Citation: Samantha B. Gacasan, Daniel L. Baker, Abby L. Parrill. G protein-coupled receptors: the evolution of structural insight[J]. AIMS Biophysics, 2017, 4(3): 491-527. doi: 10.3934/biophy.2017.3.491
| [1] | Martin Burger, Marco Di Francesco . Large time behavior of nonlocal aggregation models with nonlinear diffusion. Networks and Heterogeneous Media, 2008, 3(4): 749-785. doi: 10.3934/nhm.2008.3.749 |
| [2] | Iryna Pankratova, Andrey Piatnitski . Homogenization of convection-diffusion equation in infinite cylinder. Networks and Heterogeneous Media, 2011, 6(1): 111-126. doi: 10.3934/nhm.2011.6.111 |
| [3] | Alexander Mielke, Sina Reichelt, Marita Thomas . Two-scale homogenization of nonlinear reaction-diffusion systems with slow diffusion. Networks and Heterogeneous Media, 2014, 9(2): 353-382. doi: 10.3934/nhm.2014.9.353 |
| [4] | Patrick Henning . Convergence of MsFEM approximations for elliptic, non-periodic homogenization problems. Networks and Heterogeneous Media, 2012, 7(3): 503-524. doi: 10.3934/nhm.2012.7.503 |
| [5] | Feiyang Peng, Yanbin Tang . Inverse problem of determining diffusion matrix between different structures for time fractional diffusion equation. Networks and Heterogeneous Media, 2024, 19(1): 291-304. doi: 10.3934/nhm.2024013 |
| [6] | Elisabeth Logak, Isabelle Passat . An epidemic model with nonlocal diffusion on networks. Networks and Heterogeneous Media, 2016, 11(4): 693-719. doi: 10.3934/nhm.2016014 |
| [7] | Patrick Henning, Mario Ohlberger . The heterogeneous multiscale finite element method for advection-diffusion problems with rapidly oscillating coefficients and large expected drift. Networks and Heterogeneous Media, 2010, 5(4): 711-744. doi: 10.3934/nhm.2010.5.711 |
| [8] | José Antonio Carrillo, Yingping Peng, Aneta Wróblewska-Kamińska . Relative entropy method for the relaxation limit of hydrodynamic models. Networks and Heterogeneous Media, 2020, 15(3): 369-387. doi: 10.3934/nhm.2020023 |
| [9] | Martin Heida, Benedikt Jahnel, Anh Duc Vu . Regularized homogenization on irregularly perforated domains. Networks and Heterogeneous Media, 2025, 20(1): 165-212. doi: 10.3934/nhm.2025010 |
| [10] | Mogtaba Mohammed, Mamadou Sango . Homogenization of nonlinear hyperbolic stochastic partial differential equations with nonlinear damping and forcing. Networks and Heterogeneous Media, 2019, 14(2): 341-369. doi: 10.3934/nhm.2019014 |
NICM: nonischaemic cardiomyopathy; PBMCs: peripheral blood mononuclear cells; DEGs: differentially expressed genes; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein-protein interaction; HF: heart failure; IL-6:interleukin-6; IFN-γ: interferon-gamma; GEO: Gene Expression Omnibus; BP: biological process; MF: molecular function; CC: cellular component; NCBI: National Center for Biotechnology Information; CT: cycle threshold; IBD: inflammatory bowel disease
Heart failure (HF), a chronic cardiac syndrome, could give rise to a mean survival of 5 years once it is diagnosed, placing millions of individuals globally at risk of death at present, despite the vital progress that has been made in its diagnosis and treatment [1,2]. Nonischaemic cardiomyopathy (NICM) is one of the most common causes of HF [3]. At present, patients diagnosed with HF due to NICM, especially if conventional therapy shows no effect for them, require further diagnostic and therapeutic procedures involving endomyocardial biopsy. However, endomyocardial biopsy is invasive and thus may easily cause adverse complications [4]. Hence, it is of great necessity to establish a series of non-invasive approaches that could be operated by routine assays in the clinic.
Increasing evidence has demonstrated that dysregulated levels of circulating pro-inflammatory cytokines in peripheral blood mononuclear cells (PBMCs) from patients with NICM exhibit a strong relationship with the disease severity and/or prognosis [5]. Pressure and volume overload trigger profound alterations in cardiac extracellular matrix components, which eventually induce cardiac remodelling by affecting the removal of pro-inflammatory cytokines [6]. Pro-inflammatory monocytes and T-cell infiltration are also observed when myocardial dysfunction and cardiac remodelling occur [7]. These immune cells could mediate hypertension, cardiac hypertrophy and fibrosis by releasing certain cytokines, such as interleukin-6 (IL-6) and interferon-gamma (IFN-γ) [8,9]. PBMCs are a group of immune cells, mainly including T cells, B cells and monocytes/macrophages, the profiling of which has drawn particular attention in a number of diseases due to the convenience and noninvasiveness of PBMCs assessments in recent years [10,11].
Bioinformatic analysis and gene expression profiling analysis could help us screen molecular markers between patients and healthy individuals, thereby providing novel insights into diseases at multiple levels ranging from the alterations of copy number at the genome level to gene expression at the transcriptome level and even epigenetic alterations [12]. In this study, we sought to determine the differentially expressed genes (DEGs) in PBMCs between patients with NICM and healthy controls based on the gene expression profiles from GSE9128, which was downloaded from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). Meanwhile, we analysed gene ontology (GO) involving biological process (BP), molecular function (MF), cellular component (CC), and the KEGG pathways of these DEGs. Furthermore, we constructed a protein-protein interaction (PPI) network of these DEGs and identified the top 15 hub genes with a high degree of connectivity. Finally, we detected the mRNA levels of the top 5 upregulated and downregulated DEGs in PBMCs from a mouse model induced by isoproterenol.
Gene expression profiles from the GSE9128 dataset were downloaded from the National Center for Biotechnology Information (NCBI) GEO database, which is free and publicly available [13]. The dataset, based on the GPL96 platform [HG-U133A] Affymetrix Human Genome U133A Array, contains samples of PBMCs from 4 NICM patients and 3 normal individuals. Meanwhile, the Series Matrix File of GSE9128 from the GEO database in PubMed was also downloaded. The peripheral blood samples in this dataset were from Caucasian patients with NICM and healthy controls. Chronic HF was classified according to New York Heart Association (NYHA) functional class Ⅲ/Ⅳ as lasting for more than 6 months; the left ventricular ejection fraction (LVEF) of these patients was less than 36%. All of these NICM patients had no history of angiographically abnormal coronary arteries and myocardial infarction. Gene expression profiling was performed by Affymetrix microarrays.
We screened the DEGs between NICM and healthy samples using GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r), an interactive analysis tool for the GEO database based on the R language. We modified the DEGs as differentially expressed with a logFC < 1 (upregulated genes) or logFC > 1 (downregulated genes) according to the criteria described in the references ADDIN EN.CITE [1,2]. Meanwhile, a P value < 0.05 was regarded as significantly different, aiming to reduce the false positive rate. Finally, after downloading the relatively raw TXT data, we employed visual hierarchical cluster analysis to display the volcano plot and heat map of these DEGs in PBMCs between the NICM and healthy samples using ImageGP (http://www.ehbio.com/ImageGP/index.php/Home/Index/index.html).
Gene ontology (GO) analysis is a common framework that can annotate genes and gene products involving functions of cellular components (CC), biological pathways (BP) and molecular function (MF) [14]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a collection of databases that help to handle genomes, biological pathways, diseases, chemical substances and drugs [15]. In our study, GO and KEGG analysis was performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.ncifcrf.gov) (version 6.7), which is an online biological function database integrating considerable biological data and analysis tools [16]. P < 0.05 was regarded as the cut-off criterion for significant difference. After the key BP, MF, CC and KEGG pathways of these DEGs were determined using DAVID, a scatterplot was generated by ImageGP according to the results of the GO and KEGG pathway analyses.
The Search Tool for the Retrieval of Interacting Genes (STRING) is an online tool to assess and integrate protein-protein interaction (PPI) information, containing physical and functional associations [17]. The PPI network can then identify the core hub genes and key gene modules between patients and healthy individuals. To find the potential correlations of the DEGs, we used STRING to map the PPI network of these DEGs. Subsequently, we used the Cytoscape software platform on the basis of the PPI associations to select 15 hub genes according to their ranked order of connectivity degree. Moreover, the Molecular Complex Detection (MCODE) app was also utilized to screen the typical modules of the PPI network in Cytoscape in line with degree cutoff = 2, k-core = 2, node score cutoff = 0.2 and max depth = 100.
All animal experimental procedures in this study were approved by the Animal Care and Use Committee of Renmin Hospital of Wuhan University and were performed in accordance with the Care and Use of Laboratory Animals published by the US National Institute of Health (Revised 2011) (approval number: WDRX-2017K012). C57/B6 male mice (8-10 weeks old) weighing 25.1 ± 1.6 g were obtained from the Institute of Laboratory Animal Science of the Chinese Academy of Medical Sciences (Beijing, China). The animals were randomly divided into two groups (20 per group) for treatment, the control group and the NICM group. For the mouse model of NICM, isoprenaline (dissolved in sterile saline) was injected (60 mg/kg/day) intraperitoneally once daily for 2 consecutive weeks [18]. Success for HF model creation was assessed by serial echocardiography demonstrating dilation, hypertrophy, and ventricular dysfunction [19].
Blood samples were collected by intracardiac puncture (3–5 mL per animal). As described previously [20], the PBMCs of the mice were isolated from whole blood by density gradient centrifugation with Ficoll-Paque Plus reagent and suspended in QIAzol Lysis Reagent (Qiagen, Dusseldorf, Germany). Subsequently, the layer with the PBMCs was removed and washed twice with saline (0.9%), and the recovered cells were used for total RNA extraction.
Total RNA was isolated using a TRIzol (Invitrogen, Carlsbad, CA, USA) assay, the concentrations and purities of which were quantified using an ultraviolet spectrophotometer. The RNA was then reverse transcribed according to the previous description [21]. The expression levels of the top 5 upregulated genes and the top 5 downregulated genes in PBMCs were normalized to GAPDH. Relative mRNA expression levels were analysed by the 2-∆∆ cycle threshold (CT) method. Apart from detecting the mRNA levels of the top 5 upregulated genes and the top 5 downregulated genes in the control group and NICM group, we also measured the mRNA levels of these genes in the isolated different cell types, the T cells (CD4+ and CD8+), B cells (CD20+) and monocytes (CD14+), from the obtained PBMCs of the mice by positive selection (Anti-PE MicroBeads UltraPure, Miltenyi Biotec, Teterow, Germany).
The obtained data are presented as the mean ± SD (standard deviation) and were assessed by a two-tailed Student's t-test. P < 0 05 was considered statistically significant.
In the present study, a comparison of 3 healthy samples with 4 NICM samples was executed by employing the GEO2R online analysis tool based on a P value < 0.05 and logFC ≤ −1 or logFC ≥ 1 criteria. After analysing the GSE9128 microarray, a total of 371 DEGs were selected, consisting of 288 upregulated DEGs and 83 downregulated DEGs. The expression proportion of these DEGs is displayed in the volcano plot (Figure 1A). The expression levels of the top 10 upregulated and top 10 downregulated DEGs are displayed in a heat map to visualize the results (Figure 1B). Among these 371 DEGs, the top 5 upregulated DEGs were EGR3, MBNL1, PTGS2, EGR2 and TXNIP, while the top 5 downregulated DEGs were GABARAPL3, NPIPB3, RGS1, CREM and JUN. The biological functions and gene titles of these 10 DEGs are presented in Table 1.
| DEGs | Gene title | Gene symbol | LogFC | Biological function | ||||
| Up-regulated | early growth response 3 | EGR3 | 3.25296217 | Regulating systemic autoimmunity | ||||
| muscleblind like splicing regulator 1 | MBNL1 | 3.22538031 | Regulating pre-mRNA alternative splicing | |||||
| prostaglandin-endoperoxide synthase 2 | PTGS2 | 3.21406038 | Catalyzings the conversion of arachidonic acid to prostaglandins; Immune response suppression | |||||
| early growth response 2 | EGR2 | 2.96759548 | Intrinsic negative regulator of DC immunogenicity; Mmonocyte/macrophage cell fate determination | |||||
| thioredoxin interacting protein | TXNIP | 2.76192581 | Linking oxidative stress to inflammasome activation | |||||
| Down-regulated | GABA type A receptor associated protein like 3 pseudogene | GABARAPL3 | −1.78817386 | Inhibiting inflammasome | ||||
| nuclear pore complex interacting protein family member B3 | NPIPB3 | −1.82835573 | Modulating Type I interferon | |||||
| regulator of G-protein signaling 1 | RGS1 | −1.87910047 | Regulating chemokine signalling | |||||
| cAMP responsive element modulator | CREM | −1.93814192 | Suppresseing IL-2 and TCRζ chain gene transcription | |||||
| Jun proto-oncogene, AP-1 transcription factor subunit | JUN | −2.12183042 | Modulating inflammation | |||||
DownLoad:
CSV
To gain a more extensive and in-depth knowledge of the selected DEGs, we used DAVID to analyse significantly enriched GO functions. By analysing GO enrichment of all upregulated and downregulated DEGs via DAVID, we discovered that the upregulated DEGs in BP were principally enriched in inflammatory response, immune response, response to lipopolysaccharide, cellular response to mechanical stimulus and negative regulation of translation, while the downregulated DEGs were enriched in circadian regulation of gene expression, negative regulation of sequence-specific DNA binding transcription factor activity, innate immune response, cellular response to hydrogen peroxide and negative regulation of I-kappaB kinase/NF-kappaB signaling. For CC, the upregulated DEGs were mainly enriched in extracellular exosome, nucleus, membrane and nucleoplasm and cytosol, while the downregulated DEGs were enriched in nucleus, nucleoplasm, centrosome and transcription factor complex. MF analysis revealed that the upregulated DEGs were mainly responsible for ATP binding, poly(A) RNA binding, chromatin binding, nucleotide binding and GTP binding. By contrast, the downregulated DEGs may function in protein binding, mitochondrial uncoupling and cytokine activity. The details of the GO analysis are presented in Figure 2 A–C and Table 2.
| Expression | Category | Term | Count | PValue |
| Up-regulated | GOTERM_BP_DIRECT | GO:0006954~inflammatory response | 9 | 0.008 |
| GOTERM_BP_DIRECT | GO:0006955~immune response | 8 | 0.012 | |
| GOTERM_BP_DIRECT | GO:0032496~response to lipopolysaccharide | 7 | 0.001 | |
| GOTERM_BP_DIRECT | GO:0071260~cellular response to mechanical stimulus | 6 | 2.66E-04 | |
| GOTERM_BP_DIRECT | GO:0017148~negative regulation of translation | 5 | 0.001 | |
| GOTERM_CC_DIRECT | GO:0070062~extracellular exosome | 51 | 1.41E-04 | |
| GOTERM_CC_DIRECT | GO:0005634~nucleus | 49 | 0.011 | |
| GOTERM_CC_DIRECT | GO:0016020~membrane | 27 | 5.87E-05 | |
| GOTERM_CC_DIRECT | GO:0005654~nucleoplasm | 27 | 0.021 | |
| GOTERM_CC_DIRECT | GO:0005829~cytosol | 19 | 0.049 | |
| GOTERM_MF_DIRECT | GO:0005524~ATP binding | 25 | 0.039 | |
| GOTERM_MF_DIRECT | GO:0044822~poly(A) RNA binding | 23 | 0.006 | |
| GOTERM_MF_DIRECT | GO:0003682~chromatin binding | 11 | 0.008 | |
| GOTERM_MF_DIRECT | GO:0000166~nucleotide binding | 10 | 0.016 | |
| GOTERM_MF_DIRECT | GO:0005525~GTP binding | 10 | 0.044 | |
| Down-regulated | GOTERM_BP_DIRECT | GO:0032922~circadian regulation of gene expression | 3 | 0.008 |
| GOTERM_BP_DIRECT | GO:0043433~negative regulation of sequence-specific DNA binding transcription factor activity | 3 | 0.011 | |
| GOTERM_BP_DIRECT | GO:0045087~innate immune response | 3 | 0.001 | |
| GOTERM_BP_DIRECT | GO:0070301~cellular response to hydrogen peroxide | 2 | 0.003 | |
| GOTERM_BP_DIRECT | GO:0043124~negative regulation of I-kappaB kinase/NF-kappaB signaling | 2 | ≤0.001 | |
| GOTERM_CC_DIRECT | GO:0005634~nucleus | 14 | 0.032 | |
| GOTERM_CC_DIRECT | GO:0005654~nucleoplasm | 13 | 7.15E-04 | |
| GOTERM_CC_DIRECT | GO:0005813~centrosome | 5 | 0.019 | |
| GOTERM_CC_DIRECT | GO:0005667~transcription factor complex | 3 | 0.007 | |
| GOTERM_MF_DIRECT | GO:0005515~protein binding | 58 | 0.003 | |
| GOTERM_MF_DIRECT | GO:0008083~mitochondrial uncoupling | 5 | 0.031 | |
| GOTERM_MF_DIRECT | GO:0005125~cytokine activity | 5 | 0.032 | |
| GO: Gene Ontology. | ||||
DownLoad:
CSV
To obtain more integrated information regarding the crucial pathways of these selected DEGs, KEGG pathways were also analysed via DAVID, the result of which is presented in Table 3 and Figure 2D. The downregulated DEGs were enriched in protein processing in the endoplasmic reticulum, legionellosis, NF-kappa B signaling pathway, NOD-like receptor signaling pathway, estrogen signaling pathway while the upregulated DEGs were enriched in inflammatory bowel disease (IBD), TGF-beta signaling pathway, neurotrophin signaling pathway and Epstein-Barr virus infection.
| Category | Term | Count | % | P-Value | Genes |
| Down-regulated | ptr04141:Protein processing in endoplasmic reticulum | 11 | 0.030 | 5.49E-04 | SEC23A, HSP90AB1, HSP90B1, HSP90AA1, UBE2J1, MAN1A1, EDEM3, UBE2D1, SAR1B, TRAM1, SSR1 |
| ptr05134:Legionellosis | 7 | 0.020 | 5.25E-04 | CXCL2, CXCL8, IL1B, TLR4, HSPD1, CASP1, SAR1B | |
| ptr04064:NF-kappa B signaling pathway | 7 | 0.020 | 0.004304358 | PTGS2, BCL2A1, CXCL8, IL1B, TLR4, TAB2, ATM | |
| ptr04621:NOD-like receptor signaling pathway | 6 | 0.017 | 0.002195688 | HSP90AB1, HSP90AA1, CXCL8, IL1B, CASP1, TAB2 | |
| ptr04915:Estrogen signaling pathway | 6 | 0.017 | 0.023154474 | HSP90AB1, HSP90B1, HSP90AA1, FKBP5, CREB1, PRKACB | |
| Up-regulated | cjc05321:Inflammatory bowel disease (IBD) | 3 | 0.030 | 0.000636646 | JUN, SMAD3, RORA |
| cjc04350:TGF-beta signaling pathway | 3 | 0.030 | 0.03910693 | SMAD7, ID1, SMAD3 | |
| cjc04722:Neurotrophin signaling pathway | 3 | 0.030 | 0.000546883 | NFKBIE, JUN, MAPKAPK2 | |
| cjc05169:Epstein-Barr virus infection | 3 | 0.030 | 0.000983148 | NFKBIE, JUN, TNFAIP3 | |
| KEGG: Kyoto Encyclopedia of Genes and Genomes; FDR: False Discovery Rate. | |||||
DownLoad:
CSV
Furthermore, 15 hub genes identified from the Cytoscape software, which occupy the core position in the PPI network, were found in accordance with a high degree of connectivity (Table 4 & Figure 3A). We built the PPI network from the top 15 hub genes via the information from the STRING protein query of public databases. The top 15 hub genes, possessing a high degree of connectivity in NICM, are as follows: JUN, MYC, HSP90AA1, PCNA, CREB1, IL1B, IL8, SMARCA2, TLR4, RB1, RANBP2, EGR1, PTGS2, ENO1 and XPO1. Among the top 15 hub genes, only one gene (JUN) was significantly downregulated in the PBMCs from the patients with NICM, while the others were significantly increased in the PBMCs. Intriguingly, JUN is not only the DEG with the highest connectivity but is also the downregulated DEG with the highest fold change. Additionally, the top 15 hub genes could interact with 149 DEGs directly. These hub genes could also interact with each other and exhibited a strong correlation. For example, JUN could directly interact with IL8, RB1, EGR1, PTGS2, IL1B, SMARCA2, CREB1, HSP90AA1, TLR4 and MYC, while CREB1 interacts with IL1B, PTGS2, JUN, SMARCA2, RB1, MYC, XPO1, TLR4 and HSP90AA1. Moreover, we used the MCODE plug-in to detect the highest modules in the PPI network. We identified the top 2 modules of the network; Module 1 (Figure 3B) was associated with immune response and protein processing in the endoplasmic reticulum, while Module 2 (Figure 3C) was associated with the signaling pathway and cellular response to inflammation. Collectively, the PPI network suggested that the top 15 hub genes, especially JUN and CREB, may play essential roles in the immune response of NICM.
| Gene | Degree of connectivity | P value | Log FC |
| JUN | 47 | ≤0.001 | -2.121 |
| MYC | 44 | ≤0.001 | 1.932 |
| HSP90AA1 | 40 | 0.040 | 1.155 |
| PCNA | 35 | 0.004 | 1.649 |
| CREB1 | 33 | 0.006 | 1.617 |
| IL1B | 33 | 0.009 | 1.351 |
| IL8 | 33 | ≤0.001 | 1.241 |
| SMARCA2 | 30 | 0.002 | 1.131 |
| TLR4 | 30 | 0.008 | 1.821 |
| RB1 | 28 | 0.003 | 1.176 |
| RANBP2 | 28 | 0.002 | 1.855 |
| EGR1 | 24 | 0.003 | 1.721 |
| PTGS2 | 24 | 0.001 | 3.214 |
| ENO1 | 23 | 0.013 | 1.953 |
| XPO1 | 22 | 0.015 | 1.067 |
| Top 15 hub genes with higher degree of connectivity | |||
DownLoad:
CSV
To ensure the credibility of the GSE9128 microarray and obtain further credible analysis, we re-identified the top 5 upregulated genes and the top 5 downregulated genes via qPCR in a mouse model induced by isoprenaline. The results from PCR (Figure 4) demonstrated that the mRNA expression levels of EGR3, MBNL1, PTGS2, EGR2 and TXNIP in PBMCs from mice with NICM were significantly higher than those in control mice (P < 0.05), while the mRNA expression levels of GABARAPL3, NPIPB3, RGS1, CREM and JUN in the NICM group were statistically lower than those in the control group (P < 0.05).
Furthermore, we detected the mRNA levels of the top 5 upregulated and top 5 downregulated genes in the subtypes of the PBMCs. To our knowledge, the main subtypes of PBMCs consists of T cells, B cells and monocytes. Therefore, we principally explored the mRNA levels of these 10 genes in CD4+ T cells, CD8+ T cells, CD14+ monocytes and CD20+ B cells (Figure 5). EGR3 was mainly expressed in T cells in mice with NICM. MBNL, PTGS2 and EGR2 were consistently higher in monocytes; however, their expression was significantly higher in mice with NICM. TXNIP was mainly expressed in CD4+ T cells, CD8+ T cells and CD20+ B cells, the expression of which was significantly increased in the presence of NICM. Regarding the downregulated genes, we found that JUN was highly expressed in T cells in normal subjects, while its level was reduced in T cells but increased in B cells in mice with NICM. CREM was principally expressed in T cells, while its level was decreased in T cells in mice with NICM. RGS1 was primarily expressed in B cells in normal mice. The level of NPIPB3 was higher in T cells and monocytes of normal mice compared with the mice with NICM. The mRNA levels of GABARAPL3 in the four types of immune cells were consistently higher in normal mice than in mice with NICM.
Patients with left ventricular dysfunction resulting from NICM possess an annual rate of death from any cause of approximately 7 percent when they received standard medical therapy for HF [22]. The aetiology of NICM is complicated and multifactorial, and at present, it is recognized that NICM is usually an integrated pathologic change implicated with genetic factors, mechanical stress, metabolic disorders, and infectious (mainly viral) causes, as well as toxicity-related causes [23]. The potential pathophysiological mechanisms underlying these alterations in NICM remain incompletely explored, and it is believed that the interplay among altered sarcomeric and cytoskeletal proteins, autoimmune mechanisms, postinfection immune mechanisms, direct pathogen damage, and free oxygen radical species are associated with the development and progression of NICM [24]. In particular, immune mechanisms in NICM have received increasing attention and have been unveiled in recent years. Some therapeutic strategies have also been proposed to indirectly modulate the inflammatory and immune status of patients with NICM. However, to date, our knowledge of immune mediators and mechanisms in NICM is still too superficial. Hence, some key immune-related diagnostic biomarkers and therapeutic targets in PBMCs should be verified as early as possible.
In this study, we first performed a comprehensive investigation on the expression profile of PBMCs obtained from patients with NICM and healthy individuals. Our study included 3 normal individuals and 4 patients with NICM from the GSE9128 GEO database. In our analysis, a total of 371 DEGs (accounting for 2.1 % of all detected genes) were found: 288 upregulated genes and 83 downregulated genes. Among the 371 DEGs, we identified the top 5 upregulated (EGR3, MBNL1, PTGS2, EGR2 and TXNIP) and top 5 downregulated (GABARAPL3, NPIPB3, RGS1, CREM and JUN) genes between healthy individuals and patients with NICM. Furthermore, we detected their mRNA levels in PBMCs in a mouse model induced by isoprenaline, and the results based on our experiments validated the bioinformatics analysis. Thus, we hypothesize that these 10 DEGs are expected to be promising biomarkers for the diagnosis of NICM. Meanwhile, based on a PPI network, we selected 15 hub genes with high connectivity, which all occupied the core node of the network, indicating that these hub genes may exert pivotal influence in the immune response of NICM. Furthermore, by annotating and analysing all DEGs in the PBMCs, we found that immune response, inflammatory response, extracellular exosome and cell differentiation were enriched in the progression of NICM. In fact, most of the DEGs, especially the top 5 upregulated/downregulated genes and the top 15 hub genes, perform vital immunoregulatory functions.
EGR3 is a transcription factor of the Cys2His2-type zinc finger transcription factor family that can inhibit IL-2 and interferon (IFN)-γ secretion in T cells and upregulate the level of the E3 ligase Cbl-b, which is important for the modulation of T cell energy and tolerance [25]. In a NICM model induced by transverse aortic constriction (TAC), IL-2 was significantly downregulated, and pretreatment with IL-2 was effective in the selective induction of regulatory T cells (Tregs) and in alleviating the development of cardiac remodelling through decreasing cardiac systemic inflammation [26]. On the other hand, the frequency of T helper (Th) 17 cells, which are derived from naive Th cells, was significantly elevated in patients with HF [27]. Meanwhile, EGR3 could trigger a Th17 response by enhancing the development of γδ T cells, suggesting that EGR3 may mediate the activation of Th17 cells and induce HF [28]. In our study, EGR3 was significantly increased in T cells of patients and mice with NICM. Thus, we speculate that EGR3 may suppress the secretion of IL2 by inhibiting Tregs and promoting Th17 cells, thereby deteriorating cardiac injury and dysfunction.
Excessive inflammation and monocyte/macrophage activation are featured in cardiac remodelling associated with NICM [29]. However, the roles of monocytes/macrophages in cardiac remodelling and HF remain controversial. For example, inhibition of macrophages in hypertensive rats resulted in an acceleration of cardiomyopathy, which was evidenced by left ventricle systolic dysfunction [30]. Other studies have provided primary evidence that the localization of macrophages may contribute to inflammation, fibrosis and cardiac injury induced by TAC [31,32]. RGS1 was reported to inactivate G-protein signalling and suppress the response to continuous chemokine stimulation. A recent study reported that in non-activated monocytes, RGS1 was low, the expression of which was immediately upregulated upon the activation of monocytes by pro-inflammatory signals during the recruitment phase. Subsequently, monocytes began to differentiate into macrophages, resulting in the increased expression of RGS1. The increased RGS1 further terminated chemokine signalling, eventually reducing the capacity for inflammatory cell migration and increasing the accumulation of macrophages [33]. In our study, we found that RGS1 was highly expressed in monocytes in the control group and that the mRNA level of RGS1 was significantly decreased in patients and mice with NICM, which hinted to us that RGS1 may exert a protective role in NICM by blocking the differentiation of monocytes/macrophages. In fact, another top 5 upregulated gene in our study, EGR2 was also uncovered to regulate the differentiation of monocytes into macrophages by modulating the expression of colony-stimulating factor-1 (CSF-1). In the inflammatory response, the hub gene (TLR4) has been reported to be significantly activated in monocytes and macrophages in atherosclerosis [34]. Collectively, our study suggested that the DEGs we screened participated in the differentiation of monocytes into macrophages in the development of NICM from the perspective of bioinformatics analysis.
Another finding in our study was that the inflammatory response played an essential role in the development of NICM [35]. Although a vast number of studies have proven the biological status of inflammation in NICM, few studies have provided evidence using bioinformatics analysis. The GO and KEGG pathway analysis in our study revealed that many inflammatory pathways in patients with NICM were activated, including the NF-kappa B signaling pathway, mitochondrial uncoupling, Toll-like receptor signaling, NOD-like receptor signaling pathway and TGF-beta signaling pathway. Additionally, many of the screened DEGs and hub genes were also associated with inflammation, including TXNIP, GABARAPL3, CREM, JUN, PCNA, TLR4, IL8, IL1B and HSP90AA1. To our knowledge, activation of the immune system is not independent of inflammation in the progression of HF. In chronic HF, activating the immune system can usually contribute to the activation of the complement system, secretion of pro-inflammatory cytokines, and production and release of autoantibodies, indicating that immune-inflammatory activation in patients with NICM may occur synchronously but not in isolation [36].
Taken together, using a series of bioinformatics analyses, we performed an integrated and novel illustration of mRNA expression profiles to identify DEGs expressed in PBMCs, which may play critical roles in the occurrence and development in patients with NICM. Genes and pathways implicated in inflammation and the immune response were significantly altered in the PBMCs of NICM. These findings will greatly contribute to unveiling the molecular mechanisms of NICM and promoting a non-invasive diagnosis. To allow these biomarkers and targets to be used more routinely in the clinic, further investigations into these genes in PBMCs should be performed.
All procedures performed in study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.
This work was supported by grants from the National Natural Science Foundation of China (No. 81470516, 81530012, 81300070, and 81770399), the Key Project of the National Natural Science Foundation (No.81530012) and Fundamental Research Funds for the Central Universities of China (2042018kf0121).
The data used to support the findings of this study are available from the corresponding author upon request.
ZJ conceived the experiments. LN conducted the experiments. LN analyzed the data. ZJ wrote the paper.
The authors declare that they have no competing interests.
| [1] |
Venter JC, Adams MD, Myers EW, et al. (2001) The sequence of the human genome. Science 291: 1304–1351. doi: 10.1126/science.1058040
|
| [2] |
Lander ES, Linton LM, Birren B, et al. (2001) Initial sequencing and analysis of the human genome. Nature 409: 860–921. doi: 10.1038/35057062
|
| [3] | Fredriksson R, Lagerstrom MC, Lundin LG, et al. (2003) The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Mol Pharmacol 63: 1256–1272. |
| [4] |
Pierce KL, Premont RT, Lefkowitz RJ (2002) Seven-transmembrane receptors. Nat Rev Mol Cell Biol 3: 639–650. doi: 10.1038/nrm908
|
| [5] |
Lagerstrom MC, Schioth HB (2008) Structural diversity of G protein-coupled receptors and significance for drug discovery. Nat Rev Drug Discov 7: 339–357. doi: 10.1038/nrd2518
|
| [6] |
Chien EY, Liu W, Zhao Q, et al. (2010) Structure of the human dopamine D3 receptor in complex with a D2/D3 selective antagonist. Science 330: 1091–1095. doi: 10.1126/science.1197410
|
| [7] |
Yang J, Villar VA, Armando I, et al. (2016) G protein-coupled receptor kinases: Crucial regulators of blood pressure. J Am Heart Assoc 5: e003519. doi: 10.1161/JAHA.116.003519
|
| [8] |
Bar-Shavit R, Maoz M, Kancharla A, et al. (2016) G protein-coupled receptors in cancer. Int J Mol Sci 17: 1320. doi: 10.3390/ijms17081320
|
| [9] |
Flower DR (1999) Modelling G-protein-coupled receptors for drug design. Biochim Biophys Acta 1422: 207–234. doi: 10.1016/S0304-4157(99)00006-4
|
| [10] |
Katritch V, Cherezov V, Stevens RC (2012) Diversity and modularity of G protein-coupled receptor structures. Trends Pharmacol Sci 33: 17–27. doi: 10.1016/j.tips.2011.09.003
|
| [11] | Gether U, Ballesteros JA, Seifert R, et al. (1997) Structural instability of a constitutively active G protein-coupled receptor. Agonist-independent activation due to conformational flexibility. J Biol Chem 272: 2587–2590. |
| [12] |
Seifert R, Wenzel-Seifert K (2002) Constitutive activity of G-protein-coupled receptors: cause of disease and common property of wild-type receptors. Naunyn Schmiedebergs Arch Pharmacol 366: 381–416. doi: 10.1007/s00210-002-0588-0
|
| [13] |
Nelson G, Hoon MA, Chandrashekar J, et al. (2001) Mammalian sweet taste receptors. Cell 106: 381–390. doi: 10.1016/S0092-8674(01)00451-2
|
| [14] |
Nelson G, Chandrashekar J, Hoon MA, et al. (2002) An amino-acid taste receptor. Nature 416: 199–202. doi: 10.1038/nature726
|
| [15] | Milligan G (2004) G protein-coupled receptor dimerization: Function and ligand pharmacology. Mol Pharmacol 66. |
| [16] |
Syrovatkina V, Alegre KO, Dey R, et al. (2016) Regulation, signaling, and physiological functions of G-proteins. J Mol Biol 428: 3850–3868. doi: 10.1016/j.jmb.2016.08.002
|
| [17] | Wess J (1997) G-protein-coupled receptors: molecular mechanisms involved in receptor activation and selectivity of G-protein recognition. FASEB J 11: 346–354. |
| [18] |
Rasumussen SG, DeVree BT, Zou Y, et al. (2011) Crystal structure of the beta2 adrenergic receptor-Gs protein complex. Nature 477: 549–555. doi: 10.1038/nature10361
|
| [19] |
Gilman AG (1987) G proteins: transducers of receptor-generated signals. Annu Rev Biochem 56: 615–649. doi: 10.1146/annurev.bi.56.070187.003151
|
| [20] | Nie J, Lewis DL (2001) Structural domains of the CB1 cannabinoid receptor that contribute to constitutive activity and G-protein sequestration. J Neurosci 21: 8758–8764. |
| [21] |
Kobilka BK, Deupi X (2007) Conformational complexity of G-protein-coupled receptors. Trends Pharmacol Sci 28: 397–406. doi: 10.1016/j.tips.2007.06.003
|
| [22] |
Ross EM, Wilkie TM (2000) GTPase-activating proteins for heterotrimeric G proteins: regulators of G protein signaling (RGS) and RGS-like proteins. Annu Rev Biochem 69: 795–827. doi: 10.1146/annurev.biochem.69.1.795
|
| [23] |
Claing A, Laporte SA, Caron MG, et al. (2002) Endocytosis of G protein-coupled receptors: roles of G protein-coupled receptor kinases and beta-arrestin proteins. Prog Neurobiol 66: 61–79. doi: 10.1016/S0301-0082(01)00023-5
|
| [24] |
Okada T, Le TI, Fox BA, et al. (2000) X-Ray diffraction analysis of three-dimensional crystals of bovine rhodopsin obtained from mixed micelles. J Struct Biol 130: 73–80. doi: 10.1006/jsbi.1999.4209
|
| [25] |
Palczewski K, Kumasaka T, Hori T, et al. (2000) Crystal structure of rhodopsin: A G protein-coupled receptor. Science 289: 739–745. doi: 10.1126/science.289.5480.739
|
| [26] |
Cherezov V, Rosenbaum DM, Hanson MA, et al. (2007) High resolution crystal structure of an engineered human beta2-adrenergic G protein-couple receptor. Science 318: 1258–1265. doi: 10.1126/science.1150577
|
| [27] |
Rasmussen SG, Choi HJ, Rosenbaum DM, et al. (2007) Crystal structure of the human beta2 adrenergic G-protein-coupled receptor. Nature 450: 383–387. doi: 10.1038/nature06325
|
| [28] | Stroud RM (2011) New tools in membrane protein determination. F1000 Biol Rep 3: 8. |
| [29] |
Ujwal R, Bowie JU (2011) Crystallizing membrane proteins using lipidic bicelles. Methods 55: 337–341. doi: 10.1016/j.ymeth.2011.09.020
|
| [30] |
Denisov IG, Sligar SG (2016) Nanodiscs for structural and functional studies of membrane proteins. Nat Struct Mol Biol 23: 481–486. doi: 10.1038/nsmb.3195
|
| [31] |
Xiang J, Chun E, Liu C, et al. (2016) Successful strategies to determine high-resolution structures of GPCRs. Trends Pharmacol Sci 37: 1055–1069. doi: 10.1016/j.tips.2016.09.009
|
| [32] |
Rawlings AE (2016) Membrane proteins: always an insoluble problem? Biochem Soc Trans 44: 790–795. doi: 10.1042/BST20160025
|
| [33] |
Neubig RR, Siderovski DP (2002) Regulators of G-protein signalling as new central nervous system drug targets. Nat Rev Drug Discov 1: 187–197. doi: 10.1038/nrd747
|
| [34] |
Bayburt TH, Grinkova YV, Sligar SG (2002) Self-assembly of discoidal phospholipid bilayer nanoparticles with membrane scaffold proteins. Nano Lett 2: 853–856. doi: 10.1021/nl025623k
|
| [35] |
Delmar JA, Bolla JR, Su CC, et al. (2015) Crystallization of membrane proteins by vapor diffusion. Methods Enzymol 557: 363–392. doi: 10.1016/bs.mie.2014.12.018
|
| [36] | Jaakola VP, Griffith MT, Hanson MA, et al. (2008) The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 322: 1211–1217. |
| [37] |
Dore AS, Okrasa K, Patel JC, et al. (2014) Structure of class C GPCR metabotropic glutamate receptor 5 transmembrane domain. Nature 511: 557–562. doi: 10.1038/nature13396
|
| [38] |
Thompson AA, Liu W, Chun E, et al. (2012) Structure of the nociceptin/orphanin FQ receptor in complex with a peptide mimetic. Nature 485: 395–399. doi: 10.1038/nature11085
|
| [39] |
Wang C, Wu H, Katritch V, et al. (2013) Structure of the human smoothened receptor bound to an antitumour agent. Nature 497: 338–343. doi: 10.1038/nature12167
|
| [40] |
Yin J, Babaoglu K, Brautigam CA, et al. (2016) Structure and ligand-binding mechanism of the human OX1 and OX2 orexin receptors. Nat Struct Mol Biol 23: 293–299. doi: 10.1038/nsmb.3183
|
| [41] | Zhang K, Zhang J, Gao Z, et al. (2014) Structure of the human P2Y12 receptor in complex with an antithrombotic drug. Nature 509. |
| [42] | Isberg V, Mordalski S, Munk C, et al. (2016) GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Res. |
| [43] |
Berman HM, Westbrook J, Feng Z, et al. (2000) The protein data bank. Nucl Acids Res 28: 235–242. doi: 10.1093/nar/28.1.235
|
| [44] |
Teller DC, Okada T, Behnke CA, et al. (2001) Advances in determination of a high-resolution three-dimensional structure of rhodopsin, a model of G-protein-coupled receptors (GPCRs). Biochemistry 40: 7761–7772. doi: 10.1021/bi0155091
|
| [45] |
Yeagle PL, Choi G, Albert AD (2001) Studies on the structure of the G-protein-coupled receptor rhodopsin including the putative G-protein binding site in unactivated and activated forms. Biochemistry 40: 11932–11937. doi: 10.1021/bi015543f
|
| [46] |
Okada T, Fujiyoshi Y, Silow M, et al. (2002) Functional role of internal water molecules in rhodopsin revealed by X-ray crystallography. Proc Natl Acad Sci USA 99: 5982–5987. doi: 10.1073/pnas.082666399
|
| [47] |
Choi G, Landin J, Galan JF, et al. (2002) Structural studies of metarhodopsin II, the activated form of the G-protein coupled receptor, rhodopsin. Biochemistry 41: 7318–7324. doi: 10.1021/bi025507w
|
| [48] |
Li J, Edwards PC, Burghammer M, et al. (2004) Structure of bovine rhodopsin in a trigonal crystal form. J Mol Biol 343: 1409–1438. doi: 10.1016/j.jmb.2004.08.090
|
| [49] |
Okada T, Sugihara M, Bondar AN, et al. (2004) The retinal conformation and its environment in rhodopsin in light of a new 22 A crystal structure. J Mol Biol 342: 571–583. doi: 10.1016/j.jmb.2004.07.044
|
| [50] |
Nakamichi H, Okada T (2006) Crystallographic analysis of primary visual photochemistry. Angew Chem Int Ed Engl 45: 4270–4273. doi: 10.1002/anie.200600595
|
| [51] |
Nakamichi H, Okada T (2006) Local peptide movement in the photoreaction intermediate of rhodopsin. Proc Natl Acad Sci USA 103: 12729–12734. doi: 10.1073/pnas.0601765103
|
| [52] |
Salom D, Lodowski DT, Stenkamp RE, et al. (2006) Crystal structure of a photoactivated deprotonated intermediate of rhodopsin. Proc Natl Acad Sci USA 103: 16123–16128. doi: 10.1073/pnas.0608022103
|
| [53] |
Standfuss J, Xie G, Edwards PC, et al. (2007) Crystal structure of a thermally stable rhodopsin mutant. J Mol Biol 372: 1179–1188. doi: 10.1016/j.jmb.2007.03.007
|
| [54] |
Nakamichi H, Buss V, Okada T (2007) Photoisomerization mechanism of rhodopsin and 9-cis-rhodopsin revealed by x-ray crystallography. Biophys J 92: L106–L108. doi: 10.1529/biophysj.107.108225
|
| [55] | Stenkamp RE (2008) Alternative models for two crystal structures of bovine rhodopsin. Acta Crystallogr D D64: 902–904. |
| [56] |
Park JH, Scheerer P, Hofmann KP, et al. (2008) Crystal structure of the ligand-free G-protein-coupled receptor opsin. Nature 454: 183–187. doi: 10.1038/nature07063
|
| [57] |
Scheerer P, Park JH, Hildebrand PW, et al. (2008) Crystal structure of opsin in its G-protein-interacting conformation. Nature 455: 497–502. doi: 10.1038/nature07330
|
| [58] |
Standfuss J, Edwards PC, D'Antona A, et al. (2011) The structural basis of agonist-induced activation in constitutively active rhodopsin. Nature 471: 656–660. doi: 10.1038/nature09795
|
| [59] |
Makino CL, Riley CK, Looney J, et al. (2010) Binding of more than one retinoid to visual opsins. Biophys J 99: 2366–2373. doi: 10.1016/j.bpj.2010.08.003
|
| [60] |
Choe HW, Kim YJ, Park JH, et al. (2011) Crystal structure of metarhodopsin II. Nature 471: 651–655. doi: 10.1038/nature09789
|
| [61] |
Deupi X, Edwards P, Singhal A, et al. (2012) Stabilized G protein binding site in the structure of constitutively active metarhodopsin-II. Proc Natl Acad Sci USA 109: 119–124. doi: 10.1073/pnas.1114089108
|
| [62] |
Park JH, Morizumi T, Li Y, et al. (2013) Opsin, a structural model for olfactory receptors? Angew Chem Int Ed Engl 52: 11021–11024. doi: 10.1002/anie.201302374
|
| [63] |
Singhal A, Ostermaier MK, Vishnivetskiy SA, et al. (2013) Insights into congenital stationary night blindness based on the structure of G90D rhodopsin. EMBO Rep 14: 520–526. doi: 10.1038/embor.2013.44
|
| [64] |
Szczepek M, Beyriere F, Hofmann KP, et al. (2014) Crystal structure of a common GPCR-binding interface for G protein and arrestin. Nat Commun 5: 4801. doi: 10.1038/ncomms5801
|
| [65] |
Blankenship E, Vahedi-Faridi A, Lodowski DT (2015) The high-resolution structure of activated opsin reveals a conserved solvent network in the transmembrane region essential for activation. Structure 23: 2358–2364. doi: 10.1016/j.str.2015.09.015
|
| [66] |
Kang Y, Zhou XE, Gao X, et al. (2015) Crystal structure of rhodopsin bound to arrestin by femtosecond X-ray laser. Nature 523: 561–567. doi: 10.1038/nature14656
|
| [67] |
Singhal A, Guo Y, Matkovic M, et al. (2016) Structural role of the T94I rhodopsin mutation in congenital stationary night blindness. EMBO Rep 17: 1431–1440. doi: 10.15252/embr.201642671
|
| [68] |
Gulati S, Jastrzebska B, Banerjee S, et al. (2017) Photocyclic behavior of rhodopsin induced by an atypical isomerization mechanism. Proc Natl Acad Sci USA 114: E2608–E2615. doi: 10.1073/pnas.1617446114
|
| [69] |
Warne T, Serrano-Vega MJ, Baker JG, et al. (2008) Structure of a beta1-adrenergic G-protein-coupled receptor. Nature 454: 486–491. doi: 10.1038/nature07101
|
| [70] |
Warne T, Moukhametzianov R, Baker JG, et al. (2011) The structural basis for agonist and partial agonist action on a beta(1)-adrenergic receptor. Nature 469: 241–244. doi: 10.1038/nature09746
|
| [71] |
Moukhametzianov R, Warne T, Edwards PC, et al. (2011) Two distinct conformations of helix 6 observed in antagonist-bound structures of a beta-1 adrenergic receptor. Proc Natl Acad Sci USA 108: 8228–8232. doi: 10.1073/pnas.1100185108
|
| [72] |
Christopher JA, Brown J, Dore AS, et al. (2013) Biophysical fragment screening of the beta1-adrenergic receptor: identification of high affinity arylpiperazine leads using structure-based drug design. J Med Chem 56: 3446–3455. doi: 10.1021/jm400140q
|
| [73] |
Warne T, Edwards PC, Leslie AG, et al. (2012) Crystal structures of a stabilized beta1-adrenoceptor bound to the biased agonists bucindolol and carvedilol. Structure 20: 841–849. doi: 10.1016/j.str.2012.03.014
|
| [74] | Miller-Gallacher JL, Nehme R, Warne T, et al. (2014) The 2.1 A resolution structure of cyanopindolol-bound beta1-adrenoceptor identifies an intramembrane Na+ ion that stabilises the ligand-free receptor. PLoS One 9: e92727. |
| [75] |
Huang J, Chen S, Zhang JJ, et al. (2013) Crystal structure of oligomeric beta1-adrenergic G protein-coupled receptors in ligand-free basal state. Nat Struct Mol Biol 20: 419–425. doi: 10.1038/nsmb.2504
|
| [76] |
Sato T, Baker J, Warne T, et al. (2015) Pharmacological analysis and structure determination of 7-Methylcyanopindolol-bound beta1-adrenergic receptor. Mol Pharmacol 88: 1024–1034. doi: 10.1124/mol.115.101030
|
| [77] |
Leslie AG, Warne T, Tate CG (2015) Ligand occupancy in crystal structure of beta1-adrenergic G protein-coupled receptor. Nat Struct Mol Biol 22: 941–942. doi: 10.1038/nsmb.3130
|
| [78] | Hanson MA, Cherezov V, Griffith MT, et al. (2008) A specific cholesterol binding site is established by the 2.8 A structure of the human beta2-adrenergic receptor. Structure 6: 897–905. |
| [79] |
Bokoch MP, Zou Y, Rasmussen SG, et al. (2010) Ligand-specific regulation of the extracellular surface of a G-protein-coupled receptor. Nature 463: 108–112. doi: 10.1038/nature08650
|
| [80] |
Wacker D, Fenalti G, Brown MA, et al. (2010) Conserved binding mode of human beta2 adrenergic receptor inverse agonists and antagonist revealed by X-ray crystallography. J Am Chem Soc 132: 11443–11445. doi: 10.1021/ja105108q
|
| [81] |
Rasmussen SG, Choi HJ, Fung JJ, et al. (2011) Structure of a nanobody-stabilized active state of the beta(2) adrenoceptor. Nature 469: 175–180. doi: 10.1038/nature09648
|
| [82] |
Rosenbaum DM, Zhang C, Lyons JA, et al. (2011) Structure and function of an irreversible agonist-beta(2) adrenoceptor complex. Nature 469: 236–240. doi: 10.1038/nature09665
|
| [83] | Zou Y, Weis WI, Kobilka BK (2012) N-terminal T4 lysozyme fusion facilitates crystallization of a G protein coupled receptor. PLos One 7. |
| [84] |
Ring AM, Manglik A, Kruse AC, et al. (2013) Adrenaline-activated structure of beta2-adrenoreceptor stabilized by an engineered nanobody. Nature 502: 575–579. doi: 10.1038/nature12572
|
| [85] |
Weichert D, Kruse AC, Manglik A, et al. (2014) Covalent agonists for studying G protein-coupled receptor activation. Proc Natl Acad Sci USA 111: 10744–10748. doi: 10.1073/pnas.1410415111
|
| [86] |
Huang CY, Olieric V, Ma P, et al. (2016) In meso in situ serial X-ray crystallography of soluble and membrane proteins at cryogenic temperatures. Acta Crystallogr D 72: 93–112. doi: 10.1107/S2059798315021683
|
| [87] |
Staus DP, Strachan RT, Manglik A, et al. (2016) Allosteric nanobodies reveal the dynamic range and diverse mechanisms of G-protein-coupled receptor activation. Nature 535: 448–452. doi: 10.1038/nature18636
|
| [88] |
Shimamura T, Shiroishi M, Weyand S, et al. (2011) Structure of the human histamine H1 receptor complex with doxepin. Nature 475: 65–70. doi: 10.1038/nature10236
|
| [89] |
Wang C, Jiang Y, Ma J, et al. (2013) Structural basis for molecular recognition at serotonin receptors. Science 340: 610–614. doi: 10.1126/science.1232807
|
| [90] |
Wacker D, Wang C, Katritch V, et al. (2013) Structural features for functional selectivity at serotonin receptors. Science 340: 615–619. doi: 10.1126/science.1232808
|
| [91] |
Liu W, Wacker D, Gati C, et al. (2013) Serial femtosecond crystallography of G protein-coupled receptors. Science 342: 1521–1524. doi: 10.1126/science.1244142
|
| [92] |
Wacker D, Wang S, McCorvy JD, et al. (2017) Crystal structure of an LSD-bound human serotonin receptor. Cell 168: 377–389. doi: 10.1016/j.cell.2016.12.033
|
| [93] |
Thal DM, Sun B, Feng D, et al. (2016) Crystal structures of the M1 and M4 muscarinic acetylcholine receptors. Nature 531: 335–340. doi: 10.1038/nature17188
|
| [94] |
Haga K, Kruse AC, Asada H, et al. (2012) Structure of the human M2 muscarinic acetylcholine receptor bound to an antagonist. Nature 482: 547–551. doi: 10.1038/nature10753
|
| [95] |
Kruse AC, Ring AM, Manglik A, et al. (2013) Activation and allosteric modulation of a muscarinic acetylcholine receptor. Nature 504: 101–106. doi: 10.1038/nature12735
|
| [96] |
Kruse AC, Hu J, Pan AC, et al. (2012) Structure and dynamics of the M3 muscarinic acetylcholine receptor. Nature 482: 552–556. doi: 10.1038/nature10867
|
| [97] |
Thorsen TS, Matt R, Weis WI, et al. (2014) Modified T4 lysozyme fusion proteins facilitate G protein-coupled receptor crystallogenesis. Structure 22: 1657–1664. doi: 10.1016/j.str.2014.08.022
|
| [98] |
Glukhova A, Thal DM, Nguyen AT, et al. (2017) Structure of the adenosine A1 receptor reveals the basis for subtype selectivity. Cell 168: 867–877. doi: 10.1016/j.cell.2017.01.042
|
| [99] |
Dore AS, Robertson N, Errey JC, et al. (2011) Structure of the adenosine A(2A) receptor in complex with ZM241385 and the xanthines XAC and caffeine. Structure 19: 1283–1293. doi: 10.1016/j.str.2011.06.014
|
| [100] |
Xu F, Wu H, Katritch V, et al. (2011) Structure of an agonist-bound human A2A adenosine receptor. Science 332: 322–327. doi: 10.1126/science.1202793
|
| [101] |
Lebon G, Warne T, Edwards PC, et al. (2011) Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation. Nature 474: 521–525. doi: 10.1038/nature10136
|
| [102] | Hino T, Arakawa T, Iwanari H, et al. (2012) G-protein-coupled receptor inactivation by an allosteric inverse-agonist antibody. Nature 482: 237–240. |
| [103] |
Congreve M, Andrews SP, Dore AS, et al. (2012) Discovery of 1,2,4-triazine derivatives as adenosine A(2A) antagonists using structure based drug design. J Med Chem 55: 1898–1903. doi: 10.1021/jm201376w
|
| [104] |
Liu W, Chun E, Thompson AA, et al. (2012) Structural basis for allosteric regulation of GPCRs by sodium ions. Science 337: 232–236. doi: 10.1126/science.1219218
|
| [105] |
Lebon G, Edwards PC, Leslie AG, et al. (2015) Molecular determinants of CGS21680 binding to the human adenosine A2A receptor. Mol Pharmacol 87: 907–915. doi: 10.1124/mol.114.097360
|
| [106] |
Segala E, Guo D, Cheng RK, et al. (2016) Controlling the dissociation of ligands from the adenosine A2A receptor through modulation of salt bridge strength. J Med Chem 59: 6470–6479. doi: 10.1021/acs.jmedchem.6b00653
|
| [107] |
Batyuk A, Galli L, Ishchenko A, et al. (2016) Native phasing of x-ray free-electron laser data for a G protein-coupled receptor. Sci Adv 2: e1600292. doi: 10.1126/sciadv.1600292
|
| [108] |
Carpenter B, Nehme R, Warne T, et al. (2016) Structure of the adenosine A(2A) receptor bound to an engineered G protein. Nature 536: 104–107. doi: 10.1038/nature18966
|
| [109] |
Sun B, Bachhawat P, Chu ML, et al. (2017) Crystal structure of the adenosine A2A receptor bound to an antagonist reveals a potential allosteric pocket. Proc Natl Acad Sci USA 114: 2066–2071. doi: 10.1073/pnas.1621423114
|
| [110] |
Hanson MA, Roth CB, Jo E, et al. (2012) Crystal structure of a lipid G protein-coupled receptor. Science 335: 851–855. doi: 10.1126/science.1215904
|
| [111] |
Chrencik JE, Roth CB, Terakado M, et al. (2015) Crystal structure of antagonist bound human lysophosphatidic acid receptor 1. Cell 161: 1633–1643. doi: 10.1016/j.cell.2015.06.002
|
| [112] |
Hua T, Vemuri K, Pu M, et al. (2016) Crystal structure of the human cannabinoid receptor CB1. Cell 167: 750–762. doi: 10.1016/j.cell.2016.10.004
|
| [113] | Shao Z, Yin J, Chapman K, et al. (2016) High-resolution crystal structure of the human CB1 cannabinoid receptor. Nature. |
| [114] |
White JF, Noinaj N, Shibata Y, et al. (2012) Structure of the agonist-bound neurotensin receptor. Nature 490: 508–513. doi: 10.1038/nature11558
|
| [115] |
Egloff P, Hillenbrand M, Klenk C, et al. (2014) Structure of signaling-competent neurotensin receptor 1 obtained by directed evolution in Escherichia coli. Proc Natl Acad Sci USA 111: E655–E662. doi: 10.1073/pnas.1317903111
|
| [116] |
Krumm BE, White JF, Shah P, et al. (2015) Structural prerequisites for G-protein activation by the neurotensin receptor. Nat Commun 6: 7895. doi: 10.1038/ncomms8895
|
| [117] |
Krumm BE, Lee S, Bhattacharya S, et al. (2016) Structure and dynamics of a constitutively active neurotensin receptor. Sci Rep 6: 38564. doi: 10.1038/srep38564
|
| [118] | Yin J, Mobarec JC, Kolb P, et al. (2015) Crystal structure of the human OX2 orexin receptor bound to the insomnia drug suvorexant. Nature 519: 247–250. |
| [119] |
Gayen A, Goswami SK, Mukhopadhyay C (2011) NMR evidence of GM1-induced conformational change of Substance P using isotropic bicelles. Biochim Biophys Acta 1808: 127–139. doi: 10.1016/j.bbamem.2010.09.023
|
| [120] |
Shihoya W, Nishizawa T, Okuta A, et al. (2016) Activation mechanism of endothelin ETB receptor by endothelin-1. Nature 537: 363–368. doi: 10.1038/nature19319
|
| [121] | Park SH, Das BB, Casagrande F, et al. (2012) Structure of the chemokine receptor CXCR1 in phospholipid bilayers. Nature 491: 779–783. |
| [122] |
Zheng Y, Qin L, Zacarias NV, et al. (2016) Structure of CC chemokine receptor 2 with orthosteric and allosteric antagonists. Nature 540: 458–461. doi: 10.1038/nature20605
|
| [123] |
Wu B, Chien EY, Mol CD, et al. (2010) Structures of the CXCR4 chemokine GPCR with small-molecule and cyclic peptide antagonists. Science 330: 1066–1071. doi: 10.1126/science.1194396
|
| [124] | Qin L, Kufareva I, Holden LG, et al. (2015) Structural biology. Crystal structure of the chemokine receptor CXCR4 in complex with a viral chemokine. Science 347: 1117–1122. |
| [125] |
Tan Q, Zhu Y, Li J, et al. (2013) Structure of the CCR5 chemokine receptor-HIV entry inhibitor maraviroc complex. Science 341: 1387–1390. doi: 10.1126/science.1241475
|
| [126] |
Oswald C, Rappas M, Kean J, et al. (2016) Intracellular allosteric antagonism of the CCR9 receptor. Nature 540: 462–465. doi: 10.1038/nature20606
|
| [127] |
Miller RL, Thompson AA, Trapella C, et al. (2015) The importance of ligand-receptor conformational pairs in stabilization: Spotlight on the N/OFQ G protein-coupled receptor. Structure 23: 2291–2299. doi: 10.1016/j.str.2015.07.024
|
| [128] |
Wu H, Wacker D, Mileni M, et al. (2012) Structure of the human kappa-opioid receptor in complex with JDTic. Nature 485: 327–332. doi: 10.1038/nature10939
|
| [129] |
Manglik A, Kruse AC, Kobilka TS, et al. (2012) Crystal structure of the mu-opioid receptor bound to a morphinan antagonist. Nature 485: 321–326. doi: 10.1038/nature10954
|
| [130] |
Huang W, Manglik A, Venkatakrishnan AJ, et al. (2015) Structural insights into micro-opioid receptor activation. Nature 524: 315–321. doi: 10.1038/nature14886
|
| [131] |
Granier S, Manglik A, Kruse AC, et al. (2012) Structure of the delta-opioid receptor bound to naltrindole. Nature 485: 400–404. doi: 10.1038/nature11111
|
| [132] |
Fenalti G, Giguere PM, Katritch V, et al. (2014) Molecular control of delta-opioid receptor signaling. Nature 506: 191–196. doi: 10.1038/nature12944
|
| [133] |
Fenalti G, Zatsepin NA, Betti C, et al. (2015) Structural basis for bifunctional peptide recognition at human delta-opioid receptor. Nat Struct Mol Biol 22: 265–268. doi: 10.1038/nsmb.2965
|
| [134] |
Zhang H, Unal H, Gati C, et al. (2015) Structure of the Angiotensin receptor revealed by serial femtosecond crystallography. Cell 161: 833–844. doi: 10.1016/j.cell.2015.04.011
|
| [135] |
Zhang H, Unal H, Desnoyer R, et al. (2015) Structural basis for ligand recognition and functional selectivity at angiotensin receptor. J Biol Chem 290: 29127–29139. doi: 10.1074/jbc.M115.689000
|
| [136] | Zhang H, Han GW, Batyuk A, et al. (2017) Structural basis for selectivity and diversity in angiotensin II receptors. Nature. |
| [137] | Burg JS, Ingram JR, Venkatakrishnan AJ, et al. (2015) Structural biology. Structural basis for chemokine recognition and activation of a viral G protein-coupled receptor. Science 347: 1113–1117. |
| [138] |
Zhang D, Gao ZG, Zhang K, et al. (2015) Two disparate ligand-binding sites in the human P2Y1 receptor. Nature 520: 317–321. doi: 10.1038/nature14287
|
| [139] |
Zhang J, Zhang K, Gao ZG, et al. (2014) Agonist-bound structure of the human P2Y12 receptor. Nature 509: 119–122. doi: 10.1038/nature13288
|
| [140] |
Zhang C, Srinivasan Y, Arlow DH, et al. (2012) High-resolution crystal structure of human protease-activated receptor 1. Nature 492: 387–392. doi: 10.1038/nature11701
|
| [141] |
Srivastava A, Yano J, Hirozane Y, et al. (2014) High-resolution structure of the human GPR40 receptor bound to allosteric agonist TAK-875. Nature 513: 124–127. doi: 10.1038/nature13494
|
| [142] |
Siu FY, He M, de Graaf C, et al. (2013) Structure of the human glucagon class B G-protein-coupled receptor. Nature 499: 444–449. doi: 10.1038/nature12393
|
| [143] |
Jazayeri A, Dore AS, Lamb D, et al. (2016) Extra-helical binding site of a glucagon receptor antagonist. Nature 533: 274–277. doi: 10.1038/nature17414
|
| [144] |
Hollenstein K, Kean J, Bortolato A, et al. (2013) Structure of class B GPCR corticotropin-releasing factor receptor 1. Nature 499: 438–443. doi: 10.1038/nature12357
|
| [145] | Dore AS, Bortolato A, Hollenstein K, et al. (2017) Decoding corticotropin-releasing factor receptor type 1 crystal structures. Curr Mol Pharmacol: Epub ahead of print, DOI: 10.2174/1874467210666170110114727. |
| [146] |
Wu H, Wang C, Gregory KJ, et al. (2014) Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator. Science 344: 58–64. doi: 10.1126/science.1249489
|
| [147] |
Christopher JA, Aves SJ, Bennett KA, et al. (2015) Fragment and structure-based drug discovery for a class C GPCR: Discovery of the mGlu5 negative allosteric modulator HTL14242 (3-Chloro-5-[6-(5-fluoropyridin-2-yl)pyrimidin-4-yl]benzonitrile). J Med Chem 58: 6653–6664. doi: 10.1021/acs.jmedchem.5b00892
|
| [148] | Wang C, Wu H, Evron T, et al. (2014) Structural basis for Smoothened receptor modulation and chemoresistance to anticancer drugs. Nat Commun 5: 4355. |
| [149] |
Byrne EF, Sircar R, Miller PS, et al. (2016) Structural basis of Smoothened regulation by its extracellular domains. Nature 535: 517–522. doi: 10.1038/nature18934
|
| [150] |
Lundstrom K (2006) Latest development in drug discovery on G protein-coupled receptors. Curr Protein Pept Sci 7: 465–470. doi: 10.2174/138920306778559403
|
| [151] |
Shonberg J, Kling RC, Gmeiner P, et al. (2015) GPCR crystal structures: Medicinal chemistry in the pocket. Bioorg Med Chem 23: 3880–3906. doi: 10.1016/j.bmc.2014.12.034
|
| [152] |
Attwood TK, Findlay JB (1993) Design of a discriminating fingerprint for G-protein-coupled receptors. Protein Eng 6: 167–176. doi: 10.1093/protein/6.2.167
|
| [153] |
Chee MS, Satchwell SC, Preddie E, et al. (1990) Human cytomegalovirus encodes three G protein-coupled receptor homologues. Nature 344: 774–777. doi: 10.1038/344774a0
|
| [154] |
Attwood TK, Findlay JB (1994) Fingerprinting G-protein-coupled receptors. Protein Eng 7: 195–203. doi: 10.1093/protein/7.2.195
|
| [155] | Strader CD, Sigal IS, Dixon RA (1989) Structural basis of beta-adrenergic receptor function. FASEB J 3: 1825–1832. |
| [156] | Liapakis G, Ballesteros JA, Papachristou S, et al. (2000) The forgotten serine. A critical role for Ser-2035.42 in ligand binding to and activation of the beta 2-adrenergic receptor. J Biol Chem 275: 37779–37788. |
| [157] | Swaminath G, Xiang Y, Lee TW, et al. (2004) Sequential binding of agonists to the beta2 adrenoceptor. Kinetic evidence for intermediate conformational states. J Biol Chem 279: 686–691. |
| [158] |
Baldwin JM (1994) Structure and function of receptors coupled to G proteins. Curr Opin Cell Biol 6: 180–190. doi: 10.1016/0955-0674(94)90134-1
|
| [159] |
Tyndall JD, Sandilya R (2005) GPCR agonists and antagonists in the clinic. Med Chem 1: 405–421. doi: 10.2174/1573406054368675
|
| [160] | Jacoby E, Bouhelal R, Gerspacher M, et al. (2006) The 7 TM G-protein-coupled receptor target family. Chem Med Chem 1: 761–782. |
| [161] | Spiss CK, Maze M (1985) Adrenoreceptors. Anaesthesist 34: 1–10. |
| [162] |
Civelli O, Reinscheid RK, Zhang Y, et al. (2013) G protein-coupled receptor deorphanizations. Annu Rev Pharmacol Toxicol 53: 127–146. doi: 10.1146/annurev-pharmtox-010611-134548
|
| [163] |
Barst RJ, Langleben D, Frost A, et al. (2004) Sitaxsentan therapy for pulmonary arterial hypertension. Am J Respir Crit Care Med 169: 441–447. doi: 10.1164/rccm.200307-957OC
|
| [164] |
Kotake T, Usami M, Akaza H, et al. (1999) Goserelin acetate with or without antiandrogen or estrogen in the treatment of patients with advanced prostate cancer: a multicenter, randomized, controlled trial in Japan. Zoladex Study Group. Jpn J Clin Oncol 29: 562–570. doi: 10.1093/jjco/29.11.562
|
| [165] |
Onuffer JJ, Horuk R (2002) Chemokines, chemokine receptors and small-molecule antagonists: recent developments. Trends Pharmacol Sci 23: 459–467. doi: 10.1016/S0165-6147(02)02064-3
|
| [166] |
Fatkenheuer G, Pozniak AL, Johnson MA, et al. (2005) Efficacy of short-term monotherapy with maraviroc, a new CCR5 antagonist, in patients infected with HIV-1. Nat Med 11: 1170–1172. doi: 10.1038/nm1319
|
| [167] |
Lu M, Wu B (2016) Structural studies of G protein-coupled receptors. IUBMB Life 68: 894–903. doi: 10.1002/iub.1578
|
| [168] |
Strotmann R, Schrock K, Boselt I, et al. (2011) Evolution of GPCR: change and continuity. Mol Cell Endocrinol 331: 170–178. doi: 10.1016/j.mce.2010.07.012
|
| [169] |
Ballesteros JA, Weinstein H (1995) Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Method Neurosci 25: 366–428. doi: 10.1016/S1043-9471(05)80049-7
|
| [170] |
Zhang D, Weinstein H (1994) Polarity conserved positions in transmembrane domains of G-protein coupled receptors and bacteriorhodopsin. FEBS Lett 337: 207–212. doi: 10.1016/0014-5793(94)80274-2
|
| [171] |
Katritch V, Fenalti G, Abola EE, et al. (2014) Allosteric sodium in class A GPCR signaling. Trends Biochem Sci 39: 233–244. doi: 10.1016/j.tibs.2014.03.002
|
| [172] |
Bjarnadottir TK, Geirardsdottir K, Ingemansson M, et al. (2007) Identification of novel splice variants of Adhesion G protein-coupled receptors. Gene 387: 38–48. doi: 10.1016/j.gene.2006.07.039
|
| [173] |
Lin HH, Chang GW, Davies JQ, et al. (2004) Autocatalytic cleavage of the EMR2 receptor occurs at a conserved G protein-coupled receptor proteolytic site motif. J Biol Chem 279: 31823–31832. doi: 10.1074/jbc.M402974200
|
| [174] |
Isberg V, Vroling B, Van DKR, et al. (2014) GPCRDB: an information system for G protein-coupled receptors. Nucleic Acids Res 42: D422–D425. doi: 10.1093/nar/gkt1255
|
| [175] |
Gasparini F, Kuhn R, Pin JP (2002) Allosteric modulators of group I metabotropic glutamate receptors: novel subtype-selective ligands and therapeutic perspectives. Curr Opin Pharmacol 2: 43–49. doi: 10.1016/S1471-4892(01)00119-9
|
| [176] |
Malherbe P, Kratochwil N, Zenner MT, et al. (2003) Mutational analysis and molecular modeling of the binding pocket of the metabotropic glutamate 5 receptor negative modulator 2-methyl-6-(phenylethynyl)-pyridine. Mol Pharmacol 64: 823–832. doi: 10.1124/mol.64.4.823
|
| [177] | Litschig S, Gasparini F, Rueegg D, et al. (1999) CPCCOEt, a noncompetitive metabotropic glutamate receptor 1 antagonist, inhibits receptor signaling without affecting glutamate binding. Mol Pharmacol 55: 453–461. |
| [178] |
Silve C, Petrel C, Leroy C, et al. (2005) Delineating a Ca2+ binding pocket within the venus flytrap module of the human calcium-sensing receptor. J Biol Chem 280: 37917–37923. doi: 10.1074/jbc.M506263200
|
| [179] |
Hermans E, Challiss RA (2001) Structural, signalling and regulatory properties of the group I metabotropic glutamate receptors: prototypic family C G-protein-coupled receptors. Biochem J 359: 465–484. doi: 10.1042/bj3590465
|
| [180] |
Nakanishi S (1992) Molecular diversity of glutamate receptors and implications for brain function. Science 258: 597–603. doi: 10.1126/science.1329206
|
| [181] |
Riedel G, Platt B, Micheau J (2003) Glutamate receptor function in learning and memory. Behav Brain Res 140: 1–47. doi: 10.1016/S0166-4328(02)00272-3
|
| [182] |
Kunishima N, Shimada Y, Tsuji Y, et al. (2000) Structural basis of glutamate recognition by a dimeric metabotropic glutamate receptor. Nature 407: 971–977. doi: 10.1038/35039564
|
| [183] |
Niswender CM, Conn PJ (2010) Metabotropic glutamate receptors: physiology, pharmacology, and disease. Annu Rev Pharmacol Toxicol 50: 295–322. doi: 10.1146/annurev.pharmtox.011008.145533
|
| [184] |
Bhanot P, Brink M, Samos CH, et al. (1996) A new member of the frizzled family from Drosophila functions as a Wingless receptor. Nature 382: 225–230. doi: 10.1038/382225a0
|
| [185] |
Murone M, Rosenthal A, de Sauvage FJ (1999) Sonic hedgehog signaling by the patched-smoothened receptor complex. Curr Biol 9: 76–84. doi: 10.1016/S0960-9822(99)80018-9
|
| [186] |
Chen CM, Strapps W, Tomlinson A, et al. (2004) Evidence that the cysteine-rich domain of Drosophila Frizzled family receptors is dispensable for transducing Wingless. Proc Natl Acad Sci USA 101: 15961–15966. doi: 10.1073/pnas.0407103101
|
| [187] |
Nakano Y, Nystedt S, Shivdasani AA, et al. (2004) Functional domains and sub-cellular distribution of the Hedgehog transducing protein Smoothened in Drosophila. Mech Dev 121: 507–518. doi: 10.1016/j.mod.2004.04.015
|
| [188] |
Isberg V, de Graaf C, Bortolato A, et al. (2015) Generic GPCR residue numbers-Aligning topology maps minding the gaps. Trends Pharmacol Sci 36: 22–31. doi: 10.1016/j.tips.2014.11.001
|
| 1. | Hongru Ma, Yanbin Tang, Homogenization of a semilinear elliptic problem in a thin composite domain with an imperfect interface, 2023, 46, 0170-4214, 19329, 10.1002/mma.9628 | |
| 2. | Yongqiang Zhao, Yanbin Tang, Approximation of solutions to integro-differential time fractional order parabolic equations in Lp-spaces, 2023, 2023, 1029-242X, 10.1186/s13660-023-03057-2 | |
| 3. | Caihong Gu, Yanbin Tang, Well-posedness of Cauchy problem of fractional drift diffusion system in non-critical spaces with power-law nonlinearity, 2024, 13, 2191-950X, 10.1515/anona-2024-0023 | |
| 4. | Junlong Chen, Yanbin Tang, Homogenization of non-local nonlinear p-Laplacian equation with variable index and periodic structure, 2023, 64, 0022-2488, 10.1063/5.0091156 | |
| 5. | Feiyang Peng, Yanbin Tang, Inverse problem of determining diffusion matrix between different structures for time fractional diffusion equation, 2024, 19, 1556-1801, 291, 10.3934/nhm.2024013 | |
| 6. | Hongru Ma, Yanbin Tang, Homogenization of a nonlinear reaction‐diffusion problem in a perforated domain with different size obstacles, 2024, 104, 0044-2267, 10.1002/zamm.202300333 | |
| 7. | Tomasz Klimsiak, Tomasz Komorowski, Lorenzo Marino, Homogenization of stable-like operators with random, ergodic coefficients, 2025, 00220396, 10.1016/j.jde.2025.02.054 |
Figures(7) / Tables(2)
Samantha B. Gacasan, Daniel L. Baker, Abby L. Parrill. G protein-coupled receptors: the evolution of structural insight[J]. AIMS Biophysics, 2017, 4(3): 491-527. doi: 10.3934/biophy.2017.3.491
| DEGs | Gene title | Gene symbol | LogFC | Biological function | ||||
| Up-regulated | early growth response 3 | EGR3 | 3.25296217 | Regulating systemic autoimmunity | ||||
| muscleblind like splicing regulator 1 | MBNL1 | 3.22538031 | Regulating pre-mRNA alternative splicing | |||||
| prostaglandin-endoperoxide synthase 2 | PTGS2 | 3.21406038 | Catalyzings the conversion of arachidonic acid to prostaglandins; Immune response suppression | |||||
| early growth response 2 | EGR2 | 2.96759548 | Intrinsic negative regulator of DC immunogenicity; Mmonocyte/macrophage cell fate determination | |||||
| thioredoxin interacting protein | TXNIP | 2.76192581 | Linking oxidative stress to inflammasome activation | |||||
| Down-regulated | GABA type A receptor associated protein like 3 pseudogene | GABARAPL3 | −1.78817386 | Inhibiting inflammasome | ||||
| nuclear pore complex interacting protein family member B3 | NPIPB3 | −1.82835573 | Modulating Type I interferon | |||||
| regulator of G-protein signaling 1 | RGS1 | −1.87910047 | Regulating chemokine signalling | |||||
| cAMP responsive element modulator | CREM | −1.93814192 | Suppresseing IL-2 and TCRζ chain gene transcription | |||||
| Jun proto-oncogene, AP-1 transcription factor subunit | JUN | −2.12183042 | Modulating inflammation | |||||
DownLoad:
CSV
| Expression | Category | Term | Count | PValue |
| Up-regulated | GOTERM_BP_DIRECT | GO:0006954~inflammatory response | 9 | 0.008 |
| GOTERM_BP_DIRECT | GO:0006955~immune response | 8 | 0.012 | |
| GOTERM_BP_DIRECT | GO:0032496~response to lipopolysaccharide | 7 | 0.001 | |
| GOTERM_BP_DIRECT | GO:0071260~cellular response to mechanical stimulus | 6 | 2.66E-04 | |
| GOTERM_BP_DIRECT | GO:0017148~negative regulation of translation | 5 | 0.001 | |
| GOTERM_CC_DIRECT | GO:0070062~extracellular exosome | 51 | 1.41E-04 | |
| GOTERM_CC_DIRECT | GO:0005634~nucleus | 49 | 0.011 | |
| GOTERM_CC_DIRECT | GO:0016020~membrane | 27 | 5.87E-05 | |
| GOTERM_CC_DIRECT | GO:0005654~nucleoplasm | 27 | 0.021 | |
| GOTERM_CC_DIRECT | GO:0005829~cytosol | 19 | 0.049 | |
| GOTERM_MF_DIRECT | GO:0005524~ATP binding | 25 | 0.039 | |
| GOTERM_MF_DIRECT | GO:0044822~poly(A) RNA binding | 23 | 0.006 | |
| GOTERM_MF_DIRECT | GO:0003682~chromatin binding | 11 | 0.008 | |
| GOTERM_MF_DIRECT | GO:0000166~nucleotide binding | 10 | 0.016 | |
| GOTERM_MF_DIRECT | GO:0005525~GTP binding | 10 | 0.044 | |
| Down-regulated | GOTERM_BP_DIRECT | GO:0032922~circadian regulation of gene expression | 3 | 0.008 |
| GOTERM_BP_DIRECT | GO:0043433~negative regulation of sequence-specific DNA binding transcription factor activity | 3 | 0.011 | |
| GOTERM_BP_DIRECT | GO:0045087~innate immune response | 3 | 0.001 | |
| GOTERM_BP_DIRECT | GO:0070301~cellular response to hydrogen peroxide | 2 | 0.003 | |
| GOTERM_BP_DIRECT | GO:0043124~negative regulation of I-kappaB kinase/NF-kappaB signaling | 2 | ≤0.001 | |
| GOTERM_CC_DIRECT | GO:0005634~nucleus | 14 | 0.032 | |
| GOTERM_CC_DIRECT | GO:0005654~nucleoplasm | 13 | 7.15E-04 | |
| GOTERM_CC_DIRECT | GO:0005813~centrosome | 5 | 0.019 | |
| GOTERM_CC_DIRECT | GO:0005667~transcription factor complex | 3 | 0.007 | |
| GOTERM_MF_DIRECT | GO:0005515~protein binding | 58 | 0.003 | |
| GOTERM_MF_DIRECT | GO:0008083~mitochondrial uncoupling | 5 | 0.031 | |
| GOTERM_MF_DIRECT | GO:0005125~cytokine activity | 5 | 0.032 | |
| GO: Gene Ontology. | ||||
DownLoad:
CSV
| Category | Term | Count | % | P-Value | Genes |
| Down-regulated | ptr04141:Protein processing in endoplasmic reticulum | 11 | 0.030 | 5.49E-04 | SEC23A, HSP90AB1, HSP90B1, HSP90AA1, UBE2J1, MAN1A1, EDEM3, UBE2D1, SAR1B, TRAM1, SSR1 |
| ptr05134:Legionellosis | 7 | 0.020 | 5.25E-04 | CXCL2, CXCL8, IL1B, TLR4, HSPD1, CASP1, SAR1B | |
| ptr04064:NF-kappa B signaling pathway | 7 | 0.020 | 0.004304358 | PTGS2, BCL2A1, CXCL8, IL1B, TLR4, TAB2, ATM | |
| ptr04621:NOD-like receptor signaling pathway | 6 | 0.017 | 0.002195688 | HSP90AB1, HSP90AA1, CXCL8, IL1B, CASP1, TAB2 | |
| ptr04915:Estrogen signaling pathway | 6 | 0.017 | 0.023154474 | HSP90AB1, HSP90B1, HSP90AA1, FKBP5, CREB1, PRKACB | |
| Up-regulated | cjc05321:Inflammatory bowel disease (IBD) | 3 | 0.030 | 0.000636646 | JUN, SMAD3, RORA |
| cjc04350:TGF-beta signaling pathway | 3 | 0.030 | 0.03910693 | SMAD7, ID1, SMAD3 | |
| cjc04722:Neurotrophin signaling pathway | 3 | 0.030 | 0.000546883 | NFKBIE, JUN, MAPKAPK2 | |
| cjc05169:Epstein-Barr virus infection | 3 | 0.030 | 0.000983148 | NFKBIE, JUN, TNFAIP3 | |
| KEGG: Kyoto Encyclopedia of Genes and Genomes; FDR: False Discovery Rate. | |||||
DownLoad:
CSV
| Gene | Degree of connectivity | P value | Log FC |
| JUN | 47 | ≤0.001 | -2.121 |
| MYC | 44 | ≤0.001 | 1.932 |
| HSP90AA1 | 40 | 0.040 | 1.155 |
| PCNA | 35 | 0.004 | 1.649 |
| CREB1 | 33 | 0.006 | 1.617 |
| IL1B | 33 | 0.009 | 1.351 |
| IL8 | 33 | ≤0.001 | 1.241 |
| SMARCA2 | 30 | 0.002 | 1.131 |
| TLR4 | 30 | 0.008 | 1.821 |
| RB1 | 28 | 0.003 | 1.176 |
| RANBP2 | 28 | 0.002 | 1.855 |
| EGR1 | 24 | 0.003 | 1.721 |
| PTGS2 | 24 | 0.001 | 3.214 |
| ENO1 | 23 | 0.013 | 1.953 |
| XPO1 | 22 | 0.015 | 1.067 |
| Top 15 hub genes with higher degree of connectivity | |||
DownLoad:
CSV
| DEGs | Gene title | Gene symbol | LogFC | Biological function | ||||
| Up-regulated | early growth response 3 | EGR3 | 3.25296217 | Regulating systemic autoimmunity | ||||
| muscleblind like splicing regulator 1 | MBNL1 | 3.22538031 | Regulating pre-mRNA alternative splicing | |||||
| prostaglandin-endoperoxide synthase 2 | PTGS2 | 3.21406038 | Catalyzings the conversion of arachidonic acid to prostaglandins; Immune response suppression | |||||
| early growth response 2 | EGR2 | 2.96759548 | Intrinsic negative regulator of DC immunogenicity; Mmonocyte/macrophage cell fate determination | |||||
| thioredoxin interacting protein | TXNIP | 2.76192581 | Linking oxidative stress to inflammasome activation | |||||
| Down-regulated | GABA type A receptor associated protein like 3 pseudogene | GABARAPL3 | −1.78817386 | Inhibiting inflammasome | ||||
| nuclear pore complex interacting protein family member B3 | NPIPB3 | −1.82835573 | Modulating Type I interferon | |||||
| regulator of G-protein signaling 1 | RGS1 | −1.87910047 | Regulating chemokine signalling | |||||
| cAMP responsive element modulator | CREM | −1.93814192 | Suppresseing IL-2 and TCRζ chain gene transcription | |||||
| Jun proto-oncogene, AP-1 transcription factor subunit | JUN | −2.12183042 | Modulating inflammation | |||||
| Expression | Category | Term | Count | PValue |
| Up-regulated | GOTERM_BP_DIRECT | GO:0006954~inflammatory response | 9 | 0.008 |
| GOTERM_BP_DIRECT | GO:0006955~immune response | 8 | 0.012 | |
| GOTERM_BP_DIRECT | GO:0032496~response to lipopolysaccharide | 7 | 0.001 | |
| GOTERM_BP_DIRECT | GO:0071260~cellular response to mechanical stimulus | 6 | 2.66E-04 | |
| GOTERM_BP_DIRECT | GO:0017148~negative regulation of translation | 5 | 0.001 | |
| GOTERM_CC_DIRECT | GO:0070062~extracellular exosome | 51 | 1.41E-04 | |
| GOTERM_CC_DIRECT | GO:0005634~nucleus | 49 | 0.011 | |
| GOTERM_CC_DIRECT | GO:0016020~membrane | 27 | 5.87E-05 | |
| GOTERM_CC_DIRECT | GO:0005654~nucleoplasm | 27 | 0.021 | |
| GOTERM_CC_DIRECT | GO:0005829~cytosol | 19 | 0.049 | |
| GOTERM_MF_DIRECT | GO:0005524~ATP binding | 25 | 0.039 | |
| GOTERM_MF_DIRECT | GO:0044822~poly(A) RNA binding | 23 | 0.006 | |
| GOTERM_MF_DIRECT | GO:0003682~chromatin binding | 11 | 0.008 | |
| GOTERM_MF_DIRECT | GO:0000166~nucleotide binding | 10 | 0.016 | |
| GOTERM_MF_DIRECT | GO:0005525~GTP binding | 10 | 0.044 | |
| Down-regulated | GOTERM_BP_DIRECT | GO:0032922~circadian regulation of gene expression | 3 | 0.008 |
| GOTERM_BP_DIRECT | GO:0043433~negative regulation of sequence-specific DNA binding transcription factor activity | 3 | 0.011 | |
| GOTERM_BP_DIRECT | GO:0045087~innate immune response | 3 | 0.001 | |
| GOTERM_BP_DIRECT | GO:0070301~cellular response to hydrogen peroxide | 2 | 0.003 | |
| GOTERM_BP_DIRECT | GO:0043124~negative regulation of I-kappaB kinase/NF-kappaB signaling | 2 | ≤0.001 | |
| GOTERM_CC_DIRECT | GO:0005634~nucleus | 14 | 0.032 | |
| GOTERM_CC_DIRECT | GO:0005654~nucleoplasm | 13 | 7.15E-04 | |
| GOTERM_CC_DIRECT | GO:0005813~centrosome | 5 | 0.019 | |
| GOTERM_CC_DIRECT | GO:0005667~transcription factor complex | 3 | 0.007 | |
| GOTERM_MF_DIRECT | GO:0005515~protein binding | 58 | 0.003 | |
| GOTERM_MF_DIRECT | GO:0008083~mitochondrial uncoupling | 5 | 0.031 | |
| GOTERM_MF_DIRECT | GO:0005125~cytokine activity | 5 | 0.032 | |
| GO: Gene Ontology. | ||||
| Category | Term | Count | % | P-Value | Genes |
| Down-regulated | ptr04141:Protein processing in endoplasmic reticulum | 11 | 0.030 | 5.49E-04 | SEC23A, HSP90AB1, HSP90B1, HSP90AA1, UBE2J1, MAN1A1, EDEM3, UBE2D1, SAR1B, TRAM1, SSR1 |
| ptr05134:Legionellosis | 7 | 0.020 | 5.25E-04 | CXCL2, CXCL8, IL1B, TLR4, HSPD1, CASP1, SAR1B | |
| ptr04064:NF-kappa B signaling pathway | 7 | 0.020 | 0.004304358 | PTGS2, BCL2A1, CXCL8, IL1B, TLR4, TAB2, ATM | |
| ptr04621:NOD-like receptor signaling pathway | 6 | 0.017 | 0.002195688 | HSP90AB1, HSP90AA1, CXCL8, IL1B, CASP1, TAB2 | |
| ptr04915:Estrogen signaling pathway | 6 | 0.017 | 0.023154474 | HSP90AB1, HSP90B1, HSP90AA1, FKBP5, CREB1, PRKACB | |
| Up-regulated | cjc05321:Inflammatory bowel disease (IBD) | 3 | 0.030 | 0.000636646 | JUN, SMAD3, RORA |
| cjc04350:TGF-beta signaling pathway | 3 | 0.030 | 0.03910693 | SMAD7, ID1, SMAD3 | |
| cjc04722:Neurotrophin signaling pathway | 3 | 0.030 | 0.000546883 | NFKBIE, JUN, MAPKAPK2 | |
| cjc05169:Epstein-Barr virus infection | 3 | 0.030 | 0.000983148 | NFKBIE, JUN, TNFAIP3 | |
| KEGG: Kyoto Encyclopedia of Genes and Genomes; FDR: False Discovery Rate. | |||||
| Gene | Degree of connectivity | P value | Log FC |
| JUN | 47 | ≤0.001 | -2.121 |
| MYC | 44 | ≤0.001 | 1.932 |
| HSP90AA1 | 40 | 0.040 | 1.155 |
| PCNA | 35 | 0.004 | 1.649 |
| CREB1 | 33 | 0.006 | 1.617 |
| IL1B | 33 | 0.009 | 1.351 |
| IL8 | 33 | ≤0.001 | 1.241 |
| SMARCA2 | 30 | 0.002 | 1.131 |
| TLR4 | 30 | 0.008 | 1.821 |
| RB1 | 28 | 0.003 | 1.176 |
| RANBP2 | 28 | 0.002 | 1.855 |
| EGR1 | 24 | 0.003 | 1.721 |
| PTGS2 | 24 | 0.001 | 3.214 |
| ENO1 | 23 | 0.013 | 1.953 |
| XPO1 | 22 | 0.015 | 1.067 |
| Top 15 hub genes with higher degree of connectivity | |||
