Genetic and functional variants of the TBX20 gene promoter in dilated cardiomyopathy.

BACKGROUND: Dilated cardiomyopathy (DCM) is a major cause of heart failure and sudden cardiac death. As DCM is a genetically heterogeneous disease, genetic variants of cardiac transcription factor genes may play an important role. Transcription factor TBX20, an indispensable factor in normal heart development, is involved in the regulation of cardiac structure and function. Although the TBX20 gene is associated with the occurrence and development of DCM, the influence of genetic variants of the TBX20 gene promoter region on DCM has not been reported. METHODS: We conducted a case-control study consisting of 107 DCM patients and 210 healthy controls. Genetic variants within TBX20 gene promoter region were identified using sequencing techniques and were functionally analyzed by dual-luciferase reporting assay. Electrophoretic mobility shift assay (EMSA) was used to investigate DNA-protein interactions. RESULTS: In this study cohort (n = 317), we identified eight variants within TBX20 gene promoter. One novel DNA sequence variants (DSV) (g.4275G>T) and four single-nucleotide polymorphisms (SNPs) [g.4169G>A (rs1263874255), g.4949C>T (rs1191745927), g.5114G>A (rs112076877), g.5252C>T (rs1356932911)] were identified in DCM patients, but in none of controls. Among them, the DSV (g.4275G>T) and three SNPs [g.4949C>T (rs1191745927), g.5114G>A (rs112076877) and g.5252C>T (rs1356932911)] significantly altered the transcription activity of TBX20 gene promoter by dual-luciferase reporting assay (p < 0.05). Further, EMSA assay indicated that the DSV (g.4275G>T) and three SNPs [g.4949C>T (rs1191745927), g.5114G>A (rs112076877) and g.5252C>T (rs1356932911)] affected the binding of transcription factors. CONCLUSIONS: These data indicate that the DSV (g.4275G>T) and three SNPs [g.4949C>T (rs1191745927), g.5114G>A (rs112076877) and g.5252C>T (rs1356932911)] increase transcription activity of TBX20 gene promoter in both HEK-293 and neonatal rat cardiomyocytes (NRCMs) cell lines by affecting the binding of transcription factors. But the mechanism remains to be verified in vivo.

The Gut Microbiome Affects Atherosclerosis by Regulating Reverse Cholesterol Transport.

The human system's secret organ, the gut microbiome, has received considerable attention. Emerging research has yielded substantial scientific evidence indicating that changes in gut microbial composition and microbial metabolites may contribute to the development of atherosclerotic cardiovascular disease. The burden of cardiovascular disease on healthcare systems is exacerbated by atherosclerotic cardiovascular disease, which continues to be the leading cause of mortality globally. Reverse cholesterol transport is a powerful protective mechanism that effectively prevents excessive accumulation of cholesterol for atherosclerotic cardiovascular disease. It has been revealed how the gut microbiota modulates reverse cholesterol transport in patients with atherosclerotic risk. In this review, we highlight the complex interactions between microbes, their metabolites, and their potential impacts in reverse cholesterol transport. We also explore the feasibility of modulating gut microbes and metabolites to facilitate reverse cholesterol transport as a novel therapy for atherosclerosis.

Molecular Genetic Study on HAND2 Gene Promoter in Ventricular Septal Defect.

Ventricular septal defect (VSD), the most common type of congenital heart disease (CHD), is primarily caused by cardiac dysplasia. Heart and neural crest derivatives expressed 2 (HAND2) participates in developing the right heart. The loss of HAND2 expression in humans is closely connected with ventricular septal defects. We used a case-control study to analyze the genetic variations in the HAND2 promoter region in VSD patients and controls. Some statistical analysis methods were used to analyze the association of single nucleotide polymorphisms (SNPs) with VSD. The dual-luciferase reporter assay and electrophoretic mobility shift assay (EMSA) were used to conduct functional analysis and molecular mechanism study of genetic variations. Through sequencing, we identified nine genetic variants in patients with VSD. The SNP rs2276940 G>T and rs2276941 G>A were associated with an increased risk of VSD. The dual-luciferase reporter assay showed that SNP rs2276940 G>T and rs138531627 C>G decreased the transcriptional activity of the HAND2 promoter. Transcription factors (TFs) predicting suggested that all three SNPs may change the binding of TFs. The result of EMSA showed that rs138531627 C>G may create a new binding site for TFs while rs2276940 G>T enhanced the binding affinity for TFs. These results indicated that genetic variants of the HAND2 promoter may increase the risk of VSD, and the molecular mechanism may be the change of the binding affinity of TFs.

Genetically encoded protein sensors for metal ion detection in biological systems: a review and bibliometric analysis.

Metal ions are indispensable elements in living organisms and are associated with regulating various biological processes. An imbalance in metal ion content can lead to disorders in normal physiological functions of the human body and cause various diseases. Genetically encoded fluorescent protein sensors have the advantages of low biotoxicity, high specificity, and a long imaging time in vivo and have become a powerful tool to visualize or quantify the concentration level of biomolecules in vivo and in vitro, temporal and spatial distribution, and life activity process. This review analyzes the development status and current research hotspots in the field of genetically encoded fluorescent protein sensors by bibliometric analysis. Based on the results of bibliometric analysis, the research progress of genetically encoded fluorescent protein sensors for metal ion detection is reviewed, and the construction strategies, physicochemical properties, and applications of such sensors in biological imaging are summarized.

The Role of Macrophages in Atherosclerosis: Participants and Therapists.

Currently, atherosclerosis, characterized by the dysfunction of lipid metabolism and chronic inflammation in the intimal space of the vessel, is considered to be a metabolic disease. As the most abundant innate immune cells in the body, macrophages play a key role in the onset, progression, or regression of atherosclerosis. For example, macrophages exhibit several polarization states in response to microenvironmental stimuli; an increasing proportion of macrophages, polarized toward M2, can suppress inflammation, scavenge cell debris and apoptotic cells, and contribute to tissue repair and fibrosis. Additionally, specific exosomes, generated by macrophages containing certain miRNAs and effective efferocytosis of macrophages, are crucial for atherosclerosis. Therefore, macrophages have emerged as a novel potential target for anti-atherosclerosis therapy. This article reviews the role of macrophages in atherosclerosis from different aspects: origin, phenotype, exosomes, and efferocytosis, and discusses new approaches for the treatment of atherosclerosis.

[Association of ventricular septal defect with rare variations of the HAND2 gene]. / 室间隔缺损与HAND2åŸºå› ç½•è§å˜å¼‚çš„å ³è”åˆ†æž.

OBJECTIVES: To study the association of ventricular septal defect (VSD) with rare variations in the promoter region of HAND2 gene, as well as related molecular mechanisms. METHODS: Blood samples were collected from 349 children with VSD and 345 healthy controls. The target fragments were amplified by polymerase chain reaction and sequenced to identify the rare variation sites in the promoter region of the HAND2 gene. Dual-luciferase reporter assay was used to perform a functional analysis of the variation sites. Electrophoretic mobility shift assay (EMSA) was used to investigate related molecular mechanisms. TRANSFAC and JASPAR databases were used to predict transcription factors. RESULTS: Sequencing revealed that three variation sites (g.173530852A>G, g.173531173A>G, and g.173531213C>G) were only observed in the promoter region of the HAND2 gene in 10 children with VSD, among whom 4 children had only one variation site. The dual-luciferase reporter assay revealed that g.173531213C>G reduced the transcriptional activity of the HAND2 gene promoter. EMSA and transcription factor prediction revealed that g.173531213C>G created a binding site for transcription factor. CONCLUSIONS: The rare variation, g.173531213C>G, in the promoter region of the HAND2 gene participates in the development and progression of VSD possibly by affecting the binding of transcription factors.

Correlation Analysis of CTSB Promoter Polymorphism and Function in Patients with Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is caused by a combination of genetic susceptibility and environmental factors. Cathepsin B affects the pathogenesis of DCM; however, its molecular mechanism is still unclear. In this study, we examined the association of rare CTSB variants with the occurrence of DCM. This case-control study involved 394 participants: 142 patients with DCM and 252 healthy controls. DNA was extracted from the peripheral leukocytes of all participants, and CTSB variants were analyzed and identified using polymerase chain reaction amplification. Functional analysis was performed using the dual-luciferase reporter assay, and the ability of genetic CTSB variants to bind to transcription factors (TFs) was analyzed and validated using the electrophoretic mobility shift assay (EMSA). Two single-nucleotide polymorphisms (SNPs) were identified in the study population. One SNP, g.4803 T > C (rs1293312), was more common in patients with DCM. A second SNP, g.4954 T > A (rs942670850), was identified in two patients with DCM. Both SNPs significantly enhanced the transcriptional activity of CTSB promoters. An analysis using the TRANSFAC database revealed that these SNPs affect TF binding, which was confirmed using the EMSA. Our results demonstrate that within the CTSB promoter, the genetic variants g.4803T>C (rs1293312) and g.4954 T > A (rs942670850) are rare risk factors for DCM development.

Genetic Variants of SIRT1 Gene Promoter in Type 2 Diabetes.

Type 2 diabetes (T2D) is a highly heterogeneous and polygenic disease. To date, genetic causes and underlying mechanisms for T2D remain unclear. SIRT1, one member of highly conserved NAD-dependent class III deacetylases, has been implicated in many human diseases. Accumulating evidence indicates that SIRT1 is involved in insulin resistance and impaired pancreatic ß-cell function, the two hallmarks of T2D. Thus, we speculated that altered SIRT1 levels, resulting from the genetic variants within its regulatory region of SIRT1 gene, may contribute to the T2D development. In this study, the SIRT1 gene promoter was genetically analyzed in T2D patients (n = 218) and healthy controls (n = 358). A total of 20 genetic variants, including 7 single-nucleotide polymorphisms (SNPs), were identified. Five heterozygous genetic variants (g.4114-15InsA, g.4801G > A, g.4816G > C, g.4934G > T, and g.4963_64Ins17bp) and one SNP (g.4198A > C (rs35706870)) were identified in T2D patients, but in none of the controls. The frequencies of two SNPs (g.4540A > G (rs3740051) (OR: 1.75, 95% CI: 1.24-2.47, P < 0.001 in dominant genetic model) and g.4821G > T (rs35995735)) (OR: 3.58, 95% CI: 1.94-6.60, P < 0.001 in dominant genetic model) were significantly higher in T2D patients. Further association and haplotype analyses confirmed that these two SNPs were strongly linked, contributing to the T2D (OR: 1.442, 95% CI: 1.080-1.927, P < 0.05). Moreover, most of the genetic variants identified in T2D were disease-specific. Taken together, the genetic variants within SIRT1 gene promoter might contribute to the T2D development by altering SIRT1 levels. Underlying molecular mechanism needs to be further explored.

A review of the current research on in vivo and in vitro detection for alpha-synuclein: a biomarker of Parkinson's disease.

Parkinson's disease is a health-threatening neurodegenerative disease of the elderly with clinical manifestations of motor and non-motor deficits such as tremor palsy and loss of smell. Alpha-synuclein (α-Syn) is the pathological basis of PD, it can abnormally aggregate into insoluble forms such as oligomers, fibrils, and plaques, causing degeneration of nigrostriatal dopaminergic neurons in the substantia nigra in the patient's brain and the formation of Lewy bodies (LBs) and Lewy neuritis (LN) inclusions. As a result, achieving α-Syn aggregate detection in the early stages of PD can effectively stop or delay the progression of the disease. In this paper, we provide a brief overview and analysis of the molecular structures and α-Syn in vivo and in vitro detection methods, such as mass spectrometry, antigen-antibody recognition, electrochemical sensors, and imaging techniques, intending to provide more technological support for detecting α-Syn early in the disease and intervening in the progression of Parkinson's disease.

Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation.

Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because, although the human visual system can detect symmetries in 2-D images effectively, regular CNNs can only exploit translation invariance, overlooking further inherent symmetries existing in medical images, such as rotations and reflections. To solve this problem, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations. First, kernel-based equivariant operations are devised on each orientation, which allows it to effectively address the gaps of learning symmetries in existing approaches. Then, to keep segmentation networks globally equivariant, we design distinctive group layers with layer-wise symmetry constraints. Finally, based on our novel framework, extensive experiments conducted on real-world clinical data demonstrate that a group equivariant Res-UNet (called GER-UNet) outperforms its regular CNN-based counterpart and the state-of-the-art segmentation methods in the tasks of hepatic tumor segmentation, COVID-19 lung infection segmentation, and retinal vessel detection. More importantly, the newly built GER-UNet also shows potential in reducing the sample complexity and the redundancy of filters, upgrading current segmentation CNNs, and delineating organs on other medical imaging modalities.

Preparation and Evaluation of Animal Models of Cardiotoxicity in Antineoplastic Therapy.

The continuous development of antineoplastic therapy has significantly reduced the mortality of patients with malignant tumors, but its induced cardiotoxicity has become the primary cause of long-term death in patients with malignant tumors. However, the pathogenesis of cardiotoxicity of antineoplastic therapy is currently unknown, and practical means of prevention and treatment are lacking in clinical practice. Therefore, how to effectively prevent and treat cardiotoxicity while treating tumors is a major challenge. Animal models are important tools for studying cardiotoxicity in antitumor therapy and are of great importance in elucidating pathophysiological mechanisms and developing and evaluating modality drugs. In this paper, we summarize the existing animal models in antitumor therapeutic cardiotoxicity studies and evaluate the models by observing the macroscopic signs, echocardiography, and pathological morphology of the animals, aiming to provide a reference for subsequent experimental development and clinical application.

Training radiomics-based CNNs for clinical outcome prediction: Challenges, strategies and findings.

Radiological images play a central role in radiotherapy, especially in target volume delineation. Radiomic feature extraction has demonstrated its potential for predicting patient outcome and cancer risk assessment prior to treatment. However, inherent methodological challenges such as severe class imbalance, small training sample size, multi-centre data and weak correlation of image representations to outcomes are yet to be addressed adequately. Current radiomic analysis relies on segmented images (e.g., of tumours) for feature extraction, leading to loss of important context information in surrounding tissue. In this work, we examine the correlation between radiomics and clinical outcomes by combining two data modalities: pre-treatment computerized tomography (CT) imaging data and contours of segmented gross tumour volumes (GTVs). We focus on a clinical head & neck cancer dataset and design an efficient convolutional neural network (CNN) architecture together with appropriate machine learning strategies to cope with the challenges. During the training process on two cohorts, our algorithm learns to produce clinical outcome predictions by automatically extracting radiomic features. Test results on two other cohorts show state-of-the-art performance in predicting different clinical endpoints (i.e., distant metastasis: AUC = 0.91; loco-regional failure: AUC = 0.78; overall survival: AUC = 0.70 on segmented CT data) compared to prior studies. Furthermore, we also conduct extensive experiments both on the whole CT dataset and a combination of CT and GTV contours to investigate different learning strategies for this task. For example, further experiments indicate that overall survival prediction significantly improves to 0.83 AUC by combining CT and GTV contours as inputs, and the combination provides more intuitive visual explanations for patient outcome predictions.

2D medical image segmentation via learning multi-scale contextual dependencies.

Automatic medical image segmentation plays an important role as a diagnostic aid in the identification of diseases and their treatment in clinical settings. Recently proposed methods based on Convolutional Neural Networks (CNNs) have demonstrated their potential in image processing tasks, including some medical image analysis tasks. Those methods can learn various feature representations with numerous weight-shared convolutional kernels, however, the missed diagnosis rate of regions of interest (ROIs) is still high in medical image segmentation. Two crucial factors behind this shortcoming, which have been overlooked, are small ROIs from medical images and the limited context information from the existing network models. In order to reduce the missed diagnosis rate of ROIs from medical images, we propose a new segmentation framework which enhances the representative capability of small ROIs (particularly in deep layers) and explicitly learns global contextual dependencies in multi-scale feature spaces. In particular, the local features and their global dependencies from each feature space are adaptively aggregated based on both the spatial and the channel dimensions. Moreover, some visualization comparisons of the learned features from our framework further boost neural networks' interpretability. Experimental results show that, in comparison to some popular medical image segmentation and general image segmentation methods, our proposed framework achieves the state-of-the-art performance on the liver tumor segmentation task with 91.18% Sensitivity, the COVID-19 lung infection segmentation task with 75.73% Sensitivity and the retinal vessel detection task with 82.68% Sensitivity. Moreover, it is possible to integrate (parts of) the proposed framework into most of the recently proposed Fully CNN-based models, in order to improve their effectiveness in medical image segmentation tasks.

Genetic Variants and Functional Analyses of the ATG16L1 Gene Promoter in Acute Myocardial Infarction.

BACKGROUND: Acute myocardial infarction (AMI), a common complex disease caused by an interaction between genetic and environmental factors, is a serious type of coronary artery disease and is also a leading cause of death worldwide. Autophagy-related 16-like 1 (ATG16L1) is a key regulatory factor of autophagy and plays an important role in induced autophagy. In the cardiovascular system, autophagy is essential to preserve the homeostasis and function of the heart and blood vessels. No studies have hitherto examined the association between AMI and ATG16L1 gene promoter. METHODS: We conducted a case-control study, using polymerase chain reaction and sequencing techniques, dual luciferase reporter assay, and electrophoretic mobility shift assay, to analyze genetic and functional variation in the ATG16L1 gene promoter between AMI and controls. A variety of statistical analyses were used to analyze the allele and genotype frequencies and the relationship between single-nucleotide polymorphisms (SNPs) and AMI. RESULTS: In all, 10 SNPs and two DNA-sequence variants (DSVs) were identified in 688 subjects, and three ATG16L1 gene promoter mutations [g.233250693 T > C (rs185213911), g.233250946 G > A (rs568956599), g.233251133 C > G (rs1301744254)] that were identified in AMI patients significantly altered the transcriptional activity of ATG16L1 gene promoter in HEH2, HEK-293, and H9c2 cells (P < 0.05). Further electrophoretic mobility shift assays indicated that the SNPs affected the binding of transcription factors (P < 0.01). CONCLUSION: ATG16L1 gene promoter mutations in AMI patients may affect the binding of transcription factors and change the transcriptional activity of the ATG16L1 gene, changing the level of autophagy and contributing to the occurrence and development of AMI as rare and low-frequency risk factors.

Identification and functional study of GATA4 gene regulatory variants in atrial septal defects.

BACKGROUND: Congenital heart disease (CHD) is the leading cause of mortality from birth defects. In adult CHD patients with successful surgical repair, cardiac complications including heart failure develop at late stage, likely due to genetic causes. To date, many mutations in cardiac developmental genes have been associated with CHD. Recently, regulatory variants in genes have been linked to many human diseases. Although mutations and splicing variants in GATA4 gene have been reported in CHD patients, few regulatory variants of GATA4 gene are identified in CHD patients. METHODS: GATA4 gene regulatory region was investigated in the patients with atrial septal defects (ASD) (n = 332) and ethnic-matched controls (n = 336). RESULTS: Five heterozygous regulatory variants including four SNPs [g.31360 T>C (rs372004083), g.31436G>A, g.31437C>A (rs769262495), g.31487C>G (rs1053351749) and g.31856C>T (rs1385460518)] were only identified in ASD patients. Functional analysis indicated that the regulatory variants significantly affected the transcriptional activity of GATA4 gene promoter. Furthermore, two of the five regulatory variants have evidently effected on transcription factor binding sites. CONCLUSIONS: Our data suggested that GATA4 gene regulatory variants may confer ASD susceptibility by decreasing GATA4 levels.

A potential association between immunosenescence and high COVID-19 related mortality among elderly patients with cardiovascular diseases.

Elderly patients with cardiovascular diseases account for a large proportion of Corona virus Disease 2019(COVID-19)related deaths. COVID-19, as a new coronavirus, mainly targets the patient's lung triggering a cascade of innate and adaptive immune responses in the host. The principal causes of death among COVID-19 patients, especially elderly subjects with cardiovascular diseases, are acute respiratory distress syndrome(ARDS), multiple organ dysfunction syndrome (MODS), and microvascular thrombosis. All prompted by an excessive uncontrolled systemic inflammatory response. Immunosenescence, characterized by systemic and chronic inflammation as well as innate/adaptive immune imbalance, presents both in the elderly and cardiovascular patients. COVID-19 infection further aggravates the existing inflammatory process and lymphocyte depletion leading to uncontrollable systemic inflammatory responses, which is the primary cause of death. Based on the higher mortality, this study attempts to elucidate the pathophysiological mechanisms of COVID-19 in elderly subjects with cardiovascular diseases as well as the cause of the high mortality result from COVID-19.

Identification and functional study of GATA4 gene regulatory variants in type 2 diabetes mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2D) is a common and complex disease. Dysfunction of pancreatic ß cells, which cannot release sufficient insulin, plays a central role in T2D. Genetics plays a critical role in T2D etiology. Transcription factor GATA4 is required for the pancreatic development, and GATA4 gene mutations are implicated in neonatal or childhood-onset diabetes. In this study, we aimed to investigate whether regulatory variants in GATA4 gene may change GATA4 levels, conferring susceptibility to T2D development. METHODS: The promoter region of GATA4 gene was analyzed by targeted sequencing in T2D patients (n = 255) and ethnic-matched controls (n = 371). Dual luciferase activity assay was used for functional study, and EMSA (electrophoretic mobility shift assay) was performed for detecting transcription factor binding. RESULTS: Thirteen regulatory variants including 5 SNPs were identified. A novel heterozygous variant (32124C > T) and one SNP [31487C > G (rs1053351749)] were only identified in T2D. Both regulatory variants significantly affected GATA4 gene promoter activity in cultured HEK-293 and INS-1 cells. Furthermore, the variant (32124C > T) evidently enhanced the binding of unknown transcriptional activator. CONCLUSIONS: Our data suggested that GATA4 gene regulatory variants may contribute to T2D development as a rare risk factor.

Tumor attention networks: Better feature selection, better tumor segmentation.

Compared with the traditional analysis of computed tomography scans, automatic liver tumor segmentation can supply precise tumor volumes and reduce the inter-observer variability in estimating the tumor size and the tumor burden, which could further assist physicians to make better therapeutic choices for hepatic diseases and monitoring treatment. Among current mainstream segmentation approaches, multi-layer and multi-kernel convolutional neural networks (CNNs) have attracted much attention in diverse biomedical/medical image segmentation tasks with remarkable performance. However, an arbitrary stacking of feature maps makes CNNs quite inconsistent in imitating the cognition and the visual attention of human beings for a specific visual task. To mitigate the lack of a reasonable feature selection mechanism in CNNs, we exploit a novel and effective network architecture, called Tumor Attention Networks (TA-Net), for mining adaptive features by embedding Tumor Attention layers with multi-functional modules to assist the liver tumor segmentation task. In particular, each tumor attention layer can adaptively highlight valuable tumor features and suppress unrelated ones among feature maps from 3D and 2D perspectives. Moreover, an analysis of visualization results illustrates the effectiveness of our tumor attention modules and the interpretability of CNNs for liver tumor segmentation. Furthermore, we explore different arrangements of skip connections in information fusion. A deep ablation study is also conducted to illustrate the effects of different attention strategies for hepatic tumors. The results of extensive experiments demonstrate that the proposed TA-Net increases the liver tumor segmentation performance with a lower computation cost and a small parameter overhead over the state-of-the-art methods, under various evaluation metrics on clinical benchmark data. In addition, two additional medical image datasets are used to evaluate generalization capability of TA-Net, including the comparison with general semantic segmentation methods and a non-tumor segmentation task. All the program codes have been released at https://github.com/shuchao1212/TA-Net.

Molecular genetic study on GATA5 gene promoter in acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) is a severe type of coronary artery disease, caused by coronary occlusion and followed by cardiac ischaemia. GATA binding protein 5 (GATA5) is an important member of GATA family and plays an important role in vascular inflammation, endothelial function, oxidative stress and cell metabolism. Previous studies have shown that the DNA sequence variants (DSVs) in GATA4 and GATA6 promoter can increase susceptibility to AMI. In this study, we explored the relationship between GATA5 promoter and AMI for the first time, hoping to provide a new genetic basis for understanding the pathogenesis of AMI. METHODS: GATA5 promoter was sequenced in 683 individuals (332 AMI patients and 351 controls). The transcriptional activity of the GATA5 promoter with or without DSVs in HEK-293 cells, H9c2 cells and primary neonatal rat cardiomyocytes were examined by Promega Dual-Luciferase® Reporter Assay system. Electrophoretic mobility shift assay (EMSA) was performed to explore whether the DSVs interfered with the binding of transcription factors (TFs). RESULTS: Nine mutations have been found in GATA5 promoter, eight of them evidently altered the transcriptional activity of the GATA5 promoter, five of them disrupted the binding of TFs (such as farnesoid X receptor). Furthermore, haplotype AT (across rs80197101 and rs77067995) is a dangerous haplotype of AMI. Genotype GA and allele A of rs80197101 and genotype CT and allele T of rs77067995 are the risk factors of AMI. CONCLUSIONS: DSVs in GATA5 promoter can increase susceptibility to AMI. But the mechanism remains to be verified in vivo.

TFEB Gene Promoter Variants Effect on Gene Expression in Acute Myocardial Infarction.

Autophagy is involved in many physiological processes. Transcription factor EB (TFEB) is a master regulator of autophagy and coordinates the expression of autophagic proteins, lysosomal hydrolases, and lysosomal membrane proteins. Though autophagy has been implicated in several human diseases, little is known regarding TFEB gene expression and regulation in the process. Since dysfunctional autophagy plays critical roles in acute myocardial infarction (AMI), dysregulated TFEB gene expression may be associated with AMI by regulating autophagy. In this study, the TFEB gene promoter was genetically and functionally analyzed in AMI patients (n = 352) and ethnic-matched controls (n = 337). A total of fifteen regulatory variants of the TFEB gene, including eight single-nucleotide polymorphisms (SNPs), were identified in this population. Among these, six regulatory variants [g.41737274T>C (rs533895008), g.41737144A>G, g.41736987C > T (rs760293138), g.41736806C > T (rs748537297), g.41736635T > C (rs975050638), and g.41736544C > T] were only identified in AMI patients. These regulatory variants significantly altered the transcriptional activity of the TFEB gene promoter. Further electrophoretic mobility shift assay revealed that three of the variants evidently affected the binding of transcription factors. Therefore, this study identified novel TFEB gene regulatory variants which affect the gene expression. These TFEB gene regulatory variants may contribute to AMI development as a rare risk factor.