Ontology highlight
ABSTRACT:
PROVIDER: PRJNA723886 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Bacteriophage – host dynamics and interactions are important for microbial community composition and ecosystem function. Nonetheless, empirical evidence in engineered environment is scarce. Here, we examined phage and prokaryotic community composition of four anaerobic digestors in full-scale wastewater treatment plants (WWTPs) across China. Despite relatively stable process performance in biogas production, both phage and prokaryotic groups fluctuated monthly over a year of study period. Nonetheless, there were significant correlations in their α- and β-diversities between phage and prokaryotes. Phages explained 40.6% of total prokaryotic community composition, much higher than the explainable power by abiotic factors (14.5%). Consequently, phages were significantly (P<0.010) linked to parameters related to process performance including biogas production and volatile solid concentrations. Association network analyses showed that phage-prokaryote pairs were deeply rooted, and two network modules were exclusively comprised of phages, suggesting a possibility of co-infection. Those results collectively demonstrate phages as a major biotic factor in controlling bacterial composition. Therefore, phages may play a larger role in shaping prokaryotic dynamics and process performance of WWTPs than currently appreciated, enabling reliable prediction of microbial communities across time and space.
2016-12-29 | GSE92978 | GEO
Project description:Transcriptome profiling of pyrethroid resistant field populations of Anopheles funestus across Uganda and neighboring Kenya from Uganda and Kenya compared to a susceptible lab strain FANG
2020-10-19 | E-MTAB-9529 | biostudies-arrayexpress
Project description:This study used an emerging analytical technology (cDNA microarrays) to assess the potential effects of PFC exposure on largemouth bass in TCMA lakes. Microarrays simultaneously measure the expression of thousands of genes in various tissues from organisms exposed to different environmental conditions. From this large data set, biomarkers (i.e., genes that are expressed in response to an exposure to known stressors) and bioindicators (e.g., suites of genes that correspond to changes in organism health) can be simultaneously measured to clarify the relationship between contaminant exposure and organism health. Based on current scientific literature, we hypothesized that gene expression patterns would be altered in fish exposed to PFCs (as compared with fish from reference lakes), and that the magnitude of these changes would correspond to the concentrations of PFCs present throughout TCMA lakes. Patterns of gene expression in largemouth bass observed across the TCMA lakes corresponded closely with PFC concentration. Concentrations of PFCs in largemouth bass varied significantly across the sampled lakes, where the lowest concentrations were found in Steiger and Upper Prior Lakes and the highest concentrations were found in Calhoun and Twin Lakes. Patterns of gene expression were most different (relative to controls) in fish with the highest PFC tissue concentrations, where fish from Twin and Calhoun Lakes were observed to have between 5437 and 5936 differentially expressed genes in liver and gonad tissues. Although gene expression patterns demonstrated a high degree of correlation with PFC concentrations, microarray data also suggest there are likely additional factors influencing gene expression patterns in largemouth bass in TCMA lakes.
2014-07-31 | GSE57684 | GEO
Project description:We evaluated transcriptional profiles in peripheral blood mononuclear cells (PBMCs) from 54 pregnant women in Kenya, 19 of whom delivered preterm.
2019-12-01 | GSE140939 | GEO
Project description:Contaminants of Emerging Concern (CECs) can be measured in waters across the United States, including the tributaries of the Great Lakes. The extent to which these contaminants affect gene expression in aquatic wildlife is unclear. This dataset presents the full hepatic transcriptomes of laboratory reared fathead minnows (Pimephales promelas) caged at multiple sites within the Milwaukee Estuary area of concern and control sites. Following 4 days of in situ exposure, liver tissue was removed from males at each site for RNA extraction and sequencing, yielding a total of 116 samples from which libraries were prepared, pooled, and sequenced. For each exposure site, 179 chemical analytes were also assessed. These data were created with the intention of inviting research on possible transcriptomic changes observed in aquatic species exposed to CECs. Access to both full sequencing reads of animal samples as well as water contaminant data across multiple Great Lakes sites will allow others to explore the health of these ecosystems, in support of the aims of the Great Lakes Restoration Initiative.
2020-01-31 | GSE144301 | GEO
Project description:Prokaryotic cell transcriptomics have been limited to mixed or sub-population dynamics and individuality of cells within heterogeneous populations. This significantly hampers further knowledge into spatiotemporal and stage-specific processes of prokaryotic cells within complex environments. Herein, we developed a ‘TRANSITomic’ approach to profile transcriptomes of single Burkholderia pseudomallei (Bp) cells as they transit through host cell infection at defined stages, yielding pathophysiological insights. Bp transits through host cells during infection in three observable stages: i) vacuole entry; ii) cytoplasmic escape and replication; and iii) membrane protrusion, promoting cell-to-cell spread. The Bp ‘TRANSITome’ revealed dynamic gene-expression flux during transit in host cells. Some genes undergoing changes are required for pathogenesis. We discovered several hypothetical proteins and assigned them to virulence mechanisms such as attachment, cytoskeletal modulation, and autophagy evasion. The Bp ‘TRANSITome’ has provided high-resolution understanding of host-pathogen interactions and opens the door for future single-cell transcriptomic analysis of other prokaryotic processes.
2021-02-10 | GSE156938 | GEO