Wheat (Triticum aestivum L.) is one of the world\'s most important staple crops, whose production is critical to feed the expanding population worldwide. The 90-kDa Heat Shock Protein 90 (HSP90) is a highly abundant chaperone protein involved in multiple cellular processes. It facilitates the folding of nascent preproteins for their maturation and functioning. This data described HSP90.2 clients identified from the whole genome of wheat. The HSP90.2 chaperome contains over 1500 proteins, most detected by the C terminus and full-length of HSP90.2. Over 60 % of the clients reside in the cytosol, nucleus, and chloroplasts. Cytoskeleton-related proteins are enriched in the chaperome of the N terminus of HSP90.2. The clients of the middle part of HSP90.2 contains several factors involved in ethylene biosynthesis and extracellular vesicle or organelle-related activities. Some clients related to plant hypersensitive response are induced by stripe rust. The presented dataset could isolate proteins regulated by HSP90.2 at the post-translational level.

There is a substantial unmet need for effective and patient-acceptable drugs to treat severe mental illnesses like schizophrenia. Computational analysis of genomic, transcriptomic, and pharmacologic data generated in the past two decades enables repurposing of drugs or compounds with acceptable safety profiles, namely those that are FDA-approved or reached late stages in clinical trials. We developed a rational approach to achieve this computationally for schizophrenia by studying drugs that target the proteins in its protein interaction network (\'interactome\'). This involved contrasting the transcriptomic modulations observed in the disorder and the drug; our analyses resulted in 12 candidate drugs, 9 of which had additional supportive evidence: their target networks were enriched for pathways relevant to schizophrenia etiology or for genes that had an association with diseases pathogenically similar to schizophrenia. To translate these computational results to the clinic, these shortlisted drugs must be tested empirically through randomized controlled trials (RCT), where their prior safety approvals obviate the need for time-consuming phase I and II studies. We selected two among the shortlisted candidates based on likely adherence and side effect profiles. We are testing them through adjunctive RCTs for patients with schizophrenia or schizoaffective disorder who experienced incomplete resolution of psychotic features with conventional treatment. The integrated computational analysis for identifying and ranking drugs for clinical trials can be iterated as additional data are obtained. Our approach could be expanded to enable disease subtype-specific drug discovery in future and should also be exploited for other psychiatric disorders.

Plasma membrane proteins (PMPs) play critical roles in a myriad of physiological and disease conditions. A unique subset of PMPs functions through interacting with each other in trans at the interface between two contacting cells. These trans-interacting PMPs (tiPMPs) include adhesion molecules and ligands/receptors that facilitate cell-cell contact and direct communication between cells. Among the tiPMPs, a significant number have apparent extracellular binding domains but remain orphans with no known binding partners. Identification of their potential binding partners is therefore important for the understanding of processes such as organismal development and immune cell activation. While a number of methods have been developed for the identification of protein binding partners in general, very few are applicable to tiPMPs, which interact in a two-dimensional fashion with low intrinsic binding affinities. In this review, we present the significance of tiPMP interactions, the challenges of identifying binding partners for tiPMPs, and the landscape of method development. We describe current avidity-based screening approaches for identifying novel tiPMP binding partners and discuss their advantages and limitations. We conclude by highlighting the importance of developing novel methods of identifying new tiPMP interactions for deciphering the complex protein interactome and developing targeted therapeutics for diseases.

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Cells continuously remodel their intracellular proteins with the monosaccharide O-linked N-acetylglucosamine (O-GlcNAc) to regulate metabolism, signaling, and stress. This protocol describes the use of GlycoID tools to capture O-GlcNAc dynamics in live cells. GlycoID constructs contain an O-GlcNAc binding domain linked to a proximity labeling domain and a subcellular localization sequence. When expressed in mammalian cells, GlycoID tracks changes in O-GlcNAc-modified proteins and their interactomes in response to chemical induction with biotin over time. Pairing the subcellular localization of GlycoID with the chemical induction of activity enables spatiotemporal studies of O-GlcNAc biology during cellular events such as insulin signaling. However, optimizing intracellular labeling experiments requires attention to several variables. Here, we describe two protocols to adapt GlycoID methods to a cell line and biological process of interest. Next, we describe how to conduct a semiquantitative proteomic analysis of O-GlcNAcylated proteins and their interactomes using insulin versus glucagon signaling as a sample application. This articles aims to establish baseline GlycoID protocols for new users and set the stage for widespread use over diverse cellular applications for the functional study of O-GlcNAc glycobiology. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Expression of targeted GlycoID constructs to verify subcellular location and labeling activity in mammalian cells Basic Protocol 2: GlycoID labeling in live HeLa cells for O-GlcNAc proteomic comparisons.

Moderate-to-severe psoriasis (Ps) treatment includes systemic drugs and biological agents. Apremilast, a small molecule primarily metabolized by cytochrome CYP3A4, modulates the immune system by specifically inhibiting phosphodiesterase type 4 (PDE4) isoforms and is currently used for the treatment of Ps and psoriatic arthritis (PsA). Clinical trials and real-world data showed variable efficacy in response among Ps patients underlying the need for personalized therapy. This study implements a candidate-gene and a network-based approach to identify genetic markers associated with apremilast response in forty-nine Greek Ps patients. Our data revealed an association of sixty-four SNPs within or near PDE4 and CYP3A4 genes, four SNPs in ncRNAs ANRIL, LINC00941 and miR4706, which influence the abundance or function of PDE4s, and thirty-three SNPs within fourteen genes whose protein products either interact directly with PDE4 proteins or constitute components of the cAMP signaling pathway which is modulated by PDE4s. Notably, fifty-six of the aforementioned SNPs constitute eQTLs for the respective genes in relevant to psoriasis tissues/cells implying that these variants could be causal. Our analysis provides a number of novel genetic variants that, upon validation in larger cohorts, could be utilized as predictive markers regarding the response of Ps patients to apremilast treatment.

Homocysteine-inducible endoplasmic reticulum protein (HERP) is an endoplasmic reticulum (ER)-resident protein and important for the adaptation of cellular protein homeostasis by ER-associated degradation (ERAD) system. HERP interactors are critical for cellular viability and the reaction to ER stress. To explore the exact mechanisms by which HERP performed the biological functions, we conducted an interaction analysis of HERP protein in HeLa cells by co-immunoprecipitation (Co-IP) and liquid chromatography-mass spectrometer (LC-MS)/MS coupled with label-free quantification (LFQ). Among the interactome results, 123 proteins significantly interacted with HERP, which leads to numerous biological processes including protein import into nucleus, ubiquitin-dependent ERAD pathway, negative regulation of apoptotic process, and protein transport from ER, along with multiple pathways including several diseases, protein processing in ER, fatty acid metabolism, and steroid biosynthesis. Furthermore, we selected several prey proteins from the interactome data and confirmed that HERP interacted with ancient ubiquitous protein 1 (AUP1), Fas-associated factor family member 2 (FAF2), tripartite motif containing 47 (TRIM47), acyl-CoA synthetase long-chain family member 3 (ACSL3), sequestosome 1 (SQSTM1), and poly(rC) binding protein 2 (PCBP2) by Co-IP and confocal microscopy experiments, respectively. Moreover, the expression and location of several interacted proteins were obviously altered in response to ER stress induced by Thapsigargin stimulation and Enterovirus 71 infection. In conclusion, our findings revealed that the vital proteins interacted with HERP to mediate signaling transduction, thus providing novel clues for the mechanisms of HERP associated with ERAD and metabolism in response to ER stress under physiological and pathological conditions.

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

Lysine fatty acylation is a protein posttranslational modification (PTM) that has been linked to various important biological processes. HDAC11, the sole member of class IV of histone deacetylases (HDACs), has been shown to have high lysine defatty-acylase activity. In order to better understand the functions of lysine fatty acylation and its regulation by HDAC11, it is important to identify the physiological substrates of HDAC11. This can be achieved through profiling the interactome of HDAC11 using a stable isotope labeling with amino acids in cell culture (SILAC) proteomics strategy. Here we describe a detailed method on using SILAC to identify the interactome of HDAC11. This method can be similarly used to identify the interactome, and thus potential substrates, of other PTM enzymes.

Plant pathogens secrete effectors, which target host proteins to facilitate infection. The Ustilago maydis effector UmSee1 is required for tumor formation in the leaf during infection of maize. UmSee1 interacts with maize SGT1 (suppressor of G2 allele of skp1) and blocks its phosphorylation in vivo. In the absence of UmSee1, U. maydis cannot trigger tumor formation in the bundle sheath. However, it remains unclear which host processes are manipulated by UmSee1 and the UmSee1-SGT1 interaction to cause the observed phenotype. Proximity-dependent protein labeling involving the turbo biotin ligase tag (TurboID) for proximal labeling of proteins is a powerful tool for identifying the protein interactome. We have generated transgenic U. maydis that secretes biotin ligase-fused See1 effector (UmSee1-TurboID-3HA) directly into maize cells. This approach, in combination with conventional co-immunoprecipitation, allowed the identification of additional UmSee1 interactors in maize cells. Collectively, our data identified three ubiquitin-proteasome pathway-related proteins (ZmSIP1, ZmSIP2, and ZmSIP3) that either interact with or are close to UmSee1 during host infection of maize with U. maydis. ZmSIP3 represents a cell cycle regulator whose degradation appears to be promoted in the presence of UmSee1. Our data provide a possible explanation of the requirement for UmSee1 in tumor formation during U. maydis-Zea mays interaction.