Hasil untuk "Microbial ecology"

P. Keeling, P. Keeling, Fabien Burki et al.

Current sampling of genomic sequence data from eukaryotes is relatively poor, biased, and inadequate to address important questions about their biology, evolution, and ecology; this Community Page describes a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans.

J. Schimel, S. Schaeffer

A major thrust of terrestrial microbial ecology is focused on understanding when and how the composition of the microbial community affects the functioning of biogeochemical processes at the ecosystem scale (meters-to-kilometers and days-to-years). While research has demonstrated these linkages for physiologically and phylogenetically “narrow” processes such as trace gas emissions and nitrification, there is less conclusive evidence that microbial community composition influences the “broad” processes of decomposition and organic matter (OM) turnover in soil. In this paper, we consider how soil microbial community structure influences C cycling. We consider the phylogenetic level at which microbes form meaningful guilds, based on overall life history strategies, and suggest that these are associated with deep evolutionary divergences, while much of the species-level diversity probably reflects functional redundancy. We then consider under what conditions it is possible for differences among microbes to affect process dynamics, and argue that while microbial community structure may be important in the rate of OM breakdown in the rhizosphere and in detritus, it is likely not important in the mineral soil. In mineral soil, physical access to occluded or sorbed substrates is the rate-limiting process. Microbial community influences on OM turnover in mineral soils are based on how organisms allocate the C they take up – not only do the fates of the molecules differ, but they can affect the soil system differently as well. For example, extracellular enzymes and extracellular polysaccharides can be key controls on soil structure and function. How microbes allocate C may also be particularly important for understanding the long-term fate of C in soil – is it sequestered or not?

C. Lozupone, M. Hamady, R. Knight

BackgroundMoving beyond pairwise significance tests to compare many microbial communities simultaneously is critical for understanding large-scale trends in microbial ecology and community assembly. Techniques that allow microbial communities to be compared in a phylogenetic context are rapidly gaining acceptance, but the widespread application of these techniques has been hindered by the difficulty of performing the analyses.ResultsWe introduce UniFrac, a web application available at http://bmf.colorado.edu/unifrac, that allows several phylogenetic tests for differences among communities to be easily applied and interpreted. We demonstrate the use of UniFrac to cluster multiple environments, and to test which environments are significantly different. We show that analysis of previously published sequences from the Columbia river, its estuary, and the adjacent coastal ocean using the UniFrac interface provided insights that were not apparent from the initial data analysis, which used other commonly employed techniques to compare the communities.ConclusionUniFrac provides easy access to powerful multivariate techniques for comparing microbial communities in a phylogenetic context. We thus expect that it will provide a completely new picture of many microbial interactions and processes in both environmental and medical contexts.

R. Gibbons, J. Houte

G. Muyzer, T. Brinkhoff, U. Nübel et al.

J. Garland

R. Conrad

Rebecca J. Case, Y. Boucher, I. Dahllöf et al.

Diandong Wang, Jingjing Liao, Zhaoming Cai et al.

Disease caused by <i>Plasmodiophora brassicae</i> severely disrupts cruciferous crops by altering root physiology and rhizosphere ecology. While pathogen-induced shifts in rhizosphere microbiomes are documented, the mechanisms linking root exudate reprogramming to microbial community remodeling remain poorly understood. Here, we integrated untargeted metabolomics and 16S rRNA sequencing to investigate how root exudates reshape the rhizosphere microbiome of tumorous stem mustard (<i>Brassica juncea</i> var. tumida) through <i>P. brassicae</i> infection. Metabolomic profiling identified 1718 root exudate metabolites, with flavones (e.g., apigenin 7-<i>O</i>-β-D-rutinoside, VIP > 1.5) and phenolic derivatives (e.g., gastrodin) being selectively enriched in infected plants. <i>P. brassicae</i> infection significantly increased rhizobacterial richness (ACE index, <i>p</i> < 0.05) and restructured the community composition, marked by enrichment of <i>Paenibacillus</i> (LDA score > 3.0). Procrustes analysis revealed tight coupling between microbial community shifts and metabolic reprogramming (M² = 0.446, <i>p</i> = 0.005), while Spearman correlations implicated pathogen-induced metabolites like geniposidic acid in recruiting beneficial <i>Paenibacillus</i>. Our results reveal that plant hosts dynamically secrete defense-related root metabolites to remodel the rhizosphere microbiome in response to <i>P. brassicae</i> infection. This dual-omics approach elucidates a chemical dialogue mediating plant–microbe–pathogen interactions, offering novel insights for engineering disease-suppressive microbiomes through root exudate manipulation.

Eun Ji Kang, Min‑Gi Cha, Goo‑Hyun Kwon et al.

Vitor Heidrich, Gloria Fackelmann, Milka Malesevic et al.

Abstract Understudied pet-associated microbiomes represent a rich source for the discovery of microbial taxa important for pet and human health. From a cohort of 23 dogs, we sampled and metagenomically sequenced 64 dental plaque microbiomes, generating 1945 metagenome-assembled genomes spanning 347 microbial species, including 277 undercharacterized species without cultivated representatives. Integration with human microbiome data revealed the dog plaque microbiome is more diverse than – and shows little overlap (5.9% species in common) with – the human plaque microbiome, even though some shared periodontal pathobionts arise as a potential concern.

Hemanoel Passarelli-Araujo, Thiago M. Venancio, William P. Hanage

Abstract Background Genetic discontinuity represents abrupt breaks in genomic identity among species. Advances in genome sequencing have enhanced our ability to track and characterize genetic discontinuity in bacterial populations. However, exploring the degree to which bacterial diversity exists as a continuum or sorted into discrete and readily defined species remains a challenge in microbial ecology. Here, we aim to quantify the genetic discontinuity ( $$\delta$$ δ ) and investigate how this metric is related to ecology. Results We harness a dataset comprising 210,129 genomes to systematically explore genetic discontinuity patterns across several distantly related species, finding clear breakpoints which vary depending on the taxa in question. By delving into pangenome characteristics, we uncover a significant association between pangenome saturation and genetic discontinuity. Closed pangenomes are associated with more pronounced breaks, exemplified by Mycobacterium tuberculosis. Additionally, through a machine learning approach, we detect key features such as gene conservation patterns and functional annotations that significantly impact genetic discontinuity prediction. Conclusions Our study clarifies bacterial genetic patterns and their ecological impacts, enhancing the delineation of species boundaries and deepening our understanding of microbial diversity.

Madison Moore, Hunter D. Whittington, Rebecca Knickmeyer et al.

Diet is one of the main factors shaping the human microbiome, yet our understanding of how specific dietary components influence microbial consortia assembly and subsequent stability in response to press disturbances – such as increasing resource availability (feeding rate) – is still incomplete. This study explores the reproducible re-assembly, metabolic interplay, and compositional stability within microbial consortia derived from pooled stool samples of three healthy infants. Using a single-step packed-bed reactor (PBR) system, we assessed the reassembly and metabolic output of consortia exposed to lactose, glucose, galacto-oligosaccharides (GOS), and humanized GOS (hGOS). Our findings reveal that complex carbohydrates, especially those containing low inclusion (~1.25 gL−1) components present in human milk, such as N-acetyl-lactosamine (LacNAc), promote taxonomic, and metabolic stability under varying feeding rates, as shown by diversity metrics and network analysis. Targeted metabolomics highlighted distinct metabolic responses to different carbohydrates: GOS was linked to increased lactate, lactose to propionate, sucrose to butyrate, and CO2, and the introduction of bile salts with GOS or hGOS resulted in butyrate reduction and increased hydrogen production. This study validates the use of single-step PBRs for reliably studying microbial consortium stability and functionality in response to nutritional press disturbances, offering insights into the dietary modulation of microbial consortia and their ecological dynamics.

Mingzhi Dai, Weiwei Cai, Xiang Feng et al.

Microbiomes not only underpin Earth's biogeochemical cycles but also play crucial roles in both engineered and natural ecosystems, such as the soil, wastewater treatment, and the human gut. However, microbiome engineering faces significant obstacles to surmount to deliver the desired improvements in microbiome control. Here, we use the backpropagation neural network (BPNN), optimized through differential evolution (DE-BP), to predict the microbial composition of activated sludge (AS) systems collected from wastewater treatment plants (WWTPs) located worldwide. Furthermore, we introduce a novel clustering algorithm termed Directional Position Nonlinear Emotional Preference Migration Behavior Clustering (DPNG-EPMC). This method is applied to conduct a clustering analysis of WWTPs across various feature attributes. Finally, we employ the Similar Time Generative Adversarial Networks (SiTime-GAN), to synthesize novel microbial compositions and feature attributes data. As a result, we demonstrate that the DE-BP model can provide superior predictions of the microbial composition. Additionally, we show that the DPNG-EPMC can be applied to the analysis of WWTPs under various feature attributes. Finally, we demonstrate that the SiTime-GAN model can generate valuable incremental synthetic data. Our results, obtained through predicting the microbial community and conducting analysis of WWTPs under various feature attributes, develop an understanding of the factors influencing AS communities.

Alejandra Hernández‐Agreda, R. Gates, T. Ainsworth

Janina Rahlff, George Westmeijer, Julia Weissenbach et al.

Abstract Background Aquatic viruses act as key players in shaping microbial communities. In polar environments, they face significant challenges such as limited host availability and harsh conditions. However, due to the restricted accessibility of these ecosystems, our understanding of viral diversity, abundance, adaptations, and host interactions remains limited. Results To fill this knowledge gap, we studied viruses from atmosphere-close aquatic ecosystems in the Central Arctic and Northern Greenland. Aquatic samples for virus-host analysis were collected from ~60 cm depth and the submillimeter surface microlayer (SML) during the Synoptic Arctic Survey 2021 on icebreaker Oden in the Arctic summer. Water was sampled from a melt pond and open water before undergoing size-fractioned filtration, followed by genome-resolved metagenomic and cultivation investigations. The prokaryotic diversity in the melt pond was considerably lower compared to that of open water. The melt pond was dominated by a Flavobacterium sp. and Aquiluna sp., the latter having a relatively small genome size of 1.2 Mb and the metabolic potential to generate ATP using the phosphate acetyltransferase-acetate kinase pathway. Viral diversity on the host fraction (0.2–5 µm) of the melt pond was strikingly limited compared to that of open water. From the 1154 viral operational taxonomic units (vOTUs), of which two-thirds were predicted bacteriophages, 17.2% encoded for auxiliary metabolic genes (AMGs) with metabolic functions. Some AMGs like glycerol-3-phosphate cytidylyltransferase and ice-binding like proteins might serve to provide cryoprotection for the host. Prophages were often associated with SML genomes, and two active prophages of new viral genera from the Arctic SML strain Leeuwenhoekiella aequorea Arc30 were induced. We found evidence that vOTU abundance in the SML compared to that of ~60 cm depth was more positively correlated with the distribution of a vOTU across five different Arctic stations. Conclusions The results indicate that viruses employ elaborate strategies to endure in extreme, host-limited environments. Moreover, our observations suggest that the immediate air-sea interface serves as a platform for viral distribution in the Central Arctic. Video Abstract

Elena A. Alexa, José F. Cobo-Díaz, Erica Renes et al.

Abstract Background Artisanal cheeses usually contain a highly diverse microbial community which can significantly impact their quality and safety. Here, we describe a detailed longitudinal study assessing the impact of ripening in three natural caves on the microbiome and resistome succession across three different producers of Cabrales blue-veined cheese. Results Both the producer and cave in which cheeses were ripened significantly influenced the cheese microbiome. Lactococcus and the former Lactobacillus genus, among other taxa, showed high abundance in cheeses at initial stages of ripening, either coming from the raw material, starter culture used, and/or the environment of processing plants. Along cheese ripening in caves, these taxa were displaced by other bacteria, such as Tetragenococcus, Corynebacterium, Brevibacterium, Yaniella, and Staphylococcus, predominantly originating from cave environments (mainly food contact surfaces), as demonstrated by source-tracking analysis, strain analysis at read level, and the characterization of 613 metagenome-assembled genomes. The high abundance of Tetragenococcus koreensis and Tetragenococcus halophilus detected in cheese has not been found previously in cheese metagenomes. Furthermore, Tetragenococcus showed a high level of horizontal gene transfer with other members of the cheese microbiome, mainly with Lactococcus and Staphylococcus, involving genes related to carbohydrate metabolism functions. The resistome analysis revealed that raw milk and the associated processing environments are a rich reservoir of antimicrobial resistance determinants, mainly associated with resistance to aminoglycosides, tetracyclines, and β-lactam antibiotics and harbored by aerobic gram-negative bacteria of high relevance from a safety point of view, such as Escherichia coli, Salmonella enterica, Acinetobacter, and Klebsiella pneumoniae, and that the displacement of most raw milk-associated taxa by cave-associated taxa during ripening gave rise to a significant decrease in the load of ARGs and, therefore, to a safer end product. Conclusion Overall, the cave environments represented an important source of non-starter microorganisms which may play a relevant role in the quality and safety of the end products. Among them, we have identified novel taxa and taxa not previously regarded as being dominant components of the cheese microbiome (Tetragenococcus spp.), providing very valuable information for the authentication of this protected designation of origin artisanal cheese. Video Abstract

Run-Xuan Tang

The surging demand for new energy vehicles is driven by the imperative to conserve energy, reduce emissions, and enhance the ecological ambiance. By conducting behavioral analysis and mining usage patterns of new energy vehicles, particular patterns can be identified. For instance, overloading the battery, operating with low battery power, and driving at excessive speeds can all detrimentally affect the battery's performance. To assess the impact of such driving behavior on the urban ecology, an environmental computational modeling method has been proposed to simulate the interaction between new energy vehicles and the environment. To extend the time series data of the vehicle's entire life cycle and the ecological environment within the model sequence data, the LSTM model with Bayesian optimizer is utilized for simulation. The analysis revealed the detrimental effects of poor driving behavior on the environment.

Julien Milli, Merlin Boribon, Fabien Malbet et al.

We present an example of low-carbon research activity carried out by astrophysicists and focused on ecology and environmental protection, with direct impacts on territories and society. This project serves as an illustration of action research for an astrophysics lab in the context of the current ecological crisis.

Haiyang Shi

Due to the heterogeneity of the global distribution of ecological and hydrological ground-truth observations, machine learning models can have limited adaptability when applied to unknown locations, which is referred to as weak extrapolability. Domain adaptation techniques have been widely used in machine learning domains such as image classification, which can improve the model generalization ability by adjusting the difference or inconsistency of the domain distribution between the training and test sets. However, this approach has rarely been used explicitly in machine learning models in ecology and hydrology at the global scale, although these models have often been questioned due to geographic extrapolability issues. This paper briefly describes the shortcomings of current machine learning models of ecology and hydrology in terms of the global representativeness of the distribution of observations and the resulting limitations of the lack of extrapolability and suggests that future related modelling efforts should consider the use of domain adaptation techniques to improve extrapolability.