Transcriptional changes in macaques exposed to Sudan virus and treated with a vehicle controls or obeldesivir for 5 or 10 days
Data files
Feb 07, 2024 version files 1.18 MB
Abstract
Normalized Nanostring transcriptomic data (fold2-change- and Benjamini–Hochberg adjusted p-values) were exported as an .xlsx file. Groups include vehicle control (N=3), treated fatal (N=2), and treated survivor subjects administered ODV for 5 (N=3) or 10 days (N=5) compared against a pre-challenge baseline (0 DPI) at each collection timepoint. Any differentially expressed transcripts with a Benjamini-Hochberg false discovery rate (FDR) corrected p-value less than 0.05 were deemed significant. ODV, obeldesivir; DPI, days post infection.
README: Data S1-S3
https://doi.org/10.5061/dryad.wdbrv15vn
To determine immune correlates associated with obeldesivir-mediated protection, we performed targeted transcriptomics on whole blood RNA samples from Sudan virus-exposed macaques using a Nanostring nCounter® SPRINT Profiler and Nanostring NHPV2_Immunology reporter and capture probe sets. Normalized data (fold2-change- and Benjamini–Hochberg adjusted p-values) were exported as an .xlsx file (Data S1).
To capture shifts in circulating immune cell populations for each vehicle control (N=3), treated fatal (N=2), and treated survivor (N=3 for 5-day treatment; N=5 for 10-day treatment) group, we performed digital cell quantitation (DCQ) via transcriptional profiling. Predicted cell-type scores were exported as an .xlsx file (Data S2).
Pathway analysis was conducted to compare z-scores from canonical pathway, upstream analysis, disease and function, and tox function analyses among groups. Z-scores were exported as an .xlsx file (Data S3).
Description of the data and file structure
Data S1. Normalized fold2-change- and Benjamini–Hochberg adjusted p-values in vehicle control and obeldesivir-treated groups. Values were derived from macaque blood samples in vehicle control (N=3), treated fatal (N=2), and treated survivor (N=3 for 5-day treatment; N=5 for 10-day treatment) groups at each timepoint. (e.g., Control : 01 vs. Control : 00 means a comparison was made between the control group at 1 day post infection versus the day 0 pre-challenge baseline). Comparisons were made at each collection timepoint among vehicle control (N=3), treated fatal (N=2 for 5-day treatment), and treated survivor (N=3 for 5-day treatment; N=5 for 10-day treatment) groups. The Treated 1-5 groups (fatal, survivor) indicate that subjects were treated with obeldesivir for 5 consecutive days. The Treated 1-10 group (all survivors) indicates subjects were treated with obeldesivir for 10 consecutive days. Any differentially expressed transcripts with a Benjamini-Hochberg false discovery rate (FDR) corrected p-value less than 0.05 were deemed significant. Empty columns denote a group size n < 3 for that particular time point (i.e., the vehicle control group at days 1, 3, 5, and 9 days post infection versus a day 0 post infection pre-challenge baseline) due to inconsistent sampling at that timepoint for the control group. Other empty cells denote that statistics could not be calculated due to the lack of detectable transcripts for that particular probe. Please also note that annotations are not provided for non-human primate probe sets and so that column is empty. Abbreviations: mRNA, messenger RNA.
Data S2. Cell-type trend scores in individual vehicle control and obeldesivir-treated subjects. Raw abundance values of cell types were derived from blood samples from individual macaque subjects treated with a vehicle control (N=3) or obeldesivir (N=10) (continuous data). Human annotations were added for each respective mRNA to perform immune cell profiling within nSolver. Categorial data variables include cytotoxic cells, T-cells, exhausted CD8 T-cells, B-cells, NK CD56dim, macrophages, neutrophils, CD8 T cells, NK cells, mast cells, and Th1 cells. The continuous variable includes cell-type trend scores. Empty cells denote a cell-type score could not be derived due to the lack of detectable transcripts for that particular probe (score of 0). Note that our methods do not support the conclusion that one cell type is more abundant than another. Rather, they support the claim that a given cell type is more abundant in one sample than in another. Abbreviations: C, control subject; T, treated subject; D, day post infection; NK, natural killer; Th1, T helper 1.
Data S3. Enrichment Z-scores in vehicle control and obeldesivir-treated groups. Z-scores were derived using Ingenuity Pathway Analysis (Qiagen) for canonical pathway, upstream analysis, disease and function, and tox function analyses. Comparisons were made at each collection timepoint among vehicle control (N=3), treated fatal (N=2), and treated survivor (N=3 for 5-day treatment; N=5 for 10-day treatment) groups. For example, "Control : 04 vs. Control : 00" means a comparison was made between the control group at 4 days post infection versus the day 0 pre-challenge baseline. The Treated 1-5 groups (fatal, survivor) indicate that subjects were treated with obeldesivir for 5 consecutive days. The Treated 1-10 group (all survivors) indicates subjects were treated with obeldesivir for 10 consecutive days. A cell with N/A indicates that an insufficient number of transcripts mapped to that term and so z-scores could not be calculated.
Sharing/Access information
All data are provided as .xlsx files.
Code/Software
The data was analyzed with NanoString nSolver and the Advanced Analysis 2.0 package for differential expression.
Methods
NHPV2_Immunology reporter and capture probe sets (NanoString Technologies) were hybridized with 3 µL of each RNA sample for ~24 hours at 65°C. The RNA:probe set complexes were subsequently loaded onto an nCounter microfluidics cartridge and assayed using a NanoString nCounter SPRINT Profiler. Samples with an image binding density greater than 2.0 were re-analyzed with 1 µL of RNA to meet quality control criteria.
Briefly, nCounter .RCC files were imported into NanoString nSolver 4.0 software. To compensate for varying RNA inputs and reaction efficiency, an array of 10 housekeeping genes and spiked-in positive and negative controls were used to normalize the raw read counts. The array and number of housekeeping mRNAs are selected by default within the Nanostring nSolver Advanced Analysis module. As both sample input and reaction efficiency are expected to affect all probes uniformly, normalization for run-to-run and sample-to-sample variability is performed by dividing counts within a lane by the geometric mean of the reference/normalizer probes from the same lane (i.e., all probes/count levels within a lane are adjusted by the same factor). The ideal normalization genes are automatically determined by selecting those that minimize the pairwise variation statistic and are selected using the widely used geNorm algorithm as implemented in the Bioconductor package NormqPCR. The data was analyzed with NanoString nSolver Advanced Analysis 2.0 package for differential expression. Normalized data (fold2-change- and Benjamini–Hochberg adjusted p-values) were exported as an .xlsx file (Data S1). Groups include vehicle control (N=3), treated fatal (N=2), and treated survivor subjects administered ODV for 5 (N=3) or 10 days (N=5) compared against a pre-challenge baseline (0 DPI) at each collection timepoint. Any differentially expressed transcripts with a Benjamini-Hochberg false discovery rate (FDR) corrected p-value less than 0.05 were deemed significant.
Human annotations were added for each respective mRNA to perform immune cell profiling within nSolver (Data S2). For the heatmaps, groups of vehicle control (N=3), treated fatal (N=2), and treated survivor subjects administered ODV for 5 (N=3) or 10 days (N=5) were compared against their pre-challenge baseline (0 DPI) at each collection timepoint.
For enrichment analysis, differentially expressed transcripts and adjusted p-values from the Data S1 file were imported into Ingenuity Pathway Analysis (IPA; Qiagen) for canonical pathway, upstream analysis, disease and function, and tox function analyses with respect to a pre-challenge baseline (Data S3). The topmost significant pathways based on z-scores were imported into GraphPad Prism version 10.0.1 to produce heatmaps.