Altered microRNA expression in COVID-19 patients enables identification of SARS-CoV-2 infection.

The host response to SARS-CoV-2 infection provide insights into both viral pathogenesis and patient management. The host-encoded microRNA (miRNA) response to SARS-CoV-2 infection, however, remains poorly defined. Here we profiled circulating miRNAs from ten COVID-19 patients sampled longitudinally and ten age and gender matched healthy donors. We observed 55 miRNAs that were altered in COVID-19 patients during early-stage disease, with the inflammatory miR-31-5p the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-423-5p, miR-23a-3p and miR-195-5p) independently classified COVID-19 cases with an accuracy of 99.9%. In a ferret COVID-19 model, the three-miRNA signature again detected SARS-CoV-2 infection with 99.7% accuracy, and distinguished SARS-CoV-2 infection from influenza A (H1N1) infection and healthy controls with 95% accuracy. Distinct miRNA profiles were also observed in COVID-19 patients requiring oxygenation. This study demonstrates that SARS-CoV-2 infection induces a robust host miRNA response that could improve COVID-19 detection and patient management.

Circulating non-coding RNAs as biomarkers of beta cell death in diabetes.

Death of pancreatic islet beta cells is a common feature of type 1 and 2 diabetes and often follows islet cell transplantation. Measurement of blood glucose is currently the only blunt instrument available to diagnose diabetes mellitus, and we lack tools to quantify islet cell loss or protection thereof. A class of RNA molecules (called microRNAs/miRNAs/miRs) that regulate endogenous gene expression via mRNA cleavage or translational arrest have been identified to be critical for birth, maintenance and regeneration of pancreatic beta cells. Recent demonstration that microRNAs can potentially be utilised as biomarkers due to their serum stability, has triggered increasing interest in understanding their role as regulators or biomarkers of disease. This review aims to delve into the potential of miRNA biomarkers, and whether miRNA profiles are indicators or effector of disease pathology. Furthermore, an outline for identifying and confirming islet-specific miRNA biomarkers is discussed.

Profiling Host MicroRNA Responses to Henipavirus Infection.

Diseases caused by henipaviruses feature incubation periods of up to 16 days, during which infected animals may show no apparent signs of disease yet be capable of transmitting the virus to humans. This risk has prompted research into host-derived biomarkers for early disease detection. Here, we describe a methodology for the assaying of host microRNAs (miRs), small non-coding RNAs that show promise as biomarkers for several human diseases and are responsive during early-stage henipavirus infection. In addition to their potential as disease biomarkers, miRNA profiling of henipavirus infections provides insight into cellular and immune pathways associated with disease pathogenesis.

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.

Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract.

Host biomarkers are increasingly being considered as tools for improved COVID-19 detection and prognosis. We recently profiled circulating host-encoded microRNA (miRNAs) during SARS-CoV-2 infection, revealing a signature that classified COVID-19 cases with 99.9% accuracy. Here we sought to develop a signature suited for clinical application by analyzing specimens collected using minimally invasive procedures. Eight miRNAs displayed altered expression in anterior nasal tissues from COVID-19 patients, with miR-142-3p, a negative regulator of interleukin-6 (IL-6) production, the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-30c-2-3p, miR-628-3p and miR-93-5p) independently classifies COVID-19 cases with 100% accuracy. This study further defines the host miRNA response to SARS-CoV-2 infection and identifies candidate biomarkers for improved COVID-19 detection.

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.

Machine Learning Identifies Cellular and Exosomal MicroRNA Signatures of Lyssavirus Infection in Human Stem Cell-Derived Neurons.

Despite being vaccine preventable, rabies (lyssavirus) still has a significant impact on global mortality, disproportionally affecting children under 15 years of age. This neurotropic virus is deft at avoiding the immune system while travelling through neurons to the brain. Until recently, research efforts into the role of non-coding RNAs in rabies pathogenicity and detection have been hampered by a lack of human in vitro neuronal models. Here, we utilized our previously described human stem cell-derived neural model to investigate the effect of lyssavirus infection on microRNA (miRNA) expression in human neural cells and their secreted exosomes. Conventional differential expression analysis identified 25 cellular and 16 exosomal miRNAs that were significantly altered (FDR adjusted P-value <0.05) in response to different lyssavirus strains. Supervised machine learning algorithms determined 6 cellular miRNAs (miR-99b-5p, miR-346, miR-5701, miR-138-2-3p, miR-651-5p, and miR-7977) were indicative of lyssavirus infection (100% accuracy), with the first four miRNAs having previously established roles in neuronal function, or panic and impulsivity-related behaviors. Another 4-miRNA signatures in exosomes (miR-25-3p, miR-26b-5p, miR-218-5p, miR-598-3p) can independently predict lyssavirus infected cells with >99% accuracy. Identification of these robust lyssavirus miRNA signatures offers further insight into neural lineage responses to infection and provides a foundation for utilizing exosome miRNAs in the development of next-generation molecular diagnostics for rabies.

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.

Modelling Lyssavirus Infections in Human Stem Cell-Derived Neural Cultures.

Rabies is a zoonotic neurological infection caused by lyssavirus that continues to result in devastating loss of human life. Many aspects of rabies pathogenesis in human neurons are not well understood. Lack of appropriate ex-vivo models for studying rabies infection in human neurons has contributed to this knowledge gap. In this study, we utilize advances in stem cell technology to characterize rabies infection in human stem cell-derived neurons. We show key cellular features of rabies infection in our human neural cultures, including upregulation of inflammatory chemokines, lack of neuronal apoptosis, and axonal transmission of viruses in neuronal networks. In addition, we highlight specific differences in cellular pathogenesis between laboratory-adapted and field strain lyssavirus. This study therefore defines the first stem cell-derived ex-vivo model system to study rabies pathogenesis in human neurons. This new model system demonstrates the potential for enabling an increased understanding of molecular mechanisms in human rabies, which could lead to improved control methods.

MicroRNA Biomarkers for Infectious Diseases: From Basic Research to Biosensing.

In the pursuit of improved diagnostic tests for infectious diseases, several classes of molecules have been scrutinized as prospective biomarkers. Small (18-22 nucleotide), non-coding RNA transcripts called microRNAs (miRNAs) have emerged as promising candidates with extensive diagnostic potential, due to their role in numerous diseases, previously established methods for quantitation and their stability within biofluids. Despite efforts to identify, characterize and apply miRNA signatures as diagnostic markers in a range of non-infectious diseases, their application in infectious disease has advanced relatively slowly. Here, we outline the benefits that miRNA biomarkers offer to the diagnosis, management, and treatment of infectious diseases. Investigation of these novel biomarkers could advance the use of personalized medicine in infectious disease treatment, which raises important considerations for validating their use as diagnostic or prognostic markers. Finally, we discuss new and emerging miRNA detection platforms, with a focus on rapid, point-of-care testing, to evaluate the benefits and obstacles of miRNA biomarkers for infectious disease.

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.

Circulating microRNAs in Diabetes Progression: Discovery, Validation, and Research Translation.

Diabetes, in all of its forms, is a disease state that demonstrates wide ranging pathological effects throughout the body. Until now, the only method of diagnosing and monitoring the progression of diabetes was through the measurement of blood glucose. Unfortunately, beta cell dysfunction initiates well before the clinical onset of diabetes, and so the development of an effective biomarker signature is of paramount importance to predict and monitor the progression of this disease. MicroRNAs (miRNAs/miRs) are small (18-22 nucleotide) noncoding (nc)RNAs that post-transcriptionally regulate endogenous gene expression by targeted inhibition or degradation of messenger (m)RNA. Recently, miRNAs have shown great promise as biomarkers as some exhibit differential expression in multiple disease states, including type 1 and type 2 diabetes (T1D/T2D). Furthermore, miRNAs are quite stable in circulation, resistant to freeze-thaw and pH-mediated degradation, and are relatively easy to detect using quantitative (q)PCR. Here, we discuss microRNAs that may form a diabetes biomarker signature. To identify these transcripts we outline miRNAs that play a central role in pancreas development and diabetes, as well as previously identified miRNAs with differential expression in individuals with T1D and T2D. Validation and refinement of a miRNA biomarker signature for diabetes would allow identification and intervention of individuals at risk of this disease, as well as stratification and monitoring of patients with established diabetes.

A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy.

MicroRNAs are now increasingly recognized as biomarkers of disease progression. Several quantitative real-time PCR (qPCR) platforms have been developed to determine the relative levels of microRNAs in biological fluids. We systematically compared the detection of cellular and circulating microRNA using a standard 96-well platform, a high-content microfluidics platform and two ultra-high content platforms. We used extensive analytical tools to compute inter- and intra-run variability and concordance measured using fidelity scoring, coefficient of variation and cluster analysis. We carried out unprejudiced next generation sequencing to identify a microRNA signature for Diabetic Retinopathy (DR) and systematically assessed the validation of this signature on clinical samples using each of the above four qPCR platforms. The results indicate that sensitivity to measure low copy number microRNAs is inversely related to qPCR reaction volume and that the choice of platform for microRNA biomarker validation should be made based on the abundance of miRNAs of interest.