Disease signs correlate with viral replication

Ten male cynomolgus macaques were challenged with 1000 PFU of ZEBOV-Makona strain C07. We selected this isolate since it is one of the earliest and better characterized isolates from Guinea that was also used in a recent NHP study12. Fever (temperatures 2 °F higher than baseline) was evident on or after 4 days post infection (DPI). Anorexia and mild to moderate depression were noted 6 DPI in 4/4 animals, whereas mild petechial rashes on arms, chest, and groin regions was evident in 3/4 animals. Two animals on day 6 exhibited a hunched posture and general weakness and one monkey had rectal bleeding (Fig. 1a). However, these clinical signs became scorable only 6 DPI (Supplementary Table S1, Fig. 1b). Viral titers were measured in plasma by plaque assay and viral RNA was measured in whole blood using one-step RT-qPCR. Viremia was detected 3 DPI and significantly increased as infection progressed (Fig. 1b).

Figure 1 Disease signs correlate with viral replication. (a) Study time line and clinical observations. (b) Infectious virus was quantified by plaque assay on Vero cells and viral genome copies were measured using RT-qPCR with primers/probe targeting VP30. Average clinical scores as obtained by a scoring sheet (Supplementary Table S1). (c) Amylase (AMY) and C-reactive protein (CRP) plasma levels. (d) Percentage hematocrit and platelets counts. (e) Blood urea nitrogen (BUN) and creatinine (CRE) levels. (f) Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyltransferase (GGT) levels. (g) White blood cells (WBC), lymphocyte (LY), monocyte (MO), and granulocyte (GR) counts throughout infection. A linear model was used to perform statistical analysis; p-values listed for each parameter represent overall effect throughout infection. Full size image

Levels of circulating C-reactive protein (CRP), indicative of inflammation, correlated with viremia, and were increased slightly 4 DPI, followed by a large increase at 5 and 6 DPI (Fig. 1c). Similarly, changes in blood amylase levels, characteristic of pancreatic injury, weren’t detected until 5-6 DPI, when they significantly decreased (Fig. 1c). Hematocrit and platelet numbers also decreased 5-6 DPI, which may be associated with coagulopathy and detrimental changes in microcirculation (Fig. 1d). Levels of blood creatinine (CRE), indicative of kidney function, as well as liver enzymes (alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), and gamma-glutamyl transpeptidase (GGT)) significantly increased only 6 DPI when disease signs were more evident (Fig. 1e and f). Total white blood cell numbers (WBC) increased 4-5 DPI driven by a significant increase in granulocytes (neutrophils) before declining 6 DPI due to loss of both granulocytes and lymphocytes, while monocyte numbers remained relatively consistent throughout the study (Fig. 1g).

Changes in circulating immune mediators correlated with disease progression. In plasma, we detected significantly increased levels of inflammatory cytokines 6 DPI including IL-1β, IL-18, IFNγ, and IL-6 as well as regulatory cytokines IL-1RA and IL-4 (Supplementary Fig. S1a,b). We also observed a sharp increase in IFNα, a potent antiviral cytokine, 6 DPI (Supplementary Fig. S1b). Lymphocyte populations were also likely impacted by significant decreases in levels of IL-7, which plays a role in B and T-cell development and homeostasis, by 5-6 DPI (Supplementary Fig. S1b). Furthermore, ZEBOV-Makona infection resulted in upregulation of several chemokines 6 DPI (Supplementary Fig. S1c) including T-cell attractants, I-TAC (CXCL11) and MIG (CXCL9); leukocyte attractant, MIP1α; monocyte attractant, MCP-1; eosinophil attractant, eotaxin; and B-cell attractant, CXCL13. We also detected a significant decrease in growth factor PDGF-BB, which also coincides with characteristic thrombocytopenia seen in filovirus infection (Supplementary Fig. S1c).

ZEBOV-Makona infection results in early activation of innate immune cells

To characterize the immune response to ZEBOV-Makona, we used flow cytometry to measure changes in frequency and phenotype of circulating immune cells in PBMC (gating strategy described in Supplementary Fig. S2). Frequency of monocytes (defined as CD3−CD20−CD14+) remained relatively stable throughout infection with the exception of a small decrease 5 DPI (Fig. 2a). Further analysis indicates a transient yet significant increased frequency of intermediate/non-classical monocytes (CD16+) (p = 0.005) 4 DPI (Fig. 2b). This increased frequency of CD16+ monocytes returned to baseline values 6 DPI.

Figure 2 ZEBOV-Makona infection results in early activation of innate immune cells and lymphopenia. (a) Frequency of dendritic cells (DCs, CD14−HLA-DR+), monocytes (CD14+HLA-DR+/−), and NK cells (CD3−CD20−CD14−CD8a+) were measured by flow cytometry (FCM). (b) Frequency of classical (CD16−) and intermediate/non-classical (CD16+) monocytes. (c) Frequencies of myeloid DCs (mDC, CD123−CD11c+), plasmacytoid DCs (pDC, CD123+CD11c−) and other DCs (CD123−CD11c−). (d) Frequency of DC subsets expressing CD80. (e) Frequency of CD20+ B-cells, CD4+ and CD8+ T-cells. (f,g) Frequency of naïve (CD28+CD95−) and memory (CD28+/−CD95+) T-cells within CD4 (f) and CD8 (g) subsets. (h) Frequency of naïve (CD27−) and memory (CD27+) B-cells. For frequency changes in total monocyte(s), DCs, NK cells, CD4 T-cells, CD8 T-cells and B-cells, a linear model was used to perform statistical analysis; p-values listed for each parameter represent overall effect throughout infection. For changes in subset frequencies within monocytes, DC, and NK cells, a nonparametric trend where each time point is modeled by its own mean was assumed for statistical analysis; symbols (*,†,#) denote p-value ≤ 0.05 at the indicated time point compared to 0 DPI: *for CD16+ Monocytes and pDCs; †for CD16− Monocytes and other DCs; #for mDCs. Full size image

Total number of DCs (CD3−CD20−CD14−HLA-DR+) also remained stable with the exception of a small increase 4 DPI followed by a decline 5 DPI (Fig. 2a). We then analyzed changes in the three distinct DC subsets: myeloid dendritic cells (mDC; CD11c+CD123−), plasmacytoid dendritic cells (pDC; CD11c−CD123+), and other dendritic cells (CD11c−CD123−) (Fig. 2c). This analysis revealed that the increase in DC numbers 4 DPI was driven by a slight increase in the frequency of mDCs (p = 0.116) and a significant increase in pDCs 3 DPI (p = 0.0222) and 4 DPI (p = 0.0775). The increase in mDC and pDC numbers 4 DPI was accompanied by increased expression of the activation marker CD80 by all three DC subsets (Fig. 2d). The number of other DCs, in contrast, declined 6 DPI (p = 0.033) (Fig. 2c).

We detected a small decrease in the total number of natural killer (NK) cells (CD3−CD20−CD14−CD8α+) at the later stages of the disease 5-6 DPI (Fig. 2a). Although frequencies of cytotoxic NK cells (CD16+) and cytokine-producing NK cells (CD16−) remained stable throughout infection (Supplementary Fig. S3a), the frequency of cytokine-producing NK cells expressing CD159 was transiently increased 1 DPI (p = 0.0072) (Supplementary Fig. S3b).

ZEBOV-Makona infection results in lymphopenia

PBMC were also analyzed by flow cytometry to determine changes in frequency of circulating lymphocytes (Fig. 2e–h). CD4+ T-cells (CD3+CD20−CD4+) and CD8+ T-cells (CD3+CD20−CD8+) were classified into naïve (CD28+CD95−) and memory (CD28+/−CD95+) subsets (Fig. 2f,g). Similarly, B-cells (CD3−CD20+) were also classified as naïve (CD27−) or memory (CD27+) (Fig. 2h). Overall, T-cell numbers, both memory and naïve, declined 5 and 6 DPI (Fig. 2e–g). Although the number of total B-cells remained stable, memory B-cells significantly decreased 5-6 DPI (Fig. 2h). In addition to analyzing frequency of T and B-cell subsets, we assessed proliferation by measuring changes in the expression of Ki67, a nuclear protein associated with DNA replication. Memory CD4+ and CD8+ T-cells were further divided into three groups: central memory (CM; CD28+CD95+CCR7+), effector memory (EM; CD28−CD95+CCR7−) and transitional effector memory (TEM; CD28+CD95+CCR7−). In contrast to lymphocyte numbers, the proliferation of memory CD4+ and CD8+ T-cell subsets significantly increased 4-6 DPI while that of naïve T-cells remained unchanged (Supplementary Fig. S3c,d).

ZEBOV-Makona induces early and sustained upregulation of innate immune genes

We next determined longitudinal gene expression profiles in WB using RNA sequencing (RNA-Seq). The number of differentially expressed genes (DEGs) correlated very tightly with viral replication with large changes observed 5 and 6 DPI (Fig. 3a). The majority of DEGs mapped to human homologues (60–90%) with some DEGs remaining uncharacterized (10–40%) and a few mapping to non-coding RNA (ncRNA; 0.2–0.5%) (Supplementary Table S2). We also determined the number of ZEBOV transcripts mapping to each ZEBOV open reading frame (ORF) and intergenic region (IGR) (Supplementary Fig. S4a). We detected a significant increase in transcripts from all ORFs and IGRs 5-6 DPI. Furthermore, NP had the highest number of transcripts while VP24 and L had the lowest, indicative of a productive ZEBOV transcriptional gradient. To understand the biological impact of the gene expression changes detected each DPI, we performed functional enrichment using MetaCoreTM 13 to identify gene ontology (GO) processes, which are defined terms representing the biological processes of a gene set. DEGs upregulated 3 to 6 DPI enriched to GO processes associated with host defense (Fig. 3b). As infection progressed, both the number of DEGs mapping to these GO processes and the false discovery rate (FDR)-corrected p-values became more significant (Fig. 3b). Additionally, DEGs upregulated 5 and 6 DPI, when clinical signs are detected (Fig. 1a,b), enriched to metabolism and leukocyte activation GO processes (Fig. 3b). A significant number of downregulated DEGs was only detected 6 DPI and most of those genes enriched to GO processes associated with T-cell activation and metabolic processes (Fig. 3c).

Figure 3 ZEBOV-Makona infection induces sustained large transcriptional changes of innate immune genes. (a) Bar graph depicts number of protein-coding differentially expressed genes (DEGs; defined as those ≥log2 fold change compared to 0 DPI and FDR-corrected p-value ≤ 0.05) that have human homologues. Line graph indicates number of viral transcripts reported as normalized by reads per kilobase per million mapped (RPKM); the EdgeR package was used to determine statistically significant changes in viral reads; *denote p-value ≤ 0.05 at the indicated time point compared to 0 DPI. (b) Heatmap representing functional enrichment of DEGs upregulated 3-6 DPI; color intensity represents the statistical significance (shown as −log 10 of the FDR-corrected p-value); range of colors is based on the lowest and highest −log 10 (FDR) values for the entire set of GO processes; the number of DEGs enriching to each GO process each day is listed within each box; blank boxes represent no statistical significance. (c) Bar graph depicting statistically significant GO processes to which downregulated genes 6 DPI enriched; the line graph represents -log 10 (FDR) of the enriched term. (d) 4-way venn diagram shows overlap of DEGs (protein-coding human homologs) detected 3, 4, 5 and 6 DPI. (e) Heatmap representing gene expression (shown as absolute normalized RPKM values) of the common 30 DEGs first detected 3DPI and upregulated throughout infection; range of colors is based on scaled and centered RPKM values of the entire set of genes (red represents increased expression while blue represents decreased expression); each column represents the median RPKM values for each DPI. Full size image

The 30 DEGs that were detected 3 DPI remained upregulated throughout infection with fold changes that increased from ~20 at 3 DPI to ~150 at 6 DPI (Fig. 3d). These 30 genes play a role in antiviral defense including: interferon-stimulated genes such as OAS1 (2′-5′-Oligoadenylate Synthetase 1; fold change (FC) = 31.2), MX1 (MX Dynamin-Like GTPase 1; FC = 18.2), IFI44 (Interferon-Induced Protein 44; FC = 16.6); and sensors of viral nucleic acids such as DHX58 (DEXH Box Polypeptide 58; FC = 7.7) and IFIH1 (Interferon-Induced with Helicase C Domain 1; FC = 5.3) (Fig. 3e). Similarly, the 68 upregulated genes that were newly detected 4 DPI remained upregulated 5-6 DPI (Fig. 3d). Many of these genes were involved in inflammation including SERPING1 (Serpin Family G Member 1; FC = 202.8), CXCL10 (FC = 38.0), S100A8 (S100 Calcium Binding Protein A8; FC = 6.5), and TIFA (TRAF Interacting Protein with Forkhead Associated Domain; FC = 33.8) (Fig. 4a). Other upregulated genes in this group activate innate immunity such as TLR3 (Toll-Like Receptor 3; FC = 34.6) and CD177 (FC = 9.4), while others encode negative regulators e.g. IL1RN (Interleukin 1 Receptor Antagonist; FC = 41.3), SOCS3 (Suppressor of Cytokine Signaling 3; FC = 23.9) and TRAFD1 (TRAF-Type Zinc Finger Domain Containing 1; FC = 6.9). Finally, some of these genes play a role in cell death such as TNFSF10 (FC = 11.0), CASP5 (caspase 5; FC = 46.6), and CD274 (Programmed Death Ligand 1; FC = 12.5) (Fig. 4a).

Figure 4 Gene expression changes 5 DPI reflect increased number of granulocytes. (a) Heatmap representing gene expression (shown as absolute normalized RPKM values) of DEGs upregulated 4 and 5 DPI with a FC ≥ 6.5; range of colors is based on scaled and centered RPKM values of the entire set of genes (red represents increased expression while blue represents decreased expression); day 0 is represented by the median RPKM value, while each column represents 1 animal for 4 and 5 DPI. (b) Network depicting direct interactions of DEGs upregulated 5 DPI that map to GO process “Leukocyte Activation”. Full size image

End-stage disease is characterized by upregulation of genes important for cell death and inflammation and downregulation of those important for T-cell activation and translation

A substantial number of DEGs was detected 5 DPI (801). These DEGs enriched to GO processes associated with host defense as well as apoptosis and metabolism (Fig. 3b). A network analysis of the DEGs that enriched to GO processes “Leukocyte activation” showed that several were regulated by a number of transcription factors critical to the inflammatory response, notably components of the NFκB complex RELA/RELB (V-Rel Avian Reticuloendotheliosis Viral Oncogene Homolog A and B; FC = 5.0 and FC = 5.9) and SPI1 (SPI-1 Proto-Oncogene; FC = 6.1) (Fig. 4b). Interestingly, genes involved in B-cell development such as BCL6 (B-Cell CLL/Lymphoma 6; FC = 8.3) and BTK (Bruton Agammaglobulinemia Tyrosine Kinase; FC = 3.4) as well as T-cell activation including LCP (Lymphocyte Cytosolic Protein 1; FC = 7.4) and FYB (FYN Binding Protein; FC = 8.2) were upregulated. Moreover, Toll-Like Receptors (TLR) 1-4 and 6 were upregulated (FC = 9.5, 10.1, 102.9, 12.1, and 8.2, respectively) (Fig. 4b).

Similarly, DEGs upregulated 6 DPI enriched to GO processes associated with host defense, cell death, and metabolic processes (Fig. 3b). Of the 409 genes that mapped to “Immune system process”, 110 with known roles in inflammation directly interacted with one another (Fig. 5a). Expression of these genes is regulated by: NFKB1 (Nuclear Factor of Kappa Light Polypeptide Gene Enhancer in B-Cells 1; FC = 3.0); STAT1/STAT2 (Signal Transducer and Activator of Transcription 1 and 2; FC = 7.3 and FC = 12.5); and CEBPB (CCAAT/Enhancer Binding Protein, Beta; FC = 10.0). DEGs regulated by these transcription factors included chemokines/cytokines such as CCL2 (FC = 123.7), CXCL10 (FC = 217.3), TNF (Tumor Necrosis Factor; FC = 8.3), and IL1B (FC = 7.2); as well as their receptors, notably C3AR1 (Complement Component 3a Receptor 1; FC = 39.7), CXCR1 (FC = 4.7), and IL1RAP (Interleukin 1 Receptor Accessory Protein; FC = 19.9). Several IFN-stimulated genes (ISG15, IFIT2) and RNA helicases (HERC5 and DHX58) were also part of this network. Other DEGs in this network play a role in generating or clearing reactive oxygen species e.g. CYBB (Cytochrome B-245, Beta; FC = 5.9) and SOD2 (Superoxide Dismutase 2; FC = 9.6) (Fig. 5a). These upregulated genes are in line with the dysregulated immune activation often attributed to Ebola hemorrhagic fever (EHF). DEGs upregulated 6 DPI also play a role in 1) cell death including FAS (Fas Cell Surface Death Receptor; FC = 11.2), TNFSF10 (FC = 16.6), BCL2A1 (BCL2 Related Protein A1; FC = 18.7), TRPM2 (Transient Receptor Potential Cation Channel Subfamily M Member 2; FC = 47.9); or 2) cell cycle progression such as TCF7L1 (Transcription Factor 7 Like 1; FC = 32.1) and OLFM4 (Olfactomedin 4; FC = 981.8) (Fig. 5a,b).

Figure 5 End-stage disease is characterized by upregulation of genes important for cell death and inflammation. (a) Network depicting direct interactions of DEGs upregulated 6 DPI that map to “Immune system response” with a FC ≥ 5.6. (b) Heatmap representing gene expression (shown as absolute normalized RPKM values) of DEGs upregulated 6 DPI that map to “Regulation of cell death” with a FC ≥ 16; range of colors is based on scaled and centered RPKM values of the entire set of genes (red represents increased expression while blue represents decreased expression); day 0 is represented by the median RPKM value, while each column represents 1 animal for 6 DPI. Full size image

At day 6, changes in gene expression were indicative of a suppressed adaptive immune response and cellular homeostasis (Fig. 3c). Specifically, several of the 48 downregulated genes mapping to “Immune system process” play a role in T-cell responses: CD3 (FC = 6.8), CD8 (FC = 4.8), IL2RB (FC = 9.3), TRAC (T-Cell Receptor Alpha Constant; FC = 6.7), and ZAP70 (Zeta-Chain (TCR) Associated Protein Kinase; FC = 4.9) (Fig. 6a). Additionally, genes encoding effector molecules GZMB (Granzyme B; FC = 5.2), PRF1 (Perforin; FC = 7.1), and CD244 (Natural Killer Cell Receptor 2B4; FC = 7.3) were downregulated (Fig. 6a). The second major group of downregulated genes enriched to GO process “translation” (Fig. 3c) and contained translation initiation factors e.g. EIF3E (Eukaryotic translation initiation factor 3 subunit E; FC = 2.6); elongation factors such as EEF2 (Elongation Factor 2; FC = 2.5); and ribosomal proteins e.g. RPL22L1 (Ribosomal protein L22 Like 1; FC = 12.9) (Fig. 6b). Interestingly, several of the genes that enriched to the GO term “viral process” were also ribosomal proteins e.g. RPS27 (FC = 9.6) as well as genes that regulate nuclear import such as importins (e.g., IPO5; FC = 2.6) and nucleoporins (e.g., NUP210; FC = 2.8) (Fig. 6c).

Figure 6 ZEBOV-Makona pathogenesis is characterized by downregulation of genes critical for T-cell activation and translation. (a) Network depicting direct interactions of DEGs downregulated 6 DPI that map to “Immune system response”. (b,c) Heatmap representing gene expression (shown as absolute normalized RPKM values) of DEGs downregulated 6 DPI that map to “Translation” with a FC ≥ 2.5 (b) and DEGs downregulated 6 DPI that map to “Viral process” with a FC ≥ 3.0 (c); range of colors is based on scaled and centered RPKM values of the entire set of genes (red represents increased expression while blue represents decreased expression); day 0 is represented by the median RPKM value, while each column represents 1 animal for 6 DPI. Full size image

DEGs detected only in PBMC fraction play a role in regulating blood clotting, response to oxidative stress and vasculature development

In order to get a better understanding of the contributions of lymphocytes and antigen-presenting cells to gene expression changes, we next identified transcriptional changes within PBMC, which are devoid of granulocytes, erythrocytes, and platelets. The kinetics of host and viral gene expression changes in PBMC were similar as those described for WB, but the number of DEGs and viral reads was significantly smaller (Fig. 7a and Supplementary Fig. S4b). As described for WB, 45 mostly interferon-stimulated genes were upregulated 3-6 DPI in PBMC with fold changes that increased from ~10 at 3 DPI to ~70 at 6 DPI as infection progressed (Supplementary Fig. S5a,b). Of these 45 DEGs, 27 were also detected in WB 3 DPI (Supplementary Fig. S5b). The remaining 18 DEGs were detected in WB 4-5 DPI. Overall, a substantial number (50–75%) of DEGs detected in PBMC throughout infection were also detected in WB (Supplementary Fig. S5c–e and Fig. 7b).

Figure 7 DEGs detected only in PBMC fraction play a role in regulating blood clotting, response to oxidative stress, and vasculature development (a) Bar graph depicts number of protein-coding differentially expressed genes (DEGs; defined as those ≥log2 fold change compared to 0 DPI and FDR-corrected p-value ≤ 0.05) that have human homologues. Line graph indicates number of viral transcripts reported as normalized by reads per kilobase per million mapped (RPKM); the EdgeR package was used to determine statistically significant changes in viral reads; *denote p-value ≤ 0.05 at the indicated time point compared to 0 DPI. (b) Venn diagram shows overlap between DEGs in PBMC and WB 6 DPI. (c,d) Bar graph depicting statistically significant GO processes to which upregulated (c) and downregulated (d) genes found exclusively in PBMC 6 DPI enriched; the line graph represents −log 10 (FDR) of the enriched term. (e,f) Heatmap representing gene expression (shown as absolute normalized RPKM values) of DEGs upregulated 6 DPI that map to “Coagulation” and/or “Immune system process” (e) and DEGs downregulated 6 DPI that map to “Immune system process” (f); range of colors is based on scaled and centered RPKM values of the entire set of genes (red represents increased expression while blue represents decreased expression); day 0 is represented by the median RPKM value, while each column represents 1 animal for 6 DPI. Full size image

Most of the 59 DEGs detected only in PBMC 4 DPI, were detected in WB 5-6 DPI such as C1QC (Complement Component 1, Q Subcomponent, C Chain; FC = 6.6), BCL2A1 (FC = 3.8), and CYBB (FC = 3.1) (Supplementary Fig. S5f). DEGs detected only in PBMC 5 DPI enriched to GO processes associated with blood regulation and response to oxidative stress (Supplementary Fig. S5g) including ADAMDEC1 (A Disintegrin and Metalloproteinase Domain-Like Protein Decysin-1; FC = 20.2), NCF1 (Neutrophil Cytosolic Factor 1; FC = 3.1), ATP2A2 (ATPase Sarcoplasmic/Endoplasmic Reticulum Ca2+ Transporting 2; FC = 2.5), THBS1 (Thrombospondin 1; FC = 3.3), and F13A1 (Coagulation Factor XIII A Chain; FC = 4.4) (Supplementary Fig. S5h).

Differences between the PBMC and WB gene expression profiles were most prominent 6 DPI (Fig. 7b–d). DEGs upregulated only in PBMC enriched to immune related GO processes such as host defense and coagulation (Fig. 7c). Upregulated genes that enriched to “Immune system process” include MNDA (Myeloid Cell Nuclear Differentiation Antigen; FC = 2.6) and NLRP12 (PYRIN-Containing APAF1-Like Protein 7; FC = 8.9). Genes that play a role in coagulation include PTAFR (Platelet Activating Factor Receptor; FC = 6.7) and THBD (Thrombomodulin; FC = 15.8) (Fig. 7e). Some of the downregulated genes detected only in PBMC 6 DPI play a role in host defense such as: CD96 (FC = 3.5), CD8B (FC = 2.6), and SLAMF6 (Activating NK Receptor; FC = 2.1) (Fig. 7d,f). Others were involved in vasculature development such as CYSLTR2 (Cysteinyl Leukotriene Receptor 2; FC = 3.4) and ADRB2 (Beta-2 Adrenoreceptor; FC = 3.3), as well as coagulation e.g. PRKCA (Protein Kinase C, Alpha; FC = 2.9) and ENPP4 (Ectonucleotide Pyrophosphatase 4; FC = 2.4) (Fig. 7f). To better understand the source of the DEGs detected only in PBMC 6 DPI, we used the Immunological Genome Project Consortium database (ImmGen), a collaborative effort to delineate gene expression patterns across different leukocyte subsets14 (Supplementary Fig. S6). This analysis revealed that most of these DEGs are expressed by DCs and monocytes/macrophages.

ZEBOV-Makona induces overlapping but distinct gene expression changes compared to ZEBOV-Kikwit

To better understand the differences in pathogenesis caused by ZEBOV-Makona compared to the previously identified ZEBOV variant Kikwit, we compared our transcriptome results to those obtained from a recent study in which male cynomolgus macaques were challenged intramuscularly with 1000 PFU of Kikwit15, 16. Protocols for library preparation, sequencing, and bioinformatics analysis were the same, making these comparisons feasible. This comparison further supports delayed disease progression following challenge with Makona relative to Kikwit (Supplementary Fig. S7). Specifically, we observed significantly larger gene expression changes in both WB and PBMC 4 DPI following Kikwit infection compared to Makona (Supplementary Fig. S7a,b). Functional enrichment showed that DEGs detected 4 days following both Kikwit and Makona infection enriched to innate immunity while lymphocyte related transcripts were only downregulated following Kikwit infection, indicating lymphopenia occurs earlier compared to Makona infection17,18,19 (Supplementary Fig. S7c,d).

By 6 DPI, widespread transcriptional responses were detected following infection with either variant and although there was significant overlap, we identified distinct gene expression profiles unique to Kikwit and Makona infection (Fig. 8a,b). By 6 DPI, DEGs associated with inflammation and lymphopenia were detected with either variant. Since whole blood encompasses PBMC and functional enrichment was similar for both, we focused our analysis on differences between Kikwit and Makona infection in WB 6 DPI. Infection with either strain increased expression of ISGs (GBP1, IFIT1, IRF7) and reduced expression of lymphocyte related genes (CD3D, CD8A, ZAP70) (Fig. 8c). However, DEGs unique to Makona infection suggest a more robust immune dysregulation (Fig. 8d,e), while DEGs found exclusively during Kikwit infection were mostly involved with dysregulation of metabolism, cell cycle, and translation (Fig. 8f,g).