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Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is essential for the downstream complex biological functional studies. However, combining different data sources for clustering analysis of single-cell multimodal data remains a statistical and computational challenge. Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive simulation and real-data experiments reveal that scMDC outperforms existing single-cell single-modal and multimodal clustering methods on different single-cell multimodal datasets. The linear scalability of running time makes scMDC a promising method for analyzing large multimodal datasets. Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. Here the authors develops a multimodal deep clustering method for the analysis of single-cell multi-omics data that supports clustering different types of multi-omics data and multi-batch data, as well as downstream differential expression analysis.

Despite exceptional experimental efforts to map out the human interactome, the continued data incompleteness limits our ability to understand the molecular roots of human disease. Computational tools offer a promising alternative, helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or network-based similarity, interacting proteins are not necessarily similar and similar proteins do not necessarily interact. Here, we offer structural and evolutionary evidence that proteins interact not if they are similar to each other, but if one of them is similar to the other’s partners. This approach, that mathematically relies on network paths of length three (L3), significantly outperforms all existing link prediction methods. Given its high accuracy, we show that L3 can offer mechanistic insights into disease mechanisms and can complement future experimental efforts to complete the human interactome. Computational protein-protein interaction (PPI) prediction has the potential to complement experimental efforts to map interactomes. Here, the authors show that proteins tend to interact if one is similar to the other’s partners and that PPI prediction based on this principle is highly accurate.

Leaf rust, caused by Puccinia triticina Eriksson ( Pt ), is one of the most severe foliar diseases of wheat. Breeding for leaf rust resistance is a practical and sustainable method to control this devastating disease. Here, we report the identification of Lr47 , a broadly effective leaf rust resistance gene introgressed into wheat from Aegilops speltoides . Lr47 encodes a coiled-coil nucleotide-binding leucine-rich repeat protein that is both necessary and sufficient to confer Pt resistance, as demonstrated by loss-of-function mutations and transgenic complementation. Lr47 introgression lines with no or reduced linkage drag are generated using the Pairing homoeologous1 mutation, and a diagnostic molecular marker for Lr47 is developed. The coiled-coil domain of the Lr47 protein is unable to induce cell death, nor does it have self-protein interaction. The cloning of Lr47 expands the number of leaf rust resistance genes that can be incorporated into multigene transgenic cassettes to control this devastating disease. Leaf rust is one of the most severe foliar diseases of wheat. Here, the authors report the cloning of Lr47 , a broadly effective leaf rust resistance gene introgressed into wheat from Aegilops speltoides , and show it encodes a coiled-coil nucleotide-binding leucine-rich repeat protein.

The development of potent strigolactone (SL) agonists as suicidal germination inducers could be a useful strategy for controlling root parasitic weeds, but uncertainty about the SL perception mechanism impedes real progress. Here we describe small-molecule agonists that efficiently stimulate Phelipanchce aegyptiaca , and Striga hermonthica , germination in concentrations as low as 10 −8 to 10 −17 M. We show that full efficiency of synthetic SL agonists in triggering signaling through the Striga SL receptor, ShHTL7, depends on the receptor-catalyzed hydrolytic reaction of the agonists. Additionally, we reveal that the stereochemistry of synthetic SL analogs affects the hydrolytic ability of ShHTL7 by influencing the probability of the privileged conformations of ShHTL7. Importantly, an alternative ShHTL7-mediated hydrolysis mechanism, proceeding via nucleophilic attack of the NE2 atom of H246 to the 2′C of the D-ring, is reported. Together, our findings provide insight into SL hydrolysis and structure-perception mechanisms, and potent suicide germination stimulants, which would contribute to the elimination of the noxious parasitic weeds. Strigolactone agonists could potentially help control noxious weeds by promoting suicidal germination. Here the authors describe a series of small molecule agonists that stimulate germination via the Striga ShHTL7 receptor and show that stereochemistry and hydrolysis-independent signalling mediate potency.

Sulfur-sulfur motifs widely occur in vital function and drug design, which yearns for polysulfide construction in an efficient manner. However, it is a great challenge to install desired functional groups on both sides of sulfur-sulfur bonds at liberty. Herein, we designed a mesocyclic bilateral disulfurating reagent for sequential assembly and modular installation of polysulfides. Based on S-O bond dissociation energy imparity (mesocyclic compared to linear imparity is at least 5.34 kcal mol −1 higher), diverse types of functional molecules can be bridged via sulfur-sulfur bonds distinctly. With these stable reagents, excellent reactivities with nucleophiles including C, N and S are comprehensively demonstrated, sequentially installing on both sides of sulfur-sulfur motif with various substituents to afford six species of unsymmetrical polysulfides including di-, tri- and even tetra-sulfides. Life-related molecules, natural products and pharmaceuticals can be successively cross-linked with sulfur-sulfur bond. Remarkably, the cyclization of tri- and tetra-peptides affords 15- and 18-membered cyclic disulfide peptides with this reagent, respectively. The functionalization of a sulfur-sulfur motif is synthetically challenging but highly desired for the production of bioactive compounds. Here, the authors report a disulfurating reagent for sequential and modular assembly of polysulfides where the S-S motif is functionalized with different C-, N- and S-nucleophiles.

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster . Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the ‘FlyBi’ dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings ( dwg ) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function. Maps of protein-protein interactions (PPIs) help identify new components of pathways, complexes, and processes. In this work, state-of-the-art methods are used to identify binary Drosophila PPIs, generating broadly useful physical and data resources.

Broad-scale protein-protein interaction mapping is a major challenge given the cost, time, and sensitivity constraints of existing technologies. Here, we present a massively multiplexed yeast two-hybrid method, CrY2H-seq, which uses a Cre recombinase interaction reporter to intracellularly fuse the coding sequences of two interacting proteins and next-generation DNA sequencing to identify these interactions en masse. We applied CrY2H-seq to investigate sparsely annotated Arabidopsis thaliana transcription factors interactions. By performing ten independent screens testing a total of 36 million binary interaction combinations, and uncovering a network of 8,577 interactions among 1,453 transcription factors, we demonstrate CrY2H-seq's improved screening capacity, efficiency, and sensitivity over those of existing technologies. The deep-coverage network resource we call AtTFIN-1 recapitulates one-third of previously reported interactions derived from diverse methods, expands the number of known plant transcription factor interactions by three-fold, and reveals previously unknown family-specific interaction module associations with plant reproductive development, root architecture, and circadian coordination.

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. Pathogens secrete effectors to promote disease, symbionts might use them to confer benefits. Here, the authors identify 106 candidate effectors from the symbiont Serendipita indica, characterise their interactions, and reveal their roles in regulating phytohormone signalling and promoting growth.

Hexokinase 2 (HK2), which catalyzes the first committed step in glucose metabolism, is induced in cancer cells. HK2’s role in tumorigenesis has been attributed to its glucose kinase activity. Here, we describe a kinase independent HK2 activity, which contributes to metastasis. HK2 binds and sequesters glycogen synthase kinase 3 (GSK3) and acts as a scaffold forming a ternary complex with the regulatory subunit of protein kinase A (PRKAR1a) and GSK3β to facilitate GSK3β phosphorylation and inhibition by PKA. Thus, HK2 functions as an A-kinase anchoring protein (AKAP). Phosphorylation by GSK3β targets proteins for degradation. Consistently, HK2 increases the level and stability of GSK3 targets, MCL1, NRF2, and particularly SNAIL. In addition to GSK3 inhibition, HK2 kinase activity mediates SNAIL glycosylation, which prohibits its phosphorylation by GSK3. Finally, in mouse models of breast cancer metastasis, HK2 deficiency decreases SNAIL protein levels and inhibits SNAIL-mediated epithelial mesenchymal transition and metastasis. Hexokinase 2 expression is markedly induced in cancer cells and contributes to cancer cell metabolism. Here, the authors show that hexokinase 2 can contribute to the metastatic spread of cancer cells independently of its glycolytic function via inhibiting the activity of GSK3β, which in turn elevates the protein levels of the EMT transcription factor SNAIL.

Chromosomes pair and synapse with their homologous partners to segregate correctly at the first meiotic division. Association of telomeres with the LINC (Linker of Nucleoskeleton and Cytoskeleton) complex composed of SUN1 and KASH5 enables telomere-led chromosome movements and telomere bouquet formation, facilitating precise pairwise alignment of homologs. Here, we identify a direct interaction between SUN1 and Speedy A (SPDYA) and determine the crystal structure of human SUN1-SPDYA-CDK2 ternary complex. Analysis of meiosis prophase I process in SPDYA-binding-deficient SUN1 mutant mice reveals that the SUN1-SPDYA interaction is required for the telomere-LINC complex connection and the assembly of a ring-shaped telomere supramolecular architecture at the nuclear envelope, which is critical for efficient homologous pairing and synapsis. Overall, our results provide structural insights into meiotic telomere structure that is essential for meiotic prophase I progression. Telomeres attach to the nuclear envelope to facilitate homolog pairing during meiosis prophase I. Here, the authors show that SUN1 and SPDYA interact, and demonstrate that this interaction is important for telomere structure and function, and essential to mice gametogenesis.