Human gut metagenome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA691455 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13423223_1.fastq.gz | Fastqsanger.gz | |||
| SRR13423223_2.fastq.gz | Fastqsanger.gz | |||
| SRR13423224_1.fastq.gz | Fastqsanger.gz | |||
| SRR13423224_2.fastq.gz | Fastqsanger.gz | |||
| SRR13423225_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 98 |
Similar Datasets
Project description:<p>The intestinal microflora and metabolites produced by these microbes serve as important regulators of the development of sepsis. Accordingly, this study was designed to systematically explore the relationships between the regulation of septicemia and both the intestinal flora and fecal metabolites by examining the functional roles of metabolites in the protection against sepsis-associated intestinal damage. To that end, fecal and peripheral blood mononuclear cell (PBMC) samples were collected from sepsis patients and healthy controls. A series of longitudinal multi-omics analyses were then used to assess the links between the intestinal flora or associated metabolites and PBMCs in sepsis patients, while animal model studies were further used to probe the protective effects of intestinal flora-derived metabolites on intestinal damage and immunity in the context of sepsis. These analyses revealed that intestinal dysbiosis was a common finding in sepsis patients, which commonly exhibited higher levels of deleterious bacteria and/or reductions in beneficial bacteria. A machine learning approach was used to identify samples from sepsis patients, revealing that at the genus level, sepsis samples could be distinguished by the presence of Bifidobacterium, Bacteroides, Porphyromonas, Prevotell, Enterococcus, Anaerococcus and Veillonella species. Metabolomics analyses indicated that there were significant differences in the levels of intestinal flora-derived metabolites including L-serine, L-valine and L-tyrosine when comparing samples from the sepsis and control groups, while corresponding transcriptomic analyses of PBMC samples using an ImmunecellAI analytical approach revealed a significant sepsis-related increase in the abundance of T cells and Th17 cells. Single-cell sequencing data from sepsis-associated PBMCs was also downloaded from the GEO database, confirming the observation that Th17 cell levels and those of other immune cells rose significantly in the context of septicemia. Animal model experiments revealed that intestinal microbiota-derived L-valine was able to alleviate inflammation and protest against sepsis-induced intestinal damage by inhibiting Th17 cell activation. Overall, these results thus highlight the successful application of machine learning to distinguish between sepsis and control samples based on the composition of the intestinal flora while demonstrating the potential therapeutic benefits of L-valine as an inhibitor of Th17 cell activity that may offer value as a means of alleviating or preventing intestinal damage in treated individuals. </p>
2023-07-28 | MTBLS7866 | MetaboLights
Project description:Sepsis is a syndromic response to infection and is frequently a final common pathway to death from many infectious diseases worldwide. The global burden of sepsis is difficult to ascertain, although a recent scientific publication estimated that there were 48.9 million cases and 11 million sepsis-related deaths worldwide, which accounted for almost 20% of all global deaths. Here we analyzed the whole blood transcriptome of 5 healthy controls and 5 patients with confirmed sepsis from the Department of Emergency, Division of Surgical critical care, Tongji Trauma Center, Tongji Hospital, Tongji Medical College. This research aims to understand the expression of immune cells in sepsis patients and to construct an immune cell landscape of sepsis.
2023-02-05 | GSE224095 | GEO
Project description:Introduction: Sepsis is a complex immunological response to infection characterized by early hyperinflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on SIRS differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. Methods: This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. Results: Based on preliminary microarray analyses comparing HC and sepsis groups. A panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. AUC ROC curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 - 92%. Conclusions: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. GEO Note: Data made available represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20). This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. The GEO data represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).
2011-11-01 | E-GEOD-28750 | biostudies-arrayexpress
Project description:Systemic infections, especially in patients with chronic diseases, result in sepsis: an explosive, uncoordinated immune response that can lead to multisystem organ failure with a high mortality rate. Sepsis survivors and non-survivors oftentimes have similar clinical phenotypes or sepsis biomarker expression upon diagnosis, suggesting that the dynamics of sepsis in the critical early stage may have an impact on these opposite outcomes. To investigate this, we designed a within-subject study of patients with systemic gram-negative bacterial sepsis with surviving and fatal outcomes and performed single-cell transcriptomic analyses of peripheral blood mononuclear cells (PBMC) collected during the critical period between sepsis recognition and 6 hours. We observed that the largest sepsis-induced expression changes over time in surviving versus fatal sepsis were in CD14+ monocytes, including gene signatures previously reported for sepsis outcomes. We further identify changes in the metabolic pathways of both monocytes and platelets, the emergence of erythroid precursors, and T cell exhaustion signatures, with the most extreme differences occurring between the non-sepsis control and the sepsis non-survivor. Our single-cell observations are consistent with trends from public datasets but also reveal specific effects in individual immune cell populations, which change within hours. In conclusion, this pilot study provides the first single-cell results with a repeated measures design in sepsis to analyze the temporal changes in the immune cell population behavior in surviving or fatal sepsis. These findings indicate that tracking temporal expression changes in specific cell-types could lead to more accurate predictions of sepsis outcomes. We also identify molecular pathways that could be therapeutically controlled to improve the sepsis trajectory toward better outcomes.
2021-03-03 | GSE167363 | GEO
Project description:The host response in critically ill patients with sepsis, septic shock remains poorly defined. Considerable research has been conducted to accurately distinguish patients with sepsis from those with non-infectious causes of disease. Technological innovations have positioned systems biology at the forefront of biomarker discovery. Analysis of the whole-blood leukocyte transcriptome enables the assessment of thousands of molecular signals beyond simply measuring several proteins in plasma, which for use as biomarkers is important since combinations of biomarkers likely provide more diagnostic accuracy than the measurement of single ones or a few. Evidence suggests that genome-wide transcriptional profiling of blood leukocytes can assist in differentiating between infection and non-infectious causes of severe disease. Of importance, RNA biomarkers have the potential advantage that they can be measured reliably in rapid quantitative reverse transcriptase polymerase chain reaction (qRT-PCR)-based point of care tests. PAXgene blood RNA was isolated at intensive-care unit (ICU) admission and throughout ICU length-of-stay. Through the use of genome-wide microarrays we aimed to identify molecular features that enbale the adequate discrimination of infectious and non-infectious sources of critical illness. Moreover, biological pathway analysis was used to tease out the most relevant biological units in sepsis and septic shock.
2015-07-06 | E-GEOD-65682 | biostudies-arrayexpress
Project description:We assayed leukocyte global gene expression for a prospective discovery cohort of 106 adult patients admitted to UK intensive care units with sepsis due to community acquired pneumonia or faecal peritonitis. We assigned all samples to sepsis response signature groups after performing unsupervised analysis of the transcriptomic data.
2017-05-01 | E-MTAB-5274 | biostudies-arrayexpress
Project description:During extreme physiological stress, the intestinal tract can be transformed into a harsh environment characterized by regio- spatial alterations in oxygen, pH, and phosphate concentration. When the human intestine is exposed to extreme medical interventions, the normal flora becomes replaced by pathogenic species whose virulence can be triggered by various physico-chemical cues leading to lethal sepsis. We previously demonstrated that phosphate depletion develops in the mouse intestine following surgical injury and triggers intestinal P. aeruginosa to express a lethal phenotype that can be prevented by oral phosphate ([Pi]) supplementation.
2011-10-06 | GSE29789 | GEO