Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA378875 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR5401050.fastq.gz | Fastqsanger.gz | |||
| SRR5401051.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 2 of 2 |
Similar Datasets
Project description:Background and aims: Gene mutations or variants leading to insufficient reactive oxygen species (ROS) production have been associated with inflammatory bowel disease (IBD). In particular, 40-50% of patients with chronic granulomatous disease have IBD (CGD-IBD). CGD is caused by inherited defects in any one of the 5 subunits forming the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex 2 (NOX2), leading to severely reduced or absent phagocyte-derived ROS production. While conventional IBD therapies can treat CGD-IBD, their benefits must be weighed against the risk of infection in this immune compromised population. Understanding the impact of NOX2 defects on the composition and function of the intestinal microbiota may lead to the identification of treatments for CGD-IBD. Methods: We evaluated GI symptom and quality of life scores, and clinical biomarkers of local (i.e. fecal occult blood and calprotectin) and systemic (i.e. CBC, CRP, ESR, and albumin) inflammation in a cohort of 79 patients with CGD, 8 mutation carriers and 17 healthy controls followed at the National Institutes of Health (NIH). We profiled the intestinal microbiome by 16S rRNA (V4 region) sequencing and the stool metabolome by mass spectrometry in all fecal samples, and further validated our findings by profiling the stool microbiome in a second cohort of 36 patients with CGD recruited from 11 centers across North-America through the Primary Immune Deficiency Treatment Consortium (PIDTC). Predictive functional profiling of the microbial communities based on 16S rRNA sequencing was also performed. Results: After controlling for significant variables, we show decreased alpha diversity and identified distinct intestinal microbiome and metabolomic profiles in patients with CGD compared to healthy individuals. In particular, we observed enrichment for Erysipelatoclostridium spp., Sellimonas spp. and Lachnoclostridium spp. in stool samples from patients with CGD. Despite differences in alpha and beta diversity in samples from the NIH compared to the PIDTC cohort, there were several bacterial taxa that correlated significantly between both cohorts. We further demonstrate that patients with active IBD and/or a history of IBD have a distinct microbiome and metabolomic profile compared to patients without CGD-IBD and identified bacterial taxa to be evaluated as potential markers of disease severity, as well as targets for future therapeutic interventions. Conclusions: Intestinal microbiome and metabolomic signatures distinguished patients with CGD and CGD-IBD and identified microbial and metabolomic candidates to be further evaluated as potential targets to improve the management of patients with CGD-IBD.
2023-12-04 | GSE220260 | GEO
Project description:The joint disease rheumatoid arthritis (RA) is characterized by persistent synovitis, leading to cartilage damage, bone erosion, and ultimately impaired joint function. The disease affects 0.5 to 1.0% of adults in developed countries, and is three times more frequent in women than in men. A number of autoantibodies can be detected in RA patient’s serum targeting the patient’s own proteins. Several of these proteins, including rheumatoid factor, can also be detected in patients suffering from other autoimmune diseases, including the inflammatory bowel diseases (IBD). IBD and RA share several genetic risk logi, an altered gut microbiota, and environmental risk factors. Articular involvement is the most common extra-intestinal manifestation in patients diagnosed with IBD, with a prevalence between 17 to 39%. Additionally, methotrexate (MTX) is the most frequently prescribed immunosuppressive drug for RA and the second most for the IBD, indicating close similarities between the two diseases. We, therefore, characterized the protein content (the proteome) of the colon mucosa of gastrointestinal healthy RA patients, to investigate if we could detect IBD-related changes. The LC-MS/MS analysis was conducted as part of a previous study (ProteomeXChange submission PXD001608), enabling a comparison between the two datasets, containing the colon mucosal proteome of 11 RA patients, 10 IBD (ulcerative colitis) patients, and 10 controls. This data submission covers the triplicate proteome analysis of the colon mucosa of 11 gastrointestinal healthy RA patients.
2016-09-26 | PXD003082 | Pride
Project description:The joint disease rheumatoid arthritis (RA) is characterized by persistent synovitis, leading to cartilage damage, bone erosion, and ultimately impaired joint function. The disease affects 0.5 to 1.0% of adults in developed countries, and is three times more frequent in women than in men. A number of autoantibodies can be detected in RA patient’s serum targeting the patient’s own proteins. Several of these proteins, including rheumatoid factor, can also be detected in patients suffering from other autoimmune diseases, including the inflammatory bowel diseases (IBD). IBD and RA share several genetic risk logi, an altered gut microbiota, and environmental risk factors. Articular involvement is the most common extra-intestinal manifestation in patients diagnosed with IBD, with a prevalence between 17 to 39%. Additionally, methotrexate (MTX) is the most frequently prescribed immunosuppressive drug for RA and the second most for the IBD, indicating close similarities between the two diseases. We, therefore, characterized the protein content (the proteome) of the colon mucosa of gastrointestinal healthy RA patients, to investigate if we could detect IBD-related changes. The LC-MS/MS analysis was conducted as part of a previous study (ProteomeXChange submission PXD001608), enabling a comparison between the two datasets, containing the colon mucosal proteome of 11 RA patients, 10 IBD (ulcerative colitis) patients, and 10 controls. This data submission covers the triplicate proteome analysis of the colon mucosa of 11 gastrointestinal healthy RA patients.
2017-04-01 | MSV000080861 | MassIVE
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:We used microarrays to identify mucosal gene signatures predictive of response to infliximab (IFX) in patients with inflammatory bowel disease (IBD) and to gain more insight into the pathogenesis of IBD. Keywords: drug response and treatment effect Mucosal biopsies were obtained at endoscopy in actively inflamed mucosa from 61 IBD patients (24 ulcerative colitis (UC), 19 Crohnâs colitis (CDc) and 18 Crohnâs ileitis (CDi)), refractory to corticosteroids and/or immunosuppression, before and 4-6 weeks after (except for 1 CDc patient) their first infliximab infusion and in normal mucosa from 12 control patients (6 colon and 6 ileum). The patients were classified for response to infliximab based on endoscopic and histologic findings at 4-6 weeks after first infliximab treatment. Total RNA was isolated from intestinal mucosal biopsies, labelled and hybridized to Affymetrix Human Genome U133 Plus 2.0 Arrays.
2009-11-02 | E-GEOD-16879 | biostudies-arrayexpress
Project description:In this study we developed metaproteomics based methods for quantifying taxonomic composition of microbiomes (microbial communities). We also compared metaproteomics based quantification to other quantification methods, namely metagenomics and 16S rRNA gene amplicon sequencing. The metagenomic and 16S rRNA data can be found in the European Nucleotide Archive (Study number: PRJEB19901). For the method development and comparison of the methods we analyzed three types of mock communities with all three methods. The communities contain between 28 to 32 species and strains of bacteria, archaea, eukaryotes and bacteriophage. For each community type 4 biological replicate communities were generated. All four replicates were analyzed by 16S rRNA sequencing and metaproteomics. Three replicates of each community type were analyzed with metagenomics. The "C" type communities have same cell/phage particle number for all community members (C1 to C4). The "P" type communities have the same protein content for all community members (P1 to P4). The "U" (UNEVEN) type communities cover a large range of protein amounts and cell numbers (U1 to U4). We also generated proteomic data for four pure cultures to test the specificity of the protein inference method. This data is also included in this submission.
2017-05-12 | PXD006118 | Pride
Project description:Inflammatory bowel disease (IBD) is associated with altered microbiota composition and metabolism, but it is unclear whether these changes precede inflammation or are the result of it since current studies have mainly focused on changes after the onset of disease. We previously showed differences in mucus gut microbiota composition preceded colitis-induced inflammation and stool microbial differences only became apparent at colitis onset. In the present study, we aimed to investigate whether microbial dysbiosis was associated with differences in both predicted microbial gene content and endogenous metabolite profiles. We examined the functional potential of mucus and stool microbial communities in the mdr1a -/- mouse model of colitis and littermate controls using PICRUSt on 16S rRNA sequencing data. Our findings indicate that despite changes in microbial composition, microbial functional pathways were stable before and during the development of mucosal inflammation. LC-MS-based metabolic phenotyping (metabotyping) in urine samples confirmed that metabolite profiles in mdr1a -/- mice were remarkably unaffected by development of intestinal inflammation and there were no differences in previously published metabolic markers of IBD. Metabolic profiles did, however, discriminate the colitis-prone mdr1a -/- genotype from controls. Our results indicate resilience of the metabolic network irrespective of inflammation. Importantly as metabolites differentiated genotype, genotype-differentiating metabolites could potentially predict IBD risk.
2017-07-14 | MTBLS432 | MetaboLights
Project description:Inflammatory bowel diseases (IBD) are believed to be driven by dysregulated interactions between the host and the gut microbiota. Our goal is to characterize the relationships between mucosal T cells, the host tissue environment and microbial communities in IBD patients to identify new therapeutic targets. We identified 26 predictors from our combined data set that were effective in distinguishing between regions of the intestine undergoing active inflammation and regions that were normal. Network analysis on these 26 predictors revealed SAA1 as the most connected node linking the abundance of the genus Bacteroides with the production of IL17 and IL22 by CD4+ T cells. The SAA1-linked microbial and transcriptome interactions were further validated with data from the pediatric IBD RISK cohort. This study identifies expression of SAA1 as an important link between mucosal T cells, microbial communities and their tissue environment in IBD patients. A combination of FACS, gene expression and microbial profiling can distinguish between intestinal inflammatory states in IBD regardless of disease types.
2017-11-11 | GSE96665 | GEO
Project description:Expression data of epithelial organoid cultures generated from intestinal mucosa of non-IBD controls and patients with ulcerative colitis
| PRJNA305704 | ENA
Project description:In this study, we performed a comparative analysis of gut microbiota composition and gut microbiome-derived bacterial extracellular vesicles (bEVs) isolated from patients with solid tumours and healthy controls. After isolating bEVs from the faeces of solid tumour patients and healthy controls, we performed spectrometry analysis of their proteomes and next-generation sequencing (NGS) of the 16S gene. We also investigated the gut microbiomes of faeces from patientsand controls using 16S rRNA sequencing. Machine learning was used to classify the samples into patients and controls based on their bEVs and faecal microbiomes.
2024-08-08 | PXD047510 | Pride