Full genome sequences are increasingly used to track the geographic spread and transmission dynamics of viral pathogens. Here, with a focus on Israel, we sequenced 212 SARS-CoV-2 sequences and use them to perform a comprehensive analysis to trace the origins and spread of the virus. A phylogenetic analysis including thousands of globally sampled sequences allowed us to infer multiple independent introductions into Israel, followed by local transmission. Returning travelers from the U.S. contributed dramatically more to viral spread relative to their proportion in incoming infected travelers. Using phylodynamic analysis, we estimated that the basic reproduction number of the virus was initially around ~2.0-2.6, dropping by two-thirds following the implementation of social distancing measures. A comparison between reported and model-estimated case numbers indicated high levels of transmission heterogeneity in SARS-CoV-2 spread, with between 1-10% of infected individuals resulting in 80% of secondary infections. Overall, our findings underscore the ability of this virus to efficiently transmit between and within countries, as well as demonstrate the effectiveness of social distancing measures for reducing its spread.

Full genome sequences are increasingly used to track the geographic spread and transmission dynamics of viral pathogens. Here, with a focus on Israel, we sequence 212 SARS-CoV-2 sequences and use them to perform a comprehensive analysis to trace the origins and spread of the virus. We find that travelers returning from the United States of America significantly contributed to viral spread in Israel, more than their proportion in incoming infected travelers. Using phylodynamic analysis, we estimate that the basic reproduction number of the virus was initially around 2.5, dropping by more than two-thirds following the implementation of social distancing measures. We further report high levels of transmission heterogeneity in SARS-CoV-2 spread, with between 2-10% of infected individuals resulting in 80% of secondary infections. Overall, our findings demonstrate the effectiveness of social distancing measures for reducing viral spread.

Cheater viruses, also known as defective interfering viruses, cannot replicate on their own yet replicate faster than the wild type upon coinfection. While there is growing interest in using cheaters as antiviral therapeutics, the mechanisms underlying cheating have been rarely explored. During experimental evolution of MS2 phage, we observed the parallel emergence of two independent cheater mutants. The first, a point deletion mutant, lacked polymerase activity but was advantageous in viral packaging. The second synonymous mutant cheater displayed a completely different cheating mechanism, involving an altered RNA structure. Continued evolution revealed the demise of the deletion cheater and rise of the synonymous cheater. A mathematical model inferred that while a single cheater is expected to reach an equilibrium with the wild type, cheater demise arises from antagonistic interactions between coinfecting cheaters. These findings highlight layers of parasitism: viruses parasitizing cells, cheaters parasitizing intact viruses, and cheaters may parasitize other cheaters.

One of the key challenges in the field of genetics is the inference of haplotypes from next generation sequencing data. The MinION Oxford Nanopore sequencer allows sequencing long reads, with the potential of sequencing complete genes, and even complete genomes of viruses, in individual reads. However, MinION suffers from high error rates, rendering the detection of true variants difficult. Here, we propose a new statistical approach named AssociVar, which differentiates between true mutations and sequencing errors from direct RNA/DNA sequencing using MinION. Our strategy relies on the assumption that sequencing errors will be dispersed randomly along sequencing reads, and hence will not be associated with each other, whereas real mutations will display a non-random pattern of association with other mutations. We demonstrate our approach using direct RNA sequencing data from evolved populations of the MS2 bacteriophage, whose small genome makes it ideal for MinION sequencing. AssociVar inferred several mutations in the phage genome, which were corroborated using parallel Illumina sequencing. This allowed us to reconstruct full genome viral haplotypes constituting different strains that were present in the sample. Our approach is applicable to long read sequencing data from any organism for accurate detection of bona fide mutations and interstrain polymorphisms.

These authors contributed equally One of the key challenges in the field of RNA virus genetics is the inference of full haplotypes from next generation sequencing data. The MinION Oxford Nanopore sequencer allows sequencing very long reads, with the potential of sequencing the complete genomes of RNA viruses in individual reads. However, MinION suffers from high error rates, rendering the detection of true viral mutations very difficult. Here we propose a new statistical approach to differentiate between true mutations and sequencing errors from direct RNA sequencing using MinION. Our strategy relies on the assumption that sequencing errors will be dispersed randomly along sequencing reads, and hence will not be associated with each other, whereas real mutations will display a non-random pattern of association with other mutations. We demonstrate our approach using direct RNA sequencing data from an evolved population of the MS2 bacteriophage, whose genome length of 3,569 base pairs makes it ideal for MinION. Our results pinpointed several mutations in the phage genome that were corroborated using parallel Illumina sequencing, allowing us to reconstruct associations between mutations. Our approach is amenable to long read sequencing data from any organism for accurate detection of bona fide mutations.