Posted in Resource, shingles vaccine

Metabolic Phenotypes Of Response to Vaccination in Humans

Metabolic responses to Zostavax vaccines over time (days 1, 3, 7). Source: Li et al., Cell, 2018

Zostavax is a licensed live-attenuated vaccine for prevention of HZ (shingles) in individuals aged 50 and above. HZ is caused by varicella zoster virus (VZV) reactivation, and Zostavax has been previously shown to confer ~50% reduction in HZ. Interestingly, the efficacy against HZ was 63.9% in subjects who were 60–69 years old but only 37.6% in subjects older than 70 years. In this article published in Cell, Li et al investigates why the efficacy of Zostavax could be different in the young and old subjects, using a multi-omics approach. Main findings are summarised below:

77 participants enrolled. 33 were young adults between the ages of 25 and 40 years and 44 were older subjects between the ages of 60 and 79 years.

PBMCs collected at baseline prior to vaccination and at days 1, 3, 7, 14, 28, 90, and 180 post-vaccination

Weak CD8+ T-cell responses seen in all subjects, but CD4 T-cell responses detected in the majority of subjects. Blood Tfh-like cells (CD4+CXCR5+CXCR3+ICOS+ T cells), known to be important in providing B-cell help, was also increased in both the young and elderly adults after vaccination.

The younger subjects had a greater increase of VZV-specific IgG antibody after vaccination compared to the elderly subjects.

Transcriptomics show increased interferon-stimulated genes at days 1 and 3, but the induction of these innate immune genes were of a smaller magnitude than the Yellow Fever live-attenuated vaccine. This is then followed by increased immunoglobulin transcripts at day 7. Of note, the increase in immunoglobulin genes coincided with the increase in antigen-specific plasmablast cells.

Comparing between young and elderly subjects, most differences were seen at baseline, including increased expression of genes in gene modules related to inflammation, cytosolic DNA sensing, and NK cells.

Among the different cell population phenotypes measured, day 7 IFN-γ+ T cells is the most significant predictor of IgG response. High activity in inositol phosphate metabolism is also associated with reduced T cell and B cell responses.

Sterol regulatory binding protein 1 and its targets is most predictive of Tfh response, IgG response, and the associations between genes and metabolite networks, including chemokine signaling (CCL23, CCL19, and CCR3), TNF/MAPK signaling (TNFSF11, VACM1, and DUSP6), complement genes (C1QA and C1QB), killer cell immunoglobulin-like receptors (KIR3DL3, KIR2DL3, and KIR2DS5), and lipid metabolic enzymes (ACSM2A, ACSM2B, and DGAT2).

Data is available at GEO: GSE79396

Posted in Clustergrammer, Data visualisation

Clustergrammer: A great online tool for plotting clustergrams and heatmaps

Clustergrammer is an online tool can be used to visualise gene expression patterns. Red indicates increased expression whereas blue indicates reduced expression. Distinct gene clusters are depicted at the bottom of the heatmap. Source from Fernandez et al., scientific data, 2017.

A clustergram or a heatmap is one of several techniques that can directly visualise data without the need for dimensionality reduction. As clustergrams are easy to interpret, they are widely used to visualise biological data in print publications. Based on similarities and differences in gene expression patterns, clustergrams can also allow direct visualisation of clusters.

In this entry, I will introduce Clustergrammer, which is a user-friendly webtool for plotting clustergrams. The loading of the data into Clustergrammer can be summarised in 3 basic steps:

  1. Normalise the gene expression data by performing a Z score transformation. This ensures that the grand mean of each gene will be centralised at value of 0, with standard deviation of 1.
  2. Make sure that the samples are arranged in columns and the genes are arranged in rows. I recommend ordering the samples in the same way as how you would want your data to be published (e.g. controls on the extreme left and the other samples on the right), as proper ordering of the variables allows Clustergrammer to perform supervised clustering. Finally, if you have multiple conditions, you may assign the clusters beforehand by inserting additional rows at the top. You may also consider adding additional columns on the left to assign genes that perform similar functions (see detailed instructions within website).
  3. Save file in .txt format and upload file in Clustergrammer.

By default, Clustergrammer performs an unsupervised clustering on both rows and columns, and clusters can be visualised by the small arrowheads at the bottom and right of the heatmap. A single-click on the arrowhead reveals the genes within the cluster, allowing you to query their functions directly in Enrichr. A double-click allows you to zoom into the heatmap within the cluster. To further examine the expression levels at the individual level, you can move your mouse cursor within the heatmap and use the mouse scroll to zoom in or zoom out.

For supervised clustering, you can choose to arrange the rows and columns according to the sample order originally assigned. The sidebar is located at the top left hand side of the website. If you have pre-assigned your clusters by adding additional rows, you may choose to click on the category you have classified.

Finally, to determine the relatedness between the different conditions, Clustergrammer also plots the co-expression matrix. The applications of Clustergrammer are not just limited to analysing gene expression studies, but can be extended to proteomics, metabolomics, virus-host interactions and cyTOF analyses. The ease of use, interactive interface and the ability to directly visualise gene expression patterns makes Clustergrammer my top choice in analysing omics datasets.

Posted in About me

Introduction about myself

Welcome to my blog! I am Kuan Rong, currently a principal research scientist in Duke-NUS medical school. My scientific career begun in 2008 , interrogating how antibodies can influence outcome of dengue virus infection. In my PhD with Prof. Ooi Eng Eong, we identified the molecular mechanisms involved in dengue virus neutralisation and enhancement (Chan et al., PNAS, 2011; Chan et al., PNAS, 2014). After graduation, I had the unique opportunity to work with Prof. Ooi Eng Eong and Dr. Jenny Low to investigate if antibodies can impact the outcome of the live-attenuated Yellow Fever virus infection. Since then, I became very interested in data science. By integrating various omics platforms (e.g. genomics, proteomics and metabolomics), we were able to understand how human variations and host responses affect immunogenicity and adverse events to the Yellow Fever vaccine (Chan et al., Nature Microbiology, 2016; Chan et al., Nature Medicine, 2019). 

My motivation for setting up this blog is to share my knowledge on big data analysis. With the advent of high-throughput genomics, proteomics and metabolomics platforms, these tools are increasingly used to provide insights of biological processes involved in virus infection and disease manifestation, which can help guide the development of therapeutics against viral infections. However, to a virologist, extracting biologically meaningful data from omics data can be challenging, as many of us do not have formal training in bioinformatics. Fortunately, many experts in the field have developed tools that are free and easy to use. I hope that the information provided in this website will be useful for individuals with a passion for infectious diseases and systems biology.