Ofer Isakov

Desmoglein 1 deficiency results in severe dermatitis, multiple allergies and metabolic wasting

The relative contribution of immunological dysregulation and impaired epithelial barrier function to allergic diseases is still a matter of debate. Here we describe a new syndrome featuring severe dermatitis, multiple allergies and metabolic wasting (SAM syndrome) caused by homozygous mutations in DSG1. DSG1 encodes desmoglein 1, a major constituent of desmosomes, which connect the cell surface to the keratin cytoskeleton and have a crucial role in maintaining epidermal integrity and barrier function. Mutations causing SAM syndrome resulted in lack of membrane expression of DSG1, leading to loss of cell-cell adhesion. In addition, DSG1 deficiency was associated with increased expression of a number of genes encoding allergy-related cytokines. Our deciphering of the pathogenesis of SAM syndrome substantiates the notion that allergy may result from a primary structural epidermal defect.

Familial Pityriasis Rubra Pilaris Is Caused by Mutations in CARD14

Pityriasis rubra pilaris (PRP) is a papulosquamous disorder phenotypically related to psoriasis. The disease has been occasionally shown to be inherited in an autosomal-dominant fashion. To identify the genetic cause of familial PRP, we ascertained four unrelated families affected by autosomal-dominant PRP. We initially mapped PRP to 17q25.3, a region overlapping with psoriasis susceptibility locus 2 (PSORS2 [MIM 602723]). Using a combination of linkage analysis followed by targeted whole-exome sequencing and candidate-gene screening, we identified three different heterozygous mutations in CARD14, which encodes caspase recruitment domain family, member 14. CARD14 was found to be specifically expressed in the skin. CARD14 is a known activator of nuclear factor kappa B signaling, which has been implicated in inflammatory disorders. Accordingly, CARD14 levels were increased, and p65 was found to be activated in the skin of PRP-affected individuals. The present data demonstrate that autosomal-dominant PRP is allelic to familial psoriasis, which was recently shown to also be caused by mutations in CARD14.

Analysis of insertion–deletion from deep-sequencing data: software evaluation for optimal detection

Insertion and deletion (indel) mutations, the most common type of structural variance in the human genome, affect a multitude of human traits and diseases. New sequencing technologies, such as deep sequencing, allow massive throughput of sequence data and greatly contribute to the field of disease causing mutation detection, in general, and indel detection, specifically. In order to infer indel presence (indel calling), the deep-sequencing data have to undergo comprehensive computational analysis. Selecting which indel calling software to use can often skew the results and inherent tool limitations may affect downstream analysis. In order to better understand these inter-software differences, we evaluated the performance of several indel calling software for short indel (1-10 nt) detection. We compared the softwares sensitivity and predictive values in the presence of varying parameters such as read depth (coverage), read length, indel size and frequency. We pinpoint several key features that assist successful experimental design and appropriate tool selection. Our study may also serve as a basis for future evaluation of additional indel calling methods.

Pathogen detection using short-RNA deep sequencing subtraction and assembly

Motivation: Early and accurate detection of human pathogen infection is critical for treatment and therapeutics. Here we describe pathogen identification using short RNA subtraction and assembly (SRSA), a detection method that overcomes the requirement of prior knowledge and culturing of pathogens, by using degraded small RNA and deep sequencing technology. We prove our approachs efficiency through identification of a combined viral and bacterial infection in human cells. Contact: nshomron@post.tau.ac.il

Group Selection and Contribution of Minority Variants during Virus Adaptation Determines Virus Fitness and Phenotype.

Understanding how a pathogen colonizes and adapts to a new host environment is a primary aim in studying emerging infectious diseases. Adaptive mutations arise among the thousands of variants generated during RNA virus infection, and identifying these variants will shed light onto how changes in tropism and species jumps can occur. Here, we adapted Coxsackie virus B3 to a highly permissive and less permissive environment. Using deep sequencing and bioinformatics, we identified a multi-step adaptive process to adaptation involving residues in the receptor footprints that correlated with receptor availability and with increase in virus fitness in an environment-specific manner. We show that adaptation occurs by selection of a dominant mutation followed by group selection of minority variants that together, confer the fitness increase observed in the population, rather than selection of a single dominant genotype.

Emergence and Transmission of Arbovirus Evolutionary Intermediates with Epidemic Potential

The high replication and mutation rates of RNA viruses can result in the emergence of new epidemic variants. Thus, the ability to follow host-specific evolutionary trajectories of viruses is essential to predict and prevent epidemics. By studying the spatial and temporal evolution of chikungunya virus during natural transmission between mosquitoes and mammals, we have identified viral evolutionary intermediates prior to emergence. Analysis of virus populations at anatomical barriers revealed that the mosquito midgut and salivary gland pose population bottlenecks. By focusing on virus subpopulations in the saliva of multiple mosquito strains, we recapitulated the emergence of a recent epidemic strain of chikungunya and identified E1 glycoprotein mutations with potential to emerge in the future. These mutations confer fitness advantages in mosquito and mammalian hosts by altering virion stability and fusogenic activity. Thus, virus evolutionary trajectories can be predicted and studied in the short term before new variants displace currently circulating strains.

Whole-Genome Sequencing Analysis from the Chikungunya Virus Caribbean Outbreak Reveals Novel Evolutionary Genomic Elements.

Background Chikungunya virus (CHIKV), an alphavirus and member of the Togaviridae family, is capable of causing severe febrile disease in humans. In December of 2013 the Asian Lineage of CHIKV spread from the Old World to the Americas, spreading rapidly throughout the New World. Given this new emergence in naïve populations we studied the viral genetic diversity present in infected individuals to understand how CHIKV may have evolved during this continuing outbreak. Methodology/Principle Findings We used deep-sequencing technologies coupled with well-established bioinformatics pipelines to characterize the minority variants and diversity present in CHIKV infected individuals from Guadeloupe and Martinique, two islands in the center of the epidemic. We observed changes in the consensus sequence as well as a diverse range of minority variants present at various levels in the population. Furthermore, we found that overall diversity was dramatically reduced after single passages in cell lines. Finally, we constructed an infectious clone from this outbreak and identified a novel 3’ untranslated region (UTR) structure, not previously found in nature, that led to increased replication in insect cells. Conclusions/Significance Here we preformed an intrahost quasispecies analysis of the new CHIKV outbreak in the Caribbean. We identified novel variants present in infected individuals, as well as a new 3’UTR structure, suggesting that CHIKV has rapidly evolved in a short period of time once it entered this naïve population. These studies highlight the need to continue viral diversity surveillance over time as this epidemic evolves in order to understand the evolutionary potential of CHIKV.

The microRNA Transcriptome of Human Cytomegalovirus (HCMV)

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.

Deep sequencing analysis of viral infection and evolution allows rapid and detailed characterization of viral mutant spectrum

Abstract Motivation: The study of RNA virus populations is a challenging task. Each population of RNA virus is composed of a collection of different, yet related genomes often referred to as mutant spectra or quasispecies. Virologists using deep sequencing technologies face major obstacles when studying virus population dynamics, both experimentally and in natural settings due to the relatively high error rates of these technologies and the lack of high performance pipelines. In order to overcome these hurdles we developed a computational pipeline, termed ViVan (Viral Variance Analysis). ViVan is a complete pipeline facilitating the identification, characterization and comparison of sequence variance in deep sequenced virus populations. Results: Applying ViVan on deep sequenced data obtained from samples that were previously characterized by more classical approaches, we uncovered novel and potentially crucial aspects of virus populations. With our experimental work, we illustrate how ViVan can be used for studies ranging from the more practical, detection of resistant mutations and effects of antiviral treatments, to the more theoretical temporal characterization of the population in evolutionary studies. Availability and implementation: Freely available on the web at http://www.vivanbioinfo.org Contact: nshomron@post.tau.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.

Phylogenetic and Genome-Wide Deep-Sequencing Analyses of Canine Parvovirus Reveal Co-Infection with Field Variants and Emergence of a Recent Recombinant Strain

Canine parvovirus (CPV), a fast-evolving single-stranded DNA virus, comprises three antigenic variants (2a, 2b, and 2c) with different frequencies and genetic variability among countries. The contribution of co-infection and recombination to the genetic variability of CPV is far from being fully elucidated. Here we took advantage of a natural CPV population, recently formed by the convergence of divergent CPV-2c and CPV-2a strains, to study co-infection and recombination. Complete sequences of the viral coding region of CPV-2a and CPV-2c strains from 40 samples were generated and analyzed using phylogenetic tools. Two samples showed co-infection and were further analyzed by deep sequencing. The sequence profile of one of the samples revealed the presence of CPV-2c and CPV-2a strains that differed at 29 nucleotides. The other sample included a minor CPV-2a strain (13.3% of the viral population) and a major recombinant strain (86.7%). The recombinant strain arose from inter-genotypic recombination between CPV-2c and CPV-2a strains within the VP1/VP2 gene boundary. Our findings highlight the importance of deep-sequencing analysis to provide a better understanding of CPV molecular diversity.