Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Progression of whole blood transcriptional signatures from interferon-induced to neutrophil-associated patterns in patients with severe influenza

ABSTRACT: Transcriptional profiles are increasingly used to investigate the severity, subtype and pathogenesis of disease. We now describe whole blood RNA signatures and local and systemic immune mediator levels in a large cohort of adults hospitalised with influenza from which extensive clinical and investigational data was obtained. Signatures reflecting interferon-related antiviral pathways were common up to day 4 of symptoms in cases not requiring mechanical ventilatory support; in those needing mechanical ventilation, an inflammatory, activated neutrophil and cell stress/death (‘bacterial’) pattern was seen, even early after disease onset. Identifiable bacterial co-infection was not necessary for this ‘bacterial’ signature but could enhance its development while attenuating the early ‘viral’ signature. Our findings emphasise the importance of timing and severity in the interpretation of transcriptomic profiles and soluble mediator levels, and identify specific patterns of immune activation that may enable the development of novel diagnostics and therapeutics

ORGANISM(S): Homo sapiens

PROVIDER: GSE111368 | GEO | 2018/03/03

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Mechanisms of variable ventilation in experimental lung injury

Project description:In the present study we seek to identify changes in lung gene expression under mechanical ventilation in uninjured as well as acutely and chronically injured lungs. A standard volume-controlled lung-protective ventilatory protocol is compared to a concept of mechanical ventilation using variable tidal volumes.

2017-02-17 | GSE78008 | GEO

miRNA expression in tracheal aspirates in mild/moderate and severe BPD

Project description:Extreme preterm infants are a growing population in neonatal intensive care units who carry a high mortality and morbidity. Multiple factors play a role in preterm birth, resulting in major impact on organogenesis leading to complications including bronchopulmonary dysplasia (BPD). The goal of this study was to identify biomarker signatures associated with BPD severity. We analyzed profiles in tracheal aspirates (TAs) from 25 extremely preterm infants receiving invasive mechanical ventilation. Eight infants were diagnosed with mild/moderate BPD, and 17 were diagnosed with severe BPD, according to the NHLBI consensus conference classification . We found specific miRNA signatures in TAs that may serve as biomarkers for BPD severity.

2021-01-30 | GSE165828 | GEO

Human lung organoid for modeling infection and disease conditions (Lung II)

Project description:We sought to define the host immune response, a.k.a, the “cytokine storm” that has been implicated in fatal COVID-19 using an AI-based approach. Over 45,000 publicly available transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a ‘seed’ gene; ACE2 was rationalized because the receptor it encodes enables the virus that causes Covid-19, SARS-CoV-2, to enter host cells. The signature was surprisingly conserved in all viral pandemics, including COVID-19, inspiring the nomenclature ViP-signature. A subset of 20-genes classified disease severity in respiratory pandemics. The ViP signatures pinpointed airway epithelial and myeloid cells as the major contributors of an IL-15 cytokine storm, and epithelial and NK cell destruction as determinants of severity/fatality. They also helped formulate precise therapeutic goals to reduce disease symptoms and severity. Thus, the ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool in our armamentarium to rapidly assess disease severity and vet candidate drugs.

2020-12-31 | GSE157057 | GEO

Human lung organoid for modeling infection and disease conditions (Lung I)

2020-12-31 | GSE157055 | GEO

AI-guided Discovery of a Paradoxical Immune Response in Fatal COVID-19 (Ileum)

2020-12-07 | GSE157059 | GEO

AI-guided Discovery of a Paradoxical Immune Response in Fatal COVID-19 (Hamster)

2020-12-07 | GSE157058 | GEO

Transcription profiling of human chronic lymphocytic leukemia samples from patients with stable or progressive disease

Project description:Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patientsâ lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients. Experiment Overall Design: For the prognostic genomic signature, 68 CLL leukemia samples were used (36 from patients with stable disease and 32 from patients with progressive disease).

2009-11-11 | E-GEOD-10138 | biostudies-arrayexpress

Transcription profiling of human chronic lymphocytic leukemia samples to improve prognosis and predict therapeutic response

Project description:Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patientsâ lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients. Experiment Overall Design: For the predictive genomic signature or response to pentostatin, cyclophosphamide, and rituximab, 20 CLL leukemia samples were used in the training set, and 20 CLL leukemia samples were used in the validation set

2009-11-11 | E-GEOD-10137 | biostudies-arrayexpress

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures

Project description:Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.

2022-12-18 | GSE221234 | GEO

Project description:Microbiota-signatures of GVHD severity

| PRJNA602484 | ENA

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data