Regulation of adaptive growth decisions via phosphorylation of the TRAPPII complex in Arabidopsis.

Plants often adapt to adverse or stress conditions via differential growth. The trans-Golgi network (TGN) has been implicated in stress responses, but it is not clear in what capacity it mediates adaptive growth decisions. In this study, we assess the role of the TGN in stress responses by exploring the previously identified interactome of the Transport Protein Particle II (TRAPPII) complex required for TGN structure and function. We identified physical and genetic interactions between AtTRAPPII and shaggy-like kinases (GSK3/AtSKs) and provided in vitro and in vivo evidence that the TRAPPII phosphostatus mediates adaptive responses to abiotic cues. AtSKs are multifunctional kinases that integrate a broad range of signals. Similarly, the AtTRAPPII interactome is vast and considerably enriched in signaling components. An AtSK-TRAPPII interaction would integrate all levels of cellular organization and instruct the TGN, a central and highly discriminate cellular hub, as to how to mobilize and allocate resources to optimize growth and survival under limiting or adverse conditions.

SARS-CoV-2 NSP14 MTase activity is critical for inducing canonical NF-κB activation.

Upon SARS-CoV-2 infection, patients with severe forms of COVID-19 often suffer from a dysregulated immune response and hyperinflammation. Aberrant expression of cytokines and chemokines is associated with strong activation of the immunoregulatory transcription factor NF-κB, which can be directly induced by the SARS-CoV-2 protein NSP14. Here, we use NSP14 mutants and generated cells with host factor knockouts (KOs) in the NF-κB signaling pathways to characterize the molecular mechanism of NSP14-induced NF-κB activation. We demonstrate that full-length NSP14 requires methyltransferase (MTase) activity to drive NF-κB induction. NSP14 WT, but not an MTase-defective mutant, is poorly expressed and inherent post-translational instability is mediated by proteasomal degradation. Binding of SARS-CoV-2 NSP10 or addition of the co-factor S-adenosylmethionine (SAM) stabilizes NSP14 and augments its potential to activate NF-κB. Using CRISPR/Cas9-engineered KO cells, we demonstrate that NSP14 stimulation of canonical NF-κB activation relies on NF-κB factor p65/RELA downstream of the NEMO/IKK complex, while c-Rel or non-canonical RelB are not required to induce NF-κB transcriptional activity. However, NSP14 overexpression is unable to induce canonical IκB kinase ß (IKKß)/NF-κB signaling and in co-immunoprecipitation assays we do not detect stable associations between NSP14 and NEMO or p65, suggesting that NSP14 activates NF-κB indirectly through its methyltransferase activity. Taken together, our data provide a framework how NSP14 can augment basal NF-κB activation, which may enhance cytokine expression in SARS-CoV-2 infected cells.

Regulation of adaptive growth decisions via phosphorylation of the TRAPPII complex in Arabidopsis.

Plants often adapt to adverse or stress conditions via differential growth. The trans-Golgi Network (TGN) has been implicated in stress responses, but it is not clear in what capacity it mediates adaptive growth decisions. In this study, we assess the role of the TGN in stress responses by exploring the interactome of the Transport Protein Particle II (TRAPPII) complex, required for TGN structure and function. We identified physical and genetic interactions between TRAPPII and shaggy-like kinases (GSK3/AtSKs). Kinase assays and pharmacological inhibition provided in vitro and in vivo evidence that AtSKs target the TRAPPII-specific subunit AtTRS120/TRAPPC9. GSK3/AtSK phosphorylation sites in AtTRS120/TRAPPC9 were mutated, and the resulting AtTRS120 phosphovariants subjected to a variety of single and multiple stress conditions in planta . The non-phosphorylatable TRS120 mutant exhibited enhanced adaptation to multiple stress conditions and to osmotic stress whereas the phosphomimetic version was less resilient. Higher order inducible trappii atsk mutants had a synthetically enhanced defect in root gravitropism. Our results suggest that the TRAPPII phosphostatus mediates adaptive responses to abiotic cues. AtSKs are multifunctional kinases that integrate a broad range of signals. Similarly, the TRAPPII interactome is vast and considerably enriched in signaling components. An AtSK-TRAPPII interaction would integrate all levels of cellular organization and instruct the TGN, a central and highly discriminate cellular hub, as to how to mobilize and allocate resources to optimize growth and survival under limiting or adverse conditions.

Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases.

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.

Symbiont-host interactome mapping reveals effector-targeted modulation of hormone networks and activation of growth promotion.

Plants have benefited from interactions with symbionts for coping with challenging environments since the colonisation of land. The mechanisms of symbiont-mediated beneficial effects and similarities and differences to pathogen strategies are mostly unknown. Here, we use 106 (effector-) proteins, secreted by the symbiont Serendipita indica (Si) to modulate host physiology, to map interactions with Arabidopsis thaliana host proteins. Using integrative network analysis, we show significant convergence on target-proteins shared with pathogens and exclusive targeting of Arabidopsis proteins in the phytohormone signalling network. Functional in planta screening and phenotyping of Si effectors and interacting proteins reveals previously unknown hormone functions of Arabidopsis proteins and direct beneficial activities mediated by effectors in Arabidopsis. Thus, symbionts and pathogens target a shared molecular microbe-host interface. At the same time Si effectors specifically target the plant hormone network and constitute a powerful resource for elucidating the signalling network function and boosting plant productivity.

A resource of human coronavirus protein-coding sequences in a flexible, multipurpose Gateway Entry clone collection.

The COVID-19 pandemic has catalyzed unprecedented scientific data and reagent sharing and collaboration, which enabled understanding the virology of the SARS-CoV-2 virus and vaccine development at record speed. The pandemic, however, has also raised awareness of the danger posed by the family of coronaviruses, of which 7 are known to infect humans and dozens have been identified in reservoir species, such as bats, rodents, or livestock. To facilitate understanding the commonalities and specifics of coronavirus infections and aspects of viral biology that determine their level of lethality to the human host, we have generated a collection of freely available clones encoding nearly all human coronavirus proteins known to date. We hope that this flexible, Gateway-compatible vector collection will encourage further research into the interactions of coronaviruses with their human host, to increase preparedness for future zoonotic viral outbreaks.

Exploiting breakdown in nonhost effector-target interactions to boost host disease resistance.

Plants are resistant to most microbial species due to nonhost resistance (NHR), providing broad-spectrum and durable immunity. However, the molecular components contributing to NHR are poorly characterised. We address the question of whether failure of pathogen effectors to manipulate nonhost plants plays a critical role in NHR. RxLR (Arg-any amino acid-Leu-Arg) effectors from two oomycete pathogens, Phytophthora infestans and Hyaloperonospora arabidopsidis, enhanced pathogen infection when expressed in host plants (Nicotiana benthamiana and Arabidopsis, respectively) but the same effectors performed poorly in distantly related nonhost pathosystems. Putative target proteins in the host plant potato were identified for 64 P. infestans RxLR effectors using yeast 2-hybrid (Y2H) screens. Candidate orthologues of these target proteins in the distantly related non-host plant Arabidopsis were identified and screened using matrix Y2H for interaction with RxLR effectors from both P. infestans and H. arabidopsidis. Few P. infestans effector-target protein interactions were conserved from potato to candidate Arabidopsis target orthologues (cAtOrths). However, there was an enrichment of H. arabidopsidis RxLR effectors interacting with cAtOrths. We expressed the cAtOrth AtPUB33, which unlike its potato orthologue did not interact with P. infestans effector PiSFI3, in potato and Nicotiana benthamiana. Expression of AtPUB33 significantly reduced P. infestans colonization in both host plants. Our results provide evidence that failure of pathogen effectors to interact with and/or correctly manipulate target proteins in distantly related non-host plants contributes to NHR. Moreover, exploiting this breakdown in effector-nonhost target interaction, transferring effector target orthologues from non-host to host plants is a strategy to reduce disease.

Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC.

BACKGROUND: High-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed. RESULTS: We present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae, histone Htb1 concentrations decrease with replicative age. CONCLUSIONS: Cell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC.

Novel Pseudomonas sp. SCA7 Promotes Plant Growth in Two Plant Families and Induces Systemic Resistance in Arabidopsis thaliana.

Pseudomonas sp. SCA7, characterized in this study, was isolated from roots of the bread wheat Triticum aestivum. Sequencing and annotation of the complete SCA7 genome revealed that it represents a potential new Pseudomonas sp. with a remarkable repertoire of plant beneficial functions. In vitro and in planta experiments with the reference dicot plant A. thaliana and the original monocot host T. aestivum were conducted to identify the functional properties of SCA7. The isolate was able to colonize roots, modify root architecture, and promote growth in A. thaliana. Moreover, the isolate increased plant fresh weight in T. aestivum under unchallenged conditions. Gene expression analysis of SCA7-inoculated A. thaliana indicated a role of SCA7 in nutrient uptake and priming of plants. Moreover, confrontational assays of SCA7 with fungal and bacterial plant pathogens revealed growth restriction of the pathogens by SCA7 in direct as well as indirect contact. The latter indicated involvement of microbial volatile organic compounds (mVOCs) in this interaction. Gas chromatography-mass spectrometry (GC-MS) analyses revealed 1-undecene as the major mVOC, and octanal and 1,4-undecadiene as minor abundant compounds in the emission pattern of SCA7. Additionally, SCA7 enhanced resistance of A. thaliana against infection with the plant pathogen Pseudomonas syringae pv. tomato DC3000. In line with these results, SA- and JA/ET-related gene expression in A. thaliana during infection with Pst DC3000 was upregulated upon treatment with SCA7, indicating the ability of SCA7 to induce systemic resistance. The thorough characterization of the novel Pseudomonas sp. SCA7 showed a remarkable genomic and functional potential of plant beneficial traits, rendering it a promising candidate for application as a biocontrol or a biostimulation agent.

Chemokine-like MDL proteins modulate flowering time and innate immunity in plants.

Human macrophage migration inhibitory factor (MIF) is an atypical chemokine implicated in intercellular signaling and innate immunity. MIF orthologs (MIF/D-DT-like proteins, MDLs) are present throughout the plant kingdom, but remain experimentally unexplored in these organisms. Here, we provide an in planta characterization and functional analysis of the three-member gene/protein MDL family in Arabidopsis thaliana. Subcellular localization experiments indicated a nucleo-cytoplasmic distribution of MDL1 and MDL2, while MDL3 is localized to peroxisomes. Protein-protein interaction assays revealed the in vivo formation of MDL1, MDL2, and MDL3 homo-oligomers, as well as the formation of MDL1-MDL2 hetero-oligomers. Functionally, Arabidopsismdl mutants exhibited a delayed transition from vegetative to reproductive growth (flowering) under long-day conditions, but not in a short-day environment. In addition, mdl mutants were more resistant to colonization by the bacterial pathogen Pseudomonas syringae pv. maculicola. The latter phenotype was compromised by the additional mutation of SALICYLIC ACID INDUCTION DEFICIENT 2 (SID2), a gene implicated in the defense-induced biosynthesis of the key signaling molecule salicylic acid. However, the enhanced antibacterial immunity was not associated with any constitutive or pathogen-induced alterations in the levels of characteristic phytohormones or defense-associated metabolites. Interestingly, bacterial infection triggered relocalization and accumulation of MDL1 and MDL2 at the peripheral lobes of leaf epidermal cells. Collectively, our data indicate redundant functionality and a complex interplay between the three chemokine-like Arabidopsis MDL proteins in the regulation of both developmental and immune-related processes. These insights expand the comparative cross-kingdom analysis of MIF/MDL signaling in human and plant systems.

ARMADILLO REPEAT ONLY proteins confine Rho GTPase signalling to polar growth sites.

Polar growth requires the precise tuning of Rho GTPase signalling at distinct plasma membrane domains. The activity of Rho of plant (ROP) GTPases is regulated by the opposing action of guanine nucleotide-exchange factors (GEFs) and GTPase-activating proteins (GAPs). Whereas plant-specific ROPGEFs have been shown to be embedded in higher-level regulatory mechanisms involving membrane-bound receptor-like kinases, the regulation of GAPs has remained enigmatic. Here, we show that three Arabidopsis ARMADILLO REPEAT ONLY (ARO) proteins are essential for the stabilization of growth sites in root hair cells and trichomes. AROs interact with ROP1 enhancer GAPs (RENGAPs) and bind to the plasma membrane via a conserved polybasic region at the ARO amino terminus. The ectopic spreading of ROP2 in aro2/3/4 mutant root hair cells and the preferential interaction of AROs with active ROPs and anionic phospholipids suggests that AROs recruit RENGAPs into complexes with ROPs to confine ROP signalling to distinct membrane regions.

Extensive signal integration by the phytohormone protein network.

Plant hormones coordinate responses to environmental cues with developmental programs1, and are fundamental for stress resilience and agronomic yield2. The core signalling pathways underlying the effects of phytohormones have been elucidated by genetic screens and hypothesis-driven approaches, and extended by interactome studies of select pathways3. However, fundamental questions remain about how information from different pathways is integrated. Genetically, most phenotypes seem to be regulated by several hormones, but transcriptional profiling suggests that hormones trigger largely exclusive transcriptional programs4. We hypothesized that protein-protein interactions have an important role in phytohormone signal integration. Here, we experimentally generated a systems-level map of the Arabidopsis phytohormone signalling network, consisting of more than 2,000 binary protein-protein interactions. In the highly interconnected network, we identify pathway communities and hundreds of previously unknown pathway contacts that represent potential points of crosstalk. Functional validation of candidates in seven hormone pathways reveals new functions for 74% of tested proteins in 84% of candidate interactions, and indicates that a large majority of signalling proteins function pleiotropically in several pathways. Moreover, we identify several hundred largely small-molecule-dependent interactions of hormone receptors. Comparison with previous reports suggests that noncanonical and nontranscription-mediated receptor signalling is more common than hitherto appreciated.

Publisher Correction: Extensive signal integration by the phytohormone protein network.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice.

Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we develop PCR-mediated Linkage of barcoded Adapters To nucleic acid Elements for sequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome for Oryza sativa covering 2,300 genes and constructing a high-quality protein-protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein-protein interactions in a wide range of organisms.

TRIPP Is a Plant-Specific Component of the Arabidopsis TRAPPII Membrane Trafficking Complex with Important Roles in Plant Development.

How the membrane trafficking system spatially organizes intracellular activities and intercellular signaling networks in plants is not well understood. Transport Protein Particle (TRAPP) complexes play key roles in the selective delivery of membrane vesicles to various subcellular compartments in yeast and animals but remain to be fully characterized in plants. Here, we investigated TRAPP complexes in Arabidopsis (Arabidopsis thaliana) using immunoprecipitation followed by quantitative mass spectrometry analysis of AtTRS33, a conserved core component of all TRAPP complexes. We identified 14 AtTRS33-interacting proteins, including homologs of all 13 TRAPP components in mammals and a protein that has homologs only in multicellular photosynthetic organisms and is thus named TRAPP-Interacting Plant Protein (TRIPP). TRIPP specifically associates with the TRAPPII complex through binary interactions with two TRAPPII-specific subunits. TRIPP colocalized with a subset of TRS33 compartments and trans-Golgi network markers in a TRS33-dependent manner. Loss-of-function tripp mutants exhibited dwarfism, sterility, partial photomorphogenesis in the dark, reduced polarity of the auxin transporter PIN2, incomplete cross wall formation, and altered localization of a TRAPPII-specific component. Therefore, TRIPP is a plant-specific component of the TRAPPII complex with important functions in trafficking, plant growth, and development.

Mass-spectrometry-based draft of the Arabidopsis proteome.

Plants are essential for life and are extremely diverse organisms with unique molecular capabilities1. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions.

GROWTH-REGULATING FACTORS Interact with DELLAs and Regulate Growth in Cold Stress.

DELLA proteins are repressors of the gibberellin (GA) hormone signaling pathway that act mainly by regulating transcription factor activities in plants. GAs induce DELLA repressor protein degradation and thereby control a number of critical developmental processes as well as responses to stresses such as cold. The strong effect of cold temperatures on many physiological processes has rendered it difficult to assess, based on phenotypic criteria, the role of GA and DELLAs in plant growth during cold stress. Here, we uncover substantial differences in the GA transcriptomes between plants grown at ambient temperature (21°C) and plants exposed to cold stress (4°C) in Arabidopsis (Arabidopsis thaliana). We further identify over 250, to the largest extent previously unknown, DELLA-transcription factor interactions using the yeast two-hybrid system. By integrating both data sets, we reveal that most members of the nine-member GRF (GROWTH REGULATORY FACTOR) transcription factor family are DELLA interactors and, at the same time, that several GRF genes are targets of DELLA-modulated transcription after exposure to cold stress. We find that plants with altered GRF dosage are differentially sensitive to the manipulation of GA and hence DELLA levels, also after cold stress, and identify a subset of cold stress-responsive genes that qualify as targets of this DELLA-GRF regulatory module.

Interactions between Transport Protein Particle (TRAPP) complexes and Rab GTPases in Arabidopsis.

Transport Protein Particle II (TRAPPII) is essential for exocytosis, endocytosis, protein sorting and cytokinesis. In spite of a considerable understanding of its biological role, little information is known about Arabidopsis TRAPPII complex topology and molecular function. In this study, independent proteomic approaches initiated with TRAPP components or Rab-A GTPase variants converge on the TRAPPII complex. We show that the Arabidopsis genome encodes the full complement of 13 TRAPPC subunits, including four previously unidentified components. A dimerization model is proposed to account for binary interactions between TRAPPII subunits. Preferential binding to dominant negative (GDP-bound) versus wild-type or constitutively active (GTP-bound) RAB-A2a variants discriminates between TRAPPII and TRAPPIII subunits and shows that Arabidopsis complexes differ from yeast but resemble metazoan TRAPP complexes. Analyzes of Rab-A mutant variants in trappii backgrounds provide genetic evidence that TRAPPII functions upstream of RAB-A2a, allowing us to propose that TRAPPII is likely to behave as a guanine nucleotide exchange factor (GEF) for the RAB-A2a GTPase. GEFs catalyze exchange of GDP for GTP; the GTP-bound, activated, Rab then recruits a diverse local network of Rab effectors to specify membrane identity in subsequent vesicle fusion events. Understanding GEF-Rab interactions will be crucial to unravel the co-ordination of plant membrane traffic.