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Reaction of the immune system and the body to a Coronavirus-19 (COVID-19) vaccination is so different and ultimately unpredictable has not yet been clarified. It is also not yet known why people who have been vaccinated react to a vaccination with sometimes serious side effects. Using high-throughput dissecting (analytical) methods with the suffix OMICS ("Multi-OMICS" methods, collective characterization and quantification of pools of biological molecules) used in this study on the basis of blood tests, data from several molecular levels can be recorded and a holistic picture can be created from this, which can depict the connections between these levels.
Various vaccines against COVID-19 ("CORONA") have now been approved in Germany. How and why the reaction of the immune system and the body to a COVID-19 vaccination is so different and ultimately unpredictable has not yet been clarified. It is also not yet known why people who have been vaccinated react to a vaccination with sometimes serious side effects. There are now initial indications that genetic prerequisites can play a role in the development of the immune response. Furthermore, we want to examine the long-term protection against Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) infection through vaccination and learn to understand it better.
Using high-throughput dissecting (analytical) methods with the suffix OMICS ("Multi-OMICS" method) used in this study on the basis of blood tests, data from several molecular levels can be recorded and a holistic picture can be created from this, which can depict the connections between these levels.
The expected results can help to gain a better understanding of the underlying reactions to a COVID-19 vaccination and the functioning of the body (pathophysiology) in the future, which could enable the basis for the development of causal therapeutic approaches and improved vaccines.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Healthy adults | Experimental | Healthy adults who had no infection with SARS-CoV-2 virus before or had recovered from COVID-19 and plan to take the various COVID-19 experimental vaccine candidates or had already one dose of COVID-19 vaccine and are going to have the second vaccination with a different vaccine. Study related procedures: 1-3 days before application of the first vaccine dose (blood sample 1) 7-10 days after first dose of vaccine (blood sample 2), 1-3 days before second dose of vaccine (blood sample 3) 7-10 days after second vaccine dose (blood sample 4) 6-12 Months after second vaccine dose (blood sample 5, optional) Next Generation Sequencing (NGS) analysis and other omics analysis (transcriptomics, proteomics, metabolomics). |
|
| COVID-19 vaccinated subjects with side effects | Experimental | COVID-19 vaccinated subjects with diagnosed central thrombosis, anaphylactic shock or other major or minor complications such as dermatitis. Study related procedures: Blood sample will be taken without time frame 1-3 days after admission to the hospital (severe side effects) or consulting a doctor (mild side effects) (blood sample 1) during treatment (blood sample 2) After subject is recovered (blood sample 3) 6-12 Months after recovering (blood sample 4, optional) Next Generation Sequencing (NGS) analysis and other omics analysis (transcriptomics, proteomics, metabolomics). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Multi-OMICS | Genetic | Measurement of gene expression in immune cells (Human Peripheral Blood Mononuclear Cell (PBMCs) or total blood) using functional genomics, proteomics, metabolomics and lipidomics tools and compare the dynamics of immune response before and after vaccination against COVID-19. |
| Measure | Description | Time Frame |
|---|---|---|
| Toll Like Receptor 7 (TLR7) | TLR7 (used method: Whole Genome Sequencing (WGS) using ordinal logistic regression) measured in Single Nucleotide Polymorphism (SNP) | Day 1-3 before 1. vaccination |
| Toll Like Receptor 7 (TLR7) | TLR7 (used method: Whole Genome Sequencing (WGS) using ordinal logistic regression) measured in Single Nucleotide Polymorphism (SNP) | Day 7-10 after 1. vaccination |
| Toll Like Receptor 7 (TLR7) | TLR7 (used method: Whole Genome Sequencing (WGS) using ordinal logistic regression) measured in Single Nucleotide Polymorphism (SNP) | Day 1-3 before 2. vaccination |
| Toll Like Receptor 7 (TLR7) | TLR7 (used method: Whole Genome Sequencing (WGS) using ordinal logistic regression) measured in Single Nucleotide Polymorphism (SNP) | Day 7-10 after 2. vaccination |
| Toll Like Receptor 7 (TLR7) | TLR7 (used method: Whole Genome Sequencing (WGS) using ordinal logistic regression) measured in Single Nucleotide Polymorphism (SNP) | Month 6-12 after 2. vaccination |
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Inclusion Criteria:
Group 1
Group 2
Exclusion Criteria:
Group 1 and 2
- Missing informed consent of the subject
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Olaf Rieß, Prof. Dr. | Contact | +49 7071 29 | 72323 | olaf.riess@med.uni-tuebingen.de |
| Yogesh Singh, Dr. | Contact | +49 7071 29 | 72257 | yogesh.singh@med.uni-tuebingen.de |
| Name | Affiliation | Role |
|---|---|---|
| Olaf Rieß | University Hospital Tübingen | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Hospital Tübingen | Recruiting | Tübingen | 72076 | Germany |
The COVID19-VAC study will provide data in a pseudonymised manner to national and international databases set up to increase the diagnostic yield through advanced analysis tools and matchmaking against other cohorts
Data will become available after analysis and unlimited.
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| ID | Term |
|---|---|
| D020022 | Genetic Predisposition to Disease |
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D004198 | Disease Susceptibility |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |