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Mizrahi, I.

Publications and source records attributed to Mizrahi, I..

2 recordsLinked to original sources

Segregational drift and the interplay between plasmid copy number and evolvability

The ubiquity of plasmids in all prokaryotic phyla and habitats and their ability to transfer between cells marks them as prominent constituents of prokaryotic genomes. Many plasmids are found in their host cell in multiple copies. This leads to an increased mutational supply of plasmid-encoded genes and genetically heterogeneous plasmid genomes. Nonetheless, the segregation of plasmid copies into daughter cells during cell division is considered to occur in the absence of selection on the plasmid alleles. We investigate the implications of random genetic drift of multicopy plasmids during cell division - termed here segregational drift - to plasmid evolution. Performing experimental evolution of low- and high-copy non-mobile plasmids in Escherichia coli, we find that the evolutionary rate of multicopy plasmids does not reflect the increased mutational supply expected according to their copy number. In addition, simulated evolution of multicopy plasmid alleles demonstrates that segregational drift leads to increased loss frequency and extended fixation time of plasmid mutations in comparison to haploid chromosomes. Furthermore, an examination of the experimentally evolved hosts reveals a significant impact of the plasmid type on the host chromosome evolution. Our study demonstrates that segregational drift of multicopy plasmids interferes with the retention and fixation of novel plasmid variants. Depending on the selection pressure on newly emerging variants, plasmid genomes may evolve slower than haploid chromosomes, regardless of their higher mutational supply. We suggest that plasmid copy number is an important determinant of plasmid evolvability due to the manifestation of segregational drift.

microbiology

Modeling the temporal dynamics of the gut microbial community in adults and infants

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and function of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of the operational taxonomic units (OTUs) depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of the microbial community temporal dynamics based on the community composition at previous time stamps. MTV-LMM can identify time-dependent microbes in time series datasets, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on three human microbiome time series datasets, and found that MTV-LMM significantly outperforms all existing methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance levels of an OTU at a later time point is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable proportion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.

microbiology