Biology subjects

Dominguez-Mirazo, M.

Publications and source records attributed to Dominguez-Mirazo, M..

4 recordsLinked to original sources

bioRxiv2025

Viral community diversity in the rhizosphere of the foundation salt marsh plant Spartina alterniflora

Viruses of microorganisms impact microbial population dynamics, community structure, nutrient cycling, gene transfer, and genomic innovation. In wetlands, root-associated microbial communities mediate key biogeochemical processes important for plants involved in ecosystem maintenance. Nonetheless, the presence and role of microbial viruses in salt marshes remains poorly understood. In this study, we analyzed 24 metagenomes retrieved from the root zone of Spartina alterniflora, a foundation plant in salt marshes of the eastern and Gulf coasts of the U.S. The samples span three plant compartments--bulk sediment, rhizosphere, and root--and two cordgrass plant phenotypes: short and tall. We observed differentiation between phenotypes and increased similarity in viral communities between the root and rhizosphere, indicating that plant compartment and phenotype shape viral community composition. The majority of viral populations characterized are novel at the genus level, with a subset predicted to target microorganisms known to carry out key biogeochemical functions. The findings provide a holistic assessment of plant-associated viral diversity and community composition as well as identifying potential targets for exploring viral modulation of microbially-mediated ecosystem functioning in intertidal wetlands. ImportanceSalt marshes are vital coastal ecosystems. Microbes in these environments drive nutrient cycling and support plant health, with Spartina alterniflora serving as a foundation species. This study explores viral communities associated with S. alterniflora, revealing how plant compartment and phenotype shape viral composition. The discovery of numerous novel viruses, some potentially influencing microbes involved in key biogeochemical processes, highlights their ecological significance. Given the increasing pressures on coastal ecosystems, understanding virus-microbe-plant interactions is essential for predicting and managing ecosystem responses to environmental change.

genomics↗

bioRxiv2025

Inferring single-cell heterogeneity of bacteriophage life-history traits from population-scale dynamics

Phage-induced lysis of bacteria transforms population dynamics, community structure, and ecosystem functioning. Scaling up infected cell fate to quantify population- and ecosystem-scale impacts requires estimates of viral life history traits, including underlying heterogeneity in the timing, efficiency, and outcome of lytic infections. However, the variability of lysis-associated phage traits remains poorly characterized. Here, we infer single-cell heterogeneity in lysis-associated traits for an ecologically relevant system: Syn9, a T4-like cyanophage infecting Synechococcus strain WH8109, a representative of globally abundant marine cyanobacteria. We estimate the heterogeneous distribution of latent period and burst size using a nonlinear model of infection dynamics applied to population-scale time series data. We then validate our inference approach using a single-cell assay - demonstrating the feasibility of inferring phage trait heterogeneity from population data even in the absence of single-cell experiments. The variation in Syn9s latent period exceeds that previously found in coliphages and reinforces the limitations of representing viral traits with a single value. Moreover, by partitioning lytic events via the inferred heterogeneous latent period distribution, we show that realized burst size variability is largely explained by differences in latent period, providing a path forward to measure and integrate trait (co)variation into population and ecosystem models.

microbiology↗

bioRxiv2024

Accounting for Cellular-Level Variation in Lysis: Implications for Virus-Host Dynamics

Viral impacts on microbial populations depend on interaction phenotypes - including viral traits spanning adsorption rate, latent period, and burst size. The latent period is a key viral trait in lytic infections. Defined as the time from viral adsorption to viral progeny release, the latent period of bacteriophage is conventionally inferred via one-step growth curves in which the accumulation of free virus is measured over time in a population of infected cells. Developed more than 80 years ago, one-step growth curves do not account for cellular-level variability in the timing of lysis, potentially biasing inference of viral traits. Here, we use nonlinear dynamical models to understand how individual-level variation of the latent period impacts virus-host dynamics. Our modeling approach shows that inference of latent period via one-step growth curves is systematically biased - generating estimates of shorter latent periods than the underlying population-level mean. The bias arises because variability in lysis timing at the cellular level leads to a fraction of early burst events which are interpreted, artefactually, as an earlier mean time of viral release. We develop a computational framework to estimate latent period variability from joint measurements of host and free virus populations. Our computational framework recovers both the mean and variance of the latent period within simulated infections including realistic measurement noise. This work suggests that reframing the latent period as a distribution to account for variability in the population will improve the study of viral traits and their role in shaping microbial populations. ImportanceQuantifying viral traits - including the adsorption rate, burst size, and latent period - is critical to characterize viral infection dynamics and to develop predictive models of viral impacts across scales from cells to ecosystems. Here, we revisit the gold standard of viral trait estimation - the one-step growth curve - to assess the extent to which assumptions at the core of viral infection dynamics lead to ongoing and systematic biases in inferences of viral traits. We show that latent period estimates obtained via one-step growth curves systematically under-estimate the mean latent period and, in turn, over-estimate the rate of viral killing at population scales. By explicitly incorporating trait variability into a dynamical inference framework that leverages both virus and host time series we provide a practical route to improve estimates of the mean and variance of viral traits across diverse virus-microbe systems.

ecology↗

bioRxiv2019

Functional and comparative genomic analysis of integrated prophage-like sequences in Candidatus Liberibacter asiaticus

Huanglongbing (HLB; yellow shoot disease) is a severe worldwide infectious disease for citrus family plants. The pathogen Candidatus Liberibacter asiaticus (CLas) is an alphapro-teobacterium of the Rhizobiaceae family that has been identified as the cause. The virulence of CLas has been attributed, in part, to prophage encoded genes. Prophage and prophage like elements have been identified in 12 of the 15 CLas available genomes, and are classified into three prophage types. Here, we re-examined all 15 CLas genomes using a de novo prediction approach and expanded the number of prophage like elements from 16 to 33. Further, we find that all CLas contain at least one prophage-like sequence. Comparative analysis reveals a prevalent, albeit previously unknown, prophage-like sequence type that is a remnant of an integrated prophage. Notably, this remnant prophage is found in the Ishi-1 CLas strain that had previously been reported as lacking prophages. Our findings provide both a resource and new insights into the evolutionary relationship between phage and CLas pathogenicity.

bioinformatics↗