Biology subjects

Riedel, M.

Publications and source records attributed to Riedel, M..

9 recordsLinked to original sources

bioRxiv2026

Global integrators of light signalling and nutrient homeostasis safeguard symbiosis under light stress

Their large, protein-rich seeds render many legume plants like beans, peas or soy attractive food or fodder crops. This is thanks to nitrogen-fixing root nodulation symbiosis (RNS) with rhizobial bacteria, a mutualistic association enabling legumes to not only form storage seeds, but also thrive in nitrogen poor habitats. These benefits come at a cost: accommodating millions of bacteria that actively fix N2 requires substantial nutrient and energy input. Evolutionarily successful hosts employ a multi-layered regulatory system balancing endogenous nutrient status and interorganismal nutrient exchange with symbiosis establishment and progression. Root infection as well as nodule organogenesis and -lifespan are tightly regulated, ensuring a mutualistic status. Under adverse conditions, established nodules can be deactivated by induction of nodule senescence in a process involving the transcription factor genes NAM ATAF CUC (NAC) 094 (Wang et al., 2023) and FIXATION UNDER NITRATE (FUN) (Lin et al., 2024). The molecular basis and role of senescence induction in RNS regulation is still far from understood. Here, we demonstrate that in Lotus japonicus, the two photomorphogenesis genes ELONGATED HYPOCOTYL5 (HY5) and the B-Box-containing zinc finger transcription factor gene BBX21 are required and sufficient for maintaining nodule function under high light intensities. Loss of either HY5 or BBX21 results in a light-dependent increase of NAC094 transcript levels, nodule necrosis and early senescence, and enhanced nodule proliferation. Moreover, expression of dominant negative HY5 or BBX21 is sufficient to induce nodule necrosis and hypernodulation. Our data establish LjHY5 and LjBBX21 as a novel gene pair safeguarding nodule function and nitrogen fixation by preventing light induced senescence. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/741514v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@b16552org.highwire.dtl.DTLVardef@72f82eorg.highwire.dtl.DTLVardef@3cfbcorg.highwire.dtl.DTLVardef@3b542a_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

plant biology↗

bioRxiv2025

Virtual World Coupling with Photosynthesis Evaluation for Synthetic Data Production

In this work, we couple the functional-structural plant model CPlantBox to the Unreal Engine by exploiting the implemented raytracing pipeline to evaluate light influx on the plant surface. There are many approaches for photosynthesis computation and light evaluation, though they typically are limited by versatility, compute speed, or operate on much coarser resolutions. This work specifically addresses the concern that data generation pipelines tend to be unresponsive and do not include model-based knowledge as part of the generation pipeline. Using established photosynthesis solvers, we model the interaction between the Unreal Engine and the FSPM to measure physical properties in the virtual world. This is successful if we are able to reproduce experimental results using an in silico model. As part of the pipeline, we generate a surface geometry and utilize material shaders that are designed to establish a baseline surface model for light interception and transmission, based on simple parameter sets that can be calibrated. Using a Selhausen field experiment as baseline, we reproduce the photosynthesis effectiveness of the plants in the 2016 winter wheat experiments. Our pipeline is deeply intertwined with data generation and has been proven to perform well at scale. In this work, we build on our previous work by showcasing both a simulation study of a light evaluation as well as quantifying how well our system performs on high-performance computing systems.

bioinformatics↗

bioRxiv2024

VRoot: An XR-Based Application for Manual Root System Architecture Reconstruction

This article describes an immersive extended reality reconstruction tool for root system architectures from 3D volumetric scans of soil columns. We have conducted a laboratory user study to assess the performance of new users with our software in comparison to classical and established desktop software. We utilize a functional-structural plant model to derive a synthetic root architecture that serves as objective quantification for the root system architecture reconstruction. Additionally, we have collected quantitative feedback on our software in the form of standardized questionnaires. This work provides an overview of the extended reality software and the advantage of using immersive techniques for 3D data extraction in plant science. Through our formal study, we further provide a quantification of manual root system reconstruction accuracy. We observe an increase in root system architecture reconstruction accuracy (F1) compared to state-of-the-art desktop software and a more robust extraction quality.

bioinformatics↗

bioRxiv2024

Rapid UPF1 depletion illuminates the temporal dynamics of the NMD-regulated transcriptome in human cells

The helicase UPF1 acts as the central essential factor in human nonsense-mediated mRNA decay (NMD) and is involved in various other mRNA degradation processes. Given its multifunctionality, distinguishing between mRNAs regulated directly and indirectly by UPF1 remains a critical challenge. We engineered two different conditional degron tags into endogenous UPF1 in human cell lines to probe the consequences of UPF1 rapid depletion. UPF1 degradation inhibits NMD within hours and strongly stabilizes endogenous NMD substrates, which can be classified into different groups based on their expression kinetics. Extended UPF1 depletion results in massive transcript and isoform alterations, partially driven by secondary effects. We define a high-confidence UPF1-regulated core set of transcripts, which consists mostly of NMD substrates. NMD-regulated genes are involved in brain development and the integrated stress response, among other biological processes. In summary, UPF1 degron systems rapidly inhibit NMD, providing valuable insights into its roles across various experimental systems.

molecular biology↗

bioRxiv2023

Geographically Biased Composition of NetMHCpan Training Datasets and Evaluation of MHC-Peptide Binding Prediction Accuracy on Novel Alleles

Bias in neural network model training datasets has been observed to decrease prediction accuracy for groups underrepresented in training data. Thus, investigating the composition of training datasets used in machine learning models with health-care applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. As antigen presentation is a critical step in mounting the adaptive immune response, previous work has used these or similar predictions models in a broad array of applications, from explaining asymptomatic viral infection to cancer neoantigen prediction. However, these models have also been shown to be biased toward hydrophobic peptides, suggesting the network could also contain other sources of bias. Here, we report the composition of the networks training datasets are heavily biased toward European Caucasian individuals and against Asian and Pacific Islander individuals. We test the ability of NetMHCpan-4.1 and NetMHCpan-4.0 to distinguish true binders from randomly generated peptides on alleles not included in the training datasets. Unexpectedly, we fail to find evidence that the disparities in training data lead to a meaningful difference in prediction quality for alleles not present in the training data. We attempt to explain this result by mapping the HLA sequence space to determine the sequence diversity of the training dataset. Furthermore, we link the residues which have the greatest impact on NetMHCpan predictions to structural features for three alleles (HLA-A*34:01, HLA-C*04:03, HLA-DRB1*12:02).

immunology↗

bioRxiv2023

Cardiopulmonary structural, functional and immune-alterations in a Down syndrome mouse model and upon modulation of EGCG

In individuals with Down syndrome (DS), cardiovascular and pulmonary diseases are the most common health problem and result in increased mortality and morbidity. Although these clinical comorbidities are well described, no preclinical models for DS are fully characterized for cardiopulmonary alterations, preventing research to understanding the development and pharmacological modulation of lungs, heart and immune system. Our objective is to characterize the cardiopulmonary and immunological phenotype in Ts65Dn mice and investigate the modulatory effects green tea extract enriched in epigallocatechin 3 gallate (GTE-EGCG). GTE-EGCG administration started at embryonic day 9 and was discontinued at postnatal day (PD) 180. Newborns were longitudinally monitored until PD210 using micro-computed tomography. At endpoint, we characterized the structural, functional and immunological alterations and persistent effects of GTE-EGCG administration. This study revealed normal lung development in the Ts65Dn mice and highlighted RV hypertrophy and immunological alterations. GTE-EGCG administration resulted in genotype-specific and genotype-independent alterations resulting in lung immaturation and airway hyperreactivity. Our results highlight the cardiovascular and immunological phenotype of Ts65Dn mice and potential use for safety studies of therapeutic agents in a DS-specific context. Summary statementThis study longitudinally follows respiratory and cardiac alterations in the Ts65Dn mouse model and describes the impact of prenatal EGCG modulation on the euploid and trisomic phenotype

systems biology↗

bioRxiv2022

Evasive spike variants elucidate the preservation of T cell immune response to the SARS-CoV-2 omicron variant

The Omicron variants boast the highest infectivity rates among all SARS-CoV-2 variants. Despite their lower disease severity, they can reinfect COVID-19 patients and infect vaccinated individuals as well. The high number of mutations in these variants render them resistant to antibodies that otherwise neutralize the spike protein of the original SARS-CoV-2 spike protein. Recent research has shown that despite its strong immune evasion, Omicron still induces strong T Cell responses similar to the original variant. This work investigates the molecular basis for this observation using the neural network tools NetMHCpan-4.1 and NetMHCiipan-4.0. The antigens presented through the MHC Class I and Class II pathways from all the notable SARS-CoV-2 variants were compared across numerous high frequency HLAs. All variants were observed to have equivalent T cell antigenicity. A novel positive control system was engineered in the form of spike variants that did evade T Cell responses, unlike Omicron. These evasive spike proteins were used to statistically confirm that the Omicron variants did not exhibit lower antigenicity in the MHC pathways. These results suggest that T Cell immunity mounts a strong defense against COVID-19 which is difficult for SARS-CoV-2 to overcome through mere evolution. Author summary

immunology↗

bioRxiv2019

Completing Linnaeus’s Inventory of the Swedish Insect Fauna: Only 5000 Species Left

Despite more than 250 years of taxonomic research, we still have only a vague idea about the true size and composition of the faunas and floras of the planet. Many biodiversity inventories provide limited insight because they focus on a small taxonomic subsample or a tiny geographic area. Here, we report on the size and composition of the Swedish insect fauna, thought to represent roughly half of the diversity of multicellular life in one of the largest European countries. Our results are based on more than a decade of data from the Swedish Taxonomy Initiative and its massive inventory of the countrys insect fauna, the Swedish Malaise Trap Project The fauna is considered one of the best known in the world, but the initiative has nevertheless revealed a surprising amount of hidden diversity: more than 3,000 new species (301 new to science) have been documented so far. Here, we use three independent methods to analyze the true size and composition of the fauna at the family or subfamily level: (1) assessments by experts who have been working on the most poorly known groups in the fauna; (2) estimates based on the proportion of new species discovered in the Malaise trap inventory; and (3) extrapolations based on species abundance and incidence data from the inventory. For the last method, we develop a new estimator, the combined non-parametric estimator, which we show is less sensitive to poor coverage of the species pool than other popular estimators. The three methods converge on similar estimates of the size and composition of the fauna, suggesting that it comprises around 33,000 species. Of those, 8,600 (26%) were unknown at the start of the inventory and 5,000 (15%) still await discovery. We analyze the taxonomic and ecological composition of the estimated fauna, and show that most of the new species belong to Hymenoptera and Diptera groups that are decomposers or parasitoids. Thus, current knowledge of the Swedish insect fauna is strongly biased taxonomically and ecologically, and we show that similar but even stronger biases have distorted our understanding of the fauna in the past. We analyze latitudinal gradients in the size and composition of known European insect faunas and show that several of the patterns contradict the Swedish data, presumably due to similar knowledge biases. Addressing these biases is critical in understanding insect biomes and the ecosystem services they provide. Our results emphasize the need to broaden the taxonomic scope of current insect monitoring efforts, a task that is all the more urgent as recent studies indicate a possible worldwide decline in insect faunas.

ecology↗

bioRxiv2019

Ontological Dimensions of Cognitive-Neural Mappings

The growing literature reporting results of cognitive-neural mappings has increased calls for an adequate organizing ontology, or taxonomy, of these mappings. This enterprise is non-trivial, as relevant dimensions that might contribute to such an ontology are not yet agreed upon. We propose that any candidate dimensions should be evaluated on their ability to explain observed differences in functional neuroimaging activation patterns. In this study, we use a large sample of task-based functional magnetic resonance imaging (task-fMRI) results and a data-driven strategy to identify these dimensions. First, using a data-driven dimension reduction approach and multivariate distance matrix regression (MDMR), we quantify the variance among activation maps that is explained by existing ontological dimensions. We find that task paradigm categories explain the most variance among task-activation maps than other dimensions, including latent cognitive categories. Surprisingly, study ID, or the study from which each activation map was reported, explained close to 50% of the variance in activation patterns. Using a clustering approach that allows for overlapping clusters, we derived data-driven latent activation states, associated with re-occurring configurations of the canonical fronto-parietal/salience, sensory-motor, and default mode network activation patterns. Importantly, with only four data-driven latent dimensions, one can explain greater variance among activation maps than all conventional ontological dimensions combined. These latent dimensions may inform a data-driven cognitive ontology, and suggest that current descriptions of cognitive processes and the tasks used to elicit them do not accurately reflect activation patterns commonly observed in the human brain.

neuroscience↗