Abstract: Immunotherapy became a key pillar of cancer therapeutics with the approvals of ipilimumab, nivolumab, and pembrolizumab, which inhibit either cytotoxic T-lymphocyte antigen-4 (CTLA-4) or programmed death-1 (PD-1) that are negative regulators of T-cell activation. However, boosting T-cell activation is often accompanied by autoimmunity, leading to adverse drug reactions (ADRs), including high grade 3-4 colitis and its severe complications whose prevalence may reach 14% for combination checkpoint inhibitors. In this research, we investigated how mechanistic differences between anti-CTLA-4 (ipilimumab) and anti-PD-1 (nivolumab and pembrolizumab) affect colitis, a general class toxicity. The data analytical platform Molecular Health Effect was utilized to map population ADR data from the US Food and Drug Administration (FDA) Adverse Event Reporting System to chemical and biological databases for hypothesis generation regarding the underlying molecular mechanisms causing colitis. Disproportionality analysis was used to assess the statistical relevance between adverse events of interest and molecular causation. We verified that the anti-CTLA-4 drug is associated with an approximately three-fold higher proportional reporting ratio associated with colitis than those of the anti-PD-1 drugs. The signal of the molecular mechanisms, including signaling pathways of inflammatory cytokines, was statistically insignificant to test the hypothesis that the severer rate of colitis associated with ipilimumab would be due to a greater magnitude of T-cell activation as a result of earlier response of the anti-CTLA-4 drug in the immune response. This patient-centered systems-based approach provides an exploratory process to better understand drug pair adverse events at pathway and target levels through reverse translation from postmarket surveillance safety reports.

Abstract: The dataset represented in this article is referred to by the review article entitled "Topical drug delivery: history, percutaneous absorption, and product development" (MS Roberts et al., 2021) [1]. The dataset contains maximal flux (Jmax ), and permeability coefficient (kp ) values collated from In Vitro human skin Permeation Test (IVPT) reports published to date for various drugs, xenobiotics, and other solutes applied to human epidermis from aqueous solutions. Also included are each solute's physicochemical properties and the experimental conditions, such as temperature, skin thickness, and skin integrity, under which the data was generated. This database is limited to diluted or saturated aqueous solutions of solutes applied on human epidermal membranes or isolated stratum corneum in large volumes so that there was minimal change in the donor phase concentration. Included in this paper are univariate Quantitative Structure-epidermal Permeability Relationships (QSPR) in which the solute epidermal permeation parameters (kp , and Jmax ) are related to potential individual solute physicochemical properties, such as molecular weight (MW), log octanol-water partition coefficient (log P), melting point (MP), hydrogen bonding (acceptor - Ha , donor - Hd ), by scatter plots. This data was used in the associated review article to externally validate existing QSPR regression equations used to forecast the kp and Jmax for new therapeutic agents and chemicals. The data may also be useful in developing new QSPRs that may aid in: (1) drug choice and (2) product design for both topical and transdermal delivery, as well as (3) characterizing the potential skin exposure of hazardous substances.

Abstract: Background: Patients with rheumatoid arthritis (RA) may be more susceptible to infection by coronavirus disease-19 (COVID-19) due to immune system dysfunction. However, there are still insufficient treatment strategies for patients with RA and COVID-19. Since Jingulian is a traditional Chinese medicine (TCM) with anti-viral and immune regulatory functions, our study aims to explore the detailed mechanisms of Jingulian in treating patients with RA and COVID-19. Methods: All the components of Jingulian were retrieved from pharmacology databases. Then, a series of network pharmacology-based analyses and molecular docking were used to understand the molecular functions, core targets, related pathways, and potential therapeutic targets of Jingulian in patients with RA/COVID-19. Results: A total of 93 genes were identified according to the disease-compound-target network. We investigated that the main targets, signaling pathways, and biological functions of Jingulian in RA and COVID-19. Our results indicated that Jingulian may treat patients with RA/COVID-19 through immune processes and viral processes. Moreover, the results of molecular docking revealed that tormentic acid was one of the top compounds of Jingulian, which had high affinity with Janus kinase 1 (JAK1), signal transducer and activator of transcription 3 (STAT3), and epidermal growth factor receptor (EGFR) in patients with RA/COVID-19. Furthermore, 5 core targets of Jingulian were also identified, including JAK1, Janus kinase 2 (JAK2), STAT3, lymphocyte specific protein tyrosine kinase (LCK), and EGFR. Conclusions: Tormentic acid in Jingulian may regulate JAK1, STAT3, and EGFR, and might play a critical role in RA/COVID-19.

Abstract: OBJECTIVE: This study identified underlying genetic molecules associated with histologically unstable carotid atherosclerotic plaques through bioinformatics analysis that may be potential biomarkers and therapeutic targets. METHODS: Three transcriptome datasets (GSE41571, GSE120521 and E-MTAB-2055) and one non-coding RNA dataset (GSE111794) that met histological grouping criteria of unstable plaque were downloaded. The common differentially expressed genes (co-DEGs) of unstable plaques identified from three mRNA datasets were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). A protein-protein interaction (PPI) network was constructed to present the interaction between co-DEGs and screen out hub genes. MiRNet database and GSE111794 dataset were used to identify the miRNAs targeting hub genes. Associated transcription factors (TFs) and drugs were also predicted. These predicted results were used to construct miRNA/TFs-hub gene and drug-hub gene regulatory networks. RESULTS: A total of 105 co-DEGs were identified, including 42 up-regulated genes and 63 down-regulated genes, which were mainly enriched in collagen-containing extracellular matrix, focal adhesion, actin filament bundle, chemokine signaling pathway and regulates of actin cytoskeleton. Ten hub genes (up-regulated: HCK, C1QC, CD14, FCER1G, LCP1 and RAC2; down-regulated: TPM1, MYH10, PLS3 and FMOD) were screened. HCK and RAC2 were involved in chemokine signaling pathway, MYH10 and RAC2 were involved in regulation of actin cytoskeleton. We also predicted 12 miRNAs, top5 TFs and 25 drugs targeting hub genes. In the miRNA/TF-hub gene regulatory network, PLS3 was the most connected hub genes and was targeted by six miRNAs and all five screened TFs. In the drug-hub gene regulatory network, HCK was targeted by 20 drugs including 10 inhibitors. CONCLUSIONS: We screened 10 hub genes and predicted miRNAs and TFs targeting them. These molecules may play a crucial role in the progression of histologically unstable carotid plaques and serve as potential biomarkers and therapeutic targets.

Abstract: Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

Abstract: Drug discovery often requires the identification of off-targets as the binding of a compound to targets other than the intended target(s) can be beneficial in some cases or detrimental in other situations (e.g., binding to anti-targets). Such investigations are also of importance during the early stage of a project, for example when the target is not known (e.g., phenotypic screening). Target identification can be performed in-vitro, but various in-silico methods have also been developed in recent years to facilitate target identification and help generate ideas. FastTargetPred is one such approach, it is a freely available Python/C program that attempts to predict putative macromolecular targets (i.e., target fishing) for a single input small molecule query or an entire compound collection using established chemical similarity search approaches. Indeed, the putative macromolecular target(s) of a small chemical compound can be predicted by identifying ligands that are known experimentally to bind to some targets and that are structurally similar to the input query chemical compound. Therefore, this type of target fishing approach relies on a large collection of experimentally validated macromolecule-chemical compound binding data. The small chemical compounds can be described as molecular fingerprints encoding their structural characteristics as a vector. The published version of FastTargetPred used ligand-target binding data extracted from the release 25 (2019) of the ChEMBL database. Here we provide a new dataset for FastTargetPred extracted from the last ChEMBL release, namely, at the time of writing, ChEMBL29 (2021). Four fingerprints were computed (ECFP4, ECFP6, MACCS and PL) for the extracted compound dataset (714,780 unique ChEMBL29 compounds while the entire ChEMBL29 database contained about 2.1 million compounds). However, it was not possible to compute fingerprints for 19 molecules because of their unusual chemistry (complex macrocycles). These data files were then prepared so as to be compatible with FastTargetPred requirements. The 714,761 ChEMBL chemical compounds with computed fingerprints hit 6,477 macromolecular targets based on the selected criteria. For these ChEMBL compounds a ChEMBL target ID is reported and these target IDs were matched with the corresponding UniProt IDs. Thus, when available, the UniProt ID is provided, the protein UniProt name, the gene name, the organism as well as annotated involvement in diseases, gene ontology data, and cross-references to the Reactome pathway database. As short peptides can be of interest for drug discovery and chemical biology endeavours, we were interested in attempting to predict putative macromolecular targets for a previously reported exhaustive combination of peptides containing four natural amino acids (i.e., 20 x 20 x 20 x 20 = 160,000 linear tetrapeptides) using FastTargetPred and the presently generated ChEMBL29 dataset. With the parameters used, putative targets are reported for 63,944 unique query peptides. These target predictions are provided in two different searchable files with hyperlinks to the ChEMBL, UniProt and Reactome databases.

Abstract: The plasma membrane proteome resides at the interface between the extra- and intra-cellular environment and through its various roles in signal transduction, immune recognition, nutrient transport, and cell-cell/cell-matrix interactions plays an absolutely critical role in determining the fate of a cell. Our work was aimed at exploring the cell-membrane proteome of a HER2+ breast-cancer cell line (SKBR3) to identify triggers responsible for uncontrolled cell proliferation and intrinsic resources that enable detection and therapeutic interventions. To mimic environmental conditions that enable cancer cells to evolve adaptation/survival traits, cell culture was performed under serum-rich and serum-deprived conditions. Proteomic analysis enabled the identification of ~ 2000 cell-membrane proteins. Classification into proteins with receptor/enzymatic activity, CD antigens, transporters, and cell adhesion/junction proteins uncovered overlapping roles in processes that drive cell growth, apoptosis, differentiation, immune response, adhesion and migration, as well as alternate pathways for proliferation. The large number of tumor markers (> 50) and putative drug targets (> 100) exposed a vast potential for yet unexplored detection and targeting opportunities, whereas the presence of 15 antigen immunological markers enabled an assessment of epithelial, mesenchymal or stemness characteristics. Serum-starved cells displayed altered processes related to mitochondrial OXPHOS/ATP synthesis, protein folding and localization, while serum-treated cells exhibited attributes that support tissue invasion and metastasis. Altogether, our findings advance the understanding of the biological triggers that sustain aberrant cancer cell proliferation, survival and development of resistance to therapeutic drugs, and reveal vast innate opportunities for guiding immunological profiling and precision medicine applications aimed at target selection or drug discovery.

Abstract: OBJECTIVES: Autoimmune and allergic diseases are outcomes of the dysregulation of the immune system. Our study aimed to elucidate differences or shared components in genetic backgrounds between autoimmune and allergic diseases. METHODS: We estimated genetic correlation and performed multi-trait and cross-population genome-wide association study (GWAS) meta-analysis of six immune-related diseases: rheumatoid arthritis, Graves' disease, type 1 diabetes for autoimmune diseases and asthma, atopic dermatitis and pollinosis for allergic diseases. By integrating large-scale biobank resources (Biobank Japan and UK biobank), our study included 105 721 cases and 433 663 controls. Newly identified variants were evaluated in 21 778 cases and 712 767 controls for two additional autoimmune diseases: psoriasis and systemic lupus erythematosus. We performed enrichment analyses of cell types and biological pathways to highlight shared and distinct perspectives. RESULTS: Autoimmune and allergic diseases were not only mutually classified based on genetic backgrounds but also they had multiple positive genetic correlations beyond the classifications. Multi-trait GWAS meta-analysis newly identified six allergic disease-associated loci. We identified four loci shared between the six autoimmune and allergic diseases (rs10803431 at PRDM2, OR=1.07, p=2.3x10(-8), rs2053062 at G3BP1, OR=0.90, p=2.9x10(-8), rs2210366 at HBS1L, OR=1.07, p=2.5x10(-8) in Japanese and rs4529910 at POU2AF1, OR=0.96, p=1.9x10(-10) across ancestries). Associations of rs10803431 and rs4529910 were confirmed at the two additional autoimmune diseases. Enrichment analysis demonstrated link to T cells, natural killer cells and various cytokine signals, including innate immune pathways. CONCLUSION: Our multi-trait and cross-population study should elucidate complex pathogenesis shared components across autoimmune and allergic diseases.

Abstract: Pseudomonas aeruginosa is an opportunistic pathogen that can cause acute and severe infections. Increasing resistance to antibiotics has given rise to the urgent need for an alternative antimicrobial agent. A promising strategy is the inhibition of iron sequestration in the bacteria. The current work aimed to screen for inhibitors of pyoverdine-mediated iron sequestration in P. aeruginosa. As a drug target, we choose l-ornithine-N5-monooxygenase (PvdA), an enzyme involved in the biosynthesis of pyoverdine that catalyzes the FAD-dependent hydroxylation of the side chain amine of ornithine. As drug repurposing is a fast and cost-efficient way of discovering new applications for known drugs, the approach may help to solve emerging clinical problems. In this study, we use data about molecules from drug banks for screening. A total of 15 drugs that are similar in structure to l-ornithine, the substrate of PvdA, and 30 drugs that are sub-structures of l-ornithine were virtually docked against PvdA. N-2-succinyl ornithine and cilazapril were found to be the top binders with a binding energy of -12.8 and -9.1 kcal mol(-1), respectively. As the drug-likeness and ADME properties of the drugs were also found to be promising, molecular dynamics studies were performed to further confirm the stability of the complexes. The results of this in silico study indicate that N-2-succinyl ornithine could potentially be explored as a drug for the treatment of P. aeruginosa infections.

Abstract: Mechanistic study and precision treatment of primary liver cancer (PLC) are hindered by marked heterogeneity, which is challenging to recapitulate in any given liver cancer mouse model. Here, we report the generation of 25 mouse models of PLC by in situ genome editing of hepatocytes recapitulating 25 single or combinations of human cancer driver genes. These mouse tumors represent major histopathological types of human PLCs and could be divided into three human-matched molecular subtypes based on transcriptomic and proteomic profiles. Phenotypical characterization identified subtype- or genotype-specific alterations in immune microenvironment, metabolic reprogramming, cell proliferation, and expression of drug targets. Furthermore, single-cell analysis and expression tracing revealed spatial and temporal dynamics in expression of pyruvate kinase M2 (Pkm2). Tumor-specific knockdown of Pkm2 by multiplexed genome editing reversed the Warburg effect and suppressed tumorigenesis in a genotype-specific manner. Our study provides mouse PLC models with defined genetic drivers and characterized phenotypical heterogeneity suitable for mechanistic investigation and preclinical testing.