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Register a new userCellular reproduction number, generation time and growth rate differ between human- and avian-adapted influenza strains
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When analysing in vitro data, growth kinetics of influenza strains are often compared by computing their growth rates, which are sometimes used as proxies for fitness. However, analogous to mechanistic epidemic models, the growth rate can be defined as a function of two parameters: the basic reproduction number (the average number of cells each infected cell infects) and the mean generation time (the average length of a replication cycle). Using a mechanistic model, previously published data from experiments in human lung cells, and newly generated data, we compared estimates of all three parameters for six influenza A strains. Using previously published data, we found that the two human-adapted strains (pre-2009 seasonal H1N1, and pandemic H1N1) had a lower basic reproduction number, shorter mean generation time and slower growth rate than the two avian-adapted strains (H5N1 and H7N9). These same differences were then observed in data from new experiments where two strains were engineered to have different internal proteins (pandemic H1N1 and H5N1), but the same surface proteins (PR8), confirming our initial findings and implying that differences between strains were driven by internal genes. Also, the model predicted that the human-adapted strains underwent more replication cycles than the avian-adapted strains by the time of peak viral load, potentially accumulating mutations more quickly. These results suggest that the in vitro reproduction number, generation time and growth rate differ between human-adapted and avian-adapted influenza strains, and thus could be used to assess host adaptation of internal proteins to inform pandemic risk assessment.
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This paper develops a simplified set of models describing asexual and sexual replication in unicel- lular diploid organisms. The models assume organisms whose genomes consist of two chromosomes, where each chromosome is assumed to be functional if it is equal to some master sequence $ sigma_0 $, and non-functional otherwise. The first-order growth rate constant, or fitness, of an organism, is determined by whether it has zero, one, or two functional chromosomes in its genome. For a population replicating asexually, a given cell replicates both of its chromosomes, and splits its genetic material evenly between the two cells. For a population replicating sexually, a given cell first divides into two haploids, which enter a haploid pool, fuse into diploids, and then divide via the normal mitotic process. Haploid fusion is modeled as a second-order rate process. When the cost for sex is small, as measured by the ratio of the characteristic haploid fusion time to the characteristic growth time, we find that sexual replication with random haploid fusion leads to a greater mean fitness for the population than a purely asexual strategy. However, independently of the cost for sex, we find that sexual replication with a selective mating strategy leads to a higher mean fitness than the random mating strategy. This result is based on the assumption that a selective mating strategy does not have any additional time or energy costs over the random mating strategy, an assumption that is discussed in the paper. The results of this paper are consistent with previous studies suggesting that sex is favored at intermediate mutation rates, for slowly replicating organisms, and at high population densities.
Increasing number in global COVID-19 cases demands for mathematical model to analyze the interaction between the virus dynamics and the response of innate and adaptive immunity. Here, based on the assumption of a weak and delayed response of the innate and adaptive immunity in SARS-CoV-2 infection, we constructed a mathematical model to describe the dynamic processes of immune system. Integrating theoretical results with clinical COVID-19 patients data, we classified the COVID-19 development processes into three typical modes of immune responses, correlated with the clinical classification of mild & moderate, severe and critical patients. We found that the immune efficacy (the ability of host to clear virus and kill infected cells) and the lymphocyte supply (the abundance and pool of naive T and B cell) play important roles in the dynamic process and determine the clinical outcome, especially for the severe and critical patients. Furthermore, we put forward possible treatment strategies for the three typical modes of immune response. We hope our results can help to understand the dynamical mechanism of the immune response against SARS-CoV-2 infection, and to be useful for the treatment strategies and vaccine design.
Objectives Influenza outbreaks have been widely studied. However, the patterns between influenza and religious festivals remained unexplored. This study examined the patterns of influenza and Hanukkah in Israel, and that of influenza and Hajj in Bahrain, Egypt, Iraq, Jordan, Oman and Qatar. Method Influenza surveillance data of these seven countries from 2009 to 2017 were downloaded from the FluNet of the World Health Organization. Secondary data were collected for the countries population, and the dates of Hajj and Hanukkah. We aggregated the weekly influenza A and B laboratory confirmations for each country over the study period. Weekly influenza A patterns and religious festival dates were further explored across the study period. Results We found that influenza A peaks closely followed Hanukkah in Israel in six out of seven years from 2010 to 2017. Aggregated influenza A peaks of the other six Middle East countries also occurred right after Hajj every year during the study period. Conclusions We predict that unless there is an emergence of new influenza strain, such influenza patterns are likely to persist in future years. Our results suggested that the optimal timing of mass influenza vaccination should take into considerations of the dates of these religious festivals.
Generally, genotypes and phenotypes are expected to be spatially congruent, however, in widespread species complexes with few barriers to dispersal, multiple contact zones, and limited reproductive isolation, discordance between phenotypes and phylogeographic groups is more probable. Wagtails (Aves: Motacilla) are a genus of birds with striking plumage pattern variation across Eurasia. Up to 13 subspecies are recognized within a single species, yet previous studies using mitochondrial DNA have supported phylogeographic groups that are inconsistent with subspecies plumage characteristics. In this study, we investigate the link between phenotypes and genotype by comparing populations thought to be at different stages along the speciation continuum. We take a phylogeographic approach by estimating population structure, testing for isolation by distance, conducting demographic modeling, and estimating the first time-calibrated species tree for the genus. Our study provides strong evidence for species-level patterns of differentiation in wagtails, however population-level differentiation is less pronounced. We find evidence that three of four widespread Eurasian species exhibit an east-west divide that contradicts both subspecies taxonomy and phenotypic variation. Both the geographic location of this divide and time estimates from demographic models are overlapping in two sympatric species, indicating that coincident Pleistocene events shaped their histories.
Viral kinetics have been extensively studied in the past through the use of spatially homogeneous ordinary differential equations describing the time evolution of the diseased state. However, spatial characteristics such as localized populations of dead cells might adversely affect the spread of infection, similar to the manner in which a counter-fire can stop a forest fire from spreading. In order to investigate the influence of spatial heterogeneities on viral spread, a simple 2-D cellular automaton (CA) model of a viral infection has been developed. In this initial phase of the investigation, the CA model is validated against clinical immunological data for uncomplicated influenza A infections. Our results will be shown and discussed.
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