Biology subjects

Weitz, J. S.

Publications and source records attributed to Weitz, J. S..

36 records · Page 2Linked to original sources

bioRxiv2024

Diel Partitioning in Microbial Phosphorus Acquisition in the Sargasso Sea

The daily cycle of photosynthetic primary production at the base of marine food webs is often limited by the availability of scarce nutrients. According to temporal niche partitioning theory, competition for scarce resources can be alleviated insofar as the intensity of nutrient uptake and assimilation activities are distributed heterogeneously across organisms over periodic input cycles. Recent analysis of community transcriptional dynamics in the nitrogen-limited subtropical North Pacific gyre revealed evidence of temporal partitioning of nitrogen uptake and assimilation between eukaryotic phytoplankton, cyanobacteria, and heterotrophic bacteria over day-night cycles. Here, we present results from a Lagrangian metatranscriptomic time series survey in the Sargasso Sea and demonstrate temporally partitioned phosphorus uptake in this phosphorus-limited environment. In the Sargasso, heterotrophic bacteria, eukaryotic phytoplankton, and cyanobacteria express genes for phosphorus assimilation during the morning, day, and dusk, respectively. These results support the generality of temporal niche partitioning as an emergent mechanism structuring uptake of limiting nutrients and facilitating coexistence of diverse microbes in open ocean ecosystems.

ecology↗

bioRxiv2024

Metapopulation model of phage therapy of an acute Pseudomonas aeruginosa lung infection

Infections caused by multi-drug resistant (MDR) pathogenic bacteria are a global health threat. Phage therapy, which uses phage to kill bacterial pathogens, is increasingly used to treat patients infected by MDR bacteria. However, the therapeutic outcome of phage therapy may be limited by the emergence of phage resistance during treatment and/or by physical constraints that impede phage-bacteria interactions in vivo. In this work, we evaluate the role of lung spatial structure on the efficacy of phage therapy for Pseudomonas aeruginosa infection. To do so, we developed a spatially structured metapopulation network model based on the geometry of the bronchial tree, and included the emergence of phage-resistant bacterial mutants and host innate immune responses. We model the ecological interactions between bacteria, phage, and the host innate immune system at the airway (node) level. The model predicts the synergistic elimination of a P. aeruginosa infection due to the combined effects of phage and neutrophils given sufficiently active immune states and suitable phage life history traits. Moreover, the metapopulation model simulations predict that local MDR pathogens are cleared faster at distal nodes of the bronchial tree. Notably, image analysis of lung tissue time series from wild-type and lymphocyte-depleted mice (n=13) revealed a concordant, statistically significant pattern: infection intensity cleared in the bottom before the top of the lungs. Overall, the combined use of simulations and image analysis of in vivo experiments further supports the use of phage therapy for treating acute lung infections caused by P. aeruginosa while highlighting potential limits to therapy given a spatially structured environment, such as impaired innate immune responses and low phage efficacy.

microbiology↗

bioRxiv2024

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

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary - and largely uncharacterized - genetics of adsorption, injection, cell take-over and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage{lambda} strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. We found that the most effective approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, accurately predicting 86% of interactions while reducing the relative error in the estimated strength of the infection phenotype by 40%. Feature selection revealed key phage{lambda} and E. coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage{lambda} infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. The methods success in recapitulating strain-level infection outcomes arising during coevolutionary dynamics may also help inform generalized approaches for imputing genetic drivers of interaction phenotypes in complex communities of phage and bacteria.

bioinformatics↗

bioRxiv2022

Microbial and Viral Genome and Proteome Nitrogen Demand Varies Across Multiple Spatial Scales Within a Marine Oxygen Minimum Zone

Nutrient availability can significantly influence microbial genomic and proteomic streamlining, for example by selecting for lower nitrogen to carbon ratios. Oligotrophic open ocean microbes have streamlined genomic nitrogen requirements relative to their counterparts in nutrient-rich coastal waters. However, steep gradients in nutrient availability occur at meter- and even micron-level spatial scales. It is unclear if such gradients also structure genomic and proteomic stoichiometry. Focusing on the eastern tropical North Pacific oxygen minimum zone (OMZ), we use comparative metagenomics to examine how nitrogen availability shapes microbial and viral genome properties along the vertical gradient across the OMZ and between two size fractions distinguishing free-living versus particle-associated microbes. We find a substantial increase in nitrogen content of encoded proteins in particle-associated over free-living bacteria and archaea across nitrogen availability regimes over depth. Within each size-fraction, we find that bacterial and viral genomic nitrogen tends to increase with increasing nitrate concentrations with depth. In contrast to cellular genes, the nitrogen content of virus proteins does not differ between size fractions. We identified arginine as a key amino acid in modulating the C:N ratio of core genes for bacteria, archaea, and viruses. Functional analysis reveals that particle-associated bacterial metagenomes are enriched for genes involved in arginine metabolism and organic nitrogen compound catabolism. Our results are consistent with nitrogen streamlining in both cellular and viral genomes on spatial scales of meters to microns. These effects are similar in magnitude to those previously reported across scales of thousands of kilometers. IMPORTANCEThe genomes of marine microbes can be shaped by nutrient cycles, with ocean-scale gradients in nitrogen availability known to influence microbial amino acid usage. It is unclear, however, how genomic properties are shaped by nutrient changes over much smaller spatial scales, for example along the vertical transition into oxygen minimum zones (OMZs) or from the exterior to interior of detrital particles. Here, we measure protein nitrogen usage by marine bacteria, archaea, and viruses using metagenomes from the nitracline of the eastern tropical North Pacific OMZ including both particle-associated and non-associated biomass. Our results show higher genomic and proteomic nitrogen content in particle-associated microbes and at depths with higher nitrogen availability for cellular and viral genomes. This discovery suggests that stoichiometry influences microbial and viral evolution across multiple scales, including the micro- to millimeter scale associated with particle-associated versus free-living lifestyles.

microbiology↗

bioRxiv2021

Time-scales modulate optimal lysis-lysogeny decision switches and near-term phage fitness

Temperate phage can initiate lysis or lysogeny after infecting a bacterial host. The genetic switch between lysis and lysogeny is mediated by phage regulatory genes as well as host and environmental factors. Recently, a new class of decision switches was identified in phage of the SPbeta group, mediated by the extracellular release of small, phage-encoded peptides termed arbitrium. Arbitrium peptides can be taken up by bacteria prior to infection, modulating the decision switch in the event of a subsequent phage infection. Increasing concentration of arbitrium increases the chance that a phage infection will lead to lysogeny, rather than lysis. Although prior work has centered on the molecular mechanisms of arbitrium-induced switching, here we focus on how selective pressures impact the benefits of plasticity in switching responses. In this work, we examine the possible advantages of near-term adaptation of communication-based decision switches used by the SPbeta-like group. We combine a nonlinear population model with a control theoretic approach to evaluate the relationship between a putative phage reaction norm (i.e., the probability of lysogeny as a function of arbitrium) and the near-term time horizon. We show the adaptive potential of communication-based lysis-lysogeny responses and find that optimal switching between lysis to lysogeny increases near-term fitness compared to fixed responses. We further find that plastic responses are robust to the inclusion of cellular-level stochasticity. These findings provide a principled basis to explore the long-term evolution of phage-encoded decision systems mediated by extracellular decision-signaling molecules, and the feedback between phage reaction norms and ecological context.

ecology↗

bioRxiv2021

Diel population dynamics and mortality of Prochlorococcus in the North Pacific Subtropical Gyre

Photosynthesis fuels primary production at the base of marine food webs. Yet, in many surface ocean ecosystems, diel-driven primary production is tightly coupled to daily loss. This tight coupling raises the question: which top-down drivers predominate in maintaining persistently stable picocyanobacterial populations over longer time scales? Motivated by high-frequency surface water measurements taken in the North Pacific Subtropical Gyre (NPSG), we developed multitrophic models to investigate bottom-up and top-down mechanisms underlying the balanced control of Prochlorococcus populations. We find that incorporating photosynthetic growth with viral- and predator-induced mortality is sufficient to recapitulate daily oscillations of Prochlorococcus abundances with baseline community abundances. In doing so, we infer that grazers function as the primary top-down factor despite high standing viral particle densities while identifying the potential for light-dependent viral traits and non-canonical loss factors to shape the structure and function of marine microbial communities.

ecology↗

bioRxiv2020

Community-scale Synchronization and Temporal Partitioning of Gene Expression, Metabolism, and Lipid Biosynthesis in Oligotrophic Ocean Surface Waters

Sunlight drives daily rhythms of photosynthesis, growth, and division of photoautotrophs throughout the surface oceans. However, the cascading impacts of oscillatory light input on diverse microbial communities and community-scale metabolism remains unclear. Here we use an unsupervised machine learning approach to show that a small number of diel archetypes can explain pervasive periodic dynamics amongst more than 65,000 distinct time series, including transcriptional activity, macromolecules, lipids, and metabolites from the North Pacific Subtropical Gyre. Overall, we find evidence for synchronous timing of carbon-cycle gene expression that underlie daily oscillations in the concentrations of particulate organic carbon. In contrast, we find evidence of asynchronous timing in gene transcription related to nitrogen metabolism and related metabolic processes consistent with temporal niche partitioning amongst microorganisms in the bacterial and eukaryotic domains.

ecology↗

bioRxiv2020

Combined pigment and metatranscriptomic analysis reveals synchronized diel patterns of phenotypic light response across domains in the open ocean

Sunlight is the most important environmental control on diel fluctuations in phytoplankton activity, and understanding diel microbial processes is essential to the study of oceanic biogeochemical cycles. Yet, little is known about the in situ frequency of phytoplankton metabolic activities and their coordination across different populations. We investigated the diel orchestration of phytoplankton activity involved in photosynthesis, photoacclimation, and photoprotection by analyzing the pigment and quinone distribution in combination with metatranscriptomes in the surface waters of the North Pacific Subtropical Gyre (NPSG). We found diel cycles in pigment abundances resulting from the balance of their synthesis and consumption. The night represents a metabolic recovery phase to refill cellular pigment stores, while the photosystems are remodeled towards photoprotection during the day. Transcript levels of genes involved in photosynthesis and pigment metabolism had highly synchronized diel expression patterns among all taxa, suggesting that there are similar regulatory mechanisms for light and energy metabolism across domains, and that other environmental factors drive niche differentiation. Observed decoupling of diel oscillations in transcripts and related pigments in the NPSG indicates that pigment abundance is modulated by environmental factors extending beyond gene expression/regulation, showing that metatranscriptomes may provide only limited insights on real-time photophysiological metabolism.

molecular biology↗

bioRxiv2019

Topological constraints in early multicellularity favor reproductive division of labor

Reproductive division of labor (e.g., germ-soma specialization) is a hallmark of the evolution of multicellularity, signifying the emergence of a new type of individual and facilitating the evolution of increased organismal complexity. A large body of work from evolutionary biology, economics, and ecology has shown that specialization is beneficial when further division of labor produces an accelerating increase in absolute productivity (i.e., productivity is a convex function of specialization). Here we show that reproductive specialization is qualitatively different from classical models of resource sharing, and can evolve even when the benefits of specialization are saturating (i.e., productivity is a concave function of specialization). Through analytical theory and evolutionary individual based simulations, our work demonstrates that reproductive specialization is strongly favored in sparse networks of cellular interactions, such as trees and filaments, that reflect the morphology of early, simple multicellular organisms, highlighting the importance of restricted social interactions in the evolution of reproductive specialization. More broadly, we find that specialization is strongly favored, despite saturating returns on investment, in a wide range of scenarios in which sharing is asymmetric.

evolutionary biology↗

bioRxiv2019

When be Temperate: On the Fitness Benefits of Lysis vs. Lysogeny

Bacterial viruses, i.e., bacteriophage or phage, can infect and lyse their bacterial hosts, releasing new viral progeny. In addition to the lytic pathway, certain bacteriophage (i.e., temperate bacteriophage) can also initiate lysogeny, a latent mode of infection in which the viral genome is integrated into and replicated with the bacterial chromosome. Subsequently, the integrated viral genome, i.e., the prophage, can induce and restart the lytic pathway. Here, we explore the relationship between infection mode, ecological context, and viral fitness, in essence asking: when should viruses be temperate? To do so, we use network loop analysis to quantify fitness in terms of network paths through the life history of an infectious pathogen that start and end with infected cells. This analysis reveals that temperate strategies, particularly those with direct benefits to cellular fitness, should be favored at low host abundances. This finding applies to a spectrum of mechanistic models of phage-bacteria dynamics spanning both explicit and implicit representations of intracellular infection dynamics. However, the same analysis reveals that temperate strategies, in and of themselves, do not provide an advantage when infection imposes a cost to cellular fitness. Hence, we use evolutionary invasion analysis to explore when temperate phage can invade microbial communities with circulating lytic phage. We find that lytic phage can drive down niche competition amongst microbial cells, facilitating the subsequent invasion of latent strategies that increase cellular resistance and/or immunity to infection by lytic viruses - notably this finding holds even when the prophage comes at a direct fitness cost to cellular reproduction. Altogether, our analysis identifies broad ecological conditions that favor latency and provide a principled framework for exploring the impacts of ecological context on both the short- and long-term benefits of being temperate.

ecology↗

bioRxiv2019

Infect While the Iron is Scarce: Nutrient Explicit Phage-Bacteria Games

Marine microbial primary production is influenced by the availability and uptake of essential nutrients, including iron. Although marine microbes have evolved mechanisms to scavenge sub-nanomolar concentrations of iron, recent observations suggest that viruses may co-opt these very same mechanisms to facilitate infection. The \"Ferrojan Horse Hypothesis\" proposes that viruses incorporate iron atoms into their tail fiber proteins to adsorb to target host receptors. Here, we propose an evolutionary game theoretic approach to consider the joint strategies of hosts and viruses in environments with limited nutrients (like iron). We analyze the bimatrix game and find that evolutionarily stable strategies depend on the stability and quality of nutrient conditions. For example, in highly stable iron conditions, virus pressure does not change host uptake strategies. However, when iron levels are dynamic, virus pressure can lead to fluctuations in the extent to which hosts invest in metabolic machinery that increases both iron uptake and susceptibility to viral infection. Altogether, this evolutionary game model provides further evidence that viral infection and nutrient dynamics jointly shape the fate of microbial populations.

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↗

bioRxiv2019

Allelic polymorphism shapes collective phenotypes in evolving Pseudomonas aeruginosa populations

Pseudomonas aeruginosa is an opportunistic pathogen that chronically infects the lungs of individuals with cystic fibrosis (CF) by forming antibiotic resistant biofilms. Emergence of phenotypically diverse isolates within CF P. aeruginosa populations has previously been reported, however, the impact of heterogeneity on social behaviors and community function is poorly understood. Here we describe how this heterogeneity impacts on behavioral traits by evolving the strain PAO1 in biofilms grown in a synthetic sputum medium for 50 days. We measured social trait production and antibiotic tolerance and used a metagenomic approach to analyze and assess genomic changes over the duration of the evolution experiment. We found that (i) evolutionary trajectories were reproducible in independently evolving populations; (ii) over 60% of genomic diversity occurred within the first 10 days of selection. We then focused on quorum sensing (QS), a well-studied P. aeruginosa trait that is commonly mutated in strains isolated from CF lungs. We found that at the population level (i) evolution in sputum medium selected for decreased production of QS and QS-dependent traits; (ii) there was a significant correlation between lasR mutant frequency, the loss of protease and the 3O-C12-HSL signal, and an increase in resistance to clinically relevant {beta}-lactam antibiotics, despite no previous antibiotic exposure. Overall, our findings provide insights into the effect of allelic polymorphism on community functions in diverse P. aeruginosa populations. Further, we demonstrate that P. aeruginosa population and evolutionary dynamics can impact on traits important for virulence and can lead to increased tolerance to {beta}-lactam antibiotics.

microbiology↗

bioRxiv2019

Quantitative Models of Phage-Antibiotics Combination Therapy

The spread of multi-drug resistant (MDR) bacteria is a global public health crisis. Bacteriophage therapy (or \"phage therapy\") constitutes a potential alternative approach to treat MDR infections. However, the effective use of phage therapy may be limited when phage-resistant bacterial mutants evolve and proliferate during treatment. Here, we develop a nonlinear population dynamics model of combination therapy that accounts for the system-level interactions between bacteria, phage and antibiotics for in-vivo application given an immune response against bacteria. We simulate the combination therapy model for two strains of Pseudomonas aeruginosa, one which is phage-sensitive (and antibiotic resistant) and one which is antibiotic-sensitive (and phage-resistant). We find that combination therapy outperforms either phage or antibiotic alone, and that therapeutic effectiveness is enhanced given interaction with innate immune responses. Notably, therapeutic success can be achieved even at sub-inhibitory concentrations of antibiotics, e.g., ciprofloxacin. These in-silico findings provide further support to the nascent application of combination therapy to treat MDR bacterial infections, while highlighting the role of innate immunity in shaping therapeutic outcomes.

microbiology↗

bioRxiv2018

Viral Fitness Across a Continuum from Lysis to Latency

The prevailing paradigm in ecological studies of viruses and their microbial hosts is that the reproductive success of viruses depends on the proliferation of the \"predator\", i.e., the virus particle. Yet, viruses are obligate intracellular parasites, and the virus genome - the actual unit of selection - can persist and proliferate from one cell generation to the next without lysis or the production of new virus particles. Here, we propose a theoretical framework to quantify the invasion fitness of viruses using an epidemiological cell-centric metric that focuses on the proliferation of viral genomes inside cells instead of virus particles outside cells. This cell-centric metric enables direct comparison of viral strategies characterized by obligate killing of hosts (e.g., via lysis), persistence of viral genomes inside hosts (e.g., via lysogeny), and strategies along a continuum between these extremes (e.g., via chronic infections). As a result, we can identify environmental drivers, life history traits, and key feedbacks that govern variation in viral propagation in nonlinear population models. For example, we identify threshold conditions given relatively low densities of susceptible cells and relatively high growth rates of infected cells in which lysogenic and other chronic strategies have higher potential viral reproduction than lytic strategies. Altogether, the theoretical framework helps unify the ongoing study of eco-evolutionary drivers of viral strategies in natural environments.

ecology↗

bioRxiv2018

The effect of strain level diversity on robust inference of virus-induced mortality

Infection and lysis of phytoplankton by viruses affects population dynamics and nutrient cycles within oceanic microbial communities. However, estimating the quantitative rates of viral-induced lysis remains challenging in situ. The modified dilution method is the most commonly utilised empirical approach to estimate virus-induced killing rates of phytoplankton. The lysis rate estimates of the modified dilution method are based on models of virus-host interactions involving only a single virus and a single host population. Here, using modelling approaches, we examine the robustness of the modified dilution method in multi-strain, complex communities. We assume that strains differ in their life history traits, including growth rates (of hosts) and lysis rates (by viruses). We show that trait differences affect resulting experimental dynamics such that lysis rates measured using the modified dilution method may be driven by the fastest replicating strains; which are not necessarily the most abundant in situ. We discuss the implications of using the modified dilution method and alternative dilution-based approaches for estimating viral-induced lysis rates in marine microbial communities.

microbiology↗

bioRxiv2018

Information sharing for a coordination game in fluctuating environments

Collective action dilemmas pervade the social and biological sciences - from human decision-making to bacterial quorum sensing. In these scenarios, individuals sense cues from the environment to adopt a suitable phenotype or change in behavior. However, when cues include signals from other individuals, then the appropriate behavior of each individual is linked. Here, we develop a framework to quantify the influence of information sharing on individual behavior in the context of two player coordination games. In this framework, the environment stochastically switches between two states, and the state determines which one of two actions players must coordinate on. Given a stochastically switching environment, we then consider two versions of the game that differ in the way players acquire information. In the first model, players independently sense private environmental cues, but do not communicate with each other. We find there are two types of strategies that emerge as Nash equilibria and fitness maximizers - players prefer to commit to one particular action when private information is poor, or prefer to employ phenotypic plasticity when it is good. The second model adds an additional layer of communication, where players share social cues as well. When the quality of social information is high, we find the socially optimal strategy is a novel \"majority logic\" strategy that bases decision-making on social cues. Our game-theoretic approach offers a principled way of investigating the role of communication in group decision-making under uncertain conditions.

animal behavior and cognition↗

bioRxiv2017

Correlations, interactions, and predictability in virus-microbe networks

1Microbes are present in high abundances in the environment and in human-associated microbiomes, often exceeding one million per milliliter. Viruses of microbes are present in even higher abundances and are important in shaping microbial populations, communities, and ecosystems. Given the relative specificity of viral infection, it is essential to identify the functional linkages between viruses and their microbial hosts, particularly given dynamic changes in virus and host abundances. Multiple approaches have been proposed to infer infection networks from time-series of in situ communities, among which correlation-based approaches have emerged as the de facto standard. In this work, we evaluate the accuracy of correlation-based inference methods using an in silico approach. In doing so, we compare predicted networks to actual networks to assess the self-consistency of correlation-based inference. At odds with assumptions underlying its widespread use, we find that correlation is a poor predictor of interactions in the context of viral infection and lysis of microbial hosts. The failure to predict interactions holds for methods which leverage product-moment, time-lagged, and relative-abundance based correlations. In closing, we discuss alternative inference methods, particularly model-based methods, as a means to infer interactions in complex microbial communities with viruses.\n\n2 ImportanceInferring interactions from population time-series is an active and ongoing area of research. It is relevant across many biological systems - in particular in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference - using correlations to predict interactions - is widespread. However, it is well known that \"correlation does not imply causation\". Despite this, many studies apply correlation-based inference methods to experimental time-series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions within in silica virus-microbe communities, raising questions on their relevance when applied in situ.

ecology↗