The Arabidopsis thaliana–Streptomyces Interaction Is Controlled by the Metabolic Status of the Holobiont

How specific interactions between plant and pathogenic, commensal, or mutualistic microorganisms are mediated and how bacteria are selected by a plant are important questions to address. Here, an Arabidopsis thaliana mutant called chs5 partially deficient in the biogenesis of isoprenoid precursors was shown to extend its metabolic remodeling to phenylpropanoids and lipids in addition to carotenoids, chlorophylls, and terpenoids. Such a metabolic profile was concomitant to increased colonization of the phyllosphere by the pathogenic strain Pseudomonas syringae pv. tomato DC3000. A thorough microbiome analysis by 16S sequencing revealed that Streptomyces had a reduced colonization potential in chs5. This study revealed that the bacteria–Arabidopsis interaction implies molecular processes impaired in the chs5 mutant. Interestingly, our results revealed that the metabolic status of A. thaliana was crucial for the specific recruitment of Streptomyces into the microbiota. More generally, this study highlights specific as well as complex molecular interactions that shape the plant microbiota.     

Microbial Contributions for Rice Production: From Conventional Crop Management to the Use of ‘Omics’ Technologies

Rice, the main staple food for about half of the world’s population, has had the growth of its production stagnate in the last two decades. One of the ways to further improve rice production is to enhance the associations between rice plants and the microbiome that exists around, on, and inside the plant. This article reviews recent developments in understanding how microorganisms exert positive influences on plant growth, production, and health, focusing particularly on rice. A variety of microbial species and taxa reside in the rhizosphere and the phyllosphere of plants and also have multiple roles as symbiotic endophytes while living within plant tissues and even cells. They alter the morphology of host plants, enhance their growth, health, and yield, and reduce their vulnerability to biotic and abiotic stresses. The findings of both agronomic and molecular analysis show ways in which microorganisms regulate the growth, physiological traits, and molecular signaling within rice plants. However, many significant scientific questions remain to be resolved. Advancements in high-throughput multi-omics technologies can be used to elucidate mechanisms involved in microbial–rice plant associations. Prospectively, the use of microbial inoculants and associated approaches offers some new, cost-effective, and more eco-friendly practices for increasing rice production.     

Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI)

Modifications of the activity of Cytochrome 450 (CYP) enzymes by compounds in food might impair medical treatments. These CYP-mediated food–drug interactions (FDI) play a major role in drug clearance in the intestine and liver. Inter-individual variation in both CYP expression and structure is an important determinant of FDI. Traditional targeted approaches have highlighted a limited number of dietary inhibitors and single-nucleotide variations (SNVs), each determining personal CYP activity and inhibition. These approaches are costly in time, money and labor. Here, we review computational tools and databases that are already available and are relevant to predicting CYP-mediated FDIs. Computer-aided approaches such as protein–ligand interaction modeling and the virtual screening of big data narrow down hundreds of thousands of items in databanks to a few putative targets, to which the research resources could be further directed. Structure-based methods are used to explore the structural nature of the interaction between compounds and CYP enzymes. However, while collections of chemical, biochemical and genetic data are available today and call for the implementation of big-data approaches, ligand-based machine-learning approaches for virtual screening are still scarcely used for FDI studies. This review of CYP-mediated FDIs promises to attract scientists and the general public.     

Challenges in Discovering Drugs That Target the Protein–Protein Interactions of Disordered Proteins

Protein–protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.     

CRISPR/Cas9–Mediated Genome Editing for Pseudomonas fulva,a Novel Pseudomonas Species with Clinical,Animal, and Plant–Associated Isolates

As one of the most widespread groups of Gram–negative bacteria, Pseudomonas bacteria are prevalent in almost all natural environments, where they have developed intimate associations with plants and animals. Pseudomonas fulva is a novel species of Pseudomonas with clinical, animal, and plant–associated isolates, closely related to human and animal health, plant growth, and bioremediation. Although genetic manipulations have been proven as powerful tools for understanding bacterial biological and biochemical characteristics and the evolutionary origins, native isolates are often difficult to genetically manipulate, thereby making it a time–consuming and laborious endeavor. Here, by using the CRISPR–Cas system, a versatile gene–editing tool with a two–plasmid strategy was developed for a native P. fulva strain isolated from the model organism silkworm (Bombyx mori) gut. We harmonized and detailed the experimental setup and clarified the optimal conditions for bacteria transformation, competent cell preparation, and higher editing efficiency. Furthermore, we provided some case studies, testing and validating this approach. An antibiotic–related gene, oqxB, was knocked out, resulting in the slow growth of the P. fulva deletion mutant in LB containing chloramphenicol. Fusion constructs with knocked–in gfp exhibited intense fluorescence. Altogether, the successful construction and application of new genetic editing approaches gave us more powerful tools to investigate the functionalities of the novel Pseudomonas species.     

Receptor–Receptor Interactions and Glial Cell Functions with a Special Focus on G Protein-Coupled Receptors

The discovery that receptors from all families can establish allosteric receptor–receptor interactions and variably associate to form receptor complexes operating as integrative input units endowed with a high functional and structural plasticity has expanded our understanding of intercellular communication. Regarding the nervous system, most research in the field has focused on neuronal populations and has led to the identification of many receptor complexes representing an important mechanism to fine-tune synaptic efficiency. Receptor–receptor interactions, however, also modulate glia–neuron and glia–glia intercellular communication, with significant consequences on synaptic activity and brain network plasticity. The research on this topic is probably still at the beginning and, here, available evidence will be reviewed and discussed. It may also be of potential interest from a pharmacological standpoint, opening the possibility to explore, inter alia, glia-based neuroprotective therapeutic strategies.     

Thermodynamics of π–π Interactions of Benzene and Phenol in Water

The π–π interaction is a major driving force that stabilizes protein assemblies during protein folding. Recent studies have additionally demonstrated its involvement in the liquid–liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs). As the participating residues in IDPs are exposed to water, π–π interactions for LLPS must be modeled in water, as opposed to the interactions that are often established at the hydrophobic domains of folded proteins. Thus, we investigated the association of free energies of benzene and phenol dimers in water by integrating van der Waals (vdW)-corrected density functional theory (DFT) and DFT in classical explicit solvents (DFT-CES). By comparing the vdW-corrected DFT and DFT-CES results with high-level wavefunction calculations and experimental solvation free energies, respectively, we established the quantitative credibility of these approaches, enabling a reliable prediction of the benzene and phenol dimer association free energies in water. We discovered that solvation influences dimer association free energies, but not significantly when no direct hydrogen-bond-type interaction exists between two monomeric units, which can be explained by the enthalpy–entropy compensation. Our comprehensive computational study of the solvation effect on π–π interactions in water could help us understand the molecular-level driving mechanism underlying the IDP phase behaviors.     

Lights,Camera, Interaction: Studying Protein–Protein Interactions of the ER Protein Translocase in Living Cells

Various landmark studies have revealed structures and functions of the Sec61/SecY complex in all domains of live demonstrating the conserved nature of this ancestral protein translocase. While the bacterial homolog of the Sec61 complex resides in the plasma membrane, the eukaryotic counterpart manages the transfer of precursor proteins into or across the membrane of the endoplasmic reticulum (ER). Sec61 complexes are accompanied by a set of dynamically recruited auxiliary proteins assisting the transport of certain precursor polypeptides. TRAP and Sec62/Sec63 are two auxiliary protein complexes in mammalian cells that have been characterized by structural and biochemical methods. Using these ER membrane protein complexes for our proof-of-concept study, we aimed to detect interactions of membrane proteins in living mammalian cells under physiological conditions. Bimolecular luminescence complementation and competition was used to demonstrate multiple protein–protein interactions of different topological layouts. In addition to the interaction of the soluble catalytic and regulatory subunits of the cytosolic protein kinase A, we detected interactions of ER membrane proteins that either belong to the same multimeric protein complex (intra-complex interactions: Sec61α–Sec61β, TRAPα–TRAPβ) or protein complexes in juxtaposition (inter-complex interactions: Sec61α–TRAPα, Sec61α–Sec63, and Sec61β–Sec63). In the process, we established further control elements like synthetic peptide complementation for expression profiling of fusion constructs and protease-mediated reporter degradation demonstrating the cytosolic localization of a reporter complementation. Ease of use and flexibility of the approach presented here will spur further research regarding the dynamics of protein–protein interactions in response to changing cellular conditions in living cells.     

Lorentzian-Corrected Apparent Exchange-Dependent Relaxation (LAREX) Ω-Plot Analysis—An Adaptation for qCEST in a Multi-Pool System: Comprehensive In Silico,In Situ,and In Vivo Studies

Based on in silico, in situ, and in vivo studies, this study aims to develop a new method for the quantitative chemical exchange saturation transfer (qCEST) technique considering multi-pool systems. To this end, we extended the state-of-the-art apparent exchange-dependent relaxation (AREX) method with a Lorentzian correction (LAREX). We then validated this new method with in situ and in vivo experiments on human intervertebral discs (IVDs) using the Kendall-Tau correlation coefficient. In the in silico experiments, we observed significant deviations of the AREX method as a function of the underlying exchange rate (k_ba) and fractional concentration (f_b) compared to the ground truth due to the influence of other exchange pools. In comparison to AREX, the LAREX-based Ω-plot approach yielded a substantial improvement. In the subsequent in situ and in vivo experiments on human IVDs, no correlation to the histological reference standard or Pfirrmann classification could be found for the f_b (in situ: τ = −0.17 p = 0.51; in vivo: τ = 0.13 p = 0.30) and k_ba (in situ: τ = 0.042 p = 0.87; in vivo: τ = −0.26 p = 0.04) of Glycosaminoglycan (GAG) with AREX. In contrast, the influence of interfering pools could be corrected by LAREX, and a moderate to strong correlation was observed for the fractional concentration of GAG for both in situ (τ = −0.71 p = 0.005) and in vivo (τ = −0.49 p < 0.001) experiments. The study presented here is the first to introduce a new qCEST method that enables qCEST imaging in systems with multiple proton pools.     

Acceptive Immunity: The Role of Fucosylated Glycans in Human Host–Microbiome Interactions

The growth in the number of chronic non-communicable diseases in the second half of the past century and in the first two decades of the new century is largely due to the disruption of the relationship between the human body and its symbiotic microbiota, and not pathogens. The interaction of the human immune system with symbionts is not accompanied by inflammation, but is a physiological norm. This is achieved via microbiota control by the immune system through a complex balance of pro-inflammatory and suppressive responses, and only a disturbance of this balance can trigger pathophysiological mechanisms. This review discusses the establishment of homeostatic relationships during immune system development and intestinal bacterial colonization through the interaction of milk glycans, mucins, and secretory immunoglobulins. In particular, the role of fucose and fucosylated glycans in the mechanism of interactions between host epithelial and immune cells is discussed.     

cpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein–Protein Interactions

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach (cpxDeepMSA. We used different protein monomer databases and incorporated the three strategies (genomic distance, phylogeny information, and STRING interaction network) used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis.     

Fabrication of CL-20/HMX Cocrystal@Melamine–Formaldehyde Resin Core–Shell Composites Featuring Enhanced Thermal and Safety Performance via In Situ Polymerization

Safety concerns remain a bottleneck for the application of 2,4,6,8,10,12-hexanitro- 2,4,6,8,10,12-hexaazaisowurtzitane (CL-20)/1,3,5,7-tetranitro-1,3,5,7-tetrazacyclooctane (HMX) cocrystal. Melamine–formaldehyde (MF) resin was chosen to fabricate CL-20/HMX cocrystal-based core–shell composites (CH@MF composites) via a facile in situ polymerization method. The resulted CH@MF composites were comprehensively characterized, and a compact core–shell structure was confirmed. The effects of the shell content on the properties of the composites were explored as well. As a result, we found that, except for CH@MF–2 with a 1% shell content, the increase in shell content led to a rougher surface morphology and more close-packed structure. The thermal decomposition peak temperature improved by 5.3 °C for the cocrystal enabled in 1.0 wt% MF resin. Regarding the sensitivity, the CH@MF composites exhibited a significantly reduced impact and friction sensitivity with negligible energy loss compared with the raw cocrystal and physical mixtures due to the cushioning and insulation effects of the MF coating. The formation mechanism of the core–shell micro-composites was further clarified. Overall, this work provides a green, facile and industrially potential strategy for the desensitization of energetic cocrystals. The CH@MF composites with high thermal stability and low sensitivity are promising to be applied in propellants and polymer-bonded explosive (PBX) formulations.     

Predicting Drug–Target Interactions Based on the Ensemble Models of Multiple Feature Pairs

Backgroud: The prediction of drug–target interactions (DTIs) is of great significance in drug development. It is time-consuming and expensive in traditional experimental methods. Machine learning can reduce the cost of prediction and is limited by the characteristics of imbalanced datasets and problems of essential feature selection. Methods: The prediction method based on the Ensemble model of Multiple Feature Pairs (Ensemble-MFP) is introduced. Firstly, three negative sets are generated according to the Euclidean distance of three feature pairs. Then, the negative samples of the validation set/test set are randomly selected from the union set of the three negative sets in the validation set/test set. At the same time, the ensemble model with weight is optimized and applied to the test set. Results: The area under the receiver operating characteristic curve (area under ROC, AUC) in three out of four sub-datasets in gold standard datasets was more than 94.0% in the prediction of new drugs. The effectiveness of the proposed method is also shown with the comparison of state-of-the-art methods and demonstration of predicted drug–target pairs. Conclusion: The Ensemble-MFP can weigh the existing feature pairs and has a good prediction effect for general prediction on new drugs.     

alfaNET: A Database of Alfalfa-Bacterial Stem Blight Protein–Protein Interactions Revealing the Molecular Features of the Disease-causing Bacteria

Alfalfa has emerged as one of the most important forage crops, owing to its wide adaptation and high biomass production worldwide. In the last decade, the emergence of bacterial stem blight (caused by Pseudomonas syringae pv. syringae ALF3) in alfalfa has caused around 50% yield losses in the United States. Studies are being conducted to decipher the roles of the key genes and pathways regulating the disease, but due to the sparse knowledge about the infection mechanisms of Pseudomonas, the development of resistant cultivars is hampered. The database alfaNET is an attempt to assist researchers by providing comprehensive Pseudomonas proteome annotations, as well as a host–pathogen interactome tool, which predicts the interactions between host and pathogen based on orthology. alfaNET is a user-friendly and efficient tool and includes other features such as subcellular localization annotations of pathogen proteins, gene ontology (GO) annotations, network visualization, and effector protein prediction. Users can also browse and search the database using particular keywords or proteins with a specific length. Additionally, the BLAST search tool enables the user to perform a homology sequence search against the alfalfa and Pseudomonas proteomes. With the successful implementation of these attributes, alfaNET will be a beneficial resource to the research community engaged in implementing molecular strategies to mitigate the disease. alfaNET is freely available for public use at http://bioinfo.usu.edu/alfanet/.     

Functional Delineation of a Protein–Membrane Interaction Hotspot Site on the HIV-1 Neutralizing Antibody 10E8

Antibody engagement with the membrane-proximal external region (MPER) of the envelope glycoprotein (Env) of HIV-1 constitutes a distinctive molecular recognition phenomenon, the full appreciation of which is crucial for understanding the mechanisms that underlie the broad neutralization of the virus. Recognition of the HIV-1 Env antigen seems to depend on two specific features developed by antibodies with MPER specificity: (i) a large cavity at the antigen-binding site that holds the epitope amphipathic helix; and (ii) a membrane-accommodating Fab surface that engages with viral phospholipids. Thus, besides the main Fab–peptide interaction, molecular recognition of MPER depends on semi-specific (electrostatic and hydrophobic) interactions with membranes and, reportedly, on specific binding to the phospholipid head groups. Here, based on available cryo-EM structures of Fab–Env complexes of the anti-MPER antibody 10E8, we sought to delineate the functional antibody–membrane interface using as the defining criterion the neutralization potency and binding affinity improvements induced by Arg substitutions. This rational, Arg-based mutagenesis strategy revealed the position-dependent contribution of electrostatic interactions upon inclusion of Arg-s at the CDR1, CDR2 or FR3 of the Fab light chain. Moreover, the contribution of the most effective Arg-s increased the potency enhancement induced by inclusion of a hydrophobic-at-interface Phe at position 100c of the heavy chain CDR3. In combination, the potency and affinity improvements by Arg residues delineated a protein–membrane interaction site, whose surface and position support a possible mechanism of action for 10E8-induced neutralization. Functional delineation of membrane-interacting patches could open new lines of research to optimize antibodies of therapeutic interest that target integral membrane epitopes.     

Diversity and Taxonomic Distribution of Endophytic Bacterial Community in the Rice Plant and Its Prospective

Endophytic bacterial communities are beneficial communities for host plants that exist inside the surfaces of plant tissues, and their application improves plant growth. They benefit directly from the host plant by enhancing the nutrient amount of the plant’s intake and influencing the phytohormones, which are responsible for growth promotion and stress. Endophytic bacteria play an important role in plant-growth promotion (PGP) by regulating the indirect mechanism targeting pest and pathogens through hydrolytic enzymes, antibiotics, biocontrol potential, and nutrient restriction for pathogens. To attain these benefits, firstly bacterial communities must be colonized by plant tissues. The nature of colonization can be achieved by using a set of traits, including attachment behavior and motility speed, degradation of plant polymers, and plant defense evasion. The diversity of bacterial endophytes colonization depends on various factors, such as plants’ relationship with environmental factors. Generally, each endophytic bacteria has a wide host range, and they are used as bio-inoculants in the form of synthetic applications for sustainable agriculture systems and to protect the environment from chemical hazards. This review discusses and explores the taxonomic distribution of endophytic bacteria associated with different genotypes of rice plants and their origin, movement, and mechanism of PGP. In addition, this review accentuates compressive meta data of endophytic bacteria communities associated with different genotypes of rice plants, retrieves their plant-growth-promoting properties and their antagonism against plant pathogens, and discusses the indication of endophytic bacterial flora in rice plant tissues using various methods. The future direction deepens the study of novel endophytic bacterial communities and their identification from rice plants through innovative techniques and their application for sustainable agriculture systems.