A three-tiered integrative analysis of transcriptional data reveals the shared pathways related to heart failure from different aetiologies

TRIM28 Fosters Microglia Ferroptosis via Autophagy Modulation to Enhance Neuropathic Pain and Neuroinflammation

This study explores the molecular underpinnings of neuropathic pain (NPP) and neuroinflammation, focusing on the role of TRIM28 in the regulation of autophagy and microglia ferroptosis. Leveraging transcriptomic data associated with NPP, we identified TRIM28 as a critical regulator of ferroptosis. Through comprehensive analysis, including Gene Ontology enrichment and protein-protein interaction network assessments, we unveiled GSK3B as a downstream target of TRIM28. Experimental validation confirmed the capacity of TRIM28 to suppress GSK3B expression and attenuate autophagic processes in microglia. We probed the consequences of autophagy and ferroptosis on microglia physiology, iron homeostasis, oxidative stress, and the release of proinflammatory cytokines. In a murine model, we validated the pivotal role of TRIM28 in NPP and neuroinflammation. Our analysis identified 20 ferroptosis regulatory factors associated with NPP, with TRIM28 emerging as a central orchestrator. Experimental evidence affirmed that TRIM28 governs microglial iron homeostasis and cell fate by downregulating GSK3B expression and modulating autophagy. Notably, autophagy was found to influence oxidative stress and proinflammatory cytokine release through the iron metabolism pathway, ultimately fueling neuroinflammation. In vivo experiments provided conclusive evidence of TRIM28-mediated pathways contributing to heightened pain sensitivity in neuroinflammatory states. The effect of TRIM28 on autophagy and microglia ferroptosis drives NPP and neuroinflammation. These findings offer promising avenues for identifying novel therapeutic targets to manage NPP and neuroinflammation.

Exploring novel biomarkers in dilated cardiomyopathy‑induced heart failure by integrated analysis and in vitro experiments

Despite the availability of several effective and promising treatment methods, heart failure (HF) remains a significant public health concern that requires advanced therapeutic strategies and techniques. Dilated cardiomyopathy (DCM) is a crucial factor that contributes to the development and deterioration of HF. The aim of the present study was to identify novel biomarkers and biological pathways to enhance the diagnosis and treatment of patients with DCM-induced HF using weighted gene co-expression network analysis (WGCNA). A total of 24 co-expressed gene modules connected with DCM-induced HF were obtained by WGCNA. Among these, the blue module had the highest correlation with DCM-induced HF (r=0.91; P<0.001) and was enriched in the AGE-RAGE signaling pathway in diabetic complications, the p53 and MAPK signaling pathway, adrenergic signaling in cardiomyocytes, the Janus kinase-STAT signaling pathway and cGMP/PKG signaling. Eight key genes, including secreted protein acidic and rich in cysteine-related modular calcium-binding protein 2 (SMOC2), serpin family A member 3 (SERPINA3), myosin heavy chain 6 (MYH6), S100 calcium binding protein A9 (S100A9), tubulin α (TUBA)3E, TUBA3D, lymphatic vessel endothelial hyaluronic acid receptor 1 (LYVE1) and phospholipase C ε1 (PLCE1), were selected as the therapeutic targets of DCM-induced HF based on WGCNA and differentially expressed gene analysis. Immune cell infiltration analysis revealed that the proportion of naive B cells and CD4-activated memory T cells was markedly upregulated in DCM-induced HF tissues compared with tissues from healthy controls. Furthermore, reverse transcription-quantitative PCR in AC16 human cardiomyocyte cells treated with doxorubicin showed that among the eight key genes, only SERPINA3, MYH6, S100A9, LYVE1 and PLCE1 exhibited expression levels identical to those revealed by bioinformatics analysis, suggesting that these genes may be involved in the development of DCM-induced HF.

Comparative study of disease progression for heart failure with different etiologies via time-ordered network analysis

OBJECTIVES

Heart failure (HF), the primary end-stage manifestation of multiple cardiovascular diseases, has become a global epidemic with high morbidity and mortality. However, the mechanisms underlying the pathogenesis of HF with different etiologies have yet to be fully elucidated.

METHODS

In this study, we developed a novel method to determine the dysregulated lncRNA-mRNA regulation pairs (LMRPs) in the different causes that lead to HF. Time-ordered dysregulated lncRNA-mRNA regulation networks were constructed for comparing the HF progression initiated from different causes. Additionally, the random forest and support vector machine classification algorithm were applied to identify HF-related diagnostic biomarkers.

RESULTS

Biological functional analysis indicated that similar functions were detected at the late stage across different causes of HF, whereas different characteristics were revealed during disease progression. Specifically, the disturbance of myocardial energy metabolism might be a cause of dilated cardiomyopathy (DCM) and peripartum cardiomyopathy (PPCM), while immune response appeared earlier in hypertrophic cardiomyopathy (HCM). Inflammatory response during HCM and PPCM progression might be mediated by complement system, whereas ischemic cardiomyopathy (ICM) might be induced by cytokines. Finally, we identified several panels of diagnostic biomarkers for distinguishing HF patients of different etiologies from non-heart failure (NF) controls.

CONCLUSIONS

This study revealed distinct functional characteristics during the progression of HF from different causes and facilitated the discovery of candidate diagnostic biomarkers for HF.

Identification of the distinctive role of DPT in dilated cardiomyopathy: a study based on bulk and single-cell transcriptomic analysis

Background

Dilated cardiomyopathy (DCM) is a common cause of heart failure. Cardiac remodeling is the main pathological change in DCM, yet the molecular mechanism is still unclear. Therefore, the present study aims to find potential crucial genes and regulators through bulk and single-cell transcriptomic analysis.

Methods

Three microarray datasets of DCM (GSE57338, GSE42955, GSE79962) were chosen from gene expression omnibus (GEO) to analyze the differentially expressed genes (DEGs). LASSO regression, SVM-RFE, and PPI network methods were then carried out to identify key genes. Another dataset (GSE116250) was used to validate these findings. To further identify DCM-associated specific cell types, transcription factors, and cell-cell interaction networks, GSEA, SCENIC, and CellPhoneDB were conducted on public datasets for single-cell RNA sequencing analysis of DCM (GSE109816 and GSE121893). Finally, reverse transcription-polymerase chain reaction (RT-PCR), western blot, and immunohistochemical were performed to validate DPT expression in fibroblasts and DCM.

Results

There were 281 DEGs between DCM and non-failing donors. CCL5 and DPT were identified to be key genes and both genes had a 0.844 area under the receiver operating curve (AUC) in the validation dataset. Further single-cell sequencing analysis revealed three main findings: (I) DPT was mainly expressed in fibroblasts and was significantly upregulated in DCM fibroblasts; (II) DPT⁺ fibroblasts were involved in the organization of the extracellular matrix (ECM) and collagen fibrils and were regulated by the transcription factor STAT3; and (III) DPT⁺ fibroblasts had high interactions with endothelial cells through including Ephrin-Eph, ACKR-CXCL, and JAG-NOTCH signal pathways. RT-PCR, western blot, and immunohistochemical confirmed that DPT was highly expressed and co-localized with Vimentin and p-STAT3 in DCM patients. STAT3 inhibitor S3I-201 reduced the expression of DPT in mouse cardiac fibroblasts.

Conclusions

DPT could be used as a diagnostic marker and therapeutic target of DCM. DPT⁺ fibroblasts could be a novel regulator of the cardiac remodeling process in DCM.

Pan-Tissue Aging Clock Genes That Have Intimate Connections with the Immune System and Age-Related Disease

In our recent transcriptomic meta-analysis, we used random forest machine learning to accurately predict age in human blood, bone, brain, heart, and retina tissues given gene inputs. Although each tissue-specific model utilized a unique number of genes for age prediction, we found that the following six genes were prioritized in all five tissues: CHI3L2, CIDEC, FCGR3A, RPS4Y1, SLC11A1, and VTCN1. Since being selected for age prediction in multiple tissues is unique, we decided to explore these pan-tissue clock genes in greater detail. In the present study, we began by performing over-representation and network topology-based enrichment analyses in the Gene Ontology Biological Process database. These analyses revealed that the immunological terms "response to protozoan," "immune response," and "positive regulation of immune system process" were significantly enriched by these clock inputs. Expression analyses in mouse and human tissues identified that these inputs are frequently upregulated or downregulated with age. A detailed literature search showed that all six genes had noteworthy connections to age-related disease. For example, mice deficient in Cidec are protected against various metabolic defects, while suppressing VTCN1 inhibits age-related cancers in mouse models. Using a large multitissue transcriptomic dataset, we additionally generate a novel, minimalistic aging clock that can predict human age using just these six genes as inputs. Taken all together, these six genes are connected to diverse aspects of aging.

Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure

Heart failure (HF) is a serious condition in which the support of blood pumped by the heart is insufficient to meet the demands of body at a normal cardiac filling pressure. Approximately 26 million patients worldwide are suffering from heart failure and about 17–45% of patients with heart failure die within 1-year, and the majority die within 5-years admitted to a hospital. The molecular mechanisms underlying the progression of heart failure have been poorly studied. We compared the gene expression profiles between patients with heart failure (n = 177) and without heart failure (n = 136) using multiple feature selection strategies and identified 38 HF signature genes. The support vector machine (SVM) classifier based on these 38 genes evaluated with leave-one-out cross validation (LOOCV) achieved great performance with sensitivity of 0.983 and specificity of 0.963. The network analysis suggested that the hub gene SMOC2 may play important roles in HF. Other genes, such as FCN3, HMGN2, and SERPINA3, also showed great promises. Our results can facilitate the early detection of heart failure and can reveal its molecular mechanisms.

A three-tiered integrative analysis of transcriptional data reveals the shared pathways related to heart failure from different aetiologies

Heart failure (HF) is the end stage of most heart disease cases and can be initiated from multiple aetiologies. However, whether the molecular basis of HF has a commonality between different aetiologies has not been elucidated. To address this lack, we performed a three‐tiered analysis by integrating transcriptional data and pathway information to explore the commonalities of HF from different aetiologies. First, through differential expression analysis, we obtained 111 genes that were frequently differentially expressed in HF from 11 different aetiologies. Several genes, such as NPPA and NPPB, are early and accurate biomarkers for HF. We also provided candidates for further experimental verification, such as SERPINA3 and STAT4. Then, using gene set enrichment analysis, we successfully identified 19 frequently dysregulated pathways. In particular, we found that pathways related to immune system signalling, the extracellular matrix and metabolism were critical in the development of HF. Finally, we successfully acquired 241 regulatory relationships between 64 transcriptional factors (TFs) and 17 frequently dysregulated pathways by integrating a regulatory network, and some of the identified TFs have already been proven to play important roles in HF. Taken together, the three‐tiered analysis of HF provided a systems biology perspective on HF and emphasized the molecular commonality of HF from different aetiologies.