bioRxiv Science⌕ Search

Biology subjects

Smug, B. J.

Publications and source records attributed to Smug, B. J..

2 recordsLinked to original sources

Structural modularity of receptor-binding proteins underlies host-range strategy diversification in Klebsiella pneumoniae phages

Bacteriophage receptor-binding proteins (RBPs) determine bacterial host recognition and are central to phage host-range evolution, yet the structural principles governing how RBPs diversify and expand host range remain not fully understood. Although phage RBPs are thought to evolve through modular exchange of receptor-binding domains, it is unclear how this modularity is organised across diverse RBP architectures, at what structural scales recombination operates, and how it shapes host-range breadth. Here we combined large-scale AlphaFold3 structural modelling, domain-level annotation, de novo pseudo-domain segmentation, sequence modularity analysis and experimentally determined host-range phenotypes across 192 Klebsiella pneumoniae phages and 382 high-confidence RBPs spanning up to 96 K-types. We generated the first system-wide structural atlas of K. pneumoniae phage RBPs, resolving 39 structurally distinct RBP-classes. Although capsule-degrading beta-helix depolymerases dominated numerically, 161 RBPs across 37 RBP-classes employed non-depolymerase architectures, including 18 novel RBP-classes. Structural and sequence analyses show that this diversity arose through modular reuse of structural domains rather than independent invention. Conserved N-terminal scaffolds linked depolymerase and non-depolymerase RBPs across morphotypes, while receptor-binding regions diversified through recombination operating at and beyond domain boundaries. We found recent modular exchange both within genera, where capsule-specific and capsule-independent RBPs can be swapped to alter host-range strategy, and across morphotypes, where depolymerase modules moved between distant lineages altering capsule specificity. Together, these architectures resolve into six receptor-recognition strategies, establishing multi-scale modularity as the primary organising principle of RBP diversification and a structure-informed framework for guiding phage isolation and engineering against K. pneumoniae. The complete atlas is freely accessible as an interactive community resource at Klebsiella-Phage-RBP-Atlas.

evolutionary biology↗

How to determine microbial lag phase duration?

The lag phase is a temporary non-replicative period observed when a microbial population is introduced to a new nutrient-rich environment. Its duration can have a pronounced effect on population fitness, and it is often measured in laboratory conditions. However, calculating the lag phase length may be challenging and method and parameters dependent. Moreover, the details of these methods and parameters used throughout experimental studies are often under-reported. Here we discuss the most frequently used methods in experimental and theoretical studies, and we point out some inconsistencies between them. Using experimental and simulated data we study the performance of these methods depending on the frequency of population size measurements, and parameters determining the growth curve shape, such as growth rate. It turns out that the sensitivity to each of these parameters depends on the lag calculation methods. For example, lag duration calculation by parameter fitting to a logistic model is very robust to low frequency of measurements, but it may be highly biased for growth curves with low growth rate. On the contrary, the method based on finding the point where growth acceleration is the highest, is robust to low growth rate, but highly sensitive to low frequency of measurements and the level of noise in the data. Based on our results, we propose a decision tree to choose a method most suited to ones data. Finally, we developed a web tool where the lag duration can be calculated based on the user-specified growth curve data, and for various explicitly specified methods, parameters, and data pre-processing techniques. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/516631v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1867dc4org.highwire.dtl.DTLVardef@ab04fborg.highwire.dtl.DTLVardef@1d43aaeorg.highwire.dtl.DTLVardef@58963f_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗