Postpartum circulating microRNA enhances prediction of future type 2 diabetes in women with previous gestational diabetes

A risk prediction model of gestational diabetes mellitus based on traditional and genetic factors

BACKGROUND

Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication during pregnancy. We aimed to evaluate a risk prediction model of GDM based on traditional and genetic factors.

METHODS

A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to gather general data. Serum test results were collected from the laboratory information system. Independent risk factors for GDM were identified using univariate and multivariate logistic regression analyses. A GDM risk prediction model was constructed and evaluated with the Hosmer-Lemeshow goodness-of-fit test, goodness-of-fit calibration plot, receiver operating characteristic curve and area under the curve.

RESULTS

Among traditional factors, age ≥30 years, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms (SNPs) (rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657) were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk.

CONCLUSIONS

Both genetic and traditional factors contribute to the risk of GDM in women, operating through diverse mechanisms. Strengthening the risk prediction of SNPs for postpartum DM among women with GDM history is crucial for maternal and child health protection.

The role of microRNAs in pregnancies complicated by maternal diabetes

With the global prevalence of diabetes increasing, more people of reproductive age are experiencing hyperglycaemic pregnancies. Maternal Type 1 (T1DM) or Type 2 (T2DM) diabetes mellitus, and gestational diabetes mellitus (GDM) are associated with maternal cardiovascular and metabolic complications. Pregnancies complicated by maternal diabetes also increase the risk of short- and long-term health complications for the offspring, including altered fetal growth and the onset of T2DM and cardiometabolic diseases throughout life. Despite advanced methods for improving maternal glucose control, the prevalence of adverse maternal and offspring outcomes associated with maternal diabetes remains high. The placenta is a key organ at the maternal-fetal interface that regulates fetal growth and development. In pregnancies complicated by maternal diabetes, altered placental development and function has been linked to adverse outcomes in both mother and fetus. Emerging evidence suggests that microRNAs (miRNAs) are key molecules involved in mediating these changes. In this review, we describe the role of miRNAs in normal pregnancy and discuss how miRNA dysregulation in the placenta and maternal circulation is associated with suboptimal placental development and pregnancy outcomes in individuals with maternal diabetes. We also discuss evidence demonstrating that miRNA dysregulation may affect the long-term health of mothers and their offspring. As such, miRNAs are potential candidates as biomarkers and therapeutic targets in diabetic pregnancies at risk of adverse outcomes.

Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores

Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.

Risk prediction models for postpartum glucose intolerance in women with a history of gestational diabetes mellitus: a scoping review

Objective

The objective of this scoping review was to investigate the effectiveness and limitations of risk prediction models for postpartum glucose intolerance in women with gestational diabetes mellitus (GDM). The aim was to provide valuable insights for healthcare professionals in the development of robust risk prediction models.

Methods

A comprehensive literature search was conducted across multiple databases, including PubMed, EBSCO, Web of Science Core Collection, Ovid Full-Text Medical Journal Database, ProQuest, Elsevier ClinicalKey, China National Knowledge Infrastructure, China Biology Medicine, and WanFang Database, spanning from January 1990 to July 2023. To assess the quality of the included models, the Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed.

Results

Fourteen relevant studies were identified and included in the final review, all focusing on model development. The discrimination ability of the included models ranged from 0.725 to 0.940, indicating satisfactory prediction accuracy. However, a notable limitation was that nine of these models (64.3%) did not provide clear guidelines on the selection of potential predictors. Furthermore, only six models (42.86%) underwent internal validation, with none undergoing external validation. A high risk of bias was observed across the included models. Logistic regression, Cox regression, and machine learning were the primary methods employed in the construction of these models.

Conclusion

The risk prediction models included in this review demonstrated favorable prediction accuracy. However, due to variations in construction methodologies, direct comparison of their performance is challenging. These models exhibited certain shortcomings, such as inadequate handling of missing data and a lack of internal and external validation, resulting in a high risk of bias. Therefore, it is recommended that these models be updated and externally validated. The development of prospective, multi-center studies is encouraged to construct predictive models with low risk of bias and high clinical applicability, ultimately guiding evidence-based clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01330-1.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

Models Predicting Postpartum Glucose Intolerance Among Women with a History of Gestational Diabetes Mellitus: a Systematic Review

PURPOSE OF REVIEW

Despite the crucial role that prediction models play in guiding early risk stratification and timely intervention to prevent type 2 diabetes after gestational diabetes mellitus (GDM), their use is not widespread in clinical practice. The purpose of this review is to examine the methodological characteristics and quality of existing prognostic models predicting postpartum glucose intolerance following GDM.

RECENT FINDINGS

A systematic review was conducted on relevant risk prediction models, resulting in 15 eligible publications from research groups in various countries. Our review found that traditional statistical models were more common than machine learning models, and only two were assessed to have a low risk of bias. Seven were internally validated, but none were externally validated. Model discrimination and calibration were done in 13 and four studies, respectively. Various predictors were identified, including body mass index, fasting glucose concentration during pregnancy, maternal age, family history of diabetes, biochemical variables, oral glucose tolerance test, use of insulin in pregnancy, postnatal fasting glucose level, genetic risk factors, hemoglobin A1c, and weight. The existing prognostic models for glucose intolerance following GDM have various methodological shortcomings, with only a few models being assessed to have low risk of bias and validated internally. Future research should prioritize the development of robust, high-quality risk prediction models that follow appropriate guidelines, in order to advance this area and improve early risk stratification and intervention for glucose intolerance and type 2 diabetes among women who have had GDM.

Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles

BACKGROUND

Preterm birth (PTB) is the main driver of newborn deaths. The identification of pregnancies at risk of PTB remains challenging, as the incomplete understanding of molecular mechanisms associated with PTB. Although several transcriptome studies have been done on the placenta and plasma from PTB women, a comprehensive description of the RNA profiles from plasma and placenta associated with PTB remains lacking.

METHODS

Candidate markers with consistent trends in the placenta and plasma were identified by implementing differential expression analysis using placental tissue and maternal plasma RNA-seq datasets, and then validated by RT-qPCR in an independent cohort. In combination with bioinformatics analysis tools, we set up two protein-protein interaction networks of the significant PTB-related modules. The support vector machine (SVM) model was used to verify the prediction potential of cell free RNAs (cfRNAs) in plasma for PTB and late PTB.

RESULTS

We identified 15 genes with consistent regulatory trends in placenta and plasma of PTB while the full term birth (FTB) acts as a control. Subsequently, we verified seven cfRNAs in an independent cohort by RT-qPCR in maternal plasma. The cfRNA ARHGEF28 showed consistence in the experimental validation and performed excellently in prediction of PTB in the model. The AUC achieved 0.990 for whole PTB and 0.986 for late PTB.

CONCLUSIONS

In a comparison of PTB versus FTB, the combined investigation of placental and plasma RNA profiles has shown a further understanding of the mechanism of PTB. Then, the cfRNA identified has the capacity of predicting whole PTB and late PTB.

Genetics and Epigenetics: Implications for the Life Course of Gestational Diabetes

Gestational diabetes (GDM) is one of the most common complications of pregnancy, affecting as many as one in six pregnancies. It is associated with both short- and long-term adverse outcomes for the mother and fetus and has important implications for the life course of affected women. Advances in genetics and epigenetics have not only provided new insight into the pathophysiology of GDM but have also provided new approaches to identify women at high risk for progression to postpartum cardiometabolic disease. GDM and type 2 diabetes share similarities in their pathophysiology, suggesting that they also share similarities in their genetic architecture. Candidate gene and genome-wide association studies have identified susceptibility genes that are shared between GDM and type 2 diabetes. Despite these similarities, a much greater effect size for MTNR1B in GDM compared to type 2 diabetes and association of HKDC1, which encodes a hexokinase, with GDM but not type 2 diabetes suggest some differences in the genetic architecture of GDM. Genetic risk scores have shown some efficacy in identifying women with a history of GDM who will progress to type 2 diabetes. The association of epigenetic changes, including DNA methylation and circulating microRNAs, with GDM has also been examined. Targeted and epigenome-wide approaches have been used to identify DNA methylation in circulating blood cells collected during early, mid-, and late pregnancy that is associated with GDM. DNA methylation in early pregnancy had some ability to identify women who progressed to GDM, while DNA methylation in blood collected at 26-30 weeks gestation improved upon the ability of clinical factors alone to identify women at risk for progression to abnormal glucose tolerance post-partum. Finally, circulating microRNAs and long non-coding RNAs that are present in early or mid-pregnancy and associated with GDM have been identified. MicroRNAs have also proven efficacious in predicting both the development of GDM as well as its long-term cardiometabolic complications. Studies performed to date have demonstrated the potential for genetic and epigenetic technologies to impact clinical care, although much remains to be done.

The link between glycemic control measures and eye microvascular complications in a clinical cohort of type 2 diabetes with microRNA-223-3p signature

BACKGROUND

Type 2 diabetes (T2D) is a critical healthcare challenge and priority in Qatar which is listed amongst the top 10 countries in the world, with its prevalence presently at 17% double the global average. MicroRNAs (miRNAs) are implicated in the pathogenesis of (T2D) and long-term microvascular complications including diabetic retinopathy (DR).

METHODS

In this study, a T2D cohort that accurately matches the characteristics of the general population was employed to find microRNA (miRNA) signatures that are correlated with glycemic and β cell function measurements. Targeted miRNA profiling was performed in (471) T2D individuals with or without DR and (491) (non-diabetic) healthy controls from the Qatar Biobank. Discovery analysis identified 20 differentially expressed miRNAs in T2D compared to controls, of which miR-223-3p was significantly upregulated (fold change:5.16, p = 3.6e-02) and positively correlated with glucose and hemoglobin A1c (HbA1c) levels (p-value = 9.88e-04 and 1.64e-05, respectively), but did not show any significant associations with insulin or C-peptide. Accordingly, we performed functional validation using a miR-223-3p mimic (overexpression) under control and hyperglycemia-induced conditions in a zebrafish model.

RESULTS

Over-expression of miR-223-3p alone was associated with significantly higher glucose (42.7 mg/dL, n = 75 vs 38.7 mg/dL, n = 75, p = 0.02) and degenerated retinal vasculature, and altered retinal morphology involving changes in the ganglion cell layer and inner and outer nuclear layers. Assessment of retinal angiogenesis revealed significant upregulation in the expression of vascular endothelial growth factor and its receptors, including kinase insert domain receptor. Further, the pancreatic markers, pancreatic and duodenal homeobox 1, and the insulin gene expressions were upregulated in the miR-223-3p group.

CONCLUSION

Our zebrafish model validates a novel correlation between miR-223-3p and DR development. Targeting miR-223-3p in T2D patients may serve as a promising therapeutic strategy to control DR in at-risk individuals.

miR-24-3p and Body Mass Index as Type 2 Diabetes Risk Factors in Spanish Women 15 Years after Gestational Diabetes Mellitus Diagnosis

Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance that is diagnosed for the first time during pregnancy. The objective of this study is to know the glucose tolerance status after 15 years of pregnancy in patients diagnosed with gestational diabetes and to assess the long-term effect of GDM on the circulating miRNA profile of these women. To answer these, 30 randomly selected women diagnosed with GDM during 2005-2006 were included in the study, and glucose tolerance was measured using the National Diabetes Data Group criteria. Additionally, four miRNAs (hsa-miR-1-3p, hsa-miR-24-3p, hsa-miR-329-3p, hsa-miR-543) were selected for their analysis in the plasma of women 15 years after the diagnosis of GDM. In our study we discovered that, fifteen years after the diagnosis of GDM, 50% of women have some degree of glucose intolerance directly related to body weight and body mass index during pregnancy. Dysglycemic women also showed a significantly increased level of circulating hsa-miR-24-3p. Thus, we can conclude that initial weight and BMI, together with circulating expression levels of hsa-miR-24-3p, could be good predictors of the future development of dysglycemia in women with a previous diagnosis of GDM.

Pregnancy as an opportunity to prevent type 2 diabetes mellitus: FIGO Best Practice Advice

Gestational diabetes (GDM) impacts approximately 17 million pregnancies worldwide. Women with a history of GDM have an 8-10-fold higher risk of developing type 2 diabetes and a 2-fold higher risk of developing cardiovascular disease (CVD) compared with women without prior GDM. Although it is possible to prevent and/or delay progression of GDM to type 2 diabetes, this is not widely undertaken. Considering the increasing global rates of type 2 diabetes and CVD in women, it is essential to utilize pregnancy as an opportunity to identify women at risk and initiate preventive intervention. This article reviews existing clinical guidelines for postpartum identification and management of women with previous GDM and identifies key recommendations for the prevention and/or delayed progression to type 2 diabetes for global clinical practice.

Circulating microRNAs from early childhood and adolescence are associated with pre-diabetes at 18 years of age in women from the PMNS cohort

With type 2 diabetes presenting at younger ages, there is a growing need to identify biomarkers of future glucose intolerance. A high (20%) prevalence of glucose intolerance at 18 years was seen in women from the Pune Maternal Nutrition Study (PMNS) birth cohort. We investigated the potential of circulating microRNAs in risk stratification for future pre-diabetes in these women. Here, we provide preliminary longitudinal analyses of circulating microRNAs in normal glucose tolerant (NGT@18y, N = 10) and glucose intolerant (N = 8) women (ADA criteria) at 6, 12 and 17 years of their age using discovery analysis (OpenArray™ platform). Machine-learning workflows involving Lasso with bootstrapping/leave-one-out cross-validation identified microRNAs associated with glucose intolerance at 18 years of age. Several microRNAs, including miR-212-3p, miR-30e-3p and miR-638, stratified glucose-intolerant women from NGT at childhood. Our results suggest that circulating microRNAs, longitudinally assessed over 17 years of life, are dynamic biomarkers associated with and predictive of pre-diabetes at 18 years of age. Validation of these findings in males and remaining participants from the PMNS birth cohort will provide a unique opportunity to study novel epigenetic mechanisms in the life-course progression of glucose intolerance and enhance current clinical risk prediction of pre-diabetes and progression to type 2 diabetes.

Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study

BACKGROUND

The increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening.

OBJECTIVE

In this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin.

METHODS

Prospective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study-Growing Up in Singapore Towards healthy Outcomes-were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters.

RESULTS

A high performance prediction model for postpartum T2D comprising of 2 midgestation features-midpregnancy BMI after gestational weight gain and diagnosis of GDM-was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P=.02; OR 0.88, 95% CI 0.79-0.98).

CONCLUSIONS

Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01174875; https://clinicaltrials.gov/ct2/show/NCT01174875.

Analysis of Half a Billion Datapoints Across Ten Machine-Learning Algorithms Identifies Key Elements Associated With Insulin Transcription in Human Pancreatic Islet Cells

Machine learning (ML)-workflows enable unprejudiced/robust evaluation of complex datasets. Here, we analyzed over 490,000,000 data points to compare 10 different ML-workflows in a large (N=11,652) training dataset of human pancreatic single-cell (sc-)transcriptomes to identify genes associated with the presence or absence of insulin transcript(s). Prediction accuracy/sensitivity of each ML-workflow was tested in a separate validation dataset (N=2,913). Ensemble ML-workflows, in particular Random Forest ML-algorithm delivered high predictive power (AUC=0.83) and sensitivity (0.98), compared to other algorithms. The transcripts identified through these analyses also demonstrated significant correlation with insulin in bulk RNA-seq data from human islets. The top-10 features, (including IAPP, ADCYAP1, LDHA and SST) common to the three Ensemble ML-workflows were significantly dysregulated in scRNA-seq datasets from Ire-1αβ-/- mice that demonstrate dedifferentiation of pancreatic β-cells in a model of type 1 diabetes (T1D) and in pancreatic single cells from individuals with type 2 Diabetes (T2D). Our findings provide direct comparison of ML-workflows in big data analyses, identify key elements associated with insulin transcription and provide workflows for future analyses.

Decrease in Plasma miR-27a and miR-221 After Concussion in Australian Football Players

Introduction:

Sports-related concussion (SRC) is a common form of brain injury that lacks reliable methods to guide clinical decisions. MicroRNAs (miRNAs) can influence biological processes involved in SRC, and measurement of miRNAs in biological fluids may provide objective diagnostic and return to play/recovery biomarkers. Therefore, this prospective study investigated the temporal profile of circulating miRNA levels in concussed male and female athletes.

Methods:

Pre-season baseline blood samples were collected from amateur Australian rules football players (82 males, 45 females). Of these, 20 males and 8 females sustained an SRC during the subsequent season and underwent blood sampling at 2-, 6- and 13-days post-injury. A miRNA discovery Open Array was conducted on plasma to assess the expression of 754 known/validated miRNAs. miRNA target identified were further investigated with quantitative real-time PCR (qRT-PCR) in a validation study. Data pertaining to SRC symptoms, demographics, sporting history, education history and concussion history were also collected.

Results:

Discovery analysis identified 18 candidate miRNA. The consequent validation study found that plasma miR-221-3p levels were decreased at 6d and 13d, and that miR-27a-3p levels were decreased at 6d, when compared to baseline. Moreover, miR-27a and miR-221-3p levels were inversely correlated with SRC symptom severity.

Conclusion:

Circulating levels of miR-27a-3p and miR-221-3p were decreased in the sub-acute stages after SRC, and were inversely correlated with SRC symptom severity. Although further studies are required, these analyses have identified miRNA biomarker candidates of SRC severity and recovery that may one day assist in its clinical management.

Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows

MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a machine learning algorithm. We then provide optimized techniques to validate the identified miRNAs through over-expression/loss-of-function studies. Overall, these protocols apply to any field in biology where high-dimensional data are produced.

For complete details on the use and execution of this protocol, please refer to Wong et al. (2021a).

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LASSO and bootstrapping identify important miRNAs associated with gene of interest

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Generating puromycin resistant, doxycycline inducible, miRNA overexpressing cells

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Transient miRNA knockdown using LNA inhibitors in human primary cells

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Optimized reagent concentrations and cell densities for miRNA manipulation

Photoactivated DNA Walker Based on DNA Nanoflares for Signal-Amplified MicroRNA Imaging in Single Living Cells

Specific and sensitive detection and imaging of cancer-related miRNA in living cells are desirable for cancer diagnosis and treatment. Because of the spatiotemporal variability of miRNA expression level during different cell cycles, signal amplification strategies that can be activated by external stimuli are required to image miRNAs on demand at desired times and selected locations. Herein, we develop a signal amplification strategy termed as the photoactivated DNA walker based on DNA nanoflares, which enables photocontrollable signal amplification imaging of cancer-related miRNA in single living cells. The developed method is achieved via combining photoactivated nucleic acid displacement reaction with the traditional exonuclease III (EXO III)-assisted DNA walker based on DNA nanoflares. This method is capable of on-demand activation of the DNA walker for dictated signal amplification imaging of cancer-related miRNA in single living cells. The developed method was demonstrated as a proof of concept to achieve photoactivated signal amplification imaging of miRNA-21 in single living HeLa cells via selective two-photon irradiation (λ = 740 nm) of single living HeLa cells by using confocal microscopy equipped with a femtosecond laser.

Significance of Sex Differences in ncRNAs Expression and Function in Pregnancy and Related Complications

In the era of personalized medicine, fetal sex-specific research is of utmost importance for comprehending the mechanisms governing pregnancy and pregnancy-related complications. In recent times, noncoding RNAs (ncRNAs) have gained increasing attention as critical players in gene regulation and disease pathogenesis, and as candidate biomarkers in human diseases as well. Different types of ncRNAs, including microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), participate in every step of pregnancy progression, although studies taking into consideration fetal sex as a central variable are still limited. To date, most of the available data have been obtained investigating sex-specific placental miRNA expression. Several studies revealed that miRNAs regulate the (patho)-physiological processes in a sexually dimorphic manner, ensuring normal fetal development, successful pregnancy, and susceptibility to diseases. Moreover, the observation that ncRNA profiles differ according to cells, tissues, and developmental stages of pregnancy, along with the complex interactions among different types of ncRNAs in regulating gene expression, strongly indicates that more studies are needed to understand the role of sex-specific ncRNA in pregnancy and associated disorders.