Maoz Gelbart

Background: Data on pediatric recurrent acute mastoiditis are lacking, despite its morbidity and clinical significance. Our aim was to describe the incidence, characteristics, and associated factors of recurrent mastoiditis in hospitalized children. Methods: Using a case–control design, analyzing electronic data of hospitalized children with acute mastoiditis between June 2011 and December 2018, children with recurrent mastoiditis were compared to children with a single episode at the time of hospitalization. Recurrent episodes of mastoiditis were compared to the first episodes. Recurrent acute mastoiditis was defined as recurring mastoiditis ≥4‐weeks after a completely resolved event. Results: Of 347 children hospitalized with acute mastoiditis, 22 (6.3%) had recurrent mastoiditis; the median interval between episodes was 3 months (range: 1–36). The mean ± SD age was 2.3 ± 2.25 years. A comparison of first episodes in recurring cases to single episodes by univariate and multivariate analysis, showed no differences in the pre‐admission management or in the isolated pathogens; however, a history of atopic dermatitis and percutaneous abscess drainage were more frequent in first episodes of recurring cases (27.3% vs. 1.2%, p < 0.001, and 27.3% vs. 10.0%, p = 0.026, respectively). The second episode of acute mastoiditis was characterized by a milder clinical course and shorter durations from symptoms to hospitalization, intravenous antibiotic therapy, and length of hospital stay. Linear regression showed that an increased interval from symptoms to hospitalization significantly increased length of hospital stay (regression coefficient of 0.215 [95% CI: 0.114‐0.317], p < 0.001). Conclusions: Recurrent episodes of mastoiditis were clinically milder, with shorter length of hospital stay compared to first episodes, possibly because of early admission.

Site-specific evolutionary rate shifts are defined as protein sites, where the rate of substitution has changed dramatically across the phylogeny. With respect to a given clade, sites may either undergo a rate acceleration or a rate deceleration, reflecting a site that was conserved and became variable, or vice-versa, respectively. Sites displaying such a dramatic evolutionary change may point to a loss or gain of function at the protein site, reflecting adaptation, or they may indicate epistatic interactions among sites. Here, we analyzed full genomes of HIV and SIV-1 and identified 271 rate-shifting sites along the HIV-1/SIV phylogeny. The majority of rate shifts occurred at long branches, often corresponding to cross-species transmission branches. We noted that in most proteins, the number of rate accelerations and decelerations was equal, and we suggest that this reflects epistatic interactions among sites. However, several accessory proteins were enriched for either accelerations or decelerations, and we suggest that this may be a signature of adaptation to new hosts. Interestingly, the non-pandemic HIV-1 group O clade exhibited a substantially higher number of rate-shift events than the pandemic group M clade. We propose that this may be a reflection of the height of the species barrier between gorillas and humans versus chimpanzees and humans. Our results provide a genome-wide view of the constraints operating on proteins of HIV-1 and SIV.

Genetic diversity is the fuel of evolution and facilitates adaptation to novel environments. However, our understanding of what drives differences in the genetic diversity during the early stages of viral infection is somewhat limited. Here, we use ultra-deep sequencing to interrogate 43 clinical samples taken from early infections of the human-infecting viruses HIV, RSV and CMV. Hundreds to thousands of virus templates were sequenced per sample, allowing us to reveal dramatic differences in within-host genetic diversity among virus populations. We found that increased diversity was mostly driven by presence of multiple divergent genotypes in HIV and CMV samples, which we suggest reflect multiple transmitted/founder viruses. Conversely, we detected an abundance of low frequency hyper-edited genomes in RSV samples, presumably reflecting defective virus genomes (DVGs). We suggest that RSV is characterized by higher levels of cellular co-infection, which allow for complementation and hence elevated levels of DVGs.

Conflict between mutant viruses is driven by cheater-of-cheater dynamics, leading to the fall of one cheater and rise of another.

With the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here, we present Flexible Inference from Time-Series (FITS)—a computational tool that allows inference of one of three parameters: the fitness of a specific 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 rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on simulated data and highlight its ability to infer the fitness/mutation rate/population size. We further show that FITS can infer meaningful information even when the input parameters are inexact. In particular, FITS is able to successfully categorize a mutation as advantageous or deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate high accuracy in inference.

HIV has a high mutation rate, which contributes to its ability to evolve quickly. However, we know little about the fitness costs of individual HIV mutations in vivo, their distribution and the different factors shaping the viral fitness landscape. We calculated the mean frequency of transition mutations at 870 sites of the pol gene in 160 patients, allowing us to determine the cost of these mutations. As expected, we found high costs for non-synonymous and nonsense mutations as compared to synonymous mutations. In addition, we found that non-synonymous mutations that lead to drastic amino acid changes are twice as costly as those that do not and mutations that create new CpG dinucleotides are also twice as costly as those that do not. We also found that G→A and C→T mutations are more costly than A→G mutations. We anticipate that our new in vivo frequency-based approach will provide insights into the fitness landscape and evolvability of not only HIV, but a variety of microbes. HIV’s high mutation rate allows it to evolve quickly. However, most mutations probably reduce the virus’ ability to replicate —they are costly to the virus. Until now, the actual cost of mutations is not well understood. We used within-patient mutation frequencies to estimate the cost of 870 HIV mutations in vivo. As expected, we found high costs for non-synonymous and nonsense mutations. In addition, we found surprisingly high costs for mutations that lead to drastic amino acid changes, mutations that create new CpG sites (possibly because they trigger the host’s immune system), and G→A and C→T mutations. Our results demonstrate the power of analyzing mutant frequencies from in vivo viral populations to study costs of mutations. A better understanding of fitness costs will help to predict the evolution of HIV.

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.

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.