A curated transcriptome dataset collection to investigate the immunobiology of HIV infection

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 lymphomas¹¹, and four datasets addressed HIV infected patients with neurological disorders, such as HIV related fatigue syndrome¹², 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 infection¹⁶ [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.

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 publication⁵. 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).