Protein-protein Interaction Modules Research Articles

Construction of miRNA-mRNA network for the identification of key biological markers and their associated pathways in IgA nephropathy by employing the integrated bioinformatics analysis.

BackgroundAbout half-century ago, Immunoglobulin A nephropathy (IgAN) was discovered as a complicated disease with frequent clinical symptoms. Until now, exact mechanism underlying the pathogenesis of IgAN is poorly known. Therefore, current study was aimed to understand the molecular mechanism of IgAN by identifying the key miRNAs and their targeted hub genes. The key miRNAs might contribute to the diagnosis and therapy of IgAN, and could turn out to be a new star in the field of IgAN.MethodsThe microarray datasets were downloaded from Gene Expresssion Omnibus (GEO) database and analyzed using R package (LIMMA) in order to obtain differential expressed genes (DEGs). Then, the hub genes were identified using cytoHubba plugin of cytoscpae tool and other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, and miRNA-hub gene network construction was also performed.ResultsA total of 348 DEGs were identified, of which 107 were upregulated genes and 241 were downregulated genes. Subsequently, the 12 overlapped genes were predicted from cytoHubba, and considered as hub genes. Moreover, a network among miRNA-hub genes was created to explore the correlation between the hub genes and their targeted miRNAs. Network construction ultimately lead to the identification of nine gene named FN1, EGR1, FOS, JUN, SERPINE1, MMP2, ATF3, MYC, and IL1B and one novel key miRNA namely, has-miR-144-3p as biomarker for diagnosis and therapy of IgAN.ConclusionThis study updates the information and yield a new perspective in context of understanding the pathogenesis and development of IgAN. In future, key miRNAs might be capable of improving the personalized detection and therapies for IgAN. In vivo and in vitro investigation of miRNAs and pathway interaction is essential to delineate the specific roles of the novel miRNAs, which may help to further reveal the mechanisms underlying IgAN.

Identification of key genes in allergic rhinitis by bioinformatics analysis.

ObjectiveThis study aimed to explore the potential molecular mechanism of allergic rhinitis (AR) and identify gene signatures by analyzing microarray data using bioinformatics methods.MethodsThe dataset GSE19187 was used to screen differentially expressed genes (DEGs) between samples from patients with AR and healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied for the DEGs. Subsequently, a protein–protein interaction (PPI) network was constructed to identify hub genes. GSE44037 and GSE43523 datasets were screened to validate critical genes.ResultsA total of 156 DEGs were identified. GO analysis verified that the DEGs were enriched in antigen processing and presentation, the immune response, and antigen binding. KEGG analysis demonstrated that the DEGs were enriched in Staphylococcus aureus infection, rheumatoid arthritis, and allograft rejection. PPI network and module analysis predicted seven hub genes, of which six (CD44, HLA-DPA1, HLA-DRB1, HLA-DRB5, MUC5B, and CD274) were identified in the validation dataset.ConclusionsOur findings suggest that hub genes play important roles in the development of AR.

Development of Allosteric Modulators of Voltage-Gated Na+ Channels: A Novel Approach for an Old Target.

Given their primacy in governing the action potential (AP) of excitable cells, voltage-gated Na+ (Nav) channels are important pharmacological targets of therapeutics for a diverse array of clinical indications. Despite historically being a traditional drug target, therapeutics targeting Nav channels lack isoform selectivity, giving rise to off-target side effects. To develop isoform-selective modulators of Nav channels with improved target-specificity, the identification and pharmacological targeting of allosteric sites that display structural divergence among Nav channel isoforms represents an attractive approach. Despite the high homology among Nav channel α subunit isoforms (Nav1.1-Nav1.9), there is considerable amino acid sequence divergence among their constituent C-terminal domains (CTD), which enables structurally and functionally specific protein: protein interactions (PPI) with auxiliary proteins. Although pharmacological targeting of such PPI interfaces between the CTDs of Nav channels and auxiliary proteins represents an innovate approach for developing isoform-selective modulators of Nav channels, appreciable modulation of PPIs using small molecules has conventionally been difficult to achieve. After briefly discussing the challenges of modulating PPIs using small molecules, this current frontier review that follows subsequently expounds on approaches for circumventing such difficulties in the context of developing small molecule modulators of PPIs between transmembrane ion channels and their auxiliary proteins. In addition to broadly discussing such approaches, the implementation of such approaches is specifically discussed in the context of developing small molecule modulators between the CTD of Nav channels and auxiliary proteins. Developing allosteric modulators of ion channels by targeting their PPI interfaces with auxiliary proteins represents an innovative and promising strategy in ion channel drug discovery that could expand the "druggable genome" and usher in first-in-class PPI-targeting therapeutics for a multitude of channelopathies.

PPI Modulators of E6 as Potential Targeted Therapeutics for Cervical Cancer: Progress and Challenges in Targeting E6.

Advanced cervical cancer is primarily managed using cytotoxic therapies, despite evidence of limited efficacy and known toxicity. There is a current lack of alternative therapeutics to treat the disease more effectively. As such, there have been more research endeavors to develop targeted therapies directed at oncogenic host cellular targets over the past 4 decades, but thus far, only marginal gains in survival have been realized. The E6 oncoprotein, a protein of human papillomavirus origin that functionally inactivates various cellular antitumor proteins through protein–protein interactions (PPIs), represents an alternative target and intriguing opportunity to identify novel and potentially effective therapies to treat cervical cancer. Published research has reported a number of peptide and small-molecule modulators targeting the PPIs of E6 in various cell-based models. However, the reported compounds have rarely been well characterized in animal or human subjects. This indicates that while notable progress has been made in targeting E6, more extensive research is needed to accelerate the optimization of leads. In this review, we summarize the current knowledge and understanding of specific E6 PPI inhibition, the progress and challenges being faced, and potential approaches that can be utilized to identify novel and potent PPI inhibitors for cervical cancer treatment.

Diacylglycerol kinase ζ interacts with sphingomyelin synthase 1 and sphingomyelin synthase‐related protein

Introduction Mammalian sphingomyelin (SM) synthase (SMS) consists of two isoform (SMS1, SMS2). SMS produces diacylglycerol (DG) by the transfer of phosphocholine from phosphatidylcholine (PC) to ceramide. SMS-related protein (SMSr) also generates DG via synthesis of ceramide phosphoethanolamine, a SM analog, using phosphatidylethanolamine. Mammalian DG kinase (DGK) consists of ten isozymes (α, β, γ, δ, η, ϵ, κ, ι, ζ, θ) and governs a wide range of physiological and pathological events such as type 2 diabetes. Among them, SMS1, SMSr, DGKδ and DGKη contain a steraile a motif domain (SAMD), a protein-protein interaction module. We previously reported that DGKδ interacts with SMSr, but not SMS1, via SAMDs [Murakami et al. J. Biol. Chem. 295, 2932–47, 2020]. Moreover, SMSr provided DG to DGKδ. However, it is still unclear whether other DGK isozymes interact with SMSs. In the present study, we analyzed interaction between DGKs and SMSs by co-immunoprecipitation analysis. Results and Conclusion First, we determined whether SAMD-containing DGKs (DGKδ and DGKη) interact with SAMD-containing SMSs (SMS1 and SMSr). Although DGKη-SAMD co-sedimented with DGKη-SAMD itself, SMS1- and SMSr-SAMD failed to interact with DGKη-SAMD. Furthermore, only the combination of full-length DGKδ and full-length SMSr could form a heteromeric complex. These results suggest that the interaction via heteromerization of SAMDs is specific to DGKδ and SMSr. Next, we analyzed whether other DGKs, which contain no SAMD, interact with SMSs. We found that DGKζ was strongly co-immunoprecipitated with SMSr and SMS1, but not SMS2. These results suggest that DGKζ can interact with SMS1 and SMSr without heteromerization between SAMDs. Taken together, these results suggest that, in addition to DGKδ, DGKζ is a new candidate that acts downstream of SMSr and SMS1. SMSr produces palmitic acid (16:0)-containing DG molecular species and SMS1 generates PC-derived DG species including 16:0-containing DGs. Moreover, DGKζ preferably phosphorylates 16:0-containing DG species during neuronal cell differentiation. Therefore, it is possible that these enzymes compose a new pathway independent of the phosphatidylinositol turnover, which primary utilizes arachidonic acid (20:4)-containing DG species.

Sphingomyelin synthase‐related protein generates diacylglycerol via hydrolysis of phosphatidic acid, phosphatidylcholine, phosphatidylinositol and phosphatidylethanolamine without ceramide

Introduction Sphingomyelin synthase-related protein (SMSr) produces diacylglycerol (DG) and ceramide phosphoethanolamine (CPE) by the transfer of phosphoethanolamine from phosphatidylethanolamine (PE) to ceramide. We previously reported that SMSr functionally interacts with DG kinase (DGK) δ via steraile α motif domains (SAMD), protein-protein interaction module [Murakami et al. J. Biol. Chem. 295, 2932–47, 2020]. Moreover, SMSr provided DG to DGKδ. Puzzlingly, mammalian SMSr produces only trace amounts of CPE and DG. However, we previously reported that DG levels, particularly saturated fatty acid (SFA)- and/or monounsaturated fatty acid (MUFA)-containing DG molecular species, were significantly increased in COS-7 cells overexpressing human SMSr. To clarify this discrepancy, we purified human SMSr and determined whether purified SMSr showed other DG-generating activity via hydrolyis of phospholipids without ceramide in the present study. Results and Conclusion Human SMSr was expressed using the baculovirus-insect cell expression system and highly purified. Interestingly, purified SMSr showed DG-generating activity by hydrolysis of PE, phosphatidylcholine (PC), phosphatidylinositol (PI) and phosphatidic acid (PA) in the absence of ceramide. PA phosphatase (PAP) activity of SMSr was about 300-fold stronger than CPE synthase activity. Moreover, SMSr showed substrate selectivity for SFA- and/or MUFA-containing PA molecular species, but not arachidonic acid-containing PA, which is exclusively generated in the PI(4,5)P2 cycle. We confirmed that SMSr expressed in COS-7 cells showed PAP and PI-phospholipase C (PLC) activities. Collectively, our study indicated that SMSr possesses previously unrecognized enzyme activities (multi-glycerophospholipid PLC hydrolase) and constitutes a novel DG signaling pathway together with DGKd independent of the PI(4,5)P2 cycle.

Hub Genes Associated with the Diagnosis of Diabetic Retinopathy.

PurposeThis study aimed to identify genes that may be effective in diagnosing or treating diabetic retinopathy (DR), the most common complication of diabetes mellitus (DM).MethodsDifferentially expressed genes (DEGs) were identified between DR and DM in GSE146615 dataset. DEGs that were consistently up- or down-regulated under both standard glucose and high glucose conditions were identified as common genes and used to generate a protein–protein interaction network and modules. The module genes were assessed for the area under the receiver operating characteristic curve (AUC), leading to the identification of hub genes. Differentially methylated probes in GSE76169 were also compared with common DEGs to identify specific methylation markers of DR. Enrichment analysis was used to explore the biological characteristics. The Short Time-series Expression Miner algorithm was used to identify genes that were progressively dysregulated in the sequence: healthy controls < DM < DR.ResultsA total of 1917 common genes were identified for seven modules. The eight genes with AUC > 0.8 under high glucose and standard glucose conditions were considered as hub genes. The module genes were significantly enriched during vascular smooth muscle cell development and regulation of oxygen metabolism, while 92 methylation markers were involved in the similar terms. Among the progressively dysregulated genes, three intersection genes under both standard glucose and high glucose conditions were found to be module genes and were considered as key genes.ConclusionWe identified eight potential DR-specific diagnostic and therapeutic genes, whose abnormal expression can cause oxidative stress, thus favoring the course of the disease.

S-Nitrosylation in Tumor Microenvironment.

S-nitrosylation is a selective and reversible post-translational modification of protein thiols by nitric oxide (NO), which is a bioactive signaling molecule, to exert a variety of effects. These effects include the modulation of protein conformation, activity, stability, and protein-protein interactions. S-nitrosylation plays a central role in propagating NO signals within a cell, tissue, and tissue microenvironment, as the nitrosyl moiety can rapidly be transferred from one protein to another upon contact. This modification has also been reported to confer either tumor-suppressing or tumor-promoting effects and is portrayed as a process involved in every stage of cancer progression. In particular, S-nitrosylation has recently been found as an essential regulator of the tumor microenvironment (TME), the environment around a tumor governing the disease pathogenesis. This review aims to outline the effects of S-nitrosylation on different resident cells in the TME and the diverse outcomes in a context-dependent manner. Furthermore, we will discuss the therapeutic potentials of modulating S-nitrosylation levels in tumors.

CDC20 and PTTG1 are Important Biomarkers and Potential Therapeutic Targets for Metastatic Prostate Cancer.

Metastatic prostate cancer (mPCa) is responsible for most prostate cancer (PCa) deaths worldwide. The present study aims to explore the molecular differences between mPCa and PCa. The authors downloaded GSE6752, GSE6919, and GSE32269 from the Gene Expression Omnibus and employed integrated analysis to identify differentially expressed genes (DEGs) between mPCa and PCa. Functional and pathway-enrichment analyses were performed, and a protein-protein interaction (PPI) network and modules were constructed. Clinical mPCa specimens were collected to verify the results by performing RT-qPCR. The Cancer Genome Atlas database was used to conduct a survival analysis, and an immunohistochemical assay was performed. The invasion ability of PCa cells was verified by Transwell assay. One-hundred six consistently DEGs were found in mPCa compared with PCa. DEGs significantly enriched the positive regulation of cell proliferation, cell division, and cell adhesion in small cell lung cancer and PCa. Cell division, nucleoplasm, and cell cycle were selected from the PPI network, and the top 10 hub genes were selected. CDC20 and PTTG1 with genetic alterations were significantly associated with poorer disease-free survival. Immunohistochemical assay results showed that the expression levels of CDC20 and PTTG1 in mPCa were higher than those in PCa. The results of the migration assay indicated that CDC20 and PTTG1 could enhance the migration ability of PCa cells. The present study revealed that CDC20 and PTTG1 contribute more to migration, progression, and poorer prognoses in mPCa compared with PCa. CDC20 and PTTG1 could represent therapeutic targets in mPCa medical research and clinical studies.

The Novel Regulatory Role of lncRNA-miRNA-mRNA Axis in Amyotrophic Lateral Sclerosis: An Integrated Bioinformatics Analysis.

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease that primarily affects motor neurons, causing muscle atrophy, bulbar palsy, and pyramidal tract signs. However, the aetiology and pathogenesis of ALS have not been elucidated to date. In this study, a competitive endogenous RNA (ceRNA) network was constructed by analyzing the expression profiles of messenger RNAs (mRNAs) and long noncoding RNAs (lncRNAs) that were matched by 7 ALS samples and 4 control samples, and then a protein-protein interaction (PPI) network was constructed to identify the genes related to ALS. Gene Ontology (GO) was used to study the potential functions of differentially expressed mRNAs (DEmRNAs) in the ceRNA network. For the ALS and control groups, 247177 potential lncRNA-mRNA ceRNA relationship pairs were screened. Analysis of significant relationship pairs demonstrated that the PPI modules formed by the MALAT1-regulated SYNRG, ITSN2, PICALM, AP3B1, and AAK1 genes may play important roles in the pathogenesis of ALS, and these results may help to characterize the pathogenesis of ALS.

SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2.

Small molecule modulators of protein–protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine learning classifiers for prediction of new small molecule modulators of PPI. Our analysis reveals that using random forest (RF) classifier, general PPI Modulators independent of PPI family can be predicted with ROC-AUC higher than 0.9, when training and test sets are generated by random split. The performance of the classifier on data sets very different from those used in training has also been estimated by using different state of the art protocols for removing various types of bias in division of data into training and test sets. The family-specific PPIM predictors developed in this work for 11 clinically important PPI families also have prediction accuracies of above 90% in majority of the cases. All these ML-based predictors have been implemented in a freely available software named SMMPPI for prediction of small molecule modulators for clinically relevant PPIs like RBD:hACE2, Bromodomain_Histone, BCL2-Like_BAX/BAK, LEDGF_IN, LFA_ICAM, MDM2-Like_P53, RAS_SOS1, XIAP_Smac, WDR5_MLL1, KEAP1_NRF2 and CD4_gp120. We have identified novel chemical scaffolds as inhibitors for RBD_hACE PPI involved in host cell entry of SARS-CoV-2. Docking studies for some of the compounds reveal that they can inhibit RBD_hACE2 interaction by high affinity binding to interaction hotspots on RBD. Some of these new scaffolds have also been found in SARS-CoV-2 viral growth inhibitors reported recently; however, it is not known if these molecules inhibit the entry phase.

The Influence of Let-7b-5p Target Gene in Cholangiocarcinoma and its Bioinformatic Analysis

Objective: To analyze the biological functions and its clinical significance of let-7b-5p target gene in cholangiocarcinoma utilizing bioinformatics. Methods: The paper focuses on the let-7b-5p target gene, and predicts its biological functions as well as related signal pathways through GO biological function and KEGG signal pathway enrichment analysis. The STRING database and Cytoscape are used to construct a protein-protein interaction network to screen core genes. Results: The results of GO analysis showed that let-7b-5p target gene was mainly enriched in biological processes such as Small GTPase binding, Rho GTPase binding, and Rac GTPase binding. The results of KEGG analysis showed that let-7b-5p target gene was significantly enriched in key signaling pathways including Focal adhesion and ECM-receptor interaction. Through protein-protein interaction network and module analysis, CXCL8 and SDC2 were screened as the core site. Conclusion: let-7b-5p can participate in the regulation of biological functions of tumor cells in cholangiocarcinoma, suggesting that it may play an important role as a tumor suppressor gene and biomarker in the occurrence and development of cholangiocarcinoma, which provides new ideas for its diagnosis and treatment.

Whole-Transcriptome RNA Sequencing Reveals Significant Differentially Expressed mRNAs, miRNAs, and lncRNAs and Related Regulating Biological Pathways in the Peripheral Blood of COVID-19 Patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was initially identified in China and currently worldwide dispersed, resulting in the coronavirus disease 2019 (COVID-19) pandemic. Notably, COVID-19 is characterized by systemic inflammation. However, the potential mechanisms of the “cytokine storm” of COVID-19 are still limited. In this study, fourteen peripheral blood samples from COVID-19 patients (n = 10) and healthy donors (n = 4) were collected to perform the whole-transcriptome sequencing. Lung tissues of COVID-19 patients (70%) presenting with ground-glass opacity. Also, the leukocytes and lymphocytes were significantly decreased in COVID-19 compared with the control group (p < 0.05). In total, 25,482 differentially expressed messenger RNAs (DE mRNA), 23 differentially expressed microRNAs (DE miRNA), and 410 differentially expressed long noncoding RNAs (DE lncRNAs) were identified in the COVID-19 samples compared to the healthy controls. Gene Ontology (GO) analysis showed that the upregulated DE mRNAs were mainly involved in antigen processing and presentation of endogenous antigen, positive regulation of T cell mediated cytotoxicity, and positive regulation of gamma-delta T cell activation. The downregulated DE mRNAs were mainly concentrated in the glycogen biosynthetic process. We also established the protein-protein interaction (PPI) networks of up/downregulated DE mRNAs and identified 4 modules. Functional enrichment analyses indicated that these module targets were associated with positive regulation of cytokine production, cytokine-mediated signaling pathway, leukocyte differentiation, and migration. A total of 6 hub genes were selected in the PPI module networks including AKT1, TNFRSF1B, FCGR2A, CXCL8, STAT3, and TLR2. Moreover, a competing endogenous RNA network showed the interactions between lncRNAs, mRNAs, and miRNAs. Our results highlight the potential pathogenesis of excessive cytokine production such as MSTRG.119845.30/hsa-miR-20a-5p/TNFRSF1B, MSTRG.119845.30/hsa-miR-29b-2-5p/FCGR2A, and MSTRG.106112.2/hsa-miR-6501-5p/STAT3 axis, which may also play an important role in the development of ground-glass opacity in COVID-19 patients. This study gives new insights into inflammation regulatory mechanisms of coding and noncoding RNAs in COVID-19, which may provide novel diagnostic biomarkers and therapeutic avenues for COVID-19 patients.

Prognostic significance and immune infiltration of microenvironment-related signatures in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is 1 of the highly fatal and most aggressive types of malignancies and accounts for the vast majority of Pancreatic Cancer. Numerous studies have reported that the tumor microenvironment (TME) was significantly correlated with the oncogenesis, progress, and prognosis of various malignancies. Therefore, mining of TME-related genes is reasonably important to improve the overall survival of patients with PDAC.The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm was applied to identify differential expressed genes. Functional and pathway enrichment analyses, protein–protein interaction network construction and module analysis, overall survival analysis and tumor immune estimation resource database analysis were then performed on differential expressed genes.Data analysis indicated that higher immune scores were correlated with better overall survival (P = 0.033). Differential expression analysis obtained 90 intersection genes influencing both stromal and immune scores. Among these intersection genes, CA9, EBI3, SPOCK2, WDFY4, CD1D, and CCL22 were significantly correlated with overall survival in PDAC patients. Moreover, multivariate Cox analysis revealed that CA9, SPOCK2, and CD1D were the most significant prognostic genes, and were closely correlated with immune infiltration in TCGA cohort. Further analysis indicated that CD1D were significantly related with immune cell biomarkers for PDAC patients.In summary, our findings provide a more comprehensive insight into TME and show a list of prognostic immune associated genes in PDAC. However, further studies on these genes need to be performed to gain additional understanding of the association between TME and prognosis in PDAC.

Research on Potential Pathogenesis of Advanced Diabetic Nephropathy based on Bioinformatic Analysis

Objective: Through the bioinformatic analysis of gene chips related to advanced diabetic nephropathy in the GEO database, the key genes of advanced diabetic nephropathy are screened, whose biological functions and signal pathways are predicted as well. Methods: The gene chips related to advanced diabetic nephropathy from the GEO expression profile database was downloaded, and the differentially expressed genes in patients with advanced diabetic nephropathy and normal people were analyzed through R. For the screened differentially expressed genes, the biological function of GO and the enrichment analysis of KEGG signal pathway were used to predict their biological functions and related signal pathways. In addition, a protein-protein interaction network was constructed, so as to screen core pathogenic genes utilizing STRING database and Cytoscape. Results: By analyzing the chip GSE142025, 301 differential genes were obtained, including 197 up-regulated genes and 104 down-regulated genes. Both GO annotation and enrichment analysis suggested that differential genes were mainly involved in immune-inflammatory response and cytokine action. Furthermore, KEGG pathway analysis suggested that the most important pathway related to advanced diabetic nephropathy was MAPK signaling pathway. Through protein-protein interaction network and module analysis, C3, CCR2, CCL19, and SAA1 were selected as the core sites of the interaction. Conclusions: Differential genes participate in the pathogenesis of advanced diabetic nephropathy through the KEGG pathway, the immune inflammatory response and cytokine action, which provides new ways for the diagnosis and treatment of advanced diabetic nephropathy.

Exploring Molecular Contacts of MUC1 at CIN85 Binding Interface to Address Future Drug Design Efforts.

The modulation of protein-protein interactions (PPIs) by small molecules represents a valuable strategy for pharmacological intervention in several human diseases. In this context, computer-aided drug discovery techniques offer useful resources to predict the network of interactions governing the recognition process between protein partners, thus furnishing relevant information for the design of novel PPI modulators. In this work, we focused our attention on the MUC1-CIN85 complex as a crucial PPI controlling cancer progression and metastasis. MUC1 is a transmembrane glycoprotein whose extracellular domain contains a variable number of tandem repeats (VNTRs) regions that are highly glycosylated in normal cells and under-glycosylated in cancer. The hypo-glycosylation fosters the exposure of the backbone to new interactions with other proteins, such as CIN85, that alter the intracellular signalling in tumour cells. Herein, different computational approaches were combined to investigate the molecular recognition pattern of MUC1-CIN85 PPI thus unveiling new structural information useful for the design of MUC1-CIN85 PPI inhibitors as potential anti-metastatic agents.

T cell immune regulator 1 is a prognostic marker associated with immune infiltration in glioblastoma multiforme.

Glioma is the most common primary brain tumor and glioblastoma multiforme (GBM) is the most malignant brain glioma with the worst prognosis. T cell immune regulator 1 (TCIRG1) constitutes the V0a3 subunit of vacuolar ATPase (V-ATPase), and the function of V-ATPase in malignant tumors, such as breast cancer, melanoma and hepatocellular carcinoma, has been reported. However, the effect of the TCIRG1 subunit on GBM remains to be fully elucidated. mRNA levels of TCIRG1 in different cancer types and the corresponding normal tissues were extracted from the Oncomine and Tumor Immune Estimation Resource (TIMER) databases. The Gene Expression Omnibus (access number: GSE16011), the Chinese Glioma Genome Atlas and The Cancer Genome Atlas were used to investigate the mRNA level of TCIRG1 in glioma. Protein level validation in glioma was performed using western blotting. The Database for Annotation, Visualization and Integrated Discovery was used to analyze Gene Ontology (GO) categories for genes correlated with TCIRG1 in GBM. Protein-protein interaction (PPI) networks and module analyses were performed using Cytoscape software and the MCODE plugin. The correlation between tumor immune cell infiltration and TCIRG1 expression was explored using the TIMER database. Additionally, the correlation between TCIRG1 and the gene signature of immune infiltration was explored through TIMER and Gene Expression Profiling Interactive Analysis. External validation of TCIRG1 expression according to immune signatures in GBM was performed using the GSE16011 dataset with the GlioVis online tool. It was found that TCIRG1 expression was increased in GBM and numerous malignant tumors and may serve as a biomarker of the mesenchymal subtype of GBM. GO category analysis of positively correlated genes revealed that TCIRG1 was correlated with the immune response in GBM. PPI network and module analyses also supported the potential function of TCIRG1 in the local immune response. The expression of TCIRG1 was associated with various immune markers. It was therefore speculated that TCIRG1 is associated with glioma malignancy and may be a marker of unfavorable prognosis in patients with GBM, and it could be regarded as a prognostic biomarker and an indicator of immune infiltration in GBM.

Applications of Solution NMR in Drug Discovery.

During the past decades, solution nuclear magnetic resonance (NMR) spectroscopy has demonstrated itself as a promising tool in drug discovery. Especially, fragment-based drug discovery (FBDD) has benefited a lot from the NMR development. Multiple candidate compounds and FDA-approved drugs derived from FBDD have been developed with the assistance of NMR techniques. NMR has broad applications in different stages of the FBDD process, which includes fragment library construction, hit generation and validation, hit-to-lead optimization and working mechanism elucidation, etc. In this manuscript, we reviewed the current progresses of NMR applications in fragment-based drug discovery, which were illustrated by multiple reported cases. Moreover, the NMR applications in protein-protein interaction (PPI) modulators development and the progress of in-cell NMR for drug discovery were also briefly summarized.

Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations.

Protein-protein interactions are vital to biological processes, but the shape and size of their interfaces make them hard to target using small molecules. Cyclic peptides have shown promise as protein-protein interaction modulators, as they can bind protein surfaces with high affinity and specificity. Dozens of cyclic peptides are already FDA approved, and many more are in various stages of development as immunosuppressants, antibiotics, antivirals, or anticancer drugs. However, most cyclic peptide drugs so far have been natural products or derivatives thereof, with de novo design having proven challenging. A key obstacle is structural characterization: cyclic peptides frequently adopt multiple conformations in solution, which are difficult to resolve using techniques like NMR spectroscopy. The lack of solution structural information prevents a thorough understanding of cyclic peptides' sequence-structure-function relationship. Here we review recent development and application of molecular dynamics simulations with enhanced sampling to studying the solution structures of cyclic peptides. We describe novel computational methods capable of sampling cyclic peptides' conformational space and provide examples of computational studies that relate peptides' sequence and structure to biological activity. We demonstrate that molecular dynamics simulations have grown from an explanatory technique to a full-fledged tool for systematic studies at the forefront of cyclic peptide therapeutic design.

The iPPI-DB initiative: a community-centered database of protein-protein interaction modulators.

MotivationOne avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets.ResultsHere, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets and extended our focus to stabilizers of PPIs as well.Availability and implementationThe iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files.Supplementary information Supplementary data are available at Bioinformatics online.