Thermal Sensitivity of Phage-Host Interactions and Climate Impact: A Short Review.

Marine bacteriophages, the most abundant biological entities in marine ecosystems, are essential in biogeochemical cycling. Despite extensive genomic data, many phage genes remain uncharacterised, creating a gap between genomic diversity and gene function knowledge. This gap limits our understanding of phage life cycles, assembly, and host interactions. In this study, we used mass spectrometry to profile the proteomes of 13 marine phages from diverse lifestyles and hosts. The analysis accurately annotated hypothetical genes, mapped virion protein arrangements, and revealed structural similarities among phages infecting the same host, particularly in tail fibre proteins. Protein structure comparisons showed conservation and variability in head and tail proteins, particularly in key domains involved in virion stabilisation and host recognition. For the first time, we identified post-translational modifications (PTMs) in marine phage proteins, which may enhance phage adaptability and help evade host immune systems. These findings suggest that phages optimise their infection strategies through structural variations and PTM modifications, improving their adaptability and host interactions.

Almost a century has elapsed since the discovery of bacteriophages (phages), and 85 years have passed since the emergence of evidence that phages can infect starter cultures, thereby impacting dairy fermentations. Soon afterward, research efforts were undertaken to investigate phage interactions regarding starter strains. Investigations into phage biology and morphology and phage-host relationships have been aimed at mitigating the negative impact phages have on the fermented dairy industry. From the viewpoint of a supplier of dairy starter cultures, this review examines the composition of an industrial phage collection, providing insight into the development of starter strains and cultures and the evolution of phages in the industry. Research advances in the diversity of phages and structural bases for phage-host recognition and an overview of the perpetual arms race between phage virulence and host defense are presented, with a perspective toward the development of improved phage-resistant starter culture systems.

(Bacterio)phages (viruses that infect bacteria) are the most abundant entities on earth. Phages are the natural predators of bacteria and therefore have a great antibacterial potential. Immediately after their discovery, in the early twentieth century and before the antibiotic era, they were extensively used to treat infectious diseases. Now, on account of the spread of antibiotic resistance, the interest in phage therapy has been reborn and the recent advances on phage biology and host interactions have reinforced their therapeutic potential. This chapter describes the most important features of phages that make them valuable alternatives to antibiotics in controlling infectious diseases and the challenges that phage therapy is facing for them to be brought into clinical practice.

Under natural conditions phytoplankton are often simultaneously subjected to phosphorus (P) limitation and suboptimal light levels. Potential interacting effects of P-limitation and light availability on phytoplankton virus-host interactions have thus far not been reported. We studied the influence of three environmentally relevant light levels (low; 25, medium; 100 and high; 250 µmol quanta m-2 s-1) in combination with P-limitation (vs. P-replete conditions) on virus proliferation in the key phytoplankton species Micromonas pusilla and Phaeocystis globosa. Cultures were acclimated to balanced P-limited growth at 3 light levels by semi-continuous culturing, before one-step infection experiments were carried out in batch mode. Under optimal conditions (medium light, P-replete), the latent period (time until first release of progeny viruses) was 6-9 h and 9-12 h, and the burst size (number of viruses released per lysed host cell) was 241±5 and 690±28 for M. pusilla virus MpV and P. globosa virus PgV, respectively. Low light intensity under P-replete conditions prolonged the latent period of PgV (with maximally 3 h). The PgV burst size was 2.8-fold reduced under low light and 2.2-fold reduced under high light. The 10-fold range in light intensity did not affect viral latent period or burst size in P-replete M. pusilla. However, P-limitation (under optimal light) also led to elongated latent periods (with maximally 3 h compared to P-replete) and the viral burst sizes decreased by 2.7-fold for MpV and 3.5-fold for PgV. Finally, infectivity assays showed that PgV progeny from the P-limited high and low light cultures largely lost their infectivity, reducing their infective burst sizes to only 2-4 infective viruses per lysed host cell. Our study demonstrates that the effects of specific light and P-availability on virus-phytoplankton interaction are not only species specific, but can also strengthen each other’s effects. Relatively small differences in environmental conditions with depth, geography or time have the potential to drastically affect viral infection of phytoplankton, with consequent effects on host species composition and biogeochemical fluxes.

Microbes drive the biogeochemical cycles that fuel planet Earth, and their viruses (phages) alter microbial population structure, genome repertoire, and metabolic capacity. However, our ability to understand and quantify phage–host interactions is technique-limited. Here, we introduce phageFISH – a markedly improved geneFISH protocol that increases gene detection efficiency from 40% to > 92% and is optimized for detection and visualization of intra- and extracellular phage DNA. The application of phageFISH to characterize infection dynamics in a marine podovirus–gammaproteobacterial host model system corroborated classical metrics (qPCR, plaque assay, FVIC, DAPI) and outperformed most of them to reveal new biology. PhageFISH detected both replicating and encapsidated (intracellular and extracellular) phage DNA, while simultaneously identifying and quantifying host cells during all stages of infection. Additionally, phageFISH allowed per-cell relative measurements of phage DNA, enabling single-cell documentation of infection status (e.g. early vs late stage infections). Further, it discriminated between two waves of infection, which no other measurement could due to population-averaged signals. Together, these findings richly characterize the infection dynamics of a novel model phage–host system, and debut phageFISH as a much-needed tool for studying phage–host interactions in the laboratory, with great promise for environmental surveys and lineage-specific population ecology of free phages.

The influence of environmental factors on the interactions between phages and bacteria, particularly single-stranded DNA (ssDNA) phages, has been largely unexplored. In this study, we used Finnlakevirus FLiP, the first known ssDNA phage species with a lipid membrane, as our model phage. We examined the infectivity of FLiP with three Flavobacterium host strains, B330, B167 and B114. We discovered that FLiP infection is contingent on the host strain and conditions such as temperature and bacterial growth phase. FLiP can infect its hosts across a wide temperature range, but optimal phage replication varies with each host. We uncovered some unique aspects of phage infectivity: FLiP has limited infectivity in liquid-suspended cells, but it improves when cells are surface-attached. Moreover, FLiP infects stationary phase B167 and B114 cells more rapidly and efficiently than exponentially growing cells, a pattern not observed with the B330 host. We also present the first experimental evidence of endolysin function in ssDNA phages. The activity of FLiP's lytic enzymes was found to be condition-dependent. Our findings underscore the importance of studying phage ecology in contexts that are relevant to the environment, as both the host and the surrounding conditions can significantly alter the outcome of phage-host interactions.

Biochar amendment reshapes microbial community dynamics in vermicomposting, but the mechanism of how phages respond to this anthropogenic intervention and regulate the dissemination of antibiotic resistance genes (ARGs) remains unclear. In this study, we used metagenomics, viromics, and laboratory validation to explore how nano-biochar affects phage-host interactions and ARGs dissemination in vermicomposting. Our results revealed distinct niche-specific phage life strategies. In vermicompost, lytic phages dominated and used a "kill-the-winner" strategy to suppress antibiotic-resistant bacteria (ARB). In contrast, lysogenic phages prevailed in the earthworm gut, adopting a "piggyback-the-winner" strategy that promoted ARGs transduction through mutualistic host interactions. Nano-biochar induced the conversion of lysogenic to lytic phages in the earthworm gut, while concurrently reducing the abundance of lysogenic phages and their encoded auxiliary metabolic genes carried by ARB. This shift disrupted phage-host mutualism and inhibited ARGs transmission via a "phage shunting" mechanism. In vitro validation with batch culture experiments further confirmed that lysogenic phages increased transduction of ARGs in the earthworm gut, while nano-biochar reduced the spread of ARGs by enhancing lysis infectivity. Our study constructs a mechanistic framework linking nano-biochar induced shifts in phage lifestyles that suppress ARG spread, offering insights into phage-host coadaptation and resistance mitigation strategies in organic waste treatment ecosystems.

The volumes of novel phages obtained by metagenomics demand computational tools to predict phage–host interactions. Compared with the experimental approach, the identification of phage–host interactions by computational method can save time and reduce costs. In this paper, we present a computational method for predicting potential phage-host interactions by network fusion and graph mining, named PHIHNE. Unlike existing methods, PHIHNE constructs two different viral host heterogeneous networks by similarity network fusion and graph embedding techniques. Then, PHIHNE introduces two meta-path scores to extract features from each viral host heterogeneous graph. Based on this graph mining approach, a hybrid feature vector of phage-host pairs can be obtained to predict potential phage-host interactions using a machine learning classifier. PHIHNE is validated on four datasets and its performance shows the potential of PHIHNE in predicting phage-host interaction. Some of the novel phage-host interactions predicted by PHIHNE have been verified by existing in biological experiments.

Large-scale metagenomic and data-mining efforts have revealed an expansive diversity of bacteriophages (phages) within the human gut1–3. However, functional understanding of phage–host interactions within this complex environment is limited, largely due to a lack of cultured isolates available for experimental validation. Here we characterize 134 inducible prophages originating from 252 human gut bacterial isolates using 10 different induction conditions to expand the experimentally validated temperate phage–host pairs originating from the human gut. Importantly, only 18% of computationally predicted prophages could be induced in pure cultures. Moreover, we construct a 78-member synthetic microbiome that, when co-cultured in the presence of human colonic cells (Caco2), led to the induction of 35% phage species. Using cultured isolates, we demonstrate that human host-associated cellular products may act as induction agents, providing a possible link between gastrointestinal cell lysis and temperate phage populations4,5. We provide key insights into prophage diversity and genetics, including a genetic pathway for domestication, finding that polylysogeny was common and resulted in coordinated prophage induction, and that differential induction can be influenced by divergent prophage integration sites. More broadly, our study highlights the importance of culture-based techniques, alongside experimental validation, genomics and computational prediction, to understand the biology and function of temperate phages in the human gut microbiome. These culture-based approaches will enable applications across synthetic biology, biotechnology and microbiome fields.

Viral burst size (BS), i.e. the number of viruses released during cell lysis, is a critical parameter for assessing the ecological and biogeochemical role of viruses in aquatic systems. Burst size is typically estimated by enumerating the viral particles in bacteria using transmission electron microscopy. Here, we review the average BS reported for different aquatic systems, present several hypotheses on the control of the BS and evaluate whether there are relationships between BS and bacterial activity parameters across systems. Based on reports from a variety of different aquatic environments, we calculated a mean BS of 24 and 34 for marine and freshwater environments, respectively. Generally, the BS increased with the trophic status of the environment and with the percentage of infected cells in marine populations. When diel dynamics were investigated or averages from large-scale environments were used, BS was positively related to bacterial production but no trend was detectable across systems. The across systems' finding that BS was significantly related to the frequency of infected cells (FIC) could be due to co-infection or superinfection. At any given site, BS seems to be influenced by a number of factors such as the size of the host cell and the viruses, the metabolic activity of the host and phage and host diversity. Thus, based on the available data collected over the past two decades on a variety of aquatic systems, some relations between BS and bacterial variables were detectable.

Complex interactions of bacteriophages with their bacterial hosts determine phage host range and infectivity. While phage defense systems and host factors have been identified in model bacteria, they remain challenging to predict in non-model bacteria. In this paper, we integrate functional genomics and transcriptomics to investigate phage-host interactions, revealing active phage resistance and host factor genes in Burkholderia cenocepacia K56-2. Burkholderia cepacia complex species are commonly found in soil and are opportunistic pathogens in immunocompromised patients. We studied infection of B. cenocepacia K56-2 with Bcep176, a temperate phage isolated from Burkholderia multivorans. A genome-wide dCas9 knockdown library targeting B. cenocepacia K56-2 was constructed, and a pooled infection experiment identified 63 novel genes or operons coding for candidate host factors or phage resistance genes. The activities of a subset of candidate host factor and resistance genes were validated via single-gene knockdowns. Transcriptomics of B. cenocepacia K56-2 during Bcep176 infection revealed that expression of genes coding for host factor and resistance candidates identified in this screen was significantly altered during infection by 4 h post-infection. Identifying which bacterial genes are involved in phage infection is important to understand the ecological niches of B. cenocepacia and its phages, and for designing phage therapies.IMPORTANCEBurkholderia cepacia complex bacteria are opportunistic pathogens inherently resistant to antibiotics, and phage therapy is a promising alternative treatment for chronically infected patients. Burkholderia bacteria are also ubiquitous in soil microbiomes. To develop improved phage therapies for pathogenic Burkholderia bacteria, or engineer phages for applications, such as microbiome editing, it's essential to know the bacterial host factors required by the phage to kill bacteria, as well as how the bacteria prevent phage infection. This work identified 65 genes involved in phage-host interactions in Burkholderia cenocepacia K56-2 and tracked their expression during infection. These findings establish a knowledge base to select and engineer phages infecting or transducing Burkholderia bacteria.

Bacteriophages, the viruses infecting bacteria, hold great potential for the treatment of multidrug-resistant bacterial infections and other applications due to their unparalleled diversity and recent breakthroughs in their genetic engineering. However, fundamental knowledge of the molecular mechanisms underlying phage–host interactions is mostly confined to a few traditional model systems and did not keep pace with the recent massive expansion of the field. The true potential of molecular biology encoded by these viruses has therefore remained largely untapped, and phages for therapy or other applications are often still selected empirically. We therefore sought to promote a systematic exploration of phage–host interactions by composing a well-assorted library of 68 newly isolated phages infecting the model organism Escherichia coli that we share with the community as the BASEL (BActeriophage SElection for your Laboratory) collection. This collection is largely representative of natural E. coli phage diversity and was intensively characterized phenotypically and genomically alongside 10 well-studied traditional model phages. We experimentally determined essential host receptors of all phages, quantified their sensitivity to 11 defense systems across different layers of bacterial immunity, and matched these results to the phages’ host range across a panel of pathogenic enterobacterial strains. Clear patterns in the distribution of phage phenotypes and genomic features highlighted systematic differences in the potency of different immunity systems and suggested the molecular basis of receptor specificity in several phage groups. Our results also indicate strong trade-offs between fitness traits like broad host recognition and resistance to bacterial immunity that might drive the divergent adaptation of different phage groups to specific ecological niches. We envision that the BASEL collection will inspire future work exploring the biology of bacteriophages and their hosts by facilitating the discovery of underlying molecular mechanisms as the basis for an effective translation into biotechnology or therapeutic applications.

Bacteriophages, the viruses infecting bacteria, hold great potential for the treatment of multidrug-resistant bacterial infections and other applications due to their unparalleled diversity and recent breakthroughs in their genetic engineering. However, fundamental knowledge of the molecular mechanisms underlying phage-host interactions is mostly confined to a few traditional model systems and did not keep pace with the recent massive expansion of the field. The true potential of molecular biology encoded by these viruses has therefore remained largely untapped, and phages for therapy or other applications are often still selected empirically. We therefore sought to promote a systematic exploration of phage-host interactions by composing a well-assorted library of 68 newly isolated phages infecting the model organism Escherichia coli that we share with the community as the BASEL (BActeriophage SElection for your Laboratory) collection. This collection is largely representative of natural E. coli phage diversity and was intensively characterized phenotypically and genomically alongside 10 well-studied traditional model phages. We experimentally determined essential host receptors of all phages, quantified their sensitivity to 11 defense systems across different layers of bacterial immunity, and matched these results to the phages' host range across a panel of pathogenic enterobacterial strains. Clear patterns in the distribution of phage phenotypes and genomic features highlighted systematic differences in the potency of different immunity systems and suggested the molecular basis of receptor specificity in several phage groups. Our results also indicate strong trade-offs between fitness traits like broad host recognition and resistance to bacterial immunity that might drive the divergent adaptation of different phage groups to specific ecological niches. We envision that the BASEL collection will inspire future work exploring the biology of bacteriophages and their hosts by facilitating the discovery of underlying molecular mechanisms as the basis for an effective translation into biotechnology or therapeutic applications.

PHISDetector: A Tool to Detect Diverse In Silico Phage–host Interaction Signals for Virome Studies

Bacterial resistance has emerged as one of the greatest threats to human health, and phages have shown tremendous potential in addressing the issue of drug-resistant bacteria by lysing host. The identification of phage-host interactions (PHI) is crucial for addressing bacterial infections. Some existing computational methods for predicting PHI are suboptimal in terms of prediction efficiency due to the limited types of available information. Despite the emergence of some supporting information, the generalizability of models using this information is limited by the small scale of the databases. Additionally, most existing models overlook the sparsity of association data, which severely impacts their predictive performance as well. In this study, we propose a dual-view sparse network model (DSPHI) to predict PHI, which leverages logical probability theory and network sparsification. Specifically, we first constructed similarity networks using the sequences of phages and hosts respectively, and then sparsified these networks, enabling the model to focus more on key information during the learning process, thereby improving prediction efficiency. Next, we utilize logical probability theory to compute high-order logical information between phages (hosts), which is known as mutual information. Subsequently, we connect this information in node form to the sparse phage (host) similarity network, resulting in a phage (host) heterogeneous network that better integrates the two information views, thereby reducing the complexity of model computation and enhancing information aggregation capabilities. The hidden features of phages and hosts are explored through graph learning algorithms. Experimental results demonstrate that mutual information is effective information in predicting PHI, and the sparsification procedure of similarity networks significantly improves the model's predictive performance.