Ontology highlight
ABSTRACT:
PROVIDER: PRJNA779228 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We established simple synthetic microbial communities in a microcosm model system to determine the mechanisms that underlay cross-feeding in microbial methane-consuming communities. Co-occurring strains from Lake Washington sediment were used that are involved in methane consumption, a methanotroph and two non-methanotrophic methylotrophs.
2016-12-26 | GSE85736 | GEO
Project description:Drought represents a significant stress to microorganisms and is known to reduce microbial activity and organic matter decomposition in Mediterranean ecosystems. However, we lack a detailed understanding of the drought stress response of microbial decomposers. Here we present metatranscriptomic data on the physiological response of in situ microbial communities on plant litter to long-term drought in Californian grass and shrub ecosystems.
2020-05-01 | GSE148618 | GEO
Project description:Microorganisms are key contributors to biogeochemical flux in estuarine ecosystems. In this study, we conducted proteogenomic characterization of microbial communities from estuarine ecosystems.
2018-07-04 | PXD008093 | Pride
Project description:We are using a minimal model of interacting multispecies ecological communities that incorporates competition, immigration, and demographic noise. Importantly, the dynamics of the system are described by a birth-death process with interactions, whereby the abundance (the number of individuals) of any species is discrete, where the number of individuals from a given species increases or decreases by one following given birth or death rates (respectively).
We find rich behavior with many unexpected regimes. We apply the insights and implications of our model to the range of behaviors observed experimentally in different ecosystems—from bacteria to the immune system.
This repository stores the code used to study the population dynamics described in the PNAS publication: Phenomenology and Dynamics of Competitive Ecosystems Beyond the Niche-Neutral Regimes by Nava Leibovich, Jeremy Rothschild, Sidhartha Goyal and Anton Zilman. See more at (https://github.com/jbRothschild/project-abundance/tree/PNAS)
2023-03-17 | MODEL2212080001 | BioModels
Project description:Desert microbial communities live in a pulsed ecosystem shaped by isolated and rare precipitation events. The Namib desert is one of the oldest continuously hyperarid ecosystems on Earth. In this study, surface microbial communities of open soils (without sheltering features like rocks, vegetation or biological soil crusts) are analysed. We designed an artificial rainfall experiment where a 7x7 (3.5 x 3.5 m) plot remained dry while an adjacent one received a 30 mm simulated rain. Samples were taken randomly in parallel from both plots at 10 min, 1 h, 3 h, 7 h, 24 h and 7 days after the watering moment. Duplicate libraries were generated from total (rRNA depleted) RNA and sequenced 2x150 bp in an Illumina Hiseq 4000 instrument.
2021-06-01 | E-MTAB-9439 | biostudies-arrayexpress
Project description:To effectively monitor microbial populations in acidic environments and bioleaching systems, a comprehensive 50-mer-based oligonucleotide microarray was developed based on most of the known genes associated with the acidophiles. This array contained 1,072 probes in which there were 571 related to 16S rRNA and 501 related to functional genes. Acid mine drainage (AMD) presents numerous problems to the aquatic life and surrounding ecosystems. However, little is known about the geographic distribution, diversity, composition, structure and function of AMD microbial communities. In this study, we analyzed the geographic distribution of AMD microbial communities from twenty sites using restriction fragment length polymorphism (RFLP) analysis of 16S rRNA genes, and the results showed that AMD microbial communities were geographically distributed and had high variations among different sites. Then an AMD-specific microarray was used to further analyze nine AMD microbial communities, and showed that those nine AMD microbial communities had high variations measured by the number of detected genes, overlapping genes between samples, unique genes, and diversity indices. Statistical analyses indicated that the concentrations of Fe, S, Ca, Mg, Zn, Cu and pH had strong impacts on both phylogenetic and functional diversity, composition, and structure of AMD microbial communities. This study provides insights into our understanding of the geographic distribution, diversity, composition, structure and functional potential of AMD microbial communities and key environmental factors shaping them.
2011-03-08 | GSE27542 | GEO
Project description:To effectively monitor microbial populations in acidic environments and bioleaching systems, a comprehensive 50-mer-based oligonucleotide microarray was developed based on most of the known genes associated with the acidophiles. This array contained 1,072 probes in which there were 571 related to 16S rRNA and 501 related to functional genes. Acid mine drainage (AMD) presents numerous problems to the aquatic life and surrounding ecosystems. However, little is known about the geographic distribution, diversity, composition, structure and function of AMD microbial communities. In this study, we analyzed the geographic distribution of AMD microbial communities from twenty sites using restriction fragment length polymorphism (RFLP) analysis of 16S rRNA genes, and the results showed that AMD microbial communities were geographically distributed and had high variations among different sites. Then an AMD-specific microarray was used to further analyze nine AMD microbial communities, and showed that those nine AMD microbial communities had high variations measured by the number of detected genes, overlapping genes between samples, unique genes, and diversity indices. Statistical analyses indicated that the concentrations of Fe, S, Ca, Mg, Zn, Cu and pH had strong impacts on both phylogenetic and functional diversity, composition, and structure of AMD microbial communities. This study provides insights into our understanding of the geographic distribution, diversity, composition, structure and functional potential of AMD microbial communities and key environmental factors shaping them. This study investigated the geographic distribution of Acid Mine Drainages microbial communities using a 16S rRNA gene-based RFLP method and the diversity, composition and structure of AMD microbial communities phylogenetically and functionally using an AMD-specific microarray which contained 1,072 probes ( 571 related to 16S rRNA and 501 related to functional genes). The functional genes in the microarray were involved in carbon metabolism (158), nitrogen metabolism (72), sulfur metabolism (39), iron metabolism (68), DNA replication and repair (97), metal-resistance (27), membrane-relate gene (16), transposon (13) and IST sequence (11).
2011-03-08 | E-GEOD-27542 | biostudies-arrayexpress
Project description:Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a new machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially-resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally-defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell state co-occurrence patterns, we discovered 10 clinically-distinct multicellular communities with unexpectedly strong conservation, including four with unique myeloid and stromal elements, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue (https://ecotyper.stanford.edu).
2021-08-12 | GSE181817 | GEO