Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA323822 | ENA |
REPOSITORIES: ENA
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Project description:IFI27 is a gene-expression biomarker that identifies influenza-specific immune response in symptomatic patients
2017-05-11 | GSE82050 | GEO
Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19). A better definition of the pulmonary host response to SARS-CoV-2 infection is required to understand viral pathogenesis and to validate putative COVID-19 biomarkers that have been proposed in clinical studies. Here, we use targeted transcriptomics of FFPE tissue using the Nanostring GeoMX™ platform to generate an in-depth picture of the pulmonary transcriptional landscape of COVID-19, pandemic H1N1 influenza and uninfected control patients. Host transcriptomics showed a significant upregulation of genes associated with inflammation, type I interferon production, coagulation and angiogenesis in the lungs of COVID-19 patients compared to non-infected controls. SARS-CoV-2 was non-uniformly distributed in lungs (emphasising the advantages of spatial transcriptomics) with the areas of high viral load associated with an increased type I interferon response. Once the dominant cell type present in the sample, within patient correlations and patient-patient variation had been controlled for, only a very limited number of genes were differentially expressed between the lungs of fatal influenza and COVID-19 patients. Strikingly, the interferon-associated gene IFI27, previously identified as a useful blood biomarker to differentiate bacterial and viral lung infections, was significantly upregulated in the lungs of COVID-19 patients compared to patients with influenza. Collectively, these data demonstrate that spatial transcriptomics is a powerful tool to identify novel gene signatures within tissues, offering new insights into the pathogenesis of SARS-COV-2 to aid in patient triage and treatment
2022-07-29 | GSE209537 | GEO
Project description:Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design and personalized medicine approaches for COVID-19.
2022-02-22 | PXD023175 | Pride
Project description:The pathogenesis of avian influenza A H5N1 virus in human has not been clearly elucidated. There have been increasing evidence suggesting a role for virus-induced cytokine dysregulation in contributing to the pathogenesis of human H5N1 disease. However, the role of aberrant innate immune response in human lungs infected by avian influenza H5N1 virus has not been explored and direct evidence for inappropriate innate responses in lungs of avian influenza H5N1 virus infected patients is lacking. In order to obtain evidences for the proposed role of aberrant innate immune response in avian influenza H5N1 virus pathogenesis in human, we analyzed expression profile of lung tissues from two fatal cases of avian influenza H5N1 virus infected patients in comparison to normal human lung using an expression microarray.
2010-06-16 | E-GEOD-22212 | biostudies-arrayexpress
Project description:Immune response biomarker profiling of serum from prostate cancer patients treated with anti-CTLA-4 immunotherapy.
| PRJNA171428 | ENA
Project description:This ordinary differential equation model simulating the mechanisms that govern cancer-immune dynamics and their role in tumor responses to immunotherapy is described by the publication:
Creemers JHA, Lesterhuis WJ, Mehra N, et al.
"A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery."
Journal for ImmunoTherapy of Cancer 2021;9:e002032.
doi:10.1136/jitc-2020-002032
Comment:
Simulation parameters in supplementary table 1 mismatch with figure 1 in manuscript. Therefore, to clarify, the following parameter values were used:
Reproduction of Fig. 1(C), xi = 0.0005
Reproduction of Fig. 1(D), xi = 0.00025
Abstract:
Background: Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.
Methods: A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.
Results: Our model shows that a tipping point—a sharp state transition between immune control and immune evasion—induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.
Conclusion: These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient’s distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.
2024-09-02 | BIOMD0000001022 | BioModels
Project description:Miao2010 - Innate and adaptive immune
responses to primary Influenza A Virus infection
This model is described in the article:
Quantifying the early immune
response and adaptive immune response kinetics in mice infected
with influenza A virus.
Miao H, Hollenbaugh JA, Zand MS,
Holden-Wiltse J, Mosmann TR, Perelson AS, Wu H, Topham DJ.
J. Virol. 2010 Jul; 84(13):
6687-6698
Abstract:
Seasonal and pandemic influenza A virus (IAV) continues to
be a public health threat. However, we lack a detailed and
quantitative understanding of the immune response kinetics to
IAV infection and which biological parameters most strongly
influence infection outcomes. To address these issues, we use
modeling approaches combined with experimental data to
quantitatively investigate the innate and adaptive immune
responses to primary IAV infection. Mathematical models were
developed to describe the dynamic interactions between target
(epithelial) cells, influenza virus, cytotoxic T lymphocytes
(CTLs), and virus-specific IgG and IgM. IAV and immune kinetic
parameters were estimated by fitting models to a large data set
obtained from primary H3N2 IAV infection of 340 mice. Prior to
a detectable virus-specific immune response (before day 5), the
estimated half-life of infected epithelial cells is
approximately 1.2 days, and the half-life of free infectious
IAV is approximately 4 h. During the adaptive immune response
(after day 5), the average half-life of infected epithelial
cells is approximately 0.5 days, and the average half-life of
free infectious virus is approximately 1.8 min. During the
adaptive phase, model fitting confirms that CD8(+) CTLs are
crucial for limiting infected cells, while virus-specific IgM
regulates free IAV levels. This may imply that CD4 T cells and
class-switched IgG antibodies are more relevant for generating
IAV-specific memory and preventing future infection via a more
rapid secondary immune response. Also, simulation studies were
performed to understand the relative contributions of
biological parameters to IAV clearance. This study provides a
basis to better understand and predict influenza virus
immunity.
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2024-09-02 | BIOMD0000000546 | BioModels
Project description:The existence of asymptomatic and re-detectable positive COVID-19 patients presents the disease control challenges of COVID-19. Most studies on immune response of COVID-19 have focused on the moderately or severely symptomatic patients, however little is known about the immune response in asymptomatic and re-detectable positive patients. Here we performed a comprehensive analysis of the transcriptomic profiles of PBMCs from 48 COVID-19 patients.
2021-07-27 | GSE179627 | GEO
Project description:Diagnosis of influenza A infection is currently based on clinical symptoms and pathogen detection. Use of host peripheral blood gene expression data to classify individuals with influenza A virus infection represents a novel approach to infection diagnosis We used microarrays to assay peripheral blood gene expression at baseline and every 8 hours for 7 days following intranasal influenza A H1N1 or H3N2 inoculation in healthy volunteers. We determined groups of coexpressed genes that classified symptomatic influenza infection. We then tested this gene expression classifier in patients with naturally acquired respiratory illness.
2013-11-16 | GSE52428 | GEO
Project description:Diagnosis of influenza A infection is currently based on clinical symptoms and pathogen detection. Use of host peripheral blood gene expression data to classify individuals with influenza A virus infection represents a novel approach to infection diagnosis We used microarrays to assay peripheral blood gene expression at baseline and every 8 hours for 7 days following intranasal influenza A H1N1 or H3N2 inoculation in healthy volunteers. We determined groups of coexpressed genes that classified symptomatic influenza infection. We then tested this gene expression classifier in patients with naturally acquired respiratory illness. We experimentally inoculated healthy volunteers with intranasal influenza A H1N1 and H3N2. Symptoms were documented and peripheral blood samples drawn into PAXgene RNA tubes for RNA isolation. We further enrolled patients presenting to the Emergency Department with naturally acquired respiratory illness, and documented symptoms and collected PAXgene RNA samples for RNA isolation.
2013-11-16 | E-GEOD-52428 | biostudies-arrayexpress