Abstract: This study was performed to investigate the herb-drug interactions (HDIs) of citrus herbs (CHs), which was inspired by the "grapefruit (GF) juice effect". Based on network analysis, a total of 249 components in GF and 159 compounds in CHs exhibited great potential as active ingredients. Moreover, 360 GF-related genes, 422 CH-related genes, and 111 genes associated with drug transport and metabolism were collected, while 25 and 26 overlapping genes were identified. In compound-target networks, the degrees of naringenin, isopimpinellin, apigenin, sinensetin, and isoimperatorin were higher, and the results of protein-protein interaction indicated the hub role of UGT1A1 and CYP3A4. Conventional drugs such as erlotinib, nilotinib, tamoxifen, theophylline, venlafaxine, and verapamil were associated with GF and CHs via multiple drug transporters and drug-metabolizing enzymes. Remarkably, GF and CHs shared 48 potential active compounds, among which naringenin, tangeretin, nobiletin, and apigenin possessed more interactions with targets. Drug metabolism by cytochrome P450 stood out in the mutual mechanism of GF and CHs. Molecular docking was utilized to elevate the protein-ligand binding potential of naringenin, tangeretin, nobiletin, and apigenin with UGT1A1 and CYP3A4. Furthermore, in vitro experiments demonstrated their regulating effect. Overall, this approach provided predictions on the HDIs of CHs, and they were tentatively verified through molecular docking and cell tests. Moreover, there is a demand for clinical and experimental evidence to support the prediction.

Abstract: A major complication in COVID-19 infection consists in the onset of acute respiratory distress fueled by a dysregulation of the host immune network that leads to a run-away cytokine storm. Here, we present an in silico approach that captures the host immune system's complex regulatory dynamics, allowing us to identify and rank candidate drugs and drug pairs that engage with minimal subsets of immune mediators such that their downstream interactions effectively disrupt the signaling cascades driving cytokine storm. Drug-target regulatory interactions are extracted from peer-reviewed literature using automated text-mining for over 5000 compounds associated with COVID-induced cytokine storm and elements of the underlying biology. The targets and mode of action of each compound, as well as combinations of compounds, were scored against their functional alignment with sets of competing model-predicted optimal intervention strategies, as well as the availability of like-acting compounds and known off-target effects. Top-ranking individual compounds identified included a number of known immune suppressors such as calcineurin and mTOR inhibitors as well as compounds less frequently associated for their immune-modulatory effects, including antimicrobials, statins, and cholinergic agonists. Pairwise combinations of drugs targeting distinct biological pathways tended to perform significantly better than single drugs with dexamethasone emerging as a frequent high-ranking companion. While these predicted drug combinations aim to disrupt COVID-induced acute respiratory distress syndrome, the approach itself can be applied more broadly to other diseases and may provide a standard tool for drug discovery initiatives in evaluating alternative targets and repurposing approved drugs.

Abstract: Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Abstract: Brain metastases (BrMs) are a common occurrence in lung cancer with a dismal outcome. To understand the mechanism of metastasis to inform prognosis and treatment, here we analyze primary and metastasized tumor specimens from 44 non-small cell lung cancer patients by spatial RNA sequencing, affording a whole transcriptome map of metastasis resolved with morphological markers for the tumor core, tumor immune microenvironment (TIME), and tumor brain microenvironment (TBME). Our data indicate that the tumor microenvironment (TME) in the brain, including the TIME and TBME, undergoes extensive remodeling to create an immunosuppressive and fibrogenic niche for the BrMs. Specifically, the brain TME is characterized with reduced antigen presentation and B/T cell function, increased neutrophils and M2-type macrophages, immature microglia, and reactive astrocytes. Differential gene expression and network analysis identify fibrosis and immune regulation as the major functional modules disrupted in both the lung and brain TME. Besides providing systems-level insights into the mechanism of lung cancer brain metastasis, our study uncovers potential prognostic biomarkers and suggests that therapeutic strategies should be tailored to the immune and fibrosis status of the BrMs.

Abstract: Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/ .

Abstract: Recent experiments show that adaptive Darwinian evolution in one environment can lead to the emergence of multiple new traits that provide no immediate benefit in this environment. Such latent non-adaptive traits, however, can become adaptive in future environments. We do not know whether mutation or environment-driven selection is more important for the emergence of such traits. To find out, we evolve multiple wild-type and mutator E. coli populations under two mutation rates in simple (single antibiotic) environments and in complex (multi-antibiotic) environments. We then assay the viability of evolved populations in dozens of new environments and show that all populations become viable in multiple new environments different from those they had evolved in. The number of these new environments increases with environmental complexity but not with the mutation rate. Genome sequencing demonstrates the reason: Different environments affect pleiotropic mutations differently. Our experiments show that the selection pressure provided by an environment can be more important for the evolution of novel traits than the mutational supply experienced by a wild-type and a mutator strain of E. coli.

Abstract: BACKGROUND: The Bacillus cereus group, also known as B. cereus sensu lato (s.l.) contains ubiquitous spore-forming bacteria found in the environment including strains from the B. cereus sensu stricto (s.s.) species. They occur naturally in a wide range of raw materials and in consumer products. Characterizing isolates that have survived in consumer products allows us to better understand the mechanisms that permit spores to persist and potentially cause illness. Here we characterize the draft genome sequence of B. cereus s. s. 3A-ES, originally isolated from eye shadow and since investigated in several cosmetic studies and compared it to other top ten published complete genome sequences of B. cereus s.l. members. RESULTS: The draft genome sequence of B. cereus s.s. 3A ES consisted of an average of 90 contigs comprising approximately 5,335,727 bp and a GC content of 34,988%, and with 5509 predicted coding sequences. Based on the annotation statistics and comparison to other genomes within the same species archived in the Pathosystems Resource Integration Center (PATRIC), this genome "was of good quality. Annotation of B. cereus s.s. 3A ES revealed a variety of subsystem features, virulence factors and antibiotic resistant genes. The phylogenetic analysis of ten B. cereus group members showed B. cereus s.s. 3A-ES to be a closely related homolog of B. cereus s.s. ATCC 14,579, an established reference strain that is not adapted for cosmetic microbiological studies. Survival of 3A-ES in eye shadow could be linked to predicted stress-response genes and strengthened by additional stress-response genes such as VanB-type, VanRB, CAT15/16, BcrA, BcrB, Lsa(B), and recA that are lacking in B. cereus s.s. ATCC 14,579. CONCLUSION: Our genomic analysis of B. cereus s.s. 3A-ES revealed genes, which may allow this bacterium to withstand the action of preservatives and inhibitors in cosmetics, as well as virulence factors that could contribute to its pathogenicity. Having a well-characterized strain obtained from eye-shadow may be useful for establishing a reference strain for cosmetics testing.

Abstract: The remarkable rise in antimicrobial resistance is alarming for Acinetobacter baumannii, which necessitates effective strategies for the discovery of promising anti-acinetobacter agents. We used a subtractive proteomics approach to identify unique protein drug targets. Shortlisted targets passed through subtractive channels, including essentiality, non-homology to the human proteome, druggability, sub-cellular localization prediction and conservation. Sixty-eight drug targets were shortlisted; among these, glutamine synthetase, dihydrodipicolinate reductase, UDP-N-acetylglucosamine acyltransferase, aspartate 1-decarboxylase and bifunctional UDP-N-acetylglucosamine diphosphorylase/glucosamine-1-phosphate N-acetyltransferase were evaluated in vitro by determining the minimum inhibitory concentration (MIC) of candidate ligands, citric acid, dipicolinic acid, D-tartaric acid, malonic acid and 2-(N-morpholino)ethanesulfonic acid (MES), respectively, which ranged from 325 to 1500 mug/mL except for MES (25 mg/mL). The candidate ligands, citric acid, D-tartaric acid and malonic acid, showed good binding energy scores to their targets upon applying molecular docking, in addition to a significant reduction in A. baumannii microbial load in the wound infection mouse model. These ligands also exhibited good tolerability to human skin fibroblast. The significant increase in the MIC of malonic acid in beta-alanine and pantothenate-supplemented media confirmed its selective inhibition to aspartate 1-decarboxylase. In conclusion, three out of sixty-eight potential A. baumannii drug targets were effectively inhibited in vitro and in vivo by promising ligands.

Abstract: The last big outbreaks of Ebola fever in Africa, the thousands of avian influenza outbreaks across Europe, Asia, North America and Africa, the emergence of monkeypox virus in Europe and specially the COVID-19 pandemics have globally stressed the need for efficient, cost-effective vaccines against infectious diseases. Ideally, they should be based on transversal technologies of wide applicability. In this context, and pushed by the above-mentioned epidemiological needs, new and highly sophisticated DNA-or RNA-based vaccination strategies have been recently developed and applied at large-scale. Being very promising and effective, they still need to be assessed regarding the level of conferred long-term protection. Despite these fast-developing approaches, subunit vaccines, based on recombinant proteins obtained by conventional genetic engineering, still show a wide spectrum of interesting potentialities and an important margin for further development. In the 80's, the first vaccination attempts with recombinant vaccines consisted in single structural proteins from viral pathogens, administered as soluble plain versions. In contrast, more complex formulations of recombinant antigens with particular geometries are progressively generated and explored in an attempt to mimic the multifaceted set of stimuli offered to the immune system by replicating pathogens. The diversity of recombinant antimicrobial vaccines and vaccine prototypes is revised here considering the cell factory types, through relevant examples of prototypes under development as well as already approved products.

Abstract: The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.