Short-chain fatty acid concentrations in the incidence and risk-stratification of colorectal cancer: a systematic review and meta-analysis.

BACKGROUND: The beneficial role of gut microbiota and bacterial metabolites, including short-chain fatty acids (SCFAs), is well recognized, although the available literature around their role in colorectal cancer (CRC) has been inconsistent. METHODS: We performed a systematic review and meta-analysis to examine the associations of fecal SCFA concentrations to the incidence and risk of CRC. Data extraction through Medline, Embase, and Web of Science was carried out from database conception to June 29, 2022. Predefined inclusion/exclusion criteria led to the selection of 17 case-control and six cross-sectional studies for quality assessment and analyses. Studies were categorized for CRC risk or incidence, and RevMan 5.4 was used to perform the meta-analyses. Standardized mean differences (SMD) with 95% confidence intervals (CI) were calculated using a random-effects model. Studies lacking quantitation were included in qualitative analyses. RESULTS: Combined analysis of acetic, propionic, and butyric acid revealed significantly lower concentrations of these SCFAs in individuals with a high-risk of CRC (SMD = 2.02, 95% CI 0.31 to 3.74, P = 0.02). Additionally, CRC incidence was higher in individuals with lower levels of SCFAs (SMD = 0.45, 95% CI 0.19 to 0.72, P = 0.0009), compared to healthy individuals. Qualitative analyses identified 70.4% of studies reporting significantly lower concentrations of fecal acetic, propionic, butyric acid, or total SCFAs in those at higher risk of CRC, while 66.7% reported significantly lower concentrations of fecal acetic and butyric acid in CRC patients compared to healthy controls. CONCLUSIONS: Overall, lower fecal concentrations of the three major SCFAs are associated with higher risk of CRC and incidence of CRC.

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

AIMS/HYPOTHESIS: Type 2 diabetes mellitus is a major cause of morbidity and death worldwide. Women with gestational diabetes mellitus (GDM) have greater than a sevenfold higher risk of developing type 2 diabetes in later life. Accurate methods for postpartum type 2 diabetes risk stratification are lacking. Circulating microRNAs (miRNAs) are well recognised as biomarkers/mediators of metabolic disease. We aimed to determine whether postpartum circulating miRNAs can predict the development of type 2 diabetes in women with previous GDM. METHODS: In an observational study, plasma samples were collected at 12 weeks postpartum from 103 women following GDM pregnancy. Utilising a discovery approach, we measured 754 miRNAs in plasma from type 2 diabetes non-progressors (n = 11) and type 2 diabetes progressors (n = 10) using TaqMan-based real-time PCR on an OpenArray platform. Machine learning algorithms involving penalised logistic regression followed by bootstrapping were implemented. RESULTS: Fifteen miRNAs were selected based on their importance in discriminating type 2 diabetes progressors from non-progressors in our discovery cohort. The levels of miRNA miR-369-3p remained significantly different (p < 0.05) between progressors and non-progressors in the validation sample set (n = 82; 71 non-progressors, 11 progressors) after adjusting for age and correcting for multiple comparisons. In a clinical model of prediction of type 2 diabetes that included six traditional risk factors (age, BMI, pregnancy fasting glucose, postpartum fasting glucose, cholesterol and triacylglycerols), the addition of the circulating miR-369-3p measured at 12 weeks postpartum improved the prediction of future type 2 diabetes from traditional AUC 0.83 (95% CI 0.68, 0.97) to an AUC 0.92 (95% CI 0.84, 1.00). CONCLUSIONS: This is the first demonstration of miRNA-based type 2 diabetes prediction in women with previous GDM. Improved prediction will facilitate early lifestyle/drug intervention for type 2 diabetes prevention.

Expression of miR-206 in human islets and its role in glucokinase regulation.

Inappropriate insulin secretion from ß-cells is considered as an early sign of impaired glucose tolerance and type 2 diabetes (T2D). Glucokinase (GCK) is an important enzyme that regulates glucose metabolism and ensures that the normal circulating glucose concentrations are maintained. GCK expression is induced by glucose and regulated via transcription factors and regulatory proteins. Recently, microRNA-206 (miR-206) was reported to regulate GCK and alter glucose tolerance in normal and high-fat diet-fed mice. Although the study findings have implications for human diabetes, studies in human islets are lacking. Here, we analyze human islets from individuals without or with T2D, using TaqMan-based real-time qPCR at the tissue (isolated islet) level as well as at single cell resolution, to assess the relationship between miR-206 and GCK expression in normal and T2D human islets. Our data suggest that, unlike mouse islets, human islets do not exhibit any correlation between miR-206 and GCK transcripts. These data implicate the need for further studies aimed toward exploring its potential role(s) in human islets.

Short-chain fatty acids and insulin sensitivity: a systematic review and meta-analysis.

CONTEXT: There is substantial evidence that reduced short-chain fatty acids (SCFAs) in the gut are associated with obesity and type 2 diabetes, although findings from clinical interventions that can increase SCFAs are inconsistent. OBJECTIVE: This systematic review and meta-analysis aimed to assess the effect of SCFA interventions on fasting glucose, fasting insulin, and homeostatic model assessment of insulin resistance (HOMA-IR). DATA SOURCES: Relevant articles published up to July 28, 2022, were extracted from PubMed and Embase using the MeSH (Medical Subject Headings) terms of the defined keywords [(short-chain fatty acids) AND (obesity OR diabetes OR insulin sensitivity)] and their synonyms. Data analyses were performed independently by two researchers who used the Cochrane meta-analysis checklist and the PRISMA guidelines. DATA EXTRACTION: Clinical studies and trials that measured SCFAs and reported glucose homeostasis parameters were included in the analysis. Standardized mean differences (SMDs) with 95%CIs were calculated using a random-effects model in the data extraction tool Review Manager version 5.4 (RevMan 5.4). The risk-of-bias assessment was performed following the Cochrane checklist for randomized and crossover studies. DATA ANALYSIS: In total, 6040 nonduplicate studies were identified, 23 of which met the defined criteria, reported fasting insulin, fasting glucose, or HOMA-IR values, and reported change in SCFA concentrations post intervention. Meta-analyses of these studies indicated that fasting insulin concentrations were significantly reduced (overall effect: SMD = -0.15; 95%CI = -0.29 to -0.01, P = 0.04) in treatment groups, relative to placebo groups, at the end of the intervention. Studies with a confirmed increase in SCFAs at the end of intervention also had a significant effect on lowering fasting insulin (P = 0.008). Elevated levels of SCFAs, compared with baseline levels, were associated with beneficial effects on HOMA-IR (P < 0.00001). There was no significant change in fasting glucose concentrations. CONCLUSION: Increased postintervention levels of SCFAs are associated with lower fasting insulin concentrations, offering a beneficial effect on insulin sensitivity. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration number CRD42021257248.

MicroRNA Profiling from Tears as a Potential Non-invasive Method for Early Detection of Diabetic Retinopathy.

Diabetic retinopathy (DR) is a vascular complication of diabetes that can lead to partial or complete loss of vision. Early detection and treatment of DR can prevent blindness. Regular clinical examination is recommended for DR diagnosis; however, it is not always possible or feasible due to limited resources, expertise, time, and infrastructure. Several clinical and molecular biomarkers are proposed for the prediction of DR including microRNAs. MicroRNAs are a class of small non-coding RNAs that are found in biofluids and can be measured using reliable and sensitive methods. The most commonly used biofluid for microRNA profiling is plasma or serum; however, tear fluid (tears) is also demonstrated to contain microRNAs. MicroRNAs isolated from tears present a non-invasive source for DR detection. Different methods of microRNA profiling are available including digital PCR-based methods that can detect up to a single copy of microRNA in the biofluids. Here, we describe microRNA isolation from tears using manual method as well as using a high-throughput automated platform followed by microRNA profiling using digital PCR system.

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.

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-1αß-/- mice that demonstrate dedifferentiation of pancreatic ß-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.

A Pro-Endocrine Pancreatic Islet Transcriptional Program Established During Development Is Retained in Human Gallbladder Epithelial Cells.

BACKGROUND & AIMS: Pancreatic islet ß-cells are factories for insulin production; however, ectopic expression of insulin also is well recognized. The gallbladder is a next-door neighbor to the developing pancreas. Here, we wanted to understand if gallbladders contain functional insulin-producing cells. METHODS: We compared developing and adult mouse as well as human gallbladder epithelial cells and islets using immunohistochemistry, flow cytometry, enzyme-linked immunosorbent assays, RNA sequencing, real-time polymerase chain reaction, chromatin immunoprecipitation, and functional studies. RESULTS: We show that the epithelial lining of developing, as well as adult, mouse and human gallbladders naturally contain interspersed cells that retain the capacity to actively transcribe, translate, package, and release insulin. We show that human gallbladders also contain functional insulin-secreting cells with the potential to naturally respond to glucose in vitro and in situ. Notably, in a non-obese diabetic (NOD) mouse model of type 1 diabetes, we observed that insulin-producing cells in the gallbladder are not targeted by autoimmune cells. Interestingly, in human gallbladders, insulin splice variants are absent, although insulin splice forms are observed in human islets. CONCLUSIONS: In summary, our biochemical, transcriptomic, and functional data in mouse and human gallbladder epithelial cells collectively show the evolutionary and developmental similarities between gallbladder and the pancreas that allow gallbladder epithelial cells to continue insulin production in adult life. Understanding the mechanisms regulating insulin transcription and translation in gallbladder epithelial cells would help guide future studies in type 1 diabetes therapy.

Promoting Pro-Endocrine Differentiation and Graft Maturation Following Surgical Resection of the Mouse Pancreas.

Type 1 diabetes (T1D) is an autoimmune disease, where insulin-producing ß-cells in the pancreas are inappropriately recognized and destroyed by immune cells. Islet transplantation is the most successful cell-based therapy for T1D individuals who experience frequent and severe life-threatening hypoglycemia. However, this therapy is extremely restricted owing to the limited availability of donor pancreas. In recent years, significant progress has been made in generating ß-cells from stem/progenitor cells using different approaches of in vitro differentiation. The insulin production from such in vitro generated ß-cells is still far less than that observed in islet ß-cells. We employed a novel strategy to improve the efficiency of progenitor cell differentiation by performing partial mouse pancreas resection after transplanting in vitro generated insulin-producing cells under the kidney capsule of these mice. Pancreas resection (pancreatectomy) has been shown to induce regenerative pathways, leading to regeneration of almost the entire resected pancreas over 3-5 weeks in mice. We found that in our method, regenerating mouse pancreas promotes better graft differentiation/maturation and insulin production from transplanted cells. In this chapter, we detail the protocols used for transplantation of in vitro differentiated cells in immunocompromised mice, partial pancreatectomy in host (NOD scid) mice, and assessment of graft function. We believe that our protocols provide a solid platform for further studies aimed at understanding growth/differentiation molecules secreted from regenerating pancreas that promote graft maturation.

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).

Urinary microRNAs as non-invasive biomarkers for toxic acute kidney injury in humans.

MicroRNAs in biofluids are potential biomarkers for detecting kidney and other organ injuries. We profiled microRNAs in urine samples from patients with Russell's viper envenoming or acute self-poisoning following paraquat, glyphosate, or oxalic acid [with and without acute kidney injury (AKI)] and on healthy controls. Discovery analysis profiled for 754 microRNAs using TaqMan OpenArray qPCR with three patients per group (12 samples in each toxic agent). From these, 53 microRNAs were selected and validated in a larger cohort of patients (Russell's viper envenoming = 53, paraquat = 51, glyphosate = 51, oxalic acid = 40) and 27 healthy controls. Urinary microRNAs had significantly higher expression in patients poisoned/envenomed by different nephrotoxic agents in both discovery and validation cohorts. Seven microRNAs discriminated severe AKI patients from no AKI for all four nephrotoxic agents. Four microRNAs (miR-30a-3p, miR-30a-5p, miR-92a, and miR-204) had > 17 fold change (p < 0.0001) and receiver operator characteristics area-under-curve (ROC-AUC) > 0.72. Pathway analysis of target mRNAs of these differentially expressed microRNAs showed association with the regulation of different nephrotoxic signaling pathways. In conclusion, human urinary microRNAs could identify toxic AKI early after acute injury. These urinary microRNAs have potential clinical application as early non-invasive diagnostic AKI biomarkers.

Insulin micro-secretion in Type 1 diabetes and related microRNA profiles.

The aim of this cross-sectional study was to compare plasma C-peptide presence and levels in people without diabetes (CON) and with Type 1 diabetes and relate C-peptide status to clinical factors. In a subset we evaluated 50 microRNAs (miRs) previously implicated in beta-cell death and associations with clinical status and C-peptide levels. Diabetes age of onset was stratified as adult (≥ 18 y.o) or childhood (< 18 y.o.), and diabetes duration was stratified as ≤ 10 years, 10-20 years and > 20 years. Plasma C-peptide was measured by ultrasensitive ELISA. Plasma miRs were quantified using TaqMan probe-primer mix on an OpenArray platform. C-peptide was detectable in 55.3% of (n = 349) people with diabetes, including 64.1% of adults and 34.0% of youth with diabetes, p < 0.0001 and in all (n = 253) participants without diabetes (CON). C-peptide levels, when detectable, were lower in the individuals with diabetes than in the CON group [median lower quartile (LQ)-upper quartile (UQ)] 5.0 (2.6-28.7) versus 650.9 (401.2-732.4) pmol/L respectively, p < 0.0001 and lower in childhood versus adult-onset diabetes [median (LQ-UQ) 4.2 (2.6-12.2) pmol/L vs. 8.0 (2.3-80.5) pmol/L, p = 0.02, respectively]. In the childhood-onset group more people with longer diabetes duration (> 20 years) had detectable C-peptide (60%) than in those with shorter diabetes duration (39%, p for trend < 0.05). Nine miRs significantly correlated with detectable C-peptide levels in people with diabetes and 16 miRs correlated with C-peptide levels in CON. Our cross-sectional study results are supportive of (a) greater beta-cell function loss in younger onset Type 1 diabetes; (b) persistent insulin secretion in adult-onset diabetes and possibly regenerative secretion in childhood-onset long diabetes duration; and (c) relationships of C-peptide levels with circulating miRs. Confirmatory clinical studies and related basic science studies are merited.

Machine learning workflows identify a microRNA signature of insulin transcription in human tissues.

Dicer knockout mouse models demonstrated a key role for microRNAs in pancreatic ß-cell function. Studies to identify specific microRNA(s) associated with human (pro-)endocrine gene expression are needed. We profiled microRNAs and key pancreatic genes in 353 human tissue samples. Machine learning workflows identified microRNAs associated with (pro-)insulin transcripts in a discovery set of islets (n = 30) and insulin-negative tissues (n = 62). This microRNA signature was validated in remaining 261 tissues that include nine islet samples from individuals with type 2 diabetes. Top eight microRNAs (miR-183-5p, -375-3p, 216b-5p, 183-3p, -7-5p, -217-5p, -7-2-3p, and -429-3p) were confirmed to be associated with and predictive of (pro-)insulin transcript levels. Use of doxycycline-inducible microRNA-overexpressing human pancreatic duct cell lines confirmed the regulatory roles of these microRNAs in (pro-)endocrine gene expression. Knockdown of these microRNAs in human islet cells reduced (pro-)insulin transcript abundance. Our data provide specific microRNAs to further study microRNA-mRNA interactions in regulating insulin transcription.

Directed differentiation into insulin-producing cells using microRNA manipulation.

Our commentary is focused on three studies that used microRNA overexpression methods for directed differentiation of stem cells into insulin-producing cells. Islet transplantation is the only cell-based therapy used to treat type 1 diabetes mellitus. However, due to the scarcity of cadaveric donors and limited availability of good quality and quantity of islets for transplant, alternate sources of insulin-producing cells are being studied and used by researchers. This commentary provides an overview of distinct studies focused on manipulating microRNA expression to optimize differentiation of embryonic stem cells or induced pluripotent stem cells into insulin-producing cells. These studies have used different approaches to overexpress microRNAs that are highly abundant in human islets (such as miR-375 and miR-7) in their differentiation protocol to achieve better differentiation into functional islet beta (ß)-cells.

An Optimised Step-by-Step Protocol for Measuring Relative Telomere Length.

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.

Droplet Digital PCR for Measuring Absolute Copies of Gene Transcripts in Human Islet-Derived Progenitor Cells.

Transcript analysis is a routinely used method to assess the expression profile of progenitor cells at different stages starting from their isolation to differentiation into specific lineages. It is a powerful way to understand similarities and differences between different cell types as well to estimate successful differentiation process. Transcript measurement is most commonly done using polymerase chain reaction (PCR) but other methods such as in situ hybridization, RNA sequencing are available. The quantitative PCR using TaqMan chemistry is a highly sensitive and reproducible method that measures gene transcripts as a relative abundance. With recent advances in technology, absolute quantitation of genes to single copy level is possible using digital PCR platforms.Digital PCR is an improved method of PCR in which a single reaction is partitioned into multiple mini reactions. Gene transcripts are measured in each of these mini reactions thereby improving assay sensitivity and making absolute quantitation possible. Here we describe the generation of human islet-derived progenitor cells and measuring gene transcripts in these cells at different passages using digital droplet PCR.

Comparative analysis of diagnostic platforms for measurement of differentially methylated insulin DNA.

Circulating cell-free DNA (cfDNA) has been intensively investigated as a diagnostic and prognostic marker for various cancers. In recent years, presence of unmethylated insulin cfDNA in the circulation has been correlated with pancreatic ß-cell death and risk of developing type 1 diabetes. Digital (d)PCR is an increasingly popular method of quantifying insulin cfDNA due to its ability to determine absolute copy numbers, and its increased sensitivity when compared to the more routinely used quantitative PCR. Multiple platforms have been developed to carry out dPCR. However, not all technologies perform comparably, thereby necessitating evaluation of each platform. Here, we compare two dPCR platforms: the QuantStudio 3D (QS3D, Applied Biosystems) and the QX200 (Bio-Rad), to measure copies of unmethylated/methylated insulin plasmids. The QS3D detected greater copy numbers of the plasmids than the QX200 (manual mode), whereas QX200 demonstrated minimal replicate variability, increased throughput, ease of use and the potential for automation. Overall, the performance of QX200, in our hands, was better suited to measure differentially methylated insulin cfDNA.

Non-Coding RNA in Pancreas and ß-Cell Development.

In this review, we provide an overview of the current knowledge on the role of different classes of non-coding RNAs for islet and ß-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 ß-cell maturation and function are now emerging. Specific miRNAs are dynamically regulated throughout the period of pancreas development, during islet and ß-cell differentiation as well as in the perinatal period, where a burst of ß-cell replication takes place. The role of long non-coding RNAs (lncRNA) in islet and ß-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 ß-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 ß-cell super-enhancers and regulate neighboring gene activity. Moreover, ncRNAs present in imprinted regions are involved in pancreas development and ß-cell function. Altogether, these observations support significant and important actions of ncRNAs in ß-cell development and function.