Biology subjects

Leitner, D.

Publications and source records attributed to Leitner, D..

12 recordsLinked to original sources

bioRxiv2026

Root Age Structure Governs Long-Term Architecture and Hydraulic Function in Perennial Plants: a Modelling Framework

Background and aims: Perennial root systems develop over decades and exhibit continuous turnover of structural and absorptive components, yet most root architecture models treat woody roots as permanent and neglect root age structure. We developed an empirically constrained modelling framework that explicitly represents age-structured demography, growth regulation and dormancy in perennial root systems. Methods: We extended the functional-structural plant model CPlantBox to simulate perennial root development over decades by integrating age-dependent survival, secondary radial growth and climate-driven dormancy. The model was parameterised using unique three-dimensional root system data from young and mature grapevines, together with dendrochronological root age measurements, and coupled to root hydraulic properties. Key results: Empirical analyses revealed multi-cohort age distributions and substantial turnover of lignified structural roots, contradicting the assumption of a static woody scaffold. The model reproduced age distributions, order-specific survival probabilities and fine-to-structural root length ratios, and generated realistic, genotype-specific three-dimensional architectures. Simulated uptake profiles and hydraulic scenarios showed strong age- and architecture-driven effects. Conclusions: Explicitly representing woody root age distributions and age-dependent processes may fundamentally change predictions of perennial root system architecture and function. The framework advances root modelling beyond annual paradigms and provides a transferable tool for analysing long-term architectural and hydraulic consequences of genotype x climate interactions in perennial crops.

plant biology↗

bioRxiv2026

Short T1 Fraction as a Marker of Myelin Content: Evidence from Postmortem MRI and Histology

PurposeTo investigate the short-T1 fraction as a potential biomarker of white matter myelin integrity, using myelin histology as ground truth. MethodsMulti-inversion-time MRI data were acquired from four postmortem brain hemisphere specimens from donors with Alzheimers disease on a clinical 3T scanner, and from five dissected tissue blocks containing white matter hyperintensities (WMHs) on a preclinical 3T system. Short-T1 fraction maps were generated using inverse Laplace transform to isolate short-T1 components and were compared with T2-based myelin water imaging metrics through joint T1-T2 correlation analysis. Short-T1 fraction maps from the tissue blocks were further compared with myelin-stained histology. In addition, in vivo short-T1 fraction data were acquired from two elderly volunteers with WMHs to demonstrate translational feasibility. ResultsPostmortem MRI revealed reduced short-T1 fractions in WMHs. T1-T2 correlation analysis showed that the short-T1 fraction was closely associated with the short-T2 (myelin water) component. Strong correlations were observed between short-T1 fractions and optical densities from both Luxol Fast Blue- and myelin basic protein-stained histological sections, supporting the link between the short-T1 signal and myelin content. Consistent findings were also observed in vivo, where significant reductions in short-T1 fractions were detected within WMHs. ConclusionThe short-T1 fraction correlated with myelin content in postmortem brain white matter, supporting its potential as a clinically translatable biomarker of myelin integrity.

neuroscience↗

bioRxiv2025

Alphavirus replicon particle expressing IL-12 reprograms tumor-associated macrophages and neutrophils and induces anti-tumor immunity

Cancers evade anti-tumor immunity by the infiltration of immunosuppressive myeloid cells such as macrophages and neutrophils into the tumor microenvironment (TME). The limited efficacy of current immunotherapy is attributed to the presence of these immunosuppressive cells. Here, we describe a novel immunotherapy approach, using a single-cycle alphavirus replicon particle (VRP) that carries a self-amplifying RNA encoding interleukin-12 (IL-12). Leveraging the natural tropism of alphavirus to myeloid cells, single cell transcriptomic analyses of mouse tumor tissues demonstrated that intratumoral (i.t.) injection of the vector effectively reprogrammed tumor-associated macrophages and neutrophils toward a pro-inflammatory, anti-tumor phenotype via interferon signaling, further enhanced by IL-12 expression. The treatment also induced cytotoxic NK and T cell responses, resulting in a significant reduction of tumor growth and effectively preventing nodal and distant metastases in solid tumor mouse models. By targeting tumor-supporting myeloid cells to transform the TME into an immune-active state, our myeloid modulation approach has the potential to enhance the efficacy of current immunotherapies.

cancer biology↗

bioRxiv2023

Peripheral serotonergic neurons regulate anxiety-like behavior and intestinal barrier immunity

Serotonergic circuits in the central nervous system play important roles in regulating mood and behavior, yet the functions of peripheral serotonergic neurons are less understood. Here, we engineered mice lacking the serotonin-producing enzyme Tph2 in peripheral neurons but with intact Tph2 in central neurons. In contrast to mice lacking Tph2 in all neurons, mice lacking Tph2 in peripheral serotonergic neurons did not exhibit increased territorial aggression. However, similar to the total body Tph2 KO mice, the conditional KO animals, exhibited reduced gut motility and decreased anxiety-like behavior. These observations reveal that peripheral serotonergic neurons contribute to control of intestinal motility and anxiety-like behavior and suggest that therapeutics targeting this subset of peripheral neurons could be beneficial.

immunology↗

bioRxiv2023

Development and calibration of the FSPM CPlantBox to represent the interactions between water and carbon fluxes in the soil-plant-atmosphere continuum

A plants development is strongly linked to the water and carbon flows in the soil-plant-atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models which simulate mechanistically and dynamically the feedback loops between a plants three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype-environment-management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional-Structural Plant Model CPlantBox with PiafMunch and DuMux coupling. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of one week (no precipitation). We compared the results for dry spells starting on different days (day 11 or 18) and with different climates (wetter and colder against drier and warmer atmospheric and initial soil conditions). Compared with the wetter and colder climate, the dry spell with the drier and warmer climate led to a lower instantaneous water use efficiency. Moreover, the lower symplasm turgor for the drier and warmer climate limited the growth, which made the sucrose available for other processes, such as maintenance respiration. Both of these effects were stronger for the later dry spell compared with the early dry spell under the drier and warmer climate. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants phenotypic plasticity response to their environment.

plant biology↗

bioRxiv2022

Phloem anatomy restricts root system architecture development: theoretical clues from in silico experiments.

Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plants life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging. Here we used a combination of computational plant models to help understand experimental findings about how local phloem anatomical features influence the root system architecture. In particular, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results. Our in silico study highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level. Our results open the door to a better representation of growth processes in plant computational models.

plant biology↗

bioRxiv2021

Simulating rhizodeposition patterns around growing and exuding root systems

1In this study, we developed a novel model approach to compute the spatio-temporal distribution patterns of rhizodeposits around growing root systems in three dimensions. This model approach allows us for the first time to study the evolution of rhizodeposition patterns around complex three-dimensional root systems. Root systems were generated using the root architecture model CPlantBox. The concentration of rhizodeposits at a given location in the soil domain was computed analytically. To simulate the spread of rhizodeposits in the soil, we considered rhizodeposit release from the roots, rhizodeposit diffusion into the soil, rhizodeposit sorption to soil particles, and rhizodeposit degradation by microorganisms. To demonstrate the capabilities of our new model approach, we performed simulations for the two example rhizodeposits mucilage and cit-rate and the example root system Vicia faba. The rhizodeposition model was parameterized using values from the literature. Our simulations showed that the rhizosphere soil volume with rhizodeposit concentrations above a defined threshold value (i.e., the rhizodeposit hotspot volume), exhibited a maximum at intermediate root growth rates. Root branching allowed the rhizospheres of individual roots to overlap, resulting in a greater volume of rhizodeposit hotspots. This was particularly important in the case of citrate, where overlap of rhizodeposition zones accounted for more than half of the total rhizodeposit hotspot volumes. Coupling a root architecture model with a rhizodeposition model allowed us to get a better understanding of the influence of root architecture as well as rhizodeposit properties on the evolution of the spatio-temporal distribution patterns of rhizodeposits around growing root systems.

plant biology↗

bioRxiv2020

Proteomic Differences in the Hippocampus and Cortex of Epilepsy Brain Tissue

Epilepsy is a common neurological disorder affecting over 70 million people worldwide, with a high rate of pharmaco-resistance, diverse comorbidities including progressive cognitive and behavioral disorders, and increased mortality from direct (e.g., Sudden Unexpected Death in Epilepsy [SUDEP], accidents, drowning) or indirect effects of seizures and therapies. Extensive research with animal models and human studies provides limited insights into the mechanisms underlying seizures and epileptogenesis, and these have not translated into significant reductions in pharmaco-resistance, morbidities or mortality. To help define changes in molecular signaling networks associated with epilepsy, we examined the proteome of brain samples from epilepsy and control cases. Label-free quantitative mass spectrometry (MS) was performed on the hippocampal CA1-3 region, frontal cortex, and dentate gyrus microdissected from epilepsy and control cases (n=14/group). Epilepsy cases had significant differences in the expression of 777 proteins in the hippocampal CA1-3 region, 296 proteins in the frontal cortex, and 49 proteins in the dentate gyrus in comparison to control cases. Network analysis showed that proteins involved in protein synthesis, mitochondrial function, G-protein signaling, and synaptic plasticity were particularly altered in epilepsy. While protein differences were most pronounced in the hippocampus, similar changes were observed in other brain regions indicating broad proteomic abnormalities in epilepsy. Among the most significantly altered proteins, G-protein Subunit Beta 1 (GNB1) was one of the most significantly decreased proteins in epilepsy in all regions studied, highlighting the importance of G-protein subunit signaling and G-protein-coupled receptors (GPCRs) in epilepsy. Our results provide insights into the molecular mechanisms underlying epilepsy, which may allow for novel targeted therapeutic strategies.

neuroscience↗

bioRxiv2019

CPlantBox, a whole plant modeling framework for the simulation of water and carbon related processes

The interaction between carbon and flows within the plant is at the center of most growth and developmental processes. Understanding how these fluxes influence each other, and how they respond to heterogeneous environmental conditions, is important to answer diverse questions in forest, agriculture and environmental sciences. However, due to the high complexity of the plant-environment system, specific tools are needed to perform such quantitative analyses.\n\nHere we present CPlantBox, full plant modelling framework based on the root system model CRootBox. CPlantbox is capable of simulating the growth and development of a variety of plant architectures (root and shoot). In addition, the flexibility of CPlantBox enables its coupling with external modeling tools. Here, we connected it to an existing mechanistic model of water and carbon flows in the plant, PiafMunch.\n\nThe usefulness of the CPlantBox modelling framework is exemplified in four case studies. Firstly, we illustrate the range of plant structures that can be simulated using CPlantBox. In the second example, we simulated diurnal carbon and water flows, which corroborates published experimental data. In the third case study, we simulated impacts of heterogeneous environment on carbon and water flows. Finally, we showed that our modelling framework can be used to fit phloem pressure and flow speed to (published) experimental data.\n\nThe CPlantBox modelling framework is open-source, highly accessible and flexible. Its aim is to provide a quantitative framework for the understanding of plant-environment interaction.

plant biology↗

bioRxiv2019

Call for participation: Collaborative benchmarking of functional-structural root architecture models. The case of root water uptake.

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute towards improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development.

plant biology↗

bioRxiv2018

Connecting the dots between computational tools to analyse soil-root water relations

In the recent years, many computational tools, such as image analysis, data management, process-based simulation and upscaling tools, were developed to help quantify and understand water flow in the soil-root system, at multiple scales (tissue, organ, plant and population). Several of these tools work together or, at least, are compatible. However, for the un-informed researcher, they might seem disconnected, forming a unclear and disorganised succession of tools.\n\nIn this article, we present how different pieces of work can be further developed by connecting them to analyse soil-root-water relations in a comprehensive and structured network. This \"explicit network of soil-root computational tools\" informs the reader about existing tools and help them understand how their data (past and future) might fit within the network. We also demonstrate the novel possibilities of scale-consistent parameterizations made possible by the network with a set of case studies from the literature. Finally, we discuss existing gaps in the network and how we can move forward to fill them.\n\nHighlightsMany computational tools exist to quantify water flow in the soil-root system. These tools can be arranged in a comprehensive network that can be leveraged to better interpret experimental data.

plant biology↗

bioRxiv2017

CRootBox: A Structural-Functional Modelling Framework For Root Systems

Background and AimsRoot architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architectural models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. We present a root architectural model, CRootBox, as a flexible framework to model architecture and its interactions with static and dynamic soil environments.\n\nMethodsCRootBox is a C++ -based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and CSV. We further created a database of published root architectural parameters. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted into a freely available web application.\n\nKey resultsWe demonstrate the use of CRootBox for 5 different cases (1) free growth of individual root systems (2) growth of root systems in containers as a way to mimic experimental setups, (3), field scale simulation, (4) root growth as affected by heterogeneous, static soil conditions, and (5) coupling CRootBox with Soil Physics with Python code to dynamically compute water flow in soil, root water uptake, and water flow inside roots.\n\nConclusionsIn conclusion, we present a fast and flexible functional-structural root model which is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of root on soil. In the future, we plan to extend this approach to the aboveground part of the plant.

plant biology↗