Protein-protein Interaction Modules Research Articles

Gene Expression Microarray Data Identify Hub Genes Involved in Osteoarthritis

The present study was performed to explore the underlying molecular mechanisms and screen hub genes of osteoarthritis (OA) via bioinformatics analysis. In total, twenty-five OA synovial tissue samples and 25 normal synovial tissue samples were derived from three datasets, namely, GSE55457, GSE55235, and GSE1919, and were used to identify the differentially expressed genes (DEGs) of OA by R language. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A Venn diagram was built to show the potential hub genes identified in all three datasets. The STRING database was used for constructing the protein–protein interaction (PPI) networks and submodules of DEGs. We identified 507 upregulated and 620 downregulated genes. Upregulated DEGs were significantly involved in immune response, MHC class II receptor activity, and presented in the extracellular region, while downregulated DEGs were mainly enriched in response to organic substances, extracellular region parts, and cadmium ion binding. Results of KEGG analysis indicated that the upregulated DEGs mainly existed in cell adhesion molecules (CAMs), while downregulated DEGs were significantly involved in the MAPK signaling pathway. A total of eighteen intersection genes were identified across the three datasets. These include Nell-1, ATF3, RhoB, STC1, and VEGFA. In addition, 10 hub genes including CXCL12, CXCL8, CCL20, and CCL4 were found in the PPI network and module construction. Identification of DEGs and hub genes associated with OA may be helpful for revealing the molecular mechanisms of OA and further promotes the development of relevant biomarkers and drug targets.

Helical Foldamers and Stapled Peptides as New Modalities in Drug Discovery: Modulators of Protein-Protein Interactions

A “foldamer” is an artificial oligomeric molecule with a regular secondary or tertiary structure consisting of various building blocks. A “stapled peptide” is a peptide with stabilized secondary structures, in particular, helical structures by intramolecular covalent side-chain cross-linking. Helical foldamers and stapled peptides are potential drug candidates that can target protein-protein interactions because they enable multipoint molecular recognition, which is difficult to achieve with low-molecular-weight compounds. This mini-review describes a variety of peptide-based foldamers and stapled peptides with a view to their applications in drug discovery, including our recent progress.

The therapeutic mechanism of PuRenDan for the treatment of diabetic nephropathy: Network pharmacology and experimental verification

Ethnopharmacological relevancePurendan (PRD), as a Chinese medicinal formula, behaves remarkable therapeutic effects on diabetes and complications in clinical and experimental research. However, the underlying pharmacological mechanism in the treatment of diabetic nephropathy (DN) is still unclear. AimsTo investigate the therapeutical effects of PRD on DN and to explore its pharmacological mechanisms using network pharmacology and experimental verification. Materials and methodsThe active compounds and putative targets in PRD, and disease-related targets of DN were extracted from public databases. The key targets were identified through the protein-protein interaction (PPI) network and module analysis. The GO and KEGG enrichment analysis were performed to discover potential pharmacological mechanisms. The expression of the key targets was detected in kidney tissue in Gene Expression Omnibus (GEO) dataset. The affinity between key proteins and corresponding compounds was evaluated by molecular docking and validated by the surface plasmon resonance (SPR) assay. The indicators on major pathways and hub genes were verified by in vivo experiments. ResultsIn network pharmacology, 137 common targets in PRD for DN treatment were screened. The key targets and main signaling pathways including AGE-RAGE and lipid pathways were identified. The statistical difference in the expression of the key targets was verified in GSE96804 database, confirming the association with DN. The docking scores obtained from molecular docking illustrated good binding force between hub proteins and active compounds. And the good component-protein affinities were validated by SPR assay. Furthermore, the results of animal experiment indicated that PRD could ameliorate the level of serum glucose and renal function in rat model. It could regulate the expression of hub targets (AKT1, MAPK3, and STAT3) and improve indicators related with oxidative stress and lipid metabolism. ConclusionThe key targets and major signaling pathways in the treatment of PRD on DN were identified. The mechanism might relate to regulation of oxidative stress and lipid metabolism.

Proteomic Tools for the Quantitative Analysis of Artificial Peptide Libraries: Detection and Characterization of Target‐Amplified PD‐1 Inhibitors

We report a quantitative proteomics data analysis pipeline, which coupled to protein-directed dynamic combinatorial chemistry (DDC) experiments, enables the rapid discovery and direct characterization of protein-protein interaction (PPI) modulators. A low-affinity PD-1 binder was incubated with a library of >100 D-peptides under thiol-exchange favoring conditions, in the presence of the target protein PD-1, and we determined the S-linked dimeric species that resulted, amplified in the protein samples versus the controls. We chemically synthesized the target dimer candidates and validated them by thermophoresis binding and protein-protein interaction assays. The results provide a proof-of-concept for using this strategy in the high-throughput search of improved drug-like peptide binders that block therapeutically relevant protein-protein interactions.

Identification of key candidate genes and biological pathways in the synovial tissue of patients with rheumatoid arthritis.

The aim of the present study was to identify potential key candidate genes and mechanisms associated with rheumatoid arthritis (RA). Gene expression data from GSE55235, GSE55457 and GSE1919 datasets were downloaded from the Gene Expression Omnibus database. These datasets comprised 78 tissue samples collectively, including 25 healthy synovial membrane samples and 28 RA synovial membrane samples, whilst the 25 osteoarthritis (OA) samples were not included in the analysis. The differentially expressed genes (DEGs) between the two types of samples were identified with the Linear Models for Microarray Analysis package in R. Gene Ontology (GO) functional term and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses were also performed. In addition, Protein-Protein Interaction (PPI) network and module analyses were visualized using Cytoscape, and subsequent hub gene identification as well as GO and KEGG enrichment analyses of the modules was performed. Finally, reverse transcription-quantitative PCR (RT-qPCR) was used to validate the expression of the DEGs identified by GO and KEGG analysis in vitro. The analysis identified 491 DEGs, including 289 upregulated and 202 downregulated genes, which were mainly enriched in the following pathways: ‘Cytokine-cytokine receptor interaction’, ‘Rheumatoid arthritis’, ‘Chemokine signaling pathway’, ‘Intestinal immune network for IgA production’ and ‘Primary immunodeficiency’. The top 10 hub genes identified from the PPI network were IL-6, protein tyrosine phosphatase receptor type C, VEGFA, CD86, EGFR, C-X-C chemokine receptor type 4, matrix metalloproteinase 9, CC-chemokine receptor type (CCR)7, CCR5 and selectin L. KEGG signaling pathway enrichment analysis of the top two modules identified from the PPI network revealed that the genes in Module 1 were mainly enriched in the ‘Cytokine-cytokine receptor interaction’ and ‘Chemokine signaling pathway’, whereas analysis of Module 2 revealed that the genes were mainly enriched in ‘Primary immunodeficiency’ and ‘Cytokine-cytokine receptor interaction’. Finally, the results of the RT-qPCR and western blot analysis demonstrated that the expression levels of inflammation and NF-κB signaling pathway-related mRNAs were significantly upregulated following lipopolysaccharide stimulation. In conclusion, the findings of the present study identified key genes and signaling pathways associated with RA, which may improve the current understanding of the molecular mechanisms underlying its development and progression. The identified hub genes may also be used as potential targets for RA diagnosis and treatment.

Targeted Protein Degradation and Regulation with Molecular Glue: Past and Recent Discoveries.

The evolution in research and clinical settings of targeted therapies has been inspired by the progress of cancer chemotherapy to use small molecules and monoclonal antibodies for targeting specific disease-associated genes and proteins for noninfectious chronic diseases. In addition to conventional protein inhibition and activation strategies as drug discovery modalities, new methods of targeted protein degradation and regulation using molecular glues have become an attractive approach for drug discovery. Mechanistically, molecular glues trigger interactions between the proteins that originally did not interact by forming ternary complexes as protein-protein interaction (PPI) modulators. New molecular glues and their mechanisms of action have been actively investigated in the past decades. An immunomodulatory imide drug, thalidomide, and its derivatives have been used in the clinic and are a class of molecular glue that induces degradation of several neo-substrates. In this review, we summarize the development of molecular glues and share our opinions on the identification of novel molecular glues in an attempt to promote the concept and inspire further investigations.

In silico identification of small molecule protein-protein interaction inhibitors: targeting hotspot regions at the interface of MXRA8 and CHIKV envelope protein

Chikungunya virus (CHIKV) is an arthritogenic arbovirus responsible for re-emerging epidemics of Chikungunya fever around the world for centuries. Chikungunya has become endemic in Africa, Southeast Asia, the Indian subcontinent, and subtropical regions of the Americas. The unavailability of antiviral therapy or vaccine against the CHIKV and its continuous re-emergence demands an urgent need to develop potential candidate therapeutics. CHIKV entry into the host cell is mediated by its envelope proteins engaging the cellular receptor MXRA8 to invade the susceptible cells. We report here two essential target binding sites at the CHIKV E1-E2 proteins by identifying hotspot regions at the E1-E2-MXRA8 binding interface. Further, we employed high throughput computational screening to identify potential small molecule protein-protein interaction (PPI) modulators which could effectively bind at the identified target sites. Molecular dynamics simulations and binding free energy calculations confirmed the stability of three compounds, viz., ZINC299817498, ZINC584908978, and LAS52155651, at both the predicted interface binding sites. The polar and charged residues at the interface were responsible for energetically holding the ligands at the binding sites. Altogether, our findings suggest that the predicted target binding sites at the E1-E2 dimer could be essential to block the receptor interaction as well as the fusion process of the CHIKV particles. Thus, we identified a few small molecule PPI inhibitors with great potential to block the E1-E2-MXRA8 interaction and act as promising templates to design anti-CHIKV drugs. Communicated by Ramaswamy H. Sarma

A Bionic-Homodimerization Strategy for Optimizing Modulators of Protein-Protein Interactions: From Statistical Mechanics Theory to Potential Clinical Translation.

Emerging protein–protein interaction (PPI) modulators have brought out exciting ability as therapeutics in human diseases, but its clinical translation has been greatly hampered by the limited affinity. Inspired by the homodimerize structure of antibody, the homodimerization contributes hugely to generating the optimized affinity is conjectured. Herein, a statistical‐mechanics‐theory‐guided method is established to quantize the affinity of ligands with different topologies through analyzing the change of enthalpy and the loss of translational and rotational entropies. A peptide modulator for p53‐MDM2 termed CPAP is used to homodimerize connecting, and this simple homodimerization can significantly increase the affinity. To realize the cellular internalization and tumor accumulation, DimerCPAP and MonoCPAP are nanoengineered into gold(I)‐CPAP supermolecule by the aurophilic interaction‐driven self‐assembly. Nano‐DimerCPAP potently suppressed tumor growth in lung cancer allograft model and a patient‐derived xenograft model in more action than Nano‐MonoCPAP, while keeping a favorable drug safety profile. This work not only presents a physico‐mechanical method for calculating the affinity of PPI modulators, but also provides a simple yet robust homodimerization strategy to optimize the affinity of PPI modulators.

Identification of biomarkers and regulatory networks for cartilage damage patients.

The aim of this study was to mine cartilage damage and regeneration-related biomarkers and identify the gene regulatory networks of cartilage damage. A gene expression data set (GSE129147) containing damaged and control samples collected from the knee of the same patients was employed. R package limma was used to identify differentially expressed genes (DEGs), and clusterProfiler was performed for the GO and KEGG functional enrichment analysis. Cytoscape plug-ins of CytoHubba and MCODE were applied to investigate protein-protein interaction (PPI) network, modules, and hub genes. We identified 422 DEGs that were involved in skeletal system development, bone development, ossification, mesenchyme development, mesenchymal cell differentiation, connective tissue development, osteoblast differentiation, and extracellular matrix. We dug out 30 hub genes, identified three PPI modules, and constructed a miRNA regulatory network for DEGs. The miRNAs of the DEGs were predicted by miRNet, and the miRNA-mRNA network displayed some important miRNAs such as miR-335-5p, miR-92a-3p, and miR-98-5p. Collectively, these results have the potential to clarify the mechanism of cartilage damage and to assist us in discovering the damage and repair-related biomarkers.

Investigating the molecular mechanism of a pH sensitive proteome

Proteostasis (protein homeostasis) represents a network of regulatory mechanisms that aid the synthesis, folding, remodeling, and degradation of proteins within a cell. The abundance of some proteins has been shown to be regulated by intracellular pH (pHi) via pH-dependent modulation of protein-protein interactions in the ubiquitin-proteosome system. For example, β-catenin is a recently identified pH-sensor that has increased binding to its bona fide E3-ubiquitin ligase (β-TrCP) at high pH, resulting in decreased protein abundance of β-catenin.

Modeling the MreB-CbtA Interaction to Facilitate the Prediction and Design of Candidate Antibacterial Peptides.

Protein-protein interactions (PPIs) have emerged as promising targets for PPI modulators as alternative drugs because they are essential for most biochemical processes in living organisms. In recent years, a spotlight has been put on the development of peptide-based PPI inhibitors as the next-generation therapeutics to combat antimicrobial resistance taking cognizance of protein-based PPI-modulators that interact with target proteins to inhibit function. Although protein-based PPI inhibitors are not effective therapeutic agents because of their high molecular weights, they could serve as sources for peptide-based pharmaceutics if the target-inhibitor complex is accessible and well characterized. The Escherichia coli (E. coli) toxin protein, CbtA, has been identified as a protein-based PPI modulator that binds to the bacterial actin homolog MreB leading to the perturbation of its polymerization dynamics; and consequently has been suggested to have antibacterial properties. Unfortunately, however, the three-dimensional structures of CbtA and the MreB-CbtA complex are currently not available to facilitate the optimization process of the pharmacological properties of CbtA. In this study, computer modeling strategies were used to predict key MreB-CbtA interactions to facilitate the design of antiMreB peptide candidates. A model of the E. coli CbtA was built using the trRosetta software and its stability was assessed through molecular dynamics (MD) simulations. The modeling and simulations data pointed to a model with reasonable quality and stability. Also, the HADDOCK software was used to predict a possible MreB-CbtA complex, which was characterized through MD simulations and compared with MreB-MreB dimmer. The results suggest that CbtA inhibits MreB through the competitive mechanism whereby CbtA competes with MreB monomers for the interprotofilament interface leading to interference with double protofilament formation. Additionally, by using the antiBP software to predict antibacterial peptides in CbtA, and the MreB-CbtA complex as the reference structure to determine important interactions and contacts, candidate antiMreB peptides were suggested. The peptide sequences could be useful in a rational antimicrobial peptide hybridization strategy to design novel antibiotics. All-inclusive, the data reveal the molecular basis of MreB inhibition by CbtA and can be incorporated in the design/development of the next-generation antibacterial peptides targeting MreB.

Cooperativity as quantification and optimization paradigm for nuclear receptor modulators.

Nuclear Receptors (NRs) are highly relevant drug targets, for which small molecule modulation goes beyond a simple ligand/receptor interaction. NR–ligands modulate Protein–Protein Interactions (PPIs) with coregulator proteins. Here we bring forward a cooperativity mechanism for small molecule modulation of NR PPIs, using the Peroxisome Proliferator Activated Receptor γ (PPARγ), which describes NR–ligands as allosteric molecular glues. The cooperativity framework uses a thermodynamic model based on three-body binding events, to dissect and quantify reciprocal effects of NR–coregulator binding (KID) and NR–ligand binding (KIID), jointly recapitulated in the cooperativity factor (α) for each specific ternary ligand·NR·coregulator complex formation. These fundamental thermodynamic parameters allow for a conceptually new way of thinking about structure–activity-relationships for NR–ligands and can steer NR modulator discovery and optimization via a completely novel approach.

Modulating Angiogenesis by Proteomimetics of Vascular Endothelial Growth Factor.

Angiogenesis, formation of new blood vessels from the existing vascular network, is a hallmark of cancer cells that leads to tumor vascular proliferation and metastasis. This process is mediated through the binding interaction of VEGF-A with VEGF receptors. However, the balance between pro-angiogenic and anti-angiogenic effect after ligand binding yet remains elusive and is therefore challenging to manipulate. To interrogate this interaction, herein we designed a few sulfono-γ-AA peptide based helical peptidomimetics that could effectively mimic a key binding interface on VEGF (helix-α1) for VEGFR recognition. Intriguingly, although both sulfono-γ-AA peptide sequences V2 and V3 bound to VEGF receptors tightly, in vitro angiogenesis assays demonstrated that V3 potently inhibited angiogenesis, whereas V2 activated angiogenesis effectively instead. Our findings suggested that this distinct modulation of angiogenesis might be due to the result of selective binding of V2 to VEGFR-1 and V3 to VEGFR-2, respectively. These molecules thus provide us a key to switch the angiogenic signaling, a biological process that balances the effects of pro-angiogenic and anti-angiogenic factors, where imbalances lead to several diseases including cancer. In addition, both V2 and V3 exhibited remarkable stability toward proteolytic hydrolysis, suggesting that V2 and V3 are promising therapeutic agents for the intervention of disease conditions arising due to angiogenic imbalances and could also be used as novel molecular switching probes to interrogate the mechanism of VEGFR signaling. The findings also further demonstrated the potential of sulfono-γ-AA peptides to mimic the α-helical domain for protein recognition and modulation of protein-protein interactions.

Fragment-based exploration of the 14-3-3/Amot-p130 interface.

The modulation of protein-protein interactions (PPIs) has developed into a well-established field of drug discovery. Despite the advances achieved in the field, many PPIs are still deemed as ‘undruggable’ targets and the design of PPIs stabilizers remains a significant challenge. The application of fragment-based methods for the identification of drug leads and to evaluate the ‘tractability’ of the desired protein target has seen a remarkable development in recent years. In this study, we explore the molecular characteristics of the 14-3-3/Amot-p130 PPI and the conceptual possibility of targeting this interface using X-ray crystallography fragment-based screening. We report the first structural elucidation of the 14-3-3 binding motif of Amot-p130 and the characterization of the binding mode and affinities involved. We made use of fragments to probe the ‘ligandability’ of the 14-3-3/Amot-p130 composite binding pocket. Here we disclose initial hits with promising stabilizing activity and an early-stage selectivity toward the Amot-p130 motifs over other representatives 14-3-3 partners. Our findings highlight the potential of using fragments to characterize and explore proteins' surfaces and might provide a starting point toward the development of small molecules capable of acting as molecular glues.

Long Non-Coding RNA- Associated Competing Endogenous RNA Axes in T-Cells in Multiple Sclerosis.

Multiple sclerosis (MS) is an immune-mediated demyelinating and degenerative disease with unknown etiology. Inappropriate response of T-cells to myelin antigens has an essential role in the pathophysiology of MS. The clinical and pathophysiological complications of MS necessitate identification of potential molecular targets to understand the pathogenic events of MS. Since the functions and regulatory mechanisms of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in MS are yet uncertain, we conducted a bioinformatics analysis to explain the lncRNA-associated ceRNA axes to clarify molecular regulatory mechanisms involved in T-cells responses in MS. Two microarray datasets of peripheral blood T-cell from subjects with relapsing-remitting MS and matched controls containing data about miRNAs (GSE43590), mRNAs and lncRNAs (GSE43591) were downloaded from the Gene Expression Omnibus database. Differentially expressed miRNAs (DEmiRNAs), mRNAs (DEmRNAs), and lncRNAs (DElncRNAs) were identified by the limma package of the R software. Protein-protein interaction (PPI) network and module were developed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the Molecular Complex Detection (MCODE) Cytoscape plugin, respectively. Using DIANA-LncBase and miRTarBase, the lncRNA-associated ceRNA axes was constructed. We conducted a Pearson correlation analysis and selected the positive correlations among the lncRNAs and mRNAs in the ceRNA axes. Lastly, DEmRNAs pathway enrichment was conducted by the Enrichr tool. A ceRNA regulatory relationship among Small nucleolar RNA host gene 1 (SNHG1), hsa-miR-197-3p, YOD1 deubiquitinase (YOD1) and zinc finger protein 101 (ZNF101) and downstream connected genes was identified. Pathway enrichment analysis showed that DEmRNAs were enriched in “Protein processing in endoplasmic reticulum” and “Herpes simplex virus 1 infection” pathways. To our knowledge, this would be the first report of a possible role of SNHG1/hsa-miR-197-3p/YOD1/ZNF101 axes in the pathogenesis of MS. This research remarks on the significance of ceRNAs and prepares new perceptions for discovering the molecular mechanism of MS.

Abstract P098: A chemoproteomic platform for identifying small-molecule modulators of protein-protein interactions, discovering new cancer targets, and revealing previously unknown targets for well-known drugs

Abstract BridGene Biosciences has developed a novel chemoproteomic platform IMTACTM (Isobaric Mass-Tagged Affinity Characterization) for discovering small-molecule inhibitors of protein-protein interactions, disclosing new cancer targets, and identifying previously unknown targets for known drugs. The key components of the IMTACTM platform include a unique library of drug-like covalent small molecules containing an alkyne tag, live-cell screening, and quantitative mass spec analysis. The IMTACTM analysis begins with treating live cells with probes from the covalent library, proceeds with enrichment of the probe-modified proteins, and then employs Mass Spec to identify the probe-modified proteins and determine the relative binding affinity. This powerful chemoproteomic platform enables systematic mapping of the direct interactions of small-molecules across the proteome. A prominent application of BridGene’s IMTACTM platform is to identify hits for “hard to drug” targets from live-cell screening. IMTAC TM screening is well suited for identifying drug-like ligands for such hard-to-drug targets, including those with shallow binding pockets or temporary pockets formed under certain cellular settings. Lead optimization can be immediately initiated after confirmation of the ligand’s binding to relevant targets. Using the IMTACTM platform, BridGene has discovered small-molecule ligands for a number of “hard-to-drug” targets as well as oncogenic mutants, including GTPases (e.g. RhoA), transcription factors (e.g. TEAD), splicing factors (e.g. SRSF1), epigenetic modulators (e.g. WDR5), E3 ligases, etc. The potency of the hits ranges from low nanomolar to micromolar. Because of the IMTACTM platform’s proteome-wide profiling capability, BridGene has also, for the first time, revealed new targets for some well-known drugs. Combining IMTAC TM screening with phenotypic screening, BridGene is also discovering new/unknown targets that drive certain disease phenotypic changes. IMTACTM is a novel platform to discover small molecule drugs for hard-to-drug targets. It allows the mapping of targets for small molecules on a proteome-wide scale, which can provide comprehensive selectivity information to facilitate lead optimization and lower off-target toxicity. IMTACTM has the potential to redefine precision medicine, discover new drugs and new targets, and identify new indications of known drugs. Citation Format: Cindy Huang, Vivian Zhang, Ning Deng, Irene Yuan, Linda Pullan, C. Glenn Begley, Ping Cao. A chemoproteomic platform for identifying small-molecule modulators of protein-protein interactions, discovering new cancer targets, and revealing previously unknown targets for well-known drugs [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P098.

The Identification of Key Genes and Biological Pathways in Heart Failure by Integrated Bioinformatics Analysis.

Purpose Heart failure (HF) is a clinical syndrome caused by ventricular insufficiency. In order to further explore the biomarkers related to HF, we apply the high-throughput database. Materials and Methods The GSE21610 was applied for the differentially expressed gene (DEG) analysis. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The Gene Set Enrichment Analysis (GSEA) was used for gene expression profile GSE21610. The Protein-Protein Interaction (PPI) network and modules were also constructed for research. These hub gene function pathways were estimated in HF progression. Result We have identified 434 DEGs in total, including 304 downregulated DEGs and 130 upregulated DEGs. GO and KEGG illustrated that DEGs in HF were significantly enriched in G protein-coupled receptor binding, peroxisome, and cAMP signaling pathway. GSEA results showed gene set GSE21610 was gathered in lipid digestion, defense response to fungus, and intestinal lipid absorption. Finally, through analyzing the PPI network, we screened hub genes CDH1, TFRC, CCL2, BUB1B, and CD19 by the Cytoscape software. Conclusion This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to HF. These analysis results provide us with new ideas for finding biomarkers and treatment methods for HF.

Potential Molecular Targets of Tenofovir Disoproxil Fumarate for Alleviating Chronic Liver Diseases via a Non-Antiviral Effect in a Normal Mouse Model.

Accumulating evidence suggests that tenofovir disoproxil fumarate (TDF) can attenuate liver fibrosis directly, the mechanism of which, however, has not been fully elucidated, and there is a paucity of data concerning whether TDF can also mitigate other chronic liver diseases (CLDs). We aimed to identify the molecular targets and potential mechanism of TDF itself in ameliorating CLDs. RNA-sequencing was performed on mouse liver tissues treated with TDF or normal saline. Then the differentially expressed genes (DEGs) were screened, and enrichment analyses of the function and signaling pathways of DEGs were performed with Database for Annotation, Visualization, and Integrated Discovery (DAVID) and Metascape. Next, protein-protein interaction (PPI) networks were constructed and module analyses were utilized to identify significant genes. Subsequently, the DisGeNET platform was used to identify the potential target genes of TDF in mitigating these diseases. Finally, prediction of the transcription factors (TFs) and microRNAs (miRNAs) of the target genes was done to conjecture the underlying mechanism by which TDF relieved CLDs. As a result, a total of 854 DEGs were identified, and the DEGs were involved mainly in “immunity,” “inflammation,” and “metabolism” processes. In addition, 50 significant genes were obtained via PPI construction and module analyses. Furthermore, by means of DisGeNET, 19 genes (Adra2a, Cxcl1, Itgam, Cxcl2, Ccr1, Ccl5, Cxcl5, Fabp5, Sell, Lilr4b, Ccr2, Tlr2, Lilrb4a, Tnf, Itgb2, Lgals3, Cxcr4, Sucnr1, and Mme) were identified to be associated with nine CLDs. Finally, 34 miRNAs (especially mmu-miR-155-5p) and 12 TFs (especially Nfkb1) were predicted to be upstream of the nine target genes (Cxcl1, Cxcl2, Ccl5, Ccr2, Sell, Tlr2, Tnf, Cxcr4, and Mme) of TDF in ameliorating CLDs. In conclusion, our study suggests that TDF have the potential to ameliorate CLDs independently of its antiviral activity by affecting the expression of genes involved in hepatic immune, inflammatory, and metabolic processes via mmu-miR-155-5p-NF-κB signaling. These findings provided prima facie evidence for using TDF in CHB patients with concurrent CLDs.

Between Interactions and Aggregates: The PolyQ Balance.

Polyglutamine (polyQ) regions are highly abundant consecutive runs of glutamine residues. They have been generally studied in relation to the so-called polyQ-associated diseases, characterized by protein aggregation caused by the expansion of the polyQ tract via a CAG-slippage mechanism. However, more than 4,800 human proteins contain a polyQ, and only nine of these regions are known to be associated with disease. Computational sequence studies and experimental structure determinations are completing a more interesting picture in which polyQ emerge as a motif for modulation of protein–protein interactions. But long polyQ regions may lead to an excess of interactions, and produce aggregates. Within this mechanistic perspective of polyQ function and malfunction, we discuss polyQ definition and properties such as variable codon usage, sequence and context structure imposition, functional relevance, evolutionary patterns in species-centered analyses, and open resources.

Computationally Reconstructed Interactome of Bradyrhizobium diazoefficiens USDA110 Reveals Novel Functional Modules and Protein Hubs for Symbiotic Nitrogen Fixation.

Symbiotic nitrogen fixation is an important part of the nitrogen biogeochemical cycles and the main nitrogen source of the biosphere. As a classical model system for symbiotic nitrogen fixation, rhizobium-legume systems have been studied elaborately for decades. Details about the molecular mechanisms of the communication and coordination between rhizobia and host plants is becoming clearer. For more systematic insights, there is an increasing demand for new studies integrating multiomics information. Here, we present a comprehensive computational framework integrating the reconstructed protein interactome of B. diazoefficiens USDA110 with its transcriptome and proteome data to study the complex protein-protein interaction (PPI) network involved in the symbiosis system. We reconstructed the interactome of B. diazoefficiens USDA110 by computational approaches. Based on the comparison of interactomes between B. diazoefficiens USDA110 and other rhizobia, we inferred that the slow growth of B. diazoefficiens USDA110 may be due to the requirement of more protein modifications, and we further identified 36 conserved functional PPI modules. Integrated with transcriptome and proteome data, interactomes representing free-living cell and symbiotic nitrogen-fixing (SNF) bacteroid were obtained. Based on the SNF interactome, a core-sub-PPI-network for symbiotic nitrogen fixation was determined and nine novel functional modules and eleven key protein hubs playing key roles in symbiosis were identified. The reconstructed interactome of B. diazoefficiens USDA110 may serve as a valuable reference for studying the mechanism underlying the SNF system of rhizobia and legumes.