Upregulation of Mir342 in Diet-Induced Obesity Mouse and the Hypothalamic Appetite Control

Epigenetic regulation in metabolic diseases: mechanisms and advances in clinical study

Epigenetics regulates gene expression and has been confirmed to play a critical role in a variety of metabolic diseases, such as diabetes, obesity, non-alcoholic fatty liver disease (NAFLD), osteoporosis, gout, hyperthyroidism, hypothyroidism and others. The term 'epigenetics' was firstly proposed in 1942 and with the development of technologies, the exploration of epigenetics has made great progresses. There are four main epigenetic mechanisms, including DNA methylation, histone modification, chromatin remodelling, and noncoding RNA (ncRNA), which exert different effects on metabolic diseases. Genetic and non-genetic factors, including ageing, diet, and exercise, interact with epigenetics and jointly affect the formation of a phenotype. Understanding epigenetics could be applied to diagnosing and treating metabolic diseases in the clinic, including epigenetic biomarkers, epigenetic drugs, and epigenetic editing. In this review, we introduce the brief history of epigenetics as well as the milestone events since the proposal of the term 'epigenetics'. Moreover, we summarise the research methods of epigenetics and introduce four main general mechanisms of epigenetic modulation. Furthermore, we summarise epigenetic mechanisms in metabolic diseases and introduce the interaction between epigenetics and genetic or non-genetic factors. Finally, we introduce the clinical trials and applications of epigenetics in metabolic diseases.

Non-coding RNAs: The link between maternal malnutrition and offspring metabolism

Early life nutrition is associated with the development and metabolism in later life, which is known as the Developmental Origin of Health and Diseases (DOHaD). Epigenetics have been proposed as an important explanation for this link between early life malnutrition and long-term diseases. Non-coding RNAs (ncRNAs) may play a role in this epigenetic programming. The expression of ncRNAs (such as long non-coding RNA H19, microRNA-122, and circular RNA-SETD2) was significantly altered in specific tissues of offspring exposed to maternal malnutrition. Changes in these downstream targets of ncRNAs lead to abnormal development and metabolism. This review aims to summarize the existing knowledge on ncRNAs linking the maternal nutrition condition and offspring metabolic diseases, such as obesity, type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD).

Neuroendocrine microRNAs linked to energy homeostasis: future therapeutic potential

The brain orchestrates whole-body metabolism through an intricate system involving interneuronal crosstalk and communication. Specifically, a key player in this complex circuitry is the hypothalamus that controls feeding behaviour, energy expenditure, body weight and metabolism, whereby hypothalamic neurons sense and respond to circulating hormones, nutrients, and chemicals. Dysregulation of these neurons contributes to the development of metabolic disorders, such as obesity and type 2 diabetes. The involvement of hypothalamic microRNAs, post-transcriptional regulators of gene expression, in the central regulation of energy homeostasis has become increasingly apparent, although not completely delineated. This review summarizes current evidence demonstrating the regulation of feeding-related neuropeptides by brain-derived microRNAs as well as the regulation of specific miRNAs by nutrients and other peripheral signals. Moreover, the involvement of microRNAs in the central nervous system control of insulin, leptin, and estrogen signal transduction is examined. Finally, the therapeutic and diagnostic potential of microRNAs for metabolic disorders will be discussed and the regulation of brain-derived microRNAs by nutrients and other peripheral signals is considered. Demonstrating a critical role of microRNAs in hypothalamic regulation of energy homeostasis is an innovative route to uncover novel biomarkers and therapeutic candidates for metabolic disorders.

Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus

PURPOSE

Gestational diabetes (GDM) is associated with increased risk for preterm birth and related complications for both the pregnant person and newborn. Changes in gene expression have the potential to characterize complex interactions between genetic and behavioral/environmental risk factors for GDM. Our goal was to summarize the state of the science about changes in gene expression and GDM.

DESIGN

The systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

METHODS

PubMed articles about humans, in English, from any date were included if they described mRNA transcriptome or microRNA findings from blood samples in adults with GDM compared with adults without GDM.

RESULTS

Sixteen articles were found representing 1355 adults (n=674 with GDM, n=681 controls) from 12 countries. Three studies reported transcriptome results and thirteen reported microRNA findings. Identified pathways described various aspects of diabetes pathogenesis, including glucose and insulin signaling, regulation, and transport; natural killer cell mediated cytotoxicity; and fatty acid biosynthesis and metabolism. Studies described 135 unique miRNAs that were associated with GDM, of which eight (miR-16-5p, miR-17-5p, miR-20a-5p, miR-29a-3p, miR-195-5p, miR-222-3p, miR-210-3p, and miR-342-3p) were described in 2 or more studies. Findings suggest that miRNA levels vary based on the time in pregnancy when GDM develops, the time point at which they were measured, sex assigned at birth of the offspring, and both the pre-pregnancy and gestational body mass index of the pregnant person.

CONCLUSIONS

The mRNA, miRNA, gene targets, and pathways identified in this review contribute to our understanding of GDM pathogenesis; however, further research is warranted to validate previous findings. In particular, longitudinal repeated-measures designs are needed that control for participant characteristics (e.g., weight), use standardized data collection methods and analysis tools, and are sufficiently powered to detect differences between subgroups. Findings may be used to improve early diagnosis, prevention, medication choice and/or clinical treatment of patients with GDM.