A transcriptome analysis of basal and stimulated VWF release from endothelial cells derived from patients with type 1 VWD.

Type 1 von Willebrand disease (VWD) is associated with a reduction in qualitatively normal von Willebrand factor (VWF). Current diagnostic guidelines only take into consideration the contribution of basal VWF levels, despite a lack of correlation with bleeding severity. Defects in stimulated VWF release, which occurs after hemostatic challenge, may contribute to bleeding in type 1 VWD, but the pathogenic mechanisms are poorly defined. In this study, a layered multiomic approach including messenger RNA (mRNA) and microRNA (miRNA) sequencing was used to evaluate transcriptome-wide differences between type 1 VWD- and control-derived endothelial colony forming cells (ECFCs) during basal and stimulated VWF release. ECFCs from 8 patients with type 1 VWD and 4 other patients were included in this study as controls. VWF protein analysis revealed heterogenous responses to stimulation among type 1 VWD and control ECFCs. During basal VWF release, 64 mRNAs and 7 miRNAs were differentially regulated between type 1 VWD and control ECFCs, and 65 putatively pathogenic miRNA-mRNA interactions were identified. During stimulated VWF release, 190 mRNAs and 5 mRNAs were differentially regulated between type 1 VWD and control ECFCs, and 110 putatively pathogenic miRNA-mRNA interactions were identified. Five gene ontology terms including coagulation, regulation of cell shape, and regulation of cell signaling were also differentially regulated in type 1 VWD ECFCs during stimulated release. To our knowledge, we have shown for the first time that transcriptome-wide differences exist between type 1 VWD and control ECFCs. These differences may contribute to bleeding in type 1 VWD, and further investigation may reveal novel biomarkers and therapeutic targets.

A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data.

Complex high-dimensional datasets that are challenging to analyze are frequently produced through '-omics' profiling. Typically, these datasets contain more genomic features than samples, limiting the use of multivariable statistical and machine learning-based approaches to analysis. Therefore, effective alternative approaches are urgently needed to identify features-of-interest in '-omics' data. In this study, we present the molecular feature selection tool, a novel, ensemble-based, feature selection application for identifying candidate biomarkers in '-omics' data. As proof-of-principle, we applied the molecular feature selection tool to identify a small set of immune-related genes as potential biomarkers of three prostate adenocarcinoma subtypes. Furthermore, we tested the selected genes in a model to classify the three subtypes and compared the results to models built using all genes and all differentially expressed genes. Genes identified with the molecular feature selection tool performed better than the other models in this study in all comparison metrics: accuracy, precision, recall, and F1-score using a significantly smaller set of genes. In addition, we developed a simple graphical user interface for the molecular feature selection tool, which is available for free download. This user-friendly interface is a valuable tool for the identification of potential biomarkers in gene expression datasets and is an asset for biomarker discovery studies.

Emerging Evidence of Noncoding RNAs in Bleb Scarring after Glaucoma Filtration Surgery.

PURPOSE: To conduct a narrative review of research articles on the potential anti- and pro-fibrotic mechanisms of noncoding RNAs following glaucoma filtration surgery. METHODS: Keyword searches of PubMed, and Medline databases were conducted for articles discussing post-glaucoma filtration surgeries and noncoding RNA. Additional manual searches of reference lists of primary articles were performed. RESULTS: Fifteen primary research articles were identified. Four of the included papers used microarrays and qRT-PCR to identify up- or down-regulated microRNA (miRNA, miR) profiles and direct further study, with the remainder focusing on miRNAs or long noncoding RNAs (lncRNAs) based on previous work in other organs or disease processes. The results of the reviewed papers identified miR-26a, -29b, -139, -155, and -200a as having anti-fibrotic effects. In contrast, miRs-200b and -216b may play pro-fibrotic roles in filtration surgery fibrosis. lncRNAs including H19, NR003923, and 00028 have demonstrated pro-fibrotic effects. CONCLUSIONS: Noncoding RNAs including miRNAs and lncRNAs are emerging and promising therapeutic targets in the prevention of post-glaucoma filtration surgery fibrosis.

Topology preserving stratification of tissue neoplasticity using Deep Neural Maps and microRNA signatures.

BACKGROUND: Accurate cancer classification is essential for correct treatment selection and better prognostication. microRNAs (miRNAs) are small RNA molecules that negatively regulate gene expression, and their dyresgulation is a common disease mechanism in many cancers. Through a clearer understanding of miRNA dysregulation in cancer, improved mechanistic knowledge and better treatments can be sought. RESULTS: We present a topology-preserving deep learning framework to study miRNA dysregulation in cancer. Our study comprises miRNA expression profiles from 3685 cancer and non-cancer tissue samples and hierarchical annotations on organ and neoplasticity status. Using unsupervised learning, a two-dimensional topological map is trained to cluster similar tissue samples. Labelled samples are used after training to identify clustering accuracy in terms of tissue-of-origin and neoplasticity status. In addition, an approach using activation gradients is developed to determine the attention of the networks to miRNAs that drive the clustering. Using this deep learning framework, we classify the neoplasticity status of held-out test samples with an accuracy of 91.07%, the tissue-of-origin with 86.36%, and combined neoplasticity status and tissue-of-origin with an accuracy of 84.28%. The topological maps display the ability of miRNAs to recognize tissue types and neoplasticity status. Importantly, when our approach identifies samples that do not cluster well with their respective classes, activation gradients provide further insight in cancer subtypes or grades. CONCLUSIONS: An unsupervised deep learning approach is developed for cancer classification and interpretation. This work provides an intuitive approach for understanding molecular properties of cancer and has significant potential for cancer classification and treatment selection.

Discriminating Neoplastic from Nonneoplastic Tissues Using an miRNA-Based Deep Cancer Classifier.

Next-generation sequencing has enabled the collection of large biological data sets, allowing novel molecular-based classification methods to be developed for increased understanding of disease. miRNAs are small regulatory RNA molecules that can be quantified using next-generation sequencing and are excellent classificatory markers. Herein, a deep cancer classifier (DCC) was adapted to differentiate neoplastic from nonneoplastic samples using comprehensive miRNA expression profiles from 1031 human breast and skin tissue samples. The classifier was fine-tuned and evaluated using 750 neoplastic and 281 nonneoplastic breast and skin tissue samples. Performance of the DCC was compared with two machine-learning classifiers: support vector machine and random forests. In addition, performance of feature extraction through the DCC was also compared with a developed feature selection algorithm, cancer specificity. The DCC had the highest performance of area under the receiver operating curve and high performance in both sensitivity and specificity, unlike machine-learning and feature selection models, which often performed well in one metric compared with the other. In particular, deep learning had noticeable advantages with highly heterogeneous data sets. In addition, our cancer specificity algorithm identified candidate biomarkers for differentiating neoplastic and nonneoplastic tissue samples (eg, miR-144 and miR-375 in breast cancer and miR-375 and miR-451 in skin cancer).

Multi-Omic Graph Transformers for Cancer Classification and Interpretation.

Next-generation sequencing has provided rapid collection and quantification of 'big' biological data. In particular, multi-omics and integration of different molecular data such as miRNA and mRNA can provide important insights to disease classification and processes. There is a need for computational methods that can correctly model and interpret these relationships, and handle the difficulties of large-scale data. In this study, we develop a novel method of representing miRNA-mRNA interactions to classify cancer. Specifically, graphs are designed to account for the interactions and biological communication between miRNAs and mRNAs, using message-passing and attention mechanisms. Patient-matched miRNA and mRNA expression data is obtained from The Cancer Genome Atlas for 12 cancers, and targeting information is incorporated from TargetScan. A Graph Transformer Network (GTN) is selected to provide high interpretability of classification through self-attention mechanisms. The GTN is able to classify the 12 different cancers with an accuracy of 93.56% and is compared to a Graph Convolutional Network, Random Forest, Support Vector Machine, and Multilayer Perceptron. While the GTN does not outperform all of the other classifiers in terms of accuracy, it allows high interpretation of results. Multi-omics models are compared and generally outperform their respective single-omics performance. Extensive analysis of attention identifies important targeting pathways and molecular biomarkers based on integrated miRNA and mRNA expression.

The Non-Coding RNA Journal Club: Highlights on Recent Papers-9.

We are delighted to share with you our ninth Journal Club and highlight some of the most interesting papers published recently [...].

A miR-375/YAP axis regulates neuroendocrine differentiation and tumorigenesis in lung carcinoid cells.

Lung carcinoids are variably aggressive and mechanistically understudied neuroendocrine neoplasms (NENs). Here, we identified and elucidated the function of a miR-375/yes-associated protein (YAP) axis in lung carcinoid (H727) cells. miR-375 and YAP are respectively high and low expressed in wild-type H727 cells. Following lentiviral CRISPR/Cas9-mediated miR-375 depletion, we identified distinct transcriptomic changes including dramatic YAP upregulation. We also observed a significant decrease in neuroendocrine differentiation and substantial reductions in cell proliferation, transformation, and tumor growth in cell culture and xenograft mouse disease models. Similarly, YAP overexpression resulted in distinct and partially overlapping transcriptomic changes, phenocopying the effects of miR-375 depletion in the same models as above. Transient YAP knockdown in miR-375-depleted cells reversed the effects of miR-375 on neuroendocrine differentiation and cell proliferation. Pathways analysis and confirmatory real-time PCR studies of shared dysregulated target genes indicate that this axis controls neuroendocrine related functions such as neural differentiation, exocytosis, and secretion. Taken together, we provide compelling evidence that a miR-375/YAP axis is a critical mediator of neuroendocrine differentiation and tumorigenesis in lung carcinoid cells.

Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining.

Lung neuroendocrine neoplasms (NENs) can be challenging to classify due to subtle histologic differences between pathological types. MicroRNAs (miRNAs) are small RNA molecules that are valuable markers in many neoplastic diseases. To evaluate miRNAs as classificatory markers for lung NENs, we generated comprehensive miRNA expression profiles from 14 typical carcinoid (TC), 15 atypical carcinoid (AC), 11 small cell lung carcinoma (SCLC), and 15 large cell neuroendocrine carcinoma (LCNEC) samples, through barcoded small RNA sequencing. Following sequence annotation and data preprocessing, we randomly assigned these profiles to discovery and validation sets. Through high expression analyses, we found that miR-21 and -375 are abundant in all lung NENs, and that miR-21/miR-375 expression ratios are significantly lower in carcinoids (TC and AC) than in neuroendocrine carcinomas (NECs; SCLC and LCNEC). Subsequently, we ranked and selected miRNAs for use in miRNA-based classification, to discriminate carcinoids from NECs. Using miR-18a and -155 expression, our classifier discriminated these groups in discovery and validation sets, with 93% and 100% accuracy. We also identified miR-17, -103, and -127, and miR-301a, -106b, and -25, as candidate markers for discriminating TC from AC, and SCLC from LCNEC, respectively. However, these promising findings require external validation due to sample size.

Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining.

Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomical sites was proposed by experts, with the requirement of improved molecular data integration. Here, we compiled 378 miRNA expression profiles to examine NEN classification through comprehensive miRNA profiling and data mining. Following data preprocessing, our final study cohort included 221 NEN and 114 non-NEN samples, representing 15 NEN pathological types and 5 site-matched non-NEN control groups. Unsupervised hierarchical clustering of miRNA expression profiles clearly separated NENs from non-NENs. Comparative analyses showed that miR-375 and miR-7 expression is substantially higher in NEN cases than non-NEN controls. Correlation analyses showed that NENs from diverse anatomical sites have convergent miRNA expression programs, likely reflecting morphological and functional similarities. Using machine learning approaches, we identified 17 miRNAs to discriminate 15 NEN pathological types and subsequently constructed a multilayer classifier, correctly identifying 217 (98%) of 221 samples and overturning one histological diagnosis. Through our research, we have identified common and type-specific miRNA tissue markers and constructed an accurate miRNA-based classifier, advancing our understanding of NEN diversity.

Distinguishing Tumor and Stromal Sources of MicroRNAs Linked to Metastasis in Cutaneous Melanoma.

MicroRNA (miRNA) dysregulation in cancer causes changes in gene expression programs regulating tumor progression and metastasis. Candidate metastasis suppressor miRNA are often identified by differential expression in primary tumors compared to metastases. Here, we performed comprehensive analysis of miRNA expression in The Cancer Genome Atlas (TCGA) skin cutaneous melanoma (SKCM) tumors (97 primary, 350 metastatic), and identified candidate metastasis-suppressor miRNAs. Differential expression analysis revealed miRNA significantly downregulated in metastatic tumors, including miR-205, miR-203, miR-200a-c, and miR-141. Furthermore, sequential feature selection and classification analysis identified miR-205 and miR-203 as the miRNA best able to discriminate between primary and metastatic tumors. However, cell-type enrichment analysis revealed that gene expression signatures for epithelial cells, including keratinocytes and sebocytes, were present in primary tumors and significantly correlated with expression of the candidate metastasis-suppressor miRNA. Examination of miRNA expression in cell lines revealed that candidate metastasis-suppressor miRNA identified in the SKCM tumors, were largely absent in melanoma cells or melanocytes, and highly restricted to keratinocytes and other epithelial cell types. Indeed, the differences in stromal cell composition between primary and metastatic tumor tissues is the main basis for identification of differential miRNA that were previously classified as metastasis-suppressor miRNAs. We conclude that future studies must consider tumor-intrinsic and stromal sources of miRNA in their workflow to identify bone fide metastasis-suppressor miRNA in cutaneous melanoma and other cancers.

Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier.

BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNA that regulate gene expression through post-transcriptional silencing. Differential expression observed in miRNAs, combined with advancements in deep learning (DL), have the potential to improve cancer classification by modelling non-linear miRNA-phenotype associations. We propose a novel miRNA-based deep cancer classifier (DCC) incorporating genomic and hierarchical tissue annotation, capable of accurately predicting the presence of cancer in wide range of human tissues. METHODS: miRNA expression profiles were analyzed for 1746 neoplastic and 3871 normal samples, across 26 types of cancer involving six organ sub-structures and 68 cell types. miRNAs were ranked and filtered using a specificity score representing their information content in relation to neoplasticity, incorporating 3 levels of hierarchical biological annotation. A DL architecture composed of stacked autoencoders (AE) and a multi-layer perceptron (MLP) was trained to predict neoplasticity using 497 abundant and informative miRNAs. Additional DCCs were trained using expression of miRNA cistrons and sequence families, and combined as a diagnostic ensemble. Important miRNAs were identified using backpropagation, and analyzed in Cytoscape using iCTNet and BiNGO. RESULTS: Nested four-fold cross-validation was used to assess the performance of the DL model. The model achieved an accuracy, AUC/ROC, sensitivity, and specificity of 94.73%, 98.6%, 95.1%, and 94.3%, respectively. CONCLUSION: Deep autoencoder networks are a powerful tool for modelling complex miRNA-phenotype associations in cancer. The proposed DCC improves classification accuracy by learning from the biological context of both samples and miRNAs, using anatomical and genomic annotation. Analyzing the deep structure of DCCs with backpropagation can also facilitate biological discovery, by performing gene ontology searches on the most highly significant features.

Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing.

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) can be challenging to evaluate histologically. MicroRNAs (miRNAs) are small RNA molecules that often are excellent biomarkers due to their abundance, cell-type and disease stage specificity and stability. To evaluate miRNAs as adjunct tissue markers for classifying and grading well-differentiated GEP-NETs, we generated and compared miRNA expression profiles from four pathological types of GEP-NETs. Using quantitative barcoded small RNA sequencing and state-of-the-art sequence annotation, we generated comprehensive miRNA expression profiles from archived pancreatic, ileal, appendiceal and rectal NETs. Following data preprocessing, we randomly assigned sample profiles to discovery (80%) and validation (20%) sets prior to data mining using machine-learning techniques. High expression analyses indicated that miR-375 was the most abundant individual miRNA and miRNA cistron in all samples. Leveraging prior knowledge that GEP-NET behavior is influenced by embryonic derivation, we developed a dual-layer hierarchical classifier for differentiating GEP-NET types. In the first layer, our classifier discriminated midgut (ileum, appendix) from non-midgut (rectum, pancreas) NETs based on miR-615 and -92b expression. In the second layer, our classifier discriminated ileal from appendiceal NETs based on miR-125b, -192 and -149 expression, and rectal from pancreatic NETs based on miR-429 and -487b expression. Our classifier achieved overall accuracies of 98.5% and 94.4% in discovery and validation sets, respectively. We also found provisional evidence that low- and intermediate-grade pancreatic NETs can be discriminated based on miR-328 expression. GEP-NETs can be reliably classified and potentially graded using a limited panel of miRNA markers, complementing morphological and immunohistochemistry-based approaches to histologic evaluation.

MicroRNA-206 suppresses TGF-ß signalling to limit tumor growth and metastasis in lung adenocarcinoma.

MicroRNA-206 (miR-206) has demonstrated tumor suppressive effects in a variety of cancers. Numerous studies have identified aberrantly expressed targets of miR-206 that contribute to tumor progression and metastasis, however, the broader gene-networks and pathways regulated by miR-206 remain poorly defined. Here, we have ectopically expressed miR-206 in lung adenocarcinoma cell lines and tumors to identify differentially expressed genes, and study the effects on tumor growth and metastasis. In H1299 tumor xenograft assays, stable expression of miR-206 suppressed both tumor growth and metastasis in mice. Profiling of xenograft tumors using small RNA sequencing and a targeted panel of tumor progression and metastasis-related genes revealed a network of genes involved in TGF-ß signalling that were regulated by miR-206. Among these were the TGFB1 ligand, as well as direct transcriptional targets of Smad3. Other differentially expressed genes included components of the extracellular matrix involved in TGF-ß activation and signalling, including Thrombospondin-1, which is responsible for the activation of latent TGF-ß in the stroma. In cultured lung adenocarcinoma cells treated with recombinant TGF-ß, ectopic expression of miR-206 impaired canonical signalling, and expression of TGF-ß target genes linked to epithelial-mesenchymal transition. This was due at least in part to the suppression of Smad3 protein levels in lung adenocarcinoma cells with ectopic miR-206 expression. Together, these findings indicate that miR-206 can suppress tumor progression and metastasis by limiting autocrine production of TGF-ß, and highlight the potential utility of TGF-ß inhibitors for the treatment of lung adenocarcinomas.

microRNA-guided diagnostics in clinical samples.

miRNA-guided diagnostics is a powerful molecular approach for evaluating clinical samples through miRNA detection and/or visualization. To date, this approach has been successfully used to diagnose, manage, and/or monitor a wide range of neoplastic and non-neoplastic diseases. Despite the promise of miRNA-guided diagnostics, particularly in the field of minimally invasive biomarkers, several knowledge and practical issues confound or hinder translation into routine clinical practice including: miRNA sequence database errors, suboptimal RNA extraction methods, detection assay variability, a vast array of online resources for bioinformatic analyses, and non-standardized statistical analyses for miRNA clinical testing. In this review, we raise awareness of these issues and recommend research directions to help specialists in endocrinology and metabolism integrate miRNA testing into clinical decision-making.

Genetic fitness and selection intensity in a population affected with high-incidence spinocerebellar ataxia type 1.

Spinocerebellar ataxia type 1 (SCA1) is the major and likely the only type of autosomal dominant cerebellar ataxia in the Sakha (Yakut) people of Eastern Siberia. The prevalence rate of SCA1 has doubled over the past 21 years peaking at 46 cases per 100,000 rural population. The age at death correlates closely with the number of CAG triplet repeats in the mutant ATXN1 gene (r = -0.81); most patients with low-medium (39-55) repeat numbers survived until the end of reproductive age. The number of CAG repeats expands in meiosis, particularly in paternal transmissions; the average total increase in intergenerational transmissions in our cohort was estimated at 1.6 CAG repeats. The fertility rates of heterozygous carriers of 39-55 CAG repeats in women were no different from those of the general Sakha population. Overall, the survival of mutation carriers through reproductive age, unaltered fertility rates, low childhood mortality in SCA1-affected families, and intergenerational transmission of increasing numbers of CAG repeats in the ATXN1 gene indicate that SCA1 in the Sakha population will be maintained at high prevalence levels. The low (0.19) Crow's index of total selection intensity in our SCA1 cohort implies that this mutation is unlikely to be eliminated through natural selection alone.

Adult-onset autosomal dominant spastic paraplegia linked to a GTPase-effector domain mutation of dynamin 2.

BACKGROUND: Hereditary Spastic Paraplegia (HSP) represents a large group of clinically and genetically heterogeneous disorders linked to over 70 different loci and more than 60 recognized disease-causing genes. A heightened vulnerability to disruption of various cellular processes inherent to the unique function and morphology of corticospinal neurons may account, at least in part, for the genetic heterogeneity. METHODS: Whole exome sequencing was utilized to identify candidate genetic variants in a four-generation Siberian kindred that includes nine individuals showing clinical features of HSP. Segregation of candidate variants within the family yielded a disease-associated mutation. Functional as well as in-silico structural analyses confirmed the selected candidate variant to be causative. RESULTS: Nine known patients had young-adult onset of bilateral slowly progressive lower-limb spasticity, weakness and hyperreflexia progressing over two-to-three decades to wheel-chair dependency. In the advanced stage of the disease, some patients also had distal wasting of lower leg muscles, pes cavus, mildly decreased vibratory sense in the ankles, and urinary urgency along with electrophysiological evidence of a mild distal motor/sensory axonopathy. Molecular analyses uncovered a missense c.2155C > T, p.R719W mutation in the highly conserved GTP-effector domain of dynamin 2. The mutant DNM2 co-segregated with HSP and affected endocytosis when expressed in HeLa cells. In-silico modeling indicated that this HSP-associated dynamin 2 mutation is located in a highly conserved bundle-signaling element of the protein while dynamin 2 mutations associated with other disorders are located in the stalk and PH domains; p.R719W potentially disrupts dynamin 2 assembly. CONCLUSION: This is the first report linking a mutation in dynamin 2 to a HSP phenotype. Dynamin 2 mutations have previously been associated with other phenotypes including two forms of Charcot-Marie-Tooth neuropathy and centronuclear myopathy. These strikingly different pathogenic effects may depend on structural relationships the mutations disrupt. Awareness of this distinct association between HSP and c.2155C > T, p.R719W mutation will facilitate ascertainment of additional DNM2 HSP families and will direct future research toward better understanding of cell biological processes involved in these partly overlapping clinical syndromes.

Unique microRNAs appear at different times during the course of a delayed-type hypersensitivity reaction in human skin.

Diphencyprone (DPCP) is a hapten that induces delayed-type hypersensitivity (DTH) reactions. MicroRNAs (miRNAs) are short non-coding RNAs that negatively regulate gene expression and have been implicated in various inflammatory skin diseases, but their role in DTH reactions is not well understood. We generated global miRNA expression profiles (using next-generation sequencing) of DPCP reactions in skin of seven healthy volunteers at 3, 14 and 120 days after challenge. Compared to placebo-treated sites, DPCP-challenged skin at 3 days (peak inflammation) had 127 miRNAs significantly deregulated. At 14 days (during resolution of inflammation), 43 miRNAs were deregulated and, at 120 days (when inflammation had completely resolved), six miRNAs were upregulated. While some miRNAs have been observed in psoriasis or atopic dermatitis, most of the deregulated miRNAs have not yet been studied in the context of skin biology or immunology. Across the three time points studied, many but not all miRNAs were uniquely expressed. As various miRNAs may influence T cell activation, this may indicate that the miRNAs exclusively expressed at different time points function to promote or resolve skin inflammation, and therefore, may inform on the paradoxical ability of DPCP to treat both autoimmune conditions (alopecia areata) and conditions of ineffective immunity (melanoma).

miR-375 gene dosage in pancreatic ß-cells: implications for regulation of ß-cell mass and biomarker development.

UNLABELLED: MicroRNAs play a crucial role in the regulation of cell growth and differentiation. Mice with genetic deletion of miR-375 exhibit impaired glycemic control due to decreased ß-cell and increased α-cell mass and function. The relative importance of these processes for the overall phenotype of miR-375KO mice is unknown. Here, we show that mice overexpressing miR-375 exhibit normal ß-cell mass and function. Selective re-expression of miR-375 in ß-cells of miR-375KO mice normalizes both, α- and ß-cell phenotypes as well as glucose metabolism. Using this model, we also analyzed the contribution of ß-cells to the total plasma miR-375 levels. Only a small proportion (≈1 %) of circulating miR-375 originates from ß-cells. Furthermore, acute and profound ß-cell destruction is sufficient to detect elevations of miR-375 levels in the blood. These findings are supported by higher miR-375 levels in the circulation of type 1 diabetes (T1D) subjects but not mature onset diabetes of the young (MODY) and type 2 diabetes (T2D) patients. Together, our data support an essential role for miR-375 in the maintenance of ß-cell mass and provide in vivo evidence for release of miRNAs from pancreatic ß-cells. The small contribution of ß-cells to total plasma miR-375 levels make this miRNA an unlikely biomarker for ß-cell function but suggests a utility for the detection of acute ß-cell death for autoimmune diabetes. KEY MESSAGES: • Overexpression of miR-375 in ß-cells does not influence ß-cell mass and function. • Increased α-cell mass in miR-375KO arises secondarily to loss of miR-375 in ß-cells. • Only a small proportion of circulating miR-375 levels originates from ß-cells. • Acute ß-cell destruction results in measurable increases of miR-375 in the blood. Circulating miR-375 levels are not a biomarker for pancreatic ß-cell function.

Dysregulation of microRNA-219 promotes neurodegeneration through post-transcriptional regulation of tau.

Tau is a highly abundant and multifunctional brain protein that accumulates in neurofibrillary tangles (NFTs), most commonly in Alzheimer's disease (AD) and primary age-related tauopathy. Recently, microRNAs (miRNAs) have been linked to neurodegeneration; however, it is not clear whether miRNA dysregulation contributes to tau neurotoxicity. Here, we determined that the highly conserved brain miRNA miR-219 is downregulated in brain tissue taken at autopsy from patients with AD and from those with severe primary age-related tauopathy. In a Drosophila model that produces human tau, reduction of miR-219 exacerbated tau toxicity, while overexpression of miR-219 partially abrogated toxic effects. Moreover, we observed a bidirectional modulation of tau levels in the Drosophila model that was dependent on miR-219 expression or neutralization, demonstrating that miR-219 regulates tau in vivo. In mammalian cellular models, we found that miR-219 binds directly to the 3'-UTR of the tau mRNA and represses tau synthesis at the post-transcriptional level. Together, our data indicate that silencing of tau by miR-219 is an ancient regulatory mechanism that may become perturbed during neurofibrillary degeneration and suggest that this regulatory pathway may be useful for developing therapeutics for tauopathies.