Ontology highlight
ABSTRACT:
PROVIDER: PRJNA768945 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Full-scale wastewater treatment plant bioreactor metagenomic sequencing
| PRJNA691751 | ENA
Project description:Nucleic acids in wastewater provide a rich source of data for detection and surveillance of microbes. We have longitudinally collected 116 RNA samples from a wastewater treatment plant in Berlin/Germany, from March 2021 to July 2022, and 24 DNA samples from May to July 2022. We tracked human astroviruses, enteroviruses, noroviruses and adenoviruses over time to the level of strains or even individual nucleotide variations, showing how detailed human pathogens can be observed using wastewater. For respiratory pathogens, a broad enrichment panel enabled us to detect waves of RSV, influenza, or common cold coronaviruses in high agreement with clinical data. By applying a profile Hidden Markov Model-based search for novel viruses, we identified more than 100 thousand novel transcript assemblies likely not belonging to known virus species, thus substantially expanding our knowledge of virus diversity. Phylogenetic analysis is shown for bunyaviruses and parvoviruses. Finally, we identify Hundreds of novel protein sequences for CRISPR-associated proteins such as Transposase B, a class of small RNA-guided DNA editing enzymes. Taken together, we present a longitudinal and deep investigation into wastewater-derived genomic sequencing data that underlines the value of sewage surveillance for public health, planetary virome research, and biotechnological potential.
2023-04-13 | GSE228220 | GEO
Project description:In this study, we exposed Caenorhabditis elegans wild types N2 to water collected from six sources in the Dutch village Sneek. The sources were: wastewater from a hospital, a community (80 households), a nursing home, influent into the local municipal wastewater treatment plant, effluent of the wastewater treatment plant, and surface water samples. The goal of the experiment was to determine if C. elegans can be used to identify pollutants in the water by transcriptional profiling. Age synchronized worms at developmental L4 larval stage were exposed to treatment for 24 hours. After flash freezing the samples, RNA was isolated, labeled and hybridized on oligo microarray (Agilent) slides.
2022-12-03 | E-MTAB-11260 | biostudies-arrayexpress
Project description:Recently, intensive global climate change has become a major factor impacting plant survival during the winter. Freezing cold temperatures during the winter and abnormal temperature fluctuations during the winter and early spring are the most harmful ambient factors threatening tea plant winter survival and currently cause marked economic losses in tea production. In this study, by simulating natural climate change, we established cold acclimation (CA) and rapid cold stress (after CA) conditions to comprehensively investigate the transcriptome changes involved in CA and rapid cold stress. Electrolyte leakage (EL) rate and expression profile clustering analyses confirmed that the experimental design was valid. Comparative transcription analysis identified many differentially expressed genes (DEGs) involved in both processes. Time course and pathway enrichment analyses further revealed the physiological changes that occur during the initial period of CA and the cell wall changes that occur throughout the entire CA process; these changes play crucial roles in increasing freezing tolerance during this process. Compared with CA, different cold response mechanisms were rapidly activated under cold stress; however, the subsequent accumulation of reactive oxygen species, which affect multiple aspects, caused by freezing cold could be the harshest factor impairing tea leaves. Moreover, we investigated 60 DEGs shared by both processes and highlighted the importance of KCSs, HXXXD-type acyl-transferase family proteins, NAC080, SWEETs and ENOs in the responses to various cold conditions. These results greatly improve our knowledge of cold response mechanisms in tea plants and provide meaningful information for functional studies investigating cold tolerance-related genes.
2017-09-29 | GSE104390 | GEO
Project description:Characterization of microbial communities at the genomic, transcriptomic, proteomic and metabolomic levels, with a special interest on lipid accumulating bacterial populations, which are naturally enriched in biological wastewater treatment systems and may be harnessed for the conversion of mixed lipid substrates (wastewater) into biodiesel. The project aims to elucidate the genetic blueprints and the functional relevance of specific populations within the community. It focuses on within-population genetic and functional heterogeneity, trying to understand how fine-scale variations contribute to differing lipid accumulating phenotypes. Insights from this project will contribute to the understanding the functioning of microbial ecosystems, and improve optimization and modeling strategies for current and future biological wastewater treatment processes. This project contains datasets derived from the same biological wastewater treatment plant. The data includes metagenomes, metatranscriptomes, metaproteomes and organisms isolated in pure cultures. Characterization of microbial communities at the genomic, transcriptomic, proteomic and metabolomic levels, with a special interest on lipid accumulating bacterial populations, which are naturally enriched in biological wastewater treatment systems and may be harnessed for the conversion of mixed lipid substrates (wastewater) into biodiesel. The project aims to elucidate the genetic blueprints and the functional relevance of specific populations within the community. It focuses on within-population genetic and functional heterogeneity, trying to understand how fine-scale variations contribute to differing lipid accumulating phenotypes. Insights from this project will contribute to the understanding the functioning of microbial ecosystems, and improve optimization and modeling strategies for current and future biological wastewater treatment processes. This project contains datasets derived from the same biological wastewater treatment plant. The data includes metagenomes, metatranscriptomes, metaproteomes and organisms isolated in pure cultures.
2020-08-19 | PXD013655 | Pride