Alternating lysis and lysogeny is a winning strategy in bacteriophages due to Parrondo's paradox

Lytic/Lysogenic Transition as a Life-History Switch

The transition between lytic and lysogenic life cycles is the most important feature of the life-history of temperate viruses. To explain this transition, an optimal life-history model is offered based a discrete-time formulation of phage/bacteria population dynamics that features infection of bacteria by Poisson sampling of virions from the environment. The time step is the viral latency period. In this model, density-dependent viral absorption onto the bacterial surface produces virus/bacteria coexistence and density dependence in bacterial growth is not needed. The formula for the transition between lytic and lysogenic phases is termed the 'fitness switch'. According to the model, the virus switches from lytic to lysogenic when its population grows faster as prophage than as virions produced by lysis of the infected cells, and conversely for the switch from lysogenic to lytic. A prophage that benefits the bacterium it infects automatically incurs lower fitness upon exiting the bacterial genome, resulting in its becoming locked into the bacterial genome in what is termed here as a 'prophage lock'. The fitness switch qualitatively predicts the ecogeographic rule that environmental enrichment leads to microbialization with a concomitant increase in lysogeny, fluctuating environmental conditions promote virus-mediated horizontal gene transfer, and prophage-containing bacteria can integrate into the microbiome of a eukaryotic host forming a functionally integrated tripartite holobiont. These predictions accord more with the 'Piggyback-the-Winner' hypothesis than with the 'Kill-the-Winner' hypothesis in virus ecology.

An Eco-evolutionary Model on Surviving Lysogeny Through Grounding and Accumulation of Prophages

Temperate phages integrate into the bacterial genomes propagating along with the bacterial genomes. Multiple phage elements, representing diverse prophages, are present in most bacterial genomes. The evolutionary events and the ecological dynamics underlying the accumulation of prophage elements in bacterial genomes have yet to be understood. Here, we show that the local wastewater had 7% of lysogens (hosting mitomycin C-inducible prophages), and they showed resistance to superinfection by their corresponding lysates. Genomic analysis of four lysogens and four non-lysogens revealed the presence of multiple prophages (belonging to Myoviridae and Siphoviridae) in both lysogens and non-lysogens. For large-scale comparison, 2180 Escherichia coli genomes isolated from various sources across the globe and 523 genomes specifically isolated from diverse wastewaters were analyzed. A total of 15,279 prophages were predicted among 2180 E. coli genomes and 2802 prophages among 523 global wastewater isolates, with a mean of ~ 5 prophages per genome. These observations indicate that most putative prophages are relics of past bacteria-phage conflicts; they are "grounded" prophages that cannot excise from the bacterial genome. Prophage distribution analysis based on the sequence homology suggested the random distribution of E. coli prophages within and between E. coli clades. The independent occurrence pattern of these prophages indicates extensive horizontal transfers across the genomes. We modeled the eco-evolutionary dynamics to reconstruct the events that could have resulted in the prophage accumulation accounting for infection, superinfection immunity, and grounding. In bacteria-phage conflicts, the bacteria win by grounding the prophage, which could confer superinfection immunity.

Optimizing crop rotations via Parrondo's paradox for sustainable agriculture

Crop rotation, a sustainable agricultural technique, has been at humanity's disposal since time immemorial and is practised globally. Switching between cover crops and cash crops helps avoid the adverse effects of intensive farming. Determining the optimum cash-cover rotation schedule for maximizing yield has been tackled on multiple fronts by agricultural scientists, economists, biologists and computer scientists, to name a few. However, considering the uncertainty due to diseases, pests, droughts, floods and impending effects of climate change is essential when designing rotation strategies. Analysing this time-tested technique of crop rotations with a new lens of Parrondo's paradox allows us to optimally use the rotation technique in synchrony with uncertainty. While previous approaches are reactive to the diversity of crop types and environmental uncertainties, we make use of the said uncertainties to enhance crop rotation schedules. We calculate optimum switching probabilities in a randomized cropping sequence and suggest optimum deterministic sequences and judicious use of fertilizers. Our methods demonstrate strategies to enhance crop yield and the eventual profit margins for farmers. Conforming to translational biology, we extend Parrondo's paradox, where two losing situations can be combined eventually into a winning scenario, to agriculture.

Identification of integrons and gene cassette-associated recombination sites in bacteriophage genomes

Bacteriophages are versatile mobile genetic elements that play key roles in driving the evolution of their bacterial hosts through horizontal gene transfer. Phages co-evolve with their bacterial hosts and have plastic genomes with extensive mosaicism. In this study, we present bioinformatic and experimental evidence that temperate and virulent (lytic) phages carry integrons, including integron-integrase genes, attC/attI recombination sites and gene cassettes. Integrons are normally found in Bacteria, where they capture, express and re-arrange mobile gene cassettes via integron-integrase activity. We demonstrate experimentally that a panel of attC sites carried in virulent phage can be recognized by the bacterial class 1 integron-integrase (IntI1) and then integrated into the paradigmatic attI1 recombination site using an attC x attI recombination assay. With an increasing number of phage genomes projected to become available, more phage-associated integrons and their components will likely be identified in the future. The discovery of integron components in bacteriophages establishes a new route for lateral transfer of these elements and their cargo genes between bacterial host cells.

Visibility graph for time series prediction and image classification: a review

The analysis of time series and images is significant across different fields due to their widespread applications. In the past few decades, many approaches have been developed, including data-driven artificial intelligence methods, mechanism-driven physical methods, and hybrid mechanism and data-driven models. Complex networks have been used to model numerous complex systems due to its characteristics, including time series prediction and image classification. In order to map time series and images into complex networks, many visibility graph algorithms have been developed, such as horizontal visibility graph, limited penetrable visibility graph, multiplex visibility graph, and image visibility graph. The family of visibility graph algorithms will construct different types of complex networks, including (un-) weighted, (un-) directed, and (single-) multi-layered networks, thereby focusing on different kinds of properties. Different types of visibility graph algorithms will be reviewed in this paper. Through exploring the topological structure and information in the network based on statistical physics, the property of time series and images can be discovered. In order to forecast (multivariate) time series, several variations of local random walk algorithms and different information fusion approaches are applied to measure the similarity between nodes in the network. Different forecasting frameworks are also proposed to consider the information in the time series based on the similarity. In order to classify the image, several machine learning models (such as support vector machine and linear discriminant) are used to classify images based on global features, local features, and multiplex features. Through various simulations on a variety of datasets, researchers have found that the visibility graph algorithm outperformed existing algorithms, both in time series prediction and image classification. Clearly, complex networks are closely connected with time series and images by visibility graph algorithms, rendering complex networks to be an important tool for understanding the characteristics of time series and images. Finally, we conclude in the last section with future outlooks for the visibility graph.

A comprehensive framework for preference aggregation Parrondo's paradox

Individuals can make choices for themselves that are beneficial or detrimental to the entire group. Consider two losing choices that some individuals have to make on behalf of the group. Is it possible that the losing choices combine to give a winning outcome? We show that it is possible through a variant of Parrondo's paradox-the preference aggregation Parrondo's paradox (PAPP). This new variant of Parrondo's paradox makes use of an aggregate rule that combines with a decision-making heuristic that can be applied to individuals or parts of the social group. The aim of this work is to discuss this PAPP framework and exemplify it on a social network. This work enhances existing research by constructing a feedback loop that allows individuals in the social network to adapt its behavior according to the outcome of the Parrondo's games played.