Endoplasmic reticulum stress response and bile acid signatures associate with multi-strain seroresponsiveness during elderly influenza vaccination

Carré, Christophe; Wong, Glenn; Narang, Vipin; Tan, Crystal TY; Chong, Joni; Chin, Hui Xian; Xu, Weili; Lu, Yanxia; Chua, Michelle; Tambyah, Paul A.; Nyunt, Ma Shwe Zin; Ng, Tze Pin; Larocque, Daniel; Hessler, Catherine; Bosco, Nabil; Quéméneur, Laurence; Larbi, Anis

doi:10.1016/j.isci.2021.102970

Cited by 8 publications

(7 citation statements)

References 80 publications

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“…Furthermore, the observed differences in functionality might represent one facet of the immune senescence process, thus indicating a higher degree of immune senescence in the non-responders as compared to the responders. A similar observation was made in the context of an elderly influenza vaccination cohort, where incomplete vaccine responders showed signs of accelerated T cell aging 85 . However, it would be important to gather information on responsiveness to other vaccines in order to dissect general vs. vaccine-specific effects.…”

Section: Discussionsupporting

confidence: 64%

Distinct immunological and molecular signatures underpinning influenza vaccine responsiveness in the elderly

et al. 2022

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Seasonal influenza outbreaks, especially in high-risk groups such as the elderly, represent an important public health problem. Prevailing inadequate efficacy of seasonal vaccines is a crucial bottleneck. Understanding the immunological and molecular mechanisms underpinning differential influenza vaccine responsiveness is essential to improve vaccination strategies. Here we show comprehensive characterization of the immune response of randomly selected elderly participants (≥ 65 years), immunized with the adjuvanted influenza vaccine Fluad. In-depth analyses by serology, multi-parametric flow cytometry, multiplex and transcriptome analysis, coupled to bioinformatics and mathematical modelling, reveal distinguishing immunological and molecular features between responders and non-responders defined by vaccine-induced seroconversion. Non-responders are specifically characterized by multiple suppressive immune mechanisms. The generated comprehensive high dimensional dataset enables the identification of putative mechanisms and nodes responsible for vaccine non-responsiveness independently of confounding age-related effects, with the potential to facilitate development of tailored vaccination strategies for the elderly.

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Section: Discussionsupporting

confidence: 64%

Distinct immunological and molecular signatures underpinning influenza vaccine responsiveness in the elderly

et al. 2022

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“…In this section, the performance of BayesDeBulk is compared with Plier [15], Cibersort [18], xCell [1], CibersortX [19], EPIC [25] and MCP-counter [2] based on transcriptomic data from peripheral blood mononuclear cells. For this comparison, we used two public gene expression data based on influenza vaccination cohorts referred to as influenza cohort 1 [33] and influenza cohort 2 [3, 16, 17]. As additional cohort, we used the gene expression of a peripheral blood data cohort involving 20 patients [18].…”

Section: Validation Based On Flow Cytometrymentioning

confidence: 99%

“…Let I k be the set containing 20 cell-type specific markers for the k -th cell type; which were randomly sampled from the full list of markers. The mean of cell-type specific markers for a particular cell type k , i.e., μ k,j with j ∈ I k , was drawn from a Gaussian distribution with mean uniformly sampled from the interval [1, 3] and standard deviation 0.5; while the mean of other markers, i.e., μ k,j for j ∉ I k , from a Gaussian distribution centered on zero and standard deviation 0.5. The fraction of different cell types, i.e., ( π 1 i , … π K,i ), was randomly generated from a Dirichlet distribution with parameter 0.5.…”

Section: Validation Based On Synthetic Datamentioning

confidence: 99%

BayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data

Petralia

Krek

Calinawan

et al. 2021

Preprint

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Characterizing the tumor microenvironment is crucial in order to improve responsiveness to immunotherapy and develop new therapeutic strategies. The fraction of different cell-types in the tumor microenvironment can be estimated based on transcriptomic profiling of bulk tumor data via deconvolution algorithms. One class of such algorithms, known as reference-based, rely on a reference signature containing gene expression data for various cell-types. The limitation of these methods is that such a signature is derived from the gene expression of pure cell-types, which might not be consistent with the transcriptomic profiling in solid tumors. On the other hand, reference-free methods usually require only a set of cell-specific markers to perform deconvolution; however, once the different components have been estimated from the data, their labeling can be problematic. To overcome these limitations, we propose BayesDeBulk - a new reference-free Bayesian method for bulk deconvolution based on gene expression data. Given a list of markers expressed in each cell-type (cell-specific markers), a repulsive prior is placed on the mean of gene expression in different cell-types to ensure that cell-specific markers are upregulated in a particular component. Contrary to existing reference-free methods, the labeling of different components is decided a priori through a repulsive prior. Furthermore, the advantage over reference-based algorithms is that the cell fractions as well as the gene expression of different cells are estimated from the data, simultaneously. Given its flexibility, BayesDeBulk can be utilized to perform bulk deconvolution beyond transcriptomic data, based on other data types such as proteomic profiles or the integration of both transcriptomic and proteomic profiles.

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“…Despite the importance of identifying biomarkers that predict influenza vaccine responses, the number of studies on this topic is limited. Previous studies have described potential pre‐vaccination biomarkers such as CD4+ T memory (Furman et al., 2013 ; Tomic et al., 2019 ; Tsang et al., 2014 ), CD8+ T memory (Carre et al., 2021 ; Furman et al., 2013 ; Tomic et al., 2019 ) and B memory (Tomic et al., 2019 ; Tsang et al., 2014 ) cells. Although these subsets are potential predictors of influenza vaccine responses, they exhibit high inter‐individual variation influenced by factors such as age and chronic viral infections like cytomegalovirus (CMV) (Cevirgel et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Pre‐vaccination immunotypes reveal weak and robust antibody responders to influenza vaccination

Cevirgel,

Shetty,

Vos

et al. 2023

Aging Cell

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Effective vaccine‐induced immune responses are particularly essential in older adults who face an increased risk of immunosenescence. However, the complexity and variability of the human immune system make predicting vaccine responsiveness challenging. To address this knowledge gap, our study aimed to characterize immune profiles that are predictive of vaccine responsiveness using “immunotypes” as an innovative approach. We analyzed an extensive set of innate and adaptive immune cell subsets in the whole blood of 307 individuals (aged 25–92) pre‐ and post‐influenza vaccination which we associated with day 28 hemagglutination inhibition (HI) antibody titers. Building on our previous work that stratified individuals into nine immunotypes based on immune cell subsets, we identified two pre‐vaccination immunotypes associated with weak and one showing robust day 28 antibody response. Notably, the weak responders demonstrated HLA‐DR+ T‐cell signatures, while the robust responders displayed a high naïve‐to‐memory T‐cell ratio and percentage of nonclassical monocytes. These specific signatures deepen our understanding of the relationship between the baseline of the immune system and its functional potential. This approach could enhance our ability to identify individuals at risk of immunosenescence. Our findings highlight the potential of pre‐vaccination immunotypes as an innovative tool for informing personalized vaccination strategies and improving health outcomes, particularly for aging populations.

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Endoplasmic reticulum stress response and bile acid signatures associate with multi-strain seroresponsiveness during elderly influenza vaccination

Cited by 8 publications

References 80 publications

Distinct immunological and molecular signatures underpinning influenza vaccine responsiveness in the elderly

Distinct immunological and molecular signatures underpinning influenza vaccine responsiveness in the elderly

BayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data

Pre‐vaccination immunotypes reveal weak and robust antibody responders to influenza vaccination

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