Oviductal Glycoprotein 1 Promotes Hypertension by Inducing Vascular Remodeling Through an Interaction With MYH9

MiRNA-miRNA interaction network in peripheral blood of patients with myocardial infarction: a gene expression meta-analysis

In recent years, investigations have revealed that microRNAs (miRNAs) can bind together and form a miRNA-miRNA-mRNA regulatory network that alters the consequence of miRNA-mRNA interaction. If we consider the miRNA that binds to mRNA as the primary miRNA and the miRNA that binds to the primary miRNA as the secondary one, secondry miRNAs can act as master regulators upstream of primary miRNAs and their target mRNAs. One of the distinguishing characteristics of secondary miRNAs as master regulators within a diverse set of differentially expressed genes is the absence of direct target mRNA for them. Instead, these master regulators exclusively govern the regulation of miRNAs that target specific mRNAs. Through in silico analysis, we identified 18 miRNAs among 385 differentially expressed miRNAs (DEmiRNAs) with no direct target mRNAs among 58 differentially expressed mRNAs (DEmRNAs) in peripheral blood of patients with myocardial infarction (MI). Instead, these secondary miRNAs targeted 9 primary miRNAs that had 36 direct targets among 58 DEmRNAs. We found that one primary miRNA might be regulated by more than one secondary miRNAs and each secondary miRNA can target more than one primary miRNAs. Among identified miRNA-miRNA-mRNA networks miR-188-5p/miR-299-3p/natural killer cell granule protein (NKG7), miR-200a-3p/miR-199b-5p/granzyme B (GZMB), and miR-377-3p/miR-581/oviductal glycoprotein 1 (OVGP1) exhibited higher scors in terms of expression levels (>2-fold increase or decrease) and strengh of interactions (ΔG < -5). Given the extensive network of miRNA interactions, focusing on master regulators opens up avenues for identifying key regulatory nodes for more effective therapeutic strategies.

Causality-enriched epigenetic age uncouples damage and adaptation

Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

Disease Diagnosis Based on Nucleic Acid Modifications

Nucleic acid modifications include a wide range of epigenetic and epitranscriptomic factors and impact a wide range of nucleic acids due to their profound influence on biological inheritance, growth, and metabolism. The recently developed methods of mapping and characterizing these modifications have promoted their discovery as well as large-scale studies in eukaryotes, especially in humans. Because of these pioneering strategies, nucleic acid modifications have been shown to have a great impact on human disorders such as cancer. Therefore, whether nucleic acid modifications could become a new type of biomarker remains an open question. In this review, we briefly look back at classical nucleic acid modifications and then focus on the progress made in investigating these modifications as diagnostic biomarkers in clinical therapy and present our perspective on their development prospects.

circACTA2 inhibits NLRP3 inflammasome-mediated inflammation via interacting with NF-κB in vascular smooth muscle cells

circACTA2 derived from the smooth muscle α-actin gene plays an important role in the regulation of vascular smooth muscle cell (VSMC) phenotype. The activation of NLRP3 inflammasome is involved in VSMC phenotypic switching. However, the mechanistic relationship between circACTA2 and NLRP3 inflammasome during vascular remodeling remains poorly understood. Here, we showed that circACTA2 was down-regulated in human intimal hyperplasia. circACTA2 overexpression in circACTA2 transgenic mice significantly decreased the neointimal hyperplasia induced by vascular injury, which is concomitant with a decrease in IL-18, IL-1β, TNF-α, and IL-6 levels. Gain- and loss-of-function studies revealed that circACTA2 alleviated VSMC inflammation by suppressing the activation of NLRP3 inflammasome. Mechanistically, circACTA2 inhibited the expression of NF-κB p65 and p50 subunits and interacted with p50, which impedes the formation of the p50/p65 heterodimer and nuclear translocation induced by TNF-α, thus resulting in the suppression of NLRP3 gene transcription and inflammasome activation. Furthermore, circACTA2 overexpression mitigated inflammation via repressing NLRP3 inflammasome-mediated VSMC pyroptosis. Importantly, employing a decoy oligonucleotide to compete with circACTA2 for binding to p50 could attenuate the expression of NLRP3, ASC, and caspase-1. These findings provide a novel insight into the functional roles of circACTA2 in VSMCs, and targeting the circACTA2-NF-κB-NLRP3 axis represents a promising therapeutic strategy for vascular remodeling.

Epigenetic Signatures in Hypertension

Clear epigenetic signatures were found in hypertensive and pre-hypertensive patients using DNA methylation data and neural networks in a classification algorithm. It is shown how by selecting an appropriate subset of CpGs it is possible to achieve a mean accuracy classification of 86% for distinguishing control and hypertensive (and pre-hypertensive) patients using only 2239 CpGs. Furthermore, it is also possible to obtain a statistically comparable model achieving an 83% mean accuracy using only 22 CpGs. Both of these approaches represent a substantial improvement over using the entire amount of available CpGs, which resulted in the neural network not generating accurate classifications. An optimization approach is followed to select the CpGs to be used as the base for a model distinguishing between hypertensive and pre-hypertensive individuals. It is shown that it is possible to find methylation signatures using machine learning techniques, which can be applied to distinguish between control (healthy) individuals, pre-hypertensive individuals and hypertensive individuals, illustrating an associated epigenetic impact. Identifying epigenetic signatures might lead to more targeted treatments for patients in the future.