Lysis inhibition (LIN) in bacteriophage is a strategy to maximize progeny production. A clear plaque-forming mutant, CSP1C, was isolated from the turbid plaque-forming CSP1 phage. CSP1C exhibited an adsorption rate and replication dynamics similar to CSP1. Approximately 90% of the phages were adsorbed to the host cell within 12 min, and both phages had a latent period of 25 min. Burst sizes were 171.42 ± 31.75 plaque-forming units (PFU) per infected cell for CSP1 and 168.94 ± 51.67 PFU per infected cell for CSP1C. Both phages caused comparable reductions in viable E. coli cell counts at a low multiplicity of infection (MOI). However, CSP1 infection did not reduce turbidity, suggesting a form of LIN distinct from the well-characterized LIN of T4 phage. Genomic analysis revealed that a 4,672-base pairs (bp) DNA region, encompassing part of the tail fiber gene, CSP1_020, along with three hypothetical genes, CSP1_021, CSP1_022, and part of CSP1_023, was deleted from CSP1 to make CSP1C. Complementation analysis in CSP1C identified CSP1_020, CSP1_021, and CSP1_022 as a minimal gene set required for the lysis suppression in CSP1. Co-expression of these genes in E. coli with holin (CSP1_092) and endolysin (CSP1_091) resulted in lysis suppression. Lysis suppression was abolished by disrupting the proton motive force (PMF), supporting their potential role as antiholin. Additionally, CSP1_021 directly interacts with holin, suggesting that it may function as an antiholin. These findings identify new genetic factors involved in lysis suppression in CSP1, providing broader insights into phage strategies for modulating host cell lysis.
This review explores current advancements in microbiome functional analysis enabled by next-generation sequencing technologies, which have transformed our understanding of microbial communities from mere taxonomic composition to their functional potential. We examine approaches that move beyond species identification to characterize microbial activities, interactions, and their roles in host health and disease. Genome-scale metabolic models allow for in-depth simulations of metabolic networks, enabling researchers to predict microbial metabolism, growth, and interspecies interactions in diverse environments. Additionally, computational methods for predicting metabolite profiles offer indirect insights into microbial metabolic outputs, which is crucial for identifying biomarkers and potential therapeutic targets. Functional pathway analysis tools further reveal microbial contributions to metabolic pathways, highlighting alterations in response to environmental changes and disease states. Together, these methods offer a powerful framework for understanding the complex metabolic interactions within microbial communities and their impact on host physiology. While significant progress has been made, challenges remain in the accuracy of predictive models and the completeness of reference databases, which limit the applicability of these methods in under-characterized ecosystems. The integration of these computational tools with multi-omic data holds promise for personalized approaches in precision medicine, allowing for targeted interventions that modulate the microbiome to improve health outcomes. This review highlights recent advances in microbiome functional analysis, providing a roadmap for future research and translational applications in human health and environmental microbiology.
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