Ask about this productRelated genes to: HPSE Blocking Peptide
- Gene:
- HPSE NIH gene
- Name:
- heparanase
- Previous symbol:
- -
- Synonyms:
- HPA, HSE1, HPSE1
- Chromosome:
- 4q21.23
- Locus Type:
- gene with protein product
- Date approved:
- 1999-09-28
- Date modifiied:
- 2016-10-05
Related products to: HPSE Blocking Peptide
Related articles to: HPSE Blocking Peptide
- Polysaccharides (PS) are key building blocks for bioactive nanomaterials in oncology, as they enhance colloidal stability, enable chemical functionalization, support controlled delivery systems, and, for selected families, provide targeting or therapeutic activities. However, despite their high biomedical potential, PS families such as λ-carrageenan (λ-CAR) have remained virtually unexplored in nano-oncology, as poor solubility, restrictive rheological properties, and undesired anticoagulant and pro-inflammatory effects constitute a major barrier to systemic use in vivo. In a recent study, we showed that depolymerized λ-carrageenan oligosaccharides (λ-COS) overcome these limitations and enable the design of stable hybrid nanoparticles embedding free Mn²⁺ ions and ultrasmall ferrite cores (λ-COS NP), exhibiting excellent colloidal stability, in vivo compatibility, and efficient MRI contrast capabilities. Building on this work, the present study investigates the inherent antitumor potential of these "unlocked" λ-COS NP, harnessing the known complementary bioactive properties of the λ-COS scaffold and those of the ferrite/Mn cargo. The λ-COS NP effectively inhibited the migration of MDA-MB-231 triple-negative breast cancer cells and suppressed heparanase (HPSE) activity, a key enzyme involved in tumor invasiveness. Moreover, λ-COS NP were efficiently internalized by RAW 264.7 macrophages and reprogrammed M2-like polarized cells toward an antitumor phenotype by restoring TNF-α and nitric oxide production. Preclinical studies in an MDA-MB-231 xenograft mouse model revealed efficient tumor accumulation and MRI detectability following intravenous administration, and a 50% reduction in tumor growth at the end of the therapeutic assays. Collectively, these results highlight λ-COS NP as promising tumor microenvironment-targeted nanotherapeutics with integrated bioactivities and imaging-tracking potential. - Source: PubMed
Publication date: 2026/06/14
Saliba JenniferPorta Zapata ManonMiranda-Perez de Alejo ClaudiaMersni RachidaUrkola-Arsuaga AinhizeMusnier BenjaminCherfan JulienSánchez-Téllez AlmaMaugard ThierryBiard DenisBousset LucBaranger KevinTrinh Le Vi Kieu CélineGigoux VéroniqueRuiz-Cabello JesùsFruitier-Arnaudin IngridCarregal-Romero SusanaGroult Hugo - Chimeric antigen receptor (CAR) T cell therapy has shown promise in solid tumours, but its efficacy is limited by dense extracellular matrix (ECM) that blocks T cell entry. We engineered mesothelin-targeted CAR T cells to express heparanase (HPSE) fused to the truncated hepatitis A virus pX domain (pX-Δ1-30), enabling surface display of HPSE on extracellular vesicles for localized, pH-dependent ECM degradation within the acidic tumour microenvironment while limiting systemic exposure. HPSE-pX-Δ1-30 CAR T (referred to as HPSE CAR T) cells penetrated ECM mimics nearly fourfold more effectively than standard CAR T cells. They also expressed more TNF-related apoptosis-inducing ligand (TRAIL), Fas ligand (FasL), and perforin, leading to stronger tumour killing in 2D and 3D colorectal cancer models. HPSE-pX-Δ1-30 CAR T-derived extracellular vesicles (EVs) retained CAR and chemokine receptors (CCR5/CCR7), carried apoptotic ligands, and were efficiently taken up by tumour cells and T cells. EV exposure promoted T cell proliferation, CCR5 expression, and central/stem-like memory formation while lowering PD-1 and CD57. In HCT116 xenografts, HPSE CAR T cells showed increased intratumoral infiltration, and EVs from these cells promoted infiltration of host T cells. Treatment reduced tumour burden, extended survival beyond 70 days, and did not cause systemic toxicity. These results highlight a dual strategy of ECM remodelling and immune modulation, offering a translational approach to overcome barriers to CAR T therapy in colorectal cancer. - Source: PubMed
Zhu SongshanYin JunYang WeiqiangFu XinZeng YiweiHuang NaZhang LingYu CongOuyang PingHuang KaisongChen RuiZhao XiaoleiJiang DanXu Guangxian - The infiltration of pro-inflammatory macrophages and the enzymatic degradation of the protective intra-islet heparan sulfate (HS) barrier are established pathological hallmarks of type 1 diabetes (T1D). While we previously identified myeloid-derived heparanase (HPSE) as the primary enzyme responsible for intra-islet HS cleavage, the transcriptional mechanisms driving its aberrant upregulation in macrophages remain unknown. By integrating single-cell RNA sequencing of T1D immune cells, we identified the transcription factor ETS proto-oncogene 1 (ETS1) as a key upstream regulator of in T1D-specific macrophages. Mechanistically, Cleavage Under Targets and Tagmentation (CUT&Tag) and luciferase reporter assays confirmed that ETS1 directly binds to the promoter and activates its expression in macrophages. , myeloid-specific knockout ( -mKO) mice exhibited profound resistance to multiple low-dose streptozotocin (MLD-STZ)-induced T1D insulitis. This protection was driven by the marked suppression of myeloid HPSE expression, which preserved intra-islet HS levels, reduced inflammatory cell infiltration, and enhanced β-cell survival compared with that in wild-type littermates. In conclusion, our findings define a previously unrecognized ETS1-HPSE-HS signaling axis that is involved in macrophage-mediated islet damage in T1D. We demonstrate that ETS1 is a crucial driver of the enzymatic breakdown of the islet basement membrane and the subsequent progression of insulitis, suggesting that targeting the ETS1-mediated transcriptional activation of HPSE offers a novel therapeutic strategy to safeguard the islet microenvironment and slow the progression of T1D. - Source: PubMed
Publication date: 2026/06/25
Zhang JiaNi HailingHu YourongZhou XiaohangHuang YiyueHu XinLin HuangmoCao XinyuanLi KaiHan XiaoSun Peng - - Source: PubMed
Publication date: 2026/06/03
de Melo Inês GuerraTavares ValériaSavva-Bordalo JoanaRei MarianaLiz-Pimenta JoanaPereira DeolindaMedeiros Rui - Human Heparanase (HPSE), the only mammalian endo-β-D-glucuronidase, plays an important role in extracellular matrix remodeling and the release of heparin-bound growth factors. Its overexpression is strongly correlated with increased tumor growth, angiogenesis, metastasis, and inflammation, highlighting HPSE as a compelling therapeutic target for oncology and inflammatory diseases. This study aimed to develop and validate a robust computational workflow for predicting the activity class of potential HPSE inhibitors using curated data from the ChEMBL database. Bioactivity data ([Formula: see text]) for known HPSE inhibitors were extracted and put through a meticulous data curation process, which included chemical structure standardization, molecular weight filtering, and final deduplication based on standardized isomeric SMILES to ensure structural uniqueness. Continuous [Formula: see text] values (nM) were converted to [Formula: see text] and subsequently categorized into three activity classes: A ([Formula: see text] and [Formula: see text]), B ([Formula: see text] and [Formula: see text]), and C ([Formula: see text] and [Formula: see text]) for multi-class classification. Molecular representations included two-dimensional physicochemical descriptors, Morgan fingerprints, and three-dimensional descriptors derived from optimized low-energy conformers generated using ETKDGv3 and MMFF94s. Multiple machine learning classifiers were evaluated using pipelines incorporating imputation, scaling, optional Principal Component Analysis (PCA) dimensionality reduction applied to the combined feature sets, and SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance. Models were trained and optimized using randomized search cross-validation on an 80% training split, maximizing balanced accuracy. The best-performing model pipeline (RF_B, a Random Forest with PCA on 2D+Morgan Fingerprints+3D features) achieved approximately 80% accuracy and 78.5% balanced accuracy on the held-out 20% test set. The final validated model was successfully utilized to predict the activity classes of new, unseen compounds. This comprehensive pipeline provides a validated tool for classifying HPSE inhibitors derived from ChEMBL data, potentially aiding virtual screening efforts and guiding hit prioritization in drug discovery campaigns targeting HPSE. - Source: PubMed
Publication date: 2026/05/29
Shanbhogue Rachana VGandhi Neha SB Shanthi PNavada Sandhyalaxmi G