Antibody and transcription landscape in peripheral blood mononuclear cells of elderly adults over 70 years of age with third dose of COVID-19 BBIBP-CorV and ZF2001 booster vaccine

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Abstract

Background

In the context of the COVID-19 pandemic and extensive vaccination, it is important to explore the immune response of elderly adults to homologous and heterologous booster vaccines of COVID-19. At this point, we detected serum IgG antibodies and PBMC sample transcriptome profiles in 46 participants under 70 years old and 25 participants over 70 years old who received the third dose of the BBIBP-CorV and ZF2001 vaccines.

Results

On day 7, the antibody levels of people over 70 years old after the third dose of booster vaccine were lower than those of young people, and the transcriptional responses of innate and adaptive immunity were also weak. The age of the participants showed a significant negative correlation with functions related to T-cell differentiation and costimulation. Nevertheless, 28 days after the third dose, the IgG antibodies of elderly adults reached equivalence to those of younger adults, and immune-related transcriptional regulation was significantly improved. The age showed a significant positive correlation with functions related to "chemokine receptor binding", "chemokine activity", and "chemokine-mediated signaling pathway".

Conclusions

Our results document that the response of elderly adults to the third dose of the vaccine was delayed, but still able to achieve comparable immune effects compared to younger adults, in regard to antibody responses as well as at the transcript level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12979-023-00408-x.

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Most cited references 42

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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Author and article information

Contributors

Xiaolin Jiang: jxl198607@126.com

Journal

Journal ID (nlm-ta): Immun Ageing

Journal ID (iso-abbrev): Immun Ageing

Title: Immunity & Ageing : I & A

Publisher: BioMed Central (London )

ISSN (Electronic): 1742-4933

Publication date (Electronic): 27 January 2024

Publication date PMC-release: 27 January 2024

Publication date Collection: 2024

Volume: 21

Electronic Location Identifier: 11

Affiliations

[1 ]Infectious Disease Prevention and Control Section, Shandong Center for Disease Control and Prevention, ( https://ror.org/027a61038) Jinan, Shandong Province China

[2 ]School of Public Health and Management, Binzhou Medical University, ( https://ror.org/008w1vb37) Yantai , Shandong Province China

[3 ]Liaocheng Center for Disease Control and Prevention, ( https://ror.org/02yr91f43) Liaocheng, Shandong Province China

[4 ]School of Public Health and Health Management, Shandong First Medical University & Shandong Academy of Medical Sciences, ( https://ror.org/05jb9pq57) Jinan, Shandong Province China

[5 ]Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, ( https://ror.org/027a61038) 16992 Jingshi Road , Jinan, 250014 Shandong Province China

Article

Publisher ID: 408

DOI: 10.1186/s12979-023-00408-x

PMC ID: 10821575

PubMed ID: 38280989

SO-VID: 29c8d6dd-b799-4b19-ba1a-6a9a5bfa51a7

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 22 August 2023

Date accepted : 20 December 2023

Funding

Funded by: the Major Scientific and Technological Innovation Project in Shandong Province

Award ID: 2020SFXGFY02-1

Funded by: the Key Research and Development Plan of Shandong Province

Award ID: 2021RZA01021

Funded by: the Natural Science Foundation of Shandong Province

Award ID: ZR202112040005

Custom metadata

ScienceOpen disciplines: Immunology

Keywords: covid-19,third booster vaccine,aging,systems biology analysis,antibody responses,rna-seq,transcriptome analysis

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ScienceOpen disciplines: Immunology

Keywords: covid-19, third booster vaccine, aging, systems biology analysis, antibody responses, rna-seq, transcriptome analysis

Antibody and transcription landscape in peripheral blood mononuclear cells of elderly adults over 70 years of age with third dose of COVID-19 BBIBP-CorV and ZF2001 booster vaccine

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Abstract

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Supplementary Information

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Novel Coronavirus Disease COVID-19

Most cited references 42

limma powers differential expression analyses for RNA-sequencing and microarray studies

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

WGCNA: an R package for weighted correlation network analysis

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