mosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data.

Multi-omic data can better characterize complex cellular signaling pathways from multiple views compared to individual omic data. However, integrative multi-omic data analysis to rank key disease biomarkers and infer core signaling pathways remains an open problem. In this study, our novel contributions are that we developed a novel graph AI model, mosGraphFlow, for analyzing multi-omic signaling graphs (mosGraphs), 2) analyzed multi-omic mosGraph datasets of AD, and 3) identified, visualized and evaluated a set of AD associated signaling biomarkers and network. The comparison results show that the proposed model not only achieves the best classification accuracy but also identifies important AD disease biomarkers and signaling interactions. Moreover, the signaling sources are highlighted at specific omic levels to facilitate the understanding of the pathogenesis of AD. The proposed model can also be applied and expanded for other studies using multi-omic data. Model code is accessible via GitHub: https://github.com/FuhaiLiAiLab/mosGraphFlow.

Case Report: A Chinese child with Barth syndrome caused by a novel TAFAZZIN mutation.

Barth syndrome (BTHS) is a rare X-linked recessive genetic disorder characterized by a broad spectrum of clinical features including cardiomyopathy, skeletal myopathy, neutropenia, growth delay, and 3-methylglutaconic aciduria. This disease is caused by loss-of-function mutations in the TAFAZZIN gene located on chromosome Xq28, resulting in cardiolipin deficiency. Most patients are diagnosed in childhood, and the mortality rate is highest in the early years. We report a case of acute, life-threatening metabolic decompensation occurring one day after birth. A novel TAFAZZIN splice site mutation was identified in the patient, marking the first reported case of such a mutation in BTHS identified in China. The report aims to expand our understanding of the spectrum of TAFAZZIN mutations in BTHS.

M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis.

Multi-omic data-driven studies, characterizing complex disease signaling system from multiple levels, are at the forefront of precision medicine and healthcare. The integration and interpretation of multi-omic data are essential for identifying molecular targets and deciphering core signaling pathways of complex diseases. However, it remains an open problem due the large number of biomarkers and complex interactions among them. In this study, we propose a novel Multi-scale Multi-hop Multi-omic graph model, M3NetFlow, to facilitate generic multi-omic data analysis to rank targets and infer core signaling flows/pathways. To evaluate M3NetFlow, we applied it in two independent multi-omic case studies: 1) uncovering mechanisms of synergistic drug combination response (defined as anchor-target guided learning), and 2) identifying biomarkers and pathways of Alzheimer 's disease (AD). The evaluation and comparison results showed M3NetFlow achieves the best prediction accuracy (accurate), and identifies a set of essential targets and core signaling pathways (interpretable). The model can be directly applied to other multi-omic data-driven studies. The code is publicly accessible at: https://github.com/FuhaiLiAiLab/M3NetFlow.

Deep learning models to predict primary open-angle glaucoma.

Glaucoma is a major cause of blindness and vision impairment worldwide, and visual field (VF) tests are essential for monitoring the conversion of glaucoma. While previous studies have primarily focused on using VF data at a single time point for glaucoma prediction, there has been limited exploration of longitudinal trajectories. Additionally, many deep learning techniques treat the time-to-glaucoma prediction as a binary classification problem (glaucoma Yes/No), resulting in the misclassification of some censored subjects into the nonglaucoma category and decreased power. To tackle these challenges, we propose and implement several deep-learning approaches that naturally incorporate temporal and spatial information from longitudinal VF data to predict time-to-glaucoma. When evaluated on the Ocular Hypertension Treatment Study (OHTS) dataset, our proposed convolutional neural network (CNN)-long short-term memory (LSTM) emerged as the top-performing model among all those examined. The implementation code can be found online (https://github.com/rivenzhou/VF_prediction).

mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI.

Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model, mosGraphGPT, for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer's Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link: https://github.com/mosGraph/mosGraphGPT.

Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails.

Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD, and the second is a human cirrhosis cohort. The evaluation confirms the promising potential of using PathFinder as a general signaling network inference model.

mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omics data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining key disease targets and signaling pathways. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of TCGA and Alzheimer's disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen.

Factors predicting resolution of left ventricular thrombus in different time windows after myocardial infarction.

BACKGROUND: Left ventricular thrombus (LVT) is a serious complication after myocardial infarction. However, due to its asymptomatic nature, early detection is challenging. We aimed to explore the differences in clinical correlates of LVT found in acute to subacute and chronic phases of myocardial infarction. METHODS: We collected data from 153 patients who were diagnosed with LVT after myocardial infarction at the Affiliated Hospital of Qingdao University from January 2013 to December 2022. Baseline information, inflammatory markers, transthoracic echocardiograph (TTE) data and other clinical correlates were collected. Patients were categorized into acute to subacute phase group (< 30 days) and chronic phase group (30 days and after) according to the time at which echocardiograph was performed. The resolution of thrombus within 90 days is regarded as the primary endpoint event. We fitted logistic regression models to relating clinical correlates with phase-specific thrombus resolution. RESULTS: For acute to subacute phase thrombus patients: C-reactive protein levels (OR: 0.95, 95% CI: 0.918-0.983, p = 0.003) were significantly associated with thrombus resolution. For chronic phase thrombus patients: anticoagulant treatment was associated with 5.717-fold odds of thrombus resolution (OR: 5.717, 95% CI: 1.543-21.18, p = 0.009). CONCLUSIONS: Higher levels of CRP were associated with lower likelihood of LVT resolution in acute phase myocardial infarction; Anticoagulant therapy is still needed for thrombus in the chronic stage of myocardial infarction.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex and poorly understood signaling mechanisms within microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. Though a set of targets have been identified, however, it remains a challenging to infer the core intra- and inter-multi-cell signaling communication networks using the scRNA-seq data, considering the complex and highly interactive background signaling network. Herein, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and signaling communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy, which divides the complex signaling networks into signaling paths, and then score and rank them using a novel graph transformer architecture to infer the intra- and inter-cell signaling communications. We evaluated PathFinder using scRNA-seq data of APOE4-genotype specific AD mice models and identified novel APOE4 altered intra- and inter-cell interaction networks among neurons, astrocytes, and microglia. PathFinder is a general signaling network inference model and can be applied to other omics data-driven signaling network inference.

Proteo-genomics of soluble TREM2 in cerebrospinal fluid provides novel insights and identifies novel modulators for Alzheimer's disease.

Triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in microglial activation, survival, and apoptosis, as well as in Alzheimer's disease (AD) pathogenesis. We previously reported the MS4A locus as a key modulator for soluble TREM2 (sTREM2) in cerebrospinal fluid (CSF). To identify additional novel genetic modifiers of sTREM2, we performed the largest genome-wide association study (GWAS) and identified four loci for CSF sTREM2 in 3,350 individuals of European ancestry. Through multi-ethnic fine mapping, we identified two independent missense variants (p.M178V in MS4A4A and p.A112T in MS4A6A) that drive the association in MS4A locus and showed an epistatic effect for sTREM2 levels and AD risk. The novel TREM2 locus on chr 6 contains two rare missense variants (rs75932628 p.R47H, P=7.16×10-19; rs142232675 p.D87N, P=2.71×10-10) associated with sTREM2 and AD risk. The third novel locus in the TGFBR2 and RBMS3 gene region (rs73823326, P=3.86×10-9) included a regulatory variant with a microglia-specific chromatin loop for the promoter of TGFBR2. Using cell-based assays we demonstrate that overexpression and knock-down of TGFBR2, but not RBMS3, leads to significant changes of sTREM2. The last novel locus is located on the APOE region (rs11666329, P=2.52×10-8), but we demonstrated that this signal was independent of APOE genotype. This signal colocalized with cis-eQTL of NECTIN2 in the brain cortex and cis-pQTL of NECTIN2 in CSF. Overexpression of NECTIN2 led to an increase of sTREM2 supporting the genetic findings. To our knowledge, this is the largest study to date aimed at identifying genetic modifiers of CSF sTREM2. This study provided novel insights into the MS4A and TREM2 loci, two well-known AD risk genes, and identified TGFBR2 and NECTIN2 as additional modulators involved in TREM2 biology.

sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

Ablation for atrial fibrillation improves the outcomes in patients with heart failure with preserved ejection fraction.

AIMS: Patients with heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF) have worse clinical outcomes than those with sinus rhythm (SR). We aim to investigate whether maintaining SR in patients with HFpEF through a strategy such as AF ablation would improve outcomes. METHODS AND RESULTS: This is a cohort study that analysed 1034 patients (median age 69 [63-76] years, 46.2% [478/1034] female) with HFpEF and AF. Of these, 392 patients who underwent first-time AF ablation were assigned to the ablation group, and the remaining 642 patients, who received only medical therapy, were assigned to the no ablation group. The primary endpoint was a composite of all-cause death or rehospitalization for worsening heart failure. After a median follow-up of 39 months, the cumulative incidence of the primary endpoint was significantly lower in the ablation group compared to the no ablation group (adjusted hazard ratio [HR], 0.55 [95% CI, 0.37-0.82], P = 0.003) in the propensity score-matched model. Secondary endpoint analysis showed that the benefit of AF ablation was mainly driven by a reduction in rehospitalization for worsening heart failure (adjusted HR, 0.52 [95% CI, 0.34-0.80], P = 0.003). Patients in the ablation group showed a 33% relative decrease in atrial tachycardia/AF recurrence compared to the no ablation group (adjusted HR, 0.67 [95% CI, 0.54-0.84], P < 0.001). CONCLUSION: Among patients with HFpEF and AF, the strategy of AF ablation to maintain SR was associated with a lower risk of the composite outcome of all-cause death or rehospitalization for worsening heart failure.

The TFPI2-PPARγ axis induces M2 polarization and inhibits fibroblast activation to promote recovery from post-myocardial infarction in diabetic mice.

BACKGROUND: Diabetes mellitus is one of the causes of poor ventricular remodelling and poor cardiac recovery after myocardial infarction (MI). We previously reported that tissue factor pathway inhibitor-2 (TFPI2) was downregulated in response to hyperglycaemia and that it played a pivotal role in extracellular matrix (ECM) degradation and cell migration. Nonetheless, the function and mechanism of TFPI2 in post-MI remodelling under diabetic conditions remain unclear. Therefore, in the present study, we investigated the role of TFPI2 in post-MI effects in a diabetic mouse model. RESULTS: TFPI2 expression was markedly decreased in the infarcted myocardium of diabetic MI mice compared with that in non-diabetic mice. TFPI2 knockdown in the MI mouse model promoted fibroblast activation and migration as well as matrix metalloproteinase (MMP) expression, leading to disproportionate fibrosis remodelling and poor cardiac recovery. TFPI2 silencing promoted pro-inflammatory M1 macrophage polarization, which is consistent with the results of TFPI2 downregulation and M1 polarization under diabetic conditions. In contrast, TFPI2 overexpression in diabetic MI mice protected against adverse cardiac remodelling and functional deterioration. TFPI2 overexpression also inhibited MMP2 and MMP9 expression and attenuated fibroblast activation and migration, as well as excessive collagen production, in the infarcted myocardium of diabetic mice. TFPI2 promoted an earlier phenotype transition of pro-inflammatory M1 macrophages to reparative M2 macrophages via activation of peroxisome proliferator-activated receptor gamma. CONCLUSIONS: This study highlights TFPI2 as a promising therapeutic target for early resolution of post-MI inflammation and disproportionate ECM remodelling under diabetic conditions.

Highly accurate disease diagnosis and highly reproducible biomarker identification with PathFormer.

Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limitations of existing GNNs in omics data analysis, i.e., limited-prediction/diagnosis accuracy and limited-reproducible biomarker identification capacity across multiple datasets. The root of the challenges is the unique graph structure of biological signaling pathways, which consists of a large number of targets and intensive and complex signaling interactions among these targets. To resolve these two challenges, in this study, we presented a novel GNN model architecture, named PathFormer, which systematically integrate signaling network, priori knowledge and omics data to rank biomarkers and predict disease diagnosis. In the comparison results, PathFormer outperformed existing GNN models significantly in terms of highly accurate prediction capability (~30% accuracy improvement in disease diagnosis compared with existing GNN models) and high reproducibility of biomarker ranking across different datasets. The improvement was confirmed using two independent Alzheimer's Disease (AD) and cancer transcriptomic datasets. The PathFormer model can be directly applied to other omics data analysis studies.

Association of body fat distribution with all-cause and cardiovascular mortality in US adults: a secondary analysis using NHANES.

OBJECTIVE: To investigate the association of fat and lean mass in specific regions with all-cause and cardiovascular-related mortality. DESIGN: Population based cohort study. SETTING: US National Health and Nutrition Examination Survey (2003-2006 and 2011-2018). PARTICIPANTS: 22 652 US adults aged 20 years or older. EXPOSURES: Fat and lean mass in specific regions obtained from the whole-body dual-energy X-ray absorptiometry. MAIN OUTCOME MEASURES: All-cause and cardiovascular-related mortality. RESULTS: During a median of 83 months of follow-up, 1432 deaths were identified. Associations between body composition metrics and mortality risks were evident above specific thresholds. For all-cause mortality, Android fat mass showed elevated HRs above 2.46 kg (HR: 1.17, 95% CI 1.02 to 1.34), while Android lean mass (ALM) had similar trends above 2.75 kg (HR: 1.17, 95% CI 1.03 to 1.33), and Android total mass above 5.75 kg (HR: 1.08, 95% CI 1.01 to 1.16). Conversely, lower HRs were observed below certain thresholds: Gynoid fat mass (GFM) below 3.71 kg (HR: 0.72, 95% CI 0.56 to 0.93), Gynoid lean mass below 6.44 kg (HR: 0.77, 95% CI 0.64 to 0.92), and Gynoid total mass below 11.78 kg (HR: 0.76, 95% CI 0.70 to 0.84). Notably, below 0.722 kg, the HR of visceral adipose tissue mass (VATM) was 1.25 (95% CI 1.04 to 1.48) for all-cause mortality, and above 3.18 kg, the HR of total abdominal fat mass was 2.41 (95% CI 1.15 to 5.05). Cardiovascular-related mortality exhibited associations as well, particularly for Android fat mass (AFM) above 1.78 kg (HR: 1.22, 95% CI 1.01 to 1.47) and below 7.16 kg (HR: 0.50, 95% CI 0.36 to 0.69). HRs varied for Gynoid total mass below and above 10.98 kg (HRs: 0.70, 95% CI 0.54 to 0.93, and 1.12, 95% CI 1.02 to 1.23). Android per cent fat, subcutaneous fat mass (SFM), AFM/GFM, and VATM/SFM were not statistically associated with all-cause mortality. Android per cent fat, Gynoid per cent fat, AFM/GFM, and VATM/SFM were not statistically associated with cardiovascular-related mortality. Conicity index showed that the ALM/GLM had the highest performance for all-cause and cardiovascular-related mortality with AUCs of 0.785, and 0.746, respectively. CONCLUSIONS: The relationship between fat or lean mass and all-cause mortality varies by region. Fat mass was positively correlated with cardiovascular mortality, regardless of the region in which they located. ALM/GLM might be a better predictor of all-cause and cardiovascular-related mortality than other body components or body mass index.

HNEAP Regulates Necroptosis of Cardiomyocytes by Suppressing the m5 C Methylation of Atf7 mRNA.

PIWI-interacting RNAs (piRNAs) are highly expressed in various cardiovascular diseases. However, their role in cardiomyocyte death caused by ischemia/reperfusion (I/R) injury, especially necroptosis, remains elusive. In this study, a heart necroptosis-associated piRNA (HNEAP) is found that regulates cardiomyocyte necroptosis by targeting DNA methyltransferase 1 (DNMT1)-mediated 5-methylcytosine (m5 C) methylation of the activating transcription factor 7 (Atf7) mRNA transcript. HNEAP expression level is significantly elevated in hypoxia/reoxygenation (H/R)-exposed cardiomyocytes and I/R-injured mouse hearts. Loss of HNEAP inhibited cardiomyocyte necroptosis and ameliorated cardiac function in mice. Mechanistically, HNEAP directly interacts with DNMT1 and attenuates m5 C methylation of the Atf7 mRNA transcript, which increases Atf7 expression level. ATF7 can further downregulate the transcription of Chmp2a, an inhibitor of necroptosis, resulting in the reduction of Chmp2a level and the progression of cardiomyocyte necroptosis. The findings reveal that piRNA-mediated m5 C methylation is involved in the regulation of cardiomyocyte necroptosis. Thus, the HNEAP-DNMT1-ATF7-CHMP2A axis may be a potential target for attenuating cardiac injury caused by necroptosis in ischemic heart disease.

Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling.

Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.

Circular RNA FEACR inhibits ferroptosis and alleviates myocardial ischemia/reperfusion injury by interacting with NAMPT.

BACKGROUND: Emerging research has reported that circular RNAs (circRNAs) play important roles in cardiac cell death after myocardial ischemia and reperfusion (I/R). Ferroptosis, a new form of cell death discovered in recent years, has been proven to participate in the regulation of myocardial I/R. This study used circRNA sequencing to explore the key circRNA in the regulation of cardiac ferroptosis after I/R and study the mechanisms of potential circRNA function. METHODS: We performed circRNA sequencing to explore circRNAs differentially expressed after myocardial I/R. We used quantitative polymerase chain reactions to determine the circRNA expression in different tissues and detect the circRNA subcellular localization in the cardiomyocyte. Gain- and loss-of-function experiments were aimed to examine the function of circRNAs in cardiomyocyte ferroptosis and cardiac tissue damage after myocardial I/R. RNA pull-down was applied to explore proteins interacting with circRNA. RESULTS: Here, we identified a ferroptosis-associated circRNA (FEACR) that has an underlying regulatory role in cardiomyocyte ferroptosis. FEACR overexpression suppressed I/R-induced myocardial infarction and ameliorated cardiac function. FEACR inhibition induces ferroptosis in cardiomyocytes and FEACR overexpression inhibits hypoxia and reoxygenation-induced ferroptosis. Mechanistically, FEACR directly bound to nicotinamide phosphoribosyltransferase (NAMPT) and enhanced the protein stability of NAMPT, which increased NAMPT-dependent Sirtuin1 (Sirt1) expression, which promoted the transcriptional activity of forkhead box protein O1 (FOXO1) by reducing FOXO1 acetylation levels. FOXO1 further upregulated the transcription of ferritin heavy chain 1 (Fth1), a ferroptosis suppressor, which resulted in the inhibition of cardiomyocyte ferroptosis. CONCLUSIONS: Our finding reveals that the circRNA FEACR-mediated NAMPT-Sirt1-FOXO1-FTH1 signaling axis participates in the regulation of cardiomyocyte ferroptosis and protects the heart function against I/R injury. Thus, FEACR and its downstream factors could be novel targets for alleviating ferroptosis-related myocardial injury in ischemic heart diseases.

Semaphorin3A Exacerbates Cardiac Microvascular Rarefaction in Pressure Overload-Induced Heart Disease.

Microvascular endothelial cells (MiVECs) impair angiogenic potential, leading to microvascular rarefaction, which is a characteristic feature of chronic pressure overload-induced cardiac dysfunction. Semaphorin3A (Sema3A) is a secreted protein upregulated in MiVECs following angiotensin II (Ang II) activation and pressure overload stimuli. However, its role and mechanism in microvascular rarefaction remain elusive. The function and mechanism of action of Sema3A in pressure overload-induced microvascular rarefaction, is explored, through an Ang II-induced animal model of pressure overload. RNA sequencing, immunoblotting analysis, enzyme-linked immunosorbent assay, quantitative reverse transcription polymerase chain reaction (qRT-PCR), and immunofluorescence staining results indicate that Sema3A is predominantly expressed and significantly upregulated in MiVECs under pressure overload. Immunoelectron microscopy and nano-flow cytometry analyses indicate small extracellular vesicles (sEVs), with surface-attached Sema3A, to be a novel tool for efficient release and delivery of Sema3A from the MiVECs to extracellular microenvironment. To investigate pressure overload-mediated cardiac microvascular rarefaction and cardiac fibrosis in vivo, endothelial-specific Sema3A knockdown mice are established. Mechanistically, serum response factor (transcription factor) promotes the production of Sema3A; Sema3A-positive sEVs compete with vascular endothelial growth factor A to bind to neuropilin-1. Therefore, MiVECs lose their ability to respond to angiogenesis. In conclusion, Sema3A is a key pathogenic mediator that impairs the angiogenic potential of MiVECs, which leads to cardiac microvascular rarefaction in pressure overload-induced heart disease.