‘miR2Disease’, a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA–disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA–disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA–disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA–disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org.

BackgroundThe identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.ResultsHerein, we devised a computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs.ConclusionsWe presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.

ciated viruses are dependoviruses. The autonomous parvoviruses, which replicate in the absence of other viruses, are common animal pathogens; feline panleucopenia virus, Aleutian mink disease virus, minute virus of mice, and Kilham rat virus are examples of members of this genus. Despite differences in their biological behavior, both types of parvovirus have similar genetic organizations. The coding potential of the short genome is increased by utilization of overlapping transcripts and multiple reading frames. Structurally, their DNA is characterized by the presence of terminal hairpin structures that serve as initiation sites for replication through double-stranded intermediate forms. Two or three structurally similar capsid proteins are encoded by the right side of the genome by separate but overlapping RNA species. One to three noncapsid proteins that are thought to provide replicative functions are encoded by the left side. In the parvoviruses studied so far, separate promoters drive transcription from the left side and the middle of the genome. The pathogenic B19 human virus is, for a member of the Parvoviridae family, remarkable in its highly restricted tissue range. The molecular basis of this unusual biologic behavior remains uncertain. In erythroid bone marrow cells cultured in vitro, the pattern of B19 DNA replication resembles that of other parvoviruses (26). The protein species that compose the capsid and the noncapsid proteins also are analogous in number and size to those of other parvoviruses (13, 27). As we report here, however, the B19 transcription map differs in several fundamental aspects from that of other Parvoviridae. MATERIALS AND METHODS Cell culture. B19 parvovirus was propagated in human erythroid bone marrow cells that were obtained from patients with sickle cell disease after informed consent and 2395

Introduction: The Afirma® Xpression Atlas (XA) detects gene variants and fusions in thyroid nodule FNA samples from a curated panel of 511 genes using whole-transcriptome RNA-sequencing. Its intended use is among cytologically indeterminate nodules that are Afirma GSC suspicious, Bethesda V/VI nodules, or known thyroid metastases. Here we report its analytical and clinical validation.Methods: DNA and RNA were purified from the same sample across 943 blinded FNAs and compared by multiple methodologies, including whole-transcriptome RNA-seq, targeted RNA-seq, and targeted DNA-seq. An additional 695 blinded FNAs were used to define performance for fusions between whole-transcriptome RNA-seq and targeted RNA-seq. We quantified the reproducibility of the whole-transcriptome RNA-seq assay across laboratories and reagent lots. Finally, variants and fusions were compared to histopathology results.Results: Of variants detected in DNA at 5 or 20% variant allele frequency, 74 and 88% were also detected by XA, respectively. XA variant detection was 89% when compared to an alternative RNA-based detection method. Low levels of expression of the DNA allele carrying the variant, compared with the wild-type allele, was found in some variants not detected by XA. 82% of gene fusions detected in a targeted RNA fusion assay were detected by XA. Conversely, nearly all variants or fusions detected by XA were confirmed by an alternative method. Analytical validation studies demonstrated high intra-plate reproducibility (89%-94%), inter-plate reproducibility (86–91%), and inter-lab accuracy (90%). Multiple variants and fusions previously described across the spectrum of thyroid cancers were identified by XA, including some with approved or investigational targeted therapies. Among 190 Bethesda III/IV nodules, the sensitivity of XA as a standalone test was 49%.Conclusion: When the Afirma Genomic Sequencing Classifier (GSC) is used first among Bethesda III/IV nodules as a rule-out test, XA supplements genomic insight among those that are GSC suspicious. Our data clinically and analytically validate XA for use among GSC suspicious, or Bethesda V/VI nodules. Genomic information provided by XA may inform clinical decision-making with precision medicine insights across a broad range of FNA sample types encountered in the care of patients with thyroid nodules and thyroid cancer.

Impaired synaptic plasticity and neuron loss are hallmarks of Alzheimer’s disease and vascular dementia. Here, we found that chronic brain hypoperfusion (CBH) by bilateral common carotid artery occlusion (2VO) decreased the total length, numbers and crossings of dendrites and caused neuron death in rat hippocampi and cortices. It also led to increase in N-terminal β-amyloid precursor protein (N-APP) and death receptor-6 (DR6) protein levels and in the activation of caspase-3 and caspase-6. Further study showed that DR6 protein was downregulated by miR-195 overexpression, upregulated by miR-195 inhibition, and unchanged by binding-site mutation and miR-masks. Knockdown of endogenous miR-195 by lentiviral vector-mediated overexpression of its antisense molecule (lenti-pre-AMO-miR-195) decreased the total length, numbers and crossings of dendrites and neuron death, upregulated N-APP and DR6 levels, and elevated cleaved caspase-3 and caspase-6 levels. Overexpression of miR-195 using lenti-pre-miR-195 prevented these changes triggered by 2VO. We conclude that miR-195 is involved in CBH-induced dendritic degeneration and neuron death through activation of the N-APP/DR6/caspase pathway.

Context Broad genomic analyses among thyroid histologies have been described from relatively small cohorts. Objective Investigate the molecular findings across a large, real-world cohort of thyroid fine needle aspiration (FNA) samples. Design Retrospective analysis of RNA sequencing data files. Setting CLIA laboratory performing Afirma Genomic Sequencing Classifier (GSC) and Xpression Atlas (XA) testing. Participants 50,644 consecutive Bethesda III-VI nodules. Intervention none. Main Outcome Measures Molecular test results. Results Of 48,952 Bethesda III/IV FNAs studied, 66% were benign by Afirma GSC. The prevalence of BRAF V600E was 2% among all Bethesda III/IV FNAs and 76% among Bethesda VI FNAs. Fusions involving NTRK, RET, BRAF, and ALK were most prevalent in Bethesda V (10%), and 130 different gene partners were identified. Among small consecutive Bethesda III/IV sample cohorts with one of these fusions and available surgical pathology excision data, the positive predictive value of an NTRK or RET fusion for carcinoma or non-invasive follicular thyroid neoplasm with papillary-like nuclear features was >95%, while for BRAF and ALK fusions it was 81% and 67%, respectively. At least one genomic alteration was identified by the expanded Afirma XA panel in 70% of Medullary Thyroid Carcinoma Classifier positive FNAs, 44% of Bethesda III or IV Afirma GSC suspicious FNAs, 64% of Bethesda V FNAs, and 87% of Bethesda VI FNAs. Conclusions This large study demonstrates that almost half of Bethesda III/IV Afirma GSC suspicious and most Bethesda V/VI nodules had at least 1 genomic variant or fusion identified, which may optimize personalized treatment decisions.

Background Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingly, non-cancerous Hürthle lesions were conventionally distinguished from Hürthle cell cancers by histopathological examination of tissue following surgical resection. Reliance on histopathological evaluation requires patients to undergo surgery to obtain a diagnosis despite most being non-cancerous. It is highly desirable to avoid surgery and to provide accurate classification of benignity versus malignancy from FNAB preoperatively. In our first-generation algorithm, Gene Expression Classifier (GEC), we achieved this goal by using machine learning (ML) on gene expression features. The classifier is sensitive, but not specific due in part to the presence of non-neoplastic benign Hürthle cells in many FNAB. Results We sought to overcome this low-specificity limitation by expanding the feature set for ML using next-generation whole transcriptome RNA sequencing and called the improved algorithm the Genomic Sequencing Classifier (GSC). The Hürthle identification leverages mitochondrial expression and we developed novel feature extraction mechanisms to measure chromosomal and genomic level loss-of-heterozygosity (LOH) for the algorithm. Additionally, we developed a multi-layered system of cascading classifiers to sequentially triage Hürthle cell-containing FNAB, including: 1. presence of Hürthle cells, 2. presence of neoplastic Hürthle cells, and 3. presence of benign Hürthle cells. The final Hürthle cell Index utilizes 1048 nuclear and mitochondrial genes; and Hürthle cell Neoplasm Index leverages LOH features as well as 2041 genes. Both indices are Support Vector Machine (SVM) based. The third classifier, the GSC Benign/Suspicious classifier, utilizes 1115 core genes and is an ensemble classifier incorporating 12 individual models. Conclusions The accurate algorithmic depiction of this complex biological system among Hürthle subtypes results in a dramatic improvement of classification performance; specificity among Hürthle cell neoplasms increases from 11.8% with the GEC to 58.8% with the GSC, while maintaining the same sensitivity of 89%. Electronic supplementary material The online version of this article (10.1186/s12918-019-0693-z) contains supplementary material, which is available to authorized users.

Small insertions/deletions (INDELs) of ≤21 bp comprise 18% of all recorded mutations causing human inherited disease and are evident in 24% of documented Mendelian diseases. INDELs affect gene function in multiple ways: for example, by introducing premature stop codons that either lead to the production of truncated proteins or affect transcriptional efficiency. However, the means by which they impact post-transcriptional regulation, including alternative splicing, have not been fully evaluated. In this study, we collate disease-causing INDELs from the Human Gene Mutation Database (HGMD) and neutral INDELs from the 1000 Genomes Project. The potential of these two types of INDELs to affect binding-site affinity of RNA-binding proteins (RBPs) was then evaluated. We identified several sequence features that can distinguish disease-causing INDELs from neutral INDELs. Moreover, we built a machine-learning predictor called PinPor (predicting pathogenic small insertions and deletions affecting post-transcriptional regulation, http://watson.compbio.iupui.edu/pinpor/) to ascertain which newly observed INDELs are likely to be pathogenic. Our results show that disease-causing INDELs are more likely to ablate RBP-binding sites and tend to affect more RBP-binding sites than neutral INDELs. Additionally, disease-causing INDELs give rise to greater deviations in binding affinity than neutral INDELs. We also demonstrated that disease-causing INDELs may be distinguished from neutral INDELs by several sequence features, such as their proximity to splice sites and their potential effects on RNA secondary structure. This predictor showed satisfactory performance in identifying numerous pathogenic INDELs, with a Matthews correlation coefficient (MCC) value of 0.51 and an accuracy of 0.75.