Ontology highlight
ABSTRACT:
PROVIDER: PRJNA646809 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12245222_1.fastq.gz | Fastqsanger.gz | |||
| SRR12245222_2.fastq.gz | Fastqsanger.gz | |||
| SRR12245223_1.fastq.gz | Fastqsanger.gz | |||
| SRR12245223_2.fastq.gz | Fastqsanger.gz | |||
| SRR12245224_1.fastq.gz | Fastqsanger.gz |
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Project description:Bacteriophage lambda is one of the most extensively studied organisms, and has been a primary model for understanding basic modes of genetic regulation. Here we examine the progress of lambda gene expression during phage development by ribosome profiling, and thereby provide a very high resolution view of lambda gene expression. The known genes are expressed in a predictable fashion, authenticating the analysis. But many previously unappreciated potential open reading frames become apparent in the expression analysis, revealing an unexpected complexity in the pattern of lambda gene function.
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Project description:Optimality of the spontaneous prophage induction rate.
Cortes MG1, Krog J2, Balázsi G3.
1 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.
2 The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.
3 Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. Electronic address: gabor.balazsi@stonybrook.edu.
Abstract
Lysogens are bacterial cells that have survived after genomically incorporating the DNA of temperate bacteriophages infecting them. If an infection results in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome known as a prophage. However, the prophage can still have an impact on the host's phenotype and overall fitness in certain environments. Additionally, the prophage within the lysogen can activate the lytic pathway via spontaneous prophage induction (SPI), killing the lysogen and releasing new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it kills the host cell but also attacks nonlysogenic competitors, either lysing or lysogenizing them. Here we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on SPI rates for lysogens to displace nonlysogens. We show that there exists an optimal SPI rate for bacteriophage λ and explain why it is so low. We also investigate the impact of stochasticity and conclude that even at low cell numbers SPI can still provide an advantage to the lysogens. These results corroborate recent experimental studies showing that lower SPI rates are advantageous for phage-phage competition, and establish theoretical bounds on the SPI rate in terms of ecological and environmental variables associated with lysogens having a competitive advantage over their nonlysogenic counterparts.
Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.
2024-09-02 | BIOMD0000000884 | BioModels
Project description:The ancestral SARS-CoV-2 strain that initiated the Covid-19 pandemic at the end of 2019 has rapidly mutated into multiple variants of concern with variable pathogenicity and increasing immune escape strategies. However, differences in host cellular antiviral responses upon infection with SARS-CoV-2 variants remains elusive. Leveraging whole cell proteomics, we determined host signalling pathways that are differentially modulated upon infection with the clinical isolates of the ancestral SARS-CoV-2 B.1 and the variants of concern Delta and Omicron BA.1. Our findings illustrate alterations in the global host proteome landscape upon infection with SARS-CoV-2 variants and the resulting host immune responses. Additionally, viral proteome kinetics reveal declining levels of viral protein expression during Omicron BA.1 infection when compared to ancestral B.1 and Delta variants, consistent with its reduced replication rates. Moreover, molecular assays reveal deferral activation of specific host antiviral signalling upon Omicron BA.1 and BA.2 infection. Our study provides an overview of host proteome profile of multiple SARS-CoV-2 variants and brings forth a better understanding of the instigation of key immune signalling pathways causative for the differential pathogenicity of SARS-CoV-2 variants.
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