Biology subjects

Iqbal, A. J.

Publications and source records attributed to Iqbal, A. J..

4 recordsLinked to original sources

bioRxiv2026

Galectin-9 drives thrombo-inflammatory circuits initiating venous thrombosis

Background: Venous thromboembolism is increasingly recognized as a thrombo-inflammatory disease, yet the pathways linking inflammation to venous thrombus initiation remain incompletely defined. Galectin-9 (Gal-9), a {beta}-galactoside-binding lectin elevated in inflammatory disorders, has no defined role in venous thrombosis. Methods: Using plasma from patients with deep vein thrombosis (DVT), Gal-9-deficient mice, an inferior vena cava stenosis model, intravital microscopy, microfluidics, and ex vivo platelet and neutrophil assays, we assessed the functional contribution of Gal-9 to venous thrombosis. Results: Plasma Gal-9 was elevated in DVT patients, correlated with D-dimer at presentation, and declined over 3-month follow-up. Gal-9 deletion did not alter hemostasis but markedly impaired thrombus initiation, with reduced platelet and neutrophil recruitment to the vessel wall and reduced intra-thrombus neutrophil extracellular trap (NET) formation. TNF-/IFN-{gamma}-stimulated endothelial cells upregulated Gal-9 and promoted platelet adhesion and fibrin(ogen) deposition, which Gal-9 neutralization partially reversed. Under venous shear, immobilized recombinant Gal-9 potentiated platelet activation and thrombus formation, particularly alongside collagen, whereas Gal-9 inhibition suppressed these responses. Gal-9-activated platelets promoted NETosis and shifted thrombus composition toward a NET-rich inflammatory phenotype. Conclusions: Gal-9 is a previously unrecognized driver of venous thrombo-inflammation, linking vascular inflammation to platelet activation, NET formation, and thrombosis. Genetic deletion attenuated these responses in vivo, and antibody-mediated Gal-9 neutralization suppressed platelet activation and thrombus formation in vitro, identifying the Gal-9 glycan-binding pathway as a therapeutic target that disrupts thrombosis without impairing hemostasis.

immunology↗

bioRxiv2026

Single-cell analysis of an adult IBD INCEPTION cohort reveals Galectin-linked disease mechanisms

Background and AimsThe molecular pathogenesis of Inflammatory Bowel Disease (IBD) remains unclear. We aimed to establish a high-resolution immune landscape of treatment-naive IBD to identify central drivers of disease onset and early pathogenic signalling. MethodsWe generated a single-cell atlas using intestinal biopsies from a large adult inception cohort of 137 individuals, including treatment-naive Crohns disease (CD), ulcerative colitis (UC), and symptomatic non-IBD controls. We integrated scRNA-seq (1 million cells) with co-varying neighbourhood analysis (CNA) and unbiased tensor decomposition of cell-cell communication (CCC) networks. Findings were validated in vitro macrophage stimulation model and using serum from patients. ResultsThe inception cohort exhibited significantly more homogenous compartmental diversity compared to benchmark reference studies (p < 0.001). Inflammation in both CD and UC was characterized by a marked expansion of inflammatory monocytes. Unbiased CCC analysis identified a dominant disease-specific signalling module centred on the Galectin family (LGALS1 and LGALS9). Galectin-9 expression was specifically enriched in inflammatory monocytes, which exhibited distinct transcriptional programs linked to antigen presentation and microbial sensing. In vitro, Galectin-9 acted as a potent stimulus, driving macrophages toward a pro-inflammatory phenotype. Clinically, serum Galectin-9 levels were significantly elevated in IBD patients and correlated with systemic inflammatory markers and treatment response. ConclusionsOur data identify a galectin-monocyte signalling axis as a unifying inflammatory hallmark of early IBD. Galectin-9 serves as both a functional driver of mucosal inflammation and a dynamic biomarker, offering new opportunities for therapeutic targeting and disease monitoring from diagnosis.

immunology↗

bioRxiv2024

PEPITEM Tripeptides and Peptidomimetics: Next-Generation Modulators of Inflammation in Immune-Mediated Conditions

PEPITEM is an immune-modulatory peptide that effectively regulates inflammation and mitigates immune-mediated inflammatory diseases (IMIDs). Here we identify two independently active tripeptide pharmacophores within PEPITEM and engineered peptidomimetics with enhanced pharmacodynamic properties. These peptidomimetics regulate T-cell trafficking in vitro and reduce T-cell, neutrophil and macrophage numbers in the inflamed peritoneal cavity in vivo. In a plaque psoriasis model, topical administration reduced disease severity, inflammation and immune cell infiltration, while regulating cytokine release in macrophages and fibroblasts, as well as keratinocyte proliferation. Th1 and Th17 cell abundance, along with their cytokines, was reduced in secondary lymphoid organs. This expanded functional repertoire of PEPITEM and its derivatives provides innovative tools for countering immune and stromal cell-induced pathology in IMIDs, paving the way for a novel class of anti-inflammatory agents.

immunology↗

bioRxiv2020

CASTLE: Cell Adhesion with Supervised Training and Learning Environment

Different types of microscopy are used to uncover signatures of cell adhesion and mechanics. Automating the identification and analysis often involve sacrificial routines of cell manipulation such as in vitro staining. Phase-contrast microscopy (PCM) is rarely used in automation due to the difficulties with poor quality images. However, it is the least intrusive method to provide insights into the dynamics of cells, where other types of microscopy are too destructive to monitor. In this study, we propose an efficient workflow to automate cell counting and morphology in PCM images. We introduce Cell Adhesion with Supervised Training and Learning Environment (CASTLE), available as a series of additional plugins to ImageJ. CASTLE combines effective techniques for phase-contrast image processing with statistical analysis and machine learning algorithms to interpret the results. The proposed workflow was validated by comparing the results to a manual count and manual segmentation of cells in images investigating different adherent cell types, including monocytes, neutrophils and platelets. In addition, the effect of different molecules on cell adhesion was characterised using CASTLE. For example, we demonstate that Galectin-9 leads to differences in adhesion of leukocytes. CASTLE also provides information using machine learning techniques, namely principal component analysis and k-means clustering, to distinguish morphology currently inaccessible with manual methods. All scripts and documentation is open-source and available at the corresponding GitLab project.

immunology↗