AI Article Synopsis

  • Research shows a significant link between specific gene variants (SNPs) of the HPSE gene and the risk of developing graft-versus-host disease (GVHD) after stem cell transplants.
  • The expression of heparanase changes in response to pretransplant conditioning, and differences in HPSE gene variants between donors and recipients influence recovery times and GVHD risk, highlighting the role of these genetic factors in the success of transplants.

Article Abstract

Heparanase is an endo-β-glucuronidase that specifically cleaves the saccharide chains of HSPGs, important structural and functional components of the ECM. Cleavage of HS leads to loss of the structural integrity of the ECM and release of HS-bound cytokines, chemokines, and bioactive angiogenic- and growth-promoting factors. Our previous study revealed a highly significant correlation of HPSE gene SNPs rs4693608 and rs4364254 and their combination with the risk of developing GVHD. We now demonstrate that HPSE is up-regulated in response to pretransplantation conditioning, followed by a gradual decrease thereafter. Expression of heparanase correlated with the rs4693608 HPSE SNP before and after conditioning. Moreover, a positive correlation was found between recipient and donor rs4693608 SNP discrepancy and the time of neutrophil and platelet recovery. Similarly, the discrepancy in rs4693608 HPSE SNP between recipients and donors was found to be a more significant factor for the risk of aGVHD than patient genotype. The rs4693608 SNP also affected HPSE gene expression in LPS-treated MNCs from PB and CB. Possessors of the AA genotype exhibited up-regulation of heparanase with a high ratio in the LPS-treated MNCs, whereas individuals with genotype GG showed down-regulation or no effect on HPSE gene expression. HPSE up-regulation was mediated by TLR4. The study emphasizes the importance of rs4693608 SNP for HPSE gene expression in activated MNCs, indicating a role in allogeneic stem cell transplantation, including postconditioning, engraftment, and GVHD.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4046248PMC
http://dx.doi.org/10.1189/jlb.0313147DOI Listing

Publication Analysis

Top Keywords

gene expression
16
rs4693608 snp
16
hpse gene
16
hpse
8
rs4693608 hpse
8
hpse snp
8
snp hpse
8
lps-treated mncs
8
rs4693608
7
snp
6

Similar Publications

Pathway metrics accurately stratify T cells to their cells states.

BioData Min

December 2024

The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.

Pathway analysis is a powerful approach for elucidating insights from gene expression data and associating such changes with cellular phenotypes. The overarching objective of pathway research is to identify critical molecular drivers within a cellular context and uncover novel signaling networks from groups of relevant biomolecules. In this work, we present PathSingle, a Python-based pathway analysis tool tailored for single-cell data analysis.

View Article and Find Full Text PDF

Objective: The evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi-omics analysis have shown great potential in the field of cancer diagnosis and treatment.

Methods: A GNNs model was constructed to predict the immunotherapy response and identify key pathways.

View Article and Find Full Text PDF

Background: CRISPR is widely used to silence genes by inducing mutations expected to nullify their expression. While numerous computational tools have been developed to design single-guide RNAs (sgRNAs) with high cutting efficiency and minimal off-target effects, only a few tools focus specifically on predicting gene knockouts following CRISPR. These tools consider factors like conservation, amino acid composition, and frameshift likelihood.

View Article and Find Full Text PDF

Attenuated sex-related DNA methylation differences in cancer highlight the magnitude bias mediating existing disparities.

Biol Sex Differ

December 2024

State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China.

Background: DNA methylation (DNAm) influences both sex differences and cancer development, yet the mechanisms connecting these factors remain unclear.

Methods: Utilizing data from The Cancer Genome Atlas, we conducted a comprehensive analysis of sex-related DNAm effects in nine non-reproductive cancers, compared to paired normal adjacent tissues (NATs), and validated the results using independent datasets. First, we assessed the extent of sex differential DNAm between cancers and NATs to explore how sex-related DNAm differences change in cancerous tissues.

View Article and Find Full Text PDF

Background: Calcium-dependent protein kinases (CDPKs), play multiple roles in plant development, growth and response to bio- or abiotic stresses. Calmodulin-like domains typically contain four EF-hand motifs for Ca²⁺ binding. The CDPK gene family can be divided into four subgroups in Arabidopsis, and it has been identified in many plants, such as rice, tomato, but has not been investigated in alfalfa (Medicago sativa subsp.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!

A PHP Error was encountered

Severity: Notice

Message: fwrite(): Write of 34 bytes failed with errno=28 No space left on device

Filename: drivers/Session_files_driver.php

Line Number: 272

Backtrace:

A PHP Error was encountered

Severity: Warning

Message: session_write_close(): Failed to write session data using user defined save handler. (session.save_path: /var/lib/php/sessions)

Filename: Unknown

Line Number: 0

Backtrace: