Summary: Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Climate change is projected to lead to an increase in the burden of vector- and water-borne, as well as zoonotic infectious diseases including COVID-19. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts. We applied cutting edge multi-omics approaches to characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final hepatitis B antibody titres. Using both a molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we overcame the p>>>n problem and uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Correlations were identified with signalling pathways such as JAK-STAT and interleukin signalling, Toll-Like Receptor Cascades, interferon signalling and Th17 cell differentiation. This study provides further evidence that baseline cellular and molecular characteristics of an individual’s immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets
Overall Design: A total of 75 samples (15 participants, each sampled at five time points) representing both pre- and post-vaccination blood draws
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Growth Protocol: | - |
Treatment Protocol: | - |
Extract Protocol: | PAXGene RNA purification kit was used on collected whole blood as per manufacturer's instructions |
Library Construction Protocol: | KAPA stranded RNA-Seq library preparation kit was used as per manufacturer's guidelines |
Molecule Type: | Poly(A)+ RNA |
Library Source: | |
Library Layout: | SINGLE |
Library Strand: | Forward |
Platform: | ILLUMINA |
Instrument Model: | Illumina HiSeq 2500 |
Strand-Specific: | Specific |
Data Resource | GEN Sample ID | GEN Dataset ID | Project ID | BioProject ID | Sample ID | Sample Name | BioSample ID | Sample Accession | Experiment Accession | Release Date | Submission Date | Update Date | Species | Race | Ethnicity | Age | Age Unit | Gender | Source Name | Tissue | Cell Type | Cell Subtype | Cell Line | Disease | Disease State | Development Stage | Mutation | Phenotype | Case Detail | Control Detail | Growth Protocol | Treatment Protocol | Extract Protocol | Library Construction Protocol | Molecule Type | Library Layout | Strand-Specific | Library Strand | Spike-In | Strategy | Platform | Instrument Model | Cell Number | Reads Number | Gbases | AvgSpotLen1 | AvgSpotLen2 | Uniq Mapping Rate | Multiple Mapping Rate | Coverage Rate |
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