Membrane-Mediated Cooperative Interactions of CD47 and SIRPα.

The specific binding of the ubiquitous 'marker of self' protein CD47 to the SIRPα protein anchored in the macrophage plasma membrane results in the inhibition of the engulfment of 'self' cells by macrophages and thus constitutes a key checkpoint of our innate immune system. Consequently, the CD47-SIRPα protein complex has been recognized as a potential therapeutic target in cancer and inflammation. Here, we introduce a lattice-based mesoscale model for the biomimetic system studied recently in fluorescence microscopy experiments where GFP-tagged CD47 proteins on giant plasma membrane vesicles bind to SIRPα proteins immobilized on a surface. Computer simulations of the lattice-based mesoscale model allow us to study the biomimetic system on multiple length scales, ranging from single nanometers to several micrometers and simultaneously keep track of single CD47-SIRPα binding and unbinding events. Our simulations not only reproduce data from the fluorescence microscopy experiments but also are consistent with results of several other experiments, which validates our numerical approach. In addition, our simulations yield quantitative predictions on the magnitude and range of effective, membrane-mediated attraction between CD47-SIRPα complexes. Such detailed information on CD47-SIRPα interactions cannot be obtained currently from experiments alone. Our simulation results thus extend the present understanding of cooperative effects in CD47-SIRPα interactions and may have an influence on the advancement of new cancer treatments.

Interplay of receptor-ligand binding and lipid domain formation during cell adhesion.

Cell adhesion involved in biological processes such as cell migration, immune responses, and cancer metastasis, is mediated by the specific binding of receptor and ligand proteins. Some of these proteins exhibit affinity for nanoscale lipid clusters in cell membranes. A key question is how these nanoscale lipid clusters influence and react to the receptor-ligand binding during cell adhesion. In this article, we review recent computational studies that shed new light on the interplay of the receptor-ligand binding and the formation of lipid domains in adhering membranes. These studies indicate that the receptor-ligand binding promotes coalescence of lipid clusters into mesoscale domains, which, in turn, enhances both the affinity and cooperativity of the receptor-ligand binding in cell-cell adhesion with mobile ligands. In contrast, in the case of cell-extracellular matrix adhesion with immobile ligands, the receptor-ligand binding and the lipid cluster coalescence can be correlated or anti-correlated, depending strongly on the ligand distribution. These findings deepen our understanding of correlations between cell adhesion and membrane heterogeneities.

An integrated prognosis model of pharmacogenomic gene signature and clinical information for diffuse large B-cell lymphoma patients following CHOP-like chemotherapy.

BACKGROUND: As the most common form of lymphoma, diffuse large B-cell lymphoma (DLBCL) is a clinical highly heterogeneous disease with variability in therapeutic outcomes and biological features. It is a challenge to identify of clinically meaningful tools for outcome prediction. In this study, we developed a prognosis model fused clinical characteristics with drug resistance pharmacogenomic signature to identify DLBCL prognostic subgroups for CHOP-based treatment. METHODS: The expression microarray data and clinical characteristics of 791 DLBCL patients from two Gene Expression Omnibus (GEO) databases were used to establish and validate this model. By using univariate Cox regression, eight clinical or genetic signatures were analyzed. The elastic net-regulated Cox regression analysis was used to select the best prognosis related factors into the predictive model. To estimate the prognostic capability of the model, Kaplan-Meier curve and the area under receiver operating characteristic (ROC) curve (AUC) were performed. RESULTS: A predictive model comprising 4 clinical factors and 2 pharmacogenomic gene signatures was established after 1000 times cross validation in the training dataset. The AUC of the comprehensive risk model was 0.78, whereas AUC value was lower for the clinical only model (0.68) or the gene only model (0.67). Compared with low-risk patients, the overall survival (OS) of DLBCL patients with high-risk scores was significantly decreased (HR = 4.55, 95% CI 3.14-6.59, log-rank p value = 1.06 × 10-15). The signature also enables to predict prognosis within different molecular subtypes of DLBCL. The reliability of the integrated model was confirmed by independent validation dataset (HR = 3.47, 95% CI 2.42-4.97, log rank p value = 1.53 × 10-11). CONCLUSIONS: This integrated model has a better predictive capability to ascertain the prognosis of DLBCL patients prior to CHOP-like treatment, which may improve the clinical management of DLBCL patients and provide theoretical basis for individualized treatment.

Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy.

Diffuse Large B-cell Lymphoma (DLBCL), a heterogeneous disease, is influenced by complex network of gene interactions. Most previous studies focused on individual genes, but ignored the importance of intergenic correlations. In current study, we aimed to explore the association between gene networks and overall survival (OS) of DLBCL patients treated with CHOP-based chemotherapy (cyclophosphamide combination with doxorubicin, vincristine and prednisone). Weighted gene co-expression network analysis was conducted to obtain insights into the molecular characteristics of DLBCL. Ten co-expression gene networks (modules) were identified in training dataset (n = 470), and their associations with patients' OS after chemotherapy were tested. The results were validated in four independent datasets (n = 802). Gene ontology (GO) biological function enrichment analysis was conducted with Metascape. Three modules (purple, brown and red), which were enriched in T-cell immune, cell-cell adhesion and extracellular matrix (ECM), respectively, were found to be related to longer OS. Higher expression of several hub genes within these three co-expression modules, for example, LCP2 (HR = 0.77, p = 5.40 × 10-2), CD2 (HR = 0.87, p = 6.31 × 10-2), CD3D (HR = 0.83, p = 6.94 × 10-3), FYB (HR = 0.82, p = 1.40 × 10-2), GZMK (HR = 0.92, p = 1.19 × 10-1), FN1 (HR = 0.88, p = 7.06 × 10-2), SPARC (HR = 0.82, p = 2.06 × 10-2), were found to be associated with favourable survival. Moreover, the associations of the modules and hub genes with OS in different molecular subtypes and different chemotherapy groups were also revealed. In general, our research revealed the key gene modules and several hub genes were upregulated correlated with good survival of DLBCL patients, which might provide potential therapeutic targets for future clinical research.

Long Non-coding RNA Expression Profiling in Biopsy to Identify Renal Allograft at Risk of Chronic Damage and Future Graft Loss.

The loss of allograft from chronic damage is still the major risk that renal transplant recipients face today. Biomarkers for early detection of chronic damage are needed to improve the long-term graft survival. This study aimed to identify long non-coding RNA (lncRNA) biomarkers associated with chronic damage and graft loss after renal transplantation. Gene Expression Omnibus (GEO) datasets including GSE57387 (n = 101), GSE21374 (n = 282), and GSE25902 (n = 24) from three high-quality studies were analyzed. By repurposing the publicly available array-based data coupled with Affymetrix Human Exon 1.0 ST and Human U133 Plus 2.0 arrays, we obtained expression profiles of 11323 and 3383 lncRNAs in biopsies after renal transplantation, respectively. The logistic regression model and Cox regression model were applied to identify lncRNAs associated with chronic damage and graft survival. High AC093673.5 expression was identified as significantly associated with the three endpoints including chronic damage, progressive chronic histological damage, and graft failure across these three datasets. A six-lncRNA signature was created to predict renal allograft at risk of chronic damage with a high predictive ability (AUC = 0.94). Gene set enrichment analysis (GSEA) indicated that our lncRNA signature was related with allograft rejection and immunity. Our study highlights the importance of lncRNAs in chronic graft damage and allograft loss, supporting their potential role as prognosis biomarkers.

RasGRP1 is a target for VEGF to induce angiogenesis and involved in the endothelial-protective effects of metformin under high glucose in HUVECs.

Impaired angiogenesis in endothelial cells is a hallmark of diabetes vascular complications. Ras guanine-releasing protein 1 (RasGRP1) is a guanine nucleotide exchange factor for Ras, and its role in endothelial angiogenesis has not been investigated. Given the importance of Ras in vascular endothelial growth factor (VEGF)-induced angiogenesis, we hypothesized that RasGRP1 may be a critical pathway downstream of VEGF and involved in endothelial angiogenesis. Furthermore, we investigate whether RasGRP1-dependent VEGF signaling was downregulated under high glucose conditions mimicking diabetes and required for the endothelial protective action of metformin in human umbilical vein endothelial cells (HUVECs). HUVECs were transfected with either RasGRP1 small interfering RNA (siRNA) or pEnter-RasGRP1 plasmid to down- and upregulate RasGRP1 expression before different treatments, such as added VEGF or not, exposed to high glucose (35 mM) or normal glucose (5 mM) in the presence or absence of metformin. Expression of VEGF, RasGRP1, and their signaling targets were analyzed by Western blot; migration and tube formation were detected by transwell chamber assay and Matrigel angiogenesis assay, respectively. Knockdown of RasGRP1 significantly attenuated VEGF-induced migration and tube formation activities of HUVECs and activation of AKT pathway. The expression of VEGF, RasGRP1, and AKT phosphorylation was downregulated in HUVECs exposed to high glucose compared with normal glucose, whereas metformin upregulated the RasGRP1-dependent VEGF signaling and ameliorates the impaired angiogenesis caused by high glucose. RasGRP1 is involved in the VEGF-induced angiogenesis and the pro-angiogenesis effects of metformin under hyperglycemia. © 2019 IUBMB Life, 71(9):1391-1400, 2019.

The glycocalyx promotes cooperative binding and clustering of adhesion receptors.

Cell adhesion plays a pivotal role in various biological processes, e.g., immune responses, cancer metastasis, and stem cell differentiation. The adhesion behaviors depend subtly on the binding kinetics of receptors and ligands restricted at the cell-substrate interfaces. Although much effort has been directed toward investigating the kinetics of adhesion molecules, the role of the glycocalyx, anchored on cell surfaces as an exterior layer, is still unclear. In this paper, we propose a theoretical approach to study the collective binding kinetics of a few and a large number of binders in the presence of the glycocalyx, representing the cases of initial and mature adhesions of cells, respectively. The analytical results are validated by finding good agreement with our Monte Carlo simulations. In the force loading case, the on-rate and affinity increase as more bonds form, whereas this cooperative effect is not observed in the displacement loading case. The increased thickness and stiffness of the glycocalyx tend to decrease the affinity for a few bonds, while they have less influence on the affinity for a large number of bonds. Moreover, for a flexible membrane with thermally-excited shape fluctuations, the glycocalyx is exhibited to promote the formation of bond clusters, mainly due to the cooperative binding of binders. This study helps to understand the cooperative kinetics of adhesion receptors under physiologically relevant loading conditions and sheds light on the novel role of the glycocalyx in cell adhesion.