The temperature sensor TWA1 is required for thermotolerance in Arabidopsis.

Plants exposed to incidences of excessive temperatures activate heat-stress responses to cope with the physiological challenge and stimulate long-term acclimation1,2. The mechanism that senses cellular temperature for inducing thermotolerance is still unclear3. Here we show that TWA1 is a temperature-sensing transcriptional co-regulator that is needed for basal and acquired thermotolerance in Arabidopsis thaliana. At elevated temperatures, TWA1 changes its conformation and allows physical interaction with JASMONATE-ASSOCIATED MYC-LIKE (JAM) transcription factors and TOPLESS (TPL) and TOPLESS-RELATED (TPR) proteins for repressor complex assembly. TWA1 is a predicted intrinsically disordered protein that has a key thermosensory role functioning through an amino-terminal highly variable region. At elevated temperatures, TWA1 accumulates in nuclear subdomains, and physical interactions with JAM2 and TPL appear to be restricted to these nuclear subdomains. The transcriptional upregulation of the heat shock transcription factor A2 (HSFA2) and heat shock proteins depended on TWA1, and TWA1 orthologues provided different temperature thresholds, consistent with the sensor function in early signalling of heat stress. The identification of the plant thermosensors offers a molecular tool for adjusting thermal acclimation responses of crops by breeding and biotechnology, and a sensitive temperature switch for thermogenetics.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Independent yet overlapping pathways ensure the robustness and responsiveness of trans-Golgi network functions in Arabidopsis.

The trans-Golgi-network (TGN) has essential housekeeping functions in secretion, endocytosis and protein sorting, but also more specialized functions in plant development. How the robustness of basal TGN function is ensured while specialized functions are differentially regulated is poorly understood. Here, we investigate two key regulators of TGN structure and function, ECHIDNA and the Transport Protein Particle II (TRAPPII) tethering complex. An analysis of physical, network and genetic interactions suggests that two network communities are implicated in TGN function and that ECHIDNA and TRAPPII belong to distinct yet overlapping pathways. Whereas ECHIDNA and TRAPPII colocalized at the TGN in interphase cells, their localization diverged in dividing cells. Moreover, ECHIDNA and TRAPPII localization patterns were mutually independent. TGN structure, endocytosis and sorting decisions were differentially impacted in echidna and trappii mutants. Our analyses point to a partitioning of specialized TGN functions, with ECHIDNA being required for cell elongation and TRAPPII for cytokinesis. Two independent pathways able to compensate for each other might contribute to the robustness of TGN housekeeping functions and to the responsiveness and fine tuning of its specialized functions.

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.

Drought resistance is mediated by divergent strategies in closely related Brassicaceae.

Droughts cause severe crop losses worldwide and climate change is projected to increase their prevalence in the future. Similar to the situation for many crops, the reference plant Arabidopsis thaliana (Ath) is considered drought-sensitive, whereas, as we demonstrate, its close relatives Arabidopsis lyrata (Aly) and Eutrema salsugineum (Esa) are drought-resistant. To understand the molecular basis for this plasticity we conducted a deep phenotypic, biochemical and transcriptomic comparison using developmentally matched plants. We demonstrate that Aly responds most sensitively to decreasing water availability with early growth reduction, metabolic adaptations and signaling network rewiring. By contrast, Esa is in a constantly prepared mode as evidenced by high basal proline levels, ABA signaling transcripts and late growth responses. The stress-sensitive Ath responds later than Aly and earlier than Esa, although its responses tend to be more extreme. All species detect water scarcity with similar sensitivity; response differences are encoded in downstream signaling and response networks. Moreover, several signaling genes expressed at higher basal levels in both Aly and Esa have been shown to increase water-use efficiency and drought resistance when overexpressed in Ath. Our data demonstrate contrasting strategies of closely related Brassicaceae to achieve drought resistance.

LSU network hubs integrate abiotic and biotic stress responses via interaction with the superoxide dismutase FSD2.

In natural environments, plants often experience different stresses simultaneously, and adverse abiotic conditions can weaken the plant immune system. Interactome mapping revealed that the LOW SULPHUR UPREGULATED (LSU) proteins are hubs in an Arabidopsis protein interaction network that are targeted by virulence effectors from evolutionarily diverse pathogens. Here we show that LSU proteins are up-regulated in several abiotic and biotic stress conditions, such as nutrient depletion or salt stress, by both transcriptional and post-translational mechanisms. Interference with LSU expression prevents chloroplastic reactive oxygen species (ROS) production and proper stomatal closure during sulphur stress. We demonstrate that LSU1 interacts with the chloroplastic superoxide dismutase FSD2 and stimulates its enzymatic activity in vivo and in vitro. Pseudomonas syringae virulence effectors interfere with this interaction and preclude re-localization of LSU1 to chloroplasts. We demonstrate that reduced LSU levels cause a moderately enhanced disease susceptibility in plants exposed to abiotic stresses such as nutrient deficiency, high salinity, or heavy metal toxicity, whereas LSU1 overexpression confers significant disease resistance in several of these conditions. Our data suggest that the network hub LSU1 plays an important role in co-ordinating plant immune responses across a spectrum of abiotic stress 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 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.

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.