Abstract
Compendia of large-scale datasets available in public repositories provide an opportunity to identify and fill current gaps in biomedical knowledge. But first, these data need to be readily accessible to research investigators for interpretation. Here, we make available a collection of transcriptome datasets relevant to HIV infection. A total of 2717 unique transcriptional profiles distributed among 34 datasets were identified, retrieved from the NCBI Gene Expression Omnibus (GEO), and loaded in a custom web application, the Gene Expression Browser (GXB), designed for interactive query and visualization of integrated large-scale data. Multiple sample groupings and rank lists were created to facilitate dataset query and interpretation via this interface. Web links to customized graphical views can be generated by users and subsequently inserted in manuscripts reporting novel findings, such as discovery notes. The tool also enables browsing of a single gene across projects, which can provide new perspectives on the role of a given molecule across biological systems. This curated dataset collection is available at: http://hiv.gxbsidra.org/dm3/geneBrowser/list.
Keywords: Transcriptomics, Bioinformatics, Software, HIV, Immune Response, Big Data
Introduction
Uncovering the gene transcription signature associated with different outcomes of HIV infection is paramount to a deeper understanding of HIV pathogenesis and to identifying potential therapeutic targets for improving immunological response and for eradicating HIV infection 1. HIV has a complex life cycle during which it engages multiple host cellular components, including the immune cells in which it replicates, undermining immune functions. It also highjacks host transcription factors and enzymes to assure viral production and subsequent infections 2. HIV dysregulates host genes resulting in aberrant immune response, disease progression, and opportunistic infections 3, 4. The ability to pool and analyze samples across various groups of HIV infected individuals with different disease outcomes and across various cell types or tissues, offers a unique opportunity to define common denominators of the immune control of HIV infection, the regulation of HIV replication, and/or the virus-host interaction. With this in mind, we make available, via an interactive web application, a curated collection of transcriptome datasets relevant to HIV infection.
With over 65,000 studies deposited in the NCBI Gene Expression Omnibus (GEO), a public repository of transcriptome profiles, the identification of datasets relevant to a particular research area is not straightforward. Furthermore, GEO is primarily designed as a repository for storing data, rather than for browsing and interacting with the data. Thus, we used a custom web application, the gene expression browser (GXB), to host a collection of datasets that we identified as particularly relevant to the study of the immunobiology of HIV infection. This tool has been described in detail and the source code released as part of a recent publication 5. It allows seamless browsing and interactive visualization of large volumes of heterogeneous data. Users can easily customize data plots by adding multiple layers of information, modifying the sample order and generating links that capture these settings and can be inserted in email communications or in publications. Accessing the tool via these links also provides access to rich contextual information essential for data interpretation. This includes for instance access to gene information and relevant literature, study design, and detailed sample information.
Material and methods
Identification of relevant datasets
Potentially relevant datasets deposited in GEO were identified using an advanced query based on the Bioconductor package GEOmetadb, version 1.30.0, and on the SQLite database that captures detailed information on GEO data structure ( https://www.bioconductor.org/packages/release/bioc/html/GEOmetadb.html) 6. The search query was designed to retrieve entries where the title or summary contained the word HIV, and were generated from human samples using Illumina or Affymetrix commercial platforms.
The relevance of each entry returned by this query was assessed individually. This process involved reading through the descriptions and examining the list of available samples and their annotations. Sometimes it was also necessary to review the original published report in which the design of the study and generation of the dataset are described in more details. We identified 87 datasets meeting the search criteria and containing HIV infected samples (some studies related to HIV problematics contained uninfected samples only). Out of the 87 datasets, 41 were generated from tissues or cells isolated from HIV infected individuals, 46 contained cell lines or primary cells infected in vitro. Since molecular, cellular and physiological processes involved in the context of in vivo and in vitro infections are dramatically different, we decided to create two separate collections. Here we describe the “ in vivo collection” composed of 34 curated datasets (after filtering out datasets that did not meet quality control criteria, as described in “Dataset Validation” section, or datasets generated using an unsupported array platform). Of the 34 datasets, 7 are from whole blood, 7 from peripheral blood mononuclear cells (PBMCs), 8 from CD4 + and/or CD8 + T-cells, 4 from monocytes, 1 from dendritic cells (DCs), and 7 from tissues different from blood ( Figure 1). Four datasets comprise samples from patients co-infected with tuberculosis (TB) 7– 10, one dataset comprises samples from AIDS related lymphomas 11, and four datasets addressed HIV infected patients with neurological disorders, such as HIV related fatigue syndrome 12, major depression disorder (MDD) 13, or HIV-Associated Neurocognitive Disorder (HAND) 14, 15. Among the many noteworthy datasets, several stood out, such as the extensive study of the transcriptional signature of early acute HIV infection in whole blood samples of both antiretroviral-treated and untreated populations over the course of infection 16 [GXB: GSE29429-GPL10558 and GSE29429-GPL6947]. Several datasets investigate differences in gene expression between distinct stages of HIV infection (early/acute, chronic) 17, 18 [GXB: GSE6740, GSE16363], or different host responses to infection (progressors, non-progressors, elite controllers) 19– 23 [GXB: GSE28128, GSE24081, GSE56837, GSE23879, GSE18233]. Other studies address different stages or responses to antiretroviral therapy 24– 26 [GXB: GSE44228, GSE19087, GSE52900], or transcriptional changes after therapy interruption 27– 29 [GXB: GSE10924, GSE28177, GSE5220]. The entirety of the datasets that makes up our collection is listed in Table 1. Thematic composition of our collection is illustrated by a graphical representation of relative occurrences of terms in the list of titles loaded into the GXB tool ( Figure 2).
Figure 1. Sample source composition of the dataset collection.
Pie charts representing the numbers of datasets ( a) or transcriptome profiles ( b) for different cell types and tissues.
Figure 2. Thematic composition of the dataset collection.
Word frequencies extracted from titles of the studies loaded into the GXB tool are depicted as a word cloud. The size of the word is proportional to its frequency.
Table 1. List of datasets constituting the collection, also available at http://hiv.gxbsidra.org/dm3/geneBrowser/list.
Copyright: © 2016 Blazkova J et al.
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Gene expression browser (GXB) – dataset upload and annotation
Once a final selection had been made, each dataset was downloaded from GEO as a Simple Omnibus Format in Text (SOFT) file. It was in turn uploaded on a dedicated instance of the GXB, an interactive web application developed at the Benaroya Research Institute, hosted on the Amazon Web Services cloud. Available sample and study information were also uploaded. Samples were grouped according to possible interpretations of study results and gene rankings were computed based on different group comparisons (e.g. comparing samples form HIV negative vs HIV positive patients, with or without antiretroviral therapy, in different stages of disease progression, or with or without co-infection, depending on the focus of respective studies).
GXB – short tutorial
The GXB software has been described in detail in a recent publication 5. This custom software interface provides users with a means to easily navigate and filter the dataset collection available at http://hiv.gxbsidra.org/dm3/geneBrowser/list. A web tutorial is also available online: https://gxb.benaroyaresearch.org/dm3/tutorials.gsp#gxbtut. Briefly, datasets of interest can be quickly identified either by filtering on criteria from pre-defined lists on the left side of the dataset navigation page, 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 to 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 the rank list by selecting different comparisons (in cases where the dataset is split in more than two groups), or to only include genes that are selected for specific biological interest. b) To change sample grouping (Group Set button); in some datasets, user can switch between interpretations where samples are grouped based on cell type or disease, 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 a bar plot 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 plot or a box plot. e) To provide a color legend for the sample groups. f) To select categorical information 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 or the table with expression values as a comma separated values (csv) file. 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 “hide/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. Information available about individual samples is provided under the “Sample” tab. Rolling the mouse cursor over a bar plot, 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. These functionalities include notably: a) “Annotations”, which provides access to all the ancillary information about the study, samples and the dataset, organized across different tabs; b) “Cross Project View”, which provides the ability to browse across all available studies for a given gene; 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); and d) “Chart Options”, which gives user the option to customize chart labels.
Dataset validation
Quality control checks were performed by examination of profiles of relevant biological markers. Known leukocyte surface markers were used to verify consistency of the information provided by dataset depositors, and to identify instances where contamination of samples by other leukocyte populations may be confounding. The markers that were used include: CD3 (CD3D), a T-cell marker; CD4 and CD8 (CD8A), markers of CD4 + and CD8 + T cells respectively; CD11c (ITGAX), an mDC marker; CD14, expressed by monocytes and macrophages; or Adiponectin (ADIPOQ), expressed in adipose tissue. Expression of the XIST transcripts, which expression is gender-specific, was also examined in datasets containing relevant information, to determine its concordance with demographic information provided with the GEO submission (respective links in Table 1).
Data availability
The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2016 Blazkova J et al.
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). http://creativecommons.org/publicdomain/zero/1.0/
All datasets included in our curated collection are also available publically via the NCBI GEO website: www.ncbi.gov/geo; and are referenced throughout the manuscript by their GEO accession numbers (e.g. GSE44228). Signal files and sample description files can also be downloaded from the GXB tool under the “downloads” tab.
F1000Research: Dataset 1. Raw data for Figure 1, 10.5256/f1000research.8204.d115581 39
Acknowledgments
We would like to thank all the investigators who decided to make their datasets publically available by depositing them in GEO.
Funding Statement
JB, SB and DC were supported by the Qatar Foundation.
I confirm that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
[version 1; referees: 3 approved]
References
- 1. Martin AR, Siliciano RF: Progress Toward HIV Eradication: Case Reports, Current Efforts, and the Challenges Associated with Cure. Annu Rev Med. 2016;67:215–28. 10.1146/annurev-med-011514-023043 [DOI] [PubMed] [Google Scholar]
- 2. Moir S, Chun TW, Fauci AS: Pathogenic mechanisms of HIV disease. Annu Rev Pathol. 2011;6:223–48. 10.1146/annurev-pathol-011110-130254 [DOI] [PubMed] [Google Scholar]
- 3. Sauter D, Kirchhoff F: HIV replication: a game of hide and sense. Curr Opin HIV AIDS. 2016;11(2):173–81. 10.1097/COH.0000000000000233 [DOI] [PubMed] [Google Scholar]
- 4. Mohan T, Bhatnagar S, Gupta DL, et al. : Current understanding of HIV-1 and T-cell adaptive immunity: progress to date. Microb Pathog. 2014;73:60–9. 10.1016/j.micpath.2014.06.003 [DOI] [PubMed] [Google Scholar]
- 5. Speake C, Presnell S, Domico K, et al. : An interactive web application for the dissemination of human systems immunology data. J Transl Med. 2015;13:196. 10.1186/s12967-015-0541-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Zhu Y, Davis S, Stephens R, et al. : GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus. Bioinformatics. 2008;24(23):2798–800. 10.1093/bioinformatics/btn520 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Lai RP, Meintjes G, Wilkinson KA, et al. : HIV-tuberculosis-associated immune reconstitution inflammatory syndrome is characterized by Toll-like receptor and inflammasome signalling. Nat Commun. 2015;6: 8451. 10.1038/ncomms9451 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Dawany N, Showe LC, Kossenkov AV, et al. : Identification of a 251 gene expression signature that can accurately detect M. tuberculosis in patients with and without HIV co-infection. PLoS One. 2014;9(2):e89925. 10.1371/journal.pone.0089925 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Anderson ST, Kaforou M, Brent AJ, et al. : Diagnosis of childhood tuberculosis and host RNA expression in Africa. N Engl J Med. 2014;370(18):1712–23. 10.1056/NEJMoa1303657 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Kaforou M, Wright VJ, Oni T, et al. : Detection of tuberculosis in HIV-infected and -uninfected African adults using whole blood RNA expression signatures: a case-control study. PLoS Med. 2013;10(10):e1001538. 10.1371/journal.pmed.1001538 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Deffenbacher KE, Iqbal J, Liu Z, et al. : Recurrent chromosomal alterations in molecularly classified AIDS-related lymphomas: an integrated analysis of DNA copy number and gene expression. J Acquir Immune Defic Syndr. 2010;54(1):18–26. [DOI] [PubMed] [Google Scholar]
- 12. Voss JG, Dobra A, Morse C, et al. : Fatigue-related gene networks identified in CD14 + cells isolated from HIV-infected patients: part II: statistical analysis. Biol Res Nurs. 2013;15(2):152–9. 10.1177/1099800411423307 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Tatro ET, Scott ER, Nguyen TB, et al. : Evidence for Alteration of Gene Regulatory Networks through MicroRNAs of the HIV-infected brain: novel analysis of retrospective cases. PLoS One. 2010;5(4):e10337. 10.1371/journal.pone.0010337 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Gelman BB, Chen T, Lisinicchia JG, et al. : The National NeuroAIDS Tissue Consortium brain gene array: two types of HIV-associated neurocognitive impairment. PLoS One. 2012;7(9):e46178. 10.1371/journal.pone.0046178 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Levine AJ, Horvath S, Miller EN, et al. : Transcriptome analysis of HIV-infected peripheral blood monocytes: gene transcripts and networks associated with neurocognitive functioning. J Neuroimmunol. 2013;265(1–2):96–105. 10.1016/j.jneuroim.2013.09.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Chang HH, Soderberg K, Skinner JA, et al. : Transcriptional network predicts viral set point during acute HIV-1 infection. J Am Med Inform Assoc. 2012;19(6):1103–9. 10.1136/amiajnl-2012-000867 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Hyrcza MD, Kovacs C, Loutfy M, et al. : Distinct transcriptional profiles in ex vivo CD4 + and CD8 + T cells are established early in human immunodeficiency virus type 1 infection and are characterized by a chronic interferon response as well as extensive transcriptional changes in CD8 + T cells. J Virol. 2007;81(7):3477–86. 10.1128/JVI.01552-06 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Li Q, Smith AJ, Schacker TW, et al. : Microarray analysis of lymphatic tissue reveals stage-specific, gene expression signatures in HIV-1 infection. J Immunol. 2009;183(3):1975–82. 10.4049/jimmunol.0803222 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Rotger M, Dalmau J, Rauch A, et al. : Comparative transcriptomics of extreme phenotypes of human HIV-1 infection and SIV infection in sooty mangabey and rhesus macaque. J Clin Invest. 2011;121(6):2391–400. 10.1172/JCI45235 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Quigley M, Pereyra F, Nilsson B, et al. : Transcriptional analysis of HIV-specific CD8 + T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat Med. 2010;16(10):1147–51. 10.1038/nm.2232 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Xu X, Qiu C, Zhu L, et al. : IFN-stimulated gene LY6E in monocytes regulates the CD14/TLR4 pathway but inadequately restrains the hyperactivation of monocytes during chronic HIV-1 infection. J Immunol. 2014;193(8):4125–36. 10.4049/jimmunol.1401249 [DOI] [PubMed] [Google Scholar]
- 22. Vigneault F, Woods M, Buzon MJ, et al. : Transcriptional profiling of CD4 T cells identifies distinct subgroups of HIV-1 elite controllers. J Virol. 2011;85(6):3015–9. 10.1128/JVI.01846-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Rotger M, Dang KK, Fellay J, et al. : Genome-wide mRNA expression correlates of viral control in CD4 + T-cells from HIV-1-infected individuals. PLoS Pathog. 2010;6(2):e1000781. 10.1371/journal.ppat.1000781 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Massanella M, Singhania A, Beliakova-Bethell N, et al. : Differential gene expression in HIV-infected individuals following ART. Antiviral Res. 2013;100(2):420–8. 10.1016/j.antiviral.2013.07.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Woelk CH, Beliakova-Bethell N, Goicoechea M, et al. : Gene expression before HAART initiation predicts HIV-infected individuals at risk of poor CD4 + T-cell recovery. AIDS. 2010;24(2):217–22. 10.1097/QAD.0b013e328334f1f0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Wu JQ, Sassé TR, Saksena MM, et al. : Transcriptome analysis of primary monocytes from HIV-positive patients with differential responses to antiretroviral therapy. Virol J. 2013;10:361. 10.1186/1743-422X-10-361 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Vahey MT, Wang Z, Su Z, et al. : CD4 + T-cell decline after the interruption of antiretroviral therapy in ACTG A5170 is predicted by differential expression of genes in the ras signaling pathway. AIDS Res Hum Retroviruses. 2008;24(8):1047–66. 10.1089/aid.2008.0059 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Lerner P, Guadalupe M, Donovan R, et al. : The gut mucosal viral reservoir in HIV-infected patients is not the major source of rebound plasma viremia following interruption of highly active antiretroviral therapy. J Virol. 2011;85(10):4772–82. 10.1128/JVI.02409-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Tilton JC, Johnson AJ, Luskin MR, et al. : Diminished production of monocyte proinflammatory cytokines during human immunodeficiency virus viremia is mediated by type I interferons. J Virol. 2006;80(23):11486–97. 10.1128/JVI.00324-06 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Beliakova-Bethell N, Jain S, Woelk CH, et al. : Maraviroc intensification in patients with suppressed HIV viremia has limited effects on CD4 + T cell recovery and gene expression. Antiviral Res. 2014;107:42–9. 10.1016/j.antiviral.2014.04.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Sedaghat AR, German J, Teslovich TM, et al. : Chronic CD4 + T-cell activation and depletion in human immunodeficiency virus type 1 infection: type I interferon-mediated disruption of T-cell dynamics. J Virol. 2008;82(4):1870–83. 10.1128/JVI.02228-07 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. McLaren PJ, Ball TB, Wachihi C, et al. : HIV-exposed seronegative commercial sex workers show a quiescent phenotype in the CD4 + T cell compartment and reduced expression of HIV-dependent host factors. J Infect Dis. 2010;202(Suppl 3):S339–44. 10.1086/655968 [DOI] [PubMed] [Google Scholar]
- 33. Katz BZ, Salimi B, Gadd SL, et al. : Differential gene expression of soluble CD8 + T-cell mediated suppression of HIV replication in three older children. J Med Virol. 2011;83(1):24–32. 10.1002/jmv.21933 [DOI] [PubMed] [Google Scholar]
- 34. Nagy LH, Grishina I, Macal M, et al. : Chronic HIV infection enhances the responsiveness of antigen presenting cells to commensal Lactobacillus. PLoS One. 2013;8(8):e72789. 10.1371/journal.pone.0072789 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Songok EM, Luo M, Liang B, et al. : Microarray analysis of HIV resistant female sex workers reveal a gene expression signature pattern reminiscent of a lowered immune activation state. PLoS One. 2012;7(1):e30048. 10.1371/journal.pone.0030048 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Montano M, Rarick M, Sebastiani P, et al. : Gene-expression profiling of HIV-1 infection and perinatal transmission in Botswana. Genes Immun. 2006;7(4):298–309. 10.1038/sj.gene.6364297 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Ockenhouse CF, Bernstein WB, Wang Z, et al. : Functional genomic relationships in HIV-1 disease revealed by gene-expression profiling of primary human peripheral blood mononuclear cells. J Infect Dis. 2005;191(12):2064–74. 10.1086/430321 [DOI] [PubMed] [Google Scholar]
- 38. Smith AJ, Li Q, Wietgrefe SW, et al. : Host genes associated with HIV-1 replication in lymphatic tissue. J Immunol. 2010;185(9):5417–24. 10.4049/jimmunol.1002197 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Blazkova J, Boughorbel S, Presnell S, et al. : Dataset 1 in: A curated transcriptome dataset collection to investigate the immunobiology of HIV infection. F1000Research. 2016. Data Source [DOI] [PMC free article] [PubMed]