Biology subjects

Gaiser, T.

Publications and source records attributed to Gaiser, T..

8 recordsLinked to original sources

bioRxiv2026

Macrophages target PVR/CD155 on colorectal cancer cells via REVERBalpha

Background&AimsPatients with microsatellite-instable (MSI) colorectal cancer (CRC) benefit from immune checkpoint therapy. For patients with microsatellite-stable (MSS) non-MSI tumors targeting alternative immune checkpoints, such as the Poliovirus receptor (PVR/CD155), may extend response to checkpoint inhibitors and thereby improve outcomes. The drugable transcription factor REVERB (NR1D1) is a master repressor of macrophage function. We hypothesized that regulation of PVR allows elimination of tumor cells by macrophage-directed precision therapy. MethodsHuman CRC cell lines (MSS+: HT29, SW480; MSI+: HCT116), patient-derived organoids (MSS+ PDOs) and tissues were assessed by PCR, Western blot, immunohistochemistry and flow cytometry. 3D co-cultures of CRC cells with macrophages derived from THP1 monocytic leukemia cells or peripheral blood of healthy donors were analysed by microscopy and viability assays. Functional perturbation of PVR and REVERB was achieved by CRISPR/Cas9-sgRNA gene modification, synthetic ligands or blocking antibodies (Abs). ResultsREVERB bound a cognate DNA-element in the -1 kb human PVR gene promoter, and its agonist (SR9009) super-repressed, whereas its antagonist (SR8278) de-repressed transcription of PVR mRNA in macrophages. Macrophages with CRISPR-modified REVERB were unresponsive to ligand, devoid of PVR protein, showed more phagocytosis, tumor cell efferocytosis and expression of genes related to host immunity (PDL1, TLR4 e.a.) than clones with the wild-type receptor. Macrophages lowered the viability of tumor cells, potentiated by PVR blocking Ab or PVR knock-out in tumor cells. Consistently, REVERB antagonist augmented tumor cell death in co-cultures with macrophages and PVR blocking Ab. ConclusionCo-addressing the checkpoint axis REVERB-PVR may represent a novel intervention strategy for patients with MSS+ CRC.

cancer biology↗

bioRxiv2024

The evolution of root hydraulic traits in wheat over 100 years of breeding

Wheat (Triticum aestivum L.) plays a vital role in global food security, and understanding its root traits is essential for improving water uptake under varying environmental conditions. This study investigates how breeding over a century has influenced root morphological and hydraulic properties in six German winter wheat cultivars released between 1895 and 2002. Field and hydroponic experiments were used to measure root diameter, root number, branching density, and whole root system hydraulic conductance (Krs). Results showed a significant decline in root axes number and Krs over time, while root diameter remained stable across cultivars. Additionally, dynamic functional-structural modeling using the whole-plant model CPlantBox was employed to simulate the development of Krs with root system growth, revealing that older cultivars consistently had higher hydraulic conductance than modern ones. The combined approach of field phenotyping and modeling provided a comprehensive view of the changes in root traits with breeding. These findings suggest that breeding may have unintentionally favored cultivars with smaller root systems and more conservative water uptake strategies, under the high-input, high-density conditions of modern agriculture. The lessons from this study may inform future breeding efforts aimed at optimizing wheat root systems, helping to develop cultivars with water uptake better tailored to locally changing environmental conditions.

plant biology↗

bioRxiv2023

Root hydraulic properties: an exploration of their variability across scales

Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment-, individual root-, or root system scale and determined its variability and the main factors contributing to it. We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients). Furthermore, whole root system conductance increased significantly with root system age across several crop species, causing very large variation in the data (> 2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. This behaviour was also observed in simulations with computational plant models, suggesting common patterns across studies and species. These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database (https://roothydraulic-properties.shinyapps.io/database/), fostering scientific exchange.

plant biology↗

bioRxiv2022

Calibration of crop phenology models: Going beyond recommendations

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are "obligatory" parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their "usual" calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.

plant biology↗

bioRxiv2020

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. HighlightsO_LIWe propose a new approach to deriving calibration recommendations for system models C_LIO_LIApproach is based on analyzing calibration in multi-model simulation exercises C_LIO_LIResulting recommendations are holistic and anchored in actual practice C_LIO_LIWe apply the approach to calibration of crop models used to simulate phenology C_LIO_LIRecommendations concern: objective function, parameters to estimate, software used C_LI

plant biology↗

bioRxiv2020

Multi-model evaluation of phenology prediction for wheat in Australia

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

plant biology↗

bioRxiv2019

How well do crop models predict phenology, with emphasis on the effect of calibration?

Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

plant biology↗

bioRxiv2019

Multiparametric phenotyping of compound effects on patient derived organoids

Patient derived organoids resemble the biology of tissues and tumors, enabling ex vivo modeling of human diseases from primary patient samples. Organoids can be used as models for drug discovery and are being explored to guide clinical decision making. Patient derived organoids can have heterogeneous morphologies with unclear biological causes and relationship to treatment response. Here, we used high-throughput, image-based profiling to quantify phenotypes of over 5 million individual colorectal cancer organoids after treatment with more than 500 small molecules. Integration of data using a joint multi-omics modelling framework identified organoid size and cystic vs. solid organoid architecture as axes of morphological variation across organoids. Mechanistically, we found that organoid size was linked to IGF1 receptor signaling, while a cystic organoid architecture was associated with an LGR5+ stemness program. Treatment-induced organoid morphology reflected organoid viability, drug mechanism of action, and was biologically interpretable using joint modelling. Inhibition of MEK led to cystic reorganization of organoids and increased expression of LGR5, while inhibition of mTOR induced IGF1 receptor signaling. In conclusion, we identified shared axes of variation for colorectal cancer organoid morphology, their underlying biological mechanisms, and pharmacological interventions with the ability to move organoids along them. Image-based profiling of patient derived organoids coupled with multi-omics integration facilitates drug discovery by linking drug responses with underlying biological mechanisms.

genomics↗