BHLHE23

Last update 08 May 2025

Basic Info

Synonyms

bA305P22.3, basic helix-loop-helix family member e23, Beta4

+ [4]

Introduction

May function as transcriptional repressor. May modulate the expression of genes required for the differentiation and/or maintenance of pancreatic and neuronal cell types. May be important for rod bipolar cell maturation (By similarity).

100 Clinical Results associated with BHLHE23

100 Translational Medicine associated with BHLHE23

0 Patents (Medical) associated with BHLHE23

806

Literatures (Medical) associated with BHLHE23

01 Mar 2024·Bioactive Materials

Hypoxia endothelial cells-derived exosomes facilitate diabetic wound healing through improving endothelial cell function and promoting M2 macrophages polarization

Article

Author: Zha, Kangkang ; Mi, Bobin ; Zhang, Yingze ; Hu, Hankun ; Xiong, Yuan ; Hu, Yixin ; Cheng, Peng ; Xue, Hang ; Hu, Liangcong ; Chen, Lang ; Liu, Guohui ; Lin, Ze ; Lin, Chuanlu ; Zhou, Wu ; Zhang, Yuxiao ; Hu, Yiqiang ; Dai, Guandong ; Lv, Bin ; Yu, Tengbo ; Xie, Xudong ; Zhang, Kunyu

It is imperative to develop and implement newer, more effective strategies to address refractory diabetic wounds. As of now, there is currently no optimal solution for these wounds. Hypoxic human umbilical vein endothelial cells (HUVECs)-derived exosomes have been postulated to promote diabetic wound healing, however, its effect and molecular mechanism need further study. In this study, we aimed to investigate whether hypoxic exosomes enhance wound healing in diabetics. Based on our high-throughput sequencing, differentially expressed lncRNAs (including 64 upregulated lncRNAs and 94 downregulated lncRNAs) were found in hypoxic exosomes compared to normoxic exosomes. Interestingly, lncHAR1B was one of the prominently upregulated lncRNAs in hypoxic exosomes, showing a notable correlation with diabetic wound healing. More specifically, hypoxic exosomes were transmitted to surrounding cells, which resulted in a significant increase in lncHAR1B level, thereby relieving the dysfunction of endothelial cells and promoting the switch from M1 to M2 macrophages under high glucose conditions. Mechanistically, lncHAR1B directly interacted with the transcription factor basic helix-loop-helix family member e23 (BHLHE23), which subsequently led to its binding to the KLF transcription factor 4 (KLF4) and promoted KLF4 expression. In our in vivo experiments, the use of hypoxic exosomes-loaded HGM-QCS hydrogels (Gel-H-Exos) resulted in rapid wound healing compared to that of normoxic exosomes-loaded HGM-QCS hydrogels (Gel-N-Exos) and diabetic groups. Consequently, our study provides potentially novel therapeutic approaches aimed at accelerating wound healing and developing a practical exosomes delivery platform.

01 Jan 2023·Frontiers in aging neuroscience

Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer's disease.

Article

Author: Yuan, Yulin ; Wu, Chunle ; Lai, Lichuan ; Wei, Huangwei

BackgroundAlzheimer's disease (AD) is an age-associated neurodegenerative disease, and the currently available diagnostic modalities and therapeutic agents are unsatisfactory due to its high clinical heterogeneity. Necroptosis is a common type of programmed cell death that has been shown to be activated in AD.MethodsIn this study, we first investigated the expression profiles of necroptosis-related genes (NRGs) and the immune landscape of AD based on GSE33000 dataset. Next, the AD samples in the GSE33000 dataset were extracted and subjected to consensus clustering based upon the differentially expressed NRGs. Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. Finally, we developed a diagnostic model for AD by comparing four different machine learning approaches. The discrimination performance and clinical relevance of the diagnostic model were assessed using various evaluation metrics, including the nomogram, calibration plot, decision curve analysis (DCA), and independent validation datasets.ResultsAberrant expression patterns of NRGs and specific immune landscape were identified in the AD samples. Consensus clustering revealed that patients in the GSE33000 dataset could be classified into two necroptosis clusters, each with distinct immune landscapes and enriched pathways. The Extreme Gradient Boosting (XGB) was found to be the most optimal diagnostic model for the AD based on the predictive ability and reliability of the models constructed by four machine learning approaches. The five most important variables, including ACAA2, BHLHB4, CACNA2D3, NRN1, and TAC1, were used to construct a five-gene diagnostic model. The constructed nomogram, calibration plot, DCA, and external independent validation datasets exhibited outstanding diagnostic performance for AD and were closely related with the pathologic hallmarks of AD.ConclusionThis work presents a novel diagnostic model that may serve as a framework to study disease heterogeneity and provide a plausible mechanism underlying neuronal loss in AD.

01 Sep 2022·Diabetes

Heterogeneous Development of β-Cell Populations in Diabetes-Resistant and -Susceptible Mice

Article

Author: Lickert, Heiko ; Gottmann, Pascal ; Jähnert, Markus ; Zuljan, Erika ; Sterr, Michael ; Santos, Patrícia Martínez ; Stadion, Mandy ; Aga, Heja ; Speckmann, Thilo ; Bornstein, Stefan R. ; Theis, Fabian J. ; Büttner, Maren ; Schürmann, Annette

Progressive dysfunction and failure of insulin-releasing β-cells are a hallmark of type 2 diabetes (T2D). To study mechanisms of β-cell loss in T2D, we performed islet single-cell RNA sequencing of two obese mouse strains differing in their diabetes susceptibility. With mice on a control diet, we identified six β-cell clusters with similar abundance in both strains. However, after feeding of a diabetogenic diet for 2 days, β-cell cluster composition markedly differed between strains. Islets of diabetes-resistant mice developed into a protective β-cell cluster (Beta4), whereas those of diabetes-prone mice progressed toward stress-related clusters with a strikingly different expression pattern. Interestingly, the protective cluster showed indications of reduced β-cell identity, such as downregulation of GLUT2, GLP1R, and MafA, and in vitro knockdown of GLUT2 in β-cells—mimicking its phenotype—decreased stress response and apoptosis. This might explain enhanced β-cell survival of diabetes-resistant islets. In contrast, β-cells of diabetes-prone mice responded with expression changes indicating metabolic pressure and endoplasmic reticulum stress, presumably leading to later β-cell loss. In conclusion, failure of diabetes-prone mice to adapt gene expression toward a more dedifferentiated state in response to rising blood glucose levels leads to β-cell failure and diabetes development.

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

BHLHE23

Basic Info

Related

Analysis