A Genetic Model of Constitutively Active Integrin CD11b/CD18

Pharmacological activation of integrin CD11b/CD18 (α β , Mac-1, and CR3) shows anti-inflammatory benefits in a variety of animal models of human disease, and it is a novel therapeutic strategy. Reasoning that genetic models can provide an orthogonal and direct system for the mechanistic study of CD11b agonism, we present in this study, to our knowledge, a novel knock-in model of constitutive active CD11b in mice. We genetically targeted the gene (which codes for CD11b) to introduce a point mutation that results in the I332G substitution in the protein. The I332G mutation in CD11b promotes an active, higher-affinity conformation of the ligand-binding I/A-domain (CD11b αA-domain). In vitro, this mutation increased adhesion of knock-in neutrophils to fibrinogen and decreased neutrophil chemotaxis to a formyl-Met-Leu-Phe gradient. In vivo, CD11b animals showed a reduction in recruitment of neutrophils and macrophages in a model of sterile peritonitis. This genetic activation of CD11b also protected against development of atherosclerosis in the setting of hyperlipidemia via reduction of macrophage recruitment into atherosclerotic lesions. Thus, our animal model of constitutive genetic activation of CD11b can be a useful tool for the study of integrin activation and its potential contribution to modulating leukocyte recruitment and alleviating different inflammatory diseases

The intricate cellular ecosystem of human peripheral veins as revealed by single-cell transcriptomic analysis.

The venous system has been historically understudied despite its critical roles in blood distribution, heart function, and systemic immunity. This study dissects the microanatomy of upper arm veins at the single cell level, and how it relates to wall structure, remodeling processes, and inflammatory responses to injury. We applied single-cell RNA sequencing to 4 non-diseased human veins (3 basilic, 1 cephalic) obtained from organ donors, followed by bioinformatic and histological analyses. Unsupervised clustering of 20,006 cells revealed a complex ecosystem of endothelial cell (EC) types, smooth muscle cell (SMCs) and pericytes, various types of fibroblasts, and immune cell populations. The venous endothelium showed significant upregulation of cell adhesion genes, with arteriovenous zonation EC phenotypes highlighting the heterogeneity of vasa vasorum (VV) microvessels. Venous SMCs had atypical contractile phenotypes and showed widespread localization in the intima and media. MYH11+DESlo SMCs were transcriptionally associated with negative regulation of contraction and pro-inflammatory gene expression. MYH11+DEShi SMCs showed significant upregulation of extracellular matrix genes and pro-migratory mediators. Venous fibroblasts ranging from secretory to myofibroblastic phenotypes were 4X more abundant than SMCs and widely distributed throughout the wall. Fibroblast-derived angiopoietin-like factors were identified as versatile signaling hubs to regulate angiogenesis and SMC proliferation. An abundant monocyte/macrophage population was detected and confirmed by histology, including pro-inflammatory and homeostatic phenotypes, with cell counts positively correlated with age. Ligand-receptor interactome networks identified the venous endothelium in the main lumen and the VV as a niche for monocyte recruitment and infiltration. This study underscores the transcriptional uniqueness of venous cells and their relevance for vascular inflammation and remodeling processes. Findings from this study may be relevant for molecular investigations of upper arm veins used for vascular access creation, where single-cell analyses of cell composition and phenotypes are currently lacking

The Transcriptomics of the Human Vein Transformation After Arteriovenous Fistula Anastomosis Uncovers Layer-Specific Remodeling and Hallmarks of Maturation Failure

The molecular transformation of the human preaccess vein after arteriovenous fistula (AVF) creation is poorly understood. This limits our ability to design efficacious therapies to improve maturation outcomes. Bulk RNA sequencing (RNA-seq) followed by paired bioinformatic analyses and validation assays were performed in 76 longitudinal vascular biopsies (veins and AVFs) from 38 patients with stage 5 chronic kidney disease or end-stage kidney disease undergoing surgeries for 2-stage AVF creation (19 matured, 19 failed). A total of 3637 transcripts were differentially expressed between veins and AVFs independent of maturation outcomes, with 80% upregulated in fistulas. The postoperative transcriptome demonstrated transcriptional activation of basement membrane and interstitial extracellular matrix (ECM) components, including preexisting and novel collagens, proteoglycans, hemostasis factors, and angiogenesis regulators. A postoperative intramural cytokine storm involved >80 chemokines, interleukins, and growth factors. Postoperative changes in ECM expression were differentially distributed in the AVF wall, with proteoglycans and fibrillar collagens predominantly found in the intima and media, respectively. Interestingly, upregulated matrisome genes were enough to make a crude separation of AVFs that failed from those with successful maturation. We identified 102 differentially expressed genes (DEGs) in association with AVF maturation failure, including upregulation of network collagen VIII in medial smooth muscle cells (SMCs) and downregulation of endothelial-predominant transcripts and ECM regulators. This work delineates the molecular changes that characterize venous remodeling after AVF creation and those relevant to maturation failure. We provide an essential framework to streamline translational models and our search for antistenotic therapies. [Display omitted

Immune cells in veins.

A) Gene ontology pathway analysis of genes expressed in immune cell populations in veins. Mo: monocyte/macrophages, NK: natural killer cells, Mast: mast cells, Neu: neutrophils. B-C) Representative stainings of CD163+ monocyte/macrophages (B) and CD8+ T cells (C) in basilic veins from the validation cohort. Scale bars represent 50 μm in all panels. A: adventitia, M: media. D) Donor’s age is positively correlated with CD163+ cell counts, but not CD8+ counts, in 6–7 basilic veins analyzed by IHC. (PDF)</p

Quality control (QC) metrics of single-cell RNA libraries.

A) Number of genes versus number of UMIs (transcripts) per cell after initial QC filters with color legend indicating mitochondrial read fractions (mitoRatio). B) Cell density histograms indicating distribution of UMI counts and gene counts per cell in the four vein samples. Cells with 0.15 mitoRatio, or predicted as doublets were filtered out from downstream bioinformatic analyses. C) UMAP plot color coded by sample demonstrating cell integration of individual single cell libraries. (PDF)</p

Transcriptional profiling of endothelial cell (EC) populations in veins.

A) Heatmap of single-cell gene expression data for the top differentially expressed genes among EC subclusters in veins. Genes are shown in rows and cells in columns, color-coded according to the main arteriovenous zonation phenotypes (EC1: arteriolar-like, EC2: capillary-like, EC3: venous-like, EC4: valvular-like, and EC5: lymphatic-like). B) Feature plots indicating the relative expressions of transcription factors and ephrinB2 ligand and receptor markers across a pseudotime defined by arteriovenous zonation phenotypes. The pseudotime direction goes from EC1 to EC4 followed by EC5, with the Moran’s I statistics of spatial autocorrelation shown for each gene. C) Gene expression levels of positive (SNAI1) and negative regulators (EPB41L3) of endothelial to mesenchymal transition in the five EC subclusters. (PDF)</p

Venous fibroblasts.

A) Representative IHC of PDGFRA+ fibroblasts in veins demonstrates widespread distribution in the wall. Scale bars = 50 μm. B) Immunofluorescence of APOD in the intima (I), media (M), and adventitia (A). APOD+ fibroblasts appear in pink while extracellular APOD is shown in red. Scale bars = 20 μm. C) The Fib1 cluster is most closely related to SMC1 cells as predicted by pseudotime trajectory analysis. D) IGFBP5 in SMCs can potentiate the effects of insulin-like growth factor 1 (IGF1) from fibroblasts to promote SMC proliferation. (PDF)</p