CropXR Conference 2025: Abstracts and sessions

CropXR employs an innovative ‘smart-data’ strategy to enhance plant resilience. Accordingly, we use diverse computational approaches to understand, optimize, and translate the mechanisms that plants use to respond to the environment, and store data and models efficiently to facilitate their reusability.

This satellite session concerns the latest computational research for the integration of mechanistic and machine learning models in Arabidopsis (WP-C1), how to translate these models to other plants (WP-T), modelling of data from large potato field experiments (WP-S1), and the development of the Resilience Hub data infrastructure to facilitate the exchange of research outputs between work packages (WP-D).

Most biologists have had some training in statistics. The typical curriculum will have included ANOVA and regression and some non-parametric procedures. These methods almost always will have been taught from a classical frequentist perspective. In the last decades Bayesian alternatives to frequentist methods have gained in popularity. A fundamental difference between frequentist and Bayesian statistics lies in the definition of probability. As a consequence parameters are also treated differently. We will describe the frequentist and Bayesian views on statistical modelling and inference and illustrate those on relatively simple models. Within the context of integrating genetics and physiology we will look at a more advanced application of Bayesian statistics where we model plant growth for large sets of genotypes across multiple experiments in the presence of genotype by environment interactions.

This session highlights how plant roots respond to their environment, with a focus on the role of the microbiome and nitrogen availability. Speakers will present recent insights from Arabidopsis and potato, ranging from root–shoot communication and methods to test root behavior under different conditions, to physiological responses to nitrogen deficiency and the influence of microbiomes across soil types. Genetic approaches such as GWAS will also be discussed to link microbiome effects with plant performance. The session offers both fundamental perspectives and practical applications for developing resilient crops.

By Mark Roosjen and Rob Schuurink

Plant breeding is an ancient practice and has been a cornerstone of agriculture. Classical mass selection and crossbreeding strategies are now being accompanied by advanced molecular methods to increase the speed and precision of beneficial trait selection. Within the different work packages of CropXR state of the art phenotyping, omics and gene-editing technologies are being employed to get a grasp on the molecular framework of plant resilience. In this parallel session, speakers will showcase how specific technologies can advance our understanding of plant resilience.

By Ivo Rieu and Martijn van Zanten

Temperature-related research is omnipresent in the CropXR program with researchers working on diverse aspects ranging from seedling establishment to reproductive processes and from mild high ambient temperature to severe heat stress. Goal of this session is to provide an oversight of temperature-related topics that are covered within CropXR by showcasing examples from each WP. Together we will define where we stand by discussing what the different WPs need from each other, what they can offer to others and how we ultimately translate and integrate fundamental findings to develop thermotolerant crops.

Program:

11:00-11:10      Welcome and introduction to the session

11:10-12:00      10 minute pitches of researchers representing the WPs that involve temperature research (C1, C2, C3, C5, T).

12:00-12:10      2 minute pitches of each speaker: what does the WP/researcher need and what can the WP/researcher offer to other WPs/researchers

12:10-12:30      Plenary discussion between audience and panel

Authors: Alysha Somer¹, Ronald Pierik¹

Affilliation of authors  
¹Wageningen University and Research 

Abstract 
Root plasticity is an important trait for crop resilience. Although we know that signaling between shoot and root occurs, the role of the shoot in regulating root architecture plasticity is not understood in great detail. In drought stress, root growth and development are strongly affected, resulting in reduced primary root growth and lateral root development. The objectives of this project are to identify and verify shoot-derived mobile signals involved in root growth inhibition under drought stress. We will functionally validate shoot-derived signals by characterizing their shoot-to-root mobility, impact and mode-of-action in root architecture regulation.   

Authors : Anna Neefjes¹, Xenja Ploeger¹, Saskia van Wees², Rob Schuurink¹ & Petra Bleeker¹ 

Affilliation of authors 
¹ University of Amsterdam, Sils, Plant Physiology 
² University of Utrecht, Plant-Microbe Interactions 

Abstract 
In nature, plants often face multiple concurrent stresses, with diseases caused by pest and pathogens representing a major contstraint for plant performance. These problems are aggrevated by changes in climate, with elevated temperature as a prominent factor. These (a)biotic stress combinations can trigger antagonistic and syntergistic signaling responses. Plants can steer the production of defence metabolites in response to environmental cues, in particular hormonal regulation that balances the output of gene regulatory networks. Breeding for pest resistance by optimizing the production of specialized metabolites remains difficult, as the level of resistance relies on different genes that impact both gene regulation and metabolic rates. To breed for quantitative resistance, we thus need to be able to predict the outcome of combinatorial (a)biotic stresses on hormonal and metabolic fluxes.  

Glucosinolates (GLS) are specialised metabolites specific to plants of the Brassicales order that play an important role in stress signaling and defence against biotic stressors like the pest insect thrips. Upon herbivory the enzyme myrosinase hydrolyses GLSs releasing bioactive products such as isothiocyanates, that exhibit anti-insect properties. Here, we aim to characterise Arabidopsis thaliana GLS profiles and determine the changes in these profiles in response to damage through time, by jasmonic acid, using targetd MS/MS methodology. We will also analyse the GLS response to thrips infestation and validate the role of these defence metabolites in Arabidopsis against thrips.  

This project is part of the CropXR consoritum. Ultimately this work will contribute to the development of a dynamic metabolic network model, coupled to a gene regulatory network in response to different biotic and temperature stress combinations. 

Authors: Arnoud Glasbeek¹, Corné Verburg¹, Fred van Eeuwijk², Neil Budko¹

Affilliation of authors 
¹ Numerical Analysis, Delft Institute of Applied Mathematics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
² Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands

Abstract
To shorten the breeding cycle of potatoes, it is desirable to be able to model growth of existing and new genotypes in arbitrary environments without having to rely on field trials. This can be achieved by combining Dynamic Crop Growth Models (D-CGM) with the methods of Whole Genome Prediction (WGP). We employ a novel D-CGM that describes the genotype-by-environment interaction using a set of basis functions depending on environmental variables, with the influence of each basis function being determined by genotype-specific coefficients (GSC’s). Further, the methods of WGP, such as mixed models, can provide estimates of these GSC’s, e.g.
using SNP marker data. Apart from reducing the need for expensive field trials to evaluate genotypes in the breeding cycle, combining D-CGM with WGP may also assist making better-informed selection decisions by helping identify which regions of the DNA should be prioritised.

Authors: Thijs van Loo^*1, Ben Noordijk^*2, Milan van Hoek³, Marcel Reinders⁴, Aalt Dirk Jan van Dijk⁵, Monica Garcia Gomez^1,6, Kirsten ten Tusscher^1,6, Dick de Ridder².

Affilliation of authors 
¹Theoretical Biology, Institute of Biodynamics and Biocomplexity, Department of Biology, Utrecht University, Utrecht, Netherlands 

²Bioinformatics Group, Wageningen University & Research 

³Keygene, Wageningen 

⁴Pattern Recognition & Bioinformatics Group, Delft University of Technology 

⁵Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam 

⁶Experimental and Computational Plant Development, Institute of Environmental Biology, Department of Biology, Utrecht University, Utrecht, Netherlands 

^*These authors contributed equally to this work 

Abstract 

Models  of plants have to strike a balance between different goals. Pure Mechanistic Modelling (MM) excels at interpretability, allowing inspection of underlying mechanisms and causes. However, MM is limited in its maximum predictive performance because the required detailed knowledge of interactions and mechanisms in plants and other complex organisms is often limited. On the other hand, Machine Learning (ML) models excel at learning patterns from data, but their performance often suffers when they are applied to new unseen variations on the data. On top of that, most ML techniques are limited in their interpretability, not allowing intuitive understanding of the plants’ mechanisms and limiting their applications for breeding. One solution to this is to combine the two techniques in order to gain the benefits of the interpretability of MM with the data fitting performance from ML. In this project we plan to train such hybrid models on simulated RNA expression data and study how they perform on unseen variations of the data by simulating knockout and overexpression experiments. The hybrid model should preserve both the pattern fitting performance and interpretability of it constituent models, allowing insight into what regulatory parts and mechanism are most promising as targets in plant breeding and what results might be possible. 

Author: Víctor Betriu Yáñez 

Affiliation of author  
Wageningen University, Philosophy Group 

Abstract 
The development of AI-enabled plant breeding tools for achieving crop resilience under the threat of climate change presents itself as an innovation with a societal and environmental motivation, insofar as it is addressed to make agriculture more sustainable and strives towards food security. Does this good intention suffice to call such an innovation “responsible”? Insofar as the digitalization of plant breeding entails the datafication of natural resources, and this process of datafication implies the conversion of natural fluxes into capitalizable data, then the emergence of AI-enabled plant breeding would arguably be indissociable from a process of capitalization. This would show that, behind a social narrative of innovation such as the attempt to use AI to solve a societal grand challenge, an economic logic prevails dominant in the name of the good. Now, from a Responsible Research and Innovation (RRI), the criterion to guarantee responsible scientific developments is that of “responsiveness”—the capacity for structural change in science, technology and innovation, such that the social is not subjected to the economic nor scientific. In the past decades, literature on technological and scientific governance has mainly concretized this praiseworthy ideal through the promise of a deliberative democracy, based on the ideal of communicative action and a politics of talk between science and society. However, communication, consensus and transparency risk being the means through which the privilege of the economic in contemporary technoscientific innovation is perpetuated. In light of this problem, this poster proposes that the first philosophical question we should be asking ourselves is why our understandings of responsiveness have turn out to be complicit with the techno-economic paradigm of innovation if their original intention was to dethrone it. In other words, we must identify what aspects of our ways of thinking have transformed a praiseworthy notion of responsiveness into a toothless and cosmetic one. Wasn’t responsiveness all about steering, rather than stirring? 

Authors: Bianca-Maria Cosma¹, Thomas Abeel^1,2 

¹Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands 

²Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, USA 

Abstract 
In times of drought, plants rely on their root microbes for help. Unsurprisingly, introducing beneficial microbes in root communities can result in more drought-resilient plants – but successful microbial inoculation in agricultural soils remains a difficult task.  

We investigated the relationship between microbes affected by drought and those affected by inoculation, using a meta-analysis approach. We extracted rhizosphere, endosphere and bulk soil microbial drought signatures from nine studies in which different plants were exposed to drought. Based on data from four other studies, we then connected these signature taxa to those affected by inoculation.  

Across studies, distinct microbial genera were differentially abundant between drought and well-watered controls, for each compartment, with the rhizosphere and endosphere showing more diverse drought signatures than bulk soil samples (23 and 13, compared to 7 genera). Among all genera, Kribella was drought-enriched in all compartments. Under drought, successful inoculation experiments resulted in more significant shifts in the microbiome; we also observed a depletion of Nitrospira in both successful inoculation experiments, mirroring the genus’ behavior in the drought signature.  

Authors: C. Verburg¹, A. Heinlein¹, F.A. van Eeuwijk², N.V. Budko¹

Affilliation of authors
¹ Numerical Analysis, Delft Institute of Applied Mathematics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
²Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, TheNetherlands

Abstract
Understanding genotype-by-environment interactions is important for plant breeding and agronomic decision-making. Mechanistic models need extensive calibration, while black-box machine learning approaches lack interpretability. Therefore, we propose a middle-ground approach based on the Sparse Identification of Nonlinear Dynamics (SINDy) method to derive compact ODEs linking physiological traits (e.g., leaf area index or weight of storage organs) to environmental factors such as rainfall and temperature. Using in-silico, WOFOST-generated data, we demonstrate that SINDy is
able to recover interpretable growth dynamics even with sparse data and allows extracting genotypespecific stress responses. This approach
combines physiological knowledge with analytical tractability, offering a step toward interpretable data-driven crop models

Authors: Eva Eleonora Ferradosa¹, Christoph Lofi¹ 

Affiliation of authors
¹Web Information Systems Group, Software Technology Department, TU Delft 

Abstract 

High-quality metadata is crucial for research data reuse. Metadata standards support FAIR principles by providing structure, often tailored to specific domains. However, in complex domains like applied plant systems biology, standard metadata may fall short. Contextual metadata—capturing meaning, motivations, and nuances—enhances understanding across disciplines but is harder to formalize. While structured metadata ensures consistency, contextual metadata enhances understanding and adaptability. This project explores how CropXR data can be reused across research contexts and what metadata is needed. Through surveys and interviews, we aim to bridge the gap between current standards and researchers’ practical needs. 

Authors: Fotios Palaiochorinos¹, Jemima Deakin¹, David Torres Sanchez¹, Gerard van der Linden¹ 

Affilliation of authors 
¹ Plant Breeding, Wageningen University and Research, Wageningen, Netherlands 

Abstract 
Water scarcity poses a major challenge for onion (Allium cepa L.) cultivation worldwide, underscoring the need to elucidate the traits that contribute to drought resilience. In this greenhouse study, eight diverse onion varieties were evaluated under controlled water-deficit conditions to assess their physiological, morphological, and transcriptional responses. A suite of high-throughput phenotyping tools, including imaging-based approaches, was applied to refine drought assessment methods and identify key resilience traits. This work provides a framework for characterizing drought adaptation in onion, laying the groundwork for future research and breeding strategies aimed at improving onion performance in water-limited environments. 

Authors: Francesca Giaume¹, Siwi Sekar¹, Pinelopi Kokkinopoulou¹, Christa Testerink¹

Affilliation of authors
¹Laboratory of Plant Physiology, Wageningen University and Research, The Netherlands

Abstract
Developing climate-resilient crops able to succeed in abiotic stress conditions, including heat and drought, is becoming deeply demanding yet
necessary. This study aims at identifying resilience key players involved in early perception and later response to drought and temperature stresses, together with their combinations. To test this, we performed RNA-seq on Arabidopsis whole seedlings, as well as separate tissues, sampled at 2,3,4,7 and 14 DAS (days after the sowing) grown in soil and exposed to a
9-condition matrix of different isolated and combined stresses.
Concurrently, analysis of phenotypic measurements were carried out on main traits as root length, hypocotyl length and canopy area, both at the beginning and at the end of the experiments, to correlate transcriptomic with phenomic data and to assess trait plasticity over time.

Authors: Jenske Aben¹, Marcello Gazale², Francisco Pinto Espinosa³, Lucia Perez Borroto⁴, Richard Visser⁵, Gerard van der Linden⁶ 

Affilliation of authors  
^1-6: Wageningen University and Research 

Abstract 
The primary objective of this PhD study is to elucidate the physiological and molecular mechanisms underlying the formation, growth and yield of potato tubers under varying nitrogen availability. As part of this research, a greenhouse experiment was conducted in 2024. The aim of this experiment was to look at the growth of 8 different cultivars under two different levels of nitrogen, and take samples for both RNA and biochemical analysis for later analysis. Under low nitrogen availability, a reduction in plant biomass, chlorophyll content and leaf area was observed, along with altered biomass partitioning within the plants.  

Authors: Jielin Wang¹, Francesca Giaume¹, Christa Testerink¹ 

Affilliation of authors
¹ Laboratory of Plant Physiology, Wageningen University 

Abstract
Plants have evolved a series of plasticity strategies to adapt to the drought stress. Particularly, the remodeling of root system architecture (RSA) is an effective response for plants to optimize water supply, and to balance the trade-off between growth and defense. As part of the CropXR work package C3, my PhD project aims to map Arabidopsis root developmental dynamics in response to drought stress, with a focus on identifying the cell type-specific regulatory networks that drive root plasticity. This knowledge will help us design precise strategies to improve plant stress resilience. 

Authors : Jochem Huijben, Stijn Nagelkerke, Roeland Berendsen, Corné Pieterse 

Affilliation of authors  
Utrecht University, Plant Microbe Interactions. 

Abstract 
The plant microbiome can enhance plant resilience through diverse mechanisms such as improving nutrient uptake, priming plant defenses via induced systemic resistance, and increasing drought tolerance. In this project, we aim to unravel the genetic basis of potato-microbiome interactions. The ability of potatoes to shape and benefit from a rich microbiome is not uniform. Plant-microbiome interactions vary significantly across genotypes, which suggests a genetic basis for these relationships, prompting the question: Can breeding programs target traits that improve potato-microbiome cooperation?  

Authors: Jordan Earle_¹, Anna Neefjes_¹, Huub Hoefsloot_¹, Aalt-Jan van Dijk_¹, Petra Bleeker_¹, Julia Ruiz Capella_², Saskia van Wees_² 

Affilliation of authors  
¹University of Amsterdam 
²Utrecht University 

Abstract 
In plant defense, many different systems must change rapidly to coordinate a response to potential stressors, from hormones signaling, to the mRNA response, to the increase in key secondary metabolites. To gain a better understanding of these dynamics, we model the production of Glucosinolates in response to stress from pests and heat. These systems typically have too little data for a single solution model, so using Approximate Bayesian Computing, we examine the range of parameters and responses which could give rise to the behaviors observed experimentally and use these to both examine the initial assumptions made and guide future investigations. 

Authors: Jordi Alonso Esteve¹, Niels Aerts¹, Saskia C.M. van Wees², Dick de Ridder², Aalt D.J. van Dijk¹

Affilliation of authors
¹University of Amsterdam, Biosystems Data Analysis
²Utrecht University, Plant-Microbe Interactions
³Wageningen Universiteit and Research, Bioinformatics Group

Abstract
Recent advances in machine learning have fueled interest in predicting gene expression from genomic sequence, with implications for biology and crop improvement. However, the ability of current methods to capture
condition-specific responses remains unclear. Here, we evaluate approaches ranging from linear regression to convolutional neural networks and genomic language models, focusing on predicting gene expression
under biotic and abiotic stress treatments. Using mRNA time-series data, we extract condition-specific responses and test prediction on unseen genes. Our results show this task is harder than predicting basal expression. Modern models improve accuracy but often at the expense of interpretability.

Authors: Josias Kern¹, Francisco Pinto¹, Jochem Evers¹ 

Affiliation of authors  
Wageningen University¹ 

Abstract 
Crop growth models are an important tool for understanding how the interaction between genotype, environment, and management defines potato tolerance and yield response to drought and nitrogen stress. However, insights remain limited due to the need for laborious genotype-specific parameterisation. Combining model calibration with high-throughput phenotyping shows promise for generating the required parameter sets. Yet, the performance and data requirements of calibration methods remain unclear. We aim to identify suitable calibration methods, input traits, and number and timing of observations for generating parameter sets for the 200 genotypes in CropXR’s S1-Potato field trial in the WOFOST crop growth model. 

Authors: Julia Ruiz Capella, Niels Aerts and Saskia van Wees 

Affilliation of authors  
University of Utrecht, Institute of Environmental Biology, Plant-Microbe Interactions Group, Utrecht, The Netherlands. 

Abstract 
Plant diseases caused by pests and pathogens represent a major constraint for agriculture worldwide. These problems are aggravated by a changing climate, particularly harsh conditions such as drought, flooding, and high temperatures. These abiotic factors negatively impact plant performance, by affecting growth, yield, and by influencing plant immunity levels. Plant performance under various stress conditions relies on precise and timely activation and repression of different hormone-inducible gene regulatory networks (GRNs). While the role of individual hormone GRNs in controlling plant resistance to single stresses is well studied, much less is known about the interaction between these hormone GRNs when plants experience multiple stresses at the same time. Deciphering the key regulators involved in the immunity-high temperature interaction would bring the scientific community closer to the growth of crops resilient to the challenges associated to climate change.  

This project aims to study the effect of elevated temperature on plant immune responses by identifying which sectors within the hormone GRNs are differentially engaged under dual stress conditions and pinpoint key genes in early signalling that can be used to improve resilience. To this end, data from existing RNA-seq time series of plants treated with plant hormones (JA, SA, ABA, JA+SA, JA+ABA) together with two additional generated treatments from the same time series (ET and ET+MeJA) will be combined with an elevated temperature treatment at a limited number of time points.  

Additionally, different regimes of elevated temperature are being tested on plants exposed to phytohormones or pathogens attacks. Expression levels of stress-responsive marker genes are being analyzed to potentially identify main regulators of the immune response towards the exposure of the dual stress and response differences among the different applied regimes. These results have been used as input to define which temperature regime was used for the previously mentioned RNA-seq experiment. The plant treatments have been recently finalized and RNA isolation of those samples is planned to be done soon.  

Authors : Linfei Guo¹; Mark Roosjen¹; Dolf Weijers¹  

Affilliation of authors  
¹Laboratory of Biochemistry, Wageningen University, Wageningen, the Netherlands 

Abstract 
Plants, as sessile organisms, integrate various internal and environmental signals, such as hormones, light, and temperature, through intracellular networks that regulate growth and defense. This project aims to elucidate the role of key protein complexes, specifically BAP-D and COP1/SPA, in modulating these processes. By focusing on the composition and post-translational modifications of these complexes in response to growth cues and stress, we aim to understand how they mediate signal integration. Using protein proximity labeling, we will map interactions and modifications to uncover the regulatory mechanisms that govern plant growth under stress conditions. 

Authors: Lotte van der Krabben¹, Victoria Mironova¹, Ivo Rieu¹ 

Affilliation of authors  
¹Department of Plant & Animal Biology, Radboud Institute for Biological and Environmental Sciences, Radboud University, Nijmegen, The Netherlands 

Abstract 
While the effects of extreme heat stresses on pollen development have been widely studied, the mechanisms underlying reduced pollen viability in mild heat (MH) remain poorly understood. Here, we investigate the effects of MH on pollen and anther development. Using MTT viability staining, we investigate the developmental window most sensitive to MH, providing temporal resolution to the stress response. We apply snRNA-seq to resolve transcriptional responses at the cellular level during male reproductive development. Our integrated analysis offers new insights into the genetic and cellular mechanisms governing male reproductive resilience under environmental stress, critical knowledge for improving crop fertility and yield stability in a changing climate. 

Authors: Mahshad Keshtiarast Esfahani¹, Luca Laurenti¹, Manuel Mazo¹ 

Affilliation of authors  
¹ Delft Center for Systems and Control, Delft University of Technology, The Netherlands 

Abstract 
Characterizing the long-term behavior of biological models is essential for identifying the ranges of states a system can reliably occupy. While ordinary differential equation (ODE) models capture detailed dynamics, it remains difficult to rigorously ensure that the system trajectories stay within biologically meaningful ranges. Here, we present a computational framework that automatically constructs barrier functions—mathematical certificates separating safe from unsafe regions in the state space of nonlinear systems. Our method combines optimization with formal verification to identify forward-invariant sets: regions where the system state is guaranteed to remain once entered. We illustrate this workflow on representative biological network models, showing how it provides interpretable guarantees about system behaviour. This approach bridges control theory and systems biology, equipping modelers with rigorous tools to analyze the safe operating regimes of complex regulatory networks. 

Authors: Marcello Gazale¹, Frans Mangnus¹, Jenske Aben¹, Richard Visser¹, Niels Anten², Lucia Perez-Borroto¹, Gerard van der Linden¹ 

Affiliation of authors
¹ Laboratory of Plant Breeding (PBR, Wageningen University and Research)  
² Centre for Crop System Analysis (CSA, Wageningen University and Research) 

Abstract 
Drought alters resource allocation between source and sink tissues and significantly reduces potato yield. This project investigates how potato cultivars adapt to drought and subsequent rewatering, focusing on carbon partitioning dynamics. Trials are conducted in controlled greenhouse conditions (2024) and in tunnel settings (2025) that better mimic field environments. Results from 2024 show that drought decreases stomatal conductance and photosynthetic efficiency, limiting carbon assimilation for growth and development. Consequently, tuberization is impaired, leading to fewer and smaller tubers. Upon rewatering, tuberization and key physiological processes were restored to different extents across cultivars. 

Authors: Stijn Nagelkerke, Jochem Huijben, Corné Pieterse & Roeland Berendsen 

Affilliation of authors  
Utrecht University, Plant microbe interactions 

Abstract 
Plants can recruit microbes that assemble on the root system into a functional microbiome that improves root architecture, fosters enhanced nutrient uptake from the soil, and stimulates plant adaptive mechanisms to cope with various biotic and abiotic stresses. In this project, we aim to develop a deeper understanding of the relevance of the potato microbiome for potato performance. For this, we are investigating the microbiome profiles of 102 Dutch potato soils with diverse backgrounds and their effect on potato growth using an experimental setup in the Netherlands Plant-Ecophenotyping Centre (NPEC). Using machine learning we aim to link the root microbiome to plant growth and develop a microbiome-informed prediction model for potato growth. 

Authors: Thijs Stegmann, Yvet Boele, Fengjiao Lu, Joseph Peller, Anneke Horstman, Viola Willemsen.

Abstract 
Roots are in constant contact with their direct environment and are the direct supply for plants in terms of resources. Therefore, roots must be ultimately plastic: the development of the root system architecture (RSA) of a plant can change drastically depending on the environment. Such changes can also be observed when a plant is exposed to a drying soil, where under mild drought, the root growth phenotype is characteristically enhanced, as opposed to the response under severe drought. How the plant responds to mild drought can often be indicative of its resilience to drought over its whole life cycle. Thus, my research focuses on the development of the root system under differing water conditions. To do so, I use a collection of Arabidopsis thaliana accessions selected from various climactic regions. These accessions are grown on soil in rhizotrons, which allow for non-invasive imaging of root growth. Once this experiment is complete, I will be able to correlate different rooting strategies to different success rates, as well as look for genetic adaptation found in Arabidopsis, which may lead to interesting insights in the plasticity and adaptation of root systems. As of now, the first data has been collected and annotated, and some preliminary data can be shown, in which we already see visible differences.  

Authors: Samara Almeida Landman¹, Silvia Coolen¹, Guido Van den Ackerveken¹

Affilliation of authors
¹Translational Plant Biology, Utrecht University, Institute of Environmental Biology, Utrecht, The Netherlands

Abstract

Plant immune responses can be compromised under elevated temperatures, increasing susceptibility to pests and pathogens and posing a growing challenge for global agriculture in a changing climate. Plants can counteract threats such as pests and pathogens through pattern-triggered immunity (PTI), initiated by pattern recognition receptors (PRRs) that detect molecular patterns. While PTI signaling is well-studied, its modulation under elevated temperature remains unclear. This raises the key question: How does temperature affects pattern-triggered immune responses? Here we investigate how elevated temperatures affect pattern-triggered networks using time-series RNA sequencing in Arabidopsis thaliana. Pilot experiments defined key parameters, including temperature duration, pattern selection and concentration, and sampling time points.