Hasil untuk "Microbial ecology"

D. Ning, Ye Deng, J. Tiedje et al.

Significance An ecological community is a dynamic complex system with a myriad of interacting species, which are controlled by various scale-dependent deterministic and stochastic forces. With rapid advances in genomics technologies, categorizing biological diversity, particularly microbial diversity, becomes relatively easy, but the great challenge is to disentangle the mechanisms controlling biological diversity. The general null model-based framework developed in this study provides an effective and robust tool to ecologists for quantitatively assessing ecological stochasticity. By highlighting the caveats such as model selection, similarity metrics, and spatial scales, this study provides guidance for appropriate use of null model-based approaches for examining community assembly processes. Although this framework was tested with microbial data, it should also be applicable to plant and animal ecology. Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. Here we propose a general mathematical framework to quantify ecological stochasticity under different situations in which deterministic factors drive the communities more similar or dissimilar than null expectation. An index, normalized stochasticity ratio (NST), was developed with 50% as the boundary point between more deterministic (50%) assembly. NST was tested with simulated communities by considering abiotic filtering, competition, environmental noise, and spatial scales. All tested approaches showed limited performance at large spatial scales or under very high environmental noise. However, in all of the other simulated scenarios, NST showed high accuracy (0.90 to 1.00) and precision (0.91 to 0.99), with averages of 0.37 higher accuracy (0.1 to 0.7) and 0.33 higher precision (0.0 to 1.8) than previous approaches. NST was also applied to estimate stochasticity in the succession of a groundwater microbial community in response to organic carbon (vegetable oil) injection. Our results showed that community assembly was shifted from more deterministic (NST = 21%) to more stochastic (NST = 70%) right after organic carbon input. As the vegetable oil was consumed, the community gradually returned to be more deterministic (NST = 27%). In addition, our results demonstrated that null model algorithms and community similarity metrics had strong effects on quantifying ecological stochasticity.

Christoph A. Thaiss, N. Zmora, M. Levy et al.

N. Ling, Tingting Wang, Y. Kuzyakov

Microbial composition and functions in the rhizosphere—an important microbial hotspot—are among the most fascinating yet elusive topics in microbial ecology. We used 557 pairs of published 16S rDNA amplicon sequences from the bulk soils and rhizosphere in different ecosystems around the world to generalize bacterial characteristics with respect to community diversity, composition, and functions. The rhizosphere selects microorganisms from bulk soil to function as a seed bank, reducing microbial diversity. The rhizosphere is enriched in Bacteroidetes, Proteobacteria, and other copiotrophs. Highly modular but unstable bacterial networks in the rhizosphere (common for r -strategists) reflect the interactions and adaptations of microorganisms to dynamic conditions. Dormancy strategies in the rhizosphere are dominated by toxin–antitoxin systems, while sporulation is common in bulk soils. Functional predictions showed that genes involved in organic compound conversion, nitrogen fixation, and denitrification were strongly enriched in the rhizosphere (11–182%), while genes involved in nitrification were strongly depleted. Understanding soil microbiota dynamics is key the development of soil-based sustainable agriculture and conservation strategies. This meta-analysis shows that bulk soil functions as a seed bank for the rhizosphere, which encompasses a rich microbiota adapted to dynamic conditions in hotpots.

Shuo Jiao, Yunfeng Yang, Yiqin Xu et al.

F. Cardona, C. Andrés-Lacueva, S. Tulipani et al.

The biological properties of dietary polyphenols are greatly dependent on their bioavailability that, in turn, is largely influenced by their degree of polymerization. The gut microbiota play a key role in modulating the production, bioavailability and, thus, the biological activities of phenolic metabolites, particularly after the intake of food containing high-molecular-weight polyphenols. In addition, evidence is emerging on the activity of dietary polyphenols on the modulation of the colonic microbial population composition or activity. However, although the great range of health-promoting activities of dietary polyphenols has been widely investigated, their effect on the modulation of the gut ecology and the two-way relationship "polyphenols ↔ microbiota" are still poorly understood. Only a few studies have examined the impact of dietary polyphenols on the human gut microbiota, and most were focused on single polyphenol molecules and selected bacterial populations. This review focuses on the reciprocal interactions between the gut microbiota and polyphenols, the mechanisms of action and the consequences of these interactions on human health.

Ye Deng, Yi-Huei Jiang, Yunfeng Yang et al.

BackgroundUnderstanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.ResultsHere, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).ConclusionsThe RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Ilseung Cho, M. Blaser

E. Costello, Keaton Stagaman, L. Dethlefsen et al.

F. Escudié, Lucas Auer, M. Bernard et al.

J. M. Mark Welch, B. Rossetti, C. Rieken et al.

T. Thomas, J. Gilbert, Folker Meyer

Metagenomics applies a suite of genomic technologies and bioinformatics tools to directly access the genetic content of entire communities of organisms. The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now. With the growing numbers of activities also comes a plethora of methodological knowledge and expertise that should guide future developments in the field. This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing. As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared.

T. Stoeck, D. Bass, M. Nebel et al.

Sequencing of ribosomal DNA clone libraries amplified from environmental DNA has revolutionized our understanding of microbial eukaryote diversity and ecology. The results of these analyses have shown that protist groups are far more genetically heterogeneous than their morphological diversity suggests. However, the clone library approach is labour‐intensive, relatively expensive, and methodologically biased. Therefore, even the most intensive rDNA library analyses have recovered only small samples of much larger assemblages, indicating that global environments harbour a vast array of unexplored biodiversity. High‐throughput parallel tag 454 sequencing offers an unprecedented scale of sampling for molecular detection of microbial diversity. Here, we report a 454 protocol for sampling and characterizing assemblages of eukaryote microbes. We use this approach to sequence two SSU rDNA diversity markers—the variable V4 and V9 regions—from 10 L of anoxic Norwegian fjord water. We identified 38 116 V4 and 15 156 V9 unique sequences. Both markers detect a wide range of taxonomic groups but in both cases the diversity detected was dominated by dinoflagellates and close relatives. Long‐tailed rank abundance curves suggest that the 454 sequencing approach provides improved access to rare genotypes. Most tags detected represent genotypes not currently in GenBank, although many are similar to database sequences. We suggest that current understanding of the ecological complexity of protist communities, genetic diversity, and global species richness are severely limited by the sequence data hitherto available, and we discuss the biological significance of this high amplicon diversity.

H. Flint, E. Bayer, M. Rincón et al.

Jillian L. Waters, R. Ley

The Christensenellaceae, a recently described family in the phylum Firmicutes, is emerging as an important player in human health. The relative abundance of Christensenellaceae in the human gut is inversely related to host body mass index (BMI) in different populations and multiple studies, making its relationship with BMI the most robust and reproducible link between the microbial ecology of the human gut and metabolic disease reported to date. The family is also related to a healthy status in a number of other different disease contexts, including obesity and inflammatory bowel disease. In addition, Christensenellaceae is highly heritable across multiple populations, although specific human genes underlying its heritability have so far been elusive. Further research into the microbial ecology and metabolism of these bacteria should reveal mechanistic underpinnings of their host-health associations and enable their development as therapeutics.

J. Cole, Benli Chai, Ryan J. Farris et al.

Substantial new features have been implemented at the Ribosomal Database Project in response to the increased importance of high-throughput rRNA sequence analysis in microbial ecology and related disciplines. The most important changes include quality analysis, including chimera detection, for all available rRNA sequences and the introduction of myRDP Space, a new web component designed to help researchers place their own data in context with the RDP's data. In addition, new video tutorials describe how to use RDP features. Details about RDP data and analytical functions can be found at the RDP-II website ().

J. Handelsman

S. Pryde, S. Duncan, G. Hold et al.

K. S. Andersen, R. Kirkegaard, S. Karst et al.

Summary Microbial community analysis using 16S rRNA gene amplicon sequencing is the backbone of many microbial ecology studies. Several approaches and pipelines exist for processing the raw data generated through DNA sequencing and convert the data into OTU-tables. Here we present ampvis2, an R package designed for analysis of microbial community data in OTU-table format with focus on simplicity, reproducibility, and sample metadata integration, with a minimal set of intuitive commands. Unique features include flexible heatmaps and simplified ordination. By generating plots using the ggplot2 package, ampvis2 produces publication-ready figures that can be easily customised. Furthermore, ampvis2 includes features for interactive visualisation, which can be convenient for larger, more complex data. Availability ampvis2 is implemented in the R statistical language and is released under the GNU A-GPL license. Documentation website and source code is maintained at: https://github.com/MadsAlbertsen/ampvis2 Contact Mads Albertsen (ma@bio.aau.dk)

C. McSweeney, B. Palmer, D. McNeill et al.

Tianhao Shi, Yang Zhang, Xiaoyan Zhao et al.

The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its behavioral and distributional implications. Leveraging a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform, this study elucidated the distinct creation and consumption behaviors characterizing AIGC versus Human-Generated Content (HGC). We identified a prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC. Deeper analysis uncovered the ability of the algorithmic content distribution mechanism in moderating these competing interests regarding AIGC. These findings advocated for the implementation of AIGC-sensitive distribution algorithms and precise governance frameworks to ensure the long-term health of the online content platforms.