Ongoing shuffling of protein fragments diversifies core viral functions linked to interactions with bacterial hosts.

Biological modularity enhances evolutionary adaptability. This principle is vividly exemplified by bacterial viruses (phages), which display extensive genomic modularity. Phage genomes are composed of independent functional modules that evolve separately and recombine in various configurations. While genomic modularity in phages has been extensively studied, less attention has been paid to protein modularity-proteins consisting of distinct building blocks that can evolve and recombine, enhancing functional and genetic diversity. Here, we use a set of 133,574 representative phage proteins and highly sensitive homology detection to capture instances of domain mosaicism, defined as fragment sharing between two otherwise unrelated proteins, and to understand its relationship with functional diversity in phage genomes. We discover that unrelated proteins from diverse functional classes frequently share homologous domains. This phenomenon is particularly pronounced within receptor-binding proteins, endolysins, and DNA polymerases. We also identify multiple instances of recent diversification via domain shuffling in receptor-binding proteins, neck passage structures, endolysins and some members of the core replication machinery, often transcending distant taxonomic and ecological boundaries. Our findings suggest that ongoing diversification via domain shuffling is reflective of a co-evolutionary arms race, driven by the need to overcome various bacterial resistance mechanisms against phages.

A Systematic Review on Quiescent State Research Approaches in S. cerevisiae.

Quiescence, the temporary and reversible arrest of cell growth, is a fundamental biological process. However, the lack of standardization in terms of reporting the experimental details of quiescent cells and populations can cause confusion and hinder knowledge transfer. We employ the systematic review methodology to comprehensively analyze the diversity of approaches used to study the quiescent state, focusing on all published research addressing the budding yeast Saccharomyces cerevisiae. We group research articles into those that consider all cells comprising the stationary-phase (SP) population as quiescent and those that recognize heterogeneity within the SP by distinguishing phenotypically distinct subpopulations. Furthermore, we investigate the chronological age of the quiescent populations under study and the methods used to induce the quiescent state, such as gradual starvation or abrupt environmental change. We also assess whether the strains used in research are prototrophic or auxotrophic. By combining the above features, we identify 48 possible experimental setups that can be used to study quiescence, which can be misleading when drawing general conclusions. We therefore summarize our review by proposing guidelines and recommendations pertaining to the information included in research articles. We believe that more rigorous reporting on the features of quiescent populations will facilitate knowledge transfer within and between disciplines, thereby stimulating valuable scientific discussion.

PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment.

Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the treated bacterial population can be postponed or reduced by the utilization of an effective phage cocktail. In this work, we present a publicly available web tool PhREEPred (Phage Resistance Emergence Prediction) (https://phartner.shinyapps.io/PhREEPred/), which will allow an informed choice of the composition of phage cocktails by predicting the outcome of phage cocktail or phage/depolymerase combination treatments against encapsulated bacterial pathogens given a mutating population that escapes single phage treatment. PhREEPred simulates solutions of our mathematical model calibrated and tested on the experimental Klebsiella pneumoniae setup and Klebsiella-specific lytic phages: K63 type-specific phage KP34 equipped with a capsule-degrading enzyme (KP34p57), capsule-independent myoviruses KP15 and KP27, and recombinant capsule depolymerase KP34p57. The model can calculate the phage-resistance emergence depending on the bacterial growth rate and initial density, the multiplicity of infection, phage latent period, its infectiveness and the cocktail composition, as well as initial depolymerase concentration and activity rate. This model reproduced the experimental results and showed that (i) the phage cocktail of parallelly infecting phages is less effective than the one composed of sequentially infecting phages; (ii) depolymerase can delay or prevent bacterial resistance by unveiling an alternative receptor for initially inactive phages. In our opinion, this customer-friendly web tool will allow for the primary design of the phage cocktail and phage-depolymerase combination effectiveness against encapsulated pathogens.

Would that it were so simple: Interactions between multiple traits undermine classical single-trait-based predictions of microbial community function and evolution.

Understanding how microbial traits affect the evolution and functioning of microbial communities is fundamental for improving the management of harmful microorganisms, while promoting those that are beneficial. Decades of evolutionary ecology research has focused on examining microbial cooperation, diversity, productivity and virulence but with one crucial limitation. The traits under consideration, such as public good production and resistance to antibiotics or predation, are often assumed to act in isolation. Yet, in reality, multiple traits frequently interact, which can lead to unexpected and undesired outcomes for the health of macroorganisms and ecosystem functioning. This is because many predictions generated in a single-trait context aimed at promoting diversity, reducing virulence or controlling antibiotic resistance can fail for systems where multiple traits interact. Here, we provide a much needed discussion and synthesis of the most recent research to reveal the widespread and diverse nature of multi-trait interactions and their consequences for predicting and controlling microbial community dynamics. Importantly, we argue that synthetic microbial communities and multi-trait mathematical models are powerful tools for managing the beneficial and detrimental impacts of microbial communities, such that past mistakes, like those made regarding the stewardship of antimicrobials, are not repeated.