Biology subjects

Stern, A.

Publications and source records attributed to Stern, A..

3 recordsLinked to original sources

bioRxiv2018

Inferring Population Genetics Parameters of Evolving Viruses Using Time-series Data

1With the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here we present FITS (Flexible Inference from Time-Series) - a computational framework that allows inference of either the fitness of a mutation, the mutation rate or the population size from genomic time-series sequencing data. FITS was designed first and foremost for analysis of either short-term Evolve & Resequence (E&R) experiments, or for rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on noisy simulated data, and highlight its ability to infer meaningful information even in those circumstances. In particular FITS is able to categorize a mutation as Advantageous, Neutral or Deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate extremely high accuracy in inference. We highlight the ease of use of FITS for step-wise or iterative inference of mutation rates, population size, and fitness values for each mutation sequenced, when deep sequencing data is available at multiple time-points.\n\nAvailabilityFITS is written in C++ and is available both with a highly user friendly graphical user interface but also as a command line program that allows parallel high throughput analyses. Source code, binaries (Windows and Mac) and complementary scripts, are available from GitHub at https://github.com/SternLabTAU/FITS.\n\nContactsternadi@tau.ac.il

evolutionary biology↗

bioRxiv2018

AccuNGS: a sequencing protocol for detection of ultra-rare variants reveals extensive variation in HIV during first days of infection

Mutations fuel evolution and facilitate adaptation to novel environments. However, characterizing the spectrum of mutations in a population is obscured by high error rates of next generation sequencing. Here, we present AccuNGS, a novel in vivo sequencing approach that detects variants as rare as 1:10,000. Applying it to 46 clinical samples taken from early infections of the human-infecting viruses HIV, RSV and CMV, revealed large differences in within-host genetic diversity among virus populations. Haplotype reconstruction revealed that increased diversity was mostly driven by multiple transmitted/founder viruses in HIV and CMV samples. Conversely, we detected an abundance of defective virus genomes (DVGs) in RSV samples, including hyper-edited genomes, nonsense mutations and single point deletions. Higher proportions of DVGs correlated with increased viral loads, suggesting increased cellular co-infection rates, which enable DVG persistence. AccuNGS establishes a general platform that allows detecting DVGs, and in general, rare variants that drive evolution.

bioinformatics↗

bioRxiv2017

Evolutionary rate shifts suggest species-specific adaptation events in HIV-1 and SIV

The process of molecular adaptation following a cross-species virus transmission event is currently poorly understood. Here, we identified 137 protein sites that experienced deceleration in their rate of evolution along the HIV-1/SIV phylogeny, likely indicating gain-of-function and consequent adaptation. The majority of such events occurred in parallel to cross-species transmission events and varied between HIV-1 groups, indicating independent adaptation strategies. The evolutionary rate decelerations we found were particularly prominent in accessory proteins that counteract host antiviral restriction factors, suggesting that these factors are a major barrier to viral adaptation to a new host. Surprisingly, we observed that the non-pandemic HIV-1 group O, derived from gorillas, exhibited more rate deceleration events than the pandemic group M, derived from chimpanzees. We suggest that the species barrier is higher when the genetic distance of the hosts increases. Our approach paves the way for subsequent studies on cross-species transfers in other major pathogens.

evolutionary biology↗