Abstract: Drug combinations can increase the therapeutic effect by reducing the level of toxicity and the occurrence of drug resistance. Therefore, several drug combinations are often used in the management of complex diseases. However, due to the exponential growth in drug development, it would be impractical to evaluate all combinations through experiments. In view of this, we developed Pathway Interaction Network (PINet) biological model to estimate the optimal drug combinations for various diseases. The random walk with restart (RWR) algorithm was used to capture the "disease state" and "drug state," while PINet was used to evaluate the optimal drug combinations and the high-order drug combination. The model achieved a mean area under the curve of a receiver operating characteristic curve of 0.885. In addition, for some diseases, PINet predicted the optimal drug combination. For example, in the case of acute myeloid leukemia, PINet correctly predicted midostaurin and gemtuzumab as effective drug combinations, as demonstrated by the results of a Phase-I clinical trial. Moreover, PINet also correctly predicted the potential drug combinations for diseases that lacked a training dataset that could not be predicted using standard machine learning models.

Abstract: Complex diseases are associated with a wide range of cellular, physiological, and clinical phenotypes. To advance our understanding of disease mechanisms and our ability to treat these diseases, it is critical to delineate the molecular basis and therapeutic avenues of specific disease phenotypes, especially those that are associated with multiple diseases. Inflammatory processes constitute one such prominent phenotype, being involved in a wide range of health problems including ischemic heart disease, stroke, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease, and autoimmune and neurodegenerative conditions. While hundreds of genes might play a role in the etiology of each of these diseases, isolating the genes involved in the specific phenotype (e.g., inflammation "component") could help us understand the genes and pathways underlying this phenotype across diseases and predict potential drugs to target the phenotype. Here, we present a computational approach that integrates gene interaction networks, disease-/trait-gene associations, and drug-target information to accomplish this goal. We apply this approach to isolate gene signatures of complex diseases that correspond to chronic inflammation and use SAveRUNNER to prioritize drugs to reveal new therapeutic opportunities.

Abstract: Objective: Erchen Decoction (ECD), a well-known traditional Chinese medicine, exerts metabolism-regulatory, immunoregulation, and anti-tumor effects. However, the action and pharmacological mechanism of ECD remain largely unclear. In the present study, we explored the effects and mechanisms of ECD in the treatment of CRC using network pharmacology, molecular docking, and systematic experimental validation. Methods: The active components of ECD were obtained from the TCMSP database and the potential targets of them were annotated by the STRING database. The CRC-related targets were identified from different databases (OMIM, DisGeNet, GeneCards, and DrugBank). The interactive targets of ECD and CRC were screened and the protein-protein interaction (PPI) networks were constructed. Then, the hub interactive targets were calculated and visualized from the PPI network using the Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. In addition, the molecular docking was performed. Finally, systematic in vitro, in vivo and molecular biology experiments were performed to further explore the anti-tumor effects and underlying mechanisms of ECD in CRC. Results: A total of 116 active components and 246 targets of ECD were predicted based on the component-target network analysis. 2406 CRC-related targets were obtained from different databases and 140 intersective targets were identified between ECD and CRC. 12 hub molecules (STAT3, JUN, MAPK3, TP53, MAPK1, RELA, FOS, ESR1, IL6, MAPK14, MYC, and CDKN1A) were finally screened from PPI network. GO and KEGG pathway enrichment analyses demonstrated that the biological discrepancy was mainly focused on the tumorigenesis-, immune-, and mechanism-related pathways. Based on the experimental validation, ECD could suppress the proliferation of CRC cells by inhibiting cell cycle and promoting cell apoptosis. In addition, ECD could inhibit tumor growth in mice. Finally, the results of molecular biology experiments suggested ECD could regulate the transcriptional levels of several hub molecules during the development of CRC, including MAPKs, PPARs, TP53, and STATs. Conclusion: This study revealed the potential pharmacodynamic material basis and underlying molecular mechanisms of ECD in the treatment of CRC, providing a novel insight for us to find more effective anti-CRC drugs.

Abstract: Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide and the human papillomavirus (HPV(+))-driven subtype is the fastest rising cancer in North America. Although most cases of HPV(+) HNSCC respond favorably to the treatment via surgery followed by radiochemotherapy, up to 20% recur with a poor prognosis. The molecular and cellular mechanisms of recurrence are not fully understood. Methods: To gain insights into the mechanisms of recurrence and to inform patient stratification and personalized treatment, we compared the proteome and phosphoproteome of recurrent and non-recurrent tumors by quantitative mass spectrometry. Results: We observe significant differences between the recurrent and non-recurrent tumors in cellular composition, function, and signaling. The recurrent tumors are characterized by a pro-fibrotic and immunosuppressive tumor microenvironment (TME) featuring markedly more abundant cancer-associated fibroblasts, extracellular matrix (ECM), neutrophils, and suppressive myeloid cells. Defective T cell function and increased epithelial-mesenchymal transition potential are also associated with recurrence. These cellular changes in the TME are accompanied by reprogramming of the kinome and the signaling networks that regulate the ECM, cytoskeletal reorganization, cell adhesion, neutrophil function, and coagulation. Conclusions: In addition to providing systems-level insights into the molecular basis of recurrence, our work identifies numerous mechanism-based, candidate biomarkers and therapeutic targets that may aid future endeavors to develop prognostic biomarkers and precision-targeted treatment for recurrent HPV(+) HNSCC.

Abstract: Septic cardiomyopathy (SCM) is severe organ dysfunction caused by sepsis that is associated with poor prognosis, and its pathobiological mechanisms remain unclear. Autophagy is a biological process that has recently been focused on SCM, yet the current understanding of the role of dysregulated autophagy in the pathogenesis of SCM remains limited and uncertain. Exploring the molecular mechanisms of disease based on the transcriptomes of human pathological samples may bring the closest insights. In this study, we analyzed the differential expression of autophagy-related genes in SCM based on the transcriptomes of human septic hearts, and further explored their potential crosstalk and functional pathways. Key functional module and hub genes were identified by constructing a protein-protein interaction network. Eight key genes (CCL2, MYC, TP53, SOD2, HIF1A, CTNNB1, CAT, and ADIPOQ) that regulate autophagy in SCM were identified after validation in a lipopolysaccharide (LPS)-induced H9c2 cardiomyoblast injury model, as well as the autophagic characteristic features. Furthermore, we found that key genes were associated with abnormal immune infiltration in septic hearts and have the potential to serve as biomarkers. Finally, we predicted drugs that may play a protective role in SCM by regulating autophagy based on our results. Our study provides evidence and new insights into the role of autophagy in SCM based on human septic heart transcriptomes, which would be of great benefit to reveal the molecular pathological mechanisms and explore the diagnostic and therapeutic targets for SCM.

Abstract: Streptococcus pneumoniae (S. pneumoniae), the major etiological agent of community-acquired pneumonia (CAP) contributes significantly to the global burden of infectious diseases which is getting resistant day by day. Nearly 30% of the S. pneumoniae genomes encode hypothetical proteins (HPs), and better understandings of these HPs in virulence and pathogenicity plausibly decipher new treatments. Some of the HPs are present across many Streptococcus species, systematic assessment of these unexplored HPs will disclose prospective drug targets. In this study, through a stringent bioinformatics analysis of the core genome and proteome of S. pneumoniae PCS8235, we identified and analyzed 28 HPs that are common in many Streptococcus species and might have a potential role in the virulence or pathogenesis of the bacteria. Functional annotations of the proteins were conducted based on the physicochemical properties, subcellular localization, virulence prediction, protein-protein interactions, and identification of essential genes, to find potentially druggable proteins among 28 HPs. The majority of the HPs are involved in bacterial transcription and translation. Besides, some of them were homologs of enzymes, binding proteins, transporters, and regulators. Protein-protein interactions revealed HP PCS8235_RS05845 made the highest interactions with other HPs and also has TRP structural motif along with virulent and pathogenic properties indicating it has critical cellular functions and might go under unconventional protein secretions. The second highest interacting protein HP PCS8235_RS02595 interacts with the Regulator of chromosomal segregation (RocS) which participates in chromosome segregation and nucleoid protection in S. pneumoniae. In this interacting network, 54% of protein members have virulent properties and 40% contain pathogenic properties. Among them, most of these proteins circulate in the cytoplasmic area and have hydrophilic properties. Finally, molecular docking and dynamics simulation demonstrated that the antimalarial drug Artenimol can act as a drug repurposing candidate against HP PCS8235_RS 04650 of S. pneumoniae. Hence, the present study could aid in drugs against S. pneumoniae.

Abstract: Drug-Induced Liver Injury (DILI), despite its low occurrence rate, can cause severe side effects or even lead to death. Thus, it is one of the leading causes for terminating the development of new, and restricting the use of already-circulating, drugs. Moreover, its multifactorial nature, combined with a clinical presentation that often mimics other liver diseases, complicate the identification of DILI-related (or "positive") literature, which remains the main medium for sourcing results from the clinical practice and experimental studies. This work-contributing to the "Literature AI for DILI Challenge" of the Critical Assessment of Massive Data Analysis (CAMDA) 2021- presents an automated pipeline for distinguishing between DILI-positive and negative publications. We used Natural Language Processing (NLP) to filter out the uninformative parts of a text, and identify and extract mentions of chemicals and diseases. We combined that information with small-molecule and disease embeddings, which are capable of capturing chemical and disease similarities, to improve classification performance. The former were directly sourced from the Chemical Checker (CC). For the latter, we collected data that encode different aspects of disease similarity from the National Library of Medicine's (NLM) Medical Subject Headings (MeSH) thesaurus and the Comparative Toxicogenomics Database (CTD). Following a similar procedure as the one used in the CC, vector representations for diseases were learnt and evaluated. Two Neural Network (NN) classifiers were developed: a baseline model that accepts texts as input and an augmented, extended, model that also utilises chemical and disease embeddings. We trained, validated, and tested the classifiers through a Nested Cross-Validation (NCV) scheme with 10 outer and 5 inner folds. During this, the baseline and extended models performed virtually identically, with F1-scores of 95.04 +/- 0.61% and 94.80 +/- 0.41%, respectively. Upon validation on an external, withheld, dataset that is meant to assess classifier generalisability, the extended model achieved an F1-score of 91.14 +/- 1.62%, outperforming its baseline counterpart which received a lower score of 88.30 +/- 2.44%. We make further comparisons between the classifiers and discuss future improvements and directions, including utilising chemical and disease embeddings for visualisation and exploratory analysis of the DILI-positive literature.

Abstract: Objectives: Chronic pain, the main symptom of knee osteoarthritis (OA), remains the primary reason for decreased functional capacity. Quzhi decoction, a TCM prescription, is effective in treating chronic pain in OA, but the potential mechanisms require further exploration. Methods: An anterior cruciate ligament transection (ACLT) rat model was established, and pain-like behavior was evaluated. Metabolomics analysis of serum samples was performed to identify differential metabolites, and network pharmacology was used to identify potential targets of Quzhi decoction for the treatment of OA. Finally, we constructed a comprehensive network of serum metabolomics and network pharmacology. At the same time, the obtained key targets were verified by molecular docking. Results: Quzhi decoction was shown to attenuate pain-like behavior and joint inflammation in OA rats. Through serum metabolomics, thirty potentially significant metabolites were found to be involved in the therapeutic effects of Quzhi decoction against OA pain. According to network pharmacology, 107 active drug components were matched with 115 disease targets, which was partly consistent with the metabolomics findings. Further analysis focused on 6 key targets, including CYP3A4, PLA2G4A, PTGS1, PTGS2, TYR, and ALOX5, and their associated core metabolites and pathways. Molecular docking results showed that the related targets had high affinity with the active pharmaceutical ingredients in Quzhi decoction. Conclusion: The effect of Quzhi decoction on OA pain may be related to the inhibition of joint inflammation, mainly through disturbing arachidonic acid metabolism, tyrosine metabolism, and leukotriene metabolism. Further systematic molecular biology experiments are needed to verify the accurate mechanism.

Abstract: Primary hypertension is understood as a disease with diverse etiology, a complicated pathological mechanism, and progressive changes. Gedan Jiangya Decoction (GJD), with the patent publication number CN114246896A, was designed to treat primary hypertension. It contains six botanical drugs; however, the underlying mechanism is uncertain. We utilized network pharmacology to predict the active components, targets, and signaling pathways of GJD in the treatment of primary hypertension. We also investigated the potential molecular mechanism using molecular docking and animal experiments. The Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), the Protein Database (UniProt), and a literature review were used to identify the active components and related targets of GJD's pharmacological effects. The GeneCards, Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), and DrugBank databases were utilized to identify hypertension-related targets. Based on a Venn diagram of designed intersection targets, 214 intersection targets were obtained and 35 key targets for the treatment of hypertension were determined using the STRING data platform and Cytoscape software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of key targets revealed that the relevant molecular action pathways of GJD in the treatment of hypertension include the Toll-like receptor, MAPK, PI3K-Akt, and renin-angiotensin signaling pathways. A GJD active ingredient-key target-pathway connection diagram was created using Cytoscape software, and 11 essential active components were selected. Molecular docking was then used to verify the binding activity of key targets and key active ingredients in GJD to treat primary hypertension. The results of this study indicate that AGTR1, AKT1 with puerarin, EDNRA with tanshinone IIA, MAPK14 with daidzein, MAPK8 with ursolic acid, and CHRM2 with cryptotanshinone had high binding activity to the targets with active components, whereas AGTR1 was selected as target genes verified by our experiment. HPLC was utilized to identify the five active ingredients. Experiments in high-salt rats demonstrated that GJD might decrease the expression of AGTR1 in the kidney and thoracic aorta while increasing the expression of eNOS by preventing the activation of the renin-angiotensin pathway, thereby reducing lowering systolic and diastolic blood pressure.

Abstract: Purpose: This study was the first to screen the active compounds of Jian Aikang Concentrated Pill (JAKCP) with network pharmacology, predict its potential targets, screen the signaling pathways, and combine with cellular experimental validation to explore the potential mechanism of JAKCP for the treatment of acquired immunodeficiency syndrome (AIDS). Methods: The main compounds and targets of Chinese herbs in JAKCP were identified by TCMSP; the targets of AIDS were collected from Genecards, Online Mendelian Inheritance in Man (OMIM), Disgenet, Therapeutic Target Database (TTD) and Drugbank; the network of "Chinese herbs-active compounds-targets" for JAKCP was constructed by Cytoscape, and protein-protein interaction (PPI) network was constructed using STRING to generate the intersection targets, Metascape was conducted to analyze the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and the network of "main active compounds-core targets-pathways" was constructed by Cytoscape. Finally, the effect of JAKCP on the survival rate of HIV pseudovirus-infected MT-4 cells was investigated by CCK-8 assay, and the predicted targets were verified by ELISA, qPCR and Western blot. Results: A total of 147 active compounds of JAKCP were screened covering 351 targets and 416 AIDS disease targets were obtained, besides 140 intersection targets and 321 KEGG pathways were collected. Ultimately, quercetin, kaempferol, stigmasterol, beta-sitosterol, epigallocatechin gallate were identified as the important compounds, the core targets are HSP90AA1, IL-10, IL-6, TNF, IL-1beta, TP53, and IL-1a, and the biological pathways and processes mainly include T cell activation, regulation of DNA-binding transcription factor activity and apoptotic signaling pathway. Experiments on the targets of "T cell activation" demonstrated that JAKCP promotes the survival of HIV pseudovirus-infected MT-4 cells. Also, JAKCP down-regulated mRNA and protein levels of IL-1a, IL-1beta, and IL-6 while up-regulated mRNA and protein levels of IL-2, IL-6ST, and IL-10 in vitro. Conclusion: JAKCP exerted regulatory immune functions through multi-component, multi-target and multi-pathway, thereby providing novel ideas and clues for the treatment of AIDS.