Human coronaviruses (HCoVs) cause mild to severe respiratory diseases. Six types of HCoVs have been discovered, the most recent one termed the Middle East respiratory syndrome coronavirus (MERS-CoV). The aim of this study is to monitor the circulation of HCoV types in the population during 2015–2016 in Israel. HCoVs were detected by real-time PCR analysis in 1910 respiratory samples, collected from influenza-like illness (ILI) patients during the winter sentinel influenza survey across Israel. Moreover, 195 HCoV-positive samples from hospitalized patients were detected during one year at Soroka University Medical Center. While no MERS-CoV infections were detected, 10.36% of patients in the survey were infected with HCoV-OC43 (43.43%), HCoV-NL63 (44.95%), and HCoV-229E (11.62%) viruses. The HCoVs were shown to co-circulate with respiratory syncytial virus (RSV) and to appear prior to influenza virus infections. HCoV clinical symptoms were more severe than those of RSV infections but milder than influenza symptoms. Hospitalized patients had similar HCoV types percentages. However, while it was absent from the public winter survey, 22.6% of the patients were HCoV-HKU1 positives, mainly during the spring-summer period.

The purpose of the present study was to characterize the microRNA transcriptome (miRNAome) of the human cytomegalovirus (HCMV or HHV5). We used deep sequencing and real time PCR (qPCR) together with bioinformatics to analyze the pattern of small RNA expression in cells infected with low-passage isolates of HCMV as well as in plasma and amniotic fluid. We report here on the discovery of four new precursors and ten new miRNAs as well as eleven microRNA-offset-RNAs (moRs) that are all encoded by HCMV. About eighty percent of the total HCMV reads were perfectly mapped to HCMV miRNAs, strongly suggestive of their important biological role that in large remains still to be defined and characterized. Taken altogether, the results of this study demonstrate the power and usefulness of the combined bioinformatics/biological approach in discovering additional important members of HCMV- encoded small RNAs and can be applied to the study of other viruses as well.

Background: MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs (20-24 nts) that can affect gene expression by post-transcriptional regulation of mRNAs. They play important roles in several biological processes (e.g., development and cell cycle regulation). Numerous bioinformatics methods have been developed to identify the function of miRNAs by predicting their target mRNAs. Some viral organisms also encode miRNAs, a fact that contributes to the complex interactions between viruses and their hosts. A need arises to understand the functional relationship between viral and host miRNAs and their effect on viral and host genes. Our approach to meet this challenge is to identify modules where viral and host miRNAs cooperatively regulate host gene expression. Results: We present a method to identify groups of viral and host miRNAs that cooperate in post-transcriptional gene regulation, and their target genes that are involved in similar biological processes. We call these groups (genes and miRNAs of human and viral origin) - modules. The modules are found in a new two-stage procedure, which we call bi-targeting, and is presented in this paper. The stages are (i) a new and efficient target prediction, and (ii) a new method for clustering objects of three different data types. In this work we integrate multiple information sources, including miRNA-target binding information, miRNA expression profiles, and GO annotations. Our hypotheses and the methods have been tested on human and Epstein Barr virus (EBV) miRNAs and human genes, for which we found 34 modules. We provide supporting evidence from biological and medical literature for two of our modules. Our code and data are available at http://www.cs.bgu.ac.il/~vaksler/BiTargeting.htm Conclusions: The presented algorithm, which makes use of diverse biological data, is demonstrated to be an efficient approach for finding bi-targeting modules of viral and human miRNAs. These modules can contribute to a better understanding of viral-host interactions and the role that miRNAs play in them.

Background.Transmission of hepatitis C virus (HCV) from infected health care workers to patients rarely occurs. In 2003, a cluster of patients with HCV infection was identified at a medical center in Israel. All patients had a common history of various surgical procedures performed during the period 2001–2003. All patients had been anesthetized by an anesthesiologist who was an injection drug user and was infected with genotype 2a HCV. Screening was initiated by the hospital to identify newly infected patients with HCV infection and to determine the source of the iatrogenic HCV infection outbreak using comparative molecular analysis of the HCV E1 and HCV E2 hypervariable regions (HVR1 and HVR2). Methods.A total of 1200 patients who were anesthetized by the anesthesiologist (the related group) and 873 hospital personnel and patients anesthetized by other anesthetists (the unrelated group) were examined. Serum samples were screened for anti-HCV antibodies, HCV RNA, and genotype. Sequence analysis of HVR1 and HVR2 was performed after reverse-transcriptase polymerase chain reaction. Results.HCV type 2a was found in 33 patients in the related group but in only 1 patient in the unrelated group. The differences between the sequences isolated from the related group serum samples and the sequences isolated from genotype 2a control group serum samples (obtained from 15 patients) were highly statistically significant. The genetic distances from the anesthesiologist sequence were 1.4%–4.4% in the HVR1 and 0%–3% in the HVR2 in the related group serum samples, whereas in the HCV genotype 2a control group serum samples, the genetic distances were 22%–45% and 10%–35%, respectively. Conclusions.Molecular analysis revealed sequence similarity of HVR1 and HVR2 in the related group, suggesting that the anesthesiologist with chronic HCV infection may have transmitted HCV to 33 patients.

The COVID-19 pandemic is rapidly spreading throughout the world. Recent reports suggest that 10-30% of SARS-CoV-2 infected patients are asymptomatic. Other studies report that some subjects have significant viral shedding prior to symptom onset. Since both asymptomatic and pre-symptomatic subjects can spread the disease, identifying such individuals is critical for effective control of the SARS-CoV-2 pandemic. Therefore, there is an urgent need to increase diagnostic testing capabilities in order to also screen asymptomatic carriers. In fact, such tests will be routinely required until a vaccine is developed. Yet, a major bottleneck of managing the COVID-19 pandemic in many countries is diagnostic testing, due to limited laboratory capabilities as well as limited access to genome-extraction and Polymerase Chain Reaction (PCR) reagents. We developed P-BEST - a method for Pooling-Based Efficient SARS-CoV-2 Testing, using a non-adaptive group-testing approach, which significantly reduces the number of tests required to identify all positive subjects within a large set of samples. Instead of testing each sample separately, samples are pooled into groups and each pool is tested for SARS-CoV-2 using the standard clinically approved PCR-based diagnostic assay. Each sample is part of multiple pools, using a combinatorial pooling strategy based on compressed sensing designed for maximizing the ability to identify all positive individuals. We evaluated P-BEST using leftover samples that were previously clinically tested for COVID-19. In our current proof-of-concept study we pooled 384 patient samples into 48 pools providing an 8-fold increase in testing efficiency. Five sets of 384 samples, containing 1-5 positive carriers were screened and all positive carriers in each set were correctly identified. P-BEST provides an efficient and easy-to-implement solution for increasing testing capacity that will work with any clinically approved genome-extraction and PCR-based diagnostic methodologies.