SDSL

Last update 08 May 2025

Basic Info

Synonyms

cSDH, L-serine deaminase, L-serine dehydratase/L-threonine deaminase

+ [8]

Introduction

Catalyzes the pyridoxal-phosphate-dependent dehydrative deamination of L-threonine and L-serine to ammonia and alpha-ketobutyrate and pyruvate, respectively (PubMed:16580895, PubMed:18342636). Also exhibits racemase activity towards L-glutamate and D-glutamate (By similarity).

100 Clinical Results associated with SDSL

100 Translational Medicine associated with SDSL

0 Patents (Medical) associated with SDSL

Literatures (Medical) associated with SDSL

01 Aug 2024·Journal of Cellular and Molecular Medicine

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers

Article

Author: Hu, Chengcheng ; Dai, Hualei ; Liu, Ying ; Zhu, Meng ; Luo, Peng ; Zhang, Guimin ; Jiang, Aimin ; Tao, Siming

AbstractHypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contributor to mortality. While the involvement of immune inflammation in the development of cardiac ailments is well‐documented, its specific impact on HCM pathogenesis remains uncertain. Five distinct machine learning algorithms, namely LASSO, SVM, RF, Boruta and XGBoost, were utilized to discover new biomarkers associated with HCM. A unique nomogram was developed using two newly identified biomarkers and subsequently validated. Furthermore, samples of HCM and normal heart tissues were gathered from our institution to confirm the variance in expression levels and prognostic significance of GATM and MGST1. Five novel biomarkers (DARS2, GATM, MGST1, SDSL and ARG2) associated with HCM were identified. Subsequent validation revealed that GATM and MGST1 exhibited significant diagnostic utility for HCM in both the training and test cohorts, with all AUC values exceeding 0.8. Furthermore, a novel risk assessment model for HCM patients based on the expression levels of GATM and MGST1 demonstrated favourable performance in both the training (AUC = 0.88) and test cohorts (AUC = 0.9). Furthermore, our study revealed that GATM and MGST1 exhibited elevated expression levels in HCM tissues, demonstrating strong discriminatory ability between HCM and normal cardiac tissues (AUC of GATM = 0.79; MGST1 = 0.86). Our findings suggest that two specific cell types, monocytes and multipotent progenitors (MPP), may play crucial roles in the pathogenesis of HCM. Notably, GATM and MGST1 were found to be highly expressed in various tumours and showed significant prognostic implications. Functionally, GATM and MGST1 are likely involved in xenobiotic metabolism and epithelial mesenchymal transition in a wide range of cancer types. GATM and MGST1 have been identified as novel biomarkers implicated in the progression of both HCM and cancer. Additionally, monocytes and MPP may also play a role in facilitating the progression of HCM.

01 Mar 2024·International Journal of Cardiology

Identification of cuproptosis-related genes and immune infiltration in dilated cardiomyopathy

Article

Author: Chen, Pengxiao ; Qian, Zhi Rong ; Zhang, Tong ; Guo, Jinhua ; Lin, Yixuan ; Chen, Kaicong

BACKGROUNDDilated cardiomyopathy (DCM) is a leading cause of heart failure. Cuproptosis is involved in various diseases, although its role in DCM is still unclear. Here, this study aims to investigate the feasibility of using genes related to cuproptosis as diagnostic biomarkers for DCM and the association of their expression with immune infiltration and drug target in cardiac tissue.METHODSGene expression data from nonfailure (NF) and DCM samples were retrieved from the GEO database. Cuproptosis scores were calculated using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) was used to screen key modules associated with DCM and cuproptosis. Random forest and least absolute shrinkage and selection operator (LASSO) were applied to identify signature genes. Finally, immune cell infiltration was assessed using ssGSEA. mRNA-miRNA-lncRNA regulatory networks and chemical-drug regulatory networks based on signature genes were analyzed by Cytoscape.RESULTS8 modules were aggregated by WGCNA, among which MEblue was significantly associated with cuproptosis scores and DCM. A diagnostic model made up of six signature genes including SEPTIN1, CLEC11A, ISG15, P3H3, SDSL, and INKA1 was selected. Furthermore, immune infiltration studies showed significant differences between DCM and NF. Drugs networks and ceRNA regulatory network based on six signature genes were successfully constructed.CONCLUSIONSix signature genes (SEPTIN1, CLEC11A, ISG15, P3H3, SDSL, and INKA1) were identified as novel diagnostic biomarkers in DCM. In addition, the expression of these genes was associated with immune cell infiltration, suggesting that cuproptosis may be involved in the immune regulation of DCM.

01 Oct 2023·Apoptosis

Comprehensive analysis of mitochondrial dysfunction and necroptosis in intracranial aneurysms from the perspective of predictive, preventative, and personalized medicine

Article

Author: Peng, Renjun ; Zhang, Liyang ; Yang, Liting ; Chen, Bo ; Xie, Kang ; Zhang, Jianzhong ; Zhou, Hongshu

AbstractMitochondrial dysfunction and necroptosis are closely associated, and play vital roles in the medical strategy of multiple cardiovascular diseases. However, their implications in intracranial aneurysms (IAs) remain unclear. In this study, we aimed to explore whether mitochondrial dysfunction and necroptosis could be identified as valuable starting points for predictive, preventive, and personalized medicine for IAs. The transcriptional profiles of 75 IAs and 37 control samples were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis, and least absolute shrinkage and selection operator (LASSO) regression were used to screen key genes. The ssGSEA algorithm was performed to establish phenotype scores. The correlation between mitochondrial dysfunction and necroptosis was evaluated using functional enrichment crossover, phenotype score correlation, immune infiltration, and interaction network construction. The IA diagnostic values of key genes were identified using machine learning. Finally, we performed the single-cell sequencing (scRNA-seq) analysis to explore mitochondrial dysfunction and necroptosis at the cellular level. In total, 42 IA-mitochondrial DEGs and 15 IA-necroptosis DEGs were identified. Screening revealed seven key genes invovled in mitochondrial dysfunction (KMO, HADH, BAX, AADAT, SDSL, PYCR1, and MAOA) and five genes involved in necroptosis (IL1B, CAMK2G, STAT1, NLRP3, and BAX). Machine learning confirmed the high diagnostic value of these key genes for IA. The IA samples showed higher expression of mitochondrial dysfunction and necroptosis. Mitochondrial dysfunction and necroptosis exhibited a close association. Furthermore, scRNA-seq indicated that mitochondrial dysfunction and necroptosis were preferentially up-regulated in monocytes/macrophages and vascular smooth muscle cells (VSMCs) within IA lesions. In conclusion, mitochondria-induced necroptosis was involved in IA formation, and was mainly up-regulated in monocytes/macrophages and VSMCs within IA lesions. Mitochondria-induced necroptosis may be a novel potential target for diagnosis, prevention, and treatment of IA.

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