Neonatal BCG vaccination is associated with a long-term DNA methylation signature in circulating monocytes

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Abstract

Trained immunity describes the capacity of innate immune cells to develop heterologous memory in response to certain exogenous exposures. This phenomenon mediates, at least in part, the beneficial off-target effects of the BCG vaccine. Using an in vitro model of trained immunity, we show that BCG exposure induces a persistent change in active histone modifications, DNA methylation, transcription, and adenosine-to-inosine RNA modification in human monocytes. By profiling DNA methylation of circulating monocytes from infants in the MIS BAIR clinical trial, we identify a BCG-associated DNA methylation signature that persisted more than 12 months after neonatal BCG vaccination. Genes associated with this epigenetic signature are involved in viral response pathways, consistent with the reported off-target protection against viral infections in neonates, adults, and the elderly. Our findings indicate that the off-target effects of BCG in infants are accompanied by epigenetic remodeling of circulating monocytes that lasts more than 1 year.

Abstract

BCG vaccine–induced DNA methylation signature can be observed in circulating monocytes more than a year after vaccination.

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The Sequence Alignment/Map format and SAMtools

Heng Li, Bob Handsaker, Alec Wysoker … (2009)

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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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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Author and article information

Contributors

Samantha Bannister:

ORCID: https://orcid.org/0000-0001-7507-3704

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Bowon Kim: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: Writing - review & editing

Jorge Domínguez-Andrés:

ORCID: https://orcid.org/0000-0002-9091-1961

Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: VisualizationRole: Writing - review & editing

Gizem Kilic:

ORCID: https://orcid.org/0000-0002-8251-1909

Role: InvestigationRole: ResourcesRole: ValidationRole: VisualizationRole: Writing - review & editing

Brendan R. E. Ansell:

ORCID: https://orcid.org/0000-0003-0297-897X

Role: Formal analysisRole: ResourcesRole: SoftwareRole: Visualization

Melanie R. Neeland:

ORCID: https://orcid.org/0000-0001-7301-9982

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing - review & editing

Simone J. C. F. M. Moorlag:

ORCID: https://orcid.org/0000-0001-8884-8417

Role: InvestigationRole: Writing - review & editing

Vasiliki Matzaraki:

ORCID: https://orcid.org/0000-0002-5852-6801

Role: Data curationRole: Formal analysis

Amanda Vlahos: Role: Investigation

Rebecca Shepherd: Role: InvestigationRole: Project administrationRole: ValidationRole: Writing - review & editing

Susie Germano:

ORCID: https://orcid.org/0000-0001-9924-7545

Role: Investigation

Melanie Bahlo:

ORCID: https://orcid.org/0000-0001-5132-0774

Role: Supervision

Nicole L. Messina: Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing - review & editing

Richard Saffery: Role: ConceptualizationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - review & editing

Mihai G. Netea: Role: ConceptualizationRole: Funding acquisitionRole: ResourcesRole: SupervisionRole: ValidationRole: Writing - review & editing

Nigel Curtis:

ORCID: https://orcid.org/0000-0003-3446-4594

Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing - review & editing

Boris Novakovic:

ORCID: https://orcid.org/0000-0002-5623-9008

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: August 2022

Publication date (Electronic, pub): 05 August 2022

Volume: 8

Issue: 31

Electronic Location Identifier: eabn4002

Affiliations

[ ¹ ]Infectious Diseases, Infection and Immunity Theme, Murdoch Children’s Research Institute, Parkville, VIC, Australia.

[ ² ]Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia.

[ ³ ]Infectious Diseases Unit, The Royal Children’s Hospital, Parkville, VIC, Australia.

[ ⁴ ]Molecular Immunity, Infection and Immunity Theme, Murdoch Children’s Research Institute, Parkville, VIC, Australia.

[ ⁵ ]Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Nijmegen Medical Center, 6500HB Nijmegen, Netherlands.

[ ⁶ ]Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.

[ ⁷ ]Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.

[ ⁸ ]Department for Immunology and Metabolism, Life and Medical Science Institute (LIMES), University of Bonn, 53115 Bonn, Germany.

Author notes

[* ]Corresponding author. Email: boris.novakovic@ 123456mcri.edu.au (B.N.); nigel.curtis@ 123456mcri.edu.au (N.C.)

[†]

These authors contributed equally to this work.

Author information

Samantha Bannister https://orcid.org/0000-0001-7507-3704

Jorge Domínguez-Andrés https://orcid.org/0000-0002-9091-1961

Gizem Kilic https://orcid.org/0000-0002-8251-1909

Brendan R. E. Ansell https://orcid.org/0000-0003-0297-897X

Melanie R. Neeland https://orcid.org/0000-0001-7301-9982

Simone J. C. F. M. Moorlag https://orcid.org/0000-0001-8884-8417

Vasiliki Matzaraki https://orcid.org/0000-0002-5852-6801

Susie Germano https://orcid.org/0000-0001-9924-7545

Melanie Bahlo https://orcid.org/0000-0001-5132-0774

Nigel Curtis https://orcid.org/0000-0003-3446-4594

Boris Novakovic https://orcid.org/0000-0002-5623-9008

Article

Publisher ID: abn4002

DOI: 10.1126/sciadv.abn4002

PMC ID: 9355358

PubMed ID: 35930640

SO-VID: 3ddb4364-7cf4-40ab-83c5-47b79ff7e8a2

Copyright © Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 23 November 2021

Date accepted : 23 June 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;

Award ID: 833247

Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;

Award ID: 833247