This year, I was very interested in systems vaccinology, and have placed a lot of my efforts summarising various systems vaccinology papers which I found interesting. However, the current layout of my blog didn’t allow presentation of all these publications in a format that users can quickly access to them. I have thus compiled them in my medium publication account, and in a systematic fashion in my medium website. Feel free to visit these small compilations of my blog entries from the hyperlink. Cheers!
Identifying signatures of host genome-wide transcriptional patterns can be a tool for biomarker discovery as well as for understanding molecular mechanisms and pathophysiological signatures of disease states.
In this study by Loke et al., transcriptional profiling analysis of pediatric patients from Nicaragua with a predominantly DENV-1 infection was performed, and the gene signatures between healthy, dengue fever (DF), dengue haemorrhaigic fever (DHF) and dengue shock syndrome (DSS) were compared. Enrolment criteria consisted of hospitalised patients younger than 15 years of age. Whole blood was collected during acute illness (days 3-6).
Unsupervised clustering reveal that DHF and DF patients cluster distinctly from the DSS patients. Interestingly, many of the genes that separate these two groups are involved in ‘protein biosynthesis’ and ‘protein metabolism and modification’. A large number of mitochondrial ribosomal proteins and ‘nucleic acid binding’ were also flagged (See Figure above).
Genes related to metabolism, oxidative phosphorylation, protein targeting, nucleic acid metabolism, purine and pyrimidine metabolism, electron transport, DNA metabolism and replication, and protein metabolism and modification were differentially regulated by DF, DHF and DSS patients, reflecting a shared signature of DENV-1 infection.
On the other hand, the biological processes differentially expressed by DSS patients were protein metabolism and modification, intracellular protein traffic, pre-mRNA processing, mRNA splicing, nuclear transport, protein-lipid modification and protein folding.
Of note, the changes in metabolism genes cannot be seen in vitro. Instead, interferon signatures were upregulated.
Data is deposited in Gene Expression Omnibus (GEO) under GSE25226.
Predicting and achieving vaccine efficacy remains a major challenge. Here, Rechtien et al used a systems vaccinology approach to disentangle the early innate immune responses elicited by the Ebola vaccine rVSV-Zaire Ebola virus (ZEBOV) to identify innate immune responses correlating with Ebola virus (EBOV)-glycoprotein (GP)-specific antibody induction. Of note, this replication-competent recombinant vaccine candidate is based on the vesicular stomatitis virus (rVSV)-based vaccine vector, which has been shown safe and immunogenic in a number of phase I trials.
The vaccine rVSV-ZEBOV induced a rapid and robust increase in cytokine levels, with a maximum peak at day 1, especially for CXCL10, MCP-1 and MIP-1β. Assessment of PBMCs revealed significant induction of co-stimulatory molecules, monocyte/DC activation and NK cell activation at day 1 post-vaccination. The expression of these molecules begin to decline at day 3.
Interestingly, CXCL10 plasma levels and frequency of activated NK cells at day 3 were found to be positively correlated with antibody responses. CD86+ expression in monocytes and mDCs at day 3 are negatively correlated with antibody responses (See figure on top).
The most number of upregulated genes were detected at day 1 post-vaccination. Critically, the early gene signature linked to CXCL10 pathway, including TIFA (TRAF-interacting protein with forkhead-associated domain) on day 1, SLC6A9 (solute carrier family 6 member 9) on day 3, NFKB1 and NFKB2 were most predictive of antibody responses.
Data is stored under NCBI GEO: GSE97590.
The gene signatures predictive of severe dengue disease progression is poorly understood.
The study by Robinson et al., utilise 10 publicly available datasets and divided them into 7 “discovery” and 3 “validation” datasets. In the discovery datasets, a total of 59 differentially expressed genes (FDR < 10%, effect size > 1.3-fold) was detected between patients who progress to DHF and/or DSS (DHF/DSS) versus patients with an uncomplicated course (dengue fever).
An iterative greedy forward search to the 59 genes revealed a final set of and 20 differentially expressed genes (3 over-expressed, 17 under-expressed) in DHF/DSS (Gene list as shown in figure above). A dengue score for each sample was obtained by subtracting the geometric mean expression of the 17 under-expressed genes from the geometric mean expression of the 3 over-expressed genes.
The 20-gene dengue severity scores distinguished DHF/DSS from dengue fever upon presentation and prior to the onset of severe complications with a summary area under the curve (AUC) = 0.79 in the discovery datasets. The 20-gene dengue scores also accurately identified dengue patients who will develop DHF/DSS in all three validation datasets.
To further validate this signature, the authors tested a cohort of prospectively enrolled dengue patients in Colombia. The 20-gene dengue score, measured by qPCR, distinguished severe dengue from dengue with or without warning signs (AUC = 0.89) and even severe dengue from dengue with warning signs (AUC = 0.85).
Finally, the 20-gene set is significantly downregulated in natural killer (NK) and NK T (NKT) cells, indicating the role of NK and NKT cells in modulating severe disease outcome.
Dataset deposited under Gene Expression Omnibus (GEO): GSE124046
rDEN2Δ30 is a recombinant serotype 2 virus based on the American genotype 1974 Tonga DENV2 virus, which has been partially attenuated by deletion of 30 nucleotides in the 3′ untranslated region of the RNA genome (Δ30). rDEN2Δ30 infection is known to induce modest viremia in all flavivirus-naive subjects and a mild, transient non-pruritic rash in 80% of recipients.
rDEN2Δ30 infection could hence be a suitable model to evaluate molecular signatures responsible for asymptomatic or mild DENV-2 infection.
In this study by Hanley JP et al., RNA-seq was performed on whole blood collected from rDEN2Δ30-infected subjects at 0, 8, and 28 days post infection. rDEN2Δ30-induced reproducible but modest viremia and a mild rash as the only clinically significant finding in DENV-naive subjects.
Principal component analysis reveal minimal overlap between baseline (day 0) and peak viremia (day 8). The day 28 data (post viremia) partially overlapped with the baseline (day 0) and acute (day 8) timepoints. Pathways enriched in the type I and type II interferon and antiviral responses were upregulated at day 8, whereas pathways controlling translational initiation were downregulated. NF-κB, IL-17 signaling pathways, apoptosis, toll-like receptor signaling, response to viruses, ribosomes, and defense responses were also differentially regulated at day 28.
Myeloid cells including monocytes and activated dendritic cells were significantly increased during acute infection and returned to baseline. In contrast, regulatory T cells (Tregs) were significantly decreased during acute stage.
Gene ontology pathway analysis revealed that the viremia-tracking set of genes was enriched for both response to and regulation of type I and II interferon pathways, including JAK/STAT signaling. Genes encoding for proteins that directly inhibit viral genome replication and involved in protein ubiquitination and catabolism, especially ISG15 pathway, tracked with viremia. Day 28 revealed more varied pathways, including protein ubiquitination, cell migration, cytoskeletal reorganization, and angiogenesis.
Baseline transcript signatures can potentially predict whether the subjects would develop rash after rDEN2Δ30 infection. Higher baseline expression of myeloid nuclear differentiation antigen (MNDA), and cell surface associated cellular processes such as tetraspanin CD37, integral membrane 2B (ITM2B), and genes involved in autophagy (VMP1) was associated with protection from rash. These genes are mostly related to myeloid responses, membrane regulation, autophagy, K63 ubiquitination, and cell morphogenesis.
Transcriptomic signatures modulated by rDEN2Δ30 infection and severe dengue are distinct. Only one gene family, the guanine binding protein (GBP1/2) genes was differentially regulated in both severe dengue and during mild rDEN2Δ30 infection.
Data deposited im Gene Expression Omnibus under accession number GSE152255
Dengue infections can be asymptomatic, symptomatic, or occasionally progress to severe dengue, a life-threatening condition characterised by a cytokine storm, vascular leakage, and shock. However, the molecular and immunological mechanisms underlying asymptomatic dengue virus (DENV) infection remains largely unknown.
In the publication, E Simon-Lorière et al recruited DENV infected children in Cambodia. Nine individuals remained strictly asymptomatic at the time of inclusion and during the 10-day follow-up period. PBMCs from 8 asymptomatic DENV-1 viremic individuals and 25 symptomatic dengue patients were used for further gene expression analysis.
Asymptomatic individuals have an increase in the percentage of CD4+ T cells and a decrease in CD8+ T cells compared to symptomatic dengue individiuals. However, CD14+ monocytes, Lin-CD11c+ dendritic cells, CD19+ B cells, and CD335+ natural killer cells are not significantly different between asymptomatic and symptomatic individuals.
Transcriptomic signatures were distinct between asymptomatic and symptomatic individuals. The top pathways that diverge the most between asymptomatic and clinical dengue individuals were related to immune processes. Notably, the transcriptomic differences cannot be explained by differences in viral load or immune status.
The innate immune responses were not significantly different between the asymptomatic and symptomatic individuals. Instead, the most significantly activated pathway in asymptomatic individuals was related to “nuclear factor of activated T-cells (NFAT) mediated regulation of immune response.” These genes include CIITA, CD74 and various human leukocyte antigen (HLA) genes, where their expression differences were also validated at the protein levels (See figure on top).
Protein kinase Cq (PKCq) signaling in T lymphocytes was also highly activated in asymptomatic viremic individuals. Genes upregulated included AKT3, SOS1, PAK1, and SLAMF6, as well as T-cell costimulatory pathways such as ICOS-ICOSL, and CD28 and CTLA4 signaling in cytotoxic T-cells.
In contrast, genes related to B-cell activation, differentiation and plasma cell development (BLIMP-1, IRF4) were downregulated in asymptomatic individuals. This finding is correlated with the reduction in antibody production in the asymptomatic individuals.
Data is saved in Gene Expression Omnibus under accession number GSE100299
There are generally 4 categories of immune functions that relate to protection:
|Correlate||A specific immune response to a vaccine that is closely related to protection against infection, disease or other defined end point|
|Absolute correlate||A quantity of a specific immune response to a vaccine that always provide near 100%|
|Relative correlate||A quantity of a specific immune response to a vaccine that usually (not always) provides protection|
|Cocorrelate||A quantity of a specific immune response to a vaccine that is 1 of >=2 correlates of protection, and that must be synergistic with other correlates|
|Surrogate||A quantified specific immune response to a vaccine that is not itself protective but that substitutes for the true (perhaps unknown) correlate|
Some important pointers that I learnt from the article published by Stanley Plotkin, CID, 2008:
1. The correlate of protection induced by vaccination may not necessarily be the same correlate that operates to close off infection. An example of this principle is measles vaccine. Titers <200 mIU/mL of antibody after vaccination are protective against infection, whereas titers between 120 and 200 mIU/mL protect against clinical signs of disease but not against infection. Titers <120 mIU/mL are not protective at all. Another consideration is the cellular immunity to measles, which is critical in recovery from disease, as CD8+ cells are needed to control viremia and consequent infection of organs. Another example is cytomegalovirus, where antibodies are a correlate of protection against infection, whereas T cell immunity is a correlate of protection against disease.
2. Correlate of protection may be either absolute and relative. Examples of absolute correlates (situations in which a certain level of response almost guarantees protection) include diphtheria, tetanus, measles, rubella and hepatitis A. While absolute correlation is highly desired, many correlates are relative. In these cases, although protection is usually conferred at a certain level of responses, breakthrough infections are possible. An example is the influenza vaccine, where a hemagglutination-inhibition antibody titer of 1/40 is associated with 70% clinical efficacy.
3. While antibodies are often used as measures of correlates of protection, not all antibodies neutralise infections in the same way. An example is the Meningococcal polysaccharide vaccines which give notoriously poor protection in young children, although children do have significant ELISA antibody responses. Other functions, including opsonophagocytosis, ADCC and complement activation could also be important for protection.
4. In some cases, antibodies are surrogates, rather as a true correlate of protection. This means that the antibodies could be indirectly related to the true correlate of protection. Examples provided were the rotavirus and varicella vaccine, where cell-mediated immunity is clearly required for protection against viral infection and disease.
5. Emerging evidence suggest the possibility of organ-specific correlates. Based on experimental studies, it appears that CD4+ cells are key to the prevention of brain pathology after measles and in helping CD8+ cells to close off West Nile virus CNS infection. More work will be needed to define correlates of protection that are organ-specific.
6. Correlates of immunity may differ between different age groups. An example is the influenza vaccine, where antibody production is critical to prevent primary influenza infection in the young, but CD4+ cells may be more important for immunologically experienced individuals undergoing heterosubtypic infection.
7. Cellular responses are increasingly recognised as correlates or cocorrelates of protection. Given that CD4+ cells must be present to help antibodies to develop, and CD8+ cells are needed for virus clearance, emerging evidence now suggest that cellular responses are critical in limiting viral pathogenesis and dissemination. However, more work will be needed to uncover the parameters that are essential for cell-mediated protection.
Immunisation with the stable trimeric recombinant HIV-1 envelope glycoprotein, CN54gp140, has been shown to induce potent humoral immune responses, especially when adjuvanted with TLR4 agonist adjuvants, such as monophosphoryl lipid A or GLA-AF (glucopyranosol lipid adjuvant-aqueous formulation). These adjuvants exert their adjuvanticity, at least in part, by activating the myeloid differentiation factor 88 (MyD88) and toll-interleukin 1 receptor domain-containing adapter inducing interferon-β (TRIF) pathways. However, the clinical efficacy to the CN54gp140 adjuvanted with GLA-AF is variable between individuals. Anderson et al characterised the host responses after vaccinating subjects with CN54gp140 adjuvanted with GLA-AF, and examined the gene signatures linked to vaccine immunogenicity.
Healthy male (n = 8) or female (n = 6) volunteers aged between 18 and 45 and with no history of HIV-1 and HIV-2 infection received the vaccine, and whole blood was collected from these subjects at 6 hours, 1, 3 and 7 days after vaccination.
Majority of total DEGs were observed within 24 h post vaccination compared to later time points at 3 days and 7 days post-immunisation.
The DEGs reveal an enrichment of BTMs related to cell cycle regulation and signaling as well as those related to innate and adaptive immune responses.
NK cell-related enriched BTMs (M7.2, M61.0, and S1) were significantly repressed in the gene expression profiles from individuals with either late high serum IgA or IgG4 responders (See Figure on top).
In particular, we identified a repression in BTM modules related to NK cells, especially at 3 and 7 days post-vaccination, for high serum IgM, IgA, and IgG4 antibody responders.
Flow cytometry was performed to determine that the changes were due to NK cell numbers or expression levels of proteins upon vaccination.
In the limited number of analyzed samples, frequency of CD3–CD56dim NK cell population in the blood of high antibody responder subjects was increased on 14 days post vaccination compared to the 0 h baseline. While more studies need to be done, the authors speculate that the repression of BTMs related to NK cells observed in the first 7 days post-vaccination reflects NK cells leaving the circulation early in the response. Given that NK cells are short lived, the enhanced frequency of NK cells for 14 days post vaccination is presumably attributed to secondary induction of NK cell differentiation processes in response to vaccination.
Although vaccination is considered the most effective method for preventing influenza, it shows limited efficacy in the elderly. Here, Nakaya et al used a systems vaccinology approach to understand the mechanisms behind poor vaccine efficacy in the inactivated influenza vaccine (TIV) in the elderly.
212 individuals from the current study and 218 individuals from a previously published study (Franco et al., 2013) were included in analyses. 54 of these were elderly (>65 years old). As expected, antibody responses to the Influenza vaccine (TIV) decrease with age.
Blood Transcriptomic Modules related to the induction of interferons and activation of dendritic cells were enriched on days 1 and 3 after TIV vaccination, whereas modules related to T cells at these time points were negatively associated with the antibody response. On day 7, there was a robust enrichment of antibody secreting cells (ASC) and cell cycle-related modules. The enriched modules in young and elderly were similar, particularly those related to the interferon response and activation of dendritic cells on day 1. However, the magnitude of expression of interferon-related genes was significantly higher in young individuals (See top figure).
Combining all datasets, several B-cell- and T-cell-related modules at pre-vaccination was positively correlated with an increased antibody response to vaccination. In contrast, modules related to monocytes were negatively correlated with antibody responses, supporting the concept that inflammatory responses at baseline might be detrimental to the induction of vaccine-induced antibody responses.
Consistent with the neutralizing antibody responses, B-cell and plasmablast modules (BTM S3) showed reduced expression in the elderly compared to the young on day 7. However, Natural killer (NK) cell and monocyte modules were enriched in the elderly at day 3 and day 7 after vaccination.
To examine if the transcriptional changes were due to changes in these specific cell types, flow cytometry was performed. Proportions of total NK cells in elderly subjects were higher than those of young subjects at baseline and all time points studied (days 0–14). The NK-cell activation markers were also more prominent in the elderly. Similarly, increased quantities of monocytes were seen in the elderly, with higher CCR5 expression at all time-points tested.
Differential expression of miRNA is also evident between the elderly and young, which suggests that miRNA could be important regulators of the immune response to influenza vaccination.
Data is deposited in GEO as GSE74817.
rVSV∆G-ZEBOV-GP is a recombinant vaccine based on the Vesicular Stomatitis Virus (VSV), where the original VSV glycoprotein encoding gene was deleted and replaced with the surface glycoprotein (GP) encoding gene from the Ebolavirus Zaire strain (ZEBOV).
The vaccine was shown to be safe, although occasionally associated with transient reactogenicity. However, the host response to the vaccine has not been thoroughly investigated.
In this manuscript by F Santoro et al., 2021, the blood transcriptomic response to high dose vaccination (107 and 5 × 107 pfu) with rVSV∆G- ZEBOV-GP was analysed in 51 volunteers. Whole blood data was taken from day 0, 1, 2, 3, 7, 14, 21 and 28.
Vaccination resulted in greatest host transcriptomic changes at day 1, which lasts till day 3 (see top figure). Notably, the massive transcriptomic changes on days 1-2 corresponds to the timing of occurrence of mild to moderate reactogenicity events (chills, fever, headache, fatigue or myalgia) in 50 out of 51 vaccinees
Viral load differences did not affect host responses to vaccine, except for the MZB1 gene, coding for Marginal Zone B And B1 Cell Specific Protein.
Most blood transcriptomic module correlations with anti-ZEBOV GP IgGs were detected at day 14 post-vaccination. As expected, B cell activation and BCR signaling modules were observed to correlate with vaccine immunogenicity. Other modules that were significantly correlated at day 14 involve pathways such as calcium signalling, cell adhesion and activating transcription factor networks, which are possibly related to signal transduction.
Transcriptomic data are available in the Zenodo database.