Ontology highlight
ABSTRACT:
PROVIDER: PRJNA704313 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13771000_1.fastq.gz | Fastqsanger.gz | |||
| SRR13771000_2.fastq.gz | Fastqsanger.gz | |||
| SRR13770977_1.fastq.gz | Fastqsanger.gz | |||
| SRR13770977_2.fastq.gz | Fastqsanger.gz | |||
| SRR13770978_1.fastq.gz | Fastqsanger.gz |
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Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
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Project description:Metagenomic next-generation sequencing (mNGS) is increasingly being applied in clinical laboratories for unbiased culture-independent diagnosis. We use the mNGS as a routine diagnostic test in clinical.
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Project description:To compare tumor associated macrophage (TAM) from naïve and sepsis surviving mice we have employed Agilent microarrays slides with almost 60,000 genes (39,430 mRNA and 16,251 long non coding RNAs). Other experiments we conducted demonstrated TAM accumulation was increased in post-sepsis subjects. For this reason, we asked if TAM from post-sepsis mice could also exhibit a different gene expression profile. Sepsis was induced by cecal and ligation puncture. Naïve mice were used as control group. All animals were treated with ertapenem (20 mg/kg, i.p., 6 hours after surgery, and then each 12 hours for 3 days). B16-F10 melanoma (30,000 cells) were injected subcutaneously at day 14 after sepsis induction. Fourteen days after tumor inoculation, animals were killed and tumors were harvested and digested (collagenase and DNAse). TAM was isolated by a Percoll gradient (70/30) followed by a 1-hour adhesion protocol, reaching a purity of ~75%. For comparative reasons, we assessed TAM from post-sepsis (n = 4), TAM from naïve mice (n = 4), bone marrow derived macrophage from naïve (n = 4) and from post-sepsis (n = 4), M1-polarized macrophage (n = 4) and M2-polarized macrophage (n = 4). We found only minor gene expression differences between TAM from naïve and from post-sepsis mice (61 genes were up-regulated and 98 genes were down-regulated, fold-change > 0.58 or < -0.58, and p < 0.01). We found genes related to leukocyte activation were down-regulated in TAM from post-sepsis mice (e.g. Ccr7, Cd86, H2-Ab1), as well as genes related to antigen processing and presentation of peptide or polysaccharide antigen via MHC class II (H2-DMb1, Cd74, H2-Eb1, H2-Ob). A gene related to M2 polarization was up-regulated (Marco). Also, we found a down-regulation of Nfkbid in post-sepsis-derived TAM. This led us to hypothesize TAM from post-sepsis mice exhibit a more M2-like phenotype, which may in part contribute to post-sepsis tumor expansion. Three independent experiments were conducted for TAM obtaining, each experiment using n = 4 for naïve and n = 4 for post-sepsis. We selected the 4 best within a group of 12 samples, following A260/280 and A260/230 ratios. For bone marrow derived macrophage from naïve and from post-sepsis, and for M1 and M2-polarized macrophage, we conducted two independent experiments using n = 3 per group. The best 4 samples in each group was selected to microarray processing and analysis.
2014-12-25 | E-GEOD-64498 | biostudies-arrayexpress
Project description:Sepsis, the dysregulated host response to infection leading to organ dysfunction, arises from diverse mechanisms that are poorly resolved by sepsis as a syndromic classification, confounding immunotherapy trials. Here we delineate neutrophil and granulopoietic disturbances underlying sepsis pathophysiology. We present a whole blood single-cell multiomic sepsis response atlas (272,993 cells, n=39 individuals), revealing multiple immature neutrophil populations that were collectively immunosuppressive, inhibiting CD4+ T cell proliferation and activation in co-culture. We traced this to altered granulopoiesis using single-cell multiomic mapping of circulating hematopoietic stem cells (HSCs) (29,366 cells, n=27). We show how these features are enriched in a subset of patients, resolving a specific poor outcome endotype (sepsis response signature SRS1). SRS1 patients have increased IL1R2+ immature neutrophils, epigenetic signatures of emergency granulopoiesis transcription factors in HSCs, and STAT3 features across infectious disease settings. Our findings delineate an immunohematopoietic axis, therapeutic targets and stratified medicine approach to sepsis.
2024-10-16 | GSE216007 | GEO
Project description:Sepsis, the dysregulated host response to infection leading to organ dysfunction, arises from diverse mechanisms that are poorly resolved by sepsis as a syndromic classification, confounding immunotherapy trials. Here we delineate neutrophil and granulopoietic disturbances underlying sepsis pathophysiology. We present a whole blood single-cell multiomic sepsis response atlas (272,993 cells, n=39 individuals), revealing multiple immature neutrophil populations that were collectively immunosuppressive, inhibiting CD4+ T cell proliferation and activation in co-culture. We traced this to altered granulopoiesis using single-cell multiomic mapping of circulating hematopoietic stem cells (HSCs) (29,366 cells, n=27). We show how these features are enriched in a subset of patients, resolving a specific poor outcome endotype (sepsis response signature SRS1). SRS1 patients have increased IL1R2+ immature neutrophils, epigenetic signatures of emergency granulopoiesis transcription factors in HSCs, and STAT3 features across infectious disease settings. Our findings delineate an immunohematopoietic axis, therapeutic targets and stratified medicine approach to sepsis.
2024-10-16 | GSE216009 | GEO
Project description:1364 plasma proteome samples taken at time-of-admission to the emergency department from patients suspected of sepsis.
Used initially with the following abstract:
Sepsis is one of the leading causes of mortality in the world. Currently, the heterogeneity of sepsis makes it challenging to determine the molecular mechanisms that define the syndrome. Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. We identified panels of proteins using explainable artificial intelligence that predict clinical outcomes and applied these panels to reduce high-dimensional proteomics data to a low-dimensional interpretable latent space (ILS). Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk patients using only data available at time-of-admission. In addition to being highly effective for investigating sepsis, this approach supports the flexible incorporation of new data and can generalize to other diseases to aid in translational research and the development of precision medicine.
2024-04-08 | MSV000094486 | MassIVE
Project description:Background: Sepsis involves aberrant immune responses to infection, but the exact nature of this immune dysfunction remains poorly defined. Bacterial endotoxins like lipopolysaccharide (LPS) are potent inducers of inflammation, which has been associated with the pathophysiology of sepsis, but repeated exposure can also induce a suppressive effect known as endotoxin tolerance or cellular reprogramming. It has been proposed that endotoxin tolerance might be associated with the immunosuppressive state that was primarily observed during late-stage sepsis. However, this relationship remains poorly characterised. Here we clarify the underlying mechanisms and timing of immune dysfunction in sepsis. Methods: We defined a gene expression signature characteristic of endotoxin tolerance. Gene-set test approaches were used to correlate this signature with early sepsis, both newly and retrospectively analysing microarrays from 593 patients in 11 cohorts. Then we recruited a unique cohort of possible sepsis patients at first clinical presentation in an independent blinded controlled observational study to determine whether this signature was associated with the development of confirmed sepsis and organ dysfunction. Findings: All sepsis patients presented an expression profile strongly associated with the endotoxin tolerance signature (p < 0.01; AUC 96.1%). Importantly, this signature further differentiated between suspected sepsis patients who did, or did not, go on to develop confirmed sepsis, and predicted the development of organ dysfunction. Interpretation: Our data support an updated model of sepsis pathogenesis in which endotoxin tolerance-mediated immune dysfunction (cellular reprogramming) is present throughout the clinical course of disease and related to disease severity. Thus endotoxin tolerance might offer new insights guiding the development of new therapies and diagnostics for early sepsis. For the RNA-Seq study reported here, 73 patients were recruited with deferred consent at the time of first examination in an emergency ward based on the opinion of physicians that there was a potential for the patient's condition to develop into sepsis. These were retrospectively divided into groups based on clinical features and compared to 11 non-urgent surgical controls.
2014-11-14 | E-GEOD-63311 | biostudies-arrayexpress