailed biochemical and kinetic information is unavailable. In such settings, biological pathways are represented as networks capturing system components and their interactions, providing a simplified yet informative abstraction of system behavior. While the structural topology of these networks is often well characterized, the absence of mechanistic detail limits the applicability of parameter-dependent modeling frameworks. To address this, we present BoolDog, a Python package for the construction, simulation, and analysis of Boolean and semi-quantitative Boolean networks. BoolDog supports synchronous simulation with events, attractor and steady-state identification, network visualization, and the systematic transformation of logic-based models into continuous ordinary differential equation (ODE) systems -- enabling the seamless integration of discrete and continuous modeling paradigms. Networks can be imported and exported across standard formats, and BoolDog integrates natively with established Python libraries for network analysis and visualisation, including NetworkX, igraph, and py4Cytoscape. Together, these capabilities provide a flexible, accessible, and interoperable platform for logic-based modeling of complex biological systems. Availability and implementationBoolDog is implemented in Python and available at https://github.com/NIB-SI/BoolDog/.

systems biology↗

bioRxiv2026

Reliable quantification of multiplexed genetically encoded biosensors responsiveness in plant tissues

Genetically encoded biosensors are one of the essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and five fluorescent proteins. We further validated this approach in virus-infected cells by following organelle dynamics in vivo. Finally, we demonstrate the feasibility of high-throughput segmentation and quantification with a custom MATLAB workflow for nuclei, chloroplasts, and cytoplasm signal analysis. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap. Significance statementMultiplexing genetically encoded biosensors in plants has been limited by overlapping fluorescent signals and strong autofluorescence. This study presents an optimized framework for linear unmixing and provides a MATLAB-based organelle segmentation tool, allowing precise quantification of multiple fluorescent reporters in vivo and advancing real-time visualization of complex cellular processes in plants.

plant biology↗

bioRxiv2024

Integration of multi-omics and deep phenotyping provides novel insights into multiple abiotic stress responses in potato

Potato is highly water and space efficient but susceptible to abiotic stresses such as heat, drought, or flooding, which are severely exacerbated by climate change. Understanding of crop acclimation to abiotic stress, however, remains limited. Here, we present a comprehensive molecular and physiological high-throughput profiling of potato (Solanum tuberosum, cv. Desiree) under heat, drought and waterlogging applied as single stresses or in combinations designed to mimic realistic future scenarios. Stress-responses were monitored via daily phenotyping and multi-omics analyses of leaf samples comprising transcriptomics, proteomics, metabolomics and hormonomics at several timepoints during and after stress treatments. Additionally, critical metabolites of tuber samples were analysed at the end of the stress period. Integrative analysis of multi-omics data was performed using a bioinformatic pipeline, which was established here, based on machine learning and knowledge networks. Overall, waterlogging had the most immediate and dramatic effects on potato plants, interestingly activating ABA-responses similar to drought stress. In addition, we observed distinct stress signatures at multiple molecular levels in response to heat or drought and to a combination of both. In response to all treatments, we found a downregulation of photosynthesis at different molecular levels, an accumulation of minor amino acids and diverse stress induced hormones. Our integrative multi-omics analysis provides global insights into plant stress responses, facilitating improved breeding strategies towards climate-adapted potato varieties. One Sentence SummaryIntegrated multi-omics analysis of high-throughput phenotyping in potato reveals distinct molecular signatures of acclimation to single and combined abiotic stresses related to climate change.

plant biology↗

bioRxiv2023

Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses

Stress Knowledge Map (SKM, https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs describing current knowledge of biochemical, signalling, and regulatory molecular interactions in plants: a highly curated model of plant stress signalling (PSS, 543 reactions) and a large comprehensive knowledge network (CKN, 488,390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entrypoint for plant stress response investigations and the related growth tradeoffs. SKM provides interactive exploration of current knowledge. PSS is also formulated as qualitative and quantitative models for systems biology, and thus represents a starting point of a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation, design of validation experiments, or to gain new insights into experimental observations in plant biology.

plant biology↗

bioRxiv2021

Chloroplast redox state changes indicate cell-to-cell signalling during the hypersensitive response

Hypersensitive response (HR)-conferred resistance is associated with an induction of programmed cell death (PCD) and pathogen spread restriction in its proximity. While the pivotal role of salicylic acid (SA) in the restriction of pathogen spread during HR has been confirmed, the exact role of chloroplastic reactive oxygen species and the link between SA signalling and chloroplast redox state during HR remain unexplained. To unravel these relationships, we performed detailed spatiotemporal analysis of chloroplast redox response to potato virus Y (PVY) infection in resistant Ny-1-gene-bearing potato and its transgenic counterpart with impaired SA accumulation and compromised resistance. We found that the chloroplasts are highly oxidized in the cells adjacent to the cell death zone at different stages after virus inoculation in both genotypes. Moreover, we detected individual cells with moderately oxidized chloroplasts, which we call "signalling cells", in close proximity as well as farther from the cell death zone. These are relatively rare in SA-deficient plants, suggesting their role in signalling for HR-conferred resistance. This hypothesis is further supported by highly induced formation of stroma filled tubules that extend from chloroplasts (stromules) in the cells adjacent to signalling cells. Unexpectedly, the cells with the highest occurrence of stromules have chloroplasts in more reduced state than the adjacent ones. In addition, we show that stromules are induced also at the edge of PVY multiplication zone. We conclude that chloroplast redox state and stromule formation are tightly spatiotemporally regulated by SA-signalling which leads to effective HR response.

plant biology↗