Hasil untuk "Microbial ecology"

M. Derrien, C. Belzer, W. D. de Vos

Connor R. Fitzpatrick, J. Copeland, Pauline W. Wang et al.

C. Nadell, K. Drescher, K. Foster

A. Shade, J. Handelsman

Hannah G. Hampton, B. N. Watson, Peter C. Fineran

Stephen Nayfach, S. Roux, Rekha Seshadri et al.

The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth’s continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes. Cataloging microbial genomes from Earth’s environments expands the known phylogenetic diversity of bacteria and archaea.

M. Strickland, J. Rousk

Anders F. Andersson, Mathilda Lindberg, Hedvig E. Jakobsson et al.

Humans host complex microbial communities believed to contribute to health maintenance and, when in imbalance, to the development of diseases. Determining the microbial composition in patients and healthy controls may thus provide novel therapeutic targets. For this purpose, high-throughput, cost-effective methods for microbiota characterization are needed. We have employed 454-pyrosequencing of a hyper-variable region of the 16S rRNA gene in combination with sample-specific barcode sequences which enables parallel in-depth analysis of hundreds of samples with limited sample processing. In silico modeling demonstrated that the method correctly describes microbial communities down to phylotypes below the genus level. Here we applied the technique to analyze microbial communities in throat, stomach and fecal samples. Our results demonstrate the applicability of barcoded pyrosequencing as a high-throughput method for comparative microbial ecology.

Jizhong Zhou, Ye Deng, Ping Zhang et al.

M. Rondon, P. August, A. Bettermann et al.

K. Kümmerer

A. Willis

Understanding the drivers of microbial diversity is a fundamental question in microbial ecology. Extensive literature discusses different methods for describing microbial diversity and documenting its effects on ecosystem function. However, it is widely believed that diversity depends on the number of reads that are sequenced. I discuss a statistical perspective on diversity, framing the diversity of an environment as an unknown parameter, and discussing the bias and variance of plug-in and rarefied estimates. I argue that by failing to account for both bias and variance, we invalidate analysis of alpha diversity. I describe the state of the statistical literature for addressing these problems, and suggest that measurement error modeling can address issues with variance, but bias corrections need to be utilized as well. I encourage microbial ecologists to avoid motivating their investigations with alpha diversity analyses that do not use valid statistical methodology.

M. Hall, R. Beiko

Environmental Microbiology: Advanced Research and Multidisciplinary Applications focus on the current research on microorganisms in the environment. Contributions in the volume cover several aspects of applied microbial research, basic research on microbial ecology and molecular genetics. The reader will find a collection of topics with theoretical and practical value, allowing them to connect environmental microbiology to a variety of subjects in life sciences, ecology, and environmental science topics. Advanced topics including biogeochemical cycling, microbial biosensors, bioremediation, application of microbial biofilms in bioremediation, application of microbial surfactants, microbes for mining and metallurgical operations, valorization of waste, and biodegradation of aromatic waste, microbial communication, nutrient cycling and biotransformation are also covered. The content is designed for advanced undergraduate students, graduate students, and environmental professionals, with a comprehensive and up-to-date discussion of environmental microbiology as a discipline that has greatly expanded in scope and interest over the past several decades.

Narges Kakavand, Anupam Sengupta

Microorganisms live in inherently dynamic environments where fluctuations in biotic and abiotic factors shape their behaviour, physiology, and fitness. The concept of ecological memory: the lasting imprint of prior environmental cues, suggests that past exposures can exert prolonged effects on microbial growth, resilience, and phenotypic expressions. For motile microbes in aquatic ecosystems, environmental variability is mediated by fluid motion, which may engender a form of hydrodynamic memory, whereby prior exposure to specific spatio-temporal cues influence future growth and migratory behaviour. Yet, the emergence of such flow-induced memory, or its long-term consequences for trait evolution and population dynamics, remain unexplored. We integrate millifluidic flow control, high-resolution cell tracking, and tunable hydrodynamic cues to quantify growth and migration of Heterosigma akashiwo, a model microbe, across growth stages. Using two complementary perturbation scenarios: standard (flow after static conditions) and reverse (flow before static growth), we test how the temporal structure of forcing shapes multigenerational responses. This combinatorial design disentangles exposure history from its duration, and reveals how prior flow modulates sensitivity, generating legacy effects. Compared with static controls, repeated hydrodynamic exposure alters doubling time, carrying capacity, gravitactic stability, and swimming speed distributions; shifting growth phase progression and tolerance to subsequent perturbations. These results establish a mechanistic framework for flow-induced memory in motile microbes, revealing how past fluidic cues shape future growth and migration. Our study advances predictive understanding of motile microbes in natural and engineered hydrodynamic systems experiencing increasing variability under global environmental changes.

Yuyuan Ma, Jian Ding, Zhixing Nie et al.

Eggplant cultivation faces major challenges from continuous cropping obstacles, which degrade soil health and limit sustainable production. Microbial inoculants offer a promising strategy for addressing such issues by modifying the soil environment and rhizosphere ecology. In this study, a field experiment was conducted to evaluate the effects of three bacterial inoculants, including <i>Bacillus zhangzhouensis</i> (BF1), <i>Bacillus mobilis</i> (BF2), and <i>Zhihengliuella halotolerans</i> (BF3), on soil properties, microbial community structure, and crop performance in a continuously cropped eggplant system. The results showed that three inoculants exerted strain-specific effects: BF1 significantly promoted eggplant vegetative growth and yield, increasing plant height by 32.1%, stem diameter by 28.7%, and total yield by 142.4% relative to the control; BF3 selectively improved fruit quality and soil nutrient status, elevating eggplant fruit total amino acid, soluble protein, and soluble sugar contents by 68.9%, 52.3%, and 41.2%, respectively, and increasing soil organic carbon (SOC), total nitrogen (TN), and available nitrogen (AN) by 13.73%, 18.03%, and 84.92% compared with the control. BF2 showed limited efficacy relative to the control. All inoculants enhanced the abundance of beneficial bacteria and reshaped the rhizosphere microbial community structure. The findings demonstrate the potential of strain-specific microbial inoculants to alleviate continuous cropping obstacles and promote sustainable eggplant production.

Lars Vogt, Birgitta König-Ries, Tim Alamenciak et al.

Ecological research increasingly relies on integrating heterogeneous datasets and knowledge to explain and predict complex phenomena. Yet, differences in data types, terminology, and documentation often hinder interoperability, reuse, and causal understanding. We present the Semantic Units Framework, a novel, domain-agnostic semantic modelling approach applied here to ecological data and knowledge in compliance with the FAIR (Findable, Accessible, Interoperable, Reusable) and CLEAR (Cognitively interoperable, semantically Linked, contextually Explorable, easily Accessible, human-Readable and -interpretable) Principles. The framework models data and knowledge as modular, logic-aware semantic units: single propositions (statement units) or coherent groups of propositions (compound units). Statement units can model measurements, observations, or universal relationships, including causal ones, and link to methods and evidence. Compound units group related statement units into reusable, semantically coherent knowledge objects. Implemented using RDF, OWL, and knowledge graphs, semantic units can be serialized as FAIR Digital Objects with persistent identifiers, provenance, and semantic interoperability. We show how universal statement units build ecological causal networks, which can be composed into causal maps and perspective-specific subnetworks. These support causal reasoning, confounder detection (back-door), effect identification with unobserved confounders (front-door), application of do-calculus, and alignment with Bayesian networks, structural equation models, and structural causal models. By linking fine-grained empirical data to high-level causal reasoning, the Semantic Units Framework provides a foundation for ecological knowledge synthesis, evidence annotation, cross-domain integration, reproducible workflows, and AI-ready ecological research.

Chandler Ross, Ville Laitinen, Moein Khalighi et al.

Ecosystems tend to fluctuate around stable equilibria in response to internal dynamics and environmental factors. Occasionally, they enter an unstable tipping region and collapse into an alternative stable state. Our understanding of how ecological communities vary over time and respond to perturbations depends on our ability to quantify and predict these dynamics. However, the scarcity of long, dense time series data poses a severe bottleneck for characterising community dynamics using existing methods. We overcome this limitation by combining information across multiple short time series using Bayesian inference. By decomposing dynamics into deterministic and stochastic components using Gaussian process priors, we predict stable and tipping regions along the community landscape and quantify resilience while addressing uncertainty. After validation with simulated and real ecological time series, we use the model to question common assumptions underlying classical potential analysis and re-evaluate the stability of previously proposed "tipping elements" in the human gut microbiota.

Davi Arrais Nobre, Karen C. Abbott, Jonathan Machta et al.

Many ecological populations are known to display a cyclic behavior with period 2. Previous work has shown that when a metapopulation (group of coupled populations) with such dynamics is allowed to interact via nearest neighbor dispersal in two dimensions, it undergoes a phase transition from disordered (spatially asynchronous) to ordered (spatially synchronous) that falls under the 2-D Ising universality class. While nearest neighbor dispersal may satisfactorily describe how most individuals migrate between habitats, we should expect a small fraction of individuals to venture on a journey to further locations. We model this behavior by considering ecological oscillators on dynamically rewired small-world networks, in which at each time step a fraction $p$ of the nearest neighbor interactions is replaced by a new interaction with a random node on the network. We measure how this connectivity change affects the critical point for synchronizing ecological oscillators. Our results indicate that increasing the amount of long-range interaction (increasing $p$) favors the ordered regime, but the presence of memory in ecological oscillators leads to quantitative differences in how much long-range dispersal is needed to order the network, relative to an analogous network of Ising spins. We also show that, even for very small values of $p$, the phase transition falls into the mean-field universality class, and argue that ecosystems where dispersal can occasionally happen across the system's length scale will display a phase transition in the mean-field universality class.

Andrea Marchetti, Henrik Jeldtoft Jensen

The myriad microscopic interactions among the individual organisms that constitute an ecological system collectively give rise, at the macroscopic scale, to evolutionary trends. The ability to detect the directionality of such trends is crucial for understanding and managing the dynamics of natural systems. Nevertheless, identifying the key observable quantities that capture such directional behaviour poses a major challenge. In this study, we propose that translating ecological data into a network framework is a valuable strategy to measure system stability and evolution. We examine the Tangled Nature model as a test case, evaluating network entropy, species diversity, and the clustering coefficient as metrics of network stability and directionality.