Biology subjects

Matuszynska, A. B.

Publications and source records attributed to Matuszynska, A. B..

9 recordsLinked to original sources

bioRxiv2026

Chilling Nights Do Not Cause Starch Over-Accumulation and Trigger a Shift in Carbon Partitioning via SPS Toward Sucrose in Arabidopsis - Differing from Acclimation to Permanent Cold

Plants acclimate to low temperatures by remodeling carbohydrate metabolism. Permanent cold at 4/5{whitebullet} C induces well-characterized cold acclimation in plants, including accumulation of starch and soluble sugars. In natural environments, however, annual plants in their vegetative phase more frequently experience chilling nights (0-6{whitebullet} C nights followed by days at least 12{whitebullet} C warmer), whose metabolic consequences remain poorly understood. We investigated Col-0 Arabidopsis thaliana exposed to one or seven chilling nights and quantified central carbohydrate metabolites, starch, photosynthetic parameters, and maximal activities of enzymes linking sucrose synthesis and cleavage. We integrated these time-series data into a biologically constrained augmented neural ordinary differential equation (ANODE) model to infer diurnal reaction-rate dynamics. Chilling nights induced strong accumulation of sucrose, glucose, and fructose, particularly after seven nights. Both measured sucrose phosphate synthase (SPS) capacity and ANODE-predicted SPS rates increased. In contrast to permanent cold acclimation, daytime starch over-accumulation was absent. Thus, lacking starch over-accumulation together with increased SPS rates, chilling nights invoke distinct carbohydrate-partitioning patterns towards sucrose, indicating a fundamentally different acclimation strategy compared to well described permanent cold.

plant biology↗

bioRxiv2026

Disentangling the sucrose metabolism of the corn smut Ustilago maydis reveals unexpected complexity

The race for carbohydrates shapes organismic interactions. In plant pathogenic fungi, sucrose is a key nutrient as it constitutes the major transport sugar in plants. Here, we investigate sucrose acquisition in the corn smut fungus Ustilago maydis, a biotrophic pathogen that transitions from yeast-like to hyphal growth for infection. We establish that the fungus encodes a secreted acidic invertase, Suc2, with a dimeric canonical glycoside hydrolase 32 architecture. Comparative biochemical analyses across fungal homologs indicate that this dimeric architecture represents the predominant state, whereas higher-order oligomers, as initially described for Saccharomyces cerevisiae Suc2, are restricted to a subset of lineages. Unexpectedly, elimination of Suc2 did not impair yeast-like growth on sucrose. Similarly, deletion of genes for sucrose transporter Srt1 and cytosolic hydrolase Suc1, typically associated with intracellular sucrose metabolism, did not abolish growth. Instead, sucrose utilization during yeast-like growth depended on a repurposed non-canonical module comprising maltose transporter Agt1 and intracellular (iso)maltases. In contrast, pathogenic development strongly relied on the canonical intracellular sucrose utilization pathway mediated by Srt1 and Agt1. Infection was strongly diminished in strains unable to metabolise sucrose, confirming its central nutritional role for the fungus. Together, our work defines the complete sucrose utilization repertoire of U. maydis and uncovers a lifestyle-dependent metabolic switch between alternative sucrose acquisition strategies. Flexible carbon acquisition might represent a widespread adaptive strategy in basidiomycete pathogenic fungi.

microbiology↗

bioRxiv2026

Context-dependent siderophore exploitability shapes microbial community structure

1)Siderophores are classically viewed as shared iron-scavenging public goods, yet their ecological roles in multispecies communities remain poorly defined. Here, we establish a synthetic microbial community to dissect how different siderophores, their uptake compatibility and spatial structure shape iron competition. Using Corynebacterium glutamicum as a model, we show that this siderophore non-producer accesses diverse xenosiderophores, including enterobactin secreted by Escherichia coli. However, exploitation was constrained and co-cultures converged to stable compositions. Dose-response experiments combined with mathematical modelling indicated that the producer retains more effective access to enterobactin than the exploiter. Presence of Pseudomonas putida altered this interaction, as it exploited enterobactin while producing pyoverdine, a siderophore inaccessible to the other community members that restricted their iron access. Across different cultivation scales, community dynamics was strongly influenced by spatial organization and initial composition. These findings identify siderophores as context-dependent iron-allocation agents that can promote microbial coexistence or exclusion.

microbiology↗

bioRxiv2025

Assessing the redox state of the plastoquinone pool in algae and cyanobacteria via OJIP fluorescence: perspectives and limitations

The redox state of the plastoquinone pool (PQ-redox) acts as a central element in a variety of intracellular signal pathways. Several methods for determining PQ-redox have been established. Although some of these methods may be quantitative, such as those based on liquid chromatography, they are typically sensitive to sample preparation. Here, we critically evaluate the use of fast chlorophyll a fluorescence induction kinetics (the so-called OJIP transient) for semi-quantitative PQ-redox estimation in green algae (Chlorella vulgaris) and cyanobacteria (Synechocystis sp. PCC 6803). The method, based on the evaluation of relative fluorescence yield at the J-step of the OJIP transient (VJ, VJ), has already been reported; however, thus far, it has been used mostly for studying dark-acclimated leaves, which limits its range of application. Here, we show that the OJIP transient can be used for semi-quantitative estimation of PQ-redox in algal and cyanobacterial cell cultures, in addition to plants. We further show that it can reflect PQ-redox in both dark-acclimated and light-acclimated samples. Our systematic comparison of Multi-Color PAM, AquaPen, and FL 6000 fluorometers demonstrates that accurate measurement of VJ and VJ parameters in suspension cultures requires low culture density and a high-intensity saturation pulse. We further show that with increasing light intensity to which the cells are exposed, the state of photosystem II (PSII) changes due to light-induced reduction of quinone A (QA-) and conformational changes, which in turn influence both the sensitivity and dynamic range of the VJ parameter towards PQ-redox estimation. A comparison of fluorescence transients in Chlorella and Synechocystis revealed high homeostatic control over PQ-redox in Synechocystis, maintained by terminal oxidases present at the thylakoid membrane. While we discuss certain limitations, our systematic assessment suggests that the OJIP method has great potential to become a routine tool for semi-quantitative PQ-redox estimation under a wide range of experimental conditions in green algae and cyanobacteria.

microbiology↗

bioRxiv2025

MxlPy - Python Package for Mechanistic Learning in Life Science

SummaryRecent advances in artificial intelligence have accelerated the adoption of ML in biology, enabling powerful predictive models across diverse applications. However, in scientific research, the need for interpretability and mechanistic insight remains crucial. To address this, we introduce MxlPy, a Python package that combines mechanistic modelling with ML to deliver explainable, data-informed solutions. MxlPy facilitates mechanistic learning, an emerging approach that integrates the transparency of mathematical models with the flexibility of data-driven methods. By streamlining tasks such as data integration, model formulation, output analysis, and surrogate modelling, MxlPy enhances the modelling experience without sacrificing interpretability. Designed for both computational biologists and interdisciplinary researchers, it supports the development of accurate, efficient, and explainable models, making it a valuable tool for advancing bioinformatics, systems biology, and biomedical research. AvailabilityMxlPy source code is freely available at https://github.com/Computational-Biology-Aachen/MxlPy. The full documentation with features and examples can be found here https://computational-biology-aachen.github.io/MxlPy.

systems biology↗

bioRxiv2025

Hackathons as essential tools in an interdisciplinary biological training - report from trainings for Sub-Saharan students

Hackathons are collaborative, fast-paced events where participants from various fields work together to solve real-world problems. They are increasingly used in higher education to foster collaboration, problem-solving and applied computational skills, yet their role in interdisciplinary biological training remains under-documented. We report on the design and implementation of two computational biology summer schools in Kenya (2022, 2023), each culminating in a hackathon that integrated biological problems, quantitative methods, and coding. Both events targeted early-career researchers from multiple sub-Saharan countries and combined intensive teaching in programming and modelling with a time-bound group challenge using authentic marine conservation and synthetic epidemiological datasets. We describe the educational design, including its grounding in project-based learning, authentic learning, and Self-Determination Theory, and we document how performance-based assessment and structured participant feedback were used to evaluate learning outcomes. We present a critical reflective account of what worked, our teaching philosophy, and how hackathons can be embedded responsibly within biological curricula. We argue that, when embedded in sustained training and supported by appropriate mentoring, hackathons provide a practical and effective way to help biologists build computational skills, communicate across disciplines, and gain confidence in shaping their own research.

scientific communication and education↗

bioRxiv2023

Shedding Light On Blue-Green Photosynthesis: A Wavelength-Dependent Mathematical Model Of Photosynthesis In Synechocystis sp. PCC 6803

Cyanobacteria hold great potential to revolutionize conventional industries and farming practices with their light-driven chemical production. To fully exploit their photosynthetic capacity and enhance product yield, it is crucial to investigate their intricate interplay with the environment including the light intensity and spectrum. Mathematical models provide valuable insights for optimizing strategies in this pursuit. In this study, we present an ordinary differential equation-based model for the cyanobacterium Synechocystis sp. PCC 6803 to assess its performance under various light sources, including monochromatic light. Our model can reproduce a variety of physiologically measured quantities, e.g. experimentally reported partitioning of electrons through four main pathways, O2 evolution, and the rate of carbon fixation for ambient and saturated CO2. By capturing the interactions between different components of a photosynthetic system, our model helps in understanding the underlying mechanisms driving system behavior. Our model qualitatively reproduces fluorescence emitted under various light regimes, replicating Pulse-amplitude modulation (PAM) fluorometry experiments with saturating pulses. Using our model, we test four hypothesized mechanisms of cyanobacterial state transitions. Moreover, we evaluate metabolic control for biotechnological production under diverse light colors and irradiances. By offering a comprehensive computational model of cyanobacterial photosynthesis, our work enhances the basic understanding of light-dependent cyanobacterial behavior and sets the first wavelength-dependent framework to systematically test their producing capacity for biocatalysis.

plant biology↗

bioRxiv2022

Bioenergetics of the secondary metabolites production in photosynthetic glandular trichomes

Several commercially important secondary metabolites are produced and accumulated in high amounts by glandular trichomes, giving the prospect of using them as metabolic cell factories. Due to extremely high metabolic fluxes through glandular trichomes, previous research focused on how such flows are achieved. The question regarding their bioenergetics became even more interesting with the discovery of photosynthetic activity in some glandular trichomes. Despite recent advances, how primary metabolism contributes to the high metabolic fluxes in glandular trichomes is still not fully elucidated. Using computational methods and available multi-omics data, we first developed a quantitative framework to investigate the possible role of photosynthetic energy supply in terpenoid production and next tested experimentally the simulation-driven hypothesis. With this work, we provide the first reconstruction of specialised metabolism in Type-VI photosynthetic glandular trichomes of Solanum lycopersicum. Our model predicted that increasing light intensities results in a shift of carbon partitioning from catabolic to anabolic reactions driven by the energy availability of the cell. Moreover, we show the benefit of shifting between isoprenoid pathways under different light regimes, leading to a production of different classes of terpenes. Our computational predictions were confirmed in vivo, demonstrating a significant increase in production of monoterpenoids while the sesquiterpenes remained unchanged under higher light intensities. The outcomes of this research provide quantitative measures to assess the beneficial role of chloroplast in glandular trichomes for enhanced production of secondary metabolites and can guide the design of new experiments that aim at modulating terpenoid production. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/510054v2_ufig1.gif" ALT="Figure 1"> View larger version (9K): org.highwire.dtl.DTLVardef@2ec6f4org.highwire.dtl.DTLVardef@107d0c1org.highwire.dtl.DTLVardef@1a545corg.highwire.dtl.DTLVardef@98747b_HPS_FORMAT_FIGEXP M_FIG C_FIG Please check the journals author guidelines for whether a graphical abstract, key points, new findings, or other items are required for display in the Table of Contents.

plant biology↗

bioRxiv2020

Constructing and analysing dynamic models withn modelbasev1.0 - a software update

BackgroundComputational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Unfortunately, despite community efforts leading to the development of SBML or the BioModels database, many published models have not been fully exploited, largely due to lack of proper documentation or the dependence on proprietary software. To facilitate synergies within the emerging research fields of systems biology and medicine by reusing and further developing existing models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent and reproducible is desired. Results and DiscussionWe provide here the update on the development of modelbase, a free expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow convenient analyses of structural and dynamic properties of the models. Specifying reaction stoichiometries and rate equations, the system of differential equations is assembled automatically. A newly provided library of common kinetic rate laws highly reduces the repetitiveness of the computer programming code, and provides full SBML compatibility. Previous versions provided functions for automatic construction of networks for isotope labelling studies. Using user-provided label maps, modelbase v1.0 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in modelbase is continuously growing. Ranging from photosynthesis over tumour cell growth to viral infection evolution, all models are available now in a transparent, reusable and unified format using modelbase. ConclusionWith the small price of learning a new software package, which is written in Python, currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others, repeating and reproducing models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their label specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.

systems biology↗