Analysis of Protein-Protein Functional Associations by Using Gene Ontology and KEGG Pathway

Mechanism of action of the Banxia-Xiakucao herb pair in sleep deprivation: New comprehensive evidence from network pharmacology, transcriptomics and molecular biology experiments

ETHNOPHARMACOLOGICAL RELEVANCE

Chinese herb pairs are the most basic and compressed examples of Chinese herbal combinations and can be used to effectively explain the fundamental concepts of traditional Chinese medicine prescriptions. These pairings have gained significant interest due to their subtle therapeutic benefits, minimal side effects, and efficacy in treating complicated chronic conditions. The Banxia-Xiakucao Chinese herb pair (BXHP) consists of Pinellia ternata (Thunb.) Breit. (Banxia) and Prunella vulgaris L. (Xiakucao). This formula was documented in The Medical Classic of the Yellow Emperor approximately 2000 years ago，and clinical research has demonstrated that BXHP effectively treats insomnia.

AIM OF THE STUDY

This study aimed to evaluate the efficacy and therapeutic mechanism of the BXHP through a comprehensive strategy involving network pharmacology, molecular docking, transcriptomics, and molecular biology experimental validation.

MATERIALS AND METHODS

The composition of BXHP was characterized using the UPLC-Q-TOF-MS. The active compounds were screened to find drug-likeness compounds by analyzing the ADME data. To predict the molecular mechanism of BXHP in sleep deprivation (SD) by network pharmacology and molecular docking. We established a rat model of SD and the in vivo efficacy of BXHP was verified through the pentobarbital sodium righting reflex test, behavioral assays, enzyme-linked immunosorbent assay, transmission electron microscopy, HE staining, and Nissl staining, and the underlying molecular mechanism of BXHP in SD was revealed through transcriptomic and bioinformatic analyses in conjunction with quantitative real-time PCR, Western blot, and immunofluorescence staining.

RESULTS

In the present study, we showed for the first time that BXHP reduced sleep latency, prolongs sleep duration, and improves anxiety; lowered serum CORT, IL6, TNF-α and MDA levels; decreased hypothalamic Glu levels; and elevated hypothalamic GABA and 5-HT levels in SD rats. We found 16 active compounds that acted on 583 targets, 145 of which are related to SD. By modularly dissecting the PPI network, we discovered three critical targets, Akt1, CREB1, and PRKACA, all of which play important roles in the effects of BXHP on SD. Molecular docking resulted in the identification of 16 active compounds that strongly bind to key targets. The results of GO and KEGG enrichment analyses of network pharmacology and transcriptomics focused on both the regulation of circadian rhythm and the cAMP signaling pathway, which strongly demonstrated that BXHP affects SD via the cAMP-PKA-CREB-Circadian rhythm pathway. Molecular biology experiments verified this hypothesis. Following BXHP administration, PKA and CREB phosphorylation levels were elevated in SD rats, the cAMP-PKA-CREB signaling pathway was activated, the expression levels of the biological clock genes CLOCK, p-BMAL1/BMAL1, and PER3 were increased, and the rhythmicity of the biological clock was improved.

CONCLUSIONS

The active compounds in BXHP can activate the cAMP-PKA-CREB-Circadian rhythm pathway, improve the rhythmicity of the biological clock, promote sleep and ameliorate anxiety, which suggests that BXHP improves SD through a multicomponent, multitarget, multipathway mechanism. This study is important for the development of herbal medicines and clinical therapies for improving sleep deprivation.

Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene-gene interaction. The main purpose of this study is to develop extraction methods for gene-gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene-gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene-gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene-gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene-gene interactions. In this way, the results of this study are expected to help achieve precision medicine.

Identification of Protein-Protein Interaction Associated Functions Based on Gene Ontology

Protein-protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontology (GO) terms are related to the determination of PPIs, suggesting the special patterns on the GO terms of proteins in PPIs. In this study, we explored the special GO term patterns on human PPIs, trying to uncover the underlying functional mechanism of PPIs. The experimental validated human PPIs were retrieved from STRING database, which were termed as positive samples. Additionally, we randomly paired proteins occurring in positive samples, yielding lots of negative samples. A simple calculation was conducted to count the number of positive samples for each GO term pair, where proteins in samples were annotated by GO terms in the pair individually. The similar number for negative samples was also counted and further adjusted due to the great gap between the numbers of positive and negative samples. The difference of the above two numbers and the relative ratio compared with the number on positive samples were calculated. This ratio provided a precise evaluation of the occurrence of GO term pairs for positive samples and negative samples, indicating the latent GO term patterns for PPIs. Our analysis unveiled several nuclear biological processes, including gene transcription, cell proliferation, and nutrient metabolism, as key biological functions. Interactions between major proliferative or metabolic GO terms consistently correspond with significantly reported PPIs in recent literature.

Immunomodulatory potential of bioactive glycoside syringin: a network pharmacology and molecular modeling approach

Many diseases, such as rheumatoid arthritis, neurodegenerative disease, lupus, autoimmune disease, and cancer, are described by chronic inflammation following tissue damage. Anti-inflammatory drugs like non-steroidal anti-inflammatory drugs and other steroids cause many side effects and generally need careful consideration and monitoring during usage. In recent years, a significant interest in plant-derived approaches has been warranted. The bioactive glycoside syringin might be one of the effective immunomodulatory agents. However, its immunomodulatory potential needs to be better known. In this study, we evaluated the immunomodulatory potential of syringin using network pharmacology, molecular docking, and molecular dynamics simulation-based approaches. First, we applied the GeneCards and OMIM databases to acquire the immunomodulatory agents. Then, the STRING database was utilized to get the hub genes. Interaction analysis and molecular docking described strong binding of the active site of immunomodulatory proteins with the bioactive syringin. Molecular dynamics simulations (200 ns) showed a very stable interaction of syringin with the immunomodulatory protein. Further, the optimized structure and molecular electrostatic potential of the syringin were calculated by a density-functional theory utilizing basis levels of B3LYP/6-31. Syringin investigated in this study holds the required drug-likeness characteristics and follows Lipinski's rule of five. However, quantum-chemical estimations show the syringin has potent reactivity, demonstrating a lower energy gap. Furthermore, the gap between ELUMO and EHOMO was low, suggesting the excellent affinity of syringin towards the immunomodulatory proteins. The present study shows that syringin might be an effective immunomodulatory agent and can be further explored using different experimental methods.Communicated by Ramaswamy H. Sarma.

Identification of protein–protein interaction associated functions based on gene ontology and KEGG pathway

Protein–protein interactions (PPIs) are extremely important for gaining mechanistic insights into the functional organization of the proteome. The resolution of PPI functions can help in the identification of novel diagnostic and therapeutic targets with medical utility, thus facilitating the development of new medications. However, the traditional methods for resolving PPI functions are mainly experimental methods, such as co-immunoprecipitation, pull-down assays, cross-linking, label transfer, and far-Western blot analysis, that are not only expensive but also time-consuming. In this study, we constructed an integrated feature selection scheme for the large-scale selection of the relevant functions of PPIs by using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations of PPI participants. First, we encoded the proteins in each PPI with their gene ontologies and KEGG pathways. Then, the encoded protein features were refined as features of both positive and negative PPIs. Subsequently, Boruta was used for the initial filtering of features to obtain 5684 features. Three feature ranking algorithms, namely, least absolute shrinkage and selection operator, light gradient boosting machine, and max-relevance and min-redundancy, were applied to evaluate feature importance. Finally, the top-ranked features derived from multiple datasets were comprehensively evaluated, and the intersection of results mined by three feature ranking algorithms was taken to identify the features with high correlation with PPIs. Some functional terms were identified in our study, including cytokine–cytokine receptor interaction (hsa04060), intrinsic component of membrane (GO:0031224), and protein-binding biological process (GO:0005515). Our newly proposed integrated computational approach offers a novel perspective of the large-scale mining of biological functions linked to PPI.

Tandem Mass Tagging Based Identification of Proteome Signatures for Reductive Stress Cardiomyopathy

Nuclear factor erythroid 2-related factor 2 (NRF2), a redox sensor, is vital for cellular redox homeostasis. We reported that transgenic mice expressing constitutively active Nrf2 (CaNrf2-TG) exhibit reductive stress (RS). In this study, we identified novel protein signature for RS-induced cardiomyopathy using Tandem Mass Tag (TMT) proteomic analysis in heart tissues of TG (CaNrf2-TG) mice at 6–7 months of age. A total of 1,105 proteins were extracted from 22,544 spectra. About 560 proteins were differentially expressed in TG vs. NTg hearts, indicating a global impact of RS on the myocardial proteome. Over 32 proteins were significantly altered in response to RS -20 were upregulated and 12 were downregulated in the hearts of TG vs. NTg mice, suggesting that these proteins could be putative signatures of RS. Scaffold analysis revealed a clear distinction between TG vs. NTg hearts. The majority of the differentially expressed proteins (DEPs) that were significantly altered in RS mice were found to be involved in stress related pathways such as antioxidants, NADPH, protein quality control, etc. Interestingly, proteins that were involved in mitochondrial respiration, lipophagy and cardiac rhythm were dramatically decreased in TG hearts. Of note, we identified the glutathione family of proteins as the significantly changed subset of the proteome in TG heart. Surprisingly, our comparative analysis of NGS based transcriptome and TMT-proteome indicated that ~50% of the altered proteins in TG myocardium was found to be negatively correlated with their transcript levels. In association with the altered proteome the TG mice displayed pathological cardiac remodeling.

Developing a Novel Indium(III) Agent Based on Human Serum Albumin Nanoparticles: Integrating Bioimaging and Therapy

To effectively integrate diagnosis and therapy for tumors, we proposed to develop an indium (In) agent based on the unique property of human serum albumin (HSA) nanoparticles (NPs). A novel In(III) quinoline-2-formaldehyde thiosemicarbazone compound (C5) was optimized with remarkable cytotoxicity and fluorescence to cancer cells in vitro. An HSA-C5 complex NP delivery system was then successfully constructed. Importantly, the HSA-C5 complex NPs have stronger bioimaging and therapeutic efficiency relative to C5 alone in vivo. Besides, the results of gene chip analysis revealed that C5/HSA-C5 complex NPs act on cancer cells through multiple mechanisms: inducing autophagy, apoptosis, and inhibiting the PI3K-Akt signaling pathway.

Circular RNA expression profiles in the plasma of patients with infantile hemangioma determined using microarray analysis

Circular RNAs (circRNAs) are noncoding RNAs that have important roles in tumor progression. Previous studies have examined the circRNAs involved in infantile hemangioma (IH) tumors. The present study compared the circRNA levels in plasma samples from patients with IH and control individuals. The circRNA expression profiles were determined using microarray in three pairs of plasma samples from patients with proliferative IH and healthy control subjects. Expression of the identified differentially expressed circRNAs was verified using reverse transcription-quantitative PCR (RT-qPCR) and a bioinformatics analysis was performed to predict the microRNAs targeted by the validated circRNAs. From the circRNA expression profiles in the plasma of patients with IHs, 128 differentially expressed circRNAs were identified, of which 72 were upregulated and 56 were downregulated. The downregulated expression of three circRNAs [Homo sapiens (hsa)_circRNA_101566, hsa_circRNA_103546 and hsa_circRNA_103573] was verified using RT-qPCR. Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that all identified networks participated in angiogenesis and tumor formation and progression. It was determined that hsa_circRNA_101566, which is able to regulate the mTOR signaling pathway, may be an important regulatory molecule in IH development and that targeting of hsa_miR_520c is able to indirectly regulate the vascular endothelial growth factor signaling pathway. Further studies are required to clarify these effects and the underlying mechanisms.

Determining protein-protein functional associations by functional rules based on gene ontology and KEGG pathway

Protein-protein interactions (PPIs) describe the direct physical contact of two proteins that usually results in specific biological functions or regulatory processes. The characterization and study of PPIs through the investigation of their pattern and principle have remained a question in biological studies. Various experimental and computational methods have been used for PPI studies, but most of them are based on the sequence similarity with current validated PPI participators or cellular localization patterns. Most methods ignore the fact that PPIs are defined by their specific biological functions. In this study, we constructed a novel rule-based computational method using gene ontology and KEGG pathway annotation of PPI participators that correspond to the complicated biological effects of PPIs. Our newly presented computational method identified a group of biological functions that are tightly associated with PPIs and provided a new function-based tool for PPI studies in a rule manner.

Key regulatory genes and signaling pathways involved in islet culture: a bioinformatic analysis

Type 1 diabetes (T1D) is characterized by non-ideal mass and low survival rate of islets. Therefore, it is necessary to find intrinsic factors that prolong the survival of islets. This study aimed to track out hub genes and pathways in the process of islet culture by bioinformatic analysis. We downloaded the gene expression microarray of GSE42591 from the Gene Expression Omnibus (GEO). Aberrant Differentially methylated genes (DMGs) were obtained using the GEO2R tool. Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses were performed on selected genes by using the Database for Annotation Visualization and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed with the Retrieval of Interacting Genes (STRING) and visualized in Cytoscape 3.7.2. A total of 434 genes were overexpressed and 114 genes underexpressed in fresh to cultured 4 h tissue. KEGG pathway enrichment analyses revealed the TGF-beta signaling pathway, MAPK signaling pathway, or VEGF signaling pathway. The genes FN1, MKI67, IGF1, MAPK14, COL1A1 might be involved in islet culture. In general, this work scrutinized islet culture-relevant knowledge and provided insight into the regulation and mediation of islet survival.

Investigation of miRNA and mRNA Co-expression Network in Ependymoma

Ependymoma (EPN) is a rare primary tumor of the central nervous system (CNS) that affects both children and adults. Despite the definition and classification of distinct molecular subgroups, there remains a group of EPNs with a balanced genome, which makes it difficult to predict a prognosis of patients with EPN. The role of miRNA-mRNA network on EPN is still poorly understood. We assessed the involvement of miRNA-mRNA pairs in EPN by applying a weighted co-expression network analysis (WGCNA) approach. Using whole genome expression profile analysis followed by functional enrichment, we detected hub genes involved in active proliferation and DNA replication of nerve cells. Key genes including CYP11B1, KRT33B, RUNX1T1, SIK1, MAP3K4, MLANA, and SFRP5 identified in co-expression networks were regulated by miR-15a and miR-24-1. These seven miRNA-mRNA pairs were considered to influence not only pathways in cancer and tumor suppression process, but also MAPK, NF-kappaB, and WNT signaling pathways which were associated with tumorigenesis and development. This study provides a novel insight into potential diagnostic biomarkers of EPN and may have value in choosing therapeutic targets with clinical utility.