Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27256300_1.fastq.gz | Fastqsanger.gz | |||
| SRR27256300_2.fastq.gz | Fastqsanger.gz | |||
| SRR27256301_1.fastq.gz | Fastqsanger.gz | |||
| SRR27256301_2.fastq.gz | Fastqsanger.gz | |||
| SRR27256302_1.fastq.gz | Fastqsanger.gz |
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Project description:The clinical importance of microbiomes to the chronicity of wounds is widely appreciated, yet little is understood about patient-specific processes shaping wound microbiome composition. Here, a two-cohort microbiome-genome wide association study is presented through which patient genomic loci associated with chronic wound microbiome diversity were identified. Further investigation revealed that alternative TLN2 and ZNF521 genotypes explained significant inter-patient variation in relative abundance of two key pathogens, Pseudomonas aeruginosa and Staphylococcus epidermidis. Wound diversity was lowest in Pseudomonas aeruginosa infected wounds, and decreasing wound diversity had a significant negative linear relationship with healing rate. In addition to microbiome characteristics, age, diabetic status, and genetic ancestry all significantly influenced healing. Using structural equation modeling to identify common variance among SNPs, six loci were sufficient to explain 53% of variation in wound microbiome diversity, which was a 10% increase over traditional multiple regression. Focusing on TLN2, genotype at rs8031916 explained expression differences of alternative transcripts that differ in inclusion of important focal adhesion binding domains. Such differences are hypothesized to relate to wound microbiomes and healing through effects on bacterial exploitation of focal adhesions and/or cellular migration. Related, other associated loci were functionally enriched, often with roles in cytoskeletal dynamics. This study, being the first to identify patient genetic determinants for wound microbiomes and healing, implicates genetic variation determining cellular adhesion phenotypes as important drivers of infection type. The identification of predictive biomarkers for chronic wound microbiomes may serve as risk factors and guide treatment by informing patient-specific tendencies of infection.
2020-04-25 | GSE149314 | GEO
Project description:Marine sediment microbiomes in East Siberian Sea
| PRJEB76672 | ENA
Project description:modENCODE_submission_3082 This submission comes from a modENCODE project of Michael Snyder. For full list of modENCODE projects, see http://www.genome.gov/26524648 Project Goal: We are identifying the DNA binding sites for 300 transcription factors in C. elegans. Each transcription factor gene is tagged with the same GFP fusion protein, permitting validation of the gene's correct spatio-temporal expression pattern in transgenic animals. Chromatin immunoprecipitation on each strain is peformed using an anti-GFP antibody, and any bound DNA is deep-sequenced using Solexa GA2 technology. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf EXPERIMENT TYPE: CHIP-seq. BIOLOGICAL SOURCE: Strain: OP193(official name : OP193 genotype : unc-119(ed3); wgIs193(sea-2::TY1 EGFP FLAG C; unc-119) outcross : 3 mutagen : Bombard tags : GFP::3xFlag description : This strain's transgene was constructed by Mihail Sarov at the Max Planck Institute for Cell Biology in Tubiginen using Tony Hyman's recombineering pipeline. The resulting plasmid was used for biolistic transformation of an unc-119(ed3) strain. The SEA-2::EGFP fusion protein is expressed in the correct sea-2 spatio-temporal expression pattern. This strain was used for ChIP-seq experiments to map the in vivo binding sites for the SEA-2 transcription factor. made_by : ); Developmental Stage: L3; Genotype: unc-119(ed3); wgIs193(sea-2::TY1 EGFP FLAG C; unc-119); Sex: Hermaphrodite; EXPERIMENTAL FACTORS: Developmental Stage L3; Target gene sea-2; Strain OP193(official name : OP193 genotype : unc-119(ed3); wgIs193(sea-2::TY1 EGFP FLAG C; unc-119) outcross : 3 mutagen : Bombard tags : GFP::3xFlag description : This strain's transgene was constructed by Mihail Sarov at the Max Planck Institute for Cell Biology in Tubiginen using Tony Hyman's recombineering pipeline. The resulting plasmid was used for biolistic transformation of an unc-119(ed3) strain. The SEA-2::EGFP fusion protein is expressed in the correct sea-2 spatio-temporal expression pattern. This strain was used for ChIP-seq experiments to map the in vivo binding sites for the SEA-2 transcription factor. made_by : ); temp (temperature) 20 degree celsius
2012-05-07 | E-GEOD-37806 | biostudies-arrayexpress
Project description:We identified cis-regulatory elements based on their dynamic chromatin accessibility during the gastrula-larva stages of sea urchin and sea star and studied their evolution in these echinoderm species
2021-10-24 | GSE186363 | GEO
Project description:HuMiChip was used to analyze human oral and gut microbiomes, showing significantly different functional gene profiles between oral and gut microbiome. The results were used to demonstarte the usefulness of applying HuMiChip to human microbiome studies.
2014-02-04 | E-GEOD-54290 | biostudies-arrayexpress
Project description:HuMiChip was used to analyze human oral and gut microbiomes, showing significantly different functional gene profiles between oral and gut microbiome.
2014-02-04 | GSE54290 | GEO
Project description:Transcription profiling by RNA-seq of six African green monkey tissues at different ages (Chlorocebus aethiops sabaeus)
2016-09-23 | E-MTAB-4404 | ExpressionAtlas
Project description:We developed an approach named Rapid Assay of Individual Microbiome (RapidAIM) to screen xenobiotics against individual microbiomes, and conducted a proof-of-concept (POC) study on the use of RapidAIM. We tested 43 compounds against five individual microbiomes. The individual microbiomes are cultured in 96-well plates for 24 hours and the samples are then analyzed using a metaproteomics-based analytical approach to gain functional insight into the individual microbiomes responses following drug treatments.The tested compounds significantly affected overall microbiome abundance, microbiome composition and functional pathways at multiple taxonomic levels. The microbiome responses to berberine, metformin, diclofenac, fructooligosaccharide and most antibiotics were consistent among most individual microbiomes. Interestingly, most of our tested NSAIDs, statins, and histamine-2 blockers induced individually distinct responses. Our workflow offers an effective solution to systematically study the effects of many different compounds on individual microbiomes.
2020-05-26 | PXD012724 | Pride