Integrated framework of the immune-defense transcriptional signatures in the arabidopsis shoot apical meristem

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The growing tips of plants grow sterile; therefore, disease-free plants can be generated from them. How plants safeguard growing apices from pathogen infection is still a mystery. The shoot apical meristem (SAM) is one of the three stem cells niches that give rise to the above ground plant organs. This is very well explored; however, how signaling networks orchestrate immune responses against pathogen infections in the SAM remains unclear. To reconstruct a transcriptional framework of the differentially expressed genes (DEGs) pertaining to various SAM cellular populations, we acquired large-scale transcriptome datasets from the public repository Gene Expression Omnibus (GEO). We identify here distinct sets of genes for various SAM cellular populations that are enriched in immune functions, such as immune defense, pathogen infection, biotic stress, and response to salicylic acid and jasmonic acid and their biosynthetic pathways in the SAM. We further linked those immune genes to their respective proteins and identify interactions among them by mapping a transcriptome-guided SAM-interactome. Furthermore, we compared stem-cells regulated transcriptome with innate immune responses in plants showing transcriptional separation among their DEGs in Arabidopsis. Besides unleashing a repertoire of immune-related genes in the SAM, our analysis provides a SAM-interactome that will help the community in designing functional experiments to study the specific defense dynamics of the SAM-cellular populations. Moreover, our study promotes the essence of large-scale omics data re-analysis, allowing a fresh look at the SAM-cellular transcriptome repurposing data-sets for new questions

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors

Analysis of Regulatory Networks during Cell Differentiation and in Infection Biology

Das zentrale Paradigma der Systembiologie zielt auf ein möglichst umfassendes Ver-ständnis der komplexen Zusammenhänge biologischer Systeme. Die in dieser Arbeit angewandten Methoden folgen diesem Grundsatz. Am Beispiel von drei auf Basis von Datenbanken und aktueller Literatur rekonstruier-ten Netzwerkmodellen konnte in der hier vorliegenden Arbeit die Gültigkeit analyti-scher und prädiktiver Algorithmen nachgewiesen werden, die in Form der Analy-sesoftware Jimena angewandt wurden. Die daraus resultierenden Ergebnisse sowohl für die Berechnung von stabilen Systemzuständen, der dynamischen Simulation, als auch der Identifikation zentraler Kontrollknoten konnten experimentell validiert wer-den. Die Ergebnisse wurden in einem iterativen Prozess verwendet werden um das entsprechende Netzwerkmodell zu optimieren. Beim Vergleich des Verhaltens des semiquantitativ ausgewerteten regulatorischen Netzwerks zur Kontrolle der Differenzierung humaner mesenchymaler Stammzellen in Chondrozyten (Knorpelbildung), Osteoblasten (Knochenbildung) und Adipozyten (Fett-zellbildung) konnten 12 wichtige Faktoren (darunter: RUNX2, OSX/SP7, SOX9, TP53) mit Hilfe der Berechnung der Bedeutung (Kontrollzentralität der Netzwerkknoten identifi-ziert werden). Der Abgleich des simulierten Verhaltens dieses Netzwerkes ergab eine Übereinstimmung mit experimentellen Daten von 47,2%, bei einem widersprüchlichen Verhalten von ca. 25%, dass unter anderem durch die temporäre Natur experimentel-ler Messungen im Vergleich zu den terminalen Bedingungen des Berechnung der stabilen Systemzustände erklärt werden kann. Bei der Analyse des Netzwerkmodells der menschlichen Immunantwort auf eine Infek-tion durch A. fumigatus konnten vier Hauptregulatoren identifiziert werden (A. fumi-gatus, Blutplättchen, hier Platelets genannt, und TNF), die im Zusammenspiel mit wei-teren Faktoren mit hohen Zentralitätswerten (CCL5, IL1, IL6, Dectin-1, TLR2 und TLR4) fähig sind das gesamte Netzwerkverhalten zu beeinflussen. Es konnte gezeigt werden, dass sich das Aktivitätsverhalten von IL6 in Reaktion auf A. fumigatus und die regulato-rische Wirkung von Blutplättchen mit den entsprechenden experimentellen Resultaten deckt. Die Simulation, sowie die Berechnung der stabilen Systemzustände der Immunantwort von A. thaliana auf eine Infektion durch Pseudomonas syringae konnte zeigen, dass die in silico Ergebnisse mit den experimentellen Ergebnissen übereinstimmen. Zusätzlich konnten mit Hilfe der Analyse der Zentralitätswerte des Netzwerkmodells fünf Master-regulatoren identifiziert werden: TGA Transkriptionsfaktor, Jasmonsäure, Ent-Kaurenoate-Oxidase, Ent-kaurene-Synthase und Aspartat-Semialdehyd-Dehydrogenase. Während die ersteren beiden bereits lange als wichtige Regulatoren für die Gib-berellin-Synthese bekannt sind, ist die immunregulatorische Funktion von Aspartat-Semialdehyd-Dehydrogenase bisher weitgehend unbekannt.The central paradigm of systems biology aims at a comprehensive understanding in complex relationships of biological systems. The methods used in this work support this aim. By the example of three network models reconstructed on the basis of databases and current literature, the validity of analytical and predictive algorithms could be demon-strated in this work. As simulation software the framework Jimena was applied. The results for the calculation of stable system states, the dynamic simulation as well as the identification of central control nodes could be validated experimentally. The re-sults were used in an iterative process to further optimize the corresponding network model. Comparing the behavior of the semi-quantitatively evaluated regulatory network to control the differentiation of human mesenchymal stem cells into chondrocytes (carti-lage formation), osteoblasts (bone formation) and adipocytes (fatty cell formation), 12 important factors (including: RUNX2, OSX/SP7, SOX9, TP53) could be identified by the calculation of the control centralities of the network nodes. The comparison of the simulated behavior of these nodes showed an agreement with experimental data of 47.2%. We found a contradictory behavior of approximately 25%. Differing results can be explained due to the temporary nature of experimental measurements compared to the terminal conditions of the calculation the stable system states. Analyzing the network model of the human immune response to A. fumigatus infec-tion, four major regulators could be identified (A. fumigatus, platelets, and TNF), which interact with other factors with high control centrality values (CCL5, IL1, IL6, Dectin1). TLR2 and TLR4) are capable of affecting the overall network behavior. It could be shown that the activity behavior of IL6 in response to the modular activity of the plate-lets as well as A. fumigatus coincides with the corresponding experimental results. The simulation, as well as the calculation of the stable system states of the immune response of A. thaliana to an infection by Pseudomonas syringae, showed that in silico results are in agreement with the experimental results. By analyzing the control cen-trality values of the network model, five main regulators could be: TGA transcription factor, jasmonic acid, ent-kaurene-Oxidase, ent-kaurene synthase and aspartate semi-aldehyde. While the former two have long been recognized as important regulators of gibberel-lin synthesis, the immunoregulatory function of aspartate semialdehyde dehydrogen-ase has been largely unknown

Integrated Framework of the Immune-Defense Transcriptional Signatures in the Arabidopsis Shoot Apical Meristem

The growing tips of plants grow sterile; therefore, disease-free plants can be generated from them. How plants safeguard growing apices from pathogen infection is still a mystery. The shoot apical meristem (SAM) is one of the three stem cells niches that give rise to the above ground plant organs. This is very well explored; however, how signaling networks orchestrate immune responses against pathogen infections in the SAM remains unclear. To reconstruct a transcriptional framework of the differentially expressed genes (DEGs) pertaining to various SAM cellular populations, we acquired large-scale transcriptome datasets from the public repository Gene Expression Omnibus (GEO). We identify here distinct sets of genes for various SAM cellular populations that are enriched in immune functions, such as immune defense, pathogen infection, biotic stress, and response to salicylic acid and jasmonic acid and their biosynthetic pathways in the SAM. We further linked those immune genes to their respective proteins and identify interactions among them by mapping a transcriptome-guided SAM-interactome. Furthermore, we compared stem-cells regulated transcriptome with innate immune responses in plants showing transcriptional separation among their DEGs in Arabidopsis. Besides unleashing a repertoire of immune-related genes in the SAM, our analysis provides a SAM-interactome that will help the community in designing functional experiments to study the specific defense dynamics of the SAM-cellular populations. Moreover, our study promotes the essence of large-scale omics data re-analysis, allowing a fresh look at the SAM-cellular transcriptome repurposing data-sets for new questions

Nanocellulose composites as smart devices with chassis, light-directed DNA Storage, engineered electronic properties, and chip integration

The rapid development of green and sustainable materials opens up new possibilities in the field of applied research. Such materials include nanocellulose composites that can integrate many components into composites and provide a good chassis for smart devices. In our study, we evaluate four approaches for turning a nanocellulose composite into an information storage or processing device: 1) nanocellulose can be a suitable carrier material and protect information stored in DNA. 2) Nucleotide-processing enzymes (polymerase and exonuclease) can be controlled by light after fusing them with light-gating domains; nucleotide substrate specificity can be changed by mutation or pH change (read-in and read-out of the information). 3) Semiconductors and electronic capabilities can be achieved: we show that nanocellulose is rendered electronic by iodine treatment replacing silicon including microstructures. Nanocellulose semiconductor properties are measured, and the resulting potential including single-electron transistors (SET) and their properties are modeled. Electric current can also be transported by DNA through G-quadruplex DNA molecules; these as well as classical silicon semiconductors can easily be integrated into the nanocellulose composite. 4) To elaborate upon miniaturization and integration for a smart nanocellulose chip device, we demonstrate pH-sensitive dyes in nanocellulose, nanopore creation, and kinase micropatterning on bacterial membranes as well as digital PCR micro-wells. Future application potential includes nano-3D printing and fast molecular processors (e.g., SETs) integrated with DNA storage and conventional electronics. This would also lead to environment-friendly nanocellulose chips for information processing as well as smart nanocellulose composites for biomedical applications and nano-factories

Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach

New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness (“hubs”), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines