Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.

Adenosine-to-inosine (A-to-I) editing modifies RNA transcripts from their genomic blueprint. A prerequisite for this process is a double-stranded RNA (dsRNA) structure. Such dsRNAs are formed as part of the microRNA (miRNA) maturation process, and it is therefore expected that miRNAs are affected by A-to-I editing. Editing of miRNAs has the potential to add another layer of complexity to gene regulation pathways, especially if editing occurs within the miRNA–mRNA recognition site. Thus, it is of interest to study the extent of this phenomenon. Current reports in the literature disagree on its extent; while some reports claim that it may be widespread, others deem the reported events as rare. Utilizing a next-generation sequencing (NGS) approach supplemented by an extensive bioinformatic analysis, we were able to systematically identify A-to-I editing events in mature miRNAs derived from human brain tissues. Our algorithm successfully identified many of the known editing sites in mature miRNAs and revealed 17 novel human sites, 12 of which are in the recognition sites of the miRNAs. We confirmed most of the editing events using in vitro ADAR overexpression assays. The editing efficiency of most sites identified is very low. Similar results are obtained for publicly available data sets of mouse brain-regions tissues. Thus, we find that A-to-I editing does alter several miRNAs, but it is not widespread.

Background: Identification of genes responsible for medically important traits is a major challenge in human genetics. Due to the genetic heterogeneity of hearing loss, targeted DNA capture and massively parallel sequencing are ideal tools to address this challenge. Our subjects for genome analysis are Israeli Jewish and Palestinian Arab families with hearing loss that varies in mode of inheritance and severity. Results: A custom 1.46 MB design of cRNA oligonucleotides was constructed containing 246 genes responsible for either human or mouse deafness. Paired-end libraries were prepared from 11 probands and bar-coded multiplexed samples were sequenced to high depth of coverage. Rare single base pair and indel variants were identified by filtering sequence reads against polymorphisms in dbSNP132 and the 1000 Genomes Project. We identified deleterious mutations in CDH23, MYO15A, TECTA, TMC1, and WFS1. Critical mutations of the probands co-segregated with hearing loss. Screening of additional families in a relevant population was performed. TMC1 p.S647P proved to be a founder allele, contributing to 34% of genetic hearing loss in the Moroccan Jewish population. Conclusions: Critical mutations were identified in 6 of the 11 original probands and their families, leading to the identification of causative alleles in 20 additional probands and their families. The integration of genomic analysis into early clinical diagnosis of hearing loss will enable prediction of related phenotypes and enhance rehabilitation. Characterization of the proteins encoded by these genes will enable an understanding of the biological mechanisms involved in hearing loss.

Metastasis is the primary cause for mortality in breast cancer. MicroRNAs, gene expression master regulators, constitute an attractive candidate to control metastasis. Here we show that breast cancer metastasis can be prevented by miR-96 or miR-182 treatment, and decipher the mechanism of action. We found that miR-96/miR-182 downregulate Palladin protein levels, thereby reducing breast cancer cell migration and invasion. A common SNP, rs1071738, at the miR-96/miR-182-binding site within the Palladin 3′-UTR abolishes miRNA:mRNA binding, thus diminishing Palladin regulation by these miRNAs. Regulation is successfully restored by applying complimentary miRNAs. A hydrogel-embedded, gold-nanoparticle-based delivery vehicle provides efficient local, selective, and sustained release of miR-96/miR-182, markedly suppressing metastasis in a breast cancer mouse model. Combined delivery of the miRNAs with a chemotherapy drug, cisplatin, enables significant primary tumour shrinkage and metastasis prevention. Our data corroborate the role of miRNAs in metastasis, and suggest miR-96/miR-182 delivery as a potential anti-metastatic drug.

MicroRNAs (miRNAs) are short regulatory RNAs that down-regulate gene expression. They are essential for cell homeostasis and active in many disease states. A major discovery is the ability of miRNAs to determine the efficacy of drugs, which has given rise to the field of ‘miRNA pharmacogenomics’ through ‘Pharmaco-miRs’. miRNAs play a significant role in pharmacogenomics by down-regulating genes that are important for drug function. These interactions can be described as triplet sets consisting of a miRNA, a target gene and a drug associated with the gene. We have developed a web server which links miRNA expression and drug function by combining data on miRNA targeting and protein–drug interactions. miRNA targeting information derive from both experimental data and computational predictions, and protein–drug interactions are annotated by the Pharmacogenomics Knowledge base (PharmGKB). Pharmaco-miR’s input consists of miRNAs, genes and/or drug names and the output consists of miRNA pharmacogenomic sets or a list of unique associated miRNAs, genes and drugs. We have furthermore built a database, named Pharmaco-miR Verified Sets (VerSe), which contains miRNA pharmacogenomic data manually curated from the literature, can be searched and downloaded via Pharmaco-miR and informs on trends and generalities published in the field. Overall, we present examples of how Pharmaco-miR provides possible explanations for previously published observations, including how the cisplatin and 5-fluorouracil resistance induced by miR-148a may be caused by miR-148a targeting of the gene KIT. The information is available at www.Pharmaco-miR.org.