Functional characterization of the immune receptor RLP32 and its ligand IF1

Pflanzen besitzen Immunrezeptoren, sogenannte pattern recognition receptors (PRRs), über die molekulare Muster von Pathogenen erkannt werden (pathogen-associated molecular patterns (PAMPs)). Durch die Erkennung von Pathogenen werden in der Pflanze Abwehrmechanismen (PAMP triggered immunity (PTI)) induziert. Vor Beginn dieser Doktorarbeit wurde der Translationsinitiationsfaktor 1 (IF1) aus bakteriellem Extrakt aufgereinigt. IF1initiiert frühe Immunreaktionen in A. thaliana. Das Rezeptorprotein RLP32 wurde als möglicher IF1-Rezeptor in A. thaliana identifiziert. In dieser Arbeit konnte RLP32 als Rezeptor von IF1 durch biochemische und immunologische Analysen bestätigt werden. RLP32 ist nur in einigen Mitgliedern der Brassicaceae-Familie zu finden. Die Komplementation von IF1-insensitiven Pflanzen mit RLP32 verhalf den Pflanzen dazu auf IF1 zu reagieren. Ebenso konnte gezeigt werden, dass IF1 und RLP32 in planta direkt und spezifisch miteinander interagieren. Ko-Immunopräzipitationsexperimente zeigten, dass RLP32 konstitutiv mit der Adapterkinase SOBIR1 interagiert. Der RLP32/SOBIR1-Komplex interagiert IF1-abhängig mit dem Ko-Rezeptor BAK1/SERK3, so wie es bereits für andere PRR/ Ligandensysteme beschrieben wurde. IF1 konnte als Ligand von RLP32 bestätigt werden. IF1 ist in Proteobakterien konserviert. IF1 aus verschiedenen Proteobacteria wiesen ähnliche immunologische Aktivitäten auf. Die Analyse von Deletionskonstrukten ergab, dass fast das ganze IF1-Protein (oder dessen Tertiärstrukturfaltung) nötig sind, für dessen immunologische Aktivität ist. Kulturpflanzen, die zu den Solanaceae gehören, verfügen nicht über ein RLP32-Homolog. Sie sind anfällig gegenüber proteobakeriellen Pathogenen. Nachdem RLP32 stabil in N. benthamiana transformiert wurde, konnte eine Immunantwort durch IF1 induziert werden. N. benthamiana, die RLP32 exprimieren, zeigten eine erhöhte Resistenz nach Infektion mit dem Pseudomonas syringae hrcC- Mutanten-Stamm, was darauf schließen lässt, dass N. benthamiana Reaktionsfähigkeit auf pathogene Proteobakterien erlangte. IF1 konnte erfolgreich das Immunsystem in A. thaliana primen, was zu einer erhöhten Resistenz gegenüber P. syringae führte. Im Gegensatz dazu führte eine Vorbehandlung von rlp32-knock-out Mutanten mit IF1 nicht zu Resistenz gegenüber P. syringae. Die Entdeckung des neuen Rezeptor-Liganden-Paares RLP32/IF1 kann in modernen Züchtungsmethoden impliziert werden, um Kulturpflanzen zu entwickeln, die gegenüber proteobakteriellen Pathogenen resistent sind.Plants can detect pathogens via pattern recognition receptors (PRRs) that bind pathogen-associated molecular patterns (PAMPs), thereby inducing PAMP triggered immunity (PTI). Prior to this study, the translation initiation factor IF1 was purified from bacterial extract and was shown to trigger early immune responses in A. thaliana. The receptor-like protein RLP32 was identified as the putative pattern recognition receptor of IF1 in A. thaliana. In this study, RLP32 was confirmed as the IF1 receptor using a variety of biochemical and immunological analyses. RLP32 is only present in some members of the Brassicaceae family. Complementation of IF1-insensitive plants with the RLP32 conferred the plants with the ability to respond to IF1. IF1 and RLP32 were further shown to directly and specifically interact in planta. Co-immunoprecipitation experiments showed constitutive interaction of RLP32 with the adaptor kinase SOBIR1. The RLP32/SOBIR1 complex interacts with the coreceptor BAK1/SERK3 in an IF1 dependent manner, analogous to what is reported for other PRR/ligand systems. IF1 was confirmed to be the ligand of RLP32. IF1 is conserved among Proteobacteria. IF1 derived from different Proteobacteria showed similar immunogenic activity. Deletion construct analysis revealed that virtually the entire IF1 protein (or its tertiary structure fold) are required for its immunogenic activity. Solanaceous crops lack an RLP32-homolog and are susceptible towards many pathogenic Proteobacteria. After stable transformation of N. benthamiana with RLP32, immune responses are induced when elicited with IF1. N. benthamiana expressing RLP32 showed enhanced resistance after infection with the hrcC- mutant strain of Pseudomonas syringae, indicating that N. benthamiana gained responsiveness to pathogenic proteobacteria. IF1 successfully primed immunity in A. thaliana leading to an enhanced resistance to P. syringae. In contrast, pretreatment with IF1 did not protect rlp32 knock-out mutant genotypes. The discovery of the new receptor-ligand pair RLP32/IF1 can be implemented in modern breeding techniques in crop plants to potentially improve resistance against proteobacterial pathogens

Modelling and Optimisation of Laser-Structured Battery Electrodes

An electrochemical multi-scale model framework for the simulation of arbitrarily three-dimensional structured electrodes for lithium-ion batteries is presented. For the parameterisation, the electrodes are structured via laser ablation, and the model is fit to four different, experimentally electrochemically tested cells. The parameterised model is used to optimise the parameters of three different pattern designs, namely linear, gridwise, and pinhole geometries. The simulations are performed via a finite element implementation in two and three dimensions. The presented model is well suited to depict the experimental cells, and the virtual optimisation delivers optimal geometrical parameters for different C-rates based on the respective discharge capacities. These virtually optimised cells will help in the reduction of prototyping cost and speed up production process parameterisation

Electro-Chemical Modelling of Laser Structured Electrodes

A simulation study performed in the scope of the project RealLi! is presented. One of the project’s main goals is to improve NMC811 and graphite electrode cycling capacities at high C-rates. The rapid charging and discharging capability of batteries is improved using laser ablation to introduce structures into the surface of the electrode composite layers. Due to improved transport kinetics, this not only improves the electrochemical properties in the high-current range, but also homogenizes and accelerates the electrolyte wetting during production as a side effect. This is particularly advantageous in thick-film electrodes for providing high energy densities. This study supports the laser structuring process of battery electrodes [1][2] via a virtual optimisation, based on electro-chemical battery models. The electrodes are structured by ultrafast laser ablation, with parallel channels being introduced along the electrode surface. This modification enables an easier electrolyte penetration, a reduced charge transfer resistance, and shortened lithium-ion transport pathways which finally leads to a reduced diffusion overpotential at high C-rates. The geometrical parameters of this process (pitch distance, width, and cross-sectional shape of laser-generated micro-channels) and their impact on cell performance are virtually optimised by simulations. The simulations are based on a homogenised multi-scale model, applied in 2D/3D macroscopic cuts, coupled with 1D microscopic particle cuts. The 2D/3D macroscopic electrolyte transport equations are common concentrated electrolyte equations. The microscopic particle transport equations are either a set of non-linear Fick’s Diffusion equations [3] that are used to describe spherical symmetric NMC811 materials or a set of Cahn-Hilliard equations [4] that consistently describe the phase separating nature of graphite anodes in cylindrically symmetric particles. The underlying numerical method is an implicit-multi-scale finite-element-method [3] that allows for a flexible implementation of such models. The first results of this ongoing project will be presented along with the overall structure of the method and its implementation. The results include geometrical as well as electro-chemical parameter variations and their respective sensitivity analysis. Furthermore, in the discussed electrode geometry the possible anisotropic structure of an electrode (due to particle shape and distribution) has a bigger impact than in unstructured electrodes. The improved transport pathways along the channels, therefore, imply the necessity of a more thorough homogenisation than it is usually done, for example in a Newman-Model approach. A long-term goal of this work is to enable a significant increase in areal energy density, i.e., the use of thicker electrode films and the use of advanced high energy materials in battery electrodes. [1]3D silicon/graphite composite electrodes for high-energy lithium-ion batteries, W. Pfleging et.al., Electrochimica Acta, Volume 317, 2019, Pages 502-508, J Power Sources 145 (5), 2345-2356 [2]Recent progress in laser texturing of battery materials: a review of tuning electrochemical performances, related material development, and prospects for large-scale manufacturing,W. Pfleging,International Journal of Extreme Manufacturing, Vol 3, 2020 [3]Derivation of a multi-scale battery model and its high-performance computing implementation, F. Pichler, Doctoral Thesis, Graz, 2018 [4]Phase Transformation Dynamics in Porous Battery Electrodes, R. Ferguson, M. Z. Bazant, Electrochimica Acta, Volume 146, Pages 89-97, 2014 Figure

The immunity-related GTPase Irga6 dimerizes in a parallel head-to-head fashion

The immunity-related GTPases (IRGs) constitute a powerful cell-autonomous resistance system against several intracellular pathogens. Irga6 is a dynamin-like protein that oligomerizes at the parasitophorous vacuolar membrane (PVM) of Toxoplasma gondii leading to its vesiculation. Based on a previous biochemical analysis, it has been proposed that the GTPase domains of Irga6 dimerize in an antiparallel fashion during oligomerization.Leibniz Graduate School grants: (SFB958, SFB635)

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best

Genotyping-by-sequencing-based identification of Arabidopsis pattern recognition receptor RLP32 recognizing proteobacterial translation initiation factor IF1

Activation of plant pattern-triggered immunity (PTI) relies on the recognition of microbe-derived structures, termed patterns, through plant-encoded surface-resident pattern recognition receptors (PRRs). We show that proteobacterial translation initiation factor 1 (IF1) triggers PTI in Arabidopsis thaliana and related Brassicaceae species. Unlike for most other immunogenic patterns, IF1 elicitor activity cannot be assigned to a small peptide epitope, suggesting that tertiary fold features are required for IF1 receptor activation. We have deployed natural variation in IF1 sensitivity to identify Arabidopsis leucine-rich repeat (LRR) receptor-like protein 32 (RLP32) as IF1 receptor using a restriction site-associated DNA sequencing approach. RLP32 confers IF1 sensitivity to rlp32 mutants, IF1-insensitive Arabidopsis accessions and IF1-insensitive Nicotiana benthamiana, binds IF1 specifically and forms complexes with LRR receptor kinases SOBIR1 and BAK1 to mediate signaling. Similar to other PRRs, RLP32 confers resistance to Pseudomonas syringae, highlighting an unexpectedly complex array of bacterial pattern sensors within a single plant species

Genotyping-by-sequencing-based identification of Arabidopsis pattern recognition receptor RLP32 recognizing proteobacterial translation initiation factor IF1

Activation of plant pattern-triggered immunity (PTI) relies on the recognition of microbe-derived structures, termed patterns, through plant-encoded surface-resident pattern recognition receptors (PRRs). We show that proteobacterial translation initiation factor 1 (IF1) triggers PTI in Arabidopsis thaliana and related Brassicaceae species. Unlike for most other immunogenic patterns, IF1 elicitor activity cannot be assigned to a small peptide epitope, suggesting that tertiary fold features are required for IF1 receptor activation. We have deployed natural variation in IF1 sensitivity to identify Arabidopsis leucine-rich repeat (LRR) receptor-like protein 32 (RLP32) as IF1 receptor using a restriction site-associated DNA sequencing approach. RLP32 confers IF1 sensitivity to rlp32 mutants, IF1-insensitive Arabidopsis accessions and IF1-insensitive Nicotiana benthamiana, binds IF1 specifically and forms complexes with LRR receptor kinases SOBIR1 and BAK1 to mediate signaling. Similar to other PRRs, RLP32 confers resistance to Pseudomonas syringae, highlighting an unexpectedly complex array of bacterial pattern sensors within a single plant species