Coevolutionary diversification creates nested-modular structure in phage-bacteria interaction networks

Critically evaluating the relative importance of phage in shaping microbial community composition

The ubiquity of bacteriophages (phages) and the major evolutionary and ecological impacts they can have on their microbial hosts has resulted in phages often cited as key drivers shaping microbial community composition (the relative abundances of species). However, the evidence for the importance of phages is mixed. Here, we critically review the theory and data exploring the role of phages in communities, identifying the conditions when phages are likely to be important drivers of community composition. At ecological scales, we conclude that phages are often followers rather than drivers of microbial population and community dynamics. While phages can affect strain diversity within species, there is yet to be strong evidence suggesting that fluctuations in species' strains affects community composition.

The evolutionary adaptation of wood-decay macrofungi to host gymnosperms differs from that to host angiosperms

Wood-decay macrofungi play a vital role in forest ecosystems by promoting nutrient cycling and soil structure, and their evolution is closely related to their host plants. This study investigates the potential evolutionary adaptation of wood-decay macrofungi to their host plants, focusing on whether these relationships differ between gymnosperms and angiosperms. While previous research has suggested non-random associations between specific fungi and plant deadwood, direct evidence of evolutionary adaptation has been lacking. Our study, conducted in a subtropical region, utilized metabarcoding techniques to identify deadwood species and associated fungi. We found significant evidence of evolutionary adaptation when considering all sampled species collectively. However, distinct patterns emerged when comparing angiosperms and gymnosperms: a significant evolutionary adaptation was observed of wood-decay macrofungi to angiosperms, but not to gymnosperms. This variation may be due to the longer evolutionary history and more stable species interactions of gymnosperms, as indicated by a higher modularity coefficient (r = .452), suggesting greater specialization. In contrast, angiosperms, being evolutionarily younger, displayed less stable and more coevolving interactions with fungi, reflected in a lower modularity coefficient (r = .387). Our findings provide the first direct evidence of differential evolutionary adaptation dynamics of these fungi to angiosperms versus gymnosperms, enhancing our understanding of forest ecosystem carbon cycling and resource management.

Restoring ecological complexity in a changing environment

As land use leaves massive tracts of land vacant for recovery, restoration must undergo a substantial shift to incorporate a complexity perspective beyond the traditional community, biodiversity or functional views. With an interaction-function perspective, we may be able to achieve ecosystems with better chances to adapt to current environmental changes and, especially, to climate change. We explore combined approaches that include still unused and underexplored techniques that will soon go mainstream and produce massive amounts of information to address the complexity gap. As we understand how complexity reassembles after the end of agriculture, we will be able to design actions to restore or enhance it at unprecedented spatial scales while increasing its adaptability to environmental changes.

Mechanistic interactions as the origin of modularity in biological networks

Biological networks are often modular. Explanations for this peculiarity either assume an adaptive advantage of a modular design such as higher robustness, or attribute it to neutral factors such as constraints underlying network assembly. Interestingly, most insights on the origin of modularity stem from models in which interactions are either determined by highly simplistic mechanisms, or have no mechanistic basis at all. Yet, empirical knowledge suggests that biological interactions are often mediated by complex structural or behavioural traits. Here, we investigate the origins of modularity using a model in which interactions are determined by potentially complex traits. Specifically, we model system elements-such as the species in an ecosystem-as finite-state machines (FSMs), and determine their interactions by means of communication between the corresponding FSMs. Using this model, we show that modularity probably emerges for free. We further find that the more modular an interaction network is, the less complex are the traits that mediate the interactions. Altogether, our results suggest that the conditions for modularity to evolve may be much broader than previously thought.

Disentangling top-down drivers of mortality underlying diel population dynamics of Prochlorococcus in the North Pacific Subtropical Gyre

Photosynthesis fuels primary production at the base of marine food webs. Yet, in many surface ocean ecosystems, diel-driven primary production is tightly coupled to daily loss. This tight coupling raises the question: which top-down drivers predominate in maintaining persistently stable picocyanobacterial populations over longer time scales? Motivated by high-frequency surface water measurements taken in the North Pacific Subtropical Gyre (NPSG), we developed multitrophic models to investigate bottom-up and top-down mechanisms underlying the balanced control of Prochlorococcus populations. We find that incorporating photosynthetic growth with viral- and predator-induced mortality is sufficient to recapitulate daily oscillations of Prochlorococcus abundances with baseline community abundances. In doing so, we infer that grazers in this environment function as the predominant top-down factor despite high standing viral particle densities. The model-data fits also reveal the ecological relevance of light-dependent viral traits and non-canonical factors to cellular loss. Finally, we leverage sensitivity analyses to demonstrate how variation in life history traits across distinct oceanic contexts, including variation in viral adsorption and grazer clearance rates, can transform the quantitative and even qualitative importance of top-down controls in shaping Prochlorococcus population dynamics.

The dimensionality of infection networks among viruses infecting microbial eukaryotes and bacteria

Diverse viruses and their hosts are interconnected through complex networks of infection, which are thought to influence ecological and evolutionary processes, but the principles underlying infection network structure are not well understood. Here we focus on network dimensionality and how it varies across 37 networks of viruses infecting eukaryotic phytoplankton and bacteria. We find that dimensionality is often strikingly low, with most networks being one- or two-dimensional, although dimensionality increases with network richness, suggesting that the true dimensionality of natural systems is higher. Low-dimensional networks generally exhibit a mixture of host partitioning among viruses and nestededness of host ranges. Networks of bacteria-infecting and eukaryote-infecting viruses possess comparable distributions of dimensionality and prevalence of nestedness, indicating that fundamentals of network structure are similar among domains of life and different viral lineages. The relative simplicity of many infection networks suggests that coevolutionary dynamics are often driven by a modest number of underlying mechanisms.

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary -and largely uncharacterized- genetics of adsorption, injection, and cell take-over. Here we present a machine learning (ML) approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. The most effective ML approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, predicting phage host range with 86% mean classification accuracy while reducing the relative error in the estimated strength of the infection phenotype by 40%. Further, transparent feature selection in the predictive model revealed 18 of 176 phage λ and 6 of 18 E. coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. While the genetic variation studied was limited to a focal, coevolved phage-bacteria system, the method's success at recapitulating strain-level infection outcomes provides a path forward towards developing strategies for inferring interactions in non-model systems, including those of therapeutic significance.

Rapid bacteria-phage coevolution drives the emergence of multiscale networks

Interactions between species catalyze the evolution of multiscale ecological networks, including both nested and modular elements that regulate the function of diverse communities. One common assumption is that such complex pattern formation requires spatial isolation or long evolutionary timescales. We show that multiscale network structure can evolve rapidly under simple ecological conditions without spatial structure. In just 21 days of laboratory coevolution, Escherichia coli and bacteriophage Φ21 coevolve and diversify to form elaborate cross-infection networks. By measuring ~10,000 phage-bacteria infections and testing the genetic basis of interactions, we identify the mechanisms that create each component of the multiscale pattern. Our results demonstrate how multiscale networks evolve in parasite-host systems, illustrating Darwin's idea that simple adaptive processes can generate entangled banks of ecological interactions.

Conceptualizing microbe-plasmid communities as complex adaptive systems

Plasmids shape microbial communities' diversity, structure, and function. Nevertheless, we lack a mechanistic understanding of how community structure and dynamics emerge from local microbe-plasmid interactions and coevolution. Addressing this gap is challenging because multiple processes operate simultaneously at multiple levels of organization. For example, immunity operates between a plasmid and a cell, but incompatibility mechanisms regulate coexistence between plasmids. Conceptualizing microbe-plasmid communities as complex adaptive systems is a promising approach to overcoming these challenges. I illustrate how agent-based evolutionary modeling, extended by network analysis, can be used to quantify the relative importance of local processes governing community dynamics. These theoretical developments can advance our understanding of plasmid ecology and evolution, especially when combined with empirical data.

The dynamic interplay of bacteriophage, bacteria and the mammalian host during phage therapy

For decades, biomedically centered studies of bacteria have focused on mechanistic drivers of disease in their mammalian hosts. Likewise, molecular studies of bacteriophage have centered on understanding mechanisms by which bacteriophage exploit the intracellular environment of their bacterial hosts. These binary interactions - bacteriophage infect bacteria and bacteria infect eukaryotic hosts - have remained largely separate lines of inquiry. However, recent evidence demonstrates how tripartite interactions between bacteriophage, bacteria and the eukaryotic host shape the dynamics and fate of each component. In this perspective, we provide an overview of different ways in which bacteriophage ecology modulates bacterial infections along a spectrum of positive to negative impacts on a mammalian host. We also examine how coevolutionary processes over longer timescales may change the valence of these interactions. We argue that anticipating both ecological and evolutionary dynamics is key to understand and control tripartite interactions and ultimately to the success or failure of phage therapy.

Marine viruses and climate change: Virioplankton, the carbon cycle, and our future ocean

Interactions between marine viruses and microbes are a critical part of the oceanic carbon cycle. The impacts of virus-host interactions range from short-term disruptions in the mobility of microbial biomass carbon to higher trophic levels through cell lysis (i.e., the viral shunt) to long-term reallocation of microbial biomass carbon to the deep sea through accelerating the biological pump (i.e., the viral shuttle). The biogeochemical backdrop of the ocean-the physical, chemical, and biological landscape-influences the likelihood of both virus-host interactions and particle formation, and the fate and flow of carbon. As climate change reshapes the oceanic landscape through large-scale shifts in temperature, circulation, stratification, and acidification, virus-mediated carbon flux is likely to shift in response. Dynamics in the directionality and magnitude of changes in how, where, and when viruses mediate the recycling or storage of microbial biomass carbon is largely unknown. Integrating viral infection dynamics data obtained from experimental models and field systems, with particle motion microphysics and global observations of oceanic biogeochemistry, into improved ecosystem models will enable viral oceanographers to better predict the role of viruses in marine carbon cycling in the future ocean.

From kill the winner to eliminate the winner in open phage-bacteria systems

Phages and bacteria manage to coexist and sustain ecosystems with a high diversity of strains, despite limited resources and heavy predation. This diversity can be explained by the “kill the winner” model where virulent phages predominantly prey on fast-growing bacteria and thereby suppress the competitive exclusion of slower-growing bacteria. Here we computationally investigate the robustness of these systems against invasions, where new phages or bacteria may interact with more than one of the resident strains. The resulting interaction networks were found to self-organize into a network with strongly interacting specialized predator-prey pairs, resembling that of the “kill the winner” model. Furthermore, the “kill the winner” dynamics is enforced with the occasional elimination of even the fastest-growing bacteria strains due to a phage infecting the fast and slow growers. The frequency of slower-growing strains was increased with the introduction of even a few non-diagonal interactions. Hence, phages capable of infecting multiple hosts play significant roles both in the evolution of the ecosystem by eliminating the winner and in supporting diversity by allowing slow growers to coexist with faster growers.

We demonstrate that in an open system of phages and bacteria with very limited resources, a bacterial strain that has a high growth rate can still be outcompeted by a slower-growing strain if they have a common phage. The impact of this on ecosystem structure is significant as soon as there is a small probability to have a common phage among bacterial strains. Furthermore, by analysing the structure of the interaction network we show that it self-organizes into a network with strongly interacting specialized predator-prey pairs, in order to reduce phages competition. Nevertheless, the presence of the remaining links is very important for the network dynamics since even a few of them significantly enhance the frequency of slower-growing strains.

Butterfly–parasitoid–hostplant interactions in Western Palaearctic Hesperiidae: a DNA barcoding reference library

The study of ecological interactions between plants, phytophagous insects and their natural enemies is an essential but challenging component for understanding ecosystem dynamics. Molecular methods such as DNA barcoding can help elucidate these interactions. In this study, we employed DNA barcoding to establish hostplant and parasitoid interactions with hesperiid butterflies, using a complete reference library for Hesperiidae of continental Europe and north-western Africa (53 species, 100% of those recorded) based on 2934 sequences from 38 countries. A total of 233 hostplant and parasitoid interactions are presented, some recovered by DNA barcoding larval remains or parasitoid cocoons. Combining DNA barcode results with other lines of evidence allowed 94% species-level identification for Hesperiidae, but success was lower for parasitoids, in part due to unresolved taxonomy. Potential cases of cryptic diversity, both in Hesperiidae and Microgastrinae, are discussed. We briefly analyse the resulting interaction networks. Future DNA barcoding initiatives in this region should focus attention on north-western Africa and on parasitoids, because in these cases barcode reference libraries and taxonomy are less well developed.

Growth rate determines prokaryote-provirus network modulated by temperature and host genetic traits

BACKGROUND

Prokaryote-virus interactions play key roles in driving biogeochemical cycles. However, little is known about the drivers shaping their interaction network structures, especially from the host features. Here, we compiled 7656 species-level genomes in 39 prokaryotic phyla across environments globally and explored how their interaction specialization is constrained by host life history traits, such as growth rate.

RESULTS

We first reported that host growth rate indicated by the reverse of minimal doubling time was negatively related to interaction specialization for host in host-provirus network across various ecosystems and taxonomy groups. Such a negative linear growth rate-specialization relationship (GrSR) was dependent on host optimal growth temperature (OGT), and stronger toward the two gradient ends of OGT. For instance, prokaryotic species with an OGT ≥ 40 °C showed a stronger GrSR (Pearson's r = -0.525, P < 0.001). Significant GrSRs were observed with the presences of host genes in promoting the infection cycle at stages of adsorption, establishment, and viral release, but nonsignificant with the presence of immune systems, such as restriction-modification systems and CRISPR-Cas systems. Moreover, GrSR strength was increased with the presence of temperature-dependent lytic switches, which was also confirmed by mathematical modeling.

CONCLUSIONS

Together, our results advance our understanding of the interactions between prokaryotes and proviruses and highlight the importance of host growth rate in interaction specialization during lysogenization. Video Abstract.

PhREEPred: Phage Resistance Emergence Prediction web 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 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.

Modeling Virus and Bacteria Populations in Europa’s Subsurface Ocean

The search for life in the universe is often informed by the study of “extreme” environments on Earth, which provide analogs for habitable locations in the Solar System, and whose microbial inhabitants may therefore also serve as analogs for potential life forms in extraterrestrial milieus. Recent work has highlighted the ubiquity and importance of viral entities in terrestrial ecosystems, which calls for a greater understanding of the roles that viruses might play in hypothetical extraterrestrial biomes. While some studies have modeled the dynamics of viral and bacterial populations in icy ocean environments on Earth, previous work has yet to apply these findings to icy ocean worlds such as Jupiter’s moon Europa. It is commonly theorized that hydrothermal vents on Europa could produce the necessary reductants for chemosynthesis to take place on the ocean bottom. In the case that Europa’s ocean is a reductant-limited environment, how might reductants and organic matter reach the sub-ice region to power a more easily accessible ecosystem? Here, we propose a ‘viral elevator,’ a mechanism that functions similarly to the ‘viral shunt’ in Earth’s oceans, which could create and shuttle dissolved organic matter (DOM) to a hypothetical sub-ice biosphere through viral carriers. Current models of Europa’s ocean currents and stratification support the movement of DOM to the sub-ice biosphere. We adapt an existing model for bacterial and viral population dynamics in Earth’s Arctic sea ice to Europa and use parameters from various Arctic-based studies as proxies for Europa’s environment. We find that viral burst size has the most significant effect on the virus-to-bacteria ratio (VBR) and system longevity in closed systems (such as brine pockets within Europa’s icy crust), with higher burst sizes clearly increasing both. When applying our model to an open system with an influx of DOM from the viral elevator, we found that a steady-state system is attainable, with resulting sub-ice biofilms on the order of 0.1 mm thick (global equivalent layer). This has implications for future searches for life on Europa, given that life directly under the ice will be easier to detect and observe than life near the ocean bottom.

Systematic analysis of putative phage-phage interactions on minimum-sized phage cocktails

The application of bacteriophages as antibacterial agents has many benefits in the “post-antibiotic age”. To increase the number of successfully targeted bacterial strains, phage cocktails, instead of a single phage, are commonly formulated. Nevertheless, there is currently no consensus pipeline for phage cocktail development. Thus, although large cocktails increase the spectrum of activity, they could produce side effects such as the mobilization of virulence or antibiotic resistance genes. On the other hand, coinfection (simultaneous infection of one host cell by several phages) might reduce the potential for bacteria to evolve phage resistance, but some antagonistic interactions amongst phages might be detrimental for the outcome of phage cocktail application. With this in mind, we introduce here a new method, which considers the host range and each individual phage-host interaction, to design the phage mixtures that best suppress the target bacteria while minimizing the number of phages to restrict manufacturing costs. Additionally, putative phage-phage interactions in cocktails and phage-bacteria networks are compared as the understanding of the complex interactions amongst bacteriophages could be critical in the development of realistic phage therapy models in the future.

Defensive Symbionts and the Evolution of Parasitoid Host Specialization

Insect host-parasitoid interactions abound in nature and are characterized by a high degree of host specialization. In addition to their behavioral and immune defenses, many host species rely on heritable bacterial endosymbionts for defense against parasitoids. Studies on aphids and flies show that resistance conferred by symbionts can be very strong and highly specific, possibly as a result of variation in symbiont-produced toxins. I argue that defensive symbionts are therefore an important source of diversifying selection, promoting the evolution of host specialization by parasitoids. This is likely to affect the structure of host-parasitoid food webs. I consider potential changes in terms of food web complexity, although the nature of these effects will also be influenced by whether maternally transmitted symbionts have some capacity for lateral transfer. This is discussed in the light of available evidence for horizontal transmission routes. Finally, I propose that defensive mutualisms other than microbial endosymbionts may also exert diversifying selection on insect parasitoids.

Computational design of phage cocktails based on phage-bacteria infection networks

The misuse and overuse of antibiotics have boosted the proliferation of multidrug-resistant (MDR) bacteria, which are considered a major public health issue in the twenty-first century. Phage therapy may be a promising way in the treatment of infections caused by MDR pathogens, without the side effects of the current available antimicrobials. Phage therapy is based on phage cocktails, that is, combinations of phages able to lyse the target bacteria. In this work, we present and explain in detail two innovative computational methods to design phage cocktails taking into account a given phage-bacteria infection network. One of the methods (Exhaustive Search) always generates the best possible phage cocktail, while the other method (Network Metrics) always keeps a very reduced runtime (a few milliseconds). Both methods have been included in a Cytoscape application that is available for any user. A complete experimental study has been performed, evaluating and comparing the biological quality, runtime, and the impact when additional phages are included in the cocktail.

Investigation of Salmonella Phage-Bacteria Infection Profiles: Network Structure Reveals a Gradient of Target-Range from Generalist to Specialist Phage Clones in Nested Subsets

Bacteriophages that lyse Salmonella enterica are potential tools to target and control Salmonella infections. Investigating the host range of Salmonella phages is a key to understand their impact on bacterial ecology, coevolution and inform their use in intervention strategies. Virus-host infection networks have been used to characterize the "predator-prey" interactions between phages and bacteria and provide insights into host range and specificity. Here, we characterize the target-range and infection profiles of 13 Salmonella phage clones against a diverse set of 141 Salmonella strains. The environmental source and taxonomy contributed to the observed infection profiles, and genetically proximal phages shared similar infection profiles. Using in vitro infection data, we analyzed the structure of the Salmonella phage-bacteria infection network. The network has a non-random nested organization and weak modularity suggesting a gradient of target-range from generalist to specialist species with nested subsets, which are also observed within and across the different phage infection profile groups. Our results have implications for our understanding of the coevolutionary mechanisms shaping the ecological interactions between Salmonella phages and their bacterial hosts and can inform strategies for targeting Salmonella enterica with specific phage preparations.

How does feedback from phage infections influence the evolution of phase variation in Campylobacter?

Campylobacter jejuni (C. jejuni) causes gastroenteritis following the consumption of contaminated poultry meat, resulting in a large health and economic burden worldwide. Phage therapy is a promising technique for eradicating C. jejuni from poultry flocks and chicken carcasses. However, C. jejuni can resist infections by some phages through stochastic, phase-variable ON/OFF switching of the phage receptors mediated by simple sequence repeats (SSR). While selection strength and exposure time influence the evolution of SSR-mediated phase variation (PV), phages offer a more complex evolutionary environment as phage replication depends on having a permissive host organism. Here, we build and explore several continuous culture bacteria-phage computational models, each analysing different phase-variable scenarios calibrated to the experimental SSR rates of C. jejuni loci and replication parameters for the F336 phage. We simulate the evolution of PV rates via the adaptive dynamics framework for varying levels of selective pressures that act on the phage-resistant state. Our results indicate that growth reducing counter-selection on a single PV locus results in the stable maintenance of the phage, while compensatory selection between bacterial states affects the evolutionary stable mutation rates (i.e. very high and very low mutation rates are evolutionarily disadvantageous), whereas, in the absence of either selective pressure the evolution of PV rates results in mutation rates below the basal values. Contrastingly, a biologically-relevant model with two phase-variable loci resulted in phage extinction and locking of the bacteria into a phage-resistant state suggesting that another counter-selective pressure is required, instance, the use of a distinct phage whose receptor is an F336-phage-resistant state. We conclude that a delicate balance between counter-selection and phage-attack can result in both the evolution of phase-variable phage receptors and persistence of PV-receptor-specific phage.

Globally rising rates of antibiotic resistance have renewed interest in phage therapy. Bacteriophages (phages) act on bacteria to select for resistance mechanisms such as loss of phage receptors by phase variation (PV). Phase-variable genes mediate rapid adaption by stochastic switching of gene expression. Campylobacter jejuni is a common commensal of birds but also causes serious gastrointestinal infections in humans. Optimisation of phage therapy against C. jejuni requires an in-depth understanding of how PV has evolved and mediates phage resistance. Here, we use a detailed continuous culture model for nutrient-limited bacteria-phage interactions, with PV rates calibrated to match the experimental observations for C.jejuni and phage F336. Evolution within a model accounting for two phase-variable loci closely matches the experimental results when growth reducing counter-selection is imposed on all phage-resistant states, but, not when restricted to the particular states associated with resistance to immune effectors. Our results emphasize that delicate balancing of selective pressures, imposed by single and multiple distinct phages, are necessary for effective use of phage therapy against C. jejuni.

An ecological approach to structural flexibility in online communication systems

Human cognitive abilities are limited resources. Today, in the age of cheap information-cheap to produce, to manipulate, to disseminate-this cognitive bottleneck translates into hypercompetition for rewarding outcomes among actors. These incentives push actors to mutualistically interact with specific memes, seeking the virality of their messages. In turn, memes' chances to persist and spread are subject to changes in the communication environment. In spite of all this complexity, here we show that the underlying architecture of empirical actor-meme information ecosystems evolves into recurring emergent patterns. We then propose an ecology-inspired modelling framework, bringing to light the precise mechanisms causing the observed flexible structural reorganisation. The model predicts-and the data confirm-that users' struggle for visibility induces a re-equilibration of the network's mesoscale towards self-similar nested arrangements. Our final microscale insights suggest that flexibility at the structural level is not mirrored at the dynamical one.

A New Pipeline for Designing Phage Cocktails Based on Phage-Bacteria Infection Networks

In recent years, the spread of antibiotic-resistant bacteria and efforts to preserve food microbiota have induced renewed interest in phage therapy. Phage cocktails, instead of a single phage, are commonly used as antibacterial agents since the hosts are unlikely to become resistant to several phages simultaneously. While the spectrum of activity might increase with cocktail complexity, excessive phages could produce side effects, such as the horizontal transfer of genes that augment the fitness of host strains, dysbiosis or high manufacturing costs. Therefore, cocktail formulation represents a compromise between achieving substantial reduction in the bacterial loads and restricting its complexity. Despite the abovementioned points, the observed bacterial load reduction does not increase significantly with the size of phage cocktails, indicating the requirement for a systematic approach to their design. In this work, the information provided by host range matrices was analyzed after building phage-bacteria infection networks (PBINs). To this end, we conducted a meta-analysis of 35 host range matrices, including recently published studies and new datasets comprising Escherichia coli strains isolated during ripening of artisanal raw milk cheese and virulent coliphages from ewes' feces. The nestedness temperature, which reflects the host range hierarchy of the phages, was determined from bipartite host range matrices using heuristic (Nestedness Temperature Calculator) and genetic (BinMatNest) algorithms. The latter optimizes matrix packing, leading to lower temperatures, i.e., it simplifies the identification of the phages with the broadest host range. The structure of infection networks suggests that generalist phages (and not specialist phages) tend to succeed in infecting less susceptible bacteria. A new metric (Φ), which considers some properties of the host range matrices (fill, temperature, and number of bacteria), is proposed as an estimator of phage cocktail size. To identify the best candidates, agglomerative hierarchical clustering using Ward's method was implemented. Finally, a cocktail was formulated for the biocontrol of cheese-isolated E. coli, reducing bacterial counts by five orders of magnitude.

The Russian Doll Model: How Bacteria Shape Successful and Sustainable Inter-Kingdom Relationships

Successful inter-kingdom relationships are based upon a dynamic balance between defense and cooperation. A certain degree of competition is necessary to guarantee life spread and development. On the other hand, cooperation is a powerful tool to ensure a long lasting adaptation to changing environmental conditions and to support evolution to a higher level of complexity. Bacteria can interact with their (true or potential) parasites (i.e., phages) and with their multicellular hosts. In these model interactions, bacteria learnt how to cope with their inner and outer host, transforming dangerous signals into opportunities and modulating responses in order to achieve an agreement that is beneficial for the overall participants, thus giving rise to a more complex "organism" or ecosystem. In this review, particular attention will be addressed to underline the minimal energy expenditure required for these successful interactions [e.g., moonlighting proteins, post-translational modifications (PTMs), and multitasking signals] and the systemic vision of these processes and ways of life in which the system proves to be more than the sum of the single components. Using an inside-out perspective, I will examine the possibility of multilevel interactions, in which viruses help bacteria to cope with the animal host and bacteria support the human immune system to counteract viral infection in a circular vision. In this sophisticated network, bacteria represent the precious link that insures system stability with relative low energy expenditure.

The network structure and eco-evolutionary dynamics of CRISPR-induced immune diversification

As a heritable sequence-specific adaptive immune system, CRISPR-Cas is a powerful force shaping strain diversity in host-virus systems. While the diversity of CRISPR alleles has been explored, the associated structure and dynamics of host-virus interactions have not. We explore the role of CRISPR in mediating the interplay between host-virus interaction structure and eco-evolutionary dynamics in a computational model and compare the results with three empirical datasets from natural systems. We show that the structure of the networks describing who infects whom and the degree to which strains are immune, are respectively modular (containing groups of hosts and viruses that interact strongly) and weighted-nested (specialist hosts are more susceptible to subsets of viruses that in turn also infect the more generalist hosts with many spacers matching many viruses). The dynamic interplay between these networks influences transitions between dynamical regimes of virus diversification and host control. The three empirical systems exhibit weighted-nested immunity networks, a pattern our theory shows is indicative of hosts able to suppress virus diversification. Previously missing from studies of microbial host-pathogen systems, the immunity network plays a key role in the coevolutionary dynamics.

Diversity and Local Coadaptation of Escherichia coli and Coliphages From Small Ruminants

Bacteriophages are highly specific predators that drive bacterial diversity through coevolution while striking tradeoffs among preserving host populations for long-term exploitation and increasing their virulence, structural stability, or host range. Escherichia coli and other coliform bacteria present in the microbiota of milk and during early ripening of raw milk cheeses have been linked to the production of gas, manifested by the appearance of eyes, and the development of off-flavors; thus, they might cause early blowing and cheese spoilage. Here, we report the characterization of coliphages isolated from manure from small ruminant farms and E. coli strains isolated from goat and sheep raw milk cheese. Additionally, the virulence and host range of locally isolated and laboratory collection phages were determined by comparing the susceptibility of E. coli strains from different sources. In agreement with the high genetic diversity found within the species E. coli, clustering analysis of whole-cell protein revealed a total of 13 distinct profiles but none of the raw milk cheese isolates showed inhibition of growth by reference or water-isolated coliphages. Conversely, 10 newly isolated phages had a broad host range (i.e., able to lyse ≥50% of bacterial hosts tested), thus exhibiting utility for biocontrol and only one cheese-isolated E. coli strain was resistant to all the phages. Whereas there was a high positive correlation between bacterial susceptibility range and lysis intensity, the phages virulence decreased as range increased until reaching a plateau. These results suggest local gene-for-gene coevolution between hosts and phages with selective tradeoffs for both resistance and competitive ability of the bacteria and host-range extension and virulence of the phage populations. Hence, different phage cocktail formulations might be required when devising long-term and short-term biocontrol strategies.

An individual-based model for the eco-evolutionary emergence of bipartite interaction networks

How ecological interaction networks emerge on evolutionary time scales remains unclear. Here we build an individual-based eco-evolutionary model for the emergence of mutualistic, antagonistic and neutral bipartite interaction networks. Exploring networks evolved under these scenarios, we find three main results. First, antagonistic interactions tend to foster species and trait diversity, while mutualistic interactions reduce diversity. Second, antagonistic interactors evolve higher specialisation, which results in networks that are often more modular than neutral ones; resource species in these networks often display phylogenetic conservatism in interaction partners. Third, mutualistic interactions lead to networks that are more nested than neutral ones, with low phylogenetic conservatism in interaction partners. These results tend to match overall empirical trends, demonstrating that structures of empirical networks that have most often been explained by ecological processes can result from an evolutionary emergence. Our model contributes to the ongoing effort of better integrating ecological interactions and macroevolution.

Coexistence of nestedness and modularity in host-pathogen infection networks

The long-term coevolution of hosts and pathogens in their environment forms a complex web of multi-scale interactions. Understanding how environmental heterogeneity affects the structure of host-pathogen networks is a prerequisite for predicting disease dynamics and emergence. Although nestedness is common in ecological networks, and theory suggests that nested ecosystems are less prone to dynamic instability, why nestedness varies in time and space is not fully understood. Many studies have been limited by a focus on single habitats and the absence of a link between spatial variation and structural heterogeneity such as nestedness and modularity. Here we propose a neutral model for the evolution of host-pathogen networks in multiple habitats. In contrast to previous studies, our study proposes that local modularity can coexist with global nestedness, and shows that real ecosystems are found in a continuum between nested-modular and nested networks driven by intraspecific competition. Nestedness depends on neutral mechanisms of community assembly, whereas modularity is contingent on local adaptation and competition. The structural pattern may change spatially and temporally but remains stable over evolutionary timescales. We validate our theoretical predictions with a longitudinal study of plant-virus interactions in a heterogeneous agricultural landscape.

The Role of Evolution in Shaping Ecological Networks

The structure of ecological networks reflects the evolutionary history of their biotic components, and their dynamics are strongly driven by ecoevolutionary processes. Here, we present an appraisal of recent relevant research, in which the pervasive role of evolution within ecological networks is manifest. Although evolutionary processes are most evident at macroevolutionary scales, they are also important drivers of local network structure and dynamics. We propose components of a blueprint for further research, emphasising process-based models, experimental evolution, and phenotypic variation, across a range of distinct spatial and temporal scales. Evolutionary dimensions are required to advance our understanding of foundational properties of community assembly and to enhance our capability of predicting how networks will respond to impending changes.

Macro- and mesoscale pattern interdependencies in complex networks

Identifying and explaining the structure of complex networks at different scales has become an important problem across disciplines. At the mesoscale, modular architecture has attracted most of the attention. At the macroscale, other arrangements-e.g. nestedness or core-periphery-have been studied in parallel, but to a much lesser extent. However, empirical evidence increasingly suggests that characterizing a network with a unique pattern typology may be too simplistic, since a system can integrate properties from distinct organizations at different scales. Here, we explore the relationship between some of these different organizational patterns: two at the mesoscale (modularity and in-block nestedness); and one at the macroscale (nestedness). We show experimentally and analytically that nestedness imposes bounds to modularity, with exact analytical results in idealized scenarios. Specifically, we show that nestedness and modularity are interdependent. Furthermore, we analytically evidence that in-block nestedness provides a natural combination between nested and modular networks, taking structural properties of both. Far from a mere theoretical exercise, understanding the boundaries that discriminate each architecture is fundamental, to the extent that modularity and nestedness are known to place heavy dynamical effects on processes, such as species abundances and stability in ecology.

Online division of labour: emergent structures in Open Source Software

The development Open Source Software fundamentally depends on the participation and commitment of volunteer developers to progress on a particular task. Several works have presented strategies to increase the on-boarding and engagement of new contributors, but little is known on how these diverse groups of developers self-organise to work together. To understand this, one must consider that, on one hand, platforms like GitHub provide a virtually unlimited development framework: any number of actors can potentially join to contribute in a decentralised, distributed, remote, and asynchronous manner. On the other, however, it seems reasonable that some sort of hierarchy and division of labour must be in place to meet human biological and cognitive limits, and also to achieve some level of efficiency. These latter features (hierarchy and division of labour) should translate into detectable structural arrangements when projects are represented as developer-file bipartite networks. Thus, in this paper we analyse a set of popular open source projects from GitHub, placing the accent on three key properties: nestedness, modularity and in-block nestedness -which typify the emergence of heterogeneities among contributors, the emergence of subgroups of developers working on specific subgroups of files, and a mixture of the two previous, respectively. These analyses show that indeed projects evolve into internally organised blocks. Furthermore, the distribution of sizes of such blocks is bounded, connecting our results to the celebrated Dunbar number both in off- and on-line environments. Our conclusions create a link between bio-cognitive constraints, group formation and online working environments, opening up a rich scenario for future research on (online) work team assembly (e.g. size, composition, and formation). From a complex network perspective, our results pave the way for the study of time-resolved datasets, and the design of suitable models that can mimic the growth and evolution of OSS projects.

Revealing biases in the sampling of ecological interaction networks

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases with respect to both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These ecological and statistical issues directly affect ecologists’ abilities to accurately construct ecological networks. However, statistical biases introduced by sampling are difficult to quantify in the absence of full knowledge of the underlying ecological network’s structure. To explore properties of large-scale ecological networks, we developed the software EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different mathematical sampling designs that correspond to methods used in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties depends strongly both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, modules with nested structure were the easiest to detect, regardless of the sampling design used. Sampling a network starting with any species that had a high degree (e.g., abundant generalist species) was consistently found to be the most accurate strategy to estimate network structure. Because high-degree species tend to be generalists, abundant in natural communities relative to specialists, and connected to each other, sampling by degree may therefore be common but unintentional in empirical sampling of networks. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. To reduce biases introduced by sampling methods, we recommend that these findings be incorporated into field design considerations for projects aiming to characterize large species interaction networks.

A new model explaining the origin of different topologies in interaction networks

Nestedness and modularity have been recurrently observed in species interaction networks. Some studies argue that those topologies result from selection against unstable networks, and others propose that they likely emerge from processes driving the interactions between pairs of species. Here we present a model that simulates the evolution of consumer species using resource species following simple rules derived from the integrative hypothesis of specialization (IHS). Without any selection on stability, our model reproduced all commonly observed network topologies. Our simulations demonstrate that resource heterogeneity drives network topology. On the one hand, systems containing only homogeneous resources form generalized nested networks, in which generalist consumers have higher performance on each resource than specialists. On the other hand, heterogeneous systems tend to have a compound topology: modular with internally nested modules, in which generalists that divide their interactions between modules have low performance. Our results demonstrate that all real-world topologies likely emerge through processes driving interactions between pairs of species. Additionally, our simulations suggest that networks containing similar species differ from heterogeneous networks and that modules may not present the topology of entire networks.

Coevolutionary dynamics shape the structure of bacteria-phage infection networks

Coevolution-reciprocal evolutionary change among interacting species driven by natural selection-is thought to be an important force in shaping biodiversity. This ongoing process takes place within tangled networks of species interactions. In microbial communities, evolutionary change between hosts and parasites occurs at the same time scale as ecological change. Yet, we still lack experimental evidence of the role of coevolution in driving changes in the structure of such species interaction networks. Filling this gap is important because network structure influences community persistence through indirect effects. Here, we quantified experimentally to what extent coevolutionary dynamics lead to contrasting patterns in the architecture of bacteria-phage infection networks. Specifically, we look at the tendency of these networks to be organized in a nested pattern by which the more specialist phages tend to infect only a proper subset of those bacteria infected by the most generalist phages. We found that interactions between coevolving bacteria and phages become less nested over time under fluctuating dynamics, and more nested under arms race dynamics. Moreover, when coevolution results in high average infectivity, phages and bacteria differ more from each other over time under arms race dynamics than under fluctuating dynamics. The tradeoff between the fitness benefits of evolving resistance/infectivity traits and the costs of maintaining them might explain these differences in network structure. Our study shows that the interaction pattern between bacteria and phages at the community level depends on the way coevolution unfolds.

Dynamics of Bacterial and Viral Communities in Paddy Soil with Irrigation and Urea Application

Viruses are ubiquitous in natural systems. By influencing bacterial abundance (BA) and community structure through lysis-lysogenic conversion, viruses are involved in various ecological processes. In agricultural management, nitrogen addition and irrigation should be considered as important factors that can modify soil viral dynamics but have been ignored. In our study, short-term dynamics of autochthonous soil viral and bacterial abundance and diversity after irrigation and urea application were examined in a long-term experimental paddy field. Urea addition delayed the emergence of peak viral abundance for three days, suggesting that viruses are sensitive to N addition. Under short-term eutrophic conditions through urea application, viruses undertake a lysogenic-biased strategy. Moreover, nitrogen-fixing bacteria were most likely specifically lysed in urea-treated soil, which suggests that soil viruses block N accumulation by killing nitrogen-fixing bacteria. To the best of our knowledge, this study is the first to investigate dynamic changes in autochthonous viruses in paddy fields.

The first known virus isolates from Antarctic sea ice have complex infection patterns

Viruses are recognized as important actors in ocean ecology and biogeochemical cycles, but many details are not yet understood. We participated in a winter expedition to the Weddell Sea, Antarctica, to isolate viruses and to measure virus-like particle abundance (flow cytometry) in sea ice. We isolated 59 bacterial strains and the first four Antarctic sea-ice viruses known (PANV1, PANV2, OANV1 and OANV2), which grow in bacterial hosts belonging to the typical sea-ice genera Paraglaciecola and Octadecabacter. The viruses were specific for bacteria at the strain level, although OANV1 was able to infect strains from two different classes. Both PANV1 and PANV2 infected 11/15 isolated Paraglaciecola strains that had almost identical 16S rRNA gene sequences, but the plating efficiencies differed among the strains, whereas OANV1 infected 3/7 Octadecabacter and 1/15 Paraglaciecola strains and OANV2 1/7 Octadecabacter strains. All the phages were cold-active and able to infect their original host at 0°C and 4°C, but not at higher temperatures. The results showed that virus-host interactions can be very complex and that the viral community can also be dynamic in the winter-sea ice.

Computational prediction of inter-species relationships through omics data analysis and machine learning

BACKGROUND

Antibiotic resistance and its rapid dissemination around the world threaten the efficacy of currently-used medical treatments and call for novel, innovative approaches to manage multi-drug resistant infections. Phage therapy, i.e., the use of viruses (phages) to specifically infect and kill bacteria during their life cycle, is one of the most promising alternatives to antibiotics. It is based on the correct matching between a target pathogenic bacteria and the therapeutic phage. Nevertheless, correctly matching them is a major challenge. Currently, there is no systematic method to efficiently predict whether phage-bacterium interactions exist and these pairs must be empirically tested in laboratory. Herein, we present our approach for developing a computational model able to predict whether a given phage-bacterium pair can interact based on their genome.

RESULTS

Based on public data from GenBank and phagesDB.org, we collected more than a thousand positive phage-bacterium interactions with their complete genomes. In addition, we generated putative negative (i.e., non-interacting) pairs. We extracted, from the collected genomes, a set of informative features based on the distribution of predictive protein-protein interactions and on their primary structure (e.g. amino-acid frequency, molecular weight and chemical composition of each protein). With these features, we generated multiple candidate datasets to train our algorithms. On this base, we built predictive models exhibiting predictive performance of around 90% in terms of F1-score, sensitivity, specificity, and accuracy, obtained on the test set with 10-fold cross-validation.

CONCLUSION

These promising results reinforce the hypothesis that machine learning techniques may produce highly-predictive models accelerating the search of interacting phage-bacteria pairs.

Cross-resistance is modular in bacteria-phage interactions

Phages shape the structure of natural bacterial communities and can be effective therapeutic agents. Bacterial resistance to phage infection, however, limits the usefulness of phage therapies and could destabilise community structures, especially if individual resistance mutations provide cross-resistance against multiple phages. We currently understand very little about the evolution of cross-resistance in bacteria–phage interactions. Here we show that the network structure of cross-resistance among spontaneous resistance mutants of Pseudomonas aeruginosa evolved against each of 27 phages is highly modular. The cross-resistance network contained both symmetric (reciprocal) and asymmetric (nonreciprocal) cross-resistance, forming two cross-resistance modules defined by high within- but low between-module cross-resistance. Mutations conferring cross-resistance within modules targeted either lipopolysaccharide or type IV pilus biosynthesis, suggesting that the modularity of cross-resistance was structured by distinct phage receptors. In contrast, between-module cross-resistance was provided by mutations affecting the alternative sigma factor, RpoN, which controls many lifestyle-associated functions, including motility, biofilm formation, and quorum sensing. Broader cross-resistance range was not associated with higher fitness costs or weaker resistance against the focal phage used to select resistance. However, mutations in rpoN, providing between-module cross-resistance, were associated with higher fitness costs than mutations associated with within-module cross-resistance, i.e., in genes encoding either lipopolysaccharide or type IV pilus biosynthesis. The observed structure of cross-resistance predicted both the frequency of resistance mutations and the ability of phage combinations to suppress bacterial growth. These findings suggest that the evolution of cross-resistance is common, is likely to play an important role in the dynamic structure of bacteria–phage communities, and could inform the design principles for phage therapy treatments.

Phage therapy is a promising alternative to antibiotics for treating bacterial infections. Yet as with antibiotics, bacteria readily evolve resistance to phage attack, including cross-resistance that protects against multiple phages at once and so limits the usefulness of phage cocktails. Here we show, using laboratory experimental evolution of resistance against 27 phages in P. aeruginosa, that cross-resistance is common and determines the ability of phage combinations to suppress bacterial growth. Using whole-genome sequencing, we show that cross-resistance is most common against multiple phages that use the same receptor but that global regulator mutations provide generalist resistance, probably by simultaneously affecting the expression of multiple different phage receptors. Future trials should test if these features of cross-resistance evolution translate to more complex in vivo environments and can therefore be exploited to design more effective phage therapies for the clinic.

Temperature-dependent virus lifecycle choices may reveal and predict facets of the biology of opportunistic pathogenic bacteria

Melioidosis, a serious illness caused by Burkholderia pseudomallei, results in up to 40% fatality in infected patients. The pathogen is found in tropical water and soil. Recent findings demonstrated that bacterial numbers can be regulated by a novel clade of phages that are abundant in soil and water. These phages differentially infect their bacterial hosts causing lysis at high temperatures and lysogeny at lower temperatures. Thus seasonal and daily temperature variations would cause switches in phage-bacteria interactions. We developed mathematical models using realistic parameters to explore the impact of phages on B. pseudomallei populations in the surface water of rice fields over time and under seasonally changing environmental conditions. Historical records were used to provide UV radiation levels and temperature for two Thailand provinces. The models predict seasonal variation of phage-free bacterial numbers correlates with the higher risk of melioidosis acquisition during the “warm and wet” season. We find that enrichment of the environment may lead to irregular large amplitude pulses of bacterial numbers that could significantly increase the probability of disease acquisition. Our results suggest that the phages may regulate B. pseudomallei populations throughout the seasons, and these data can potentially help improve the melioidosis prevention efforts in Southeast Asia.

Revealing in-block nestedness: Detection and benchmarking

As new instances of nested organization-beyond ecological networks-are discovered, scholars are debating the coexistence of two apparently incompatible macroscale architectures: nestedness and modularity. The discussion is far from being solved, mainly for two reasons. First, nestedness and modularity appear to emerge from two contradictory dynamics, cooperation and competition. Second, existing methods to assess the presence of nestedness and modularity are flawed when it comes to the evaluation of concurrently nested and modular structures. In this work, we tackle the latter problem, presenting the concept of in-block nestedness, a structural property determining to what extent a network is composed of blocks whose internal connectivity exhibits nestedness. We then put forward a set of optimization methods that allow us to identify such organization successfully, in synthetic and in a large number of real networks. These findings challenge our understanding of the topology of ecological and social systems, calling for new models to explain how such patterns emerge.

Modeling Bacteria-Phage Interactions and Its Implications for Phage Therapy

Bacteriophages are more abundant than any other organism on our planet. The interaction of bacteriophages and bacteria and their coevolution is well known. In this chapter, we describe various aspects of modeling such systems and their dynamics. We explore their interaction in: (i) liquid media, which leads to well-mixed populations and (ii) solid media, where their interaction is spatially restricted. Such modeling, when used in conjunction with experiments would not only shed deep insight into the underlying dynamics but also provide useful clues toward potential therapeutic applications.

Coevolution Maintains Diversity in the Stochastic "Kill the Winner" Model

The "kill the winner" hypothesis is an attempt to address the problem of diversity in biology. It argues that host-specific predators control the population of each prey, preventing a winner from emerging and thus maintaining the coexistence of all species in the system. We develop a stochastic model for the kill the winner paradigm and show that the stable coexistence state of the deterministic kill the winner model is destroyed by demographic stochasticity, through a cascade of extinction events. We formulate an individual-level stochastic model in which predator-prey coevolution promotes the high diversity of the ecosystem by generating a persistent population flux of species.

The Gut Microbiota Facilitates Drifts in the Genetic Diversity and Infectivity of Bacterial Viruses

The intestinal microbiota and human health are intimately linked, but interactions between bacteria and bacteriophages in the context of the mammalian intestine remain largely unexplored. We used comparative population genomics to study a tripartite network consisting of a virulent bacteriophage, its bacterial host, and a phage-insensitive bacterial strain both in vitro and within the murine gut. The bacteriophage adapted to infect the insensitive strain when the three partners co-existed in the gut of conventional mice, but not in dixenic mice or in planktonic cultures. The molecular changes associated with modifications in the bacteriophage host spectrum included single amino acid substitutions and an unusual homologous intragenomic recombination event within the genome of the bacteriophage. An intermediate bacterial host isolated from the murine microbiota mediated bacteriophage adaptation. Our data indicate that by offering access to new hosts, the microbiota shifts the genetic diversity of bacteriophages, thereby promoting long-term persistence of bacteriophage populations.

Viruses in the environment - presence and diversity of bacteriophage and enteric virus populations in the Umhlangane River, Durban, South Africa

Due to the continued persistence of waterborne viral-associated infections, the presence of enteric viruses is a concern. Notwithstanding the health implications, viral diversity and abundance is an indicator of water quality declination in the environment. The aim of this study was to evaluate the presence of viruses (bacteriophage and enteric viruses) in a highly polluted, anthropogenic-influenced river system over a 6-month period at five sampling points. Cytopathic-based tissue culture assays revealed that the isolated viruses were infectious when tested on Hep-G2, HEK293 and Vero cells. While transmission electron microscopy (TEM) revealed that the majority of the viruses were bacteriophages, a number of presumptive enteric virus families were visualized, some of which include Picornaviridae, Adenoviridae, Polyomaviridae and Reoviridae. Finally, primer specific nested polymerase chain reaction (nested-PCR)/reverse transcription-polymerase chain reaction (RT-PCR) coupled with BLAST analysis identified human adenovirus, polyomavirus and hepatitis A and C virus genomes in river water samples. Taken together, the complexity of both bacteriophage and enteric virus populations in the river has potential health implications. Finally, a systematic integrated risk assessment and management plan to identify and minimize sources of faecal contamination is the most effective way of ensuring water safety and should be established in all future guidelines.