Protein-protein Interaction Modules Research Articles

Integrated network pharmacology analysis and in vitro validation revealed the underlying mechanism of Xiyanping injection in treating coronavirus disease 2019.

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, leading to a pandemic. In China, Xiyanping injection (XYP) has been recommended as a drug for COVID-19 treatment in the Guideline on Diagnosis and Treatment of COVID-19 by the National Health Commission of the People Republic of China and National Administration of Traditional Chinese Medicine (Trial eighth Edition). However, the relevant mechanisms at the molecular-level need to be further elucidated. In this study, XYP related active ingredients, potential targets and COVID-19 related genes were searched in public databases. Protein-protein interaction network and module analyzes were used to screen for key targets. gene ontology and Kyoto encyclopedia of genes and genomes were performed to investigate the potentially relevant signaling pathways. Molecular docking was performed using Autodock Tools and Vina. For the validation of potential mechanism, PolyI:C was used to induce human lung epithelial cells for an inflammation model. Subsequently, CCK-8 assays, enzyme-linked immunosorbent assay, reverse transcription quantitative polymerase chain reaction and western blot were employed to determine the effect of XYP on the expression of key genes. Seven effective active ingredients in XYP were searched for 123 targets in the relevant databases. Furthermore, 6446 COVID-19 disease targets were identified. Sodium 9-dehydro-17-hydro-andrographolide-19-yl sulfate was identified as the vital active compounds, and IL-6, TNF, IL-1β, CXCL8, STAT3, MAPK1, MAPK14, and MAPK8 were considered as the key targets. In addition, molecular docking revealed that the active compound and the targets showed good binding affinities. The enrichment analysis predicted that the XYP could regulate the IL-17, Toll-like receptor, PI3K-Akt and JAK-STAT signaling pathways. Consistently, further in vitro experiments demonstrated that XYP could slow down the cytokine storm in the lung tissue of COVID-19 patients by down-regulating IL-6, TNF-α, IL-1β, CXCL8, and p-STAT3. Through effective network pharmacology analysis and molecular docking, this study suggests that XYP contains many effective compounds that may target COVID-19 related signaling pathways. Moreover, the in vitro experiment confirmed that XYP could inhibit the cytokine storm by regulating genes or proteins related to immune and inflammatory responses.

PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease.

Identification of Alzheimer's Disease (AD)-related transcriptomic signatures from blood is important for early diagnosis of the disease. Deep learning techniques are potent classifiers for AD diagnosis, but most have been unable to identify biomarkers because of their lack of interpretability. To address these challenges, we propose a pathway information-based neural network (PINNet) to predict AD patients and analyze blood and brain transcriptomic signatures using an interpretable deep learning model. PINNet is a deep neural network (DNN) model with pathway prior knowledge from either the Gene Ontology or Kyoto Encyclopedia of Genes and Genomes databases. Then, a backpropagation-based model interpretation method was applied to reveal essential pathways and genes for predicting AD. The performance of PINNet was compared with a DNN model without a pathway. Performances of PINNet outperformed or were similar to those of DNN without a pathway using blood and brain gene expressions, respectively. Moreover, PINNet considers more AD-related genes as essential features than DNN without a pathway in the learning process. Pathway analysis of protein-protein interaction modules of highly contributed genes showed that AD-related genes in blood were enriched with cell migration, PI3K-Akt, MAPK signaling, and apoptosis in blood. The pathways enriched in the brain module included cell migration, PI3K-Akt, MAPK signaling, apoptosis, protein ubiquitination, and t-cell activation. By integrating prior knowledge about pathways, PINNet can reveal essential pathways related to AD. The source codes are available at https://github.com/DMCB-GIST/PINNet.

Exploring the pathogenesis of colorectal carcinoma complicated with hepatocellular carcinoma via microarray data analysis.

Background: Despite the increasing number of research endeavors dedicated to investigating the relationship between colorectal carcinoma (CRC) and hepatocellular carcinoma (HCC), the underlying pathogenic mechanism remains largely elusive. The aim of this study is to shed light on the molecular mechanism involved in the development of this comorbidity. Methods: The gene expression profiles of CRC (GSE90627) and HCC (GSE45267) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) of psoriasis and atherosclerosis, three kinds of analyses were performed, namely, functional annotation, protein-protein interaction (PPI) network and module construction, and hub gene identification, survival analysis and co-expression analysis. Results: A total of 150 common downregulated differentially expressed genes and 148 upregulated differentially expressed genes were selected for subsequent analyses. The significance of chemokines and cytokines in the pathogenesis of these two ailments is underscored by functional analysis. Seven gene modules that were closely connected were identified. Moreover, the lipopolysaccharide-mediated signaling pathway is intricately linked to the development of both diseases. Finally, 10 important hub genes were identified using cytoHubba, including CDK1, KIF11, CDC20, CCNA2, TOP2A, CCNB1, NUSAP1, BUB1B, ASPM, and MAD2L1. Conclusion: Our study reveals the common pathogenesis of colorectal carcinoma and hepatocellular carcinoma. These common pathways and hub genes may provide new ideas for further mechanism research.

Identification of crucial genes of pediatric inflammatory bowel disease in remission by protein-protein interaction network and module analyses.

Although the main treatments of inflammatory bowel disease (IBD) aim at the induction and maintenance of clinical remission, several studies have demonstrated inflammation still present in clinical remission. The goal of this study was to analyze the gene expression profiles between the pediatric IBD and control samples, aiming to further investigate the underlying therapeutic target in remission. Gene expression profiles data of GSE33943 were downloaded from Gene Expression Omnibus, which included 45 pediatric IBD samples and 13 control samples. The differentially expressed genes (DEGs) between IBD and control samples were identified by LIMMA package in R and the function of DEGs were predicted by Gene Ontology and KEGG pathway enrichment analyses using GeneAnswer package. Furthermore, a protein-protein interaction (PPI) network was constructed through the STRING database and functional module was obtained using ClusterONE. A total of 224 DEGs were screened between IBD and control samples. These DEGs (e.g. up-regulated FAS and down-regulated CCL5) were mainly enriched in cytokine‒cytokine receptor interaction and chemokine signaling pathway. In addition, some hub genes (e.g. up-regulated PSMA2 and PSMA6) were obtained through PPI network and functional module. These two genes were involved in Proteasome alpha-subunit and conserved site by functional module analysis. The immune and Proteasome mechanisms are still active during remission and FAS, PMSF6 and PMSF2 may be underlying targets for therapy of this disease.

Stabilization of 14-3-3 protein-protein interactions with Fusicoccin-A decreases alpha-synuclein dependent cell-autonomous death in neuronal and mouse models

Synucleinopathies are a group of neurodegenerative diseases without effective treatment characterized by the abnormal aggregation of alpha-synuclein (aSyn) protein. Changes in levels or in the amino acid sequence of aSyn (by duplication/triplication of the aSyn gene or point mutations in the encoding region) cause familial cases of synucleinopathies. However, the specific molecular mechanisms of aSyn-dependent toxicity remain unclear. Increased aSyn protein levels or pathological mutations may favor abnormal protein-protein interactions (PPIs) that could either promote neuronal death or belong to a coping response program against neurotoxicity. Therefore, the identification and modulation of aSyn-dependent PPIs can provide new therapeutic targets for these diseases. To identify aSyn-dependent PPIs we performed a proximity biotinylation assay based on the promiscuous biotinylase BioID2. When expressed as a fusion protein, BioID2 biotinylates by proximity stable and transient interacting partners, allowing their identification by streptavidin affinity purification and mass spectrometry. The aSyn interactome was analyzed using BioID2-tagged wild-type (WT) and pathological mutant E46K aSyn versions in HEK293 cells. We found the 14-3-3 epsilon isoform as a common protein interactor for WT and E46K aSyn. 14‐3-3 epsilon correlates with aSyn protein levels in brain regions of a transgenic mouse model overexpressing WT human aSyn. Using a neuronal model in which aSyn cell-autonomous toxicity is quantitatively scored by longitudinal survival analysis, we found that stabilization of 14‐3-3 protein-proteins interactions with Fusicoccin-A (FC-A) decreases aSyn-dependent toxicity. Furthermore, FC-A treatment protects dopaminergic neuronal somas in the substantia nigra of a Parkinson's disease mouse model. Based on these results, we propose that the stabilization of 14‐3-3 epsilon interaction with aSyn might reduce aSyn toxicity, and highlight FC-A as a potential therapeutic compound for synucleinopathies.

Using a stacked ensemble learning framework to predict modulators of protein–protein interactions

Identifying small molecule protein–protein interaction modulators (PPIMs) is a highly promising and meaningful research direction for drug discovery, cancer treatment, and other fields. In this study, we developed a stacking ensemble computational framework, SELPPI, based on a genetic algorithm and tree-based machine learning method for effectively predicting new modulators targeting protein–protein interactions. More specifically, extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), random forest (RF), cascade forest, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) were used as basic learners. Seven types of chemical descriptors were taken as the input characteristic parameters. Primary predictions were obtained with each basic learner–descriptor pair. Then, the 6 methods mentioned above were used as meta learners and trained on the primary prediction in turn. The most efficient method was utilized as the meta learner. Finally, the genetic algorithm was used to select the optimal primary prediction output as the input of the meta learner for secondary prediction to obtain the final result. We systematically evaluated our model on the pdCSM-PPI datasets. To our knowledge, our model outperformed all existing models, which demonstrates its great power.

Transcriptome Analysis Identifies Biomarkers for the Diagnosis and Management of Psoriasis Complicated with Depression.

Psoriasis is a systemic inflammatory disease, and the mechanism that links psoriasis to depression is still elusive. Hence, this study aimed to elucidate the potential pathogenesis of psoriasis and depression comorbidity. The gene expression profiles of psoriasis (GSE34248, GSE78097 and GSE161683) and depression (GSE39653) were downloaded from the Gene Expression Omnibus (GEO) DataSets. Functional annotation, protein-protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis were performed, following identification of the common differentially expressed genes (DEGs) of psoriasis and depression. A total of 115 common DEGs (55 up-regulated and 60 down-regulated) were identified between psoriasis and depression. Functional analysis indicated that T cell activation and differentiation were predominantly implicated in the potential pathogenesis of these two diseases. In addition, Th17 cell differentiation and cytokines is closely related to both. Finally, 17 hub genes were screened, including CTLA4, LCK, ITK, IL7R, CD3D, SOCS1, IL4R, PRKCQ, SOCS3, IL23A, PDGFB, PAG1, TGFA, FGFR1, RELN, ITGB5 and TNXB, which re-emphasized the importance of the immune system in psoriasis and depression. Our study reveals the common pathogenesis of psoriasis and depression. These common pathways and hub genes may apply to a molecular screening tool for depression in psoriasis patients, which could help dermatologists optimize patient management in routine care.

Research progress in small-molecule modulators of protein-protein interactions

Research progress in small-molecule modulators of protein-protein interactions

Hub genes and pathways in gastric cancer: A comparison between studies that used normal tissues adjacent to the tumour and studies that used healthy tissues as calibrator.

Several bioinformatics studies have been performed on high-throughput expression data to determine the cellular pathways and hub genes affected by Gastric cancer (GC). However, these studies differ in using a healthy tissue or normal tissue adjacent to the tumour (NAT) as calibrator tissues. This study was designed to find how using healthy or NAT tissues as calibrator tissues could affect pathway enrichment data and hub genes in GC. Two gene expression datasets with NAT tissues (GSE79973 and GSE118916) and one dataset with healthy tissues (GSE54129) were downloaded and processed by the limma package to screen the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were performed by the Enrichr online tool. Protein-protein interaction network construction, module analysis, and hub genes selection were performed by Cytoscape software, Molecular Complex Detection plugin, and cytoHubba plugin, respectively. The gene expression profiling interactive analysis web server was used to analyse RNA sequencing expression data from The Cancer Genome Atlas Program. The Kaplan-Meier plotter was used to perform survival analysis. Our results showed that some KEGG and GO pathways were shared between studies with NAT and the study with healthy tissues. However, some terms, especially inflammation-related terms, were missed when NAT tissues were used as calibrator tissues. Also, only FN1 and COL1A1 are common hub genes between DEGs of the studies with NAT and healthy tissues. Since hub genes are usually extracted and suggested as candidate targets for GC diagnosis, prognosis, or treatment, selecting healthy or NAT tissues may affect the hub genes selection.

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches.

Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protein surfaces and the development of modulators of protein-protein interactions still represent a major challenge due to their highly dynamic and extensive interfacial areas. Over the years, multiple strategies including structural, computational, and combinatorial approaches have been developed to characterize PPI and to date, several successful examples of small molecules, antibodies, peptides, and aptamers able to modulate these interfaces have been determined. Notably, peptides are a particularly useful tool for inhibiting PPIs due to their exquisite potency, specificity, and selectivity. Here, after an overview of PPIs and of the commonly used approaches to identify and characterize them, we describe and evaluate the impact of chemical peptide libraries in medicinal chemistry with a special focus on the results achieved through recent applications of this methodology. Finally, we also discuss the role that this methodology can have in the framework of the opportunities, and challenges that the application of new predictive approaches based on artificial intelligence is generating in structural biology.

Abstract 464: AAC-11 survival pathways as therapeutic target in cancer: AAC-11 leucine-zipper domain derived peptides exert potent antitumor effects and exhibit favorable stability, pharmacokinetic and toxicology profiles

Abstract Background: Due to their basal stress phenotype associated to transformation, cancer cells are addicted to non-mutated, non-oncogenic proteins that do not bear such vital functions in normal cells, a phenomenon referred as Non-Oncogenic Addiction (NOA). Targeting these NOAs could therefore induce selective killing of cancer cells, opening several therapeutic opportunities. Recent data suggest that the stress-related scaffold protein AAC-11 (anti-apoptosis clone 11, also known as Api5) is critically involved in cancer cells resistance to chemotherapies, metastatic potential and escape from the immune system. Methods: We have developed inactivating peptides based on the fusion of a cell penetrating sequence and portions of the leucine-zipper domain of AAC-11, which functions as a protein-protein interaction module. These peptides induce cancer cells death, through the inhibition of protein-protein interactions between AAC-11 and its partners, while sparing normal cells. We now describe further characterization of our lead peptide, JRT39, which contains residues 377 to 379 of AAC-11 linked to the cell-penetrating peptide “penetratin”. Results: In vitro, JRT39 causes cell death in a wide spectrum of cancer cell lines with IC50 ranging from 5 µM to 30 µM depending on tumor cell type. In particular, JRT39 showed selective efficacy towards primary cells from Acute Myeloid Leukemia (AML), Acute Lymphocytic Leukemia (ALL) or Sézary syndrome (SS) patients, while sparing normal hematopoietic cells, with an IC50 of 5-15 µM. Mechanistically, JRT39 induces membranolysis of cancer cells through binding to p21-activated kinase 1 (PAK1) in AML or SS cells plasma membrane, where PAK1 is overexpressed. In addition, JRT39 exerted potent anti-tumor activity in vivo in disseminated or subcutaneous AML, APL (Acute Promyelocytic Leukemia) and SS-patient derived preclinical murine models. Preliminary pharmacokinetic studies revealed that JRT39 is stable in human serum and has a plasma half-life of ~1.5-2.5 hours after intravenous (IV) administration to dogs or cynomolgus monkeys, with concentration-time data fitting 2-compartment model. Finally, JRT39 was well tolerated at 5 and 10 mg/kg after single or repeated (daily) IV injections. Conclusions: Combined, our preclinical data confirm that interfering with AAC-11-related survival pathways is a promising novel anticancer strategy and support the development of JRT39 for the treatment of cancer. Citation Format: Louise Jeammet, Emile Adicéam, Justine Habault, Anna Kaci, Jeannig Berrou, Mélanie Dupont, Nicolas Thonnart, Ewa Pasquereau-Kotula, Anne Marie-Cardine, Armand Bensussan, Marika Pla, Hervé Dombret, Claude Gardin, Martine Bagot, Jean-Christophe Rain, Hélène Sicard, Jérôme Tiollier, Thorsten Braun, Jean-Luc Poyet. AAC-11 survival pathways as therapeutic target in cancer: AAC-11 leucine-zipper domain derived peptides exert potent antitumor effects and exhibit favorable stability, pharmacokinetic and toxicology profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 464.

Going beyond Binary: Rapid Identification of Protein-Protein Interaction Modulators Using a Multifragment Kinetic Target-Guided Synthesis Approach.

Kinetic target-guided synthesis (KTGS) is a powerful screening approach that enables identification of small molecule modulators for biomolecules. While many KTGS variants have emerged, a majority of the examples suffer from limited throughput and a poor signal/noise ratio, hampering reliable hit detection. Herein, we present our optimized multifragment KTGS screening strategy that tackles these limitations. This approach utilizes selected reaction monitoring liquid chromatography tandem mass spectrometry for hit detection, enabling the incubation of 190 fragment combinations per screening well. Consequentially, our fragment library was expanded from 81 possible combinations to 1710, representing the largest KTGS screening library assembled to date. The expanded library was screened against Mcl-1, leading to the discovery of 24 inhibitors. This work unveils the true potential of KTGS with respect to the rapid and reliable identification of hits, further highlighting its utility as a complement to the existing repertoire of screening methods used in drug discovery.

Discovering genetic linkage between periodontitis and type 1 diabetes: A bioinformatics study.

Background: Relationship between periodontitis (PD) and type 1 diabetes (T1D) has been reported, but the detailed pathogenesis requires further elucidation. This study aimed to reveal the genetic linkage between PD and T1D through bioinformatics analysis, thereby providing novel insights into scientific research and clinical treatment of the two diseases. Methods: PD-related datasets (GSE10334, GSE16134, GSE23586) and T1D-related datasets(GSE162689)were downloaded from NCBI Gene Expression Omnibus (GEO). Following batch correction and merging of PD-related datasets as one cohort, differential expression analysis was performed (adjusted p-value <0.05 and ∣log2 fold change| > 0.5), and common differentially expressed genes (DEGs) between PD and T1D were extracted. Functional enrichment analysis was conducted via Metascape website. The protein-protein interaction (PPI) network of common DEGs was generated in The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Hub genes were selected by Cytoscape software and validated by receiver operating characteristic (ROC) curve analysis. Results: 59 common DEGs of PD and T1D were identified. Among these DEGs, 23 genes were commonly upregulated, and 36 genes were commonly downregulated in both PD- and T1D-related cohorts. Functional enrichment analysis indicated that common DEGs were mainly enriched in tube morphogenesis, supramolecular fiber organization, 9 + 0 non-motile cilium, plasma membrane bounded cell projection assembly, glomerulus development, enzyme-linked receptor protein signaling pathway, endochondral bone morphogenesis, positive regulation of kinase activity, cell projection membrane and regulation of lipid metabolic process. After PPI construction and modules selection, 6 hub genes (CD34, EGR1, BBS7, FMOD, IGF2, TXN) were screened out and expected to be critical in linking PD and T1D. ROC analysis showed that the AUC values of hub genes were all greater than 70% in PD-related cohort and greater than 60% in T1D-related datasets. Conclusion: Shared molecular mechanisms between PD and T1D were revealed in this study, and 6 hub genes were identified as potential targets in treating PD and T1D.

Stretching Peptides to Generate Small Molecule β-Strand Mimics.

Advances in the modulation of protein-protein interactions (PPIs) enable both characterization of PPI networks that govern diseases and design of therapeutics and probes. The shallow protein surfaces that dominate PPIs are challenging to target using standard methods, and approaches for accessing extended backbone structures are limited. Here, we incorporate a rigid, linear, diyne brace between side chains at the i to i+2 positions to generate a family of low-molecular-weight, extended-backbone peptide macrocycles. NMR and density functional theory studies show that these stretched peptides adopt stable, rigid conformations in solution and can be tuned to explore extended peptide conformational space. The diyne brace is formed in excellent conversions (>95%) and amenable to high-throughput synthesis. The minimalist structure-inducing tripeptide core (<300 Da) is amenable to further synthetic elaboration. Diyne-braced inhibitors of bacterial type 1 signal peptidase demonstrate the utility of the technique.

Identification of GNG7 as a novel biomarker and potential therapeutic target for gastric cancer via bioinformatic analysis and in vitro experiments.

Gastric cancer (GC) is one of the most common malignancies with unfavorable prognoses. The present study aimed to identify novel biomarkers or potential therapeutic targets in GC via bioinformatic analysis and in vitro experiments. The Gene Expression Omnibus and The Cancer Genome Atlas databases were used to screen the differentially expressed genes (DEGs). After protein-protein interaction network construction, both module and prognostic analyses were performed to identify prognosis-related genes in GC. The expression patterns and functions of G protein γ subunit 7 (GNG7) in GC were then visualized in multiple databases and further verified using in vitro experiments. A total of 897 overlapping DEGs were detected and 20 hub genes were identified via systematic analysis. After accessing the prognostic value of the hub genes using the online server Kaplan-Meier plotter, a six-gene prognostic signature was identified, which was also significantly correlated with the process of immune infiltration in GC. The results of open-access database analyses suggested that GNG7 is downregulated in GC; this downregulation was associated with tumor progression. Furthermore, the functional enrichment analysis unveiled that the GNG7-coexpressed genes or gene sets were closely correlated with the proliferation and cell cycle processes of GC cells. Finally, in vitro experiments further confirmed that GNG7 overexpression inhibited GC cell proliferation, colony formation, and cell cycle progression and induced apoptosis. As a tumor suppressor gene, GNG7 suppressed the growth of GC cells via cell cycle blockade and apoptosis induction and thus may be used as a potential biomarker and therapeutic target for GC.

Progression of Thoracic Aortic Dissection Is Aggravated by the hsa_circ_0007386/miR-1271-5P/IGF1R/AKT Axis via Induction of Arterial Smooth Muscle Cell Apoptosis

The molecular mechanisms associated with thoracic aortic dissection (TAD) remain poorly understood. A comprehensive high-throughput sequencing-based analysis of the circRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) regulatory network in TAD has not been conducted. The purpose of this study is to identify and verify the key ceRNA networks which may have crucial biological functions in the pathogenesis of TAD. Gene expression profiles of the GSE97745, GSE98770, and GSE52093 datasets were acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the GEO2R tools. Protein-protein interaction (PPI) networks of the hub genes were constructed using STRING; the hub genes and modules were identified by MCODE and CytoHubba plugins of the Cytoscape. We analyzed the hub genes using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The functions of these hub genes were assessed using Cytoscape software. Our data-along with data from GSE97745, GSE98770, and GSE52093-were used to verify the findings. Upon combined biological prediction, a total of 11 ce-circRNAs, 11 ce-miRNAs, and 26 ce-mRNAs were screened to construct a circRNA-miRNA-mRNA ceRNA network. PPI network and module analysis identified four hub nodes, including IGF1R, JAK2, CSF1, and GAB1. Genes associated with the Ras and PI3K-Akt signaling pathways were clustered in the four hub node modules in TAD. The node degrees were most significant for IGF1R, which were also the most significant in the two modules (up module and hub module). IGF1R was selected as a key gene, and the hsa_circ_0007386/miR-1271-5P/IGF1R/AKT regulatory axis was established. The relative expression levels of the regulatory axis members were confirmed by RT-PCR in 12 samples, including TAD tissues and normal tissues. Downregulation of IGF1R expression in smooth muscle cells (SMCs) was found to induce apoptosis by regulating the AKT levels. In addition, IGF1R showed high diagnostic efficacy in both AD tissue and blood samples. The hsa_circ_0007386/miR-1271-5P/IGF1R/AKT axis may aggravate the progression of TAD by inducing VSMCs apoptosis. CeRNA networks could provide new insights into the underlying molecular mechanisms of TAD. In addition, IGF1R showed high diagnostic efficacy in both tissue and plasma samples in TAD, which can be considered as a diagnostic marker for TAD.

Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan-Meier curves analysis.

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets GSE89076, GSE73360, and GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan-Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.

Investigation of the Pathogenic Mechanism of Ciprofloxacin in Aortic Aneurysm and Dissection by an Integrated Proteomics and Network Pharmacology Strategy.

Aortic aneurysm and dissection (AAD) is a life-threatening disease worldwide. Recently, fluoroquinolones have been reported to significantly increase the risk of AAD. This study aimed to investigate the potential functional mechanism and molecular targets of fluoroquinolones in relation to AAD by an integrated proteomic and network pharmacology strategy. A total of 1351 differentially expressed proteins were identified in human aortic vascular smooth muscle cells (VSMCs) after ciprofloxacin (CIP) stimulation. The functional analysis emphasized the important roles of metabolism, extracellular matrix homeostasis, mitochondrial damage, focal adhesion, and apoptosis in CIP-stimulated VSMCs. CIP targets were predicted with online databases and verified by molecular docking. Protein-protein interaction (PPI) analysis and module construction of the 34 potential CIP targets and 37 selected hub molecules after CIP stimulation identified four critical target proteins in the module: PARP1, RAC1, IGF1R and MKI67. Functional analysis of the PPI module showed that the MAPK signalling pathway, focal adhesion, apoptosis, regulation of actin cytoskeleton, and PI3K-Akt signalling pathway were significantly enriched. Our results will provide novel insights into the pathogenic mechanism of fluoroquinolones in aortic diseases.

Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions.

Design of peptide binders is an attractive strategy for targeting "undruggable" protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.

Stabilized cyclic peptides as modulators of protein-protein interactions: promising strategies and biological evaluation.

Protein-protein interactions (PPIs) control many essential biological pathways which are often misregulated in disease. As such, selective PPI modulators are desirable to unravel complex functions of PPIs and thus expand the repertoire of therapeutic targets. However, the large size and relative flatness of PPI interfaces make them challenging molecular targets for conventional drug modalities, rendering most PPIs "undruggable". Therefore, there is a growing need to discover innovative molecules that are able to modulate crucial PPIs. Peptides are ideal candidates to deliver such therapeutics attributed to their ability to closely mimic structural features of protein interfaces. However, their inherently poor proteolysis resistance and cell permeability inevitably hamper their biomedical applications. The introduction of a constraint (i.e., peptide cyclization) to stabilize peptides' secondary structure is a promising strategy to address this problem as witnessed by the rapid development of cyclic peptide drugs in the past two decades. Here, we comprehensively review the recent progress on stabilized cyclic peptides in targeting challenging PPIs. Technological advancements and emerging chemical approaches for stabilizing active peptide conformations are categorized in terms of α-helix stapling, β-hairpin mimetics and macrocyclization. To discover potent and selective ligands, cyclic peptide library technologies were updated based on genetic, biochemical or synthetic methodologies. Moreover, several advances to improve the permeability and oral bioavailability of biologically active cyclic peptides enable the de novo development of cyclic peptide ligands with pharmacological properties. In summary, the development of cyclic peptide-based PPI modulators carries tremendous promise for the next generation of therapeutic agents to target historically "intractable" PPI systems.