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Data Note

A curated transcriptome dataset collection to investigate the blood transcriptional response to viral respiratory tract infection and vaccination.

[version 1; peer review: 2 approved]
PUBLISHED 13 Mar 2019
Author details Author details
OPEN PEER REVIEW
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This article is included in the Data: Use and Reuse collection.

Abstract

The human immune defense mechanisms and factors associated with good versus poor health outcomes following viral respiratory tract infections (VRTI), as well as correlates of protection following vaccination against respiratory viruses, remain incompletely understood. To shed further light into these mechanisms, a number of systems-scale studies have been conducted to measure transcriptional changes in blood leukocytes of either naturally or experimentally infected individuals, or in individual’s post-vaccination. Here we are making available a public repository, for research investigators for interpretation, a collection of transcriptome datasets obtained from human whole blood and peripheral blood mononuclear cells (PBMC) to investigate the transcriptional responses following viral respiratory tract infection or vaccination against respiratory viruses. In total, Thirty one31 datasets, associated to viral respiratory tract infections and their related vaccination studies, were identified and retrieved from the NCBI Gene Expression Omnibus (GEO) and loaded in a custom web application designed for interactive query and visualization of integrated large-scale data. Quality control checks, using relevant biological markers, were performed. Multiple sample groupings and rank lists were created to facilitate dataset query and interpretation. Via this interface, users can generate web links to customized graphical views, which may be subsequently inserted into manuscripts to report novel findings. The GXB tool enables browsing of a single gene across projects, providing new perspectives on the role of a given molecule across biological systems in the diagnostic and prognostic following VRTI but also in identifying new correlates of protection. This dataset collection is available at: http://vri1.gxbsidra.org/dm3/geneBrowser/list.

Keywords

Transcriptomics, Bioinformatics, Software, Viral respiratory infection, Influenza viruses, Respiratory syncytial viruses (RSV), Rhinoviruses, Whole Blood, PBMC.

Introduction

Viral respiratory tract infections (VRTI) are responsible for the majority hospitalizations among infants and the elderly. They are caused mainly by a heterogeneous group of viruses, including rhinoviruses, influenza viruses, parainfluenza viruses, respiratory syncytial virus (RSV), enteroviruses, coronaviruses, and certain strains of adenovirus1,2. Few antiviral therapies are currently approved and routinely used for VRTI. Most of these are specific inhibitors of influenza viruses3. Moreover, for most respiratory viruses, there is no licensed vaccine available4,5, with the exception of flu vaccines for which protection generally lasts only one flu season. Consequently, clinical management of individuals with VRTI is mostly restricted to supportive care5.

As clinical symptoms are often overlapping and are not specific for any of the viral species, it is difficult to establish a clinical diagnosis without laboratory testing1. Furthermore, clinical manifestations of VRTI are highly variable, ranging from asymptomatic infections or illness with mild symptoms (a common cold) to clinically severe disease with life-threatening complications, such as respiratory failure and in some cases may have a fatal outcome6. Infants, the elderly and patients with chronic lung or heart diseases in particular are at high risk7.

Thus, there is an evident need to better understand the molecular mechanisms underlying the disease pathogenesis, progression as well as severity of, and immunity against, VRTI among humans8. In this context, different large scale gene expression studies have been conducted using whole blood or peripheral blood mononuclear cells (PBMCs), to assess the human immune response to natural911 and experimental viral respiratory infections12,13; in particular, to influenza and RSV infections, and also to vaccination1417.

Here, we make available, through an interactive web application, a curated collection of datasets that were obtained from pediatric and adult patients with natural VRTI, volunteers with experimental exposition to respiratory viruses and also vaccinated volunteers. Transcriptomics datasets were obtained from whole blood and PBMCs.

A total of 31 datasets were retrieved and selected from the NCBI Gene Expression Omnibus (GEO), a public repository of transcriptome profiles. The identified datasets are particularly relevant to our interest in understanding the pathobiology of VRTI and vaccination. As described in recent publications18,19, these datasets were loaded into a custom interactive web application, the Gene Expression Browser (GXB), which enables easy access to large datasets and interactive visualization of our dataset collection related to VRTI and vaccination against respiratory viruses. It also provides access to demographic and clinical information. Importantly, the user can customize data plots by adding multiple layers of parameters (e.g. age, gender, sample type, type of infection, type of vaccine, sample collection time), modify the sample ordering and genes, and generate links (mini URL) that can be shared via e-mail or used in publications. Therefore, we are providing here a resource enabling browsing of datasets relevant to blood transcriptional responses to VRTI and vaccination that offers a unique opportunity to identify host genes and their regulation that may be of diagnostic and/or prognostic value, or that may be tested as novel correlates of protection in subsequent studies. For example, a comparative approach of the transcriptional response signatures between experimentally infected and vaccinated individuals could be used to identify common mechanisms that define the poor health outcomes versus strong protection. The ability to pool, compare and analyze the immune responses to different infections and vaccines, in different individuals and at various age, offers a unique opportunity for a better understanding of the pathophysiology of VRTI.

Methods

A total of 120 datasets, potentially relevant to human immune responses to VRTI and vaccination, were identified in GEO using the following search query:

Homo sapiens[Organism] AND ((“respiratory syncytial virus”[DESC] OR RSV[DESC] OR metapneumovirus[DESC] OR hMPV[DESC] OR influenza[DESC] OR parainfluenza[DESC] OR rhinovirus[DESC] OR rhinoviruses[DESC] OR adenovirus[DESC] OR adenoviruses[DESC] OR HAdV[DESC] OR coronaviruses[DESC] OR HCoV[DESC]) OR (vaccine OR vaccines OR vaccination) AND (blood[DESC] OR PBMC[DESC] OR PBMCs[DESC] OR lymphocyte[DESC] OR lymphocytes[DESC] OR “B cell”[DESC] OR “B cells”[DESC] OR “plasma cells”[DESC] OR “T cell”[DESC] OR “T cells”[DESC] OR Treg[DESC] OR Tregs[DESC] OR monocyte[DESC] OR monocytes[DESC] OR dendritic[DESC] OR DC[DESC] OR DCs[DESC] OR "natural killer"[DESC] OR NK[DESC] OR NKT[DESC] OR neutrophil[DESC] OR neutrophils[DESC]) AND (“Expression profiling by array”[gdsType] OR “Expression profiling by high throughput sequencing”[gdsType]).

Most of retrieved datasets were generated from human blood and human PBMC, using Illumina or Affymetrix commercial platforms or RNA-sequencing. All the entries that were returned with this query were manually curated. The process involved reading all the descriptions available of the datasets, the study design and the GEO-linked article in pubmed. Finally, only studies using human whole blood and human PBMCs, associated with natural or experimental VRTI, or vaccination against VRTI, were retained for our dataset collection. For the retained datasets, if the platform used to generate the transcriptome profiles was not supported by GXB or if from an in vitro study, they were exlcluded from our dataset collection. Based on these criteria, 31 datasets were retained. These include datasets that were generated from whole blood or PBMCs of individuals who were either naturally (12) or experimentally infected (3) (with influenza viruses, RSV, Rhinovirus, Rotavirus) as well as from healthy, uninfected (age-matched) volunteers. The remaining 16 datasets were generated from whole blood or PBMCs of individuals who had received flu vaccines (Figure 1). The datasets that comprise our collection are listed in Table 1.

3d43edce-10c9-40e9-af37-f5c00699a764_figure1.gif

Figure 1. Break down of the dataset collection by category.

The pie chart indicates the type of studies carried out for the 31 datasets.

Table 1. List of datasets constituting the collection.

TitlePlatforms usedResponse toVirus/VaccineCell type/
Tissues
Number of
samples
Selected
marker for
QC*
Citation #GEO ID
Blood transcriptome of human bacterial and influenza A pneumonia.Illumina
HumanHT-12 v3
Natural
infection
Influenza
H1N1
Whole
blood
190Xist9GSE40012
Expression profiling of critically ill influenza and bacterial pneumonia patients, also influenza vaccination recipients.Illumina
HumanHT-12 v3
Natural
infection and
vaccination
Influenza
H1N1
Whole
blood
81ND* 14GSE20346
FACS-sorted cells from Young Adults Vaccinated with Influenza TIV or LAIV Vaccines during 2008/09 Flu SeasonAffymetrix HG-
U133A
VaccinationInfluenza
TIV vaccine
PBMC84ND*16GSE29618
Gene expression analysis in children with complex seizures by influenza A (H1N1)pdm09 or rotavirus gastroenteritis.Affymetrix HG
U133_Plus2
Natural
infection
Influenza
(H1N1)
Rotavirus
Whole
blood
32Xist 10GSE50628
Gene expression analysis of Influenza vaccine response in Young and Old - Year 1.Illumina
HumanHT-12 v4
VaccinationInfluenza
TIV vaccine
PBMC72ND*23GSE59635
Gene expression analysis of Influenza vaccine response in Young and Old - Year 2 Illumina
HumanHT-12 v4
VaccinationInfluenza
TIV vaccine
PBMC156ND*23 GSE59654
Gene expression signatures of symptomatic respiratory viral infection in adults.Affymetrix HG-
U133A
Experimental
infection
Influenza
(H3N2)
RSV
Rhinovirus
Whole
blood
113ND*13GSE17156
Genome-wide analysis of whole blood transcriptional response to Respiratory Syncytial Virus (RSV), Influenza and Rhinovirus lower respiratory tract infection (LRTI) in children.Illumina
HumanHT-12 v4
Natural
infection
Influenza, RSV,
rhinovirus
Whole
blood
36 Xist 8GSE38900 (GPL10558)
Genome-wide analysis of whole blood transcriptional response to Respiratory Syncytial Virus, Influenza and Rhinovirus lower respiratory tract infection (LRTI) in children.Illumina
HumanWG-6 v3
Natural
infection
Influenza, RSV,
rhinovirus
Whole
blood
205Xist8GSE38900 (GPL6884)
Genome-wide profiling of whole blood from healthy adult volunteers before and after receiving non-live vaccines including seasonal influenza or pneumococcal vaccine or placebo (saline) injections I.Illumina
HumanHT-12 v3
VaccinationInfluenza
TIV vaccine
Whole
blood
72 Xist 24 GSE30059
Genome-wide profiling of whole blood from healthy adult volunteers before and after receiving non-live vaccines including seasonal influenza or pneumococcal vaccine or placebo (saline) injections II. Finger prick-Training Set Illumina
HumanHT-12 v3
VaccinationInfluenza
TIV vaccine
Whole
blood
185 Xist 24 GSE48762
Genome-wide profiling of whole blood from healthy adult volunteers before and after receiving non-live vaccines including seasonal influenza or pneumococcal vaccine or placebo (saline) injections II. Venipuncture-Training Set.Illumina
HumanHT-12 v3
VaccinationInfluenza
QIV vaccine
Whole
blood
214 Xist 24 GSE48762
Genome-wide profiling of whole blood from healthy adult volunteers before and after receiving non-live vaccines including seasonal influenza or pneumococcal vaccine or placebo (saline) injections II. Venipuncture-Validation Set.Illumina
HumanHT-12 v3
VaccinationInfluenza
QIV vaccine
Whole
blood
30 Xist 24 GSE48762
Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination Responses.Affymetrix
HuGene 1.0 ST v1
VaccinationInfluenza
QIV vaccine
PBMC292 ND* 17 GSE47353
Host gene expression signatures of influenza A H1N1 and H3N2 virus infection in adults.Affymetrix
HG-U133A
Experimental
infection
Influenza
H1N1, H3N2
Whole
blood
649 ND* 12 GSE52428
Host transcriptional response to influenza and other acute respiratory viral infections – a prospective cohort study.Illumina
HumanHT-12 v4
Natural
infection
Influenza
H1N1
Whole
blood
880 Xist 11 GSE68310
Hosts responses in critical disease caused by pandemic H1N1.Illumina
HumanWG-6 v2
Natural
infection
Influenza
H1N1
Whole
blood
40 ND* 25 GSE21802
Influenzavirus serotype association to global whole blood transcriptional changes.Illumina
HumanHT-12 v4
Natural
infection
Influenza
H1N1, H3N2
Whole
blood
465 ND* NA GSE29385
Olfactomedin 4 serves as a marker for disease severity in pediatric Respiratory Syncytial Virus (RSV) infection.Affymetrix
HG-U133_Plus_2
Natural
infection
RSVPBMC43 ND* 26 GSE69606
Patient-based transcriptome-wide analysis identify interferon and ubiquination pathways as potential predictors of Influenza A disease severity.Illumina
HumanHT-12 v4
Natural
infection
Influenza
H1N1, H3N2
Whole
blood
402 ND* 27 GSE61821
Peripheral blood cells expression data form 7 patients with severe pdm(H1N1) influensa and 7 gender and age matched healthy controls.Affymetrix
HuGene 1.0 ST v1
Natural
infection
Influenza
H1N1
Whole
blood
21 ND* 28 GSE27131
Systems biology of vaccination for seasonal influenza in humans.Affymetrix
HT_HG-U133_Plus_PM v1
VaccinationInfluenza
TIV, LAIV vaccines
PBMC163 ND* 16 GSE29619
Systems biology of vaccination for seasonal influenza in humans.Affymetrix
HG-U133A
VaccinationInfluenza
TIV, LAIV vaccine
PBMC84 ND* 16 GSE29619 (GPL3921)
T cell responses to H1N1v and a longitudinal study of seasonal influenza vaccination - 2009.Illumina
HumanHT-12 v4
VaccinationInfluenza
QIV vaccine
Whole
blood
72 ND* NA GSE58943
T cell responses to H1N1v and a longitudinal study of seasonal influenza vaccination - 2010.Illumina
HumanHT-12 v4
VaccinationInfluenza
TIV vaccine
Whole
blood
75 Xist NA GSE58898
T cell responses to H1N1v and a longitudinal study of seasonal influenza vaccination - 2012.Affymetrix
PrimeView v1
VaccinationInfluenza
TIV vaccine
Whole
blood
71 Xist NA GSE64514
Time Course of Adults Vaccinated with Influenza TIV Vaccine 2009-2012.Affymetrix
HT_HG-U133_Plus_PM v1
VaccinationInfluenza
TIV vaccine
Whole
blood
621 ND* 15 GSE74817
Time series of global gene expression after trivalent influenza vaccination in humans (female cohort).Illumina
HumanHT-12 v4
VaccinationInfluenza
TIV vaccine
Whole
blood
417 Xist 29 GSE48023
Time series of global gene expression after trivalent influenza vaccination in humans (male cohort).Illumina
HumanHT-12 v3
VaccinationInfluenza
TIV vaccine
Whole
blood
431 Xist 29 GSE48018
Transcriptional profile of PBMCs in patients with acute RSV or Influenza infection.Illumina
HumanHT-12 v4
Natural
infection
Influenza
(H1N1, H3N2), RSV
PBMC101 Xist 30 GSE34205
Transcriptomic profiling facilitates classification of response to influenza challenge Illumina
HumanHT-12 v4
Experimental
infection
Influenza
H3N2
Whole
blood
88 ND* 31 GSE61754
Transcriptomic profiling in childhood H1N1/09 influenza reveals reduced expression of protein synthesis genes Illumina
HumanHT-12 v3
Natural
infection
Influenza
(H1N1), RSV
Whole
blood
92 Xist 32 GSE42026
Trivalent Inactivated Influenza Vaccine (TIV) and Live Attenuated Influenza Vaccine (LAIV) Induce Different B cell and Transcriptional Responses in Children Illumina
HumanHT-12 v4
VaccinationInfluenza
TIV, LAIV vaccines
Whole
blood
140 ND* 30 GSE52005
Whole Blood Transcriptional Response to Pediatric Influenza Infection Illumina
HumanWG-6 v3
Natural
infection
InfluenzaWhole
blood
31 Xist 8 GSE29366

*Gender information and/or Xist probe was not available. QC – quality control, ND – no data

Once the final selection had been made, each dataset was downloaded from GEO by using the SOFT file format. Then, the datasets were uploaded on the Gene Expression Browser (GXB), an interactive web application hosted on the Amazon Web Services cloud20. Information about samples and study design were also uploaded. The available samples were put into groups based on relevant study variables and genes were ranked according to the different groups comparisons. A detailed description of the GXB software tool is available from recent publications1921. This software interface allows user to easily navigate and filter the dataset collection. A web tutorial can be easily accessed online. Annotation and functionality of the web software interface were described previously by our group18,19,21, and is reproduced here so that readers can use this article as a standalone resource. Briefly, datasets of interest can be quickly identified either by filtering on criteria from pre-defined sections on the left or by entering a query term in the search box at the top of the dataset navigation page. Clicking on one of the studies listed in the dataset navigation page opens a viewer designed to provide interactive browsing and graphic representations of large-scale data in an interpretable format. This interface is designed to present ranked gene lists and display expression results graphically in a context-rich environment. Selecting a gene from the rank ordered list on the left of the data-viewing interface will display its expression values graphically in the screen’s central panel. Directly above the graphical display drop down menus give users the ability: a) To change how the gene list is ranked - this allows the user to change the method used to rank the genes, or to only include genes that are selected for specific biological interest; b) To change sample grouping (Group Set button) - in some datasets, a user can switch between groups based on cell type to groups based on disease type, for example; c) To sort individual samples within a group based on associated categorical or continuous variables (e.g. gender or age); d) To toggle between the bar chart view and a box plot view, with expression values represented as a single point for each sample. Samples are split into the same groups whether displayed as a bar chart or box plot; e) To provide a color legend for the sample groups; f) To select categorical information that is to be overlaid at the bottom of the graph - for example, the user can display gender or smoking status in this manner; g) To provide a color legend for the categorical information overlaid at the bottom of the graph; h) To download the graph as a portable network graphics (png) image. Measurements have no intrinsic utility in absence of contextual information. It is this contextual information that makes the results of a study or experiment interpretable. It is therefore important to capture, integrate and display information that will give users the ability to interpret data and gain new insights from it. We have organized this information under different tabs directly above the graphical display. The tabs can be hidden to make more room for displaying the data plots, or revealed by clicking on the blue “show info panel” button on the top right corner of the display. Information about the gene selected from the list on the left side of the display is available under the “Gene” tab. Information about the study is available under the “Study” tab. Rolling the mouse cursor over a bar chart feature while displaying the “Sample” tab lists any clinical, demographic, or laboratory information available for the selected sample. Finally, the “Downloads” tab allows advanced users to retrieve the original dataset for analysis outside this tool. It also provides all available sample annotation data for use alongside the expression data in third party analysis software. Other functionalities are provided under the “Tools” drop-down menu located in the top right corner of the user interface. Some of the notable functionalities available through this menu include: a) Annotations, which provides access to all the ancillary information about the study, samples and dataset organized across different tabs; b) Cross-project view; which provides the ability for a given gene to browse through all available studies; c) Copy link, which generates a mini-URL encapsulating information about the display settings in use and that can be saved and shared with others (clicking on the envelope icon on the toolbar inserts the URL in an email message via the local email client); d) Chart options; which gives user the option to customize chart labels.

Quality Control

Quality control checks can be performed on the datasets loaded on GXB, for example by examining concordance of the gender-specific expression of the XIST gene in those datasets for which gender information was available as metadata. The XIST gene is essential for imprinted and random X-chromosome inactivation22 and therefore, expression is expected to be high in female and low in male samples. Respective hyperlinks are found in Table 1 allow you to visualize the XIST experession based on the gender information provided with the GEO submission. Figure 2 shows XIST gene expression in a representative dataset, along with gender information available that was recorded and made available in GEO.

3d43edce-10c9-40e9-af37-f5c00699a764_figure2.gif

Figure 2. Shown are XIST gene expression levels and gender information from the venipuncture validation set of GSE48762.

Gene expression data were from whole blood of healthy adult volunteers before and after receiving either placebo (saline) injections, seasonal influenza (Fluzone) or pneumococcal (Pneumovax) vaccination.

Data availability

All datasets included in our curated collection are also available publically via the NCBI GEO website : https://www.ncbi.nlm.nih.gov/gds/; and are referenced throughout the manuscript by their GEO accession numbers (e.g. GSE17763). Signal files and sample description files can also be downloaded from the GXB tool under the “downloads” tab.

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Bougarn S, Boughorbel S, Chaussabel D and Marr N. A curated transcriptome dataset collection to investigate the blood transcriptional response to viral respiratory tract infection and vaccination. [version 1; peer review: 2 approved]. F1000Research 2019, 8:284 (https://doi.org/10.12688/f1000research.18533.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Open Peer Review

Current Reviewer Status: ?
Key to Reviewer Statuses VIEW
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 1
VERSION 1
PUBLISHED 13 Mar 2019
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Reviewer Report 13 Jun 2019
Benjamin M Tang, Department of Intensive Care Medicine, Nepean Hospital, Kingswood, NSW, Australia 
Approved
VIEWS 7
This paper represents an extension of the authors' extensive work in this area - which aims to provide an accessible web-based platform for researchers to perform multi-cohort analysis of pooled transcriptomics data.

The Introduction outlines the scientific rationale ... Continue reading
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CITE
HOW TO CITE THIS REPORT
Tang BM. Reviewer Report For: A curated transcriptome dataset collection to investigate the blood transcriptional response to viral respiratory tract infection and vaccination. [version 1; peer review: 2 approved]. F1000Research 2019, 8:284 (https://doi.org/10.5256/f1000research.20283.r48849)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Reviewer Report 26 Mar 2019
Carlos Alberto Moreira-Filho, Department of Pediatrics, Faculty of Medicine, University of São Paulo (USP), São Paulo, Brazil 
Approved
VIEWS 9
The paper by Bougarn et al. describes the use of an interactive web application, GXB (Speake et al.(2015)1), for investigating the blood transcriptional response to viral respiratory tract infection and vaccination. A curated dataset collection was established for this purpose ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Moreira-Filho CA. Reviewer Report For: A curated transcriptome dataset collection to investigate the blood transcriptional response to viral respiratory tract infection and vaccination. [version 1; peer review: 2 approved]. F1000Research 2019, 8:284 (https://doi.org/10.5256/f1000research.20283.r45689)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.

Comments on this article Comments (0)

Version 1
VERSION 1 PUBLISHED 13 Mar 2019
Comment
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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