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BACKGROUND: Myocardial mitochondrial dysfunction underpins the pathogenesis of heart failure (HF), yet therapeutic options to restore myocardial mitochondrial function are scarce. Epigenetic modifications of mitochondrial DNA (mtDNA), such as methylation, play a pivotal role in modulating mitochondrial homeostasis. However, their involvement in HF remains unclear. METHODS: Experimental HF models were established through continuous angiotensin II and phenylephrine (AngII/PE) infusion or prolonged myocardial ischemia/reperfusion injury. The landscape of N6-methyladenine (6mA) methylation within failing cardiomyocyte mtDNA was characterized using high-resolution mass spectrometry and methylated DNA immunoprecipitation sequencing. A tamoxifen-inducible cardiomyocyte-specific Mettl4 knockout mouse model and adeno-associated virus vectors designed for cardiomyocyte-targeted manipulation of METTL4 (methyltransferase-like protein 4) expression were used to ascertain the role of mtDNA 6mA and its methyltransferase METTL4 in HF. RESULTS: METTL4 was predominantly localized within adult cardiomyocyte mitochondria. 6mA modifications were significantly more abundant in mtDNA than in nuclear DNA. Postnatal cardiomyocyte maturation presented with a reduction in 6mA levels within mtDNA, coinciding with a decrease in METTL4 expression. However, an increase in both mtDNA 6mA level and METTL4 expression was observed in failing adult cardiomyocytes, suggesting a shift toward a neonatal-like state. METTL4 preferentially targeted mtDNA promoter regions, which resulted in interference with transcription initiation complex assembly, mtDNA transcriptional stalling, and ultimately mitochondrial dysfunction. Amplifying cardiomyocyte mtDNA 6mA through METTL4 overexpression led to spontaneous mitochondrial dysfunction and HF phenotypes. The transcription factor p53 was identified as a direct regulator of METTL4 transcription in response to HF-provoking stress, thereby revealing a stress-responsive mechanism that controls METTL4 expression and mtDNA 6mA. Cardiomyocyte-specific deletion of the Mettl4 gene eliminated mtDNA 6mA excess, preserved mitochondrial function, and mitigated the development of HF upon continuous infusion of AngII/PE. In addition, specific silencing of METTL4 in cardiomyocytes restored mitochondrial function and offered therapeutic relief in mice with preexisting HF, irrespective of whether the condition was induced by AngII/PE infusion or myocardial ischemia/reperfusion injury. CONCLUSIONS: Our findings identify a pivotal role of cardiomyocyte mtDNA 6mA and the corresponding methyltransferase, METTL4, in the pathogenesis of mitochondrial dysfunction and HF. Targeted suppression of METTL4 to rectify mtDNA 6mA excess emerges as a promising strategy for developing mitochondria-focused HF interventions.
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AIMS: ßII spectrin is a cytoskeletal protein known to be tightly linked to heart development and cardiovascular electrophysiology. However, the roles of ßII spectrin in cardiac contractile function and pathological post-myocardial infarction remodeling remain unclear. Here, we investigated whether and how ßII spectrin, the most common isoform of non-erythrocytic spectrin in cardiomyocytes, is involved in cardiac contractile function and ischemia/reperfusion (I/R) injury. METHODS AND RESULTS: We observed that the levels of serum ßII spectrin breakdown products (ßII SBDPs) were significantly increased in patients with acute myocardial infarction (AMI). Concordantly, ßII spectrin was degraded into ßII SBDPs by calpain in mouse hearts after I/R injury. Using tamoxifen-inducible cardiac-specific ßII spectrin knockout mice, we found that deletion of ßII spectrin in the adult heart resulted in spontaneous development of cardiac contractile dysfunction, cardiac hypertrophy and fibrosis at 5 weeks after tamoxifen treatment. Moreover, at 1 week after tamoxifen treatment, although spontaneous cardiac dysfunction in cardiac-specific ßII spectrin knockout mice had not developed, deletion of ßII spectrin in the heart exacerbated I/R-induced cardiomyocyte death and heart failure. Furthermore, restoration of ßII spectrin expression via adenoviral small activating RNA (saRNA) delivery into the heart reduced I/R injury. Immunoprecipitation coupled with mass spectrometry (IP-LC-MS/MS) analyses and functional studies revealed that ßII spectrin is indispensable for mitochondrial complex I activity and respiratory function. Mechanistically, ßII spectrin promotes translocation of NADH:ubiquinone oxidoreductase 75 kDa Fe-S protein 1 (NDUFS1) from the cytosol to mitochondria by crosslinking with actin filaments (F-actin) to maintain F-actin stability. CONCLUSION: ßII spectrin is an essential cytoskeletal element for preserving mitochondrial homeostasis and cardiac function. Defects in ßII spectrin exacerbate cardiac I/R injury.
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Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition with a high incidence and mortality rate in intensive care unit (ICU) admissions. Early identification of patients at high risk for developing ARDS is crucial for timely intervention and improved clinical outcomes. However, the complex pathophysiology of ARDS makes early prediction challenging. This study aimed to develop an artificial intelligence (AI) model for automated lung lesion segmentation and early prediction of ARDS to facilitate timely intervention in the intensive care unit. Methods: A total of 928 ICU patients with chest computed tomography (CT) scans were included from November 2018 to November 2021 at three centers in China. Patients were divided into a retrospective cohort for model development and internal validation, and three independent cohorts for external validation. A deep learning-based framework using the UNet Transformer (UNETR) model was developed to perform the segmentation of lung lesions and early prediction of ARDS. We employed various data augmentation techniques using the Medical Open Network for AI (MONAI) framework, enhancing the training sample diversity and improving the model's generalization capabilities. The performance of the deep learning-based framework was compared with a Densenet-based image classification network and evaluated in external and prospective validation cohorts. The segmentation performance was assessed using the Dice coefficient (DC), and the prediction performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The contributions of different features to ARDS prediction were visualized using Shapley Explanation Plots. This study was registered with the China Clinical Trial Registration Centre (ChiCTR2200058700). Findings: The segmentation task using the deep learning framework achieved a DC of 0.734 ± 0.137 in the validation set. For the prediction task, the deep learning-based framework achieved AUCs of 0.916 [0.858-0.961], 0.865 [0.774-0.945], 0.901 [0.835-0.955], and 0.876 [0.804-0.936] in the internal validation cohort, external validation cohort I, external validation cohort II, and prospective validation cohort, respectively. It outperformed the Densenet-based image classification network in terms of prediction accuracy. Moreover, the ARDS prediction model identified lung lesion features and clinical parameters such as C-reactive protein, albumin, bilirubin, platelet count, and age as significant contributors to ARDS prediction. Interpretation: The deep learning-based framework using the UNETR model demonstrated high accuracy and robustness in lung lesion segmentation and early ARDS prediction, and had good generalization ability and clinical applicability. Funding: This study was supported by grants from the Shanghai Renji Hospital Clinical Research Innovation and Cultivation Fund (RJPY-DZX-008) and Shanghai Science and Technology Development Funds (22YF1423300).