Role of chemokine receptor 2 in rheumatoid arthritis: A research update

Monocyte/macrophage-mediated venous thrombus resolution

Venous thromboembolism (VTE) poses a notable risk of morbidity and mortality. The natural resolution of the venous thrombus might be a potential alternative treatment strategy for VTE. Monocytes/macrophages merge as pivotal cell types in the gradual resolution of the thrombus. In this review, the vital role of macrophages in inducing inflammatory response, augmenting neovascularization, and facilitating the degradation of fibrin and collagen during thrombus resolution was described. The two phenotypes of macrophages involved in thrombus resolution and their dual functions were discussed. Macrophages expressing various factors, including cytokines and their receptors, adhesion molecules, chemokine receptors, vascular endothelial growth factor receptors, profibrinolytic- or antifibrinolytic-related enzymes, and other elements, are explored for their potential to promote or attenuate thrombus resolution. Furthermore, this review provides a comprehensive summary of new and promising therapeutic candidate drugs associated with monocytes/macrophages that have been demonstrated to promote or impair thrombus resolution. However, further clinical trials are essential to validate their efficacy in VTE therapy.

PTEN: an emerging target in rheumatoid arthritis?

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is a critical tumor suppressor protein that regulates various biological processes such as cell proliferation, apoptosis, and inflammatory responses by controlling the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (PI3K/AKT) signaling pathway. PTEN plays a crucial role in the pathogenesis of rheumatoid arthritis (RA). Loss of PTEN may contribute to survival, proliferation, and pro-inflammatory cytokine release of fibroblast-like synoviocytes (FLS). Also, persistent PI3K signaling increases myeloid cells' osteoclastic potential, enhancing localized bone destruction. Recent studies have shown that the expression of PTEN protein in the synovial lining of RA patients with aggressive FLS is minimal. Experimental upregulation of PTEN protein expression could reduce the damage caused by RA. Nonetheless, a complete comprehension of aberrant PTEN drives RA progression and its interactions with other crucial molecules remains elusive. This review is dedicated to promoting a thorough understanding of the signaling mechanisms of aberrant PTEN in RA and aims to furnish pertinent theoretical support for forthcoming endeavors in both basic and clinical research within this domain.

Metabolomic Profiling of Leptadenia reticulata: Unveiling Therapeutic Potential for Inflammatory Diseases through Network Pharmacology and Docking Studies

Medicinal plants have been utilized since ancient times for their therapeutic properties, offering potential solutions for various ailments, including epidemics. Among these, Leptadenia reticulata, a member of the Asclepiadaceae family, has been traditionally employed to address numerous conditions such as diarrhea, cancer, and fever. In this study, employing HR-LCMS/MS(Q-TOF) analysis, we identified 113 compounds from the methanolic extract of L. reticulata. Utilizing Lipinski's rule of five, we evaluated the drug-likeness of these compounds using SwissADME and ProTox II. SwissTarget Prediction facilitated the identification of potential inflammatory targets, and these targets were discerned through the Genecard, TTD, and CTD databases. A network pharmacology analysis unveiled hub proteins including CCR2, ICAM1, KIT, MPO, NOS2, and STAT3. Molecular docking studies identified various constituents of L. reticulata, exhibiting high binding affinity scores. Further investigations involving in vivo testing and genomic analyses of metabolite-encoding genes will be pivotal in developing efficacious natural-source drugs. Additionally, the potential of molecular dynamics simulations warrants exploration, offering insights into the dynamic behavior of protein-compound interactions and guiding the design of novel therapeutics.

Molecular determinants of antagonist interactions with chemokine receptors CCR2 and CCR5

By driving monocyte chemotaxis, the chemokine receptor CCR2 shapes inflammatory responses and the formation of tumor microenvironments. This makes it a promising target in inflammation and immuno-oncology; however, despite extensive efforts, there are no FDA-approved CCR2-targeting therapeutics. Cited challenges include the redundancy of the chemokine system, suboptimal properties of compound candidates, and species differences that confound the translation of results from animals to humans. Structure-based drug design can rationalize and accelerate the discovery and optimization of CCR2 antagonists to address these challenges. The prerequisites for such efforts include an atomic-level understanding of the molecular determinants of action of existing antagonists. In this study, using molecular docking and artificial-intelligence-powered compound library screening, we uncover the structural principles of small molecule antagonism and selectivity towards CCR2 and its sister receptor CCR5. CCR2 orthosteric inhibitors are shown to universally occupy an inactive-state-specific tunnel between receptor helices 1 and 7; we also discover an unexpected role for an extra-helical groove accessible through this tunnel, suggesting its potential as a new targetable interface for CCR2 and CCR5 modulation. By contrast, only shape complementarity and limited helix 8 hydrogen bonding govern the binding of various chemotypes of allosteric antagonists. CCR2 residues S1012.63 and V2446.36 are implicated as determinants of CCR2/CCR5 and human/mouse orthosteric and allosteric antagonist selectivity, respectively, and the role of S1012.63 is corroborated through experimental gain-of-function mutagenesis. We establish a critical role of induced fit in antagonist recognition, reveal strong chemotype selectivity of existing structures, and demonstrate the high predictive potential of a new deep-learning-based compound scoring function. Finally, this study expands the available CCR2 structural landscape with computationally generated chemotype-specific models well-suited for structure-based antagonist design.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Keras/TensorFlow in Drug Design for Immunity Disorders

Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3, and one CXC receptor, CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier but as an efficient multi-class classifier that can discard not only inactive compounds but also low- or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 compounds were among the top predicted compounds; thus, the benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.

Effect of NLRP3 inflammasome genes polymorphism on disease susceptibility and response to TNF-α inhibitors in Iraqi patients with rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a genetically predisposed, systemic, chronic, inflammatory disease. Immune system dysregulation and inherited susceptibility polymorphisms suggest that this type of variation is functional and may help predict disease susceptibility and develop new therapeutic strategies. Anti-TNF-alpha (TNF-α) drugs are highly effective RA treatments, but not all patients respond the same way. It's important to figure out whether RA risk alleles can identify and predict anti-TNF-α-responsiveness in RA patients.

Aims of the study

Examine the function of the NLR family pyrin domain containing 3 (NLRP3) and caspase recruitment domain family member 8 (CARD8) genes polymorphisms and their morbid genotypes and alleles in RA patients and apparently healthy controls. In addition, their role in disease susceptibility, severity, and response to anti-TNF-α therapy. Also, examine how single nucleotide polymorphisms (SNPs) affect serum levels of pro-inflammatory cytokines like TNF-α and interleukin (IL)-1β.

Materials and methods

100 RA patients (88 females, 12 males) and 100 apparently healthy people (86 females, 14 males) were examined. To measure serum TNF-α and IL-1β, Elabscience sandwich ELISA kits were used. Iraq Biotech, Turkey DNA extraction kit was used to extract genomic DNA from whole blood. CARD8 (rs2043211) and NLRP3 (rs4612666) were genotyped using Agilent, AriaMx, USA, through Tri-Plex SYBR Green-based real-time PCR allelic discrimination assays. Geneious software, version 2019.2.2, used to design primers from published sequences (GenBank accession no. GCA 009914755.1). Primer specificity was determined by NCBI's BLAST.

Results

Study found that there is association between cytokines serum level and 28-joints disease activity score (DAS-28). The level of TNF-α increases with the higher DAS-28 (r² = 0.45, P < 0.0001). Also, IL- 1β level increases with higher DAS-28 (r² = 0.51, P < 0.0001). There were no statistically significant variations between patients with RA and the control group in the distribution of CARD8 SNP rs2043211 and NLRP3 SNP rs4612666 genotypes (P = 0.17 and 0.08 respectively) as well their alleles (P = 0.059 and 0.879 respectively). CARD8 (rs2043211) TT genotype was more frequent in patients with higher DAS-28 (P < 0.0001) and higher TNF-α and IL-1β serum levels (P < 0.0001 for both). Also, NLRP3 (rs4612666) TT genotype was more frequent in patients with higher DAS-28 (P < 0.0001) and higher TNF-α and IL- 1β serum levels (P < 0.0001 for both). Interestingly, this study revealed that CARD8 (rs2043211) and NLRP3 (rs4612666) variant genotypes are associated with lower response to anti-TNF-α drugs.

Conclusions

Serum TNF-α and IL-1β correlate with DAS-28 and disease activity. Non-responders have elevated TNF-α and IL-1β. CARD8 rs2043211 and NLRP3 rs4612666 variant polymorphisms are associated with high serum TNF-α and IL-1β, active disease course, poor disease outcomes, and low response to anti-TNF-α therapy.