Abstract: MATERIALS AND METHODS: Compounds of HQHG were scanned by LC-MS/MS, and the target profiles of compounds were identified based on SwissTarget Prediction. ITP target proteins were collected from various databases. Then, KEGG pathway and GO enrichment analyses were performed to explore the signaling pathways related to HQHG for ITP. The PPI and drug-herbs-compounds-targets-pathways network were constructed using Cytoscape 3.7.2. Finally, Discovery studio software was used to confirm the key targets and active compounds from HQHG. RESULTS: A total of 187 interacting targets of 19 potentially active compounds in HQHG and 3837 ITP-related targets were collected. Then, 187 intersection targets were obtained. A total of 20 key targets including EGFR, CASP3, TNF, STAT3, and ERBB2 were identified through PPI network analysis. These targets were mainly focused on the biological processes of positive regulation of protein phosphorylation, cellular response to organonitrogen compound, and cellular response to nitrogen compound. 20 possible pathways of HQHG in the treatment of ITP were identified through KEGG enrichment. EGFR, CASP3, TNF, and STAT3 are the four most important target proteins, while adenosine, caffeic acid, ferulic acid, quercetin-3beta-D-glucoside, rutin, scopoletin, and tianshic acid are the most important active compounds, which were validated by molecular docking simulation. CONCLUSION: This study demonstrated that HQHG produced relief effects against ITP by regulating multitargets and multipathways with multicompounds. And the combined data provide novel insight of drug developing for ITP.

Abstract: Background: Several clinical trials have demonstrated that glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1RAs) reduce the incidence of non-fatal myocardial infarction (MI) in patients with type 2 diabetes mellitus (T2DM). However, the underlying mechanism remains unclear. In this study, we applied a network pharmacology method to investigate the mechanisms by which GLP-1RAs reduce MI occurrence in patients with T2DM. Methods: Targets of three GLP-1RAs (liraglutide, semaglutide, and albiglutide), T2DM, and MI were retrieved from online databases. The intersection process and associated targets retrieval were employed to obtain the related targets of GLP-1RAs against T2DM and MI. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genes (KEGG) enrichment analyses were performed. The STRING database was used to obtain the protein-protein interaction (PPI) network, and Cytoscape was used to identify core targets, transcription factors, and modules. Results: A total of 198 targets were retrieved for the three drugs and 511 targets for T2DM with MI. Finally, 51 related targets, including 31 intersection targets and 20 associated targets, were predicted to interfere with the progression of T2DM and MI on using GLP-1RAs. The STRING database was used to establish a PPI network comprising 46 nodes and 175 edges. The PPI network was analyzed using Cytoscape, and seven core targets were screened: AGT, TGFB1, STAT3, TIMP1, MMP9, MMP1, and MMP2. The transcription factor MAFB regulates all seven core targets. The cluster analysis generated three modules. The GO analysis for 51 targets indicated that the terms were mainly enriched in the extracellular matrix, angiotensin, platelets, and endopeptidase. The results of KEGG analysis revealed that the 51 targets primarily participated in the renin-angiotensin system, complement and coagulation cascades, hypertrophic cardiomyopathy, and AGE-RAGE signaling pathway in diabetic complications. Conclusion: GLP-1RAs exert multi-dimensional effects on reducing the occurrence of MI in T2DM patients by interfering with targets, biological processes, and cellular signaling pathways related to atheromatous plaque, myocardial remodeling, and thrombosis.

Abstract: The fragment approach has emerged as a method of choice for drug design, as it allows difficult therapeutic targets to be addressed. Success lies in the choice of the screened chemical library and the biophysical screening method, and also in the quality of the selected fragment and structural information used to develop a drug-like ligand. It has recently been proposed that promiscuous compounds, i.e., those that bind to several proteins, present an advantage for the fragment approach because they are likely to give frequent hits in screening. In this study, we searched the Protein Data Bank for fragments with multiple binding modes and targeting different sites. We identified 203 fragments represented by 90 scaffolds, some of which are not or hardly present in commercial fragment libraries. By contrast to other available fragment libraries, the studied set is enriched in fragments with a marked three-dimensional character (download at 10.5281/zenodo.7554649).

Abstract: Introduction: More than 50 mutations in the MAPT gene result in heterogeneous forms of frontotemporal lobar dementia with tau inclusions (FTLD-Tau). However, early pathogenic events that lead to disease and the degree to which they are common across MAPT mutations remain poorly understood. The goal of this study is to determine whether there is a common molecular signature of FTLD-Tau. Methods: We analyzed genes differentially expressed in induced pluripotent stem cell-derived neurons (iPSC-neurons) that represent the three major categories of MAPT mutations: splicing (IVS10 + 16), exon 10 (p.P301L), and C-terminal (p.R406W) compared with isogenic controls. The genes that were commonly differentially expressed in MAPT IVS10 + 16, p.P301L, and p.R406W neurons were enriched in trans-synaptic signaling, neuronal processes, and lysosomal function. Many of these pathways are sensitive to disruptions in calcium homeostasis. One gene, CALB1, was significantly reduced across the three MAPT mutant iPSC-neurons and in a mouse model of tau accumulation. We observed a significant reduction in calcium levels in MAPT mutant neurons compared with isogenic controls, pointing to a functional consequence of this disrupted gene expression. Finally, a subset of genes commonly differentially expressed across MAPT mutations were also dysregulated in brains from MAPT mutation carriers and to a lesser extent in brains from sporadic Alzheimer disease and progressive supranuclear palsy, suggesting that molecular signatures relevant to genetic and sporadic forms of tauopathy are captured in a dish. The results from this study demonstrate that iPSC-neurons capture molecular processes that occur in human brains and can be used to pinpoint common molecular pathways involving synaptic and lysosomal function and neuronal development, which may be regulated by disruptions in calcium homeostasis.

Abstract: OBJECTIVE: To explore the pharmacological mechanism and molecular targets of Tianma Gouteng Decoction (TMGTD) in the treatment of Parkinson's disease (PD). METHODS: We applied network pharmacology to screen the active components of TMGTD and predict target genes in multiple Chinese herbal medicine databases and compound databases, and built a drug-ingredient-target network. Then, we used the CytoHubba plug-in to filter out the core components of TMGTD according to the order of degree value. We screened PD-related pathogenic targets in the DrugBank, Genecard and OMIM databases from high to low in Betweenness Centrality (BC) value and Closeness Centrality (CC) value. Subsequently, we determined the intersection target of TMGTD and PD by Venn diagram and performed protein-protein interaction (PPI) analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core molecules and intersection targets. Finally, molecular docking was performed to verify the binding of the top three core molecules of TMGTD with the top three core targets of PD. RESULTS: The core components of TMGTD are quercetin, kaempferol and palmitic acid. The main targets of TMGTD in the treatment of PD are ALB, GAPDH and AKT1. GO analysis and KEGG analysis showed that the biological process of TMGTD in the treatment of PD is closely related to the activities of neurotransmitter receptors, G protein-coupled receptors and dopamine neurotransmitter receptors. TMGTD possesses therapeutic effects on PD mainly through the PI3K-Akt signaling pathway and MAPK signaling pathway. Molecular docking shows the high affinity of the quercetin, kaempferol and palmitic acid with PD core targets. CONCLUSION: TMGTD plays a pivotal role in the treatment of PD through multiple components, multiple targets and multiple pathways. The results provide a research direction for the subsequent exploration of the mechanism of TMGTD in PD treatment.

Abstract: BACKGROUND: The relationship between H. pylori infection and gastric cancer (GC) has been widely studied, and H. pylori is considered as the main factor. Utilizing bioinformatics analysis, this study examined gene signatures related to progressing H. pylori-associated GC. MATERIALS AND METHODS: The dataset GSE13195 was chosen to search for abnormally expressed genes in H. pylori-associated GC and normal tissues. The TCGA-STAD database was chosen to verify the expression of key genes in GC and normal tissues. RESULTS: In GSE13195, a total of 332 differential expression genes (DEGs) were screened. The results of weighted gene co-expression network analysis showed that the light cyan, plum2, black, and magenta4 modules were associated with stages (T3, T2, and T4), while the orangered4, salmon2, pink, and navajowhite2 modules were correlated with lymph node metastasis (N3, N2, and N0). Based on the results of DEGs and hub genes, a total of 7 key genes (ADAM28, FCER1G, MRPL14, SOSTDC1, TYROBP, C1QC, and C3) were screened out. These gene mRNA levels were able to distinguish between normal and H. pylori-associated GC tissue using receiver operating characteristic curves. After transcriptional level verification and survival analysis, ADAM28 and C1QC were excluded. An immune infiltration study revealed that key genes were involved in regulating the infiltration levels of cells associated with innate immune response, antigen presentation process, humoral immune response, or Tcell-mediated immune response. In addition, drugs targeting FCER1G and TYROBP have been approved and are under investigation. CONCLUSION: Our study identified five key genes involved in H. pylori-associated GC tumorigenesis. Patients with higher levels of C3 expression had a poorer prognosis than those with lower levels. In addition, these key genes may serve as biomarkers and therapeutic targets for H. pylori-associated GC diagnosis, targeted therapy, and immunotherapy in the future.

Abstract: Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

Abstract: The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Abstract: Background: Our aim was to determine the potential pharmacological mechanisms of the Guizhi decoction (GZD) in the treatment of osteoarthritis (OA) through an integrated approach of network pharmacological analyses, RNA sequencing (RNA-seq), and experimental validation. Methods: The quality control and identification of bioactive compounds of the GZD were carried out by using ultra-performance liquid chromatography (UPLC), and their OA-related genes were identified through overlapping traditional Chinese medicine systems pharmacology database (TCMSP), DrugBank and SEA Search Server databases, and GeneCards. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were implemented after constructing the component-target network. RNA-seq was used to screen differentially expressed genes (DEGs) under intervention conditions with and without the GZD in vitro. The crossover signaling pathways between RNA-seq and network pharmacology were then analyzed. Accordingly, protein-protein interaction (PPI) networks, GO, and KEGG analysis were performed using the Cytoscape, STRING, or DAVID database. The OA rat model was established to further verify the pharmacological effects in vivo. Hematoxylin-eosin (H&E) and safranin O/fast green (S-O) staining were used to grade the histopathological features of the cartilage. We verified the mRNA and protein expressions of the key targets related to the TNF signaling pathways in vivo and in vitro by qPCR, Western blotting (WB), and immunofluorescence assay. In addition, we also detected inflammatory cytokines in the rat serum by Luminex liquid suspension chip, which included tumor necrosis factor-alpha (TNF-alpha), interleukin-6 (IL-6), and interleukin-1beta (IL-1beta). Results: Eighteen compounds and 373 targets of the GZD were identified. A total of 2,356 OA-related genes were obtained from the GeneCards database. A total of three hub active ingredients of quercetin, kaempferol, and beta-sitosterol were determined, while 166 target genes associated with OA were finally overlapped. The RNA-seq analysis revealed 1,426 DEGs. In the KEGG intersection between network pharmacology and RNA-seq analysis, the closest screening relevant to GZD treatment was the TNF signaling pathway, of which TNF, IL-6, and IL-1beta were classified as hub genes. In consistent, H&E and S-O staining of the rat model showed that GZD could attenuate cartilage degradation. When compared with the OA group in vivo and in vitro, the mRNA levels of TNF-alpha, IL-1beta, IL-6, matrix metalloproteinase 3 (MMP3), and matrix metalloproteinase 9 (MMP9) were all downregulated in the GZD group (all p < 0.05). The expression levels of anabolic proteins (Col2alpha1 and SOX9) were all higher in the GZD group than in the OA group (p < 0.05), while the expression levels of the catabolic proteins (MMP9 and COX-2) and TNF-alpha in the GZD group were significantly lower than those in the OA group (p < 0.05). In addition, the expression levels of TNF, IL-6, and IL-1beta were upregulated in the OA group, while the GZD group prevented such aberrations (p < 0.01). Conclusion: The present study reveals that the mechanism of the GZD against OA may be related to the regulation of the TNF signaling pathway and inhibition of inflammatory response.

Abstract: BACKGROUND: Drug-induced nephrotoxicity is a relatively common preventable cause of acute kidney injury (AKI), providing early recognition and management. The pharmacokinetics or pharmacodynamics of drug-drug interactions may lead to additive or synergistic toxicity. The influx of new medications or off-label use of medications in the critical care setting can lead to additional nephrotoxicities, often challenging to predict or detect. This study evaluates the patterns of medication utilization, their combinations, and the related associations with AKI. METHODS: We utilized correlation-based network analysis (CNA) to investigate the relationship between medications or their combinations with AKI in a large cohort of critically ill patients in a tertiary medical center between 2007 and 2018. Pairwise medication-AKI correlation analysis was performed to evaluate drug synergistic or additive effects. To investigate the inherent nephrotoxicity of medications, we further analyzed medications that were not paired with any other medications within 24 hours before or after their administration time (isolated medication analysis). RESULTS: Among 147,289 ICU admissions, we identified 244 associations among 1,555 unique medication types. In pairwise analysis, 233 significant correlations were found among 13,150,198 medication pair instances. In isolated medication analysis, ten significant AKI associations were noted. When stratified by eGFR level, substantial differences between eGFR<90 vs. eGFR>/=90 patients were observed. This highlights a need to determine eGFR as a risk factor for nephrotoxicity assessment when drug interactions are considered. CONCLUSIONS: This large-scale cohort study identified an artificial intelligence model to identify patient-agnostic relationships between medication or their pairs with AKI incidence among critically ill patients. It could be used as a continuous quality assurance tool to monitor drug-associated risk nephrotoxicity.