Transcriptional changes in macaques exposed to Sudan virus and treated with a vehicle controls or obeldesivir for 5 or 10 days

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.

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.