Telomeres represent the nucleotide repeat sequences at the ends of chromosomes and are essential for chromosome stability. They can shorten at each round of DNA replication mainly because of incomplete DNA synthesis of the lagging strand. Reduced relative telomere length is associated with aging and a range of disease states. Different methods such as terminal restriction fragment analysis, real-time quantitative PCR (qPCR) and fluorescence in situ hybridization are available to measure telomere length; however, the qPCR-based method is commonly used for large population-based studies. There are multiple variations across qPCR-based methods, including the choice of the single-copy gene, primer sequences, reagents, and data analysis methods in the different reported studies so far. Here, we provide a detailed step-by-step protocol that we have optimized and successfully tested in the hands of other users. This protocol will help researchers interested in measuring relative telomere lengths in cells or across larger clinical cohort/study samples to determine associations of telomere length with health and disease.

In this review, we provide an overview of the current knowledge on the role of different classes of non-coding RNAs for islet and β-cell development, maturation and function. MicroRNAs (miRNAs), a prominent class of small RNAs, have been investigated for more than two decades and patterns of the roles of different miRNAs in pancreatic fetal development, islet and β-cell maturation and function are now emerging. Specific miRNAs are dynamically regulated throughout the period of pancreas development, during islet and β-cell differentiation as well as in the perinatal period, where a burst of β-cell replication takes place. The role of long non-coding RNAs (lncRNA) in islet and β-cells is less investigated than for miRNAs, but knowledge is increasing rapidly. The advent of ultra-deep RNA sequencing has enabled the identification of highly islet- or β-cell-selective lncRNA transcripts expressed at low levels. Their roles in islet cells are currently only characterized for a few of these lncRNAs, and these are often associated with β-cell super-enhancers and regulate neighboring gene activity. Moreover, ncRNAs present in imprinted regions are involved in pancreas development and β-cell function. Altogether, these observations support significant and important actions of ncRNAs in β-cell development and function.

Human islet isolation is a cost- and resource-intensive program for generating islets for cell therapy in type 1 diabetes. However, only one-third of cadaveric pancreases get to clinical transplantation because of low quality/number of islets. There is a need to identify biomarkers that predict the quality of islets, before initiating their isolation. Here, we sequenced transcriptomes from 18 human islet preparations stratified into 3 groups (group 1: best quality/transplantable islets; group 2: intermediary quality; and group 3: inferior quality/nontransplantable islets) based on routine measurements, including islet purity/viability. Machine-learning algorithms involving penalized regression analyses identified 10 long noncoding RNAs (lncRNAs) that were significantly different across all group-wise comparisons (group 1 vs. group 2, group 2 vs. group 3, and group 1 vs. group 3). Two variants of metastasis-associated lung adenocarcinoma transcript 1 ( MALAT1 ) lncRNA were common across all comparisons. We then confirmed RNA-Seq findings in a validation set of 75 human islet preparations. Finally, in 19 pancreas samples, we demonstrated that assessing the levels of MALAT1 variants alone (receiver operator characteristic curve AUC: 0.83) offers higher specificity in predicting postisolation islet quality, further improving the predictive potential for clinical islet transplantation when combined with Edmonton Donor Points/BMI/North American Islet Donor Score. We present this resource of islet quality–stratified lncRNA transcriptome data and identify MALAT1 as a biomarker that significantly enhances current selection methods for clinical-grade (good manufacturing practice–grade) islet isolation.

Background: The potential utility of microRNAs (miRNAs) in the diagnosis, prognosis, and treatment of multiple disease states has been an area of great interest since their discovery. In patients with cardiovascular disease, there is a large pool of literature amassed from the last decade assessing their diagnostic and prognostic potential. This systematic review sought to determine whether existing literature supports the use of miRNAs as prognostic markers after an Acute Coronary Syndrome (ACS) presentation. Methods: A systematic review of published articles from 2005–2019 using MEDLINE and EMBASE databases was undertaken independently by two reviewers. Studies addressing prognosis in an ACS population yielded 32 studies and 2 systematic reviews. Results/conclusion: 23 prospective studies reported significant differences in miRNA levels and 16 compared the predictive power of miRNAs. The most common miRNAs assessed included miR-133a, -208b, -21, -1, -34a, -150, and -423, shown to be involved in cell differentiation, apoptosis, and angiogenesis. Barriers to the use of miRNAs as prognostic markers include bias in miRNA selection, small sample size, variable normalization of data, and adjustment for confounders. Therefore, findings from this systematic review do not support the use of miRNAs for prognostication post-ACS beyond traditional cardiovascular risk factors, existing risk scores, and stratifications tools.

Women with Polycystic Ovary Syndrome (PCOS) present a heterogeneous reproductive and metabolic profile with an increased lifetime risk of Type 2 Diabetes (T2D). Early biomarkers of these metabolic disturbances in PCOS women have not been identified. The abundance of circulating insulin gene promotor cell-free DNA (INS cfDNA) was shown to be valuable as a predictive biomarker of β-cell death in individuals with Type 1 diabetes (T1D) as well as with gestational diabetes. Since β-cell death is common to the development of T1D as well as in T2D, we aimed to investigate if insulin-coding DNA is more abundant in circulation of PCOS women (vs Controls) and if their levels change after 6 yr. follow-up as a potential measure to predict future T2D. A cohort of 40 women diagnosed with PCOS according to Rotterdam 2003 criteria and eight healthy controls were examined at baseline and 6 years follow-up. Clinical measurements for evaluation of glucose homeostasis as well as blood/serum samples were obtained at each visit. Methylated and unmethylated INS cfDNA were quantified using droplet digital PCR. Differences between groups were assessed using Kruskall-Wallis test and Wilcoxon Signed rank test. At baseline, there was no detectable difference in copy number (copies/μL) of methylated (p = 0.74) or unmethylated INS cfDNA (p = 0.34) between PCOS and Control groups. At follow up, neither methylated (p = 0.50) nor unmethylated INScfDNA levels (p = 0.48) differed significantly between these groups. Likewise, when pooling the groups, there was no difference between baseline and follow up, in terms of copies of methylated or unmethylated INS cfDNA (p = 0.38 and p = 0.52, respectively). There were no significant correlations between counts of unmethylated or methylated cfDNA and the clinical measurements of β-cell function and pre-diabetes. The circulating level of unmethylated and methylated INScfDNA is similar between PCOS and Controls and cannot be used to predict islet β-cell loss and progression to Type 2 diabetes in a 6-year follow-up. The Danish Data Protection Agency (REG-31-2016. Approval: 01-12-2015) and by the Danish Scientific Ethical committee of Region Zealand (Journal no. SJ-525. Approval: 13-06-2016), Clinicaltrials.gov, ( NCT03142633 , registered 1. March, 2017, Retrospectively registered).