Biology subjects

De Meyer, S.

Publications and source records attributed to De Meyer, S..

5 recordsLinked to original sources

bioRxiv2023

Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform

BackgroundThermography is a popular tool to assess plant water use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect drought stress. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. ResultsThe sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic difference in the plants water use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple thermal infrared indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. ConclusionOverall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.

plant biology↗

bioRxiv2022

Predicting yield traits of individual field-grown Brassica napus plants from rosette-stage leaf gene expression

BackgroundIn the plant sciences, results of laboratory studies often do not translate well to the field because lab growth conditions are very different from field conditions. To help close this lab-field gap, we developed a new strategy for studying the wiring of plant traits directly in the field, based on molecular profiling and phenotyping of individual plants of the same genetic background grown in the same field. This single-plant omics strategy leverages uncontrolled micro-environmental variation across the field and stochastic variation among the individual plants as information sources, rather than controlled perturbations. Here, we use single-plant omics on winter-type Brassica napus (rapeseed) plants to investigate to what extent rosette-stage gene expression profiles can be linked to the early and late phenotypes of individual field-grown plants. ResultsWe find that rosette leaf gene expression in autumn has substantial predictive power for both autumnal leaf phenotypes and final yield in spring. Many of the top predictor genes are linked to developmental processes known to occur in autumn in winter-type B. napus accessions, such as the juvenile-to-adult and vegetative-to-reproductive phase transitions, indicating that the yield potential of winter-type B. napus is influenced by autumnal development. ConclusionsOur results show that profiling individual plants under uncontrolled field conditions is a valid strategy for identifying genes and processes influencing crop yield in the field.

systems biology↗

bioRxiv2022

Development and optimisation of a high-throughput screening assay for in vitro anti-SARS-CoV-2 activity: evaluation of 5676 phase 1 passed structures

Although vaccines are currently used to control the coronavirus disease 2019 (COVID-19) pandemic, treatment options are urgently needed for those who cannot be vaccinated and for future outbreaks involving new severe acute respiratory syndrome coronavirus virus 2 (SARS-CoV-2) strains or coronaviruses not covered by current vaccines. Thus far, few existing antivirals are known to be effective against SARS-CoV-2 and clinically successful against COVID-19. As part of an immediate response to the COVID-19 pandemic, a high-throughput, high content imaging-based SARS-CoV-2 infection assay was developed in VeroE6-eGFP cells and was used to screen a library of 5676 compounds that passed phase 1 clinical trials. Eight candidates (nelfinavir, RG-12915, itraconazole, chloroquine, hydroxychloroquine, sematilide, remdesivir, and doxorubicin) with in vitro anti-SARS-CoV-2 activity in VeroE6-eGFP and/or Caco-2 cell lines were identified. However, apart from remdesivir, toxicity and pharmacokinetic data did not support further clinical development of these compounds for COVID-19 treatment.

immunology↗

bioRxiv2020

In Vitro Activity of Itraconazole Against SARS-CoV-2

BackgroundAs long as there is no vaccine available, having access to inhibitors of SARS-CoV-2 will be of utmost importance. Antivirals against coronaviruses do not exist, hence global drug re-purposing efforts have been carried out to identify agents that may provide clinical benefit to patients with COVID-19. Itraconazole, an antifungal agent, has been reported to have potential activity against animal coronaviruses. MethodsUsing cell-based phenotypic assays, the in vitro antiviral activity of itraconazole and 17-OH itraconazole was assessed against clinical isolates from a German and Belgian patient infected with SARS-CoV-2. ResultsItraconazole demonstrated antiviral activity in human Caco-2 cells (EC50 = 2.3 M; MTT assay). Similarly, its primary metabolite, 17-OH itraconazole, showed inhibition of SARS-CoV-2 activity (EC50 = 3.6 M). Remdesivir inhibited viral replication with an EC50 = 0.4 M. Itraconazole and 17-OH itraconazole resulted in a viral yield reduction in vitro of approximately 2-log10 and approximately 1-log10, as measured in both Caco-2 cells and VeroE6-eGFP cells, respectively. The viral yield reduction brought about by remdesivir or GS-441524 (parent nucleoside of the antiviral prodrug remdesivir; positive control) was more pronounced, with an approximately 3 log10 drop and >4 log10 drop in Caco-2 cells and VeroE6-eGFP cells, respectively. DiscussionItraconazole and 17-OH itraconazole exert in vitro low micromolar activity against SARS-CoV-2. Despite the in vitro antiviral activity, itraconazole did not result in a beneficial effect in hospitalized COVID-19 patients in a clinical study (EudraCT Number: 2020-001243-15). HighlightsO_LIItraconazole exerted in vitro low micromolar activity against SARS-CoV-2 (EC50 = 2.3 M) C_LIO_LIRemdesivir demonstrated potent antiviral activity, confirming validity of the assay C_LIO_LIItraconazole has since shown no efficacy in a clinical study in hospitalized COVID-19 patients C_LI

pharmacology and toxicology↗

bioRxiv2020

Using single-plant -omics in the field to link maize genes to functions and phenotypes

Most of our current knowledge on plant molecular biology is based on experiments in controlled lab environments. Over the years, lab experiments have generated substantial insights in the molecular wiring of plant developmental processes, stress responses and phenotypes. However, translating these insights from the lab to the field is often not straightforward, in part because field growth conditions are very different from lab conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene-phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field, and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional lab-generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field-generated transcriptome and metabolome data can be used to quantitatively predict at least some individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab-field gap.

systems biology↗