Aging-dependent alterations in gene expression and a mitochondrial signature of responsiveness to human influenza vaccination

Thakar, Juilee; Mohanty, Subhasis; West, A. Phillip; Joshi, Samit R; Ueda, Ikuyo; Wilson, Judith; Meng, Hailong; Blevins, Tamara P.; Tsang, Sui; Trentalange, Mark; Siconolfi, Barbara; Park, Koonam; Gill, Thomas M.; Belshe, Robert B.; Kaech, Susan M.; Shadel, Gerald S.; Kleinstein, Steven H.; Shaw, Albert C.

doi:10.18632/aging.100720

Cited by 75 publications

(89 citation statements)

References 37 publications

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“…2A). Interestingly, the abnormally expanded "superlineages" continued to make up a similar fraction of the total repertoire at days 7 and 28 after vaccination as they did before vaccination at day 0, indicating that the size of any vaccine response [e.g., release of recalled IgG plasmablasts around day 7 (11,24,25)] was minor compared with the weight of the superlineages. Note that no CDR3 sequences were shared between superlineages, which provides confidence that superlineages did not arise from contamination.…”

Section: Resultsmentioning

confidence: 96%

Phylogenetic analysis of the human antibody repertoire reveals quantitative signatures of immune senescence and aging

Bourcy

Angel

Vollmers

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

118

123

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The elderly have reduced humoral immunity, as manifested by increased susceptibility to infections and impaired vaccine responses. To investigate the effects of aging on B-cell receptor (BCR) repertoire evolution during an immunological challenge, we used a phylogenetic distance metric to analyze Ig heavy-chain transcript sequences in both young and elderly individuals before and after influenza vaccination. We determined that BCR repertoires become increasingly specialized over a span of decades, but less plastic. In 50% of the elderly individuals, a large space in the repertoire was occupied by a small number of recall lineages that did not decline during vaccine response and contained hypermutated IgD + B cells. Relative to their younger counterparts, older subjects demonstrated a contracted naive repertoire and diminished intralineage diversification, signifying a reduced substrate for mounting novel responses and decreased fine-tuning of BCR specificities by somatic hypermutation. Furthermore, a larger proportion of the repertoire exhibited premature stop codons in some elderly subjects, indicating that aging may negatively affect the ability of B cells to discriminate between functional and nonfunctional receptors. Finally, we observed a decreased incidence of radical mutations compared with conservative mutations in elderly subjects' vaccine responses, which suggests that accumulating original antigenic sin may be limiting the accessible space for paratope evolution. Our findings shed light on the complex interplay of environmental and gerontological factors affecting immune senescence, and provide direct molecular characterization of the effects of senescence on the immune repertoire.aging | antibody repertoire | influenza vaccine | CMV | UniFrac T he deterioration of immune function with age, a process referred to as immunosenescence, is well recognized. Notable changes contributing to immunosenescence include, among others, decreased proliferation of lymphocytes, reduced T-cell receptor repertoire, and defects in antibody production (1, 2). This phenomenon contributes to an age-related increase in susceptibility to viral and bacterial infections and decreased response to vaccination (3-5). Indeed, individuals over the age of 65 y are less than half as protected by standard influenza vaccines as younger individuals (6), and pneumonia and influenza represent the fourth most common cause of death among aging individuals (4).Antibody-mediated immunity is the result of an evolutionary arms race between the pathogens to which an individual is exposed and antibody-producing B cells. A tremendous diversity of potential antibody affinities is generated by the mechanisms of V(D)J recombination, random junctional insertions/deletions, and somatic hypermutation (7). Preferential proliferation of activated B cells upon encounter with a cognate antigen then exerts a selective pressure for B-cell receptors (BCRs) with a high binding affinity to the antigen (7). The clonal history of B cells circulating in the blood c...

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Section: Resultsmentioning

confidence: 96%

Phylogenetic analysis of the human antibody repertoire reveals quantitative signatures of immune senescence and aging

Bourcy

Angel

Vollmers

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

118

123

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“…2A) and the previously published observation that both the antibody and transcriptional responses to vaccination have strong age dependencies (5, 26), we opted to divide each of the cohorts into young (35 years and below) and older (60 years and above) groups and analyzed them separately. This approach allowed us to uncover signatures beyond those driven by age, which was the focus of the original studies, as well as other existing studies (3, 5, 14). It allowed us to compare response signatures in young versus older adult participants, which is an important issue that has been largely unexplored.…”

Section: Resultsmentioning

confidence: 99%

“…Last, it is well-known that experimental measurements are subject to batch effects (33), and failure to account for these differences may hinder comparisons across studies. Although multiple studies have suggested postvaccination transcriptional signatures that are associated with titer responses to influenza vaccination (5, 12, 15, 34, 35), only a few have identified baseline signatures that are significantly associated with the antibody response (3, 7, 13, 14), and none of these baseline signatures were found and then further validated using independent cohorts. Furman et al (14) analyzed a cohort of 91 individuals (SDY212) and identified an age- and apoptosis-related gene module that was correlated with the hemagglutination inhibition assay (HAI) response.…”

Section: Discussionmentioning

confidence: 99%

“…A number of demographic factors have been associated with differential vaccination response, including age, gender, and smoking (3, 4). Furthermore, gene expression patterns can display different temporal patterns in young and older individuals (5), as well as in males and females after vaccination (4, 6). In addition, immune “states” can be highly variable across individuals (7).…”

Section: Introductionmentioning

confidence: 99%

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Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses

et al. 2017

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Annual influenza vaccinations are currently recommended for all individuals 6 months and older. Antibodies induced by vaccination are an important mechanism of protection against infection. Despite the overall public health success of influenza vaccination, many individuals fail to induce a substantial antibody response. Systems-level immune profiling studies have discerned associations between transcriptional and cell subset signatures with the success of antibody responses. However, existing signatures have relied on small cohorts and have not been validated in large independent studies. We leveraged multiple influenza vaccination cohorts spanning distinct geographical locations and seasons from the Human Immunology Project Consortium (HIPC) and the Center for Human Immunology (CHI) to identify baseline (i.e., before vaccination) predictive transcriptional signatures of influenza vaccination responses. Our multicohort analysis of HIPC data identified nine genes (RAB24, GRB2, DPP3, ACTB, MVP, DPP7, ARPC4, PLEKHB2, and ARRB1) and three gene modules that were significantly associated with the magnitude of the antibody response, and these associations were validated in the independent CHI cohort. These signatures were specific to young individuals, suggesting that distinct mechanisms underlie the lower vaccine response in older individuals. We found an inverse correlation between the effect size of signatures in young and older individuals. Although the presence of an inflammatory gene signature, for example, was associated with better antibody responses in young individuals, it was associated with worse responses in older individuals. These results point to the prospect of predicting antibody responses before vaccination and provide insights into the biological mechanisms underlying successful vaccination responses.

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“…Schoggins-gene-set defines Interferon Stimulated Genes (ISGs) and has been reported to be significantly enriched upon influenza infections by previous studies [8, 23]. 43 out of 50 FCM-gene-sets were found enriched in at least one of the pathways ( p value <0.01) (Fig.…”

Section: Resultsmentioning

confidence: 89%

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes

Khan

Katanic

Thakar³

2017

BMC Bioinformatics

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BackgroundDespite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.ResultsWe developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward’s method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes.ConclusionWe validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive ‘Fuzzy Inference of Gene-sets (FIGS)’ package (GitHub: https://github.com/Thakar-Lab/FIGS) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1669-x) contains supplementary material, which is available to authorized users.

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Aging-dependent alterations in gene expression and a mitochondrial signature of responsiveness to human influenza vaccination

Cited by 75 publications

References 37 publications

Phylogenetic analysis of the human antibody repertoire reveals quantitative signatures of immune senescence and aging

Phylogenetic analysis of the human antibody repertoire reveals quantitative signatures of immune senescence and aging

Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes

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