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.

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 β (IKKβ)/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.

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.

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.

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.

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.

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.

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.

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.

Understanding the mechanisms of coronavirus disease 2019 (COVID-19) disease severity to efficiently design therapies for emerging virus variants remains an urgent challenge of the ongoing pandemic. Infection and immune reactions are mediated by direct contacts between viral molecules and the host proteome, and the vast majority of these virus-host contacts (the 'contactome') have not been identified. Here, we present a systematic contactome map of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with the human host encompassing more than 200 binary virus-host and intraviral protein-protein interactions. We find that host proteins genetically associated with comorbidities of severe illness and long COVID are enriched in SARS-CoV-2 targeted network communities. Evaluating contactome-derived hypotheses, we demonstrate that viral NSP14 activates nuclear factor κB (NF-κB)-dependent transcription, even in the presence of cytokine signaling. Moreover, for several tested host proteins, genetic knock-down substantially reduces viral replication. Additionally, we show for USP25 that this effect is phenocopied by the small-molecule inhibitor AZ1. Our results connect viral proteins to human genetic architecture for COVID-19 severity and offer potential therapeutic targets.