Indoor microbes and chemicals
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB41403 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR5208230_1.fastq.gz | Fastqsanger.gz | |||
| ERR5208230_2.fastq.gz | Fastqsanger.gz | |||
| ERR5208240_1.fastq.gz | Fastqsanger.gz | |||
| ERR5208240_2.fastq.gz | Fastqsanger.gz | |||
| ERR5208250_1.fastq.gz | Fastqsanger.gz |
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Project description:Background - Multiple health effects are associated with moisture damage in buildings. Studies explaining these associations and cell-level mechanisms behind the observed health effects are urgently called for. Objectives - Alongside general toxicological markers, we focused on characterizing gene expression in human airway epithelium after exposure to indoor air particulate matter (PM) sampled from houses with and without moisture damage. Methods - We performed detailed technical building inspections in 25 residential houses and categorized them based on the detection of moisture damages and the probability of occupant exposure. PM sampling was complemented by microbiological and volatile organic compound assessment. We exposed airway constructs to three doses (1:16, 1:8, 1:4) of PM from moisture-damaged (index) and non-moisture-damaged (reference) houses. The RNA of exposed constructs was sequenced targeting toxicology -related genes. We did groupwise comparisons between index and reference houses and pairwise comparisons in matched index/reference houses. We analyzed general toxicological markers and imaged the constructs with electron microscopy. Results - In pairwise comparison, several genes were differentially expressed. Genes related to multiple toxicological pathways were highlighted, while none of the genes was consistently expressed in all the comparisons. In groupwise comparison, CYP1A1 was statistically significantly over-expressed in index houses at all doses (dose 1:16 log2FC = 0.007, padj = 0.03; dose 1:8 log2FC = 0.077, padj = 0.018; dose 1:4 log2FC = 0.270, padj = 0.002) and NFKB1 at the highest dose 1:4 (log2FC = 0.124, padj = 0.029). General toxicological markers did not separate the index/reference houses. Discussion - The exposure to PM from index houses activates toxicology -related genes in airway constructs. While differential expression was not consistent among all the index/reference pairs, possibly due to compositional differences of bioactive particles, our study highlights CYP1A1 and NFKB1 as potential targets in moisture damage -associated cellular responses.
2022-04-27 | GSE175878 | GEO
Project description:Modeling fungal growth in carpet dust exposed to variations in relative humidity
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Project description:Plasma membrane NADPH oxidases (NOXs) are major producers of reactive oxygen species (ROS) in plant cells under normal growth and stress conditions. Rice NOXs have multiple homologs but their functional mechanisms are largely unknown. We used microarrays to detail the global gene expression profiles in rice wild-type (WT, Dongjin) and a mutant osnox2 which loss the functions of OsNOX2 protein under drought and identified distinct classes of genes between the two type rice plants under both normal growth and drought stressed conditions. The youngest fully expanded leaves from 2.5-month-old WT and osnox2 plants (three replicates each), grown under normal growth (soil moisture, 47.3%) and drought conditions (soil moisture, 8.5%), were used for RNA extraction and hybridization on Affymetrix microarrays. Control: normal growth condition; Drought: drought stress condition.
2015-12-31 | E-GEOD-54466 | biostudies-arrayexpress
Project description:Background: Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of human biomedical science. Many such classifiers discovered thus far lack vigorous statistical and experimental validations, with their stability and reliability unknown. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors were used in this study to search for classifiers of endocrine disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and all tissue combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results: Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their intended chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions: The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. While GSEA appeared to provide a flexible and effective tool for application of gene classifiers, a similar but more refined algorithm, connectivity mapping, should also be explored for ecotoxicological applications. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions. chemical abbreviations: EE2, 17α-ethynyl estradiol; FAD, fadrozole; TRB, 17 -trenbolone; FIP, fipronil; PRO, prochloraz; FLU, flutamide; MUS, muscimol; KET, ketoconazole; TRI, trilostane; VIN, vinclozolin Since this study was conducted in several phases, three different version of Agilent zebrafish two color microarrays were used based on their availability at the time. These include G2518A (designID 013223) and G2519F (designID 015064, 019161). There were a total of 58 treatment conditions with various combinations of chemical, tissue type, exposure time, and gender. Each condition contained eight to 12 independent samples, half from chemical-treated fish and half from water- control fish.
2012-08-22 | E-GEOD-38070 | biostudies-arrayexpress
Project description:BackgroundMicroorganisms influence the chemical milieu of their environment, and chemical metabolites can affect ecological processes. In built environments, where people spend the majority of their time, very little is known about how surface-borne microorganisms influence the chemistry of the indoor spaces. Here, we applied multidisciplinary approaches to investigate aspects of chemical microbiology in a house.MethodsWe characterized the microbial and chemical composition of two common and frequently wet surfaces in a residential setting: kitchen sink and bathroom shower. Microbial communities were studied using culture-dependent and independent techniques, including targeting RNA for amplicon sequencing. Volatile and soluble chemicals from paired samples were analyzed using state-of-the-art techniques to explore the links between the observed microbiota and chemical exudates.ResultsMicrobial analysis revealed a rich biological presence on the surfaces exposed in kitchen sinks and bathroom shower stalls. Microbial composition, matched for DNA and RNA targets, varied by surface type and sampling period. Bacteria were found to have an average of 25× more gene copies than fungi. Biomass estimates based on qPCR were well correlated with measured total volatile organic compound (VOC) emissions. Abundant VOCs included products associated with fatty acid production. Molecular networking revealed a diversity of surface-borne compounds that likely originate from microbes and from household products.ConclusionsMicrobes played a role in structuring the chemical profiles on and emitted from kitchen sinks and shower stalls. Microbial VOCs (mVOCs) were predominately associated with the processing of fatty acids. The mVOC composition may be more stable than that of microbial communities, which can show temporal and spatial variation in their responses to changing environmental conditions. The mVOC output from microbial metabolism on kitchen sinks and bathroom showers should be apparent through careful measurement, even against a broader background of VOCs in homes, some of which may originate from microbes in other locations within the home. A deeper understanding of the chemical interactions between microbes on household surfaces will require experimentation under relevant environmental conditions, with a finer temporal resolution, to build on the observational study results presented here.
| S-EPMC5615633 | biostudies-literature
Project description:Indoor dust is a reservoir for commercial consumer product chemicals, including many compounds with known or suspected health effects. However, most dust exposure studies measure few chemicals in small samples. We systematically searched the U.S. indoor dust literature on phthalates, replacement flame retardants (RFRs), perfluoroalkyl substances (PFASs), synthetic fragrances, and environmental phenols and estimated pooled geometric means (GMs) and 95% confidence intervals for 45 chemicals measured in ?3 data sets. In order to rank and contextualize these results, we used the pooled GMs to calculate residential intake from dust ingestion, inhalation, and dermal uptake from air, and then identified hazard traits from the Safer Consumer Products Candidate Chemical List. Our results indicate that U.S. indoor dust consistently contains chemicals from multiple classes. Phthalates occurred in the highest concentrations, followed by phenols, RFRs, fragrance, and PFASs. Several phthalates and RFRs had the highest residential intakes. We also found that many chemicals in dust share hazard traits such as reproductive and endocrine toxicity. We offer recommendations to maximize comparability of studies and advance indoor exposure science. This information is critical in shaping future exposure and health studies, especially related to cumulative exposures, and in providing evidence for intervention development and public policy.
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Project description:Background: Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of human biomedical science. Many such classifiers discovered thus far lack vigorous statistical and experimental validations, with their stability and reliability unknown. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors were used in this study to search for classifiers of endocrine disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and all tissue combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results: Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their intended chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions: The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. While GSEA appeared to provide a flexible and effective tool for application of gene classifiers, a similar but more refined algorithm, connectivity mapping, should also be explored for ecotoxicological applications. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions. chemical abbreviations: EE2, 17α-ethynyl estradiol; FAD, fadrozole; TRB, 17 -trenbolone; FIP, fipronil; PRO, prochloraz; FLU, flutamide; MUS, muscimol; KET, ketoconazole; TRI, trilostane; VIN, vinclozolin
2012-08-22 | GSE38070 | GEO
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