Protein-protein Interaction Modules Research Articles

Saccharomyces cerevisiae Rev7 promotes non-homologous end-joining by blocking Mre11 nuclease and Rad50's ATPase activities and homologous recombination.

Recent studies have shown that, in human cancer cells, the tetrameric Shieldin complex (comprising REV7, SHLD1, SHLD2, and SHLD3) facilitates non-homologous end-joining (NHEJ) while blocking homologous recombination (HR). Surprisingly, several eukaryotic species lack SHLD1, SHLD2, and SHLD3 orthologs, suggesting that Rev7 may leverage an alternative mechanism to regulate the double-strand break (DSB) repair pathway choice. Exploring this hypothesis, we discovered that Saccharomyces cerevisiae Rev7 physically interacts with the Mre11-Rad50-Xrs2 (MRX) subunits, impedes G-quadruplex DNA synergized HU-induced toxicity, and facilitates NHEJ, while antagonizing HR. Notably, we reveal that a 42-amino acid C-terminal fragment of Rev7 binds to the subunits of MRX complex, protects rev7∆ cells from G-quadruplex DNA-HU-induced toxicity, and promotes NHEJ by blocking HR. By comparison, the N-terminal HORMA domain, a conserved protein-protein interaction module, was dispensable. We further show that the full-length Rev7 impedes Mre11 nuclease and Rad50's ATPase activities without affecting the latter's ATP-binding ability. Combined, these results provide unanticipated insights into the functional interaction between the MRX subunits and Rev7 and highlight a previously unrecognized mechanism by which Rev7 facilitates DSB repair via NHEJ, and attenuation of HR, by blocking Mre11 nuclease and Rad50's ATPase activities in S. cerevisiae.

Medical Treatment of Hypercortisolism with Relacorilant: Final Results of the Phase 3 GRACE Study

Diabetic cardiomyopathy (DCM) is a cardiometabolic syndrome, manifested by ventricular diastolic or systolic dysfunction which occurs in approximately 12% of diabetic patients. Diabetes perpetuates the development of microvascular complications that can exacerbate cardiovascular pathologies. Understanding the common molecular targets underlying these two conditions is important for designing effective interventions that can address diabetes and related complications simultaneously. Herein, we aim to investigate the underlying network of key modules associated with diabetic cardiomyopathy. In current study, microarray gene expression profiles of diabetic and cardiomyopathy patients possessing accession number (GSE30122 and GSE20966) and (GSE89714, GSE3586, and GSE1145) were retrieved from Gene Expression Omnibus (GEO) database to explore key modulators. The differentially expressed genes (DEGs) were analyzed via GEO2R tool, based on the cut off criteria (p ≤ 0.05) and log2(FC)>|±1|. 37 overlapped DEGs were identified which then subjected to miRNA target prediction, protein-protein interaction (PPI) network construction and module screening via TargetScan, ShinyGO, STRING, and Cytoscape respectively, to screen hub genes as potential targets. Functional gene enrichment analysis identified enrichment of DEGs in heart contraction, apoptosis, cytokines signaling, beta adrenergic signaling and GPCR neurotransmitter receptor activity. The miRNA-mRNA regulatory network identified 9 hub genes, including ADRB1, TGFB2, RAP2A, CALD1, WIPF1, ATP10a, AKAP1, NR4A3, EXT1 and 4 hub miRNAs including miR-199-5, miR-200a, mir141-3, and miR-19-3p based on their degree of connectivity and centrality within the network. Conclusively, our analysis unveiled hub genes and miRNAs associated with diabetes-linked cardiomyopathy that might considered as biomarkers and offer a reliable tool for developing effective therapeutic strategies in future.

Unveiling biomarker detection in Alzheimer's disease: a computational approach to microarray analysis.

Alzheimer's disease (AD) is a major neurodegenerative condition that affects a significant number of people around the world, making understanding the underlying molecular mechanisms fundamental for identifying predictive biomarkers and therapeutic targets for treating AD. Analysis of the gene expression profile GSE5281, consisting of 161 samples (87 AD and 74 control samples) revealed differentially expressed genes (DEGs) used for KEGG screening to connect dysregulated genes to metabolic pathways or other neurological diseases including Parkinson's, prion, and Huntington's and construction of a protein interaction network. Protein-protein interaction (PPI) network and module analysis uncovered the hub genes ACTB, ACTG1, ATP5A1, CCT2, CDC42, EGFR, FN1, GAPDH, GFAP, GRIA1, HSP90AB1, MAPK1, PSMA3, PSMD14, SNAP25, SNCA, SOD1, SOX2, TPI1, and YWHAZ. The analysis revealed a link between dysregulated genes and processes in AD pathology, including the promotion of osteoporosis, an altered nucleotide metabolism, microtubule stability, and the dysfunctionality of the blood-brain barrier (BBB). These targets might be used as predictive biomarkers or to develop curative and preventive therapeutic approaches for treating AD.

Structural basis of pseudoGTPase-mediated protein-protein interactions.

GTPases regulate various cellular processes through conformational changes triggered by GTP or GDP binding. Recently, pseudoGTPases, the catalytically inactive counterparts of GTPases, have been identified across species from bacteria to human, although their functions and mechanisms remain unexplored. In this study, we demonstrate that the N-terminal region of the assembly chaperone AAGAB is a type i pseudoGTPase using biochemistry and X-ray crystallography. Furthermore, we discovered that the AAGAB pseudoGTPase domain (psGD) interacts with the σ subunits of AP1 and AP2 adaptor complexes, heterotetrameric complexes involved in clathrin-mediated membrane trafficking. AAGAB psGD engages the σ subunits via a unique interface distinct from the conventional GTPase interacting regions. Further biochemical and cell-based assays confirmed the crucial role of the newly identified interface in binding and membrane trafficking. Collectively, our results establish AAGAB pseudoGTPase domain as a critical protein-protein interaction module. These findings offer new insight into the structural basis and molecular mechanisms of pseudoGTPases.

Modulators of the Hop-HSP90 Protein-Protein Interaction Disrupt KSHV Lytic Replication.

The central role of the chaperome in maintaining cellular proteostasis has seen numerous viral families evolve to parasitically exploit host chaperones in their life cycle. The HSP90 chaperone protein and its cochaperone Hop have both individually been shown to be essential factors for Kaposi sarcoma-associated herpesvirus (KSHV) lytic replication. Given the fundamental regulatory role that protein-protein interactions (PPIs) play in cellular biology, we reasoned that disrupting the Hop-HSP90 PPI may provide a new host-based target for inhibiting KSHV lytic replication. This study expands upon a previous report of non-natural peptides, which were found to disrupt the association between the HopTPR2A domain and its interacting HSP90CTD. Here, in addition to providing insight into the structure-activity relationships of PPI inhibition, we show disruption of the full-length Hop-HSP90 PPI. The inhibitory peptides selectively engaged the HopTPR2A domain in cell lysates and when tethered to a cell-penetrating peptide acted as noncytotoxic inhibitors of KSHV lytic replication by lowering the viral load, preventing the production of infectious virions, and reducing the expression of KSHV lytic genes. In addition to tentative evidence of Hop-HSP90 PPI as a much-needed target for KSHV drug discovery, this study represents an important step in understanding viral interactions with the host proteostasis machinery.

Impairing protein-protein interactions in an essential tRNA modification complex: An innovative antimicrobial strategy against Pseudomonas aeruginosa.

Protein-protein interactions (PPIs) have been recognized as a promising target for the development of new drugs, as proved by the growing number of PPI modulators reaching clinical trials. In this context, peptides represent a valid alternative to small molecules, owing to their unique ability to mimic the target protein structure and interact with wider surface areas. Among the possible fields of interest, bacterial PPIs represent an attractive target to face the urgent necessity to fight antibiotic resistance. Growing attention has been paid to the YgjD/YeaZ/YjeE complex responsible for the essential t6A37 tRNA modification in bacteria. We previously identified an α-helix on the surface of Pseudomonas aeruginosa YeaZ, crucial for the YeaZ-YeaZ homodimer formation and the conserved YeaZ-YgjD interactions. Herein, we present our studies for impairing the PPIs involved in the formation of the YeaZ dimers through synthetic peptide derivatives of this helical moiety, both in vitro with purified components and on P. aeruginosa cells. Our results proved the possibility of targeting those PPIs which are usually essential for protein functioning and thus are refractory to mutational changes and antibiotic resistance development.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

BackgroundEndometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.MethodsNext-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.ResultsA total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.ConclusionsThis investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

Exploring protein-protein interactions for the development of new analgesics.

The development of new analgesics has been challenging. Candidate drugs often have limited clinical utility due to side effects that arise because many drug targets are involved in signaling pathways other than pain transduction. Here, we explored the potential of targeting protein-protein interactions (PPIs) that mediate pain signaling as an approach to developing drugs to treat chronic pain. We reviewed the approaches used to identify small molecules and peptide modulators of PPIs and their ability to decrease pain-like behaviors in rodent animal models. We analyzed data from rodent and human sensory nerve tissues to build associated signaling networks and assessed both validated and potential interactions and the structures of the interacting domains that could inform the design of synthetic peptides and small molecules. This resource identifies PPIs that could be explored for the development of new analgesics, particularly between scaffolding proteins and receptors for various growth factors and neurotransmitters, as well as ion channels and other enzymes. Targeting the adaptor function of CBL by blocking interactions between its proline-rich carboxyl-terminal domain and its SH3-domain-containing protein partners, such as GRB2, could disrupt endosomal signaling induced by pain-associated growth factors. This approach would leave intact its E3-ligase functions, which are mediated by other domains and are critical for other cellular functions. This potential of PPI modulators to be more selective may mitigate side effects and improve the clinical management of pain.

In silico approach uncovers the shared genetic landscape of type 2 diabetes mellitus and asthenozoospermia

Asthenozoospermia (AZS) is one of the most common types of male infertility. Current evidence revealed that type 2 diabetes mellitus (T2DM) is closely associated with declining semen quality, especially for poor sperm motility. This study aimed to uncover the genetic interrelationships and important biomarkers between AZS and T2DM. Transcriptome data regarding AZS and T2DM were downloaded from the Gene Expression Omnibus (GEO) database. We performed GO and pathway analysis, and protein-protein interaction (PPI) network construction for T2DM-related differentially expressed genes (DMRGs). Moreover, we calculated receiver operator characteristic (ROC) curve and conducted external independent validation. Expression of hub DMRGs was assessed for patients using the qPCR method. MiRNA interaction and immune infiltration were subsequently characterized. A total of 554 overlapping DMRGs were identified between the AZS/T2DM and healthy groups. These overlapping DMRG participated in the DNA damage-, energy metabolism-, and immune-related biological pathways. Module function analysis discovered that the top three PPI modules were tightly correlated with DNA damage-related processes. After external validation in other independent datasets, two hub DMRGs (TBC1D12 and SCG5) were obtained. ROC analysis revealed that TBC1D12 and SCG5 had good diagnostic performance (area under the curve > 0.75). Immune infiltration profile showed that the level of T cell co-stimulation and CD8+_T_cells were negatively related to the hub DMRGs expression. Mirna interaction analysis showed 15 significant hub DMRGs-miRNA interactions. The qPCR results showed that expression of TBC1D12 and SCG5 were significantly different between sperm samples from diabetic patients with AZS and controls. The present study revealed molecular signatures and critical pathways between the AZS and T2DM, and identified two hub DMRGs of TBC1D12 and SCG5. The data would provide novel understandings of shared pathogenic mechanisms in T2DM-associated AZS.

Characterizations of Protein Arginine Deiminase 1 as a Substrate of NTMT1: Implications of Nα-Methylation in Protein Stability and Interaction.

α-N-Methylation (Nα-methylation), catalyzed by protein N-terminal methyltransferases (NTMTs), constitutes a crucial post-translational modification involving the transfer of a methyl group from S-adenosyl-l-methionine (SAM) to the Nα-terminal amino group of substrate proteins. NTMT1/2 are known to methylate canonical Nα sequences, such as X-P-K/R. With over 300 potential human protein substrates, only a small fraction has been validated, and even less is known about the functions of Nα-methylation. This study delves into the characterizations of protein arginine deiminase 1 (PAD1) as a substrate of NTMT1. By employing biochemical and cellular assays, we demonstrated NTMT1-mediated Nα-methylation of PAD1, leading to an increase in protein half-life and the modulation of protein-protein interactions in HEK293T cells. The methylation of PAD1 appears nonessential to its enzymatic activity or cellular localization. Proteomic studies revealed differential protein interactions between unmethylated and Nα-methylated PAD1, suggesting a regulatory role for Nα-methylation in modulating PAD1's protein-protein interactions. These findings shed light on the intricate molecular mechanisms governing PAD1 function and expand our knowledge of Nα-methylation in regulating protein function.

Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD

The dissociation rate, or its reciprocal, the residence time (τ), is a crucial parameter for understanding the duration and biological impact of biomolecular interactions. Accurate prediction of τ is essential for understanding protein-protein interactions (PPIs) and identifying potential drug targets or modulators for tackling diseases. Conventional molecular dynamics simulation techniques are inherently constrained by their limited timescales, making it challenging to estimate residence times, which typically range from minutes to hours. Building upon its successful application in protein-small molecule systems, τ-Random Acceleration Molecular Dynamics (τRAMD) is here investigated for estimating dissociation rates of protein-protein complexes. τRAMD enables the observation of unbinding events on the nanosecond timescale, facilitating rapid and efficient computation of relative residence times. We tested this methodology for three protein-protein complexes and their extensive mutant datasets, achieving good agreement between computed and experimental data. By combining τRAMD with MD-IFP (Interaction Fingerprint) analysis, dissociation mechanisms were characterized and their sensitivity to mutations investigated, enabling the identification of molecular hotspots for selective modulation of dissociation kinetics. In conclusion, our findings underscore the versatility of τRAMD as a simple and computationally efficient approach for computing relative protein-protein dissociation rates and investigating dissociation mechanisms, thereby aiding the design of PPI modulators.

A Novel Confocal Scanning Protein-Protein Interaction Assay (PPI-CONA) Reveals Exceptional Selectivity and Specificity of CC0651, a Small Molecule Binding Enhancer of the Weak Interaction between the E2 Ubiquitin-Conjugating Enzyme CDC34A and Ubiquitin.

Protein-protein interactions (PPIs) are some of the most challenging target classes in drug discovery. Highly sensitive detection techniques are required for the identification of chemical modulators of PPIs. Here, we introduce PPI confocal nanoscanning (PPI-CONA), a miniaturized, microbead based high-resolution fluorescence imaging assay. We demonstrate the capabilities of PPI-CONA by detecting low affinity ternary complex formation between the human CDC34A ubiquitin-conjugating (E2) enzyme, ubiquitin, and CC0651, a small molecule enhancer of the CDC34A-ubiquitin interaction. We further exemplify PPI-CONA with an E2 enzyme binding study on CC0651 and a CDC34A binding specificity study of a series of CC0651 analogues. Our results indicate that CC0651 is highly selective toward CDC34A. We further demonstrate how PPI-CONA can be applied to screening very low affinity interactions. PPI-CONA holds potential for high-throughput screening for modulators of PPI targets and characterization of their affinity, specificity, and selectivity.

Understanding the role of BRD8 in human carcinogenesis.

The bromodomain is a conserved protein-protein interaction module that functions exclusively to recognize acetylated lysine residues on histones and other proteins. It is noteworthy that bromodomain-containing proteins are involved in transcriptional modulation by recruiting various transcription factors and/or protein complexes such as ATP-dependent chromatin remodelers and acetyltransferases. Bromodomain-containing protein 8 (BRD8), a molecule initially recognized as skeletal muscle abundant protein and thyroid hormone receptor coactivating protein of 120 kDa (TrCP120), was shown to be a subunit of the NuA4/TIP60-histone acetyltransferase complex. BRD8 has been reported to be upregulated in a subset of cancers and implicated in the regulation of cell proliferation as well as in the response to cytotoxic agents. However, little is still known about the underlying molecular mechanisms. In recent years, it has become increasingly clear that the bromodomain of BRD8 recognizes acetylated and/or nonacetylated histones H4 and H2AZ, and that BRD8 is associated with cancer development in both a NuA4/TIP60 complex-dependent and -independent manner. In this review, we will provide an overview of the current knowledge on the molecular function of BRD8, focusing on the biological role of the bromodomain of BRD8 in cancer cells.

Bioinformatics study of the TNFRSF1A mechanism involved in acute liver injury in sepsis through the mTOR signaling pathway.

This study analyzed potential key genes involved in the mechanism of acute liver injury induced by sepsis through bioinformatics techniques, aiming to provide novel insights for the identification of early-stage sepsis-induced acute liver injury and its diagnosis. Gene chip data sets containing samples from acute liver injury induced by sepsis and control groups (GSE22009 and GSE60088) were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) with |log fold change| >1 and p<0.05 were screened with the GEO2R tool, which was also used for the selection of upregulated DEGs in the chips with p<0.05. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene Ontology, and protein-protein interaction (PPI) analyses were then conducted. Results were visualized using R language packages, including volcano plots, Venn diagrams, and boxplots. The intersection of candidate genes with relevant genes in the Comparative Toxicogenomics Database (CTD) was performed, and the clinical significance of these genes was explored through a literature review. A rat model of acute liver injury was developed by inducing sepsis with the cecum ligation and puncture method. Real-time PCR was performed to determine the gene expression in rat liver tissues. A total of 646 upregulated DEGs were determined in GSE22009 and 146 in GSE60088. A Venn diagram was used to find the intersection of the upregulated DEGs between the two data sets, and 67 DEGs associated with sepsis-mediated acute liver damage were obtained. Enrichment analysis from the KEGG pathway showed that DEG upregulation was primarily associated with various pathways: TNF, NF-κB, IL-17, ferroptosis, mTOR, and JAK-STAT signaling pathways. DEGs resulted in three clusters and 15 candidate genes, as revealed by the PPI network and module analyses. Intersection with sepsis-induced acute liver injury-related genes in the CTD resulted in the identification of three significant differentially co-expressed genes: CXCL1, ICAM1, and TNFRSF1A. Sepsis-induced liver tissue indicated the overexpression of CXCL1, ICAM1, and TNFRSF1A mRNA, as compared with the control group (p<0.05). The key genes identified and related signaling pathways provided insights into the molecular mechanisms of sepsis-induced acute liver injury. In vivo studies revealed the overexpression of CXCL1, ICAM1, and TNFRSF1A mRNA in sepsis-mediated injured liver tissues, providing a theoretical basis for early diagnosis and targeted treatment research.

Exploring the effects of matrix metalloproteinase-13 on the malignant biological behavior of tongue squamous cell carcinoma via the TNF signaling pathway based on bioinformatics methods.

Identification of the etiology, molecular mechanisms, and carcinogenic pathways of tongue squamous cell carcinoma (TSCC) is crucial for developing new diagnostic and therapeutic strategies. This study used bioinformatics methods to identify key genes in TSCC and explored the potential functions and pathway mechanisms related to the malignant biological behavior of TSCC. Gene chip data sets (i.e., GSE13601 and GSE34106) containing the data of both TSCC patients and normal control subjects were selected from the Gene Expression Omnibus (GEO) database. Using a gene expression analysis tool (GEO2R) of the GEO database, the differentially expressed genes (DEGs) were identified using the following criteria: |log fold change| >1, and P<0.05. The GEO2R tool was also used to select the upregulated DEGs in the chip candidates based on a P value <0.05. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Ontology (GO) function analysis, and a protein-protein interaction (PPI) network analysis were then conducted. The results were displayed using R language packages, including volcano plots, Venn diagrams, heatmaps, and enriched pathway bubble charts. Genes from the MalaCards database were compared with the candidate genes, and a thorough review of the literature was conducted to determine the clinical significance of these genes. Finally, feature gene-directed chemical drugs or targeted drugs were predicted using the Comparative Toxicogenomics Database (CTD). In total, 767 upregulated DEGs were identified from GSE13601 and 695 from GSE34106. By intersecting the upregulated DEGs from both data sets using a Venn diagram, 100 DEGs related to TSCC were identified. The enrichment analysis of the KEGG signaling pathways identified the majority of the pathways associated with the upregulated DEGs, including the Toll-like receptor signaling pathway, the extracellular matrix-receptor interaction, the tumor necrosis factor (TNF) signaling pathway, cytokine-cytokine receptor interaction, the chemokine signaling pathway, the interlukin-17 signaling pathway, and natural killer cell-mediated cytotoxicity. The PPI network and module analyses of the shared DEGs ultimately resulted in five clusters and 55 candidate genes. A further intersection analysis of the TSCC-related genes in the MalaCards database via a Venn diagram identified three important shared DEGs; that is, matrix metalloproteinase-1 (MMP1), MMP9, and MMP13. In the CTD, seven drugs related to MMP13 were identified for treating tongue tumors. This study identified key genes and signaling pathways involved in TSCC and thus extended understandings of the molecular mechanisms that underlie the development and progression of TSCC. Additionally, this study showed that MMP13 may influence the malignant biological behavior of TSCC through the TNF signaling pathway. This finding could provide a theoretical basis for research into early differential diagnosis and targeted treatment.

A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators With Functional Group Information and Hypergraph Structure.

Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective.

Uncovering periodontitis-associated markers through the aggregation of transcriptomics information from diverse sources.

Periodontitis, a common chronic inflammatory disease, significantly impacted oral health. To provide novel biological indicators for the diagnosis and treatment of periodontitis, we analyzed public microarray datasets to identify biomarkers associated with periodontitis. The Gene Expression Omnibus (GEO) datasets GSE16134 and GSE106090 were downloaded. We performed differential analysis and robust rank aggregation (RRA) to obtain a list of differential genes. To obtain the core modules and core genes related to periodontitis, we evaluated differential genes through enrichment analysis, correlation analysis, protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network analysis. Potential biomarkers for periodontitis were identified through comparative analysis of dual networks (PPI network and ceRNA network). PPI network analysis was performed in STRING. The ceRNA network consisted of RRA differentially expressed messenger RNAs (RRA_DEmRNAs) and RRA differentially expressed long non-coding RNAs (RRA_DElncRNAs), which regulated each other's expression by sharing microRNA (miRNA) target sites. RRA_DEmRNAs were significantly enriched in inflammation-related biological processes, osteoblast differentiation, inflammatory response pathways and immunomodulatory pathways. Comparing the core ceRNA module and the core PPI module, C1QA, CENPK, CENPU and BST2 were found to be the common genes of the two core modules, and C1QA was highly correlated with inflammatory functionality. C1QA and BST2 were significantly enriched in immune-regulatory pathways. Meanwhile, LINC01133 played a significant role in regulating the expression of the core genes during the pathogenesis of periodontitis. The identified biomarkers C1QA, CENPK, CENPU, BST2 and LINC01133 provided valuable insight into periodontitis pathology.

Irbesartan ameliorates diabetic kidney injury in db/db mice by restoring circadian rhythm and cell cycle.

Irbesartan has been widely used in the clinical treatment of diabetic kidney disease (DKD). However, the molecular mechanism of its delay of DKD disease progression has not been fully elucidated. The aim of the present study was to investigate the mechanism of irbesartan in the treatment of DKD. C57BL/KsJ db/db mice were randomly divided into the model group and irbesartan-treated group. After treatment with irbesartan for 12 weeks, the effects on blood glucose, body weight, 24-h urinary albumin, and renal injuries were evaluated. Microarray was used to determine the differentially expressed genes (DEGs) in the renal cortex of mice. |Log FC| <0.5 and false discovery rate (FDR) <0.25 were set as the screening criteria. Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO), protein-protein interaction (PPI) network and modules, and microRNA (miRNA)-DEGs network analysis were applied to analyze the DEGs. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the results of microarray. The present study demonstrated irbesartan could significantly improve the renal function in db/db mice through decreasing 24-h urinary albumin and alleviating the pathological injury of kidney. Irbesartan may affect the expression of numerous kidney genes involved in circadian rhythm, cell cycle, micoRNAs in cancer, and PI3K-AKT signaling pathway. In the miRNA-DEGs network, miR-1970, miR-703, miR-466f, miR-5135, and miR-132-3p were the potential targets for irbesartan treatment. The validation test confirmed that key genes regulating circadian rhythm (Arntl, Per3, and Dbp) and cell cycle (Prc1, Ccna2, and Ccnb2) were restored in db/db mice on treatment with Irbesartan. Generally, irbesartan can effectively treat DKD by regulating the circadian rhythm and cell cycle. The DEGs and pathways identified in the study will provide new insights into the potential mechanisms of irbesartan in the treatment of DKD.

Irbesartan ameliorates diabetic kidney injury in db/db mice by restoring circadian rhythm and cell cycle.

Irbesartan has been widely used in the clinical treatment of diabetic kidney disease (DKD). However, the molecular mechanism of its delay of DKD disease progression has not been fully elucidated. The aim of the present study was to investigate the mechanism of irbesartan in the treatment of DKD. C57BL/KsJ db/db mice were randomly divided into the model group and irbesartan-treated group. After treatment with irbesartan for 12 weeks, the effects on blood glucose, body weight, 24-h urinary albumin, and renal injuries were evaluated. Microarray was used to determine the differentially expressed genes (DEGs) in the renal cortex of mice. |Log FC| <0.5 and false discovery rate (FDR) <0.25 were set as the screening criteria. Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO), protein-protein interaction (PPI) network and modules, and microRNA (miRNA)-DEGs network analysis were applied to analyze the DEGs. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the results of microarray. The present study demonstrated irbesartan could significantly improve the renal function in db/db mice through decreasing 24-h urinary albumin and alleviating the pathological injury of kidney. Irbesartan may affect the expression of numerous kidney genes involved in circadian rhythm, cell cycle, micoRNAs in cancer, and PI3K-AKT signaling pathway. In the miRNA-DEGs network, miR-1970, miR-703, miR-466f, miR-5135, and miR-132-3p were the potential targets for irbesartan treatment. The validation test confirmed that key genes regulating circadian rhythm (Arntl, Per3, and Dbp) and cell cycle (Prc1, Ccna2, and Ccnb2) were restored in db/db mice on treatment with Irbesartan. Generally, irbesartan can effectively treat DKD by regulating the circadian rhythm and cell cycle. The DEGs and pathways identified in the study will provide new insights into the potential mechanisms of irbesartan in the treatment of DKD.

A Multivalent DNA Nanoparticle/Peptide Hybrid Molecular Modality for the Modulation of Protein-Protein Interactions in the Tumor Microenvironment.

Despite success in the treatment of some blood cancers and melanoma, positive response to immunotherapies remains disappointingly low in the treatment of solid tumors. The context of the molecular crosstalk within the tumor microenvironment can result in dysfunctional immune cell activation, leading to tumor tolerance and progression. Although modulating these protein-protein interactions (PPIs) is vital for appropriate immune cell activation and recognition, targeting nonenzymatic PPIs has proven to be fraught with challenges. To address this, we introduce a synthetic, multivalent molecular modality comprised of small interfering peptides precisely hybridized to a semi-rigid DNA scaffold. Herein, we describe a prototype of this modality that targets the IL-33/ST2 signaling axis, which is associated with tumor tolerance and immunotherapy treatment failure. Using peptides that mimic the specific high energy "hotspot" residues with which the IL-33/ST2 co-receptor, IL-1RAcP, interacts with the initial binary complex, we show this platform to effectively bind IL-33/ST2 with a of 110 nM. Additionally, this molecule effectively abrogates signal transduction in cell models at high nanomolar concentrations and is exquisitely selective for this complex over structurally similar PPIs within the same cytokine superfamily.