Biological context modulates virus-host dynamics and diversification

Biological context modulates virus-host dynamics and diversification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Biological context modulates virus-host dynamics and diversification Josefa Anton, Rodrigo Sanchez-Martinez, Esther Rubio-Portillo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7825215/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Virus-bacterial interactions are fundamental to microbial ecology and evolution, but they have been characterized mostly using pure cultures under simplified laboratory conditions. To better understand how ecological complexity shapes these dynamics, we examined the behavior of the extreme halophilic bacterium Salinibacter ruber strain M1 and the EM1 virus in the presence of additional Sal. ruber strains and viruses. In the short term, the presence of other strains delayed lysis and reduced EM1 virus production, indicating that community composition directly affects viral replication. In the long term, both Sal. ruber M1 and the EM1 virus persisted across all experimental conditions, but their evolutionary responses differed. The EM1 virus showed an increased mutation rate, reduced infectivity against the native host, and expanded host range when other viruses were present, suggesting a previously unrecognized form of virus-virus interaction, in which coexisting viruses influence each other’s evolutionary paths promoting viral diversification. In contrast, Sal. ruber M1 exhibited higher mutation rates evolving with other strains, indicating that in our system, intraspecific competition, rather than viral pressure, drives bacterial evolution. These findings demonstrate that incorporating biological complexity reveals distinct selective pressures acting on hosts and viruses, and is therefore essential for accurately predicting virus-host evolution in natural microbial ecosystems. Biological sciences/Evolution/Experimental evolution Biological sciences/Microbiology/Microbial communities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Natural ecosystems are highly complex networks where multiple biotic and abiotic factors interact dynamically (Fuhrman et al., 2015 ; Levin, 1998 ). This complexity makes it challenging to predict ecological and evolutionary phenomena since experimental studies are generally conducted under simplified laboratory conditions which do not reflect natural dynamics (Calisi & Bentley, 2009 ; Chen et al., 2023 ; Roberts et al., 2015 ; Zimmer & Dorea, 2023 ). To advance our understanding of microbial processes and predict how microbial communities respond to environmental changes or selective pressures, it is crucial to design experiments that more faithfully simulate natural conditions, incorporating the diversity of species and interactions that characterize real ecosystems (Benton et al., 2007 ; Blazanin & Turner, 2021 ; Castledine & Buckling, 2024 ; Jessup et al., 2004 ). Viruses act as major eco-evolutionary forces in microbial ecosystems. As (likely) the most abundant biological entities on Earth, they play a central role in regulating microbial communities through diverse infection cycles that impact nutrient cycling, genetic diversity, and ecosystem structure (Fierer, 2017 ; Middelboe & Brussaard, 2017 ; Rohwer & Thurber, 2009 ). Virus-host interactions also drive microbial evolution, triggering adaptations that influence community composition and function. Despite their importance, many aspects of these interactions remain poorly understood, particularly under complex, natural conditions. Although numerous studies have explored virus-host interactions, most have relied on simplified experimental systems involving a single virus-host pair under controlled conditions. As a result, our understanding of how these interactions operate in more realistic settings remains limited. Investigating virus-host dynamics in environments that better mimic natural complexity is essential to fully grasp their ecological and evolutionary roles (Blazanin & Turner, 2021 ; Castledine & Buckling, 2024 ; Chevallereau et al., 2022 ; Koskella et al., 2022 ). While some recent efforts have incorporated greater biological complexity (Alseth et al., 2019 , 2024 ; De Sordi et al., 2017 ; Gómez & Buckling, 2011 , 2013 ; Middelboe et al., 2001 ; Mumford & Friman, 2017 ), such studies remain scarce. This persistent gap limits our ability to understand and predict how virus-host interactions shape microbial communities in natural ecosystems. Hypersaline aquatic environments, which constitute approximately half of continental waters, harbor microbial communities that, at extreme salinities, are dominated by archaea (Burns et al., 2007 ; Shiklomanov, 1998 ; Ventosa, 2006 ). At these salinities, Salinibacter ruber emerges as the main bacterial species (Antón et al., 2002 ; Gomariz et al., 2015 ; Oren, 2011 ). Although Sal. ruber is as halophilic as the dominant archaea at close-to-saturation salinities, it is consistently less abundant, representing between 1% and 10% of total cells, indicating that it could be subjected to intense selective pressure (Mora-Ruiz et al., 2018 ; Ventosa et al., 2015 ). Cultivation and metagenomic studies reveal a high degree of intraspecific genomic diversity in Sal. ruber , including the coexistence of distinct phylogroups and adaptation to local microenvironments (Viver et al., 2015 , 2023 ). These hypersaline habitats also harbor some of the highest recorded viral concentrations, with virus-like particles (VLP) reaching up to 10 10 VLP/ml and virus-to-cell ratios ranging from 10 to 100 (Di Meglio et al., 2016 ; Santos et al., 2012 ). Given its environmental ubiquity, cultivability, and intraspecific variation, likely shaped in part by viral predation, Sal. ruber constitutes a valuable model for studying microbial ecology and virus-host interactions. In this work, we have addressed how the ecology and evolution of a virus-host pair (i.e., Sal. ruber strain M1 and the EM1 virus) are affected by the presence of additional Sal. ruber strains and their viruses. Through experiments in which biological complexity is increased, we have demonstrated that: (i) the presence of other strains and viruses delays lysis and reduces EM1 virion production; and that (ii) the biological context influences the long-term evolution of the virus-host pair and promotes changes in viral infectivity and host range, driven by a high mutation rate under more complex contexts. These findings highlight the importance of considering biological complexity in studies of virus-host interactions. Our work not only contributes to a better understanding of these dynamics in natural systems but also suggests that this complexity should be considered in practical applications such as phage therapy and microbial community engineering. RESULTS AND DISCUSSION Five parallel experiments evaluated the effects of the presence of Sal. ruber strains and viruses on the interactions and evolution of Sal. ruber strain M1 and its M1EM1 virus (referred to as EM1 hereafter) (Fig. 1 a). In experiment 1, Sal. ruber strain M1 was cultured alone. In experiment 2, Sal. ruber strains M8, M31, and P18 were added to the M1 culture. These two experiments served as virus-free controls. In experiment 3, M1 alone was infected with its EM1 virus. Experiment 4 included M1 infected with EM1, along with the presence of strains M8, M31, and P18. Finally, experiment 5 mirrored experiment 4, with the addition of M31CC1 and M31CR41-2 viruses (referred to as CC1 and CR41-2, respectively), which infect both Sal. ruber strains M31 and M8, and M8CR-4 virus (i.e. CR4), which infects Sal. ruber strain M8 (Villamor et al., 2018 ) (Supplementary Table 1). The first 9 days of incubation (“short-term experiment”), corresponding to ~ 14 generations of Sal. ruber , evaluated infection traits such as lysis timing, virion production, infectivity, and community dynamics. Thereafter, all cultures were periodically transferred to fresh medium and maintained for 150 days (~ 240 generations). This constituted the “long-term experiment”, aimed at studying possible evolutionary changes in the virus-host pair, while continuing to monitor key parameters of the infection (Fig. 1 b). Increased biological complexity leads to a delay in lysis of Sal. ruber M1 and a reduced production of EM1 virions In the short-term experiment, Sal. ruber exhibited its characteristic growth pattern in experiments 1 and 2, with a slightly higher optical density (OD) in experiment 2, likely as a consequence of additional bacterial strains (Fig. 2 a; Extended Data Fig. 1 a). In infection experiment 3, lysis of Sal. ruber M1 was observed, followed by a recovery, a pattern that mimics previous findings from our laboratory, in which that growth was mainly attributed to the emergence of pseudolysogens (Sanchez-Martinez et al., 2025 ). In experiments 4 and 5, this OD recovery was observed earlier, which could be attributed to the growth of uninfected strains. The production of infectious EM1 viral particles, measured as plaque-forming-units (PFU) on Sal. ruber M1, was significantly delayed in experiments 4 and 5 compared to experiment 3 ( t -test p-value < 0.0001) (Fig. 2 b), indicating that the presence of additional Sal. ruber strains delayed the lysis of M1. After 96 hours, a drop in the number of infectious particles was observed, more pronounced in experiments 4 and 5 than in experiment 3 (p-value < 0.01). In all the experiments a slight decline in the number of PFUs was observed over time, with no significant differences between 6 and 9 days. Total extracellular virion concentration, measured by flow cytometry, reached 9.7 x 10 10 VLP/ml within 48 hours in experiment 3, whereas in experiment 4 this concentration was 4.2 x 10 8 VLP/ml, a statistically significant reduction (p-value < 0.0001) (Fig. 2 c), consistent with the observations of PFU counts. Experiment 5 was excluded from this figure, as the presence of multiple coexisting viruses prevents attributing VLP counts specifically to EM1 (Extended Data Fig. 1 b). High throughput microfluidics-based qPCR revealed that the concentration of the M1 strain (measured as genomes/ml) varied across experiments and time points (Fig. 3 a). At 48 hours, M1 abundance dropped significantly in experiment 3 (3.9 x 10 6 genomes/ml), indicating active lysis of the population, while it remained at higher numbers in experiments 4 (1.2 x 10 9 genomes/ml) and 5 (7.6 x 10 8 genomes/ml) (p-value < 0.0001). By 72 hours, M1 levels decreased in all experiments, maintaining low values in experiment 3 (3.8 x 10 6 genomes/ml), and showing delayed but evident reductions in experiments 4 and 5 (2.2 x 10 6 and 5.2 x 10 6 genomes/ml, respectively) (Fig. 3 a; Supplementary Fig. 1). Regarding the bacterial community, in experiment 4 a decrease in the concentration of M8, M31, and P18 strains was observed at 50 hours, despite the absence of their viruses (Fig. 3 a). This pattern suggests that the growth of these strains was transiently inhibited after Sal. ruber M1 lysis. To test this, a virus-free lysate from an M1-infected culture was added to pure cultures of M8, M31, and P18. A clear, transient growth inhibition was observed, confirming that the M1 lysate affected the growth of the other strains (Extended Data Fig. 2 ). Similar results have been reported in other studies, where intracellular bacterial contents released upon lysis can inhibit the growth of other bacteria (Penchuk et al., 2023 ; Smakman & Hall, 2022 ). In experiment 5, M8 showed minimal growth. As the only strain infected by three viruses (Supplementary Table 1), M8 likely could not withstand the high viral pressure, explaining its poor performance under these conditions. When the extracellular viruses were measured by microfluidics-based qPCR, a lower concentration of EM1 was observed at 48 hours in both experiments 4 and 5 (2.2 x 10 8 and 3.3 x 10 8 genomes/ml, respectively) compared with experiment 3 (4.2 x 10 9 genomes/ml) (p-value < 0.0001) (Fig. 3 b; Supplementary Fig. 2), confirming that increased community complexity delayed the infection and lysis of Sal. ruber M1 by the EM1 virus. By 216 hours (day 9), experiment 4 yielded a significantly lower VLP concentration than experiment 3 (2.4 x 10 10 and 2.8 x 10 10 VLP/ml, respectively, p-value < 0.05) (Fig. 2 c). Microfluidics-based qPCR measurements also revealed that the concentration of extracellular EM1 viruses in experiments 4 and 5 (7.6 x 10 9 and 3.4 x 10 9 genomes/ml, respectively) was significantly lower than in experiment 3 (2.7 x 10 10 genomes/ml) at the same time point (p-value < 0.01), supporting the flow cytometry results (Fig. 3 b). These findings indicate that the presence of additional Sal. ruber strains reduced the overall production of EM1, in good agreement with some previous studies showing that the presence of other bacterial species can lead to lower viral densities (Alseth et al., 2019 ; Mumford & Friman, 2017 ). In contrast, a recent work found no differences in lysis timing or viral titers produced by Pseudomonas aeruginosa PA14 when exposed to competing bacterial strains (Alseth et al., 2024 ). Experiment 5 also revealed distinct patterns in the production of other viruses. CC1 virus, which infects both Sal. ruber M8 and M31, showed sustained high titers from 50 hours onward, consistent with ongoing replication in its hosts (Fig. 3 b). CR41-2 virus, which infects the same strains as CC1 (M8 and M31), was only detected on days 1 and 4. Adsorption assays revealed that CR41-2 adsorbs significantly less efficiently than CC1 to M31 (Supplementary Fig. 3). Thus, CC1 may have outcompeted CR41-2 for access to M31, thereby limiting CR41-2 infection. This observation aligns with a well-established principle of phage biology, that considers adsorption efficiency as a critical determinant of successful phage infection and subsequent replication (Abedon, 2023 ). Such virus-virus competition for shared hosts can strongly shape infection dynamics, allowing more efficient viruses to suppress the adsorption of less competitive ones (Bürkle et al., 2025 ; Turner, 2005 ). Finally, CR4 virus, which exclusively infects M8, exhibited an early increase followed by a sharp decline (Fig. 3 b). This decline likely reflects the lack of M8 cell growth, which limited the availability of susceptible hosts and consequently reduced further CR4 replication. Sal. ruber M1 and its EM1 virus establish a stable long-term relationship, regardless of the biological context After the initial 9-day period, all cultures were transferred to fresh medium to monitor their long-term evolution with transfers every 9–13 days over a total of 150 days. At selected time points, specific parameters were assessed, including changes in host range and infectivity toward the native host (Fig. 1 b). Sal. ruber M1 was maintained at high density (~ 10⁹ genomes/ml) across all time points, regardless of the presence of EM1 or other strains and viruses (Extended Data Fig. 3 ). The EM1 virus maintained relatively stable concentrations extracellularly across all three experiments (Extended Data Fig. 4 ; Supplementary Fig. 4). Although the EM1 titer declined by about one order of magnitude between days 52 and 84, its level remained stable until the end of the study. These results indicate that Sal. ruber M1 and its EM1 virus established a stable coexistence that persisted throughout the study, regardless of the surrounding biological context. Long-term virus-host coexistence has been widely reported in other systems driven by mechanisms such as arms-race dynamics, spatial heterogeneity, or phase variation in gene expression (Cortés-Martín et al., 2025 ; Kortright et al., 2022 ; Lourenço et al., 2020 ). However, a previous work from our group demonstrated that EM1 can be maintained in Sal. ruber M1 in a pseudolysogenic state (Sanchez-Martinez et al., 2025 ), which appears to underlie the stable coexistence observed here. Its establishment and maintenance do not seem to be affected by the presence of additional strains and viruses. Additional insights emerged from the dynamics of the other community members. In experiment 5, CC1 remained at high concentrations, as well as one of their hosts, M31, which was also detected until the end of the experiment (Extended Data Figs. 3 and 4 ). In the case of the CR4 virus, although it was not detected extracellularly after 9 days, it persisted in the total culture until 84 days, coinciding with the extinction of its host M8 (Supplementary Fig. 4). The P18 strain, the only one not infected by any virus, disappeared in experiment 5 from all 3 replicates and, although still detectable, was outcompeted by other strains in experiments 2 and 4. This indicates that viruses were not the only force structuring the community, and that competitive interactions among bacterial strains also played a crucial role. Increasing the complexity of the biological context reduces the infectivity of the EM1 virus and alters its host range On day 9, the infectivity of EM1, calculated as the ratio between PFU/ml (tested with the native M1 strain as the host) and extracellular EM1 genomes/ml, was maintained above 0.1% in the three experiments and remained stable at these levels for 52 days (Fig. 4 a). However, by day 84, significant differences (p-value < 0.0001) emerged between experiment 3 and experiments 4 and 5. In experiment 3 infectivity remained stable but, in experiment 4, where the Sal. ruber M1-EM1 pair was accompanied by other strains, infectivity dropped by almost one order of magnitude. In experiment 5, the most biologically complex, this reduction in infectivity was more drastic, with a drop of nearly 4 orders of magnitude compared to experiment 3. By day 130, infectivity had decreased further in all experiments, down to 5 orders of magnitude below the initial level. These findings suggested that the EM1 virus underwent genomic changes, as further explored below, which reduced its ability to infect the native strain in all the experiments and highlighted that the biological context significantly affected the interactions between EM1 and its host. Host range changes were also detected in the EM1 virus in experiment 5, whereas no such changes were observed in experiments 3 and 4. The evolved EM1 in experiment 5, which initially only infected the M1 strain, expanded its host range and infected the native M8 strain consistently across all three biological replicates at 84 hours (Fig. 4 b). Several studies have reported host range changes in simplified experimental evolution contexts (Bono et al., 2015 ; Holtzman et al., 2020 ; Sant et al., 2021 ; Subramanian et al., 2022 ), but only a few have explored this phenomenon under more complex conditions. In multispecies settings, viruses have been shown to broaden their host range by transiently using alternative hosts as intermediates to access new hosts (De Sordi et al., 2017 ), which may partly explain why this host range shift occurred in experiment 5. Moreover, it has been observed that the presence of multiple viruses affect the host range of a given virus by driving evolutionary changes of the host that affect its susceptibility (Avrani et al., 2011 ; Chevallereau et al., 2022 ). These findings suggest that the observed host range expansion in EM1 was a result of selective pressures arising from increased ecological complexity. Coexistence with other strains and viruses may have favored viral variants capable of infecting alternative hosts (i.e., either by exploiting new receptor variants or by overcoming host-specific resistance mechanisms (see below)). In the case of the other viruses, none of them showed any host range changes. The unequal decline in the EM1 virus infectivity across experiments, together with the shift in host range observed in experiment 5, suggested that EM1 underwent different genomic changes under the different conditions, influencing the virus-host evolutionary trajectory according to the surrounding biotic environment. To analyze whether genomic changes could be responsible for these differences, extracellular viromes and cellular pellets from experiments 3, 4 and 5 were sequenced on days 9, 84 and 150. The original viral genomes were also sequenced as references. Viral genomic diversification is enhanced by the presence of other viruses EM1 genome remained largely stable during the early stages of the experiment, with only a single mutation in a gene coding for a hypothetical protein detected across all treatments by day 9 (Supplementary Table 2). By day 84, mutation accumulation remained low in experiments 3 and 4 (15 and 14 mutations, respectively), mostly in non-coding regions (Extended Data Fig. 5 ; Supplementary Table 2). Experiment 5 showed a markedly different pattern, with 772 mutations distributed across the genome. A large proportion of these occurred in structural genes, particularly those related to tail components. Two minor tail protein genes accumulated over 190 mutations, and a long-tail fiber protein gene showed 69 changes, suggesting strong selective pressure on host recognition machinery. This genomic diversification coincided with a sharp drop in EM1 infectivity against the native M1 host, pointing to a possible link between mutation accumulation in tail-related regions and host range evolution. As the experiment progressed to day 150, the accumulation of mutations continued to diverge between treatments. EM1 genome in the sole presence of its host M1 (experiment 3) reached a total of 34 mutations, including 17 in a gene coding for a DNA methyltransferase and several nonsynonymous changes in genes for a DNA-binding protein, a minor tail protein, and a long-tail fiber protein, some of which may be linked to the observed decline in infectivity (Fig. 5 ). In contrast, EM1 genome in experiment 4, where all the Sal. ruber strains were present, accumulated 267 mutations, with a high concentration in genes related to DNA metabolism, such as a DNA primase and a single-strand DNA-binding protein. This accumulation of mutations in DNA metabolism-related genes could suggest a potential disruption in viral replication, contributing to reduced infectivity (Boddin et al., 2022 ; Jiang et al., 2007 , 2009 ). In experiment 5 (containing all the bacterial strains and their viruses), EM1 genome mutation accumulation intensified, reaching 1,331 mutations distributed across the genome. Structural and replication-related genes showed particularly high mutational loads, including 198 mutations in minor tail protein genes and over 190 combined in genes for a DNA-binding protein and a DNA primase. This translated into a mutation rate of 2.5 × 10 − 4 mutations per site per day in experiment 5, markedly higher than those estimated for experiments 3 and 4 (6.4 x 10 − 6 and 5 x 10 − 5 , respectively) (Supplementary Table 2). These differences were reflected in the average nucleotide identity of sequence alignment reads (ANIr) along the EM1 genome (the mean nucleotide-level similarity between mapped reads and the reference genome). EM1 genome in experiments 3 and 4 showed higher ANIr values (99.70% and 99.75%, respectively) indicating a more homogeneous population, compared to experiment 5 (98.13%) on day 150 (Supplementary Table 2). The lower ANIr values observed in the presence of other viruses indicated an accelerated diversification of the viral population, with the emergence of multiple distinct genotypes or “genomovars” (defined as variants with ANI < 99.5%) (Aldeguer-Riquelme et al., 2024 ). The increase in genetic variability observed under complex community conditions reveals a previously overlooked role of viral coexistence in promoting diversification. Our results demonstrate that the mere presence of other viruses can substantially reshape the genetic structure of a viral population, both in terms of mutation rate and the distribution of these mutations across the genome (Supplementary Table 2; Fig. 5 ). This diversification was accompanied by a drastic loss of infectivity and a shift in host range, likely driven by mutations in tail protein genes. While previous studies have shown that viral mutation rates can vary depending on the host genotype (Duffy, 2018 ; Longdon et al., 2014 ; Sant et al., 2021 ), and that exposure to multiple host genotypes can promote tail gene diversification (Hernandez et al., 2024 ), our findings suggest a previously unreported mechanism. Here, it is the presence of other viruses, more than the mere presence of other hosts, that significantly increased the viral mutation rate. This uncovers a novel form of virus-virus interaction, where coexisting viruses indirectly drive each other’s evolutionary trajectories. Such interactions, largely overlooked to date, could play a central role in shaping viral evolution in natural communities characterized by high viral diversity. Intracellular viral diversity exceeds the subset released extracellularly Analysis of intracellular viral populations revealed that only part of the viral diversity generated inside host cells was released extracellularly, indicating that the extracellular fraction corresponds to a selected subset of intracellular genotypes. Three genes (marked with an asterisk in Fig. 5 ) were characterized by amplicon sequencing: one encoding a hypothetical protein with minimal mutations across all experiments (used as a control), and two genes highly mutated in experiment 5, encoding a minor tail protein and a DNA-binding protein, respectively. The mutation patterns in these genes were generally similar to those observed in the extracellular viral population, especially in experiments 3 and 4, with few differences (Extended Data Fig. 6). EM1 genome in experiment 5 displayed a markedly higher number of mutations in the intracellular fraction in both the minor tail protein and DNA-binding protein genes (63 and 30 mutations on average across replicates) compared to experiment 3 (0 and 2 mutations) and experiment 4 (no mutations). When comparing intracellular and extracellular fractions some differences emerged, particularly in experiment 5. Both the minor tail protein and DNA-binding protein regions showed a high number of mutations at nearly 100% frequency in the extracellular fraction, while the intracellular fraction exhibited greater heterogeneity in mutation frequencies. These results support our earlier findings that the presence of other viruses acts as a selective force, increasing the mutation rate of EM1 and generating a larger diversity of viral genotypes. However, based on the intracellular data, only a subset of these genotypes appears to be selected and successfully replicated for virion production. Intraspecific interactions, rather than viral pressure, drive host genome evolution We also analyzed genetic mutations in the host Sal. ruber M1. As experiments 2, 4, and 5 included additional Sal. ruber strains sharing large genome regions, we focused on nine genomic regions, ranging from 1.8 to 23.5 kb, specific to Sal. ruber M1. This strategy allowed us to unambiguously track mutations in M1 despite the presence of other closely related strains in the mixed cultures. In total, 70.1 kb were analyzed, (Supplementary Fig. 5). Mutation counts at 150 hours revealed striking differences: 9 mutations in experiment 1, 224 in experiment 2, 8 in experiment 3, 266 in experiment 4, and 14 in experiment 5, with most occurring in non-coding regions (Extended Data Fig. 7; Supplementary Table 3). These findings corresponded to mutation rates of 7,6 x 10 − 7 , 2,1 x 10 − 5 , 6,7 x 10 − 7 , 5 x 10 − 5 , 5,6 x 10 − 6 mutations per site per day, respectively (Supplementary Table 3). Our results demonstrate that Sal. ruber M1 experienced significantly elevated mutation rates in specific regions in those experiments where it evolved alongside multiple strains—experiments 2 and 4 (Extended Data Fig. 3 ). In contrast, experiment 5, which also included other viruses, resulted in markedly lower host mutation rates. This suggests that the presence of viruses in a complex community context may constrain bacterial evolution, rather than accelerate it. This finding is striking and counterintuitive, as viruses are typically considered potent drivers of evolutionary change, and other studies showed that bacteria coevolving with multiple viruses exhibited higher mutation rates (Betts et al., 2018 ; Wielgoss et al., 2016 ). In our system, however, viral pressure may have limited evolutionary change in the host, possibly by imposing bottlenecks through lysis, constraining population dynamics, or reducing the strength of competition among bacterial strains. The observed increase in mutation rates under competitive conditions suggests that bacterial intraspecific competition is a strong driver of genomic evolution in the host. While EM1 viral mutations primarily emerged within infected populations, Sal. ruber M1 genetic variability was predominantly shaped by interactions with coexisting bacterial strains. This is consistent with previous work from our laboratory demonstrating weak but significant competitive interactions between Sal. ruber strains in co-culture (Peña et al., 2010). Furthermore, in a recent mesocosm experiment, we showed that strain M8 was displaced by other genotypes after being introduced at high abundance into a pond, suggesting competitive exclusion through intraspecific competition (Ramos-Barbero et al., 2024 ). Several mechanisms could account for the elevated mutation rates observed in experiments 2 and 4. These include stress-induced mutagenesis, where competition triggers cellular stress responses that increase mutation frequency (Svet et al., 2023 ; Trejo-Hernández et al., 2014 ), and the selection of hypermutator phenotypes (bacterial variants with defective DNA repair systems) previously documented in Escherichia coli during gut colonization (Giraud et al., 2001 ) and Vibrio cholerae under antibiotic pressure (Baharoglu & Mazel, 2011 ). In our system, the sharp increase in mutations specifically in the absence of viral pressure suggests that competition with other bacterial strains may trigger stress responses or transient mutagenic states, rather than the stable fixation of hypermutator lineages. This is further supported by the fact that mutation rates remained low in experiment 5, despite the presence of both other strains and viruses, indicating that viral pressure may counteract or suppress the effects of competition-induced mutagenesis. These findings support the view that microbial competition plays a central role in shaping evolutionary trajectories. Together, they underscore a critical consideration for evolutionary studies: microbial mutation rates and adaptation cannot be fully understood without accounting for the complex network of interspecies interactions that govern microbial life in natural environments. CONCLUSIONS The usefulness of studying virus-host interactions within microcosms that simulate natural conditions in a stepwise manner has become increasingly evident in recent years (Blazanin & Turner, 2021 ; Castledine & Buckling, 2024 ; Chevallereau et al., 2022 ; Koskella et al., 2022 ). Understanding the ecological and evolutionary dynamics of viruses and prokaryotes in their natural environments not only enriches our knowledge of the natural world but also enables us to manipulate these communities more predictably. In this study, we demonstrate that a semi-complex biological context (encompassing Sal. ruber strains and their viruses) substantially influence the ecology and evolution of the Sal. ruber M1-EM1 pair in controlled microcosms. This work shows how in the short-term, the presence of additional Sal. ruber strains delayed the lysis of Sal. ruber M1 and reduced EM1 virion production. These effects were consistent across different quantification methods, indicating that community composition has a measurable impact on virus replication dynamics. In the long-term, Sal. ruber M1 and the EM1 virus established a stable coexistence throughout the entire experiment regardless of community complexity. The infectivity of EM1 against its original host declined faster in the presence of other viruses, revealing that viral genomic diversification is enhanced by virus-virus interactions. This reduction in infectivity was linked to genomic changes in EM1, leading to an expanded host range and a sharply increased mutation rate in the presence of other viruses. Meanwhile, the bacterial host, Sal. ruber M1, showed elevated mutation rates evolving alongside other strains, indicating that intraspecific bacterial interactions—not viral pressure—drive host genome evolution. Overall, our results highlight the critical importance of incorporating biological complexity in studies of virus-host interactions. These findings emphasize the need to consider the multifaceted nature of microbial communities to more accurately predict virus-host dynamics and their ecological and evolutionary consequences in natural environments. METHODS Bacterial strains, viruses and experimental design Sal. ruber strains M1, M8, M31 and P18 were aerobically grown with gentle shaking (60 rpm) at 37ºC in 25% SW (sea water, a salt solution containing the salts present in seawater at a total concentration of 25% weight/volume) with 0.2% yeast extract (Rodriguez-Valera et al., 1985 ). Sal ruber viruses used in this study, named M1EM-1, M31CC-1, M8CR-4 and M31CR41-2 (referred to here as EM1, CC1, CR4 and CR41-2, respectively, for convenience), were isolated during a previous study in our laboratory (Villamor et al., 2018 ). Five experiments were conducted in parallel (Fig. 1 ). In all the experiments, 25 ml of culture media were inoculated with 7.5x10 8 cells of Sal. ruber M1. In experiments 2, 4, and 5, the cultures also included 2.5 × 10 7 cells of each of the M8, M31, and P18 strains. In experiments 3, 4 and 5, 7.5x10 7 virus-like particles (VLP) of the strictly virulent EM1 virus (multiplicity of infection = 0.01), which only infects the M1 strain, were added. Experiment 5 also contained 2.5x10 6 VLP of each one of the CC1, CR4 and CR41-2 viruses that infect M8 and M31 strains (Supplementary Table 1). After inoculation, experiments were incubated for 9 days as described above, taking aliquots once a day for culture monitoring by optical density (OD) at 600 nm and additional approaches (see below). This was defined as the short-term experiment. On day 9 (213 hours after inoculation), 250 µl of each experiment were transferred to 25 ml of fresh medium and incubated as described above. After 13 days, new aliquots were collected to quantify the bacterial and viral diversity of the cultures by microfluidics-based qPCR (see below) and transferred and incubated in the same way. This was repeated every approximately 9–13 days for 150 days. Days 9 to 150 were considered as the long-term experiment. In addition, on days 9, 52, 84, 130 and 150, aliquots were taken for plaque forming units (PFU) quantification, host range analyses of the viruses and DNA extraction and sequencing. All experiments were conducted in triplicate. Flow cytometry For each day of the short-term experiment, cells/ml and VLP/ml were quantified by flow cytometry as described in Brussaard, 2004 . Briefly, 500 µl of each sample were fixed with glutaraldehyde (0.5% final concentration) at 4ºC for 30 min, flash-frozen in liquid nitrogen, and stored at -80°C until use. Upon thawing, samples were diluted in Tris-EDTA buffer (pH 8), stained with SYBR TM Gold (0.5X final concentration), incubated for 10 min in the dark at 80°C, and cooled for 5 min at room temperature prior to analysis. The cytometer settings were as follows: the threshold was set in blue fluorescence (300 units), FITC voltage = 500, SSC voltage = 300, forward scatter voltage = 500, and the flow rate was established as low (15 µl/min). Background noise was checked on blanks, composed by TE buffer stained with SYBR Gold. Samples were recorded with an event rate of 100–1000 events per second. Cells and VLP counts were obtained by correcting the background measured in blanks. Cells and VLP abundances were respectively expressed as cells/ml or VLP/ml. Plaque assay The number of free infective EM1 viruses at each sampling point of the short- and long-term experiments was determined by plaque assay in experiments 3, 4 and 5. Aliquots of 150 µl were centrifuged at 17,000 x g for 10 min and supernatants were serially diluted in sterile SW 25%. 100 µl of dilutions 10 − 4 , 10 − 6 and 10 − 8 were mixed with 500 µl of Sal. ruber M1 in exponential phase. Four ml of soft agar (25% SW with 0.2% yeast extract and 0.7% agar) were added and this was poured on plates with solid media. After the agar had solidified, the plates were incubated at 37ºC for 10 days until plaques were visible. The number of free infective viruses (PFU/ml) was counted and represented. On days 9, 52, 84, 130 and 150, plaque assays were also carried out to obtain individual plaques of all viruses for experiment 5, using as hosts the native strains M8, M31 and P18. High throughput microfluidics-based qPCR Individual targets (i.e. the 4 strains and the 4 viruses) were quantified in each point of the short-term experiment, in both the total culture and the supernatant to quantify free viruses, using the Biomark HD high throughput microfluidics-based qPCR system (Standard Biotools, South San Francisco, USA). Primers for each target gene were designed using using Geneious software 6.1.8 (Biomatters, Auckland, New Zealand) (Supplementary Table 4). From each of the experiments, 100 µl of the culture were taken for total culture analysis and 120 µl for supernatant quantification of free viruses. In the latter, a centrifugation step consisting of 10 min at 7,197 x g and 100 µl supernatant transfer to a clean eppendorf tube was performed. Both were fixed with 0.5% (final concentration) of formaldehyde for 1 hour at 4°C, diluted 10-fold in phosphate buffered saline (PBS) 1X, and stored at 4°C. DNA targets were then pre-amplified using the PreAmp Master Mix (Standard Biotools, South San Francisco, USA) following manufacturer’s instructions. In brief, a primer pool was prepared by mixing the forward and reverse primers for all targets (Supplementary Table 4) and diluting the mixture with DNA suspension buffer (10 mM Tris/HCL and 0.1 mM EDTA pH 8) to a final concentration of 0.5 µM of each primer pair. Each sample was then pre-amplified in a total volume of 5 uL containing 1 µL PreAmp Master Mix (Standard Biotools, South San Francisco, USA), 0.5 µL of primer pool and 3.5 µL of sample. Preamplification reactions were performed on a Simpliamp Thermal Cycler (Applied Biosystems, Waltham, USA) using the following sample conditions: an initial activation cycle at 95°C for 10 min followed by 14 two-step cycles (denaturation at 95°C for 15 seconds and annealing/extension at 60°C for 4 min). The preamplified products were subjected to Exonuclease I cleanup (New England Biolabs, Massachusetts, USA) to remove any unincorporated primers (4 U/µL final concentration at 37°C for 30 min followed by inactivation at 80ºC for 15 min). A 1:5 dilution using DNA suspension buffer was then performed on the Exonuclease I treated samples and stored at -20°C. Finally, pre-amplified DNA targets were quantified by microfluidics-based qPCR on 192x24 Dynamic Array™ IFCs (Fluidigm, South San Francisco, USA) according to manufacturer's instructions. In brief, 10X assay primer mixtures for each DNA target and sample pre-mixes for each sample were prepared in triplicate. For the generation of the 10X assay primer mixtures, the forward and reverse primers for a single DNA target were pooled together at a concentration of 50 uM each. 0.15 uL of this pool were then mixed with 1.35 µl of DNA suspension buffer (Tris/HCL and 0.1 mM EDTA pH 8) and 1.5 µl of 2X Assay Loading Reagent (Standard Biotools, South San Francisco, USA). Sample pre-mixes were made by combining 1.5 µl SsoFast™ Evagreen® Supermix 2X (BioRad, California, USA) with 0.15 µl 20X GE Sample Loading Reagent (Standard Biotools, South San Francisco, USA) and 1.35 µl of pre-amplified DNA. The 192x24 Dynamic Array™ IFCs were then primed on a Juno controller (Standard Biotools, South San Francisco, USA) and 3 uL of each 10X assay primer mix and sample pre-mix were transferred to the appropriate inlets of the IFC and loaded using again the Juno controller. After loading, the IFC was transferred to a Biomark HD instrument (Standard Biotools, South San Francisco, USA) and qPCR reactions were performed using the following cycling conditions: 95°C for 1 min, followed by 30 two-step cycles (95°C for 15 seconds and 60°C for 20 seconds) and a final melt curve analysis. Data were analyzed with the Real-Time PCR Analysis Software (Standard Biotools, South San Francisco, USA) using manually defined thresholds. All samples were run in triplicate (including the standards and negative controls). All samples, standards, and negative controls were run in triplicate, with negative controls included both in the pre-amplification and in the microfluidics-based qPCR. A total of four 192x24 Dynamic Array™ IFCs were used, each with its own standard curve consisting of five concentration points for every target, and each chip included positive controls for the eight targets individually. Host range through spot test and qPCR The host range of each virus at time 0 was determined through a spot test. Briefly, 4 ml of molten 0.7% top agar of 25% SW with 0.2% yeast extract were mixed with 500 µl of bacterial cultures at exponential phase and plated on solid medium. Once solidified, 3 µl of each virus, titered at 10 10 PFUs/ml and diluted 10 2 , 10 4 and 10 6 times, were added to the corresponding lawn. The spotted plates were left to dry and incubated at 37ºC for 10 days. The detection of a clearance zone was interpreted as evidence of viral lysis while the areas exhibiting growth were attributed to resistance. This was done by exposing the 4 native strains ( Sal. ruber strains M1, M8, M31 and P18) to the 4 viruses (EM1, CC1, CR41-2 and CR4 viruses). The viral fractions from experiments 3, 4 and 5, at 9, 52, 84, 130 and 150 days, were obtained by centrifuging each replicate at 7,197 x g for 20 min and filtering the supernatant through a 0.22 µm filter (Millipore, Burlington, USA). For experiments 3 and 4, the host range of the EM1 virus over time was measured through a spot test against the 4 native hosts as described above. The determination of the host range of the 4 viruses over time in experiment 5 was performed by qPCR as follows. All the plaques obtained for each native host (see plaque assay above) for each replicate on days 9, 52, 84, 130 and 150, were picked from the soft agar and resuspended in 1 ml of ultrapure water. The presence of each virus in these mixtures of plaques were checked by qPCR with specific primers (Supplementary Table 4) using SYBR Green. The experiment was carried out with the standard run in a StepOnePlus PCR System (Life Technologies, Carlsbad, USA) in a 10 µl reaction mixture with Power SYBR Green PCR Master Mix (Applied Biosystems, Waltham, USA). The reaction contained: 5 µl of 2X Master Mix, 0.2 µl of each 10 µM primer, 1 µl of sample and ultrapure water to complete volume. Conditions are detailed in Supplementary Table 5. The results were analyzed with the Applied Biosystems StepOne™ Instrument program. All samples were run in triplicate (including the standards and negative controls). Adsorption assay An adsorption assay was performed in parallel with the CC1 and CR41-2 viruses and Sal. ruber M31 wild type. Cultures in exponential phase were diluted with fresh medium to an OD of 0.3 and 2x10 8 cells were mixed with 2x10 6 PFU (MOI = 0.01) of the virus. This was considered as the initial time. Mixtures were then incubated at 37 ºC and 60 rpm. Aliquots of 150 µl were taken along 5 h (at 1, 2, 3, 4 and 5 h), centrifuged at 17,000 x g for 8 min, and 120 µl of the cells-free supernatant were stored in ice. The numbers of PFU/ml were measured by plaque assay as described above. All the experiments were conducted in triplicate. Lysate experiment An infection of Sal. ruber M1 with the EM1 virus at a MOI of 0.1 was carried out under the same conditions as described above. After 5 days, the infection was centrifuged at 17,000 x g for 10 minutes, and the supernatant was filtered through a 0.022 µm filter (Millipore, Burlington, USA) to remove any remaining viruses. One ml of this lysate was added to 25 ml of exponential-phase cultures of M8, M31, and P18 strains. Controls received 1 ml of fresh medium instead. Optical density was measured at 600 nm. Both the lysate-treated cultures and controls were conducted in triplicate. DNA extractions and sequencing The three biological replicates of each experiment were pooled prior to DNA extraction, as previous experiments in our laboratory have shown high reproducibility in the mutational trajectories of replicate populations. Bacterial pellets from all the experiments at 150 days were obtained by centrifugation at 17,000 x g for 10 min. The cells were washed with sterile medium, the supernatant removed, and the cell pellets stored at -80ºC until extraction. DNA was extracted with the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) following the manufacturer's protocol, and nucleic acids eluted in 70 µL of ultrapure water. Viruses from the same points were obtained by centrifuging at 7.197 x g for 20 min and filtering the supernatant through a filter of 0.22 µm. Dissolved DNA was removed with DNAse I from bovine pancreas (Sigma-Aldrich, St. Louis, USA) at 37ºC for 30 min, with an inactivation at 75ºC for 10 min. DNA was extracted with the QIAamp MinElute Virus Kit (Qiagen, Hilden, Germany) following the manufacturer's protocol, and nucleic acids eluted in 35 µL of ultrapure water. DNA from the viruses used for infecting at the initial time was also extracted as a reference. All extracted cellular and viral DNAs were quantified using Qubit 2.0. Flourometer (Life Technologies, Carlsbad, USA), and sequenced on an Illumina Novaseq6000 (2 x 150 bp) at Macrogen (Macrogen, Seoul, South Korea). Metabarcoding Three genes from the EM1 virus were selected, and specific primers targeting ~ 375 bp regions were designed (Supplementary Table 6). Cell pellets were washed three times by centrifugation, and DNA was extracted and quantified as described above. Specific regions were amplified via PCR in 50 µL reactions containing: 1.5 mM MgCl₂, 10X buffer, 10 mM dNTPs, 5 U/µL Taq polymerase, 100 µM primers, 5 µg of the template DNA and water up to the volume. PCR conditions are detailed in Supplementary Table 7. Five ml of the amplified products were electrophoresed, stained with ethidium bromide (100 µg/mL), and visualized under UV light. The remaining product was sequenced on an Illumina Miseq (2 x 150 bp) at Fundació per al Foment de la Investigació Sanitària i Biomèdica de la Comunitat Valenciana (Fisabio, Valencia, Spain). Mutations were identified comparing the obtained reads with the reference genomes of the viruses at initial time using Geneious software 6.1.8 (Biomatters, Auckland, New Zealand) with a minimum variant frequency of 0.01% and a coverage > 50% to the mean coverage. Mutations were represented using the package ‘ggplot2’ v3.4.0 in R v4.2.2. Bioinformatic analysis Primers and adapters were removed from sequences, and reads were filtered based on quality scores using Trimmomatic v0.36.0 (Bolger et al., 2014 ). Trimmed reads of initial viruses were assembled using SPAdes v3.13.1 (Bankevich et al., 2012 ) with the trusted option using their genomes of NCBI as a reference (GenBank accession numbers MF580955, MF580958, MF580960 and MF580960). Mutations generated in the viruses over time were identified by comparing reads from the extracellular viromes on days 9, 84 and 150 with the reference genomes of the viruses at initial time using Geneious software 6.1.8 (Biomatters, Auckland, New Zealand) with a minimum variant frequency of 0.01% and a coverage > 50% to the mean coverage. Mutations were represented using the package ‘ggplot2’ v3.4.0 in R v4.2.2. For the analysis of M1 sequences, the chromosomes of strains M1, M8, M31 and P18 were aligned with Mauve (Darling et al., 2004 ) and 9 M1-specific regions were selected where the mutations were called in the same way as for the virus. A BLASTn was performed with the reads of each virome, using the genome of the EM1 virus as the reference. Then, the BLASTn output was filtered by best_hit option and read coverage > 70%. The ANIr (average nucleotide identity of sequence alignment reads) along the genome of EM1 was calculated with a home-made script in windows of 50 bp and plotted. Recruitment plots were carried out by enveomics tool (Rodriguez-R & Konstantinidis, 2016 ) using enve.recplot 2 (R stadistic pluging). Declarations ACKNOWLEDGEMENTS We warmly thank the Molecular Microbial Ecology group for their valuable feedback and constructive criticism throughout this work. We also acknowledge the Genomics and Proteomics Unit of the University of Alicante for their assistance with flow cytometry. Special thanks to Antonio Sanchez-Amat and Maliheh Mehrshad for their insightful feedback and suggestions. We thank Beatriz Cámara for developing the script used in the ANIr analysis and Heather Maughan for the professional English editing and critical reading of the manuscript. This research was supported by the projects "VIRHOST" CIPROM/2021/006 (PROMETEO 2022, Generalitat Valenciana) to J.A., and METACIRCLE PID2021-126114NB-C41 to F.S. and J.A. (Spanish Ministry of Science and Innovation). R.S.M. received funding for his doctoral thesis from the Spanish Ministry of Science and Innovation PRE2019-087998. R.S.M., E.R.P., F.S. and J.A. are members of the National Excellence Network FAGOMA (RED2022-134837-T). AUTHOR CONTRIBUTIONS F.S. and J.A. conceived the study. R.S.M., F.S., and J.A. designed the experimental approach. R.S.M. performed the experiments and analyzed the sequences under the inputs and supervision of F.S. and J.A. E.R.P. designed the primers and probes for each strain and virus. L.M.R., J.S. and M.E. provided advice and carried out the microfluidics-based qPCR. R.S.M. drafted the original manuscript. All authors contributed to manuscript final writing and approved the final paper. 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08:22:35","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33219,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/b176e209e22a2e32f3a757b2.png"},{"id":95275453,"identity":"ccef36d0-73f4-44e9-8b25-71130a8ff3bb","added_by":"auto","created_at":"2025-11-06 08:22:35","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28348,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/23741fe310e8cedd99434b02.png"},{"id":95275451,"identity":"8131593a-3b9b-4adc-ac2a-61600ee1dae7","added_by":"auto","created_at":"2025-11-06 08:22:35","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32107,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/412c7748fe80504f106c28d2.png"},{"id":95275456,"identity":"0bc756b5-3a3b-4238-9136-087b611a3d81","added_by":"auto","created_at":"2025-11-06 08:22:35","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":192467,"visible":true,"origin":"","legend":"","description":"","filename":"NCOMMS2583916T0structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/238d37e086808c23cf7901ee.xml"},{"id":95275459,"identity":"e3e06825-2840-4117-b342-1123ee32ed33","added_by":"auto","created_at":"2025-11-06 08:22:35","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":203307,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/9e48cbef15e3abd3e09731dd.html"},{"id":95275421,"identity":"34058f9f-0df7-420a-b56a-1d7fdd51cf08","added_by":"auto","created_at":"2025-11-06 08:22:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":189741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design.\u003c/strong\u003e \u003cstrong\u003ea) \u003c/strong\u003eFive experiments were conducted using \u003cem\u003eSal. ruber\u003c/em\u003e strain M1, with experiments 2, 4, and 5 also inoculated with M8, M31 and P18 strains. Experiments 3, 4, and 5 included the EM1 virus, which specifically infects the M1 strain. In experiment 5, the CC1 and CR41-2 viruses, which infect the M8 and M31 strains, and the CR4 virus, which infects the M8 strain, were also added. The color bars indicate the proportion of each strain (i.e. cells/ml) or virus (VLP/ml) in the respective experiments. All experiments were performed in triplicate. \u003cstrong\u003eb)\u003c/strong\u003eIn the short-term experiment, samples were incubated for 9 days with regular monitoring. In the long-term experiment, samples were transferred to fresh media every 9-13 days over a 150-day period (black and red dots), with additional parameters measured only at specific points (red dots). Extracellular viromes from experiments 3, 4, and 5 were sequenced at 9, 84, and 150 days, as well as the original viruses (asterisks denote the points chosen for sequencing). Figure created with Biorender.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/5fb06d0366727a5fece6c0a6.png"},{"id":95275423,"identity":"8be12259-f90b-45ae-8151-646ba351346a","added_by":"auto","created_at":"2025-11-06 08:22:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfluence of the biological context on the virus-host dynamics in the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSal. ruber\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e M1-EM1 pair. a) \u003c/strong\u003eOptical density (OD 600 nm) of all five short-term experiments. \u003cstrong\u003eb)\u003c/strong\u003e PFU/ml of experiments 3, 4 and 5, measured by plaque assay against the original host \u003cem\u003eSal. ruber\u003c/em\u003eM1. \u003cstrong\u003ec)\u003c/strong\u003eExtracellular VLP/ml of experiments 3 and 4 measured by flow cytometry. All the experiments were performed in triplicate. Error bars represent the standard error of the biological replicates. Asterisks indicate significant differences based on one-way ANOVA comparing experiment 3 with experiments 4 and 5. `Other strains´ refers to M8, M31 and P18 strains and `other viruses´ refers to CC1, CR4, and CR41-2 viruses.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/426e230ff7e79ae51bbc0b68.png"},{"id":95313664,"identity":"55a9d5e5-13a2-41f7-b9d2-44bbded75238","added_by":"auto","created_at":"2025-11-06 15:51:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConcentration of the different \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSal. ruber\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e strains and viruses in experiments 3, 4, and 5.\u003c/strong\u003e \u003cstrong\u003ea) \u003c/strong\u003eGenomes/ml of each strain in experiments 3, 4 and 5 (top to bottom) measured by microfluidic qPCR. \u003cstrong\u003eb\u003c/strong\u003e) Free genomes/ml in the supernatant of experiments 3, 4, and 5 (top to bottom) measured by microfluidics-based qPCR. All experiments were performed in triplicate. Error bars represent the standard error of the biological replicates. Asterisks indicate significant differences based on one-way ANOVA comparing experiment 3 with experiments 4 and 5.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/12ddc5c3964a979f199a6d75.png"},{"id":95275425,"identity":"c9cc5bf4-9918-40a0-a491-59897d54f216","added_by":"auto","created_at":"2025-11-06 08:22:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe biological context affects the evolution of the infectivity and host range of the EM1 virus. a) \u003c/strong\u003eInfectivity of EM1 virus in experiments 3, 4 and 5 in the long-term experiment, defined as the ratio between PFU/ml and extracellular genomes/ml. The y-axis represents the percentage of infectious viruses. Error bars represent the standard error of the biological replicates. Asterisks indicate significant differences based on one-way ANOVA comparing experiment 3 with experiments 4 and 5. \u003cstrong\u003eb)\u003c/strong\u003e Host range of the four viruses across the three biological replicates in experiment 5. The gray area indicates viral extinction. The time points marked in black (upper part) denote those selected for genomic analysis.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/138507cf8b34178878c1c4a8.png"},{"id":95313834,"identity":"0d6f2648-27f4-44f6-8709-8988d8063fe3","added_by":"auto","created_at":"2025-11-06 15:52:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":262471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutations in the EM1 virus genome in the three experiments at 150 days. \u003c/strong\u003eThe three panels outlined in black represent experiments 3, 4, and 5, from top to bottom. In each panel's upper section, mutations are marked as points across the EM1 genome, with the y-axis indicating mutations frequencies. Below each mutation plot is a representation of the genome of EM1, where arrows represent the open reading frames (ORFs). The middle plot depicts the ANIr along the viral genome, with the y-axis showing the ANIr. In the lower part of each panel, a fragment recruitment plot is shown, where the EM1 genome is positioned at the top as the reference, and gray points represent the reads recruited, with the y-axis showing the percentage of identity for each mapped read. A histogram on the right side of this section indicates the distribution of reads by identity. At the right of the figure, pie charts illustrate the protein-coding genes mutated in each experiment and the number of mutations in each ORF. Proteins selected for metabarcoding are marked with asterisks at the top of the first panel.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/83360fdfa16c80fb95f43dda.png"},{"id":108804230,"identity":"35e61720-fd7f-4b55-a9e7-e2f09e9ddfdd","added_by":"auto","created_at":"2026-05-08 15:18:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170141,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/f1c56497-1079-486b-aa1a-e6a54f65b3b7.pdf"},{"id":95313781,"identity":"77eb3e8e-670b-4fb4-9ca8-70185854d531","added_by":"auto","created_at":"2025-11-06 15:52:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47089,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/22b95626fefbb733f4a70ac6.docx"},{"id":95275434,"identity":"57fdd87f-187c-4fde-8650-bdbe8db9a565","added_by":"auto","created_at":"2025-11-06 08:22:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1280130,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/94d1ae8b2610a40337f6c74c.docx"},{"id":95313714,"identity":"5c05c27d-10aa-4963-9ab0-2e474a0c4c7c","added_by":"auto","created_at":"2025-11-06 15:51:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2822189,"visible":true,"origin":"","legend":"","description":"","filename":"EXTENDEDDATA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7825215/v1/7a59a31a29882a426e18283e.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Biological context modulates virus-host dynamics and diversification","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNatural ecosystems are highly complex networks where multiple biotic and abiotic factors interact dynamically (Fuhrman et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Levin, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This complexity makes it challenging to predict ecological and evolutionary phenomena since experimental studies are generally conducted under simplified laboratory conditions which do not reflect natural dynamics (Calisi \u0026amp; Bentley, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Roberts et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zimmer \u0026amp; Dorea, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To advance our understanding of microbial processes and predict how microbial communities respond to environmental changes or selective pressures, it is crucial to design experiments that more faithfully simulate natural conditions, incorporating the diversity of species and interactions that characterize real ecosystems (Benton et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Blazanin \u0026amp; Turner, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Castledine \u0026amp; Buckling, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jessup et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eViruses act as major eco-evolutionary forces in microbial ecosystems. As (likely) the most abundant biological entities on Earth, they play a central role in regulating microbial communities through diverse infection cycles that impact nutrient cycling, genetic diversity, and ecosystem structure (Fierer, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Middelboe \u0026amp; Brussaard, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rohwer \u0026amp; Thurber, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Virus-host interactions also drive microbial evolution, triggering adaptations that influence community composition and function. Despite their importance, many aspects of these interactions remain poorly understood, particularly under complex, natural conditions. Although numerous studies have explored virus-host interactions, most have relied on simplified experimental systems involving a single virus-host pair under controlled conditions. As a result, our understanding of how these interactions operate in more realistic settings remains limited. Investigating virus-host dynamics in environments that better mimic natural complexity is essential to fully grasp their ecological and evolutionary roles (Blazanin \u0026amp; Turner, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Castledine \u0026amp; Buckling, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chevallereau et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Koskella et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While some recent efforts have incorporated greater biological complexity (Alseth et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; De Sordi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; G\u0026oacute;mez \u0026amp; Buckling, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Middelboe et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Mumford \u0026amp; Friman, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), such studies remain scarce. This persistent gap limits our ability to understand and predict how virus-host interactions shape microbial communities in natural ecosystems.\u003c/p\u003e\u003cp\u003eHypersaline aquatic environments, which constitute approximately half of continental waters, harbor microbial communities that, at extreme salinities, are dominated by archaea (Burns et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Shiklomanov, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ventosa, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). At these salinities, \u003cem\u003eSalinibacter ruber\u003c/em\u003e emerges as the main bacterial species (Ant\u0026oacute;n et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gomariz et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Oren, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Although \u003cem\u003eSal. ruber\u003c/em\u003e is as halophilic as the dominant archaea at close-to-saturation salinities, it is consistently less abundant, representing between 1% and 10% of total cells, indicating that it could be subjected to intense selective pressure (Mora-Ruiz et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ventosa et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Cultivation and metagenomic studies reveal a high degree of intraspecific genomic diversity in \u003cem\u003eSal. ruber\u003c/em\u003e, including the coexistence of distinct phylogroups and adaptation to local microenvironments (Viver et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These hypersaline habitats also harbor some of the highest recorded viral concentrations, with virus-like particles (VLP) reaching up to 10\u003csup\u003e10\u003c/sup\u003e VLP/ml and virus-to-cell ratios ranging from 10 to 100 (Di Meglio et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Given its environmental ubiquity, cultivability, and intraspecific variation, likely shaped in part by viral predation, \u003cem\u003eSal. ruber\u003c/em\u003e constitutes a valuable model for studying microbial ecology and virus-host interactions.\u003c/p\u003e\u003cp\u003eIn this work, we have addressed how the ecology and evolution of a virus-host pair (i.e., \u003cem\u003eSal. ruber\u003c/em\u003e strain M1 and the EM1 virus) are affected by the presence of additional \u003cem\u003eSal. ruber\u003c/em\u003e strains and their viruses. Through experiments in which biological complexity is increased, we have demonstrated that: (i) the presence of other strains and viruses delays lysis and reduces EM1 virion production; and that (ii) the biological context influences the long-term evolution of the virus-host pair and promotes changes in viral infectivity and host range, driven by a high mutation rate under more complex contexts. These findings highlight the importance of considering biological complexity in studies of virus-host interactions. Our work not only contributes to a better understanding of these dynamics in natural systems but also suggests that this complexity should be considered in practical applications such as phage therapy and microbial community engineering.\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003eFive parallel experiments evaluated the effects of the presence of \u003cem\u003eSal. ruber\u003c/em\u003e strains and viruses on the interactions and evolution of \u003cem\u003eSal. ruber\u003c/em\u003e strain M1 and its M1EM1 virus (referred to as EM1 hereafter) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In experiment 1, \u003cem\u003eSal. ruber\u003c/em\u003e strain M1 was cultured alone. In experiment 2, \u003cem\u003eSal. ruber\u003c/em\u003e strains M8, M31, and P18 were added to the M1 culture. These two experiments served as virus-free controls. In experiment 3, M1 alone was infected with its EM1 virus. Experiment 4 included M1 infected with EM1, along with the presence of strains M8, M31, and P18. Finally, experiment 5 mirrored experiment 4, with the addition of M31CC1 and M31CR41-2 viruses (referred to as CC1 and CR41-2, respectively), which infect both \u003cem\u003eSal. ruber\u003c/em\u003e strains M31 and M8, and M8CR-4 virus (i.e. CR4), which infects \u003cem\u003eSal. ruber\u003c/em\u003e strain M8 (Villamor et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe first 9 days of incubation (\u0026ldquo;short-term experiment\u0026rdquo;), corresponding to ~\u0026thinsp;14 generations of \u003cem\u003eSal. ruber\u003c/em\u003e, evaluated infection traits such as lysis timing, virion production, infectivity, and community dynamics. Thereafter, all cultures were periodically transferred to fresh medium and maintained for 150 days (~\u0026thinsp;240 generations). This constituted the \u0026ldquo;long-term experiment\u0026rdquo;, aimed at studying possible evolutionary changes in the virus-host pair, while continuing to monitor key parameters of the infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncreased biological complexity leads to a delay in lysis of\u003c/b\u003e \u003cb\u003eSal. ruber\u003c/b\u003e \u003cb\u003eM1 and a reduced production of EM1 virions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the short-term experiment, \u003cem\u003eSal. ruber\u003c/em\u003e exhibited its characteristic growth pattern in experiments 1 and 2, with a slightly higher optical density (OD) in experiment 2, likely as a consequence of additional bacterial strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In infection experiment 3, lysis of \u003cem\u003eSal. ruber\u003c/em\u003e M1 was observed, followed by a recovery, a pattern that mimics previous findings from our laboratory, in which that growth was mainly attributed to the emergence of pseudolysogens (Sanchez-Martinez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In experiments 4 and 5, this OD recovery was observed earlier, which could be attributed to the growth of uninfected strains.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe production of infectious EM1 viral particles, measured as plaque-forming-units (PFU) on \u003cem\u003eSal. ruber\u003c/em\u003e M1, was significantly delayed in experiments 4 and 5 compared to experiment 3 (\u003cem\u003et\u003c/em\u003e-test p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), indicating that the presence of additional \u003cem\u003eSal. ruber\u003c/em\u003e strains delayed the lysis of M1. After 96 hours, a drop in the number of infectious particles was observed, more pronounced in experiments 4 and 5 than in experiment 3 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In all the experiments a slight decline in the number of PFUs was observed over time, with no significant differences between 6 and 9 days. Total extracellular virion concentration, measured by flow cytometry, reached 9.7 x 10\u003csup\u003e10\u003c/sup\u003e VLP/ml within 48 hours in experiment 3, whereas in experiment 4 this concentration was 4.2 x 10\u003csup\u003e8\u003c/sup\u003e VLP/ml, a statistically significant reduction (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), consistent with the observations of PFU counts. Experiment 5 was excluded from this figure, as the presence of multiple coexisting viruses prevents attributing VLP counts specifically to EM1 (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eHigh throughput microfluidics-based qPCR revealed that the concentration of the M1 strain (measured as genomes/ml) varied across experiments and time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). At 48 hours, M1 abundance dropped significantly in experiment 3 (3.9 x 10\u003csup\u003e6\u003c/sup\u003e genomes/ml), indicating active lysis of the population, while it remained at higher numbers in experiments 4 (1.2 x 10\u003csup\u003e9\u003c/sup\u003e genomes/ml) and 5 (7.6 x 10\u003csup\u003e8\u003c/sup\u003e genomes/ml) (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). By 72 hours, M1 levels decreased in all experiments, maintaining low values in experiment 3 (3.8 x 10\u003csup\u003e6\u003c/sup\u003e genomes/ml), and showing delayed but evident reductions in experiments 4 and 5 (2.2 x 10\u003csup\u003e6\u003c/sup\u003e and 5.2 x 10\u003csup\u003e6\u003c/sup\u003e genomes/ml, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegarding the bacterial community, in experiment 4 a decrease in the concentration of M8, M31, and P18 strains was observed at 50 hours, despite the absence of their viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This pattern suggests that the growth of these strains was transiently inhibited after \u003cem\u003eSal. ruber\u003c/em\u003e M1 lysis. To test this, a virus-free lysate from an M1-infected culture was added to pure cultures of M8, M31, and P18. A clear, transient growth inhibition was observed, confirming that the M1 lysate affected the growth of the other strains (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar results have been reported in other studies, where intracellular bacterial contents released upon lysis can inhibit the growth of other bacteria (Penchuk et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Smakman \u0026amp; Hall, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In experiment 5, M8 showed minimal growth. As the only strain infected by three viruses (Supplementary Table\u0026nbsp;1), M8 likely could not withstand the high viral pressure, explaining its poor performance under these conditions.\u003c/p\u003e\u003cp\u003eWhen the extracellular viruses were measured by microfluidics-based qPCR, a lower concentration of EM1 was observed at 48 hours in both experiments 4 and 5 (2.2 x 10\u003csup\u003e8\u003c/sup\u003e and 3.3 x 10\u003csup\u003e8\u003c/sup\u003e genomes/ml, respectively) compared with experiment 3 (4.2 x 10\u003csup\u003e9\u003c/sup\u003e genomes/ml) (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb; Supplementary Fig.\u0026nbsp;2), confirming that increased community complexity delayed the infection and lysis of \u003cem\u003eSal. ruber\u003c/em\u003e M1 by the EM1 virus. By 216 hours (day 9), experiment 4 yielded a significantly lower VLP concentration than experiment 3 (2.4 x 10\u003csup\u003e10\u003c/sup\u003e and 2.8 x 10\u003csup\u003e10\u003c/sup\u003e VLP/ml, respectively, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Microfluidics-based qPCR measurements also revealed that the concentration of extracellular EM1 viruses in experiments 4 and 5 (7.6 x 10\u003csup\u003e9\u003c/sup\u003e and 3.4 x 10\u003csup\u003e9\u003c/sup\u003e genomes/ml, respectively) was significantly lower than in experiment 3 (2.7 x 10\u003csup\u003e10\u003c/sup\u003e genomes/ml) at the same time point (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting the flow cytometry results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). These findings indicate that the presence of additional \u003cem\u003eSal. ruber\u003c/em\u003e strains reduced the overall production of EM1, in good agreement with some previous studies showing that the presence of other bacterial species can lead to lower viral densities (Alseth et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mumford \u0026amp; Friman, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, a recent work found no differences in lysis timing or viral titers produced by \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e PA14 when exposed to competing bacterial strains (Alseth et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExperiment 5 also revealed distinct patterns in the production of other viruses. CC1 virus, which infects both \u003cem\u003eSal. ruber\u003c/em\u003e M8 and M31, showed sustained high titers from 50 hours onward, consistent with ongoing replication in its hosts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). CR41-2 virus, which infects the same strains as CC1 (M8 and M31), was only detected on days 1 and 4. Adsorption assays revealed that CR41-2 adsorbs significantly less efficiently than CC1 to M31 (Supplementary Fig.\u0026nbsp;3). Thus, CC1 may have outcompeted CR41-2 for access to M31, thereby limiting CR41-2 infection. This observation aligns with a well-established principle of phage biology, that considers adsorption efficiency as a critical determinant of successful phage infection and subsequent replication (Abedon, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such virus-virus competition for shared hosts can strongly shape infection dynamics, allowing more efficient viruses to suppress the adsorption of less competitive ones (B\u0026uuml;rkle et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Turner, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Finally, CR4 virus, which exclusively infects M8, exhibited an early increase followed by a sharp decline (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This decline likely reflects the lack of M8 cell growth, which limited the availability of susceptible hosts and consequently reduced further CR4 replication.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSal. ruber\u003c/b\u003e \u003cb\u003eM1 and its EM1 virus establish a stable long-term relationship, regardless of the biological context\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter the initial 9-day period, all cultures were transferred to fresh medium to monitor their long-term evolution with transfers every 9\u0026ndash;13 days over a total of 150 days. At selected time points, specific parameters were assessed, including changes in host range and infectivity toward the native host (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003cem\u003eSal. ruber\u003c/em\u003e M1 was maintained at high density (~\u0026thinsp;10⁹ genomes/ml) across all time points, regardless of the presence of EM1 or other strains and viruses (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The EM1 virus maintained relatively stable concentrations extracellularly across all three experiments (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplementary Fig.\u0026nbsp;4). Although the EM1 titer declined by about one order of magnitude between days 52 and 84, its level remained stable until the end of the study. These results indicate that \u003cem\u003eSal. ruber\u003c/em\u003e M1 and its EM1 virus established a stable coexistence that persisted throughout the study, regardless of the surrounding biological context. Long-term virus-host coexistence has been widely reported in other systems driven by mechanisms such as arms-race dynamics, spatial heterogeneity, or phase variation in gene expression (Cort\u0026eacute;s-Mart\u0026iacute;n et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kortright et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Louren\u0026ccedil;o et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, a previous work from our group demonstrated that EM1 can be maintained in \u003cem\u003eSal. ruber\u003c/em\u003e M1 in a pseudolysogenic state (Sanchez-Martinez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which appears to underlie the stable coexistence observed here. Its establishment and maintenance do not seem to be affected by the presence of additional strains and viruses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditional insights emerged from the dynamics of the other community members. In experiment 5, CC1 remained at high concentrations, as well as one of their hosts, M31, which was also detected until the end of the experiment (Extended Data Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the case of the CR4 virus, although it was not detected extracellularly after 9 days, it persisted in the total culture until 84 days, coinciding with the extinction of its host M8 (Supplementary Fig.\u0026nbsp;4). The P18 strain, the only one not infected by any virus, disappeared in experiment 5 from all 3 replicates and, although still detectable, was outcompeted by other strains in experiments 2 and 4. This indicates that viruses were not the only force structuring the community, and that competitive interactions among bacterial strains also played a crucial role.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncreasing the complexity of the biological context reduces the infectivity of the EM1 virus and alters its host range\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOn day 9, the infectivity of EM1, calculated as the ratio between PFU/ml (tested with the native M1 strain as the host) and extracellular EM1 genomes/ml, was maintained above 0.1% in the three experiments and remained stable at these levels for 52 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). However, by day 84, significant differences (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) emerged between experiment 3 and experiments 4 and 5. In experiment 3 infectivity remained stable but, in experiment 4, where the \u003cem\u003eSal. ruber\u003c/em\u003e M1-EM1 pair was accompanied by other strains, infectivity dropped by almost one order of magnitude. In experiment 5, the most biologically complex, this reduction in infectivity was more drastic, with a drop of nearly 4 orders of magnitude compared to experiment 3. By day 130, infectivity had decreased further in all experiments, down to 5 orders of magnitude below the initial level. These findings suggested that the EM1 virus underwent genomic changes, as further explored below, which reduced its ability to infect the native strain in all the experiments and highlighted that the biological context significantly affected the interactions between EM1 and its host.\u003c/p\u003e\u003cp\u003eHost range changes were also detected in the EM1 virus in experiment 5, whereas no such changes were observed in experiments 3 and 4. The evolved EM1 in experiment 5, which initially only infected the M1 strain, expanded its host range and infected the native M8 strain consistently across all three biological replicates at 84 hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Several studies have reported host range changes in simplified experimental evolution contexts (Bono et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Holtzman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sant et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Subramanian et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but only a few have explored this phenomenon under more complex conditions. In multispecies settings, viruses have been shown to broaden their host range by transiently using alternative hosts as intermediates to access new hosts (De Sordi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which may partly explain why this host range shift occurred in experiment 5. Moreover, it has been observed that the presence of multiple viruses affect the host range of a given virus by driving evolutionary changes of the host that affect its susceptibility (Avrani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chevallereau et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings suggest that the observed host range expansion in EM1 was a result of selective pressures arising from increased ecological complexity. Coexistence with other strains and viruses may have favored viral variants capable of infecting alternative hosts (i.e., either by exploiting new receptor variants or by overcoming host-specific resistance mechanisms (see below)). In the case of the other viruses, none of them showed any host range changes.\u003c/p\u003e\u003cp\u003eThe unequal decline in the EM1 virus infectivity across experiments, together with the shift in host range observed in experiment 5, suggested that EM1 underwent different genomic changes under the different conditions, influencing the virus-host evolutionary trajectory according to the surrounding biotic environment. To analyze whether genomic changes could be responsible for these differences, extracellular viromes and cellular pellets from experiments 3, 4 and 5 were sequenced on days 9, 84 and 150. The original viral genomes were also sequenced as references.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eViral genomic diversification is enhanced by the presence of other viruses\u003c/h2\u003e\u003cp\u003eEM1 genome remained largely stable during the early stages of the experiment, with only a single mutation in a gene coding for a hypothetical protein detected across all treatments by day 9 (Supplementary Table\u0026nbsp;2). By day 84, mutation accumulation remained low in experiments 3 and 4 (15 and 14 mutations, respectively), mostly in non-coding regions (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Table\u0026nbsp;2). Experiment 5 showed a markedly different pattern, with 772 mutations distributed across the genome. A large proportion of these occurred in structural genes, particularly those related to tail components. Two minor tail protein genes accumulated over 190 mutations, and a long-tail fiber protein gene showed 69 changes, suggesting strong selective pressure on host recognition machinery. This genomic diversification coincided with a sharp drop in EM1 infectivity against the native M1 host, pointing to a possible link between mutation accumulation in tail-related regions and host range evolution.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs the experiment progressed to day 150, the accumulation of mutations continued to diverge between treatments. EM1 genome in the sole presence of its host M1 (experiment 3) reached a total of 34 mutations, including 17 in a gene coding for a DNA methyltransferase and several nonsynonymous changes in genes for a DNA-binding protein, a minor tail protein, and a long-tail fiber protein, some of which may be linked to the observed decline in infectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, EM1 genome in experiment 4, where all the \u003cem\u003eSal. ruber\u003c/em\u003e strains were present, accumulated 267 mutations, with a high concentration in genes related to DNA metabolism, such as a DNA primase and a single-strand DNA-binding protein. This accumulation of mutations in DNA metabolism-related genes could suggest a potential disruption in viral replication, contributing to reduced infectivity (Boddin et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In experiment 5 (containing all the bacterial strains and their viruses), EM1 genome mutation accumulation intensified, reaching 1,331 mutations distributed across the genome. Structural and replication-related genes showed particularly high mutational loads, including 198 mutations in minor tail protein genes and over 190 combined in genes for a DNA-binding protein and a DNA primase. This translated into a mutation rate of 2.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mutations per site per day in experiment 5, markedly higher than those estimated for experiments 3 and 4 (6.4 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e and 5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, respectively) (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eThese differences were reflected in the average nucleotide identity of sequence alignment reads (ANIr) along the EM1 genome (the mean nucleotide-level similarity between mapped reads and the reference genome). EM1 genome in experiments 3 and 4 showed higher ANIr values (99.70% and 99.75%, respectively) indicating a more homogeneous population, compared to experiment 5 (98.13%) on day 150 (Supplementary Table\u0026nbsp;2). The lower ANIr values observed in the presence of other viruses indicated an accelerated diversification of the viral population, with the emergence of multiple distinct genotypes or \u0026ldquo;genomovars\u0026rdquo; (defined as variants with ANI\u0026thinsp;\u0026lt;\u0026thinsp;99.5%) (Aldeguer-Riquelme et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe increase in genetic variability observed under complex community conditions reveals a previously overlooked role of viral coexistence in promoting diversification. Our results demonstrate that the mere presence of other viruses can substantially reshape the genetic structure of a viral population, both in terms of mutation rate and the distribution of these mutations across the genome (Supplementary Table\u0026nbsp;2; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This diversification was accompanied by a drastic loss of infectivity and a shift in host range, likely driven by mutations in tail protein genes. While previous studies have shown that viral mutation rates can vary depending on the host genotype (Duffy, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Longdon et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sant et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and that exposure to multiple host genotypes can promote tail gene diversification (Hernandez et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), our findings suggest a previously unreported mechanism. Here, it is the presence of other viruses, more than the mere presence of other hosts, that significantly increased the viral mutation rate. This uncovers a novel form of virus-virus interaction, where coexisting viruses indirectly drive each other\u0026rsquo;s evolutionary trajectories. Such interactions, largely overlooked to date, could play a central role in shaping viral evolution in natural communities characterized by high viral diversity.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIntracellular viral diversity exceeds the subset released extracellularly\u003c/h3\u003e\n\u003cp\u003eAnalysis of intracellular viral populations revealed that only part of the viral diversity generated inside host cells was released extracellularly, indicating that the extracellular fraction corresponds to a selected subset of intracellular genotypes. Three genes (marked with an asterisk in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) were characterized by amplicon sequencing: one encoding a hypothetical protein with minimal mutations across all experiments (used as a control), and two genes highly mutated in experiment 5, encoding a minor tail protein and a DNA-binding protein, respectively. The mutation patterns in these genes were generally similar to those observed in the extracellular viral population, especially in experiments 3 and 4, with few differences (Extended Data Fig.\u0026nbsp;6). EM1 genome in experiment 5 displayed a markedly higher number of mutations in the intracellular fraction in both the minor tail protein and DNA-binding protein genes (63 and 30 mutations on average across replicates) compared to experiment 3 (0 and 2 mutations) and experiment 4 (no mutations).\u003c/p\u003e\u003cp\u003eWhen comparing intracellular and extracellular fractions some differences emerged, particularly in experiment 5. Both the minor tail protein and DNA-binding protein regions showed a high number of mutations at nearly 100% frequency in the extracellular fraction, while the intracellular fraction exhibited greater heterogeneity in mutation frequencies. These results support our earlier findings that the presence of other viruses acts as a selective force, increasing the mutation rate of EM1 and generating a larger diversity of viral genotypes. However, based on the intracellular data, only a subset of these genotypes appears to be selected and successfully replicated for virion production.\u003c/p\u003e\n\u003ch3\u003eIntraspecific interactions, rather than viral pressure, drive host genome evolution\u003c/h3\u003e\n\u003cp\u003eWe also analyzed genetic mutations in the host \u003cem\u003eSal. ruber\u003c/em\u003e M1. As experiments 2, 4, and 5 included additional \u003cem\u003eSal. ruber\u003c/em\u003e strains sharing large genome regions, we focused on nine genomic regions, ranging from 1.8 to 23.5 kb, specific to \u003cem\u003eSal. ruber\u003c/em\u003e M1. This strategy allowed us to unambiguously track mutations in M1 despite the presence of other closely related strains in the mixed cultures. In total, 70.1 kb were analyzed, (Supplementary Fig.\u0026nbsp;5). Mutation counts at 150 hours revealed striking differences: 9 mutations in experiment 1, 224 in experiment 2, 8 in experiment 3, 266 in experiment 4, and 14 in experiment 5, with most occurring in non-coding regions (Extended Data Fig.\u0026nbsp;7; Supplementary Table\u0026nbsp;3). These findings corresponded to mutation rates of 7,6 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e, 2,1 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, 6,7 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e, 5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, 5,6 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mutations per site per day, respectively (Supplementary Table\u0026nbsp;3). Our results demonstrate that \u003cem\u003eSal. ruber\u003c/em\u003e M1 experienced significantly elevated mutation rates in specific regions in those experiments where it evolved alongside multiple strains\u0026mdash;experiments 2 and 4 (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, experiment 5, which also included other viruses, resulted in markedly lower host mutation rates. This suggests that the presence of viruses in a complex community context may constrain bacterial evolution, rather than accelerate it. This finding is striking and counterintuitive, as viruses are typically considered potent drivers of evolutionary change, and other studies showed that bacteria coevolving with multiple viruses exhibited higher mutation rates (Betts et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wielgoss et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In our system, however, viral pressure may have limited evolutionary change in the host, possibly by imposing bottlenecks through lysis, constraining population dynamics, or reducing the strength of competition among bacterial strains.\u003c/p\u003e\u003cp\u003eThe observed increase in mutation rates under competitive conditions suggests that bacterial intraspecific competition is a strong driver of genomic evolution in the host. While EM1 viral mutations primarily emerged within infected populations, \u003cem\u003eSal. ruber\u003c/em\u003e M1 genetic variability was predominantly shaped by interactions with coexisting bacterial strains. This is consistent with previous work from our laboratory demonstrating weak but significant competitive interactions between \u003cem\u003eSal. ruber\u003c/em\u003e strains in co-culture (Pe\u0026ntilde;a et al., 2010). Furthermore, in a recent mesocosm experiment, we showed that strain M8 was displaced by other genotypes after being introduced at high abundance into a pond, suggesting competitive exclusion through intraspecific competition (Ramos-Barbero et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral mechanisms could account for the elevated mutation rates observed in experiments 2 and 4. These include stress-induced mutagenesis, where competition triggers cellular stress responses that increase mutation frequency (Svet et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Trejo-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and the selection of hypermutator phenotypes (bacterial variants with defective DNA repair systems) previously documented in \u003cem\u003eEscherichia coli\u003c/em\u003e during gut colonization (Giraud et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and \u003cem\u003eVibrio cholerae\u003c/em\u003e under antibiotic pressure (Baharoglu \u0026amp; Mazel, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In our system, the sharp increase in mutations specifically in the absence of viral pressure suggests that competition with other bacterial strains may trigger stress responses or transient mutagenic states, rather than the stable fixation of hypermutator lineages. This is further supported by the fact that mutation rates remained low in experiment 5, despite the presence of both other strains and viruses, indicating that viral pressure may counteract or suppress the effects of competition-induced mutagenesis.\u003c/p\u003e\u003cp\u003eThese findings support the view that microbial competition plays a central role in shaping evolutionary trajectories. Together, they underscore a critical consideration for evolutionary studies: microbial mutation rates and adaptation cannot be fully understood without accounting for the complex network of interspecies interactions that govern microbial life in natural environments.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe usefulness of studying virus-host interactions within microcosms that simulate natural conditions in a stepwise manner has become increasingly evident in recent years (Blazanin \u0026amp; Turner, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Castledine \u0026amp; Buckling, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chevallereau et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Koskella et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Understanding the ecological and evolutionary dynamics of viruses and prokaryotes in their natural environments not only enriches our knowledge of the natural world but also enables us to manipulate these communities more predictably. In this study, we demonstrate that a semi-complex biological context (encompassing \u003cem\u003eSal. ruber\u003c/em\u003e strains and their viruses) substantially influence the ecology and evolution of the \u003cem\u003eSal. ruber\u003c/em\u003e M1-EM1 pair in controlled microcosms.\u003c/p\u003e\u003cp\u003eThis work shows how in the short-term, the presence of additional \u003cem\u003eSal. ruber\u003c/em\u003e strains delayed the lysis of \u003cem\u003eSal. ruber\u003c/em\u003e M1 and reduced EM1 virion production. These effects were consistent across different quantification methods, indicating that community composition has a measurable impact on virus replication dynamics. In the long-term, \u003cem\u003eSal. ruber\u003c/em\u003e M1 and the EM1 virus established a stable coexistence throughout the entire experiment regardless of community complexity. The infectivity of EM1 against its original host declined faster in the presence of other viruses, revealing that viral genomic diversification is enhanced by virus-virus interactions. This reduction in infectivity was linked to genomic changes in EM1, leading to an expanded host range and a sharply increased mutation rate in the presence of other viruses. Meanwhile, the bacterial host, \u003cem\u003eSal. ruber\u003c/em\u003e M1, showed elevated mutation rates evolving alongside other strains, indicating that intraspecific bacterial interactions\u0026mdash;not viral pressure\u0026mdash;drive host genome evolution.\u003c/p\u003e\u003cp\u003eOverall, our results highlight the critical importance of incorporating biological complexity in studies of virus-host interactions. These findings emphasize the need to consider the multifaceted nature of microbial communities to more accurately predict virus-host dynamics and their ecological and evolutionary consequences in natural environments.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBacterial strains, viruses and experimental design\u003c/h2\u003e\u003cp\u003e\u003cem\u003eSal. ruber\u003c/em\u003e strains M1, M8, M31 and P18 were aerobically grown with gentle shaking (60 rpm) at 37\u0026ordm;C in 25% SW (sea water, a salt solution containing the salts present in seawater at a total concentration of 25% weight/volume) with 0.2% yeast extract (Rodriguez-Valera et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). \u003cem\u003eSal ruber\u003c/em\u003e viruses used in this study, named M1EM-1, M31CC-1, M8CR-4 and M31CR41-2 (referred to here as EM1, CC1, CR4 and CR41-2, respectively, for convenience), were isolated during a previous study in our laboratory (Villamor et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFive experiments were conducted in parallel (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In all the experiments, 25 ml of culture media were inoculated with 7.5x10\u003csup\u003e8\u003c/sup\u003e cells of \u003cem\u003eSal. ruber\u003c/em\u003e M1. In experiments 2, 4, and 5, the cultures also included 2.5 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e cells of each of the M8, M31, and P18 strains. In experiments 3, 4 and 5, 7.5x10\u003csup\u003e7\u003c/sup\u003e virus-like particles (VLP) of the strictly virulent EM1 virus (multiplicity of infection\u0026thinsp;=\u0026thinsp;0.01), which only infects the M1 strain, were added. Experiment 5 also contained 2.5x10\u003csup\u003e6\u003c/sup\u003e VLP of each one of the CC1, CR4 and CR41-2 viruses that infect M8 and M31 strains (Supplementary Table\u0026nbsp;1). After inoculation, experiments were incubated for 9 days as described above, taking aliquots once a day for culture monitoring by optical density (OD) at 600 nm and additional approaches (see below). This was defined as the short-term experiment.\u003c/p\u003e\u003cp\u003eOn day 9 (213 hours after inoculation), 250 \u0026micro;l of each experiment were transferred to 25 ml of fresh medium and incubated as described above. After 13 days, new aliquots were collected to quantify the bacterial and viral diversity of the cultures by microfluidics-based qPCR (see below) and transferred and incubated in the same way. This was repeated every approximately 9\u0026ndash;13 days for 150 days. Days 9 to 150 were considered as the long-term experiment.\u003c/p\u003e\u003cp\u003eIn addition, on days 9, 52, 84, 130 and 150, aliquots were taken for plaque forming units (PFU) quantification, host range analyses of the viruses and DNA extraction and sequencing. All experiments were conducted in triplicate.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFlow cytometry\u003c/h3\u003e\n\u003cp\u003eFor each day of the short-term experiment, cells/ml and VLP/ml were quantified by flow cytometry as described in Brussaard, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e. Briefly, 500 \u0026micro;l of each sample were fixed with glutaraldehyde (0.5% final concentration) at 4\u0026ordm;C for 30 min, flash-frozen in liquid nitrogen, and stored at -80\u0026deg;C until use. Upon thawing, samples were diluted in Tris-EDTA buffer (pH 8), stained with SYBR\u003csup\u003eTM\u003c/sup\u003eGold (0.5X final concentration), incubated for 10 min in the dark at 80\u0026deg;C, and cooled for 5 min at room temperature prior to analysis.\u003c/p\u003e\u003cp\u003eThe cytometer settings were as follows: the threshold was set in blue fluorescence (300 units), FITC voltage\u0026thinsp;=\u0026thinsp;500, SSC voltage\u0026thinsp;=\u0026thinsp;300, forward scatter voltage\u0026thinsp;=\u0026thinsp;500, and the flow rate was established as low (15 \u0026micro;l/min). Background noise was checked on blanks, composed by TE buffer stained with SYBR Gold. Samples were recorded with an event rate of 100\u0026ndash;1000 events per second. Cells and VLP counts were obtained by correcting the background measured in blanks. Cells and VLP abundances were respectively expressed as cells/ml or VLP/ml.\u003c/p\u003e\n\u003ch3\u003ePlaque assay\u003c/h3\u003e\n\u003cp\u003eThe number of free infective EM1 viruses at each sampling point of the short- and long-term experiments was determined by plaque assay in experiments 3, 4 and 5. Aliquots of 150 \u0026micro;l were centrifuged at 17,000 x g for 10 min and supernatants were serially diluted in sterile SW 25%. 100 \u0026micro;l of dilutions 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e and 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e were mixed with 500 \u0026micro;l of \u003cem\u003eSal. ruber\u003c/em\u003e M1 in exponential phase. Four ml of soft agar (25% SW with 0.2% yeast extract and 0.7% agar) were added and this was poured on plates with solid media. After the agar had solidified, the plates were incubated at 37\u0026ordm;C for 10 days until plaques were visible. The number of free infective viruses (PFU/ml) was counted and represented. On days 9, 52, 84, 130 and 150, plaque assays were also carried out to obtain individual plaques of all viruses for experiment 5, using as hosts the native strains M8, M31 and P18.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eHigh throughput microfluidics-based qPCR\u003c/h2\u003e\u003cp\u003eIndividual targets (i.e. the 4 strains and the 4 viruses) were quantified in each point of the short-term experiment, in both the total culture and the supernatant to quantify free viruses, using the Biomark HD high throughput microfluidics-based qPCR system (Standard Biotools, South San Francisco, USA). Primers for each target gene were designed using using Geneious software 6.1.8 (Biomatters, Auckland, New Zealand) (Supplementary Table\u0026nbsp;4). From each of the experiments, 100 \u0026micro;l of the culture were taken for total culture analysis and 120 \u0026micro;l for supernatant quantification of free viruses. In the latter, a centrifugation step consisting of 10 min at 7,197 x g and 100 \u0026micro;l supernatant transfer to a clean eppendorf tube was performed. Both were fixed with 0.5% (final concentration) of formaldehyde for 1 hour at 4\u0026deg;C, diluted 10-fold in phosphate buffered saline (PBS) 1X, and stored at 4\u0026deg;C.\u003c/p\u003e\u003cp\u003eDNA targets were then pre-amplified using the PreAmp Master Mix (Standard Biotools, South San Francisco, USA) following manufacturer\u0026rsquo;s instructions. In brief, a primer pool was prepared by mixing the forward and reverse primers for all targets (Supplementary Table\u0026nbsp;4) and diluting the mixture with DNA suspension buffer (10 mM Tris/HCL and 0.1 mM EDTA pH 8) to a final concentration of 0.5 \u0026micro;M of each primer pair. Each sample was then pre-amplified in a total volume of 5 uL containing 1 \u0026micro;L PreAmp Master Mix (Standard Biotools, South San Francisco, USA), 0.5 \u0026micro;L of primer pool and 3.5 \u0026micro;L of sample. Preamplification reactions were performed on a Simpliamp Thermal Cycler (Applied Biosystems, Waltham, USA) using the following sample conditions: an initial activation cycle at 95\u0026deg;C for 10 min followed by 14 two-step cycles (denaturation at 95\u0026deg;C for 15 seconds and annealing/extension at 60\u0026deg;C for 4 min). The preamplified products were subjected to Exonuclease I cleanup (New England Biolabs, Massachusetts, USA) to remove any unincorporated primers (4 U/\u0026micro;L final concentration at 37\u0026deg;C for 30 min followed by inactivation at 80\u0026ordm;C for 15 min). A 1:5 dilution using DNA suspension buffer was then performed on the Exonuclease I treated samples and stored at -20\u0026deg;C.\u003c/p\u003e\u003cp\u003e Finally, pre-amplified DNA targets were quantified by microfluidics-based qPCR on 192x24 Dynamic Array\u0026trade; IFCs (Fluidigm, South San Francisco, USA) according to manufacturer's instructions. In brief, 10X assay primer mixtures for each DNA target and sample pre-mixes for each sample were prepared in triplicate. For the generation of the 10X assay primer mixtures, the forward and reverse primers for a single DNA target were pooled together at a concentration of 50 uM each. 0.15 uL of this pool were then mixed with 1.35 \u0026micro;l of DNA suspension buffer (Tris/HCL and 0.1 mM EDTA pH 8) and 1.5 \u0026micro;l of 2X Assay Loading Reagent (Standard Biotools, South San Francisco, USA). Sample pre-mixes were made by combining 1.5 \u0026micro;l SsoFast\u0026trade; Evagreen\u0026reg; Supermix 2X (BioRad, California, USA) with 0.15 \u0026micro;l 20X GE Sample Loading Reagent (Standard Biotools, South San Francisco, USA) and 1.35 \u0026micro;l of pre-amplified DNA. The 192x24 Dynamic Array\u0026trade; IFCs were then primed on a Juno controller (Standard Biotools, South San Francisco, USA) and 3 uL of each 10X assay primer mix and sample pre-mix were transferred to the appropriate inlets of the IFC and loaded using again the Juno controller. After loading, the IFC was transferred to a Biomark HD instrument (Standard Biotools, South San Francisco, USA) and qPCR reactions were performed using the following cycling conditions: 95\u0026deg;C for 1 min, followed by 30 two-step cycles (95\u0026deg;C for 15 seconds and 60\u0026deg;C for 20 seconds) and a final melt curve analysis. Data were analyzed with the Real-Time PCR Analysis Software (Standard Biotools, South San Francisco, USA) using manually defined thresholds. All samples were run in triplicate (including the standards and negative controls). All samples, standards, and negative controls were run in triplicate, with negative controls included both in the pre-amplification and in the microfluidics-based qPCR. A total of four 192x24 Dynamic Array\u0026trade; IFCs were used, each with its own standard curve consisting of five concentration points for every target, and each chip included positive controls for the eight targets individually.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eHost range through spot test and qPCR\u003c/h2\u003e\u003cp\u003eThe host range of each virus at time 0 was determined through a spot test. Briefly, 4 ml of molten 0.7% top agar of 25% SW with 0.2% yeast extract were mixed with 500 \u0026micro;l of bacterial cultures at exponential phase and plated on solid medium. Once solidified, 3 \u0026micro;l of each virus, titered at 10\u003csup\u003e10\u003c/sup\u003e PFUs/ml and diluted 10\u003csup\u003e2\u003c/sup\u003e, 10\u003csup\u003e4\u003c/sup\u003e and 10\u003csup\u003e6\u003c/sup\u003e times, were added to the corresponding lawn. The spotted plates were left to dry and incubated at 37\u0026ordm;C for 10 days. The detection of a clearance zone was interpreted as evidence of viral lysis while the areas exhibiting growth were attributed to resistance. This was done by exposing the 4 native strains (\u003cem\u003eSal. ruber\u003c/em\u003e strains M1, M8, M31 and P18) to the 4 viruses (EM1, CC1, CR41-2 and CR4 viruses).\u003c/p\u003e\u003cp\u003eThe viral fractions from experiments 3, 4 and 5, at 9, 52, 84, 130 and 150 days, were obtained by centrifuging each replicate at 7,197 x g for 20 min and filtering the supernatant through a 0.22 \u0026micro;m filter (Millipore, Burlington, USA). For experiments 3 and 4, the host range of the EM1 virus over time was measured through a spot test against the 4 native hosts as described above.\u003c/p\u003e\u003cp\u003eThe determination of the host range of the 4 viruses over time in experiment 5 was performed by qPCR as follows. All the plaques obtained for each native host (see plaque assay above) for each replicate on days 9, 52, 84, 130 and 150, were picked from the soft agar and resuspended in 1 ml of ultrapure water. The presence of each virus in these mixtures of plaques were checked by qPCR with specific primers (Supplementary Table\u0026nbsp;4) using SYBR Green. The experiment was carried out with the standard run in a StepOnePlus PCR System (Life Technologies, Carlsbad, USA) in a 10 \u0026micro;l reaction mixture with Power SYBR Green PCR Master Mix (Applied Biosystems, Waltham, USA). The reaction contained: 5 \u0026micro;l of 2X Master Mix, 0.2 \u0026micro;l of each 10 \u0026micro;M primer, 1 \u0026micro;l of sample and ultrapure water to complete volume. Conditions are detailed in Supplementary Table\u0026nbsp;5. The results were analyzed with the Applied Biosystems StepOne\u0026trade; Instrument program. All samples were run in triplicate (including the standards and negative controls).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAdsorption assay\u003c/h2\u003e\u003cp\u003eAn adsorption assay was performed in parallel with the CC1 and CR41-2 viruses and \u003cem\u003eSal. ruber\u003c/em\u003e M31 wild type. Cultures in exponential phase were diluted with fresh medium to an OD of 0.3 and 2x10\u003csup\u003e8\u003c/sup\u003e cells were mixed with 2x10\u003csup\u003e6\u003c/sup\u003e PFU (MOI\u0026thinsp;=\u0026thinsp;0.01) of the virus. This was considered as the initial time. Mixtures were then incubated at 37 \u0026ordm;C and 60 rpm. Aliquots of 150 \u0026micro;l were taken along 5 h (at 1, 2, 3, 4 and 5 h), centrifuged at 17,000 x g for 8 min, and 120 \u0026micro;l of the cells-free supernatant were stored in ice. The numbers of PFU/ml were measured by plaque assay as described above. All the experiments were conducted in triplicate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLysate experiment\u003c/h2\u003e\u003cp\u003eAn infection of \u003cem\u003eSal. ruber\u003c/em\u003e M1 with the EM1 virus at a MOI of 0.1 was carried out under the same conditions as described above. After 5 days, the infection was centrifuged at 17,000 x g for 10 minutes, and the supernatant was filtered through a 0.022 \u0026micro;m filter (Millipore, Burlington, USA) to remove any remaining viruses. One ml of this lysate was added to 25 ml of exponential-phase cultures of M8, M31, and P18 strains. Controls received 1 ml of fresh medium instead. Optical density was measured at 600 nm. Both the lysate-treated cultures and controls were conducted in triplicate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eDNA extractions and sequencing\u003c/h2\u003e\u003cp\u003eThe three biological replicates of each experiment were pooled prior to DNA extraction, as previous experiments in our laboratory have shown high reproducibility in the mutational trajectories of replicate populations. Bacterial pellets from all the experiments at 150 days were obtained by centrifugation at 17,000 x g for 10 min. The cells were washed with sterile medium, the supernatant removed, and the cell pellets stored at -80\u0026ordm;C until extraction. DNA was extracted with the DNeasy Blood \u0026amp; Tissue Kit (Qiagen, Hilden, Germany) following the manufacturer's protocol, and nucleic acids eluted in 70 \u0026micro;L of ultrapure water.\u003c/p\u003e\u003cp\u003eViruses from the same points were obtained by centrifuging at 7.197 x g for 20 min and filtering the supernatant through a filter of 0.22 \u0026micro;m. Dissolved DNA was removed with DNAse I from bovine pancreas (Sigma-Aldrich, St. Louis, USA) at 37\u0026ordm;C for 30 min, with an inactivation at 75\u0026ordm;C for 10 min. DNA was extracted with the QIAamp MinElute Virus Kit (Qiagen, Hilden, Germany) following the manufacturer's protocol, and nucleic acids eluted in 35 \u0026micro;L of ultrapure water. DNA from the viruses used for infecting at the initial time was also extracted as a reference.\u003c/p\u003e\u003cp\u003eAll extracted cellular and viral DNAs were quantified using Qubit 2.0. Flourometer (Life Technologies, Carlsbad, USA), and sequenced on an Illumina Novaseq6000 (2 x 150 bp) at Macrogen (Macrogen, Seoul, South Korea).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMetabarcoding\u003c/h2\u003e\u003cp\u003eThree genes from the EM1 virus were selected, and specific primers targeting\u0026thinsp;~\u0026thinsp;375 bp regions were designed (Supplementary Table\u0026nbsp;6). Cell pellets were washed three times by centrifugation, and DNA was extracted and quantified as described above. Specific regions were amplified via PCR in 50 \u0026micro;L reactions containing: 1.5 mM MgCl₂, 10X buffer, 10 mM dNTPs, 5 U/\u0026micro;L Taq polymerase, 100 \u0026micro;M primers, 5 \u0026micro;g of the template DNA and water up to the volume. PCR conditions are detailed in Supplementary Table\u0026nbsp;7. Five ml of the amplified products were electrophoresed, stained with ethidium bromide (100 \u0026micro;g/mL), and visualized under UV light. The remaining product was sequenced on an Illumina Miseq (2 x 150 bp) at Fundaci\u0026oacute; per al Foment de la Investigaci\u0026oacute; Sanit\u0026agrave;ria i Biom\u0026egrave;dica de la Comunitat Valenciana (Fisabio, Valencia, Spain).\u003c/p\u003e\u003cp\u003eMutations were identified comparing the obtained reads with the reference genomes of the viruses at initial time using Geneious software 6.1.8 (Biomatters, Auckland, New Zealand) with a minimum variant frequency of 0.01% and a coverage\u0026thinsp;\u0026gt;\u0026thinsp;50% to the mean coverage. Mutations were represented using the package \u0026lsquo;ggplot2\u0026rsquo; v3.4.0 in R v4.2.2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eBioinformatic analysis\u003c/h2\u003e\u003cp\u003ePrimers and adapters were removed from sequences, and reads were filtered based on quality scores using Trimmomatic v0.36.0 (Bolger et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Trimmed reads of initial viruses were assembled using SPAdes v3.13.1 (Bankevich et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) with the trusted option using their genomes of NCBI as a reference (GenBank accession numbers MF580955, MF580958, MF580960 and MF580960).\u003c/p\u003e\u003cp\u003eMutations generated in the viruses over time were identified by comparing reads from the extracellular viromes on days 9, 84 and 150 with the reference genomes of the viruses at initial time using Geneious software 6.1.8 (Biomatters, Auckland, New Zealand) with a minimum variant frequency of 0.01% and a coverage\u0026thinsp;\u0026gt;\u0026thinsp;50% to the mean coverage. Mutations were represented using the package \u0026lsquo;ggplot2\u0026rsquo; v3.4.0 in R v4.2.2. For the analysis of M1 sequences, the chromosomes of strains M1, M8, M31 and P18 were aligned with Mauve (Darling et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and 9 M1-specific regions were selected where the mutations were called in the same way as for the virus.\u003c/p\u003e\u003cp\u003eA BLASTn was performed with the reads of each virome, using the genome of the EM1 virus as the reference. Then, the BLASTn output was filtered by best_hit option and read coverage\u0026thinsp;\u0026gt;\u0026thinsp;70%. The ANIr (average nucleotide identity of sequence alignment reads) along the genome of EM1 was calculated with a home-made script in windows of 50 bp and plotted. Recruitment plots were carried out by enveomics tool (Rodriguez-R \u0026amp; Konstantinidis, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) using enve.recplot 2 (R stadistic pluging).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe warmly thank the Molecular Microbial Ecology group for their valuable feedback and constructive criticism throughout this work. We also acknowledge the Genomics and Proteomics Unit of the University of Alicante for their assistance with flow cytometry. Special thanks to Antonio Sanchez-Amat and Maliheh Mehrshad for their insightful feedback and suggestions. We thank Beatriz Cámara for developing the script used in the ANIr analysis and Heather Maughan for the professional English editing and critical reading of the manuscript. This research was supported by the projects \"VIRHOST\" CIPROM/2021/006 (PROMETEO 2022, Generalitat Valenciana) to J.A., and METACIRCLE PID2021-126114NB-C41 to F.S. and J.A. (Spanish Ministry of Science and Innovation). R.S.M. received funding for his doctoral thesis from the Spanish Ministry of Science and Innovation PRE2019-087998. R.S.M., E.R.P., F.S. and J.A. are members of the National Excellence Network FAGOMA (RED2022-134837-T).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.S. and J.A. conceived the study. R.S.M., F.S., and J.A. designed the experimental approach. R.S.M. performed the experiments and analyzed the sequences under the inputs and supervision of F.S. and J.A. E.R.P. designed the primers and probes for each strain and virus. L.M.R., J.S. and M.E. provided advice and carried out the microfluidics-based qPCR. R.S.M. drafted the original manuscript. All authors contributed to manuscript final writing and approved the final paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;DATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequences used were deposited in the NCBI database with BioProject ID PRJNA1294611.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;ETHICS DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbedon, S. T. (2023). Bacteriophage Adsorption: Likelihood of Virion Encounter with Bacteria and Other Factors Affecting Rates. Antibiotics, 12(4), Article 4. https://doi.org/10.3390/antibiotics12040723\u003c/li\u003e\n\u003cli\u003eAldeguer-Riquelme, B., Conrad, R. E., Ant\u0026oacute;n, J., Rossello-Mora, R., \u0026amp; Konstantinidis, K. T. (2024). 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Differences in laboratory versus field treatment performance of point-of-use drinking water treatment methods: Research gaps and ways forward. Npj Clean Water, 6(1), 1-7. https://doi.org/10.1038/s41545-023-00241-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7825215/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7825215/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVirus-bacterial interactions are fundamental to microbial ecology and evolution, but they have been characterized mostly using pure cultures under simplified laboratory conditions. To better understand how ecological complexity shapes these dynamics, we examined the behavior of the extreme halophilic bacterium \u003cem\u003eSalinibacter ruber\u003c/em\u003e strain M1 and the EM1 virus in the presence of additional \u003cem\u003eSal. ruber\u003c/em\u003e strains and viruses. In the short term, the presence of other strains delayed lysis and reduced EM1 virus production, indicating that community composition directly affects viral replication. In the long term, both \u003cem\u003eSal. ruber\u003c/em\u003e M1 and the EM1 virus persisted across all experimental conditions, but their evolutionary responses differed. The EM1 virus showed an increased mutation rate, reduced infectivity against the native host, and expanded host range when other viruses were present, suggesting a previously unrecognized form of virus-virus interaction, in which coexisting viruses influence each other\u0026rsquo;s evolutionary paths promoting viral diversification. In contrast, \u003cem\u003eSal. ruber\u003c/em\u003e M1 exhibited higher mutation rates evolving with other strains, indicating that in our system, intraspecific competition, rather than viral pressure, drives bacterial evolution. These findings demonstrate that incorporating biological complexity reveals distinct selective pressures acting on hosts and viruses, and is therefore essential for accurately predicting virus-host evolution in natural microbial ecosystems.\u003c/p\u003e","manuscriptTitle":"Biological context modulates virus-host dynamics and diversification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 08:22:29","doi":"10.21203/rs.3.rs-7825215/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15a2e8c4-76e5-4e60-ab3d-f98f64502604","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57494758,"name":"Biological sciences/Evolution/Experimental evolution"},{"id":57494759,"name":"Biological sciences/Microbiology/Microbial communities"}],"tags":[],"updatedAt":"2026-05-04T21:06:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 08:22:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7825215","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7825215","identity":"rs-7825215","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}