CSTA

Last update 08 May 2025

Basic Info

Synonyms

CSTA, cystatin A, Cystatin-A

+ [5]

Introduction

This is an intracellular thiol proteinase inhibitor. Has an important role in desmosome-mediated cell-cell adhesion in the lower levels of the epidermis.

100 Clinical Results associated with CSTA

100 Translational Medicine associated with CSTA

0 Patents (Medical) associated with CSTA

364

Literatures (Medical) associated with CSTA

09 Apr 2025·Journal of Agricultural and Food Chemistry

Plant Tannase: Evolutionary and Functional Divergence Features

Article

Author: Dai, Xinlong ; Yang, Yanfen ; Xiao, Houhong ; Zhou, Xingrong ; Deng, Tian ; Xie, Youshudi ; Dong, Xue ; Chen, Mei ; Yang, Changli ; Chen, Dingli ; Ma, Jie ; Xia, Tao ; Tian, Fengyun

In plants, α/β-hydrolase regulates the hydrolysis of ester compounds, enriching the types and functions of specialized metabolites. In this study, three genes─CsCSE, CsCXE3, and CsTA─which encode caffeoyl shikimate esterase (CSE), carboxylesterase (CXE), and tannase (TA), respectively, were isolated from the tea plant genome through multiomics correlation analysis. Evolutionary analysis showed that CSE and CXE were ancient, whereas TA emerged in core eudicots approximately 120 million years ago. Enzyme activity assays revealed that CsCSE and CsCXE3 catalyze phenolic acid and acetate ester hydrolysis, respectively. Interestingly, CsTA not only has the catalytic function of CsCSE and CsCXE3 but also catalyzes the hydrolysis of galloylated catechin, hydrolyzable tannin, and hormone ester. Overall, CsTA is a newly derived α/β-hydrolase with evolutionary and functional divergence features. This study expands our understanding of the physiological significance of plant TA and provides insights into the potential role of CsTA in the complex metabolic processes of tea plants.

01 Mar 2025·The Clinical Respiratory Journal

Bioinformatic Insights and XGBoost Identify Shared Genetics in Chronic Obstructive Pulmonary Disease and Type 2 Diabetes

Article

Author: Yi, Chao ; Zhan, Yiqiang ; Ji, Qianqian ; Chen, Xiaoliang ; Han, Xiaojie ; Meng, Yaxian

ABSTRACTBackgroundThe correlation between chronic obstructive pulmonary disease (COPD) and Type 2 diabetes mellitus (T2DM) has long been recognized, but their shared molecular underpinnings remain elusive. This study aims to uncover common genetic markers and pathways in COPD and T2DM, providing insights into their molecular crosstalk.MethodsUtilizing the Gene Expression Omnibus (GEO) database, we analyzed gene expression datasets from six COPD and five T2DM studies. A multifaceted bioinformatics approach, encompassing the limma R package, unified matrix analysis, and weighted gene co‐expression network analysis (WGCNA), was deployed to identify differentially expressed genes (DEGs) and hub genes. Functional enrichment and protein–protein interaction (PPI) analyses were conducted, followed by cross‐species validation in Mus musculus models. Machine learning techniques, including random forest and LASSO regression, were applied for further validation, culminating in the development of a prognostic model using XGBoost.ResultsOur analysis revealed shared DEGs such as KIF1C, CSTA, GMNN, and PHGDH in both COPD and T2DM. Cross‐species comparison identified common genes including PON1 and CD14, exhibiting varying expression patterns. The random forest and LASSO regression identified six critical genes, with our XGBoost model demonstrating significant predictive accuracy (AUC = 0.996 for COPD).ConclusionsThis study identifies key genetic markers shared between COPD and T2DM, providing new insights into their molecular pathways. Our XGBoost model exhibited high predictive accuracy for COPD, highlighting the potential utility of these markers. These findings offer promising biomarkers for early detection and enhance our understanding of the diseases' interplay. Further validation in larger cohorts is recommended.

01 Feb 2025·Heliyon

In silico evaluation of missense SNPs in cancer-associated Cystatin A protein and their potential to disrupt Cathepsin B interaction

Article

Author: Saleh, Md Abu ; Sultana, Tamanna ; Bin Manjur, Omar Hamza ; Shimu, Mst Sharmin Sultana ; Rahman, Tania ; Al Mamun, Abdullah ; Rahman, Md Miftaur ; Naim, Mustafizur Rahman ; Siddique, Shahariar ; Hossain, Shafaat ; Hasan, Md Rakibul

Cystatin A (CSTA) functions as a cysteine protease inhibitor by forming tight complexes with the cathepsins. Pathogenic mutations in the CSTA gene can disrupt this interaction, potentially leading to physiological ailments. In this study, eight bioinformatics tools (SIFT, PolyPhen-2, PROVEAN, P-Mut, MutPred2, SNAP2, SNPs & GO, and PHD-SNP) were implemented to analyze non-synonymous SNPs from the dbSNP database. Five mutations (Y43C, Y43N, V48F, Y53H, and E94K) located in the conserved region were found to be highly deleterious and less stabilizing. The protein-protein interaction network found that Cathepsin B (CTSB) interacts highly with CSTA. Mutated CSTAs were created by homology modeling, and their altered binding with CTSB was examined through molecular docking and dynamics simulations. Among these, the Y53H (rs1448459675) and E94K (rs200394711) mutants were recognized as weaker inhibitors because they had 2.5 % and an 8 % lower binding affinity, respectively. Moreover, the E94K-CTSB complex, with a root mean square deviation (RMSD) above 5 Å, was found to be highly unstable during molecular dynamics. The root mean square fluctuation (RMSF) of the E94K mutant showed insufficient flexibility, indicating a reduced capacity to suppress CTSB. These findings suggest that the E94K mutation could affect the protein structure and cathepsin B interaction, potentially leading to pathological consequences as evidenced by colorectal adenocarcinoma patients in the COSMIC (Catalogue of Somatic Mutations in Cancer) database.

Analysis

Perform a panoramic analysis of this field.

view full example data

AI Agents Built for Biopharma Breakthroughs

Accelerate discovery. Empower decisions. Transform outcomes.

Get started for free today!

Accelerate Strategic R&D decision making with Synapse, PatSnap’s AI-powered Connected Innovation Intelligence Platform Built for Life Sciences Professionals.

Start your data trial now!

Synapse data is also accessible to external entities via APIs or data packages. Empower better decisions with the latest in pharmaceutical intelligence.

Bio

Bio Sequences Search & Analysis

Chemical

Chemical Structures Search & Analysis

CSTA

Basic Info

Related

Analysis